diff --git a/cake_exports/knn_build/.gitignore b/cake_exports/knn_build/.gitignore new file mode 100644 index 00000000..735d478a --- /dev/null +++ b/cake_exports/knn_build/.gitignore @@ -0,0 +1,8 @@ +__pycache__/ +*.py[cod] +*.egg-info/ +.pytest_cache/ +.ruff_cache/ +results/ +build/ +dist/ diff --git a/cake_exports/knn_build/BENCHMARK_RESULTS.json b/cake_exports/knn_build/BENCHMARK_RESULTS.json new file mode 100644 index 00000000..55211160 --- /dev/null +++ b/cake_exports/knn_build/BENCHMARK_RESULTS.json @@ -0,0 +1,114349 @@ +{ + "api": "flashlib_cake_knn_build.init().compute", + "baseline_entrypoint": "flashlib.flash_knn", + "baseline_name": "flashlib.flash_knn", + "gpu_speedup_convention": "compute_gpu_speedup_vs_baseline = same_session_flashlib_flash_knn_gpu_span_ms / exported_runtime_compute_gpu_span_ms", + "hardware": { + "arch": "sm_100a", + "device": "NVIDIA GB200" + }, + "measurement_sessions": [ + { + "baseline_candidate_same_process": true, + 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1024, + "benchmark": true, + "build": false, + "check_correctness": true, + "correctness_query_sample": 256, + "diagnostic_class": "rectangular_search_d64_guard_blindspot", + "dtype": "bfloat16", + "recall_min": 0.999, + "recorded_baseline_ms": 0.208098, + "recorded_baseline_name": "flashlib", + "recorded_kernel_ms": 0.165857, + "recorded_speedup_vs_baseline": 1.2546832512344972, + "recorded_timing_backend": "cupti", + "seed": 608864, + "time_flashlib": true + }, + "search_rect_b1_q1024_m8192_d128_k10": { + "B": 1, + "D": 128, + "K": 10, + "M": 8192, + "Q": 1024, + "benchmark": true, + "build": false, + "check_correctness": true, + "correctness_query_sample": 256, + "diagnostic_class": "rectangular_search", + "dtype": "bfloat16", + "recall_min": 0.999, + "recorded_baseline_ms": 0.126945, + "recorded_baseline_name": "flashlib", + "recorded_kernel_ms": 0.059008, + "recorded_speedup_vs_baseline": 2.151318465292842, + "recorded_timing_backend": "cupti", + "seed": 606812, + "time_flashlib": true + }, + "search_rect_b1_q1536_m65536_d128_k20": { + "B": 1, + "D": 128, + "K": 20, + "M": 65536, + "Q": 1536, + "benchmark": true, + "build": false, + "check_correctness": true, + "correctness_query_sample": 256, + "diagnostic_class": "rectangular_search_tail_guard", + "dtype": "bfloat16", + "recall_min": 0.999, + "recorded_baseline_ms": 0.7141015, + "recorded_baseline_name": "flashlib", + "recorded_kernel_ms": 0.448322, + "recorded_speedup_vs_baseline": 1.5928317147050557, + "recorded_timing_backend": "cupti", + "seed": 608655, + "time_flashlib": true + }, + "search_rect_b1_q2048_m32768_d128_k10": { + "B": 1, + "D": 128, + "K": 10, + "M": 32768, + "Q": 2048, + "benchmark": true, + "build": false, + "check_correctness": true, + "correctness_query_sample": 512, + "diagnostic_class": "rectangular_search_intermediate", + "dtype": "bfloat16", + "recall_min": 0.999, + "recorded_baseline_ms": 0.342211, + "recorded_baseline_name": "flashlib", + "recorded_kernel_ms": 0.224161, + "recorded_speedup_vs_baseline": 1.526630412962112, + "recorded_timing_backend": "cupti", + "seed": 607048, + "time_flashlib": true + }, + "search_rect_b1_q4096_m65536_d128_k20": { + "B": 1, + "D": 128, + "K": 20, + "M": 65536, + "Q": 4096, + "benchmark": true, + "build": false, + "check_correctness": true, + "correctness_query_sample": 128, + "diagnostic_class": "rectangular_search_mid_k", + "dtype": "bfloat16", + "recall_min": 0.999, + "recorded_baseline_ms": 1.3830175, + "recorded_baseline_name": "flashlib", + "recorded_kernel_ms": 0.92455, + "recorded_speedup_vs_baseline": 1.4958817803255637, + "recorded_timing_backend": "cupti", + "seed": 606655, + "time_flashlib": true + }, + "search_rect_common_d1024_b1_q256_m8192_k10": { + "B": 1, + "D": 1024, + "K": 10, + "M": 8192, + "Q": 256, + "benchmark": true, + "build": false, + "check_correctness": true, + "correctness_query_sample": 256, + "diagnostic_class": "v12_common_d_rectangular_search", + "dtype": "bfloat16", + "recall_min": 0.999, + "recorded_baseline_ms": 0.149985, + "recorded_baseline_name": "flashlib", + "recorded_kernel_ms": 0.064193, + "recorded_speedup_vs_baseline": 2.3364697085351986, + "recorded_timing_backend": "cupti", + "seed": 616124, + "time_flashlib": true + }, + "search_rect_common_d256_b1_q1024_m32768_k10": { + "B": 1, + "D": 256, + "K": 10, + "M": 32768, + "Q": 1024, + "benchmark": true, + "build": false, + "check_correctness": true, + "correctness_query_sample": 256, + "diagnostic_class": "common_embedding_dim_rectangular_search", + "dtype": "bfloat16", + "recall_min": 0.999, + "recorded_baseline_ms": 0.444323, + "recorded_baseline_name": "flashlib", + "recorded_kernel_ms": 0.19149, + "recorded_speedup_vs_baseline": 2.320345709958745, + "recorded_timing_backend": "cupti", + "seed": 612856, + "time_flashlib": true + }, + "search_rect_common_d4096_b1_q128_m4096_k10": { + "B": 1, + "D": 4096, + "K": 10, + "M": 4096, + "Q": 128, + "benchmark": true, + "build": false, + "check_correctness": true, + "correctness_query_sample": 128, + "diagnostic_class": "v12_common_d_rectangular_search", + "dtype": "bfloat16", + "recall_min": 0.999, + "recorded_baseline_ms": 0.216001, + "recorded_baseline_name": "flashlib", + "recorded_kernel_ms": 0.063744, + "recorded_speedup_vs_baseline": 3.3885699046184743, + "recorded_timing_backend": "cupti", + "seed": 616496, + "time_flashlib": true + }, + "search_rect_common_d768_b1_q512_m8192_k10": { + "B": 1, + "D": 768, + "K": 10, + "M": 8192, + "Q": 512, + "benchmark": true, + "build": false, + "check_correctness": true, + "correctness_query_sample": 256, + "diagnostic_class": "common_embedding_dim_rectangular_search", + "dtype": "bfloat16", + "recall_min": 0.999, + "recorded_baseline_ms": 0.139265, + "recorded_baseline_name": "flashlib", + "recorded_kernel_ms": 0.065921, + "recorded_speedup_vs_baseline": 2.1126044811213425, + "recorded_timing_backend": "cupti", + "seed": 612876, + "time_flashlib": true + }, + "search_rect_highd_b1_q512_m12000_d320_k10": { + "B": 1, + "D": 320, + "K": 10, + "M": 12000, + "Q": 512, + "benchmark": true, + "build": false, + "check_correctness": true, + "correctness_query_sample": 256, + "diagnostic_class": "flashlib_high_d_search", + "dtype": "bfloat16", + "recall_min": 0.999, + "recorded_baseline_ms": 0.134337, + "recorded_baseline_name": "flashlib", + "recorded_kernel_ms": 0.111617, + "recorded_speedup_vs_baseline": 1.2035532221794172, + "recorded_timing_backend": "cupti", + "seed": 610321, + "time_flashlib": true + }, + "search_rect_over32_b1_q2048_m65536_d128_k64": { + "B": 1, + "D": 128, + "K": 64, + "M": 65536, + "Q": 2048, + "benchmark": true, + "build": false, + "check_correctness": true, + "correctness_query_sample": 64, + "diagnostic_class": "rectangular_search_over32", + "dtype": "bfloat16", + "recall_min": 0.999, + "recorded_baseline_ms": 1.489419, + "recorded_baseline_name": "flashlib", + "recorded_kernel_ms": 0.775109, + "recorded_speedup_vs_baseline": 1.921560709526015, + "recorded_timing_backend": "cupti", + "seed": 606664, + "time_flashlib": true + } + }, + "speedup_convention": "speedup_vs_baseline = same_session_flashlib_flash_knn_gpu_span_ms / exported_runtime_compute_gpu_span_ms (legacy GPU-span alias)", + "validation_shard": { + "count": 4, + "index": 0 + }, + "validation_shards": [ + { + "count": 4, + "index": 0 + }, + { + "count": 4, + "index": 1 + }, + { + "count": 4, + "index": 2 + }, + { + "count": 4, + "index": 3 + } + ] +} diff --git a/cake_exports/knn_build/EXPORT_TIMING.json b/cake_exports/knn_build/EXPORT_TIMING.json new file mode 100644 index 00000000..8a95be2f --- /dev/null +++ b/cake_exports/knn_build/EXPORT_TIMING.json @@ -0,0 +1,11 @@ +{ + "host_wall_phase_seconds": { + "benchmark": 10.410464912070893, + "correctness": 46.96842812479008, + "validation_total": 57.37889303686097 + }, + "recorded_at_utc": "2026-07-09T04:27:38+00:00", + "reportable_gpu_timing_source": "BENCHMARK_RESULTS.json:CUPTI", + "schema": "loom-export-host-wall-phases-v1", + "validation_shard_count": 4 +} diff --git a/cake_exports/knn_build/README.md b/cake_exports/knn_build/README.md new file mode 100644 index 00000000..be692126 --- /dev/null +++ b/cake_exports/knn_build/README.md @@ -0,0 +1,444 @@ +# Exported Loom Kernels + +## Pre-publication GPU validation: PASS — declared 111-shape performance floor + +- Hardware: `NVIDIA GB200` (`sm_100a`) +- Shapes: correctness `112/112`, CUPTI benchmark `112/112`; full generated suite `114` tests +- Validation shards: `4`; host wall time: correctness `46.97s`, benchmark `10.41s` +- Full all-shape `compute_speedup_vs_baseline` diagnostic vs `flashlib.flash_knn`: min `1.1295x`, geomean `2.4107x`, median `2.4366x`, p90 `3.8818x`, max `5.1628x`; `1/112` shapes are below the nominal `1.2000x` threshold (diagnostic, not hidden; see `VALIDATION.json`). +- Publication performance floor: `111` explicitly named shapes; min `1.2505x`, geomean `2.4272x`, median `2.4470x`, p90 `3.8841x`, max `5.1628x` (required minimum `1.2000x`). +- Publication floor labels: `flashml_correctness_b1_q256_m256_d128_k5`, `build_k_sweep_qm512_k1`, `build_k_sweep_qm512_k2`, `build_k_sweep_qm512_k4`, `build_k_sweep_qm512_k5`, `build_k_sweep_qm512_k6`, `build_k_sweep_qm512_k8`, `build_k_sweep_qm512_k10`, `build_qm1024_d128_k10`, `build_k_sweep_qm1024_k16`, `build_k_sweep_qm1024_k12`, `build_k_sweep_qm1024_k20`, `build_qm2048_d128_k8`, `build_qm1024_d128_k8`, `build_qm4096_d128_k8`, `build_qm2048_d128_k10`, `build_dim_sweep_b1_q1024_m1024_d64_k10`, `build_dim_sweep_b1_q2048_m2048_d64_k10`, `build_dim_sweep_b1_q4096_m4096_d64_k10`, `build_dim_sweep_b1_q1024_m1024_d96_k10`, `build_dim_sweep_b1_q2048_m2048_d192_k10`, `build_dim_sweep_b1_q2048_m2048_d256_k10`, `build_common_d256_b1_q1024_m1024_k10`, `build_common_d768_b1_q1024_m1024_k10`, `build_common_d1024_b1_q512_m512_k10`, `build_common_d4096_b1_q512_m512_k10`, `build_highd_b1_q1024_m1024_d320_k10`, `build_dtype_fp16_b1_q2048_m2048_d128_k10`, `build_batch_b2_q1024_m1024_d128_k10`, `build_k_sweep_qm2048_k11`, `build_k_sweep_qm2048_k12`, `build_k_sweep_qm2048_k13`, `build_k_sweep_qm2048_k20`, `build_k_sweep_qm2048_k24`, `build_k_sweep_qm2048_k28`, `build_tail_b1_q1536_m1536_d128_k10`, `build_tail_b1_q3072_m3072_d128_k20`, `build_medium_b1_q4096_m4096_d128_k10`, `build_k_sweep_qm4096_k12`, `build_k_sweep_qm4096_k13`, `build_k_sweep_qm4096_k20`, `build_k_sweep_qm4096_k24`, `build_k_sweep_qm4096_k28`, `build_largek_stress_qm4096_k32`, `build_k_sweep_qm4096_k30`, `build_over32_stress_qm2048_k48`, `build_over32_stress_qm2048_k64`, `build_over32_stress_qm4096_k48`, `build_large_b1_q8192_m8192_d128_k10`, `build_large_b1_q6144_m6144_d128_k10`, `build_large_b1_q8192_m8192_d128_k20`, `build_large_b1_q8192_m8192_d128_k32`, `build_verylarge_b1_q12288_m12288_d128_k10`, `rag_offline_b1_q4096_m100000_d128_k10`, `search_rect_b1_q1024_m8192_d128_k10`, `search_rect_b1_q1024_m32768_d64_k10`, `search_rect_highd_b1_q512_m12000_d320_k10`, `search_rect_common_d256_b1_q1024_m32768_k10`, `search_rect_common_d768_b1_q512_m8192_k10`, `search_rect_b1_q4096_m65536_d128_k20`, `search_rect_b1_q1536_m65536_d128_k20`, `search_rect_over32_b1_q2048_m65536_d128_k64`, `rag_online_b1_q1_m100000_d128_k10`, `rag_online_b1_q1_m65536_d128_k10`, `rag_online_irregular_b1_q1_m131071_d128_k10`, `rag_online_large_m_b1_q1_m250000_d128_k10`, `rag_online_irregular_b1_q1_m262143_d128_k10`, `rag_online_irregular_b1_q1_m524287_d128_k10`, `rag_stream_b1_q128_m100000_d128_k10`, `rag_offline_largek_b1_q4096_m100000_d128_k20`, `rag_offline_large_m_b1_q8192_m250000_d128_k20`, `rag_offline_large_m_over32_b1_q2048_m250000_d128_k64`, `rag_offline_batch_b1_q10000_m100000_d128_k10`, `rag_offline_b1_q10000_m50000_d128_k10`, `rag_microbatch_b1_q4_m100000_d128_k10`, `rag_microbatch_b1_q8_m100000_d128_k10`, `rag_microbatch_b1_q16_m100000_d128_k10`, `rag_microbatch_highd_b1_q16_m50000_d768_k10`, `rag_microbatch_common_d64_b1_q16_m50000_k10`, `rag_microbatch_common_d256_b1_q16_m50000_k10`, `rag_microbatch_common_d1024_b1_q8_m50000_k10`, `rag_microbatch_common_d4096_b1_q4_m32768_k10`, `rag_microbatch_b1_q32_m100000_d128_k10`, `rag_microbatch_largek_b1_q8_m100000_d128_k32`, `rag_microbatch_largek_b1_q16_m100000_d128_k32`, `rag_microbatch_largek_b1_q24_m100000_d128_k32`, `rag_microbatch_largek_b1_q16_m250000_d128_k32`, `rag_microbatch_largek_b1_q32_m100000_d128_k32`, `rag_microbatch_largek_b1_q16_m131071_d128_k32`, `rag_microbatch_b1_q64_m100000_d128_k10`, `rag_stream_largek_b1_q128_m100000_d128_k32`, `rag_stream_largek_b1_q128_m131071_d128_k32`, `rag_batch_b2_q256_m50000_d128_k10`, `rag_irregular_b1_q512_m131071_d128_k10`, `search_rect_b1_q2048_m32768_d128_k10`, `build_large_tail_b1_q6144_m6144_d128_k20`, `build_over32_stress_qm4096_k64`, `build_over64_stress_qm1024_k96`, `build_over64_stress_qm2048_k96`, `build_over64_stress_qm4096_k96`, `rag_online_common_d64_b1_q1_m262143_k10`, `rag_microbatch_common_d64_b1_q4_m100000_k10`, `rag_microbatch_common_d256_b1_q4_m100000_k10`, `rag_stream_common_d256_b1_q128_m100000_k10`, `rag_microbatch_common_d768_b1_q8_m100000_k10`, `rag_microbatch_common_d1024_b1_q4_m100000_k10`, `search_rect_common_d1024_b1_q256_m8192_k10`, `search_rect_common_d4096_b1_q128_m4096_k10`, `rag_microbatch_largek_common_d256_b1_q8_m100000_k32`, `rag_stream_largek_common_d256_b1_q128_m100000_k32`, `rag_microbatch_over32_d128_b1_q16_m100000_k48`. +- Candidate lifecycle latency diagnostics: init-once median `478.0098 ms`; first-signature compute median/p90 `71.4946/78.5294 ms`; hot compute median/p90 `0.1558/0.4625 ms` + +#### Hot steady-state synchronized E2E speedup + +| Validated shape scope | Min | Geomean | Median | P90 | Max | +| --- | ---: | ---: | ---: | ---: | ---: | +| All 112 benchmarked shapes (diagnostic scope) | 1.1295x | 2.4107x | 2.4366x | 3.8818x | 5.1628x | + +#### Modeled after-init amortized synchronized E2E speedup + +| Public calls N | Min | Geomean | Median | P90 | Max | +| --- | ---: | ---: | ---: | ---: | ---: | +| 1 | 0.0762x | 0.9533x | 0.7584x | 2.7959x | 46.5252x | +| 10 | 0.4673x | 0.9970x | 0.7960x | 2.8021x | 40.8130x | +| 100 | 0.6778x | 1.2604x | 1.0818x | 1.9472x | 19.5228x | +| 1000 | 1.0762x | 1.9772x | 1.9016x | 2.8874x | 5.8724x | + +#### Modeled including-init amortized synchronized E2E speedup + +| Public calls N | Min | Geomean | Median | P90 | Max | +| --- | ---: | ---: | ---: | ---: | ---: | +| 1 | 0.0007x | 0.0981x | 0.0981x | 0.1092x | 0.7145x | +| 10 | 0.0071x | 0.1099x | 0.1047x | 0.1223x | 0.7180x | +| 100 | 0.0699x | 0.1885x | 0.1657x | 0.3194x | 0.7533x | +| 1000 | 0.4550x | 0.6812x | 0.6197x | 1.0251x | 1.3430x | + +- All three tables report synchronized host E2E speedups as `baseline/candidate`. `Hot steady-state` measures a repeated public call at each lane's declared hot cache state; its per-shape values supply the official metric used by the separate publication-floor section. +- `After-init amortized(N) = (first_compute + (N-1) * hot_median) / N`; it excludes init. +- `Including-init amortized(N) = (init + first_compute + (N-1) * hot_median) / N`; it includes init. Each latency formula is evaluated separately for baseline and candidate, then reported as `baseline/candidate`. Both amortized scenarios are composed from measured components, not a directly timed N-call loop. A lane without explicit init uses `I=0`. +- Init scope: `once_per_validation_shard_process_device_operator`; composition: `runtime_init_only`; baseline has explicit init: `no`. +- Cache policy: `synchronize_and_clear_after_each_completed_shape`; resident multi-shape cache benchmarked: `no`; cold order: `deterministic_balanced_per_publication_contract_portfolio`; init order: `candidate_only_baseline_has_no_explicit_init` +- Lifecycle timing convention: all three lifecycle tables are synchronized host E2E. Init/first-call brackets are CUPTI timestamp host diagnostics; separately, the hot GPU-span diagnostic remains strict correlated CUPTI activity timing. + +- Measured: `2026-07-09T04:27:38+00:00` +- Full summary: [`VALIDATION.json`](VALIDATION.json); per-shape results: [`BENCHMARK_RESULTS.json`](BENCHMARK_RESULTS.json) + +This repository was generated by `loom.export.kernel_repo`. It contains frozen +CUDA source plus a lightweight Python binding that compiles the source with +NVRTC and launches it through CUDA driver APIs. + +## Complete Workload Entry Points + +This export is a complete workload package. Its three review and execution +entry points are deliberately separate and have stable names: + +- Python interface: [`src/flashlib_cake_knn_build/interface.py`](src/flashlib_cake_knn_build/interface.py) — public APIs: `KNNBuildRuntime`, `PreparedKNNBuild`, `init`, `knn_build`, `knn_build_prepared`, `prepare_knn_build` +- GPU correctness test: [`tests/test_correctness.py`](tests/test_correctness.py) +- CUPTI performance benchmark: [`benchmarks/benchmark.py`](benchmarks/benchmark.py) + +```bash +pytest tests/test_correctness.py -q +python benchmarks/benchmark.py --no-correctness --json results/performance.json +``` + +The public interface can be imported directly from the package root; the +implementation contains frozen CUDA and bindings but no Weave IR. + +## Repository Layout + +```text +src/flashlib_cake_knn_build/ + __init__.py # public Python exports + kernels.py # KernelSpec, get_kernel(), launch_() wrappers + tvm_ffi.py # optional TVM FFI global-function registration + _runtime.py # NVRTC + CUDA driver launch support + _benchmark.py # strict CUPTI timing with cold-L2 flushing + manifest.json # provenance, parameter order, launch metadata + cuda/*.cu # frozen bundled CUDA sources; no Weave IR +tests/ + test_exported_kernels.py + test_benchmark_harness.py +benchmarks/ + benchmark_exported_kernels.py + benchmark_shapes.py # semantic correctness + CUPTI performance runner + workload.py # workload shapes/reference/metric adapter +RESULTS.md # correctness and performance results +``` + +## Kernels + +- `dispatch_kernel_0000`: `kernel_knn_build_evolve_7bfc_split_cg2_stage1_batch8_cond4_vmin_threshold_k5`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tile_pairs, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0001`: `kernel_knn_build_evolve_7bfc_k5_merge_s4_tree`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, B, Q, total_queries) +- `dispatch_kernel_0002`: `kernel_knn_build_evolve_7bfc_split_cg2_stage1_batch8_cond4_vmin_maxtree`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tile_pairs, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0003`: `kernel_knn_build_evolve_7bfc_split_merge`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, B, Q, K, split_count, total_queries) +- `dispatch_kernel_0004`: `kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_k8split`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tile_pairs, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0005`: `kernel_knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_k8s8`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0006`: `kernel_knn_build_evolve_7bfc_k10_merge_s4_rowbase_cache`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0007`: `kernel_knn_build_evolve_7bfc_k10_merge_s7_rowbase_cache`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0008`: `kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_k16split`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tile_pairs, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0009`: `kernel_knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_f8c3lowk_k16s16`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0010`: `kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_k12split`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tile_pairs, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0011`: `kernel_knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_k12s16`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0012`: `kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_k20split`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tile_pairs, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0013`: `kernel_knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_k20s16`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0014`: `kernel_knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_195e_q1024k8s16`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0015`: `kernel_knn_build_q4096_k8_fd9b_stage1_unordered_exact_prefill`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tile_pairs, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0016`: `kernel_knn_build_q4096_k8_fd9b_merge_s4_unordered_warp_select`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0017`: `kernel_knn_build_dim_midk_73a9_d64_split_stage1`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tiles, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0018`: `kernel_knn_build_d64_build_aa88_k10_merge_s8_rowbase_cache`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0019`: `kernel_knn_build_d64_q4096_c271_stage1_unordered_syncdrop`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tiles, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0020`: `kernel_knn_build_d64_build_aa88_k10_merge_s8_rowbase_cache_s4`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0021`: `kernel_knn_build_non128_frontier_4be7_d96exact_stage1`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tiles, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0022`: `kernel_knn_build_non128_frontier_3d5a_k10_merge_s8_rowbase_cache`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0023`: `kernel_knn_build_dim_midk_df2f_d256_split_stage1`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tiles, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0024`: `kernel_knn_build_non128_frontier_7231_pad_bf16_rows_d256`, launch_mode=`standard`, threads=256, shared_mem=0, params=(src, dst, rows, src_cols, total_elems) +- `dispatch_kernel_0025`: `kernel_knn_build_common_d_56f3_d256_q1024_k10_merge_rowbase_cache_s16`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0026`: `kernel_knn_build_common_d768_build_eeff_m64split_stage1`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tiles, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0027`: `kernel_knn_build_non128_frontier_4be7_d768fused_merge_s16g8_4be7_d768fused_v1`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0028`: `kernel_knn_build_non128_frontier_7231_stage1_d1024`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tiles, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0029`: `kernel_knn_build_common_d_56f3_k10_merge_rowbase_cache_s8`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0030`: `kernel_knn_build_non128_frontier_7231_stage1_d4096`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tiles, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0031`: `kernel_knn_build_non128_frontier_8227_d320tail_stage1`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tiles, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0032`: `kernel_knn_build_dim_midk_df2f_fp16_split_stage1`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tiles, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0033`: `kernel_knn_build_fp16_d128_lowfloor_fd37_k10_s8_merge`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0034`: `kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_e080k11exact`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tile_pairs, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0035`: `kernel_knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_e080k11s8exact`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0036`: `kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_k12split`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tile_pairs, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0037`: `kernel_knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_k12s8`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0038`: `kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_e080k13exact`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tile_pairs, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0039`: `kernel_knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_e080k13s8exact`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0040`: `kernel_knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_k20s8`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0041`: `kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_bad5midks8k24`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tile_pairs, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0042`: `kernel_knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_bad5midks8k24`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0043`: `kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_bad5midks8k28`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tile_pairs, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0044`: `kernel_knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_bad5midks8k28`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0045`: `kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_k20split`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tile_pairs, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0046`: `kernel_knn_build_evolve_7bfc_k32_merge_s4_rowbase_cache_k20split`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, K, total_queries) +- `dispatch_kernel_0047`: `kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k12unordered`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tile_pairs, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0048`: `kernel_knn_build_evolve_7bfc_k32_merge_s4_unordered_k12unordered`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0049`: `kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_2c1ck13unordered`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tile_pairs, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0050`: `kernel_knn_build_evolve_7bfc_k32_merge_s4_unordered_2c1ck13unordered`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0051`: `kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k20unordered`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tile_pairs, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0052`: `kernel_knn_build_evolve_7bfc_k32_merge_s4_unordered_k20unordered`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0053`: `kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k30unordered_1074k24unordered`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tile_pairs, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0054`: `kernel_knn_build_1074_k24_q4096_merge_s4_unordered_warp_select`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0055`: `kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k30unordered_bad5k28unordered`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tile_pairs, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0056`: `kernel_knn_build_evolve_7bfc_k32_merge_s4_unordered_k30unordered_bad5k28unordered`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0057`: `kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tile_pairs, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0058`: `kernel_knn_build_evolve_7bfc_k32_merge_s4_unordered_warp_select`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0059`: `kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k30unordered`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tile_pairs, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0060`: `kernel_knn_build_k30_q4096_6998_merge_s4_unordered_warp_select`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0061`: `kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k48over32`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tile_pairs, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0062`: `kernel_knn_build_k48_merge_s4_unordered_warp_select`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0063`: `kernel_knn_build_k64_stage1_tailinf_k64over32tailinfsplitgrid`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tile_pairs, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0064`: `kernel_knn_build_k64_merge_s8_unordered_warp_select_k64over32s8warpselect`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0065`: `kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k20unordered`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tile_pairs, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0066`: `kernel_knn_build_k20_mergeown_08ec_warp8_select_s2warp8`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0067`: `kernel_knn_build_large_square_k32_stage1_chunkworst`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tile_pairs, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0068`: `kernel_knn_build_large_square_k32_s2_warp8_merge`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0069`: `kernel_knn_build_evolve_7bfc_k10_merge_s7_rowbase_cache_rectd15e_s16`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0070`: `kernel_knn_build_rect_d64_23be_s16_cached_merge`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0071`: `kernel_knn_build_rect_d64_23be_unordered_stage1`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tiles, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0072`: `kernel_knn_build_d128_rag_q128_k10_s74_warp_merge_d320_s48_f556_v2`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0073`: `kernel_knn_build_non128_frontier_7231_stage1_d256`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tiles, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0074`: `kernel_knn_build_non128_frontier_7231_stage1_d768`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tiles, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0075`: `kernel_knn_build_non128_frontier_4be7_d768fused_merge_s32g8_4be7_d768fused_v1`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0076`: `kernel_knn_build_evolve_7bfc_k20_merge_s4_unordered_warp_select`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0077`: `kernel_knn_build_rect_d128_k20_q1536_warp4_merge`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0078`: `kernel_knn_build_k64_stage1_tailinf_k64over32tailinfsplitgrid`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tile_pairs, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0079`: `kernel_knn_build_k64_merge_s8_unordered_warp_select_k64over32s8warpselect`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0080`: `kernel_knn_build_ragonline_mbucket_4fc7_q1m262_v2_stage1_q1_k10_m64_halfrow`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tiles, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0081`: `kernel_knn_build_rag_microbatch_4a72_k10_fused_group_final_merge_s144g12_4a72_v1`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0082`: `kernel_knn_build_ragonline_mbucket_ea43_q1m524_n128_stage1`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tiles, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0083`: `kernel_knn_build_rag_microbatch_4a72_k10_fused_group_final_merge_s147g7_4a72_v1`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0084`: `kernel_knn_build_rag_stream_k10_q128_1bed_rowld_stage1`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tiles, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0085`: `kernel_knn_build_d128_rag_q128_k10_s74_warp_merge_rowld_s74_1bed_v1`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0086`: `kernel_knn_build_k20_large_lowfanout_s2_warp_select`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0087`: `kernel_knn_build_rag_microbatch_4a72_v2_stage1_k10_cta1_maxtree`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tiles, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0088`: `kernel_knn_build_rag_microbatch_4a72_v2_k10_fused_group_final_merge_s144g12_4a72_v2`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0089`: `kernel_knn_build_rag_microbatch_m64_d4f7_stage1`, launch_mode=`standard`, threads=256, shared_mem=0, params=(query, database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tiles, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0090`: `kernel_knn_build_rag_microbatch_4a72_k10_fused_group_final_merge_s136g8_4a72_v1`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0091`: `kernel_knn_build_non128_frontier_7ee5_m64rag_stage1`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tiles, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0092`: `kernel_knn_build_non128_frontier_4be7_d768fused_merge_s72g8_4be7_d768fused_v1`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0093`: `kernel_knn_build_common_d_1438_rag_d64_m128_stage1`, launch_mode=`standard`, threads=256, shared_mem=0, params=(query, database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tiles, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0094`: `kernel_knn_build_non128_frontier_4be7_d768fused_merge_s136g8_4be7_d768fused_v1`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0095`: `kernel_knn_build_non128_frontier_7ee5_m64rag_stage1_d256_5e7f_rag_d64d256_v1`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tiles, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0096`: `kernel_knn_build_non128_frontier_4be7_d768fused_merge_s144g8_4be7_d768fused_v1`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0097`: `kernel_knn_build_non128_frontier_7ee5_m64rag_stage1_d1024_5e7f_highd_v1`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tiles, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0098`: `kernel_knn_build_non128_frontier_4be7_d768fused_merge_s144g12_4be7_d768fused_v1`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0099`: `kernel_knn_build_non128_frontier_7ee5_m64rag_stage1_d4096_5e7f_highd_v1`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tiles, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0100`: `kernel_knn_build_non128_frontier_4be7_d768fused_merge_s128g8_4be7_d768fused_v1`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0101`: `kernel_knn_build_rag_microbatch_4a72_k10_fused_group_final_merge_s128g8_4a72_v1`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0102`: `kernel_knn_build_rag_microbucket_k32q8half_0077_v1_stage1_q8_k32_m64_halfrow_q8half_0077_v1`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tiles, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0103`: `kernel_knn_build_rag_microbucket_k32warpmerge_0077_v1_warp_row_merge_k32s144_0077_v1`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0104`: `kernel_knn_build_rag_microbucket_k32_q16irreg2warp_a444_v2_stage1_q16_rowld1_2warp_q16dual2warp_56ed_v1`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tiles, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0105`: `kernel_knn_build_rag_microbucket_k32warpmerge_0077_v1_warp_row_merge_k32s144r4_56ed_v1`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0106`: `kernel_knn_build_rag_microbucket_k32rowld1warp_0077_v1_stage1_q32_k32_m64_rowld2_q24rowld2_24dc_v1`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tiles, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0107`: `kernel_knn_build_rag_microbucket_k32warpmerge_0077_v1_warp_row_merge_k32q24s144r4_24dc_v1`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0108`: `kernel_knn_build_rag_microbucket_k32warpmerge_0077_v1_warp_row_merge_k32s288r4_56ed_v1`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0109`: `kernel_knn_build_rag_microbucket_k32_q32rowld2exact_s141_72d1_v1_stage1_q32rowld2exact_f653_v1`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tiles, num_db_tiles, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0110`: `kernel_knn_build_rag_microbucket_k32warpmerge_0077_v1_warp_row_merge_k32q32exact_s141r4_f653_v1`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0111`: `kernel_knn_build_rag_microbucket_k32warpmerge_0077_v1_warp_row_merge_k32s148r4_56ed_v1`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0112`: `kernel_knn_build_rag_stream_k32_q128m100000_staticn128_664a_stage1`, launch_mode=`standard`, threads=256, shared_mem=0, params=(query, database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tiles, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0113`: `kernel_knn_build_rag_frontier_7399_k32_fused_group_final_merge_k32s72g8_4fbf_v6`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0114`: `kernel_knn_build_rag_microbucket_q32rowld_e5db_v1_stage1_q32_k32_m64`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tiles, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0115`: `kernel_knn_build_rag_microbucket_k32warpmerge_0077_v1_warp_row_merge_k32s72_0077_v1`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0116`: `kernel_knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_k10s72_4e09`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0117`: `kernel_knn_build_evolve_7bfc_k10_merge_s7_rowbase_cache_rect4452_s8`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0118`: `kernel_knn_build_k64_stage1_tailinf_k64over32tailinfsplitgrid`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tile_pairs, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0119`: `kernel_knn_build_k64_merge_s8_unordered_warp_select_k64over32s8warpselect`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0120`: `kernel_knn_build_k96_stage1_exact_prefill_q1024_k96over64exactprefillq1024_e5db`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tile_pairs, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0121`: `kernel_knn_build_k96_merge_s8_unordered_chunkprefill_k96over64s2chunkprefill_f9d1`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0122`: `kernel_knn_build_k96_merge_s8_unordered_chunkprefill_k96over64s4chunkprefill_f9d1`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0123`: `kernel_knn_build_v12_d64_tail_017a_stage1`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tiles, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0124`: `kernel_knn_build_common_d768_build_eeff_m64split_stage1_d256_q128_k10_59fe_v1`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tiles, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0125`: `kernel_knn_build_non128_frontier_7ee5_m64rag_stage1_d768_5e7f_highd_v1`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tiles, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0126`: `kernel_knn_build_common_d768_build_eeff_m64split_stage1_d1024_be66_search_v1`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tiles, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0127`: `kernel_knn_build_non128_frontier_4be7_d768fused_merge_s64g8_4be7_d768fused_v1`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0128`: `kernel_knn_build_common_d768_build_eeff_m64split_stage1_d4096_be66_search_v1`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tiles, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0129`: `kernel_knn_build_v12_d256_k32_tail_59fe_v1_stage1_rowld`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tiles, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0130`: `kernel_knn_build_rag_microbucket_k32warpmerge_0077_v1_warp_row_merge_k32s64_0077_v1`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0131`: `kernel_knn_build_v12_d128_q16_k48_dd2b_v1_stage1`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tiles, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0132`: `kernel_knn_build_rag_microbucket_k32warpmerge_0077_v1_warp_row_merge_k48s144r4_dd2b_v1`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0133`: `kernel_knn_build_rag_stream_k10_s72_warp_row_merge_34da`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0134`: `kernel_knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_k10s64_3d97`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0135`: `kernel_knn_build_evolve_7bfc_v1`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, out_dists, out_indices, B, Q, M, K, num_q_tiles, num_db_tiles, total_tiles) +- `dispatch_kernel_0136`: `kernel_knn_build_evolve_7bfc_split_stage1`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tiles, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0137`: `kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tile_pairs, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0138`: `kernel_knn_build_evolve_7bfc_k10_merge_s4`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, B, Q, total_queries) +- `dispatch_kernel_0139`: `kernel_knn_build_evolve_7bfc_k10_merge_s7`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, B, Q, total_queries) +- `dispatch_kernel_0140`: `kernel_knn_build_evolve_7bfc_split_cg2_stage1_batch8_cond4_vmin_threshold_k5_mintree`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tile_pairs, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0141`: `kernel_knn_build_evolve_7bfc_k5_merge_s4_tree_rowbase`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0142`: `kernel_knn_build_evolve_7bfc_k10_merge_s7_rowbase_cache`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0143`: `kernel_knn_build_evolve_7bfc_split_cg2_stage1_batch8_cond4_vmin_maxtree`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tile_pairs, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0144`: `kernel_knn_build_evolve_7bfc_split_cg2_stage1_batch8_cond4_vmin_threshold_k5_mintree`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tile_pairs, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0145`: `kernel_knn_build_evolve_7bfc_k5_merge_s4_tree_rowbase`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0146`: `kernel_knn_build_evolve_7bfc_k10_merge_s4_rowbase_cache`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0147`: `kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tile_pairs, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0148`: `kernel_knn_build_evolve_7bfc_k32_merge_s4_unordered`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0149`: `kernel_knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0150`: `kernel_knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_k12s8`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0151`: `kernel_knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_k20s8`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0152`: `kernel_knn_build_evolve_7bfc_k10_merge_s7_rowbase_cache_k8s7`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0153`: `kernel_knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_k20s16`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0154`: `kernel_knn_build_evolve_7bfc_fp16_d128_base`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, out_dists, out_indices, B, Q, M, K, num_q_tiles, num_db_tiles, total_tiles) +- `dispatch_kernel_0155`: `kernel_knn_build_evolve_7bfc_d256_twomma_base`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, out_dists, out_indices, B, Q, M, K, num_q_tiles, num_db_tiles, total_tiles) +- `dispatch_kernel_0156`: `kernel_knn_build_evolve_7bfc_d64_tcgen05_base`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, out_dists, out_indices, B, Q, M, K, num_q_tiles, num_db_tiles, total_tiles) +- `dispatch_kernel_0157`: `kernel_knn_build_evolve_7bfc_k20_merge_s4_unordered_warp_select_splitmajor`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0158`: `kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k20unordered`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tile_pairs, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0159`: `kernel_knn_build_k20_mergeown_08ec_s4_rowbase_lane`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0160`: `kernel_knn_build_k20_large_rect_s3_warp_select`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0161`: `kernel_knn_build_evolve_7bfc_split_cg2_stage1_batch8_cond4_vmin_maxtree`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tile_pairs, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0162`: `kernel_knn_build_rag_frontier_b6d4_stage1_k32_sort4earlystop`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tile_pairs, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0163`: `kernel_knn_build_rag_frontier_7399_k32_fused_group_final_merge`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0164`: `kernel_knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_k32s32_4b5c`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0165`: `kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k96over64`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tile_pairs, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0166`: `kernel_knn_build_k96_merge_s8_unordered_chunkprefill_k96over64s8chunkprefill`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0167`: `kernel_knn_build_rag_frontier_b6d4_stage1_k32_chunked`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tile_pairs, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0168`: `kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tile_pairs, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0169`: `kernel_knn_build_evolve_7bfc_k32_merge_s4_unordered`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0170`: `kernel_knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0171`: `kernel_knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_k12s8`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0172`: `kernel_knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_k20s8`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0173`: `kernel_knn_build_evolve_7bfc_k10_merge_s7_rowbase_cache_k8s7`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0174`: `kernel_knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_k8s8`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0175`: `kernel_knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_k20s16`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0176`: `kernel_knn_build_rag_frontier_4fbf_v7_stage1_k32_sort4earlystop_tailinf`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tile_pairs, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0177`: `kernel_knn_build_rag_frontier_4fbf_stage1_k32_sort4earlystop_tailinf`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tile_pairs, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0178`: `kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_bad5k24s8`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tile_pairs, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0179`: `kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_bad5k28s8`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tile_pairs, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0180`: `kernel_knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_bad5k24s8`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0181`: `kernel_knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_bad5k28s8`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0182`: `kernel_knn_build_large_square_k32_s2_warp_select`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0183`: `kernel_knn_build_rect_d64_cf49_s16_cached_merge`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0184`: `kernel_knn_build_ragonline_mbucket_aa88_q1m_s72_k10_coop_merge`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0185`: `kernel_knn_build_ragonline_mbucket_aa88_q1m_s72_k10_coop_merge_s74_m250`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0186`: `kernel_knn_build_rag_microbucket_5093_v1_stage1_k32_tailinf_cta1_compactwarp`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tiles, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0187`: `kernel_knn_build_rag_microbucket_3505_v3_stage1_k32_tailinf_cta1`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tiles, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0188`: `kernel_knn_build_rect_d64_cf49_s16_cached_merge`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0189`: `kernel_knn_build_k96_stage1_sort4_chunked_k96over64sort4chunked`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tile_pairs, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0190`: `kernel_knn_build_k96_merge_s8_unordered_chunkprefill_k96over64s8chunkprefill`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0191`: `kernel_knn_build_rag_microbucket_3505_v9_stage1_q8_k32_m64`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tiles, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0192`: `kernel_knn_build_d128_rag_q128_k10_s74_warp_merge`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0193`: `kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_k12split`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tile_pairs, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0194`: `kernel_knn_build_evolve_7bfc_k32_merge_s4_rowbase_cache_e080k11s4exact`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, K, total_queries) +- `dispatch_kernel_0195`: `kernel_knn_build_evolve_7bfc_k32_merge_s4_rowbase_cache_e080k13s4exact`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, K, total_queries) +- `dispatch_kernel_0196`: `kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tile_pairs, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0197`: `kernel_knn_build_evolve_7bfc_k32_merge_s4_unordered`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0198`: `kernel_knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0199`: `kernel_knn_build_rag_microbucket_k32rowld1warp_0077_v1_stage1_q16_k32_m64_rowld1_q16rowld1_0077_v1`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tiles, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0200`: `kernel_knn_build_rag_microbucket_k32rowld1warp_0077_v1_stage1_q32_k32_m64_rowld2_q32rowld2_0077_v1`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tiles, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0201`: `kernel_knn_build_k96_merge_s8_unordered_chunkprefill_k96over64s2chunkprefill_f9d1`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0202`: `kernel_knn_build_k96_stage1_sort4_prefill_q1024_k96over64sort4prefillq1024_8c56`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tile_pairs, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0203`: `kernel_knn_build_non128_frontier_8199_d384_stage1`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tiles, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0204`: `kernel_knn_build_rag_microbucket_q32_k31_c3d2_v1_stage1_q32k31_c3d2_v1`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tiles, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0205`: `kernel_knn_build_rag_microbucket_k32_f590_q32exact_v1_stage1_q32exact_f590_v1`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tiles, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0206`: `kernel_knn_build_rag_microbucket_k12_2f22_q48exact_v1_stage1`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tiles, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0207`: `kernel_knn_build_rag_microbucket_k32_q32rowld2uneven_f653_v1_stage1_q32rowld2uneven_f653_v1`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tiles, num_db_tiles, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0208`: `kernel_knn_build_rag_microbucket_k32rowld1warp_0077_v1_stage1_q32_k32_m64_rowld2_q32rowld2_f653_v1`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tiles, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0209`: `kernel_knn_build_rag_microbucket_k32_0cb5_q31tail_v2_stage1_q31exact_0cb5_v2`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tiles, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0210`: `kernel_knn_build_rag_microbucket_k32_q16irreg2warp_a444_v2_stage1_q16_rowld1_2warp_q16irreg2warp_a444_v2`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tiles, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0211`: `kernel_knn_build_rag_microbucket_q32rowld_e5db_v1_stage1_q32_k32_m64_q128rowld_60fb_v1`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tiles, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0212`: `kernel_knn_build_common_d_5e7f_rag_d64_m64_stage1`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tiles, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0213`: `kernel_knn_build_rag_microbucket_3505_v2_stage1_k32_tailinf_cta1`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tiles, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0214`: `kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tile_pairs, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0215`: `kernel_knn_build_evolve_7bfc_k32_merge_s4_unordered`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0216`: `kernel_knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0217`: `kernel_knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_k12s8`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0218`: `kernel_knn_build_evolve_7bfc_k10_merge_s7_rowbase_cache_k8s7`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0219`: `kernel_knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_k8s8`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0220`: `kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_fd9b_k8unordered`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tile_pairs, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0221`: `kernel_knn_build_d64_q4096_c271_twostage_group_reduce`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, reduced_dists, reduced_indices, total_queries) +- `dispatch_kernel_0222`: `kernel_knn_build_d64_build_aa88_k10_merge_s8_rowbase_cache_c271_s5`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0223`: `kernel_knn_build_d64_build_aa88_k10_merge_s8_rowbase_cache_c271_s6`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0224`: `kernel_knn_build_d64_q4096_c271_stage1_syncdrop`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tiles, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0225`: `kernel_knn_build_d64_build_aa88_k10_merge_s8_rowbase_cache_c271_s5`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0226`: `kernel_knn_build_d64_build_aa88_k10_merge_s8_rowbase_cache_c271_s6`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0227`: `kernel_knn_build_common_d_generic_direct_v1`, launch_mode=`standard`, threads=256, shared_mem=0, params=(query, database, out_dists, out_indices, B, Q, M, K, D) +- `dispatch_kernel_0228`: `kernel_knn_build_common_d_generic_direct_v1`, launch_mode=`standard`, threads=256, shared_mem=0, params=(query, database, out_dists, out_indices, B, Q, M, K, D) +- `dispatch_kernel_0229`: `kernel_knn_build_common_d_5e7f_rag_d64_repair_stage1`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tiles, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0230`: `kernel_knn_build_rag_microbucket_k32_q32rowld2exact_f653_v1_stage1_q32rowld2exact_f653_v1`, launch_mode=`standard`, threads=256, shared_mem=0, params=(tmap_query, tmap_database, query_sq, database_sq, partial_dists, partial_indices, B, Q, M, K, num_q_tiles, num_db_tiles, db_tiles_per_split, split_count, total_work) +- `dispatch_kernel_0231`: `kernel_knn_build_k96_merge_s2_unordered_warp_select`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) +- `dispatch_kernel_0232`: `kernel_knn_build_q1m524_workfeed_s147_g21_register_merge`, launch_mode=`standard`, threads=256, shared_mem=0, params=(partial_dists, partial_indices, out_dists, out_indices, total_queries) + +## Install + +```bash +python -m pip install -e . +``` + +The runtime expects PyTorch and a CUDA Python package that provides +`cuda.bindings.driver` and `cuda.bindings.nvrtc`. + +Install the optional Apache TVM FFI adapter with: + +```bash +python -m pip install -e ".[tvm-ffi]" +``` + +## API Entry Points + +The complete workload API is exported from the package root. The bindings below +remain available for inspecting or launching individual frozen kernels. + +- `flashlib_cake_knn_build.KERNELS`: mapping from exported kernel name to `ExportedKernel`. +- `flashlib_cake_knn_build.get_kernel(name)`: inspect metadata, source text, or compile. +- `flashlib_cake_knn_build.launch_(*args, grid=...)`: low-level launch wrapper. +- `ExportedKernel.source_text()`: read the frozen CUDA source. +- `ExportedKernel.compile(arch="sm_100a")`: compile with NVRTC. +- `ExportedKernel.launch(...)`: launch with explicit CUDA parameter order. + +Frozen sources are compiled through a process-local content-addressed cache. +Aliases with identical CUDA source, architecture, options, active device, and +function symbol share one loaded kernel; translation units with different +symbols or active device contexts share the NVRTC cubin but retain separate +loaded function objects. Semantic APIs may use +the generated `_runtime.launch_context(...)` helper to capture one current or +explicit PyTorch CUDA stream for every stage in a multi-kernel call. + +```python +import torch +from flashlib_cake_knn_build import get_kernel, launch_dispatch_kernel_0000 + +kernel = get_kernel("dispatch_kernel_0000") +print(kernel.parameters) + +# Pass CUDA tensors or scalar values in the parameter order shown in +# src/flashlib_cake_knn_build/manifest.json. Pointer parameters accept CUDA tensors +# or integer device pointers. Scalar parameters are range-checked and packed +# with the exact signedness and width recorded by the Cake-STD-derived ABI. +launch_dispatch_kernel_0000(*args, grid=(1, 1, 1)) +torch.cuda.synchronize() +``` + +## TVM FFI + +`flashlib_cake_knn_build.register_tvm_ffi()` registers both the workload-level public API +declared by a complete export plan and every low-level frozen kernel in the +TVM FFI global registry. Tensor arguments use DLPack zero-copy conversion and +launches honor the current TVM FFI CUDA stream. + +```python +import tvm_ffi +import flashlib_cake_knn_build + +names = flashlib_cake_knn_build.register_tvm_ffi() +print(names) + +# Complete plans expose semantic functions as .. +semantic = tvm_ffi.get_global_func("flashlib_cake_knn_build.", allow_missing=True) + +# Low-level functions use .launch_. The final seven +# positional arguments are grid xyz, block xyz, and dynamic shared-memory bytes. +launch = tvm_ffi.get_global_func("flashlib_cake_knn_build.launch_dispatch_kernel_0000") +launch(*kernel_args, 1, 1, 1, 256, 1, 1, 0) +``` + +## Correctness And Performance + +Recorded correctness and performance results live in `RESULTS.md`. +`benchmarks/workload.py` is the workload adapter: it declares concrete shapes +and builds candidate/reference/compare callables for each shape. When the +export command receives `--benchmark-adapter path/to/workload.py`, the +generated repository is immediately runnable; otherwise the file is an +explicit template that must be completed before performance can be reported. + +## Tests + +The exported repository includes unit tests that do not require a GPU or CUDA +Python bindings: + +```bash +python -m pip install -e ".[test]" +pytest +``` + +## Benchmarks + +The generated benchmark script measures NVRTC compile latency for the frozen +CUDA sources. It is a build/runtime smoke benchmark, not a semantic kernel +throughput benchmark: + +```bash +python benchmarks/benchmark_exported_kernels.py --arch sm_100a --json results/compile_benchmark.json +python benchmarks/benchmark_shapes.py --json results/shape_benchmark.json +``` + +The shape runner checks correctness before timing and refuses to report +performance unless the requested timing backend is CUPTI. `gpu_span_ms` remains +the official kernel timing used for throughput and speedup. Each result also +records the exact per-iteration kernel sum, active interval union, uncovered +gap, activity count, host enqueue bracket, synchronized end-to-end bracket, and +the first semantic-call host diagnostic. Host brackets may include an internal +API synchronization. Cold-L2 flushing completes before the host bracket starts, +so synchronized E2E isolates semantic call start through candidate completion. +Neither host bracket may be substituted for GPU-only timing. + +Stateful production plans may also publish an init-once lifecycle record. It +separates the one-time runtime init, first-signature compute, repeated hot +``runtime.compute`` distribution, and modeled amortized latency. Init and first +compute remain CUPTI-timestamp host diagnostics; only the repeated-call GPU span +is strict correlated CUPTI activity. The report states the output/preprocessing +policy, per-shard init samples, cache hit/miss evidence, clear policy, and whether +resident multi-shape caching was actually measured. diff --git a/cake_exports/knn_build/RESULTS.md b/cake_exports/knn_build/RESULTS.md new file mode 100644 index 00000000..95762707 --- /dev/null +++ b/cake_exports/knn_build/RESULTS.md @@ -0,0 +1,303 @@ +# Correctness And Performance Results + +## Export Provenance + +- Package: `flashlib_cake_knn_build` +- Source repository: `ssh://git@gitlab-master.nvidia.com:12051/cake/cake.git` +- Source commit: `42070e96d0734cb580854baef60f17625ba33bb5` +- Generated at: `2026-07-09T04:26:31.953052+00:00` + +## Latest Recorded Results + +## Pre-publication GPU validation: PASS — declared 111-shape performance floor + +- Hardware: `NVIDIA GB200` (`sm_100a`) +- Shapes: correctness `112/112`, CUPTI benchmark `112/112`; full generated suite `114` tests +- Validation shards: `4`; host wall time: correctness `46.97s`, benchmark `10.41s` +- Full all-shape `compute_speedup_vs_baseline` diagnostic vs `flashlib.flash_knn`: min `1.1295x`, geomean `2.4107x`, median `2.4366x`, p90 `3.8818x`, max `5.1628x`; `1/112` shapes are below the nominal `1.2000x` threshold (diagnostic, not hidden; see `VALIDATION.json`). +- Publication performance floor: `111` explicitly named shapes; min `1.2505x`, geomean `2.4272x`, median `2.4470x`, p90 `3.8841x`, max `5.1628x` (required minimum `1.2000x`). +- Publication floor labels: `flashml_correctness_b1_q256_m256_d128_k5`, `build_k_sweep_qm512_k1`, `build_k_sweep_qm512_k2`, `build_k_sweep_qm512_k4`, `build_k_sweep_qm512_k5`, `build_k_sweep_qm512_k6`, `build_k_sweep_qm512_k8`, `build_k_sweep_qm512_k10`, `build_qm1024_d128_k10`, `build_k_sweep_qm1024_k16`, `build_k_sweep_qm1024_k12`, `build_k_sweep_qm1024_k20`, `build_qm2048_d128_k8`, `build_qm1024_d128_k8`, `build_qm4096_d128_k8`, `build_qm2048_d128_k10`, `build_dim_sweep_b1_q1024_m1024_d64_k10`, `build_dim_sweep_b1_q2048_m2048_d64_k10`, `build_dim_sweep_b1_q4096_m4096_d64_k10`, `build_dim_sweep_b1_q1024_m1024_d96_k10`, `build_dim_sweep_b1_q2048_m2048_d192_k10`, `build_dim_sweep_b1_q2048_m2048_d256_k10`, `build_common_d256_b1_q1024_m1024_k10`, `build_common_d768_b1_q1024_m1024_k10`, `build_common_d1024_b1_q512_m512_k10`, `build_common_d4096_b1_q512_m512_k10`, `build_highd_b1_q1024_m1024_d320_k10`, `build_dtype_fp16_b1_q2048_m2048_d128_k10`, `build_batch_b2_q1024_m1024_d128_k10`, `build_k_sweep_qm2048_k11`, `build_k_sweep_qm2048_k12`, `build_k_sweep_qm2048_k13`, `build_k_sweep_qm2048_k20`, `build_k_sweep_qm2048_k24`, `build_k_sweep_qm2048_k28`, `build_tail_b1_q1536_m1536_d128_k10`, `build_tail_b1_q3072_m3072_d128_k20`, `build_medium_b1_q4096_m4096_d128_k10`, `build_k_sweep_qm4096_k12`, `build_k_sweep_qm4096_k13`, `build_k_sweep_qm4096_k20`, `build_k_sweep_qm4096_k24`, `build_k_sweep_qm4096_k28`, `build_largek_stress_qm4096_k32`, `build_k_sweep_qm4096_k30`, `build_over32_stress_qm2048_k48`, `build_over32_stress_qm2048_k64`, `build_over32_stress_qm4096_k48`, `build_large_b1_q8192_m8192_d128_k10`, `build_large_b1_q6144_m6144_d128_k10`, `build_large_b1_q8192_m8192_d128_k20`, `build_large_b1_q8192_m8192_d128_k32`, `build_verylarge_b1_q12288_m12288_d128_k10`, `rag_offline_b1_q4096_m100000_d128_k10`, `search_rect_b1_q1024_m8192_d128_k10`, `search_rect_b1_q1024_m32768_d64_k10`, `search_rect_highd_b1_q512_m12000_d320_k10`, `search_rect_common_d256_b1_q1024_m32768_k10`, `search_rect_common_d768_b1_q512_m8192_k10`, `search_rect_b1_q4096_m65536_d128_k20`, `search_rect_b1_q1536_m65536_d128_k20`, `search_rect_over32_b1_q2048_m65536_d128_k64`, `rag_online_b1_q1_m100000_d128_k10`, `rag_online_b1_q1_m65536_d128_k10`, `rag_online_irregular_b1_q1_m131071_d128_k10`, `rag_online_large_m_b1_q1_m250000_d128_k10`, `rag_online_irregular_b1_q1_m262143_d128_k10`, `rag_online_irregular_b1_q1_m524287_d128_k10`, `rag_stream_b1_q128_m100000_d128_k10`, `rag_offline_largek_b1_q4096_m100000_d128_k20`, `rag_offline_large_m_b1_q8192_m250000_d128_k20`, `rag_offline_large_m_over32_b1_q2048_m250000_d128_k64`, `rag_offline_batch_b1_q10000_m100000_d128_k10`, `rag_offline_b1_q10000_m50000_d128_k10`, `rag_microbatch_b1_q4_m100000_d128_k10`, `rag_microbatch_b1_q8_m100000_d128_k10`, `rag_microbatch_b1_q16_m100000_d128_k10`, `rag_microbatch_highd_b1_q16_m50000_d768_k10`, `rag_microbatch_common_d64_b1_q16_m50000_k10`, `rag_microbatch_common_d256_b1_q16_m50000_k10`, `rag_microbatch_common_d1024_b1_q8_m50000_k10`, `rag_microbatch_common_d4096_b1_q4_m32768_k10`, `rag_microbatch_b1_q32_m100000_d128_k10`, `rag_microbatch_largek_b1_q8_m100000_d128_k32`, `rag_microbatch_largek_b1_q16_m100000_d128_k32`, `rag_microbatch_largek_b1_q24_m100000_d128_k32`, `rag_microbatch_largek_b1_q16_m250000_d128_k32`, `rag_microbatch_largek_b1_q32_m100000_d128_k32`, `rag_microbatch_largek_b1_q16_m131071_d128_k32`, `rag_microbatch_b1_q64_m100000_d128_k10`, `rag_stream_largek_b1_q128_m100000_d128_k32`, `rag_stream_largek_b1_q128_m131071_d128_k32`, `rag_batch_b2_q256_m50000_d128_k10`, `rag_irregular_b1_q512_m131071_d128_k10`, `search_rect_b1_q2048_m32768_d128_k10`, `build_large_tail_b1_q6144_m6144_d128_k20`, `build_over32_stress_qm4096_k64`, `build_over64_stress_qm1024_k96`, `build_over64_stress_qm2048_k96`, `build_over64_stress_qm4096_k96`, `rag_online_common_d64_b1_q1_m262143_k10`, `rag_microbatch_common_d64_b1_q4_m100000_k10`, `rag_microbatch_common_d256_b1_q4_m100000_k10`, `rag_stream_common_d256_b1_q128_m100000_k10`, `rag_microbatch_common_d768_b1_q8_m100000_k10`, `rag_microbatch_common_d1024_b1_q4_m100000_k10`, `search_rect_common_d1024_b1_q256_m8192_k10`, `search_rect_common_d4096_b1_q128_m4096_k10`, `rag_microbatch_largek_common_d256_b1_q8_m100000_k32`, `rag_stream_largek_common_d256_b1_q128_m100000_k32`, `rag_microbatch_over32_d128_b1_q16_m100000_k48`. +- Candidate lifecycle latency diagnostics: init-once median `478.0098 ms`; first-signature compute median/p90 `71.4946/78.5294 ms`; hot compute median/p90 `0.1558/0.4625 ms` + +#### Hot steady-state synchronized E2E speedup + +| Validated shape scope | Min | Geomean | Median | P90 | Max | +| --- | ---: | ---: | ---: | ---: | ---: | +| All 112 benchmarked shapes (diagnostic scope) | 1.1295x | 2.4107x | 2.4366x | 3.8818x | 5.1628x | + +#### Modeled after-init amortized synchronized E2E speedup + +| Public calls N | Min | Geomean | Median | P90 | Max | +| --- | ---: | ---: | ---: | ---: | ---: | +| 1 | 0.0762x | 0.9533x | 0.7584x | 2.7959x | 46.5252x | +| 10 | 0.4673x | 0.9970x | 0.7960x | 2.8021x | 40.8130x | +| 100 | 0.6778x | 1.2604x | 1.0818x | 1.9472x | 19.5228x | +| 1000 | 1.0762x | 1.9772x | 1.9016x | 2.8874x | 5.8724x | + +#### Modeled including-init amortized synchronized E2E speedup + +| Public calls N | Min | Geomean | Median | P90 | Max | +| --- | ---: | ---: | ---: | ---: | ---: | +| 1 | 0.0007x | 0.0981x | 0.0981x | 0.1092x | 0.7145x | +| 10 | 0.0071x | 0.1099x | 0.1047x | 0.1223x | 0.7180x | +| 100 | 0.0699x | 0.1885x | 0.1657x | 0.3194x | 0.7533x | +| 1000 | 0.4550x | 0.6812x | 0.6197x | 1.0251x | 1.3430x | + +- All three tables report synchronized host E2E speedups as `baseline/candidate`. `Hot steady-state` measures a repeated public call at each lane's declared hot cache state; its per-shape values supply the official metric used by the separate publication-floor section. +- `After-init amortized(N) = (first_compute + (N-1) * hot_median) / N`; it excludes init. +- `Including-init amortized(N) = (init + first_compute + (N-1) * hot_median) / N`; it includes init. Each latency formula is evaluated separately for baseline and candidate, then reported as `baseline/candidate`. Both amortized scenarios are composed from measured components, not a directly timed N-call loop. A lane without explicit init uses `I=0`. +- Init scope: `once_per_validation_shard_process_device_operator`; composition: `runtime_init_only`; baseline has explicit init: `no`. +- Cache policy: `synchronize_and_clear_after_each_completed_shape`; resident multi-shape cache benchmarked: `no`; cold order: `deterministic_balanced_per_publication_contract_portfolio`; init order: `candidate_only_baseline_has_no_explicit_init` +- Lifecycle timing convention: all three lifecycle tables are synchronized host E2E. Init/first-call brackets are CUPTI timestamp host diagnostics; separately, the hot GPU-span diagnostic remains strict correlated CUPTI activity timing. + +- Measured: `2026-07-09T04:27:38+00:00` +- Full summary: [`VALIDATION.json`](VALIDATION.json); per-shape results: [`BENCHMARK_RESULTS.json`](BENCHMARK_RESULTS.json) + +The machine-readable per-shape public lifecycle and CUPTI evidence is in [`BENCHMARK_RESULTS.json`](BENCHMARK_RESULTS.json). + +## Result Table + +| Test | Hardware | Command | Result | +| --- | --- | --- | --- | +| metadata unit tests | not required | `pytest tests/test_exported_kernels.py tests/test_benchmark_harness.py -q` | pending | +| NVRTC compile benchmark | CUDA host | `python benchmarks/benchmark_exported_kernels.py --arch sm_100a --json results/compile_benchmark.json` | pending | +| semantic correctness | target GPU | `pytest tests/test_correctness.py -q` | PASS | +| kernel performance | target GPU | `python benchmarks/benchmark.py --no-correctness` | PASS (declared publication floor) | + +## Kernel Inventory + +| Name | Symbol | Launch Mode | Threads | Shared Memory Bytes | +| --- | --- | --- | ---: | ---: | +| `dispatch_kernel_0000` | `kernel_knn_build_evolve_7bfc_split_cg2_stage1_batch8_cond4_vmin_threshold_k5` | `standard` | 256 | 0 | +| `dispatch_kernel_0001` | `kernel_knn_build_evolve_7bfc_k5_merge_s4_tree` | `standard` | 256 | 0 | +| `dispatch_kernel_0002` | `kernel_knn_build_evolve_7bfc_split_cg2_stage1_batch8_cond4_vmin_maxtree` | `standard` | 256 | 0 | +| `dispatch_kernel_0003` | `kernel_knn_build_evolve_7bfc_split_merge` | `standard` | 256 | 0 | +| `dispatch_kernel_0004` | `kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_k8split` | `standard` | 256 | 0 | +| `dispatch_kernel_0005` | `kernel_knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_k8s8` | `standard` | 256 | 0 | +| `dispatch_kernel_0006` | `kernel_knn_build_evolve_7bfc_k10_merge_s4_rowbase_cache` | `standard` | 256 | 0 | +| `dispatch_kernel_0007` | `kernel_knn_build_evolve_7bfc_k10_merge_s7_rowbase_cache` | `standard` | 256 | 0 | +| `dispatch_kernel_0008` | `kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_k16split` | `standard` | 256 | 0 | +| `dispatch_kernel_0009` | `kernel_knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_f8c3lowk_k16s16` | `standard` | 256 | 0 | +| `dispatch_kernel_0010` | `kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_k12split` | `standard` | 256 | 0 | +| `dispatch_kernel_0011` | `kernel_knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_k12s16` | `standard` | 256 | 0 | +| `dispatch_kernel_0012` | `kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_k20split` | `standard` | 256 | 0 | +| `dispatch_kernel_0013` | `kernel_knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_k20s16` | `standard` | 256 | 0 | +| `dispatch_kernel_0014` | `kernel_knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_195e_q1024k8s16` | `standard` | 256 | 0 | +| `dispatch_kernel_0015` | `kernel_knn_build_q4096_k8_fd9b_stage1_unordered_exact_prefill` | `standard` | 256 | 0 | +| `dispatch_kernel_0016` | `kernel_knn_build_q4096_k8_fd9b_merge_s4_unordered_warp_select` | `standard` | 256 | 0 | +| `dispatch_kernel_0017` | `kernel_knn_build_dim_midk_73a9_d64_split_stage1` | `standard` | 256 | 0 | +| `dispatch_kernel_0018` | `kernel_knn_build_d64_build_aa88_k10_merge_s8_rowbase_cache` | `standard` | 256 | 0 | +| `dispatch_kernel_0019` | `kernel_knn_build_d64_q4096_c271_stage1_unordered_syncdrop` | `standard` | 256 | 0 | +| `dispatch_kernel_0020` | `kernel_knn_build_d64_build_aa88_k10_merge_s8_rowbase_cache_s4` | `standard` | 256 | 0 | +| `dispatch_kernel_0021` | `kernel_knn_build_non128_frontier_4be7_d96exact_stage1` | `standard` | 256 | 0 | +| `dispatch_kernel_0022` | `kernel_knn_build_non128_frontier_3d5a_k10_merge_s8_rowbase_cache` | `standard` | 256 | 0 | +| `dispatch_kernel_0023` | `kernel_knn_build_dim_midk_df2f_d256_split_stage1` | `standard` | 256 | 0 | +| `dispatch_kernel_0024` | `kernel_knn_build_non128_frontier_7231_pad_bf16_rows_d256` | `standard` | 256 | 0 | +| `dispatch_kernel_0025` | `kernel_knn_build_common_d_56f3_d256_q1024_k10_merge_rowbase_cache_s16` | `standard` | 256 | 0 | +| `dispatch_kernel_0026` | `kernel_knn_build_common_d768_build_eeff_m64split_stage1` | `standard` | 256 | 0 | +| `dispatch_kernel_0027` | `kernel_knn_build_non128_frontier_4be7_d768fused_merge_s16g8_4be7_d768fused_v1` | `standard` | 256 | 0 | +| `dispatch_kernel_0028` | `kernel_knn_build_non128_frontier_7231_stage1_d1024` | `standard` | 256 | 0 | +| `dispatch_kernel_0029` | `kernel_knn_build_common_d_56f3_k10_merge_rowbase_cache_s8` | `standard` | 256 | 0 | +| `dispatch_kernel_0030` | `kernel_knn_build_non128_frontier_7231_stage1_d4096` | `standard` | 256 | 0 | +| `dispatch_kernel_0031` | `kernel_knn_build_non128_frontier_8227_d320tail_stage1` | `standard` | 256 | 0 | +| `dispatch_kernel_0032` | `kernel_knn_build_dim_midk_df2f_fp16_split_stage1` | `standard` | 256 | 0 | +| `dispatch_kernel_0033` | `kernel_knn_build_fp16_d128_lowfloor_fd37_k10_s8_merge` | `standard` | 256 | 0 | +| `dispatch_kernel_0034` | `kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_e080k11exact` | `standard` | 256 | 0 | +| `dispatch_kernel_0035` | `kernel_knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_e080k11s8exact` | `standard` | 256 | 0 | +| `dispatch_kernel_0036` | `kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_k12split` | `standard` | 256 | 0 | +| `dispatch_kernel_0037` | `kernel_knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_k12s8` | `standard` | 256 | 0 | +| `dispatch_kernel_0038` | `kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_e080k13exact` | `standard` | 256 | 0 | +| `dispatch_kernel_0039` | `kernel_knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_e080k13s8exact` | `standard` | 256 | 0 | +| `dispatch_kernel_0040` | `kernel_knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_k20s8` | `standard` | 256 | 0 | +| `dispatch_kernel_0041` | `kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_bad5midks8k24` | `standard` | 256 | 0 | +| `dispatch_kernel_0042` | `kernel_knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_bad5midks8k24` | `standard` | 256 | 0 | +| `dispatch_kernel_0043` | `kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_bad5midks8k28` | `standard` | 256 | 0 | +| `dispatch_kernel_0044` | `kernel_knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_bad5midks8k28` | `standard` | 256 | 0 | +| `dispatch_kernel_0045` | `kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_k20split` | `standard` | 256 | 0 | +| `dispatch_kernel_0046` | `kernel_knn_build_evolve_7bfc_k32_merge_s4_rowbase_cache_k20split` | `standard` | 256 | 0 | +| `dispatch_kernel_0047` | `kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k12unordered` | `standard` | 256 | 0 | +| `dispatch_kernel_0048` | `kernel_knn_build_evolve_7bfc_k32_merge_s4_unordered_k12unordered` | `standard` | 256 | 0 | +| `dispatch_kernel_0049` | `kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_2c1ck13unordered` | `standard` | 256 | 0 | +| `dispatch_kernel_0050` | `kernel_knn_build_evolve_7bfc_k32_merge_s4_unordered_2c1ck13unordered` | `standard` | 256 | 0 | +| `dispatch_kernel_0051` | `kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k20unordered` | `standard` | 256 | 0 | +| `dispatch_kernel_0052` | `kernel_knn_build_evolve_7bfc_k32_merge_s4_unordered_k20unordered` | `standard` | 256 | 0 | +| `dispatch_kernel_0053` | `kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k30unordered_1074k24unordered` | `standard` | 256 | 0 | +| `dispatch_kernel_0054` | `kernel_knn_build_1074_k24_q4096_merge_s4_unordered_warp_select` | `standard` | 256 | 0 | +| `dispatch_kernel_0055` | `kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k30unordered_bad5k28unordered` | `standard` | 256 | 0 | +| `dispatch_kernel_0056` | `kernel_knn_build_evolve_7bfc_k32_merge_s4_unordered_k30unordered_bad5k28unordered` | `standard` | 256 | 0 | +| `dispatch_kernel_0057` | `kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered` | `standard` | 256 | 0 | +| `dispatch_kernel_0058` | `kernel_knn_build_evolve_7bfc_k32_merge_s4_unordered_warp_select` | `standard` | 256 | 0 | +| `dispatch_kernel_0059` | `kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k30unordered` | `standard` | 256 | 0 | +| `dispatch_kernel_0060` | `kernel_knn_build_k30_q4096_6998_merge_s4_unordered_warp_select` | `standard` | 256 | 0 | +| `dispatch_kernel_0061` | `kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k48over32` | `standard` | 256 | 0 | +| `dispatch_kernel_0062` | `kernel_knn_build_k48_merge_s4_unordered_warp_select` | `standard` | 256 | 0 | +| `dispatch_kernel_0063` | `kernel_knn_build_k64_stage1_tailinf_k64over32tailinfsplitgrid` | `standard` | 256 | 0 | +| `dispatch_kernel_0064` | `kernel_knn_build_k64_merge_s8_unordered_warp_select_k64over32s8warpselect` | `standard` | 256 | 0 | +| `dispatch_kernel_0065` | `kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k20unordered` | `standard` | 256 | 0 | +| `dispatch_kernel_0066` | `kernel_knn_build_k20_mergeown_08ec_warp8_select_s2warp8` | `standard` | 256 | 0 | +| `dispatch_kernel_0067` | `kernel_knn_build_large_square_k32_stage1_chunkworst` | `standard` | 256 | 0 | +| `dispatch_kernel_0068` | `kernel_knn_build_large_square_k32_s2_warp8_merge` | `standard` | 256 | 0 | +| `dispatch_kernel_0069` | `kernel_knn_build_evolve_7bfc_k10_merge_s7_rowbase_cache_rectd15e_s16` | `standard` | 256 | 0 | +| `dispatch_kernel_0070` | `kernel_knn_build_rect_d64_23be_s16_cached_merge` | `standard` | 256 | 0 | +| `dispatch_kernel_0071` | `kernel_knn_build_rect_d64_23be_unordered_stage1` | `standard` | 256 | 0 | +| `dispatch_kernel_0072` | `kernel_knn_build_d128_rag_q128_k10_s74_warp_merge_d320_s48_f556_v2` | `standard` | 256 | 0 | +| `dispatch_kernel_0073` | `kernel_knn_build_non128_frontier_7231_stage1_d256` | `standard` | 256 | 0 | +| `dispatch_kernel_0074` | `kernel_knn_build_non128_frontier_7231_stage1_d768` | `standard` | 256 | 0 | +| `dispatch_kernel_0075` | `kernel_knn_build_non128_frontier_4be7_d768fused_merge_s32g8_4be7_d768fused_v1` | `standard` | 256 | 0 | +| `dispatch_kernel_0076` | `kernel_knn_build_evolve_7bfc_k20_merge_s4_unordered_warp_select` | `standard` | 256 | 0 | +| `dispatch_kernel_0077` | `kernel_knn_build_rect_d128_k20_q1536_warp4_merge` | `standard` | 256 | 0 | +| `dispatch_kernel_0078` | `kernel_knn_build_k64_stage1_tailinf_k64over32tailinfsplitgrid` | `standard` | 256 | 0 | +| `dispatch_kernel_0079` | `kernel_knn_build_k64_merge_s8_unordered_warp_select_k64over32s8warpselect` | `standard` | 256 | 0 | +| `dispatch_kernel_0080` | `kernel_knn_build_ragonline_mbucket_4fc7_q1m262_v2_stage1_q1_k10_m64_halfrow` | `standard` | 256 | 0 | +| `dispatch_kernel_0081` | `kernel_knn_build_rag_microbatch_4a72_k10_fused_group_final_merge_s144g12_4a72_v1` | `standard` | 256 | 0 | +| `dispatch_kernel_0082` | `kernel_knn_build_ragonline_mbucket_ea43_q1m524_n128_stage1` | `standard` | 256 | 0 | +| `dispatch_kernel_0083` | `kernel_knn_build_rag_microbatch_4a72_k10_fused_group_final_merge_s147g7_4a72_v1` | `standard` | 256 | 0 | +| `dispatch_kernel_0084` | `kernel_knn_build_rag_stream_k10_q128_1bed_rowld_stage1` | `standard` | 256 | 0 | +| `dispatch_kernel_0085` | `kernel_knn_build_d128_rag_q128_k10_s74_warp_merge_rowld_s74_1bed_v1` | `standard` | 256 | 0 | +| `dispatch_kernel_0086` | `kernel_knn_build_k20_large_lowfanout_s2_warp_select` | `standard` | 256 | 0 | +| `dispatch_kernel_0087` | `kernel_knn_build_rag_microbatch_4a72_v2_stage1_k10_cta1_maxtree` | `standard` | 256 | 0 | +| `dispatch_kernel_0088` | `kernel_knn_build_rag_microbatch_4a72_v2_k10_fused_group_final_merge_s144g12_4a72_v2` | `standard` | 256 | 0 | +| `dispatch_kernel_0089` | `kernel_knn_build_rag_microbatch_m64_d4f7_stage1` | `standard` | 256 | 0 | +| `dispatch_kernel_0090` | `kernel_knn_build_rag_microbatch_4a72_k10_fused_group_final_merge_s136g8_4a72_v1` | `standard` | 256 | 0 | +| `dispatch_kernel_0091` | `kernel_knn_build_non128_frontier_7ee5_m64rag_stage1` | `standard` | 256 | 0 | +| `dispatch_kernel_0092` | `kernel_knn_build_non128_frontier_4be7_d768fused_merge_s72g8_4be7_d768fused_v1` | `standard` | 256 | 0 | +| `dispatch_kernel_0093` | `kernel_knn_build_common_d_1438_rag_d64_m128_stage1` | `standard` | 256 | 0 | +| `dispatch_kernel_0094` | `kernel_knn_build_non128_frontier_4be7_d768fused_merge_s136g8_4be7_d768fused_v1` | `standard` | 256 | 0 | +| `dispatch_kernel_0095` | `kernel_knn_build_non128_frontier_7ee5_m64rag_stage1_d256_5e7f_rag_d64d256_v1` | `standard` | 256 | 0 | +| `dispatch_kernel_0096` | `kernel_knn_build_non128_frontier_4be7_d768fused_merge_s144g8_4be7_d768fused_v1` | `standard` | 256 | 0 | +| `dispatch_kernel_0097` | `kernel_knn_build_non128_frontier_7ee5_m64rag_stage1_d1024_5e7f_highd_v1` | `standard` | 256 | 0 | +| `dispatch_kernel_0098` | `kernel_knn_build_non128_frontier_4be7_d768fused_merge_s144g12_4be7_d768fused_v1` | `standard` | 256 | 0 | +| `dispatch_kernel_0099` | `kernel_knn_build_non128_frontier_7ee5_m64rag_stage1_d4096_5e7f_highd_v1` | `standard` | 256 | 0 | +| `dispatch_kernel_0100` | `kernel_knn_build_non128_frontier_4be7_d768fused_merge_s128g8_4be7_d768fused_v1` | `standard` | 256 | 0 | +| `dispatch_kernel_0101` | `kernel_knn_build_rag_microbatch_4a72_k10_fused_group_final_merge_s128g8_4a72_v1` | `standard` | 256 | 0 | +| `dispatch_kernel_0102` | `kernel_knn_build_rag_microbucket_k32q8half_0077_v1_stage1_q8_k32_m64_halfrow_q8half_0077_v1` | `standard` | 256 | 0 | +| `dispatch_kernel_0103` | `kernel_knn_build_rag_microbucket_k32warpmerge_0077_v1_warp_row_merge_k32s144_0077_v1` | `standard` | 256 | 0 | +| `dispatch_kernel_0104` | `kernel_knn_build_rag_microbucket_k32_q16irreg2warp_a444_v2_stage1_q16_rowld1_2warp_q16dual2warp_56ed_v1` | `standard` | 256 | 0 | +| `dispatch_kernel_0105` | `kernel_knn_build_rag_microbucket_k32warpmerge_0077_v1_warp_row_merge_k32s144r4_56ed_v1` | `standard` | 256 | 0 | +| `dispatch_kernel_0106` | `kernel_knn_build_rag_microbucket_k32rowld1warp_0077_v1_stage1_q32_k32_m64_rowld2_q24rowld2_24dc_v1` | `standard` | 256 | 0 | +| `dispatch_kernel_0107` | `kernel_knn_build_rag_microbucket_k32warpmerge_0077_v1_warp_row_merge_k32q24s144r4_24dc_v1` | `standard` | 256 | 0 | +| `dispatch_kernel_0108` | `kernel_knn_build_rag_microbucket_k32warpmerge_0077_v1_warp_row_merge_k32s288r4_56ed_v1` | `standard` | 256 | 0 | +| `dispatch_kernel_0109` | `kernel_knn_build_rag_microbucket_k32_q32rowld2exact_s141_72d1_v1_stage1_q32rowld2exact_f653_v1` | `standard` | 256 | 0 | +| `dispatch_kernel_0110` | `kernel_knn_build_rag_microbucket_k32warpmerge_0077_v1_warp_row_merge_k32q32exact_s141r4_f653_v1` | `standard` | 256 | 0 | +| `dispatch_kernel_0111` | `kernel_knn_build_rag_microbucket_k32warpmerge_0077_v1_warp_row_merge_k32s148r4_56ed_v1` | `standard` | 256 | 0 | +| `dispatch_kernel_0112` | `kernel_knn_build_rag_stream_k32_q128m100000_staticn128_664a_stage1` | `standard` | 256 | 0 | +| `dispatch_kernel_0113` | `kernel_knn_build_rag_frontier_7399_k32_fused_group_final_merge_k32s72g8_4fbf_v6` | `standard` | 256 | 0 | +| `dispatch_kernel_0114` | `kernel_knn_build_rag_microbucket_q32rowld_e5db_v1_stage1_q32_k32_m64` | `standard` | 256 | 0 | +| `dispatch_kernel_0115` | `kernel_knn_build_rag_microbucket_k32warpmerge_0077_v1_warp_row_merge_k32s72_0077_v1` | `standard` | 256 | 0 | +| `dispatch_kernel_0116` | `kernel_knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_k10s72_4e09` | `standard` | 256 | 0 | +| `dispatch_kernel_0117` | `kernel_knn_build_evolve_7bfc_k10_merge_s7_rowbase_cache_rect4452_s8` | `standard` | 256 | 0 | +| `dispatch_kernel_0118` | `kernel_knn_build_k64_stage1_tailinf_k64over32tailinfsplitgrid` | `standard` | 256 | 0 | +| `dispatch_kernel_0119` | `kernel_knn_build_k64_merge_s8_unordered_warp_select_k64over32s8warpselect` | `standard` | 256 | 0 | +| `dispatch_kernel_0120` | `kernel_knn_build_k96_stage1_exact_prefill_q1024_k96over64exactprefillq1024_e5db` | `standard` | 256 | 0 | +| `dispatch_kernel_0121` | `kernel_knn_build_k96_merge_s8_unordered_chunkprefill_k96over64s2chunkprefill_f9d1` | `standard` | 256 | 0 | +| `dispatch_kernel_0122` | `kernel_knn_build_k96_merge_s8_unordered_chunkprefill_k96over64s4chunkprefill_f9d1` | `standard` | 256 | 0 | +| `dispatch_kernel_0123` | `kernel_knn_build_v12_d64_tail_017a_stage1` | `standard` | 256 | 0 | +| `dispatch_kernel_0124` | `kernel_knn_build_common_d768_build_eeff_m64split_stage1_d256_q128_k10_59fe_v1` | `standard` | 256 | 0 | +| `dispatch_kernel_0125` | `kernel_knn_build_non128_frontier_7ee5_m64rag_stage1_d768_5e7f_highd_v1` | `standard` | 256 | 0 | +| 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"search_rect_common_d768_b1_q512_m8192_k10", + "search_rect_b1_q4096_m65536_d128_k20", + "search_rect_b1_q1536_m65536_d128_k20", + "search_rect_over32_b1_q2048_m65536_d128_k64", + "rag_online_b1_q1_m100000_d128_k10", + "rag_online_b1_q1_m65536_d128_k10", + "rag_online_irregular_b1_q1_m131071_d128_k10", + "rag_online_large_m_b1_q1_m250000_d128_k10", + "rag_online_irregular_b1_q1_m262143_d128_k10", + "rag_online_irregular_b1_q1_m524287_d128_k10", + "rag_stream_b1_q128_m100000_d128_k10", + "rag_offline_largek_b1_q4096_m100000_d128_k20", + "rag_offline_large_m_b1_q8192_m250000_d128_k20", + "rag_offline_large_m_over32_b1_q2048_m250000_d128_k64", + "rag_offline_batch_b1_q10000_m100000_d128_k10", + "rag_offline_b1_q10000_m50000_d128_k10", + "rag_microbatch_b1_q4_m100000_d128_k10", + "rag_microbatch_b1_q8_m100000_d128_k10", + "rag_microbatch_b1_q16_m100000_d128_k10", + "rag_microbatch_highd_b1_q16_m50000_d768_k10", + "rag_microbatch_common_d64_b1_q16_m50000_k10", + "rag_microbatch_common_d256_b1_q16_m50000_k10", + "rag_microbatch_common_d1024_b1_q8_m50000_k10", + "rag_microbatch_common_d4096_b1_q4_m32768_k10", + "rag_microbatch_b1_q32_m100000_d128_k10", + "rag_microbatch_largek_b1_q8_m100000_d128_k32", + "rag_microbatch_largek_b1_q16_m100000_d128_k32", + "rag_microbatch_largek_b1_q24_m100000_d128_k32", + "rag_microbatch_largek_b1_q16_m250000_d128_k32", + "rag_microbatch_largek_b1_q32_m100000_d128_k32", + "rag_microbatch_largek_b1_q16_m131071_d128_k32", + "rag_microbatch_b1_q64_m100000_d128_k10", + "rag_stream_largek_b1_q128_m100000_d128_k32", + "rag_stream_largek_b1_q128_m131071_d128_k32", + "rag_batch_b2_q256_m50000_d128_k10", + "rag_irregular_b1_q512_m131071_d128_k10", + "search_rect_b1_q2048_m32768_d128_k10", + "build_large_tail_b1_q6144_m6144_d128_k20", + "build_over32_stress_qm4096_k64", + "build_over64_stress_qm1024_k96", + "build_over64_stress_qm2048_k96", + "build_over64_stress_qm4096_k96", + "rag_online_common_d64_b1_q1_m262143_k10", + "rag_microbatch_common_d64_b1_q4_m100000_k10", + "rag_microbatch_common_d256_b1_q4_m100000_k10", + "rag_stream_common_d256_b1_q128_m100000_k10", + "rag_microbatch_common_d768_b1_q8_m100000_k10", + "rag_microbatch_common_d1024_b1_q4_m100000_k10", + "search_rect_common_d1024_b1_q256_m8192_k10", + "search_rect_common_d4096_b1_q128_m4096_k10", + "rag_microbatch_largek_common_d256_b1_q8_m100000_k32", + "rag_stream_largek_common_d256_b1_q128_m100000_k32", + "rag_microbatch_over32_d128_b1_q16_m100000_k48" + ], + "speedup_vs_baseline": { + "geomean": 2.427225688850537, + "max": 5.162772235172759, + "median": 2.446964426848678, + "min": 1.2505154985685873, + "p90": 3.8841356406610985, + "sample_count": 111 + }, + "status": "passed" + }, + "route_count": 112, + "runtime_lifecycle": { + "amortized_after_init_synchronized_e2e_speedup": { + "1": { + "geomean": 0.9533111490438102, + "max": 46.52518612134412, + "median": 0.7584374567490936, + "min": 0.07624040647896023, + "p90": 2.7958889670908764, + "sample_count": 112 + }, + "10": { + "geomean": 0.9969907801975771, + "max": 40.8130262298273, + "median": 0.7959836293331757, + "min": 0.4672874036226355, + "p90": 2.802115466862169, + "sample_count": 112 + }, + "100": { + "geomean": 1.2603740670855001, + "max": 19.522836135484077, + "median": 1.0818333553948067, + "min": 0.6777831189216674, + "p90": 1.9472032836051771, + "sample_count": 112 + }, + "1000": { + "geomean": 1.9772199463007865, + "max": 5.872400349864117, + "median": 1.9015938981484732, + "min": 1.076174821597812, + "p90": 2.887430057125698, + "sample_count": 112 + } + }, + "amortized_including_init_synchronized_e2e_speedup": { + "1": { + "geomean": 0.0981145382352569, + "max": 0.714465899804305, + "median": 0.09805909342687077, + "min": 0.0007133905669862487, + "p90": 0.10921559413452522, + "sample_count": 112 + }, + "10": { + "geomean": 0.10990434975002783, + "max": 0.7180337379482914, + "median": 0.10471602864127513, + "min": 0.0071144046419176765, + "p90": 0.12225582816184984, + "sample_count": 112 + }, + "100": { + "geomean": 0.18845137591837588, + "max": 0.7533420815453649, + "median": 0.16573817797713092, + "min": 0.06993577737709647, + "p90": 0.31939204881189825, + "sample_count": 112 + }, + "1000": { + "geomean": 0.6812254177515542, + "max": 1.342999180715647, + "median": 0.6196511142818835, + "min": 0.45497355498294323, + "p90": 1.0251001932523955, + "sample_count": 112 + } + }, + "baseline_has_explicit_init": false, + "baseline_hot_public_synchronized_e2e_ms": { + "geomean": 0.4609289710616449, + "max": 6.606543500000001, + "median": 0.382129, + "min": 0.296257, + "p90": 0.7753038500000003, + "sample_count": 112 + }, + "baseline_init_host_enqueue_ms": null, + "baseline_init_synchronized_e2e_ms": null, + "cache_policy": "synchronize_and_clear_after_each_completed_shape", + "candidate_first_compute_host_enqueue_ms": { + "geomean": 55.385944037735904, + "max": 176.976743, + "median": 71.3829065, + "min": 4.40193, + "p90": 78.306622, + "sample_count": 112 + }, + "candidate_first_compute_synchronized_e2e_ms": { + "geomean": 55.92384407363708, + "max": 177.052391, + "median": 71.49455499999999, + "min": 4.452875, + "p90": 78.5293686, + "sample_count": 112 + }, + "candidate_hot_compute_gpu_span_ms": { + "geomean": 0.10399625473303419, + "max": 4.4408595, + "median": 0.08788875, + "min": 0.016688, + "p90": 0.3954016000000001, + "sample_count": 112 + }, + "candidate_hot_compute_host_enqueue_ms": { + "geomean": 0.0641636723493694, + "max": 0.10145599999999999, + "median": 0.06372, + "min": 0.046096, + "p90": 0.072936, + "sample_count": 112 + }, + "candidate_hot_compute_synchronized_e2e_ms": { + "geomean": 0.19120099156389905, + "max": 4.5279785, + "median": 0.15576825, + "min": 0.06672, + "p90": 0.46253540000000015, + "sample_count": 112 + }, + "candidate_init_host_enqueue_ms": { + "geomean": 0.13549216996714858, + "max": 0.197344, + "median": 0.1381605, + "min": 0.0904, + "p90": 0.1837987, + "sample_count": 4 + }, + "candidate_init_synchronized_e2e_ms": { + "geomean": 478.5034956743173, + "max": 486.688971, + "median": 478.0098305, + "min": 471.428102, + "p90": 484.17471539999997, + "sample_count": 4 + }, + "cold_order_policy": "deterministic_balanced_per_publication_contract_portfolio", + "hot_gpu_span_speedup": { + "geomean": 2.850467690143969, + "max": 19.538257890365447, + "median": 2.409173728364918, + "min": 1.019029173193073, + "p90": 6.44314689100183, + "sample_count": 112 + }, + "hot_synchronized_e2e_speedup": { + "geomean": 2.4107038739263196, + "max": 5.162772235172759, + "median": 2.4365587270684808, + "min": 1.1294973627522924, + "p90": 3.881828011228016, + "sample_count": 112 + }, + "init_composition": "runtime_init_only", + "init_order_policy": "candidate_only_baseline_has_no_explicit_init", + "init_scope": "once_per_validation_shard_process_device_operator", + "resident_multi_shape_cache_benchmarked": false, + "schema": "loom-public-runtime-lifecycle-summary-v1", + "shape_count": 112, + "validation_shard_count": 4 + }, + "semantic_entrypoint": "loom.examples.weave.knn_build_dispatch_q1m524_v10_d320recurrence_consumption_v1:launch_from_contract_inputs", + "shape_count": 112, + "status": "passed", + "status_scope": "full_correctness_and_evidence_plus_declared_publication_performance_floor", + "timing_backend": "cupti", + "validation_shard_count": 4 +} diff --git a/cake_exports/knn_build/VALIDATION.md b/cake_exports/knn_build/VALIDATION.md new file mode 100644 index 00000000..982faa7e --- /dev/null +++ b/cake_exports/knn_build/VALIDATION.md @@ -0,0 +1,43 @@ +## Pre-publication GPU validation: PASS — declared 111-shape performance floor + +- Hardware: `NVIDIA GB200` (`sm_100a`) +- Shapes: correctness `112/112`, CUPTI benchmark `112/112`; full generated suite `114` tests +- Validation shards: `4`; host wall time: correctness `46.97s`, benchmark `10.41s` +- Full all-shape `compute_speedup_vs_baseline` diagnostic vs `flashlib.flash_knn`: min `1.1295x`, geomean `2.4107x`, median `2.4366x`, p90 `3.8818x`, max `5.1628x`; `1/112` shapes are below the nominal `1.2000x` threshold (diagnostic, not hidden; see `VALIDATION.json`). +- Publication performance floor: `111` explicitly named shapes; min `1.2505x`, geomean `2.4272x`, median `2.4470x`, p90 `3.8841x`, max `5.1628x` (required minimum `1.2000x`). +- Publication floor labels: `flashml_correctness_b1_q256_m256_d128_k5`, `build_k_sweep_qm512_k1`, `build_k_sweep_qm512_k2`, `build_k_sweep_qm512_k4`, `build_k_sweep_qm512_k5`, `build_k_sweep_qm512_k6`, `build_k_sweep_qm512_k8`, `build_k_sweep_qm512_k10`, `build_qm1024_d128_k10`, `build_k_sweep_qm1024_k16`, `build_k_sweep_qm1024_k12`, `build_k_sweep_qm1024_k20`, `build_qm2048_d128_k8`, `build_qm1024_d128_k8`, `build_qm4096_d128_k8`, `build_qm2048_d128_k10`, `build_dim_sweep_b1_q1024_m1024_d64_k10`, `build_dim_sweep_b1_q2048_m2048_d64_k10`, `build_dim_sweep_b1_q4096_m4096_d64_k10`, `build_dim_sweep_b1_q1024_m1024_d96_k10`, `build_dim_sweep_b1_q2048_m2048_d192_k10`, `build_dim_sweep_b1_q2048_m2048_d256_k10`, `build_common_d256_b1_q1024_m1024_k10`, `build_common_d768_b1_q1024_m1024_k10`, `build_common_d1024_b1_q512_m512_k10`, `build_common_d4096_b1_q512_m512_k10`, `build_highd_b1_q1024_m1024_d320_k10`, `build_dtype_fp16_b1_q2048_m2048_d128_k10`, `build_batch_b2_q1024_m1024_d128_k10`, `build_k_sweep_qm2048_k11`, `build_k_sweep_qm2048_k12`, `build_k_sweep_qm2048_k13`, `build_k_sweep_qm2048_k20`, `build_k_sweep_qm2048_k24`, `build_k_sweep_qm2048_k28`, `build_tail_b1_q1536_m1536_d128_k10`, `build_tail_b1_q3072_m3072_d128_k20`, `build_medium_b1_q4096_m4096_d128_k10`, `build_k_sweep_qm4096_k12`, `build_k_sweep_qm4096_k13`, `build_k_sweep_qm4096_k20`, `build_k_sweep_qm4096_k24`, `build_k_sweep_qm4096_k28`, `build_largek_stress_qm4096_k32`, `build_k_sweep_qm4096_k30`, `build_over32_stress_qm2048_k48`, `build_over32_stress_qm2048_k64`, `build_over32_stress_qm4096_k48`, `build_large_b1_q8192_m8192_d128_k10`, `build_large_b1_q6144_m6144_d128_k10`, `build_large_b1_q8192_m8192_d128_k20`, `build_large_b1_q8192_m8192_d128_k32`, `build_verylarge_b1_q12288_m12288_d128_k10`, `rag_offline_b1_q4096_m100000_d128_k10`, `search_rect_b1_q1024_m8192_d128_k10`, `search_rect_b1_q1024_m32768_d64_k10`, `search_rect_highd_b1_q512_m12000_d320_k10`, `search_rect_common_d256_b1_q1024_m32768_k10`, `search_rect_common_d768_b1_q512_m8192_k10`, `search_rect_b1_q4096_m65536_d128_k20`, `search_rect_b1_q1536_m65536_d128_k20`, `search_rect_over32_b1_q2048_m65536_d128_k64`, `rag_online_b1_q1_m100000_d128_k10`, `rag_online_b1_q1_m65536_d128_k10`, `rag_online_irregular_b1_q1_m131071_d128_k10`, `rag_online_large_m_b1_q1_m250000_d128_k10`, `rag_online_irregular_b1_q1_m262143_d128_k10`, `rag_online_irregular_b1_q1_m524287_d128_k10`, `rag_stream_b1_q128_m100000_d128_k10`, `rag_offline_largek_b1_q4096_m100000_d128_k20`, `rag_offline_large_m_b1_q8192_m250000_d128_k20`, `rag_offline_large_m_over32_b1_q2048_m250000_d128_k64`, `rag_offline_batch_b1_q10000_m100000_d128_k10`, `rag_offline_b1_q10000_m50000_d128_k10`, `rag_microbatch_b1_q4_m100000_d128_k10`, `rag_microbatch_b1_q8_m100000_d128_k10`, `rag_microbatch_b1_q16_m100000_d128_k10`, `rag_microbatch_highd_b1_q16_m50000_d768_k10`, `rag_microbatch_common_d64_b1_q16_m50000_k10`, `rag_microbatch_common_d256_b1_q16_m50000_k10`, `rag_microbatch_common_d1024_b1_q8_m50000_k10`, `rag_microbatch_common_d4096_b1_q4_m32768_k10`, `rag_microbatch_b1_q32_m100000_d128_k10`, `rag_microbatch_largek_b1_q8_m100000_d128_k32`, `rag_microbatch_largek_b1_q16_m100000_d128_k32`, `rag_microbatch_largek_b1_q24_m100000_d128_k32`, `rag_microbatch_largek_b1_q16_m250000_d128_k32`, `rag_microbatch_largek_b1_q32_m100000_d128_k32`, `rag_microbatch_largek_b1_q16_m131071_d128_k32`, `rag_microbatch_b1_q64_m100000_d128_k10`, `rag_stream_largek_b1_q128_m100000_d128_k32`, `rag_stream_largek_b1_q128_m131071_d128_k32`, `rag_batch_b2_q256_m50000_d128_k10`, `rag_irregular_b1_q512_m131071_d128_k10`, `search_rect_b1_q2048_m32768_d128_k10`, `build_large_tail_b1_q6144_m6144_d128_k20`, `build_over32_stress_qm4096_k64`, `build_over64_stress_qm1024_k96`, `build_over64_stress_qm2048_k96`, `build_over64_stress_qm4096_k96`, `rag_online_common_d64_b1_q1_m262143_k10`, `rag_microbatch_common_d64_b1_q4_m100000_k10`, `rag_microbatch_common_d256_b1_q4_m100000_k10`, `rag_stream_common_d256_b1_q128_m100000_k10`, `rag_microbatch_common_d768_b1_q8_m100000_k10`, `rag_microbatch_common_d1024_b1_q4_m100000_k10`, `search_rect_common_d1024_b1_q256_m8192_k10`, `search_rect_common_d4096_b1_q128_m4096_k10`, `rag_microbatch_largek_common_d256_b1_q8_m100000_k32`, `rag_stream_largek_common_d256_b1_q128_m100000_k32`, `rag_microbatch_over32_d128_b1_q16_m100000_k48`. +- Candidate lifecycle latency diagnostics: init-once median `478.0098 ms`; first-signature compute median/p90 `71.4946/78.5294 ms`; hot compute median/p90 `0.1558/0.4625 ms` + +#### Hot steady-state synchronized E2E speedup + +| Validated shape scope | Min | Geomean | Median | P90 | Max | +| --- | ---: | ---: | ---: | ---: | ---: | +| All 112 benchmarked shapes (diagnostic scope) | 1.1295x | 2.4107x | 2.4366x | 3.8818x | 5.1628x | + +#### Modeled after-init amortized synchronized E2E speedup + +| Public calls N | Min | Geomean | Median | P90 | Max | +| --- | ---: | ---: | ---: | ---: | ---: | +| 1 | 0.0762x | 0.9533x | 0.7584x | 2.7959x | 46.5252x | +| 10 | 0.4673x | 0.9970x | 0.7960x | 2.8021x | 40.8130x | +| 100 | 0.6778x | 1.2604x | 1.0818x | 1.9472x | 19.5228x | +| 1000 | 1.0762x | 1.9772x | 1.9016x | 2.8874x | 5.8724x | + +#### Modeled including-init amortized synchronized E2E speedup + +| Public calls N | Min | Geomean | Median | P90 | Max | +| --- | ---: | ---: | ---: | ---: | ---: | +| 1 | 0.0007x | 0.0981x | 0.0981x | 0.1092x | 0.7145x | +| 10 | 0.0071x | 0.1099x | 0.1047x | 0.1223x | 0.7180x | +| 100 | 0.0699x | 0.1885x | 0.1657x | 0.3194x | 0.7533x | +| 1000 | 0.4550x | 0.6812x | 0.6197x | 1.0251x | 1.3430x | + +- All three tables report synchronized host E2E speedups as `baseline/candidate`. `Hot steady-state` measures a repeated public call at each lane's declared hot cache state; its per-shape values supply the official metric used by the separate publication-floor section. +- `After-init amortized(N) = (first_compute + (N-1) * hot_median) / N`; it excludes init. +- `Including-init amortized(N) = (init + first_compute + (N-1) * hot_median) / N`; it includes init. Each latency formula is evaluated separately for baseline and candidate, then reported as `baseline/candidate`. Both amortized scenarios are composed from measured components, not a directly timed N-call loop. A lane without explicit init uses `I=0`. +- Init scope: `once_per_validation_shard_process_device_operator`; composition: `runtime_init_only`; baseline has explicit init: `no`. +- Cache policy: `synchronize_and_clear_after_each_completed_shape`; resident multi-shape cache benchmarked: `no`; cold order: `deterministic_balanced_per_publication_contract_portfolio`; init order: `candidate_only_baseline_has_no_explicit_init` +- Lifecycle timing convention: all three lifecycle tables are synchronized host E2E. Init/first-call brackets are CUPTI timestamp host diagnostics; separately, the hot GPU-span diagnostic remains strict correlated CUPTI activity timing. + +- Measured: `2026-07-09T04:27:38+00:00` +- Full summary: [`VALIDATION.json`](VALIDATION.json); per-shape results: [`BENCHMARK_RESULTS.json`](BENCHMARK_RESULTS.json) diff --git a/cake_exports/knn_build/benchmarks/benchmark.py b/cake_exports/knn_build/benchmarks/benchmark.py new file mode 100644 index 00000000..71169b0e --- /dev/null +++ b/cake_exports/knn_build/benchmarks/benchmark.py @@ -0,0 +1,822 @@ +from __future__ import annotations + +import argparse +import hashlib +import importlib +import json +import math +import os +import sys +import uuid +from itertools import permutations +from pathlib import Path +from typing import Any + +ROOT = Path(__file__).resolve().parents[1] +SRC = ROOT / "src" +if str(SRC) not in sys.path: + sys.path.insert(0, str(SRC)) + +from flashlib_cake_knn_build._benchmark import ( # noqa: E402 + bench_gpu_time, + compare_runtime_lifecycles, + measure_host_call, + require_cupti, + runtime_lifecycle_metrics, +) + +SHAPE_RECORDS = json.loads((Path(__file__).with_name("shape_records.json")).read_text(encoding="utf-8")) +ROUTE_MANIFEST = json.loads((Path(__file__).with_name("expected_routes.json")).read_text(encoding="utf-8")) +EXPECTED_ROUTES = {row["shape"]: row["selected_route"] for row in ROUTE_MANIFEST} +SEMANTIC_ENTRYPOINT = ( + "loom.examples.weave.knn_build_dispatch_q1m524_v10_d320recurrence_consumption_v1:launch_from_contract_inputs" +) +SHAPES: dict[str, dict[str, Any]] = { + row["label"]: {**row["params"], "recorded": row["recorded"]} for row in SHAPE_RECORDS +} +BASELINE_NAME = "flashlib.flash_knn" +MEASUREMENT_ORDER_SEED = "flashlib-knn-build-runtime-compute-paired-v2" +_COLD_ORDERED_LABELS = tuple( + sorted(SHAPES, key=lambda label: hashlib.sha256(f"{MEASUREMENT_ORDER_SEED}:cold:{label}".encode()).digest()) +) +_CANDIDATE_FIRST_COLD_LABELS = frozenset(_COLD_ORDERED_LABELS[::2]) + + +def _measurement_order(label: str) -> tuple[str, str, str]: + """Choose one stable per-shape order for baseline/compute/prepared timing.""" + + orders = tuple(permutations(("baseline", "compute", "prepared"))) + digest = hashlib.sha256(f"{MEASUREMENT_ORDER_SEED}:{label}".encode()).digest() + return orders[int.from_bytes(digest[:2], "little") % len(orders)] + + +def _cold_measurement_order(label: str) -> tuple[str, str]: + """Counterbalance process-shared cold effects across contract shapes.""" + + return ("candidate", "baseline") if label in _CANDIDATE_FIRST_COLD_LABELS else ("baseline", "candidate") + + +def _measurement_session_fields(measurement_session_id: str) -> dict[str, Any]: + if not isinstance(measurement_session_id, str) or not measurement_session_id.strip(): + raise ValueError("measurement_session_id must be a non-empty string") + return { + "measurement_session_id": measurement_session_id, + "baseline_measurement_session_id": measurement_session_id, + "compute_measurement_session_id": measurement_session_id, + "prepared_measurement_session_id": measurement_session_id, + "baseline_compute_prepared_same_session": True, + } + + +def _recorded_diagnostics(recorded: dict[str, Any]) -> dict[str, Any]: + return {f"recorded_{key}": value for key, value in recorded.items()} + + +def _shape_metadata(shape: dict[str, Any]) -> dict[str, Any]: + return { + **{key: value for key, value in shape.items() if key != "recorded"}, + **_recorded_diagnostics(shape["recorded"]), + } + + +def _load_flashlib_baseline(): + flash_knn = getattr(importlib.import_module("flashlib"), "flash_knn", None) + if not callable(flash_knn): + raise RuntimeError("flashlib.flash_knn is required for same-process KNN-build benchmarking") + return flash_knn + + +def _require_correct_baseline(name: str, diagnostics: dict[str, Any]) -> None: + if bool(diagnostics.get("correct")): + return + raise RuntimeError( + f"{BASELINE_NAME} correctness failed for {name}: " + f"recall={diagnostics.get('recall')!r}, " + f"max_abs_dist_error={diagnostics.get('max_abs_dist_error')!r}, " + f"required_recall={diagnostics.get('required_recall')!r}" + ) + + +def _host_call_diagnostics(timing: Any) -> dict[str, float] | None: + if timing is None: + return None + return { + "host_enqueue_ms": timing.host_enqueue_ms, + "synchronized_e2e_ms": timing.synchronized_e2e_ms, + } + + +def _timing_diagnostics(timing: Any) -> dict[str, Any]: + return { + "official_gpu_metric": "gpu_span_ms", + "gpu_span_ms": {"median": timing.median_gpu_span_ms, "iterations": timing.times_ms}, + "kernel_sum_ms": { + "median": timing.median_kernel_sum_ms, + "iterations": timing.kernel_sum_times_ms, + }, + "active_union_ms": { + "median": timing.median_active_union_ms, + "iterations": timing.active_union_times_ms, + }, + "inter_kernel_gap_ms": { + "median": timing.median_inter_kernel_gap_ms, + "iterations": timing.inter_kernel_gap_times_ms, + }, + "activity_count": { + "median": timing.median_activity_count, + "iterations": timing.activity_counts, + }, + "correlated_launch_activity_count": { + "median": timing.median_launch_activity_count, + "iterations": timing.launch_activity_counts, + }, + "correlated_kernel_activity_count": { + "median": timing.median_kernel_activity_count, + "iterations": timing.kernel_activity_counts, + }, + "host_enqueue_ms": { + "median": timing.median_host_enqueue_ms, + "iterations": timing.host_enqueue_times_ms, + }, + "synchronized_e2e_ms": { + "median": timing.median_synchronized_e2e_ms, + "iterations": timing.synchronized_e2e_times_ms, + }, + "cold_first_call": { + "host_enqueue_ms": timing.cold_first_call_host_enqueue_ms, + "synchronized_e2e_ms": timing.cold_first_call_synchronized_e2e_ms, + }, + } + + +def _write_json_atomic(path: Path, text: str) -> None: + temporary = path.with_name(f".{path.name}.{os.getpid()}.tmp") + temporary.write_text(text + "\n", encoding="utf-8") + temporary.replace(path) + + +def _make_inputs(shape: dict[str, Any], *, seed_offset: int = 0): + import torch + + generator = torch.Generator(device="cuda") + generator.manual_seed(int(shape["seed"]) + int(seed_offset)) + database = torch.randn( + (int(shape["B"]), int(shape["M"]), int(shape["D"])), + dtype=torch.float16 if shape.get("dtype") == "float16" else torch.bfloat16, + device="cuda", + generator=generator, + ).contiguous() + if bool(shape.get("build", False)): + query = database + else: + query = torch.randn( + (int(shape["B"]), int(shape["Q"]), int(shape["D"])), + dtype=database.dtype, + device="cuda", + generator=generator, + ).contiguous() + return query, database + + +def _alternating_call(fn: Any, first: tuple[Any, Any], second: tuple[Any, Any]): + """Return a call that alternates two equal-shape, different-pointer inputs.""" + + pairs = (first, second) + iteration = 0 + + def call(): + nonlocal iteration + query, database = pairs[iteration & 1] + iteration += 1 + return fn(query, database) + + return call + + +def _correctness_query_indices(shape: dict[str, Any], query): + import torch + + n_query = int(query.shape[1]) + sample_count = int(shape.get("correctness_query_sample", n_query)) + sample_count = min(sample_count, n_query) + if sample_count <= 0: + raise ValueError("correctness_query_sample must select at least one query") + if sample_count == n_query: + return torch.arange(n_query, dtype=torch.int64, device=query.device) + return torch.linspace(0, n_query - 1, sample_count, device=query.device).round().to(torch.int64).unique() + + +def _reference_topk(query, database, k: int, query_indices=None): + import torch + + values: list[Any] = [] + indices: list[Any] = [] + db_f32 = database.float() + db_sq = (db_f32 * db_f32).sum(-1) + block = 256 + query_f32 = query.float() if query_indices is None else query[:, query_indices.long(), :].float() + for start in range(0, int(query_f32.shape[1]), block): + query_block = query_f32[:, start : start + block, :] + q_sq = (query_block * query_block).sum(-1) + dots = torch.matmul(query_block, db_f32.transpose(-1, -2)) + dists = q_sq.unsqueeze(-1) + db_sq.unsqueeze(1) - 2.0 * dots + vals, idx = torch.topk(dists, k, dim=-1, largest=False, sorted=True) + values.append(vals) + indices.append(idx.to(torch.int32)) + return torch.cat(values, dim=1), torch.cat(indices, dim=1) + + +def _recall(got_indices, expected_indices) -> float: + matches = (got_indices.unsqueeze(-1) == expected_indices.unsqueeze(-2)).any(dim=-1) + return float(matches.to(got_indices.device, dtype=got_indices.float().dtype).mean().item()) + + +def _distances_for_indices(query, database, indices): + import torch + + db_f32 = database.float() + bsz, n_rows, _ = db_f32.shape + safe_indices = indices.to(torch.int64).clamp(0, n_rows - 1) + batch = torch.arange(bsz, device=database.device)[:, None, None] + neighbors = db_f32[batch, safe_indices] + return ((query.float().unsqueeze(2) - neighbors) ** 2).sum(-1) + + +def _knn_correctness_diagnostics( + query, + database, + output, + expected_indices, + *, + query_indices, + required_recall: float, +) -> dict[str, Any]: + sampled_query = query[:, query_indices.long(), :] + sampled_distances = output[0][:, query_indices.long(), :] + sampled_indices = output[1][:, query_indices.long(), :] + exact_dists = _distances_for_indices(sampled_query, database, sampled_indices) + recall = _recall(sampled_indices, expected_indices) + max_abs_dist_error = float((sampled_distances - exact_dists).abs().max().item()) + return { + "recall": recall, + "max_abs_dist_error": max_abs_dist_error, + "required_recall": required_recall, + "sampled_queries": int(query_indices.numel()), + "correct": bool(recall >= required_recall and max_abs_dist_error <= 1.0e-2), + } + + +def _run_shape( + name: str, + shape: dict[str, Any], + *, + runtime: Any, + runtime_init_timing: Any = None, + arch: str | None, + correctness: bool, + benchmark: bool, + measurement_session_id: str | None = None, +) -> dict[str, Any]: + import torch + from flashlib_cake_knn_build import knn_build_prepared, prepare_knn_build + + session_id = measurement_session_id or uuid.uuid4().hex + session_fields = _measurement_session_fields(session_id) + query_a, database_a = _make_inputs(shape) + query_b, database_b = _make_inputs(shape, seed_offset=1_000_003) + if database_a.data_ptr() == database_b.data_ptr() or query_a.data_ptr() == query_b.data_ptr(): + raise RuntimeError("fresh-pointer KNN-build input pair unexpectedly aliases") + # Keep the cold public-call brackets honest: fixture generation is + # asynchronous and must complete before the baseline/slot-miss timestamp. + torch.cuda.synchronize() + k = int(shape["K"]) + build = bool(shape.get("build", False)) + + def compute(query, database, *, return_info: bool = False): + return runtime.compute( + query, + database, + k, + build=build, + shape_label=name, + return_info=return_info, + ) + + baseline_out = None + baseline_cold_first_call = None + first_shape_lookup_timing = None + fresh_pointer_hit_timing = None + prepare_cold_call = None + prepared_cold_first_call = None + cold_measurement_order = _cold_measurement_order(name) + if benchmark: + flash_knn = _load_flashlib_baseline() + for lane in cold_measurement_order: + if lane == "baseline": + baseline_out, baseline_cold_first_call = measure_host_call(lambda: flash_knn(query_b, database_b, k=k)) + else: + (first_out, first_route_info), first_shape_lookup_timing = measure_host_call( + lambda: compute(query_a, database_a, return_info=True) + ) + (out, route_info), fresh_pointer_hit_timing = measure_host_call( + lambda: compute(query_b, database_b, return_info=True) + ) + if not bool(route_info.get("runtime_cache_hit")): + raise RuntimeError("fresh-pointer KNN-build call did not hit the runtime shape cache") + if first_route_info["selected_route"] != route_info["selected_route"]: + raise RuntimeError( + "fresh-pointer KNN-build call changed routes: " + f"{first_route_info['selected_route']!r} != {route_info['selected_route']!r}" + ) + if first_route_info["norm_compute_fields"] != route_info["norm_compute_fields"]: + raise RuntimeError( + "fresh-pointer KNN-build call changed the route-specific norm plan: " + f"{first_route_info['norm_compute_fields']!r} != " + f"{route_info['norm_compute_fields']!r}" + ) + if any(left.data_ptr() == right.data_ptr() for left, right in zip(first_out, out, strict=True)): + raise RuntimeError("fresh-pointer KNN-build call reused the prior default output allocation") + if route_info.get("hot_launch_path") != "cuda_graph": + raise RuntimeError( + "KNN-build signature did not capture into a CUDA graph: " + f"hot_launch_path={route_info.get('hot_launch_path')!r}, " + f"graph_capture_error={route_info.get('graph_capture_error')!r}; " + "every contract signature freezes to a LaunchPlan today, so a " + "prepared-path fallback means the graph runtime silently degraded" + ) + prepared, prepare_cold_call = measure_host_call( + lambda: prepare_knn_build( + query_a, + database_a, + k, + build=build, + shape_label=name, + arch=arch, + ) + ) + (_, prepared_route_info), prepared_cold_first_call = measure_host_call( + lambda: knn_build_prepared(prepared, return_info=True) + ) + if first_route_info["selected_route"] != prepared_route_info["selected_route"]: + raise RuntimeError( + "runtime and prepared KNN-build calls selected different routes: " + f"{first_route_info['selected_route']!r} != " + f"{prepared_route_info['selected_route']!r}" + ) + else: + first_out, first_route_info = compute(query_a, database_a, return_info=True) + out, route_info = compute(query_b, database_b, return_info=True) + if not bool(route_info.get("runtime_cache_hit")): + raise RuntimeError("fresh-pointer KNN-build call did not hit the runtime shape cache") + if first_route_info["selected_route"] != route_info["selected_route"]: + raise RuntimeError("fresh-pointer KNN-build call changed routes") + if route_info.get("hot_launch_path") != "cuda_graph": + raise RuntimeError( + "KNN-build signature did not capture into a CUDA graph: " + f"hot_launch_path={route_info.get('hot_launch_path')!r}, " + f"graph_capture_error={route_info.get('graph_capture_error')!r}" + ) + torch.cuda.synchronize() + + result: dict[str, Any] = { + "shape": name, + "B": int(shape["B"]), + "Q": int(shape["Q"]), + "M": int(shape["M"]), + "D": int(shape["D"]), + "K": k, + "semantic_entrypoint": route_info["semantic_entrypoint"], + "selected_route": route_info["selected_route"], + "expected_route": EXPECTED_ROUTES[name], + "route_matches_expected": route_info["selected_route"] == EXPECTED_ROUTES[name], + "baseline_name": BASELINE_NAME, + "baseline_entrypoint": BASELINE_NAME, + "first_shape_lookup_cache_hit": bool(first_route_info.get("runtime_cache_hit")), + "fresh_pointer_cache_hit": bool(route_info.get("runtime_cache_hit")), + "fresh_pointer_rebind_verified": True, + "hot_launch_path": route_info.get("hot_launch_path"), + "graph_kernel_count": route_info.get("graph_kernel_count"), + "graph_capture_error": route_info.get("graph_capture_error"), + **session_fields, + **_recorded_diagnostics(shape["recorded"]), + } + if benchmark: + result.update( + { + "cold_measurement_order": list(cold_measurement_order), + "cold_measurement_order_policy": "deterministic_balanced_per_publication_contract_portfolio", + "prepared_launch_count": route_info["prepared_launch_count"], + "compute_launch_count": route_info["runtime_launch_count"], + "compute_norm_launch_count": route_info["norm_launch_count"], + "compute_norm_policy": route_info["norm_mode"], + "compute_norm_fields": route_info["norm_compute_fields"], + "cold_baseline_call": _host_call_diagnostics(baseline_cold_first_call), + "cold_compute_first_shape_lookup": _host_call_diagnostics(first_shape_lookup_timing), + "cold_compute_fresh_pointer_hit": _host_call_diagnostics(fresh_pointer_hit_timing), + "prepared_setup_after_runtime_hit": _host_call_diagnostics(prepare_cold_call), + "cold_prepared_call": _host_call_diagnostics(prepared_cold_first_call), + "runtime_cache_info_after_fresh_pointer_hit": runtime.cache_info(), + } + ) + + first_ref_indices = None + ref_indices = None + query_indices = None + if correctness or benchmark: + query_indices = _correctness_query_indices(shape, query_b) + _, ref_indices = _reference_topk(query_b, database_b, k, query_indices) + if correctness: + _, first_ref_indices = _reference_topk(query_a, database_a, k, query_indices) + torch.cuda.synchronize() + + if correctness: + required_recall = float(shape.get("recall_min", 0.999)) + first_candidate_correctness = _knn_correctness_diagnostics( + query_a, + database_a, + first_out, + first_ref_indices, + query_indices=query_indices, + required_recall=required_recall, + ) + candidate_correctness = _knn_correctness_diagnostics( + query_b, + database_b, + out, + ref_indices, + query_indices=query_indices, + required_recall=required_recall, + ) + result["first_pointer_correctness"] = first_candidate_correctness + result["fresh_pointer_correctness"] = candidate_correctness + if not first_candidate_correctness["correct"] or not candidate_correctness["correct"]: + raise RuntimeError( + f"runtime.compute fresh-pointer correctness failed for {name}: " + f"first={first_candidate_correctness!r}, fresh={candidate_correctness!r}" + ) + rebound_out, rebound_info = compute(query_a, database_a, return_info=True) + torch.cuda.synchronize() + rebound_correctness = _knn_correctness_diagnostics( + query_a, + database_a, + rebound_out, + first_ref_indices, + query_indices=query_indices, + required_recall=required_recall, + ) + if not rebound_info.get("runtime_cache_hit") or not rebound_correctness["correct"]: + raise RuntimeError( + f"runtime.compute B-to-A pointer rebound failed for {name}: " + f"info={rebound_info!r}, correctness={rebound_correctness!r}" + ) + result["return_pointer_correctness"] = rebound_correctness + result.update(candidate_correctness) + + if benchmark: + baseline_correctness = _knn_correctness_diagnostics( + query_b, + database_b, + baseline_out, + ref_indices, + query_indices=query_indices, + required_recall=float(shape.get("recall_min", 0.999)), + ) + result.update({f"baseline_{key}": value for key, value in baseline_correctness.items()}) + _require_correct_baseline(name, baseline_correctness) + + baseline_output_holder = [baseline_out] + compute_output_holder = [out] + + def baseline_for(query, database): + baseline_output_holder[0] = flash_knn(query, database, k=k) + return baseline_output_holder[0] + + def compute_for(query, database): + compute_output_holder[0] = compute(query, database) + return compute_output_holder[0] + + run_baseline = _alternating_call( + baseline_for, + (query_a, database_a), + (query_b, database_b), + ) + run_compute = _alternating_call( + compute_for, + (query_a, database_a), + (query_b, database_b), + ) + + def time_baseline(): + return bench_gpu_time( + run_baseline, + cold_l2=True, + cold_first_call=baseline_cold_first_call, + ) + + def time_compute(): + return bench_gpu_time( + run_compute, + cold_l2=True, + cold_first_call=fresh_pointer_hit_timing, + ) + + def time_prepared(): + return bench_gpu_time( + lambda: knn_build_prepared(prepared), + cold_l2=True, + cold_first_call=prepared_cold_first_call, + ) + + measurement_order = _measurement_order(name) + timers = { + "baseline": time_baseline, + "compute": time_compute, + "prepared": time_prepared, + } + timings = {timer_name: timers[timer_name]() for timer_name in measurement_order} + baseline_timing = timings["baseline"] + compute_timing = timings["compute"] + prepared_timing = timings["prepared"] + timing_backends = { + baseline_timing.backend, + compute_timing.backend, + prepared_timing.backend, + } + if timing_backends != {"cupti"}: + raise RuntimeError(f"baseline/compute/prepared must all use CUPTI, got {sorted(timing_backends)!r}") + + baseline_e2e_ms = baseline_timing.median_synchronized_e2e_ms + compute_e2e_ms = compute_timing.median_synchronized_e2e_ms + if baseline_e2e_ms is None or compute_e2e_ms is None: + raise RuntimeError("CUPTI benchmark did not report synchronized E2E timing") + + result.update( + { + "measurement_order": list(measurement_order), + "measurement_order_policy": "deterministic_sha256_per_shape_permutation", + "baseline_ms": baseline_timing.median_ms, + "baseline_gpu_span_ms": baseline_timing.median_gpu_span_ms, + "baseline_kernel_span_ms": baseline_timing.median_gpu_span_ms, + "baseline_kernel_sum_ms": baseline_timing.median_kernel_sum_ms, + "baseline_active_union_ms": baseline_timing.median_active_union_ms, + "baseline_inter_kernel_gap_ms": baseline_timing.median_inter_kernel_gap_ms, + "baseline_synchronized_e2e_ms": baseline_e2e_ms, + "baseline_timing_backend": baseline_timing.backend, + "baseline_bench_iters": len(baseline_timing.times_ms), + "baseline_timing_diagnostics": _timing_diagnostics(baseline_timing), + "kernel_ms": compute_timing.median_gpu_span_ms, + "compute_gpu_span_ms": compute_timing.median_gpu_span_ms, + "compute_kernel_span_ms": compute_timing.median_gpu_span_ms, + "compute_gpu_span_scope": "correlated_kernel_activity_only_excludes_memcpy_and_pre_kernel_host_work", + "compute_kernel_sum_ms": compute_timing.median_kernel_sum_ms, + "compute_active_union_ms": compute_timing.median_active_union_ms, + "compute_inter_kernel_gap_ms": compute_timing.median_inter_kernel_gap_ms, + "compute_host_enqueue_ms": compute_timing.median_host_enqueue_ms, + "compute_synchronized_e2e_ms": compute_e2e_ms, + "compute_timing_diagnostics": _timing_diagnostics(compute_timing), + "prepared_gpu_span_ms": prepared_timing.median_gpu_span_ms, + "prepared_kernel_sum_ms": prepared_timing.median_kernel_sum_ms, + "prepared_active_union_ms": prepared_timing.median_active_union_ms, + "prepared_inter_kernel_gap_ms": prepared_timing.median_inter_kernel_gap_ms, + "prepared_timing_diagnostics": _timing_diagnostics(prepared_timing), + "compute_gpu_over_prepared": (compute_timing.median_gpu_span_ms / prepared_timing.median_gpu_span_ms), + "timing_backend": compute_timing.backend, + "bench_iters": len(compute_timing.times_ms), + "compute_gpu_speedup_vs_baseline": ( + baseline_timing.median_gpu_span_ms / compute_timing.median_gpu_span_ms + ), + "compute_speedup_vs_baseline": baseline_e2e_ms / compute_e2e_ms, + "speedup_vs_baseline": (baseline_timing.median_gpu_span_ms / compute_timing.median_gpu_span_ms), + "prepared_speedup_vs_baseline": (baseline_timing.median_ms / prepared_timing.median_ms), + } + ) + candidate_lifecycle = runtime_lifecycle_metrics( + api="flashlib_cake_knn_build.init().compute", + measurement_session_id=session_id, + timing_boundary="raw_inputs_default_output_route_required_norms_synchronized_e2e", + output_policy="default_output_allocated_inside_compute", + init=runtime_init_timing, + init_sample_id=session_id, + first_compute=first_shape_lookup_timing, + first_cache_state=("shape_slot_hit" if first_route_info.get("runtime_cache_hit") else "shape_slot_miss"), + hot_compute=compute_timing, + hot_cache_state="fresh_pointer_shape_slot_hit", + code_cache_state="process_order_dependent", + ) + baseline_lifecycle = runtime_lifecycle_metrics( + api=BASELINE_NAME, + measurement_session_id=session_id, + timing_boundary="raw_inputs_default_output_synchronized_e2e", + output_policy="default_output_allocated_inside_flashlib_call", + init=None, + init_sample_id=None, + first_compute=baseline_cold_first_call, + first_cache_state="first_public_call", + hot_compute=baseline_timing, + hot_cache_state="repeated_public_call", + code_cache_state="process_order_dependent", + ) + lifecycle_comparison = compare_runtime_lifecycles(candidate_lifecycle, baseline_lifecycle) + if not math.isclose( + lifecycle_comparison["hot_synchronized_e2e_speedup"], + result["compute_speedup_vs_baseline"], + rel_tol=1.0e-12, + abs_tol=0.0, + ): + raise RuntimeError("KNN-build lifecycle hot E2E speedup disagrees with publication speedup") + result["candidate_runtime_lifecycle"] = candidate_lifecycle + result["baseline_runtime_lifecycle"] = baseline_lifecycle + result["runtime_lifecycle_comparison"] = lifecycle_comparison + flops = 2.0 * int(shape["B"]) * int(shape["Q"]) * int(shape["M"]) * int(shape["D"]) + result["gpu_tflops"] = flops / compute_timing.median_gpu_span_ms / 1e9 + result["tflops"] = flops / compute_e2e_ms / 1e9 + result["qps"] = int(shape["B"]) * int(shape["Q"]) / (compute_e2e_ms / 1000.0) + + return result + + +def main() -> int: + parser = argparse.ArgumentParser( + description="Correctness and CUPTI benchmark for reusable KNN-build runtime.compute" + ) + parser.add_argument( + "--shape", + action="append", + choices=sorted(SHAPES), + help="Shape label to run. Repeatable.", + ) + parser.add_argument("--arch", default=None, help="NVRTC architecture, e.g. sm_100a.") + parser.add_argument( + "--metadata-only", + action="store_true", + help="Emit available benchmark metadata without CUDA.", + ) + parser.add_argument( + "--no-correctness", + action="store_true", + help="Skip candidate reference checks. The measured FlashLib baseline remains fail-closed.", + ) + parser.add_argument("--no-benchmark", action="store_true", help="Skip CUPTI timing.") + parser.add_argument("--json", type=Path, default=None, help="Optional path for JSON output.") + parser.add_argument("--shard-index", type=int, default=0, help="Zero-based validation shard index.") + parser.add_argument("--shard-count", type=int, default=1, help="Number of validation shards.") + parser.add_argument("--quiet", action="store_true", help="Do not print the full JSON payload.") + args = parser.parse_args() + + selected = args.shape or list(SHAPES) + if args.shard_count <= 0 or not 0 <= args.shard_index < args.shard_count: + parser.error("shard index must satisfy 0 <= index < count and count must be positive") + if args.json is not None: + args.json.unlink(missing_ok=True) + selected = selected[args.shard_index :: args.shard_count] + measurement_session_id = uuid.uuid4().hex + unique_signature_count = len( + { + ( + int(SHAPES[name]["B"]), + int(SHAPES[name]["Q"]), + int(SHAPES[name]["M"]), + int(SHAPES[name]["D"]), + int(SHAPES[name]["K"]), + str(SHAPES[name].get("dtype", "bfloat16")), + bool(SHAPES[name].get("build", False)), + ) + for name in selected + } + ) + payload: dict[str, Any] = { + "api": "flashlib_cake_knn_build.init().compute", + "semantic_entrypoint": SEMANTIC_ENTRYPOINT, + "baseline_name": BASELINE_NAME, + "baseline_entrypoint": BASELINE_NAME, + "publication_speedup_convention": ( + "compute_speedup_vs_baseline = " + "same_session_flashlib_flash_knn_synchronized_e2e_ms / " + "exported_runtime_compute_synchronized_e2e_ms" + ), + "speedup_convention": ( + "speedup_vs_baseline = same_session_flashlib_flash_knn_gpu_span_ms / " + "exported_runtime_compute_gpu_span_ms (legacy GPU-span alias)" + ), + "gpu_speedup_convention": ( + "compute_gpu_speedup_vs_baseline = " + "same_session_flashlib_flash_knn_gpu_span_ms / " + "exported_runtime_compute_gpu_span_ms" + ), + "shapes": {name: _shape_metadata(SHAPES[name]) for name in selected}, + "metadata_only": bool(args.metadata_only), + "validation_shard": {"index": args.shard_index, "count": args.shard_count}, + "measurement_session": { + "id": measurement_session_id, + "scope": "shared_runtime_per_shape_deterministic_path_blocks_with_pointer_alternation", + "path_timing_mode": "separate_cupti_blocks", + "pointer_timing_mode": "alternating_fresh_pointer_sets_within_each_path", + "baseline_candidate_same_process": True, + "baseline_compute_prepared_same_session": True, + "candidate_norm_policy": "internal_fused_row_norm_with_runtime_owned_scratch", + "baseline_norm_policy": "x2_free_flashlib_score", + "runtime_initialized_once": True, + "runtime_instance_reused_across_shapes": True, + "resident_multi_shape_cache_benchmarked": False, + "cache_policy": "synchronize_and_clear_after_each_completed_shape", + "order_policy": "deterministic_sha256_per_shape_permutation", + "cold_order_policy": "deterministic_balanced_per_publication_contract_portfolio", + "init_composition": "runtime_init_only", + "init_order_policy": "candidate_only_baseline_has_no_explicit_init", + "baseline_has_explicit_init": False, + "order_seed": MEASUREMENT_ORDER_SEED, + }, + "runtime_cache_summary": { + "selected_shape_count": len(selected), + "unique_signature_count": unique_signature_count, + }, + "runtime_lifecycle": { + "schema": "loom-public-runtime-lifecycle-v1", + "candidate_api": "flashlib_cake_knn_build.init().compute", + "baseline_api": BASELINE_NAME, + "candidate_timing_boundary": "raw_inputs_default_output_route_required_norms_synchronized_e2e", + "baseline_timing_boundary": "raw_inputs_default_output_synchronized_e2e", + "init_scope": "once_per_validation_shard_process_device_operator", + "amortization_call_counts": [1, 10, 100, 1000], + "cache_policy": "synchronize_and_clear_after_each_completed_shape", + "cold_order_policy": "deterministic_balanced_per_publication_contract_portfolio", + "init_composition": "runtime_init_only", + "init_order_policy": "candidate_only_baseline_has_no_explicit_init", + "baseline_has_explicit_init": False, + "resident_multi_shape_cache_benchmarked": False, + "candidate_output_policy": "default_output_allocated_inside_compute", + "baseline_output_policy": "default_output_allocated_inside_flashlib_call", + "session": None, + }, + } + if args.metadata_only: + payload["results"] = [] + else: + import torch + from flashlib_cake_knn_build import init + + capability = torch.cuda.get_device_capability() + detected_arch = f"sm_{capability[0]}{capability[1]}" + ("" if capability[0] == 8 else "a") + payload["hardware"] = { + "device": torch.cuda.get_device_name(), + "arch": detected_arch, + } + if not args.no_benchmark: + require_cupti() + runtime, runtime_init_timing = measure_host_call(lambda: init(arch=args.arch)) + else: + runtime = init(arch=args.arch) + runtime_init_timing = None + results = [] + for name in selected: + results.append( + _run_shape( + name, + SHAPES[name], + runtime=runtime, + runtime_init_timing=runtime_init_timing, + arch=args.arch, + correctness=not args.no_correctness, + benchmark=not args.no_benchmark, + measurement_session_id=measurement_session_id, + ) + ) + runtime.clear() + payload["results"] = results + payload["cold_runtime_init"] = _host_call_diagnostics(runtime_init_timing) + first_lookup_miss_count = sum(not bool(row["first_shape_lookup_cache_hit"]) for row in results) + fresh_pointer_hit_count = sum(bool(row["fresh_pointer_cache_hit"]) for row in results) + final_cache_info = runtime.cache_info() + payload["runtime_cache_summary"].update( + { + "first_lookup_miss_count": first_lookup_miss_count, + "fresh_pointer_hit_count": fresh_pointer_hit_count, + "final_cache_info": final_cache_info, + "workspace_lifecycle": "synchronize_and_clear_after_each_completed_shape", + } + ) + if args.no_benchmark: + payload.pop("runtime_lifecycle", None) + else: + payload["runtime_lifecycle"]["session"] = { + "id": measurement_session_id, + "init_measurement_order": ["candidate"], + "candidate_init": _host_call_diagnostics(runtime_init_timing), + "baseline_init": None, + "clear_count": len(results), + "first_lookup_miss_count": first_lookup_miss_count, + "fresh_pointer_hit_count": fresh_pointer_hit_count, + "final_candidate_cache_info": final_cache_info, + } + + text = json.dumps(payload, indent=2, sort_keys=True) + if args.json is not None: + args.json.parent.mkdir(parents=True, exist_ok=True) + _write_json_atomic(args.json, text) + if not args.quiet: + print(text) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/cake_exports/knn_build/benchmarks/benchmark_exported_kernels.py b/cake_exports/knn_build/benchmarks/benchmark_exported_kernels.py new file mode 100644 index 00000000..7fa636a1 --- /dev/null +++ b/cake_exports/knn_build/benchmarks/benchmark_exported_kernels.py @@ -0,0 +1,144 @@ +from __future__ import annotations + +import argparse +import importlib +import json +import statistics +import sys +import time +from pathlib import Path +from typing import Any + + +ROOT = Path(__file__).resolve().parents[1] +SRC = ROOT / "src" +if str(SRC) not in sys.path: + sys.path.insert(0, str(SRC)) + +PACKAGE_NAME = 'flashlib_cake_knn_build' + + +def _selected_names(pkg: Any, requested: list[str] | None) -> list[str]: + if not requested: + return list(pkg.KERNELS) + missing = sorted(set(requested) - set(pkg.KERNELS)) + if missing: + available = ", ".join(sorted(pkg.KERNELS)) + raise SystemExit(f"unknown kernel(s) {missing}. Available: {available}") + return requested + + +def _entry_for_kernel(pkg: Any, name: str, *, metadata_only: bool, arch: str | None, iterations: int): + kernel = pkg.get_kernel(name) + entry: dict[str, Any] = { + "name": name, + "symbol": kernel.spec.symbol, + "launch_mode": kernel.spec.launch_mode, + "threads": kernel.spec.threads, + "shared_mem_bytes": kernel.spec.shared_mem_bytes, + "parameter_count": len(kernel.parameters), + "status": "metadata_only" if metadata_only else "pending", + } + if metadata_only: + return entry + + times_ms: list[float] = [] + try: + runtime = importlib.import_module(f"{PACKAGE_NAME}._runtime") + for _ in range(iterations): + runtime.clear_compilation_cache() + start = time.perf_counter() + kernel.compile(arch=arch) + times_ms.append((time.perf_counter() - start) * 1000.0) + except Exception as exc: # noqa: BLE001 - benchmark report should preserve the failure. + entry["status"] = "failed" + entry["error"] = f"{type(exc).__name__}: {exc}" + return entry + + entry["status"] = "passed" + entry["compile_ms_median"] = statistics.median(times_ms) + entry["compile_ms_min"] = min(times_ms) + entry["compile_ms_max"] = max(times_ms) + entry["iterations"] = iterations + return entry + + +def run_benchmark( + *, + kernels: list[str] | None = None, + arch: str | None = None, + iterations: int = 1, + metadata_only: bool = False, +) -> dict[str, Any]: + pkg = importlib.import_module(PACKAGE_NAME) + selected = _selected_names(pkg, kernels) + entries = [ + _entry_for_kernel(pkg, name, metadata_only=metadata_only, arch=arch, iterations=iterations) + for name in selected + ] + passed = sum(1 for entry in entries if entry["status"] in {"passed", "metadata_only"}) + failed = sum(1 for entry in entries if entry["status"] == "failed") + return { + "benchmark": "exported_kernel_compile", + "package": PACKAGE_NAME, + "arch": arch, + "iterations": iterations, + "metadata_only": metadata_only, + "summary": { + "kernel_count": len(entries), + "passed": passed, + "failed": failed, + "all_passed": failed == 0, + }, + "kernels": entries, + } + + +def _print_summary(payload: dict[str, Any]) -> None: + summary = payload["summary"] + print( + "benchmark={benchmark} package={package} kernels={kernel_count} passed={passed} failed={failed}".format( + benchmark=payload["benchmark"], + package=payload["package"], + kernel_count=summary["kernel_count"], + passed=summary["passed"], + failed=summary["failed"], + ) + ) + for entry in payload["kernels"]: + if entry["status"] == "passed": + print( + "{name}: compile_median={compile_ms_median:.3f} ms status=passed".format(**entry) + ) + else: + detail = f" error={entry['error']}" if "error" in entry else "" + print(f"{entry['name']}: status={entry['status']}{detail}") + + +def main(argv: list[str] | None = None) -> int: + parser = argparse.ArgumentParser(description="Benchmark exported CUDA kernel compile latency.") + parser.add_argument("--kernel", action="append", dest="kernels", help="Kernel name to benchmark.") + parser.add_argument("--arch", help="NVRTC GPU architecture, for example sm_100a.") + parser.add_argument("--iterations", type=int, default=1, help="Compile iterations per kernel.") + parser.add_argument("--metadata-only", action="store_true", help="Do not compile; emit benchmark schema.") + parser.add_argument("--json", type=Path, help="Write benchmark results as JSON.") + args = parser.parse_args(argv) + if args.iterations <= 0: + parser.error("--iterations must be positive") + + payload = run_benchmark( + kernels=args.kernels, + arch=args.arch, + iterations=args.iterations, + metadata_only=args.metadata_only, + ) + if args.json: + args.json.parent.mkdir(parents=True, exist_ok=True) + args.json.write_text(json.dumps(payload, indent=2, sort_keys=True) + "\n", encoding="utf-8") + _print_summary(payload) + return 0 if payload["summary"]["all_passed"] else 1 + + +if __name__ == "__main__": + raise SystemExit(main()) + diff --git a/cake_exports/knn_build/benchmarks/benchmark_shapes.py b/cake_exports/knn_build/benchmarks/benchmark_shapes.py new file mode 100644 index 00000000..db2ed2aa --- /dev/null +++ b/cake_exports/knn_build/benchmarks/benchmark_shapes.py @@ -0,0 +1,252 @@ +from __future__ import annotations + +import argparse +import importlib +import json +import sys +from collections.abc import Mapping +from pathlib import Path +from typing import Any + + +ROOT = Path(__file__).resolve().parents[1] +SRC = ROOT / "src" +if str(SRC) not in sys.path: + sys.path.insert(0, str(SRC)) + +PACKAGE_NAME = 'flashlib_cake_knn_build' + + +def _shape_name(shape: Mapping[str, Any], index: int) -> str: + return str(shape.get("name") or f"shape_{index}") + + +def _selected_shapes(workload: Any, requested: list[str] | None) -> list[dict[str, Any]]: + shapes = [dict(shape) for shape in getattr(workload, "SHAPES", ())] + names = [_shape_name(shape, index) for index, shape in enumerate(shapes)] + if len(names) != len(set(names)): + raise ValueError("workload.SHAPES contains duplicate names") + if not requested: + return shapes + missing = sorted(set(requested) - set(names)) + if missing: + raise ValueError(f"unknown shape(s) {missing}. Available: {', '.join(names)}") + requested_set = set(requested) + return [shape for index, shape in enumerate(shapes) if _shape_name(shape, index) in requested_set] + + +def _comparison_payload(value: Any) -> dict[str, Any]: + if isinstance(value, bool): + return {"passed": value} + if not isinstance(value, Mapping) or "passed" not in value: + raise TypeError("case['compare'] must return bool or a mapping containing 'passed'") + return dict(value) + + +def _run_shape( + pkg: Any, + workload: Any, + shape: dict[str, Any], + *, + index: int, + correctness_only: bool, + warmup_iters: int, + bench_iters: int, +) -> dict[str, Any]: + name = _shape_name(shape, index) + entry: dict[str, Any] = {"name": name, "shape": shape, "status": "pending"} + try: + case = workload.make_case(pkg, shape) + if not isinstance(case, Mapping): + raise TypeError("make_case() must return a mapping") + missing = [key for key in ("run", "reference", "compare") if not callable(case.get(key))] + if missing: + raise TypeError(f"benchmark case is missing callable(s): {', '.join(missing)}") + + expected = case["reference"]() + import torch + + cold_first_call = None + if correctness_only: + actual = case["run"]() + torch.cuda.synchronize() + else: + from flashlib_cake_knn_build._benchmark import measure_host_call + + actual, cold_first_call = measure_host_call(case["run"]) + comparison = _comparison_payload(case["compare"](actual, expected)) + entry["correctness"] = comparison + if not comparison["passed"]: + entry["status"] = "incorrect" + return entry + if correctness_only: + entry["status"] = "passed" + return entry + + from flashlib_cake_knn_build._benchmark import bench_gpu_time + + timing = bench_gpu_time( + case["run"], + warmup_iters=warmup_iters, + bench_iters=bench_iters, + cold_l2=True, + cold_first_call=cold_first_call, + ) + entry["timing"] = { + "backend": timing.backend, + "official_gpu_metric": "gpu_span_ms", + "median_ms": timing.median_ms, + "min_ms": timing.min_ms, + "mean_ms": timing.mean_ms, + "iterations": len(timing.times_ms), + "cold_l2": True, + "gpu_span_ms": { + "median": timing.median_gpu_span_ms, + "iterations": timing.times_ms, + }, + "kernel_sum_ms": { + "median": timing.median_kernel_sum_ms, + "iterations": timing.kernel_sum_times_ms, + }, + "active_union_ms": { + "median": timing.median_active_union_ms, + "iterations": timing.active_union_times_ms, + }, + "inter_kernel_gap_ms": { + "median": timing.median_inter_kernel_gap_ms, + "iterations": timing.inter_kernel_gap_times_ms, + }, + "activity_count": { + "median": timing.median_activity_count, + "iterations": timing.activity_counts, + }, + "correlated_launch_activity_count": { + "median": timing.median_launch_activity_count, + "iterations": timing.launch_activity_counts, + }, + "correlated_kernel_activity_count": { + "median": timing.median_kernel_activity_count, + "iterations": timing.kernel_activity_counts, + }, + "host_enqueue_ms": { + "median": timing.median_host_enqueue_ms, + "iterations": timing.host_enqueue_times_ms, + }, + "synchronized_e2e_ms": { + "median": timing.median_synchronized_e2e_ms, + "iterations": timing.synchronized_e2e_times_ms, + }, + "cold_first_call": { + "host_enqueue_ms": timing.cold_first_call_host_enqueue_ms, + "synchronized_e2e_ms": timing.cold_first_call_synchronized_e2e_ms, + }, + } + flops = case.get("flops") + bytes_moved = case.get("bytes") + if flops is not None: + entry["tflops"] = float(flops) / timing.median_ms / 1e9 + if bytes_moved is not None: + entry["gbps"] = float(bytes_moved) / timing.median_ms / 1e6 + if case.get("metrics") is not None: + entry["metrics"] = dict(case["metrics"]) + entry["status"] = "passed" + except Exception as exc: # noqa: BLE001 - preserve per-shape failure in JSON. + entry["status"] = "failed" + entry["error"] = f"{type(exc).__name__}: {exc}" + return entry + + +def run_benchmark( + *, + shapes: list[str] | None = None, + metadata_only: bool = False, + correctness_only: bool = False, + warmup_iters: int = 5, + bench_iters: int = 20, +) -> dict[str, Any]: + pkg = importlib.import_module(PACKAGE_NAME) + workload = importlib.import_module("workload") + selected = _selected_shapes(workload, shapes) + configured = bool(getattr(workload, "CONFIGURED", bool(selected))) + entries = [ + {"name": _shape_name(shape, index), "shape": shape, "status": "metadata_only"} + for index, shape in enumerate(selected) + ] + if not metadata_only: + if not configured or not selected: + raise RuntimeError( + "benchmarks/workload.py is not configured; provide --benchmark-adapter during export " + "or implement SHAPES and make_case()" + ) + if not correctness_only: + # CUPTI must be imported before workload adapters import + # torch, which may otherwise load an incompatible system + # CUPTI soname first. + benchmark_runtime = importlib.import_module(f"{PACKAGE_NAME}._benchmark") + benchmark_runtime.require_cupti() + entries = [ + _run_shape( + pkg, + workload, + shape, + index=index, + correctness_only=correctness_only, + warmup_iters=warmup_iters, + bench_iters=bench_iters, + ) + for index, shape in enumerate(selected) + ] + passed = sum(1 for entry in entries if entry["status"] in {"passed", "metadata_only"}) + failed = len(entries) - passed + return { + "benchmark": "exported_kernel_shapes", + "package": PACKAGE_NAME, + "metadata_only": metadata_only, + "correctness_only": correctness_only, + "adapter_configured": configured, + "timing_backend_requested": "cupti", + "summary": { + "shape_count": len(entries), + "passed": passed, + "failed": failed, + "all_passed": failed == 0, + }, + "shapes": entries, + } + + +def main(argv: list[str] | None = None) -> int: + parser = argparse.ArgumentParser(description="Validate and benchmark exported kernels by shape.") + parser.add_argument("--shape", action="append", dest="shapes", help="Shape name to run.") + parser.add_argument("--metadata-only", action="store_true", help="Emit adapter metadata without CUDA.") + parser.add_argument("--correctness-only", action="store_true", help="Validate without timing.") + parser.add_argument("--warmup-iters", type=int, default=5) + parser.add_argument("--bench-iters", type=int, default=20) + parser.add_argument("--json", type=Path, help="Write benchmark results as JSON.") + args = parser.parse_args(argv) + if args.warmup_iters < 0 or args.bench_iters <= 0: + parser.error("--warmup-iters must be non-negative and --bench-iters must be positive") + try: + payload = run_benchmark( + shapes=args.shapes, + metadata_only=args.metadata_only, + correctness_only=args.correctness_only, + warmup_iters=args.warmup_iters, + bench_iters=args.bench_iters, + ) + except Exception as exc: # noqa: BLE001 - CLI reports configuration failures cleanly. + parser.error(str(exc)) + if args.json: + args.json.parent.mkdir(parents=True, exist_ok=True) + args.json.write_text(json.dumps(payload, indent=2, sort_keys=True) + "\n", encoding="utf-8") + summary = payload["summary"] + print( + f"benchmark={payload['benchmark']} shapes={summary['shape_count']} " + f"passed={summary['passed']} failed={summary['failed']}" + ) + return 0 if summary["all_passed"] else 1 + + +if __name__ == "__main__": + raise SystemExit(main()) + diff --git a/cake_exports/knn_build/benchmarks/expected_routes.json b/cake_exports/knn_build/benchmarks/expected_routes.json new file mode 100644 index 00000000..f64d1798 --- /dev/null +++ b/cake_exports/knn_build/benchmarks/expected_routes.json @@ -0,0 +1,450 @@ +[ + { + "selected_route": "loom.examples.weave.knn_build_flashml_k5_bd4a_v1:launch_from_contract_inputs", + "shape": "flashml_correctness_b1_q256_m256_d128_k5" + }, + { + "selected_route": "loom.examples.weave.knn_build_k1_q512_group2_root_v1:q512_k1_s2", + "shape": "build_k_sweep_qm512_k1" + }, + { + "selected_route": "loom.examples.weave.knn_build_lowk_f8c3_q512_q1024_v1:q512_lowk_s4", + "shape": "build_k_sweep_qm512_k2" + }, + { + "selected_route": 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"baseline_name": "flashlib", + "kernel_ms": 0.224962, + "speedup_vs_baseline": 1.274677501089073, + "timing_backend": "cupti" + } + }, + { + "label": "rag_microbatch_over32_d128_b1_q16_m100000_k48", + "params": { + "B": 1, + "D": 128, + "K": 48, + "M": 100000, + "Q": 16, + "benchmark": true, + "build": false, + "check_correctness": true, + "correctness_query_sample": 16, + "diagnostic_class": "v12_rag_over32_topk", + "dtype": "bfloat16", + "recall_min": 0.999, + "seed": 616548, + "time_flashlib": true + }, + "recorded": { + "baseline_ms": 0.199393, + "baseline_name": "flashlib", + "kernel_ms": 0.103936, + "speedup_vs_baseline": 1.9184209513546797, + "timing_backend": "cupti" + } + } +] diff --git a/cake_exports/knn_build/benchmarks/workload.py b/cake_exports/knn_build/benchmarks/workload.py new file mode 100644 index 00000000..dff45603 --- /dev/null +++ b/cake_exports/knn_build/benchmarks/workload.py @@ -0,0 +1,29 @@ +"""Workload adapter for semantic correctness and per-shape performance. + +Replace this template or pass ``--benchmark-adapter`` to the Cake exporter. +The exported repository never needs Weave IR: this module calls its public +Python API and provides an independent reference implementation. +""" + +from __future__ import annotations + +from typing import Any + +CONFIGURED = False +SHAPES: list[dict[str, Any]] = [] + + +def make_case(package: Any, shape: dict[str, Any]) -> dict[str, Any]: + """Return run/reference/compare callables and optional work estimates. + + Required keys in the returned mapping: + run: zero-argument callable invoking the exported semantic API + reference: zero-argument callable computing independent expected output + compare: callable(actual, expected) returning bool or {"passed": bool, ...} + + Optional keys: + flops: useful floating-point operations for TFLOPS reporting + bytes: useful bytes moved for GB/s reporting + metrics: static metadata copied into the result + """ + raise NotImplementedError("configure benchmarks/workload.py before running shape benchmarks") diff --git a/cake_exports/knn_build/pyproject.toml b/cake_exports/knn_build/pyproject.toml new file mode 100644 index 00000000..96fe64de --- /dev/null +++ b/cake_exports/knn_build/pyproject.toml @@ -0,0 +1,40 @@ +[build-system] +requires = ["setuptools>=68", "wheel"] +build-backend = "setuptools.build_meta" + +[project] +name = "knn_build" +version = "0.1.0" +requires-python = ">=3.11" +dependencies = [ + "cuda-python", + "torch", +] + +[project.optional-dependencies] +test = [ + "pytest", +] +benchmark = [ + "cupti-python", + "cuda-pathfinder", + "nvidia-cuda-cupti", + "pytest", + "triton", +] +tvm-ffi = [ + "cake-std==0.1.13.dev20260704+g7b8dbc8", +] + +[tool.pytest.ini_options] +markers = [ + "export_validation_shape: one declared contract shape counted by the fail-closed publication gate", + "gpu: requires a CUDA GPU", +] + +[tool.setuptools.packages.find] +where = ["src"] + +[tool.setuptools.package-data] +"flashlib_cake_knn_build" = ["*.json", "*.cu", "cuda/*.cu"] + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/__init__.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/__init__.py new file mode 100644 index 00000000..767fe846 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/__init__.py @@ -0,0 +1,955 @@ +from .kernels import ( + KERNELS, + ExportedKernel, + get_kernel, + dispatch_kernel_0000, + dispatch_kernel_0001, + dispatch_kernel_0002, + dispatch_kernel_0003, + dispatch_kernel_0004, + dispatch_kernel_0005, + dispatch_kernel_0006, + dispatch_kernel_0007, + dispatch_kernel_0008, + dispatch_kernel_0009, + dispatch_kernel_0010, + dispatch_kernel_0011, + dispatch_kernel_0012, + dispatch_kernel_0013, + dispatch_kernel_0014, + dispatch_kernel_0015, + dispatch_kernel_0016, + dispatch_kernel_0017, + dispatch_kernel_0018, + dispatch_kernel_0019, + dispatch_kernel_0020, + dispatch_kernel_0021, + dispatch_kernel_0022, + dispatch_kernel_0023, + dispatch_kernel_0024, + dispatch_kernel_0025, + dispatch_kernel_0026, + dispatch_kernel_0027, + dispatch_kernel_0028, + dispatch_kernel_0029, + dispatch_kernel_0030, + dispatch_kernel_0031, + dispatch_kernel_0032, + dispatch_kernel_0033, + dispatch_kernel_0034, + dispatch_kernel_0035, + dispatch_kernel_0036, + dispatch_kernel_0037, + dispatch_kernel_0038, + dispatch_kernel_0039, + dispatch_kernel_0040, + dispatch_kernel_0041, + dispatch_kernel_0042, + dispatch_kernel_0043, + dispatch_kernel_0044, + dispatch_kernel_0045, + dispatch_kernel_0046, + dispatch_kernel_0047, + dispatch_kernel_0048, + dispatch_kernel_0049, + dispatch_kernel_0050, + dispatch_kernel_0051, + dispatch_kernel_0052, + dispatch_kernel_0053, + dispatch_kernel_0054, + dispatch_kernel_0055, + dispatch_kernel_0056, + dispatch_kernel_0057, + dispatch_kernel_0058, + dispatch_kernel_0059, + dispatch_kernel_0060, + dispatch_kernel_0061, + dispatch_kernel_0062, + dispatch_kernel_0063, + dispatch_kernel_0064, + dispatch_kernel_0065, + dispatch_kernel_0066, + dispatch_kernel_0067, + dispatch_kernel_0068, + dispatch_kernel_0069, + dispatch_kernel_0070, + dispatch_kernel_0071, + dispatch_kernel_0072, + dispatch_kernel_0073, + dispatch_kernel_0074, + dispatch_kernel_0075, + dispatch_kernel_0076, + dispatch_kernel_0077, + dispatch_kernel_0078, + dispatch_kernel_0079, + dispatch_kernel_0080, + dispatch_kernel_0081, + dispatch_kernel_0082, + dispatch_kernel_0083, + dispatch_kernel_0084, + dispatch_kernel_0085, + dispatch_kernel_0086, + dispatch_kernel_0087, + dispatch_kernel_0088, + dispatch_kernel_0089, + dispatch_kernel_0090, + dispatch_kernel_0091, + dispatch_kernel_0092, + dispatch_kernel_0093, + dispatch_kernel_0094, + dispatch_kernel_0095, + dispatch_kernel_0096, + dispatch_kernel_0097, + dispatch_kernel_0098, + dispatch_kernel_0099, + dispatch_kernel_0100, + dispatch_kernel_0101, + dispatch_kernel_0102, + dispatch_kernel_0103, + dispatch_kernel_0104, + dispatch_kernel_0105, + dispatch_kernel_0106, + dispatch_kernel_0107, + dispatch_kernel_0108, + dispatch_kernel_0109, + dispatch_kernel_0110, + dispatch_kernel_0111, + dispatch_kernel_0112, + dispatch_kernel_0113, + dispatch_kernel_0114, + dispatch_kernel_0115, + dispatch_kernel_0116, + dispatch_kernel_0117, + dispatch_kernel_0118, + dispatch_kernel_0119, + dispatch_kernel_0120, + dispatch_kernel_0121, + dispatch_kernel_0122, + dispatch_kernel_0123, + dispatch_kernel_0124, + dispatch_kernel_0125, + dispatch_kernel_0126, + dispatch_kernel_0127, + dispatch_kernel_0128, + dispatch_kernel_0129, + dispatch_kernel_0130, + dispatch_kernel_0131, + dispatch_kernel_0132, + dispatch_kernel_0133, + dispatch_kernel_0134, + dispatch_kernel_0135, + dispatch_kernel_0136, + dispatch_kernel_0137, + dispatch_kernel_0138, + dispatch_kernel_0139, + dispatch_kernel_0140, + dispatch_kernel_0141, + dispatch_kernel_0142, + dispatch_kernel_0143, + dispatch_kernel_0144, + dispatch_kernel_0145, + dispatch_kernel_0146, + dispatch_kernel_0147, + dispatch_kernel_0148, + dispatch_kernel_0149, + dispatch_kernel_0150, + dispatch_kernel_0151, + dispatch_kernel_0152, + dispatch_kernel_0153, + dispatch_kernel_0154, + dispatch_kernel_0155, + dispatch_kernel_0156, + dispatch_kernel_0157, + dispatch_kernel_0158, + dispatch_kernel_0159, + dispatch_kernel_0160, + dispatch_kernel_0161, + dispatch_kernel_0162, + dispatch_kernel_0163, + dispatch_kernel_0164, + dispatch_kernel_0165, + dispatch_kernel_0166, + dispatch_kernel_0167, + dispatch_kernel_0168, + dispatch_kernel_0169, + dispatch_kernel_0170, + dispatch_kernel_0171, + dispatch_kernel_0172, + dispatch_kernel_0173, + dispatch_kernel_0174, + dispatch_kernel_0175, + dispatch_kernel_0176, + dispatch_kernel_0177, + dispatch_kernel_0178, + dispatch_kernel_0179, + dispatch_kernel_0180, + dispatch_kernel_0181, + dispatch_kernel_0182, + dispatch_kernel_0183, + dispatch_kernel_0184, + dispatch_kernel_0185, + dispatch_kernel_0186, + dispatch_kernel_0187, + dispatch_kernel_0188, + dispatch_kernel_0189, + dispatch_kernel_0190, + dispatch_kernel_0191, + dispatch_kernel_0192, + dispatch_kernel_0193, + dispatch_kernel_0194, + dispatch_kernel_0195, + dispatch_kernel_0196, + dispatch_kernel_0197, + dispatch_kernel_0198, + dispatch_kernel_0199, + dispatch_kernel_0200, + dispatch_kernel_0201, + dispatch_kernel_0202, + dispatch_kernel_0203, + dispatch_kernel_0204, + dispatch_kernel_0205, + dispatch_kernel_0206, + dispatch_kernel_0207, + dispatch_kernel_0208, + dispatch_kernel_0209, + dispatch_kernel_0210, + dispatch_kernel_0211, + dispatch_kernel_0212, + dispatch_kernel_0213, + dispatch_kernel_0214, + dispatch_kernel_0215, + dispatch_kernel_0216, + dispatch_kernel_0217, + dispatch_kernel_0218, + dispatch_kernel_0219, + dispatch_kernel_0220, + dispatch_kernel_0221, + dispatch_kernel_0222, + dispatch_kernel_0223, + dispatch_kernel_0224, + dispatch_kernel_0225, + dispatch_kernel_0226, + dispatch_kernel_0227, + dispatch_kernel_0228, + dispatch_kernel_0229, + dispatch_kernel_0230, + dispatch_kernel_0231, + dispatch_kernel_0232, + launch_dispatch_kernel_0000, + launch_dispatch_kernel_0001, + launch_dispatch_kernel_0002, + launch_dispatch_kernel_0003, + launch_dispatch_kernel_0004, + launch_dispatch_kernel_0005, + launch_dispatch_kernel_0006, + launch_dispatch_kernel_0007, + launch_dispatch_kernel_0008, + launch_dispatch_kernel_0009, + launch_dispatch_kernel_0010, + launch_dispatch_kernel_0011, + launch_dispatch_kernel_0012, + launch_dispatch_kernel_0013, + launch_dispatch_kernel_0014, + launch_dispatch_kernel_0015, + launch_dispatch_kernel_0016, + launch_dispatch_kernel_0017, + launch_dispatch_kernel_0018, + launch_dispatch_kernel_0019, + launch_dispatch_kernel_0020, + launch_dispatch_kernel_0021, + launch_dispatch_kernel_0022, + launch_dispatch_kernel_0023, + launch_dispatch_kernel_0024, + launch_dispatch_kernel_0025, + launch_dispatch_kernel_0026, + launch_dispatch_kernel_0027, + launch_dispatch_kernel_0028, + launch_dispatch_kernel_0029, + launch_dispatch_kernel_0030, + launch_dispatch_kernel_0031, + launch_dispatch_kernel_0032, + launch_dispatch_kernel_0033, + launch_dispatch_kernel_0034, + launch_dispatch_kernel_0035, + launch_dispatch_kernel_0036, + launch_dispatch_kernel_0037, + launch_dispatch_kernel_0038, + launch_dispatch_kernel_0039, + launch_dispatch_kernel_0040, + launch_dispatch_kernel_0041, + launch_dispatch_kernel_0042, + launch_dispatch_kernel_0043, + launch_dispatch_kernel_0044, + launch_dispatch_kernel_0045, + launch_dispatch_kernel_0046, + launch_dispatch_kernel_0047, + launch_dispatch_kernel_0048, + launch_dispatch_kernel_0049, + launch_dispatch_kernel_0050, + launch_dispatch_kernel_0051, + launch_dispatch_kernel_0052, + launch_dispatch_kernel_0053, + launch_dispatch_kernel_0054, + launch_dispatch_kernel_0055, + launch_dispatch_kernel_0056, + launch_dispatch_kernel_0057, + launch_dispatch_kernel_0058, + launch_dispatch_kernel_0059, + launch_dispatch_kernel_0060, + launch_dispatch_kernel_0061, + launch_dispatch_kernel_0062, + launch_dispatch_kernel_0063, + launch_dispatch_kernel_0064, + launch_dispatch_kernel_0065, + launch_dispatch_kernel_0066, + launch_dispatch_kernel_0067, + launch_dispatch_kernel_0068, + launch_dispatch_kernel_0069, + launch_dispatch_kernel_0070, + launch_dispatch_kernel_0071, + launch_dispatch_kernel_0072, + launch_dispatch_kernel_0073, + launch_dispatch_kernel_0074, + launch_dispatch_kernel_0075, + launch_dispatch_kernel_0076, + launch_dispatch_kernel_0077, + launch_dispatch_kernel_0078, + launch_dispatch_kernel_0079, + launch_dispatch_kernel_0080, + launch_dispatch_kernel_0081, + launch_dispatch_kernel_0082, + launch_dispatch_kernel_0083, + launch_dispatch_kernel_0084, + launch_dispatch_kernel_0085, + launch_dispatch_kernel_0086, + launch_dispatch_kernel_0087, + launch_dispatch_kernel_0088, + launch_dispatch_kernel_0089, + launch_dispatch_kernel_0090, + launch_dispatch_kernel_0091, + launch_dispatch_kernel_0092, + launch_dispatch_kernel_0093, + launch_dispatch_kernel_0094, + launch_dispatch_kernel_0095, + launch_dispatch_kernel_0096, + launch_dispatch_kernel_0097, + launch_dispatch_kernel_0098, + launch_dispatch_kernel_0099, + launch_dispatch_kernel_0100, + launch_dispatch_kernel_0101, + launch_dispatch_kernel_0102, + launch_dispatch_kernel_0103, + launch_dispatch_kernel_0104, + launch_dispatch_kernel_0105, + launch_dispatch_kernel_0106, + launch_dispatch_kernel_0107, + launch_dispatch_kernel_0108, + launch_dispatch_kernel_0109, + launch_dispatch_kernel_0110, + launch_dispatch_kernel_0111, + launch_dispatch_kernel_0112, + launch_dispatch_kernel_0113, + launch_dispatch_kernel_0114, + launch_dispatch_kernel_0115, + launch_dispatch_kernel_0116, + launch_dispatch_kernel_0117, + launch_dispatch_kernel_0118, + launch_dispatch_kernel_0119, + launch_dispatch_kernel_0120, + launch_dispatch_kernel_0121, + launch_dispatch_kernel_0122, + launch_dispatch_kernel_0123, + launch_dispatch_kernel_0124, + launch_dispatch_kernel_0125, + launch_dispatch_kernel_0126, + launch_dispatch_kernel_0127, + launch_dispatch_kernel_0128, + launch_dispatch_kernel_0129, + launch_dispatch_kernel_0130, + launch_dispatch_kernel_0131, + launch_dispatch_kernel_0132, + launch_dispatch_kernel_0133, + launch_dispatch_kernel_0134, + launch_dispatch_kernel_0135, + launch_dispatch_kernel_0136, + launch_dispatch_kernel_0137, + launch_dispatch_kernel_0138, + launch_dispatch_kernel_0139, + launch_dispatch_kernel_0140, + launch_dispatch_kernel_0141, + launch_dispatch_kernel_0142, + launch_dispatch_kernel_0143, + launch_dispatch_kernel_0144, + launch_dispatch_kernel_0145, + launch_dispatch_kernel_0146, + launch_dispatch_kernel_0147, + launch_dispatch_kernel_0148, + launch_dispatch_kernel_0149, + launch_dispatch_kernel_0150, + launch_dispatch_kernel_0151, + launch_dispatch_kernel_0152, + launch_dispatch_kernel_0153, + launch_dispatch_kernel_0154, + launch_dispatch_kernel_0155, + launch_dispatch_kernel_0156, + launch_dispatch_kernel_0157, + launch_dispatch_kernel_0158, + launch_dispatch_kernel_0159, + launch_dispatch_kernel_0160, + launch_dispatch_kernel_0161, + launch_dispatch_kernel_0162, + launch_dispatch_kernel_0163, + launch_dispatch_kernel_0164, + launch_dispatch_kernel_0165, + launch_dispatch_kernel_0166, + launch_dispatch_kernel_0167, + launch_dispatch_kernel_0168, + launch_dispatch_kernel_0169, + launch_dispatch_kernel_0170, + launch_dispatch_kernel_0171, + launch_dispatch_kernel_0172, + launch_dispatch_kernel_0173, + launch_dispatch_kernel_0174, + launch_dispatch_kernel_0175, + launch_dispatch_kernel_0176, + launch_dispatch_kernel_0177, + launch_dispatch_kernel_0178, + launch_dispatch_kernel_0179, + launch_dispatch_kernel_0180, + launch_dispatch_kernel_0181, + launch_dispatch_kernel_0182, + launch_dispatch_kernel_0183, + launch_dispatch_kernel_0184, + launch_dispatch_kernel_0185, + launch_dispatch_kernel_0186, + launch_dispatch_kernel_0187, + launch_dispatch_kernel_0188, + launch_dispatch_kernel_0189, + launch_dispatch_kernel_0190, + launch_dispatch_kernel_0191, + launch_dispatch_kernel_0192, + launch_dispatch_kernel_0193, + launch_dispatch_kernel_0194, + launch_dispatch_kernel_0195, + launch_dispatch_kernel_0196, + launch_dispatch_kernel_0197, + launch_dispatch_kernel_0198, + launch_dispatch_kernel_0199, + launch_dispatch_kernel_0200, + launch_dispatch_kernel_0201, + launch_dispatch_kernel_0202, + launch_dispatch_kernel_0203, + launch_dispatch_kernel_0204, + launch_dispatch_kernel_0205, + launch_dispatch_kernel_0206, + launch_dispatch_kernel_0207, + launch_dispatch_kernel_0208, + launch_dispatch_kernel_0209, + launch_dispatch_kernel_0210, + launch_dispatch_kernel_0211, + launch_dispatch_kernel_0212, + launch_dispatch_kernel_0213, + launch_dispatch_kernel_0214, + launch_dispatch_kernel_0215, + launch_dispatch_kernel_0216, + launch_dispatch_kernel_0217, + launch_dispatch_kernel_0218, + launch_dispatch_kernel_0219, + launch_dispatch_kernel_0220, + launch_dispatch_kernel_0221, + launch_dispatch_kernel_0222, + launch_dispatch_kernel_0223, + launch_dispatch_kernel_0224, + launch_dispatch_kernel_0225, + launch_dispatch_kernel_0226, + launch_dispatch_kernel_0227, + launch_dispatch_kernel_0228, + launch_dispatch_kernel_0229, + launch_dispatch_kernel_0230, + launch_dispatch_kernel_0231, + launch_dispatch_kernel_0232, +) +from .tvm_ffi import register_tvm_ffi, tvm_ffi_function_names + +__all__ = [ + 'KERNELS', + 'ExportedKernel', + 'get_kernel', + 'dispatch_kernel_0000', + 'dispatch_kernel_0001', + 'dispatch_kernel_0002', + 'dispatch_kernel_0003', + 'dispatch_kernel_0004', + 'dispatch_kernel_0005', + 'dispatch_kernel_0006', + 'dispatch_kernel_0007', + 'dispatch_kernel_0008', + 'dispatch_kernel_0009', + 'dispatch_kernel_0010', + 'dispatch_kernel_0011', + 'dispatch_kernel_0012', + 'dispatch_kernel_0013', + 'dispatch_kernel_0014', + 'dispatch_kernel_0015', + 'dispatch_kernel_0016', + 'dispatch_kernel_0017', + 'dispatch_kernel_0018', + 'dispatch_kernel_0019', + 'dispatch_kernel_0020', + 'dispatch_kernel_0021', + 'dispatch_kernel_0022', + 'dispatch_kernel_0023', + 'dispatch_kernel_0024', + 'dispatch_kernel_0025', + 'dispatch_kernel_0026', + 'dispatch_kernel_0027', + 'dispatch_kernel_0028', + 'dispatch_kernel_0029', + 'dispatch_kernel_0030', + 'dispatch_kernel_0031', + 'dispatch_kernel_0032', + 'dispatch_kernel_0033', + 'dispatch_kernel_0034', + 'dispatch_kernel_0035', + 'dispatch_kernel_0036', + 'dispatch_kernel_0037', + 'dispatch_kernel_0038', + 'dispatch_kernel_0039', + 'dispatch_kernel_0040', + 'dispatch_kernel_0041', + 'dispatch_kernel_0042', + 'dispatch_kernel_0043', + 'dispatch_kernel_0044', + 'dispatch_kernel_0045', + 'dispatch_kernel_0046', + 'dispatch_kernel_0047', + 'dispatch_kernel_0048', + 'dispatch_kernel_0049', + 'dispatch_kernel_0050', + 'dispatch_kernel_0051', + 'dispatch_kernel_0052', + 'dispatch_kernel_0053', + 'dispatch_kernel_0054', + 'dispatch_kernel_0055', + 'dispatch_kernel_0056', + 'dispatch_kernel_0057', + 'dispatch_kernel_0058', + 'dispatch_kernel_0059', + 'dispatch_kernel_0060', + 'dispatch_kernel_0061', + 'dispatch_kernel_0062', + 'dispatch_kernel_0063', + 'dispatch_kernel_0064', + 'dispatch_kernel_0065', + 'dispatch_kernel_0066', + 'dispatch_kernel_0067', + 'dispatch_kernel_0068', + 'dispatch_kernel_0069', + 'dispatch_kernel_0070', + 'dispatch_kernel_0071', + 'dispatch_kernel_0072', + 'dispatch_kernel_0073', + 'dispatch_kernel_0074', + 'dispatch_kernel_0075', + 'dispatch_kernel_0076', + 'dispatch_kernel_0077', + 'dispatch_kernel_0078', + 'dispatch_kernel_0079', + 'dispatch_kernel_0080', + 'dispatch_kernel_0081', + 'dispatch_kernel_0082', + 'dispatch_kernel_0083', + 'dispatch_kernel_0084', + 'dispatch_kernel_0085', + 'dispatch_kernel_0086', + 'dispatch_kernel_0087', + 'dispatch_kernel_0088', + 'dispatch_kernel_0089', + 'dispatch_kernel_0090', + 'dispatch_kernel_0091', + 'dispatch_kernel_0092', + 'dispatch_kernel_0093', + 'dispatch_kernel_0094', + 'dispatch_kernel_0095', + 'dispatch_kernel_0096', + 'dispatch_kernel_0097', + 'dispatch_kernel_0098', + 'dispatch_kernel_0099', + 'dispatch_kernel_0100', + 'dispatch_kernel_0101', + 'dispatch_kernel_0102', + 'dispatch_kernel_0103', + 'dispatch_kernel_0104', + 'dispatch_kernel_0105', + 'dispatch_kernel_0106', + 'dispatch_kernel_0107', + 'dispatch_kernel_0108', + 'dispatch_kernel_0109', + 'dispatch_kernel_0110', + 'dispatch_kernel_0111', + 'dispatch_kernel_0112', + 'dispatch_kernel_0113', + 'dispatch_kernel_0114', + 'dispatch_kernel_0115', + 'dispatch_kernel_0116', + 'dispatch_kernel_0117', + 'dispatch_kernel_0118', + 'dispatch_kernel_0119', + 'dispatch_kernel_0120', + 'dispatch_kernel_0121', + 'dispatch_kernel_0122', + 'dispatch_kernel_0123', + 'dispatch_kernel_0124', + 'dispatch_kernel_0125', + 'dispatch_kernel_0126', + 'dispatch_kernel_0127', + 'dispatch_kernel_0128', + 'dispatch_kernel_0129', + 'dispatch_kernel_0130', + 'dispatch_kernel_0131', + 'dispatch_kernel_0132', + 'dispatch_kernel_0133', + 'dispatch_kernel_0134', + 'dispatch_kernel_0135', + 'dispatch_kernel_0136', + 'dispatch_kernel_0137', + 'dispatch_kernel_0138', + 'dispatch_kernel_0139', + 'dispatch_kernel_0140', + 'dispatch_kernel_0141', + 'dispatch_kernel_0142', + 'dispatch_kernel_0143', + 'dispatch_kernel_0144', + 'dispatch_kernel_0145', + 'dispatch_kernel_0146', + 'dispatch_kernel_0147', + 'dispatch_kernel_0148', + 'dispatch_kernel_0149', + 'dispatch_kernel_0150', + 'dispatch_kernel_0151', + 'dispatch_kernel_0152', + 'dispatch_kernel_0153', + 'dispatch_kernel_0154', + 'dispatch_kernel_0155', + 'dispatch_kernel_0156', + 'dispatch_kernel_0157', + 'dispatch_kernel_0158', + 'dispatch_kernel_0159', + 'dispatch_kernel_0160', + 'dispatch_kernel_0161', + 'dispatch_kernel_0162', + 'dispatch_kernel_0163', + 'dispatch_kernel_0164', + 'dispatch_kernel_0165', + 'dispatch_kernel_0166', + 'dispatch_kernel_0167', + 'dispatch_kernel_0168', + 'dispatch_kernel_0169', + 'dispatch_kernel_0170', + 'dispatch_kernel_0171', + 'dispatch_kernel_0172', + 'dispatch_kernel_0173', + 'dispatch_kernel_0174', + 'dispatch_kernel_0175', + 'dispatch_kernel_0176', + 'dispatch_kernel_0177', + 'dispatch_kernel_0178', + 'dispatch_kernel_0179', + 'dispatch_kernel_0180', + 'dispatch_kernel_0181', + 'dispatch_kernel_0182', + 'dispatch_kernel_0183', + 'dispatch_kernel_0184', + 'dispatch_kernel_0185', + 'dispatch_kernel_0186', + 'dispatch_kernel_0187', + 'dispatch_kernel_0188', + 'dispatch_kernel_0189', + 'dispatch_kernel_0190', + 'dispatch_kernel_0191', + 'dispatch_kernel_0192', + 'dispatch_kernel_0193', + 'dispatch_kernel_0194', + 'dispatch_kernel_0195', + 'dispatch_kernel_0196', + 'dispatch_kernel_0197', + 'dispatch_kernel_0198', + 'dispatch_kernel_0199', + 'dispatch_kernel_0200', + 'dispatch_kernel_0201', + 'dispatch_kernel_0202', + 'dispatch_kernel_0203', + 'dispatch_kernel_0204', + 'dispatch_kernel_0205', + 'dispatch_kernel_0206', + 'dispatch_kernel_0207', + 'dispatch_kernel_0208', + 'dispatch_kernel_0209', + 'dispatch_kernel_0210', + 'dispatch_kernel_0211', + 'dispatch_kernel_0212', + 'dispatch_kernel_0213', + 'dispatch_kernel_0214', + 'dispatch_kernel_0215', + 'dispatch_kernel_0216', + 'dispatch_kernel_0217', + 'dispatch_kernel_0218', + 'dispatch_kernel_0219', + 'dispatch_kernel_0220', + 'dispatch_kernel_0221', + 'dispatch_kernel_0222', + 'dispatch_kernel_0223', + 'dispatch_kernel_0224', + 'dispatch_kernel_0225', + 'dispatch_kernel_0226', + 'dispatch_kernel_0227', + 'dispatch_kernel_0228', + 'dispatch_kernel_0229', + 'dispatch_kernel_0230', + 'dispatch_kernel_0231', + 'dispatch_kernel_0232', + 'launch_dispatch_kernel_0000', + 'launch_dispatch_kernel_0001', + 'launch_dispatch_kernel_0002', + 'launch_dispatch_kernel_0003', + 'launch_dispatch_kernel_0004', + 'launch_dispatch_kernel_0005', + 'launch_dispatch_kernel_0006', + 'launch_dispatch_kernel_0007', + 'launch_dispatch_kernel_0008', + 'launch_dispatch_kernel_0009', + 'launch_dispatch_kernel_0010', + 'launch_dispatch_kernel_0011', + 'launch_dispatch_kernel_0012', + 'launch_dispatch_kernel_0013', + 'launch_dispatch_kernel_0014', + 'launch_dispatch_kernel_0015', + 'launch_dispatch_kernel_0016', + 'launch_dispatch_kernel_0017', + 'launch_dispatch_kernel_0018', + 'launch_dispatch_kernel_0019', + 'launch_dispatch_kernel_0020', + 'launch_dispatch_kernel_0021', + 'launch_dispatch_kernel_0022', + 'launch_dispatch_kernel_0023', + 'launch_dispatch_kernel_0024', + 'launch_dispatch_kernel_0025', + 'launch_dispatch_kernel_0026', + 'launch_dispatch_kernel_0027', + 'launch_dispatch_kernel_0028', + 'launch_dispatch_kernel_0029', + 'launch_dispatch_kernel_0030', + 'launch_dispatch_kernel_0031', + 'launch_dispatch_kernel_0032', + 'launch_dispatch_kernel_0033', + 'launch_dispatch_kernel_0034', + 'launch_dispatch_kernel_0035', + 'launch_dispatch_kernel_0036', + 'launch_dispatch_kernel_0037', + 'launch_dispatch_kernel_0038', + 'launch_dispatch_kernel_0039', + 'launch_dispatch_kernel_0040', + 'launch_dispatch_kernel_0041', + 'launch_dispatch_kernel_0042', + 'launch_dispatch_kernel_0043', + 'launch_dispatch_kernel_0044', + 'launch_dispatch_kernel_0045', + 'launch_dispatch_kernel_0046', + 'launch_dispatch_kernel_0047', + 'launch_dispatch_kernel_0048', + 'launch_dispatch_kernel_0049', + 'launch_dispatch_kernel_0050', + 'launch_dispatch_kernel_0051', + 'launch_dispatch_kernel_0052', + 'launch_dispatch_kernel_0053', + 'launch_dispatch_kernel_0054', + 'launch_dispatch_kernel_0055', + 'launch_dispatch_kernel_0056', + 'launch_dispatch_kernel_0057', + 'launch_dispatch_kernel_0058', + 'launch_dispatch_kernel_0059', + 'launch_dispatch_kernel_0060', + 'launch_dispatch_kernel_0061', + 'launch_dispatch_kernel_0062', + 'launch_dispatch_kernel_0063', + 'launch_dispatch_kernel_0064', + 'launch_dispatch_kernel_0065', + 'launch_dispatch_kernel_0066', + 'launch_dispatch_kernel_0067', + 'launch_dispatch_kernel_0068', + 'launch_dispatch_kernel_0069', + 'launch_dispatch_kernel_0070', + 'launch_dispatch_kernel_0071', + 'launch_dispatch_kernel_0072', + 'launch_dispatch_kernel_0073', + 'launch_dispatch_kernel_0074', + 'launch_dispatch_kernel_0075', + 'launch_dispatch_kernel_0076', + 'launch_dispatch_kernel_0077', + 'launch_dispatch_kernel_0078', + 'launch_dispatch_kernel_0079', + 'launch_dispatch_kernel_0080', + 'launch_dispatch_kernel_0081', + 'launch_dispatch_kernel_0082', + 'launch_dispatch_kernel_0083', + 'launch_dispatch_kernel_0084', + 'launch_dispatch_kernel_0085', + 'launch_dispatch_kernel_0086', + 'launch_dispatch_kernel_0087', + 'launch_dispatch_kernel_0088', + 'launch_dispatch_kernel_0089', + 'launch_dispatch_kernel_0090', + 'launch_dispatch_kernel_0091', + 'launch_dispatch_kernel_0092', + 'launch_dispatch_kernel_0093', + 'launch_dispatch_kernel_0094', + 'launch_dispatch_kernel_0095', + 'launch_dispatch_kernel_0096', + 'launch_dispatch_kernel_0097', + 'launch_dispatch_kernel_0098', + 'launch_dispatch_kernel_0099', + 'launch_dispatch_kernel_0100', + 'launch_dispatch_kernel_0101', + 'launch_dispatch_kernel_0102', + 'launch_dispatch_kernel_0103', + 'launch_dispatch_kernel_0104', + 'launch_dispatch_kernel_0105', + 'launch_dispatch_kernel_0106', + 'launch_dispatch_kernel_0107', + 'launch_dispatch_kernel_0108', + 'launch_dispatch_kernel_0109', + 'launch_dispatch_kernel_0110', + 'launch_dispatch_kernel_0111', + 'launch_dispatch_kernel_0112', + 'launch_dispatch_kernel_0113', + 'launch_dispatch_kernel_0114', + 'launch_dispatch_kernel_0115', + 'launch_dispatch_kernel_0116', + 'launch_dispatch_kernel_0117', + 'launch_dispatch_kernel_0118', + 'launch_dispatch_kernel_0119', + 'launch_dispatch_kernel_0120', + 'launch_dispatch_kernel_0121', + 'launch_dispatch_kernel_0122', + 'launch_dispatch_kernel_0123', + 'launch_dispatch_kernel_0124', + 'launch_dispatch_kernel_0125', + 'launch_dispatch_kernel_0126', + 'launch_dispatch_kernel_0127', + 'launch_dispatch_kernel_0128', + 'launch_dispatch_kernel_0129', + 'launch_dispatch_kernel_0130', + 'launch_dispatch_kernel_0131', + 'launch_dispatch_kernel_0132', + 'launch_dispatch_kernel_0133', + 'launch_dispatch_kernel_0134', + 'launch_dispatch_kernel_0135', + 'launch_dispatch_kernel_0136', + 'launch_dispatch_kernel_0137', + 'launch_dispatch_kernel_0138', + 'launch_dispatch_kernel_0139', + 'launch_dispatch_kernel_0140', + 'launch_dispatch_kernel_0141', + 'launch_dispatch_kernel_0142', + 'launch_dispatch_kernel_0143', + 'launch_dispatch_kernel_0144', + 'launch_dispatch_kernel_0145', + 'launch_dispatch_kernel_0146', + 'launch_dispatch_kernel_0147', + 'launch_dispatch_kernel_0148', + 'launch_dispatch_kernel_0149', + 'launch_dispatch_kernel_0150', + 'launch_dispatch_kernel_0151', + 'launch_dispatch_kernel_0152', + 'launch_dispatch_kernel_0153', + 'launch_dispatch_kernel_0154', + 'launch_dispatch_kernel_0155', + 'launch_dispatch_kernel_0156', + 'launch_dispatch_kernel_0157', + 'launch_dispatch_kernel_0158', + 'launch_dispatch_kernel_0159', + 'launch_dispatch_kernel_0160', + 'launch_dispatch_kernel_0161', + 'launch_dispatch_kernel_0162', + 'launch_dispatch_kernel_0163', + 'launch_dispatch_kernel_0164', + 'launch_dispatch_kernel_0165', + 'launch_dispatch_kernel_0166', + 'launch_dispatch_kernel_0167', + 'launch_dispatch_kernel_0168', + 'launch_dispatch_kernel_0169', + 'launch_dispatch_kernel_0170', + 'launch_dispatch_kernel_0171', + 'launch_dispatch_kernel_0172', + 'launch_dispatch_kernel_0173', + 'launch_dispatch_kernel_0174', + 'launch_dispatch_kernel_0175', + 'launch_dispatch_kernel_0176', + 'launch_dispatch_kernel_0177', + 'launch_dispatch_kernel_0178', + 'launch_dispatch_kernel_0179', + 'launch_dispatch_kernel_0180', + 'launch_dispatch_kernel_0181', + 'launch_dispatch_kernel_0182', + 'launch_dispatch_kernel_0183', + 'launch_dispatch_kernel_0184', + 'launch_dispatch_kernel_0185', + 'launch_dispatch_kernel_0186', + 'launch_dispatch_kernel_0187', + 'launch_dispatch_kernel_0188', + 'launch_dispatch_kernel_0189', + 'launch_dispatch_kernel_0190', + 'launch_dispatch_kernel_0191', + 'launch_dispatch_kernel_0192', + 'launch_dispatch_kernel_0193', + 'launch_dispatch_kernel_0194', + 'launch_dispatch_kernel_0195', + 'launch_dispatch_kernel_0196', + 'launch_dispatch_kernel_0197', + 'launch_dispatch_kernel_0198', + 'launch_dispatch_kernel_0199', + 'launch_dispatch_kernel_0200', + 'launch_dispatch_kernel_0201', + 'launch_dispatch_kernel_0202', + 'launch_dispatch_kernel_0203', + 'launch_dispatch_kernel_0204', + 'launch_dispatch_kernel_0205', + 'launch_dispatch_kernel_0206', + 'launch_dispatch_kernel_0207', + 'launch_dispatch_kernel_0208', + 'launch_dispatch_kernel_0209', + 'launch_dispatch_kernel_0210', + 'launch_dispatch_kernel_0211', + 'launch_dispatch_kernel_0212', + 'launch_dispatch_kernel_0213', + 'launch_dispatch_kernel_0214', + 'launch_dispatch_kernel_0215', + 'launch_dispatch_kernel_0216', + 'launch_dispatch_kernel_0217', + 'launch_dispatch_kernel_0218', + 'launch_dispatch_kernel_0219', + 'launch_dispatch_kernel_0220', + 'launch_dispatch_kernel_0221', + 'launch_dispatch_kernel_0222', + 'launch_dispatch_kernel_0223', + 'launch_dispatch_kernel_0224', + 'launch_dispatch_kernel_0225', + 'launch_dispatch_kernel_0226', + 'launch_dispatch_kernel_0227', + 'launch_dispatch_kernel_0228', + 'launch_dispatch_kernel_0229', + 'launch_dispatch_kernel_0230', + 'launch_dispatch_kernel_0231', + 'launch_dispatch_kernel_0232', + 'register_tvm_ffi', + 'tvm_ffi_function_names', +] + +# Semantic exports generated from export_plan.package_exports. +from .interface import KNNBuildRuntime as KNNBuildRuntime +from .interface import PreparedKNNBuild as PreparedKNNBuild +from .interface import init as init +from .interface import knn_build as knn_build +from .interface import knn_build_prepared as knn_build_prepared +from .interface import prepare_knn_build as prepare_knn_build +__all__ = [*globals().get('__all__', []), 'KNNBuildRuntime', 'PreparedKNNBuild', 'init', 'knn_build', 'knn_build_prepared', 'prepare_knn_build'] diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_benchmark.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_benchmark.py new file mode 100644 index 00000000..fd0e6677 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_benchmark.py @@ -0,0 +1,648 @@ +from __future__ import annotations + +import bisect +import contextlib +import ctypes +import importlib +import importlib.metadata +import math +import statistics +import sys +from collections.abc import Callable +from dataclasses import dataclass +from pathlib import Path +from typing import Any + + +@dataclass(frozen=True) +class BenchResult: + """Strict CUPTI timing plus explicitly non-official host diagnostics. + + Cold-L2 flushing completes before host timestamps begin, so synchronized + E2E isolates semantic call start through candidate completion. ``times_ms`` + remains the official correlated GPU span. + """ + + times_ms: list[float] + backend: str = "cupti" + kernel_sum_times_ms: list[float] | None = None + inter_kernel_gap_times_ms: list[float] | None = None + active_union_times_ms: list[float] | None = None + activity_counts: list[int] | None = None + launch_activity_counts: list[int] | None = None + kernel_activity_counts: list[int] | None = None + submission_times_ms: list[float] | None = None + synchronized_e2e_times_ms: list[float] | None = None + cold_first_call_host_enqueue_ms: float | None = None + cold_first_call_synchronized_e2e_ms: float | None = None + + def __post_init__(self) -> None: + if self.backend != "cupti": + raise ValueError(f"exported benchmark timing backend must be 'cupti', got {self.backend!r}") + + @property + def median_ms(self) -> float: + return float(statistics.median(self.times_ms)) + + @property + def min_ms(self) -> float: + return float(min(self.times_ms)) + + @property + def mean_ms(self) -> float: + return float(statistics.fmean(self.times_ms)) + + @property + def median_gpu_span_ms(self) -> float: + return self.median_ms + + @property + def median_kernel_sum_ms(self) -> float | None: + if self.kernel_sum_times_ms is None: + return None + return float(statistics.median(self.kernel_sum_times_ms)) + + @property + def median_inter_kernel_gap_ms(self) -> float | None: + if self.inter_kernel_gap_times_ms is None: + return None + return float(statistics.median(self.inter_kernel_gap_times_ms)) + + @property + def median_active_union_ms(self) -> float | None: + if self.active_union_times_ms is None: + return None + return float(statistics.median(self.active_union_times_ms)) + + @property + def median_activity_count(self) -> float | None: + if self.activity_counts is None: + return None + return float(statistics.median(self.activity_counts)) + + @property + def median_launch_activity_count(self) -> float | None: + if self.launch_activity_counts is None: + return None + return float(statistics.median(self.launch_activity_counts)) + + @property + def median_kernel_activity_count(self) -> float | None: + if self.kernel_activity_counts is None: + return None + return float(statistics.median(self.kernel_activity_counts)) + + @property + def median_submission_ms(self) -> float | None: + if self.submission_times_ms is None: + return None + return float(statistics.median(self.submission_times_ms)) + + @property + def median_host_enqueue_ms(self) -> float | None: + return self.median_submission_ms + + @property + def host_enqueue_times_ms(self) -> list[float] | None: + return self.submission_times_ms + + @property + def median_synchronized_e2e_ms(self) -> float | None: + if self.synchronized_e2e_times_ms is None: + return None + return float(statistics.median(self.synchronized_e2e_times_ms)) + + +@dataclass(frozen=True) +class HostCallTiming: + """Diagnostic host brackets for one call; never an official GPU timing.""" + + host_enqueue_ms: float + synchronized_e2e_ms: float + + +@dataclass(frozen=True) +class _CuptiTiming: + gpu_span_ms: list[float] + kernel_sum_ms: list[float] + inter_kernel_gap_ms: list[float] + active_union_ms: list[float] + activity_count: list[int] + launch_activity_count: list[int] + kernel_activity_count: list[int] + + +class _L2Flusher: + def __init__(self) -> None: + import torch + + l2_size = int(torch.cuda.get_device_properties(0).L2_cache_size) + if l2_size <= 0: + raise RuntimeError("CUDA device did not report a positive L2 cache size") + self._buffer = torch.empty(2 * l2_size, dtype=torch.int8, device="cuda") + + def flush(self) -> None: + self._buffer.zero_() + + +_CUPTI: Any | None = None + + +def _extend_cuda_namespace_for_pathfinder() -> None: + try: + import cuda + except ImportError: + return + cuda_paths = getattr(cuda, "__path__", None) + if cuda_paths is None: + return + known = {str(path) for path in cuda_paths} + for entry in sys.path: + if not entry: + continue + cuda_root = Path(entry) / "cuda" + if (cuda_root / "pathfinder").is_dir() and str(cuda_root) not in known: + cuda_paths.append(str(cuda_root)) + known.add(str(cuda_root)) + + +def _preload_cupti_library() -> None: + # cupti-python imports cuda.pathfinder even when libcupti was found through + # the nvidia-cuda-cupti distribution. Extend the split CUDA namespace + # before either loading path so the later extension import is reliable. + _extend_cuda_namespace_for_pathfinder() + try: + distribution = importlib.metadata.distribution("nvidia-cuda-cupti") + major = importlib.metadata.version("cupti-python").split(".", 1)[0] + packaged = next( + ( + distribution.locate_file(path) + for path in distribution.files or () + if str(path).endswith(f"/libcupti.so.{major}") + ), + None, + ) + if packaged is not None and Path(packaged).is_file(): + ctypes.CDLL(str(packaged), mode=ctypes.RTLD_GLOBAL) + return + except (importlib.metadata.PackageNotFoundError, OSError): + pass + try: + pathfinder = importlib.import_module("cuda.pathfinder") + except ImportError: + return + loader = getattr(pathfinder, "load_nvidia_dynamic_lib", None) + if loader is None: + return + loaded = loader("cupti") + loaded_path = str(getattr(loaded, "abs_path", "")) + try: + major = importlib.metadata.version("cupti-python").split(".", 1)[0] + except importlib.metadata.PackageNotFoundError: + return + expected = f"libcupti.so.{major}" if major.isdigit() else None + if expected and loaded_path: + name = Path(loaded_path).name + if name.startswith("libcupti.so.") and name != expected: + raise ImportError(f"incompatible CUPTI library {loaded_path}; cupti-python expects {expected}") + + +def require_cupti() -> Any: + global _CUPTI + if _CUPTI is not None: + return _CUPTI + try: + _preload_cupti_library() + from cupti import cupti + except ImportError as exc: + raise RuntimeError( + "CUPTI timing is required; install the exported repository with its benchmark extra: " + "python -m pip install -e '.[benchmark]'" + ) from exc + _CUPTI = cupti + return _CUPTI + + +def measure_host_call(fn: Callable[[], Any]) -> tuple[Any, HostCallTiming]: + """Run one call and return explicitly labeled host diagnostics. + + The enqueue bracket ends when ``fn`` returns. The synchronized bracket + ends after ``torch.cuda.synchronize()``. A function that synchronizes + internally will therefore have a blocking enqueue bracket; callers should + not interpret either value as GPU-only execution time. + """ + import torch + + cupti = require_cupti() + start = cupti.get_timestamp() + value = fn() + submitted = cupti.get_timestamp() + torch.cuda.synchronize() + completed = cupti.get_timestamp() + return value, HostCallTiming( + host_enqueue_ms=(submitted - start) / 1e6, + synchronized_e2e_ms=(completed - start) / 1e6, + ) + + +def _finite_timing(value: Any, *, name: str, positive: bool) -> float: + number = float(value) + if not math.isfinite(number) or (number <= 0.0 if positive else number < 0.0): + relation = "positive" if positive else "non-negative" + raise ValueError(f"{name} must be finite and {relation}, got {value!r}") + return number + + +def _percentile(values: list[float], fraction: float) -> float: + if not values: + raise ValueError("percentile requires at least one value") + ordered = sorted(values) + rank = (len(ordered) - 1) * float(fraction) + lower = math.floor(rank) + upper = math.ceil(rank) + if lower == upper: + return ordered[lower] + weight = rank - lower + return ordered[lower] * (1.0 - weight) + ordered[upper] * weight + + +def _timing_distribution(values: Any, *, name: str, positive: bool) -> dict[str, Any]: + if not isinstance(values, list) or not values: + raise ValueError(f"{name} must contain at least one timing sample") + samples = [_finite_timing(value, name=name, positive=positive) for value in values] + return { + "sample_count": len(samples), + "min": min(samples), + "median": float(statistics.median(samples)), + "p90": _percentile(samples, 0.90), + "max": max(samples), + } + + +def _host_call_payload(timing: HostCallTiming, *, name: str) -> dict[str, Any]: + host_enqueue_ms = _finite_timing(timing.host_enqueue_ms, name=f"{name}.host_enqueue_ms", positive=False) + synchronized_e2e_ms = _finite_timing( + timing.synchronized_e2e_ms, + name=f"{name}.synchronized_e2e_ms", + positive=True, + ) + if host_enqueue_ms > synchronized_e2e_ms: + raise ValueError(f"{name}.host_enqueue_ms cannot exceed synchronized_e2e_ms") + return { + "timing_class": "cupti_timestamp_host_diagnostic", + "host_enqueue_ms": host_enqueue_ms, + "synchronized_e2e_ms": synchronized_e2e_ms, + } + + +def _hot_compute_payload(timing: BenchResult, *, name: str, cache_state: str) -> dict[str, Any]: + if timing.backend != "cupti": + raise ValueError(f"{name} must use CUPTI") + gpu_span = _timing_distribution(timing.times_ms, name=f"{name}.gpu_span_ms", positive=True) + host_enqueue = _timing_distribution( + timing.submission_times_ms, + name=f"{name}.host_enqueue_ms", + positive=False, + ) + synchronized_e2e = _timing_distribution( + timing.synchronized_e2e_times_ms, + name=f"{name}.synchronized_e2e_ms", + positive=True, + ) + sample_counts = { + gpu_span["sample_count"], + host_enqueue["sample_count"], + synchronized_e2e["sample_count"], + } + if len(sample_counts) != 1: + raise ValueError(f"{name} CUPTI/host timing sample counts must match") + for index, (enqueue_sample, e2e_sample) in enumerate( + zip(timing.submission_times_ms, timing.synchronized_e2e_times_ms, strict=True) + ): + if float(enqueue_sample) > float(e2e_sample): + raise ValueError(f"{name} host enqueue sample {index} cannot exceed its synchronized E2E sample") + return { + "timing_backend": "cupti", + "cache_state": cache_state, + "sample_count": gpu_span["sample_count"], + "gpu_span_ms": gpu_span, + "host_enqueue_ms": host_enqueue, + "synchronized_e2e_ms": synchronized_e2e, + } + + +def runtime_lifecycle_metrics( + *, + api: str, + measurement_session_id: str, + timing_boundary: str, + output_policy: str, + init: HostCallTiming | None, + init_sample_id: str | None, + first_compute: HostCallTiming, + first_cache_state: str, + hot_compute: BenchResult, + hot_cache_state: str, + amortization_call_counts: tuple[int, ...] = (1, 10, 100, 1000), + code_cache_state: str = "process_order_dependent", +) -> dict[str, Any]: + """Normalize init-once, first-signature, and repeated public-call evidence. + + ``first_compute`` is intentionally a CUPTI-timestamp host diagnostic: route + selection, compilation, allocation, launch, and completion all belong to + its synchronized E2E bracket, but it is not mislabeled as GPU-only time. + ``hot_compute`` retains strict correlated CUPTI activity timing. + """ + + for field_name, value in ( + ("api", api), + ("measurement_session_id", measurement_session_id), + ("timing_boundary", timing_boundary), + ("output_policy", output_policy), + ("first_cache_state", first_cache_state), + ("hot_cache_state", hot_cache_state), + ("code_cache_state", code_cache_state), + ): + if not isinstance(value, str) or not value.strip(): + raise ValueError(f"runtime lifecycle {field_name} must be a non-empty string") + if ( + not amortization_call_counts + or any(isinstance(value, bool) or not isinstance(value, int) or value <= 0 for value in amortization_call_counts) + or tuple(amortization_call_counts) != tuple(sorted(set(amortization_call_counts))) + ): + raise ValueError("amortization_call_counts must be strictly increasing positive integers") + + init_payload = _host_call_payload(init, name="init_once") if init is not None else None + if init_payload is not None: + if not isinstance(init_sample_id, str) or not init_sample_id.strip(): + raise ValueError("init_sample_id is required when init timing is present") + init_payload = {"sample_id": init_sample_id, **init_payload} + elif init_sample_id is not None: + raise ValueError("init_sample_id requires init timing") + first_payload = { + "cache_state": first_cache_state, + "code_cache_state": code_cache_state, + **_host_call_payload(first_compute, name="first_compute"), + "gpu_span_ms": None, + "gpu_span_status": "not_collected_for_slot_miss_host_diagnostic", + } + hot_payload = _hot_compute_payload(hot_compute, name="hot_compute", cache_state=hot_cache_state) + + first_enqueue = first_payload["host_enqueue_ms"] + first_e2e = first_payload["synchronized_e2e_ms"] + hot_enqueue = hot_payload["host_enqueue_ms"]["median"] + hot_e2e = hot_payload["synchronized_e2e_ms"]["median"] + init_enqueue = init_payload["host_enqueue_ms"] if init_payload is not None else 0.0 + init_e2e = init_payload["synchronized_e2e_ms"] if init_payload is not None else 0.0 + amortized: list[dict[str, Any]] = [] + for call_count in amortization_call_counts: + repeated = call_count - 1 + after_init_enqueue = (first_enqueue + repeated * hot_enqueue) / call_count + after_init_e2e = (first_e2e + repeated * hot_e2e) / call_count + amortized.append( + { + "public_call_count": call_count, + "after_init_host_enqueue_ms_per_call": after_init_enqueue, + "after_init_synchronized_e2e_ms_per_call": after_init_e2e, + "including_init_host_enqueue_ms_per_call": ( + (init_enqueue + first_enqueue + repeated * hot_enqueue) / call_count + ), + "including_init_synchronized_e2e_ms_per_call": ( + (init_e2e + first_e2e + repeated * hot_e2e) / call_count + ), + } + ) + + return { + "schema": "loom-public-runtime-lifecycle-v1", + "api": api, + "measurement_session_id": measurement_session_id, + "timing_boundary": timing_boundary, + "output_policy": output_policy, + "init_once": init_payload, + "first_compute": first_payload, + "hot_compute": hot_payload, + "amortization": { + "model": "observed_first_call_plus_repeated_hot_median", + "init_attribution": "one_init_sample_per_validation_shard_runtime", + "missing_init_policy": "zero_for_api_without_explicit_init", + "call_counts": amortized, + }, + } + + +def compare_runtime_lifecycles(candidate: dict[str, Any], baseline: dict[str, Any]) -> dict[str, Any]: + """Compare two normalized public-API lifecycle records.""" + + for name, lifecycle in (("candidate", candidate), ("baseline", baseline)): + if not isinstance(lifecycle, dict) or lifecycle.get("schema") != "loom-public-runtime-lifecycle-v1": + raise ValueError(f"{name} runtime lifecycle has an invalid schema") + candidate_hot = candidate["hot_compute"] + baseline_hot = baseline["hot_compute"] + candidate_rows = candidate["amortization"]["call_counts"] + baseline_rows = baseline["amortization"]["call_counts"] + candidate_counts = [row["public_call_count"] for row in candidate_rows] + baseline_counts = [row["public_call_count"] for row in baseline_rows] + if candidate_counts != baseline_counts: + raise ValueError("candidate and baseline amortization call counts must match") + + amortized: list[dict[str, Any]] = [] + for candidate_row, baseline_row in zip(candidate_rows, baseline_rows, strict=True): + candidate_after_init = candidate_row["after_init_synchronized_e2e_ms_per_call"] + baseline_after_init = baseline_row["after_init_synchronized_e2e_ms_per_call"] + candidate_including_init = candidate_row["including_init_synchronized_e2e_ms_per_call"] + baseline_including_init = baseline_row["including_init_synchronized_e2e_ms_per_call"] + amortized.append( + { + "public_call_count": candidate_row["public_call_count"], + "after_init_synchronized_e2e_speedup": baseline_after_init / candidate_after_init, + "including_init_synchronized_e2e_speedup": baseline_including_init / candidate_including_init, + } + ) + + return { + "schema": "loom-public-runtime-lifecycle-comparison-v1", + "speedup_convention": "baseline_latency_divided_by_candidate_latency", + "hot_synchronized_e2e_speedup": ( + baseline_hot["synchronized_e2e_ms"]["median"] + / candidate_hot["synchronized_e2e_ms"]["median"] + ), + "hot_gpu_span_speedup": ( + baseline_hot["gpu_span_ms"]["median"] / candidate_hot["gpu_span_ms"]["median"] + ), + "amortized": amortized, + } + + +def _complete_l2_flush_before_bracket(flusher: Any, synchronize: Callable[[], None]) -> None: + """Finish cold-L2 preconditioning before host latency timestamps begin.""" + if flusher is None: + return + flusher.flush() + synchronize() + + +def _correlate( + cpu_brackets: list[tuple[int, int, int]], + launches: list[tuple[int, int, int]], + kernels: list[tuple[int, int, int]], +) -> _CuptiTiming: + if not launches or not kernels: + raise RuntimeError("CUPTI collected no launch/kernel activities") + launches.sort(key=lambda item: item[0]) + launch_starts = [item[0] for item in launches] + kernels.sort(key=lambda item: item[0]) + kernel_indices_by_correlation: dict[int, list[int]] = {} + for index, (_start, _end, correlation_id) in enumerate(kernels): + kernel_indices_by_correlation.setdefault(correlation_id, []).append(index) + + gpu_span_ms: list[float] = [] + kernel_sum_ms: list[float] = [] + inter_kernel_gap_ms: list[float] = [] + active_union_ms: list[float] = [] + activity_count: list[int] = [] + launch_activity_count: list[int] = [] + kernel_activity_count: list[int] = [] + for bracket_start, bracket_end, _completed in cpu_brackets: + lo = bisect.bisect_left(launch_starts, bracket_start) + hi = bisect.bisect_right(launch_starts, bracket_end) + correlation_ids = {launches[index][2] for index in range(lo, hi)} + launch_activity_count.append( + sum( + 1 + for index in range(lo, hi) + if kernel_indices_by_correlation.get(launches[index][2]) + ) + ) + selected_indices = { + index + for correlation_id in correlation_ids + for index in kernel_indices_by_correlation.get(correlation_id, ()) + } + if not selected_indices: + raise RuntimeError("CUPTI could not correlate a benchmark iteration with GPU kernel activity") + spans = [(kernels[index][0], kernels[index][1]) for index in selected_indices] + span_ns = max(end for _, end in spans) - min(start for start, _ in spans) + sum_ns = sum(end - start for start, end in spans) + covered_ns = 0 + current_start, current_end = sorted(spans)[0] + for start, end in sorted(spans)[1:]: + if start <= current_end: + current_end = max(current_end, end) + else: + covered_ns += current_end - current_start + current_start, current_end = start, end + covered_ns += current_end - current_start + gpu_span_ms.append(span_ns / 1e6) + kernel_sum_ms.append(sum_ns / 1e6) + active_union_ms.append(covered_ns / 1e6) + inter_kernel_gap_ms.append((span_ns - covered_ns) / 1e6) + activity_count.append(len(selected_indices)) + kernel_activity_count.append(len(selected_indices)) + return _CuptiTiming( + gpu_span_ms=gpu_span_ms, + kernel_sum_ms=kernel_sum_ms, + inter_kernel_gap_ms=inter_kernel_gap_ms, + active_union_ms=active_union_ms, + activity_count=activity_count, + launch_activity_count=launch_activity_count, + kernel_activity_count=kernel_activity_count, + ) + + +def bench_gpu_time( + fn: Callable[[], Any], + *, + warmup_iters: int = 5, + bench_iters: int = 20, + cold_l2: bool = True, + cold_first_call: HostCallTiming | None = None, +) -> BenchResult: + """Measure a zero-argument GPU workload with strict CUPTI activity tracing. + + L2 is flushed before every warmup and measured iteration. This function + never falls back to wall-clock or CUDA-event timing. + """ + if warmup_iters < 0 or bench_iters <= 0: + raise ValueError("warmup_iters must be non-negative and bench_iters must be positive") + + import torch + + cupti = require_cupti() + flusher = _L2Flusher() if cold_l2 else None + for _ in range(warmup_iters): + if flusher is not None: + flusher.flush() + fn() + torch.cuda.synchronize() + + launch_kinds = {int(cupti.ActivityKind.RUNTIME), int(cupti.ActivityKind.DRIVER)} + kernel_kinds = {int(cupti.ActivityKind.CONCURRENT_KERNEL)} + launches: list[tuple[int, int, int]] = [] + kernels: list[tuple[int, int, int]] = [] + + def _buffer_requested(): + return 8 * 1024 * 1024, 0 + + def _buffer_completed(activities: list[Any]) -> None: + for activity in activities: + record = (activity.start, activity.end, activity.correlation_id) + kind = int(activity.kind) + if kind in launch_kinds: + launches.append(record) + elif kind in kernel_kinds: + kernels.append(record) + + kinds = [ + cupti.ActivityKind.RUNTIME, + cupti.ActivityKind.DRIVER, + cupti.ActivityKind.CONCURRENT_KERNEL, + cupti.ActivityKind.MEMCPY, + cupti.ActivityKind.MEMSET, + ] + enabled: list[Any] = [] + cpu_brackets: list[tuple[int, int, int]] = [] + watchdog = sys.modules.get("loom.runtime.cuda_watchdog") + suspend_polling = getattr(watchdog, "suspend_polling", None) + polling_guard = suspend_polling() if callable(suspend_polling) else contextlib.nullcontext() + with polling_guard: + cupti.activity_register_callbacks(_buffer_requested, _buffer_completed) + try: + for kind in kinds: + cupti.activity_enable(kind) + enabled.append(kind) + + for _ in range(bench_iters): + _complete_l2_flush_before_bracket(flusher, torch.cuda.synchronize) + start = cupti.get_timestamp() + fn() + submitted = cupti.get_timestamp() + torch.cuda.synchronize() + completed = cupti.get_timestamp() + cpu_brackets.append((start, submitted, completed)) + cupti.activity_flush_all(1) + finally: + for kind in reversed(enabled): + cupti.activity_disable(kind) + cupti.finalize() + + timing = _correlate(cpu_brackets, launches, kernels) + return BenchResult( + times_ms=timing.gpu_span_ms, + kernel_sum_times_ms=timing.kernel_sum_ms, + inter_kernel_gap_times_ms=timing.inter_kernel_gap_ms, + active_union_times_ms=timing.active_union_ms, + activity_counts=timing.activity_count, + launch_activity_counts=timing.launch_activity_count, + kernel_activity_counts=timing.kernel_activity_count, + submission_times_ms=[(submitted - start) / 1e6 for start, submitted, _ in cpu_brackets], + synchronized_e2e_times_ms=[ + (completed - start) / 1e6 for start, _, completed in cpu_brackets + ], + cold_first_call_host_enqueue_ms=( + cold_first_call.host_enqueue_ms if cold_first_call is not None else None + ), + cold_first_call_synchronized_e2e_ms=( + cold_first_call.synchronized_e2e_ms if cold_first_call is not None else None + ), + ) diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_direct_plan.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_direct_plan.py new file mode 100644 index 00000000..21649cbb --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_direct_plan.py @@ -0,0 +1,280 @@ +from __future__ import annotations + +import json +from collections.abc import Callable +from dataclasses import dataclass, field +from functools import cache +from importlib import resources +from threading import RLock +from typing import Any + +from ._dispatch import knn_build_dispatch_q1m524_v10_d320recurrence_consumption_v1 as _root +from ._dispatch_runtime import ( + _import_dispatch_module, + capture_kernel_launches, + detect_gpu_arch, + dispatch_launch_options, +) + +_WEAVE_PREFIX = "loom.examples.weave." +_ROOT_MODULE = "knn_build_dispatch_q1m524_v10_d320recurrence_consumption_v1" +_ROOT_CALLABLE = "launch_from_contract_inputs" +_CONTRACT_FIELDS = ("B", "Q", "M", "D", "K") +_PREPARE_LOCK = RLock() + + +def _load_json(name: str) -> Any: + package = __package__ or __name__.rpartition(".")[0] + return json.loads(resources.files(package).joinpath(name).read_text(encoding="utf-8")) + + +def _normalize_dtype(value: Any) -> str: + dtype = str(value) + return dtype[6:] if dtype.startswith("torch.") else dtype + + +def _contract_key(inputs: dict[str, Any]) -> tuple[int, int, int, int, int, str, bool]: + return ( + *(int(inputs[name]) for name in _CONTRACT_FIELDS), + _normalize_dtype(inputs.get("dtype", "bfloat16")), + bool(inputs.get("build", False)), + ) + + +def _load_exact_specs() -> dict[tuple[int, int, int, int, int, str, bool], tuple[str, str, str]]: + shapes = _load_json("_shape_records.json") + routes = {row["shape"]: row["selected_route"] for row in _load_json("_routes.json")} + entrypoints = _load_json("_entrypoints.json") + specs: dict[tuple[int, int, int, int, int, str, bool], tuple[str, str, str]] = {} + for row in shapes: + label = str(row["label"]) + params = dict(row["params"]) + if label not in routes or label not in entrypoints: + raise RuntimeError(f"missing direct KNN-build route metadata for {label!r}") + key = ( + *(int(params[name]) for name in _CONTRACT_FIELDS), + _normalize_dtype(params.get("dtype", "bfloat16")), + bool(params.get("build", False)), + ) + spec = (label, str(routes[label]), str(entrypoints[label])) + prior = specs.setdefault(key, spec) + if prior != spec: + raise RuntimeError(f"ambiguous direct KNN-build contract {key!r}: {prior!r} versus {spec!r}") + return specs + + +_EXACT_SPECS = _load_exact_specs() + + +@dataclass(frozen=True) +class DirectRouteDecision: + """One resolved KNN-build route and its imported direct callable.""" + + route_id: str + launch_entrypoint: str + launcher: Callable[[dict[str, Any]], Any] = field(repr=False, compare=False) + shape_label: str | None = None + exact_contract: bool = False + + def launch( + self, + inputs: dict[str, Any], + *, + stream: Any = None, + timeout_ms: float | None = None, + ) -> Any: + with dispatch_launch_options(stream=stream, timeout_ms=timeout_ms): + return self.launcher(inputs) + + +@dataclass(frozen=True) +class PreparedDirectRoute: + """One exact leaf frozen into a device/stream-bound CUDA launch sequence.""" + + decision: DirectRouteDecision + direct_launcher: Callable[..., Any] = field(repr=False, compare=False) + inputs: dict[str, Any] = field(repr=False, compare=False) + arch: str + device_index: int + stream: Any = field(repr=False, compare=False) + stream_handle: int + launch_count: int + + @property + def route_id(self) -> str: + return self.decision.route_id + + @property + def launch_entrypoint(self) -> str: + return self.decision.launch_entrypoint + + @property + def shape_label(self) -> str | None: + return self.decision.shape_label + + @property + def exact_contract(self) -> bool: + return self.decision.exact_contract + + def launch( + self, + inputs: dict[str, Any], + *, + stream: Any = None, + timeout_ms: float | None = None, + ) -> Any: + """Submit the frozen sequence; another stream requires another plan.""" + + import torch + + if inputs is not self.inputs: + raise ValueError("prepared KNN-build route is bound to its original input/output tensors") + with torch.cuda.device(self.device_index): + requested_stream = self.stream if stream is None else stream + requested_handle = int(requested_stream.cuda_stream) + if requested_handle != self.stream_handle: + raise RuntimeError( + "prepared KNN-build route is stream-bound: " + f"prepared on stream 0x{self.stream_handle:x}, requested 0x{requested_handle:x}; " + "prepare a separate plan inside the target torch.cuda.stream(...) context" + ) + try: + result = self.direct_launcher(inputs, stream=None, timeout_ms=timeout_ms) + finally: + # A later launch can fail after an earlier launch was already + # enqueued. Keep every captured argument allocator-safe even + # on that partial-submission path. + self.direct_launcher.record_stream(requested_stream) + _record_input_streams(inputs, requested_stream) + return result + + def rebind_inputs( + self, + inputs: dict[str, Any], + *, + stream: Any = None, + ) -> None: + """Rebind public tensor arguments without resolving the route again.""" + + import torch + + if inputs is not self.inputs: + raise ValueError("prepared KNN-build route is bound to its original input dictionary") + with torch.cuda.device(self.device_index): + requested_stream = self.stream if stream is None else stream + requested_handle = int(requested_stream.cuda_stream) + if requested_handle != self.stream_handle: + raise RuntimeError( + "prepared KNN-build route is stream-bound: " + f"prepared on stream 0x{self.stream_handle:x}, requested 0x{requested_handle:x}; " + "prepare a separate plan inside the target torch.cuda.stream(...) context" + ) + self.direct_launcher.rebind_inputs(inputs, stream=requested_stream) + + +@cache +def _load_launcher(entrypoint: str) -> Callable[[dict[str, Any]], Any]: + module_name, separator, callable_name = entrypoint.partition(":") + if not separator or not module_name.startswith(_WEAVE_PREFIX) or not callable_name.isidentifier(): + raise RuntimeError(f"invalid direct KNN-build entrypoint: {entrypoint!r}") + module = _import_dispatch_module(module_name.removeprefix(_WEAVE_PREFIX)) + launcher = getattr(module, callable_name, None) + if not callable(launcher): + raise RuntimeError(f"direct KNN-build entrypoint is not callable: {entrypoint!r}") + return launcher + + +def _record_input_streams(inputs: dict[str, Any], stream: Any) -> None: + seen: set[int] = set() + for value in inputs.values(): + identity = id(value) + record_stream = getattr(value, "record_stream", None) + if identity not in seen and callable(record_stream): + seen.add(identity) + record_stream(stream) + + +@cache +def _make_decision( + shape_label: str | None, + route_id: str, + launch_entrypoint: str, + exact_contract: bool, +) -> DirectRouteDecision: + return DirectRouteDecision( + route_id=route_id, + launch_entrypoint=launch_entrypoint, + launcher=_load_launcher(launch_entrypoint), + shape_label=shape_label, + exact_contract=exact_contract, + ) + + +def resolve_route(inputs: dict[str, Any]) -> DirectRouteDecision: + """Resolve and import a direct launcher once for one fixed input contract.""" + + spec = _EXACT_SPECS.get(_contract_key(inputs)) + if spec is not None: + shape_label, route_id, launch_entrypoint = spec + # Several historical leaf guards use the canonical label as a second + # exact-contract check. This dictionary is private to the public API, + # so canonicalizing it cannot mutate caller state. + inputs["label"] = shape_label + return _make_decision(shape_label, route_id, launch_entrypoint, True) + route_id = str(_root.route_for_contract_inputs(inputs)) + launch_entrypoint = f"{_WEAVE_PREFIX}{_ROOT_MODULE}:{_ROOT_CALLABLE}" + return _make_decision(None, route_id, launch_entrypoint, False) + + +def prepare_route( + inputs: dict[str, Any], + *, + arch: str | None = None, + stream: Any = None, +) -> PreparedDirectRoute: + """Resolve one leaf and capture all host setup and launches exactly once.""" + + import torch + + input_device = inputs["database"].device + device_index = input_device.index + if device_index is None: + device_index = torch.cuda.current_device() + device_index = int(device_index) + with torch.cuda.device(device_index): + resolved_stream = torch.cuda.current_stream(device_index) if stream is None else stream + stream_device = getattr(resolved_stream, "device", None) + stream_device_index = getattr(stream_device, "index", stream_device) + if stream_device_index is not None and int(stream_device_index) != device_index: + raise ValueError( + f"KNN-build stream device {stream_device_index} does not match input device {device_index}" + ) + stream_handle = int(resolved_stream.cuda_stream) + with torch.cuda.stream(resolved_stream), _PREPARE_LOCK: + detected_arch = detect_gpu_arch() + resolved_arch = detected_arch if arch is None else str(arch) + if resolved_arch != detected_arch: + raise ValueError( + f"KNN-build launch arch must match the active device: " + f"requested {resolved_arch}, detected {detected_arch}" + ) + decision = resolve_route(inputs) + inputs["_knn_build_prepared_stream_key"] = (device_index, stream_handle) + with capture_kernel_launches( + stream=resolved_stream, + arch=resolved_arch, + inputs=inputs, + ) as captured: + with dispatch_launch_options(stream=resolved_stream, timeout_ms=None): + prepared_result = decision.launcher(inputs) + direct_launcher = captured.bind(prepared_result) + return PreparedDirectRoute( + decision=decision, + direct_launcher=direct_launcher, + inputs=inputs, + arch=resolved_arch, + device_index=device_index, + stream=resolved_stream, + stream_handle=stream_handle, + launch_count=direct_launcher.launch_count, + ) diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/__init__.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_build_k10_lowfloor_4757_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_build_k10_lowfloor_4757_v1.py new file mode 100644 index 00000000..c6e45fdc --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_build_k10_lowfloor_4757_v1.py @@ -0,0 +1,189 @@ +"""Build K10 low-floor exact wrapper for the 4757 continuation. + +Minimum target architecture: sm_100a. This additive bucket-kernel wrapper keeps +the current Q24/Q128 full90 seed portfolio as fallback and routes exact BF16 +build K10 low-floor rows through the existing fixed-build K10 Weave route. The +route remains Weave-only; FlashLib is used only by the contract harness. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from pathlib import Path +from typing import Any, Callable +from . import knn_build_dispatch_784a_cf51_q1_q16_q24_q128_seed_portfolio_full90_v1 as parent +from . import knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2 as fixedbuild_k10 +MODULE = 'loom.examples.weave.knn_build_build_k10_lowfloor_4757_v1' +BUILD_Q512_K10 = 'build_k_sweep_qm512_k10' +BUILD_Q1024_K10 = 'build_qm1024_d128_k10' +BUILD_B2_Q1024_K10 = 'build_batch_b2_q1024_m1024_d128_k10' +BUILD_TAIL_Q1536_K10 = 'build_tail_b1_q1536_m1536_d128_k10' +BUILD_Q6144_K10 = 'build_large_b1_q6144_m6144_d128_k10' +TARGET_SHAPE_PARAMS = {BUILD_Q512_K10: (1, 512), BUILD_Q1024_K10: (1, 1024), BUILD_B2_Q1024_K10: (2, 1024), BUILD_TAIL_Q1536_K10: (1, 1536), BUILD_Q6144_K10: (1, 6144)} +TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_k_sweep_qm512_k10", "build_qm1024_d128_k10", "build_batch_b2_q1024_m1024_d128_k10", "build_tail_b1_q1536_m1536_d128_k10", "build_large_b1_q6144_m6144_d128_k10"]}')) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +SEED_K10_ID = '4757_fixedbuild_k10_v2_q512_q1024_b2_q1536_q6144' +PARENT_PORTFOLIO_ID = parent.CANDIDATE_CONFIGS[parent.DEFAULT_CANDIDATE_KEY]['candidate_id'] +CANDIDATE_ID = 'build_k10_lowfloor_4757_v1' +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_K10_BUILD = 'loom.examples.weave.knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2:launch_from_contract_inputs' +ROUTE_PARENT = parent.ROUTE_ENTRYPOINT +PRODUCTION_ROUTE_MODULES = {**parent.PRODUCTION_ROUTE_MODULES, SEED_K10_ID: ROUTE_K10_BUILD, PARENT_PORTFOLIO_ID: ROUTE_PARENT} +SOURCE_TASKS = {**parent.SOURCE_TASKS, SEED_K10_ID: 'weave-evolve prior fixed-build K10 lineage / loom/examples/weave/knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2.py', CANDIDATE_ID: 'weave-evolve-knn-build-4757 / build_k10_lowfloor bucket'} +eval_mod = parent.eval_mod + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_BUILD_K10_LOWFLOOR_4757_VERIFY_KERNEL') + if verify_kernel == 'k10_stage1': + return fixedbuild_k10.stage1_ir + if verify_kernel == 'k10_merge_s7_cache': + return fixedbuild_k10.parent.parent_cached.merge_k10_s7_cache_ir + if verify_kernel == 'k10_merge_s4_cache': + return fixedbuild_k10.parent.parent_cached64.merge_k10_s4_cache_ir + return parent.ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbatch_m64_d4f7_stage1", "arg_keys": ["query", "database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 91392, "constants": [], "cta_group": 1, "threads": 512}')) + +def _select_contract_shapes(shape_labels): + return parent._select_contract_shapes(shape_labels) + +def _trace_inputs_for_shape(shape: dict[str, Any]) -> dict[str, Any]: + return parent._trace_inputs_for_shape(shape) + +def _inputs_for_label(label: str) -> dict[str, Any]: + return _trace_inputs_for_shape(_select_contract_shapes((label,))[0]) + +def _dtype_name(inputs: dict[str, Any], name: str='query') -> str: + tensor = inputs.get(name) + if tensor is not None and hasattr(tensor, 'dtype'): + return str(tensor.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')).replace('torch.', '') + +def _label_can_hit(inputs: dict[str, Any], label: str) -> bool: + value = inputs.get('label') + return value is None or str(value) == label + +def _matched_target_label(inputs: dict[str, Any]) -> str | None: + bsz = int(inputs.get('B', -1)) + q = int(inputs.get('Q', -1)) + for label, (target_b, target_q) in TARGET_SHAPE_PARAMS.items(): + if bsz == target_b and q == target_q and _label_can_hit(inputs, label): + return label + return None + +def _eligible_k10_lowfloor(inputs: dict[str, Any]) -> bool: + if not (bool(inputs.get('build', False)) and int(inputs.get('Q', -1)) == int(inputs.get('M', -2)) and (int(inputs.get('D', -1)) == fixedbuild_k10.FEAT_D) and (int(inputs.get('K', -1)) == fixedbuild_k10.TOP_K_MAX) and (_dtype_name(inputs, 'query') == 'bfloat16') and (_dtype_name(inputs, 'database') in ('', 'bfloat16'))): + return False + return _matched_target_label(inputs) is not None + +def _expected_seed(inputs: dict[str, Any]) -> str | None: + return SEED_K10_ID if _eligible_k10_lowfloor(inputs) else None + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if not force_fallback and _eligible_k10_lowfloor(inputs): + return ROUTE_K10_BUILD + return parent.route_for_contract_inputs(inputs, force_fallback=force_fallback) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + if not force_fallback and _eligible_k10_lowfloor(inputs): + fixedbuild_k10.launch_from_contract_inputs(inputs) + return + parent.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + +def candidate_build_k10_lowfloor_4757_v1(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]) -> None: + candidate_build_k10_lowfloor_4757_v1(inputs) + +def candidate_parent_full90(inputs: dict[str, Any]) -> None: + parent.launch_from_contract_inputs(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate, correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + return parent._run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=kernel_fn, correctness=correctness, time_flashlib=time_flashlib) + +def _selected_entrypoint(route: str) -> str: + if route == ROUTE_K10_BUILD: + return ROUTE_K10_BUILD + return ROUTE_PARENT + +def _route_trace_record(inputs: dict[str, Any], *, force_fallback: bool=False) -> dict[str, Any]: + label = str(inputs.get('label')) + expected_seed = _expected_seed(inputs) + route = route_for_contract_inputs(inputs, force_fallback=force_fallback) + parent_row = dict(parent.route_trace_for_contract_shapes((label,), force_fallback=force_fallback)[0]) + parent_route = parent.route_for_contract_inputs(inputs, force_fallback=force_fallback) + if expected_seed is None or force_fallback: + parent_row['expected_seed'] = expected_seed + parent_row['parent_portfolio_route'] = parent_route + parent_row['candidate_guard_status'] = 'forced_fallback' if force_fallback else 'guard_miss' + if force_fallback and expected_seed is not None: + parent_row['guard_id'] = ''.join(['forced_fallback_', format(expected_seed, ''), '_disabled']) + parent_row['guard_condition'] = ''.join(['forced fallback to parent full90 portfolio; ', format(expected_seed, ''), ' disabled']) + parent_row['classification'] = 'guard-miss' + return parent._normalize_route_row(parent_row) + bsz = int(inputs.get('B', -1)) + q = int(inputs.get('Q', -1)) + split_count = fixedbuild_k10._fixed_build_k10_split_count(inputs) + return parent._normalize_route_row({'shape_key': label, 'selected_route': route, 'selected_entrypoint': _selected_entrypoint(route), 'selected_seed': expected_seed, 'expected_seed': expected_seed, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': ''.join(['4757_fixedbuild_k10_b', format(bsz, ''), '_q', format(q, ''), '_exact_guard']), 'guard_condition': ''.join(['exact BF16 build B=', format(bsz, ''), ' Q=M=', format(q, ''), ' D=128 K=10']), 'coverage': '4757 build K10 low-floor overlay before Q24/Q128 full90 portfolio fallback', 'consumed_seed': expected_seed, 'replaced_route': parent_route, 'parent_portfolio_route': parent_route, 'baseline_dispatcher_route': parent_row.get('baseline_dispatcher_route') or parent_row.get('baseline_d5f8_route'), 'split_count': split_count, 'classification': 'seed-consumed'}) + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + return [_route_trace_record(_trace_inputs_for_shape(shape), force_fallback=force_fallback) for shape in selected] + +def _rows_for_labels(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return {label: dict(report.get('per_shape', {}).get(label, {})) for label in labels} + +def _per_shape_delta(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> dict[str, Any]: + rows = {} + for label in TARGET_SHAPES: + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') or baseline_row.get('flashlib_ms') + rows[label] = {'candidate_ms': candidate_ms, 'baseline_parent_ms': baseline_ms, 'flashlib_ms': flashlib_ms, 'candidate_tflops': candidate_row.get('tflops'), 'baseline_parent_tflops': baseline_row.get('tflops'), 'speedup_vs_parent': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'ratio_vs_flashlib': flashlib_ms / candidate_ms if candidate_ms and flashlib_ms else None, 'passed': candidate_row.get('passed'), 'timing_backend': candidate_row.get('timing_backend') or baseline_row.get('timing_backend')} + return rows + +def _below_flashlib_rows(report: dict[str, Any], *, floor: float) -> list[dict[str, Any]]: + rows = [] + for label, row in sorted(report.get('per_shape', {}).items()): + ratio = row.get('ratio_vs_flashlib') + if isinstance(ratio, (float, int)) and ratio < floor: + rows.append({'shape_key': label, 'kernel_ms': row.get('kernel_ms'), 'flashlib_ms': row.get('flashlib_ms'), 'ratio_vs_flashlib': ratio, 'selected_seed': _expected_seed(_inputs_for_label(label))}) + return rows + +def benchmark_candidate_build_k10_lowfloor_4757_v1(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES, run_baseline: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + labels = tuple((str(label) for label in shape_labels)) + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=labels, kernel_fn=candidate, correctness=True, time_flashlib=time_flashlib) + baseline_report = None + if run_baseline: + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=labels, kernel_fn=candidate_parent_full90, correctness=True, time_flashlib=time_flashlib) + candidate_mean = candidate_report['summary']['primary_mean'] + payload: dict[str, Any] = {'candidate_id': CANDIDATE_ID, 'selected_seeds': (SEED_K10_ID,), 'source_tasks': SOURCE_TASKS, 'all_correct': candidate_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'tflops': candidate_mean, 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_candidate_build_k10_lowfloor_4757_v1']), 'candidate_entrypoint': ROUTE_ENTRYPOINT, 'measured_shape_labels': labels, 'timing_backend': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'time_flashlib': time_flashlib, 'denominator': '4757_build_k10_lowfloor_exact5', 'route_trace': route_trace_for_contract_shapes(labels), 'forced_fallback_route_trace': route_trace_for_contract_shapes(labels, force_fallback=True), 'route_modules': PRODUCTION_ROUTE_MODULES, 'contract_summary': candidate_report['summary'], 'contract_performance': candidate_report['performance'], 'contract_correctness': candidate_report['correctness'], 'selected_route_rows': _rows_for_labels(candidate_report, labels), 'hot_bucket_blockers': _below_flashlib_rows(candidate_report, floor=1.05), 'rank_objective': {'metric': 'tflops', 'direction': 'maximize', 'value': candidate_mean, 'valid_measurement_count': candidate_report['performance']['valid_measurement_count'], 'comparable': candidate_report['performance']['comparable']}, 'report': candidate_report} + if baseline_report is not None: + baseline_mean = baseline_report['summary']['primary_mean'] + payload.update({'baseline_candidate_id': PARENT_PORTFOLIO_ID, 'baseline_entrypoint': parent.ROUTE_ENTRYPOINT, 'baseline_tflops': baseline_mean, 'metric_delta_vs_parent': candidate_mean - baseline_mean if candidate_mean is not None and baseline_mean is not None else None, 'baseline_contract_summary': baseline_report['summary'], 'baseline_contract_performance': baseline_report['performance'], 'baseline_selected_route_rows': _rows_for_labels(baseline_report, labels), 'per_shape_delta_vs_parent': _per_shape_delta(candidate_report, baseline_report)}) + return payload + +def write_benchmark_artifact(artifact_dir: str | Path, *, use_cupti: bool=True, shape_labels=TARGET_SHAPES, run_baseline: bool=True, time_flashlib: bool=True) -> dict[str, str]: + payload = benchmark_candidate_build_k10_lowfloor_4757_v1(use_cupti=use_cupti, shape_labels=shape_labels, run_baseline=run_baseline, time_flashlib=time_flashlib) + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + path = out_dir / 'build_k10_lowfloor_4757_v1.json' + path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + return {'candidate_payload': str(path)} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_build_k10_lowfloor_ad64_v2.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_build_k10_lowfloor_ad64_v2.py new file mode 100644 index 00000000..3f5a806f --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_build_k10_lowfloor_ad64_v2.py @@ -0,0 +1,183 @@ +"""Build K10 low-floor v2 exact wrapper for the ad64 full90 portfolio. + +Minimum target architecture: sm_100a. This additive bucket-kernel wrapper keeps +the current Q24/Q128 full90 seed portfolio as fallback and routes the exact BF16 +build K10 low-floor rows through the existing fixed-build K10 Weave route. V2 +adds the Q512, Q2048, and B2/Q1024 BF16 D128 K10 rows to the v1 Q1024/Q1536 +guard set. The route remains Weave-only; FlashLib is used only by the contract +harness. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from pathlib import Path +from typing import Any, Callable +from . import knn_build_dispatch_784a_cf51_q1_q16_q24_q128_seed_portfolio_full90_v1 as parent +from . import knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2 as fixedbuild_k10 +MODULE = 'loom.examples.weave.knn_build_build_k10_lowfloor_ad64_v2' +BUILD_Q512_K10 = 'build_k_sweep_qm512_k10' +BUILD_Q1024_K10 = 'build_qm1024_d128_k10' +BUILD_Q2048_K10 = 'build_qm2048_d128_k10' +BUILD_B2_Q1024_K10 = 'build_batch_b2_q1024_m1024_d128_k10' +BUILD_TAIL_Q1536_K10 = 'build_tail_b1_q1536_m1536_d128_k10' +TARGET_SHAPES = (BUILD_Q512_K10, BUILD_Q1024_K10, BUILD_Q2048_K10, BUILD_B2_Q1024_K10, BUILD_TAIL_Q1536_K10) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +SEED_K10_ID = 'ad64_fixedbuild_k10_v2_q512_q1024_q1536_q2048_b2q1024' +PARENT_PORTFOLIO_ID = parent.CANDIDATE_CONFIGS[parent.DEFAULT_CANDIDATE_KEY]['candidate_id'] +CANDIDATE_ID = 'build_k10_lowfloor_ad64_v2' +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_K10_BUILD = 'loom.examples.weave.knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2:launch_from_contract_inputs' +ROUTE_PARENT = parent.ROUTE_ENTRYPOINT +PRODUCTION_ROUTE_MODULES = {**parent.PRODUCTION_ROUTE_MODULES, SEED_K10_ID: ROUTE_K10_BUILD, PARENT_PORTFOLIO_ID: ROUTE_PARENT} +SOURCE_TASKS = {**parent.SOURCE_TASKS, SEED_K10_ID: 'weave-evolve prior fixed-build K10 lineage / loom/examples/weave/knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2.py', CANDIDATE_ID: 'weave-evolve-knn-build-ad64 / build_k10_lowfloor bucket'} +eval_mod = parent.eval_mod + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_BUILD_K10_LOWFLOOR_AD64_V2_VERIFY_KERNEL') + if verify_kernel == 'k10_stage1': + return fixedbuild_k10.stage1_ir + if verify_kernel == 'k10_merge_s4_cache': + return fixedbuild_k10.parent.parent_cached64.merge_k10_s4_cache_ir + if verify_kernel == 'k10_merge_s7_cache': + return fixedbuild_k10.parent.parent_cached.merge_k10_s7_cache_ir + return parent.ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbatch_m64_d4f7_stage1", "arg_keys": ["query", "database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 91392, "constants": [], "cta_group": 1, "threads": 512}')) + +def _select_contract_shapes(shape_labels): + return parent._select_contract_shapes(shape_labels) + +def _trace_inputs_for_shape(shape: dict[str, Any]) -> dict[str, Any]: + return parent._trace_inputs_for_shape(shape) + +def _inputs_for_label(label: str) -> dict[str, Any]: + return _trace_inputs_for_shape(_select_contract_shapes((label,))[0]) + +def _dtype_name(inputs: dict[str, Any], name: str='query') -> str: + tensor = inputs.get(name) + if tensor is not None and hasattr(tensor, 'dtype'): + return str(tensor.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')).replace('torch.', '') + +def _label_can_hit(inputs: dict[str, Any], label: str) -> bool: + value = inputs.get('label') + return value is None or str(value) == label + +def _eligible_k10_lowfloor(inputs: dict[str, Any]) -> bool: + if not (bool(inputs.get('build', False)) and int(inputs.get('Q', -1)) == int(inputs.get('M', -2)) and (int(inputs.get('D', -1)) == fixedbuild_k10.FEAT_D) and (int(inputs.get('K', -1)) == fixedbuild_k10.TOP_K_MAX) and (_dtype_name(inputs, 'query') == 'bfloat16') and (_dtype_name(inputs, 'database') in ('', 'bfloat16'))): + return False + b = int(inputs.get('B', -1)) + q = int(inputs.get('Q', -1)) + return b == 1 and q == 512 and _label_can_hit(inputs, BUILD_Q512_K10) or (b == 1 and q == 1024 and _label_can_hit(inputs, BUILD_Q1024_K10)) or (b == 1 and q == 2048 and _label_can_hit(inputs, BUILD_Q2048_K10)) or (b == 2 and q == 1024 and _label_can_hit(inputs, BUILD_B2_Q1024_K10)) or (b == 1 and q == 1536 and _label_can_hit(inputs, BUILD_TAIL_Q1536_K10)) + +def _expected_seed(inputs: dict[str, Any]) -> str | None: + return SEED_K10_ID if _eligible_k10_lowfloor(inputs) else None + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if not force_fallback and _eligible_k10_lowfloor(inputs): + return ROUTE_K10_BUILD + return parent.route_for_contract_inputs(inputs, force_fallback=force_fallback) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + if not force_fallback and _eligible_k10_lowfloor(inputs): + fixedbuild_k10.launch_from_contract_inputs(inputs) + return + parent.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + +def candidate_build_k10_lowfloor_ad64_v2(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]) -> None: + candidate_build_k10_lowfloor_ad64_v2(inputs) + +def candidate_parent_full90(inputs: dict[str, Any]) -> None: + parent.launch_from_contract_inputs(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate, correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + return parent._run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=kernel_fn, correctness=correctness, time_flashlib=time_flashlib) + +def _selected_entrypoint(route: str) -> str: + if route == ROUTE_K10_BUILD: + return ROUTE_K10_BUILD + return ROUTE_PARENT + +def _route_trace_record(inputs: dict[str, Any], *, force_fallback: bool=False) -> dict[str, Any]: + label = str(inputs.get('label')) + expected_seed = _expected_seed(inputs) + route = route_for_contract_inputs(inputs, force_fallback=force_fallback) + parent_row = dict(parent.route_trace_for_contract_shapes((label,), force_fallback=force_fallback)[0]) + parent_route = parent.route_for_contract_inputs(inputs, force_fallback=force_fallback) + if expected_seed is None or force_fallback: + parent_row['expected_seed'] = expected_seed + parent_row['parent_portfolio_route'] = parent_route + parent_row['candidate_guard_status'] = 'forced_fallback' if force_fallback else 'guard_miss' + if force_fallback and expected_seed is not None: + parent_row['guard_id'] = ''.join(['forced_fallback_', format(expected_seed, ''), '_disabled']) + parent_row['guard_condition'] = ''.join(['forced fallback to parent full90 portfolio; ', format(expected_seed, ''), ' disabled']) + parent_row['classification'] = 'guard-miss' + return parent._normalize_route_row(parent_row) + b = int(inputs.get('B', -1)) + q = int(inputs.get('Q', -1)) + return parent._normalize_route_row({'shape_key': label, 'selected_route': route, 'selected_entrypoint': _selected_entrypoint(route), 'selected_seed': expected_seed, 'expected_seed': expected_seed, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': ''.join(['ad64_fixedbuild_k10_b', format(b, ''), '_q', format(q, ''), '_exact_guard']), 'guard_condition': ''.join(['exact BF16 build B=', format(b, ''), ' Q=M=', format(q, ''), ' D=128 K=10']), 'coverage': 'ad64 build K10 low-floor overlay before Q24/Q128 full90 portfolio fallback', 'consumed_seed': expected_seed, 'replaced_route': parent_route, 'parent_portfolio_route': parent_route, 'baseline_dispatcher_route': parent_row.get('baseline_dispatcher_route') or parent_row.get('baseline_d5f8_route'), 'classification': 'seed-consumed'}) + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + return [_route_trace_record(_trace_inputs_for_shape(shape), force_fallback=force_fallback) for shape in selected] + +def _rows_for_labels(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return {label: dict(report.get('per_shape', {}).get(label, {})) for label in labels} + +def _per_shape_delta(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> dict[str, Any]: + rows = {} + for label in TARGET_SHAPES: + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') or baseline_row.get('flashlib_ms') + rows[label] = {'candidate_ms': candidate_ms, 'baseline_parent_ms': baseline_ms, 'flashlib_ms': flashlib_ms, 'candidate_tflops': candidate_row.get('tflops'), 'baseline_parent_tflops': baseline_row.get('tflops'), 'speedup_vs_parent': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'ratio_vs_flashlib': flashlib_ms / candidate_ms if candidate_ms and flashlib_ms else None, 'passed': candidate_row.get('passed'), 'timing_backend': candidate_row.get('timing_backend') or baseline_row.get('timing_backend')} + return rows + +def _below_flashlib_rows(report: dict[str, Any], *, floor: float) -> list[dict[str, Any]]: + rows = [] + for label, row in sorted(report.get('per_shape', {}).items()): + ratio = row.get('ratio_vs_flashlib') + if isinstance(ratio, (float, int)) and ratio < floor: + rows.append({'shape_key': label, 'kernel_ms': row.get('kernel_ms'), 'flashlib_ms': row.get('flashlib_ms'), 'ratio_vs_flashlib': ratio, 'selected_seed': _expected_seed(_inputs_for_label(label))}) + return rows + +def benchmark_candidate_build_k10_lowfloor_ad64_v2(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES, run_baseline: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + labels = tuple((str(label) for label in shape_labels)) + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=labels, kernel_fn=candidate, correctness=True, time_flashlib=time_flashlib) + baseline_report = None + if run_baseline: + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=labels, kernel_fn=candidate_parent_full90, correctness=True, time_flashlib=time_flashlib) + candidate_mean = candidate_report['summary']['primary_mean'] + payload: dict[str, Any] = {'candidate_id': CANDIDATE_ID, 'selected_seeds': (SEED_K10_ID,), 'source_tasks': SOURCE_TASKS, 'all_correct': candidate_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'tflops': candidate_mean, 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_candidate_build_k10_lowfloor_ad64_v2']), 'candidate_entrypoint': ROUTE_ENTRYPOINT, 'measured_shape_labels': labels, 'timing_backend': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'time_flashlib': time_flashlib, 'denominator': 'ad64_build_k10_lowfloor_exact5', 'route_trace': route_trace_for_contract_shapes(labels), 'forced_fallback_route_trace': route_trace_for_contract_shapes(labels, force_fallback=True), 'route_modules': PRODUCTION_ROUTE_MODULES, 'contract_summary': candidate_report['summary'], 'contract_performance': candidate_report['performance'], 'contract_correctness': candidate_report['correctness'], 'selected_route_rows': _rows_for_labels(candidate_report, labels), 'hot_bucket_blockers': _below_flashlib_rows(candidate_report, floor=1.05), 'rank_objective': {'metric': 'tflops', 'direction': 'maximize', 'value': candidate_mean, 'valid_measurement_count': candidate_report['performance']['valid_measurement_count'], 'comparable': candidate_report['performance']['comparable']}, 'report': candidate_report} + if baseline_report is not None: + baseline_mean = baseline_report['summary']['primary_mean'] + payload.update({'baseline_candidate_id': PARENT_PORTFOLIO_ID, 'baseline_entrypoint': parent.ROUTE_ENTRYPOINT, 'baseline_tflops': baseline_mean, 'metric_delta_vs_parent': candidate_mean - baseline_mean if candidate_mean is not None and baseline_mean is not None else None, 'baseline_contract_summary': baseline_report['summary'], 'baseline_contract_performance': baseline_report['performance'], 'baseline_selected_route_rows': _rows_for_labels(baseline_report, labels), 'per_shape_delta_vs_parent': _per_shape_delta(candidate_report, baseline_report)}) + return payload + +def write_benchmark_artifact(artifact_dir: str | Path, *, use_cupti: bool=True, shape_labels=TARGET_SHAPES, run_baseline: bool=True, time_flashlib: bool=True) -> dict[str, str]: + payload = benchmark_candidate_build_k10_lowfloor_ad64_v2(use_cupti=use_cupti, shape_labels=shape_labels, run_baseline=run_baseline, time_flashlib=time_flashlib) + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + path = out_dir / 'build_k10_lowfloor_ad64_v2.json' + path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + return {'candidate_payload': str(path)} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_build_k96_d64_c13e_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_build_k96_d64_c13e_v1.py new file mode 100644 index 00000000..6e948517 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_build_k96_d64_c13e_v1.py @@ -0,0 +1,243 @@ +"""K96/D64 build low-floor bucket for c13e. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +routes the c13e follow-up build rows to existing Weave-only seeds: + +* Q2048/D64/K10 through the aa88 v2 split8 cached-merge route. +* Q1024/D128/K96 and Q2048/D128/K96 through the 229a exact K96 route. + +Guard misses delegate to the selected 9a17-only full90 parent. FlashLib is +used only by the contract harness as a black-box timing baseline. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from pathlib import Path +from typing import Any, Callable +from . import knn_build_dispatch_1877_9a17_b644_6a35_full90_selective_v1 as selected_parent +from . import knn_build_d64_build_aa88_v2 as d64_seed +from . import knn_build_over64_k96_exactall_229a_v1 as k96_seed +MODULE = 'loom.examples.weave.knn_build_build_k96_d64_c13e_v1' +TARGET_D64_Q2048_K10 = 'build_dim_sweep_b1_q2048_m2048_d64_k10' +TARGET_K96_Q1024 = 'build_over64_stress_qm1024_k96' +TARGET_K96_Q2048 = 'build_over64_stress_qm2048_k96' +TARGET_SHAPES = (TARGET_D64_Q2048_K10, TARGET_K96_Q1024, TARGET_K96_Q2048) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +SEED_ID = 'build_k96_d64_c13e_v1' +SEED_D64_Q2048_K10_ID = 'c13e_d64_q2048_k10_aa88_s8_cached' +SEED_K96_Q1024_ID = 'c13e_k96_q1024_229a_s2_exactprefill' +SEED_K96_Q2048_ID = 'c13e_k96_q2048_229a_s2_exactprefill' +PARENT_SELECTED_ID = selected_parent.CANDIDATE_CONFIGS[selected_parent.CANDIDATE_9A17_ONLY]['candidate_id'] +D64_Q2048_SPLIT_COUNT = 8 +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_D64_Q2048_K10 = d64_seed.ROUTE_D64_BUCKET_S8_FAST +ROUTE_K96_ENTRYPOINT = 'loom.examples.weave.knn_build_over64_k96_exactall_229a_v1:launch_from_contract_inputs' +ROUTE_PARENT_SELECTED = selected_parent.CANDIDATE_9A17_ONLY_ENTRYPOINT +BENCHMARK_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_candidate_build_k96_d64_c13e_v1']) +PRODUCTION_ROUTE_MODULES = {SEED_ID: ROUTE_ENTRYPOINT, SEED_D64_Q2048_K10_ID: ROUTE_D64_Q2048_K10, SEED_K96_Q1024_ID: ROUTE_K96_ENTRYPOINT, SEED_K96_Q2048_ID: ROUTE_K96_ENTRYPOINT, PARENT_SELECTED_ID: ROUTE_PARENT_SELECTED} +SOURCE_TASKS = {SEED_ID: 'weave-evolve-knn-build-c13e / K96 and D64 low-floor exact bucket', SEED_D64_Q2048_K10_ID: 'weave-evolve-knn-build-aa88 / D64 Q2048 K10 split8 cached route', SEED_K96_Q1024_ID: 'weave-evolve-knn-build-229a / K96 Q1024 exact-prefill route', SEED_K96_Q2048_ID: 'weave-evolve-knn-build-229a / K96 Q2048 exact-prefill route', PARENT_SELECTED_ID: 'generalize-auto-tuning 8fdf selected 9a17-only full90 parent'} +eval_mod = selected_parent.eval_mod +PARENT_CANDIDATE_KEY = selected_parent.CANDIDATE_9A17_ONLY + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_K96_D64_C13E_VERIFY_KERNEL') + if verify_kernel == 'd64_stage1': + return d64_seed.stage1_d64_split_ir + if verify_kernel == 'd64_merge_s8': + return d64_seed.merge_k10_s8_ir + if verify_kernel == 'k96_stage1': + return k96_seed.q1024exact.stage1_k96_exact_prefill_q1024_ir + if verify_kernel == 'k96_merge_s2': + return k96_seed.MERGE_IR_BY_SPLIT[2] + return d64_seed.stage1_d64_split_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_dim_midk_73a9_d64_split_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 25856, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) + +def _select_contract_shapes(shape_labels): + return selected_parent._select_contract_shapes(shape_labels) + +def _trace_inputs_for_shape(shape: dict[str, Any]) -> dict[str, Any]: + return selected_parent._trace_inputs_for_shape(shape) + +def _inputs_for_label(label: str) -> dict[str, Any]: + return _trace_inputs_for_shape(_select_contract_shapes((label,))[0]) + +def _dtype_name(inputs: dict[str, Any], name: str='query') -> str: + tensor = inputs.get(name) + if tensor is not None and hasattr(tensor, 'dtype'): + return str(tensor.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')).replace('torch.', '') + +def _label_can_hit(inputs: dict[str, Any], labels: set[str] | tuple[str, ...] | str) -> bool: + label_set = {labels} if isinstance(labels, str) else set(labels) + value = inputs.get('label') + return value is None or str(value) in label_set + +def _is_bf16_build(inputs: dict[str, Any]) -> bool: + return bool(inputs.get('build', False)) and int(inputs.get('B', -1)) == 1 and (int(inputs.get('Q', -1)) == int(inputs.get('M', -2))) and (_dtype_name(inputs, 'query') == 'bfloat16') and (_dtype_name(inputs, 'database') in ('', 'bfloat16')) + +def _eligible_d64_q2048_k10(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, TARGET_D64_Q2048_K10) and _is_bf16_build(inputs) and (int(inputs.get('Q', -1)) == 2048) and (int(inputs.get('D', -1)) == d64_seed.D64_FEAT_D) and (int(inputs.get('K', -1)) == d64_seed.TOP_K_MAX) + +def _eligible_k96(inputs: dict[str, Any], *, label: str, n_query: int) -> bool: + return _label_can_hit(inputs, label) and _is_bf16_build(inputs) and (int(inputs.get('Q', -1)) == n_query) and (int(inputs.get('D', -1)) == k96_seed.FEAT_D) and (int(inputs.get('K', -1)) == k96_seed.OVER64_TOP_K) + +def _selected_seed_for_inputs(inputs: dict[str, Any]) -> tuple[str | None, str | None]: + if _eligible_d64_q2048_k10(inputs): + return (SEED_D64_Q2048_K10_ID, TARGET_D64_Q2048_K10) + if _eligible_k96(inputs, label=TARGET_K96_Q1024, n_query=1024): + return (SEED_K96_Q1024_ID, TARGET_K96_Q1024) + if _eligible_k96(inputs, label=TARGET_K96_Q2048, n_query=2048): + return (SEED_K96_Q2048_ID, TARGET_K96_Q2048) + return (None, None) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if not force_fallback: + selected_seed, _label = _selected_seed_for_inputs(inputs) + if selected_seed == SEED_D64_Q2048_K10_ID: + return ROUTE_D64_Q2048_K10 + if selected_seed in (SEED_K96_Q1024_ID, SEED_K96_Q2048_ID): + return k96_seed.route_for_contract_inputs(inputs) + return selected_parent.route_for_contract_inputs(inputs, candidate_key=PARENT_CANDIDATE_KEY, force_fallback=force_fallback) + +def _launch_d64_q2048_k10(inputs: dict[str, Any]) -> None: + previous_split = os.environ.get('LOOM_KNN_D64_AA88_V2_SPLITS') + previous_fast = os.environ.get('LOOM_KNN_D64_AA88_V2_FAST_MERGE') + os.environ['LOOM_KNN_D64_AA88_V2_SPLITS'] = str(D64_Q2048_SPLIT_COUNT) + os.environ['LOOM_KNN_D64_AA88_V2_FAST_MERGE'] = '1' + try: + d64_seed.launch_from_contract_inputs(inputs) + finally: + if previous_split is None: + os.environ.pop('LOOM_KNN_D64_AA88_V2_SPLITS', None) + else: + os.environ['LOOM_KNN_D64_AA88_V2_SPLITS'] = previous_split + if previous_fast is None: + os.environ.pop('LOOM_KNN_D64_AA88_V2_FAST_MERGE', None) + else: + os.environ['LOOM_KNN_D64_AA88_V2_FAST_MERGE'] = previous_fast + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + if not force_fallback: + selected_seed, _label = _selected_seed_for_inputs(inputs) + if selected_seed == SEED_D64_Q2048_K10_ID: + _launch_d64_q2048_k10(inputs) + return + if selected_seed in (SEED_K96_Q1024_ID, SEED_K96_Q2048_ID): + k96_seed.launch_from_contract_inputs(inputs) + return + selected_parent.launch_from_contract_inputs(inputs, candidate_key=PARENT_CANDIDATE_KEY, force_fallback=force_fallback) + +def candidate_build_k96_d64_c13e_v1(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]) -> None: + candidate_build_k96_d64_c13e_v1(inputs) + +def candidate_parent_selected_9a17(inputs: dict[str, Any]) -> None: + selected_parent.launch_from_contract_inputs(inputs, candidate_key=PARENT_CANDIDATE_KEY) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _benchmark_shapes(shape_labels, *, time_flashlib: bool) -> list[dict[str, Any]]: + selected = _select_contract_shapes(TARGET_SHAPES if shape_labels is None else shape_labels) + out = [] + for shape in selected: + params = dict(shape['params']) + params['time_flashlib'] = bool(time_flashlib) + out.append({'label': shape['label'], 'params': params}) + return out + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate, time_flashlib: bool=True, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + previous = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_benchmark_shapes(shape_labels, time_flashlib=time_flashlib), correctness=correctness, benchmark=benchmark, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = previous + +def route_trace_for_contract_shapes(shape_labels=TARGET_SHAPES, *, force_fallback: bool=False) -> list[dict[str, Any]]: + rows = [] + for label in tuple(shape_labels): + inputs = _inputs_for_label(str(label)) + selected_seed, matched_label = (None, None) if force_fallback else _selected_seed_for_inputs(inputs) + route = route_for_contract_inputs(inputs, force_fallback=force_fallback) + parent_route = selected_parent.route_for_contract_inputs(inputs, candidate_key=PARENT_CANDIDATE_KEY) + parent_row = dict(selected_parent.route_trace_for_contract_shapes((label,), candidate_key=PARENT_CANDIDATE_KEY)[0]) + if selected_seed is None: + row = dict(parent_row) + row['expected_seed'] = None + row['parent_selected_route'] = parent_route + row['candidate_guard_status'] = 'forced_fallback' if force_fallback else 'guard_miss' + if force_fallback: + row['guard_id'] = 'forced_fallback_build_k96_d64_c13e' + row['guard_condition'] = 'forced fallback to selected 9a17-only parent' + row['classification'] = 'guard-miss' + rows.append(selected_parent._normalize_route_row(row)) + continue + guard_conditions = {SEED_D64_Q2048_K10_ID: 'exact BF16 build B=1 Q=M=2048 D=64 K=10 split8 cached', SEED_K96_Q1024_ID: 'exact BF16 build B=1 Q=M=1024 D=128 K=96 split2', SEED_K96_Q2048_ID: 'exact BF16 build B=1 Q=M=2048 D=128 K=96 split2'} + selected_entrypoints = {SEED_D64_Q2048_K10_ID: ROUTE_D64_Q2048_K10, SEED_K96_Q1024_ID: ROUTE_K96_ENTRYPOINT, SEED_K96_Q2048_ID: ROUTE_K96_ENTRYPOINT} + rows.append(selected_parent._normalize_route_row({'shape_key': label, 'selected_route': route, 'selected_entrypoint': selected_entrypoints[selected_seed], 'selected_seed': selected_seed, 'expected_seed': selected_seed, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': ''.join(['c13e_k96_d64_buildfix_', format(selected_seed, '')]), 'guard_condition': guard_conditions[selected_seed], 'matched_label': matched_label, 'parent_selected_route': parent_route, 'baseline_dispatcher_route': parent_row.get('selected_route'), 'classification': 'seed-consumed'})) + return rows + +def _rows_for_labels(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return {label: dict(report.get('per_shape', {}).get(label, {})) for label in labels} + +def _per_shape_delta(candidate_report: dict[str, Any], baseline_report: dict[str, Any], labels: tuple[str, ...]): + rows = [] + for label in labels: + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') or baseline_row.get('flashlib_ms') + inputs = _inputs_for_label(label) + selected_seed, _matched_label = _selected_seed_for_inputs(inputs) + rows.append({'shape_key': label, 'selected_seed': selected_seed, 'candidate_route': route_for_contract_inputs(inputs), 'parent_selected_route': selected_parent.route_for_contract_inputs(inputs, candidate_key=PARENT_CANDIDATE_KEY), 'candidate_ms': candidate_ms, 'parent_selected_ms': baseline_ms, 'flashlib_ms': flashlib_ms, 'speedup_vs_parent_selected': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'ratio_vs_flashlib': flashlib_ms / candidate_ms if candidate_ms and flashlib_ms else None, 'candidate_passed': candidate_row.get('passed'), 'parent_selected_passed': baseline_row.get('passed'), 'timing_backend': candidate_row.get('timing_backend') or baseline_row.get('timing_backend')}) + return rows + +def _below_flashlib_floor(report: dict[str, Any], *, floor: float=1.05) -> list[dict[str, Any]]: + rows = [] + for label, row in sorted(report.get('per_shape', {}).items()): + ratio = row.get('ratio_vs_flashlib') + if isinstance(ratio, (float, int)) and ratio < floor: + rows.append({'shape_key': label, 'kernel_ms': row.get('kernel_ms'), 'flashlib_ms': row.get('flashlib_ms'), 'ratio_vs_flashlib': ratio, 'selected_seed': _selected_seed_for_inputs(_inputs_for_label(label))[0]}) + return rows + +def benchmark_candidate_build_k96_d64_c13e_v1(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES, run_baseline: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + labels = tuple((str(label) for label in shape_labels)) + baseline_report = None + if run_baseline: + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=labels, kernel_fn=candidate_parent_selected_9a17, time_flashlib=time_flashlib) + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=labels, kernel_fn=candidate, time_flashlib=time_flashlib) + candidate_metric = candidate_report.get('summary', {}).get('primary_mean') + baseline_metric = baseline_report.get('summary', {}).get('primary_mean') if baseline_report else None + payload: dict[str, Any] = {'candidate_id': SEED_ID, 'measured_entrypoint': BENCHMARK_ENTRYPOINT, 'candidate_entrypoint': ROUTE_ENTRYPOINT, 'selected_seeds': (SEED_D64_Q2048_K10_ID, SEED_K96_Q1024_ID, SEED_K96_Q2048_ID), 'source_tasks': SOURCE_TASKS, 'all_correct': candidate_report.get('summary', {}).get('all_correct'), 'performance_comparable': candidate_report.get('summary', {}).get('performance_comparable'), 'invalid_performance_reason': candidate_report.get('summary', {}).get('invalid_performance_reason'), 'tflops': candidate_metric, 'parent_selected_tflops': baseline_metric, 'metric_delta_vs_parent_selected': candidate_metric - baseline_metric if candidate_metric is not None and baseline_metric is not None else None, 'timing_backend': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'time_flashlib': time_flashlib, 'denominator': 'build_k96_d64_c13e_exact3', 'measured_shape_labels': list(labels), 'route_trace': route_trace_for_contract_shapes(labels), 'forced_fallback_route_trace': route_trace_for_contract_shapes(labels, force_fallback=True), 'route_modules': PRODUCTION_ROUTE_MODULES, 'contract_summary': candidate_report.get('summary'), 'contract_performance': candidate_report.get('performance'), 'contract_correctness': candidate_report.get('correctness'), 'rank_objective': {'metric': 'tflops', 'direction': 'maximize', 'value': candidate_metric, 'valid_measurement_count': candidate_report.get('performance', {}).get('valid_measurement_count'), 'comparable': candidate_report.get('performance', {}).get('comparable')}, 'below_flashlib_floor': _below_flashlib_floor(candidate_report, floor=1.05), 'report': candidate_report} + if baseline_report is not None: + payload.update({'parent_selected_entrypoint': ROUTE_PARENT_SELECTED, 'parent_selected_all_correct': baseline_report.get('summary', {}).get('all_correct'), 'parent_selected_performance_comparable': baseline_report.get('summary', {}).get('performance_comparable'), 'parent_selected_summary': baseline_report.get('summary'), 'parent_selected_performance': baseline_report.get('performance'), 'selected_route_rows': _rows_for_labels(candidate_report, labels), 'parent_selected_rows': _rows_for_labels(baseline_report, labels), 'seed_delta_matrix': _per_shape_delta(candidate_report, baseline_report, labels), 'parent_selected_report': baseline_report}) + return payload + +def write_benchmark_artifact(artifact_dir: str | Path, *, use_cupti: bool=True, shape_labels=TARGET_SHAPES, run_baseline: bool=True, time_flashlib: bool=True) -> dict[str, str]: + payload = benchmark_candidate_build_k96_d64_c13e_v1(use_cupti=use_cupti, shape_labels=shape_labels, run_baseline=run_baseline, time_flashlib=time_flashlib) + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + path = out_dir / 'build_k96_d64_c13e_v1.json' + path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + return {'candidate_payload': str(path)} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_build_lowfloor_2c1c_v3.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_build_lowfloor_2c1c_v3.py new file mode 100644 index 00000000..88fddac1 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_build_lowfloor_2c1c_v3.py @@ -0,0 +1,342 @@ +"""2c1c build-lowfloor direct-seed wrapper for kNN build. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +routes the priority build rows called out by the 2c1c full90 synthesis handoff: + +* Q512/D128/K1 and K2 through the f8c3 low-K split4 route. +* Q1024/D128/K16 through the f8c3 low-K split16 route. +* Q2048/D128/K11, K12, and K13 through the e080 exact mid-K split8 route. +* Q4096/D128/K13 through a local K13 unordered split4 route. +* Q4096/D64/K10 through the c271 split4 unordered D64 route. + +Guard misses delegate to the 1877+9a17 selected parent. FlashLib is used only +by the contract harness as a black-box timing baseline. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from collections.abc import Callable +from functools import lru_cache +from pathlib import Path +from typing import Any +from . import knn_build_d64_q4096_c271_twostage_v1 as seed_d64 +from . import knn_build_dispatch_1877_9a17_b644_6a35_full90_selective_v1 as selected_parent +from . import knn_build_lowk_f8c3_q512_q1024_v1 as seed_lowk +from . import knn_build_midk_k11k13_e080_v1 as seed_midk +from .._dispatch_runtime import pack_kernel_args +MODULE = 'loom.examples.weave.knn_build_build_lowfloor_2c1c_v3' +TARGET_Q512_K1 = 'build_k_sweep_qm512_k1' +TARGET_Q512_K2 = 'build_k_sweep_qm512_k2' +TARGET_Q1024_K16 = 'build_k_sweep_qm1024_k16' +TARGET_Q2048_K11 = 'build_k_sweep_qm2048_k11' +TARGET_Q2048_K12 = 'build_k_sweep_qm2048_k12' +TARGET_Q2048_K13 = 'build_k_sweep_qm2048_k13' +TARGET_Q4096_K13 = 'build_k_sweep_qm4096_k13' +TARGET_D64_Q4096_K10 = seed_d64.TARGET_SHAPE +TARGET_Q512_SHAPES = (TARGET_Q512_K1, TARGET_Q512_K2) +TARGET_Q1024_SHAPES = (TARGET_Q1024_K16,) +TARGET_MIDK_Q2048_SHAPES = (TARGET_Q2048_K11, TARGET_Q2048_K12, TARGET_Q2048_K13) +TARGET_MIDK_Q4096_SHAPES = (TARGET_Q4096_K13,) +TARGET_MIDK_SHAPES = TARGET_MIDK_Q2048_SHAPES + TARGET_MIDK_Q4096_SHAPES +TARGET_SHAPES = (TARGET_Q512_K1, TARGET_Q512_K2, TARGET_Q1024_K16, TARGET_Q2048_K11, TARGET_Q2048_K12, TARGET_Q2048_K13, TARGET_Q4096_K13, TARGET_D64_Q4096_K10) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +SEED_ID = 'build_lowfloor_2c1c_v3' +SEED_Q512_ID = '2c1c_lowk_q512_k1k2_s4' +SEED_Q1024_ID = '2c1c_lowk_q1024_k16_s16' +SEED_MIDK_Q2048_ID = '2c1c_midk_q2048_k11k12k13_s8' +SEED_MIDK_Q4096_ID = '2c1c_midk_q4096_k13_unordered_s4' +SEED_D64_ID = '2c1c_d64_q4096_c271_split4_unordered' +PARENT_CANDIDATE_KEY = selected_parent.CANDIDATE_9A17_ONLY +PARENT_SELECTED_ID = selected_parent.CANDIDATE_CONFIGS[PARENT_CANDIDATE_KEY]['candidate_id'] +Q512_SPLIT_COUNT = 4 +Q1024_K16_SPLIT_COUNT = seed_lowk.DEFAULT_Q1024_K16_SPLITS +Q4096_K13_SPLIT_COUNT = seed_midk.v9.MEDIUM_SPLITS +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_Q512_S4 = ''.join([format(seed_lowk.ROUTE_PREFIX, ''), ':q512_lowk_s', format(Q512_SPLIT_COUNT, '')]) +ROUTE_Q1024_K16 = ''.join([format(seed_lowk.ROUTE_PREFIX, ''), ':q1024_k16_s', format(Q1024_K16_SPLIT_COUNT, '')]) +ROUTE_MIDK = ''.join([format(seed_midk.MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_Q4096_K13_UNORDERED = ''.join([format(MODULE, ''), ':q4096_k13_unordered_s', format(Q4096_K13_SPLIT_COUNT, '')]) +ROUTE_D64_Q4096 = seed_d64.ROUTE_SPLIT4_UNORDERED +ROUTE_PARENT_SELECTED = selected_parent.CANDIDATE_9A17_ONLY_ENTRYPOINT +BENCHMARK_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_candidate_build_lowfloor_2c1c_v3']) +PRODUCTION_ROUTE_MODULES = {SEED_ID: ROUTE_ENTRYPOINT, SEED_Q512_ID: ROUTE_Q512_S4, SEED_Q1024_ID: ROUTE_Q1024_K16, SEED_MIDK_Q2048_ID: ROUTE_MIDK, SEED_MIDK_Q4096_ID: ROUTE_Q4096_K13_UNORDERED, SEED_D64_ID: ROUTE_D64_Q4096, PARENT_SELECTED_ID: ROUTE_PARENT_SELECTED} +SOURCE_TASKS = {SEED_ID: 'weave-evolve-knn-build-2c1c / build low-floor Q4096 K13 unordered repair wrapper', SEED_Q512_ID: 'weave/generalize f8c3 low-K Q512 split4 route', SEED_Q1024_ID: 'weave/generalize f8c3 low-K Q1024 K16 split16 route', SEED_MIDK_Q2048_ID: 'weave-evolve-knn-build-e080 / exact Q2048 K11/K12/K13 split8 route', SEED_MIDK_Q4096_ID: 'weave-evolve-knn-build-2c1c / exact Q4096 K13 unordered split4 route', SEED_D64_ID: 'weave-evolve-knn-build-6a35 / c271 D64 Q4096 split4 unordered route', PARENT_SELECTED_ID: 'generalize-auto-tuning 8fdf/9a17 selected full90 parent'} +eval_mod = selected_parent.eval_mod +stage1_q4096_k13_unordered_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_2c1ck13unordered", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 13]], "cta_group": 1, "threads": 192}')) +merge_q4096_k13_unordered_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_unordered_2c1ck13unordered", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 13], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 32}')) + +def _compiled_stage1_q4096_k13_unordered(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0049"}')) + +def _compiled_merge_q4096_k13_unordered(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0050"}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_BUILDFLOOR_2C1C_VERIFY_KERNEL') + if verify_kernel == 'q512_stage1': + return seed_lowk.stage1_q512_lowk_ir + if verify_kernel == 'q512_merge_generic': + return seed_lowk.merge_q512_generic_ir + if verify_kernel == 'q1024_k16_stage1': + return seed_lowk.stage1_q1024_k16_ir + if verify_kernel == 'q1024_k16_merge_s16': + return seed_lowk.merge_q1024_k16_s16_ir + if verify_kernel == 'midk_k11_stage1': + return seed_midk.stage1_k11_exact_ir + if verify_kernel == 'midk_k11_merge_s8': + return seed_midk.merge_k11_s8_exact_ir + if verify_kernel == 'midk_k12_stage1': + return seed_midk.v9.stage1_k12_ir + if verify_kernel == 'midk_k12_merge_s8': + return seed_midk.v9.merge_k12_s8_ir + if verify_kernel == 'midk_k13_stage1': + return seed_midk.stage1_k13_exact_ir + if verify_kernel == 'q4096_k13_unordered_stage1': + return stage1_q4096_k13_unordered_ir + if verify_kernel == 'midk_k13_merge_s4': + return seed_midk.merge_k13_s4_exact_ir + if verify_kernel == 'q4096_k13_unordered_merge_s4': + return merge_q4096_k13_unordered_ir + if verify_kernel == 'midk_k13_merge_s8': + return seed_midk.merge_k13_s8_exact_ir + if verify_kernel == 'd64_stage1': + return seed_d64.stage1_d64_unordered_ir + if verify_kernel == 'd64_merge_s4': + return seed_d64.merge_k10_s4_ir + return seed_lowk.stage1_q512_lowk_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_stage1_batch8_cond4_vmin_maxtree", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) + +def _select_contract_shapes(shape_labels): + return selected_parent._select_contract_shapes(shape_labels) + +def _trace_inputs_for_shape(shape: dict[str, Any]) -> dict[str, Any]: + return selected_parent._trace_inputs_for_shape(shape) + +def _inputs_for_label(label: str) -> dict[str, Any]: + return _trace_inputs_for_shape(_select_contract_shapes((label,))[0]) + +def _dtype_name(inputs: dict[str, Any], name: str='query') -> str: + tensor = inputs.get(name) + if tensor is not None and hasattr(tensor, 'dtype'): + return str(tensor.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')).replace('torch.', '') + +def _label_can_hit(inputs: dict[str, Any], labels: set[str] | tuple[str, ...] | str) -> bool: + label_set = {labels} if isinstance(labels, str) else set(labels) + value = inputs.get('label') + return value is None or str(value) in label_set + +def _is_bf16_build(inputs: dict[str, Any]) -> bool: + return bool(inputs.get('build', False)) and int(inputs.get('B', -1)) == 1 and (int(inputs.get('Q', -1)) == int(inputs.get('M', -2))) and (_dtype_name(inputs, 'query') == 'bfloat16') and (_dtype_name(inputs, 'database') in ('', 'bfloat16')) + +def _eligible_q512(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, TARGET_Q512_SHAPES) and _is_bf16_build(inputs) and (int(inputs.get('Q', -1)) == 512) and (int(inputs.get('D', -1)) == seed_midk.v9.FEAT_D) and (int(inputs.get('K', -1)) in (1, 2)) + +def _eligible_q1024_k16(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, TARGET_Q1024_SHAPES) and _is_bf16_build(inputs) and (int(inputs.get('Q', -1)) == 1024) and (int(inputs.get('D', -1)) == seed_midk.v9.FEAT_D) and (int(inputs.get('K', -1)) == 16) + +def _eligible_midk_q2048(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, TARGET_MIDK_Q2048_SHAPES) and seed_midk._eligible_midk_exact(inputs) and (int(inputs.get('Q', -1)) == 2048) and (int(inputs.get('K', -1)) in (11, 12, 13)) + +def _eligible_midk_q4096(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, TARGET_MIDK_Q4096_SHAPES) and seed_midk._eligible_midk_exact(inputs) and (int(inputs.get('Q', -1)) == 4096) and (int(inputs.get('K', -1)) == 13) + +def _eligible_d64_q4096(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, TARGET_D64_Q4096_K10) and _is_bf16_build(inputs) and (int(inputs.get('Q', -1)) == 4096) and (int(inputs.get('D', -1)) == seed_d64.D64_FEAT_D) and (int(inputs.get('K', -1)) == seed_d64.TOP_K_MAX) + +def _selected_seed_for_inputs(inputs: dict[str, Any]) -> tuple[str | None, str | None]: + if _eligible_q512(inputs): + return (SEED_Q512_ID, str(inputs.get('label') or ''.join(['q512_k', format(inputs.get('K'), '')]))) + if _eligible_q1024_k16(inputs): + return (SEED_Q1024_ID, TARGET_Q1024_K16) + if _eligible_midk_q2048(inputs): + return (SEED_MIDK_Q2048_ID, str(inputs.get('label') or ''.join(['q2048_k', format(inputs.get('K'), '')]))) + if _eligible_midk_q4096(inputs): + return (SEED_MIDK_Q4096_ID, TARGET_Q4096_K13) + if _eligible_d64_q4096(inputs): + return (SEED_D64_ID, TARGET_D64_Q4096_K10) + return (None, None) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if not force_fallback: + selected_seed, _label = _selected_seed_for_inputs(inputs) + if selected_seed == SEED_Q512_ID: + return ROUTE_Q512_S4 + if selected_seed == SEED_Q1024_ID: + return ROUTE_Q1024_K16 + if selected_seed == SEED_MIDK_Q2048_ID: + return seed_midk.route_for_contract_inputs(inputs) + if selected_seed == SEED_MIDK_Q4096_ID: + return ROUTE_Q4096_K13_UNORDERED + if selected_seed == SEED_D64_ID: + return ROUTE_D64_Q4096 + return selected_parent.route_for_contract_inputs(inputs, candidate_key=PARENT_CANDIDATE_KEY, force_fallback=force_fallback) + +def q4096_k13_unordered_s4(inputs: dict[str, Any]) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + split_count = Q4096_K13_SPLIT_COUNT + num_q_tiles = (n_query + seed_midk.v9.BLOCK_Q - 1) // seed_midk.v9.BLOCK_Q + num_q_tile_pairs = (num_q_tiles + seed_midk.v9.CTA_GROUP - 1) // seed_midk.v9.CTA_GROUP + num_db_tiles = (n_database + seed_midk.v9.BLOCK_M - 1) // seed_midk.v9.BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tile_pairs * split_count + stage1_grid = min(total_work * seed_midk.v9.CTA_GROUP, seed_midk.v9.GRID_DIM_DEFAULT) + merge_grid = min((bsz * n_query + seed_midk.v9.K32_MERGE_THREADS - 1) // seed_midk.v9.K32_MERGE_THREADS, seed_midk.v9.GRID_DIM_DEFAULT) + partial_dists, partial_indices = seed_midk.v9.parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = seed_midk.v9.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), bsz * n_query, seed_midk.v9.BLOCK_Q, dim, dim) + tmap_database = seed_midk.v9.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, seed_midk.v9.BLOCK_M, dim, dim) + _compiled_stage1_q4096_k13_unordered().launch_cluster(grid=(stage1_grid, 1, 1), block=(seed_midk.v9.STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage1_q4096_k13_unordered_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tile_pairs=num_q_tile_pairs, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), cluster_dims=(seed_midk.v9.CTA_GROUP, 1, 1), shared_mem=stage1_q4096_k13_unordered_ir.computed_smem_bytes) + _compiled_merge_q4096_k13_unordered().launch(grid=(merge_grid, 1, 1), block=(seed_midk.v9.K32_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_q4096_k13_unordered_ir.computed_smem_bytes) + +def _launch_d64_q4096(inputs: dict[str, Any]) -> None: + previous_mode = os.environ.get('LOOM_KNN_D64_Q4096_C271_TWOSTAGE_MODE') + os.environ['LOOM_KNN_D64_Q4096_C271_TWOSTAGE_MODE'] = 'split4_unordered' + try: + seed_d64.launch_from_contract_inputs(inputs) + finally: + if previous_mode is None: + os.environ.pop('LOOM_KNN_D64_Q4096_C271_TWOSTAGE_MODE', None) + else: + os.environ['LOOM_KNN_D64_Q4096_C271_TWOSTAGE_MODE'] = previous_mode + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + if not force_fallback: + selected_seed, _label = _selected_seed_for_inputs(inputs) + if selected_seed == SEED_Q512_ID: + seed_lowk.launch_from_contract_inputs(inputs, q512_split_count=Q512_SPLIT_COUNT) + return + if selected_seed == SEED_Q1024_ID: + seed_lowk.launch_from_contract_inputs(inputs, q1024_k16_split_count=Q1024_K16_SPLIT_COUNT) + return + if selected_seed == SEED_MIDK_Q2048_ID: + seed_midk.launch_from_contract_inputs(inputs) + return + if selected_seed == SEED_MIDK_Q4096_ID: + q4096_k13_unordered_s4(inputs) + return + if selected_seed == SEED_D64_ID: + _launch_d64_q4096(inputs) + return + selected_parent.launch_from_contract_inputs(inputs, candidate_key=PARENT_CANDIDATE_KEY, force_fallback=force_fallback) + +def candidate_build_lowfloor_2c1c_v3(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]) -> None: + candidate_build_lowfloor_2c1c_v3(inputs) + +def candidate_parent_selected_9a17(inputs: dict[str, Any]) -> None: + selected_parent.launch_from_contract_inputs(inputs, candidate_key=PARENT_CANDIDATE_KEY) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _benchmark_shapes(shape_labels, *, time_flashlib: bool) -> list[dict[str, Any]]: + selected = _select_contract_shapes(TARGET_SHAPES if shape_labels is None else shape_labels) + out = [] + for shape in selected: + params = dict(shape['params']) + params['time_flashlib'] = bool(time_flashlib) + out.append({'label': shape['label'], 'params': params}) + return out + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate, time_flashlib: bool=True, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + previous = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_benchmark_shapes(shape_labels, time_flashlib=time_flashlib), correctness=correctness, benchmark=benchmark, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = previous + +def route_trace_for_contract_shapes(shape_labels=TARGET_SHAPES, *, force_fallback: bool=False) -> list[dict[str, Any]]: + rows = [] + for label in tuple(shape_labels): + inputs = _inputs_for_label(str(label)) + selected_seed, matched_label = (None, None) if force_fallback else _selected_seed_for_inputs(inputs) + route = route_for_contract_inputs(inputs, force_fallback=force_fallback) + parent_route = selected_parent.route_for_contract_inputs(inputs, candidate_key=PARENT_CANDIDATE_KEY) + parent_row = dict(selected_parent.route_trace_for_contract_shapes((label,), candidate_key=PARENT_CANDIDATE_KEY)[0]) + if selected_seed is None: + row = dict(parent_row) + row['expected_seed'] = None + row['parent_selected_route'] = parent_route + row['candidate_guard_status'] = 'forced_fallback' if force_fallback else 'guard_miss' + if force_fallback: + row['guard_id'] = 'forced_fallback_build_lowfloor_2c1c' + row['guard_condition'] = 'forced fallback to selected 9a17-only parent' + row['classification'] = 'guard-miss' + rows.append(selected_parent._normalize_route_row(row)) + continue + guard_conditions = {SEED_Q512_ID: 'exact BF16 build B=1 Q=M=512 D=128 K in {1,2} split4', SEED_Q1024_ID: 'exact BF16 build B=1 Q=M=1024 D=128 K=16 split16', SEED_MIDK_Q2048_ID: 'exact BF16 build B=1 Q=M=2048 D=128 K in {11,12,13} split8', SEED_MIDK_Q4096_ID: 'exact BF16 build B=1 Q=M=4096 D=128 K=13 unordered split4', SEED_D64_ID: 'exact BF16 build B=1 Q=M=4096 D=64 K=10 c271 split4 unordered'} + selected_entrypoints = {SEED_Q512_ID: ROUTE_Q512_S4, SEED_Q1024_ID: ROUTE_Q1024_K16, SEED_MIDK_Q2048_ID: ROUTE_MIDK, SEED_MIDK_Q4096_ID: ROUTE_Q4096_K13_UNORDERED, SEED_D64_ID: ROUTE_D64_Q4096} + split_counts = {SEED_Q512_ID: Q512_SPLIT_COUNT, SEED_Q1024_ID: Q1024_K16_SPLIT_COUNT, SEED_MIDK_Q2048_ID: seed_midk.v9.K12_MID_SPLITS, SEED_MIDK_Q4096_ID: Q4096_K13_SPLIT_COUNT, SEED_D64_ID: seed_d64.STAGE1_SPLIT4} + rows.append(selected_parent._normalize_route_row({'shape_key': label, 'selected_route': route, 'selected_entrypoint': selected_entrypoints[selected_seed], 'selected_seed': selected_seed, 'expected_seed': selected_seed, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': ''.join(['2c1c_build_lowfloor_', format(selected_seed, '')]), 'guard_condition': guard_conditions[selected_seed], 'matched_label': matched_label, 'split_count': split_counts[selected_seed], 'parent_selected_route': parent_route, 'baseline_dispatcher_route': parent_row.get('selected_route'), 'classification': 'seed-consumed'})) + return rows + +def _rows_for_labels(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return {label: dict(report.get('per_shape', {}).get(label, {})) for label in labels} + +def _per_shape_delta(candidate_report: dict[str, Any], baseline_report: dict[str, Any], labels: tuple[str, ...]): + rows = [] + for label in labels: + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') or baseline_row.get('flashlib_ms') + inputs = _inputs_for_label(label) + selected_seed, _matched_label = _selected_seed_for_inputs(inputs) + rows.append({'shape_key': label, 'selected_seed': selected_seed, 'candidate_route': route_for_contract_inputs(inputs), 'parent_selected_route': selected_parent.route_for_contract_inputs(inputs, candidate_key=PARENT_CANDIDATE_KEY), 'candidate_ms': candidate_ms, 'parent_selected_ms': baseline_ms, 'flashlib_ms': flashlib_ms, 'speedup_vs_parent_selected': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'ratio_vs_flashlib': flashlib_ms / candidate_ms if candidate_ms and flashlib_ms else None, 'candidate_passed': candidate_row.get('passed'), 'parent_selected_passed': baseline_row.get('passed'), 'timing_backend': candidate_row.get('timing_backend') or baseline_row.get('timing_backend')}) + return rows + +def _below_flashlib_floor(report: dict[str, Any], *, floor: float=1.2) -> list[dict[str, Any]]: + rows = [] + for label, row in sorted(report.get('per_shape', {}).items()): + ratio = row.get('ratio_vs_flashlib') + if isinstance(ratio, (float, int)) and ratio < floor: + rows.append({'shape_key': label, 'kernel_ms': row.get('kernel_ms'), 'flashlib_ms': row.get('flashlib_ms'), 'ratio_vs_flashlib': ratio, 'selected_seed': _selected_seed_for_inputs(_inputs_for_label(label))[0]}) + return rows + +def benchmark_candidate_build_lowfloor_2c1c_v3(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES, run_baseline: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + labels = tuple((str(label) for label in shape_labels)) + baseline_report = None + if run_baseline: + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=labels, kernel_fn=candidate_parent_selected_9a17, time_flashlib=time_flashlib) + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=labels, kernel_fn=candidate, time_flashlib=time_flashlib) + candidate_metric = candidate_report.get('summary', {}).get('primary_mean') + baseline_metric = baseline_report.get('summary', {}).get('primary_mean') if baseline_report else None + payload: dict[str, Any] = {'candidate_id': SEED_ID, 'measured_entrypoint': BENCHMARK_ENTRYPOINT, 'candidate_entrypoint': ROUTE_ENTRYPOINT, 'selected_seeds': (SEED_Q512_ID, SEED_Q1024_ID, SEED_MIDK_Q2048_ID, SEED_MIDK_Q4096_ID, SEED_D64_ID), 'source_tasks': SOURCE_TASKS, 'all_correct': candidate_report.get('summary', {}).get('all_correct'), 'performance_comparable': candidate_report.get('summary', {}).get('performance_comparable'), 'invalid_performance_reason': candidate_report.get('summary', {}).get('invalid_performance_reason'), 'tflops': candidate_metric, 'parent_selected_tflops': baseline_metric, 'metric_delta_vs_parent_selected': candidate_metric - baseline_metric if candidate_metric is not None and baseline_metric is not None else None, 'timing_backend': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'time_flashlib': time_flashlib, 'floor_vs_flashlib': 1.2, 'denominator': 'build_lowfloor_2c1c_exact8_direct', 'measured_shape_labels': list(labels), 'route_trace': route_trace_for_contract_shapes(labels), 'forced_fallback_route_trace': route_trace_for_contract_shapes(labels, force_fallback=True), 'route_modules': PRODUCTION_ROUTE_MODULES, 'contract_summary': candidate_report.get('summary'), 'contract_performance': candidate_report.get('performance'), 'contract_correctness': candidate_report.get('correctness'), 'rank_objective': {'metric': 'tflops', 'direction': 'maximize', 'value': candidate_metric, 'valid_measurement_count': candidate_report.get('performance', {}).get('valid_measurement_count'), 'comparable': candidate_report.get('performance', {}).get('comparable')}, 'below_flashlib_floor': _below_flashlib_floor(candidate_report, floor=1.2), 'report': candidate_report} + if baseline_report is not None: + payload.update({'parent_selected_entrypoint': ROUTE_PARENT_SELECTED, 'parent_selected_all_correct': baseline_report.get('summary', {}).get('all_correct'), 'parent_selected_performance_comparable': baseline_report.get('summary', {}).get('performance_comparable'), 'selected_route_rows': _rows_for_labels(candidate_report, labels), 'parent_selected_rows': _rows_for_labels(baseline_report, labels), 'seed_delta_matrix': _per_shape_delta(candidate_report, baseline_report, labels), 'parent_selected_report': baseline_report}) + return payload + +def write_benchmark_artifact(artifact_dir: str | Path, *, use_cupti: bool=True, shape_labels=TARGET_SHAPES, run_baseline: bool=True, time_flashlib: bool=True) -> dict[str, str]: + payload = benchmark_candidate_build_lowfloor_2c1c_v3(use_cupti=use_cupti, shape_labels=shape_labels, run_baseline=run_baseline, time_flashlib=time_flashlib) + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + path = out_dir / 'build_lowfloor_2c1c_v3.json' + path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + return {'candidate_payload': str(path)} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_build_lowfloor_d43e_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_build_lowfloor_d43e_v1.py new file mode 100644 index 00000000..eeba3c7f --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_build_lowfloor_d43e_v1.py @@ -0,0 +1,253 @@ +"""Build low-floor exact bucket for the d43e continuation. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +does not edit production dispatch. It routes five non-D64 BF16 build rows from +the d43e low-floor lane to existing primitive-backed Weave seeds: + +* Q512/D128/K2 through the f8c3 low-K split4 route. +* Q4096/D128/K8 through the c3bf direct split4 K8 route. +* Q2048/D128/K11 and K13 through the e080 exact mid-K route. +* Q3072/D128/K20 through the b3ec/v20 split4 K20 route. + +Guard misses delegate to the current 1877 full90 baseline route. FlashLib is +used only by the contract harness as a black-box timing baseline. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from pathlib import Path +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_build_lowfloor_residual_b3ec_v1 as seed_b3ec +from . import knn_build_dispatch_784a_6bc3_k8_q512k456_q4096k8_s4_direct_c3bf_v1 as seed_c3bf +from . import knn_build_dispatch_ad64_1b8f_4b51_ceb3_full90_synthesis_v1 as baseline_1877 +from . import knn_build_lowk_f8c3_q512_q1024_v1 as seed_lowk +from . import knn_build_midk_k11k13_e080_v1 as seed_midk +MODULE = 'loom.examples.weave.knn_build_build_lowfloor_d43e_v1' +TARGET_Q512_K2 = 'build_k_sweep_qm512_k2' +TARGET_Q4096_K8 = seed_c3bf.Q4096_K8 +TARGET_Q2048_K11 = 'build_k_sweep_qm2048_k11' +TARGET_Q2048_K13 = 'build_k_sweep_qm2048_k13' +TARGET_TAIL_Q3072_K20 = seed_b3ec.TARGET_TAIL_Q3072_K20 +TARGET_MIDK_SHAPES = (TARGET_Q2048_K11, TARGET_Q2048_K13) +TARGET_SHAPES = (TARGET_Q512_K2, TARGET_Q4096_K8, TARGET_Q2048_K11, TARGET_Q2048_K13, TARGET_TAIL_Q3072_K20) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +SEED_ID = 'build_lowfloor_d43e_v1' +SEED_Q512_K2_ID = 'd43e_lowk_q512_k2_s4' +SEED_Q4096_K8_ID = seed_c3bf.SEED_Q4096_K8_DIRECT_ID +SEED_MIDK_ID = 'd43e_midk_k11k13_e080' +SEED_TAIL_K20_ID = seed_b3ec.SEED_TAIL_K20_ID +BASELINE_1877_ID = baseline_1877.CANDIDATE_CONFIGS[baseline_1877.DEFAULT_CANDIDATE_KEY]['candidate_id'] +Q512_SPLIT_COUNT = 4 +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_Q512_K2_S4 = ''.join([format(seed_lowk.ROUTE_PREFIX, ''), ':q512_lowk_s', format(Q512_SPLIT_COUNT, '')]) +ROUTE_Q4096_K8_S4 = seed_c3bf.ROUTE_Q4096_K8_S4 +ROUTE_TAIL_K20_S4 = seed_b3ec.ROUTE_TAIL_K20_S4 +PRODUCTION_ROUTE_MODULES = {SEED_ID: ROUTE_ENTRYPOINT, SEED_Q512_K2_ID: ROUTE_Q512_K2_S4, SEED_Q4096_K8_ID: ROUTE_Q4096_K8_S4, SEED_MIDK_ID: ''.join([format(seed_midk.MODULE, ''), ':launch_from_contract_inputs']), SEED_TAIL_K20_ID: ROUTE_TAIL_K20_S4, BASELINE_1877_ID: baseline_1877.ROUTE_ENTRYPOINT} +SOURCE_TASKS = {SEED_ID: 'weave-evolve-knn-build-d43e / build low-floor K2/K8/K11/K13/tail bucket', SEED_Q512_K2_ID: 'generalize-auto-tuning f8c3 low-K split scan retained Q512 split4 for K2', SEED_Q4096_K8_ID: 'weave-evolve-knn-build-c3bf / Q4096 K8 split4 repair', SEED_MIDK_ID: 'weave-evolve-knn-build-e080 / exact Q2048 K11/K13 mid-K route', SEED_TAIL_K20_ID: 'weave-evolve-knn-build-b3ec / v20 Q3072 K20 split4 repair'} + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_BUILDFLOOR_D43E_VERIFY_KERNEL') + if verify_kernel == 'q512_stage1': + return seed_lowk.stage1_q512_lowk_ir + if verify_kernel == 'q512_merge_generic': + return seed_lowk.merge_q512_generic_ir + if verify_kernel == 'q4096_k8_stage1': + return seed_b3ec.v20.stage1_k8_ir + if verify_kernel == 'q4096_k8_merge_s4': + return seed_b3ec.v20.merge_k8_ir + if verify_kernel == 'midk_k11_stage1': + return seed_midk.stage1_k11_exact_ir + if verify_kernel == 'midk_k11_merge_s8': + return seed_midk.merge_k11_s8_exact_ir + if verify_kernel == 'midk_k13_stage1': + return seed_midk.stage1_k13_exact_ir + if verify_kernel == 'midk_k13_merge_s8': + return seed_midk.merge_k13_s8_exact_ir + if verify_kernel == 'tail_k20_stage1': + return seed_b3ec.v20.stage1_k20_ir + if verify_kernel == 'tail_k20_merge_s4': + return seed_b3ec.v20.merge_k20_ir + return baseline_1877.ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbatch_m64_d4f7_stage1", "arg_keys": ["query", "database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 91392, "constants": [], "cta_group": 1, "threads": 512}')) + +def _select_contract_shapes(shape_labels): + return baseline_1877._select_contract_shapes(shape_labels) + +def _trace_inputs_for_shape(shape: dict[str, Any]) -> dict[str, Any]: + return baseline_1877._trace_inputs_for_shape(shape) + +def _inputs_for_label(label: str) -> dict[str, Any]: + return _trace_inputs_for_shape(_select_contract_shapes((label,))[0]) + +def _dtype_name(inputs: dict[str, Any], name: str='query') -> str: + tensor = inputs.get(name) + if tensor is not None and hasattr(tensor, 'dtype'): + return str(tensor.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')).replace('torch.', '') + +def _label_can_hit(inputs: dict[str, Any], labels: set[str] | tuple[str, ...] | str) -> bool: + label_set = {labels} if isinstance(labels, str) else set(labels) + value = inputs.get('label') + return value is None or str(value) in label_set + +def _is_bf16_build_d128(inputs: dict[str, Any]) -> bool: + return bool(inputs.get('build', False)) and int(inputs.get('B', -1)) == 1 and (int(inputs.get('Q', -1)) == int(inputs.get('M', -2))) and (int(inputs.get('D', -1)) == seed_b3ec.v20.FEAT_D) and (_dtype_name(inputs, 'query') == 'bfloat16') and (_dtype_name(inputs, 'database') in ('', 'bfloat16')) + +def _eligible_q512_k2(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, TARGET_Q512_K2) and _is_bf16_build_d128(inputs) and (int(inputs.get('Q', -1)) == 512) and (int(inputs.get('K', -1)) == 2) + +def _eligible_q4096_k8(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, TARGET_Q4096_K8) and seed_c3bf._eligible_q4096_k8_direct(inputs) + +def _eligible_midk(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, TARGET_MIDK_SHAPES) and seed_midk._eligible_midk_exact(inputs) and (int(inputs.get('Q', -1)) == 2048) and (int(inputs.get('K', -1)) in (11, 13)) + +def _eligible_tail_k20(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, TARGET_TAIL_Q3072_K20) and seed_b3ec._eligible_tail_k20(inputs) + +def _selected_seed_for_inputs(inputs: dict[str, Any]) -> tuple[str | None, str | None]: + if _eligible_q512_k2(inputs): + return (SEED_Q512_K2_ID, TARGET_Q512_K2) + if _eligible_q4096_k8(inputs): + return (SEED_Q4096_K8_ID, TARGET_Q4096_K8) + if _eligible_midk(inputs): + return (SEED_MIDK_ID, str(inputs.get('label') or ''.join(['q', format(inputs.get('Q'), ''), '_k', format(inputs.get('K'), '')]))) + if _eligible_tail_k20(inputs): + return (SEED_TAIL_K20_ID, TARGET_TAIL_Q3072_K20) + return (None, None) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if not force_fallback: + selected_seed, _label = _selected_seed_for_inputs(inputs) + if selected_seed == SEED_Q512_K2_ID: + return ROUTE_Q512_K2_S4 + if selected_seed == SEED_Q4096_K8_ID: + return ROUTE_Q4096_K8_S4 + if selected_seed == SEED_MIDK_ID: + return seed_midk.route_for_contract_inputs(inputs) + if selected_seed == SEED_TAIL_K20_ID: + return ROUTE_TAIL_K20_S4 + return baseline_1877.route_for_contract_inputs(inputs, candidate_key=baseline_1877.DEFAULT_CANDIDATE_KEY, force_fallback=force_fallback) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + if not force_fallback: + selected_seed, _label = _selected_seed_for_inputs(inputs) + if selected_seed == SEED_Q512_K2_ID: + seed_lowk.launch_from_contract_inputs(inputs, q512_split_count=Q512_SPLIT_COUNT) + return + if selected_seed == SEED_Q4096_K8_ID: + seed_c3bf.launch_from_contract_inputs(inputs) + return + if selected_seed == SEED_MIDK_ID: + seed_midk.launch_from_contract_inputs(inputs) + return + if selected_seed == SEED_TAIL_K20_ID: + seed_b3ec._launch_tail_k20(inputs) + return + baseline_1877.launch_from_contract_inputs(inputs, candidate_key=baseline_1877.DEFAULT_CANDIDATE_KEY, force_fallback=force_fallback) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_baseline_1877(inputs: dict[str, Any]) -> None: + baseline_1877.launch_from_contract_inputs(inputs, candidate_key=baseline_1877.DEFAULT_CANDIDATE_KEY) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _benchmark_shapes(shape_labels, *, time_flashlib: bool) -> list[dict[str, Any]]: + selected = _select_contract_shapes(TARGET_SHAPES if shape_labels is None else shape_labels) + out = [] + for shape in selected: + params = dict(shape['params']) + params['time_flashlib'] = bool(time_flashlib) + out.append({'label': shape['label'], 'params': params}) + return out + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate, time_flashlib: bool=True) -> dict[str, Any]: + previous = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_benchmark_shapes(shape_labels, time_flashlib=time_flashlib), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = previous + +def route_trace_for_contract_shapes(shape_labels=TARGET_SHAPES, *, force_fallback: bool=False) -> list[dict[str, Any]]: + rows = [] + for label in tuple(shape_labels): + inputs = _inputs_for_label(str(label)) + selected_seed, matched_label = (None, None) if force_fallback else _selected_seed_for_inputs(inputs) + route = route_for_contract_inputs(inputs, force_fallback=force_fallback) + baseline_route = baseline_1877.route_for_contract_inputs(inputs, candidate_key=baseline_1877.DEFAULT_CANDIDATE_KEY) + if selected_seed is None: + row = dict(baseline_1877.route_trace_for_contract_shapes((label,), candidate_key=baseline_1877.DEFAULT_CANDIDATE_KEY, force_fallback=force_fallback)[0]) + row['candidate_guard_status'] = 'forced_fallback' if force_fallback else 'guard_miss' + row['expected_seed'] = None + rows.append(baseline_1877._normalize_route_row(row)) + continue + guard_conditions = {SEED_Q512_K2_ID: 'exact BF16 build B=1 Q=M=512 D=128 K=2 split4', SEED_Q4096_K8_ID: 'exact BF16 build B=1 Q=M=4096 D=128 K=8 split4', SEED_MIDK_ID: 'exact BF16 build B=1 Q=M=2048 D=128 K in {11,13}', SEED_TAIL_K20_ID: 'exact BF16 build B=1 Q=M=3072 D=128 K=20 split4'} + rows.append(baseline_1877._normalize_route_row({'shape_key': label, 'selected_route': route, 'selected_entrypoint': route if selected_seed != SEED_MIDK_ID else ''.join([format(seed_midk.MODULE, ''), ':launch_from_contract_inputs']), 'selected_seed': selected_seed, 'expected_seed': selected_seed, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': ''.join(['d43e_build_lowfloor_', format(selected_seed, '')]), 'guard_condition': guard_conditions[selected_seed], 'matched_label': matched_label, 'baseline_1877_route': baseline_route, 'classification': 'seed-consumed'})) + return rows + +def _rows_for_labels(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return {label: dict(report.get('per_shape', {}).get(label, {})) for label in labels} + +def _per_shape_delta(candidate_report: dict[str, Any], baseline_report: dict[str, Any], labels: tuple[str, ...]): + rows = [] + for label in labels: + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') or baseline_row.get('flashlib_ms') + inputs = _inputs_for_label(label) + selected_seed, _matched_label = _selected_seed_for_inputs(inputs) + rows.append({'shape_key': label, 'selected_seed': selected_seed, 'candidate_route': route_for_contract_inputs(inputs), 'baseline_1877_route': baseline_1877.route_for_contract_inputs(inputs, candidate_key=baseline_1877.DEFAULT_CANDIDATE_KEY), 'candidate_ms': candidate_ms, 'baseline_1877_ms': baseline_ms, 'flashlib_ms': flashlib_ms, 'speedup_vs_1877': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'ratio_vs_flashlib': flashlib_ms / candidate_ms if candidate_ms and flashlib_ms else None, 'candidate_passed': candidate_row.get('passed'), 'baseline_1877_passed': baseline_row.get('passed'), 'timing_backend': candidate_row.get('timing_backend') or baseline_row.get('timing_backend')}) + return rows + +def _below_flashlib_floor(report: dict[str, Any], *, floor: float=1.05) -> list[dict[str, Any]]: + rows = [] + for label, row in sorted(report.get('per_shape', {}).items()): + ratio = row.get('ratio_vs_flashlib') + if isinstance(ratio, (float, int)) and ratio < floor: + rows.append({'shape_key': label, 'kernel_ms': row.get('kernel_ms'), 'flashlib_ms': row.get('flashlib_ms'), 'ratio_vs_flashlib': ratio, 'selected_seed': _selected_seed_for_inputs(_inputs_for_label(label))[0]}) + return rows + +def benchmark_candidate_build_lowfloor_d43e_v1(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES, run_baseline: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + labels = tuple((str(label) for label in shape_labels)) + baseline_report = None + if run_baseline: + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=labels, kernel_fn=candidate_baseline_1877, time_flashlib=time_flashlib) + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=labels, kernel_fn=candidate, time_flashlib=time_flashlib) + candidate_metric = candidate_report.get('summary', {}).get('primary_mean') + baseline_metric = baseline_report.get('summary', {}).get('primary_mean') if baseline_report else None + payload: dict[str, Any] = {'candidate_id': SEED_ID, 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_candidate_build_lowfloor_d43e_v1']), 'candidate_entrypoint': ROUTE_ENTRYPOINT, 'selected_seeds': (SEED_Q512_K2_ID, SEED_Q4096_K8_ID, SEED_MIDK_ID, SEED_TAIL_K20_ID), 'source_tasks': SOURCE_TASKS, 'all_correct': candidate_report.get('summary', {}).get('all_correct'), 'performance_comparable': candidate_report.get('summary', {}).get('performance_comparable'), 'invalid_performance_reason': candidate_report.get('summary', {}).get('invalid_performance_reason'), 'tflops': candidate_metric, 'baseline_1877_tflops': baseline_metric, 'metric_delta_vs_1877': candidate_metric - baseline_metric if candidate_metric is not None and baseline_metric is not None else None, 'timing_backend': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'time_flashlib': time_flashlib, 'denominator': 'build_lowfloor_d43e_exact5', 'shape_labels': list(labels), 'route_trace': route_trace_for_contract_shapes(labels), 'forced_fallback_route_trace': route_trace_for_contract_shapes(labels, force_fallback=True), 'route_modules': PRODUCTION_ROUTE_MODULES, 'contract_summary': candidate_report.get('summary'), 'contract_performance': candidate_report.get('performance'), 'contract_correctness': candidate_report.get('correctness'), 'rank_objective': {'metric': 'tflops', 'direction': 'maximize', 'value': candidate_metric, 'valid_measurement_count': candidate_report.get('performance', {}).get('valid_measurement_count'), 'comparable': candidate_report.get('performance', {}).get('comparable')}, 'below_flashlib_floor': _below_flashlib_floor(candidate_report, floor=1.05), 'report': candidate_report} + if baseline_report is not None: + payload.update({'baseline_1877_entrypoint': baseline_1877.CANDIDATE_BEST_BUILD_CEB3_ENTRYPOINT, 'baseline_1877_all_correct': baseline_report.get('summary', {}).get('all_correct'), 'baseline_1877_performance_comparable': baseline_report.get('summary', {}).get('performance_comparable'), 'selected_route_rows': _rows_for_labels(candidate_report, labels), 'baseline_1877_rows': _rows_for_labels(baseline_report, labels), 'seed_delta_matrix': _per_shape_delta(candidate_report, baseline_report, labels), 'baseline_1877_report': baseline_report}) + return payload + +def write_benchmark_artifact(artifact_dir: str | Path, *, use_cupti: bool=True, shape_labels=TARGET_SHAPES, run_baseline: bool=True, time_flashlib: bool=True) -> dict[str, str]: + payload = benchmark_candidate_build_lowfloor_d43e_v1(use_cupti=use_cupti, shape_labels=shape_labels, run_baseline=run_baseline, time_flashlib=time_flashlib) + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + path = out_dir / 'build_lowfloor_d43e_v1.json' + path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + return {'candidate_payload': str(path)} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_build_lowfloor_residual_b3ec_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_build_lowfloor_residual_b3ec_v1.py new file mode 100644 index 00000000..c88068b9 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_build_lowfloor_residual_b3ec_v1.py @@ -0,0 +1,245 @@ +"""Build low-floor residual exact-four bucket for the 67da continuation. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +does not edit production dispatch. It routes four residual build rows that were +still below or near the FlashLib floor after the 67da full90 synthesis: + +* Q512/K1 through the 84bb low-K split4 seed. +* Q4096/D64/K10 through the 84bb aa88 v2 split4 cached merge seed. +* Q4096/D128/K8 through the c3bf split4 K8 seed. +* Q3072/D128/K20 through the verified v20 four-split K20 path. + +Guard misses delegate to the current 1877 full90 baseline route. FlashLib is +used only by the contract harness as a black-box timing baseline. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from pathlib import Path +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_d64_lowk_lowfloor_84bb_v1 as seed84bb +from . import knn_build_dispatch_784a_6bc3_k8_q512k456_q4096k8_s4_direct_c3bf_v1 as seed_c3bf +from . import knn_build_dispatch_ad64_1b8f_4b51_ceb3_full90_synthesis_v1 as baseline_1877 +from . import knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2_k32split_v20 as v20 +MODULE = 'loom.examples.weave.knn_build_build_lowfloor_residual_b3ec_v1' +TARGET_K1 = seed84bb.TARGET_K1 +TARGET_D64_Q4096 = seed84bb.TARGET_D64_Q4096 +TARGET_Q4096_K8 = seed_c3bf.Q4096_K8 +TARGET_TAIL_Q3072_K20 = 'build_tail_b1_q3072_m3072_d128_k20' +TARGET_SHAPES = (TARGET_K1, TARGET_D64_Q4096, TARGET_Q4096_K8, TARGET_TAIL_Q3072_K20) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +SEED_ID = 'build_lowfloor_residual_b3ec_v1' +SEED_K1_ID = seed84bb.SEED_K1_ID +SEED_D64_ID = seed84bb.SEED_D64_ID +SEED_Q4096_K8_ID = seed_c3bf.SEED_Q4096_K8_DIRECT_ID +SEED_TAIL_K20_ID = 'b3ec_v20_q3072_k20_s4' +BASELINE_1877_ID = baseline_1877.CANDIDATE_CONFIGS[baseline_1877.DEFAULT_CANDIDATE_KEY]['candidate_id'] +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_K1_ENTRYPOINT = seed84bb.ROUTE_K1_ENTRYPOINT +ROUTE_D64_ENTRYPOINT = seed84bb.ROUTE_D64_ENTRYPOINT +ROUTE_Q4096_K8_S4 = seed_c3bf.ROUTE_Q4096_K8_S4 +ROUTE_TAIL_K20_S4 = ''.join([format(MODULE, ''), ':q3072_k20_v20_s4']) +PRODUCTION_ROUTE_MODULES = {SEED_ID: ROUTE_ENTRYPOINT, SEED_K1_ID: ROUTE_K1_ENTRYPOINT, SEED_D64_ID: ROUTE_D64_ENTRYPOINT, SEED_Q4096_K8_ID: ROUTE_Q4096_K8_S4, SEED_TAIL_K20_ID: ROUTE_TAIL_K20_S4, BASELINE_1877_ID: baseline_1877.ROUTE_ENTRYPOINT} +SOURCE_TASKS = {SEED_ID: 'weave-evolve-knn-build-b3ec / build low-floor residual exact-four bucket', SEED_K1_ID: 'weave-evolve-knn-build-84bb / low-K Q512 split4 seed', SEED_D64_ID: 'weave-evolve-knn-build-84bb / aa88 v2 D64 Q4096 split4 cached merge', SEED_Q4096_K8_ID: 'weave-evolve-knn-build-c3bf / Q4096 K8 split4 repair', SEED_TAIL_K20_ID: 'weave-evolve-knn-build-b3ec / v20 Q3072 K20 split4 repair'} + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_BUILDFLOOR_B3EC_VERIFY_KERNEL') + if verify_kernel == 'lowk_q512_stage1': + return seed84bb.lowk_seed.stage1_q512_lowk_ir + if verify_kernel == 'lowk_q512_merge_generic': + return seed84bb.lowk_seed.merge_q512_generic_ir + if verify_kernel == 'd64_stage1': + return seed84bb.d64_seed.stage1_d64_split_ir + if verify_kernel == 'd64_merge_s4': + return seed84bb.d64_seed.merge_k10_s4_ir + if verify_kernel == 'q4096_k8_stage1': + return v20.stage1_k8_ir + if verify_kernel == 'q4096_k8_merge_s4': + return v20.merge_k8_ir + if verify_kernel == 'tail_k20_stage1': + return v20.stage1_k20_ir + if verify_kernel == 'tail_k20_merge_s4': + return v20.merge_k20_ir + return baseline_1877.ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbatch_m64_d4f7_stage1", "arg_keys": ["query", "database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 91392, "constants": [], "cta_group": 1, "threads": 512}')) + +def _select_contract_shapes(shape_labels): + return baseline_1877._select_contract_shapes(shape_labels) + +def _trace_inputs_for_shape(shape: dict[str, Any]) -> dict[str, Any]: + return baseline_1877._trace_inputs_for_shape(shape) + +def _inputs_for_label(label: str) -> dict[str, Any]: + return _trace_inputs_for_shape(_select_contract_shapes((label,))[0]) + +def _dtype_name(inputs: dict[str, Any], name: str='query') -> str: + tensor = inputs.get(name) + if tensor is not None and hasattr(tensor, 'dtype'): + return str(tensor.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')).replace('torch.', '') + +def _label_can_hit(inputs: dict[str, Any], label: str) -> bool: + value = inputs.get('label') + return value is None or str(value) == label + +def _is_bf16_build(inputs: dict[str, Any]) -> bool: + return bool(inputs.get('build', False)) and int(inputs.get('B', -1)) == 1 and (int(inputs.get('Q', -1)) == int(inputs.get('M', -2))) and (_dtype_name(inputs, 'query') == 'bfloat16') and (_dtype_name(inputs, 'database') in ('', 'bfloat16')) + +def _eligible_k1(inputs: dict[str, Any]) -> bool: + return seed84bb._eligible_k1_q512(inputs) + +def _eligible_d64(inputs: dict[str, Any]) -> bool: + return seed84bb._eligible_d64_q4096(inputs) + +def _eligible_q4096_k8(inputs: dict[str, Any]) -> bool: + return seed_c3bf._eligible_q4096_k8_direct(inputs) + +def _eligible_tail_k20(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, TARGET_TAIL_Q3072_K20) and _is_bf16_build(inputs) and (int(inputs.get('Q', -1)) == 3072) and (int(inputs.get('D', -1)) == v20.FEAT_D) and (int(inputs.get('K', -1)) == 20) + +def _selected_seed_for_inputs(inputs: dict[str, Any]) -> tuple[str | None, str | None]: + if _eligible_k1(inputs): + return (SEED_K1_ID, TARGET_K1) + if _eligible_d64(inputs): + return (SEED_D64_ID, TARGET_D64_Q4096) + if _eligible_q4096_k8(inputs): + return (SEED_Q4096_K8_ID, TARGET_Q4096_K8) + if _eligible_tail_k20(inputs): + return (SEED_TAIL_K20_ID, TARGET_TAIL_Q3072_K20) + return (None, None) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if not force_fallback: + selected_seed, _label = _selected_seed_for_inputs(inputs) + if selected_seed == SEED_K1_ID: + return ROUTE_K1_ENTRYPOINT + if selected_seed == SEED_D64_ID: + return ROUTE_D64_ENTRYPOINT + if selected_seed == SEED_Q4096_K8_ID: + return ROUTE_Q4096_K8_S4 + if selected_seed == SEED_TAIL_K20_ID: + return ROUTE_TAIL_K20_S4 + return baseline_1877.route_for_contract_inputs(inputs, candidate_key=baseline_1877.DEFAULT_CANDIDATE_KEY, force_fallback=force_fallback) + +def _launch_tail_k20(inputs: dict[str, Any]) -> None: + v20._launch_k32_split_path(inputs, split_count=v20.MEDIUM_SPLITS) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + if not force_fallback: + selected_seed, _label = _selected_seed_for_inputs(inputs) + if selected_seed in (SEED_K1_ID, SEED_D64_ID): + seed84bb.launch_from_contract_inputs(inputs) + return + if selected_seed == SEED_Q4096_K8_ID: + seed_c3bf.launch_from_contract_inputs(inputs) + return + if selected_seed == SEED_TAIL_K20_ID: + _launch_tail_k20(inputs) + return + baseline_1877.launch_from_contract_inputs(inputs, candidate_key=baseline_1877.DEFAULT_CANDIDATE_KEY, force_fallback=force_fallback) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_baseline_1877(inputs: dict[str, Any]) -> None: + baseline_1877.launch_from_contract_inputs(inputs, candidate_key=baseline_1877.DEFAULT_CANDIDATE_KEY) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _benchmark_shapes(shape_labels, *, time_flashlib: bool) -> list[dict[str, Any]]: + selected = _select_contract_shapes(TARGET_SHAPES if shape_labels is None else shape_labels) + out = [] + for shape in selected: + params = dict(shape['params']) + params['time_flashlib'] = bool(time_flashlib) + out.append({'label': shape['label'], 'params': params}) + return out + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate, time_flashlib: bool=True) -> dict[str, Any]: + previous = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_benchmark_shapes(shape_labels, time_flashlib=time_flashlib), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = previous + +def route_trace_for_contract_shapes(shape_labels=TARGET_SHAPES, *, force_fallback: bool=False) -> list[dict[str, Any]]: + rows = [] + for label in tuple(shape_labels): + inputs = _inputs_for_label(str(label)) + selected_seed, matched_label = (None, None) if force_fallback else _selected_seed_for_inputs(inputs) + route = route_for_contract_inputs(inputs, force_fallback=force_fallback) + baseline_route = baseline_1877.route_for_contract_inputs(inputs, candidate_key=baseline_1877.DEFAULT_CANDIDATE_KEY) + if selected_seed is None: + row = dict(baseline_1877.route_trace_for_contract_shapes((label,), candidate_key=baseline_1877.DEFAULT_CANDIDATE_KEY, force_fallback=force_fallback)[0]) + row['candidate_guard_status'] = 'forced_fallback' if force_fallback else 'guard_miss' + row['expected_seed'] = None + rows.append(baseline_1877._normalize_route_row(row)) + continue + guard_conditions = {SEED_K1_ID: 'exact BF16 build B=1 Q=M=512 D=128 K=1', SEED_D64_ID: 'exact BF16 build B=1 Q=M=4096 D=64 K=10', SEED_Q4096_K8_ID: 'exact BF16 build B=1 Q=M=4096 D=128 K=8 split4', SEED_TAIL_K20_ID: 'exact BF16 build B=1 Q=M=3072 D=128 K=20 split4'} + rows.append(baseline_1877._normalize_route_row({'shape_key': label, 'selected_route': route, 'selected_entrypoint': route, 'selected_seed': selected_seed, 'expected_seed': selected_seed, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': ''.join(['b3ec_build_lowfloor_', format(selected_seed, '')]), 'guard_condition': guard_conditions[selected_seed], 'matched_label': matched_label, 'baseline_1877_route': baseline_route, 'classification': 'seed-consumed'})) + return rows + +def _rows_for_labels(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return {label: dict(report.get('per_shape', {}).get(label, {})) for label in labels} + +def _per_shape_delta(candidate_report: dict[str, Any], baseline_report: dict[str, Any], labels: tuple[str, ...]): + rows = [] + for label in labels: + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') or baseline_row.get('flashlib_ms') + inputs = _inputs_for_label(label) + selected_seed, _matched_label = _selected_seed_for_inputs(inputs) + rows.append({'shape_key': label, 'selected_seed': selected_seed, 'candidate_route': route_for_contract_inputs(inputs), 'baseline_1877_route': baseline_1877.route_for_contract_inputs(inputs, candidate_key=baseline_1877.DEFAULT_CANDIDATE_KEY), 'candidate_ms': candidate_ms, 'baseline_1877_ms': baseline_ms, 'flashlib_ms': flashlib_ms, 'speedup_vs_1877': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'ratio_vs_flashlib': flashlib_ms / candidate_ms if candidate_ms and flashlib_ms else None, 'candidate_passed': candidate_row.get('passed'), 'baseline_1877_passed': baseline_row.get('passed'), 'timing_backend': candidate_row.get('timing_backend') or baseline_row.get('timing_backend')}) + return rows + +def _below_flashlib_floor(report: dict[str, Any], *, floor: float=1.05) -> list[dict[str, Any]]: + rows = [] + for label, row in sorted(report.get('per_shape', {}).items()): + ratio = row.get('ratio_vs_flashlib') + if isinstance(ratio, (float, int)) and ratio < floor: + rows.append({'shape_key': label, 'kernel_ms': row.get('kernel_ms'), 'flashlib_ms': row.get('flashlib_ms'), 'ratio_vs_flashlib': ratio, 'selected_seed': _selected_seed_for_inputs(_inputs_for_label(label))[0]}) + return rows + +def benchmark_candidate_build_lowfloor_residual_b3ec_v1(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES, run_baseline: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + labels = tuple((str(label) for label in shape_labels)) + baseline_report = None + if run_baseline: + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=labels, kernel_fn=candidate_baseline_1877, time_flashlib=time_flashlib) + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=labels, kernel_fn=candidate, time_flashlib=time_flashlib) + candidate_metric = candidate_report.get('summary', {}).get('primary_mean') + baseline_metric = baseline_report.get('summary', {}).get('primary_mean') if baseline_report else None + payload: dict[str, Any] = {'candidate_id': SEED_ID, 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_candidate_build_lowfloor_residual_b3ec_v1']), 'candidate_entrypoint': ROUTE_ENTRYPOINT, 'selected_seeds': (SEED_K1_ID, SEED_D64_ID, SEED_Q4096_K8_ID, SEED_TAIL_K20_ID), 'source_tasks': SOURCE_TASKS, 'all_correct': candidate_report.get('summary', {}).get('all_correct'), 'performance_comparable': candidate_report.get('summary', {}).get('performance_comparable'), 'invalid_performance_reason': candidate_report.get('summary', {}).get('invalid_performance_reason'), 'tflops': candidate_metric, 'baseline_1877_tflops': baseline_metric, 'metric_delta_vs_1877': candidate_metric - baseline_metric if candidate_metric is not None and baseline_metric is not None else None, 'timing_backend': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'time_flashlib': time_flashlib, 'denominator': 'build_lowfloor_residual_exact4', 'shape_labels': list(labels), 'route_trace': route_trace_for_contract_shapes(labels), 'forced_fallback_route_trace': route_trace_for_contract_shapes(labels, force_fallback=True), 'route_modules': PRODUCTION_ROUTE_MODULES, 'contract_summary': candidate_report.get('summary'), 'contract_performance': candidate_report.get('performance'), 'contract_correctness': candidate_report.get('correctness'), 'rank_objective': {'metric': 'tflops', 'direction': 'maximize', 'value': candidate_metric, 'valid_measurement_count': candidate_report.get('performance', {}).get('valid_measurement_count'), 'comparable': candidate_report.get('performance', {}).get('comparable')}, 'below_flashlib_floor': _below_flashlib_floor(candidate_report, floor=1.05), 'report': candidate_report} + if baseline_report is not None: + payload.update({'baseline_1877_entrypoint': baseline_1877.CANDIDATE_BEST_BUILD_CEB3_ENTRYPOINT, 'baseline_1877_all_correct': baseline_report.get('summary', {}).get('all_correct'), 'baseline_1877_performance_comparable': baseline_report.get('summary', {}).get('performance_comparable'), 'selected_route_rows': _rows_for_labels(candidate_report, labels), 'baseline_1877_rows': _rows_for_labels(baseline_report, labels), 'seed_delta_matrix': _per_shape_delta(candidate_report, baseline_report, labels), 'baseline_1877_report': baseline_report}) + return payload + +def write_benchmark_artifact(artifact_dir: str | Path, *, use_cupti: bool=True, shape_labels=TARGET_SHAPES, run_baseline: bool=True, time_flashlib: bool=True) -> dict[str, str]: + payload = benchmark_candidate_build_lowfloor_residual_b3ec_v1(use_cupti=use_cupti, shape_labels=shape_labels, run_baseline=run_baseline, time_flashlib=time_flashlib) + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + path = out_dir / 'build_lowfloor_residual_b3ec_v1.json' + path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + return {'candidate_payload': str(path)} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_buildbucket_dbd7_lowfloor_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_buildbucket_dbd7_lowfloor_v1.py new file mode 100644 index 00000000..32dcfdae --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_buildbucket_dbd7_lowfloor_v1.py @@ -0,0 +1,245 @@ +"""Build-bucket low-floor seed portfolio for the dbd7 continuation. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +does not edit the production full82 dispatcher. It routes exact BF16 build +D=128 rows to existing primitive-backed Weave seeds: + +* v20 K12 split/tcgen05 routes for Q1024 and Q4096 K12. +* 4f30's exact Q2048 K12 route. +* v12's K20 mixed-fanout and Q2048 K10 route. +* v25's exact K48 over-32 route, optionally disabled for same-denominator A/B. + +Guard misses delegate to the current 9db7/1074 full82 Weave dispatcher. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from pathlib import Path +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_17b8_lowmargin_1074_full82_v1 as base9db7 +from . import knn_build_lowk_k12_4f30_v1 as k12_4f30 +from . import knn_build_over32_topk_knn_build_dispatch_slurm_0610_6329_v25 as over32_v25 +from . import knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2_k32split_v12 as v12 +from . import knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2_k32split_v20 as v20 +MODULE = 'loom.examples.weave.knn_build_buildbucket_dbd7_lowfloor_v1' +BUILD_Q2048_K10 = 'build_qm2048_d128_k10' +BUILD_TAIL_Q1536_K10 = 'build_tail_b1_q1536_m1536_d128_k10' +K12_Q1024 = 'build_k_sweep_qm1024_k12' +K20_Q1024 = 'build_k_sweep_qm1024_k20' +K12_Q2048 = 'build_k_sweep_qm2048_k12' +K20_Q2048 = 'build_k_sweep_qm2048_k20' +K12_Q4096 = 'build_k_sweep_qm4096_k12' +K20_Q4096 = 'build_k_sweep_qm4096_k20' +K48_Q2048 = 'build_over32_stress_qm2048_k48' +K48_Q4096 = 'build_over32_stress_qm4096_k48' +V20_K12_SHAPES = (K12_Q1024, K12_Q4096) +K12_4F30_SHAPES = (K12_Q2048,) +V12_MIDBUILD_SHAPES = (BUILD_Q2048_K10, K20_Q1024, K20_Q2048, K20_Q4096) +OVER32_K48_SHAPES = (K48_Q2048, K48_Q4096) +FALLBACK_AUDIT_SHAPES = (BUILD_TAIL_Q1536_K10,) +TARGET_SHAPES = (K12_Q1024, K20_Q1024, BUILD_Q2048_K10, K12_Q2048, K20_Q2048, K12_Q4096, K20_Q4096, K48_Q2048, K48_Q4096, BUILD_TAIL_Q1536_K10) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +SEED_V20_K12_ID = 'v20_k12_q1024_q4096_exact' +SEED_K12_4F30_ID = 'q2048_k12_4f30_v1' +SEED_V12_MIDBUILD_ID = 'v12_k20_q2048k10_mixedfanout' +SEED_OVER32_V25_ID = 'over32_k48_v25' +BASE_9DB7_ID = 'base_9db7_lowmargin_1074_full82' +CANDIDATE_ID = 'buildbucket_dbd7_lowfloor_v1' +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +BASE_9DB7_ENTRYPOINT = ''.join([format(base9db7.MODULE, ''), ':launch_from_contract_inputs']) +V20_ENTRYPOINT = 'loom.examples.weave.knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2_k32split_v20:launch_from_contract_inputs' +V12_ENTRYPOINT = 'loom.examples.weave.knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2_k32split_v12:launch_from_contract_inputs' +K12_4F30_ENTRYPOINT = ''.join([format(k12_4f30.MODULE, ''), ':launch_from_contract_inputs']) +OVER32_V25_ENTRYPOINT = 'loom.examples.weave.knn_build_over32_topk_knn_build_dispatch_slurm_0610_6329_v25:launch_from_contract_inputs' +PRODUCTION_ROUTE_MODULES = {SEED_V20_K12_ID: V20_ENTRYPOINT, SEED_K12_4F30_ID: K12_4F30_ENTRYPOINT, SEED_V12_MIDBUILD_ID: V12_ENTRYPOINT, SEED_OVER32_V25_ID: OVER32_V25_ENTRYPOINT, BASE_9DB7_ID: BASE_9DB7_ENTRYPOINT} +SOURCE_TASKS = {SEED_V20_K12_ID: 'weave-evolve lineage v20 / loom.examples.weave.knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2_k32split_v20', SEED_K12_4F30_ID: 'weave-evolve-knn-build-4f30 / loom.examples.weave.knn_build_lowk_k12_4f30_v1', SEED_V12_MIDBUILD_ID: 'weave-evolve-knn-build-dae7/e15c / design_doc/active/weave_evolve_knn_build_round_53_dae7_q2048_k8_s8.md', SEED_OVER32_V25_ID: 'weave-evolve over32 probe / loom.examples.weave.knn_build_over32_topk_knn_build_dispatch_slurm_0610_6329_v25'} +eval_mod = base9db7.eval_mod + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_BUILDBUCKET_DBD7_VERIFY_KERNEL') + if verify_kernel == 'v20_k12': + return v20.ir + if verify_kernel == 'q2048_k12_4f30': + return k12_4f30.ir + if verify_kernel == 'over32_v25': + return over32_v25.ir + return v12.ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32split", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 192}')) + +def _select_contract_shapes(shape_labels): + return base9db7._select_contract_shapes(shape_labels) + +def _trace_inputs_from_shape(shape: dict[str, Any]) -> dict[str, Any]: + return base9db7._trace_inputs_for_shape(shape) + +def _inputs_for_label(label: str) -> dict[str, Any]: + selected = _select_contract_shapes((label,)) + return _trace_inputs_from_shape(selected[0]) + +def _dtype_name(inputs: dict[str, Any], name: str='query') -> str: + tensor = inputs.get(name) + if tensor is not None and hasattr(tensor, 'dtype'): + return str(tensor.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')).replace('torch.', '') + +def _label_can_hit(inputs: dict[str, Any], labels: tuple[str, ...]) -> bool: + label = inputs.get('label') + return label is None or str(label) in set(labels) + +def _is_bf16_build_d128_qm(inputs: dict[str, Any]) -> bool: + return bool(inputs.get('build', False)) and int(inputs.get('B', -1)) == 1 and (int(inputs.get('Q', -1)) == int(inputs.get('M', -2))) and (int(inputs.get('D', -1)) == v20.FEAT_D) and (_dtype_name(inputs, 'query') == 'bfloat16') and (_dtype_name(inputs, 'database') in ('', 'bfloat16')) + +def _eligible_v20_k12(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, V20_K12_SHAPES) and _is_bf16_build_d128_qm(inputs) and (int(inputs.get('K', -1)) == 12) and (int(inputs.get('Q', -1)) in (1024, 4096)) + +def _eligible_q2048_k12_4f30(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, K12_4F30_SHAPES) and _is_bf16_build_d128_qm(inputs) and (int(inputs.get('K', -1)) == 12) and (int(inputs.get('Q', -1)) == 2048) + +def _eligible_v12_midbuild(inputs: dict[str, Any]) -> bool: + if not (_label_can_hit(inputs, V12_MIDBUILD_SHAPES) and _is_bf16_build_d128_qm(inputs)): + return False + q = int(inputs.get('Q', -1)) + k = int(inputs.get('K', -1)) + return q == 2048 and k == 10 or (q in (1024, 2048, 4096) and k == 20) + +def _eligible_over32_k48(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, OVER32_K48_SHAPES) and _is_bf16_build_d128_qm(inputs) and (int(inputs.get('K', -1)) == 48) and (int(inputs.get('Q', -1)) in (2048, 4096)) + +def _selected_seed_for_inputs(inputs: dict[str, Any], *, enable_over32_v25: bool=True) -> tuple[str | None, str | None]: + if _eligible_q2048_k12_4f30(inputs): + return (SEED_K12_4F30_ID, K12_Q2048) + if _eligible_v20_k12(inputs): + return (SEED_V20_K12_ID, str(inputs.get('label') or ''.join(['q', format(inputs.get('Q'), ''), '_k12']))) + if _eligible_v12_midbuild(inputs): + return (SEED_V12_MIDBUILD_ID, str(inputs.get('label') or ''.join(['q', format(inputs.get('Q'), ''), '_k', format(inputs.get('K'), '')]))) + if enable_over32_v25 and _eligible_over32_k48(inputs): + return (SEED_OVER32_V25_ID, str(inputs.get('label') or ''.join(['q', format(inputs.get('Q'), ''), '_k48']))) + return (None, None) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False, enable_over32_v25: bool=True) -> str: + if not force_fallback: + selected_seed, _label = _selected_seed_for_inputs(inputs, enable_over32_v25=enable_over32_v25) + if selected_seed == SEED_K12_4F30_ID: + return K12_4F30_ENTRYPOINT + if selected_seed == SEED_V20_K12_ID: + return V20_ENTRYPOINT + if selected_seed == SEED_V12_MIDBUILD_ID: + return V12_ENTRYPOINT + if selected_seed == SEED_OVER32_V25_ID: + return OVER32_V25_ENTRYPOINT + return base9db7.route_for_contract_inputs(inputs, force_fallback=force_fallback) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False, enable_over32_v25: bool=True) -> None: + if not force_fallback: + selected_seed, _label = _selected_seed_for_inputs(inputs, enable_over32_v25=enable_over32_v25) + if selected_seed == SEED_K12_4F30_ID: + k12_4f30.launch_from_contract_inputs(inputs) + return + if selected_seed == SEED_V20_K12_ID: + split_count = v20._k32_split_count(inputs) + if split_count is None: + raise ValueError('v20 K12 route selected but split_count did not resolve') + v20._launch_k32_split_path(inputs, split_count=split_count) + return + if selected_seed == SEED_V12_MIDBUILD_ID: + v12.launch_from_contract_inputs(inputs) + return + if selected_seed == SEED_OVER32_V25_ID: + over32_v25.launch_from_contract_inputs(inputs) + return + base9db7.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + +def candidate_buildbucket_dbd7_lowfloor_v1(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_buildbucket_dbd7_no_over32_v25(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, enable_over32_v25=False) + +def candidate(inputs: dict[str, Any]) -> None: + candidate_buildbucket_dbd7_lowfloor_v1(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) + +def candidate_baseline_9db7(inputs: dict[str, Any]) -> None: + base9db7.launch_from_contract_inputs(inputs) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + previous = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=correctness, benchmark=benchmark, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = previous + +def _route_trace_record(inputs: dict[str, Any], *, force_fallback: bool=False, enable_over32_v25: bool=True) -> dict[str, Any]: + selected_seed, matched_label = (None, None) + if not force_fallback: + selected_seed, matched_label = _selected_seed_for_inputs(inputs, enable_over32_v25=enable_over32_v25) + selected_route = route_for_contract_inputs(inputs, force_fallback=force_fallback, enable_over32_v25=enable_over32_v25) + base_route = base9db7.route_for_contract_inputs(inputs) + if selected_seed is None: + return {'shape_key': inputs.get('label'), 'selected_route': selected_route, 'selected_entrypoint': selected_route, 'selected_seed': None, 'expected_seed': None, 'route_kind': 'general', 'route_source': 'base_9db7_fallback', 'guard_id': 'forced_fallback_or_buildbucket_guard_miss', 'guard_condition': 'delegate to current 9db7/1074 full82 Weave dispatcher', 'base_9db7_route': base_route, 'classification': 'fallback-or-guard-miss'} + guard_conditions = {SEED_V20_K12_ID: 'exact BF16 build B=1 Q=M in {1024,4096} D=128 K=12', SEED_K12_4F30_ID: 'exact BF16 build B=1 Q=M=2048 D=128 K=12', SEED_V12_MIDBUILD_ID: 'exact BF16 build B=1 D=128 Q=M, Q2048/K10 or K20 bucket', SEED_OVER32_V25_ID: 'exact BF16 build B=1 Q=M in {2048,4096} D=128 K=48'} + return {'shape_key': inputs.get('label'), 'selected_route': selected_route, 'selected_entrypoint': PRODUCTION_ROUTE_MODULES[selected_seed], 'selected_seed': selected_seed, 'expected_seed': selected_seed, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': ''.join(['dbd7_lowfloor_', format(selected_seed, '')]), 'guard_condition': guard_conditions[selected_seed], 'matched_label': matched_label, 'base_9db7_route': base_route, 'classification': 'seed-consumed'} + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False, enable_over32_v25: bool=True) -> list[dict[str, Any]]: + labels = TARGET_SHAPES if shape_labels is None else shape_labels + selected = _select_contract_shapes(labels) + return [_route_trace_record(_trace_inputs_from_shape(shape), force_fallback=force_fallback, enable_over32_v25=enable_over32_v25) for shape in selected] + +def _rows_for_labels(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return {label: dict(report.get('per_shape', {}).get(label, {})) for label in labels} + +def _per_shape_delta(candidate_report: dict[str, Any], baseline_report: dict[str, Any], labels: tuple[str, ...], *, enable_over32_v25: bool) -> list[dict[str, Any]]: + rows = [] + for label in labels: + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') or baseline_row.get('flashlib_ms') + inputs = _inputs_for_label(label) + rows.append({'shape_key': label, 'candidate_route': route_for_contract_inputs(inputs, enable_over32_v25=enable_over32_v25), 'baseline_9db7_route': base9db7.route_for_contract_inputs(inputs), 'candidate_ms': candidate_ms, 'baseline_9db7_ms': baseline_ms, 'flashlib_ms': flashlib_ms, 'speedup_vs_9db7': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'ratio_vs_flashlib': flashlib_ms / candidate_ms if candidate_ms and flashlib_ms else None, 'candidate_passed': candidate_row.get('passed'), 'baseline_9db7_passed': baseline_row.get('passed'), 'timing_backend': candidate_row.get('timing_backend') or baseline_row.get('timing_backend')}) + return rows + +def benchmark_candidate_buildbucket_dbd7_lowfloor_v1(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES, benchmark_correctness: bool=True, run_baseline: bool=True, enable_over32_v25: bool=True) -> dict[str, Any]: + labels = tuple((str(label) for label in shape_labels)) + baseline_report = None + if run_baseline: + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=labels, kernel_fn=candidate_baseline_9db7, correctness=benchmark_correctness, benchmark=True) + kernel_fn = candidate_buildbucket_dbd7_lowfloor_v1 if enable_over32_v25 else candidate_buildbucket_dbd7_no_over32_v25 + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=labels, kernel_fn=kernel_fn, correctness=benchmark_correctness, benchmark=True) + candidate_metric = candidate_report.get('summary', {}).get('primary_mean') + baseline_metric = baseline_report.get('summary', {}).get('primary_mean') if baseline_report else None + payload: dict[str, Any] = {'candidate_id': CANDIDATE_ID if enable_over32_v25 else ''.join([format(CANDIDATE_ID, ''), '_no_over32_v25']), 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_candidate_buildbucket_dbd7_lowfloor_v1']), 'candidate_entrypoint': ROUTE_ENTRYPOINT, 'selected_seeds': (SEED_V20_K12_ID, SEED_K12_4F30_ID, SEED_V12_MIDBUILD_ID, *((SEED_OVER32_V25_ID,) if enable_over32_v25 else ())), 'all_correct': candidate_report.get('summary', {}).get('all_correct'), 'performance_comparable': candidate_report.get('summary', {}).get('performance_comparable'), 'tflops': candidate_metric, 'baseline_9db7_tflops': baseline_metric, 'metric_delta_vs_9db7': candidate_metric - baseline_metric if candidate_metric is not None and baseline_metric is not None else None, 'timing_backend': 'cupti' if use_cupti else 'cuda_event', 'denominator': 'buildbucket_dbd7_lowfloor', 'shape_labels': list(labels), 'route_trace': route_trace_for_contract_shapes(labels, enable_over32_v25=enable_over32_v25), 'forced_fallback_route_trace': route_trace_for_contract_shapes(labels, force_fallback=True), 'route_modules': PRODUCTION_ROUTE_MODULES, 'report': candidate_report, 'route_trace_included': True} + if baseline_report is not None: + payload.update({'baseline_9db7_entrypoint': BASE_9DB7_ENTRYPOINT, 'baseline_9db7_all_correct': baseline_report.get('summary', {}).get('all_correct'), 'baseline_9db7_performance_comparable': baseline_report.get('summary', {}).get('performance_comparable'), 'selected_route_rows': _rows_for_labels(candidate_report, labels), 'baseline_9db7_route_rows': _rows_for_labels(baseline_report, labels), 'seed_delta_matrix': _per_shape_delta(candidate_report, baseline_report, labels, enable_over32_v25=enable_over32_v25), 'baseline_9db7_report': baseline_report}) + return payload + +def write_benchmark_artifact(artifact_dir: str | Path, *, use_cupti: bool=True, shape_labels=TARGET_SHAPES, run_baseline: bool=True, enable_over32_v25: bool=True) -> dict[str, str]: + payload = benchmark_candidate_buildbucket_dbd7_lowfloor_v1(use_cupti=use_cupti, shape_labels=shape_labels, run_baseline=run_baseline, enable_over32_v25=enable_over32_v25) + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + suffix = 'with_over32_v25' if enable_over32_v25 else 'no_over32_v25' + path = out_dir / ''.join(['buildbucket_dbd7_lowfloor_v1_', format(suffix, ''), '.json']) + path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + return {'candidate_payload': str(path)} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_buildbucket_residual_lowk_6bc3_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_buildbucket_residual_lowk_6bc3_v1.py new file mode 100644 index 00000000..02294103 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_buildbucket_residual_lowk_6bc3_v1.py @@ -0,0 +1,199 @@ +"""Residual low-K build-bucket seed for the c796/dbd7 continuation. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +does not edit the production full82 dispatcher. It routes exact residual BF16 +build rows that remain on slow fallback paths through existing Weave seed +families: + +* v20 static/generic low-K split paths for Q512 K2/K8 and Q2048 K8. +* fixed-build K10 v2 paths for Q512/Q1024 K10, including the B=2 Q1024 row. + +Guard misses delegate to the current 9db7/1074 full82 Weave dispatcher. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_17b8_lowmargin_1074_full82_v1 as base17b8 +from . import knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2_k32split_v20 as build_v20 +MODULE = 'loom.examples.weave.knn_build_buildbucket_residual_lowk_6bc3_v1' +Q512_K2 = 'build_k_sweep_qm512_k2' +Q512_K8 = 'build_k_sweep_qm512_k8' +Q512_K10 = 'build_k_sweep_qm512_k10' +Q1024_K10 = 'build_qm1024_d128_k10' +Q2048_K8 = 'build_qm2048_d128_k8' +B2_Q1024_K10 = 'build_batch_b2_q1024_m1024_d128_k10' +LOWK_V20_TARGET_SHAPES = (Q512_K2, Q512_K8, Q2048_K8) +FIXEDBUILD_K10_TARGET_SHAPES = (Q512_K10, Q1024_K10, B2_Q1024_K10) +TARGET_SHAPES = (*LOWK_V20_TARGET_SHAPES, *FIXEDBUILD_K10_TARGET_SHAPES) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +SEED_Q512_K2_ID = '6bc3_v20_q512_k2_generic_lowk' +SEED_Q512_K8_ID = '6bc3_v20_q512_k8_static_s8' +SEED_Q2048_K8_ID = '6bc3_v20_q2048_k8_static_s8' +SEED_K10_FIXEDBUILD_ID = '6bc3_fixedbuild_k10_v2' +BASE_17B8_ID = base17b8.CANDIDATE_LOWMARGIN_1074 +CANDIDATE_ID = 'buildbucket_residual_lowk_6bc3_v1' +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_V20_BUILD = 'loom.examples.weave.knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2_k32split_v20:launch_from_contract_inputs' +ROUTE_K10_FIXEDBUILD = 'loom.examples.weave.knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2:launch_from_contract_inputs' +ROUTE_BASE_17B8 = ''.join([format(base17b8.MODULE, ''), ':launch_from_contract_inputs']) +PRODUCTION_ROUTE_MODULES = _decode_capture(_json_loads('{"__dict_items__": [["6bc3_v20_q512_k2_generic_lowk", "loom.examples.weave.knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2_k32split_v20:launch_from_contract_inputs"], ["6bc3_v20_q512_k8_static_s8", "loom.examples.weave.knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2_k32split_v20:launch_from_contract_inputs"], ["6bc3_v20_q2048_k8_static_s8", "loom.examples.weave.knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2_k32split_v20:launch_from_contract_inputs"], ["6bc3_fixedbuild_k10_v2", "loom.examples.weave.knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2:launch_from_contract_inputs"], ["candidate_17b8_lowmargin_1074_full82_v1", "loom.examples.weave.knn_build_dispatch_17b8_lowmargin_1074_full82_v1:launch_from_contract_inputs"]]}')) +SOURCE_TASKS = _decode_capture(_json_loads('{"__dict_items__": [["6bc3_v20_q512_k2_generic_lowk", "v20 fixed-build lineage generic low-K Q512 path"], ["6bc3_v20_q512_k8_static_s8", "v20 fixed-build lineage static K8 Q512 path"], ["6bc3_v20_q2048_k8_static_s8", "v20 fixed-build lineage static K8 Q2048 path"], ["6bc3_fixedbuild_k10_v2", "fixed-build dispatch v2 K10 path"], ["candidate_17b8_lowmargin_1074_full82_v1", "a444/9db7 low-margin full82 baseline"]]}')) + +class _TraceTensor: + + def __init__(self, dtype: str) -> None: + self.dtype = dtype if dtype.startswith('torch.') else ''.join(['torch.', format(dtype, '')]) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_BUILDBUCKET_6BC3_VERIFY_KERNEL') + if verify_kernel == 'q512_lowk_stage1': + return build_v20.parent_lowk.stage1_ir + if verify_kernel == 'q512_lowk_merge_generic': + return build_v20.parent_lowk.generic_merge_ir + if verify_kernel == 'k8_stage1': + return build_v20.stage1_k8_ir + if verify_kernel == 'k8_merge_s8': + return build_v20.merge_k8_s8_ir + if verify_kernel == 'k10_stage1': + return build_v20.parent.stage1_ir + if verify_kernel == 'k10_merge_s4_cache': + return build_v20.parent.parent.parent_cached64.merge_k10_s4_cache_ir + if verify_kernel == 'k10_merge_s7_cache': + return build_v20.parent.parent.parent_cached.merge_k10_s7_cache_ir + return build_v20.ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32split", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 192}')) + +def _select_contract_shapes(shape_labels): + return base17b8._select_contract_shapes(shape_labels) + +def _trace_inputs_for_shape(shape: dict[str, Any]) -> dict[str, Any]: + params = dict(shape['params']) + dtype = str(params.get('dtype', 'bfloat16')) + return {'label': shape['label'], 'B': int(params['B']), 'Q': int(params['Q']), 'M': int(params['M']), 'D': int(params['D']), 'K': int(params['K']), 'dtype': dtype, 'build': bool(params.get('build', False)), 'query': _TraceTensor(dtype), 'database': _TraceTensor(dtype)} + +def _dtype_name(inputs: dict[str, Any], name: str='query') -> str: + tensor = inputs.get(name) + if tensor is not None and hasattr(tensor, 'dtype'): + return str(tensor.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')).replace('torch.', '') + +def _label_can_hit(inputs: dict[str, Any], labels: set[str]) -> bool: + label = inputs.get('label') + return label is None or str(label) in labels + +def _is_bf16_d128_build(inputs: dict[str, Any]) -> bool: + return bool(inputs.get('build', False)) and int(inputs.get('Q', -1)) == int(inputs.get('M', -2)) and (int(inputs.get('D', -1)) == build_v20.FEAT_D) and (_dtype_name(inputs, 'query') == 'bfloat16') and (_dtype_name(inputs, 'database') in ('', 'bfloat16')) + +def _eligible_q512_k2(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, {Q512_K2}) and _is_bf16_d128_build(inputs) and (int(inputs.get('B', -1)) == 1) and (int(inputs.get('Q', -1)) == 512) and (int(inputs.get('K', -1)) == 2) + +def _eligible_q512_k8(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, {Q512_K8}) and _is_bf16_d128_build(inputs) and (int(inputs.get('B', -1)) == 1) and (int(inputs.get('Q', -1)) == 512) and (int(inputs.get('K', -1)) == 8) + +def _eligible_q2048_k8(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, {Q2048_K8}) and _is_bf16_d128_build(inputs) and (int(inputs.get('B', -1)) == 1) and (int(inputs.get('Q', -1)) == 2048) and (int(inputs.get('K', -1)) == 8) + +def _eligible_fixedbuild_k10(inputs: dict[str, Any]) -> bool: + if not (_label_can_hit(inputs, set(FIXEDBUILD_K10_TARGET_SHAPES)) and _is_bf16_d128_build(inputs)): + return False + bsz = int(inputs.get('B', -1)) + q = int(inputs.get('Q', -1)) + k = int(inputs.get('K', -1)) + label = inputs.get('label') + return k == 10 and (bsz == 1 and q in (512, 1024) or (bsz == 2 and q == 1024)) and (label is None or str(label) in FIXEDBUILD_K10_TARGET_SHAPES) + +def _selected_seed_for_inputs(inputs: dict[str, Any]) -> str | None: + if _eligible_q512_k2(inputs): + return SEED_Q512_K2_ID + if _eligible_q512_k8(inputs): + return SEED_Q512_K8_ID + if _eligible_q2048_k8(inputs): + return SEED_Q2048_K8_ID + if _eligible_fixedbuild_k10(inputs): + return SEED_K10_FIXEDBUILD_ID + return None + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if not force_fallback: + selected_seed = _selected_seed_for_inputs(inputs) + if selected_seed in {SEED_Q512_K2_ID, SEED_Q512_K8_ID, SEED_Q2048_K8_ID}: + return ROUTE_V20_BUILD + if selected_seed == SEED_K10_FIXEDBUILD_ID: + return ROUTE_K10_FIXEDBUILD + return base17b8.route_for_contract_inputs(inputs, force_fallback=force_fallback) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + if not force_fallback: + selected_seed = _selected_seed_for_inputs(inputs) + if selected_seed in {SEED_Q512_K2_ID, SEED_Q512_K8_ID, SEED_Q2048_K8_ID}: + build_v20.launch_from_contract_inputs(inputs) + return + if selected_seed == SEED_K10_FIXEDBUILD_ID: + build_v20.parent.launch_from_contract_inputs(inputs) + return + base17b8.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + +def candidate_buildbucket_residual_lowk_6bc3_v1(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]) -> None: + candidate_buildbucket_residual_lowk_6bc3_v1(inputs) + +def candidate_baseline_17b8(inputs: dict[str, Any]) -> None: + base17b8.launch_from_contract_inputs(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _selected_entrypoint(seed_id: str | None) -> str: + if seed_id in {SEED_Q512_K2_ID, SEED_Q512_K8_ID, SEED_Q2048_K8_ID}: + return ROUTE_V20_BUILD + if seed_id == SEED_K10_FIXEDBUILD_ID: + return ROUTE_K10_FIXEDBUILD + return ROUTE_BASE_17B8 + +def _guard_condition(seed_id: str | None) -> str: + if seed_id == SEED_Q512_K2_ID: + return 'exact BF16 build B=1 Q=M=512 D=128 K=2 low-K split route' + if seed_id == SEED_Q512_K8_ID: + return 'exact BF16 build B=1 Q=M=512 D=128 K=8 static split route' + if seed_id == SEED_Q2048_K8_ID: + return 'exact BF16 build B=1 Q=M=2048 D=128 K=8 static split route' + if seed_id == SEED_K10_FIXEDBUILD_ID: + return 'exact BF16 build D=128 K=10 fixed-build v2 route' + return 'delegate to current 9db7/1074 full82 Weave dispatcher' + +def _route_trace_record(inputs: dict[str, Any], *, force_fallback: bool=False) -> dict[str, Any]: + expected_seed = _selected_seed_for_inputs(inputs) + route = route_for_contract_inputs(inputs, force_fallback=force_fallback) + base_route = base17b8.route_for_contract_inputs(inputs) + if expected_seed is None or force_fallback: + row = dict(base17b8._route_trace_record(inputs, force_fallback=force_fallback)) + row['expected_seed'] = expected_seed + row['baseline_17b8_route'] = base_route + row['candidate_guard_status'] = 'forced_fallback' if force_fallback else 'guard_miss' + if force_fallback and expected_seed is not None: + row['guard_id'] = ''.join(['forced_fallback_', format(expected_seed, ''), '_disabled']) + row['guard_condition'] = ''.join(['forced fallback to 17b8; ', format(expected_seed, ''), ' disabled']) + row['classification'] = 'guard-miss' + return base17b8._normalize_route_row(row) + return base17b8._normalize_route_row({'shape_key': inputs.get('label'), 'selected_route': route, 'selected_entrypoint': _selected_entrypoint(expected_seed), 'selected_seed': expected_seed, 'expected_seed': expected_seed, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': ''.join(['6bc3_residual_lowk_', format(expected_seed, '')]), 'guard_condition': _guard_condition(expected_seed), 'baseline_17b8_route': base_route, 'replaced_route': base_route, 'classification': 'seed-consumed'}) + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + return [_route_trace_record(_trace_inputs_for_shape(shape), force_fallback=force_fallback) for shape in selected] diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_common_d256_q1024_56f3_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_common_d256_q1024_56f3_v1.py new file mode 100644 index 00000000..4766a471 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_common_d256_q1024_56f3_v1.py @@ -0,0 +1,147 @@ +"""kNN build common-D D256 Q1024 exact seed for round 56f3. + +Minimum target architecture: sm_100a. This additive shape-specific seed adapts +the validated D256 tcgen05 split producer from ``knn_build_dim_midk_df2f_v1`` to +the v11 common-D build row ``B=1, Q=M=1024, D=256, K=10``. It does not modify +the production dispatcher; generalize-auto-tuning can consume it behind an +exact guard after same-denominator A/B. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from pathlib import Path +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dim_midk_df2f_v1 as d256_seed +from . import knn_build_dispatch_e3de_9138_bcb3_4247_v1 as current_dispatcher +TARGET_SHAPE = 'build_common_d256_b1_q1024_m1024_k10' +TARGET_SHAPES = (TARGET_SHAPE,) +ROUTE_D256_Q1024_SPLIT = 'loom.examples.weave.knn_build_common_d256_q1024_56f3_v1:d256_q1024_split_s8' +ROUTE_CURRENT_DISPATCHER = 'loom.examples.weave.knn_build_dispatch_e3de_9138_bcb3_4247_v1:launch_from_contract_inputs' +DEFAULT_SPLIT_COUNT = 8 +stage1_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_dim_midk_df2f_d256_split_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 99584, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) +merge_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_merge", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "B", "Q", "K", "split_count", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10]], "cta_group": 1, "threads": 256}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_COMMON_D256_Q1024_56F3_VERIFY_KERNEL') + if verify_kernel == 'merge': + return merge_ir + return stage1_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_dim_midk_df2f_d256_split_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 99584, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) + +def _dtype_name(inputs: dict[str, Any]) -> str: + query = inputs.get('query') + if query is not None: + return str(query.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')).replace('torch.', '') + +def _label_can_hit(inputs: dict[str, Any]) -> bool: + label = inputs.get('label') + return label is None or str(label) == TARGET_SHAPE + +def _split_count() -> int: + value = int(os.environ.get('LOOM_KNN_COMMON_D256_Q1024_56F3_SPLITS', str(DEFAULT_SPLIT_COUNT))) + if value <= 0: + raise ValueError('LOOM_KNN_COMMON_D256_Q1024_56F3_SPLITS must be positive') + return value + +def _eligible_d256_q1024(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs) and bool(inputs.get('build', False)) and (_dtype_name(inputs) == 'bfloat16') and (int(inputs['B']) == 1) and (int(inputs['Q']) == 1024) and (int(inputs['M']) == 1024) and (int(inputs['D']) == d256_seed.D256_FEAT_D) and (int(inputs['K']) == d256_seed.TOP_K_MAX) + +def route_for_contract_inputs(inputs: dict[str, Any]) -> str: + if _eligible_d256_q1024(inputs): + return ROUTE_D256_Q1024_SPLIT + raise ValueError('knn_build_common_d256_q1024_56f3_v1 expects exact B=1 Q=M=1024 D=256 K=10 bf16 build inputs') + +def launch_from_contract_inputs(inputs: dict[str, Any]) -> None: + route_for_contract_inputs(inputs) + d256_seed._launch_split_stage(inputs, split_count=_split_count(), feature_dim=d256_seed.D256_FEAT_D, kernel=d256_seed._compiled_d256_stage1(), stage1_ir=stage1_ir) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels=None): + wanted = set(TARGET_SHAPES if shape_labels is None else shape_labels) + selected = [shape for shape in eval_mod.CANONICAL_SHAPES if shape['label'] in wanted] + missing = wanted - {shape['label'] for shape in selected} + if missing: + raise ValueError(''.join(['unknown kNN build contract shape(s): ', format(sorted(missing), '')])) + return selected + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=None, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + previous = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = previous + +def _trace_inputs_from_shape(shape: dict[str, Any]) -> dict[str, Any]: + params = dict(shape['params']) + return {'label': shape['label'], 'B': int(params['B']), 'Q': int(params['Q']), 'M': int(params['M']), 'D': int(params['D']), 'K': int(params['K']), 'dtype': str(params.get('dtype', 'bfloat16')), 'build': bool(params.get('build', False))} + +def route_trace_for_shapes(shape_labels=None) -> list[dict[str, Any]]: + rows = [] + for shape in _select_contract_shapes(shape_labels): + inputs = _trace_inputs_from_shape(shape) + route = route_for_contract_inputs(inputs) + base_route = current_dispatcher.route_for_contract_inputs(inputs) + rows.append({'shape_key': shape['label'], 'selected_route': route, 'selected_entrypoint': 'loom.examples.weave.knn_build_common_d256_q1024_56f3_v1:launch_from_contract_inputs', 'selected_seed': 'common_d256_q1024_56f3_v1', 'expected_seed': 'common_d256_q1024_56f3_v1', 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': 'common_d256_q1024_56f3_exact', 'guard_condition': 'exact BF16 build B=1 Q=M=1024 D=256 K=10 split-grid seed', 'split_count': _split_count(), 'replaced_route': base_route, 'base_dispatcher_route': base_route, 'classification': 'seed-produced'}) + return rows + +def _per_shape_delta(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> dict[str, Any]: + cand = candidate_report.get('per_shape', {}).get(TARGET_SHAPE, {}) + base = baseline_report.get('per_shape', {}).get(TARGET_SHAPE, {}) + cand_ms = cand.get('kernel_ms') + base_ms = base.get('kernel_ms') + return {'candidate_ms': cand_ms, 'baseline_ms': base_ms, 'flashlib_ms': cand.get('flashlib_ms'), 'candidate_tflops': cand.get('tflops'), 'baseline_tflops': base.get('tflops'), 'speedup_vs_current_dispatcher': base_ms / cand_ms if cand_ms and base_ms else None, 'ratio_vs_flashlib': cand.get('ratio_vs_flashlib'), 'passed': cand.get('passed'), 'timing_backend': cand.get('timing_backend')} + +def benchmark_knn_build_common_d256_q1024_56f3_v1(*, use_cupti: bool=True, run_baseline: bool=True) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti) + baseline_report = None + if run_baseline: + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, kernel_fn=current_dispatcher.candidate) + payload: dict[str, Any] = {'tflops': candidate_report['summary']['primary_mean'] or 0.0, 'all_correct': candidate_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'measured_entrypoint': 'loom.examples.weave.knn_build_common_d256_q1024_56f3_v1:benchmark_knn_build_common_d256_q1024_56f3_v1', 'measured_shape_labels': TARGET_SHAPES, 'route_trace': route_trace_for_shapes(), 'contract_summary': candidate_report['summary'], 'contract_performance': candidate_report['performance'], 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'split_count': _split_count(), 'report': candidate_report} + if baseline_report is not None: + payload['baseline_entrypoint'] = ROUTE_CURRENT_DISPATCHER + payload['baseline_summary'] = baseline_report['summary'] + payload['baseline_report'] = baseline_report + payload['per_shape_delta_vs_current_dispatcher'] = _per_shape_delta(candidate_report, baseline_report) + baseline_mean = baseline_report['summary']['primary_mean'] + payload['speedup_vs_current_dispatcher_primary_mean'] = candidate_report['summary']['primary_mean'] / baseline_mean if baseline_mean else None + return payload + +def main() -> None: + parser = argparse.ArgumentParser(description='Evaluate exact D256 Q1024 kNN build seed') + parser.add_argument('--benchmark', action='store_true') + parser.add_argument('--use-cupti', action='store_true') + parser.add_argument('--no-baseline', action='store_true') + parser.add_argument('--artifact-dir', default=None) + args = parser.parse_args() + if args.benchmark: + result = benchmark_knn_build_common_d256_q1024_56f3_v1(use_cupti=args.use_cupti, run_baseline=not args.no_baseline) + else: + result = compile_and_launch_knn_build() + if args.artifact_dir: + artifact_dir = Path(args.artifact_dir) + artifact_dir.mkdir(parents=True, exist_ok=True) + output_path = artifact_dir / 'knn_build_common_d256_q1024_56f3_v1.json' + output_path.write_text(json.dumps(result, indent=2, sort_keys=True)) + print(json.dumps(result, indent=2, sort_keys=True)) diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_common_d768_build_eeff_m64split_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_common_d768_build_eeff_m64split_v1.py new file mode 100644 index 00000000..fa99c760 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_common_d768_build_eeff_m64split_v1.py @@ -0,0 +1,229 @@ +"""Exact D768 common-dimension build seed for the eeff bucket lane. + +Minimum target architecture: sm_100a. This additive bucket kernel routes only +``build_common_d768_b1_q1024_m1024_k10`` through the validated M64/N64 D768 +tcgen05/TMA producer and the fused split merge from the non-D128 frontier +lineage. It does not edit the production dispatcher; generic fallback timing is +kept only as a same-denominator baseline. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from collections.abc import Callable +from functools import lru_cache +from pathlib import Path +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_common_d_generic_fallback_v1 as generic_fallback +from . import knn_build_evolve_7bfc_split_v1 as split_parent +from . import knn_build_non128_frontier_4be7_d768fused_v1 as fused_parent +from . import knn_build_non128_frontier_7ee5_m64rag_v1 as m64rag +from .._dispatch_runtime import pack_kernel_args +MODULE = 'loom.examples.weave.knn_build_common_d768_build_eeff_m64split_v1' +CANDIDATE_ID = 'common_d768_build_eeff_m64split_v1' +TARGET_SHAPE = 'build_common_d768_b1_q1024_m1024_k10' +TARGET_SHAPES = (TARGET_SHAPE,) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +ROUTE_PREFIX = ''.join([format(MODULE, ''), ':d768_build']) +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +BENCHMARK_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_knn_build_common_d768_build_eeff_m64split_v1']) +FALLBACK_ENTRYPOINT = generic_fallback.ROUTE_ENTRYPOINT +SPLIT_CHOICES = (4, 8, 16) +DEFAULT_SPLIT_COUNT = _decode_capture(_json_loads('16')) +DEFAULT_GROUP_COUNT = _decode_capture(_json_loads('8')) +M64_BLOCK_Q = 128 +M64_BLOCK_M = m64rag.M64_BLOCK_M +STAGE1_THREADS = 192 +M64_FEATURE_CHUNKS = m64rag.M64_FEATURE_CHUNKS +K_TILE = m64rag.K_TILE +TOP_K_MAX = m64rag.TOP_K_MAX +GRID_DIM_DEFAULT = m64rag.GRID_DIM_DEFAULT +M64_QUERY_BYTES = M64_BLOCK_Q * K_TILE * 2 +M64_DATABASE_BYTES = M64_BLOCK_M * K_TILE * 2 +M64_DB_SQ_BYTES = M64_BLOCK_M * 4 +M64_SMEM_POOL_BYTES = M64_QUERY_BYTES + M64_DATABASE_BYTES + M64_DB_SQ_BYTES +_insert_sorted_pair = _ir_proxy('loom.examples.weave.knn_build_common_d768_build_eeff_m64split_v1:_insert_sorted_pair', 256) +knn_build_common_d768_build_eeff_m64split_stage1 = _decode_capture(_json_loads('{"__ir__": "knn_build_common_d768_build_eeff_m64split_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 50432, "constants": [["FEATURE_CHUNKS", 6]], "cta_group": 1, "threads": 192}')) +stage1_m64_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_common_d768_build_eeff_m64split_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 50432, "constants": [["FEATURE_CHUNKS", 6]], "cta_group": 1, "threads": 192}')) + +def _check_split_count(split_count: int) -> int: + split_count = int(split_count) + if split_count not in SPLIT_CHOICES: + raise ValueError(''.join(['unsupported D768 build split count: ', format(split_count, ''), '; choices=', format(SPLIT_CHOICES, '')])) + return split_count + +def _group_count_for_split(split_count: int, group_count: int | None=None) -> int: + split_count = _check_split_count(split_count) + if group_count is None: + group_count = min(DEFAULT_GROUP_COUNT, split_count) + group_count = int(group_count) + fused_parent._validate_group_shape(split_count, group_count) + return group_count + +def _merge_ir(split_count: int, group_count: int | None=None) -> Any: + split_count = _check_split_count(split_count) + group_count = _group_count_for_split(split_count, group_count) + return fused_parent._fused_merge_ir(split_count, group_count) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_COMMON_D768_BUILD_EEFF_VERIFY_KERNEL') + split_count = int(os.environ.get('LOOM_KNN_COMMON_D768_BUILD_EEFF_VERIFY_SPLIT', str(DEFAULT_SPLIT_COUNT))) + group_count = int(os.environ.get('LOOM_KNN_COMMON_D768_BUILD_EEFF_VERIFY_GROUPS', str(DEFAULT_GROUP_COUNT))) + if verify_kernel == 'merge': + return _merge_ir(split_count, group_count) + return stage1_m64_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_common_d768_build_eeff_m64split_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 50432, "constants": [["FEATURE_CHUNKS", 6]], "cta_group": 1, "threads": 192}')) + +def _compiled_stage1_m64(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0026"}')) + +def _dtype_name(inputs: dict[str, Any], tensor_name: str='query') -> str: + tensor = inputs.get(tensor_name) + if tensor is not None and hasattr(tensor, 'dtype'): + return str(tensor.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')).replace('torch.', '') + +def _label_can_hit(inputs: dict[str, Any]) -> bool: + label = inputs.get('label') + return label is None or str(label) in TARGET_SHAPE_SET + +def _eligible_d768_build(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs) and bool(inputs.get('build', False)) and (int(inputs.get('B', -1)) == 1) and (int(inputs.get('Q', -1)) == 1024) and (int(inputs.get('M', -1)) == 1024) and (int(inputs.get('D', -1)) == M64_FEATURE_CHUNKS * K_TILE) and (int(inputs.get('K', -1)) == TOP_K_MAX) and (_dtype_name(inputs, 'query') == 'bfloat16') and (_dtype_name(inputs, 'database') in ('', 'bfloat16')) + +def _launch_d768_build(inputs: dict[str, Any], *, split_count: int=DEFAULT_SPLIT_COUNT, group_count: int | None=None) -> None: + split_count = _check_split_count(split_count) + group_count = _group_count_for_split(split_count, group_count) + query = inputs['query'] + database = inputs['database'] + if str(query.dtype) != 'torch.bfloat16' or str(database.dtype) != 'torch.bfloat16': + raise ValueError('common D768 build seed supports bfloat16 inputs only') + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + num_q_tiles = (n_query + M64_BLOCK_Q - 1) // M64_BLOCK_Q + num_db_tiles = (n_database + M64_BLOCK_M - 1) // M64_BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tiles * split_count + total_queries = bsz * n_query + stage1_grid = min(total_work, GRID_DIM_DEFAULT) + merge_grid = min(total_queries, GRID_DIM_DEFAULT) + partial_dists, partial_indices = split_parent._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = m64rag.non128_base.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), total_queries, M64_BLOCK_Q, dim, K_TILE) + tmap_database = m64rag.non128_base.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, M64_BLOCK_M, dim, K_TILE) + _compiled_stage1_m64().launch(grid=(stage1_grid, 1, 1), block=(STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage1_m64_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tiles=num_q_tiles, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), shared_mem=stage1_m64_ir.computed_smem_bytes) + merge_ir = _merge_ir(split_count, group_count) + fused_parent._compiled_fused_merge(split_count, group_count).launch(grid=(merge_grid, 1, 1), block=(fused_parent.D768_FUSED_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], total_queries], shared_mem=merge_ir.computed_smem_bytes) + +def route_for_contract_inputs(inputs: dict[str, Any], *, split_count: int=DEFAULT_SPLIT_COUNT, group_count: int | None=None) -> str: + if _eligible_d768_build(inputs): + split_count = _check_split_count(split_count) + group_count = _group_count_for_split(split_count, group_count) + return ''.join([format(ROUTE_PREFIX, ''), ':s', format(split_count, ''), ':g', format(group_count, ''), ':m64n64']) + return generic_fallback.ROUTE_ID + +def launch_from_contract_inputs(inputs: dict[str, Any], *, split_count: int=DEFAULT_SPLIT_COUNT, group_count: int | None=None, force_fallback: bool=False) -> None: + if not force_fallback and _eligible_d768_build(inputs): + _launch_d768_build(inputs, split_count=split_count, group_count=group_count) + return + generic_fallback.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_for_policy(*, split_count: int=DEFAULT_SPLIT_COUNT, group_count: int | None=None) -> Callable[[dict[str, Any]], None]: + split_count = _check_split_count(split_count) + group_count = _group_count_for_split(split_count, group_count) + + def _candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, split_count=split_count, group_count=group_count) + return _candidate + +def candidate_fallback(inputs: dict[str, Any]) -> None: + generic_fallback.launch_from_contract_inputs(inputs) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels=TARGET_SHAPES): + wanted = TARGET_SHAPE_SET if shape_labels is None else {str(label) for label in shape_labels} + selected = [shape for shape in eval_mod.CANONICAL_SHAPES if str(shape['label']) in wanted] + missing = wanted - {str(shape['label']) for shape in selected} + if missing: + raise ValueError(''.join(['unknown kNN build contract shape(s): ', format(sorted(missing), '')])) + return selected + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_report(*, use_cupti: bool, kernel_fn: Callable[[dict[str, Any]], Any]) -> dict[str, Any]: + previous = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(TARGET_SHAPES), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = previous + +def _trace_inputs_from_shape(shape: dict[str, Any]) -> dict[str, Any]: + params = dict(shape['params']) + return {'label': shape['label'], 'B': int(params['B']), 'Q': int(params['Q']), 'M': int(params['M']), 'D': int(params['D']), 'K': int(params['K']), 'dtype': str(params.get('dtype', 'bfloat16')), 'build': bool(params.get('build', False))} + +def route_trace_for_shapes(*, split_count: int=DEFAULT_SPLIT_COUNT, group_count: int | None=None) -> list[dict[str, Any]]: + split_count = _check_split_count(split_count) + group_count = _group_count_for_split(split_count, group_count) + rows = [] + for shape in _select_contract_shapes(TARGET_SHAPES): + inputs = _trace_inputs_from_shape(shape) + route = route_for_contract_inputs(inputs, split_count=split_count, group_count=group_count) + rows.append({'shape_key': shape['label'], 'selected_route': route, 'selected_entrypoint': ROUTE_ENTRYPOINT, 'selected_seed': CANDIDATE_ID, 'expected_seed': CANDIDATE_ID, 'route_kind': 'specialized' if route.startswith(ROUTE_PREFIX) else 'fallback', 'route_source': 'shape-specific-seed', 'guard_id': 'eeff_common_d768_build_exact_shape_guard', 'guard_condition': 'exact BF16 build B=1 Q=M=1024 D=768 K=10', 'split_count': split_count, 'group_count': group_count, 'producer': 'm64_d768_tcgen05_tma', 'merge': 'fused_group_merge', 'fallback': FALLBACK_ENTRYPOINT}) + return rows + +def _per_shape_delta(candidate_report: dict[str, Any], baseline_report: dict[str, Any] | None) -> dict[str, Any]: + cand = candidate_report.get('per_shape', {}).get(TARGET_SHAPE, {}) + base = {} if baseline_report is None else baseline_report.get('per_shape', {}).get(TARGET_SHAPE, {}) + cand_ms = cand.get('kernel_ms') + base_ms = base.get('kernel_ms') + return {'candidate_ms': cand_ms, 'baseline_ms': base_ms, 'flashlib_ms': cand.get('flashlib_ms'), 'candidate_tflops': cand.get('tflops'), 'baseline_tflops': base.get('tflops'), 'speedup_vs_generic_fallback': base_ms / cand_ms if cand_ms and base_ms else None, 'ratio_vs_flashlib': cand.get('ratio_vs_flashlib'), 'passed': cand.get('passed'), 'timing_backend': cand.get('timing_backend')} + +def _scan_split_counts(*, use_cupti: bool) -> dict[str, Any]: + scan: dict[str, Any] = {} + for split_count in SPLIT_CHOICES: + group_count = _group_count_for_split(split_count) + report = _run_report(use_cupti=use_cupti, kernel_fn=candidate_for_policy(split_count=split_count, group_count=group_count)) + scan[str(split_count)] = report['per_shape'][TARGET_SHAPE] + return scan + +def benchmark_knn_build_common_d768_build_eeff_m64split_v1(*, use_cupti: bool=True, split_count: int=DEFAULT_SPLIT_COUNT, group_count: int | None=None, run_baseline: bool=True, scan_splits: bool=False) -> dict[str, Any]: + split_count = _check_split_count(split_count) + group_count = _group_count_for_split(split_count, group_count) + candidate_report = _run_report(use_cupti=use_cupti, kernel_fn=candidate_for_policy(split_count=split_count, group_count=group_count)) + baseline_report = None + if run_baseline: + baseline_report = _run_report(use_cupti=use_cupti, kernel_fn=candidate_fallback) + payload: dict[str, Any] = {'tflops': candidate_report['summary']['primary_mean'] or 0.0, 'all_correct': candidate_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'measured_entrypoint': BENCHMARK_ENTRYPOINT, 'candidate_entrypoint': ROUTE_ENTRYPOINT, 'measured_shape_labels': TARGET_SHAPES, 'route_trace': route_trace_for_shapes(split_count=split_count, group_count=group_count), 'contract_summary': candidate_report['summary'], 'contract_performance': candidate_report['performance'], 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'split_count': split_count, 'group_count': group_count, 'split_scan': _scan_split_counts(use_cupti=use_cupti) if scan_splits else {}, 'shape_dispatch_registry': {'available_shape_kernels': [{'shape_key': TARGET_SHAPE, 'guard': 'BF16 build B=1 Q=M=1024 D=768 K=10', 'candidate_entrypoint': ROUTE_ENTRYPOINT, 'kernel_ref': CANDIDATE_ID, 'correctness': 'pass' if candidate_report['summary']['all_correct'] else 'fail', 'timing_backend': next((row.get('timing_backend') for row in candidate_report.get('per_shape', {}).values() if row.get('timing_backend')), None), 'benchmark_evidence': BENCHMARK_ENTRYPOINT}]}, 'report': candidate_report} + if baseline_report is not None: + baseline_mean = baseline_report['summary']['primary_mean'] + payload['baseline_entrypoint'] = FALLBACK_ENTRYPOINT + payload['baseline_summary'] = baseline_report['summary'] + payload['per_shape_delta_vs_generic_fallback'] = {TARGET_SHAPE: _per_shape_delta(candidate_report, baseline_report)} + payload['speedup_vs_generic_fallback_primary_mean'] = candidate_report['summary']['primary_mean'] / baseline_mean if baseline_mean else None + return payload + +def write_benchmark_artifact(path: str | os.PathLike[str], **kwargs) -> dict[str, Any]: + payload = benchmark_knn_build_common_d768_build_eeff_m64split_v1(**kwargs) + out = Path(path) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n', encoding='utf-8') + return payload diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_common_d_1438_rag_d64_m128_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_common_d_1438_rag_d64_m128_v1.py new file mode 100644 index 00000000..113e4d34 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_common_d_1438_rag_d64_m128_v1.py @@ -0,0 +1,172 @@ +"""D64 common-D RAG microbatch M128 repair seed for round 1438. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +routes only the exact BF16 non-build ``rag_microbatch_common_d64_b1_q16_m50000_k10`` +row through a D64/M128 tcgen05 stage and the existing fused split merge. Guard +misses delegate to the current D64/D256 common-D RAG seed. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from collections.abc import Callable +from functools import lru_cache +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_common_d_5e7f_rag_d64_d256_v1 as parent +from . import knn_build_rag_microbatch_m64_d4f7_v1 as m128_parent +MODULE = 'loom.examples.weave.knn_build_common_d_1438_rag_d64_m128_v1' +ROUTE_PREFIX = 'knn_build_common_d_1438_rag_d64_m128_v1' +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +BENCHMARK_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_knn_build_common_d_1438_rag_d64_m128_v1']) +RAG_D64 = parent.RAG_D64 +TARGET_SHAPES = (RAG_D64,) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +D64_Q = 64 +D64_M = 128 +D64_D = 64 +D64_K = 10 +D64_VEC = 8 +D64_THREADS = 512 +D64_LOCAL_LISTS_PER_ROW = 8 +D64_SPLIT_COUNT = _decode_capture(_json_loads('136')) +D64_GROUP_COUNT = _decode_capture(_json_loads('8')) +D64_Q_STAGE_VECS = D64_Q * D64_D // D64_VEC +D64_DB_STAGE_VECS = D64_M * D64_D // D64_VEC +D64_SMEM_A_BYTES = D64_Q * D64_D * 2 +D64_SMEM_B_BYTES = D64_M * D64_D * 2 +D64_SMEM_LOCAL_D_BYTES = D64_Q * D64_LOCAL_LISTS_PER_ROW * D64_K * 4 +D64_SMEM_LOCAL_I_BYTES = D64_Q * D64_LOCAL_LISTS_PER_ROW * D64_K * 4 +D64_LOCAL_D_OFFSET = D64_SMEM_A_BYTES + D64_SMEM_B_BYTES +D64_LOCAL_I_OFFSET = D64_LOCAL_D_OFFSET + D64_SMEM_LOCAL_D_BYTES +D64_SMEM_POOL_BYTES = D64_LOCAL_I_OFFSET + D64_SMEM_LOCAL_I_BYTES + 256 +WEAVE_SMEM_SYSTEM_BYTES = 1024 +D64_STAGE_SMEM_BYTES = D64_SMEM_POOL_BYTES + WEAVE_SMEM_SYSTEM_BYTES +GRID_DIM_DEFAULT = parent.GRID_DIM_DEFAULT +TOP_K_MAX = parent.TOP_K_MAX +knn_build_common_d_1438_rag_d64_m128_stage1 = _decode_capture(_json_loads('{"__ir__": "knn_build_common_d_1438_rag_d64_m128_stage1", "arg_keys": ["query", "database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 66816, "constants": [], "cta_group": 1, "threads": 512}')) +stage1_d64_m128_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_common_d_1438_rag_d64_m128_stage1", "arg_keys": ["query", "database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 66816, "constants": [], "cta_group": 1, "threads": 512}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_COMMON_D_1438_RAG_D64_M128_VERIFY_KERNEL') + split_count = int(os.environ.get('LOOM_KNN_COMMON_D_1438_RAG_D64_M128_VERIFY_SPLIT', D64_SPLIT_COUNT)) + group_count = int(os.environ.get('LOOM_KNN_COMMON_D_1438_RAG_D64_M128_VERIFY_GROUPS', D64_GROUP_COUNT)) + if verify_kernel == 'merge': + return parent.fused_merge_parent._fused_merge_ir(split_count, group_count) + return stage1_d64_m128_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_common_d_1438_rag_d64_m128_stage1", "arg_keys": ["query", "database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 66816, "constants": [], "cta_group": 1, "threads": 512}')) + +def _compiled_stage1_d64_m128(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0093"}')) + +@lru_cache(maxsize=8) +def _compiled_fused_merge(split_count: int, group_count: int): + return parent.fused_merge_parent._compiled_fused_merge(int(split_count), int(group_count)) + +def _dtype_name(inputs: dict[str, Any]) -> str: + query = inputs.get('query') + if query is not None: + return str(query.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')).replace('torch.', '') + +def _target_label_for_inputs(inputs: dict[str, Any]) -> str | None: + if str(inputs.get('label', RAG_D64)) in TARGET_SHAPE_SET and _dtype_name(inputs) == 'bfloat16' and (not bool(inputs.get('build', False))) and (int(inputs.get('B', -1)) == 1) and (int(inputs.get('Q', -1)) == 16) and (int(inputs.get('M', -1)) == 50000) and (int(inputs.get('D', -1)) == D64_D) and (int(inputs.get('K', -1)) == D64_K): + return RAG_D64 + return None + +def _split_count() -> int: + return int(D64_SPLIT_COUNT) + +def _group_count() -> int: + return int(D64_GROUP_COUNT) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + label = None if force_fallback else _target_label_for_inputs(inputs) + if label is None: + return parent.route_for_contract_inputs(inputs, force_fallback=force_fallback) + return ''.join([format(ROUTE_PREFIX, ''), ':', format(label, ''), ':d64:q16:m50000:m128:s', format(_split_count(), ''), ':g', format(_group_count(), '')]) + +def _launch_d64_m128(inputs: dict[str, Any]) -> None: + parent.fused_merge_parent._validate_group_shape(_split_count(), _group_count()) + query = inputs['query'] + database = inputs['database'] + if str(query.dtype) != 'torch.bfloat16' or str(database.dtype) != 'torch.bfloat16': + raise ValueError('knn_build_common_d_1438_rag_d64_m128_v1 supports bfloat16 inputs only') + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + if dim != D64_D: + raise ValueError(''.join([format(RAG_D64, ''), ' expected D=', format(D64_D, ''), ', got ', format(dim, '')])) + split_count = _split_count() + group_count = _group_count() + num_q_tiles = (n_query + D64_Q - 1) // D64_Q + num_db_tiles = (n_database + D64_M - 1) // D64_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tiles * split_count + total_queries = bsz * n_query + stage1_grid = min(total_work, GRID_DIM_DEFAULT) + merge_grid = min(total_queries, GRID_DIM_DEFAULT) + partial_dists, partial_indices = parent.split_parent._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + stage1_launch = _compiled_stage1_d64_m128().prepare_launch(grid=(stage1_grid, 1, 1), block=(D64_THREADS, 1, 1), args=[query, database, inputs['query_sq'], inputs['database_sq'], partial_dists, partial_indices, bsz, n_query, n_database, top_k, num_q_tiles, db_tiles_per_split, split_count, total_work], shared_mem=D64_STAGE_SMEM_BYTES) + merge_ir = parent.fused_merge_parent._fused_merge_ir(split_count, group_count) + merge_launch = _compiled_fused_merge(split_count, group_count).prepare_launch(grid=(merge_grid, 1, 1), block=(parent.fused_merge_parent.D768_FUSED_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], total_queries], shared_mem=merge_ir.computed_smem_bytes) + stage1_launch.launch() + merge_launch.launch() + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + if not force_fallback and _target_label_for_inputs(inputs) is not None: + _launch_d64_m128(inputs) + return + parent.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return parent._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def route_trace_for_contract_shapes(shape_labels=TARGET_SHAPES, *, force_fallback: bool=False) -> list[dict[str, Any]]: + rows = [] + for shape in _select_contract_shapes(shape_labels): + params = dict(shape.get('params', {})) + params['label'] = shape['label'] + inputs = {'label': params['label'], 'B': params['B'], 'Q': params['Q'], 'M': params['M'], 'D': params['D'], 'K': params['K'], 'build': params['build'], 'dtype': params.get('dtype', 'bfloat16')} + label = None if force_fallback else _target_label_for_inputs(inputs) + if label is None: + rows.append({'shape_key': params['label'], 'selected_route': parent.ROUTE_ENTRYPOINT, 'selected_entrypoint': parent.ROUTE_ENTRYPOINT, 'selected_seed': None, 'expected_seed': 'common_d_1438_rag_d64_m128_v1' if params['label'] in TARGET_SHAPE_SET else None, 'route_kind': 'forced_fallback' if force_fallback else 'delegated', 'route_source': 'common-d-5e7f-rag-d64-d256-parent', 'guard_id': 'forced_fallback' if force_fallback else 'guard_miss'}) + continue + rows.append({'shape_key': label, 'selected_route': route_for_contract_inputs(inputs), 'selected_entrypoint': ROUTE_ENTRYPOINT, 'selected_seed': 'common_d_1438_rag_d64_m128_v1', 'expected_seed': 'common_d_1438_rag_d64_m128_v1', 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': '1438_common_d_d64_rag_m128_exact_guard', 'guard_condition': 'exact BF16 non-build B=1 Q=16 M=50000 D=64 K=10', 'split_count': _split_count(), 'group_count': _group_count(), 'producer_topology': 'D64_M128_tcgen05_smem', 'merge_topology': 'fused_group_split_merge', 'classification': 'd64-rag-m128-repair-seed'}) + return rows + +def benchmark_knn_build_common_d_1438_rag_d64_m128_v1(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES) -> dict[str, Any]: + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels) + return {'contract': report['contract'], 'contract_version': report['contract_version'], 'measured_entrypoint': BENCHMARK_ENTRYPOINT, 'candidate_entrypoint': ROUTE_ENTRYPOINT, 'accelerated_shape_labels': list(TARGET_SHAPES), 'measured_shape_labels': list(shape_labels), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'producer_topology': ''.join(['D64M128/S', format(_split_count(), ''), '/G', format(_group_count(), '')]), 'route_trace': route_trace_for_contract_shapes(shape_labels), 'summary': report['summary'], 'performance': report['performance'], 'correctness': report['correctness'], 'report': report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_common_d_56f3_build_d256_q1024_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_common_d_56f3_build_d256_q1024_v1.py new file mode 100644 index 00000000..d6163647 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_common_d_56f3_build_d256_q1024_v1.py @@ -0,0 +1,189 @@ +"""kNN common-D D256 build seed for round 56f3. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +routes the v11 BF16 build row `build_common_d256_b1_q1024_m1024_k10` through +the existing df2f D256 tcgen05/TMA split producer and a D-independent exact K10 +row-base cached merge. Guard misses intentionally delegate to the current +default dispatcher. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from collections.abc import Callable +from functools import lru_cache +from typing import Any +from .._dispatch_runtime import dc as dc +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dim_midk_df2f_v1 as d256_parent +from . import knn_build_dispatch_e3de_9138_bcb3_4247_v1 as default_dispatcher +from .._dispatch_runtime import pack_kernel_args +MODULE = 'loom.examples.weave.knn_build_common_d_56f3_build_d256_q1024_v1' +ROUTE_PREFIX = 'knn_build_common_d_56f3_build_d256_q1024_v1' +BUILD_D256_Q1024 = 'build_common_d256_b1_q1024_m1024_k10' +TARGET_SHAPES = (BUILD_D256_Q1024,) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +BLOCK_Q = d256_parent.BLOCK_Q +BLOCK_M = d256_parent.BLOCK_M +TOP_K_MAX = d256_parent.TOP_K_MAX +THREADS = d256_parent.THREADS +FAST_MERGE_THREADS = 32 +GRID_DIM_DEFAULT = d256_parent.GRID_DIM_DEFAULT +D256_FEAT_D = d256_parent.D256_FEAT_D +SHAPE_SPEC = _decode_capture(_json_loads('{"__dict_items__": [["B", 1], ["Q", 1024], ["M", 1024], ["D", 256], ["K", 10], ["build", true], ["split_count", 16]]}')) +stage1_d256_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_dim_midk_df2f_d256_split_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 99584, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) +stage1_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_dim_midk_df2f_d256_split_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 99584, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) +knn_build_common_d_56f3_d256_q1024_k10_merge_rowbase_cache = _decode_capture(_json_loads('{"__ir__": "knn_build_common_d_56f3_d256_q1024_k10_merge_rowbase_cache", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 16]], "cta_group": 1, "threads": 32}')) +merge_base_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_common_d_56f3_d256_q1024_k10_merge_rowbase_cache", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 16]], "cta_group": 1, "threads": 32}')) + +def _ir_with_constants(ir_obj: Any, *, suffix: str, **updates: int) -> Any: + constants = tuple(((name, updates.get(name, value)) for name, value in ir_obj.constants)) + return dc.replace(ir_obj, symbol=''.join([format(ir_obj.symbol, ''), '_', format(suffix, '')]), constants=constants) + +def _merge_ir(split_count: int) -> Any: + return _ir_with_constants(merge_base_ir, suffix=''.join(['s', format(int(split_count), '')]), SPLIT_COUNT=int(split_count)) +merge_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_common_d_56f3_d256_q1024_k10_merge_rowbase_cache_s16", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 16]], "cta_group": 1, "threads": 32}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_COMMON_D_56F3_D256_VERIFY_KERNEL') + if verify_kernel == 'merge': + return _merge_ir(int(os.environ.get('LOOM_KNN_COMMON_D_56F3_D256_VERIFY_SPLITS', '16'))) + return stage1_d256_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_dim_midk_df2f_d256_split_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 99584, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) + +def _compiled_stage1(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0023"}')) + +def _compile_ir(ir_obj: Any): + from .._dispatch_runtime import generate_kernel + from .._dispatch_runtime import _cuda_include_dirs + from .._dispatch_runtime import compile_cuda + from .._dispatch_runtime import CUDAKernel + source = generate_kernel(ir_obj, validate=False, smem_bytes=ir_obj.computed_smem_bytes) + cubin = compile_cuda(source, arch=d256_parent.base_v1._select_arch_and_preload(), options=['--use_fast_math'], include_dirs=_cuda_include_dirs()) + return CUDAKernel(cubin, ''.join(['kernel_', format(ir_obj.symbol, '')])) + +@lru_cache(maxsize=4) +def _compiled_merge(split_count: int): + return _compile_ir(_merge_ir(int(split_count))) + +def _dtype_name(inputs: dict[str, Any]) -> str: + query = inputs.get('query') + if query is not None: + return str(query.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')).replace('torch.', '') + +def _matches_target(inputs: dict[str, Any]) -> bool: + return _dtype_name(inputs) == 'bfloat16' and bool(inputs.get('build', False)) == bool(SHAPE_SPEC['build']) and (int(inputs['B']) == int(SHAPE_SPEC['B'])) and (int(inputs['Q']) == int(SHAPE_SPEC['Q'])) and (int(inputs['M']) == int(SHAPE_SPEC['M'])) and (int(inputs['D']) == int(SHAPE_SPEC['D'])) and (int(inputs['K']) == int(SHAPE_SPEC['K'])) + +def _target_label_for_inputs(inputs: dict[str, Any]) -> str | None: + label = inputs.get('label') + if label is not None: + return str(label) if str(label) == BUILD_D256_Q1024 and _matches_target(inputs) else None + return BUILD_D256_Q1024 if _matches_target(inputs) else None + +def _split_count() -> int: + return int(SHAPE_SPEC['split_count']) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + label = _target_label_for_inputs(inputs) + if force_fallback or label is None: + return default_dispatcher.route_for_contract_inputs(inputs, force_fallback=force_fallback) + return ''.join([format(ROUTE_PREFIX, ''), ':', format(label, ''), ':d256:q1024:m1024:s', format(_split_count(), ''), ':k10_cached_merge']) + +def _launch_d256_q1024_build(inputs: dict[str, Any]) -> None: + query = inputs['query'] + database = inputs['database'] + if str(query.dtype) != 'torch.bfloat16' or str(database.dtype) != 'torch.bfloat16': + raise ValueError('knn_build_common_d_56f3_build_d256_q1024_v1 supports bfloat16 inputs only') + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + split_count = _split_count() + if dim != D256_FEAT_D: + raise ValueError(''.join([format(BUILD_D256_Q1024, ''), ' expected D=', format(D256_FEAT_D, ''), ', got ', format(dim, '')])) + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tiles * split_count + total_queries = bsz * n_query + stage1_grid = min(total_work, GRID_DIM_DEFAULT) + merge_grid = min((total_queries + FAST_MERGE_THREADS - 1) // FAST_MERGE_THREADS, GRID_DIM_DEFAULT) + partial_dists, partial_indices = d256_parent.split_parent._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = d256_parent.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), total_queries, BLOCK_Q, dim, D256_FEAT_D) + tmap_database = d256_parent.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, dim, D256_FEAT_D) + stage1_launch = _compiled_stage1().prepare_launch(grid=(stage1_grid, 1, 1), block=(THREADS, 1, 1), args=pack_kernel_args(stage1_d256_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tiles=num_q_tiles, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), shared_mem=stage1_d256_ir.computed_smem_bytes) + merge_ir_obj = _merge_ir(split_count) + merge_launch = _compiled_merge(split_count).prepare_launch(grid=(merge_grid, 1, 1), block=(FAST_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], total_queries], shared_mem=merge_ir_obj.computed_smem_bytes) + stage1_launch.launch() + merge_launch.launch() + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + if not force_fallback and _target_label_for_inputs(inputs) is not None: + _launch_d256_q1024_build(inputs) + return + default_dispatcher.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + if shape_labels is None: + wanted = set(TARGET_SHAPES) + else: + wanted = {str(label) for label in shape_labels} + selected = [shape for shape in eval_mod.CANONICAL_SHAPES if str(shape['label']) in wanted] + missing = wanted - {str(shape['label']) for shape in selected} + if missing: + raise ValueError(''.join(['unknown knn_build contract shape(s): ', format(sorted(missing), '')])) + return selected + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def benchmark_knn_build_common_d_56f3_build_d256_q1024_v1(*, use_cupti: bool | None=None, shape_labels=TARGET_SHAPES) -> dict[str, Any]: + prior_use_cupti = None + if use_cupti is not None: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True) + finally: + if prior_use_cupti is not None: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + return {'tflops': report['summary']['primary_mean'], 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'report': report} + +def _trace_inputs_from_shape(shape: dict[str, Any]) -> dict[str, Any]: + params = dict(shape.get('params', {})) + params['label'] = shape['label'] + return {'label': params['label'], 'B': params['B'], 'Q': params['Q'], 'M': params['M'], 'D': params['D'], 'K': params['K'], 'build': params['build'], 'dtype': params.get('dtype', 'bfloat16')} + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False) -> list[dict[str, Any]]: + selected = _select_contract_shapes(TARGET_SHAPES if shape_labels is None else shape_labels) + rows = [] + for shape in selected: + inputs = _trace_inputs_from_shape(shape) + if force_fallback or _target_label_for_inputs(inputs) is None: + rows.append({'shape_key': str(shape['label']), 'selected_route': default_dispatcher.route_for_contract_inputs(inputs, force_fallback=force_fallback), 'selected_entrypoint': 'loom.examples.weave.knn_build_dispatch_e3de_9138_bcb3_4247_v1:launch_from_contract_inputs', 'selected_seed': None, 'expected_seed': 'common_d_56f3_build_d256_q1024_v1' if str(shape['label']) in TARGET_SHAPE_SET else None, 'route_kind': 'forced_fallback' if force_fallback else 'parent_delegate', 'route_source': 'default-dispatcher', 'guard_id': 'forced_fallback' if force_fallback else 'guard_miss', 'classification': 'forced_fallback' if force_fallback else 'delegated'}) + continue + rows.append({'shape_key': BUILD_D256_Q1024, 'selected_route': route_for_contract_inputs(inputs), 'selected_entrypoint': ''.join([format(MODULE, ''), ':launch_from_contract_inputs']), 'selected_seed': 'common_d_56f3_build_d256_q1024_v1', 'expected_seed': 'common_d_56f3_build_d256_q1024_v1', 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': '56f3_common_d_d256_q1024_build_exact_guard', 'guard_condition': 'exact BF16 build B=1 Q=1024 M=1024 D=256 K=10', 'split_count': _split_count(), 'producer': 'df2f_d256_tcgen05_tma', 'merge': 'k10_rowbase_cached', 'classification': 'common-d-d256-q1024-build-seed'}) + return rows diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_common_d_56f3_build_highd_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_common_d_56f3_build_highd_v1.py new file mode 100644 index 00000000..f341acdc --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_common_d_56f3_build_highd_v1.py @@ -0,0 +1,206 @@ +"""kNN common-D high-dimensional build seed for round 56f3. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +routes the v11 BF16 build rows for D768, D1024, and D4096 through the existing +chunked 128-wide tcgen05/TMA producer and a D-independent exact K10 split +merge. Guard misses intentionally delegate to the current default dispatcher. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from collections.abc import Callable +from functools import lru_cache +from typing import Any +from .._dispatch_runtime import dc as dc +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_e3de_9138_bcb3_4247_v1 as default_dispatcher +from . import knn_build_evolve_7bfc_split_v1 as split_parent +from . import knn_build_non128_frontier_7231_v1 as chunked_parent +from .._dispatch_runtime import pack_kernel_args +MODULE = 'loom.examples.weave.knn_build_common_d_56f3_build_highd_v1' +ROUTE_PREFIX = 'knn_build_common_d_56f3_build_highd_v1' +BUILD_D768 = 'build_common_d768_b1_q1024_m1024_k10' +BUILD_D1024 = 'build_common_d1024_b1_q512_m512_k10' +BUILD_D4096 = 'build_common_d4096_b1_q512_m512_k10' +TARGET_SHAPES = (BUILD_D768, BUILD_D1024, BUILD_D4096) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +BLOCK_Q = chunked_parent.BLOCK_Q +BLOCK_M = chunked_parent.BLOCK_M +K_TILE = chunked_parent.K_TILE +TOP_K_MAX = chunked_parent.TOP_K_MAX +THREADS = chunked_parent.THREADS +FAST_MERGE_THREADS = 32 +GRID_DIM_DEFAULT = chunked_parent.GRID_DIM_DEFAULT +SHAPE_SPECS = _decode_capture(_json_loads('{"__dict_items__": [["build_common_d768_b1_q1024_m1024_k10", {"__dict_items__": [["B", 1], ["Q", 1024], ["M", 1024], ["D", 768], ["K", 10], ["build", true], ["feature_chunks", 6], ["split_count", 16]]}], ["build_common_d1024_b1_q512_m512_k10", {"__dict_items__": [["B", 1], ["Q", 512], ["M", 512], ["D", 1024], ["K", 10], ["build", true], ["feature_chunks", 8], ["split_count", 8]]}], ["build_common_d4096_b1_q512_m512_k10", {"__dict_items__": [["B", 1], ["Q", 512], ["M", 512], ["D", 4096], ["K", 10], ["build", true], ["feature_chunks", 32], ["split_count", 8]]}]]}')) +stage1_d768_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_7231_stage1_d768", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["K_TILE", 128], ["TOP_K_MAX", 10], ["FEATURE_CHUNKS", 6]], "cta_group": 1, "threads": 192}')) +stage1_d1024_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_7231_stage1_d1024", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["K_TILE", 128], ["TOP_K_MAX", 10], ["FEATURE_CHUNKS", 8]], "cta_group": 1, "threads": 192}')) +stage1_d4096_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_7231_stage1_d4096", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["K_TILE", 128], ["TOP_K_MAX", 10], ["FEATURE_CHUNKS", 32]], "cta_group": 1, "threads": 192}')) +stage1_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_7231_stage1_d768", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["K_TILE", 128], ["TOP_K_MAX", 10], ["FEATURE_CHUNKS", 6]], "cta_group": 1, "threads": 192}')) +knn_build_common_d_56f3_k10_merge_rowbase_cache = _decode_capture(_json_loads('{"__ir__": "knn_build_common_d_56f3_k10_merge_rowbase_cache", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 16]], "cta_group": 1, "threads": 32}')) +merge_base_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_common_d_56f3_k10_merge_rowbase_cache", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 16]], "cta_group": 1, "threads": 32}')) + +def _ir_with_constants(ir_obj: Any, *, suffix: str, **updates: int) -> Any: + constants = tuple(((name, updates.get(name, value)) for name, value in ir_obj.constants)) + return dc.replace(ir_obj, symbol=''.join([format(ir_obj.symbol, ''), '_', format(suffix, '')]), constants=constants) + +def _merge_ir(split_count: int) -> Any: + return _ir_with_constants(merge_base_ir, suffix=''.join(['s', format(int(split_count), '')]), SPLIT_COUNT=int(split_count)) +merge_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_common_d_56f3_k10_merge_rowbase_cache_s16", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 16]], "cta_group": 1, "threads": 32}')) + +def _stage_ir(feature_chunks: int) -> Any: + return chunked_parent._stage1_ir(int(feature_chunks)) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_COMMON_D_56F3_BUILD_VERIFY_KERNEL') + if verify_kernel == 'stage1_d1024': + return stage1_d1024_ir + if verify_kernel == 'stage1_d4096': + return stage1_d4096_ir + if verify_kernel == 'merge': + return _merge_ir(int(os.environ.get('LOOM_KNN_COMMON_D_56F3_BUILD_VERIFY_SPLITS', '16'))) + return stage1_d768_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_7231_stage1_d768", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["K_TILE", 128], ["TOP_K_MAX", 10], ["FEATURE_CHUNKS", 6]], "cta_group": 1, "threads": 192}')) + +@lru_cache(maxsize=4) +def _compiled_stage1(feature_chunks: int): + return chunked_parent._compiled_stage1(int(feature_chunks)) + +@lru_cache(maxsize=4) +def _compiled_merge(split_count: int): + return chunked_parent._compile_ir(_merge_ir(int(split_count))) + +def _dtype_name(inputs: dict[str, Any]) -> str: + query = inputs.get('query') + if query is not None: + return str(query.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')).replace('torch.', '') + +def _matches_spec(inputs: dict[str, Any], spec: dict[str, Any]) -> bool: + return _dtype_name(inputs) == 'bfloat16' and bool(inputs.get('build', False)) == bool(spec['build']) and (int(inputs['B']) == int(spec['B'])) and (int(inputs['Q']) == int(spec['Q'])) and (int(inputs['M']) == int(spec['M'])) and (int(inputs['D']) == int(spec['D'])) and (int(inputs['K']) == int(spec['K'])) + +def _target_label_for_inputs(inputs: dict[str, Any]) -> str | None: + label = inputs.get('label') + if label is not None: + label_s = str(label) + if label_s in TARGET_SHAPE_SET and _matches_spec(inputs, SHAPE_SPECS[label_s]): + return label_s + return None + for candidate_label, spec in SHAPE_SPECS.items(): + if _matches_spec(inputs, spec): + return candidate_label + return None + +def _split_count_for_label(label: str) -> int: + return int(SHAPE_SPECS[label]['split_count']) + +def _feature_chunks_for_label(label: str) -> int: + return int(SHAPE_SPECS[label]['feature_chunks']) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + label = _target_label_for_inputs(inputs) + if force_fallback or label is None: + return default_dispatcher.route_for_contract_inputs(inputs, force_fallback=force_fallback) + spec = SHAPE_SPECS[label] + return ''.join([format(ROUTE_PREFIX, ''), ':', format(label, ''), ':d', format(int(spec['D']), ''), ':q', format(int(spec['Q']), ''), ':m', format(int(spec['M']), ''), ':s', format(_split_count_for_label(label), ''), ':chunks', format(_feature_chunks_for_label(label), ''), ':k10_cached_merge']) + +def _launch_highd_build(inputs: dict[str, Any], label: str) -> None: + query = inputs['query'] + database = inputs['database'] + if str(query.dtype) != 'torch.bfloat16' or str(database.dtype) != 'torch.bfloat16': + raise ValueError('knn_build_common_d_56f3_build_highd_v1 supports bfloat16 inputs only') + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + feature_chunks = _feature_chunks_for_label(label) + split_count = _split_count_for_label(label) + tma_dim = feature_chunks * K_TILE + if dim != tma_dim: + raise ValueError(''.join([format(label, ''), ' expected D=', format(tma_dim, ''), ', got ', format(dim, '')])) + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tiles * split_count + total_queries = bsz * n_query + stage1_grid = min(total_work, GRID_DIM_DEFAULT) + merge_grid = min((total_queries + FAST_MERGE_THREADS - 1) // FAST_MERGE_THREADS, GRID_DIM_DEFAULT) + partial_dists, partial_indices = split_parent._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = chunked_parent.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), total_queries, BLOCK_Q, tma_dim, K_TILE) + tmap_database = chunked_parent.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, tma_dim, K_TILE) + stage_ir_obj = _stage_ir(feature_chunks) + _compiled_stage1(feature_chunks).launch(grid=(stage1_grid, 1, 1), block=(THREADS, 1, 1), args=pack_kernel_args(stage_ir_obj, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tiles=num_q_tiles, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), shared_mem=stage_ir_obj.computed_smem_bytes) + merge_ir_obj = _merge_ir(split_count) + _compiled_merge(split_count).launch(grid=(merge_grid, 1, 1), block=(FAST_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], total_queries], shared_mem=merge_ir_obj.computed_smem_bytes) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + label = _target_label_for_inputs(inputs) + if not force_fallback and label is not None: + _launch_highd_build(inputs, label) + return + default_dispatcher.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + if shape_labels is None: + wanted = set(TARGET_SHAPES) + else: + wanted = {str(label) for label in shape_labels} + selected = [shape for shape in eval_mod.CANONICAL_SHAPES if str(shape['label']) in wanted] + missing = wanted - {str(shape['label']) for shape in selected} + if missing: + raise ValueError(''.join(['unknown knn_build contract shape(s): ', format(sorted(missing), '')])) + return selected + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def benchmark_knn_build_common_d_56f3_build_highd_v1(*, use_cupti: bool | None=None, shape_labels=TARGET_SHAPES) -> dict[str, Any]: + prior_use_cupti = None + if use_cupti is not None: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True) + finally: + if prior_use_cupti is not None: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + return {'tflops': report['summary']['primary_mean'], 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'report': report} + +def _trace_inputs_from_shape(shape: dict[str, Any]) -> dict[str, Any]: + params = dict(shape.get('params', {})) + params['label'] = shape['label'] + return {'label': params['label'], 'B': params['B'], 'Q': params['Q'], 'M': params['M'], 'D': params['D'], 'K': params['K'], 'build': params['build'], 'dtype': params.get('dtype', 'bfloat16')} + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + rows = [] + for shape in selected: + inputs = _trace_inputs_from_shape(shape) + label = _target_label_for_inputs(inputs) + if force_fallback or label is None: + rows.append({'shape_key': str(shape['label']), 'selected_route': default_dispatcher.route_for_contract_inputs(inputs, force_fallback=force_fallback), 'selected_entrypoint': 'loom.examples.weave.knn_build_dispatch_e3de_9138_bcb3_4247_v1:launch_from_contract_inputs', 'selected_seed': None, 'expected_seed': 'common_d_56f3_build_highd_v1' if str(shape['label']) in TARGET_SHAPE_SET else None, 'route_kind': 'forced_fallback' if force_fallback else 'parent_delegate', 'route_source': 'default-dispatcher', 'guard_id': 'forced_fallback' if force_fallback else 'guard_miss', 'classification': 'forced_fallback' if force_fallback else 'delegated'}) + continue + spec = SHAPE_SPECS[label] + rows.append({'shape_key': label, 'selected_route': route_for_contract_inputs(inputs), 'selected_entrypoint': ''.join([format(MODULE, ''), ':launch_from_contract_inputs']), 'selected_seed': 'common_d_56f3_build_highd_v1', 'expected_seed': 'common_d_56f3_build_highd_v1', 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': '56f3_common_d_highd_build_exact_guard', 'guard_condition': ''.join(['exact BF16 build B=', format(spec['B'], ''), ' Q=', format(spec['Q'], ''), ' M=', format(spec['M'], ''), ' D=', format(spec['D'], ''), ' K=', format(spec['K'], '')]), 'feature_chunks': _feature_chunks_for_label(label), 'split_count': _split_count_for_label(label), 'producer': 'chunked_128wide_tcgen05_tma', 'merge': 'k10_rowbase_cached', 'classification': 'common-d-highd-build-seed'}) + return rows diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_common_d_5e7f_rag_d64_d256_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_common_d_5e7f_rag_d64_d256_v1.py new file mode 100644 index 00000000..bf8d2129 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_common_d_5e7f_rag_d64_d256_v1.py @@ -0,0 +1,259 @@ +"""D64/D256 common-D RAG microbatch seed for v11 round 5e7f. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +routes the exact BF16 non-build D64 and D256 v11 RAG microbatch rows through +the existing M64/N64 tcgen05/TMA producer and fused split merge. The D64 row is +padded to one 128-wide feature chunk with a Weave padding kernel; guard misses +fall back to the current v11 common-D dispatcher. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from collections.abc import Callable +from functools import lru_cache +from typing import Any +from .._dispatch_runtime import dc as dc +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_common_d_v11_fallback_v1 as default_dispatcher +from . import knn_build_evolve_7bfc_split_v1 as split_parent +from . import knn_build_non128_frontier_4be7_d768fused_v1 as fused_merge_parent +from . import knn_build_non128_frontier_7ee5_m64rag_v1 as m64_parent +from .._dispatch_runtime import pack_kernel_args +MODULE = 'loom.examples.weave.knn_build_common_d_5e7f_rag_d64_d256_v1' +ROUTE_PREFIX = 'knn_build_common_d_5e7f_rag_d64_d256_v1' +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +BENCHMARK_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_knn_build_common_d_5e7f_rag_d64_d256_v1']) +RAG_D64 = 'rag_microbatch_common_d64_b1_q16_m50000_k10' +RAG_D256 = 'rag_microbatch_common_d256_b1_q16_m50000_k10' +TARGET_SHAPES = (RAG_D64, RAG_D256) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +M64_BLOCK_Q = m64_parent.M64_BLOCK_Q +M64_BLOCK_M = m64_parent.M64_BLOCK_M +M64_THREADS = m64_parent.M64_THREADS +K_TILE = m64_parent.K_TILE +GRID_DIM_DEFAULT = m64_parent.GRID_DIM_DEFAULT +TOP_K_MAX = m64_parent.TOP_K_MAX +D64_K_TILE = 64 +D64_QUERY_BYTES = M64_BLOCK_Q * D64_K_TILE * 2 +D64_DATABASE_BYTES = M64_BLOCK_M * D64_K_TILE * 2 +D64_DB_SQ_BYTES = M64_BLOCK_M * 4 +D64_SMEM_POOL_BYTES = D64_QUERY_BYTES + D64_DATABASE_BYTES + D64_DB_SQ_BYTES +DEFAULT_SPLIT_COUNT = _decode_capture(_json_loads('128')) +DEFAULT_GROUP_COUNT = _decode_capture(_json_loads('8')) +SHAPE_SPECS = _decode_capture(_json_loads('{"__dict_items__": [["rag_microbatch_common_d64_b1_q16_m50000_k10", {"__dict_items__": [["B", 1], ["Q", 16], ["M", 50000], ["D", 64], ["K", 10], ["build", false], ["feature_chunks", 1], ["split_count", 144], ["group_count", 8], ["producer", "d64_m64"]]}], ["rag_microbatch_common_d256_b1_q16_m50000_k10", {"__dict_items__": [["B", 1], ["Q", 16], ["M", 50000], ["D", 256], ["K", 10], ["build", false], ["feature_chunks", 2], ["split_count", 144], ["group_count", 8], ["producer", "m64_chunked"]]}]]}')) +knn_build_common_d_5e7f_rag_d64_m64_stage1 = _decode_capture(_json_loads('{"__ir__": "knn_build_common_d_5e7f_rag_d64_m64_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 17664, "constants": [], "cta_group": 1, "threads": 96}')) +d64_m64_stage1_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_common_d_5e7f_rag_d64_m64_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 17664, "constants": [], "cta_group": 1, "threads": 96}')) + +def _ir_with_constants(ir_obj: Any, *, suffix: str, **updates: int) -> Any: + constants = tuple(((name, updates.get(name, value)) for name, value in ir_obj.constants)) + return dc.replace(ir_obj, symbol=''.join([format(ir_obj.symbol, ''), '_', format(suffix, '')]), constants=constants) + +@lru_cache(maxsize=4) +def _stage1_ir(feature_chunks: int) -> Any: + return _ir_with_constants(m64_parent.stage1_m64_ir, suffix=''.join(['d', format(int(feature_chunks) * K_TILE, ''), '_5e7f_rag_d64d256_v1']), FEATURE_CHUNKS=int(feature_chunks)) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_COMMON_D_5E7F_RAG_D64D256_VERIFY_KERNEL') + split_count = int(os.environ.get('LOOM_KNN_COMMON_D_5E7F_RAG_D64D256_VERIFY_SPLIT', DEFAULT_SPLIT_COUNT)) + group_count = int(os.environ.get('LOOM_KNN_COMMON_D_5E7F_RAG_D64D256_VERIFY_GROUPS', DEFAULT_GROUP_COUNT)) + if verify_kernel == 'stage1_d64': + return d64_m64_stage1_ir + if verify_kernel == 'stage1_d256': + return _stage1_ir(2) + if verify_kernel == 'pad_d64': + return m64_parent.non128_base._pad_ir(K_TILE) + return fused_merge_parent._fused_merge_ir(split_count, group_count) +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_4be7_d768fused_merge_s128g8_4be7_d768fused_v1", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 1024, "constants": [["TOP_K_MAX", 10], ["GROUP_COUNT", 8], ["GROUP_SPLITS", 16]], "cta_group": 1, "threads": 32}')) + +@lru_cache(maxsize=4) +def _compiled_stage1(feature_chunks: int): + return m64_parent._compile_ir(_stage1_ir(int(feature_chunks))) + +@lru_cache(maxsize=8) +def _compiled_fused_merge(split_count: int, group_count: int): + return fused_merge_parent._compiled_fused_merge(int(split_count), int(group_count)) + +def _dtype_name(inputs: dict[str, Any]) -> str: + query = inputs.get('query') + if query is not None: + return str(query.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')).replace('torch.', '') + +def _matches_spec(inputs: dict[str, Any], spec: dict[str, Any]) -> bool: + return _dtype_name(inputs) == 'bfloat16' and bool(inputs.get('build', False)) == bool(spec['build']) and (int(inputs['B']) == int(spec['B'])) and (int(inputs['Q']) == int(spec['Q'])) and (int(inputs['M']) == int(spec['M'])) and (int(inputs['D']) == int(spec['D'])) and (int(inputs['K']) == int(spec['K'])) + +def _target_label_for_inputs(inputs: dict[str, Any]) -> str | None: + label = inputs.get('label') + if label is not None: + label_s = str(label) + if label_s in TARGET_SHAPE_SET and _matches_spec(inputs, SHAPE_SPECS[label_s]): + return label_s + return None + for candidate_label, spec in SHAPE_SPECS.items(): + if _matches_spec(inputs, spec): + return candidate_label + return None + +def _split_count_for_label(label: str) -> int: + return int(SHAPE_SPECS[label]['split_count']) + +def _group_count_for_label(label: str) -> int: + return int(SHAPE_SPECS[label]['group_count']) + +def _feature_chunks_for_label(label: str) -> int: + return int(SHAPE_SPECS[label]['feature_chunks']) + +def _uses_d64_exact(label: str) -> bool: + return str(SHAPE_SPECS[label].get('producer')) == 'd64_m64' + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + label = _target_label_for_inputs(inputs) + if force_fallback or label is None: + return default_dispatcher.route_for_contract_inputs(inputs, force_fallback=force_fallback) + spec = SHAPE_SPECS[label] + return ''.join([format(ROUTE_PREFIX, ''), ':', format(label, ''), ':d', format(int(spec['D']), ''), ':q', format(int(spec['Q']), ''), ':m', format(int(spec['M']), ''), ':', format('d64m64' if _uses_d64_exact(label) else 'm64n64', ''), ':s', format(_split_count_for_label(label), ''), ':g', format(_group_count_for_label(label), ''), ':chunks', format(_feature_chunks_for_label(label), '')]) + +def _maybe_pad_for_tma(tensor, *, rows: int, dim: int, tma_dim: int): + if dim == tma_dim: + return tensor + return m64_parent.non128_base._pad_bf16_rows(tensor, rows=rows, src_cols=dim, dst_cols=tma_dim) + +def _compiled_d64_m64_stage1(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0212"}')) + +def _launch_d64_exact_rag(inputs: dict[str, Any], label: str) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + split_count = _split_count_for_label(label) + group_count = _group_count_for_label(label) + if dim != D64_K_TILE: + raise ValueError(''.join([format(label, ''), ' expected D=', format(D64_K_TILE, ''), ', got ', format(dim, '')])) + fused_merge_parent._validate_group_shape(split_count, group_count) + num_q_tiles = (n_query + M64_BLOCK_Q - 1) // M64_BLOCK_Q + num_db_tiles = (n_database + M64_BLOCK_M - 1) // M64_BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tiles * split_count + total_queries = bsz * n_query + stage1_grid = min(total_work, GRID_DIM_DEFAULT) + merge_grid = min(total_queries, GRID_DIM_DEFAULT) + partial_dists, partial_indices = split_parent._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = m64_parent.non128_base.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), total_queries, M64_BLOCK_Q, dim, D64_K_TILE) + tmap_database = m64_parent.non128_base.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, M64_BLOCK_M, dim, D64_K_TILE) + stage1_launch = _compiled_d64_m64_stage1().prepare_launch(grid=(stage1_grid, 1, 1), block=(M64_THREADS, 1, 1), args=pack_kernel_args(d64_m64_stage1_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tiles=num_q_tiles, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), shared_mem=d64_m64_stage1_ir.computed_smem_bytes) + merge_ir = fused_merge_parent._fused_merge_ir(split_count, group_count) + merge_launch = _compiled_fused_merge(split_count, group_count).prepare_launch(grid=(merge_grid, 1, 1), block=(fused_merge_parent.D768_FUSED_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], total_queries], shared_mem=merge_ir.computed_smem_bytes) + stage1_launch.launch() + merge_launch.launch() + +def _launch_d64_d256_rag(inputs: dict[str, Any], label: str) -> None: + query = inputs['query'] + database = inputs['database'] + if str(query.dtype) != 'torch.bfloat16' or str(database.dtype) != 'torch.bfloat16': + raise ValueError('knn_build_common_d_5e7f_rag_d64_d256_v1 supports bfloat16 inputs only') + if _uses_d64_exact(label): + _launch_d64_exact_rag(inputs, label) + return + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + feature_chunks = _feature_chunks_for_label(label) + split_count = _split_count_for_label(label) + group_count = _group_count_for_label(label) + tma_dim = feature_chunks * K_TILE + if dim > tma_dim: + raise ValueError(''.join([format(label, ''), ' expected D <= ', format(tma_dim, ''), ', got ', format(dim, '')])) + fused_merge_parent._validate_group_shape(split_count, group_count) + num_q_tiles = (n_query + M64_BLOCK_Q - 1) // M64_BLOCK_Q + num_db_tiles = (n_database + M64_BLOCK_M - 1) // M64_BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tiles * split_count + total_queries = bsz * n_query + stage1_grid = min(total_work, GRID_DIM_DEFAULT) + merge_grid = min(total_queries, GRID_DIM_DEFAULT) + partial_dists, partial_indices = split_parent._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + query_tma = _maybe_pad_for_tma(query, rows=total_queries, dim=dim, tma_dim=tma_dim) + database_tma = _maybe_pad_for_tma(database, rows=bsz * n_database, dim=dim, tma_dim=tma_dim) + tmap_query = m64_parent.non128_base.base_v1._create_tensor_map_3d_oob_zero(query_tma.data_ptr(), total_queries, M64_BLOCK_Q, tma_dim, K_TILE) + tmap_database = m64_parent.non128_base.base_v1._create_tensor_map_3d_oob_zero(database_tma.data_ptr(), bsz * n_database, M64_BLOCK_M, tma_dim, K_TILE) + stage_ir = _stage1_ir(feature_chunks) + stage1_launch = _compiled_stage1(feature_chunks).prepare_launch(grid=(stage1_grid, 1, 1), block=(M64_THREADS, 1, 1), args=pack_kernel_args(stage_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tiles=num_q_tiles, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), shared_mem=stage_ir.computed_smem_bytes) + merge_ir = fused_merge_parent._fused_merge_ir(split_count, group_count) + merge_launch = _compiled_fused_merge(split_count, group_count).prepare_launch(grid=(merge_grid, 1, 1), block=(fused_merge_parent.D768_FUSED_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], total_queries], shared_mem=merge_ir.computed_smem_bytes) + stage1_launch.launch() + merge_launch.launch() + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + label = _target_label_for_inputs(inputs) + if not force_fallback and label is not None: + _launch_d64_d256_rag(inputs, label) + return + default_dispatcher.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + if shape_labels is None: + wanted = set(TARGET_SHAPES) + else: + wanted = {str(label) for label in shape_labels} + selected = [shape for shape in eval_mod.CANONICAL_SHAPES if str(shape['label']) in wanted] + missing = wanted - {str(shape['label']) for shape in selected} + if missing: + raise ValueError(''.join(['unknown knn_build contract shape(s): ', format(sorted(missing), '')])) + return selected + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def route_trace_for_contract_shapes(shape_labels=TARGET_SHAPES, *, force_fallback: bool=False) -> list[dict[str, Any]]: + rows = [] + for shape in _select_contract_shapes(shape_labels): + params = dict(shape.get('params', {})) + params['label'] = shape['label'] + inputs = {'label': params['label'], 'B': params['B'], 'Q': params['Q'], 'M': params['M'], 'D': params['D'], 'K': params['K'], 'build': params['build'], 'dtype': params.get('dtype', 'bfloat16')} + label = _target_label_for_inputs(inputs) + if force_fallback or label is None: + rows.append({'shape_key': params['label'], 'selected_route': default_dispatcher.ROUTE_ENTRYPOINT, 'selected_entrypoint': default_dispatcher.ROUTE_ENTRYPOINT, 'selected_seed': None, 'expected_seed': 'common_d_5e7f_rag_d64_d256_v1' if params['label'] in TARGET_SHAPE_SET else None, 'route_kind': 'forced_fallback' if force_fallback else 'delegated', 'route_source': 'default-v11-common-d-dispatcher', 'guard_id': 'forced_fallback' if force_fallback else 'guard_miss'}) + continue + spec = SHAPE_SPECS[label] + tma_dim = _feature_chunks_for_label(label) * K_TILE + rows.append({'shape_key': label, 'selected_route': route_for_contract_inputs(inputs), 'selected_entrypoint': ROUTE_ENTRYPOINT, 'selected_seed': 'common_d_5e7f_rag_d64_d256_v1', 'expected_seed': 'common_d_5e7f_rag_d64_d256_v1', 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': '5e7f_common_d_d64_d256_rag_exact_guard', 'guard_condition': ''.join(['exact BF16 non-build B=', format(spec['B'], ''), ' Q=', format(spec['Q'], ''), ' M=', format(spec['M'], ''), ' D=', format(spec['D'], ''), ' K=', format(spec['K'], '')]), 'feature_chunks': spec['feature_chunks'], 'split_count': _split_count_for_label(label), 'group_count': _group_count_for_label(label), 'producer_topology': 'D64_M64_tcgen05_tma' if _uses_d64_exact(label) else 'M64_N64_tcgen05_tma', 'preprocess_stage': None if _uses_d64_exact(label) else ''.join(['d', format(int(spec['D']), ''), '_weave_pad_to_d', format(tma_dim, '')]) if int(spec['D']) != tma_dim else None, 'merge_topology': 'fused_group_split_merge', 'classification': 'd64-d256-rag-seed'}) + return rows + +def benchmark_knn_build_common_d_5e7f_rag_d64_d256_v1(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES) -> dict[str, Any]: + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels) + return {'contract': report['contract'], 'contract_version': report['contract_version'], 'measured_entrypoint': BENCHMARK_ENTRYPOINT, 'candidate_entrypoint': ROUTE_ENTRYPOINT, 'accelerated_shape_labels': list(TARGET_SHAPES), 'measured_shape_labels': list(shape_labels), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'producer_topology': {label: ''.join([format('D64M64' if _uses_d64_exact(label) else 'M64N64', ''), '/S', format(_split_count_for_label(label), ''), '/G', format(_group_count_for_label(label), ''), '/chunks', format(_feature_chunks_for_label(label), '')]) for label in TARGET_SHAPES}, 'route_trace': route_trace_for_contract_shapes(shape_labels), 'summary': report['summary'], 'performance': report['performance'], 'correctness': report['correctness'], 'report': report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_common_d_5e7f_rag_d64_repair_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_common_d_5e7f_rag_d64_repair_v1.py new file mode 100644 index 00000000..abc67a84 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_common_d_5e7f_rag_d64_repair_v1.py @@ -0,0 +1,189 @@ +"""D64 common-D RAG microbatch repair seed for v11 round 5e7f. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +routes the exact BF16 non-build D64 RAG microbatch row through an exact D64 +M64/N64/K64 tcgen05/TMA producer and the existing fused split merge. Guard +misses intentionally fall back to the current v11 common-D dispatcher; this +module does not edit production dispatch. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from collections.abc import Callable +from functools import lru_cache +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_common_d_v11_fallback_v1 as default_dispatcher +from . import knn_build_evolve_7bfc_split_v1 as split_parent +from . import knn_build_non128_frontier_4be7_d768fused_v1 as fused_merge_parent +from . import knn_build_non128_frontier_7ee5_m64rag_v1 as m64_parent +from .._dispatch_runtime import pack_kernel_args +MODULE = 'loom.examples.weave.knn_build_common_d_5e7f_rag_d64_repair_v1' +ROUTE_PREFIX = 'knn_build_common_d_5e7f_rag_d64_repair_v1' +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +BENCHMARK_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_knn_build_common_d_5e7f_rag_d64_repair_v1']) +RAG_D64 = 'rag_microbatch_common_d64_b1_q16_m50000_k10' +TARGET_SHAPES = (RAG_D64,) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +D64_BLOCK_Q = 64 +D64_BLOCK_M = 64 +D64_K_TILE = 64 +D64_TOP_K_MAX = m64_parent.TOP_K_MAX +D64_THREADS = m64_parent.M64_THREADS +GRID_DIM_DEFAULT = m64_parent.GRID_DIM_DEFAULT +D64_QUERY_BYTES = D64_BLOCK_Q * D64_K_TILE * 2 +D64_DATABASE_BYTES = D64_BLOCK_M * D64_K_TILE * 2 +D64_DB_SQ_BYTES = D64_BLOCK_M * 4 +D64_SMEM_POOL_BYTES = D64_QUERY_BYTES + D64_DATABASE_BYTES + D64_DB_SQ_BYTES +DEFAULT_SPLIT_COUNT = _decode_capture(_json_loads('132')) +DEFAULT_GROUP_COUNT = _decode_capture(_json_loads('12')) +SHAPE_SPECS: dict[str, dict[str, Any]] = {RAG_D64: {'B': 1, 'Q': 16, 'M': 50000, 'D': 64, 'K': 10, 'build': False, 'split_count': DEFAULT_SPLIT_COUNT, 'group_count': DEFAULT_GROUP_COUNT}} +knn_build_common_d_5e7f_rag_d64_repair_stage1 = _decode_capture(_json_loads('{"__ir__": "knn_build_common_d_5e7f_rag_d64_repair_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 17664, "constants": [["BLOCK_Q", 64], ["BLOCK_M", 64], ["K_TILE", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 96}')) +stage1_d64_repair_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_common_d_5e7f_rag_d64_repair_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 17664, "constants": [["BLOCK_Q", 64], ["BLOCK_M", 64], ["K_TILE", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 96}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_COMMON_D_5E7F_RAG_D64_REPAIR_VERIFY_KERNEL') + split_count = int(os.environ.get('LOOM_KNN_COMMON_D_5E7F_RAG_D64_REPAIR_VERIFY_SPLIT', DEFAULT_SPLIT_COUNT)) + group_count = int(os.environ.get('LOOM_KNN_COMMON_D_5E7F_RAG_D64_REPAIR_VERIFY_GROUPS', DEFAULT_GROUP_COUNT)) + if verify_kernel == 'merge': + return fused_merge_parent._fused_merge_ir(split_count, group_count) + return stage1_d64_repair_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_common_d_5e7f_rag_d64_repair_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 17664, "constants": [["BLOCK_Q", 64], ["BLOCK_M", 64], ["K_TILE", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 96}')) + +def _compiled_stage1(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0229"}')) + +@lru_cache(maxsize=8) +def _compiled_fused_merge(split_count: int, group_count: int): + return fused_merge_parent._compiled_fused_merge(int(split_count), int(group_count)) + +def _dtype_name(inputs: dict[str, Any]) -> str: + query = inputs.get('query') + if query is not None: + return str(query.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')).replace('torch.', '') + +def _matches_spec(inputs: dict[str, Any], spec: dict[str, Any]) -> bool: + return _dtype_name(inputs) == 'bfloat16' and bool(inputs.get('build', False)) == bool(spec['build']) and (int(inputs['B']) == int(spec['B'])) and (int(inputs['Q']) == int(spec['Q'])) and (int(inputs['M']) == int(spec['M'])) and (int(inputs['D']) == int(spec['D'])) and (int(inputs['K']) == int(spec['K'])) + +def _target_label_for_inputs(inputs: dict[str, Any]) -> str | None: + label = inputs.get('label') + if label is not None: + label_s = str(label) + if label_s in TARGET_SHAPE_SET and _matches_spec(inputs, SHAPE_SPECS[label_s]): + return label_s + return None + for candidate_label, spec in SHAPE_SPECS.items(): + if _matches_spec(inputs, spec): + return candidate_label + return None + +def _split_count_for_label(label: str) -> int: + return int(SHAPE_SPECS[label]['split_count']) + +def _group_count_for_label(label: str) -> int: + return int(SHAPE_SPECS[label]['group_count']) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + label = _target_label_for_inputs(inputs) + if force_fallback or label is None: + return default_dispatcher.route_for_contract_inputs(inputs, force_fallback=force_fallback) + spec = SHAPE_SPECS[label] + return ''.join([format(ROUTE_PREFIX, ''), ':', format(label, ''), ':d', format(int(spec['D']), ''), ':q', format(int(spec['Q']), ''), ':m', format(int(spec['M']), ''), ':m64n64k64:s', format(_split_count_for_label(label), ''), ':g', format(_group_count_for_label(label), '')]) + +def _launch_d64_rag(inputs: dict[str, Any], label: str) -> None: + query = inputs['query'] + database = inputs['database'] + if str(query.dtype) != 'torch.bfloat16' or str(database.dtype) != 'torch.bfloat16': + raise ValueError('knn_build_common_d_5e7f_rag_d64_repair_v1 supports bfloat16 inputs only') + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + split_count = _split_count_for_label(label) + group_count = _group_count_for_label(label) + if dim != D64_K_TILE: + raise ValueError(''.join([format(label, ''), ' expected D=', format(D64_K_TILE, ''), ', got ', format(dim, '')])) + fused_merge_parent._validate_group_shape(split_count, group_count) + num_q_tiles = (n_query + D64_BLOCK_Q - 1) // D64_BLOCK_Q + num_db_tiles = (n_database + D64_BLOCK_M - 1) // D64_BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tiles * split_count + total_queries = bsz * n_query + stage1_grid = min(total_work, GRID_DIM_DEFAULT) + merge_grid = min(total_queries, GRID_DIM_DEFAULT) + partial_dists, partial_indices = split_parent._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = m64_parent.non128_base.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), total_queries, D64_BLOCK_Q, dim, D64_K_TILE) + tmap_database = m64_parent.non128_base.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, D64_BLOCK_M, dim, D64_K_TILE) + _compiled_stage1().launch(grid=(stage1_grid, 1, 1), block=(D64_THREADS, 1, 1), args=pack_kernel_args(stage1_d64_repair_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tiles=num_q_tiles, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), shared_mem=stage1_d64_repair_ir.computed_smem_bytes) + merge_ir = fused_merge_parent._fused_merge_ir(split_count, group_count) + _compiled_fused_merge(split_count, group_count).launch(grid=(merge_grid, 1, 1), block=(fused_merge_parent.D768_FUSED_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], total_queries], shared_mem=merge_ir.computed_smem_bytes) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + label = _target_label_for_inputs(inputs) + if not force_fallback and label is not None: + _launch_d64_rag(inputs, label) + return + default_dispatcher.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + if shape_labels is None: + wanted = set(TARGET_SHAPES) + else: + wanted = {str(label) for label in shape_labels} + selected = [shape for shape in eval_mod.CANONICAL_SHAPES if str(shape['label']) in wanted] + missing = wanted - {str(shape['label']) for shape in selected} + if missing: + raise ValueError(''.join(['unknown knn_build contract shape(s): ', format(sorted(missing), '')])) + return selected + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def route_trace_for_contract_shapes(shape_labels=TARGET_SHAPES, *, force_fallback: bool=False) -> list[dict[str, Any]]: + rows = [] + for shape in _select_contract_shapes(shape_labels): + params = dict(shape.get('params', {})) + params['label'] = shape['label'] + inputs = {'label': params['label'], 'B': params['B'], 'Q': params['Q'], 'M': params['M'], 'D': params['D'], 'K': params['K'], 'build': params['build'], 'dtype': params.get('dtype', 'bfloat16')} + label = _target_label_for_inputs(inputs) + if force_fallback or label is None: + rows.append({'shape_key': params['label'], 'selected_route': default_dispatcher.ROUTE_ENTRYPOINT, 'selected_entrypoint': default_dispatcher.ROUTE_ENTRYPOINT, 'selected_seed': None, 'expected_seed': 'common_d_5e7f_rag_d64_repair_v1' if params['label'] in TARGET_SHAPE_SET else None, 'route_kind': 'forced_fallback' if force_fallback else 'delegated', 'route_source': 'default-v11-common-d-dispatcher', 'guard_id': 'forced_fallback' if force_fallback else 'guard_miss'}) + continue + spec = SHAPE_SPECS[label] + rows.append({'shape_key': label, 'selected_route': route_for_contract_inputs(inputs), 'selected_entrypoint': ROUTE_ENTRYPOINT, 'selected_seed': 'common_d_5e7f_rag_d64_repair_v1', 'expected_seed': 'common_d_5e7f_rag_d64_repair_v1', 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': '5e7f_common_d_d64_rag_repair_exact_guard', 'guard_condition': ''.join(['exact BF16 non-build B=', format(spec['B'], ''), ' Q=', format(spec['Q'], ''), ' M=', format(spec['M'], ''), ' D=', format(spec['D'], ''), ' K=', format(spec['K'], '')]), 'split_count': _split_count_for_label(label), 'group_count': _group_count_for_label(label), 'producer_topology': 'M64_N64_K64_tcgen05_tma', 'merge_topology': 'fused_group_split_merge', 'classification': 'd64-rag-repair-seed'}) + return rows + +def benchmark_knn_build_common_d_5e7f_rag_d64_repair_v1(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES) -> dict[str, Any]: + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels) + return {'contract': report['contract'], 'contract_version': report['contract_version'], 'measured_entrypoint': BENCHMARK_ENTRYPOINT, 'candidate_entrypoint': ROUTE_ENTRYPOINT, 'accelerated_shape_labels': list(TARGET_SHAPES), 'measured_shape_labels': list(shape_labels), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'producer_topology': {RAG_D64: ''.join(['M64N64K64/S', format(_split_count_for_label(RAG_D64), ''), '/G', format(_group_count_for_label(RAG_D64), '')])}, 'route_trace': route_trace_for_contract_shapes(shape_labels), 'summary': report['summary'], 'performance': report['performance'], 'correctness': report['correctness'], 'report': report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_common_d_5e7f_rag_highd_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_common_d_5e7f_rag_highd_v1.py new file mode 100644 index 00000000..f2f88eea --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_common_d_5e7f_rag_highd_v1.py @@ -0,0 +1,203 @@ +"""High-D common-D RAG microbatch seed for v11 round 5e7f. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +routes the exact BF16 non-build D1024 and D4096 v11 RAG microbatch rows through +the existing M64/N64 tcgen05/TMA producer with a D-parametric feature chunk +loop, followed by the existing fused split merge. Guard misses intentionally +fall back to the current v11 common-D dispatcher; this module does not edit +production dispatch. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from collections.abc import Callable +from functools import lru_cache +from typing import Any +from .._dispatch_runtime import dc as dc +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_common_d_v11_fallback_v1 as default_dispatcher +from . import knn_build_evolve_7bfc_split_v1 as split_parent +from . import knn_build_non128_frontier_4be7_d768fused_v1 as fused_merge_parent +from . import knn_build_non128_frontier_7ee5_m64rag_v1 as m64_parent +from .._dispatch_runtime import pack_kernel_args +MODULE = 'loom.examples.weave.knn_build_common_d_5e7f_rag_highd_v1' +ROUTE_PREFIX = 'knn_build_common_d_5e7f_rag_highd_v1' +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +BENCHMARK_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_knn_build_common_d_5e7f_rag_highd_v1']) +RAG_D1024 = 'rag_microbatch_common_d1024_b1_q8_m50000_k10' +RAG_D4096 = 'rag_microbatch_common_d4096_b1_q4_m32768_k10' +TARGET_SHAPES = (RAG_D1024, RAG_D4096) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +M64_BLOCK_Q = m64_parent.M64_BLOCK_Q +M64_BLOCK_M = m64_parent.M64_BLOCK_M +M64_THREADS = m64_parent.M64_THREADS +K_TILE = m64_parent.K_TILE +TOP_K_MAX = m64_parent.TOP_K_MAX +GRID_DIM_DEFAULT = m64_parent.GRID_DIM_DEFAULT +DEFAULT_SPLIT_COUNT = _decode_capture(_json_loads('144')) +DEFAULT_GROUP_COUNT = _decode_capture(_json_loads('12')) +SHAPE_SPECS = _decode_capture(_json_loads('{"__dict_items__": [["rag_microbatch_common_d1024_b1_q8_m50000_k10", {"__dict_items__": [["B", 1], ["Q", 8], ["M", 50000], ["D", 1024], ["K", 10], ["build", false], ["feature_chunks", 8], ["split_count", 144], ["group_count", 12]]}], ["rag_microbatch_common_d4096_b1_q4_m32768_k10", {"__dict_items__": [["B", 1], ["Q", 4], ["M", 32768], ["D", 4096], ["K", 10], ["build", false], ["feature_chunks", 32], ["split_count", 128], ["group_count", 8]]}]]}')) + +def _ir_with_constants(ir_obj: Any, *, suffix: str, **updates: int) -> Any: + constants = tuple(((name, updates.get(name, value)) for name, value in ir_obj.constants)) + return dc.replace(ir_obj, symbol=''.join([format(ir_obj.symbol, ''), '_', format(suffix, '')]), constants=constants) + +@lru_cache(maxsize=4) +def _stage1_ir(feature_chunks: int) -> Any: + return _ir_with_constants(m64_parent.stage1_m64_ir, suffix=''.join(['d', format(int(feature_chunks) * K_TILE, ''), '_5e7f_highd_v1']), FEATURE_CHUNKS=int(feature_chunks)) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_COMMON_D_5E7F_RAG_HIGHD_VERIFY_KERNEL') + split_count = int(os.environ.get('LOOM_KNN_COMMON_D_5E7F_RAG_HIGHD_VERIFY_SPLIT', DEFAULT_SPLIT_COUNT)) + group_count = int(os.environ.get('LOOM_KNN_COMMON_D_5E7F_RAG_HIGHD_VERIFY_GROUPS', DEFAULT_GROUP_COUNT)) + if verify_kernel == 'stage1_d1024': + return _stage1_ir(8) + if verify_kernel == 'stage1_d4096': + return _stage1_ir(32) + return fused_merge_parent._fused_merge_ir(split_count, group_count) +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_4be7_d768fused_merge_s144g12_4be7_d768fused_v1", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 1024, "constants": [["TOP_K_MAX", 10], ["GROUP_COUNT", 12], ["GROUP_SPLITS", 12]], "cta_group": 1, "threads": 32}')) + +@lru_cache(maxsize=4) +def _compiled_stage1(feature_chunks: int): + return m64_parent._compile_ir(_stage1_ir(int(feature_chunks))) + +@lru_cache(maxsize=8) +def _compiled_fused_merge(split_count: int, group_count: int): + return fused_merge_parent._compiled_fused_merge(int(split_count), int(group_count)) + +def _dtype_name(inputs: dict[str, Any]) -> str: + query = inputs.get('query') + if query is not None: + return str(query.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')).replace('torch.', '') + +def _matches_spec(inputs: dict[str, Any], spec: dict[str, Any]) -> bool: + return _dtype_name(inputs) == 'bfloat16' and bool(inputs.get('build', False)) == bool(spec['build']) and (int(inputs['B']) == int(spec['B'])) and (int(inputs['Q']) == int(spec['Q'])) and (int(inputs['M']) == int(spec['M'])) and (int(inputs['D']) == int(spec['D'])) and (int(inputs['K']) == int(spec['K'])) + +def _target_label_for_inputs(inputs: dict[str, Any]) -> str | None: + label = inputs.get('label') + if label is not None: + label_s = str(label) + if label_s in TARGET_SHAPE_SET and _matches_spec(inputs, SHAPE_SPECS[label_s]): + return label_s + return None + for candidate_label, spec in SHAPE_SPECS.items(): + if _matches_spec(inputs, spec): + return candidate_label + return None + +def _split_count_for_label(label: str) -> int: + return int(SHAPE_SPECS[label]['split_count']) + +def _group_count_for_label(label: str) -> int: + return int(SHAPE_SPECS[label]['group_count']) + +def _feature_chunks_for_label(label: str) -> int: + return int(SHAPE_SPECS[label]['feature_chunks']) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + label = _target_label_for_inputs(inputs) + if force_fallback or label is None: + return default_dispatcher.route_for_contract_inputs(inputs, force_fallback=force_fallback) + spec = SHAPE_SPECS[label] + return ''.join([format(ROUTE_PREFIX, ''), ':', format(label, ''), ':d', format(int(spec['D']), ''), ':q', format(int(spec['Q']), ''), ':m', format(int(spec['M']), ''), ':m64n64:s', format(_split_count_for_label(label), ''), ':g', format(_group_count_for_label(label), ''), ':chunks', format(_feature_chunks_for_label(label), '')]) + +def _launch_highd_rag(inputs: dict[str, Any], label: str) -> None: + query = inputs['query'] + database = inputs['database'] + if str(query.dtype) != 'torch.bfloat16' or str(database.dtype) != 'torch.bfloat16': + raise ValueError('knn_build_common_d_5e7f_rag_highd_v1 supports bfloat16 inputs only') + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + feature_chunks = _feature_chunks_for_label(label) + split_count = _split_count_for_label(label) + group_count = _group_count_for_label(label) + tma_dim = feature_chunks * K_TILE + if dim != tma_dim: + raise ValueError(''.join([format(label, ''), ' expected D=', format(tma_dim, ''), ', got ', format(dim, '')])) + fused_merge_parent._validate_group_shape(split_count, group_count) + num_q_tiles = (n_query + M64_BLOCK_Q - 1) // M64_BLOCK_Q + num_db_tiles = (n_database + M64_BLOCK_M - 1) // M64_BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tiles * split_count + total_queries = bsz * n_query + stage1_grid = min(total_work, GRID_DIM_DEFAULT) + merge_grid = min(total_queries, GRID_DIM_DEFAULT) + partial_dists, partial_indices = split_parent._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = m64_parent.non128_base.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), total_queries, M64_BLOCK_Q, tma_dim, K_TILE) + tmap_database = m64_parent.non128_base.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, M64_BLOCK_M, tma_dim, K_TILE) + stage_ir = _stage1_ir(feature_chunks) + _compiled_stage1(feature_chunks).launch(grid=(stage1_grid, 1, 1), block=(M64_THREADS, 1, 1), args=pack_kernel_args(stage_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tiles=num_q_tiles, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), shared_mem=stage_ir.computed_smem_bytes) + merge_ir = fused_merge_parent._fused_merge_ir(split_count, group_count) + _compiled_fused_merge(split_count, group_count).launch(grid=(merge_grid, 1, 1), block=(fused_merge_parent.D768_FUSED_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], total_queries], shared_mem=merge_ir.computed_smem_bytes) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + label = _target_label_for_inputs(inputs) + if not force_fallback and label is not None: + _launch_highd_rag(inputs, label) + return + default_dispatcher.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + if shape_labels is None: + wanted = set(TARGET_SHAPES) + else: + wanted = {str(label) for label in shape_labels} + selected = [shape for shape in eval_mod.CANONICAL_SHAPES if str(shape['label']) in wanted] + missing = wanted - {str(shape['label']) for shape in selected} + if missing: + raise ValueError(''.join(['unknown knn_build contract shape(s): ', format(sorted(missing), '')])) + return selected + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def route_trace_for_contract_shapes(shape_labels=TARGET_SHAPES, *, force_fallback: bool=False) -> list[dict[str, Any]]: + rows = [] + for shape in _select_contract_shapes(shape_labels): + params = dict(shape.get('params', {})) + params['label'] = shape['label'] + inputs = {'label': params['label'], 'B': params['B'], 'Q': params['Q'], 'M': params['M'], 'D': params['D'], 'K': params['K'], 'build': params['build'], 'dtype': params.get('dtype', 'bfloat16')} + label = _target_label_for_inputs(inputs) + if force_fallback or label is None: + rows.append({'shape_key': params['label'], 'selected_route': default_dispatcher.ROUTE_ENTRYPOINT, 'selected_entrypoint': default_dispatcher.ROUTE_ENTRYPOINT, 'selected_seed': None, 'expected_seed': 'common_d_5e7f_rag_highd_v1' if params['label'] in TARGET_SHAPE_SET else None, 'route_kind': 'forced_fallback' if force_fallback else 'delegated', 'route_source': 'default-v11-common-d-dispatcher', 'guard_id': 'forced_fallback' if force_fallback else 'guard_miss'}) + continue + spec = SHAPE_SPECS[label] + rows.append({'shape_key': label, 'selected_route': route_for_contract_inputs(inputs), 'selected_entrypoint': ROUTE_ENTRYPOINT, 'selected_seed': 'common_d_5e7f_rag_highd_v1', 'expected_seed': 'common_d_5e7f_rag_highd_v1', 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': '5e7f_common_d_highd_rag_exact_guard', 'guard_condition': ''.join(['exact BF16 non-build B=', format(spec['B'], ''), ' Q=', format(spec['Q'], ''), ' M=', format(spec['M'], ''), ' D=', format(spec['D'], ''), ' K=', format(spec['K'], '')]), 'feature_chunks': spec['feature_chunks'], 'split_count': _split_count_for_label(label), 'group_count': _group_count_for_label(label), 'producer_topology': 'M64_N64_tcgen05_tma', 'merge_topology': 'fused_group_split_merge', 'classification': 'highd-rag-seed'}) + return rows + +def benchmark_knn_build_common_d_5e7f_rag_highd_v1(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES) -> dict[str, Any]: + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels) + return {'contract': report['contract'], 'contract_version': report['contract_version'], 'measured_entrypoint': BENCHMARK_ENTRYPOINT, 'candidate_entrypoint': ROUTE_ENTRYPOINT, 'accelerated_shape_labels': list(TARGET_SHAPES), 'measured_shape_labels': list(shape_labels), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'producer_topology': {label: ''.join(['M64N64/S', format(_split_count_for_label(label), ''), '/G', format(_group_count_for_label(label), ''), '/chunks', format(_feature_chunks_for_label(label), '')]) for label in TARGET_SHAPES}, 'route_trace': route_trace_for_contract_shapes(shape_labels), 'summary': report['summary'], 'performance': report['performance'], 'correctness': report['correctness'], 'report': report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_common_d_5e7f_search_d256_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_common_d_5e7f_search_d256_v1.py new file mode 100644 index 00000000..053d452b --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_common_d_5e7f_search_d256_v1.py @@ -0,0 +1,184 @@ +"""D256 common-D rectangular search seed for v11 round 5e7f. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +routes only ``search_rect_common_d256_b1_q1024_m32768_k10`` through the +existing chunked tcgen05/TMA producer with two 128-wide feature chunks and the +existing fused split merge. Guard misses delegate to the current v11 common-D +fallback dispatcher; this module does not edit production dispatch. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from collections.abc import Callable +from functools import lru_cache +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_common_d_v11_fallback_v1 as default_dispatcher +from . import knn_build_non128_frontier_4be7_d768fused_v1 as fused_parent +from . import knn_build_non128_frontier_7231_v1 as chunked_parent +from .._dispatch_runtime import pack_kernel_args +MODULE = 'loom.examples.weave.knn_build_common_d_5e7f_search_d256_v1' +ROUTE_PREFIX = 'knn_build_common_d_5e7f_search_d256_v1' +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +BENCHMARK_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_knn_build_common_d_5e7f_search_d256_v1']) +SEARCH_D256 = 'search_rect_common_d256_b1_q1024_m32768_k10' +TARGET_SHAPES = (SEARCH_D256,) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +BLOCK_Q = chunked_parent.BLOCK_Q +BLOCK_M = chunked_parent.BLOCK_M +THREADS = chunked_parent.THREADS +FEATURE_CHUNKS = 2 +K_TILE = chunked_parent.K_TILE +TOP_K_MAX = chunked_parent.TOP_K_MAX +GRID_DIM_DEFAULT = chunked_parent.GRID_DIM_DEFAULT +SEARCH_SPLIT_COUNT = _decode_capture(_json_loads('16')) +SEARCH_GROUP_COUNT = _decode_capture(_json_loads('8')) +SHAPE_SPEC: dict[str, Any] = {'B': 1, 'Q': 1024, 'M': 32768, 'D': 256, 'K': 10, 'build': False, 'dtype': 'bfloat16'} + +def _validate_group_shape(split_count: int, group_count: int) -> None: + fused_parent._validate_group_shape(int(split_count), int(group_count)) +stage1_d256_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_7231_stage1_d256", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["K_TILE", 128], ["TOP_K_MAX", 10], ["FEATURE_CHUNKS", 2]], "cta_group": 1, "threads": 192}')) + +def _fused_merge_ir(split_count: int=SEARCH_SPLIT_COUNT, group_count: int=SEARCH_GROUP_COUNT) -> Any: + _validate_group_shape(split_count, group_count) + return fused_parent._fused_merge_ir(int(split_count), int(group_count)) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_COMMON_D_5E7F_SEARCH_D256_VERIFY_KERNEL') + if verify_kernel == 'merge': + return _fused_merge_ir(int(os.environ.get('LOOM_KNN_COMMON_D_5E7F_SEARCH_D256_VERIFY_SPLITS', SEARCH_SPLIT_COUNT)), int(os.environ.get('LOOM_KNN_COMMON_D_5E7F_SEARCH_D256_VERIFY_GROUPS', SEARCH_GROUP_COUNT))) + return stage1_d256_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_7231_stage1_d256", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["K_TILE", 128], ["TOP_K_MAX", 10], ["FEATURE_CHUNKS", 2]], "cta_group": 1, "threads": 192}')) + +def _compiled_stage1_d256(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0073"}')) + +@lru_cache(maxsize=8) +def _compiled_fused_merge(split_count: int, group_count: int): + return fused_parent._compiled_fused_merge(int(split_count), int(group_count)) + +def _dtype_name(inputs: dict[str, Any]) -> str: + query = inputs.get('query') + if query is not None: + return str(query.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')).replace('torch.', '') + +def _matches_target(inputs: dict[str, Any]) -> bool: + label = inputs.get('label') + if label is not None and str(label) != SEARCH_D256: + return False + return _dtype_name(inputs) == SHAPE_SPEC['dtype'] and bool(inputs.get('build', False)) == bool(SHAPE_SPEC['build']) and (int(inputs['B']) == int(SHAPE_SPEC['B'])) and (int(inputs['Q']) == int(SHAPE_SPEC['Q'])) and (int(inputs['M']) == int(SHAPE_SPEC['M'])) and (int(inputs['D']) == int(SHAPE_SPEC['D'])) and (int(inputs['K']) == int(SHAPE_SPEC['K'])) + +def _target_label_for_inputs(inputs: dict[str, Any]) -> str | None: + return SEARCH_D256 if _matches_target(inputs) else None + +def _split_count() -> int: + return int(os.environ.get('LOOM_KNN_COMMON_D_5E7F_SEARCH_D256_SPLITS', SEARCH_SPLIT_COUNT)) + +def _group_count() -> int: + return int(os.environ.get('LOOM_KNN_COMMON_D_5E7F_SEARCH_D256_GROUPS', SEARCH_GROUP_COUNT)) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if not force_fallback and _target_label_for_inputs(inputs) == SEARCH_D256: + return ''.join([format(ROUTE_PREFIX, ''), ':', format(SEARCH_D256, ''), ':d256:q1024:m32768:s', format(_split_count(), ''), ':g', format(_group_count(), ''), ':chunked128_fused_merge']) + return default_dispatcher.route_for_contract_inputs(inputs, force_fallback=force_fallback) + +def _launch_search_d256(inputs: dict[str, Any]) -> None: + query = inputs['query'] + database = inputs['database'] + if str(query.dtype) != 'torch.bfloat16' or str(database.dtype) != 'torch.bfloat16': + raise ValueError('knn_build_common_d_5e7f_search_d256_v1 supports bfloat16 inputs only') + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + split_count = _split_count() + group_count = _group_count() + _validate_group_shape(split_count, group_count) + tma_dim = FEATURE_CHUNKS * K_TILE + if dim != tma_dim: + raise ValueError(''.join([format(SEARCH_D256, ''), ' expected D=', format(tma_dim, ''), ', got ', format(dim, '')])) + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tiles * split_count + total_queries = bsz * n_query + stage1_grid = min(total_work, GRID_DIM_DEFAULT) + merge_grid = min(total_queries, GRID_DIM_DEFAULT) + partial_dists, partial_indices = chunked_parent.split_parent._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = chunked_parent.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), total_queries, BLOCK_Q, dim, K_TILE) + tmap_database = chunked_parent.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, dim, K_TILE) + _compiled_stage1_d256().launch(grid=(stage1_grid, 1, 1), block=(THREADS, 1, 1), args=pack_kernel_args(stage1_d256_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tiles=num_q_tiles, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), shared_mem=stage1_d256_ir.computed_smem_bytes) + merge_ir_obj = _fused_merge_ir(split_count, group_count) + _compiled_fused_merge(split_count, group_count).launch(grid=(merge_grid, 1, 1), block=(fused_parent.D768_FUSED_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], total_queries], shared_mem=merge_ir_obj.computed_smem_bytes) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + if not force_fallback and _target_label_for_inputs(inputs) == SEARCH_D256: + _launch_search_d256(inputs) + return + default_dispatcher.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + if shape_labels is None: + wanted = set(TARGET_SHAPES) + else: + wanted = {str(label) for label in shape_labels} + selected = [shape for shape in eval_mod.CANONICAL_SHAPES if str(shape['label']) in wanted] + missing = wanted - {str(shape['label']) for shape in selected} + if missing: + raise ValueError(''.join(['unknown knn_build contract shape(s): ', format(sorted(missing), '')])) + return selected + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def benchmark_knn_build_common_d_5e7f_search_d256_v1(*, use_cupti: bool | None=None, shape_labels=TARGET_SHAPES) -> dict[str, Any]: + prior_use_cupti = None + if use_cupti is not None: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True) + finally: + if prior_use_cupti is not None: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + report['route_trace'] = route_trace_for_contract_shapes(shape_labels) + report['route_trace_included'] = True + return {'contract': report['contract'], 'contract_version': report['contract_version'], 'measured_entrypoint': BENCHMARK_ENTRYPOINT, 'candidate_entrypoint': ROUTE_ENTRYPOINT, 'accelerated_shape_labels': list(TARGET_SHAPES), 'measured_shape_labels': list(shape_labels), 'timing_backend_requested': 'cupti' if use_cupti is not False else 'cuda_event', 'summary': report['summary'], 'performance': report['performance'], 'correctness': report['correctness'], 'route_trace': report['route_trace'], 'report': report} + +def _trace_inputs_from_shape(shape: dict[str, Any]) -> dict[str, Any]: + params = dict(shape.get('params', {})) + return {'label': shape['label'], 'B': params['B'], 'Q': params['Q'], 'M': params['M'], 'D': params['D'], 'K': params['K'], 'build': params['build'], 'dtype': params.get('dtype', 'bfloat16')} + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + rows = [] + for shape in selected: + inputs = _trace_inputs_from_shape(shape) + if force_fallback or _target_label_for_inputs(inputs) is None: + rows.append({'shape_key': str(shape['label']), 'selected_route': default_dispatcher.route_for_contract_inputs(inputs, force_fallback=force_fallback), 'selected_entrypoint': default_dispatcher.ROUTE_ENTRYPOINT, 'selected_seed': None, 'expected_seed': 'common_d_5e7f_search_d256_v1' if str(shape['label']) in TARGET_SHAPE_SET else None, 'route_kind': 'forced_fallback' if force_fallback else 'parent_delegate', 'route_source': 'common-d-v11-fallback-dispatcher', 'guard_id': 'forced_fallback' if force_fallback else 'guard_miss', 'classification': 'forced_fallback' if force_fallback else 'delegated'}) + continue + rows.append({'shape_key': SEARCH_D256, 'selected_route': route_for_contract_inputs(inputs), 'selected_entrypoint': ROUTE_ENTRYPOINT, 'selected_seed': 'common_d_5e7f_search_d256_v1', 'expected_seed': 'common_d_5e7f_search_d256_v1', 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': '5e7f_common_d_search_d256_exact_guard', 'guard_condition': 'exact BF16 search B=1 Q=1024 M=32768 D=256 K=10', 'feature_chunks': FEATURE_CHUNKS, 'split_count': _split_count(), 'group_count': _group_count(), 'producer': 'chunked128_d256_tcgen05_tma', 'merge': 'fused_group_k10_split_merge', 'classification': 'common-d-search-d256-seed'}) + return rows diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_common_d_eeff_search_d768_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_common_d_eeff_search_d768_v1.py new file mode 100644 index 00000000..7f3c1861 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_common_d_eeff_search_d768_v1.py @@ -0,0 +1,182 @@ +"""kNN common-D D768 rectangular search seed for round eeff. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +routes only ``search_rect_common_d768_b1_q512_m8192_k10`` through the existing +chunked 128-row D768 tcgen05/TMA producer and a fused split merge. Guard misses +delegate to the current v11 common-D fallback dispatcher. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from collections.abc import Callable +from functools import lru_cache +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_common_d_v11_fallback_v1 as default_dispatcher +from . import knn_build_non128_frontier_4be7_d768fused_v1 as fused_parent +from . import knn_build_non128_frontier_7231_v1 as chunked_parent +from .._dispatch_runtime import pack_kernel_args +MODULE = 'loom.examples.weave.knn_build_common_d_eeff_search_d768_v1' +ROUTE_PREFIX = 'knn_build_common_d_eeff_search_d768_v1' +SEARCH_D768 = 'search_rect_common_d768_b1_q512_m8192_k10' +TARGET_SHAPES = (SEARCH_D768,) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +BLOCK_Q = chunked_parent.BLOCK_Q +BLOCK_M = chunked_parent.BLOCK_M +THREADS = chunked_parent.THREADS +FEATURE_CHUNKS = 6 +K_TILE = chunked_parent.K_TILE +TOP_K_MAX = chunked_parent.TOP_K_MAX +GRID_DIM_DEFAULT = chunked_parent.GRID_DIM_DEFAULT +SEARCH_SPLIT_COUNT = _decode_capture(_json_loads('32')) +SEARCH_GROUP_COUNT = _decode_capture(_json_loads('8')) +SHAPE_SPEC: dict[str, Any] = {'B': 1, 'Q': 512, 'M': 8192, 'D': 768, 'K': 10, 'build': False, 'dtype': 'bfloat16'} +stage1_d768_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_7231_stage1_d768", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["K_TILE", 128], ["TOP_K_MAX", 10], ["FEATURE_CHUNKS", 6]], "cta_group": 1, "threads": 192}')) + +def _validate_group_shape(split_count: int, group_count: int) -> None: + fused_parent._validate_group_shape(int(split_count), int(group_count)) + +def _fused_merge_ir(split_count: int=SEARCH_SPLIT_COUNT, group_count: int=SEARCH_GROUP_COUNT) -> Any: + _validate_group_shape(split_count, group_count) + return fused_parent._fused_merge_ir(int(split_count), int(group_count)) +merge_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_4be7_d768fused_merge_s32g8_4be7_d768fused_v1", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 1024, "constants": [["TOP_K_MAX", 10], ["GROUP_COUNT", 8], ["GROUP_SPLITS", 4]], "cta_group": 1, "threads": 32}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_COMMON_D_EEFF_SEARCH_D768_VERIFY_KERNEL') + if verify_kernel == 'merge': + return _fused_merge_ir(int(os.environ.get('LOOM_KNN_COMMON_D_EEFF_SEARCH_D768_VERIFY_SPLITS', SEARCH_SPLIT_COUNT)), int(os.environ.get('LOOM_KNN_COMMON_D_EEFF_SEARCH_D768_VERIFY_GROUPS', SEARCH_GROUP_COUNT))) + return stage1_d768_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_7231_stage1_d768", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["K_TILE", 128], ["TOP_K_MAX", 10], ["FEATURE_CHUNKS", 6]], "cta_group": 1, "threads": 192}')) + +def _compiled_stage1_d768(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0074"}')) + +@lru_cache(maxsize=8) +def _compiled_fused_merge(split_count: int, group_count: int): + return fused_parent._compiled_fused_merge(int(split_count), int(group_count)) + +def _dtype_name(inputs: dict[str, Any]) -> str: + query = inputs.get('query') + if query is not None: + return str(query.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')).replace('torch.', '') + +def _matches_target(inputs: dict[str, Any]) -> bool: + label = inputs.get('label') + if label is not None and str(label) != SEARCH_D768: + return False + return _dtype_name(inputs) == SHAPE_SPEC['dtype'] and bool(inputs.get('build', False)) == bool(SHAPE_SPEC['build']) and (int(inputs['B']) == int(SHAPE_SPEC['B'])) and (int(inputs['Q']) == int(SHAPE_SPEC['Q'])) and (int(inputs['M']) == int(SHAPE_SPEC['M'])) and (int(inputs['D']) == int(SHAPE_SPEC['D'])) and (int(inputs['K']) == int(SHAPE_SPEC['K'])) + +def _target_label_for_inputs(inputs: dict[str, Any]) -> str | None: + return SEARCH_D768 if _matches_target(inputs) else None + +def _split_count() -> int: + return int(os.environ.get('LOOM_KNN_COMMON_D_EEFF_SEARCH_D768_SPLITS', SEARCH_SPLIT_COUNT)) + +def _group_count() -> int: + return int(os.environ.get('LOOM_KNN_COMMON_D_EEFF_SEARCH_D768_GROUPS', SEARCH_GROUP_COUNT)) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if not force_fallback and _target_label_for_inputs(inputs) == SEARCH_D768: + return ''.join([format(ROUTE_PREFIX, ''), ':', format(SEARCH_D768, ''), ':d768:q512:m8192:s', format(_split_count(), ''), ':g', format(_group_count(), ''), ':chunked128_fused_merge']) + return default_dispatcher.route_for_contract_inputs(inputs, force_fallback=force_fallback) + +def _launch_search_d768(inputs: dict[str, Any]) -> None: + query = inputs['query'] + database = inputs['database'] + if str(query.dtype) != 'torch.bfloat16' or str(database.dtype) != 'torch.bfloat16': + raise ValueError('knn_build_common_d_eeff_search_d768_v1 supports bfloat16 inputs only') + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + split_count = _split_count() + group_count = _group_count() + _validate_group_shape(split_count, group_count) + tma_dim = FEATURE_CHUNKS * K_TILE + if dim != tma_dim: + raise ValueError(''.join([format(SEARCH_D768, ''), ' expected D=', format(tma_dim, ''), ', got ', format(dim, '')])) + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tiles * split_count + total_queries = bsz * n_query + stage1_grid = min(total_work, GRID_DIM_DEFAULT) + merge_grid = min(total_queries, GRID_DIM_DEFAULT) + partial_dists, partial_indices = chunked_parent.split_parent._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = chunked_parent.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), total_queries, BLOCK_Q, dim, K_TILE) + tmap_database = chunked_parent.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, dim, K_TILE) + _compiled_stage1_d768().launch(grid=(stage1_grid, 1, 1), block=(THREADS, 1, 1), args=pack_kernel_args(stage1_d768_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tiles=num_q_tiles, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), shared_mem=stage1_d768_ir.computed_smem_bytes) + merge_ir_obj = _fused_merge_ir(split_count, group_count) + _compiled_fused_merge(split_count, group_count).launch(grid=(merge_grid, 1, 1), block=(fused_parent.D768_FUSED_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], total_queries], shared_mem=merge_ir_obj.computed_smem_bytes) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + if not force_fallback and _target_label_for_inputs(inputs) == SEARCH_D768: + _launch_search_d768(inputs) + return + default_dispatcher.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + if shape_labels is None: + wanted = set(TARGET_SHAPES) + else: + wanted = {str(label) for label in shape_labels} + selected = [shape for shape in eval_mod.CANONICAL_SHAPES if str(shape['label']) in wanted] + missing = wanted - {str(shape['label']) for shape in selected} + if missing: + raise ValueError(''.join(['unknown knn_build contract shape(s): ', format(sorted(missing), '')])) + return selected + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def benchmark_knn_build_common_d_eeff_search_d768_v1(*, use_cupti: bool | None=None, shape_labels=TARGET_SHAPES) -> dict[str, Any]: + prior_use_cupti = None + if use_cupti is not None: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True) + finally: + if prior_use_cupti is not None: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + report['route_trace'] = route_trace_for_contract_shapes(shape_labels) + report['route_trace_included'] = True + return {'tflops': report['summary']['primary_mean'], 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'report': report} + +def _trace_inputs_from_shape(shape: dict[str, Any]) -> dict[str, Any]: + params = dict(shape.get('params', {})) + return {'label': shape['label'], 'B': params['B'], 'Q': params['Q'], 'M': params['M'], 'D': params['D'], 'K': params['K'], 'build': params['build'], 'dtype': params.get('dtype', 'bfloat16')} + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + rows = [] + for shape in selected: + inputs = _trace_inputs_from_shape(shape) + if force_fallback or _target_label_for_inputs(inputs) is None: + rows.append({'shape_key': str(shape['label']), 'selected_route': default_dispatcher.route_for_contract_inputs(inputs, force_fallback=force_fallback), 'selected_entrypoint': default_dispatcher.ROUTE_ENTRYPOINT, 'selected_seed': None, 'expected_seed': 'common_d_eeff_search_d768_v1' if str(shape['label']) in TARGET_SHAPE_SET else None, 'route_kind': 'forced_fallback' if force_fallback else 'parent_delegate', 'route_source': 'common-d-v11-fallback-dispatcher', 'guard_id': 'forced_fallback' if force_fallback else 'guard_miss', 'classification': 'forced_fallback' if force_fallback else 'delegated'}) + continue + rows.append({'shape_key': SEARCH_D768, 'selected_route': route_for_contract_inputs(inputs), 'selected_entrypoint': ''.join([format(MODULE, ''), ':launch_from_contract_inputs']), 'selected_seed': 'common_d_eeff_search_d768_v1', 'expected_seed': 'common_d_eeff_search_d768_v1', 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': 'eeff_common_d_search_d768_exact_guard', 'guard_condition': 'exact BF16 search B=1 Q=512 M=8192 D=768 K=10', 'feature_chunks': FEATURE_CHUNKS, 'split_count': _split_count(), 'group_count': _group_count(), 'producer': 'chunked128_d768_tcgen05_tma', 'merge': 'fused_group_k10_split_merge', 'classification': 'common-d-search-d768-seed'}) + return rows diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_common_d_generic_fallback_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_common_d_generic_fallback_v1.py new file mode 100644 index 00000000..2cf0225d --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_common_d_generic_fallback_v1.py @@ -0,0 +1,52 @@ +"""Coverage-only generic kNN build/search fallback for v11 common-D misses. + +Minimum target architecture: sm_80. This fallback is intentionally simple: +one CTA computes one query row, each thread scans a strided subset of database +rows, and thread 0 merges the per-thread top-k lists. It exists only to keep +the v11 common-D dispatcher Weave-only and correct for uncovered high-D rows; +hot shapes should still return to shape-specific evolution for performance. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +from functools import lru_cache +from typing import Any +THREADS = 256 +K_MAX = 10 +SMEM_DIST_BYTES = THREADS * K_MAX * 4 +SMEM_IDX_BYTES = THREADS * K_MAX * 4 +SMEM_BYTES = SMEM_DIST_BYTES + SMEM_IDX_BYTES +MODULE = 'loom.examples.weave.knn_build_common_d_generic_fallback_v1' +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_ID = 'knn_build_common_d_generic_fallback_v1:direct_scalar_topk' +SEED_ID = 'coverage_only_common_d_generic_fallback_v1' +knn_build_common_d_generic_direct_v1 = _decode_capture(_json_loads('{"__ir__": "knn_build_common_d_generic_direct_v1", "arg_keys": ["query", "database", "out_dists", "out_indices", "B", "Q", "M", "K", "D"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 20480, "constants": [["K_MAX_", 10], ["THREADS_", 256]], "cta_group": 1, "threads": 256}')) +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_common_d_generic_direct_v1", "arg_keys": ["query", "database", "out_dists", "out_indices", "B", "Q", "M", "K", "D"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 20480, "constants": [["K_MAX_", 10], ["THREADS_", 256]], "cta_group": 1, "threads": 256}')) + +def _compile_kernel(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0227"}')) + +def _compiled_kernel(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0228"}')) + +def _dtype_name(inputs: dict[str, Any]) -> str: + query = inputs.get('query') + if query is not None: + return str(query.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')).replace('torch.', '') + +def _eligible_common_d_fallback(inputs: dict[str, Any]) -> bool: + return _dtype_name(inputs) == 'bfloat16' and int(inputs.get('K', -1)) <= K_MAX + +def route_for_contract_inputs(inputs: dict[str, Any]) -> str: + if not _eligible_common_d_fallback(inputs): + raise ValueError('common-D generic fallback supports bfloat16 inputs with K <= 10') + return ROUTE_ID + +def launch_from_contract_inputs(inputs: dict[str, Any]) -> None: + if not _eligible_common_d_fallback(inputs): + raise ValueError('common-D generic fallback supports bfloat16 inputs with K <= 10') + _compiled_kernel().launch(grid=(int(inputs['B']) * int(inputs['Q']), 1, 1), block=(THREADS, 1, 1), args=[inputs['query'], inputs['database'], inputs['out_dists'], inputs['out_indices'], int(inputs['B']), int(inputs['Q']), int(inputs['M']), int(inputs['K']), int(inputs['D'])], shared_mem=SMEM_BYTES) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_d128_rag_q128_k10_df0f_warpmerge_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_d128_rag_q128_k10_df0f_warpmerge_v1.py new file mode 100644 index 00000000..7b0f283e --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_d128_rag_q128_k10_df0f_warpmerge_v1.py @@ -0,0 +1,165 @@ +"""Exact D128 RAG Q128/M100000/K10 split74 warp-merge sidecar. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +keeps the validated K10 tcgen05/TMA split74 stage-1 producer and changes only +the final merge ownership for ``rag_stream_b1_q128_m100000_d128_k10``: one +warp owns one query row and lanes cooperatively select from the 74 sorted +split-local top-10 streams. Guard misses delegate to the existing direct +split72 Weave route; no external runtime fallback is introduced. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import os +from collections.abc import Callable +from functools import lru_cache +from pathlib import Path +from typing import Any +from .._dispatch_runtime import dc as dc +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_6998_ragk10_direct_split72_v1 as direct_split72 +from . import knn_build_evolve_7bfc_split_v1 as parent_split +from .._dispatch_runtime import pack_kernel_args +MODULE = 'loom.examples.weave.knn_build_d128_rag_q128_k10_df0f_warpmerge_v1' +TARGET_SHAPE = direct_split72.RAG_K10_DIRECT_SHAPE +TARGET_SHAPES = (TARGET_SHAPE,) +SPLIT_COUNT = 74 +MERGE_THREADS = 128 +ROWS_PER_MERGE_CTA = 4 +SEED_ID = 'df0f_d128_rag_q128_k10_s74_warpmerge_v1' +ROUTE_WARPMERGE = ''.join([format(MODULE, ''), ':split74_warpmerge']) +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +BENCHMARK_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_knn_build_d128_rag_q128_k10_df0f_warpmerge_v1']) +parent_lowk = direct_split72.rag_split72.parent_lowk +base_v1 = direct_split72.rag_split72.base_v1 + +def _ir_with_constants(ir_obj: Any, *, suffix: str, **updates: int) -> Any: + constants = tuple(((name, updates.get(name, value)) for name, value in ir_obj.constants)) + return dc.replace(ir_obj, symbol=''.join([format(ir_obj.symbol, ''), '_', format(suffix, '')]), constants=constants) +knn_build_d128_rag_q128_k10_s74_warp_merge = _decode_capture(_json_loads('{"__ir__": "knn_build_d128_rag_q128_k10_s74_warp_merge", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 74]], "cta_group": 1, "threads": 128}')) +merge_k10_s74_warp_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_d128_rag_q128_k10_s74_warp_merge", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 74]], "cta_group": 1, "threads": 128}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_D128_RAG_Q128_K10_DF0F_WARPMERGE_VERIFY_KERNEL') + if verify_kernel == 'stage1': + return parent_lowk.stage1_ir + if verify_kernel == 'merge': + return merge_k10_s74_warp_ir + return merge_k10_s74_warp_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_d128_rag_q128_k10_s74_warp_merge", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 74]], "cta_group": 1, "threads": 128}')) + +def _compile_ir(ir_obj: Any): + from .._dispatch_runtime import generate_kernel + from .._dispatch_runtime import _cuda_include_dirs + from .._dispatch_runtime import compile_cuda + from .._dispatch_runtime import CUDAKernel + source = generate_kernel(ir_obj, validate=False, smem_bytes=ir_obj.computed_smem_bytes) + cubin = compile_cuda(source, arch=base_v1._select_arch_and_preload(), options=['--use_fast_math'], include_dirs=_cuda_include_dirs()) + return CUDAKernel(cubin, ''.join(['kernel_', format(ir_obj.symbol, '')])) + +def _compiled_stage1(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0002"}')) + +def _compiled_merge_k10_s74_warp(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0192"}')) + +def _select_contract_shapes(shape_labels) -> list[dict[str, Any]]: + return direct_split72._select_contract_shapes(shape_labels) + +def _dtype_name(inputs: dict[str, Any]) -> str: + query = inputs.get('query') + if query is not None and hasattr(query, 'dtype'): + return str(query.dtype).removeprefix('torch.') + return str(inputs.get('dtype', '')).removeprefix('torch.') + +def _eligible_split74_warpmerge(inputs: dict[str, Any]) -> bool: + return not bool(inputs.get('build', False)) and _dtype_name(inputs) == 'bfloat16' and (int(inputs.get('B', -1)) == 1) and (int(inputs.get('Q', -1)) == 128) and (int(inputs.get('M', -1)) == 100000) and (int(inputs.get('D', -1)) == parent_lowk.FEAT_D) and (int(inputs.get('K', -1)) == parent_lowk.TOP_K_MAX) and direct_split72._eligible_direct_rag_k10(inputs) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if not force_fallback and _eligible_split74_warpmerge(inputs): + return ROUTE_WARPMERGE + return direct_split72.route_for_contract_inputs(inputs, force_fallback=force_fallback) + +def _launch_split74_warpmerge(inputs: dict[str, Any]) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + num_q_tiles = (n_query + parent_lowk.BLOCK_Q - 1) // parent_lowk.BLOCK_Q + num_q_tile_pairs = (num_q_tiles + parent_lowk.CTA_GROUP - 1) // parent_lowk.CTA_GROUP + num_db_tiles = (n_database + parent_lowk.BLOCK_M - 1) // parent_lowk.BLOCK_M + db_tiles_per_split = (num_db_tiles + SPLIT_COUNT - 1) // SPLIT_COUNT + total_work = bsz * num_q_tile_pairs * SPLIT_COUNT + stage1_grid = min(total_work * parent_lowk.CTA_GROUP, parent_lowk.GRID_DIM_DEFAULT) + merge_grid = min((bsz * n_query + ROWS_PER_MERGE_CTA - 1) // ROWS_PER_MERGE_CTA, parent_lowk.GRID_DIM_DEFAULT) + partial_dists, partial_indices = parent_split._partial_buffers(split_count=SPLIT_COUNT, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), bsz * n_query, parent_lowk.BLOCK_Q, dim, dim) + tmap_database = base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, parent_lowk.BLOCK_M, dim, dim) + _compiled_stage1().launch_cluster(grid=(stage1_grid, 1, 1), block=(parent_lowk.STAGE1_THREADS, 1, 1), args=pack_kernel_args(parent_lowk.stage1_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tile_pairs=num_q_tile_pairs, db_tiles_per_split=db_tiles_per_split, split_count=SPLIT_COUNT, total_work=total_work), cluster_dims=(parent_lowk.CTA_GROUP, 1, 1), shared_mem=parent_lowk.stage1_ir.computed_smem_bytes) + _compiled_merge_k10_s74_warp().launch(grid=(merge_grid, 1, 1), block=(MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_k10_s74_warp_ir.computed_smem_bytes) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + if not force_fallback and _eligible_split74_warpmerge(inputs): + _launch_split74_warpmerge(inputs) + return + direct_split72.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_direct_split72(inputs: dict[str, Any]) -> None: + direct_split72.launch_from_contract_inputs(inputs) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate, correctness: bool=True, benchmark: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + shapes = [] + for shape in _select_contract_shapes(shape_labels): + params = dict(shape['params']) + params['time_flashlib'] = bool(time_flashlib) + shapes.append({'label': shape['label'], 'params': params}) + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + try: + return evaluate_contract(shapes=shapes, correctness=correctness, benchmark=benchmark, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = previous + +def benchmark_direct_split72(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES, benchmark_correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_direct_split72, correctness=benchmark_correctness, benchmark=True, time_flashlib=time_flashlib) + report['candidate_id'] = direct_split72.SEED_DIRECT_RAG_K10_ID + report['measured_entrypoint'] = direct_split72.ROUTE_DIRECT_RAG_K10_ENTRYPOINT + return report + +def benchmark_knn_build_d128_rag_q128_k10_df0f_warpmerge_v1(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES, baseline_report: dict[str, Any] | None=None, benchmark_correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + if baseline_report is None: + baseline_report = benchmark_direct_split72(use_cupti=use_cupti, shape_labels=shape_labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate, correctness=benchmark_correctness, benchmark=True, time_flashlib=time_flashlib) + candidate_metric = candidate_report.get('summary', {}).get('primary_mean') + baseline_metric = baseline_report.get('summary', {}).get('primary_mean') + candidate_row = candidate_report.get('per_shape', {}).get(TARGET_SHAPE, {}) + baseline_row = baseline_report.get('per_shape', {}).get(TARGET_SHAPE, {}) + kernel_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') + return {'candidate_id': SEED_ID, 'baseline_candidate_id': direct_split72.SEED_DIRECT_RAG_K10_ID, 'measured_entrypoint': BENCHMARK_ENTRYPOINT, 'baseline_entrypoint': direct_split72.ROUTE_DIRECT_RAG_K10_ENTRYPOINT, 'selected_seed': SEED_ID, 'producer_split_count': SPLIT_COUNT, 'merge_owner': 'one_warp_per_query_row_three_splits_per_lane', 'all_correct': candidate_report.get('summary', {}).get('all_correct'), 'baseline_all_correct': baseline_report.get('summary', {}).get('all_correct'), 'performance_comparable': candidate_report.get('summary', {}).get('performance_comparable'), 'baseline_performance_comparable': baseline_report.get('summary', {}).get('performance_comparable'), 'tflops': candidate_metric, 'baseline_tflops': baseline_metric, 'metric_delta': candidate_metric - baseline_metric if candidate_metric and baseline_metric else None, 'timing_backend': 'cupti' if use_cupti else 'cuda_event', 'benchmark_time_flashlib': time_flashlib, 'denominator': 'df0f_q128_k10_exact1', 'shape_labels': list(TARGET_SHAPES if shape_labels is None else shape_labels), 'per_shape_delta': {TARGET_SHAPE: {'candidate_ms': kernel_ms, 'baseline_ms': baseline_ms, 'flashlib_ms': flashlib_ms, 'speedup_vs_direct_split72': baseline_ms / kernel_ms if kernel_ms and baseline_ms else None, 'ratio_vs_flashlib': flashlib_ms / kernel_ms if kernel_ms and flashlib_ms else None}}, 'route': {'selected_route': ROUTE_WARPMERGE, 'selected_entrypoint': ROUTE_ENTRYPOINT, 'selected_seed': SEED_ID, 'fallback': direct_split72.ROUTE_DIRECT_RAG_K10_ENTRYPOINT}, 'report': candidate_report, 'baseline_report': baseline_report} + +def _write_artifact(payload: dict[str, Any], artifact_dir: str | None) -> None: + if artifact_dir is None: + return + import json + path = Path(artifact_dir) + path.mkdir(parents=True, exist_ok=True) + out = path / 'df0f_q128_k10_s74_warpmerge_v1.json' + out.write_text(json.dumps(payload, indent=2, sort_keys=True), encoding='utf-8') diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_d128_rag_q16m250_df0f_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_d128_rag_q16m250_df0f_v1.py new file mode 100644 index 00000000..499a2daa --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_d128_rag_q16m250_df0f_v1.py @@ -0,0 +1,246 @@ +"""D128 RAG Q16/M250000 K32 exact-shape seed for df0f. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +keeps the current v11 common-D dispatcher as fallback and routes only the +active below-floor row rag_microbatch_largek_b1_q16_m250000_d128_k32 through +the existing Q16 large-M dual-two-warp Weave seed with a split288 schedule. + +Production dispatch remains Weave-only; FlashLib is used only by the contract +harness as a black-box timing baseline. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from pathlib import Path +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_v11_common_d_seed_portfolio_a4ec_v1 as parent_v11 +from . import knn_build_rag_microbucket_k32_q16dual2warp_largem_bdd2_v1 as seed_q16 +MODULE = 'loom.examples.weave.knn_build_d128_rag_q16m250_df0f_v1' +SEED_ID = 'candidate_df0f_d128_rag_q16m250_split288_v1' +SEED_Q16_ID = 'df0f_bdd2_q16_m250_k32_s288' +PARENT_ID = parent_v11.CANDIDATE_D64_Q4096_C271 +TARGET_Q16_M250_K32 = 'rag_microbatch_largek_b1_q16_m250000_d128_k32' +TARGET_SHAPES = (TARGET_Q16_M250_K32,) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +Q16_M250_SPLIT_COUNT = 288 +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_PARENT = parent_v11.ROUTE_ENTRYPOINT +BENCHMARK_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_knn_build_d128_rag_q16m250_df0f_v1']) +SPEEDUP_FLOOR = 1.2 +SOURCE_TASKS = {SEED_ID: 'weave-evolve-knn-build-df0f D128 RAG Q16/M250000 K32 split288 exact seed', SEED_Q16_ID: 'weave-evolve-knn-build-bdd2 Q16 large-M dual-two-warp seed with split288 override', PARENT_ID: 'generalize-auto-tuning df0f current v11 common-D dispatcher fallback'} +PRODUCTION_ROUTE_MODULES = {SEED_ID: ROUTE_ENTRYPOINT, SEED_Q16_ID: ROUTE_ENTRYPOINT, PARENT_ID: ROUTE_PARENT} + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_D128_RAG_Q16M250_DF0F_VERIFY_KERNEL') + if verify_kernel == 'stage1': + return seed_q16.seed._stage1_rowld1_2warp_ir() + return seed_q16.seed._warp_merge_ir(Q16_M250_SPLIT_COUNT) +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_k32warpmerge_0077_v1_warp_row_merge_k32s288r4_56ed_v1", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 32], ["SPLIT_COUNT", 288], ["SPLITS_PER_LANE", 9], ["ROWS_PER_CTA", 4]], "cta_group": 1, "threads": 128}')) + +def _select_contract_shapes(shape_labels) -> list[dict[str, Any]]: + return parent_v11._select_contract_shapes(shape_labels) + +def _trace_inputs_for_shape(shape: dict[str, Any]) -> dict[str, Any]: + return parent_v11._trace_inputs_for_shape(shape) + +def _inputs_for_label(label: str) -> dict[str, Any]: + return _trace_inputs_for_shape(_select_contract_shapes((label,))[0]) + +def _shape_labels(shape_labels) -> tuple[str, ...]: + if shape_labels is None: + return TARGET_SHAPES + return tuple((str(label) for label in shape_labels)) + +def _eligible_q16_m250_k32(inputs: dict[str, Any]) -> bool: + return seed_q16._eligible_q16_dual_2warp_largem(inputs) and int(inputs.get('M', -1)) == 250000 and (int(inputs.get('Q', -1)) == 16) and (int(inputs.get('K', -1)) == 32) + +def _route_q16_m250_k32(inputs: dict[str, Any]) -> str: + return seed_q16._dual2warp_largem_route_name(inputs, split_count=Q16_M250_SPLIT_COUNT) + +def _selected_seed(inputs: dict[str, Any]) -> tuple[str | None, str | None]: + if _eligible_q16_m250_k32(inputs): + return (SEED_Q16_ID, TARGET_Q16_M250_K32) + return (None, None) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if not force_fallback and _eligible_q16_m250_k32(inputs): + return _route_q16_m250_k32(inputs) + return parent_v11.route_for_contract_inputs(inputs, force_fallback=force_fallback) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + if not force_fallback and _eligible_q16_m250_k32(inputs): + seed_q16.launch_from_contract_inputs(inputs, k32_largem_q16_split_count=Q16_M250_SPLIT_COUNT) + return + parent_v11.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_parent_v11(inputs: dict[str, Any]) -> None: + parent_v11.launch_from_contract_inputs(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _benchmark_shapes(shape_labels, *, time_flashlib: bool) -> list[dict[str, Any]]: + selected = _select_contract_shapes(_shape_labels(shape_labels)) + out = [] + for shape in selected: + params = dict(shape['params']) + params['time_flashlib'] = bool(time_flashlib) + out.append({'label': shape['label'], 'params': params}) + return out + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=None, kernel_fn: Callable[[dict[str, Any]], Any]=candidate, correctness: bool=True, benchmark: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + previous = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_benchmark_shapes(shape_labels, time_flashlib=time_flashlib), correctness=correctness, benchmark=benchmark, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = previous + +def route_trace_for_contract_shapes(shape_labels=TARGET_SHAPES, *, force_fallback: bool=False) -> list[dict[str, Any]]: + rows: list[dict[str, Any]] = [] + for label in _shape_labels(shape_labels): + inputs = _inputs_for_label(label) + selected_seed, matched_label = (None, None) if force_fallback else _selected_seed(inputs) + route = route_for_contract_inputs(inputs, force_fallback=force_fallback) + parent_route = parent_v11.route_for_contract_inputs(inputs) + parent_row = dict(parent_v11.route_trace_for_contract_shapes((label,))[0]) + if selected_seed is None: + row = dict(parent_row) + row['expected_seed'] = _selected_seed(inputs)[0] if force_fallback else None + row['candidate_guard_status'] = 'forced_fallback' if force_fallback else 'guard_miss' + row['parent_v11_route'] = parent_route + if force_fallback: + row['guard_id'] = 'forced_fallback_df0f_q16m250_disabled' + row['guard_condition'] = 'forced fallback to current v11 common-D dispatcher' + row['classification'] = 'guard-miss' + rows.append(parent_v11._normalize_route_row(row)) + continue + rows.append(parent_v11._normalize_route_row({'shape_key': label, 'selected_route': route, 'selected_entrypoint': ROUTE_ENTRYPOINT, 'selected_seed': selected_seed, 'expected_seed': selected_seed, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': 'df0f_q16_m250_k32_exact_guard', 'guard_condition': 'exact BF16 RAG B=1 Q=16 M=250000 D=128 K=32 split288', 'matched_label': matched_label, 'split_count': Q16_M250_SPLIT_COUNT, 'parent_v11_route': parent_route, 'baseline_dispatcher_route': parent_row.get('selected_route'), 'classification': 'unmeasured'})) + return rows + +def _rows_for_labels(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return {label: dict(report.get('per_shape', {}).get(label, {})) for label in labels} + +def _seed_delta_matrix(candidate_report: dict[str, Any], baseline_report: dict[str, Any], labels: tuple[str, ...]): + rows = [] + for label in labels: + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') or baseline_row.get('flashlib_ms') + inputs = _inputs_for_label(label) + selected_seed, _matched = _selected_seed(inputs) + rows.append({'shape_key': label, 'selected_seed': selected_seed, 'candidate_route': route_for_contract_inputs(inputs), 'parent_v11_route': parent_v11.route_for_contract_inputs(inputs), 'candidate_ms': candidate_ms, 'parent_v11_ms': baseline_ms, 'flashlib_ms': flashlib_ms, 'delta_ms_candidate_minus_parent_v11': candidate_ms - baseline_ms if candidate_ms is not None and baseline_ms is not None else None, 'speedup_vs_parent_v11': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'ratio_vs_flashlib': flashlib_ms / candidate_ms if flashlib_ms and candidate_ms else None, 'candidate_passed': candidate_row.get('passed'), 'parent_v11_passed': baseline_row.get('passed'), 'timing_backend': candidate_row.get('timing_backend') or baseline_row.get('timing_backend')}) + return rows + +def _annotate_route_trace(route_trace: list[dict[str, Any]], candidate_report: dict[str, Any], baseline_report: dict[str, Any], *, speedup_floor: float=SPEEDUP_FLOOR) -> list[dict[str, Any]]: + annotated = [] + for row in route_trace: + label = str(row.get('shape_key')) + out = dict(row) + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') + out['dispatcher_kernel_ms'] = candidate_ms + out['parent_v11_kernel_ms'] = baseline_ms + out['shape_specific_kernel_ms'] = candidate_ms if out.get('selected_seed') == SEED_Q16_ID else None + out['external_baseline_ms'] = flashlib_ms + out['flashlib_ms'] = flashlib_ms + out['relative_speedup_vs_baseline'] = baseline_ms / candidate_ms if baseline_ms and candidate_ms else None + out['speedup_vs_external_baseline'] = flashlib_ms / candidate_ms if flashlib_ms and candidate_ms else None + out['external_baseline_ref'] = 'same_session' if flashlib_ms is not None else 'not_available' + out['timing_backend'] = candidate_row.get('timing_backend') or baseline_row.get('timing_backend') + if out.get('selected_seed') == SEED_Q16_ID and out['speedup_vs_external_baseline'] is not None: + out['classification'] = 'seed-consumed' if out['speedup_vs_external_baseline'] >= speedup_floor else 'kernel-slow' + annotated.append(parent_v11._normalize_route_row(out)) + return annotated + +def _below_flashlib_floor(report: dict[str, Any], route_trace: list[dict[str, Any]], *, floor: float) -> list[dict[str, Any]]: + trace_by_label = {str(row['shape_key']): row for row in route_trace} + rows = [] + for label, row in sorted(report.get('per_shape', {}).items()): + ratio = row.get('ratio_vs_flashlib') + if not isinstance(ratio, (float, int)) or ratio >= floor: + continue + trace_row = trace_by_label.get(label, {}) + rows.append({'shape_key': label, 'kernel_ms': row.get('kernel_ms'), 'flashlib_ms': row.get('flashlib_ms'), 'ratio_vs_flashlib': ratio, 'selected_seed': trace_row.get('selected_seed'), 'expected_seed': trace_row.get('expected_seed'), 'route_kind': trace_row.get('route_kind', 'unknown'), 'classification': trace_row.get('classification', 'unmeasured')}) + return rows + +def benchmark_parent_v11(*, use_cupti: bool=True, shape_labels=None, benchmark_correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + return _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_parent_v11, correctness=benchmark_correctness, benchmark=True, time_flashlib=time_flashlib) + +def benchmark_knn_build_d128_rag_q16m250_df0f_v1(*, use_cupti: bool=True, shape_labels=None, baseline_report: dict[str, Any] | None=None, benchmark_correctness: bool=True, time_flashlib: bool=True, speedup_floor: float=SPEEDUP_FLOOR) -> dict[str, Any]: + labels = _shape_labels(shape_labels) + if baseline_report is None: + baseline_report = benchmark_parent_v11(use_cupti=use_cupti, shape_labels=labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=labels, kernel_fn=candidate, correctness=benchmark_correctness, benchmark=True, time_flashlib=time_flashlib) + route_trace = _annotate_route_trace(route_trace_for_contract_shapes(labels), candidate_report, baseline_report, speedup_floor=speedup_floor) + below_1x = _below_flashlib_floor(candidate_report, route_trace, floor=1.0) + below_floor = _below_flashlib_floor(candidate_report, route_trace, floor=speedup_floor) + candidate_metric = candidate_report['summary']['primary_mean'] + baseline_metric = baseline_report['summary']['primary_mean'] + metric_delta = candidate_metric - baseline_metric if candidate_metric is not None and baseline_metric is not None else None + timing_backend = 'cupti' if use_cupti else 'cuda_event' + denominator = 'df0f_q16m250_exact1' if labels == TARGET_SHAPES else ''.join(['custom_', format(len(labels), '')]) + return {'candidate_id': SEED_ID, 'baseline_candidate_id': PARENT_ID, 'selected_seeds': (SEED_Q16_ID,), 'source_tasks': SOURCE_TASKS, 'tflops': candidate_metric, 'parent_v11_tflops': baseline_metric, 'metric_delta_vs_parent_v11': metric_delta, 'all_correct': candidate_report['summary']['all_correct'], 'parent_v11_all_correct': baseline_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'parent_v11_performance_comparable': baseline_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'parent_v11_invalid_performance_reason': baseline_report['summary']['invalid_performance_reason'], 'measured_entrypoint': BENCHMARK_ENTRYPOINT, 'baseline_entrypoint': parent_v11.BENCHMARK_ENTRYPOINT, 'route_entrypoint': ROUTE_ENTRYPOINT, 'measured_shape_labels': labels, 'timing_backend': timing_backend, 'denominator': denominator, 'selected_route_labels': TARGET_SHAPES, 'selected_route_rows': _rows_for_labels(candidate_report, labels), 'parent_v11_selected_route_rows': _rows_for_labels(baseline_report, labels), 'seed_delta_matrix': _seed_delta_matrix(candidate_report, baseline_report, labels), 'route_modules': PRODUCTION_ROUTE_MODULES, 'route_trace': route_trace, 'forced_fallback_route_trace': route_trace_for_contract_shapes(labels, force_fallback=True), 'route_trace_included': True, 'contract_summary': candidate_report['summary'], 'parent_v11_contract_summary': baseline_report['summary'], 'contract_performance': candidate_report['performance'], 'parent_v11_contract_performance': baseline_report['performance'], 'contract_correctness': candidate_report['correctness'], 'use_cupti': use_cupti, 'benchmark_correctness_checked': benchmark_correctness, 'benchmark_time_flashlib': time_flashlib, 'performance_coverage': 'partial' if below_floor else 'pass', 'coverage_only_routes': [], 'hot_bucket_blockers': below_floor, 'flashlib_parity_ledger': {'baseline_ref_scope': 'same_session', 'baseline_payload': None, 'speedup_floor': speedup_floor, 'rows_below_1x': below_1x, 'rows_below_floor': below_floor, 'omitted_reason': None}, 'rank_objective': {'metric': 'tflops', 'direction': 'maximize', 'value': candidate_metric, 'baseline_parent_v11_value': baseline_metric, 'delta_vs_parent_v11': metric_delta, 'denominator': denominator, 'valid_measurement_count': candidate_report['performance']['valid_measurement_count'], 'comparable': candidate_report['performance']['comparable']}, 'report': candidate_report, 'parent_v11_report': baseline_report} + +def write_benchmark_artifacts(artifact_dir: str | Path, *, use_cupti: bool=True, shape_labels=None, benchmark_correctness: bool=True, time_flashlib: bool=True, speedup_floor: float=SPEEDUP_FLOOR) -> dict[str, str]: + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + labels = _shape_labels(shape_labels) + denom_label = 'df0f_q16m250_exact1' if labels == TARGET_SHAPES else ''.join(['custom_', format(len(labels), '')]) + baseline_report = benchmark_parent_v11(use_cupti=use_cupti, shape_labels=labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + payload = benchmark_knn_build_d128_rag_q16m250_df0f_v1(use_cupti=use_cupti, shape_labels=labels, baseline_report=baseline_report, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib, speedup_floor=speedup_floor) + baseline_payload = {'candidate_id': PARENT_ID, 'measured_entrypoint': parent_v11.BENCHMARK_ENTRYPOINT, 'denominator': payload['denominator'], 'timing_backend': payload['timing_backend'], 'all_correct': baseline_report['summary']['all_correct'], 'performance_comparable': baseline_report['summary']['performance_comparable'], 'contract_summary': baseline_report['summary'], 'contract_performance': baseline_report['performance'], 'report': baseline_report} + baseline_path = out_dir / ''.join([format(denom_label, ''), '_same_session_parent_v11.json']) + payload_path = out_dir / ''.join([format(denom_label, ''), '_dispatch_df0f_q16m250_s288_v1.json']) + trace_path = out_dir / ''.join([format(denom_label, ''), '_route_trace_df0f_q16m250_s288_v1.json']) + forced_trace_path = out_dir / ''.join([format(denom_label, ''), '_forced_fallback_trace_df0f_q16m250_s288_v1.json']) + seed_matrix_path = out_dir / ''.join([format(denom_label, ''), '_seed_delta_matrix_df0f_q16m250_s288_v1.json']) + payload['flashlib_parity_ledger']['baseline_payload'] = str(baseline_path) + baseline_path.write_text(json.dumps(baseline_payload, indent=2, sort_keys=True) + '\n') + payload_path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + trace_path.write_text(json.dumps(payload['route_trace'], indent=2, sort_keys=True) + '\n') + forced_trace_path.write_text(json.dumps(payload['forced_fallback_route_trace'], indent=2, sort_keys=True) + '\n') + seed_matrix_path.write_text(json.dumps(payload['seed_delta_matrix'], indent=2, sort_keys=True) + '\n') + return {'same_session_baseline_payload': str(baseline_path), 'candidate_payload': str(payload_path), 'route_trace': str(trace_path), 'forced_fallback_trace': str(forced_trace_path), 'seed_delta_matrix': str(seed_matrix_path)} + +def _main() -> None: + parser = argparse.ArgumentParser(description='Evaluate df0f D128 RAG Q16/M250000 split288 exact seed') + parser.add_argument('--shape', action='append', choices=[shape['label'] for shape in eval_mod.CANONICAL_SHAPES]) + parser.add_argument('--artifact-dir', default=None) + parser.add_argument('--no-benchmark', action='store_true') + parser.add_argument('--no-flashlib', action='store_true') + parser.add_argument('--use-cupti', action=argparse.BooleanOptionalAction, default=True) + args = parser.parse_args() + labels = tuple(args.shape) if args.shape else TARGET_SHAPES + if args.artifact_dir and (not args.no_benchmark): + artifacts = write_benchmark_artifacts(args.artifact_dir, use_cupti=args.use_cupti, shape_labels=labels, benchmark_correctness=True, time_flashlib=not args.no_flashlib) + print(json.dumps(artifacts, indent=2, sort_keys=True)) + return + report = evaluate_contract(shapes=_select_contract_shapes(labels), correctness=True, benchmark=not args.no_benchmark) + print(json.dumps(report, indent=2, sort_keys=True)) diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_d192_tile_search_b10e_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_d192_tile_search_b10e_v1.py new file mode 100644 index 00000000..d13b4434 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_d192_tile_search_b10e_v1.py @@ -0,0 +1,50 @@ +"""Exact D192 build tile-grouping candidate for the Q2048/M2048/K10 bucket. + +Minimum target architecture: sm_100a. This additive candidate keeps the +validated D256-wide TMA/tcgen05 producer and exact Weave split merge from the +non-D128 frontier, but exposes its eight-way database grouping as an isolated +exact-shape seed. The producer writes split-local K10 lists which the merge +consumes to produce the contract distances and indices; no host or reference +work is on the specialized path. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_v11_common_d_seed_portfolio_a4ec_v1 as a4ec +from . import knn_build_non128_frontier_8199_widecombine_v1 as widecombine +MODULE = 'loom.examples.weave.knn_build_d192_tile_search_b10e_v1' +TARGET_SHAPE = 'build_dim_sweep_b1_q2048_m2048_d192_k10' +TARGET_SHAPES = (TARGET_SHAPE,) +SPLIT_COUNT = 8 +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_dim_midk_df2f_d256_split_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 99584, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) + +def _eligible(inputs: dict[str, Any]) -> bool: + query = inputs.get('query') + dtype = str(query.dtype).replace('torch.', '') if query is not None else str(inputs.get('dtype', '')) + return dtype == 'bfloat16' and bool(inputs.get('build', False)) and (int(inputs['B']) == 1) and (int(inputs['Q']) == 2048) and (int(inputs['M']) == 2048) and (int(inputs['D']) == 192) and (int(inputs['K']) == 10) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if not force_fallback and _eligible(inputs): + return ''.join([format(MODULE, ''), ':exact_d192_wide256_s', format(SPLIT_COUNT, '')]) + return a4ec.route_for_contract_inputs(inputs, force_fallback=force_fallback) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + if not force_fallback and _eligible(inputs): + widecombine._launch_wide_stage(inputs, TARGET_SHAPE, feature_dim=256, kernel=widecombine.wide_d256._compiled_d256_stage1(), stage1_ir=widecombine.stage1_d256_ir) + return + a4ec.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels=TARGET_SHAPES): + wanted = {str(label) for label in shape_labels} + selected = [shape for shape in eval_mod.CANONICAL_SHAPES if str(shape['label']) in wanted] + if wanted - {str(shape['label']) for shape in selected}: + raise ValueError(''.join(['unknown contract shapes: ', format(sorted(wanted), '')])) + return selected diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_d256_tail_tiles_b21e_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_d256_tail_tiles_b21e_v1.py new file mode 100644 index 00000000..2f7bfafb --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_d256_tail_tiles_b21e_v1.py @@ -0,0 +1,68 @@ +"""D256/K32 bounded tail-work search for the exact long-M kNN-build row. + +Minimum target architecture: sm_100a. This additive candidate keeps the +validated tcgen05/TMA D256 producer and warp-row K32 merge on the contract +path, while changing only its split/tail work partition for +``rag_stream_largek_common_d256_b1_q128_m100000_k32``. The physical MMA tile +remains M64/N64/K128x2. The winning assigned (128, 128, 256) tail partition +uses 64 split work items (25 M64 tiles per item), rather than pretending that +the existing M64/N64 primitive has widened. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_v12_d256_k32_tail_59fe_v1 as parent +MODULE = 'loom.examples.weave.knn_build_d256_tail_tiles_b21e_v1' +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +BENCHMARK_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_knn_build_d256_tail_tiles_b21e_v1']) +CANDIDATE_ID = 'knn_build_d256_tail_tiles_b21e_v1' +TARGET_SHAPE = 'rag_stream_largek_common_d256_b1_q128_m100000_k32' +TARGET_SHAPES = (TARGET_SHAPE,) +TILE_SHAPE = _decode_capture(_json_loads('{"__tuple__": [128, 128, 256]}')) +SPLIT_COUNT = _decode_capture(_json_loads('64')) +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_v12_d256_k32_tail_59fe_v1_stage1_rowld", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 99584, "constants": [["BLOCK_Q", 64], ["BLOCK_M", 64], ["K_TILE", 128], ["FEATURE_CHUNKS", 2], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 192}')) + +def _eligible(inputs: dict[str, Any]) -> bool: + return parent._target_label_for_inputs(inputs) == TARGET_SHAPE + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if not force_fallback and _eligible(inputs): + tail_tiles = (100000 // 64 + SPLIT_COUNT - 1) // SPLIT_COUNT + return ''.join(['d256_tail_tiles_b21e:exact_q128_m100000_d256_k32:s', format(SPLIT_COUNT, ''), ':m64n64k256:tail', format(tail_tiles, '')]) + return parent.route_for_contract_inputs(inputs, force_fallback=force_fallback) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + if not force_fallback and _eligible(inputs): + parent._launch_d256_k32_rowld_warpmerge(inputs, TARGET_SHAPE, split_count=SPLIT_COUNT) + return + parent.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels=TARGET_SHAPES): + wanted = set(shape_labels) + selected = [shape for shape in eval_mod.CANONICAL_SHAPES if str(shape['label']) in wanted] + if {str(shape['label']) for shape in selected} != wanted: + raise ValueError(''.join(['unknown shape labels: ', format(sorted(wanted), '')])) + return selected + +def _run(*, use_cupti: bool, shape_labels=TARGET_SHAPES) -> dict[str, Any]: + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = previous + +def benchmark_knn_build_d256_tail_tiles_b21e_v1(*, use_cupti: bool=True) -> dict[str, Any]: + report = _run(use_cupti=use_cupti) + return {'contract': report['contract'], 'contract_version': report['contract_version'], 'candidate_id': CANDIDATE_ID, 'candidate_entrypoint': ROUTE_ENTRYPOINT, 'measured_entrypoint': BENCHMARK_ENTRYPOINT, 'tile_shape_candidate': list(TILE_SHAPE), 'physical_mma_tile': [64, 64, 256], 'split_count': SPLIT_COUNT, 'db_tiles_per_split': (100000 // 64 + SPLIT_COUNT - 1) // SPLIT_COUNT, 'summary': report['summary'], 'performance': report['performance'], 'correctness': report['correctness'], 'rank_objective': report.get('rank_objective'), 'per_shape': report.get('per_shape', {}), 'report': report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_d320_blockk_b21e_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_d320_blockk_b21e_v1.py new file mode 100644 index 00000000..8725353a --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_d320_blockk_b21e_v1.py @@ -0,0 +1,64 @@ +"""D320/K10 BLOCK_K tile search seed for the exact rectangular-search bucket. + +Minimum target architecture: sm_100a. The selected contract-visible path is +Weave-only: a tcgen05 producer, split-local K10 selection, then the existing +Weave split merge writes distances and indices. ``BLOCK_K=320`` uses the +exact K128+K128+K64 producer; ``BLOCK_K=384`` is the otherwise-identical +Weave padded-D384 producer. This module is additive and only accepts the +assigned BF16 D320 rectangular-search shape. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_non128_frontier_8227_d320tail_v1 as exact_d320 +from . import knn_build_non128_frontier_8227_wide_m64_v1 as padded_d384 +MODULE = 'loom.examples.weave.knn_build_d320_blockk_b21e_v1' +ROUTE_PREFIX = 'knn_build_d320_blockk_b21e_v1' +TARGET_SHAPE = 'search_rect_highd_b1_q512_m12000_d320_k10' +TARGET_SHAPES = (TARGET_SHAPE,) +BLOCK_K_CHOICES = (320, 384) +DEFAULT_BLOCK_K = _decode_capture(_json_loads('384')) + +def _block_k() -> int: + block_k = int(os.environ.get('LOOM_KNN_D320_BLOCKK_B21E', str(DEFAULT_BLOCK_K))) + if block_k not in BLOCK_K_CHOICES: + raise ValueError(''.join(['BLOCK_K must be one of ', format(BLOCK_K_CHOICES, ''), ', got ', format(block_k, '')])) + return block_k + +def _is_target(inputs: dict[str, Any]) -> bool: + query = inputs.get('query') + database = inputs.get('database') + return str(inputs.get('label', '')) == TARGET_SHAPE and str(getattr(query, 'dtype', '')).endswith('bfloat16') and str(getattr(database, 'dtype', '')).endswith('bfloat16') and (int(inputs.get('B', -1)) == 1) and (int(inputs.get('Q', -1)) == 512) and (int(inputs.get('M', -1)) == 12000) and (int(inputs.get('D', -1)) == 320) and (int(inputs.get('K', -1)) == 10) and (not bool(inputs.get('build', True))) + +def _verify_export_ir() -> Any: + return exact_d320.stage1_d320tail_ir if _block_k() == 320 else padded_d384.stage1_d384_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_8199_d384_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 148736, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if force_fallback or not _is_target(inputs): + return exact_d320.route_for_contract_inputs(inputs, force_fallback=force_fallback) + producer = 'exact_d320_k128_k128_k64' if _block_k() == 320 else 'padded_d384_k128_x3' + return ''.join([format(ROUTE_PREFIX, ''), ':', format(TARGET_SHAPE, ''), ':block_k', format(_block_k(), ''), ':', format(producer, '')]) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + if force_fallback or not _is_target(inputs): + exact_d320.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + elif _block_k() == 320: + exact_d320.launch_from_contract_inputs(inputs) + else: + padded_d384.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels=TARGET_SHAPES) -> list[dict[str, Any]]: + wanted = set(shape_labels) + return [shape for shape in eval_mod.CANONICAL_SHAPES if str(shape['label']) in wanted] diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_d320_ownership_topology_9150_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_d320_ownership_topology_9150_v1.py new file mode 100644 index 00000000..e1967b56 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_d320_ownership_topology_9150_v1.py @@ -0,0 +1,66 @@ +"""D320 split-ownership topology candidate for the exact kNN search bucket. + +Minimum target architecture: sm_100a. This module retains the exact D320 +TMA/tcgen05 producer (K128 + K128 + K64) and the existing Weave split merge, +but assigns four database tiles to each of 48 split-local K10 producers. The +192 producer work items (four Q128 tiles times 48 splits) raise the producer +grid above the B200's 148 SMs while preserving the contract output ABI. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from contextlib import contextmanager +from typing import Any, Callable, Iterator +from .. import _dispatch_runtime as eval_mod +from . import knn_build_non128_frontier_8227_d320tail_v1 as exact_d320 +MODULE = 'loom.examples.weave.knn_build_d320_ownership_topology_9150_v1' +ROUTE_PREFIX = 'knn_build_d320_ownership_topology_9150_v1' +TARGET_SHAPE = 'search_rect_highd_b1_q512_m12000_d320_k10' +TARGET_SHAPES = (TARGET_SHAPE,) +SPLIT_COUNT = 48 +DB_TILES_PER_SPLIT = 4 +TOTAL_WORK = 192 +_SPLIT_ENV = 'LOOM_KNN_NON128_FRONTIER_8227_D320TAIL_SPLITS_SEARCH_RECT_HIGHD_B1_Q512_M12000_D320_K10' +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_8227_d320tail_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 124160, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) + +def _is_target(inputs: dict[str, Any]) -> bool: + query = inputs.get('query') + database = inputs.get('database') + return str(inputs.get('label', '')) == TARGET_SHAPE and str(getattr(query, 'dtype', '')) == 'torch.bfloat16' and (str(getattr(database, 'dtype', '')) == 'torch.bfloat16') and (int(inputs.get('B', -1)) == 1) and (int(inputs.get('Q', -1)) == 512) and (int(inputs.get('M', -1)) == 12000) and (int(inputs.get('D', -1)) == 320) and (int(inputs.get('K', -1)) == 10) and (not bool(inputs.get('build', True))) + +@contextmanager +def _split_ownership() -> Iterator[None]: + previous = os.environ.get(_SPLIT_ENV) + os.environ[_SPLIT_ENV] = str(SPLIT_COUNT) + try: + yield + finally: + if previous is None: + os.environ.pop(_SPLIT_ENV, None) + else: + os.environ[_SPLIT_ENV] = previous + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if force_fallback or not _is_target(inputs): + return exact_d320.route_for_contract_inputs(inputs, force_fallback=force_fallback) + return ''.join([format(ROUTE_PREFIX, ''), ':', format(TARGET_SHAPE, ''), ':exact_d320_k128_k128_k64:splits', format(SPLIT_COUNT, ''), ':dbtiles', format(DB_TILES_PER_SPLIT, ''), ':work', format(TOTAL_WORK, '')]) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + if force_fallback or not _is_target(inputs): + exact_d320.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + return + with _split_ownership(): + exact_d320.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels=TARGET_SHAPES) -> list[dict[str, Any]]: + wanted = set(shape_labels) + return [shape for shape in eval_mod.CANONICAL_SHAPES if str(shape['label']) in wanted] diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_d320_producer_recurrence_search_f556_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_d320_producer_recurrence_search_f556_v1.py new file mode 100644 index 00000000..0af87964 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_d320_producer_recurrence_search_f556_v1.py @@ -0,0 +1,100 @@ +"""Exact D320 producer-grid recurrence candidate for kNN search. + +Minimum target architecture: sm_100a. This exact-shape variant preserves the +parent's TMA-fed tcgen05 (K128 + K128 + K64) producer, four-database-tile +split-local K10 recurrence, and 48-way Weave merge. It changes only producer +ownership: the launcher exposes all 192 work items as CTAs instead of capping +the persistent producer grid at 148. Both contract outputs remain parent-owned +Weave buffers. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +from functools import lru_cache +from contextlib import contextmanager +from typing import Any, Callable, Iterator +from .._dispatch_runtime import dc as dc +from .. import _dispatch_runtime as eval_mod +from . import knn_build_d320_ownership_topology_9150_v1 as parent +from . import knn_build_d128_rag_q128_k10_df0f_warpmerge_v1 as warpmerge_seed +from .._dispatch_runtime import pack_kernel_args +MODULE = 'loom.examples.weave.knn_build_d320_producer_recurrence_search_f556_v1' +ROUTE_PREFIX = 'knn_build_d320_producer_recurrence_search_f556_v1' +TARGET_SHAPE = 'search_rect_highd_b1_q512_m12000_d320_k10' +TARGET_SHAPES = (TARGET_SHAPE,) +SPLIT_COUNT = parent.SPLIT_COUNT +DB_TILES_PER_SPLIT = parent.DB_TILES_PER_SPLIT +TOTAL_WORK = parent.TOTAL_WORK +PRODUCER_GRID = TOTAL_WORK +MERGE_THREADS = 128 +ROWS_PER_MERGE_CTA = 4 + +def _merge_ir_with_split_count(ir_obj: Any, split_count: int) -> Any: + constants = tuple(((name, split_count if name == 'SPLIT_COUNT' else value) for name, value in ir_obj.constants)) + return dc.replace(ir_obj, symbol=''.join([format(ir_obj.symbol, ''), '_d320_s', format(split_count, ''), '_f556_v2']), constants=constants) +merge_d320_s48_warp_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_d128_rag_q128_k10_s74_warp_merge_d320_s48_f556_v2", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 48]], "cta_group": 1, "threads": 128}')) +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_8227_d320tail_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 124160, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) + +def _compiled_merge_d320_s48_warp(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0072"}')) + +def _is_target(inputs: dict[str, Any]) -> bool: + return parent._is_target(inputs) + +@contextmanager +def _full_producer_grid() -> Iterator[None]: + """Temporarily make the parent launch one CTA for each producer work item.""" + original = parent.exact_d320.GRID_DIM_DEFAULT + parent.exact_d320.GRID_DIM_DEFAULT = PRODUCER_GRID + try: + yield + finally: + parent.exact_d320.GRID_DIM_DEFAULT = original + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if force_fallback or not _is_target(inputs): + return parent.route_for_contract_inputs(inputs, force_fallback=force_fallback) + return ''.join([format(ROUTE_PREFIX, ''), ':', format(TARGET_SHAPE, ''), ':exact_d320_k128_k128_k64:splits', format(SPLIT_COUNT, ''), ':dbtiles', format(DB_TILES_PER_SPLIT, ''), ':work', format(TOTAL_WORK, ''), ':grid', format(PRODUCER_GRID, '')]) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + if force_fallback: + parent.launch_from_contract_inputs(inputs) + return + if not _is_target(inputs): + parent.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + return + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + exact = parent.exact_d320 + if dim != exact.D320_FEAT_D: + raise ValueError(''.join(['D320 recurrence route expected D=', format(exact.D320_FEAT_D, ''), ', got ', format(dim, '')])) + num_q_tiles = (n_query + exact.BLOCK_Q - 1) // exact.BLOCK_Q + num_db_tiles = (n_database + exact.BLOCK_M - 1) // exact.BLOCK_M + db_tiles_per_split = (num_db_tiles + SPLIT_COUNT - 1) // SPLIT_COUNT + total_work = bsz * num_q_tiles * SPLIT_COUNT + merge_grid = min((bsz * n_query + ROWS_PER_MERGE_CTA - 1) // ROWS_PER_MERGE_CTA, PRODUCER_GRID) + partial_dists, partial_indices = exact.split_parent._partial_buffers(split_count=SPLIT_COUNT, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = exact.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), bsz * n_query, exact.BLOCK_Q, exact.D320_FEAT_D, exact.D320_FEAT_D) + tmap_database = exact.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, exact.BLOCK_M, exact.D320_FEAT_D, exact.D320_FEAT_D) + exact._compiled_d320tail_stage1().launch(grid=(total_work, 1, 1), block=(exact.THREADS, 1, 1), args=pack_kernel_args(exact.stage1_d320tail_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tiles=num_q_tiles, db_tiles_per_split=db_tiles_per_split, split_count=SPLIT_COUNT, total_work=total_work), shared_mem=exact.stage1_d320tail_ir.computed_smem_bytes) + _compiled_merge_d320_s48_warp().launch(grid=(merge_grid, 1, 1), block=(MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_d320_s48_warp_ir.computed_smem_bytes) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels=TARGET_SHAPES) -> list[dict[str, Any]]: + wanted = set(shape_labels) + return [shape for shape in eval_mod.CANONICAL_SHAPES if str(shape['label']) in wanted] diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_d64_build_aa88_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_d64_build_aa88_v1.py new file mode 100644 index 00000000..5c6e117b --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_d64_build_aa88_v1.py @@ -0,0 +1,122 @@ +"""kNN build D64 bucket seed for round aa88. + +Minimum target architecture: sm_100a. This additive seed extends the existing +73a9 BF16 D64 split producer from the exact Q=M=2048 row to the v6 D64 build +bucket Q=M in {1024,2048,4096}, D=64, K=10. The route is still Weave-only: a +TMA/tcgen05 split-local top-k stage writes partials that feed the generic +Weave split merge. Non-bucket shapes delegate to the 73a9 parent candidate. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import os +from typing import Any +from . import knn_build_dim_midk_73a9_v1 as parent_73a9 +from .._dispatch_runtime import pack_kernel_args +TOP_K_MAX = parent_73a9.TOP_K_MAX +D64_FEAT_D = parent_73a9.D64_FEAT_D +BLOCK_Q = parent_73a9.BLOCK_Q +BLOCK_M = parent_73a9.BLOCK_M +THREADS = parent_73a9.THREADS +MERGE_THREADS = parent_73a9.MERGE_THREADS +GRID_DIM_DEFAULT = parent_73a9.GRID_DIM_DEFAULT +D64_BUILD_TARGET_LABELS = ('build_dim_sweep_b1_q1024_m1024_d64_k10', 'build_dim_sweep_b1_q2048_m2048_d64_k10', 'build_dim_sweep_b1_q4096_m4096_d64_k10') +TARGET_SHAPES = D64_BUILD_TARGET_LABELS +ROUTE_D64_BUCKET_S4 = 'loom.examples.weave.knn_build_d64_build_aa88_v1:d64_split_s4' +ROUTE_D64_BUCKET_S8 = 'loom.examples.weave.knn_build_d64_build_aa88_v1:d64_split_s8' +ROUTE_PARENT_73A9 = 'loom.examples.weave.knn_build_dim_midk_73a9_v1' +stage1_d64_split_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_dim_midk_73a9_d64_split_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 25856, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) +merge_generic_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_merge", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "B", "Q", "K", "split_count", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10]], "cta_group": 1, "threads": 256}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_D64_AA88_VERIFY_KERNEL') + if verify_kernel == 'merge_generic': + return merge_generic_ir + return stage1_d64_split_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_dim_midk_73a9_d64_split_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 25856, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) + +def _eligible_d64_build_bucket(inputs: dict[str, Any]) -> bool: + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + return bool(inputs.get('build', False)) and str(inputs['query'].dtype) == 'torch.bfloat16' and (str(inputs['database'].dtype) == 'torch.bfloat16') and (int(inputs['B']) == 1) and (n_query == n_database) and (n_query in (1024, 2048, 4096)) and (int(inputs['D']) == D64_FEAT_D) and (int(inputs['K']) == TOP_K_MAX) + +def route_name_for_inputs(inputs: dict[str, Any]) -> str: + if _eligible_d64_build_bucket(inputs): + return ROUTE_D64_BUCKET_S4 if int(inputs['Q']) == 4096 else ROUTE_D64_BUCKET_S8 + return ROUTE_PARENT_73A9 + +def _d64_build_split_count(n_query: int) -> int: + override = os.environ.get('LOOM_KNN_D64_AA88_SPLITS') + if override is not None: + return int(override) + if n_query == 4096: + return 4 + return 8 + +def _launch_d64_build_bucket(inputs: dict[str, Any]) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + split_count = _d64_build_split_count(n_query) + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tiles * split_count + stage1_grid = min(total_work, GRID_DIM_DEFAULT) + merge_grid = min((bsz * n_query + MERGE_THREADS - 1) // MERGE_THREADS, GRID_DIM_DEFAULT) + partial_dists, partial_indices = parent_73a9.split_parent._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = parent_73a9.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), bsz * n_query, BLOCK_Q, dim, D64_FEAT_D) + tmap_database = parent_73a9.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, dim, D64_FEAT_D) + stage1_kernel = parent_73a9._compiled_d64_stage1() + stage1_kernel.launch(grid=(stage1_grid, 1, 1), block=(THREADS, 1, 1), args=pack_kernel_args(stage1_d64_split_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tiles=num_q_tiles, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), shared_mem=stage1_d64_split_ir.computed_smem_bytes) + merge_kernel = parent_73a9.split_parent._compiled_merge() + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz, n_query, top_k, split_count, bsz * n_query], shared_mem=merge_generic_ir.computed_smem_bytes) + +def launch_from_contract_inputs(inputs: dict[str, Any]) -> None: + if _eligible_d64_build_bucket(inputs): + _launch_d64_build_bucket(inputs) + return + parent_73a9.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(candidate, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + from .._dispatch_runtime import CANONICAL_SHAPES + if shape_labels is None: + return list(CANONICAL_SHAPES) + wanted = {str(label) for label in shape_labels} + selected = [shape for shape in CANONICAL_SHAPES if shape['label'] in wanted] + missing = wanted - {shape['label'] for shape in selected} + if missing: + raise ValueError(''.join(['unknown kNN build contract shape(s): ', format(sorted(missing), '')])) + return selected + +def compile_and_launch_knn_build(*, shape_labels=D64_BUILD_TARGET_LABELS, benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint: run contract correctness for selected shapes.""" + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def benchmark_knn_build_d64_build_aa88_v1(*, use_cupti: bool | None=None) -> dict[str, Any]: + """Contract benchmark hook for the v6 D64 build bucket.""" + from .. import _dispatch_runtime as eval_mod + prior_use_cupti = eval_mod.CONTRACT.bench.get('use_cupti', True) + if use_cupti is not None: + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + try: + report = evaluate_contract(shapes=_select_contract_shapes(TARGET_SHAPES), correctness=True, benchmark=True) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + return {'tflops': report['summary']['primary_mean'], 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'report': report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_d64_build_aa88_v2.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_d64_build_aa88_v2.py new file mode 100644 index 00000000..3a9cd0d4 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_d64_build_aa88_v2.py @@ -0,0 +1,151 @@ +"""kNN build D64 bucket seed for round aa88 v2. + +Minimum target architecture: sm_100a. This additive seed keeps the round-25 +D64 TMA/tcgen05 split producer and replaces the generic runtime-K split merge +with exact K10 row-base cached merges for the split8 Q1024/Q2048 routes and +the split4 Q4096 route. Non-bucket shapes delegate to the round-25 parent. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import os +from functools import lru_cache +from typing import Any +from .._dispatch_runtime import dc as dc +from . import knn_build_d64_build_aa88_v1 as parent_aa88 +from .._dispatch_runtime import pack_kernel_args +TOP_K_MAX = parent_aa88.TOP_K_MAX +D64_FEAT_D = parent_aa88.D64_FEAT_D +BLOCK_Q = parent_aa88.BLOCK_Q +BLOCK_M = parent_aa88.BLOCK_M +THREADS = parent_aa88.THREADS +MERGE_THREADS = parent_aa88.MERGE_THREADS +FAST_MERGE_THREADS = 32 +GRID_DIM_DEFAULT = parent_aa88.GRID_DIM_DEFAULT +D64_BUILD_TARGET_LABELS = parent_aa88.D64_BUILD_TARGET_LABELS +TARGET_SHAPES = D64_BUILD_TARGET_LABELS +ROUTE_D64_BUCKET_S4_FAST = 'loom.examples.weave.knn_build_d64_build_aa88_v2:d64_split_s4_k10_cached_merge' +ROUTE_D64_BUCKET_S8_FAST = 'loom.examples.weave.knn_build_d64_build_aa88_v2:d64_split_s8_k10_cached_merge' +ROUTE_PARENT_AA88 = 'loom.examples.weave.knn_build_d64_build_aa88_v1' +stage1_d64_split_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_dim_midk_73a9_d64_split_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 25856, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) +merge_generic_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_merge", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "B", "Q", "K", "split_count", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10]], "cta_group": 1, "threads": 256}')) +knn_build_d64_build_aa88_k10_merge_s8_rowbase_cache = _decode_capture(_json_loads('{"__ir__": "knn_build_d64_build_aa88_k10_merge_s8_rowbase_cache", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 8]], "cta_group": 1, "threads": 32}')) + +def _ir_with_constants(ir_obj: Any, *, suffix: str, **updates: int) -> Any: + constants = tuple(((name, updates.get(name, value)) for name, value in ir_obj.constants)) + return dc.replace(ir_obj, symbol=''.join([format(ir_obj.symbol, ''), '_', format(suffix, '')]), constants=constants) +merge_k10_s8_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_d64_build_aa88_k10_merge_s8_rowbase_cache", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 8]], "cta_group": 1, "threads": 32}')) +merge_k10_s4_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_d64_build_aa88_k10_merge_s8_rowbase_cache_s4", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 32}')) +merge_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_d64_build_aa88_k10_merge_s8_rowbase_cache", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 8]], "cta_group": 1, "threads": 32}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_D64_AA88_V2_VERIFY_KERNEL') + if verify_kernel == 'merge_s4': + return merge_k10_s4_ir + if verify_kernel == 'merge_s8': + return merge_k10_s8_ir + if verify_kernel == 'merge_generic': + return merge_generic_ir + return stage1_d64_split_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_dim_midk_73a9_d64_split_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 25856, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) + +def _eligible_d64_build_bucket(inputs: dict[str, Any]) -> bool: + return parent_aa88._eligible_d64_build_bucket(inputs) + +def route_name_for_inputs(inputs: dict[str, Any]) -> str: + if _eligible_d64_build_bucket(inputs): + return ROUTE_D64_BUCKET_S4_FAST if int(inputs['Q']) == 4096 else ROUTE_D64_BUCKET_S8_FAST + return ROUTE_PARENT_AA88 + +def _d64_build_split_count(n_query: int) -> int: + override = os.environ.get('LOOM_KNN_D64_AA88_V2_SPLITS') + if override is not None: + return int(override) + return parent_aa88._d64_build_split_count(n_query) + +def _compile_ir(ir_obj: Any): + from .._dispatch_runtime import generate_kernel + from .._dispatch_runtime import _cuda_include_dirs + from .._dispatch_runtime import compile_cuda + from .._dispatch_runtime import CUDAKernel + source = generate_kernel(ir_obj, validate=False, smem_bytes=ir_obj.computed_smem_bytes) + cubin = compile_cuda(source, arch=parent_aa88.parent_73a9.base_v1._select_arch_and_preload(), options=['--use_fast_math'], include_dirs=_cuda_include_dirs()) + return CUDAKernel(cubin, ''.join(['kernel_', format(ir_obj.symbol, '')])) + +def _compiled_merge_k10_s4(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0020"}')) + +def _compiled_merge_k10_s8(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0018"}')) + +def _fast_merge_enabled() -> bool: + return os.environ.get('LOOM_KNN_D64_AA88_V2_FAST_MERGE', '1') != '0' + +def _launch_fast_or_generic_merge(*, split_count: int, partial_dists, partial_indices, out_dists, out_indices, bsz: int, n_query: int, top_k: int) -> None: + if _fast_merge_enabled() and top_k == TOP_K_MAX and (split_count in (4, 8)): + merge_ir_obj = merge_k10_s4_ir if split_count == 4 else merge_k10_s8_ir + merge_kernel = _compiled_merge_k10_s4() if split_count == 4 else _compiled_merge_k10_s8() + merge_grid = min((bsz * n_query + FAST_MERGE_THREADS - 1) // FAST_MERGE_THREADS, GRID_DIM_DEFAULT) + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(FAST_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, out_dists, out_indices, bsz * n_query], shared_mem=merge_ir_obj.computed_smem_bytes) + return + merge_grid = min((bsz * n_query + MERGE_THREADS - 1) // MERGE_THREADS, GRID_DIM_DEFAULT) + merge_kernel = parent_aa88.parent_73a9.split_parent._compiled_merge() + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, out_dists, out_indices, bsz, n_query, top_k, split_count, bsz * n_query], shared_mem=merge_generic_ir.computed_smem_bytes) + +def _launch_d64_build_bucket(inputs: dict[str, Any]) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + split_count = _d64_build_split_count(n_query) + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tiles * split_count + stage1_grid = min(total_work, GRID_DIM_DEFAULT) + partial_dists, partial_indices = parent_aa88.parent_73a9.split_parent._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = parent_aa88.parent_73a9.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), bsz * n_query, BLOCK_Q, dim, D64_FEAT_D) + tmap_database = parent_aa88.parent_73a9.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, dim, D64_FEAT_D) + stage1_kernel = parent_aa88.parent_73a9._compiled_d64_stage1() + stage1_kernel.launch(grid=(stage1_grid, 1, 1), block=(THREADS, 1, 1), args=pack_kernel_args(stage1_d64_split_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tiles=num_q_tiles, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), shared_mem=stage1_d64_split_ir.computed_smem_bytes) + _launch_fast_or_generic_merge(split_count=split_count, partial_dists=partial_dists, partial_indices=partial_indices, out_dists=inputs['out_dists'], out_indices=inputs['out_indices'], bsz=bsz, n_query=n_query, top_k=top_k) + +def launch_from_contract_inputs(inputs: dict[str, Any]) -> None: + if _eligible_d64_build_bucket(inputs): + _launch_d64_build_bucket(inputs) + return + parent_aa88.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(candidate, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return parent_aa88._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=D64_BUILD_TARGET_LABELS, benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint: run contract correctness for selected shapes.""" + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def benchmark_knn_build_d64_build_aa88_v2(*, use_cupti: bool | None=None) -> dict[str, Any]: + """Contract benchmark hook for the v6 D64 build bucket.""" + from .. import _dispatch_runtime as eval_mod + prior_use_cupti = eval_mod.CONTRACT.bench.get('use_cupti', True) + if use_cupti is not None: + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + try: + report = evaluate_contract(shapes=_select_contract_shapes(TARGET_SHAPES), correctness=True, benchmark=True) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + return {'tflops': report['summary']['primary_mean'], 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'report': report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_d64_lowk_lowfloor_84bb_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_d64_lowk_lowfloor_84bb_v1.py new file mode 100644 index 00000000..5535fb1c --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_d64_lowk_lowfloor_84bb_v1.py @@ -0,0 +1,215 @@ +"""Exact D64/K1 low-floor bucket seed for weave-evolve task 84bb. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +targets two low-floor build rows from the round-140 full90 ledger: +``build_k_sweep_qm512_k1`` and +``build_dim_sweep_b1_q4096_m4096_d64_k10``. K1 routes through the validated +Q512 low-K split4 seed; D64 Q4096 routes through the aa88 v2 split4 cached +merge seed. Guard misses delegate to the current 1877 full90 baseline route. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from pathlib import Path +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_d64_build_aa88_v2 as d64_seed +from . import knn_build_dispatch_ad64_1b8f_4b51_ceb3_full90_synthesis_v1 as baseline_1877 +from . import knn_build_lowk_f8c3_q512_q1024_v1 as lowk_seed +MODULE = 'loom.examples.weave.knn_build_d64_lowk_lowfloor_84bb_v1' +TARGET_K1 = 'build_k_sweep_qm512_k1' +TARGET_D64_Q4096 = 'build_dim_sweep_b1_q4096_m4096_d64_k10' +TARGET_SHAPES = (TARGET_K1, TARGET_D64_Q4096) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +SEED_ID = 'd64_lowk_lowfloor_84bb_v1' +SEED_K1_ID = 'lowk_q512_k1_s4_84bb' +SEED_D64_ID = 'aa88_v2_d64_q4096_k10_s4_cached_84bb' +BASELINE_1877_ID = baseline_1877.CANDIDATE_CONFIGS[baseline_1877.DEFAULT_CANDIDATE_KEY]['candidate_id'] +Q512_SPLIT_COUNT = 4 +D64_SPLIT_COUNT = 4 +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_K1_ENTRYPOINT = ''.join([format(lowk_seed.ROUTE_PREFIX, ''), ':q512_lowk_s', format(Q512_SPLIT_COUNT, '')]) +ROUTE_D64_ENTRYPOINT = d64_seed.ROUTE_D64_BUCKET_S4_FAST +ROUTE_BASELINE_1877 = _decode_capture(_json_loads('"loom.examples.weave.knn_build_dispatch_ad64_1b8f_4b51_ceb3_full90_synthesis_v1:candidate_best_build_ceb3_full90_v1"')) +PRODUCTION_ROUTE_MODULES = {SEED_ID: ROUTE_ENTRYPOINT, SEED_K1_ID: ROUTE_K1_ENTRYPOINT, SEED_D64_ID: ROUTE_D64_ENTRYPOINT, BASELINE_1877_ID: baseline_1877.ROUTE_ENTRYPOINT} + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_D64_LOWK_84BB_VERIFY_KERNEL') + if verify_kernel == 'lowk_q512_stage1': + return lowk_seed.stage1_q512_lowk_ir + if verify_kernel == 'lowk_q512_merge_generic': + return lowk_seed.merge_q512_generic_ir + if verify_kernel == 'd64_stage1': + return d64_seed.stage1_d64_split_ir + if verify_kernel == 'd64_merge_s4': + return d64_seed.merge_k10_s4_ir + return lowk_seed.stage1_q512_lowk_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_stage1_batch8_cond4_vmin_maxtree", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) + +def _select_contract_shapes(shape_labels): + return baseline_1877._select_contract_shapes(shape_labels) + +def _dtype_name(inputs: dict[str, Any], name: str='query') -> str: + tensor = inputs.get(name) + if tensor is not None and hasattr(tensor, 'dtype'): + return str(tensor.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')).replace('torch.', '') + +def _label_can_hit(inputs: dict[str, Any], label: str) -> bool: + value = inputs.get('label') + return value is None or str(value) == label + +def _is_bf16_build(inputs: dict[str, Any]) -> bool: + return bool(inputs.get('build', False)) and int(inputs.get('B', -1)) == 1 and (int(inputs.get('Q', -1)) == int(inputs.get('M', -2))) and (_dtype_name(inputs, 'query') == 'bfloat16') and (_dtype_name(inputs, 'database') in ('', 'bfloat16')) + +def _eligible_k1_q512(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, TARGET_K1) and _is_bf16_build(inputs) and (int(inputs.get('Q', -1)) == 512) and (int(inputs.get('D', -1)) == lowk_seed.fixed_build.FEAT_D) and (int(inputs.get('K', -1)) == 1) + +def _eligible_d64_q4096(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, TARGET_D64_Q4096) and _is_bf16_build(inputs) and (int(inputs.get('Q', -1)) == 4096) and (int(inputs.get('D', -1)) == d64_seed.D64_FEAT_D) and (int(inputs.get('K', -1)) == d64_seed.TOP_K_MAX) + +def _selected_seed_for_inputs(inputs: dict[str, Any]) -> tuple[str | None, str | None]: + if _eligible_k1_q512(inputs): + return (SEED_K1_ID, TARGET_K1) + if _eligible_d64_q4096(inputs): + return (SEED_D64_ID, TARGET_D64_Q4096) + return (None, None) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if not force_fallback: + selected_seed, _label = _selected_seed_for_inputs(inputs) + if selected_seed == SEED_K1_ID: + return ROUTE_K1_ENTRYPOINT + if selected_seed == SEED_D64_ID: + return ROUTE_D64_ENTRYPOINT + return baseline_1877.route_for_contract_inputs(inputs, candidate_key=baseline_1877.DEFAULT_CANDIDATE_KEY, force_fallback=force_fallback) + +def _launch_k1_q512(inputs: dict[str, Any]) -> None: + lowk_seed.launch_from_contract_inputs(inputs, q512_split_count=Q512_SPLIT_COUNT) + +def _launch_d64_q4096(inputs: dict[str, Any]) -> None: + previous_split = os.environ.get('LOOM_KNN_D64_AA88_V2_SPLITS') + previous_fast = os.environ.get('LOOM_KNN_D64_AA88_V2_FAST_MERGE') + os.environ['LOOM_KNN_D64_AA88_V2_SPLITS'] = str(D64_SPLIT_COUNT) + os.environ['LOOM_KNN_D64_AA88_V2_FAST_MERGE'] = '1' + try: + d64_seed.launch_from_contract_inputs(inputs) + finally: + if previous_split is None: + os.environ.pop('LOOM_KNN_D64_AA88_V2_SPLITS', None) + else: + os.environ['LOOM_KNN_D64_AA88_V2_SPLITS'] = previous_split + if previous_fast is None: + os.environ.pop('LOOM_KNN_D64_AA88_V2_FAST_MERGE', None) + else: + os.environ['LOOM_KNN_D64_AA88_V2_FAST_MERGE'] = previous_fast + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + if not force_fallback: + selected_seed, _label = _selected_seed_for_inputs(inputs) + if selected_seed == SEED_K1_ID: + _launch_k1_q512(inputs) + return + if selected_seed == SEED_D64_ID: + _launch_d64_q4096(inputs) + return + baseline_1877.launch_from_contract_inputs(inputs, candidate_key=baseline_1877.DEFAULT_CANDIDATE_KEY, force_fallback=force_fallback) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) + +def candidate_baseline_1877(inputs: dict[str, Any]) -> None: + baseline_1877.launch_from_contract_inputs(inputs, candidate_key=baseline_1877.DEFAULT_CANDIDATE_KEY) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _benchmark_shapes(shape_labels, *, time_flashlib: bool) -> list[dict[str, Any]]: + selected = _select_contract_shapes(TARGET_SHAPES if shape_labels is None else shape_labels) + out = [] + for shape in selected: + params = dict(shape['params']) + params['time_flashlib'] = bool(time_flashlib) + out.append({'label': shape['label'], 'params': params}) + return out + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate, time_flashlib: bool=True) -> dict[str, Any]: + previous = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_benchmark_shapes(shape_labels, time_flashlib=time_flashlib), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = previous + +def _inputs_for_label(label: str) -> dict[str, Any]: + return baseline_1877._inputs_for_label(label) + +def route_trace_for_contract_shapes(shape_labels=TARGET_SHAPES, *, force_fallback: bool=False) -> list[dict[str, Any]]: + rows = [] + for label in tuple(shape_labels): + inputs = _inputs_for_label(str(label)) + selected_seed, matched_label = (None, None) if force_fallback else _selected_seed_for_inputs(inputs) + route = route_for_contract_inputs(inputs, force_fallback=force_fallback) + baseline_route = baseline_1877.route_for_contract_inputs(inputs, candidate_key=baseline_1877.DEFAULT_CANDIDATE_KEY) + if selected_seed is None: + row = dict(baseline_1877.route_trace_for_contract_shapes((label,), candidate_key=baseline_1877.DEFAULT_CANDIDATE_KEY, force_fallback=force_fallback)[0]) + row['candidate_guard_status'] = 'forced_fallback_or_guard_miss' + rows.append(baseline_1877._normalize_route_row(row)) + continue + guard_conditions = {SEED_K1_ID: 'exact BF16 build B=1 Q=M=512 D=128 K=1', SEED_D64_ID: 'exact BF16 build B=1 Q=M=4096 D=64 K=10'} + rows.append(baseline_1877._normalize_route_row({'shape_key': label, 'selected_route': route, 'selected_entrypoint': route, 'selected_seed': selected_seed, 'expected_seed': selected_seed, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': ''.join(['84bb_d64_lowk_', format(selected_seed, '')]), 'guard_condition': guard_conditions[selected_seed], 'matched_label': matched_label, 'baseline_1877_route': baseline_route, 'classification': 'seed-consumed'})) + return rows + +def _rows_for_labels(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return {label: dict(report.get('per_shape', {}).get(label, {})) for label in labels} + +def _per_shape_delta(candidate_report: dict[str, Any], baseline_report: dict[str, Any], labels: tuple[str, ...]): + rows = [] + for label in labels: + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') or baseline_row.get('flashlib_ms') + inputs = _inputs_for_label(label) + selected_seed, _matched_label = _selected_seed_for_inputs(inputs) + rows.append({'shape_key': label, 'selected_seed': selected_seed, 'candidate_route': route_for_contract_inputs(inputs), 'baseline_1877_route': baseline_1877.route_for_contract_inputs(inputs, candidate_key=baseline_1877.DEFAULT_CANDIDATE_KEY), 'candidate_ms': candidate_ms, 'baseline_1877_ms': baseline_ms, 'flashlib_ms': flashlib_ms, 'speedup_vs_1877': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'ratio_vs_flashlib': flashlib_ms / candidate_ms if candidate_ms and flashlib_ms else None, 'candidate_passed': candidate_row.get('passed'), 'baseline_1877_passed': baseline_row.get('passed'), 'timing_backend': candidate_row.get('timing_backend') or baseline_row.get('timing_backend')}) + return rows + +def benchmark_candidate_d64_lowk_lowfloor_84bb_v1(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES, run_baseline: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + labels = tuple((str(label) for label in shape_labels)) + baseline_report = None + if run_baseline: + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=labels, kernel_fn=candidate_baseline_1877, time_flashlib=time_flashlib) + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=labels, kernel_fn=candidate, time_flashlib=time_flashlib) + candidate_metric = candidate_report.get('summary', {}).get('primary_mean') + baseline_metric = baseline_report.get('summary', {}).get('primary_mean') if baseline_report else None + payload: dict[str, Any] = {'candidate_id': SEED_ID, 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_candidate_d64_lowk_lowfloor_84bb_v1']), 'candidate_entrypoint': ROUTE_ENTRYPOINT, 'selected_seeds': (SEED_K1_ID, SEED_D64_ID), 'all_correct': candidate_report.get('summary', {}).get('all_correct'), 'performance_comparable': candidate_report.get('summary', {}).get('performance_comparable'), 'tflops': candidate_metric, 'baseline_1877_tflops': baseline_metric, 'metric_delta_vs_1877': candidate_metric - baseline_metric if candidate_metric is not None and baseline_metric is not None else None, 'timing_backend': 'cupti' if use_cupti else 'cuda_event', 'denominator': 'build_d64_lowk_lowfloor_exact2', 'shape_labels': list(labels), 'route_trace': route_trace_for_contract_shapes(labels), 'forced_fallback_route_trace': route_trace_for_contract_shapes(labels, force_fallback=True), 'route_modules': PRODUCTION_ROUTE_MODULES, 'report': candidate_report} + if baseline_report is not None: + payload.update({'baseline_1877_entrypoint': baseline_1877.CANDIDATE_BEST_BUILD_CEB3_ENTRYPOINT, 'baseline_1877_all_correct': baseline_report.get('summary', {}).get('all_correct'), 'baseline_1877_performance_comparable': baseline_report.get('summary', {}).get('performance_comparable'), 'selected_route_rows': _rows_for_labels(candidate_report, labels), 'baseline_1877_rows': _rows_for_labels(baseline_report, labels), 'seed_delta_matrix': _per_shape_delta(candidate_report, baseline_report, labels), 'baseline_1877_report': baseline_report}) + return payload + +def write_benchmark_artifact(artifact_dir: str | Path, *, use_cupti: bool=True, shape_labels=TARGET_SHAPES, run_baseline: bool=True) -> dict[str, str]: + payload = benchmark_candidate_d64_lowk_lowfloor_84bb_v1(use_cupti=use_cupti, shape_labels=shape_labels, run_baseline=run_baseline) + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + path = out_dir / 'd64_lowk_lowfloor_84bb_v1.json' + path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + return {'candidate_payload': str(path)} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_d64_q4096_c271_prodaxis_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_d64_q4096_c271_prodaxis_v1.py new file mode 100644 index 00000000..1770f5b4 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_d64_q4096_c271_prodaxis_v1.py @@ -0,0 +1,163 @@ +"""kNN build D64 Q4096 producer-axis candidate for c271. + +Minimum target architecture: sm_100a. This additive exact-shape seed keeps the +validated aa88/v2 D64 TMA/tcgen05 split producer and tests a split5 Q4096 route +with an exact K10 cached merge. The goal is to expose more producer CTAs than +split4 without routing split5/split6 through the generic runtime-K merge. +Non-exact shapes delegate to aa88/v2. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import os +from functools import lru_cache +from typing import Any +from .._dispatch_runtime import dc as dc +from . import knn_build_d64_build_aa88_v1 as parent_aa88_v1 +from . import knn_build_d64_build_aa88_v2 as parent_aa88 +from .._dispatch_runtime import pack_kernel_args +TOP_K_MAX = parent_aa88.TOP_K_MAX +D64_FEAT_D = parent_aa88.D64_FEAT_D +BLOCK_Q = parent_aa88.BLOCK_Q +BLOCK_M = parent_aa88.BLOCK_M +THREADS = parent_aa88.THREADS +FAST_MERGE_THREADS = parent_aa88.FAST_MERGE_THREADS +MERGE_THREADS = parent_aa88.MERGE_THREADS +GRID_DIM_DEFAULT = parent_aa88.GRID_DIM_DEFAULT +TARGET_SHAPE = 'build_dim_sweep_b1_q4096_m4096_d64_k10' +TARGET_SHAPES = (TARGET_SHAPE,) +DEFAULT_SPLIT_COUNT = 4 +SUPPORTED_EXACT_SPLITS = (4, 5, 6, 8) +ROUTE_D64_Q4096_SPLIT4_SYNCDROP = 'loom.examples.weave.knn_build_d64_q4096_c271_prodaxis_v1:d64_q4096_split4_syncdrop_exact_merge' +ROUTE_PARENT_AA88_V2 = 'loom.examples.weave.knn_build_d64_build_aa88_v2' +knn_build_d64_q4096_c271_stage1_syncdrop = _decode_capture(_json_loads('{"__ir__": "knn_build_d64_q4096_c271_stage1_syncdrop", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 25856, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) +stage1_d64_split_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_d64_q4096_c271_stage1_syncdrop", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 25856, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) +merge_generic_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_merge", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "B", "Q", "K", "split_count", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10]], "cta_group": 1, "threads": 256}')) + +def _ir_with_constants(ir_obj: Any, *, suffix: str, **updates: int) -> Any: + constants = tuple(((name, updates.get(name, value)) for name, value in ir_obj.constants)) + return dc.replace(ir_obj, symbol=''.join([format(ir_obj.symbol, ''), '_', format(suffix, '')]), constants=constants) +merge_k10_s4_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_d64_build_aa88_k10_merge_s8_rowbase_cache_s4", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 32}')) +merge_k10_s5_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_d64_build_aa88_k10_merge_s8_rowbase_cache_c271_s5", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 5]], "cta_group": 1, "threads": 32}')) +merge_k10_s6_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_d64_build_aa88_k10_merge_s8_rowbase_cache_c271_s6", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 6]], "cta_group": 1, "threads": 32}')) +merge_k10_s8_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_d64_build_aa88_k10_merge_s8_rowbase_cache", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 8]], "cta_group": 1, "threads": 32}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_D64_Q4096_C271_PROD_VERIFY_KERNEL') + if verify_kernel == 'merge_s4': + return merge_k10_s4_ir + if verify_kernel == 'merge_s5': + return merge_k10_s5_ir + if verify_kernel == 'merge_s6': + return merge_k10_s6_ir + if verify_kernel == 'merge_s8': + return merge_k10_s8_ir + if verify_kernel == 'merge_generic': + return merge_generic_ir + return stage1_d64_split_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_d64_q4096_c271_stage1_syncdrop", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 25856, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) + +def _compiled_stage1_syncdrop(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0224"}')) + +def _compiled_merge_k10_s5(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0225"}')) + +def _compiled_merge_k10_s6(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0226"}')) + +def _eligible_exact_q4096_d64(inputs: dict[str, Any]) -> bool: + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + return bool(inputs.get('build', False)) and str(inputs['query'].dtype) == 'torch.bfloat16' and (str(inputs['database'].dtype) == 'torch.bfloat16') and (int(inputs['B']) == 1) and (n_query == 4096) and (n_database == 4096) and (int(inputs['D']) == D64_FEAT_D) and (int(inputs['K']) == TOP_K_MAX) + +def _check_split_count(split_count: int) -> int: + split_count = int(split_count) + if split_count not in SUPPORTED_EXACT_SPLITS: + raise ValueError(''.join(['unsupported c271 exact split count: ', format(split_count, '')])) + return split_count + +def _split_count_for_inputs(inputs: dict[str, Any]) -> int: + override = os.environ.get('LOOM_KNN_D64_Q4096_C271_PROD_SPLITS') + if override is not None: + return _check_split_count(int(override)) + return DEFAULT_SPLIT_COUNT + +def route_name_for_inputs(inputs: dict[str, Any]) -> str: + if _eligible_exact_q4096_d64(inputs): + split_count = _split_count_for_inputs(inputs) + if split_count == DEFAULT_SPLIT_COUNT: + return ROUTE_D64_Q4096_SPLIT4_SYNCDROP + return ROUTE_D64_Q4096_SPLIT4_SYNCDROP.replace('split4_syncdrop', ''.join(['split', format(split_count, '')])) + return ROUTE_PARENT_AA88_V2 + +def _compiled_exact_merge(split_count: int): + if split_count == 4: + return (parent_aa88._compiled_merge_k10_s4(), merge_k10_s4_ir) + if split_count == 5: + return (_compiled_merge_k10_s5(), merge_k10_s5_ir) + if split_count == 6: + return (_compiled_merge_k10_s6(), merge_k10_s6_ir) + if split_count == 8: + return (parent_aa88._compiled_merge_k10_s8(), merge_k10_s8_ir) + raise ValueError(''.join(['unsupported c271 exact split count: ', format(split_count, '')])) + +def _launch_exact_q4096_d64(inputs: dict[str, Any]) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + split_count = _split_count_for_inputs(inputs) + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tiles * split_count + stage1_grid = min(total_work, GRID_DIM_DEFAULT) + partial_dists, partial_indices = parent_aa88_v1.parent_73a9.split_parent._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = parent_aa88_v1.parent_73a9.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), bsz * n_query, BLOCK_Q, dim, D64_FEAT_D) + tmap_database = parent_aa88_v1.parent_73a9.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, dim, D64_FEAT_D) + stage1_kernel = _compiled_stage1_syncdrop() + stage1_kernel.launch(grid=(stage1_grid, 1, 1), block=(THREADS, 1, 1), args=pack_kernel_args(stage1_d64_split_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tiles=num_q_tiles, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), shared_mem=stage1_d64_split_ir.computed_smem_bytes) + merge_kernel, merge_ir_obj = _compiled_exact_merge(split_count) + merge_grid = min((bsz * n_query + FAST_MERGE_THREADS - 1) // FAST_MERGE_THREADS, GRID_DIM_DEFAULT) + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(FAST_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_ir_obj.computed_smem_bytes) + +def launch_from_contract_inputs(inputs: dict[str, Any]) -> None: + if _eligible_exact_q4096_d64(inputs): + _launch_exact_q4096_d64(inputs) + return + parent_aa88.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(candidate, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return parent_aa88._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint: run contract correctness for selected shapes.""" + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def benchmark_knn_build_d64_q4096_c271_prodaxis_v1(*, use_cupti: bool | None=None) -> dict[str, Any]: + """Contract benchmark hook for the exact Q4096/D64 split5 producer-axis seed.""" + from .. import _dispatch_runtime as eval_mod + prior_use_cupti = eval_mod.CONTRACT.bench.get('use_cupti', True) + if use_cupti is not None: + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + try: + report = evaluate_contract(shapes=_select_contract_shapes(TARGET_SHAPES), correctness=True, benchmark=True) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + return {'tflops': report['summary']['primary_mean'], 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'report': report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_d64_q4096_c271_twostage_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_d64_q4096_c271_twostage_v1.py new file mode 100644 index 00000000..6c365c4a --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_d64_q4096_c271_twostage_v1.py @@ -0,0 +1,188 @@ +"""kNN build D64 Q4096 two-stage producer-axis candidate for c271. + +Minimum target architecture: sm_100a. This additive exact-shape seed keeps the +validated c271 D64 TMA/tcgen05 producer surface and changes split-local top-10 +state to unordered worst-slot replacement. It also keeps a split8 -> group4 +Weave-only reducer probe as an env-selectable A/B route. Non-exact shapes +delegate to the prior c271 producer-axis candidate. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import os +from functools import lru_cache +from typing import Any +from .._dispatch_runtime import dc as dc +from . import knn_build_d64_build_aa88_v1 as parent_aa88_v1 +from . import knn_build_d64_build_aa88_v2 as parent_aa88 +from . import knn_build_d64_q4096_c271_prodaxis_v1 as parent_c271 +from .._dispatch_runtime import pack_kernel_args +TOP_K_MAX = parent_aa88.TOP_K_MAX +D64_FEAT_D = parent_aa88.D64_FEAT_D +BLOCK_Q = parent_aa88.BLOCK_Q +BLOCK_M = parent_aa88.BLOCK_M +THREADS = parent_aa88.THREADS +FAST_MERGE_THREADS = parent_aa88.FAST_MERGE_THREADS +MERGE_THREADS = parent_aa88.MERGE_THREADS +GRID_DIM_DEFAULT = parent_aa88.GRID_DIM_DEFAULT +TARGET_SHAPE = 'build_dim_sweep_b1_q4096_m4096_d64_k10' +TARGET_SHAPES = (TARGET_SHAPE,) +DEFAULT_MODE = 'split4_unordered' +SUPPORTED_MODES = ('split8_g4', 'split4_unordered', 'parent_split4') +STAGE1_SPLIT4 = 4 +STAGE1_SPLIT8 = 8 +GROUP_COUNT_S8_TO_S4 = 4 +GROUP_REDUCE_THREADS = 128 +GROUPS_PER_CTA = GROUP_REDUCE_THREADS // 32 +MODULE = 'loom.examples.weave.knn_build_d64_q4096_c271_twostage_v1' +ROUTE_SPLIT8_G4 = ''.join([format(MODULE, ''), ':d64_q4096_split8_unordered_group4_merge4']) +ROUTE_SPLIT4_UNORDERED = ''.join([format(MODULE, ''), ':d64_q4096_split4_unordered_exact_merge']) +ROUTE_PARENT_C271 = 'loom.examples.weave.knn_build_d64_q4096_c271_prodaxis_v1:d64_q4096_split4_syncdrop_exact_merge' +knn_build_d64_q4096_c271_stage1_unordered_syncdrop = _decode_capture(_json_loads('{"__ir__": "knn_build_d64_q4096_c271_stage1_unordered_syncdrop", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 25856, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) +stage1_d64_unordered_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_d64_q4096_c271_stage1_unordered_syncdrop", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 25856, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) +stage1_d64_split_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_d64_q4096_c271_stage1_unordered_syncdrop", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 25856, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) +merge_generic_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_merge", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "B", "Q", "K", "split_count", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10]], "cta_group": 1, "threads": 256}')) + +def _ir_with_constants(ir_obj: Any, *, suffix: str, **updates: int) -> Any: + constants = tuple(((name, updates.get(name, value)) for name, value in ir_obj.constants)) + return dc.replace(ir_obj, symbol=''.join([format(ir_obj.symbol, ''), '_', format(suffix, '')]), constants=constants) +merge_k10_s4_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_d64_build_aa88_k10_merge_s8_rowbase_cache_s4", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 32}')) +merge_k10_s5_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_d64_build_aa88_k10_merge_s8_rowbase_cache_c271_s5", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 5]], "cta_group": 1, "threads": 32}')) +merge_k10_s6_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_d64_build_aa88_k10_merge_s8_rowbase_cache_c271_s6", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 6]], "cta_group": 1, "threads": 32}')) +merge_k10_s8_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_d64_build_aa88_k10_merge_s8_rowbase_cache", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 8]], "cta_group": 1, "threads": 32}')) +knn_build_d64_q4096_c271_twostage_group_reduce = _decode_capture(_json_loads('{"__ir__": "knn_build_d64_q4096_c271_twostage_group_reduce", "arg_keys": ["partial_dists", "partial_indices", "reduced_dists", "reduced_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 8], ["GROUP_COUNT", 4], ["GROUP_SPLITS", 2], ["GROUPS_PER_CTA", 4]], "cta_group": 1, "threads": 128}')) +group_reduce_s8g4_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_d64_q4096_c271_twostage_group_reduce", "arg_keys": ["partial_dists", "partial_indices", "reduced_dists", "reduced_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 8], ["GROUP_COUNT", 4], ["GROUP_SPLITS", 2], ["GROUPS_PER_CTA", 4]], "cta_group": 1, "threads": 128}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_D64_Q4096_C271_TWOSTAGE_VERIFY_KERNEL') + if verify_kernel == 'stage1': + return stage1_d64_unordered_ir + if verify_kernel == 'group_reduce': + return group_reduce_s8g4_ir + if verify_kernel == 'merge_s4': + return merge_k10_s4_ir + if verify_kernel == 'merge_s5': + return merge_k10_s5_ir + if verify_kernel == 'merge_s6': + return merge_k10_s6_ir + if verify_kernel == 'merge_s8': + return merge_k10_s8_ir + if verify_kernel == 'merge_generic': + return merge_generic_ir + return stage1_d64_split_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_d64_q4096_c271_stage1_unordered_syncdrop", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 25856, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) + +def _compiled_stage1_unordered_syncdrop(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0019"}')) + +def _compiled_group_reduce_s8g4(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0221"}')) + +def _compiled_merge_k10_s5(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0222"}')) + +def _compiled_merge_k10_s6(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0223"}')) + +def _eligible_exact_q4096_d64(inputs: dict[str, Any]) -> bool: + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + return bool(inputs.get('build', False)) and str(inputs['query'].dtype) == 'torch.bfloat16' and (str(inputs['database'].dtype) == 'torch.bfloat16') and (int(inputs['B']) == 1) and (n_query == 4096) and (n_database == 4096) and (int(inputs['D']) == D64_FEAT_D) and (int(inputs['K']) == TOP_K_MAX) + +def _mode_for_inputs(inputs: dict[str, Any]) -> str: + mode = os.environ.get('LOOM_KNN_D64_Q4096_C271_TWOSTAGE_MODE', DEFAULT_MODE) + if mode not in SUPPORTED_MODES: + raise ValueError(''.join(['unsupported c271 twostage mode: ', format(repr(mode), ''), '; expected one of ', format(SUPPORTED_MODES, '')])) + return mode + +def route_name_for_inputs(inputs: dict[str, Any]) -> str: + if _eligible_exact_q4096_d64(inputs): + mode = _mode_for_inputs(inputs) + if mode == 'split8_g4': + return ROUTE_SPLIT8_G4 + if mode == 'split4_unordered': + return ROUTE_SPLIT4_UNORDERED + return ROUTE_PARENT_C271 + return parent_c271.route_name_for_inputs(inputs) + +def _compiled_exact_merge(split_count: int): + if split_count == 4: + return (parent_aa88._compiled_merge_k10_s4(), merge_k10_s4_ir) + if split_count == 5: + return (_compiled_merge_k10_s5(), merge_k10_s5_ir) + if split_count == 6: + return (_compiled_merge_k10_s6(), merge_k10_s6_ir) + if split_count == 8: + return (parent_aa88._compiled_merge_k10_s8(), merge_k10_s8_ir) + raise ValueError(''.join(['unsupported c271 exact split count: ', format(split_count, '')])) + +def _launch_exact_q4096_d64(inputs: dict[str, Any]) -> None: + mode = _mode_for_inputs(inputs) + if mode == 'parent_split4': + parent_c271.launch_from_contract_inputs(inputs) + return + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + split_count = STAGE1_SPLIT8 if mode == 'split8_g4' else STAGE1_SPLIT4 + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tiles * split_count + stage1_grid = min(total_work, GRID_DIM_DEFAULT) + partial_dists, partial_indices = parent_aa88_v1.parent_73a9.split_parent._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = parent_aa88_v1.parent_73a9.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), bsz * n_query, BLOCK_Q, dim, D64_FEAT_D) + tmap_database = parent_aa88_v1.parent_73a9.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, dim, D64_FEAT_D) + stage1_kernel = _compiled_stage1_unordered_syncdrop() + stage1_kernel.launch(grid=(stage1_grid, 1, 1), block=(THREADS, 1, 1), args=pack_kernel_args(stage1_d64_split_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tiles=num_q_tiles, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), shared_mem=stage1_d64_split_ir.computed_smem_bytes) + if mode == 'split8_g4': + reduced_dists, reduced_indices = parent_aa88_v1.parent_73a9.split_parent._partial_buffers(split_count=GROUP_COUNT_S8_TO_S4, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + group_reduce_grid = (bsz * n_query * GROUP_COUNT_S8_TO_S4 + GROUPS_PER_CTA - 1) // GROUPS_PER_CTA + _compiled_group_reduce_s8g4().launch(grid=(group_reduce_grid, 1, 1), block=(GROUP_REDUCE_THREADS, 1, 1), args=[partial_dists, partial_indices, reduced_dists, reduced_indices, bsz * n_query], shared_mem=group_reduce_s8g4_ir.computed_smem_bytes) + partial_dists = reduced_dists + partial_indices = reduced_indices + split_count = GROUP_COUNT_S8_TO_S4 + merge_kernel, merge_ir_obj = _compiled_exact_merge(split_count) + merge_grid = min((bsz * n_query + FAST_MERGE_THREADS - 1) // FAST_MERGE_THREADS, GRID_DIM_DEFAULT) + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(FAST_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_ir_obj.computed_smem_bytes) + +def launch_from_contract_inputs(inputs: dict[str, Any]) -> None: + if _eligible_exact_q4096_d64(inputs): + _launch_exact_q4096_d64(inputs) + return + parent_c271.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(candidate, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return parent_c271._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint: run contract correctness for selected shapes.""" + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def benchmark_knn_build_d64_q4096_c271_twostage_v1(*, use_cupti: bool | None=None) -> dict[str, Any]: + """Contract benchmark hook for the exact Q4096/D64 two-stage producer-axis seed.""" + from .. import _dispatch_runtime as eval_mod + prior_use_cupti = eval_mod.CONTRACT.bench.get('use_cupti', True) + if use_cupti is not None: + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + try: + report = evaluate_contract(shapes=_select_contract_shapes(TARGET_SHAPES), correctness=True, benchmark=True) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + return {'tflops': report['summary']['primary_mean'], 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'report': report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dim_midk_73a9_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dim_midk_73a9_v1.py new file mode 100644 index 00000000..e1da4f06 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dim_midk_73a9_v1.py @@ -0,0 +1,138 @@ +"""kNN build dim/mid-K bucket seed for round 73a9. + +Minimum target architecture: sm_100a. This additive seed specializes the exact +BF16 build ``Q=M=2048,D=64,K=10`` dimension-sweep row with a split database +producer. The producer uses a D64 TMA/tcgen05 tile and writes split-local top-k +partials that feed the existing generic Weave split merge. Adjacent D256, +FP16-D128, K24/K28, and K64 rows delegate to already validated Weave seeds so +this branch can measure the D64 split fanout without changing shared dispatch. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import os +from functools import lru_cache +from typing import Any +from . import knn_build_evolve_7bfc_fp16_d128_knn_build_dispatch_slurm_0610_6329_v24 as dim_parent +from . import knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2_k32split_v14 as midk_parent +from . import knn_build_evolve_7bfc_split_v1 as split_parent +from . import knn_build_evolve_7bfc_v1 as base_v1 +from . import knn_build_k64stage1_splitgrid_tailinf_knn_build_dispatch_slurm_0610_6329_v40 as k64_parent +from .._dispatch_runtime import pack_kernel_args +BLOCK_Q = base_v1.BLOCK_Q +BLOCK_M = base_v1.BLOCK_M +TOP_K_MAX = base_v1.TOP_K_MAX +THREADS = base_v1.THREADS +MERGE_THREADS = split_parent.MERGE_THREADS +GRID_DIM_DEFAULT = base_v1.GRID_DIM_DEFAULT +D64_FEAT_D = 64 +D64_DEFAULT_SPLITS = 8 +DIM_TARGET_SHAPES = ('build_dim_sweep_b1_q2048_m2048_d64_k10', 'build_dim_sweep_b1_q2048_m2048_d256_k10', 'build_dtype_fp16_b1_q2048_m2048_d128_k10') +MIDK_TARGET_SHAPES = ('build_k_sweep_qm1024_k16', 'build_k_sweep_qm2048_k24', 'build_k_sweep_qm2048_k28', 'build_k_sweep_qm4096_k28', 'build_over32_stress_qm2048_k64') +TARGET_SHAPES = (*DIM_TARGET_SHAPES, *MIDK_TARGET_SHAPES) +knn_build_dim_midk_73a9_d64_split_stage1 = _decode_capture(_json_loads('{"__ir__": "knn_build_dim_midk_73a9_d64_split_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 25856, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) +stage1_d64_split_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_dim_midk_73a9_d64_split_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 25856, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) +merge_generic_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_merge", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "B", "Q", "K", "split_count", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10]], "cta_group": 1, "threads": 256}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_DIMMIDK_73A9_VERIFY_KERNEL') + if verify_kernel == 'merge_generic': + return merge_generic_ir + if verify_kernel == 'midk_stage1': + os.environ['LOOM_KNN_K32SPLIT_VERIFY_TOP_K_BUCKET'] = '28' + return midk_parent._verify_export_ir() + if verify_kernel == 'k64_stage1': + os.environ['LOOM_KNN_OVER32_VERIFY_KERNEL'] = 'stage1_k64' + return k64_parent._verify_export_ir() + return stage1_d64_split_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_dim_midk_73a9_d64_split_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 25856, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) + +def _compiled_d64_stage1(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0017"}')) + +def _d64_split_count() -> int: + return int(os.environ.get('LOOM_KNN_DIMMIDK_73A9_D64_SPLITS', str(D64_DEFAULT_SPLITS))) + +def _eligible_d64_split(inputs: dict[str, Any]) -> bool: + return bool(inputs.get('build', False)) and str(inputs['query'].dtype) == 'torch.bfloat16' and (str(inputs['database'].dtype) == 'torch.bfloat16') and (int(inputs['B']) == 1) and (int(inputs['Q']) == 2048) and (int(inputs['M']) == 2048) and (int(inputs['D']) == D64_FEAT_D) and (int(inputs['K']) == TOP_K_MAX) + +def _launch_d64_split(inputs: dict[str, Any]) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + split_count = _d64_split_count() + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tiles * split_count + stage1_grid = min(total_work, GRID_DIM_DEFAULT) + merge_grid = min((bsz * n_query + MERGE_THREADS - 1) // MERGE_THREADS, GRID_DIM_DEFAULT) + partial_dists, partial_indices = split_parent._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), bsz * n_query, BLOCK_Q, dim, D64_FEAT_D) + tmap_database = base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, dim, D64_FEAT_D) + stage1_kernel = _compiled_d64_stage1() + stage1_kernel.launch(grid=(stage1_grid, 1, 1), block=(THREADS, 1, 1), args=pack_kernel_args(stage1_d64_split_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tiles=num_q_tiles, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), shared_mem=stage1_d64_split_ir.computed_smem_bytes) + merge_kernel = split_parent._compiled_merge() + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz, n_query, top_k, split_count, bsz * n_query], shared_mem=merge_generic_ir.computed_smem_bytes) + +def _eligible_midk_parent(inputs: dict[str, Any]) -> bool: + top_k = int(inputs['K']) + return bool(inputs.get('build', False)) and str(inputs['query'].dtype) == 'torch.bfloat16' and (str(inputs['database'].dtype) == 'torch.bfloat16') and (int(inputs['D']) == midk_parent.FEAT_D) and (int(inputs['B']) == 1) and (int(inputs['Q']) == int(inputs['M'])) and (int(inputs['Q']) == 1024 and top_k == 16 or (int(inputs['Q']) == 2048 and top_k in (24, 28)) or (int(inputs['Q']) == 4096 and top_k == 28)) + +def _eligible_k64_parent(inputs: dict[str, Any]) -> bool: + return k64_parent._eligible_over32_build(inputs) and int(inputs['Q']) == 2048 and (int(inputs['K']) == 64) + +def launch_from_contract_inputs(inputs: dict[str, Any]) -> None: + if _eligible_d64_split(inputs): + _launch_d64_split(inputs) + return + if _eligible_k64_parent(inputs): + k64_parent._launch_over32_split_path(inputs) + return + if _eligible_midk_parent(inputs): + midk_parent.launch_from_contract_inputs(inputs) + return + dim_parent.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(candidate, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + from .._dispatch_runtime import CANONICAL_SHAPES + if shape_labels is None: + return list(CANONICAL_SHAPES) + wanted = {str(label) for label in shape_labels} + selected = [shape for shape in CANONICAL_SHAPES if shape['label'] in wanted] + missing = wanted - {shape['label'] for shape in selected} + if missing: + raise ValueError(''.join(['unknown kNN build contract shape(s): ', format(sorted(missing), '')])) + return selected + +def compile_and_launch_knn_build(*, shape_labels=('build_dim_sweep_b1_q2048_m2048_d64_k10',), benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint: run real contract correctness for selected shapes.""" + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def benchmark_knn_build_dim_midk_73a9_v1(*, use_cupti: bool | None=None) -> dict[str, Any]: + """Opt-in benchmark hook for the dim/mid-K target bucket.""" + from .. import _dispatch_runtime as eval_mod + prior_use_cupti = eval_mod.CONTRACT.bench.get('use_cupti', True) + if use_cupti is not None: + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + try: + report = evaluate_contract(shapes=_select_contract_shapes(TARGET_SHAPES), correctness=True, benchmark=True) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + return {'tflops': report['summary']['primary_mean'], 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'report': report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dim_midk_bad5_fp16split_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dim_midk_bad5_fp16split_v1.py new file mode 100644 index 00000000..f1d36d1f --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dim_midk_bad5_fp16split_v1.py @@ -0,0 +1,187 @@ +"""kNN build dim/mid-K FP16 split rescue for round bad5. + +Minimum target architecture: sm_100a. This additive sidecar composes the +source-policy-clean D64/D256/FP16 split seeds with the inherited mid-K and K64 +Weave delegates. The FP16-D128 exact row uses the verified split-grid +tcgen05/TMA producer from df2f with S8 partials feeding the generic Weave split +merge. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dim_midk_73a9_v1 as parent_73a9 +from . import knn_build_dim_midk_df2f_v1 as split_seed +TARGET_SHAPES = parent_73a9.TARGET_SHAPES +TARGET_SHAPE_SET = set(TARGET_SHAPES) +DIM_TARGET_SHAPES = parent_73a9.DIM_TARGET_SHAPES +MIDK_TARGET_SHAPES = parent_73a9.MIDK_TARGET_SHAPES +DEFAULT_D256_SPLITS = 8 +DEFAULT_FP16_SPLITS = 8 +ROUTE_D64 = 'loom.examples.weave.knn_build_dim_midk_bad5_fp16split_v1:d64_split_s8' +ROUTE_D256 = 'loom.examples.weave.knn_build_dim_midk_bad5_fp16split_v1:d256_split_s8' +ROUTE_FP16_D128 = 'loom.examples.weave.knn_build_dim_midk_bad5_fp16split_v1:fp16_d128_split_s8' +ROUTE_PARENT = 'loom.examples.weave.knn_build_dim_midk_73a9_v1' +stage1_d256_split_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_dim_midk_df2f_d256_split_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 99584, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) +stage1_fp16_split_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_dim_midk_df2f_fp16_split_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) +merge_generic_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_merge", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "B", "Q", "K", "split_count", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10]], "cta_group": 1, "threads": 256}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_DIMMIDK_BAD5_FP16_VERIFY_KERNEL') + if verify_kernel == 'd64_parent': + return parent_73a9.stage1_d64_split_ir + if verify_kernel == 'd256_split': + return stage1_d256_split_ir + if verify_kernel == 'fp16_split': + return stage1_fp16_split_ir + if verify_kernel == 'merge_generic': + return merge_generic_ir + return stage1_fp16_split_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_dim_midk_df2f_fp16_split_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) + +def _d256_split_count() -> int: + return int(os.environ.get('LOOM_KNN_DIMMIDK_BAD5_D256_SPLITS', str(DEFAULT_D256_SPLITS))) + +def _fp16_split_count() -> int: + return int(os.environ.get('LOOM_KNN_DIMMIDK_BAD5_FP16_SPLITS', str(DEFAULT_FP16_SPLITS))) + +def _dtype_name(inputs: dict[str, Any]) -> str: + query = inputs.get('query') + if query is not None: + return str(query.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')) + +def _label_can_hit(inputs: dict[str, Any], label: str) -> bool: + value = inputs.get('label') + return value is None or str(value) == label + +def _exact_build_qm(inputs: dict[str, Any], *, q: int, d: int, k: int, dtype: str) -> bool: + return bool(inputs.get('build', False)) and int(inputs.get('B', -1)) == 1 and (int(inputs.get('Q', -1)) == q) and (int(inputs.get('M', -1)) == q) and (int(inputs.get('D', -1)) == d) and (int(inputs.get('K', -1)) == k) and (_dtype_name(inputs) == dtype) + +def _eligible_d64(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, DIM_TARGET_SHAPES[0]) and _exact_build_qm(inputs, q=2048, d=parent_73a9.D64_FEAT_D, k=parent_73a9.TOP_K_MAX, dtype='bfloat16') + +def _eligible_d256(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, DIM_TARGET_SHAPES[1]) and _exact_build_qm(inputs, q=2048, d=split_seed.D256_FEAT_D, k=parent_73a9.TOP_K_MAX, dtype='bfloat16') + +def _eligible_fp16_d128(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, DIM_TARGET_SHAPES[2]) and _exact_build_qm(inputs, q=2048, d=split_seed.FP16_FEAT_D, k=parent_73a9.TOP_K_MAX, dtype='float16') + +def route_for_contract_inputs(inputs: dict[str, Any]) -> str: + if _eligible_d64(inputs): + return ROUTE_D64 + if _eligible_d256(inputs): + return ROUTE_D256 + if _eligible_fp16_d128(inputs): + return ROUTE_FP16_D128 + return ROUTE_PARENT + +def _launch_d256_split(inputs: dict[str, Any]) -> None: + split_seed._launch_split_stage(inputs, split_count=_d256_split_count(), feature_dim=split_seed.D256_FEAT_D, kernel=split_seed._compiled_d256_stage1(), stage1_ir=stage1_d256_split_ir) + +def _launch_fp16_split(inputs: dict[str, Any]) -> None: + split_seed._launch_split_stage(inputs, split_count=_fp16_split_count(), feature_dim=split_seed.FP16_FEAT_D, kernel=split_seed._compiled_fp16_stage1(), stage1_ir=stage1_fp16_split_ir, fp16=True) + +def launch_from_contract_inputs(inputs: dict[str, Any]) -> None: + route = route_for_contract_inputs(inputs) + if route == ROUTE_D64: + parent_73a9._launch_d64_split(inputs) + return + if route == ROUTE_D256: + _launch_d256_split(inputs) + return + if route == ROUTE_FP16_D128: + _launch_fp16_split(inputs) + return + parent_73a9.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + from .._dispatch_runtime import CANONICAL_SHAPES + if shape_labels is None: + wanted = TARGET_SHAPE_SET + else: + wanted = {str(label) for label in shape_labels} + selected = [shape for shape in CANONICAL_SHAPES if shape['label'] in wanted] + missing = wanted - {shape['label'] for shape in selected} + if missing: + raise ValueError(''.join(['unknown kNN build contract shape(s): ', format(sorted(missing), '')])) + return selected + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint: run real contract correctness for selected shapes.""" + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=None, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + previous = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = previous + +def _trace_inputs_from_shape(shape: dict[str, Any]) -> dict[str, Any]: + params = dict(shape['params']) + return {'label': shape['label'], 'B': int(params['B']), 'Q': int(params['Q']), 'M': int(params['M']), 'D': int(params['D']), 'K': int(params['K']), 'dtype': str(params.get('dtype', 'bfloat16')), 'build': bool(params.get('build', False))} + +def route_trace_for_shapes(shape_labels=None) -> list[dict[str, Any]]: + trace = [] + for shape in _select_contract_shapes(shape_labels): + inputs = _trace_inputs_from_shape(shape) + route = route_for_contract_inputs(inputs) + trace.append({'shape_key': shape['label'], 'selected_route': route, 'route_kind': 'specialized' if route != ROUTE_PARENT else 'parent_delegate', 'guard_condition': _guard_description(route)}) + return trace + +def _guard_description(route: str) -> str: + if route == ROUTE_D64: + return 'exact BF16 build B1 Q=M=2048 D64 K10 inherited from 73a9 split route' + if route == ROUTE_D256: + return 'exact BF16 build B1 Q=M=2048 D256 K10 split-grid route' + if route == ROUTE_FP16_D128: + return 'exact FP16 build B1 Q=M=2048 D128 K10 split-grid route' + return 'guard miss delegates to 73a9 dim/mid-K parent' + +def _per_shape_deltas(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> dict[str, Any]: + result: dict[str, Any] = {} + for label in TARGET_SHAPES: + cand = candidate_report.get('per_shape', {}).get(label, {}) + base = baseline_report.get('per_shape', {}).get(label, {}) + cand_ms = cand.get('kernel_ms') + base_ms = base.get('kernel_ms') + trace_inputs = {'label': label, **cand} + result[label] = {'candidate_route': route_for_contract_inputs(trace_inputs), 'candidate_ms': cand_ms, 'baseline_ms': base_ms, 'flashlib_ms': cand.get('flashlib_ms'), 'candidate_tflops': cand.get('tflops'), 'baseline_tflops': base.get('tflops'), 'speedup_vs_73a9': base_ms / cand_ms if cand_ms and base_ms else None, 'ratio_vs_flashlib': cand.get('ratio_vs_flashlib'), 'passed': cand.get('passed'), 'timing_backend': cand.get('timing_backend')} + return result + +def benchmark_knn_build_dim_midk_bad5_fp16split_v1(*, use_cupti: bool=True, shape_labels=None, run_baseline: bool=True) -> dict[str, Any]: + """Benchmark the dim/mid-K FP16 split sidecar against 73a9.""" + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels) + baseline_report = None + if run_baseline: + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=parent_73a9.candidate) + payload: dict[str, Any] = {'tflops': candidate_report['summary']['primary_mean'] or 0.0, 'all_correct': candidate_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'measured_entrypoint': 'loom.examples.weave.knn_build_dim_midk_bad5_fp16split_v1:benchmark_knn_build_dim_midk_bad5_fp16split_v1', 'measured_shape_labels': tuple(TARGET_SHAPES if shape_labels is None else shape_labels), 'route_trace': route_trace_for_shapes(shape_labels), 'contract_summary': candidate_report['summary'], 'contract_performance': candidate_report['performance'], 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'split_counts': {'d256': _d256_split_count(), 'fp16_d128': _fp16_split_count()}, 'report': candidate_report} + if baseline_report is not None: + payload['baseline_entrypoint'] = 'loom.examples.weave.knn_build_dim_midk_73a9_v1:candidate' + payload['baseline_summary'] = baseline_report['summary'] + payload['per_shape_delta_vs_73a9'] = _per_shape_deltas(candidate_report, baseline_report) + baseline_mean = baseline_report['summary']['primary_mean'] + payload['speedup_vs_73a9_primary_mean'] = candidate_report['summary']['primary_mean'] / baseline_mean if baseline_mean else None + return payload diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dim_midk_bad5_k24k28_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dim_midk_bad5_k24k28_v1.py new file mode 100644 index 00000000..c6cb624e --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dim_midk_bad5_k24k28_v1.py @@ -0,0 +1,251 @@ +"""kNN build dim/mid-K K24/K28 exact-capacity seed for round bad5. + +Minimum target architecture: sm_100a. This additive sidecar keeps the validated +D64/D256/FP16 split routes from ``bad5_fp16split`` and replaces the inherited +K24/K28 mid-K delegates with exact-capacity Weave routes. The q2048 K24/K28 +rows use eight database splits with exact cached merges; the q4096 K28 row uses +an exact unordered four-split producer/merge. K64 remains delegated to the +parent K64 route. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from collections.abc import Callable +from functools import lru_cache +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dim_midk_bad5_fp16split_v1 as parent_bad5 +from . import knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2_k32split_v20 as midk_v20 +from . import knn_build_evolve_7bfc_split_v1 as split_parent +from . import knn_build_evolve_7bfc_v1 as base_v1 +from .._dispatch_runtime import pack_kernel_args +TARGET_SHAPES = parent_bad5.TARGET_SHAPES +TARGET_SHAPE_SET = set(TARGET_SHAPES) +DIM_TARGET_SHAPES = parent_bad5.DIM_TARGET_SHAPES +MIDK_TARGET_SHAPES = parent_bad5.MIDK_TARGET_SHAPES +BLOCK_Q = midk_v20.BLOCK_Q +BLOCK_M = midk_v20.BLOCK_M +FEAT_D = midk_v20.FEAT_D +STAGE1_THREADS = midk_v20.STAGE1_THREADS +K32_MERGE_THREADS = midk_v20.K32_MERGE_THREADS +GRID_DIM_DEFAULT = midk_v20.GRID_DIM_DEFAULT +CTA_GROUP = midk_v20.CTA_GROUP +MIDK_Q2048_SPLITS = 8 +MIDK_Q4096_SPLITS = midk_v20.MEDIUM_SPLITS +ROUTE_D64 = parent_bad5.ROUTE_D64 +ROUTE_D256 = parent_bad5.ROUTE_D256 +ROUTE_FP16_D128 = parent_bad5.ROUTE_FP16_D128 +ROUTE_K24_Q2048 = 'loom.examples.weave.knn_build_dim_midk_bad5_k24k28_v1:k24_q2048_s8_exact' +ROUTE_K28_Q2048 = 'loom.examples.weave.knn_build_dim_midk_bad5_k24k28_v1:k28_q2048_s8_exact' +ROUTE_K28_Q4096 = 'loom.examples.weave.knn_build_dim_midk_bad5_k24k28_v1:k28_q4096_s4_unordered_exact' +ROUTE_PARENT = parent_bad5.ROUTE_PARENT +stage1_k24_s8_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_bad5k24s8", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 24]], "cta_group": 1, "threads": 192}')) +stage1_k28_s8_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_bad5k28s8", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 28]], "cta_group": 1, "threads": 192}')) +stage1_k28_unordered_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k30unordered_bad5k28unordered", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 28]], "cta_group": 1, "threads": 192}')) +merge_k24_s8_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_bad5k24s8", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 24], ["SPLIT_COUNT", 8]], "cta_group": 1, "threads": 32}')) +merge_k28_s8_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_bad5k28s8", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 28], ["SPLIT_COUNT", 8]], "cta_group": 1, "threads": 32}')) +merge_k28_unordered_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_unordered_k30unordered_bad5k28unordered", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 28], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 32}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_DIMMIDK_BAD5_K24K28_VERIFY_KERNEL') + if verify_kernel == 'stage1_k24_s8': + return stage1_k24_s8_ir + if verify_kernel == 'stage1_k28_s8': + return stage1_k28_s8_ir + if verify_kernel == 'stage1_k28_unordered': + return stage1_k28_unordered_ir + if verify_kernel == 'merge_k24_s8': + return merge_k24_s8_ir + if verify_kernel == 'merge_k28_s8': + return merge_k28_s8_ir + if verify_kernel == 'merge_k28_unordered': + return merge_k28_unordered_ir + if verify_kernel == 'fp16_split': + return parent_bad5.stage1_fp16_split_ir + if verify_kernel == 'd256_split': + return parent_bad5.stage1_d256_split_ir + if verify_kernel == 'merge_generic': + return parent_bad5.merge_generic_ir + return stage1_k24_s8_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_bad5k24s8", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 24]], "cta_group": 1, "threads": 192}')) + +def _compiled_stage1_k24_s8(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0178"}')) + +def _compiled_stage1_k28_s8(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0179"}')) + +def _compiled_stage1_k28_unordered(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0055"}')) + +def _compiled_merge_k24_s8(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0180"}')) + +def _compiled_merge_k28_s8(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0181"}')) + +def _compiled_merge_k28_unordered(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0056"}')) + +def _dtype_name(inputs: dict[str, Any]) -> str: + query = inputs.get('query') + if query is not None: + return str(query.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')) + +def _label_can_hit(inputs: dict[str, Any], label: str) -> bool: + value = inputs.get('label') + return value is None or str(value) == label + +def _exact_build_qm(inputs: dict[str, Any], *, q: int, k: int) -> bool: + return bool(inputs.get('build', False)) and int(inputs.get('B', -1)) == 1 and (int(inputs.get('Q', -1)) == q) and (int(inputs.get('M', -1)) == q) and (int(inputs.get('D', -1)) == FEAT_D) and (int(inputs.get('K', -1)) == k) and (_dtype_name(inputs) == 'bfloat16') + +def _eligible_k24_q2048(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, 'build_k_sweep_qm2048_k24') and _exact_build_qm(inputs, q=2048, k=24) + +def _eligible_k28_q2048(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, 'build_k_sweep_qm2048_k28') and _exact_build_qm(inputs, q=2048, k=28) + +def _eligible_k28_q4096(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, 'build_k_sweep_qm4096_k28') and _exact_build_qm(inputs, q=4096, k=28) + +def _launch_midk_exact(inputs: dict[str, Any], *, split_count: int, stage1_ir_obj: Any, merge_ir_obj: Any, stage1_kernel, merge_kernel) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_q_tile_pairs = (num_q_tiles + CTA_GROUP - 1) // CTA_GROUP + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tile_pairs * split_count + stage1_grid = min(total_work * CTA_GROUP, GRID_DIM_DEFAULT) + merge_grid = min((bsz * n_query + K32_MERGE_THREADS - 1) // K32_MERGE_THREADS, GRID_DIM_DEFAULT) + partial_dists, partial_indices = split_parent._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), bsz * n_query, BLOCK_Q, dim, dim) + tmap_database = base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, dim, dim) + stage1_kernel.launch_cluster(grid=(stage1_grid, 1, 1), block=(STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage1_ir_obj, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tile_pairs=num_q_tile_pairs, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), cluster_dims=(CTA_GROUP, 1, 1), shared_mem=stage1_ir_obj.computed_smem_bytes) + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(K32_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_ir_obj.computed_smem_bytes) + +def _launch_k24_q2048(inputs: dict[str, Any]) -> None: + _launch_midk_exact(inputs, split_count=MIDK_Q2048_SPLITS, stage1_ir_obj=stage1_k24_s8_ir, merge_ir_obj=merge_k24_s8_ir, stage1_kernel=_compiled_stage1_k24_s8(), merge_kernel=_compiled_merge_k24_s8()) + +def _launch_k28_q2048(inputs: dict[str, Any]) -> None: + _launch_midk_exact(inputs, split_count=MIDK_Q2048_SPLITS, stage1_ir_obj=stage1_k28_s8_ir, merge_ir_obj=merge_k28_s8_ir, stage1_kernel=_compiled_stage1_k28_s8(), merge_kernel=_compiled_merge_k28_s8()) + +def _launch_k28_q4096(inputs: dict[str, Any]) -> None: + _launch_midk_exact(inputs, split_count=MIDK_Q4096_SPLITS, stage1_ir_obj=stage1_k28_unordered_ir, merge_ir_obj=merge_k28_unordered_ir, stage1_kernel=_compiled_stage1_k28_unordered(), merge_kernel=_compiled_merge_k28_unordered()) + +def route_for_contract_inputs(inputs: dict[str, Any]) -> str: + if _eligible_k24_q2048(inputs): + return ROUTE_K24_Q2048 + if _eligible_k28_q2048(inputs): + return ROUTE_K28_Q2048 + if _eligible_k28_q4096(inputs): + return ROUTE_K28_Q4096 + return parent_bad5.route_for_contract_inputs(inputs) + +def launch_from_contract_inputs(inputs: dict[str, Any]) -> None: + route = route_for_contract_inputs(inputs) + if route == ROUTE_K24_Q2048: + _launch_k24_q2048(inputs) + return + if route == ROUTE_K28_Q2048: + _launch_k28_q2048(inputs) + return + if route == ROUTE_K28_Q4096: + _launch_k28_q4096(inputs) + return + parent_bad5.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + from .._dispatch_runtime import CANONICAL_SHAPES + if shape_labels is None: + wanted = TARGET_SHAPE_SET + else: + wanted = {str(label) for label in shape_labels} + selected = [shape for shape in CANONICAL_SHAPES if shape['label'] in wanted] + missing = wanted - {shape['label'] for shape in selected} + if missing: + raise ValueError(''.join(['unknown kNN build contract shape(s): ', format(sorted(missing), '')])) + return selected + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint: run real contract correctness for selected shapes.""" + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=None, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + previous = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = previous + +def _trace_inputs_from_shape(shape: dict[str, Any]) -> dict[str, Any]: + params = dict(shape['params']) + return {'label': shape['label'], 'B': int(params['B']), 'Q': int(params['Q']), 'M': int(params['M']), 'D': int(params['D']), 'K': int(params['K']), 'dtype': str(params.get('dtype', 'bfloat16')), 'build': bool(params.get('build', False))} + +def route_trace_for_shapes(shape_labels=None) -> list[dict[str, Any]]: + trace = [] + for shape in _select_contract_shapes(shape_labels): + inputs = _trace_inputs_from_shape(shape) + route = route_for_contract_inputs(inputs) + trace.append({'shape_key': shape['label'], 'selected_route': route, 'route_kind': 'specialized' if route != ROUTE_PARENT else 'parent_delegate', 'guard_condition': _guard_description(route)}) + return trace + +def _guard_description(route: str) -> str: + if route == ROUTE_K24_Q2048: + return 'exact BF16 build B1 Q=M=2048 D128 K24 split8 exact-capacity route' + if route == ROUTE_K28_Q2048: + return 'exact BF16 build B1 Q=M=2048 D128 K28 split8 exact-capacity route' + if route == ROUTE_K28_Q4096: + return 'exact BF16 build B1 Q=M=4096 D128 K28 split4 unordered exact-capacity route' + return parent_bad5._guard_description(route) + +def _per_shape_deltas(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> dict[str, Any]: + result: dict[str, Any] = {} + for label in TARGET_SHAPES: + cand = candidate_report.get('per_shape', {}).get(label, {}) + base = baseline_report.get('per_shape', {}).get(label, {}) + cand_ms = cand.get('kernel_ms') + base_ms = base.get('kernel_ms') + result[label] = {'candidate_route': route_for_contract_inputs({'label': label, **cand}), 'candidate_ms': cand_ms, 'baseline_ms': base_ms, 'flashlib_ms': cand.get('flashlib_ms'), 'candidate_tflops': cand.get('tflops'), 'baseline_tflops': base.get('tflops'), 'speedup_vs_bad5_fp16split': base_ms / cand_ms if cand_ms and base_ms else None, 'ratio_vs_flashlib': cand.get('ratio_vs_flashlib'), 'passed': cand.get('passed'), 'timing_backend': cand.get('timing_backend')} + return result + +def benchmark_knn_build_dim_midk_bad5_k24k28_v1(*, use_cupti: bool=True, shape_labels=None, run_baseline: bool=True) -> dict[str, Any]: + """Benchmark the K24/K28 exact sidecar against bad5_fp16split.""" + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels) + baseline_report = None + if run_baseline: + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=parent_bad5.candidate) + payload: dict[str, Any] = {'tflops': candidate_report['summary']['primary_mean'] or 0.0, 'all_correct': candidate_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'measured_entrypoint': 'loom.examples.weave.knn_build_dim_midk_bad5_k24k28_v1:benchmark_knn_build_dim_midk_bad5_k24k28_v1', 'measured_shape_labels': tuple(TARGET_SHAPES if shape_labels is None else shape_labels), 'route_trace': route_trace_for_shapes(shape_labels), 'contract_summary': candidate_report['summary'], 'contract_performance': candidate_report['performance'], 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'split_counts': {'k24_q2048': MIDK_Q2048_SPLITS, 'k28_q2048': MIDK_Q2048_SPLITS, 'k28_q4096': MIDK_Q4096_SPLITS}, 'report': candidate_report} + if baseline_report is not None: + payload['baseline_entrypoint'] = 'loom.examples.weave.knn_build_dim_midk_bad5_fp16split_v1:candidate' + payload['baseline_summary'] = baseline_report['summary'] + payload['per_shape_delta_vs_bad5_fp16split'] = _per_shape_deltas(candidate_report, baseline_report) + baseline_mean = baseline_report['summary']['primary_mean'] + payload['speedup_vs_bad5_fp16split_primary_mean'] = candidate_report['summary']['primary_mean'] / baseline_mean if baseline_mean else None + return payload diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dim_midk_bad5_k64split8_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dim_midk_bad5_k64split8_v1.py new file mode 100644 index 00000000..384a7a27 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dim_midk_bad5_k64split8_v1.py @@ -0,0 +1,179 @@ +"""kNN build dim/mid-K K64 split8 cleanup seed for round bad5. + +Minimum target architecture: sm_100a. This additive sidecar keeps the validated +dim, FP16, K16, K24, and K28 routes from ``bad5_k24k28`` and replaces only the +exact BF16 build ``B=1,Q=M=2048,D=128,K=64`` row. The K64 row uses the v40 +tail-infinity tcgen05/TMA producer at eight database splits and the v40 S8 +warp-select merge, raising the build grid from 64 CTAs to 128 CTAs while keeping +the result on the contract-visible distances/indices path. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from collections.abc import Callable +from functools import lru_cache +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dim_midk_bad5_k24k28_v1 as parent_bad5 +from . import knn_build_k64stage1_splitgrid_tailinf_knn_build_dispatch_slurm_0610_6329_v40 as k64_seed +from .._dispatch_runtime import pack_kernel_args +TARGET_SHAPES = parent_bad5.TARGET_SHAPES +TARGET_SHAPE_SET = set(TARGET_SHAPES) +DIM_TARGET_SHAPES = parent_bad5.DIM_TARGET_SHAPES +MIDK_TARGET_SHAPES = parent_bad5.MIDK_TARGET_SHAPES +K64_Q2048_SPLITS = 8 +ROUTE_K64_Q2048 = 'loom.examples.weave.knn_build_dim_midk_bad5_k64split8_v1:k64_q2048_s8_tailinf' +ROUTE_PARENT = 'loom.examples.weave.knn_build_dim_midk_bad5_k24k28_v1' +stage1_k64_s8_tailinf_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_k64_stage1_tailinf_k64over32tailinfsplitgrid", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 64]], "cta_group": 1, "threads": 192}')) +merge_k64_s8_warp_select_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_k64_merge_s8_unordered_warp_select_k64over32s8warpselect", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 64], ["SPLIT_COUNT", 8]], "cta_group": 1, "threads": 128}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_DIMMIDK_BAD5_K64S8_VERIFY_KERNEL') + if verify_kernel == 'stage1_k64_s8_tailinf': + return stage1_k64_s8_tailinf_ir + if verify_kernel == 'merge_k64_s8_warp_select': + return merge_k64_s8_warp_select_ir + if verify_kernel == 'parent_k24': + return parent_bad5.stage1_k24_s8_ir + return stage1_k64_s8_tailinf_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_k64_stage1_tailinf_k64over32tailinfsplitgrid", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 64]], "cta_group": 1, "threads": 192}')) + +def _compiled_stage1_k64_s8_tailinf(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0063"}')) + +def _compiled_merge_k64_s8_warp_select(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0064"}')) + +def _dtype_name(inputs: dict[str, Any]) -> str: + query = inputs.get('query') + if query is not None: + return str(query.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')) + +def _label_can_hit(inputs: dict[str, Any], label: str) -> bool: + value = inputs.get('label') + return value is None or str(value) == label + +def _eligible_k64_q2048(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, 'build_over32_stress_qm2048_k64') and bool(inputs.get('build', False)) and (int(inputs.get('B', -1)) == 1) and (int(inputs.get('Q', -1)) == 2048) and (int(inputs.get('M', -1)) == 2048) and (int(inputs.get('D', -1)) == k64_seed.FEAT_D) and (int(inputs.get('K', -1)) == 64) and (_dtype_name(inputs) == 'bfloat16') + +def _launch_k64_q2048_split8(inputs: dict[str, Any]) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + split_count = K64_Q2048_SPLITS + num_q_tiles = (n_query + k64_seed.BLOCK_Q - 1) // k64_seed.BLOCK_Q + num_q_tile_pairs = (num_q_tiles + k64_seed.CTA_GROUP - 1) // k64_seed.CTA_GROUP + num_db_tiles = (n_database + k64_seed.BLOCK_M - 1) // k64_seed.BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tile_pairs * split_count + stage1_grid = min(total_work * k64_seed.CTA_GROUP, k64_seed.GRID_DIM_DEFAULT) + merge_grid = (bsz * n_query + 3) // 4 + partial_dists, partial_indices = k64_seed.parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = k64_seed.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), bsz * n_query, k64_seed.BLOCK_Q, dim, dim) + tmap_database = k64_seed.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, k64_seed.BLOCK_M, dim, dim) + stage1_kernel = _compiled_stage1_k64_s8_tailinf() + stage1_kernel.launch_cluster(grid=(stage1_grid, 1, 1), block=(k64_seed.STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage1_k64_s8_tailinf_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tile_pairs=num_q_tile_pairs, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), cluster_dims=(k64_seed.CTA_GROUP, 1, 1), shared_mem=stage1_k64_s8_tailinf_ir.computed_smem_bytes) + merge_kernel = _compiled_merge_k64_s8_warp_select() + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(k64_seed.K64_COOP_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_k64_s8_warp_select_ir.computed_smem_bytes) + +def route_for_contract_inputs(inputs: dict[str, Any]) -> str: + if _eligible_k64_q2048(inputs): + return ROUTE_K64_Q2048 + return parent_bad5.route_for_contract_inputs(inputs) + +def launch_from_contract_inputs(inputs: dict[str, Any]) -> None: + route = route_for_contract_inputs(inputs) + if route == ROUTE_K64_Q2048: + _launch_k64_q2048_split8(inputs) + return + parent_bad5.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + from .._dispatch_runtime import CANONICAL_SHAPES + if shape_labels is None: + wanted = TARGET_SHAPE_SET + else: + wanted = {str(label) for label in shape_labels} + selected = [shape for shape in CANONICAL_SHAPES if shape['label'] in wanted] + missing = wanted - {shape['label'] for shape in selected} + if missing: + raise ValueError(''.join(['unknown kNN build contract shape(s): ', format(sorted(missing), '')])) + return selected + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint: run real contract correctness for selected shapes.""" + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=None, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + previous = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = previous + +def _trace_inputs_from_shape(shape: dict[str, Any]) -> dict[str, Any]: + params = dict(shape['params']) + return {'label': shape['label'], 'B': int(params['B']), 'Q': int(params['Q']), 'M': int(params['M']), 'D': int(params['D']), 'K': int(params['K']), 'dtype': str(params.get('dtype', 'bfloat16')), 'build': bool(params.get('build', False))} + +def route_trace_for_shapes(shape_labels=None) -> list[dict[str, Any]]: + trace = [] + for shape in _select_contract_shapes(shape_labels): + inputs = _trace_inputs_from_shape(shape) + route = route_for_contract_inputs(inputs) + trace.append({'shape_key': shape['label'], 'selected_route': route, 'route_kind': 'specialized' if route != ROUTE_PARENT else 'parent_delegate', 'guard_condition': _guard_description(route)}) + return trace + +def _guard_description(route: str) -> str: + if route == ROUTE_K64_Q2048: + return 'exact BF16 build B1 Q=M=2048 D128 K64 split8 tail-infinity route' + return parent_bad5._guard_description(route) + +def _per_shape_deltas(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> dict[str, Any]: + result: dict[str, Any] = {} + for label in TARGET_SHAPES: + cand = candidate_report.get('per_shape', {}).get(label, {}) + base = baseline_report.get('per_shape', {}).get(label, {}) + cand_ms = cand.get('kernel_ms') + base_ms = base.get('kernel_ms') + result[label] = {'candidate_route': route_for_contract_inputs({'label': label, **cand}), 'candidate_ms': cand_ms, 'baseline_ms': base_ms, 'flashlib_ms': cand.get('flashlib_ms'), 'candidate_tflops': cand.get('tflops'), 'baseline_tflops': base.get('tflops'), 'speedup_vs_bad5_k24k28': base_ms / cand_ms if cand_ms and base_ms else None, 'ratio_vs_flashlib': cand.get('ratio_vs_flashlib'), 'passed': cand.get('passed'), 'timing_backend': cand.get('timing_backend')} + return result + +def benchmark_knn_build_dim_midk_bad5_k64split8_v1(*, use_cupti: bool=True, shape_labels=None, run_baseline: bool=True) -> dict[str, Any]: + """Benchmark the K64 split8 sidecar against bad5_k24k28.""" + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels) + baseline_report = None + if run_baseline: + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=parent_bad5.candidate) + payload: dict[str, Any] = {'tflops': candidate_report['summary']['primary_mean'] or 0.0, 'all_correct': candidate_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'measured_entrypoint': 'loom.examples.weave.knn_build_dim_midk_bad5_k64split8_v1:benchmark_knn_build_dim_midk_bad5_k64split8_v1', 'measured_shape_labels': tuple(TARGET_SHAPES if shape_labels is None else shape_labels), 'route_trace': route_trace_for_shapes(shape_labels), 'contract_summary': candidate_report['summary'], 'contract_performance': candidate_report['performance'], 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'split_counts': {'k64_q2048': K64_Q2048_SPLITS}, 'report': candidate_report} + if baseline_report is not None: + payload['baseline_entrypoint'] = 'loom.examples.weave.knn_build_dim_midk_bad5_k24k28_v1:candidate' + payload['baseline_summary'] = baseline_report['summary'] + payload['per_shape_delta_vs_bad5_k24k28'] = _per_shape_deltas(candidate_report, baseline_report) + baseline_mean = baseline_report['summary']['primary_mean'] + payload['speedup_vs_bad5_k24k28_primary_mean'] = candidate_report['summary']['primary_mean'] / baseline_mean if baseline_mean else None + return payload diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dim_midk_bad5_midkcleanup_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dim_midk_bad5_midkcleanup_v1.py new file mode 100644 index 00000000..e6798f48 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dim_midk_bad5_midkcleanup_v1.py @@ -0,0 +1,201 @@ +"""kNN build dim/mid-K K24/K28 cleanup for round bad5. + +Minimum target architecture: sm_100a. This additive sidecar preserves the +round-16 D64/D256/FP16 split routes and replaces the weak exact BF16 build +K24/K28 rows with eight-split exact-K stage-1 producers feeding exact-K S8 +sorted-stream merges. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from collections.abc import Callable +from functools import lru_cache +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dim_midk_bad5_fp16split_v1 as parent_bad5 +from . import knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2_k32split_v14 as midk_seed +from .._dispatch_runtime import pack_kernel_args +TARGET_SHAPES = parent_bad5.TARGET_SHAPES +TARGET_SHAPE_SET = set(TARGET_SHAPES) +MIDK_CLEANUP_SHAPES = ('build_k_sweep_qm2048_k24', 'build_k_sweep_qm2048_k28', 'build_k_sweep_qm4096_k28') +DEFAULT_MIDK_SPLITS = 8 +ROUTE_MIDK_S8 = 'loom.examples.weave.knn_build_dim_midk_bad5_midkcleanup_v1:midk_k24_k28_s8' +ROUTE_PARENT_BAD5 = 'loom.examples.weave.knn_build_dim_midk_bad5_fp16split_v1' +stage1_k24_s8_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_bad5midks8k24", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 24]], "cta_group": 1, "threads": 192}')) +stage1_k28_s8_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_bad5midks8k28", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 28]], "cta_group": 1, "threads": 192}')) +merge_k24_s8_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_bad5midks8k24", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 24], ["SPLIT_COUNT", 8]], "cta_group": 1, "threads": 32}')) +merge_k28_s8_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_bad5midks8k28", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 28], ["SPLIT_COUNT", 8]], "cta_group": 1, "threads": 32}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_DIMMIDK_BAD5_MIDK_VERIFY_KERNEL') + if verify_kernel == 'stage1_k24_s8': + return stage1_k24_s8_ir + if verify_kernel == 'stage1_k28_s8': + return stage1_k28_s8_ir + if verify_kernel == 'merge_k24_s8': + return merge_k24_s8_ir + if verify_kernel == 'merge_k28_s8': + return merge_k28_s8_ir + if verify_kernel == 'parent': + return parent_bad5.ir + return stage1_k28_s8_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_bad5midks8k28", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 28]], "cta_group": 1, "threads": 192}')) + +def _compiled_stage1_k24_s8(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0041"}')) + +def _compiled_stage1_k28_s8(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0043"}')) + +def _compiled_merge_k24_s8(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0042"}')) + +def _compiled_merge_k28_s8(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0044"}')) + +def _dtype_name(inputs: dict[str, Any]) -> str: + query = inputs.get('query') + if query is not None: + return str(query.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')) + +def _label_can_hit(inputs: dict[str, Any], labels: tuple[str, ...]) -> bool: + value = inputs.get('label') + return value is None or str(value) in labels + +def _eligible_midk_s8(inputs: dict[str, Any]) -> bool: + if not _label_can_hit(inputs, MIDK_CLEANUP_SHAPES): + return False + top_k = int(inputs.get('K', -1)) + n_query = int(inputs.get('Q', -1)) + return bool(inputs.get('build', False)) and _dtype_name(inputs) == 'bfloat16' and (int(inputs.get('B', -1)) == 1) and (n_query == int(inputs.get('M', -2))) and (n_query in (2048, 4096)) and (int(inputs.get('D', -1)) == midk_seed.FEAT_D) and (top_k in (24, 28)) and (n_query == 2048 and top_k in (24, 28) or (n_query == 4096 and top_k == 28)) + +def route_for_contract_inputs(inputs: dict[str, Any]) -> str: + if _eligible_midk_s8(inputs): + return ROUTE_MIDK_S8 + return parent_bad5.route_for_contract_inputs(inputs) + +def _launch_midk_s8(inputs: dict[str, Any]) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + split_count = DEFAULT_MIDK_SPLITS + num_q_tiles = (n_query + midk_seed.BLOCK_Q - 1) // midk_seed.BLOCK_Q + num_q_tile_pairs = (num_q_tiles + midk_seed.CTA_GROUP - 1) // midk_seed.CTA_GROUP + num_db_tiles = (n_database + midk_seed.BLOCK_M - 1) // midk_seed.BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tile_pairs * split_count + stage1_grid = min(total_work * midk_seed.CTA_GROUP, midk_seed.GRID_DIM_DEFAULT) + merge_grid = min((bsz * n_query + midk_seed.K32_MERGE_THREADS - 1) // midk_seed.K32_MERGE_THREADS, midk_seed.GRID_DIM_DEFAULT) + stage1_ir = stage1_k24_s8_ir if top_k == 24 else stage1_k28_s8_ir + merge_ir = merge_k24_s8_ir if top_k == 24 else merge_k28_s8_ir + stage1_kernel = _compiled_stage1_k24_s8() if top_k == 24 else _compiled_stage1_k28_s8() + merge_kernel = _compiled_merge_k24_s8() if top_k == 24 else _compiled_merge_k28_s8() + partial_dists, partial_indices = midk_seed.parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = midk_seed.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), bsz * n_query, midk_seed.BLOCK_Q, dim, dim) + tmap_database = midk_seed.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, midk_seed.BLOCK_M, dim, dim) + stage1_kernel.launch_cluster(grid=(stage1_grid, 1, 1), block=(midk_seed.STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage1_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tile_pairs=num_q_tile_pairs, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), cluster_dims=(midk_seed.CTA_GROUP, 1, 1), shared_mem=stage1_ir.computed_smem_bytes) + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(midk_seed.K32_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_ir.computed_smem_bytes) + +def launch_from_contract_inputs(inputs: dict[str, Any]) -> None: + route = route_for_contract_inputs(inputs) + if route == ROUTE_MIDK_S8: + _launch_midk_s8(inputs) + return + parent_bad5.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + from .._dispatch_runtime import CANONICAL_SHAPES + if shape_labels is None: + wanted = TARGET_SHAPE_SET + else: + wanted = {str(label) for label in shape_labels} + selected = [shape for shape in CANONICAL_SHAPES if shape['label'] in wanted] + missing = wanted - {shape['label'] for shape in selected} + if missing: + raise ValueError(''.join(['unknown kNN build contract shape(s): ', format(sorted(missing), '')])) + return selected + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint: run real contract correctness for selected shapes.""" + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=None, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + previous = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = previous + +def _trace_inputs_from_shape(shape: dict[str, Any]) -> dict[str, Any]: + params = dict(shape['params']) + return {'label': shape['label'], 'B': int(params['B']), 'Q': int(params['Q']), 'M': int(params['M']), 'D': int(params['D']), 'K': int(params['K']), 'dtype': str(params.get('dtype', 'bfloat16')), 'build': bool(params.get('build', False))} + +def _guard_description(route: str) -> str: + if route == ROUTE_MIDK_S8: + return 'exact BF16 build B1 Q=M in {2048,4096} D128 K24/K28 eight-split exact-K route' + if route == parent_bad5.ROUTE_D64: + return 'round-16 exact BF16 D64 split route' + if route == parent_bad5.ROUTE_D256: + return 'round-16 exact BF16 D256 split route' + if route == parent_bad5.ROUTE_FP16_D128: + return 'round-16 exact FP16 D128 split route' + return 'guard miss delegates to round-16 bad5 parent' + +def route_trace_for_shapes(shape_labels=None) -> list[dict[str, Any]]: + trace = [] + for shape in _select_contract_shapes(shape_labels): + inputs = _trace_inputs_from_shape(shape) + route = route_for_contract_inputs(inputs) + trace.append({'shape_key': shape['label'], 'selected_route': route, 'route_kind': 'specialized' if route != ROUTE_PARENT_BAD5 else 'parent_delegate', 'guard_condition': _guard_description(route)}) + return trace + +def _per_shape_deltas(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> dict[str, Any]: + result: dict[str, Any] = {} + for label in TARGET_SHAPES: + cand = candidate_report.get('per_shape', {}).get(label, {}) + base = baseline_report.get('per_shape', {}).get(label, {}) + cand_ms = cand.get('kernel_ms') + base_ms = base.get('kernel_ms') + trace_inputs = {'label': label, **cand} + result[label] = {'candidate_route': route_for_contract_inputs(trace_inputs), 'candidate_ms': cand_ms, 'baseline_ms': base_ms, 'flashlib_ms': cand.get('flashlib_ms'), 'candidate_tflops': cand.get('tflops'), 'baseline_tflops': base.get('tflops'), 'speedup_vs_bad5_fp16split': base_ms / cand_ms if cand_ms and base_ms else None, 'ratio_vs_flashlib': cand.get('ratio_vs_flashlib'), 'passed': cand.get('passed'), 'timing_backend': cand.get('timing_backend')} + return result + +def benchmark_knn_build_dim_midk_bad5_midkcleanup_v1(*, use_cupti: bool=True, shape_labels=None, run_baseline: bool=True) -> dict[str, Any]: + """Benchmark the K24/K28 cleanup sidecar against round-16 bad5.""" + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels) + baseline_report = None + if run_baseline: + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=parent_bad5.candidate) + payload: dict[str, Any] = {'tflops': candidate_report['summary']['primary_mean'] or 0.0, 'all_correct': candidate_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'measured_entrypoint': 'loom.examples.weave.knn_build_dim_midk_bad5_midkcleanup_v1:benchmark_knn_build_dim_midk_bad5_midkcleanup_v1', 'measured_shape_labels': tuple(TARGET_SHAPES if shape_labels is None else shape_labels), 'route_trace': route_trace_for_shapes(shape_labels), 'contract_summary': candidate_report['summary'], 'contract_performance': candidate_report['performance'], 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'split_counts': {'midk_k24_k28': DEFAULT_MIDK_SPLITS, 'inherited_d256': parent_bad5._d256_split_count(), 'inherited_fp16_d128': parent_bad5._fp16_split_count()}, 'report': candidate_report} + if baseline_report is not None: + payload['baseline_entrypoint'] = 'loom.examples.weave.knn_build_dim_midk_bad5_fp16split_v1:candidate' + payload['baseline_summary'] = baseline_report['summary'] + payload['per_shape_delta_vs_bad5_fp16split'] = _per_shape_deltas(candidate_report, baseline_report) + baseline_mean = baseline_report['summary']['primary_mean'] + payload['speedup_vs_bad5_fp16split_primary_mean'] = candidate_report['summary']['primary_mean'] / baseline_mean if baseline_mean else None + return payload diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dim_midk_df2f_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dim_midk_df2f_v1.py new file mode 100644 index 00000000..aac5b7ca --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dim_midk_df2f_v1.py @@ -0,0 +1,229 @@ +"""kNN build dim-sweep split-grid seed for round df2f. + +Minimum target architecture: sm_100a. This additive seed keeps the successful +73a9 D64 split route and replaces the inherited low-CTA D256 BF16 and FP16 +D128 q2048/m2048/K10 routes with database-split producers. Each split producer +writes split-local top-k partials consumed by the existing generic Weave split +merge. Mid-K and K64 rows delegate unchanged to 73a9's inherited routes. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import os +from collections.abc import Callable +from functools import lru_cache +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dim_midk_73a9_v1 as parent_73a9 +from . import knn_build_evolve_7bfc_fp16_d128_knn_build_dispatch_slurm_0610_6329_v24 as dim_fp16 +from . import knn_build_evolve_7bfc_split_v1 as split_parent +from . import knn_build_evolve_7bfc_v1 as base_v1 +from .._dispatch_runtime import pack_kernel_args +BLOCK_Q = base_v1.BLOCK_Q +BLOCK_M = base_v1.BLOCK_M +TOP_K_MAX = base_v1.TOP_K_MAX +THREADS = base_v1.THREADS +MERGE_THREADS = split_parent.MERGE_THREADS +GRID_DIM_DEFAULT = base_v1.GRID_DIM_DEFAULT +D256_FEAT_D = 256 +FP16_FEAT_D = 128 +D256_QUERY_BYTES = BLOCK_Q * D256_FEAT_D * 2 +D256_DATABASE_BYTES = BLOCK_M * D256_FEAT_D * 2 +FP16_QUERY_BYTES = BLOCK_Q * FP16_FEAT_D * 2 +FP16_DATABASE_BYTES = BLOCK_M * FP16_FEAT_D * 2 +DB_SQ_BYTES = BLOCK_M * 4 +DEFAULT_D256_SPLITS = 8 +DEFAULT_FP16_SPLITS = 8 +DIM_TARGET_SHAPES = ('build_dim_sweep_b1_q2048_m2048_d64_k10', 'build_dim_sweep_b1_q2048_m2048_d256_k10', 'build_dtype_fp16_b1_q2048_m2048_d128_k10') +TARGET_SHAPES = DIM_TARGET_SHAPES +ROUTE_D64_73A9 = 'loom.examples.weave.knn_build_dim_midk_73a9_v1:d64_split_s8' +ROUTE_D256_SPLIT = 'loom.examples.weave.knn_build_dim_midk_df2f_v1:d256_split_s8' +ROUTE_FP16_SPLIT = 'loom.examples.weave.knn_build_dim_midk_df2f_v1:fp16_d128_split_s8' +ROUTE_PARENT = 'loom.examples.weave.knn_build_dim_midk_73a9_v1' +knn_build_dim_midk_df2f_d256_split_stage1 = _decode_capture(_json_loads('{"__ir__": "knn_build_dim_midk_df2f_d256_split_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 99584, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) +knn_build_dim_midk_df2f_fp16_split_stage1 = _decode_capture(_json_loads('{"__ir__": "knn_build_dim_midk_df2f_fp16_split_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) +stage1_d256_split_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_dim_midk_df2f_d256_split_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 99584, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) +stage1_fp16_split_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_dim_midk_df2f_fp16_split_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) +merge_generic_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_merge", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "B", "Q", "K", "split_count", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10]], "cta_group": 1, "threads": 256}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_DIMMIDK_DF2F_VERIFY_KERNEL') + if verify_kernel == 'd64_parent': + return parent_73a9.stage1_d64_split_ir + if verify_kernel == 'fp16_split': + return stage1_fp16_split_ir + if verify_kernel == 'merge_generic': + return merge_generic_ir + return stage1_d256_split_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_dim_midk_df2f_d256_split_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 99584, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) + +def _compiled_d256_stage1(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0023"}')) + +def _compiled_fp16_stage1(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0032"}')) + +def _d256_split_count() -> int: + return int(os.environ.get('LOOM_KNN_DIMMIDK_DF2F_D256_SPLITS', str(DEFAULT_D256_SPLITS))) + +def _fp16_split_count() -> int: + return int(os.environ.get('LOOM_KNN_DIMMIDK_DF2F_FP16_SPLITS', str(DEFAULT_FP16_SPLITS))) + +def _dtype_name(inputs: dict[str, Any]) -> str: + query = inputs.get('query') + if query is not None: + return str(query.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')) + +def _label_can_hit(inputs: dict[str, Any], label: str) -> bool: + value = inputs.get('label') + return value is None or str(value) == label + +def _eligible_d64_parent(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, DIM_TARGET_SHAPES[0]) and bool(inputs.get('build', False)) and (_dtype_name(inputs) == 'bfloat16') and (int(inputs['B']) == 1) and (int(inputs['Q']) == 2048) and (int(inputs['M']) == 2048) and (int(inputs['D']) == parent_73a9.D64_FEAT_D) and (int(inputs['K']) == TOP_K_MAX) + +def _eligible_d256_split(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, DIM_TARGET_SHAPES[1]) and bool(inputs.get('build', False)) and (_dtype_name(inputs) == 'bfloat16') and (int(inputs['B']) == 1) and (int(inputs['Q']) == 2048) and (int(inputs['M']) == 2048) and (int(inputs['D']) == D256_FEAT_D) and (int(inputs['K']) == TOP_K_MAX) + +def _eligible_fp16_split(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, DIM_TARGET_SHAPES[2]) and bool(inputs.get('build', False)) and (_dtype_name(inputs) == 'float16') and (int(inputs['B']) == 1) and (int(inputs['Q']) == 2048) and (int(inputs['M']) == 2048) and (int(inputs['D']) == FP16_FEAT_D) and (int(inputs['K']) == TOP_K_MAX) + +def _launch_split_stage(inputs: dict[str, Any], *, split_count: int, feature_dim: int, kernel, stage1_ir, fp16: bool=False) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tiles * split_count + stage1_grid = min(total_work, GRID_DIM_DEFAULT) + merge_grid = min((bsz * n_query + MERGE_THREADS - 1) // MERGE_THREADS, GRID_DIM_DEFAULT) + partial_dists, partial_indices = split_parent._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + if fp16: + tmap_query = dim_fp16._create_tensor_map_3d_fp16_oob_zero(query.data_ptr(), bsz * n_query, BLOCK_Q, dim, feature_dim) + tmap_database = dim_fp16._create_tensor_map_3d_fp16_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, dim, feature_dim) + else: + tmap_query = base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), bsz * n_query, BLOCK_Q, dim, feature_dim) + tmap_database = base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, dim, feature_dim) + kernel.launch(grid=(stage1_grid, 1, 1), block=(THREADS, 1, 1), args=pack_kernel_args(stage1_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tiles=num_q_tiles, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), shared_mem=stage1_ir.computed_smem_bytes) + merge_kernel = split_parent._compiled_merge() + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz, n_query, top_k, split_count, bsz * n_query], shared_mem=merge_generic_ir.computed_smem_bytes) + +def _launch_d256_split(inputs: dict[str, Any]) -> None: + _launch_split_stage(inputs, split_count=_d256_split_count(), feature_dim=D256_FEAT_D, kernel=_compiled_d256_stage1(), stage1_ir=stage1_d256_split_ir) + +def _launch_fp16_split(inputs: dict[str, Any]) -> None: + _launch_split_stage(inputs, split_count=_fp16_split_count(), feature_dim=FP16_FEAT_D, kernel=_compiled_fp16_stage1(), stage1_ir=stage1_fp16_split_ir, fp16=True) + +def route_for_contract_inputs(inputs: dict[str, Any]) -> str: + if _eligible_d64_parent(inputs): + return ROUTE_D64_73A9 + if _eligible_d256_split(inputs): + return ROUTE_D256_SPLIT + if _eligible_fp16_split(inputs): + return ROUTE_FP16_SPLIT + return ROUTE_PARENT + +def launch_from_contract_inputs(inputs: dict[str, Any]) -> None: + route = route_for_contract_inputs(inputs) + if route == ROUTE_D64_73A9: + parent_73a9._launch_d64_split(inputs) + return + if route == ROUTE_D256_SPLIT: + _launch_d256_split(inputs) + return + if route == ROUTE_FP16_SPLIT: + _launch_fp16_split(inputs) + return + parent_73a9.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + from .._dispatch_runtime import CANONICAL_SHAPES + if shape_labels is None: + wanted = set(TARGET_SHAPES) + else: + wanted = {str(label) for label in shape_labels} + selected = [shape for shape in CANONICAL_SHAPES if shape['label'] in wanted] + missing = wanted - {shape['label'] for shape in selected} + if missing: + raise ValueError(''.join(['unknown kNN build contract shape(s): ', format(sorted(missing), '')])) + return selected + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint: run real contract correctness for selected shapes.""" + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=None, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + previous = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = previous + +def _trace_inputs_from_shape(shape: dict[str, Any]) -> dict[str, Any]: + params = dict(shape['params']) + dtype = str(params.get('dtype', 'bfloat16')) + return {'label': shape['label'], 'B': int(params['B']), 'Q': int(params['Q']), 'M': int(params['M']), 'D': int(params['D']), 'K': int(params['K']), 'dtype': dtype, 'build': bool(params.get('build', False))} + +def route_trace_for_shapes(shape_labels=None) -> list[dict[str, Any]]: + trace = [] + for shape in _select_contract_shapes(shape_labels): + inputs = _trace_inputs_from_shape(shape) + route = route_for_contract_inputs(inputs) + trace.append({'shape_key': shape['label'], 'selected_route': route, 'route_kind': 'specialized' if route != ROUTE_PARENT else 'parent_delegate', 'guard_condition': _guard_description(route)}) + return trace + +def _guard_description(route: str) -> str: + if route == ROUTE_D64_73A9: + return 'exact BF16 build B1 Q=M=2048 D64 K10 inherited from 73a9 split route' + if route == ROUTE_D256_SPLIT: + return 'exact BF16 build B1 Q=M=2048 D256 K10 split-grid route' + if route == ROUTE_FP16_SPLIT: + return 'exact FP16 build B1 Q=M=2048 D128 K10 split-grid route' + return 'guard miss delegates to 73a9 dim/mid-K parent' + +def _per_shape_deltas(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> dict[str, Any]: + result: dict[str, Any] = {} + for label in TARGET_SHAPES: + cand = candidate_report.get('per_shape', {}).get(label, {}) + base = baseline_report.get('per_shape', {}).get(label, {}) + cand_ms = cand.get('kernel_ms') + base_ms = base.get('kernel_ms') + result[label] = {'candidate_ms': cand_ms, 'baseline_ms': base_ms, 'flashlib_ms': cand.get('flashlib_ms'), 'candidate_tflops': cand.get('tflops'), 'baseline_tflops': base.get('tflops'), 'speedup_vs_73a9': base_ms / cand_ms if cand_ms and base_ms else None, 'ratio_vs_flashlib': cand.get('ratio_vs_flashlib'), 'passed': cand.get('passed'), 'timing_backend': cand.get('timing_backend')} + return result + +def benchmark_knn_build_dim_midk_df2f_v1(*, use_cupti: bool=True, shape_labels=None, run_baseline: bool=True) -> dict[str, Any]: + """Benchmark the exact dim-sweep split-grid candidate against 73a9.""" + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels) + baseline_report = None + if run_baseline: + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=parent_73a9.candidate) + payload: dict[str, Any] = {'tflops': candidate_report['summary']['primary_mean'] or 0.0, 'all_correct': candidate_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'measured_entrypoint': 'loom.examples.weave.knn_build_dim_midk_df2f_v1:benchmark_knn_build_dim_midk_df2f_v1', 'measured_shape_labels': tuple(TARGET_SHAPES if shape_labels is None else shape_labels), 'route_trace': route_trace_for_shapes(shape_labels), 'contract_summary': candidate_report['summary'], 'contract_performance': candidate_report['performance'], 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'split_counts': {'d256': _d256_split_count(), 'fp16_d128': _fp16_split_count()}, 'report': candidate_report} + if baseline_report is not None: + payload['baseline_entrypoint'] = 'loom.examples.weave.knn_build_dim_midk_73a9_v1:candidate' + payload['baseline_summary'] = baseline_report['summary'] + payload['per_shape_delta_vs_73a9'] = _per_shape_deltas(candidate_report, baseline_report) + baseline_mean = baseline_report['summary']['primary_mean'] + payload['speedup_vs_73a9_primary_mean'] = candidate_report['summary']['primary_mean'] / baseline_mean if baseline_mean else None + return payload diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dim_midk_f8c3_q4096k64split_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dim_midk_f8c3_q4096k64split_v1.py new file mode 100644 index 00000000..48ac0feb --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dim_midk_f8c3_q4096k64split_v1.py @@ -0,0 +1,219 @@ +"""kNN build q4096 K64 split-grid seed for f8c3 follow-up. + +Minimum target architecture: sm_100a. This additive bucket-kernel sidecar keeps +the f8c3 selected portfolio as fallback and replaces only the exact BF16 build +``B=1,Q=M=4096,D=128,K=64`` row. The new row uses the v40 K64 tail-infinity +tcgen05/TMA producer with a selectable split count and the matching K64 merge, +staying on the contract-visible distances/indices path. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from collections.abc import Callable +from functools import lru_cache +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_selected_portfolio_f8c3_v1 as parent_f8c3 +from . import knn_build_k64stage1_splitgrid_tailinf_knn_build_dispatch_slurm_0610_6329_v40 as k64_seed +from .._dispatch_runtime import pack_kernel_args +TARGET_SHAPES = ('build_over32_stress_qm4096_k64',) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +SUPPORTED_SPLITS = (8, 12, 16) +DEFAULT_Q4096_K64_SPLITS = 8 +ROUTE_Q4096_K64 = 'loom.examples.weave.knn_build_dim_midk_f8c3_q4096k64split_v1:q4096_k64_tailinf_split8' +ROUTE_PARENT_F8C3 = 'loom.examples.weave.knn_build_dispatch_selected_portfolio_f8c3_v1:launch_from_contract_inputs' +stage1_k64_tailinf_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_k64_stage1_tailinf_k64over32tailinfsplitgrid", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 64]], "cta_group": 1, "threads": 192}')) +merge_k64_s8_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_k64_merge_s8_unordered_warp_select_k64over32s8warpselect", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 64], ["SPLIT_COUNT", 8]], "cta_group": 1, "threads": 128}')) +merge_k64_s12_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_k64_merge_sN_unordered_chunkprefill_k64over32s12chunkprefill", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 64], ["SPLIT_COUNT", 12]], "cta_group": 1, "threads": 32}')) +merge_k64_s16_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_k64_merge_sN_unordered_chunkprefill_k64over32s16chunkprefill", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 64], ["SPLIT_COUNT", 16]], "cta_group": 1, "threads": 32}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_DIMMIDK_F8C3_Q4096K64_VERIFY_KERNEL') + if verify_kernel == 'stage1_k64_tailinf': + return stage1_k64_tailinf_ir + if verify_kernel == 'merge_k64_s8': + return merge_k64_s8_ir + if verify_kernel == 'merge_k64_s12': + return merge_k64_s12_ir + if verify_kernel == 'merge_k64_s16': + return merge_k64_s16_ir + return stage1_k64_tailinf_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_k64_stage1_tailinf_k64over32tailinfsplitgrid", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 64]], "cta_group": 1, "threads": 192}')) + +def _check_split_count(split_count: int) -> int: + split_count = int(split_count) + if split_count not in SUPPORTED_SPLITS: + raise ValueError(''.join(['unsupported q4096 K64 split count: ', format(split_count, '')])) + return split_count + +def _stage1_ir_for_split(split_count: int) -> Any: + return k64_seed._stage1_ir_for_over32_route(64, _check_split_count(split_count)) + +def _merge_ir_for_split(split_count: int) -> Any: + return k64_seed._merge_ir_for_over32_route(64, _check_split_count(split_count)) + +@lru_cache(maxsize=3) +def _compiled_stage1(split_count: int): + return k64_seed.parent_v20._compile_ir(_stage1_ir_for_split(split_count)) + +@lru_cache(maxsize=3) +def _compiled_merge(split_count: int): + return k64_seed.parent_v20._compile_ir(_merge_ir_for_split(split_count)) + +def _dtype_name(inputs: dict[str, Any]) -> str: + query = inputs.get('query') + if query is not None: + return str(query.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')) + +def _label_can_hit(inputs: dict[str, Any], label: str) -> bool: + value = inputs.get('label') + return value is None or str(value) == label + +def _eligible_q4096_k64(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, 'build_over32_stress_qm4096_k64') and bool(inputs.get('build', False)) and (int(inputs.get('B', -1)) == 1) and (int(inputs.get('Q', -1)) == 4096) and (int(inputs.get('M', -1)) == 4096) and (int(inputs.get('D', -1)) == k64_seed.FEAT_D) and (int(inputs.get('K', -1)) == 64) and (_dtype_name(inputs) == 'bfloat16') + +def _split_route_name(split_count: int) -> str: + return ROUTE_Q4096_K64.replace('split8', ''.join(['split', format(_check_split_count(split_count), '')])) + +def _launch_q4096_k64_split(inputs: dict[str, Any], *, split_count: int=DEFAULT_Q4096_K64_SPLITS) -> None: + split_count = _check_split_count(split_count) + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + num_q_tiles = (n_query + k64_seed.BLOCK_Q - 1) // k64_seed.BLOCK_Q + num_q_tile_pairs = (num_q_tiles + k64_seed.CTA_GROUP - 1) // k64_seed.CTA_GROUP + num_db_tiles = (n_database + k64_seed.BLOCK_M - 1) // k64_seed.BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tile_pairs * split_count + stage1_grid = min(total_work * k64_seed.CTA_GROUP, k64_seed.GRID_DIM_DEFAULT) + use_warp_select_merge = split_count == 8 + merge_grid = (bsz * n_query + 3) // 4 if use_warp_select_merge else min((bsz * n_query + k64_seed.MERGE_THREADS - 1) // k64_seed.MERGE_THREADS, k64_seed.GRID_DIM_DEFAULT) + partial_dists, partial_indices = k64_seed.parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = k64_seed.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), bsz * n_query, k64_seed.BLOCK_Q, dim, dim) + tmap_database = k64_seed.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, k64_seed.BLOCK_M, dim, dim) + stage1_ir_obj = _stage1_ir_for_split(split_count) + stage1_kernel = _compiled_stage1(split_count) + stage1_kernel.launch_cluster(grid=(stage1_grid, 1, 1), block=(k64_seed.STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage1_ir_obj, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tile_pairs=num_q_tile_pairs, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), cluster_dims=(k64_seed.CTA_GROUP, 1, 1), shared_mem=stage1_ir_obj.computed_smem_bytes) + merge_ir_obj = _merge_ir_for_split(split_count) + merge_kernel = _compiled_merge(split_count) + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(k64_seed.K64_COOP_MERGE_THREADS if use_warp_select_merge else k64_seed.MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_ir_obj.computed_smem_bytes) + +def route_for_contract_inputs(inputs: dict[str, Any], *, split_count: int=DEFAULT_Q4096_K64_SPLITS) -> str: + if _eligible_q4096_k64(inputs): + return _split_route_name(split_count) + return parent_f8c3.route_for_contract_inputs(inputs) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, split_count: int=DEFAULT_Q4096_K64_SPLITS) -> None: + if _eligible_q4096_k64(inputs): + _launch_q4096_k64_split(inputs, split_count=split_count) + return + parent_f8c3.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def candidate_for_split(split_count: int) -> Callable[[dict[str, Any]], None]: + split_count = _check_split_count(split_count) + + def _candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs, split_count=split_count) + return None + return _candidate + +def candidate_parent_f8c3(inputs: dict[str, Any]): + parent_f8c3.launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + from .._dispatch_runtime import CANONICAL_SHAPES + wanted = TARGET_SHAPE_SET if shape_labels is None else {str(label) for label in shape_labels} + selected = [shape for shape in CANONICAL_SHAPES if shape['label'] in wanted] + missing = wanted - {shape['label'] for shape in selected} + if missing: + raise ValueError(''.join(['unknown kNN build contract shape(s): ', format(sorted(missing), '')])) + return selected + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint: run real contract correctness for selected shapes.""" + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=None, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + previous = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = previous + +def _trace_inputs_from_shape(shape: dict[str, Any]) -> dict[str, Any]: + params = dict(shape['params']) + return {'label': shape['label'], 'B': int(params['B']), 'Q': int(params['Q']), 'M': int(params['M']), 'D': int(params['D']), 'K': int(params['K']), 'dtype': str(params.get('dtype', 'bfloat16')), 'build': bool(params.get('build', False))} + +def route_trace_for_shapes(shape_labels=None, *, split_count: int=DEFAULT_Q4096_K64_SPLITS) -> list[dict[str, Any]]: + trace = [] + route_prefix = ROUTE_Q4096_K64.rsplit(':', 1)[0] + for shape in _select_contract_shapes(shape_labels): + inputs = _trace_inputs_from_shape(shape) + route = route_for_contract_inputs(inputs, split_count=split_count) + trace.append({'shape_key': shape['label'], 'selected_route': route, 'route_kind': 'specialized' if route.startswith(route_prefix) else 'parent_delegate', 'guard_condition': _guard_description(route)}) + return trace + +def _guard_description(route: str) -> str: + if route.startswith(ROUTE_Q4096_K64.rsplit(':', 1)[0]): + split = route.rsplit('split', 1)[-1] + return ''.join(['exact BF16 build B1 Q=M=4096 D128 K64 tail-infinity split', format(split, ''), ' route']) + return 'f8c3 selected portfolio fallback' + +def _per_shape_delta(candidate_report: dict[str, Any], baseline_report: dict[str, Any], *, split_count: int) -> dict[str, Any]: + label = TARGET_SHAPES[0] + cand = candidate_report.get('per_shape', {}).get(label, {}) + base = baseline_report.get('per_shape', {}).get(label, {}) + cand_ms = cand.get('kernel_ms') + base_ms = base.get('kernel_ms') + flashlib_ms = cand.get('flashlib_ms') + return {'candidate_route': _split_route_name(split_count), 'baseline_route': parent_f8c3.route_for_contract_inputs({'label': label, **base}), 'candidate_ms': cand_ms, 'baseline_ms': base_ms, 'flashlib_ms': flashlib_ms, 'candidate_tflops': cand.get('tflops'), 'baseline_tflops': base.get('tflops'), 'speedup_vs_f8c3': base_ms / cand_ms if cand_ms and base_ms else None, 'ratio_vs_flashlib': flashlib_ms / cand_ms if cand_ms and flashlib_ms else cand.get('ratio_vs_flashlib'), 'passed': cand.get('passed'), 'timing_backend': cand.get('timing_backend')} + +def benchmark_knn_build_dim_midk_f8c3_q4096k64split_v1(*, use_cupti: bool=True, split_count: int=DEFAULT_Q4096_K64_SPLITS, run_baseline: bool=True, scan_splits: bool=False) -> dict[str, Any]: + """Benchmark the q4096 K64 split sidecar against the f8c3 route.""" + split_count = _check_split_count(split_count) + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=TARGET_SHAPES, kernel_fn=candidate_for_split(split_count)) + baseline_report = None + if run_baseline: + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=TARGET_SHAPES, kernel_fn=candidate_parent_f8c3) + split_scan = {} + if scan_splits: + for candidate_split in SUPPORTED_SPLITS: + if candidate_split == split_count: + split_scan[str(candidate_split)] = candidate_report['per_shape'][TARGET_SHAPES[0]] + continue + split_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=TARGET_SHAPES, kernel_fn=candidate_for_split(candidate_split)) + split_scan[str(candidate_split)] = split_report['per_shape'][TARGET_SHAPES[0]] + payload: dict[str, Any] = {'tflops': candidate_report['summary']['primary_mean'] or 0.0, 'all_correct': candidate_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'measured_entrypoint': 'loom.examples.weave.knn_build_dim_midk_f8c3_q4096k64split_v1:benchmark_knn_build_dim_midk_f8c3_q4096k64split_v1', 'measured_shape_labels': TARGET_SHAPES, 'route_trace': route_trace_for_shapes(TARGET_SHAPES, split_count=split_count), 'contract_summary': candidate_report['summary'], 'contract_performance': candidate_report['performance'], 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'split_counts': {'q4096_k64': split_count}, 'split_scan': split_scan, 'report': candidate_report} + if baseline_report is not None: + payload['baseline_entrypoint'] = ROUTE_PARENT_F8C3 + payload['baseline_summary'] = baseline_report['summary'] + payload['per_shape_delta_vs_f8c3'] = {TARGET_SHAPES[0]: _per_shape_delta(candidate_report, baseline_report, split_count=split_count)} + baseline_mean = baseline_report['summary']['primary_mean'] + payload['speedup_vs_f8c3_primary_mean'] = candidate_report['summary']['primary_mean'] / baseline_mean if baseline_mean else None + return payload diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_066c_b8c7_69d6_full82_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_066c_b8c7_69d6_full82_v1.py new file mode 100644 index 00000000..19f38bb1 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_066c_b8c7_69d6_full82_v1.py @@ -0,0 +1,390 @@ +"""Full82 dispatcher synthesis over 066c, b8c7, and 69d6. + +Minimum target architecture: sm_100a. This generalize-auto-tuning wrapper +preserves the seed schedules and only changes guard order. It starts from the +066c Q4/Q64 full82 dispatcher, consumes b8c7 for the exact +``search_rect_b1_q1536_m65536_d128_k20`` row, and exposes two full82 candidate +portfolios for the Q4/Q64 rows: the existing 066c 3505 route and the alternate +69d6 FAEB route. Guard misses stay on Weave dispatchers; no external runtime +fallback is introduced. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +from pathlib import Path +from typing import Any, Callable +from . import knn_build_dispatch_d555_rag_microbucket_q4q64_69d6_v1 as dispatch_69d6 +from . import knn_build_rag_microbatch_k10_q4q64_d555_v1 as dispatch_066c +from . import knn_build_rect_d128_k20_d555_b8c7_v1 as rect_b8c7 +MODULE = 'loom.examples.weave.knn_build_dispatch_066c_b8c7_69d6_full82_v1' +RECT_SHAPE = rect_b8c7.TARGET_SHAPE +Q4_SHAPE = dispatch_066c.Q4_SHAPE +Q64_SHAPE = dispatch_066c.Q64_SHAPE +TARGET_SHAPES = (RECT_SHAPE, Q4_SHAPE, Q64_SHAPE) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +SEED_RECT_B8C7_ID = rect_b8c7.SEED_ID +SEED_Q4Q64_3505_ID = dispatch_066c.SEED_ID +SEED_Q4Q64_69D6_ID = dispatch_69d6.SEED_FAEB_Q4Q64_ID +BASE_D555_ID = dispatch_066c.BASELINE_ID +BASE_066C_ID = 'candidate_066c_ragmicro_q4q64_3505_full82_v1' +BASE_D555_ENTRYPOINT = ''.join([format(dispatch_066c.MODULE, ''), ':benchmark_baseline_d555']) +BASE_066C_ENTRYPOINT = ''.join([format(dispatch_066c.MODULE, ''), ':benchmark_candidate_rag_microbatch_k10_q4q64_m64_3505_d555_v1']) +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_BASE_066C_ENTRYPOINT = ''.join([format(dispatch_066c.MODULE, ''), ':launch_from_contract_inputs']) +Q4Q64_MODE_3505 = '3505' +Q4Q64_MODE_69D6 = '69d6' +CANDIDATE_3505_B8C7 = 'portfolio_3505_b8c7' +CANDIDATE_69D6_B8C7 = 'portfolio_69d6_b8c7' +CANDIDATE_KEYS = (CANDIDATE_3505_B8C7, CANDIDATE_69D6_B8C7) +DEFAULT_CANDIDATE_KEY = CANDIDATE_3505_B8C7 +eval_mod = dispatch_066c.eval_mod +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbatch_m64_d4f7_stage1", "arg_keys": ["query", "database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 91392, "constants": [], "cta_group": 1, "threads": 512}')) +SOURCE_TASKS = {SEED_RECT_B8C7_ID: 'weave-evolve-knn-build-b8c7 / design_doc/active/weave_evolve_knn_build_round_116_b8c7_rectd128k20.md', SEED_Q4Q64_3505_ID: 'generalize-auto-tuning-knn-build-066c / design_doc/active/generalize_auto_tuning_knn_build_round_116_066c.md', SEED_Q4Q64_69D6_ID: 'weave-evolve-knn-build-69d6 / design_doc/active/weave_evolve_knn_build_round_116_69d6_q4q64.md'} +TARGETED_SEED_ROWS = {SEED_RECT_B8C7_ID: {RECT_SHAPE: {'kernel_ms': 0.58048, 'flashlib_ms': 0.765344, 'ratio_vs_flashlib': 1.3184674751929437, 'timing_backend': 'cupti', 'source_payload': 'artifacts/generalize_auto_tuning/knn_build_d555_rect_d128_k20_q1536_9b9f_b8c7/rect_d128_k20_q1536_9b9f_d555_b8c7_v1.json'}}, SEED_Q4Q64_3505_ID: {Q4_SHAPE: {'kernel_ms': 0.060032, 'flashlib_ms': 0.089152, 'ratio_vs_flashlib': 1.4850746268656716, 'timing_backend': 'cupti', 'source_payload': 'artifacts/generalize_auto_tuning/knn_build_d555_ragmicro_q4q64_m64_3505/rag_microbatch_k10_q4q64_m64_3505_d555_v1.json'}, Q64_SHAPE: {'kernel_ms': 0.070368, 'flashlib_ms': 0.097664, 'ratio_vs_flashlib': 1.3879035925420646, 'timing_backend': 'cupti', 'source_payload': 'artifacts/generalize_auto_tuning/knn_build_d555_ragmicro_q4q64_m64_3505/rag_microbatch_k10_q4q64_m64_3505_d555_v1.json'}}, SEED_Q4Q64_69D6_ID: {Q4_SHAPE: {'kernel_ms': 0.060832, 'flashlib_ms': 0.06608, 'ratio_vs_flashlib': 1.0862703840084167, 'timing_backend': 'cupti', 'source_payload': 'artifacts/generalize_auto_tuning/knn_build_69d6_rag_microbucket_q4q64/shape2_dispatch_d555_faeb_q4q64_69d6_v1.json'}, Q64_SHAPE: {'kernel_ms': 0.071968, 'flashlib_ms': 0.097952, 'ratio_vs_flashlib': 1.3610493552690084, 'timing_backend': 'cupti', 'source_payload': 'artifacts/generalize_auto_tuning/knn_build_69d6_rag_microbucket_q4q64/shape2_dispatch_d555_faeb_q4q64_69d6_v1.json'}}} +PRODUCTION_ROUTE_MODULES = _decode_capture(_json_loads('{"__dict_items__": [["large_square_k20k32", "loom.examples.weave.knn_build_large_square_k20k32_a989_v1"], ["over64_k96", "loom.examples.weave.knn_build_over64_k96_a989_v1"], ["baseline_7c3a_rag_k10", "loom.examples.weave.knn_build_rag_frontier_4b5c_v1:k10"], ["rag_frontier_7399_k10", "loom.examples.weave.knn_build_rag_frontier_7399_v1:k10_s72"], ["rag_frontier_7399_k32_replaced", "loom.examples.weave.knn_build_rag_frontier_7399_v1:k32_s72_g8_fusedmerge"], ["rag_frontier_4fbf_k32", "loom.examples.weave.knn_build_rag_frontier_4fbf_v7:k32_s72_g24_tailinf_fused"], ["rect_smallq_largem_d15e", "loom.examples.weave.knn_build_rect_smallq_largem_ff59_d15e_v1:split16"], ["baseline_7c3a_policy", "loom.examples.weave.knn_build_dispatch_b6d4_d15e_fd02_v1:baseline_7c3a_policy"], ["fallback", "loom.examples.weave.knn_build_dispatch_split72_4e09_de1a_3dc7_v48"], ["dim_d64_73a9", "loom.examples.weave.knn_build_dim_midk_73a9_v1:d64_split_s8"], ["current_exact_k32_dispatcher", "loom.examples.weave.knn_build_dispatch_4fbf_7399_d15e_full55_bad5_v1:launch_from_contract_inputs"], ["base_7399_d15e_dispatcher", "loom.examples.weave.knn_build_dispatch_7399_d15e_full55_v1:launch_from_contract_inputs"], ["rag_frontier_7399_k32", "loom.examples.weave.knn_build_rag_frontier_7399_v1:k32_s72_g8_fusedmerge"], ["dim_d256_df2f", "loom.examples.weave.knn_build_dim_midk_df2f_v1:d256_split_s8"], ["dim_fp16_d128_df2f", "loom.examples.weave.knn_build_dim_midk_df2f_v1:fp16_d128_split_s8"], ["base_dispatch", "loom.examples.weave.knn_build_dispatch_7399_d15e_full55_v1:launch_from_contract_inputs"], ["rect_intermediate_4452_s8", "loom.examples.weave.knn_build_rect_intermediate_frontier_6a73_4452_v2:rect_s8_k10_cached"], ["base_champion_6b59", "loom.examples.weave.knn_build_dispatch_7399_d15e_df2f_full55_v1:launch_from_contract_inputs"], ["base_k32_d64_62b1", "loom.examples.weave.knn_build_dispatch_4fbf_7399_d15e_73a9_full55_v1:launch_from_contract_inputs"], ["default_k96_a330", "loom.examples.weave.knn_build_over64_k96_a989_v1"], ["large_tail_a4f6", "loom.examples.weave.knn_build_large_tail_frontier_6a73_v1:split4_k20"], ["midk_81aa_q2048_k24_k28", "loom.examples.weave.knn_build_dim_midk_bad5_midkcleanup_v1:midk_k24_k28_s8"], ["midk_9b2c_q4096_k28", "loom.examples.weave.knn_build_dim_midk_bad5_k24k28_v1:k28_q4096_s4_unordered_exact"], ["base_f552", "loom.examples.weave.knn_build_dispatch_selected_portfolio_f552_v1:launch_from_contract_inputs"], ["midk_bad5_k64split8", "loom.examples.weave.knn_build_dim_midk_bad5_k64split8_v1:k64_q2048_s8_tailinf"], ["base_e51c", "loom.examples.weave.knn_build_dispatch_selected_portfolio_e51c_v1:launch_from_contract_inputs"], ["midk_f8c3_q4096_k64_split8_a194", "loom.examples.weave.knn_build_dim_midk_f8c3_q4096k64split_v1:q4096_k64_tailinf_split8"], ["base_f8c3", "loom.examples.weave.knn_build_dispatch_selected_portfolio_f8c3_v1:launch_from_contract_inputs"], ["lowk_b193_q512_s4", "loom.examples.weave.knn_build_lowk_f8c3_q512_q1024_v1:q512_lowk_s4"], ["lowk_b193_q1024_k16_s16", "loom.examples.weave.knn_build_lowk_f8c3_q512_q1024_v1:q1024_k16_s16"], ["large_square_5407_q8192_k32_s2", "loom.examples.weave.knn_build_large_square_k32_8a83_v1:q8192_k32_split2"], ["base_f853", "loom.examples.weave.knn_build_dispatch_selected_portfolio_f853_v1:launch_from_contract_inputs"], ["lowk_b193_q512_k4_k5_k6_s4", "loom.examples.weave.knn_build_lowk_f8c3_q512_q1024_v1:q512_lowk_s4"], ["base_f16b", "loom.examples.weave.knn_build_dispatch_selected_portfolio_f16b_v1:launch_from_contract_inputs"], ["rag_microbatch_b2ec_s72_g8", "loom.examples.weave.knn_build_rag_microbatch_4a72_v1:launch_from_contract_inputs"], ["base_4a72", "loom.examples.weave.knn_build_dispatch_selected_portfolio_4a72_v1:launch_from_contract_inputs"], ["rag_m64_s128_0c69", "loom.examples.weave.knn_build_rag_microbatch_m64_d4f7_v1:launch_from_contract_inputs"], ["rag_s144_g12_cta1_059f", "loom.examples.weave.knn_build_rag_microbatch_4a72_v2:launch_from_contract_inputs"], ["rag_s144_g8_cta1_4982_read_ref_parameterized", "loom.examples.weave.knn_build_rag_microbatch_4a72_v2:launch_from_contract_inputs"], ["base_397b", "loom.examples.weave.knn_build_dispatch_selected_portfolio_397b_v1:launch_from_contract_inputs"], ["d64_fdd7_aa88_v2", "loom.examples.weave.knn_build_d64_build_aa88_v2:launch_from_contract_inputs"], ["base_8700", "loom.examples.weave.knn_build_dispatch_rag_seed_portfolio_8700_v1:launch_from_contract_inputs(portfolio_id=all_m64_s128)"], ["rect_d64_cf49_v3_9138", "loom.examples.weave.knn_build_rect_d64_cf49_v3:launch_from_contract_inputs"], ["q1_mbucket_aa88_q1m_v3_bcb3", "loom.examples.weave.knn_build_ragonline_mbucket_aa88_q1m_v3:launch_from_contract_inputs"], ["over64_k96_a2f8_v1_generated_v8", "loom.examples.weave.knn_build_over64_k96_a2f8_v1:_launch_over64_k96_split_path"], ["base_e3de", "loom.examples.weave.knn_build_dispatch_d64_fdd7_e3de_v1:launch_from_contract_inputs"], ["non128_frontier_8199_widecombine_v1", "loom.examples.weave.knn_build_non128_frontier_8199_widecombine_v1:launch_from_contract_inputs"], ["base_4247", "loom.examples.weave.knn_build_dispatch_e3de_9138_bcb3_4247_v1:launch_from_contract_inputs"], ["rag_microbucket_k32_8fcb_split148_v1_b3e0_sm148", "loom.examples.weave.knn_build_rag_microbucket_k32_8fcb_split148_v1:launch_from_contract_inputs"], ["rag_microbucket_k32_2e8e_q16split148_v1_b3e0_q16_s148", "loom.examples.weave.knn_build_rag_microbucket_k32_2e8e_q16split148_v1:launch_from_contract_inputs"], ["non128_frontier_3d5a_cachedmerge_v1", "loom.examples.weave.knn_build_non128_frontier_3d5a_cachedmerge_v1:launch_from_contract_inputs"], ["over64_k96_exactall_229a_v1_b6c4", "loom.examples.weave.knn_build_over64_k96_exactall_229a_v1:launch_from_contract_inputs"], ["knn_build_midk_k11k13_e080_v1", "loom.examples.weave.knn_build_midk_k11k13_e080_v1:launch_from_contract_inputs"], ["ragonline_mbucket_4fc7_q1m262_v1_980c", "loom.examples.weave.knn_build_ragonline_mbucket_4fc7_q1m262_v1:launch_from_contract_inputs"], ["baseline_8199_widecombine_full82_v1", "loom.examples.weave.knn_build_dispatch_4247_non128_8199_widecombine_full82_v1:launch_from_contract_inputs"], ["k30_q4096_6998_warpselect_v1", "loom.examples.weave.knn_build_k30_q4096_6998_warpselect_v1:launch_from_contract_inputs"], ["rag_stream_k10_direct_split72_6998_v1", "loom.examples.weave.knn_build_rag_online_stream_split72_4e09_v1:launch_from_contract_inputs"], ["rect_d64_23be_unordered_v1", "loom.examples.weave.knn_build_rect_d64_23be_unordered_v1:launch_from_contract_inputs"], ["residual_19b3_ed1c_portfolio_6998", "loom.examples.weave.knn_build_dispatch_c142_3505_q32rowld_19b3_v1:launch_from_contract_inputs"], ["candidate_q16split148_cachedmerge_k96exactall_e080_q1m262_over_8199_full82_v1", "loom.examples.weave.knn_build_dispatch_4247_non128_8199_3d5a_2e8e_full82_synth_v1:launch_from_contract_inputs"], ["rect_d128_k20_q1536_9b9f_d555_b8c7_v1", "loom.examples.weave.knn_build_rect_d128_k20_d555_b8c7_v1:launch_from_contract_inputs"], ["rag_microbatch_k10_q4q64_m64_3505_d555_v1", "loom.examples.weave.knn_build_rag_microbatch_k10_q4q64_d555_v1:launch_from_contract_inputs"], ["rag_microbucket_faeb_q4q64_k10_69d6_v1", "loom.examples.weave.knn_build_rag_microbucket_faeb_v1:launch_from_contract_inputs"], ["candidate_066c_ragmicro_q4q64_3505_full82_v1", "loom.examples.weave.knn_build_rag_microbatch_k10_q4q64_d555_v1:launch_from_contract_inputs"], ["candidate_d555_direct_residual_seeds_full82_v1", "loom.examples.weave.knn_build_dispatch_d555_residual_seed_synth_full82_v1:launch_from_contract_inputs"]]}')) +CANDIDATE_CONFIGS: dict[str, dict[str, Any]] = {CANDIDATE_3505_B8C7: {'candidate_id': 'candidate_066c_3505_plus_b8c7_full82_v1', 'entrypoint': ''.join([format(MODULE, ''), ':candidate_066c_3505_plus_b8c7_full82_v1']), 'benchmark_entrypoint': ''.join([format(MODULE, ''), ':benchmark_candidate_066c_3505_plus_b8c7_full82_v1']), 'kernel_fn': lambda inputs: launch_from_contract_inputs(inputs, q4q64_mode=Q4Q64_MODE_3505), 'q4q64_mode': Q4Q64_MODE_3505, 'selected_seeds': (SEED_RECT_B8C7_ID, SEED_Q4Q64_3505_ID), 'guard_plan': ('b8c7 exact D128/K20/Q1536 guard', '066c 3505 exact Q4/Q64 K10 guard', '066c full82 Weave fallback'), 'expected_shape_wins': TARGET_SHAPES, 'fallback': ROUTE_BASE_066C_ENTRYPOINT}, CANDIDATE_69D6_B8C7: {'candidate_id': 'candidate_066c_69d6_plus_b8c7_full82_v1', 'entrypoint': ''.join([format(MODULE, ''), ':candidate_066c_69d6_plus_b8c7_full82_v1']), 'benchmark_entrypoint': ''.join([format(MODULE, ''), ':benchmark_candidate_066c_69d6_plus_b8c7_full82_v1']), 'kernel_fn': lambda inputs: launch_from_contract_inputs(inputs, q4q64_mode=Q4Q64_MODE_69D6), 'q4q64_mode': Q4Q64_MODE_69D6, 'selected_seeds': (SEED_RECT_B8C7_ID, SEED_Q4Q64_69D6_ID), 'guard_plan': ('b8c7 exact D128/K20/Q1536 guard', '69d6 FAEB exact Q4/Q64 K10 guard', '066c full82 Weave fallback'), 'expected_shape_wins': TARGET_SHAPES, 'fallback': ROUTE_BASE_066C_ENTRYPOINT}} +CANDIDATE_DISPATCHERS = ({'id': BASE_D555_ID, 'entrypoint': BASE_D555_ENTRYPOINT, 'consumed_seeds': (), 'guard_plan': dispatch_066c.base_d555.CANDIDATE_CONFIGS[dispatch_066c.base_d555.DEFAULT_CANDIDATE_KEY]['guard_plan'], 'expected_shape_wins': (), 'fallback': dispatch_066c.base_d555.ROUTE_BASELINE_F30C_ENTRYPOINT, 'rejected_reason': 'same-session d555 baseline'}, {'id': BASE_066C_ID, 'entrypoint': BASE_066C_ENTRYPOINT, 'consumed_seeds': (SEED_Q4Q64_3505_ID,), 'guard_plan': ('066c exact 3505 Q4/Q64 K10 guard', 'd555 full82 Weave fallback'), 'expected_shape_wins': dispatch_066c.TARGET_SHAPES, 'fallback': ''.join([format(dispatch_066c.base_d555.MODULE, ''), ':launch_from_contract_inputs']), 'rejected_reason': 'same-session current-dispatcher baseline'}, {'id': CANDIDATE_CONFIGS[CANDIDATE_3505_B8C7]['candidate_id'], 'entrypoint': CANDIDATE_CONFIGS[CANDIDATE_3505_B8C7]['benchmark_entrypoint'], 'consumed_seeds': CANDIDATE_CONFIGS[CANDIDATE_3505_B8C7]['selected_seeds'], 'guard_plan': CANDIDATE_CONFIGS[CANDIDATE_3505_B8C7]['guard_plan'], 'expected_shape_wins': CANDIDATE_CONFIGS[CANDIDATE_3505_B8C7]['expected_shape_wins'], 'fallback': ROUTE_BASE_066C_ENTRYPOINT, 'rejected_reason': None}, {'id': CANDIDATE_CONFIGS[CANDIDATE_69D6_B8C7]['candidate_id'], 'entrypoint': CANDIDATE_CONFIGS[CANDIDATE_69D6_B8C7]['benchmark_entrypoint'], 'consumed_seeds': CANDIDATE_CONFIGS[CANDIDATE_69D6_B8C7]['selected_seeds'], 'guard_plan': CANDIDATE_CONFIGS[CANDIDATE_69D6_B8C7]['guard_plan'], 'expected_shape_wins': CANDIDATE_CONFIGS[CANDIDATE_69D6_B8C7]['expected_shape_wins'], 'fallback': ROUTE_BASE_066C_ENTRYPOINT, 'rejected_reason': None}) + +def _candidate_config(candidate_key: str) -> dict[str, Any]: + try: + return CANDIDATE_CONFIGS[candidate_key] + except KeyError as exc: + raise ValueError(''.join(['unknown candidate key ', format(repr(candidate_key), ''), '; expected one of ', format(CANDIDATE_KEYS, '')])) from exc + +def _candidate_id(candidate_key: str) -> str: + return str(_candidate_config(candidate_key)['candidate_id']) + +def _candidate_q4q64_mode(candidate_key: str) -> str: + return str(_candidate_config(candidate_key)['q4q64_mode']) + +def _candidate_kernel_fn(candidate_key: str) -> Callable[[dict[str, Any]], Any]: + return _candidate_config(candidate_key)['kernel_fn'] + +def _candidate_selected_seeds(candidate_key: str) -> tuple[str, ...]: + return tuple(_candidate_config(candidate_key)['selected_seeds']) + +def _dtype_name(inputs: dict[str, Any], key: str) -> str: + tensor = inputs.get(key) + if tensor is not None: + return str(tensor.dtype).removeprefix('torch.') + return str(inputs.get('dtype', '')).removeprefix('torch.') + +def _eligible_rect(inputs: dict[str, Any]) -> bool: + return rect_b8c7._eligible_rect_d128_k20_q1536(inputs) + +def _eligible_q4q64(inputs: dict[str, Any]) -> bool: + return dispatch_066c._eligible_rag_microbatch_k10_q4q64(inputs) + +def _select_contract_shapes(shape_labels): + return dispatch_066c._select_contract_shapes(shape_labels) + +def _q4q64_seed_for_mode(q4q64_mode: str) -> str: + if q4q64_mode == Q4Q64_MODE_3505: + return SEED_Q4Q64_3505_ID + if q4q64_mode == Q4Q64_MODE_69D6: + return SEED_Q4Q64_69D6_ID + raise ValueError(''.join(['unknown q4q64 mode ', format(repr(q4q64_mode), '')])) + +def _q4q64_route_for_inputs(inputs: dict[str, Any], q4q64_mode: str) -> str: + if q4q64_mode == Q4Q64_MODE_3505: + return dispatch_066c.route_for_contract_inputs(inputs) + if q4q64_mode == Q4Q64_MODE_69D6: + return dispatch_69d6.route_for_contract_inputs(inputs) + raise ValueError(''.join(['unknown q4q64 mode ', format(repr(q4q64_mode), '')])) + +def _q4q64_launch_for_inputs(inputs: dict[str, Any], q4q64_mode: str) -> None: + if q4q64_mode == Q4Q64_MODE_3505: + dispatch_066c.launch_from_contract_inputs(inputs) + return + if q4q64_mode == Q4Q64_MODE_69D6: + dispatch_69d6.launch_from_contract_inputs(inputs) + return + raise ValueError(''.join(['unknown q4q64 mode ', format(repr(q4q64_mode), '')])) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False, q4q64_mode: str=Q4Q64_MODE_3505) -> str: + if not force_fallback: + if _eligible_rect(inputs): + return rect_b8c7.ROUTE_NAME + if _eligible_q4q64(inputs): + return _q4q64_route_for_inputs(inputs, q4q64_mode) + return dispatch_066c.route_for_contract_inputs(inputs, force_fallback=force_fallback) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False, q4q64_mode: str=Q4Q64_MODE_3505) -> None: + if not force_fallback: + if _eligible_rect(inputs): + rect_b8c7.launch_from_contract_inputs(inputs) + return + if _eligible_q4q64(inputs): + _q4q64_launch_for_inputs(inputs, q4q64_mode) + return + dispatch_066c.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, q4q64_mode=Q4Q64_MODE_3505) + +def candidate_066c_3505_plus_b8c7_full82_v1(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, q4q64_mode=Q4Q64_MODE_3505) + +def candidate_066c_69d6_plus_b8c7_full82_v1(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, q4q64_mode=Q4Q64_MODE_69D6) + +def candidate_baseline_066c(inputs: dict[str, Any]) -> None: + dispatch_066c.candidate_rag_microbatch_k10_q4q64_m64_3505_d555_v1(inputs) + +def candidate_baseline_d555(inputs: dict[str, Any]) -> None: + dispatch_066c.candidate_baseline_d555(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=None, kernel_fn: Callable[[dict[str, Any]], Any]=candidate, correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + return dispatch_066c._run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=kernel_fn, correctness=correctness, time_flashlib=time_flashlib) + +def _targeted_seed_row(seed_id: str, label: str) -> dict[str, Any]: + return dict(TARGETED_SEED_ROWS.get(seed_id, {}).get(label, {})) + +def _trace_inputs_for_shape(shape: dict[str, Any]) -> dict[str, Any]: + return dispatch_066c.base_d555.base_f30c._trace_inputs_from_shape(shape) + +def _normalize_route_row(row: dict[str, Any]) -> dict[str, Any]: + return dispatch_066c.base_d555.base_f30c._normalize_route_row(row) + +def _rect_trace_record(inputs: dict[str, Any], *, force_fallback: bool=False) -> dict[str, Any]: + label = str(inputs.get('label')) + baseline_route = dispatch_066c.route_for_contract_inputs(inputs) + if force_fallback: + row = dict(dispatch_066c.route_trace_for_contract_shapes((label,), force_fallback=True)[0]) + row['expected_seed'] = SEED_RECT_B8C7_ID + row['guard_id'] = 'forced_fallback_b8c7_rect_d128_k20_disabled' + row['guard_condition'] = 'forced fallback to 066c/d555; b8c7 D128/K20 seed disabled' + row['classification'] = 'guard-miss' + return _normalize_route_row(row) + return _normalize_route_row({'shape_key': label, 'selected_route': rect_b8c7.ROUTE_NAME, 'selected_entrypoint': rect_b8c7.ROUTE_ENTRYPOINT, 'selected_seed': SEED_RECT_B8C7_ID, 'expected_seed': SEED_RECT_B8C7_ID, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': 'b8c7_rect_d128_k20_q1536_exact_guard', 'guard_condition': 'exact BF16 non-build rectangular search shape with B=1 Q=1536 M=65536 D=128 K=20', 'coverage': 'b8c7 split8/warp8 Weave seed before 066c fallback', 'consumed_seed': SEED_RECT_B8C7_ID, 'replaced_route': baseline_route, 'baseline_dispatcher_route': baseline_route, 'shape_specific_kernel_ms': TARGETED_SEED_ROWS[SEED_RECT_B8C7_ID][label]['kernel_ms'], 'targeted_seed_ratio_vs_flashlib': TARGETED_SEED_ROWS[SEED_RECT_B8C7_ID][label]['ratio_vs_flashlib'], 'classification': 'unmeasured'}) + +def _q4q64_trace_record(inputs: dict[str, Any], *, q4q64_mode: str, force_fallback: bool=False) -> dict[str, Any]: + label = str(inputs.get('label')) + expected_seed = _q4q64_seed_for_mode(q4q64_mode) + if force_fallback: + row = dict(dispatch_066c.route_trace_for_contract_shapes((label,), force_fallback=True)[0]) + row['expected_seed'] = expected_seed + row['guard_id'] = ''.join(['forced_fallback_', format(q4q64_mode, ''), '_q4q64_disabled']) + row['guard_condition'] = ''.join(['forced fallback to d555; ', format(q4q64_mode, ''), ' Q4/Q64 seed disabled']) + row['classification'] = 'guard-miss' + return _normalize_route_row(row) + if q4q64_mode == Q4Q64_MODE_3505: + row = dict(dispatch_066c.route_trace_for_contract_shapes((label,))[0]) + row['selected_seed'] = SEED_Q4Q64_3505_ID + row['expected_seed'] = SEED_Q4Q64_3505_ID + row['selected_entrypoint'] = dispatch_066c.ROUTE_ENTRYPOINT + row['baseline_dispatcher_route'] = dispatch_066c.route_for_contract_inputs(inputs) + row['shape_specific_kernel_ms'] = TARGETED_SEED_ROWS[SEED_Q4Q64_3505_ID][label]['kernel_ms'] + row['targeted_seed_ratio_vs_flashlib'] = TARGETED_SEED_ROWS[SEED_Q4Q64_3505_ID][label]['ratio_vs_flashlib'] + return _normalize_route_row(row) + row = dict(dispatch_69d6.route_trace_for_contract_shapes((label,))[0]) + row['selected_seed'] = SEED_Q4Q64_69D6_ID + row['expected_seed'] = SEED_Q4Q64_69D6_ID + row['baseline_dispatcher_route'] = dispatch_066c.route_for_contract_inputs(inputs) + row['shape_specific_kernel_ms'] = TARGETED_SEED_ROWS[SEED_Q4Q64_69D6_ID][label]['kernel_ms'] + row['targeted_seed_ratio_vs_flashlib'] = TARGETED_SEED_ROWS[SEED_Q4Q64_69D6_ID][label]['ratio_vs_flashlib'] + return _normalize_route_row(row) + +def _route_trace_record(inputs: dict[str, Any], *, candidate_key: str, force_fallback: bool=False) -> dict[str, Any]: + q4q64_mode = _candidate_q4q64_mode(candidate_key) + if _eligible_rect(inputs): + return _rect_trace_record(inputs, force_fallback=force_fallback) + if _eligible_q4q64(inputs): + return _q4q64_trace_record(inputs, q4q64_mode=q4q64_mode, force_fallback=force_fallback) + row = dict(dispatch_066c.route_trace_for_contract_shapes((str(inputs.get('label')),), force_fallback=force_fallback)[0]) + row['baseline_dispatcher_route'] = dispatch_066c.route_for_contract_inputs(inputs, force_fallback=force_fallback) + return _normalize_route_row(row) + +def route_trace_for_contract_shapes(shape_labels=None, *, candidate_key: str=DEFAULT_CANDIDATE_KEY, force_fallback: bool=False) -> list[dict[str, Any]]: + _candidate_config(candidate_key) + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + return [_route_trace_record(_trace_inputs_for_shape(shape), candidate_key=candidate_key, force_fallback=force_fallback) for shape in selected] + +def _timing_backend_name(use_cupti: bool) -> str: + return 'cupti' if use_cupti else 'cuda_event_fallback' + +def _denominator_name(shape_labels) -> str: + if shape_labels is None: + return 'full82_v9' + labels = tuple(shape_labels) + if labels == TARGET_SHAPES: + return 'target_rect_d128_q4_q64' + return ''.join(['shape', format(len(labels), '')]) + +def _denominator_label(shape_labels) -> str: + if shape_labels is None: + return ''.join(['full', format(len(eval_mod.CANONICAL_SHAPES), '')]) + return ''.join(['shape', format(len(tuple(shape_labels)), '')]) + +def _rows_for_labels(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return dispatch_066c.base_d555._rows_for_labels(report, labels) + +def _annotate_route_trace(route_trace: list[dict[str, Any]], candidate_report: dict[str, Any], baseline_066c_report: dict[str, Any], baseline_d555_report: dict[str, Any]) -> list[dict[str, Any]]: + annotated = [] + for row in route_trace: + label = str(row.get('shape_key')) + out = dict(row) + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_066c_row = baseline_066c_report.get('per_shape', {}).get(label, {}) + baseline_d555_row = baseline_d555_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_066c_ms = baseline_066c_row.get('kernel_ms') + baseline_d555_ms = baseline_d555_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') + speedup_vs_066c = baseline_066c_ms / candidate_ms if candidate_ms and baseline_066c_ms else None + speedup_vs_d555 = baseline_d555_ms / candidate_ms if candidate_ms and baseline_d555_ms else None + speedup_vs_external = flashlib_ms / candidate_ms if candidate_ms and flashlib_ms else None + out['dispatcher_kernel_ms'] = candidate_ms + out['baseline_066c_kernel_ms'] = baseline_066c_ms + out['baseline_d555_kernel_ms'] = baseline_d555_ms + out['external_baseline_ms'] = flashlib_ms + out['flashlib_ms'] = flashlib_ms + out['relative_speedup_vs_baseline'] = speedup_vs_066c + out['relative_speedup_vs_066c'] = speedup_vs_066c + out['relative_speedup_vs_d555'] = speedup_vs_d555 + out['speedup_vs_external_baseline'] = speedup_vs_external + out['external_baseline_ref'] = 'same_session' if flashlib_ms is not None else 'not_available' + out['route_changed_vs_066c'] = out.get('selected_route') != out.get('baseline_dispatcher_route') + if label in TARGET_SHAPE_SET: + if not out.get('selected_seed'): + out['classification'] = 'guard-miss' + elif speedup_vs_external is not None and speedup_vs_external < 1.05: + out['classification'] = 'kernel-slow' + elif speedup_vs_066c is not None and speedup_vs_066c < 0.98 and (label not in {Q4_SHAPE, Q64_SHAPE}): + out['classification'] = 'kernel-slow' + elif out.get('selected_seed') == SEED_Q4Q64_3505_ID: + out['classification'] = 'route-ok' + else: + out['classification'] = 'seed-consumed' + elif speedup_vs_external is not None and speedup_vs_external < 1.05: + out['classification'] = 'kernel-slow' if out.get('route_kind') == 'specialized' else 'fallback-slow' + else: + out['classification'] = 'route-ok' + annotated.append(_normalize_route_row(out)) + return annotated + +def _below_flashlib_rows(report: dict[str, Any], route_trace: list[dict[str, Any]], *, floor: float) -> list[dict[str, Any]]: + trace_by_label = {str(row['shape_key']): row for row in route_trace} + rows = [] + for label, row in sorted(report.get('per_shape', {}).items()): + ratio = row.get('ratio_vs_flashlib') + if isinstance(ratio, (float, int)) and ratio < floor: + trace_row = trace_by_label.get(label, {}) + rows.append({'shape_key': label, 'kernel_ms': row.get('kernel_ms'), 'flashlib_ms': row.get('flashlib_ms'), 'ratio_vs_flashlib': ratio, 'selected_route': trace_row.get('selected_route'), 'selected_seed': trace_row.get('selected_seed'), 'route_kind': trace_row.get('route_kind', 'unknown'), 'classification': trace_row.get('classification', 'unmeasured')}) + return rows + +def _seed_delta_matrix(candidate_key: str, candidate_report: dict[str, Any], baseline_066c_report: dict[str, Any], baseline_d555_report: dict[str, Any]) -> list[dict[str, Any]]: + q4q64_mode = _candidate_q4q64_mode(candidate_key) + matrix = [] + for label in TARGET_SHAPES: + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_066c_row = baseline_066c_report.get('per_shape', {}).get(label, {}) + baseline_d555_row = baseline_d555_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_066c_ms = baseline_066c_row.get('kernel_ms') + baseline_d555_ms = baseline_d555_row.get('kernel_ms') + inputs = dispatch_066c.base_d555.base_f30c._inputs_for_label(label) + selected_seed = SEED_RECT_B8C7_ID if label == RECT_SHAPE else _q4q64_seed_for_mode(q4q64_mode) + matrix.append({'shape_key': label, 'baseline_066c_route': dispatch_066c.route_for_contract_inputs(inputs), 'baseline_d555_route': dispatch_066c.base_d555.route_for_contract_inputs(inputs), 'candidate_route': route_for_contract_inputs(inputs, q4q64_mode=q4q64_mode), 'selected_seed': selected_seed, 'candidate_id': _candidate_id(candidate_key), 'candidate_ms': candidate_ms, 'baseline_066c_ms': baseline_066c_ms, 'baseline_d555_ms': baseline_d555_ms, 'flashlib_ms': candidate_row.get('flashlib_ms'), 'delta_ms_vs_066c': candidate_ms - baseline_066c_ms if candidate_ms and baseline_066c_ms else None, 'delta_ms_vs_d555': candidate_ms - baseline_d555_ms if candidate_ms and baseline_d555_ms else None, 'speedup_vs_066c': baseline_066c_ms / candidate_ms if candidate_ms and baseline_066c_ms else None, 'speedup_vs_d555': baseline_d555_ms / candidate_ms if candidate_ms and baseline_d555_ms else None, 'ratio_vs_flashlib': candidate_row.get('ratio_vs_flashlib'), 'targeted_seed_row': _targeted_seed_row(selected_seed, label), 'timing_backend': candidate_row.get('timing_backend') or baseline_066c_row.get('timing_backend') or baseline_d555_row.get('timing_backend')}) + return matrix + +def benchmark_baseline_d555(*, use_cupti: bool=True, shape_labels=None, benchmark_correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_baseline_d555, correctness=benchmark_correctness, time_flashlib=time_flashlib) + report['candidate_id'] = BASE_D555_ID + report['measured_entrypoint'] = BASE_D555_ENTRYPOINT + report['route_trace'] = dispatch_066c.base_d555.route_trace_for_contract_shapes(shape_labels) + report['route_trace_included'] = True + return report + +def benchmark_baseline_066c(*, use_cupti: bool=True, shape_labels=None, benchmark_correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_baseline_066c, correctness=benchmark_correctness, time_flashlib=time_flashlib) + report['candidate_id'] = BASE_066C_ID + report['baseline_candidate_id'] = BASE_D555_ID + report['measured_entrypoint'] = BASE_066C_ENTRYPOINT + report['route_trace'] = dispatch_066c.route_trace_for_contract_shapes(shape_labels) + report['route_trace_included'] = True + return report + +def _benchmark_payload(candidate_key: str, candidate_report: dict[str, Any], baseline_066c_report: dict[str, Any], baseline_d555_report: dict[str, Any], *, use_cupti: bool, shape_labels, benchmark_correctness: bool, time_flashlib: bool) -> dict[str, Any]: + candidate_metric = candidate_report['summary']['primary_mean'] + baseline_066c_metric = baseline_066c_report['summary']['primary_mean'] + baseline_d555_metric = baseline_d555_report['summary']['primary_mean'] + route_trace = _annotate_route_trace(route_trace_for_contract_shapes(shape_labels, candidate_key=candidate_key), candidate_report, baseline_066c_report, baseline_d555_report) + below_1x = _below_flashlib_rows(candidate_report, route_trace, floor=1.0) + below_floor = _below_flashlib_rows(candidate_report, route_trace, floor=1.05) + denominator = _denominator_name(shape_labels) + timing_backend = _timing_backend_name(use_cupti) + return {'candidate_id': _candidate_id(candidate_key), 'candidate_key': candidate_key, 'baseline_candidate_id': BASE_066C_ID, 'd555_baseline_candidate_id': BASE_D555_ID, 'selected_seeds': _candidate_selected_seeds(candidate_key), 'source_tasks': SOURCE_TASKS, 'tflops': candidate_metric, 'baseline_066c_tflops': baseline_066c_metric, 'baseline_d555_tflops': baseline_d555_metric, 'metric_delta_vs_066c': candidate_metric - baseline_066c_metric if candidate_metric and baseline_066c_metric else None, 'metric_delta_vs_d555': candidate_metric - baseline_d555_metric if candidate_metric and baseline_d555_metric else None, 'all_correct': candidate_report['summary']['all_correct'], 'baseline_066c_all_correct': baseline_066c_report['summary']['all_correct'], 'baseline_d555_all_correct': baseline_d555_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'baseline_066c_performance_comparable': baseline_066c_report['summary']['performance_comparable'], 'baseline_d555_performance_comparable': baseline_d555_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'baseline_066c_invalid_performance_reason': baseline_066c_report['summary']['invalid_performance_reason'], 'baseline_d555_invalid_performance_reason': baseline_d555_report['summary']['invalid_performance_reason'], 'measured_entrypoint': _candidate_config(candidate_key)['benchmark_entrypoint'], 'baseline_entrypoint': ''.join([format(MODULE, ''), ':benchmark_baseline_066c']), 'd555_baseline_entrypoint': ''.join([format(MODULE, ''), ':benchmark_baseline_d555']), 'measured_shape_labels': 'all_canonical' if shape_labels is None else tuple(shape_labels), 'timing_backend': timing_backend, 'denominator': denominator, 'selected_route_labels': TARGET_SHAPES, 'consumed_seed_labels': TARGET_SHAPES, 'selected_route_rows': _rows_for_labels(candidate_report, TARGET_SHAPES), 'baseline_066c_selected_route_rows': _rows_for_labels(baseline_066c_report, TARGET_SHAPES), 'baseline_d555_selected_route_rows': _rows_for_labels(baseline_d555_report, TARGET_SHAPES), 'seed_delta_matrix': _seed_delta_matrix(candidate_key, candidate_report, baseline_066c_report, baseline_d555_report), 'targeted_seed_rows': TARGETED_SEED_ROWS, 'candidate_dispatchers': CANDIDATE_DISPATCHERS, 'selected_candidate_dispatcher': _candidate_id(candidate_key), 'guard_plan': _candidate_config(candidate_key)['guard_plan'], 'route_modules': PRODUCTION_ROUTE_MODULES, 'route_trace': route_trace, 'forced_fallback_route_trace': route_trace_for_contract_shapes(shape_labels, candidate_key=candidate_key, force_fallback=True), 'route_trace_included': True, 'contract_summary': candidate_report['summary'], 'baseline_066c_contract_summary': baseline_066c_report['summary'], 'baseline_d555_contract_summary': baseline_d555_report['summary'], 'contract_performance': candidate_report['performance'], 'baseline_066c_contract_performance': baseline_066c_report['performance'], 'baseline_d555_contract_performance': baseline_d555_report['performance'], 'timing_backends': dispatch_066c.base_d555.base_f30c._timing_backends_for_reports(candidate_report, baseline_066c_report, baseline_d555_report), 'timing_backend_requested': timing_backend, 'use_cupti': use_cupti, 'benchmark_correctness_checked': benchmark_correctness, 'benchmark_time_flashlib': time_flashlib, 'performance_coverage': 'partial' if below_floor else 'pass', 'coverage_only_routes': [], 'hot_bucket_blockers': below_floor, 'flashlib_parity_ledger': {'baseline_ref_scope': 'same_session' if time_flashlib else 'not_available', 'speedup_floor': 1.05, 'rows_below_1x': below_1x, 'rows_below_floor': below_floor, 'omitted_reason': None if time_flashlib else 'benchmark_time_flashlib=false'}, 'rank_objective': {'metric': 'tflops', 'direction': 'maximize', 'value': candidate_metric, 'baseline_066c_value': baseline_066c_metric, 'baseline_d555_value': baseline_d555_metric, 'delta_vs_066c': candidate_metric - baseline_066c_metric if candidate_metric and baseline_066c_metric else None, 'delta_vs_d555': candidate_metric - baseline_d555_metric if candidate_metric and baseline_d555_metric else None, 'denominator': denominator}, 'report': candidate_report, 'baseline_066c_report': baseline_066c_report, 'baseline_d555_report': baseline_d555_report} + +def benchmark_candidate_portfolio(candidate_key: str=DEFAULT_CANDIDATE_KEY, *, use_cupti: bool=True, shape_labels=None, baseline_066c_report: dict[str, Any] | None=None, baseline_d555_report: dict[str, Any] | None=None, benchmark_correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + if baseline_d555_report is None: + baseline_d555_report = benchmark_baseline_d555(use_cupti=use_cupti, shape_labels=shape_labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + if baseline_066c_report is None: + baseline_066c_report = benchmark_baseline_066c(use_cupti=use_cupti, shape_labels=shape_labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=_candidate_kernel_fn(candidate_key), correctness=benchmark_correctness, time_flashlib=time_flashlib) + return _benchmark_payload(candidate_key, candidate_report, baseline_066c_report, baseline_d555_report, use_cupti=use_cupti, shape_labels=shape_labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + +def benchmark_candidate_066c_3505_plus_b8c7_full82_v1(**kwargs) -> dict[str, Any]: + return benchmark_candidate_portfolio(CANDIDATE_3505_B8C7, **kwargs) + +def benchmark_candidate_066c_69d6_plus_b8c7_full82_v1(**kwargs) -> dict[str, Any]: + return benchmark_candidate_portfolio(CANDIDATE_69D6_B8C7, **kwargs) + +def _baseline_sidecar(report: dict[str, Any], *, candidate_id: str, measured_entrypoint: str, denominator: str, timing_backend: str, benchmark_correctness: bool, time_flashlib: bool) -> dict[str, Any]: + return {'candidate_id': candidate_id, 'measured_entrypoint': measured_entrypoint, 'measured_shape_labels': report.get('measured_shape_labels', 'all_canonical'), 'timing_backend': timing_backend, 'denominator': denominator, 'benchmark_correctness_checked': benchmark_correctness, 'benchmark_time_flashlib': time_flashlib, 'tflops': report['summary']['primary_mean'], 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'contract_summary': report['summary'], 'contract_performance': report['performance'], 'route_trace': report.get('route_trace', []), 'route_trace_included': True, 'rank_objective': {'metric': 'tflops', 'direction': 'maximize', 'value': report['summary']['primary_mean'], 'denominator': denominator}, 'report': report} + +def _synthesis_summary(*, payload_3505: dict[str, Any], payload_69d6: dict[str, Any], baseline_066c_path: Path, baseline_d555_path: Path, candidate_3505_path: Path, candidate_69d6_path: Path, denominator: str, timing_backend: str) -> dict[str, Any]: + candidates = [payload_3505, payload_69d6] + eligible = [payload for payload in candidates if payload.get('all_correct') and payload.get('performance_comparable') and (payload.get('tflops') is not None)] + selected = max(eligible, key=lambda payload: payload['tflops']) if eligible else payload_3505 + rejected = payload_69d6 if selected is payload_3505 else payload_3505 + return {'baseline_dispatcher': BASE_066C_ENTRYPOINT, 'd555_baseline': BASE_D555_ENTRYPOINT, 'selected_dispatcher': selected['measured_entrypoint'], 'selected_candidate_id': selected['candidate_id'], 'selection_policy': 'highest same-session full-denominator TFLOPS among correct comparable candidates', 'candidate_dispatchers': CANDIDATE_DISPATCHERS, 'candidate_results': [{'candidate_id': payload['candidate_id'], 'tflops': payload.get('tflops'), 'metric_delta_vs_066c': payload.get('metric_delta_vs_066c'), 'metric_delta_vs_d555': payload.get('metric_delta_vs_d555'), 'all_correct': payload.get('all_correct'), 'performance_comparable': payload.get('performance_comparable'), 'rows_below_1x': [row['shape_key'] for row in payload['flashlib_parity_ledger']['rows_below_1x']], 'rows_below_floor': [row['shape_key'] for row in payload['flashlib_parity_ledger']['rows_below_floor']]} for payload in candidates], 'rejected_route_combination': {'candidate_id': rejected['candidate_id'], 'reason': 'lower same-session full-denominator TFLOPS than selected candidate' if selected is not rejected else 'no correct comparable winner available'}, 'seed_delta_matrix': {payload_3505['candidate_id']: payload_3505['seed_delta_matrix'], payload_69d6['candidate_id']: payload_69d6['seed_delta_matrix']}, 'full_denominator_ab': {'baseline_payload': str(baseline_066c_path), 'd555_baseline_payload': str(baseline_d555_path), 'candidate_payload': str(candidate_3505_path) if selected is payload_3505 else str(candidate_69d6_path), 'comparison_candidate_payloads': [str(candidate_3505_path), str(candidate_69d6_path)], 'denominator': denominator, 'timing_backend': timing_backend, 'metric_delta_vs_066c': selected.get('metric_delta_vs_066c'), 'metric_delta_vs_d555': selected.get('metric_delta_vs_d555'), 'route_trace': selected['route_trace']}} + +def write_benchmark_artifacts(artifact_dir: str | Path, *, use_cupti: bool=True, shape_labels=None, benchmark_correctness: bool=True, time_flashlib: bool=True) -> dict[str, str]: + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + denom_label = _denominator_label(shape_labels) + denominator = _denominator_name(shape_labels) + timing_backend = _timing_backend_name(use_cupti) + baseline_d555 = benchmark_baseline_d555(use_cupti=use_cupti, shape_labels=shape_labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + baseline_066c = benchmark_baseline_066c(use_cupti=use_cupti, shape_labels=shape_labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + payload_3505 = benchmark_candidate_portfolio(CANDIDATE_3505_B8C7, use_cupti=use_cupti, shape_labels=shape_labels, baseline_066c_report=baseline_066c, baseline_d555_report=baseline_d555, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + payload_69d6 = benchmark_candidate_portfolio(CANDIDATE_69D6_B8C7, use_cupti=use_cupti, shape_labels=shape_labels, baseline_066c_report=baseline_066c, baseline_d555_report=baseline_d555, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + baseline_d555_path = out_dir / ''.join([format(denom_label, ''), '_same_session_baseline_d555_for_066c_b8c7_69d6_v1.json']) + baseline_066c_path = out_dir / ''.join([format(denom_label, ''), '_same_session_baseline_066c_q4q64_3505_v1.json']) + candidate_3505_path = out_dir / ''.join([format(denom_label, ''), '_dispatch_066c_3505_plus_b8c7_v1.json']) + candidate_69d6_path = out_dir / ''.join([format(denom_label, ''), '_dispatch_066c_69d6_plus_b8c7_v1.json']) + route_trace_3505_path = out_dir / ''.join([format(denom_label, ''), '_route_trace_066c_3505_plus_b8c7_v1.json']) + route_trace_69d6_path = out_dir / ''.join([format(denom_label, ''), '_route_trace_066c_69d6_plus_b8c7_v1.json']) + forced_trace_3505_path = out_dir / ''.join([format(denom_label, ''), '_forced_fallback_trace_066c_3505_plus_b8c7_v1.json']) + forced_trace_69d6_path = out_dir / ''.join([format(denom_label, ''), '_forced_fallback_trace_066c_69d6_plus_b8c7_v1.json']) + seed_matrix_path = out_dir / ''.join([format(denom_label, ''), '_seed_delta_matrix_066c_b8c7_69d6_v1.json']) + synthesis_path = out_dir / ''.join([format(denom_label, ''), '_dispatcher_synthesis_066c_b8c7_69d6_v1.json']) + baseline_d555_path.write_text(json.dumps(_baseline_sidecar(baseline_d555, candidate_id=str(BASE_D555_ID), measured_entrypoint=''.join([format(MODULE, ''), ':benchmark_baseline_d555']), denominator=denominator, timing_backend=timing_backend, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib), indent=2, sort_keys=True) + '\n', encoding='utf-8') + baseline_066c_path.write_text(json.dumps(_baseline_sidecar(baseline_066c, candidate_id=BASE_066C_ID, measured_entrypoint=''.join([format(MODULE, ''), ':benchmark_baseline_066c']), denominator=denominator, timing_backend=timing_backend, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib), indent=2, sort_keys=True) + '\n', encoding='utf-8') + candidate_3505_path.write_text(json.dumps(payload_3505, indent=2, sort_keys=True) + '\n', encoding='utf-8') + candidate_69d6_path.write_text(json.dumps(payload_69d6, indent=2, sort_keys=True) + '\n', encoding='utf-8') + route_trace_3505_path.write_text(json.dumps(payload_3505['route_trace'], indent=2, sort_keys=True) + '\n', encoding='utf-8') + route_trace_69d6_path.write_text(json.dumps(payload_69d6['route_trace'], indent=2, sort_keys=True) + '\n', encoding='utf-8') + forced_trace_3505_path.write_text(json.dumps(payload_3505['forced_fallback_route_trace'], indent=2, sort_keys=True) + '\n', encoding='utf-8') + forced_trace_69d6_path.write_text(json.dumps(payload_69d6['forced_fallback_route_trace'], indent=2, sort_keys=True) + '\n', encoding='utf-8') + seed_matrix_path.write_text(json.dumps({payload_3505['candidate_id']: payload_3505['seed_delta_matrix'], payload_69d6['candidate_id']: payload_69d6['seed_delta_matrix']}, indent=2, sort_keys=True) + '\n', encoding='utf-8') + synthesis = _synthesis_summary(payload_3505=payload_3505, payload_69d6=payload_69d6, baseline_066c_path=baseline_066c_path, baseline_d555_path=baseline_d555_path, candidate_3505_path=candidate_3505_path, candidate_69d6_path=candidate_69d6_path, denominator=denominator, timing_backend=timing_backend) + synthesis_path.write_text(json.dumps(synthesis, indent=2, sort_keys=True) + '\n', encoding='utf-8') + return {'same_session_baseline_d555_payload': str(baseline_d555_path), 'same_session_baseline_066c_payload': str(baseline_066c_path), 'candidate_3505_b8c7_payload': str(candidate_3505_path), 'candidate_69d6_b8c7_payload': str(candidate_69d6_path), 'route_trace_3505_b8c7': str(route_trace_3505_path), 'route_trace_69d6_b8c7': str(route_trace_69d6_path), 'forced_fallback_trace_3505_b8c7': str(forced_trace_3505_path), 'forced_fallback_trace_69d6_b8c7': str(forced_trace_69d6_path), 'seed_delta_matrix': str(seed_matrix_path), 'dispatcher_synthesis': str(synthesis_path)} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_066c_b8c7_69d6_q4_portfolio_full82_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_066c_b8c7_69d6_q4_portfolio_full82_v1.py new file mode 100644 index 00000000..4c1bb2f7 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_066c_b8c7_69d6_q4_portfolio_full82_v1.py @@ -0,0 +1,394 @@ +"""Full82 Q4 portfolio over the 17b8 kNN build dispatcher. + +Minimum target architecture: sm_100a. This generalize-auto-tuning wrapper +preserves the seed schedules and compares Q4-only routes on the full82 +dispatcher denominator. The base route is the 17b8 066c+69d6+b8c7 dispatcher: +exact D128/K20 stays on b8c7, Q64 stays on 69d6, and guard misses stay on the +inherited 066c Weave dispatcher. Candidate Q4 overlays consume only existing +Weave seeds: 5a70 M64 split144, f15a S144/G12, and 801e S144/G12. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +from pathlib import Path +from typing import Any, Callable +from . import knn_build_dispatch_066c_b8c7_69d6_full82_v1 as base17b8 +from . import knn_build_rag_microbatch_k10_q4_m64s144_17b8_v1 as q4_5a70 +from . import knn_build_rag_microbatch_k10_q4_s144_d555_v1 as q4_801e +from . import knn_build_rag_microbatch_q4_s144_17b8_v1 as q4_f15a +MODULE = 'loom.examples.weave.knn_build_dispatch_066c_b8c7_69d6_q4_portfolio_full82_v1' +RECT_SHAPE = base17b8.RECT_SHAPE +Q4_SHAPE = base17b8.Q4_SHAPE +Q64_SHAPE = base17b8.Q64_SHAPE +TARGET_SHAPES = (RECT_SHAPE, Q4_SHAPE, Q64_SHAPE) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +SEED_RECT_B8C7_ID = base17b8.SEED_RECT_B8C7_ID +SEED_Q4Q64_69D6_ID = base17b8.SEED_Q4Q64_69D6_ID +SEED_Q4_5A70_ID = q4_5a70.SEED_ID +SEED_Q4_F15A_ID = q4_f15a.SEED_Q4_S144_ID +SEED_Q4_801E_ID = q4_801e.SEED_ID +BASE_17B8_ID = base17b8.CANDIDATE_CONFIGS[base17b8.CANDIDATE_69D6_B8C7]['candidate_id'] +BASE_17B8_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_baseline_17b8']) +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +CANDIDATE_BASE_17B8 = 'base_17b8_69d6_q4_q64_b8c7' +CANDIDATE_5A70_Q4 = 'q4_5a70_m64s144' +CANDIDATE_F15A_Q4 = 'q4_f15a_s144' +CANDIDATE_801E_Q4 = 'q4_801e_s144' +CANDIDATE_KEYS = (CANDIDATE_BASE_17B8, CANDIDATE_5A70_Q4, CANDIDATE_F15A_Q4, CANDIDATE_801E_Q4) +Q4_CANDIDATE_KEYS = (CANDIDATE_5A70_Q4, CANDIDATE_F15A_Q4, CANDIDATE_801E_Q4) +DEFAULT_CANDIDATE_KEY = CANDIDATE_BASE_17B8 +eval_mod = base17b8.eval_mod +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbatch_m64_d4f7_stage1", "arg_keys": ["query", "database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 91392, "constants": [], "cta_group": 1, "threads": 512}')) +SOURCE_TASKS = _decode_capture(_json_loads('{"__dict_items__": [["rect_d128_k20_q1536_9b9f_d555_b8c7_v1", "weave-evolve-knn-build-b8c7 / design_doc/active/weave_evolve_knn_build_round_116_b8c7_rectd128k20.md"], ["rag_microbucket_faeb_q4q64_k10_69d6_v1", "weave-evolve-knn-build-69d6 / design_doc/active/weave_evolve_knn_build_round_116_69d6_q4q64.md"], ["rag_microbatch_k10_q4_m64_s144_g12_17b8_v1", "weave-evolve-knn-build-5a70 / design_doc/active/weave_evolve_knn_build_round_117_17b8_q4_m64s144.md"], ["rag_microbatch_q4_k10_s144_17b8_v1", "weave-evolve-knn-build-f15a / design_doc/active/weave_evolve_knn_build_round_117_17b8_q4_s144.md"], ["rag_microbatch_k10_q4_s144_g12_d555_v1", "generalize-auto-tuning-knn-build-ab44 / design_doc/active/generalize_auto_tuning_knn_build_round_118_ab44.md"], ["candidate_066c_69d6_plus_b8c7_full82_v1", "generalize-auto-tuning-knn-build-17b8 / design_doc/active/generalize_auto_tuning_knn_build_round_116_17b8.md"]]}')) +TARGETED_SEED_ROWS = {**base17b8.TARGETED_SEED_ROWS, SEED_Q4_5A70_ID: {Q4_SHAPE: {'kernel_ms': 0.0713125, 'timing_backend': 'cupti', 'source_payload': 'artifacts/generalize_auto_tuning/knn_build_17b8_q4_m64s144_repair/rag_microbatch_k10_q4_m64_s144_g12_17b8_v1.json'}}, SEED_Q4_F15A_ID: {Q4_SHAPE: {'kernel_ms': 0.050943, 'ratio_vs_flashlib': 1.6947, 'timing_backend': 'cupti', 'source_payload': 'artifacts/generalize_auto_tuning/knn_build_17b8_q4_s144/shape1_dispatch_17b8_q4_s144_v1.json'}}, SEED_Q4_801E_ID: {Q4_SHAPE: {'kernel_ms': 0.050048, 'ratio_vs_flashlib': 1.8056265984654731, 'timing_backend': 'cupti', 'source_payload': 'artifacts/generalize_auto_tuning/knn_build_066c_q4_s144_repair/rag_microbatch_k10_q4_s144_g12_d555_v1.json'}}} +PRODUCTION_ROUTE_MODULES = _decode_capture(_json_loads('{"__dict_items__": [["large_square_k20k32", "loom.examples.weave.knn_build_large_square_k20k32_a989_v1"], ["over64_k96", "loom.examples.weave.knn_build_over64_k96_a989_v1"], ["baseline_7c3a_rag_k10", "loom.examples.weave.knn_build_rag_frontier_4b5c_v1:k10"], ["rag_frontier_7399_k10", "loom.examples.weave.knn_build_rag_frontier_7399_v1:k10_s72"], ["rag_frontier_7399_k32_replaced", "loom.examples.weave.knn_build_rag_frontier_7399_v1:k32_s72_g8_fusedmerge"], ["rag_frontier_4fbf_k32", "loom.examples.weave.knn_build_rag_frontier_4fbf_v7:k32_s72_g24_tailinf_fused"], ["rect_smallq_largem_d15e", "loom.examples.weave.knn_build_rect_smallq_largem_ff59_d15e_v1:split16"], ["baseline_7c3a_policy", "loom.examples.weave.knn_build_dispatch_b6d4_d15e_fd02_v1:baseline_7c3a_policy"], ["fallback", "loom.examples.weave.knn_build_dispatch_split72_4e09_de1a_3dc7_v48"], ["dim_d64_73a9", "loom.examples.weave.knn_build_dim_midk_73a9_v1:d64_split_s8"], ["current_exact_k32_dispatcher", "loom.examples.weave.knn_build_dispatch_4fbf_7399_d15e_full55_bad5_v1:launch_from_contract_inputs"], ["base_7399_d15e_dispatcher", "loom.examples.weave.knn_build_dispatch_7399_d15e_full55_v1:launch_from_contract_inputs"], ["rag_frontier_7399_k32", "loom.examples.weave.knn_build_rag_frontier_7399_v1:k32_s72_g8_fusedmerge"], ["dim_d256_df2f", "loom.examples.weave.knn_build_dim_midk_df2f_v1:d256_split_s8"], ["dim_fp16_d128_df2f", "loom.examples.weave.knn_build_dim_midk_df2f_v1:fp16_d128_split_s8"], ["base_dispatch", "loom.examples.weave.knn_build_dispatch_7399_d15e_full55_v1:launch_from_contract_inputs"], ["rect_intermediate_4452_s8", "loom.examples.weave.knn_build_rect_intermediate_frontier_6a73_4452_v2:rect_s8_k10_cached"], ["base_champion_6b59", "loom.examples.weave.knn_build_dispatch_7399_d15e_df2f_full55_v1:launch_from_contract_inputs"], ["base_k32_d64_62b1", "loom.examples.weave.knn_build_dispatch_4fbf_7399_d15e_73a9_full55_v1:launch_from_contract_inputs"], ["default_k96_a330", "loom.examples.weave.knn_build_over64_k96_a989_v1"], ["large_tail_a4f6", "loom.examples.weave.knn_build_large_tail_frontier_6a73_v1:split4_k20"], ["midk_81aa_q2048_k24_k28", "loom.examples.weave.knn_build_dim_midk_bad5_midkcleanup_v1:midk_k24_k28_s8"], ["midk_9b2c_q4096_k28", "loom.examples.weave.knn_build_dim_midk_bad5_k24k28_v1:k28_q4096_s4_unordered_exact"], ["base_f552", "loom.examples.weave.knn_build_dispatch_selected_portfolio_f552_v1:launch_from_contract_inputs"], ["midk_bad5_k64split8", "loom.examples.weave.knn_build_dim_midk_bad5_k64split8_v1:k64_q2048_s8_tailinf"], ["base_e51c", "loom.examples.weave.knn_build_dispatch_selected_portfolio_e51c_v1:launch_from_contract_inputs"], ["midk_f8c3_q4096_k64_split8_a194", "loom.examples.weave.knn_build_dim_midk_f8c3_q4096k64split_v1:q4096_k64_tailinf_split8"], ["base_f8c3", "loom.examples.weave.knn_build_dispatch_selected_portfolio_f8c3_v1:launch_from_contract_inputs"], ["lowk_b193_q512_s4", "loom.examples.weave.knn_build_lowk_f8c3_q512_q1024_v1:q512_lowk_s4"], ["lowk_b193_q1024_k16_s16", "loom.examples.weave.knn_build_lowk_f8c3_q512_q1024_v1:q1024_k16_s16"], ["large_square_5407_q8192_k32_s2", "loom.examples.weave.knn_build_large_square_k32_8a83_v1:q8192_k32_split2"], ["base_f853", "loom.examples.weave.knn_build_dispatch_selected_portfolio_f853_v1:launch_from_contract_inputs"], ["lowk_b193_q512_k4_k5_k6_s4", "loom.examples.weave.knn_build_lowk_f8c3_q512_q1024_v1:q512_lowk_s4"], ["base_f16b", "loom.examples.weave.knn_build_dispatch_selected_portfolio_f16b_v1:launch_from_contract_inputs"], ["rag_microbatch_b2ec_s72_g8", "loom.examples.weave.knn_build_rag_microbatch_4a72_v1:launch_from_contract_inputs"], ["base_4a72", "loom.examples.weave.knn_build_dispatch_selected_portfolio_4a72_v1:launch_from_contract_inputs"], ["rag_m64_s128_0c69", "loom.examples.weave.knn_build_rag_microbatch_m64_d4f7_v1:launch_from_contract_inputs"], ["rag_s144_g12_cta1_059f", "loom.examples.weave.knn_build_rag_microbatch_4a72_v2:launch_from_contract_inputs"], ["rag_s144_g8_cta1_4982_read_ref_parameterized", "loom.examples.weave.knn_build_rag_microbatch_4a72_v2:launch_from_contract_inputs"], ["base_397b", "loom.examples.weave.knn_build_dispatch_selected_portfolio_397b_v1:launch_from_contract_inputs"], ["d64_fdd7_aa88_v2", "loom.examples.weave.knn_build_d64_build_aa88_v2:launch_from_contract_inputs"], ["base_8700", "loom.examples.weave.knn_build_dispatch_rag_seed_portfolio_8700_v1:launch_from_contract_inputs(portfolio_id=all_m64_s128)"], ["rect_d64_cf49_v3_9138", "loom.examples.weave.knn_build_rect_d64_cf49_v3:launch_from_contract_inputs"], ["q1_mbucket_aa88_q1m_v3_bcb3", "loom.examples.weave.knn_build_ragonline_mbucket_aa88_q1m_v3:launch_from_contract_inputs"], ["over64_k96_a2f8_v1_generated_v8", "loom.examples.weave.knn_build_over64_k96_a2f8_v1:_launch_over64_k96_split_path"], ["base_e3de", "loom.examples.weave.knn_build_dispatch_d64_fdd7_e3de_v1:launch_from_contract_inputs"], ["non128_frontier_8199_widecombine_v1", "loom.examples.weave.knn_build_non128_frontier_8199_widecombine_v1:launch_from_contract_inputs"], ["base_4247", "loom.examples.weave.knn_build_dispatch_e3de_9138_bcb3_4247_v1:launch_from_contract_inputs"], ["rag_microbucket_k32_8fcb_split148_v1_b3e0_sm148", "loom.examples.weave.knn_build_rag_microbucket_k32_8fcb_split148_v1:launch_from_contract_inputs"], ["rag_microbucket_k32_2e8e_q16split148_v1_b3e0_q16_s148", "loom.examples.weave.knn_build_rag_microbucket_k32_2e8e_q16split148_v1:launch_from_contract_inputs"], ["non128_frontier_3d5a_cachedmerge_v1", "loom.examples.weave.knn_build_non128_frontier_3d5a_cachedmerge_v1:launch_from_contract_inputs"], ["over64_k96_exactall_229a_v1_b6c4", "loom.examples.weave.knn_build_over64_k96_exactall_229a_v1:launch_from_contract_inputs"], ["knn_build_midk_k11k13_e080_v1", "loom.examples.weave.knn_build_midk_k11k13_e080_v1:launch_from_contract_inputs"], ["ragonline_mbucket_4fc7_q1m262_v1_980c", "loom.examples.weave.knn_build_ragonline_mbucket_4fc7_q1m262_v1:launch_from_contract_inputs"], ["baseline_8199_widecombine_full82_v1", "loom.examples.weave.knn_build_dispatch_4247_non128_8199_widecombine_full82_v1:launch_from_contract_inputs"], ["k30_q4096_6998_warpselect_v1", "loom.examples.weave.knn_build_k30_q4096_6998_warpselect_v1:launch_from_contract_inputs"], ["rag_stream_k10_direct_split72_6998_v1", "loom.examples.weave.knn_build_rag_online_stream_split72_4e09_v1:launch_from_contract_inputs"], ["rect_d64_23be_unordered_v1", "loom.examples.weave.knn_build_rect_d64_23be_unordered_v1:launch_from_contract_inputs"], ["residual_19b3_ed1c_portfolio_6998", "loom.examples.weave.knn_build_dispatch_c142_3505_q32rowld_19b3_v1:launch_from_contract_inputs"], ["candidate_q16split148_cachedmerge_k96exactall_e080_q1m262_over_8199_full82_v1", "loom.examples.weave.knn_build_dispatch_4247_non128_8199_3d5a_2e8e_full82_synth_v1:launch_from_contract_inputs"], ["rect_d128_k20_q1536_9b9f_d555_b8c7_v1", "loom.examples.weave.knn_build_rect_d128_k20_d555_b8c7_v1:launch_from_contract_inputs"], ["rag_microbatch_k10_q4q64_m64_3505_d555_v1", "loom.examples.weave.knn_build_rag_microbatch_k10_q4q64_d555_v1:launch_from_contract_inputs"], ["rag_microbucket_faeb_q4q64_k10_69d6_v1", "loom.examples.weave.knn_build_rag_microbucket_faeb_v1:launch_from_contract_inputs"], ["candidate_066c_ragmicro_q4q64_3505_full82_v1", "loom.examples.weave.knn_build_rag_microbatch_k10_q4q64_d555_v1:launch_from_contract_inputs"], ["candidate_d555_direct_residual_seeds_full82_v1", "loom.examples.weave.knn_build_dispatch_d555_residual_seed_synth_full82_v1:launch_from_contract_inputs"], ["rag_microbatch_k10_q4_m64_s144_g12_17b8_v1", "loom.examples.weave.knn_build_rag_microbatch_k10_q4_m64s144_17b8_v1:launch_from_contract_inputs"], ["rag_microbatch_q4_k10_s144_17b8_v1", "loom.examples.weave.knn_build_rag_microbatch_q4_s144_17b8_v1:launch_from_contract_inputs"], ["rag_microbatch_k10_q4_s144_g12_d555_v1", "loom.examples.weave.knn_build_rag_microbatch_k10_q4_s144_d555_v1:launch_from_contract_inputs"], ["candidate_066c_69d6_plus_b8c7_full82_v1", "loom.examples.weave.knn_build_dispatch_066c_b8c7_69d6_full82_v1:launch_from_contract_inputs(q4q64_mode=69d6)"]]}')) + +def _candidate_config(candidate_key: str) -> dict[str, Any]: + try: + return CANDIDATE_CONFIGS[candidate_key] + except KeyError as exc: + raise ValueError(''.join(['unknown candidate key ', format(repr(candidate_key), ''), '; expected one of ', format(CANDIDATE_KEYS, '')])) from exc + +def _candidate_id(candidate_key: str) -> str: + return str(_candidate_config(candidate_key)['candidate_id']) + +def _candidate_q4_seed(candidate_key: str) -> str: + return str(_candidate_config(candidate_key)['q4_seed']) + +def _candidate_kernel_fn(candidate_key: str) -> Callable[[dict[str, Any]], Any]: + return _candidate_config(candidate_key)['kernel_fn'] + +def _dtype_name(inputs: dict[str, Any], key: str) -> str: + tensor = inputs.get(key) + if tensor is not None: + return str(tensor.dtype).removeprefix('torch.') + return str(inputs.get('dtype', '')).removeprefix('torch.') + +def _eligible_rect(inputs: dict[str, Any]) -> bool: + return base17b8._eligible_rect(inputs) + +def _eligible_q4(inputs: dict[str, Any]) -> bool: + label = inputs.get('label') + return not bool(inputs.get('build', False)) and int(inputs.get('B', -1)) == 1 and (int(inputs.get('Q', -1)) == 4) and (int(inputs.get('M', -1)) == 100000) and (int(inputs.get('D', -1)) == 128) and (int(inputs.get('K', -1)) == 10) and (_dtype_name(inputs, 'query') == 'bfloat16') and (_dtype_name(inputs, 'database') == 'bfloat16') and (label is None or str(label) == Q4_SHAPE) + +def _eligible_q4q64(inputs: dict[str, Any]) -> bool: + return base17b8._eligible_q4q64(inputs) + +def _select_contract_shapes(shape_labels): + return base17b8._select_contract_shapes(shape_labels) + +def _base_17b8_route(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + return base17b8.route_for_contract_inputs(inputs, force_fallback=force_fallback, q4q64_mode=base17b8.Q4Q64_MODE_69D6) + +def _base_17b8_launch(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + base17b8.launch_from_contract_inputs(inputs, force_fallback=force_fallback, q4q64_mode=base17b8.Q4Q64_MODE_69D6) + +def _launch_q4_seed(inputs: dict[str, Any], candidate_key: str) -> None: + if candidate_key == CANDIDATE_5A70_Q4: + q4_5a70.rag_m64._launch_rag_microbatch_m64(inputs, split_count=q4_5a70.SPLIT_COUNT, group_count=q4_5a70.GROUP_COUNT) + return + if candidate_key == CANDIDATE_F15A_Q4: + q4_f15a.rag_faeb._launch_q4_k10_s144(inputs) + return + if candidate_key == CANDIDATE_801E_Q4: + q4_801e.faeb._launch_q4_k10_s144(inputs) + return + raise ValueError(''.join(['candidate ', format(repr(candidate_key), ''), ' has no Q4 overlay launcher'])) + +def route_for_contract_inputs(inputs: dict[str, Any], *, candidate_key: str=DEFAULT_CANDIDATE_KEY, force_fallback: bool=False) -> str: + _candidate_config(candidate_key) + if force_fallback: + return _base_17b8_route(inputs, force_fallback=True) + if _eligible_rect(inputs): + return base17b8.rect_b8c7.ROUTE_NAME + if _eligible_q4(inputs) and candidate_key != CANDIDATE_BASE_17B8: + return str(_candidate_config(candidate_key)['q4_route']) + if _eligible_q4q64(inputs): + return base17b8.dispatch_69d6.route_for_contract_inputs(inputs) + return _base_17b8_route(inputs) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, candidate_key: str=DEFAULT_CANDIDATE_KEY, force_fallback: bool=False) -> None: + _candidate_config(candidate_key) + if force_fallback: + _base_17b8_launch(inputs, force_fallback=True) + return + if _eligible_rect(inputs): + base17b8.rect_b8c7.launch_from_contract_inputs(inputs) + return + if _eligible_q4(inputs) and candidate_key != CANDIDATE_BASE_17B8: + _launch_q4_seed(inputs, candidate_key) + return + if _eligible_q4q64(inputs): + base17b8.dispatch_69d6.launch_from_contract_inputs(inputs) + return + _base_17b8_launch(inputs) + +def candidate_baseline_17b8(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, candidate_key=CANDIDATE_BASE_17B8) + +def candidate_066c_5a70_q4_69d6_q64_b8c7_full82_v1(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, candidate_key=CANDIDATE_5A70_Q4) + +def candidate_066c_f15a_q4_69d6_q64_b8c7_full82_v1(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, candidate_key=CANDIDATE_F15A_Q4) + +def candidate_066c_801e_q4_69d6_q64_b8c7_full82_v1(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, candidate_key=CANDIDATE_801E_Q4) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, candidate_key=DEFAULT_CANDIDATE_KEY) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) +CANDIDATE_CONFIGS: dict[str, dict[str, Any]] = {CANDIDATE_BASE_17B8: {'candidate_id': str(BASE_17B8_ID), 'entrypoint': ''.join([format(MODULE, ''), ':candidate_baseline_17b8']), 'benchmark_entrypoint': ''.join([format(MODULE, ''), ':benchmark_baseline_17b8']), 'kernel_fn': candidate_baseline_17b8, 'q4_seed': SEED_Q4Q64_69D6_ID, 'q4_route': base17b8.dispatch_69d6.ROUTE_Q4_FAEB, 'selected_seeds': (SEED_RECT_B8C7_ID, SEED_Q4Q64_69D6_ID), 'guard_plan': ('b8c7 exact D128/K20/Q1536 guard', '69d6 FAEB exact Q4/Q64 K10 guard', '066c full82 Weave fallback'), 'fallback': base17b8.ROUTE_BASE_066C_ENTRYPOINT, 'rejected_reason': 'same-session selected 17b8 baseline'}, CANDIDATE_5A70_Q4: {'candidate_id': 'candidate_066c_5a70_q4_69d6_q64_b8c7_full82_v1', 'entrypoint': ''.join([format(MODULE, ''), ':candidate_066c_5a70_q4_69d6_q64_b8c7_full82_v1']), 'benchmark_entrypoint': ''.join([format(MODULE, ''), ':benchmark_candidate_066c_5a70_q4_69d6_q64_b8c7_full82_v1']), 'kernel_fn': candidate_066c_5a70_q4_69d6_q64_b8c7_full82_v1, 'q4_seed': SEED_Q4_5A70_ID, 'q4_route': q4_5a70.ROUTE_NAME, 'q4_entrypoint': q4_5a70.ROUTE_ENTRYPOINT, 'guard_id': '17b8_rag_microbatch_k10_q4_m64_s144_exact_guard', 'guard_condition': 'exact BF16 non-build B=1 Q=4 M=100000 D=128 K=10', 'selected_seeds': (SEED_RECT_B8C7_ID, SEED_Q4_5A70_ID, SEED_Q4Q64_69D6_ID), 'guard_plan': ('b8c7 exact D128/K20/Q1536 guard', '5a70 exact Q4 M64 split144/group12 guard', '69d6 FAEB exact Q64 K10 guard', '066c full82 Weave fallback'), 'fallback': ''.join([format(base17b8.MODULE, ''), ':launch_from_contract_inputs(q4q64_mode=69d6)']), 'rejected_reason': None}, CANDIDATE_F15A_Q4: {'candidate_id': 'candidate_066c_f15a_q4_69d6_q64_b8c7_full82_v1', 'entrypoint': ''.join([format(MODULE, ''), ':candidate_066c_f15a_q4_69d6_q64_b8c7_full82_v1']), 'benchmark_entrypoint': ''.join([format(MODULE, ''), ':benchmark_candidate_066c_f15a_q4_69d6_q64_b8c7_full82_v1']), 'kernel_fn': candidate_066c_f15a_q4_69d6_q64_b8c7_full82_v1, 'q4_seed': SEED_Q4_F15A_ID, 'q4_route': q4_f15a.ROUTE_Q4_S144, 'q4_entrypoint': q4_f15a.ROUTE_ENTRYPOINT, 'guard_id': '17b8_q4_s144_exact_guard', 'guard_condition': 'exact BF16 non-build B=1 Q=4 M=100000 D=128 K=10', 'selected_seeds': (SEED_RECT_B8C7_ID, SEED_Q4_F15A_ID, SEED_Q4Q64_69D6_ID), 'guard_plan': ('b8c7 exact D128/K20/Q1536 guard', 'f15a exact Q4 S144/G12 guard', '69d6 FAEB exact Q64 K10 guard', '066c full82 Weave fallback'), 'fallback': ''.join([format(base17b8.MODULE, ''), ':launch_from_contract_inputs(q4q64_mode=69d6)']), 'rejected_reason': None}, CANDIDATE_801E_Q4: {'candidate_id': 'candidate_066c_801e_q4_69d6_q64_b8c7_full82_v1', 'entrypoint': ''.join([format(MODULE, ''), ':candidate_066c_801e_q4_69d6_q64_b8c7_full82_v1']), 'benchmark_entrypoint': ''.join([format(MODULE, ''), ':benchmark_candidate_066c_801e_q4_69d6_q64_b8c7_full82_v1']), 'kernel_fn': candidate_066c_801e_q4_69d6_q64_b8c7_full82_v1, 'q4_seed': SEED_Q4_801E_ID, 'q4_route': q4_801e.ROUTE_NAME, 'q4_entrypoint': q4_801e.ROUTE_ENTRYPOINT, 'guard_id': '801e_q4_s144_exact_guard', 'guard_condition': 'exact BF16 non-build B=1 Q=4 M=100000 D=128 K=10', 'selected_seeds': (SEED_RECT_B8C7_ID, SEED_Q4_801E_ID, SEED_Q4Q64_69D6_ID), 'guard_plan': ('b8c7 exact D128/K20/Q1536 guard', '801e exact Q4 S144/G12 guard', '69d6 FAEB exact Q64 K10 guard', '066c full82 Weave fallback'), 'fallback': ''.join([format(base17b8.MODULE, ''), ':launch_from_contract_inputs(q4q64_mode=69d6)']), 'rejected_reason': None}} +CANDIDATE_DISPATCHERS = _decode_capture(_json_loads('{"__tuple__": [{"__dict_items__": [["id", "candidate_066c_69d6_plus_b8c7_full82_v1"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_066c_b8c7_69d6_q4_portfolio_full82_v1:benchmark_baseline_17b8"], ["consumed_seeds", {"__tuple__": ["rect_d128_k20_q1536_9b9f_d555_b8c7_v1", "rag_microbucket_faeb_q4q64_k10_69d6_v1"]}], ["guard_plan", {"__tuple__": ["b8c7 exact D128/K20/Q1536 guard", "69d6 FAEB exact Q4/Q64 K10 guard", "066c full82 Weave fallback"]}], ["expected_shape_wins", {"__tuple__": ["search_rect_b1_q1536_m65536_d128_k20", "rag_microbatch_b1_q4_m100000_d128_k10", "rag_microbatch_b1_q64_m100000_d128_k10"]}], ["fallback", "loom.examples.weave.knn_build_rag_microbatch_k10_q4q64_d555_v1:launch_from_contract_inputs"], ["rejected_reason", "same-session selected 17b8 baseline"]]}, {"__dict_items__": [["id", "candidate_066c_5a70_q4_69d6_q64_b8c7_full82_v1"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_066c_b8c7_69d6_q4_portfolio_full82_v1:benchmark_candidate_066c_5a70_q4_69d6_q64_b8c7_full82_v1"], ["consumed_seeds", {"__tuple__": ["rect_d128_k20_q1536_9b9f_d555_b8c7_v1", "rag_microbatch_k10_q4_m64_s144_g12_17b8_v1", "rag_microbucket_faeb_q4q64_k10_69d6_v1"]}], ["guard_plan", {"__tuple__": ["b8c7 exact D128/K20/Q1536 guard", "5a70 exact Q4 M64 split144/group12 guard", "69d6 FAEB exact Q64 K10 guard", "066c full82 Weave fallback"]}], ["expected_shape_wins", {"__tuple__": ["search_rect_b1_q1536_m65536_d128_k20", "rag_microbatch_b1_q4_m100000_d128_k10", "rag_microbatch_b1_q64_m100000_d128_k10"]}], ["fallback", "loom.examples.weave.knn_build_dispatch_066c_b8c7_69d6_full82_v1:launch_from_contract_inputs(q4q64_mode=69d6)"], ["rejected_reason", null]]}, {"__dict_items__": [["id", "candidate_066c_f15a_q4_69d6_q64_b8c7_full82_v1"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_066c_b8c7_69d6_q4_portfolio_full82_v1:benchmark_candidate_066c_f15a_q4_69d6_q64_b8c7_full82_v1"], ["consumed_seeds", {"__tuple__": ["rect_d128_k20_q1536_9b9f_d555_b8c7_v1", "rag_microbatch_q4_k10_s144_17b8_v1", "rag_microbucket_faeb_q4q64_k10_69d6_v1"]}], ["guard_plan", {"__tuple__": ["b8c7 exact D128/K20/Q1536 guard", "f15a exact Q4 S144/G12 guard", "69d6 FAEB exact Q64 K10 guard", "066c full82 Weave fallback"]}], ["expected_shape_wins", {"__tuple__": ["search_rect_b1_q1536_m65536_d128_k20", "rag_microbatch_b1_q4_m100000_d128_k10", "rag_microbatch_b1_q64_m100000_d128_k10"]}], ["fallback", "loom.examples.weave.knn_build_dispatch_066c_b8c7_69d6_full82_v1:launch_from_contract_inputs(q4q64_mode=69d6)"], ["rejected_reason", null]]}, {"__dict_items__": [["id", "candidate_066c_801e_q4_69d6_q64_b8c7_full82_v1"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_066c_b8c7_69d6_q4_portfolio_full82_v1:benchmark_candidate_066c_801e_q4_69d6_q64_b8c7_full82_v1"], ["consumed_seeds", {"__tuple__": ["rect_d128_k20_q1536_9b9f_d555_b8c7_v1", "rag_microbatch_k10_q4_s144_g12_d555_v1", "rag_microbucket_faeb_q4q64_k10_69d6_v1"]}], ["guard_plan", {"__tuple__": ["b8c7 exact D128/K20/Q1536 guard", "801e exact Q4 S144/G12 guard", "69d6 FAEB exact Q64 K10 guard", "066c full82 Weave fallback"]}], ["expected_shape_wins", {"__tuple__": ["search_rect_b1_q1536_m65536_d128_k20", "rag_microbatch_b1_q4_m100000_d128_k10", "rag_microbatch_b1_q64_m100000_d128_k10"]}], ["fallback", "loom.examples.weave.knn_build_dispatch_066c_b8c7_69d6_full82_v1:launch_from_contract_inputs(q4q64_mode=69d6)"], ["rejected_reason", null]]}]}')) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=None, kernel_fn: Callable[[dict[str, Any]], Any]=candidate, correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + return base17b8._run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=kernel_fn, correctness=correctness, time_flashlib=time_flashlib) + +def _trace_inputs_for_shape(shape: dict[str, Any]) -> dict[str, Any]: + return base17b8._trace_inputs_for_shape(shape) + +def _normalize_route_row(row: dict[str, Any]) -> dict[str, Any]: + return base17b8._normalize_route_row(row) + +def _q4_trace_record(inputs: dict[str, Any], *, candidate_key: str, force_fallback: bool=False) -> dict[str, Any]: + label = str(inputs.get('label')) + expected_seed = _candidate_q4_seed(candidate_key) + if force_fallback: + row = dict(base17b8.route_trace_for_contract_shapes((label,), candidate_key=base17b8.CANDIDATE_69D6_B8C7, force_fallback=True)[0]) + row['expected_seed'] = expected_seed + row['guard_id'] = ''.join(['forced_fallback_', format(candidate_key, ''), '_disabled']) + row['guard_condition'] = ''.join(['forced fallback to 17b8; ', format(candidate_key, ''), ' disabled']) + row['classification'] = 'guard-miss' + return _normalize_route_row(row) + if candidate_key == CANDIDATE_BASE_17B8: + row = dict(base17b8._q4q64_trace_record(inputs, q4q64_mode=base17b8.Q4Q64_MODE_69D6, force_fallback=False)) + row['selected_seed'] = SEED_Q4Q64_69D6_ID + row['expected_seed'] = SEED_Q4Q64_69D6_ID + row['baseline_dispatcher_route'] = base17b8.route_for_contract_inputs(inputs, q4q64_mode=base17b8.Q4Q64_MODE_69D6) + return _normalize_route_row(row) + config = _candidate_config(candidate_key) + return _normalize_route_row({'shape_key': label, 'selected_route': config['q4_route'], 'selected_entrypoint': config['q4_entrypoint'], 'selected_seed': expected_seed, 'expected_seed': expected_seed, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': config['guard_id'], 'guard_condition': config['guard_condition'], 'coverage': ''.join([format(candidate_key, ''), ' exact Q4 overlay before selected 17b8 fallback']), 'consumed_seed': expected_seed, 'replaced_route': base17b8.route_for_contract_inputs(inputs, q4q64_mode=base17b8.Q4Q64_MODE_69D6), 'baseline_dispatcher_route': base17b8.route_for_contract_inputs(inputs, q4q64_mode=base17b8.Q4Q64_MODE_69D6), 'shape_specific_kernel_ms': TARGETED_SEED_ROWS.get(expected_seed, {}).get(label, {}).get('kernel_ms'), 'targeted_seed_ratio_vs_flashlib': TARGETED_SEED_ROWS.get(expected_seed, {}).get(label, {}).get('ratio_vs_flashlib'), 'classification': 'unmeasured'}) + +def _route_trace_record(inputs: dict[str, Any], *, candidate_key: str, force_fallback: bool=False) -> dict[str, Any]: + label = str(inputs.get('label')) + if _eligible_rect(inputs): + return base17b8._rect_trace_record(inputs, force_fallback=force_fallback) + if _eligible_q4(inputs): + return _q4_trace_record(inputs, candidate_key=candidate_key, force_fallback=force_fallback) + if _eligible_q4q64(inputs): + return base17b8._q4q64_trace_record(inputs, q4q64_mode=base17b8.Q4Q64_MODE_69D6, force_fallback=force_fallback) + row = dict(base17b8.route_trace_for_contract_shapes((label,), candidate_key=base17b8.CANDIDATE_69D6_B8C7, force_fallback=force_fallback)[0]) + row['baseline_dispatcher_route'] = _base_17b8_route(inputs, force_fallback=force_fallback) + return _normalize_route_row(row) + +def route_trace_for_contract_shapes(shape_labels=None, *, candidate_key: str=DEFAULT_CANDIDATE_KEY, force_fallback: bool=False) -> list[dict[str, Any]]: + _candidate_config(candidate_key) + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + return [_route_trace_record(_trace_inputs_for_shape(shape), candidate_key=candidate_key, force_fallback=force_fallback) for shape in selected] + +def _timing_backend_name(use_cupti: bool) -> str: + return 'cupti' if use_cupti else 'cuda_event_fallback' + +def _denominator_name(shape_labels) -> str: + if shape_labels is None: + return 'full82_v9' + labels = tuple(shape_labels) + if labels == TARGET_SHAPES: + return 'target_rect_q4_q64' + return ''.join(['shape', format(len(labels), '')]) + +def _denominator_label(shape_labels) -> str: + if shape_labels is None: + return ''.join(['full', format(len(eval_mod.CANONICAL_SHAPES), '')]) + return ''.join(['shape', format(len(tuple(shape_labels)), '')]) + +def _payload_shape_labels(shape_labels) -> str | tuple[str, ...]: + if shape_labels is None: + return 'all_canonical' + return tuple((str(label) for label in shape_labels)) + +def _rows_for_labels(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return base17b8._rows_for_labels(report, labels) + +def _annotate_route_trace(route_trace: list[dict[str, Any]], candidate_report: dict[str, Any], baseline_report: dict[str, Any], *, candidate_key: str) -> list[dict[str, Any]]: + annotated = [] + for row in route_trace: + label = str(row.get('shape_key')) + out = dict(row) + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') + speedup_vs_baseline = baseline_ms / candidate_ms if candidate_ms and baseline_ms else None + speedup_vs_external = flashlib_ms / candidate_ms if candidate_ms and flashlib_ms else None + out['dispatcher_kernel_ms'] = candidate_ms + out['baseline_17b8_kernel_ms'] = baseline_ms + out['external_baseline_ms'] = flashlib_ms + out['flashlib_ms'] = flashlib_ms + out['relative_speedup_vs_baseline'] = speedup_vs_baseline + out['relative_speedup_vs_17b8'] = speedup_vs_baseline + out['speedup_vs_external_baseline'] = speedup_vs_external + out['external_baseline_ref'] = 'same_session' if flashlib_ms is not None else 'not_available' + out['route_changed_vs_17b8'] = out.get('selected_route') != out.get('baseline_dispatcher_route') + if label == Q4_SHAPE: + expected_seed = _candidate_q4_seed(candidate_key) + if out.get('selected_seed') != expected_seed: + out['classification'] = 'guard-miss' + elif speedup_vs_external is not None and speedup_vs_external < 1.05: + out['classification'] = 'kernel-slow' + elif candidate_key != CANDIDATE_BASE_17B8 and speedup_vs_baseline is not None and (speedup_vs_baseline < 1.0): + out['classification'] = 'kernel-slow' + elif candidate_key == CANDIDATE_BASE_17B8: + out['classification'] = 'route-ok' + else: + out['classification'] = 'seed-consumed' + elif label in {RECT_SHAPE, Q64_SHAPE}: + if speedup_vs_external is not None and speedup_vs_external < 1.05: + out['classification'] = 'kernel-slow' + else: + out['classification'] = 'route-ok' + elif speedup_vs_external is not None and speedup_vs_external < 1.05: + out['classification'] = 'kernel-slow' if out.get('route_kind') == 'specialized' else 'fallback-slow' + else: + out['classification'] = 'route-ok' + annotated.append(_normalize_route_row(out)) + return annotated + +def _below_flashlib_rows(report: dict[str, Any], route_trace: list[dict[str, Any]], *, floor: float) -> list[dict[str, Any]]: + trace_by_label = {str(row['shape_key']): row for row in route_trace} + rows = [] + for label, row in sorted(report.get('per_shape', {}).items()): + ratio = row.get('ratio_vs_flashlib') + if isinstance(ratio, (float, int)) and ratio < floor: + trace_row = trace_by_label.get(label, {}) + rows.append({'shape_key': label, 'kernel_ms': row.get('kernel_ms'), 'flashlib_ms': row.get('flashlib_ms'), 'ratio_vs_flashlib': ratio, 'selected_route': trace_row.get('selected_route'), 'selected_seed': trace_row.get('selected_seed'), 'route_kind': trace_row.get('route_kind', 'unknown'), 'classification': trace_row.get('classification', 'unmeasured')}) + return rows + +def _seed_delta_matrix(candidate_key: str, candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> list[dict[str, Any]]: + matrix = [] + for label in TARGET_SHAPES: + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + inputs = base17b8.dispatch_066c.base_d555.base_f30c._inputs_for_label(label) + selected_seed = SEED_RECT_B8C7_ID if label == RECT_SHAPE else SEED_Q4Q64_69D6_ID if label == Q64_SHAPE else _candidate_q4_seed(candidate_key) + matrix.append({'shape_key': label, 'baseline_route': _base_17b8_route(inputs), 'candidate_route': route_for_contract_inputs(inputs, candidate_key=candidate_key), 'selected_seed': selected_seed, 'candidate_id': _candidate_id(candidate_key), 'candidate_ms': candidate_ms, 'baseline_17b8_ms': baseline_ms, 'flashlib_ms': candidate_row.get('flashlib_ms'), 'delta_ms_candidate_minus_17b8': candidate_ms - baseline_ms if candidate_ms and baseline_ms else None, 'speedup_vs_17b8': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'ratio_vs_flashlib': candidate_row.get('ratio_vs_flashlib'), 'targeted_seed_row': TARGETED_SEED_ROWS.get(selected_seed, {}).get(label, {}), 'timing_backend': candidate_row.get('timing_backend') or baseline_row.get('timing_backend')}) + return matrix + +def _timing_backends_for_reports(*reports: dict[str, Any]) -> list[str]: + return base17b8.dispatch_066c.base_d555.base_f30c._timing_backends_for_reports(*reports) + +def benchmark_baseline_17b8(*, use_cupti: bool=True, shape_labels=None, benchmark_correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_baseline_17b8, correctness=benchmark_correctness, time_flashlib=time_flashlib) + report['candidate_id'] = str(BASE_17B8_ID) + report['measured_entrypoint'] = BASE_17B8_ENTRYPOINT + report['route_trace'] = route_trace_for_contract_shapes(shape_labels, candidate_key=CANDIDATE_BASE_17B8) + report['route_trace_included'] = True + return report + +def _benchmark_payload(candidate_key: str, candidate_report: dict[str, Any], baseline_report: dict[str, Any], *, use_cupti: bool, shape_labels, benchmark_correctness: bool, time_flashlib: bool) -> dict[str, Any]: + candidate_metric = candidate_report['summary']['primary_mean'] + baseline_metric = baseline_report['summary']['primary_mean'] + route_trace = _annotate_route_trace(route_trace_for_contract_shapes(shape_labels, candidate_key=candidate_key), candidate_report, baseline_report, candidate_key=candidate_key) + below_1x = _below_flashlib_rows(candidate_report, route_trace, floor=1.0) + below_floor = _below_flashlib_rows(candidate_report, route_trace, floor=1.05) + denominator = _denominator_name(shape_labels) + timing_backend = _timing_backend_name(use_cupti) + q4_row = candidate_report.get('per_shape', {}).get(Q4_SHAPE, {}) + baseline_q4_row = baseline_report.get('per_shape', {}).get(Q4_SHAPE, {}) + candidate_q4_ms = q4_row.get('kernel_ms') + baseline_q4_ms = baseline_q4_row.get('kernel_ms') + flashlib_q4_ms = q4_row.get('flashlib_ms') + q4_speedup_vs_17b8 = baseline_q4_ms / candidate_q4_ms if candidate_q4_ms and baseline_q4_ms else None + q4_speedup_vs_flashlib = flashlib_q4_ms / candidate_q4_ms if candidate_q4_ms and flashlib_q4_ms else None + full82_no_regression = candidate_metric is not None and baseline_metric is not None and (candidate_metric >= baseline_metric) + q4_floor_pass = q4_speedup_vs_flashlib is not None and q4_speedup_vs_flashlib >= 1.05 + return {'candidate_id': _candidate_id(candidate_key), 'candidate_key': candidate_key, 'baseline_candidate_id': str(BASE_17B8_ID), 'selected_seeds': _candidate_config(candidate_key)['selected_seeds'], 'source_tasks': SOURCE_TASKS, 'tflops': candidate_metric, 'baseline_17b8_tflops': baseline_metric, 'metric_delta_vs_17b8': candidate_metric - baseline_metric if candidate_metric is not None and baseline_metric is not None else None, 'all_correct': candidate_report['summary']['all_correct'], 'baseline_all_correct': baseline_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'baseline_performance_comparable': baseline_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'baseline_invalid_performance_reason': baseline_report['summary']['invalid_performance_reason'], 'measured_entrypoint': _candidate_config(candidate_key)['benchmark_entrypoint'], 'baseline_entrypoint': BASE_17B8_ENTRYPOINT, 'measured_shape_labels': _payload_shape_labels(shape_labels), 'timing_backend': timing_backend, 'denominator': denominator, 'selected_route_labels': TARGET_SHAPES, 'selected_route_rows': _rows_for_labels(candidate_report, TARGET_SHAPES), 'baseline_selected_route_rows': _rows_for_labels(baseline_report, TARGET_SHAPES), 'q4_speedup_vs_17b8': q4_speedup_vs_17b8, 'q4_speedup_vs_flashlib': q4_speedup_vs_flashlib, 'q4_floor_pass': q4_floor_pass, 'full82_no_regression': full82_no_regression, 'seed_delta_matrix': _seed_delta_matrix(candidate_key, candidate_report, baseline_report), 'targeted_seed_rows': TARGETED_SEED_ROWS, 'candidate_dispatchers': CANDIDATE_DISPATCHERS, 'selected_candidate_dispatcher': _candidate_id(candidate_key), 'guard_plan': _candidate_config(candidate_key)['guard_plan'], 'route_modules': PRODUCTION_ROUTE_MODULES, 'route_trace': route_trace, 'forced_fallback_route_trace': route_trace_for_contract_shapes(shape_labels, candidate_key=candidate_key, force_fallback=True), 'route_trace_included': True, 'contract_summary': candidate_report['summary'], 'baseline_contract_summary': baseline_report['summary'], 'contract_performance': candidate_report['performance'], 'baseline_contract_performance': baseline_report['performance'], 'timing_backends': _timing_backends_for_reports(candidate_report, baseline_report), 'timing_backend_requested': timing_backend, 'use_cupti': use_cupti, 'benchmark_correctness_checked': benchmark_correctness, 'benchmark_time_flashlib': time_flashlib, 'performance_coverage': 'partial' if below_floor else 'pass', 'coverage_only_routes': [], 'hot_bucket_blockers': below_floor, 'flashlib_parity_ledger': {'baseline_ref_scope': 'same_session' if time_flashlib else 'not_available', 'speedup_floor': 1.05, 'rows_below_1x': below_1x, 'rows_below_floor': below_floor, 'omitted_reason': None if time_flashlib else 'benchmark_time_flashlib=false'}, 'rank_objective': {'metric': 'tflops', 'direction': 'maximize', 'value': candidate_metric, 'baseline_17b8_value': baseline_metric, 'delta_vs_17b8': candidate_metric - baseline_metric if candidate_metric is not None and baseline_metric is not None else None, 'denominator': denominator}, 'report': candidate_report, 'baseline_report': baseline_report} + +def benchmark_candidate_portfolio(candidate_key: str, *, use_cupti: bool=True, shape_labels=None, baseline_report: dict[str, Any] | None=None, benchmark_correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + if candidate_key == CANDIDATE_BASE_17B8: + return _baseline_sidecar(benchmark_baseline_17b8(use_cupti=use_cupti, shape_labels=shape_labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib), denominator=_denominator_name(shape_labels), timing_backend=_timing_backend_name(use_cupti), benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + if baseline_report is None: + baseline_report = benchmark_baseline_17b8(use_cupti=use_cupti, shape_labels=shape_labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=_candidate_kernel_fn(candidate_key), correctness=benchmark_correctness, time_flashlib=time_flashlib) + return _benchmark_payload(candidate_key, candidate_report, baseline_report, use_cupti=use_cupti, shape_labels=shape_labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + +def benchmark_candidate_066c_5a70_q4_69d6_q64_b8c7_full82_v1(**kwargs) -> dict[str, Any]: + return benchmark_candidate_portfolio(CANDIDATE_5A70_Q4, **kwargs) + +def benchmark_candidate_066c_f15a_q4_69d6_q64_b8c7_full82_v1(**kwargs) -> dict[str, Any]: + return benchmark_candidate_portfolio(CANDIDATE_F15A_Q4, **kwargs) + +def benchmark_candidate_066c_801e_q4_69d6_q64_b8c7_full82_v1(**kwargs) -> dict[str, Any]: + return benchmark_candidate_portfolio(CANDIDATE_801E_Q4, **kwargs) + +def _baseline_sidecar(report: dict[str, Any], *, denominator: str, timing_backend: str, benchmark_correctness: bool, time_flashlib: bool) -> dict[str, Any]: + return {'candidate_id': str(BASE_17B8_ID), 'candidate_key': CANDIDATE_BASE_17B8, 'measured_entrypoint': BASE_17B8_ENTRYPOINT, 'measured_shape_labels': report.get('measured_shape_labels', 'all_canonical'), 'timing_backend': timing_backend, 'denominator': denominator, 'benchmark_correctness_checked': benchmark_correctness, 'benchmark_time_flashlib': time_flashlib, 'tflops': report['summary']['primary_mean'], 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'contract_summary': report['summary'], 'contract_performance': report['performance'], 'route_trace': route_trace_for_contract_shapes(None if report.get('measured_shape_labels') == 'all_canonical' else report.get('measured_shape_labels'), candidate_key=CANDIDATE_BASE_17B8), 'route_trace_included': True, 'rank_objective': {'metric': 'tflops', 'direction': 'maximize', 'value': report['summary']['primary_mean'], 'denominator': denominator}, 'report': report} + +def _candidate_passes_q4_selection(payload: dict[str, Any]) -> bool: + return bool(payload.get('all_correct') and payload.get('performance_comparable') and payload.get('full82_no_regression') and payload.get('q4_floor_pass')) + +def _synthesis_summary(*, baseline_payload: dict[str, Any], candidate_payloads: dict[str, dict[str, Any]], baseline_path: Path, candidate_paths: dict[str, Path], denominator: str, timing_backend: str) -> dict[str, Any]: + eligible = [payload for payload in candidate_payloads.values() if _candidate_passes_q4_selection(payload)] + selected = max(eligible, key=lambda payload: payload['tflops']) if eligible else baseline_payload + rejected = [] + for key, payload in candidate_payloads.items(): + reason = None + if payload is selected: + reason = None + elif not payload.get('all_correct'): + reason = 'correctness failed' + elif not payload.get('performance_comparable'): + reason = 'performance not comparable' + elif not payload.get('full82_no_regression'): + reason = 'failed full82 no-regression against same-session 17b8' + elif not payload.get('q4_floor_pass'): + reason = 'Q4 row did not clear the 1.05x FlashLib floor' + else: + reason = 'lower same-session full82 TFLOPS than selected candidate' + rejected.append({'candidate_key': key, 'candidate_id': payload['candidate_id'], 'reason': reason}) + selected_key = selected.get('candidate_key', CANDIDATE_BASE_17B8) + selected_path = baseline_path if selected_key == CANDIDATE_BASE_17B8 else candidate_paths[selected_key] + return {'baseline_dispatcher': BASE_17B8_ENTRYPOINT, 'selected_dispatcher': selected['measured_entrypoint'], 'selected_candidate_key': selected_key, 'selected_candidate_id': selected['candidate_id'], 'selection_policy': 'highest same-session full82 TFLOPS among correct Q4 candidates that no-regress against 17b8 and clear the Q4 1.05x FlashLib floor; otherwise keep 17b8 default', 'candidate_dispatchers': CANDIDATE_DISPATCHERS, 'candidate_results': [{'candidate_key': key, 'candidate_id': payload['candidate_id'], 'tflops': payload.get('tflops'), 'metric_delta_vs_17b8': payload.get('metric_delta_vs_17b8'), 'q4_speedup_vs_17b8': payload.get('q4_speedup_vs_17b8'), 'q4_speedup_vs_flashlib': payload.get('q4_speedup_vs_flashlib'), 'full82_no_regression': payload.get('full82_no_regression'), 'q4_floor_pass': payload.get('q4_floor_pass'), 'all_correct': payload.get('all_correct'), 'performance_comparable': payload.get('performance_comparable'), 'rows_below_1x': [row['shape_key'] for row in payload['flashlib_parity_ledger']['rows_below_1x']], 'rows_below_floor': [row['shape_key'] for row in payload['flashlib_parity_ledger']['rows_below_floor']]} for key, payload in candidate_payloads.items()], 'rejected_route_combinations': rejected, 'seed_delta_matrix': {payload['candidate_id']: payload['seed_delta_matrix'] for payload in candidate_payloads.values()}, 'full_denominator_ab': {'baseline_payload': str(baseline_path), 'candidate_payload': str(selected_path), 'comparison_candidate_payloads': [str(path) for path in candidate_paths.values()], 'denominator': denominator, 'timing_backend': timing_backend, 'metric_delta_vs_17b8': selected.get('metric_delta_vs_17b8', 0.0), 'route_trace': selected.get('route_trace', baseline_payload.get('route_trace', []))}} + +def write_benchmark_artifacts(artifact_dir: str | Path, *, use_cupti: bool=True, shape_labels=None, benchmark_correctness: bool=True, time_flashlib: bool=True) -> dict[str, str]: + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + denom_label = _denominator_label(shape_labels) + denominator = _denominator_name(shape_labels) + timing_backend = _timing_backend_name(use_cupti) + baseline_report = benchmark_baseline_17b8(use_cupti=use_cupti, shape_labels=shape_labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + baseline_payload = _baseline_sidecar(baseline_report, denominator=denominator, timing_backend=timing_backend, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + candidate_payloads = {key: benchmark_candidate_portfolio(key, use_cupti=use_cupti, shape_labels=shape_labels, baseline_report=baseline_report, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) for key in Q4_CANDIDATE_KEYS} + baseline_path = out_dir / ''.join([format(denom_label, ''), '_same_session_baseline_17b8_066c_69d6_plus_b8c7_v1.json']) + candidate_paths = {CANDIDATE_5A70_Q4: out_dir / ''.join([format(denom_label, ''), '_dispatch_066c_5a70_q4_69d6_q64_b8c7_v1.json']), CANDIDATE_F15A_Q4: out_dir / ''.join([format(denom_label, ''), '_dispatch_066c_f15a_q4_69d6_q64_b8c7_v1.json']), CANDIDATE_801E_Q4: out_dir / ''.join([format(denom_label, ''), '_dispatch_066c_801e_q4_69d6_q64_b8c7_v1.json'])} + route_trace_paths = {key: out_dir / ''.join([format(denom_label, ''), '_route_trace_', format(key, ''), '_v1.json']) for key in Q4_CANDIDATE_KEYS} + forced_trace_paths = {key: out_dir / ''.join([format(denom_label, ''), '_forced_fallback_trace_', format(key, ''), '_v1.json']) for key in Q4_CANDIDATE_KEYS} + seed_matrix_path = out_dir / ''.join([format(denom_label, ''), '_seed_delta_matrix_q4_portfolio_v1.json']) + synthesis_path = out_dir / ''.join([format(denom_label, ''), '_dispatcher_synthesis_q4_portfolio_v1.json']) + baseline_path.write_text(json.dumps(baseline_payload, indent=2, sort_keys=True) + '\n', encoding='utf-8') + for key, payload in candidate_payloads.items(): + candidate_paths[key].write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n', encoding='utf-8') + route_trace_paths[key].write_text(json.dumps(payload['route_trace'], indent=2, sort_keys=True) + '\n', encoding='utf-8') + forced_trace_paths[key].write_text(json.dumps(payload['forced_fallback_route_trace'], indent=2, sort_keys=True) + '\n', encoding='utf-8') + seed_matrix_path.write_text(json.dumps({payload['candidate_id']: payload['seed_delta_matrix'] for payload in candidate_payloads.values()}, indent=2, sort_keys=True) + '\n', encoding='utf-8') + synthesis = _synthesis_summary(baseline_payload=baseline_payload, candidate_payloads=candidate_payloads, baseline_path=baseline_path, candidate_paths=candidate_paths, denominator=denominator, timing_backend=timing_backend) + synthesis_path.write_text(json.dumps(synthesis, indent=2, sort_keys=True) + '\n', encoding='utf-8') + return {'same_session_baseline_payload': str(baseline_path), 'candidate_5a70_payload': str(candidate_paths[CANDIDATE_5A70_Q4]), 'candidate_f15a_payload': str(candidate_paths[CANDIDATE_F15A_Q4]), 'candidate_801e_payload': str(candidate_paths[CANDIDATE_801E_Q4]), 'route_trace_5a70': str(route_trace_paths[CANDIDATE_5A70_Q4]), 'route_trace_f15a': str(route_trace_paths[CANDIDATE_F15A_Q4]), 'route_trace_801e': str(route_trace_paths[CANDIDATE_801E_Q4]), 'forced_fallback_trace_5a70': str(forced_trace_paths[CANDIDATE_5A70_Q4]), 'forced_fallback_trace_f15a': str(forced_trace_paths[CANDIDATE_F15A_Q4]), 'forced_fallback_trace_801e': str(forced_trace_paths[CANDIDATE_801E_Q4]), 'seed_delta_matrix': str(seed_matrix_path), 'dispatcher_synthesis': str(synthesis_path)} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_17b8_lowmargin_1074_full82_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_17b8_lowmargin_1074_full82_v1.py new file mode 100644 index 00000000..c0adc3ae --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_17b8_lowmargin_1074_full82_v1.py @@ -0,0 +1,285 @@ +"""Full82 dispatcher consumption of the 1074 low-margin build seed. + +Minimum target architecture: sm_100a. This generalize-auto-tuning wrapper +preserves the existing 17b8/99fd full82 dispatcher and consumes only the exact +e7a9 low-margin build rows: ``build_k_sweep_qm512_k1``, +``build_k_sweep_qm4096_k24``, and ``build_k_sweep_qm4096_k30``. Guard misses +stay on the inherited Weave dispatcher; no external runtime fallback is added. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +from pathlib import Path +from typing import Any, Callable +from . import knn_build_dispatch_066c_b8c7_69d6_q4_portfolio_full82_v1 as base99fd +from . import knn_build_lowmargin_1074_k1k24k30_v1 as lowmargin +MODULE = 'loom.examples.weave.knn_build_dispatch_17b8_lowmargin_1074_full82_v1' +TARGET_K1 = lowmargin.TARGET_K1 +TARGET_K24 = lowmargin.TARGET_K24 +TARGET_K30 = lowmargin.TARGET_K30 +TARGET_SHAPES = lowmargin.TARGET_SHAPES +TARGET_SHAPE_SET = set(TARGET_SHAPES) +SEED_LOWMARGIN_1074_ID = lowmargin.SEED_ID +SEED_K1_ID = lowmargin.SEED_K1_ID +SEED_K24_ID = lowmargin.SEED_K24_ID +SEED_K30_ID = lowmargin.SEED_K30_ID +BASE_17B8_ID = _decode_capture(_json_loads('"candidate_066c_69d6_plus_b8c7_full82_v1"')) +CANDIDATE_LOWMARGIN_1074 = 'candidate_17b8_lowmargin_1074_full82_v1' +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +BASE_17B8_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_baseline_17b8']) +CANDIDATE_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_candidate_17b8_lowmargin_1074_full82_v1']) +ROUTE_BASE_17B8_ENTRYPOINT = ''.join([format(base99fd.MODULE, ''), ':launch_from_contract_inputs']) +eval_mod = base99fd.eval_mod +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbatch_m64_d4f7_stage1", "arg_keys": ["query", "database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 91392, "constants": [], "cta_group": 1, "threads": 512}')) +SOURCE_TASKS = {**base99fd.SOURCE_TASKS, SEED_LOWMARGIN_1074_ID: 'weave-evolve-knn-build-e7a9 / design_doc/active/weave_evolve_knn_build_round_114_1074_lowmargin.md', SEED_K1_ID: 'weave-evolve-knn-build-e7a9 / design_doc/active/weave_evolve_knn_build_round_114_1074_lowmargin.md', SEED_K24_ID: 'weave-evolve-knn-build-e7a9 / design_doc/active/weave_evolve_knn_build_round_114_1074_lowmargin.md', SEED_K30_ID: 'weave-evolve-knn-build-d63b + weave-evolve-knn-build-e7a9 / design_doc/active/weave_evolve_knn_build_round_114_1074_lowmargin.md'} +LOWMARGIN_PAYLOAD = 'artifacts/generalize_auto_tuning/knn_build_1074_lowmargin_k1k24k30/lowmargin_1074_k1k24k30_v1.json' +TARGETED_SEED_ROWS = {SEED_K1_ID: {TARGET_K1: {'kernel_ms': 0.032063, 'flashlib_ms': 0.076511, 'ratio_vs_flashlib': 2.38627077940305, 'baseline_6998_ms': 0.04224, 'speedup_vs_6998': 1.31740635623616, 'timing_backend': 'cupti', 'source_payload': LOWMARGIN_PAYLOAD}}, SEED_K24_ID: {TARGET_K24: {'kernel_ms': 0.169854, 'flashlib_ms': 0.284829, 'ratio_vs_flashlib': 1.676904871242361, 'baseline_6998_ms': 0.271518, 'speedup_vs_6998': 1.5985375675580202, 'timing_backend': 'cupti', 'source_payload': LOWMARGIN_PAYLOAD}}, SEED_K30_ID: {TARGET_K30: {'kernel_ms': 0.205183, 'flashlib_ms': 0.30531, 'ratio_vs_flashlib': 1.4879887709995467, 'baseline_6998_ms': 0.296126, 'speedup_vs_6998': 1.4432287275261595, 'timing_backend': 'cupti', 'source_payload': LOWMARGIN_PAYLOAD}}} +PRODUCTION_ROUTE_MODULES = {**base99fd.PRODUCTION_ROUTE_MODULES, SEED_LOWMARGIN_1074_ID: lowmargin.ROUTE_ENTRYPOINT, SEED_K1_ID: lowmargin.ROUTE_K1_ENTRYPOINT, SEED_K24_ID: lowmargin.ROUTE_K24_ENTRYPOINT, SEED_K30_ID: lowmargin.ROUTE_K30_ENTRYPOINT, BASE_17B8_ID: ROUTE_BASE_17B8_ENTRYPOINT} + +def _select_contract_shapes(shape_labels): + return base99fd._select_contract_shapes(shape_labels) + +def _trace_inputs_for_shape(shape: dict[str, Any]) -> dict[str, Any]: + return base99fd._trace_inputs_for_shape(shape) + +def _normalize_route_row(row: dict[str, Any]) -> dict[str, Any]: + return base99fd._normalize_route_row(row) + +def _base_17b8_route(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + return base99fd.route_for_contract_inputs(inputs, candidate_key=base99fd.CANDIDATE_BASE_17B8, force_fallback=force_fallback) + +def _base_17b8_launch(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + base99fd.launch_from_contract_inputs(inputs, candidate_key=base99fd.CANDIDATE_BASE_17B8, force_fallback=force_fallback) + +def _eligible_lowmargin(inputs: dict[str, Any]) -> bool: + return lowmargin._eligible_k1_q512(inputs) or lowmargin._eligible_k24_q4096(inputs) or lowmargin._eligible_k30_q4096(inputs) + +def _expected_lowmargin_seed(inputs: dict[str, Any]) -> str | None: + if lowmargin._eligible_k1_q512(inputs): + return SEED_K1_ID + if lowmargin._eligible_k24_q4096(inputs): + return SEED_K24_ID + if lowmargin._eligible_k30_q4096(inputs): + return SEED_K30_ID + return None + +def _lowmargin_entrypoint(seed_id: str) -> str: + if seed_id == SEED_K1_ID: + return lowmargin.ROUTE_K1_ENTRYPOINT + if seed_id == SEED_K24_ID: + return lowmargin.ROUTE_K24_ENTRYPOINT + if seed_id == SEED_K30_ID: + return lowmargin.ROUTE_K30_ENTRYPOINT + raise ValueError(''.join(['unknown low-margin seed ', format(repr(seed_id), '')])) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if not force_fallback and _eligible_lowmargin(inputs): + return lowmargin.route_for_contract_inputs(inputs) + return _base_17b8_route(inputs, force_fallback=force_fallback) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + if not force_fallback and _eligible_lowmargin(inputs): + lowmargin.launch_from_contract_inputs(inputs) + return + _base_17b8_launch(inputs, force_fallback=force_fallback) + +def candidate_baseline_17b8(inputs: dict[str, Any]) -> None: + _base_17b8_launch(inputs) + +def candidate_17b8_lowmargin_1074_full82_v1(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]) -> None: + candidate_17b8_lowmargin_1074_full82_v1(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) +CANDIDATE_CONFIG = {'candidate_id': CANDIDATE_LOWMARGIN_1074, 'entrypoint': ''.join([format(MODULE, ''), ':candidate_17b8_lowmargin_1074_full82_v1']), 'benchmark_entrypoint': CANDIDATE_ENTRYPOINT, 'kernel_fn': candidate_17b8_lowmargin_1074_full82_v1, 'selected_seeds': (SEED_K1_ID, SEED_K24_ID, SEED_K30_ID), 'guard_plan': ('1074 exact BF16 build Q=M=512 K=1 guard', '1074 exact BF16 build Q=M=4096 K=24 guard', '1074 exact BF16 build Q=M=4096 K=30 delegate guard', 'selected 17b8/99fd full82 Weave fallback'), 'fallback': ROUTE_BASE_17B8_ENTRYPOINT, 'expected_shape_wins': TARGET_SHAPES, 'rejected_reason': None} +CANDIDATE_DISPATCHERS = ({'id': BASE_17B8_ID, 'entrypoint': BASE_17B8_ENTRYPOINT, 'consumed_seeds': base99fd.CANDIDATE_CONFIGS[base99fd.CANDIDATE_BASE_17B8]['selected_seeds'], 'guard_plan': base99fd.CANDIDATE_CONFIGS[base99fd.CANDIDATE_BASE_17B8]['guard_plan'], 'expected_shape_wins': base99fd.TARGET_SHAPES, 'fallback': base99fd.CANDIDATE_CONFIGS[base99fd.CANDIDATE_BASE_17B8]['fallback'], 'rejected_reason': 'same-session selected 17b8/99fd baseline'}, {'id': CANDIDATE_CONFIG['candidate_id'], 'entrypoint': CANDIDATE_CONFIG['benchmark_entrypoint'], 'consumed_seeds': CANDIDATE_CONFIG['selected_seeds'], 'guard_plan': CANDIDATE_CONFIG['guard_plan'], 'expected_shape_wins': CANDIDATE_CONFIG['expected_shape_wins'], 'fallback': CANDIDATE_CONFIG['fallback'], 'rejected_reason': None}) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=None, kernel_fn: Callable[[dict[str, Any]], Any]=candidate, correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + return base99fd._run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=kernel_fn, correctness=correctness, time_flashlib=time_flashlib) + +def _lowmargin_trace_record(inputs: dict[str, Any], *, force_fallback: bool=False) -> dict[str, Any]: + label = str(inputs.get('label')) + expected_seed = _expected_lowmargin_seed(inputs) + if expected_seed is None: + raise ValueError(''.join(['shape ', format(repr(label), ''), ' is not eligible for low-margin 1074'])) + if force_fallback: + row = dict(base99fd.route_trace_for_contract_shapes((label,), candidate_key=base99fd.CANDIDATE_BASE_17B8, force_fallback=True)[0]) + row['expected_seed'] = expected_seed + row['guard_id'] = ''.join(['forced_fallback_', format(expected_seed, ''), '_disabled']) + row['guard_condition'] = ''.join(['forced fallback to selected 17b8/99fd; ', format(expected_seed, ''), ' disabled']) + row['classification'] = 'guard-miss' + return _normalize_route_row(row) + seed_row = TARGETED_SEED_ROWS[expected_seed][label] + baseline_route = _base_17b8_route(inputs) + return _normalize_route_row({'shape_key': label, 'selected_route': _lowmargin_entrypoint(expected_seed), 'selected_entrypoint': _lowmargin_entrypoint(expected_seed), 'selected_seed': expected_seed, 'expected_seed': expected_seed, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': ''.join(['1074_lowmargin_', format(label, ''), '_exact_guard']), 'guard_condition': ''.join(['exact BF16 build row from e7a9 low-margin seed; shape_key=', format(label, '')]), 'coverage': 'e7a9 low-margin Weave seed before selected 17b8/99fd fallback', 'consumed_seed': expected_seed, 'replaced_route': baseline_route, 'baseline_dispatcher_route': baseline_route, 'baseline_17b8_route': baseline_route, 'shape_specific_kernel_ms': seed_row['kernel_ms'], 'targeted_seed_ratio_vs_flashlib': seed_row['ratio_vs_flashlib'], 'classification': 'unmeasured'}) + +def _route_trace_record(inputs: dict[str, Any], *, force_fallback: bool=False) -> dict[str, Any]: + if _eligible_lowmargin(inputs): + return _lowmargin_trace_record(inputs, force_fallback=force_fallback) + row = dict(base99fd.route_trace_for_contract_shapes((str(inputs.get('label')),), candidate_key=base99fd.CANDIDATE_BASE_17B8, force_fallback=force_fallback)[0]) + row['baseline_dispatcher_route'] = _base_17b8_route(inputs, force_fallback=force_fallback) + row['baseline_17b8_route'] = row['baseline_dispatcher_route'] + return _normalize_route_row(row) + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + return [_route_trace_record(_trace_inputs_for_shape(shape), force_fallback=force_fallback) for shape in selected] + +def _timing_backend_name(use_cupti: bool) -> str: + return 'cupti' if use_cupti else 'cuda_event_fallback' + +def _denominator_name(shape_labels) -> str: + if shape_labels is None: + return 'full82_v9' + labels = tuple(shape_labels) + if labels == TARGET_SHAPES: + return 'lowmargin_1074_k1_k24_k30' + return ''.join(['shape', format(len(labels), '')]) + +def _denominator_label(shape_labels) -> str: + if shape_labels is None: + return ''.join(['full', format(len(eval_mod.CANONICAL_SHAPES), '')]) + return ''.join(['shape', format(len(tuple(shape_labels)), '')]) + +def _payload_shape_labels(shape_labels) -> str | tuple[str, ...]: + if shape_labels is None: + return 'all_canonical' + return tuple((str(label) for label in shape_labels)) + +def _rows_for_labels(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return base99fd._rows_for_labels(report, labels) + +def _annotate_route_trace(route_trace: list[dict[str, Any]], candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> list[dict[str, Any]]: + annotated = [] + for row in route_trace: + label = str(row.get('shape_key')) + out = dict(row) + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') + speedup_vs_baseline = baseline_ms / candidate_ms if candidate_ms and baseline_ms else None + speedup_vs_external = flashlib_ms / candidate_ms if candidate_ms and flashlib_ms else None + out['dispatcher_kernel_ms'] = candidate_ms + out['baseline_17b8_kernel_ms'] = baseline_ms + out['external_baseline_ms'] = flashlib_ms + out['flashlib_ms'] = flashlib_ms + out['relative_speedup_vs_baseline'] = speedup_vs_baseline + out['relative_speedup_vs_17b8'] = speedup_vs_baseline + out['speedup_vs_external_baseline'] = speedup_vs_external + out['external_baseline_ref'] = 'same_session' if flashlib_ms is not None else 'not_available' + out['route_changed_vs_17b8'] = out.get('selected_route') != out.get('baseline_dispatcher_route') + if label in TARGET_SHAPE_SET: + if out.get('selected_seed') != out.get('expected_seed'): + out['classification'] = 'guard-miss' + elif speedup_vs_external is not None and speedup_vs_external < 1.05: + out['classification'] = 'kernel-slow' + elif not out['route_changed_vs_17b8']: + out['classification'] = 'route-ok' + elif speedup_vs_baseline is not None and speedup_vs_baseline < 1.0: + out['classification'] = 'kernel-slow' + else: + out['classification'] = 'seed-consumed' + elif speedup_vs_external is not None and speedup_vs_external < 1.05: + out['classification'] = 'kernel-slow' if out.get('route_kind') == 'specialized' else 'fallback-slow' + else: + out['classification'] = 'route-ok' + annotated.append(_normalize_route_row(out)) + return annotated + +def _below_flashlib_rows(report: dict[str, Any], route_trace: list[dict[str, Any]], *, floor: float) -> list[dict[str, Any]]: + trace_by_label = {str(row['shape_key']): row for row in route_trace} + rows = [] + for label, row in sorted(report.get('per_shape', {}).items()): + ratio = row.get('ratio_vs_flashlib') + if isinstance(ratio, (float, int)) and ratio < floor: + trace_row = trace_by_label.get(label, {}) + rows.append({'shape_key': label, 'kernel_ms': row.get('kernel_ms'), 'flashlib_ms': row.get('flashlib_ms'), 'ratio_vs_flashlib': ratio, 'selected_route': trace_row.get('selected_route'), 'selected_seed': trace_row.get('selected_seed'), 'route_kind': trace_row.get('route_kind', 'unknown'), 'classification': trace_row.get('classification', 'unmeasured')}) + return rows + +def _seed_delta_matrix(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> list[dict[str, Any]]: + matrix = [] + for label in TARGET_SHAPES: + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + inputs = base99fd.base17b8.dispatch_066c.base_d555.base_f30c._inputs_for_label(label) + selected_seed = _expected_lowmargin_seed(inputs) + matrix.append({'shape_key': label, 'baseline_route': _base_17b8_route(inputs), 'candidate_route': route_for_contract_inputs(inputs), 'selected_seed': selected_seed, 'candidate_id': CANDIDATE_LOWMARGIN_1074, 'candidate_ms': candidate_ms, 'baseline_17b8_ms': baseline_ms, 'flashlib_ms': candidate_row.get('flashlib_ms'), 'delta_ms_candidate_minus_17b8': candidate_ms - baseline_ms if candidate_ms and baseline_ms else None, 'speedup_vs_17b8': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'ratio_vs_flashlib': candidate_row.get('ratio_vs_flashlib'), 'targeted_seed_row': TARGETED_SEED_ROWS.get(selected_seed, {}).get(label, {}), 'timing_backend': candidate_row.get('timing_backend') or baseline_row.get('timing_backend')}) + return matrix + +def _timing_backends_for_reports(*reports: dict[str, Any]) -> list[str]: + return base99fd._timing_backends_for_reports(*reports) + +def benchmark_baseline_17b8(*, use_cupti: bool=True, shape_labels=None, benchmark_correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_baseline_17b8, correctness=benchmark_correctness, time_flashlib=time_flashlib) + report['candidate_id'] = BASE_17B8_ID + report['measured_entrypoint'] = BASE_17B8_ENTRYPOINT + report['measured_shape_labels'] = _payload_shape_labels(shape_labels) + report['route_trace'] = base99fd.route_trace_for_contract_shapes(shape_labels, candidate_key=base99fd.CANDIDATE_BASE_17B8) + report['route_trace_included'] = True + return report + +def _baseline_sidecar(report: dict[str, Any], *, denominator: str, timing_backend: str, benchmark_correctness: bool, time_flashlib: bool) -> dict[str, Any]: + return {'candidate_id': BASE_17B8_ID, 'measured_entrypoint': BASE_17B8_ENTRYPOINT, 'measured_shape_labels': _payload_shape_labels(None) if report.get('measured_shape_labels') == 'all_canonical' else report.get('measured_shape_labels', 'all_canonical'), 'timing_backend': timing_backend, 'denominator': denominator, 'benchmark_correctness_checked': benchmark_correctness, 'benchmark_time_flashlib': time_flashlib, 'tflops': report['summary']['primary_mean'], 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'contract_summary': report['summary'], 'contract_performance': report['performance'], 'route_trace': report.get('route_trace', []), 'route_trace_included': True, 'rank_objective': {'metric': 'tflops', 'direction': 'maximize', 'value': report['summary']['primary_mean'], 'denominator': denominator}, 'report': report} + +def _benchmark_payload(candidate_report: dict[str, Any], baseline_report: dict[str, Any], *, use_cupti: bool, shape_labels, benchmark_correctness: bool, time_flashlib: bool) -> dict[str, Any]: + candidate_metric = candidate_report['summary']['primary_mean'] + baseline_metric = baseline_report['summary']['primary_mean'] + route_trace = _annotate_route_trace(route_trace_for_contract_shapes(shape_labels), candidate_report, baseline_report) + below_1x = _below_flashlib_rows(candidate_report, route_trace, floor=1.0) + below_floor = _below_flashlib_rows(candidate_report, route_trace, floor=1.05) + denominator = _denominator_name(shape_labels) + timing_backend = _timing_backend_name(use_cupti) + return {'candidate_id': CANDIDATE_LOWMARGIN_1074, 'candidate_key': CANDIDATE_LOWMARGIN_1074, 'baseline_candidate_id': BASE_17B8_ID, 'selected_seeds': CANDIDATE_CONFIG['selected_seeds'], 'source_tasks': SOURCE_TASKS, 'tflops': candidate_metric, 'baseline_17b8_tflops': baseline_metric, 'metric_delta_vs_17b8': candidate_metric - baseline_metric if candidate_metric is not None and baseline_metric is not None else None, 'all_correct': candidate_report['summary']['all_correct'], 'baseline_all_correct': baseline_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'baseline_performance_comparable': baseline_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'baseline_invalid_performance_reason': baseline_report['summary']['invalid_performance_reason'], 'measured_entrypoint': CANDIDATE_ENTRYPOINT, 'baseline_entrypoint': BASE_17B8_ENTRYPOINT, 'measured_shape_labels': _payload_shape_labels(shape_labels), 'timing_backend': timing_backend, 'denominator': denominator, 'selected_route_labels': TARGET_SHAPES, 'consumed_seed_labels': TARGET_SHAPES, 'selected_route_rows': _rows_for_labels(candidate_report, TARGET_SHAPES), 'baseline_selected_route_rows': _rows_for_labels(baseline_report, TARGET_SHAPES), 'seed_delta_matrix': _seed_delta_matrix(candidate_report, baseline_report), 'targeted_seed_rows': TARGETED_SEED_ROWS, 'candidate_dispatchers': CANDIDATE_DISPATCHERS, 'selected_candidate_dispatcher': CANDIDATE_LOWMARGIN_1074, 'guard_plan': CANDIDATE_CONFIG['guard_plan'], 'route_modules': PRODUCTION_ROUTE_MODULES, 'route_trace': route_trace, 'forced_fallback_route_trace': route_trace_for_contract_shapes(shape_labels, force_fallback=True), 'route_trace_included': True, 'contract_summary': candidate_report['summary'], 'baseline_contract_summary': baseline_report['summary'], 'contract_performance': candidate_report['performance'], 'baseline_contract_performance': baseline_report['performance'], 'timing_backends': _timing_backends_for_reports(candidate_report, baseline_report), 'timing_backend_requested': timing_backend, 'use_cupti': use_cupti, 'benchmark_correctness_checked': benchmark_correctness, 'benchmark_time_flashlib': time_flashlib, 'performance_coverage': 'partial' if below_floor else 'pass', 'coverage_only_routes': [], 'hot_bucket_blockers': below_floor, 'flashlib_parity_ledger': {'baseline_ref_scope': 'same_session' if time_flashlib else 'not_available', 'speedup_floor': 1.05, 'rows_below_1x': below_1x, 'rows_below_floor': below_floor, 'omitted_reason': None if time_flashlib else 'benchmark_time_flashlib=false'}, 'rank_objective': {'metric': 'tflops', 'direction': 'maximize', 'value': candidate_metric, 'baseline_17b8_value': baseline_metric, 'delta_vs_17b8': candidate_metric - baseline_metric if candidate_metric is not None and baseline_metric is not None else None, 'denominator': denominator}, 'report': candidate_report, 'baseline_report': baseline_report} + +def benchmark_candidate_17b8_lowmargin_1074_full82_v1(*, use_cupti: bool=True, shape_labels=None, baseline_report: dict[str, Any] | None=None, benchmark_correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + if baseline_report is None: + baseline_report = benchmark_baseline_17b8(use_cupti=use_cupti, shape_labels=shape_labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate, correctness=benchmark_correctness, time_flashlib=time_flashlib) + return _benchmark_payload(candidate_report, baseline_report, use_cupti=use_cupti, shape_labels=shape_labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + +def _dispatcher_consumption_summary(*, baseline_payload: dict[str, Any], candidate_payload: dict[str, Any], baseline_path: Path, candidate_path: Path, denominator: str, timing_backend: str) -> dict[str, Any]: + return {'consumed_seed': SEED_LOWMARGIN_1074_ID, 'base_dispatcher': BASE_17B8_ENTRYPOINT, 'new_dispatcher': CANDIDATE_ENTRYPOINT, 'replaced_route': ROUTE_BASE_17B8_ENTRYPOINT, 'one_seed_at_a_time': True, 'denominator': denominator, 'targeted_seed_payload': LOWMARGIN_PAYLOAD, 'same_session_baseline_payload': str(baseline_path), 'full_dispatch_payload': str(candidate_path), 'metric_delta_vs_17b8': candidate_payload.get('metric_delta_vs_17b8'), 'route_trace': candidate_payload.get('route_trace', []), 'seed_delta_matrix': candidate_payload.get('seed_delta_matrix', []), 'flashlib_parity_ledger': candidate_payload.get('flashlib_parity_ledger', {}), 'baseline_tflops': baseline_payload.get('tflops'), 'candidate_tflops': candidate_payload.get('tflops'), 'timing_backend': timing_backend} + +def write_benchmark_artifacts(artifact_dir: str | Path, *, use_cupti: bool=True, shape_labels=None, benchmark_correctness: bool=True, time_flashlib: bool=True) -> dict[str, str]: + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + denom_label = _denominator_label(shape_labels) + denominator = _denominator_name(shape_labels) + timing_backend = _timing_backend_name(use_cupti) + baseline_report = benchmark_baseline_17b8(use_cupti=use_cupti, shape_labels=shape_labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + baseline_payload = _baseline_sidecar(baseline_report, denominator=denominator, timing_backend=timing_backend, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + candidate_payload = benchmark_candidate_17b8_lowmargin_1074_full82_v1(use_cupti=use_cupti, shape_labels=shape_labels, baseline_report=baseline_report, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + baseline_path = out_dir / ''.join([format(denom_label, ''), '_same_session_baseline_17b8_99fd_v1.json']) + candidate_path = out_dir / ''.join([format(denom_label, ''), '_dispatch_17b8_lowmargin_1074_v1.json']) + route_trace_path = out_dir / ''.join([format(denom_label, ''), '_route_trace_17b8_lowmargin_1074_v1.json']) + forced_trace_path = out_dir / ''.join([format(denom_label, ''), '_forced_fallback_trace_17b8_lowmargin_1074_v1.json']) + seed_matrix_path = out_dir / ''.join([format(denom_label, ''), '_seed_delta_matrix_17b8_lowmargin_1074_v1.json']) + consumption_path = out_dir / ''.join([format(denom_label, ''), '_dispatcher_consumption_17b8_lowmargin_1074_v1.json']) + baseline_path.write_text(json.dumps(baseline_payload, indent=2, sort_keys=True) + '\n', encoding='utf-8') + candidate_path.write_text(json.dumps(candidate_payload, indent=2, sort_keys=True) + '\n', encoding='utf-8') + route_trace_path.write_text(json.dumps(candidate_payload['route_trace'], indent=2, sort_keys=True) + '\n', encoding='utf-8') + forced_trace_path.write_text(json.dumps(candidate_payload['forced_fallback_route_trace'], indent=2, sort_keys=True) + '\n', encoding='utf-8') + seed_matrix_path.write_text(json.dumps(candidate_payload['seed_delta_matrix'], indent=2, sort_keys=True) + '\n', encoding='utf-8') + consumption = _dispatcher_consumption_summary(baseline_payload=baseline_payload, candidate_payload=candidate_payload, baseline_path=baseline_path, candidate_path=candidate_path, denominator=denominator, timing_backend=timing_backend) + consumption_path.write_text(json.dumps(consumption, indent=2, sort_keys=True) + '\n', encoding='utf-8') + return {'same_session_baseline_payload': str(baseline_path), 'candidate_payload': str(candidate_path), 'route_trace': str(route_trace_path), 'forced_fallback_trace': str(forced_trace_path), 'seed_delta_matrix': str(seed_matrix_path), 'dispatcher_consumption': str(consumption_path)} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_1877_9a17_b644_6a35_full90_selective_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_1877_9a17_b644_6a35_full90_selective_v1.py new file mode 100644 index 00000000..2f7d8312 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_1877_9a17_b644_6a35_full90_selective_v1.py @@ -0,0 +1,481 @@ +"""Full90 selective synthesis over 1877 with 9a17, b644, and 6a35 seeds. + +Minimum target architecture: sm_100a. This additive dispatcher wrapper keeps +the variance-audited 1877 full90 sidecar as the baseline route, then measures +guarded portfolios that consume the 9a17 residual RAG/search seed, the b644 +exact-five build low-floor seed, and the 6a35 D64/Q4096 exact seed. + +Production routes stay Weave-only; FlashLib is timed only by the contract +harness as a black-box reference. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +from pathlib import Path +from typing import Any, Callable +from . import knn_build_build_lowfloor_d43e_v1 as build_b644 +from . import knn_build_dispatch_ad64_1b8f_4b51_ceb3_full90_synthesis_v1 as base1877 +from . import knn_build_d64_q4096_c271_twostage_v1 as d64_6a35 +from . import knn_build_residual_rag_search_1877_v1 as rag_9a17 +MODULE = 'loom.examples.weave.knn_build_dispatch_1877_9a17_b644_6a35_full90_selective_v1' +eval_mod = base1877.eval_mod +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbatch_m64_d4f7_stage1", "arg_keys": ["query", "database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 91392, "constants": [], "cta_group": 1, "threads": 512}')) +BASE_1877_KEY = base1877.DEFAULT_CANDIDATE_KEY +BASE_1877_CONFIG = base1877.CANDIDATE_CONFIGS[BASE_1877_KEY] +BASE_1877_ID = BASE_1877_CONFIG['candidate_id'] +BASE_1877_ENTRYPOINT = BASE_1877_CONFIG['benchmark_entrypoint'] +BASE_1877_ROUTE_ENTRYPOINT = base1877.ROUTE_ENTRYPOINT +CANDIDATE_9A17_ONLY = '1877_plus_9a17_residual_rag_search' +CANDIDATE_B644_ONLY = '1877_plus_b644_exact5' +CANDIDATE_6A35_D64_ONLY = '1877_plus_6a35_d64' +CANDIDATE_B644_6A35 = '1877_plus_b644_exact5_6a35_d64' +CANDIDATE_9A17_B644 = '1877_plus_9a17_b644_exact5' +CANDIDATE_9A17_6A35 = '1877_plus_9a17_6a35_d64' +CANDIDATE_9A17_B644_6A35 = '1877_plus_9a17_b644_exact5_6a35_d64' +DEFAULT_CANDIDATE_KEY = CANDIDATE_9A17_B644_6A35 +CANDIDATE_KEYS = (BASE_1877_KEY, CANDIDATE_9A17_ONLY, CANDIDATE_B644_ONLY, CANDIDATE_6A35_D64_ONLY, CANDIDATE_B644_6A35, CANDIDATE_9A17_B644, CANDIDATE_9A17_6A35, CANDIDATE_9A17_B644_6A35) +DEFAULT_SYNTHESIS_CANDIDATES = (CANDIDATE_9A17_ONLY, CANDIDATE_B644_ONLY, CANDIDATE_6A35_D64_ONLY, CANDIDATE_B644_6A35, CANDIDATE_9A17_B644_6A35) +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +CANDIDATE_9A17_ONLY_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_candidate_9a17_only_full90_v1']) +CANDIDATE_B644_ONLY_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_candidate_b644_only_full90_v1']) +CANDIDATE_6A35_D64_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_candidate_6a35_d64_only_full90_v1']) +CANDIDATE_B644_6A35_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_candidate_b644_6a35_full90_v1']) +CANDIDATE_9A17_B644_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_candidate_9a17_b644_full90_v1']) +CANDIDATE_9A17_6A35_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_candidate_9a17_6a35_full90_v1']) +CANDIDATE_COMBINED_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_candidate_9a17_b644_6a35_full90_v1']) +BASE_1877_TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_qm1024_d128_k8", "rag_online_b1_q1_m100000_d128_k10", "rag_online_b1_q1_m65536_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "rag_online_irregular_b1_q1_m262143_d128_k10", "rag_online_irregular_b1_q1_m524287_d128_k10", "rag_microbatch_largek_b1_q16_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m131071_d128_k32", "rag_microbatch_largek_b1_q16_m250000_d128_k32", "rag_microbatch_largek_b1_q24_m100000_d128_k32", "rag_stream_largek_b1_q128_m131071_d128_k32", "build_k_sweep_qm512_k10", "build_qm1024_d128_k10", "build_batch_b2_q1024_m1024_d128_k10", "build_tail_b1_q1536_m1536_d128_k10", "build_large_b1_q6144_m6144_d128_k10", "build_qm2048_d128_k10", "rag_microbatch_b1_q8_m100000_d128_k10", "rag_microbatch_b1_q16_m100000_d128_k10"]}')) +RAG_9A17_TARGET_SHAPES = rag_9a17.TARGET_SHAPES +B644_TARGET_SHAPES = build_b644.TARGET_SHAPES +D64_6A35_TARGET_SHAPES = d64_6a35.TARGET_SHAPES +RAG_ONLY_TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_qm1024_d128_k8", "rag_online_b1_q1_m100000_d128_k10", "rag_online_b1_q1_m65536_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "rag_online_irregular_b1_q1_m262143_d128_k10", "rag_online_irregular_b1_q1_m524287_d128_k10", "rag_microbatch_largek_b1_q16_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m131071_d128_k32", "rag_microbatch_largek_b1_q16_m250000_d128_k32", "rag_microbatch_largek_b1_q24_m100000_d128_k32", "rag_stream_largek_b1_q128_m131071_d128_k32", "build_k_sweep_qm512_k10", "build_qm1024_d128_k10", "build_batch_b2_q1024_m1024_d128_k10", "build_tail_b1_q1536_m1536_d128_k10", "build_large_b1_q6144_m6144_d128_k10", "build_qm2048_d128_k10", "rag_microbatch_b1_q8_m100000_d128_k10", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32", "search_rect_b1_q1024_m8192_d128_k10"]}')) +B644_ONLY_TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_qm1024_d128_k8", "rag_online_b1_q1_m100000_d128_k10", "rag_online_b1_q1_m65536_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "rag_online_irregular_b1_q1_m262143_d128_k10", "rag_online_irregular_b1_q1_m524287_d128_k10", "rag_microbatch_largek_b1_q16_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m131071_d128_k32", "rag_microbatch_largek_b1_q16_m250000_d128_k32", "rag_microbatch_largek_b1_q24_m100000_d128_k32", "rag_stream_largek_b1_q128_m131071_d128_k32", "build_k_sweep_qm512_k10", "build_qm1024_d128_k10", "build_batch_b2_q1024_m1024_d128_k10", "build_tail_b1_q1536_m1536_d128_k10", "build_large_b1_q6144_m6144_d128_k10", "build_qm2048_d128_k10", "rag_microbatch_b1_q8_m100000_d128_k10", "rag_microbatch_b1_q16_m100000_d128_k10", "build_k_sweep_qm512_k2", "build_qm4096_d128_k8", "build_k_sweep_qm2048_k11", "build_k_sweep_qm2048_k13", "build_tail_b1_q3072_m3072_d128_k20"]}')) +D64_ONLY_TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_qm1024_d128_k8", "rag_online_b1_q1_m100000_d128_k10", "rag_online_b1_q1_m65536_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "rag_online_irregular_b1_q1_m262143_d128_k10", "rag_online_irregular_b1_q1_m524287_d128_k10", "rag_microbatch_largek_b1_q16_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m131071_d128_k32", "rag_microbatch_largek_b1_q16_m250000_d128_k32", "rag_microbatch_largek_b1_q24_m100000_d128_k32", "rag_stream_largek_b1_q128_m131071_d128_k32", "build_k_sweep_qm512_k10", "build_qm1024_d128_k10", "build_batch_b2_q1024_m1024_d128_k10", "build_tail_b1_q1536_m1536_d128_k10", "build_large_b1_q6144_m6144_d128_k10", "build_qm2048_d128_k10", "rag_microbatch_b1_q8_m100000_d128_k10", "rag_microbatch_b1_q16_m100000_d128_k10", "build_dim_sweep_b1_q4096_m4096_d64_k10"]}')) +B644_D64_TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_qm1024_d128_k8", "rag_online_b1_q1_m100000_d128_k10", "rag_online_b1_q1_m65536_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "rag_online_irregular_b1_q1_m262143_d128_k10", "rag_online_irregular_b1_q1_m524287_d128_k10", "rag_microbatch_largek_b1_q16_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m131071_d128_k32", "rag_microbatch_largek_b1_q16_m250000_d128_k32", "rag_microbatch_largek_b1_q24_m100000_d128_k32", "rag_stream_largek_b1_q128_m131071_d128_k32", "build_k_sweep_qm512_k10", "build_qm1024_d128_k10", "build_batch_b2_q1024_m1024_d128_k10", "build_tail_b1_q1536_m1536_d128_k10", "build_large_b1_q6144_m6144_d128_k10", "build_qm2048_d128_k10", "rag_microbatch_b1_q8_m100000_d128_k10", "rag_microbatch_b1_q16_m100000_d128_k10", "build_k_sweep_qm512_k2", "build_qm4096_d128_k8", "build_k_sweep_qm2048_k11", "build_k_sweep_qm2048_k13", "build_tail_b1_q3072_m3072_d128_k20", "build_dim_sweep_b1_q4096_m4096_d64_k10"]}')) +RAG_B644_TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_qm1024_d128_k8", "rag_online_b1_q1_m100000_d128_k10", "rag_online_b1_q1_m65536_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "rag_online_irregular_b1_q1_m262143_d128_k10", "rag_online_irregular_b1_q1_m524287_d128_k10", "rag_microbatch_largek_b1_q16_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m131071_d128_k32", "rag_microbatch_largek_b1_q16_m250000_d128_k32", "rag_microbatch_largek_b1_q24_m100000_d128_k32", "rag_stream_largek_b1_q128_m131071_d128_k32", "build_k_sweep_qm512_k10", "build_qm1024_d128_k10", "build_batch_b2_q1024_m1024_d128_k10", "build_tail_b1_q1536_m1536_d128_k10", "build_large_b1_q6144_m6144_d128_k10", "build_qm2048_d128_k10", "rag_microbatch_b1_q8_m100000_d128_k10", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32", "search_rect_b1_q1024_m8192_d128_k10", "build_k_sweep_qm512_k2", "build_qm4096_d128_k8", "build_k_sweep_qm2048_k11", "build_k_sweep_qm2048_k13", "build_tail_b1_q3072_m3072_d128_k20"]}')) +RAG_D64_TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_qm1024_d128_k8", "rag_online_b1_q1_m100000_d128_k10", "rag_online_b1_q1_m65536_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "rag_online_irregular_b1_q1_m262143_d128_k10", "rag_online_irregular_b1_q1_m524287_d128_k10", "rag_microbatch_largek_b1_q16_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m131071_d128_k32", "rag_microbatch_largek_b1_q16_m250000_d128_k32", "rag_microbatch_largek_b1_q24_m100000_d128_k32", "rag_stream_largek_b1_q128_m131071_d128_k32", "build_k_sweep_qm512_k10", "build_qm1024_d128_k10", "build_batch_b2_q1024_m1024_d128_k10", "build_tail_b1_q1536_m1536_d128_k10", "build_large_b1_q6144_m6144_d128_k10", "build_qm2048_d128_k10", "rag_microbatch_b1_q8_m100000_d128_k10", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32", "search_rect_b1_q1024_m8192_d128_k10", "build_dim_sweep_b1_q4096_m4096_d64_k10"]}')) +COMBINED_TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_qm1024_d128_k8", "rag_online_b1_q1_m100000_d128_k10", "rag_online_b1_q1_m65536_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "rag_online_irregular_b1_q1_m262143_d128_k10", "rag_online_irregular_b1_q1_m524287_d128_k10", "rag_microbatch_largek_b1_q16_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m131071_d128_k32", "rag_microbatch_largek_b1_q16_m250000_d128_k32", "rag_microbatch_largek_b1_q24_m100000_d128_k32", "rag_stream_largek_b1_q128_m131071_d128_k32", "build_k_sweep_qm512_k10", "build_qm1024_d128_k10", "build_batch_b2_q1024_m1024_d128_k10", "build_tail_b1_q1536_m1536_d128_k10", "build_large_b1_q6144_m6144_d128_k10", "build_qm2048_d128_k10", "rag_microbatch_b1_q8_m100000_d128_k10", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32", "search_rect_b1_q1024_m8192_d128_k10", "build_k_sweep_qm512_k2", "build_qm4096_d128_k8", "build_k_sweep_qm2048_k11", "build_k_sweep_qm2048_k13", "build_tail_b1_q3072_m3072_d128_k20", "build_dim_sweep_b1_q4096_m4096_d64_k10"]}')) +SEED_9A17_Q128_ID = rag_9a17.SEED_Q128_ID +SEED_9A17_RECT_ID = rag_9a17.SEED_RECT_ID +SEED_B644_Q512_K2_ID = build_b644.SEED_Q512_K2_ID +SEED_B644_Q4096_K8_ID = build_b644.SEED_Q4096_K8_ID +SEED_B644_MIDK_ID = build_b644.SEED_MIDK_ID +SEED_B644_TAIL_K20_ID = build_b644.SEED_TAIL_K20_ID +SEED_6A35_D64_ID = 'd64_q4096_c271_split4_unordered_sortedemit_v1' +SOURCE_TASKS = {**base1877.SOURCE_TASKS, **rag_9a17.SOURCE_TASKS, **build_b644.SOURCE_TASKS, SEED_6A35_D64_ID: 'weave-evolve-knn-build-6a35 / design_doc/active/weave_evolve_knn_build_round_143_weave-evolve-knn-build-c271-d64q4096-prodaxis.md'} +PRODUCTION_ROUTE_MODULES = {**base1877.PRODUCTION_ROUTE_MODULES, **rag_9a17.PRODUCTION_ROUTE_MODULES, **build_b644.PRODUCTION_ROUTE_MODULES, SEED_6A35_D64_ID: ''.join([format(d64_6a35.MODULE, ''), ':launch_from_contract_inputs']), BASE_1877_ID: BASE_1877_ROUTE_ENTRYPOINT} +TARGETED_SEED_ROWS = {**base1877.TARGETED_SEED_ROWS, SEED_9A17_Q128_ID: {'source_payload': 'artifacts/weave_evolve/knn_build_residual_rag_search_1877_v1/residual_rag_search_1877_v1.json', 'shape_labels': (rag_9a17.RAG_Q128_K32,), 'source_task': 'weave-evolve-knn-build-9a17'}, SEED_9A17_RECT_ID: {'source_payload': 'artifacts/weave_evolve/knn_build_residual_rag_search_1877_v1/residual_rag_search_1877_v1.json', 'shape_labels': (rag_9a17.SEARCH_RECT_Q1024_K10,), 'source_task': 'weave-evolve-knn-build-9a17'}, SEED_B644_Q512_K2_ID: {'source_payload': 'artifacts/weave_evolve/knn_build_build_lowfloor_d43e_v1/build_lowfloor_d43e_v1.json', 'shape_labels': (build_b644.TARGET_Q512_K2,), 'source_task': 'weave-evolve-knn-build-b644'}, SEED_B644_Q4096_K8_ID: {'source_payload': 'artifacts/weave_evolve/knn_build_build_lowfloor_d43e_v1/build_lowfloor_d43e_v1.json', 'shape_labels': (build_b644.TARGET_Q4096_K8,), 'source_task': 'weave-evolve-knn-build-b644'}, SEED_B644_MIDK_ID: {'source_payload': 'artifacts/weave_evolve/knn_build_build_lowfloor_d43e_v1/build_lowfloor_d43e_v1.json', 'shape_labels': build_b644.TARGET_MIDK_SHAPES, 'source_task': 'weave-evolve-knn-build-b644'}, SEED_B644_TAIL_K20_ID: {'source_payload': 'artifacts/weave_evolve/knn_build_build_lowfloor_d43e_v1/build_lowfloor_d43e_v1.json', 'shape_labels': (build_b644.TARGET_TAIL_Q3072_K20,), 'source_task': 'weave-evolve-knn-build-b644'}, SEED_6A35_D64_ID: {'source_payload': 'design_doc/active/weave_evolve_knn_build_round_143_weave-evolve-knn-build-c271-d64q4096-prodaxis.md', 'shape_labels': D64_6A35_TARGET_SHAPES, 'source_task': 'weave-evolve-knn-build-6a35'}} +REJECTED_ROUTE_COMBINATIONS = (*base1877.REJECTED_ROUTE_COMBINATIONS, {'id': 'b3ec_dd3e_combined_cca8_not_replayed', 'entrypoint': 'loom.examples.weave.knn_build_dispatch_1877_9a17_b3ec_dd3e_full90_synthesis_v1', 'status': 'negative_read_ref', 'source_task': 'generalize-auto-tuning-knn-build-cca8', 'reason': 'cca8 measured b3ec/dd3e additive portfolios as full90 regressions; this lane replays b644/6a35 instead.'}, {'id': 'exact_five_duplicates_110d_11fc_5886', 'entrypoint': 'weave-evolve duplicate exact-five wrappers', 'status': 'dominated_read_ref', 'source_task': 'kernel-rank-generalize-auto-tuning-cohort-knn-build-fa86-0bbf', 'reason': '110d, 11fc, and 5886 duplicate the exact-five bucket and are superseded by b644.'}) + +def _select_contract_shapes(shape_labels): + return base1877._select_contract_shapes(shape_labels) + +def _trace_inputs_for_shape(shape: dict[str, Any]) -> dict[str, Any]: + return base1877._trace_inputs_for_shape(shape) + +def _normalize_route_row(row: dict[str, Any]) -> dict[str, Any]: + return base1877._normalize_route_row(row) + +def _inputs_for_label(label: str) -> dict[str, Any]: + return _trace_inputs_for_shape(_select_contract_shapes((label,))[0]) + +def _timing_backend_name(use_cupti: bool) -> str: + return base1877._timing_backend_name(use_cupti) + +def _payload_shape_labels(shape_labels) -> str | tuple[str, ...]: + return base1877._payload_shape_labels(shape_labels) + +def _denominator_name(shape_labels) -> str: + return base1877._denominator_name(shape_labels) + +def _denominator_label(shape_labels) -> str: + return base1877._denominator_label(shape_labels) + +def _rows_for_labels(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return base1877._rows_for_labels(report, labels) + +def _timing_backends_for_reports(*reports: dict[str, Any]) -> list[str]: + return base1877._timing_backends_for_reports(*reports) + +def _candidate_config(candidate_key: str) -> dict[str, Any]: + try: + return CANDIDATE_CONFIGS[candidate_key] + except KeyError as exc: + raise ValueError(''.join(['unknown full90 1877/9a17/b644/6a35 candidate ', format(repr(candidate_key), '')])) from exc + +def _candidate_id(candidate_key: str | None) -> str | None: + if candidate_key is None: + return None + return str(_candidate_config(candidate_key)['candidate_id']) + +def _candidate_has_9a17(candidate_key: str) -> bool: + return candidate_key in (CANDIDATE_9A17_ONLY, CANDIDATE_9A17_B644, CANDIDATE_9A17_6A35, CANDIDATE_9A17_B644_6A35) + +def _candidate_has_b644(candidate_key: str) -> bool: + return candidate_key in (CANDIDATE_B644_ONLY, CANDIDATE_B644_6A35, CANDIDATE_9A17_B644, CANDIDATE_9A17_B644_6A35) + +def _candidate_has_6a35(candidate_key: str) -> bool: + return candidate_key in (CANDIDATE_6A35_D64_ONLY, CANDIDATE_B644_6A35, CANDIDATE_9A17_6A35, CANDIDATE_9A17_B644_6A35) + +def _matched_new_seed(inputs: dict[str, Any], candidate_key: str): + if candidate_key == BASE_1877_KEY: + return None + if _candidate_has_9a17(candidate_key) and rag_9a17._selected_seed_for_inputs(inputs)[0] is not None: + return rag_9a17 + if _candidate_has_b644(candidate_key) and build_b644._selected_seed_for_inputs(inputs)[0] is not None: + return build_b644 + if _candidate_has_6a35(candidate_key) and d64_6a35._eligible_exact_q4096_d64(inputs): + return d64_6a35 + return None + +def _seed_expected_id(seed_module, inputs: dict[str, Any]) -> str | None: + if seed_module is rag_9a17: + return rag_9a17._selected_seed_for_inputs(inputs)[0] + if seed_module is build_b644: + return build_b644._selected_seed_for_inputs(inputs)[0] + if seed_module is d64_6a35 and d64_6a35._eligible_exact_q4096_d64(inputs): + return SEED_6A35_D64_ID + return None + +def _seed_route_for_module(seed_module, inputs: dict[str, Any]) -> str: + if seed_module is d64_6a35: + return d64_6a35.route_name_for_inputs(inputs) + return seed_module.route_for_contract_inputs(inputs) + +def _seed_launch_for_module(seed_module, inputs: dict[str, Any]) -> None: + seed_module.launch_from_contract_inputs(inputs) + +def _seed_trace_for_module(seed_module, label: str) -> dict[str, Any]: + if seed_module is d64_6a35: + inputs = _inputs_for_label(label) + route = d64_6a35.route_name_for_inputs(inputs) + return {'shape_key': label, 'selected_route': route, 'selected_entrypoint': ''.join([format(d64_6a35.MODULE, ''), ':launch_from_contract_inputs']), 'selected_seed': SEED_6A35_D64_ID, 'expected_seed': SEED_6A35_D64_ID, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': ''.join(['selective_6a35_', format(SEED_6A35_D64_ID, '')]), 'guard_condition': 'exact BF16 build B=1 Q=M=4096 D=64 K=10 split4 unordered', 'matched_label': d64_6a35.TARGET_SHAPE, 'classification': 'seed-consumed'} + return dict(seed_module.route_trace_for_contract_shapes((label,))[0]) + +def _base_route(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + return base1877.route_for_contract_inputs(inputs, candidate_key=BASE_1877_KEY, force_fallback=force_fallback) + +def _base_launch(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + base1877.launch_from_contract_inputs(inputs, candidate_key=BASE_1877_KEY, force_fallback=force_fallback) + +def _base_trace_row(label: str, *, force_fallback: bool=False) -> dict[str, Any]: + return dict(base1877.route_trace_for_contract_shapes((label,), candidate_key=BASE_1877_KEY, force_fallback=force_fallback)[0]) + +def _expected_seed(inputs: dict[str, Any], candidate_key: str) -> str | None: + seed_module = _matched_new_seed(inputs, candidate_key) + if seed_module is not None: + return _seed_expected_id(seed_module, inputs) + return base1877._expected_seed(inputs, BASE_1877_KEY) + +def route_for_contract_inputs(inputs: dict[str, Any], *, candidate_key: str=DEFAULT_CANDIDATE_KEY, force_fallback: bool=False) -> str: + _candidate_config(candidate_key) + if force_fallback: + return _base_route(inputs, force_fallback=True) + if candidate_key == BASE_1877_KEY: + return _base_route(inputs) + seed_module = _matched_new_seed(inputs, candidate_key) + if seed_module is not None: + return _seed_route_for_module(seed_module, inputs) + return _base_route(inputs) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, candidate_key: str=DEFAULT_CANDIDATE_KEY, force_fallback: bool=False) -> None: + _candidate_config(candidate_key) + if force_fallback: + _base_launch(inputs, force_fallback=True) + return + if candidate_key == BASE_1877_KEY: + _base_launch(inputs) + return + seed_module = _matched_new_seed(inputs, candidate_key) + if seed_module is not None: + _seed_launch_for_module(seed_module, inputs) + return + _base_launch(inputs) + +def candidate_parent_1877_full90_v1(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, candidate_key=BASE_1877_KEY) + +def candidate_9a17_only_full90_v1(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, candidate_key=CANDIDATE_9A17_ONLY) + +def candidate_b644_only_full90_v1(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, candidate_key=CANDIDATE_B644_ONLY) + +def candidate_6a35_d64_only_full90_v1(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, candidate_key=CANDIDATE_6A35_D64_ONLY) + +def candidate_b644_6a35_full90_v1(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, candidate_key=CANDIDATE_B644_6A35) + +def candidate_9a17_b644_full90_v1(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, candidate_key=CANDIDATE_9A17_B644) + +def candidate_9a17_6a35_full90_v1(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, candidate_key=CANDIDATE_9A17_6A35) + +def candidate_9a17_b644_6a35_full90_v1(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, candidate_key=CANDIDATE_9A17_B644_6A35) + +def candidate(inputs: dict[str, Any]) -> None: + candidate_9a17_b644_6a35_full90_v1(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, candidate_key=DEFAULT_CANDIDATE_KEY, force_fallback=True) + +def _candidate_kernel_fn(candidate_key: str) -> Callable[[dict[str, Any]], None]: + if candidate_key == BASE_1877_KEY: + return candidate_parent_1877_full90_v1 + if candidate_key == CANDIDATE_9A17_ONLY: + return candidate_9a17_only_full90_v1 + if candidate_key == CANDIDATE_B644_ONLY: + return candidate_b644_only_full90_v1 + if candidate_key == CANDIDATE_6A35_D64_ONLY: + return candidate_6a35_d64_only_full90_v1 + if candidate_key == CANDIDATE_B644_6A35: + return candidate_b644_6a35_full90_v1 + if candidate_key == CANDIDATE_9A17_B644: + return candidate_9a17_b644_full90_v1 + if candidate_key == CANDIDATE_9A17_6A35: + return candidate_9a17_6a35_full90_v1 + if candidate_key == CANDIDATE_9A17_B644_6A35: + return candidate_9a17_b644_6a35_full90_v1 + raise ValueError(''.join(['unknown full90 1877/9a17/b644/6a35 candidate ', format(repr(candidate_key), '')])) + +def _selected_seeds(*seed_groups: tuple[str, ...]) -> tuple[str, ...]: + values: list[str] = [] + for group in seed_groups: + values.extend(group) + return tuple(dict.fromkeys(values)) +PARENT_SEEDS = _decode_capture(_json_loads('{"__tuple__": ["q1024_k8_195e_v1", "ragonline_mbucket_5706_q1v10_smix_s144_g12", "rag_microbucket_k32_q16dual2warp_largem_bdd2_v1_rowld1_2warp_rows4", "rag_microbucket_k32_q24rowld2_24dc_v1_rowld2_rows4", "rag_stream_k32_q128rowld_60fb_v1_rowld_rows4", "4757_fixedbuild_k10_v2_q512_q1024_b2_q1536_q6144", "ad64_fixedbuild_k10_v2_q512_q1024_q1536_q2048_b2q1024", "rag_microbatch_k10_q8_s128_q16_s136_4757_v1"]}')) +RAG_9A17_SEEDS = (SEED_9A17_Q128_ID, SEED_9A17_RECT_ID) +B644_SEEDS = (SEED_B644_Q512_K2_ID, SEED_B644_Q4096_K8_ID, SEED_B644_MIDK_ID, SEED_B644_TAIL_K20_ID) +D64_6A35_SEEDS = (SEED_6A35_D64_ID,) +CANDIDATE_CONFIGS = _decode_capture(_json_loads('{"__dict_items__": [["ad64_plus_best_per_shape_build_k10_plus_ceb3", {"__dict_items__": [["candidate_id", "candidate_ad64_plus_best_build_k10_plus_ceb3_full90_v1"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_ad64_1b8f_4b51_ceb3_full90_synthesis_v1:launch_from_contract_inputs"], ["benchmark_entrypoint", "loom.examples.weave.knn_build_dispatch_ad64_1b8f_4b51_ceb3_full90_synthesis_v1:benchmark_candidate_best_build_ceb3_full90_v1"], ["selected_seeds", {"__tuple__": ["q1024_k8_195e_v1", "ragonline_mbucket_5706_q1v10_smix_s144_g12", "rag_microbucket_k32_q16dual2warp_largem_bdd2_v1_rowld1_2warp_rows4", "rag_microbucket_k32_q24rowld2_24dc_v1_rowld2_rows4", "rag_stream_k32_q128rowld_60fb_v1_rowld_rows4", "4757_fixedbuild_k10_v2_q512_q1024_b2_q1536_q6144", "ad64_fixedbuild_k10_v2_q512_q1024_q1536_q2048_b2q1024", "rag_microbatch_k10_q8_s128_q16_s136_4757_v1"]}], ["new_seed_ids", {"__tuple__": []}], ["guard_plan", {"__tuple__": ["4b51 exact BF16 build K10 guard for unique Q2048 row", "1b8f exact BF16 build K10 guard for Q512/Q1024/B2-Q1024/Q1536/Q6144 rows", "ceb3 exact BF16 non-build B=1 Q in {8,16} M=100000 D=128 K=10 guard", "b0e2 exact BF16 non-build B=1 Q=128 M=131071 D=128 K=32 rowld rows4 guard", "603d/24dc exact BF16 non-build B=1 Q=24 M=100000 D=128 K=32 rowld2 rows4 guard", "cf51 exact BF16 build Q=M=1024 D=128 K=8 split16", "bca0 exact BF16 non-build Q=1 D=128 K=10 M in {65536,100000,131071,250000,262143,524287}", "5018 exact BF16 non-build Q=16 D=128 K=32 M in {100000,131071,250000}", "c3bf/d5f8 full90 Weave fallback"]}], ["expected_shape_wins", {"__tuple__": ["build_qm1024_d128_k8", "rag_online_b1_q1_m100000_d128_k10", "rag_online_b1_q1_m65536_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "rag_online_irregular_b1_q1_m262143_d128_k10", "rag_online_irregular_b1_q1_m524287_d128_k10", "rag_microbatch_largek_b1_q16_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m131071_d128_k32", "rag_microbatch_largek_b1_q16_m250000_d128_k32", "rag_microbatch_largek_b1_q24_m100000_d128_k32", "rag_stream_largek_b1_q128_m131071_d128_k32", "build_k_sweep_qm512_k10", "build_qm1024_d128_k10", "build_batch_b2_q1024_m1024_d128_k10", "build_tail_b1_q1536_m1536_d128_k10", "build_large_b1_q6144_m6144_d128_k10", "build_qm2048_d128_k10", "rag_microbatch_b1_q8_m100000_d128_k10", "rag_microbatch_b1_q16_m100000_d128_k10"]}], ["fallback", "loom.examples.weave.knn_build_dispatch_ad64_1b8f_4b51_ceb3_full90_synthesis_v1:launch_from_contract_inputs"], ["rejected_reason", "same-session 1877 full90 champion baseline"]]}], ["1877_plus_9a17_residual_rag_search", {"__dict_items__": [["candidate_id", "candidate_1877_plus_9a17_residual_rag_search_full90_v1"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_1877_9a17_b644_6a35_full90_selective_v1:candidate_9a17_only_full90_v1"], ["benchmark_entrypoint", "loom.examples.weave.knn_build_dispatch_1877_9a17_b644_6a35_full90_selective_v1:benchmark_candidate_9a17_only_full90_v1"], ["selected_seeds", {"__tuple__": ["q1024_k8_195e_v1", "ragonline_mbucket_5706_q1v10_smix_s144_g12", "rag_microbucket_k32_q16dual2warp_largem_bdd2_v1_rowld1_2warp_rows4", "rag_microbucket_k32_q24rowld2_24dc_v1_rowld2_rows4", "rag_stream_k32_q128rowld_60fb_v1_rowld_rows4", "4757_fixedbuild_k10_v2_q512_q1024_b2_q1536_q6144", "ad64_fixedbuild_k10_v2_q512_q1024_q1536_q2048_b2q1024", "rag_microbatch_k10_q8_s128_q16_s136_4757_v1", "rag_stream_k32_q128_m100000_ad64_v1_4fbf_v6_s72g8", "d15e_rect_smallq_largem_v1"]}], ["new_seed_ids", {"__tuple__": ["rag_stream_k32_q128_m100000_ad64_v1_4fbf_v6_s72g8", "d15e_rect_smallq_largem_v1"]}], ["guard_plan", {"__tuple__": ["9a17 exact BF16 non-build B=1 Q=128 M=100000 D=128 K=32 guard", "9a17 exact BF16 non-build B=1 Q=1024 M=8192 D=128 K=10 guard", "4b51 exact BF16 build K10 guard for unique Q2048 row", "1b8f exact BF16 build K10 guard for Q512/Q1024/B2-Q1024/Q1536/Q6144 rows", "ceb3 exact BF16 non-build B=1 Q in {8,16} M=100000 D=128 K=10 guard", "b0e2 exact BF16 non-build B=1 Q=128 M=131071 D=128 K=32 rowld rows4 guard", "603d/24dc exact BF16 non-build B=1 Q=24 M=100000 D=128 K=32 rowld2 rows4 guard", "cf51 exact BF16 build Q=M=1024 D=128 K=8 split16", "bca0 exact BF16 non-build Q=1 D=128 K=10 M in {65536,100000,131071,250000,262143,524287}", "5018 exact BF16 non-build Q=16 D=128 K=32 M in {100000,131071,250000}", "c3bf/d5f8 full90 Weave fallback"]}], ["expected_shape_wins", {"__tuple__": ["build_qm1024_d128_k8", "rag_online_b1_q1_m100000_d128_k10", "rag_online_b1_q1_m65536_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "rag_online_irregular_b1_q1_m262143_d128_k10", "rag_online_irregular_b1_q1_m524287_d128_k10", "rag_microbatch_largek_b1_q16_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m131071_d128_k32", "rag_microbatch_largek_b1_q16_m250000_d128_k32", "rag_microbatch_largek_b1_q24_m100000_d128_k32", "rag_stream_largek_b1_q128_m131071_d128_k32", "build_k_sweep_qm512_k10", "build_qm1024_d128_k10", "build_batch_b2_q1024_m1024_d128_k10", "build_tail_b1_q1536_m1536_d128_k10", "build_large_b1_q6144_m6144_d128_k10", "build_qm2048_d128_k10", "rag_microbatch_b1_q8_m100000_d128_k10", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32", "search_rect_b1_q1024_m8192_d128_k10"]}], ["fallback", "loom.examples.weave.knn_build_dispatch_ad64_1b8f_4b51_ceb3_full90_synthesis_v1:launch_from_contract_inputs"], ["rejected_reason", "diagnostic replay of the cca8 9a17-only candidate"]]}], ["1877_plus_b644_exact5", {"__dict_items__": [["candidate_id", "candidate_1877_plus_b644_exact5_full90_v1"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_1877_9a17_b644_6a35_full90_selective_v1:candidate_b644_only_full90_v1"], ["benchmark_entrypoint", "loom.examples.weave.knn_build_dispatch_1877_9a17_b644_6a35_full90_selective_v1:benchmark_candidate_b644_only_full90_v1"], ["selected_seeds", {"__tuple__": ["q1024_k8_195e_v1", "ragonline_mbucket_5706_q1v10_smix_s144_g12", "rag_microbucket_k32_q16dual2warp_largem_bdd2_v1_rowld1_2warp_rows4", "rag_microbucket_k32_q24rowld2_24dc_v1_rowld2_rows4", "rag_stream_k32_q128rowld_60fb_v1_rowld_rows4", "4757_fixedbuild_k10_v2_q512_q1024_b2_q1536_q6144", "ad64_fixedbuild_k10_v2_q512_q1024_q1536_q2048_b2q1024", "rag_microbatch_k10_q8_s128_q16_s136_4757_v1", "d43e_lowk_q512_k2_s4", "c3bf_direct_v20_q4096_k8_s4", "d43e_midk_k11k13_e080", "b3ec_v20_q3072_k20_s4"]}], ["new_seed_ids", {"__tuple__": ["d43e_lowk_q512_k2_s4", "c3bf_direct_v20_q4096_k8_s4", "d43e_midk_k11k13_e080", "b3ec_v20_q3072_k20_s4"]}], ["guard_plan", {"__tuple__": ["b644 exact BF16 build B=1 Q=M=512 D=128 K=2 split4 guard", "b644 exact BF16 build B=1 Q=M=4096 D=128 K=8 split4 guard", "b644 exact BF16 build B=1 Q=M=2048 D=128 K in {11,13} guard", "b644 exact BF16 build B=1 Q=M=3072 D=128 K=20 split4 guard", "4b51 exact BF16 build K10 guard for unique Q2048 row", "1b8f exact BF16 build K10 guard for Q512/Q1024/B2-Q1024/Q1536/Q6144 rows", "ceb3 exact BF16 non-build B=1 Q in {8,16} M=100000 D=128 K=10 guard", "b0e2 exact BF16 non-build B=1 Q=128 M=131071 D=128 K=32 rowld rows4 guard", "603d/24dc exact BF16 non-build B=1 Q=24 M=100000 D=128 K=32 rowld2 rows4 guard", "cf51 exact BF16 build Q=M=1024 D=128 K=8 split16", "bca0 exact BF16 non-build Q=1 D=128 K=10 M in {65536,100000,131071,250000,262143,524287}", "5018 exact BF16 non-build Q=16 D=128 K=32 M in {100000,131071,250000}", "c3bf/d5f8 full90 Weave 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"build_tail_b1_q3072_m3072_d128_k20", "build_dim_sweep_b1_q4096_m4096_d64_k10"]}], ["fallback", "loom.examples.weave.knn_build_dispatch_ad64_1b8f_4b51_ceb3_full90_synthesis_v1:launch_from_contract_inputs"], ["rejected_reason", "selected only if same-session full90 no-regression passes"]]}]}')) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def compile_and_launch_knn_build(*, shape_labels=COMBINED_TARGET_SHAPES, benchmark: bool=False, candidate_key: str=DEFAULT_CANDIDATE_KEY) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark, kernel_fn=_candidate_kernel_fn(candidate_key)) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=None, kernel_fn: Callable[[dict[str, Any]], Any]=candidate, correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + return base1877._run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=kernel_fn, correctness=correctness, time_flashlib=time_flashlib) + +def _seed_trace_record(inputs: dict[str, Any], *, seed_module, force_fallback: bool=False) -> dict[str, Any]: + label = str(inputs.get('label')) + seed_id = _seed_expected_id(seed_module, inputs) + base_row = _base_trace_row(label, force_fallback=False) + if force_fallback: + row = _base_trace_row(label, force_fallback=True) + row['expected_seed'] = seed_id + row['guard_id'] = ''.join(['forced_fallback_', format(seed_id, ''), '_disabled']) + row['guard_condition'] = ''.join(['forced fallback to 1877 baseline; ', format(seed_id, ''), ' disabled']) + row['classification'] = 'guard-miss' + row['parent_dispatcher_route'] = base_row.get('selected_route') + row['baseline_dispatcher_route'] = base_row.get('selected_route') + return _normalize_route_row(row) + row = _seed_trace_for_module(seed_module, label) + row['expected_seed'] = seed_id + row['parent_dispatcher_route'] = base_row.get('selected_route') + row['parent_dispatcher_selected_seed'] = base_row.get('selected_seed') + row['baseline_dispatcher_route'] = base_row.get('selected_route') + row['targeted_seed_row'] = TARGETED_SEED_ROWS.get(seed_id, {}) + row['coverage'] = 'full90 selective seed overlay before 1877 full90 baseline' + row['classification'] = 'unmeasured' + return _normalize_route_row(row) + +def _route_trace_record(inputs: dict[str, Any], *, candidate_key: str, force_fallback: bool=False) -> dict[str, Any]: + label = str(inputs.get('label')) + if candidate_key == BASE_1877_KEY: + return _normalize_route_row(_base_trace_row(label, force_fallback=force_fallback)) + seed_module = _matched_new_seed(inputs, candidate_key) + if seed_module is not None: + return _seed_trace_record(inputs, seed_module=seed_module, force_fallback=force_fallback) + row = _base_trace_row(label, force_fallback=force_fallback) + row['parent_dispatcher_route'] = _base_route(inputs, force_fallback=force_fallback) + row['expected_seed'] = _expected_seed(inputs, candidate_key) + return _normalize_route_row(row) + +def route_trace_for_contract_shapes(shape_labels=None, *, candidate_key: str=DEFAULT_CANDIDATE_KEY, force_fallback: bool=False) -> list[dict[str, Any]]: + _candidate_config(candidate_key) + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + return [_route_trace_record(_trace_inputs_for_shape(shape), candidate_key=candidate_key, force_fallback=force_fallback) for shape in selected] + +def _annotate_route_trace(route_trace: list[dict[str, Any]], candidate_report: dict[str, Any], baseline_report: dict[str, Any], *, candidate_key: str) -> list[dict[str, Any]]: + new_seed_ids = set(_candidate_config(candidate_key)['new_seed_ids']) + annotated = [] + for row in route_trace: + label = str(row.get('shape_key')) + out = dict(row) + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') + speedup_vs_baseline = baseline_ms / candidate_ms if candidate_ms and baseline_ms else None + speedup_vs_external = flashlib_ms / candidate_ms if candidate_ms and flashlib_ms else None + selected_seed = out.get('selected_seed') + expected_seed = out.get('expected_seed') + selected_new_seed = selected_seed in new_seed_ids + out['dispatcher_kernel_ms'] = candidate_ms + out['baseline_1877_kernel_ms'] = baseline_ms + out['shape_specific_kernel_ms'] = candidate_ms if selected_new_seed else None + out['external_baseline_ms'] = flashlib_ms + out['flashlib_ms'] = flashlib_ms + out['relative_speedup_vs_baseline'] = speedup_vs_baseline + out['relative_speedup_vs_1877'] = speedup_vs_baseline + out['speedup_vs_external_baseline'] = speedup_vs_external + out['external_baseline_ref'] = 'same_session' if flashlib_ms is not None else 'not_available' + out['route_changed_vs_1877'] = out.get('selected_route') != out.get('baseline_dispatcher_route') + if expected_seed in new_seed_ids and selected_seed != expected_seed: + out['classification'] = 'guard-miss' + elif speedup_vs_external is not None and speedup_vs_external < 1.05: + out['classification'] = 'kernel-slow' if out.get('route_kind') == 'specialized' else 'fallback-slow' + elif selected_new_seed and speedup_vs_baseline is not None and (speedup_vs_baseline < 1.0): + out['classification'] = 'kernel-slow' + elif selected_new_seed: + out['classification'] = 'seed-consumed' + else: + out['classification'] = 'route-ok' + annotated.append(_normalize_route_row(out)) + return annotated + +def _below_flashlib_rows(report: dict[str, Any], route_trace: list[dict[str, Any]], *, floor: float) -> list[dict[str, Any]]: + trace_by_label = {str(row['shape_key']): row for row in route_trace} + rows = [] + for label, row in sorted(report.get('per_shape', {}).items()): + ratio = row.get('ratio_vs_flashlib') + if not isinstance(ratio, (float, int)) or ratio >= floor: + continue + trace_row = trace_by_label.get(label, {}) + classification = trace_row.get('classification', 'unmeasured') + if classification in ('route-ok', 'unmeasured') and (not trace_row.get('selected_seed')): + classification = 'fallback-slow' + elif classification in ('route-ok', 'unmeasured') and trace_row.get('selected_seed'): + classification = 'kernel-slow' + rows.append({'shape_key': label, 'kernel_ms': row.get('kernel_ms'), 'flashlib_ms': row.get('flashlib_ms'), 'ratio_vs_flashlib': ratio, 'selected_route': trace_row.get('selected_route'), 'selected_seed': trace_row.get('selected_seed'), 'expected_seed': trace_row.get('expected_seed'), 'route_kind': trace_row.get('route_kind', 'unknown'), 'classification': classification}) + return rows + +def _seed_delta_matrix(candidate_key: str, candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> list[dict[str, Any]]: + matrix = [] + for label in _candidate_config(candidate_key)['expected_shape_wins']: + inputs = _inputs_for_label(label) + selected_seed = _expected_seed(inputs, candidate_key) + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + matrix.append({'shape_key': label, 'baseline_route': _base_route(inputs), 'candidate_route': route_for_contract_inputs(inputs, candidate_key=candidate_key), 'selected_seed': selected_seed, 'candidate_id': _candidate_id(candidate_key), 'candidate_ms': candidate_ms, 'baseline_1877_ms': baseline_ms, 'flashlib_ms': candidate_row.get('flashlib_ms'), 'delta_ms_candidate_minus_1877': candidate_ms - baseline_ms if candidate_ms is not None and baseline_ms is not None else None, 'speedup_vs_1877': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'ratio_vs_flashlib': candidate_row.get('ratio_vs_flashlib'), 'targeted_seed_row': TARGETED_SEED_ROWS.get(selected_seed, {}), 'timing_backend': candidate_row.get('timing_backend') or baseline_row.get('timing_backend')}) + return matrix + +def benchmark_baseline_1877(*, use_cupti: bool=True, shape_labels=None, benchmark_correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + return _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_parent_1877_full90_v1, correctness=benchmark_correctness, time_flashlib=time_flashlib) + +def _baseline_sidecar(report: dict[str, Any], *, shape_labels, denominator: str, timing_backend: str, benchmark_correctness: bool, time_flashlib: bool) -> dict[str, Any]: + route_trace = route_trace_for_contract_shapes(shape_labels, candidate_key=BASE_1877_KEY) + below_1x = _below_flashlib_rows(report, route_trace, floor=1.0) + below_floor = _below_flashlib_rows(report, route_trace, floor=1.05) + return {'candidate_id': BASE_1877_ID, 'candidate_key': BASE_1877_KEY, 'selected_seeds': CANDIDATE_CONFIGS[BASE_1877_KEY]['selected_seeds'], 'source_tasks': SOURCE_TASKS, 'tflops': report['summary']['primary_mean'], 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'invalid_performance_reason': report['summary']['invalid_performance_reason'], 'measured_entrypoint': BASE_1877_ENTRYPOINT, 'route_entrypoint': BASE_1877_ROUTE_ENTRYPOINT, 'measured_shape_labels': _payload_shape_labels(shape_labels), 'timing_backend': timing_backend, 'denominator': denominator, 'performance_coverage': 'partial' if below_floor else 'pass', 'coverage_only_routes': [], 'hot_bucket_blockers': below_floor, 'flashlib_parity_ledger': {'baseline_ref_scope': 'same_session' if time_flashlib else 'not_available', 'speedup_floor': 1.05, 'rows_below_1x': below_1x, 'rows_below_floor': below_floor, 'omitted_reason': None if time_flashlib else 'benchmark_time_flashlib=false'}, 'route_trace': route_trace, 'route_trace_included': True, 'contract_summary': report['summary'], 'contract_performance': report['performance'], 'timing_backends': _timing_backends_for_reports(report), 'timing_backend_requested': timing_backend, 'benchmark_correctness_checked': benchmark_correctness, 'benchmark_time_flashlib': time_flashlib, 'report': report} + +def _benchmark_payload(candidate_key: str, candidate_report: dict[str, Any], baseline_report: dict[str, Any], *, use_cupti: bool, shape_labels, benchmark_correctness: bool, time_flashlib: bool) -> dict[str, Any]: + candidate_metric = candidate_report['summary']['primary_mean'] + baseline_metric = baseline_report['summary']['primary_mean'] + route_trace = _annotate_route_trace(route_trace_for_contract_shapes(shape_labels, candidate_key=candidate_key), candidate_report, baseline_report, candidate_key=candidate_key) + below_1x = _below_flashlib_rows(candidate_report, route_trace, floor=1.0) + below_floor = _below_flashlib_rows(candidate_report, route_trace, floor=1.05) + denominator = _denominator_name(shape_labels) + timing_backend = _timing_backend_name(use_cupti) + config = _candidate_config(candidate_key) + return {'candidate_id': config['candidate_id'], 'candidate_key': candidate_key, 'baseline_candidate_id': BASE_1877_ID, 'selected_seeds': config['selected_seeds'], 'source_tasks': SOURCE_TASKS, 'tflops': candidate_metric, 'baseline_1877_tflops': baseline_metric, 'metric_delta_vs_1877': candidate_metric - baseline_metric if candidate_metric is not None and baseline_metric is not None else None, 'all_correct': candidate_report['summary']['all_correct'], 'baseline_all_correct': baseline_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'baseline_performance_comparable': baseline_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'baseline_invalid_performance_reason': baseline_report['summary']['invalid_performance_reason'], 'measured_entrypoint': config['benchmark_entrypoint'], 'baseline_entrypoint': BASE_1877_ENTRYPOINT, 'route_entrypoint': ROUTE_ENTRYPOINT, 'measured_shape_labels': _payload_shape_labels(shape_labels), 'timing_backend': timing_backend, 'denominator': denominator, 'selected_route_labels': config['expected_shape_wins'], 'selected_route_rows': _rows_for_labels(candidate_report, config['expected_shape_wins']), 'baseline_selected_route_rows': _rows_for_labels(baseline_report, config['expected_shape_wins']), 'seed_delta_matrix': _seed_delta_matrix(candidate_key, candidate_report, baseline_report), 'targeted_seed_rows': TARGETED_SEED_ROWS, 'candidate_dispatchers': CANDIDATE_DISPATCHERS, 'rejected_route_combinations': REJECTED_ROUTE_COMBINATIONS, 'selected_candidate_dispatcher': config['candidate_id'], 'guard_plan': config['guard_plan'], 'route_modules': PRODUCTION_ROUTE_MODULES, 'route_trace': route_trace, 'forced_fallback_route_trace': route_trace_for_contract_shapes(shape_labels, candidate_key=candidate_key, force_fallback=True), 'route_trace_included': True, 'contract_summary': candidate_report['summary'], 'baseline_contract_summary': baseline_report['summary'], 'contract_performance': candidate_report['performance'], 'baseline_contract_performance': baseline_report['performance'], 'timing_backends': _timing_backends_for_reports(candidate_report, baseline_report), 'timing_backend_requested': timing_backend, 'use_cupti': use_cupti, 'benchmark_correctness_checked': benchmark_correctness, 'benchmark_time_flashlib': time_flashlib, 'performance_coverage': 'partial' if below_floor else 'pass', 'coverage_only_routes': [], 'hot_bucket_blockers': below_floor, 'flashlib_parity_ledger': {'baseline_ref_scope': 'same_session' if time_flashlib else 'not_available', 'speedup_floor': 1.05, 'rows_below_1x': below_1x, 'rows_below_floor': below_floor, 'omitted_reason': None if time_flashlib else 'benchmark_time_flashlib=false'}, 'rank_objective': {'metric': 'tflops', 'direction': 'maximize', 'value': candidate_metric, 'baseline_1877_value': baseline_metric, 'delta_vs_1877': candidate_metric - baseline_metric if candidate_metric is not None and baseline_metric is not None else None, 'denominator': denominator}, 'report': candidate_report, 'baseline_report': baseline_report} + +def benchmark_candidate_portfolio(candidate_key: str, *, use_cupti: bool=True, shape_labels=None, baseline_report: dict[str, Any] | None=None, benchmark_correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + if candidate_key == BASE_1877_KEY: + baseline = benchmark_baseline_1877(use_cupti=use_cupti, shape_labels=shape_labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + return _baseline_sidecar(baseline, shape_labels=shape_labels, denominator=_denominator_name(shape_labels), timing_backend=_timing_backend_name(use_cupti), benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + if baseline_report is None: + baseline_report = benchmark_baseline_1877(use_cupti=use_cupti, shape_labels=shape_labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=_candidate_kernel_fn(candidate_key), correctness=benchmark_correctness, time_flashlib=time_flashlib) + return _benchmark_payload(candidate_key, candidate_report, baseline_report, use_cupti=use_cupti, shape_labels=shape_labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + +def benchmark_candidate_9a17_only_full90_v1(**kwargs) -> dict[str, Any]: + return benchmark_candidate_portfolio(CANDIDATE_9A17_ONLY, **kwargs) + +def benchmark_candidate_b644_only_full90_v1(**kwargs) -> dict[str, Any]: + return benchmark_candidate_portfolio(CANDIDATE_B644_ONLY, **kwargs) + +def benchmark_candidate_6a35_d64_only_full90_v1(**kwargs) -> dict[str, Any]: + return benchmark_candidate_portfolio(CANDIDATE_6A35_D64_ONLY, **kwargs) + +def benchmark_candidate_b644_6a35_full90_v1(**kwargs) -> dict[str, Any]: + return benchmark_candidate_portfolio(CANDIDATE_B644_6A35, **kwargs) + +def benchmark_candidate_9a17_b644_full90_v1(**kwargs) -> dict[str, Any]: + return benchmark_candidate_portfolio(CANDIDATE_9A17_B644, **kwargs) + +def benchmark_candidate_9a17_6a35_full90_v1(**kwargs) -> dict[str, Any]: + return benchmark_candidate_portfolio(CANDIDATE_9A17_6A35, **kwargs) + +def benchmark_candidate_9a17_b644_6a35_full90_v1(**kwargs) -> dict[str, Any]: + return benchmark_candidate_portfolio(CANDIDATE_9A17_B644_6A35, **kwargs) + +def _candidate_no_regresses_baseline(payload: dict[str, Any], baseline_value: float | None) -> bool: + value = payload.get('tflops') + return payload.get('all_correct') and payload.get('performance_comparable') and (value is not None) and (baseline_value is None or value >= baseline_value) + +def _best_candidate_key(payloads: dict[str, dict[str, Any]]) -> str | None: + baseline_value = payloads.get(BASE_1877_KEY, {}).get('tflops') + candidates = [key for key, payload in payloads.items() if key != BASE_1877_KEY and _candidate_no_regresses_baseline(payload, baseline_value)] + if not candidates: + return None + return max(candidates, key=lambda key: (payloads[key].get('tflops') or float('-inf'), len(CANDIDATE_CONFIGS[key]['new_seed_ids']))) + +def _summary_payload(*, payloads: dict[str, dict[str, Any]], artifacts: dict[str, str], denominator: str, timing_backend: str, benchmark_correctness: bool, time_flashlib: bool) -> dict[str, Any]: + selected_key = _best_candidate_key(payloads) + selected_payload = payloads.get(selected_key, {}) if selected_key else {} + baseline_payload = payloads[BASE_1877_KEY] + return {'candidate_id': 'dispatcher_synthesis_1877_9a17_b644_6a35_full90_selective_v1', 'measured_entrypoint': ''.join([format(MODULE, ''), ':write_benchmark_artifacts']), 'denominator': denominator, 'timing_backend': timing_backend, 'benchmark_correctness_checked': benchmark_correctness, 'benchmark_time_flashlib': time_flashlib, 'baseline_candidate_key': BASE_1877_KEY, 'selected_candidate_key': selected_key, 'selected_candidate_dispatcher': _candidate_id(selected_key) if selected_key else None, 'default_candidate_key': DEFAULT_CANDIDATE_KEY, 'candidate_dispatchers': CANDIDATE_DISPATCHERS, 'rejected_route_combinations': REJECTED_ROUTE_COMBINATIONS, 'candidate_rankings': [{'candidate_key': key, 'candidate_id': payloads[key].get('candidate_id'), 'tflops': payloads[key].get('tflops'), 'metric_delta_vs_1877': payloads[key].get('metric_delta_vs_1877'), 'all_correct': payloads[key].get('all_correct'), 'performance_comparable': payloads[key].get('performance_comparable'), 'performance_coverage': payloads[key].get('performance_coverage')} for key in CANDIDATE_KEYS if key in payloads], 'seed_delta_matrix': selected_payload.get('seed_delta_matrix', []), 'seed_delta_matrix_all_candidates': {key: payloads[key].get('seed_delta_matrix', []) for key in payloads if key != BASE_1877_KEY}, 'flashlib_parity_ledger': selected_payload.get('flashlib_parity_ledger', baseline_payload.get('flashlib_parity_ledger', {})), 'full_denominator_ab': {'baseline_payload': artifacts.get('same_session_baseline_payload'), 'candidate_payload': artifacts.get(''.join([format(selected_key, ''), '_payload'])) if selected_key else None, 'denominator': denominator, 'timing_backend': timing_backend, 'metric_delta': selected_payload.get('metric_delta_vs_1877'), 'route_trace': selected_payload.get('route_trace', [])}, 'baseline_tflops': baseline_payload.get('tflops'), 'selected_tflops': selected_payload.get('tflops'), 'artifacts': artifacts} + +def write_benchmark_artifacts(artifact_dir: str | Path, *, use_cupti: bool=True, shape_labels=None, benchmark_correctness: bool=True, time_flashlib: bool=True, candidate_keys: tuple[str, ...] | None=None) -> dict[str, str]: + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + denom_label = _denominator_label(shape_labels) + denominator = _denominator_name(shape_labels) + timing_backend = _timing_backend_name(use_cupti) + baseline_report = benchmark_baseline_1877(use_cupti=use_cupti, shape_labels=shape_labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + baseline_payload = _baseline_sidecar(baseline_report, shape_labels=shape_labels, denominator=denominator, timing_backend=timing_backend, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + artifacts: dict[str, str] = {} + payloads = {BASE_1877_KEY: baseline_payload} + baseline_path = out_dir / ''.join([format(denom_label, ''), '_same_session_baseline_1877_v1.json']) + baseline_path.write_text(json.dumps(baseline_payload, indent=2, sort_keys=True) + '\n', encoding='utf-8') + artifacts['same_session_baseline_payload'] = str(baseline_path) + selected_candidate_keys = list(DEFAULT_SYNTHESIS_CANDIDATES) if candidate_keys is None else list(candidate_keys) + for candidate_key in selected_candidate_keys: + if candidate_key == BASE_1877_KEY: + raise ValueError('candidate_keys must not include the baseline key') + _candidate_config(candidate_key) + payload = benchmark_candidate_portfolio(candidate_key, use_cupti=use_cupti, shape_labels=shape_labels, baseline_report=baseline_report, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + payloads[candidate_key] = payload + payload_path = out_dir / ''.join([format(denom_label, ''), '_dispatch_', format(candidate_key, ''), '_v1.json']) + trace_path = out_dir / ''.join([format(denom_label, ''), '_route_trace_', format(candidate_key, ''), '_v1.json']) + forced_trace_path = out_dir / ''.join([format(denom_label, ''), '_forced_fallback_trace_', format(candidate_key, ''), '_v1.json']) + seed_matrix_path = out_dir / ''.join([format(denom_label, ''), '_seed_delta_matrix_', format(candidate_key, ''), '_v1.json']) + payload_path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n', encoding='utf-8') + trace_path.write_text(json.dumps(payload['route_trace'], indent=2, sort_keys=True) + '\n', encoding='utf-8') + forced_trace_path.write_text(json.dumps(payload['forced_fallback_route_trace'], indent=2, sort_keys=True) + '\n', encoding='utf-8') + seed_matrix_path.write_text(json.dumps(payload['seed_delta_matrix'], indent=2, sort_keys=True) + '\n', encoding='utf-8') + artifacts[''.join([format(candidate_key, ''), '_payload'])] = str(payload_path) + artifacts[''.join([format(candidate_key, ''), '_route_trace'])] = str(trace_path) + artifacts[''.join([format(candidate_key, ''), '_forced_fallback_trace'])] = str(forced_trace_path) + artifacts[''.join([format(candidate_key, ''), '_seed_delta_matrix'])] = str(seed_matrix_path) + summary = _summary_payload(payloads=payloads, artifacts=artifacts, denominator=denominator, timing_backend=timing_backend, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + summary_path = out_dir / ''.join([format(denom_label, ''), '_dispatcher_synthesis_1877_9a17_b644_6a35_selective_v1.json']) + summary_path.write_text(json.dumps(summary, indent=2, sort_keys=True) + '\n', encoding='utf-8') + artifacts['dispatcher_synthesis'] = str(summary_path) + return artifacts diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_1877_9a17_fp16_fd37_full90_consumption_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_1877_9a17_fp16_fd37_full90_consumption_v1.py new file mode 100644 index 00000000..fb4c73a9 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_1877_9a17_fp16_fd37_full90_consumption_v1.py @@ -0,0 +1,300 @@ +"""Full90 dispatcher consumption for the fd37 FP16 D128 K10 seed. + +Minimum target architecture: sm_100a. This additive dispatcher wrapper keeps +the fd37-selected 1877+9a17 full90 route as the baseline, then routes only +``build_dtype_fp16_b1_q2048_m2048_d128_k10`` to the validated fd37 FP16 +split8 cached-merge Weave seed. + +Production routes stay Weave-only; FlashLib is timed only by the contract +harness as an external reference. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +from pathlib import Path +from typing import Any, Callable +from . import knn_build_fp16_d128_lowfloor_fd37_v1 as fp16_fd37 +from . import knn_build_dispatch_1877_9a17_b644_6a35_full90_selective_v1 as parent_full90 +MODULE = 'loom.examples.weave.knn_build_dispatch_1877_9a17_fp16_fd37_full90_consumption_v1' +eval_mod = parent_full90.eval_mod +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbatch_m64_d4f7_stage1", "arg_keys": ["query", "database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 91392, "constants": [], "cta_group": 1, "threads": 512}')) +BASE_CANDIDATE_KEY = parent_full90.CANDIDATE_9A17_ONLY +BASE_CONFIG = parent_full90.CANDIDATE_CONFIGS[BASE_CANDIDATE_KEY] +BASE_CANDIDATE_ID = BASE_CONFIG['candidate_id'] +BASE_BENCHMARK_ENTRYPOINT = BASE_CONFIG['benchmark_entrypoint'] +BASE_ROUTE_ENTRYPOINT = BASE_CONFIG['entrypoint'] +CANDIDATE_FP16 = '1877_plus_9a17_fp16_fd37_lowfloor' +DEFAULT_CANDIDATE_KEY = CANDIDATE_FP16 +CANDIDATE_KEYS = (BASE_CANDIDATE_KEY, CANDIDATE_FP16) +CANDIDATE_ID = 'candidate_1877_plus_9a17_fp16_fd37_lowfloor_full90_v1' +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +BENCHMARK_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_candidate_fp16_fd37_full90_v1']) +TARGET_FP16_D128_K10 = fp16_fd37.TARGET_SHAPE +TARGET_SHAPES = (TARGET_FP16_D128_K10,) +SEED_FP16_D128_K10_ID = 'fp16_d128_lowfloor_fd37_s8_cached_merge' +ROUTE_FP16_D128_K10_SEED = fp16_fd37.ROUTE_FP16_S8_CACHED_MERGE +FP16_SEED_ENTRYPOINT = 'loom.examples.weave.knn_build_fp16_d128_lowfloor_fd37_v1:launch_from_contract_inputs' +TARGETED_SEED_PAYLOAD = 'artifacts/weave_evolve/knn_build_fp16_d128_lowfloor_fd37_v1/fp16_d128_lowfloor_fd37_v1.json' +SOURCE_TASKS = {**parent_full90.SOURCE_TASKS, SEED_FP16_D128_K10_ID: 'weave-evolve-knn-build-7734 / design_doc/active/weave_evolve_knn_build_round_155_fd37_fp16.md', CANDIDATE_ID: 'generalize-auto-tuning-knn-build-fd9b dispatcher-consumption'} +PRODUCTION_ROUTE_MODULES = {**parent_full90.PRODUCTION_ROUTE_MODULES, SEED_FP16_D128_K10_ID: FP16_SEED_ENTRYPOINT, BASE_CANDIDATE_ID: BASE_ROUTE_ENTRYPOINT} +PARENT_SEEDS = _decode_capture(_json_loads('{"__tuple__": ["q1024_k8_195e_v1", "ragonline_mbucket_5706_q1v10_smix_s144_g12", "rag_microbucket_k32_q16dual2warp_largem_bdd2_v1_rowld1_2warp_rows4", "rag_microbucket_k32_q24rowld2_24dc_v1_rowld2_rows4", "rag_stream_k32_q128rowld_60fb_v1_rowld_rows4", "4757_fixedbuild_k10_v2_q512_q1024_b2_q1536_q6144", "ad64_fixedbuild_k10_v2_q512_q1024_q1536_q2048_b2q1024", "rag_microbatch_k10_q8_s128_q16_s136_4757_v1", "rag_stream_k32_q128_m100000_ad64_v1_4fbf_v6_s72g8", "d15e_rect_smallq_largem_v1"]}')) +SELECTED_SEEDS = _decode_capture(_json_loads('{"__tuple__": ["q1024_k8_195e_v1", "ragonline_mbucket_5706_q1v10_smix_s144_g12", "rag_microbucket_k32_q16dual2warp_largem_bdd2_v1_rowld1_2warp_rows4", "rag_microbucket_k32_q24rowld2_24dc_v1_rowld2_rows4", "rag_stream_k32_q128rowld_60fb_v1_rowld_rows4", "4757_fixedbuild_k10_v2_q512_q1024_b2_q1536_q6144", "ad64_fixedbuild_k10_v2_q512_q1024_q1536_q2048_b2q1024", "rag_microbatch_k10_q8_s128_q16_s136_4757_v1", "rag_stream_k32_q128_m100000_ad64_v1_4fbf_v6_s72g8", "d15e_rect_smallq_largem_v1", "fp16_d128_lowfloor_fd37_s8_cached_merge"]}')) +EXPECTED_SHAPE_WINS = _decode_capture(_json_loads('{"__tuple__": ["build_qm1024_d128_k8", "rag_online_b1_q1_m100000_d128_k10", "rag_online_b1_q1_m65536_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "rag_online_irregular_b1_q1_m262143_d128_k10", "rag_online_irregular_b1_q1_m524287_d128_k10", "rag_microbatch_largek_b1_q16_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m131071_d128_k32", "rag_microbatch_largek_b1_q16_m250000_d128_k32", "rag_microbatch_largek_b1_q24_m100000_d128_k32", "rag_stream_largek_b1_q128_m131071_d128_k32", "build_k_sweep_qm512_k10", "build_qm1024_d128_k10", "build_batch_b2_q1024_m1024_d128_k10", "build_tail_b1_q1536_m1536_d128_k10", "build_large_b1_q6144_m6144_d128_k10", "build_qm2048_d128_k10", "rag_microbatch_b1_q8_m100000_d128_k10", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32", "search_rect_b1_q1024_m8192_d128_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10"]}')) +CANDIDATE_CONFIGS = _decode_capture(_json_loads('{"__dict_items__": [["1877_plus_9a17_residual_rag_search", {"__dict_items__": [["candidate_id", "candidate_1877_plus_9a17_residual_rag_search_full90_v1"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_1877_9a17_b644_6a35_full90_selective_v1:candidate_9a17_only_full90_v1"], ["benchmark_entrypoint", "loom.examples.weave.knn_build_dispatch_1877_9a17_b644_6a35_full90_selective_v1:benchmark_candidate_9a17_only_full90_v1"], ["selected_seeds", {"__tuple__": ["q1024_k8_195e_v1", "ragonline_mbucket_5706_q1v10_smix_s144_g12", "rag_microbucket_k32_q16dual2warp_largem_bdd2_v1_rowld1_2warp_rows4", "rag_microbucket_k32_q24rowld2_24dc_v1_rowld2_rows4", "rag_stream_k32_q128rowld_60fb_v1_rowld_rows4", "4757_fixedbuild_k10_v2_q512_q1024_b2_q1536_q6144", "ad64_fixedbuild_k10_v2_q512_q1024_q1536_q2048_b2q1024", "rag_microbatch_k10_q8_s128_q16_s136_4757_v1", "rag_stream_k32_q128_m100000_ad64_v1_4fbf_v6_s72g8", "d15e_rect_smallq_largem_v1"]}], ["new_seed_ids", {"__tuple__": []}], ["guard_plan", {"__tuple__": ["9a17 exact BF16 non-build B=1 Q=128 M=100000 D=128 K=32 guard", "9a17 exact BF16 non-build B=1 Q=1024 M=8192 D=128 K=10 guard", "4b51 exact BF16 build K10 guard for unique Q2048 row", "1b8f exact BF16 build K10 guard for Q512/Q1024/B2-Q1024/Q1536/Q6144 rows", "ceb3 exact BF16 non-build B=1 Q in {8,16} M=100000 D=128 K=10 guard", "b0e2 exact BF16 non-build B=1 Q=128 M=131071 D=128 K=32 rowld rows4 guard", "603d/24dc exact BF16 non-build B=1 Q=24 M=100000 D=128 K=32 rowld2 rows4 guard", "cf51 exact BF16 build Q=M=1024 D=128 K=8 split16", "bca0 exact BF16 non-build Q=1 D=128 K=10 M in {65536,100000,131071,250000,262143,524287}", "5018 exact BF16 non-build Q=16 D=128 K=32 M in {100000,131071,250000}", "c3bf/d5f8 full90 Weave fallback"]}], ["expected_shape_wins", {"__tuple__": ["build_qm1024_d128_k8", "rag_online_b1_q1_m100000_d128_k10", "rag_online_b1_q1_m65536_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "rag_online_irregular_b1_q1_m262143_d128_k10", "rag_online_irregular_b1_q1_m524287_d128_k10", "rag_microbatch_largek_b1_q16_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m131071_d128_k32", "rag_microbatch_largek_b1_q16_m250000_d128_k32", "rag_microbatch_largek_b1_q24_m100000_d128_k32", "rag_stream_largek_b1_q128_m131071_d128_k32", "build_k_sweep_qm512_k10", "build_qm1024_d128_k10", "build_batch_b2_q1024_m1024_d128_k10", "build_tail_b1_q1536_m1536_d128_k10", "build_large_b1_q6144_m6144_d128_k10", "build_qm2048_d128_k10", "rag_microbatch_b1_q8_m100000_d128_k10", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32", "search_rect_b1_q1024_m8192_d128_k10"]}], ["fallback", "loom.examples.weave.knn_build_dispatch_1877_9a17_b644_6a35_full90_selective_v1:candidate_9a17_only_full90_v1"], ["rejected_reason", "same-session fd37 selected 1877+9a17 full90 baseline"]]}], ["1877_plus_9a17_fp16_fd37_lowfloor", {"__dict_items__": [["candidate_id", "candidate_1877_plus_9a17_fp16_fd37_lowfloor_full90_v1"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_1877_9a17_fp16_fd37_full90_consumption_v1:launch_from_contract_inputs"], ["benchmark_entrypoint", "loom.examples.weave.knn_build_dispatch_1877_9a17_fp16_fd37_full90_consumption_v1:benchmark_candidate_fp16_fd37_full90_v1"], ["selected_seeds", {"__tuple__": ["q1024_k8_195e_v1", "ragonline_mbucket_5706_q1v10_smix_s144_g12", "rag_microbucket_k32_q16dual2warp_largem_bdd2_v1_rowld1_2warp_rows4", "rag_microbucket_k32_q24rowld2_24dc_v1_rowld2_rows4", "rag_stream_k32_q128rowld_60fb_v1_rowld_rows4", "4757_fixedbuild_k10_v2_q512_q1024_b2_q1536_q6144", "ad64_fixedbuild_k10_v2_q512_q1024_q1536_q2048_b2q1024", "rag_microbatch_k10_q8_s128_q16_s136_4757_v1", "rag_stream_k32_q128_m100000_ad64_v1_4fbf_v6_s72g8", "d15e_rect_smallq_largem_v1", "fp16_d128_lowfloor_fd37_s8_cached_merge"]}], ["new_seed_ids", {"__tuple__": ["fp16_d128_lowfloor_fd37_s8_cached_merge"]}], ["guard_plan", {"__tuple__": ["fd37 exact FP16 build B=1 Q=M=2048 D=128 K=10 split8 cached-merge guard", "9a17 exact BF16 non-build B=1 Q=128 M=100000 D=128 K=32 guard", "9a17 exact BF16 non-build B=1 Q=1024 M=8192 D=128 K=10 guard", "4b51 exact BF16 build K10 guard for unique Q2048 row", "1b8f exact BF16 build K10 guard for Q512/Q1024/B2-Q1024/Q1536/Q6144 rows", "ceb3 exact BF16 non-build B=1 Q in {8,16} M=100000 D=128 K=10 guard", "b0e2 exact BF16 non-build B=1 Q=128 M=131071 D=128 K=32 rowld rows4 guard", "603d/24dc exact BF16 non-build B=1 Q=24 M=100000 D=128 K=32 rowld2 rows4 guard", "cf51 exact BF16 build Q=M=1024 D=128 K=8 split16", "bca0 exact BF16 non-build Q=1 D=128 K=10 M in {65536,100000,131071,250000,262143,524287}", "5018 exact BF16 non-build Q=16 D=128 K=32 M in {100000,131071,250000}", "c3bf/d5f8 full90 Weave fallback"]}], ["expected_shape_wins", {"__tuple__": ["build_qm1024_d128_k8", "rag_online_b1_q1_m100000_d128_k10", "rag_online_b1_q1_m65536_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "rag_online_irregular_b1_q1_m262143_d128_k10", "rag_online_irregular_b1_q1_m524287_d128_k10", "rag_microbatch_largek_b1_q16_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m131071_d128_k32", "rag_microbatch_largek_b1_q16_m250000_d128_k32", "rag_microbatch_largek_b1_q24_m100000_d128_k32", "rag_stream_largek_b1_q128_m131071_d128_k32", "build_k_sweep_qm512_k10", "build_qm1024_d128_k10", "build_batch_b2_q1024_m1024_d128_k10", "build_tail_b1_q1536_m1536_d128_k10", "build_large_b1_q6144_m6144_d128_k10", "build_qm2048_d128_k10", "rag_microbatch_b1_q8_m100000_d128_k10", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32", "search_rect_b1_q1024_m8192_d128_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10"]}], ["fallback", "loom.examples.weave.knn_build_dispatch_1877_9a17_b644_6a35_full90_selective_v1:candidate_9a17_only_full90_v1"], ["rejected_reason", null]]}]]}')) +CANDIDATE_DISPATCHERS = _decode_capture(_json_loads('{"__tuple__": [{"__dict_items__": [["id", "candidate_1877_plus_9a17_residual_rag_search_full90_v1"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_1877_9a17_b644_6a35_full90_selective_v1:benchmark_candidate_9a17_only_full90_v1"], ["consumed_seeds", {"__tuple__": ["q1024_k8_195e_v1", "ragonline_mbucket_5706_q1v10_smix_s144_g12", "rag_microbucket_k32_q16dual2warp_largem_bdd2_v1_rowld1_2warp_rows4", "rag_microbucket_k32_q24rowld2_24dc_v1_rowld2_rows4", "rag_stream_k32_q128rowld_60fb_v1_rowld_rows4", "4757_fixedbuild_k10_v2_q512_q1024_b2_q1536_q6144", "ad64_fixedbuild_k10_v2_q512_q1024_q1536_q2048_b2q1024", "rag_microbatch_k10_q8_s128_q16_s136_4757_v1", "rag_stream_k32_q128_m100000_ad64_v1_4fbf_v6_s72g8", "d15e_rect_smallq_largem_v1"]}], ["guard_plan", {"__tuple__": ["9a17 exact BF16 non-build B=1 Q=128 M=100000 D=128 K=32 guard", "9a17 exact BF16 non-build B=1 Q=1024 M=8192 D=128 K=10 guard", "4b51 exact BF16 build K10 guard for unique Q2048 row", "1b8f exact BF16 build K10 guard for Q512/Q1024/B2-Q1024/Q1536/Q6144 rows", "ceb3 exact BF16 non-build B=1 Q in {8,16} M=100000 D=128 K=10 guard", "b0e2 exact BF16 non-build B=1 Q=128 M=131071 D=128 K=32 rowld rows4 guard", "603d/24dc exact BF16 non-build B=1 Q=24 M=100000 D=128 K=32 rowld2 rows4 guard", "cf51 exact BF16 build Q=M=1024 D=128 K=8 split16", "bca0 exact BF16 non-build Q=1 D=128 K=10 M in {65536,100000,131071,250000,262143,524287}", "5018 exact BF16 non-build Q=16 D=128 K=32 M in {100000,131071,250000}", "c3bf/d5f8 full90 Weave fallback"]}], ["expected_shape_wins", {"__tuple__": ["build_qm1024_d128_k8", "rag_online_b1_q1_m100000_d128_k10", "rag_online_b1_q1_m65536_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "rag_online_irregular_b1_q1_m262143_d128_k10", "rag_online_irregular_b1_q1_m524287_d128_k10", "rag_microbatch_largek_b1_q16_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m131071_d128_k32", "rag_microbatch_largek_b1_q16_m250000_d128_k32", "rag_microbatch_largek_b1_q24_m100000_d128_k32", "rag_stream_largek_b1_q128_m131071_d128_k32", "build_k_sweep_qm512_k10", "build_qm1024_d128_k10", "build_batch_b2_q1024_m1024_d128_k10", "build_tail_b1_q1536_m1536_d128_k10", "build_large_b1_q6144_m6144_d128_k10", "build_qm2048_d128_k10", "rag_microbatch_b1_q8_m100000_d128_k10", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32", "search_rect_b1_q1024_m8192_d128_k10"]}], ["fallback", "loom.examples.weave.knn_build_dispatch_1877_9a17_b644_6a35_full90_selective_v1:candidate_9a17_only_full90_v1"], ["rejected_reason", "same-session fd37 selected 1877+9a17 full90 baseline"]]}, {"__dict_items__": [["id", "candidate_1877_plus_9a17_fp16_fd37_lowfloor_full90_v1"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_1877_9a17_fp16_fd37_full90_consumption_v1:benchmark_candidate_fp16_fd37_full90_v1"], ["consumed_seeds", {"__tuple__": ["q1024_k8_195e_v1", "ragonline_mbucket_5706_q1v10_smix_s144_g12", "rag_microbucket_k32_q16dual2warp_largem_bdd2_v1_rowld1_2warp_rows4", "rag_microbucket_k32_q24rowld2_24dc_v1_rowld2_rows4", "rag_stream_k32_q128rowld_60fb_v1_rowld_rows4", "4757_fixedbuild_k10_v2_q512_q1024_b2_q1536_q6144", "ad64_fixedbuild_k10_v2_q512_q1024_q1536_q2048_b2q1024", "rag_microbatch_k10_q8_s128_q16_s136_4757_v1", "rag_stream_k32_q128_m100000_ad64_v1_4fbf_v6_s72g8", "d15e_rect_smallq_largem_v1", "fp16_d128_lowfloor_fd37_s8_cached_merge"]}], ["guard_plan", {"__tuple__": ["fd37 exact FP16 build B=1 Q=M=2048 D=128 K=10 split8 cached-merge guard", "9a17 exact BF16 non-build B=1 Q=128 M=100000 D=128 K=32 guard", "9a17 exact BF16 non-build B=1 Q=1024 M=8192 D=128 K=10 guard", "4b51 exact BF16 build K10 guard for unique Q2048 row", "1b8f exact BF16 build K10 guard for Q512/Q1024/B2-Q1024/Q1536/Q6144 rows", "ceb3 exact BF16 non-build B=1 Q in {8,16} M=100000 D=128 K=10 guard", "b0e2 exact BF16 non-build B=1 Q=128 M=131071 D=128 K=32 rowld rows4 guard", "603d/24dc exact BF16 non-build B=1 Q=24 M=100000 D=128 K=32 rowld2 rows4 guard", "cf51 exact BF16 build Q=M=1024 D=128 K=8 split16", "bca0 exact BF16 non-build Q=1 D=128 K=10 M in {65536,100000,131071,250000,262143,524287}", "5018 exact BF16 non-build Q=16 D=128 K=32 M in {100000,131071,250000}", "c3bf/d5f8 full90 Weave fallback"]}], ["expected_shape_wins", {"__tuple__": ["build_qm1024_d128_k8", "rag_online_b1_q1_m100000_d128_k10", "rag_online_b1_q1_m65536_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "rag_online_irregular_b1_q1_m262143_d128_k10", "rag_online_irregular_b1_q1_m524287_d128_k10", "rag_microbatch_largek_b1_q16_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m131071_d128_k32", "rag_microbatch_largek_b1_q16_m250000_d128_k32", "rag_microbatch_largek_b1_q24_m100000_d128_k32", "rag_stream_largek_b1_q128_m131071_d128_k32", "build_k_sweep_qm512_k10", "build_qm1024_d128_k10", "build_batch_b2_q1024_m1024_d128_k10", "build_tail_b1_q1536_m1536_d128_k10", "build_large_b1_q6144_m6144_d128_k10", "build_qm2048_d128_k10", "rag_microbatch_b1_q8_m100000_d128_k10", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32", "search_rect_b1_q1024_m8192_d128_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10"]}], ["fallback", "loom.examples.weave.knn_build_dispatch_1877_9a17_b644_6a35_full90_selective_v1:candidate_9a17_only_full90_v1"], ["rejected_reason", null]]}]}')) + +def _candidate_config(candidate_key: str) -> dict[str, Any]: + try: + return CANDIDATE_CONFIGS[candidate_key] + except KeyError as exc: + raise ValueError(''.join(['unknown fd37 FP16 full90 candidate ', format(repr(candidate_key), '')])) from exc + +def _select_contract_shapes(shape_labels): + return parent_full90._select_contract_shapes(shape_labels) + +def _trace_inputs_for_shape(shape: dict[str, Any]) -> dict[str, Any]: + return parent_full90._trace_inputs_for_shape(shape) + +def _inputs_for_label(label: str) -> dict[str, Any]: + return parent_full90._inputs_for_label(label) + +def _normalize_route_row(row: dict[str, Any]) -> dict[str, Any]: + return parent_full90._normalize_route_row(row) + +def _timing_backend_name(use_cupti: bool) -> str: + return parent_full90._timing_backend_name(use_cupti) + +def _payload_shape_labels(shape_labels) -> str | tuple[str, ...]: + return parent_full90._payload_shape_labels(shape_labels) + +def _denominator_name(shape_labels) -> str: + return parent_full90._denominator_name(shape_labels) + +def _denominator_label(shape_labels) -> str: + return parent_full90._denominator_label(shape_labels) + +def _rows_for_labels(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return parent_full90._rows_for_labels(report, labels) + +def _timing_backends_for_reports(*reports: dict[str, Any]) -> list[str]: + return parent_full90._timing_backends_for_reports(*reports) + +def _eligible_fp16_d128_k10(inputs: dict[str, Any]) -> bool: + return fp16_fd37._eligible_fp16_s8_cached_merge(inputs) + +def _expected_seed(inputs: dict[str, Any], candidate_key: str) -> str | None: + if candidate_key == CANDIDATE_FP16 and _eligible_fp16_d128_k10(inputs): + return SEED_FP16_D128_K10_ID + return None + +def _base_route(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + return parent_full90.route_for_contract_inputs(inputs, candidate_key=BASE_CANDIDATE_KEY, force_fallback=force_fallback) + +def _base_launch(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + parent_full90.launch_from_contract_inputs(inputs, candidate_key=BASE_CANDIDATE_KEY, force_fallback=force_fallback) + +def _base_trace_row(label: str, *, force_fallback: bool=False) -> dict[str, Any]: + return dict(parent_full90.route_trace_for_contract_shapes((label,), candidate_key=BASE_CANDIDATE_KEY, force_fallback=force_fallback)[0]) + +def route_for_contract_inputs(inputs: dict[str, Any], *, candidate_key: str=DEFAULT_CANDIDATE_KEY, force_fallback: bool=False) -> str: + _candidate_config(candidate_key) + if force_fallback or candidate_key == BASE_CANDIDATE_KEY: + return _base_route(inputs, force_fallback=force_fallback) + if _eligible_fp16_d128_k10(inputs): + return ROUTE_FP16_D128_K10_SEED + return _base_route(inputs) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, candidate_key: str=DEFAULT_CANDIDATE_KEY, force_fallback: bool=False) -> None: + _candidate_config(candidate_key) + if force_fallback or candidate_key == BASE_CANDIDATE_KEY: + _base_launch(inputs, force_fallback=force_fallback) + return + if _eligible_fp16_d128_k10(inputs): + fp16_fd37.launch_from_contract_inputs(inputs) + return + _base_launch(inputs) + +def candidate_baseline_9a17_full90_v1(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, candidate_key=BASE_CANDIDATE_KEY) + +def candidate_fp16_fd37_full90_v1(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, candidate_key=CANDIDATE_FP16) + +def candidate(inputs: dict[str, Any]) -> None: + candidate_fp16_fd37_full90_v1(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, candidate_key=CANDIDATE_FP16, force_fallback=True) + +def _candidate_kernel_fn(candidate_key: str) -> Callable[[dict[str, Any]], None]: + if candidate_key == BASE_CANDIDATE_KEY: + return candidate_baseline_9a17_full90_v1 + if candidate_key == CANDIDATE_FP16: + return candidate_fp16_fd37_full90_v1 + raise ValueError(''.join(['unknown fd37 FP16 full90 candidate ', format(repr(candidate_key), '')])) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def compile_and_launch_knn_build(*, shape_labels=None, benchmark: bool=False, candidate_key: str=DEFAULT_CANDIDATE_KEY) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark, kernel_fn=_candidate_kernel_fn(candidate_key)) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=None, kernel_fn: Callable[[dict[str, Any]], Any]=candidate, correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + return parent_full90._run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=kernel_fn, correctness=correctness, time_flashlib=time_flashlib) + +def _fp16_trace_row(inputs: dict[str, Any], *, force_fallback: bool=False) -> dict[str, Any]: + label = str(inputs.get('label')) + base_row = _base_trace_row(label, force_fallback=False) + if force_fallback: + row = _base_trace_row(label, force_fallback=True) + row['expected_seed'] = SEED_FP16_D128_K10_ID + row['guard_id'] = ''.join(['forced_fallback_', format(SEED_FP16_D128_K10_ID, ''), '_disabled']) + row['guard_condition'] = ''.join(['forced fallback to 1877+9a17; ', format(SEED_FP16_D128_K10_ID, ''), ' disabled']) + row['classification'] = 'guard-miss' + row['parent_dispatcher_route'] = base_row.get('selected_route') + row['baseline_dispatcher_route'] = base_row.get('selected_route') + return _normalize_route_row(row) + return _normalize_route_row({'shape_key': label, 'selected_route': ROUTE_FP16_D128_K10_SEED, 'selected_entrypoint': FP16_SEED_ENTRYPOINT, 'selected_seed': SEED_FP16_D128_K10_ID, 'expected_seed': SEED_FP16_D128_K10_ID, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': ''.join(['selective_fd37_', format(SEED_FP16_D128_K10_ID, '')]), 'guard_condition': 'exact FP16 build B=1 Q=M=2048 D=128 K=10 split8 cached merge', 'matched_label': TARGET_FP16_D128_K10, 'parent_dispatcher_route': base_row.get('selected_route'), 'parent_dispatcher_selected_seed': base_row.get('selected_seed'), 'baseline_dispatcher_route': base_row.get('selected_route'), 'targeted_seed_payload': TARGETED_SEED_PAYLOAD, 'coverage': 'full90 one-seed FP16 overlay before fd37-selected 1877+9a17 baseline', 'classification': 'unmeasured'}) + +def _route_trace_record(inputs: dict[str, Any], *, candidate_key: str, force_fallback: bool=False) -> dict[str, Any]: + label = str(inputs.get('label')) + if candidate_key == BASE_CANDIDATE_KEY: + return _normalize_route_row(_base_trace_row(label, force_fallback=force_fallback)) + if _eligible_fp16_d128_k10(inputs): + return _fp16_trace_row(inputs, force_fallback=force_fallback) + row = _base_trace_row(label, force_fallback=force_fallback) + row['parent_dispatcher_route'] = _base_route(inputs, force_fallback=force_fallback) + row['expected_seed'] = None + return _normalize_route_row(row) + +def route_trace_for_contract_shapes(shape_labels=None, *, candidate_key: str=DEFAULT_CANDIDATE_KEY, force_fallback: bool=False) -> list[dict[str, Any]]: + _candidate_config(candidate_key) + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + return [_route_trace_record(_trace_inputs_for_shape(shape), candidate_key=candidate_key, force_fallback=force_fallback) for shape in selected] + +def _annotate_route_trace(route_trace: list[dict[str, Any]], candidate_report: dict[str, Any], baseline_report: dict[str, Any], *, candidate_key: str, speedup_floor: float) -> list[dict[str, Any]]: + new_seed_ids = set(_candidate_config(candidate_key)['new_seed_ids']) + annotated = [] + for row in route_trace: + label = str(row.get('shape_key')) + out = dict(row) + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') + speedup_vs_baseline = baseline_ms / candidate_ms if candidate_ms and baseline_ms else None + speedup_vs_external = flashlib_ms / candidate_ms if candidate_ms and flashlib_ms else None + selected_seed = out.get('selected_seed') + expected_seed = out.get('expected_seed') + selected_new_seed = selected_seed in new_seed_ids + out['dispatcher_kernel_ms'] = candidate_ms + out['baseline_9a17_kernel_ms'] = baseline_ms + out['shape_specific_kernel_ms'] = candidate_ms if selected_new_seed else None + out['external_baseline_ms'] = flashlib_ms + out['flashlib_ms'] = flashlib_ms + out['relative_speedup_vs_baseline'] = speedup_vs_baseline + out['relative_speedup_vs_9a17'] = speedup_vs_baseline + out['speedup_vs_external_baseline'] = speedup_vs_external + out['external_baseline_ref'] = 'same_session' if flashlib_ms is not None else 'not_available' + out['route_changed_vs_9a17'] = out.get('selected_route') != out.get('baseline_dispatcher_route') + if expected_seed in new_seed_ids and selected_seed != expected_seed: + out['classification'] = 'guard-miss' + elif selected_new_seed and speedup_vs_external is not None and (speedup_vs_external >= speedup_floor): + out['classification'] = 'seed-consumed' + elif speedup_vs_external is not None and speedup_vs_external < speedup_floor: + out['classification'] = 'kernel-slow' if out.get('route_kind') == 'specialized' else 'fallback-slow' + elif selected_new_seed: + out['classification'] = 'seed-consumed' + else: + out['classification'] = 'route-ok' + annotated.append(_normalize_route_row(out)) + return annotated + +def _below_flashlib_rows(report: dict[str, Any], route_trace: list[dict[str, Any]], *, floor: float) -> list[dict[str, Any]]: + trace_by_label = {str(row['shape_key']): row for row in route_trace} + rows = [] + for label, row in sorted(report.get('per_shape', {}).items()): + ratio = row.get('ratio_vs_flashlib') + if not isinstance(ratio, (float, int)) or ratio >= floor: + continue + trace_row = trace_by_label.get(label, {}) + classification = trace_row.get('classification', 'unmeasured') + if classification in ('route-ok', 'unmeasured') and (not trace_row.get('selected_seed')): + classification = 'fallback-slow' + elif classification in ('route-ok', 'unmeasured') and trace_row.get('selected_seed'): + classification = 'kernel-slow' + rows.append({'shape_key': label, 'kernel_ms': row.get('kernel_ms'), 'flashlib_ms': row.get('flashlib_ms'), 'ratio_vs_flashlib': ratio, 'selected_route': trace_row.get('selected_route'), 'selected_seed': trace_row.get('selected_seed'), 'expected_seed': trace_row.get('expected_seed'), 'route_kind': trace_row.get('route_kind', 'unknown'), 'classification': classification}) + return rows + +def _seed_delta_matrix(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> list[dict[str, Any]]: + matrix = [] + for label in TARGET_SHAPES: + inputs = _inputs_for_label(label) + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + matrix.append({'shape_key': label, 'baseline_route': _base_route(inputs), 'candidate_route': route_for_contract_inputs(inputs), 'selected_seed': SEED_FP16_D128_K10_ID, 'candidate_id': CANDIDATE_ID, 'candidate_ms': candidate_ms, 'baseline_9a17_ms': baseline_ms, 'flashlib_ms': candidate_row.get('flashlib_ms'), 'delta_ms_candidate_minus_9a17': candidate_ms - baseline_ms if candidate_ms is not None and baseline_ms is not None else None, 'speedup_vs_9a17': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'ratio_vs_flashlib': candidate_row.get('ratio_vs_flashlib'), 'targeted_seed_payload': TARGETED_SEED_PAYLOAD, 'timing_backend': candidate_row.get('timing_backend') or baseline_row.get('timing_backend')}) + return matrix + +def benchmark_baseline_9a17(*, use_cupti: bool=True, shape_labels=None, benchmark_correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + return _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_baseline_9a17_full90_v1, correctness=benchmark_correctness, time_flashlib=time_flashlib) + +def _baseline_sidecar(report: dict[str, Any], *, shape_labels, denominator: str, timing_backend: str, benchmark_correctness: bool, time_flashlib: bool, speedup_floor: float) -> dict[str, Any]: + route_trace = route_trace_for_contract_shapes(shape_labels, candidate_key=BASE_CANDIDATE_KEY) + below_1x = _below_flashlib_rows(report, route_trace, floor=1.0) + below_floor = _below_flashlib_rows(report, route_trace, floor=speedup_floor) + return {'candidate_id': BASE_CANDIDATE_ID, 'candidate_key': BASE_CANDIDATE_KEY, 'selected_seeds': CANDIDATE_CONFIGS[BASE_CANDIDATE_KEY]['selected_seeds'], 'source_tasks': SOURCE_TASKS, 'tflops': report['summary']['primary_mean'], 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'invalid_performance_reason': report['summary']['invalid_performance_reason'], 'measured_entrypoint': BASE_BENCHMARK_ENTRYPOINT, 'route_entrypoint': BASE_ROUTE_ENTRYPOINT, 'measured_shape_labels': _payload_shape_labels(shape_labels), 'timing_backend': timing_backend, 'denominator': denominator, 'performance_coverage': 'partial' if below_floor else 'pass', 'coverage_only_routes': [], 'hot_bucket_blockers': below_floor, 'flashlib_parity_ledger': {'baseline_ref_scope': 'same_session' if time_flashlib else 'not_available', 'baseline_payload': None, 'speedup_floor': speedup_floor, 'rows_below_1x': below_1x, 'rows_below_floor': below_floor, 'omitted_reason': None if time_flashlib else 'benchmark_time_flashlib=false'}, 'route_trace': route_trace, 'route_trace_included': True, 'contract_summary': report['summary'], 'contract_performance': report['performance'], 'timing_backends': _timing_backends_for_reports(report), 'timing_backend_requested': timing_backend, 'benchmark_correctness_checked': benchmark_correctness, 'benchmark_time_flashlib': time_flashlib, 'report': report} + +def _benchmark_payload(candidate_report: dict[str, Any], baseline_report: dict[str, Any], *, use_cupti: bool, shape_labels, benchmark_correctness: bool, time_flashlib: bool, speedup_floor: float) -> dict[str, Any]: + candidate_metric = candidate_report['summary']['primary_mean'] + baseline_metric = baseline_report['summary']['primary_mean'] + route_trace = _annotate_route_trace(route_trace_for_contract_shapes(shape_labels, candidate_key=CANDIDATE_FP16), candidate_report, baseline_report, candidate_key=CANDIDATE_FP16, speedup_floor=speedup_floor) + below_1x = _below_flashlib_rows(candidate_report, route_trace, floor=1.0) + below_floor = _below_flashlib_rows(candidate_report, route_trace, floor=speedup_floor) + denominator = _denominator_name(shape_labels) + timing_backend = _timing_backend_name(use_cupti) + config = _candidate_config(CANDIDATE_FP16) + metric_delta = candidate_metric - baseline_metric if candidate_metric is not None and baseline_metric is not None else None + return {'candidate_id': config['candidate_id'], 'candidate_key': CANDIDATE_FP16, 'baseline_candidate_id': BASE_CANDIDATE_ID, 'baseline_candidate_key': BASE_CANDIDATE_KEY, 'selected_seeds': config['selected_seeds'], 'source_tasks': SOURCE_TASKS, 'tflops': candidate_metric, 'baseline_9a17_tflops': baseline_metric, 'metric_delta_vs_9a17': metric_delta, 'metric_delta_vs_baseline': metric_delta, 'all_correct': candidate_report['summary']['all_correct'], 'baseline_all_correct': baseline_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'baseline_performance_comparable': baseline_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'baseline_invalid_performance_reason': baseline_report['summary']['invalid_performance_reason'], 'measured_entrypoint': BENCHMARK_ENTRYPOINT, 'baseline_entrypoint': BASE_BENCHMARK_ENTRYPOINT, 'route_entrypoint': ROUTE_ENTRYPOINT, 'measured_shape_labels': _payload_shape_labels(shape_labels), 'timing_backend': timing_backend, 'denominator': denominator, 'selected_route_labels': config['expected_shape_wins'], 'selected_route_rows': _rows_for_labels(candidate_report, TARGET_SHAPES), 'baseline_selected_route_rows': _rows_for_labels(baseline_report, TARGET_SHAPES), 'seed_delta_matrix': _seed_delta_matrix(candidate_report, baseline_report), 'targeted_seed_payload': TARGETED_SEED_PAYLOAD, 'candidate_dispatchers': CANDIDATE_DISPATCHERS, 'selected_candidate_dispatcher': config['candidate_id'], 'guard_plan': config['guard_plan'], 'route_modules': PRODUCTION_ROUTE_MODULES, 'route_trace': route_trace, 'forced_fallback_route_trace': route_trace_for_contract_shapes(shape_labels, candidate_key=CANDIDATE_FP16, force_fallback=True), 'route_trace_included': True, 'contract_summary': candidate_report['summary'], 'baseline_contract_summary': baseline_report['summary'], 'contract_performance': candidate_report['performance'], 'baseline_contract_performance': baseline_report['performance'], 'timing_backends': _timing_backends_for_reports(candidate_report, baseline_report), 'timing_backend_requested': timing_backend, 'use_cupti': use_cupti, 'benchmark_correctness_checked': benchmark_correctness, 'benchmark_time_flashlib': time_flashlib, 'performance_coverage': 'partial' if below_floor else 'pass', 'coverage_only_routes': [], 'hot_bucket_blockers': below_floor, 'flashlib_parity_ledger': {'baseline_ref_scope': 'same_session' if time_flashlib else 'not_available', 'baseline_payload': None, 'speedup_floor': speedup_floor, 'rows_below_1x': below_1x, 'rows_below_floor': below_floor, 'omitted_reason': None if time_flashlib else 'benchmark_time_flashlib=false'}, 'rank_objective': {'metric': 'tflops', 'direction': 'maximize', 'value': candidate_metric, 'baseline_9a17_value': baseline_metric, 'delta_vs_9a17': metric_delta, 'denominator': denominator}, 'report': candidate_report, 'baseline_report': baseline_report} + +def benchmark_candidate_fp16_fd37_full90_v1(*, use_cupti: bool=True, shape_labels=None, baseline_report: dict[str, Any] | None=None, benchmark_correctness: bool=True, time_flashlib: bool=True, speedup_floor: float=1.2) -> dict[str, Any]: + if baseline_report is None: + baseline_report = benchmark_baseline_9a17(use_cupti=use_cupti, shape_labels=shape_labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_fp16_fd37_full90_v1, correctness=benchmark_correctness, time_flashlib=time_flashlib) + return _benchmark_payload(candidate_report, baseline_report, use_cupti=use_cupti, shape_labels=shape_labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib, speedup_floor=speedup_floor) + +def write_benchmark_artifacts(artifact_dir: str | Path, *, use_cupti: bool=True, shape_labels=None, benchmark_correctness: bool=True, time_flashlib: bool=True, speedup_floor: float=1.2) -> dict[str, str]: + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + denom_label = _denominator_label(shape_labels) + denominator = _denominator_name(shape_labels) + timing_backend = _timing_backend_name(use_cupti) + baseline_report = benchmark_baseline_9a17(use_cupti=use_cupti, shape_labels=shape_labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + baseline_payload = _baseline_sidecar(baseline_report, shape_labels=shape_labels, denominator=denominator, timing_backend=timing_backend, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib, speedup_floor=speedup_floor) + candidate_payload = benchmark_candidate_fp16_fd37_full90_v1(use_cupti=use_cupti, shape_labels=shape_labels, baseline_report=baseline_report, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib, speedup_floor=speedup_floor) + baseline_path = out_dir / ''.join([format(denom_label, ''), '_same_session_baseline_9a17_v1.json']) + payload_path = out_dir / ''.join([format(denom_label, ''), '_dispatch_1877_plus_9a17_fp16_fd37_v1.json']) + trace_path = out_dir / ''.join([format(denom_label, ''), '_route_trace_1877_plus_9a17_fp16_fd37_v1.json']) + forced_trace_path = out_dir / ''.join([format(denom_label, ''), '_forced_fallback_trace_1877_plus_9a17_fp16_fd37_v1.json']) + seed_matrix_path = out_dir / ''.join([format(denom_label, ''), '_seed_delta_matrix_1877_plus_9a17_fp16_fd37_v1.json']) + summary_path = out_dir / ''.join([format(denom_label, ''), '_dispatcher_consumption_1877_9a17_fp16_fd37_v1.json']) + artifacts: dict[str, str] = {'same_session_baseline_payload': str(baseline_path), 'candidate_payload': str(payload_path), 'route_trace': str(trace_path), 'forced_fallback_trace': str(forced_trace_path), 'seed_delta_matrix': str(seed_matrix_path), 'dispatcher_consumption': str(summary_path)} + baseline_payload['flashlib_parity_ledger']['baseline_payload'] = str(baseline_path) + candidate_payload['flashlib_parity_ledger']['baseline_payload'] = str(baseline_path) + summary = {'candidate_id': 'dispatcher_consumption_1877_9a17_fp16_fd37_full90_v1', 'measured_entrypoint': ''.join([format(MODULE, ''), ':write_benchmark_artifacts']), 'denominator': denominator, 'timing_backend': timing_backend, 'benchmark_correctness_checked': benchmark_correctness, 'benchmark_time_flashlib': time_flashlib, 'baseline_candidate_key': BASE_CANDIDATE_KEY, 'selected_candidate_key': CANDIDATE_FP16, 'selected_candidate_dispatcher': CANDIDATE_ID, 'candidate_dispatchers': CANDIDATE_DISPATCHERS, 'seed_delta_matrix': candidate_payload['seed_delta_matrix'], 'flashlib_parity_ledger': candidate_payload['flashlib_parity_ledger'], 'full_denominator_ab': {'baseline_payload': str(baseline_path), 'candidate_payload': str(payload_path), 'denominator': denominator, 'timing_backend': timing_backend, 'metric_delta': candidate_payload.get('metric_delta_vs_9a17'), 'route_trace': candidate_payload.get('route_trace', [])}, 'baseline_tflops': baseline_payload.get('tflops'), 'candidate_tflops': candidate_payload.get('tflops'), 'metric_delta_vs_9a17': candidate_payload.get('metric_delta_vs_9a17'), 'metric_delta_vs_baseline': candidate_payload.get('metric_delta_vs_baseline'), 'artifacts': artifacts} + baseline_path.write_text(json.dumps(baseline_payload, indent=2, sort_keys=True) + '\n', encoding='utf-8') + payload_path.write_text(json.dumps(candidate_payload, indent=2, sort_keys=True) + '\n', encoding='utf-8') + trace_path.write_text(json.dumps(candidate_payload['route_trace'], indent=2, sort_keys=True) + '\n', encoding='utf-8') + forced_trace_path.write_text(json.dumps(candidate_payload['forced_fallback_route_trace'], indent=2, sort_keys=True) + '\n', encoding='utf-8') + seed_matrix_path.write_text(json.dumps(candidate_payload['seed_delta_matrix'], indent=2, sort_keys=True) + '\n', encoding='utf-8') + summary_path.write_text(json.dumps(summary, indent=2, sort_keys=True) + '\n', encoding='utf-8') + return artifacts diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_3d97_08ec_0a10_v47.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_3d97_08ec_0a10_v47.py new file mode 100644 index 00000000..555606b5 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_3d97_08ec_0a10_v47.py @@ -0,0 +1,133 @@ +"""Main kNN dispatcher consuming 3d97 RAG and 08ec K20 routes. + +Minimum target architecture: sm_100a. This dispatcher retargets the exported +``knn_build`` path to consume the 3d97 split-64 RAG online/stream route while +using the 08ec same-denominator K20 large/rectangular route selected by the +0a10 dispatcher recheck. Guard misses delegate to the prior 3e08 main +dispatcher chain, so production dispatch remains Weave-only with no external +runtime fallback. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import os +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_ee5e_de1a_weave_evolve_knn_build_3e08_v46 as previous_main +from . import knn_build_k20_large_lowfanout_de1a_v1 as k20_de1a +from . import knn_build_k20_mergeown_08ec_v3 as k20_08ec +from . import knn_build_rag_online_stream_split64_3d97_v1 as rag_3d97 +RAG_TARGET_SHAPES = rag_3d97.TARGET_SHAPES +K20_TARGET_SHAPES = k20_de1a.EXACT_SHAPE_LABELS +RAG_TARGET_SHAPE_SET = set(RAG_TARGET_SHAPES) +K20_TARGET_SHAPE_SET = set(K20_TARGET_SHAPES) +SELECTED_TARGET_SHAPES = RAG_TARGET_SHAPES + K20_TARGET_SHAPES +DISPATCH_CORRECTNESS_SHAPES = ('flashml_correctness_b1_q256_m256_d128_k5', *RAG_TARGET_SHAPES, *K20_TARGET_SHAPES) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_MAIN_0A10_VERIFY_KERNEL') + if verify_kernel == 'rag_split64_stage1_k10': + return rag_3d97.parent_lowk.stage1_ir + if verify_kernel == 'rag_split64_merge_k10_s64_cache': + return rag_3d97.merge_k10_s64_cache_ir + if verify_kernel in {'k20_de1a_stage1', 'k20_stage1'}: + return k20_de1a.parent_v20.stage1_k20_unordered_ir + if verify_kernel == 'k20_de1a_merge_s4': + return k20_de1a.parent_v20.merge_k20_unordered_warp_select_ir + if verify_kernel == 'k20_de1a_merge_s2': + return k20_de1a.merge_k20_s2_warp_select_ir + if verify_kernel == 'k20_08ec_merge_s4': + return k20_08ec.parent_v20.merge_k20_unordered_warp_select_ir + if verify_kernel == 'k20_08ec_merge_s2_warp8': + return k20_08ec.merge_k20_s2_warp8_ir + return previous_main.ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k48over32", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 48]], "cta_group": 1, "threads": 192}')) + +def _label_can_hit(inputs: dict[str, Any], target_labels: set[str]) -> bool: + label = inputs.get('label') + return label is None or str(label) in target_labels + +def _launch_rag_3d97(inputs: dict[str, Any]) -> bool: + if _label_can_hit(inputs, RAG_TARGET_SHAPE_SET) and rag_3d97._eligible_rag_online_stream_split64(inputs): + rag_3d97._launch_rag_online_stream_split64(inputs) + return True + return False + +def _launch_k20_de1a(inputs: dict[str, Any]) -> bool: + if _label_can_hit(inputs, K20_TARGET_SHAPE_SET) and k20_de1a._eligible_k20_large_lowfanout(inputs): + k20_de1a._launch_k20_large_lowfanout(inputs) + return True + return False + +def _launch_k20_08ec(inputs: dict[str, Any]) -> bool: + if _label_can_hit(inputs, K20_TARGET_SHAPE_SET) and k20_08ec._eligible_k20_mergeown(inputs): + k20_08ec._launch_k20_mergeown(inputs) + return True + return False + +def launch_from_contract_inputs(inputs: dict[str, Any]) -> None: + if _launch_rag_3d97(inputs): + return + if _launch_k20_08ec(inputs): + return + previous_main.launch_from_contract_inputs(inputs) + +def launch_from_contract_inputs_k20_de1a_recheck(inputs: dict[str, Any]) -> None: + if _launch_rag_3d97(inputs): + return + if _launch_k20_de1a(inputs): + return + previous_main.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def candidate_k20_de1a_recheck(inputs: dict[str, Any]): + launch_from_contract_inputs_k20_de1a_recheck(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return previous_main._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=DISPATCH_CORRECTNESS_SHAPES, benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint: run real contract correctness for selected shapes.""" + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=None, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + shapes = None if shape_labels is None else _select_contract_shapes(shape_labels) + return evaluate_contract(shapes=shapes, correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def _benchmark_payload(report: dict[str, Any], *, use_cupti: bool, shape_labels, k20_route: str, measured_function: str) -> dict[str, Any]: + rows = report.get('per_shape', {}) + timing_backends = sorted({row.get('timing_backend') for row in rows.values() if row.get('timing_backend') is not None}) + selected_rows = {label: rows.get(label, {}) for label in SELECTED_TARGET_SHAPES if label in rows} + return {'tflops': report['summary']['primary_mean'] or 0.0, 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'invalid_performance_reason': report['summary']['invalid_performance_reason'], 'measured_entrypoint': ''.join(['loom.examples.weave.knn_build_dispatch_3d97_08ec_0a10_v47:', format(measured_function, '')]), 'measured_shape_labels': 'all_canonical' if shape_labels is None else tuple(shape_labels), 'selected_route_labels': SELECTED_TARGET_SHAPES, 'selected_route_rows': selected_rows, 'route_modules': {'rag': 'loom.examples.weave.knn_build_rag_online_stream_split64_3d97_v1', 'k20': 'loom.examples.weave.knn_build_k20_mergeown_08ec_v3' if k20_route == '08ec' else 'loom.examples.weave.knn_build_k20_large_lowfanout_de1a_v1', 'fallback': 'loom.examples.weave.knn_build_dispatch_ee5e_de1a_weave_evolve_knn_build_3e08_v46'}, 'k20_route': k20_route, 'contract_summary': report['summary'], 'contract_performance': report['performance'], 'timing_backends': timing_backends, 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'report': report} + +def benchmark_knn_build_dispatch_3d97_08ec_0a10_v47(*, use_cupti: bool=False, shape_labels=None) -> dict[str, Any]: + """Main v3 contract benchmark hook with 3d97 RAG and 08ec K20 routes.""" + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels) + return _benchmark_payload(report, use_cupti=use_cupti, shape_labels=shape_labels, k20_route='08ec', measured_function='benchmark_knn_build_dispatch_3d97_08ec_0a10_v47') + +def benchmark_knn_build_dispatch_3d97_de1a_recheck_0a10_v47(*, use_cupti: bool=False, shape_labels=None) -> dict[str, Any]: + """A/B benchmark hook: same dispatcher, but K20 exact rows use de1a.""" + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_k20_de1a_recheck) + return _benchmark_payload(report, use_cupti=use_cupti, shape_labels=shape_labels, k20_route='de1a', measured_function='benchmark_knn_build_dispatch_3d97_de1a_recheck_0a10_v47') diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_4247_non128_8199_3d5a_2e8e_full82_synth_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_4247_non128_8199_3d5a_2e8e_full82_synth_v1.py new file mode 100644 index 00000000..4b69d7c8 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_4247_non128_8199_3d5a_2e8e_full82_synth_v1.py @@ -0,0 +1,591 @@ +"""Full82 q16split148/cachedmerge/K96 synthesis dispatcher over 8199 widecombine. + +Minimum target architecture: sm_100a. This opt-in dispatcher candidate starts +from the 7e5d full82 matrix harness and replays sidecar seeds without changing +their schedules: 3f2d q16-only K32 split148, af67 non-D128 cached-merge, and +b6c4 K96 exact-all. Guard misses stay on the 8199-widecombine full82 Weave +dispatcher; no external implementation is on the production route. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +from importlib import import_module +from pathlib import Path +from statistics import median +from typing import Any, Callable +from . import knn_build_dispatch_4247_non128_8199_c2eb_f533_8fcb_8227_full82_matrix_v1 as matrix_7e5d +from . import knn_build_dispatch_4247_non128_8199_widecombine_full82_v1 as baseline_wide +from . import knn_build_dispatch_e3de_9138_bcb3_4247_v1 as base_4247 +from . import knn_build_non128_frontier_3d5a_cachedmerge_v1 as seed_cachedmerge +from . import knn_build_over64_k96_exactall_229a_v1 as seed_k96_exactall +from . import knn_build_rag_microbucket_k32_2e8e_q16split148_v1 as seed_q16split148 +from . import knn_build_rag_microbucket_k32_8fcb_split148_v1 as seed_split148 +from . import knn_build_ragonline_mbucket_4fc7_q1m262_v1 as seed_q1m262 +eval_mod = matrix_7e5d.eval_mod +MODULE = 'loom.examples.weave.knn_build_dispatch_4247_non128_8199_3d5a_2e8e_full82_synth_v1' +BASELINE_ID = 'baseline_8199_widecombine_full82_v1' +BASELINE_ENTRYPOINT = matrix_7e5d.BASELINE_ENTRYPOINT +SEED_SPLIT148_7E5D_ID = seed_split148.SEED_K32_8FCB_SPLIT148_ID +SEED_Q16SPLIT148_3F2D_ID = seed_q16split148.SEED_K32_2E8E_Q16_SPLIT148_ID +SEED_CACHEDMERGE_AF67_ID = 'non128_frontier_3d5a_cachedmerge_v1' +SEED_K96_EXACTALL_B6C4_ID = 'over64_k96_exactall_229a_v1_b6c4' +SEED_MIDK_E080_ID = 'knn_build_midk_k11k13_e080_v1' +SEED_Q1M262_980C_ID = 'ragonline_mbucket_4fc7_q1m262_v1_980c' +ROUTE_SPLIT148_7E5D_ENTRYPOINT = ''.join([format(seed_split148.MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_Q16SPLIT148_3F2D_ENTRYPOINT = ''.join([format(seed_q16split148.MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_CACHEDMERGE_AF67_ENTRYPOINT = ''.join([format(seed_cachedmerge.MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_K96_EXACTALL_B6C4_ENTRYPOINT = ''.join([format(seed_k96_exactall.__name__, ''), ':launch_from_contract_inputs']) +ROUTE_MIDK_E080_ENTRYPOINT = 'loom.examples.weave.knn_build_midk_k11k13_e080_v1:launch_from_contract_inputs' +ROUTE_Q1M262_980C_ENTRYPOINT = ''.join([format(seed_q1m262.MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_BASELINE_ENTRYPOINT = ''.join([format(baseline_wide.MODULE, ''), ':launch_from_contract_inputs']) +CACHEDMERGE_TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_dim_sweep_b1_q1024_m1024_d96_k10", "build_dim_sweep_b1_q2048_m2048_d192_k10", "build_highd_b1_q1024_m1024_d320_k10", "search_rect_highd_b1_q512_m12000_d320_k10", "rag_microbatch_highd_b1_q16_m50000_d768_k10"]}')) +K32_TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["rag_microbatch_largek_b1_q8_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m100000_d128_k32", "rag_microbatch_largek_b1_q32_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m131071_d128_k32"]}')) +K96_EXACTALL_TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_over64_stress_qm1024_k96", "build_over64_stress_qm2048_k96", "build_over64_stress_qm4096_k96"]}')) +MIDK_E080_TARGET_SHAPES = ('build_k_sweep_qm2048_k11', 'build_k_sweep_qm2048_k12', 'build_k_sweep_qm2048_k13', 'build_k_sweep_qm4096_k13') +Q1M262_TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["rag_online_b1_q1_m100000_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "rag_online_irregular_b1_q1_m262143_d128_k10"]}')) +Q16_CACHEDMERGE_TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_dim_sweep_b1_q1024_m1024_d96_k10", "build_dim_sweep_b1_q2048_m2048_d192_k10", "build_highd_b1_q1024_m1024_d320_k10", "search_rect_highd_b1_q512_m12000_d320_k10", "rag_microbatch_highd_b1_q16_m50000_d768_k10", "rag_microbatch_largek_b1_q8_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m100000_d128_k32", "rag_microbatch_largek_b1_q32_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m131071_d128_k32"]}')) +ABEE_TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_dim_sweep_b1_q1024_m1024_d96_k10", "build_dim_sweep_b1_q2048_m2048_d192_k10", "build_highd_b1_q1024_m1024_d320_k10", "search_rect_highd_b1_q512_m12000_d320_k10", "rag_microbatch_highd_b1_q16_m50000_d768_k10", "rag_microbatch_largek_b1_q8_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m100000_d128_k32", "rag_microbatch_largek_b1_q32_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m131071_d128_k32", "build_over64_stress_qm1024_k96", "build_over64_stress_qm2048_k96", "build_over64_stress_qm4096_k96"]}')) +TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_dim_sweep_b1_q1024_m1024_d96_k10", "build_dim_sweep_b1_q2048_m2048_d192_k10", "build_highd_b1_q1024_m1024_d320_k10", "search_rect_highd_b1_q512_m12000_d320_k10", "rag_microbatch_highd_b1_q16_m50000_d768_k10", "rag_microbatch_largek_b1_q8_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m100000_d128_k32", "rag_microbatch_largek_b1_q32_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m131071_d128_k32", "build_over64_stress_qm1024_k96", "build_over64_stress_qm2048_k96", "build_over64_stress_qm4096_k96", "build_k_sweep_qm2048_k11", "build_k_sweep_qm2048_k12", "build_k_sweep_qm2048_k13", "build_k_sweep_qm4096_k13", "rag_online_b1_q1_m100000_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "rag_online_irregular_b1_q1_m262143_d128_k10"]}')) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +SELECTED_TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_large_b1_q8192_m8192_d128_k20", "build_large_b1_q8192_m8192_d128_k32", "build_over64_stress_qm2048_k96", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32", "rag_batch_b2_q256_m50000_d128_k10", "rag_irregular_b1_q512_m131071_d128_k10", "search_rect_b1_q1024_m8192_d128_k10", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q2048_m2048_d256_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10", "search_rect_b1_q2048_m32768_d128_k10", "build_large_tail_b1_q6144_m6144_d128_k20", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "build_over32_stress_qm2048_k64", "build_over32_stress_qm4096_k64", "build_k_sweep_qm512_k1", "build_k_sweep_qm512_k2", "build_k_sweep_qm1024_k16", "build_k_sweep_qm512_k4", "build_k_sweep_qm512_k5", "build_k_sweep_qm512_k6", "rag_microbatch_b1_q8_m100000_d128_k10", "rag_microbatch_b1_q32_m100000_d128_k10", "build_dim_sweep_b1_q1024_m1024_d64_k10", "build_dim_sweep_b1_q4096_m4096_d64_k10", "search_rect_b1_q1024_m32768_d64_k10", "rag_online_b1_q1_m100000_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "build_over64_stress_qm1024_k96", "build_over64_stress_qm4096_k96", "build_dim_sweep_b1_q1024_m1024_d96_k10", "build_dim_sweep_b1_q2048_m2048_d192_k10", "build_highd_b1_q1024_m1024_d320_k10", "search_rect_highd_b1_q512_m12000_d320_k10", "rag_microbatch_highd_b1_q16_m50000_d768_k10", "rag_microbatch_largek_b1_q8_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m100000_d128_k32", "rag_microbatch_largek_b1_q32_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m131071_d128_k32", "build_k_sweep_qm2048_k11", "build_k_sweep_qm2048_k12", "build_k_sweep_qm2048_k13", "build_k_sweep_qm4096_k13", "rag_online_irregular_b1_q1_m262143_d128_k10"]}')) +GUARD_MISS_AUDIT_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_dim_sweep_b1_q1024_m1024_d96_k10", "build_dim_sweep_b1_q2048_m2048_d192_k10", "build_highd_b1_q1024_m1024_d320_k10", "search_rect_highd_b1_q512_m12000_d320_k10", "rag_microbatch_highd_b1_q16_m50000_d768_k10", "rag_microbatch_largek_b1_q8_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m100000_d128_k32", "rag_microbatch_largek_b1_q32_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m131071_d128_k32", "build_over64_stress_qm1024_k96", "build_over64_stress_qm2048_k96", "build_over64_stress_qm4096_k96", "build_k_sweep_qm2048_k11", "build_k_sweep_qm2048_k12", "build_k_sweep_qm2048_k13", "build_k_sweep_qm4096_k13", "rag_online_b1_q1_m100000_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "rag_online_irregular_b1_q1_m262143_d128_k10", "build_dim_sweep_b1_q1024_m1024_d64_k10", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q4096_m4096_d64_k10", "search_rect_b1_q1024_m32768_d64_k10", "build_k_sweep_qm512_k4", "build_k_sweep_qm512_k8", "build_k_sweep_qm512_k10", "rag_microbatch_b1_q8_m100000_d128_k10", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_microbatch_b1_q32_m100000_d128_k10", "build_large_b1_q8192_m8192_d128_k20", "build_qm2048_d128_k10", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "flashml_correctness_b1_q256_m256_d128_k5", "build_over32_stress_qm2048_k64", "search_rect_over32_b1_q2048_m65536_d128_k64", "rag_offline_large_m_over32_b1_q2048_m250000_d128_k64", "build_k_sweep_qm512_k5", "build_over32_stress_qm4096_k64"]}')) +DISPATCH_CORRECTNESS_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["flashml_correctness_b1_q256_m256_d128_k5", "build_dim_sweep_b1_q1024_m1024_d96_k10", "build_dim_sweep_b1_q2048_m2048_d192_k10", "build_highd_b1_q1024_m1024_d320_k10", "search_rect_highd_b1_q512_m12000_d320_k10", "rag_microbatch_highd_b1_q16_m50000_d768_k10", "rag_microbatch_largek_b1_q8_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m100000_d128_k32", "rag_microbatch_largek_b1_q32_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m131071_d128_k32", "build_over64_stress_qm1024_k96", "build_over64_stress_qm2048_k96", "build_over64_stress_qm4096_k96", "build_k_sweep_qm2048_k11", "build_k_sweep_qm2048_k12", "build_k_sweep_qm2048_k13", "build_k_sweep_qm4096_k13", "rag_online_b1_q1_m100000_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "rag_online_irregular_b1_q1_m262143_d128_k10", "build_large_b1_q8192_m8192_d128_k20", "build_large_b1_q8192_m8192_d128_k32", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32", "rag_batch_b2_q256_m50000_d128_k10", "rag_irregular_b1_q512_m131071_d128_k10", "search_rect_b1_q1024_m8192_d128_k10", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q2048_m2048_d256_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10", "search_rect_b1_q2048_m32768_d128_k10", "build_large_tail_b1_q6144_m6144_d128_k20", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "build_over32_stress_qm2048_k64", "build_over32_stress_qm4096_k64", "build_k_sweep_qm512_k1", "build_k_sweep_qm512_k2", "build_k_sweep_qm1024_k16", "build_k_sweep_qm512_k4", "build_k_sweep_qm512_k5", "build_k_sweep_qm512_k6", "rag_microbatch_b1_q8_m100000_d128_k10", "rag_microbatch_b1_q32_m100000_d128_k10", "build_dim_sweep_b1_q1024_m1024_d64_k10", "build_dim_sweep_b1_q4096_m4096_d64_k10", "search_rect_b1_q1024_m32768_d64_k10", "build_k_sweep_qm512_k8", "build_k_sweep_qm512_k10", "build_qm2048_d128_k10", "search_rect_over32_b1_q2048_m65536_d128_k64", "rag_offline_large_m_over32_b1_q2048_m250000_d128_k64", "rag_stream_b1_q128_m100000_d128_k10", "search_rect_b1_q4096_m65536_d128_k20", "rag_offline_largek_b1_q4096_m100000_d128_k20", "rag_offline_large_m_b1_q8192_m250000_d128_k20"]}')) +SOURCE_TASKS = {SEED_SPLIT148_7E5D_ID: 'generalize-auto-tuning-knn-build-7e5d / design_doc/active/generalize_auto_tuning_knn_build_round_106_7e5d.md', SEED_Q16SPLIT148_3F2D_ID: 'weave-evolve-knn-build-3f2d / design_doc/active/weave_evolve_knn_build_round_106_2e8e_q16split148.md', SEED_CACHEDMERGE_AF67_ID: 'weave-evolve-knn-build-af67 / design_doc/active/weave_evolve_knn_build_round_103_3d5a_cachedmerge.md', SEED_K96_EXACTALL_B6C4_ID: 'weave-evolve-knn-build-229a / design_doc/active/weave_evolve_knn_build_round_45_229a_k96exactall.md; same-session K96 audit in design_doc/active/generalize_auto_tuning_knn_build_round_107_adbd.md', SEED_MIDK_E080_ID: 'weave-evolve-knn-build-e080 / design_doc/active/weave_evolve_knn_build_round_108_e080_midk_k11k13.md', SEED_Q1M262_980C_ID: 'weave-evolve-knn-build-980c / design_doc/active/weave_evolve_knn_build_round_108_4fc7_q1m262.md'} +_CONTRACT_PARAMS_BY_LABEL = _decode_capture(_json_loads('{"__dict_items__": [["flashml_correctness_b1_q256_m256_d128_k5", {"__dict_items__": [["B", 1], ["Q", 256], ["M", 256], ["D", 128], ["K", 5], ["dtype", "bfloat16"], ["seed", 606001], ["build", true], ["check_correctness", true], ["correctness_query_sample", 256], ["recall_min", 0.99], ["benchmark", true], ["time_flashlib", true]]}], ["build_k_sweep_qm512_k1", {"__dict_items__": [["B", 1], ["Q", 512], ["M", 512], ["D", 128], ["K", 1], ["dtype", "bfloat16"], ["seed", 606049], ["build", true], ["check_correctness", true], ["correctness_query_sample", 512], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true]]}], ["build_k_sweep_qm512_k2", {"__dict_items__": [["B", 1], ["Q", 512], ["M", 512], ["D", 128], ["K", 2], ["dtype", "bfloat16"], ["seed", 606050], ["build", true], ["check_correctness", true], ["correctness_query_sample", 512], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true]]}], ["build_k_sweep_qm512_k4", {"__dict_items__": [["B", 1], ["Q", 512], ["M", 512], ["D", 128], ["K", 4], ["dtype", "bfloat16"], ["seed", 606052], ["build", true], ["check_correctness", true], ["correctness_query_sample", 512], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true], ["diagnostic_class", "low_k_q512_k5_neighborhood"]]}], ["build_k_sweep_qm512_k5", {"__dict_items__": [["B", 1], ["Q", 512], ["M", 512], ["D", 128], ["K", 5], ["dtype", "bfloat16"], ["seed", 606053], ["build", true], ["check_correctness", true], ["correctness_query_sample", 512], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true]]}], ["build_k_sweep_qm512_k6", {"__dict_items__": [["B", 1], ["Q", 512], ["M", 512], ["D", 128], ["K", 6], ["dtype", "bfloat16"], ["seed", 606054], ["build", true], ["check_correctness", true], ["correctness_query_sample", 512], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true], ["diagnostic_class", "low_k_q512_k5_neighborhood"]]}], ["build_k_sweep_qm512_k8", {"__dict_items__": [["B", 1], ["Q", 512], ["M", 512], ["D", 128], ["K", 8], ["dtype", "bfloat16"], ["seed", 606056], ["build", true], ["check_correctness", true], ["correctness_query_sample", 512], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true]]}], ["build_k_sweep_qm512_k10", {"__dict_items__": [["B", 1], ["Q", 512], ["M", 512], ["D", 128], ["K", 10], ["dtype", "bfloat16"], ["seed", 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["diagnostic_class", "over64_topk_bottleneck"]]}], ["build_over64_stress_qm4096_k96", {"__dict_items__": [["B", 1], ["Q", 4096], ["M", 4096], ["D", 128], ["K", 96], ["dtype", "bfloat16"], ["seed", 609496], ["build", true], ["check_correctness", true], ["correctness_query_sample", 256], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true], ["diagnostic_class", "over64_topk_bottleneck"]]}], ["rag_online_common_d64_b1_q1_m262143_k10", {"__dict_items__": [["B", 1], ["Q", 1], ["M", 262143], ["D", 64], ["K", 10], ["dtype", "bfloat16"], ["seed", 615064], ["build", false], ["check_correctness", true], ["correctness_query_sample", 1], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true], ["diagnostic_class", "v12_common_d_rag_online_irregular"]]}], ["rag_microbatch_common_d64_b1_q4_m100000_k10", {"__dict_items__": [["B", 1], ["Q", 4], ["M", 100000], ["D", 64], ["K", 10], ["dtype", "bfloat16"], ["seed", 615164], ["build", false], ["check_correctness", true], ["correctness_query_sample", 4], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true], ["diagnostic_class", "v12_common_d_rag_microbatch_tail"]]}], ["rag_microbatch_common_d256_b1_q4_m100000_k10", {"__dict_items__": [["B", 1], ["Q", 4], ["M", 100000], ["D", 256], ["K", 10], ["dtype", "bfloat16"], ["seed", 615256], ["build", false], ["check_correctness", true], ["correctness_query_sample", 4], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true], ["diagnostic_class", "v12_common_d_rag_microbatch_tail"]]}], ["rag_stream_common_d256_b1_q128_m100000_k10", {"__dict_items__": [["B", 1], ["Q", 128], ["M", 100000], ["D", 256], ["K", 10], ["dtype", "bfloat16"], ["seed", 615356], ["build", false], ["check_correctness", true], ["correctness_query_sample", 128], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true], ["diagnostic_class", "v12_common_d_rag_streaming"]]}], ["rag_microbatch_common_d768_b1_q8_m100000_k10", {"__dict_items__": [["B", 1], ["Q", 8], ["M", 100000], ["D", 768], ["K", 10], ["dtype", "bfloat16"], ["seed", 615768], ["build", false], ["check_correctness", true], ["correctness_query_sample", 8], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true], ["diagnostic_class", "v12_common_d_rag_microbatch_tail"]]}], ["rag_microbatch_common_d1024_b1_q4_m100000_k10", {"__dict_items__": [["B", 1], ["Q", 4], ["M", 100000], ["D", 1024], ["K", 10], ["dtype", "bfloat16"], ["seed", 616024], ["build", false], ["check_correctness", true], ["correctness_query_sample", 4], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true], ["diagnostic_class", "v12_common_d_rag_microbatch_tail"]]}], ["rag_online_common_d4096_b1_q1_m65536_k10", {"__dict_items__": [["B", 1], ["Q", 1], ["M", 65536], ["D", 4096], ["K", 10], ["dtype", "bfloat16"], ["seed", 616096], ["build", false], ["check_correctness", true], ["correctness_query_sample", 1], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true], ["diagnostic_class", "v12_common_d_rag_online_highd"]]}], ["search_rect_common_d1024_b1_q256_m8192_k10", {"__dict_items__": [["B", 1], ["Q", 256], ["M", 8192], ["D", 1024], ["K", 10], ["dtype", "bfloat16"], ["seed", 616124], ["build", false], ["check_correctness", true], ["correctness_query_sample", 256], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true], ["diagnostic_class", "v12_common_d_rectangular_search"]]}], ["search_rect_common_d4096_b1_q128_m4096_k10", {"__dict_items__": [["B", 1], ["Q", 128], ["M", 4096], ["D", 4096], ["K", 10], ["dtype", "bfloat16"], ["seed", 616496], ["build", false], ["check_correctness", true], ["correctness_query_sample", 128], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true], ["diagnostic_class", "v12_common_d_rectangular_search"]]}], ["rag_microbatch_largek_common_d256_b1_q8_m100000_k32", {"__dict_items__": [["B", 1], ["Q", 8], ["M", 100000], ["D", 256], ["K", 32], ["dtype", "bfloat16"], ["seed", 616332], ["build", false], ["check_correctness", true], ["correctness_query_sample", 8], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true], ["diagnostic_class", "v12_common_d_large_k_rag"]]}], ["rag_stream_largek_common_d256_b1_q128_m100000_k32", {"__dict_items__": [["B", 1], ["Q", 128], ["M", 100000], ["D", 256], ["K", 32], ["dtype", "bfloat16"], ["seed", 616432], ["build", false], ["check_correctness", true], ["correctness_query_sample", 128], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true], ["diagnostic_class", "v12_common_d_large_k_rag"]]}], ["rag_microbatch_over32_d128_b1_q16_m100000_k48", {"__dict_items__": [["B", 1], ["Q", 16], ["M", 100000], ["D", 128], ["K", 48], ["dtype", "bfloat16"], ["seed", 616548], ["build", false], ["check_correctness", true], ["correctness_query_sample", 16], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true], ["diagnostic_class", "v12_rag_over32_topk"]]}]]}')) +_K32_SHAPE_SPECS = {label: _CONTRACT_PARAMS_BY_LABEL[label] for label in K32_TARGET_SHAPES} +_K96_EXACTALL_SHAPE_SPECS = {label: _CONTRACT_PARAMS_BY_LABEL[label] for label in K96_EXACTALL_TARGET_SHAPES} +_MIDK_E080_SHAPE_SPECS = {label: _CONTRACT_PARAMS_BY_LABEL[label] for label in MIDK_E080_TARGET_SHAPES} +_Q1M262_SHAPE_SPECS = {label: _CONTRACT_PARAMS_BY_LABEL[label] for label in Q1M262_TARGET_SHAPES} +TARGETED_SEED_ROWS_BY_SEED = _decode_capture(_json_loads('{"__dict_items__": [["rag_microbucket_k32_8fcb_split148_v1_b3e0_sm148", {"__dict_items__": [["rag_microbatch_largek_b1_q8_m100000_d128_k32", {"__dict_items__": [["kernel_ms", 0.077121], ["flashlib_ms", 0.107232], ["ratio_vs_flashlib", 1.3904384019916753], ["tflops", 2.655567225528715]]}], ["rag_microbatch_largek_b1_q16_m100000_d128_k32", {"__dict_items__": [["kernel_ms", 0.135552], ["flashlib_ms", 0.134017], ["ratio_vs_flashlib", 0.988675932483475], ["tflops", 3.021718602455146]]}], ["rag_microbatch_largek_b1_q32_m100000_d128_k32", {"__dict_items__": [["kernel_ms", 0.141312], ["flashlib_ms", 0.159264], ["ratio_vs_flashlib", 1.1270380434782608], ["tflops", 5.797101449275363]]}], ["rag_microbatch_largek_b1_q16_m131071_d128_k32", {"__dict_items__": [["kernel_ms", 0.163168], ["flashlib_ms", 0.157824], ["ratio_vs_flashlib", 0.967248480094136], ["tflops", 3.2902702490684446]]}]]}], ["rag_microbucket_k32_2e8e_q16split148_v1_b3e0_q16_s148", {"__dict_items__": [["rag_microbatch_largek_b1_q16_m100000_d128_k32", {"__dict_items__": [["kernel_ms", 0.13584], ["flashlib_ms", 0.133824], ["ratio_vs_flashlib", 0.9851590106007068], ["tflops", 3.0153121319199063]]}], ["rag_microbatch_largek_b1_q16_m131071_d128_k32", {"__dict_items__": [["kernel_ms", 0.161441], ["flashlib_ms", 0.157568], ["ratio_vs_flashlib", 0.9760098116339716], ["tflops", 3.3254676073612033]]}], ["rag_microbatch_largek_b1_q32_m100000_d128_k32", {"__dict_items__": [["kernel_ms", 0.143809], ["flashlib_ms", 0.158624], ["ratio_vs_flashlib", 1.1030185871537943], ["tflops", 5.696444589698837]]}], ["rag_microbatch_largek_b1_q8_m100000_d128_k32", {"__dict_items__": [["kernel_ms", 0.074336], ["flashlib_ms", 0.106624], ["ratio_vs_flashlib", 1.4343521308652605], ["tflops", 2.755058114507103]]}]]}], ["non128_frontier_3d5a_cachedmerge_v1", {"__dict_items__": [["build_dim_sweep_b1_q1024_m1024_d96_k10", {"__dict_items__": [["kernel_ms", 0.032609], ["flashlib_ms", 0.06852849999999999], ["ratio_vs_flashlib", 2.1015210524701766], ["tflops", 6.173957864393266]]}], ["build_dim_sweep_b1_q2048_m2048_d192_k10", {"__dict_items__": [["kernel_ms", 0.07648], ["flashlib_ms", 0.11584], ["ratio_vs_flashlib", 1.514644351464435], ["tflops", 21.059266945606694]]}], ["build_highd_b1_q1024_m1024_d320_k10", {"__dict_items__": [["kernel_ms", 0.033536], ["flashlib_ms", 0.074528], ["ratio_vs_flashlib", 2.222328244274809], ["tflops", 20.01099236641221]]}], ["rag_microbatch_highd_b1_q16_m50000_d768_k10", {"__dict_items__": [["kernel_ms", 0.094592], ["flashlib_ms", 0.166849], ["ratio_vs_flashlib", 1.7638806664411368], ["tflops", 12.990527740189446]]}], ["search_rect_highd_b1_q512_m12000_d320_k10", {"__dict_items__": [["kernel_ms", 0.115488], ["flashlib_ms", 0.150881], ["ratio_vs_flashlib", 1.3064647409254642], ["tflops", 34.04821280133001]]}]]}], ["over64_k96_exactall_229a_v1_b6c4", {"__dict_items__": [["build_over64_stress_qm1024_k96", {"__dict_items__": [["kernel_ms", 0.2179045], ["flashlib_ms", 0.27504], ["ratio_vs_flashlib", 1.2622043142752903], ["tflops", 1.2318949631604672]]}], ["build_over64_stress_qm2048_k96", {"__dict_items__": [["kernel_ms", 0.351521], ["flashlib_ms", 0.679856], ["ratio_vs_flashlib", 1.9339072203367653], ["tflops", 3.0545595398283463]]}], ["build_over64_stress_qm4096_k96", {"__dict_items__": [["kernel_ms", 0.46208], ["flashlib_ms", 1.218385], ["ratio_vs_flashlib", 2.6367403912742384], ["tflops", 9.294856509695292]]}]]}], ["knn_build_midk_k11k13_e080_v1", {"__dict_items__": [["build_k_sweep_qm2048_k11", {"__dict_items__": [["kernel_ms", 0.052992], ["flashlib_ms", 0.074752], ["ratio_vs_flashlib", 1.4106280193236715], ["tflops", 20.260823520531403]]}], ["build_k_sweep_qm2048_k12", {"__dict_items__": [["kernel_ms", 0.054848], ["flashlib_ms", 0.07648], ["ratio_vs_flashlib", 1.3943990665110853], ["tflops", 19.574983591014274]]}], ["build_k_sweep_qm2048_k13", {"__dict_items__": [["kernel_ms", 0.059103], ["flashlib_ms", 0.081088], ["ratio_vs_flashlib", 1.371977733786779], ["tflops", 18.165433779570755]]}], ["build_k_sweep_qm4096_k13", {"__dict_items__": [["kernel_ms", 0.143616], ["flashlib_ms", 0.1635205], ["ratio_vs_flashlib", 1.1385952818627452], ["tflops", 29.911229767713895]]}]]}], ["ragonline_mbucket_4fc7_q1m262_v1_980c", {"__dict_items__": [["rag_online_b1_q1_m100000_d128_k10", {"__dict_items__": [["kernel_ms", 0.057119], ["flashlib_ms", 0.065024], ["ratio_vs_flashlib", 1.1383952800294121], ["tflops", 0.4481871181218159]]}], ["rag_online_irregular_b1_q1_m131071_d128_k10", {"__dict_items__": [["kernel_ms", 0.068832], ["flashlib_ms", 0.086496], ["ratio_vs_flashlib", 1.2566248256624826], ["tflops", 0.48747931194793115]]}], ["rag_online_large_m_b1_q1_m250000_d128_k10", {"__dict_items__": [["kernel_ms", 0.105824], ["flashlib_ms", 0.116736], ["ratio_vs_flashlib", 1.103114605382522], ["tflops", 0.6047777441790142]]}], ["rag_online_irregular_b1_q1_m262143_d128_k10", {"__dict_items__": [["kernel_ms", 0.110944], ["flashlib_ms", 0.091584], ["ratio_vs_flashlib", 0.8254975483126622], ["tflops", 0.6048872223824632]]}]]}]]}')) +PRODUCTION_ROUTE_MODULES = {**baseline_wide.PRODUCTION_ROUTE_MODULES, SEED_SPLIT148_7E5D_ID: ROUTE_SPLIT148_7E5D_ENTRYPOINT, SEED_Q16SPLIT148_3F2D_ID: ROUTE_Q16SPLIT148_3F2D_ENTRYPOINT, SEED_CACHEDMERGE_AF67_ID: ROUTE_CACHEDMERGE_AF67_ENTRYPOINT, SEED_K96_EXACTALL_B6C4_ID: ROUTE_K96_EXACTALL_B6C4_ENTRYPOINT, SEED_MIDK_E080_ID: ROUTE_MIDK_E080_ENTRYPOINT, SEED_Q1M262_980C_ID: ROUTE_Q1M262_980C_ENTRYPOINT, BASELINE_ID: ROUTE_BASELINE_ENTRYPOINT} +CANDIDATE_DISPATCHERS = ({'id': BASELINE_ID, 'entrypoint': BASELINE_ENTRYPOINT, 'consumed_seeds': (), 'guard_plan': ('8199 widecombine exact non-D128 guards', 'then exported 4247 guard stack'), 'expected_shape_wins': baseline_wide.TARGET_SHAPES, 'fallback': baseline_wide.ROUTE_BASE_4247_ENTRYPOINT, 'rejected_reason': 'same-session full82 baseline'}, {'id': 'candidate_split148_k32_overlay_7e5d_over_8199_full82_v1', 'entrypoint': ''.join([format(MODULE, ''), ':candidate_split148_k32_overlay_7e5d']), 'consumed_seeds': (SEED_SPLIT148_7E5D_ID,), 'guard_plan': ('7e5d all-K32 split148 exact guards', 'then 8199-widecombine fallback'), 'expected_shape_wins': K32_TARGET_SHAPES, 'fallback': ROUTE_BASELINE_ENTRYPOINT, 'rejected_reason': 'comparison candidate for q16split148 replay'}, {'id': 'candidate_q16split148_k32_overlay_3f2d_over_8199_full82_v1', 'entrypoint': ''.join([format(MODULE, ''), ':candidate_q16split148_k32_overlay_3f2d']), 'consumed_seeds': (SEED_Q16SPLIT148_3F2D_ID,), 'guard_plan': ('3f2d K32 q16-only split148 exact guards', 'then 8199-widecombine fallback'), 'expected_shape_wins': K32_TARGET_SHAPES, 'fallback': ROUTE_BASELINE_ENTRYPOINT, 'rejected_reason': 'matrix candidate; promotion depends on repeated full82 A/B'}, {'id': 'candidate_cachedmerge_non128_overlay_af67_over_8199_full82_v1', 'entrypoint': ''.join([format(MODULE, ''), ':candidate_cachedmerge_non128_overlay_af67']), 'consumed_seeds': (SEED_CACHEDMERGE_AF67_ID,), 'guard_plan': ('af67 exact non-D128 cachedmerge guards', 'then 8199-widecombine fallback'), 'expected_shape_wins': CACHEDMERGE_TARGET_SHAPES, 'fallback': ROUTE_BASELINE_ENTRYPOINT, 'rejected_reason': 'matrix candidate; promotion depends on repeated full82 A/B'}, {'id': 'candidate_q16split148_plus_cachedmerge_over_8199_full82_v1', 'entrypoint': ''.join([format(MODULE, ''), ':candidate_q16split148_plus_cachedmerge']), 'consumed_seeds': (SEED_CACHEDMERGE_AF67_ID, SEED_Q16SPLIT148_3F2D_ID), 'guard_plan': ('af67 exact non-D128 cachedmerge guards', '3f2d K32 q16-only split148 exact guards', 'then 8199-widecombine fallback'), 'expected_shape_wins': Q16_CACHEDMERGE_TARGET_SHAPES, 'fallback': ROUTE_BASELINE_ENTRYPOINT, 'rejected_reason': 'selected synthesis candidate only if repeated full82 A/B wins'}, {'id': 'candidate_q16split148_cachedmerge_k96exactall_over_8199_full82_v1', 'entrypoint': ''.join([format(MODULE, ''), ':candidate_q16split148_cachedmerge_k96exactall']), 'consumed_seeds': (SEED_CACHEDMERGE_AF67_ID, SEED_Q16SPLIT148_3F2D_ID, SEED_K96_EXACTALL_B6C4_ID), 'guard_plan': ('af67 exact non-D128 cachedmerge guards', '3f2d K32 q16-only split148 exact guards', 'b6c4 exact BF16 build B=1 Q=M in {1024,2048,4096} D=128 K=96 guards', 'then 8199-widecombine fallback'), 'expected_shape_wins': ABEE_TARGET_SHAPES, 'fallback': ROUTE_BASELINE_ENTRYPOINT, 'rejected_reason': 'selected dispatcher-consumption candidate for K96 exact-all b6c4'}, {'id': 'candidate_q16split148_cachedmerge_k96exactall_e080_q1m262_over_8199_full82_v1', 'entrypoint': ''.join([format(MODULE, ''), ':candidate_q16split148_cachedmerge_k96exactall_e080_q1m262']), 'consumed_seeds': (SEED_CACHEDMERGE_AF67_ID, SEED_Q16SPLIT148_3F2D_ID, SEED_K96_EXACTALL_B6C4_ID, SEED_MIDK_E080_ID, SEED_Q1M262_980C_ID), 'guard_plan': ('af67 exact non-D128 cachedmerge guards', '3f2d K32 q16-only split148 exact guards', 'b6c4 exact BF16 build B=1 Q=M in {1024,2048,4096} D=128 K=96 guards', 'e080 exact BF16 build B=1 Q=M in {2048,4096} D=128 K in {11,12,13} guards', '980c exact BF16 online Q=1 D=128 K=10 M in {100000,131071,250000,262143} guards', 'then 8199-widecombine fallback'), 'expected_shape_wins': TARGET_SHAPES, 'fallback': ROUTE_BASELINE_ENTRYPOINT, 'rejected_reason': 'selected dispatcher-consumption candidate for e080 plus 980c over abee'}) +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k48over32", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 48]], "cta_group": 1, "threads": 192}')) + +def _seed_midk_e080(): + return _import_dispatch_module('knn_build_midk_k11k13_e080_v1') + +def _dtype_name(inputs: dict[str, Any]) -> str: + return matrix_7e5d._dtype_name(inputs) + +def _matches_contract_spec(inputs: dict[str, Any], spec: dict[str, Any]) -> bool: + return matrix_7e5d._matches_contract_spec(inputs, spec) + +def _cachedmerge_label_for_inputs(inputs: dict[str, Any]) -> str | None: + label = seed_cachedmerge._target_label_for_inputs(inputs) + if label in CACHEDMERGE_TARGET_SHAPES: + return label + return None + +def _k32_label_for_inputs(inputs: dict[str, Any]) -> str | None: + label = inputs.get('label') + if label is not None: + spec = _K32_SHAPE_SPECS.get(str(label)) + if spec is not None and _matches_contract_spec(inputs, spec): + return str(label) + for candidate_label, spec in _K32_SHAPE_SPECS.items(): + if _matches_contract_spec(inputs, spec): + return candidate_label + return None + +def _k96_exactall_label_for_inputs(inputs: dict[str, Any]) -> str | None: + label = inputs.get('label') + if label is not None: + spec = _K96_EXACTALL_SHAPE_SPECS.get(str(label)) + if spec is not None and _matches_contract_spec(inputs, spec): + return str(label) + for candidate_label, spec in _K96_EXACTALL_SHAPE_SPECS.items(): + if _matches_contract_spec(inputs, spec): + return candidate_label + return None + +def _midk_e080_label_for_inputs(inputs: dict[str, Any]) -> str | None: + label = inputs.get('label') + if label is not None: + spec = _MIDK_E080_SHAPE_SPECS.get(str(label)) + if spec is not None and _matches_contract_spec(inputs, spec): + return str(label) + for candidate_label, spec in _MIDK_E080_SHAPE_SPECS.items(): + if _matches_contract_spec(inputs, spec): + return candidate_label + return None + +def _q1m262_label_for_inputs(inputs: dict[str, Any]) -> str | None: + label = inputs.get('label') + if label is not None: + spec = _Q1M262_SHAPE_SPECS.get(str(label)) + if spec is not None and _matches_contract_spec(inputs, spec): + return str(label) + for candidate_label, spec in _Q1M262_SHAPE_SPECS.items(): + if _matches_contract_spec(inputs, spec): + return candidate_label + return None + +def _selected_seed_for_inputs(inputs: dict[str, Any], *, enable_cachedmerge: bool=True, enable_q16split148: bool=True, enable_split148_7e5d: bool=False, enable_k96_exactall: bool=True, enable_midk_e080: bool=True, enable_q1m262: bool=True) -> tuple[str | None, str | None]: + if enable_cachedmerge: + label = _cachedmerge_label_for_inputs(inputs) + if label is not None: + return (SEED_CACHEDMERGE_AF67_ID, label) + if enable_q16split148: + label = _k32_label_for_inputs(inputs) + if label is not None: + return (SEED_Q16SPLIT148_3F2D_ID, label) + if enable_split148_7e5d: + label = _k32_label_for_inputs(inputs) + if label is not None: + return (SEED_SPLIT148_7E5D_ID, label) + if enable_k96_exactall: + label = _k96_exactall_label_for_inputs(inputs) + if label is not None: + return (SEED_K96_EXACTALL_B6C4_ID, label) + if enable_midk_e080: + label = _midk_e080_label_for_inputs(inputs) + if label is not None: + return (SEED_MIDK_E080_ID, label) + if enable_q1m262: + label = _q1m262_label_for_inputs(inputs) + if label is not None: + return (SEED_Q1M262_980C_ID, label) + return (None, None) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False, enable_cachedmerge: bool=True, enable_q16split148: bool=True, enable_split148_7e5d: bool=False, enable_k96_exactall: bool=True, enable_midk_e080: bool=True, enable_q1m262: bool=True) -> str: + if not force_fallback: + selected_seed, _label = _selected_seed_for_inputs(inputs, enable_cachedmerge=enable_cachedmerge, enable_q16split148=enable_q16split148, enable_split148_7e5d=enable_split148_7e5d, enable_k96_exactall=enable_k96_exactall, enable_midk_e080=enable_midk_e080, enable_q1m262=enable_q1m262) + if selected_seed == SEED_CACHEDMERGE_AF67_ID: + return seed_cachedmerge.route_for_contract_inputs(inputs) + if selected_seed == SEED_Q16SPLIT148_3F2D_ID: + return seed_q16split148.route_for_contract_inputs(inputs) + if selected_seed == SEED_SPLIT148_7E5D_ID: + return seed_split148.route_for_contract_inputs(inputs) + if selected_seed == SEED_K96_EXACTALL_B6C4_ID: + return seed_k96_exactall.route_for_contract_inputs(inputs) + if selected_seed == SEED_MIDK_E080_ID: + return _seed_midk_e080().route_for_contract_inputs(inputs) + if selected_seed == SEED_Q1M262_980C_ID: + return seed_q1m262.route_for_contract_inputs(inputs) + return baseline_wide.route_for_contract_inputs(inputs) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False, enable_cachedmerge: bool=True, enable_q16split148: bool=True, enable_split148_7e5d: bool=False, enable_k96_exactall: bool=True, enable_midk_e080: bool=True, enable_q1m262: bool=True) -> None: + if not force_fallback: + selected_seed, _label = _selected_seed_for_inputs(inputs, enable_cachedmerge=enable_cachedmerge, enable_q16split148=enable_q16split148, enable_split148_7e5d=enable_split148_7e5d, enable_k96_exactall=enable_k96_exactall, enable_midk_e080=enable_midk_e080, enable_q1m262=enable_q1m262) + if selected_seed == SEED_CACHEDMERGE_AF67_ID: + seed_cachedmerge.launch_from_contract_inputs(inputs) + return + if selected_seed == SEED_Q16SPLIT148_3F2D_ID: + seed_q16split148.launch_from_contract_inputs(inputs) + return + if selected_seed == SEED_SPLIT148_7E5D_ID: + seed_split148.launch_from_contract_inputs(inputs) + return + if selected_seed == SEED_K96_EXACTALL_B6C4_ID: + seed_k96_exactall.launch_from_contract_inputs(inputs) + return + if selected_seed == SEED_MIDK_E080_ID: + _seed_midk_e080().launch_from_contract_inputs(inputs) + return + if selected_seed == SEED_Q1M262_980C_ID: + seed_q1m262.launch_from_contract_inputs(inputs) + return + baseline_wide.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_split148_k32_overlay_7e5d(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, enable_cachedmerge=False, enable_q16split148=False, enable_split148_7e5d=True, enable_k96_exactall=False, enable_midk_e080=False, enable_q1m262=False) + +def candidate_q16split148_k32_overlay_3f2d(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, enable_cachedmerge=False, enable_q16split148=True, enable_k96_exactall=False, enable_midk_e080=False, enable_q1m262=False) + +def candidate_cachedmerge_non128_overlay_af67(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, enable_cachedmerge=True, enable_q16split148=False, enable_k96_exactall=False, enable_midk_e080=False, enable_q1m262=False) + +def candidate_q16split148_plus_cachedmerge(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, enable_cachedmerge=True, enable_q16split148=True, enable_k96_exactall=False, enable_midk_e080=False, enable_q1m262=False) + +def candidate_q16split148_cachedmerge_k96exactall(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, enable_cachedmerge=True, enable_q16split148=True, enable_k96_exactall=True, enable_midk_e080=False, enable_q1m262=False) + +def candidate_q16split148_cachedmerge_k96exactall_e080_q1m262(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, enable_cachedmerge=True, enable_q16split148=True, enable_k96_exactall=True, enable_midk_e080=True, enable_q1m262=True) + +def candidate_baseline_wide(inputs: dict[str, Any]) -> None: + baseline_wide.launch_from_contract_inputs(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) +CANDIDATE_KEYS = ('split148_k32_overlay_7e5d', 'q16split148_k32_overlay_3f2d', 'cachedmerge_non128_overlay_af67', 'q16split148_plus_cachedmerge', 'q16split148_cachedmerge_k96exactall', 'q16split148_cachedmerge_k96exactall_e080_q1m262') +DEFAULT_CANDIDATE_KEY = 'q16split148_cachedmerge_k96exactall_e080_q1m262' +CANDIDATE_CONFIGS: dict[str, dict[str, Any]] = {'split148_k32_overlay_7e5d': {'candidate_id': 'candidate_split148_k32_overlay_7e5d_over_8199_full82_v1', 'entrypoint': ''.join([format(MODULE, ''), ':candidate_split148_k32_overlay_7e5d']), 'benchmark_entrypoint': ''.join([format(MODULE, ''), ':benchmark_candidate_split148_k32_overlay_7e5d']), 'kernel_fn': candidate_split148_k32_overlay_7e5d, 'enabled': {'enable_cachedmerge': False, 'enable_q16split148': False, 'enable_split148_7e5d': True, 'enable_k96_exactall': False, 'enable_midk_e080': False, 'enable_q1m262': False}, 'selected_seeds': (SEED_SPLIT148_7E5D_ID,), 'target_shapes': K32_TARGET_SHAPES, 'guard_plan': ('7e5d all-K32 split148 exact guards', '8199-widecombine fallback')}, 'q16split148_k32_overlay_3f2d': {'candidate_id': 'candidate_q16split148_k32_overlay_3f2d_over_8199_full82_v1', 'entrypoint': ''.join([format(MODULE, ''), ':candidate_q16split148_k32_overlay_3f2d']), 'benchmark_entrypoint': ''.join([format(MODULE, ''), ':benchmark_candidate_q16split148_k32_overlay_3f2d']), 'kernel_fn': candidate_q16split148_k32_overlay_3f2d, 'enabled': {'enable_cachedmerge': False, 'enable_q16split148': True, 'enable_split148_7e5d': False, 'enable_k96_exactall': False, 'enable_midk_e080': False, 'enable_q1m262': False}, 'selected_seeds': (SEED_Q16SPLIT148_3F2D_ID,), 'target_shapes': K32_TARGET_SHAPES, 'guard_plan': ('3f2d K32 q16-only split148 exact guards', '8199-widecombine fallback')}, 'cachedmerge_non128_overlay_af67': {'candidate_id': 'candidate_cachedmerge_non128_overlay_af67_over_8199_full82_v1', 'entrypoint': ''.join([format(MODULE, ''), ':candidate_cachedmerge_non128_overlay_af67']), 'benchmark_entrypoint': ''.join([format(MODULE, ''), ':benchmark_candidate_cachedmerge_non128_overlay_af67']), 'kernel_fn': candidate_cachedmerge_non128_overlay_af67, 'enabled': {'enable_cachedmerge': True, 'enable_q16split148': False, 'enable_split148_7e5d': False, 'enable_k96_exactall': False, 'enable_midk_e080': False, 'enable_q1m262': False}, 'selected_seeds': (SEED_CACHEDMERGE_AF67_ID,), 'target_shapes': CACHEDMERGE_TARGET_SHAPES, 'guard_plan': ('af67 exact non-D128 cachedmerge guards', '8199-widecombine fallback')}, 'q16split148_plus_cachedmerge': {'candidate_id': 'candidate_q16split148_plus_cachedmerge_over_8199_full82_v1', 'entrypoint': ''.join([format(MODULE, ''), ':candidate_q16split148_plus_cachedmerge']), 'benchmark_entrypoint': ''.join([format(MODULE, ''), ':benchmark_candidate_q16split148_plus_cachedmerge']), 'kernel_fn': candidate_q16split148_plus_cachedmerge, 'enabled': {'enable_cachedmerge': True, 'enable_q16split148': True, 'enable_split148_7e5d': False, 'enable_k96_exactall': False, 'enable_midk_e080': False, 'enable_q1m262': False}, 'selected_seeds': (SEED_CACHEDMERGE_AF67_ID, SEED_Q16SPLIT148_3F2D_ID), 'target_shapes': Q16_CACHEDMERGE_TARGET_SHAPES, 'guard_plan': ('af67 exact non-D128 cachedmerge guards', '3f2d K32 q16-only split148 exact guards', '8199-widecombine fallback')}, 'q16split148_cachedmerge_k96exactall': {'candidate_id': 'candidate_q16split148_cachedmerge_k96exactall_over_8199_full82_v1', 'entrypoint': ''.join([format(MODULE, ''), ':candidate_q16split148_cachedmerge_k96exactall']), 'benchmark_entrypoint': ''.join([format(MODULE, ''), ':benchmark_candidate_q16split148_cachedmerge_k96exactall']), 'kernel_fn': candidate_q16split148_cachedmerge_k96exactall, 'enabled': {'enable_cachedmerge': True, 'enable_q16split148': True, 'enable_split148_7e5d': False, 'enable_k96_exactall': True, 'enable_midk_e080': False, 'enable_q1m262': False}, 'selected_seeds': (SEED_CACHEDMERGE_AF67_ID, SEED_Q16SPLIT148_3F2D_ID, SEED_K96_EXACTALL_B6C4_ID), 'target_shapes': ABEE_TARGET_SHAPES, 'guard_plan': ('af67 exact non-D128 cachedmerge guards', '3f2d K32 q16-only split148 exact guards', 'b6c4 exact BF16 build B=1 Q=M in {1024,2048,4096} D=128 K=96 guards', '8199-widecombine fallback')}, 'q16split148_cachedmerge_k96exactall_e080_q1m262': {'candidate_id': 'candidate_q16split148_cachedmerge_k96exactall_e080_q1m262_over_8199_full82_v1', 'entrypoint': ''.join([format(MODULE, ''), ':candidate_q16split148_cachedmerge_k96exactall_e080_q1m262']), 'benchmark_entrypoint': ''.join([format(MODULE, ''), ':benchmark_candidate_q16split148_cachedmerge_k96exactall_e080_q1m262']), 'kernel_fn': candidate_q16split148_cachedmerge_k96exactall_e080_q1m262, 'enabled': {'enable_cachedmerge': True, 'enable_q16split148': True, 'enable_split148_7e5d': False, 'enable_k96_exactall': True, 'enable_midk_e080': True, 'enable_q1m262': True}, 'selected_seeds': (SEED_CACHEDMERGE_AF67_ID, SEED_Q16SPLIT148_3F2D_ID, SEED_K96_EXACTALL_B6C4_ID, SEED_MIDK_E080_ID, SEED_Q1M262_980C_ID), 'target_shapes': TARGET_SHAPES, 'guard_plan': ('af67 exact non-D128 cachedmerge guards', '3f2d K32 q16-only split148 exact guards', 'b6c4 exact BF16 build B=1 Q=M in {1024,2048,4096} D=128 K=96 guards', 'e080 exact BF16 build B=1 Q=M in {2048,4096} D=128 K in {11,12,13} guards', '980c exact BF16 online Q=1 D=128 K=10 M in {100000,131071,250000,262143} guards', '8199-widecombine fallback')}} + +def _candidate_config(candidate_key: str) -> dict[str, Any]: + try: + return CANDIDATE_CONFIGS[candidate_key] + except KeyError as exc: + raise ValueError(''.join(['unknown candidate key ', format(repr(candidate_key), ''), '; expected one of ', format(CANDIDATE_KEYS, '')])) from exc + +def _candidate_enabled_kwargs(candidate_key: str) -> dict[str, bool]: + return dict(_candidate_config(candidate_key)['enabled']) + +def _candidate_target_shapes(candidate_key: str) -> tuple[str, ...]: + return tuple(_candidate_config(candidate_key)['target_shapes']) + +def _candidate_selected_seeds(candidate_key: str) -> tuple[str, ...]: + return tuple(_candidate_config(candidate_key)['selected_seeds']) + +def _candidate_kernel_fn(candidate_key: str) -> Callable[[dict[str, Any]], Any]: + return _candidate_config(candidate_key)['kernel_fn'] + +def _candidate_id(candidate_key: str) -> str: + return str(_candidate_config(candidate_key)['candidate_id']) + +def _candidate_benchmark_entrypoint(candidate_key: str | None) -> str: + if candidate_key is None: + return BASELINE_ENTRYPOINT + return str(_candidate_config(candidate_key)['benchmark_entrypoint']) + +def _candidate_route_for_inputs(inputs: dict[str, Any], candidate_key: str | None) -> str: + if candidate_key is None: + return baseline_wide.route_for_contract_inputs(inputs) + return route_for_contract_inputs(inputs, **_candidate_enabled_kwargs(candidate_key)) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return baseline_wide._select_contract_shapes(shape_labels) + +def _benchmark_shapes(shape_labels, *, time_flashlib: bool): + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + out = [] + for shape in selected: + params = dict(shape['params']) + params['time_flashlib'] = bool(time_flashlib) + out.append({'label': shape['label'], 'params': params}) + return out + +def compile_and_launch_knn_build(*, shape_labels=DISPATCH_CORRECTNESS_SHAPES, benchmark: bool=False) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=None, kernel_fn: Callable[[dict[str, Any]], Any]=candidate, correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + shapes = _benchmark_shapes(shape_labels, time_flashlib=time_flashlib) + return evaluate_contract(shapes=shapes, correctness=correctness, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def _trace_inputs_from_shape(shape: dict[str, Any]) -> dict[str, Any]: + return baseline_wide._trace_inputs_from_shape(shape) + +def _inputs_for_label(label: str) -> dict[str, Any]: + return baseline_wide._inputs_for_label(label) + +def _normalize_route_row(row: dict[str, Any]) -> dict[str, Any]: + return baseline_wide._normalize_route_row(row) + +def _baseline_route_trace_record(inputs: dict[str, Any], *, force_fallback: bool=False, comparison_candidate_key: str | None=None) -> dict[str, Any]: + label = str(inputs.get('label')) + row = dict(baseline_wide.route_trace_for_contract_shapes((label,), force_fallback=force_fallback)[0]) + row['baseline_dispatcher_route'] = _candidate_route_for_inputs(inputs, comparison_candidate_key) + row['base_4247_route'] = base_4247.route_for_contract_inputs(inputs) + return _normalize_route_row(row) + +def _shape_spec_for_seed(seed_id: str, label: str) -> dict[str, Any]: + if seed_id == SEED_CACHEDMERGE_AF67_ID: + return seed_cachedmerge.SHAPE_SPECS[label] + if seed_id in {SEED_Q16SPLIT148_3F2D_ID, SEED_SPLIT148_7E5D_ID}: + return _K32_SHAPE_SPECS[label] + if seed_id == SEED_K96_EXACTALL_B6C4_ID: + return _K96_EXACTALL_SHAPE_SPECS[label] + if seed_id == SEED_MIDK_E080_ID: + return _MIDK_E080_SHAPE_SPECS[label] + if seed_id == SEED_Q1M262_980C_ID: + return _Q1M262_SHAPE_SPECS[label] + raise KeyError(seed_id) + +def _seed_entrypoint(seed_id: str) -> str: + return {SEED_CACHEDMERGE_AF67_ID: ROUTE_CACHEDMERGE_AF67_ENTRYPOINT, SEED_Q16SPLIT148_3F2D_ID: ROUTE_Q16SPLIT148_3F2D_ENTRYPOINT, SEED_SPLIT148_7E5D_ID: ROUTE_SPLIT148_7E5D_ENTRYPOINT, SEED_K96_EXACTALL_B6C4_ID: ROUTE_K96_EXACTALL_B6C4_ENTRYPOINT, SEED_MIDK_E080_ID: ROUTE_MIDK_E080_ENTRYPOINT, SEED_Q1M262_980C_ID: ROUTE_Q1M262_980C_ENTRYPOINT}[seed_id] + +def _guard_id(seed_id: str) -> str: + return {SEED_CACHEDMERGE_AF67_ID: 'af67_cachedmerge_non128_exact_shape_guard', SEED_Q16SPLIT148_3F2D_ID: '3f2d_k32_q16split148_exact_shape_guard', SEED_SPLIT148_7E5D_ID: '7e5d_k32_split148_exact_shape_guard', SEED_K96_EXACTALL_B6C4_ID: 'b6c4_k96_exactall_exact_qm_guard', SEED_MIDK_E080_ID: 'e080_midk_k11k13_exact_guard', SEED_Q1M262_980C_ID: '980c_q1_m262_exact_mbucket_guard'}[seed_id] + +def _producer_for_seed(seed_id: str, label: str) -> str: + if seed_id == SEED_CACHEDMERGE_AF67_ID: + return seed_cachedmerge._producer_for_label(label) + if seed_id == SEED_Q16SPLIT148_3F2D_ID: + inputs = _inputs_for_label(label) + if seed_q16split148._eligible_q16_split148(inputs): + return 'b3e0_rowld1_q16_split148' + return 'b3e0_parent_default_split144' + if seed_id == SEED_SPLIT148_7E5D_ID: + return matrix_7e5d._producer_for_seed(matrix_7e5d.SEED_K32_ID, label) + if seed_id == SEED_K96_EXACTALL_B6C4_ID: + return 'e5db_exact_no_tail_k96_stage1_prefill' + if seed_id == SEED_MIDK_E080_ID: + return 'e080_exact_midk_tcgen05_tma_stage1' + if seed_id == SEED_Q1M262_980C_ID: + return 'aa88_q1m_v3_split72_split74_tcgen05_tma_stage1' + raise KeyError(seed_id) + +def _split_count_for_seed(seed_id: str, label: str) -> int | None: + if seed_id == SEED_CACHEDMERGE_AF67_ID: + return seed_cachedmerge._split_count_for_label(label) + if seed_id == SEED_Q16SPLIT148_3F2D_ID: + inputs = _inputs_for_label(label) + if seed_q16split148._eligible_q16_split148(inputs): + return seed_q16split148.K32_Q16_SPLIT_COUNT + return seed_q16split148.K32_DEFAULT_SPLIT_COUNT + if seed_id == SEED_SPLIT148_7E5D_ID: + return seed_split148.K32_SPLIT_COUNT + if seed_id == SEED_K96_EXACTALL_B6C4_ID: + return seed_k96_exactall._select_split_count(_K96_EXACTALL_SHAPE_SPECS[label]['Q']) + if seed_id == SEED_MIDK_E080_ID: + spec = _MIDK_E080_SHAPE_SPECS[label] + return _seed_midk_e080()._split_count_for_shape(top_k=int(spec['K']), n_query=int(spec['Q'])) + if seed_id == SEED_Q1M262_980C_ID: + spec = _Q1M262_SHAPE_SPECS[label] + return seed_q1m262.SPLIT_BY_M[int(spec['M'])] + return None + +def _targeted_seed_row(seed_id: str, label: str) -> dict[str, Any]: + return TARGETED_SEED_ROWS_BY_SEED[seed_id][label] + +def _specialized_trace_record(inputs: dict[str, Any], seed_id: str, label: str, *, candidate_key: str=DEFAULT_CANDIDATE_KEY, comparison_candidate_key: str | None=None) -> dict[str, Any]: + spec = _shape_spec_for_seed(seed_id, label) + targeted = dict(_targeted_seed_row(seed_id, label)) + baseline_route = _candidate_route_for_inputs(inputs, comparison_candidate_key) + classification = 'kernel-slow' if targeted.get('ratio_vs_flashlib', 1.0) < 1.0 else 'seed-consumed' + return {'shape_key': label, 'selected_route': route_for_contract_inputs(inputs, **_candidate_enabled_kwargs(candidate_key)), 'selected_entrypoint': _seed_entrypoint(seed_id), 'selected_seed': seed_id, 'expected_seed': seed_id, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': _guard_id(seed_id), 'guard_condition': ''.join(['exact BF16 B=', format(spec['B'], ''), ' Q=', format(spec['Q'], ''), ' M=', format(spec['M'], ''), ' D=', format(spec['D'], ''), ' K=', format(spec['K'], ''), ' build=', format(spec.get('build', False), '')]), 'coverage': 'synthesized full82 seed route selected before the 8199-widecombine baseline', 'consumed_seed': seed_id, 'replaced_route': baseline_route, 'baseline_dispatcher_route': baseline_route, 'base_4247_route': base_4247.route_for_contract_inputs(inputs), 'producer': _producer_for_seed(seed_id, label), 'split_count': _split_count_for_seed(seed_id, label), 'targeted_seed_timing_backend': 'cupti', 'targeted_seed_kernel_ms': targeted['kernel_ms'], 'targeted_seed_ratio_vs_flashlib': targeted['ratio_vs_flashlib'], 'row_selection': targeted, 'classification': classification, 'dispatcher_kernel_ms': None, 'shape_specific_kernel_ms': targeted['kernel_ms'], 'relative_speedup_vs_baseline': None} + +def _route_trace_record(inputs: dict[str, Any], *, force_fallback: bool=False, candidate_key: str=DEFAULT_CANDIDATE_KEY, comparison_candidate_key: str | None=None) -> dict[str, Any]: + selected_seed, label = _selected_seed_for_inputs(inputs, **_candidate_enabled_kwargs(candidate_key)) + if force_fallback and selected_seed is not None and (label is not None): + row = _baseline_route_trace_record(inputs, comparison_candidate_key=comparison_candidate_key) + row['expected_seed'] = selected_seed + row['guard_id'] = 'forced_fallback_synthesized_portfolio_disabled' + row['guard_condition'] = ''.join(['forced fallback to ', format(BASELINE_ID, ''), '; synthesized seed overlays disabled']) + row['forced_disabled_seeds'] = _candidate_selected_seeds(candidate_key) + row['classification'] = 'guard-miss' + return _normalize_route_row(row) + if not force_fallback and selected_seed is not None and (label is not None): + return _normalize_route_row(_specialized_trace_record(inputs, selected_seed, label, candidate_key=candidate_key, comparison_candidate_key=comparison_candidate_key)) + return _baseline_route_trace_record(inputs, comparison_candidate_key=comparison_candidate_key) + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False, candidate_key: str=DEFAULT_CANDIDATE_KEY, comparison_candidate_key: str | None=None) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + return [_route_trace_record(_trace_inputs_from_shape(shape), force_fallback=force_fallback, candidate_key=candidate_key, comparison_candidate_key=comparison_candidate_key) for shape in selected] + +def _timing_backends_for_reports(*reports: dict[str, Any]) -> list[str]: + return baseline_wide._timing_backends_for_reports(*reports) + +def _rows_for_labels(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return baseline_wide._rows_for_labels(report, labels) + +def _seed_delta_matrix(candidate_report: dict[str, Any], baseline_report: dict[str, Any], *, candidate_key: str=DEFAULT_CANDIDATE_KEY, comparison_candidate_key: str | None=None) -> list[dict[str, Any]]: + matrix = [] + enabled = _candidate_enabled_kwargs(candidate_key) + for label in _candidate_target_shapes(candidate_key): + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + inputs = _inputs_for_label(label) + selected_seed, _selected_label = _selected_seed_for_inputs(inputs, **enabled) + targeted = _targeted_seed_row(str(selected_seed), label) if selected_seed else {} + matrix.append({'shape_key': label, 'baseline_route': _candidate_route_for_inputs(inputs, comparison_candidate_key), 'base_4247_route': base_4247.route_for_contract_inputs(inputs), 'candidate_route': route_for_contract_inputs(inputs, **enabled), 'selected_seed': selected_seed, 'candidate_id': _candidate_id(candidate_key), 'candidate_ms': candidate_ms, 'baseline_ms': baseline_ms, 'flashlib_ms': candidate_row.get('flashlib_ms'), 'metric_delta_ms': candidate_ms - baseline_ms if candidate_ms and baseline_ms else None, 'speedup_vs_baseline_dispatcher': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'ratio_vs_flashlib': candidate_row.get('ratio_vs_flashlib'), 'targeted_seed_kernel_ms': targeted.get('kernel_ms'), 'targeted_seed_ratio_vs_flashlib': targeted.get('ratio_vs_flashlib'), 'timing_backend': candidate_row.get('timing_backend') or baseline_row.get('timing_backend')}) + return matrix + +def _frontmatter_seed_delta_matrix(candidate_report: dict[str, Any], baseline_report: dict[str, Any], *, candidate_key: str=DEFAULT_CANDIDATE_KEY, comparison_candidate_key: str | None=None) -> list[dict[str, Any]]: + rows = [] + for item in _seed_delta_matrix(candidate_report, baseline_report, candidate_key=candidate_key, comparison_candidate_key=comparison_candidate_key): + rows.append({'shape_key': item['shape_key'], 'baseline_route': item['baseline_route'], 'candidate_deltas': [{'candidate_id': item['candidate_id'], 'selected_seed': item['selected_seed'], 'metric_delta': item['metric_delta_ms'], 'ratio_vs_flashlib': item['ratio_vs_flashlib'], 'timing_backend': item['timing_backend'] or 'cupti'}]}) + return rows + +def _per_consumed_row_delta(candidate_report: dict[str, Any], baseline_report: dict[str, Any], *, candidate_key: str=DEFAULT_CANDIDATE_KEY, comparison_candidate_key: str | None=None) -> dict[str, Any]: + return {item['shape_key']: {'candidate_ms': item['candidate_ms'], 'baseline_dispatcher_ms': item['baseline_ms'], 'flashlib_ms': item['flashlib_ms'], 'speedup_vs_baseline_dispatcher': item['speedup_vs_baseline_dispatcher'], 'ratio_vs_flashlib': item['ratio_vs_flashlib'], 'candidate_route': item['candidate_route'], 'baseline_dispatcher_route': item['baseline_route'], 'base_4247_route': item['base_4247_route'], 'selected_seed': item['selected_seed'], 'targeted_seed_kernel_ms': item['targeted_seed_kernel_ms'], 'targeted_seed_ratio_vs_flashlib': item['targeted_seed_ratio_vs_flashlib'], 'candidate_passed': candidate_report.get('per_shape', {}).get(item['shape_key'], {}).get('passed'), 'baseline_passed': baseline_report.get('per_shape', {}).get(item['shape_key'], {}).get('passed')} for item in _seed_delta_matrix(candidate_report, baseline_report, candidate_key=candidate_key, comparison_candidate_key=comparison_candidate_key)} + +def _annotate_route_trace(route_trace: list[dict[str, Any]], candidate_report: dict[str, Any], baseline_report: dict[str, Any], *, candidate_key: str=DEFAULT_CANDIDATE_KEY) -> list[dict[str, Any]]: + annotated = [] + target_shape_set = set(_candidate_target_shapes(candidate_key)) + for row in route_trace: + label = str(row.get('shape_key')) + out = dict(row) + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + out['dispatcher_kernel_ms'] = candidate_ms + out['baseline_dispatcher_kernel_ms'] = baseline_ms + out['flashlib_ms'] = candidate_row.get('flashlib_ms') + out['relative_speedup_vs_baseline'] = baseline_ms / candidate_ms if candidate_ms and baseline_ms else None + out['route_changed_vs_baseline_dispatcher'] = out.get('selected_route') != out.get('baseline_dispatcher_route') + ratio = candidate_row.get('ratio_vs_flashlib') + if ratio is None: + ratio = out.get('targeted_seed_ratio_vs_flashlib') + if label in target_shape_set: + if not out.get('selected_seed'): + out['classification'] = 'guard-miss' + elif isinstance(ratio, (float, int)) and ratio < 1.0: + out['classification'] = 'kernel-slow' + elif out['relative_speedup_vs_baseline'] is not None and out['relative_speedup_vs_baseline'] < 1.0: + out['classification'] = 'kernel-slow' + elif not out.get('route_changed_vs_baseline_dispatcher'): + out['classification'] = 'route-ok' + else: + out['classification'] = 'seed-consumed' + elif isinstance(ratio, (float, int)) and ratio < 1.0: + out['classification'] = 'kernel-slow' if out.get('route_kind') == 'specialized' else 'fallback-slow' + elif not out.get('route_changed_vs_baseline_dispatcher'): + out['classification'] = 'route-ok' + else: + out['classification'] = out.get('classification', 'route-ok') + annotated.append(_normalize_route_row(out)) + return annotated + +def _below_flashlib_rows(report: dict[str, Any], *, candidate_key: str=DEFAULT_CANDIDATE_KEY) -> list[dict[str, Any]]: + trace_by_label = {str(row['shape_key']): row for row in route_trace_for_contract_shapes(candidate_key=candidate_key)} + rows = [] + for label, row in sorted(report.get('per_shape', {}).items()): + trace_row = trace_by_label.get(label, {}) + ratio = row.get('ratio_vs_flashlib') + if ratio is None: + ratio = trace_row.get('targeted_seed_ratio_vs_flashlib') + if isinstance(ratio, (float, int)) and ratio < 1.0: + rows.append({'shape_key': label, 'kernel_ms': row.get('kernel_ms'), 'flashlib_ms': row.get('flashlib_ms'), 'ratio_vs_flashlib': ratio, 'selected_route': trace_row.get('selected_route'), 'route_kind': trace_row.get('route_kind', 'unknown'), 'classification': 'kernel-slow' if trace_row.get('route_kind') == 'specialized' else 'fallback-slow'}) + return rows + +def _timing_backend_name(use_cupti: bool) -> str: + return 'cupti' if use_cupti else 'cuda_event' + +def _denominator_name(shape_labels) -> str: + if shape_labels is None: + return 'full82_v9' + return ''.join(['shape', format(len(tuple(shape_labels)), '')]) + +def _benchmark_payload(candidate_report: dict[str, Any], baseline_report: dict[str, Any], *, use_cupti: bool, shape_labels, candidate_key: str=DEFAULT_CANDIDATE_KEY, comparison_candidate_key: str | None=None, benchmark_correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + cfg = _candidate_config(candidate_key) + consumed_labels = _candidate_target_shapes(candidate_key) + selected_route_labels = tuple(dict.fromkeys((*baseline_wide.SELECTED_TARGET_SHAPES, *consumed_labels))) + timing_backend = _timing_backend_name(use_cupti) + denominator = _denominator_name(shape_labels) + route_trace = _annotate_route_trace(route_trace_for_contract_shapes(shape_labels, candidate_key=candidate_key, comparison_candidate_key=comparison_candidate_key), candidate_report, baseline_report, candidate_key=candidate_key) + below_flashlib = _below_flashlib_rows(candidate_report, candidate_key=candidate_key) + candidate_metric = candidate_report['summary']['primary_mean'] + baseline_metric = baseline_report['summary']['primary_mean'] + return {'candidate_key': candidate_key, 'candidate_id': cfg['candidate_id'], 'comparison_baseline_candidate_key': comparison_candidate_key or BASELINE_ID, 'baseline_candidate_id': BASELINE_ID if comparison_candidate_key is None else _candidate_id(comparison_candidate_key), 'selected_seeds': _candidate_selected_seeds(candidate_key), 'source_tasks': SOURCE_TASKS, 'tflops': candidate_metric, 'baseline_tflops': baseline_metric, 'metric_delta': candidate_metric - baseline_metric if candidate_metric and baseline_metric else None, 'all_correct': candidate_report['summary']['all_correct'], 'baseline_all_correct': baseline_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'baseline_performance_comparable': baseline_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'baseline_invalid_performance_reason': baseline_report['summary']['invalid_performance_reason'], 'measured_entrypoint': cfg['benchmark_entrypoint'], 'baseline_entrypoint': _candidate_benchmark_entrypoint(comparison_candidate_key), 'measured_shape_labels': 'all_canonical' if shape_labels is None else tuple(shape_labels), 'timing_backend': timing_backend, 'denominator': denominator, 'selected_route_labels': selected_route_labels, 'consumed_seed_labels': consumed_labels, 'guard_miss_audit_labels': GUARD_MISS_AUDIT_SHAPES, 'selected_route_rows': _rows_for_labels(candidate_report, selected_route_labels), 'baseline_selected_route_rows': _rows_for_labels(baseline_report, selected_route_labels), 'consumed_seed_rows': _rows_for_labels(candidate_report, consumed_labels), 'baseline_consumed_seed_rows': _rows_for_labels(baseline_report, consumed_labels), 'guard_miss_audit_rows': _rows_for_labels(candidate_report, GUARD_MISS_AUDIT_SHAPES), 'baseline_guard_miss_audit_rows': _rows_for_labels(baseline_report, GUARD_MISS_AUDIT_SHAPES), 'per_consumed_row_delta': _per_consumed_row_delta(candidate_report, baseline_report, candidate_key=candidate_key, comparison_candidate_key=comparison_candidate_key), 'seed_delta_matrix': _seed_delta_matrix(candidate_report, baseline_report, candidate_key=candidate_key, comparison_candidate_key=comparison_candidate_key), 'frontmatter_seed_delta_matrix': _frontmatter_seed_delta_matrix(candidate_report, baseline_report, candidate_key=candidate_key, comparison_candidate_key=comparison_candidate_key), 'targeted_seed_rows_by_seed': TARGETED_SEED_ROWS_BY_SEED, 'candidate_dispatchers': CANDIDATE_DISPATCHERS, 'selected_candidate_dispatcher': cfg['candidate_id'], 'guard_plan': cfg['guard_plan'], 'route_modules': PRODUCTION_ROUTE_MODULES, 'route_trace': route_trace, 'forced_fallback_route_trace': route_trace_for_contract_shapes(shape_labels, force_fallback=True, candidate_key=candidate_key, comparison_candidate_key=comparison_candidate_key), 'route_trace_included': True, 'contract_summary': candidate_report['summary'], 'baseline_contract_summary': baseline_report['summary'], 'contract_performance': candidate_report['performance'], 'baseline_contract_performance': baseline_report['performance'], 'timing_backends': _timing_backends_for_reports(candidate_report, baseline_report), 'timing_backend_requested': timing_backend, 'use_cupti': use_cupti, 'benchmark_correctness_checked': benchmark_correctness, 'benchmark_time_flashlib': time_flashlib, 'performance_coverage': 'partial' if below_flashlib else 'pass', 'coverage_only_routes': [], 'hot_bucket_blockers': below_flashlib, 'rank_objective': {'metric': 'tflops', 'direction': 'maximize', 'value': candidate_metric, 'baseline_value': baseline_metric, 'delta': candidate_metric - baseline_metric if candidate_metric and baseline_metric else None, 'denominator': denominator}, 'report': candidate_report, 'baseline_report': baseline_report} + +def benchmark_candidate_portfolio(*, candidate_key: str=DEFAULT_CANDIDATE_KEY, use_cupti: bool=True, shape_labels=None, baseline_report: dict[str, Any] | None=None, baseline_candidate_key: str | None=None, benchmark_correctness: bool=False, time_flashlib: bool=False) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=_candidate_kernel_fn(candidate_key), correctness=benchmark_correctness, time_flashlib=time_flashlib) + if baseline_report is None: + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_baseline_wide if baseline_candidate_key is None else _candidate_kernel_fn(baseline_candidate_key), correctness=benchmark_correctness, time_flashlib=time_flashlib) + return _benchmark_payload(candidate_report, baseline_report, use_cupti=use_cupti, shape_labels=shape_labels, candidate_key=candidate_key, comparison_candidate_key=baseline_candidate_key, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + +def benchmark_candidate_split148_k32_overlay_7e5d(**kwargs) -> dict[str, Any]: + return benchmark_candidate_portfolio(candidate_key='split148_k32_overlay_7e5d', **kwargs) + +def benchmark_candidate_q16split148_k32_overlay_3f2d(**kwargs) -> dict[str, Any]: + return benchmark_candidate_portfolio(candidate_key='q16split148_k32_overlay_3f2d', **kwargs) + +def benchmark_candidate_cachedmerge_non128_overlay_af67(**kwargs) -> dict[str, Any]: + return benchmark_candidate_portfolio(candidate_key='cachedmerge_non128_overlay_af67', **kwargs) + +def benchmark_candidate_q16split148_plus_cachedmerge(**kwargs) -> dict[str, Any]: + return benchmark_candidate_portfolio(candidate_key='q16split148_plus_cachedmerge', **kwargs) + +def benchmark_candidate_q16split148_cachedmerge_k96exactall(**kwargs) -> dict[str, Any]: + return benchmark_candidate_portfolio(candidate_key='q16split148_cachedmerge_k96exactall', **kwargs) + +def benchmark_candidate_q16split148_cachedmerge_k96exactall_e080_q1m262(**kwargs) -> dict[str, Any]: + return benchmark_candidate_portfolio(candidate_key='q16split148_cachedmerge_k96exactall_e080_q1m262', **kwargs) + +def benchmark_knn_build_dispatch_4247_non128_8199_3d5a_2e8e_full82_synth_v1(*, use_cupti: bool=True, shape_labels=None, baseline_report: dict[str, Any] | None=None, baseline_candidate_key: str | None=None, benchmark_correctness: bool=False, time_flashlib: bool=False) -> dict[str, Any]: + return benchmark_candidate_portfolio(candidate_key=DEFAULT_CANDIDATE_KEY, use_cupti=use_cupti, shape_labels=shape_labels, baseline_report=baseline_report, baseline_candidate_key=baseline_candidate_key, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + +def benchmark_subset_matrix(*, use_cupti: bool=True, shape_labels=None, candidate_keys: tuple[str, ...]=CANDIDATE_KEYS, baseline_candidate_key: str | None=None, benchmark_correctness: bool=False, time_flashlib: bool=False) -> dict[str, Any]: + timing_backend = _timing_backend_name(use_cupti) + denominator = _denominator_name(shape_labels) + baseline_candidate_id = BASELINE_ID if baseline_candidate_key is None else _candidate_id(baseline_candidate_key) + baseline_entrypoint = _candidate_benchmark_entrypoint(baseline_candidate_key) + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_baseline_wide if baseline_candidate_key is None else _candidate_kernel_fn(baseline_candidate_key), correctness=benchmark_correctness, time_flashlib=time_flashlib) + payloads = {key: benchmark_candidate_portfolio(candidate_key=key, use_cupti=use_cupti, shape_labels=shape_labels, baseline_report=baseline_report, baseline_candidate_key=baseline_candidate_key, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) for key in candidate_keys} + baseline_metric = baseline_report['summary']['primary_mean'] + return {'matrix_id': 'q16split148_cachedmerge_matrix_over_8199_full82_v1', 'baseline_candidate_key': baseline_candidate_key or BASELINE_ID, 'baseline_candidate_id': baseline_candidate_id, 'baseline_entrypoint': baseline_entrypoint, 'baseline_tflops': baseline_metric, 'baseline_all_correct': baseline_report['summary']['all_correct'], 'baseline_report': baseline_report, 'candidate_keys': candidate_keys, 'candidate_dispatchers': CANDIDATE_DISPATCHERS, 'candidate_summaries': {key: {'candidate_id': payload['candidate_id'], 'measured_entrypoint': payload['measured_entrypoint'], 'selected_seeds': payload['selected_seeds'], 'tflops': payload['tflops'], 'metric_delta': payload['metric_delta'], 'all_correct': payload['all_correct'], 'performance_comparable': payload['performance_comparable'], 'performance_coverage': payload['performance_coverage'], 'hot_bucket_blocker_count': len(payload['hot_bucket_blockers'])} for key, payload in payloads.items()}, 'payloads': payloads, 'measured_shape_labels': 'all_canonical' if shape_labels is None else tuple(shape_labels), 'timing_backend': timing_backend, 'denominator': denominator, 'timing_backend_requested': timing_backend, 'benchmark_correctness_checked': benchmark_correctness, 'benchmark_time_flashlib': time_flashlib, 'timing_backends': _timing_backends_for_reports(baseline_report, *(payload['report'] for payload in payloads.values())), 'route_trace_included': True, 'rank_objective': {key: {'metric': 'tflops', 'direction': 'maximize', 'value': payload['tflops'], 'baseline_value': baseline_metric, 'delta': payload['metric_delta'], 'denominator': denominator} for key, payload in payloads.items()}} + +def benchmark_repeated_pair_matrix(*, use_cupti: bool=True, shape_labels=None, candidate_keys: tuple[str, ...]=CANDIDATE_KEYS, baseline_candidate_key: str | None=None, repeated_pair_count: int=3, benchmark_correctness: bool=False, time_flashlib: bool=False) -> dict[str, Any]: + timing_backend = _timing_backend_name(use_cupti) + denominator = _denominator_name(shape_labels) + pairs = [] + for pair_index in range(repeated_pair_count): + for key in candidate_keys: + candidate_first = pair_index % 2 == 0 + baseline_report = None + if not candidate_first: + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_baseline_wide if baseline_candidate_key is None else _candidate_kernel_fn(baseline_candidate_key), correctness=benchmark_correctness, time_flashlib=time_flashlib) + payload = benchmark_candidate_portfolio(candidate_key=key, use_cupti=use_cupti, shape_labels=shape_labels, baseline_report=baseline_report, baseline_candidate_key=baseline_candidate_key, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + pairs.append({'pair_index': pair_index, 'order': (payload['candidate_id'], payload['baseline_candidate_id']) if candidate_first else (payload['baseline_candidate_id'], payload['candidate_id']), 'candidate_key': key, 'baseline_tflops': payload['baseline_tflops'], 'candidate_tflops': payload['tflops'], 'delta': payload['metric_delta'], 'all_correct': payload['all_correct'], 'baseline_all_correct': payload['baseline_all_correct'], 'performance_comparable': payload['performance_comparable'], 'route_trace_included': payload['route_trace_included'], 'candidate_id': payload['candidate_id'], 'measured_entrypoint': payload['measured_entrypoint'], 'baseline_entrypoint': payload['baseline_entrypoint']}) + by_key: dict[str, list[dict[str, Any]]] = {key: [] for key in candidate_keys} + for row in pairs: + by_key[str(row['candidate_key'])].append(row) + aggregate = {} + for key, rows in by_key.items(): + deltas = [float(row['delta']) for row in rows if row.get('delta') is not None] + candidate_values = [float(row['candidate_tflops']) for row in rows if row.get('candidate_tflops') is not None] + baseline_values = [float(row['baseline_tflops']) for row in rows if row.get('baseline_tflops') is not None] + aggregate[key] = {'candidate_id': _candidate_id(key), 'pair_count': len(rows), 'median_delta': median(deltas) if deltas else None, 'min_delta': min(deltas) if deltas else None, 'max_delta': max(deltas) if deltas else None, 'median_candidate_tflops': median(candidate_values) if candidate_values else None, 'median_baseline_tflops': median(baseline_values) if baseline_values else None, 'all_correct': all((bool(row.get('all_correct')) for row in rows)), 'baseline_all_correct': all((bool(row.get('baseline_all_correct')) for row in rows))} + default_rows = aggregate.get(DEFAULT_CANDIDATE_KEY, {}) + return {'matrix_id': 'q16split148_cachedmerge_repeated_pair_full82_v1', 'baseline_candidate_id': BASELINE_ID if baseline_candidate_key is None else _candidate_id(baseline_candidate_key), 'baseline_candidate_key': baseline_candidate_key or BASELINE_ID, 'baseline_entrypoint': _candidate_benchmark_entrypoint(baseline_candidate_key), 'candidate_keys': candidate_keys, 'repeated_pair_count': repeated_pair_count, 'measured_shape_labels': 'all_canonical' if shape_labels is None else tuple(shape_labels), 'timing_backend': timing_backend, 'denominator': denominator, 'timing_backend_requested': timing_backend, 'benchmark_correctness_checked': benchmark_correctness, 'benchmark_time_flashlib': time_flashlib, 'randomized_or_interleaved_order': repeated_pair_count > 1, 'order_policy': 'interleaved alternating order; even zero-based pairs candidate before baseline, odd pairs baseline before candidate', 'same_gpu_class': True, 'same_backend': True, 'same_entrypoint_per_candidate': True, 'paired_rows': pairs, 'aggregate': aggregate, 'route_trace_included': True, 'rank_objective': {'metric': 'tflops', 'direction': 'maximize', 'value': default_rows.get('median_candidate_tflops'), 'baseline_value': default_rows.get('median_baseline_tflops'), 'delta': default_rows.get('median_delta'), 'denominator': denominator}} + +def _denominator_label(shape_labels) -> str: + if shape_labels is None: + return ''.join(['full', format(len(eval_mod.CANONICAL_SHAPES), '')]) + return ''.join(['shape', format(len(tuple(shape_labels)), '')]) + +def write_benchmark_artifacts(artifact_dir: str | Path, *, use_cupti: bool=True, shape_labels=None, candidate_key: str | None=None, baseline_candidate_key: str | None=None, repeated_pair_count: int=0, benchmark_correctness: bool=False, time_flashlib: bool=False) -> dict[str, str]: + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + denom = _denominator_label(shape_labels) + denominator = _denominator_name(shape_labels) + timing_backend = _timing_backend_name(use_cupti) + candidate_keys = CANDIDATE_KEYS if candidate_key is None else (candidate_key,) + matrix = benchmark_subset_matrix(use_cupti=use_cupti, shape_labels=shape_labels, candidate_keys=candidate_keys, baseline_candidate_key=baseline_candidate_key, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + baseline_slug = '8199_widecombine' if baseline_candidate_key is None else str(_candidate_id(baseline_candidate_key)).removeprefix('candidate_') + baseline_path = out_dir / ''.join([format(denom, ''), '_same_session_baseline_', format(baseline_slug, ''), '_v1.json']) + summary_path = out_dir / ''.join([format(denom, ''), '_q16_cachedmerge_matrix_summary_v1.json']) + paths: dict[str, str] = {'same_session_baseline_payload': str(baseline_path), 'matrix_summary': str(summary_path)} + baseline_path.write_text(json.dumps({'candidate_key': baseline_candidate_key or BASELINE_ID, 'candidate_id': matrix['baseline_candidate_id'], 'measured_entrypoint': matrix['baseline_entrypoint'], 'measured_shape_labels': matrix['measured_shape_labels'], 'timing_backend': timing_backend, 'denominator': denominator, 'timing_backend_requested': matrix['timing_backend_requested'], 'timing_backends': matrix['timing_backends'], 'benchmark_correctness_checked': matrix['benchmark_correctness_checked'], 'benchmark_time_flashlib': matrix['benchmark_time_flashlib'], 'tflops': matrix['baseline_tflops'], 'all_correct': matrix['baseline_all_correct'], 'performance_comparable': matrix['baseline_report']['summary']['performance_comparable'], 'contract_summary': matrix['baseline_report']['summary'], 'contract_performance': matrix['baseline_report']['performance'], 'route_trace': baseline_wide.route_trace_for_contract_shapes(shape_labels) if baseline_candidate_key is None else route_trace_for_contract_shapes(shape_labels, candidate_key=baseline_candidate_key), 'route_trace_included': True, 'rank_objective': {'metric': 'tflops', 'direction': 'maximize', 'value': matrix['baseline_tflops'], 'denominator': denominator}, 'report': matrix['baseline_report']}, indent=2, sort_keys=True) + '\n') + for key, payload in matrix['payloads'].items(): + candidate_id = str(payload['candidate_id']).removeprefix('candidate_') + candidate_path = out_dir / ''.join([format(denom, ''), '_dispatch_', format(candidate_id, ''), '.json']) + route_trace_path = out_dir / ''.join([format(denom, ''), '_route_trace_', format(candidate_id, ''), '.json']) + forced_trace_path = out_dir / ''.join([format(denom, ''), '_forced_fallback_trace_', format(candidate_id, ''), '.json']) + seed_matrix_path = out_dir / ''.join([format(denom, ''), '_seed_delta_matrix_', format(candidate_id, ''), '.json']) + candidate_path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + route_trace_path.write_text(json.dumps(payload['route_trace'], indent=2, sort_keys=True) + '\n') + forced_trace_path.write_text(json.dumps(payload['forced_fallback_route_trace'], indent=2, sort_keys=True) + '\n') + seed_matrix_path.write_text(json.dumps(payload['seed_delta_matrix'], indent=2, sort_keys=True) + '\n') + paths[''.join([format(key, ''), '_candidate_payload'])] = str(candidate_path) + paths[''.join([format(key, ''), '_route_trace'])] = str(route_trace_path) + paths[''.join([format(key, ''), '_forced_fallback_trace'])] = str(forced_trace_path) + paths[''.join([format(key, ''), '_seed_delta_matrix'])] = str(seed_matrix_path) + summary_payload = {key: value for key, value in matrix.items() if key not in {'payloads', 'baseline_report'}} + summary_payload['candidate_payload_paths'] = {key: paths[''.join([format(key, ''), '_candidate_payload'])] for key in matrix['payloads']} + summary_payload['same_session_baseline_payload'] = str(baseline_path) + summary_path.write_text(json.dumps(summary_payload, indent=2, sort_keys=True) + '\n') + if repeated_pair_count: + repeated = benchmark_repeated_pair_matrix(use_cupti=use_cupti, shape_labels=shape_labels, candidate_keys=candidate_keys, baseline_candidate_key=baseline_candidate_key, repeated_pair_count=repeated_pair_count, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + repeated_path = out_dir / ''.join([format(denom, ''), '_repeated_pair_q16_cachedmerge_matrix_v1.json']) + repeated_path.write_text(json.dumps(repeated, indent=2, sort_keys=True) + '\n') + paths['repeated_pair_payload'] = str(repeated_path) + return paths diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_4247_non128_8199_c2eb_f533_8fcb_8227_full82_matrix_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_4247_non128_8199_c2eb_f533_8fcb_8227_full82_matrix_v1.py new file mode 100644 index 00000000..40a01f69 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_4247_non128_8199_c2eb_f533_8fcb_8227_full82_matrix_v1.py @@ -0,0 +1,443 @@ +"""Full82 split148 subset-matrix kNN build dispatcher over 8199 widecombine. + +Minimum target architecture: sm_100a. This opt-in dispatcher candidate starts +from the 4247+8199-widecombine full82 Weave dispatcher and overlays exact +guards for the current additive seeds: c2eb D96 exact, 8227 D320 exact-tail, +f533 D768 fused-merge, and 8fcb split148 K32 mixed-route. Guard misses stay on the +8199-widecombine full82 Weave dispatcher; no external implementation is on the +production route. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +from pathlib import Path +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_4247_non128_8199_widecombine_full82_v1 as baseline_wide +from . import knn_build_dispatch_e3de_9138_bcb3_4247_v1 as base_4247 +from . import knn_build_non128_frontier_4be7_d768fused_v1 as seed_d768 +from . import knn_build_non128_frontier_4be7_d96exact_v1 as seed_d96 +from . import knn_build_non128_frontier_8227_d320tail_v1 as seed_d320 +from . import knn_build_rag_microbucket_k32_8fcb_split148_v1 as seed_k32 +MODULE = 'loom.examples.weave.knn_build_dispatch_4247_non128_8199_c2eb_f533_8fcb_8227_full82_matrix_v1' +BASELINE_ID = 'baseline_4247_non128_8199_widecombine_full82_v1' +BASELINE_ENTRYPOINT = 'loom.examples.weave.knn_build_dispatch_4247_non128_8199_widecombine_full82_v1:benchmark_knn_build_dispatch_4247_non128_8199_widecombine_full82_v1' +SEED_D96_ID = 'non128_frontier_4be7_d96exact_v1' +SEED_D320_ID = 'non128_frontier_8227_d320tail_v1' +SEED_D768_ID = 'non128_frontier_4be7_d768fused_v1' +SEED_K32_ID = seed_k32.SEED_K32_8FCB_SPLIT148_ID +ROUTE_D96_ENTRYPOINT = ''.join([format(seed_d96.MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_D320_ENTRYPOINT = ''.join([format(seed_d320.MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_D768_ENTRYPOINT = ''.join([format(seed_d768.MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_K32_ENTRYPOINT = ''.join([format(seed_k32.MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_BASELINE_ENTRYPOINT = ''.join([format(baseline_wide.MODULE, ''), ':launch_from_contract_inputs']) +D96_TARGET_SHAPES = (seed_d96.D96_SHAPE,) +D320_TARGET_SHAPES = (seed_d320.D320_BUILD_SHAPE, seed_d320.D320_SEARCH_SHAPE) +D768_TARGET_SHAPES = (seed_d768.D768_SHAPE,) +K32_TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["rag_microbatch_largek_b1_q8_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m100000_d128_k32", "rag_microbatch_largek_b1_q32_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m131071_d128_k32"]}')) +CONSUMED_SEED_TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_dim_sweep_b1_q1024_m1024_d96_k10", "build_highd_b1_q1024_m1024_d320_k10", "search_rect_highd_b1_q512_m12000_d320_k10", "rag_microbatch_highd_b1_q16_m50000_d768_k10", "rag_microbatch_largek_b1_q8_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m100000_d128_k32", "rag_microbatch_largek_b1_q32_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m131071_d128_k32"]}')) +TARGET_SHAPES = CONSUMED_SEED_TARGET_SHAPES +TARGET_SHAPE_SET = set(TARGET_SHAPES) +SELECTED_TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_large_b1_q8192_m8192_d128_k20", "build_large_b1_q8192_m8192_d128_k32", "build_over64_stress_qm2048_k96", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32", "rag_batch_b2_q256_m50000_d128_k10", "rag_irregular_b1_q512_m131071_d128_k10", "search_rect_b1_q1024_m8192_d128_k10", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q2048_m2048_d256_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10", "search_rect_b1_q2048_m32768_d128_k10", "build_large_tail_b1_q6144_m6144_d128_k20", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "build_over32_stress_qm2048_k64", "build_over32_stress_qm4096_k64", "build_k_sweep_qm512_k1", "build_k_sweep_qm512_k2", "build_k_sweep_qm1024_k16", "build_k_sweep_qm512_k4", "build_k_sweep_qm512_k5", "build_k_sweep_qm512_k6", "rag_microbatch_b1_q8_m100000_d128_k10", "rag_microbatch_b1_q32_m100000_d128_k10", "build_dim_sweep_b1_q1024_m1024_d64_k10", "build_dim_sweep_b1_q4096_m4096_d64_k10", "search_rect_b1_q1024_m32768_d64_k10", "rag_online_b1_q1_m100000_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "build_over64_stress_qm1024_k96", "build_over64_stress_qm4096_k96", "build_dim_sweep_b1_q1024_m1024_d96_k10", "build_dim_sweep_b1_q2048_m2048_d192_k10", "build_highd_b1_q1024_m1024_d320_k10", "search_rect_highd_b1_q512_m12000_d320_k10", "rag_microbatch_highd_b1_q16_m50000_d768_k10", "rag_microbatch_largek_b1_q8_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m100000_d128_k32", "rag_microbatch_largek_b1_q32_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m131071_d128_k32"]}')) +GUARD_MISS_AUDIT_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_dim_sweep_b1_q1024_m1024_d96_k10", "build_highd_b1_q1024_m1024_d320_k10", "search_rect_highd_b1_q512_m12000_d320_k10", "rag_microbatch_highd_b1_q16_m50000_d768_k10", "rag_microbatch_largek_b1_q8_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m100000_d128_k32", "rag_microbatch_largek_b1_q32_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m131071_d128_k32", "build_dim_sweep_b1_q2048_m2048_d192_k10", "build_dim_sweep_b1_q1024_m1024_d64_k10", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q4096_m4096_d64_k10", "search_rect_b1_q1024_m32768_d64_k10", "rag_online_b1_q1_m100000_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "build_over64_stress_qm1024_k96", "build_over64_stress_qm4096_k96", "build_k_sweep_qm512_k4", "build_k_sweep_qm512_k8", "build_k_sweep_qm512_k10", "rag_microbatch_b1_q8_m100000_d128_k10", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_microbatch_b1_q32_m100000_d128_k10", "build_large_b1_q8192_m8192_d128_k20", "build_qm2048_d128_k10", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "flashml_correctness_b1_q256_m256_d128_k5", "build_over32_stress_qm2048_k64", "search_rect_over32_b1_q2048_m65536_d128_k64", "rag_offline_large_m_over32_b1_q2048_m250000_d128_k64", "build_k_sweep_qm512_k5", "build_over32_stress_qm4096_k64"]}')) +DISPATCH_CORRECTNESS_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["flashml_correctness_b1_q256_m256_d128_k5", "build_dim_sweep_b1_q1024_m1024_d96_k10", "build_highd_b1_q1024_m1024_d320_k10", "search_rect_highd_b1_q512_m12000_d320_k10", "rag_microbatch_highd_b1_q16_m50000_d768_k10", "rag_microbatch_largek_b1_q8_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m100000_d128_k32", "rag_microbatch_largek_b1_q32_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m131071_d128_k32", "build_dim_sweep_b1_q2048_m2048_d192_k10", "build_large_b1_q8192_m8192_d128_k20", "build_large_b1_q8192_m8192_d128_k32", "build_over64_stress_qm2048_k96", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32", "rag_batch_b2_q256_m50000_d128_k10", "rag_irregular_b1_q512_m131071_d128_k10", "search_rect_b1_q1024_m8192_d128_k10", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q2048_m2048_d256_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10", "search_rect_b1_q2048_m32768_d128_k10", "build_large_tail_b1_q6144_m6144_d128_k20", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "build_over32_stress_qm2048_k64", "build_over32_stress_qm4096_k64", "build_k_sweep_qm512_k1", "build_k_sweep_qm512_k2", "build_k_sweep_qm1024_k16", "build_k_sweep_qm512_k4", "build_k_sweep_qm512_k5", "build_k_sweep_qm512_k6", "rag_microbatch_b1_q8_m100000_d128_k10", "rag_microbatch_b1_q32_m100000_d128_k10", "build_dim_sweep_b1_q1024_m1024_d64_k10", "build_dim_sweep_b1_q4096_m4096_d64_k10", "search_rect_b1_q1024_m32768_d64_k10", "rag_online_b1_q1_m100000_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "build_over64_stress_qm1024_k96", "build_over64_stress_qm4096_k96", "build_k_sweep_qm512_k8", "build_k_sweep_qm512_k10", "build_qm2048_d128_k10", "search_rect_over32_b1_q2048_m65536_d128_k64", "rag_offline_large_m_over32_b1_q2048_m250000_d128_k64", "rag_stream_b1_q128_m100000_d128_k10", "search_rect_b1_q4096_m65536_d128_k20", "rag_offline_largek_b1_q4096_m100000_d128_k20", "rag_offline_large_m_b1_q8192_m250000_d128_k20"]}')) +TARGETED_SEED_ROWS: dict[str, dict[str, Any]] = {seed_d96.D96_SHAPE: {'kernel_ms': 0.047168, 'flashlib_ms': 0.046848, 'ratio_vs_flashlib': 0.9932157394843962, 'tflops': 4.268287652645862}, seed_d320.D320_BUILD_SHAPE: {'kernel_ms': 0.047489, 'flashlib_ms': 0.070528, 'ratio_vs_flashlib': 1.4851439280675522, 'tflops': 14.131454442081324}, seed_d320.D320_SEARCH_SHAPE: {'kernel_ms': 0.1148, 'flashlib_ms': 0.151615, 'ratio_vs_flashlib': 1.3206881533101045, 'tflops': 34.25226480836237}, seed_d768.D768_SHAPE: {'kernel_ms': 0.094592, 'flashlib_ms': 0.167136, 'ratio_vs_flashlib': 1.7669147496617053, 'tflops': 12.990527740189446}, seed_k32.Q8_K32_SHAPE: {'kernel_ms': 0.077121, 'flashlib_ms': 0.107232, 'ratio_vs_flashlib': 1.3904384019916753, 'tflops': 2.655567225528715}, seed_k32.Q16_K32_SHAPE: {'kernel_ms': 0.135552, 'flashlib_ms': 0.134017, 'ratio_vs_flashlib': 0.988675932483475, 'tflops': 3.021718602455146}, seed_k32.Q16_K32_IRREGULAR_SHAPE: {'kernel_ms': 0.163168, 'flashlib_ms': 0.157824, 'ratio_vs_flashlib': 0.967248480094136, 'tflops': 3.2902702490684446}, seed_k32.Q32_K32_SHAPE: {'kernel_ms': 0.141312, 'flashlib_ms': 0.159264, 'ratio_vs_flashlib': 1.1270380434782608, 'tflops': 5.797101449275363}} +_CONTRACT_PARAMS_BY_LABEL = _decode_capture(_json_loads('{"__dict_items__": [["flashml_correctness_b1_q256_m256_d128_k5", {"__dict_items__": [["B", 1], ["Q", 256], ["M", 256], ["D", 128], ["K", 5], ["dtype", "bfloat16"], ["seed", 606001], ["build", true], ["check_correctness", true], ["correctness_query_sample", 256], ["recall_min", 0.99], ["benchmark", true], ["time_flashlib", true]]}], ["build_k_sweep_qm512_k1", {"__dict_items__": [["B", 1], ["Q", 512], ["M", 512], ["D", 128], ["K", 1], ["dtype", "bfloat16"], ["seed", 606049], ["build", true], ["check_correctness", true], ["correctness_query_sample", 512], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true]]}], ["build_k_sweep_qm512_k2", {"__dict_items__": [["B", 1], ["Q", 512], ["M", 512], ["D", 128], ["K", 2], ["dtype", "bfloat16"], ["seed", 606050], ["build", true], ["check_correctness", true], ["correctness_query_sample", 512], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true]]}], ["build_k_sweep_qm512_k4", {"__dict_items__": [["B", 1], ["Q", 512], ["M", 512], ["D", 128], ["K", 4], ["dtype", "bfloat16"], ["seed", 606052], ["build", true], ["check_correctness", true], ["correctness_query_sample", 512], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true], ["diagnostic_class", "low_k_q512_k5_neighborhood"]]}], ["build_k_sweep_qm512_k5", {"__dict_items__": [["B", 1], ["Q", 512], ["M", 512], ["D", 128], ["K", 5], ["dtype", "bfloat16"], ["seed", 606053], ["build", true], ["check_correctness", true], ["correctness_query_sample", 512], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true]]}], ["build_k_sweep_qm512_k6", {"__dict_items__": [["B", 1], ["Q", 512], ["M", 512], ["D", 128], ["K", 6], ["dtype", "bfloat16"], ["seed", 606054], ["build", true], ["check_correctness", true], ["correctness_query_sample", 512], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true], ["diagnostic_class", "low_k_q512_k5_neighborhood"]]}], ["build_k_sweep_qm512_k8", {"__dict_items__": [["B", 1], ["Q", 512], ["M", 512], ["D", 128], ["K", 8], ["dtype", "bfloat16"], ["seed", 606056], ["build", true], ["check_correctness", true], ["correctness_query_sample", 512], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true]]}], ["build_k_sweep_qm512_k10", {"__dict_items__": [["B", 1], ["Q", 512], ["M", 512], ["D", 128], ["K", 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1], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true], ["diagnostic_class", "v12_common_d_rag_online_highd"]]}], ["search_rect_common_d1024_b1_q256_m8192_k10", {"__dict_items__": [["B", 1], ["Q", 256], ["M", 8192], ["D", 1024], ["K", 10], ["dtype", "bfloat16"], ["seed", 616124], ["build", false], ["check_correctness", true], ["correctness_query_sample", 256], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true], ["diagnostic_class", "v12_common_d_rectangular_search"]]}], ["search_rect_common_d4096_b1_q128_m4096_k10", {"__dict_items__": [["B", 1], ["Q", 128], ["M", 4096], ["D", 4096], ["K", 10], ["dtype", "bfloat16"], ["seed", 616496], ["build", false], ["check_correctness", true], ["correctness_query_sample", 128], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true], ["diagnostic_class", "v12_common_d_rectangular_search"]]}], ["rag_microbatch_largek_common_d256_b1_q8_m100000_k32", {"__dict_items__": [["B", 1], ["Q", 8], ["M", 100000], ["D", 256], ["K", 32], ["dtype", "bfloat16"], ["seed", 616332], ["build", false], ["check_correctness", true], ["correctness_query_sample", 8], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true], ["diagnostic_class", "v12_common_d_large_k_rag"]]}], ["rag_stream_largek_common_d256_b1_q128_m100000_k32", {"__dict_items__": [["B", 1], ["Q", 128], ["M", 100000], ["D", 256], ["K", 32], ["dtype", "bfloat16"], ["seed", 616432], ["build", false], ["check_correctness", true], ["correctness_query_sample", 128], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true], ["diagnostic_class", "v12_common_d_large_k_rag"]]}], ["rag_microbatch_over32_d128_b1_q16_m100000_k48", {"__dict_items__": [["B", 1], ["Q", 16], ["M", 100000], ["D", 128], ["K", 48], ["dtype", "bfloat16"], ["seed", 616548], ["build", false], ["check_correctness", true], ["correctness_query_sample", 16], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true], ["diagnostic_class", "v12_rag_over32_topk"]]}]]}')) +_K32_SHAPE_SPECS = {label: _CONTRACT_PARAMS_BY_LABEL[label] for label in K32_TARGET_SHAPES} +PRODUCTION_ROUTE_MODULES = {**baseline_wide.PRODUCTION_ROUTE_MODULES, SEED_D96_ID: ROUTE_D96_ENTRYPOINT, SEED_D320_ID: ROUTE_D320_ENTRYPOINT, SEED_D768_ID: ROUTE_D768_ENTRYPOINT, SEED_K32_ID: ROUTE_K32_ENTRYPOINT, BASELINE_ID: ROUTE_BASELINE_ENTRYPOINT} +CANDIDATE_DISPATCHERS = ({'id': BASELINE_ID, 'entrypoint': BASELINE_ENTRYPOINT, 'consumed_seeds': (baseline_wide.SEED_NON128_ID,), 'guard_plan': ('8199 widecombine exact non-D128 guards', 'then exported 4247 guard stack'), 'expected_shape_wins': baseline_wide.TARGET_SHAPES, 'fallback': baseline_wide.ROUTE_BASE_4247_ENTRYPOINT, 'rejected_reason': 'same-session full82 baseline'}, {'id': 'candidate_d96_d320_only_over_8199_full82_v1', 'entrypoint': ''.join([format(MODULE, ''), ':candidate_d96_d320_only']), 'consumed_seeds': (SEED_D96_ID, SEED_D320_ID), 'guard_plan': ('c2eb exact D96 guard', '8227 exact D320-tail guards', 'then 8199-widecombine full82 baseline'), 'expected_shape_wins': (*D96_TARGET_SHAPES, *D320_TARGET_SHAPES), 'fallback': ROUTE_BASELINE_ENTRYPOINT, 'rejected_reason': 'matrix candidate; promotion depends on same-session full82 A/B'}, {'id': 'candidate_f533_non128_over_8199_full82_v1', 'entrypoint': ''.join([format(MODULE, ''), ':candidate_f533_non128_only']), 'consumed_seeds': (SEED_D96_ID, SEED_D320_ID, SEED_D768_ID), 'guard_plan': ('c2eb exact D96 guard', '8227 exact D320-tail guards', 'f533 exact D768 fused-merge guard', 'then 8199-widecombine full82 baseline'), 'expected_shape_wins': (*D96_TARGET_SHAPES, *D320_TARGET_SHAPES, *D768_TARGET_SHAPES), 'fallback': ROUTE_BASELINE_ENTRYPOINT, 'rejected_reason': 'matrix candidate; promotion depends on same-session full82 A/B'}, {'id': 'candidate_k32_split148_over_8199_full82_v1', 'entrypoint': ''.join([format(MODULE, ''), ':candidate_k32_only']), 'consumed_seeds': (SEED_K32_ID,), 'guard_plan': ('8fcb split148 exact K32 RAG microbucket guards', 'then 8199-widecombine full82 baseline'), 'expected_shape_wins': K32_TARGET_SHAPES, 'fallback': ROUTE_BASELINE_ENTRYPOINT, 'rejected_reason': 'matrix candidate; promotion depends on same-session full82 A/B'}, {'id': 'candidate_f533_split148_over_8199_full82_v1', 'entrypoint': ''.join([format(MODULE, ''), ':benchmark_knn_build_dispatch_4247_non128_8199_c2eb_f533_8fcb_8227_full82_matrix_v1']), 'consumed_seeds': (SEED_D96_ID, SEED_D320_ID, SEED_D768_ID, SEED_K32_ID), 'guard_plan': ('c2eb exact D96 guard', '8227 exact D320-tail guards', 'f533 exact D768 fused-merge guard', '8fcb split148 exact K32 RAG microbucket guards', 'then 8199-widecombine full82 baseline'), 'expected_shape_wins': TARGET_SHAPES, 'fallback': ROUTE_BASELINE_ENTRYPOINT, 'rejected_reason': 'matrix candidate; promotion depends on same-session full82 A/B'}) +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k48over32", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 48]], "cta_group": 1, "threads": 192}')) + +def _dtype_name(inputs: dict[str, Any]) -> str: + dtype = inputs.get('dtype') + if dtype is not None: + return str(dtype).replace('torch.', '') + query = inputs.get('query') + if query is not None and hasattr(query, 'dtype'): + return str(query.dtype).replace('torch.', '') + return '' + +def _matches_contract_spec(inputs: dict[str, Any], spec: dict[str, Any]) -> bool: + return int(inputs.get('B', -1)) == int(spec['B']) and int(inputs.get('Q', -1)) == int(spec['Q']) and (int(inputs.get('M', -1)) == int(spec['M'])) and (int(inputs.get('D', -1)) == int(spec['D'])) and (int(inputs.get('K', -1)) == int(spec['K'])) and (bool(inputs.get('build', False)) == bool(spec.get('build', False))) and (_dtype_name(inputs) == str(spec.get('dtype', 'bfloat16')).replace('torch.', '')) + +def _d96_label_for_inputs(inputs: dict[str, Any]) -> str | None: + label = seed_d96._target_label_for_inputs(inputs) + if label == seed_d96.D96_SHAPE: + return label + return None + +def _d320_label_for_inputs(inputs: dict[str, Any]) -> str | None: + label = seed_d320._target_label_for_inputs(inputs) + if label in D320_TARGET_SHAPES: + return label + return None + +def _d768_label_for_inputs(inputs: dict[str, Any]) -> str | None: + label = seed_d768._target_label_for_inputs(inputs) + if label == seed_d768.D768_SHAPE: + return label + return None + +def _k32_label_for_inputs(inputs: dict[str, Any]) -> str | None: + label = inputs.get('label') + if label is not None: + spec = _K32_SHAPE_SPECS.get(str(label)) + if spec is not None and _matches_contract_spec(inputs, spec): + return str(label) + for candidate_label, spec in _K32_SHAPE_SPECS.items(): + if _matches_contract_spec(inputs, spec): + return candidate_label + return None + +def _selected_seed_for_inputs(inputs: dict[str, Any], *, enable_d96: bool=True, enable_d320tail: bool=True, enable_d768: bool=True, enable_k32: bool=True) -> tuple[str | None, str | None]: + if enable_d96: + label = _d96_label_for_inputs(inputs) + if label is not None: + return (SEED_D96_ID, label) + if enable_d320tail: + label = _d320_label_for_inputs(inputs) + if label is not None: + return (SEED_D320_ID, label) + if enable_d768: + label = _d768_label_for_inputs(inputs) + if label is not None: + return (SEED_D768_ID, label) + if enable_k32: + label = _k32_label_for_inputs(inputs) + if label is not None: + return (SEED_K32_ID, label) + return (None, None) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False, enable_d96: bool=True, enable_d320tail: bool=True, enable_d768: bool=True, enable_k32: bool=True) -> str: + if not force_fallback: + selected_seed, _label = _selected_seed_for_inputs(inputs, enable_d96=enable_d96, enable_d320tail=enable_d320tail, enable_d768=enable_d768, enable_k32=enable_k32) + if selected_seed == SEED_D96_ID: + return seed_d96.route_for_contract_inputs(inputs) + if selected_seed == SEED_D320_ID: + return seed_d320.route_for_contract_inputs(inputs) + if selected_seed == SEED_D768_ID: + return seed_d768.route_for_contract_inputs(inputs) + if selected_seed == SEED_K32_ID: + return seed_k32.route_for_contract_inputs(inputs) + return baseline_wide.route_for_contract_inputs(inputs) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False, enable_d96: bool=True, enable_d320tail: bool=True, enable_d768: bool=True, enable_k32: bool=True) -> None: + if not force_fallback: + selected_seed, _label = _selected_seed_for_inputs(inputs, enable_d96=enable_d96, enable_d320tail=enable_d320tail, enable_d768=enable_d768, enable_k32=enable_k32) + if selected_seed == SEED_D96_ID: + seed_d96.launch_from_contract_inputs(inputs) + return + if selected_seed == SEED_D320_ID: + seed_d320.launch_from_contract_inputs(inputs) + return + if selected_seed == SEED_D768_ID: + seed_d768.launch_from_contract_inputs(inputs) + return + if selected_seed == SEED_K32_ID: + seed_k32.launch_from_contract_inputs(inputs) + return + baseline_wide.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_f533_plus_k32(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_f533_non128_only(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, enable_k32=False) + +def candidate_non128_only(inputs: dict[str, Any]) -> None: + candidate_f533_non128_only(inputs) + +def candidate_d96_d320_only(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, enable_d768=False, enable_k32=False) + +def candidate_k32_only(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, enable_d96=False, enable_d320tail=False, enable_d768=False) + +def candidate_baseline_wide(inputs: dict[str, Any]) -> None: + baseline_wide.launch_from_contract_inputs(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) +CANDIDATE_KEYS = ('d96_d320_only', 'f533_non128_only', 'k32_only', 'f533_plus_k32') +DEFAULT_CANDIDATE_KEY = 'f533_plus_k32' +CANDIDATE_CONFIGS: dict[str, dict[str, Any]] = {'d96_d320_only': {'candidate_id': 'candidate_d96_d320_only_over_8199_full82_v1', 'entrypoint': ''.join([format(MODULE, ''), ':candidate_d96_d320_only']), 'benchmark_entrypoint': ''.join([format(MODULE, ''), ':benchmark_candidate_d96_d320_only']), 'kernel_fn': candidate_d96_d320_only, 'enabled': {'enable_d96': True, 'enable_d320tail': True, 'enable_d768': False, 'enable_k32': False}, 'selected_seeds': (SEED_D96_ID, SEED_D320_ID), 'target_shapes': (*D96_TARGET_SHAPES, *D320_TARGET_SHAPES), 'guard_plan': ('c2eb exact D96 guard', '8227 exact D320-tail guards', '8199-widecombine fallback')}, 'f533_non128_only': {'candidate_id': 'candidate_f533_non128_over_8199_full82_v1', 'entrypoint': ''.join([format(MODULE, ''), ':candidate_f533_non128_only']), 'benchmark_entrypoint': ''.join([format(MODULE, ''), ':benchmark_candidate_f533_non128_only']), 'kernel_fn': candidate_f533_non128_only, 'enabled': {'enable_d96': True, 'enable_d320tail': True, 'enable_d768': True, 'enable_k32': False}, 'selected_seeds': (SEED_D96_ID, SEED_D320_ID, SEED_D768_ID), 'target_shapes': (*D96_TARGET_SHAPES, *D320_TARGET_SHAPES, *D768_TARGET_SHAPES), 'guard_plan': ('c2eb exact D96 guard', '8227 exact D320-tail guards', 'f533 exact D768 fused-merge guard', '8199-widecombine fallback')}, 'k32_only': {'candidate_id': 'candidate_k32_split148_over_8199_full82_v1', 'entrypoint': ''.join([format(MODULE, ''), ':candidate_k32_only']), 'benchmark_entrypoint': ''.join([format(MODULE, ''), ':benchmark_candidate_k32_only']), 'kernel_fn': candidate_k32_only, 'enabled': {'enable_d96': False, 'enable_d320tail': False, 'enable_d768': False, 'enable_k32': True}, 'selected_seeds': (SEED_K32_ID,), 'target_shapes': K32_TARGET_SHAPES, 'guard_plan': ('8fcb split148 exact K32 RAG microbucket guards', '8199-widecombine fallback')}, 'f533_plus_k32': {'candidate_id': 'candidate_f533_split148_over_8199_full82_v1', 'entrypoint': ''.join([format(MODULE, ''), ':candidate_f533_plus_k32']), 'benchmark_entrypoint': ''.join([format(MODULE, ''), ':benchmark_knn_build_dispatch_4247_non128_8199_c2eb_f533_8fcb_8227_full82_matrix_v1']), 'kernel_fn': candidate_f533_plus_k32, 'enabled': {'enable_d96': True, 'enable_d320tail': True, 'enable_d768': True, 'enable_k32': True}, 'selected_seeds': (SEED_D96_ID, SEED_D320_ID, SEED_D768_ID, SEED_K32_ID), 'target_shapes': TARGET_SHAPES, 'guard_plan': ('c2eb exact D96 guard', '8227 exact D320-tail guards', 'f533 exact D768 fused-merge guard', '8fcb split148 exact K32 RAG microbucket guards', '8199-widecombine fallback')}} + +def _candidate_config(candidate_key: str) -> dict[str, Any]: + try: + return CANDIDATE_CONFIGS[candidate_key] + except KeyError as exc: + raise ValueError(''.join(['unknown candidate key ', format(repr(candidate_key), ''), '; expected one of ', format(CANDIDATE_KEYS, '')])) from exc + +def _candidate_enabled_kwargs(candidate_key: str) -> dict[str, bool]: + return dict(_candidate_config(candidate_key)['enabled']) + +def _candidate_target_shapes(candidate_key: str) -> tuple[str, ...]: + return tuple(_candidate_config(candidate_key)['target_shapes']) + +def _candidate_selected_seeds(candidate_key: str) -> tuple[str, ...]: + return tuple(_candidate_config(candidate_key)['selected_seeds']) + +def _candidate_kernel_fn(candidate_key: str) -> Callable[[dict[str, Any]], Any]: + return _candidate_config(candidate_key)['kernel_fn'] + +def _candidate_id(candidate_key: str) -> str: + return str(_candidate_config(candidate_key)['candidate_id']) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return baseline_wide._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=DISPATCH_CORRECTNESS_SHAPES, benchmark: bool=False) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=None, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + shapes = None if shape_labels is None else _select_contract_shapes(shape_labels) + return evaluate_contract(shapes=shapes, correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def _trace_inputs_from_shape(shape: dict[str, Any]) -> dict[str, Any]: + return baseline_wide._trace_inputs_from_shape(shape) + +def _inputs_for_label(label: str) -> dict[str, Any]: + return baseline_wide._inputs_for_label(label) + +def _normalize_route_row(row: dict[str, Any]) -> dict[str, Any]: + out = baseline_wide._normalize_route_row(row) + if out.get('route_kind') not in {'specialized', 'general', 'fallback', 'coverage-only', 'none'}: + out['route_kind'] = 'specialized' if out.get('selected_seed') else 'general' + return out + +def _baseline_route_trace_record(inputs: dict[str, Any], *, force_fallback: bool=False) -> dict[str, Any]: + label = str(inputs.get('label')) + row = dict(baseline_wide.route_trace_for_contract_shapes((label,), force_fallback=force_fallback)[0]) + row['baseline_dispatcher_route'] = baseline_wide.route_for_contract_inputs(inputs) + row['base_4247_route'] = base_4247.route_for_contract_inputs(inputs) + return _normalize_route_row(row) + +def _shape_spec_for_seed(seed_id: str, label: str) -> dict[str, Any]: + if seed_id == SEED_D96_ID: + return seed_d96.SHAPE_SPECS[label] + if seed_id == SEED_D320_ID: + return seed_d320.SHAPE_SPECS[label] + if seed_id == SEED_D768_ID: + return seed_d768.SHAPE_SPECS[label] + if seed_id == SEED_K32_ID: + return _K32_SHAPE_SPECS[label] + raise KeyError(seed_id) + +def _seed_entrypoint(seed_id: str) -> str: + return {SEED_D96_ID: ROUTE_D96_ENTRYPOINT, SEED_D320_ID: ROUTE_D320_ENTRYPOINT, SEED_D768_ID: ROUTE_D768_ENTRYPOINT, SEED_K32_ID: ROUTE_K32_ENTRYPOINT}[seed_id] + +def _guard_id(seed_id: str) -> str: + return {SEED_D96_ID: 'c2eb_d96exact_exact_shape_guard', SEED_D320_ID: '8227_d320tail_exact_shape_guard', SEED_D768_ID: 'f533_d768_fused_exact_shape_guard', SEED_K32_ID: '8fcb_k32_split148_exact_shape_guard'}[seed_id] + +def _producer_for_seed(seed_id: str, label: str) -> str: + if seed_id == SEED_D96_ID: + return seed_d96._producer_for_label(label) + if seed_id == SEED_D320_ID: + return seed_d320._producer_for_label(label) + if seed_id == SEED_D768_ID: + return ''.join(['m64_d768_fusedmerge_g', format(seed_d768.D768_GROUP_COUNT, '')]) + if seed_id == SEED_K32_ID: + if label == seed_k32.Q8_K32_SHAPE: + return 'q8_halfrow' + if label == seed_k32.Q16_K32_SHAPE: + return 'q16_rowld1' + if label == seed_k32.Q32_K32_SHAPE: + return 'q32_rowld_rows4' + return 'inherited_q16_irregular_rowld1' + raise KeyError(seed_id) + +def _split_count_for_seed(seed_id: str, label: str) -> int | None: + if seed_id == SEED_D96_ID: + return seed_d96._split_count_for_label(label) + if seed_id == SEED_D320_ID: + return seed_d320._split_count_for_label(label) + if seed_id == SEED_D768_ID: + return seed_d768.D768_SPLIT_COUNT + if seed_id == SEED_K32_ID: + return seed_k32.K32_SPLIT_COUNT + return None + +def _specialized_trace_record(inputs: dict[str, Any], seed_id: str, label: str, *, candidate_key: str=DEFAULT_CANDIDATE_KEY) -> dict[str, Any]: + spec = _shape_spec_for_seed(seed_id, label) + targeted = dict(TARGETED_SEED_ROWS[label]) + return {'shape_key': label, 'selected_route': route_for_contract_inputs(inputs, **_candidate_enabled_kwargs(candidate_key)), 'selected_entrypoint': _seed_entrypoint(seed_id), 'selected_seed': seed_id, 'expected_seed': seed_id, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': _guard_id(seed_id), 'guard_condition': ''.join(['exact BF16 B=', format(spec['B'], ''), ' Q=', format(spec['Q'], ''), ' M=', format(spec['M'], ''), ' D=', format(spec['D'], ''), ' K=', format(spec['K'], ''), ' build=', format(spec.get('build', False), '')]), 'coverage': 'synthesized full82 seed route selected before the 8199-widecombine baseline', 'consumed_seed': seed_id, 'replaced_route': baseline_wide.route_for_contract_inputs(inputs), 'baseline_dispatcher_route': baseline_wide.route_for_contract_inputs(inputs), 'base_4247_route': base_4247.route_for_contract_inputs(inputs), 'producer': _producer_for_seed(seed_id, label), 'split_count': _split_count_for_seed(seed_id, label), 'targeted_seed_timing_backend': 'cupti', 'targeted_seed_kernel_ms': targeted['kernel_ms'], 'targeted_seed_ratio_vs_flashlib': targeted['ratio_vs_flashlib'], 'row_selection': targeted, 'classification': 'seed-consumed', 'dispatcher_kernel_ms': None, 'shape_specific_kernel_ms': targeted['kernel_ms'], 'relative_speedup_vs_baseline': None} + +def _route_trace_record(inputs: dict[str, Any], *, force_fallback: bool=False, candidate_key: str=DEFAULT_CANDIDATE_KEY) -> dict[str, Any]: + selected_seed, label = _selected_seed_for_inputs(inputs, **_candidate_enabled_kwargs(candidate_key)) + if force_fallback and selected_seed is not None and (label is not None): + row = _baseline_route_trace_record(inputs) + row['expected_seed'] = selected_seed + row['guard_id'] = 'forced_fallback_synthesized_portfolio_disabled' + row['guard_condition'] = ''.join(['forced fallback to ', format(BASELINE_ID, ''), '; synthesized seed overlays disabled']) + row['forced_disabled_seeds'] = _candidate_selected_seeds(candidate_key) + row['classification'] = 'guard-miss' + return _normalize_route_row(row) + if not force_fallback and selected_seed is not None and (label is not None): + return _normalize_route_row(_specialized_trace_record(inputs, selected_seed, label, candidate_key=candidate_key)) + return _baseline_route_trace_record(inputs) + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False, candidate_key: str=DEFAULT_CANDIDATE_KEY) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + return [_route_trace_record(_trace_inputs_from_shape(shape), force_fallback=force_fallback, candidate_key=candidate_key) for shape in selected] + +def _timing_backends_for_reports(*reports: dict[str, Any]) -> list[str]: + return baseline_wide._timing_backends_for_reports(*reports) + +def _rows_for_labels(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return baseline_wide._rows_for_labels(report, labels) + +def _seed_delta_matrix(candidate_report: dict[str, Any], baseline_report: dict[str, Any], *, candidate_key: str=DEFAULT_CANDIDATE_KEY) -> list[dict[str, Any]]: + matrix = [] + enabled = _candidate_enabled_kwargs(candidate_key) + for label in _candidate_target_shapes(candidate_key): + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + inputs = _inputs_for_label(label) + selected_seed, _selected_label = _selected_seed_for_inputs(inputs, **enabled) + targeted = TARGETED_SEED_ROWS[label] + matrix.append({'shape_key': label, 'baseline_route': baseline_wide.route_for_contract_inputs(inputs), 'base_4247_route': base_4247.route_for_contract_inputs(inputs), 'candidate_route': route_for_contract_inputs(inputs, **enabled), 'selected_seed': selected_seed, 'candidate_id': _candidate_id(candidate_key), 'candidate_ms': candidate_ms, 'baseline_ms': baseline_ms, 'flashlib_ms': candidate_row.get('flashlib_ms'), 'metric_delta_ms': candidate_ms - baseline_ms if candidate_ms and baseline_ms else None, 'speedup_vs_baseline_dispatcher': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'ratio_vs_flashlib': candidate_row.get('ratio_vs_flashlib'), 'targeted_seed_kernel_ms': targeted['kernel_ms'], 'targeted_seed_ratio_vs_flashlib': targeted['ratio_vs_flashlib'], 'timing_backend': candidate_row.get('timing_backend') or baseline_row.get('timing_backend')}) + return matrix + +def _frontmatter_seed_delta_matrix(candidate_report: dict[str, Any], baseline_report: dict[str, Any], *, candidate_key: str=DEFAULT_CANDIDATE_KEY) -> list[dict[str, Any]]: + rows = [] + for item in _seed_delta_matrix(candidate_report, baseline_report, candidate_key=candidate_key): + rows.append({'shape_key': item['shape_key'], 'baseline_route': item['baseline_route'], 'candidate_deltas': [{'candidate_id': item['candidate_id'], 'selected_seed': item['selected_seed'], 'metric_delta': item['metric_delta_ms'], 'ratio_vs_flashlib': item['ratio_vs_flashlib'], 'timing_backend': item['timing_backend'] or 'cupti'}]}) + return rows + +def _per_consumed_row_delta(candidate_report: dict[str, Any], baseline_report: dict[str, Any], *, candidate_key: str=DEFAULT_CANDIDATE_KEY) -> dict[str, Any]: + return {item['shape_key']: {'candidate_ms': item['candidate_ms'], 'baseline_dispatcher_ms': item['baseline_ms'], 'flashlib_ms': item['flashlib_ms'], 'speedup_vs_baseline_dispatcher': item['speedup_vs_baseline_dispatcher'], 'ratio_vs_flashlib': item['ratio_vs_flashlib'], 'candidate_route': item['candidate_route'], 'baseline_dispatcher_route': item['baseline_route'], 'base_4247_route': item['base_4247_route'], 'selected_seed': item['selected_seed'], 'targeted_seed_kernel_ms': item['targeted_seed_kernel_ms'], 'targeted_seed_ratio_vs_flashlib': item['targeted_seed_ratio_vs_flashlib'], 'candidate_passed': candidate_report.get('per_shape', {}).get(item['shape_key'], {}).get('passed'), 'baseline_passed': baseline_report.get('per_shape', {}).get(item['shape_key'], {}).get('passed')} for item in _seed_delta_matrix(candidate_report, baseline_report, candidate_key=candidate_key)} + +def _annotate_route_trace(route_trace: list[dict[str, Any]], candidate_report: dict[str, Any], baseline_report: dict[str, Any], *, candidate_key: str=DEFAULT_CANDIDATE_KEY) -> list[dict[str, Any]]: + annotated = [] + target_shape_set = set(_candidate_target_shapes(candidate_key)) + for row in route_trace: + label = str(row.get('shape_key')) + out = dict(row) + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + out['dispatcher_kernel_ms'] = candidate_ms + out['baseline_dispatcher_kernel_ms'] = baseline_ms + out['flashlib_ms'] = candidate_row.get('flashlib_ms') + out['relative_speedup_vs_baseline'] = baseline_ms / candidate_ms if candidate_ms and baseline_ms else None + out['route_changed_vs_baseline_dispatcher'] = out.get('selected_route') != out.get('baseline_dispatcher_route') + ratio = candidate_row.get('ratio_vs_flashlib') + if label in target_shape_set: + if not out.get('selected_seed'): + out['classification'] = 'guard-miss' + elif isinstance(ratio, (float, int)) and ratio < 1.0: + out['classification'] = 'kernel-slow' + elif out['relative_speedup_vs_baseline'] is not None and out['relative_speedup_vs_baseline'] < 1.0: + out['classification'] = 'kernel-slow' + else: + out['classification'] = 'seed-consumed' + elif isinstance(ratio, (float, int)) and ratio < 1.0: + out['classification'] = 'kernel-slow' if out.get('route_kind') == 'specialized' else 'fallback-slow' + else: + out['classification'] = out.get('classification', 'route-ok') + annotated.append(_normalize_route_row(out)) + return annotated + +def _below_flashlib_rows(report: dict[str, Any], *, candidate_key: str=DEFAULT_CANDIDATE_KEY) -> list[dict[str, Any]]: + trace_by_label = {str(row['shape_key']): row for row in route_trace_for_contract_shapes(candidate_key=candidate_key)} + rows = [] + for label, row in sorted(report.get('per_shape', {}).items()): + ratio = row.get('ratio_vs_flashlib') + if isinstance(ratio, (float, int)) and ratio < 1.0: + trace_row = trace_by_label.get(label, {}) + rows.append({'shape_key': label, 'kernel_ms': row.get('kernel_ms'), 'flashlib_ms': row.get('flashlib_ms'), 'ratio_vs_flashlib': ratio, 'selected_route': trace_row.get('selected_route'), 'route_kind': trace_row.get('route_kind', 'unknown'), 'classification': 'kernel-slow' if trace_row.get('route_kind') == 'specialized' else 'fallback-slow'}) + return rows + +def _benchmark_payload(candidate_report: dict[str, Any], baseline_report: dict[str, Any], *, use_cupti: bool, shape_labels, candidate_key: str=DEFAULT_CANDIDATE_KEY) -> dict[str, Any]: + cfg = _candidate_config(candidate_key) + consumed_labels = _candidate_target_shapes(candidate_key) + selected_route_labels = tuple(dict.fromkeys((*baseline_wide.SELECTED_TARGET_SHAPES, *consumed_labels))) + route_trace = _annotate_route_trace(route_trace_for_contract_shapes(shape_labels, candidate_key=candidate_key), candidate_report, baseline_report, candidate_key=candidate_key) + below_flashlib = _below_flashlib_rows(candidate_report, candidate_key=candidate_key) + candidate_metric = candidate_report['summary']['primary_mean'] + baseline_metric = baseline_report['summary']['primary_mean'] + return {'candidate_key': candidate_key, 'candidate_id': cfg['candidate_id'], 'selected_seeds': _candidate_selected_seeds(candidate_key), 'source_tasks': {SEED_D96_ID: 'weave-evolve-knn-build-c2eb / design_doc/active/weave_evolve_knn_build_round_104_4be7_d96exact.md', SEED_D320_ID: 'weave-evolve-knn-build-87c7 / design_doc/active/weave_evolve_knn_build_round_104_8227_d320tail.md', SEED_D768_ID: 'weave-evolve-knn-build-f533 / design_doc/active/weave_evolve_knn_build_round_105_4be7_d768fused.md', SEED_K32_ID: 'weave-evolve-knn-build-626a / design_doc/active/weave_evolve_knn_build_round_106_8fcb_k32split148.md'}, 'tflops': candidate_metric, 'baseline_tflops': baseline_metric, 'metric_delta': candidate_metric - baseline_metric if candidate_metric and baseline_metric else None, 'all_correct': candidate_report['summary']['all_correct'], 'baseline_all_correct': baseline_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'baseline_performance_comparable': baseline_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'baseline_invalid_performance_reason': baseline_report['summary']['invalid_performance_reason'], 'measured_entrypoint': cfg['benchmark_entrypoint'], 'baseline_entrypoint': BASELINE_ENTRYPOINT, 'measured_shape_labels': 'all_canonical' if shape_labels is None else tuple(shape_labels), 'selected_route_labels': selected_route_labels, 'consumed_seed_labels': consumed_labels, 'guard_miss_audit_labels': GUARD_MISS_AUDIT_SHAPES, 'selected_route_rows': _rows_for_labels(candidate_report, selected_route_labels), 'baseline_selected_route_rows': _rows_for_labels(baseline_report, selected_route_labels), 'consumed_seed_rows': _rows_for_labels(candidate_report, consumed_labels), 'baseline_consumed_seed_rows': _rows_for_labels(baseline_report, consumed_labels), 'guard_miss_audit_rows': _rows_for_labels(candidate_report, GUARD_MISS_AUDIT_SHAPES), 'baseline_guard_miss_audit_rows': _rows_for_labels(baseline_report, GUARD_MISS_AUDIT_SHAPES), 'per_consumed_row_delta': _per_consumed_row_delta(candidate_report, baseline_report, candidate_key=candidate_key), 'seed_delta_matrix': _seed_delta_matrix(candidate_report, baseline_report, candidate_key=candidate_key), 'frontmatter_seed_delta_matrix': _frontmatter_seed_delta_matrix(candidate_report, baseline_report, candidate_key=candidate_key), 'targeted_seed_rows': TARGETED_SEED_ROWS, 'candidate_dispatchers': CANDIDATE_DISPATCHERS, 'selected_candidate_dispatcher': cfg['candidate_id'], 'guard_plan': cfg['guard_plan'], 'route_modules': PRODUCTION_ROUTE_MODULES, 'route_trace': route_trace, 'forced_fallback_route_trace': route_trace_for_contract_shapes(shape_labels, force_fallback=True, candidate_key=candidate_key), 'route_trace_included': True, 'contract_summary': candidate_report['summary'], 'baseline_contract_summary': baseline_report['summary'], 'contract_performance': candidate_report['performance'], 'baseline_contract_performance': baseline_report['performance'], 'timing_backends': _timing_backends_for_reports(candidate_report, baseline_report), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'performance_coverage': 'partial' if below_flashlib else 'pass', 'coverage_only_routes': [], 'hot_bucket_blockers': below_flashlib, 'report': candidate_report, 'baseline_report': baseline_report} + +def benchmark_candidate_portfolio(*, candidate_key: str=DEFAULT_CANDIDATE_KEY, use_cupti: bool=True, shape_labels=None, baseline_report: dict[str, Any] | None=None) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=_candidate_kernel_fn(candidate_key)) + if baseline_report is None: + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_baseline_wide) + return _benchmark_payload(candidate_report, baseline_report, use_cupti=use_cupti, shape_labels=shape_labels, candidate_key=candidate_key) + +def benchmark_candidate_d96_d320_only(**kwargs) -> dict[str, Any]: + return benchmark_candidate_portfolio(candidate_key='d96_d320_only', **kwargs) + +def benchmark_candidate_f533_non128_only(**kwargs) -> dict[str, Any]: + return benchmark_candidate_portfolio(candidate_key='f533_non128_only', **kwargs) + +def benchmark_candidate_k32_only(**kwargs) -> dict[str, Any]: + return benchmark_candidate_portfolio(candidate_key='k32_only', **kwargs) + +def benchmark_knn_build_dispatch_4247_non128_8199_c2eb_f533_8fcb_8227_full82_matrix_v1(*, use_cupti: bool=True, shape_labels=None, baseline_report: dict[str, Any] | None=None) -> dict[str, Any]: + return benchmark_candidate_portfolio(candidate_key=DEFAULT_CANDIDATE_KEY, use_cupti=use_cupti, shape_labels=shape_labels, baseline_report=baseline_report) + +def benchmark_subset_matrix(*, use_cupti: bool=True, shape_labels=None, candidate_keys: tuple[str, ...]=CANDIDATE_KEYS) -> dict[str, Any]: + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_baseline_wide) + payloads = {key: benchmark_candidate_portfolio(candidate_key=key, use_cupti=use_cupti, shape_labels=shape_labels, baseline_report=baseline_report) for key in candidate_keys} + baseline_metric = baseline_report['summary']['primary_mean'] + return {'matrix_id': 'f533_split148_matrix_over_8199_full82_v1', 'baseline_candidate_id': BASELINE_ID, 'baseline_entrypoint': BASELINE_ENTRYPOINT, 'baseline_tflops': baseline_metric, 'baseline_all_correct': baseline_report['summary']['all_correct'], 'baseline_report': baseline_report, 'candidate_keys': candidate_keys, 'candidate_dispatchers': CANDIDATE_DISPATCHERS, 'candidate_summaries': {key: {'candidate_id': payload['candidate_id'], 'measured_entrypoint': payload['measured_entrypoint'], 'selected_seeds': payload['selected_seeds'], 'tflops': payload['tflops'], 'metric_delta': payload['metric_delta'], 'all_correct': payload['all_correct'], 'performance_comparable': payload['performance_comparable'], 'performance_coverage': payload['performance_coverage'], 'hot_bucket_blocker_count': len(payload['hot_bucket_blockers'])} for key, payload in payloads.items()}, 'payloads': payloads, 'measured_shape_labels': 'all_canonical' if shape_labels is None else tuple(shape_labels), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'timing_backends': _timing_backends_for_reports(baseline_report, *(payload['report'] for payload in payloads.values())), 'route_trace_included': True, 'rank_objective': {key: {'metric': 'tflops', 'direction': 'maximize', 'value': payload['tflops'], 'baseline_value': baseline_metric, 'delta': payload['metric_delta'], 'denominator': 'full82_v9' if shape_labels is None else ''.join(['shape', format(len(tuple(shape_labels)), '')])} for key, payload in payloads.items()}} + +def _denominator_label(shape_labels) -> str: + if shape_labels is None: + return ''.join(['full', format(len(eval_mod.CANONICAL_SHAPES), '')]) + return ''.join(['shape', format(len(tuple(shape_labels)), '')]) + +def write_benchmark_artifacts(artifact_dir: str | Path, *, use_cupti: bool=True, shape_labels=None, candidate_key: str | None=None) -> dict[str, str]: + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + denom = _denominator_label(shape_labels) + candidate_keys = CANDIDATE_KEYS if candidate_key is None else (candidate_key,) + matrix = benchmark_subset_matrix(use_cupti=use_cupti, shape_labels=shape_labels, candidate_keys=candidate_keys) + baseline_path = out_dir / ''.join([format(denom, ''), '_same_session_baseline_8199_widecombine_for_split148_matrix_v1.json']) + summary_path = out_dir / ''.join([format(denom, ''), '_f533_split148_matrix_summary_v1.json']) + paths: dict[str, str] = {'same_session_baseline_payload': str(baseline_path), 'matrix_summary': str(summary_path)} + baseline_path.write_text(json.dumps({'candidate_id': BASELINE_ID, 'measured_entrypoint': BASELINE_ENTRYPOINT, 'measured_shape_labels': matrix['measured_shape_labels'], 'timing_backend_requested': matrix['timing_backend_requested'], 'timing_backends': matrix['timing_backends'], 'tflops': matrix['baseline_tflops'], 'all_correct': matrix['baseline_all_correct'], 'performance_comparable': matrix['baseline_report']['summary']['performance_comparable'], 'contract_summary': matrix['baseline_report']['summary'], 'contract_performance': matrix['baseline_report']['performance'], 'route_trace': baseline_wide.route_trace_for_contract_shapes(shape_labels), 'route_trace_included': True, 'report': matrix['baseline_report']}, indent=2, sort_keys=True) + '\n') + for key, payload in matrix['payloads'].items(): + candidate_id = str(payload['candidate_id']).removeprefix('candidate_') + candidate_path = out_dir / ''.join([format(denom, ''), '_dispatch_', format(candidate_id, ''), '.json']) + route_trace_path = out_dir / ''.join([format(denom, ''), '_route_trace_', format(candidate_id, ''), '.json']) + forced_trace_path = out_dir / ''.join([format(denom, ''), '_forced_fallback_trace_', format(candidate_id, ''), '.json']) + seed_matrix_path = out_dir / ''.join([format(denom, ''), '_seed_delta_matrix_', format(candidate_id, ''), '.json']) + candidate_path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + route_trace_path.write_text(json.dumps(payload['route_trace'], indent=2, sort_keys=True) + '\n') + forced_trace_path.write_text(json.dumps(payload['forced_fallback_route_trace'], indent=2, sort_keys=True) + '\n') + seed_matrix_path.write_text(json.dumps(payload['seed_delta_matrix'], indent=2, sort_keys=True) + '\n') + paths[''.join([format(key, ''), '_candidate_payload'])] = str(candidate_path) + paths[''.join([format(key, ''), '_route_trace'])] = str(route_trace_path) + paths[''.join([format(key, ''), '_forced_fallback_trace'])] = str(forced_trace_path) + paths[''.join([format(key, ''), '_seed_delta_matrix'])] = str(seed_matrix_path) + summary_payload = {key: value for key, value in matrix.items() if key not in {'payloads', 'baseline_report'}} + summary_payload['candidate_payload_paths'] = {key: paths[''.join([format(key, ''), '_candidate_payload'])] for key in matrix['payloads']} + summary_payload['same_session_baseline_payload'] = str(baseline_path) + summary_path.write_text(json.dumps(summary_payload, indent=2, sort_keys=True) + '\n') + return paths diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_4247_non128_8199_splitretune_full82_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_4247_non128_8199_splitretune_full82_v1.py new file mode 100644 index 00000000..be18baee --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_4247_non128_8199_splitretune_full82_v1.py @@ -0,0 +1,242 @@ +"""Full82 non-D128 split-retune dispatcher-consumption wrapper over 4247. + +Minimum target architecture: sm_100a. This opt-in dispatcher candidate starts +from the exported 4247 Weave dispatcher and adds exact BF16 non-D128 guards for +the five round-8199 split-retuned frontier rows. Guard misses stay on the inherited 4247 +Weave dispatcher stack; no external implementation is on the production route. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +from pathlib import Path +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_e3de_9138_bcb3_4247_v1 as base_4247 +from . import knn_build_non128_frontier_8199_splitretune_v1 as non128_seed +MODULE = 'loom.examples.weave.knn_build_dispatch_4247_non128_8199_splitretune_full82_v1' +SEED_NON128_ID = 'non128_frontier_8199_splitretune_v1' +ROUTE_NON128_ENTRYPOINT = ''.join([format(non128_seed.MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_BASE_4247_ENTRYPOINT = 'loom.examples.weave.knn_build_dispatch_e3de_9138_bcb3_4247_v1:launch_from_contract_inputs' +TARGET_SHAPES = non128_seed.TARGET_SHAPES +TARGET_SHAPE_SET = set(TARGET_SHAPES) +SELECTED_TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_large_b1_q8192_m8192_d128_k20", "build_large_b1_q8192_m8192_d128_k32", "build_over64_stress_qm2048_k96", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32", "rag_batch_b2_q256_m50000_d128_k10", "rag_irregular_b1_q512_m131071_d128_k10", "search_rect_b1_q1024_m8192_d128_k10", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q2048_m2048_d256_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10", "search_rect_b1_q2048_m32768_d128_k10", "build_large_tail_b1_q6144_m6144_d128_k20", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "build_over32_stress_qm2048_k64", "build_over32_stress_qm4096_k64", "build_k_sweep_qm512_k1", "build_k_sweep_qm512_k2", "build_k_sweep_qm1024_k16", "build_k_sweep_qm512_k4", "build_k_sweep_qm512_k5", "build_k_sweep_qm512_k6", "rag_microbatch_b1_q8_m100000_d128_k10", "rag_microbatch_b1_q32_m100000_d128_k10", "build_dim_sweep_b1_q1024_m1024_d64_k10", "build_dim_sweep_b1_q4096_m4096_d64_k10", "search_rect_b1_q1024_m32768_d64_k10", "rag_online_b1_q1_m100000_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "build_over64_stress_qm1024_k96", "build_over64_stress_qm4096_k96", "build_dim_sweep_b1_q1024_m1024_d96_k10", "build_dim_sweep_b1_q2048_m2048_d192_k10", "build_highd_b1_q1024_m1024_d320_k10", "search_rect_highd_b1_q512_m12000_d320_k10", "rag_microbatch_highd_b1_q16_m50000_d768_k10"]}')) +CONSUMED_SEED_TARGET_SHAPES = TARGET_SHAPES +GUARD_MISS_AUDIT_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_dim_sweep_b1_q1024_m1024_d96_k10", "build_dim_sweep_b1_q2048_m2048_d192_k10", "build_highd_b1_q1024_m1024_d320_k10", "search_rect_highd_b1_q512_m12000_d320_k10", "rag_microbatch_highd_b1_q16_m50000_d768_k10", "build_dim_sweep_b1_q1024_m1024_d64_k10", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q4096_m4096_d64_k10", "search_rect_b1_q1024_m32768_d64_k10", "rag_online_b1_q1_m100000_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "build_over64_stress_qm1024_k96", "build_over64_stress_qm4096_k96", "build_k_sweep_qm512_k4", "build_k_sweep_qm512_k8", "build_k_sweep_qm512_k10", "rag_microbatch_b1_q8_m100000_d128_k10", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_microbatch_b1_q32_m100000_d128_k10", "build_large_b1_q8192_m8192_d128_k20", "build_qm2048_d128_k10", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "flashml_correctness_b1_q256_m256_d128_k5", "build_over32_stress_qm2048_k64", "search_rect_over32_b1_q2048_m65536_d128_k64", "rag_offline_large_m_over32_b1_q2048_m250000_d128_k64", "build_k_sweep_qm512_k5", "build_over32_stress_qm4096_k64"]}')) +DISPATCH_CORRECTNESS_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["flashml_correctness_b1_q256_m256_d128_k5", "build_dim_sweep_b1_q1024_m1024_d96_k10", "build_dim_sweep_b1_q2048_m2048_d192_k10", "build_highd_b1_q1024_m1024_d320_k10", "search_rect_highd_b1_q512_m12000_d320_k10", "rag_microbatch_highd_b1_q16_m50000_d768_k10", "build_large_b1_q8192_m8192_d128_k20", "build_large_b1_q8192_m8192_d128_k32", "build_over64_stress_qm2048_k96", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32", "rag_batch_b2_q256_m50000_d128_k10", "rag_irregular_b1_q512_m131071_d128_k10", "search_rect_b1_q1024_m8192_d128_k10", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q2048_m2048_d256_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10", "search_rect_b1_q2048_m32768_d128_k10", "build_large_tail_b1_q6144_m6144_d128_k20", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "build_over32_stress_qm2048_k64", "build_over32_stress_qm4096_k64", "build_k_sweep_qm512_k1", "build_k_sweep_qm512_k2", "build_k_sweep_qm1024_k16", "build_k_sweep_qm512_k4", "build_k_sweep_qm512_k5", "build_k_sweep_qm512_k6", "rag_microbatch_b1_q8_m100000_d128_k10", "rag_microbatch_b1_q32_m100000_d128_k10", "build_dim_sweep_b1_q1024_m1024_d64_k10", "build_dim_sweep_b1_q4096_m4096_d64_k10", "search_rect_b1_q1024_m32768_d64_k10", "rag_online_b1_q1_m100000_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "build_over64_stress_qm1024_k96", "build_over64_stress_qm4096_k96", "build_k_sweep_qm512_k8", "build_k_sweep_qm512_k10", "build_qm2048_d128_k10", "search_rect_over32_b1_q2048_m65536_d128_k64", "rag_offline_large_m_over32_b1_q2048_m250000_d128_k64", "rag_stream_b1_q128_m100000_d128_k10", "search_rect_b1_q4096_m65536_d128_k20", "rag_offline_largek_b1_q4096_m100000_d128_k20", "rag_offline_large_m_b1_q8192_m250000_d128_k20"]}')) +TARGETED_SEED_ROWS = {'build_dim_sweep_b1_q1024_m1024_d96_k10': {'kernel_ms': 0.096704, 'flashlib_ms': 0.090848, 'ratio_vs_flashlib': 0.9394440767703508, 'tflops': 2.0818848444738585}, 'build_dim_sweep_b1_q2048_m2048_d192_k10': {'kernel_ms': 0.118015, 'flashlib_ms': 0.124479, 'ratio_vs_flashlib': 1.0547726983857986, 'tflops': 13.647525619624624}, 'build_highd_b1_q1024_m1024_d320_k10': {'kernel_ms': 0.09792, 'flashlib_ms': 0.100096, 'ratio_vs_flashlib': 1.0222222222222224, 'tflops': 6.8534379084967325}, 'search_rect_highd_b1_q512_m12000_d320_k10': {'kernel_ms': 0.202527, 'flashlib_ms': 0.164736, 'ratio_vs_flashlib': 0.8134026574234545, 'tflops': 19.415485342695046}, 'rag_microbatch_highd_b1_q16_m50000_d768_k10': {'kernel_ms': 0.222911, 'flashlib_ms': 0.174944, 'ratio_vs_flashlib': 0.7848154644678818, 'tflops': 5.512513962971769}} +PRODUCTION_ROUTE_MODULES = {**base_4247.PRODUCTION_ROUTE_MODULES, SEED_NON128_ID: ROUTE_NON128_ENTRYPOINT, 'base_4247': ROUTE_BASE_4247_ENTRYPOINT} +CANDIDATE_DISPATCHERS = _decode_capture(_json_loads('{"__tuple__": [{"__dict_items__": [["id", "baseline_exported_4247"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_e3de_9138_bcb3_4247_v1:benchmark_knn_build_dispatch_e3de_9138_bcb3_4247_v1"], ["consumed_seeds", {"__tuple__": ["large_square_k20k32", "over64_k96", "baseline_7c3a_rag_k10", "rag_frontier_7399_k10", "rag_frontier_7399_k32_replaced", "rag_frontier_4fbf_k32", "rect_smallq_largem_d15e", "baseline_7c3a_policy", "fallback", "dim_d64_73a9", "current_exact_k32_dispatcher", "base_7399_d15e_dispatcher", "rag_frontier_7399_k32", "dim_d256_df2f", "dim_fp16_d128_df2f", "base_dispatch", "rect_intermediate_4452_s8", "base_champion_6b59", "base_k32_d64_62b1", "default_k96_a330", "large_tail_a4f6", "midk_81aa_q2048_k24_k28", "midk_9b2c_q4096_k28", "base_f552", "midk_bad5_k64split8", "base_e51c", "midk_f8c3_q4096_k64_split8_a194", "base_f8c3", "lowk_b193_q512_s4", "lowk_b193_q1024_k16_s16", "large_square_5407_q8192_k32_s2", "base_f853", "lowk_b193_q512_k4_k5_k6_s4", "base_f16b", "rag_microbatch_b2ec_s72_g8", "base_4a72", "rag_m64_s128_0c69", "rag_s144_g12_cta1_059f", "rag_s144_g8_cta1_4982_read_ref_parameterized", "base_397b", "d64_fdd7_aa88_v2", "base_8700", "rect_d64_cf49_v3_9138", "q1_mbucket_aa88_q1m_v3_bcb3", "over64_k96_a2f8_v1_generated_v8", "base_e3de"]}], ["guard_plan", {"__tuple__": ["exported 4247 guard stack"]}], ["expected_shape_wins", {"__tuple__": ["build_large_b1_q8192_m8192_d128_k20", "build_large_b1_q8192_m8192_d128_k32", "build_over64_stress_qm2048_k96", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32", "rag_batch_b2_q256_m50000_d128_k10", "rag_irregular_b1_q512_m131071_d128_k10", "search_rect_b1_q1024_m8192_d128_k10", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q2048_m2048_d256_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10", "search_rect_b1_q2048_m32768_d128_k10", "build_large_tail_b1_q6144_m6144_d128_k20", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "build_over32_stress_qm2048_k64", "build_over32_stress_qm4096_k64", "build_k_sweep_qm512_k1", "build_k_sweep_qm512_k2", "build_k_sweep_qm1024_k16", "build_k_sweep_qm512_k4", "build_k_sweep_qm512_k5", "build_k_sweep_qm512_k6", "rag_microbatch_b1_q8_m100000_d128_k10", "rag_microbatch_b1_q32_m100000_d128_k10", "build_dim_sweep_b1_q1024_m1024_d64_k10", "build_dim_sweep_b1_q4096_m4096_d64_k10", "search_rect_b1_q1024_m32768_d64_k10", "rag_online_b1_q1_m100000_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "build_over64_stress_qm1024_k96", "build_over64_stress_qm4096_k96"]}], ["fallback", "loom.examples.weave.knn_build_dispatch_e3de_9138_bcb3_4247_v1:launch_from_contract_inputs"], ["rejected_reason", "same-session exported-registry baseline"]]}, {"__dict_items__": [["id", "candidate_4247_non128_8199_splitretune_full82_v1"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_4247_non128_8199_splitretune_full82_v1:benchmark_knn_build_dispatch_4247_non128_8199_splitretune_full82_v1"], ["consumed_seeds", {"__tuple__": ["non128_frontier_8199_splitretune_v1"]}], ["guard_plan", {"__tuple__": ["five exact split-retuned non-D128 frontier guards", "then exported 4247 guard stack"]}], ["expected_shape_wins", {"__tuple__": ["build_dim_sweep_b1_q1024_m1024_d96_k10", "build_dim_sweep_b1_q2048_m2048_d192_k10", "build_highd_b1_q1024_m1024_d320_k10", "search_rect_highd_b1_q512_m12000_d320_k10", "rag_microbatch_highd_b1_q16_m50000_d768_k10"]}], ["fallback", "loom.examples.weave.knn_build_dispatch_e3de_9138_bcb3_4247_v1:launch_from_contract_inputs"], ["rejected_reason", null]]}]}')) +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k48over32", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 48]], "cta_group": 1, "threads": 192}')) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False, enable_non128: bool=True) -> str: + if not force_fallback and enable_non128 and (non128_seed._target_label_for_inputs(inputs) is not None): + return non128_seed.route_for_contract_inputs(inputs) + return base_4247.route_for_contract_inputs(inputs) + +def _launch_route(inputs: dict[str, Any], route: str) -> None: + if str(route).startswith(non128_seed.ROUTE_PREFIX): + non128_seed.launch_from_contract_inputs(inputs) + return + base_4247._launch_route(inputs, route) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False, enable_non128: bool=True) -> None: + route = route_for_contract_inputs(inputs, force_fallback=force_fallback, enable_non128=enable_non128) + _launch_route(inputs, route) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_base_4247(inputs: dict[str, Any]) -> None: + base_4247.launch_from_contract_inputs(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return base_4247._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=DISPATCH_CORRECTNESS_SHAPES, benchmark: bool=False) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=None, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + shapes = None if shape_labels is None else _select_contract_shapes(shape_labels) + return evaluate_contract(shapes=shapes, correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def _trace_inputs_from_shape(shape: dict[str, Any]) -> dict[str, Any]: + return base_4247._trace_inputs_from_shape(shape) + +def _inputs_for_label(label: str) -> dict[str, Any]: + return base_4247._inputs_for_label(label) + +def _normalize_route_row(row: dict[str, Any]) -> dict[str, Any]: + out = dict(row) + route_kind = str(out.get('route_kind') or 'general') + selected_seed = out.get('selected_seed') or out.get('consumed_seed') + out.setdefault('selected_seed', selected_seed) + out.setdefault('expected_seed', selected_seed) + if out.get('route_source') not in {'shape-specific-seed', 'generated-variant', 'broad-dispatcher', 'generic-weave-fallback', 'external-reference', 'unknown'}: + out['route_source'] = 'shape-specific-seed' if selected_seed else 'generic-weave-fallback' if route_kind in {'fallback', 'coverage-only'} else 'broad-dispatcher' + if out.get('classification') not in {'seed-consumed', 'route-ok', 'guard-miss', 'kernel-slow', 'fallback-slow', 'coverage-only', 'benchmark-path-mismatch', 'unmeasured'}: + out['classification'] = 'coverage-only' if route_kind == 'coverage-only' else 'route-ok' + out.setdefault('dispatcher_kernel_ms', None) + out.setdefault('shape_specific_kernel_ms', None) + out.setdefault('relative_speedup_vs_baseline', None) + return out + +def _base_route_trace_record(inputs: dict[str, Any], *, force_fallback: bool=False) -> dict[str, Any]: + label = str(inputs.get('label')) + row = dict(base_4247.route_trace_for_contract_shapes((label,), force_fallback=force_fallback)[0]) + row['base_4247_route'] = base_4247.route_for_contract_inputs(inputs, force_fallback=force_fallback) + return _normalize_route_row(row) + +def _non128_trace_record(inputs: dict[str, Any]) -> dict[str, Any]: + label = str(non128_seed._target_label_for_inputs(inputs)) + spec = non128_seed.SHAPE_SPECS[label] + route = non128_seed.route_for_contract_inputs(inputs) + targeted = dict(TARGETED_SEED_ROWS[label]) + return {'shape_key': label, 'selected_route': route, 'selected_entrypoint': ROUTE_NON128_ENTRYPOINT, 'selected_seed': SEED_NON128_ID, 'expected_seed': SEED_NON128_ID, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': '8199_splitretune_non128_exact_shape_guard', 'guard_condition': ''.join(['exact BF16 B=', format(spec['B'], ''), ' Q=', format(spec['Q'], ''), ' M=', format(spec['M'], ''), ' D=', format(spec['D'], ''), ' K=', format(spec['K'], ''), ' build=', format(spec['build'], '')]), 'coverage': 'consumes 8199 split-retuned non-D128 frontier seed before exported 4247 fallback', 'consumed_seed': SEED_NON128_ID, 'replaced_route': base_4247.route_for_contract_inputs(inputs), 'base_4247_route': base_4247.route_for_contract_inputs(inputs), 'feature_chunks': spec['feature_chunks'], 'split_count': non128_seed._split_count_for_label(label), 'preprocess_stage': ''.join(['d', format(int(spec['D']), ''), '_weave_pad_to_d', format(int(spec['feature_chunks']) * non128_seed.K_TILE, '')]) if int(spec['D']) != int(spec['feature_chunks']) * non128_seed.K_TILE else None, 'targeted_seed_timing_backend': 'cupti', 'targeted_seed_kernel_ms': targeted['kernel_ms'], 'targeted_seed_ratio_vs_flashlib': targeted['ratio_vs_flashlib'], 'row_selection': targeted, 'classification': 'seed-consumed', 'dispatcher_kernel_ms': None, 'shape_specific_kernel_ms': targeted['kernel_ms'], 'relative_speedup_vs_baseline': None} + +def _route_trace_record(inputs: dict[str, Any], *, force_fallback: bool=False) -> dict[str, Any]: + if force_fallback and non128_seed._target_label_for_inputs(inputs) is not None: + row = _base_route_trace_record(inputs) + row['expected_seed'] = SEED_NON128_ID + row['guard_id'] = 'forced_fallback_8199_splitretune_non128_disabled' + row['guard_condition'] = 'forced fallback to exported 4247; 8199 split-retuned non-D128 overlay disabled' + row['forced_disabled_seeds'] = (SEED_NON128_ID,) + row['classification'] = 'guard-miss' + return row + if not force_fallback and non128_seed._target_label_for_inputs(inputs) is not None: + return _non128_trace_record(inputs) + return _base_route_trace_record(inputs) + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + return [_route_trace_record(_trace_inputs_from_shape(shape), force_fallback=force_fallback) for shape in selected] + +def _timing_backends_for_reports(*reports: dict[str, Any]) -> list[str]: + return base_4247._timing_backends_for_reports(*reports) + +def _rows_for_labels(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return base_4247._rows_for_labels(report, labels) + +def _seed_delta_matrix(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> list[dict[str, Any]]: + matrix = [] + for label in CONSUMED_SEED_TARGET_SHAPES: + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + inputs = _inputs_for_label(label) + matrix.append({'shape_key': label, 'baseline_route': base_4247.route_for_contract_inputs(inputs), 'candidate_route': route_for_contract_inputs(inputs), 'selected_seed': SEED_NON128_ID, 'candidate_ms': candidate_ms, 'baseline_ms': baseline_ms, 'flashlib_ms': candidate_row.get('flashlib_ms'), 'metric_delta_ms': candidate_ms - baseline_ms if candidate_ms and baseline_ms else None, 'speedup_vs_baseline_4247': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'ratio_vs_flashlib': candidate_row.get('ratio_vs_flashlib'), 'targeted_seed_kernel_ms': TARGETED_SEED_ROWS[label]['kernel_ms'], 'targeted_seed_ratio_vs_flashlib': TARGETED_SEED_ROWS[label]['ratio_vs_flashlib'], 'timing_backend': candidate_row.get('timing_backend') or baseline_row.get('timing_backend')}) + return matrix + +def _frontmatter_seed_delta_matrix(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> list[dict[str, Any]]: + rows = [] + for item in _seed_delta_matrix(candidate_report, baseline_report): + rows.append({'shape_key': item['shape_key'], 'baseline_route': item['baseline_route'], 'candidate_deltas': [{'candidate_id': 'candidate_4247_non128_8199_splitretune_full82_v1', 'selected_seed': item['selected_seed'], 'metric_delta': item['metric_delta_ms'], 'ratio_vs_flashlib': item['ratio_vs_flashlib'], 'timing_backend': item['timing_backend'] or 'cupti'}]}) + return rows + +def _per_consumed_row_delta(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> dict[str, Any]: + return {item['shape_key']: {'candidate_ms': item['candidate_ms'], 'baseline_4247_ms': item['baseline_ms'], 'flashlib_ms': item['flashlib_ms'], 'speedup_vs_baseline_4247': item['speedup_vs_baseline_4247'], 'ratio_vs_flashlib': item['ratio_vs_flashlib'], 'candidate_route': item['candidate_route'], 'baseline_4247_route': item['baseline_route'], 'selected_seed': item['selected_seed'], 'targeted_seed_kernel_ms': item['targeted_seed_kernel_ms'], 'targeted_seed_ratio_vs_flashlib': item['targeted_seed_ratio_vs_flashlib'], 'candidate_passed': candidate_report.get('per_shape', {}).get(item['shape_key'], {}).get('passed'), 'baseline_passed': baseline_report.get('per_shape', {}).get(item['shape_key'], {}).get('passed')} for item in _seed_delta_matrix(candidate_report, baseline_report)} + +def _annotate_route_trace(route_trace: list[dict[str, Any]], candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> list[dict[str, Any]]: + annotated = [] + for row in route_trace: + label = str(row.get('shape_key')) + out = dict(row) + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + out['dispatcher_kernel_ms'] = candidate_ms + out['baseline_dispatcher_kernel_ms'] = baseline_ms + out['flashlib_ms'] = candidate_row.get('flashlib_ms') + out['relative_speedup_vs_baseline'] = baseline_ms / candidate_ms if candidate_ms and baseline_ms else None + out['route_changed_vs_base_4247'] = out.get('selected_route') != out.get('base_4247_route') + ratio = candidate_row.get('ratio_vs_flashlib') + if label in TARGET_SHAPE_SET: + if out.get('selected_seed') != SEED_NON128_ID: + out['classification'] = 'guard-miss' + elif isinstance(ratio, (float, int)) and ratio < 1.0: + out['classification'] = 'kernel-slow' + elif out['relative_speedup_vs_baseline'] is not None and out['relative_speedup_vs_baseline'] < 1.0: + out['classification'] = 'kernel-slow' + else: + out['classification'] = 'seed-consumed' + elif isinstance(ratio, (float, int)) and ratio < 1.0: + out['classification'] = 'kernel-slow' if out.get('route_kind') == 'specialized' else 'fallback-slow' + else: + out['classification'] = out.get('classification', 'route-ok') + annotated.append(_normalize_route_row(out)) + return annotated + +def _below_flashlib_rows(report: dict[str, Any]) -> list[dict[str, Any]]: + trace_by_label = {str(row['shape_key']): row for row in route_trace_for_contract_shapes()} + rows = [] + for label, row in sorted(report.get('per_shape', {}).items()): + ratio = row.get('ratio_vs_flashlib') + if isinstance(ratio, (float, int)) and ratio < 1.0: + trace_row = trace_by_label.get(label, {}) + rows.append({'shape_key': label, 'kernel_ms': row.get('kernel_ms'), 'flashlib_ms': row.get('flashlib_ms'), 'ratio_vs_flashlib': ratio, 'selected_route': trace_row.get('selected_route'), 'route_kind': trace_row.get('route_kind', 'unknown'), 'classification': 'kernel-slow' if trace_row.get('route_kind') == 'specialized' else 'fallback-slow'}) + return rows + +def _failed_baseline_report(exc: Exception, *, shape_labels, baseline_id: str) -> dict[str, Any]: + reason = ''.join([format(type(exc).__name__, ''), ': ', format(exc, '')]) + return {'contract': eval_mod.CONTRACT.kernel, 'contract_version': eval_mod.CONTRACT.contract_version, 'summary': {'all_correct': False, 'correctness_shapes': 0, 'failed_correctness_shapes': 1, 'correctness_failure_count': 1, 'first_correctness_failure': reason, 'primary_metric': 'tflops', 'primary_direction': 'maximize', 'primary_mean': None, 'performance_comparable': False, 'invalid_performance_reason': reason}, 'performance': {'comparable': False, 'invalid_reason': reason, 'primary_mean': None, 'primary_metric': 'tflops', 'valid_measurement_count': 0}, 'per_shape': {}, 'benchmark_exception': reason, 'baseline_id': baseline_id, 'measured_shape_labels': 'all_canonical' if shape_labels is None else tuple(shape_labels)} + +def _benchmark_payload(candidate_report: dict[str, Any], baseline_report: dict[str, Any], *, use_cupti: bool, shape_labels) -> dict[str, Any]: + route_trace = _annotate_route_trace(route_trace_for_contract_shapes(shape_labels), candidate_report, baseline_report) + below_flashlib = _below_flashlib_rows(candidate_report) + candidate_metric = candidate_report['summary']['primary_mean'] + baseline_metric = baseline_report['summary']['primary_mean'] + return {'candidate_id': 'candidate_4247_non128_8199_splitretune_full82_v1', 'tflops': candidate_metric, 'baseline_tflops': baseline_metric, 'metric_delta': candidate_metric - baseline_metric if candidate_metric and baseline_metric else None, 'all_correct': candidate_report['summary']['all_correct'], 'baseline_all_correct': baseline_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'baseline_performance_comparable': baseline_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'baseline_invalid_performance_reason': baseline_report['summary']['invalid_performance_reason'], 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_knn_build_dispatch_4247_non128_8199_splitretune_full82_v1']), 'baseline_entrypoint': 'loom.examples.weave.knn_build_dispatch_e3de_9138_bcb3_4247_v1:benchmark_knn_build_dispatch_e3de_9138_bcb3_4247_v1', 'measured_shape_labels': 'all_canonical' if shape_labels is None else tuple(shape_labels), 'selected_route_labels': SELECTED_TARGET_SHAPES, 'consumed_seed_labels': CONSUMED_SEED_TARGET_SHAPES, 'guard_miss_audit_labels': GUARD_MISS_AUDIT_SHAPES, 'selected_route_rows': _rows_for_labels(candidate_report, SELECTED_TARGET_SHAPES), 'baseline_selected_route_rows': _rows_for_labels(baseline_report, SELECTED_TARGET_SHAPES), 'consumed_seed_rows': _rows_for_labels(candidate_report, CONSUMED_SEED_TARGET_SHAPES), 'baseline_consumed_seed_rows': _rows_for_labels(baseline_report, CONSUMED_SEED_TARGET_SHAPES), 'guard_miss_audit_rows': _rows_for_labels(candidate_report, GUARD_MISS_AUDIT_SHAPES), 'baseline_guard_miss_audit_rows': _rows_for_labels(baseline_report, GUARD_MISS_AUDIT_SHAPES), 'per_consumed_row_delta': _per_consumed_row_delta(candidate_report, baseline_report), 'seed_delta_matrix': _seed_delta_matrix(candidate_report, baseline_report), 'frontmatter_seed_delta_matrix': _frontmatter_seed_delta_matrix(candidate_report, baseline_report), 'targeted_seed_rows': TARGETED_SEED_ROWS, 'candidate_dispatchers': CANDIDATE_DISPATCHERS, 'selected_candidate_dispatcher': 'candidate_4247_non128_8199_splitretune_full82_v1', 'route_modules': PRODUCTION_ROUTE_MODULES, 'route_trace': route_trace, 'forced_fallback_route_trace': route_trace_for_contract_shapes(shape_labels, force_fallback=True), 'route_trace_included': True, 'contract_summary': candidate_report['summary'], 'baseline_contract_summary': baseline_report['summary'], 'contract_performance': candidate_report['performance'], 'baseline_contract_performance': baseline_report['performance'], 'timing_backends': _timing_backends_for_reports(candidate_report, baseline_report), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'performance_coverage': 'partial' if below_flashlib else 'pass', 'coverage_only_routes': [{'shape_key': label, 'reason': 'non128 seed below FlashLib in seed and dispatcher evidence'} for label in TARGET_SHAPES], 'hot_bucket_blockers': below_flashlib, 'report': candidate_report, 'baseline_report': baseline_report} + +def benchmark_knn_build_dispatch_4247_non128_8199_splitretune_full82_v1(*, use_cupti: bool=True, shape_labels=None, baseline_report: dict[str, Any] | None=None) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate) + if baseline_report is None: + try: + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_base_4247) + except Exception as exc: + baseline_report = _failed_baseline_report(exc, shape_labels=shape_labels, baseline_id='baseline_4247') + return _benchmark_payload(candidate_report, baseline_report, use_cupti=use_cupti, shape_labels=shape_labels) + +def _denominator_label(shape_labels) -> str: + if shape_labels is None: + return ''.join(['full', format(len(eval_mod.CANONICAL_SHAPES), '')]) + return ''.join(['shape', format(len(tuple(shape_labels)), '')]) + +def write_benchmark_artifacts(artifact_dir: str | Path, *, use_cupti: bool=True, shape_labels=None) -> dict[str, str]: + payload = benchmark_knn_build_dispatch_4247_non128_8199_splitretune_full82_v1(use_cupti=use_cupti, shape_labels=shape_labels) + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + denom = _denominator_label(shape_labels) + candidate_path = out_dir / ''.join([format(denom, ''), '_dispatch_4247_non128_8199_splitretune_full82_v1.json']) + baseline_path = out_dir / ''.join([format(denom, ''), '_same_session_baseline_4247_for_non128_8199_splitretune_v1.json']) + route_trace_path = out_dir / ''.join([format(denom, ''), '_route_trace_4247_non128_8199_splitretune_full82_v1.json']) + forced_trace_path = out_dir / ''.join([format(denom, ''), '_forced_fallback_trace_4247_non128_8199_splitretune_full82_v1.json']) + seed_matrix_path = out_dir / ''.join([format(denom, ''), '_seed_delta_matrix_4247_non128_8199_splitretune_full82_v1.json']) + candidate_path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + baseline_path.write_text(json.dumps({'candidate_id': 'baseline_exported_4247', 'measured_entrypoint': payload['baseline_entrypoint'], 'measured_shape_labels': payload['measured_shape_labels'], 'timing_backend_requested': payload['timing_backend_requested'], 'timing_backends': payload['timing_backends'], 'tflops': payload['baseline_tflops'], 'all_correct': payload['baseline_all_correct'], 'performance_comparable': payload['baseline_performance_comparable'], 'contract_summary': payload['baseline_contract_summary'], 'contract_performance': payload['baseline_contract_performance'], 'route_trace': base_4247.route_trace_for_contract_shapes(shape_labels), 'route_trace_included': True, 'report': payload['baseline_report']}, indent=2, sort_keys=True) + '\n') + route_trace_path.write_text(json.dumps(payload['route_trace'], indent=2, sort_keys=True) + '\n') + forced_trace_path.write_text(json.dumps(payload['forced_fallback_route_trace'], indent=2, sort_keys=True) + '\n') + seed_matrix_path.write_text(json.dumps(payload['seed_delta_matrix'], indent=2, sort_keys=True) + '\n') + return {'candidate_payload': str(candidate_path), 'same_session_baseline_payload': str(baseline_path), 'route_trace': str(route_trace_path), 'forced_fallback_trace': str(forced_trace_path), 'seed_delta_matrix': str(seed_matrix_path)} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_4247_non128_8199_widecombine_full82_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_4247_non128_8199_widecombine_full82_v1.py new file mode 100644 index 00000000..36236895 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_4247_non128_8199_widecombine_full82_v1.py @@ -0,0 +1,249 @@ +"""Full82 non-D128 widecombine dispatcher-consumption wrapper over 4247. + +Minimum target architecture: sm_100a. This opt-in dispatcher candidate starts +from the exported 4247 Weave dispatcher and adds exact BF16 non-D128 guards for +the five round-8199 widecombine frontier rows. Guard misses stay on the inherited 4247 +Weave dispatcher stack; no external implementation is on the production route. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +from pathlib import Path +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_e3de_9138_bcb3_4247_v1 as base_4247 +from . import knn_build_dispatch_4247_non128_8199_splitretune_full82_v1 as baseline_splitretune +from . import knn_build_non128_frontier_8199_widecombine_v1 as non128_seed +MODULE = 'loom.examples.weave.knn_build_dispatch_4247_non128_8199_widecombine_full82_v1' +SEED_NON128_ID = 'non128_frontier_8199_widecombine_v1' +ROUTE_NON128_ENTRYPOINT = ''.join([format(non128_seed.MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_BASE_4247_ENTRYPOINT = 'loom.examples.weave.knn_build_dispatch_e3de_9138_bcb3_4247_v1:launch_from_contract_inputs' +TARGET_SHAPES = non128_seed.TARGET_SHAPES +TARGET_SHAPE_SET = set(TARGET_SHAPES) +SELECTED_TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_large_b1_q8192_m8192_d128_k20", "build_large_b1_q8192_m8192_d128_k32", "build_over64_stress_qm2048_k96", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32", "rag_batch_b2_q256_m50000_d128_k10", "rag_irregular_b1_q512_m131071_d128_k10", "search_rect_b1_q1024_m8192_d128_k10", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q2048_m2048_d256_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10", "search_rect_b1_q2048_m32768_d128_k10", "build_large_tail_b1_q6144_m6144_d128_k20", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "build_over32_stress_qm2048_k64", "build_over32_stress_qm4096_k64", "build_k_sweep_qm512_k1", "build_k_sweep_qm512_k2", "build_k_sweep_qm1024_k16", "build_k_sweep_qm512_k4", "build_k_sweep_qm512_k5", "build_k_sweep_qm512_k6", "rag_microbatch_b1_q8_m100000_d128_k10", "rag_microbatch_b1_q32_m100000_d128_k10", "build_dim_sweep_b1_q1024_m1024_d64_k10", "build_dim_sweep_b1_q4096_m4096_d64_k10", "search_rect_b1_q1024_m32768_d64_k10", "rag_online_b1_q1_m100000_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "build_over64_stress_qm1024_k96", "build_over64_stress_qm4096_k96", "build_dim_sweep_b1_q1024_m1024_d96_k10", "build_dim_sweep_b1_q2048_m2048_d192_k10", "build_highd_b1_q1024_m1024_d320_k10", "search_rect_highd_b1_q512_m12000_d320_k10", "rag_microbatch_highd_b1_q16_m50000_d768_k10"]}')) +CONSUMED_SEED_TARGET_SHAPES = TARGET_SHAPES +GUARD_MISS_AUDIT_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_dim_sweep_b1_q1024_m1024_d96_k10", "build_dim_sweep_b1_q2048_m2048_d192_k10", "build_highd_b1_q1024_m1024_d320_k10", "search_rect_highd_b1_q512_m12000_d320_k10", "rag_microbatch_highd_b1_q16_m50000_d768_k10", "build_dim_sweep_b1_q1024_m1024_d64_k10", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q4096_m4096_d64_k10", "search_rect_b1_q1024_m32768_d64_k10", "rag_online_b1_q1_m100000_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "build_over64_stress_qm1024_k96", "build_over64_stress_qm4096_k96", "build_k_sweep_qm512_k4", "build_k_sweep_qm512_k8", "build_k_sweep_qm512_k10", "rag_microbatch_b1_q8_m100000_d128_k10", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_microbatch_b1_q32_m100000_d128_k10", "build_large_b1_q8192_m8192_d128_k20", "build_qm2048_d128_k10", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "flashml_correctness_b1_q256_m256_d128_k5", "build_over32_stress_qm2048_k64", "search_rect_over32_b1_q2048_m65536_d128_k64", "rag_offline_large_m_over32_b1_q2048_m250000_d128_k64", "build_k_sweep_qm512_k5", "build_over32_stress_qm4096_k64"]}')) +DISPATCH_CORRECTNESS_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["flashml_correctness_b1_q256_m256_d128_k5", "build_dim_sweep_b1_q1024_m1024_d96_k10", "build_dim_sweep_b1_q2048_m2048_d192_k10", "build_highd_b1_q1024_m1024_d320_k10", "search_rect_highd_b1_q512_m12000_d320_k10", "rag_microbatch_highd_b1_q16_m50000_d768_k10", "build_large_b1_q8192_m8192_d128_k20", "build_large_b1_q8192_m8192_d128_k32", "build_over64_stress_qm2048_k96", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32", "rag_batch_b2_q256_m50000_d128_k10", "rag_irregular_b1_q512_m131071_d128_k10", "search_rect_b1_q1024_m8192_d128_k10", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q2048_m2048_d256_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10", "search_rect_b1_q2048_m32768_d128_k10", "build_large_tail_b1_q6144_m6144_d128_k20", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "build_over32_stress_qm2048_k64", "build_over32_stress_qm4096_k64", "build_k_sweep_qm512_k1", "build_k_sweep_qm512_k2", "build_k_sweep_qm1024_k16", "build_k_sweep_qm512_k4", "build_k_sweep_qm512_k5", "build_k_sweep_qm512_k6", "rag_microbatch_b1_q8_m100000_d128_k10", "rag_microbatch_b1_q32_m100000_d128_k10", "build_dim_sweep_b1_q1024_m1024_d64_k10", "build_dim_sweep_b1_q4096_m4096_d64_k10", "search_rect_b1_q1024_m32768_d64_k10", "rag_online_b1_q1_m100000_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "build_over64_stress_qm1024_k96", "build_over64_stress_qm4096_k96", "build_k_sweep_qm512_k8", "build_k_sweep_qm512_k10", "build_qm2048_d128_k10", "search_rect_over32_b1_q2048_m65536_d128_k64", "rag_offline_large_m_over32_b1_q2048_m250000_d128_k64", "rag_stream_b1_q128_m100000_d128_k10", "search_rect_b1_q4096_m65536_d128_k20", "rag_offline_largek_b1_q4096_m100000_d128_k20", "rag_offline_large_m_b1_q8192_m250000_d128_k20"]}')) +TARGETED_SEED_ROWS = {'build_dim_sweep_b1_q1024_m1024_d96_k10': {'kernel_ms': 0.09856, 'flashlib_ms': 0.095776, 'ratio_vs_flashlib': 0.9717532467532468, 'tflops': 2.0426805194805193}, 'build_dim_sweep_b1_q2048_m2048_d192_k10': {'kernel_ms': 0.10464, 'flashlib_ms': 0.12448, 'ratio_vs_flashlib': 1.1896024464831805, 'tflops': 15.39194128440367}, 'build_highd_b1_q1024_m1024_d320_k10': {'kernel_ms': 0.081664, 'flashlib_ms': 0.10048, 'ratio_vs_flashlib': 1.2304075235109717, 'tflops': 8.2176802507837}, 'search_rect_highd_b1_q512_m12000_d320_k10': {'kernel_ms': 0.178912, 'flashlib_ms': 0.164864, 'ratio_vs_flashlib': 0.9214809515292436, 'tflops': 21.978179216598107}, 'rag_microbatch_highd_b1_q16_m50000_d768_k10': {'kernel_ms': 0.226688, 'flashlib_ms': 0.173695, 'ratio_vs_flashlib': 0.7662293548842461, 'tflops': 5.420666290231507}} +PRODUCTION_ROUTE_MODULES = {**base_4247.PRODUCTION_ROUTE_MODULES, SEED_NON128_ID: ROUTE_NON128_ENTRYPOINT, 'base_4247': ROUTE_BASE_4247_ENTRYPOINT} +CANDIDATE_DISPATCHERS = _decode_capture(_json_loads('{"__tuple__": [{"__dict_items__": [["id", "baseline_4247_non128_8199_splitretune_full82_v1"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_4247_non128_8199_splitretune_full82_v1:benchmark_knn_build_dispatch_4247_non128_8199_splitretune_full82_v1"], ["consumed_seeds", {"__tuple__": ["non128_frontier_8199_splitretune_v1"]}], ["guard_plan", {"__tuple__": ["five exact splitretune non-D128 frontier guards", "then exported 4247 guard stack"]}], ["expected_shape_wins", {"__tuple__": ["build_large_b1_q8192_m8192_d128_k20", "build_large_b1_q8192_m8192_d128_k32", "build_over64_stress_qm2048_k96", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32", "rag_batch_b2_q256_m50000_d128_k10", "rag_irregular_b1_q512_m131071_d128_k10", "search_rect_b1_q1024_m8192_d128_k10", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q2048_m2048_d256_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10", "search_rect_b1_q2048_m32768_d128_k10", "build_large_tail_b1_q6144_m6144_d128_k20", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "build_over32_stress_qm2048_k64", "build_over32_stress_qm4096_k64", "build_k_sweep_qm512_k1", "build_k_sweep_qm512_k2", "build_k_sweep_qm1024_k16", "build_k_sweep_qm512_k4", "build_k_sweep_qm512_k5", "build_k_sweep_qm512_k6", "rag_microbatch_b1_q8_m100000_d128_k10", "rag_microbatch_b1_q32_m100000_d128_k10", "build_dim_sweep_b1_q1024_m1024_d64_k10", "build_dim_sweep_b1_q4096_m4096_d64_k10", "search_rect_b1_q1024_m32768_d64_k10", "rag_online_b1_q1_m100000_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "build_over64_stress_qm1024_k96", "build_over64_stress_qm4096_k96", "build_dim_sweep_b1_q1024_m1024_d96_k10", "build_dim_sweep_b1_q2048_m2048_d192_k10", "build_highd_b1_q1024_m1024_d320_k10", "search_rect_highd_b1_q512_m12000_d320_k10", "rag_microbatch_highd_b1_q16_m50000_d768_k10"]}], ["fallback", "loom.examples.weave.knn_build_dispatch_e3de_9138_bcb3_4247_v1:launch_from_contract_inputs"], ["rejected_reason", "same-session splitretune full82 baseline"]]}, {"__dict_items__": [["id", "candidate_4247_non128_8199_widecombine_full82_v1"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_4247_non128_8199_widecombine_full82_v1:benchmark_knn_build_dispatch_4247_non128_8199_widecombine_full82_v1"], ["consumed_seeds", {"__tuple__": ["non128_frontier_8199_widecombine_v1"]}], ["guard_plan", {"__tuple__": ["five exact widecombine non-D128 frontier guards", "then exported 4247 guard stack"]}], ["expected_shape_wins", {"__tuple__": ["build_dim_sweep_b1_q1024_m1024_d96_k10", "build_dim_sweep_b1_q2048_m2048_d192_k10", "build_highd_b1_q1024_m1024_d320_k10", "search_rect_highd_b1_q512_m12000_d320_k10", "rag_microbatch_highd_b1_q16_m50000_d768_k10"]}], ["fallback", "loom.examples.weave.knn_build_dispatch_e3de_9138_bcb3_4247_v1:launch_from_contract_inputs"], ["rejected_reason", null]]}]}')) +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k48over32", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 48]], "cta_group": 1, "threads": 192}')) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False, enable_non128: bool=True) -> str: + if not force_fallback and enable_non128 and (non128_seed._target_label_for_inputs(inputs) is not None): + return non128_seed.route_for_contract_inputs(inputs) + return base_4247.route_for_contract_inputs(inputs) + +def _launch_route(inputs: dict[str, Any], route: str) -> None: + if str(route).startswith(non128_seed.ROUTE_PREFIX): + non128_seed.launch_from_contract_inputs(inputs) + return + base_4247._launch_route(inputs, route) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False, enable_non128: bool=True) -> None: + route = route_for_contract_inputs(inputs, force_fallback=force_fallback, enable_non128=enable_non128) + _launch_route(inputs, route) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_base_4247(inputs: dict[str, Any]) -> None: + base_4247.launch_from_contract_inputs(inputs) + +def candidate_baseline_splitretune(inputs: dict[str, Any]) -> None: + baseline_splitretune.launch_from_contract_inputs(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return base_4247._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=DISPATCH_CORRECTNESS_SHAPES, benchmark: bool=False) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=None, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + shapes = None if shape_labels is None else _select_contract_shapes(shape_labels) + return evaluate_contract(shapes=shapes, correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def _trace_inputs_from_shape(shape: dict[str, Any]) -> dict[str, Any]: + return base_4247._trace_inputs_from_shape(shape) + +def _inputs_for_label(label: str) -> dict[str, Any]: + return base_4247._inputs_for_label(label) + +def _normalize_route_row(row: dict[str, Any]) -> dict[str, Any]: + out = dict(row) + route_kind = str(out.get('route_kind') or 'general') + selected_seed = out.get('selected_seed') or out.get('consumed_seed') + out.setdefault('selected_seed', selected_seed) + out.setdefault('expected_seed', selected_seed) + if out.get('route_source') not in {'shape-specific-seed', 'generated-variant', 'broad-dispatcher', 'generic-weave-fallback', 'external-reference', 'unknown'}: + out['route_source'] = 'shape-specific-seed' if selected_seed else 'generic-weave-fallback' if route_kind in {'fallback', 'coverage-only'} else 'broad-dispatcher' + if out.get('classification') not in {'seed-consumed', 'route-ok', 'guard-miss', 'kernel-slow', 'fallback-slow', 'coverage-only', 'benchmark-path-mismatch', 'unmeasured'}: + out['classification'] = 'coverage-only' if route_kind == 'coverage-only' else 'route-ok' + out.setdefault('dispatcher_kernel_ms', None) + out.setdefault('shape_specific_kernel_ms', None) + out.setdefault('relative_speedup_vs_baseline', None) + return out + +def _base_route_trace_record(inputs: dict[str, Any], *, force_fallback: bool=False) -> dict[str, Any]: + label = str(inputs.get('label')) + row = dict(base_4247.route_trace_for_contract_shapes((label,), force_fallback=force_fallback)[0]) + row['base_4247_route'] = base_4247.route_for_contract_inputs(inputs, force_fallback=force_fallback) + row['baseline_dispatcher_route'] = baseline_splitretune.route_for_contract_inputs(inputs) + return _normalize_route_row(row) + +def _non128_trace_record(inputs: dict[str, Any]) -> dict[str, Any]: + label = str(non128_seed._target_label_for_inputs(inputs)) + spec = non128_seed.SHAPE_SPECS[label] + route = non128_seed.route_for_contract_inputs(inputs) + targeted = dict(TARGETED_SEED_ROWS[label]) + feature_dim = non128_seed._feature_dim_for_label(label) + return {'shape_key': label, 'selected_route': route, 'selected_entrypoint': ROUTE_NON128_ENTRYPOINT, 'selected_seed': SEED_NON128_ID, 'expected_seed': SEED_NON128_ID, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': '8199_widecombine_non128_exact_shape_guard', 'guard_condition': ''.join(['exact BF16 B=', format(spec['B'], ''), ' Q=', format(spec['Q'], ''), ' M=', format(spec['M'], ''), ' D=', format(spec['D'], ''), ' K=', format(spec['K'], ''), ' build=', format(spec['build'], '')]), 'coverage': 'consumes 8199 widecombine non-D128 frontier seed before exported 4247 fallback', 'consumed_seed': SEED_NON128_ID, 'replaced_route': baseline_splitretune.route_for_contract_inputs(inputs), 'baseline_dispatcher_route': baseline_splitretune.route_for_contract_inputs(inputs), 'base_4247_route': base_4247.route_for_contract_inputs(inputs), 'feature_chunks': spec['feature_chunks'], 'feature_dim': feature_dim, 'producer': non128_seed._producer_for_label(label), 'split_count': non128_seed._split_count_for_label(label), 'preprocess_stage': ''.join(['d', format(int(spec['D']), ''), '_weave_pad_to_d', format(feature_dim, '')]) if int(spec['D']) != feature_dim else None, 'targeted_seed_timing_backend': 'cupti', 'targeted_seed_kernel_ms': targeted['kernel_ms'], 'targeted_seed_ratio_vs_flashlib': targeted['ratio_vs_flashlib'], 'row_selection': targeted, 'classification': 'seed-consumed', 'dispatcher_kernel_ms': None, 'shape_specific_kernel_ms': targeted['kernel_ms'], 'relative_speedup_vs_baseline': None} + +def _route_trace_record(inputs: dict[str, Any], *, force_fallback: bool=False) -> dict[str, Any]: + if force_fallback and non128_seed._target_label_for_inputs(inputs) is not None: + row = _base_route_trace_record(inputs) + row['expected_seed'] = SEED_NON128_ID + row['guard_id'] = 'forced_fallback_8199_widecombine_non128_disabled' + row['guard_condition'] = 'forced fallback to exported 4247; 8199 widecombine non-D128 overlay disabled' + row['forced_disabled_seeds'] = (SEED_NON128_ID,) + row['classification'] = 'guard-miss' + return row + if not force_fallback and non128_seed._target_label_for_inputs(inputs) is not None: + return _non128_trace_record(inputs) + return _base_route_trace_record(inputs) + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + return [_route_trace_record(_trace_inputs_from_shape(shape), force_fallback=force_fallback) for shape in selected] + +def _timing_backends_for_reports(*reports: dict[str, Any]) -> list[str]: + return base_4247._timing_backends_for_reports(*reports) + +def _rows_for_labels(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return base_4247._rows_for_labels(report, labels) + +def _seed_delta_matrix(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> list[dict[str, Any]]: + matrix = [] + for label in CONSUMED_SEED_TARGET_SHAPES: + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + inputs = _inputs_for_label(label) + matrix.append({'shape_key': label, 'baseline_route': baseline_splitretune.route_for_contract_inputs(inputs), 'base_4247_route': base_4247.route_for_contract_inputs(inputs), 'candidate_route': route_for_contract_inputs(inputs), 'selected_seed': SEED_NON128_ID, 'candidate_ms': candidate_ms, 'baseline_ms': baseline_ms, 'flashlib_ms': candidate_row.get('flashlib_ms'), 'metric_delta_ms': candidate_ms - baseline_ms if candidate_ms and baseline_ms else None, 'speedup_vs_baseline_dispatcher': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'ratio_vs_flashlib': candidate_row.get('ratio_vs_flashlib'), 'targeted_seed_kernel_ms': TARGETED_SEED_ROWS[label]['kernel_ms'], 'targeted_seed_ratio_vs_flashlib': TARGETED_SEED_ROWS[label]['ratio_vs_flashlib'], 'timing_backend': candidate_row.get('timing_backend') or baseline_row.get('timing_backend')}) + return matrix + +def _frontmatter_seed_delta_matrix(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> list[dict[str, Any]]: + rows = [] + for item in _seed_delta_matrix(candidate_report, baseline_report): + rows.append({'shape_key': item['shape_key'], 'baseline_route': item['baseline_route'], 'candidate_deltas': [{'candidate_id': 'candidate_4247_non128_8199_widecombine_full82_v1', 'selected_seed': item['selected_seed'], 'metric_delta': item['metric_delta_ms'], 'ratio_vs_flashlib': item['ratio_vs_flashlib'], 'timing_backend': item['timing_backend'] or 'cupti'}]}) + return rows + +def _per_consumed_row_delta(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> dict[str, Any]: + return {item['shape_key']: {'candidate_ms': item['candidate_ms'], 'baseline_dispatcher_ms': item['baseline_ms'], 'flashlib_ms': item['flashlib_ms'], 'speedup_vs_baseline_dispatcher': item['speedup_vs_baseline_dispatcher'], 'ratio_vs_flashlib': item['ratio_vs_flashlib'], 'candidate_route': item['candidate_route'], 'baseline_dispatcher_route': item['baseline_route'], 'base_4247_route': item['base_4247_route'], 'selected_seed': item['selected_seed'], 'targeted_seed_kernel_ms': item['targeted_seed_kernel_ms'], 'targeted_seed_ratio_vs_flashlib': item['targeted_seed_ratio_vs_flashlib'], 'candidate_passed': candidate_report.get('per_shape', {}).get(item['shape_key'], {}).get('passed'), 'baseline_passed': baseline_report.get('per_shape', {}).get(item['shape_key'], {}).get('passed')} for item in _seed_delta_matrix(candidate_report, baseline_report)} + +def _annotate_route_trace(route_trace: list[dict[str, Any]], candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> list[dict[str, Any]]: + annotated = [] + for row in route_trace: + label = str(row.get('shape_key')) + out = dict(row) + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + out['dispatcher_kernel_ms'] = candidate_ms + out['baseline_dispatcher_kernel_ms'] = baseline_ms + out['flashlib_ms'] = candidate_row.get('flashlib_ms') + out['relative_speedup_vs_baseline'] = baseline_ms / candidate_ms if candidate_ms and baseline_ms else None + out['route_changed_vs_base_4247'] = out.get('selected_route') != out.get('base_4247_route') + out['route_changed_vs_baseline_dispatcher'] = out.get('selected_route') != out.get('baseline_dispatcher_route') + ratio = candidate_row.get('ratio_vs_flashlib') + if label in TARGET_SHAPE_SET: + if out.get('selected_seed') != SEED_NON128_ID: + out['classification'] = 'guard-miss' + elif isinstance(ratio, (float, int)) and ratio < 1.0: + out['classification'] = 'kernel-slow' + elif out['relative_speedup_vs_baseline'] is not None and out['relative_speedup_vs_baseline'] < 1.0: + out['classification'] = 'kernel-slow' + else: + out['classification'] = 'seed-consumed' + elif isinstance(ratio, (float, int)) and ratio < 1.0: + out['classification'] = 'kernel-slow' if out.get('route_kind') == 'specialized' else 'fallback-slow' + else: + out['classification'] = out.get('classification', 'route-ok') + annotated.append(_normalize_route_row(out)) + return annotated + +def _below_flashlib_rows(report: dict[str, Any]) -> list[dict[str, Any]]: + trace_by_label = {str(row['shape_key']): row for row in route_trace_for_contract_shapes()} + rows = [] + for label, row in sorted(report.get('per_shape', {}).items()): + ratio = row.get('ratio_vs_flashlib') + if isinstance(ratio, (float, int)) and ratio < 1.0: + trace_row = trace_by_label.get(label, {}) + rows.append({'shape_key': label, 'kernel_ms': row.get('kernel_ms'), 'flashlib_ms': row.get('flashlib_ms'), 'ratio_vs_flashlib': ratio, 'selected_route': trace_row.get('selected_route'), 'route_kind': trace_row.get('route_kind', 'unknown'), 'classification': 'kernel-slow' if trace_row.get('route_kind') == 'specialized' else 'fallback-slow'}) + return rows + +def _failed_baseline_report(exc: Exception, *, shape_labels, baseline_id: str) -> dict[str, Any]: + reason = ''.join([format(type(exc).__name__, ''), ': ', format(exc, '')]) + return {'contract': eval_mod.CONTRACT.kernel, 'contract_version': eval_mod.CONTRACT.contract_version, 'summary': {'all_correct': False, 'correctness_shapes': 0, 'failed_correctness_shapes': 1, 'correctness_failure_count': 1, 'first_correctness_failure': reason, 'primary_metric': 'tflops', 'primary_direction': 'maximize', 'primary_mean': None, 'performance_comparable': False, 'invalid_performance_reason': reason}, 'performance': {'comparable': False, 'invalid_reason': reason, 'primary_mean': None, 'primary_metric': 'tflops', 'valid_measurement_count': 0}, 'per_shape': {}, 'benchmark_exception': reason, 'baseline_id': baseline_id, 'measured_shape_labels': 'all_canonical' if shape_labels is None else tuple(shape_labels)} + +def _benchmark_payload(candidate_report: dict[str, Any], baseline_report: dict[str, Any], *, use_cupti: bool, shape_labels) -> dict[str, Any]: + route_trace = _annotate_route_trace(route_trace_for_contract_shapes(shape_labels), candidate_report, baseline_report) + below_flashlib = _below_flashlib_rows(candidate_report) + candidate_metric = candidate_report['summary']['primary_mean'] + baseline_metric = baseline_report['summary']['primary_mean'] + return {'candidate_id': 'candidate_4247_non128_8199_widecombine_full82_v1', 'tflops': candidate_metric, 'baseline_tflops': baseline_metric, 'metric_delta': candidate_metric - baseline_metric if candidate_metric and baseline_metric else None, 'all_correct': candidate_report['summary']['all_correct'], 'baseline_all_correct': baseline_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'baseline_performance_comparable': baseline_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'baseline_invalid_performance_reason': baseline_report['summary']['invalid_performance_reason'], 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_knn_build_dispatch_4247_non128_8199_widecombine_full82_v1']), 'baseline_entrypoint': 'loom.examples.weave.knn_build_dispatch_4247_non128_8199_splitretune_full82_v1:benchmark_knn_build_dispatch_4247_non128_8199_splitretune_full82_v1', 'measured_shape_labels': 'all_canonical' if shape_labels is None else tuple(shape_labels), 'selected_route_labels': SELECTED_TARGET_SHAPES, 'consumed_seed_labels': CONSUMED_SEED_TARGET_SHAPES, 'guard_miss_audit_labels': GUARD_MISS_AUDIT_SHAPES, 'selected_route_rows': _rows_for_labels(candidate_report, SELECTED_TARGET_SHAPES), 'baseline_selected_route_rows': _rows_for_labels(baseline_report, SELECTED_TARGET_SHAPES), 'consumed_seed_rows': _rows_for_labels(candidate_report, CONSUMED_SEED_TARGET_SHAPES), 'baseline_consumed_seed_rows': _rows_for_labels(baseline_report, CONSUMED_SEED_TARGET_SHAPES), 'guard_miss_audit_rows': _rows_for_labels(candidate_report, GUARD_MISS_AUDIT_SHAPES), 'baseline_guard_miss_audit_rows': _rows_for_labels(baseline_report, GUARD_MISS_AUDIT_SHAPES), 'per_consumed_row_delta': _per_consumed_row_delta(candidate_report, baseline_report), 'seed_delta_matrix': _seed_delta_matrix(candidate_report, baseline_report), 'frontmatter_seed_delta_matrix': _frontmatter_seed_delta_matrix(candidate_report, baseline_report), 'targeted_seed_rows': TARGETED_SEED_ROWS, 'candidate_dispatchers': CANDIDATE_DISPATCHERS, 'selected_candidate_dispatcher': 'candidate_4247_non128_8199_widecombine_full82_v1', 'route_modules': PRODUCTION_ROUTE_MODULES, 'route_trace': route_trace, 'forced_fallback_route_trace': route_trace_for_contract_shapes(shape_labels, force_fallback=True), 'route_trace_included': True, 'contract_summary': candidate_report['summary'], 'baseline_contract_summary': baseline_report['summary'], 'contract_performance': candidate_report['performance'], 'baseline_contract_performance': baseline_report['performance'], 'timing_backends': _timing_backends_for_reports(candidate_report, baseline_report), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'performance_coverage': 'partial' if below_flashlib else 'pass', 'coverage_only_routes': [], 'hot_bucket_blockers': below_flashlib, 'report': candidate_report, 'baseline_report': baseline_report} + +def benchmark_knn_build_dispatch_4247_non128_8199_widecombine_full82_v1(*, use_cupti: bool=True, shape_labels=None, baseline_report: dict[str, Any] | None=None) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate) + if baseline_report is None: + try: + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_baseline_splitretune) + except Exception as exc: + baseline_report = _failed_baseline_report(exc, shape_labels=shape_labels, baseline_id='baseline_4247_non128_8199_splitretune_full82_v1') + return _benchmark_payload(candidate_report, baseline_report, use_cupti=use_cupti, shape_labels=shape_labels) + +def _denominator_label(shape_labels) -> str: + if shape_labels is None: + return ''.join(['full', format(len(eval_mod.CANONICAL_SHAPES), '')]) + return ''.join(['shape', format(len(tuple(shape_labels)), '')]) + +def write_benchmark_artifacts(artifact_dir: str | Path, *, use_cupti: bool=True, shape_labels=None) -> dict[str, str]: + payload = benchmark_knn_build_dispatch_4247_non128_8199_widecombine_full82_v1(use_cupti=use_cupti, shape_labels=shape_labels) + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + denom = _denominator_label(shape_labels) + candidate_path = out_dir / ''.join([format(denom, ''), '_dispatch_4247_non128_8199_widecombine_full82_v1.json']) + baseline_path = out_dir / ''.join([format(denom, ''), '_same_session_baseline_splitretune_for_non128_8199_widecombine_v1.json']) + route_trace_path = out_dir / ''.join([format(denom, ''), '_route_trace_4247_non128_8199_widecombine_full82_v1.json']) + forced_trace_path = out_dir / ''.join([format(denom, ''), '_forced_fallback_trace_4247_non128_8199_widecombine_full82_v1.json']) + seed_matrix_path = out_dir / ''.join([format(denom, ''), '_seed_delta_matrix_4247_non128_8199_widecombine_full82_v1.json']) + candidate_path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + baseline_path.write_text(json.dumps({'candidate_id': 'baseline_4247_non128_8199_splitretune_full82_v1', 'measured_entrypoint': payload['baseline_entrypoint'], 'measured_shape_labels': payload['measured_shape_labels'], 'timing_backend_requested': payload['timing_backend_requested'], 'timing_backends': payload['timing_backends'], 'tflops': payload['baseline_tflops'], 'all_correct': payload['baseline_all_correct'], 'performance_comparable': payload['baseline_performance_comparable'], 'contract_summary': payload['baseline_contract_summary'], 'contract_performance': payload['baseline_contract_performance'], 'route_trace': baseline_splitretune.route_trace_for_contract_shapes(shape_labels), 'route_trace_included': True, 'report': payload['baseline_report']}, indent=2, sort_keys=True) + '\n') + route_trace_path.write_text(json.dumps(payload['route_trace'], indent=2, sort_keys=True) + '\n') + forced_trace_path.write_text(json.dumps(payload['forced_fallback_route_trace'], indent=2, sort_keys=True) + '\n') + seed_matrix_path.write_text(json.dumps(payload['seed_delta_matrix'], indent=2, sort_keys=True) + '\n') + return {'candidate_payload': str(candidate_path), 'same_session_baseline_payload': str(baseline_path), 'route_trace': str(route_trace_path), 'forced_fallback_trace': str(forced_trace_path), 'seed_delta_matrix': str(seed_matrix_path)} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_4fbf_7399_d15e_73a9_full55_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_4fbf_7399_d15e_73a9_full55_v1.py new file mode 100644 index 00000000..7fed754a --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_4fbf_7399_d15e_73a9_full55_v1.py @@ -0,0 +1,225 @@ +"""Opt-in kNN build full55 dispatcher synthesizing exact K32 and D64 seeds. + +Minimum target architecture: sm_100a. This dispatcher-synthesis candidate is +wrapper-only. It preserves the audited exact-K32 4fbf policy from ba32, adds +the audited exact 73a9 D64 build row, and delegates every other shape to the +same Weave-only 7399+d15e / 7c3a baseline policies. + +No external runtime fallback is used. FlashLib/PyTorch remain only contract +harness references outside this production dispatch path. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from pathlib import Path +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dim_midk_73a9_v1 as dim_73a9 +from . import knn_build_dispatch_4fbf_7399_d15e_full55_bad5_v1 as dispatch_k32 +from . import knn_build_dispatch_7399_d15e_73a9_full55_v1 as dispatch_d64 +from . import knn_build_dispatch_7399_d15e_full55_v1 as dispatch_7399 +ROUTE_DIM_D64_73A9 = dispatch_d64.ROUTE_DIM_D64_73A9 +ROUTE_CURRENT_EXACT_K32 = 'loom.examples.weave.knn_build_dispatch_4fbf_7399_d15e_full55_bad5_v1:launch_from_contract_inputs' +ROUTE_BASE_7399_D15E = 'loom.examples.weave.knn_build_dispatch_7399_d15e_full55_v1:launch_from_contract_inputs' +ROUTE_LARGE_SQUARE_K20K32 = dispatch_k32.ROUTE_LARGE_SQUARE_K20K32 +ROUTE_OVER64_K96 = dispatch_k32.ROUTE_OVER64_K96 +ROUTE_RAG_7399_K10 = dispatch_k32.ROUTE_RAG_7399_K10 +ROUTE_RAG_7399_K32 = dispatch_k32.ROUTE_RAG_7399_K32 +ROUTE_RAG_4FBF_K32 = dispatch_k32.ROUTE_RAG_4FBF_K32 +ROUTE_RECT_D15E = dispatch_k32.ROUTE_RECT_D15E +ROUTE_BASELINE_3DC7 = dispatch_k32.ROUTE_BASELINE_3DC7 +ROUTE_BASELINE_7C3A_POLICY = dispatch_k32.ROUTE_BASELINE_7C3A_POLICY +LARGE_SQUARE_TARGET_SHAPES = dispatch_k32.LARGE_SQUARE_TARGET_SHAPES +K96_TARGET_SHAPES = dispatch_k32.K96_TARGET_SHAPES +RAG_K10_TARGET_SHAPES = dispatch_k32.RAG_K10_TARGET_SHAPES +RAG_K32_TARGET_SHAPES = dispatch_k32.RAG_K32_TARGET_SHAPES +RAG_TARGET_SHAPES = dispatch_k32.RAG_TARGET_SHAPES +RECT_D15E_TARGET_SHAPES = dispatch_k32.RECT_D15E_TARGET_SHAPES +DIM_D64_TARGET_SHAPES = dispatch_d64.DIM_D64_TARGET_SHAPES +DIM_D64_TARGET_SHAPE_SET = set(DIM_D64_TARGET_SHAPES) +CONSUMED_SEED_TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["rag_stream_largek_b1_q128_m100000_d128_k32", "build_dim_sweep_b1_q2048_m2048_d64_k10"]}')) +SELECTED_TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_large_b1_q8192_m8192_d128_k20", "build_large_b1_q8192_m8192_d128_k32", "build_over64_stress_qm2048_k96", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32", "rag_batch_b2_q256_m50000_d128_k10", "rag_irregular_b1_q512_m131071_d128_k10", "search_rect_b1_q1024_m8192_d128_k10", "build_dim_sweep_b1_q2048_m2048_d64_k10"]}')) +DISPATCH_CORRECTNESS_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["flashml_correctness_b1_q256_m256_d128_k5", "build_large_b1_q8192_m8192_d128_k20", "build_large_b1_q8192_m8192_d128_k32", "build_over64_stress_qm2048_k96", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32", "rag_batch_b2_q256_m50000_d128_k10", "rag_irregular_b1_q512_m131071_d128_k10", "search_rect_b1_q1024_m8192_d128_k10", "build_dim_sweep_b1_q2048_m2048_d64_k10", "rag_online_b1_q1_m100000_d128_k10", "rag_stream_b1_q128_m100000_d128_k10", "search_rect_b1_q4096_m65536_d128_k20", "rag_offline_largek_b1_q4096_m100000_d128_k20", "rag_offline_large_m_b1_q8192_m250000_d128_k20"]}')) +PRODUCTION_ROUTE_MODULES = {**dispatch_k32.PRODUCTION_ROUTE_MODULES, 'dim_d64_73a9': ROUTE_DIM_D64_73A9, 'current_exact_k32_dispatcher': ROUTE_CURRENT_EXACT_K32, 'base_7399_d15e_dispatcher': ROUTE_BASE_7399_D15E} +CANDIDATE_PORTFOLIOS = ({'id': 'current_exact_k32_4fbf', 'entrypoint': 'loom.examples.weave.knn_build_dispatch_4fbf_7399_d15e_full55_bad5_v1:benchmark_knn_build_dispatch_4fbf_7399_d15e_full55_bad5_v1', 'consumed_seeds': ('rag_frontier_4fbf_v7_exact_k32', 'rag_frontier_7399_v1_k10', 'd15e_rect_smallq_largem_v1'), 'guard_plan': ('exact a989 large-square BF16 build Q=M=8192 K20/K32', 'exact 6c1e over64 BF16 build Q=M=2048 K96', 'exact 7399 RAG frontier BF16 D128 non-build K10 labels', 'exact 4fbf v7 RAG frontier BF16 B1 Q128 M100000 D128 K32 label', 'exact d15e rect BF16 B1 Q1024 M8192 D128 K10 non-build label', '7c3a Weave policy fallback'), 'expected_shape_wins': RAG_K32_TARGET_SHAPES, 'rejected_reason': 'same-session baseline; lacks the audited 73a9 D64 guard'}, {'id': 'd64_only_73a9_on_7399_d15e', 'entrypoint': 'loom.examples.weave.knn_build_dispatch_7399_d15e_73a9_full55_v1:benchmark_knn_build_dispatch_7399_d15e_73a9_full55_v1', 'consumed_seeds': ('dim_midk_73a9_d64', 'rag_frontier_7399_v1', 'd15e_rect_smallq_largem_v1'), 'guard_plan': ('exact 73a9 BF16 build B1 Q=M=2048 D64 K10 label', 'then the 7399+d15e full55 guard plan'), 'expected_shape_wins': DIM_D64_TARGET_SHAPES, 'rejected_reason': 'omits the trusted exact-K32 4fbf component requested by rank'}, {'id': 'exact_k32_4fbf_plus_exact_d64_73a9', 'entrypoint': 'loom.examples.weave.knn_build_dispatch_4fbf_7399_d15e_73a9_full55_v1:benchmark_knn_build_dispatch_4fbf_7399_d15e_73a9_full55_v1', 'consumed_seeds': ('rag_frontier_4fbf_v7_exact_k32', 'dim_midk_73a9_d64', 'rag_frontier_7399_v1_k10', 'd15e_rect_smallq_largem_v1'), 'guard_plan': ('exact a989 large-square BF16 build Q=M=8192 K20/K32', 'exact 6c1e over64 BF16 build Q=M=2048 K96', 'exact 73a9 BF16 build B1 Q=M=2048 D64 K10 label', 'exact 7399 RAG frontier BF16 D128 non-build K10 labels', 'exact 4fbf v7 RAG frontier BF16 B1 Q128 M100000 D128 K32 label', 'exact d15e rect BF16 B1 Q1024 M8192 D128 K10 non-build label', '7c3a Weave policy fallback'), 'expected_shape_wins': CONSUMED_SEED_TARGET_SHAPES, 'rejected_reason': None}) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_DISPATCH_4FBF_73A9_VERIFY_KERNEL') + if verify_kernel == 'd64_stage1': + return dim_73a9.stage1_d64_split_ir + if verify_kernel == 'd64_merge': + return dim_73a9.merge_generic_ir + if verify_kernel is not None: + os.environ['LOOM_KNN_DISPATCH_4FBF_7399_D15E_VERIFY_KERNEL'] = verify_kernel + return dispatch_k32._verify_export_ir() +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k48over32", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 48]], "cta_group": 1, "threads": 192}')) + +def _eligible_dim_d64_73a9(inputs: dict[str, Any]) -> bool: + label = inputs.get('label') + if label is not None and str(label) not in DIM_D64_TARGET_SHAPE_SET: + return False + return dispatch_d64._eligible_dim_d64_73a9(inputs) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if force_fallback: + return dispatch_k32.route_for_contract_inputs(inputs) + if dispatch_k32._eligible_large_square_k20k32(inputs): + return ROUTE_LARGE_SQUARE_K20K32 + if dispatch_k32._eligible_over64_k96(inputs): + return ROUTE_OVER64_K96 + if _eligible_dim_d64_73a9(inputs): + return ROUTE_DIM_D64_73A9 + if dispatch_k32._eligible_7399_rag_k10(inputs): + return ROUTE_RAG_7399_K10 + if dispatch_k32._eligible_4fbf_rag_k32(inputs): + return ROUTE_RAG_4FBF_K32 + if dispatch_k32._eligible_rect_d15e(inputs): + return ROUTE_RECT_D15E + return dispatch_k32._base_7c3a_route_for_contract_inputs(inputs) + +def _launch_route(inputs: dict[str, Any], route: str) -> None: + if route == ROUTE_DIM_D64_73A9: + dim_73a9._launch_d64_split(inputs) + return + dispatch_k32._launch_route(inputs, route) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + _launch_route(inputs, route_for_contract_inputs(inputs, force_fallback=force_fallback)) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def candidate_current_exact_k32(inputs: dict[str, Any]): + dispatch_k32.launch_from_contract_inputs(inputs) + return None + +def candidate_base_7399_d15e(inputs: dict[str, Any]): + dispatch_7399.launch_from_contract_inputs(inputs) + return None + +def candidate_force_fallback(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs, force_fallback=True) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return dispatch_k32._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=DISPATCH_CORRECTNESS_SHAPES, benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint: run real contract correctness for selected shapes.""" + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + return dispatch_k32._set_bench_backend(use_cupti) + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=None, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + shapes = None if shape_labels is None else _select_contract_shapes(shape_labels) + return evaluate_contract(shapes=shapes, correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def _inputs_for_label(label: str) -> dict[str, Any]: + return dispatch_k32._inputs_for_label(label) + +def _baseline_7399_route(inputs: dict[str, Any]) -> str: + return dispatch_7399.route_for_contract_inputs(inputs) + +def _current_exact_k32_route(inputs: dict[str, Any]) -> str: + return dispatch_k32.route_for_contract_inputs(inputs) + +def _route_trace_record(inputs: dict[str, Any], *, force_fallback: bool=False) -> dict[str, Any]: + if force_fallback: + row = dispatch_k32._route_trace_record(inputs) + row['guard_condition'] = 'forced fallback to current exact-K32 dispatcher; 73a9 D64 guard disabled' + row['coverage'] = 'forced candidate fallback for the consumed 73a9 D64 seed' + row['current_exact_k32_route'] = _current_exact_k32_route(inputs) + row['baseline_7399_d15e_route'] = _baseline_7399_route(inputs) + return row + route = route_for_contract_inputs(inputs) + if route != ROUTE_DIM_D64_73A9: + row = dispatch_k32._route_trace_record(inputs) + row['current_exact_k32_route'] = _current_exact_k32_route(inputs) + row['baseline_7399_d15e_route'] = _baseline_7399_route(inputs) + return row + current_route = _current_exact_k32_route(inputs) + return {'shape_key': inputs.get('label'), 'selected_route': route, 'guard_condition': 'exact 73a9 BF16 build B1 Q=M=2048 D64 K10 label', 'route_kind': 'specialized', 'coverage': 'exact 73a9 D64 split S8 seed consumed ahead of current exact-K32 dispatcher', 'consumed_seed': 'dim_midk_73a9_d64', 'replaced_route': current_route, 'current_exact_k32_route': current_route, 'baseline_7399_d15e_route': _baseline_7399_route(inputs), 'baseline_7c3a_route': dispatch_k32._base_7c3a_route_for_contract_inputs(inputs), 'inherited_route': dispatch_k32._baseline_inherited_route(inputs), 'parity_status': 'pass', 'parity_reason': '73a9 D64 CUPTI ratio_vs_flashlib is 1.5121055366591083 in the source seed payload'} + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + return [_route_trace_record(dispatch_k32._trace_inputs_from_shape(shape), force_fallback=force_fallback) for shape in selected] + +def _timing_backends_for_reports(*reports: dict[str, Any]) -> list[str]: + return dispatch_k32._timing_backends_for_reports(*reports) + +def _rows_for_labels(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return dispatch_k32._rows_for_labels(report, labels) + +def _per_consumed_row_delta(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> dict[str, Any]: + deltas = {} + for label in CONSUMED_SEED_TARGET_SHAPES: + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') + inputs = _inputs_for_label(label) + deltas[label] = {'candidate_ms': candidate_ms, 'current_exact_k32_ms': baseline_ms, 'flashlib_ms': flashlib_ms, 'speedup_vs_current_exact_k32': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'ratio_vs_flashlib': flashlib_ms / candidate_ms if candidate_ms and flashlib_ms else None, 'candidate_route': route_for_contract_inputs(inputs), 'current_exact_k32_route': _current_exact_k32_route(inputs), 'baseline_7399_d15e_route': _baseline_7399_route(inputs)} + return deltas + +def _seed_delta_matrix(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> list[dict[str, Any]]: + matrix = [] + for label in SELECTED_TARGET_SHAPES: + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + inputs = _inputs_for_label(label) + matrix.append({'shape_key': label, 'baseline_route': _current_exact_k32_route(inputs), 'candidate_route': route_for_contract_inputs(inputs), 'candidate_ms': candidate_ms, 'baseline_ms': baseline_ms, 'metric_delta_ms': candidate_ms - baseline_ms if candidate_ms and baseline_ms else None, 'speedup_vs_current_exact_k32': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'timing_backend': candidate_row.get('timing_backend') or baseline_row.get('timing_backend')}) + return matrix + +def _frontmatter_seed_delta_matrix(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> list[dict[str, Any]]: + rows = [] + for item in _seed_delta_matrix(candidate_report, baseline_report): + delta = item['metric_delta_ms'] + rows.append({'shape_key': item['shape_key'], 'baseline_route': item['baseline_route'], 'candidate_deltas': [{'candidate_id': 'exact_k32_4fbf_plus_exact_d64_73a9', 'metric_delta': 0.0 if delta is None else float(delta), 'timing_backend': item['timing_backend'] or 'cuda_event'}]}) + return rows + +def _benchmark_payload(candidate_report: dict[str, Any], baseline_report: dict[str, Any], *, use_cupti: bool, shape_labels, measured_function: str) -> dict[str, Any]: + candidate_metric = candidate_report['summary']['primary_mean'] or 0.0 + baseline_metric = baseline_report['summary']['primary_mean'] or 0.0 + route_trace = route_trace_for_contract_shapes(shape_labels) + return {'tflops': candidate_metric, 'baseline_tflops': baseline_metric, 'metric_delta': candidate_metric - baseline_metric, 'all_correct': candidate_report['summary']['all_correct'], 'baseline_all_correct': baseline_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'baseline_performance_comparable': baseline_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'baseline_invalid_performance_reason': baseline_report['summary']['invalid_performance_reason'], 'measured_entrypoint': ''.join(['loom.examples.weave.knn_build_dispatch_4fbf_7399_d15e_73a9_full55_v1:', format(measured_function, '')]), 'baseline_entrypoint': 'loom.examples.weave.knn_build_dispatch_4fbf_7399_d15e_full55_bad5_v1:launch_from_contract_inputs', 'baseline_entrypoint_note': 'same-session current exact-K32 dispatcher; this candidate adds only the 73a9 D64 guard', 'measured_shape_labels': 'all_canonical' if shape_labels is None else tuple(shape_labels), 'selected_route_labels': SELECTED_TARGET_SHAPES, 'consumed_seed_labels': CONSUMED_SEED_TARGET_SHAPES, 'selected_route_rows': _rows_for_labels(candidate_report, SELECTED_TARGET_SHAPES), 'baseline_selected_route_rows': _rows_for_labels(baseline_report, SELECTED_TARGET_SHAPES), 'consumed_seed_rows': _rows_for_labels(candidate_report, CONSUMED_SEED_TARGET_SHAPES), 'baseline_consumed_seed_rows': _rows_for_labels(baseline_report, CONSUMED_SEED_TARGET_SHAPES), 'per_consumed_row_delta': _per_consumed_row_delta(candidate_report, baseline_report), 'seed_delta_matrix': _seed_delta_matrix(candidate_report, baseline_report), 'frontmatter_seed_delta_matrix': _frontmatter_seed_delta_matrix(candidate_report, baseline_report), 'candidate_dispatchers': CANDIDATE_PORTFOLIOS, 'selected_candidate_dispatcher': 'exact_k32_4fbf_plus_exact_d64_73a9', 'route_modules': PRODUCTION_ROUTE_MODULES, 'route_trace': route_trace, 'forced_fallback_route_trace': route_trace_for_contract_shapes(shape_labels, force_fallback=True), 'route_trace_included': True, 'contract_summary': candidate_report['summary'], 'baseline_contract_summary': baseline_report['summary'], 'contract_performance': candidate_report['performance'], 'baseline_contract_performance': baseline_report['performance'], 'timing_backends': _timing_backends_for_reports(candidate_report, baseline_report), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'hot_bucket_parity': {'rag_k32': 'pass_preserved', 'dim_sweep_qm2048_d64_k10': 'pass', 'd256_fp16_midk_k64': 'inherited_fail', 'reason': 'combined route preserves exact-K32 4fbf and adds exact 73a9 D64; remaining dim/midK rows are inherited blockers.'}, 'performance_coverage': 'partial', 'coverage_only_routes': [], 'hot_bucket_blockers': ['dim_sweep_qm2048_d256_fp16', 'midk_k24_k28_over32_k64', 'default_k96_registry_hard_gate'], 'report': candidate_report, 'baseline_report': baseline_report} + +def benchmark_knn_build_dispatch_4fbf_7399_d15e_73a9_full55_v1(*, use_cupti: bool=False, shape_labels=None) -> dict[str, Any]: + """Full-denominator A/B against the current exact-K32 dispatcher.""" + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels) + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_current_exact_k32) + return _benchmark_payload(candidate_report, baseline_report, use_cupti=use_cupti, shape_labels=shape_labels, measured_function='benchmark_knn_build_dispatch_4fbf_7399_d15e_73a9_full55_v1') + +def write_benchmark_artifacts(artifact_dir: str | os.PathLike[str], *, use_cupti: bool=False, shape_labels=None) -> dict[str, str]: + payload = benchmark_knn_build_dispatch_4fbf_7399_d15e_73a9_full55_v1(use_cupti=use_cupti, shape_labels=shape_labels) + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + candidate_path = out_dir / 'full55_dispatch_4fbf_7399_d15e_73a9_full55_v1.json' + baseline_path = out_dir / 'full55_same_session_baseline_4fbf_7399_d15e_bad5_v1.json' + route_trace_path = out_dir / 'full55_route_trace_4fbf_7399_d15e_73a9_full55_v1.json' + forced_trace_path = out_dir / 'full55_forced_fallback_trace_4fbf_7399_d15e_73a9_full55_v1.json' + candidate_path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + baseline_path.write_text(json.dumps({'measured_entrypoint': payload['baseline_entrypoint'], 'measured_shape_labels': payload['measured_shape_labels'], 'timing_backend_requested': payload['timing_backend_requested'], 'timing_backends': payload['timing_backends'], 'tflops': payload['baseline_tflops'], 'all_correct': payload['baseline_all_correct'], 'performance_comparable': payload['baseline_performance_comparable'], 'contract_summary': payload['baseline_contract_summary'], 'contract_performance': payload['baseline_contract_performance'], 'route_trace': dispatch_k32.route_trace_for_contract_shapes(shape_labels), 'route_trace_included': True, 'report': payload['baseline_report']}, indent=2, sort_keys=True) + '\n') + route_trace_path.write_text(json.dumps(payload['route_trace'], indent=2, sort_keys=True) + '\n') + forced_trace_path.write_text(json.dumps(payload['forced_fallback_route_trace'], indent=2, sort_keys=True) + '\n') + return {'candidate_payload': str(candidate_path), 'baseline_payload': str(baseline_path), 'route_trace': str(route_trace_path), 'forced_fallback_trace': str(forced_trace_path)} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_4fbf_7399_d15e_full55_bad5_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_4fbf_7399_d15e_full55_bad5_v1.py new file mode 100644 index 00000000..94ece808 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_4fbf_7399_d15e_full55_bad5_v1.py @@ -0,0 +1,299 @@ +"""Opt-in kNN build full55 dispatcher consuming the 4fbf RAG K32 route. + +Minimum target architecture: sm_100a. This dispatcher-synthesis candidate is a +wrapper-only portfolio. It starts from the repeated b0e0/fd02 guard scaffold, +keeps the existing a989 large-square, 6c1e K96, 7399 RAG K10, and d15e exact +routes, replaces only the exact RAG K32 frontier row with the 4fbf v7 seed, and +delegates every other row to the same Weave-only baseline policy used by +7c3a/fd02. + +No external runtime fallback is used. FlashLib/PyTorch remain only contract +harness references outside this production dispatch path. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from pathlib import Path +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_7399_d15e_full55_v1 as dispatch_7399 +from . import knn_build_dispatch_b6d4_d15e_fd02_v1 as fd02 +from . import knn_build_rag_frontier_4fbf_v7 as rag_4fbf +from . import knn_build_rag_frontier_7399_v1 as rag_7399 +large_square = fd02.large_square +over64_k96 = fd02.over64_k96 +rect_d15e = fd02.rect_d15e +baseline_3dc7 = fd02.baseline_3dc7 +ROUTE_LARGE_SQUARE_K20K32 = fd02.ROUTE_LARGE_SQUARE_K20K32 +ROUTE_OVER64_K96 = fd02.ROUTE_OVER64_K96 +ROUTE_RAG_7C3A_K10 = fd02.ROUTE_RAG_7C3A_K10 +ROUTE_RAG_7399_K10 = 'loom.examples.weave.knn_build_rag_frontier_7399_v1:k10_s72' +ROUTE_RAG_7399_K32 = 'loom.examples.weave.knn_build_rag_frontier_7399_v1:k32_s72_g8_fusedmerge' +ROUTE_RAG_4FBF_K32 = 'loom.examples.weave.knn_build_rag_frontier_4fbf_v7:k32_s72_g24_tailinf_fused' +ROUTE_RECT_D15E = fd02.ROUTE_RECT_D15E +ROUTE_BASELINE_3DC7 = fd02.ROUTE_BASELINE_3DC7 +ROUTE_BASELINE_7C3A_POLICY = fd02.ROUTE_BASELINE_7C3A_POLICY +LARGE_SQUARE_TARGET_SHAPES = fd02.LARGE_SQUARE_TARGET_SHAPES +K96_TARGET_SHAPES = fd02.K96_TARGET_SHAPES +RAG_7C3A_K10_TARGET_SHAPES = fd02.RAG_7C3A_K10_TARGET_SHAPES +RAG_K10_TARGET_SHAPES = rag_7399.K10_TARGET_SHAPES +RAG_K32_TARGET_SHAPES = rag_4fbf.K32_TARGET_SHAPES +RAG_TARGET_SHAPES = rag_7399.TARGET_SHAPES +RECT_D15E_TARGET_SHAPES = fd02.RECT_D15E_TARGET_SHAPES +LARGE_SQUARE_TARGET_SHAPE_SET = set(LARGE_SQUARE_TARGET_SHAPES) +K96_TARGET_SHAPE_SET = set(K96_TARGET_SHAPES) +RAG_7C3A_K10_TARGET_SHAPE_SET = set(RAG_7C3A_K10_TARGET_SHAPES) +RAG_K10_TARGET_SHAPE_SET = set(RAG_K10_TARGET_SHAPES) +RAG_K32_TARGET_SHAPE_SET = set(RAG_K32_TARGET_SHAPES) +RAG_TARGET_SHAPE_SET = set(RAG_TARGET_SHAPES) +RECT_D15E_TARGET_SHAPE_SET = set(RECT_D15E_TARGET_SHAPES) +BASE_7C3A_TARGET_SHAPES = (*LARGE_SQUARE_TARGET_SHAPES, *K96_TARGET_SHAPES, *RAG_7C3A_K10_TARGET_SHAPES) +CONSUMED_SEED_TARGET_SHAPES = (*RAG_K32_TARGET_SHAPES,) +SELECTED_TARGET_SHAPES = (*LARGE_SQUARE_TARGET_SHAPES, *K96_TARGET_SHAPES, *RAG_TARGET_SHAPES, *RECT_D15E_TARGET_SHAPES) +DISPATCH_CORRECTNESS_SHAPES = ('flashml_correctness_b1_q256_m256_d128_k5', *SELECTED_TARGET_SHAPES, *baseline_3dc7.SELECTED_TARGET_SHAPES) +PRODUCTION_ROUTE_MODULES = {'large_square_k20k32': ROUTE_LARGE_SQUARE_K20K32, 'over64_k96': ROUTE_OVER64_K96, 'baseline_7c3a_rag_k10': ROUTE_RAG_7C3A_K10, 'rag_frontier_7399_k10': ROUTE_RAG_7399_K10, 'rag_frontier_7399_k32_replaced': ROUTE_RAG_7399_K32, 'rag_frontier_4fbf_k32': ROUTE_RAG_4FBF_K32, 'rect_smallq_largem_d15e': ROUTE_RECT_D15E, 'baseline_7c3a_policy': ROUTE_BASELINE_7C3A_POLICY, 'fallback': ROUTE_BASELINE_3DC7} +CANDIDATE_PORTFOLIOS = ({'id': 'base_7399_d15e_plus_4fbf_k32', 'entrypoint': 'loom.examples.weave.knn_build_dispatch_4fbf_7399_d15e_full55_bad5_v1:benchmark_knn_build_dispatch_4fbf_7399_d15e_full55_bad5_v1', 'consumed_seeds': ('rag_frontier_4fbf_v7_k32',), 'guard_plan': ('exact a989 large-square BF16 build Q=M=8192 K20/K32', 'exact 6c1e over64 BF16 build Q=M=2048 K96', 'exact 7399 RAG frontier BF16 D128 non-build K10 labels', 'exact 4fbf v7 RAG frontier BF16 B1 Q128 M100000 D128 K32 label', 'exact d15e rect BF16 B1 Q1024 M8192 D128 K10 non-build label', '7399+d15e / 7c3a Weave policy fallback'), 'expected_shape_wins': RAG_K32_TARGET_SHAPES, 'rejected_reason': None}, {'id': 'base_7c3a_plus_7399_rag_plus_d15e', 'entrypoint': 'loom.examples.weave.knn_build_dispatch_7399_d15e_full55_v1:benchmark_knn_build_dispatch_7399_d15e_full55_v1', 'consumed_seeds': ('rag_frontier_7399_v1', 'd15e_rect_smallq_largem_v1'), 'guard_plan': ('exact a989 large-square BF16 build Q=M=8192 K20/K32', 'exact 6c1e over64 BF16 build Q=M=2048 K96', 'exact 7399 RAG frontier BF16 D128 non-build K10/K32 labels', 'exact d15e rect BF16 B1 Q1024 M8192 D128 K10 non-build label', '7c3a Weave policy fallback'), 'expected_shape_wins': (), 'rejected_reason': 'baseline for this 4fbf K32 consumption lane'}, {'id': 'base_7c3a_plus_7259_v5_plus_d15e', 'entrypoint': 'loom.examples.weave.knn_build_dispatch_b6d4v5_d15e_3156_v1:benchmark_knn_build_dispatch_b6d4v5_d15e_3156_v1', 'consumed_seeds': ('rag_frontier_b6d4_v5_7259', 'd15e_rect_smallq_largem_v1'), 'guard_plan': ('same guards as selected candidate, but RAG K32 uses b6d4 v5 two-merge topology',), 'expected_shape_wins': (), 'rejected_reason': 'kept as same-session comparator; 4fbf v7 has faster K32 CUPTI seed evidence'}, {'id': 'fd02_b6d4_v4_plus_d15e', 'entrypoint': 'loom.examples.weave.knn_build_dispatch_b6d4_d15e_fd02_v1:benchmark_knn_build_dispatch_b6d4_d15e_fd02_v1', 'consumed_seeds': ('b6d4_rag_frontier_v4', 'd15e_rect_smallq_largem_v1'), 'guard_plan': ('same large-square/K96/d15e guards as selected candidate, but RAG uses b6d4 v4',), 'expected_shape_wins': (), 'rejected_reason': 'current same-denominator champion baseline, not selected for 4fbf-consumption lane'}) + +class _TraceTensor: + + def __init__(self, dtype: str) -> None: + self.dtype = dtype if dtype.startswith('torch.') else ''.join(['torch.', format(dtype, '')]) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_DISPATCH_4FBF_7399_D15E_VERIFY_KERNEL') + if verify_kernel == 'large_square_stage1_k20': + os.environ['LOOM_KNN_LARGE_SQUARE_A989_VERIFY_KERNEL'] = 'stage1_k20' + return large_square._verify_export_ir() + if verify_kernel == 'large_square_stage1_k32': + os.environ['LOOM_KNN_LARGE_SQUARE_A989_VERIFY_KERNEL'] = 'stage1_k32' + return large_square._verify_export_ir() + if verify_kernel == 'over64_k96_stage1': + return over64_k96.stage1_k96_over64_ir + if verify_kernel == 'rag_4fbf_k32_stage1': + os.environ['LOOM_KNN_RAG_FRONTIER_4FBF_V7_VERIFY_KERNEL'] = 'k32_stage1' + return rag_4fbf._verify_export_ir() + if verify_kernel == 'rag_4fbf_k32_fused_merge': + os.environ['LOOM_KNN_RAG_FRONTIER_4FBF_V7_VERIFY_KERNEL'] = 'k32_fused_merge' + return rag_4fbf._verify_export_ir() + if verify_kernel == 'rect_d15e_stage1': + os.environ['LOOM_KNN_RECT_D15E_VERIFY_KERNEL'] = 'stage1' + return rect_d15e._verify_export_ir() + return baseline_3dc7.ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k48over32", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 48]], "cta_group": 1, "threads": 192}')) + +def _label_can_hit(inputs: dict[str, Any], target_labels: set[str]) -> bool: + label = inputs.get('label') + return label is None or str(label) in target_labels + +def _eligible_large_square_k20k32(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, LARGE_SQUARE_TARGET_SHAPE_SET) and large_square._eligible_large_square_k20k32(inputs) + +def _eligible_over64_k96(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, K96_TARGET_SHAPE_SET) and over64_k96._eligible_over64_k96_build(inputs) + +def _eligible_7c3a_rag_k10(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, RAG_7C3A_K10_TARGET_SHAPE_SET) and fd02.rag_7c3a._eligible_k10_rag_frontier(inputs) + +def _eligible_7399_rag_k10(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, RAG_K10_TARGET_SHAPE_SET) and rag_7399._eligible_k10_rag_frontier(inputs) + +def _eligible_4fbf_rag_k32(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, RAG_K32_TARGET_SHAPE_SET) and rag_4fbf._eligible_k32_rag_frontier(inputs) + +def _eligible_rect_d15e(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, RECT_D15E_TARGET_SHAPE_SET) and rect_d15e._eligible_rect_smallq_largem(inputs) + +def _base_7c3a_route_for_contract_inputs(inputs: dict[str, Any]) -> str: + if _eligible_large_square_k20k32(inputs): + return ROUTE_LARGE_SQUARE_K20K32 + if _eligible_over64_k96(inputs): + return ROUTE_OVER64_K96 + if _eligible_7c3a_rag_k10(inputs): + return ROUTE_RAG_7C3A_K10 + return ROUTE_BASELINE_3DC7 + +def _base_7399_route_for_contract_inputs(inputs: dict[str, Any]) -> str: + return dispatch_7399.route_for_contract_inputs(inputs) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if force_fallback: + return _base_7c3a_route_for_contract_inputs(inputs) + if _eligible_large_square_k20k32(inputs): + return ROUTE_LARGE_SQUARE_K20K32 + if _eligible_over64_k96(inputs): + return ROUTE_OVER64_K96 + if _eligible_7399_rag_k10(inputs): + return ROUTE_RAG_7399_K10 + if _eligible_4fbf_rag_k32(inputs): + return ROUTE_RAG_4FBF_K32 + if _eligible_rect_d15e(inputs): + return ROUTE_RECT_D15E + return _base_7c3a_route_for_contract_inputs(inputs) + +def _launch_base_7c3a_route(inputs: dict[str, Any], route: str) -> None: + fd02._launch_base_7c3a_route(inputs, route) + +def _launch_route(inputs: dict[str, Any], route: str) -> None: + if route == ROUTE_RAG_7399_K10: + rag_7399._launch_k10_rag_frontier_s72(inputs) + return + if route == ROUTE_RAG_4FBF_K32: + rag_4fbf._launch_k32_rag_frontier_sort4earlystop_stage(inputs) + return + if route == ROUTE_RECT_D15E: + rect_d15e._launch_rect_smallq_largem(inputs) + return + _launch_base_7c3a_route(inputs, route) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + _launch_route(inputs, route_for_contract_inputs(inputs, force_fallback=force_fallback)) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def candidate_without_d15e(inputs: dict[str, Any]): + route = route_for_contract_inputs(inputs) + if route == ROUTE_RECT_D15E: + route = _base_7c3a_route_for_contract_inputs(inputs) + _launch_route(inputs, route) + return None + +def candidate_baseline_7c3a(inputs: dict[str, Any]): + _launch_base_7c3a_route(inputs, _base_7c3a_route_for_contract_inputs(inputs)) + return None + +def candidate_baseline_7399_d15e(inputs: dict[str, Any]): + dispatch_7399.launch_from_contract_inputs(inputs) + return None + +def candidate_force_fallback(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs, force_fallback=True) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return baseline_3dc7._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=DISPATCH_CORRECTNESS_SHAPES, benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint: run real contract correctness for selected shapes.""" + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=None, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + shapes = None if shape_labels is None else _select_contract_shapes(shape_labels) + return evaluate_contract(shapes=shapes, correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def _trace_inputs_from_shape(shape: dict[str, Any]) -> dict[str, Any]: + params = dict(shape['params']) + dtype = str(params.get('dtype', 'bfloat16')) + return {'label': shape['label'], 'B': int(params['B']), 'Q': int(params['Q']), 'M': int(params['M']), 'D': int(params['D']), 'K': int(params['K']), 'dtype': dtype, 'build': bool(params.get('build', False)), 'query': _TraceTensor(dtype), 'database': _TraceTensor(dtype)} + +def _baseline_inherited_route(inputs: dict[str, Any]) -> str: + try: + return baseline_3dc7.route_for_contract_inputs(inputs) + except Exception: + return baseline_3dc7.ROUTE_PREVIOUS_MAIN + +def _route_kind_for_base(route: str) -> str: + return 'general' if route == ROUTE_BASELINE_3DC7 else 'specialized' + +def _route_trace_record(inputs: dict[str, Any], *, force_fallback: bool=False) -> dict[str, Any]: + route = route_for_contract_inputs(inputs, force_fallback=force_fallback) + base_route = _base_7c3a_route_for_contract_inputs(inputs) + inherited_route = _baseline_inherited_route(inputs) + if force_fallback: + return {'shape_key': inputs.get('label'), 'selected_route': route, 'guard_condition': 'forced fallback to baseline 7c3a policy', 'route_kind': _route_kind_for_base(route), 'coverage': 'forced candidate fallback; 4fbf, 7399, and d15e guards disabled', 'consumed_seed': None, 'replaced_route': None, 'baseline_7c3a_route': base_route, 'baseline_7399_route': _base_7399_route_for_contract_inputs(inputs), 'inherited_route': inherited_route} + if route == ROUTE_LARGE_SQUARE_K20K32: + return {'shape_key': inputs.get('label'), 'selected_route': route, 'guard_condition': 'exact BF16 B1 Q=M=8192 D128 build=true K in {20,32}', 'route_kind': 'specialized', 'coverage': 'baseline 7c3a exact a989 large-square K20/K32 seed', 'consumed_seed': 'a989_large_square_k20k32', 'replaced_route': base_route, 'baseline_7c3a_route': base_route, 'inherited_route': inherited_route, 'parity_status': 'pass'} + if route == ROUTE_OVER64_K96: + return {'shape_key': inputs.get('label'), 'selected_route': route, 'guard_condition': 'exact BF16 B1 Q=M=2048 D128 build=true K=96', 'route_kind': 'specialized', 'coverage': 'baseline 7c3a exact 6c1e over64 K96 seed', 'consumed_seed': '6c1e_over64_k96', 'replaced_route': base_route, 'baseline_7c3a_route': base_route, 'inherited_route': inherited_route, 'parity_status': 'pass'} + if route == ROUTE_RAG_7399_K10: + return {'shape_key': inputs.get('label'), 'selected_route': route, 'guard_condition': 'exact 7399 RAG frontier BF16 D128 non-build K10 label', 'route_kind': 'specialized', 'coverage': 'exact 7399 inherited b6d4/v3 RAG K10 split-72 seed', 'consumed_seed': 'rag_frontier_7399_v1', 'replaced_route': base_route, 'baseline_7c3a_route': base_route, 'baseline_route_kind': _route_kind_for_base(base_route), 'inherited_route': inherited_route, 'parity_status': 'pass', 'parity_reason': '7399 K10 CUPTI ratio_vs_flashlib range is 1.0443 to 1.2952 except microbatch 1.0630'} + if route == ROUTE_RAG_4FBF_K32: + return {'shape_key': inputs.get('label'), 'selected_route': route, 'guard_condition': 'exact 4fbf v7 RAG frontier BF16 B1 Q128 M100000 D128 K32 non-build label', 'route_kind': 'specialized', 'coverage': 'exact 4fbf v7 RAG K32 S72/G24 tail-sentinel fused cooperative merge seed', 'consumed_seed': 'rag_frontier_4fbf_v7', 'replaced_route': ROUTE_RAG_7399_K32, 'baseline_7c3a_route': base_route, 'baseline_7399_route': _base_7399_route_for_contract_inputs(inputs), 'baseline_route_kind': _route_kind_for_base(base_route), 'inherited_route': inherited_route, 'parity_status': 'pass', 'parity_reason': '4fbf v7 K32 CUPTI ratio_vs_flashlib is 1.0955379235836016 in the source seed payload'} + if route == ROUTE_RECT_D15E: + return {'shape_key': inputs.get('label'), 'selected_route': route, 'guard_condition': 'exact d15e rect BF16 B1 Q1024 M8192 D128 K10 non-build label', 'route_kind': 'specialized', 'coverage': 'exact d15e rectangular small-Q large-M K10 seed', 'consumed_seed': 'd15e_rect_smallq_largem_v1', 'replaced_route': base_route, 'baseline_7c3a_route': base_route, 'baseline_route_kind': _route_kind_for_base(base_route), 'inherited_route': inherited_route, 'parity_status': 'pass', 'parity_reason': 'd15e target-bucket CUPTI ratio_vs_flashlib is 1.4187 in the source seed payload'} + return {'shape_key': inputs.get('label'), 'selected_route': route, 'guard_condition': 'synthesized guard miss; delegate to baseline 7c3a Weave policy', 'route_kind': _route_kind_for_base(route), 'coverage': 'baseline 7c3a policy or inherited split72/de1a/3dc7 Weave dispatcher fallback', 'consumed_seed': None, 'replaced_route': None, 'baseline_7c3a_route': base_route, 'baseline_7399_route': _base_7399_route_for_contract_inputs(inputs), 'inherited_route': inherited_route} + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + return [_route_trace_record(_trace_inputs_from_shape(shape), force_fallback=force_fallback) for shape in selected] + +def _timing_backends_for_reports(*reports: dict[str, Any]) -> list[str]: + return fd02._timing_backends_for_reports(*reports) + +def _rows_for_labels(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return fd02._rows_for_labels(report, labels) + +def _params_for_label(label: str) -> dict[str, Any]: + return fd02._params_for_label(label) + +def _inputs_for_label(label: str) -> dict[str, Any]: + return _trace_inputs_from_shape({'label': label, 'params': _params_for_label(label)}) + +def _per_consumed_row_delta(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> dict[str, Any]: + deltas = {} + for label in CONSUMED_SEED_TARGET_SHAPES: + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') + inputs = _inputs_for_label(label) + deltas[label] = {'candidate_ms': candidate_ms, 'baseline_7399_d15e_ms': baseline_ms, 'flashlib_ms': flashlib_ms, 'speedup_vs_baseline_7399_d15e': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'ratio_vs_flashlib': flashlib_ms / candidate_ms if candidate_ms and flashlib_ms else None, 'candidate_route': route_for_contract_inputs(inputs), 'baseline_7399_route': _base_7399_route_for_contract_inputs(inputs), 'baseline_7c3a_route': _base_7c3a_route_for_contract_inputs(inputs)} + return deltas + +def _seed_delta_matrix(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> list[dict[str, Any]]: + matrix = [] + for label in SELECTED_TARGET_SHAPES: + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + inputs = _inputs_for_label(label) + matrix.append({'shape_key': label, 'baseline_route': _base_7399_route_for_contract_inputs(inputs), 'candidate_route': route_for_contract_inputs(inputs), 'candidate_ms': candidate_ms, 'baseline_ms': baseline_ms, 'metric_delta_ms': candidate_ms - baseline_ms if candidate_ms and baseline_ms else None, 'speedup_vs_baseline_7399_d15e': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'timing_backend': candidate_row.get('timing_backend') or baseline_row.get('timing_backend')}) + return matrix + +def _benchmark_payload(candidate_report: dict[str, Any], baseline_report: dict[str, Any], *, use_cupti: bool, shape_labels, measured_function: str) -> dict[str, Any]: + candidate_metric = candidate_report['summary']['primary_mean'] or 0.0 + baseline_metric = baseline_report['summary']['primary_mean'] or 0.0 + route_trace = route_trace_for_contract_shapes(shape_labels) + return {'tflops': candidate_metric, 'baseline_tflops': baseline_metric, 'metric_delta': candidate_metric - baseline_metric, 'all_correct': candidate_report['summary']['all_correct'], 'baseline_all_correct': baseline_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'baseline_performance_comparable': baseline_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'baseline_invalid_performance_reason': baseline_report['summary']['invalid_performance_reason'], 'measured_entrypoint': ''.join(['loom.examples.weave.knn_build_dispatch_4fbf_7399_d15e_full55_bad5_v1:', format(measured_function, '')]), 'baseline_entrypoint': 'loom.examples.weave.knn_build_dispatch_7399_d15e_full55_v1:benchmark_knn_build_dispatch_7399_d15e_full55_v1', 'baseline_entrypoint_note': 'same-session in-module 7399+d15e policy; this lane replaces only its exact RAG K32 route with 4fbf v7', 'measured_shape_labels': 'all_canonical' if shape_labels is None else tuple(shape_labels), 'selected_route_labels': SELECTED_TARGET_SHAPES, 'consumed_seed_labels': CONSUMED_SEED_TARGET_SHAPES, 'selected_route_rows': _rows_for_labels(candidate_report, SELECTED_TARGET_SHAPES), 'baseline_selected_route_rows': _rows_for_labels(baseline_report, SELECTED_TARGET_SHAPES), 'consumed_seed_rows': _rows_for_labels(candidate_report, CONSUMED_SEED_TARGET_SHAPES), 'baseline_consumed_seed_rows': _rows_for_labels(baseline_report, CONSUMED_SEED_TARGET_SHAPES), 'per_consumed_row_delta': _per_consumed_row_delta(candidate_report, baseline_report), 'seed_delta_matrix': _seed_delta_matrix(candidate_report, baseline_report), 'candidate_dispatchers': CANDIDATE_PORTFOLIOS, 'selected_candidate_dispatcher': 'base_7399_d15e_plus_4fbf_k32', 'route_modules': PRODUCTION_ROUTE_MODULES, 'route_trace': route_trace, 'forced_fallback_route_trace': route_trace_for_contract_shapes(shape_labels, force_fallback=True), 'route_trace_included': True, 'contract_summary': candidate_report['summary'], 'baseline_contract_summary': baseline_report['summary'], 'contract_performance': candidate_report['performance'], 'baseline_contract_performance': baseline_report['performance'], 'timing_backends': _timing_backends_for_reports(candidate_report, baseline_report), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'hot_bucket_parity': {'rag_k10': 'pass', 'rag_k32': 'pass', 'rect_smallq_largem_k10': 'pass', 'reason': '4fbf v7 K32 source-seed CUPTI timing beats FlashLib and replaces the 7399 K32 route only.'}, 'performance_coverage': 'partial', 'coverage_only_routes': [], 'hot_bucket_blockers': ['dim_sweep_qm2048_k10', 'midk_over32_cleanup'], 'report': candidate_report, 'baseline_report': baseline_report} + +def benchmark_knn_build_dispatch_4fbf_7399_d15e_full55_bad5_v1(*, use_cupti: bool=False, shape_labels=None) -> dict[str, Any]: + """Full-denominator A/B against the 7399+d15e baseline policy.""" + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels) + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_baseline_7399_d15e) + return _benchmark_payload(candidate_report, baseline_report, use_cupti=use_cupti, shape_labels=shape_labels, measured_function='benchmark_knn_build_dispatch_4fbf_7399_d15e_full55_bad5_v1') + +def write_benchmark_artifacts(artifact_dir: str | os.PathLike[str], *, use_cupti: bool=False, shape_labels=None) -> dict[str, str]: + payload = benchmark_knn_build_dispatch_4fbf_7399_d15e_full55_bad5_v1(use_cupti=use_cupti, shape_labels=shape_labels) + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + candidate_path = out_dir / 'full55_dispatch_4fbf_7399_d15e_full55_bad5_v1.json' + baseline_path = out_dir / 'full55_same_session_baseline_7399_d15e_for_4fbf_bad5_v1.json' + route_trace_path = out_dir / 'full55_route_trace_4fbf_7399_d15e_full55_bad5_v1.json' + candidate_path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + baseline_path.write_text(json.dumps({'measured_entrypoint': payload['baseline_entrypoint'], 'measured_shape_labels': payload['measured_shape_labels'], 'timing_backend_requested': payload['timing_backend_requested'], 'timing_backends': payload['timing_backends'], 'tflops': payload['baseline_tflops'], 'all_correct': payload['baseline_all_correct'], 'performance_comparable': payload['baseline_performance_comparable'], 'contract_summary': payload['baseline_contract_summary'], 'contract_performance': payload['baseline_contract_performance'], 'report': payload['baseline_report']}, indent=2, sort_keys=True) + '\n') + route_trace_path.write_text(json.dumps(payload['route_trace'], indent=2, sort_keys=True) + '\n') + return {'candidate_payload': str(candidate_path), 'baseline_payload': str(baseline_path), 'route_trace': str(route_trace_path)} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_6998_ragk10_direct_split72_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_6998_ragk10_direct_split72_v1.py new file mode 100644 index 00000000..5204f04e --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_6998_ragk10_direct_split72_v1.py @@ -0,0 +1,153 @@ +"""Direct split72 repair overlay for the 6998 residual RAG K10 row. + +Minimum target architecture: sm_100a. This additive dispatcher candidate keeps +the round-113 6998 residual 19b3 overlay as the default route, but sends only +``rag_stream_b1_q128_m100000_d128_k10`` directly to the validated split72 +Weave seed. The direct route avoids the older 19b3/ed1c portfolio chain for +this one residual bucket row and still writes contract-visible distances and +indices through the split72 tcgen05/TMA producer and cached merge. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import os +from typing import Any, Callable +from . import knn_build_dispatch_6998_residual_19b3_overlay_v1 as base_6998 +from . import knn_build_rag_online_stream_split72_4e09_v1 as rag_split72 +MODULE = 'loom.examples.weave.knn_build_dispatch_6998_ragk10_direct_split72_v1' +RAG_K10_DIRECT_SHAPE = 'rag_stream_b1_q128_m100000_d128_k10' +TARGET_SHAPES = (RAG_K10_DIRECT_SHAPE,) +SEED_DIRECT_RAG_K10_ID = 'rag_stream_k10_direct_split72_6998_v1' +ROUTE_DIRECT_RAG_K10 = 'loom.examples.weave.knn_build_rag_online_stream_split72_4e09_v1:direct_split72' +ROUTE_DIRECT_RAG_K10_ENTRYPOINT = 'loom.examples.weave.knn_build_rag_online_stream_split72_4e09_v1:launch_from_contract_inputs' +BASELINE_ID = 'candidate_residual_19b3_overlay_6998_v1' +BASELINE_ENTRYPOINT = ''.join([format(base_6998.MODULE, ''), ':benchmark_candidate_residual_19b3_overlay_6998_v1']) +PRODUCTION_ROUTE_MODULES = {**base_6998.PRODUCTION_ROUTE_MODULES, SEED_DIRECT_RAG_K10_ID: ROUTE_DIRECT_RAG_K10_ENTRYPOINT, BASELINE_ID: ''.join([format(base_6998.MODULE, ''), ':launch_from_contract_inputs'])} +CANDIDATE_DISPATCHERS = (*base_6998.CANDIDATE_DISPATCHERS, {'id': 'candidate_6998_ragk10_direct_split72_v1', 'entrypoint': ''.join([format(MODULE, ''), ':benchmark_candidate_6998_ragk10_direct_split72_v1']), 'consumed_seeds': (SEED_DIRECT_RAG_K10_ID,), 'guard_plan': ('exact residual BF16 RAG stream K10 guard', 'direct split72 Weave seed launcher for matching row', 'fall through to 6998 residual 19b3 overlay for every other shape'), 'expected_shape_wins': TARGET_SHAPES, 'fallback': ''.join([format(base_6998.MODULE, ''), ':launch_from_contract_inputs']), 'rejected_reason': None}) +eval_mod = base_6998.eval_mod + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_6998_RAGK10_VERIFY_KERNEL') + if verify_kernel == 'rag_split72_stage1': + return rag_split72.parent_lowk.stage1_ir + if verify_kernel == 'rag_split72_merge': + return rag_split72.merge_k10_s72_cache_ir + return base_6998.ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k48over32", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 48]], "cta_group": 1, "threads": 192}')) + +def _dtype_name(inputs: dict[str, Any]) -> str: + query = inputs.get('query') + if query is not None: + return str(query.dtype).removeprefix('torch.') + return str(inputs.get('dtype', '')).removeprefix('torch.') + +def _eligible_direct_rag_k10(inputs: dict[str, Any]) -> bool: + return not bool(inputs.get('build', False)) and _dtype_name(inputs) == 'bfloat16' and (int(inputs.get('B', -1)) == 1) and (int(inputs.get('Q', -1)) == 128) and (int(inputs.get('M', -1)) == 100000) and (int(inputs.get('D', -1)) == rag_split72.parent_lowk.FEAT_D) and (int(inputs.get('K', -1)) == rag_split72.parent_lowk.TOP_K_MAX) and rag_split72._eligible_rag_online_stream_split72(inputs) + +def _select_contract_shapes(shape_labels): + return base_6998._select_contract_shapes(shape_labels) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False, enable_direct_rag_k10: bool=True, enable_residual_19b3: bool=True) -> str: + if not force_fallback and enable_direct_rag_k10 and _eligible_direct_rag_k10(inputs): + return ROUTE_DIRECT_RAG_K10 + return base_6998.route_for_contract_inputs(inputs, force_fallback=force_fallback, enable_residual_19b3=enable_residual_19b3) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False, enable_direct_rag_k10: bool=True, enable_residual_19b3: bool=True) -> None: + if not force_fallback and enable_direct_rag_k10 and _eligible_direct_rag_k10(inputs): + rag_split72._launch_rag_online_stream_split72(inputs) + return + base_6998.launch_from_contract_inputs(inputs, force_fallback=force_fallback, enable_residual_19b3=enable_residual_19b3) + +def candidate_6998_ragk10_direct_split72_v1(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_baseline_6998(inputs: dict[str, Any]) -> None: + base_6998.candidate_residual_19b3_overlay_6998_v1(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=None, kernel_fn: Callable[[dict[str, Any]], Any], correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + return base_6998._run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=kernel_fn, correctness=correctness, time_flashlib=time_flashlib) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = eval_mod.evaluate(candidate, shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _direct_trace_record(inputs: dict[str, Any]) -> dict[str, Any]: + label = str(inputs.get('label')) + base_row = dict(base_6998.route_trace_for_contract_shapes((label,))[0]) + base_route = base_6998.route_for_contract_inputs(inputs) + base_row.update({'shape_key': label, 'selected_route': ROUTE_DIRECT_RAG_K10, 'selected_entrypoint': ROUTE_DIRECT_RAG_K10_ENTRYPOINT, 'selected_seed': SEED_DIRECT_RAG_K10_ID, 'expected_seed': SEED_DIRECT_RAG_K10_ID, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': '6998_ragk10_direct_split72_exact_guard', 'guard_condition': 'exact residual BF16 shape label=rag_stream_b1_q128_m100000_d128_k10 B=1 Q=128 M=100000 D=128 K=10 build=False', 'coverage': 'direct split72 RAG stream K10 seed before 6998 residual 19b3 overlay', 'consumed_seed': SEED_DIRECT_RAG_K10_ID, 'replaced_route': base_route, 'baseline_6998_route': base_route, 'wrapper_entrypoint': ROUTE_DIRECT_RAG_K10_ENTRYPOINT, 'classification': 'unmeasured'}) + return base_6998.base_f30c._normalize_route_row(base_row) + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False, enable_direct_rag_k10: bool=True, enable_residual_19b3: bool=True) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + rows = [] + for shape in selected: + inputs = base_6998.base_f30c._trace_inputs_from_shape(shape) + if force_fallback and _eligible_direct_rag_k10(inputs): + row = dict(base_6998.route_trace_for_contract_shapes((str(shape['label']),), force_fallback=True)[0]) + row['expected_seed'] = SEED_DIRECT_RAG_K10_ID + row['guard_id'] = 'forced_fallback_6998_ragk10_direct_split72_disabled' + row['guard_condition'] = 'forced fallback to 6998 base route; direct split72 RAG K10 guard disabled' + row['classification'] = 'guard-miss' + elif enable_direct_rag_k10 and _eligible_direct_rag_k10(inputs): + row = _direct_trace_record(inputs) + else: + row = dict(base_6998.route_trace_for_contract_shapes((str(shape['label']),), force_fallback=force_fallback, enable_residual_19b3=enable_residual_19b3)[0]) + rows.append(base_6998.base_f30c._normalize_route_row(row)) + return rows + +def _rows_for_labels(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return base_6998._rows_for_labels(report, labels) + +def _annotate_direct_trace(route_trace: list[dict[str, Any]], candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> list[dict[str, Any]]: + annotated = [] + for row in route_trace: + label = str(row.get('shape_key')) + out = dict(row) + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') + speedup_vs_baseline = baseline_ms / candidate_ms if candidate_ms and baseline_ms else None + speedup_vs_external = flashlib_ms / candidate_ms if candidate_ms and flashlib_ms else None + out['dispatcher_kernel_ms'] = candidate_ms + out['baseline_dispatcher_kernel_ms'] = baseline_ms + out['external_baseline_ms'] = flashlib_ms + out['flashlib_ms'] = flashlib_ms + out['relative_speedup_vs_baseline'] = speedup_vs_baseline + out['speedup_vs_external_baseline'] = speedup_vs_external + out['external_baseline_ref'] = 'same_session' if flashlib_ms is not None else 'not_available' + if label == RAG_K10_DIRECT_SHAPE: + if speedup_vs_external is not None and speedup_vs_external < 1.05: + out['classification'] = 'kernel-slow' + else: + out['classification'] = 'seed-consumed' + annotated.append(base_6998.base_f30c._normalize_route_row(out)) + return annotated + +def benchmark_baseline_6998(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES, benchmark_correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_baseline_6998, correctness=benchmark_correctness, time_flashlib=time_flashlib) + report['candidate_id'] = BASELINE_ID + report['measured_entrypoint'] = BASELINE_ENTRYPOINT + report['route_trace'] = base_6998.route_trace_for_contract_shapes(shape_labels) + return report + +def benchmark_candidate_6998_ragk10_direct_split72_v1(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES, baseline_report: dict[str, Any] | None=None, benchmark_correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + if baseline_report is None: + baseline_report = benchmark_baseline_6998(use_cupti=use_cupti, shape_labels=shape_labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_6998_ragk10_direct_split72_v1, correctness=benchmark_correctness, time_flashlib=time_flashlib) + baseline_metric = baseline_report.get('summary', {}).get('primary_mean') + candidate_metric = candidate_report.get('summary', {}).get('primary_mean') + route_trace = _annotate_direct_trace(route_trace_for_contract_shapes(shape_labels), candidate_report, baseline_report) + return {'candidate_id': 'candidate_6998_ragk10_direct_split72_v1', 'baseline_candidate_id': BASELINE_ID, 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_candidate_6998_ragk10_direct_split72_v1']), 'baseline_entrypoint': ''.join([format(MODULE, ''), ':benchmark_baseline_6998']), 'selected_seeds': (SEED_DIRECT_RAG_K10_ID,), 'all_correct': candidate_report.get('summary', {}).get('all_correct'), 'baseline_all_correct': baseline_report.get('summary', {}).get('all_correct'), 'performance_comparable': candidate_report.get('summary', {}).get('performance_comparable'), 'baseline_performance_comparable': baseline_report.get('summary', {}).get('performance_comparable'), 'tflops': candidate_metric, 'baseline_tflops': baseline_metric, 'metric_delta': candidate_metric - baseline_metric if candidate_metric and baseline_metric else None, 'timing_backend': 'cupti' if use_cupti else 'cuda_event_fallback', 'benchmark_time_flashlib': time_flashlib, 'denominator': 'rag_stream_k10_exact', 'shape_labels': list(TARGET_SHAPES if shape_labels is None else shape_labels), 'selected_route_rows': _rows_for_labels(candidate_report, tuple(TARGET_SHAPES)), 'baseline_selected_route_rows': _rows_for_labels(baseline_report, tuple(TARGET_SHAPES)), 'route_trace': route_trace, 'forced_fallback_route_trace': route_trace_for_contract_shapes(shape_labels, force_fallback=True), 'candidate_dispatchers': CANDIDATE_DISPATCHERS, 'route_modules': PRODUCTION_ROUTE_MODULES, 'report': candidate_report, 'baseline_report': baseline_report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_6998_residual_19b3_overlay_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_6998_residual_19b3_overlay_v1.py new file mode 100644 index 00000000..d255316d --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_6998_residual_19b3_overlay_v1.py @@ -0,0 +1,200 @@ +"""Residual-bucket 19b3 overlay over the f30c full82 dispatcher. + +Minimum target architecture: sm_100a. This opt-in dispatcher candidate keeps +the f30c full82 portfolio as the default route and delegates only exact +residual below-floor rows to the older 19b3/ed1c Weave portfolio. The overlay +does not change seed schedules; it only tests whether the previously validated +19b3 dispatcher path is a better production route for these bucket rows. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +from typing import Any, Callable +from . import knn_build_dispatch_4247_non128_8199_3d5a_2e8e_full82_synth_v1 as base_f30c +from . import knn_build_dispatch_c142_3505_q32rowld_19b3_v1 as portfolio_19b3 +MODULE = 'loom.examples.weave.knn_build_dispatch_6998_residual_19b3_overlay_v1' +BASELINE_ID = 'candidate_q16split148_cachedmerge_k96exactall_e080_q1m262_over_8199_full82_v1' +BASELINE_ENTRYPOINT = ''.join([format(base_f30c.MODULE, ''), ':benchmark_candidate_q16split148_cachedmerge_k96exactall_e080_q1m262']) +SEED_RESIDUAL_19B3_ID = 'residual_19b3_ed1c_portfolio_6998' +ROUTE_RESIDUAL_19B3_ENTRYPOINT = ''.join([format(portfolio_19b3.MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_BASELINE_F30C_ENTRYPOINT = ''.join([format(base_f30c.MODULE, ''), ':launch_from_contract_inputs']) +RESIDUAL_TARGET_SHAPES = ('build_k_sweep_qm512_k1', 'build_k_sweep_qm4096_k24', 'build_k_sweep_qm4096_k30', 'build_over32_stress_qm2048_k48', 'build_over32_stress_qm4096_k48', 'search_rect_b1_q1024_m32768_d64_k10', 'rag_stream_b1_q128_m100000_d128_k10', 'rag_stream_largek_b1_q128_m100000_d128_k32') +RESIDUAL_TARGET_SHAPE_SET = set(RESIDUAL_TARGET_SHAPES) +_CONTRACT_PARAMS_BY_LABEL = _decode_capture(_json_loads('{"__dict_items__": [["flashml_correctness_b1_q256_m256_d128_k5", {"__dict_items__": [["B", 1], ["Q", 256], ["M", 256], ["D", 128], ["K", 5], ["dtype", "bfloat16"], ["seed", 606001], ["build", true], ["check_correctness", true], ["correctness_query_sample", 256], ["recall_min", 0.99], ["benchmark", true], ["time_flashlib", true]]}], ["build_k_sweep_qm512_k1", {"__dict_items__": [["B", 1], ["Q", 512], ["M", 512], ["D", 128], ["K", 1], ["dtype", "bfloat16"], ["seed", 606049], ["build", true], ["check_correctness", true], ["correctness_query_sample", 512], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true]]}], ["build_k_sweep_qm512_k2", {"__dict_items__": [["B", 1], ["Q", 512], ["M", 512], ["D", 128], ["K", 2], ["dtype", "bfloat16"], ["seed", 606050], ["build", true], ["check_correctness", true], ["correctness_query_sample", 512], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true]]}], ["build_k_sweep_qm512_k4", {"__dict_items__": [["B", 1], ["Q", 512], ["M", 512], ["D", 128], ["K", 4], ["dtype", "bfloat16"], ["seed", 606052], ["build", true], ["check_correctness", true], ["correctness_query_sample", 512], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true], ["diagnostic_class", "low_k_q512_k5_neighborhood"]]}], ["build_k_sweep_qm512_k5", {"__dict_items__": [["B", 1], ["Q", 512], ["M", 512], ["D", 128], ["K", 5], ["dtype", "bfloat16"], ["seed", 606053], ["build", true], ["check_correctness", true], ["correctness_query_sample", 512], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true]]}], ["build_k_sweep_qm512_k6", {"__dict_items__": [["B", 1], ["Q", 512], ["M", 512], ["D", 128], ["K", 6], ["dtype", "bfloat16"], ["seed", 606054], ["build", true], ["check_correctness", true], ["correctness_query_sample", 512], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true], ["diagnostic_class", "low_k_q512_k5_neighborhood"]]}], ["build_k_sweep_qm512_k8", {"__dict_items__": [["B", 1], ["Q", 512], ["M", 512], ["D", 128], ["K", 8], ["dtype", "bfloat16"], ["seed", 606056], ["build", true], ["check_correctness", true], ["correctness_query_sample", 512], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true]]}], ["build_k_sweep_qm512_k10", {"__dict_items__": [["B", 1], ["Q", 512], ["M", 512], ["D", 128], ["K", 10], ["dtype", "bfloat16"], ["seed", 606058], ["build", true], ["check_correctness", true], ["correctness_query_sample", 512], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true]]}], ["build_qm1024_d128_k10", {"__dict_items__": [["B", 1], ["Q", 1024], ["M", 1024], ["D", 128], ["K", 10], ["dtype", "bfloat16"], ["seed", 606104], ["build", true], ["check_correctness", true], ["correctness_query_sample", 512], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true]]}], ["build_k_sweep_qm1024_k16", {"__dict_items__": [["B", 1], ["Q", 1024], ["M", 1024], ["D", 128], ["K", 16], ["dtype", "bfloat16"], ["seed", 606116], ["build", true], ["check_correctness", true], ["correctness_query_sample", 512], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true], ["diagnostic_class", "mid_k_topk_bucket"]]}], ["build_k_sweep_qm1024_k12", {"__dict_items__": [["B", 1], ["Q", 1024], ["M", 1024], ["D", 128], ["K", 12], ["dtype", "bfloat16"], ["seed", 606112], ["build", true], ["check_correctness", true], ["correctness_query_sample", 512], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true]]}], ["build_k_sweep_qm1024_k20", {"__dict_items__": [["B", 1], ["Q", 1024], ["M", 1024], ["D", 128], ["K", 20], ["dtype", "bfloat16"], ["seed", 606120], ["build", true], ["check_correctness", true], ["correctness_query_sample", 512], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true]]}], ["build_qm2048_d128_k8", {"__dict_items__": [["B", 1], ["Q", 2048], ["M", 2048], ["D", 128], ["K", 8], ["dtype", "bfloat16"], ["seed", 606208], ["build", true], ["check_correctness", true], ["correctness_query_sample", 512], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true]]}], ["build_qm1024_d128_k8", {"__dict_items__": [["B", 1], ["Q", 1024], ["M", 1024], ["D", 128], ["K", 8], ["dtype", "bfloat16"], ["seed", 611108], ["build", true], ["check_correctness", true], ["correctness_query_sample", 512], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true], ["diagnostic_class", "low_k_build_frontier"]]}], ["build_qm4096_d128_k8", {"__dict_items__": [["B", 1], ["Q", 4096], ["M", 4096], ["D", 128], ["K", 8], ["dtype", "bfloat16"], ["seed", 611408], ["build", true], ["check_correctness", true], ["correctness_query_sample", 512], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true], ["diagnostic_class", "low_k_build_frontier"]]}], ["build_qm2048_d128_k10", {"__dict_items__": [["B", 1], ["Q", 2048], ["M", 2048], ["D", 128], ["K", 10], ["dtype", "bfloat16"], ["seed", 606210], ["build", true], ["check_correctness", true], ["correctness_query_sample", 512], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true]]}], ["build_dim_sweep_b1_q1024_m1024_d64_k10", {"__dict_items__": [["B", 1], ["Q", 1024], ["M", 1024], ["D", 64], ["K", 10], ["dtype", "bfloat16"], ["seed", 608164], ["build", true], ["check_correctness", true], ["correctness_query_sample", 512], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true], ["diagnostic_class", "d64_dispatch_guard_blindspot"]]}], ["build_dim_sweep_b1_q2048_m2048_d64_k10", {"__dict_items__": [["B", 1], ["Q", 2048], ["M", 2048], ["D", 64], ["K", 10], ["dtype", "bfloat16"], ["seed", 606264], ["build", true], ["check_correctness", true], ["correctness_query_sample", 512], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true], ["diagnostic_class", "d_generalization"]]}], ["build_dim_sweep_b1_q4096_m4096_d64_k10", {"__dict_items__": [["B", 1], ["Q", 4096], ["M", 4096], ["D", 64], ["K", 10], ["dtype", "bfloat16"], ["seed", 608464], ["build", true], ["check_correctness", true], ["correctness_query_sample", 256], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true], ["diagnostic_class", "d64_dispatch_guard_blindspot"]]}], ["build_dim_sweep_b1_q1024_m1024_d96_k10", {"__dict_items__": [["B", 1], ["Q", 1024], ["M", 1024], ["D", 96], ["K", 10], ["dtype", "bfloat16"], ["seed", 610096], ["build", true], ["check_correctness", true], 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["build_common_d4096_b1_q512_m512_k10", {"__dict_items__": [["B", 1], ["Q", 512], ["M", 512], ["D", 4096], ["K", 10], ["dtype", "bfloat16"], ["seed", 614096], ["build", true], ["check_correctness", true], ["correctness_query_sample", 256], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true], ["diagnostic_class", "common_embedding_dim_frontier"]]}], ["build_highd_b1_q1024_m1024_d320_k10", {"__dict_items__": [["B", 1], ["Q", 1024], ["M", 1024], ["D", 320], ["K", 10], ["dtype", "bfloat16"], ["seed", 610320], ["build", true], ["check_correctness", true], ["correctness_query_sample", 512], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true], ["diagnostic_class", "high_d_split_generalization"]]}], ["build_dtype_fp16_b1_q2048_m2048_d128_k10", {"__dict_items__": [["B", 1], ["Q", 2048], ["M", 2048], ["D", 128], ["K", 10], ["dtype", "float16"], ["seed", 606216], ["build", true], ["check_correctness", true], ["correctness_query_sample", 512], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true], ["diagnostic_class", "dtype_generalization"]]}], ["build_batch_b2_q1024_m1024_d128_k10", {"__dict_items__": [["B", 2], ["Q", 1024], ["M", 1024], ["D", 128], ["K", 10], ["dtype", "bfloat16"], ["seed", 606211], ["build", true], ["check_correctness", true], ["correctness_query_sample", 512], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true], ["diagnostic_class", "batch_generalization"]]}], ["build_k_sweep_qm2048_k11", {"__dict_items__": [["B", 1], ["Q", 2048], ["M", 2048], ["D", 128], ["K", 11], ["dtype", "bfloat16"], ["seed", 609211], ["build", true], ["check_correctness", true], ["correctness_query_sample", 512], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true], ["diagnostic_class", "mid_k_boundary_guard"]]}], ["build_k_sweep_qm2048_k12", {"__dict_items__": [["B", 1], ["Q", 2048], ["M", 2048], ["D", 128], ["K", 12], ["dtype", "bfloat16"], ["seed", 606212], ["build", true], ["check_correctness", true], ["correctness_query_sample", 512], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true], ["diagnostic_class", "mid_k_boundary_guard"]]}], ["build_k_sweep_qm2048_k13", {"__dict_items__": [["B", 1], ["Q", 2048], ["M", 2048], ["D", 128], ["K", 13], ["dtype", "bfloat16"], ["seed", 609213], ["build", true], ["check_correctness", true], ["correctness_query_sample", 512], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true], ["diagnostic_class", "mid_k_boundary_guard"]]}], ["build_k_sweep_qm2048_k20", {"__dict_items__": [["B", 1], ["Q", 2048], ["M", 2048], ["D", 128], ["K", 20], ["dtype", "bfloat16"], ["seed", 606220], ["build", true], ["check_correctness", true], ["correctness_query_sample", 512], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true]]}], ["build_k_sweep_qm2048_k24", {"__dict_items__": [["B", 1], ["Q", 2048], ["M", 2048], ["D", 128], ["K", 24], ["dtype", "bfloat16"], ["seed", 606224], ["build", true], ["check_correctness", true], ["correctness_query_sample", 512], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true], ["diagnostic_class", "large_k_topk_ramp"]]}], ["build_k_sweep_qm2048_k28", {"__dict_items__": [["B", 1], ["Q", 2048], ["M", 2048], ["D", 128], ["K", 28], ["dtype", "bfloat16"], ["seed", 606228], ["build", true], ["check_correctness", true], ["correctness_query_sample", 512], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true], ["diagnostic_class", "large_k_topk_ramp"]]}], ["build_tail_b1_q1536_m1536_d128_k10", {"__dict_items__": [["B", 1], ["Q", 1536], ["M", 1536], ["D", 128], ["K", 10], ["dtype", "bfloat16"], ["seed", 606153], ["build", true], ["check_correctness", true], ["correctness_query_sample", 512], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true], ["diagnostic_class", "non_power_of_two_build"]]}], ["build_tail_b1_q3072_m3072_d128_k20", {"__dict_items__": [["B", 1], ["Q", 3072], ["M", 3072], ["D", 128], ["K", 20], ["dtype", "bfloat16"], ["seed", 606327], ["build", true], ["check_correctness", true], ["correctness_query_sample", 256], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true], ["diagnostic_class", "non_power_of_two_mid_k_build"]]}], ["build_medium_b1_q4096_m4096_d128_k10", {"__dict_items__": [["B", 1], ["Q", 4096], ["M", 4096], ["D", 128], ["K", 10], ["dtype", "bfloat16"], ["seed", 606002], ["build", true], ["check_correctness", true], ["correctness_query_sample", 256], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true]]}], ["build_k_sweep_qm4096_k12", {"__dict_items__": [["B", 1], ["Q", 4096], ["M", 4096], ["D", 128], ["K", 12], ["dtype", "bfloat16"], ["seed", 606412], ["build", true], ["check_correctness", true], ["correctness_query_sample", 256], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true], ["diagnostic_class", "mid_k_boundary_guard"]]}], ["build_k_sweep_qm4096_k13", {"__dict_items__": [["B", 1], ["Q", 4096], ["M", 4096], ["D", 128], ["K", 13], 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{"__dict_items__": [["B", 1], ["Q", 4096], ["M", 4096], ["D", 128], ["K", 64], ["dtype", "bfloat16"], ["seed", 607464], ["build", true], ["check_correctness", true], ["correctness_query_sample", 512], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true], ["diagnostic_class", "over32_topk_bottleneck"]]}], ["build_over64_stress_qm1024_k96", {"__dict_items__": [["B", 1], ["Q", 1024], ["M", 1024], ["D", 128], ["K", 96], ["dtype", "bfloat16"], ["seed", 609196], ["build", true], ["check_correctness", true], ["correctness_query_sample", 512], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true], ["diagnostic_class", "over64_topk_bottleneck"]]}], ["build_over64_stress_qm2048_k96", {"__dict_items__": [["B", 1], ["Q", 2048], ["M", 2048], ["D", 128], ["K", 96], ["dtype", "bfloat16"], ["seed", 607296], ["build", true], ["check_correctness", true], ["correctness_query_sample", 512], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true], ["diagnostic_class", "over64_topk_bottleneck"]]}], ["build_over64_stress_qm4096_k96", {"__dict_items__": [["B", 1], ["Q", 4096], ["M", 4096], ["D", 128], ["K", 96], ["dtype", "bfloat16"], ["seed", 609496], ["build", true], ["check_correctness", true], ["correctness_query_sample", 256], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true], ["diagnostic_class", "over64_topk_bottleneck"]]}], ["rag_online_common_d64_b1_q1_m262143_k10", {"__dict_items__": [["B", 1], ["Q", 1], ["M", 262143], ["D", 64], ["K", 10], ["dtype", "bfloat16"], ["seed", 615064], ["build", false], ["check_correctness", true], ["correctness_query_sample", 1], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true], ["diagnostic_class", "v12_common_d_rag_online_irregular"]]}], ["rag_microbatch_common_d64_b1_q4_m100000_k10", {"__dict_items__": [["B", 1], ["Q", 4], ["M", 100000], ["D", 64], ["K", 10], ["dtype", "bfloat16"], ["seed", 615164], ["build", false], ["check_correctness", true], ["correctness_query_sample", 4], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true], ["diagnostic_class", "v12_common_d_rag_microbatch_tail"]]}], ["rag_microbatch_common_d256_b1_q4_m100000_k10", {"__dict_items__": [["B", 1], ["Q", 4], ["M", 100000], ["D", 256], ["K", 10], ["dtype", "bfloat16"], ["seed", 615256], ["build", false], ["check_correctness", true], ["correctness_query_sample", 4], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true], ["diagnostic_class", "v12_common_d_rag_microbatch_tail"]]}], ["rag_stream_common_d256_b1_q128_m100000_k10", {"__dict_items__": [["B", 1], ["Q", 128], ["M", 100000], ["D", 256], ["K", 10], ["dtype", "bfloat16"], ["seed", 615356], ["build", false], ["check_correctness", true], ["correctness_query_sample", 128], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true], ["diagnostic_class", "v12_common_d_rag_streaming"]]}], ["rag_microbatch_common_d768_b1_q8_m100000_k10", {"__dict_items__": [["B", 1], ["Q", 8], ["M", 100000], ["D", 768], ["K", 10], ["dtype", "bfloat16"], ["seed", 615768], ["build", false], ["check_correctness", true], ["correctness_query_sample", 8], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true], ["diagnostic_class", "v12_common_d_rag_microbatch_tail"]]}], ["rag_microbatch_common_d1024_b1_q4_m100000_k10", {"__dict_items__": [["B", 1], ["Q", 4], ["M", 100000], ["D", 1024], ["K", 10], ["dtype", "bfloat16"], ["seed", 616024], ["build", false], ["check_correctness", true], ["correctness_query_sample", 4], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true], ["diagnostic_class", "v12_common_d_rag_microbatch_tail"]]}], ["rag_online_common_d4096_b1_q1_m65536_k10", {"__dict_items__": [["B", 1], ["Q", 1], ["M", 65536], ["D", 4096], ["K", 10], ["dtype", "bfloat16"], ["seed", 616096], ["build", false], ["check_correctness", true], ["correctness_query_sample", 1], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true], ["diagnostic_class", "v12_common_d_rag_online_highd"]]}], ["search_rect_common_d1024_b1_q256_m8192_k10", {"__dict_items__": [["B", 1], ["Q", 256], ["M", 8192], ["D", 1024], ["K", 10], ["dtype", "bfloat16"], ["seed", 616124], ["build", false], ["check_correctness", true], ["correctness_query_sample", 256], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true], ["diagnostic_class", "v12_common_d_rectangular_search"]]}], ["search_rect_common_d4096_b1_q128_m4096_k10", {"__dict_items__": [["B", 1], ["Q", 128], ["M", 4096], ["D", 4096], ["K", 10], ["dtype", "bfloat16"], ["seed", 616496], ["build", false], ["check_correctness", true], ["correctness_query_sample", 128], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true], ["diagnostic_class", "v12_common_d_rectangular_search"]]}], ["rag_microbatch_largek_common_d256_b1_q8_m100000_k32", {"__dict_items__": [["B", 1], ["Q", 8], ["M", 100000], ["D", 256], ["K", 32], ["dtype", "bfloat16"], ["seed", 616332], ["build", false], ["check_correctness", true], ["correctness_query_sample", 8], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true], ["diagnostic_class", "v12_common_d_large_k_rag"]]}], ["rag_stream_largek_common_d256_b1_q128_m100000_k32", {"__dict_items__": [["B", 1], ["Q", 128], ["M", 100000], ["D", 256], ["K", 32], ["dtype", "bfloat16"], ["seed", 616432], ["build", false], ["check_correctness", true], ["correctness_query_sample", 128], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true], ["diagnostic_class", "v12_common_d_large_k_rag"]]}], ["rag_microbatch_over32_d128_b1_q16_m100000_k48", {"__dict_items__": [["B", 1], ["Q", 16], ["M", 100000], ["D", 128], ["K", 48], ["dtype", "bfloat16"], ["seed", 616548], ["build", false], ["check_correctness", true], ["correctness_query_sample", 16], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true], ["diagnostic_class", "v12_rag_over32_topk"]]}]]}')) +_RESIDUAL_SHAPE_SPECS = {label: _CONTRACT_PARAMS_BY_LABEL[label] for label in RESIDUAL_TARGET_SHAPES} +PRODUCTION_ROUTE_MODULES = {**base_f30c.PRODUCTION_ROUTE_MODULES, SEED_RESIDUAL_19B3_ID: ROUTE_RESIDUAL_19B3_ENTRYPOINT, BASELINE_ID: ROUTE_BASELINE_F30C_ENTRYPOINT} +CANDIDATE_DISPATCHERS = ({'id': BASELINE_ID, 'entrypoint': BASELINE_ENTRYPOINT, 'consumed_seeds': (), 'guard_plan': base_f30c.CANDIDATE_CONFIGS[base_f30c.DEFAULT_CANDIDATE_KEY]['guard_plan'], 'expected_shape_wins': base_f30c.TARGET_SHAPES, 'fallback': base_f30c.ROUTE_BASELINE_ENTRYPOINT, 'rejected_reason': 'same-session f30c baseline'}, {'id': 'candidate_residual_19b3_overlay_6998_v1', 'entrypoint': ''.join([format(MODULE, ''), ':candidate_residual_19b3_overlay_6998_v1']), 'consumed_seeds': (SEED_RESIDUAL_19B3_ID,), 'guard_plan': ('6998 exact residual below-floor bucket guard', 'delegate matching rows to the 19b3/ed1c Weave portfolio', 'fall through to f30c full82 dispatcher for every other shape'), 'expected_shape_wins': RESIDUAL_TARGET_SHAPES, 'fallback': ROUTE_BASELINE_F30C_ENTRYPOINT, 'rejected_reason': None}) +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k48over32", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 48]], "cta_group": 1, "threads": 192}')) +eval_mod = base_f30c.eval_mod + +def _matches_contract_spec(inputs: dict[str, Any], spec: dict[str, Any]) -> bool: + return base_f30c._matches_contract_spec(inputs, spec) + +def _residual_label_for_inputs(inputs: dict[str, Any]) -> str | None: + label = inputs.get('label') + if label is not None: + spec = _RESIDUAL_SHAPE_SPECS.get(str(label)) + if spec is not None and _matches_contract_spec(inputs, spec): + return str(label) + for candidate_label, spec in _RESIDUAL_SHAPE_SPECS.items(): + if _matches_contract_spec(inputs, spec): + return candidate_label + return None + +def _select_contract_shapes(shape_labels): + return base_f30c._select_contract_shapes(shape_labels) + +def _benchmark_shapes(shape_labels, *, time_flashlib: bool): + return base_f30c._benchmark_shapes(shape_labels, time_flashlib=time_flashlib) + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=None, kernel_fn: Callable[[dict[str, Any]], Any], correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + return base_f30c._run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=kernel_fn, correctness=correctness, time_flashlib=time_flashlib) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False, enable_residual_19b3: bool=True) -> str: + if not force_fallback and enable_residual_19b3 and (_residual_label_for_inputs(inputs) is not None): + return portfolio_19b3.route_for_contract_inputs(inputs) + return base_f30c.route_for_contract_inputs(inputs) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False, enable_residual_19b3: bool=True) -> None: + if not force_fallback and enable_residual_19b3 and (_residual_label_for_inputs(inputs) is not None): + portfolio_19b3.launch_from_contract_inputs(inputs) + return + base_f30c.launch_from_contract_inputs(inputs) + +def candidate_residual_19b3_overlay_6998_v1(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_baseline_f30c(inputs: dict[str, Any]) -> None: + base_f30c.candidate_q16split148_cachedmerge_k96exactall_e080_q1m262(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) + +def compile_and_launch_knn_build(*, shape_labels=base_f30c.DISPATCH_CORRECTNESS_SHAPES, benchmark: bool=False) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = eval_mod.evaluate(candidate, shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _selected_entrypoint_for_row(row: dict[str, Any]) -> str: + selected = row.get('selected_entrypoint') + if selected: + return str(selected) + return ROUTE_RESIDUAL_19B3_ENTRYPOINT + +def _residual_trace_record(inputs: dict[str, Any]) -> dict[str, Any]: + label = str(inputs.get('label')) + base_row = dict(portfolio_19b3.route_trace_for_contract_shapes((label,))[0]) + spec = _RESIDUAL_SHAPE_SPECS[label] + selected_seed = base_row.get('selected_seed') or SEED_RESIDUAL_19B3_ID + route_kind = base_row.get('route_kind') or 'general' + route_source = base_row.get('route_source') or ('shape-specific-seed' if selected_seed != SEED_RESIDUAL_19B3_ID else 'broad-dispatcher') + if selected_seed == SEED_RESIDUAL_19B3_ID: + route_source = 'broad-dispatcher' + route_kind = 'general' + base_row.update({'shape_key': label, 'selected_entrypoint': _selected_entrypoint_for_row(base_row), 'selected_seed': selected_seed, 'expected_seed': selected_seed, 'route_kind': route_kind, 'route_source': route_source, 'guard_id': '6998_residual_19b3_exact_bucket_guard', 'guard_condition': ''.join(['exact residual BF16 shape label=', format(label, ''), ' B=', format(spec['B'], ''), ' Q=', format(spec['Q'], ''), ' M=', format(spec['M'], ''), ' D=', format(spec['D'], ''), ' K=', format(spec['K'], ''), ' build=', format(spec.get('build', False), '')]), 'wrapper_entrypoint': ROUTE_RESIDUAL_19B3_ENTRYPOINT, 'baseline_dispatcher_route': base_f30c.route_for_contract_inputs(inputs), 'base_f30c_route': base_f30c.route_for_contract_inputs(inputs), 'classification': 'unmeasured'}) + return base_f30c._normalize_route_row(base_row) + +def _baseline_trace_record(inputs: dict[str, Any]) -> dict[str, Any]: + label = str(inputs.get('label')) + row = dict(base_f30c.route_trace_for_contract_shapes((label,), candidate_key=base_f30c.DEFAULT_CANDIDATE_KEY)[0]) + row['baseline_dispatcher_route'] = row.get('selected_route') + row['base_f30c_route'] = row.get('selected_route') + return base_f30c._normalize_route_row(row) + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False, enable_residual_19b3: bool=True) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + rows = [] + for shape in selected: + inputs = base_f30c._trace_inputs_from_shape(shape) + label = _residual_label_for_inputs(inputs) + if force_fallback and label is not None: + row = _baseline_trace_record(inputs) + row['expected_seed'] = SEED_RESIDUAL_19B3_ID + row['guard_id'] = 'forced_fallback_6998_residual_19b3_disabled' + row['guard_condition'] = 'forced fallback to f30c; residual 19b3 overlay disabled' + row['classification'] = 'guard-miss' + elif enable_residual_19b3 and label is not None: + row = _residual_trace_record(inputs) + else: + row = _baseline_trace_record(inputs) + rows.append(base_f30c._normalize_route_row(row)) + return rows + +def _rows_for_labels(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return base_f30c._rows_for_labels(report, labels) + +def _classify_row(row: dict[str, Any], *, speedup_vs_external: float | None, speedup_vs_baseline: float | None) -> str: + route_kind = row.get('route_kind') + route_source = row.get('route_source') + if speedup_vs_external is not None and speedup_vs_external < 1.0: + return 'kernel-slow' if route_kind == 'specialized' else 'fallback-slow' + if speedup_vs_external is not None and speedup_vs_external < 1.05: + return 'kernel-slow' if route_kind == 'specialized' else 'fallback-slow' + if speedup_vs_baseline is not None and speedup_vs_baseline < 1.0: + return 'kernel-slow' if route_kind == 'specialized' else 'fallback-slow' + if route_source == 'shape-specific-seed': + return 'seed-consumed' + return 'route-ok' + +def _annotate_route_trace(route_trace: list[dict[str, Any]], candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> list[dict[str, Any]]: + annotated = [] + for row in route_trace: + label = str(row.get('shape_key')) + out = dict(row) + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') + speedup_vs_baseline = baseline_ms / candidate_ms if candidate_ms and baseline_ms else None + speedup_vs_external = flashlib_ms / candidate_ms if candidate_ms and flashlib_ms else None + out['dispatcher_kernel_ms'] = candidate_ms + out['baseline_dispatcher_kernel_ms'] = baseline_ms + out['external_baseline_ms'] = flashlib_ms + out['flashlib_ms'] = flashlib_ms + out['relative_speedup_vs_baseline'] = speedup_vs_baseline + out['speedup_vs_external_baseline'] = speedup_vs_external + out['external_baseline_ref'] = 'same_session' if flashlib_ms is not None else 'not_available' + if label in RESIDUAL_TARGET_SHAPE_SET: + out['classification'] = _classify_row(out, speedup_vs_external=speedup_vs_external, speedup_vs_baseline=speedup_vs_baseline) + elif speedup_vs_external is not None and speedup_vs_external < 1.0: + out['classification'] = 'kernel-slow' if out.get('route_kind') == 'specialized' else 'fallback-slow' + else: + out['classification'] = out.get('classification', 'route-ok') + annotated.append(base_f30c._normalize_route_row(out)) + return annotated + +def _seed_delta_matrix(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> list[dict[str, Any]]: + matrix = [] + for label in RESIDUAL_TARGET_SHAPES: + inputs = base_f30c._inputs_for_label(label) + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + matrix.append({'shape_key': label, 'baseline_route': base_f30c.route_for_contract_inputs(inputs), 'candidate_route': route_for_contract_inputs(inputs), 'candidate_id': 'candidate_residual_19b3_overlay_6998_v1', 'candidate_ms': candidate_ms, 'baseline_ms': baseline_ms, 'flashlib_ms': candidate_row.get('flashlib_ms'), 'metric_delta_ms': candidate_ms - baseline_ms if candidate_ms and baseline_ms else None, 'speedup_vs_baseline_dispatcher': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'ratio_vs_flashlib': candidate_row.get('ratio_vs_flashlib'), 'timing_backend': candidate_row.get('timing_backend') or baseline_row.get('timing_backend')}) + return matrix + +def benchmark_baseline_f30c(*, use_cupti: bool=True, shape_labels=None, benchmark_correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_baseline_f30c, correctness=benchmark_correctness, time_flashlib=time_flashlib) + report['candidate_id'] = BASELINE_ID + report['measured_entrypoint'] = BASELINE_ENTRYPOINT + report['route_trace'] = base_f30c.route_trace_for_contract_shapes(shape_labels, candidate_key=base_f30c.DEFAULT_CANDIDATE_KEY) + return report + +def benchmark_candidate_residual_19b3_overlay_6998_v1(*, use_cupti: bool=True, shape_labels=None, baseline_report: dict[str, Any] | None=None, benchmark_correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + if baseline_report is None: + baseline_report = benchmark_baseline_f30c(use_cupti=use_cupti, shape_labels=shape_labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_residual_19b3_overlay_6998_v1, correctness=benchmark_correctness, time_flashlib=time_flashlib) + route_trace = _annotate_route_trace(route_trace_for_contract_shapes(shape_labels), candidate_report, baseline_report) + baseline_metric = baseline_report.get('summary', {}).get('primary_mean') + candidate_metric = candidate_report.get('summary', {}).get('primary_mean') + payload = {'candidate_id': 'candidate_residual_19b3_overlay_6998_v1', 'baseline_candidate_id': BASELINE_ID, 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_candidate_residual_19b3_overlay_6998_v1']), 'baseline_entrypoint': ''.join([format(MODULE, ''), ':benchmark_baseline_f30c']), 'selected_seeds': (SEED_RESIDUAL_19B3_ID,), 'all_correct': candidate_report.get('summary', {}).get('all_correct'), 'baseline_all_correct': baseline_report.get('summary', {}).get('all_correct'), 'performance_comparable': candidate_report.get('summary', {}).get('performance_comparable'), 'baseline_performance_comparable': baseline_report.get('summary', {}).get('performance_comparable'), 'tflops': candidate_metric, 'baseline_tflops': baseline_metric, 'metric_delta': candidate_metric - baseline_metric if candidate_metric and baseline_metric else None, 'timing_backend': 'cupti' if use_cupti else 'cuda_event_fallback', 'benchmark_time_flashlib': time_flashlib, 'denominator': 'custom_residual_bucket' if shape_labels is not None else 'full82_v9', 'shape_labels': list(RESIDUAL_TARGET_SHAPES if shape_labels is None else shape_labels), 'selected_route_rows': _rows_for_labels(candidate_report, tuple(RESIDUAL_TARGET_SHAPES)), 'baseline_selected_route_rows': _rows_for_labels(baseline_report, tuple(RESIDUAL_TARGET_SHAPES)), 'seed_delta_matrix': _seed_delta_matrix(candidate_report, baseline_report), 'route_trace': route_trace, 'forced_fallback_route_trace': route_trace_for_contract_shapes(shape_labels, force_fallback=True), 'candidate_dispatchers': CANDIDATE_DISPATCHERS, 'route_modules': PRODUCTION_ROUTE_MODULES, 'report': candidate_report, 'baseline_report': baseline_report} + payload['route_trace_included'] = True + return payload diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_7399_d15e_73a9_full55_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_7399_d15e_73a9_full55_v1.py new file mode 100644 index 00000000..92758c77 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_7399_d15e_73a9_full55_v1.py @@ -0,0 +1,180 @@ +"""Opt-in kNN build full55 dispatcher consuming the exact 73a9 D64 seed. + +Minimum target architecture: sm_100a. This dispatcher-consumption candidate is +wrapper-only: it starts from the 7399+d15e full55 portfolio and adds one exact +guard for the 73a9 BF16 build ``B=1,Q=M=2048,D=64,K=10`` seed. Every other +shape delegates unchanged to the Weave-only 7399+d15e base dispatcher. + +No external runtime fallback is used. FlashLib/PyTorch remain only contract +harness references outside this production dispatch path. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from pathlib import Path +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dim_midk_73a9_v1 as dim_73a9 +from . import knn_build_dispatch_7399_d15e_full55_v1 as base_dispatch +ROUTE_DIM_D64_73A9 = 'loom.examples.weave.knn_build_dim_midk_73a9_v1:d64_split_s8' +ROUTE_BASE_7399_D15E = 'loom.examples.weave.knn_build_dispatch_7399_d15e_full55_v1:launch_from_contract_inputs' +DIM_D64_TARGET_SHAPES = ('build_dim_sweep_b1_q2048_m2048_d64_k10',) +DIM_D64_TARGET_SHAPE_SET = set(DIM_D64_TARGET_SHAPES) +BASE_SELECTED_TARGET_SHAPES = base_dispatch.SELECTED_TARGET_SHAPES +CONSUMED_SEED_TARGET_SHAPES = DIM_D64_TARGET_SHAPES +SELECTED_TARGET_SHAPES = (*BASE_SELECTED_TARGET_SHAPES, *DIM_D64_TARGET_SHAPES) +DISPATCH_CORRECTNESS_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["flashml_correctness_b1_q256_m256_d128_k5", "build_large_b1_q8192_m8192_d128_k20", "build_large_b1_q8192_m8192_d128_k32", "build_over64_stress_qm2048_k96", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32", "rag_batch_b2_q256_m50000_d128_k10", "rag_irregular_b1_q512_m131071_d128_k10", "search_rect_b1_q1024_m8192_d128_k10", "build_dim_sweep_b1_q2048_m2048_d64_k10", "rag_online_b1_q1_m100000_d128_k10", "rag_stream_b1_q128_m100000_d128_k10", "search_rect_b1_q4096_m65536_d128_k20", "rag_offline_largek_b1_q4096_m100000_d128_k20", "rag_offline_large_m_b1_q8192_m250000_d128_k20"]}')) +PRODUCTION_ROUTE_MODULES = {**base_dispatch.PRODUCTION_ROUTE_MODULES, 'dim_d64_73a9': ROUTE_DIM_D64_73A9, 'base_dispatch': ROUTE_BASE_7399_D15E} +CANDIDATE_PORTFOLIOS = ({'id': 'base_7399_d15e_full55', 'entrypoint': 'loom.examples.weave.knn_build_dispatch_7399_d15e_full55_v1:benchmark_knn_build_dispatch_7399_d15e_full55_v1', 'consumed_seeds': ('rag_frontier_7399_v1', 'd15e_rect_smallq_largem_v1'), 'guard_plan': base_dispatch.CANDIDATE_PORTFOLIOS[1]['guard_plan'], 'expected_shape_wins': base_dispatch.CONSUMED_SEED_TARGET_SHAPES, 'rejected_reason': 'baseline for this one-seed D64 consumption round'}, {'id': 'base_7399_d15e_plus_73a9_d64', 'entrypoint': 'loom.examples.weave.knn_build_dispatch_7399_d15e_73a9_full55_v1:benchmark_knn_build_dispatch_7399_d15e_73a9_full55_v1', 'consumed_seeds': ('dim_midk_73a9_d64',), 'guard_plan': ('exact 73a9 BF16 build B1 Q=M=2048 D64 K10 label', 'then the unchanged 7399+d15e full55 guard plan'), 'expected_shape_wins': DIM_D64_TARGET_SHAPES, 'rejected_reason': None}) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_DISPATCH_7399_D15E_73A9_VERIFY_KERNEL') + if verify_kernel == 'd64_stage1': + return dim_73a9.stage1_d64_split_ir + if verify_kernel == 'd64_merge': + return dim_73a9.merge_generic_ir + return base_dispatch.ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k48over32", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 48]], "cta_group": 1, "threads": 192}')) + +def _label_can_hit(inputs: dict[str, Any], target_labels: set[str]) -> bool: + label = inputs.get('label') + return label is None or str(label) in target_labels + +def _eligible_dim_d64_73a9(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, DIM_D64_TARGET_SHAPE_SET) and dim_73a9._eligible_d64_split(inputs) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if not force_fallback and _eligible_dim_d64_73a9(inputs): + return ROUTE_DIM_D64_73A9 + return base_dispatch.route_for_contract_inputs(inputs) + +def _launch_base_dispatcher_route(inputs: dict[str, Any], route: str) -> None: + base_dispatch._launch_route(inputs, route) + +def _launch_route(inputs: dict[str, Any], route: str) -> None: + if route == ROUTE_DIM_D64_73A9: + dim_73a9._launch_d64_split(inputs) + return + _launch_base_dispatcher_route(inputs, route) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + _launch_route(inputs, route_for_contract_inputs(inputs, force_fallback=force_fallback)) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def candidate_base_dispatcher(inputs: dict[str, Any]): + base_dispatch.launch_from_contract_inputs(inputs) + return None + +def candidate_force_fallback(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs, force_fallback=True) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return base_dispatch._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=DISPATCH_CORRECTNESS_SHAPES, benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint: run real contract correctness for selected shapes.""" + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=None, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + shapes = None if shape_labels is None else _select_contract_shapes(shape_labels) + return evaluate_contract(shapes=shapes, correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def _inputs_for_label(label: str) -> dict[str, Any]: + params = base_dispatch._params_for_label(label) + return base_dispatch._trace_inputs_from_shape({'label': label, 'params': params}) + +def _route_trace_record(inputs: dict[str, Any], *, force_fallback: bool=False) -> dict[str, Any]: + if force_fallback: + row = base_dispatch._route_trace_record(inputs) + row['guard_condition'] = 'forced fallback to 7399+d15e base dispatcher; 73a9 D64 guard disabled' + row['coverage'] = 'forced candidate fallback for the consumed 73a9 D64 seed' + return row + route = route_for_contract_inputs(inputs) + base_route = base_dispatch.route_for_contract_inputs(inputs) + base_row = base_dispatch._route_trace_record(inputs) + if route != ROUTE_DIM_D64_73A9: + base_row['base_dispatcher_route'] = base_route + return base_row + return {'shape_key': inputs.get('label'), 'selected_route': route, 'guard_condition': 'exact 73a9 BF16 build B1 Q=M=2048 D64 K10 label', 'route_kind': 'specialized', 'coverage': 'exact 73a9 D64 split S8 seed consumed ahead of 7399+d15e base dispatcher', 'consumed_seed': 'dim_midk_73a9_d64', 'replaced_route': base_route, 'baseline_7399_d15e_route': base_route, 'baseline_7c3a_route': base_dispatch._base_7c3a_route_for_contract_inputs(inputs), 'inherited_route': base_dispatch._baseline_inherited_route(inputs), 'parity_status': 'pass', 'parity_reason': '73a9 D64 CUPTI ratio_vs_flashlib is 1.5121055366591083 in the source seed payload'} + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + return [_route_trace_record(base_dispatch._trace_inputs_from_shape(shape), force_fallback=force_fallback) for shape in selected] + +def _timing_backends_for_reports(*reports: dict[str, Any]) -> list[str]: + return base_dispatch._timing_backends_for_reports(*reports) + +def _rows_for_labels(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return base_dispatch._rows_for_labels(report, labels) + +def _per_consumed_row_delta(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> dict[str, Any]: + deltas = {} + for label in CONSUMED_SEED_TARGET_SHAPES: + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') + inputs = _inputs_for_label(label) + deltas[label] = {'candidate_ms': candidate_ms, 'baseline_7399_d15e_ms': baseline_ms, 'flashlib_ms': flashlib_ms, 'speedup_vs_baseline_7399_d15e': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'ratio_vs_flashlib': flashlib_ms / candidate_ms if candidate_ms and flashlib_ms else None, 'candidate_route': route_for_contract_inputs(inputs), 'baseline_7399_d15e_route': base_dispatch.route_for_contract_inputs(inputs)} + return deltas + +def _seed_delta_matrix(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> list[dict[str, Any]]: + matrix = [] + for label in SELECTED_TARGET_SHAPES: + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + inputs = _inputs_for_label(label) + matrix.append({'shape_key': label, 'baseline_route': base_dispatch.route_for_contract_inputs(inputs), 'candidate_route': route_for_contract_inputs(inputs), 'candidate_ms': candidate_ms, 'baseline_ms': baseline_ms, 'metric_delta_ms': candidate_ms - baseline_ms if candidate_ms and baseline_ms else None, 'speedup_vs_baseline_7399_d15e': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'timing_backend': candidate_row.get('timing_backend') or baseline_row.get('timing_backend')}) + return matrix + +def _benchmark_payload(candidate_report: dict[str, Any], baseline_report: dict[str, Any], *, use_cupti: bool, shape_labels, measured_function: str) -> dict[str, Any]: + candidate_metric = candidate_report['summary']['primary_mean'] or 0.0 + baseline_metric = baseline_report['summary']['primary_mean'] or 0.0 + route_trace = route_trace_for_contract_shapes(shape_labels) + return {'tflops': candidate_metric, 'baseline_tflops': baseline_metric, 'metric_delta': candidate_metric - baseline_metric, 'all_correct': candidate_report['summary']['all_correct'], 'baseline_all_correct': baseline_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'baseline_performance_comparable': baseline_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'baseline_invalid_performance_reason': baseline_report['summary']['invalid_performance_reason'], 'measured_entrypoint': ''.join(['loom.examples.weave.knn_build_dispatch_7399_d15e_73a9_full55_v1:', format(measured_function, '')]), 'baseline_entrypoint': 'loom.examples.weave.knn_build_dispatch_7399_d15e_73a9_full55_v1:candidate_base_dispatcher', 'baseline_entrypoint_note': 'same-session 7399+d15e base dispatcher measured in the same wrapper module', 'measured_shape_labels': 'all_canonical' if shape_labels is None else tuple(shape_labels), 'selected_route_labels': SELECTED_TARGET_SHAPES, 'consumed_seed_labels': CONSUMED_SEED_TARGET_SHAPES, 'selected_route_rows': _rows_for_labels(candidate_report, SELECTED_TARGET_SHAPES), 'baseline_selected_route_rows': _rows_for_labels(baseline_report, SELECTED_TARGET_SHAPES), 'consumed_seed_rows': _rows_for_labels(candidate_report, CONSUMED_SEED_TARGET_SHAPES), 'baseline_consumed_seed_rows': _rows_for_labels(baseline_report, CONSUMED_SEED_TARGET_SHAPES), 'per_consumed_row_delta': _per_consumed_row_delta(candidate_report, baseline_report), 'seed_delta_matrix': _seed_delta_matrix(candidate_report, baseline_report), 'candidate_dispatchers': CANDIDATE_PORTFOLIOS, 'selected_candidate_dispatcher': 'base_7399_d15e_plus_73a9_d64', 'route_modules': PRODUCTION_ROUTE_MODULES, 'route_trace': route_trace, 'forced_fallback_route_trace': route_trace_for_contract_shapes(shape_labels, force_fallback=True), 'route_trace_included': True, 'contract_summary': candidate_report['summary'], 'baseline_contract_summary': baseline_report['summary'], 'contract_performance': candidate_report['performance'], 'baseline_contract_performance': baseline_report['performance'], 'timing_backends': _timing_backends_for_reports(candidate_report, baseline_report), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'hot_bucket_parity': {'dim_sweep_qm2048_d64_k10': 'pass', 'rag_k32': 'inherited_fail', 'd256_fp16_midk_k64': 'inherited_fail', 'reason': '73a9 closes D64 parity, but inherited RAG K32 and dim/midK rows remain blockers.'}, 'performance_coverage': 'partial', 'coverage_only_routes': [], 'hot_bucket_blockers': ['rag_frontier_real_calls_k32_flashlib_parity', 'dim_sweep_qm2048_d256_fp16', 'midk_k24_k28_over32_k64'], 'report': candidate_report, 'baseline_report': baseline_report} + +def benchmark_knn_build_dispatch_7399_d15e_73a9_full55_v1(*, use_cupti: bool=False, shape_labels=None) -> dict[str, Any]: + """Full-denominator A/B against the 7399+d15e base dispatcher.""" + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels) + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_base_dispatcher) + return _benchmark_payload(candidate_report, baseline_report, use_cupti=use_cupti, shape_labels=shape_labels, measured_function='benchmark_knn_build_dispatch_7399_d15e_73a9_full55_v1') + +def write_benchmark_artifacts(artifact_dir: str | os.PathLike[str], *, use_cupti: bool=False, shape_labels=None) -> dict[str, str]: + payload = benchmark_knn_build_dispatch_7399_d15e_73a9_full55_v1(use_cupti=use_cupti, shape_labels=shape_labels) + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + candidate_path = out_dir / 'full55_dispatch_7399_d15e_73a9_full55_v1.json' + baseline_path = out_dir / 'full55_same_session_baseline_7399_d15e_for_73a9_v1.json' + route_trace_path = out_dir / 'full55_route_trace_7399_d15e_73a9_full55_v1.json' + candidate_path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + baseline_path.write_text(json.dumps({'measured_entrypoint': payload['baseline_entrypoint'], 'measured_shape_labels': payload['measured_shape_labels'], 'timing_backend_requested': payload['timing_backend_requested'], 'timing_backends': payload['timing_backends'], 'tflops': payload['baseline_tflops'], 'all_correct': payload['baseline_all_correct'], 'performance_comparable': payload['baseline_performance_comparable'], 'contract_summary': payload['baseline_contract_summary'], 'contract_performance': payload['baseline_contract_performance'], 'route_trace': base_dispatch.route_trace_for_contract_shapes(shape_labels), 'route_trace_included': True, 'report': payload['baseline_report']}, indent=2, sort_keys=True) + '\n') + route_trace_path.write_text(json.dumps(payload['route_trace'], indent=2, sort_keys=True) + '\n') + return {'candidate_payload': str(candidate_path), 'baseline_payload': str(baseline_path), 'route_trace': str(route_trace_path)} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_7399_d15e_df2f_full55_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_7399_d15e_df2f_full55_v1.py new file mode 100644 index 00000000..36ff0ccd --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_7399_d15e_df2f_full55_v1.py @@ -0,0 +1,199 @@ +"""Opt-in kNN build full55 dispatcher consuming the 099f D256/FP16 dim seeds. + +Minimum target architecture: sm_100a. This dispatcher-consumption candidate is +wrapper-only: it starts from the 7399+d15e full55 portfolio and adds exact +guards for the 099f BF16 D256 and FP16-D128 build ``B=1,Q=M=2048,K=10`` seeds. +Every other shape delegates unchanged to the Weave-only 7399+d15e base +dispatcher. The inherited D64, K24/K28, and K64 rows intentionally remain guard +misses in this lane so their performance blockers stay visible. + +No external runtime fallback is used. FlashLib/PyTorch remain only contract +harness references outside this production dispatch path. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from pathlib import Path +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dim_midk_df2f_v1 as dim_df2f +from . import knn_build_dispatch_7399_d15e_full55_v1 as base_dispatch +ROUTE_DIM_D256_DF2F = 'loom.examples.weave.knn_build_dim_midk_df2f_v1:d256_split_s8' +ROUTE_DIM_FP16_DF2F = 'loom.examples.weave.knn_build_dim_midk_df2f_v1:fp16_d128_split_s8' +ROUTE_BASE_7399_D15E = 'loom.examples.weave.knn_build_dispatch_7399_d15e_full55_v1:launch_from_contract_inputs' +DIM_D256_TARGET_SHAPES = ('build_dim_sweep_b1_q2048_m2048_d256_k10',) +DIM_FP16_TARGET_SHAPES = ('build_dtype_fp16_b1_q2048_m2048_d128_k10',) +CONSUMED_SEED_TARGET_SHAPES = (*DIM_D256_TARGET_SHAPES, *DIM_FP16_TARGET_SHAPES) +CONSUMED_SEED_TARGET_SHAPE_SET = set(CONSUMED_SEED_TARGET_SHAPES) +GUARD_MISS_AUDIT_SHAPES = ('build_dim_sweep_b1_q2048_m2048_d64_k10', 'build_qm2048_d128_k10', 'build_k_sweep_qm2048_k24', 'build_k_sweep_qm2048_k28', 'build_k_sweep_qm4096_k28', 'build_over32_stress_qm2048_k64', 'build_over32_stress_qm4096_k64') +BASE_SELECTED_TARGET_SHAPES = base_dispatch.SELECTED_TARGET_SHAPES +SELECTED_TARGET_SHAPES = (*BASE_SELECTED_TARGET_SHAPES, *CONSUMED_SEED_TARGET_SHAPES) +DISPATCH_CORRECTNESS_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["flashml_correctness_b1_q256_m256_d128_k5", "build_large_b1_q8192_m8192_d128_k20", "build_large_b1_q8192_m8192_d128_k32", "build_over64_stress_qm2048_k96", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32", "rag_batch_b2_q256_m50000_d128_k10", "rag_irregular_b1_q512_m131071_d128_k10", "search_rect_b1_q1024_m8192_d128_k10", "build_dim_sweep_b1_q2048_m2048_d256_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_qm2048_d128_k10", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "build_over32_stress_qm2048_k64", "build_over32_stress_qm4096_k64", "rag_online_b1_q1_m100000_d128_k10", "rag_stream_b1_q128_m100000_d128_k10", "search_rect_b1_q4096_m65536_d128_k20", "rag_offline_largek_b1_q4096_m100000_d128_k20", "rag_offline_large_m_b1_q8192_m250000_d128_k20"]}')) +PRODUCTION_ROUTE_MODULES = {**base_dispatch.PRODUCTION_ROUTE_MODULES, 'dim_d256_df2f': ROUTE_DIM_D256_DF2F, 'dim_fp16_d128_df2f': ROUTE_DIM_FP16_DF2F, 'base_dispatch': ROUTE_BASE_7399_D15E} +DIAGNOSTIC_SEED_REFERENCES = ({'id': 'dim_midk_bad5_d256', 'entrypoint': 'loom.examples.weave.knn_build_dim_midk_bad5_v1:launch_from_contract_inputs', 'source_task': 'weave-evolve-knn-build-9f7e', 'production_route': False, 'reason': 'faster D256 CUPTI sidecar, but not promoted into this worktree and does not cover FP16'},) +CANDIDATE_PORTFOLIOS = ({'id': 'base_7399_d15e_full55', 'entrypoint': 'loom.examples.weave.knn_build_dispatch_7399_d15e_full55_v1:benchmark_knn_build_dispatch_7399_d15e_full55_v1', 'consumed_seeds': ('rag_frontier_7399_v1', 'd15e_rect_smallq_largem_v1'), 'guard_plan': base_dispatch.CANDIDATE_PORTFOLIOS[1]['guard_plan'], 'expected_shape_wins': base_dispatch.CONSUMED_SEED_TARGET_SHAPES, 'rejected_reason': 'baseline for this one-seed dim consumption round'}, {'id': 'base_7399_d15e_plus_df2f_d256_fp16', 'entrypoint': 'loom.examples.weave.knn_build_dispatch_7399_d15e_df2f_full55_v1:benchmark_knn_build_dispatch_7399_d15e_df2f_full55_v1', 'consumed_seeds': ('dim_midk_df2f_d256', 'dim_midk_df2f_fp16_d128'), 'guard_plan': ('exact 099f BF16 build B1 Q=M=2048 D256 K10 label', 'exact 099f FP16 build B1 Q=M=2048 D128 K10 label', 'then the unchanged 7399+d15e full55 guard plan'), 'expected_shape_wins': CONSUMED_SEED_TARGET_SHAPES, 'rejected_reason': None}) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_DISPATCH_7399_D15E_DF2F_VERIFY_KERNEL') + if verify_kernel == 'd256_stage1': + return dim_df2f.stage1_d256_split_ir + if verify_kernel == 'fp16_stage1': + return dim_df2f.stage1_fp16_split_ir + if verify_kernel == 'merge_generic': + return dim_df2f.merge_generic_ir + return base_dispatch.ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k48over32", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 48]], "cta_group": 1, "threads": 192}')) + +def _label_can_hit(inputs: dict[str, Any], target_labels: set[str]) -> bool: + label = inputs.get('label') + return label is None or str(label) in target_labels + +def _eligible_dim_d256_df2f(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, set(DIM_D256_TARGET_SHAPES)) and dim_df2f._eligible_d256_split(inputs) + +def _eligible_dim_fp16_df2f(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, set(DIM_FP16_TARGET_SHAPES)) and dim_df2f._eligible_fp16_split(inputs) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if not force_fallback and _eligible_dim_d256_df2f(inputs): + return ROUTE_DIM_D256_DF2F + if not force_fallback and _eligible_dim_fp16_df2f(inputs): + return ROUTE_DIM_FP16_DF2F + return base_dispatch.route_for_contract_inputs(inputs) + +def _launch_base_dispatcher_route(inputs: dict[str, Any], route: str) -> None: + base_dispatch._launch_route(inputs, route) + +def _launch_route(inputs: dict[str, Any], route: str) -> None: + if route == ROUTE_DIM_D256_DF2F: + dim_df2f._launch_d256_split(inputs) + return + if route == ROUTE_DIM_FP16_DF2F: + dim_df2f._launch_fp16_split(inputs) + return + _launch_base_dispatcher_route(inputs, route) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + _launch_route(inputs, route_for_contract_inputs(inputs, force_fallback=force_fallback)) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def candidate_base_dispatcher(inputs: dict[str, Any]): + base_dispatch.launch_from_contract_inputs(inputs) + return None + +def candidate_force_fallback(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs, force_fallback=True) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return base_dispatch._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=DISPATCH_CORRECTNESS_SHAPES, benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint: run real contract correctness for selected shapes.""" + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=None, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + shapes = None if shape_labels is None else _select_contract_shapes(shape_labels) + return evaluate_contract(shapes=shapes, correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def _inputs_for_label(label: str) -> dict[str, Any]: + params = base_dispatch._params_for_label(label) + return base_dispatch._trace_inputs_from_shape({'label': label, 'params': params}) + +def _route_trace_record(inputs: dict[str, Any], *, force_fallback: bool=False) -> dict[str, Any]: + if force_fallback: + row = base_dispatch._route_trace_record(inputs) + row['guard_condition'] = 'forced fallback to 7399+d15e base dispatcher; df2f dim guards disabled' + row['coverage'] = 'forced candidate fallback for the consumed 099f D256/FP16 seeds' + return row + route = route_for_contract_inputs(inputs) + base_route = base_dispatch.route_for_contract_inputs(inputs) + base_row = base_dispatch._route_trace_record(inputs) + if route not in (ROUTE_DIM_D256_DF2F, ROUTE_DIM_FP16_DF2F): + base_row['base_dispatcher_route'] = base_route + base_row['candidate_guard_status'] = 'guard_miss' + return base_row + if route == ROUTE_DIM_D256_DF2F: + return {'shape_key': inputs.get('label'), 'selected_route': route, 'guard_condition': 'exact 099f BF16 build B1 Q=M=2048 D256 K10 label', 'route_kind': 'specialized', 'coverage': 'exact 099f D256 split S8 seed consumed ahead of 7399+d15e base dispatcher', 'consumed_seed': 'dim_midk_df2f_d256', 'replaced_route': base_route, 'baseline_7399_d15e_route': base_route, 'baseline_7c3a_route': base_dispatch._base_7c3a_route_for_contract_inputs(inputs), 'inherited_route': base_dispatch._baseline_inherited_route(inputs), 'parity_status': 'pass', 'parity_reason': '099f D256 CUPTI sidecar beats 73a9 and FlashLib on the target row'} + return {'shape_key': inputs.get('label'), 'selected_route': route, 'guard_condition': 'exact 099f FP16 build B1 Q=M=2048 D128 K10 label', 'route_kind': 'specialized', 'coverage': 'exact 099f FP16-D128 split S8 seed consumed ahead of 7399+d15e base dispatcher', 'consumed_seed': 'dim_midk_df2f_fp16_d128', 'replaced_route': base_route, 'baseline_7399_d15e_route': base_route, 'baseline_7c3a_route': base_dispatch._base_7c3a_route_for_contract_inputs(inputs), 'inherited_route': base_dispatch._baseline_inherited_route(inputs), 'parity_status': 'pass', 'parity_reason': '099f FP16-D128 CUPTI sidecar beats 73a9 and FlashLib on the target row'} + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + return [_route_trace_record(base_dispatch._trace_inputs_from_shape(shape), force_fallback=force_fallback) for shape in selected] + +def _timing_backends_for_reports(*reports: dict[str, Any]) -> list[str]: + return base_dispatch._timing_backends_for_reports(*reports) + +def _rows_for_labels(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return base_dispatch._rows_for_labels(report, labels) + +def _per_consumed_row_delta(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> dict[str, Any]: + deltas = {} + for label in CONSUMED_SEED_TARGET_SHAPES: + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') + inputs = _inputs_for_label(label) + deltas[label] = {'candidate_ms': candidate_ms, 'baseline_7399_d15e_ms': baseline_ms, 'flashlib_ms': flashlib_ms, 'speedup_vs_baseline_7399_d15e': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'ratio_vs_flashlib': flashlib_ms / candidate_ms if candidate_ms and flashlib_ms else None, 'candidate_route': route_for_contract_inputs(inputs), 'baseline_7399_d15e_route': base_dispatch.route_for_contract_inputs(inputs)} + return deltas + +def _seed_delta_matrix(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> list[dict[str, Any]]: + matrix = [] + for label in SELECTED_TARGET_SHAPES: + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + inputs = _inputs_for_label(label) + matrix.append({'shape_key': label, 'baseline_route': base_dispatch.route_for_contract_inputs(inputs), 'candidate_route': route_for_contract_inputs(inputs), 'candidate_ms': candidate_ms, 'baseline_ms': baseline_ms, 'metric_delta_ms': candidate_ms - baseline_ms if candidate_ms and baseline_ms else None, 'speedup_vs_baseline_7399_d15e': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'timing_backend': candidate_row.get('timing_backend') or baseline_row.get('timing_backend')}) + return matrix + +def _benchmark_payload(candidate_report: dict[str, Any], baseline_report: dict[str, Any], *, use_cupti: bool, shape_labels, measured_function: str) -> dict[str, Any]: + candidate_metric = candidate_report['summary']['primary_mean'] or 0.0 + baseline_metric = baseline_report['summary']['primary_mean'] or 0.0 + route_trace = route_trace_for_contract_shapes(shape_labels) + return {'tflops': candidate_metric, 'baseline_tflops': baseline_metric, 'metric_delta': candidate_metric - baseline_metric, 'all_correct': candidate_report['summary']['all_correct'], 'baseline_all_correct': baseline_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'baseline_performance_comparable': baseline_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'baseline_invalid_performance_reason': baseline_report['summary']['invalid_performance_reason'], 'measured_entrypoint': ''.join(['loom.examples.weave.knn_build_dispatch_7399_d15e_df2f_full55_v1:', format(measured_function, '')]), 'baseline_entrypoint': 'loom.examples.weave.knn_build_dispatch_7399_d15e_df2f_full55_v1:candidate_base_dispatcher', 'baseline_entrypoint_note': 'same-session 7399+d15e base dispatcher measured in the same wrapper module', 'measured_shape_labels': 'all_canonical' if shape_labels is None else tuple(shape_labels), 'selected_route_labels': SELECTED_TARGET_SHAPES, 'consumed_seed_labels': CONSUMED_SEED_TARGET_SHAPES, 'guard_miss_audit_labels': GUARD_MISS_AUDIT_SHAPES, 'selected_route_rows': _rows_for_labels(candidate_report, SELECTED_TARGET_SHAPES), 'baseline_selected_route_rows': _rows_for_labels(baseline_report, SELECTED_TARGET_SHAPES), 'consumed_seed_rows': _rows_for_labels(candidate_report, CONSUMED_SEED_TARGET_SHAPES), 'baseline_consumed_seed_rows': _rows_for_labels(baseline_report, CONSUMED_SEED_TARGET_SHAPES), 'guard_miss_audit_rows': _rows_for_labels(candidate_report, GUARD_MISS_AUDIT_SHAPES), 'baseline_guard_miss_audit_rows': _rows_for_labels(baseline_report, GUARD_MISS_AUDIT_SHAPES), 'per_consumed_row_delta': _per_consumed_row_delta(candidate_report, baseline_report), 'seed_delta_matrix': _seed_delta_matrix(candidate_report, baseline_report), 'candidate_dispatchers': CANDIDATE_PORTFOLIOS, 'diagnostic_seed_references': DIAGNOSTIC_SEED_REFERENCES, 'selected_candidate_dispatcher': 'base_7399_d15e_plus_df2f_d256_fp16', 'route_modules': PRODUCTION_ROUTE_MODULES, 'route_trace': route_trace, 'forced_fallback_route_trace': route_trace_for_contract_shapes(shape_labels, force_fallback=True), 'route_trace_included': True, 'contract_summary': candidate_report['summary'], 'baseline_contract_summary': baseline_report['summary'], 'contract_performance': candidate_report['performance'], 'baseline_contract_performance': baseline_report['performance'], 'timing_backends': _timing_backends_for_reports(candidate_report, baseline_report), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'hot_bucket_parity': {'dim_sweep_qm2048_d256_k10': 'pass_full55_ab', 'dim_sweep_qm2048_fp16_d128_k10': 'pass_full55_ab', 'dim_sweep_qm2048_d64_k10': 'inherited_guard_miss', 'midk_k24_k28_over32_k64': 'inherited_fail', 'reason': 'This lane consumes only 099f D256/FP16; K24/K28/K64 stay visible as inherited blockers.'}, 'performance_coverage': 'partial', 'coverage_only_routes': [], 'hot_bucket_blockers': ['midk_k24_k28_over32_k64', 'rag_frontier_real_calls_k32_flashlib_parity', 'default_k96_registry_gate'], 'report': candidate_report, 'baseline_report': baseline_report} + +def benchmark_knn_build_dispatch_7399_d15e_df2f_full55_v1(*, use_cupti: bool=False, shape_labels=None) -> dict[str, Any]: + """Full-denominator A/B against the 7399+d15e base dispatcher.""" + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels) + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_base_dispatcher) + return _benchmark_payload(candidate_report, baseline_report, use_cupti=use_cupti, shape_labels=shape_labels, measured_function='benchmark_knn_build_dispatch_7399_d15e_df2f_full55_v1') + +def write_benchmark_artifacts(artifact_dir: str | os.PathLike[str], *, use_cupti: bool=False, shape_labels=None) -> dict[str, str]: + payload = benchmark_knn_build_dispatch_7399_d15e_df2f_full55_v1(use_cupti=use_cupti, shape_labels=shape_labels) + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + candidate_path = out_dir / 'full55_dispatch_7399_d15e_df2f_full55_v1.json' + baseline_path = out_dir / 'full55_same_session_baseline_7399_d15e_for_df2f_v1.json' + route_trace_path = out_dir / 'full55_route_trace_7399_d15e_df2f_full55_v1.json' + candidate_path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + baseline_path.write_text(json.dumps({'measured_entrypoint': payload['baseline_entrypoint'], 'measured_shape_labels': payload['measured_shape_labels'], 'timing_backend_requested': payload['timing_backend_requested'], 'timing_backends': payload['timing_backends'], 'tflops': payload['baseline_tflops'], 'all_correct': payload['baseline_all_correct'], 'performance_comparable': payload['baseline_performance_comparable'], 'contract_summary': payload['baseline_contract_summary'], 'contract_performance': payload['baseline_contract_performance'], 'route_trace': base_dispatch.route_trace_for_contract_shapes(shape_labels), 'route_trace_included': True, 'report': payload['baseline_report']}, indent=2, sort_keys=True) + '\n') + route_trace_path.write_text(json.dumps(payload['route_trace'], indent=2, sort_keys=True) + '\n') + return {'candidate_payload': str(candidate_path), 'baseline_payload': str(baseline_path), 'route_trace': str(route_trace_path)} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_7399_d15e_df2f_large_tail_a4f6_full55_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_7399_d15e_df2f_large_tail_a4f6_full55_v1.py new file mode 100644 index 00000000..b3ac94fc --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_7399_d15e_df2f_large_tail_a4f6_full55_v1.py @@ -0,0 +1,207 @@ +"""Opt-in kNN build full55 dispatcher consuming the a4f6 large-tail K20 seed. + +Minimum target architecture: sm_100a. This dispatcher-consumption candidate is +wrapper-only: it starts from the 6b59 ``7399+d15e+df2f`` full55 champion and +adds one exact guard for ``build_large_tail_b1_q6144_m6144_d128_k20``. Every +other shape delegates unchanged to the Weave-only base dispatcher. + +No external runtime fallback is used. FlashLib/PyTorch remain only contract +harness references outside this production dispatch path. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from pathlib import Path +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_7399_d15e_df2f_full55_v1 as base_dispatch +from . import knn_build_large_tail_frontier_6a73_v1 as large_tail +ROUTE_LARGE_TAIL_A4F6 = 'loom.examples.weave.knn_build_large_tail_frontier_6a73_v1:split4_k20' +ROUTE_BASE_7399_D15E_DF2F = 'loom.examples.weave.knn_build_dispatch_7399_d15e_df2f_full55_v1:launch_from_contract_inputs' +LARGE_TAIL_TARGET_SHAPES = large_tail.TARGET_SHAPES +LARGE_TAIL_TARGET_SHAPE_SET = set(LARGE_TAIL_TARGET_SHAPES) +BASE_SELECTED_TARGET_SHAPES = base_dispatch.SELECTED_TARGET_SHAPES +SELECTED_TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_large_b1_q8192_m8192_d128_k20", "build_large_b1_q8192_m8192_d128_k32", "build_over64_stress_qm2048_k96", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32", "rag_batch_b2_q256_m50000_d128_k10", "rag_irregular_b1_q512_m131071_d128_k10", "search_rect_b1_q1024_m8192_d128_k10", "build_dim_sweep_b1_q2048_m2048_d256_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10", "build_large_tail_b1_q6144_m6144_d128_k20"]}')) +DISPATCH_CORRECTNESS_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["flashml_correctness_b1_q256_m256_d128_k5", "build_large_b1_q8192_m8192_d128_k20", "build_large_b1_q8192_m8192_d128_k32", "build_over64_stress_qm2048_k96", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32", "rag_batch_b2_q256_m50000_d128_k10", "rag_irregular_b1_q512_m131071_d128_k10", "search_rect_b1_q1024_m8192_d128_k10", "build_dim_sweep_b1_q2048_m2048_d256_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_qm2048_d128_k10", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "build_over32_stress_qm2048_k64", "build_over32_stress_qm4096_k64", "rag_online_b1_q1_m100000_d128_k10", "rag_stream_b1_q128_m100000_d128_k10", "search_rect_b1_q4096_m65536_d128_k20", "rag_offline_largek_b1_q4096_m100000_d128_k20", "rag_offline_large_m_b1_q8192_m250000_d128_k20", "build_large_tail_b1_q6144_m6144_d128_k20"]}')) +PRODUCTION_ROUTE_MODULES = {**base_dispatch.PRODUCTION_ROUTE_MODULES, 'large_tail_a4f6': ROUTE_LARGE_TAIL_A4F6, 'base_dispatch': ROUTE_BASE_7399_D15E_DF2F} +CANDIDATE_PORTFOLIOS = ({'id': 'base_7399_d15e_df2f_full55', 'entrypoint': 'loom.examples.weave.knn_build_dispatch_7399_d15e_df2f_full55_v1:benchmark_knn_build_dispatch_7399_d15e_df2f_full55_v1', 'consumed_seeds': ('rag_frontier_7399_v1', 'd15e_rect_smallq_largem_v1', 'dim_midk_df2f_099f'), 'guard_plan': base_dispatch.CANDIDATE_PORTFOLIOS[1]['guard_plan'], 'expected_shape_wins': base_dispatch.CONSUMED_SEED_TARGET_SHAPES, 'rejected_reason': 'same-session baseline for this one-seed large-tail consumption round'}, {'id': 'base_7399_d15e_df2f_plus_large_tail_a4f6', 'entrypoint': 'loom.examples.weave.knn_build_dispatch_7399_d15e_df2f_large_tail_a4f6_full55_v1:benchmark_knn_build_dispatch_7399_d15e_df2f_large_tail_a4f6_full55_v1', 'consumed_seeds': ('large_tail_a4f6_k20',), 'guard_plan': ('exact a4f6 BF16 build B1 Q=M=6144 D128 K20 label', 'then the unchanged 7399+d15e+df2f full55 guard plan'), 'expected_shape_wins': LARGE_TAIL_TARGET_SHAPES, 'rejected_reason': None}) + +class _TraceTensor: + + def __init__(self, dtype: str) -> None: + self.dtype = dtype if dtype.startswith('torch.') else ''.join(['torch.', format(dtype, '')]) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_DISPATCH_7399_D15E_DF2F_LARGETAIL_VERIFY_KERNEL') + if verify_kernel == 'large_tail_stage1': + os.environ['LOOM_KNN_LARGE_TAIL_6A73_VERIFY_KERNEL'] = 'stage1' + os.environ.setdefault('LOOM_KNN_LARGE_TAIL_6A73_SPLIT_COUNT', '4') + return large_tail._verify_export_ir() + if verify_kernel == 'large_tail_merge': + os.environ['LOOM_KNN_LARGE_TAIL_6A73_VERIFY_KERNEL'] = 'merge' + os.environ.setdefault('LOOM_KNN_LARGE_TAIL_6A73_SPLIT_COUNT', '4') + return large_tail._verify_export_ir() + return base_dispatch.ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k48over32", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 48]], "cta_group": 1, "threads": 192}')) + +def _label_can_hit(inputs: dict[str, Any], target_labels: set[str]) -> bool: + label = inputs.get('label') + return label is None or str(label) in target_labels + +def _eligible_large_tail_a4f6(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, LARGE_TAIL_TARGET_SHAPE_SET) and large_tail._eligible_large_tail(inputs) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if not force_fallback and _eligible_large_tail_a4f6(inputs): + return ROUTE_LARGE_TAIL_A4F6 + return base_dispatch.route_for_contract_inputs(inputs) + +def _launch_base_dispatcher_route(inputs: dict[str, Any], route: str) -> None: + base_dispatch._launch_route(inputs, route) + +def _launch_route(inputs: dict[str, Any], route: str) -> None: + if route == ROUTE_LARGE_TAIL_A4F6: + large_tail._launch_large_tail(inputs) + return + _launch_base_dispatcher_route(inputs, route) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + _launch_route(inputs, route_for_contract_inputs(inputs, force_fallback=force_fallback)) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def candidate_base_dispatcher(inputs: dict[str, Any]): + base_dispatch.launch_from_contract_inputs(inputs) + return None + +def candidate_force_fallback(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs, force_fallback=True) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return base_dispatch._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=DISPATCH_CORRECTNESS_SHAPES, benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint: run real contract correctness for selected shapes.""" + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=None, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + shapes = None if shape_labels is None else _select_contract_shapes(shape_labels) + return evaluate_contract(shapes=shapes, correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def _trace_inputs_from_shape(shape: dict[str, Any]) -> dict[str, Any]: + params = dict(shape['params']) + dtype = str(params.get('dtype', 'bfloat16')) + return {'label': shape['label'], 'B': int(params['B']), 'Q': int(params['Q']), 'M': int(params['M']), 'D': int(params['D']), 'K': int(params['K']), 'dtype': dtype, 'build': bool(params.get('build', False)), 'query': _TraceTensor(dtype), 'database': _TraceTensor(dtype)} + +def _inputs_for_label(label: str) -> dict[str, Any]: + params = base_dispatch.base_dispatch._params_for_label(label) + return _trace_inputs_from_shape({'label': label, 'params': params}) + +def _baseline_7399_d15e_route(inputs: dict[str, Any]) -> str: + return base_dispatch.base_dispatch.route_for_contract_inputs(inputs) + +def _baseline_7c3a_route(inputs: dict[str, Any]) -> str: + return base_dispatch.base_dispatch._base_7c3a_route_for_contract_inputs(inputs) + +def _baseline_inherited_route(inputs: dict[str, Any]) -> str: + return base_dispatch.base_dispatch._baseline_inherited_route(inputs) + +def _route_trace_record(inputs: dict[str, Any], *, force_fallback: bool=False) -> dict[str, Any]: + if force_fallback: + row = base_dispatch._route_trace_record(inputs) + row['guard_condition'] = 'forced fallback to 7399+d15e+df2f base dispatcher; a4f6 large-tail guard disabled' + row['coverage'] = 'forced candidate fallback for the consumed a4f6 large-tail seed' + row['base_dispatcher_route'] = base_dispatch.route_for_contract_inputs(inputs) + return row + route = route_for_contract_inputs(inputs) + base_route = base_dispatch.route_for_contract_inputs(inputs) + base_row = base_dispatch._route_trace_record(inputs) + if route != ROUTE_LARGE_TAIL_A4F6: + base_row['base_dispatcher_route'] = base_route + base_row['candidate_guard_status'] = 'guard_miss' + return base_row + return {'shape_key': inputs.get('label'), 'selected_route': route, 'guard_condition': 'exact a4f6 BF16 build B1 Q=M=6144 D128 K20 label', 'route_kind': 'specialized', 'coverage': 'exact a4f6 large-tail split4 K20 seed consumed ahead of 7399+d15e+df2f base dispatcher', 'consumed_seed': 'large_tail_a4f6_k20', 'replaced_route': base_route, 'baseline_7399_d15e_df2f_route': base_route, 'baseline_7399_d15e_route': _baseline_7399_d15e_route(inputs), 'baseline_7c3a_route': _baseline_7c3a_route(inputs), 'inherited_route': _baseline_inherited_route(inputs), 'parity_status': 'pass', 'parity_reason': 'a4f6 source seed measured 0.412324 ms, 23.437094 TFLOPS, and 1.185953x FlashLib on CUPTI'} + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + return [_route_trace_record(_trace_inputs_from_shape(shape), force_fallback=force_fallback) for shape in selected] + +def _timing_backends_for_reports(*reports: dict[str, Any]) -> list[str]: + return base_dispatch._timing_backends_for_reports(*reports) + +def _rows_for_labels(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return base_dispatch._rows_for_labels(report, labels) + +def _per_consumed_row_delta(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> dict[str, Any]: + deltas = {} + for label in LARGE_TAIL_TARGET_SHAPES: + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') + inputs = _inputs_for_label(label) + deltas[label] = {'candidate_ms': candidate_ms, 'baseline_7399_d15e_df2f_ms': baseline_ms, 'flashlib_ms': flashlib_ms, 'speedup_vs_baseline_7399_d15e_df2f': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'ratio_vs_flashlib': flashlib_ms / candidate_ms if candidate_ms and flashlib_ms else None, 'candidate_route': route_for_contract_inputs(inputs), 'baseline_7399_d15e_df2f_route': base_dispatch.route_for_contract_inputs(inputs)} + return deltas + +def _seed_delta_matrix(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> list[dict[str, Any]]: + matrix = [] + for label in SELECTED_TARGET_SHAPES: + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + inputs = _inputs_for_label(label) + matrix.append({'shape_key': label, 'baseline_route': base_dispatch.route_for_contract_inputs(inputs), 'candidate_route': route_for_contract_inputs(inputs), 'candidate_ms': candidate_ms, 'baseline_ms': baseline_ms, 'metric_delta_ms': candidate_ms - baseline_ms if candidate_ms and baseline_ms else None, 'speedup_vs_baseline_7399_d15e_df2f': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'timing_backend': candidate_row.get('timing_backend') or baseline_row.get('timing_backend')}) + return matrix + +def _benchmark_payload(candidate_report: dict[str, Any], baseline_report: dict[str, Any], *, use_cupti: bool, shape_labels, measured_function: str) -> dict[str, Any]: + candidate_metric = candidate_report['summary']['primary_mean'] or 0.0 + baseline_metric = baseline_report['summary']['primary_mean'] or 0.0 + route_trace = route_trace_for_contract_shapes(shape_labels) + forced_fallback_route_trace = route_trace_for_contract_shapes(shape_labels, force_fallback=True) + return {'tflops': candidate_metric, 'baseline_tflops': baseline_metric, 'metric_delta': candidate_metric - baseline_metric, 'all_correct': candidate_report['summary']['all_correct'], 'baseline_all_correct': baseline_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'baseline_performance_comparable': baseline_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'baseline_invalid_performance_reason': baseline_report['summary']['invalid_performance_reason'], 'measured_entrypoint': ''.join(['loom.examples.weave.knn_build_dispatch_7399_d15e_df2f_large_tail_a4f6_full55_v1:', format(measured_function, '')]), 'baseline_entrypoint': 'loom.examples.weave.knn_build_dispatch_7399_d15e_df2f_large_tail_a4f6_full55_v1:candidate_base_dispatcher', 'baseline_entrypoint_note': 'same-session 7399+d15e+df2f base dispatcher measured in the same wrapper module', 'measured_shape_labels': 'all_canonical' if shape_labels is None else tuple(shape_labels), 'selected_route_labels': SELECTED_TARGET_SHAPES, 'consumed_seed_labels': LARGE_TAIL_TARGET_SHAPES, 'selected_route_rows': _rows_for_labels(candidate_report, SELECTED_TARGET_SHAPES), 'baseline_selected_route_rows': _rows_for_labels(baseline_report, SELECTED_TARGET_SHAPES), 'consumed_seed_rows': _rows_for_labels(candidate_report, LARGE_TAIL_TARGET_SHAPES), 'baseline_consumed_seed_rows': _rows_for_labels(baseline_report, LARGE_TAIL_TARGET_SHAPES), 'per_consumed_row_delta': _per_consumed_row_delta(candidate_report, baseline_report), 'seed_delta_matrix': _seed_delta_matrix(candidate_report, baseline_report), 'candidate_dispatchers': CANDIDATE_PORTFOLIOS, 'selected_candidate_dispatcher': 'base_7399_d15e_df2f_plus_large_tail_a4f6', 'route_modules': PRODUCTION_ROUTE_MODULES, 'route_trace': route_trace, 'forced_fallback_route_trace': forced_fallback_route_trace, 'route_trace_included': True, 'contract_summary': candidate_report['summary'], 'baseline_contract_summary': baseline_report['summary'], 'contract_performance': candidate_report['performance'], 'baseline_contract_performance': baseline_report['performance'], 'timing_backends': _timing_backends_for_reports(candidate_report, baseline_report), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'hot_bucket_parity': {'large_tail_b1_q6144_m6144_d128_k20': 'pass', 'dim_sweep_qm2048_d256_k10': 'pass_preserved', 'dtype_fp16_qm2048_d128_k10': 'pass_preserved', 'midk_k24_k28_over32_k64': 'inherited_fail', 'rag_frontier_real_calls_k32': 'inherited_fail', 'default_k96_registry_gate': 'inherited_open'}, 'performance_coverage': 'partial', 'coverage_only_routes': [], 'hot_bucket_blockers': ['midk_k24_k28_over32_k64', 'rag_frontier_real_calls_k32_flashlib_parity', 'default_k96_registry_gate'], 'report': candidate_report, 'baseline_report': baseline_report} + +def benchmark_knn_build_dispatch_7399_d15e_df2f_large_tail_a4f6_full55_v1(*, use_cupti: bool=False, shape_labels=None) -> dict[str, Any]: + """Full-denominator A/B against the 7399+d15e+df2f base dispatcher.""" + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels) + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_base_dispatcher) + return _benchmark_payload(candidate_report, baseline_report, use_cupti=use_cupti, shape_labels=shape_labels, measured_function='benchmark_knn_build_dispatch_7399_d15e_df2f_large_tail_a4f6_full55_v1') + +def write_benchmark_artifacts(artifact_dir: str | os.PathLike[str], *, use_cupti: bool=False, shape_labels=None) -> dict[str, str]: + payload = benchmark_knn_build_dispatch_7399_d15e_df2f_large_tail_a4f6_full55_v1(use_cupti=use_cupti, shape_labels=shape_labels) + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + candidate_path = out_dir / 'full55_dispatch_7399_d15e_df2f_large_tail_a4f6_full55_v1.json' + baseline_path = out_dir / 'full55_same_session_baseline_7399_d15e_df2f_for_large_tail_a4f6_v1.json' + route_trace_path = out_dir / 'full55_route_trace_7399_d15e_df2f_large_tail_a4f6_full55_v1.json' + forced_trace_path = out_dir / 'full55_forced_fallback_trace_7399_d15e_df2f_large_tail_a4f6_full55_v1.json' + candidate_path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + baseline_path.write_text(json.dumps({'measured_entrypoint': payload['baseline_entrypoint'], 'measured_shape_labels': payload['measured_shape_labels'], 'timing_backend_requested': payload['timing_backend_requested'], 'timing_backends': payload['timing_backends'], 'tflops': payload['baseline_tflops'], 'all_correct': payload['baseline_all_correct'], 'performance_comparable': payload['baseline_performance_comparable'], 'contract_summary': payload['baseline_contract_summary'], 'contract_performance': payload['baseline_contract_performance'], 'route_trace': base_dispatch.route_trace_for_contract_shapes(shape_labels), 'route_trace_included': True, 'report': payload['baseline_report']}, indent=2, sort_keys=True) + '\n') + route_trace_path.write_text(json.dumps(payload['route_trace'], indent=2, sort_keys=True) + '\n') + forced_trace_path.write_text(json.dumps(payload['forced_fallback_route_trace'], indent=2, sort_keys=True) + '\n') + return {'candidate_payload': str(candidate_path), 'baseline_payload': str(baseline_path), 'route_trace': str(route_trace_path), 'forced_fallback_trace': str(forced_trace_path)} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_7399_d15e_full55_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_7399_d15e_full55_v1.py new file mode 100644 index 00000000..bf16a0eb --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_7399_d15e_full55_v1.py @@ -0,0 +1,289 @@ +"""Opt-in kNN build full55 dispatcher consuming 7399 RAG and d15e routes. + +Minimum target architecture: sm_100a. This dispatcher-synthesis candidate is a +wrapper-only portfolio. It starts from the repeated b0e0/fd02 guard scaffold, +keeps the existing a989 large-square and 6c1e K96 exact routes, replaces the +four exact RAG frontier rows with the 7399 RAG seed, adds the exact d15e +rectangular ``search_rect_b1_q1024_m8192_d128_k10`` seed, and delegates every +other row to the same Weave-only baseline policy used by 7c3a/fd02. + +No external runtime fallback is used. FlashLib/PyTorch remain only contract +harness references outside this production dispatch path. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from pathlib import Path +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_b6d4_d15e_fd02_v1 as fd02 +from . import knn_build_rag_frontier_7399_v1 as rag_7399 +large_square = fd02.large_square +over64_k96 = fd02.over64_k96 +rect_d15e = fd02.rect_d15e +baseline_3dc7 = fd02.baseline_3dc7 +ROUTE_LARGE_SQUARE_K20K32 = fd02.ROUTE_LARGE_SQUARE_K20K32 +ROUTE_OVER64_K96 = fd02.ROUTE_OVER64_K96 +ROUTE_RAG_7C3A_K10 = fd02.ROUTE_RAG_7C3A_K10 +ROUTE_RAG_7399_K10 = 'loom.examples.weave.knn_build_rag_frontier_7399_v1:k10_s72' +ROUTE_RAG_7399_K32 = 'loom.examples.weave.knn_build_rag_frontier_7399_v1:k32_s72_g8_fusedmerge' +ROUTE_RECT_D15E = fd02.ROUTE_RECT_D15E +ROUTE_BASELINE_3DC7 = fd02.ROUTE_BASELINE_3DC7 +ROUTE_BASELINE_7C3A_POLICY = fd02.ROUTE_BASELINE_7C3A_POLICY +LARGE_SQUARE_TARGET_SHAPES = fd02.LARGE_SQUARE_TARGET_SHAPES +K96_TARGET_SHAPES = fd02.K96_TARGET_SHAPES +RAG_7C3A_K10_TARGET_SHAPES = fd02.RAG_7C3A_K10_TARGET_SHAPES +RAG_K10_TARGET_SHAPES = rag_7399.K10_TARGET_SHAPES +RAG_K32_TARGET_SHAPES = rag_7399.K32_TARGET_SHAPES +RAG_TARGET_SHAPES = rag_7399.TARGET_SHAPES +RECT_D15E_TARGET_SHAPES = fd02.RECT_D15E_TARGET_SHAPES +LARGE_SQUARE_TARGET_SHAPE_SET = set(LARGE_SQUARE_TARGET_SHAPES) +K96_TARGET_SHAPE_SET = set(K96_TARGET_SHAPES) +RAG_7C3A_K10_TARGET_SHAPE_SET = set(RAG_7C3A_K10_TARGET_SHAPES) +RAG_K10_TARGET_SHAPE_SET = set(RAG_K10_TARGET_SHAPES) +RAG_K32_TARGET_SHAPE_SET = set(RAG_K32_TARGET_SHAPES) +RAG_TARGET_SHAPE_SET = set(RAG_TARGET_SHAPES) +RECT_D15E_TARGET_SHAPE_SET = set(RECT_D15E_TARGET_SHAPES) +BASE_7C3A_TARGET_SHAPES = (*LARGE_SQUARE_TARGET_SHAPES, *K96_TARGET_SHAPES, *RAG_7C3A_K10_TARGET_SHAPES) +CONSUMED_SEED_TARGET_SHAPES = (*RAG_TARGET_SHAPES, *RECT_D15E_TARGET_SHAPES) +SELECTED_TARGET_SHAPES = (*LARGE_SQUARE_TARGET_SHAPES, *K96_TARGET_SHAPES, *RAG_TARGET_SHAPES, *RECT_D15E_TARGET_SHAPES) +DISPATCH_CORRECTNESS_SHAPES = ('flashml_correctness_b1_q256_m256_d128_k5', *SELECTED_TARGET_SHAPES, *baseline_3dc7.SELECTED_TARGET_SHAPES) +PRODUCTION_ROUTE_MODULES = {'large_square_k20k32': ROUTE_LARGE_SQUARE_K20K32, 'over64_k96': ROUTE_OVER64_K96, 'baseline_7c3a_rag_k10': ROUTE_RAG_7C3A_K10, 'rag_frontier_7399_k10': ROUTE_RAG_7399_K10, 'rag_frontier_7399_k32': ROUTE_RAG_7399_K32, 'rect_smallq_largem_d15e': ROUTE_RECT_D15E, 'baseline_7c3a_policy': ROUTE_BASELINE_7C3A_POLICY, 'fallback': ROUTE_BASELINE_3DC7} +CANDIDATE_PORTFOLIOS = ({'id': 'base_7c3a_plus_7399_rag', 'entrypoint': 'loom.examples.weave.knn_build_dispatch_7399_d15e_full55_v1:candidate_without_d15e', 'consumed_seeds': ('rag_frontier_7399_v1',), 'guard_plan': ('exact a989 large-square BF16 build Q=M=8192 K20/K32', 'exact 6c1e over64 BF16 build Q=M=2048 K96', 'exact 7399 RAG frontier BF16 D128 non-build K10/K32 labels', '7c3a Weave policy fallback'), 'expected_shape_wins': RAG_TARGET_SHAPES, 'rejected_reason': 'lower coverage than selected 7399+d15e portfolio; leaves rect_smallq_largem row on fallback'}, {'id': 'base_7c3a_plus_7399_rag_plus_d15e', 'entrypoint': 'loom.examples.weave.knn_build_dispatch_7399_d15e_full55_v1:benchmark_knn_build_dispatch_7399_d15e_full55_v1', 'consumed_seeds': ('rag_frontier_7399_v1', 'd15e_rect_smallq_largem_v1'), 'guard_plan': ('exact a989 large-square BF16 build Q=M=8192 K20/K32', 'exact 6c1e over64 BF16 build Q=M=2048 K96', 'exact 7399 RAG frontier BF16 D128 non-build K10/K32 labels', 'exact d15e rect BF16 B1 Q1024 M8192 D128 K10 non-build label', '7c3a Weave policy fallback'), 'expected_shape_wins': CONSUMED_SEED_TARGET_SHAPES, 'rejected_reason': None}, {'id': 'base_7c3a_plus_7259_v5_plus_d15e', 'entrypoint': 'loom.examples.weave.knn_build_dispatch_b6d4v5_d15e_3156_v1:benchmark_knn_build_dispatch_b6d4v5_d15e_3156_v1', 'consumed_seeds': ('rag_frontier_b6d4_v5_7259', 'd15e_rect_smallq_largem_v1'), 'guard_plan': ('same guards as selected candidate, but RAG K32 uses b6d4 v5 two-merge topology',), 'expected_shape_wins': (), 'rejected_reason': 'kept as same-session comparator; 7399 has faster K32 CUPTI seed evidence'}, {'id': 'fd02_b6d4_v4_plus_d15e', 'entrypoint': 'loom.examples.weave.knn_build_dispatch_b6d4_d15e_fd02_v1:benchmark_knn_build_dispatch_b6d4_d15e_fd02_v1', 'consumed_seeds': ('b6d4_rag_frontier_v4', 'd15e_rect_smallq_largem_v1'), 'guard_plan': ('same large-square/K96/d15e guards as selected candidate, but RAG uses b6d4 v4',), 'expected_shape_wins': (), 'rejected_reason': 'current same-denominator champion baseline, not selected for 7399-consumption lane'}) + +class _TraceTensor: + + def __init__(self, dtype: str) -> None: + self.dtype = dtype if dtype.startswith('torch.') else ''.join(['torch.', format(dtype, '')]) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_DISPATCH_7399_D15E_VERIFY_KERNEL') + if verify_kernel == 'large_square_stage1_k20': + os.environ['LOOM_KNN_LARGE_SQUARE_A989_VERIFY_KERNEL'] = 'stage1_k20' + return large_square._verify_export_ir() + if verify_kernel == 'large_square_stage1_k32': + os.environ['LOOM_KNN_LARGE_SQUARE_A989_VERIFY_KERNEL'] = 'stage1_k32' + return large_square._verify_export_ir() + if verify_kernel == 'over64_k96_stage1': + return over64_k96.stage1_k96_over64_ir + if verify_kernel == 'rag_7399_k32_stage1': + os.environ['LOOM_KNN_RAG_FRONTIER_7399_V1_VERIFY_KERNEL'] = 'stage1' + return rag_7399._verify_export_ir() + if verify_kernel == 'rag_7399_k32_fused_merge': + os.environ['LOOM_KNN_RAG_FRONTIER_7399_V1_VERIFY_KERNEL'] = 'fused_merge' + return rag_7399._verify_export_ir() + if verify_kernel == 'rect_d15e_stage1': + os.environ['LOOM_KNN_RECT_D15E_VERIFY_KERNEL'] = 'stage1' + return rect_d15e._verify_export_ir() + return baseline_3dc7.ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k48over32", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 48]], "cta_group": 1, "threads": 192}')) + +def _label_can_hit(inputs: dict[str, Any], target_labels: set[str]) -> bool: + label = inputs.get('label') + return label is None or str(label) in target_labels + +def _eligible_large_square_k20k32(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, LARGE_SQUARE_TARGET_SHAPE_SET) and large_square._eligible_large_square_k20k32(inputs) + +def _eligible_over64_k96(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, K96_TARGET_SHAPE_SET) and over64_k96._eligible_over64_k96_build(inputs) + +def _eligible_7c3a_rag_k10(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, RAG_7C3A_K10_TARGET_SHAPE_SET) and fd02.rag_7c3a._eligible_k10_rag_frontier(inputs) + +def _eligible_7399_rag_k10(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, RAG_K10_TARGET_SHAPE_SET) and rag_7399._eligible_k10_rag_frontier(inputs) + +def _eligible_7399_rag_k32(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, RAG_K32_TARGET_SHAPE_SET) and rag_7399._eligible_k32_rag_frontier(inputs) + +def _eligible_rect_d15e(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, RECT_D15E_TARGET_SHAPE_SET) and rect_d15e._eligible_rect_smallq_largem(inputs) + +def _base_7c3a_route_for_contract_inputs(inputs: dict[str, Any]) -> str: + if _eligible_large_square_k20k32(inputs): + return ROUTE_LARGE_SQUARE_K20K32 + if _eligible_over64_k96(inputs): + return ROUTE_OVER64_K96 + if _eligible_7c3a_rag_k10(inputs): + return ROUTE_RAG_7C3A_K10 + return ROUTE_BASELINE_3DC7 + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if force_fallback: + return _base_7c3a_route_for_contract_inputs(inputs) + if _eligible_large_square_k20k32(inputs): + return ROUTE_LARGE_SQUARE_K20K32 + if _eligible_over64_k96(inputs): + return ROUTE_OVER64_K96 + if _eligible_7399_rag_k10(inputs): + return ROUTE_RAG_7399_K10 + if _eligible_7399_rag_k32(inputs): + return ROUTE_RAG_7399_K32 + if _eligible_rect_d15e(inputs): + return ROUTE_RECT_D15E + return _base_7c3a_route_for_contract_inputs(inputs) + +def _launch_base_7c3a_route(inputs: dict[str, Any], route: str) -> None: + fd02._launch_base_7c3a_route(inputs, route) + +def _launch_route(inputs: dict[str, Any], route: str) -> None: + if route == ROUTE_RAG_7399_K10: + rag_7399._launch_k10_rag_frontier_s72(inputs) + return + if route == ROUTE_RAG_7399_K32: + rag_7399._launch_k32_rag_frontier_fused_merge(inputs) + return + if route == ROUTE_RECT_D15E: + rect_d15e._launch_rect_smallq_largem(inputs) + return + _launch_base_7c3a_route(inputs, route) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + _launch_route(inputs, route_for_contract_inputs(inputs, force_fallback=force_fallback)) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def candidate_without_d15e(inputs: dict[str, Any]): + route = route_for_contract_inputs(inputs) + if route == ROUTE_RECT_D15E: + route = _base_7c3a_route_for_contract_inputs(inputs) + _launch_route(inputs, route) + return None + +def candidate_baseline_7c3a(inputs: dict[str, Any]): + _launch_base_7c3a_route(inputs, _base_7c3a_route_for_contract_inputs(inputs)) + return None + +def candidate_force_fallback(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs, force_fallback=True) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return baseline_3dc7._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=DISPATCH_CORRECTNESS_SHAPES, benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint: run real contract correctness for selected shapes.""" + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=None, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + shapes = None if shape_labels is None else _select_contract_shapes(shape_labels) + return evaluate_contract(shapes=shapes, correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def _trace_inputs_from_shape(shape: dict[str, Any]) -> dict[str, Any]: + params = dict(shape['params']) + dtype = str(params.get('dtype', 'bfloat16')) + return {'label': shape['label'], 'B': int(params['B']), 'Q': int(params['Q']), 'M': int(params['M']), 'D': int(params['D']), 'K': int(params['K']), 'dtype': dtype, 'build': bool(params.get('build', False)), 'query': _TraceTensor(dtype), 'database': _TraceTensor(dtype)} + +def _baseline_inherited_route(inputs: dict[str, Any]) -> str: + try: + return baseline_3dc7.route_for_contract_inputs(inputs) + except Exception: + return baseline_3dc7.ROUTE_PREVIOUS_MAIN + +def _route_kind_for_base(route: str) -> str: + return 'general' if route == ROUTE_BASELINE_3DC7 else 'specialized' + +def _route_trace_record(inputs: dict[str, Any], *, force_fallback: bool=False) -> dict[str, Any]: + route = route_for_contract_inputs(inputs, force_fallback=force_fallback) + base_route = _base_7c3a_route_for_contract_inputs(inputs) + inherited_route = _baseline_inherited_route(inputs) + if force_fallback: + return {'shape_key': inputs.get('label'), 'selected_route': route, 'guard_condition': 'forced fallback to baseline 7c3a policy', 'route_kind': _route_kind_for_base(route), 'coverage': 'forced candidate fallback; 7399 and d15e guards disabled', 'consumed_seed': None, 'replaced_route': None, 'baseline_7c3a_route': base_route, 'inherited_route': inherited_route} + if route == ROUTE_LARGE_SQUARE_K20K32: + return {'shape_key': inputs.get('label'), 'selected_route': route, 'guard_condition': 'exact BF16 B1 Q=M=8192 D128 build=true K in {20,32}', 'route_kind': 'specialized', 'coverage': 'baseline 7c3a exact a989 large-square K20/K32 seed', 'consumed_seed': 'a989_large_square_k20k32', 'replaced_route': base_route, 'baseline_7c3a_route': base_route, 'inherited_route': inherited_route, 'parity_status': 'pass'} + if route == ROUTE_OVER64_K96: + return {'shape_key': inputs.get('label'), 'selected_route': route, 'guard_condition': 'exact BF16 B1 Q=M=2048 D128 build=true K=96', 'route_kind': 'specialized', 'coverage': 'baseline 7c3a exact 6c1e over64 K96 seed', 'consumed_seed': '6c1e_over64_k96', 'replaced_route': base_route, 'baseline_7c3a_route': base_route, 'inherited_route': inherited_route, 'parity_status': 'pass'} + if route == ROUTE_RAG_7399_K10: + return {'shape_key': inputs.get('label'), 'selected_route': route, 'guard_condition': 'exact 7399 RAG frontier BF16 D128 non-build K10 label', 'route_kind': 'specialized', 'coverage': 'exact 7399 inherited b6d4/v3 RAG K10 split-72 seed', 'consumed_seed': 'rag_frontier_7399_v1', 'replaced_route': base_route, 'baseline_7c3a_route': base_route, 'baseline_route_kind': _route_kind_for_base(base_route), 'inherited_route': inherited_route, 'parity_status': 'pass', 'parity_reason': '7399 K10 CUPTI ratio_vs_flashlib range is 1.0443 to 1.2952 except microbatch 1.0630'} + if route == ROUTE_RAG_7399_K32: + return {'shape_key': inputs.get('label'), 'selected_route': route, 'guard_condition': 'exact 7399 RAG frontier BF16 B1 Q128 M100000 D128 K32 non-build label', 'route_kind': 'specialized', 'coverage': 'exact 7399 RAG K32 S72/G8 fused cooperative group/final merge seed', 'consumed_seed': 'rag_frontier_7399_v1', 'replaced_route': base_route, 'baseline_7c3a_route': base_route, 'baseline_route_kind': _route_kind_for_base(base_route), 'inherited_route': inherited_route, 'parity_status': 'fail', 'parity_reason': '7399 K32 CUPTI ratio_vs_flashlib is 0.9436378734204341 in the source seed payload'} + if route == ROUTE_RECT_D15E: + return {'shape_key': inputs.get('label'), 'selected_route': route, 'guard_condition': 'exact d15e rect BF16 B1 Q1024 M8192 D128 K10 non-build label', 'route_kind': 'specialized', 'coverage': 'exact d15e rectangular small-Q large-M K10 seed', 'consumed_seed': 'd15e_rect_smallq_largem_v1', 'replaced_route': base_route, 'baseline_7c3a_route': base_route, 'baseline_route_kind': _route_kind_for_base(base_route), 'inherited_route': inherited_route, 'parity_status': 'pass', 'parity_reason': 'd15e target-bucket CUPTI ratio_vs_flashlib is 1.4187 in the source seed payload'} + return {'shape_key': inputs.get('label'), 'selected_route': route, 'guard_condition': 'synthesized guard miss; delegate to baseline 7c3a Weave policy', 'route_kind': _route_kind_for_base(route), 'coverage': 'baseline 7c3a policy or inherited split72/de1a/3dc7 Weave dispatcher fallback', 'consumed_seed': None, 'replaced_route': None, 'baseline_7c3a_route': base_route, 'inherited_route': inherited_route} + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + return [_route_trace_record(_trace_inputs_from_shape(shape), force_fallback=force_fallback) for shape in selected] + +def _timing_backends_for_reports(*reports: dict[str, Any]) -> list[str]: + return fd02._timing_backends_for_reports(*reports) + +def _rows_for_labels(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return fd02._rows_for_labels(report, labels) + +def _params_for_label(label: str) -> dict[str, Any]: + return fd02._params_for_label(label) + +def _inputs_for_label(label: str) -> dict[str, Any]: + return _trace_inputs_from_shape({'label': label, 'params': _params_for_label(label)}) + +def _per_consumed_row_delta(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> dict[str, Any]: + deltas = {} + for label in CONSUMED_SEED_TARGET_SHAPES: + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') + inputs = _inputs_for_label(label) + deltas[label] = {'candidate_ms': candidate_ms, 'baseline_7c3a_ms': baseline_ms, 'flashlib_ms': flashlib_ms, 'speedup_vs_baseline_7c3a': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'ratio_vs_flashlib': flashlib_ms / candidate_ms if candidate_ms and flashlib_ms else None, 'candidate_route': route_for_contract_inputs(inputs), 'baseline_7c3a_route': _base_7c3a_route_for_contract_inputs(inputs)} + return deltas + +def _seed_delta_matrix(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> list[dict[str, Any]]: + matrix = [] + for label in SELECTED_TARGET_SHAPES: + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + inputs = _inputs_for_label(label) + matrix.append({'shape_key': label, 'baseline_route': _base_7c3a_route_for_contract_inputs(inputs), 'candidate_route': route_for_contract_inputs(inputs), 'candidate_ms': candidate_ms, 'baseline_ms': baseline_ms, 'metric_delta_ms': candidate_ms - baseline_ms if candidate_ms and baseline_ms else None, 'speedup_vs_baseline_7c3a': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'timing_backend': candidate_row.get('timing_backend') or baseline_row.get('timing_backend')}) + return matrix + +def _benchmark_payload(candidate_report: dict[str, Any], baseline_report: dict[str, Any], *, use_cupti: bool, shape_labels, measured_function: str) -> dict[str, Any]: + candidate_metric = candidate_report['summary']['primary_mean'] or 0.0 + baseline_metric = baseline_report['summary']['primary_mean'] or 0.0 + route_trace = route_trace_for_contract_shapes(shape_labels) + return {'tflops': candidate_metric, 'baseline_tflops': baseline_metric, 'metric_delta': candidate_metric - baseline_metric, 'all_correct': candidate_report['summary']['all_correct'], 'baseline_all_correct': baseline_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'baseline_performance_comparable': baseline_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'baseline_invalid_performance_reason': baseline_report['summary']['invalid_performance_reason'], 'measured_entrypoint': ''.join(['loom.examples.weave.knn_build_dispatch_7399_d15e_full55_v1:', format(measured_function, '')]), 'baseline_entrypoint': 'loom.examples.weave.knn_build_dispatch_default_7c3a_v1:benchmark_knn_build_dispatch_default_7c3a_v1', 'baseline_entrypoint_note': 'same-session in-module 7c3a-equivalent policy; production route table matches 7c3a/fd02 source wrapper', 'measured_shape_labels': 'all_canonical' if shape_labels is None else tuple(shape_labels), 'selected_route_labels': SELECTED_TARGET_SHAPES, 'consumed_seed_labels': CONSUMED_SEED_TARGET_SHAPES, 'selected_route_rows': _rows_for_labels(candidate_report, SELECTED_TARGET_SHAPES), 'baseline_selected_route_rows': _rows_for_labels(baseline_report, SELECTED_TARGET_SHAPES), 'consumed_seed_rows': _rows_for_labels(candidate_report, CONSUMED_SEED_TARGET_SHAPES), 'baseline_consumed_seed_rows': _rows_for_labels(baseline_report, CONSUMED_SEED_TARGET_SHAPES), 'per_consumed_row_delta': _per_consumed_row_delta(candidate_report, baseline_report), 'seed_delta_matrix': _seed_delta_matrix(candidate_report, baseline_report), 'candidate_dispatchers': CANDIDATE_PORTFOLIOS, 'selected_candidate_dispatcher': 'base_7c3a_plus_7399_rag_plus_d15e', 'route_modules': PRODUCTION_ROUTE_MODULES, 'route_trace': route_trace, 'forced_fallback_route_trace': route_trace_for_contract_shapes(shape_labels, force_fallback=True), 'route_trace_included': True, 'contract_summary': candidate_report['summary'], 'baseline_contract_summary': baseline_report['summary'], 'contract_performance': candidate_report['performance'], 'baseline_contract_performance': baseline_report['performance'], 'timing_backends': _timing_backends_for_reports(candidate_report, baseline_report), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'hot_bucket_parity': {'rag_k10': 'pass', 'rag_k32': 'fail', 'rect_smallq_largem_k10': 'pass', 'reason': '7399 K32 is faster than b6d4 v5 and inherited 7c3a fallback but remains below FlashLib parity.'}, 'performance_coverage': 'partial', 'coverage_only_routes': [], 'hot_bucket_blockers': ['rag_frontier_real_calls_k32_flashlib_parity', 'dim_sweep_qm2048_k10', 'midk_over32_cleanup'], 'report': candidate_report, 'baseline_report': baseline_report} + +def benchmark_knn_build_dispatch_7399_d15e_full55_v1(*, use_cupti: bool=False, shape_labels=None) -> dict[str, Any]: + """Full-denominator A/B against the 7c3a-equivalent baseline policy.""" + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels) + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_baseline_7c3a) + return _benchmark_payload(candidate_report, baseline_report, use_cupti=use_cupti, shape_labels=shape_labels, measured_function='benchmark_knn_build_dispatch_7399_d15e_full55_v1') + +def write_benchmark_artifacts(artifact_dir: str | os.PathLike[str], *, use_cupti: bool=False, shape_labels=None) -> dict[str, str]: + payload = benchmark_knn_build_dispatch_7399_d15e_full55_v1(use_cupti=use_cupti, shape_labels=shape_labels) + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + candidate_path = out_dir / 'full55_dispatch_7399_d15e_full55_v1.json' + baseline_path = out_dir / 'full55_same_session_baseline_7c3a_for_7399_d15e_v1.json' + route_trace_path = out_dir / 'full55_route_trace_7399_d15e_full55_v1.json' + candidate_path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + baseline_path.write_text(json.dumps({'measured_entrypoint': payload['baseline_entrypoint'], 'measured_shape_labels': payload['measured_shape_labels'], 'timing_backend_requested': payload['timing_backend_requested'], 'timing_backends': payload['timing_backends'], 'tflops': payload['baseline_tflops'], 'all_correct': payload['baseline_all_correct'], 'performance_comparable': payload['baseline_performance_comparable'], 'contract_summary': payload['baseline_contract_summary'], 'contract_performance': payload['baseline_contract_performance'], 'report': payload['baseline_report']}, indent=2, sort_keys=True) + '\n') + route_trace_path.write_text(json.dumps(payload['route_trace'], indent=2, sort_keys=True) + '\n') + return {'candidate_payload': str(candidate_path), 'baseline_payload': str(baseline_path), 'route_trace': str(route_trace_path)} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_784a_6bc3_k8_q512k456_direct_fcf2_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_784a_6bc3_k8_q512k456_direct_fcf2_v1.py new file mode 100644 index 00000000..c11e92e9 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_784a_6bc3_k8_q512k456_direct_fcf2_v1.py @@ -0,0 +1,347 @@ +"""Selective fcf2 dispatcher with direct Q512 K4/K5/K6 low-K launch. + +Minimum target architecture: sm_100a. This additive dispatcher-consumption +wrapper starts from the fcf2 784a + selective 6bc3 K8 wrapper and adds exact +BF16 build guards for ``B=1,Q=M=512,D=128,K in {4,5,6}``. Those rows route +directly to the already validated low-K split4 Weave seed, avoiding the older +full82 fallback chain. All other rows delegate to the fcf2 wrapper unchanged. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from pathlib import Path +from typing import Any, Callable +from . import knn_build_dispatch_784a_6bc3_k8_selective_full82_v1 as parent_fcf2 +from . import knn_build_lowk_f8c3_q512_q1024_v1 as lowk_seed +MODULE = 'loom.examples.weave.knn_build_dispatch_784a_6bc3_k8_q512k456_direct_fcf2_v1' +eval_mod = parent_fcf2.eval_mod +BASE_784A_KEY = parent_fcf2.BASE_784A_KEY +PARENT_FCF2_KEY = 'parent_784a_plus_6bc3_k8_selective' +CANDIDATE_Q512K456_DIRECT = '784a_plus_direct_q512_k456_plus_6bc3_k8' +DEFAULT_CANDIDATE_KEY = CANDIDATE_Q512K456_DIRECT +CANDIDATE_KEYS = (BASE_784A_KEY, PARENT_FCF2_KEY, CANDIDATE_Q512K456_DIRECT) +Q512_K4 = 'build_k_sweep_qm512_k4' +Q512_K5 = 'build_k_sweep_qm512_k5' +Q512_K6 = 'build_k_sweep_qm512_k6' +Q512_K456_TARGET_SHAPES = (Q512_K4, Q512_K5, Q512_K6) +Q512_K456_TARGET_SHAPE_SET = set(Q512_K456_TARGET_SHAPES) +TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_k_sweep_qm512_k4", "build_k_sweep_qm512_k5", "build_k_sweep_qm512_k6", "build_k_sweep_qm512_k8", "build_qm2048_d128_k8"]}')) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +SEED_Q512_K456_DIRECT_ID = 'fcf2_direct_lowk_q512_k4_k5_k6_s4' +ROUTE_LOWK_Q512_K456_S4 = _decode_capture(_json_loads('"loom.examples.weave.knn_build_lowk_f8c3_q512_q1024_v1:q512_lowk_s4"')) +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +PARENT_FCF2_ENTRYPOINT = parent_fcf2.ROUTE_ENTRYPOINT +CANDIDATE_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_candidate_784a_direct_q512_k456_plus_6bc3_k8_full82_v1']) +PARENT_FCF2_BENCH_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_parent_784a_plus_6bc3_k8_selective']) +BASE_784A_ID = parent_fcf2.BASE_784A_ID +PARENT_FCF2_ID = parent_fcf2.CANDIDATE_CONFIGS[parent_fcf2.CANDIDATE_6BC3_K8]['candidate_id'] +CANDIDATE_ID = 'candidate_784a_direct_q512_k456_plus_6bc3_k8_full82_v1' +PRODUCTION_ROUTE_MODULES = {**parent_fcf2.PRODUCTION_ROUTE_MODULES, SEED_Q512_K456_DIRECT_ID: ROUTE_LOWK_Q512_K456_S4, PARENT_FCF2_ID: PARENT_FCF2_ENTRYPOINT, CANDIDATE_ID: ROUTE_ENTRYPOINT} +SOURCE_TASKS = {**parent_fcf2.SOURCE_TASKS, SEED_Q512_K456_DIRECT_ID: 'fcf2 direct low-K repair probe / loom.examples.weave.knn_build_lowk_f8c3_q512_q1024_v1:_launch_q512_lowk_split'} + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_FCF2_Q512K456_DIRECT_VERIFY_KERNEL') + if verify_kernel == 'q512_stage1': + return lowk_seed.stage1_q512_lowk_ir + if verify_kernel == 'q512_merge_generic': + return lowk_seed.merge_q512_generic_ir + return parent_fcf2.ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbatch_m64_d4f7_stage1", "arg_keys": ["query", "database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 91392, "constants": [], "cta_group": 1, "threads": 512}')) + +def _select_contract_shapes(shape_labels): + return parent_fcf2._select_contract_shapes(shape_labels) + +def _trace_inputs_for_shape(shape: dict[str, Any]) -> dict[str, Any]: + return parent_fcf2._trace_inputs_for_shape(shape) + +def _normalize_route_row(row: dict[str, Any]) -> dict[str, Any]: + return parent_fcf2._normalize_route_row(row) + +def _inputs_for_label(label: str) -> dict[str, Any]: + return _trace_inputs_for_shape(_select_contract_shapes((label,))[0]) + +def _dtype_name(inputs: dict[str, Any], name: str='query') -> str: + tensor = inputs.get(name) + if tensor is not None and hasattr(tensor, 'dtype'): + return str(tensor.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')).replace('torch.', '') + +def _label_can_hit(inputs: dict[str, Any], labels: set[str]) -> bool: + label = inputs.get('label') + return label is None or str(label) in labels + +def _eligible_q512_k456_direct(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, Q512_K456_TARGET_SHAPE_SET) and bool(inputs.get('build', False)) and (int(inputs.get('B', -1)) == 1) and (int(inputs.get('Q', -1)) == 512) and (int(inputs.get('M', -2)) == 512) and (int(inputs.get('D', -1)) == 128) and (int(inputs.get('K', -1)) in (4, 5, 6)) and (_dtype_name(inputs, 'query') == 'bfloat16') and (_dtype_name(inputs, 'database') in ('', 'bfloat16')) + +def _candidate_config(candidate_key: str) -> dict[str, Any]: + try: + return CANDIDATE_CONFIGS[candidate_key] + except KeyError as exc: + raise ValueError(''.join(['unknown fcf2 direct-Q512K456 candidate ', format(repr(candidate_key), '')])) from exc + +def _candidate_id(candidate_key: str | None) -> str | None: + if candidate_key is None: + return None + return str(_candidate_config(candidate_key)['candidate_id']) + +def _selected_direct_seed(inputs: dict[str, Any]) -> str | None: + return SEED_Q512_K456_DIRECT_ID if _eligible_q512_k456_direct(inputs) else None + +def route_for_contract_inputs(inputs: dict[str, Any], *, candidate_key: str=DEFAULT_CANDIDATE_KEY, force_fallback: bool=False) -> str: + _candidate_config(candidate_key) + if force_fallback or candidate_key == BASE_784A_KEY: + return parent_fcf2.route_for_contract_inputs(inputs, candidate_key=parent_fcf2.BASE_784A_KEY, force_fallback=force_fallback) + if candidate_key == PARENT_FCF2_KEY: + return parent_fcf2.route_for_contract_inputs(inputs) + if _eligible_q512_k456_direct(inputs): + return ROUTE_LOWK_Q512_K456_S4 + return parent_fcf2.route_for_contract_inputs(inputs) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, candidate_key: str=DEFAULT_CANDIDATE_KEY, force_fallback: bool=False) -> None: + _candidate_config(candidate_key) + if force_fallback or candidate_key == BASE_784A_KEY: + parent_fcf2.launch_from_contract_inputs(inputs, candidate_key=parent_fcf2.BASE_784A_KEY, force_fallback=force_fallback) + return + if candidate_key == PARENT_FCF2_KEY: + parent_fcf2.launch_from_contract_inputs(inputs) + return + if _eligible_q512_k456_direct(inputs): + lowk_seed._launch_q512_lowk_split(inputs, split_count=lowk_seed.DEFAULT_Q512_SPLITS) + return + parent_fcf2.launch_from_contract_inputs(inputs) + +def candidate_baseline_784a_005f(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, candidate_key=BASE_784A_KEY) + +def candidate_parent_784a_plus_6bc3_k8_selective(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, candidate_key=PARENT_FCF2_KEY) + +def candidate_784a_direct_q512_k456_plus_6bc3_k8_full82_v1(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, candidate_key=CANDIDATE_Q512K456_DIRECT) + +def candidate(inputs: dict[str, Any]) -> None: + candidate_784a_direct_q512_k456_plus_6bc3_k8_full82_v1(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, candidate_key=DEFAULT_CANDIDATE_KEY, force_fallback=True) + +def _candidate_kernel_fn(candidate_key: str) -> Callable[[dict[str, Any]], None]: + _candidate_config(candidate_key) + if candidate_key == BASE_784A_KEY: + return candidate_baseline_784a_005f + if candidate_key == PARENT_FCF2_KEY: + return candidate_parent_784a_plus_6bc3_k8_selective + if candidate_key == CANDIDATE_Q512K456_DIRECT: + return candidate_784a_direct_q512_k456_plus_6bc3_k8_full82_v1 + raise ValueError(''.join(['unknown fcf2 direct-Q512K456 candidate ', format(repr(candidate_key), '')])) +_PARENT_SELECTED_SEEDS = _decode_capture(_json_loads('{"__tuple__": ["v20_k12_q1024_q4096_exact", "q2048_k12_4f30_v1", "v12_k20_q2048k10_mixedfanout", "over32_k48_v25", "6bc3_v20_q512_k8_static_s8", "6bc3_v20_q2048_k8_static_s8"]}')) +CANDIDATE_CONFIGS = _decode_capture(_json_loads('{"__dict_items__": [["base_784a_005f", {"__dict_items__": [["candidate_id", "candidate_dbd7_005f_buildbucket_full82_v1"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_dbd7_lowfloor_005f_8a78_full82_v1:launch_from_contract_inputs"], ["benchmark_entrypoint", "loom.examples.weave.knn_build_dispatch_784a_6bc3_k8_selective_full82_v1:benchmark_baseline_784a_005f"], ["selected_seeds", {"__tuple__": ["v20_k12_q1024_q4096_exact", "q2048_k12_4f30_v1", "v12_k20_q2048k10_mixedfanout", "over32_k48_v25"]}], ["guard_plan", {"__tuple__": ["005f exact BF16 build low-floor portfolio for K10/K12/K20/K48 rows", "a444/9db7 full82 Weave fallback for guard misses and Q1536/K10 tail"]}], ["expected_shape_wins", {"__tuple__": ["build_k_sweep_qm1024_k12", "build_k_sweep_qm1024_k20", "build_qm2048_d128_k10", "build_k_sweep_qm2048_k12", "build_k_sweep_qm2048_k20", "build_tail_b1_q1536_m1536_d128_k10", "build_k_sweep_qm4096_k12", "build_k_sweep_qm4096_k20", "build_over32_stress_qm2048_k48", "build_over32_stress_qm4096_k48"]}], ["fallback", "loom.examples.weave.knn_build_dispatch_dbd7_lowfloor_005f_8a78_full82_v1:launch_from_contract_inputs"], ["rejected_reason", "same-session 784a baseline"]]}], ["parent_784a_plus_6bc3_k8_selective", {"__dict_items__": [["candidate_id", "candidate_784a_plus_6bc3_k8_selective_full82_v1"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_784a_6bc3_k8_selective_full82_v1:benchmark_candidate_784a_plus_6bc3_k8_selective_full82_v1"], ["benchmark_entrypoint", "loom.examples.weave.knn_build_dispatch_784a_6bc3_k8_q512k456_direct_fcf2_v1:benchmark_parent_784a_plus_6bc3_k8_selective"], ["selected_seeds", {"__tuple__": ["v20_k12_q1024_q4096_exact", "q2048_k12_4f30_v1", "v12_k20_q2048k10_mixedfanout", "over32_k48_v25", "6bc3_v20_q512_k8_static_s8", "6bc3_v20_q2048_k8_static_s8"]}], ["guard_plan", {"__tuple__": ["6bc3 exact BF16 build Q512/K8 and Q2048/K8 guards only", "then 784a 005f build-lowfloor portfolio", "then inherited a444/9db7 full82 Weave fallback"]}], ["expected_shape_wins", {"__tuple__": ["build_k_sweep_qm512_k8", "build_qm2048_d128_k8"]}], ["fallback", "loom.examples.weave.knn_build_dispatch_dbd7_lowfloor_005f_8a78_full82_v1:launch_from_contract_inputs"], ["rejected_reason", "parent fcf2 baseline"]]}], ["784a_plus_direct_q512_k456_plus_6bc3_k8", {"__dict_items__": [["candidate_id", "candidate_784a_direct_q512_k456_plus_6bc3_k8_full82_v1"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_784a_6bc3_k8_q512k456_direct_fcf2_v1:candidate_784a_direct_q512_k456_plus_6bc3_k8_full82_v1"], ["benchmark_entrypoint", "loom.examples.weave.knn_build_dispatch_784a_6bc3_k8_q512k456_direct_fcf2_v1:benchmark_candidate_784a_direct_q512_k456_plus_6bc3_k8_full82_v1"], ["selected_seeds", {"__tuple__": ["v20_k12_q1024_q4096_exact", "q2048_k12_4f30_v1", "v12_k20_q2048k10_mixedfanout", "over32_k48_v25", "6bc3_v20_q512_k8_static_s8", "6bc3_v20_q2048_k8_static_s8", "fcf2_direct_lowk_q512_k4_k5_k6_s4"]}], ["guard_plan", {"__tuple__": ["direct exact BF16 build Q512 K4/K5/K6 low-K split4 guard", "then fcf2 selective 6bc3 exact Q512/Q2048 K8 guards", "then 784a 005f build-lowfloor portfolio", "then inherited a444/9db7 full82 Weave fallback"]}], ["expected_shape_wins", {"__tuple__": ["build_k_sweep_qm512_k4", "build_k_sweep_qm512_k5", "build_k_sweep_qm512_k6", "build_k_sweep_qm512_k8", "build_qm2048_d128_k8"]}], ["fallback", "loom.examples.weave.knn_build_dispatch_784a_6bc3_k8_selective_full82_v1:launch_from_contract_inputs"], ["rejected_reason", null]]}]]}')) +CANDIDATE_DISPATCHERS = _decode_capture(_json_loads('{"__tuple__": [{"__dict_items__": [["id", "candidate_dbd7_005f_buildbucket_full82_v1"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_784a_6bc3_k8_selective_full82_v1:benchmark_baseline_784a_005f"], ["consumed_seeds", {"__tuple__": ["v20_k12_q1024_q4096_exact", "q2048_k12_4f30_v1", "v12_k20_q2048k10_mixedfanout", "over32_k48_v25"]}], ["guard_plan", {"__tuple__": ["005f exact BF16 build low-floor portfolio for K10/K12/K20/K48 rows", "a444/9db7 full82 Weave fallback for guard misses and Q1536/K10 tail"]}], ["expected_shape_wins", {"__tuple__": ["build_k_sweep_qm1024_k12", "build_k_sweep_qm1024_k20", "build_qm2048_d128_k10", "build_k_sweep_qm2048_k12", "build_k_sweep_qm2048_k20", "build_tail_b1_q1536_m1536_d128_k10", "build_k_sweep_qm4096_k12", "build_k_sweep_qm4096_k20", "build_over32_stress_qm2048_k48", "build_over32_stress_qm4096_k48"]}], ["fallback", "loom.examples.weave.knn_build_dispatch_dbd7_lowfloor_005f_8a78_full82_v1:launch_from_contract_inputs"], ["rejected_reason", "same-session 784a baseline"]]}, {"__dict_items__": [["id", "candidate_784a_plus_6bc3_k8_selective_full82_v1"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_784a_6bc3_k8_q512k456_direct_fcf2_v1:benchmark_parent_784a_plus_6bc3_k8_selective"], ["consumed_seeds", {"__tuple__": ["v20_k12_q1024_q4096_exact", "q2048_k12_4f30_v1", "v12_k20_q2048k10_mixedfanout", "over32_k48_v25", "6bc3_v20_q512_k8_static_s8", "6bc3_v20_q2048_k8_static_s8"]}], ["guard_plan", {"__tuple__": ["6bc3 exact BF16 build Q512/K8 and Q2048/K8 guards only", "then 784a 005f build-lowfloor portfolio", "then inherited a444/9db7 full82 Weave fallback"]}], ["expected_shape_wins", {"__tuple__": ["build_k_sweep_qm512_k8", "build_qm2048_d128_k8"]}], ["fallback", "loom.examples.weave.knn_build_dispatch_dbd7_lowfloor_005f_8a78_full82_v1:launch_from_contract_inputs"], ["rejected_reason", "parent fcf2 baseline"]]}, {"__dict_items__": [["id", "candidate_784a_direct_q512_k456_plus_6bc3_k8_full82_v1"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_784a_6bc3_k8_q512k456_direct_fcf2_v1:benchmark_candidate_784a_direct_q512_k456_plus_6bc3_k8_full82_v1"], ["consumed_seeds", {"__tuple__": ["v20_k12_q1024_q4096_exact", "q2048_k12_4f30_v1", "v12_k20_q2048k10_mixedfanout", "over32_k48_v25", "6bc3_v20_q512_k8_static_s8", "6bc3_v20_q2048_k8_static_s8", "fcf2_direct_lowk_q512_k4_k5_k6_s4"]}], ["guard_plan", {"__tuple__": ["direct exact BF16 build Q512 K4/K5/K6 low-K split4 guard", "then fcf2 selective 6bc3 exact Q512/Q2048 K8 guards", "then 784a 005f build-lowfloor portfolio", "then inherited a444/9db7 full82 Weave fallback"]}], ["expected_shape_wins", {"__tuple__": ["build_k_sweep_qm512_k4", "build_k_sweep_qm512_k5", "build_k_sweep_qm512_k6", "build_k_sweep_qm512_k8", "build_qm2048_d128_k8"]}], ["fallback", "loom.examples.weave.knn_build_dispatch_784a_6bc3_k8_selective_full82_v1:launch_from_contract_inputs"], ["rejected_reason", null]]}]}')) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False, candidate_key: str=DEFAULT_CANDIDATE_KEY) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark, kernel_fn=_candidate_kernel_fn(candidate_key)) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=None, kernel_fn: Callable[[dict[str, Any]], Any]=candidate, correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + return parent_fcf2._run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=kernel_fn, correctness=correctness, time_flashlib=time_flashlib) + +def _timing_backend_name(use_cupti: bool) -> str: + return 'cupti' if use_cupti else 'cuda_event_fallback' + +def _payload_shape_labels(shape_labels) -> str | tuple[str, ...]: + if shape_labels is None: + return 'all_canonical' + return tuple((str(label) for label in shape_labels)) + +def _denominator_name(shape_labels) -> str: + if shape_labels is None: + return ''.join(['full', format(len(eval_mod.CANONICAL_SHAPES), ''), '_v10']) + labels = tuple((str(label) for label in shape_labels)) + if labels == TARGET_SHAPES: + return 'q512k456_plus_6bc3_k8_target_rows' + return ''.join(['shape', format(len(labels), '')]) + +def _denominator_label(shape_labels) -> str: + if shape_labels is None: + return ''.join(['full', format(len(eval_mod.CANONICAL_SHAPES), '')]) + return ''.join(['shape', format(len(tuple(shape_labels)), '')]) + +def _rows_for_labels(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return {label: dict(report.get('per_shape', {}).get(label, {})) for label in labels} + +def _timing_backends_for_reports(*reports: dict[str, Any]) -> list[str]: + return parent_fcf2._timing_backends_for_reports(*reports) + +def _parent_route_trace_row(label: str) -> dict[str, Any]: + row = dict(parent_fcf2.route_trace_for_contract_shapes((label,))[0]) + row['parent_fcf2_route'] = row.get('selected_route') + row['parent_fcf2_selected_seed'] = row.get('selected_seed') + return _normalize_route_row(row) + +def _guard_condition(seed_id: str | None) -> str: + if seed_id == SEED_Q512_K456_DIRECT_ID: + return 'exact BF16 build B=1 Q=M=512 D=128 K in {4,5,6} direct low-K split4 route' + return parent_fcf2._guard_condition(seed_id) + +def _route_trace_record(inputs: dict[str, Any], *, candidate_key: str, force_fallback: bool=False) -> dict[str, Any]: + label = str(inputs.get('label')) + direct_seed = _selected_direct_seed(inputs) if candidate_key == CANDIDATE_Q512K456_DIRECT else None + parent_row = _parent_route_trace_row(label) + if force_fallback or candidate_key != CANDIDATE_Q512K456_DIRECT or direct_seed is None: + if candidate_key == PARENT_FCF2_KEY: + row = dict(parent_row) + else: + row = dict(parent_fcf2.route_trace_for_contract_shapes((label,), candidate_key=parent_fcf2.BASE_784A_KEY if candidate_key == BASE_784A_KEY else parent_fcf2.DEFAULT_CANDIDATE_KEY, force_fallback=force_fallback)[0]) + row['expected_seed'] = direct_seed or row.get('expected_seed') + row['parent_fcf2_route'] = parent_row.get('selected_route') + row['parent_fcf2_selected_seed'] = parent_row.get('selected_seed') + if force_fallback and direct_seed is not None: + row['selected_route'] = parent_fcf2.route_for_contract_inputs(inputs, candidate_key=parent_fcf2.BASE_784A_KEY, force_fallback=True) + row['selected_entrypoint'] = parent_fcf2.BASE_784A_ROUTE_ENTRYPOINT + row['guard_id'] = ''.join(['forced_fallback_', format(direct_seed, ''), '_disabled']) + row['guard_condition'] = ''.join(['forced fallback to 784a baseline; ', format(direct_seed, ''), ' disabled']) + row['classification'] = 'guard-miss' + return _normalize_route_row(row) + return _normalize_route_row({'shape_key': label, 'selected_route': ROUTE_LOWK_Q512_K456_S4, 'selected_entrypoint': ROUTE_LOWK_Q512_K456_S4, 'selected_seed': direct_seed, 'expected_seed': direct_seed, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': ''.join([format(candidate_key, ''), '_', format(direct_seed, ''), '_guard']), 'guard_condition': _guard_condition(direct_seed), 'coverage': 'direct Q512 K4/K5/K6 low-K split4 route before fcf2 parent', 'consumed_seed': direct_seed, 'replaced_route': parent_row.get('selected_route'), 'parent_fcf2_route': parent_row.get('selected_route'), 'parent_fcf2_selected_seed': parent_row.get('selected_seed'), 'baseline_784a_route': parent_row.get('baseline_784a_route'), 'base_784a_route': parent_row.get('base_784a_route'), 'shape_specific_kernel_ms': None, 'classification': 'unmeasured'}) + +def route_trace_for_contract_shapes(shape_labels=None, *, candidate_key: str=DEFAULT_CANDIDATE_KEY, force_fallback: bool=False) -> list[dict[str, Any]]: + _candidate_config(candidate_key) + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + return [_route_trace_record(_trace_inputs_for_shape(shape), candidate_key=candidate_key, force_fallback=force_fallback) for shape in selected] + +def _annotate_route_trace(route_trace: list[dict[str, Any]], candidate_report: dict[str, Any], parent_report: dict[str, Any], *, candidate_key: str) -> list[dict[str, Any]]: + annotated = [] + for row in route_trace: + label = str(row.get('shape_key')) + out = dict(row) + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + parent_row = parent_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + parent_ms = parent_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') + speedup_vs_parent = parent_ms / candidate_ms if candidate_ms and parent_ms else None + speedup_vs_external = flashlib_ms / candidate_ms if candidate_ms and flashlib_ms else None + out['dispatcher_kernel_ms'] = candidate_ms + out['parent_fcf2_kernel_ms'] = parent_ms + out['external_baseline_ms'] = flashlib_ms + out['flashlib_ms'] = flashlib_ms + out['relative_speedup_vs_parent_fcf2'] = speedup_vs_parent + out['speedup_vs_external_baseline'] = speedup_vs_external + out['external_baseline_ref'] = 'same_session' if flashlib_ms is not None else 'not_available' + out['route_changed_vs_parent_fcf2'] = out.get('selected_route') != out.get('parent_fcf2_route') + expected_seed = out.get('expected_seed') + if candidate_key == CANDIDATE_Q512K456_DIRECT and expected_seed == SEED_Q512_K456_DIRECT_ID: + if out.get('selected_seed') != expected_seed: + out['classification'] = 'guard-miss' + elif speedup_vs_external is not None and speedup_vs_external < 1.05: + out['classification'] = 'kernel-slow' + elif speedup_vs_parent is not None and speedup_vs_parent < 1.0: + out['classification'] = 'kernel-slow' + else: + out['classification'] = 'seed-consumed' + elif speedup_vs_external is not None and speedup_vs_external < 1.05: + out['classification'] = 'kernel-slow' if out.get('route_kind') == 'specialized' else 'fallback-slow' + else: + out['classification'] = 'route-ok' + annotated.append(_normalize_route_row(out)) + return annotated + +def _below_flashlib_rows(report: dict[str, Any], route_trace: list[dict[str, Any]], *, floor: float) -> list[dict[str, Any]]: + trace_by_label = {str(row['shape_key']): row for row in route_trace} + rows = [] + for label, row in sorted(report.get('per_shape', {}).items()): + ratio = row.get('ratio_vs_flashlib') + if isinstance(ratio, (float, int)) and ratio < floor: + trace_row = trace_by_label.get(label, {}) + rows.append({'shape_key': label, 'kernel_ms': row.get('kernel_ms'), 'flashlib_ms': row.get('flashlib_ms'), 'ratio_vs_flashlib': ratio, 'selected_route': trace_row.get('selected_route'), 'selected_seed': trace_row.get('selected_seed'), 'expected_seed': trace_row.get('expected_seed'), 'route_kind': trace_row.get('route_kind', 'unknown'), 'classification': trace_row.get('classification', 'unmeasured')}) + return rows + +def _seed_delta_matrix(candidate_key: str, candidate_report: dict[str, Any], parent_report: dict[str, Any]) -> list[dict[str, Any]]: + matrix = [] + for label in TARGET_SHAPES: + inputs = _inputs_for_label(label) + selected_seed = _selected_direct_seed(inputs) + if selected_seed is None and candidate_key != BASE_784A_KEY: + selected_seed = parent_fcf2._selected_6bc3_k8_seed(inputs) + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + parent_row = parent_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + parent_ms = parent_row.get('kernel_ms') + matrix.append({'shape_key': label, 'parent_fcf2_route': parent_fcf2.route_for_contract_inputs(inputs), 'candidate_route': route_for_contract_inputs(inputs, candidate_key=candidate_key), 'selected_seed': selected_seed, 'candidate_id': _candidate_id(candidate_key), 'candidate_ms': candidate_ms, 'parent_fcf2_ms': parent_ms, 'flashlib_ms': candidate_row.get('flashlib_ms'), 'delta_ms_candidate_minus_parent_fcf2': candidate_ms - parent_ms if candidate_ms and parent_ms else None, 'speedup_vs_parent_fcf2': parent_ms / candidate_ms if candidate_ms and parent_ms else None, 'ratio_vs_flashlib': candidate_row.get('ratio_vs_flashlib'), 'timing_backend': candidate_row.get('timing_backend') or parent_row.get('timing_backend')}) + return matrix + +def benchmark_parent_784a_plus_6bc3_k8_selective(*, use_cupti: bool=True, shape_labels=None, benchmark_correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_parent_784a_plus_6bc3_k8_selective, correctness=benchmark_correctness, time_flashlib=time_flashlib) + report['candidate_id'] = PARENT_FCF2_ID + report['measured_entrypoint'] = PARENT_FCF2_BENCH_ENTRYPOINT + report['measured_shape_labels'] = _payload_shape_labels(shape_labels) + report['route_trace'] = route_trace_for_contract_shapes(shape_labels, candidate_key=PARENT_FCF2_KEY) + report['route_trace_included'] = True + return report + +def _benchmark_payload(candidate_key: str, candidate_report: dict[str, Any], parent_report: dict[str, Any], *, use_cupti: bool, shape_labels, benchmark_correctness: bool, time_flashlib: bool, baseline_784a_report: dict[str, Any] | None=None) -> dict[str, Any]: + candidate_metric = candidate_report['summary']['primary_mean'] + parent_metric = parent_report['summary']['primary_mean'] + baseline_784a_metric = baseline_784a_report['summary']['primary_mean'] if baseline_784a_report is not None else None + route_trace = _annotate_route_trace(route_trace_for_contract_shapes(shape_labels, candidate_key=candidate_key), candidate_report, parent_report, candidate_key=candidate_key) + below_1x = _below_flashlib_rows(candidate_report, route_trace, floor=1.0) + below_floor = _below_flashlib_rows(candidate_report, route_trace, floor=1.05) + denominator = _denominator_name(shape_labels) + timing_backend = _timing_backend_name(use_cupti) + config = _candidate_config(candidate_key) + return {'candidate_id': config['candidate_id'], 'candidate_key': candidate_key, 'parent_candidate_id': PARENT_FCF2_ID, 'baseline_784a_candidate_id': BASE_784A_ID, 'selected_seeds': config['selected_seeds'], 'source_tasks': SOURCE_TASKS, 'tflops': candidate_metric, 'parent_fcf2_tflops': parent_metric, 'baseline_784a_tflops': baseline_784a_metric, 'metric_delta_vs_parent_fcf2': candidate_metric - parent_metric if candidate_metric is not None and parent_metric is not None else None, 'metric_delta_vs_784a': candidate_metric - baseline_784a_metric if candidate_metric is not None and baseline_784a_metric is not None else None, 'all_correct': candidate_report['summary']['all_correct'], 'parent_all_correct': parent_report['summary']['all_correct'], 'baseline_784a_all_correct': baseline_784a_report['summary']['all_correct'] if baseline_784a_report is not None else None, 'performance_comparable': candidate_report['summary']['performance_comparable'], 'parent_performance_comparable': parent_report['summary']['performance_comparable'], 'baseline_784a_performance_comparable': baseline_784a_report['summary']['performance_comparable'] if baseline_784a_report is not None else None, 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'parent_invalid_performance_reason': parent_report['summary']['invalid_performance_reason'], 'measured_entrypoint': config['benchmark_entrypoint'], 'parent_entrypoint': PARENT_FCF2_BENCH_ENTRYPOINT, 'baseline_784a_entrypoint': parent_fcf2.BASE_784A_ENTRYPOINT, 'route_entrypoint': ROUTE_ENTRYPOINT, 'measured_shape_labels': _payload_shape_labels(shape_labels), 'timing_backend': timing_backend, 'denominator': denominator, 'selected_route_labels': config['expected_shape_wins'], 'selected_route_rows': _rows_for_labels(candidate_report, TARGET_SHAPES), 'parent_selected_route_rows': _rows_for_labels(parent_report, TARGET_SHAPES), 'seed_delta_matrix': _seed_delta_matrix(candidate_key, candidate_report, parent_report), 'candidate_dispatchers': CANDIDATE_DISPATCHERS, 'selected_candidate_dispatcher': config['candidate_id'], 'guard_plan': config['guard_plan'], 'route_modules': PRODUCTION_ROUTE_MODULES, 'route_trace': route_trace, 'forced_fallback_route_trace': route_trace_for_contract_shapes(shape_labels, candidate_key=candidate_key, force_fallback=True), 'route_trace_included': True, 'contract_summary': candidate_report['summary'], 'parent_contract_summary': parent_report['summary'], 'baseline_784a_contract_summary': baseline_784a_report['summary'] if baseline_784a_report else None, 'contract_performance': candidate_report['performance'], 'parent_contract_performance': parent_report['performance'], 'baseline_784a_contract_performance': baseline_784a_report['performance'] if baseline_784a_report else None, 'timing_backends': _timing_backends_for_reports(candidate_report, parent_report, *((baseline_784a_report,) if baseline_784a_report is not None else ())), 'timing_backend_requested': timing_backend, 'use_cupti': use_cupti, 'benchmark_correctness_checked': benchmark_correctness, 'benchmark_time_flashlib': time_flashlib, 'performance_coverage': 'partial' if below_floor else 'pass', 'coverage_only_routes': [], 'hot_bucket_blockers': below_floor, 'flashlib_parity_ledger': {'baseline_ref_scope': 'same_session' if time_flashlib else 'not_available', 'speedup_floor': 1.05, 'rows_below_1x': below_1x, 'rows_below_floor': below_floor, 'omitted_reason': None if time_flashlib else 'benchmark_time_flashlib=false'}, 'rank_objective': {'metric': 'tflops', 'direction': 'maximize', 'value': candidate_metric, 'parent_fcf2_value': parent_metric, 'baseline_784a_value': baseline_784a_metric, 'delta_vs_parent_fcf2': candidate_metric - parent_metric if candidate_metric is not None and parent_metric is not None else None, 'delta_vs_784a': candidate_metric - baseline_784a_metric if candidate_metric is not None and baseline_784a_metric is not None else None, 'denominator': denominator}, 'report': candidate_report, 'parent_report': parent_report, 'baseline_784a_report': baseline_784a_report} + +def benchmark_candidate_portfolio(candidate_key: str, *, use_cupti: bool=True, shape_labels=None, parent_report: dict[str, Any] | None=None, baseline_784a_report: dict[str, Any] | None=None, benchmark_correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + if candidate_key == BASE_784A_KEY: + return parent_fcf2.benchmark_baseline_784a_005f(use_cupti=use_cupti, shape_labels=shape_labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + if candidate_key == PARENT_FCF2_KEY: + return benchmark_parent_784a_plus_6bc3_k8_selective(use_cupti=use_cupti, shape_labels=shape_labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + if parent_report is None: + parent_report = benchmark_parent_784a_plus_6bc3_k8_selective(use_cupti=use_cupti, shape_labels=shape_labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=_candidate_kernel_fn(candidate_key), correctness=benchmark_correctness, time_flashlib=time_flashlib) + return _benchmark_payload(candidate_key, candidate_report, parent_report, use_cupti=use_cupti, shape_labels=shape_labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib, baseline_784a_report=baseline_784a_report) + +def benchmark_candidate_784a_direct_q512_k456_plus_6bc3_k8_full82_v1(**kwargs) -> dict[str, Any]: + return benchmark_candidate_portfolio(CANDIDATE_Q512K456_DIRECT, **kwargs) + +def write_benchmark_artifacts(artifact_dir: str | Path, *, use_cupti: bool=True, shape_labels=None, benchmark_correctness: bool=True, time_flashlib: bool=True) -> dict[str, str]: + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + denom_label = _denominator_label(shape_labels) + baseline_784a_report = parent_fcf2.benchmark_baseline_784a_005f(use_cupti=use_cupti, shape_labels=shape_labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + parent_report = benchmark_parent_784a_plus_6bc3_k8_selective(use_cupti=use_cupti, shape_labels=shape_labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + payload = benchmark_candidate_portfolio(CANDIDATE_Q512K456_DIRECT, use_cupti=use_cupti, shape_labels=shape_labels, parent_report=parent_report, baseline_784a_report=baseline_784a_report, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + artifacts: dict[str, str] = {} + baseline_path = out_dir / ''.join([format(denom_label, ''), '_same_session_baseline_784a_005f_for_q512k456_direct_v1.json']) + parent_path = out_dir / ''.join([format(denom_label, ''), '_same_session_parent_fcf2_6bc3_k8_for_q512k456_direct_v1.json']) + payload_path = out_dir / ''.join([format(denom_label, ''), '_dispatch_784a_direct_q512k456_plus_6bc3_k8_v1.json']) + trace_path = out_dir / ''.join([format(denom_label, ''), '_route_trace_784a_direct_q512k456_plus_6bc3_k8_v1.json']) + forced_trace_path = out_dir / ''.join([format(denom_label, ''), '_forced_fallback_trace_784a_direct_q512k456_plus_6bc3_k8_v1.json']) + seed_matrix_path = out_dir / ''.join([format(denom_label, ''), '_seed_delta_matrix_784a_direct_q512k456_plus_6bc3_k8_v1.json']) + baseline_path.write_text(json.dumps(baseline_784a_report, indent=2, sort_keys=True) + '\n', encoding='utf-8') + parent_path.write_text(json.dumps(parent_report, indent=2, sort_keys=True) + '\n', encoding='utf-8') + payload_path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n', encoding='utf-8') + trace_path.write_text(json.dumps(payload['route_trace'], indent=2, sort_keys=True) + '\n', encoding='utf-8') + forced_trace_path.write_text(json.dumps(payload['forced_fallback_route_trace'], indent=2, sort_keys=True) + '\n', encoding='utf-8') + seed_matrix_path.write_text(json.dumps(payload['seed_delta_matrix'], indent=2, sort_keys=True) + '\n', encoding='utf-8') + artifacts['same_session_baseline_784a_payload'] = str(baseline_path) + artifacts['same_session_parent_fcf2_payload'] = str(parent_path) + artifacts[''.join([format(CANDIDATE_Q512K456_DIRECT, ''), '_payload'])] = str(payload_path) + artifacts[''.join([format(CANDIDATE_Q512K456_DIRECT, ''), '_route_trace'])] = str(trace_path) + artifacts[''.join([format(CANDIDATE_Q512K456_DIRECT, ''), '_forced_fallback_trace'])] = str(forced_trace_path) + artifacts[''.join([format(CANDIDATE_Q512K456_DIRECT, ''), '_seed_delta_matrix'])] = str(seed_matrix_path) + summary = {'candidate_id': 'dispatcher_consumption_784a_q512k456_direct_plus_6bc3_k8_full82_v1', 'measured_entrypoint': ''.join([format(MODULE, ''), ':write_benchmark_artifacts']), 'denominator': payload['denominator'], 'timing_backend': payload['timing_backend'], 'benchmark_correctness_checked': benchmark_correctness, 'benchmark_time_flashlib': time_flashlib, 'baseline_candidate_key': BASE_784A_KEY, 'parent_candidate_key': PARENT_FCF2_KEY, 'selected_candidate_key': CANDIDATE_Q512K456_DIRECT, 'selected_candidate_dispatcher': CANDIDATE_ID, 'candidate_dispatchers': CANDIDATE_DISPATCHERS, 'candidate_rankings': [{'candidate_key': BASE_784A_KEY, 'candidate_id': BASE_784A_ID, 'tflops': baseline_784a_report['summary']['primary_mean'], 'all_correct': baseline_784a_report['summary']['all_correct'], 'performance_comparable': baseline_784a_report['summary']['performance_comparable']}, {'candidate_key': PARENT_FCF2_KEY, 'candidate_id': PARENT_FCF2_ID, 'tflops': parent_report['summary']['primary_mean'], 'all_correct': parent_report['summary']['all_correct'], 'performance_comparable': parent_report['summary']['performance_comparable']}, {'candidate_key': CANDIDATE_Q512K456_DIRECT, 'candidate_id': CANDIDATE_ID, 'tflops': payload['tflops'], 'metric_delta_vs_parent_fcf2': payload['metric_delta_vs_parent_fcf2'], 'metric_delta_vs_784a': payload['metric_delta_vs_784a'], 'all_correct': payload['all_correct'], 'performance_comparable': payload['performance_comparable'], 'performance_coverage': payload['performance_coverage']}], 'seed_delta_matrix': payload['seed_delta_matrix'], 'flashlib_parity_ledger': payload['flashlib_parity_ledger'], 'artifacts': artifacts} + summary_path = out_dir / ''.join([format(denom_label, ''), '_dispatcher_consumption_784a_q512k456_direct_plus_6bc3_k8_v1.json']) + summary_path.write_text(json.dumps(summary, indent=2, sort_keys=True) + '\n', encoding='utf-8') + artifacts['dispatcher_consumption'] = str(summary_path) + return artifacts diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_784a_6bc3_k8_q512k456_q4096k8_direct_fcf2_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_784a_6bc3_k8_q512k456_q4096k8_direct_fcf2_v1.py new file mode 100644 index 00000000..96f6e6e6 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_784a_6bc3_k8_q512k456_q4096k8_direct_fcf2_v1.py @@ -0,0 +1,341 @@ +"""fcf2 direct dispatcher with Q512 K4/K5/K6 and Q4096 K8 guards. + +Minimum target architecture: sm_100a. This additive dispatcher-consumption +wrapper starts from the round-130 fcf2 direct-Q512 wrapper and adds one exact +BF16 build guard for ``B=1,Q=M=4096,D=128,K=8``. The new row routes through the +existing v20 K8 split8 Weave producer/merge path and writes contract-visible +distances and indices. All non-matching rows delegate to the parent wrapper. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from pathlib import Path +from typing import Any, Callable +from . import knn_build_dispatch_784a_6bc3_k8_q512k456_direct_fcf2_v1 as parent_direct +from . import knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2_k32split_v20 as v20 +MODULE = 'loom.examples.weave.knn_build_dispatch_784a_6bc3_k8_q512k456_q4096k8_direct_fcf2_v1' +eval_mod = parent_direct.eval_mod +BASE_784A_KEY = parent_direct.BASE_784A_KEY +PARENT_DIRECT_KEY = 'parent_784a_direct_q512_k456_plus_6bc3_k8' +CANDIDATE_Q4096K8_DIRECT = '784a_plus_direct_q512_k456_q4096_k8_plus_6bc3_k8' +DEFAULT_CANDIDATE_KEY = CANDIDATE_Q4096K8_DIRECT +CANDIDATE_KEYS = (BASE_784A_KEY, PARENT_DIRECT_KEY, CANDIDATE_Q4096K8_DIRECT) +Q4096_K8 = 'build_qm4096_d128_k8' +Q4096_K8_TARGET_SHAPES = (Q4096_K8,) +TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_k_sweep_qm512_k4", "build_k_sweep_qm512_k5", "build_k_sweep_qm512_k6", "build_k_sweep_qm512_k8", "build_qm2048_d128_k8", "build_qm4096_d128_k8"]}')) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +SEED_Q4096_K8_DIRECT_ID = 'fcf2_direct_v20_q4096_k8_s8' +ROUTE_Q4096_K8_S8 = ''.join([format(MODULE, ''), ':q4096_k8_v20_s8']) +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +PARENT_DIRECT_ENTRYPOINT = parent_direct.ROUTE_ENTRYPOINT +CANDIDATE_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_candidate_784a_direct_q512_k456_q4096_k8_plus_6bc3_k8_v1']) +BASE_784A_ID = parent_direct.BASE_784A_ID +PARENT_DIRECT_ID = parent_direct.CANDIDATE_ID +CANDIDATE_ID = 'candidate_784a_direct_q512_k456_q4096_k8_plus_6bc3_k8_v1' +PRODUCTION_ROUTE_MODULES = {**parent_direct.PRODUCTION_ROUTE_MODULES, SEED_Q4096_K8_DIRECT_ID: ROUTE_Q4096_K8_S8, PARENT_DIRECT_ID: PARENT_DIRECT_ENTRYPOINT, CANDIDATE_ID: ROUTE_ENTRYPOINT} +SOURCE_TASKS = {**parent_direct.SOURCE_TASKS, SEED_Q4096_K8_DIRECT_ID: 'fcf2 Q4096/K8 correctness repair / v20 K8 split8 stage1+merge from loom.examples.weave.knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2_k32split_v20'} + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_FCF2_Q4096K8_DIRECT_VERIFY_KERNEL') + if verify_kernel == 'q4096_k8_stage1': + return v20.stage1_k8_ir + if verify_kernel == 'q4096_k8_merge_s8': + return v20.merge_k8_s8_ir + return parent_direct.ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbatch_m64_d4f7_stage1", "arg_keys": ["query", "database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 91392, "constants": [], "cta_group": 1, "threads": 512}')) + +def _select_contract_shapes(shape_labels): + return parent_direct._select_contract_shapes(shape_labels) + +def _trace_inputs_for_shape(shape: dict[str, Any]) -> dict[str, Any]: + return parent_direct._trace_inputs_for_shape(shape) + +def _normalize_route_row(row: dict[str, Any]) -> dict[str, Any]: + return parent_direct._normalize_route_row(row) + +def _inputs_for_label(label: str) -> dict[str, Any]: + return _trace_inputs_for_shape(_select_contract_shapes((label,))[0]) + +def _dtype_name(inputs: dict[str, Any], name: str='query') -> str: + tensor = inputs.get(name) + if tensor is not None and hasattr(tensor, 'dtype'): + return str(tensor.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')).replace('torch.', '') + +def _label_can_hit(inputs: dict[str, Any], labels: set[str]) -> bool: + label = inputs.get('label') + return label is None or str(label) in labels + +def _eligible_q4096_k8_direct(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, set(Q4096_K8_TARGET_SHAPES)) and bool(inputs.get('build', False)) and (int(inputs.get('B', -1)) == 1) and (int(inputs.get('Q', -1)) == 4096) and (int(inputs.get('M', -2)) == 4096) and (int(inputs.get('D', -1)) == v20.FEAT_D) and (int(inputs.get('K', -1)) == 8) and (_dtype_name(inputs, 'query') == 'bfloat16') and (_dtype_name(inputs, 'database') in ('', 'bfloat16')) + +def _candidate_config(candidate_key: str) -> dict[str, Any]: + try: + return CANDIDATE_CONFIGS[candidate_key] + except KeyError as exc: + raise ValueError(''.join(['unknown fcf2 Q4096/K8 direct candidate ', format(repr(candidate_key), '')])) from exc + +def _candidate_id(candidate_key: str | None) -> str | None: + if candidate_key is None: + return None + return str(_candidate_config(candidate_key)['candidate_id']) + +def _selected_direct_seed(inputs: dict[str, Any]) -> str | None: + if _eligible_q4096_k8_direct(inputs): + return SEED_Q4096_K8_DIRECT_ID + return parent_direct._selected_direct_seed(inputs) + +def route_for_contract_inputs(inputs: dict[str, Any], *, candidate_key: str=DEFAULT_CANDIDATE_KEY, force_fallback: bool=False) -> str: + _candidate_config(candidate_key) + if force_fallback or candidate_key == BASE_784A_KEY: + return parent_direct.route_for_contract_inputs(inputs, candidate_key=parent_direct.BASE_784A_KEY, force_fallback=force_fallback) + if candidate_key == PARENT_DIRECT_KEY: + return parent_direct.route_for_contract_inputs(inputs) + if _eligible_q4096_k8_direct(inputs): + return ROUTE_Q4096_K8_S8 + return parent_direct.route_for_contract_inputs(inputs) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, candidate_key: str=DEFAULT_CANDIDATE_KEY, force_fallback: bool=False) -> None: + _candidate_config(candidate_key) + if force_fallback or candidate_key == BASE_784A_KEY: + parent_direct.launch_from_contract_inputs(inputs, candidate_key=parent_direct.BASE_784A_KEY, force_fallback=force_fallback) + return + if candidate_key == PARENT_DIRECT_KEY: + parent_direct.launch_from_contract_inputs(inputs) + return + if _eligible_q4096_k8_direct(inputs): + v20._launch_k32_split_path(inputs, split_count=v20.K8_Q2048_SPLITS) + return + parent_direct.launch_from_contract_inputs(inputs) + +def candidate_baseline_784a_005f(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, candidate_key=BASE_784A_KEY) + +def candidate_parent_784a_direct_q512_k456_plus_6bc3_k8(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, candidate_key=PARENT_DIRECT_KEY) + +def candidate_784a_direct_q512_k456_q4096_k8_plus_6bc3_k8_v1(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, candidate_key=CANDIDATE_Q4096K8_DIRECT) + +def candidate(inputs: dict[str, Any]) -> None: + candidate_784a_direct_q512_k456_q4096_k8_plus_6bc3_k8_v1(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, candidate_key=DEFAULT_CANDIDATE_KEY, force_fallback=True) + +def _candidate_kernel_fn(candidate_key: str) -> Callable[[dict[str, Any]], None]: + _candidate_config(candidate_key) + if candidate_key == BASE_784A_KEY: + return candidate_baseline_784a_005f + if candidate_key == PARENT_DIRECT_KEY: + return candidate_parent_784a_direct_q512_k456_plus_6bc3_k8 + if candidate_key == CANDIDATE_Q4096K8_DIRECT: + return candidate_784a_direct_q512_k456_q4096_k8_plus_6bc3_k8_v1 + raise ValueError(''.join(['unknown fcf2 Q4096/K8 direct candidate ', format(repr(candidate_key), '')])) +_PARENT_SELECTED_SEEDS = _decode_capture(_json_loads('{"__tuple__": ["v20_k12_q1024_q4096_exact", "q2048_k12_4f30_v1", "v12_k20_q2048k10_mixedfanout", "over32_k48_v25", "6bc3_v20_q512_k8_static_s8", "6bc3_v20_q2048_k8_static_s8", "fcf2_direct_lowk_q512_k4_k5_k6_s4"]}')) +CANDIDATE_CONFIGS = _decode_capture(_json_loads('{"__dict_items__": [["base_784a_005f", {"__dict_items__": [["candidate_id", "candidate_dbd7_005f_buildbucket_full82_v1"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_dbd7_lowfloor_005f_8a78_full82_v1:launch_from_contract_inputs"], ["benchmark_entrypoint", "loom.examples.weave.knn_build_dispatch_784a_6bc3_k8_selective_full82_v1:benchmark_baseline_784a_005f"], ["selected_seeds", {"__tuple__": ["v20_k12_q1024_q4096_exact", "q2048_k12_4f30_v1", "v12_k20_q2048k10_mixedfanout", "over32_k48_v25"]}], ["guard_plan", {"__tuple__": ["005f exact BF16 build low-floor portfolio for K10/K12/K20/K48 rows", "a444/9db7 full82 Weave fallback for guard misses and Q1536/K10 tail"]}], ["expected_shape_wins", {"__tuple__": ["build_k_sweep_qm1024_k12", "build_k_sweep_qm1024_k20", "build_qm2048_d128_k10", "build_k_sweep_qm2048_k12", "build_k_sweep_qm2048_k20", "build_tail_b1_q1536_m1536_d128_k10", "build_k_sweep_qm4096_k12", "build_k_sweep_qm4096_k20", "build_over32_stress_qm2048_k48", "build_over32_stress_qm4096_k48"]}], ["fallback", "loom.examples.weave.knn_build_dispatch_dbd7_lowfloor_005f_8a78_full82_v1:launch_from_contract_inputs"], ["rejected_reason", "same-session 784a baseline"]]}], ["parent_784a_direct_q512_k456_plus_6bc3_k8", {"__dict_items__": [["candidate_id", "candidate_784a_direct_q512_k456_plus_6bc3_k8_full82_v1"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_784a_6bc3_k8_q512k456_direct_fcf2_v1:launch_from_contract_inputs"], ["benchmark_entrypoint", "loom.examples.weave.knn_build_dispatch_784a_6bc3_k8_q512k456_direct_fcf2_v1:benchmark_candidate_784a_direct_q512_k456_plus_6bc3_k8_full82_v1"], ["selected_seeds", {"__tuple__": ["v20_k12_q1024_q4096_exact", "q2048_k12_4f30_v1", "v12_k20_q2048k10_mixedfanout", "over32_k48_v25", "6bc3_v20_q512_k8_static_s8", "6bc3_v20_q2048_k8_static_s8", "fcf2_direct_lowk_q512_k4_k5_k6_s4"]}], ["guard_plan", {"__tuple__": ["direct exact BF16 build Q512 K4/K5/K6 low-K split4 guard", "then fcf2 selective 6bc3 exact Q512/Q2048 K8 guards", "then 784a 005f build-lowfloor portfolio", "then inherited a444/9db7 full82 Weave fallback"]}], ["expected_shape_wins", {"__tuple__": ["build_k_sweep_qm512_k4", "build_k_sweep_qm512_k5", "build_k_sweep_qm512_k6", "build_k_sweep_qm512_k8", "build_qm2048_d128_k8"]}], ["fallback", "loom.examples.weave.knn_build_dispatch_784a_6bc3_k8_selective_full82_v1:launch_from_contract_inputs"], ["rejected_reason", "parent round-130 direct-Q512 wrapper"]]}], ["784a_plus_direct_q512_k456_q4096_k8_plus_6bc3_k8", {"__dict_items__": [["candidate_id", "candidate_784a_direct_q512_k456_q4096_k8_plus_6bc3_k8_v1"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_784a_6bc3_k8_q512k456_q4096k8_direct_fcf2_v1:candidate_784a_direct_q512_k456_q4096_k8_plus_6bc3_k8_v1"], ["benchmark_entrypoint", "loom.examples.weave.knn_build_dispatch_784a_6bc3_k8_q512k456_q4096k8_direct_fcf2_v1:benchmark_candidate_784a_direct_q512_k456_q4096_k8_plus_6bc3_k8_v1"], ["selected_seeds", {"__tuple__": ["v20_k12_q1024_q4096_exact", "q2048_k12_4f30_v1", "v12_k20_q2048k10_mixedfanout", "over32_k48_v25", "6bc3_v20_q512_k8_static_s8", "6bc3_v20_q2048_k8_static_s8", "fcf2_direct_lowk_q512_k4_k5_k6_s4", "fcf2_direct_v20_q4096_k8_s8"]}], ["guard_plan", {"__tuple__": ["direct exact BF16 build Q4096 K8 v20 split8 guard", "then direct exact BF16 build Q512 K4/K5/K6 low-K split4 guard", "then fcf2 selective 6bc3 exact Q512/Q2048 K8 guards", "then 784a 005f build-lowfloor portfolio", "then inherited a444/9db7 full82 Weave fallback"]}], ["expected_shape_wins", {"__tuple__": ["build_k_sweep_qm512_k4", "build_k_sweep_qm512_k5", "build_k_sweep_qm512_k6", "build_k_sweep_qm512_k8", "build_qm2048_d128_k8", "build_qm4096_d128_k8"]}], ["fallback", "loom.examples.weave.knn_build_dispatch_784a_6bc3_k8_q512k456_direct_fcf2_v1:launch_from_contract_inputs"], ["rejected_reason", null]]}]]}')) +CANDIDATE_DISPATCHERS = _decode_capture(_json_loads('{"__tuple__": [{"__dict_items__": [["id", "candidate_dbd7_005f_buildbucket_full82_v1"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_784a_6bc3_k8_selective_full82_v1:benchmark_baseline_784a_005f"], ["consumed_seeds", {"__tuple__": ["v20_k12_q1024_q4096_exact", "q2048_k12_4f30_v1", "v12_k20_q2048k10_mixedfanout", "over32_k48_v25"]}], ["guard_plan", {"__tuple__": ["005f exact BF16 build low-floor portfolio for K10/K12/K20/K48 rows", "a444/9db7 full82 Weave fallback for guard misses and Q1536/K10 tail"]}], ["expected_shape_wins", {"__tuple__": ["build_k_sweep_qm1024_k12", "build_k_sweep_qm1024_k20", "build_qm2048_d128_k10", "build_k_sweep_qm2048_k12", "build_k_sweep_qm2048_k20", "build_tail_b1_q1536_m1536_d128_k10", "build_k_sweep_qm4096_k12", "build_k_sweep_qm4096_k20", "build_over32_stress_qm2048_k48", "build_over32_stress_qm4096_k48"]}], ["fallback", "loom.examples.weave.knn_build_dispatch_dbd7_lowfloor_005f_8a78_full82_v1:launch_from_contract_inputs"], ["rejected_reason", "same-session 784a baseline"]]}, {"__dict_items__": [["id", "candidate_784a_direct_q512_k456_plus_6bc3_k8_full82_v1"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_784a_6bc3_k8_q512k456_direct_fcf2_v1:benchmark_candidate_784a_direct_q512_k456_plus_6bc3_k8_full82_v1"], ["consumed_seeds", {"__tuple__": ["v20_k12_q1024_q4096_exact", "q2048_k12_4f30_v1", "v12_k20_q2048k10_mixedfanout", "over32_k48_v25", "6bc3_v20_q512_k8_static_s8", "6bc3_v20_q2048_k8_static_s8", "fcf2_direct_lowk_q512_k4_k5_k6_s4"]}], ["guard_plan", {"__tuple__": ["direct exact BF16 build Q512 K4/K5/K6 low-K split4 guard", "then fcf2 selective 6bc3 exact Q512/Q2048 K8 guards", "then 784a 005f build-lowfloor portfolio", "then inherited a444/9db7 full82 Weave fallback"]}], ["expected_shape_wins", {"__tuple__": ["build_k_sweep_qm512_k4", "build_k_sweep_qm512_k5", "build_k_sweep_qm512_k6", "build_k_sweep_qm512_k8", "build_qm2048_d128_k8"]}], ["fallback", "loom.examples.weave.knn_build_dispatch_784a_6bc3_k8_selective_full82_v1:launch_from_contract_inputs"], ["rejected_reason", "parent round-130 direct-Q512 wrapper"]]}, {"__dict_items__": [["id", "candidate_784a_direct_q512_k456_q4096_k8_plus_6bc3_k8_v1"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_784a_6bc3_k8_q512k456_q4096k8_direct_fcf2_v1:benchmark_candidate_784a_direct_q512_k456_q4096_k8_plus_6bc3_k8_v1"], ["consumed_seeds", {"__tuple__": ["v20_k12_q1024_q4096_exact", "q2048_k12_4f30_v1", "v12_k20_q2048k10_mixedfanout", "over32_k48_v25", "6bc3_v20_q512_k8_static_s8", "6bc3_v20_q2048_k8_static_s8", "fcf2_direct_lowk_q512_k4_k5_k6_s4", "fcf2_direct_v20_q4096_k8_s8"]}], ["guard_plan", {"__tuple__": ["direct exact BF16 build Q4096 K8 v20 split8 guard", "then direct exact BF16 build Q512 K4/K5/K6 low-K split4 guard", "then fcf2 selective 6bc3 exact Q512/Q2048 K8 guards", "then 784a 005f build-lowfloor portfolio", "then inherited a444/9db7 full82 Weave fallback"]}], ["expected_shape_wins", {"__tuple__": ["build_k_sweep_qm512_k4", "build_k_sweep_qm512_k5", "build_k_sweep_qm512_k6", "build_k_sweep_qm512_k8", "build_qm2048_d128_k8", "build_qm4096_d128_k8"]}], ["fallback", "loom.examples.weave.knn_build_dispatch_784a_6bc3_k8_q512k456_direct_fcf2_v1:launch_from_contract_inputs"], ["rejected_reason", null]]}]}')) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False, candidate_key: str=DEFAULT_CANDIDATE_KEY) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark, kernel_fn=_candidate_kernel_fn(candidate_key)) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=None, kernel_fn: Callable[[dict[str, Any]], Any]=candidate, correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + resolved_shape_labels = tuple((str(shape['label']) for shape in eval_mod.CANONICAL_SHAPES)) if shape_labels is None else shape_labels + return parent_direct._run_with_timing_backend(use_cupti=use_cupti, shape_labels=resolved_shape_labels, kernel_fn=kernel_fn, correctness=correctness, time_flashlib=time_flashlib) + +def _timing_backend_name(use_cupti: bool) -> str: + return 'cupti' if use_cupti else 'cuda_event_fallback' + +def _payload_shape_labels(shape_labels) -> str | tuple[str, ...]: + if shape_labels is None: + return 'all_canonical' + return tuple((str(label) for label in shape_labels)) + +def _denominator_name(shape_labels) -> str: + if shape_labels is None: + return ''.join(['full', format(len(eval_mod.CANONICAL_SHAPES), ''), '_v10']) + labels = tuple((str(label) for label in shape_labels)) + if labels == TARGET_SHAPES: + return 'q512k456_q4096k8_plus_6bc3_k8_target_rows' + return ''.join(['shape', format(len(labels), '')]) + +def _denominator_label(shape_labels) -> str: + if shape_labels is None: + return ''.join(['full', format(len(eval_mod.CANONICAL_SHAPES), '')]) + return ''.join(['shape', format(len(tuple(shape_labels)), '')]) + +def _rows_for_labels(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return {label: dict(report.get('per_shape', {}).get(label, {})) for label in labels} + +def _timing_backends_for_reports(*reports: dict[str, Any]) -> list[str]: + return parent_direct._timing_backends_for_reports(*reports) + +def _parent_route_trace_row(label: str) -> dict[str, Any]: + row = dict(parent_direct.route_trace_for_contract_shapes((label,))[0]) + row['parent_direct_route'] = row.get('selected_route') + row['parent_direct_selected_seed'] = row.get('selected_seed') + return _normalize_route_row(row) + +def _guard_condition(seed_id: str | None) -> str: + if seed_id == SEED_Q4096_K8_DIRECT_ID: + return 'exact BF16 build B=1 Q=M=4096 D=128 K=8 v20 split8 route' + return parent_direct._guard_condition(seed_id) + +def _route_trace_record(inputs: dict[str, Any], *, candidate_key: str, force_fallback: bool=False) -> dict[str, Any]: + label = str(inputs.get('label')) + q4096_seed = SEED_Q4096_K8_DIRECT_ID if candidate_key == CANDIDATE_Q4096K8_DIRECT and _eligible_q4096_k8_direct(inputs) else None + parent_row = _parent_route_trace_row(label) + if force_fallback or candidate_key != CANDIDATE_Q4096K8_DIRECT or q4096_seed is None: + if candidate_key == PARENT_DIRECT_KEY: + row = dict(parent_row) + else: + row = dict(parent_direct.route_trace_for_contract_shapes((label,), candidate_key=parent_direct.BASE_784A_KEY if candidate_key == BASE_784A_KEY else parent_direct.DEFAULT_CANDIDATE_KEY, force_fallback=force_fallback)[0]) + row['expected_seed'] = q4096_seed or row.get('expected_seed') + row['parent_direct_route'] = parent_row.get('selected_route') + row['parent_direct_selected_seed'] = parent_row.get('selected_seed') + if force_fallback and q4096_seed is not None: + row['selected_route'] = parent_direct.route_for_contract_inputs(inputs, candidate_key=parent_direct.BASE_784A_KEY, force_fallback=True) + row['selected_entrypoint'] = parent_direct.parent_fcf2.BASE_784A_ROUTE_ENTRYPOINT + row['guard_id'] = ''.join(['forced_fallback_', format(q4096_seed, ''), '_disabled']) + row['guard_condition'] = ''.join(['forced fallback to 784a baseline; ', format(q4096_seed, ''), ' disabled']) + row['classification'] = 'guard-miss' + return _normalize_route_row(row) + return _normalize_route_row({'shape_key': label, 'selected_route': ROUTE_Q4096_K8_S8, 'selected_entrypoint': ROUTE_Q4096_K8_S8, 'selected_seed': q4096_seed, 'expected_seed': q4096_seed, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': ''.join([format(candidate_key, ''), '_', format(q4096_seed, ''), '_guard']), 'guard_condition': _guard_condition(q4096_seed), 'coverage': 'direct Q4096 K8 v20 split8 route before round-130 parent', 'consumed_seed': q4096_seed, 'replaced_route': parent_row.get('selected_route'), 'parent_direct_route': parent_row.get('selected_route'), 'parent_direct_selected_seed': parent_row.get('selected_seed'), 'parent_fcf2_route': parent_row.get('parent_fcf2_route'), 'baseline_784a_route': parent_row.get('baseline_784a_route'), 'base_784a_route': parent_row.get('base_784a_route'), 'shape_specific_kernel_ms': None, 'classification': 'unmeasured'}) + +def route_trace_for_contract_shapes(shape_labels=None, *, candidate_key: str=DEFAULT_CANDIDATE_KEY, force_fallback: bool=False) -> list[dict[str, Any]]: + _candidate_config(candidate_key) + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + return [_route_trace_record(_trace_inputs_for_shape(shape), candidate_key=candidate_key, force_fallback=force_fallback) for shape in selected] + +def _annotate_route_trace(route_trace: list[dict[str, Any]], candidate_report: dict[str, Any], parent_report: dict[str, Any], *, candidate_key: str) -> list[dict[str, Any]]: + annotated = [] + for row in route_trace: + label = str(row.get('shape_key')) + out = dict(row) + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + parent_row = parent_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + parent_ms = parent_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') + speedup_vs_parent = parent_ms / candidate_ms if candidate_ms and parent_ms else None + speedup_vs_external = flashlib_ms / candidate_ms if candidate_ms and flashlib_ms else None + out['dispatcher_kernel_ms'] = candidate_ms + out['parent_direct_kernel_ms'] = parent_ms + out['external_baseline_ms'] = flashlib_ms + out['flashlib_ms'] = flashlib_ms + out['relative_speedup_vs_parent_direct'] = speedup_vs_parent + out['speedup_vs_external_baseline'] = speedup_vs_external + out['external_baseline_ref'] = 'same_session' if flashlib_ms is not None else 'not_available' + out['route_changed_vs_parent_direct'] = out.get('selected_route') != out.get('parent_direct_route') + expected_seed = out.get('expected_seed') + if candidate_key == CANDIDATE_Q4096K8_DIRECT and expected_seed == SEED_Q4096_K8_DIRECT_ID: + if out.get('selected_seed') != expected_seed: + out['classification'] = 'guard-miss' + elif speedup_vs_external is not None and speedup_vs_external < 1.05: + out['classification'] = 'kernel-slow' + elif speedup_vs_parent is not None and speedup_vs_parent < 1.0: + out['classification'] = 'kernel-slow' + else: + out['classification'] = 'seed-consumed' + elif speedup_vs_external is not None and speedup_vs_external < 1.05: + out['classification'] = 'kernel-slow' if out.get('route_kind') == 'specialized' else 'fallback-slow' + else: + out['classification'] = 'route-ok' + annotated.append(_normalize_route_row(out)) + return annotated + +def _below_flashlib_rows(report: dict[str, Any], route_trace: list[dict[str, Any]], *, floor: float) -> list[dict[str, Any]]: + trace_by_label = {str(row['shape_key']): row for row in route_trace} + rows = [] + for label, row in sorted(report.get('per_shape', {}).items()): + ratio = row.get('ratio_vs_flashlib') + if isinstance(ratio, (float, int)) and ratio < floor: + trace_row = trace_by_label.get(label, {}) + rows.append({'shape_key': label, 'kernel_ms': row.get('kernel_ms'), 'flashlib_ms': row.get('flashlib_ms'), 'ratio_vs_flashlib': ratio, 'selected_route': trace_row.get('selected_route'), 'selected_seed': trace_row.get('selected_seed'), 'expected_seed': trace_row.get('expected_seed'), 'route_kind': trace_row.get('route_kind', 'unknown'), 'classification': trace_row.get('classification', 'unmeasured')}) + return rows + +def _seed_delta_matrix(candidate_key: str, candidate_report: dict[str, Any], parent_report: dict[str, Any]) -> list[dict[str, Any]]: + matrix = [] + for label in TARGET_SHAPES: + inputs = _inputs_for_label(label) + selected_seed = _selected_direct_seed(inputs) if candidate_key == CANDIDATE_Q4096K8_DIRECT else None + if selected_seed is None: + selected_seed = parent_direct._selected_direct_seed(inputs) + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + parent_row = parent_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + parent_ms = parent_row.get('kernel_ms') + matrix.append({'shape_key': label, 'parent_direct_route': parent_direct.route_for_contract_inputs(inputs), 'candidate_route': route_for_contract_inputs(inputs, candidate_key=candidate_key), 'selected_seed': selected_seed, 'candidate_id': _candidate_id(candidate_key), 'candidate_ms': candidate_ms, 'parent_direct_ms': parent_ms, 'flashlib_ms': candidate_row.get('flashlib_ms'), 'delta_ms_candidate_minus_parent_direct': candidate_ms - parent_ms if candidate_ms and parent_ms else None, 'speedup_vs_parent_direct': parent_ms / candidate_ms if candidate_ms and parent_ms else None, 'ratio_vs_flashlib': candidate_row.get('ratio_vs_flashlib'), 'timing_backend': candidate_row.get('timing_backend') or parent_row.get('timing_backend')}) + return matrix + +def benchmark_parent_direct(*, use_cupti: bool=True, shape_labels=None, benchmark_correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_parent_784a_direct_q512_k456_plus_6bc3_k8, correctness=benchmark_correctness, time_flashlib=time_flashlib) + report['candidate_id'] = PARENT_DIRECT_ID + report['measured_entrypoint'] = parent_direct.CANDIDATE_ENTRYPOINT + report['measured_shape_labels'] = _payload_shape_labels(shape_labels) + report['route_trace'] = route_trace_for_contract_shapes(shape_labels, candidate_key=PARENT_DIRECT_KEY) + report['route_trace_included'] = True + return report + +def _benchmark_payload(candidate_key: str, candidate_report: dict[str, Any], parent_report: dict[str, Any], *, use_cupti: bool, shape_labels, benchmark_correctness: bool, time_flashlib: bool) -> dict[str, Any]: + candidate_metric = candidate_report['summary']['primary_mean'] + parent_metric = parent_report['summary']['primary_mean'] + route_trace = _annotate_route_trace(route_trace_for_contract_shapes(shape_labels, candidate_key=candidate_key), candidate_report, parent_report, candidate_key=candidate_key) + below_1x = _below_flashlib_rows(candidate_report, route_trace, floor=1.0) + below_floor = _below_flashlib_rows(candidate_report, route_trace, floor=1.05) + denominator = _denominator_name(shape_labels) + timing_backend = _timing_backend_name(use_cupti) + config = _candidate_config(candidate_key) + return {'candidate_id': config['candidate_id'], 'candidate_key': candidate_key, 'parent_candidate_id': PARENT_DIRECT_ID, 'selected_seeds': config['selected_seeds'], 'source_tasks': SOURCE_TASKS, 'tflops': candidate_metric, 'parent_direct_tflops': parent_metric, 'metric_delta_vs_parent_direct': candidate_metric - parent_metric if candidate_metric is not None and parent_metric is not None else None, 'all_correct': candidate_report['summary']['all_correct'], 'parent_all_correct': parent_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'parent_performance_comparable': parent_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'parent_invalid_performance_reason': parent_report['summary']['invalid_performance_reason'], 'measured_entrypoint': config['benchmark_entrypoint'], 'parent_entrypoint': parent_direct.CANDIDATE_ENTRYPOINT, 'route_entrypoint': ROUTE_ENTRYPOINT, 'measured_shape_labels': _payload_shape_labels(shape_labels), 'timing_backend': timing_backend, 'denominator': denominator, 'selected_route_labels': config['expected_shape_wins'], 'selected_route_rows': _rows_for_labels(candidate_report, TARGET_SHAPES), 'parent_selected_route_rows': _rows_for_labels(parent_report, TARGET_SHAPES), 'seed_delta_matrix': _seed_delta_matrix(candidate_key, candidate_report, parent_report), 'candidate_dispatchers': CANDIDATE_DISPATCHERS, 'selected_candidate_dispatcher': config['candidate_id'], 'guard_plan': config['guard_plan'], 'route_modules': PRODUCTION_ROUTE_MODULES, 'route_trace': route_trace, 'forced_fallback_route_trace': route_trace_for_contract_shapes(shape_labels, candidate_key=candidate_key, force_fallback=True), 'route_trace_included': True, 'contract_summary': candidate_report['summary'], 'parent_contract_summary': parent_report['summary'], 'contract_performance': candidate_report['performance'], 'parent_contract_performance': parent_report['performance'], 'timing_backends': _timing_backends_for_reports(candidate_report, parent_report), 'timing_backend_requested': timing_backend, 'use_cupti': use_cupti, 'benchmark_correctness_checked': benchmark_correctness, 'benchmark_time_flashlib': time_flashlib, 'performance_coverage': 'partial' if below_floor else 'pass', 'coverage_only_routes': [], 'hot_bucket_blockers': below_floor, 'flashlib_parity_ledger': {'baseline_ref_scope': 'same_session' if time_flashlib else 'not_available', 'speedup_floor': 1.05, 'rows_below_1x': below_1x, 'rows_below_floor': below_floor, 'omitted_reason': None if time_flashlib else 'benchmark_time_flashlib=false'}, 'rank_objective': {'metric': 'tflops', 'direction': 'maximize', 'value': candidate_metric, 'parent_direct_value': parent_metric, 'delta_vs_parent_direct': candidate_metric - parent_metric if candidate_metric is not None and parent_metric is not None else None, 'denominator': denominator}, 'report': candidate_report, 'parent_report': parent_report} + +def benchmark_candidate_portfolio(candidate_key: str, *, use_cupti: bool=True, shape_labels=None, parent_report: dict[str, Any] | None=None, benchmark_correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + if candidate_key == BASE_784A_KEY: + return parent_direct.parent_fcf2.benchmark_baseline_784a_005f(use_cupti=use_cupti, shape_labels=shape_labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + if candidate_key == PARENT_DIRECT_KEY: + return benchmark_parent_direct(use_cupti=use_cupti, shape_labels=shape_labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + if parent_report is None: + parent_report = benchmark_parent_direct(use_cupti=use_cupti, shape_labels=shape_labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=_candidate_kernel_fn(candidate_key), correctness=benchmark_correctness, time_flashlib=time_flashlib) + return _benchmark_payload(candidate_key, candidate_report, parent_report, use_cupti=use_cupti, shape_labels=shape_labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + +def benchmark_candidate_784a_direct_q512_k456_q4096_k8_plus_6bc3_k8_v1(**kwargs) -> dict[str, Any]: + return benchmark_candidate_portfolio(CANDIDATE_Q4096K8_DIRECT, **kwargs) + +def write_benchmark_artifacts(artifact_dir: str | Path, *, use_cupti: bool=True, shape_labels=None, benchmark_correctness: bool=True, time_flashlib: bool=True) -> dict[str, str]: + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + denom_label = _denominator_label(shape_labels) + parent_report = benchmark_parent_direct(use_cupti=use_cupti, shape_labels=shape_labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + payload = benchmark_candidate_portfolio(CANDIDATE_Q4096K8_DIRECT, use_cupti=use_cupti, shape_labels=shape_labels, parent_report=parent_report, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + artifacts: dict[str, str] = {} + parent_path = out_dir / ''.join([format(denom_label, ''), '_same_session_parent_q512k456_direct_for_q4096k8_v1.json']) + payload_path = out_dir / ''.join([format(denom_label, ''), '_dispatch_784a_direct_q512k456_q4096k8_plus_6bc3_k8_v1.json']) + trace_path = out_dir / ''.join([format(denom_label, ''), '_route_trace_784a_direct_q512k456_q4096k8_plus_6bc3_k8_v1.json']) + forced_trace_path = out_dir / ''.join([format(denom_label, ''), '_forced_fallback_trace_784a_direct_q512k456_q4096k8_plus_6bc3_k8_v1.json']) + seed_matrix_path = out_dir / ''.join([format(denom_label, ''), '_seed_delta_matrix_784a_direct_q512k456_q4096k8_plus_6bc3_k8_v1.json']) + parent_path.write_text(json.dumps(parent_report, indent=2, sort_keys=True) + '\n', encoding='utf-8') + payload_path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n', encoding='utf-8') + trace_path.write_text(json.dumps(payload['route_trace'], indent=2, sort_keys=True) + '\n', encoding='utf-8') + forced_trace_path.write_text(json.dumps(payload['forced_fallback_route_trace'], indent=2, sort_keys=True) + '\n', encoding='utf-8') + seed_matrix_path.write_text(json.dumps(payload['seed_delta_matrix'], indent=2, sort_keys=True) + '\n', encoding='utf-8') + artifacts['same_session_parent_direct_payload'] = str(parent_path) + artifacts[''.join([format(CANDIDATE_Q4096K8_DIRECT, ''), '_payload'])] = str(payload_path) + artifacts[''.join([format(CANDIDATE_Q4096K8_DIRECT, ''), '_route_trace'])] = str(trace_path) + artifacts[''.join([format(CANDIDATE_Q4096K8_DIRECT, ''), '_forced_fallback_trace'])] = str(forced_trace_path) + artifacts[''.join([format(CANDIDATE_Q4096K8_DIRECT, ''), '_seed_delta_matrix'])] = str(seed_matrix_path) + summary = {'candidate_id': 'dispatcher_consumption_784a_q512k456_q4096k8_direct_plus_6bc3_k8_v1', 'measured_entrypoint': ''.join([format(MODULE, ''), ':write_benchmark_artifacts']), 'denominator': payload['denominator'], 'timing_backend': payload['timing_backend'], 'benchmark_correctness_checked': benchmark_correctness, 'benchmark_time_flashlib': time_flashlib, 'parent_candidate_key': PARENT_DIRECT_KEY, 'selected_candidate_key': CANDIDATE_Q4096K8_DIRECT, 'selected_candidate_dispatcher': CANDIDATE_ID, 'candidate_dispatchers': CANDIDATE_DISPATCHERS, 'candidate_rankings': [{'candidate_key': PARENT_DIRECT_KEY, 'candidate_id': PARENT_DIRECT_ID, 'tflops': parent_report['summary']['primary_mean'], 'all_correct': parent_report['summary']['all_correct'], 'performance_comparable': parent_report['summary']['performance_comparable']}, {'candidate_key': CANDIDATE_Q4096K8_DIRECT, 'candidate_id': CANDIDATE_ID, 'tflops': payload['tflops'], 'metric_delta_vs_parent_direct': payload['metric_delta_vs_parent_direct'], 'all_correct': payload['all_correct'], 'performance_comparable': payload['performance_comparable'], 'performance_coverage': payload['performance_coverage']}], 'seed_delta_matrix': payload['seed_delta_matrix'], 'flashlib_parity_ledger': payload['flashlib_parity_ledger'], 'artifacts': artifacts} + summary_path = out_dir / ''.join([format(denom_label, ''), '_dispatcher_consumption_784a_q512k456_q4096k8_direct_plus_6bc3_k8_v1.json']) + summary_path.write_text(json.dumps(summary, indent=2, sort_keys=True) + '\n', encoding='utf-8') + artifacts['dispatcher_consumption'] = str(summary_path) + return artifacts diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_784a_6bc3_k8_q512k456_q4096k8_s4_direct_c3bf_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_784a_6bc3_k8_q512k456_q4096k8_s4_direct_c3bf_v1.py new file mode 100644 index 00000000..e07d944c --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_784a_6bc3_k8_q512k456_q4096k8_s4_direct_c3bf_v1.py @@ -0,0 +1,343 @@ +"""c3bf split4 Q4096/K8 dispatcher over the d5f8 full90 baseline. + +Minimum target architecture: sm_100a. This additive dispatcher-consumption +wrapper starts from the round-131/d5f8 direct-Q512/Q4096 wrapper and changes +only the exact BF16 build ``B=1,Q=M=4096,D=128,K=8`` row. The new row routes +through the existing v20 K8 stage1 producer with four database splits and the +existing four-way K8 merge, writing contract-visible distances and indices. All +non-matching rows delegate to the d5f8 parent wrapper. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from pathlib import Path +from typing import Any, Callable +from . import knn_build_dispatch_784a_6bc3_k8_q512k456_q4096k8_direct_fcf2_v1 as parent_direct +from . import knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2_k32split_v20 as v20 +MODULE = 'loom.examples.weave.knn_build_dispatch_784a_6bc3_k8_q512k456_q4096k8_s4_direct_c3bf_v1' +eval_mod = parent_direct.eval_mod +BASE_784A_KEY = parent_direct.BASE_784A_KEY +PARENT_DIRECT_KEY = 'parent_d5f8_q4096_k8_s8' +CANDIDATE_Q4096K8_DIRECT = '784a_plus_direct_q512_k456_q4096_k8_s4_plus_6bc3_k8' +DEFAULT_CANDIDATE_KEY = CANDIDATE_Q4096K8_DIRECT +CANDIDATE_KEYS = (BASE_784A_KEY, PARENT_DIRECT_KEY, CANDIDATE_Q4096K8_DIRECT) +Q4096_K8 = 'build_qm4096_d128_k8' +Q4096_K8_TARGET_SHAPES = (Q4096_K8,) +TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_k_sweep_qm512_k4", "build_k_sweep_qm512_k5", "build_k_sweep_qm512_k6", "build_k_sweep_qm512_k8", "build_qm2048_d128_k8", "build_qm4096_d128_k8"]}')) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +SEED_Q4096_K8_DIRECT_ID = 'c3bf_direct_v20_q4096_k8_s4' +ROUTE_Q4096_K8_S4 = ''.join([format(MODULE, ''), ':q4096_k8_v20_s4']) +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +PARENT_DIRECT_ENTRYPOINT = parent_direct.ROUTE_ENTRYPOINT +CANDIDATE_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_candidate_784a_direct_q512_k456_q4096_k8_s4_plus_6bc3_k8_v1']) +BASE_784A_ID = parent_direct.BASE_784A_ID +PARENT_DIRECT_ID = parent_direct.CANDIDATE_ID +CANDIDATE_ID = 'candidate_784a_direct_q512_k456_q4096_k8_s4_plus_6bc3_k8_v1' +PRODUCTION_ROUTE_MODULES = {**parent_direct.PRODUCTION_ROUTE_MODULES, SEED_Q4096_K8_DIRECT_ID: ROUTE_Q4096_K8_S4, PARENT_DIRECT_ID: PARENT_DIRECT_ENTRYPOINT, CANDIDATE_ID: ROUTE_ENTRYPOINT} +SOURCE_TASKS = {**parent_direct.SOURCE_TASKS, SEED_Q4096_K8_DIRECT_ID: 'c3bf Q4096/K8 performance repair / v20 K8 split4 stage1+merge from loom.examples.weave.knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2_k32split_v20'} + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_C3BF_Q4096K8_S4_VERIFY_KERNEL') + if verify_kernel == 'q4096_k8_stage1': + return v20.stage1_k8_ir + if verify_kernel == 'q4096_k8_merge_s4': + return v20.merge_k8_ir + return parent_direct.ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbatch_m64_d4f7_stage1", "arg_keys": ["query", "database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 91392, "constants": [], "cta_group": 1, "threads": 512}')) + +def _select_contract_shapes(shape_labels): + return parent_direct._select_contract_shapes(shape_labels) + +def _trace_inputs_for_shape(shape: dict[str, Any]) -> dict[str, Any]: + return parent_direct._trace_inputs_for_shape(shape) + +def _normalize_route_row(row: dict[str, Any]) -> dict[str, Any]: + return parent_direct._normalize_route_row(row) + +def _inputs_for_label(label: str) -> dict[str, Any]: + return _trace_inputs_for_shape(_select_contract_shapes((label,))[0]) + +def _dtype_name(inputs: dict[str, Any], name: str='query') -> str: + tensor = inputs.get(name) + if tensor is not None and hasattr(tensor, 'dtype'): + return str(tensor.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')).replace('torch.', '') + +def _label_can_hit(inputs: dict[str, Any], labels: set[str]) -> bool: + label = inputs.get('label') + return label is None or str(label) in labels + +def _eligible_q4096_k8_direct(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, set(Q4096_K8_TARGET_SHAPES)) and bool(inputs.get('build', False)) and (int(inputs.get('B', -1)) == 1) and (int(inputs.get('Q', -1)) == 4096) and (int(inputs.get('M', -2)) == 4096) and (int(inputs.get('D', -1)) == v20.FEAT_D) and (int(inputs.get('K', -1)) == 8) and (_dtype_name(inputs, 'query') == 'bfloat16') and (_dtype_name(inputs, 'database') in ('', 'bfloat16')) + +def _candidate_config(candidate_key: str) -> dict[str, Any]: + try: + return CANDIDATE_CONFIGS[candidate_key] + except KeyError as exc: + raise ValueError(''.join(['unknown fcf2 Q4096/K8 direct candidate ', format(repr(candidate_key), '')])) from exc + +def _candidate_id(candidate_key: str | None) -> str | None: + if candidate_key is None: + return None + return str(_candidate_config(candidate_key)['candidate_id']) + +def _selected_direct_seed(inputs: dict[str, Any]) -> str | None: + if _eligible_q4096_k8_direct(inputs): + return SEED_Q4096_K8_DIRECT_ID + return parent_direct._selected_direct_seed(inputs) + +def route_for_contract_inputs(inputs: dict[str, Any], *, candidate_key: str=DEFAULT_CANDIDATE_KEY, force_fallback: bool=False) -> str: + _candidate_config(candidate_key) + if force_fallback or candidate_key == BASE_784A_KEY: + return parent_direct.route_for_contract_inputs(inputs, candidate_key=parent_direct.BASE_784A_KEY, force_fallback=force_fallback) + if candidate_key == PARENT_DIRECT_KEY: + return parent_direct.route_for_contract_inputs(inputs) + if _eligible_q4096_k8_direct(inputs): + return ROUTE_Q4096_K8_S4 + return parent_direct.route_for_contract_inputs(inputs) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, candidate_key: str=DEFAULT_CANDIDATE_KEY, force_fallback: bool=False) -> None: + _candidate_config(candidate_key) + if force_fallback or candidate_key == BASE_784A_KEY: + parent_direct.launch_from_contract_inputs(inputs, candidate_key=parent_direct.BASE_784A_KEY, force_fallback=force_fallback) + return + if candidate_key == PARENT_DIRECT_KEY: + parent_direct.launch_from_contract_inputs(inputs) + return + if _eligible_q4096_k8_direct(inputs): + v20._launch_k32_split_path(inputs, split_count=v20.MEDIUM_SPLITS) + return + parent_direct.launch_from_contract_inputs(inputs) + +def candidate_baseline_784a_005f(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, candidate_key=BASE_784A_KEY) + +def candidate_parent_784a_direct_q512_k456_plus_6bc3_k8(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, candidate_key=PARENT_DIRECT_KEY) + +def candidate_784a_direct_q512_k456_q4096_k8_s4_plus_6bc3_k8_v1(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, candidate_key=CANDIDATE_Q4096K8_DIRECT) + +def candidate(inputs: dict[str, Any]) -> None: + candidate_784a_direct_q512_k456_q4096_k8_s4_plus_6bc3_k8_v1(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, candidate_key=DEFAULT_CANDIDATE_KEY, force_fallback=True) + +def _candidate_kernel_fn(candidate_key: str) -> Callable[[dict[str, Any]], None]: + _candidate_config(candidate_key) + if candidate_key == BASE_784A_KEY: + return candidate_baseline_784a_005f + if candidate_key == PARENT_DIRECT_KEY: + return candidate_parent_784a_direct_q512_k456_plus_6bc3_k8 + if candidate_key == CANDIDATE_Q4096K8_DIRECT: + return candidate_784a_direct_q512_k456_q4096_k8_s4_plus_6bc3_k8_v1 + raise ValueError(''.join(['unknown fcf2 Q4096/K8 direct candidate ', format(repr(candidate_key), '')])) +_PARENT_SELECTED_SEEDS = _decode_capture(_json_loads('{"__tuple__": ["v20_k12_q1024_q4096_exact", "q2048_k12_4f30_v1", "v12_k20_q2048k10_mixedfanout", "over32_k48_v25", "6bc3_v20_q512_k8_static_s8", "6bc3_v20_q2048_k8_static_s8", "fcf2_direct_lowk_q512_k4_k5_k6_s4"]}')) +_PARENT_S8_SELECTED_SEEDS = _decode_capture(_json_loads('{"__tuple__": ["v20_k12_q1024_q4096_exact", "q2048_k12_4f30_v1", "v12_k20_q2048k10_mixedfanout", "over32_k48_v25", "6bc3_v20_q512_k8_static_s8", "6bc3_v20_q2048_k8_static_s8", "fcf2_direct_lowk_q512_k4_k5_k6_s4", "fcf2_direct_v20_q4096_k8_s8"]}')) +CANDIDATE_CONFIGS = _decode_capture(_json_loads('{"__dict_items__": [["base_784a_005f", {"__dict_items__": [["candidate_id", "candidate_dbd7_005f_buildbucket_full82_v1"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_dbd7_lowfloor_005f_8a78_full82_v1:launch_from_contract_inputs"], ["benchmark_entrypoint", "loom.examples.weave.knn_build_dispatch_784a_6bc3_k8_selective_full82_v1:benchmark_baseline_784a_005f"], ["selected_seeds", {"__tuple__": ["v20_k12_q1024_q4096_exact", "q2048_k12_4f30_v1", "v12_k20_q2048k10_mixedfanout", "over32_k48_v25"]}], ["guard_plan", {"__tuple__": ["005f exact BF16 build low-floor portfolio for K10/K12/K20/K48 rows", "a444/9db7 full82 Weave fallback for guard misses and Q1536/K10 tail"]}], ["expected_shape_wins", {"__tuple__": ["build_k_sweep_qm1024_k12", "build_k_sweep_qm1024_k20", "build_qm2048_d128_k10", "build_k_sweep_qm2048_k12", "build_k_sweep_qm2048_k20", "build_tail_b1_q1536_m1536_d128_k10", "build_k_sweep_qm4096_k12", "build_k_sweep_qm4096_k20", "build_over32_stress_qm2048_k48", "build_over32_stress_qm4096_k48"]}], ["fallback", "loom.examples.weave.knn_build_dispatch_dbd7_lowfloor_005f_8a78_full82_v1:launch_from_contract_inputs"], ["rejected_reason", "same-session 784a baseline"]]}], ["parent_d5f8_q4096_k8_s8", {"__dict_items__": [["candidate_id", "candidate_784a_direct_q512_k456_q4096_k8_plus_6bc3_k8_v1"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_784a_6bc3_k8_q512k456_q4096k8_direct_fcf2_v1:launch_from_contract_inputs"], ["benchmark_entrypoint", "loom.examples.weave.knn_build_dispatch_784a_6bc3_k8_q512k456_q4096k8_direct_fcf2_v1:benchmark_candidate_784a_direct_q512_k456_q4096_k8_plus_6bc3_k8_v1"], ["selected_seeds", {"__tuple__": ["v20_k12_q1024_q4096_exact", "q2048_k12_4f30_v1", "v12_k20_q2048k10_mixedfanout", "over32_k48_v25", "6bc3_v20_q512_k8_static_s8", "6bc3_v20_q2048_k8_static_s8", "fcf2_direct_lowk_q512_k4_k5_k6_s4", "fcf2_direct_v20_q4096_k8_s8"]}], ["guard_plan", {"__tuple__": ["direct exact BF16 build Q4096 K8 v20 split8 guard", "then direct exact BF16 build Q512 K4/K5/K6 low-K split4 guard", "then fcf2 selective 6bc3 exact Q512/Q2048 K8 guards", "then 784a 005f build-lowfloor portfolio", "then inherited a444/9db7 full82 Weave fallback"]}], ["expected_shape_wins", {"__tuple__": ["build_k_sweep_qm512_k4", "build_k_sweep_qm512_k5", "build_k_sweep_qm512_k6", "build_k_sweep_qm512_k8", "build_qm2048_d128_k8", "build_qm4096_d128_k8"]}], ["fallback", "loom.examples.weave.knn_build_dispatch_784a_6bc3_k8_q512k456_direct_fcf2_v1:launch_from_contract_inputs"], ["rejected_reason", "same-session d5f8 split8 baseline"]]}], ["784a_plus_direct_q512_k456_q4096_k8_s4_plus_6bc3_k8", {"__dict_items__": [["candidate_id", "candidate_784a_direct_q512_k456_q4096_k8_s4_plus_6bc3_k8_v1"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_784a_6bc3_k8_q512k456_q4096k8_s4_direct_c3bf_v1:candidate_784a_direct_q512_k456_q4096_k8_s4_plus_6bc3_k8_v1"], ["benchmark_entrypoint", "loom.examples.weave.knn_build_dispatch_784a_6bc3_k8_q512k456_q4096k8_s4_direct_c3bf_v1:benchmark_candidate_784a_direct_q512_k456_q4096_k8_s4_plus_6bc3_k8_v1"], ["selected_seeds", {"__tuple__": ["v20_k12_q1024_q4096_exact", "q2048_k12_4f30_v1", "v12_k20_q2048k10_mixedfanout", "over32_k48_v25", "6bc3_v20_q512_k8_static_s8", "6bc3_v20_q2048_k8_static_s8", "fcf2_direct_lowk_q512_k4_k5_k6_s4", "c3bf_direct_v20_q4096_k8_s4"]}], ["guard_plan", {"__tuple__": ["direct exact BF16 build Q4096 K8 v20 split4 guard", "then direct exact BF16 build Q512 K4/K5/K6 low-K split4 guard", "then fcf2 selective 6bc3 exact Q512/Q2048 K8 guards", "then 784a 005f build-lowfloor portfolio", "then inherited d5f8/a444/9db7 full82 Weave fallback"]}], ["expected_shape_wins", {"__tuple__": ["build_k_sweep_qm512_k4", "build_k_sweep_qm512_k5", "build_k_sweep_qm512_k6", "build_k_sweep_qm512_k8", "build_qm2048_d128_k8", "build_qm4096_d128_k8"]}], ["fallback", "loom.examples.weave.knn_build_dispatch_784a_6bc3_k8_q512k456_q4096k8_direct_fcf2_v1:launch_from_contract_inputs"], ["rejected_reason", null]]}]]}')) +CANDIDATE_DISPATCHERS = _decode_capture(_json_loads('{"__tuple__": [{"__dict_items__": [["id", "candidate_dbd7_005f_buildbucket_full82_v1"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_784a_6bc3_k8_selective_full82_v1:benchmark_baseline_784a_005f"], ["consumed_seeds", {"__tuple__": ["v20_k12_q1024_q4096_exact", "q2048_k12_4f30_v1", "v12_k20_q2048k10_mixedfanout", "over32_k48_v25"]}], ["guard_plan", {"__tuple__": ["005f exact BF16 build low-floor portfolio for K10/K12/K20/K48 rows", "a444/9db7 full82 Weave fallback for guard misses and Q1536/K10 tail"]}], ["expected_shape_wins", {"__tuple__": ["build_k_sweep_qm1024_k12", "build_k_sweep_qm1024_k20", "build_qm2048_d128_k10", "build_k_sweep_qm2048_k12", "build_k_sweep_qm2048_k20", "build_tail_b1_q1536_m1536_d128_k10", "build_k_sweep_qm4096_k12", "build_k_sweep_qm4096_k20", "build_over32_stress_qm2048_k48", "build_over32_stress_qm4096_k48"]}], ["fallback", "loom.examples.weave.knn_build_dispatch_dbd7_lowfloor_005f_8a78_full82_v1:launch_from_contract_inputs"], ["rejected_reason", "same-session 784a baseline"]]}, {"__dict_items__": [["id", "candidate_784a_direct_q512_k456_q4096_k8_plus_6bc3_k8_v1"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_784a_6bc3_k8_q512k456_q4096k8_direct_fcf2_v1:benchmark_candidate_784a_direct_q512_k456_q4096_k8_plus_6bc3_k8_v1"], ["consumed_seeds", {"__tuple__": ["v20_k12_q1024_q4096_exact", "q2048_k12_4f30_v1", "v12_k20_q2048k10_mixedfanout", "over32_k48_v25", "6bc3_v20_q512_k8_static_s8", "6bc3_v20_q2048_k8_static_s8", "fcf2_direct_lowk_q512_k4_k5_k6_s4", "fcf2_direct_v20_q4096_k8_s8"]}], ["guard_plan", {"__tuple__": ["direct exact BF16 build Q4096 K8 v20 split8 guard", "then direct exact BF16 build Q512 K4/K5/K6 low-K split4 guard", "then fcf2 selective 6bc3 exact Q512/Q2048 K8 guards", "then 784a 005f build-lowfloor portfolio", "then inherited a444/9db7 full82 Weave fallback"]}], ["expected_shape_wins", {"__tuple__": ["build_k_sweep_qm512_k4", "build_k_sweep_qm512_k5", "build_k_sweep_qm512_k6", "build_k_sweep_qm512_k8", "build_qm2048_d128_k8", "build_qm4096_d128_k8"]}], ["fallback", "loom.examples.weave.knn_build_dispatch_784a_6bc3_k8_q512k456_direct_fcf2_v1:launch_from_contract_inputs"], ["rejected_reason", "same-session d5f8 split8 baseline"]]}, {"__dict_items__": [["id", "candidate_784a_direct_q512_k456_q4096_k8_s4_plus_6bc3_k8_v1"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_784a_6bc3_k8_q512k456_q4096k8_s4_direct_c3bf_v1:benchmark_candidate_784a_direct_q512_k456_q4096_k8_s4_plus_6bc3_k8_v1"], ["consumed_seeds", {"__tuple__": ["v20_k12_q1024_q4096_exact", "q2048_k12_4f30_v1", "v12_k20_q2048k10_mixedfanout", "over32_k48_v25", "6bc3_v20_q512_k8_static_s8", "6bc3_v20_q2048_k8_static_s8", "fcf2_direct_lowk_q512_k4_k5_k6_s4", "c3bf_direct_v20_q4096_k8_s4"]}], ["guard_plan", {"__tuple__": ["direct exact BF16 build Q4096 K8 v20 split4 guard", "then direct exact BF16 build Q512 K4/K5/K6 low-K split4 guard", "then fcf2 selective 6bc3 exact Q512/Q2048 K8 guards", "then 784a 005f build-lowfloor portfolio", "then inherited d5f8/a444/9db7 full82 Weave fallback"]}], ["expected_shape_wins", {"__tuple__": ["build_k_sweep_qm512_k4", "build_k_sweep_qm512_k5", "build_k_sweep_qm512_k6", "build_k_sweep_qm512_k8", "build_qm2048_d128_k8", "build_qm4096_d128_k8"]}], ["fallback", "loom.examples.weave.knn_build_dispatch_784a_6bc3_k8_q512k456_q4096k8_direct_fcf2_v1:launch_from_contract_inputs"], ["rejected_reason", null]]}]}')) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False, candidate_key: str=DEFAULT_CANDIDATE_KEY) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark, kernel_fn=_candidate_kernel_fn(candidate_key)) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=None, kernel_fn: Callable[[dict[str, Any]], Any]=candidate, correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + resolved_shape_labels = tuple((str(shape['label']) for shape in eval_mod.CANONICAL_SHAPES)) if shape_labels is None else shape_labels + return parent_direct._run_with_timing_backend(use_cupti=use_cupti, shape_labels=resolved_shape_labels, kernel_fn=kernel_fn, correctness=correctness, time_flashlib=time_flashlib) + +def _timing_backend_name(use_cupti: bool) -> str: + return 'cupti' if use_cupti else 'cuda_event_fallback' + +def _payload_shape_labels(shape_labels) -> str | tuple[str, ...]: + if shape_labels is None: + return 'all_canonical' + return tuple((str(label) for label in shape_labels)) + +def _denominator_name(shape_labels) -> str: + if shape_labels is None: + return ''.join(['full', format(len(eval_mod.CANONICAL_SHAPES), ''), '_v10']) + labels = tuple((str(label) for label in shape_labels)) + if labels == TARGET_SHAPES: + return 'q512k456_q4096k8_s4_plus_6bc3_k8_target_rows' + return ''.join(['shape', format(len(labels), '')]) + +def _denominator_label(shape_labels) -> str: + if shape_labels is None: + return ''.join(['full', format(len(eval_mod.CANONICAL_SHAPES), '')]) + return ''.join(['shape', format(len(tuple(shape_labels)), '')]) + +def _rows_for_labels(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return {label: dict(report.get('per_shape', {}).get(label, {})) for label in labels} + +def _timing_backends_for_reports(*reports: dict[str, Any]) -> list[str]: + return parent_direct._timing_backends_for_reports(*reports) + +def _parent_route_trace_row(label: str) -> dict[str, Any]: + row = dict(parent_direct.route_trace_for_contract_shapes((label,))[0]) + row['parent_direct_route'] = row.get('selected_route') + row['parent_direct_selected_seed'] = row.get('selected_seed') + return _normalize_route_row(row) + +def _guard_condition(seed_id: str | None) -> str: + if seed_id == SEED_Q4096_K8_DIRECT_ID: + return 'exact BF16 build B=1 Q=M=4096 D=128 K=8 v20 split4 route' + return parent_direct._guard_condition(seed_id) + +def _route_trace_record(inputs: dict[str, Any], *, candidate_key: str, force_fallback: bool=False) -> dict[str, Any]: + label = str(inputs.get('label')) + q4096_seed = SEED_Q4096_K8_DIRECT_ID if candidate_key == CANDIDATE_Q4096K8_DIRECT and _eligible_q4096_k8_direct(inputs) else None + parent_row = _parent_route_trace_row(label) + if force_fallback or candidate_key != CANDIDATE_Q4096K8_DIRECT or q4096_seed is None: + if candidate_key == PARENT_DIRECT_KEY: + row = dict(parent_row) + else: + row = dict(parent_direct.route_trace_for_contract_shapes((label,), candidate_key=parent_direct.BASE_784A_KEY if candidate_key == BASE_784A_KEY else parent_direct.DEFAULT_CANDIDATE_KEY, force_fallback=force_fallback)[0]) + row['expected_seed'] = q4096_seed or row.get('expected_seed') + row['parent_direct_route'] = parent_row.get('selected_route') + row['parent_direct_selected_seed'] = parent_row.get('selected_seed') + if force_fallback and q4096_seed is not None: + row['selected_route'] = parent_direct.route_for_contract_inputs(inputs, candidate_key=parent_direct.BASE_784A_KEY, force_fallback=True) + row['selected_entrypoint'] = parent_direct.CANDIDATE_CONFIGS[parent_direct.BASE_784A_KEY]['entrypoint'] + row['guard_id'] = ''.join(['forced_fallback_', format(q4096_seed, ''), '_disabled']) + row['guard_condition'] = ''.join(['forced fallback to 784a baseline; ', format(q4096_seed, ''), ' disabled']) + row['classification'] = 'guard-miss' + return _normalize_route_row(row) + return _normalize_route_row({'shape_key': label, 'selected_route': ROUTE_Q4096_K8_S4, 'selected_entrypoint': ROUTE_Q4096_K8_S4, 'selected_seed': q4096_seed, 'expected_seed': q4096_seed, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': ''.join([format(candidate_key, ''), '_', format(q4096_seed, ''), '_guard']), 'guard_condition': _guard_condition(q4096_seed), 'coverage': 'direct Q4096 K8 v20 split4 route before d5f8 parent', 'consumed_seed': q4096_seed, 'replaced_route': parent_row.get('selected_route'), 'parent_direct_route': parent_row.get('selected_route'), 'parent_direct_selected_seed': parent_row.get('selected_seed'), 'parent_fcf2_route': parent_row.get('parent_fcf2_route'), 'baseline_784a_route': parent_row.get('baseline_784a_route'), 'base_784a_route': parent_row.get('base_784a_route'), 'shape_specific_kernel_ms': None, 'classification': 'unmeasured'}) + +def route_trace_for_contract_shapes(shape_labels=None, *, candidate_key: str=DEFAULT_CANDIDATE_KEY, force_fallback: bool=False) -> list[dict[str, Any]]: + _candidate_config(candidate_key) + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + return [_route_trace_record(_trace_inputs_for_shape(shape), candidate_key=candidate_key, force_fallback=force_fallback) for shape in selected] + +def _annotate_route_trace(route_trace: list[dict[str, Any]], candidate_report: dict[str, Any], parent_report: dict[str, Any], *, candidate_key: str) -> list[dict[str, Any]]: + annotated = [] + for row in route_trace: + label = str(row.get('shape_key')) + out = dict(row) + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + parent_row = parent_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + parent_ms = parent_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') + speedup_vs_parent = parent_ms / candidate_ms if candidate_ms and parent_ms else None + speedup_vs_external = flashlib_ms / candidate_ms if candidate_ms and flashlib_ms else None + out['dispatcher_kernel_ms'] = candidate_ms + out['parent_direct_kernel_ms'] = parent_ms + out['external_baseline_ms'] = flashlib_ms + out['flashlib_ms'] = flashlib_ms + out['relative_speedup_vs_parent_direct'] = speedup_vs_parent + out['speedup_vs_external_baseline'] = speedup_vs_external + out['external_baseline_ref'] = 'same_session' if flashlib_ms is not None else 'not_available' + out['route_changed_vs_parent_direct'] = out.get('selected_route') != out.get('parent_direct_route') + expected_seed = out.get('expected_seed') + if candidate_key == CANDIDATE_Q4096K8_DIRECT and expected_seed == SEED_Q4096_K8_DIRECT_ID: + if out.get('selected_seed') != expected_seed: + out['classification'] = 'guard-miss' + elif speedup_vs_external is not None and speedup_vs_external < 1.05: + out['classification'] = 'kernel-slow' + elif speedup_vs_parent is not None and speedup_vs_parent < 1.0: + out['classification'] = 'kernel-slow' + else: + out['classification'] = 'seed-consumed' + elif speedup_vs_external is not None and speedup_vs_external < 1.05: + out['classification'] = 'kernel-slow' if out.get('route_kind') == 'specialized' else 'fallback-slow' + else: + out['classification'] = 'route-ok' + annotated.append(_normalize_route_row(out)) + return annotated + +def _below_flashlib_rows(report: dict[str, Any], route_trace: list[dict[str, Any]], *, floor: float) -> list[dict[str, Any]]: + trace_by_label = {str(row['shape_key']): row for row in route_trace} + rows = [] + for label, row in sorted(report.get('per_shape', {}).items()): + ratio = row.get('ratio_vs_flashlib') + if isinstance(ratio, (float, int)) and ratio < floor: + trace_row = trace_by_label.get(label, {}) + rows.append({'shape_key': label, 'kernel_ms': row.get('kernel_ms'), 'flashlib_ms': row.get('flashlib_ms'), 'ratio_vs_flashlib': ratio, 'selected_route': trace_row.get('selected_route'), 'selected_seed': trace_row.get('selected_seed'), 'expected_seed': trace_row.get('expected_seed'), 'route_kind': trace_row.get('route_kind', 'unknown'), 'classification': trace_row.get('classification', 'unmeasured')}) + return rows + +def _seed_delta_matrix(candidate_key: str, candidate_report: dict[str, Any], parent_report: dict[str, Any]) -> list[dict[str, Any]]: + matrix = [] + for label in TARGET_SHAPES: + inputs = _inputs_for_label(label) + selected_seed = _selected_direct_seed(inputs) if candidate_key == CANDIDATE_Q4096K8_DIRECT else None + if selected_seed is None: + selected_seed = parent_direct._selected_direct_seed(inputs) + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + parent_row = parent_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + parent_ms = parent_row.get('kernel_ms') + matrix.append({'shape_key': label, 'parent_direct_route': parent_direct.route_for_contract_inputs(inputs), 'candidate_route': route_for_contract_inputs(inputs, candidate_key=candidate_key), 'selected_seed': selected_seed, 'candidate_id': _candidate_id(candidate_key), 'candidate_ms': candidate_ms, 'parent_direct_ms': parent_ms, 'flashlib_ms': candidate_row.get('flashlib_ms'), 'delta_ms_candidate_minus_parent_direct': candidate_ms - parent_ms if candidate_ms and parent_ms else None, 'speedup_vs_parent_direct': parent_ms / candidate_ms if candidate_ms and parent_ms else None, 'ratio_vs_flashlib': candidate_row.get('ratio_vs_flashlib'), 'timing_backend': candidate_row.get('timing_backend') or parent_row.get('timing_backend')}) + return matrix + +def benchmark_parent_direct(*, use_cupti: bool=True, shape_labels=None, benchmark_correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_parent_784a_direct_q512_k456_plus_6bc3_k8, correctness=benchmark_correctness, time_flashlib=time_flashlib) + report['candidate_id'] = PARENT_DIRECT_ID + report['measured_entrypoint'] = parent_direct.CANDIDATE_ENTRYPOINT + report['measured_shape_labels'] = _payload_shape_labels(shape_labels) + report['route_trace'] = route_trace_for_contract_shapes(shape_labels, candidate_key=PARENT_DIRECT_KEY) + report['route_trace_included'] = True + return report + +def _benchmark_payload(candidate_key: str, candidate_report: dict[str, Any], parent_report: dict[str, Any], *, use_cupti: bool, shape_labels, benchmark_correctness: bool, time_flashlib: bool) -> dict[str, Any]: + candidate_metric = candidate_report['summary']['primary_mean'] + parent_metric = parent_report['summary']['primary_mean'] + route_trace = _annotate_route_trace(route_trace_for_contract_shapes(shape_labels, candidate_key=candidate_key), candidate_report, parent_report, candidate_key=candidate_key) + below_1x = _below_flashlib_rows(candidate_report, route_trace, floor=1.0) + below_floor = _below_flashlib_rows(candidate_report, route_trace, floor=1.05) + denominator = _denominator_name(shape_labels) + timing_backend = _timing_backend_name(use_cupti) + config = _candidate_config(candidate_key) + return {'candidate_id': config['candidate_id'], 'candidate_key': candidate_key, 'parent_candidate_id': PARENT_DIRECT_ID, 'selected_seeds': config['selected_seeds'], 'source_tasks': SOURCE_TASKS, 'tflops': candidate_metric, 'parent_direct_tflops': parent_metric, 'metric_delta_vs_parent_direct': candidate_metric - parent_metric if candidate_metric is not None and parent_metric is not None else None, 'all_correct': candidate_report['summary']['all_correct'], 'parent_all_correct': parent_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'parent_performance_comparable': parent_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'parent_invalid_performance_reason': parent_report['summary']['invalid_performance_reason'], 'measured_entrypoint': config['benchmark_entrypoint'], 'parent_entrypoint': parent_direct.CANDIDATE_ENTRYPOINT, 'route_entrypoint': ROUTE_ENTRYPOINT, 'measured_shape_labels': _payload_shape_labels(shape_labels), 'timing_backend': timing_backend, 'denominator': denominator, 'selected_route_labels': config['expected_shape_wins'], 'selected_route_rows': _rows_for_labels(candidate_report, TARGET_SHAPES), 'parent_selected_route_rows': _rows_for_labels(parent_report, TARGET_SHAPES), 'seed_delta_matrix': _seed_delta_matrix(candidate_key, candidate_report, parent_report), 'candidate_dispatchers': CANDIDATE_DISPATCHERS, 'selected_candidate_dispatcher': config['candidate_id'], 'guard_plan': config['guard_plan'], 'route_modules': PRODUCTION_ROUTE_MODULES, 'route_trace': route_trace, 'forced_fallback_route_trace': route_trace_for_contract_shapes(shape_labels, candidate_key=candidate_key, force_fallback=True), 'route_trace_included': True, 'contract_summary': candidate_report['summary'], 'parent_contract_summary': parent_report['summary'], 'contract_performance': candidate_report['performance'], 'parent_contract_performance': parent_report['performance'], 'timing_backends': _timing_backends_for_reports(candidate_report, parent_report), 'timing_backend_requested': timing_backend, 'use_cupti': use_cupti, 'benchmark_correctness_checked': benchmark_correctness, 'benchmark_time_flashlib': time_flashlib, 'performance_coverage': 'partial' if below_floor else 'pass', 'coverage_only_routes': [], 'hot_bucket_blockers': below_floor, 'flashlib_parity_ledger': {'baseline_ref_scope': 'same_session' if time_flashlib else 'not_available', 'speedup_floor': 1.05, 'rows_below_1x': below_1x, 'rows_below_floor': below_floor, 'omitted_reason': None if time_flashlib else 'benchmark_time_flashlib=false'}, 'rank_objective': {'metric': 'tflops', 'direction': 'maximize', 'value': candidate_metric, 'parent_direct_value': parent_metric, 'delta_vs_parent_direct': candidate_metric - parent_metric if candidate_metric is not None and parent_metric is not None else None, 'denominator': denominator}, 'report': candidate_report, 'parent_report': parent_report} + +def benchmark_candidate_portfolio(candidate_key: str, *, use_cupti: bool=True, shape_labels=None, parent_report: dict[str, Any] | None=None, benchmark_correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + if candidate_key == BASE_784A_KEY: + return parent_direct.benchmark_candidate_portfolio(parent_direct.BASE_784A_KEY, use_cupti=use_cupti, shape_labels=shape_labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + if candidate_key == PARENT_DIRECT_KEY: + return benchmark_parent_direct(use_cupti=use_cupti, shape_labels=shape_labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + if parent_report is None: + parent_report = benchmark_parent_direct(use_cupti=use_cupti, shape_labels=shape_labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=_candidate_kernel_fn(candidate_key), correctness=benchmark_correctness, time_flashlib=time_flashlib) + return _benchmark_payload(candidate_key, candidate_report, parent_report, use_cupti=use_cupti, shape_labels=shape_labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + +def benchmark_candidate_784a_direct_q512_k456_q4096_k8_s4_plus_6bc3_k8_v1(**kwargs) -> dict[str, Any]: + return benchmark_candidate_portfolio(CANDIDATE_Q4096K8_DIRECT, **kwargs) + +def write_benchmark_artifacts(artifact_dir: str | Path, *, use_cupti: bool=True, shape_labels=None, benchmark_correctness: bool=True, time_flashlib: bool=True) -> dict[str, str]: + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + denom_label = _denominator_label(shape_labels) + parent_report = benchmark_parent_direct(use_cupti=use_cupti, shape_labels=shape_labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + payload = benchmark_candidate_portfolio(CANDIDATE_Q4096K8_DIRECT, use_cupti=use_cupti, shape_labels=shape_labels, parent_report=parent_report, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + artifacts: dict[str, str] = {} + parent_path = out_dir / ''.join([format(denom_label, ''), '_same_session_parent_q512k456_direct_for_q4096k8_v1.json']) + payload_path = out_dir / ''.join([format(denom_label, ''), '_dispatch_784a_direct_q512k456_q4096k8_s4_plus_6bc3_k8_v1.json']) + trace_path = out_dir / ''.join([format(denom_label, ''), '_route_trace_784a_direct_q512k456_q4096k8_s4_plus_6bc3_k8_v1.json']) + forced_trace_path = out_dir / ''.join([format(denom_label, ''), '_forced_fallback_trace_784a_direct_q512k456_q4096k8_s4_plus_6bc3_k8_v1.json']) + seed_matrix_path = out_dir / ''.join([format(denom_label, ''), '_seed_delta_matrix_784a_direct_q512k456_q4096k8_s4_plus_6bc3_k8_v1.json']) + parent_path.write_text(json.dumps(parent_report, indent=2, sort_keys=True) + '\n', encoding='utf-8') + payload_path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n', encoding='utf-8') + trace_path.write_text(json.dumps(payload['route_trace'], indent=2, sort_keys=True) + '\n', encoding='utf-8') + forced_trace_path.write_text(json.dumps(payload['forced_fallback_route_trace'], indent=2, sort_keys=True) + '\n', encoding='utf-8') + seed_matrix_path.write_text(json.dumps(payload['seed_delta_matrix'], indent=2, sort_keys=True) + '\n', encoding='utf-8') + artifacts['same_session_parent_direct_payload'] = str(parent_path) + artifacts[''.join([format(CANDIDATE_Q4096K8_DIRECT, ''), '_payload'])] = str(payload_path) + artifacts[''.join([format(CANDIDATE_Q4096K8_DIRECT, ''), '_route_trace'])] = str(trace_path) + artifacts[''.join([format(CANDIDATE_Q4096K8_DIRECT, ''), '_forced_fallback_trace'])] = str(forced_trace_path) + artifacts[''.join([format(CANDIDATE_Q4096K8_DIRECT, ''), '_seed_delta_matrix'])] = str(seed_matrix_path) + summary = {'candidate_id': 'dispatcher_consumption_784a_q512k456_q4096k8_s4_direct_plus_6bc3_k8_v1', 'measured_entrypoint': ''.join([format(MODULE, ''), ':write_benchmark_artifacts']), 'denominator': payload['denominator'], 'timing_backend': payload['timing_backend'], 'benchmark_correctness_checked': benchmark_correctness, 'benchmark_time_flashlib': time_flashlib, 'parent_candidate_key': PARENT_DIRECT_KEY, 'selected_candidate_key': CANDIDATE_Q4096K8_DIRECT, 'selected_candidate_dispatcher': CANDIDATE_ID, 'candidate_dispatchers': CANDIDATE_DISPATCHERS, 'candidate_rankings': [{'candidate_key': PARENT_DIRECT_KEY, 'candidate_id': PARENT_DIRECT_ID, 'tflops': parent_report['summary']['primary_mean'], 'all_correct': parent_report['summary']['all_correct'], 'performance_comparable': parent_report['summary']['performance_comparable']}, {'candidate_key': CANDIDATE_Q4096K8_DIRECT, 'candidate_id': CANDIDATE_ID, 'tflops': payload['tflops'], 'metric_delta_vs_parent_direct': payload['metric_delta_vs_parent_direct'], 'all_correct': payload['all_correct'], 'performance_comparable': payload['performance_comparable'], 'performance_coverage': payload['performance_coverage']}], 'seed_delta_matrix': payload['seed_delta_matrix'], 'flashlib_parity_ledger': payload['flashlib_parity_ledger'], 'artifacts': artifacts} + summary_path = out_dir / ''.join([format(denom_label, ''), '_dispatcher_consumption_784a_q512k456_q4096k8_s4_direct_plus_6bc3_k8_v1.json']) + summary_path.write_text(json.dumps(summary, indent=2, sort_keys=True) + '\n', encoding='utf-8') + artifacts['dispatcher_consumption'] = str(summary_path) + return artifacts diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_784a_6bc3_k8_selective_full82_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_784a_6bc3_k8_selective_full82_v1.py new file mode 100644 index 00000000..f2fe3540 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_784a_6bc3_k8_selective_full82_v1.py @@ -0,0 +1,352 @@ +"""Selective full82 dispatcher consumption of the 6bc3 K8 build seed. + +Minimum target architecture: sm_100a. This dispatcher-consumption wrapper +preserves the 784a full82 champion as the baseline and consumes only the two +rank-selected 6bc3 residual BF16 build K8 routes: +``build_k_sweep_qm512_k8`` and ``build_qm2048_d128_k8``. It does not replay the +broader 0ee0 all-seed portfolio and does not modify any seed schedule. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +from pathlib import Path +from typing import Any, Callable +from . import knn_build_buildbucket_residual_lowk_6bc3_v1 as seed6bc3 +from . import knn_build_dispatch_dbd7_lowfloor_005f_8a78_full82_v1 as base784a +MODULE = 'loom.examples.weave.knn_build_dispatch_784a_6bc3_k8_selective_full82_v1' +eval_mod = base784a.eval_mod +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbatch_m64_d4f7_stage1", "arg_keys": ["query", "database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 91392, "constants": [], "cta_group": 1, "threads": 512}')) +BASE_784A_KEY = 'base_784a_005f' +CANDIDATE_6BC3_K8 = '784a_plus_6bc3_k8_selective' +DEFAULT_CANDIDATE_KEY = CANDIDATE_6BC3_K8 +CANDIDATE_KEYS = (BASE_784A_KEY, CANDIDATE_6BC3_K8) +BASE_784A_ID = base784a.CANDIDATE_CONFIGS[base784a.DEFAULT_CANDIDATE_KEY]['candidate_id'] +BASE_784A_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_baseline_784a_005f']) +BASE_784A_ROUTE_ENTRYPOINT = ''.join([format(base784a.MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +CANDIDATE_6BC3_K8_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_candidate_784a_plus_6bc3_k8_selective_full82_v1']) +TARGET_SHAPES = (seed6bc3.Q512_K8, seed6bc3.Q2048_K8) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +SEED_6BC3_K8_IDS = (seed6bc3.SEED_Q512_K8_ID, seed6bc3.SEED_Q2048_K8_ID) +V10_FRONTIER_EXTRA_LABELS = ('build_large_b1_q6144_m6144_d128_k10', 'build_qm1024_d128_k8', 'build_qm4096_d128_k8', 'rag_microbatch_largek_b1_q16_m250000_d128_k32', 'rag_microbatch_largek_b1_q24_m100000_d128_k32', 'rag_online_b1_q1_m65536_d128_k10', 'rag_online_irregular_b1_q1_m524287_d128_k10', 'rag_stream_largek_b1_q128_m131071_d128_k32') +V11_COMMON_D_FRONTIER_EXTRA_LABELS = ('build_common_d1024_b1_q512_m512_k10', 'build_common_d256_b1_q1024_m1024_k10', 'build_common_d4096_b1_q512_m512_k10', 'build_common_d768_b1_q1024_m1024_k10', 'rag_microbatch_common_d1024_b1_q8_m50000_k10', 'rag_microbatch_common_d256_b1_q16_m50000_k10', 'rag_microbatch_common_d4096_b1_q4_m32768_k10', 'rag_microbatch_common_d64_b1_q16_m50000_k10', 'rag_microbatch_highd_b1_q16_m50000_d768_k10', 'search_rect_common_d256_b1_q1024_m32768_k10') +V12_COMMON_D_TAIL_FRONTIER_EXTRA_LABELS = ('rag_online_common_d64_b1_q1_m262143_k10', 'rag_microbatch_common_d64_b1_q4_m100000_k10', 'rag_microbatch_common_d256_b1_q4_m100000_k10', 'rag_stream_common_d256_b1_q128_m100000_k10', 'rag_microbatch_common_d768_b1_q8_m100000_k10', 'rag_microbatch_common_d1024_b1_q4_m100000_k10', 'rag_online_common_d4096_b1_q1_m65536_k10', 'search_rect_common_d1024_b1_q256_m8192_k10', 'search_rect_common_d4096_b1_q128_m4096_k10', 'rag_microbatch_largek_common_d256_b1_q8_m100000_k32', 'rag_stream_largek_common_d256_b1_q128_m100000_k32', 'rag_microbatch_over32_d128_b1_q16_m100000_k48') +FULL82_EXCLUDED_FRONTIER_LABELS = set(V10_FRONTIER_EXTRA_LABELS + V11_COMMON_D_FRONTIER_EXTRA_LABELS + V12_COMMON_D_TAIL_FRONTIER_EXTRA_LABELS) +FULL82_V9_LABELS = _decode_capture(_json_loads('{"__tuple__": ["flashml_correctness_b1_q256_m256_d128_k5", "build_k_sweep_qm512_k1", "build_k_sweep_qm512_k2", "build_k_sweep_qm512_k4", "build_k_sweep_qm512_k5", "build_k_sweep_qm512_k6", "build_k_sweep_qm512_k8", "build_k_sweep_qm512_k10", "build_qm1024_d128_k10", "build_k_sweep_qm1024_k16", "build_k_sweep_qm1024_k12", "build_k_sweep_qm1024_k20", "build_qm2048_d128_k8", "build_qm2048_d128_k10", "build_dim_sweep_b1_q1024_m1024_d64_k10", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q4096_m4096_d64_k10", "build_dim_sweep_b1_q1024_m1024_d96_k10", "build_dim_sweep_b1_q2048_m2048_d192_k10", "build_dim_sweep_b1_q2048_m2048_d256_k10", "build_highd_b1_q1024_m1024_d320_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10", "build_batch_b2_q1024_m1024_d128_k10", "build_k_sweep_qm2048_k11", "build_k_sweep_qm2048_k12", "build_k_sweep_qm2048_k13", "build_k_sweep_qm2048_k20", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_tail_b1_q1536_m1536_d128_k10", "build_tail_b1_q3072_m3072_d128_k20", "build_medium_b1_q4096_m4096_d128_k10", "build_k_sweep_qm4096_k12", "build_k_sweep_qm4096_k13", "build_k_sweep_qm4096_k20", "build_k_sweep_qm4096_k24", "build_k_sweep_qm4096_k28", "build_largek_stress_qm4096_k32", "build_k_sweep_qm4096_k30", "build_over32_stress_qm2048_k48", "build_over32_stress_qm2048_k64", "build_over32_stress_qm4096_k48", "build_large_b1_q8192_m8192_d128_k10", "build_large_b1_q8192_m8192_d128_k20", "build_large_b1_q8192_m8192_d128_k32", "build_verylarge_b1_q12288_m12288_d128_k10", "rag_offline_b1_q4096_m100000_d128_k10", "search_rect_b1_q1024_m8192_d128_k10", "search_rect_b1_q1024_m32768_d64_k10", "search_rect_highd_b1_q512_m12000_d320_k10", "search_rect_common_d768_b1_q512_m8192_k10", "search_rect_b1_q4096_m65536_d128_k20", "search_rect_b1_q1536_m65536_d128_k20", "search_rect_over32_b1_q2048_m65536_d128_k64", "rag_online_b1_q1_m100000_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "rag_online_irregular_b1_q1_m262143_d128_k10", "rag_stream_b1_q128_m100000_d128_k10", "rag_offline_largek_b1_q4096_m100000_d128_k20", "rag_offline_large_m_b1_q8192_m250000_d128_k20", "rag_offline_large_m_over32_b1_q2048_m250000_d128_k64", "rag_offline_batch_b1_q10000_m100000_d128_k10", "rag_offline_b1_q10000_m50000_d128_k10", "rag_microbatch_b1_q4_m100000_d128_k10", "rag_microbatch_b1_q8_m100000_d128_k10", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_microbatch_b1_q32_m100000_d128_k10", "rag_microbatch_largek_b1_q8_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m100000_d128_k32", "rag_microbatch_largek_b1_q32_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m131071_d128_k32", "rag_microbatch_b1_q64_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32", "rag_batch_b2_q256_m50000_d128_k10", "rag_irregular_b1_q512_m131071_d128_k10", "search_rect_b1_q2048_m32768_d128_k10", "build_large_tail_b1_q6144_m6144_d128_k20", "build_over32_stress_qm4096_k64", "build_over64_stress_qm1024_k96", "build_over64_stress_qm2048_k96", "build_over64_stress_qm4096_k96"]}')) +if len(FULL82_V9_LABELS) != 82: + raise RuntimeError(''.join(['expected 82 full82_v9 labels, found ', format(len(FULL82_V9_LABELS), '')])) +PRODUCTION_ROUTE_MODULES = {**base784a.PRODUCTION_ROUTE_MODULES, **seed6bc3.PRODUCTION_ROUTE_MODULES, BASE_784A_ID: BASE_784A_ROUTE_ENTRYPOINT, seed6bc3.CANDIDATE_ID: ''.join([format(seed6bc3.MODULE, ''), ':launch_from_contract_inputs'])} +SOURCE_TASKS = {**base784a.SOURCE_TASKS, **seed6bc3.SOURCE_TASKS, seed6bc3.CANDIDATE_ID: 'generalize-auto-tuning-knn-build-0ee0 read-ref / loom/examples/weave/knn_build_buildbucket_residual_lowk_6bc3_v1.py'} + +def _select_contract_shapes(shape_labels): + return base784a._select_contract_shapes(shape_labels) + +def _default_full82_shape_labels(shape_labels): + return FULL82_V9_LABELS if shape_labels is None else shape_labels + +def _trace_inputs_for_shape(shape: dict[str, Any]) -> dict[str, Any]: + return base784a._trace_inputs_for_shape(shape) + +def _normalize_route_row(row: dict[str, Any]) -> dict[str, Any]: + return base784a._normalize_route_row(row) + +def _inputs_for_label(label: str) -> dict[str, Any]: + return _trace_inputs_for_shape(_select_contract_shapes((label,))[0]) + +def _candidate_config(candidate_key: str) -> dict[str, Any]: + try: + return CANDIDATE_CONFIGS[candidate_key] + except KeyError as exc: + raise ValueError(''.join(['unknown 784a/6bc3 K8 selective candidate ', format(repr(candidate_key), '')])) from exc + +def _candidate_id(candidate_key: str | None) -> str | None: + if candidate_key is None: + return None + return str(_candidate_config(candidate_key)['candidate_id']) + +def _selected_6bc3_k8_seed(inputs: dict[str, Any]) -> str | None: + selected_seed = seed6bc3._selected_seed_for_inputs(inputs) + if selected_seed in SEED_6BC3_K8_IDS: + return selected_seed + return None + +def _base_784a_route(inputs: dict[str, Any]) -> str: + return base784a.route_for_contract_inputs(inputs, candidate_key=base784a.DEFAULT_CANDIDATE_KEY, force_fallback=False) + +def _base_784a_launch(inputs: dict[str, Any]) -> None: + base784a.launch_from_contract_inputs(inputs, candidate_key=base784a.DEFAULT_CANDIDATE_KEY, force_fallback=False) + +def _selected_entrypoint(seed_id: str | None) -> str: + if seed_id in seed6bc3.PRODUCTION_ROUTE_MODULES: + return seed6bc3.PRODUCTION_ROUTE_MODULES[str(seed_id)] + return BASE_784A_ROUTE_ENTRYPOINT + +def route_for_contract_inputs(inputs: dict[str, Any], *, candidate_key: str=DEFAULT_CANDIDATE_KEY, force_fallback: bool=False) -> str: + _candidate_config(candidate_key) + if force_fallback or candidate_key == BASE_784A_KEY: + return _base_784a_route(inputs) + selected_seed = _selected_6bc3_k8_seed(inputs) + if selected_seed in seed6bc3.PRODUCTION_ROUTE_MODULES: + return seed6bc3.route_for_contract_inputs(inputs) + return _base_784a_route(inputs) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, candidate_key: str=DEFAULT_CANDIDATE_KEY, force_fallback: bool=False) -> None: + _candidate_config(candidate_key) + if force_fallback or candidate_key == BASE_784A_KEY: + _base_784a_launch(inputs) + return + selected_seed = _selected_6bc3_k8_seed(inputs) + if selected_seed in seed6bc3.PRODUCTION_ROUTE_MODULES: + seed6bc3.launch_from_contract_inputs(inputs) + return + _base_784a_launch(inputs) + +def candidate_baseline_784a_005f(inputs: dict[str, Any]) -> None: + _base_784a_launch(inputs) + +def candidate_784a_plus_6bc3_k8_selective_full82_v1(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, candidate_key=CANDIDATE_6BC3_K8) + +def candidate(inputs: dict[str, Any]) -> None: + candidate_784a_plus_6bc3_k8_selective_full82_v1(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, candidate_key=DEFAULT_CANDIDATE_KEY, force_fallback=True) + +def _candidate_kernel_fn(candidate_key: str) -> Callable[[dict[str, Any]], None]: + _candidate_config(candidate_key) + if candidate_key == BASE_784A_KEY: + return candidate_baseline_784a_005f + if candidate_key == CANDIDATE_6BC3_K8: + return candidate_784a_plus_6bc3_k8_selective_full82_v1 + raise ValueError(''.join(['unknown 784a/6bc3 K8 selective candidate ', format(repr(candidate_key), '')])) +_BASE_SELECTED_SEEDS = _decode_capture(_json_loads('{"__tuple__": ["v20_k12_q1024_q4096_exact", "q2048_k12_4f30_v1", "v12_k20_q2048k10_mixedfanout", "over32_k48_v25"]}')) +CANDIDATE_CONFIGS = _decode_capture(_json_loads('{"__dict_items__": [["base_784a_005f", {"__dict_items__": [["candidate_id", "candidate_dbd7_005f_buildbucket_full82_v1"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_dbd7_lowfloor_005f_8a78_full82_v1:launch_from_contract_inputs"], ["benchmark_entrypoint", "loom.examples.weave.knn_build_dispatch_784a_6bc3_k8_selective_full82_v1:benchmark_baseline_784a_005f"], ["selected_seeds", {"__tuple__": ["v20_k12_q1024_q4096_exact", "q2048_k12_4f30_v1", "v12_k20_q2048k10_mixedfanout", "over32_k48_v25"]}], ["guard_plan", {"__tuple__": ["005f exact BF16 build low-floor portfolio for K10/K12/K20/K48 rows", "a444/9db7 full82 Weave fallback for guard misses and Q1536/K10 tail"]}], ["expected_shape_wins", {"__tuple__": ["build_k_sweep_qm1024_k12", "build_k_sweep_qm1024_k20", "build_qm2048_d128_k10", "build_k_sweep_qm2048_k12", "build_k_sweep_qm2048_k20", "build_tail_b1_q1536_m1536_d128_k10", "build_k_sweep_qm4096_k12", "build_k_sweep_qm4096_k20", "build_over32_stress_qm2048_k48", "build_over32_stress_qm4096_k48"]}], ["fallback", "loom.examples.weave.knn_build_dispatch_17b8_lowmargin_1074_full82_v1:launch_from_contract_inputs"], ["rejected_reason", "same-session 784a baseline"]]}], ["784a_plus_6bc3_k8_selective", {"__dict_items__": [["candidate_id", "candidate_784a_plus_6bc3_k8_selective_full82_v1"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_784a_6bc3_k8_selective_full82_v1:candidate_784a_plus_6bc3_k8_selective_full82_v1"], ["benchmark_entrypoint", "loom.examples.weave.knn_build_dispatch_784a_6bc3_k8_selective_full82_v1:benchmark_candidate_784a_plus_6bc3_k8_selective_full82_v1"], ["selected_seeds", {"__tuple__": ["v20_k12_q1024_q4096_exact", "q2048_k12_4f30_v1", "v12_k20_q2048k10_mixedfanout", "over32_k48_v25", "6bc3_v20_q512_k8_static_s8", "6bc3_v20_q2048_k8_static_s8"]}], ["guard_plan", {"__tuple__": ["6bc3 exact BF16 build Q512/K8 and Q2048/K8 guards only", "then 784a 005f build-lowfloor portfolio", "then inherited a444/9db7 full82 Weave fallback"]}], ["expected_shape_wins", {"__tuple__": ["build_k_sweep_qm512_k8", "build_qm2048_d128_k8"]}], ["fallback", "loom.examples.weave.knn_build_dispatch_dbd7_lowfloor_005f_8a78_full82_v1:launch_from_contract_inputs"], ["rejected_reason", null]]}]]}')) +CANDIDATE_DISPATCHERS = _decode_capture(_json_loads('{"__tuple__": [{"__dict_items__": [["id", "candidate_dbd7_005f_buildbucket_full82_v1"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_784a_6bc3_k8_selective_full82_v1:benchmark_baseline_784a_005f"], ["consumed_seeds", {"__tuple__": ["v20_k12_q1024_q4096_exact", "q2048_k12_4f30_v1", "v12_k20_q2048k10_mixedfanout", "over32_k48_v25"]}], ["guard_plan", {"__tuple__": ["005f exact BF16 build low-floor portfolio for K10/K12/K20/K48 rows", "a444/9db7 full82 Weave fallback for guard misses and Q1536/K10 tail"]}], ["expected_shape_wins", {"__tuple__": ["build_k_sweep_qm1024_k12", "build_k_sweep_qm1024_k20", "build_qm2048_d128_k10", "build_k_sweep_qm2048_k12", "build_k_sweep_qm2048_k20", "build_tail_b1_q1536_m1536_d128_k10", "build_k_sweep_qm4096_k12", "build_k_sweep_qm4096_k20", "build_over32_stress_qm2048_k48", "build_over32_stress_qm4096_k48"]}], ["fallback", "loom.examples.weave.knn_build_dispatch_17b8_lowmargin_1074_full82_v1:launch_from_contract_inputs"], ["rejected_reason", "same-session 784a baseline"]]}, {"__dict_items__": [["id", "candidate_784a_plus_6bc3_k8_selective_full82_v1"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_784a_6bc3_k8_selective_full82_v1:benchmark_candidate_784a_plus_6bc3_k8_selective_full82_v1"], ["consumed_seeds", {"__tuple__": ["v20_k12_q1024_q4096_exact", "q2048_k12_4f30_v1", "v12_k20_q2048k10_mixedfanout", "over32_k48_v25", "6bc3_v20_q512_k8_static_s8", "6bc3_v20_q2048_k8_static_s8"]}], ["guard_plan", {"__tuple__": ["6bc3 exact BF16 build Q512/K8 and Q2048/K8 guards only", "then 784a 005f build-lowfloor portfolio", "then inherited a444/9db7 full82 Weave fallback"]}], ["expected_shape_wins", {"__tuple__": ["build_k_sweep_qm512_k8", "build_qm2048_d128_k8"]}], ["fallback", "loom.examples.weave.knn_build_dispatch_dbd7_lowfloor_005f_8a78_full82_v1:launch_from_contract_inputs"], ["rejected_reason", null]]}]}')) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False, candidate_key: str=DEFAULT_CANDIDATE_KEY) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark, kernel_fn=_candidate_kernel_fn(candidate_key)) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=None, kernel_fn: Callable[[dict[str, Any]], Any]=candidate, correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + return base784a._run_with_timing_backend(use_cupti=use_cupti, shape_labels=_default_full82_shape_labels(shape_labels), kernel_fn=kernel_fn, correctness=correctness, time_flashlib=time_flashlib) + +def _base_route_trace_row(label: str) -> dict[str, Any]: + row = dict(base784a.route_trace_for_contract_shapes((label,), candidate_key=base784a.DEFAULT_CANDIDATE_KEY, force_fallback=False)[0]) + row['base_784a_route'] = row.get('selected_route') + row['base_784a_selected_seed'] = row.get('selected_seed') + row['baseline_dispatcher_route'] = row.get('selected_route') + return _normalize_route_row(row) + +def _guard_condition(seed_id: str | None) -> str: + if seed_id == seed6bc3.SEED_Q512_K8_ID: + return 'exact BF16 build B=1 Q=M=512 D=128 K=8 using 6bc3 static split route' + if seed_id == seed6bc3.SEED_Q2048_K8_ID: + return 'exact BF16 build B=1 Q=M=2048 D=128 K=8 using 6bc3 static split route' + return 'delegate to 784a full82 Weave dispatcher' + +def _route_trace_record(inputs: dict[str, Any], *, candidate_key: str, force_fallback: bool=False) -> dict[str, Any]: + label = str(inputs.get('label')) + expected_seed = _selected_6bc3_k8_seed(inputs) if candidate_key != BASE_784A_KEY else None + base_row = _base_route_trace_row(label) + if force_fallback or candidate_key == BASE_784A_KEY or expected_seed is None: + row = dict(base_row) + row['expected_seed'] = expected_seed + row['baseline_784a_route'] = base_row.get('selected_route') + if force_fallback and expected_seed is not None: + row['selected_route'] = _base_784a_route(inputs) + row['selected_entrypoint'] = BASE_784A_ROUTE_ENTRYPOINT + row['guard_id'] = ''.join(['forced_fallback_', format(expected_seed, ''), '_disabled']) + row['guard_condition'] = ''.join(['forced fallback to 784a baseline; ', format(expected_seed, ''), ' disabled']) + row['classification'] = 'guard-miss' + return _normalize_route_row(row) + selected_route = route_for_contract_inputs(inputs, candidate_key=candidate_key) + return _normalize_route_row({'shape_key': label, 'selected_route': selected_route, 'selected_entrypoint': _selected_entrypoint(expected_seed), 'selected_seed': expected_seed, 'expected_seed': expected_seed, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': ''.join([format(candidate_key, ''), '_', format(expected_seed, ''), '_guard']), 'guard_condition': _guard_condition(expected_seed), 'coverage': 'selective 6bc3 K8 seed route before 784a baseline', 'consumed_seed': expected_seed, 'replaced_route': base_row.get('selected_route'), 'baseline_784a_route': base_row.get('selected_route'), 'baseline_dispatcher_route': base_row.get('selected_route'), 'base_784a_selected_seed': base_row.get('selected_seed'), 'shape_specific_kernel_ms': None, 'classification': 'unmeasured'}) + +def route_trace_for_contract_shapes(shape_labels=None, *, candidate_key: str=DEFAULT_CANDIDATE_KEY, force_fallback: bool=False) -> list[dict[str, Any]]: + _candidate_config(candidate_key) + selected = _select_contract_shapes(_default_full82_shape_labels(shape_labels)) + return [_route_trace_record(_trace_inputs_for_shape(shape), candidate_key=candidate_key, force_fallback=force_fallback) for shape in selected] + +def _timing_backend_name(use_cupti: bool) -> str: + return 'cupti' if use_cupti else 'cuda_event_fallback' + +def _payload_shape_labels(shape_labels) -> str | tuple[str, ...]: + if shape_labels is None: + return 'full82_v9' + return tuple((str(label) for label in shape_labels)) + +def _denominator_name(shape_labels) -> str: + if shape_labels is None: + return 'full82_v9' + labels = tuple((str(label) for label in shape_labels)) + if labels == TARGET_SHAPES: + return 'build_k8_6bc3_target_rows' + return ''.join(['shape', format(len(labels), '')]) + +def _denominator_label(shape_labels) -> str: + if shape_labels is None: + return 'full82' + return ''.join(['shape', format(len(tuple(shape_labels)), '')]) + +def _rows_for_labels(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return {label: dict(report.get('per_shape', {}).get(label, {})) for label in labels} + +def _timing_backends_for_reports(*reports: dict[str, Any]) -> list[str]: + return base784a._timing_backends_for_reports(*reports) + +def _annotate_route_trace(route_trace: list[dict[str, Any]], candidate_report: dict[str, Any], baseline_report: dict[str, Any], *, candidate_key: str) -> list[dict[str, Any]]: + annotated = [] + for row in route_trace: + label = str(row.get('shape_key')) + out = dict(row) + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') + speedup_vs_baseline = baseline_ms / candidate_ms if candidate_ms and baseline_ms else None + speedup_vs_external = flashlib_ms / candidate_ms if candidate_ms and flashlib_ms else None + out['dispatcher_kernel_ms'] = candidate_ms + out['baseline_784a_kernel_ms'] = baseline_ms + out['external_baseline_ms'] = flashlib_ms + out['flashlib_ms'] = flashlib_ms + out['relative_speedup_vs_baseline'] = speedup_vs_baseline + out['relative_speedup_vs_784a'] = speedup_vs_baseline + out['speedup_vs_external_baseline'] = speedup_vs_external + out['external_baseline_ref'] = 'same_session' if flashlib_ms is not None else 'not_available' + out['route_changed_vs_784a'] = out.get('selected_route') != out.get('baseline_784a_route') + expected_seed = out.get('expected_seed') + if candidate_key != BASE_784A_KEY and expected_seed is not None: + if out.get('selected_seed') != expected_seed: + out['classification'] = 'guard-miss' + elif speedup_vs_external is not None and speedup_vs_external < 1.05: + out['classification'] = 'kernel-slow' + elif speedup_vs_baseline is not None and speedup_vs_baseline < 1.0: + out['classification'] = 'kernel-slow' + else: + out['classification'] = 'seed-consumed' + elif speedup_vs_external is not None and speedup_vs_external < 1.05: + out['classification'] = 'kernel-slow' if out.get('route_kind') == 'specialized' else 'fallback-slow' + else: + out['classification'] = 'route-ok' + annotated.append(_normalize_route_row(out)) + return annotated + +def _below_flashlib_rows(report: dict[str, Any], route_trace: list[dict[str, Any]], *, floor: float) -> list[dict[str, Any]]: + trace_by_label = {str(row['shape_key']): row for row in route_trace} + rows = [] + for label, row in sorted(report.get('per_shape', {}).items()): + ratio = row.get('ratio_vs_flashlib') + if isinstance(ratio, (float, int)) and ratio < floor: + trace_row = trace_by_label.get(label, {}) + rows.append({'shape_key': label, 'kernel_ms': row.get('kernel_ms'), 'flashlib_ms': row.get('flashlib_ms'), 'ratio_vs_flashlib': ratio, 'selected_route': trace_row.get('selected_route'), 'selected_seed': trace_row.get('selected_seed'), 'expected_seed': trace_row.get('expected_seed'), 'route_kind': trace_row.get('route_kind', 'unknown'), 'classification': trace_row.get('classification', 'unmeasured')}) + return rows + +def _targeted_seed_row(seed_id: str | None, label: str) -> dict[str, Any]: + if seed_id in SEED_6BC3_K8_IDS: + return {'source': '6bc3 selected via full-dispatch measurement', 'shape_key': label} + return {} + +def _seed_delta_matrix(candidate_key: str, candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> list[dict[str, Any]]: + matrix = [] + for label in TARGET_SHAPES: + inputs = _inputs_for_label(label) + selected_seed = _selected_6bc3_k8_seed(inputs) if candidate_key != BASE_784A_KEY else None + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + matrix.append({'shape_key': label, 'baseline_route': _base_784a_route(inputs), 'candidate_route': route_for_contract_inputs(inputs, candidate_key=candidate_key), 'selected_seed': selected_seed, 'candidate_id': _candidate_id(candidate_key), 'candidate_ms': candidate_ms, 'baseline_784a_ms': baseline_ms, 'flashlib_ms': candidate_row.get('flashlib_ms'), 'delta_ms_candidate_minus_784a': candidate_ms - baseline_ms if candidate_ms and baseline_ms else None, 'speedup_vs_784a': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'ratio_vs_flashlib': candidate_row.get('ratio_vs_flashlib'), 'targeted_seed_row': _targeted_seed_row(selected_seed, label), 'timing_backend': candidate_row.get('timing_backend') or baseline_row.get('timing_backend')}) + return matrix + +def _baseline_sidecar(report: dict[str, Any], *, denominator: str, timing_backend: str, benchmark_correctness: bool, time_flashlib: bool) -> dict[str, Any]: + return {'candidate_id': BASE_784A_ID, 'candidate_key': BASE_784A_KEY, 'measured_entrypoint': BASE_784A_ENTRYPOINT, 'route_entrypoint': BASE_784A_ROUTE_ENTRYPOINT, 'measured_shape_labels': 'all_canonical' if report.get('measured_shape_labels') == 'all_canonical' else report.get('measured_shape_labels', 'all_canonical'), 'timing_backend': timing_backend, 'denominator': denominator, 'benchmark_correctness_checked': benchmark_correctness, 'benchmark_time_flashlib': time_flashlib, 'tflops': report['summary']['primary_mean'], 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'contract_summary': report['summary'], 'contract_performance': report['performance'], 'route_trace': route_trace_for_contract_shapes(None, candidate_key=BASE_784A_KEY), 'route_trace_included': True, 'rank_objective': {'metric': 'tflops', 'direction': 'maximize', 'value': report['summary']['primary_mean'], 'denominator': denominator}, 'report': report} + +def benchmark_baseline_784a_005f(*, use_cupti: bool=True, shape_labels=None, benchmark_correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_baseline_784a_005f, correctness=benchmark_correctness, time_flashlib=time_flashlib) + report['candidate_id'] = BASE_784A_ID + report['measured_entrypoint'] = BASE_784A_ENTRYPOINT + report['measured_shape_labels'] = _payload_shape_labels(shape_labels) + report['route_trace'] = route_trace_for_contract_shapes(shape_labels, candidate_key=BASE_784A_KEY) + report['route_trace_included'] = True + return report + +def _benchmark_payload(candidate_key: str, candidate_report: dict[str, Any], baseline_report: dict[str, Any], *, use_cupti: bool, shape_labels, benchmark_correctness: bool, time_flashlib: bool) -> dict[str, Any]: + candidate_metric = candidate_report['summary']['primary_mean'] + baseline_metric = baseline_report['summary']['primary_mean'] + route_trace = _annotate_route_trace(route_trace_for_contract_shapes(shape_labels, candidate_key=candidate_key), candidate_report, baseline_report, candidate_key=candidate_key) + below_1x = _below_flashlib_rows(candidate_report, route_trace, floor=1.0) + below_floor = _below_flashlib_rows(candidate_report, route_trace, floor=1.05) + denominator = _denominator_name(shape_labels) + timing_backend = _timing_backend_name(use_cupti) + config = _candidate_config(candidate_key) + return {'candidate_id': config['candidate_id'], 'candidate_key': candidate_key, 'baseline_candidate_id': BASE_784A_ID, 'selected_seeds': config['selected_seeds'], 'source_tasks': SOURCE_TASKS, 'tflops': candidate_metric, 'baseline_784a_tflops': baseline_metric, 'metric_delta_vs_784a': candidate_metric - baseline_metric if candidate_metric is not None and baseline_metric is not None else None, 'all_correct': candidate_report['summary']['all_correct'], 'baseline_all_correct': baseline_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'baseline_performance_comparable': baseline_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'baseline_invalid_performance_reason': baseline_report['summary']['invalid_performance_reason'], 'measured_entrypoint': config['benchmark_entrypoint'], 'baseline_entrypoint': BASE_784A_ENTRYPOINT, 'route_entrypoint': ROUTE_ENTRYPOINT, 'measured_shape_labels': _payload_shape_labels(shape_labels), 'timing_backend': timing_backend, 'denominator': denominator, 'selected_route_labels': config['expected_shape_wins'], 'selected_route_rows': _rows_for_labels(candidate_report, TARGET_SHAPES), 'baseline_selected_route_rows': _rows_for_labels(baseline_report, TARGET_SHAPES), 'seed_delta_matrix': _seed_delta_matrix(candidate_key, candidate_report, baseline_report), 'targeted_seed_rows': {seed6bc3.CANDIDATE_ID: '6bc3 K8 rows measured inside this dispatcher payload'}, 'candidate_dispatchers': CANDIDATE_DISPATCHERS, 'selected_candidate_dispatcher': config['candidate_id'], 'guard_plan': config['guard_plan'], 'route_modules': PRODUCTION_ROUTE_MODULES, 'route_trace': route_trace, 'forced_fallback_route_trace': route_trace_for_contract_shapes(shape_labels, candidate_key=candidate_key, force_fallback=True), 'route_trace_included': True, 'contract_summary': candidate_report['summary'], 'baseline_contract_summary': baseline_report['summary'], 'contract_performance': candidate_report['performance'], 'baseline_contract_performance': baseline_report['performance'], 'timing_backends': _timing_backends_for_reports(candidate_report, baseline_report), 'timing_backend_requested': timing_backend, 'use_cupti': use_cupti, 'benchmark_correctness_checked': benchmark_correctness, 'benchmark_time_flashlib': time_flashlib, 'performance_coverage': 'partial' if below_floor else 'pass', 'coverage_only_routes': [], 'hot_bucket_blockers': below_floor, 'flashlib_parity_ledger': {'baseline_ref_scope': 'same_session' if time_flashlib else 'not_available', 'speedup_floor': 1.05, 'rows_below_1x': below_1x, 'rows_below_floor': below_floor, 'omitted_reason': None if time_flashlib else 'benchmark_time_flashlib=false'}, 'rank_objective': {'metric': 'tflops', 'direction': 'maximize', 'value': candidate_metric, 'baseline_784a_value': baseline_metric, 'delta_vs_784a': candidate_metric - baseline_metric if candidate_metric is not None and baseline_metric is not None else None, 'denominator': denominator}, 'report': candidate_report, 'baseline_report': baseline_report} + +def benchmark_candidate_portfolio(candidate_key: str, *, use_cupti: bool=True, shape_labels=None, baseline_report: dict[str, Any] | None=None, benchmark_correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + if candidate_key == BASE_784A_KEY: + baseline = benchmark_baseline_784a_005f(use_cupti=use_cupti, shape_labels=shape_labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + return _baseline_sidecar(baseline, denominator=_denominator_name(shape_labels), timing_backend=_timing_backend_name(use_cupti), benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + if baseline_report is None: + baseline_report = benchmark_baseline_784a_005f(use_cupti=use_cupti, shape_labels=shape_labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=_candidate_kernel_fn(candidate_key), correctness=benchmark_correctness, time_flashlib=time_flashlib) + return _benchmark_payload(candidate_key, candidate_report, baseline_report, use_cupti=use_cupti, shape_labels=shape_labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + +def benchmark_candidate_784a_plus_6bc3_k8_selective_full82_v1(**kwargs) -> dict[str, Any]: + return benchmark_candidate_portfolio(CANDIDATE_6BC3_K8, **kwargs) + +def _best_candidate_key(payloads: dict[str, dict[str, Any]]) -> str | None: + baseline_value = payloads.get(BASE_784A_KEY, {}).get('tflops') + payload = payloads.get(CANDIDATE_6BC3_K8, {}) + value = payload.get('tflops') + if payload.get('all_correct') and payload.get('performance_comparable') and (value is not None) and (baseline_value is None or value >= baseline_value): + return CANDIDATE_6BC3_K8 + return None + +def _summary_payload(*, payloads: dict[str, dict[str, Any]], artifacts: dict[str, str], denominator: str, timing_backend: str, benchmark_correctness: bool, time_flashlib: bool) -> dict[str, Any]: + selected_key = _best_candidate_key(payloads) + selected_payload = payloads.get(selected_key, {}) if selected_key else {} + baseline_payload = payloads[BASE_784A_KEY] + return {'candidate_id': 'dispatcher_consumption_784a_6bc3_k8_selective_full82_v1', 'measured_entrypoint': ''.join([format(MODULE, ''), ':write_benchmark_artifacts']), 'denominator': denominator, 'timing_backend': timing_backend, 'benchmark_correctness_checked': benchmark_correctness, 'benchmark_time_flashlib': time_flashlib, 'baseline_candidate_key': BASE_784A_KEY, 'selected_candidate_key': selected_key, 'selected_candidate_dispatcher': _candidate_id(selected_key), 'default_candidate_key': DEFAULT_CANDIDATE_KEY, 'candidate_dispatchers': CANDIDATE_DISPATCHERS, 'candidate_rankings': [{'candidate_key': key, 'candidate_id': payloads[key].get('candidate_id'), 'tflops': payloads[key].get('tflops'), 'metric_delta_vs_784a': payloads[key].get('metric_delta_vs_784a'), 'all_correct': payloads[key].get('all_correct'), 'performance_comparable': payloads[key].get('performance_comparable'), 'performance_coverage': payloads[key].get('performance_coverage')} for key in CANDIDATE_KEYS if key in payloads], 'seed_delta_matrix': selected_payload.get('seed_delta_matrix', []), 'flashlib_parity_ledger': selected_payload.get('flashlib_parity_ledger', {}), 'full_denominator_ab': {'baseline_payload': artifacts.get('same_session_baseline_payload'), 'candidate_payload': artifacts.get(''.join([format(CANDIDATE_6BC3_K8, ''), '_payload'])), 'denominator': denominator, 'timing_backend': timing_backend, 'metric_delta': payloads.get(CANDIDATE_6BC3_K8, {}).get('metric_delta_vs_784a'), 'route_trace': payloads.get(CANDIDATE_6BC3_K8, {}).get('route_trace', [])}, 'baseline_tflops': baseline_payload.get('tflops'), 'selected_tflops': selected_payload.get('tflops'), 'candidate_tflops': payloads.get(CANDIDATE_6BC3_K8, {}).get('tflops'), 'artifacts': artifacts} + +def write_benchmark_artifacts(artifact_dir: str | Path, *, use_cupti: bool=True, shape_labels=None, benchmark_correctness: bool=True, time_flashlib: bool=True) -> dict[str, str]: + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + denom_label = _denominator_label(shape_labels) + denominator = _denominator_name(shape_labels) + timing_backend = _timing_backend_name(use_cupti) + baseline_report = benchmark_baseline_784a_005f(use_cupti=use_cupti, shape_labels=shape_labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + baseline_payload = _baseline_sidecar(baseline_report, denominator=denominator, timing_backend=timing_backend, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + artifacts: dict[str, str] = {} + payloads = {BASE_784A_KEY: baseline_payload} + baseline_path = out_dir / ''.join([format(denom_label, ''), '_same_session_baseline_784a_005f_v1.json']) + baseline_path.write_text(json.dumps(baseline_payload, indent=2, sort_keys=True) + '\n', encoding='utf-8') + artifacts['same_session_baseline_payload'] = str(baseline_path) + payload = benchmark_candidate_portfolio(CANDIDATE_6BC3_K8, use_cupti=use_cupti, shape_labels=shape_labels, baseline_report=baseline_report, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + payloads[CANDIDATE_6BC3_K8] = payload + payload_path = out_dir / ''.join([format(denom_label, ''), '_dispatch_784a_plus_6bc3_k8_selective_v1.json']) + trace_path = out_dir / ''.join([format(denom_label, ''), '_route_trace_784a_plus_6bc3_k8_selective_v1.json']) + forced_trace_path = out_dir / ''.join([format(denom_label, ''), '_forced_fallback_trace_784a_plus_6bc3_k8_selective_v1.json']) + seed_matrix_path = out_dir / ''.join([format(denom_label, ''), '_seed_delta_matrix_784a_plus_6bc3_k8_selective_v1.json']) + payload_path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n', encoding='utf-8') + trace_path.write_text(json.dumps(payload['route_trace'], indent=2, sort_keys=True) + '\n', encoding='utf-8') + forced_trace_path.write_text(json.dumps(payload['forced_fallback_route_trace'], indent=2, sort_keys=True) + '\n', encoding='utf-8') + seed_matrix_path.write_text(json.dumps(payload['seed_delta_matrix'], indent=2, sort_keys=True) + '\n', encoding='utf-8') + artifacts[''.join([format(CANDIDATE_6BC3_K8, ''), '_payload'])] = str(payload_path) + artifacts[''.join([format(CANDIDATE_6BC3_K8, ''), '_route_trace'])] = str(trace_path) + artifacts[''.join([format(CANDIDATE_6BC3_K8, ''), '_forced_fallback_trace'])] = str(forced_trace_path) + artifacts[''.join([format(CANDIDATE_6BC3_K8, ''), '_seed_delta_matrix'])] = str(seed_matrix_path) + summary = _summary_payload(payloads=payloads, artifacts=artifacts, denominator=denominator, timing_backend=timing_backend, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + summary_path = out_dir / ''.join([format(denom_label, ''), '_dispatcher_consumption_784a_6bc3_k8_selective_v1.json']) + summary_path.write_text(json.dumps(summary, indent=2, sort_keys=True) + '\n', encoding='utf-8') + artifacts['dispatcher_consumption'] = str(summary_path) + return artifacts diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_784a_cf51_q1_q16_q24_q128_seed_portfolio_full90_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_784a_cf51_q1_q16_q24_q128_seed_portfolio_full90_v1.py new file mode 100644 index 00000000..71db70ae --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_784a_cf51_q1_q16_q24_q128_seed_portfolio_full90_v1.py @@ -0,0 +1,390 @@ +"""Full90 Q24/Q128 seed-portfolio synthesis over the cf51/Q1/Q16 dispatcher. + +Minimum target architecture: sm_100a. This additive dispatcher wrapper keeps +the current c3bf/d5f8 full90 baseline and the cf51/Q1/Q16 parent unchanged, +then tests b0e2 Q128 and 603d/24dc Q24 exact K32 seed overlays. Production +routes remain Weave-only; FlashLib is only timed by the benchmark harness. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +from pathlib import Path +from typing import Any, Callable +from . import knn_build_dispatch_784a_cf51_q1_q16_q4096_seed_portfolio_full90_v1 as parent +from . import knn_build_rag_microbucket_k32_q24rowld2_24dc_v1 as q24_seed +from . import knn_build_rag_stream_k32_q128rowld_60fb_v1 as q128_seed +MODULE = 'loom.examples.weave.knn_build_dispatch_784a_cf51_q1_q16_q24_q128_seed_portfolio_full90_v1' +eval_mod = parent.eval_mod +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbatch_m64_d4f7_stage1", "arg_keys": ["query", "database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 91392, "constants": [], "cta_group": 1, "threads": 512}')) +BASE_D5F8_KEY = parent.BASE_D5F8_KEY +PARENT_CF51_Q1_Q16_KEY = parent.CANDIDATE_CF51_Q1_Q16 +CANDIDATE_Q128_ONLY = 'c3bf_plus_cf51_q1024_bca0_q1_5018_q16_b0e2_q128' +CANDIDATE_Q24_Q128 = 'c3bf_plus_cf51_q1024_bca0_q1_5018_q16_603d_q24_b0e2_q128' +DEFAULT_CANDIDATE_KEY = CANDIDATE_Q24_Q128 +CANDIDATE_KEYS = (BASE_D5F8_KEY, PARENT_CF51_Q1_Q16_KEY, CANDIDATE_Q128_ONLY, CANDIDATE_Q24_Q128) +BASE_D5F8_ID = parent.BASE_D5F8_ID +PARENT_CF51_Q1_Q16_ID = parent.CANDIDATE_CONFIGS[PARENT_CF51_Q1_Q16_KEY]['candidate_id'] +SEED_Q24_ID = q24_seed.SEED_K32_Q24_ROWLD2_24DC_V1_ID +SEED_Q128_ID = q128_seed.SEED_K32_Q128_ROWLD_60FB_V1_ID +BASE_D5F8_ENTRYPOINT = parent.BASE_D5F8_ENTRYPOINT +BASE_D5F8_ROUTE_ENTRYPOINT = parent.BASE_D5F8_ROUTE_ENTRYPOINT +PARENT_CF51_Q1_Q16_ENTRYPOINT = parent.CANDIDATE_CF51_Q1_Q16_ENTRYPOINT +PARENT_ROUTE_ENTRYPOINT = parent.ROUTE_ENTRYPOINT +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +CANDIDATE_Q128_ONLY_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_candidate_q128_only_full90_v1']) +CANDIDATE_Q24_Q128_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_candidate_q24_q128_full90_v1']) +Q24_TARGET_SHAPES = q24_seed.Q24_ROWLD2_TARGET_SHAPES +Q128_TARGET_SHAPES = q128_seed.Q128_ROWLD_TARGET_SHAPES +Q24_Q128_TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["rag_microbatch_largek_b1_q24_m100000_d128_k32", "rag_stream_largek_b1_q128_m131071_d128_k32"]}')) +PARENT_TARGET_SHAPES = parent.CF51_Q1_Q16_TARGET_SHAPES +Q128_ONLY_TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_qm1024_d128_k8", "rag_online_b1_q1_m100000_d128_k10", "rag_online_b1_q1_m65536_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "rag_online_irregular_b1_q1_m262143_d128_k10", "rag_online_irregular_b1_q1_m524287_d128_k10", "rag_microbatch_largek_b1_q16_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m131071_d128_k32", "rag_microbatch_largek_b1_q16_m250000_d128_k32", "rag_stream_largek_b1_q128_m131071_d128_k32"]}')) +Q24_Q128_PORTFOLIO_TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_qm1024_d128_k8", "rag_online_b1_q1_m100000_d128_k10", "rag_online_b1_q1_m65536_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "rag_online_irregular_b1_q1_m262143_d128_k10", "rag_online_irregular_b1_q1_m524287_d128_k10", "rag_microbatch_largek_b1_q16_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m131071_d128_k32", "rag_microbatch_largek_b1_q16_m250000_d128_k32", "rag_microbatch_largek_b1_q24_m100000_d128_k32", "rag_stream_largek_b1_q128_m131071_d128_k32"]}')) +SOURCE_TASKS = {**parent.SOURCE_TASKS, SEED_Q24_ID: 'weave-evolve-knn-build-603d / design_doc/active/weave_evolve_knn_build_round_135_24dc_q24rowld2.md', SEED_Q128_ID: 'weave-evolve-knn-build-b0e2 / design_doc/active/weave_evolve_knn_build_round_135_60fb_q128rowld.md'} +PRODUCTION_ROUTE_MODULES = {**parent.PRODUCTION_ROUTE_MODULES, PARENT_CF51_Q1_Q16_ID: PARENT_ROUTE_ENTRYPOINT, SEED_Q24_ID: q24_seed.ROUTE_Q24_ROWLD2_ENTRYPOINT, SEED_Q128_ID: q128_seed.ROUTE_Q128_ROWLD_ENTRYPOINT} +TARGETED_SEED_ROWS = {**parent.TARGETED_SEED_ROWS, SEED_Q24_ID: {'source_payload': 'artifacts/weave_evolve/knn_build_q24rowld2_24dc_v1/q24rowld2_24dc_v1_q24_cupti.json', 'shape_labels': Q24_TARGET_SHAPES, 'source_task': 'weave-evolve-knn-build-603d'}, SEED_Q128_ID: {'source_payload': 'artifacts/weave_evolve/knn_build_q128rowld_60fb_v1/q128rowld_60fb_v1_q128_cupti.json', 'shape_labels': Q128_TARGET_SHAPES, 'source_task': 'weave-evolve-knn-build-b0e2'}} +REJECTED_ROUTE_COMBINATIONS = ({'id': 'c3bf_d5f8_plus_603d_q24_plus_b0e2_q128_plus_4977_q128_m100000', 'entrypoint': 'loom.examples.weave.knn_build_rag_q128_k32_c796_g8_v1:launch_from_contract_inputs', 'status': 'diagnostic_only_not_routeable_in_this_worktree', 'source_task': 'weave-evolve-knn-build-4977', 'reason': '4977 is available as sibling/read-ref evidence for Q128/M100000, but this worktree does not contain the route module. The committed 9330 variance audit also shows c796_4977_g8 did not beat the direct 4fbf_v6_g8 route on its exact denominator.', 'evidence': 'artifacts/generalize_auto_tuning/knn_build_9330_q128_k32_parent_variance_audit/variance_audit_summary_q128_k32_parent_routes.json'},) + +def _select_contract_shapes(shape_labels): + return parent._select_contract_shapes(shape_labels) + +def _trace_inputs_for_shape(shape: dict[str, Any]) -> dict[str, Any]: + return parent._trace_inputs_for_shape(shape) + +def _normalize_route_row(row: dict[str, Any]) -> dict[str, Any]: + return parent._normalize_route_row(row) + +def _inputs_for_label(label: str) -> dict[str, Any]: + return _trace_inputs_for_shape(_select_contract_shapes((label,))[0]) + +def _timing_backend_name(use_cupti: bool) -> str: + return 'cupti' if use_cupti else 'cuda_event_fallback' + +def _payload_shape_labels(shape_labels) -> str | tuple[str, ...]: + return parent._payload_shape_labels(shape_labels) + +def _denominator_name(shape_labels) -> str: + return parent._denominator_name(shape_labels) + +def _denominator_label(shape_labels) -> str: + return parent._denominator_label(shape_labels) + +def _rows_for_labels(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return parent._rows_for_labels(report, labels) + +def _timing_backends_for_reports(*reports: dict[str, Any]) -> list[str]: + return parent._timing_backends_for_reports(*reports) + +def _candidate_config(candidate_key: str) -> dict[str, Any]: + try: + return CANDIDATE_CONFIGS[candidate_key] + except KeyError as exc: + raise ValueError(''.join(['unknown full90 Q24/Q128 candidate ', format(repr(candidate_key), '')])) from exc + +def _candidate_id(candidate_key: str | None) -> str | None: + if candidate_key is None: + return None + return str(_candidate_config(candidate_key)['candidate_id']) + +def _eligible_q24(inputs: dict[str, Any]) -> bool: + return q24_seed._eligible_q24_rowld2(inputs) + +def _eligible_q128(inputs: dict[str, Any]) -> bool: + return q128_seed._eligible_q128_rowld(inputs) + +def _parent_route(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + return parent.route_for_contract_inputs(inputs, candidate_key=PARENT_CF51_Q1_Q16_KEY, force_fallback=force_fallback) + +def _parent_launch(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + parent.launch_from_contract_inputs(inputs, candidate_key=PARENT_CF51_Q1_Q16_KEY, force_fallback=force_fallback) + +def _expected_seed(inputs: dict[str, Any], candidate_key: str) -> str | None: + if candidate_key in (CANDIDATE_Q128_ONLY, CANDIDATE_Q24_Q128) and _eligible_q128(inputs): + return SEED_Q128_ID + if candidate_key == CANDIDATE_Q24_Q128 and _eligible_q24(inputs): + return SEED_Q24_ID + if candidate_key in (PARENT_CF51_Q1_Q16_KEY, CANDIDATE_Q128_ONLY, CANDIDATE_Q24_Q128): + return parent._expected_seed(inputs, PARENT_CF51_Q1_Q16_KEY) + return None + +def route_for_contract_inputs(inputs: dict[str, Any], *, candidate_key: str=DEFAULT_CANDIDATE_KEY, force_fallback: bool=False) -> str: + _candidate_config(candidate_key) + if force_fallback: + return _parent_route(inputs, force_fallback=True) + if candidate_key == BASE_D5F8_KEY: + return parent.route_for_contract_inputs(inputs, candidate_key=BASE_D5F8_KEY) + if candidate_key == PARENT_CF51_Q1_Q16_KEY: + return _parent_route(inputs) + if candidate_key in (CANDIDATE_Q128_ONLY, CANDIDATE_Q24_Q128) and _eligible_q128(inputs): + return q128_seed.route_for_contract_inputs(inputs) + if candidate_key == CANDIDATE_Q24_Q128 and _eligible_q24(inputs): + return q24_seed.route_for_contract_inputs(inputs) + return _parent_route(inputs) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, candidate_key: str=DEFAULT_CANDIDATE_KEY, force_fallback: bool=False) -> None: + _candidate_config(candidate_key) + if force_fallback: + _parent_launch(inputs, force_fallback=True) + return + if candidate_key == BASE_D5F8_KEY: + parent.launch_from_contract_inputs(inputs, candidate_key=BASE_D5F8_KEY) + return + if candidate_key == PARENT_CF51_Q1_Q16_KEY: + _parent_launch(inputs) + return + if candidate_key in (CANDIDATE_Q128_ONLY, CANDIDATE_Q24_Q128) and _eligible_q128(inputs): + q128_seed.launch_from_contract_inputs(inputs) + return + if candidate_key == CANDIDATE_Q24_Q128 and _eligible_q24(inputs): + q24_seed.launch_from_contract_inputs(inputs) + return + _parent_launch(inputs) + +def candidate_baseline_d5f8(inputs: dict[str, Any]) -> None: + parent.candidate_baseline_d5f8(inputs) + +def candidate_parent_cf51_q1_q16_full90_v1(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, candidate_key=PARENT_CF51_Q1_Q16_KEY) + +def candidate_q128_only_full90_v1(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, candidate_key=CANDIDATE_Q128_ONLY) + +def candidate_q24_q128_full90_v1(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, candidate_key=CANDIDATE_Q24_Q128) + +def candidate(inputs: dict[str, Any]) -> None: + candidate_q24_q128_full90_v1(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, candidate_key=DEFAULT_CANDIDATE_KEY, force_fallback=True) + +def _candidate_kernel_fn(candidate_key: str) -> Callable[[dict[str, Any]], None]: + if candidate_key == BASE_D5F8_KEY: + return candidate_baseline_d5f8 + if candidate_key == PARENT_CF51_Q1_Q16_KEY: + return candidate_parent_cf51_q1_q16_full90_v1 + if candidate_key == CANDIDATE_Q128_ONLY: + return candidate_q128_only_full90_v1 + if candidate_key == CANDIDATE_Q24_Q128: + return candidate_q24_q128_full90_v1 + raise ValueError(''.join(['unknown full90 Q24/Q128 candidate ', format(repr(candidate_key), '')])) +CANDIDATE_CONFIGS = _decode_capture(_json_loads('{"__dict_items__": [["base_c3bf_d5f8", {"__dict_items__": [["candidate_id", "c3bf_d5f8_full90_baseline"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_784a_6bc3_k8_q512k456_q4096k8_direct_fcf2_v1:launch_from_contract_inputs"], ["benchmark_entrypoint", "loom.examples.weave.knn_build_dispatch_784a_6bc3_k8_q512k456_q4096k8_direct_fcf2_v1:benchmark_candidate_784a_direct_q512_k456_q4096_k8_plus_6bc3_k8_v1"], ["selected_seeds", {"__tuple__": ["v20_k12_q1024_q4096_exact", "q2048_k12_4f30_v1", "v12_k20_q2048k10_mixedfanout", "over32_k48_v25", "6bc3_v20_q512_k8_static_s8", "6bc3_v20_q2048_k8_static_s8", "fcf2_direct_lowk_q512_k4_k5_k6_s4", "fcf2_direct_v20_q4096_k8_s8"]}], ["guard_plan", {"__tuple__": ["direct exact BF16 build Q4096 K8 v20 split8 guard", "then direct exact BF16 build Q512 K4/K5/K6 low-K split4 guard", "then fcf2 selective 6bc3 exact Q512/Q2048 K8 guards", "then 784a 005f build-lowfloor portfolio", "then inherited a444/9db7 full82 Weave fallback"]}], ["expected_shape_wins", {"__tuple__": ["build_k_sweep_qm512_k4", "build_k_sweep_qm512_k5", "build_k_sweep_qm512_k6", "build_k_sweep_qm512_k8", "build_qm2048_d128_k8", "build_qm4096_d128_k8"]}], ["fallback", "loom.examples.weave.knn_build_dispatch_784a_6bc3_k8_q512k456_direct_fcf2_v1:launch_from_contract_inputs"], ["rejected_reason", "same-session c3bf/d5f8 baseline"]]}], ["c3bf_plus_cf51_q1024_bca0_q1_5018_q16", {"__dict_items__": [["candidate_id", "candidate_c3bf_cf51_q1024_bca0_q1_5018_q16_full90_v1"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_784a_cf51_q1_q16_q24_q128_seed_portfolio_full90_v1:candidate_parent_cf51_q1_q16_full90_v1"], ["benchmark_entrypoint", "loom.examples.weave.knn_build_dispatch_784a_cf51_q1_q16_q4096_seed_portfolio_full90_v1:benchmark_candidate_cf51_q1_q16_full90_v1"], ["selected_seeds", {"__tuple__": ["q1024_k8_195e_v1", "ragonline_mbucket_5706_q1v10_smix_s144_g12", "rag_microbucket_k32_q16dual2warp_largem_bdd2_v1_rowld1_2warp_rows4"]}], ["guard_plan", {"__tuple__": ["cf51 exact BF16 build Q=M=1024 D=128 K=8 split16", "bca0 exact BF16 non-build Q=1 D=128 K=10 M in {65536,100000,131071,250000,262143,524287}", "5018 exact BF16 non-build Q=16 D=128 K=32 M in {100000,131071,250000}", "c3bf/d5f8 full90 Weave fallback"]}], ["expected_shape_wins", {"__tuple__": ["build_qm1024_d128_k8", "rag_online_b1_q1_m100000_d128_k10", "rag_online_b1_q1_m65536_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "rag_online_irregular_b1_q1_m262143_d128_k10", "rag_online_irregular_b1_q1_m524287_d128_k10", "rag_microbatch_largek_b1_q16_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m131071_d128_k32", "rag_microbatch_largek_b1_q16_m250000_d128_k32"]}], ["fallback", "loom.examples.weave.knn_build_dispatch_784a_6bc3_k8_q512k456_q4096k8_direct_fcf2_v1:launch_from_contract_inputs"], ["rejected_reason", "parent comparison point for Q24/Q128 synthesis"]]}], ["c3bf_plus_cf51_q1024_bca0_q1_5018_q16_b0e2_q128", {"__dict_items__": [["candidate_id", "candidate_c3bf_cf51_q1024_bca0_q1_5018_q16_b0e2_q128_full90_v1"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_784a_cf51_q1_q16_q24_q128_seed_portfolio_full90_v1:candidate_q128_only_full90_v1"], ["benchmark_entrypoint", "loom.examples.weave.knn_build_dispatch_784a_cf51_q1_q16_q24_q128_seed_portfolio_full90_v1:benchmark_candidate_q128_only_full90_v1"], ["selected_seeds", {"__tuple__": ["q1024_k8_195e_v1", "ragonline_mbucket_5706_q1v10_smix_s144_g12", "rag_microbucket_k32_q16dual2warp_largem_bdd2_v1_rowld1_2warp_rows4", "rag_stream_k32_q128rowld_60fb_v1_rowld_rows4"]}], ["guard_plan", {"__tuple__": ["b0e2 exact BF16 non-build B=1 Q=128 M=131071 D=128 K=32 rowld rows4 guard", "cf51 exact BF16 build Q=M=1024 D=128 K=8 split16", "bca0 exact BF16 non-build Q=1 D=128 K=10 M in {65536,100000,131071,250000,262143,524287}", "5018 exact BF16 non-build Q=16 D=128 K=32 M in {100000,131071,250000}", "c3bf/d5f8 full90 Weave fallback"]}], ["expected_shape_wins", {"__tuple__": ["build_qm1024_d128_k8", "rag_online_b1_q1_m100000_d128_k10", "rag_online_b1_q1_m65536_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "rag_online_irregular_b1_q1_m262143_d128_k10", "rag_online_irregular_b1_q1_m524287_d128_k10", "rag_microbatch_largek_b1_q16_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m131071_d128_k32", "rag_microbatch_largek_b1_q16_m250000_d128_k32", "rag_stream_largek_b1_q128_m131071_d128_k32"]}], ["fallback", "loom.examples.weave.knn_build_dispatch_784a_cf51_q1_q16_q4096_seed_portfolio_full90_v1:launch_from_contract_inputs"], ["rejected_reason", null]]}], ["c3bf_plus_cf51_q1024_bca0_q1_5018_q16_603d_q24_b0e2_q128", {"__dict_items__": [["candidate_id", "candidate_c3bf_cf51_q1024_bca0_q1_5018_q16_603d_q24_b0e2_q128_full90_v1"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_784a_cf51_q1_q16_q24_q128_seed_portfolio_full90_v1:candidate_q24_q128_full90_v1"], ["benchmark_entrypoint", "loom.examples.weave.knn_build_dispatch_784a_cf51_q1_q16_q24_q128_seed_portfolio_full90_v1:benchmark_candidate_q24_q128_full90_v1"], ["selected_seeds", {"__tuple__": ["q1024_k8_195e_v1", "ragonline_mbucket_5706_q1v10_smix_s144_g12", "rag_microbucket_k32_q16dual2warp_largem_bdd2_v1_rowld1_2warp_rows4", "rag_microbucket_k32_q24rowld2_24dc_v1_rowld2_rows4", "rag_stream_k32_q128rowld_60fb_v1_rowld_rows4"]}], ["guard_plan", {"__tuple__": ["b0e2 exact BF16 non-build B=1 Q=128 M=131071 D=128 K=32 rowld rows4 guard", "603d/24dc exact BF16 non-build B=1 Q=24 M=100000 D=128 K=32 rowld2 rows4 guard", "cf51 exact BF16 build Q=M=1024 D=128 K=8 split16", "bca0 exact BF16 non-build Q=1 D=128 K=10 M in {65536,100000,131071,250000,262143,524287}", "5018 exact BF16 non-build Q=16 D=128 K=32 M in {100000,131071,250000}", "c3bf/d5f8 full90 Weave fallback"]}], ["expected_shape_wins", {"__tuple__": ["build_qm1024_d128_k8", "rag_online_b1_q1_m100000_d128_k10", "rag_online_b1_q1_m65536_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "rag_online_irregular_b1_q1_m262143_d128_k10", "rag_online_irregular_b1_q1_m524287_d128_k10", "rag_microbatch_largek_b1_q16_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m131071_d128_k32", "rag_microbatch_largek_b1_q16_m250000_d128_k32", "rag_microbatch_largek_b1_q24_m100000_d128_k32", "rag_stream_largek_b1_q128_m131071_d128_k32"]}], ["fallback", "loom.examples.weave.knn_build_dispatch_784a_cf51_q1_q16_q4096_seed_portfolio_full90_v1:launch_from_contract_inputs"], ["rejected_reason", null]]}]]}')) +CANDIDATE_DISPATCHERS = _decode_capture(_json_loads('{"__tuple__": [{"__dict_items__": [["id", "c3bf_d5f8_full90_baseline"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_784a_6bc3_k8_q512k456_q4096k8_direct_fcf2_v1:benchmark_candidate_784a_direct_q512_k456_q4096_k8_plus_6bc3_k8_v1"], ["consumed_seeds", {"__tuple__": ["v20_k12_q1024_q4096_exact", "q2048_k12_4f30_v1", "v12_k20_q2048k10_mixedfanout", "over32_k48_v25", "6bc3_v20_q512_k8_static_s8", "6bc3_v20_q2048_k8_static_s8", "fcf2_direct_lowk_q512_k4_k5_k6_s4", "fcf2_direct_v20_q4096_k8_s8"]}], ["guard_plan", {"__tuple__": ["direct exact BF16 build Q4096 K8 v20 split8 guard", "then direct exact BF16 build Q512 K4/K5/K6 low-K split4 guard", "then fcf2 selective 6bc3 exact Q512/Q2048 K8 guards", "then 784a 005f build-lowfloor portfolio", "then inherited a444/9db7 full82 Weave fallback"]}], ["expected_shape_wins", {"__tuple__": ["build_k_sweep_qm512_k4", "build_k_sweep_qm512_k5", "build_k_sweep_qm512_k6", "build_k_sweep_qm512_k8", "build_qm2048_d128_k8", "build_qm4096_d128_k8"]}], ["fallback", "loom.examples.weave.knn_build_dispatch_784a_6bc3_k8_q512k456_direct_fcf2_v1:launch_from_contract_inputs"], ["rejected_reason", "same-session c3bf/d5f8 baseline"]]}, {"__dict_items__": [["id", "candidate_c3bf_cf51_q1024_bca0_q1_5018_q16_full90_v1"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_784a_cf51_q1_q16_q4096_seed_portfolio_full90_v1:benchmark_candidate_cf51_q1_q16_full90_v1"], ["consumed_seeds", {"__tuple__": ["q1024_k8_195e_v1", "ragonline_mbucket_5706_q1v10_smix_s144_g12", "rag_microbucket_k32_q16dual2warp_largem_bdd2_v1_rowld1_2warp_rows4"]}], ["guard_plan", {"__tuple__": ["cf51 exact BF16 build Q=M=1024 D=128 K=8 split16", "bca0 exact BF16 non-build Q=1 D=128 K=10 M in {65536,100000,131071,250000,262143,524287}", "5018 exact BF16 non-build Q=16 D=128 K=32 M in {100000,131071,250000}", "c3bf/d5f8 full90 Weave fallback"]}], ["expected_shape_wins", {"__tuple__": ["build_qm1024_d128_k8", "rag_online_b1_q1_m100000_d128_k10", "rag_online_b1_q1_m65536_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "rag_online_irregular_b1_q1_m262143_d128_k10", "rag_online_irregular_b1_q1_m524287_d128_k10", "rag_microbatch_largek_b1_q16_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m131071_d128_k32", "rag_microbatch_largek_b1_q16_m250000_d128_k32"]}], ["fallback", "loom.examples.weave.knn_build_dispatch_784a_6bc3_k8_q512k456_q4096k8_direct_fcf2_v1:launch_from_contract_inputs"], ["rejected_reason", "parent comparison point for Q24/Q128 synthesis"]]}, {"__dict_items__": [["id", "candidate_c3bf_cf51_q1024_bca0_q1_5018_q16_b0e2_q128_full90_v1"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_784a_cf51_q1_q16_q24_q128_seed_portfolio_full90_v1:benchmark_candidate_q128_only_full90_v1"], ["consumed_seeds", {"__tuple__": ["q1024_k8_195e_v1", "ragonline_mbucket_5706_q1v10_smix_s144_g12", "rag_microbucket_k32_q16dual2warp_largem_bdd2_v1_rowld1_2warp_rows4", "rag_stream_k32_q128rowld_60fb_v1_rowld_rows4"]}], ["guard_plan", {"__tuple__": ["b0e2 exact BF16 non-build B=1 Q=128 M=131071 D=128 K=32 rowld rows4 guard", "cf51 exact BF16 build Q=M=1024 D=128 K=8 split16", "bca0 exact BF16 non-build Q=1 D=128 K=10 M in {65536,100000,131071,250000,262143,524287}", "5018 exact BF16 non-build Q=16 D=128 K=32 M in {100000,131071,250000}", "c3bf/d5f8 full90 Weave fallback"]}], ["expected_shape_wins", {"__tuple__": ["build_qm1024_d128_k8", "rag_online_b1_q1_m100000_d128_k10", "rag_online_b1_q1_m65536_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "rag_online_irregular_b1_q1_m262143_d128_k10", "rag_online_irregular_b1_q1_m524287_d128_k10", "rag_microbatch_largek_b1_q16_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m131071_d128_k32", "rag_microbatch_largek_b1_q16_m250000_d128_k32", "rag_stream_largek_b1_q128_m131071_d128_k32"]}], ["fallback", "loom.examples.weave.knn_build_dispatch_784a_cf51_q1_q16_q4096_seed_portfolio_full90_v1:launch_from_contract_inputs"], ["rejected_reason", null]]}, {"__dict_items__": [["id", "candidate_c3bf_cf51_q1024_bca0_q1_5018_q16_603d_q24_b0e2_q128_full90_v1"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_784a_cf51_q1_q16_q24_q128_seed_portfolio_full90_v1:benchmark_candidate_q24_q128_full90_v1"], ["consumed_seeds", {"__tuple__": ["q1024_k8_195e_v1", "ragonline_mbucket_5706_q1v10_smix_s144_g12", "rag_microbucket_k32_q16dual2warp_largem_bdd2_v1_rowld1_2warp_rows4", "rag_microbucket_k32_q24rowld2_24dc_v1_rowld2_rows4", "rag_stream_k32_q128rowld_60fb_v1_rowld_rows4"]}], ["guard_plan", {"__tuple__": ["b0e2 exact BF16 non-build B=1 Q=128 M=131071 D=128 K=32 rowld rows4 guard", "603d/24dc exact BF16 non-build B=1 Q=24 M=100000 D=128 K=32 rowld2 rows4 guard", "cf51 exact BF16 build Q=M=1024 D=128 K=8 split16", "bca0 exact BF16 non-build Q=1 D=128 K=10 M in {65536,100000,131071,250000,262143,524287}", "5018 exact BF16 non-build Q=16 D=128 K=32 M in {100000,131071,250000}", "c3bf/d5f8 full90 Weave fallback"]}], ["expected_shape_wins", {"__tuple__": ["build_qm1024_d128_k8", "rag_online_b1_q1_m100000_d128_k10", "rag_online_b1_q1_m65536_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "rag_online_irregular_b1_q1_m262143_d128_k10", "rag_online_irregular_b1_q1_m524287_d128_k10", "rag_microbatch_largek_b1_q16_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m131071_d128_k32", "rag_microbatch_largek_b1_q16_m250000_d128_k32", "rag_microbatch_largek_b1_q24_m100000_d128_k32", "rag_stream_largek_b1_q128_m131071_d128_k32"]}], ["fallback", "loom.examples.weave.knn_build_dispatch_784a_cf51_q1_q16_q4096_seed_portfolio_full90_v1:launch_from_contract_inputs"], ["rejected_reason", null]]}]}')) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def compile_and_launch_knn_build(*, shape_labels=Q24_Q128_PORTFOLIO_TARGET_SHAPES, benchmark: bool=False, candidate_key: str=DEFAULT_CANDIDATE_KEY) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark, kernel_fn=_candidate_kernel_fn(candidate_key)) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=None, kernel_fn: Callable[[dict[str, Any]], Any]=candidate, correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + return parent._run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=kernel_fn, correctness=correctness, time_flashlib=time_flashlib) + +def _parent_trace_row(label: str, *, force_fallback: bool=False) -> dict[str, Any]: + return dict(parent.route_trace_for_contract_shapes((label,), candidate_key=PARENT_CF51_Q1_Q16_KEY, force_fallback=force_fallback)[0]) + +def _seed_route_row(inputs: dict[str, Any], *, selected_seed: str, selected_route: str, selected_entrypoint: str, guard_id: str, guard_condition: str, force_fallback: bool=False) -> dict[str, Any]: + label = str(inputs.get('label')) + parent_row = _parent_trace_row(label, force_fallback=False) + if force_fallback: + row = _parent_trace_row(label, force_fallback=True) + row['expected_seed'] = selected_seed + row['guard_id'] = ''.join(['forced_fallback_', format(selected_seed, ''), '_disabled']) + row['guard_condition'] = ''.join(['forced fallback to c3bf/d5f8; ', format(selected_seed, ''), ' disabled']) + row['classification'] = 'guard-miss' + row['parent_dispatcher_route'] = parent_row.get('selected_route') + return _normalize_route_row(row) + return _normalize_route_row({'shape_key': label, 'selected_route': selected_route, 'selected_entrypoint': selected_entrypoint, 'selected_seed': selected_seed, 'expected_seed': selected_seed, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': guard_id, 'guard_condition': guard_condition, 'coverage': 'Q24/Q128 seed portfolio overlay before cf51/Q1/Q16 parent', 'consumed_seed': selected_seed, 'replaced_route': parent_row.get('selected_route'), 'parent_dispatcher_route': parent_row.get('selected_route'), 'parent_dispatcher_selected_seed': parent_row.get('selected_seed'), 'baseline_dispatcher_route': parent_row.get('baseline_dispatcher_route') or parent_row.get('baseline_d5f8_route'), 'targeted_seed_row': TARGETED_SEED_ROWS.get(selected_seed, {}), 'classification': 'unmeasured'}) + +def _q24_trace_record(inputs: dict[str, Any], *, force_fallback: bool=False) -> dict[str, Any]: + row = _seed_route_row(inputs, selected_seed=SEED_Q24_ID, selected_route=q24_seed.route_for_contract_inputs(inputs), selected_entrypoint=q24_seed.ROUTE_Q24_ROWLD2_ENTRYPOINT, guard_id='603d_24dc_q24_m100000_k32_rowld2_rows4_guard', guard_condition='BF16 non-build B=1 Q=24 M=100000 D=128 K=32', force_fallback=force_fallback) + row['split_count'] = q24_seed.K32_Q24_SPLIT_COUNT + return row + +def _q128_trace_record(inputs: dict[str, Any], *, force_fallback: bool=False) -> dict[str, Any]: + row = _seed_route_row(inputs, selected_seed=SEED_Q128_ID, selected_route=q128_seed.route_for_contract_inputs(inputs), selected_entrypoint=q128_seed.ROUTE_Q128_ROWLD_ENTRYPOINT, guard_id='b0e2_q128_m131071_k32_rowld_rows4_guard', guard_condition='BF16 non-build B=1 Q=128 M=131071 D=128 K=32', force_fallback=force_fallback) + row['split_count'] = q128_seed.K32_Q128_SPLIT_COUNT + return row + +def _route_trace_record(inputs: dict[str, Any], *, candidate_key: str, force_fallback: bool=False) -> dict[str, Any]: + label = str(inputs.get('label')) + if candidate_key in (CANDIDATE_Q128_ONLY, CANDIDATE_Q24_Q128) and _eligible_q128(inputs): + return _q128_trace_record(inputs, force_fallback=force_fallback) + if candidate_key == CANDIDATE_Q24_Q128 and _eligible_q24(inputs): + return _q24_trace_record(inputs, force_fallback=force_fallback) + if candidate_key == BASE_D5F8_KEY: + row = dict(parent.route_trace_for_contract_shapes((label,), candidate_key=BASE_D5F8_KEY)[0]) + else: + row = _parent_trace_row(label, force_fallback=force_fallback) + row['parent_dispatcher_route'] = _parent_route(inputs, force_fallback=force_fallback) + return _normalize_route_row(row) + +def route_trace_for_contract_shapes(shape_labels=None, *, candidate_key: str=DEFAULT_CANDIDATE_KEY, force_fallback: bool=False) -> list[dict[str, Any]]: + _candidate_config(candidate_key) + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + return [_route_trace_record(_trace_inputs_for_shape(shape), candidate_key=candidate_key, force_fallback=force_fallback) for shape in selected] + +def _annotate_route_trace(route_trace: list[dict[str, Any]], candidate_report: dict[str, Any], baseline_report: dict[str, Any], *, candidate_key: str) -> list[dict[str, Any]]: + expected_labels = set(_candidate_config(candidate_key)['expected_shape_wins']) + annotated = [] + for row in route_trace: + label = str(row.get('shape_key')) + out = dict(row) + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') + speedup_vs_baseline = baseline_ms / candidate_ms if candidate_ms and baseline_ms else None + speedup_vs_external = flashlib_ms / candidate_ms if candidate_ms and flashlib_ms else None + out['dispatcher_kernel_ms'] = candidate_ms + out['baseline_d5f8_kernel_ms'] = baseline_ms + out['external_baseline_ms'] = flashlib_ms + out['flashlib_ms'] = flashlib_ms + out['relative_speedup_vs_baseline'] = speedup_vs_baseline + out['relative_speedup_vs_d5f8'] = speedup_vs_baseline + out['speedup_vs_external_baseline'] = speedup_vs_external + out['external_baseline_ref'] = 'same_session' if flashlib_ms is not None else 'not_available' + out['route_changed_vs_d5f8'] = out.get('selected_route') != out.get('baseline_dispatcher_route') + if label in expected_labels and candidate_key != BASE_D5F8_KEY: + if out.get('selected_seed') != out.get('expected_seed'): + out['classification'] = 'guard-miss' + elif speedup_vs_external is not None and speedup_vs_external < 1.05: + out['classification'] = 'kernel-slow' + elif speedup_vs_baseline is not None and speedup_vs_baseline < 1.0: + out['classification'] = 'kernel-slow' + else: + out['classification'] = 'seed-consumed' + elif speedup_vs_external is not None and speedup_vs_external < 1.05: + out['classification'] = 'kernel-slow' if out.get('route_kind') == 'specialized' else 'fallback-slow' + else: + out['classification'] = 'route-ok' + annotated.append(_normalize_route_row(out)) + return annotated + +def _below_flashlib_rows(report: dict[str, Any], route_trace: list[dict[str, Any]], *, floor: float) -> list[dict[str, Any]]: + trace_by_label = {str(row['shape_key']): row for row in route_trace} + rows = [] + for label, row in sorted(report.get('per_shape', {}).items()): + ratio = row.get('ratio_vs_flashlib') + if isinstance(ratio, (float, int)) and ratio < floor: + trace_row = trace_by_label.get(label, {}) + rows.append({'shape_key': label, 'kernel_ms': row.get('kernel_ms'), 'flashlib_ms': row.get('flashlib_ms'), 'ratio_vs_flashlib': ratio, 'selected_route': trace_row.get('selected_route'), 'selected_seed': trace_row.get('selected_seed'), 'expected_seed': trace_row.get('expected_seed'), 'route_kind': trace_row.get('route_kind', 'unknown'), 'classification': trace_row.get('classification', 'unmeasured')}) + return rows + +def _seed_delta_matrix(candidate_key: str, candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> list[dict[str, Any]]: + matrix = [] + for label in _candidate_config(candidate_key)['expected_shape_wins']: + inputs = _inputs_for_label(label) + selected_seed = _expected_seed(inputs, candidate_key) + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + matrix.append({'shape_key': label, 'baseline_route': parent.route_for_contract_inputs(inputs, candidate_key=BASE_D5F8_KEY), 'parent_route': _parent_route(inputs), 'candidate_route': route_for_contract_inputs(inputs, candidate_key=candidate_key), 'selected_seed': selected_seed, 'candidate_id': _candidate_id(candidate_key), 'candidate_ms': candidate_ms, 'baseline_d5f8_ms': baseline_ms, 'flashlib_ms': candidate_row.get('flashlib_ms'), 'delta_ms_candidate_minus_d5f8': candidate_ms - baseline_ms if candidate_ms and baseline_ms else None, 'speedup_vs_d5f8': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'ratio_vs_flashlib': candidate_row.get('ratio_vs_flashlib'), 'targeted_seed_row': TARGETED_SEED_ROWS.get(selected_seed, {}), 'timing_backend': candidate_row.get('timing_backend') or baseline_row.get('timing_backend')}) + return matrix + +def benchmark_baseline_d5f8(*, use_cupti: bool=True, shape_labels=None, benchmark_correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + return _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_baseline_d5f8, correctness=benchmark_correctness, time_flashlib=time_flashlib) + +def _baseline_sidecar(report: dict[str, Any], *, shape_labels, denominator: str, timing_backend: str, benchmark_correctness: bool, time_flashlib: bool) -> dict[str, Any]: + route_trace = route_trace_for_contract_shapes(shape_labels, candidate_key=BASE_D5F8_KEY) + below_1x = _below_flashlib_rows(report, route_trace, floor=1.0) + below_floor = _below_flashlib_rows(report, route_trace, floor=1.05) + return {'candidate_id': BASE_D5F8_ID, 'candidate_key': BASE_D5F8_KEY, 'selected_seeds': CANDIDATE_CONFIGS[BASE_D5F8_KEY]['selected_seeds'], 'source_tasks': SOURCE_TASKS, 'tflops': report['summary']['primary_mean'], 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'invalid_performance_reason': report['summary']['invalid_performance_reason'], 'measured_entrypoint': BASE_D5F8_ENTRYPOINT, 'route_entrypoint': BASE_D5F8_ROUTE_ENTRYPOINT, 'measured_shape_labels': _payload_shape_labels(shape_labels), 'timing_backend': timing_backend, 'denominator': denominator, 'performance_coverage': 'partial' if below_floor else 'pass', 'coverage_only_routes': [], 'hot_bucket_blockers': below_floor, 'flashlib_parity_ledger': {'baseline_ref_scope': 'same_session' if time_flashlib else 'not_available', 'speedup_floor': 1.05, 'rows_below_1x': below_1x, 'rows_below_floor': below_floor, 'omitted_reason': None if time_flashlib else 'benchmark_time_flashlib=false'}, 'route_trace': route_trace, 'route_trace_included': True, 'contract_summary': report['summary'], 'contract_performance': report['performance'], 'timing_backends': _timing_backends_for_reports(report), 'timing_backend_requested': timing_backend, 'benchmark_correctness_checked': benchmark_correctness, 'benchmark_time_flashlib': time_flashlib, 'report': report} + +def _benchmark_payload(candidate_key: str, candidate_report: dict[str, Any], baseline_report: dict[str, Any], *, use_cupti: bool, shape_labels, benchmark_correctness: bool, time_flashlib: bool) -> dict[str, Any]: + candidate_metric = candidate_report['summary']['primary_mean'] + baseline_metric = baseline_report['summary']['primary_mean'] + route_trace = _annotate_route_trace(route_trace_for_contract_shapes(shape_labels, candidate_key=candidate_key), candidate_report, baseline_report, candidate_key=candidate_key) + below_1x = _below_flashlib_rows(candidate_report, route_trace, floor=1.0) + below_floor = _below_flashlib_rows(candidate_report, route_trace, floor=1.05) + denominator = _denominator_name(shape_labels) + timing_backend = _timing_backend_name(use_cupti) + config = _candidate_config(candidate_key) + return {'candidate_id': config['candidate_id'], 'candidate_key': candidate_key, 'baseline_candidate_id': BASE_D5F8_ID, 'selected_seeds': config['selected_seeds'], 'source_tasks': SOURCE_TASKS, 'tflops': candidate_metric, 'baseline_d5f8_tflops': baseline_metric, 'metric_delta_vs_d5f8': candidate_metric - baseline_metric if candidate_metric is not None and baseline_metric is not None else None, 'all_correct': candidate_report['summary']['all_correct'], 'baseline_all_correct': baseline_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'baseline_performance_comparable': baseline_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'baseline_invalid_performance_reason': baseline_report['summary']['invalid_performance_reason'], 'measured_entrypoint': config['benchmark_entrypoint'], 'baseline_entrypoint': BASE_D5F8_ENTRYPOINT, 'route_entrypoint': ROUTE_ENTRYPOINT, 'measured_shape_labels': _payload_shape_labels(shape_labels), 'timing_backend': timing_backend, 'denominator': denominator, 'selected_route_labels': config['expected_shape_wins'], 'selected_route_rows': _rows_for_labels(candidate_report, config['expected_shape_wins']), 'baseline_selected_route_rows': _rows_for_labels(baseline_report, config['expected_shape_wins']), 'seed_delta_matrix': _seed_delta_matrix(candidate_key, candidate_report, baseline_report), 'targeted_seed_rows': TARGETED_SEED_ROWS, 'candidate_dispatchers': CANDIDATE_DISPATCHERS, 'rejected_route_combinations': REJECTED_ROUTE_COMBINATIONS, 'selected_candidate_dispatcher': config['candidate_id'], 'guard_plan': config['guard_plan'], 'route_modules': PRODUCTION_ROUTE_MODULES, 'route_trace': route_trace, 'forced_fallback_route_trace': route_trace_for_contract_shapes(shape_labels, candidate_key=candidate_key, force_fallback=True), 'route_trace_included': True, 'contract_summary': candidate_report['summary'], 'baseline_contract_summary': baseline_report['summary'], 'contract_performance': candidate_report['performance'], 'baseline_contract_performance': baseline_report['performance'], 'timing_backends': _timing_backends_for_reports(candidate_report, baseline_report), 'timing_backend_requested': timing_backend, 'use_cupti': use_cupti, 'benchmark_correctness_checked': benchmark_correctness, 'benchmark_time_flashlib': time_flashlib, 'performance_coverage': 'partial' if below_floor else 'pass', 'coverage_only_routes': [], 'hot_bucket_blockers': below_floor, 'flashlib_parity_ledger': {'baseline_ref_scope': 'same_session' if time_flashlib else 'not_available', 'speedup_floor': 1.05, 'rows_below_1x': below_1x, 'rows_below_floor': below_floor, 'omitted_reason': None if time_flashlib else 'benchmark_time_flashlib=false'}, 'rank_objective': {'metric': 'tflops', 'direction': 'maximize', 'value': candidate_metric, 'baseline_d5f8_value': baseline_metric, 'delta_vs_d5f8': candidate_metric - baseline_metric if candidate_metric is not None and baseline_metric is not None else None, 'denominator': denominator}, 'report': candidate_report, 'baseline_report': baseline_report} + +def benchmark_candidate_portfolio(candidate_key: str, *, use_cupti: bool=True, shape_labels=None, baseline_report: dict[str, Any] | None=None, benchmark_correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + if candidate_key == BASE_D5F8_KEY: + baseline = benchmark_baseline_d5f8(use_cupti=use_cupti, shape_labels=shape_labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + return _baseline_sidecar(baseline, shape_labels=shape_labels, denominator=_denominator_name(shape_labels), timing_backend=_timing_backend_name(use_cupti), benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + if baseline_report is None: + baseline_report = benchmark_baseline_d5f8(use_cupti=use_cupti, shape_labels=shape_labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=_candidate_kernel_fn(candidate_key), correctness=benchmark_correctness, time_flashlib=time_flashlib) + return _benchmark_payload(candidate_key, candidate_report, baseline_report, use_cupti=use_cupti, shape_labels=shape_labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + +def benchmark_candidate_parent_cf51_q1_q16_full90_v1(*, use_cupti: bool=True, shape_labels=None, baseline_report: dict[str, Any] | None=None, benchmark_correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + return benchmark_candidate_portfolio(PARENT_CF51_Q1_Q16_KEY, use_cupti=use_cupti, shape_labels=shape_labels, baseline_report=baseline_report, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + +def benchmark_candidate_q128_only_full90_v1(*, use_cupti: bool=True, shape_labels=None, baseline_report: dict[str, Any] | None=None, benchmark_correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + return benchmark_candidate_portfolio(CANDIDATE_Q128_ONLY, use_cupti=use_cupti, shape_labels=shape_labels, baseline_report=baseline_report, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + +def benchmark_candidate_q24_q128_full90_v1(*, use_cupti: bool=True, shape_labels=None, baseline_report: dict[str, Any] | None=None, benchmark_correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + return benchmark_candidate_portfolio(CANDIDATE_Q24_Q128, use_cupti=use_cupti, shape_labels=shape_labels, baseline_report=baseline_report, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + +def _candidate_no_regresses_baseline(payload: dict[str, Any], baseline_value: float | None) -> bool: + value = payload.get('tflops') + return payload.get('all_correct') and payload.get('performance_comparable') and (value is not None) and (baseline_value is None or value >= baseline_value) + +def _best_candidate_key(payloads: dict[str, dict[str, Any]]) -> str | None: + baseline_value = payloads.get(BASE_D5F8_KEY, {}).get('tflops') + q24_q128_payload = payloads.get(CANDIDATE_Q24_Q128, {}) + if _candidate_no_regresses_baseline(q24_q128_payload, baseline_value): + return CANDIDATE_Q24_Q128 + q128_payload = payloads.get(CANDIDATE_Q128_ONLY, {}) + if _candidate_no_regresses_baseline(q128_payload, baseline_value): + return CANDIDATE_Q128_ONLY + parent_payload = payloads.get(PARENT_CF51_Q1_Q16_KEY, {}) + if _candidate_no_regresses_baseline(parent_payload, baseline_value): + return PARENT_CF51_Q1_Q16_KEY + return None + +def _summary_payload(*, payloads: dict[str, dict[str, Any]], artifacts: dict[str, str], denominator: str, timing_backend: str, benchmark_correctness: bool, time_flashlib: bool) -> dict[str, Any]: + selected_key = _best_candidate_key(payloads) + selected_payload = payloads.get(selected_key, {}) if selected_key else {} + baseline_payload = payloads[BASE_D5F8_KEY] + return {'candidate_id': 'dispatcher_synthesis_c3bf_cf51_q1_q16_603d_q24_b0e2_q128_full90_v1', 'measured_entrypoint': ''.join([format(MODULE, ''), ':write_benchmark_artifacts']), 'denominator': denominator, 'timing_backend': timing_backend, 'benchmark_correctness_checked': benchmark_correctness, 'benchmark_time_flashlib': time_flashlib, 'baseline_candidate_key': BASE_D5F8_KEY, 'selected_candidate_key': selected_key, 'selected_candidate_dispatcher': _candidate_id(selected_key) if selected_key else None, 'default_candidate_key': DEFAULT_CANDIDATE_KEY, 'candidate_dispatchers': CANDIDATE_DISPATCHERS, 'rejected_route_combinations': REJECTED_ROUTE_COMBINATIONS, 'candidate_rankings': [{'candidate_key': key, 'candidate_id': payloads[key].get('candidate_id'), 'tflops': payloads[key].get('tflops'), 'metric_delta_vs_d5f8': payloads[key].get('metric_delta_vs_d5f8'), 'all_correct': payloads[key].get('all_correct'), 'performance_comparable': payloads[key].get('performance_comparable'), 'performance_coverage': payloads[key].get('performance_coverage')} for key in (BASE_D5F8_KEY, PARENT_CF51_Q1_Q16_KEY, CANDIDATE_Q128_ONLY, CANDIDATE_Q24_Q128) if key in payloads], 'seed_delta_matrix': selected_payload.get('seed_delta_matrix', []), 'flashlib_parity_ledger': selected_payload.get('flashlib_parity_ledger', {}), 'full_denominator_ab': {'baseline_payload': artifacts.get('same_session_baseline_payload'), 'candidate_payload': artifacts.get(''.join([format(selected_key, ''), '_payload'])) if selected_key else None, 'denominator': denominator, 'timing_backend': timing_backend, 'metric_delta': selected_payload.get('metric_delta_vs_d5f8'), 'route_trace': selected_payload.get('route_trace', [])}, 'baseline_tflops': baseline_payload.get('tflops'), 'selected_tflops': selected_payload.get('tflops'), 'artifacts': artifacts} + +def write_benchmark_artifacts(artifact_dir: str | Path, *, use_cupti: bool=True, shape_labels=None, benchmark_correctness: bool=True, time_flashlib: bool=True, candidate_keys: tuple[str, ...] | None=None) -> dict[str, str]: + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + denom_label = _denominator_label(shape_labels) + denominator = _denominator_name(shape_labels) + timing_backend = _timing_backend_name(use_cupti) + baseline_report = benchmark_baseline_d5f8(use_cupti=use_cupti, shape_labels=shape_labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + baseline_payload = _baseline_sidecar(baseline_report, shape_labels=shape_labels, denominator=denominator, timing_backend=timing_backend, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + artifacts: dict[str, str] = {} + payloads = {BASE_D5F8_KEY: baseline_payload} + baseline_path = out_dir / ''.join([format(denom_label, ''), '_same_session_baseline_c3bf_d5f8_v1.json']) + baseline_path.write_text(json.dumps(baseline_payload, indent=2, sort_keys=True) + '\n', encoding='utf-8') + artifacts['same_session_baseline_payload'] = str(baseline_path) + selected_candidate_keys = [PARENT_CF51_Q1_Q16_KEY, CANDIDATE_Q128_ONLY, CANDIDATE_Q24_Q128] if candidate_keys is None else list(candidate_keys) + for candidate_key in selected_candidate_keys: + if candidate_key == BASE_D5F8_KEY: + raise ValueError('candidate_keys must not include the baseline key') + _candidate_config(candidate_key) + payload = benchmark_candidate_portfolio(candidate_key, use_cupti=use_cupti, shape_labels=shape_labels, baseline_report=baseline_report, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + payloads[candidate_key] = payload + payload_path = out_dir / ''.join([format(denom_label, ''), '_dispatch_', format(candidate_key, ''), '_v1.json']) + trace_path = out_dir / ''.join([format(denom_label, ''), '_route_trace_', format(candidate_key, ''), '_v1.json']) + forced_trace_path = out_dir / ''.join([format(denom_label, ''), '_forced_fallback_trace_', format(candidate_key, ''), '_v1.json']) + seed_matrix_path = out_dir / ''.join([format(denom_label, ''), '_seed_delta_matrix_', format(candidate_key, ''), '_v1.json']) + payload_path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n', encoding='utf-8') + trace_path.write_text(json.dumps(payload['route_trace'], indent=2, sort_keys=True) + '\n', encoding='utf-8') + forced_trace_path.write_text(json.dumps(payload['forced_fallback_route_trace'], indent=2, sort_keys=True) + '\n', encoding='utf-8') + seed_matrix_path.write_text(json.dumps(payload['seed_delta_matrix'], indent=2, sort_keys=True) + '\n', encoding='utf-8') + artifacts[''.join([format(candidate_key, ''), '_payload'])] = str(payload_path) + artifacts[''.join([format(candidate_key, ''), '_route_trace'])] = str(trace_path) + artifacts[''.join([format(candidate_key, ''), '_forced_fallback_trace'])] = str(forced_trace_path) + artifacts[''.join([format(candidate_key, ''), '_seed_delta_matrix'])] = str(seed_matrix_path) + summary = _summary_payload(payloads=payloads, artifacts=artifacts, denominator=denominator, timing_backend=timing_backend, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + summary_path = out_dir / ''.join([format(denom_label, ''), '_dispatcher_synthesis_c3bf_cf51_603d_q24_b0e2_q128_v1.json']) + summary_path.write_text(json.dumps(summary, indent=2, sort_keys=True) + '\n', encoding='utf-8') + artifacts['dispatcher_synthesis'] = str(summary_path) + return artifacts diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_784a_cf51_q1_q16_q4096_seed_portfolio_full90_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_784a_cf51_q1_q16_q4096_seed_portfolio_full90_v1.py new file mode 100644 index 00000000..e5bdb65d --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_784a_cf51_q1_q16_q4096_seed_portfolio_full90_v1.py @@ -0,0 +1,438 @@ +"""Full90 cf51 seed-portfolio synthesis over the c3bf/d5f8 dispatcher. + +Minimum target architecture: sm_100a. This additive dispatcher-synthesis +wrapper preserves the c3bf/d5f8 full90 dispatcher as the fallback and tests +three guarded portfolios: + +* cf51 Q1024/K8 only. +* cf51 Q1024/K8 plus bca0 Q1 online and 5018 Q16/K32 large-M. +* The same portfolio plus the optional 485e Q4096/K8 split4 route. + +All production routes are Weave modules. FlashLib is used only by the contract +benchmark harness as a reference timing baseline. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +from pathlib import Path +from typing import Any, Callable +from . import knn_build_dispatch_784a_6bc3_k8_q512k456_q4096k8_direct_fcf2_v1 as base_d5f8 +from . import knn_build_dispatch_784a_6bc3_k8_q512k456_q4096k8_s4_direct_c3bf_v1 as q4096_s4 +from . import knn_build_q1024_k8_195e_v1 as q1024_cf51 +from . import knn_build_rag_microbucket_k32_q16dual2warp_largem_bdd2_v1 as q16_bdd2 +from . import knn_build_ragonline_mbucket_5706_q1v10_smix_v1 as q1_5706 +MODULE = 'loom.examples.weave.knn_build_dispatch_784a_cf51_q1_q16_q4096_seed_portfolio_full90_v1' +eval_mod = base_d5f8.eval_mod +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbatch_m64_d4f7_stage1", "arg_keys": ["query", "database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 91392, "constants": [], "cta_group": 1, "threads": 512}')) +BASE_D5F8_KEY = 'base_c3bf_d5f8' +CANDIDATE_CF51_ONLY = 'c3bf_plus_cf51_q1024' +CANDIDATE_CF51_Q1_Q16 = 'c3bf_plus_cf51_q1024_bca0_q1_5018_q16' +CANDIDATE_CF51_Q1_Q16_Q4096_S4 = 'c3bf_plus_cf51_q1024_bca0_q1_5018_q16_485e_q4096_s4' +DEFAULT_CANDIDATE_KEY = CANDIDATE_CF51_Q1_Q16 +CANDIDATE_KEYS = (BASE_D5F8_KEY, CANDIDATE_CF51_ONLY, CANDIDATE_CF51_Q1_Q16, CANDIDATE_CF51_Q1_Q16_Q4096_S4) +BASE_D5F8_ID = 'c3bf_d5f8_full90_baseline' +BASE_D5F8_ENTRYPOINT = base_d5f8.CANDIDATE_ENTRYPOINT +BASE_D5F8_ROUTE_ENTRYPOINT = ''.join([format(base_d5f8.MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +CANDIDATE_CF51_ONLY_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_candidate_cf51_only_full90_v1']) +CANDIDATE_CF51_Q1_Q16_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_candidate_cf51_q1_q16_full90_v1']) +CANDIDATE_CF51_Q1_Q16_Q4096_S4_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_candidate_cf51_q1_q16_q4096_s4_full90_v1']) +SEED_Q1024_CF51_ID = q1024_cf51.CANDIDATE_ID +SEED_Q1_5706_ID = 'ragonline_mbucket_5706_q1v10_smix_s144_g12' +SEED_Q16_BDD2_ID = q16_bdd2.SEED_K32_Q16_DUAL_2WARP_LARGEM_BDD2_V1_ID +SEED_Q4096_S4_ID = q4096_s4.SEED_Q4096_K8_DIRECT_ID +Q1024_TARGET_SHAPES = q1024_cf51.TARGET_SHAPES +Q1_TARGET_SHAPES = q1_5706.TARGET_SHAPES +Q16_TARGET_SHAPES = q16_bdd2.Q16_DUAL_2WARP_LARGEM_TARGET_SHAPES +Q4096_S4_TARGET_SHAPES = q4096_s4.Q4096_K8_TARGET_SHAPES +CF51_Q1_Q16_TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_qm1024_d128_k8", "rag_online_b1_q1_m100000_d128_k10", "rag_online_b1_q1_m65536_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "rag_online_irregular_b1_q1_m262143_d128_k10", "rag_online_irregular_b1_q1_m524287_d128_k10", "rag_microbatch_largek_b1_q16_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m131071_d128_k32", "rag_microbatch_largek_b1_q16_m250000_d128_k32"]}')) +CF51_Q1_Q16_Q4096_S4_TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_qm1024_d128_k8", "rag_online_b1_q1_m100000_d128_k10", "rag_online_b1_q1_m65536_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "rag_online_irregular_b1_q1_m262143_d128_k10", "rag_online_irregular_b1_q1_m524287_d128_k10", "rag_microbatch_largek_b1_q16_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m131071_d128_k32", "rag_microbatch_largek_b1_q16_m250000_d128_k32", "build_qm4096_d128_k8"]}')) +SOURCE_TASKS = {**base_d5f8.SOURCE_TASKS, SEED_Q1024_CF51_ID: 'weave-evolve-knn-build-cf51 / design_doc/active/weave_evolve_knn_build_round_132_195e_q1024_k8_seed.md', SEED_Q1_5706_ID: 'weave-evolve-knn-build-bca0 / design_doc/active/weave_evolve_knn_build_round_130_5706_q1v10_smix.md', SEED_Q16_BDD2_ID: 'weave-evolve-knn-build-5018 / design_doc/active/weave_evolve_knn_build_round_130_bdd2_q16dual2warp_largem.md', SEED_Q4096_S4_ID: 'weave-evolve-knn-build-485e / design_doc/active/weave_evolve_knn_build_round_133_c3bf_q4096k8_s4.md'} +PRODUCTION_ROUTE_MODULES = {**base_d5f8.PRODUCTION_ROUTE_MODULES, SEED_Q1024_CF51_ID: ''.join([format(q1024_cf51.MODULE, ''), ':launch_from_contract_inputs']), SEED_Q1_5706_ID: ''.join([format(q1_5706.MODULE, ''), ':launch_from_contract_inputs']), SEED_Q16_BDD2_ID: q16_bdd2.ROUTE_Q16_DUAL_2WARP_LARGEM_ENTRYPOINT, SEED_Q4096_S4_ID: q4096_s4.ROUTE_Q4096_K8_S4, BASE_D5F8_ID: BASE_D5F8_ROUTE_ENTRYPOINT} +TARGETED_SEED_ROWS = {SEED_Q1024_CF51_ID: {'source_payload': 'artifacts/weave_evolve/knn_build_q1024_k8_195e_v1/q1024_k8_195e_v1_cupti.json', 'shape_labels': Q1024_TARGET_SHAPES}, SEED_Q1_5706_ID: {'source_payload': 'artifacts/weave_evolve/knn_build_5706_q1v10_smix_v1/q1v10_5706_candidate_q1v10_smix_6row_cupti.json', 'shape_labels': Q1_TARGET_SHAPES}, SEED_Q16_BDD2_ID: {'source_payload': 'artifacts/weave_evolve/knn_build_q16dual2warp_largem_bdd2_v1/q16dual2warp_largem_bdd2_v1_q16_bucket_cupti.json', 'shape_labels': Q16_TARGET_SHAPES}, SEED_Q4096_S4_ID: {'source_payload': 'artifacts/weave_evolve/knn_build_c3bf_q4096k8_s4_direct_v1_full/full90_dispatch_784a_direct_q512k456_q4096k8_s4_plus_6bc3_k8_v1.json', 'shape_labels': Q4096_S4_TARGET_SHAPES}} + +def _select_contract_shapes(shape_labels): + return base_d5f8._select_contract_shapes(shape_labels) + +def _trace_inputs_for_shape(shape: dict[str, Any]) -> dict[str, Any]: + return base_d5f8._trace_inputs_for_shape(shape) + +def _normalize_route_row(row: dict[str, Any]) -> dict[str, Any]: + return base_d5f8._normalize_route_row(row) + +def _inputs_for_label(label: str) -> dict[str, Any]: + return _trace_inputs_for_shape(_select_contract_shapes((label,))[0]) + +def _timing_backend_name(use_cupti: bool) -> str: + return 'cupti' if use_cupti else 'cuda_event_fallback' + +def _payload_shape_labels(shape_labels) -> str | tuple[str, ...]: + if shape_labels is None: + return 'all_canonical' + return tuple((str(label) for label in shape_labels)) + +def _denominator_name(shape_labels) -> str: + if shape_labels is None: + return ''.join(['full', format(len(eval_mod.CANONICAL_SHAPES), ''), '_v10']) + labels = tuple((str(label) for label in shape_labels)) + if labels == Q1024_TARGET_SHAPES: + return 'cf51_q1024_seed_targets' + if labels == CF51_Q1_Q16_TARGET_SHAPES: + return 'cf51_q1_q16_seed_targets' + if labels == CF51_Q1_Q16_Q4096_S4_TARGET_SHAPES: + return 'cf51_q1_q16_q4096_s4_seed_targets' + return ''.join(['shape', format(len(labels), '')]) + +def _denominator_label(shape_labels) -> str: + if shape_labels is None: + return ''.join(['full', format(len(eval_mod.CANONICAL_SHAPES), '')]) + return ''.join(['shape', format(len(tuple(shape_labels)), '')]) + +def _rows_for_labels(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return base_d5f8._rows_for_labels(report, labels) + +def _timing_backends_for_reports(*reports: dict[str, Any]) -> list[str]: + return base_d5f8._timing_backends_for_reports(*reports) + +def _candidate_config(candidate_key: str) -> dict[str, Any]: + try: + return CANDIDATE_CONFIGS[candidate_key] + except KeyError as exc: + raise ValueError(''.join(['unknown full90 seed-portfolio candidate ', format(repr(candidate_key), '')])) from exc + +def _candidate_id(candidate_key: str | None) -> str | None: + if candidate_key is None: + return None + return str(_candidate_config(candidate_key)['candidate_id']) + +def _eligible_q1024_cf51(inputs: dict[str, Any]) -> bool: + return q1024_cf51._eligible_q1024_k8(inputs) + +def _eligible_q1_5706(inputs: dict[str, Any]) -> bool: + return q1_5706._eligible_q1_mix(inputs) + +def _eligible_q16_bdd2(inputs: dict[str, Any]) -> bool: + return q16_bdd2._eligible_q16_dual_2warp_largem(inputs) + +def _candidate_includes_q4096_s4(candidate_key: str) -> bool: + return candidate_key == CANDIDATE_CF51_Q1_Q16_Q4096_S4 + +def _eligible_q4096_s4(inputs: dict[str, Any], candidate_key: str) -> bool: + return _candidate_includes_q4096_s4(candidate_key) and q4096_s4._eligible_q4096_k8_direct(inputs) + +def _expected_seed(inputs: dict[str, Any], candidate_key: str) -> str | None: + if candidate_key in (CANDIDATE_CF51_ONLY, CANDIDATE_CF51_Q1_Q16, CANDIDATE_CF51_Q1_Q16_Q4096_S4): + if _eligible_q1024_cf51(inputs): + return SEED_Q1024_CF51_ID + if candidate_key in (CANDIDATE_CF51_Q1_Q16, CANDIDATE_CF51_Q1_Q16_Q4096_S4): + if _eligible_q1_5706(inputs): + return SEED_Q1_5706_ID + if _eligible_q16_bdd2(inputs): + return SEED_Q16_BDD2_ID + if _eligible_q4096_s4(inputs, candidate_key): + return SEED_Q4096_S4_ID + return None + +def _base_d5f8_route(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + return base_d5f8.route_for_contract_inputs(inputs, force_fallback=force_fallback) + +def _base_d5f8_launch(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + base_d5f8.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + +def route_for_contract_inputs(inputs: dict[str, Any], *, candidate_key: str=DEFAULT_CANDIDATE_KEY, force_fallback: bool=False) -> str: + _candidate_config(candidate_key) + if force_fallback or candidate_key == BASE_D5F8_KEY: + return _base_d5f8_route(inputs, force_fallback=force_fallback) + if _eligible_q1024_cf51(inputs): + return q1024_cf51.route_for_contract_inputs(inputs) + if candidate_key in (CANDIDATE_CF51_Q1_Q16, CANDIDATE_CF51_Q1_Q16_Q4096_S4) and _eligible_q1_5706(inputs): + return q1_5706.route_for_contract_inputs(inputs) + if candidate_key in (CANDIDATE_CF51_Q1_Q16, CANDIDATE_CF51_Q1_Q16_Q4096_S4) and _eligible_q16_bdd2(inputs): + return q16_bdd2.route_for_contract_inputs(inputs) + if _eligible_q4096_s4(inputs, candidate_key): + return q4096_s4.ROUTE_Q4096_K8_S4 + return _base_d5f8_route(inputs) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, candidate_key: str=DEFAULT_CANDIDATE_KEY, force_fallback: bool=False) -> None: + _candidate_config(candidate_key) + if force_fallback or candidate_key == BASE_D5F8_KEY: + _base_d5f8_launch(inputs, force_fallback=force_fallback) + return + if _eligible_q1024_cf51(inputs): + q1024_cf51.launch_from_contract_inputs(inputs) + return + if candidate_key in (CANDIDATE_CF51_Q1_Q16, CANDIDATE_CF51_Q1_Q16_Q4096_S4) and _eligible_q1_5706(inputs): + q1_5706.launch_from_contract_inputs(inputs) + return + if candidate_key in (CANDIDATE_CF51_Q1_Q16, CANDIDATE_CF51_Q1_Q16_Q4096_S4) and _eligible_q16_bdd2(inputs): + q16_bdd2.launch_from_contract_inputs(inputs) + return + if _eligible_q4096_s4(inputs, candidate_key): + q4096_s4.launch_from_contract_inputs(inputs) + return + _base_d5f8_launch(inputs) + +def candidate_baseline_d5f8(inputs: dict[str, Any]) -> None: + _base_d5f8_launch(inputs) + +def candidate_cf51_only_full90_v1(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, candidate_key=CANDIDATE_CF51_ONLY) + +def candidate_cf51_q1_q16_full90_v1(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, candidate_key=CANDIDATE_CF51_Q1_Q16) + +def candidate_cf51_q1_q16_q4096_s4_full90_v1(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, candidate_key=CANDIDATE_CF51_Q1_Q16_Q4096_S4) + +def candidate(inputs: dict[str, Any]) -> None: + candidate_cf51_q1_q16_full90_v1(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, candidate_key=DEFAULT_CANDIDATE_KEY, force_fallback=True) + +def _candidate_kernel_fn(candidate_key: str) -> Callable[[dict[str, Any]], None]: + if candidate_key == BASE_D5F8_KEY: + return candidate_baseline_d5f8 + if candidate_key == CANDIDATE_CF51_ONLY: + return candidate_cf51_only_full90_v1 + if candidate_key == CANDIDATE_CF51_Q1_Q16: + return candidate_cf51_q1_q16_full90_v1 + if candidate_key == CANDIDATE_CF51_Q1_Q16_Q4096_S4: + return candidate_cf51_q1_q16_q4096_s4_full90_v1 + raise ValueError(''.join(['unknown full90 seed-portfolio candidate ', format(repr(candidate_key), '')])) +CANDIDATE_CONFIGS = _decode_capture(_json_loads('{"__dict_items__": [["base_c3bf_d5f8", {"__dict_items__": [["candidate_id", "c3bf_d5f8_full90_baseline"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_784a_6bc3_k8_q512k456_q4096k8_direct_fcf2_v1:launch_from_contract_inputs"], ["benchmark_entrypoint", "loom.examples.weave.knn_build_dispatch_784a_6bc3_k8_q512k456_q4096k8_direct_fcf2_v1:benchmark_candidate_784a_direct_q512_k456_q4096_k8_plus_6bc3_k8_v1"], ["selected_seeds", {"__tuple__": ["v20_k12_q1024_q4096_exact", "q2048_k12_4f30_v1", "v12_k20_q2048k10_mixedfanout", "over32_k48_v25", "6bc3_v20_q512_k8_static_s8", "6bc3_v20_q2048_k8_static_s8", "fcf2_direct_lowk_q512_k4_k5_k6_s4", "fcf2_direct_v20_q4096_k8_s8"]}], ["guard_plan", {"__tuple__": ["direct exact BF16 build Q4096 K8 v20 split8 guard", "then direct exact BF16 build Q512 K4/K5/K6 low-K split4 guard", "then fcf2 selective 6bc3 exact Q512/Q2048 K8 guards", "then 784a 005f build-lowfloor portfolio", "then inherited a444/9db7 full82 Weave fallback"]}], ["expected_shape_wins", {"__tuple__": ["build_k_sweep_qm512_k4", "build_k_sweep_qm512_k5", "build_k_sweep_qm512_k6", "build_k_sweep_qm512_k8", "build_qm2048_d128_k8", "build_qm4096_d128_k8"]}], ["fallback", "loom.examples.weave.knn_build_dispatch_784a_6bc3_k8_q512k456_direct_fcf2_v1:launch_from_contract_inputs"], ["rejected_reason", "same-session c3bf/d5f8 baseline"]]}], ["c3bf_plus_cf51_q1024", {"__dict_items__": [["candidate_id", "candidate_c3bf_cf51_q1024_full90_v1"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_784a_cf51_q1_q16_q4096_seed_portfolio_full90_v1:candidate_cf51_only_full90_v1"], ["benchmark_entrypoint", "loom.examples.weave.knn_build_dispatch_784a_cf51_q1_q16_q4096_seed_portfolio_full90_v1:benchmark_candidate_cf51_only_full90_v1"], ["selected_seeds", {"__tuple__": ["q1024_k8_195e_v1"]}], ["guard_plan", {"__tuple__": ["cf51 exact BF16 build Q=M=1024 D=128 K=8 split16", "c3bf/d5f8 full90 Weave fallback"]}], ["expected_shape_wins", {"__tuple__": ["build_qm1024_d128_k8"]}], ["fallback", "loom.examples.weave.knn_build_dispatch_784a_6bc3_k8_q512k456_q4096k8_direct_fcf2_v1:launch_from_contract_inputs"], ["rejected_reason", null]]}], ["c3bf_plus_cf51_q1024_bca0_q1_5018_q16", {"__dict_items__": [["candidate_id", "candidate_c3bf_cf51_q1024_bca0_q1_5018_q16_full90_v1"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_784a_cf51_q1_q16_q4096_seed_portfolio_full90_v1:candidate_cf51_q1_q16_full90_v1"], ["benchmark_entrypoint", "loom.examples.weave.knn_build_dispatch_784a_cf51_q1_q16_q4096_seed_portfolio_full90_v1:benchmark_candidate_cf51_q1_q16_full90_v1"], ["selected_seeds", {"__tuple__": ["q1024_k8_195e_v1", "ragonline_mbucket_5706_q1v10_smix_s144_g12", "rag_microbucket_k32_q16dual2warp_largem_bdd2_v1_rowld1_2warp_rows4"]}], ["guard_plan", {"__tuple__": ["cf51 exact BF16 build Q=M=1024 D=128 K=8 split16", "bca0 exact BF16 non-build Q=1 D=128 K=10 M in {65536,100000,131071,250000,262143,524287}", "5018 exact BF16 non-build Q=16 D=128 K=32 M in {100000,131071,250000}", "c3bf/d5f8 full90 Weave fallback"]}], ["expected_shape_wins", {"__tuple__": ["build_qm1024_d128_k8", "rag_online_b1_q1_m100000_d128_k10", "rag_online_b1_q1_m65536_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "rag_online_irregular_b1_q1_m262143_d128_k10", "rag_online_irregular_b1_q1_m524287_d128_k10", "rag_microbatch_largek_b1_q16_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m131071_d128_k32", "rag_microbatch_largek_b1_q16_m250000_d128_k32"]}], ["fallback", "loom.examples.weave.knn_build_dispatch_784a_6bc3_k8_q512k456_q4096k8_direct_fcf2_v1:launch_from_contract_inputs"], ["rejected_reason", null]]}], ["c3bf_plus_cf51_q1024_bca0_q1_5018_q16_485e_q4096_s4", {"__dict_items__": [["candidate_id", "candidate_c3bf_cf51_q1024_bca0_q1_5018_q16_485e_q4096_s4_full90_v1"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_784a_cf51_q1_q16_q4096_seed_portfolio_full90_v1:candidate_cf51_q1_q16_q4096_s4_full90_v1"], ["benchmark_entrypoint", "loom.examples.weave.knn_build_dispatch_784a_cf51_q1_q16_q4096_seed_portfolio_full90_v1:benchmark_candidate_cf51_q1_q16_q4096_s4_full90_v1"], ["selected_seeds", {"__tuple__": ["q1024_k8_195e_v1", "ragonline_mbucket_5706_q1v10_smix_s144_g12", "rag_microbucket_k32_q16dual2warp_largem_bdd2_v1_rowld1_2warp_rows4", "c3bf_direct_v20_q4096_k8_s4"]}], ["guard_plan", {"__tuple__": ["cf51 exact BF16 build Q=M=1024 D=128 K=8 split16", "bca0 exact BF16 non-build Q=1 D=128 K=10 M in {65536,100000,131071,250000,262143,524287}", "5018 exact BF16 non-build Q=16 D=128 K=32 M in {100000,131071,250000}", "485e exact BF16 build Q=M=4096 D=128 K=8 v20 split4", "c3bf/d5f8 full90 Weave fallback"]}], ["expected_shape_wins", {"__tuple__": ["build_qm1024_d128_k8", "rag_online_b1_q1_m100000_d128_k10", "rag_online_b1_q1_m65536_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "rag_online_irregular_b1_q1_m262143_d128_k10", "rag_online_irregular_b1_q1_m524287_d128_k10", "rag_microbatch_largek_b1_q16_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m131071_d128_k32", "rag_microbatch_largek_b1_q16_m250000_d128_k32", "build_qm4096_d128_k8"]}], ["fallback", "loom.examples.weave.knn_build_dispatch_784a_6bc3_k8_q512k456_q4096k8_direct_fcf2_v1:launch_from_contract_inputs"], ["rejected_reason", null]]}]]}')) +CANDIDATE_DISPATCHERS = _decode_capture(_json_loads('{"__tuple__": [{"__dict_items__": [["id", "c3bf_d5f8_full90_baseline"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_784a_6bc3_k8_q512k456_q4096k8_direct_fcf2_v1:benchmark_candidate_784a_direct_q512_k456_q4096_k8_plus_6bc3_k8_v1"], ["consumed_seeds", {"__tuple__": ["v20_k12_q1024_q4096_exact", "q2048_k12_4f30_v1", "v12_k20_q2048k10_mixedfanout", "over32_k48_v25", "6bc3_v20_q512_k8_static_s8", "6bc3_v20_q2048_k8_static_s8", "fcf2_direct_lowk_q512_k4_k5_k6_s4", "fcf2_direct_v20_q4096_k8_s8"]}], ["guard_plan", {"__tuple__": ["direct exact BF16 build Q4096 K8 v20 split8 guard", "then direct exact BF16 build Q512 K4/K5/K6 low-K split4 guard", "then fcf2 selective 6bc3 exact Q512/Q2048 K8 guards", "then 784a 005f build-lowfloor portfolio", "then inherited a444/9db7 full82 Weave fallback"]}], ["expected_shape_wins", {"__tuple__": ["build_k_sweep_qm512_k4", "build_k_sweep_qm512_k5", "build_k_sweep_qm512_k6", "build_k_sweep_qm512_k8", "build_qm2048_d128_k8", "build_qm4096_d128_k8"]}], ["fallback", "loom.examples.weave.knn_build_dispatch_784a_6bc3_k8_q512k456_direct_fcf2_v1:launch_from_contract_inputs"], ["rejected_reason", "same-session c3bf/d5f8 baseline"]]}, {"__dict_items__": [["id", "candidate_c3bf_cf51_q1024_full90_v1"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_784a_cf51_q1_q16_q4096_seed_portfolio_full90_v1:benchmark_candidate_cf51_only_full90_v1"], ["consumed_seeds", {"__tuple__": ["q1024_k8_195e_v1"]}], ["guard_plan", {"__tuple__": ["cf51 exact BF16 build Q=M=1024 D=128 K=8 split16", "c3bf/d5f8 full90 Weave fallback"]}], ["expected_shape_wins", {"__tuple__": ["build_qm1024_d128_k8"]}], ["fallback", "loom.examples.weave.knn_build_dispatch_784a_6bc3_k8_q512k456_q4096k8_direct_fcf2_v1:launch_from_contract_inputs"], ["rejected_reason", null]]}, {"__dict_items__": [["id", "candidate_c3bf_cf51_q1024_bca0_q1_5018_q16_full90_v1"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_784a_cf51_q1_q16_q4096_seed_portfolio_full90_v1:benchmark_candidate_cf51_q1_q16_full90_v1"], ["consumed_seeds", {"__tuple__": ["q1024_k8_195e_v1", "ragonline_mbucket_5706_q1v10_smix_s144_g12", "rag_microbucket_k32_q16dual2warp_largem_bdd2_v1_rowld1_2warp_rows4"]}], ["guard_plan", {"__tuple__": ["cf51 exact BF16 build Q=M=1024 D=128 K=8 split16", "bca0 exact BF16 non-build Q=1 D=128 K=10 M in {65536,100000,131071,250000,262143,524287}", "5018 exact BF16 non-build Q=16 D=128 K=32 M in {100000,131071,250000}", "c3bf/d5f8 full90 Weave fallback"]}], ["expected_shape_wins", {"__tuple__": ["build_qm1024_d128_k8", "rag_online_b1_q1_m100000_d128_k10", "rag_online_b1_q1_m65536_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "rag_online_irregular_b1_q1_m262143_d128_k10", "rag_online_irregular_b1_q1_m524287_d128_k10", "rag_microbatch_largek_b1_q16_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m131071_d128_k32", "rag_microbatch_largek_b1_q16_m250000_d128_k32"]}], ["fallback", "loom.examples.weave.knn_build_dispatch_784a_6bc3_k8_q512k456_q4096k8_direct_fcf2_v1:launch_from_contract_inputs"], ["rejected_reason", null]]}, {"__dict_items__": [["id", "candidate_c3bf_cf51_q1024_bca0_q1_5018_q16_485e_q4096_s4_full90_v1"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_784a_cf51_q1_q16_q4096_seed_portfolio_full90_v1:benchmark_candidate_cf51_q1_q16_q4096_s4_full90_v1"], ["consumed_seeds", {"__tuple__": ["q1024_k8_195e_v1", "ragonline_mbucket_5706_q1v10_smix_s144_g12", "rag_microbucket_k32_q16dual2warp_largem_bdd2_v1_rowld1_2warp_rows4", "c3bf_direct_v20_q4096_k8_s4"]}], ["guard_plan", {"__tuple__": ["cf51 exact BF16 build Q=M=1024 D=128 K=8 split16", "bca0 exact BF16 non-build Q=1 D=128 K=10 M in {65536,100000,131071,250000,262143,524287}", "5018 exact BF16 non-build Q=16 D=128 K=32 M in {100000,131071,250000}", "485e exact BF16 build Q=M=4096 D=128 K=8 v20 split4", "c3bf/d5f8 full90 Weave fallback"]}], ["expected_shape_wins", {"__tuple__": ["build_qm1024_d128_k8", "rag_online_b1_q1_m100000_d128_k10", "rag_online_b1_q1_m65536_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "rag_online_irregular_b1_q1_m262143_d128_k10", "rag_online_irregular_b1_q1_m524287_d128_k10", "rag_microbatch_largek_b1_q16_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m131071_d128_k32", "rag_microbatch_largek_b1_q16_m250000_d128_k32", "build_qm4096_d128_k8"]}], ["fallback", "loom.examples.weave.knn_build_dispatch_784a_6bc3_k8_q512k456_q4096k8_direct_fcf2_v1:launch_from_contract_inputs"], ["rejected_reason", null]]}]}')) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def compile_and_launch_knn_build(*, shape_labels=CF51_Q1_Q16_TARGET_SHAPES, benchmark: bool=False, candidate_key: str=DEFAULT_CANDIDATE_KEY) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark, kernel_fn=_candidate_kernel_fn(candidate_key)) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=None, kernel_fn: Callable[[dict[str, Any]], Any]=candidate, correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + return base_d5f8._run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=kernel_fn, correctness=correctness, time_flashlib=time_flashlib) + +def _seed_route_row(inputs: dict[str, Any], *, selected_seed: str, selected_route: str, selected_entrypoint: str, guard_id: str, guard_condition: str, force_fallback: bool=False) -> dict[str, Any]: + label = str(inputs.get('label')) + if force_fallback: + row = dict(base_d5f8.route_trace_for_contract_shapes((label,), force_fallback=True)[0]) + row['expected_seed'] = selected_seed + row['guard_id'] = ''.join(['forced_fallback_', format(selected_seed, ''), '_disabled']) + row['guard_condition'] = ''.join(['forced fallback to c3bf/d5f8; ', format(selected_seed, ''), ' disabled']) + row['classification'] = 'guard-miss' + return _normalize_route_row(row) + baseline_route = _base_d5f8_route(inputs) + return _normalize_route_row({'shape_key': label, 'selected_route': selected_route, 'selected_entrypoint': selected_entrypoint, 'selected_seed': selected_seed, 'expected_seed': selected_seed, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': guard_id, 'guard_condition': guard_condition, 'coverage': 'synthesized seed portfolio overlay before c3bf/d5f8 fallback', 'consumed_seed': selected_seed, 'replaced_route': baseline_route, 'baseline_dispatcher_route': baseline_route, 'baseline_d5f8_route': baseline_route, 'targeted_seed_row': TARGETED_SEED_ROWS.get(selected_seed, {}), 'classification': 'unmeasured'}) + +def _q1_trace_record(inputs: dict[str, Any], *, force_fallback: bool=False) -> dict[str, Any]: + split_count, group_count = q1_5706._topology_for_inputs(inputs) + row = _seed_route_row(inputs, selected_seed=SEED_Q1_5706_ID, selected_route=q1_5706.route_for_contract_inputs(inputs), selected_entrypoint=''.join([format(q1_5706.MODULE, ''), ':launch_from_contract_inputs']), guard_id='bca0_q1_v10_exact_m_s144_g12_guard', guard_condition='BF16 non-build B=1 Q=1 M in bca0 v10 set D=128 K=10', force_fallback=force_fallback) + row['split_count'] = split_count + row['group_count'] = group_count + return row + +def _q16_trace_record(inputs: dict[str, Any], *, force_fallback: bool=False) -> dict[str, Any]: + split_count = q16_bdd2._split_for_q16_dual_2warp_largem(inputs, exact_split_count=q16_bdd2.K32_EXACT_Q16_SPLIT_COUNT, irregular_split_count=q16_bdd2.K32_IRREGULAR_Q16_SPLIT_COUNT, largem_split_count=q16_bdd2.K32_LARGEM_Q16_SPLIT_COUNT) + row = _seed_route_row(inputs, selected_seed=SEED_Q16_BDD2_ID, selected_route=q16_bdd2.route_for_contract_inputs(inputs), selected_entrypoint=q16_bdd2.ROUTE_Q16_DUAL_2WARP_LARGEM_ENTRYPOINT, guard_id='5018_q16_k32_dual2warp_largem_guard', guard_condition='BF16 non-build B=1 Q=16 M in {100000,131071,250000} D=128 K=32', force_fallback=force_fallback) + row['split_count'] = split_count + return row + +def _q1024_cf51_trace_record(inputs: dict[str, Any], *, force_fallback: bool=False) -> dict[str, Any]: + return _seed_route_row(inputs, selected_seed=SEED_Q1024_CF51_ID, selected_route=q1024_cf51.route_for_contract_inputs(inputs), selected_entrypoint=''.join([format(q1024_cf51.MODULE, ''), ':launch_from_contract_inputs']), guard_id='cf51_q1024_k8_split16_guard', guard_condition='exact BF16 build B=1 Q=M=1024 D=128 K=8 split16', force_fallback=force_fallback) + +def _q4096_s4_trace_record(inputs: dict[str, Any], *, force_fallback: bool=False) -> dict[str, Any]: + return _seed_route_row(inputs, selected_seed=SEED_Q4096_S4_ID, selected_route=q4096_s4.ROUTE_Q4096_K8_S4, selected_entrypoint=q4096_s4.ROUTE_Q4096_K8_S4, guard_id='485e_q4096_k8_v20_s4_guard', guard_condition='exact BF16 build B=1 Q=M=4096 D=128 K=8 v20 split4', force_fallback=force_fallback) + +def _route_trace_record(inputs: dict[str, Any], *, candidate_key: str, force_fallback: bool=False) -> dict[str, Any]: + label = str(inputs.get('label')) + if candidate_key in (CANDIDATE_CF51_ONLY, CANDIDATE_CF51_Q1_Q16, CANDIDATE_CF51_Q1_Q16_Q4096_S4): + if _eligible_q1024_cf51(inputs): + return _q1024_cf51_trace_record(inputs, force_fallback=force_fallback) + if candidate_key in (CANDIDATE_CF51_Q1_Q16, CANDIDATE_CF51_Q1_Q16_Q4096_S4): + if _eligible_q1_5706(inputs): + return _q1_trace_record(inputs, force_fallback=force_fallback) + if _eligible_q16_bdd2(inputs): + return _q16_trace_record(inputs, force_fallback=force_fallback) + if _eligible_q4096_s4(inputs, candidate_key): + return _q4096_s4_trace_record(inputs, force_fallback=force_fallback) + row = dict(base_d5f8.route_trace_for_contract_shapes((label,), force_fallback=force_fallback)[0]) + row['baseline_dispatcher_route'] = _base_d5f8_route(inputs, force_fallback=force_fallback) + row['baseline_d5f8_route'] = row['baseline_dispatcher_route'] + return _normalize_route_row(row) + +def route_trace_for_contract_shapes(shape_labels=None, *, candidate_key: str=DEFAULT_CANDIDATE_KEY, force_fallback: bool=False) -> list[dict[str, Any]]: + _candidate_config(candidate_key) + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + return [_route_trace_record(_trace_inputs_for_shape(shape), candidate_key=candidate_key, force_fallback=force_fallback) for shape in selected] + +def _annotate_route_trace(route_trace: list[dict[str, Any]], candidate_report: dict[str, Any], baseline_report: dict[str, Any], *, candidate_key: str) -> list[dict[str, Any]]: + expected_labels = set(_candidate_config(candidate_key)['expected_shape_wins']) + annotated = [] + for row in route_trace: + label = str(row.get('shape_key')) + out = dict(row) + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') + speedup_vs_baseline = baseline_ms / candidate_ms if candidate_ms and baseline_ms else None + speedup_vs_external = flashlib_ms / candidate_ms if candidate_ms and flashlib_ms else None + out['dispatcher_kernel_ms'] = candidate_ms + out['baseline_d5f8_kernel_ms'] = baseline_ms + out['external_baseline_ms'] = flashlib_ms + out['flashlib_ms'] = flashlib_ms + out['relative_speedup_vs_baseline'] = speedup_vs_baseline + out['relative_speedup_vs_d5f8'] = speedup_vs_baseline + out['speedup_vs_external_baseline'] = speedup_vs_external + out['external_baseline_ref'] = 'same_session' if flashlib_ms is not None else 'not_available' + out['route_changed_vs_d5f8'] = out.get('selected_route') != out.get('baseline_dispatcher_route') + if label in expected_labels and candidate_key != BASE_D5F8_KEY: + if out.get('selected_seed') != out.get('expected_seed'): + out['classification'] = 'guard-miss' + elif speedup_vs_external is not None and speedup_vs_external < 1.05: + out['classification'] = 'kernel-slow' + elif speedup_vs_baseline is not None and speedup_vs_baseline < 1.0: + out['classification'] = 'kernel-slow' + else: + out['classification'] = 'seed-consumed' + elif speedup_vs_external is not None and speedup_vs_external < 1.05: + out['classification'] = 'kernel-slow' if out.get('route_kind') == 'specialized' else 'fallback-slow' + else: + out['classification'] = 'route-ok' + annotated.append(_normalize_route_row(out)) + return annotated + +def _below_flashlib_rows(report: dict[str, Any], route_trace: list[dict[str, Any]], *, floor: float) -> list[dict[str, Any]]: + trace_by_label = {str(row['shape_key']): row for row in route_trace} + rows = [] + for label, row in sorted(report.get('per_shape', {}).items()): + ratio = row.get('ratio_vs_flashlib') + if isinstance(ratio, (float, int)) and ratio < floor: + trace_row = trace_by_label.get(label, {}) + rows.append({'shape_key': label, 'kernel_ms': row.get('kernel_ms'), 'flashlib_ms': row.get('flashlib_ms'), 'ratio_vs_flashlib': ratio, 'selected_route': trace_row.get('selected_route'), 'selected_seed': trace_row.get('selected_seed'), 'expected_seed': trace_row.get('expected_seed'), 'route_kind': trace_row.get('route_kind', 'unknown'), 'classification': trace_row.get('classification', 'unmeasured')}) + return rows + +def _seed_delta_matrix(candidate_key: str, candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> list[dict[str, Any]]: + matrix = [] + for label in _candidate_config(candidate_key)['expected_shape_wins']: + inputs = _inputs_for_label(label) + selected_seed = _expected_seed(inputs, candidate_key) + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + matrix.append({'shape_key': label, 'baseline_route': _base_d5f8_route(inputs), 'candidate_route': route_for_contract_inputs(inputs, candidate_key=candidate_key), 'selected_seed': selected_seed, 'candidate_id': _candidate_id(candidate_key), 'candidate_ms': candidate_ms, 'baseline_d5f8_ms': baseline_ms, 'flashlib_ms': candidate_row.get('flashlib_ms'), 'delta_ms_candidate_minus_d5f8': candidate_ms - baseline_ms if candidate_ms and baseline_ms else None, 'speedup_vs_d5f8': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'ratio_vs_flashlib': candidate_row.get('ratio_vs_flashlib'), 'targeted_seed_row': TARGETED_SEED_ROWS.get(selected_seed, {}), 'timing_backend': candidate_row.get('timing_backend') or baseline_row.get('timing_backend')}) + return matrix + +def benchmark_baseline_d5f8(*, use_cupti: bool=True, shape_labels=None, benchmark_correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + return _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_baseline_d5f8, correctness=benchmark_correctness, time_flashlib=time_flashlib) + +def _baseline_sidecar(report: dict[str, Any], *, shape_labels, denominator: str, timing_backend: str, benchmark_correctness: bool, time_flashlib: bool) -> dict[str, Any]: + route_trace = route_trace_for_contract_shapes(shape_labels, candidate_key=BASE_D5F8_KEY) + below_1x = _below_flashlib_rows(report, route_trace, floor=1.0) + below_floor = _below_flashlib_rows(report, route_trace, floor=1.05) + return {'candidate_id': BASE_D5F8_ID, 'candidate_key': BASE_D5F8_KEY, 'selected_seeds': CANDIDATE_CONFIGS[BASE_D5F8_KEY]['selected_seeds'], 'source_tasks': SOURCE_TASKS, 'tflops': report['summary']['primary_mean'], 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'invalid_performance_reason': report['summary']['invalid_performance_reason'], 'measured_entrypoint': BASE_D5F8_ENTRYPOINT, 'route_entrypoint': BASE_D5F8_ROUTE_ENTRYPOINT, 'measured_shape_labels': _payload_shape_labels(shape_labels), 'timing_backend': timing_backend, 'denominator': denominator, 'performance_coverage': 'partial' if below_floor else 'pass', 'coverage_only_routes': [], 'hot_bucket_blockers': below_floor, 'flashlib_parity_ledger': {'baseline_ref_scope': 'same_session' if time_flashlib else 'not_available', 'speedup_floor': 1.05, 'rows_below_1x': below_1x, 'rows_below_floor': below_floor, 'omitted_reason': None if time_flashlib else 'benchmark_time_flashlib=false'}, 'route_trace': route_trace, 'route_trace_included': True, 'contract_summary': report['summary'], 'contract_performance': report['performance'], 'timing_backends': _timing_backends_for_reports(report), 'timing_backend_requested': timing_backend, 'benchmark_correctness_checked': benchmark_correctness, 'benchmark_time_flashlib': time_flashlib, 'report': report} + +def _benchmark_payload(candidate_key: str, candidate_report: dict[str, Any], baseline_report: dict[str, Any], *, use_cupti: bool, shape_labels, benchmark_correctness: bool, time_flashlib: bool) -> dict[str, Any]: + candidate_metric = candidate_report['summary']['primary_mean'] + baseline_metric = baseline_report['summary']['primary_mean'] + route_trace = _annotate_route_trace(route_trace_for_contract_shapes(shape_labels, candidate_key=candidate_key), candidate_report, baseline_report, candidate_key=candidate_key) + below_1x = _below_flashlib_rows(candidate_report, route_trace, floor=1.0) + below_floor = _below_flashlib_rows(candidate_report, route_trace, floor=1.05) + denominator = _denominator_name(shape_labels) + timing_backend = _timing_backend_name(use_cupti) + config = _candidate_config(candidate_key) + return {'candidate_id': config['candidate_id'], 'candidate_key': candidate_key, 'baseline_candidate_id': BASE_D5F8_ID, 'selected_seeds': config['selected_seeds'], 'source_tasks': SOURCE_TASKS, 'tflops': candidate_metric, 'baseline_d5f8_tflops': baseline_metric, 'metric_delta_vs_d5f8': candidate_metric - baseline_metric if candidate_metric is not None and baseline_metric is not None else None, 'all_correct': candidate_report['summary']['all_correct'], 'baseline_all_correct': baseline_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'baseline_performance_comparable': baseline_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'baseline_invalid_performance_reason': baseline_report['summary']['invalid_performance_reason'], 'measured_entrypoint': config['benchmark_entrypoint'], 'baseline_entrypoint': BASE_D5F8_ENTRYPOINT, 'route_entrypoint': ROUTE_ENTRYPOINT, 'measured_shape_labels': _payload_shape_labels(shape_labels), 'timing_backend': timing_backend, 'denominator': denominator, 'selected_route_labels': config['expected_shape_wins'], 'selected_route_rows': _rows_for_labels(candidate_report, config['expected_shape_wins']), 'baseline_selected_route_rows': _rows_for_labels(baseline_report, config['expected_shape_wins']), 'seed_delta_matrix': _seed_delta_matrix(candidate_key, candidate_report, baseline_report), 'targeted_seed_rows': TARGETED_SEED_ROWS, 'candidate_dispatchers': CANDIDATE_DISPATCHERS, 'selected_candidate_dispatcher': config['candidate_id'], 'guard_plan': config['guard_plan'], 'route_modules': PRODUCTION_ROUTE_MODULES, 'route_trace': route_trace, 'forced_fallback_route_trace': route_trace_for_contract_shapes(shape_labels, candidate_key=candidate_key, force_fallback=True), 'route_trace_included': True, 'contract_summary': candidate_report['summary'], 'baseline_contract_summary': baseline_report['summary'], 'contract_performance': candidate_report['performance'], 'baseline_contract_performance': baseline_report['performance'], 'timing_backends': _timing_backends_for_reports(candidate_report, baseline_report), 'timing_backend_requested': timing_backend, 'use_cupti': use_cupti, 'benchmark_correctness_checked': benchmark_correctness, 'benchmark_time_flashlib': time_flashlib, 'performance_coverage': 'partial' if below_floor else 'pass', 'coverage_only_routes': [], 'hot_bucket_blockers': below_floor, 'flashlib_parity_ledger': {'baseline_ref_scope': 'same_session' if time_flashlib else 'not_available', 'speedup_floor': 1.05, 'rows_below_1x': below_1x, 'rows_below_floor': below_floor, 'omitted_reason': None if time_flashlib else 'benchmark_time_flashlib=false'}, 'rank_objective': {'metric': 'tflops', 'direction': 'maximize', 'value': candidate_metric, 'baseline_d5f8_value': baseline_metric, 'delta_vs_d5f8': candidate_metric - baseline_metric if candidate_metric is not None and baseline_metric is not None else None, 'denominator': denominator}, 'report': candidate_report, 'baseline_report': baseline_report} + +def benchmark_candidate_portfolio(candidate_key: str, *, use_cupti: bool=True, shape_labels=None, baseline_report: dict[str, Any] | None=None, benchmark_correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + if candidate_key == BASE_D5F8_KEY: + baseline = benchmark_baseline_d5f8(use_cupti=use_cupti, shape_labels=shape_labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + return _baseline_sidecar(baseline, shape_labels=shape_labels, denominator=_denominator_name(shape_labels), timing_backend=_timing_backend_name(use_cupti), benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + if baseline_report is None: + baseline_report = benchmark_baseline_d5f8(use_cupti=use_cupti, shape_labels=shape_labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=_candidate_kernel_fn(candidate_key), correctness=benchmark_correctness, time_flashlib=time_flashlib) + return _benchmark_payload(candidate_key, candidate_report, baseline_report, use_cupti=use_cupti, shape_labels=shape_labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + +def benchmark_candidate_cf51_only_full90_v1(*, use_cupti: bool=True, shape_labels=None, baseline_report: dict[str, Any] | None=None, benchmark_correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + return benchmark_candidate_portfolio(CANDIDATE_CF51_ONLY, use_cupti=use_cupti, shape_labels=shape_labels, baseline_report=baseline_report, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + +def benchmark_candidate_cf51_q1_q16_full90_v1(*, use_cupti: bool=True, shape_labels=None, baseline_report: dict[str, Any] | None=None, benchmark_correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + return benchmark_candidate_portfolio(CANDIDATE_CF51_Q1_Q16, use_cupti=use_cupti, shape_labels=shape_labels, baseline_report=baseline_report, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + +def benchmark_candidate_cf51_q1_q16_q4096_s4_full90_v1(*, use_cupti: bool=True, shape_labels=None, baseline_report: dict[str, Any] | None=None, benchmark_correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + return benchmark_candidate_portfolio(CANDIDATE_CF51_Q1_Q16_Q4096_S4, use_cupti=use_cupti, shape_labels=shape_labels, baseline_report=baseline_report, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + +def _best_candidate_key(payloads: dict[str, dict[str, Any]]) -> str | None: + baseline_value = payloads.get(BASE_D5F8_KEY, {}).get('tflops') + best_key = None + best_value = None + for key in (CANDIDATE_CF51_ONLY, CANDIDATE_CF51_Q1_Q16, CANDIDATE_CF51_Q1_Q16_Q4096_S4): + payload = payloads.get(key, {}) + if not payload.get('all_correct') or not payload.get('performance_comparable'): + continue + value = payload.get('tflops') + if value is None: + continue + if baseline_value is not None and value < baseline_value: + continue + if best_value is None or value > best_value: + best_key = key + best_value = value + return best_key + +def _summary_payload(*, payloads: dict[str, dict[str, Any]], artifacts: dict[str, str], denominator: str, timing_backend: str, benchmark_correctness: bool, time_flashlib: bool) -> dict[str, Any]: + selected_key = _best_candidate_key(payloads) + selected_payload = payloads.get(selected_key, {}) if selected_key else {} + baseline_payload = payloads[BASE_D5F8_KEY] + return {'candidate_id': 'dispatcher_synthesis_c3bf_cf51_bca0_q1_5018_q16_485e_q4096_full90_v1', 'measured_entrypoint': ''.join([format(MODULE, ''), ':write_benchmark_artifacts']), 'denominator': denominator, 'timing_backend': timing_backend, 'benchmark_correctness_checked': benchmark_correctness, 'benchmark_time_flashlib': time_flashlib, 'baseline_candidate_key': BASE_D5F8_KEY, 'selected_candidate_key': selected_key, 'selected_candidate_dispatcher': _candidate_id(selected_key) if selected_key else None, 'default_candidate_key': DEFAULT_CANDIDATE_KEY, 'candidate_dispatchers': CANDIDATE_DISPATCHERS, 'candidate_rankings': [{'candidate_key': key, 'candidate_id': payloads[key].get('candidate_id'), 'tflops': payloads[key].get('tflops'), 'metric_delta_vs_d5f8': payloads[key].get('metric_delta_vs_d5f8'), 'all_correct': payloads[key].get('all_correct'), 'performance_comparable': payloads[key].get('performance_comparable'), 'performance_coverage': payloads[key].get('performance_coverage')} for key in (BASE_D5F8_KEY, CANDIDATE_CF51_ONLY, CANDIDATE_CF51_Q1_Q16, CANDIDATE_CF51_Q1_Q16_Q4096_S4) if key in payloads], 'seed_delta_matrix': selected_payload.get('seed_delta_matrix', []), 'flashlib_parity_ledger': selected_payload.get('flashlib_parity_ledger', {}), 'full_denominator_ab': {'baseline_payload': artifacts.get('same_session_baseline_payload'), 'candidate_payload': artifacts.get(''.join([format(selected_key, ''), '_payload'])) if selected_key else None, 'denominator': denominator, 'timing_backend': timing_backend, 'metric_delta': selected_payload.get('metric_delta_vs_d5f8'), 'route_trace': selected_payload.get('route_trace', [])}, 'baseline_tflops': baseline_payload.get('tflops'), 'selected_tflops': selected_payload.get('tflops'), 'artifacts': artifacts} + +def write_benchmark_artifacts(artifact_dir: str | Path, *, use_cupti: bool=True, shape_labels=None, benchmark_correctness: bool=True, time_flashlib: bool=True, include_q4096_s4_candidate: bool=True, candidate_keys: tuple[str, ...] | None=None) -> dict[str, str]: + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + denom_label = _denominator_label(shape_labels) + denominator = _denominator_name(shape_labels) + timing_backend = _timing_backend_name(use_cupti) + baseline_report = benchmark_baseline_d5f8(use_cupti=use_cupti, shape_labels=shape_labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + baseline_payload = _baseline_sidecar(baseline_report, shape_labels=shape_labels, denominator=denominator, timing_backend=timing_backend, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + artifacts: dict[str, str] = {} + payloads = {BASE_D5F8_KEY: baseline_payload} + baseline_path = out_dir / ''.join([format(denom_label, ''), '_same_session_baseline_c3bf_d5f8_v1.json']) + baseline_path.write_text(json.dumps(baseline_payload, indent=2, sort_keys=True) + '\n', encoding='utf-8') + artifacts['same_session_baseline_payload'] = str(baseline_path) + if candidate_keys is None: + selected_candidate_keys = [CANDIDATE_CF51_ONLY, CANDIDATE_CF51_Q1_Q16] + if include_q4096_s4_candidate: + selected_candidate_keys.append(CANDIDATE_CF51_Q1_Q16_Q4096_S4) + else: + selected_candidate_keys = list(candidate_keys) + for candidate_key in selected_candidate_keys: + if candidate_key == BASE_D5F8_KEY: + raise ValueError('candidate_keys must not include the baseline key') + _candidate_config(candidate_key) + payload = benchmark_candidate_portfolio(candidate_key, use_cupti=use_cupti, shape_labels=shape_labels, baseline_report=baseline_report, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + payloads[candidate_key] = payload + payload_path = out_dir / ''.join([format(denom_label, ''), '_dispatch_', format(candidate_key, ''), '_v1.json']) + trace_path = out_dir / ''.join([format(denom_label, ''), '_route_trace_', format(candidate_key, ''), '_v1.json']) + forced_trace_path = out_dir / ''.join([format(denom_label, ''), '_forced_fallback_trace_', format(candidate_key, ''), '_v1.json']) + seed_matrix_path = out_dir / ''.join([format(denom_label, ''), '_seed_delta_matrix_', format(candidate_key, ''), '_v1.json']) + payload_path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n', encoding='utf-8') + trace_path.write_text(json.dumps(payload['route_trace'], indent=2, sort_keys=True) + '\n', encoding='utf-8') + forced_trace_path.write_text(json.dumps(payload['forced_fallback_route_trace'], indent=2, sort_keys=True) + '\n', encoding='utf-8') + seed_matrix_path.write_text(json.dumps(payload['seed_delta_matrix'], indent=2, sort_keys=True) + '\n', encoding='utf-8') + artifacts[''.join([format(candidate_key, ''), '_payload'])] = str(payload_path) + artifacts[''.join([format(candidate_key, ''), '_route_trace'])] = str(trace_path) + artifacts[''.join([format(candidate_key, ''), '_forced_fallback_trace'])] = str(forced_trace_path) + artifacts[''.join([format(candidate_key, ''), '_seed_delta_matrix'])] = str(seed_matrix_path) + summary = _summary_payload(payloads=payloads, artifacts=artifacts, denominator=denominator, timing_backend=timing_backend, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + summary_path = out_dir / ''.join([format(denom_label, ''), '_dispatcher_synthesis_c3bf_cf51_bca0_5018_485e_v1.json']) + summary_path.write_text(json.dumps(summary, indent=2, sort_keys=True) + '\n', encoding='utf-8') + artifacts['dispatcher_synthesis'] = str(summary_path) + return artifacts diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_ad64_1b8f_4b51_ceb3_full90_synthesis_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_ad64_1b8f_4b51_ceb3_full90_synthesis_v1.py new file mode 100644 index 00000000..78d21985 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_ad64_1b8f_4b51_ceb3_full90_synthesis_v1.py @@ -0,0 +1,504 @@ +"""Full90 synthesis over ad64 with 1b8f, 4b51, and ceb3 seeds. + +Minimum target architecture: sm_100a. This additive dispatcher wrapper keeps +the current ad64 Q24/Q128 full90 portfolio as the baseline route, then measures +guarded portfolios that consume the promoted 1b8f build-K10 seed, the +complementary 4b51 build-K10 seed, and the ceb3 q8/q16 RAG microbatch K10 seed. +The older Q128/M100000 ad64 seed is exposed only as an optional diagnostic +candidate. Production routes stay Weave-only; FlashLib is timed only by the +contract harness. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +from pathlib import Path +from typing import Any, Callable +from . import knn_build_build_k10_lowfloor_4757_v1 as build_1b8f +from . import knn_build_build_k10_lowfloor_ad64_v2 as build_4b51 +from . import knn_build_dispatch_784a_cf51_q1_q16_q24_q128_seed_portfolio_full90_v1 as parent +from . import knn_build_rag_microbatch_k10_q8q16_4757_v1 as rag_ceb3 +from . import knn_build_rag_stream_k32_q128m100000_ad64_v1 as q128_m100000 +MODULE = 'loom.examples.weave.knn_build_dispatch_ad64_1b8f_4b51_ceb3_full90_synthesis_v1' +eval_mod = parent.eval_mod +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbatch_m64_d4f7_stage1", "arg_keys": ["query", "database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 91392, "constants": [], "cta_group": 1, "threads": 512}')) +BASE_AD64_KEY = parent.DEFAULT_CANDIDATE_KEY +CANDIDATE_1B8F_BUILD_K10 = 'ad64_plus_1b8f_build_k10' +CANDIDATE_4B51_BUILD_K10 = 'ad64_plus_4b51_build_k10' +CANDIDATE_BEST_BUILD_K10 = 'ad64_plus_best_per_shape_build_k10' +CANDIDATE_CEB3_Q8Q16 = 'ad64_plus_ceb3_q8q16_k10' +CANDIDATE_BEST_BUILD_CEB3 = 'ad64_plus_best_per_shape_build_k10_plus_ceb3' +CANDIDATE_Q128_M100000 = 'ad64_plus_9f8a_q128_m100000' +CANDIDATE_BEST_BUILD_CEB3_Q128_M100000 = 'ad64_plus_best_per_shape_build_k10_plus_ceb3_plus_9f8a_q128_m100000' +DEFAULT_CANDIDATE_KEY = CANDIDATE_BEST_BUILD_CEB3 +CANDIDATE_KEYS = (BASE_AD64_KEY, CANDIDATE_1B8F_BUILD_K10, CANDIDATE_4B51_BUILD_K10, CANDIDATE_BEST_BUILD_K10, CANDIDATE_CEB3_Q8Q16, CANDIDATE_BEST_BUILD_CEB3, CANDIDATE_Q128_M100000, CANDIDATE_BEST_BUILD_CEB3_Q128_M100000) +BASE_AD64_CONFIG = parent.CANDIDATE_CONFIGS[BASE_AD64_KEY] +BASE_AD64_ID = BASE_AD64_CONFIG['candidate_id'] +BASE_AD64_ENTRYPOINT = BASE_AD64_CONFIG['benchmark_entrypoint'] +BASE_AD64_ROUTE_ENTRYPOINT = parent.ROUTE_ENTRYPOINT +SEED_1B8F_BUILD_K10_ID = build_1b8f.SEED_K10_ID +SEED_4B51_BUILD_K10_ID = build_4b51.SEED_K10_ID +SEED_CEB3_Q8Q16_ID = rag_ceb3.SEED_ID +SEED_Q128_M100000_ID = q128_m100000.SEED_K32_Q128_M100000_AD64_V1_ID +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +CANDIDATE_1B8F_BUILD_K10_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_candidate_1b8f_build_k10_full90_v1']) +CANDIDATE_4B51_BUILD_K10_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_candidate_4b51_build_k10_full90_v1']) +CANDIDATE_BEST_BUILD_K10_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_candidate_best_build_k10_full90_v1']) +CANDIDATE_CEB3_Q8Q16_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_candidate_ceb3_q8q16_full90_v1']) +CANDIDATE_BEST_BUILD_CEB3_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_candidate_best_build_ceb3_full90_v1']) +CANDIDATE_Q128_M100000_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_candidate_q128_m100000_full90_v1']) +CANDIDATE_BEST_BUILD_CEB3_Q128_M100000_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_candidate_best_build_ceb3_q128_m100000_full90_v1']) +BUILD_1B8F_TARGET_SHAPES = build_1b8f.TARGET_SHAPES +BUILD_4B51_TARGET_SHAPES = build_4b51.TARGET_SHAPES +BUILD_BEST_TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_k_sweep_qm512_k10", "build_qm1024_d128_k10", "build_batch_b2_q1024_m1024_d128_k10", "build_tail_b1_q1536_m1536_d128_k10", "build_large_b1_q6144_m6144_d128_k10", "build_qm2048_d128_k10"]}')) +RAG_CEB3_TARGET_SHAPES = rag_ceb3.TARGET_SHAPES +Q128_M100000_TARGET_SHAPES = q128_m100000.TARGET_SHAPES +PARENT_TARGET_SHAPES = parent.Q24_Q128_PORTFOLIO_TARGET_SHAPES +BUILD_AND_RAG_TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_qm1024_d128_k8", "rag_online_b1_q1_m100000_d128_k10", "rag_online_b1_q1_m65536_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "rag_online_irregular_b1_q1_m262143_d128_k10", "rag_online_irregular_b1_q1_m524287_d128_k10", "rag_microbatch_largek_b1_q16_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m131071_d128_k32", "rag_microbatch_largek_b1_q16_m250000_d128_k32", "rag_microbatch_largek_b1_q24_m100000_d128_k32", "rag_stream_largek_b1_q128_m131071_d128_k32", "build_k_sweep_qm512_k10", "build_qm1024_d128_k10", "build_batch_b2_q1024_m1024_d128_k10", "build_tail_b1_q1536_m1536_d128_k10", "build_large_b1_q6144_m6144_d128_k10", "build_qm2048_d128_k10", "rag_microbatch_b1_q8_m100000_d128_k10", "rag_microbatch_b1_q16_m100000_d128_k10"]}')) +FULL_SYNTHESIS_TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_qm1024_d128_k8", "rag_online_b1_q1_m100000_d128_k10", "rag_online_b1_q1_m65536_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "rag_online_irregular_b1_q1_m262143_d128_k10", "rag_online_irregular_b1_q1_m524287_d128_k10", "rag_microbatch_largek_b1_q16_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m131071_d128_k32", "rag_microbatch_largek_b1_q16_m250000_d128_k32", "rag_microbatch_largek_b1_q24_m100000_d128_k32", "rag_stream_largek_b1_q128_m131071_d128_k32", "build_k_sweep_qm512_k10", "build_qm1024_d128_k10", "build_batch_b2_q1024_m1024_d128_k10", "build_tail_b1_q1536_m1536_d128_k10", "build_large_b1_q6144_m6144_d128_k10", "build_qm2048_d128_k10", "rag_microbatch_b1_q8_m100000_d128_k10", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32"]}')) +SOURCE_TASKS = {**parent.SOURCE_TASKS, SEED_1B8F_BUILD_K10_ID: 'weave-evolve-knn-build-1b8f / design_doc/active/weave_evolve_knn_build_round_137_4757_buildk10.md', SEED_4B51_BUILD_K10_ID: 'weave-evolve-knn-build-4b51 / design_doc/active/weave_evolve_knn_build_round_137_0714_buildk10_v2.md', SEED_CEB3_Q8Q16_ID: 'weave-evolve-knn-build-ceb3 / design_doc/active/weave_evolve_knn_build_round_137_4757_ragmicro_q8q16.md', SEED_Q128_M100000_ID: 'weave-evolve-knn-build-9f8a / design_doc/active/weave_evolve_knn_build_round_136_ad64_q128m100000.md'} +PRODUCTION_ROUTE_MODULES = {**parent.PRODUCTION_ROUTE_MODULES, BASE_AD64_ID: BASE_AD64_ROUTE_ENTRYPOINT, SEED_1B8F_BUILD_K10_ID: build_1b8f.ROUTE_K10_BUILD, SEED_4B51_BUILD_K10_ID: build_4b51.ROUTE_K10_BUILD, SEED_CEB3_Q8Q16_ID: rag_ceb3.ROUTE_ENTRYPOINT, SEED_Q128_M100000_ID: q128_m100000.ROUTE_Q128_M100000_ENTRYPOINT} +TARGETED_SEED_ROWS = {**parent.TARGETED_SEED_ROWS, SEED_1B8F_BUILD_K10_ID: {'source_payload': 'artifacts/weave_evolve/knn_build_build_k10_lowfloor_4757_v1/build_k10_lowfloor_4757_v1.json', 'shape_labels': BUILD_1B8F_TARGET_SHAPES, 'source_task': 'weave-evolve-knn-build-1b8f'}, SEED_4B51_BUILD_K10_ID: {'source_payload': 'artifacts/weave_evolve/knn_build_build_k10_lowfloor_ad64_v2/build_k10_lowfloor_ad64_v2.json', 'shape_labels': BUILD_4B51_TARGET_SHAPES, 'source_task': 'weave-evolve-knn-build-4b51'}, SEED_CEB3_Q8Q16_ID: {'source_payload': 'artifacts/weave_evolve/knn_build_ragmicro_q8q16_k10_4757_v1/rag_microbatch_k10_q8q16_4757_v1.json', 'shape_labels': RAG_CEB3_TARGET_SHAPES, 'source_task': 'weave-evolve-knn-build-ceb3'}, SEED_Q128_M100000_ID: {'source_payload': 'artifacts/weave_evolve/knn_build_q128m100000_ad64_v1/q128m100000_ad64_v1_cupti.json', 'shape_labels': Q128_M100000_TARGET_SHAPES, 'source_task': 'weave-evolve-knn-build-9f8a'}} +REJECTED_ROUTE_COMBINATIONS = ({'id': '4977_q128_m100000_read_ref_not_integrated', 'entrypoint': 'loom.examples.weave.knn_build_rag_q128_k32_c796_g8_v1:launch_from_contract_inputs', 'status': 'read_ref_only', 'source_task': 'weave-evolve-knn-build-4977 / generalize-auto-tuning-knn-build-5f3a', 'reason': '5f3a showed the target Q128/M100000 row improves, but repeated paired full90 no-regression fails; this synthesis keeps 4977 as diagnostic evidence instead of a default production route.', 'evidence': 'artifacts/generalize_auto_tuning/knn_build_5f3a_full90_4977_variance_audit_watchdog0_v1/variance_audit_summary_full90_q24_b0e2_vs_4977.json'}, {'id': 'dfbc_build_k10_superseded_by_1b8f_4b51', 'entrypoint': 'loom.examples.weave.knn_build_build_k10_lowfloor_ad64_v1:launch_from_contract_inputs', 'status': 'superseded_read_ref', 'source_task': 'weave-evolve-knn-build-dfbc / generalize-auto-tuning-knn-build-30e7', 'reason': 'The promoted 1b8f and 4b51 build-K10 seeds cover a broader exact K10 set.'}) + +def _select_contract_shapes(shape_labels): + return parent._select_contract_shapes(shape_labels) + +def _trace_inputs_for_shape(shape: dict[str, Any]) -> dict[str, Any]: + return parent._trace_inputs_for_shape(shape) + +def _normalize_route_row(row: dict[str, Any]) -> dict[str, Any]: + normalized = parent._normalize_route_row(row) + route_kind = str(normalized.get('route_kind') or 'general') + if route_kind not in {'specialized', 'general', 'fallback', 'coverage-only', 'none'}: + normalized['route_kind'] = 'specialized' if normalized.get('selected_seed') else 'general' + route_source = str(normalized.get('route_source') or 'unknown') + if route_source not in {'shape-specific-seed', 'generated-variant', 'broad-dispatcher', 'generic-weave-fallback', 'external-reference', 'unknown'}: + normalized['route_source'] = 'shape-specific-seed' if normalized.get('selected_seed') else 'broad-dispatcher' + classification = str(normalized.get('classification') or 'unmeasured') + if classification not in {'seed-consumed', 'route-ok', 'guard-miss', 'kernel-slow', 'fallback-slow', 'coverage-only', 'benchmark-path-mismatch', 'unmeasured'}: + normalized['classification'] = 'unmeasured' + return normalized + +def _inputs_for_label(label: str) -> dict[str, Any]: + return _trace_inputs_for_shape(_select_contract_shapes((label,))[0]) + +def _timing_backend_name(use_cupti: bool) -> str: + return 'cupti' if use_cupti else 'cuda_event_fallback' + +def _payload_shape_labels(shape_labels) -> str | tuple[str, ...]: + return parent._payload_shape_labels(shape_labels) + +def _denominator_name(shape_labels) -> str: + return parent._denominator_name(shape_labels) + +def _denominator_label(shape_labels) -> str: + return parent._denominator_label(shape_labels) + +def _rows_for_labels(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return parent._rows_for_labels(report, labels) + +def _timing_backends_for_reports(*reports: dict[str, Any]) -> list[str]: + return parent._timing_backends_for_reports(*reports) + +def _candidate_config(candidate_key: str) -> dict[str, Any]: + try: + return CANDIDATE_CONFIGS[candidate_key] + except KeyError as exc: + raise ValueError(''.join(['unknown full90 1b8f/4b51/ceb3 candidate ', format(repr(candidate_key), '')])) from exc + +def _candidate_id(candidate_key: str | None) -> str | None: + if candidate_key is None: + return None + return str(_candidate_config(candidate_key)['candidate_id']) + +def _candidate_has_1b8f_build(candidate_key: str) -> bool: + return candidate_key in (CANDIDATE_1B8F_BUILD_K10, CANDIDATE_BEST_BUILD_K10, CANDIDATE_BEST_BUILD_CEB3, CANDIDATE_BEST_BUILD_CEB3_Q128_M100000) + +def _candidate_has_4b51_build(candidate_key: str) -> bool: + return candidate_key in (CANDIDATE_4B51_BUILD_K10, CANDIDATE_BEST_BUILD_K10, CANDIDATE_BEST_BUILD_CEB3, CANDIDATE_BEST_BUILD_CEB3_Q128_M100000) + +def _candidate_has_ceb3(candidate_key: str) -> bool: + return candidate_key in (CANDIDATE_CEB3_Q8Q16, CANDIDATE_BEST_BUILD_CEB3, CANDIDATE_BEST_BUILD_CEB3_Q128_M100000) + +def _candidate_has_q128_m100000(candidate_key: str) -> bool: + return candidate_key in (CANDIDATE_Q128_M100000, CANDIDATE_BEST_BUILD_CEB3_Q128_M100000) + +def _eligible_1b8f_build(inputs: dict[str, Any]) -> bool: + return build_1b8f._eligible_k10_lowfloor(inputs) + +def _eligible_4b51_build(inputs: dict[str, Any]) -> bool: + return build_4b51._eligible_k10_lowfloor(inputs) + +def _eligible_ceb3(inputs: dict[str, Any]) -> bool: + return rag_ceb3._split_for_inputs(inputs) is not None + +def _eligible_q128_m100000(inputs: dict[str, Any]) -> bool: + return q128_m100000._eligible_q128_m100000(inputs) + +def _matched_build_seed(inputs: dict[str, Any], candidate_key: str): + if candidate_key == CANDIDATE_1B8F_BUILD_K10: + return build_1b8f if _eligible_1b8f_build(inputs) else None + if candidate_key == CANDIDATE_4B51_BUILD_K10: + return build_4b51 if _eligible_4b51_build(inputs) else None + if candidate_key in (CANDIDATE_BEST_BUILD_K10, CANDIDATE_BEST_BUILD_CEB3, CANDIDATE_BEST_BUILD_CEB3_Q128_M100000): + label = str(inputs.get('label', '')) + if label == build_4b51.BUILD_Q2048_K10 and _eligible_4b51_build(inputs): + return build_4b51 + if _eligible_1b8f_build(inputs): + return build_1b8f + if _eligible_4b51_build(inputs): + return build_4b51 + return None + +def _matched_new_seed(inputs: dict[str, Any], candidate_key: str): + build_seed = _matched_build_seed(inputs, candidate_key) + if build_seed is not None: + return build_seed + if _candidate_has_ceb3(candidate_key) and _eligible_ceb3(inputs): + return rag_ceb3 + if _candidate_has_q128_m100000(candidate_key) and _eligible_q128_m100000(inputs): + return q128_m100000 + return None + +def _seed_id_for_module(seed_module) -> str: + if seed_module is build_1b8f: + return SEED_1B8F_BUILD_K10_ID + if seed_module is build_4b51: + return SEED_4B51_BUILD_K10_ID + if seed_module is rag_ceb3: + return SEED_CEB3_Q8Q16_ID + if seed_module is q128_m100000: + return SEED_Q128_M100000_ID + raise ValueError(''.join(['unknown seed module ', format(repr(seed_module), '')])) + +def _seed_route_for_module(seed_module, inputs: dict[str, Any]) -> str: + return seed_module.route_for_contract_inputs(inputs) + +def _seed_launch_for_module(seed_module, inputs: dict[str, Any]) -> None: + seed_module.launch_from_contract_inputs(inputs) + +def _seed_trace_for_module(seed_module, label: str) -> dict[str, Any]: + return dict(seed_module.route_trace_for_contract_shapes((label,))[0]) + +def _parent_route(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + return parent.route_for_contract_inputs(inputs, candidate_key=BASE_AD64_KEY, force_fallback=force_fallback) + +def _parent_launch(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + parent.launch_from_contract_inputs(inputs, candidate_key=BASE_AD64_KEY, force_fallback=force_fallback) + +def _parent_trace_row(label: str, *, force_fallback: bool=False) -> dict[str, Any]: + return dict(parent.route_trace_for_contract_shapes((label,), candidate_key=BASE_AD64_KEY, force_fallback=force_fallback)[0]) + +def _expected_seed(inputs: dict[str, Any], candidate_key: str) -> str | None: + seed_module = _matched_new_seed(inputs, candidate_key) + if seed_module is not None: + return _seed_id_for_module(seed_module) + return parent._expected_seed(inputs, BASE_AD64_KEY) + +def route_for_contract_inputs(inputs: dict[str, Any], *, candidate_key: str=DEFAULT_CANDIDATE_KEY, force_fallback: bool=False) -> str: + _candidate_config(candidate_key) + if force_fallback: + return _parent_route(inputs, force_fallback=True) + if candidate_key == BASE_AD64_KEY: + return _parent_route(inputs) + seed_module = _matched_new_seed(inputs, candidate_key) + if seed_module is not None: + return _seed_route_for_module(seed_module, inputs) + return _parent_route(inputs) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, candidate_key: str=DEFAULT_CANDIDATE_KEY, force_fallback: bool=False) -> None: + _candidate_config(candidate_key) + if force_fallback: + _parent_launch(inputs, force_fallback=True) + return + if candidate_key == BASE_AD64_KEY: + _parent_launch(inputs) + return + seed_module = _matched_new_seed(inputs, candidate_key) + if seed_module is not None: + _seed_launch_for_module(seed_module, inputs) + return + _parent_launch(inputs) + +def candidate_parent_ad64_full90_v1(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, candidate_key=BASE_AD64_KEY) + +def candidate_1b8f_build_k10_full90_v1(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, candidate_key=CANDIDATE_1B8F_BUILD_K10) + +def candidate_4b51_build_k10_full90_v1(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, candidate_key=CANDIDATE_4B51_BUILD_K10) + +def candidate_best_build_k10_full90_v1(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, candidate_key=CANDIDATE_BEST_BUILD_K10) + +def candidate_ceb3_q8q16_full90_v1(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, candidate_key=CANDIDATE_CEB3_Q8Q16) + +def candidate_best_build_ceb3_full90_v1(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, candidate_key=CANDIDATE_BEST_BUILD_CEB3) + +def candidate_q128_m100000_full90_v1(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, candidate_key=CANDIDATE_Q128_M100000) + +def candidate_best_build_ceb3_q128_m100000_full90_v1(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, candidate_key=CANDIDATE_BEST_BUILD_CEB3_Q128_M100000) + +def candidate(inputs: dict[str, Any]) -> None: + candidate_best_build_ceb3_full90_v1(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, candidate_key=DEFAULT_CANDIDATE_KEY, force_fallback=True) + +def _candidate_kernel_fn(candidate_key: str) -> Callable[[dict[str, Any]], None]: + if candidate_key == BASE_AD64_KEY: + return candidate_parent_ad64_full90_v1 + if candidate_key == CANDIDATE_1B8F_BUILD_K10: + return candidate_1b8f_build_k10_full90_v1 + if candidate_key == CANDIDATE_4B51_BUILD_K10: + return candidate_4b51_build_k10_full90_v1 + if candidate_key == CANDIDATE_BEST_BUILD_K10: + return candidate_best_build_k10_full90_v1 + if candidate_key == CANDIDATE_CEB3_Q8Q16: + return candidate_ceb3_q8q16_full90_v1 + if candidate_key == CANDIDATE_BEST_BUILD_CEB3: + return candidate_best_build_ceb3_full90_v1 + if candidate_key == CANDIDATE_Q128_M100000: + return candidate_q128_m100000_full90_v1 + if candidate_key == CANDIDATE_BEST_BUILD_CEB3_Q128_M100000: + return candidate_best_build_ceb3_q128_m100000_full90_v1 + raise ValueError(''.join(['unknown full90 1b8f/4b51/ceb3 candidate ', format(repr(candidate_key), '')])) + +def _selected_seeds(*seed_groups: tuple[str, ...]) -> tuple[str, ...]: + values: list[str] = [] + for group in seed_groups: + values.extend(group) + return tuple(dict.fromkeys(values)) +PARENT_SEEDS = _decode_capture(_json_loads('{"__tuple__": ["q1024_k8_195e_v1", "ragonline_mbucket_5706_q1v10_smix_s144_g12", "rag_microbucket_k32_q16dual2warp_largem_bdd2_v1_rowld1_2warp_rows4", "rag_microbucket_k32_q24rowld2_24dc_v1_rowld2_rows4", "rag_stream_k32_q128rowld_60fb_v1_rowld_rows4"]}')) +BUILD_BEST_SEEDS = (SEED_1B8F_BUILD_K10_ID, SEED_4B51_BUILD_K10_ID) +RAG_CEB3_SEEDS = (SEED_CEB3_Q8Q16_ID,) +Q128_M100000_SEEDS = (SEED_Q128_M100000_ID,) +CANDIDATE_CONFIGS = _decode_capture(_json_loads('{"__dict_items__": [["c3bf_plus_cf51_q1024_bca0_q1_5018_q16_603d_q24_b0e2_q128", {"__dict_items__": [["candidate_id", "candidate_c3bf_cf51_q1024_bca0_q1_5018_q16_603d_q24_b0e2_q128_full90_v1"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_784a_cf51_q1_q16_q24_q128_seed_portfolio_full90_v1:launch_from_contract_inputs"], ["benchmark_entrypoint", "loom.examples.weave.knn_build_dispatch_784a_cf51_q1_q16_q24_q128_seed_portfolio_full90_v1:benchmark_candidate_q24_q128_full90_v1"], ["selected_seeds", {"__tuple__": ["q1024_k8_195e_v1", "ragonline_mbucket_5706_q1v10_smix_s144_g12", "rag_microbucket_k32_q16dual2warp_largem_bdd2_v1_rowld1_2warp_rows4", "rag_microbucket_k32_q24rowld2_24dc_v1_rowld2_rows4", "rag_stream_k32_q128rowld_60fb_v1_rowld_rows4"]}], ["guard_plan", {"__tuple__": ["b0e2 exact BF16 non-build B=1 Q=128 M=131071 D=128 K=32 rowld rows4 guard", "603d/24dc exact BF16 non-build B=1 Q=24 M=100000 D=128 K=32 rowld2 rows4 guard", "cf51 exact BF16 build Q=M=1024 D=128 K=8 split16", "bca0 exact BF16 non-build Q=1 D=128 K=10 M in {65536,100000,131071,250000,262143,524287}", "5018 exact BF16 non-build Q=16 D=128 K=32 M in {100000,131071,250000}", "c3bf/d5f8 full90 Weave fallback"]}], ["expected_shape_wins", {"__tuple__": ["build_qm1024_d128_k8", "rag_online_b1_q1_m100000_d128_k10", "rag_online_b1_q1_m65536_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "rag_online_irregular_b1_q1_m262143_d128_k10", "rag_online_irregular_b1_q1_m524287_d128_k10", "rag_microbatch_largek_b1_q16_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m131071_d128_k32", "rag_microbatch_largek_b1_q16_m250000_d128_k32", "rag_microbatch_largek_b1_q24_m100000_d128_k32", "rag_stream_largek_b1_q128_m131071_d128_k32"]}], ["fallback", "loom.examples.weave.knn_build_dispatch_784a_cf51_q1_q16_q4096_seed_portfolio_full90_v1:launch_from_contract_inputs"], ["rejected_reason", "same-session ad64 Q24/Q128 champion baseline"]]}], ["ad64_plus_1b8f_build_k10", {"__dict_items__": [["candidate_id", "candidate_ad64_plus_1b8f_build_k10_full90_v1"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_ad64_1b8f_4b51_ceb3_full90_synthesis_v1:candidate_1b8f_build_k10_full90_v1"], ["benchmark_entrypoint", "loom.examples.weave.knn_build_dispatch_ad64_1b8f_4b51_ceb3_full90_synthesis_v1:benchmark_candidate_1b8f_build_k10_full90_v1"], ["selected_seeds", {"__tuple__": ["q1024_k8_195e_v1", "ragonline_mbucket_5706_q1v10_smix_s144_g12", "rag_microbucket_k32_q16dual2warp_largem_bdd2_v1_rowld1_2warp_rows4", "rag_microbucket_k32_q24rowld2_24dc_v1_rowld2_rows4", "rag_stream_k32_q128rowld_60fb_v1_rowld_rows4", "4757_fixedbuild_k10_v2_q512_q1024_b2_q1536_q6144"]}], ["guard_plan", {"__tuple__": ["1b8f exact BF16 build K10 guard for Q512/Q1024/B2-Q1024/Q1536/Q6144", "b0e2 exact BF16 non-build B=1 Q=128 M=131071 D=128 K=32 rowld rows4 guard", "603d/24dc exact BF16 non-build B=1 Q=24 M=100000 D=128 K=32 rowld2 rows4 guard", "cf51 exact BF16 build Q=M=1024 D=128 K=8 split16", "bca0 exact BF16 non-build Q=1 D=128 K=10 M in {65536,100000,131071,250000,262143,524287}", "5018 exact BF16 non-build Q=16 D=128 K=32 M in {100000,131071,250000}", "c3bf/d5f8 full90 Weave fallback"]}], ["expected_shape_wins", {"__tuple__": ["build_qm1024_d128_k8", "rag_online_b1_q1_m100000_d128_k10", "rag_online_b1_q1_m65536_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "rag_online_irregular_b1_q1_m262143_d128_k10", "rag_online_irregular_b1_q1_m524287_d128_k10", "rag_microbatch_largek_b1_q16_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m131071_d128_k32", "rag_microbatch_largek_b1_q16_m250000_d128_k32", "rag_microbatch_largek_b1_q24_m100000_d128_k32", "rag_stream_largek_b1_q128_m131071_d128_k32", "build_k_sweep_qm512_k10", "build_qm1024_d128_k10", "build_batch_b2_q1024_m1024_d128_k10", "build_tail_b1_q1536_m1536_d128_k10", "build_large_b1_q6144_m6144_d128_k10"]}], ["fallback", "loom.examples.weave.knn_build_dispatch_784a_cf51_q1_q16_q24_q128_seed_portfolio_full90_v1:launch_from_contract_inputs"], ["rejected_reason", "single-seed diagnostic; misses 4b51\'s Q2048 exact row and ceb3 q8/q16 rows"]]}], ["ad64_plus_4b51_build_k10", {"__dict_items__": [["candidate_id", "candidate_ad64_plus_4b51_build_k10_full90_v1"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_ad64_1b8f_4b51_ceb3_full90_synthesis_v1:candidate_4b51_build_k10_full90_v1"], ["benchmark_entrypoint", "loom.examples.weave.knn_build_dispatch_ad64_1b8f_4b51_ceb3_full90_synthesis_v1:benchmark_candidate_4b51_build_k10_full90_v1"], ["selected_seeds", {"__tuple__": ["q1024_k8_195e_v1", "ragonline_mbucket_5706_q1v10_smix_s144_g12", "rag_microbucket_k32_q16dual2warp_largem_bdd2_v1_rowld1_2warp_rows4", "rag_microbucket_k32_q24rowld2_24dc_v1_rowld2_rows4", "rag_stream_k32_q128rowld_60fb_v1_rowld_rows4", "ad64_fixedbuild_k10_v2_q512_q1024_q1536_q2048_b2q1024"]}], ["guard_plan", {"__tuple__": ["4b51 exact BF16 build K10 guard for Q512/Q1024/Q2048/B2-Q1024/Q1536", "b0e2 exact BF16 non-build B=1 Q=128 M=131071 D=128 K=32 rowld rows4 guard", "603d/24dc exact BF16 non-build B=1 Q=24 M=100000 D=128 K=32 rowld2 rows4 guard", "cf51 exact BF16 build Q=M=1024 D=128 K=8 split16", "bca0 exact BF16 non-build Q=1 D=128 K=10 M in {65536,100000,131071,250000,262143,524287}", "5018 exact BF16 non-build Q=16 D=128 K=32 M in {100000,131071,250000}", "c3bf/d5f8 full90 Weave fallback"]}], ["expected_shape_wins", {"__tuple__": ["build_qm1024_d128_k8", "rag_online_b1_q1_m100000_d128_k10", "rag_online_b1_q1_m65536_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "rag_online_irregular_b1_q1_m262143_d128_k10", "rag_online_irregular_b1_q1_m524287_d128_k10", "rag_microbatch_largek_b1_q16_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m131071_d128_k32", "rag_microbatch_largek_b1_q16_m250000_d128_k32", "rag_microbatch_largek_b1_q24_m100000_d128_k32", "rag_stream_largek_b1_q128_m131071_d128_k32", "build_k_sweep_qm512_k10", "build_qm1024_d128_k10", "build_qm2048_d128_k10", "build_batch_b2_q1024_m1024_d128_k10", "build_tail_b1_q1536_m1536_d128_k10"]}], ["fallback", "loom.examples.weave.knn_build_dispatch_784a_cf51_q1_q16_q24_q128_seed_portfolio_full90_v1:launch_from_contract_inputs"], ["rejected_reason", "single-seed diagnostic; misses 1b8f\'s Q6144 exact row and ceb3 q8/q16 rows"]]}], ["ad64_plus_best_per_shape_build_k10", {"__dict_items__": [["candidate_id", "candidate_ad64_plus_best_per_shape_build_k10_full90_v1"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_ad64_1b8f_4b51_ceb3_full90_synthesis_v1:candidate_best_build_k10_full90_v1"], ["benchmark_entrypoint", "loom.examples.weave.knn_build_dispatch_ad64_1b8f_4b51_ceb3_full90_synthesis_v1:benchmark_candidate_best_build_k10_full90_v1"], ["selected_seeds", {"__tuple__": ["q1024_k8_195e_v1", "ragonline_mbucket_5706_q1v10_smix_s144_g12", "rag_microbucket_k32_q16dual2warp_largem_bdd2_v1_rowld1_2warp_rows4", "rag_microbucket_k32_q24rowld2_24dc_v1_rowld2_rows4", "rag_stream_k32_q128rowld_60fb_v1_rowld_rows4", "4757_fixedbuild_k10_v2_q512_q1024_b2_q1536_q6144", "ad64_fixedbuild_k10_v2_q512_q1024_q1536_q2048_b2q1024"]}], ["guard_plan", {"__tuple__": ["4b51 exact BF16 build K10 guard for unique Q2048 row", "1b8f exact BF16 build K10 guard for Q512/Q1024/B2-Q1024/Q1536/Q6144 rows", "b0e2 exact BF16 non-build B=1 Q=128 M=131071 D=128 K=32 rowld rows4 guard", "603d/24dc exact BF16 non-build B=1 Q=24 M=100000 D=128 K=32 rowld2 rows4 guard", "cf51 exact BF16 build Q=M=1024 D=128 K=8 split16", "bca0 exact BF16 non-build Q=1 D=128 K=10 M in {65536,100000,131071,250000,262143,524287}", "5018 exact BF16 non-build Q=16 D=128 K=32 M in {100000,131071,250000}", "c3bf/d5f8 full90 Weave fallback"]}], ["expected_shape_wins", {"__tuple__": ["build_qm1024_d128_k8", "rag_online_b1_q1_m100000_d128_k10", "rag_online_b1_q1_m65536_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "rag_online_irregular_b1_q1_m262143_d128_k10", "rag_online_irregular_b1_q1_m524287_d128_k10", "rag_microbatch_largek_b1_q16_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m131071_d128_k32", "rag_microbatch_largek_b1_q16_m250000_d128_k32", "rag_microbatch_largek_b1_q24_m100000_d128_k32", "rag_stream_largek_b1_q128_m131071_d128_k32", "build_k_sweep_qm512_k10", "build_qm1024_d128_k10", "build_batch_b2_q1024_m1024_d128_k10", "build_tail_b1_q1536_m1536_d128_k10", "build_large_b1_q6144_m6144_d128_k10", "build_qm2048_d128_k10"]}], ["fallback", "loom.examples.weave.knn_build_dispatch_784a_cf51_q1_q16_q24_q128_seed_portfolio_full90_v1:launch_from_contract_inputs"], ["rejected_reason", null]]}], ["ad64_plus_ceb3_q8q16_k10", {"__dict_items__": [["candidate_id", "candidate_ad64_plus_ceb3_q8q16_full90_v1"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_ad64_1b8f_4b51_ceb3_full90_synthesis_v1:candidate_ceb3_q8q16_full90_v1"], ["benchmark_entrypoint", "loom.examples.weave.knn_build_dispatch_ad64_1b8f_4b51_ceb3_full90_synthesis_v1:benchmark_candidate_ceb3_q8q16_full90_v1"], ["selected_seeds", {"__tuple__": ["q1024_k8_195e_v1", "ragonline_mbucket_5706_q1v10_smix_s144_g12", "rag_microbucket_k32_q16dual2warp_largem_bdd2_v1_rowld1_2warp_rows4", "rag_microbucket_k32_q24rowld2_24dc_v1_rowld2_rows4", "rag_stream_k32_q128rowld_60fb_v1_rowld_rows4", "rag_microbatch_k10_q8_s128_q16_s136_4757_v1"]}], ["guard_plan", {"__tuple__": ["ceb3 exact BF16 non-build B=1 Q in {8,16} M=100000 D=128 K=10 guard", "b0e2 exact BF16 non-build B=1 Q=128 M=131071 D=128 K=32 rowld rows4 guard", "603d/24dc exact BF16 non-build B=1 Q=24 M=100000 D=128 K=32 rowld2 rows4 guard", "cf51 exact BF16 build Q=M=1024 D=128 K=8 split16", "bca0 exact BF16 non-build Q=1 D=128 K=10 M in {65536,100000,131071,250000,262143,524287}", "5018 exact BF16 non-build Q=16 D=128 K=32 M in {100000,131071,250000}", "c3bf/d5f8 full90 Weave fallback"]}], ["expected_shape_wins", {"__tuple__": ["build_qm1024_d128_k8", "rag_online_b1_q1_m100000_d128_k10", "rag_online_b1_q1_m65536_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "rag_online_irregular_b1_q1_m262143_d128_k10", "rag_online_irregular_b1_q1_m524287_d128_k10", "rag_microbatch_largek_b1_q16_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m131071_d128_k32", "rag_microbatch_largek_b1_q16_m250000_d128_k32", "rag_microbatch_largek_b1_q24_m100000_d128_k32", "rag_stream_largek_b1_q128_m131071_d128_k32", "rag_microbatch_b1_q8_m100000_d128_k10", "rag_microbatch_b1_q16_m100000_d128_k10"]}], ["fallback", "loom.examples.weave.knn_build_dispatch_784a_cf51_q1_q16_q24_q128_seed_portfolio_full90_v1:launch_from_contract_inputs"], ["rejected_reason", "single-seed diagnostic; build K10 rows still use ad64 fallback"]]}], ["ad64_plus_best_per_shape_build_k10_plus_ceb3", {"__dict_items__": [["candidate_id", "candidate_ad64_plus_best_build_k10_plus_ceb3_full90_v1"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_ad64_1b8f_4b51_ceb3_full90_synthesis_v1:candidate_best_build_ceb3_full90_v1"], ["benchmark_entrypoint", "loom.examples.weave.knn_build_dispatch_ad64_1b8f_4b51_ceb3_full90_synthesis_v1:benchmark_candidate_best_build_ceb3_full90_v1"], ["selected_seeds", {"__tuple__": ["q1024_k8_195e_v1", "ragonline_mbucket_5706_q1v10_smix_s144_g12", "rag_microbucket_k32_q16dual2warp_largem_bdd2_v1_rowld1_2warp_rows4", "rag_microbucket_k32_q24rowld2_24dc_v1_rowld2_rows4", "rag_stream_k32_q128rowld_60fb_v1_rowld_rows4", "4757_fixedbuild_k10_v2_q512_q1024_b2_q1536_q6144", "ad64_fixedbuild_k10_v2_q512_q1024_q1536_q2048_b2q1024", "rag_microbatch_k10_q8_s128_q16_s136_4757_v1"]}], ["guard_plan", {"__tuple__": ["4b51 exact BF16 build K10 guard for unique Q2048 row", "1b8f exact BF16 build K10 guard for Q512/Q1024/B2-Q1024/Q1536/Q6144 rows", "ceb3 exact BF16 non-build B=1 Q in {8,16} M=100000 D=128 K=10 guard", "b0e2 exact BF16 non-build B=1 Q=128 M=131071 D=128 K=32 rowld rows4 guard", "603d/24dc exact BF16 non-build B=1 Q=24 M=100000 D=128 K=32 rowld2 rows4 guard", "cf51 exact BF16 build Q=M=1024 D=128 K=8 split16", "bca0 exact BF16 non-build Q=1 D=128 K=10 M in {65536,100000,131071,250000,262143,524287}", "5018 exact BF16 non-build Q=16 D=128 K=32 M in {100000,131071,250000}", "c3bf/d5f8 full90 Weave fallback"]}], ["expected_shape_wins", {"__tuple__": ["build_qm1024_d128_k8", "rag_online_b1_q1_m100000_d128_k10", "rag_online_b1_q1_m65536_d128_k10", 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"rag_microbatch_k10_q8_s128_q16_s136_4757_v1", "rag_stream_k32_q128_m100000_ad64_v1_4fbf_v6_s72g8"]}], ["guard_plan", {"__tuple__": ["4b51 exact BF16 build K10 guard for unique Q2048 row", "1b8f exact BF16 build K10 guard for Q512/Q1024/B2-Q1024/Q1536/Q6144 rows", "ceb3 exact BF16 non-build B=1 Q in {8,16} M=100000 D=128 K=10 guard", "9f8a exact BF16 non-build B=1 Q=128 M=100000 D=128 K=32 guard", "b0e2 exact BF16 non-build B=1 Q=128 M=131071 D=128 K=32 rowld rows4 guard", "603d/24dc exact BF16 non-build B=1 Q=24 M=100000 D=128 K=32 rowld2 rows4 guard", "cf51 exact BF16 build Q=M=1024 D=128 K=8 split16", "bca0 exact BF16 non-build Q=1 D=128 K=10 M in {65536,100000,131071,250000,262143,524287}", "5018 exact BF16 non-build Q=16 D=128 K=32 M in {100000,131071,250000}", "c3bf/d5f8 full90 Weave fallback"]}], ["expected_shape_wins", {"__tuple__": ["build_qm1024_d128_k8", "rag_online_b1_q1_m100000_d128_k10", "rag_online_b1_q1_m65536_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", 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benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def compile_and_launch_knn_build(*, shape_labels=BUILD_AND_RAG_TARGET_SHAPES, benchmark: bool=False, candidate_key: str=DEFAULT_CANDIDATE_KEY) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark, kernel_fn=_candidate_kernel_fn(candidate_key)) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=None, kernel_fn: Callable[[dict[str, Any]], Any]=candidate, correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + return parent._run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=kernel_fn, correctness=correctness, time_flashlib=time_flashlib) + +def _seed_trace_record(inputs: dict[str, Any], *, seed_module, force_fallback: bool=False) -> dict[str, Any]: + label = str(inputs.get('label')) + seed_id = _seed_id_for_module(seed_module) + parent_row = _parent_trace_row(label, force_fallback=False) + if force_fallback: + row = _parent_trace_row(label, force_fallback=True) + row['expected_seed'] = seed_id + row['guard_id'] = ''.join(['forced_fallback_', format(seed_id, ''), '_disabled']) + row['guard_condition'] = ''.join(['forced fallback to ad64 parent; ', format(seed_id, ''), ' disabled']) + row['classification'] = 'guard-miss' + row['parent_dispatcher_route'] = parent_row.get('selected_route') + return _normalize_route_row(row) + row = _seed_trace_for_module(seed_module, label) + row['expected_seed'] = seed_id + row['parent_dispatcher_route'] = parent_row.get('selected_route') + row['parent_dispatcher_selected_seed'] = parent_row.get('selected_seed') + row['baseline_dispatcher_route'] = parent_row.get('selected_route') + row['targeted_seed_row'] = TARGETED_SEED_ROWS.get(seed_id, {}) + row['coverage'] = '5c08 synthesized seed overlay before ad64 Q24/Q128 full90 parent' + row['classification'] = 'unmeasured' + return _normalize_route_row(row) + +def _route_trace_record(inputs: dict[str, Any], *, candidate_key: str, force_fallback: bool=False) -> dict[str, Any]: + label = str(inputs.get('label')) + if candidate_key == BASE_AD64_KEY: + return _normalize_route_row(_parent_trace_row(label, force_fallback=force_fallback)) + seed_module = _matched_new_seed(inputs, candidate_key) + if seed_module is not None: + return _seed_trace_record(inputs, seed_module=seed_module, force_fallback=force_fallback) + row = _parent_trace_row(label, force_fallback=force_fallback) + row['parent_dispatcher_route'] = _parent_route(inputs, force_fallback=force_fallback) + return _normalize_route_row(row) + +def route_trace_for_contract_shapes(shape_labels=None, *, candidate_key: str=DEFAULT_CANDIDATE_KEY, force_fallback: bool=False) -> list[dict[str, Any]]: + _candidate_config(candidate_key) + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + return [_route_trace_record(_trace_inputs_for_shape(shape), candidate_key=candidate_key, force_fallback=force_fallback) for shape in selected] + +def _annotate_route_trace(route_trace: list[dict[str, Any]], candidate_report: dict[str, Any], baseline_report: dict[str, Any], *, candidate_key: str) -> list[dict[str, Any]]: + expected_labels = set(_candidate_config(candidate_key)['expected_shape_wins']) + annotated = [] + for row in route_trace: + label = str(row.get('shape_key')) + out = dict(row) + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') + speedup_vs_baseline = baseline_ms / candidate_ms if candidate_ms and baseline_ms else None + speedup_vs_external = flashlib_ms / candidate_ms if candidate_ms and flashlib_ms else None + out['dispatcher_kernel_ms'] = candidate_ms + out['baseline_ad64_kernel_ms'] = baseline_ms + out['shape_specific_kernel_ms'] = candidate_ms if out.get('selected_seed') else None + out['external_baseline_ms'] = flashlib_ms + out['flashlib_ms'] = flashlib_ms + out['relative_speedup_vs_baseline'] = speedup_vs_baseline + out['relative_speedup_vs_ad64'] = speedup_vs_baseline + out['speedup_vs_external_baseline'] = speedup_vs_external + out['external_baseline_ref'] = 'same_session' if flashlib_ms is not None else 'not_available' + out['route_changed_vs_ad64'] = out.get('selected_route') != out.get('baseline_dispatcher_route') + if label in expected_labels and candidate_key != BASE_AD64_KEY: + if out.get('expected_seed') and out.get('selected_seed') != out.get('expected_seed'): + out['classification'] = 'guard-miss' + elif speedup_vs_external is not None and speedup_vs_external < 1.05: + out['classification'] = 'kernel-slow' if out.get('route_kind') == 'specialized' else 'fallback-slow' + elif speedup_vs_baseline is not None and speedup_vs_baseline < 1.0 and out.get('selected_seed'): + out['classification'] = 'kernel-slow' + elif out.get('selected_seed') and out.get('selected_seed') == out.get('expected_seed'): + out['classification'] = 'seed-consumed' + else: + out['classification'] = 'route-ok' + elif speedup_vs_external is not None and speedup_vs_external < 1.05: + out['classification'] = 'kernel-slow' if out.get('route_kind') == 'specialized' else 'fallback-slow' + else: + out['classification'] = 'route-ok' + annotated.append(_normalize_route_row(out)) + return annotated + +def _below_flashlib_rows(report: dict[str, Any], route_trace: list[dict[str, Any]], *, floor: float) -> list[dict[str, Any]]: + trace_by_label = {str(row['shape_key']): row for row in route_trace} + rows = [] + for label, row in sorted(report.get('per_shape', {}).items()): + ratio = row.get('ratio_vs_flashlib') + if isinstance(ratio, (float, int)) and ratio < floor: + trace_row = trace_by_label.get(label, {}) + rows.append({'shape_key': label, 'kernel_ms': row.get('kernel_ms'), 'flashlib_ms': row.get('flashlib_ms'), 'ratio_vs_flashlib': ratio, 'selected_route': trace_row.get('selected_route'), 'selected_seed': trace_row.get('selected_seed'), 'expected_seed': trace_row.get('expected_seed'), 'route_kind': trace_row.get('route_kind', 'unknown'), 'classification': trace_row.get('classification', 'unmeasured')}) + return rows + +def _seed_delta_matrix(candidate_key: str, candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> list[dict[str, Any]]: + matrix = [] + for label in _candidate_config(candidate_key)['expected_shape_wins']: + inputs = _inputs_for_label(label) + selected_seed = _expected_seed(inputs, candidate_key) + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + matrix.append({'shape_key': label, 'baseline_route': _parent_route(inputs), 'candidate_route': route_for_contract_inputs(inputs, candidate_key=candidate_key), 'selected_seed': selected_seed, 'candidate_id': _candidate_id(candidate_key), 'candidate_ms': candidate_ms, 'baseline_ad64_ms': baseline_ms, 'flashlib_ms': candidate_row.get('flashlib_ms'), 'delta_ms_candidate_minus_ad64': candidate_ms - baseline_ms if candidate_ms is not None and baseline_ms is not None else None, 'speedup_vs_ad64': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'ratio_vs_flashlib': candidate_row.get('ratio_vs_flashlib'), 'targeted_seed_row': TARGETED_SEED_ROWS.get(selected_seed, {}), 'timing_backend': candidate_row.get('timing_backend') or baseline_row.get('timing_backend')}) + return matrix + +def benchmark_baseline_ad64(*, use_cupti: bool=True, shape_labels=None, benchmark_correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + return _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_parent_ad64_full90_v1, correctness=benchmark_correctness, time_flashlib=time_flashlib) + +def _baseline_sidecar(report: dict[str, Any], *, shape_labels, denominator: str, timing_backend: str, benchmark_correctness: bool, time_flashlib: bool) -> dict[str, Any]: + route_trace = route_trace_for_contract_shapes(shape_labels, candidate_key=BASE_AD64_KEY) + below_1x = _below_flashlib_rows(report, route_trace, floor=1.0) + below_floor = _below_flashlib_rows(report, route_trace, floor=1.05) + return {'candidate_id': BASE_AD64_ID, 'candidate_key': BASE_AD64_KEY, 'selected_seeds': CANDIDATE_CONFIGS[BASE_AD64_KEY]['selected_seeds'], 'source_tasks': SOURCE_TASKS, 'tflops': report['summary']['primary_mean'], 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'invalid_performance_reason': report['summary']['invalid_performance_reason'], 'measured_entrypoint': BASE_AD64_ENTRYPOINT, 'route_entrypoint': BASE_AD64_ROUTE_ENTRYPOINT, 'measured_shape_labels': _payload_shape_labels(shape_labels), 'timing_backend': timing_backend, 'denominator': denominator, 'performance_coverage': 'partial' if below_floor else 'pass', 'coverage_only_routes': [], 'hot_bucket_blockers': below_floor, 'flashlib_parity_ledger': {'baseline_ref_scope': 'same_session' if time_flashlib else 'not_available', 'speedup_floor': 1.05, 'rows_below_1x': below_1x, 'rows_below_floor': below_floor, 'omitted_reason': None if time_flashlib else 'benchmark_time_flashlib=false'}, 'route_trace': route_trace, 'route_trace_included': True, 'contract_summary': report['summary'], 'contract_performance': report['performance'], 'timing_backends': _timing_backends_for_reports(report), 'timing_backend_requested': timing_backend, 'benchmark_correctness_checked': benchmark_correctness, 'benchmark_time_flashlib': time_flashlib, 'report': report} + +def _benchmark_payload(candidate_key: str, candidate_report: dict[str, Any], baseline_report: dict[str, Any], *, use_cupti: bool, shape_labels, benchmark_correctness: bool, time_flashlib: bool) -> dict[str, Any]: + candidate_metric = candidate_report['summary']['primary_mean'] + baseline_metric = baseline_report['summary']['primary_mean'] + route_trace = _annotate_route_trace(route_trace_for_contract_shapes(shape_labels, candidate_key=candidate_key), candidate_report, baseline_report, candidate_key=candidate_key) + below_1x = _below_flashlib_rows(candidate_report, route_trace, floor=1.0) + below_floor = _below_flashlib_rows(candidate_report, route_trace, floor=1.05) + denominator = _denominator_name(shape_labels) + timing_backend = _timing_backend_name(use_cupti) + config = _candidate_config(candidate_key) + return {'candidate_id': config['candidate_id'], 'candidate_key': candidate_key, 'baseline_candidate_id': BASE_AD64_ID, 'selected_seeds': config['selected_seeds'], 'source_tasks': SOURCE_TASKS, 'tflops': candidate_metric, 'baseline_ad64_tflops': baseline_metric, 'metric_delta_vs_ad64': candidate_metric - baseline_metric if candidate_metric is not None and baseline_metric is not None else None, 'all_correct': candidate_report['summary']['all_correct'], 'baseline_all_correct': baseline_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'baseline_performance_comparable': baseline_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'baseline_invalid_performance_reason': baseline_report['summary']['invalid_performance_reason'], 'measured_entrypoint': config['benchmark_entrypoint'], 'baseline_entrypoint': BASE_AD64_ENTRYPOINT, 'route_entrypoint': ROUTE_ENTRYPOINT, 'measured_shape_labels': _payload_shape_labels(shape_labels), 'timing_backend': timing_backend, 'denominator': denominator, 'selected_route_labels': config['expected_shape_wins'], 'selected_route_rows': _rows_for_labels(candidate_report, config['expected_shape_wins']), 'baseline_selected_route_rows': _rows_for_labels(baseline_report, config['expected_shape_wins']), 'seed_delta_matrix': _seed_delta_matrix(candidate_key, candidate_report, baseline_report), 'targeted_seed_rows': TARGETED_SEED_ROWS, 'candidate_dispatchers': CANDIDATE_DISPATCHERS, 'rejected_route_combinations': REJECTED_ROUTE_COMBINATIONS, 'selected_candidate_dispatcher': config['candidate_id'], 'guard_plan': config['guard_plan'], 'route_modules': PRODUCTION_ROUTE_MODULES, 'route_trace': route_trace, 'forced_fallback_route_trace': route_trace_for_contract_shapes(shape_labels, candidate_key=candidate_key, force_fallback=True), 'route_trace_included': True, 'contract_summary': candidate_report['summary'], 'baseline_contract_summary': baseline_report['summary'], 'contract_performance': candidate_report['performance'], 'baseline_contract_performance': baseline_report['performance'], 'timing_backends': _timing_backends_for_reports(candidate_report, baseline_report), 'timing_backend_requested': timing_backend, 'use_cupti': use_cupti, 'benchmark_correctness_checked': benchmark_correctness, 'benchmark_time_flashlib': time_flashlib, 'performance_coverage': 'partial' if below_floor else 'pass', 'coverage_only_routes': [], 'hot_bucket_blockers': below_floor, 'flashlib_parity_ledger': {'baseline_ref_scope': 'same_session' if time_flashlib else 'not_available', 'speedup_floor': 1.05, 'rows_below_1x': below_1x, 'rows_below_floor': below_floor, 'omitted_reason': None if time_flashlib else 'benchmark_time_flashlib=false'}, 'rank_objective': {'metric': 'tflops', 'direction': 'maximize', 'value': candidate_metric, 'baseline_ad64_value': baseline_metric, 'delta_vs_ad64': candidate_metric - baseline_metric if candidate_metric is not None and baseline_metric is not None else None, 'denominator': denominator}, 'report': candidate_report, 'baseline_report': baseline_report} + +def benchmark_candidate_portfolio(candidate_key: str, *, use_cupti: bool=True, shape_labels=None, baseline_report: dict[str, Any] | None=None, benchmark_correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + if candidate_key == BASE_AD64_KEY: + baseline = benchmark_baseline_ad64(use_cupti=use_cupti, shape_labels=shape_labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + return _baseline_sidecar(baseline, shape_labels=shape_labels, denominator=_denominator_name(shape_labels), timing_backend=_timing_backend_name(use_cupti), benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + if baseline_report is None: + baseline_report = benchmark_baseline_ad64(use_cupti=use_cupti, shape_labels=shape_labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=_candidate_kernel_fn(candidate_key), correctness=benchmark_correctness, time_flashlib=time_flashlib) + return _benchmark_payload(candidate_key, candidate_report, baseline_report, use_cupti=use_cupti, shape_labels=shape_labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + +def benchmark_candidate_1b8f_build_k10_full90_v1(**kwargs) -> dict[str, Any]: + return benchmark_candidate_portfolio(CANDIDATE_1B8F_BUILD_K10, **kwargs) + +def benchmark_candidate_4b51_build_k10_full90_v1(**kwargs) -> dict[str, Any]: + return benchmark_candidate_portfolio(CANDIDATE_4B51_BUILD_K10, **kwargs) + +def benchmark_candidate_best_build_k10_full90_v1(**kwargs) -> dict[str, Any]: + return benchmark_candidate_portfolio(CANDIDATE_BEST_BUILD_K10, **kwargs) + +def benchmark_candidate_ceb3_q8q16_full90_v1(**kwargs) -> dict[str, Any]: + return benchmark_candidate_portfolio(CANDIDATE_CEB3_Q8Q16, **kwargs) + +def benchmark_candidate_best_build_ceb3_full90_v1(**kwargs) -> dict[str, Any]: + return benchmark_candidate_portfolio(CANDIDATE_BEST_BUILD_CEB3, **kwargs) + +def benchmark_candidate_q128_m100000_full90_v1(**kwargs) -> dict[str, Any]: + return benchmark_candidate_portfolio(CANDIDATE_Q128_M100000, **kwargs) + +def benchmark_candidate_best_build_ceb3_q128_m100000_full90_v1(**kwargs) -> dict[str, Any]: + return benchmark_candidate_portfolio(CANDIDATE_BEST_BUILD_CEB3_Q128_M100000, **kwargs) + +def _candidate_no_regresses_baseline(payload: dict[str, Any], baseline_value: float | None) -> bool: + value = payload.get('tflops') + return payload.get('all_correct') and payload.get('performance_comparable') and (value is not None) and (baseline_value is None or value >= baseline_value) + +def _best_candidate_key(payloads: dict[str, dict[str, Any]]) -> str | None: + baseline_value = payloads.get(BASE_AD64_KEY, {}).get('tflops') + candidates = [key for key, payload in payloads.items() if key != BASE_AD64_KEY and _candidate_no_regresses_baseline(payload, baseline_value)] + if not candidates: + return None + return max(candidates, key=lambda key: (payloads[key].get('tflops') or float('-inf'), len(CANDIDATE_CONFIGS[key]['selected_seeds']))) + +def _summary_payload(*, payloads: dict[str, dict[str, Any]], artifacts: dict[str, str], denominator: str, timing_backend: str, benchmark_correctness: bool, time_flashlib: bool) -> dict[str, Any]: + selected_key = _best_candidate_key(payloads) + selected_payload = payloads.get(selected_key, {}) if selected_key else {} + baseline_payload = payloads[BASE_AD64_KEY] + return {'candidate_id': 'dispatcher_synthesis_ad64_1b8f_4b51_ceb3_full90_v1', 'measured_entrypoint': ''.join([format(MODULE, ''), ':write_benchmark_artifacts']), 'denominator': denominator, 'timing_backend': timing_backend, 'benchmark_correctness_checked': benchmark_correctness, 'benchmark_time_flashlib': time_flashlib, 'baseline_candidate_key': BASE_AD64_KEY, 'selected_candidate_key': selected_key, 'selected_candidate_dispatcher': _candidate_id(selected_key) if selected_key else None, 'default_candidate_key': DEFAULT_CANDIDATE_KEY, 'candidate_dispatchers': CANDIDATE_DISPATCHERS, 'rejected_route_combinations': REJECTED_ROUTE_COMBINATIONS, 'candidate_rankings': [{'candidate_key': key, 'candidate_id': payloads[key].get('candidate_id'), 'tflops': payloads[key].get('tflops'), 'metric_delta_vs_ad64': payloads[key].get('metric_delta_vs_ad64'), 'all_correct': payloads[key].get('all_correct'), 'performance_comparable': payloads[key].get('performance_comparable'), 'performance_coverage': payloads[key].get('performance_coverage')} for key in CANDIDATE_KEYS if key in payloads], 'seed_delta_matrix': selected_payload.get('seed_delta_matrix', []), 'seed_delta_matrix_all_candidates': {key: payloads[key].get('seed_delta_matrix', []) for key in payloads if key != BASE_AD64_KEY}, 'flashlib_parity_ledger': selected_payload.get('flashlib_parity_ledger', baseline_payload.get('flashlib_parity_ledger', {})), 'full_denominator_ab': {'baseline_payload': artifacts.get('same_session_baseline_payload'), 'candidate_payload': artifacts.get(''.join([format(selected_key, ''), '_payload'])) if selected_key else None, 'denominator': denominator, 'timing_backend': timing_backend, 'metric_delta': selected_payload.get('metric_delta_vs_ad64'), 'route_trace': selected_payload.get('route_trace', [])}, 'baseline_tflops': baseline_payload.get('tflops'), 'selected_tflops': selected_payload.get('tflops'), 'artifacts': artifacts} + +def write_benchmark_artifacts(artifact_dir: str | Path, *, use_cupti: bool=True, shape_labels=None, benchmark_correctness: bool=True, time_flashlib: bool=True, candidate_keys: tuple[str, ...] | None=None) -> dict[str, str]: + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + denom_label = _denominator_label(shape_labels) + denominator = _denominator_name(shape_labels) + timing_backend = _timing_backend_name(use_cupti) + baseline_report = benchmark_baseline_ad64(use_cupti=use_cupti, shape_labels=shape_labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + baseline_payload = _baseline_sidecar(baseline_report, shape_labels=shape_labels, denominator=denominator, timing_backend=timing_backend, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + artifacts: dict[str, str] = {} + payloads = {BASE_AD64_KEY: baseline_payload} + baseline_path = out_dir / ''.join([format(denom_label, ''), '_same_session_baseline_ad64_q24_q128_v1.json']) + baseline_path.write_text(json.dumps(baseline_payload, indent=2, sort_keys=True) + '\n', encoding='utf-8') + artifacts['same_session_baseline_payload'] = str(baseline_path) + selected_candidate_keys = [CANDIDATE_1B8F_BUILD_K10, CANDIDATE_4B51_BUILD_K10, CANDIDATE_BEST_BUILD_K10, CANDIDATE_CEB3_Q8Q16, CANDIDATE_BEST_BUILD_CEB3, CANDIDATE_Q128_M100000, CANDIDATE_BEST_BUILD_CEB3_Q128_M100000] if candidate_keys is None else list(candidate_keys) + for candidate_key in selected_candidate_keys: + if candidate_key == BASE_AD64_KEY: + raise ValueError('candidate_keys must not include the baseline key') + _candidate_config(candidate_key) + payload = benchmark_candidate_portfolio(candidate_key, use_cupti=use_cupti, shape_labels=shape_labels, baseline_report=baseline_report, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + payloads[candidate_key] = payload + payload_path = out_dir / ''.join([format(denom_label, ''), '_dispatch_', format(candidate_key, ''), '_v1.json']) + trace_path = out_dir / ''.join([format(denom_label, ''), '_route_trace_', format(candidate_key, ''), '_v1.json']) + forced_trace_path = out_dir / ''.join([format(denom_label, ''), '_forced_fallback_trace_', format(candidate_key, ''), '_v1.json']) + seed_matrix_path = out_dir / ''.join([format(denom_label, ''), '_seed_delta_matrix_', format(candidate_key, ''), '_v1.json']) + payload_path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n', encoding='utf-8') + trace_path.write_text(json.dumps(payload['route_trace'], indent=2, sort_keys=True) + '\n', encoding='utf-8') + forced_trace_path.write_text(json.dumps(payload['forced_fallback_route_trace'], indent=2, sort_keys=True) + '\n', encoding='utf-8') + seed_matrix_path.write_text(json.dumps(payload['seed_delta_matrix'], indent=2, sort_keys=True) + '\n', encoding='utf-8') + artifacts[''.join([format(candidate_key, ''), '_payload'])] = str(payload_path) + artifacts[''.join([format(candidate_key, ''), '_route_trace'])] = str(trace_path) + artifacts[''.join([format(candidate_key, ''), '_forced_fallback_trace'])] = str(forced_trace_path) + artifacts[''.join([format(candidate_key, ''), '_seed_delta_matrix'])] = str(seed_matrix_path) + summary = _summary_payload(payloads=payloads, artifacts=artifacts, denominator=denominator, timing_backend=timing_backend, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + summary_path = out_dir / ''.join([format(denom_label, ''), '_dispatcher_synthesis_ad64_1b8f_4b51_ceb3_v1.json']) + summary_path.write_text(json.dumps(summary, indent=2, sort_keys=True) + '\n', encoding='utf-8') + artifacts['dispatcher_synthesis'] = str(summary_path) + return artifacts diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_all_validated_weave_evolve_knn_build_0192_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_all_validated_weave_evolve_knn_build_0192_v1.py new file mode 100644 index 00000000..20f1f4ed --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_all_validated_weave_evolve_knn_build_0192_v1.py @@ -0,0 +1,131 @@ +"""kNN build/search full dispatcher consuming all validated round-96 routes. + +Minimum target architecture: sm_100a. This dispatcher routes exactly the four +round-96 producer labels selected by the rank-wave lane: +``rag_stream_b1_q128_m100000_d128_k10``, +``rag_online_b1_q1_m100000_d128_k10``, +``rag_offline_largek_b1_q4096_m100000_d128_k20``, and +``rag_offline_large_m_b1_q8192_m250000_d128_k20``. The existing v41 +``search_rect_b1_q4096_m65536_d128_k20`` route and all guard misses remain on +the inherited Weave dispatcher. No host, Torch, FlashLib, cuBLAS, CUTLASS, +Triton, or handwritten-CUDA runtime fallback is introduced. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import os +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatchscore_tailinf_knn_build_dispatch_slurm_0610_6329_v41 as v41 +from . import knn_build_k20_rag_large_m_7487_v42 as k20_large_m +from . import knn_build_k20_search_rect_3cef_v1 as k20_search_rect +from . import knn_build_k20raglargek_4ebb_v43 as k20_largek +from . import knn_build_rag_stream_exact_weave_evolve_knn_build_0e40_v1 as rag_stream +from . import knn_build_ragonline_exact_7c8d_v42 as rag_online +RAG_STREAM_SHAPE = rag_stream.TARGET_SHAPE_LABEL +RAG_ONLINE_SHAPE = rag_online.ONLINE_SHAPE +K20_LARGEK_SHAPE = k20_largek.TARGET_SHAPE +K20_LARGE_M_SHAPE = k20_large_m.TARGET_SHAPE +SEARCH_RECT_SHAPE = k20_search_rect.EXACT_SHAPE_LABEL +ACCELERATED_SHAPE_LABELS = (RAG_STREAM_SHAPE, RAG_ONLINE_SHAPE, K20_LARGEK_SHAPE, K20_LARGE_M_SHAPE) +DISPATCH_CORRECTNESS_SHAPES = ('flashml_correctness_b1_q256_m256_d128_k5', SEARCH_RECT_SHAPE, *ACCELERATED_SHAPE_LABELS) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_DISPATCH_ALL_VERIFY_KERNEL') + if verify_kernel == 'rag_stream_stage1': + return rag_stream.replay.parent_lowk.stage1_ir + if verify_kernel == 'rag_stream_merge': + return rag_stream.replay.parent_lowk.parent_cached.merge_k10_s7_cache_ir + if verify_kernel == 'rag_online_stage1': + return rag_online.v20.parent_lowk.stage1_ir + if verify_kernel == 'rag_online_merge': + return rag_online.v20.parent_lowk.parent_cached.merge_k10_s7_cache_ir + if verify_kernel == 'k20_largek_stage1': + return k20_largek.parent_v20.stage1_k20_unordered_ir + if verify_kernel == 'k20_largek_merge': + return k20_largek.parent_v21.merge_k20_unordered_warp_select_splitmajor_ir + if verify_kernel == 'k20_largem_stage1': + return k20_large_m.stage1_k20_rag_large_m_ir + if verify_kernel == 'k20_largem_merge_s16': + return k20_large_m.merge_k20_s16_warp_select_ir + if verify_kernel == 'search_rect_stage1': + return k20_search_rect.parent_v20.stage1_k20_unordered_ir + if verify_kernel == 'search_rect_merge': + return k20_search_rect.parent_v20.merge_k20_unordered_warp_select_ir + return v41.ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k48over32", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 48]], "cta_group": 1, "threads": 192}')) + +def _dtype_is_bf16(inputs: dict[str, Any]) -> bool: + return str(inputs['query'].dtype) == 'torch.bfloat16' and str(inputs['database'].dtype) == 'torch.bfloat16' + +def _matches_shape(inputs: dict[str, Any], *, bsz: int, n_query: int, n_database: int, dim: int, top_k: int) -> bool: + return not bool(inputs.get('build', False)) and _dtype_is_bf16(inputs) and (int(inputs['B']) == bsz) and (int(inputs['Q']) == n_query) and (int(inputs['M']) == n_database) and (int(inputs['D']) == dim) and (int(inputs['K']) == top_k) + +def _is_rag_stream(inputs: dict[str, Any]) -> bool: + return _matches_shape(inputs, bsz=1, n_query=128, n_database=100000, dim=128, top_k=10) + +def _is_rag_online(inputs: dict[str, Any]) -> bool: + return _matches_shape(inputs, bsz=1, n_query=1, n_database=100000, dim=128, top_k=10) + +def _is_k20_largek(inputs: dict[str, Any]) -> bool: + return _matches_shape(inputs, bsz=1, n_query=4096, n_database=100000, dim=128, top_k=20) + +def _is_k20_large_m(inputs: dict[str, Any]) -> bool: + return _matches_shape(inputs, bsz=1, n_query=8192, n_database=250000, dim=128, top_k=20) + +def launch_from_contract_inputs(inputs: dict[str, Any]) -> None: + if _is_rag_stream(inputs): + rag_stream.launch_from_contract_inputs(inputs) + return + if _is_rag_online(inputs): + rag_online.launch_from_contract_inputs(inputs) + return + if _is_k20_largek(inputs): + k20_largek.launch_from_contract_inputs(inputs) + return + if _is_k20_large_m(inputs): + k20_large_m.launch_from_contract_inputs(inputs) + return + v41.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(candidate, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return v41._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=DISPATCH_CORRECTNESS_SHAPES, benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint for selected contract shapes.""" + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _run_with_timing_backend(*, use_cupti: bool, shapes=None) -> dict[str, Any]: + prior_use_cupti = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + try: + return evaluate_contract(shapes=shapes, correctness=True, benchmark=True) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def _benchmark_payload(report: dict[str, Any], *, use_cupti: bool) -> dict[str, Any]: + timing_backends = sorted({result.get('timing_backend') for result in report.get('per_shape', {}).values() if result.get('timing_backend') is not None}) + per_shape = report.get('per_shape', {}) + return {'tflops': report['summary']['primary_mean'] or 0.0, 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'invalid_performance_reason': report['summary']['invalid_performance_reason'], 'measured_entrypoint': 'loom.examples.weave.knn_build_dispatch_all_validated_weave_evolve_knn_build_0192_v1:benchmark_knn_build_dispatch_all_validated_0192_v1', 'contract_summary': report['summary'], 'contract_performance': report['performance'], 'timing_backends': timing_backends, 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'accelerated_shape_labels': list(ACCELERATED_SHAPE_LABELS), 'inherited_dispatcher': 'knn_build_dispatchscore_tailinf_v41', 'inherited_shape_labels': [SEARCH_RECT_SHAPE], 'accelerated_rows': {label: per_shape.get(label, {}) for label in ACCELERATED_SHAPE_LABELS}, 'search_rect_row': per_shape.get(SEARCH_RECT_SHAPE, {}), 'report': report} + +def benchmark_knn_build_dispatch_all_validated_0192_v1(*, use_cupti: bool=False) -> dict[str, Any]: + """Full v3 contract benchmark hook for the all-validated dispatcher.""" + report = _run_with_timing_backend(use_cupti=use_cupti) + return _benchmark_payload(report, use_cupti=use_cupti) + +def benchmark_knn_build_dispatch_all_validated_rows_0192_v1(*, use_cupti: bool=False) -> dict[str, Any]: + """Targeted benchmark for the five dispatcher-consumed exact rows.""" + report = _run_with_timing_backend(use_cupti=use_cupti, shapes=_select_contract_shapes((SEARCH_RECT_SHAPE, *ACCELERATED_SHAPE_LABELS))) + return _benchmark_payload(report, use_cupti=use_cupti) diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_b6d4_d15e_fd02_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_b6d4_d15e_fd02_v1.py new file mode 100644 index 00000000..0ef804f9 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_b6d4_d15e_fd02_v1.py @@ -0,0 +1,291 @@ +"""Opt-in kNN build dispatcher synthesizing 7c3a + b6d4 + d15e routes. + +Minimum target architecture: sm_100a. This dispatcher-synthesis candidate is a +wrapper-only portfolio. It starts from the 7c3a default policy, replaces the +four exact RAG frontier rows with the b6d4 RAG seed, adds the exact d15e +rectangular ``search_rect_b1_q1024_m8192_d128_k10`` seed, and delegates every +other row to the same Weave-only fallback chain used by 7c3a. + +No external runtime fallback is used. FlashLib/PyTorch remain only contract +harness references outside this production dispatch path. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from pathlib import Path +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_split72_4e09_de1a_3dc7_v48 as baseline_3dc7 +from . import knn_build_large_square_k20k32_a989_v1 as large_square +from . import knn_build_over64_k96_a989_v1 as over64_k96 +from . import knn_build_rag_frontier_4b5c_v1 as rag_7c3a +from . import knn_build_rag_frontier_b6d4_v4 as rag_b6d4 +from . import knn_build_rect_smallq_largem_ff59_d15e_v1 as rect_d15e +ROUTE_LARGE_SQUARE_K20K32 = 'loom.examples.weave.knn_build_large_square_k20k32_a989_v1' +ROUTE_OVER64_K96 = 'loom.examples.weave.knn_build_over64_k96_a989_v1' +ROUTE_RAG_7C3A_K10 = 'loom.examples.weave.knn_build_rag_frontier_4b5c_v1:k10' +ROUTE_RAG_B6D4_K10 = 'loom.examples.weave.knn_build_rag_frontier_b6d4_v4:k10_s72' +ROUTE_RAG_B6D4_K32 = 'loom.examples.weave.knn_build_rag_frontier_b6d4_v4:k32_s72_g8_chunked' +ROUTE_RECT_D15E = 'loom.examples.weave.knn_build_rect_smallq_largem_ff59_d15e_v1:split16' +ROUTE_BASELINE_3DC7 = 'loom.examples.weave.knn_build_dispatch_split72_4e09_de1a_3dc7_v48' +ROUTE_BASELINE_7C3A_POLICY = 'loom.examples.weave.knn_build_dispatch_b6d4_d15e_fd02_v1:baseline_7c3a_policy' +LARGE_SQUARE_TARGET_SHAPES = large_square.TARGET_SHAPES +K96_TARGET_SHAPES = ('build_over64_stress_qm2048_k96',) +RAG_7C3A_K10_TARGET_SHAPES = rag_7c3a.K10_TARGET_SHAPES +RAG_K10_TARGET_SHAPES = rag_b6d4.K10_TARGET_SHAPES +RAG_K32_TARGET_SHAPES = rag_b6d4.K32_TARGET_SHAPES +RAG_TARGET_SHAPES = rag_b6d4.TARGET_SHAPES +RECT_D15E_TARGET_SHAPES = rect_d15e.TARGET_SHAPES +LARGE_SQUARE_TARGET_SHAPE_SET = set(LARGE_SQUARE_TARGET_SHAPES) +K96_TARGET_SHAPE_SET = set(K96_TARGET_SHAPES) +RAG_7C3A_K10_TARGET_SHAPE_SET = set(RAG_7C3A_K10_TARGET_SHAPES) +RAG_K10_TARGET_SHAPE_SET = set(RAG_K10_TARGET_SHAPES) +RAG_K32_TARGET_SHAPE_SET = set(RAG_K32_TARGET_SHAPES) +RAG_TARGET_SHAPE_SET = set(RAG_TARGET_SHAPES) +RECT_D15E_TARGET_SHAPE_SET = set(RECT_D15E_TARGET_SHAPES) +BASE_7C3A_TARGET_SHAPES = (*LARGE_SQUARE_TARGET_SHAPES, *K96_TARGET_SHAPES, *RAG_7C3A_K10_TARGET_SHAPES) +CONSUMED_SEED_TARGET_SHAPES = (*RAG_TARGET_SHAPES, *RECT_D15E_TARGET_SHAPES) +SELECTED_TARGET_SHAPES = (*LARGE_SQUARE_TARGET_SHAPES, *K96_TARGET_SHAPES, *RAG_TARGET_SHAPES, *RECT_D15E_TARGET_SHAPES) +DISPATCH_CORRECTNESS_SHAPES = ('flashml_correctness_b1_q256_m256_d128_k5', *SELECTED_TARGET_SHAPES, *baseline_3dc7.SELECTED_TARGET_SHAPES) +PRODUCTION_ROUTE_MODULES = {'large_square_k20k32': ROUTE_LARGE_SQUARE_K20K32, 'over64_k96': ROUTE_OVER64_K96, 'baseline_7c3a_rag_k10': ROUTE_RAG_7C3A_K10, 'rag_frontier_b6d4_k10': ROUTE_RAG_B6D4_K10, 'rag_frontier_b6d4_k32': ROUTE_RAG_B6D4_K32, 'rect_smallq_largem_d15e': ROUTE_RECT_D15E, 'baseline_7c3a_policy': ROUTE_BASELINE_7C3A_POLICY, 'fallback': ROUTE_BASELINE_3DC7} +CANDIDATE_PORTFOLIOS = ({'id': 'base_7c3a_plus_b6d4', 'consumed_seeds': ('b6d4_rag_frontier_v4',), 'guard_plan': ('exact a989 large-square BF16 build Q=M=8192 K20/K32', 'exact 6c1e over64 BF16 build Q=M=2048 K96', 'exact b6d4 RAG frontier BF16 D128 non-build K10/K32 labels', '7c3a Weave policy fallback'), 'rejected_reason': 'lower expected coverage than selected b6d4+d15e portfolio; leaves rect_smallq_largem row on fallback'}, {'id': 'base_7c3a_plus_b6d4_plus_d15e', 'consumed_seeds': ('b6d4_rag_frontier_v4', 'd15e_rect_smallq_largem_v1'), 'guard_plan': ('exact a989 large-square BF16 build Q=M=8192 K20/K32', 'exact 6c1e over64 BF16 build Q=M=2048 K96', 'exact b6d4 RAG frontier BF16 D128 non-build K10/K32 labels', 'exact d15e rect BF16 B1 Q1024 M8192 D128 K10 non-build label', '7c3a Weave policy fallback'), 'rejected_reason': None}) + +class _TraceTensor: + + def __init__(self, dtype: str) -> None: + self.dtype = dtype if dtype.startswith('torch.') else ''.join(['torch.', format(dtype, '')]) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_DISPATCH_B6D4_D15E_FD02_VERIFY_KERNEL') + if verify_kernel == 'large_square_stage1_k20': + os.environ['LOOM_KNN_LARGE_SQUARE_A989_VERIFY_KERNEL'] = 'stage1_k20' + return large_square._verify_export_ir() + if verify_kernel == 'large_square_stage1_k32': + os.environ['LOOM_KNN_LARGE_SQUARE_A989_VERIFY_KERNEL'] = 'stage1_k32' + return large_square._verify_export_ir() + if verify_kernel == 'over64_k96_stage1': + return over64_k96.stage1_k96_over64_ir + if verify_kernel == 'rag_b6d4_k32_stage1': + os.environ['LOOM_KNN_RAG_FRONTIER_B6D4_V4_VERIFY_KERNEL'] = 'k32_stage1' + return rag_b6d4._verify_export_ir() + if verify_kernel == 'rect_d15e_stage1': + os.environ['LOOM_KNN_RECT_D15E_VERIFY_KERNEL'] = 'stage1' + return rect_d15e._verify_export_ir() + return baseline_3dc7.ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k48over32", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 48]], "cta_group": 1, "threads": 192}')) + +def _label_can_hit(inputs: dict[str, Any], target_labels: set[str]) -> bool: + label = inputs.get('label') + return label is None or str(label) in target_labels + +def _eligible_large_square_k20k32(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, LARGE_SQUARE_TARGET_SHAPE_SET) and large_square._eligible_large_square_k20k32(inputs) + +def _eligible_over64_k96(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, K96_TARGET_SHAPE_SET) and over64_k96._eligible_over64_k96_build(inputs) + +def _eligible_7c3a_rag_k10(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, RAG_7C3A_K10_TARGET_SHAPE_SET) and rag_7c3a._eligible_k10_rag_frontier(inputs) + +def _eligible_b6d4_rag_k10(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, RAG_K10_TARGET_SHAPE_SET) and rag_b6d4._eligible_k10_rag_frontier(inputs) + +def _eligible_b6d4_rag_k32(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, RAG_K32_TARGET_SHAPE_SET) and rag_b6d4._eligible_k32_rag_frontier(inputs) + +def _eligible_rect_d15e(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, RECT_D15E_TARGET_SHAPE_SET) and rect_d15e._eligible_rect_smallq_largem(inputs) + +def _base_7c3a_route_for_contract_inputs(inputs: dict[str, Any]) -> str: + if _eligible_large_square_k20k32(inputs): + return ROUTE_LARGE_SQUARE_K20K32 + if _eligible_over64_k96(inputs): + return ROUTE_OVER64_K96 + if _eligible_7c3a_rag_k10(inputs): + return ROUTE_RAG_7C3A_K10 + return ROUTE_BASELINE_3DC7 + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if force_fallback: + return _base_7c3a_route_for_contract_inputs(inputs) + if _eligible_large_square_k20k32(inputs): + return ROUTE_LARGE_SQUARE_K20K32 + if _eligible_over64_k96(inputs): + return ROUTE_OVER64_K96 + if _eligible_b6d4_rag_k10(inputs): + return ROUTE_RAG_B6D4_K10 + if _eligible_b6d4_rag_k32(inputs): + return ROUTE_RAG_B6D4_K32 + if _eligible_rect_d15e(inputs): + return ROUTE_RECT_D15E + return _base_7c3a_route_for_contract_inputs(inputs) + +def _launch_base_7c3a_route(inputs: dict[str, Any], route: str) -> None: + if route == ROUTE_LARGE_SQUARE_K20K32: + large_square._launch_large_square_k20k32(inputs) + return + if route == ROUTE_OVER64_K96: + over64_k96._launch_over64_k96_split_path(inputs) + return + if route == ROUTE_RAG_7C3A_K10: + rag_7c3a._launch_k10_rag_frontier_s72(inputs) + return + baseline_3dc7.launch_from_contract_inputs(inputs) + +def _launch_route(inputs: dict[str, Any], route: str) -> None: + if route == ROUTE_RAG_B6D4_K10: + rag_b6d4._launch_k10_rag_frontier_s72(inputs) + return + if route == ROUTE_RAG_B6D4_K32: + rag_b6d4._launch_k32_rag_frontier_chunked_stage(inputs) + return + if route == ROUTE_RECT_D15E: + rect_d15e._launch_rect_smallq_largem(inputs) + return + _launch_base_7c3a_route(inputs, route) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + _launch_route(inputs, route_for_contract_inputs(inputs, force_fallback=force_fallback)) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def candidate_baseline_7c3a(inputs: dict[str, Any]): + _launch_base_7c3a_route(inputs, _base_7c3a_route_for_contract_inputs(inputs)) + return None + +def candidate_force_fallback(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs, force_fallback=True) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return baseline_3dc7._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=DISPATCH_CORRECTNESS_SHAPES, benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint: run real contract correctness for selected shapes.""" + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=None, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + shapes = None if shape_labels is None else _select_contract_shapes(shape_labels) + return evaluate_contract(shapes=shapes, correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def _trace_inputs_from_shape(shape: dict[str, Any]) -> dict[str, Any]: + params = dict(shape['params']) + dtype = str(params.get('dtype', 'bfloat16')) + return {'label': shape['label'], 'B': int(params['B']), 'Q': int(params['Q']), 'M': int(params['M']), 'D': int(params['D']), 'K': int(params['K']), 'dtype': dtype, 'build': bool(params.get('build', False)), 'query': _TraceTensor(dtype), 'database': _TraceTensor(dtype)} + +def _baseline_inherited_route(inputs: dict[str, Any]) -> str: + try: + return baseline_3dc7.route_for_contract_inputs(inputs) + except Exception: + return baseline_3dc7.ROUTE_PREVIOUS_MAIN + +def _route_kind_for_base(route: str) -> str: + return 'general' if route == ROUTE_BASELINE_3DC7 else 'specialized' + +def _route_trace_record(inputs: dict[str, Any], *, force_fallback: bool=False) -> dict[str, Any]: + route = route_for_contract_inputs(inputs, force_fallback=force_fallback) + base_route = _base_7c3a_route_for_contract_inputs(inputs) + inherited_route = _baseline_inherited_route(inputs) + if force_fallback: + return {'shape_key': inputs.get('label'), 'selected_route': route, 'guard_condition': 'forced fallback to baseline 7c3a policy', 'route_kind': _route_kind_for_base(route), 'coverage': 'forced candidate fallback; b6d4 and d15e guards disabled', 'consumed_seed': None, 'replaced_route': None, 'baseline_7c3a_route': base_route, 'inherited_route': inherited_route} + if route == ROUTE_LARGE_SQUARE_K20K32: + return {'shape_key': inputs.get('label'), 'selected_route': route, 'guard_condition': 'exact BF16 B1 Q=M=8192 D128 build=true K in {20,32}', 'route_kind': 'specialized', 'coverage': 'baseline 7c3a exact a989 large-square K20/K32 seed', 'consumed_seed': 'a989_large_square_k20k32', 'replaced_route': base_route, 'baseline_7c3a_route': base_route, 'inherited_route': inherited_route, 'parity_status': 'pass'} + if route == ROUTE_OVER64_K96: + return {'shape_key': inputs.get('label'), 'selected_route': route, 'guard_condition': 'exact BF16 B1 Q=M=2048 D128 build=true K=96', 'route_kind': 'specialized', 'coverage': 'baseline 7c3a exact 6c1e over64 K96 seed', 'consumed_seed': '6c1e_over64_k96', 'replaced_route': base_route, 'baseline_7c3a_route': base_route, 'inherited_route': inherited_route, 'parity_status': 'pass'} + if route == ROUTE_RAG_B6D4_K10: + return {'shape_key': inputs.get('label'), 'selected_route': route, 'guard_condition': 'exact b6d4 RAG frontier BF16 D128 non-build K10 label', 'route_kind': 'specialized', 'coverage': 'exact b6d4 RAG K10 seed', 'consumed_seed': 'b6d4_rag_frontier_v4', 'replaced_route': base_route, 'baseline_7c3a_route': base_route, 'baseline_route_kind': _route_kind_for_base(base_route), 'inherited_route': inherited_route, 'parity_status': 'pass'} + if route == ROUTE_RAG_B6D4_K32: + return {'shape_key': inputs.get('label'), 'selected_route': route, 'guard_condition': 'exact b6d4 RAG frontier BF16 B1 Q128 M100000 D128 K32 non-build label', 'route_kind': 'specialized', 'coverage': 'exact b6d4 RAG K32 chunked-stage S72/G8 seed', 'consumed_seed': 'b6d4_rag_frontier_v4', 'replaced_route': base_route, 'baseline_7c3a_route': base_route, 'baseline_route_kind': _route_kind_for_base(base_route), 'inherited_route': inherited_route, 'parity_status': 'fail', 'parity_reason': 'b6d4 K32 CUPTI ratio_vs_flashlib is 0.7234 in the source seed payload'} + if route == ROUTE_RECT_D15E: + return {'shape_key': inputs.get('label'), 'selected_route': route, 'guard_condition': 'exact d15e rect BF16 B1 Q1024 M8192 D128 K10 non-build label', 'route_kind': 'specialized', 'coverage': 'exact d15e rectangular small-Q large-M K10 seed', 'consumed_seed': 'd15e_rect_smallq_largem_v1', 'replaced_route': base_route, 'baseline_7c3a_route': base_route, 'baseline_route_kind': _route_kind_for_base(base_route), 'inherited_route': inherited_route, 'parity_status': 'pass', 'parity_reason': 'd15e target-bucket CUPTI ratio_vs_flashlib is 1.4187 in the source seed payload'} + return {'shape_key': inputs.get('label'), 'selected_route': route, 'guard_condition': 'synthesized guard miss; delegate to baseline 7c3a Weave policy', 'route_kind': _route_kind_for_base(route), 'coverage': 'baseline 7c3a policy or inherited split72/de1a/3dc7 Weave dispatcher fallback', 'consumed_seed': None, 'replaced_route': None, 'baseline_7c3a_route': base_route, 'inherited_route': inherited_route} + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + return [_route_trace_record(_trace_inputs_from_shape(shape), force_fallback=force_fallback) for shape in selected] + +def _timing_backends_for_reports(*reports: dict[str, Any]) -> list[str]: + return sorted({row.get('timing_backend') for report in reports for row in report.get('per_shape', {}).values() if row.get('timing_backend') is not None}) + +def _rows_for_labels(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + rows = report.get('per_shape', {}) + return {label: rows.get(label, {}) for label in labels if label in rows} + +def _params_for_label(label: str) -> dict[str, Any]: + for shape in eval_mod.CANONICAL_SHAPES: + if str(shape['label']) == str(label): + return dict(shape['params']) + raise ValueError(''.join(['unknown kNN build contract shape label: ', format(label, '')])) + +def _inputs_for_label(label: str) -> dict[str, Any]: + return _trace_inputs_from_shape({'label': label, 'params': _params_for_label(label)}) + +def _per_consumed_row_delta(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> dict[str, Any]: + deltas = {} + for label in CONSUMED_SEED_TARGET_SHAPES: + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') + inputs = _inputs_for_label(label) + deltas[label] = {'candidate_ms': candidate_ms, 'baseline_7c3a_ms': baseline_ms, 'flashlib_ms': flashlib_ms, 'speedup_vs_baseline_7c3a': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'ratio_vs_flashlib': flashlib_ms / candidate_ms if candidate_ms and flashlib_ms else None, 'candidate_route': route_for_contract_inputs(inputs), 'baseline_7c3a_route': _base_7c3a_route_for_contract_inputs(inputs)} + return deltas + +def _seed_delta_matrix(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> list[dict[str, Any]]: + matrix = [] + for label in SELECTED_TARGET_SHAPES: + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + inputs = _inputs_for_label(label) + matrix.append({'shape_key': label, 'baseline_route': _base_7c3a_route_for_contract_inputs(inputs), 'candidate_route': route_for_contract_inputs(inputs), 'candidate_ms': candidate_ms, 'baseline_ms': baseline_ms, 'metric_delta_ms': candidate_ms - baseline_ms if candidate_ms and baseline_ms else None, 'speedup_vs_baseline_7c3a': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'timing_backend': candidate_row.get('timing_backend') or baseline_row.get('timing_backend')}) + return matrix + +def _benchmark_payload(candidate_report: dict[str, Any], baseline_report: dict[str, Any], *, use_cupti: bool, shape_labels, measured_function: str) -> dict[str, Any]: + candidate_metric = candidate_report['summary']['primary_mean'] or 0.0 + baseline_metric = baseline_report['summary']['primary_mean'] or 0.0 + route_trace = route_trace_for_contract_shapes(shape_labels) + return {'tflops': candidate_metric, 'baseline_tflops': baseline_metric, 'metric_delta': candidate_metric - baseline_metric, 'all_correct': candidate_report['summary']['all_correct'], 'baseline_all_correct': baseline_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'baseline_performance_comparable': baseline_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'baseline_invalid_performance_reason': baseline_report['summary']['invalid_performance_reason'], 'measured_entrypoint': ''.join(['loom.examples.weave.knn_build_dispatch_b6d4_d15e_fd02_v1:', format(measured_function, '')]), 'baseline_entrypoint': 'loom.examples.weave.knn_build_dispatch_default_7c3a_v1:benchmark_knn_build_dispatch_default_7c3a_v1', 'baseline_entrypoint_note': 'same-session in-module 7c3a-equivalent policy; production route table matches 7c3a source wrapper', 'measured_shape_labels': 'all_canonical' if shape_labels is None else tuple(shape_labels), 'selected_route_labels': SELECTED_TARGET_SHAPES, 'consumed_seed_labels': CONSUMED_SEED_TARGET_SHAPES, 'selected_route_rows': _rows_for_labels(candidate_report, SELECTED_TARGET_SHAPES), 'baseline_selected_route_rows': _rows_for_labels(baseline_report, SELECTED_TARGET_SHAPES), 'consumed_seed_rows': _rows_for_labels(candidate_report, CONSUMED_SEED_TARGET_SHAPES), 'baseline_consumed_seed_rows': _rows_for_labels(baseline_report, CONSUMED_SEED_TARGET_SHAPES), 'per_consumed_row_delta': _per_consumed_row_delta(candidate_report, baseline_report), 'seed_delta_matrix': _seed_delta_matrix(candidate_report, baseline_report), 'candidate_dispatchers': CANDIDATE_PORTFOLIOS, 'selected_candidate_dispatcher': 'base_7c3a_plus_b6d4_plus_d15e', 'route_modules': PRODUCTION_ROUTE_MODULES, 'route_trace': route_trace, 'forced_fallback_route_trace': route_trace_for_contract_shapes(shape_labels, force_fallback=True), 'route_trace_included': True, 'contract_summary': candidate_report['summary'], 'baseline_contract_summary': baseline_report['summary'], 'contract_performance': candidate_report['performance'], 'baseline_contract_performance': baseline_report['performance'], 'timing_backends': _timing_backends_for_reports(candidate_report, baseline_report), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'hot_bucket_parity': {'rag_k10': 'pass', 'rag_k32': 'fail', 'rect_smallq_largem_k10': 'pass', 'reason': 'b6d4 K32 is faster than 7c3a inherited fallback but remains below FlashLib parity.'}, 'report': candidate_report, 'baseline_report': baseline_report} + +def benchmark_knn_build_dispatch_b6d4_d15e_fd02_v1(*, use_cupti: bool=False, shape_labels=None) -> dict[str, Any]: + """Full-denominator A/B against the 7c3a-equivalent baseline policy.""" + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels) + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_baseline_7c3a) + return _benchmark_payload(candidate_report, baseline_report, use_cupti=use_cupti, shape_labels=shape_labels, measured_function='benchmark_knn_build_dispatch_b6d4_d15e_fd02_v1') + +def write_benchmark_artifacts(artifact_dir: str | os.PathLike[str], *, use_cupti: bool=False, shape_labels=None) -> dict[str, str]: + payload = benchmark_knn_build_dispatch_b6d4_d15e_fd02_v1(use_cupti=use_cupti, shape_labels=shape_labels) + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + candidate_path = out_dir / 'full55_dispatch_b6d4_d15e_fd02_v1.json' + baseline_path = out_dir / 'full55_same_session_baseline_7c3a_for_fd02_v1.json' + route_trace_path = out_dir / 'full55_route_trace_b6d4_d15e_fd02_v1.json' + candidate_path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + baseline_path.write_text(json.dumps({'measured_entrypoint': payload['baseline_entrypoint'], 'measured_shape_labels': payload['measured_shape_labels'], 'timing_backend_requested': payload['timing_backend_requested'], 'timing_backends': payload['timing_backends'], 'tflops': payload['baseline_tflops'], 'all_correct': payload['baseline_all_correct'], 'performance_comparable': payload['baseline_performance_comparable'], 'contract_summary': payload['baseline_contract_summary'], 'contract_performance': payload['baseline_contract_performance'], 'report': payload['baseline_report']}, indent=2, sort_keys=True) + '\n') + route_trace_path.write_text(json.dumps(payload['route_trace'], indent=2, sort_keys=True) + '\n') + return {'candidate_payload': str(candidate_path), 'baseline_payload': str(baseline_path), 'route_trace': str(route_trace_path)} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_c142_3505_q32rowld_19b3_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_c142_3505_q32rowld_19b3_v1.py new file mode 100644 index 00000000..f889fa16 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_c142_3505_q32rowld_19b3_v1.py @@ -0,0 +1,279 @@ +"""19b3 dispatcher-consumption wrapper: ed1c plus the e5c1 Q32/K32 seed. + +Minimum target architecture: sm_100a. This off-registry wrapper starts from +the ed1c c142+3505v7 full77 frontier and adds only the e5c1 exact +Q32/M100000/D128/K32 ROW_16x256B microbucket seed ahead of ed1c. All other +rows fall through to the previous ed1c Weave route, and the current registry +entry remains unchanged. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +from pathlib import Path +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_c142_3505_replay_ed1c_v1 as base_ed1c +from . import knn_build_rag_microbucket_q32rowld_e5db_v1 as q32rowld_e5c1 +MODULE = 'loom.examples.weave.knn_build_dispatch_c142_3505_q32rowld_19b3_v1' +SEED_C142_ID = base_ed1c.SEED_C142_ID +SEED_3505_V7_ID = base_ed1c.SEED_3505_V7_ID +SEED_E5C1_ID = 'rag_microbucket_q32rowld_e5db_v1_q32_row16x256b' +ROUTE_E5C1_ENTRYPOINT = 'loom.examples.weave.knn_build_rag_microbucket_q32rowld_e5db_v1:launch_from_contract_inputs' +Q32_E5C1_TARGET_SHAPES = (q32rowld_e5c1.Q32_K32_SHAPE,) +Q32_E5C1_TARGET_SHAPE_SET = set(Q32_E5C1_TARGET_SHAPES) +SELECTED_TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_large_b1_q8192_m8192_d128_k20", "build_large_b1_q8192_m8192_d128_k32", "build_over64_stress_qm2048_k96", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32", "rag_batch_b2_q256_m50000_d128_k10", "rag_irregular_b1_q512_m131071_d128_k10", "search_rect_b1_q1024_m8192_d128_k10", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q2048_m2048_d256_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10", "search_rect_b1_q2048_m32768_d128_k10", "build_large_tail_b1_q6144_m6144_d128_k20", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "build_over32_stress_qm2048_k64", "build_over32_stress_qm4096_k64", "build_k_sweep_qm512_k1", "build_k_sweep_qm512_k2", "build_k_sweep_qm1024_k16", "build_k_sweep_qm512_k4", "build_k_sweep_qm512_k5", "build_k_sweep_qm512_k6", "rag_microbatch_b1_q8_m100000_d128_k10", "rag_microbatch_b1_q32_m100000_d128_k10", "build_dim_sweep_b1_q1024_m1024_d64_k10", "build_dim_sweep_b1_q4096_m4096_d64_k10", "search_rect_b1_q1024_m32768_d64_k10", "rag_online_b1_q1_m100000_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "build_over64_stress_qm1024_k96", "build_over64_stress_qm4096_k96", "rag_microbatch_largek_b1_q8_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m100000_d128_k32", "rag_microbatch_largek_b1_q32_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m131071_d128_k32", "rag_microbatch_b1_q4_m100000_d128_k10", "rag_microbatch_b1_q64_m100000_d128_k10"]}')) +DISPATCH_CORRECTNESS_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["flashml_correctness_b1_q256_m256_d128_k5", "rag_microbatch_b1_q4_m100000_d128_k10", "rag_microbatch_b1_q8_m100000_d128_k10", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_microbatch_b1_q32_m100000_d128_k10", "rag_microbatch_b1_q64_m100000_d128_k10", "rag_microbatch_largek_b1_q8_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m100000_d128_k32", "rag_microbatch_largek_b1_q32_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m131071_d128_k32", "build_large_b1_q8192_m8192_d128_k20", "build_large_b1_q8192_m8192_d128_k32", "build_over64_stress_qm2048_k96", "rag_stream_largek_b1_q128_m100000_d128_k32", "rag_batch_b2_q256_m50000_d128_k10", "rag_irregular_b1_q512_m131071_d128_k10", "search_rect_b1_q1024_m8192_d128_k10", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q2048_m2048_d256_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10", "search_rect_b1_q2048_m32768_d128_k10", "build_large_tail_b1_q6144_m6144_d128_k20", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "build_over32_stress_qm2048_k64", "build_over32_stress_qm4096_k64", "build_k_sweep_qm512_k1", "build_k_sweep_qm512_k2", "build_k_sweep_qm1024_k16", "build_k_sweep_qm512_k4", "build_k_sweep_qm512_k5", "build_k_sweep_qm512_k6", "build_dim_sweep_b1_q1024_m1024_d64_k10", "build_dim_sweep_b1_q4096_m4096_d64_k10", "search_rect_b1_q1024_m32768_d64_k10", "rag_online_b1_q1_m100000_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "build_over64_stress_qm1024_k96", "build_over64_stress_qm4096_k96", "build_k_sweep_qm512_k8", "build_k_sweep_qm512_k10", "build_qm2048_d128_k10", "search_rect_over32_b1_q2048_m65536_d128_k64", "rag_offline_large_m_over32_b1_q2048_m250000_d128_k64", "rag_stream_b1_q128_m100000_d128_k10", "search_rect_b1_q4096_m65536_d128_k20", "rag_offline_largek_b1_q4096_m100000_d128_k20", "rag_offline_large_m_b1_q8192_m250000_d128_k20"]}')) +TARGETED_E5C1_ROWS = {q32rowld_e5c1.Q32_K32_SHAPE: {'guardrail_kernel_ms': 0.143361, 'guardrail_tflops': 5.714245854869874, 'guardrail_ratio_vs_flashlib': 1.1156172180718607, 'exact_q32_kernel_ms': 0.162113, 'exact_q32_tflops': 5.053265314934643, 'exact_q32_ratio_vs_flashlib': 0.9846033, 'prior_v7_guardrail_kernel_ms': 0.145314, 'prior_v7_exact_q32_kernel_ms': 0.169248, 'split_count': q32rowld_e5c1.K32_SPLIT_COUNT, 'group_count': q32rowld_e5c1.K32_GROUP_COUNT, 'classification': 'seed-consumed'}} +PRODUCTION_ROUTE_MODULES = {**base_ed1c.PRODUCTION_ROUTE_MODULES, SEED_E5C1_ID: ROUTE_E5C1_ENTRYPOINT, 'base_ed1c': ''.join([format(base_ed1c.MODULE, ''), ':launch_from_contract_inputs'])} +CANDIDATE_DISPATCHERS = _decode_capture(_json_loads('{"__tuple__": [{"__dict_items__": [["id", "current_registry_c142"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_c142_3505_q32rowld_19b3_v1:benchmark_current_registry_c142"], ["consumed_seeds", {"__tuple__": ["registered_c142_v8_k96_coverage"]}], ["guard_plan", {"__tuple__": ["registered benchmark_data knn_build c142 guard stack"]}], ["expected_shape_wins", {"__tuple__": ["build_large_b1_q8192_m8192_d128_k20", "build_large_b1_q8192_m8192_d128_k32", "build_over64_stress_qm2048_k96", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32", "rag_batch_b2_q256_m50000_d128_k10", "rag_irregular_b1_q512_m131071_d128_k10", "search_rect_b1_q1024_m8192_d128_k10", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q2048_m2048_d256_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10", "search_rect_b1_q2048_m32768_d128_k10", "build_large_tail_b1_q6144_m6144_d128_k20", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "build_over32_stress_qm2048_k64", "build_over32_stress_qm4096_k64", "build_k_sweep_qm512_k1", "build_k_sweep_qm512_k2", "build_k_sweep_qm1024_k16", "build_k_sweep_qm512_k4", "build_k_sweep_qm512_k5", "build_k_sweep_qm512_k6", "rag_microbatch_b1_q8_m100000_d128_k10", "rag_microbatch_b1_q32_m100000_d128_k10", "build_dim_sweep_b1_q1024_m1024_d64_k10", "build_dim_sweep_b1_q4096_m4096_d64_k10", "search_rect_b1_q1024_m32768_d64_k10", "rag_online_b1_q1_m100000_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "build_over64_stress_qm1024_k96", "build_over64_stress_qm4096_k96"]}], ["fallback", "loom.examples.weave.knn_build_dispatch_e3de_9138_bcb3_4247_v1:launch_from_contract_inputs"], ["rejected_reason", "current exported dispatcher baseline; not retargeted by this lane"]]}, {"__dict_items__": [["id", "baseline_ed1c_c142_3505v7"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_c142_3505_q32rowld_19b3_v1:benchmark_baseline_ed1c_v7"], ["consumed_seeds", {"__tuple__": ["registered_c142_v8_k96_coverage", "rag_microbucket_3505_v7_752a_consumption"]}], ["guard_plan", {"__tuple__": ["3505_v7 exact microbucket guards before registered c142", "then registered c142 guard stack"]}], ["expected_shape_wins", {"__tuple__": ["rag_microbatch_b1_q4_m100000_d128_k10", "rag_microbatch_b1_q8_m100000_d128_k10", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_microbatch_b1_q32_m100000_d128_k10", "rag_microbatch_b1_q64_m100000_d128_k10", "rag_microbatch_largek_b1_q8_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m100000_d128_k32", "rag_microbatch_largek_b1_q32_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m131071_d128_k32"]}], ["fallback", "loom.examples.weave.knn_build_dispatch_c142_3505_replay_ed1c_v1:launch_from_contract_inputs"], ["rejected_reason", "current clean full77 champion baseline for e5c1 consumption"]]}, {"__dict_items__": [["id", "candidate_ed1c_plus_q32rowld_19b3"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_c142_3505_q32rowld_19b3_v1:benchmark_knn_build_dispatch_c142_3505v7_q32rowld_19b3_v1"], ["consumed_seeds", {"__tuple__": ["registered_c142_v8_k96_coverage", "rag_microbucket_3505_v7_752a_consumption", "rag_microbucket_q32rowld_e5db_v1_q32_row16x256b"]}], ["guard_plan", {"__tuple__": ["e5c1 exact BF16 non-build B=1 Q=32 M=100000 D=128 K=32 guard before ed1c", "then ed1c 3505_v7 exact microbucket guards", "then registered c142 guard stack"]}], ["expected_shape_wins", {"__tuple__": ["rag_microbatch_largek_b1_q32_m100000_d128_k32"]}], ["fallback", "loom.examples.weave.knn_build_dispatch_c142_3505_replay_ed1c_v1:launch_from_contract_inputs"], ["rejected_reason", null]]}]}')) +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k48over32", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 48]], "cta_group": 1, "threads": 192}')) + +def _label_can_hit(inputs: dict[str, Any], labels: set[str]) -> bool: + label = inputs.get('label') + return label is None or str(label) in labels + +def _eligible_e5c1_q32(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, Q32_E5C1_TARGET_SHAPE_SET) and q32rowld_e5c1._eligible_q32_k32_m64_rowld(inputs) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False, enable_q32rowld_e5c1: bool=True, enable_microbucket: bool=True) -> str: + if not force_fallback and enable_q32rowld_e5c1 and _eligible_e5c1_q32(inputs): + return q32rowld_e5c1.route_for_contract_inputs(inputs) + return base_ed1c.route_for_contract_inputs(inputs, seed_mode='v7', force_fallback=False, enable_microbucket=enable_microbucket) + +def _launch_route(inputs: dict[str, Any], route: str) -> None: + if str(route).startswith('rag_microbucket_q32rowld_e5db_v1_q32_') and _eligible_e5c1_q32(inputs): + q32rowld_e5c1.launch_from_contract_inputs(inputs) + return + base_ed1c._launch_route(inputs, route) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False, enable_q32rowld_e5c1: bool=True, enable_microbucket: bool=True) -> None: + route = route_for_contract_inputs(inputs, force_fallback=force_fallback, enable_q32rowld_e5c1=enable_q32rowld_e5c1, enable_microbucket=enable_microbucket) + _launch_route(inputs, route) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_baseline_ed1c_v7(inputs: dict[str, Any]) -> None: + base_ed1c.candidate_v7(inputs) + +def candidate_current_registry_c142(inputs: dict[str, Any]) -> None: + base_ed1c.candidate_baseline_c142(inputs) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return base_ed1c._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=DISPATCH_CORRECTNESS_SHAPES, benchmark: bool=False) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=None, kernel_fn: Callable[[dict[str, Any]], Any]) -> dict[str, Any]: + return base_ed1c._run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=kernel_fn) + +def _trace_inputs_from_shape(shape: dict[str, Any]) -> dict[str, Any]: + return base_ed1c._trace_inputs_from_shape(shape) + +def _inputs_for_label(label: str) -> dict[str, Any]: + return base_ed1c._inputs_for_label(label) + +def _selected_entrypoint_for_route(route: str) -> str: + if str(route).startswith('rag_microbucket_q32rowld_e5db_v1_q32_'): + return ROUTE_E5C1_ENTRYPOINT + return base_ed1c._selected_entrypoint_for_route(route) + +def _base_ed1c_route_trace_record(inputs: dict[str, Any]) -> dict[str, Any]: + label = str(inputs.get('label')) + row = dict(base_ed1c.route_trace_for_contract_shapes((label,), seed_mode='v7', force_fallback=False)[0]) + route = str(row.get('selected_route') or base_ed1c.route_for_contract_inputs(inputs, seed_mode='v7')) + row['selected_entrypoint'] = _selected_entrypoint_for_route(route) + row.setdefault('selected_seed', row.get('consumed_seed') or SEED_C142_ID) + row.setdefault('expected_seed', row.get('selected_seed')) + row.setdefault('dispatcher_kernel_ms', None) + row.setdefault('shape_specific_kernel_ms', None) + row.setdefault('relative_speedup_vs_baseline', None) + row['base_ed1c_route'] = base_ed1c.route_for_contract_inputs(inputs, seed_mode='v7') + row['current_registry_route'] = base_ed1c.base_c142.route_for_contract_inputs(inputs) + return row + +def _e5c1_trace_record(inputs: dict[str, Any]) -> dict[str, Any]: + label = str(inputs.get('label')) + targeted = dict(TARGETED_E5C1_ROWS[label]) + route = q32rowld_e5c1.route_for_contract_inputs(inputs) + return {'shape_key': label, 'selected_route': route, 'selected_entrypoint': ROUTE_E5C1_ENTRYPOINT, 'selected_seed': SEED_E5C1_ID, 'expected_seed': SEED_E5C1_ID, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': '19b3_e5c1_q32_k32_exact_row16x256b', 'guard_condition': 'exact BF16 non-build B=1 Q=32 M=100000 D=128 K=32', 'coverage': '19b3 consumes the e5c1 Q32 ROW_16x256B seed ahead of ed1c', 'consumed_seed': SEED_E5C1_ID, 'replaced_route': base_ed1c.route_for_contract_inputs(inputs, seed_mode='v7'), 'base_ed1c_route': base_ed1c.route_for_contract_inputs(inputs, seed_mode='v7'), 'current_registry_route': base_ed1c.base_c142.route_for_contract_inputs(inputs), 'row_selection': targeted, 'split_count': targeted['split_count'], 'group_count': targeted['group_count'], 'targeted_seed_timing_backend': 'cupti', 'targeted_seed_kernel_ms': targeted['guardrail_kernel_ms'], 'targeted_seed_ratio_vs_flashlib': targeted['guardrail_ratio_vs_flashlib'], 'classification': targeted['classification'], 'dispatcher_kernel_ms': None, 'shape_specific_kernel_ms': targeted['guardrail_kernel_ms'], 'relative_speedup_vs_baseline': None} + +def _route_trace_record(inputs: dict[str, Any], *, force_fallback: bool=False, enable_q32rowld_e5c1: bool=True, enable_microbucket: bool=True) -> dict[str, Any]: + if force_fallback and enable_q32rowld_e5c1 and _eligible_e5c1_q32(inputs): + row = _base_ed1c_route_trace_record(inputs) + row['selected_route'] = base_ed1c.route_for_contract_inputs(inputs, seed_mode='v7') + row['selected_entrypoint'] = _selected_entrypoint_for_route(str(row['selected_route'])) + row['expected_seed'] = SEED_E5C1_ID + row['guard_id'] = 'forced_fallback_19b3_e5c1_disabled' + row['guard_condition'] = 'forced fallback to ed1c; e5c1 Q32 ROW_16x256B overlay disabled' + row['forced_disabled_seeds'] = (SEED_E5C1_ID,) + row['candidate_guard_status'] = 'forced_fallback' + row['classification'] = 'route-ok' + return row + if enable_q32rowld_e5c1 and _eligible_e5c1_q32(inputs): + return _e5c1_trace_record(inputs) + return _base_ed1c_route_trace_record(inputs) + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False, enable_q32rowld_e5c1: bool=True, enable_microbucket: bool=True) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + return [_route_trace_record(_trace_inputs_from_shape(shape), force_fallback=force_fallback, enable_q32rowld_e5c1=enable_q32rowld_e5c1, enable_microbucket=enable_microbucket) for shape in selected] + +def _timing_backends_for_reports(*reports: dict[str, Any]) -> list[str]: + return base_ed1c._timing_backends_for_reports(*reports) + +def _rows_for_labels(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return base_ed1c._rows_for_labels(report, labels) + +def _shape_labels_for_matrix(shape_labels) -> tuple[str, ...]: + if shape_labels is None: + return tuple((str(shape['label']) for shape in eval_mod.CANONICAL_SHAPES)) + return tuple(shape_labels) + +def _metric_delta(candidate_row: dict[str, Any], baseline_row: dict[str, Any]) -> float: + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + if isinstance(candidate_ms, (float, int)) and isinstance(baseline_ms, (float, int)): + return float(candidate_ms) - float(baseline_ms) + return 0.0 + +def _seed_delta_matrix(*, candidate_report: dict[str, Any], baseline_c142_report: dict[str, Any], baseline_ed1c_report: dict[str, Any], shape_labels) -> list[dict[str, Any]]: + matrix = [] + for label in _shape_labels_for_matrix(shape_labels): + inputs = _inputs_for_label(label) + c142_row = baseline_c142_report.get('per_shape', {}).get(label, {}) + ed1c_row = baseline_ed1c_report.get('per_shape', {}).get(label, {}) + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + ed1c_route = base_ed1c.route_for_contract_inputs(inputs, seed_mode='v7') + candidate_route = route_for_contract_inputs(inputs) + matrix.append({'shape_key': label, 'baseline_route': base_ed1c.base_c142.route_for_contract_inputs(inputs), 'candidate_deltas': [{'candidate_id': 'current_registry_c142', 'selected_route': base_ed1c.base_c142.route_for_contract_inputs(inputs), 'selected_seed': SEED_C142_ID, 'metric_delta': 0.0, 'candidate_ms': c142_row.get('kernel_ms'), 'baseline_c142_ms': c142_row.get('kernel_ms'), 'ratio_vs_flashlib': c142_row.get('ratio_vs_flashlib'), 'timing_backend': c142_row.get('timing_backend') or 'cupti'}, {'candidate_id': 'baseline_ed1c_c142_3505v7', 'selected_route': ed1c_route, 'selected_seed': SEED_3505_V7_ID if str(ed1c_route).startswith(base_ed1c.ROUTE_3505_V7_PREFIX) else SEED_C142_ID, 'metric_delta': _metric_delta(ed1c_row, c142_row), 'candidate_ms': ed1c_row.get('kernel_ms'), 'baseline_c142_ms': c142_row.get('kernel_ms'), 'ratio_vs_flashlib': ed1c_row.get('ratio_vs_flashlib'), 'timing_backend': ed1c_row.get('timing_backend') or c142_row.get('timing_backend') or 'cupti'}, {'candidate_id': 'candidate_ed1c_plus_q32rowld_19b3', 'selected_route': candidate_route, 'selected_seed': SEED_E5C1_ID if str(candidate_route).startswith('rag_microbucket_q32rowld_e5db_v1_q32_') else SEED_3505_V7_ID if str(candidate_route).startswith(base_ed1c.ROUTE_3505_V7_PREFIX) else SEED_C142_ID, 'metric_delta': _metric_delta(candidate_row, c142_row), 'candidate_ms': candidate_row.get('kernel_ms'), 'baseline_c142_ms': c142_row.get('kernel_ms'), 'baseline_ed1c_ms': ed1c_row.get('kernel_ms'), 'delta_vs_ed1c_ms': _metric_delta(candidate_row, ed1c_row), 'ratio_vs_flashlib': candidate_row.get('ratio_vs_flashlib'), 'timing_backend': candidate_row.get('timing_backend') or c142_row.get('timing_backend') or 'cupti'}]}) + return matrix + +def _annotate_route_trace(route_trace: list[dict[str, Any]], *, candidate_report: dict[str, Any], baseline_c142_report: dict[str, Any], baseline_ed1c_report: dict[str, Any]) -> list[dict[str, Any]]: + annotated = [] + for row in route_trace: + label = str(row.get('shape_key')) + out = dict(row) + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + c142_row = baseline_c142_report.get('per_shape', {}).get(label, {}) + ed1c_row = baseline_ed1c_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + c142_ms = c142_row.get('kernel_ms') + ed1c_ms = ed1c_row.get('kernel_ms') + ratio = candidate_row.get('ratio_vs_flashlib') + out['dispatcher_kernel_ms'] = candidate_ms + out['baseline_c142_dispatcher_kernel_ms'] = c142_ms + out['baseline_ed1c_dispatcher_kernel_ms'] = ed1c_ms + out['flashlib_ms'] = candidate_row.get('flashlib_ms') + out['relative_speedup_vs_baseline'] = ed1c_ms / candidate_ms if candidate_ms and ed1c_ms else None + out['relative_speedup_vs_c142'] = c142_ms / candidate_ms if candidate_ms and c142_ms else None + same_ed1c_route = out.get('selected_route') == out.get('base_ed1c_route') + material_same_route_slowdown = bool(same_ed1c_route and isinstance(candidate_ms, (float, int)) and isinstance(ed1c_ms, (float, int)) and (candidate_ms > ed1c_ms * 1.05)) + e5c1_regressed = bool(out.get('selected_seed') == SEED_E5C1_ID and isinstance(candidate_ms, (float, int)) and isinstance(ed1c_ms, (float, int)) and (candidate_ms > ed1c_ms)) + if material_same_route_slowdown: + out['classification'] = 'benchmark-path-mismatch' + out['benchmark_path_note'] = 'selected route matches the ed1c baseline; slowdown is measurement/order variance or harness-path mismatch, not an e5c1 guard decision' + elif e5c1_regressed: + out['classification'] = 'kernel-slow' + elif isinstance(ratio, (float, int)) and ratio < 1.0: + out['classification'] = 'kernel-slow' if out.get('route_kind') == 'specialized' else 'fallback-slow' + elif out.get('selected_seed') == SEED_E5C1_ID: + out['classification'] = 'seed-consumed' + elif out.get('route_kind') == 'specialized': + out['classification'] = 'route-ok' + else: + out['classification'] = out.get('classification', 'route-ok') + annotated.append(out) + return annotated + +def _below_flashlib_rows(report: dict[str, Any], route_trace: list[dict[str, Any]]) -> list[dict[str, Any]]: + trace_by_label = {str(row['shape_key']): row for row in route_trace} + rows = [] + for label, perf_row in sorted(report.get('per_shape', {}).items()): + ratio = perf_row.get('ratio_vs_flashlib') + if isinstance(ratio, (float, int)) and ratio < 1.0: + trace_row = trace_by_label.get(label, {}) + rows.append({'shape_key': label, 'kernel_ms': perf_row.get('kernel_ms'), 'flashlib_ms': perf_row.get('flashlib_ms'), 'ratio_vs_flashlib': ratio, 'selected_route': trace_row.get('selected_route'), 'route_kind': trace_row.get('route_kind', 'unknown'), 'classification': trace_row.get('classification') or ('kernel-slow' if trace_row.get('route_kind') == 'specialized' else 'fallback-slow')}) + return rows + +def _hot_bucket_parity(report: dict[str, Any]) -> dict[str, str]: + buckets = {'rag_microbucket_q32rowld_e5c1_exact': Q32_E5C1_TARGET_SHAPES, 'rag_microbucket_3505_v7_without_q32rowld': tuple((label for label in base_ed1c.V7_TARGET_SHAPES if label not in Q32_E5C1_TARGET_SHAPE_SET)), 'registered_c142_general_portfolio': tuple((label for label in _shape_labels_for_matrix(None) if label not in set(base_ed1c.V7_TARGET_SHAPES)))} + out = {} + for bucket, labels in buckets.items(): + out[bucket] = 'pass' + for label in labels: + ratio = report.get('per_shape', {}).get(label, {}).get('ratio_vs_flashlib') + if not isinstance(ratio, (float, int)) or ratio < 1.0: + out[bucket] = 'fail' + break + return out + +def _benchmark_payload(*, candidate_report: dict[str, Any], baseline_c142_report: dict[str, Any], baseline_ed1c_report: dict[str, Any], use_cupti: bool, shape_labels) -> dict[str, Any]: + candidate_metric = candidate_report['summary']['primary_mean'] + c142_metric = baseline_c142_report['summary']['primary_mean'] + ed1c_metric = baseline_ed1c_report['summary']['primary_mean'] + route_trace = _annotate_route_trace(route_trace_for_contract_shapes(shape_labels), candidate_report=candidate_report, baseline_c142_report=baseline_c142_report, baseline_ed1c_report=baseline_ed1c_report) + below_flashlib = _below_flashlib_rows(candidate_report, route_trace) + return {'candidate_id': 'candidate_ed1c_plus_q32rowld_19b3', 'tflops': candidate_metric, 'baseline_c142_tflops': c142_metric, 'baseline_ed1c_tflops': ed1c_metric, 'metric_delta_vs_c142': candidate_metric - c142_metric if candidate_metric and c142_metric else None, 'metric_delta_vs_ed1c': candidate_metric - ed1c_metric if candidate_metric and ed1c_metric else None, 'all_correct': candidate_report['summary']['all_correct'], 'baseline_c142_all_correct': baseline_c142_report['summary']['all_correct'], 'baseline_ed1c_all_correct': baseline_ed1c_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'baseline_c142_performance_comparable': baseline_c142_report['summary']['performance_comparable'], 'baseline_ed1c_performance_comparable': baseline_ed1c_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_knn_build_dispatch_c142_3505v7_q32rowld_19b3_v1']), 'baseline_c142_entrypoint': ''.join([format(MODULE, ''), ':benchmark_current_registry_c142']), 'baseline_ed1c_entrypoint': ''.join([format(MODULE, ''), ':benchmark_baseline_ed1c_v7']), 'measured_shape_labels': 'all_canonical' if shape_labels is None else tuple(shape_labels), 'selected_route_labels': SELECTED_TARGET_SHAPES, 'consumed_seed_labels': Q32_E5C1_TARGET_SHAPES, 'route_modules': PRODUCTION_ROUTE_MODULES, 'candidate_dispatchers': CANDIDATE_DISPATCHERS, 'selected_candidate_dispatcher': 'candidate_ed1c_plus_q32rowld_19b3', 'selected_route_rows': _rows_for_labels(candidate_report, SELECTED_TARGET_SHAPES), 'baseline_c142_selected_route_rows': _rows_for_labels(baseline_c142_report, SELECTED_TARGET_SHAPES), 'baseline_ed1c_selected_route_rows': _rows_for_labels(baseline_ed1c_report, SELECTED_TARGET_SHAPES), 'seed_delta_matrix': _seed_delta_matrix(candidate_report=candidate_report, baseline_c142_report=baseline_c142_report, baseline_ed1c_report=baseline_ed1c_report, shape_labels=shape_labels), 'route_trace': route_trace, 'forced_fallback_route_trace': route_trace_for_contract_shapes(shape_labels, force_fallback=True), 'route_trace_included': True, 'contract_summary': candidate_report['summary'], 'baseline_c142_contract_summary': baseline_c142_report['summary'], 'baseline_ed1c_contract_summary': baseline_ed1c_report['summary'], 'contract_performance': candidate_report['performance'], 'baseline_c142_contract_performance': baseline_c142_report['performance'], 'baseline_ed1c_contract_performance': baseline_ed1c_report['performance'], 'timing_backends': _timing_backends_for_reports(candidate_report, baseline_c142_report, baseline_ed1c_report), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'hot_bucket_parity': _hot_bucket_parity(candidate_report), 'performance_coverage': 'partial' if below_flashlib else 'pass', 'coverage_only_routes': [], 'hot_bucket_blockers': below_flashlib, 'report': candidate_report, 'baseline_c142_report': baseline_c142_report, 'baseline_ed1c_report': baseline_ed1c_report} + +def benchmark_current_registry_c142(*, use_cupti: bool=True, shape_labels=None) -> dict[str, Any]: + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_current_registry_c142) + return {'candidate_id': 'current_registry_c142', 'tflops': report['summary']['primary_mean'], 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_current_registry_c142']), 'measured_shape_labels': 'all_canonical' if shape_labels is None else tuple(shape_labels), 'route_trace': base_ed1c.base_c142.route_trace_for_contract_shapes(shape_labels), 'route_trace_included': True, 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'report': report, 'contract_summary': report['summary'], 'contract_performance': report['performance']} + +def benchmark_baseline_ed1c_v7(*, use_cupti: bool=True, shape_labels=None) -> dict[str, Any]: + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_baseline_ed1c_v7) + return {'candidate_id': 'baseline_ed1c_c142_3505v7', 'tflops': report['summary']['primary_mean'], 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_baseline_ed1c_v7']), 'measured_shape_labels': 'all_canonical' if shape_labels is None else tuple(shape_labels), 'route_trace': base_ed1c.route_trace_for_contract_shapes(shape_labels, seed_mode='v7'), 'route_trace_included': True, 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'report': report, 'contract_summary': report['summary'], 'contract_performance': report['performance']} + +def benchmark_knn_build_dispatch_c142_3505v7_q32rowld_19b3_v1(*, use_cupti: bool=True, shape_labels=None, baseline_c142_report: dict[str, Any] | None=None, baseline_ed1c_report: dict[str, Any] | None=None) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate) + if baseline_c142_report is None: + baseline_c142_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_current_registry_c142) + if baseline_ed1c_report is None: + baseline_ed1c_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_baseline_ed1c_v7) + return _benchmark_payload(candidate_report=candidate_report, baseline_c142_report=baseline_c142_report, baseline_ed1c_report=baseline_ed1c_report, use_cupti=use_cupti, shape_labels=shape_labels) + +def benchmark_knn_build_dispatch_c142_3505_q32rowld_19b3_v1(*, use_cupti: bool=True, shape_labels=None) -> dict[str, Any]: + baseline_c142_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_current_registry_c142) + baseline_ed1c_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_baseline_ed1c_v7) + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate) + candidate_payload = _benchmark_payload(candidate_report=candidate_report, baseline_c142_report=baseline_c142_report, baseline_ed1c_report=baseline_ed1c_report, use_cupti=use_cupti, shape_labels=shape_labels) + return {'candidate_id': 'same_session_c142_ed1c_q32rowld_19b3_v1', 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_knn_build_dispatch_c142_3505_q32rowld_19b3_v1']), 'measured_shape_labels': 'all_canonical' if shape_labels is None else tuple(shape_labels), 'same_session_order': ('current_registry_c142', 'baseline_ed1c_v7', 'candidate_ed1c_plus_q32rowld'), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'timing_backends': _timing_backends_for_reports(baseline_c142_report, baseline_ed1c_report, candidate_report), 'baseline_c142_tflops': baseline_c142_report['summary']['primary_mean'], 'baseline_ed1c_tflops': baseline_ed1c_report['summary']['primary_mean'], 'candidate_q32rowld_tflops': candidate_report['summary']['primary_mean'], 'candidate_q32rowld_delta_vs_c142': candidate_report['summary']['primary_mean'] - baseline_c142_report['summary']['primary_mean'], 'candidate_q32rowld_delta_vs_ed1c': candidate_report['summary']['primary_mean'] - baseline_ed1c_report['summary']['primary_mean'], 'all_correct': bool(baseline_c142_report['summary']['all_correct'] and baseline_ed1c_report['summary']['all_correct'] and candidate_report['summary']['all_correct']), 'baseline_c142_report': baseline_c142_report, 'baseline_ed1c_report': baseline_ed1c_report, 'candidate_q32rowld_payload': candidate_payload} + +def _denominator_label(shape_labels) -> str: + if shape_labels is None: + return ''.join(['full', format(len(eval_mod.CANONICAL_SHAPES), '')]) + return ''.join(['shape', format(len(tuple(shape_labels)), '')]) + +def write_benchmark_artifacts(artifact_dir: str | Path, *, use_cupti: bool=True, shape_labels=None) -> dict[str, str]: + payload = benchmark_knn_build_dispatch_c142_3505_q32rowld_19b3_v1(use_cupti=use_cupti, shape_labels=shape_labels) + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + denom = _denominator_label(shape_labels) + combined_path = out_dir / ''.join([format(denom, ''), '_same_session_c142_ed1c_q32rowld_19b3_v1.json']) + baseline_c142_path = out_dir / ''.join([format(denom, ''), '_same_session_baseline_c142_for_19b3_v1.json']) + baseline_ed1c_path = out_dir / ''.join([format(denom, ''), '_same_session_baseline_ed1c_for_19b3_v1.json']) + candidate_path = out_dir / ''.join([format(denom, ''), '_dispatch_c142_3505v7_q32rowld_19b3_v1.json']) + route_trace_path = out_dir / ''.join([format(denom, ''), '_route_trace_c142_3505v7_q32rowld_19b3_v1.json']) + forced_trace_path = out_dir / ''.join([format(denom, ''), '_forced_fallback_trace_c142_3505v7_q32rowld_19b3_v1.json']) + seed_matrix_path = out_dir / ''.join([format(denom, ''), '_seed_delta_matrix_c142_ed1c_q32rowld_19b3_v1.json']) + baseline_c142_report = payload['baseline_c142_report'] + baseline_ed1c_report = payload['baseline_ed1c_report'] + candidate_payload = payload['candidate_q32rowld_payload'] + combined_path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + baseline_c142_path.write_text(json.dumps({'candidate_id': 'current_registry_c142', 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_current_registry_c142']), 'measured_shape_labels': payload['measured_shape_labels'], 'timing_backend_requested': payload['timing_backend_requested'], 'timing_backends': payload['timing_backends'], 'tflops': payload['baseline_c142_tflops'], 'all_correct': baseline_c142_report['summary']['all_correct'], 'performance_comparable': baseline_c142_report['summary']['performance_comparable'], 'route_trace': base_ed1c.base_c142.route_trace_for_contract_shapes(shape_labels), 'route_trace_included': True, 'report': baseline_c142_report, 'contract_summary': baseline_c142_report['summary'], 'contract_performance': baseline_c142_report['performance']}, indent=2, sort_keys=True) + '\n') + baseline_ed1c_path.write_text(json.dumps({'candidate_id': 'baseline_ed1c_c142_3505v7', 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_baseline_ed1c_v7']), 'measured_shape_labels': payload['measured_shape_labels'], 'timing_backend_requested': payload['timing_backend_requested'], 'timing_backends': payload['timing_backends'], 'tflops': payload['baseline_ed1c_tflops'], 'all_correct': baseline_ed1c_report['summary']['all_correct'], 'performance_comparable': baseline_ed1c_report['summary']['performance_comparable'], 'route_trace': base_ed1c.route_trace_for_contract_shapes(shape_labels, seed_mode='v7'), 'route_trace_included': True, 'report': baseline_ed1c_report, 'contract_summary': baseline_ed1c_report['summary'], 'contract_performance': baseline_ed1c_report['performance']}, indent=2, sort_keys=True) + '\n') + candidate_path.write_text(json.dumps(candidate_payload, indent=2, sort_keys=True) + '\n') + route_trace_path.write_text(json.dumps(candidate_payload['route_trace'], indent=2, sort_keys=True) + '\n') + forced_trace_path.write_text(json.dumps(candidate_payload['forced_fallback_route_trace'], indent=2, sort_keys=True) + '\n') + seed_matrix_path.write_text(json.dumps(candidate_payload['seed_delta_matrix'], indent=2, sort_keys=True) + '\n') + return {'combined_payload': str(combined_path), 'same_session_baseline_c142_payload': str(baseline_c142_path), 'same_session_baseline_ed1c_payload': str(baseline_ed1c_path), 'candidate_q32rowld_payload': str(candidate_path), 'candidate_q32rowld_route_trace': str(route_trace_path), 'candidate_q32rowld_forced_fallback_trace': str(forced_trace_path), 'seed_delta_matrix': str(seed_matrix_path)} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_c142_3505_replay_ed1c_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_c142_3505_replay_ed1c_v1.py new file mode 100644 index 00000000..db591e7e --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_c142_3505_replay_ed1c_v1.py @@ -0,0 +1,321 @@ +"""ed1c replay dispatcher: c142 plus 3e0c/752a RAG microbucket overlays. + +Minimum target architecture: sm_100a. This dispatcher-synthesis wrapper starts +from the c142 registered v8/full77 K96-coverage dispatcher and adds only guarded +RAG microbucket seed routes for measurement: + +* ``v6`` replays the 3e0c/41b8-equivalent K32 compact seed on top of c142. +* ``v7`` consumes the 752a seed family for the same K32 rows plus Q4/Q64 K10. + +It does not retune seed schedules or retarget the production registry. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +from pathlib import Path +from typing import Any, Callable, Literal +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_e3de_9138_bcb3_4247_v1 as base_c142 +from . import knn_build_rag_microbucket_3505_v6 as rag_3505_v6 +from . import knn_build_rag_microbucket_3505_v7 as rag_3505_v7 +MODULE = 'loom.examples.weave.knn_build_dispatch_c142_3505_replay_ed1c_v1' +SeedMode = Literal['v6', 'v7'] +SEED_C142_ID = 'registered_c142_v8_k96_coverage' +SEED_3505_V6_ID = 'rag_microbucket_3505_v6_41b8_replay' +SEED_3505_V7_ID = 'rag_microbucket_3505_v7_752a_consumption' +ROUTE_BASE_C142 = 'loom.examples.weave.knn_build_dispatch_e3de_9138_bcb3_4247_v1:launch_from_contract_inputs' +ROUTE_3505_V6_PREFIX = 'rag_microbucket_3505_v6_' +ROUTE_3505_V7_PREFIX = 'rag_microbucket_3505_v7_' +V6_TARGET_SHAPES = rag_3505_v6.K32_TARGET_SHAPES +V7_TARGET_SHAPES = rag_3505_v7.TARGET_SHAPES +V6_TARGET_SHAPE_SET = set(V6_TARGET_SHAPES) +V7_TARGET_SHAPE_SET = set(V7_TARGET_SHAPES) +NEW_CONSUMED_SEED_TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["rag_microbatch_largek_b1_q8_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m100000_d128_k32", "rag_microbatch_largek_b1_q32_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m131071_d128_k32", "rag_microbatch_b1_q4_m100000_d128_k10", "rag_microbatch_b1_q8_m100000_d128_k10", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_microbatch_b1_q32_m100000_d128_k10", "rag_microbatch_b1_q64_m100000_d128_k10"]}')) +SELECTED_TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_large_b1_q8192_m8192_d128_k20", "build_large_b1_q8192_m8192_d128_k32", "build_over64_stress_qm2048_k96", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32", "rag_batch_b2_q256_m50000_d128_k10", "rag_irregular_b1_q512_m131071_d128_k10", "search_rect_b1_q1024_m8192_d128_k10", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q2048_m2048_d256_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10", "search_rect_b1_q2048_m32768_d128_k10", "build_large_tail_b1_q6144_m6144_d128_k20", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "build_over32_stress_qm2048_k64", "build_over32_stress_qm4096_k64", "build_k_sweep_qm512_k1", "build_k_sweep_qm512_k2", "build_k_sweep_qm1024_k16", "build_k_sweep_qm512_k4", "build_k_sweep_qm512_k5", "build_k_sweep_qm512_k6", "rag_microbatch_b1_q8_m100000_d128_k10", "rag_microbatch_b1_q32_m100000_d128_k10", "build_dim_sweep_b1_q1024_m1024_d64_k10", "build_dim_sweep_b1_q4096_m4096_d64_k10", "search_rect_b1_q1024_m32768_d64_k10", "rag_online_b1_q1_m100000_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "build_over64_stress_qm1024_k96", "build_over64_stress_qm4096_k96", "rag_microbatch_largek_b1_q8_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m100000_d128_k32", "rag_microbatch_largek_b1_q32_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m131071_d128_k32", "rag_microbatch_b1_q4_m100000_d128_k10", "rag_microbatch_b1_q64_m100000_d128_k10"]}')) +DISPATCH_DELTA_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_large_b1_q8192_m8192_d128_k20", "build_large_b1_q8192_m8192_d128_k32", "build_over64_stress_qm2048_k96", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32", "rag_batch_b2_q256_m50000_d128_k10", "rag_irregular_b1_q512_m131071_d128_k10", "search_rect_b1_q1024_m8192_d128_k10", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q2048_m2048_d256_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10", "search_rect_b1_q2048_m32768_d128_k10", "build_large_tail_b1_q6144_m6144_d128_k20", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "build_over32_stress_qm2048_k64", "build_over32_stress_qm4096_k64", "build_k_sweep_qm512_k1", "build_k_sweep_qm512_k2", "build_k_sweep_qm1024_k16", "build_k_sweep_qm512_k4", "build_k_sweep_qm512_k5", "build_k_sweep_qm512_k6", "rag_microbatch_b1_q8_m100000_d128_k10", "rag_microbatch_b1_q32_m100000_d128_k10", "build_dim_sweep_b1_q1024_m1024_d64_k10", "build_dim_sweep_b1_q4096_m4096_d64_k10", "search_rect_b1_q1024_m32768_d64_k10", "rag_online_b1_q1_m100000_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "build_over64_stress_qm1024_k96", "build_over64_stress_qm4096_k96", "rag_microbatch_largek_b1_q8_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m100000_d128_k32", "rag_microbatch_largek_b1_q32_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m131071_d128_k32", "rag_microbatch_b1_q4_m100000_d128_k10", "rag_microbatch_b1_q64_m100000_d128_k10"]}')) +DISPATCH_CORRECTNESS_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["flashml_correctness_b1_q256_m256_d128_k5", "rag_microbatch_b1_q4_m100000_d128_k10", "rag_microbatch_b1_q8_m100000_d128_k10", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_microbatch_b1_q32_m100000_d128_k10", "rag_microbatch_b1_q64_m100000_d128_k10", "rag_microbatch_largek_b1_q8_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m100000_d128_k32", "rag_microbatch_largek_b1_q32_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m131071_d128_k32", "build_large_b1_q8192_m8192_d128_k20", "build_large_b1_q8192_m8192_d128_k32", "build_over64_stress_qm2048_k96", "rag_stream_largek_b1_q128_m100000_d128_k32", "rag_batch_b2_q256_m50000_d128_k10", "rag_irregular_b1_q512_m131071_d128_k10", "search_rect_b1_q1024_m8192_d128_k10", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q2048_m2048_d256_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10", "search_rect_b1_q2048_m32768_d128_k10", "build_large_tail_b1_q6144_m6144_d128_k20", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "build_over32_stress_qm2048_k64", "build_over32_stress_qm4096_k64", "build_k_sweep_qm512_k1", "build_k_sweep_qm512_k2", "build_k_sweep_qm1024_k16", "build_k_sweep_qm512_k4", "build_k_sweep_qm512_k5", "build_k_sweep_qm512_k6", "build_dim_sweep_b1_q1024_m1024_d64_k10", "build_dim_sweep_b1_q4096_m4096_d64_k10", "search_rect_b1_q1024_m32768_d64_k10", "rag_online_b1_q1_m100000_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "build_over64_stress_qm1024_k96", "build_over64_stress_qm4096_k96", "build_k_sweep_qm512_k8", "build_k_sweep_qm512_k10", "build_qm2048_d128_k10", "search_rect_over32_b1_q2048_m65536_d128_k64", "rag_offline_large_m_over32_b1_q2048_m250000_d128_k64", "rag_stream_b1_q128_m100000_d128_k10", "search_rect_b1_q4096_m65536_d128_k20", "rag_offline_largek_b1_q4096_m100000_d128_k20", "rag_offline_large_m_b1_q8192_m250000_d128_k20"]}')) +V6_TARGETED_SEED_ROWS = {'rag_microbatch_largek_b1_q8_m100000_d128_k32': {'kernel_ms': 0.122881, 'flashlib_ms': 0.107201, 'ratio_vs_flashlib': 0.8723968717702493, 'route': 'rag_microbucket_3505_v6_q8_m100000_k32_tailinf_cta1_cw1_s144_g12'}, 'rag_microbatch_largek_b1_q16_m100000_d128_k32': {'kernel_ms': 0.134529, 'flashlib_ms': 0.135137, 'ratio_vs_flashlib': 1.0045194716380854, 'route': 'rag_microbucket_3505_v6_q16_m100000_k32_tailinf_cta1_cw1_s144_g12'}, 'rag_microbatch_largek_b1_q32_m100000_d128_k32': {'kernel_ms': 0.144993, 'flashlib_ms': 0.160354, 'ratio_vs_flashlib': 1.1059430455263357, 'route': 'rag_microbucket_3505_v6_q32_m100000_k32_tailinf_cta1_cw1_s144_g12'}, 'rag_microbatch_largek_b1_q16_m131071_d128_k32': {'kernel_ms': 0.159906, 'flashlib_ms': 0.158658, 'ratio_vs_flashlib': 0.9921954148061987, 'route': 'rag_microbucket_3505_v6_q16_m131071_k32_tailinf_cta1_cw1_s144_g12'}} +PRODUCTION_ROUTE_MODULES = {**base_c142.PRODUCTION_ROUTE_MODULES, SEED_3505_V6_ID: 'loom.examples.weave.knn_build_rag_microbucket_3505_v6:launch_from_contract_inputs', SEED_3505_V7_ID: 'loom.examples.weave.knn_build_rag_microbucket_3505_v7:launch_from_contract_inputs', 'base_c142': ROUTE_BASE_C142} +CANDIDATE_DISPATCHERS = _decode_capture(_json_loads('{"__tuple__": [{"__dict_items__": [["id", "baseline_c142_registered"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_c142_3505_replay_ed1c_v1:benchmark_baseline_c142"], ["consumed_seeds", {"__tuple__": ["d64_fdd7_aa88_v2", "rect_d64_cf49_v3_9138", "q1_mbucket_aa88_q1m_v3_bcb3"]}], ["guard_plan", {"__tuple__": ["registered c142 guard stack with K96 coverage"]}], ["expected_shape_wins", {"__tuple__": ["build_large_b1_q8192_m8192_d128_k20", "build_large_b1_q8192_m8192_d128_k32", "build_over64_stress_qm2048_k96", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32", "rag_batch_b2_q256_m50000_d128_k10", "rag_irregular_b1_q512_m131071_d128_k10", "search_rect_b1_q1024_m8192_d128_k10", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q2048_m2048_d256_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10", "search_rect_b1_q2048_m32768_d128_k10", "build_large_tail_b1_q6144_m6144_d128_k20", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "build_over32_stress_qm2048_k64", "build_over32_stress_qm4096_k64", "build_k_sweep_qm512_k1", "build_k_sweep_qm512_k2", "build_k_sweep_qm1024_k16", "build_k_sweep_qm512_k4", "build_k_sweep_qm512_k5", "build_k_sweep_qm512_k6", "rag_microbatch_b1_q8_m100000_d128_k10", "rag_microbatch_b1_q32_m100000_d128_k10", "build_dim_sweep_b1_q1024_m1024_d64_k10", "build_dim_sweep_b1_q4096_m4096_d64_k10", "search_rect_b1_q1024_m32768_d64_k10", "rag_online_b1_q1_m100000_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "build_over64_stress_qm1024_k96", "build_over64_stress_qm4096_k96"]}], ["fallback", "loom.examples.weave.knn_build_dispatch_e3de_9138_bcb3_4247_v1:launch_from_contract_inputs"], ["rejected_reason", "same-session full77 baseline"]]}, {"__dict_items__": [["id", "candidate_c142_3505v6_3e0c_replay_ed1c_v1"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_c142_3505_replay_ed1c_v1:benchmark_knn_build_dispatch_c142_3505v6_replay_ed1c_v1"], ["consumed_seeds", {"__tuple__": ["rag_microbucket_3505_v6_41b8_replay"]}], ["guard_plan", {"__tuple__": ["3505_v6 exact BF16 non-build B=1 D=128 K32 Q8/Q16/Q32 guards before c142", "then registered c142 guard stack"]}], ["expected_shape_wins", {"__tuple__": ["rag_microbatch_largek_b1_q8_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m100000_d128_k32", "rag_microbatch_largek_b1_q32_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m131071_d128_k32"]}], ["fallback", "loom.examples.weave.knn_build_dispatch_e3de_9138_bcb3_4247_v1:launch_from_contract_inputs"], ["rejected_reason", null]]}, {"__dict_items__": [["id", "candidate_c142_3505v7_752a_consumption_ed1c_v1"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_c142_3505_replay_ed1c_v1:benchmark_knn_build_dispatch_c142_3505v7_replay_ed1c_v1"], ["consumed_seeds", {"__tuple__": ["rag_microbucket_3505_v7_752a_consumption"]}], ["guard_plan", {"__tuple__": ["3505_v7 exact BF16 non-build B=1 D=128 K10 Q4/Q8/Q16/Q32/Q64 guards before c142", "3505_v7 exact BF16 non-build B=1 D=128 K32 Q8/Q16/Q32 guards before c142", "then registered c142 guard stack"]}], ["expected_shape_wins", {"__tuple__": ["rag_microbatch_b1_q4_m100000_d128_k10", "rag_microbatch_b1_q8_m100000_d128_k10", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_microbatch_b1_q32_m100000_d128_k10", "rag_microbatch_b1_q64_m100000_d128_k10", "rag_microbatch_largek_b1_q8_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m100000_d128_k32", "rag_microbatch_largek_b1_q32_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m131071_d128_k32"]}], ["fallback", "loom.examples.weave.knn_build_dispatch_e3de_9138_bcb3_4247_v1:launch_from_contract_inputs"], ["rejected_reason", null]]}]}')) +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k48over32", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 48]], "cta_group": 1, "threads": 192}')) + +def _label_can_hit(inputs: dict[str, Any], target_labels: set[str]) -> bool: + label = inputs.get('label') + return label is None or str(label) in target_labels + +def _eligible_v6(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, V6_TARGET_SHAPE_SET) and rag_3505_v6._eligible_compact_k32(inputs) + +def _eligible_v7(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, V7_TARGET_SHAPE_SET) and (rag_3505_v7._eligible_m64_k10(inputs) or rag_3505_v7._eligible_compact_k32(inputs)) + +def _seed_id_for_mode(seed_mode: SeedMode) -> str: + return SEED_3505_V6_ID if seed_mode == 'v6' else SEED_3505_V7_ID + +def _route_prefix_for_mode(seed_mode: SeedMode) -> str: + return ROUTE_3505_V6_PREFIX if seed_mode == 'v6' else ROUTE_3505_V7_PREFIX + +def _target_shapes_for_mode(seed_mode: SeedMode) -> tuple[str, ...]: + return V6_TARGET_SHAPES if seed_mode == 'v6' else V7_TARGET_SHAPES + +def _eligible_for_mode(inputs: dict[str, Any], seed_mode: SeedMode) -> bool: + return _eligible_v6(inputs) if seed_mode == 'v6' else _eligible_v7(inputs) + +def route_for_contract_inputs(inputs: dict[str, Any], *, seed_mode: SeedMode='v6', force_fallback: bool=False, enable_microbucket: bool=True) -> str: + if not force_fallback and enable_microbucket and _eligible_for_mode(inputs, seed_mode): + if seed_mode == 'v6': + return rag_3505_v6.route_for_contract_inputs(inputs) + return rag_3505_v7.route_for_contract_inputs(inputs) + return base_c142.route_for_contract_inputs(inputs) + +def _launch_route(inputs: dict[str, Any], route: str) -> None: + route_text = str(route) + if route_text.startswith(ROUTE_3505_V6_PREFIX): + rag_3505_v6.launch_from_contract_inputs(inputs) + return + if route_text.startswith(ROUTE_3505_V7_PREFIX): + rag_3505_v7.launch_from_contract_inputs(inputs) + return + base_c142._launch_route(inputs, route) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, seed_mode: SeedMode='v6', force_fallback: bool=False, enable_microbucket: bool=True) -> None: + route = route_for_contract_inputs(inputs, seed_mode=seed_mode, force_fallback=force_fallback, enable_microbucket=enable_microbucket) + _launch_route(inputs, route) + +def candidate_v6(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, seed_mode='v6') + +def candidate_v7(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, seed_mode='v7') + +def candidate_baseline_c142(inputs: dict[str, Any]) -> None: + base_c142.launch_from_contract_inputs(inputs) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate_v6) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return base_c142._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=DISPATCH_CORRECTNESS_SHAPES, benchmark: bool=False) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + return base_c142._set_bench_backend(use_cupti) + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=None, kernel_fn: Callable[[dict[str, Any]], Any]) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + shapes = None if shape_labels is None else _select_contract_shapes(shape_labels) + return evaluate_contract(shapes=shapes, correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def _trace_inputs_from_shape(shape: dict[str, Any]) -> dict[str, Any]: + return base_c142._trace_inputs_from_shape(shape) + +def _inputs_for_label(label: str) -> dict[str, Any]: + return base_c142._inputs_for_label(label) + +def _selected_entrypoint_for_route(route: str) -> str: + route_text = str(route) + if route_text.startswith(ROUTE_3505_V6_PREFIX): + return 'loom.examples.weave.knn_build_rag_microbucket_3505_v6:launch_from_contract_inputs' + if route_text.startswith(ROUTE_3505_V7_PREFIX): + return 'loom.examples.weave.knn_build_rag_microbucket_3505_v7:launch_from_contract_inputs' + return base_c142._selected_entrypoint_for_route(route_text) + +def _base_route_trace_record(inputs: dict[str, Any]) -> dict[str, Any]: + label = str(inputs.get('label')) + row = dict(base_c142.route_trace_for_contract_shapes((label,))[0]) + route = str(row.get('selected_route') or base_c142.route_for_contract_inputs(inputs)) + row.setdefault('shape_key', label) + row['selected_entrypoint'] = _selected_entrypoint_for_route(route) + row.setdefault('selected_seed', row.get('consumed_seed')) + row.setdefault('expected_seed', row.get('selected_seed')) + row.setdefault('route_kind', 'general') + row.setdefault('route_source', 'shape-specific-seed' if row.get('selected_seed') else 'broad-dispatcher') + row.setdefault('guard_id', row.get('candidate_guard_status')) + row.setdefault('guard_condition', 'registered c142 guard stack') + row.setdefault('classification', 'route-ok') + if row.get('classification') in {'route-ok', 'seed-consumed', 'kernel-slow'} and (not (row.get('selected_seed') or row.get('expected_seed'))): + row['selected_seed'] = SEED_C142_ID + row['expected_seed'] = SEED_C142_ID + row.setdefault('dispatcher_kernel_ms', None) + row.setdefault('shape_specific_kernel_ms', None) + row.setdefault('relative_speedup_vs_baseline', None) + return row + +def _microbucket_trace_record(inputs: dict[str, Any], *, seed_mode: SeedMode) -> dict[str, Any]: + label = str(inputs.get('label')) + route = route_for_contract_inputs(inputs, seed_mode=seed_mode) + seed_id = _seed_id_for_mode(seed_mode) + return {'shape_key': label, 'selected_route': route, 'selected_entrypoint': _selected_entrypoint_for_route(route), 'selected_seed': seed_id, 'expected_seed': seed_id, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': ''.join(['ed1c_3505_', format(seed_mode, ''), '_exact_microbucket']), 'guard_condition': 'exact BF16 non-build B=1 D=128 with 3505_v6 K32 target rows' if seed_mode == 'v6' else 'exact BF16 non-build B=1 D=128 with 3505_v7 Q<=64 K10 or K32 target rows', 'coverage': ''.join(['ed1c consumes ', format(seed_id, ''), ' ahead of registered c142']), 'consumed_seed': seed_id, 'replaced_route': base_c142.route_for_contract_inputs(inputs), 'base_c142_route': base_c142.route_for_contract_inputs(inputs), 'row_selection': V6_TARGETED_SEED_ROWS.get(label, {}), 'split_count': getattr(rag_3505_v6 if seed_mode == 'v6' else rag_3505_v7, 'K32_SPLIT_COUNT', None), 'group_count': getattr(rag_3505_v6 if seed_mode == 'v6' else rag_3505_v7, 'K32_GROUP_COUNT', None), 'targeted_seed_timing_backend': 'cupti', 'targeted_seed_kernel_ms': V6_TARGETED_SEED_ROWS.get(label, {}).get('kernel_ms'), 'targeted_seed_ratio_vs_flashlib': V6_TARGETED_SEED_ROWS.get(label, {}).get('ratio_vs_flashlib'), 'classification': 'seed-consumed', 'dispatcher_kernel_ms': None, 'shape_specific_kernel_ms': V6_TARGETED_SEED_ROWS.get(label, {}).get('kernel_ms'), 'relative_speedup_vs_baseline': None} + +def _route_trace_record(inputs: dict[str, Any], *, seed_mode: SeedMode='v6', force_fallback: bool=False, enable_microbucket: bool=True) -> dict[str, Any]: + if force_fallback and enable_microbucket and _eligible_for_mode(inputs, seed_mode): + row = _base_route_trace_record(inputs) + row['selected_route'] = base_c142.route_for_contract_inputs(inputs) + row['selected_entrypoint'] = _selected_entrypoint_for_route(str(row['selected_route'])) + row['expected_seed'] = _seed_id_for_mode(seed_mode) + row['guard_id'] = ''.join(['forced_fallback_ed1c_3505_', format(seed_mode, ''), '_disabled']) + row['guard_condition'] = ''.join(['forced fallback to registered c142; ed1c 3505_', format(seed_mode, ''), ' overlay disabled']) + row['forced_disabled_seeds'] = (_seed_id_for_mode(seed_mode),) + row['candidate_guard_status'] = 'forced_fallback' + row['classification'] = 'route-ok' + return row + route = route_for_contract_inputs(inputs, seed_mode=seed_mode, force_fallback=force_fallback, enable_microbucket=enable_microbucket) + if enable_microbucket and _eligible_for_mode(inputs, seed_mode) and str(route).startswith(_route_prefix_for_mode(seed_mode)): + return _microbucket_trace_record(inputs, seed_mode=seed_mode) + return _base_route_trace_record(inputs) + +def route_trace_for_contract_shapes(shape_labels=None, *, seed_mode: SeedMode='v6', force_fallback: bool=False, enable_microbucket: bool=True) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + return [_route_trace_record(_trace_inputs_from_shape(shape), seed_mode=seed_mode, force_fallback=force_fallback, enable_microbucket=enable_microbucket) for shape in selected] + +def _timing_backends_for_reports(*reports: dict[str, Any]) -> list[str]: + return base_c142._timing_backends_for_reports(*reports) + +def _rows_for_labels(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return base_c142._rows_for_labels(report, labels) + +def _shape_labels_for_matrix(shape_labels) -> tuple[str, ...]: + if shape_labels is None: + return tuple((str(shape['label']) for shape in eval_mod.CANONICAL_SHAPES)) + return tuple(shape_labels) + +def _seed_delta_matrix(*, seed_mode: SeedMode, candidate_report: dict[str, Any], baseline_report: dict[str, Any], other_candidate_report: dict[str, Any] | None, shape_labels) -> list[dict[str, Any]]: + matrix = [] + for label in _shape_labels_for_matrix(shape_labels): + inputs = _inputs_for_label(label) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + other_row = (other_candidate_report or {}).get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + other_ms = other_row.get('kernel_ms') + other_mode: SeedMode = 'v7' if seed_mode == 'v6' else 'v6' + candidate_route = route_for_contract_inputs(inputs, seed_mode=seed_mode) + other_route = route_for_contract_inputs(inputs, seed_mode=other_mode) + matrix.append({'shape_key': label, 'baseline_route': base_c142.route_for_contract_inputs(inputs), 'candidate_deltas': [{'candidate_id': ''.join(['candidate_c142_3505', format(seed_mode, ''), '_ed1c_v1']), 'selected_route': candidate_route, 'selected_seed': _seed_id_for_mode(seed_mode) if str(candidate_route).startswith(_route_prefix_for_mode(seed_mode)) else SEED_C142_ID, 'metric_delta': candidate_ms - baseline_ms if candidate_ms and baseline_ms else None, 'candidate_ms': candidate_ms, 'baseline_c142_ms': baseline_ms, 'ratio_vs_flashlib': candidate_row.get('ratio_vs_flashlib'), 'timing_backend': candidate_row.get('timing_backend') or baseline_row.get('timing_backend') or 'cupti'}, {'candidate_id': ''.join(['candidate_c142_3505', format(other_mode, ''), '_ed1c_v1']), 'selected_route': other_route, 'selected_seed': _seed_id_for_mode(other_mode) if str(other_route).startswith(_route_prefix_for_mode(other_mode)) else SEED_C142_ID, 'metric_delta': other_ms - baseline_ms if other_ms and baseline_ms else None, 'candidate_ms': other_ms, 'baseline_c142_ms': baseline_ms, 'ratio_vs_flashlib': other_row.get('ratio_vs_flashlib'), 'timing_backend': other_row.get('timing_backend') or baseline_row.get('timing_backend') or 'cupti'}]}) + return matrix + +def _annotate_route_trace(route_trace: list[dict[str, Any]], *, candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> list[dict[str, Any]]: + annotated = [] + for row in route_trace: + label = str(row.get('shape_key')) + out = dict(row) + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + ratio = candidate_row.get('ratio_vs_flashlib') + out['dispatcher_kernel_ms'] = candidate_ms + out['baseline_c142_dispatcher_kernel_ms'] = baseline_ms + out['flashlib_ms'] = candidate_row.get('flashlib_ms') + out['relative_speedup_vs_baseline'] = baseline_ms / candidate_ms if candidate_ms and baseline_ms else None + if isinstance(ratio, (float, int)) and ratio < 1.0: + out['classification'] = 'kernel-slow' if out.get('route_kind') == 'specialized' else 'fallback-slow' + elif out.get('route_kind') == 'specialized': + out['classification'] = 'seed-consumed' + else: + out['classification'] = out.get('classification', 'route-ok') + annotated.append(out) + return annotated + +def _below_flashlib_rows(report: dict[str, Any], route_trace: list[dict[str, Any]]) -> list[dict[str, Any]]: + trace_by_label = {str(row['shape_key']): row for row in route_trace} + rows = [] + for label, perf_row in sorted(report.get('per_shape', {}).items()): + ratio = perf_row.get('ratio_vs_flashlib') + if isinstance(ratio, (float, int)) and ratio < 1.0: + trace_row = trace_by_label.get(label, {}) + rows.append({'shape_key': label, 'kernel_ms': perf_row.get('kernel_ms'), 'flashlib_ms': perf_row.get('flashlib_ms'), 'ratio_vs_flashlib': ratio, 'selected_route': trace_row.get('selected_route'), 'route_kind': trace_row.get('route_kind', 'unknown'), 'classification': 'kernel-slow' if trace_row.get('route_kind') == 'specialized' else 'fallback-slow'}) + return rows + +def _hot_bucket_parity(report: dict[str, Any]) -> dict[str, str]: + buckets = {'rag_microbucket_3505_v6_k32': V6_TARGET_SHAPES, 'rag_microbucket_3505_v7_full': V7_TARGET_SHAPES, 'build_over64_k96': base_c142.K96_COVERAGE_TARGET_SHAPES} + out = {} + for bucket, labels in buckets.items(): + out[bucket] = 'pass' + for label in labels: + ratio = report.get('per_shape', {}).get(label, {}).get('ratio_vs_flashlib') + if not isinstance(ratio, (float, int)) or ratio < 1.0: + out[bucket] = 'fail' + break + return out + +def _benchmark_payload(*, seed_mode: SeedMode, candidate_report: dict[str, Any], baseline_report: dict[str, Any], other_candidate_report: dict[str, Any] | None, use_cupti: bool, shape_labels, measured_function: str) -> dict[str, Any]: + candidate_metric = candidate_report['summary']['primary_mean'] + baseline_metric = baseline_report['summary']['primary_mean'] + route_trace = _annotate_route_trace(route_trace_for_contract_shapes(shape_labels, seed_mode=seed_mode), candidate_report=candidate_report, baseline_report=baseline_report) + below_flashlib = _below_flashlib_rows(candidate_report, route_trace) + target_shapes = _target_shapes_for_mode(seed_mode) + return {'candidate_id': ''.join(['candidate_c142_3505', format(seed_mode, ''), '_ed1c_v1']), 'seed_mode': seed_mode, 'tflops': candidate_metric, 'baseline_c142_tflops': baseline_metric, 'metric_delta_vs_c142': candidate_metric - baseline_metric if candidate_metric and baseline_metric else None, 'all_correct': candidate_report['summary']['all_correct'], 'baseline_c142_all_correct': baseline_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'baseline_c142_performance_comparable': baseline_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'measured_entrypoint': ''.join([format(MODULE, ''), ':', format(measured_function, '')]), 'baseline_c142_entrypoint': ''.join([format(MODULE, ''), ':benchmark_baseline_c142']), 'registered_c142_entrypoint': 'loom.examples.weave.knn_build_dispatch_e3de_9138_bcb3_4247_v1:benchmark_knn_build_dispatch_e3de_9138_bcb3_4247_v1', 'measured_shape_labels': 'all_canonical' if shape_labels is None else tuple(shape_labels), 'selected_route_labels': SELECTED_TARGET_SHAPES, 'consumed_seed_labels': target_shapes, 'route_modules': PRODUCTION_ROUTE_MODULES, 'candidate_dispatchers': CANDIDATE_DISPATCHERS, 'selected_candidate_dispatcher': ''.join(['candidate_c142_3505', format(seed_mode, ''), '_ed1c_v1']), 'selected_route_rows': _rows_for_labels(candidate_report, SELECTED_TARGET_SHAPES), 'baseline_c142_selected_route_rows': _rows_for_labels(baseline_report, SELECTED_TARGET_SHAPES), 'seed_delta_matrix': _seed_delta_matrix(seed_mode=seed_mode, candidate_report=candidate_report, baseline_report=baseline_report, other_candidate_report=other_candidate_report, shape_labels=shape_labels), 'route_trace': route_trace, 'forced_fallback_route_trace': route_trace_for_contract_shapes(shape_labels, seed_mode=seed_mode, force_fallback=True), 'route_trace_included': True, 'contract_summary': candidate_report['summary'], 'baseline_c142_contract_summary': baseline_report['summary'], 'contract_performance': candidate_report['performance'], 'baseline_c142_contract_performance': baseline_report['performance'], 'timing_backends': _timing_backends_for_reports(candidate_report, baseline_report), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'hot_bucket_parity': _hot_bucket_parity(candidate_report), 'performance_coverage': 'partial' if below_flashlib else 'pass', 'coverage_only_routes': [label for label in base_c142.K96_GENERATED_VARIANT_SHAPES if label in _shape_labels_for_matrix(shape_labels)], 'hot_bucket_blockers': below_flashlib, 'report': candidate_report, 'baseline_c142_report': baseline_report} + +def _candidate_fn_for_mode(seed_mode: SeedMode) -> Callable[[dict[str, Any]], None]: + return candidate_v6 if seed_mode == 'v6' else candidate_v7 + +def _benchmark_candidate(*, seed_mode: SeedMode, use_cupti: bool=True, shape_labels=None, baseline_report: dict[str, Any] | None=None, other_candidate_report: dict[str, Any] | None=None) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=_candidate_fn_for_mode(seed_mode)) + if baseline_report is None: + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_baseline_c142) + return _benchmark_payload(seed_mode=seed_mode, candidate_report=candidate_report, baseline_report=baseline_report, other_candidate_report=other_candidate_report, use_cupti=use_cupti, shape_labels=shape_labels, measured_function=''.join(['benchmark_knn_build_dispatch_c142_3505', format(seed_mode, ''), '_replay_ed1c_v1'])) + +def benchmark_baseline_c142(*, use_cupti: bool=True, shape_labels=None) -> dict[str, Any]: + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_baseline_c142) + return {'candidate_id': 'baseline_c142_registered', 'tflops': report['summary']['primary_mean'], 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_baseline_c142']), 'measured_shape_labels': 'all_canonical' if shape_labels is None else tuple(shape_labels), 'route_trace': base_c142.route_trace_for_contract_shapes(shape_labels), 'route_trace_included': True, 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'report': report, 'contract_summary': report['summary'], 'contract_performance': report['performance']} + +def benchmark_knn_build_dispatch_c142_3505v6_replay_ed1c_v1(*, use_cupti: bool=True, shape_labels=None, baseline_report: dict[str, Any] | None=None, other_candidate_report: dict[str, Any] | None=None) -> dict[str, Any]: + return _benchmark_candidate(seed_mode='v6', use_cupti=use_cupti, shape_labels=shape_labels, baseline_report=baseline_report, other_candidate_report=other_candidate_report) + +def benchmark_knn_build_dispatch_c142_3505v7_replay_ed1c_v1(*, use_cupti: bool=True, shape_labels=None, baseline_report: dict[str, Any] | None=None, other_candidate_report: dict[str, Any] | None=None) -> dict[str, Any]: + return _benchmark_candidate(seed_mode='v7', use_cupti=use_cupti, shape_labels=shape_labels, baseline_report=baseline_report, other_candidate_report=other_candidate_report) + +def benchmark_knn_build_dispatch_c142_3505_replay_ed1c_v1(*, use_cupti: bool=True, shape_labels=None) -> dict[str, Any]: + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_baseline_c142) + v6_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_v6) + v7_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_v7) + v6_payload = _benchmark_payload(seed_mode='v6', candidate_report=v6_report, baseline_report=baseline_report, other_candidate_report=v7_report, use_cupti=use_cupti, shape_labels=shape_labels, measured_function='benchmark_knn_build_dispatch_c142_3505v6_replay_ed1c_v1') + v7_payload = _benchmark_payload(seed_mode='v7', candidate_report=v7_report, baseline_report=baseline_report, other_candidate_report=v6_report, use_cupti=use_cupti, shape_labels=shape_labels, measured_function='benchmark_knn_build_dispatch_c142_3505v7_replay_ed1c_v1') + return {'candidate_id': 'same_session_c142_v6_v7_replay_ed1c_v1', 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_knn_build_dispatch_c142_3505_replay_ed1c_v1']), 'measured_shape_labels': 'all_canonical' if shape_labels is None else tuple(shape_labels), 'same_session_order': ('baseline_c142', 'candidate_v6', 'candidate_v7'), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'timing_backends': _timing_backends_for_reports(baseline_report, v6_report, v7_report), 'baseline_c142_tflops': baseline_report['summary']['primary_mean'], 'candidate_v6_tflops': v6_report['summary']['primary_mean'], 'candidate_v7_tflops': v7_report['summary']['primary_mean'], 'candidate_v6_delta_vs_c142': v6_report['summary']['primary_mean'] - baseline_report['summary']['primary_mean'], 'candidate_v7_delta_vs_c142': v7_report['summary']['primary_mean'] - baseline_report['summary']['primary_mean'], 'all_correct': bool(baseline_report['summary']['all_correct'] and v6_report['summary']['all_correct'] and v7_report['summary']['all_correct']), 'baseline_c142_report': baseline_report, 'candidate_v6_payload': v6_payload, 'candidate_v7_payload': v7_payload} + +def _denominator_label(shape_labels) -> str: + if shape_labels is None: + return ''.join(['full', format(len(eval_mod.CANONICAL_SHAPES), '')]) + return ''.join(['shape', format(len(tuple(shape_labels)), '')]) + +def write_benchmark_artifacts(artifact_dir: str | Path, *, use_cupti: bool=True, shape_labels=None) -> dict[str, str]: + payload = benchmark_knn_build_dispatch_c142_3505_replay_ed1c_v1(use_cupti=use_cupti, shape_labels=shape_labels) + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + denom = _denominator_label(shape_labels) + combined_path = out_dir / ''.join([format(denom, ''), '_same_session_c142_3505_replay_ed1c_v1.json']) + baseline_path = out_dir / ''.join([format(denom, ''), '_same_session_baseline_c142_for_ed1c_v1.json']) + v6_path = out_dir / ''.join([format(denom, ''), '_dispatch_c142_3505v6_replay_ed1c_v1.json']) + v7_path = out_dir / ''.join([format(denom, ''), '_dispatch_c142_3505v7_replay_ed1c_v1.json']) + v6_trace_path = out_dir / ''.join([format(denom, ''), '_route_trace_c142_3505v6_replay_ed1c_v1.json']) + v7_trace_path = out_dir / ''.join([format(denom, ''), '_route_trace_c142_3505v7_replay_ed1c_v1.json']) + v6_forced_path = out_dir / ''.join([format(denom, ''), '_forced_fallback_trace_c142_3505v6_replay_ed1c_v1.json']) + v7_forced_path = out_dir / ''.join([format(denom, ''), '_forced_fallback_trace_c142_3505v7_replay_ed1c_v1.json']) + seed_matrix_path = out_dir / ''.join([format(denom, ''), '_seed_delta_matrix_c142_3505_replay_ed1c_v1.json']) + v6_payload = payload['candidate_v6_payload'] + v7_payload = payload['candidate_v7_payload'] + baseline_report = payload['baseline_c142_report'] + combined_path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + baseline_path.write_text(json.dumps({'candidate_id': 'baseline_c142_registered', 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_baseline_c142']), 'measured_shape_labels': payload['measured_shape_labels'], 'timing_backend_requested': payload['timing_backend_requested'], 'timing_backends': payload['timing_backends'], 'tflops': payload['baseline_c142_tflops'], 'all_correct': baseline_report['summary']['all_correct'], 'performance_comparable': baseline_report['summary']['performance_comparable'], 'route_trace': base_c142.route_trace_for_contract_shapes(shape_labels), 'route_trace_included': True, 'report': baseline_report, 'contract_summary': baseline_report['summary'], 'contract_performance': baseline_report['performance']}, indent=2, sort_keys=True) + '\n') + v6_path.write_text(json.dumps(v6_payload, indent=2, sort_keys=True) + '\n') + v7_path.write_text(json.dumps(v7_payload, indent=2, sort_keys=True) + '\n') + v6_trace_path.write_text(json.dumps(v6_payload['route_trace'], indent=2, sort_keys=True) + '\n') + v7_trace_path.write_text(json.dumps(v7_payload['route_trace'], indent=2, sort_keys=True) + '\n') + v6_forced_path.write_text(json.dumps(v6_payload['forced_fallback_route_trace'], indent=2, sort_keys=True) + '\n') + v7_forced_path.write_text(json.dumps(v7_payload['forced_fallback_route_trace'], indent=2, sort_keys=True) + '\n') + seed_matrix_path.write_text(json.dumps(v7_payload['seed_delta_matrix'], indent=2, sort_keys=True) + '\n') + return {'combined_payload': str(combined_path), 'same_session_baseline_c142_payload': str(baseline_path), 'candidate_v6_payload': str(v6_path), 'candidate_v7_payload': str(v7_path), 'candidate_v6_route_trace': str(v6_trace_path), 'candidate_v7_route_trace': str(v7_trace_path), 'candidate_v6_forced_fallback_trace': str(v6_forced_path), 'candidate_v7_forced_fallback_trace': str(v7_forced_path), 'seed_delta_matrix': str(seed_matrix_path)} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_combined_k20rag_weave_evolve_knn_build_c454_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_combined_k20rag_weave_evolve_knn_build_c454_v1.py new file mode 100644 index 00000000..7e56d2f3 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_combined_k20rag_weave_evolve_knn_build_c454_v1.py @@ -0,0 +1,89 @@ +"""kNN build/search c454 dispatcher for validated K20 and RAG exact routes. + +Minimum target architecture: sm_100a. This dispatcher-only candidate starts +from the 8050 full-dispatch baseline, routes the three de1a K20 low-fanout +labels through ``knn_build_k20_large_lowfanout_de1a_v1``, and routes the two +801d RAG K10 labels through ``knn_build_rag_online_stream_801d_v45``. +All guard misses stay on Weave dispatcher code; no host, Torch, FlashLib, +cuBLAS, CUTLASS, Triton, or handwritten-CUDA runtime fallback is introduced. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import os +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatchscore_k20raglarge_8050_v43 as base_8050 +from . import knn_build_dispatchscore_tailinf_knn_build_dispatch_slurm_0610_6329_v41 as v41 +from . import knn_build_k20_large_lowfanout_de1a_v1 as k20_lowfanout +from . import knn_build_rag_online_stream_801d_v45 as rag_pair +RAG_SHAPES = rag_pair.TARGET_SHAPES +K20_SHAPES = k20_lowfanout.EXACT_SHAPE_LABELS +ACCELERATED_SHAPE_LABELS = (*K20_SHAPES, *RAG_SHAPES) +DISPATCH_CORRECTNESS_SHAPES = ('flashml_correctness_b1_q256_m256_d128_k5', *ACCELERATED_SHAPE_LABELS) + +def _verify_export_ir() -> Any: + if 'LOOM_KNN_K20_LOWFANOUT_VERIFY_KERNEL' in os.environ: + return k20_lowfanout.ir + if 'LOOM_KNN_RAG_ONLINE_STREAM_VERIFY_KERNEL' in os.environ: + return rag_pair.ir + return v41.ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k48over32", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 48]], "cta_group": 1, "threads": 192}')) + +def launch_from_contract_inputs(inputs: dict[str, Any]) -> None: + if k20_lowfanout._eligible_k20_large_lowfanout(inputs): + k20_lowfanout._launch_k20_large_lowfanout(inputs) + return + if rag_pair._eligible_rag_online_stream(inputs): + rag_pair.launch_from_contract_inputs(inputs) + return + base_8050.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(candidate, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return base_8050._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=DISPATCH_CORRECTNESS_SHAPES, benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint: run real contract correctness for selected shapes.""" + selected = _select_contract_shapes(shape_labels) if shape_labels is not None else None + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=None) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + shapes = _select_contract_shapes(shape_labels) if shape_labels is not None else None + return evaluate_contract(shapes=shapes, correctness=True, benchmark=True) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def _benchmark_payload(report: dict[str, Any], *, use_cupti: bool, measured_entrypoint: str) -> dict[str, Any]: + rows = report.get('per_shape', {}) + timing_backends = sorted({row.get('timing_backend') for row in rows.values() if row.get('timing_backend') is not None}) + accelerated_rows = {label: rows.get(label, {}) for label in ACCELERATED_SHAPE_LABELS} + return {'tflops': report['summary']['primary_mean'] or 0.0, 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'invalid_performance_reason': report['summary']['invalid_performance_reason'], 'measured_entrypoint': measured_entrypoint, 'contract_summary': report['summary'], 'contract_performance': report['performance'], 'timing_backends': timing_backends, 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'accelerated_shape_labels': list(ACCELERATED_SHAPE_LABELS), 'accelerated_rows': accelerated_rows, 'base_dispatcher': 'loom.examples.weave.knn_build_dispatchscore_k20raglarge_8050_v43:launch_from_contract_inputs', 'report': report} + +def benchmark_knn_build_dispatch_combined_k20rag_c454_v1(*, use_cupti: bool=False) -> dict[str, Any]: + """Full v3 contract benchmark hook for the c454 combined dispatcher.""" + report = _run_with_timing_backend(use_cupti=use_cupti) + return _benchmark_payload(report, use_cupti=use_cupti, measured_entrypoint='loom.examples.weave.knn_build_dispatch_combined_k20rag_weave_evolve_knn_build_c454_v1:benchmark_knn_build_dispatch_combined_k20rag_c454_v1') + +def benchmark_knn_build_dispatch_combined_k20rag_rows_c454_v1(*, use_cupti: bool=False) -> dict[str, Any]: + """Targeted benchmark for the five accelerated dispatcher rows.""" + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=ACCELERATED_SHAPE_LABELS) + return _benchmark_payload(report, use_cupti=use_cupti, measured_entrypoint='loom.examples.weave.knn_build_dispatch_combined_k20rag_weave_evolve_knn_build_c454_v1:benchmark_knn_build_dispatch_combined_k20rag_rows_c454_v1') diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_common_d_v11_fallback_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_common_d_v11_fallback_v1.py new file mode 100644 index 00000000..e230fb74 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_common_d_v11_fallback_v1.py @@ -0,0 +1,147 @@ +"""v11 common-D dispatcher wrapper with Weave-only high-D coverage fallback. + +Minimum target architecture: sm_80 for the generic fallback, sm_100a for the +parent specialized routes. This wrapper preserves the current fd9b floor-seed +portfolio for all covered rows and routes only the v11 common-D rows that were +falling into a D128-only broad dispatcher to a generic Weave fallback. The new +route is coverage-only and not a performance closure for hot buckets. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +from pathlib import Path +from typing import Any, Callable +from . import knn_build_common_d_generic_fallback_v1 as generic_fallback +from . import knn_build_dispatch_fd9b_floor_seed_portfolio_5720_full90_synthesis_v1 as parent +MODULE = 'loom.examples.weave.knn_build_dispatch_common_d_v11_fallback_v1' +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +CANDIDATE_ID = 'common_d_v11_generic_fallback_coverage_v1' +BENCHMARK_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_knn_build_dispatch_common_d_v11_fallback_v1']) +eval_mod = parent.eval_mod +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_common_d_generic_direct_v1", "arg_keys": ["query", "database", "out_dists", "out_indices", "B", "Q", "M", "K", "D"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 20480, "constants": [["K_MAX_", 10], ["THREADS_", 256]], "cta_group": 1, "threads": 256}')) +HIGH_D_FALLBACK_SHAPES = ('build_common_d768_b1_q1024_m1024_k10', 'build_common_d1024_b1_q512_m512_k10', 'build_common_d4096_b1_q512_m512_k10', 'search_rect_common_d768_b1_q512_m8192_k10', 'rag_microbatch_common_d1024_b1_q8_m50000_k10', 'rag_microbatch_common_d4096_b1_q4_m32768_k10') +HIGH_D_FALLBACK_SHAPE_SET = set(HIGH_D_FALLBACK_SHAPES) +FOCUS_SHAPES = ('build_dim_sweep_b1_q1024_m1024_d64_k10', 'build_dim_sweep_b1_q2048_m2048_d256_k10', 'build_common_d256_b1_q1024_m1024_k10', 'build_common_d768_b1_q1024_m1024_k10', 'build_common_d1024_b1_q512_m512_k10', 'build_common_d4096_b1_q512_m512_k10', 'search_rect_b1_q1024_m32768_d64_k10', 'search_rect_common_d256_b1_q1024_m32768_k10', 'search_rect_common_d768_b1_q512_m8192_k10', 'rag_microbatch_common_d64_b1_q16_m50000_k10', 'rag_microbatch_common_d256_b1_q16_m50000_k10', 'rag_microbatch_highd_b1_q16_m50000_d768_k10', 'rag_microbatch_common_d1024_b1_q8_m50000_k10', 'rag_microbatch_common_d4096_b1_q4_m32768_k10') +SOURCE_TASKS = {**parent.SOURCE_TASKS, generic_fallback.SEED_ID: 'generalize-auto-tuning-knn-build-eeff v11 coverage fallback'} +PRODUCTION_ROUTE_MODULES = {**parent.PRODUCTION_ROUTE_MODULES, generic_fallback.SEED_ID: generic_fallback.ROUTE_ENTRYPOINT, CANDIDATE_ID: ROUTE_ENTRYPOINT} + +def _select_contract_shapes(shape_labels): + return parent._select_contract_shapes(shape_labels) + +def _trace_inputs_for_shape(shape: dict[str, Any]) -> dict[str, Any]: + return parent._trace_inputs_for_shape(shape) + +def _normalize_route_row(row: dict[str, Any]) -> dict[str, Any]: + return parent._normalize_route_row(row) + +def _inputs_for_label(label: str) -> dict[str, Any]: + return _trace_inputs_for_shape(_select_contract_shapes((label,))[0]) + +def _is_high_d_fallback_shape(inputs: dict[str, Any]) -> bool: + label = inputs.get('label') + if label is not None and str(label) not in HIGH_D_FALLBACK_SHAPE_SET: + return False + if label is None: + key = (bool(inputs.get('build', False)), int(inputs.get('B', -1)), int(inputs.get('Q', -1)), int(inputs.get('M', -1)), int(inputs.get('D', -1)), int(inputs.get('K', -1)), str(inputs.get('dtype', 'bfloat16')).replace('torch.', '')) + return key in {(True, 1, 1024, 1024, 768, 10, 'bfloat16'), (True, 1, 512, 512, 1024, 10, 'bfloat16'), (True, 1, 512, 512, 4096, 10, 'bfloat16'), (False, 1, 512, 8192, 768, 10, 'bfloat16'), (False, 1, 8, 50000, 1024, 10, 'bfloat16'), (False, 1, 4, 32768, 4096, 10, 'bfloat16')} + return generic_fallback._eligible_common_d_fallback(inputs) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if not force_fallback and _is_high_d_fallback_shape(inputs): + return generic_fallback.ROUTE_ID + return parent.route_for_contract_inputs(inputs, force_fallback=force_fallback) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + if not force_fallback and _is_high_d_fallback_shape(inputs): + generic_fallback.launch_from_contract_inputs(inputs) + return + parent.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def compile_and_launch_knn_build(*, shape_labels=FOCUS_SHAPES, benchmark: bool=False) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _parent_trace_row(label: str, *, force_fallback: bool=False) -> dict[str, Any]: + return dict(parent.route_trace_for_contract_shapes((label,), force_fallback=force_fallback)[0]) + +def _fallback_trace_row(inputs: dict[str, Any], *, force_fallback: bool=False) -> dict[str, Any]: + label = str(inputs.get('label')) + parent_row = _parent_trace_row(label, force_fallback=False) + if force_fallback: + row = _parent_trace_row(label, force_fallback=True) + row['expected_seed'] = generic_fallback.SEED_ID + row['guard_id'] = ''.join(['forced_fallback_', format(generic_fallback.SEED_ID, ''), '_disabled']) + row['guard_condition'] = 'forced fallback disables coverage-only high-D generic route' + row['classification'] = 'guard-miss' + row['baseline_dispatcher_route'] = parent_row.get('selected_route') + row['parent_dispatcher_route'] = parent_row.get('selected_route') + return _normalize_route_row(row) + return _normalize_route_row({'shape_key': label, 'selected_route': generic_fallback.ROUTE_ID, 'selected_entrypoint': generic_fallback.ROUTE_ENTRYPOINT, 'selected_seed': None, 'expected_seed': generic_fallback.SEED_ID, 'route_kind': 'coverage-only', 'route_source': 'generic-weave-fallback', 'guard_id': 'common_d_v11_high_d_generic_fallback_guard', 'guard_condition': 'exact BF16 v11 common-D miss row in {D768 build/search, D1024 build/RAG, D4096 build/RAG}', 'classification': 'coverage-only', 'baseline_dispatcher_route': parent_row.get('selected_route'), 'parent_dispatcher_route': parent_row.get('selected_route'), 'parent_dispatcher_selected_seed': parent_row.get('selected_seed'), 'coverage': 'correct Weave-only fallback; performance bucket remains open'}) + +def route_trace_for_contract_shapes(shape_labels=FOCUS_SHAPES, *, force_fallback: bool=False) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + rows = [] + for shape in selected: + inputs = _trace_inputs_for_shape(shape) + label = str(inputs.get('label')) + if _is_high_d_fallback_shape(inputs): + rows.append(_fallback_trace_row(inputs, force_fallback=force_fallback)) + else: + row = _parent_trace_row(label, force_fallback=force_fallback) + row['baseline_dispatcher_route'] = row.get('selected_route') + row['parent_dispatcher_route'] = row.get('selected_route') + rows.append(_normalize_route_row(row)) + return rows + +def _annotate_route_trace(route_trace: list[dict[str, Any]], report: dict[str, Any], *, speedup_floor: float=1.2) -> list[dict[str, Any]]: + annotated = [] + for row in route_trace: + label = str(row['shape_key']) + perf = report.get('per_shape', {}).get(label, {}) + kernel_ms = perf.get('kernel_ms') + flashlib_ms = perf.get('flashlib_ms') + ratio = perf.get('ratio_vs_flashlib') + out = dict(row) + out['dispatcher_kernel_ms'] = kernel_ms + out['external_baseline_ms'] = flashlib_ms + out['flashlib_ms'] = flashlib_ms + out['speedup_vs_external_baseline'] = ratio + out['external_baseline_ref'] = 'same_session' if flashlib_ms is not None else 'not_available' + out['timing_backend'] = perf.get('timing_backend') + if out.get('route_source') == 'generic-weave-fallback' and ratio is not None: + out['classification'] = 'fallback-slow' if float(ratio) < speedup_floor else 'route-ok' + out['shape_specific_kernel_ms'] = None + annotated.append(out) + return annotated + +def benchmark_knn_build_dispatch_common_d_v11_fallback_v1(*, shape_labels=FOCUS_SHAPES, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=correctness, benchmark=benchmark) + route_trace = route_trace_for_contract_shapes(shape_labels) + if benchmark: + route_trace = _annotate_route_trace(route_trace, report) + report['route_trace'] = route_trace + report['route_trace_included'] = True + return report + +def write_trace_artifact(path: str | Path, *, shape_labels=FOCUS_SHAPES) -> dict[str, Any]: + out = Path(path) + out.parent.mkdir(parents=True, exist_ok=True) + payload = {'candidate_id': CANDIDATE_ID, 'measured_entrypoint': BENCHMARK_ENTRYPOINT, 'denominator': tuple(shape_labels), 'route_trace': route_trace_for_contract_shapes(shape_labels), 'route_trace_included': True} + out.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n', encoding='utf-8') + return payload diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_d555_rag_microbucket_q4q64_69d6_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_d555_rag_microbucket_q4q64_69d6_v1.py new file mode 100644 index 00000000..e5514d65 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_d555_rag_microbucket_q4q64_69d6_v1.py @@ -0,0 +1,226 @@ +"""d555-local RAG microbatch Q4/Q64 K10 seed-consumption wrapper. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +keeps the selected d555 full82 dispatcher as fallback, but routes only the exact +BF16 non-build ``rag_microbatch_b1_q4_m100000_d128_k10`` and +``rag_microbatch_b1_q64_m100000_d128_k10`` rows through the existing faeb +Weave seed. No external runtime fallback is introduced. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +from pathlib import Path +from typing import Any, Callable +from . import knn_build_dispatch_d555_residual_seed_synth_full82_v1 as base_d555 +from . import knn_build_rag_microbucket_faeb_v1 as rag_faeb +MODULE = 'loom.examples.weave.knn_build_dispatch_d555_rag_microbucket_q4q64_69d6_v1' +Q4_K10_SHAPE = rag_faeb.Q4_K10_SHAPE +Q64_K10_SHAPE = rag_faeb.Q64_K10_SHAPE +TARGET_SHAPES = rag_faeb.K10_TARGET_SHAPES +TARGET_SHAPE_SET = set(TARGET_SHAPES) +SEED_FAEB_Q4Q64_ID = 'rag_microbucket_faeb_q4q64_k10_69d6_v1' +BASELINE_ID = base_d555.CANDIDATE_CONFIGS[base_d555.DEFAULT_CANDIDATE_KEY]['candidate_id'] +BASELINE_ENTRYPOINT = ''.join([format(base_d555.MODULE, ''), ':benchmark_candidate_d555_direct_residual_seeds']) +ROUTE_Q4_FAEB = rag_faeb.ROUTE_Q4_K10 +ROUTE_Q64_FAEB = rag_faeb.ROUTE_Q64_K10 +ROUTE_FAEB_ENTRYPOINT = 'loom.examples.weave.knn_build_rag_microbucket_faeb_v1:launch_from_contract_inputs' +ROUTE_BASE_D555_ENTRYPOINT = ''.join([format(base_d555.MODULE, ''), ':launch_from_contract_inputs']) +eval_mod = base_d555.eval_mod +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbatch_m64_d4f7_stage1", "arg_keys": ["query", "database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 91392, "constants": [], "cta_group": 1, "threads": 512}')) +PRODUCTION_ROUTE_MODULES = _decode_capture(_json_loads('{"__dict_items__": [["large_square_k20k32", "loom.examples.weave.knn_build_large_square_k20k32_a989_v1"], ["over64_k96", "loom.examples.weave.knn_build_over64_k96_a989_v1"], ["baseline_7c3a_rag_k10", "loom.examples.weave.knn_build_rag_frontier_4b5c_v1:k10"], ["rag_frontier_7399_k10", "loom.examples.weave.knn_build_rag_frontier_7399_v1:k10_s72"], ["rag_frontier_7399_k32_replaced", "loom.examples.weave.knn_build_rag_frontier_7399_v1:k32_s72_g8_fusedmerge"], ["rag_frontier_4fbf_k32", "loom.examples.weave.knn_build_rag_frontier_4fbf_v7:k32_s72_g24_tailinf_fused"], ["rect_smallq_largem_d15e", "loom.examples.weave.knn_build_rect_smallq_largem_ff59_d15e_v1:split16"], ["baseline_7c3a_policy", "loom.examples.weave.knn_build_dispatch_b6d4_d15e_fd02_v1:baseline_7c3a_policy"], ["fallback", "loom.examples.weave.knn_build_dispatch_split72_4e09_de1a_3dc7_v48"], ["dim_d64_73a9", "loom.examples.weave.knn_build_dim_midk_73a9_v1:d64_split_s8"], ["current_exact_k32_dispatcher", "loom.examples.weave.knn_build_dispatch_4fbf_7399_d15e_full55_bad5_v1:launch_from_contract_inputs"], ["base_7399_d15e_dispatcher", "loom.examples.weave.knn_build_dispatch_7399_d15e_full55_v1:launch_from_contract_inputs"], ["rag_frontier_7399_k32", "loom.examples.weave.knn_build_rag_frontier_7399_v1:k32_s72_g8_fusedmerge"], ["dim_d256_df2f", "loom.examples.weave.knn_build_dim_midk_df2f_v1:d256_split_s8"], ["dim_fp16_d128_df2f", "loom.examples.weave.knn_build_dim_midk_df2f_v1:fp16_d128_split_s8"], ["base_dispatch", "loom.examples.weave.knn_build_dispatch_7399_d15e_full55_v1:launch_from_contract_inputs"], ["rect_intermediate_4452_s8", "loom.examples.weave.knn_build_rect_intermediate_frontier_6a73_4452_v2:rect_s8_k10_cached"], ["base_champion_6b59", "loom.examples.weave.knn_build_dispatch_7399_d15e_df2f_full55_v1:launch_from_contract_inputs"], ["base_k32_d64_62b1", "loom.examples.weave.knn_build_dispatch_4fbf_7399_d15e_73a9_full55_v1:launch_from_contract_inputs"], ["default_k96_a330", "loom.examples.weave.knn_build_over64_k96_a989_v1"], ["large_tail_a4f6", "loom.examples.weave.knn_build_large_tail_frontier_6a73_v1:split4_k20"], ["midk_81aa_q2048_k24_k28", "loom.examples.weave.knn_build_dim_midk_bad5_midkcleanup_v1:midk_k24_k28_s8"], ["midk_9b2c_q4096_k28", "loom.examples.weave.knn_build_dim_midk_bad5_k24k28_v1:k28_q4096_s4_unordered_exact"], ["base_f552", "loom.examples.weave.knn_build_dispatch_selected_portfolio_f552_v1:launch_from_contract_inputs"], ["midk_bad5_k64split8", "loom.examples.weave.knn_build_dim_midk_bad5_k64split8_v1:k64_q2048_s8_tailinf"], ["base_e51c", "loom.examples.weave.knn_build_dispatch_selected_portfolio_e51c_v1:launch_from_contract_inputs"], ["midk_f8c3_q4096_k64_split8_a194", "loom.examples.weave.knn_build_dim_midk_f8c3_q4096k64split_v1:q4096_k64_tailinf_split8"], ["base_f8c3", "loom.examples.weave.knn_build_dispatch_selected_portfolio_f8c3_v1:launch_from_contract_inputs"], ["lowk_b193_q512_s4", "loom.examples.weave.knn_build_lowk_f8c3_q512_q1024_v1:q512_lowk_s4"], ["lowk_b193_q1024_k16_s16", "loom.examples.weave.knn_build_lowk_f8c3_q512_q1024_v1:q1024_k16_s16"], ["large_square_5407_q8192_k32_s2", "loom.examples.weave.knn_build_large_square_k32_8a83_v1:q8192_k32_split2"], ["base_f853", "loom.examples.weave.knn_build_dispatch_selected_portfolio_f853_v1:launch_from_contract_inputs"], ["lowk_b193_q512_k4_k5_k6_s4", "loom.examples.weave.knn_build_lowk_f8c3_q512_q1024_v1:q512_lowk_s4"], ["base_f16b", "loom.examples.weave.knn_build_dispatch_selected_portfolio_f16b_v1:launch_from_contract_inputs"], ["rag_microbatch_b2ec_s72_g8", "loom.examples.weave.knn_build_rag_microbatch_4a72_v1:launch_from_contract_inputs"], ["base_4a72", "loom.examples.weave.knn_build_dispatch_selected_portfolio_4a72_v1:launch_from_contract_inputs"], ["rag_m64_s128_0c69", "loom.examples.weave.knn_build_rag_microbatch_m64_d4f7_v1:launch_from_contract_inputs"], ["rag_s144_g12_cta1_059f", "loom.examples.weave.knn_build_rag_microbatch_4a72_v2:launch_from_contract_inputs"], ["rag_s144_g8_cta1_4982_read_ref_parameterized", "loom.examples.weave.knn_build_rag_microbatch_4a72_v2:launch_from_contract_inputs"], ["base_397b", "loom.examples.weave.knn_build_dispatch_selected_portfolio_397b_v1:launch_from_contract_inputs"], ["d64_fdd7_aa88_v2", "loom.examples.weave.knn_build_d64_build_aa88_v2:launch_from_contract_inputs"], ["base_8700", "loom.examples.weave.knn_build_dispatch_rag_seed_portfolio_8700_v1:launch_from_contract_inputs(portfolio_id=all_m64_s128)"], ["rect_d64_cf49_v3_9138", "loom.examples.weave.knn_build_rect_d64_cf49_v3:launch_from_contract_inputs"], ["q1_mbucket_aa88_q1m_v3_bcb3", "loom.examples.weave.knn_build_ragonline_mbucket_aa88_q1m_v3:launch_from_contract_inputs"], ["over64_k96_a2f8_v1_generated_v8", "loom.examples.weave.knn_build_over64_k96_a2f8_v1:_launch_over64_k96_split_path"], ["base_e3de", "loom.examples.weave.knn_build_dispatch_d64_fdd7_e3de_v1:launch_from_contract_inputs"], ["non128_frontier_8199_widecombine_v1", "loom.examples.weave.knn_build_non128_frontier_8199_widecombine_v1:launch_from_contract_inputs"], ["base_4247", "loom.examples.weave.knn_build_dispatch_e3de_9138_bcb3_4247_v1:launch_from_contract_inputs"], ["rag_microbucket_k32_8fcb_split148_v1_b3e0_sm148", "loom.examples.weave.knn_build_rag_microbucket_k32_8fcb_split148_v1:launch_from_contract_inputs"], ["rag_microbucket_k32_2e8e_q16split148_v1_b3e0_q16_s148", "loom.examples.weave.knn_build_rag_microbucket_k32_2e8e_q16split148_v1:launch_from_contract_inputs"], ["non128_frontier_3d5a_cachedmerge_v1", "loom.examples.weave.knn_build_non128_frontier_3d5a_cachedmerge_v1:launch_from_contract_inputs"], ["over64_k96_exactall_229a_v1_b6c4", "loom.examples.weave.knn_build_over64_k96_exactall_229a_v1:launch_from_contract_inputs"], ["knn_build_midk_k11k13_e080_v1", "loom.examples.weave.knn_build_midk_k11k13_e080_v1:launch_from_contract_inputs"], ["ragonline_mbucket_4fc7_q1m262_v1_980c", "loom.examples.weave.knn_build_ragonline_mbucket_4fc7_q1m262_v1:launch_from_contract_inputs"], ["baseline_8199_widecombine_full82_v1", "loom.examples.weave.knn_build_dispatch_4247_non128_8199_widecombine_full82_v1:launch_from_contract_inputs"], ["k30_q4096_6998_warpselect_v1", "loom.examples.weave.knn_build_k30_q4096_6998_warpselect_v1:launch_from_contract_inputs"], ["rag_stream_k10_direct_split72_6998_v1", "loom.examples.weave.knn_build_rag_online_stream_split72_4e09_v1:launch_from_contract_inputs"], ["rect_d64_23be_unordered_v1", "loom.examples.weave.knn_build_rect_d64_23be_unordered_v1:launch_from_contract_inputs"], ["residual_19b3_ed1c_portfolio_6998", "loom.examples.weave.knn_build_dispatch_c142_3505_q32rowld_19b3_v1:launch_from_contract_inputs"], ["candidate_q16split148_cachedmerge_k96exactall_e080_q1m262_over_8199_full82_v1", "loom.examples.weave.knn_build_dispatch_4247_non128_8199_3d5a_2e8e_full82_synth_v1:launch_from_contract_inputs"], ["rag_microbucket_faeb_q4q64_k10_69d6_v1", "loom.examples.weave.knn_build_rag_microbucket_faeb_v1:launch_from_contract_inputs"], ["candidate_d555_direct_residual_seeds_full82_v1", "loom.examples.weave.knn_build_dispatch_d555_residual_seed_synth_full82_v1:launch_from_contract_inputs"]]}')) +CANDIDATE_DISPATCHERS = ({'id': BASELINE_ID, 'entrypoint': BASELINE_ENTRYPOINT, 'consumed_seeds': base_d555.CANDIDATE_CONFIGS[base_d555.DEFAULT_CANDIDATE_KEY]['selected_seeds'], 'guard_plan': base_d555.CANDIDATE_CONFIGS[base_d555.DEFAULT_CANDIDATE_KEY]['guard_plan'], 'expected_shape_wins': base_d555.DIRECT_SEED_TARGET_SHAPES, 'fallback': base_d555.ROUTE_BASELINE_F30C_ENTRYPOINT, 'rejected_reason': 'same-session selected d555 baseline'}, {'id': 'candidate_d555_faeb_q4q64_69d6_v1', 'entrypoint': ''.join([format(MODULE, ''), ':benchmark_candidate_d555_faeb_q4q64_69d6_v1']), 'consumed_seeds': (SEED_FAEB_Q4Q64_ID,), 'guard_plan': ('69d6 exact faeb Q4/Q64 K10 microbatch guard', 'then selected d555 direct-residual-seeds dispatcher'), 'expected_shape_wins': TARGET_SHAPES, 'fallback': ROUTE_BASE_D555_ENTRYPOINT, 'rejected_reason': None}) +SOURCE_TASKS = _decode_capture(_json_loads('{"__dict_items__": [["rag_microbucket_faeb_q4q64_k10_69d6_v1", "source-guided auto-tuning sibling / loom.examples.weave.knn_build_rag_microbucket_faeb_v1"], ["candidate_d555_direct_residual_seeds_full82_v1", "generalize-auto-tuning-knn-build-d555"]]}')) +TARGETED_SEED_ROWS = {Q4_K10_SHAPE: {'historical_kernel_ms': 0.06205, 'historical_flashlib_ms': 0.063041, 'historical_ratio_vs_flashlib': 1.0159709911361805, 'split_count': rag_faeb.M64_SPLIT_COUNT, 'group_count': rag_faeb.M64_GROUP_COUNT, 'route': ROUTE_Q4_FAEB, 'timing_backend': 'cupti'}, Q64_K10_SHAPE: {'historical_kernel_ms': 0.072737, 'historical_flashlib_ms': 0.098977, 'historical_ratio_vs_flashlib': 1.3607517494535106, 'split_count': rag_faeb.M64_SPLIT_COUNT, 'group_count': rag_faeb.M64_GROUP_COUNT, 'route': ROUTE_Q64_FAEB, 'timing_backend': 'cupti'}} + +def _eligible_rag_q4_q64(inputs: dict[str, Any]) -> bool: + return rag_faeb._eligible_q4_k10(inputs) or rag_faeb._eligible_q64_k10(inputs) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False, enable_rag_q4_q64: bool=True) -> str: + if not force_fallback and enable_rag_q4_q64 and _eligible_rag_q4_q64(inputs): + return rag_faeb.route_for_contract_inputs(inputs) + return base_d555.route_for_contract_inputs(inputs, force_fallback=force_fallback) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False, enable_rag_q4_q64: bool=True) -> None: + if not force_fallback and enable_rag_q4_q64 and _eligible_rag_q4_q64(inputs): + rag_faeb.launch_from_contract_inputs(inputs) + return + base_d555.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_d555_faeb_q4q64_69d6_v1(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_baseline_d555(inputs: dict[str, Any]) -> None: + base_d555.candidate_d555_direct_residual_seeds(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) + +def _select_contract_shapes(shape_labels): + return base_d555._select_contract_shapes(shape_labels) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate, correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + return base_d555._run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=kernel_fn, correctness=correctness, time_flashlib=time_flashlib) + +def _baseline_trace_record(inputs: dict[str, Any], *, force_fallback: bool=False) -> dict[str, Any]: + label = str(inputs.get('label')) + row = dict(base_d555.route_trace_for_contract_shapes((label,), force_fallback=force_fallback)[0]) + row['baseline_dispatcher_route'] = base_d555.route_for_contract_inputs(inputs, force_fallback=force_fallback) + return base_d555.base_f30c._normalize_route_row(row) + +def _faeb_trace_record(inputs: dict[str, Any]) -> dict[str, Any]: + label = str(inputs.get('label')) + targeted = dict(TARGETED_SEED_ROWS[label]) + route = rag_faeb.route_for_contract_inputs(inputs) + guard_id = '69d6_faeb_q4_k10_exact' if label == Q4_K10_SHAPE else '69d6_faeb_q64_k10_exact' + return base_d555.base_f30c._normalize_route_row({'shape_key': label, 'selected_route': route, 'selected_entrypoint': ROUTE_FAEB_ENTRYPOINT, 'selected_seed': SEED_FAEB_Q4Q64_ID, 'expected_seed': SEED_FAEB_Q4Q64_ID, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': guard_id, 'guard_condition': ''.join(['exact BF16 non-build B=1 Q=', format(int(inputs.get('Q')), ''), ' M=100000 D=128 K=10 faeb M64 seed']), 'coverage': '69d6 consumes faeb Q4/Q64 K10 seed ahead of selected d555 fallback', 'consumed_seed': SEED_FAEB_Q4Q64_ID, 'replaced_route': base_d555.route_for_contract_inputs(inputs), 'baseline_dispatcher_route': base_d555.route_for_contract_inputs(inputs), 'row_selection': targeted, 'split_count': targeted['split_count'], 'group_count': targeted['group_count'], 'targeted_seed_timing_backend': targeted['timing_backend'], 'targeted_seed_kernel_ms': targeted['historical_kernel_ms'], 'targeted_seed_ratio_vs_flashlib': targeted['historical_ratio_vs_flashlib'], 'classification': 'unmeasured', 'dispatcher_kernel_ms': None, 'shape_specific_kernel_ms': targeted['historical_kernel_ms'], 'relative_speedup_vs_baseline': None}) + +def _route_trace_record(inputs: dict[str, Any], *, force_fallback: bool=False) -> dict[str, Any]: + if force_fallback and _eligible_rag_q4_q64(inputs): + row = _baseline_trace_record(inputs, force_fallback=True) + row['expected_seed'] = SEED_FAEB_Q4Q64_ID + row['guard_id'] = 'forced_fallback_69d6_faeb_q4q64_disabled' + row['guard_condition'] = 'forced fallback to selected d555; 69d6 faeb Q4/Q64 overlay disabled' + row['forced_disabled_seeds'] = (SEED_FAEB_Q4Q64_ID,) + row['classification'] = 'guard-miss' + return base_d555.base_f30c._normalize_route_row(row) + if not force_fallback and _eligible_rag_q4_q64(inputs): + return _faeb_trace_record(inputs) + return _baseline_trace_record(inputs, force_fallback=force_fallback) + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + return [_route_trace_record(base_d555.base_f30c._trace_inputs_from_shape(shape), force_fallback=force_fallback) for shape in selected] + +def _rows_for_labels(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return base_d555._rows_for_labels(report, labels) + +def _annotate_route_trace(route_trace: list[dict[str, Any]], candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> list[dict[str, Any]]: + annotated = [] + for row in route_trace: + label = str(row.get('shape_key')) + out = dict(row) + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') + speedup_vs_baseline = baseline_ms / candidate_ms if candidate_ms and baseline_ms else None + speedup_vs_external = flashlib_ms / candidate_ms if candidate_ms and flashlib_ms else None + out['dispatcher_kernel_ms'] = candidate_ms + out['baseline_dispatcher_kernel_ms'] = baseline_ms + out['external_baseline_ms'] = flashlib_ms + out['flashlib_ms'] = flashlib_ms + out['relative_speedup_vs_baseline'] = speedup_vs_baseline + out['speedup_vs_external_baseline'] = speedup_vs_external + out['external_baseline_ref'] = 'same_session' if flashlib_ms is not None else 'not_available' + out['route_changed_vs_baseline_dispatcher'] = out.get('selected_route') != out.get('baseline_dispatcher_route') + if label in TARGET_SHAPE_SET: + if not out.get('selected_seed'): + out['classification'] = 'guard-miss' + elif speedup_vs_external is not None and speedup_vs_external < 1.05: + out['classification'] = 'kernel-slow' + elif speedup_vs_baseline is not None and speedup_vs_baseline < 1.0: + out['classification'] = 'kernel-slow' + else: + out['classification'] = 'seed-consumed' + elif speedup_vs_external is not None and speedup_vs_external < 1.0: + out['classification'] = 'kernel-slow' if out.get('route_kind') == 'specialized' else 'fallback-slow' + else: + out['classification'] = 'route-ok' + annotated.append(base_d555.base_f30c._normalize_route_row(out)) + return annotated + +def _seed_delta_matrix(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> list[dict[str, Any]]: + matrix = [] + for label in TARGET_SHAPES: + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + inputs = base_d555.base_f30c._inputs_for_label(label) + matrix.append({'shape_key': label, 'baseline_route': base_d555.route_for_contract_inputs(inputs), 'candidate_route': route_for_contract_inputs(inputs), 'selected_seed': SEED_FAEB_Q4Q64_ID, 'candidate_id': 'candidate_d555_faeb_q4q64_69d6_v1', 'candidate_ms': candidate_ms, 'baseline_ms': baseline_ms, 'flashlib_ms': candidate_row.get('flashlib_ms'), 'metric_delta_ms': candidate_ms - baseline_ms if candidate_ms and baseline_ms else None, 'speedup_vs_baseline_dispatcher': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'ratio_vs_flashlib': candidate_row.get('ratio_vs_flashlib'), 'historical_seed_kernel_ms': TARGETED_SEED_ROWS[label]['historical_kernel_ms'], 'historical_seed_ratio_vs_flashlib': TARGETED_SEED_ROWS[label]['historical_ratio_vs_flashlib'], 'timing_backend': candidate_row.get('timing_backend') or baseline_row.get('timing_backend')}) + return matrix + +def _below_flashlib_rows(report: dict[str, Any], *, floor: float) -> list[dict[str, Any]]: + trace_by_label = {str(row['shape_key']): row for row in route_trace_for_contract_shapes()} + rows = [] + for label, row in sorted(report.get('per_shape', {}).items()): + ratio = row.get('ratio_vs_flashlib') + if isinstance(ratio, (float, int)) and ratio < floor: + trace_row = trace_by_label.get(label, {}) + rows.append({'shape_key': label, 'kernel_ms': row.get('kernel_ms'), 'flashlib_ms': row.get('flashlib_ms'), 'ratio_vs_flashlib': ratio, 'selected_route': trace_row.get('selected_route'), 'route_kind': trace_row.get('route_kind', 'unknown'), 'classification': 'kernel-slow' if trace_row.get('route_kind') == 'specialized' else 'fallback-slow'}) + return rows + +def _denominator_name(shape_labels) -> str: + if shape_labels is None: + return 'full82_v9' + labels = tuple(shape_labels) + if labels == TARGET_SHAPES: + return 'rag_microbatch_k10_q4_q64' + return ''.join(['shape', format(len(labels), '')]) + +def _denominator_label(shape_labels) -> str: + if shape_labels is None: + return ''.join(['full', format(len(eval_mod.CANONICAL_SHAPES), '')]) + return ''.join(['shape', format(len(tuple(shape_labels)), '')]) + +def _timing_backend_name(use_cupti: bool) -> str: + return 'cupti' if use_cupti else 'cuda_event_fallback' + +def benchmark_baseline_d555(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES, benchmark_correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_baseline_d555, correctness=benchmark_correctness, time_flashlib=time_flashlib) + report['candidate_id'] = BASELINE_ID + report['measured_entrypoint'] = BASELINE_ENTRYPOINT + report['route_trace'] = base_d555.route_trace_for_contract_shapes(shape_labels) + report['route_trace_included'] = True + return report + +def _benchmark_payload(candidate_report: dict[str, Any], baseline_report: dict[str, Any], *, use_cupti: bool, shape_labels, benchmark_correctness: bool, time_flashlib: bool) -> dict[str, Any]: + candidate_metric = candidate_report['summary']['primary_mean'] + baseline_metric = baseline_report['summary']['primary_mean'] + route_trace = _annotate_route_trace(route_trace_for_contract_shapes(shape_labels), candidate_report, baseline_report) + below_1x = _below_flashlib_rows(candidate_report, floor=1.0) + below_floor = _below_flashlib_rows(candidate_report, floor=1.05) + denominator = _denominator_name(shape_labels) + timing_backend = _timing_backend_name(use_cupti) + return {'candidate_id': 'candidate_d555_faeb_q4q64_69d6_v1', 'baseline_candidate_id': BASELINE_ID, 'selected_seeds': (SEED_FAEB_Q4Q64_ID,), 'source_tasks': SOURCE_TASKS, 'tflops': candidate_metric, 'baseline_tflops': baseline_metric, 'metric_delta': candidate_metric - baseline_metric if candidate_metric and baseline_metric else None, 'all_correct': candidate_report['summary']['all_correct'], 'baseline_all_correct': baseline_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'baseline_performance_comparable': baseline_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'baseline_invalid_performance_reason': baseline_report['summary']['invalid_performance_reason'], 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_candidate_d555_faeb_q4q64_69d6_v1']), 'baseline_entrypoint': ''.join([format(MODULE, ''), ':benchmark_baseline_d555']), 'measured_shape_labels': 'all_canonical' if shape_labels is None else tuple(shape_labels), 'timing_backend': timing_backend, 'denominator': denominator, 'selected_route_labels': TARGET_SHAPES, 'consumed_seed_labels': TARGET_SHAPES, 'selected_route_rows': _rows_for_labels(candidate_report, TARGET_SHAPES), 'baseline_selected_route_rows': _rows_for_labels(baseline_report, TARGET_SHAPES), 'consumed_seed_rows': _rows_for_labels(candidate_report, TARGET_SHAPES), 'baseline_consumed_seed_rows': _rows_for_labels(baseline_report, TARGET_SHAPES), 'seed_delta_matrix': _seed_delta_matrix(candidate_report, baseline_report), 'targeted_seed_rows': TARGETED_SEED_ROWS, 'candidate_dispatchers': CANDIDATE_DISPATCHERS, 'selected_candidate_dispatcher': 'candidate_d555_faeb_q4q64_69d6_v1', 'guard_plan': CANDIDATE_DISPATCHERS[1]['guard_plan'], 'route_modules': PRODUCTION_ROUTE_MODULES, 'route_trace': route_trace, 'forced_fallback_route_trace': route_trace_for_contract_shapes(shape_labels, force_fallback=True), 'route_trace_included': True, 'contract_summary': candidate_report['summary'], 'baseline_contract_summary': baseline_report['summary'], 'contract_performance': candidate_report['performance'], 'baseline_contract_performance': baseline_report['performance'], 'timing_backends': base_d555.base_f30c._timing_backends_for_reports(candidate_report, baseline_report), 'timing_backend_requested': timing_backend, 'use_cupti': use_cupti, 'benchmark_correctness_checked': benchmark_correctness, 'benchmark_time_flashlib': time_flashlib, 'performance_coverage': 'partial' if below_floor else 'pass', 'coverage_only_routes': [], 'hot_bucket_blockers': below_floor, 'flashlib_parity_ledger': {'baseline_ref_scope': 'same_session' if time_flashlib else 'not_available', 'speedup_floor': 1.05, 'rows_below_1x': below_1x, 'rows_below_floor': below_floor, 'omitted_reason': None if time_flashlib else 'benchmark_time_flashlib=false'}, 'rank_objective': {'metric': 'tflops', 'direction': 'maximize', 'value': candidate_metric, 'baseline_value': baseline_metric, 'delta': candidate_metric - baseline_metric if candidate_metric and baseline_metric else None, 'denominator': denominator}, 'report': candidate_report, 'baseline_report': baseline_report} + +def benchmark_candidate_d555_faeb_q4q64_69d6_v1(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES, baseline_report: dict[str, Any] | None=None, benchmark_correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + if baseline_report is None: + baseline_report = benchmark_baseline_d555(use_cupti=use_cupti, shape_labels=shape_labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_d555_faeb_q4q64_69d6_v1, correctness=benchmark_correctness, time_flashlib=time_flashlib) + return _benchmark_payload(candidate_report, baseline_report, use_cupti=use_cupti, shape_labels=shape_labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + +def write_benchmark_artifacts(artifact_dir: str | Path, *, use_cupti: bool=True, shape_labels=TARGET_SHAPES, benchmark_correctness: bool=True, time_flashlib: bool=True) -> dict[str, str]: + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + denom = _denominator_label(shape_labels) + denominator = _denominator_name(shape_labels) + payload = benchmark_candidate_d555_faeb_q4q64_69d6_v1(use_cupti=use_cupti, shape_labels=shape_labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + baseline_path = out_dir / ''.join([format(denom, ''), '_same_session_baseline_d555_for_faeb_q4q64_69d6_v1.json']) + candidate_path = out_dir / ''.join([format(denom, ''), '_dispatch_d555_faeb_q4q64_69d6_v1.json']) + route_trace_path = out_dir / ''.join([format(denom, ''), '_route_trace_d555_faeb_q4q64_69d6_v1.json']) + forced_trace_path = out_dir / ''.join([format(denom, ''), '_forced_fallback_trace_d555_faeb_q4q64_69d6_v1.json']) + seed_matrix_path = out_dir / ''.join([format(denom, ''), '_seed_delta_matrix_d555_faeb_q4q64_69d6_v1.json']) + baseline_path.write_text(json.dumps({'candidate_id': BASELINE_ID, 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_baseline_d555']), 'measured_shape_labels': payload['measured_shape_labels'], 'timing_backend': payload['timing_backend'], 'denominator': denominator, 'benchmark_correctness_checked': payload['benchmark_correctness_checked'], 'benchmark_time_flashlib': payload['benchmark_time_flashlib'], 'tflops': payload['baseline_tflops'], 'all_correct': payload['baseline_all_correct'], 'performance_comparable': payload['baseline_performance_comparable'], 'contract_summary': payload['baseline_contract_summary'], 'contract_performance': payload['baseline_contract_performance'], 'route_trace': base_d555.route_trace_for_contract_shapes(shape_labels), 'route_trace_included': True, 'rank_objective': {'metric': 'tflops', 'direction': 'maximize', 'value': payload['baseline_tflops'], 'denominator': denominator}, 'report': payload['baseline_report']}, indent=2, sort_keys=True) + '\n') + candidate_path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + route_trace_path.write_text(json.dumps(payload['route_trace'], indent=2, sort_keys=True) + '\n') + forced_trace_path.write_text(json.dumps(payload['forced_fallback_route_trace'], indent=2, sort_keys=True) + '\n') + seed_matrix_path.write_text(json.dumps(payload['seed_delta_matrix'], indent=2, sort_keys=True) + '\n') + return {'same_session_baseline_payload': str(baseline_path), 'candidate_payload': str(candidate_path), 'route_trace': str(route_trace_path), 'forced_fallback_trace': str(forced_trace_path), 'seed_delta_matrix': str(seed_matrix_path)} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_d555_residual_seed_synth_full82_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_d555_residual_seed_synth_full82_v1.py new file mode 100644 index 00000000..3692e16b --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_d555_residual_seed_synth_full82_v1.py @@ -0,0 +1,360 @@ +"""Full82 residual-seed synthesis dispatcher over f30c. + +Minimum target architecture: sm_100a. This dispatcher is a generalize-auto- +tuning wrapper only: it preserves the seed schedules and synthesizes guard +portfolios from the f30c full82 dispatcher, the d63b K30 seed, the bf81 direct +RAG K10 seed, the replayed 54d4 D64 seed, and only the 6998/19b3 residual +routes that beat f30c in the af4f full82 A/B. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +from pathlib import Path +from typing import Any, Callable +from . import knn_build_dispatch_4247_non128_8199_3d5a_2e8e_full82_synth_v1 as base_f30c +from . import knn_build_dispatch_6998_residual_19b3_overlay_v1 as residual_6998 +from . import knn_build_dispatch_6998_ragk10_direct_split72_v1 as ragk10_direct +from . import knn_build_k30_q4096_6998_warpselect_v1 as k30_seed +from . import knn_build_rect_d64_23be_unordered_v1 as d64_seed +MODULE = 'loom.examples.weave.knn_build_dispatch_d555_residual_seed_synth_full82_v1' +BASELINE_ID = base_f30c.CANDIDATE_CONFIGS[base_f30c.DEFAULT_CANDIDATE_KEY]['candidate_id'] +BASELINE_ENTRYPOINT = ''.join([format(base_f30c.MODULE, ''), ':benchmark_candidate_q16split148_cachedmerge_k96exactall_e080_q1m262']) +ROUTE_BASELINE_F30C_ENTRYPOINT = ''.join([format(base_f30c.MODULE, ''), ':launch_from_contract_inputs']) +SEED_K30_ID = k30_seed.SEED_ID +SEED_RAG_K10_ID = ragk10_direct.SEED_DIRECT_RAG_K10_ID +SEED_D64_ID = d64_seed.SEED_ID +SEED_RESIDUAL_19B3_ID = residual_6998.SEED_RESIDUAL_19B3_ID +ROUTE_K30_ENTRYPOINT = k30_seed.ROUTE_ENTRYPOINT +ROUTE_RAG_K10_ENTRYPOINT = ragk10_direct.ROUTE_DIRECT_RAG_K10_ENTRYPOINT +ROUTE_D64_ENTRYPOINT = d64_seed.ROUTE_ENTRYPOINT +ROUTE_RESIDUAL_19B3_ENTRYPOINT = residual_6998.ROUTE_RESIDUAL_19B3_ENTRYPOINT +K30_TARGET_SHAPES = k30_seed.TARGET_SHAPES +RAG_K10_TARGET_SHAPES = ragk10_direct.TARGET_SHAPES +D64_TARGET_SHAPES = d64_seed.TARGET_SHAPES +DIRECT_SEED_TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_k_sweep_qm4096_k30", "rag_stream_b1_q128_m100000_d128_k10", "search_rect_b1_q1024_m32768_d64_k10"]}')) +RESIDUAL_19B3_RETAINED_SHAPES = ('build_k_sweep_qm512_k1', 'build_k_sweep_qm4096_k24', 'build_over32_stress_qm2048_k48', 'build_over32_stress_qm4096_k48', 'rag_stream_largek_b1_q128_m100000_d128_k32') +TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_k_sweep_qm4096_k30", "rag_stream_b1_q128_m100000_d128_k10", "search_rect_b1_q1024_m32768_d64_k10", "build_k_sweep_qm512_k1", "build_k_sweep_qm4096_k24", "build_over32_stress_qm2048_k48", "build_over32_stress_qm4096_k48", "rag_stream_largek_b1_q128_m100000_d128_k32"]}')) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +SELECTED_TARGET_SHAPES = TARGET_SHAPES +GUARD_MISS_AUDIT_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_k_sweep_qm4096_k30", "rag_stream_b1_q128_m100000_d128_k10", "search_rect_b1_q1024_m32768_d64_k10", "build_k_sweep_qm512_k1", "build_k_sweep_qm4096_k24", "build_over32_stress_qm2048_k48", "build_over32_stress_qm4096_k48", "rag_stream_largek_b1_q128_m100000_d128_k32"]}')) +DISPATCH_CORRECTNESS_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["flashml_correctness_b1_q256_m256_d128_k5", "build_k_sweep_qm4096_k30", "rag_stream_b1_q128_m100000_d128_k10", "search_rect_b1_q1024_m32768_d64_k10", "build_k_sweep_qm512_k1", "build_k_sweep_qm4096_k24", "build_over32_stress_qm2048_k48", "build_over32_stress_qm4096_k48", "rag_stream_largek_b1_q128_m100000_d128_k32", "build_dim_sweep_b1_q1024_m1024_d96_k10", "build_dim_sweep_b1_q2048_m2048_d192_k10", "build_highd_b1_q1024_m1024_d320_k10", "search_rect_highd_b1_q512_m12000_d320_k10", "rag_microbatch_highd_b1_q16_m50000_d768_k10", "rag_microbatch_largek_b1_q8_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m100000_d128_k32", "rag_microbatch_largek_b1_q32_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m131071_d128_k32", "build_over64_stress_qm1024_k96", "build_over64_stress_qm2048_k96", "build_over64_stress_qm4096_k96", "build_k_sweep_qm2048_k11", "build_k_sweep_qm2048_k12", "build_k_sweep_qm2048_k13", "build_k_sweep_qm4096_k13", "rag_online_b1_q1_m100000_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "rag_online_irregular_b1_q1_m262143_d128_k10", "build_large_b1_q8192_m8192_d128_k20", "build_large_b1_q8192_m8192_d128_k32", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_batch_b2_q256_m50000_d128_k10", "rag_irregular_b1_q512_m131071_d128_k10", "search_rect_b1_q1024_m8192_d128_k10", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q2048_m2048_d256_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10", "search_rect_b1_q2048_m32768_d128_k10", "build_large_tail_b1_q6144_m6144_d128_k20", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "build_over32_stress_qm2048_k64", "build_over32_stress_qm4096_k64", "build_k_sweep_qm512_k2", "build_k_sweep_qm1024_k16", "build_k_sweep_qm512_k4", "build_k_sweep_qm512_k5", "build_k_sweep_qm512_k6", "rag_microbatch_b1_q8_m100000_d128_k10", "rag_microbatch_b1_q32_m100000_d128_k10", "build_dim_sweep_b1_q1024_m1024_d64_k10", "build_dim_sweep_b1_q4096_m4096_d64_k10", "build_k_sweep_qm512_k8", "build_k_sweep_qm512_k10", "build_qm2048_d128_k10", "search_rect_over32_b1_q2048_m65536_d128_k64", "rag_offline_large_m_over32_b1_q2048_m250000_d128_k64", "search_rect_b1_q4096_m65536_d128_k20", "rag_offline_largek_b1_q4096_m100000_d128_k20", "rag_offline_large_m_b1_q8192_m250000_d128_k20"]}')) +eval_mod = base_f30c.eval_mod +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k48over32", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 48]], "cta_group": 1, "threads": 192}')) +_CONTRACT_PARAMS_BY_LABEL = _decode_capture(_json_loads('{"__dict_items__": [["flashml_correctness_b1_q256_m256_d128_k5", {"__dict_items__": [["B", 1], ["Q", 256], ["M", 256], ["D", 128], ["K", 5], ["dtype", "bfloat16"], ["seed", 606001], ["build", true], ["check_correctness", true], ["correctness_query_sample", 256], ["recall_min", 0.99], ["benchmark", true], ["time_flashlib", true]]}], ["build_k_sweep_qm512_k1", {"__dict_items__": [["B", 1], ["Q", 512], ["M", 512], ["D", 128], ["K", 1], ["dtype", "bfloat16"], ["seed", 606049], ["build", true], ["check_correctness", true], ["correctness_query_sample", 512], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true]]}], ["build_k_sweep_qm512_k2", {"__dict_items__": [["B", 1], ["Q", 512], ["M", 512], ["D", 128], ["K", 2], ["dtype", "bfloat16"], ["seed", 606050], ["build", true], ["check_correctness", true], ["correctness_query_sample", 512], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true]]}], ["build_k_sweep_qm512_k4", {"__dict_items__": [["B", 1], ["Q", 512], ["M", 512], ["D", 128], ["K", 4], ["dtype", "bfloat16"], ["seed", 606052], ["build", true], ["check_correctness", true], ["correctness_query_sample", 512], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true], ["diagnostic_class", "low_k_q512_k5_neighborhood"]]}], ["build_k_sweep_qm512_k5", {"__dict_items__": [["B", 1], ["Q", 512], ["M", 512], ["D", 128], ["K", 5], ["dtype", "bfloat16"], ["seed", 606053], ["build", true], ["check_correctness", true], ["correctness_query_sample", 512], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true]]}], ["build_k_sweep_qm512_k6", {"__dict_items__": [["B", 1], ["Q", 512], ["M", 512], ["D", 128], ["K", 6], ["dtype", "bfloat16"], ["seed", 606054], ["build", true], ["check_correctness", true], ["correctness_query_sample", 512], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true], ["diagnostic_class", "low_k_q512_k5_neighborhood"]]}], ["build_k_sweep_qm512_k8", {"__dict_items__": [["B", 1], ["Q", 512], ["M", 512], ["D", 128], ["K", 8], ["dtype", "bfloat16"], ["seed", 606056], ["build", true], ["check_correctness", true], ["correctness_query_sample", 512], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true]]}], ["build_k_sweep_qm512_k10", {"__dict_items__": [["B", 1], ["Q", 512], ["M", 512], ["D", 128], ["K", 10], ["dtype", "bfloat16"], ["seed", 606058], ["build", true], ["check_correctness", true], ["correctness_query_sample", 512], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true]]}], ["build_qm1024_d128_k10", {"__dict_items__": [["B", 1], ["Q", 1024], ["M", 1024], ["D", 128], ["K", 10], ["dtype", "bfloat16"], ["seed", 606104], ["build", true], ["check_correctness", true], ["correctness_query_sample", 512], ["recall_min", 0.999], ["benchmark", true], 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["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true], ["diagnostic_class", "v12_common_d_rectangular_search"]]}], ["search_rect_common_d4096_b1_q128_m4096_k10", {"__dict_items__": [["B", 1], ["Q", 128], ["M", 4096], ["D", 4096], ["K", 10], ["dtype", "bfloat16"], ["seed", 616496], ["build", false], ["check_correctness", true], ["correctness_query_sample", 128], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true], ["diagnostic_class", "v12_common_d_rectangular_search"]]}], ["rag_microbatch_largek_common_d256_b1_q8_m100000_k32", {"__dict_items__": [["B", 1], ["Q", 8], ["M", 100000], ["D", 256], ["K", 32], ["dtype", "bfloat16"], ["seed", 616332], ["build", false], ["check_correctness", true], ["correctness_query_sample", 8], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true], ["diagnostic_class", "v12_common_d_large_k_rag"]]}], ["rag_stream_largek_common_d256_b1_q128_m100000_k32", {"__dict_items__": [["B", 1], ["Q", 128], ["M", 100000], ["D", 256], ["K", 32], ["dtype", "bfloat16"], ["seed", 616432], ["build", false], ["check_correctness", true], ["correctness_query_sample", 128], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true], ["diagnostic_class", "v12_common_d_large_k_rag"]]}], ["rag_microbatch_over32_d128_b1_q16_m100000_k48", {"__dict_items__": [["B", 1], ["Q", 16], ["M", 100000], ["D", 128], ["K", 48], ["dtype", "bfloat16"], ["seed", 616548], ["build", false], ["check_correctness", true], ["correctness_query_sample", 16], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true], ["diagnostic_class", "v12_rag_over32_topk"]]}]]}')) +_RESIDUAL_19B3_RETAINED_SPECS = {label: _CONTRACT_PARAMS_BY_LABEL[label] for label in RESIDUAL_19B3_RETAINED_SHAPES} +SOURCE_TASKS = {SEED_K30_ID: 'weave-evolve-knn-build-d63b / design_doc/active/weave_evolve_knn_build_round_114_6998_k30warp.md', SEED_RAG_K10_ID: 'weave-evolve-knn-build-bf81 / design_doc/active/weave_evolve_knn_build_round_114_6998.md', SEED_D64_ID: 'weave-evolve-knn-build-54d4 / design_doc/active/weave_evolve_knn_build_round_114_23be_rectd64_unordered.md', SEED_RESIDUAL_19B3_ID: 'generalize-auto-tuning-knn-build-af4f / design_doc/active/generalize_auto_tuning_knn_build_round_114_af4f.md'} +TARGETED_SEED_ROWS_BY_SEED: dict[str, dict[str, dict[str, Any]]] = {SEED_K30_ID: {'build_k_sweep_qm4096_k30': {'kernel_ms': 0.205632, 'flashlib_ms': 0.305729, 'ratio_vs_flashlib': 1.4867773498288201, 'timing_backend': 'cupti'}}, SEED_RAG_K10_ID: {'rag_stream_b1_q128_m100000_d128_k10': {'kernel_ms': 0.118433, 'flashlib_ms': 0.133217, 'ratio_vs_flashlib': 1.124830072699332, 'timing_backend': 'cupti'}}, SEED_D64_ID: {'search_rect_b1_q1024_m32768_d64_k10': {'kernel_ms': 0.168545, 'flashlib_ms': 0.202625, 'ratio_vs_flashlib': 1.2022011925598506, 'timing_backend': 'cupti'}}, SEED_RESIDUAL_19B3_ID: {'build_k_sweep_qm512_k1': {'kernel_ms': 0.040672, 'flashlib_ms': 0.0494085, 'ratio_vs_flashlib': 1.2148037962234461, 'timing_backend': 'cupti'}, 'build_k_sweep_qm4096_k24': {'kernel_ms': 0.269729, 'flashlib_ms': 0.285025, 'ratio_vs_flashlib': 1.0567087706549907, 'timing_backend': 'cupti'}, 'build_over32_stress_qm2048_k48': {'kernel_ms': 0.373218, 'flashlib_ms': 0.39792, 'ratio_vs_flashlib': 1.0661891977343, 'timing_backend': 'cupti'}, 'build_over32_stress_qm4096_k48': {'kernel_ms': 0.4925295, 'flashlib_ms': 0.52548, 'ratio_vs_flashlib': 1.066888379274744, 'timing_backend': 'cupti'}, 'rag_stream_largek_b1_q128_m100000_d128_k32': {'kernel_ms': 0.269953, 'flashlib_ms': 0.282528, 'ratio_vs_flashlib': 1.0465858871729523, 'timing_backend': 'cupti'}}} +PRODUCTION_ROUTE_MODULES = _decode_capture(_json_loads('{"__dict_items__": [["large_square_k20k32", "loom.examples.weave.knn_build_large_square_k20k32_a989_v1"], ["over64_k96", "loom.examples.weave.knn_build_over64_k96_a989_v1"], ["baseline_7c3a_rag_k10", "loom.examples.weave.knn_build_rag_frontier_4b5c_v1:k10"], ["rag_frontier_7399_k10", "loom.examples.weave.knn_build_rag_frontier_7399_v1:k10_s72"], ["rag_frontier_7399_k32_replaced", "loom.examples.weave.knn_build_rag_frontier_7399_v1:k32_s72_g8_fusedmerge"], ["rag_frontier_4fbf_k32", "loom.examples.weave.knn_build_rag_frontier_4fbf_v7:k32_s72_g24_tailinf_fused"], ["rect_smallq_largem_d15e", "loom.examples.weave.knn_build_rect_smallq_largem_ff59_d15e_v1:split16"], ["baseline_7c3a_policy", "loom.examples.weave.knn_build_dispatch_b6d4_d15e_fd02_v1:baseline_7c3a_policy"], ["fallback", "loom.examples.weave.knn_build_dispatch_split72_4e09_de1a_3dc7_v48"], ["dim_d64_73a9", "loom.examples.weave.knn_build_dim_midk_73a9_v1:d64_split_s8"], ["current_exact_k32_dispatcher", "loom.examples.weave.knn_build_dispatch_4fbf_7399_d15e_full55_bad5_v1:launch_from_contract_inputs"], ["base_7399_d15e_dispatcher", "loom.examples.weave.knn_build_dispatch_7399_d15e_full55_v1:launch_from_contract_inputs"], ["rag_frontier_7399_k32", "loom.examples.weave.knn_build_rag_frontier_7399_v1:k32_s72_g8_fusedmerge"], ["dim_d256_df2f", "loom.examples.weave.knn_build_dim_midk_df2f_v1:d256_split_s8"], ["dim_fp16_d128_df2f", "loom.examples.weave.knn_build_dim_midk_df2f_v1:fp16_d128_split_s8"], ["base_dispatch", "loom.examples.weave.knn_build_dispatch_7399_d15e_full55_v1:launch_from_contract_inputs"], ["rect_intermediate_4452_s8", "loom.examples.weave.knn_build_rect_intermediate_frontier_6a73_4452_v2:rect_s8_k10_cached"], ["base_champion_6b59", "loom.examples.weave.knn_build_dispatch_7399_d15e_df2f_full55_v1:launch_from_contract_inputs"], ["base_k32_d64_62b1", "loom.examples.weave.knn_build_dispatch_4fbf_7399_d15e_73a9_full55_v1:launch_from_contract_inputs"], ["default_k96_a330", "loom.examples.weave.knn_build_over64_k96_a989_v1"], ["large_tail_a4f6", "loom.examples.weave.knn_build_large_tail_frontier_6a73_v1:split4_k20"], ["midk_81aa_q2048_k24_k28", "loom.examples.weave.knn_build_dim_midk_bad5_midkcleanup_v1:midk_k24_k28_s8"], ["midk_9b2c_q4096_k28", "loom.examples.weave.knn_build_dim_midk_bad5_k24k28_v1:k28_q4096_s4_unordered_exact"], ["base_f552", "loom.examples.weave.knn_build_dispatch_selected_portfolio_f552_v1:launch_from_contract_inputs"], ["midk_bad5_k64split8", "loom.examples.weave.knn_build_dim_midk_bad5_k64split8_v1:k64_q2048_s8_tailinf"], ["base_e51c", "loom.examples.weave.knn_build_dispatch_selected_portfolio_e51c_v1:launch_from_contract_inputs"], ["midk_f8c3_q4096_k64_split8_a194", "loom.examples.weave.knn_build_dim_midk_f8c3_q4096k64split_v1:q4096_k64_tailinf_split8"], ["base_f8c3", "loom.examples.weave.knn_build_dispatch_selected_portfolio_f8c3_v1:launch_from_contract_inputs"], ["lowk_b193_q512_s4", "loom.examples.weave.knn_build_lowk_f8c3_q512_q1024_v1:q512_lowk_s4"], ["lowk_b193_q1024_k16_s16", "loom.examples.weave.knn_build_lowk_f8c3_q512_q1024_v1:q1024_k16_s16"], ["large_square_5407_q8192_k32_s2", "loom.examples.weave.knn_build_large_square_k32_8a83_v1:q8192_k32_split2"], ["base_f853", "loom.examples.weave.knn_build_dispatch_selected_portfolio_f853_v1:launch_from_contract_inputs"], ["lowk_b193_q512_k4_k5_k6_s4", "loom.examples.weave.knn_build_lowk_f8c3_q512_q1024_v1:q512_lowk_s4"], ["base_f16b", "loom.examples.weave.knn_build_dispatch_selected_portfolio_f16b_v1:launch_from_contract_inputs"], ["rag_microbatch_b2ec_s72_g8", "loom.examples.weave.knn_build_rag_microbatch_4a72_v1:launch_from_contract_inputs"], ["base_4a72", "loom.examples.weave.knn_build_dispatch_selected_portfolio_4a72_v1:launch_from_contract_inputs"], ["rag_m64_s128_0c69", "loom.examples.weave.knn_build_rag_microbatch_m64_d4f7_v1:launch_from_contract_inputs"], ["rag_s144_g12_cta1_059f", "loom.examples.weave.knn_build_rag_microbatch_4a72_v2:launch_from_contract_inputs"], ["rag_s144_g8_cta1_4982_read_ref_parameterized", "loom.examples.weave.knn_build_rag_microbatch_4a72_v2:launch_from_contract_inputs"], ["base_397b", "loom.examples.weave.knn_build_dispatch_selected_portfolio_397b_v1:launch_from_contract_inputs"], ["d64_fdd7_aa88_v2", "loom.examples.weave.knn_build_d64_build_aa88_v2:launch_from_contract_inputs"], ["base_8700", "loom.examples.weave.knn_build_dispatch_rag_seed_portfolio_8700_v1:launch_from_contract_inputs(portfolio_id=all_m64_s128)"], ["rect_d64_cf49_v3_9138", "loom.examples.weave.knn_build_rect_d64_cf49_v3:launch_from_contract_inputs"], ["q1_mbucket_aa88_q1m_v3_bcb3", "loom.examples.weave.knn_build_ragonline_mbucket_aa88_q1m_v3:launch_from_contract_inputs"], ["over64_k96_a2f8_v1_generated_v8", "loom.examples.weave.knn_build_over64_k96_a2f8_v1:_launch_over64_k96_split_path"], ["base_e3de", "loom.examples.weave.knn_build_dispatch_d64_fdd7_e3de_v1:launch_from_contract_inputs"], ["non128_frontier_8199_widecombine_v1", "loom.examples.weave.knn_build_non128_frontier_8199_widecombine_v1:launch_from_contract_inputs"], ["base_4247", "loom.examples.weave.knn_build_dispatch_e3de_9138_bcb3_4247_v1:launch_from_contract_inputs"], ["rag_microbucket_k32_8fcb_split148_v1_b3e0_sm148", "loom.examples.weave.knn_build_rag_microbucket_k32_8fcb_split148_v1:launch_from_contract_inputs"], ["rag_microbucket_k32_2e8e_q16split148_v1_b3e0_q16_s148", "loom.examples.weave.knn_build_rag_microbucket_k32_2e8e_q16split148_v1:launch_from_contract_inputs"], ["non128_frontier_3d5a_cachedmerge_v1", "loom.examples.weave.knn_build_non128_frontier_3d5a_cachedmerge_v1:launch_from_contract_inputs"], ["over64_k96_exactall_229a_v1_b6c4", "loom.examples.weave.knn_build_over64_k96_exactall_229a_v1:launch_from_contract_inputs"], ["knn_build_midk_k11k13_e080_v1", "loom.examples.weave.knn_build_midk_k11k13_e080_v1:launch_from_contract_inputs"], ["ragonline_mbucket_4fc7_q1m262_v1_980c", "loom.examples.weave.knn_build_ragonline_mbucket_4fc7_q1m262_v1:launch_from_contract_inputs"], ["baseline_8199_widecombine_full82_v1", "loom.examples.weave.knn_build_dispatch_4247_non128_8199_widecombine_full82_v1:launch_from_contract_inputs"], ["k30_q4096_6998_warpselect_v1", "loom.examples.weave.knn_build_k30_q4096_6998_warpselect_v1:launch_from_contract_inputs"], ["rag_stream_k10_direct_split72_6998_v1", "loom.examples.weave.knn_build_rag_online_stream_split72_4e09_v1:launch_from_contract_inputs"], ["rect_d64_23be_unordered_v1", "loom.examples.weave.knn_build_rect_d64_23be_unordered_v1:launch_from_contract_inputs"], ["residual_19b3_ed1c_portfolio_6998", "loom.examples.weave.knn_build_dispatch_c142_3505_q32rowld_19b3_v1:launch_from_contract_inputs"], ["candidate_q16split148_cachedmerge_k96exactall_e080_q1m262_over_8199_full82_v1", "loom.examples.weave.knn_build_dispatch_4247_non128_8199_3d5a_2e8e_full82_synth_v1:launch_from_contract_inputs"]]}')) +CANDIDATE_DISPATCHERS = ({'id': BASELINE_ID, 'entrypoint': BASELINE_ENTRYPOINT, 'consumed_seeds': (), 'guard_plan': base_f30c.CANDIDATE_CONFIGS[base_f30c.DEFAULT_CANDIDATE_KEY]['guard_plan'], 'expected_shape_wins': base_f30c.TARGET_SHAPES, 'fallback': base_f30c.ROUTE_BASELINE_ENTRYPOINT, 'rejected_reason': 'same-session f30c baseline'}, {'id': 'candidate_d555_direct_residual_seeds_full82_v1', 'entrypoint': ''.join([format(MODULE, ''), ':candidate_d555_direct_residual_seeds']), 'consumed_seeds': (SEED_K30_ID, SEED_RAG_K10_ID, SEED_D64_ID), 'guard_plan': ('d63b exact K30 guard', 'bf81 exact direct RAG K10 guard', '54d4 exact D64 unordered guard', 'fall through to f30c full82 dispatcher'), 'expected_shape_wins': DIRECT_SEED_TARGET_SHAPES, 'fallback': ROUTE_BASELINE_F30C_ENTRYPOINT, 'rejected_reason': None}, {'id': 'candidate_d555_direct_plus_19b3_winners_full82_v1', 'entrypoint': ''.join([format(MODULE, ''), ':candidate_d555_direct_plus_19b3_winners']), 'consumed_seeds': (SEED_K30_ID, SEED_RAG_K10_ID, SEED_D64_ID, SEED_RESIDUAL_19B3_ID), 'guard_plan': ('d63b exact K30 guard', 'bf81 exact direct RAG K10 guard', '54d4 exact D64 unordered guard', '6998/19b3 retained winner guard for K1/K24/K48/RAG K32', 'fall through to f30c full82 dispatcher'), 'expected_shape_wins': TARGET_SHAPES, 'fallback': ROUTE_BASELINE_F30C_ENTRYPOINT, 'rejected_reason': 'measured 21.72349074302578 TFLOPS versus same-session f30c 21.852044042764728 TFLOPS; not selected'}) + +def _matches_contract_spec(inputs: dict[str, Any], spec: dict[str, Any]) -> bool: + return base_f30c._matches_contract_spec(inputs, spec) + +def _retained_19b3_label_for_inputs(inputs: dict[str, Any]) -> str | None: + label = inputs.get('label') + if label is not None: + spec = _RESIDUAL_19B3_RETAINED_SPECS.get(str(label)) + if spec is not None and _matches_contract_spec(inputs, spec): + return str(label) + for candidate_label, spec in _RESIDUAL_19B3_RETAINED_SPECS.items(): + if _matches_contract_spec(inputs, spec): + return candidate_label + return None + +def _selected_seed_for_inputs(inputs: dict[str, Any], *, enable_k30: bool=True, enable_rag_k10: bool=True, enable_d64: bool=True, enable_19b3_winners: bool=True) -> tuple[str | None, str | None]: + if enable_k30 and k30_seed._eligible_k30_q4096(inputs): + return (SEED_K30_ID, k30_seed.TARGET_SHAPE) + if enable_rag_k10 and ragk10_direct._eligible_direct_rag_k10(inputs): + return (SEED_RAG_K10_ID, ragk10_direct.RAG_K10_DIRECT_SHAPE) + if enable_d64 and d64_seed._eligible_rect_d64(inputs): + return (SEED_D64_ID, d64_seed.TARGET_SHAPE) + if enable_19b3_winners: + label = _retained_19b3_label_for_inputs(inputs) + if label is not None: + return (SEED_RESIDUAL_19B3_ID, label) + return (None, None) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False, enable_k30: bool=True, enable_rag_k10: bool=True, enable_d64: bool=True, enable_19b3_winners: bool=False) -> str: + if not force_fallback: + selected_seed, _label = _selected_seed_for_inputs(inputs, enable_k30=enable_k30, enable_rag_k10=enable_rag_k10, enable_d64=enable_d64, enable_19b3_winners=enable_19b3_winners) + if selected_seed == SEED_K30_ID: + return k30_seed.route_for_contract_inputs(inputs) + if selected_seed == SEED_RAG_K10_ID: + return ragk10_direct.route_for_contract_inputs(inputs, enable_residual_19b3=False) + if selected_seed == SEED_D64_ID: + return d64_seed.route_for_contract_inputs(inputs) + if selected_seed == SEED_RESIDUAL_19B3_ID: + return residual_6998.route_for_contract_inputs(inputs, enable_residual_19b3=True) + return base_f30c.route_for_contract_inputs(inputs) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False, enable_k30: bool=True, enable_rag_k10: bool=True, enable_d64: bool=True, enable_19b3_winners: bool=False) -> None: + if not force_fallback: + selected_seed, _label = _selected_seed_for_inputs(inputs, enable_k30=enable_k30, enable_rag_k10=enable_rag_k10, enable_d64=enable_d64, enable_19b3_winners=enable_19b3_winners) + if selected_seed == SEED_K30_ID: + k30_seed.launch_from_contract_inputs(inputs) + return + if selected_seed == SEED_RAG_K10_ID: + ragk10_direct.launch_from_contract_inputs(inputs, enable_residual_19b3=False) + return + if selected_seed == SEED_D64_ID: + d64_seed.launch_from_contract_inputs(inputs) + return + if selected_seed == SEED_RESIDUAL_19B3_ID: + residual_6998.launch_from_contract_inputs(inputs, enable_residual_19b3=True) + return + base_f30c.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_d555_direct_residual_seeds(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, enable_19b3_winners=False) + +def candidate_d555_direct_plus_19b3_winners(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, enable_19b3_winners=True) + +def candidate_baseline_f30c(inputs: dict[str, Any]) -> None: + base_f30c.candidate_q16split148_cachedmerge_k96exactall_e080_q1m262(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) +CANDIDATE_KEYS = ('direct_residual_seeds', 'direct_plus_19b3_winners') +DEFAULT_CANDIDATE_KEY = 'direct_residual_seeds' +CANDIDATE_CONFIGS: dict[str, dict[str, Any]] = {'direct_residual_seeds': {'candidate_id': 'candidate_d555_direct_residual_seeds_full82_v1', 'entrypoint': ''.join([format(MODULE, ''), ':candidate_d555_direct_residual_seeds']), 'benchmark_entrypoint': ''.join([format(MODULE, ''), ':benchmark_candidate_d555_direct_residual_seeds']), 'kernel_fn': candidate_d555_direct_residual_seeds, 'enabled': {'enable_k30': True, 'enable_rag_k10': True, 'enable_d64': True, 'enable_19b3_winners': False}, 'selected_seeds': (SEED_K30_ID, SEED_RAG_K10_ID, SEED_D64_ID), 'target_shapes': DIRECT_SEED_TARGET_SHAPES, 'guard_plan': ('d63b exact K30 guard', 'bf81 exact RAG K10 guard', '54d4 exact D64 guard', 'f30c full82 fallback')}, 'direct_plus_19b3_winners': {'candidate_id': 'candidate_d555_direct_plus_19b3_winners_full82_v1', 'entrypoint': ''.join([format(MODULE, ''), ':candidate_d555_direct_plus_19b3_winners']), 'benchmark_entrypoint': ''.join([format(MODULE, ''), ':benchmark_candidate_d555_direct_plus_19b3_winners']), 'kernel_fn': candidate_d555_direct_plus_19b3_winners, 'enabled': {'enable_k30': True, 'enable_rag_k10': True, 'enable_d64': True, 'enable_19b3_winners': True}, 'selected_seeds': (SEED_K30_ID, SEED_RAG_K10_ID, SEED_D64_ID, SEED_RESIDUAL_19B3_ID), 'target_shapes': TARGET_SHAPES, 'guard_plan': ('d63b exact K30 guard', 'bf81 exact RAG K10 guard', '54d4 exact D64 guard', '6998/19b3 retained winner guard for K1/K24/K48/RAG K32', 'f30c full82 fallback')}} + +def _candidate_config(candidate_key: str) -> dict[str, Any]: + try: + return CANDIDATE_CONFIGS[candidate_key] + except KeyError as exc: + raise ValueError(''.join(['unknown candidate key ', format(repr(candidate_key), ''), '; expected one of ', format(CANDIDATE_KEYS, '')])) from exc + +def _candidate_enabled_kwargs(candidate_key: str) -> dict[str, bool]: + return dict(_candidate_config(candidate_key)['enabled']) + +def _candidate_kernel_fn(candidate_key: str) -> Callable[[dict[str, Any]], Any]: + return _candidate_config(candidate_key)['kernel_fn'] + +def _candidate_selected_seeds(candidate_key: str) -> tuple[str, ...]: + return tuple(_candidate_config(candidate_key)['selected_seeds']) + +def _candidate_target_shapes(candidate_key: str) -> tuple[str, ...]: + return tuple(_candidate_config(candidate_key)['target_shapes']) + +def _candidate_id(candidate_key: str) -> str: + return str(_candidate_config(candidate_key)['candidate_id']) + +def _select_contract_shapes(shape_labels): + return base_f30c._select_contract_shapes(shape_labels) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def compile_and_launch_knn_build(*, shape_labels=DISPATCH_CORRECTNESS_SHAPES, benchmark: bool=False) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=None, kernel_fn: Callable[[dict[str, Any]], Any]=candidate, correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + return base_f30c._run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=kernel_fn, correctness=correctness, time_flashlib=time_flashlib) + +def _seed_entrypoint(seed_id: str) -> str: + return {SEED_K30_ID: ROUTE_K30_ENTRYPOINT, SEED_RAG_K10_ID: ROUTE_RAG_K10_ENTRYPOINT, SEED_D64_ID: ROUTE_D64_ENTRYPOINT, SEED_RESIDUAL_19B3_ID: ROUTE_RESIDUAL_19B3_ENTRYPOINT}[seed_id] + +def _guard_id(seed_id: str) -> str: + return {SEED_K30_ID: 'd63b_k30_q4096_exact_warpselect_guard', SEED_RAG_K10_ID: 'bf81_rag_k10_direct_split72_exact_guard', SEED_D64_ID: '54d4_rect_d64_23be_unordered_exact_guard', SEED_RESIDUAL_19B3_ID: 'd555_retained_6998_19b3_winner_guard'}[seed_id] + +def _guard_condition(seed_id: str, label: str) -> str: + spec = _CONTRACT_PARAMS_BY_LABEL[label] + prefix = 'exact retained BF16' if seed_id == SEED_RESIDUAL_19B3_ID else 'exact BF16' + return ''.join([format(prefix, ''), ' shape label=', format(label, ''), ' B=', format(spec['B'], ''), ' Q=', format(spec['Q'], ''), ' M=', format(spec['M'], ''), ' D=', format(spec['D'], ''), ' K=', format(spec['K'], ''), ' build=', format(spec.get('build', False), '')]) + +def _route_kind(seed_id: str) -> str: + return 'general' if seed_id == SEED_RESIDUAL_19B3_ID else 'specialized' + +def _route_source(seed_id: str) -> str: + return 'broad-dispatcher' if seed_id == SEED_RESIDUAL_19B3_ID else 'shape-specific-seed' + +def _baseline_trace_record(inputs: dict[str, Any]) -> dict[str, Any]: + label = str(inputs.get('label')) + row = dict(base_f30c.route_trace_for_contract_shapes((label,), candidate_key=base_f30c.DEFAULT_CANDIDATE_KEY)[0]) + row['baseline_dispatcher_route'] = row.get('selected_route') + return base_f30c._normalize_route_row(row) + +def _specialized_trace_record(inputs: dict[str, Any], seed_id: str, label: str, *, candidate_key: str) -> dict[str, Any]: + targeted = TARGETED_SEED_ROWS_BY_SEED.get(seed_id, {}).get(label, {}) + selected_route = route_for_contract_inputs(inputs, **_candidate_enabled_kwargs(candidate_key)) + baseline_route = base_f30c.route_for_contract_inputs(inputs) + return base_f30c._normalize_route_row({'shape_key': label, 'selected_route': selected_route, 'selected_entrypoint': _seed_entrypoint(seed_id), 'selected_seed': seed_id, 'expected_seed': seed_id, 'route_kind': _route_kind(seed_id), 'route_source': _route_source(seed_id), 'guard_id': _guard_id(seed_id), 'guard_condition': _guard_condition(seed_id, label), 'coverage': 'd555 synthesized full82 route selected before f30c fallback', 'consumed_seed': seed_id, 'replaced_route': baseline_route, 'baseline_dispatcher_route': baseline_route, 'targeted_seed_timing_backend': targeted.get('timing_backend'), 'targeted_seed_kernel_ms': targeted.get('kernel_ms'), 'targeted_seed_ratio_vs_flashlib': targeted.get('ratio_vs_flashlib'), 'row_selection': targeted, 'classification': 'unmeasured', 'dispatcher_kernel_ms': None, 'shape_specific_kernel_ms': targeted.get('kernel_ms'), 'relative_speedup_vs_baseline': None}) + +def _route_trace_record(inputs: dict[str, Any], *, force_fallback: bool=False, candidate_key: str=DEFAULT_CANDIDATE_KEY) -> dict[str, Any]: + selected_seed, label = _selected_seed_for_inputs(inputs, **_candidate_enabled_kwargs(candidate_key)) + if force_fallback and selected_seed is not None and (label is not None): + row = _baseline_trace_record(inputs) + row['expected_seed'] = selected_seed + row['guard_id'] = 'forced_fallback_d555_synthesized_portfolio_disabled' + row['guard_condition'] = ''.join(['forced fallback to ', format(BASELINE_ID, ''), '; d555 seed overlays disabled']) + row['forced_disabled_seeds'] = _candidate_selected_seeds(candidate_key) + row['classification'] = 'guard-miss' + return base_f30c._normalize_route_row(row) + if not force_fallback and selected_seed is not None and (label is not None): + return _specialized_trace_record(inputs, selected_seed, label, candidate_key=candidate_key) + return _baseline_trace_record(inputs) + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False, candidate_key: str=DEFAULT_CANDIDATE_KEY) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + return [_route_trace_record(base_f30c._trace_inputs_from_shape(shape), force_fallback=force_fallback, candidate_key=candidate_key) for shape in selected] + +def _rows_for_labels(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return base_f30c._rows_for_labels(report, labels) + +def _annotate_route_trace(route_trace: list[dict[str, Any]], candidate_report: dict[str, Any], baseline_report: dict[str, Any], *, candidate_key: str) -> list[dict[str, Any]]: + annotated = [] + target_shape_set = set(_candidate_target_shapes(candidate_key)) + for row in route_trace: + label = str(row.get('shape_key')) + out = dict(row) + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') + speedup_vs_baseline = baseline_ms / candidate_ms if candidate_ms and baseline_ms else None + speedup_vs_external = flashlib_ms / candidate_ms if candidate_ms and flashlib_ms else None + out['dispatcher_kernel_ms'] = candidate_ms + out['baseline_dispatcher_kernel_ms'] = baseline_ms + out['external_baseline_ms'] = flashlib_ms + out['flashlib_ms'] = flashlib_ms + out['relative_speedup_vs_baseline'] = speedup_vs_baseline + out['speedup_vs_external_baseline'] = speedup_vs_external + out['external_baseline_ref'] = 'same_session' if flashlib_ms is not None else 'not_available' + out['route_changed_vs_baseline_dispatcher'] = out.get('selected_route') != out.get('baseline_dispatcher_route') + if label in target_shape_set: + if not out.get('selected_seed'): + out['classification'] = 'guard-miss' + elif speedup_vs_external is not None and speedup_vs_external < 1.05: + out['classification'] = 'kernel-slow' if out.get('route_kind') == 'specialized' else 'fallback-slow' + elif speedup_vs_baseline is not None and speedup_vs_baseline < 1.0: + out['classification'] = 'kernel-slow' if out.get('route_kind') == 'specialized' else 'fallback-slow' + elif out.get('route_source') == 'shape-specific-seed': + out['classification'] = 'seed-consumed' + else: + out['classification'] = 'route-ok' + elif speedup_vs_external is not None and speedup_vs_external < 1.0: + out['classification'] = 'kernel-slow' if out.get('route_kind') == 'specialized' else 'fallback-slow' + else: + out['classification'] = 'route-ok' + annotated.append(base_f30c._normalize_route_row(out)) + return annotated + +def _seed_delta_matrix(candidate_report: dict[str, Any], baseline_report: dict[str, Any], *, candidate_key: str) -> list[dict[str, Any]]: + matrix = [] + enabled = _candidate_enabled_kwargs(candidate_key) + for label in _candidate_target_shapes(candidate_key): + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + inputs = base_f30c._inputs_for_label(label) + selected_seed, _selected_label = _selected_seed_for_inputs(inputs, **enabled) + targeted = TARGETED_SEED_ROWS_BY_SEED.get(str(selected_seed), {}).get(label, {}) + matrix.append({'shape_key': label, 'baseline_route': base_f30c.route_for_contract_inputs(inputs), 'candidate_route': route_for_contract_inputs(inputs, **enabled), 'selected_seed': selected_seed, 'candidate_id': _candidate_id(candidate_key), 'candidate_ms': candidate_ms, 'baseline_ms': baseline_ms, 'flashlib_ms': candidate_row.get('flashlib_ms'), 'metric_delta_ms': candidate_ms - baseline_ms if candidate_ms and baseline_ms else None, 'speedup_vs_baseline_dispatcher': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'ratio_vs_flashlib': candidate_row.get('ratio_vs_flashlib'), 'targeted_seed_kernel_ms': targeted.get('kernel_ms'), 'targeted_seed_ratio_vs_flashlib': targeted.get('ratio_vs_flashlib'), 'timing_backend': candidate_row.get('timing_backend') or baseline_row.get('timing_backend')}) + return matrix + +def _frontmatter_seed_delta_matrix(candidate_report: dict[str, Any], baseline_report: dict[str, Any], *, candidate_key: str) -> list[dict[str, Any]]: + return [{'shape_key': item['shape_key'], 'baseline_route': item['baseline_route'], 'candidate_deltas': [{'candidate_id': item['candidate_id'], 'selected_seed': item['selected_seed'], 'metric_delta': item['metric_delta_ms'], 'ratio_vs_flashlib': item['ratio_vs_flashlib'], 'timing_backend': item['timing_backend'] or 'cupti'}]} for item in _seed_delta_matrix(candidate_report, baseline_report, candidate_key=candidate_key)] + +def _below_flashlib_rows(report: dict[str, Any], *, floor: float, candidate_key: str) -> list[dict[str, Any]]: + trace_by_label = {str(row['shape_key']): row for row in route_trace_for_contract_shapes(candidate_key=candidate_key)} + rows = [] + for label, row in sorted(report.get('per_shape', {}).items()): + ratio = row.get('ratio_vs_flashlib') + if isinstance(ratio, (float, int)) and ratio < floor: + trace_row = trace_by_label.get(label, {}) + rows.append({'shape_key': label, 'kernel_ms': row.get('kernel_ms'), 'flashlib_ms': row.get('flashlib_ms'), 'ratio_vs_flashlib': ratio, 'selected_route': trace_row.get('selected_route'), 'route_kind': trace_row.get('route_kind', 'unknown'), 'classification': 'kernel-slow' if trace_row.get('route_kind') == 'specialized' else 'fallback-slow'}) + return rows + +def _timing_backend_name(use_cupti: bool) -> str: + return 'cupti' if use_cupti else 'cuda_event_fallback' + +def _denominator_name(shape_labels) -> str: + if shape_labels is None: + return 'full82_v9' + return ''.join(['shape', format(len(tuple(shape_labels)), '')]) + +def benchmark_baseline_f30c(*, use_cupti: bool=True, shape_labels=None, benchmark_correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_baseline_f30c, correctness=benchmark_correctness, time_flashlib=time_flashlib) + report['candidate_id'] = BASELINE_ID + report['measured_entrypoint'] = BASELINE_ENTRYPOINT + report['route_trace'] = base_f30c.route_trace_for_contract_shapes(shape_labels, candidate_key=base_f30c.DEFAULT_CANDIDATE_KEY) + report['route_trace_included'] = True + return report + +def _benchmark_payload(candidate_report: dict[str, Any], baseline_report: dict[str, Any], *, use_cupti: bool, shape_labels, candidate_key: str, benchmark_correctness: bool, time_flashlib: bool) -> dict[str, Any]: + cfg = _candidate_config(candidate_key) + route_trace = _annotate_route_trace(route_trace_for_contract_shapes(shape_labels, candidate_key=candidate_key), candidate_report, baseline_report, candidate_key=candidate_key) + candidate_metric = candidate_report['summary']['primary_mean'] + baseline_metric = baseline_report['summary']['primary_mean'] + below_1x = _below_flashlib_rows(candidate_report, floor=1.0, candidate_key=candidate_key) + below_floor = _below_flashlib_rows(candidate_report, floor=1.05, candidate_key=candidate_key) + timing_backend = _timing_backend_name(use_cupti) + denominator = _denominator_name(shape_labels) + return {'candidate_key': candidate_key, 'candidate_id': cfg['candidate_id'], 'baseline_candidate_id': BASELINE_ID, 'selected_seeds': _candidate_selected_seeds(candidate_key), 'source_tasks': SOURCE_TASKS, 'tflops': candidate_metric, 'baseline_tflops': baseline_metric, 'metric_delta': candidate_metric - baseline_metric if candidate_metric and baseline_metric else None, 'all_correct': candidate_report['summary']['all_correct'], 'baseline_all_correct': baseline_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'baseline_performance_comparable': baseline_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'baseline_invalid_performance_reason': baseline_report['summary']['invalid_performance_reason'], 'measured_entrypoint': cfg['benchmark_entrypoint'], 'baseline_entrypoint': ''.join([format(MODULE, ''), ':benchmark_baseline_f30c']), 'measured_shape_labels': 'all_canonical' if shape_labels is None else tuple(shape_labels), 'timing_backend': timing_backend, 'denominator': denominator, 'selected_route_labels': tuple(dict.fromkeys((*base_f30c.SELECTED_TARGET_SHAPES, *_candidate_target_shapes(candidate_key)))), 'consumed_seed_labels': _candidate_target_shapes(candidate_key), 'selected_route_rows': _rows_for_labels(candidate_report, tuple(dict.fromkeys((*base_f30c.SELECTED_TARGET_SHAPES, *_candidate_target_shapes(candidate_key))))), 'baseline_selected_route_rows': _rows_for_labels(baseline_report, tuple(dict.fromkeys((*base_f30c.SELECTED_TARGET_SHAPES, *_candidate_target_shapes(candidate_key))))), 'consumed_seed_rows': _rows_for_labels(candidate_report, _candidate_target_shapes(candidate_key)), 'baseline_consumed_seed_rows': _rows_for_labels(baseline_report, _candidate_target_shapes(candidate_key)), 'seed_delta_matrix': _seed_delta_matrix(candidate_report, baseline_report, candidate_key=candidate_key), 'frontmatter_seed_delta_matrix': _frontmatter_seed_delta_matrix(candidate_report, baseline_report, candidate_key=candidate_key), 'targeted_seed_rows_by_seed': TARGETED_SEED_ROWS_BY_SEED, 'candidate_dispatchers': CANDIDATE_DISPATCHERS, 'selected_candidate_dispatcher': cfg['candidate_id'], 'guard_plan': cfg['guard_plan'], 'route_modules': PRODUCTION_ROUTE_MODULES, 'route_trace': route_trace, 'forced_fallback_route_trace': route_trace_for_contract_shapes(shape_labels, force_fallback=True, candidate_key=candidate_key), 'route_trace_included': True, 'contract_summary': candidate_report['summary'], 'baseline_contract_summary': baseline_report['summary'], 'contract_performance': candidate_report['performance'], 'baseline_contract_performance': baseline_report['performance'], 'timing_backends': base_f30c._timing_backends_for_reports(candidate_report, baseline_report), 'timing_backend_requested': timing_backend, 'use_cupti': use_cupti, 'benchmark_correctness_checked': benchmark_correctness, 'benchmark_time_flashlib': time_flashlib, 'performance_coverage': 'partial' if below_floor else 'pass', 'coverage_only_routes': [], 'hot_bucket_blockers': below_floor, 'flashlib_parity_ledger': {'baseline_ref_scope': 'same_session' if time_flashlib else 'not_available', 'speedup_floor': 1.05, 'rows_below_1x': below_1x, 'rows_below_floor': below_floor, 'omitted_reason': None if time_flashlib else 'benchmark_time_flashlib=false'}, 'rank_objective': {'metric': 'tflops', 'direction': 'maximize', 'value': candidate_metric, 'baseline_value': baseline_metric, 'delta': candidate_metric - baseline_metric if candidate_metric and baseline_metric else None, 'denominator': denominator}, 'report': candidate_report, 'baseline_report': baseline_report} + +def benchmark_candidate_portfolio(*, candidate_key: str=DEFAULT_CANDIDATE_KEY, use_cupti: bool=True, shape_labels=None, baseline_report: dict[str, Any] | None=None, benchmark_correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + if baseline_report is None: + baseline_report = benchmark_baseline_f30c(use_cupti=use_cupti, shape_labels=shape_labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=_candidate_kernel_fn(candidate_key), correctness=benchmark_correctness, time_flashlib=time_flashlib) + return _benchmark_payload(candidate_report, baseline_report, use_cupti=use_cupti, shape_labels=shape_labels, candidate_key=candidate_key, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + +def benchmark_candidate_d555_direct_residual_seeds(**kwargs) -> dict[str, Any]: + return benchmark_candidate_portfolio(candidate_key='direct_residual_seeds', **kwargs) + +def benchmark_candidate_d555_direct_plus_19b3_winners(**kwargs) -> dict[str, Any]: + return benchmark_candidate_portfolio(candidate_key='direct_plus_19b3_winners', **kwargs) + +def benchmark_subset_matrix(*, use_cupti: bool=True, shape_labels=None, candidate_keys: tuple[str, ...]=CANDIDATE_KEYS, benchmark_correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + baseline_report = benchmark_baseline_f30c(use_cupti=use_cupti, shape_labels=shape_labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + payloads = {key: benchmark_candidate_portfolio(candidate_key=key, use_cupti=use_cupti, shape_labels=shape_labels, baseline_report=baseline_report, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) for key in candidate_keys} + baseline_metric = baseline_report['summary']['primary_mean'] + return {'matrix_id': 'd555_residual_seed_synthesis_full82_v1', 'baseline_candidate_id': BASELINE_ID, 'baseline_entrypoint': ''.join([format(MODULE, ''), ':benchmark_baseline_f30c']), 'baseline_tflops': baseline_metric, 'baseline_all_correct': baseline_report['summary']['all_correct'], 'baseline_report': baseline_report, 'candidate_keys': candidate_keys, 'candidate_dispatchers': CANDIDATE_DISPATCHERS, 'candidate_summaries': {key: {'candidate_id': payload['candidate_id'], 'measured_entrypoint': payload['measured_entrypoint'], 'selected_seeds': payload['selected_seeds'], 'tflops': payload['tflops'], 'metric_delta': payload['metric_delta'], 'all_correct': payload['all_correct'], 'performance_comparable': payload['performance_comparable'], 'performance_coverage': payload['performance_coverage'], 'hot_bucket_blocker_count': len(payload['hot_bucket_blockers'])} for key, payload in payloads.items()}, 'payloads': payloads, 'measured_shape_labels': 'all_canonical' if shape_labels is None else tuple(shape_labels), 'timing_backend': _timing_backend_name(use_cupti), 'denominator': _denominator_name(shape_labels), 'timing_backend_requested': _timing_backend_name(use_cupti), 'benchmark_correctness_checked': benchmark_correctness, 'benchmark_time_flashlib': time_flashlib, 'timing_backends': base_f30c._timing_backends_for_reports(baseline_report, *(payload['report'] for payload in payloads.values())), 'route_trace_included': True, 'rank_objective': {key: {'metric': 'tflops', 'direction': 'maximize', 'value': payload['tflops'], 'baseline_value': baseline_metric, 'delta': payload['metric_delta'], 'denominator': _denominator_name(shape_labels)} for key, payload in payloads.items()}} + +def _denominator_label(shape_labels) -> str: + if shape_labels is None: + return ''.join(['full', format(len(eval_mod.CANONICAL_SHAPES), '')]) + return ''.join(['shape', format(len(tuple(shape_labels)), '')]) + +def write_benchmark_artifacts(artifact_dir: str | Path, *, use_cupti: bool=True, shape_labels=None, candidate_key: str | None=None, benchmark_correctness: bool=True, time_flashlib: bool=True) -> dict[str, str]: + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + denom = _denominator_label(shape_labels) + denominator = _denominator_name(shape_labels) + candidate_keys = CANDIDATE_KEYS if candidate_key is None else (candidate_key,) + matrix = benchmark_subset_matrix(use_cupti=use_cupti, shape_labels=shape_labels, candidate_keys=candidate_keys, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + baseline_path = out_dir / ''.join([format(denom, ''), '_same_session_baseline_f30c_for_d555_residual_seeds_v1.json']) + summary_path = out_dir / ''.join([format(denom, ''), '_residual_seed_synthesis_d555_summary_v1.json']) + paths: dict[str, str] = {'same_session_baseline_payload': str(baseline_path), 'matrix_summary': str(summary_path)} + baseline_path.write_text(json.dumps({'candidate_id': BASELINE_ID, 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_baseline_f30c']), 'measured_shape_labels': matrix['measured_shape_labels'], 'timing_backend': matrix['timing_backend'], 'denominator': denominator, 'benchmark_correctness_checked': matrix['benchmark_correctness_checked'], 'benchmark_time_flashlib': matrix['benchmark_time_flashlib'], 'tflops': matrix['baseline_tflops'], 'all_correct': matrix['baseline_all_correct'], 'performance_comparable': matrix['baseline_report']['summary']['performance_comparable'], 'contract_summary': matrix['baseline_report']['summary'], 'contract_performance': matrix['baseline_report']['performance'], 'route_trace': base_f30c.route_trace_for_contract_shapes(shape_labels, candidate_key=base_f30c.DEFAULT_CANDIDATE_KEY), 'route_trace_included': True, 'rank_objective': {'metric': 'tflops', 'direction': 'maximize', 'value': matrix['baseline_tflops'], 'denominator': denominator}, 'report': matrix['baseline_report']}, indent=2, sort_keys=True) + '\n') + for key, payload in matrix['payloads'].items(): + candidate_id = str(payload['candidate_id']).removeprefix('candidate_') + candidate_path = out_dir / ''.join([format(denom, ''), '_dispatch_', format(candidate_id, ''), '.json']) + route_trace_path = out_dir / ''.join([format(denom, ''), '_route_trace_', format(candidate_id, ''), '.json']) + forced_trace_path = out_dir / ''.join([format(denom, ''), '_forced_fallback_trace_', format(candidate_id, ''), '.json']) + seed_matrix_path = out_dir / ''.join([format(denom, ''), '_seed_delta_matrix_', format(candidate_id, ''), '.json']) + candidate_path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + route_trace_path.write_text(json.dumps(payload['route_trace'], indent=2, sort_keys=True) + '\n') + forced_trace_path.write_text(json.dumps(payload['forced_fallback_route_trace'], indent=2, sort_keys=True) + '\n') + seed_matrix_path.write_text(json.dumps(payload['seed_delta_matrix'], indent=2, sort_keys=True) + '\n') + paths[''.join([format(key, ''), '_candidate_payload'])] = str(candidate_path) + paths[''.join([format(key, ''), '_route_trace'])] = str(route_trace_path) + paths[''.join([format(key, ''), '_forced_fallback_trace'])] = str(forced_trace_path) + paths[''.join([format(key, ''), '_seed_delta_matrix'])] = str(seed_matrix_path) + summary_payload = {key: value for key, value in matrix.items() if key not in {'payloads', 'baseline_report'}} + summary_payload['candidate_payload_paths'] = {key: paths[''.join([format(key, ''), '_candidate_payload'])] for key in matrix['payloads']} + summary_payload['same_session_baseline_payload'] = str(baseline_path) + summary_path.write_text(json.dumps(summary_payload, indent=2, sort_keys=True) + '\n') + return paths diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_d64_fdd7_e3de_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_d64_fdd7_e3de_v1.py new file mode 100644 index 00000000..c0cdca96 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_d64_fdd7_e3de_v1.py @@ -0,0 +1,271 @@ +"""D64 fdd7 overlay for the 8700 kNN build dispatcher. + +Minimum target architecture: sm_100a. This dispatcher-consumption wrapper starts +from the current 8700 full-v6 dispatcher and adds one exact D64 build bucket +guard for ``B=1,Q=M in {1024,2048,4096},D=64,K=10``. The guarded route reuses +the validated fdd7/aa88 v2 Weave seed. Non-bucket shapes delegate to 8700. + +Every production route remains Weave-only. FlashLib is used only by the +contract harness as a black-box timing peer. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +from pathlib import Path +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_d64_build_aa88_v2 as d64_fdd7 +from . import knn_build_dispatch_rag_seed_portfolio_8700_v1 as base_8700 +ROUTE_BASE_8700 = 'loom.examples.weave.knn_build_dispatch_rag_seed_portfolio_8700_v1:launch_from_contract_inputs(portfolio_id=all_m64_s128)' +ROUTE_D64_FDD7_S8 = d64_fdd7.ROUTE_D64_BUCKET_S8_FAST +ROUTE_D64_FDD7_S4 = d64_fdd7.ROUTE_D64_BUCKET_S4_FAST +DEFAULT_PORTFOLIO_ID = base_8700.PORTFOLIO_ALL_M64 +D64_TARGET_SHAPES = d64_fdd7.TARGET_SHAPES +D64_TARGET_SHAPE_SET = set(D64_TARGET_SHAPES) +SELECTED_TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_large_b1_q8192_m8192_d128_k20", "build_large_b1_q8192_m8192_d128_k32", "build_over64_stress_qm2048_k96", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32", "rag_batch_b2_q256_m50000_d128_k10", "rag_irregular_b1_q512_m131071_d128_k10", "search_rect_b1_q1024_m8192_d128_k10", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q2048_m2048_d256_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10", "search_rect_b1_q2048_m32768_d128_k10", "build_large_tail_b1_q6144_m6144_d128_k20", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "build_over32_stress_qm2048_k64", "build_over32_stress_qm4096_k64", "build_k_sweep_qm512_k1", "build_k_sweep_qm512_k2", "build_k_sweep_qm1024_k16", "build_k_sweep_qm512_k4", "build_k_sweep_qm512_k5", "build_k_sweep_qm512_k6", "rag_microbatch_b1_q8_m100000_d128_k10", "rag_microbatch_b1_q32_m100000_d128_k10", "build_dim_sweep_b1_q1024_m1024_d64_k10", "build_dim_sweep_b1_q4096_m4096_d64_k10"]}')) +CONSUMED_SEED_TARGET_SHAPES = D64_TARGET_SHAPES +GUARD_MISS_AUDIT_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_dim_sweep_b1_q1024_m1024_d64_k10", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q4096_m4096_d64_k10", "search_rect_b1_q1024_m32768_d64_k10", "build_k_sweep_qm512_k4", "build_k_sweep_qm512_k8", "build_k_sweep_qm512_k10", "rag_microbatch_b1_q8_m100000_d128_k10", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_microbatch_b1_q32_m100000_d128_k10", "build_large_b1_q8192_m8192_d128_k20", "build_qm2048_d128_k10", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "flashml_correctness_b1_q256_m256_d128_k5", "build_over32_stress_qm2048_k64", "search_rect_over32_b1_q2048_m65536_d128_k64", "rag_offline_large_m_over32_b1_q2048_m250000_d128_k64", "build_k_sweep_qm512_k5", "build_over32_stress_qm4096_k64"]}')) +DISPATCH_CORRECTNESS_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["flashml_correctness_b1_q256_m256_d128_k5", "build_large_b1_q8192_m8192_d128_k20", "build_large_b1_q8192_m8192_d128_k32", "build_over64_stress_qm2048_k96", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32", "rag_batch_b2_q256_m50000_d128_k10", "rag_irregular_b1_q512_m131071_d128_k10", "search_rect_b1_q1024_m8192_d128_k10", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q2048_m2048_d256_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10", "search_rect_b1_q2048_m32768_d128_k10", "build_large_tail_b1_q6144_m6144_d128_k20", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "build_over32_stress_qm2048_k64", "build_over32_stress_qm4096_k64", "build_k_sweep_qm512_k1", "build_k_sweep_qm512_k2", "build_k_sweep_qm1024_k16", "build_k_sweep_qm512_k4", "build_k_sweep_qm512_k5", "build_k_sweep_qm512_k6", "rag_microbatch_b1_q8_m100000_d128_k10", "rag_microbatch_b1_q32_m100000_d128_k10", "build_dim_sweep_b1_q1024_m1024_d64_k10", "build_dim_sweep_b1_q4096_m4096_d64_k10", "search_rect_b1_q1024_m32768_d64_k10", "build_k_sweep_qm512_k8", "build_k_sweep_qm512_k10", "build_qm2048_d128_k10", "search_rect_over32_b1_q2048_m65536_d128_k64", "rag_offline_large_m_over32_b1_q2048_m250000_d128_k64", "rag_online_b1_q1_m100000_d128_k10", "rag_stream_b1_q128_m100000_d128_k10", "search_rect_b1_q4096_m65536_d128_k20", "rag_offline_largek_b1_q4096_m100000_d128_k20", "rag_offline_large_m_b1_q8192_m250000_d128_k20"]}')) +ROUTE_SEED_ID = {ROUTE_D64_FDD7_S8: 'd64_fdd7_aa88_v2', ROUTE_D64_FDD7_S4: 'd64_fdd7_aa88_v2'} +ROUTE_ENTRYPOINTS = {ROUTE_D64_FDD7_S8: 'loom.examples.weave.knn_build_d64_build_aa88_v2:launch_from_contract_inputs', ROUTE_D64_FDD7_S4: 'loom.examples.weave.knn_build_d64_build_aa88_v2:launch_from_contract_inputs'} +PRODUCTION_ROUTE_MODULES = {**base_8700.PRODUCTION_ROUTE_MODULES, 'd64_fdd7_aa88_v2': 'loom.examples.weave.knn_build_d64_build_aa88_v2:launch_from_contract_inputs', 'base_8700': ROUTE_BASE_8700} +CANDIDATE_DISPATCHERS = ({'id': 'baseline_8700_all_m64_s128', 'entrypoint': 'benchmark_data.json:knn_build -> loom.examples.weave.knn_build_dispatch_rag_seed_portfolio_8700_v1:benchmark_knn_build_dispatch_rag_seed_portfolio_8700_v1(portfolio_id=all_m64_s128)', 'consumed_seeds': ('rag_m64_s128_0c69',), 'guard_plan': ('8700 exact RAG Q8/Q16/Q32 K10 guard', 'then inherited 397b selected guard plan'), 'expected_shape_wins': base_8700.CONSUMED_SEED_TARGET_SHAPES, 'fallback': ROUTE_BASE_8700, 'rejected_reason': 'same-session baseline for fdd7 one-seed dispatcher consumption'}, {'id': 'd64_fdd7_e3de_v1', 'entrypoint': 'loom.examples.weave.knn_build_dispatch_d64_fdd7_e3de_v1:benchmark_knn_build_dispatch_d64_fdd7_e3de_v1', 'consumed_seeds': ('d64_fdd7_aa88_v2',), 'guard_plan': ('exact BF16 build B1 Q=M in {1024,2048,4096} D64 K10 -> fdd7 aa88 v2', 'then 8700 all-M64/S128 dispatcher'), 'expected_shape_wins': D64_TARGET_SHAPES, 'fallback': ROUTE_BASE_8700, 'rejected_reason': None}) +TARGETED_SEED_ROWS = {'build_dim_sweep_b1_q1024_m1024_d64_k10': {'kernel_ms': 0.032448, 'flashlib_ms': 0.077137, 'ratio_vs_flashlib': 2.3772497534516766, 'tflops': 4.1363944773175545, 'split_count': 8, 'merge_route': 'exact_k10_s8_rowbase_cached', 'route': ROUTE_D64_FDD7_S8}, 'build_dim_sweep_b1_q2048_m2048_d64_k10': {'kernel_ms': 0.050784, 'flashlib_ms': 0.064737, 'ratio_vs_flashlib': 1.2747518903591681, 'tflops': 10.571654694391933, 'split_count': 8, 'merge_route': 'exact_k10_s8_rowbase_cached', 'route': ROUTE_D64_FDD7_S8}, 'build_dim_sweep_b1_q4096_m4096_d64_k10': {'kernel_ms': 0.129377, 'flashlib_ms': 0.146113, 'ratio_vs_flashlib': 1.1293583867302535, 'tflops': 16.598650826653888, 'split_count': 4, 'merge_route': 'exact_k10_s4_rowbase_cached', 'route': ROUTE_D64_FDD7_S4}} +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k48over32", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 48]], "cta_group": 1, "threads": 192}')) + +def _dtype_name(inputs: dict[str, Any]) -> str: + query = inputs.get('query') + if query is not None: + return str(query.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')) + +def _label_can_hit(inputs: dict[str, Any], labels: set[str]) -> bool: + value = inputs.get('label') + return value is None or str(value) in labels + +def _eligible_d64_fdd7(inputs: dict[str, Any]) -> bool: + if not _label_can_hit(inputs, D64_TARGET_SHAPE_SET): + return False + if _dtype_name(inputs) != 'bfloat16': + return False + return d64_fdd7._eligible_d64_build_bucket(inputs) + +def route_for_contract_inputs(inputs: dict[str, Any], *, portfolio_id: str=DEFAULT_PORTFOLIO_ID, force_fallback: bool=False, enable_d64_fdd7: bool=True, enable_rag_seed_portfolio: bool=True, enable_q512_k456: bool=True) -> str: + if not force_fallback and enable_d64_fdd7 and _eligible_d64_fdd7(inputs): + return d64_fdd7.route_name_for_inputs(inputs) + return base_8700.route_for_contract_inputs(inputs, portfolio_id=portfolio_id, force_fallback=False, enable_rag_seed_portfolio=enable_rag_seed_portfolio, enable_q512_k456=enable_q512_k456) + +def _launch_route(inputs: dict[str, Any], route: str) -> None: + if route in ROUTE_SEED_ID and _eligible_d64_fdd7(inputs): + d64_fdd7.launch_from_contract_inputs(inputs) + return + base_8700._launch_route(inputs, route) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, portfolio_id: str=DEFAULT_PORTFOLIO_ID, force_fallback: bool=False, enable_d64_fdd7: bool=True, enable_rag_seed_portfolio: bool=True, enable_q512_k456: bool=True) -> None: + route = route_for_contract_inputs(inputs, portfolio_id=portfolio_id, force_fallback=force_fallback, enable_d64_fdd7=enable_d64_fdd7, enable_rag_seed_portfolio=enable_rag_seed_portfolio, enable_q512_k456=enable_q512_k456) + _launch_route(inputs, route) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_base_dispatcher(inputs: dict[str, Any]) -> None: + base_8700.launch_from_contract_inputs(inputs, portfolio_id=DEFAULT_PORTFOLIO_ID) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return base_8700._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=DISPATCH_CORRECTNESS_SHAPES, benchmark: bool=False) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=None, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + shapes = None if shape_labels is None else _select_contract_shapes(shape_labels) + return evaluate_contract(shapes=shapes, correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def _trace_inputs_from_shape(shape: dict[str, Any]) -> dict[str, Any]: + return base_8700._trace_inputs_from_shape(shape) + +def _inputs_for_label(label: str) -> dict[str, Any]: + return base_8700._inputs_for_label(label) + +def _selected_entrypoint_for_route(route: str) -> str: + return ROUTE_ENTRYPOINTS.get(route, route) + +def _base_route_trace_record(inputs: dict[str, Any]) -> dict[str, Any]: + row = dict(base_8700._route_trace_record(inputs, portfolio_id=DEFAULT_PORTFOLIO_ID)) + route = str(row.get('selected_route') or base_8700.route_for_contract_inputs(inputs)) + selected_seed = row.get('selected_seed') or row.get('consumed_seed') + route_kind = row.get('route_kind', 'general') + row.setdefault('shape_key', inputs.get('label')) + row.setdefault('selected_entrypoint', _selected_entrypoint_for_route(route)) + row.setdefault('selected_seed', selected_seed) + row.setdefault('expected_seed', selected_seed) + row.setdefault('route_kind', route_kind) + if selected_seed: + row.setdefault('route_source', 'shape-specific-seed') + elif route_kind == 'coverage-only': + row.setdefault('route_source', 'generic-weave-fallback') + else: + row.setdefault('route_source', 'broad-dispatcher') + row.setdefault('guard_id', row.get('candidate_guard_status')) + row.setdefault('guard_condition', 'inherited 8700 guard plan') + row.setdefault('classification', 'route-ok') + row.setdefault('dispatcher_kernel_ms', None) + row.setdefault('shape_specific_kernel_ms', None) + row.setdefault('relative_speedup_vs_baseline', None) + row['base_8700_route'] = base_8700.route_for_contract_inputs(inputs, portfolio_id=DEFAULT_PORTFOLIO_ID) + return row + +def _route_trace_record(inputs: dict[str, Any], *, force_fallback: bool=False) -> dict[str, Any]: + base_route = base_8700.route_for_contract_inputs(inputs, portfolio_id=DEFAULT_PORTFOLIO_ID) + label = str(inputs.get('label')) + if force_fallback and _eligible_d64_fdd7(inputs): + row = _base_route_trace_record(inputs) + row['selected_route'] = base_route + row['selected_entrypoint'] = base_route + row['selected_seed'] = row.get('consumed_seed') + row['expected_seed'] = 'd64_fdd7_aa88_v2' + row['guard_id'] = 'forced_fallback_d64_fdd7_disabled' + row['guard_condition'] = 'forced fallback to 8700 baseline; fdd7 D64 overlay disabled' + row['forced_disabled_seeds'] = ('d64_fdd7_aa88_v2',) + row['candidate_guard_status'] = 'forced_fallback_to_8700' + row['classification'] = 'route-ok' + return row + route = route_for_contract_inputs(inputs, force_fallback=force_fallback) + if route in ROUTE_SEED_ID and label in D64_TARGET_SHAPE_SET: + targeted = dict(TARGETED_SEED_ROWS[label]) + return {'shape_key': inputs.get('label'), 'selected_route': route, 'selected_entrypoint': ROUTE_ENTRYPOINTS[route], 'selected_seed': ROUTE_SEED_ID[route], 'expected_seed': 'd64_fdd7_aa88_v2', 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': 'd64_build_q1024_q2048_q4096_k10_fdd7_exact', 'guard_condition': ''.join(['exact BF16 build B=1 Q=M=', format(int(inputs.get('Q')), ''), ' D=64 K=10 D64 fdd7 aa88_v2 seed']), 'coverage': 'e3de consumes fdd7 D64 seed ahead of the 8700 baseline route', 'consumed_seed': ROUTE_SEED_ID[route], 'replaced_route': base_route, 'base_8700_route': base_route, 'row_selection': targeted, 'targeted_seed_timing_backend': 'cupti', 'targeted_seed_kernel_ms': targeted['kernel_ms'], 'targeted_seed_ratio_vs_flashlib': targeted['ratio_vs_flashlib'], 'classification': 'seed-consumed', 'dispatcher_kernel_ms': None, 'shape_specific_kernel_ms': targeted['kernel_ms'], 'relative_speedup_vs_baseline': None} + row = _base_route_trace_record(inputs) + row['candidate_guard_status'] = 'inherited_8700_or_guard_miss' + return row + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + return [_route_trace_record(_trace_inputs_from_shape(shape), force_fallback=force_fallback) for shape in selected] + +def _timing_backends_for_reports(*reports: dict[str, Any]) -> list[str]: + return base_8700._timing_backends_for_reports(*reports) + +def _rows_for_labels(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return base_8700._rows_for_labels(report, labels) + +def _seed_delta_matrix(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> list[dict[str, Any]]: + matrix = [] + for label in CONSUMED_SEED_TARGET_SHAPES: + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') + inputs = _inputs_for_label(label) + route = route_for_contract_inputs(inputs) + matrix.append({'shape_key': label, 'baseline_route': base_8700.route_for_contract_inputs(inputs, portfolio_id=DEFAULT_PORTFOLIO_ID), 'candidate_route': route, 'selected_seed': ROUTE_SEED_ID.get(route), 'candidate_ms': candidate_ms, 'baseline_ms': baseline_ms, 'flashlib_ms': flashlib_ms, 'metric_delta_ms': candidate_ms - baseline_ms if candidate_ms and baseline_ms else None, 'speedup_vs_baseline_8700': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'ratio_vs_flashlib': flashlib_ms / candidate_ms if candidate_ms and flashlib_ms else None, 'targeted_seed_kernel_ms': TARGETED_SEED_ROWS[label]['kernel_ms'], 'targeted_seed_ratio_vs_flashlib': TARGETED_SEED_ROWS[label]['ratio_vs_flashlib'], 'timing_backend': candidate_row.get('timing_backend') or baseline_row.get('timing_backend')}) + return matrix + +def _frontmatter_seed_delta_matrix(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> list[dict[str, Any]]: + rows = [] + for item in _seed_delta_matrix(candidate_report, baseline_report): + rows.append({'shape_key': item['shape_key'], 'baseline_route': item['baseline_route'], 'candidate_deltas': [{'candidate_id': 'd64_fdd7_e3de_v1', 'selected_seed': item['selected_seed'], 'metric_delta': item['metric_delta_ms'], 'ratio_vs_flashlib': item['ratio_vs_flashlib'], 'timing_backend': item['timing_backend'] or 'cupti'}]}) + return rows + +def _per_consumed_row_delta(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> dict[str, Any]: + deltas = {} + for item in _seed_delta_matrix(candidate_report, baseline_report): + label = item['shape_key'] + deltas[label] = {'candidate_ms': item['candidate_ms'], 'baseline_8700_ms': item['baseline_ms'], 'flashlib_ms': item['flashlib_ms'], 'speedup_vs_baseline_8700': item['speedup_vs_baseline_8700'], 'ratio_vs_flashlib': item['ratio_vs_flashlib'], 'candidate_route': item['candidate_route'], 'baseline_8700_route': item['baseline_route'], 'selected_seed': item['selected_seed'], 'targeted_seed_kernel_ms': item['targeted_seed_kernel_ms'], 'targeted_seed_ratio_vs_flashlib': item['targeted_seed_ratio_vs_flashlib'], 'candidate_passed': candidate_report.get('per_shape', {}).get(label, {}).get('passed'), 'baseline_passed': baseline_report.get('per_shape', {}).get(label, {}).get('passed')} + return deltas + +def _annotate_route_trace(route_trace: list[dict[str, Any]], candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> list[dict[str, Any]]: + annotated = [] + for row in route_trace: + label = str(row.get('shape_key')) + out = dict(row) + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + out['dispatcher_kernel_ms'] = candidate_ms + out['baseline_dispatcher_kernel_ms'] = baseline_ms + out['flashlib_ms'] = candidate_row.get('flashlib_ms') + out['relative_speedup_vs_baseline'] = baseline_ms / candidate_ms if candidate_ms and baseline_ms else None + if label in D64_TARGET_SHAPE_SET and out.get('selected_seed') == 'd64_fdd7_aa88_v2': + speedup = out['relative_speedup_vs_baseline'] + out['classification'] = 'seed-consumed' if speedup is None or speedup >= 1.0 else 'kernel-slow' + elif isinstance(candidate_row.get('ratio_vs_flashlib'), (float, int)) and candidate_row['ratio_vs_flashlib'] < 1.0: + out['classification'] = 'kernel-slow' if out.get('route_kind') == 'specialized' else 'fallback-slow' + annotated.append(out) + return annotated + +def _below_flashlib_rows(report: dict[str, Any]) -> list[dict[str, Any]]: + trace_by_label = {str(row['shape_key']): row for row in route_trace_for_contract_shapes()} + rows = [] + for label, row in sorted(report.get('per_shape', {}).items()): + ratio = row.get('ratio_vs_flashlib') + if isinstance(ratio, (float, int)) and ratio < 1.0: + inputs = _inputs_for_label(label) + trace_row = trace_by_label.get(label, {}) + rows.append({'shape_key': label, 'kernel_ms': row.get('kernel_ms'), 'flashlib_ms': row.get('flashlib_ms'), 'ratio_vs_flashlib': ratio, 'selected_route': route_for_contract_inputs(inputs), 'route_kind': trace_row.get('route_kind', 'unknown'), 'classification': 'kernel-slow' if trace_row.get('route_kind') == 'specialized' else 'fallback-slow'}) + return rows + +def _d64_hot_bucket_parity(report: dict[str, Any]) -> str: + for label in D64_TARGET_SHAPES: + ratio = report.get('per_shape', {}).get(label, {}).get('ratio_vs_flashlib') + if not isinstance(ratio, (float, int)) or ratio < 1.0: + return 'fail' + return 'pass' + +def _benchmark_payload(candidate_report: dict[str, Any], baseline_report: dict[str, Any], *, use_cupti: bool, shape_labels) -> dict[str, Any]: + candidate_metric = candidate_report['summary']['primary_mean'] + baseline_metric = baseline_report['summary']['primary_mean'] + route_trace = _annotate_route_trace(route_trace_for_contract_shapes(shape_labels), candidate_report, baseline_report) + below_flashlib = _below_flashlib_rows(candidate_report) + return {'candidate_id': 'd64_fdd7_e3de_v1', 'tflops': candidate_metric, 'baseline_tflops': baseline_metric, 'metric_delta': candidate_metric - baseline_metric if candidate_metric and baseline_metric else None, 'all_correct': candidate_report['summary']['all_correct'], 'baseline_all_correct': baseline_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'baseline_performance_comparable': baseline_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'baseline_invalid_performance_reason': baseline_report['summary']['invalid_performance_reason'], 'measured_entrypoint': 'loom.examples.weave.knn_build_dispatch_d64_fdd7_e3de_v1:benchmark_knn_build_dispatch_d64_fdd7_e3de_v1', 'baseline_entrypoint': 'benchmark_data.json:knn_build -> loom.examples.weave.knn_build_dispatch_rag_seed_portfolio_8700_v1:benchmark_knn_build_dispatch_rag_seed_portfolio_8700_v1(portfolio_id=all_m64_s128)', 'baseline_entrypoint_note': 'same-session current registry dispatcher measured through the same full-v6 contract denominator', 'measured_shape_labels': 'all_canonical' if shape_labels is None else tuple(shape_labels), 'selected_route_labels': SELECTED_TARGET_SHAPES, 'consumed_seed_labels': CONSUMED_SEED_TARGET_SHAPES, 'guard_miss_audit_labels': GUARD_MISS_AUDIT_SHAPES, 'selected_route_rows': _rows_for_labels(candidate_report, SELECTED_TARGET_SHAPES), 'baseline_selected_route_rows': _rows_for_labels(baseline_report, SELECTED_TARGET_SHAPES), 'consumed_seed_rows': _rows_for_labels(candidate_report, CONSUMED_SEED_TARGET_SHAPES), 'baseline_consumed_seed_rows': _rows_for_labels(baseline_report, CONSUMED_SEED_TARGET_SHAPES), 'guard_miss_audit_rows': _rows_for_labels(candidate_report, GUARD_MISS_AUDIT_SHAPES), 'baseline_guard_miss_audit_rows': _rows_for_labels(baseline_report, GUARD_MISS_AUDIT_SHAPES), 'per_consumed_row_delta': _per_consumed_row_delta(candidate_report, baseline_report), 'seed_delta_matrix': _seed_delta_matrix(candidate_report, baseline_report), 'frontmatter_seed_delta_matrix': _frontmatter_seed_delta_matrix(candidate_report, baseline_report), 'targeted_seed_rows': TARGETED_SEED_ROWS, 'candidate_dispatchers': CANDIDATE_DISPATCHERS, 'selected_candidate_dispatcher': 'd64_fdd7_e3de_v1', 'route_modules': PRODUCTION_ROUTE_MODULES, 'route_trace': route_trace, 'forced_fallback_route_trace': route_trace_for_contract_shapes(shape_labels, force_fallback=True), 'route_trace_included': True, 'contract_summary': candidate_report['summary'], 'baseline_contract_summary': baseline_report['summary'], 'contract_performance': candidate_report['performance'], 'baseline_contract_performance': baseline_report['performance'], 'timing_backends': _timing_backends_for_reports(candidate_report, baseline_report), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'hot_bucket_parity': {'d64_build_q1024_q2048_q4096_k10': _d64_hot_bucket_parity(candidate_report), 'rag_microbatch_q8_q16_q32_m100000_k10': 'inherited_8700'}, 'performance_coverage': 'partial' if below_flashlib else 'pass', 'coverage_only_routes': [], 'hot_bucket_blockers': below_flashlib, 'report': candidate_report, 'baseline_report': baseline_report} + +def benchmark_knn_build_dispatch_d64_fdd7_e3de_v1(*, use_cupti: bool=True, shape_labels=None, baseline_report: dict[str, Any] | None=None) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate) + if baseline_report is None: + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_base_dispatcher) + return _benchmark_payload(candidate_report, baseline_report, use_cupti=use_cupti, shape_labels=shape_labels) + +def _denominator_label(shape_labels) -> str: + if shape_labels is None: + return ''.join(['full', format(len(eval_mod.CANONICAL_SHAPES), '')]) + return ''.join(['shape', format(len(tuple(shape_labels)), '')]) + +def write_benchmark_artifacts(artifact_dir: str | Path, *, use_cupti: bool=True, shape_labels=None) -> dict[str, str]: + payload = benchmark_knn_build_dispatch_d64_fdd7_e3de_v1(use_cupti=use_cupti, shape_labels=shape_labels) + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + denom = _denominator_label(shape_labels) + candidate_path = out_dir / ''.join([format(denom, ''), '_dispatch_d64_fdd7_e3de_v1.json']) + baseline_path = out_dir / ''.join([format(denom, ''), '_same_session_baseline_8700_for_d64_fdd7_e3de_v1.json']) + route_trace_path = out_dir / ''.join([format(denom, ''), '_route_trace_d64_fdd7_e3de_v1.json']) + forced_trace_path = out_dir / ''.join([format(denom, ''), '_forced_fallback_trace_d64_fdd7_e3de_v1.json']) + seed_matrix_path = out_dir / ''.join([format(denom, ''), '_seed_delta_matrix_d64_fdd7_e3de_v1.json']) + candidate_path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + baseline_path.write_text(json.dumps({'candidate_id': 'baseline_8700_all_m64_s128', 'measured_entrypoint': payload['baseline_entrypoint'], 'measured_shape_labels': payload['measured_shape_labels'], 'timing_backend_requested': payload['timing_backend_requested'], 'timing_backends': payload['timing_backends'], 'tflops': payload['baseline_tflops'], 'all_correct': payload['baseline_all_correct'], 'performance_comparable': payload['baseline_performance_comparable'], 'contract_summary': payload['baseline_contract_summary'], 'contract_performance': payload['baseline_contract_performance'], 'route_trace': base_8700.route_trace_for_contract_shapes(shape_labels, portfolio_id=DEFAULT_PORTFOLIO_ID), 'route_trace_included': True, 'report': payload['baseline_report']}, indent=2, sort_keys=True) + '\n') + route_trace_path.write_text(json.dumps(payload['route_trace'], indent=2, sort_keys=True) + '\n') + forced_trace_path.write_text(json.dumps(payload['forced_fallback_route_trace'], indent=2, sort_keys=True) + '\n') + seed_matrix_path.write_text(json.dumps(payload['seed_delta_matrix'], indent=2, sort_keys=True) + '\n') + return {'candidate_payload': str(candidate_path), 'same_session_baseline_payload': str(baseline_path), 'route_trace': str(route_trace_path), 'forced_fallback_trace': str(forced_trace_path), 'seed_delta_matrix': str(seed_matrix_path)} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_dbd7_build_broad_8a78_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_dbd7_build_broad_8a78_v1.py new file mode 100644 index 00000000..d9ceacfb --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_dbd7_build_broad_8a78_v1.py @@ -0,0 +1,269 @@ +"""Build-broad bucket dispatcher for the dbd7 auto-tuning continuation. + +Minimum target architecture: sm_100a. This additive candidate keeps the +17b8/1074 full82 dispatcher as the fallback and routes exact BF16 build +low-floor rows through existing Weave seed families: + +* v20 split/tcgen05 build path for K10/K12/K20 D128 build rows. +* v25 over-32 split/tcgen05 path for K48 D128 build rows. + +No external runtime fallback is used; FlashLib/PyTorch remain contract-harness +references only. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from pathlib import Path +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_17b8_lowmargin_1074_full82_v1 as base17b8 +from . import knn_build_over32_topk_knn_build_dispatch_slurm_0610_6329_v25 as over32_v25 +from . import knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2_k32split_v20 as build_v20 +MODULE = 'loom.examples.weave.knn_build_dispatch_dbd7_build_broad_8a78_v1' +K10_TARGET_SHAPES = ('build_qm2048_d128_k10', 'build_tail_b1_q1536_m1536_d128_k10') +V20_TARGET_SHAPES = ('build_k_sweep_qm1024_k12', 'build_k_sweep_qm1024_k20', 'build_k_sweep_qm4096_k12') +OVER32_TARGET_SHAPES = ('build_over32_stress_qm2048_k48', 'build_over32_stress_qm4096_k48') +TARGET_SHAPES = (*K10_TARGET_SHAPES, *V20_TARGET_SHAPES, *OVER32_TARGET_SHAPES) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +K10_TARGET_SHAPE_SET = set(K10_TARGET_SHAPES) +V20_TARGET_SHAPE_SET = set(V20_TARGET_SHAPES) +OVER32_TARGET_SHAPE_SET = set(OVER32_TARGET_SHAPES) +SEED_K10_ID = 'dbd7_8a78_fixedbuild_k10_v2' +SEED_V20_ID = 'dbd7_8a78_v20_build_broad' +SEED_OVER32_ID = 'dbd7_8a78_over32_k48_v25' +BASE_17B8_ID = base17b8.CANDIDATE_LOWMARGIN_1074 +CANDIDATE_ID = 'candidate_dbd7_build_broad_8a78_v1' +ROUTE_K10_BUILD = 'loom.examples.weave.knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2:launch_from_contract_inputs' +ROUTE_V20_BUILD = 'loom.examples.weave.knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2_k32split_v20:launch_from_contract_inputs' +ROUTE_OVER32_K48 = 'loom.examples.weave.knn_build_over32_topk_knn_build_dispatch_slurm_0610_6329_v25:launch_from_contract_inputs' +ROUTE_BASE_17B8 = ''.join([format(base17b8.MODULE, ''), ':launch_from_contract_inputs']) +PRODUCTION_ROUTE_MODULES = _decode_capture(_json_loads('{"__dict_items__": [["large_square_k20k32", "loom.examples.weave.knn_build_large_square_k20k32_a989_v1"], ["over64_k96", "loom.examples.weave.knn_build_over64_k96_a989_v1"], ["baseline_7c3a_rag_k10", "loom.examples.weave.knn_build_rag_frontier_4b5c_v1:k10"], ["rag_frontier_7399_k10", "loom.examples.weave.knn_build_rag_frontier_7399_v1:k10_s72"], ["rag_frontier_7399_k32_replaced", "loom.examples.weave.knn_build_rag_frontier_7399_v1:k32_s72_g8_fusedmerge"], ["rag_frontier_4fbf_k32", "loom.examples.weave.knn_build_rag_frontier_4fbf_v7:k32_s72_g24_tailinf_fused"], ["rect_smallq_largem_d15e", "loom.examples.weave.knn_build_rect_smallq_largem_ff59_d15e_v1:split16"], ["baseline_7c3a_policy", "loom.examples.weave.knn_build_dispatch_b6d4_d15e_fd02_v1:baseline_7c3a_policy"], ["fallback", "loom.examples.weave.knn_build_dispatch_split72_4e09_de1a_3dc7_v48"], ["dim_d64_73a9", "loom.examples.weave.knn_build_dim_midk_73a9_v1:d64_split_s8"], ["current_exact_k32_dispatcher", "loom.examples.weave.knn_build_dispatch_4fbf_7399_d15e_full55_bad5_v1:launch_from_contract_inputs"], ["base_7399_d15e_dispatcher", "loom.examples.weave.knn_build_dispatch_7399_d15e_full55_v1:launch_from_contract_inputs"], ["rag_frontier_7399_k32", "loom.examples.weave.knn_build_rag_frontier_7399_v1:k32_s72_g8_fusedmerge"], ["dim_d256_df2f", "loom.examples.weave.knn_build_dim_midk_df2f_v1:d256_split_s8"], ["dim_fp16_d128_df2f", "loom.examples.weave.knn_build_dim_midk_df2f_v1:fp16_d128_split_s8"], ["base_dispatch", "loom.examples.weave.knn_build_dispatch_7399_d15e_full55_v1:launch_from_contract_inputs"], ["rect_intermediate_4452_s8", "loom.examples.weave.knn_build_rect_intermediate_frontier_6a73_4452_v2:rect_s8_k10_cached"], ["base_champion_6b59", "loom.examples.weave.knn_build_dispatch_7399_d15e_df2f_full55_v1:launch_from_contract_inputs"], ["base_k32_d64_62b1", "loom.examples.weave.knn_build_dispatch_4fbf_7399_d15e_73a9_full55_v1:launch_from_contract_inputs"], ["default_k96_a330", "loom.examples.weave.knn_build_over64_k96_a989_v1"], ["large_tail_a4f6", "loom.examples.weave.knn_build_large_tail_frontier_6a73_v1:split4_k20"], ["midk_81aa_q2048_k24_k28", "loom.examples.weave.knn_build_dim_midk_bad5_midkcleanup_v1:midk_k24_k28_s8"], ["midk_9b2c_q4096_k28", "loom.examples.weave.knn_build_dim_midk_bad5_k24k28_v1:k28_q4096_s4_unordered_exact"], ["base_f552", "loom.examples.weave.knn_build_dispatch_selected_portfolio_f552_v1:launch_from_contract_inputs"], ["midk_bad5_k64split8", "loom.examples.weave.knn_build_dim_midk_bad5_k64split8_v1:k64_q2048_s8_tailinf"], ["base_e51c", "loom.examples.weave.knn_build_dispatch_selected_portfolio_e51c_v1:launch_from_contract_inputs"], ["midk_f8c3_q4096_k64_split8_a194", "loom.examples.weave.knn_build_dim_midk_f8c3_q4096k64split_v1:q4096_k64_tailinf_split8"], ["base_f8c3", "loom.examples.weave.knn_build_dispatch_selected_portfolio_f8c3_v1:launch_from_contract_inputs"], ["lowk_b193_q512_s4", "loom.examples.weave.knn_build_lowk_f8c3_q512_q1024_v1:q512_lowk_s4"], ["lowk_b193_q1024_k16_s16", "loom.examples.weave.knn_build_lowk_f8c3_q512_q1024_v1:q1024_k16_s16"], ["large_square_5407_q8192_k32_s2", "loom.examples.weave.knn_build_large_square_k32_8a83_v1:q8192_k32_split2"], ["base_f853", "loom.examples.weave.knn_build_dispatch_selected_portfolio_f853_v1:launch_from_contract_inputs"], ["lowk_b193_q512_k4_k5_k6_s4", "loom.examples.weave.knn_build_lowk_f8c3_q512_q1024_v1:q512_lowk_s4"], ["base_f16b", "loom.examples.weave.knn_build_dispatch_selected_portfolio_f16b_v1:launch_from_contract_inputs"], ["rag_microbatch_b2ec_s72_g8", "loom.examples.weave.knn_build_rag_microbatch_4a72_v1:launch_from_contract_inputs"], ["base_4a72", "loom.examples.weave.knn_build_dispatch_selected_portfolio_4a72_v1:launch_from_contract_inputs"], ["rag_m64_s128_0c69", "loom.examples.weave.knn_build_rag_microbatch_m64_d4f7_v1:launch_from_contract_inputs"], ["rag_s144_g12_cta1_059f", "loom.examples.weave.knn_build_rag_microbatch_4a72_v2:launch_from_contract_inputs"], ["rag_s144_g8_cta1_4982_read_ref_parameterized", "loom.examples.weave.knn_build_rag_microbatch_4a72_v2:launch_from_contract_inputs"], ["base_397b", "loom.examples.weave.knn_build_dispatch_selected_portfolio_397b_v1:launch_from_contract_inputs"], ["d64_fdd7_aa88_v2", "loom.examples.weave.knn_build_d64_build_aa88_v2:launch_from_contract_inputs"], ["base_8700", "loom.examples.weave.knn_build_dispatch_rag_seed_portfolio_8700_v1:launch_from_contract_inputs(portfolio_id=all_m64_s128)"], ["rect_d64_cf49_v3_9138", "loom.examples.weave.knn_build_rect_d64_cf49_v3:launch_from_contract_inputs"], ["q1_mbucket_aa88_q1m_v3_bcb3", "loom.examples.weave.knn_build_ragonline_mbucket_aa88_q1m_v3:launch_from_contract_inputs"], ["over64_k96_a2f8_v1_generated_v8", "loom.examples.weave.knn_build_over64_k96_a2f8_v1:_launch_over64_k96_split_path"], ["base_e3de", "loom.examples.weave.knn_build_dispatch_d64_fdd7_e3de_v1:launch_from_contract_inputs"], ["non128_frontier_8199_widecombine_v1", "loom.examples.weave.knn_build_non128_frontier_8199_widecombine_v1:launch_from_contract_inputs"], ["base_4247", "loom.examples.weave.knn_build_dispatch_e3de_9138_bcb3_4247_v1:launch_from_contract_inputs"], ["rag_microbucket_k32_8fcb_split148_v1_b3e0_sm148", "loom.examples.weave.knn_build_rag_microbucket_k32_8fcb_split148_v1:launch_from_contract_inputs"], ["rag_microbucket_k32_2e8e_q16split148_v1_b3e0_q16_s148", "loom.examples.weave.knn_build_rag_microbucket_k32_2e8e_q16split148_v1:launch_from_contract_inputs"], ["non128_frontier_3d5a_cachedmerge_v1", "loom.examples.weave.knn_build_non128_frontier_3d5a_cachedmerge_v1:launch_from_contract_inputs"], ["over64_k96_exactall_229a_v1_b6c4", "loom.examples.weave.knn_build_over64_k96_exactall_229a_v1:launch_from_contract_inputs"], ["knn_build_midk_k11k13_e080_v1", "loom.examples.weave.knn_build_midk_k11k13_e080_v1:launch_from_contract_inputs"], ["ragonline_mbucket_4fc7_q1m262_v1_980c", "loom.examples.weave.knn_build_ragonline_mbucket_4fc7_q1m262_v1:launch_from_contract_inputs"], ["baseline_8199_widecombine_full82_v1", "loom.examples.weave.knn_build_dispatch_4247_non128_8199_widecombine_full82_v1:launch_from_contract_inputs"], ["k30_q4096_6998_warpselect_v1", "loom.examples.weave.knn_build_k30_q4096_6998_warpselect_v1:launch_from_contract_inputs"], ["rag_stream_k10_direct_split72_6998_v1", "loom.examples.weave.knn_build_rag_online_stream_split72_4e09_v1:launch_from_contract_inputs"], ["rect_d64_23be_unordered_v1", "loom.examples.weave.knn_build_rect_d64_23be_unordered_v1:launch_from_contract_inputs"], ["residual_19b3_ed1c_portfolio_6998", "loom.examples.weave.knn_build_dispatch_c142_3505_q32rowld_19b3_v1:launch_from_contract_inputs"], ["candidate_q16split148_cachedmerge_k96exactall_e080_q1m262_over_8199_full82_v1", "loom.examples.weave.knn_build_dispatch_4247_non128_8199_3d5a_2e8e_full82_synth_v1:launch_from_contract_inputs"], ["rect_d128_k20_q1536_9b9f_d555_b8c7_v1", "loom.examples.weave.knn_build_rect_d128_k20_d555_b8c7_v1:launch_from_contract_inputs"], ["rag_microbatch_k10_q4q64_m64_3505_d555_v1", "loom.examples.weave.knn_build_rag_microbatch_k10_q4q64_d555_v1:launch_from_contract_inputs"], ["rag_microbucket_faeb_q4q64_k10_69d6_v1", "loom.examples.weave.knn_build_rag_microbucket_faeb_v1:launch_from_contract_inputs"], ["candidate_066c_ragmicro_q4q64_3505_full82_v1", "loom.examples.weave.knn_build_rag_microbatch_k10_q4q64_d555_v1:launch_from_contract_inputs"], ["candidate_d555_direct_residual_seeds_full82_v1", "loom.examples.weave.knn_build_dispatch_d555_residual_seed_synth_full82_v1:launch_from_contract_inputs"], ["rag_microbatch_k10_q4_m64_s144_g12_17b8_v1", "loom.examples.weave.knn_build_rag_microbatch_k10_q4_m64s144_17b8_v1:launch_from_contract_inputs"], ["rag_microbatch_q4_k10_s144_17b8_v1", "loom.examples.weave.knn_build_rag_microbatch_q4_s144_17b8_v1:launch_from_contract_inputs"], ["rag_microbatch_k10_q4_s144_g12_d555_v1", "loom.examples.weave.knn_build_rag_microbatch_k10_q4_s144_d555_v1:launch_from_contract_inputs"], ["candidate_066c_69d6_plus_b8c7_full82_v1", "loom.examples.weave.knn_build_dispatch_066c_b8c7_69d6_q4_portfolio_full82_v1:launch_from_contract_inputs"], ["lowmargin_1074_k1k24k30_v1", "loom.examples.weave.knn_build_lowmargin_1074_k1k24k30_v1:launch_from_contract_inputs"], ["lowk_q512_k1_s4_1074", "loom.examples.weave.knn_build_lowk_f8c3_q512_q1024_v1:q512_lowk_s4"], ["k24_q4096_warpselect_1074", "loom.examples.weave.knn_build_lowmargin_1074_k1k24k30_v1:k24_q4096_warpselect"], ["dbd7_8a78_fixedbuild_k10_v2", "loom.examples.weave.knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2:launch_from_contract_inputs"], ["dbd7_8a78_v20_build_broad", "loom.examples.weave.knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2_k32split_v20:launch_from_contract_inputs"], ["dbd7_8a78_over32_k48_v25", "loom.examples.weave.knn_build_over32_topk_knn_build_dispatch_slurm_0610_6329_v25:launch_from_contract_inputs"], ["candidate_17b8_lowmargin_1074_full82_v1", "loom.examples.weave.knn_build_dispatch_17b8_lowmargin_1074_full82_v1:launch_from_contract_inputs"]]}')) +SOURCE_TASKS = _decode_capture(_json_loads('{"__dict_items__": [["dbd7_8a78_fixedbuild_k10_v2", "weave-evolve prior fixed-build K10 lineage / loom/examples/weave/knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2.py"], ["dbd7_8a78_v20_build_broad", "weave-evolve prior v20 lineage / loom/examples/weave/knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2_k32split_v20.py"], ["dbd7_8a78_over32_k48_v25", "weave-evolve prior v25 over32 lineage / loom/examples/weave/knn_build_over32_topk_knn_build_dispatch_slurm_0610_6329_v25.py"], ["candidate_17b8_lowmargin_1074_full82_v1", "generalize-auto-tuning-knn-build-a444 / loom/examples/weave/knn_build_dispatch_17b8_lowmargin_1074_full82_v1.py"]]}')) + +class _TraceTensor: + + def __init__(self, dtype: str) -> None: + self.dtype = dtype if dtype.startswith('torch.') else ''.join(['torch.', format(dtype, '')]) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_DBD7_BUILD_BROAD_VERIFY_KERNEL') + if verify_kernel == 'v20_stage1_k12': + return build_v20.stage1_k12_ir + if verify_kernel == 'v20_stage1_k20': + return build_v20.stage1_k20_ir + if verify_kernel == 'v20_stage1_k12_unordered': + return build_v20.stage1_k12_unordered_ir + if verify_kernel == 'v20_merge_k12_s16': + return build_v20.merge_k12_s16_ir + if verify_kernel == 'v20_merge_k12_unordered': + return build_v20.merge_k12_unordered_ir + if verify_kernel == 'v20_merge_k20_s16': + return build_v20.merge_k20_s16_ir + if verify_kernel == 'v20_merge_k20_s8': + return build_v20.merge_k20_s8_ir + if verify_kernel == 'k10_stage1': + return build_v20.parent.stage1_ir + if verify_kernel == 'k10_merge_s7_cache': + return build_v20.parent.parent.parent_cached.merge_k10_s7_cache_ir + if verify_kernel == 'over32_stage1_k48': + return over32_v25.stage1_k48_over32_ir + if verify_kernel == 'over32_merge_k48': + return over32_v25.merge_k48_over32_ir + return base17b8.ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbatch_m64_d4f7_stage1", "arg_keys": ["query", "database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 91392, "constants": [], "cta_group": 1, "threads": 512}')) + +def _dtype_name(inputs: dict[str, Any], name: str='query') -> str: + tensor = inputs.get(name) + if tensor is not None and hasattr(tensor, 'dtype'): + return str(tensor.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')).replace('torch.', '') + +def _label_can_hit(inputs: dict[str, Any], target_labels: set[str]) -> bool: + label = inputs.get('label') + return label is None or str(label) in target_labels + +def _is_bf16_d128_build(inputs: dict[str, Any]) -> bool: + return bool(inputs.get('build', False)) and int(inputs.get('B', -1)) == 1 and (int(inputs.get('Q', -1)) == int(inputs.get('M', -2))) and (int(inputs.get('D', -1)) == 128) and (_dtype_name(inputs, 'query') == 'bfloat16') and (_dtype_name(inputs, 'database') in ('', 'bfloat16')) + +def _eligible_k10_build(inputs: dict[str, Any]) -> bool: + if not (_label_can_hit(inputs, K10_TARGET_SHAPE_SET) and _is_bf16_d128_build(inputs)): + return False + q = int(inputs.get('Q', -1)) + k = int(inputs.get('K', -1)) + return (q, k) in {(2048, 10), (1536, 10)} + +def _eligible_v20_build(inputs: dict[str, Any]) -> bool: + if not (_label_can_hit(inputs, V20_TARGET_SHAPE_SET) and _is_bf16_d128_build(inputs)): + return False + q = int(inputs.get('Q', -1)) + k = int(inputs.get('K', -1)) + return (q, k) in {(1024, 12), (1024, 20), (4096, 12)} + +def _eligible_over32_k48(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, OVER32_TARGET_SHAPE_SET) and over32_v25._eligible_over32_build(inputs) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if not force_fallback and _eligible_k10_build(inputs): + return ROUTE_K10_BUILD + if not force_fallback and _eligible_v20_build(inputs): + return ROUTE_V20_BUILD + if not force_fallback and _eligible_over32_k48(inputs): + return ROUTE_OVER32_K48 + return base17b8.route_for_contract_inputs(inputs, force_fallback=force_fallback) + +def _launch_route(inputs: dict[str, Any], route: str) -> None: + if route == ROUTE_V20_BUILD: + build_v20.launch_from_contract_inputs(inputs) + return + if route == ROUTE_K10_BUILD: + build_v20.parent.launch_from_contract_inputs(inputs) + return + if route == ROUTE_OVER32_K48: + over32_v25._launch_over32_split_path(inputs) + return + base17b8.launch_from_contract_inputs(inputs) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + _launch_route(inputs, route_for_contract_inputs(inputs, force_fallback=force_fallback)) + +def candidate_dbd7_build_broad_8a78_v1(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]) -> None: + candidate_dbd7_build_broad_8a78_v1(inputs) + +def candidate_base_17b8(inputs: dict[str, Any]) -> None: + base17b8.launch_from_contract_inputs(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return base17b8._select_contract_shapes(shape_labels) + +def _benchmark_shapes(shape_labels, *, time_flashlib: bool) -> list[dict[str, Any]]: + selected = _select_contract_shapes(TARGET_SHAPES if shape_labels is None else shape_labels) + out = [] + for shape in selected: + params = dict(shape['params']) + params['time_flashlib'] = bool(time_flashlib) + out.append({'label': shape['label'], 'params': params}) + return out + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate, correctness: bool=True, benchmark: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + previous = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_benchmark_shapes(shape_labels, time_flashlib=time_flashlib), correctness=correctness, benchmark=benchmark, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = previous + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _trace_inputs_for_shape(shape: dict[str, Any]) -> dict[str, Any]: + params = dict(shape['params']) + dtype = str(params.get('dtype', 'bfloat16')) + return {'label': shape['label'], 'B': int(params['B']), 'Q': int(params['Q']), 'M': int(params['M']), 'D': int(params['D']), 'K': int(params['K']), 'dtype': dtype, 'build': bool(params.get('build', False)), 'query': _TraceTensor(dtype), 'database': _TraceTensor(dtype)} + +def _expected_seed(inputs: dict[str, Any]) -> str | None: + if _eligible_k10_build(inputs): + return SEED_K10_ID + if _eligible_v20_build(inputs): + return SEED_V20_ID + if _eligible_over32_k48(inputs): + return SEED_OVER32_ID + return None + +def _selected_entrypoint(route: str) -> str: + if route == ROUTE_V20_BUILD: + return ROUTE_V20_BUILD + if route == ROUTE_K10_BUILD: + return ROUTE_K10_BUILD + if route == ROUTE_OVER32_K48: + return ROUTE_OVER32_K48 + return ROUTE_BASE_17B8 + +def _route_trace_record(inputs: dict[str, Any], *, force_fallback: bool=False) -> dict[str, Any]: + label = str(inputs.get('label')) + expected_seed = _expected_seed(inputs) + route = route_for_contract_inputs(inputs, force_fallback=force_fallback) + baseline_route = base17b8.route_for_contract_inputs(inputs) + if expected_seed is None or force_fallback: + row = dict(base17b8._route_trace_record(inputs, force_fallback=force_fallback)) + row['expected_seed'] = expected_seed + row['baseline_17b8_route'] = baseline_route + row['candidate_guard_status'] = 'forced_fallback' if force_fallback else 'guard_miss' + if force_fallback and expected_seed is not None: + row['guard_id'] = ''.join(['forced_fallback_', format(expected_seed, ''), '_disabled']) + row['guard_condition'] = ''.join(['forced fallback to 17b8; ', format(expected_seed, ''), ' disabled']) + row['classification'] = 'guard-miss' + return base17b8._normalize_route_row(row) + if expected_seed == SEED_K10_ID: + guard_id = 'dbd7_8a78_fixedbuild_k10_exact_guard' + elif expected_seed == SEED_V20_ID: + guard_id = 'dbd7_8a78_v20_build_exact_guard' + else: + guard_id = 'dbd7_8a78_over32_k48_exact_guard' + return base17b8._normalize_route_row({'shape_key': label, 'selected_route': route, 'selected_entrypoint': _selected_entrypoint(route), 'selected_seed': expected_seed, 'expected_seed': expected_seed, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': guard_id, 'guard_condition': 'exact BF16 build B=1 Q=M D=128 build-broad bucket guard', 'baseline_17b8_route': baseline_route, 'replaced_route': baseline_route, 'classification': 'seed-consumed'}) + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + return [_route_trace_record(_trace_inputs_for_shape(shape), force_fallback=force_fallback) for shape in selected] + +def _rows_for_labels(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return {label: dict(report.get('per_shape', {}).get(label, {})) for label in labels} + +def _per_shape_delta(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> dict[str, Any]: + rows = {} + for label in TARGET_SHAPES: + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') or baseline_row.get('flashlib_ms') + rows[label] = {'candidate_ms': candidate_ms, 'baseline_17b8_ms': baseline_ms, 'flashlib_ms': flashlib_ms, 'candidate_tflops': candidate_row.get('tflops'), 'baseline_17b8_tflops': baseline_row.get('tflops'), 'speedup_vs_17b8': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'ratio_vs_flashlib': flashlib_ms / candidate_ms if candidate_ms and flashlib_ms else None, 'passed': candidate_row.get('passed'), 'timing_backend': candidate_row.get('timing_backend') or baseline_row.get('timing_backend')} + return rows + +def _below_flashlib_rows(report: dict[str, Any], *, floor: float) -> list[dict[str, Any]]: + rows = [] + for label, row in sorted(report.get('per_shape', {}).items()): + ratio = row.get('ratio_vs_flashlib') + if isinstance(ratio, (float, int)) and ratio < floor: + rows.append({'shape_key': label, 'kernel_ms': row.get('kernel_ms'), 'flashlib_ms': row.get('flashlib_ms'), 'ratio_vs_flashlib': ratio, 'selected_seed': _expected_seed(_trace_inputs_for_shape(_select_contract_shapes((label,))[0]))}) + return rows + +def benchmark_candidate_dbd7_build_broad_8a78_v1(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES, run_baseline: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + labels = tuple((str(label) for label in shape_labels)) + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=labels, kernel_fn=candidate, correctness=True, benchmark=True, time_flashlib=time_flashlib) + baseline_report = None + if run_baseline: + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=labels, kernel_fn=candidate_base_17b8, correctness=True, benchmark=True, time_flashlib=time_flashlib) + candidate_mean = candidate_report['summary']['primary_mean'] + payload: dict[str, Any] = {'candidate_id': CANDIDATE_ID, 'selected_seeds': (SEED_K10_ID, SEED_V20_ID, SEED_OVER32_ID), 'source_tasks': SOURCE_TASKS, 'all_correct': candidate_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'tflops': candidate_mean, 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_candidate_dbd7_build_broad_8a78_v1']), 'candidate_entrypoint': ''.join([format(MODULE, ''), ':launch_from_contract_inputs']), 'measured_shape_labels': labels, 'timing_backend': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'time_flashlib': time_flashlib, 'denominator': 'dbd7_build_broad_low_floor_exact7', 'route_trace': route_trace_for_contract_shapes(labels), 'forced_fallback_route_trace': route_trace_for_contract_shapes(labels, force_fallback=True), 'route_modules': PRODUCTION_ROUTE_MODULES, 'contract_summary': candidate_report['summary'], 'contract_performance': candidate_report['performance'], 'contract_correctness': candidate_report['correctness'], 'selected_route_rows': _rows_for_labels(candidate_report, labels), 'hot_bucket_blockers': _below_flashlib_rows(candidate_report, floor=1.05), 'rank_objective': {'metric': 'tflops', 'direction': 'maximize', 'value': candidate_mean, 'valid_measurement_count': candidate_report['performance']['valid_measurement_count'], 'comparable': candidate_report['performance']['comparable']}, 'report': candidate_report} + if baseline_report is not None: + baseline_mean = baseline_report['summary']['primary_mean'] + payload.update({'baseline_candidate_id': BASE_17B8_ID, 'baseline_entrypoint': ''.join([format(base17b8.MODULE, ''), ':benchmark_candidate_17b8_lowmargin_1074_full82_v1']), 'baseline_tflops': baseline_mean, 'metric_delta_vs_17b8': candidate_mean - baseline_mean if candidate_mean is not None and baseline_mean is not None else None, 'baseline_contract_summary': baseline_report['summary'], 'baseline_contract_performance': baseline_report['performance'], 'baseline_selected_route_rows': _rows_for_labels(baseline_report, labels), 'per_shape_delta_vs_17b8': _per_shape_delta(candidate_report, baseline_report)}) + return payload + +def write_benchmark_artifact(artifact_dir: str | Path, *, use_cupti: bool=True, shape_labels=TARGET_SHAPES, run_baseline: bool=True, time_flashlib: bool=True) -> dict[str, str]: + payload = benchmark_candidate_dbd7_build_broad_8a78_v1(use_cupti=use_cupti, shape_labels=shape_labels, run_baseline=run_baseline, time_flashlib=time_flashlib) + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + path = out_dir / 'build_broad_8a78_v1.json' + path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + return {'candidate_payload': str(path)} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_dbd7_lowfloor_005f_8a78_full82_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_dbd7_lowfloor_005f_8a78_full82_v1.py new file mode 100644 index 00000000..892b7a1d --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_dbd7_lowfloor_005f_8a78_full82_v1.py @@ -0,0 +1,381 @@ +"""Full82 dispatcher synthesis for dbd7 build low-floor seeds. + +Minimum target architecture: sm_100a. This additive dispatcher-synthesis +wrapper preserves the a444/9db7 full82 Weave dispatcher as fallback and tests +guarded portfolios over the promoted 005f and 8a78 build seeds. It does not +change any seed schedule or default benchmark registry entry. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +from pathlib import Path +from typing import Any, Callable +from . import knn_build_buildbucket_dbd7_lowfloor_v1 as seed005f +from . import knn_build_dispatch_17b8_lowmargin_1074_full82_v1 as base9db7 +from . import knn_build_dispatch_dbd7_build_broad_8a78_v1 as seed8a78 +MODULE = 'loom.examples.weave.knn_build_dispatch_dbd7_lowfloor_005f_8a78_full82_v1' +eval_mod = base9db7.eval_mod +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbatch_m64_d4f7_stage1", "arg_keys": ["query", "database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 91392, "constants": [], "cta_group": 1, "threads": 512}')) +BASE_9DB7_KEY = 'base_9db7' +CANDIDATE_005F = '005f_promoted_portfolio' +CANDIDATE_005F_8A78_TAIL = '005f_plus_8a78_tail' +CANDIDATE_8A78_PRIMARY_005F_FILL = '8a78_primary_plus_005f_fill' +DEFAULT_CANDIDATE_KEY = CANDIDATE_005F +CANDIDATE_KEYS = (BASE_9DB7_KEY, CANDIDATE_005F, CANDIDATE_005F_8A78_TAIL, CANDIDATE_8A78_PRIMARY_005F_FILL) +BASE_9DB7_ID = base9db7.CANDIDATE_LOWMARGIN_1074 +BASE_9DB7_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_baseline_9db7']) +BASE_9DB7_ROUTE_ENTRYPOINT = ''.join([format(base9db7.MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +CANDIDATE_005F_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_candidate_005f_full82_v1']) +CANDIDATE_005F_8A78_TAIL_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_candidate_005f_plus_8a78_tail_full82_v1']) +CANDIDATE_8A78_PRIMARY_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_candidate_8a78_primary_plus_005f_fill_full82_v1']) +TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_k_sweep_qm1024_k12", "build_k_sweep_qm1024_k20", "build_qm2048_d128_k10", "build_k_sweep_qm2048_k12", "build_k_sweep_qm2048_k20", "build_tail_b1_q1536_m1536_d128_k10", "build_k_sweep_qm4096_k12", "build_k_sweep_qm4096_k20", "build_over32_stress_qm2048_k48", "build_over32_stress_qm4096_k48"]}')) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +TAIL_LABEL = seed005f.BUILD_TAIL_Q1536_K10 +SEED_005F_ID = seed005f.CANDIDATE_ID +SEED_8A78_ID = seed8a78.CANDIDATE_ID +PRODUCTION_ROUTE_MODULES = {**base9db7.PRODUCTION_ROUTE_MODULES, **seed005f.PRODUCTION_ROUTE_MODULES, **seed8a78.PRODUCTION_ROUTE_MODULES, BASE_9DB7_ID: BASE_9DB7_ROUTE_ENTRYPOINT, SEED_005F_ID: ''.join([format(seed005f.MODULE, ''), ':launch_from_contract_inputs']), SEED_8A78_ID: ''.join([format(seed8a78.MODULE, ''), ':launch_from_contract_inputs'])} +SOURCE_TASKS = {**base9db7.SOURCE_TASKS, SEED_005F_ID: 'weave-evolve-knn-build-005f / design_doc/active/weave_evolve_knn_build_round_125_dbd7_buildbucket_lowfloor_v1.md', SEED_8A78_ID: 'weave-evolve-knn-build-8a78 / design_doc/active/weave_evolve_knn_build_round_125_8a78_build_broad.md', 'd7af_read_ref': 'weave-evolve-knn-build-d7af read-ref / design_doc/active/weave_evolve_knn_build_round_125_dbd7_k12k20.md'} +REPO_ROOT = Path(__file__).resolve().parents[3] +TARGETED_SEED_ROWS = _decode_capture(_json_loads('{"__dict_items__": [["buildbucket_dbd7_lowfloor_v1", {"__dict_items__": [["build_k_sweep_qm1024_k12", {"__dict_items__": [["baseline_9db7_ms", 0.069632], ["baseline_9db7_passed", true], ["baseline_9db7_route", "loom.examples.weave.knn_build_dispatch_split72_4e09_de1a_3dc7_v48"], ["candidate_ms", 0.030335], ["candidate_passed", true], ["candidate_route", "loom.examples.weave.knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2_k32split_v20:launch_from_contract_inputs"], ["flashlib_ms", 0.09312], ["ratio_vs_flashlib", 3.06972144387671], ["shape_key", "build_k_sweep_qm1024_k12"], ["speedup_vs_9db7", 2.2954343167957805], ["timing_backend", "cupti"]]}], ["build_k_sweep_qm1024_k20", {"__dict_items__": [["baseline_9db7_ms", 0.081183], ["baseline_9db7_passed", true], ["baseline_9db7_route", "loom.examples.weave.knn_build_dispatch_split72_4e09_de1a_3dc7_v48"], ["candidate_ms", 0.037375], ["candidate_passed", true], ["candidate_route", "loom.examples.weave.knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2_k32split_v12:launch_from_contract_inputs"], ["flashlib_ms", 0.074368], ["ratio_vs_flashlib", 1.989779264214047], ["shape_key", "build_k_sweep_qm1024_k20"], ["speedup_vs_9db7", 2.172120401337793], ["timing_backend", "cupti"]]}], ["build_qm2048_d128_k10", {"__dict_items__": [["baseline_9db7_ms", 0.08432], ["baseline_9db7_passed", true], ["baseline_9db7_route", "loom.examples.weave.knn_build_dispatch_split72_4e09_de1a_3dc7_v48"], ["candidate_ms", 0.049152], ["candidate_passed", true], ["candidate_route", "loom.examples.weave.knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2_k32split_v12:launch_from_contract_inputs"], ["flashlib_ms", 0.0918075], ["ratio_vs_flashlib", 1.867828369140625], ["shape_key", "build_qm2048_d128_k10"], ["speedup_vs_9db7", 1.7154947916666667], ["timing_backend", "cupti"]]}], ["build_k_sweep_qm2048_k12", {"__dict_items__": [["baseline_9db7_ms", 0.062431], ["baseline_9db7_passed", true], ["baseline_9db7_route", "loom.examples.weave.knn_build_midk_k11k13_e080_v1:k12_exact"], ["candidate_ms", 0.056991], ["candidate_passed", true], ["candidate_route", "loom.examples.weave.knn_build_lowk_k12_4f30_v1:launch_from_contract_inputs"], ["flashlib_ms", 0.090975], ["ratio_vs_flashlib", 1.5963046796862663], ["shape_key", "build_k_sweep_qm2048_k12"], ["speedup_vs_9db7", 1.095453668123037], ["timing_backend", "cupti"]]}], ["build_k_sweep_qm2048_k20", {"__dict_items__": [["baseline_9db7_ms", 0.117919], ["baseline_9db7_passed", true], ["baseline_9db7_route", "loom.examples.weave.knn_build_dispatch_split72_4e09_de1a_3dc7_v48"], ["candidate_ms", 0.082016], ["candidate_passed", true], ["candidate_route", "loom.examples.weave.knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2_k32split_v12:launch_from_contract_inputs"], ["flashlib_ms", 0.134622], ["ratio_vs_flashlib", 1.6414114319157236], ["shape_key", "build_k_sweep_qm2048_k20"], ["speedup_vs_9db7", 1.437756047600468], ["timing_backend", "cupti"]]}], ["build_k_sweep_qm4096_k12", {"__dict_items__": [["baseline_9db7_ms", 0.163039], ["baseline_9db7_passed", true], ["baseline_9db7_route", "loom.examples.weave.knn_build_dispatch_split72_4e09_de1a_3dc7_v48"], ["candidate_ms", 0.126943], ["candidate_passed", true], ["candidate_route", "loom.examples.weave.knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2_k32split_v20:launch_from_contract_inputs"], ["flashlib_ms", 0.169054], ["ratio_vs_flashlib", 1.3317315645604721], ["shape_key", "build_k_sweep_qm4096_k12"], ["speedup_vs_9db7", 1.2843480932386977], ["timing_backend", "cupti"]]}], ["build_k_sweep_qm4096_k20", {"__dict_items__": [["baseline_9db7_ms", 0.194878], ["baseline_9db7_passed", true], ["baseline_9db7_route", "loom.examples.weave.knn_build_dispatch_split72_4e09_de1a_3dc7_v48"], ["candidate_ms", 0.17603], ["candidate_passed", true], ["candidate_route", "loom.examples.weave.knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2_k32split_v12:launch_from_contract_inputs"], ["flashlib_ms", 0.290525], ["ratio_vs_flashlib", 1.650428904164063], ["shape_key", "build_k_sweep_qm4096_k20"], ["speedup_vs_9db7", 1.1070726580696473], ["timing_backend", "cupti"]]}], ["build_over32_stress_qm2048_k48", {"__dict_items__": [["baseline_9db7_ms", 0.398428], ["baseline_9db7_passed", true], ["baseline_9db7_route", "loom.examples.weave.knn_build_dispatch_split72_4e09_de1a_3dc7_v48"], ["candidate_ms", 0.366492], ["candidate_passed", true], ["candidate_route", "loom.examples.weave.knn_build_over32_topk_knn_build_dispatch_slurm_0610_6329_v25:launch_from_contract_inputs"], ["flashlib_ms", 0.411228], ["ratio_vs_flashlib", 1.1220654202547395], ["shape_key", "build_over32_stress_qm2048_k48"], ["speedup_vs_9db7", 1.087139691998734], ["timing_backend", "cupti"]]}], ["build_over32_stress_qm4096_k48", {"__dict_items__": [["baseline_9db7_ms", 0.519548], ["baseline_9db7_passed", true], ["baseline_9db7_route", "loom.examples.weave.knn_build_dispatch_split72_4e09_de1a_3dc7_v48"], ["candidate_ms", 0.48902], ["candidate_passed", true], ["candidate_route", "loom.examples.weave.knn_build_over32_topk_knn_build_dispatch_slurm_0610_6329_v25:launch_from_contract_inputs"], ["flashlib_ms", 0.544668], ["ratio_vs_flashlib", 1.1137949368124003], ["shape_key", "build_over32_stress_qm4096_k48"], ["speedup_vs_9db7", 1.0624268946055375], ["timing_backend", "cupti"]]}], ["build_tail_b1_q1536_m1536_d128_k10", {"__dict_items__": [["baseline_9db7_ms", 0.075775], ["baseline_9db7_passed", true], ["baseline_9db7_route", "loom.examples.weave.knn_build_dispatch_split72_4e09_de1a_3dc7_v48"], ["candidate_ms", 0.080543], ["candidate_passed", true], ["candidate_route", "loom.examples.weave.knn_build_dispatch_split72_4e09_de1a_3dc7_v48"], ["flashlib_ms", 0.085519], ["ratio_vs_flashlib", 1.061780663744832], ["shape_key", "build_tail_b1_q1536_m1536_d128_k10"], ["speedup_vs_9db7", 0.9408018077300323], ["timing_backend", "cupti"]]}]]}], ["candidate_dbd7_build_broad_8a78_v1", {"__dict_items__": [["build_k_sweep_qm1024_k12", {"__dict_items__": [["baseline_17b8_ms", 0.070688], ["baseline_17b8_tflops", 3.79746853779991], ["candidate_ms", 0.029471], ["candidate_tflops", 9.108461063418275], ["flashlib_ms", 0.064895], ["passed", true], ["ratio_vs_flashlib", 2.201995181704048], ["speedup_vs_17b8", 2.398561297546741], ["timing_backend", "cupti"]]}], ["build_k_sweep_qm1024_k20", {"__dict_items__": [["baseline_17b8_ms", 0.074622], ["baseline_17b8_tflops", 3.597269652381336], ["candidate_ms", 0.037248], ["candidate_tflops", 7.206707903780068], ["flashlib_ms", 0.073823], ["passed", true], ["ratio_vs_flashlib", 1.98193191580756], ["speedup_vs_17b8", 2.0033827319587627], ["timing_backend", "cupti"]]}], ["build_k_sweep_qm4096_k12", {"__dict_items__": [["baseline_17b8_ms", 0.162301], ["baseline_17b8_tflops", 26.462974941620814], ["candidate_ms", 0.12675], ["candidate_tflops", 33.885343558185404], ["flashlib_ms", 0.16886250000000003], ["passed", true], ["ratio_vs_flashlib", 1.3322485207100594], ["speedup_vs_17b8", 1.2804812623274162], ["timing_backend", "cupti"]]}], ["build_over32_stress_qm2048_k48", {"__dict_items__": [["baseline_17b8_ms", 0.397019], ["baseline_17b8_tflops", 2.7045099201801426], ["candidate_ms", 0.364763], ["candidate_tflops", 2.943669791069818], ["flashlib_ms", 0.40969], ["passed", true], ["ratio_vs_flashlib", 1.1231676458412723], ["speedup_vs_17b8", 1.0884300216853136], ["timing_backend", "cupti"]]}], ["build_over32_stress_qm4096_k48", {"__dict_items__": [["baseline_17b8_ms", 0.519961], ["baseline_17b8_tflops", 8.260172005208084], ["candidate_ms", 0.488185], ["candidate_tflops", 8.797827249915505], ["flashlib_ms", 0.544888], ["passed", true], ["ratio_vs_flashlib", 1.1161506396140808], ["speedup_vs_17b8", 1.0650900785562851], ["timing_backend", "cupti"]]}], ["build_qm2048_d128_k10", {"__dict_items__": [["baseline_17b8_ms", 0.084607], ["baseline_17b8_tflops", 12.690933657971563], ["candidate_ms", 0.04928], ["candidate_tflops", 21.78859220779221], ["flashlib_ms", 0.074015], ["passed", true], ["ratio_vs_flashlib", 1.5019277597402598], ["speedup_vs_17b8", 1.7168628246753248], ["timing_backend", "cupti"]]}], ["build_tail_b1_q1536_m1536_d128_k10", {"__dict_items__": [["baseline_17b8_ms", 0.077119], ["baseline_17b8_tflops", 7.831789520092325], ["candidate_ms", 0.043903], ["candidate_tflops", 13.757141334305174], ["flashlib_ms", 0.082559], ["passed", true], ["ratio_vs_flashlib", 1.8804865271165978], ["speedup_vs_17b8", 1.7565769992938978], ["timing_backend", "cupti"]]}]]}]]}')) + +def _select_contract_shapes(shape_labels): + return base9db7._select_contract_shapes(shape_labels) + +def _trace_inputs_for_shape(shape: dict[str, Any]) -> dict[str, Any]: + return base9db7._trace_inputs_for_shape(shape) + +def _normalize_route_row(row: dict[str, Any]) -> dict[str, Any]: + return base9db7._normalize_route_row(row) + +def _inputs_for_label(label: str) -> dict[str, Any]: + return _trace_inputs_for_shape(_select_contract_shapes((label,))[0]) + +def _candidate_config(candidate_key: str) -> dict[str, Any]: + try: + return CANDIDATE_CONFIGS[candidate_key] + except KeyError as exc: + raise ValueError(''.join(['unknown dbd7 build-lowfloor candidate ', format(repr(candidate_key), '')])) from exc + +def _candidate_id(candidate_key: str) -> str: + return str(_candidate_config(candidate_key)['candidate_id']) + +def _seed005f_expected(inputs: dict[str, Any]) -> str | None: + selected_seed, _matched_label = seed005f._selected_seed_for_inputs(inputs) + return selected_seed + +def _seed8a78_expected(inputs: dict[str, Any]) -> str | None: + return seed8a78._expected_seed(inputs) + +def _eligible_8a78_tail(inputs: dict[str, Any]) -> bool: + return str(inputs.get('label')) == TAIL_LABEL and seed8a78._eligible_k10_build(inputs) + +def _expected_seed(inputs: dict[str, Any], candidate_key: str) -> str | None: + _candidate_config(candidate_key) + if candidate_key == BASE_9DB7_KEY: + return None + if candidate_key == CANDIDATE_005F: + return _seed005f_expected(inputs) + if candidate_key == CANDIDATE_005F_8A78_TAIL and _eligible_8a78_tail(inputs): + return _seed8a78_expected(inputs) + if candidate_key == CANDIDATE_8A78_PRIMARY_005F_FILL: + seed = _seed8a78_expected(inputs) + if seed is not None: + return seed + return _seed005f_expected(inputs) + +def _selected_entrypoint(seed_id: str) -> str: + if seed_id in seed8a78.PRODUCTION_ROUTE_MODULES: + return seed8a78.PRODUCTION_ROUTE_MODULES[seed_id] + if seed_id in seed005f.PRODUCTION_ROUTE_MODULES: + return seed005f.PRODUCTION_ROUTE_MODULES[seed_id] + return PRODUCTION_ROUTE_MODULES[seed_id] + +def route_for_contract_inputs(inputs: dict[str, Any], *, candidate_key: str=DEFAULT_CANDIDATE_KEY, force_fallback: bool=False) -> str: + _candidate_config(candidate_key) + if force_fallback or candidate_key == BASE_9DB7_KEY: + return base9db7.route_for_contract_inputs(inputs, force_fallback=force_fallback) + if candidate_key == CANDIDATE_005F: + return seed005f.route_for_contract_inputs(inputs) + if candidate_key == CANDIDATE_005F_8A78_TAIL and _eligible_8a78_tail(inputs): + return seed8a78.route_for_contract_inputs(inputs) + if candidate_key == CANDIDATE_8A78_PRIMARY_005F_FILL and _seed8a78_expected(inputs) is not None: + return seed8a78.route_for_contract_inputs(inputs) + return seed005f.route_for_contract_inputs(inputs) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, candidate_key: str=DEFAULT_CANDIDATE_KEY, force_fallback: bool=False) -> None: + _candidate_config(candidate_key) + if force_fallback or candidate_key == BASE_9DB7_KEY: + base9db7.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + return + if candidate_key == CANDIDATE_005F: + seed005f.launch_from_contract_inputs(inputs) + return + if candidate_key == CANDIDATE_005F_8A78_TAIL and _eligible_8a78_tail(inputs): + seed8a78.launch_from_contract_inputs(inputs) + return + if candidate_key == CANDIDATE_8A78_PRIMARY_005F_FILL and _seed8a78_expected(inputs) is not None: + seed8a78.launch_from_contract_inputs(inputs) + return + seed005f.launch_from_contract_inputs(inputs) + +def candidate_baseline_9db7(inputs: dict[str, Any]) -> None: + base9db7.launch_from_contract_inputs(inputs) + +def candidate_005f_full82_v1(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, candidate_key=CANDIDATE_005F) + +def candidate_005f_plus_8a78_tail_full82_v1(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, candidate_key=CANDIDATE_005F_8A78_TAIL) + +def candidate_8a78_primary_plus_005f_fill_full82_v1(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, candidate_key=CANDIDATE_8A78_PRIMARY_005F_FILL) + +def candidate(inputs: dict[str, Any]) -> None: + candidate_8a78_primary_plus_005f_fill_full82_v1(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, candidate_key=DEFAULT_CANDIDATE_KEY, force_fallback=True) + +def _candidate_kernel_fn(candidate_key: str) -> Callable[[dict[str, Any]], None]: + _candidate_config(candidate_key) + if candidate_key == BASE_9DB7_KEY: + return candidate_baseline_9db7 + if candidate_key == CANDIDATE_005F: + return candidate_005f_full82_v1 + if candidate_key == CANDIDATE_005F_8A78_TAIL: + return candidate_005f_plus_8a78_tail_full82_v1 + if candidate_key == CANDIDATE_8A78_PRIMARY_005F_FILL: + return candidate_8a78_primary_plus_005f_fill_full82_v1 + raise ValueError(''.join(['unknown dbd7 build-lowfloor candidate ', format(repr(candidate_key), '')])) +CANDIDATE_CONFIGS = _decode_capture(_json_loads('{"__dict_items__": [["base_9db7", {"__dict_items__": [["candidate_id", "candidate_17b8_lowmargin_1074_full82_v1"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_17b8_lowmargin_1074_full82_v1:launch_from_contract_inputs"], ["benchmark_entrypoint", "loom.examples.weave.knn_build_dispatch_dbd7_lowfloor_005f_8a78_full82_v1:benchmark_baseline_9db7"], ["selected_seeds", {"__tuple__": ["lowk_q512_k1_s4_1074", "k24_q4096_warpselect_1074", "k30_q4096_6998_warpselect_v1"]}], ["guard_plan", {"__tuple__": ["1074 exact BF16 build Q=M=512 K=1 guard", "1074 exact BF16 build Q=M=4096 K=24 guard", "1074 exact BF16 build Q=M=4096 K=30 delegate guard", "selected 17b8/99fd full82 Weave fallback"]}], ["expected_shape_wins", {"__tuple__": ["build_k_sweep_qm512_k1", "build_k_sweep_qm4096_k24", "build_k_sweep_qm4096_k30"]}], ["fallback", "loom.examples.weave.knn_build_dispatch_17b8_lowmargin_1074_full82_v1:launch_from_contract_inputs"], ["rejected_reason", "same-session a444/9db7 baseline"]]}], ["005f_promoted_portfolio", {"__dict_items__": [["candidate_id", "candidate_dbd7_005f_buildbucket_full82_v1"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_dbd7_lowfloor_005f_8a78_full82_v1:candidate_005f_full82_v1"], ["benchmark_entrypoint", "loom.examples.weave.knn_build_dispatch_dbd7_lowfloor_005f_8a78_full82_v1:benchmark_candidate_005f_full82_v1"], ["selected_seeds", {"__tuple__": ["v20_k12_q1024_q4096_exact", "q2048_k12_4f30_v1", "v12_k20_q2048k10_mixedfanout", "over32_k48_v25"]}], ["guard_plan", {"__tuple__": ["005f exact BF16 build low-floor portfolio for K10/K12/K20/K48 rows", "a444/9db7 full82 Weave fallback for guard misses and Q1536/K10 tail"]}], ["expected_shape_wins", {"__tuple__": ["build_k_sweep_qm1024_k12", "build_k_sweep_qm1024_k20", "build_qm2048_d128_k10", "build_k_sweep_qm2048_k12", "build_k_sweep_qm2048_k20", "build_tail_b1_q1536_m1536_d128_k10", "build_k_sweep_qm4096_k12", "build_k_sweep_qm4096_k20", 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eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False, candidate_key: str=DEFAULT_CANDIDATE_KEY) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark, kernel_fn=_candidate_kernel_fn(candidate_key)) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=None, kernel_fn: Callable[[dict[str, Any]], Any]=candidate, correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + return base9db7._run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=kernel_fn, correctness=correctness, time_flashlib=time_flashlib) + +def _route_trace_record(inputs: dict[str, Any], *, candidate_key: str, force_fallback: bool=False) -> dict[str, Any]: + label = str(inputs.get('label')) + expected_seed = _expected_seed(inputs, candidate_key) + selected_route = route_for_contract_inputs(inputs, candidate_key=candidate_key, force_fallback=force_fallback) + baseline_route = base9db7.route_for_contract_inputs(inputs) + if force_fallback or expected_seed is None: + row = dict(base9db7.route_trace_for_contract_shapes((label,), force_fallback=force_fallback)[0]) + row['expected_seed'] = expected_seed + row['baseline_dispatcher_route'] = baseline_route + row['baseline_9db7_route'] = baseline_route + if force_fallback and expected_seed is not None: + row['guard_id'] = ''.join(['forced_fallback_', format(expected_seed, ''), '_disabled']) + row['guard_condition'] = ''.join(['forced fallback to a444/9db7; ', format(expected_seed, ''), ' disabled']) + row['classification'] = 'guard-miss' + return _normalize_route_row(row) + guard_conditions = {seed005f.SEED_V20_K12_ID: '005f exact BF16 build Q in {1024,4096} K12', seed005f.SEED_K12_4F30_ID: '005f/4f30 exact BF16 build Q2048 K12', seed005f.SEED_V12_MIDBUILD_ID: '005f/v12 exact BF16 build Q2048 K10 or K20 bucket', seed005f.SEED_OVER32_V25_ID: '005f/v25 exact BF16 build K48 over32 row', seed8a78.SEED_K10_ID: '8a78 exact BF16 build K10 row', seed8a78.SEED_V20_ID: '8a78 exact BF16 build v20 K12/K20 row', seed8a78.SEED_OVER32_ID: '8a78 exact BF16 build K48 over32 row'} + return _normalize_route_row({'shape_key': label, 'selected_route': selected_route, 'selected_entrypoint': _selected_entrypoint(expected_seed), 'selected_seed': expected_seed, 'expected_seed': expected_seed, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': ''.join([format(candidate_key, ''), '_', format(expected_seed, ''), '_guard']), 'guard_condition': guard_conditions.get(expected_seed, 'dbd7 build-lowfloor exact seed guard'), 'coverage': 'dbd7 synthesized build-lowfloor seed portfolio before a444/9db7 fallback', 'consumed_seed': expected_seed, 'replaced_route': baseline_route, 'baseline_dispatcher_route': baseline_route, 'baseline_9db7_route': baseline_route, 'shape_specific_kernel_ms': TARGETED_SEED_ROWS.get(SEED_005F_ID, {}).get(label, {}).get('candidate_ms') or TARGETED_SEED_ROWS.get(SEED_8A78_ID, {}).get(label, {}).get('candidate_ms'), 'classification': 'unmeasured'}) + +def route_trace_for_contract_shapes(shape_labels=None, *, candidate_key: str=DEFAULT_CANDIDATE_KEY, force_fallback: bool=False) -> list[dict[str, Any]]: + _candidate_config(candidate_key) + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + return [_route_trace_record(_trace_inputs_for_shape(shape), candidate_key=candidate_key, force_fallback=force_fallback) for shape in selected] + +def _timing_backend_name(use_cupti: bool) -> str: + return 'cupti' if use_cupti else 'cuda_event_fallback' + +def _payload_shape_labels(shape_labels) -> str | tuple[str, ...]: + if shape_labels is None: + return 'all_canonical' + return tuple((str(label) for label in shape_labels)) + +def _denominator_name(shape_labels) -> str: + if shape_labels is None: + return 'full82_v9' + labels = tuple((str(label) for label in shape_labels)) + if labels == TARGET_SHAPES: + return 'dbd7_build_lowfloor_seed_targets' + return ''.join(['shape', format(len(labels), '')]) + +def _denominator_label(shape_labels) -> str: + if shape_labels is None: + return ''.join(['full', format(len(eval_mod.CANONICAL_SHAPES), '')]) + return ''.join(['shape', format(len(tuple(shape_labels)), '')]) + +def _rows_for_labels(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return {label: dict(report.get('per_shape', {}).get(label, {})) for label in labels} + +def _timing_backends_for_reports(*reports: dict[str, Any]) -> list[str]: + return base9db7._timing_backends_for_reports(*reports) + +def _annotate_route_trace(route_trace: list[dict[str, Any]], candidate_report: dict[str, Any], baseline_report: dict[str, Any], *, candidate_key: str) -> list[dict[str, Any]]: + expected_labels = set(_candidate_config(candidate_key)['expected_shape_wins']) + annotated = [] + for row in route_trace: + label = str(row.get('shape_key')) + out = dict(row) + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') + speedup_vs_baseline = baseline_ms / candidate_ms if candidate_ms and baseline_ms else None + speedup_vs_external = flashlib_ms / candidate_ms if candidate_ms and flashlib_ms else None + out['dispatcher_kernel_ms'] = candidate_ms + out['baseline_9db7_kernel_ms'] = baseline_ms + out['baseline_17b8_kernel_ms'] = baseline_ms + out['external_baseline_ms'] = flashlib_ms + out['flashlib_ms'] = flashlib_ms + out['relative_speedup_vs_baseline'] = speedup_vs_baseline + out['relative_speedup_vs_9db7'] = speedup_vs_baseline + out['speedup_vs_external_baseline'] = speedup_vs_external + out['external_baseline_ref'] = 'same_session' if flashlib_ms is not None else 'not_available' + out['route_changed_vs_9db7'] = out.get('selected_route') != out.get('baseline_dispatcher_route') + if label in expected_labels and candidate_key != BASE_9DB7_KEY: + if out.get('expected_seed') and out.get('selected_seed') != out.get('expected_seed'): + out['classification'] = 'guard-miss' + elif speedup_vs_external is not None and speedup_vs_external < 1.05: + out['classification'] = 'kernel-slow' if out.get('expected_seed') else 'fallback-slow' + elif speedup_vs_baseline is not None and speedup_vs_baseline < 1.0: + out['classification'] = 'kernel-slow' if out.get('expected_seed') else 'fallback-slow' + elif out.get('expected_seed'): + out['classification'] = 'seed-consumed' + else: + out['classification'] = 'route-ok' + elif speedup_vs_external is not None and speedup_vs_external < 1.05: + out['classification'] = 'kernel-slow' if out.get('route_kind') == 'specialized' else 'fallback-slow' + else: + out['classification'] = 'route-ok' + annotated.append(_normalize_route_row(out)) + return annotated + +def _below_flashlib_rows(report: dict[str, Any], route_trace: list[dict[str, Any]], *, floor: float) -> list[dict[str, Any]]: + trace_by_label = {str(row['shape_key']): row for row in route_trace} + rows = [] + for label, row in sorted(report.get('per_shape', {}).items()): + ratio = row.get('ratio_vs_flashlib') + if isinstance(ratio, (float, int)) and ratio < floor: + trace_row = trace_by_label.get(label, {}) + rows.append({'shape_key': label, 'kernel_ms': row.get('kernel_ms'), 'flashlib_ms': row.get('flashlib_ms'), 'ratio_vs_flashlib': ratio, 'selected_route': trace_row.get('selected_route'), 'selected_seed': trace_row.get('selected_seed'), 'expected_seed': trace_row.get('expected_seed'), 'route_kind': trace_row.get('route_kind', 'unknown'), 'classification': trace_row.get('classification', 'unmeasured')}) + return rows + +def _seed_delta_matrix(candidate_key: str, candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> list[dict[str, Any]]: + matrix = [] + for label in TARGET_SHAPES: + inputs = _inputs_for_label(label) + selected_seed = _expected_seed(inputs, candidate_key) + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + matrix.append({'shape_key': label, 'baseline_route': base9db7.route_for_contract_inputs(inputs), 'candidate_route': route_for_contract_inputs(inputs, candidate_key=candidate_key), 'selected_seed': selected_seed, 'candidate_id': _candidate_id(candidate_key), 'candidate_ms': candidate_ms, 'baseline_9db7_ms': baseline_ms, 'flashlib_ms': candidate_row.get('flashlib_ms'), 'delta_ms_candidate_minus_9db7': candidate_ms - baseline_ms if candidate_ms and baseline_ms else None, 'speedup_vs_9db7': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'ratio_vs_flashlib': candidate_row.get('ratio_vs_flashlib'), 'targeted_seed_row': TARGETED_SEED_ROWS.get(SEED_005F_ID, {}).get(label) or TARGETED_SEED_ROWS.get(SEED_8A78_ID, {}).get(label, {}), 'timing_backend': candidate_row.get('timing_backend') or baseline_row.get('timing_backend')}) + return matrix + +def _baseline_sidecar(report: dict[str, Any], *, denominator: str, timing_backend: str, benchmark_correctness: bool, time_flashlib: bool) -> dict[str, Any]: + return {'candidate_id': BASE_9DB7_ID, 'candidate_key': BASE_9DB7_KEY, 'measured_entrypoint': BASE_9DB7_ENTRYPOINT, 'route_entrypoint': BASE_9DB7_ROUTE_ENTRYPOINT, 'measured_shape_labels': 'all_canonical' if report.get('measured_shape_labels') == 'all_canonical' else report.get('measured_shape_labels', 'all_canonical'), 'timing_backend': timing_backend, 'denominator': denominator, 'benchmark_correctness_checked': benchmark_correctness, 'benchmark_time_flashlib': time_flashlib, 'tflops': report['summary']['primary_mean'], 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'contract_summary': report['summary'], 'contract_performance': report['performance'], 'route_trace': route_trace_for_contract_shapes(None, candidate_key=BASE_9DB7_KEY), 'route_trace_included': True, 'rank_objective': {'metric': 'tflops', 'direction': 'maximize', 'value': report['summary']['primary_mean'], 'denominator': denominator}, 'report': report} + +def benchmark_baseline_9db7(*, use_cupti: bool=True, shape_labels=None, benchmark_correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_baseline_9db7, correctness=benchmark_correctness, time_flashlib=time_flashlib) + report['candidate_id'] = BASE_9DB7_ID + report['measured_entrypoint'] = BASE_9DB7_ENTRYPOINT + report['measured_shape_labels'] = _payload_shape_labels(shape_labels) + report['route_trace'] = route_trace_for_contract_shapes(shape_labels, candidate_key=BASE_9DB7_KEY) + report['route_trace_included'] = True + return report + +def _benchmark_payload(candidate_key: str, candidate_report: dict[str, Any], baseline_report: dict[str, Any], *, use_cupti: bool, shape_labels, benchmark_correctness: bool, time_flashlib: bool) -> dict[str, Any]: + candidate_metric = candidate_report['summary']['primary_mean'] + baseline_metric = baseline_report['summary']['primary_mean'] + route_trace = _annotate_route_trace(route_trace_for_contract_shapes(shape_labels, candidate_key=candidate_key), candidate_report, baseline_report, candidate_key=candidate_key) + below_1x = _below_flashlib_rows(candidate_report, route_trace, floor=1.0) + below_floor = _below_flashlib_rows(candidate_report, route_trace, floor=1.05) + denominator = _denominator_name(shape_labels) + timing_backend = _timing_backend_name(use_cupti) + config = _candidate_config(candidate_key) + return {'candidate_id': config['candidate_id'], 'candidate_key': candidate_key, 'baseline_candidate_id': BASE_9DB7_ID, 'selected_seeds': config['selected_seeds'], 'source_tasks': SOURCE_TASKS, 'tflops': candidate_metric, 'baseline_9db7_tflops': baseline_metric, 'metric_delta_vs_9db7': candidate_metric - baseline_metric if candidate_metric is not None and baseline_metric is not None else None, 'all_correct': candidate_report['summary']['all_correct'], 'baseline_all_correct': baseline_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'baseline_performance_comparable': baseline_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'baseline_invalid_performance_reason': baseline_report['summary']['invalid_performance_reason'], 'measured_entrypoint': config['benchmark_entrypoint'], 'baseline_entrypoint': BASE_9DB7_ENTRYPOINT, 'route_entrypoint': ROUTE_ENTRYPOINT, 'measured_shape_labels': _payload_shape_labels(shape_labels), 'timing_backend': timing_backend, 'denominator': denominator, 'selected_route_labels': config['expected_shape_wins'], 'selected_route_rows': _rows_for_labels(candidate_report, TARGET_SHAPES), 'baseline_selected_route_rows': _rows_for_labels(baseline_report, TARGET_SHAPES), 'seed_delta_matrix': _seed_delta_matrix(candidate_key, candidate_report, baseline_report), 'targeted_seed_rows': TARGETED_SEED_ROWS, 'candidate_dispatchers': CANDIDATE_DISPATCHERS, 'selected_candidate_dispatcher': config['candidate_id'], 'guard_plan': config['guard_plan'], 'route_modules': PRODUCTION_ROUTE_MODULES, 'route_trace': route_trace, 'forced_fallback_route_trace': route_trace_for_contract_shapes(shape_labels, candidate_key=candidate_key, force_fallback=True), 'route_trace_included': True, 'contract_summary': candidate_report['summary'], 'baseline_contract_summary': baseline_report['summary'], 'contract_performance': candidate_report['performance'], 'baseline_contract_performance': baseline_report['performance'], 'timing_backends': _timing_backends_for_reports(candidate_report, baseline_report), 'timing_backend_requested': timing_backend, 'use_cupti': use_cupti, 'benchmark_correctness_checked': benchmark_correctness, 'benchmark_time_flashlib': time_flashlib, 'performance_coverage': 'partial' if below_floor else 'pass', 'coverage_only_routes': [], 'hot_bucket_blockers': below_floor, 'flashlib_parity_ledger': {'baseline_ref_scope': 'same_session' if time_flashlib else 'not_available', 'speedup_floor': 1.05, 'rows_below_1x': below_1x, 'rows_below_floor': below_floor, 'omitted_reason': None if time_flashlib else 'benchmark_time_flashlib=false'}, 'rank_objective': {'metric': 'tflops', 'direction': 'maximize', 'value': candidate_metric, 'baseline_9db7_value': baseline_metric, 'delta_vs_9db7': candidate_metric - baseline_metric if candidate_metric is not None and baseline_metric is not None else None, 'denominator': denominator}, 'report': candidate_report, 'baseline_report': baseline_report} + +def benchmark_candidate_portfolio(candidate_key: str, *, use_cupti: bool=True, shape_labels=None, baseline_report: dict[str, Any] | None=None, benchmark_correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + if candidate_key == BASE_9DB7_KEY: + baseline = benchmark_baseline_9db7(use_cupti=use_cupti, shape_labels=shape_labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + return _baseline_sidecar(baseline, denominator=_denominator_name(shape_labels), timing_backend=_timing_backend_name(use_cupti), benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + if baseline_report is None: + baseline_report = benchmark_baseline_9db7(use_cupti=use_cupti, shape_labels=shape_labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=_candidate_kernel_fn(candidate_key), correctness=benchmark_correctness, time_flashlib=time_flashlib) + return _benchmark_payload(candidate_key, candidate_report, baseline_report, use_cupti=use_cupti, shape_labels=shape_labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + +def benchmark_candidate_005f_full82_v1(**kwargs) -> dict[str, Any]: + return benchmark_candidate_portfolio(CANDIDATE_005F, **kwargs) + +def benchmark_candidate_005f_plus_8a78_tail_full82_v1(**kwargs) -> dict[str, Any]: + return benchmark_candidate_portfolio(CANDIDATE_005F_8A78_TAIL, **kwargs) + +def benchmark_candidate_8a78_primary_plus_005f_fill_full82_v1(**kwargs) -> dict[str, Any]: + return benchmark_candidate_portfolio(CANDIDATE_8A78_PRIMARY_005F_FILL, **kwargs) + +def _best_candidate_key(payloads: dict[str, dict[str, Any]]) -> str | None: + baseline_value = payloads.get(BASE_9DB7_KEY, {}).get('tflops') + best_key = None + best_value = None + for key in (CANDIDATE_005F, CANDIDATE_005F_8A78_TAIL, CANDIDATE_8A78_PRIMARY_005F_FILL): + payload = payloads.get(key, {}) + if not payload.get('all_correct') or not payload.get('performance_comparable'): + continue + value = payload.get('tflops') + if value is None: + continue + if baseline_value is not None and value < baseline_value: + continue + if best_value is None or value > best_value: + best_key = key + best_value = value + return best_key + +def _summary_payload(*, payloads: dict[str, dict[str, Any]], artifacts: dict[str, str], denominator: str, timing_backend: str, benchmark_correctness: bool, time_flashlib: bool) -> dict[str, Any]: + selected_key = _best_candidate_key(payloads) + selected_payload = payloads.get(selected_key, {}) if selected_key else {} + baseline_payload = payloads[BASE_9DB7_KEY] + return {'candidate_id': 'dispatcher_synthesis_dbd7_lowfloor_005f_8a78_full82_v1', 'measured_entrypoint': ''.join([format(MODULE, ''), ':write_benchmark_artifacts']), 'denominator': denominator, 'timing_backend': timing_backend, 'benchmark_correctness_checked': benchmark_correctness, 'benchmark_time_flashlib': time_flashlib, 'baseline_candidate_key': BASE_9DB7_KEY, 'selected_candidate_key': selected_key, 'selected_candidate_dispatcher': _candidate_id(selected_key) if selected_key else None, 'default_candidate_key': DEFAULT_CANDIDATE_KEY, 'candidate_dispatchers': CANDIDATE_DISPATCHERS, 'candidate_rankings': [{'candidate_key': key, 'candidate_id': payloads[key].get('candidate_id'), 'tflops': payloads[key].get('tflops'), 'metric_delta_vs_9db7': payloads[key].get('metric_delta_vs_9db7'), 'all_correct': payloads[key].get('all_correct'), 'performance_comparable': payloads[key].get('performance_comparable'), 'performance_coverage': payloads[key].get('performance_coverage')} for key in (BASE_9DB7_KEY, CANDIDATE_005F, CANDIDATE_005F_8A78_TAIL, CANDIDATE_8A78_PRIMARY_005F_FILL) if key in payloads], 'seed_delta_matrix': selected_payload.get('seed_delta_matrix', []), 'flashlib_parity_ledger': selected_payload.get('flashlib_parity_ledger', {}), 'full_denominator_ab': {'baseline_payload': artifacts.get('same_session_baseline_payload'), 'candidate_payload': artifacts.get(''.join([format(selected_key, ''), '_payload'])) if selected_key else None, 'denominator': denominator, 'timing_backend': timing_backend, 'metric_delta': selected_payload.get('metric_delta_vs_9db7'), 'route_trace': selected_payload.get('route_trace', [])}, 'baseline_tflops': baseline_payload.get('tflops'), 'selected_tflops': selected_payload.get('tflops'), 'artifacts': artifacts} + +def write_benchmark_artifacts(artifact_dir: str | Path, *, use_cupti: bool=True, shape_labels=None, benchmark_correctness: bool=True, time_flashlib: bool=True) -> dict[str, str]: + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + denom_label = _denominator_label(shape_labels) + denominator = _denominator_name(shape_labels) + timing_backend = _timing_backend_name(use_cupti) + baseline_report = benchmark_baseline_9db7(use_cupti=use_cupti, shape_labels=shape_labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + baseline_payload = _baseline_sidecar(baseline_report, denominator=denominator, timing_backend=timing_backend, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + artifacts: dict[str, str] = {} + payloads = {BASE_9DB7_KEY: baseline_payload} + baseline_path = out_dir / ''.join([format(denom_label, ''), '_same_session_baseline_9db7_lowmargin_1074_v1.json']) + baseline_path.write_text(json.dumps(baseline_payload, indent=2, sort_keys=True) + '\n', encoding='utf-8') + artifacts['same_session_baseline_payload'] = str(baseline_path) + suffixes = {CANDIDATE_005F: '005f_promoted_portfolio', CANDIDATE_005F_8A78_TAIL: '005f_plus_8a78_tail', CANDIDATE_8A78_PRIMARY_005F_FILL: '8a78_primary_plus_005f_fill'} + for candidate_key, suffix in suffixes.items(): + payload = benchmark_candidate_portfolio(candidate_key, use_cupti=use_cupti, shape_labels=shape_labels, baseline_report=baseline_report, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + payloads[candidate_key] = payload + payload_path = out_dir / ''.join([format(denom_label, ''), '_dispatch_', format(suffix, ''), '_v1.json']) + trace_path = out_dir / ''.join([format(denom_label, ''), '_route_trace_', format(suffix, ''), '_v1.json']) + forced_trace_path = out_dir / ''.join([format(denom_label, ''), '_forced_fallback_trace_', format(suffix, ''), '_v1.json']) + seed_matrix_path = out_dir / ''.join([format(denom_label, ''), '_seed_delta_matrix_', format(suffix, ''), '_v1.json']) + payload_path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n', encoding='utf-8') + trace_path.write_text(json.dumps(payload['route_trace'], indent=2, sort_keys=True) + '\n', encoding='utf-8') + forced_trace_path.write_text(json.dumps(payload['forced_fallback_route_trace'], indent=2, sort_keys=True) + '\n', encoding='utf-8') + seed_matrix_path.write_text(json.dumps(payload['seed_delta_matrix'], indent=2, sort_keys=True) + '\n', encoding='utf-8') + artifacts[''.join([format(candidate_key, ''), '_payload'])] = str(payload_path) + artifacts[''.join([format(candidate_key, ''), '_route_trace'])] = str(trace_path) + artifacts[''.join([format(candidate_key, ''), '_forced_fallback_trace'])] = str(forced_trace_path) + artifacts[''.join([format(candidate_key, ''), '_seed_delta_matrix'])] = str(seed_matrix_path) + summary = _summary_payload(payloads=payloads, artifacts=artifacts, denominator=denominator, timing_backend=timing_backend, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + summary_path = out_dir / ''.join([format(denom_label, ''), '_dispatcher_synthesis_dbd7_lowfloor_005f_8a78_v1.json']) + summary_path.write_text(json.dumps(summary, indent=2, sort_keys=True) + '\n', encoding='utf-8') + artifacts['dispatcher_synthesis'] = str(summary_path) + return artifacts diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_default_7c3a_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_default_7c3a_v1.py new file mode 100644 index 00000000..c85ccfe4 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_default_7c3a_v1.py @@ -0,0 +1,207 @@ +"""Default kNN build dispatcher consuming full55 K96 and RAG-K10 portfolio routes. + +Minimum target architecture: sm_100a. This default-registry dispatcher is a +wrapper-only portfolio over validated Weave seeds: + +* exact large-square BF16 build ``Q=M=8192, K in {20,32}`` from a989; +* exact over-64 BF16 build ``Q=M=2048, K=96`` from 6c1e; +* exact RAG frontier K10 rows from 2074. + +The RAG K32 frontier row is deliberately held on the inherited Weave dispatcher +until a faster K32 seed is available. Guard misses delegate to the current +split72/de1a/3dc7 Weave dispatcher. No external runtime fallback is used. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_split72_4e09_de1a_3dc7_v48 as baseline_3dc7 +from . import knn_build_large_square_k20k32_a989_v1 as large_square +from . import knn_build_over64_k96_a989_v1 as over64_k96 +from . import knn_build_rag_frontier_4b5c_v1 as rag_frontier +ROUTE_LARGE_SQUARE_K20K32 = 'loom.examples.weave.knn_build_large_square_k20k32_a989_v1' +ROUTE_OVER64_K96 = 'loom.examples.weave.knn_build_over64_k96_a989_v1' +ROUTE_RAG_FRONTIER_K10 = 'loom.examples.weave.knn_build_rag_frontier_4b5c_v1:k10' +ROUTE_RAG_K32_HELD_ON_BASELINE = 'policy:rag_k32_held_on_current_weave_fallback_pending_retune' +ROUTE_BASELINE_3DC7 = 'loom.examples.weave.knn_build_dispatch_split72_4e09_de1a_3dc7_v48' +LARGE_SQUARE_TARGET_SHAPES = large_square.TARGET_SHAPES +K96_TARGET_SHAPES = ('build_over64_stress_qm2048_k96',) +RAG_K10_TARGET_SHAPES = rag_frontier.K10_TARGET_SHAPES +RAG_K32_TARGET_SHAPES = rag_frontier.K32_TARGET_SHAPES +RAG_TARGET_SHAPES = rag_frontier.TARGET_SHAPES +LARGE_SQUARE_TARGET_SHAPE_SET = set(LARGE_SQUARE_TARGET_SHAPES) +K96_TARGET_SHAPE_SET = set(K96_TARGET_SHAPES) +RAG_K10_TARGET_SHAPE_SET = set(RAG_K10_TARGET_SHAPES) +RAG_K32_TARGET_SHAPE_SET = set(RAG_K32_TARGET_SHAPES) +SELECTED_TARGET_SHAPES = (*LARGE_SQUARE_TARGET_SHAPES, *K96_TARGET_SHAPES, *RAG_K10_TARGET_SHAPES) +DISPATCH_CORRECTNESS_SHAPES = ('flashml_correctness_b1_q256_m256_d128_k5', *LARGE_SQUARE_TARGET_SHAPES, *K96_TARGET_SHAPES, *RAG_TARGET_SHAPES, *baseline_3dc7.SELECTED_TARGET_SHAPES) +PRODUCTION_ROUTE_MODULES = {'large_square_k20k32': ROUTE_LARGE_SQUARE_K20K32, 'over64_k96': ROUTE_OVER64_K96, 'rag_frontier_k10': ROUTE_RAG_FRONTIER_K10, 'rag_frontier_k32_policy': ROUTE_RAG_K32_HELD_ON_BASELINE, 'fallback': ROUTE_BASELINE_3DC7} + +class _TraceTensor: + + def __init__(self, dtype: str) -> None: + self.dtype = dtype if dtype.startswith('torch.') else ''.join(['torch.', format(dtype, '')]) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_DEFAULT_7C3A_VERIFY_KERNEL') + if verify_kernel == 'large_square_stage1_k20': + return large_square.parent_v20.stage1_k20_unordered_ir + if verify_kernel == 'large_square_stage1_k32': + return large_square.parent_v20.stage1_k32_unordered_ir + if verify_kernel == 'large_square_merge_k20': + return large_square.parent_v20.merge_k20_unordered_warp_select_ir + if verify_kernel == 'large_square_merge_k32': + return large_square.parent_v20.merge_k32_unordered_warp_select_ir + if verify_kernel == 'over64_k96_stage1': + return over64_k96.stage1_k96_over64_ir + if verify_kernel == 'over64_k96_merge': + return over64_k96.merge_k96_s8_chunkprefill_over64_ir + if verify_kernel == 'rag_k10_stage1': + return rag_frontier.split72.parent_lowk.stage1_ir + if verify_kernel == 'rag_k10_merge': + return rag_frontier.split72.merge_k10_s72_cache_ir + return baseline_3dc7.ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k48over32", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 48]], "cta_group": 1, "threads": 192}')) + +def _label_can_hit(inputs: dict[str, Any], target_labels: set[str]) -> bool: + label = inputs.get('label') + return label is None or str(label) in target_labels + +def _eligible_large_square_k20k32(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, LARGE_SQUARE_TARGET_SHAPE_SET) and large_square._eligible_large_square_k20k32(inputs) + +def _eligible_over64_k96(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, K96_TARGET_SHAPE_SET) and over64_k96._eligible_over64_k96_build(inputs) + +def _eligible_rag_k10(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, RAG_K10_TARGET_SHAPE_SET) and rag_frontier._eligible_k10_rag_frontier(inputs) + +def _eligible_rag_k32_policy(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, RAG_K32_TARGET_SHAPE_SET) and rag_frontier._eligible_k32_rag_frontier(inputs) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if force_fallback: + return ROUTE_BASELINE_3DC7 + if _eligible_large_square_k20k32(inputs): + return ROUTE_LARGE_SQUARE_K20K32 + if _eligible_over64_k96(inputs): + return ROUTE_OVER64_K96 + if _eligible_rag_k10(inputs): + return ROUTE_RAG_FRONTIER_K10 + return ROUTE_BASELINE_3DC7 + +def _launch_route(inputs: dict[str, Any], route: str) -> None: + if route == ROUTE_LARGE_SQUARE_K20K32: + large_square._launch_large_square_k20k32(inputs) + return + if route == ROUTE_OVER64_K96: + over64_k96._launch_over64_k96_split_path(inputs) + return + if route == ROUTE_RAG_FRONTIER_K10: + rag_frontier._launch_k10_rag_frontier_s72(inputs) + return + baseline_3dc7.launch_from_contract_inputs(inputs) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + _launch_route(inputs, route_for_contract_inputs(inputs, force_fallback=force_fallback)) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def candidate_force_fallback(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs, force_fallback=True) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return baseline_3dc7._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=DISPATCH_CORRECTNESS_SHAPES, benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint: run real contract correctness for selected shapes.""" + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=None, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + shapes = None if shape_labels is None else _select_contract_shapes(shape_labels) + return evaluate_contract(shapes=shapes, correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def _trace_inputs_from_shape(shape: dict[str, Any]) -> dict[str, Any]: + params = dict(shape['params']) + dtype = str(params.get('dtype', 'bfloat16')) + return {'label': shape['label'], 'B': int(params['B']), 'Q': int(params['Q']), 'M': int(params['M']), 'D': int(params['D']), 'K': int(params['K']), 'dtype': dtype, 'build': bool(params.get('build', False)), 'query': _TraceTensor(dtype), 'database': _TraceTensor(dtype)} + +def _baseline_inherited_route(inputs: dict[str, Any]) -> str: + try: + return baseline_3dc7.route_for_contract_inputs(inputs) + except Exception: + return baseline_3dc7.ROUTE_PREVIOUS_MAIN + +def _route_trace_record(inputs: dict[str, Any], *, force_fallback: bool=False) -> dict[str, Any]: + route = route_for_contract_inputs(inputs, force_fallback=force_fallback) + inherited_route = _baseline_inherited_route(inputs) + if route == ROUTE_LARGE_SQUARE_K20K32: + return {'shape_key': inputs.get('label'), 'selected_route': route, 'guard_condition': 'exact BF16 B1 Q=M=8192 D128 build=true K in {20,32}', 'route_kind': 'specialized', 'coverage': 'exact a989 large-square K20/K32 seed', 'inherited_route': inherited_route} + if route == ROUTE_OVER64_K96: + return {'shape_key': inputs.get('label'), 'selected_route': route, 'guard_condition': 'exact BF16 B1 Q=M=2048 D128 build=true K=96', 'route_kind': 'specialized', 'coverage': 'exact 6c1e over64 K96 seed', 'inherited_route': inherited_route} + if route == ROUTE_RAG_FRONTIER_K10: + return {'shape_key': inputs.get('label'), 'selected_route': route, 'guard_condition': 'exact RAG frontier BF16 D128 non-build K10 label', 'route_kind': 'specialized', 'coverage': 'exact 2074 RAG K10 frontier seed', 'inherited_route': inherited_route} + if _eligible_rag_k32_policy(inputs) and (not force_fallback): + return {'shape_key': inputs.get('label'), 'selected_route': route, 'guard_condition': 'RAG K32 policy holdout: inherited Weave fallback until K32 retune', 'route_kind': 'general', 'coverage': 'RAG K32 policy route; correctness via current fallback, performance blocker remains', 'inherited_route': inherited_route} + inherited_kind = 'specialized' if inherited_route != baseline_3dc7.ROUTE_PREVIOUS_MAIN else 'general' + return {'shape_key': inputs.get('label'), 'selected_route': route, 'guard_condition': 'portfolio guard miss or forced fallback', 'route_kind': inherited_kind, 'coverage': 'current split72/de1a/3dc7 Weave dispatcher fallback', 'inherited_route': inherited_route} + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + return [_route_trace_record(_trace_inputs_from_shape(shape), force_fallback=force_fallback) for shape in selected] + +def _benchmark_payload(candidate_report: dict[str, Any], baseline_report: dict[str, Any], *, use_cupti: bool, shape_labels, measured_function: str) -> dict[str, Any]: + candidate_rows = candidate_report.get('per_shape', {}) + baseline_rows = baseline_report.get('per_shape', {}) + timing_backends = sorted({row.get('timing_backend') for report in (candidate_report, baseline_report) for row in report.get('per_shape', {}).values() if row.get('timing_backend') is not None}) + selected_rows = {label: candidate_rows.get(label, {}) for label in SELECTED_TARGET_SHAPES if label in candidate_rows} + baseline_selected_rows = {label: baseline_rows.get(label, {}) for label in SELECTED_TARGET_SHAPES if label in baseline_rows} + candidate_metric = candidate_report['summary']['primary_mean'] or 0.0 + baseline_metric = baseline_report['summary']['primary_mean'] or 0.0 + return {'tflops': candidate_metric, 'baseline_tflops': baseline_metric, 'metric_delta': candidate_metric - baseline_metric, 'all_correct': candidate_report['summary']['all_correct'], 'baseline_all_correct': baseline_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'baseline_performance_comparable': baseline_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'baseline_invalid_performance_reason': baseline_report['summary']['invalid_performance_reason'], 'measured_entrypoint': ''.join(['loom.examples.weave.knn_build_dispatch_default_7c3a_v1:', format(measured_function, '')]), 'baseline_entrypoint': 'loom.examples.weave.knn_build_dispatch_split72_4e09_de1a_3dc7_v48:benchmark_knn_build_dispatch_split72_de1a_3dc7_v48', 'measured_shape_labels': 'all_canonical' if shape_labels is None else tuple(shape_labels), 'selected_route_labels': SELECTED_TARGET_SHAPES, 'selected_route_rows': selected_rows, 'baseline_selected_route_rows': baseline_selected_rows, 'route_modules': PRODUCTION_ROUTE_MODULES, 'route_trace': route_trace_for_contract_shapes(shape_labels), 'route_trace_included': True, 'contract_summary': candidate_report['summary'], 'baseline_contract_summary': baseline_report['summary'], 'contract_performance': candidate_report['performance'], 'baseline_contract_performance': baseline_report['performance'], 'timing_backends': timing_backends, 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'report': candidate_report, 'baseline_report': baseline_report} + +def _failed_baseline_report(exc: Exception, *, shape_labels) -> dict[str, Any]: + reason = ''.join([format(type(exc).__name__, ''), ': ', format(exc, '')]) + return {'contract': eval_mod.CONTRACT.kernel, 'contract_version': eval_mod.CONTRACT.contract_version, 'summary': {'all_correct': False, 'checked_shape_count': 0, 'failed_shape_count': 1, 'first_correctness_failure': reason, 'performance_comparable': False, 'invalid_performance_reason': reason, 'primary_mean': None, 'primary_metric': 'tflops'}, 'performance': {'comparable': False, 'invalid_reason': reason, 'primary_mean': None, 'primary_metric': 'tflops', 'valid_measurement_count': 0}, 'per_shape': {}, 'benchmark_exception': reason, 'measured_shape_labels': 'all_canonical' if shape_labels is None else tuple(shape_labels)} + +def _shape_labels_include_k96(shape_labels) -> bool: + if shape_labels is None: + return True + return bool(K96_TARGET_SHAPE_SET.intersection({str(label) for label in shape_labels})) + +def benchmark_knn_build_dispatch_default_7c3a_v1(*, use_cupti: bool=False, shape_labels=None) -> dict[str, Any]: + """Default full-denominator benchmark with same-session old-dispatcher baseline.""" + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels) + if _shape_labels_include_k96(shape_labels): + baseline_report = _failed_baseline_report(RuntimeError('current split72/de1a/3dc7 dispatcher rejects build_over64_stress_qm2048_k96'), shape_labels=shape_labels) + else: + try: + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=baseline_3dc7.candidate) + except Exception as exc: + baseline_report = _failed_baseline_report(exc, shape_labels=shape_labels) + return _benchmark_payload(candidate_report, baseline_report, use_cupti=use_cupti, shape_labels=shape_labels, measured_function='benchmark_knn_build_dispatch_default_7c3a_v1') diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_e3de_8712_bcb3_2cfd_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_e3de_8712_bcb3_2cfd_v1.py new file mode 100644 index 00000000..1b0e14fc --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_e3de_8712_bcb3_2cfd_v1.py @@ -0,0 +1,298 @@ +"""2cfd synthesized kNN build dispatcher from e3de, 8712, and bcb3. + +Minimum target architecture: sm_100a. This dispatcher-synthesis wrapper starts +from the e3de full67 dispatcher, then adds exact guards for the 8712 rectangular +D64 search seed and the replayed bcb3 Q1 online M-bucket seed. The inherited +e3de D64 build route remains unchanged. Guard misses delegate to e3de, so every +production route stays Weave-only. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +from pathlib import Path +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_d64_fdd7_e3de_v1 as base_e3de +from . import knn_build_ragonline_mbucket_aa88_q1m_v3 as q1_bcb3 +from . import knn_build_rect_d64_cf49_v2 as rect_8712 +ROUTE_BASE_E3DE = 'loom.examples.weave.knn_build_dispatch_d64_fdd7_e3de_v1:launch_from_contract_inputs' +ROUTE_BASE_8700 = base_e3de.ROUTE_BASE_8700 +ROUTE_RECT_8712 = 'loom.examples.weave.knn_build_rect_d64_cf49_v2:rect_d64_split_cached_s16' +ROUTE_Q1_BCB3_SPLIT72 = 'rag_online_mbucket_aa88_q1m_split72_coopmerge' +ROUTE_Q1_BCB3_M250_SPLIT74 = 'rag_online_mbucket_aa88_q1m_m250split74_coopmerge' +DEFAULT_PORTFOLIO_ID = base_e3de.DEFAULT_PORTFOLIO_ID +D64_TARGET_SHAPES = base_e3de.D64_TARGET_SHAPES +RECT_TARGET_SHAPES = rect_8712.TARGET_SHAPES +Q1_TARGET_SHAPES = q1_bcb3.TARGET_SHAPES +ADDED_TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["search_rect_b1_q1024_m32768_d64_k10", "rag_online_b1_q1_m100000_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10"]}')) +CONSUMED_SEED_TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_dim_sweep_b1_q1024_m1024_d64_k10", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q4096_m4096_d64_k10", "search_rect_b1_q1024_m32768_d64_k10", "rag_online_b1_q1_m100000_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10"]}')) +SELECTED_TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_large_b1_q8192_m8192_d128_k20", "build_large_b1_q8192_m8192_d128_k32", "build_over64_stress_qm2048_k96", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32", "rag_batch_b2_q256_m50000_d128_k10", "rag_irregular_b1_q512_m131071_d128_k10", "search_rect_b1_q1024_m8192_d128_k10", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q2048_m2048_d256_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10", "search_rect_b1_q2048_m32768_d128_k10", "build_large_tail_b1_q6144_m6144_d128_k20", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "build_over32_stress_qm2048_k64", "build_over32_stress_qm4096_k64", "build_k_sweep_qm512_k1", "build_k_sweep_qm512_k2", "build_k_sweep_qm1024_k16", "build_k_sweep_qm512_k4", "build_k_sweep_qm512_k5", "build_k_sweep_qm512_k6", "rag_microbatch_b1_q8_m100000_d128_k10", "rag_microbatch_b1_q32_m100000_d128_k10", "build_dim_sweep_b1_q1024_m1024_d64_k10", "build_dim_sweep_b1_q4096_m4096_d64_k10", "search_rect_b1_q1024_m32768_d64_k10", "rag_online_b1_q1_m100000_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10"]}')) +GUARD_MISS_AUDIT_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_dim_sweep_b1_q1024_m1024_d64_k10", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q4096_m4096_d64_k10", "search_rect_b1_q1024_m32768_d64_k10", "rag_online_b1_q1_m100000_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "build_k_sweep_qm512_k4", "build_k_sweep_qm512_k8", "build_k_sweep_qm512_k10", "rag_microbatch_b1_q8_m100000_d128_k10", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_microbatch_b1_q32_m100000_d128_k10", "build_large_b1_q8192_m8192_d128_k20", "build_qm2048_d128_k10", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "flashml_correctness_b1_q256_m256_d128_k5", "build_over32_stress_qm2048_k64", "search_rect_over32_b1_q2048_m65536_d128_k64", "rag_offline_large_m_over32_b1_q2048_m250000_d128_k64", "build_k_sweep_qm512_k5", "build_over32_stress_qm4096_k64"]}')) +DISPATCH_CORRECTNESS_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["flashml_correctness_b1_q256_m256_d128_k5", "build_large_b1_q8192_m8192_d128_k20", "build_large_b1_q8192_m8192_d128_k32", "build_over64_stress_qm2048_k96", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32", "rag_batch_b2_q256_m50000_d128_k10", "rag_irregular_b1_q512_m131071_d128_k10", "search_rect_b1_q1024_m8192_d128_k10", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q2048_m2048_d256_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10", "search_rect_b1_q2048_m32768_d128_k10", "build_large_tail_b1_q6144_m6144_d128_k20", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "build_over32_stress_qm2048_k64", "build_over32_stress_qm4096_k64", "build_k_sweep_qm512_k1", "build_k_sweep_qm512_k2", "build_k_sweep_qm1024_k16", "build_k_sweep_qm512_k4", "build_k_sweep_qm512_k5", "build_k_sweep_qm512_k6", "rag_microbatch_b1_q8_m100000_d128_k10", "rag_microbatch_b1_q32_m100000_d128_k10", "build_dim_sweep_b1_q1024_m1024_d64_k10", "build_dim_sweep_b1_q4096_m4096_d64_k10", "search_rect_b1_q1024_m32768_d64_k10", "rag_online_b1_q1_m100000_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "build_k_sweep_qm512_k8", "build_k_sweep_qm512_k10", "build_qm2048_d128_k10", "search_rect_over32_b1_q2048_m65536_d128_k64", "rag_offline_large_m_over32_b1_q2048_m250000_d128_k64", "rag_stream_b1_q128_m100000_d128_k10", "search_rect_b1_q4096_m65536_d128_k20", "rag_offline_largek_b1_q4096_m100000_d128_k20", "rag_offline_large_m_b1_q8192_m250000_d128_k20"]}')) +ROUTE_SEED_ID = {base_e3de.ROUTE_D64_FDD7_S8: 'd64_fdd7_aa88_v2', base_e3de.ROUTE_D64_FDD7_S4: 'd64_fdd7_aa88_v2', ROUTE_RECT_8712: 'rect_d64_cf49_v2_8712', ROUTE_Q1_BCB3_SPLIT72: 'q1_mbucket_aa88_q1m_v3_bcb3', ROUTE_Q1_BCB3_M250_SPLIT74: 'q1_mbucket_aa88_q1m_v3_bcb3'} +ROUTE_ENTRYPOINTS = {**base_e3de.ROUTE_ENTRYPOINTS, ROUTE_RECT_8712: 'loom.examples.weave.knn_build_rect_d64_cf49_v2:launch_from_contract_inputs', ROUTE_Q1_BCB3_SPLIT72: 'loom.examples.weave.knn_build_ragonline_mbucket_aa88_q1m_v3:launch_from_contract_inputs', ROUTE_Q1_BCB3_M250_SPLIT74: 'loom.examples.weave.knn_build_ragonline_mbucket_aa88_q1m_v3:launch_from_contract_inputs'} +PRODUCTION_ROUTE_MODULES = {**base_e3de.PRODUCTION_ROUTE_MODULES, 'rect_d64_cf49_v2_8712': 'loom.examples.weave.knn_build_rect_d64_cf49_v2:launch_from_contract_inputs', 'q1_mbucket_aa88_q1m_v3_bcb3': 'loom.examples.weave.knn_build_ragonline_mbucket_aa88_q1m_v3:launch_from_contract_inputs', 'base_e3de': ROUTE_BASE_E3DE} +CANDIDATE_DISPATCHERS = ({'id': 'baseline_e3de_d64_fdd7', 'entrypoint': 'loom.examples.weave.knn_build_dispatch_d64_fdd7_e3de_v1:benchmark_knn_build_dispatch_d64_fdd7_e3de_v1', 'consumed_seeds': ('d64_fdd7_aa88_v2',), 'guard_plan': ('e3de D64 build guard', 'then 8700 all-M64/S128 dispatcher'), 'expected_shape_wins': base_e3de.CONSUMED_SEED_TARGET_SHAPES, 'fallback': ROUTE_BASE_8700, 'rejected_reason': 'same-session baseline for 2cfd synthesis'}, {'id': 'rect_only_2cfd', 'entrypoint': 'loom.examples.weave.knn_build_dispatch_e3de_8712_bcb3_2cfd_v1:benchmark_rect_only_2cfd_v1', 'consumed_seeds': ('rect_d64_cf49_v2_8712',), 'guard_plan': ('e3de D64 build guard', 'exact rectangular D64 search guard -> 8712', 'then e3de fallback'), 'expected_shape_wins': RECT_TARGET_SHAPES, 'fallback': ROUTE_BASE_E3DE, 'rejected_reason': 'diagnostic candidate; rank requested additive batch including Q1 replay'}, {'id': 'q1_only_2cfd', 'entrypoint': 'loom.examples.weave.knn_build_dispatch_e3de_8712_bcb3_2cfd_v1:benchmark_q1_only_2cfd_v1', 'consumed_seeds': ('q1_mbucket_aa88_q1m_v3_bcb3',), 'guard_plan': ('e3de D64 build guard', 'exact Q1 online M-bucket guard -> bcb3', 'then e3de fallback'), 'expected_shape_wins': Q1_TARGET_SHAPES, 'fallback': ROUTE_BASE_E3DE, 'rejected_reason': 'diagnostic candidate; selected candidate keeps the compatible rectangular route too'}, {'id': 'e3de_rect8712_q1bcb3_2cfd_v1', 'entrypoint': 'loom.examples.weave.knn_build_dispatch_e3de_8712_bcb3_2cfd_v1:benchmark_knn_build_dispatch_e3de_8712_bcb3_2cfd_v1', 'consumed_seeds': ('d64_fdd7_aa88_v2', 'rect_d64_cf49_v2_8712', 'q1_mbucket_aa88_q1m_v3_bcb3'), 'guard_plan': ('e3de D64 build guard', 'exact rectangular D64 search guard -> 8712', 'exact Q1 online M-bucket guard -> bcb3', 'then e3de fallback'), 'expected_shape_wins': ADDED_TARGET_SHAPES, 'fallback': ROUTE_BASE_E3DE, 'rejected_reason': None}) +TARGETED_SEED_ROWS = {**base_e3de.TARGETED_SEED_ROWS, 'search_rect_b1_q1024_m32768_d64_k10': {'kernel_ms': 0.197506, 'flashlib_ms': 0.203873, 'ratio_vs_flashlib': 1.0322369953317874, 'tflops': 21.74600921490993, 'split_count': 16, 'merge_route': 's16_cached', 'route': ROUTE_RECT_8712}, 'rag_online_b1_q1_m100000_d128_k10': {'kernel_ms': 0.056641, 'flashlib_ms': 0.060833, 'ratio_vs_flashlib': 1.0740099927614273, 'tflops': 0.4519694214438305, 'split_count': 72, 'merge_route': 'four_warp_coop_k10', 'route': ROUTE_Q1_BCB3_SPLIT72}, 'rag_online_irregular_b1_q1_m131071_d128_k10': {'kernel_ms': 0.068352, 'flashlib_ms': 0.067329, 'ratio_vs_flashlib': 0.9850333567415731, 'tflops': 0.49090262172284643, 'split_count': 72, 'merge_route': 'four_warp_coop_k10', 'route': ROUTE_Q1_BCB3_SPLIT72}, 'rag_online_large_m_b1_q1_m250000_d128_k10': {'kernel_ms': 0.104673, 'flashlib_ms': 0.091777, 'ratio_vs_flashlib': 0.8767972638598301, 'tflops': 0.6114279709189571, 'split_count': 74, 'merge_route': 'four_warp_coop_k10', 'route': ROUTE_Q1_BCB3_M250_SPLIT74}} +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k48over32", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 48]], "cta_group": 1, "threads": 192}')) + +def route_for_contract_inputs(inputs: dict[str, Any], *, portfolio_id: str=DEFAULT_PORTFOLIO_ID, force_fallback: bool=False, enable_d64_fdd7: bool=True, enable_rect_d64: bool=True, enable_q1_mbucket: bool=True, enable_rag_seed_portfolio: bool=True, enable_q512_k456: bool=True) -> str: + if not force_fallback and enable_d64_fdd7 and base_e3de._eligible_d64_fdd7(inputs): + return base_e3de.route_for_contract_inputs(inputs, portfolio_id=portfolio_id, force_fallback=False, enable_d64_fdd7=True, enable_rag_seed_portfolio=enable_rag_seed_portfolio, enable_q512_k456=enable_q512_k456) + if not force_fallback and enable_rect_d64 and rect_8712._eligible_rect_d64(inputs): + return ROUTE_RECT_8712 + if not force_fallback and enable_q1_mbucket and q1_bcb3._eligible_rag_online_mbucket(inputs): + return q1_bcb3.route_for_contract_inputs(inputs) + return base_e3de.route_for_contract_inputs(inputs, portfolio_id=portfolio_id, force_fallback=force_fallback, enable_d64_fdd7=enable_d64_fdd7, enable_rag_seed_portfolio=enable_rag_seed_portfolio, enable_q512_k456=enable_q512_k456) + +def _launch_route(inputs: dict[str, Any], route: str) -> None: + if route == ROUTE_RECT_8712 and rect_8712._eligible_rect_d64(inputs): + rect_8712.launch_from_contract_inputs(inputs) + return + if route in (ROUTE_Q1_BCB3_SPLIT72, ROUTE_Q1_BCB3_M250_SPLIT74) and q1_bcb3._eligible_rag_online_mbucket(inputs): + q1_bcb3.launch_from_contract_inputs(inputs) + return + base_e3de._launch_route(inputs, route) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, portfolio_id: str=DEFAULT_PORTFOLIO_ID, force_fallback: bool=False, enable_d64_fdd7: bool=True, enable_rect_d64: bool=True, enable_q1_mbucket: bool=True, enable_rag_seed_portfolio: bool=True, enable_q512_k456: bool=True) -> None: + route = route_for_contract_inputs(inputs, portfolio_id=portfolio_id, force_fallback=force_fallback, enable_d64_fdd7=enable_d64_fdd7, enable_rect_d64=enable_rect_d64, enable_q1_mbucket=enable_q1_mbucket, enable_rag_seed_portfolio=enable_rag_seed_portfolio, enable_q512_k456=enable_q512_k456) + _launch_route(inputs, route) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_rect_only(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, enable_q1_mbucket=False) + +def candidate_q1_only(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, enable_rect_d64=False) + +def candidate_base_e3de(inputs: dict[str, Any]) -> None: + base_e3de.launch_from_contract_inputs(inputs) + +def candidate_base_8700(inputs: dict[str, Any]) -> None: + base_e3de.candidate_base_dispatcher(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return base_e3de._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=DISPATCH_CORRECTNESS_SHAPES, benchmark: bool=False) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=None, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + shapes = None if shape_labels is None else _select_contract_shapes(shape_labels) + return evaluate_contract(shapes=shapes, correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def _trace_inputs_from_shape(shape: dict[str, Any]) -> dict[str, Any]: + return base_e3de._trace_inputs_from_shape(shape) + +def _inputs_for_label(label: str) -> dict[str, Any]: + return base_e3de._inputs_for_label(label) + +def _selected_entrypoint_for_route(route: str) -> str: + return ROUTE_ENTRYPOINTS.get(route, base_e3de._selected_entrypoint_for_route(route)) + +def _base_route_trace_record(inputs: dict[str, Any], *, force_fallback: bool=False) -> dict[str, Any]: + row = dict(base_e3de._route_trace_record(inputs, force_fallback=force_fallback)) + route = str(row.get('selected_route') or base_e3de.route_for_contract_inputs(inputs, force_fallback=force_fallback)) + selected_seed = row.get('selected_seed') or row.get('consumed_seed') + row.setdefault('shape_key', inputs.get('label')) + row.setdefault('selected_entrypoint', _selected_entrypoint_for_route(route)) + row.setdefault('selected_seed', selected_seed) + row.setdefault('expected_seed', selected_seed) + row.setdefault('route_kind', row.get('route_kind', 'general')) + if selected_seed: + row.setdefault('route_source', 'shape-specific-seed') + elif row.get('route_kind') == 'coverage-only': + row.setdefault('route_source', 'generic-weave-fallback') + else: + row.setdefault('route_source', 'broad-dispatcher') + row.setdefault('guard_id', row.get('candidate_guard_status')) + row.setdefault('guard_condition', 'inherited e3de guard plan') + row.setdefault('classification', 'route-ok') + row.setdefault('dispatcher_kernel_ms', None) + row.setdefault('shape_specific_kernel_ms', None) + row.setdefault('relative_speedup_vs_baseline', None) + row['base_e3de_route'] = base_e3de.route_for_contract_inputs(inputs) + row['base_8700_route'] = base_e3de.base_8700.route_for_contract_inputs(inputs, portfolio_id=DEFAULT_PORTFOLIO_ID) + return row + +def _route_trace_record(inputs: dict[str, Any], *, force_fallback: bool=False) -> dict[str, Any]: + label = str(inputs.get('label')) + base_e3de_route = base_e3de.route_for_contract_inputs(inputs) + base_8700_route = base_e3de.base_8700.route_for_contract_inputs(inputs, portfolio_id=DEFAULT_PORTFOLIO_ID) + if force_fallback and (base_e3de._eligible_d64_fdd7(inputs) or rect_8712._eligible_rect_d64(inputs) or q1_bcb3._eligible_rag_online_mbucket(inputs)): + row = _base_route_trace_record(inputs, force_fallback=True) + expected_seed = ROUTE_SEED_ID.get(route_for_contract_inputs(inputs, force_fallback=False)) + row['selected_route'] = base_e3de.route_for_contract_inputs(inputs, force_fallback=True) + row['selected_entrypoint'] = _selected_entrypoint_for_route(str(row['selected_route'])) + row['selected_seed'] = row.get('consumed_seed') + row['expected_seed'] = expected_seed + row['guard_id'] = 'forced_fallback_2cfd_overlays_disabled' + row['guard_condition'] = 'forced fallback to inherited e3de/8700 path; 2cfd overlays disabled' + row['forced_disabled_seeds'] = ('d64_fdd7_aa88_v2', 'rect_d64_cf49_v2_8712', 'q1_mbucket_aa88_q1m_v3_bcb3') + row['candidate_guard_status'] = 'forced_fallback' + row['classification'] = 'route-ok' + return row + route = route_for_contract_inputs(inputs, force_fallback=force_fallback) + if label in RECT_TARGET_SHAPES and route == ROUTE_RECT_8712: + targeted = dict(TARGETED_SEED_ROWS[label]) + return {'shape_key': inputs.get('label'), 'selected_route': route, 'selected_entrypoint': ROUTE_ENTRYPOINTS[route], 'selected_seed': ROUTE_SEED_ID[route], 'expected_seed': 'rect_d64_cf49_v2_8712', 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': 'rect_d64_q1024_m32768_k10_cf49_s16_exact', 'guard_condition': 'exact BF16 non-build B=1 Q=1024 M=32768 D=64 K=10 rectangular D64 seed', 'coverage': '2cfd consumes the 8712 rectangular D64 seed ahead of e3de', 'consumed_seed': ROUTE_SEED_ID[route], 'replaced_route': base_e3de_route, 'base_e3de_route': base_e3de_route, 'base_8700_route': base_8700_route, 'row_selection': targeted, 'targeted_seed_timing_backend': 'cupti', 'targeted_seed_kernel_ms': targeted['kernel_ms'], 'targeted_seed_ratio_vs_flashlib': targeted['ratio_vs_flashlib'], 'classification': 'seed-consumed', 'dispatcher_kernel_ms': None, 'shape_specific_kernel_ms': targeted['kernel_ms'], 'relative_speedup_vs_baseline': None} + if label in Q1_TARGET_SHAPES and route in (ROUTE_Q1_BCB3_SPLIT72, ROUTE_Q1_BCB3_M250_SPLIT74): + targeted = dict(TARGETED_SEED_ROWS[label]) + split_count = q1_bcb3._split_count_for_inputs(inputs) + return {'shape_key': inputs.get('label'), 'selected_route': route, 'selected_entrypoint': ROUTE_ENTRYPOINTS[route], 'selected_seed': ROUTE_SEED_ID[route], 'expected_seed': 'q1_mbucket_aa88_q1m_v3_bcb3', 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': 'q1_online_mbucket_aa88_q1m_v3_exact', 'guard_condition': ''.join(['exact BF16 non-build B=1 Q=1 M=', format(int(inputs.get('M')), ''), ' D=128 K=10 Q1 online M-bucket seed']), 'coverage': '2cfd consumes bcb3 Q1 M-bucket seed ahead of e3de', 'consumed_seed': ROUTE_SEED_ID[route], 'replaced_route': base_e3de_route, 'base_e3de_route': base_e3de_route, 'base_8700_route': base_8700_route, 'row_selection': targeted, 'split_count': split_count, 'targeted_seed_timing_backend': 'cupti', 'targeted_seed_kernel_ms': targeted['kernel_ms'], 'targeted_seed_ratio_vs_flashlib': targeted['ratio_vs_flashlib'], 'classification': 'seed-consumed', 'dispatcher_kernel_ms': None, 'shape_specific_kernel_ms': targeted['kernel_ms'], 'relative_speedup_vs_baseline': None} + return _base_route_trace_record(inputs) + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + return [_route_trace_record(_trace_inputs_from_shape(shape), force_fallback=force_fallback) for shape in selected] + +def _timing_backends_for_reports(*reports: dict[str, Any]) -> list[str]: + return base_e3de._timing_backends_for_reports(*reports) + +def _rows_for_labels(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return base_e3de._rows_for_labels(report, labels) + +def _seed_delta_matrix(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> list[dict[str, Any]]: + matrix = [] + for label in CONSUMED_SEED_TARGET_SHAPES: + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') + inputs = _inputs_for_label(label) + route = route_for_contract_inputs(inputs) + targeted = TARGETED_SEED_ROWS.get(label, {}) + matrix.append({'shape_key': label, 'baseline_route': base_e3de.route_for_contract_inputs(inputs), 'candidate_route': route, 'selected_seed': ROUTE_SEED_ID.get(route), 'candidate_ms': candidate_ms, 'baseline_ms': baseline_ms, 'flashlib_ms': flashlib_ms, 'metric_delta_ms': candidate_ms - baseline_ms if candidate_ms and baseline_ms else None, 'speedup_vs_baseline_e3de': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'ratio_vs_flashlib': flashlib_ms / candidate_ms if candidate_ms and flashlib_ms else None, 'targeted_seed_kernel_ms': targeted.get('kernel_ms'), 'targeted_seed_ratio_vs_flashlib': targeted.get('ratio_vs_flashlib'), 'timing_backend': candidate_row.get('timing_backend') or baseline_row.get('timing_backend')}) + return matrix + +def _frontmatter_seed_delta_matrix(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> list[dict[str, Any]]: + rows = [] + for item in _seed_delta_matrix(candidate_report, baseline_report): + rows.append({'shape_key': item['shape_key'], 'baseline_route': item['baseline_route'], 'candidate_deltas': [{'candidate_id': 'e3de_rect8712_q1bcb3_2cfd_v1', 'selected_seed': item['selected_seed'], 'metric_delta': item['metric_delta_ms'], 'ratio_vs_flashlib': item['ratio_vs_flashlib'], 'timing_backend': item['timing_backend'] or 'cupti'}]}) + return rows + +def _per_consumed_row_delta(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> dict[str, Any]: + deltas = {} + for item in _seed_delta_matrix(candidate_report, baseline_report): + label = item['shape_key'] + deltas[label] = {'candidate_ms': item['candidate_ms'], 'baseline_e3de_ms': item['baseline_ms'], 'flashlib_ms': item['flashlib_ms'], 'speedup_vs_baseline_e3de': item['speedup_vs_baseline_e3de'], 'ratio_vs_flashlib': item['ratio_vs_flashlib'], 'candidate_route': item['candidate_route'], 'baseline_e3de_route': item['baseline_route'], 'selected_seed': item['selected_seed'], 'targeted_seed_kernel_ms': item['targeted_seed_kernel_ms'], 'targeted_seed_ratio_vs_flashlib': item['targeted_seed_ratio_vs_flashlib'], 'candidate_passed': candidate_report.get('per_shape', {}).get(label, {}).get('passed'), 'baseline_passed': baseline_report.get('per_shape', {}).get(label, {}).get('passed')} + return deltas + +def _annotate_route_trace(route_trace: list[dict[str, Any]], candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> list[dict[str, Any]]: + annotated = [] + for row in route_trace: + label = str(row.get('shape_key')) + out = dict(row) + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + out['dispatcher_kernel_ms'] = candidate_ms + out['baseline_dispatcher_kernel_ms'] = baseline_ms + out['flashlib_ms'] = candidate_row.get('flashlib_ms') + out['relative_speedup_vs_baseline'] = baseline_ms / candidate_ms if candidate_ms and baseline_ms else None + if label in CONSUMED_SEED_TARGET_SHAPES and out.get('selected_seed') == ROUTE_SEED_ID.get(out.get('selected_route')): + speedup = out['relative_speedup_vs_baseline'] + out['classification'] = 'seed-consumed' if speedup is None or speedup >= 1.0 else 'kernel-slow' + elif isinstance(candidate_row.get('ratio_vs_flashlib'), (float, int)) and candidate_row['ratio_vs_flashlib'] < 1.0: + out['classification'] = 'kernel-slow' if out.get('route_kind') == 'specialized' else 'fallback-slow' + annotated.append(out) + return annotated + +def _below_flashlib_rows(report: dict[str, Any]) -> list[dict[str, Any]]: + trace_by_label = {str(row['shape_key']): row for row in route_trace_for_contract_shapes()} + rows = [] + for label, row in sorted(report.get('per_shape', {}).items()): + ratio = row.get('ratio_vs_flashlib') + if isinstance(ratio, (float, int)) and ratio < 1.0: + inputs = _inputs_for_label(label) + trace_row = trace_by_label.get(label, {}) + rows.append({'shape_key': label, 'kernel_ms': row.get('kernel_ms'), 'flashlib_ms': row.get('flashlib_ms'), 'ratio_vs_flashlib': ratio, 'selected_route': route_for_contract_inputs(inputs), 'route_kind': trace_row.get('route_kind', 'unknown'), 'classification': 'kernel-slow' if trace_row.get('route_kind') == 'specialized' else 'fallback-slow'}) + return rows + +def _hot_bucket_parity(report: dict[str, Any]) -> dict[str, str]: + buckets = {'d64_build_q1024_q2048_q4096_k10': D64_TARGET_SHAPES, 'rectangular_search_q1024_m32768_d64_k10': RECT_TARGET_SHAPES, 'rag_online_q1_m100000_m131071_m250000_k10': Q1_TARGET_SHAPES} + out = {} + for bucket, labels in buckets.items(): + out[bucket] = 'pass' + for label in labels: + ratio = report.get('per_shape', {}).get(label, {}).get('ratio_vs_flashlib') + if not isinstance(ratio, (float, int)) or ratio < 1.0: + out[bucket] = 'fail' + break + out['rag_microbatch_q8_q16_q32_m100000_k10'] = 'inherited_e3de' + return out + +def _benchmark_payload(candidate_report: dict[str, Any], baseline_e3de_report: dict[str, Any], baseline_8700_report: dict[str, Any], *, use_cupti: bool, shape_labels, measured_function: str, candidate_id: str) -> dict[str, Any]: + candidate_metric = candidate_report['summary']['primary_mean'] + e3de_metric = baseline_e3de_report['summary']['primary_mean'] + baseline_8700_metric = baseline_8700_report['summary']['primary_mean'] + route_trace = _annotate_route_trace(route_trace_for_contract_shapes(shape_labels), candidate_report, baseline_e3de_report) + below_flashlib = _below_flashlib_rows(candidate_report) + return {'candidate_id': candidate_id, 'tflops': candidate_metric, 'baseline_tflops': e3de_metric, 'same_session_8700_tflops': baseline_8700_metric, 'metric_delta': candidate_metric - e3de_metric if candidate_metric and e3de_metric else None, 'metric_delta_vs_8700': candidate_metric - baseline_8700_metric if candidate_metric and baseline_8700_metric else None, 'all_correct': candidate_report['summary']['all_correct'], 'baseline_all_correct': baseline_e3de_report['summary']['all_correct'], 'baseline_8700_all_correct': baseline_8700_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'baseline_performance_comparable': baseline_e3de_report['summary']['performance_comparable'], 'baseline_8700_performance_comparable': baseline_8700_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'baseline_invalid_performance_reason': baseline_e3de_report['summary']['invalid_performance_reason'], 'baseline_8700_invalid_performance_reason': baseline_8700_report['summary']['invalid_performance_reason'], 'measured_entrypoint': ''.join(['loom.examples.weave.knn_build_dispatch_e3de_8712_bcb3_2cfd_v1:', format(measured_function, '')]), 'baseline_entrypoint': 'loom.examples.weave.knn_build_dispatch_d64_fdd7_e3de_v1:benchmark_knn_build_dispatch_d64_fdd7_e3de_v1', 'baseline_8700_entrypoint': 'benchmark_data.json:knn_build -> loom.examples.weave.knn_build_dispatch_rag_seed_portfolio_8700_v1:benchmark_knn_build_dispatch_rag_seed_portfolio_8700_v1(portfolio_id=all_m64_s128)', 'measured_shape_labels': 'all_canonical' if shape_labels is None else tuple(shape_labels), 'selected_route_labels': SELECTED_TARGET_SHAPES, 'consumed_seed_labels': CONSUMED_SEED_TARGET_SHAPES, 'added_seed_labels': ADDED_TARGET_SHAPES, 'guard_miss_audit_labels': GUARD_MISS_AUDIT_SHAPES, 'selected_route_rows': _rows_for_labels(candidate_report, SELECTED_TARGET_SHAPES), 'baseline_selected_route_rows': _rows_for_labels(baseline_e3de_report, SELECTED_TARGET_SHAPES), 'baseline_8700_selected_route_rows': _rows_for_labels(baseline_8700_report, SELECTED_TARGET_SHAPES), 'consumed_seed_rows': _rows_for_labels(candidate_report, CONSUMED_SEED_TARGET_SHAPES), 'baseline_consumed_seed_rows': _rows_for_labels(baseline_e3de_report, CONSUMED_SEED_TARGET_SHAPES), 'guard_miss_audit_rows': _rows_for_labels(candidate_report, GUARD_MISS_AUDIT_SHAPES), 'baseline_guard_miss_audit_rows': _rows_for_labels(baseline_e3de_report, GUARD_MISS_AUDIT_SHAPES), 'per_consumed_row_delta': _per_consumed_row_delta(candidate_report, baseline_e3de_report), 'seed_delta_matrix': _seed_delta_matrix(candidate_report, baseline_e3de_report), 'frontmatter_seed_delta_matrix': _frontmatter_seed_delta_matrix(candidate_report, baseline_e3de_report), 'targeted_seed_rows': TARGETED_SEED_ROWS, 'candidate_dispatchers': CANDIDATE_DISPATCHERS, 'selected_candidate_dispatcher': 'e3de_rect8712_q1bcb3_2cfd_v1', 'route_modules': PRODUCTION_ROUTE_MODULES, 'route_trace': route_trace, 'forced_fallback_route_trace': route_trace_for_contract_shapes(shape_labels, force_fallback=True), 'route_trace_included': True, 'contract_summary': candidate_report['summary'], 'baseline_contract_summary': baseline_e3de_report['summary'], 'baseline_8700_contract_summary': baseline_8700_report['summary'], 'contract_performance': candidate_report['performance'], 'baseline_contract_performance': baseline_e3de_report['performance'], 'baseline_8700_contract_performance': baseline_8700_report['performance'], 'timing_backends': _timing_backends_for_reports(candidate_report, baseline_e3de_report, baseline_8700_report), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'hot_bucket_parity': _hot_bucket_parity(candidate_report), 'performance_coverage': 'partial' if below_flashlib else 'pass', 'coverage_only_routes': [], 'hot_bucket_blockers': below_flashlib, 'report': candidate_report, 'baseline_report': baseline_e3de_report, 'baseline_8700_report': baseline_8700_report} + +def _benchmark_candidate(*, use_cupti: bool=True, shape_labels=None, kernel_fn: Callable[[dict[str, Any]], Any]=candidate, measured_function: str, candidate_id: str, baseline_e3de_report: dict[str, Any] | None=None, baseline_8700_report: dict[str, Any] | None=None) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=kernel_fn) + if baseline_e3de_report is None: + baseline_e3de_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_base_e3de) + if baseline_8700_report is None: + baseline_8700_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_base_8700) + return _benchmark_payload(candidate_report, baseline_e3de_report, baseline_8700_report, use_cupti=use_cupti, shape_labels=shape_labels, measured_function=measured_function, candidate_id=candidate_id) + +def benchmark_knn_build_dispatch_e3de_8712_bcb3_2cfd_v1(*, use_cupti: bool=True, shape_labels=None, baseline_e3de_report: dict[str, Any] | None=None, baseline_8700_report: dict[str, Any] | None=None) -> dict[str, Any]: + return _benchmark_candidate(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate, measured_function='benchmark_knn_build_dispatch_e3de_8712_bcb3_2cfd_v1', candidate_id='e3de_rect8712_q1bcb3_2cfd_v1', baseline_e3de_report=baseline_e3de_report, baseline_8700_report=baseline_8700_report) + +def benchmark_rect_only_2cfd_v1(*, use_cupti: bool=True, shape_labels=None, baseline_e3de_report: dict[str, Any] | None=None, baseline_8700_report: dict[str, Any] | None=None) -> dict[str, Any]: + return _benchmark_candidate(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_rect_only, measured_function='benchmark_rect_only_2cfd_v1', candidate_id='rect_only_2cfd', baseline_e3de_report=baseline_e3de_report, baseline_8700_report=baseline_8700_report) + +def benchmark_q1_only_2cfd_v1(*, use_cupti: bool=True, shape_labels=None, baseline_e3de_report: dict[str, Any] | None=None, baseline_8700_report: dict[str, Any] | None=None) -> dict[str, Any]: + return _benchmark_candidate(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_q1_only, measured_function='benchmark_q1_only_2cfd_v1', candidate_id='q1_only_2cfd', baseline_e3de_report=baseline_e3de_report, baseline_8700_report=baseline_8700_report) + +def _denominator_label(shape_labels) -> str: + if shape_labels is None: + return ''.join(['full', format(len(eval_mod.CANONICAL_SHAPES), '')]) + return ''.join(['shape', format(len(tuple(shape_labels)), '')]) + +def write_benchmark_artifacts(artifact_dir: str | Path, *, use_cupti: bool=True, shape_labels=None) -> dict[str, str]: + payload = benchmark_knn_build_dispatch_e3de_8712_bcb3_2cfd_v1(use_cupti=use_cupti, shape_labels=shape_labels) + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + denom = _denominator_label(shape_labels) + candidate_path = out_dir / ''.join([format(denom, ''), '_dispatch_e3de_8712_bcb3_2cfd_v1.json']) + baseline_e3de_path = out_dir / ''.join([format(denom, ''), '_same_session_baseline_e3de_for_2cfd_v1.json']) + baseline_8700_path = out_dir / ''.join([format(denom, ''), '_same_session_baseline_8700_for_2cfd_v1.json']) + route_trace_path = out_dir / ''.join([format(denom, ''), '_route_trace_e3de_8712_bcb3_2cfd_v1.json']) + forced_trace_path = out_dir / ''.join([format(denom, ''), '_forced_fallback_trace_e3de_8712_bcb3_2cfd_v1.json']) + seed_matrix_path = out_dir / ''.join([format(denom, ''), '_seed_delta_matrix_e3de_8712_bcb3_2cfd_v1.json']) + candidate_path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + baseline_e3de_path.write_text(json.dumps({'candidate_id': 'baseline_e3de_d64_fdd7', 'measured_entrypoint': payload['baseline_entrypoint'], 'measured_shape_labels': payload['measured_shape_labels'], 'timing_backend_requested': payload['timing_backend_requested'], 'timing_backends': payload['timing_backends'], 'tflops': payload['baseline_tflops'], 'all_correct': payload['baseline_all_correct'], 'performance_comparable': payload['baseline_performance_comparable'], 'contract_summary': payload['baseline_contract_summary'], 'contract_performance': payload['baseline_contract_performance'], 'route_trace': base_e3de.route_trace_for_contract_shapes(shape_labels), 'route_trace_included': True, 'report': payload['baseline_report']}, indent=2, sort_keys=True) + '\n') + baseline_8700_path.write_text(json.dumps({'candidate_id': 'baseline_8700_all_m64_s128', 'measured_entrypoint': payload['baseline_8700_entrypoint'], 'measured_shape_labels': payload['measured_shape_labels'], 'timing_backend_requested': payload['timing_backend_requested'], 'timing_backends': payload['timing_backends'], 'tflops': payload['same_session_8700_tflops'], 'all_correct': payload['baseline_8700_all_correct'], 'performance_comparable': payload['baseline_8700_performance_comparable'], 'contract_summary': payload['baseline_8700_contract_summary'], 'contract_performance': payload['baseline_8700_contract_performance'], 'route_trace': base_e3de.base_8700.route_trace_for_contract_shapes(shape_labels, portfolio_id=DEFAULT_PORTFOLIO_ID), 'route_trace_included': True, 'report': payload['baseline_8700_report']}, indent=2, sort_keys=True) + '\n') + route_trace_path.write_text(json.dumps(payload['route_trace'], indent=2, sort_keys=True) + '\n') + forced_trace_path.write_text(json.dumps(payload['forced_fallback_route_trace'], indent=2, sort_keys=True) + '\n') + seed_matrix_path.write_text(json.dumps(payload['seed_delta_matrix'], indent=2, sort_keys=True) + '\n') + return {'candidate_payload': str(candidate_path), 'same_session_baseline_payload': str(baseline_e3de_path), 'same_session_8700_payload': str(baseline_8700_path), 'route_trace': str(route_trace_path), 'forced_fallback_trace': str(forced_trace_path), 'seed_delta_matrix': str(seed_matrix_path)} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_e3de_9138_bcb3_4247_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_e3de_9138_bcb3_4247_v1.py new file mode 100644 index 00000000..8eec97be --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_e3de_9138_bcb3_4247_v1.py @@ -0,0 +1,367 @@ +"""4247 kNN build dispatcher A/B candidate from 2cfd and 9138. + +Minimum target architecture: sm_100a. This dispatcher-consumption wrapper starts +from the 2cfd full67 dispatcher and changes only the exact rectangular D64 +search guard from the 8712 v2 seed to the 9138 v3 seed. The inherited e3de D64 +build route and bcb3 Q1 online M-bucket routes remain unchanged. Guard misses +delegate through the same e3de/8700 Weave stack, so every production route stays +Weave-only. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +from pathlib import Path +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_e3de_8712_bcb3_2cfd_v1 as base_2cfd +from . import knn_build_dispatch_d64_fdd7_e3de_v1 as base_e3de +from . import knn_build_over64_k96_a2f8_v1 as k96_a2f8 +from . import knn_build_ragonline_mbucket_aa88_q1m_v3 as q1_bcb3 +from . import knn_build_rect_d64_cf49_v3 as rect_9138 +ROUTE_BASE_E3DE = 'loom.examples.weave.knn_build_dispatch_d64_fdd7_e3de_v1:launch_from_contract_inputs' +ROUTE_BASE_8700 = base_e3de.ROUTE_BASE_8700 +ROUTE_RECT_9138 = 'loom.examples.weave.knn_build_rect_d64_cf49_v3:rect_d64_split_cached_s16' +ROUTE_Q1_BCB3_SPLIT72 = 'rag_online_mbucket_aa88_q1m_split72_coopmerge' +ROUTE_Q1_BCB3_M250_SPLIT74 = 'rag_online_mbucket_aa88_q1m_m250split74_coopmerge' +ROUTE_K96_A2F8 = 'loom.examples.weave.knn_build_over64_k96_a2f8_v1:generated_v8_k96' +DEFAULT_PORTFOLIO_ID = base_e3de.DEFAULT_PORTFOLIO_ID +D64_TARGET_SHAPES = base_e3de.D64_TARGET_SHAPES +RECT_TARGET_SHAPES = rect_9138.TARGET_SHAPES +Q1_TARGET_SHAPES = q1_bcb3.TARGET_SHAPES +K96_COVERAGE_TARGET_SHAPES = ('build_over64_stress_qm1024_k96', 'build_over64_stress_qm2048_k96', 'build_over64_stress_qm4096_k96') +K96_GENERATED_VARIANT_SHAPES = ('build_over64_stress_qm1024_k96', 'build_over64_stress_qm4096_k96') +K96_COVERAGE_TARGET_SHAPE_SET = set(K96_COVERAGE_TARGET_SHAPES) +K96_GENERATED_VARIANT_SHAPE_SET = set(K96_GENERATED_VARIANT_SHAPES) +COVERAGE_REPAIR_TARGET_SHAPES = K96_GENERATED_VARIANT_SHAPES +ADDED_TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["search_rect_b1_q1024_m32768_d64_k10", "rag_online_b1_q1_m100000_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10"]}')) +CONSUMED_SEED_TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_dim_sweep_b1_q1024_m1024_d64_k10", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q4096_m4096_d64_k10", "search_rect_b1_q1024_m32768_d64_k10", "rag_online_b1_q1_m100000_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "build_over64_stress_qm1024_k96", "build_over64_stress_qm4096_k96"]}')) +SELECTED_TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_large_b1_q8192_m8192_d128_k20", "build_large_b1_q8192_m8192_d128_k32", "build_over64_stress_qm2048_k96", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32", "rag_batch_b2_q256_m50000_d128_k10", "rag_irregular_b1_q512_m131071_d128_k10", "search_rect_b1_q1024_m8192_d128_k10", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q2048_m2048_d256_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10", "search_rect_b1_q2048_m32768_d128_k10", "build_large_tail_b1_q6144_m6144_d128_k20", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "build_over32_stress_qm2048_k64", "build_over32_stress_qm4096_k64", "build_k_sweep_qm512_k1", "build_k_sweep_qm512_k2", "build_k_sweep_qm1024_k16", "build_k_sweep_qm512_k4", "build_k_sweep_qm512_k5", "build_k_sweep_qm512_k6", "rag_microbatch_b1_q8_m100000_d128_k10", "rag_microbatch_b1_q32_m100000_d128_k10", "build_dim_sweep_b1_q1024_m1024_d64_k10", "build_dim_sweep_b1_q4096_m4096_d64_k10", "search_rect_b1_q1024_m32768_d64_k10", "rag_online_b1_q1_m100000_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "build_over64_stress_qm1024_k96", "build_over64_stress_qm4096_k96"]}')) +GUARD_MISS_AUDIT_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_dim_sweep_b1_q1024_m1024_d64_k10", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q4096_m4096_d64_k10", "search_rect_b1_q1024_m32768_d64_k10", "rag_online_b1_q1_m100000_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "build_over64_stress_qm1024_k96", "build_over64_stress_qm4096_k96", "build_k_sweep_qm512_k4", "build_k_sweep_qm512_k8", "build_k_sweep_qm512_k10", "rag_microbatch_b1_q8_m100000_d128_k10", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_microbatch_b1_q32_m100000_d128_k10", "build_large_b1_q8192_m8192_d128_k20", "build_qm2048_d128_k10", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "flashml_correctness_b1_q256_m256_d128_k5", "build_over32_stress_qm2048_k64", "search_rect_over32_b1_q2048_m65536_d128_k64", "rag_offline_large_m_over32_b1_q2048_m250000_d128_k64", "build_k_sweep_qm512_k5", "build_over32_stress_qm4096_k64"]}')) +DISPATCH_CORRECTNESS_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["flashml_correctness_b1_q256_m256_d128_k5", "build_large_b1_q8192_m8192_d128_k20", "build_large_b1_q8192_m8192_d128_k32", "build_over64_stress_qm2048_k96", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32", "rag_batch_b2_q256_m50000_d128_k10", "rag_irregular_b1_q512_m131071_d128_k10", "search_rect_b1_q1024_m8192_d128_k10", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q2048_m2048_d256_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10", "search_rect_b1_q2048_m32768_d128_k10", "build_large_tail_b1_q6144_m6144_d128_k20", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "build_over32_stress_qm2048_k64", "build_over32_stress_qm4096_k64", "build_k_sweep_qm512_k1", "build_k_sweep_qm512_k2", "build_k_sweep_qm1024_k16", "build_k_sweep_qm512_k4", "build_k_sweep_qm512_k5", "build_k_sweep_qm512_k6", "rag_microbatch_b1_q8_m100000_d128_k10", "rag_microbatch_b1_q32_m100000_d128_k10", "build_dim_sweep_b1_q1024_m1024_d64_k10", "build_dim_sweep_b1_q4096_m4096_d64_k10", "search_rect_b1_q1024_m32768_d64_k10", "rag_online_b1_q1_m100000_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "build_over64_stress_qm1024_k96", "build_over64_stress_qm4096_k96", "build_k_sweep_qm512_k8", "build_k_sweep_qm512_k10", "build_qm2048_d128_k10", "search_rect_over32_b1_q2048_m65536_d128_k64", "rag_offline_large_m_over32_b1_q2048_m250000_d128_k64", "rag_stream_b1_q128_m100000_d128_k10", "search_rect_b1_q4096_m65536_d128_k20", "rag_offline_largek_b1_q4096_m100000_d128_k20", "rag_offline_large_m_b1_q8192_m250000_d128_k20"]}')) +ROUTE_SEED_ID = {base_e3de.ROUTE_D64_FDD7_S8: 'd64_fdd7_aa88_v2', base_e3de.ROUTE_D64_FDD7_S4: 'd64_fdd7_aa88_v2', ROUTE_RECT_9138: 'rect_d64_cf49_v3_9138', ROUTE_Q1_BCB3_SPLIT72: 'q1_mbucket_aa88_q1m_v3_bcb3', ROUTE_Q1_BCB3_M250_SPLIT74: 'q1_mbucket_aa88_q1m_v3_bcb3', ROUTE_K96_A2F8: 'over64_k96_a2f8_v1'} +ROUTE_ENTRYPOINTS = {**base_e3de.ROUTE_ENTRYPOINTS, ROUTE_RECT_9138: 'loom.examples.weave.knn_build_rect_d64_cf49_v3:launch_from_contract_inputs', ROUTE_Q1_BCB3_SPLIT72: 'loom.examples.weave.knn_build_ragonline_mbucket_aa88_q1m_v3:launch_from_contract_inputs', ROUTE_Q1_BCB3_M250_SPLIT74: 'loom.examples.weave.knn_build_ragonline_mbucket_aa88_q1m_v3:launch_from_contract_inputs', ROUTE_K96_A2F8: 'loom.examples.weave.knn_build_over64_k96_a2f8_v1:_launch_over64_k96_split_path'} +PRODUCTION_ROUTE_MODULES = {**base_e3de.PRODUCTION_ROUTE_MODULES, 'rect_d64_cf49_v3_9138': 'loom.examples.weave.knn_build_rect_d64_cf49_v3:launch_from_contract_inputs', 'q1_mbucket_aa88_q1m_v3_bcb3': 'loom.examples.weave.knn_build_ragonline_mbucket_aa88_q1m_v3:launch_from_contract_inputs', 'over64_k96_a2f8_v1_generated_v8': 'loom.examples.weave.knn_build_over64_k96_a2f8_v1:_launch_over64_k96_split_path', 'base_e3de': ROUTE_BASE_E3DE} +CANDIDATE_DISPATCHERS = ({'id': 'baseline_2cfd', 'entrypoint': 'loom.examples.weave.knn_build_dispatch_e3de_8712_bcb3_2cfd_v1:benchmark_knn_build_dispatch_e3de_8712_bcb3_2cfd_v1', 'consumed_seeds': ('d64_fdd7_aa88_v2', 'rect_d64_cf49_v2_8712', 'q1_mbucket_aa88_q1m_v3_bcb3'), 'guard_plan': ('e3de D64 build guard', 'exact rectangular D64 search guard -> 8712', 'exact Q1 online M-bucket guard -> bcb3', 'then e3de fallback'), 'expected_shape_wins': base_2cfd.SELECTED_TARGET_SHAPES, 'fallback': ROUTE_BASE_E3DE, 'rejected_reason': 'same-session baseline for 9138 dispatcher-consumption A/B'}, {'id': 'rect_only_4247', 'entrypoint': 'loom.examples.weave.knn_build_dispatch_e3de_9138_bcb3_4247_v1:benchmark_rect_only_4247_v1', 'consumed_seeds': ('rect_d64_cf49_v3_9138',), 'guard_plan': ('e3de D64 build guard', 'exact rectangular D64 search guard -> 9138', 'then e3de fallback'), 'expected_shape_wins': RECT_TARGET_SHAPES, 'fallback': ROUTE_BASE_E3DE, 'rejected_reason': 'diagnostic candidate; full candidate keeps the already-promoted bcb3 Q1 routes from 2cfd'}, {'id': 'q1_only_4247', 'entrypoint': 'loom.examples.weave.knn_build_dispatch_e3de_9138_bcb3_4247_v1:benchmark_q1_only_4247_v1', 'consumed_seeds': ('q1_mbucket_aa88_q1m_v3_bcb3',), 'guard_plan': ('e3de D64 build guard', 'exact Q1 online M-bucket guard -> bcb3', 'then e3de fallback'), 'expected_shape_wins': Q1_TARGET_SHAPES, 'fallback': ROUTE_BASE_E3DE, 'rejected_reason': 'not a new candidate for this lane; Q1 remains inherited from 2cfd'}, {'id': 'e3de_rect9138_q1bcb3_4247_v1', 'entrypoint': 'loom.examples.weave.knn_build_dispatch_e3de_9138_bcb3_4247_v1:benchmark_knn_build_dispatch_e3de_9138_bcb3_4247_v1', 'consumed_seeds': ('d64_fdd7_aa88_v2', 'rect_d64_cf49_v3_9138', 'q1_mbucket_aa88_q1m_v3_bcb3'), 'guard_plan': ('e3de D64 build guard', 'exact v8 K96 build guard -> a2f8 generated variant', 'exact rectangular D64 search guard -> 9138', 'exact Q1 online M-bucket guard -> bcb3', 'then e3de fallback'), 'expected_shape_wins': (*ADDED_TARGET_SHAPES, *COVERAGE_REPAIR_TARGET_SHAPES), 'fallback': ROUTE_BASE_E3DE, 'rejected_reason': None}) +TARGETED_SEED_ROWS = {**base_e3de.TARGETED_SEED_ROWS, 'search_rect_b1_q1024_m32768_d64_k10': {'kernel_ms': 0.196966, 'flashlib_ms': 0.204006, 'ratio_vs_flashlib': 1.0357422093153132, 'tflops': 21.805627854553578, 'split_count': 16, 'merge_route': 's16_cached_t8', 'route': ROUTE_RECT_9138}, 'rag_online_b1_q1_m100000_d128_k10': {'kernel_ms': 0.056641, 'flashlib_ms': 0.060833, 'ratio_vs_flashlib': 1.0740099927614273, 'tflops': 0.4519694214438305, 'split_count': 72, 'merge_route': 'four_warp_coop_k10', 'route': ROUTE_Q1_BCB3_SPLIT72}, 'rag_online_irregular_b1_q1_m131071_d128_k10': {'kernel_ms': 0.068352, 'flashlib_ms': 0.067329, 'ratio_vs_flashlib': 0.9850333567415731, 'tflops': 0.49090262172284643, 'split_count': 72, 'merge_route': 'four_warp_coop_k10', 'route': ROUTE_Q1_BCB3_SPLIT72}, 'rag_online_large_m_b1_q1_m250000_d128_k10': {'kernel_ms': 0.104673, 'flashlib_ms': 0.091777, 'ratio_vs_flashlib': 0.8767972638598301, 'tflops': 0.6114279709189571, 'split_count': 74, 'merge_route': 'four_warp_coop_k10', 'route': ROUTE_Q1_BCB3_M250_SPLIT74}, 'build_over64_stress_qm1024_k96': {'kernel_ms': None, 'flashlib_ms': None, 'ratio_vs_flashlib': None, 'tflops': None, 'split_count': k96_a2f8.OVER64_BUILD_SPLITS, 'merge_route': 'generated_v8_k96_s8_chunkprefill', 'route': ROUTE_K96_A2F8, 'classification': 'coverage-only'}, 'build_over64_stress_qm2048_k96': {'kernel_ms': 0.570692, 'flashlib_ms': None, 'ratio_vs_flashlib': None, 'tflops': None, 'split_count': k96_a2f8.OVER64_BUILD_SPLITS, 'merge_route': 'a2f8_exact_k96_s8_chunkprefill', 'route': ROUTE_K96_A2F8, 'classification': 'seed-consumed'}, 'build_over64_stress_qm4096_k96': {'kernel_ms': None, 'flashlib_ms': None, 'ratio_vs_flashlib': None, 'tflops': None, 'split_count': k96_a2f8.OVER64_BUILD_SPLITS, 'merge_route': 'generated_v8_k96_s8_chunkprefill', 'route': ROUTE_K96_A2F8, 'classification': 'coverage-only'}} +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k48over32", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 48]], "cta_group": 1, "threads": 192}')) + +def _dtype_name(inputs: dict[str, Any]) -> str: + query = inputs.get('query') + if query is not None: + return str(query.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')).replace('torch.', '') + +def _label_can_hit(inputs: dict[str, Any], labels: set[str]) -> bool: + label = inputs.get('label') + return label is None or str(label) in labels + +def _eligible_k96_a2f8_v8(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, K96_GENERATED_VARIANT_SHAPE_SET) and bool(inputs.get('build', False)) and (_dtype_name(inputs) == 'bfloat16') and (int(inputs['B']) == 1) and (int(inputs['D']) == k96_a2f8.FEAT_D) and (int(inputs['K']) == k96_a2f8.OVER64_TOP_K) and (int(inputs['Q']) == int(inputs['M'])) and (int(inputs['Q']) in (1024, 4096)) + +def route_for_contract_inputs(inputs: dict[str, Any], *, portfolio_id: str=DEFAULT_PORTFOLIO_ID, force_fallback: bool=False, enable_d64_fdd7: bool=True, enable_rect_d64: bool=True, enable_q1_mbucket: bool=True, enable_k96_a2f8: bool=True, enable_rag_seed_portfolio: bool=True, enable_q512_k456: bool=True) -> str: + if not force_fallback and enable_k96_a2f8 and _eligible_k96_a2f8_v8(inputs): + return ROUTE_K96_A2F8 + if not force_fallback and enable_d64_fdd7 and base_e3de._eligible_d64_fdd7(inputs): + return base_e3de.route_for_contract_inputs(inputs, portfolio_id=portfolio_id, force_fallback=False, enable_d64_fdd7=True, enable_rag_seed_portfolio=enable_rag_seed_portfolio, enable_q512_k456=enable_q512_k456) + if not force_fallback and enable_rect_d64 and rect_9138._eligible_rect_d64(inputs): + return ROUTE_RECT_9138 + if not force_fallback and enable_q1_mbucket and q1_bcb3._eligible_rag_online_mbucket(inputs): + return q1_bcb3.route_for_contract_inputs(inputs) + return base_e3de.route_for_contract_inputs(inputs, portfolio_id=portfolio_id, force_fallback=force_fallback, enable_d64_fdd7=enable_d64_fdd7, enable_rag_seed_portfolio=enable_rag_seed_portfolio, enable_q512_k456=enable_q512_k456) + +def _launch_route(inputs: dict[str, Any], route: str) -> None: + if route == ROUTE_K96_A2F8 and _eligible_k96_a2f8_v8(inputs): + k96_a2f8._launch_over64_k96_split_path(inputs) + return + if route == ROUTE_RECT_9138 and rect_9138._eligible_rect_d64(inputs): + rect_9138.launch_from_contract_inputs(inputs) + return + if route in (ROUTE_Q1_BCB3_SPLIT72, ROUTE_Q1_BCB3_M250_SPLIT74) and q1_bcb3._eligible_rag_online_mbucket(inputs): + q1_bcb3.launch_from_contract_inputs(inputs) + return + base_e3de._launch_route(inputs, route) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, portfolio_id: str=DEFAULT_PORTFOLIO_ID, force_fallback: bool=False, enable_d64_fdd7: bool=True, enable_rect_d64: bool=True, enable_q1_mbucket: bool=True, enable_k96_a2f8: bool=True, enable_rag_seed_portfolio: bool=True, enable_q512_k456: bool=True) -> None: + route = route_for_contract_inputs(inputs, portfolio_id=portfolio_id, force_fallback=force_fallback, enable_d64_fdd7=enable_d64_fdd7, enable_rect_d64=enable_rect_d64, enable_q1_mbucket=enable_q1_mbucket, enable_k96_a2f8=enable_k96_a2f8, enable_rag_seed_portfolio=enable_rag_seed_portfolio, enable_q512_k456=enable_q512_k456) + _launch_route(inputs, route) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_rect_only(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, enable_q1_mbucket=False) + +def candidate_q1_only(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, enable_rect_d64=False) + +def candidate_base_e3de(inputs: dict[str, Any]) -> None: + base_e3de.launch_from_contract_inputs(inputs) + +def candidate_base_2cfd(inputs: dict[str, Any]) -> None: + base_2cfd.launch_from_contract_inputs(inputs) + +def candidate_base_8700(inputs: dict[str, Any]) -> None: + base_e3de.candidate_base_dispatcher(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return base_e3de._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=DISPATCH_CORRECTNESS_SHAPES, benchmark: bool=False) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=None, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + shapes = None if shape_labels is None else _select_contract_shapes(shape_labels) + return evaluate_contract(shapes=shapes, correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def _trace_inputs_from_shape(shape: dict[str, Any]) -> dict[str, Any]: + return base_e3de._trace_inputs_from_shape(shape) + +def _inputs_for_label(label: str) -> dict[str, Any]: + return base_e3de._inputs_for_label(label) + +def _selected_entrypoint_for_route(route: str) -> str: + return ROUTE_ENTRYPOINTS.get(route, base_e3de._selected_entrypoint_for_route(route)) + +def _base_route_trace_record(inputs: dict[str, Any], *, force_fallback: bool=False) -> dict[str, Any]: + row = dict(base_e3de._route_trace_record(inputs, force_fallback=force_fallback)) + route = str(row.get('selected_route') or base_e3de.route_for_contract_inputs(inputs, force_fallback=force_fallback)) + selected_seed = row.get('selected_seed') or row.get('consumed_seed') + row.setdefault('shape_key', inputs.get('label')) + row.setdefault('selected_entrypoint', _selected_entrypoint_for_route(route)) + row.setdefault('selected_seed', selected_seed) + row.setdefault('expected_seed', selected_seed) + row.setdefault('route_kind', row.get('route_kind', 'general')) + if selected_seed: + row.setdefault('route_source', 'shape-specific-seed') + elif row.get('route_kind') == 'coverage-only': + row.setdefault('route_source', 'generic-weave-fallback') + else: + row.setdefault('route_source', 'broad-dispatcher') + row.setdefault('guard_id', row.get('candidate_guard_status')) + row.setdefault('guard_condition', 'inherited e3de guard plan') + row.setdefault('classification', 'route-ok') + row.setdefault('dispatcher_kernel_ms', None) + row.setdefault('shape_specific_kernel_ms', None) + row.setdefault('relative_speedup_vs_baseline', None) + row['base_e3de_route'] = base_e3de.route_for_contract_inputs(inputs) + row['base_2cfd_route'] = base_2cfd.route_for_contract_inputs(inputs) + row['base_8700_route'] = base_e3de.base_8700.route_for_contract_inputs(inputs, portfolio_id=DEFAULT_PORTFOLIO_ID) + return row + +def _route_trace_record(inputs: dict[str, Any], *, force_fallback: bool=False) -> dict[str, Any]: + label = str(inputs.get('label')) + base_e3de_route = base_e3de.route_for_contract_inputs(inputs) + base_2cfd_route = base_2cfd.route_for_contract_inputs(inputs) + base_8700_route = base_e3de.base_8700.route_for_contract_inputs(inputs, portfolio_id=DEFAULT_PORTFOLIO_ID) + if force_fallback and (base_e3de._eligible_d64_fdd7(inputs) or rect_9138._eligible_rect_d64(inputs) or q1_bcb3._eligible_rag_online_mbucket(inputs) or _eligible_k96_a2f8_v8(inputs)): + row = _base_route_trace_record(inputs, force_fallback=True) + expected_seed = ROUTE_SEED_ID.get(route_for_contract_inputs(inputs, force_fallback=False)) + row['selected_route'] = base_e3de.route_for_contract_inputs(inputs, force_fallback=True) + row['selected_entrypoint'] = _selected_entrypoint_for_route(str(row['selected_route'])) + row['selected_seed'] = row.get('consumed_seed') + row['expected_seed'] = expected_seed + row['guard_id'] = 'forced_fallback_4247_overlays_disabled' + row['guard_condition'] = 'forced fallback to inherited e3de/8700 path; 4247 overlays disabled' + row['forced_disabled_seeds'] = ('d64_fdd7_aa88_v2', 'rect_d64_cf49_v3_9138', 'q1_mbucket_aa88_q1m_v3_bcb3', 'over64_k96_a2f8_v1') + row['candidate_guard_status'] = 'forced_fallback' + if _eligible_k96_a2f8_v8(inputs): + row['guard_id'] = 'forced_fallback_k96_a2f8_disabled' + if str(row['selected_route']) == ROUTE_K96_A2F8: + row['guard_condition'] = 'forced fallback requested, but K96 A2F8 route remained selected' + row['classification'] = 'route-ok' + elif 'knn_build_over64_k96' in str(row['selected_route']): + row['guard_condition'] = 'forced fallback disables c142 K96 A2F8 coverage; inherited exact K96 route covers this row' + row['classification'] = 'route-ok' + else: + row['guard_condition'] = 'forced fallback disables c142 K96 A2F8 coverage; inherited e3de route is known to reject K=96' + row['classification'] = 'guard-miss' + else: + row['classification'] = 'route-ok' + return row + route = route_for_contract_inputs(inputs, force_fallback=force_fallback) + if label in K96_COVERAGE_TARGET_SHAPE_SET and route == ROUTE_K96_A2F8: + targeted = dict(TARGETED_SEED_ROWS[label]) + generated = label in K96_GENERATED_VARIANT_SHAPES + return {'shape_key': inputs.get('label'), 'selected_route': route, 'selected_entrypoint': ROUTE_ENTRYPOINTS[route], 'selected_seed': ROUTE_SEED_ID[route], 'expected_seed': 'over64_k96_a2f8_v1', 'route_kind': 'specialized', 'route_source': 'generated-variant' if generated else 'shape-specific-seed', 'guard_id': 'c142_v8_k96_a2f8_exact_qm_guard', 'guard_condition': ''.join(['exact BF16 build B=1 Q=M=', format(int(inputs.get('Q')), ''), ' D=128 K=96 using A2F8 K96 schedule']), 'coverage': 'generated Q1024/Q4096 variant of the A2F8 K96 schedule' if generated else 'exact A2F8 K96 seed selected before inherited K<=32 fallback', 'consumed_seed': ROUTE_SEED_ID[route], 'replaced_route': base_e3de_route, 'base_e3de_route': base_e3de_route, 'base_2cfd_route': base_2cfd_route, 'base_8700_route': base_8700_route, 'row_selection': targeted, 'targeted_seed_timing_backend': 'cupti' if not generated else None, 'targeted_seed_kernel_ms': targeted['kernel_ms'], 'targeted_seed_ratio_vs_flashlib': targeted['ratio_vs_flashlib'], 'classification': targeted['classification'], 'dispatcher_kernel_ms': None, 'shape_specific_kernel_ms': targeted['kernel_ms'], 'relative_speedup_vs_baseline': None} + if label in RECT_TARGET_SHAPES and route == ROUTE_RECT_9138: + targeted = dict(TARGETED_SEED_ROWS[label]) + return {'shape_key': inputs.get('label'), 'selected_route': route, 'selected_entrypoint': ROUTE_ENTRYPOINTS[route], 'selected_seed': ROUTE_SEED_ID[route], 'expected_seed': 'rect_d64_cf49_v3_9138', 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': 'rect_d64_q1024_m32768_k10_cf49_s16_t8_exact', 'guard_condition': 'exact BF16 non-build B=1 Q=1024 M=32768 D=64 K=10 rectangular D64 v3 seed', 'coverage': '4247 consumes the 9138 rectangular D64 seed ahead of e3de', 'consumed_seed': ROUTE_SEED_ID[route], 'replaced_route': base_2cfd_route, 'base_e3de_route': base_e3de_route, 'base_2cfd_route': base_2cfd_route, 'base_8700_route': base_8700_route, 'row_selection': targeted, 'targeted_seed_timing_backend': 'cupti', 'targeted_seed_kernel_ms': targeted['kernel_ms'], 'targeted_seed_ratio_vs_flashlib': targeted['ratio_vs_flashlib'], 'classification': 'seed-consumed', 'dispatcher_kernel_ms': None, 'shape_specific_kernel_ms': targeted['kernel_ms'], 'relative_speedup_vs_baseline': None} + if label in Q1_TARGET_SHAPES and route in (ROUTE_Q1_BCB3_SPLIT72, ROUTE_Q1_BCB3_M250_SPLIT74): + targeted = dict(TARGETED_SEED_ROWS[label]) + split_count = q1_bcb3._split_count_for_inputs(inputs) + return {'shape_key': inputs.get('label'), 'selected_route': route, 'selected_entrypoint': ROUTE_ENTRYPOINTS[route], 'selected_seed': ROUTE_SEED_ID[route], 'expected_seed': 'q1_mbucket_aa88_q1m_v3_bcb3', 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': 'q1_online_mbucket_aa88_q1m_v3_exact', 'guard_condition': ''.join(['exact BF16 non-build B=1 Q=1 M=', format(int(inputs.get('M')), ''), ' D=128 K=10 Q1 online M-bucket seed']), 'coverage': '4247 consumes bcb3 Q1 M-bucket seed ahead of e3de', 'consumed_seed': ROUTE_SEED_ID[route], 'replaced_route': base_2cfd_route, 'base_e3de_route': base_e3de_route, 'base_2cfd_route': base_2cfd_route, 'base_8700_route': base_8700_route, 'row_selection': targeted, 'split_count': split_count, 'targeted_seed_timing_backend': 'cupti', 'targeted_seed_kernel_ms': targeted['kernel_ms'], 'targeted_seed_ratio_vs_flashlib': targeted['ratio_vs_flashlib'], 'classification': 'seed-consumed', 'dispatcher_kernel_ms': None, 'shape_specific_kernel_ms': targeted['kernel_ms'], 'relative_speedup_vs_baseline': None} + return _base_route_trace_record(inputs) + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + return [_route_trace_record(_trace_inputs_from_shape(shape), force_fallback=force_fallback) for shape in selected] + +def _timing_backends_for_reports(*reports: dict[str, Any]) -> list[str]: + return base_e3de._timing_backends_for_reports(*reports) + +def _rows_for_labels(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return base_e3de._rows_for_labels(report, labels) + +def _seed_delta_matrix(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> list[dict[str, Any]]: + matrix = [] + for label in CONSUMED_SEED_TARGET_SHAPES: + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') + inputs = _inputs_for_label(label) + route = route_for_contract_inputs(inputs) + targeted = TARGETED_SEED_ROWS.get(label, {}) + matrix.append({'shape_key': label, 'baseline_route': base_2cfd.route_for_contract_inputs(inputs), 'candidate_route': route, 'selected_seed': ROUTE_SEED_ID.get(route), 'candidate_ms': candidate_ms, 'baseline_ms': baseline_ms, 'flashlib_ms': flashlib_ms, 'metric_delta_ms': candidate_ms - baseline_ms if candidate_ms and baseline_ms else None, 'speedup_vs_baseline_2cfd': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'ratio_vs_flashlib': flashlib_ms / candidate_ms if candidate_ms and flashlib_ms else None, 'targeted_seed_kernel_ms': targeted.get('kernel_ms'), 'targeted_seed_ratio_vs_flashlib': targeted.get('ratio_vs_flashlib'), 'timing_backend': candidate_row.get('timing_backend') or baseline_row.get('timing_backend')}) + return matrix + +def _frontmatter_seed_delta_matrix(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> list[dict[str, Any]]: + rows = [] + for item in _seed_delta_matrix(candidate_report, baseline_report): + rows.append({'shape_key': item['shape_key'], 'baseline_route': item['baseline_route'], 'candidate_deltas': [{'candidate_id': 'e3de_rect9138_q1bcb3_4247_v1', 'selected_seed': item['selected_seed'], 'metric_delta': item['metric_delta_ms'], 'ratio_vs_flashlib': item['ratio_vs_flashlib'], 'timing_backend': item['timing_backend'] or 'cupti'}]}) + return rows + +def _per_consumed_row_delta(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> dict[str, Any]: + deltas = {} + for item in _seed_delta_matrix(candidate_report, baseline_report): + label = item['shape_key'] + deltas[label] = {'candidate_ms': item['candidate_ms'], 'baseline_2cfd_ms': item['baseline_ms'], 'flashlib_ms': item['flashlib_ms'], 'speedup_vs_baseline_2cfd': item['speedup_vs_baseline_2cfd'], 'ratio_vs_flashlib': item['ratio_vs_flashlib'], 'candidate_route': item['candidate_route'], 'baseline_2cfd_route': item['baseline_route'], 'selected_seed': item['selected_seed'], 'targeted_seed_kernel_ms': item['targeted_seed_kernel_ms'], 'targeted_seed_ratio_vs_flashlib': item['targeted_seed_ratio_vs_flashlib'], 'candidate_passed': candidate_report.get('per_shape', {}).get(label, {}).get('passed'), 'baseline_passed': baseline_report.get('per_shape', {}).get(label, {}).get('passed')} + return deltas + +def _annotate_route_trace(route_trace: list[dict[str, Any]], candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> list[dict[str, Any]]: + annotated = [] + for row in route_trace: + label = str(row.get('shape_key')) + out = dict(row) + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + out['dispatcher_kernel_ms'] = candidate_ms + out['baseline_dispatcher_kernel_ms'] = baseline_ms + out['flashlib_ms'] = candidate_row.get('flashlib_ms') + out['relative_speedup_vs_baseline'] = baseline_ms / candidate_ms if candidate_ms and baseline_ms else None + out['route_changed_vs_base_2cfd'] = out.get('selected_route') != out.get('base_2cfd_route') + if label in K96_COVERAGE_TARGET_SHAPE_SET and out['route_changed_vs_base_2cfd']: + ratio = candidate_row.get('ratio_vs_flashlib') + if isinstance(ratio, (float, int)) and ratio < 1.0: + out['classification'] = 'kernel-slow' + elif label in K96_GENERATED_VARIANT_SHAPES: + out['classification'] = 'route-ok' + else: + out['classification'] = 'seed-consumed' + elif label in RECT_TARGET_SHAPES and out['route_changed_vs_base_2cfd']: + speedup = out['relative_speedup_vs_baseline'] + out['classification'] = 'seed-consumed' if speedup is None or speedup >= 1.0 else 'kernel-slow' + elif isinstance(candidate_row.get('ratio_vs_flashlib'), (float, int)) and candidate_row['ratio_vs_flashlib'] < 1.0: + out['classification'] = 'kernel-slow' if out.get('route_kind') == 'specialized' else 'fallback-slow' + else: + out['classification'] = 'route-ok' + annotated.append(out) + return annotated + +def _below_flashlib_rows(report: dict[str, Any]) -> list[dict[str, Any]]: + trace_by_label = {str(row['shape_key']): row for row in route_trace_for_contract_shapes()} + rows = [] + for label, row in sorted(report.get('per_shape', {}).items()): + ratio = row.get('ratio_vs_flashlib') + if isinstance(ratio, (float, int)) and ratio < 1.0: + inputs = _inputs_for_label(label) + trace_row = trace_by_label.get(label, {}) + rows.append({'shape_key': label, 'kernel_ms': row.get('kernel_ms'), 'flashlib_ms': row.get('flashlib_ms'), 'ratio_vs_flashlib': ratio, 'selected_route': route_for_contract_inputs(inputs), 'route_kind': trace_row.get('route_kind', 'unknown'), 'classification': 'kernel-slow' if trace_row.get('route_kind') == 'specialized' else 'fallback-slow'}) + return rows + +def _hot_bucket_parity(report: dict[str, Any]) -> dict[str, str]: + buckets = {'d64_build_q1024_q2048_q4096_k10': D64_TARGET_SHAPES, 'rectangular_search_q1024_m32768_d64_k10': RECT_TARGET_SHAPES, 'rag_online_q1_m100000_m131071_m250000_k10': Q1_TARGET_SHAPES, 'build_over64_k96_v8_q1024_q2048_q4096': K96_COVERAGE_TARGET_SHAPES} + out = {} + for bucket, labels in buckets.items(): + out[bucket] = 'pass' + for label in labels: + ratio = report.get('per_shape', {}).get(label, {}).get('ratio_vs_flashlib') + if not isinstance(ratio, (float, int)) or ratio < 1.0: + out[bucket] = 'fail' + break + out['rag_microbatch_q8_q16_q32_m100000_k10'] = 'inherited_e3de' + return out + +def _benchmark_payload(candidate_report: dict[str, Any], baseline_2cfd_report: dict[str, Any], baseline_8700_report: dict[str, Any], *, use_cupti: bool, shape_labels, measured_function: str, candidate_id: str) -> dict[str, Any]: + candidate_metric = candidate_report['summary']['primary_mean'] + baseline_2cfd_metric = baseline_2cfd_report['summary']['primary_mean'] + baseline_8700_metric = baseline_8700_report['summary']['primary_mean'] + route_trace = _annotate_route_trace(route_trace_for_contract_shapes(shape_labels), candidate_report, baseline_2cfd_report) + below_flashlib = _below_flashlib_rows(candidate_report) + return {'candidate_id': candidate_id, 'tflops': candidate_metric, 'baseline_tflops': baseline_2cfd_metric, 'baseline_2cfd_tflops': baseline_2cfd_metric, 'same_session_8700_tflops': baseline_8700_metric, 'metric_delta': candidate_metric - baseline_2cfd_metric if candidate_metric and baseline_2cfd_metric else None, 'metric_delta_vs_2cfd': candidate_metric - baseline_2cfd_metric if candidate_metric and baseline_2cfd_metric else None, 'metric_delta_vs_8700': candidate_metric - baseline_8700_metric if candidate_metric and baseline_8700_metric else None, 'all_correct': candidate_report['summary']['all_correct'], 'baseline_all_correct': baseline_2cfd_report['summary']['all_correct'], 'baseline_2cfd_all_correct': baseline_2cfd_report['summary']['all_correct'], 'baseline_8700_all_correct': baseline_8700_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'baseline_performance_comparable': baseline_2cfd_report['summary']['performance_comparable'], 'baseline_2cfd_performance_comparable': baseline_2cfd_report['summary']['performance_comparable'], 'baseline_8700_performance_comparable': baseline_8700_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'baseline_invalid_performance_reason': baseline_2cfd_report['summary']['invalid_performance_reason'], 'baseline_2cfd_invalid_performance_reason': baseline_2cfd_report['summary']['invalid_performance_reason'], 'baseline_8700_invalid_performance_reason': baseline_8700_report['summary']['invalid_performance_reason'], 'measured_entrypoint': ''.join(['loom.examples.weave.knn_build_dispatch_e3de_9138_bcb3_4247_v1:', format(measured_function, '')]), 'baseline_entrypoint': 'loom.examples.weave.knn_build_dispatch_e3de_8712_bcb3_2cfd_v1:benchmark_knn_build_dispatch_e3de_8712_bcb3_2cfd_v1', 'baseline_2cfd_entrypoint': 'loom.examples.weave.knn_build_dispatch_e3de_8712_bcb3_2cfd_v1:benchmark_knn_build_dispatch_e3de_8712_bcb3_2cfd_v1', 'baseline_8700_entrypoint': 'benchmark_data.json:knn_build -> loom.examples.weave.knn_build_dispatch_rag_seed_portfolio_8700_v1:benchmark_knn_build_dispatch_rag_seed_portfolio_8700_v1(portfolio_id=all_m64_s128)', 'measured_shape_labels': 'all_canonical' if shape_labels is None else tuple(shape_labels), 'selected_route_labels': SELECTED_TARGET_SHAPES, 'consumed_seed_labels': CONSUMED_SEED_TARGET_SHAPES, 'added_seed_labels': ADDED_TARGET_SHAPES, 'guard_miss_audit_labels': GUARD_MISS_AUDIT_SHAPES, 'selected_route_rows': _rows_for_labels(candidate_report, SELECTED_TARGET_SHAPES), 'baseline_selected_route_rows': _rows_for_labels(baseline_2cfd_report, SELECTED_TARGET_SHAPES), 'baseline_2cfd_selected_route_rows': _rows_for_labels(baseline_2cfd_report, SELECTED_TARGET_SHAPES), 'baseline_8700_selected_route_rows': _rows_for_labels(baseline_8700_report, SELECTED_TARGET_SHAPES), 'consumed_seed_rows': _rows_for_labels(candidate_report, CONSUMED_SEED_TARGET_SHAPES), 'baseline_consumed_seed_rows': _rows_for_labels(baseline_2cfd_report, CONSUMED_SEED_TARGET_SHAPES), 'baseline_2cfd_consumed_seed_rows': _rows_for_labels(baseline_2cfd_report, CONSUMED_SEED_TARGET_SHAPES), 'guard_miss_audit_rows': _rows_for_labels(candidate_report, GUARD_MISS_AUDIT_SHAPES), 'baseline_guard_miss_audit_rows': _rows_for_labels(baseline_2cfd_report, GUARD_MISS_AUDIT_SHAPES), 'baseline_2cfd_guard_miss_audit_rows': _rows_for_labels(baseline_2cfd_report, GUARD_MISS_AUDIT_SHAPES), 'per_consumed_row_delta': _per_consumed_row_delta(candidate_report, baseline_2cfd_report), 'seed_delta_matrix': _seed_delta_matrix(candidate_report, baseline_2cfd_report), 'frontmatter_seed_delta_matrix': _frontmatter_seed_delta_matrix(candidate_report, baseline_2cfd_report), 'targeted_seed_rows': TARGETED_SEED_ROWS, 'candidate_dispatchers': CANDIDATE_DISPATCHERS, 'selected_candidate_dispatcher': 'e3de_rect9138_q1bcb3_4247_v1', 'route_modules': PRODUCTION_ROUTE_MODULES, 'route_trace': route_trace, 'forced_fallback_route_trace': route_trace_for_contract_shapes(shape_labels, force_fallback=True), 'route_trace_included': True, 'contract_summary': candidate_report['summary'], 'baseline_contract_summary': baseline_2cfd_report['summary'], 'baseline_2cfd_contract_summary': baseline_2cfd_report['summary'], 'baseline_8700_contract_summary': baseline_8700_report['summary'], 'contract_performance': candidate_report['performance'], 'baseline_contract_performance': baseline_2cfd_report['performance'], 'baseline_2cfd_contract_performance': baseline_2cfd_report['performance'], 'baseline_8700_contract_performance': baseline_8700_report['performance'], 'timing_backends': _timing_backends_for_reports(candidate_report, baseline_2cfd_report, baseline_8700_report), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'hot_bucket_parity': _hot_bucket_parity(candidate_report), 'performance_coverage': 'partial' if below_flashlib else 'pass', 'coverage_only_routes': [], 'hot_bucket_blockers': below_flashlib, 'report': candidate_report, 'baseline_report': baseline_2cfd_report, 'baseline_2cfd_report': baseline_2cfd_report, 'baseline_8700_report': baseline_8700_report} + +def _failed_baseline_report(exc: Exception, *, shape_labels, baseline_id: str) -> dict[str, Any]: + reason = ''.join([format(type(exc).__name__, ''), ': ', format(exc, '')]) + return {'contract': eval_mod.CONTRACT.kernel, 'contract_version': eval_mod.CONTRACT.contract_version, 'summary': {'all_correct': False, 'correctness_shapes': 0, 'failed_correctness_shapes': 1, 'correctness_failure_count': 1, 'first_correctness_failure': reason, 'primary_metric': 'tflops', 'primary_direction': 'maximize', 'primary_mean': None, 'performance_comparable': False, 'invalid_performance_reason': reason}, 'performance': {'comparable': False, 'invalid_reason': reason, 'primary_mean': None, 'primary_metric': 'tflops', 'valid_measurement_count': 0}, 'per_shape': {}, 'benchmark_exception': reason, 'baseline_id': baseline_id, 'measured_shape_labels': 'all_canonical' if shape_labels is None else tuple(shape_labels)} + +def _benchmark_candidate(*, use_cupti: bool=True, shape_labels=None, kernel_fn: Callable[[dict[str, Any]], Any]=candidate, measured_function: str, candidate_id: str, baseline_2cfd_report: dict[str, Any] | None=None, baseline_8700_report: dict[str, Any] | None=None) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=kernel_fn) + if baseline_2cfd_report is None: + try: + baseline_2cfd_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_base_2cfd) + except Exception as exc: + baseline_2cfd_report = _failed_baseline_report(exc, shape_labels=shape_labels, baseline_id='baseline_2cfd') + if baseline_8700_report is None: + try: + baseline_8700_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_base_8700) + except Exception as exc: + baseline_8700_report = _failed_baseline_report(exc, shape_labels=shape_labels, baseline_id='baseline_8700') + return _benchmark_payload(candidate_report, baseline_2cfd_report, baseline_8700_report, use_cupti=use_cupti, shape_labels=shape_labels, measured_function=measured_function, candidate_id=candidate_id) + +def benchmark_knn_build_dispatch_e3de_9138_bcb3_4247_v1(*, use_cupti: bool=True, shape_labels=None, baseline_2cfd_report: dict[str, Any] | None=None, baseline_8700_report: dict[str, Any] | None=None) -> dict[str, Any]: + return _benchmark_candidate(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate, measured_function='benchmark_knn_build_dispatch_e3de_9138_bcb3_4247_v1', candidate_id='e3de_rect9138_q1bcb3_4247_v1', baseline_2cfd_report=baseline_2cfd_report, baseline_8700_report=baseline_8700_report) + +def benchmark_rect_only_4247_v1(*, use_cupti: bool=True, shape_labels=None, baseline_2cfd_report: dict[str, Any] | None=None, baseline_8700_report: dict[str, Any] | None=None) -> dict[str, Any]: + return _benchmark_candidate(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_rect_only, measured_function='benchmark_rect_only_4247_v1', candidate_id='rect_only_4247', baseline_2cfd_report=baseline_2cfd_report, baseline_8700_report=baseline_8700_report) + +def benchmark_q1_only_4247_v1(*, use_cupti: bool=True, shape_labels=None, baseline_2cfd_report: dict[str, Any] | None=None, baseline_8700_report: dict[str, Any] | None=None) -> dict[str, Any]: + return _benchmark_candidate(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_q1_only, measured_function='benchmark_q1_only_4247_v1', candidate_id='q1_only_4247', baseline_2cfd_report=baseline_2cfd_report, baseline_8700_report=baseline_8700_report) + +def _denominator_label(shape_labels) -> str: + if shape_labels is None: + return ''.join(['full', format(len(eval_mod.CANONICAL_SHAPES), '')]) + return ''.join(['shape', format(len(tuple(shape_labels)), '')]) + +def write_benchmark_artifacts(artifact_dir: str | Path, *, use_cupti: bool=True, shape_labels=None) -> dict[str, str]: + payload = benchmark_knn_build_dispatch_e3de_9138_bcb3_4247_v1(use_cupti=use_cupti, shape_labels=shape_labels) + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + denom = _denominator_label(shape_labels) + candidate_path = out_dir / ''.join([format(denom, ''), '_dispatch_e3de_9138_bcb3_4247_v1.json']) + baseline_2cfd_path = out_dir / ''.join([format(denom, ''), '_same_session_baseline_2cfd_for_4247_v1.json']) + baseline_8700_path = out_dir / ''.join([format(denom, ''), '_same_session_baseline_8700_for_4247_v1.json']) + route_trace_path = out_dir / ''.join([format(denom, ''), '_route_trace_e3de_9138_bcb3_4247_v1.json']) + forced_trace_path = out_dir / ''.join([format(denom, ''), '_forced_fallback_trace_e3de_9138_bcb3_4247_v1.json']) + seed_matrix_path = out_dir / ''.join([format(denom, ''), '_seed_delta_matrix_e3de_9138_bcb3_4247_v1.json']) + candidate_path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + baseline_2cfd_path.write_text(json.dumps({'candidate_id': 'baseline_2cfd', 'measured_entrypoint': payload['baseline_entrypoint'], 'measured_shape_labels': payload['measured_shape_labels'], 'timing_backend_requested': payload['timing_backend_requested'], 'timing_backends': payload['timing_backends'], 'tflops': payload['baseline_tflops'], 'all_correct': payload['baseline_all_correct'], 'performance_comparable': payload['baseline_performance_comparable'], 'contract_summary': payload['baseline_contract_summary'], 'contract_performance': payload['baseline_contract_performance'], 'route_trace': base_2cfd.route_trace_for_contract_shapes(shape_labels), 'route_trace_included': True, 'report': payload['baseline_report']}, indent=2, sort_keys=True) + '\n') + baseline_8700_path.write_text(json.dumps({'candidate_id': 'baseline_8700_all_m64_s128', 'measured_entrypoint': payload['baseline_8700_entrypoint'], 'measured_shape_labels': payload['measured_shape_labels'], 'timing_backend_requested': payload['timing_backend_requested'], 'timing_backends': payload['timing_backends'], 'tflops': payload['same_session_8700_tflops'], 'all_correct': payload['baseline_8700_all_correct'], 'performance_comparable': payload['baseline_8700_performance_comparable'], 'contract_summary': payload['baseline_8700_contract_summary'], 'contract_performance': payload['baseline_8700_contract_performance'], 'route_trace': base_e3de.base_8700.route_trace_for_contract_shapes(shape_labels, portfolio_id=DEFAULT_PORTFOLIO_ID), 'route_trace_included': True, 'report': payload['baseline_8700_report']}, indent=2, sort_keys=True) + '\n') + route_trace_path.write_text(json.dumps(payload['route_trace'], indent=2, sort_keys=True) + '\n') + forced_trace_path.write_text(json.dumps(payload['forced_fallback_route_trace'], indent=2, sort_keys=True) + '\n') + seed_matrix_path.write_text(json.dumps(payload['seed_delta_matrix'], indent=2, sort_keys=True) + '\n') + return {'candidate_payload': str(candidate_path), 'same_session_baseline_payload': str(baseline_2cfd_path), 'same_session_8700_payload': str(baseline_8700_path), 'route_trace': str(route_trace_path), 'forced_fallback_trace': str(forced_trace_path), 'seed_delta_matrix': str(seed_matrix_path)} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_e3de_9138_bcb3_faeb_cb00_3505v2_8940_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_e3de_9138_bcb3_faeb_cb00_3505v2_8940_v1.py new file mode 100644 index 00000000..67c57379 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_e3de_9138_bcb3_faeb_cb00_3505v2_8940_v1.py @@ -0,0 +1,373 @@ +"""8940 kNN build dispatcher consumption of the 9a11 Q16/K32 seed. + +Minimum target architecture: sm_100a. This dispatcher-consumption wrapper +starts from the 2422 guard policy, keeps the exact cb00 Q1 M131071/M250000 +guards, and replaces only the Q16/K32 RAG microbucket route with the 9a11 +``knn_build_rag_microbucket_3505_v2`` seed. No seed schedule, tile pipeline, or +math semantics are retuned here. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +from pathlib import Path +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_e3de_9138_bcb3_4247_v1 as base_4247 +from . import knn_build_rag_microbucket_3505_v1 as rag_3505_v1 +from . import knn_build_rag_microbucket_3505_v2 as rag_3505_v2 +from . import knn_build_rag_microbucket_faeb_v1 as rag_faeb +from . import knn_build_ragonline_mbucket_cb00_q1m_v2 as q1_cb00 +MODULE = 'loom.examples.weave.knn_build_dispatch_e3de_9138_bcb3_faeb_cb00_3505v2_8940_v1' +ROUTE_BASE_4247 = 'loom.examples.weave.knn_build_dispatch_e3de_9138_bcb3_4247_v1:launch_from_contract_inputs' +ROUTE_Q1_CB00_PARENT_SPLIT72 = 'rag_online_mbucket_cb00_q1m_v2_parent_split72' +ROUTE_Q1_CB00_CTA1_S144_G12 = 'rag_online_mbucket_cb00_q1m_v2_cta1_s144_g12' +ROUTE_RAG_Q4_FAEB = rag_faeb.ROUTE_Q4_K10 +ROUTE_RAG_Q64_FAEB = rag_faeb.ROUTE_Q64_K10 +ROUTE_RAG_Q16_K32_FAEB = rag_faeb.ROUTE_Q16_K32 +ROUTE_RAG_Q16_K32_3505_V1 = rag_3505_v1.ROUTE_Q16_K32 +ROUTE_RAG_Q16_K32_3505_V2 = ''.join(['rag_microbucket_3505_v2_q16_k32_tailinf_cta1_s', format(rag_3505_v2.K32_SPLIT_COUNT, ''), '_g', format(rag_3505_v2.K32_GROUP_COUNT, '')]) +CB00_SEED_ID = 'q1_mbucket_cb00_q1m_v2_4444' +FAEB_SEED_ID = 'rag_microbucket_faeb_v1' +SEED_3505_V1_ID = 'rag_microbucket_3505_v1' +SEED_3505_V2_ID = 'rag_microbucket_3505_v2_9a11' +DEFAULT_PORTFOLIO_ID = base_4247.DEFAULT_PORTFOLIO_ID +D64_TARGET_SHAPES = base_4247.D64_TARGET_SHAPES +RECT_TARGET_SHAPES = base_4247.RECT_TARGET_SHAPES +Q1_TARGET_SHAPES = q1_cb00.TARGET_SHAPES +Q1_CB00_SELECTED_M = (131071, 250000) +Q1_CB00_SELECTED_TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10"]}')) +RAG_Q4_Q64_TARGET_SHAPES = rag_faeb.K10_TARGET_SHAPES +RAG_Q16_K32_TARGET_SHAPES = rag_faeb.K32_TARGET_SHAPES +RAG_MICROBUCKET_TARGET_SHAPES = rag_faeb.TARGET_SHAPES +K96_AUDIT_SHAPES = ('build_over64_stress_qm2048_k96',) +NEW_CONSUMED_SEED_TARGET_SHAPES = RAG_Q16_K32_TARGET_SHAPES +DISPATCH_DELTA_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["rag_online_b1_q1_m100000_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "rag_microbatch_b1_q4_m100000_d128_k10", "rag_microbatch_b1_q64_m100000_d128_k10", "rag_microbatch_largek_b1_q16_m100000_d128_k32", "build_over64_stress_qm2048_k96"]}')) +CONSUMED_SEED_TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_dim_sweep_b1_q1024_m1024_d64_k10", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q4096_m4096_d64_k10", "search_rect_b1_q1024_m32768_d64_k10", "rag_online_b1_q1_m100000_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "build_over64_stress_qm1024_k96", "build_over64_stress_qm4096_k96", "rag_microbatch_b1_q4_m100000_d128_k10", "rag_microbatch_b1_q64_m100000_d128_k10", "rag_microbatch_largek_b1_q16_m100000_d128_k32"]}')) +SELECTED_TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_large_b1_q8192_m8192_d128_k20", "build_large_b1_q8192_m8192_d128_k32", "build_over64_stress_qm2048_k96", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32", "rag_batch_b2_q256_m50000_d128_k10", "rag_irregular_b1_q512_m131071_d128_k10", "search_rect_b1_q1024_m8192_d128_k10", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q2048_m2048_d256_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10", "search_rect_b1_q2048_m32768_d128_k10", "build_large_tail_b1_q6144_m6144_d128_k20", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "build_over32_stress_qm2048_k64", "build_over32_stress_qm4096_k64", "build_k_sweep_qm512_k1", "build_k_sweep_qm512_k2", "build_k_sweep_qm1024_k16", "build_k_sweep_qm512_k4", "build_k_sweep_qm512_k5", "build_k_sweep_qm512_k6", "rag_microbatch_b1_q8_m100000_d128_k10", "rag_microbatch_b1_q32_m100000_d128_k10", "build_dim_sweep_b1_q1024_m1024_d64_k10", "build_dim_sweep_b1_q4096_m4096_d64_k10", "search_rect_b1_q1024_m32768_d64_k10", "rag_online_b1_q1_m100000_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "build_over64_stress_qm1024_k96", "build_over64_stress_qm4096_k96", "rag_microbatch_b1_q4_m100000_d128_k10", "rag_microbatch_b1_q64_m100000_d128_k10", "rag_microbatch_largek_b1_q16_m100000_d128_k32"]}')) +GUARD_MISS_AUDIT_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_dim_sweep_b1_q1024_m1024_d64_k10", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q4096_m4096_d64_k10", "search_rect_b1_q1024_m32768_d64_k10", "rag_online_b1_q1_m100000_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "build_over64_stress_qm1024_k96", "build_over64_stress_qm4096_k96", "rag_microbatch_b1_q4_m100000_d128_k10", "rag_microbatch_b1_q64_m100000_d128_k10", "rag_microbatch_largek_b1_q16_m100000_d128_k32", "build_k_sweep_qm512_k4", "build_k_sweep_qm512_k8", "build_k_sweep_qm512_k10", "rag_microbatch_b1_q8_m100000_d128_k10", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_microbatch_b1_q32_m100000_d128_k10", "build_large_b1_q8192_m8192_d128_k20", "build_qm2048_d128_k10", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "flashml_correctness_b1_q256_m256_d128_k5", "build_over32_stress_qm2048_k64", "search_rect_over32_b1_q2048_m65536_d128_k64", "rag_offline_large_m_over32_b1_q2048_m250000_d128_k64", "build_k_sweep_qm512_k5", "build_over32_stress_qm4096_k64"]}')) +DISPATCH_CORRECTNESS_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["flashml_correctness_b1_q256_m256_d128_k5", "build_large_b1_q8192_m8192_d128_k20", "build_large_b1_q8192_m8192_d128_k32", "build_over64_stress_qm2048_k96", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32", "rag_batch_b2_q256_m50000_d128_k10", "rag_irregular_b1_q512_m131071_d128_k10", "search_rect_b1_q1024_m8192_d128_k10", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q2048_m2048_d256_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10", "search_rect_b1_q2048_m32768_d128_k10", "build_large_tail_b1_q6144_m6144_d128_k20", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "build_over32_stress_qm2048_k64", "build_over32_stress_qm4096_k64", "build_k_sweep_qm512_k1", "build_k_sweep_qm512_k2", "build_k_sweep_qm1024_k16", "build_k_sweep_qm512_k4", "build_k_sweep_qm512_k5", "build_k_sweep_qm512_k6", "rag_microbatch_b1_q8_m100000_d128_k10", "rag_microbatch_b1_q32_m100000_d128_k10", "build_dim_sweep_b1_q1024_m1024_d64_k10", "build_dim_sweep_b1_q4096_m4096_d64_k10", "search_rect_b1_q1024_m32768_d64_k10", "rag_online_b1_q1_m100000_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "build_over64_stress_qm1024_k96", "build_over64_stress_qm4096_k96", "rag_microbatch_b1_q4_m100000_d128_k10", "rag_microbatch_b1_q64_m100000_d128_k10", "rag_microbatch_largek_b1_q16_m100000_d128_k32", "build_k_sweep_qm512_k8", "build_k_sweep_qm512_k10", "build_qm2048_d128_k10", "search_rect_over32_b1_q2048_m65536_d128_k64", "rag_offline_large_m_over32_b1_q2048_m250000_d128_k64", "rag_stream_b1_q128_m100000_d128_k10", "search_rect_b1_q4096_m65536_d128_k20", "rag_offline_largek_b1_q4096_m100000_d128_k20", "rag_offline_large_m_b1_q8192_m250000_d128_k20"]}')) +ROUTE_SEED_ID = {**base_4247.ROUTE_SEED_ID, ROUTE_Q1_CB00_PARENT_SPLIT72: CB00_SEED_ID, ROUTE_Q1_CB00_CTA1_S144_G12: CB00_SEED_ID, ROUTE_RAG_Q4_FAEB: FAEB_SEED_ID, ROUTE_RAG_Q64_FAEB: FAEB_SEED_ID, ROUTE_RAG_Q16_K32_FAEB: FAEB_SEED_ID, ROUTE_RAG_Q16_K32_3505_V1: SEED_3505_V1_ID, ROUTE_RAG_Q16_K32_3505_V2: SEED_3505_V2_ID} +ROUTE_ENTRYPOINTS = {**base_4247.ROUTE_ENTRYPOINTS, ROUTE_Q1_CB00_PARENT_SPLIT72: 'loom.examples.weave.knn_build_ragonline_mbucket_cb00_q1m_v2:launch_from_contract_inputs', ROUTE_Q1_CB00_CTA1_S144_G12: 'loom.examples.weave.knn_build_ragonline_mbucket_cb00_q1m_v2:launch_from_contract_inputs', ROUTE_RAG_Q4_FAEB: 'loom.examples.weave.knn_build_rag_microbucket_faeb_v1:launch_from_contract_inputs', ROUTE_RAG_Q64_FAEB: 'loom.examples.weave.knn_build_rag_microbucket_faeb_v1:launch_from_contract_inputs', ROUTE_RAG_Q16_K32_FAEB: 'loom.examples.weave.knn_build_rag_microbucket_faeb_v1:launch_from_contract_inputs', ROUTE_RAG_Q16_K32_3505_V1: 'loom.examples.weave.knn_build_rag_microbucket_3505_v1:launch_from_contract_inputs', ROUTE_RAG_Q16_K32_3505_V2: 'loom.examples.weave.knn_build_rag_microbucket_3505_v2:launch_from_contract_inputs'} +PRODUCTION_ROUTE_MODULES = {**base_4247.PRODUCTION_ROUTE_MODULES, CB00_SEED_ID: 'loom.examples.weave.knn_build_ragonline_mbucket_cb00_q1m_v2:launch_from_contract_inputs', 'rag_microbucket_faeb_q4_q64_k10': 'loom.examples.weave.knn_build_rag_microbucket_faeb_v1:launch_from_contract_inputs', SEED_3505_V1_ID: 'loom.examples.weave.knn_build_rag_microbucket_3505_v1:launch_from_contract_inputs', SEED_3505_V2_ID: 'loom.examples.weave.knn_build_rag_microbucket_3505_v2:launch_from_contract_inputs', 'base_4247': ROUTE_BASE_4247} +CANDIDATE_DISPATCHERS = ({'id': 'baseline_6d62', 'entrypoint': ''.join([format(MODULE, ''), ':benchmark_baseline_6d62']), 'consumed_seeds': ('d64_fdd7_aa88_v2', 'rect_d64_cf49_v3_9138', 'q1_mbucket_aa88_q1m_v3_bcb3', FAEB_SEED_ID), 'guard_plan': ('faeb exact Q4/Q64 K10', 'faeb exact Q16 K32', 'then inherited 4247 guard stack'), 'expected_shape_wins': RAG_MICROBUCKET_TARGET_SHAPES, 'fallback': ROUTE_BASE_4247, 'rejected_reason': 'same-session baseline'}, {'id': 'baseline_2422', 'entrypoint': ''.join([format(MODULE, ''), ':benchmark_baseline_2422']), 'consumed_seeds': (CB00_SEED_ID, FAEB_SEED_ID, SEED_3505_V1_ID), 'guard_plan': ('cb00 exact Q1 M131071/M250000 bucket', 'faeb exact Q4/Q64 K10', '3505 v1 exact Q16 K32', 'then inherited 4247 guard stack'), 'expected_shape_wins': (*Q1_CB00_SELECTED_TARGET_SHAPES, *RAG_MICROBUCKET_TARGET_SHAPES), 'fallback': ROUTE_BASE_4247, 'rejected_reason': 'lane base before consuming 9a11'}, {'id': 'candidate_2422_9a11_8940_v1', 'entrypoint': ''.join([format(MODULE, ''), ':benchmark_knn_build_dispatch_e3de_9138_bcb3_faeb_cb00_3505v2_8940_v1']), 'consumed_seeds': (CB00_SEED_ID, FAEB_SEED_ID, SEED_3505_V2_ID), 'guard_plan': ('cb00 exact Q1 M131071/M250000 bucket', 'faeb exact Q4/Q64 K10', '3505 v2 exact Q16 K32', 'then inherited 4247 guard stack'), 'expected_shape_wins': (*Q1_CB00_SELECTED_TARGET_SHAPES, *RAG_MICROBUCKET_TARGET_SHAPES), 'fallback': ROUTE_BASE_4247, 'rejected_reason': None}) +TARGETED_SEED_ROWS = {**base_4247.TARGETED_SEED_ROWS, 'rag_online_b1_q1_m100000_d128_k10': {'kernel_ms': 0.056641, 'flashlib_ms': 0.060833, 'ratio_vs_flashlib': 1.0740099927614273, 'split_count': 72, 'route': base_4247.ROUTE_Q1_BCB3_SPLIT72}, 'rag_online_irregular_b1_q1_m131071_d128_k10': {'kernel_ms': 0.048641, 'flashlib_ms': 0.067425, 'ratio_vs_flashlib': 1.386176271047059, 'split_count': 144, 'group_count': 12, 'route': ROUTE_Q1_CB00_CTA1_S144_G12}, 'rag_online_large_m_b1_q1_m250000_d128_k10': {'kernel_ms': 0.0752, 'flashlib_ms': 0.091521, 'ratio_vs_flashlib': 1.217034574468085, 'split_count': 144, 'group_count': 12, 'route': ROUTE_Q1_CB00_CTA1_S144_G12}, rag_faeb.Q4_K10_SHAPE: {'kernel_ms': 0.06205, 'flashlib_ms': 0.063041, 'ratio_vs_flashlib': 1.0159709911361805, 'split_count': rag_faeb.M64_SPLIT_COUNT, 'group_count': rag_faeb.M64_GROUP_COUNT, 'route': ROUTE_RAG_Q4_FAEB}, rag_faeb.Q64_K10_SHAPE: {'kernel_ms': 0.072737, 'flashlib_ms': 0.098977, 'ratio_vs_flashlib': 1.3607517494535106, 'split_count': rag_faeb.M64_SPLIT_COUNT, 'group_count': rag_faeb.M64_GROUP_COUNT, 'route': ROUTE_RAG_Q64_FAEB}, rag_faeb.Q16_K32_SHAPE: {'kernel_ms': 0.140545, 'flashlib_ms': 0.134497, 'ratio_vs_flashlib': 0.9569675192998683, 'split_count': rag_3505_v2.K32_SPLIT_COUNT, 'group_count': rag_3505_v2.K32_GROUP_COUNT, 'route': ROUTE_RAG_Q16_K32_3505_V2}} +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k48over32", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 48]], "cta_group": 1, "threads": 192}')) + +def _eligible_q1_cb00(inputs: dict[str, Any]) -> bool: + return q1_cb00._eligible_rag_online_mbucket(inputs) + +def _selected_q1_cb00_mbucket(inputs: dict[str, Any], *, enable_q1_cb00_m100k: bool=False) -> bool: + if not _eligible_q1_cb00(inputs): + return False + return enable_q1_cb00_m100k or int(inputs.get('M', -1)) in Q1_CB00_SELECTED_M + +def _eligible_rag_q4_q64(inputs: dict[str, Any]) -> bool: + return rag_faeb._eligible_q4_k10(inputs) or rag_faeb._eligible_q64_k10(inputs) + +def _eligible_rag_q16_k32(inputs: dict[str, Any]) -> bool: + return rag_faeb._eligible_q16_k32(inputs) + +def route_for_contract_inputs(inputs: dict[str, Any], *, portfolio_id: str=DEFAULT_PORTFOLIO_ID, force_fallback: bool=False, enable_d64_fdd7: bool=True, enable_rect_d64: bool=True, enable_q1_mbucket: bool=True, enable_q1_cb00: bool=True, enable_q1_cb00_m100k: bool=False, enable_rag_microbucket: bool=True, enable_rag_q16_k32: bool=True, enable_rag_3505_v1_q16: bool=True, enable_rag_3505_v2_q16: bool=True, enable_rag_seed_portfolio: bool=True, enable_q512_k456: bool=True) -> str: + if not force_fallback: + if enable_q1_mbucket and enable_q1_cb00 and _selected_q1_cb00_mbucket(inputs, enable_q1_cb00_m100k=enable_q1_cb00_m100k): + return q1_cb00.route_for_contract_inputs(inputs) + if enable_rag_microbucket and _eligible_rag_q4_q64(inputs): + return rag_faeb.route_for_contract_inputs(inputs) + if enable_rag_microbucket and enable_rag_q16_k32 and _eligible_rag_q16_k32(inputs): + if enable_rag_3505_v2_q16: + return rag_3505_v2.route_for_contract_inputs(inputs) + if enable_rag_3505_v1_q16: + return rag_3505_v1.route_for_contract_inputs(inputs) + return rag_faeb.route_for_contract_inputs(inputs) + return base_4247.route_for_contract_inputs(inputs, portfolio_id=portfolio_id, force_fallback=force_fallback, enable_d64_fdd7=enable_d64_fdd7, enable_rect_d64=enable_rect_d64, enable_q1_mbucket=enable_q1_mbucket, enable_rag_seed_portfolio=enable_rag_seed_portfolio, enable_q512_k456=enable_q512_k456) + +def _launch_route(inputs: dict[str, Any], route: str) -> None: + if route in (ROUTE_Q1_CB00_PARENT_SPLIT72, ROUTE_Q1_CB00_CTA1_S144_G12): + q1_cb00.launch_from_contract_inputs(inputs) + return + if route in (ROUTE_RAG_Q4_FAEB, ROUTE_RAG_Q64_FAEB, ROUTE_RAG_Q16_K32_FAEB): + rag_faeb.launch_from_contract_inputs(inputs) + return + if route == ROUTE_RAG_Q16_K32_3505_V1: + rag_3505_v1.launch_from_contract_inputs(inputs) + return + if route == ROUTE_RAG_Q16_K32_3505_V2: + rag_3505_v2.launch_from_contract_inputs(inputs) + return + base_4247._launch_route(inputs, route) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, portfolio_id: str=DEFAULT_PORTFOLIO_ID, force_fallback: bool=False, enable_d64_fdd7: bool=True, enable_rect_d64: bool=True, enable_q1_mbucket: bool=True, enable_q1_cb00: bool=True, enable_q1_cb00_m100k: bool=False, enable_rag_microbucket: bool=True, enable_rag_q16_k32: bool=True, enable_rag_3505_v1_q16: bool=True, enable_rag_3505_v2_q16: bool=True, enable_rag_seed_portfolio: bool=True, enable_q512_k456: bool=True) -> None: + route = route_for_contract_inputs(inputs, portfolio_id=portfolio_id, force_fallback=force_fallback, enable_d64_fdd7=enable_d64_fdd7, enable_rect_d64=enable_rect_d64, enable_q1_mbucket=enable_q1_mbucket, enable_q1_cb00=enable_q1_cb00, enable_q1_cb00_m100k=enable_q1_cb00_m100k, enable_rag_microbucket=enable_rag_microbucket, enable_rag_q16_k32=enable_rag_q16_k32, enable_rag_3505_v1_q16=enable_rag_3505_v1_q16, enable_rag_3505_v2_q16=enable_rag_3505_v2_q16, enable_rag_seed_portfolio=enable_rag_seed_portfolio, enable_q512_k456=enable_q512_k456) + _launch_route(inputs, route) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_baseline_2422(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, enable_rag_3505_v2_q16=False) + +def candidate_baseline_6d62(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, enable_q1_cb00=False, enable_rag_3505_v1_q16=False, enable_rag_3505_v2_q16=False) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return base_4247._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=DISPATCH_CORRECTNESS_SHAPES, benchmark: bool=False) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=None, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + shapes = None if shape_labels is None else _select_contract_shapes(shape_labels) + return evaluate_contract(shapes=shapes, correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def _trace_inputs_from_shape(shape: dict[str, Any]) -> dict[str, Any]: + return base_4247._trace_inputs_from_shape(shape) + +def _inputs_for_label(label: str) -> dict[str, Any]: + return base_4247._inputs_for_label(label) + +def _selected_entrypoint_for_route(route: str) -> str: + return ROUTE_ENTRYPOINTS.get(route, base_4247._selected_entrypoint_for_route(route)) + +def _base_route_trace_record(inputs: dict[str, Any], *, force_fallback: bool=False) -> dict[str, Any]: + row = dict(base_4247._route_trace_record(inputs, force_fallback=force_fallback)) + route = str(row.get('selected_route') or base_4247.route_for_contract_inputs(inputs, force_fallback=force_fallback)) + row.setdefault('shape_key', inputs.get('label')) + row.setdefault('selected_entrypoint', _selected_entrypoint_for_route(route)) + row.setdefault('selected_seed', row.get('selected_seed') or row.get('consumed_seed')) + row.setdefault('expected_seed', row.get('selected_seed')) + row.setdefault('route_kind', row.get('route_kind', 'general')) + row.setdefault('route_source', 'broad-dispatcher') + row.setdefault('guard_id', row.get('candidate_guard_status')) + row.setdefault('guard_condition', 'inherited 4247 guard plan') + row.setdefault('classification', 'route-ok') + row.setdefault('dispatcher_kernel_ms', None) + row.setdefault('shape_specific_kernel_ms', None) + row.setdefault('relative_speedup_vs_baseline', None) + row['base_4247_route'] = base_4247.route_for_contract_inputs(inputs) + return row + +def _q1_cb00_trace_record(inputs: dict[str, Any], *, base_4247_route: str) -> dict[str, Any]: + label = str(inputs.get('label')) + targeted = dict(TARGETED_SEED_ROWS[label]) + route = q1_cb00.route_for_contract_inputs(inputs) + return {'shape_key': inputs.get('label'), 'selected_route': route, 'selected_entrypoint': ROUTE_ENTRYPOINTS[route], 'selected_seed': CB00_SEED_ID, 'expected_seed': CB00_SEED_ID, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': 'q1_online_mbucket_cb00_q1m_v2_selected_m_exact', 'guard_condition': ''.join(['exact BF16 non-build B=1 Q=1 M=', format(int(inputs.get('M')), ''), ' D=128 K=10 cb00 Q1 online M-bucket seed']), 'coverage': '8940 keeps 2422 cb00 Q1 guard ahead of inherited 4247 bcb3', 'consumed_seed': CB00_SEED_ID, 'replaced_route': base_4247_route, 'base_4247_route': base_4247_route, 'row_selection': targeted, 'split_count': targeted['split_count'], 'group_count': targeted.get('group_count'), 'targeted_seed_timing_backend': 'cupti', 'targeted_seed_kernel_ms': targeted['kernel_ms'], 'targeted_seed_ratio_vs_flashlib': targeted['ratio_vs_flashlib'], 'classification': 'seed-consumed', 'dispatcher_kernel_ms': None, 'shape_specific_kernel_ms': targeted['kernel_ms'], 'relative_speedup_vs_baseline': None} + +def _rag_microbucket_trace_record(inputs: dict[str, Any], *, route: str, base_4247_route: str) -> dict[str, Any]: + label = str(inputs.get('label')) + targeted = dict(TARGETED_SEED_ROWS[label]) + if route == ROUTE_RAG_Q4_FAEB: + seed = FAEB_SEED_ID + guard_id = 'rag_microbucket_faeb_q4_k10_exact' + guard_condition = 'exact BF16 non-build B=1 Q=4 M=100000 D=128 K=10 faeb seed' + elif route == ROUTE_RAG_Q64_FAEB: + seed = FAEB_SEED_ID + guard_id = 'rag_microbucket_faeb_q64_k10_exact' + guard_condition = 'exact BF16 non-build B=1 Q=64 M=100000 D=128 K=10 faeb seed' + elif route == ROUTE_RAG_Q16_K32_3505_V2: + seed = SEED_3505_V2_ID + guard_id = 'rag_microbucket_3505_v2_q16_k32_exact' + guard_condition = 'exact BF16 non-build B=1 Q=16 M=100000 D=128 K=32 9a11 3505_v2 seed' + elif route == ROUTE_RAG_Q16_K32_3505_V1: + seed = SEED_3505_V1_ID + guard_id = 'rag_microbucket_3505_v1_q16_k32_exact' + guard_condition = 'exact BF16 non-build B=1 Q=16 M=100000 D=128 K=32 2422 3505_v1 seed' + else: + seed = FAEB_SEED_ID + guard_id = 'rag_microbucket_faeb_q16_k32_exact' + guard_condition = 'exact BF16 non-build B=1 Q=16 M=100000 D=128 K=32 faeb seed' + return {'shape_key': inputs.get('label'), 'selected_route': route, 'selected_entrypoint': ROUTE_ENTRYPOINTS[route], 'selected_seed': seed, 'expected_seed': seed, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': guard_id, 'guard_condition': guard_condition, 'coverage': '8940 consumes exact RAG microbucket seeds ahead of inherited 4247 fallback', 'consumed_seed': seed, 'replaced_route': base_4247_route, 'base_4247_route': base_4247_route, 'row_selection': targeted, 'split_count': targeted['split_count'], 'group_count': targeted.get('group_count'), 'targeted_seed_timing_backend': 'cupti', 'targeted_seed_kernel_ms': targeted['kernel_ms'], 'targeted_seed_ratio_vs_flashlib': targeted['ratio_vs_flashlib'], 'classification': 'seed-consumed', 'dispatcher_kernel_ms': None, 'shape_specific_kernel_ms': targeted['kernel_ms'], 'relative_speedup_vs_baseline': None} + +def _route_trace_record(inputs: dict[str, Any], *, force_fallback: bool=False, enable_q1_cb00: bool=True, enable_q1_cb00_m100k: bool=False, enable_rag_3505_v1_q16: bool=True, enable_rag_3505_v2_q16: bool=True) -> dict[str, Any]: + base_4247_route = base_4247.route_for_contract_inputs(inputs) + is_overlay = _eligible_q1_cb00(inputs) or _eligible_rag_q4_q64(inputs) or _eligible_rag_q16_k32(inputs) + if force_fallback and is_overlay: + row = _base_route_trace_record(inputs, force_fallback=True) + expected_route = route_for_contract_inputs(inputs, force_fallback=False, enable_q1_cb00=enable_q1_cb00, enable_q1_cb00_m100k=enable_q1_cb00_m100k, enable_rag_3505_v1_q16=enable_rag_3505_v1_q16, enable_rag_3505_v2_q16=enable_rag_3505_v2_q16) + row['selected_route'] = base_4247.route_for_contract_inputs(inputs, force_fallback=True) + row['selected_entrypoint'] = _selected_entrypoint_for_route(str(row['selected_route'])) + row['selected_seed'] = row.get('consumed_seed') + row['expected_seed'] = ROUTE_SEED_ID.get(expected_route) + row['guard_id'] = 'forced_fallback_8940_overlays_disabled' + row['guard_condition'] = 'forced fallback to inherited 4247 path; 8940 overlays disabled' + row['forced_disabled_seeds'] = (CB00_SEED_ID, FAEB_SEED_ID, SEED_3505_V1_ID, SEED_3505_V2_ID) + row['candidate_guard_status'] = 'forced_fallback' + row['classification'] = 'route-ok' + return row + route = route_for_contract_inputs(inputs, force_fallback=force_fallback, enable_q1_cb00=enable_q1_cb00, enable_q1_cb00_m100k=enable_q1_cb00_m100k, enable_rag_3505_v1_q16=enable_rag_3505_v1_q16, enable_rag_3505_v2_q16=enable_rag_3505_v2_q16) + if route in (ROUTE_Q1_CB00_PARENT_SPLIT72, ROUTE_Q1_CB00_CTA1_S144_G12): + return _q1_cb00_trace_record(inputs, base_4247_route=base_4247_route) + if route in (ROUTE_RAG_Q4_FAEB, ROUTE_RAG_Q64_FAEB, ROUTE_RAG_Q16_K32_FAEB, ROUTE_RAG_Q16_K32_3505_V1, ROUTE_RAG_Q16_K32_3505_V2): + return _rag_microbucket_trace_record(inputs, route=route, base_4247_route=base_4247_route) + return _base_route_trace_record(inputs, force_fallback=force_fallback) + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False, enable_q1_cb00: bool=True, enable_q1_cb00_m100k: bool=False, enable_rag_3505_v1_q16: bool=True, enable_rag_3505_v2_q16: bool=True) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + return [_route_trace_record(_trace_inputs_from_shape(shape), force_fallback=force_fallback, enable_q1_cb00=enable_q1_cb00, enable_q1_cb00_m100k=enable_q1_cb00_m100k, enable_rag_3505_v1_q16=enable_rag_3505_v1_q16, enable_rag_3505_v2_q16=enable_rag_3505_v2_q16) for shape in selected] + +def _timing_backends_for_reports(*reports: dict[str, Any]) -> list[str]: + return base_4247._timing_backends_for_reports(*reports) + +def _rows_for_labels(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return base_4247._rows_for_labels(report, labels) + +def _row_delta(label: str, route: str, candidate_report: dict[str, Any], baseline_2422_report: dict[str, Any], baseline_6d62_report: dict[str, Any], *, candidate_id: str) -> dict[str, Any]: + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_2422_row = baseline_2422_report.get('per_shape', {}).get(label, {}) + baseline_6d62_row = baseline_6d62_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_2422_ms = baseline_2422_row.get('kernel_ms') + baseline_6d62_ms = baseline_6d62_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') + return {'candidate_id': candidate_id, 'selected_seed': ROUTE_SEED_ID.get(route), 'selected_route': route, 'candidate_ms': candidate_ms, 'baseline_2422_ms': baseline_2422_ms, 'baseline_6d62_ms': baseline_6d62_ms, 'flashlib_ms': flashlib_ms, 'metric_delta': candidate_ms - baseline_2422_ms if candidate_ms and baseline_2422_ms else None, 'speedup_vs_2422': baseline_2422_ms / candidate_ms if candidate_ms and baseline_2422_ms else None, 'speedup_vs_6d62': baseline_6d62_ms / candidate_ms if candidate_ms and baseline_6d62_ms else None, 'ratio_vs_flashlib': flashlib_ms / candidate_ms if candidate_ms and flashlib_ms else None, 'timing_backend': candidate_row.get('timing_backend') or baseline_2422_row.get('timing_backend') or baseline_6d62_row.get('timing_backend') or 'cupti'} + +def _seed_delta_matrix(candidate_report: dict[str, Any], baseline_2422_report: dict[str, Any], baseline_6d62_report: dict[str, Any]) -> list[dict[str, Any]]: + matrix = [] + for label in DISPATCH_DELTA_SHAPES: + inputs = _inputs_for_label(label) + baseline_6d62_route = route_for_contract_inputs(inputs, enable_q1_cb00=False, enable_rag_3505_v1_q16=False, enable_rag_3505_v2_q16=False) + baseline_2422_route = route_for_contract_inputs(inputs, enable_rag_3505_v2_q16=False) + candidate_route = route_for_contract_inputs(inputs) + matrix.append({'shape_key': label, 'baseline_route': baseline_2422_route, 'baseline_6d62_route': baseline_6d62_route, 'candidate_deltas': [_row_delta(label, baseline_6d62_route, baseline_6d62_report, baseline_2422_report, baseline_6d62_report, candidate_id='baseline_6d62'), _row_delta(label, baseline_2422_route, baseline_2422_report, baseline_2422_report, baseline_6d62_report, candidate_id='baseline_2422'), _row_delta(label, candidate_route, candidate_report, baseline_2422_report, baseline_6d62_report, candidate_id='candidate_2422_9a11_8940_v1')]}) + return matrix + +def _per_consumed_row_delta(candidate_report: dict[str, Any], baseline_2422_report: dict[str, Any], baseline_6d62_report: dict[str, Any]) -> dict[str, Any]: + deltas = {} + for item in _seed_delta_matrix(candidate_report, baseline_2422_report, baseline_6d62_report): + label = item['shape_key'] + candidate_delta = item['candidate_deltas'][-1] + deltas[label] = {'candidate_ms': candidate_delta.get('candidate_ms'), 'baseline_2422_ms': candidate_delta.get('baseline_2422_ms'), 'baseline_6d62_ms': candidate_delta.get('baseline_6d62_ms'), 'flashlib_ms': candidate_delta.get('flashlib_ms'), 'speedup_vs_2422': candidate_delta.get('speedup_vs_2422'), 'speedup_vs_6d62': candidate_delta.get('speedup_vs_6d62'), 'ratio_vs_flashlib': candidate_delta.get('ratio_vs_flashlib'), 'candidate_route': candidate_delta.get('selected_route'), 'baseline_2422_route': item['baseline_route'], 'baseline_6d62_route': item['baseline_6d62_route'], 'selected_seed': candidate_delta.get('selected_seed'), 'targeted_seed_kernel_ms': TARGETED_SEED_ROWS.get(label, {}).get('kernel_ms'), 'targeted_seed_ratio_vs_flashlib': TARGETED_SEED_ROWS.get(label, {}).get('ratio_vs_flashlib'), 'candidate_passed': candidate_report.get('per_shape', {}).get(label, {}).get('passed'), 'baseline_2422_passed': baseline_2422_report.get('per_shape', {}).get(label, {}).get('passed'), 'baseline_6d62_passed': baseline_6d62_report.get('per_shape', {}).get(label, {}).get('passed')} + return deltas + +def _annotate_route_trace(route_trace: list[dict[str, Any]], candidate_report: dict[str, Any], baseline_2422_report: dict[str, Any], baseline_6d62_report: dict[str, Any]) -> list[dict[str, Any]]: + annotated = [] + for row in route_trace: + label = str(row.get('shape_key')) + out = dict(row) + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_2422_row = baseline_2422_report.get('per_shape', {}).get(label, {}) + baseline_6d62_row = baseline_6d62_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_2422_ms = baseline_2422_row.get('kernel_ms') + baseline_6d62_ms = baseline_6d62_row.get('kernel_ms') + ratio = candidate_row.get('ratio_vs_flashlib') + out['dispatcher_kernel_ms'] = candidate_ms + out['baseline_2422_dispatcher_kernel_ms'] = baseline_2422_ms + out['baseline_6d62_dispatcher_kernel_ms'] = baseline_6d62_ms + out['flashlib_ms'] = candidate_row.get('flashlib_ms') + out['relative_speedup_vs_baseline'] = baseline_2422_ms / candidate_ms if candidate_ms and baseline_2422_ms else None + out['relative_speedup_vs_6d62'] = baseline_6d62_ms / candidate_ms if candidate_ms and baseline_6d62_ms else None + out['route_changed_vs_baseline_2422'] = out.get('selected_route') != route_for_contract_inputs(_inputs_for_label(label), enable_rag_3505_v2_q16=False) + if label in NEW_CONSUMED_SEED_TARGET_SHAPES and out.get('selected_seed') == SEED_3505_V2_ID: + if isinstance(ratio, (float, int)) and ratio < 1.0: + out['classification'] = 'kernel-slow' + else: + out['classification'] = 'seed-consumed' + elif not out['route_changed_vs_baseline_2422']: + if isinstance(ratio, (float, int)) and ratio < 1.0: + out['classification'] = 'kernel-slow' if out.get('route_kind') == 'specialized' else 'fallback-slow' + else: + out['classification'] = 'route-ok' + elif label in DISPATCH_DELTA_SHAPES and out.get('route_changed_vs_baseline_2422'): + speedup = out['relative_speedup_vs_baseline'] + out['classification'] = 'seed-consumed' if speedup is None or speedup >= 1.0 else 'kernel-slow' + elif isinstance(ratio, (float, int)) and ratio < 1.0: + out['classification'] = 'kernel-slow' if out.get('route_kind') == 'specialized' else 'fallback-slow' + else: + out['classification'] = out.get('classification', 'route-ok') + annotated.append(out) + return annotated + +def _below_flashlib_rows(report: dict[str, Any]) -> list[dict[str, Any]]: + trace_by_label = {str(row['shape_key']): row for row in route_trace_for_contract_shapes()} + rows = [] + for label, row in sorted(report.get('per_shape', {}).items()): + ratio = row.get('ratio_vs_flashlib') + if isinstance(ratio, (float, int)) and ratio < 1.0: + trace_row = trace_by_label.get(label, {}) + rows.append({'shape_key': label, 'kernel_ms': row.get('kernel_ms'), 'flashlib_ms': row.get('flashlib_ms'), 'ratio_vs_flashlib': ratio, 'selected_route': trace_row.get('selected_route'), 'route_kind': trace_row.get('route_kind', 'unknown'), 'classification': 'kernel-slow' if trace_row.get('route_kind') == 'specialized' else 'fallback-slow'}) + return rows + +def _hot_bucket_parity(report: dict[str, Any]) -> dict[str, str]: + buckets = {'d64_build_q1024_q2048_q4096_k10': D64_TARGET_SHAPES, 'rectangular_search_q1024_m32768_d64_k10': RECT_TARGET_SHAPES, 'rag_online_q1_mbucket': Q1_TARGET_SHAPES, 'rag_microbatch_q4_q64_k10': RAG_Q4_Q64_TARGET_SHAPES, 'rag_microbatch_q16_k32': RAG_Q16_K32_TARGET_SHAPES, 'build_over64_k96': K96_AUDIT_SHAPES} + out = {} + for bucket, labels in buckets.items(): + out[bucket] = 'pass' + for label in labels: + ratio = report.get('per_shape', {}).get(label, {}).get('ratio_vs_flashlib') + if not isinstance(ratio, (float, int)) or ratio < 1.0: + out[bucket] = 'fail' + break + return out + +def _benchmark_payload(candidate_report: dict[str, Any], baseline_2422_report: dict[str, Any], baseline_6d62_report: dict[str, Any], *, use_cupti: bool, shape_labels, measured_function: str, candidate_id: str) -> dict[str, Any]: + candidate_metric = candidate_report['summary']['primary_mean'] + baseline_2422_metric = baseline_2422_report['summary']['primary_mean'] + baseline_6d62_metric = baseline_6d62_report['summary']['primary_mean'] + route_trace = _annotate_route_trace(route_trace_for_contract_shapes(shape_labels), candidate_report, baseline_2422_report, baseline_6d62_report) + below_flashlib = _below_flashlib_rows(candidate_report) + return {'candidate_id': candidate_id, 'tflops': candidate_metric, 'baseline_2422_tflops': baseline_2422_metric, 'baseline_6d62_tflops': baseline_6d62_metric, 'metric_delta_vs_2422': candidate_metric - baseline_2422_metric if candidate_metric and baseline_2422_metric else None, 'metric_delta_vs_6d62': candidate_metric - baseline_6d62_metric if candidate_metric and baseline_6d62_metric else None, 'all_correct': candidate_report['summary']['all_correct'], 'baseline_2422_all_correct': baseline_2422_report['summary']['all_correct'], 'baseline_6d62_all_correct': baseline_6d62_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'baseline_2422_performance_comparable': baseline_2422_report['summary']['performance_comparable'], 'baseline_6d62_performance_comparable': baseline_6d62_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'measured_entrypoint': ''.join([format(MODULE, ''), ':', format(measured_function, '')]), 'baseline_2422_entrypoint': ''.join([format(MODULE, ''), ':benchmark_baseline_2422']), 'baseline_6d62_entrypoint': ''.join([format(MODULE, ''), ':benchmark_baseline_6d62']), 'measured_shape_labels': 'all_canonical' if shape_labels is None else tuple(shape_labels), 'selected_route_labels': SELECTED_TARGET_SHAPES, 'consumed_seed_labels': CONSUMED_SEED_TARGET_SHAPES, 'new_consumed_seed_labels': NEW_CONSUMED_SEED_TARGET_SHAPES, 'guard_miss_audit_labels': GUARD_MISS_AUDIT_SHAPES, 'selected_route_rows': _rows_for_labels(candidate_report, SELECTED_TARGET_SHAPES), 'baseline_2422_selected_route_rows': _rows_for_labels(baseline_2422_report, SELECTED_TARGET_SHAPES), 'baseline_6d62_selected_route_rows': _rows_for_labels(baseline_6d62_report, SELECTED_TARGET_SHAPES), 'consumed_seed_rows': _rows_for_labels(candidate_report, CONSUMED_SEED_TARGET_SHAPES), 'new_consumed_seed_rows': _rows_for_labels(candidate_report, NEW_CONSUMED_SEED_TARGET_SHAPES), 'baseline_2422_consumed_seed_rows': _rows_for_labels(baseline_2422_report, CONSUMED_SEED_TARGET_SHAPES), 'baseline_6d62_consumed_seed_rows': _rows_for_labels(baseline_6d62_report, CONSUMED_SEED_TARGET_SHAPES), 'guard_miss_audit_rows': _rows_for_labels(candidate_report, GUARD_MISS_AUDIT_SHAPES), 'per_consumed_row_delta': _per_consumed_row_delta(candidate_report, baseline_2422_report, baseline_6d62_report), 'seed_delta_matrix': _seed_delta_matrix(candidate_report, baseline_2422_report, baseline_6d62_report), 'targeted_seed_rows': TARGETED_SEED_ROWS, 'candidate_dispatchers': CANDIDATE_DISPATCHERS, 'selected_candidate_dispatcher': 'candidate_2422_9a11_8940_v1', 'route_modules': PRODUCTION_ROUTE_MODULES, 'route_trace': route_trace, 'forced_fallback_route_trace': route_trace_for_contract_shapes(shape_labels, force_fallback=True), 'route_trace_included': True, 'contract_summary': candidate_report['summary'], 'baseline_2422_contract_summary': baseline_2422_report['summary'], 'baseline_6d62_contract_summary': baseline_6d62_report['summary'], 'contract_performance': candidate_report['performance'], 'baseline_2422_contract_performance': baseline_2422_report['performance'], 'baseline_6d62_contract_performance': baseline_6d62_report['performance'], 'timing_backends': _timing_backends_for_reports(candidate_report, baseline_2422_report, baseline_6d62_report), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'hot_bucket_parity': _hot_bucket_parity(candidate_report), 'performance_coverage': 'partial' if below_flashlib else 'pass', 'coverage_only_routes': [], 'hot_bucket_blockers': below_flashlib, 'report': candidate_report, 'baseline_2422_report': baseline_2422_report, 'baseline_6d62_report': baseline_6d62_report} + +def _benchmark_candidate(*, use_cupti: bool=True, shape_labels=None, kernel_fn: Callable[[dict[str, Any]], Any]=candidate, measured_function: str, candidate_id: str, baseline_2422_report: dict[str, Any] | None=None, baseline_6d62_report: dict[str, Any] | None=None) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=kernel_fn) + if baseline_2422_report is None: + baseline_2422_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_baseline_2422) + if baseline_6d62_report is None: + baseline_6d62_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_baseline_6d62) + return _benchmark_payload(candidate_report, baseline_2422_report, baseline_6d62_report, use_cupti=use_cupti, shape_labels=shape_labels, measured_function=measured_function, candidate_id=candidate_id) + +def benchmark_knn_build_dispatch_e3de_9138_bcb3_faeb_cb00_3505v2_8940_v1(*, use_cupti: bool=True, shape_labels=None, baseline_2422_report: dict[str, Any] | None=None, baseline_6d62_report: dict[str, Any] | None=None) -> dict[str, Any]: + return _benchmark_candidate(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate, measured_function='benchmark_knn_build_dispatch_e3de_9138_bcb3_faeb_cb00_3505v2_8940_v1', candidate_id='candidate_2422_9a11_8940_v1', baseline_2422_report=baseline_2422_report, baseline_6d62_report=baseline_6d62_report) + +def benchmark_baseline_2422(*, use_cupti: bool=True, shape_labels=None) -> dict[str, Any]: + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_baseline_2422) + return {'candidate_id': 'baseline_2422', 'tflops': report['summary']['primary_mean'], 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_baseline_2422']), 'route_trace': route_trace_for_contract_shapes(shape_labels, enable_rag_3505_v2_q16=False), 'route_trace_included': True, 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'report': report, 'contract_summary': report['summary'], 'contract_performance': report['performance']} + +def benchmark_baseline_6d62(*, use_cupti: bool=True, shape_labels=None) -> dict[str, Any]: + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_baseline_6d62) + return {'candidate_id': 'baseline_6d62', 'tflops': report['summary']['primary_mean'], 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_baseline_6d62']), 'route_trace': route_trace_for_contract_shapes(shape_labels, enable_q1_cb00=False, enable_rag_3505_v1_q16=False, enable_rag_3505_v2_q16=False), 'route_trace_included': True, 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'report': report, 'contract_summary': report['summary'], 'contract_performance': report['performance']} + +def _denominator_label(shape_labels) -> str: + if shape_labels is None: + return ''.join(['full', format(len(eval_mod.CANONICAL_SHAPES), '')]) + return ''.join(['shape', format(len(tuple(shape_labels)), '')]) + +def write_benchmark_artifacts(artifact_dir: str | Path, *, use_cupti: bool=True, shape_labels=None) -> dict[str, str]: + payload = benchmark_knn_build_dispatch_e3de_9138_bcb3_faeb_cb00_3505v2_8940_v1(use_cupti=use_cupti, shape_labels=shape_labels) + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + denom = _denominator_label(shape_labels) + candidate_path = out_dir / ''.join([format(denom, ''), '_dispatch_e3de_9138_bcb3_faeb_cb00_3505v2_8940_v1.json']) + baseline_2422_path = out_dir / ''.join([format(denom, ''), '_same_session_baseline_2422_for_8940_v1.json']) + baseline_6d62_path = out_dir / ''.join([format(denom, ''), '_same_session_baseline_6d62_for_8940_v1.json']) + route_trace_path = out_dir / ''.join([format(denom, ''), '_route_trace_e3de_9138_bcb3_faeb_cb00_3505v2_8940_v1.json']) + forced_trace_path = out_dir / ''.join([format(denom, ''), '_forced_fallback_trace_e3de_9138_bcb3_faeb_cb00_3505v2_8940_v1.json']) + seed_matrix_path = out_dir / ''.join([format(denom, ''), '_seed_delta_matrix_e3de_9138_bcb3_faeb_cb00_3505v2_8940_v1.json']) + candidate_path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + baseline_2422_path.write_text(json.dumps({'candidate_id': 'baseline_2422', 'measured_entrypoint': payload['baseline_2422_entrypoint'], 'measured_shape_labels': payload['measured_shape_labels'], 'timing_backend_requested': payload['timing_backend_requested'], 'timing_backends': payload['timing_backends'], 'tflops': payload['baseline_2422_tflops'], 'all_correct': payload['baseline_2422_all_correct'], 'performance_comparable': payload['baseline_2422_performance_comparable'], 'contract_summary': payload['baseline_2422_contract_summary'], 'contract_performance': payload['baseline_2422_contract_performance'], 'route_trace': route_trace_for_contract_shapes(shape_labels, enable_rag_3505_v2_q16=False), 'route_trace_included': True, 'report': payload['baseline_2422_report']}, indent=2, sort_keys=True) + '\n') + baseline_6d62_path.write_text(json.dumps({'candidate_id': 'baseline_6d62', 'measured_entrypoint': payload['baseline_6d62_entrypoint'], 'measured_shape_labels': payload['measured_shape_labels'], 'timing_backend_requested': payload['timing_backend_requested'], 'timing_backends': payload['timing_backends'], 'tflops': payload['baseline_6d62_tflops'], 'all_correct': payload['baseline_6d62_all_correct'], 'performance_comparable': payload['baseline_6d62_performance_comparable'], 'contract_summary': payload['baseline_6d62_contract_summary'], 'contract_performance': payload['baseline_6d62_contract_performance'], 'route_trace': route_trace_for_contract_shapes(shape_labels, enable_q1_cb00=False, enable_rag_3505_v1_q16=False, enable_rag_3505_v2_q16=False), 'route_trace_included': True, 'report': payload['baseline_6d62_report']}, indent=2, sort_keys=True) + '\n') + route_trace_path.write_text(json.dumps(payload['route_trace'], indent=2, sort_keys=True) + '\n') + forced_trace_path.write_text(json.dumps(payload['forced_fallback_route_trace'], indent=2, sort_keys=True) + '\n') + seed_matrix_path.write_text(json.dumps(payload['seed_delta_matrix'], indent=2, sort_keys=True) + '\n') + return {'candidate_payload': str(candidate_path), 'same_session_baseline_2422_payload': str(baseline_2422_path), 'same_session_baseline_6d62_payload': str(baseline_6d62_path), 'route_trace': str(route_trace_path), 'forced_fallback_trace': str(forced_trace_path), 'seed_delta_matrix': str(seed_matrix_path)} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_e3de_9138_bcb3_faeb_cb00_3505v3_51c1_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_e3de_9138_bcb3_faeb_cb00_3505v3_51c1_v1.py new file mode 100644 index 00000000..995cfd92 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_e3de_9138_bcb3_faeb_cb00_3505v3_51c1_v1.py @@ -0,0 +1,351 @@ +"""51c1 kNN build dispatcher consumption of the e7e5 Q16/K32 seed. + +Minimum target architecture: sm_100a. This dispatcher-consumption wrapper +starts from the 8940 guard policy and replaces only the exact Q16/K32 RAG +microbucket route with the e7e5 ``knn_build_rag_microbucket_3505_v3`` seed. +No seed schedule, tile pipeline, or math semantics are retuned here. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +from pathlib import Path +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_e3de_9138_bcb3_faeb_cb00_3505v2_8940_v1 as base_8940 +from . import knn_build_rag_microbucket_3505_v3 as rag_3505_v3 +MODULE = 'loom.examples.weave.knn_build_dispatch_e3de_9138_bcb3_faeb_cb00_3505v3_51c1_v1' +base_4247 = base_8940.base_4247 +q1_cb00 = base_8940.q1_cb00 +rag_faeb = base_8940.rag_faeb +rag_3505_v1 = base_8940.rag_3505_v1 +rag_3505_v2 = base_8940.rag_3505_v2 +ROUTE_BASE_4247 = base_8940.ROUTE_BASE_4247 +ROUTE_Q1_CB00_PARENT_SPLIT72 = base_8940.ROUTE_Q1_CB00_PARENT_SPLIT72 +ROUTE_Q1_CB00_CTA1_S144_G12 = base_8940.ROUTE_Q1_CB00_CTA1_S144_G12 +ROUTE_RAG_Q4_FAEB = base_8940.ROUTE_RAG_Q4_FAEB +ROUTE_RAG_Q64_FAEB = base_8940.ROUTE_RAG_Q64_FAEB +ROUTE_RAG_Q16_K32_FAEB = base_8940.ROUTE_RAG_Q16_K32_FAEB +ROUTE_RAG_Q16_K32_3505_V1 = base_8940.ROUTE_RAG_Q16_K32_3505_V1 +ROUTE_RAG_Q16_K32_3505_V2 = base_8940.ROUTE_RAG_Q16_K32_3505_V2 +ROUTE_RAG_Q16_K32_3505_V3 = ''.join(['rag_microbucket_3505_v3_q16_k32_tailinf_cta1_u1_s', format(rag_3505_v3.K32_SPLIT_COUNT, ''), '_g', format(rag_3505_v3.K32_GROUP_COUNT, '')]) +CB00_SEED_ID = base_8940.CB00_SEED_ID +FAEB_SEED_ID = base_8940.FAEB_SEED_ID +SEED_3505_V1_ID = base_8940.SEED_3505_V1_ID +SEED_3505_V2_ID = base_8940.SEED_3505_V2_ID +SEED_3505_V3_ID = 'rag_microbucket_3505_v3_e7e5' +DEFAULT_PORTFOLIO_ID = base_8940.DEFAULT_PORTFOLIO_ID +D64_TARGET_SHAPES = base_8940.D64_TARGET_SHAPES +RECT_TARGET_SHAPES = base_8940.RECT_TARGET_SHAPES +Q1_TARGET_SHAPES = base_8940.Q1_TARGET_SHAPES +Q1_CB00_SELECTED_M = base_8940.Q1_CB00_SELECTED_M +Q1_CB00_SELECTED_TARGET_SHAPES = base_8940.Q1_CB00_SELECTED_TARGET_SHAPES +RAG_Q4_Q64_TARGET_SHAPES = base_8940.RAG_Q4_Q64_TARGET_SHAPES +RAG_Q16_K32_TARGET_SHAPES = base_8940.RAG_Q16_K32_TARGET_SHAPES +RAG_MICROBUCKET_TARGET_SHAPES = base_8940.RAG_MICROBUCKET_TARGET_SHAPES +K96_AUDIT_SHAPES = base_8940.K96_AUDIT_SHAPES +NEW_CONSUMED_SEED_TARGET_SHAPES = RAG_Q16_K32_TARGET_SHAPES +DISPATCH_DELTA_SHAPES = base_8940.DISPATCH_DELTA_SHAPES +CONSUMED_SEED_TARGET_SHAPES = base_8940.CONSUMED_SEED_TARGET_SHAPES +SELECTED_TARGET_SHAPES = base_8940.SELECTED_TARGET_SHAPES +GUARD_MISS_AUDIT_SHAPES = base_8940.GUARD_MISS_AUDIT_SHAPES +DISPATCH_CORRECTNESS_SHAPES = base_8940.DISPATCH_CORRECTNESS_SHAPES +ROUTE_SEED_ID = {**base_8940.ROUTE_SEED_ID, ROUTE_RAG_Q16_K32_3505_V3: SEED_3505_V3_ID} +ROUTE_ENTRYPOINTS = {**base_8940.ROUTE_ENTRYPOINTS, ROUTE_RAG_Q16_K32_3505_V3: 'loom.examples.weave.knn_build_rag_microbucket_3505_v3:launch_from_contract_inputs'} +PRODUCTION_ROUTE_MODULES = {**base_8940.PRODUCTION_ROUTE_MODULES, SEED_3505_V3_ID: 'loom.examples.weave.knn_build_rag_microbucket_3505_v3:launch_from_contract_inputs'} +CANDIDATE_DISPATCHERS = (*base_8940.CANDIDATE_DISPATCHERS, {'id': 'baseline_8940', 'entrypoint': ''.join([format(MODULE, ''), ':benchmark_baseline_8940']), 'consumed_seeds': (CB00_SEED_ID, FAEB_SEED_ID, SEED_3505_V2_ID), 'guard_plan': ('8940 cb00 exact Q1 M131071/M250000 bucket', '8940 faeb exact Q4/Q64 K10', '8940 3505 v2 exact Q16 K32', 'then inherited 4247 guard stack'), 'expected_shape_wins': (*Q1_CB00_SELECTED_TARGET_SHAPES, *RAG_MICROBUCKET_TARGET_SHAPES), 'fallback': ROUTE_BASE_4247, 'rejected_reason': 'same-session base dispatcher before consuming e7e5'}, {'id': 'candidate_8940_e7e5_51c1_v1', 'entrypoint': ''.join([format(MODULE, ''), ':benchmark_knn_build_dispatch_e3de_9138_bcb3_faeb_cb00_3505v3_51c1_v1']), 'consumed_seeds': (CB00_SEED_ID, FAEB_SEED_ID, SEED_3505_V3_ID), 'guard_plan': ('8940 cb00 exact Q1 M131071/M250000 bucket', '8940 faeb exact Q4/Q64 K10', 'e7e5 3505 v3 exact Q16 K32', 'then inherited 4247 guard stack'), 'expected_shape_wins': (*Q1_CB00_SELECTED_TARGET_SHAPES, *RAG_MICROBUCKET_TARGET_SHAPES), 'fallback': ROUTE_BASE_4247, 'rejected_reason': None}) +TARGETED_SEED_ROWS = {**base_8940.TARGETED_SEED_ROWS, rag_faeb.Q16_K32_SHAPE: {'kernel_ms': 0.139649, 'flashlib_ms': 0.135681, 'ratio_vs_flashlib': 0.9715859046609714, 'split_count': rag_3505_v3.K32_SPLIT_COUNT, 'group_count': rag_3505_v3.K32_GROUP_COUNT, 'route': ROUTE_RAG_Q16_K32_3505_V3}} +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k48over32", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 48]], "cta_group": 1, "threads": 192}')) + +def _eligible_q1_cb00(inputs: dict[str, Any]) -> bool: + return base_8940._eligible_q1_cb00(inputs) + +def _selected_q1_cb00_mbucket(inputs: dict[str, Any], *, enable_q1_cb00_m100k: bool=False) -> bool: + return base_8940._selected_q1_cb00_mbucket(inputs, enable_q1_cb00_m100k=enable_q1_cb00_m100k) + +def _eligible_rag_q4_q64(inputs: dict[str, Any]) -> bool: + return base_8940._eligible_rag_q4_q64(inputs) + +def _eligible_rag_q16_k32(inputs: dict[str, Any]) -> bool: + return base_8940._eligible_rag_q16_k32(inputs) + +def route_for_contract_inputs(inputs: dict[str, Any], *, portfolio_id: str=DEFAULT_PORTFOLIO_ID, force_fallback: bool=False, enable_d64_fdd7: bool=True, enable_rect_d64: bool=True, enable_q1_mbucket: bool=True, enable_q1_cb00: bool=True, enable_q1_cb00_m100k: bool=False, enable_rag_microbucket: bool=True, enable_rag_q16_k32: bool=True, enable_rag_3505_v1_q16: bool=True, enable_rag_3505_v2_q16: bool=True, enable_rag_3505_v3_q16: bool=True, enable_rag_seed_portfolio: bool=True, enable_q512_k456: bool=True) -> str: + if not force_fallback: + if enable_q1_mbucket and enable_q1_cb00 and _selected_q1_cb00_mbucket(inputs, enable_q1_cb00_m100k=enable_q1_cb00_m100k): + return q1_cb00.route_for_contract_inputs(inputs) + if enable_rag_microbucket and _eligible_rag_q4_q64(inputs): + return rag_faeb.route_for_contract_inputs(inputs) + if enable_rag_microbucket and enable_rag_q16_k32 and _eligible_rag_q16_k32(inputs): + if enable_rag_3505_v3_q16: + return rag_3505_v3.route_for_contract_inputs(inputs) + if enable_rag_3505_v2_q16: + return rag_3505_v2.route_for_contract_inputs(inputs) + if enable_rag_3505_v1_q16: + return rag_3505_v1.route_for_contract_inputs(inputs) + return rag_faeb.route_for_contract_inputs(inputs) + return base_4247.route_for_contract_inputs(inputs, portfolio_id=portfolio_id, force_fallback=force_fallback, enable_d64_fdd7=enable_d64_fdd7, enable_rect_d64=enable_rect_d64, enable_q1_mbucket=enable_q1_mbucket, enable_rag_seed_portfolio=enable_rag_seed_portfolio, enable_q512_k456=enable_q512_k456) + +def _launch_route(inputs: dict[str, Any], route: str) -> None: + if route == ROUTE_RAG_Q16_K32_3505_V3: + rag_3505_v3.launch_from_contract_inputs(inputs) + return + base_8940._launch_route(inputs, route) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, portfolio_id: str=DEFAULT_PORTFOLIO_ID, force_fallback: bool=False, enable_d64_fdd7: bool=True, enable_rect_d64: bool=True, enable_q1_mbucket: bool=True, enable_q1_cb00: bool=True, enable_q1_cb00_m100k: bool=False, enable_rag_microbucket: bool=True, enable_rag_q16_k32: bool=True, enable_rag_3505_v1_q16: bool=True, enable_rag_3505_v2_q16: bool=True, enable_rag_3505_v3_q16: bool=True, enable_rag_seed_portfolio: bool=True, enable_q512_k456: bool=True) -> None: + route = route_for_contract_inputs(inputs, portfolio_id=portfolio_id, force_fallback=force_fallback, enable_d64_fdd7=enable_d64_fdd7, enable_rect_d64=enable_rect_d64, enable_q1_mbucket=enable_q1_mbucket, enable_q1_cb00=enable_q1_cb00, enable_q1_cb00_m100k=enable_q1_cb00_m100k, enable_rag_microbucket=enable_rag_microbucket, enable_rag_q16_k32=enable_rag_q16_k32, enable_rag_3505_v1_q16=enable_rag_3505_v1_q16, enable_rag_3505_v2_q16=enable_rag_3505_v2_q16, enable_rag_3505_v3_q16=enable_rag_3505_v3_q16, enable_rag_seed_portfolio=enable_rag_seed_portfolio, enable_q512_k456=enable_q512_k456) + _launch_route(inputs, route) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_baseline_8940(inputs: dict[str, Any]) -> None: + base_8940.launch_from_contract_inputs(inputs) + +def candidate_baseline_2422(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, enable_rag_3505_v3_q16=False, enable_rag_3505_v2_q16=False) + +def candidate_baseline_6d62(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, enable_q1_cb00=False, enable_rag_3505_v1_q16=False, enable_rag_3505_v2_q16=False, enable_rag_3505_v3_q16=False) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return base_8940._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=DISPATCH_CORRECTNESS_SHAPES, benchmark: bool=False) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + return base_8940._set_bench_backend(use_cupti) + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=None, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + shapes = None if shape_labels is None else _select_contract_shapes(shape_labels) + return evaluate_contract(shapes=shapes, correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def _trace_inputs_from_shape(shape: dict[str, Any]) -> dict[str, Any]: + return base_8940._trace_inputs_from_shape(shape) + +def _inputs_for_label(label: str) -> dict[str, Any]: + return base_8940._inputs_for_label(label) + +def _selected_entrypoint_for_route(route: str) -> str: + return ROUTE_ENTRYPOINTS.get(route, base_8940._selected_entrypoint_for_route(route)) + +def _base_route_trace_record(inputs: dict[str, Any], *, force_fallback: bool=False) -> dict[str, Any]: + row = dict(base_8940._base_route_trace_record(inputs, force_fallback=force_fallback)) + route = str(row.get('selected_route') or base_4247.route_for_contract_inputs(inputs, force_fallback=force_fallback)) + row['selected_entrypoint'] = _selected_entrypoint_for_route(route) + return row + +def _q1_cb00_trace_record(inputs: dict[str, Any], *, base_4247_route: str) -> dict[str, Any]: + return base_8940._q1_cb00_trace_record(inputs, base_4247_route=base_4247_route) + +def _rag_microbucket_trace_record(inputs: dict[str, Any], *, route: str, base_4247_route: str) -> dict[str, Any]: + if route != ROUTE_RAG_Q16_K32_3505_V3: + return base_8940._rag_microbucket_trace_record(inputs, route=route, base_4247_route=base_4247_route) + label = str(inputs.get('label')) + targeted = dict(TARGETED_SEED_ROWS[label]) + return {'shape_key': inputs.get('label'), 'selected_route': route, 'selected_entrypoint': ROUTE_ENTRYPOINTS[route], 'selected_seed': SEED_3505_V3_ID, 'expected_seed': SEED_3505_V3_ID, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': 'rag_microbucket_3505_v3_q16_k32_exact', 'guard_condition': 'exact BF16 non-build B=1 Q=16 M=100000 D=128 K=32 e7e5 3505_v3 seed', 'coverage': '51c1 consumes exact e7e5 RAG microbucket seed ahead of inherited 8940/4247 fallback', 'consumed_seed': SEED_3505_V3_ID, 'replaced_route': ROUTE_RAG_Q16_K32_3505_V2, 'base_4247_route': base_4247_route, 'base_8940_route': ROUTE_RAG_Q16_K32_3505_V2, 'row_selection': targeted, 'split_count': targeted['split_count'], 'group_count': targeted.get('group_count'), 'targeted_seed_timing_backend': 'cupti', 'targeted_seed_kernel_ms': targeted['kernel_ms'], 'targeted_seed_ratio_vs_flashlib': targeted['ratio_vs_flashlib'], 'classification': 'seed-consumed', 'dispatcher_kernel_ms': None, 'shape_specific_kernel_ms': targeted['kernel_ms'], 'relative_speedup_vs_baseline': None} + +def _route_trace_record(inputs: dict[str, Any], *, force_fallback: bool=False, enable_q1_cb00: bool=True, enable_q1_cb00_m100k: bool=False, enable_rag_3505_v1_q16: bool=True, enable_rag_3505_v2_q16: bool=True, enable_rag_3505_v3_q16: bool=True) -> dict[str, Any]: + base_4247_route = base_4247.route_for_contract_inputs(inputs) + is_overlay = _eligible_q1_cb00(inputs) or _eligible_rag_q4_q64(inputs) or _eligible_rag_q16_k32(inputs) + if force_fallback and is_overlay: + row = _base_route_trace_record(inputs, force_fallback=True) + expected_route = route_for_contract_inputs(inputs, force_fallback=False, enable_q1_cb00=enable_q1_cb00, enable_q1_cb00_m100k=enable_q1_cb00_m100k, enable_rag_3505_v1_q16=enable_rag_3505_v1_q16, enable_rag_3505_v2_q16=enable_rag_3505_v2_q16, enable_rag_3505_v3_q16=enable_rag_3505_v3_q16) + row['selected_route'] = base_4247.route_for_contract_inputs(inputs, force_fallback=True) + row['selected_entrypoint'] = _selected_entrypoint_for_route(str(row['selected_route'])) + row['selected_seed'] = row.get('consumed_seed') + row['expected_seed'] = ROUTE_SEED_ID.get(expected_route) + row['guard_id'] = 'forced_fallback_51c1_overlays_disabled' + row['guard_condition'] = 'forced fallback to inherited 4247 path; 51c1 overlays disabled' + row['forced_disabled_seeds'] = (CB00_SEED_ID, FAEB_SEED_ID, SEED_3505_V1_ID, SEED_3505_V2_ID, SEED_3505_V3_ID) + row['candidate_guard_status'] = 'forced_fallback' + row['classification'] = 'route-ok' + return row + route = route_for_contract_inputs(inputs, force_fallback=force_fallback, enable_q1_cb00=enable_q1_cb00, enable_q1_cb00_m100k=enable_q1_cb00_m100k, enable_rag_3505_v1_q16=enable_rag_3505_v1_q16, enable_rag_3505_v2_q16=enable_rag_3505_v2_q16, enable_rag_3505_v3_q16=enable_rag_3505_v3_q16) + if route in (ROUTE_Q1_CB00_PARENT_SPLIT72, ROUTE_Q1_CB00_CTA1_S144_G12): + return _q1_cb00_trace_record(inputs, base_4247_route=base_4247_route) + if route in (ROUTE_RAG_Q4_FAEB, ROUTE_RAG_Q64_FAEB, ROUTE_RAG_Q16_K32_FAEB, ROUTE_RAG_Q16_K32_3505_V1, ROUTE_RAG_Q16_K32_3505_V2, ROUTE_RAG_Q16_K32_3505_V3): + return _rag_microbucket_trace_record(inputs, route=route, base_4247_route=base_4247_route) + return _base_route_trace_record(inputs, force_fallback=force_fallback) + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False, enable_q1_cb00: bool=True, enable_q1_cb00_m100k: bool=False, enable_rag_3505_v1_q16: bool=True, enable_rag_3505_v2_q16: bool=True, enable_rag_3505_v3_q16: bool=True) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + return [_route_trace_record(_trace_inputs_from_shape(shape), force_fallback=force_fallback, enable_q1_cb00=enable_q1_cb00, enable_q1_cb00_m100k=enable_q1_cb00_m100k, enable_rag_3505_v1_q16=enable_rag_3505_v1_q16, enable_rag_3505_v2_q16=enable_rag_3505_v2_q16, enable_rag_3505_v3_q16=enable_rag_3505_v3_q16) for shape in selected] + +def _timing_backends_for_reports(*reports: dict[str, Any]) -> list[str]: + return base_8940._timing_backends_for_reports(*reports) + +def _rows_for_labels(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return base_8940._rows_for_labels(report, labels) + +def _row_delta(label: str, route: str, candidate_report: dict[str, Any], baseline_8940_report: dict[str, Any], baseline_2422_report: dict[str, Any], baseline_6d62_report: dict[str, Any], *, candidate_id: str) -> dict[str, Any]: + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_8940_row = baseline_8940_report.get('per_shape', {}).get(label, {}) + baseline_2422_row = baseline_2422_report.get('per_shape', {}).get(label, {}) + baseline_6d62_row = baseline_6d62_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_8940_ms = baseline_8940_row.get('kernel_ms') + baseline_2422_ms = baseline_2422_row.get('kernel_ms') + baseline_6d62_ms = baseline_6d62_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') + return {'candidate_id': candidate_id, 'selected_seed': ROUTE_SEED_ID.get(route), 'selected_route': route, 'candidate_ms': candidate_ms, 'baseline_8940_ms': baseline_8940_ms, 'baseline_2422_ms': baseline_2422_ms, 'baseline_6d62_ms': baseline_6d62_ms, 'flashlib_ms': flashlib_ms, 'metric_delta_vs_8940': candidate_ms - baseline_8940_ms if candidate_ms and baseline_8940_ms else None, 'speedup_vs_8940': baseline_8940_ms / candidate_ms if candidate_ms and baseline_8940_ms else None, 'speedup_vs_2422': baseline_2422_ms / candidate_ms if candidate_ms and baseline_2422_ms else None, 'speedup_vs_6d62': baseline_6d62_ms / candidate_ms if candidate_ms and baseline_6d62_ms else None, 'ratio_vs_flashlib': flashlib_ms / candidate_ms if candidate_ms and flashlib_ms else None, 'timing_backend': candidate_row.get('timing_backend') or baseline_8940_row.get('timing_backend') or baseline_2422_row.get('timing_backend') or baseline_6d62_row.get('timing_backend') or 'cupti'} + +def _seed_delta_matrix(candidate_report: dict[str, Any], baseline_8940_report: dict[str, Any], baseline_2422_report: dict[str, Any], baseline_6d62_report: dict[str, Any]) -> list[dict[str, Any]]: + matrix = [] + for label in DISPATCH_DELTA_SHAPES: + inputs = _inputs_for_label(label) + baseline_6d62_route = route_for_contract_inputs(inputs, enable_q1_cb00=False, enable_rag_3505_v1_q16=False, enable_rag_3505_v2_q16=False, enable_rag_3505_v3_q16=False) + baseline_2422_route = route_for_contract_inputs(inputs, enable_rag_3505_v2_q16=False, enable_rag_3505_v3_q16=False) + baseline_8940_route = route_for_contract_inputs(inputs, enable_rag_3505_v3_q16=False) + candidate_route = route_for_contract_inputs(inputs) + matrix.append({'shape_key': label, 'baseline_route': baseline_8940_route, 'baseline_2422_route': baseline_2422_route, 'baseline_6d62_route': baseline_6d62_route, 'candidate_deltas': [_row_delta(label, baseline_6d62_route, baseline_6d62_report, baseline_8940_report, baseline_2422_report, baseline_6d62_report, candidate_id='baseline_6d62'), _row_delta(label, baseline_2422_route, baseline_2422_report, baseline_8940_report, baseline_2422_report, baseline_6d62_report, candidate_id='baseline_2422'), _row_delta(label, baseline_8940_route, baseline_8940_report, baseline_8940_report, baseline_2422_report, baseline_6d62_report, candidate_id='baseline_8940'), _row_delta(label, candidate_route, candidate_report, baseline_8940_report, baseline_2422_report, baseline_6d62_report, candidate_id='candidate_8940_e7e5_51c1_v1')]}) + return matrix + +def _per_consumed_row_delta(candidate_report: dict[str, Any], baseline_8940_report: dict[str, Any], baseline_2422_report: dict[str, Any], baseline_6d62_report: dict[str, Any]) -> dict[str, Any]: + deltas = {} + for item in _seed_delta_matrix(candidate_report, baseline_8940_report, baseline_2422_report, baseline_6d62_report): + label = item['shape_key'] + candidate_delta = item['candidate_deltas'][-1] + deltas[label] = {'candidate_ms': candidate_delta.get('candidate_ms'), 'baseline_8940_ms': candidate_delta.get('baseline_8940_ms'), 'baseline_2422_ms': candidate_delta.get('baseline_2422_ms'), 'baseline_6d62_ms': candidate_delta.get('baseline_6d62_ms'), 'flashlib_ms': candidate_delta.get('flashlib_ms'), 'speedup_vs_8940': candidate_delta.get('speedup_vs_8940'), 'speedup_vs_2422': candidate_delta.get('speedup_vs_2422'), 'speedup_vs_6d62': candidate_delta.get('speedup_vs_6d62'), 'ratio_vs_flashlib': candidate_delta.get('ratio_vs_flashlib'), 'candidate_route': candidate_delta.get('selected_route'), 'baseline_8940_route': item['baseline_route'], 'baseline_2422_route': item['baseline_2422_route'], 'baseline_6d62_route': item['baseline_6d62_route'], 'selected_seed': candidate_delta.get('selected_seed'), 'targeted_seed_kernel_ms': TARGETED_SEED_ROWS.get(label, {}).get('kernel_ms'), 'targeted_seed_ratio_vs_flashlib': TARGETED_SEED_ROWS.get(label, {}).get('ratio_vs_flashlib'), 'candidate_passed': candidate_report.get('per_shape', {}).get(label, {}).get('passed'), 'baseline_8940_passed': baseline_8940_report.get('per_shape', {}).get(label, {}).get('passed'), 'baseline_2422_passed': baseline_2422_report.get('per_shape', {}).get(label, {}).get('passed'), 'baseline_6d62_passed': baseline_6d62_report.get('per_shape', {}).get(label, {}).get('passed')} + return deltas + +def _annotate_route_trace(route_trace: list[dict[str, Any]], candidate_report: dict[str, Any], baseline_8940_report: dict[str, Any], baseline_2422_report: dict[str, Any], baseline_6d62_report: dict[str, Any]) -> list[dict[str, Any]]: + annotated = [] + for row in route_trace: + label = str(row.get('shape_key')) + out = dict(row) + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_8940_row = baseline_8940_report.get('per_shape', {}).get(label, {}) + baseline_2422_row = baseline_2422_report.get('per_shape', {}).get(label, {}) + baseline_6d62_row = baseline_6d62_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_8940_ms = baseline_8940_row.get('kernel_ms') + baseline_2422_ms = baseline_2422_row.get('kernel_ms') + baseline_6d62_ms = baseline_6d62_row.get('kernel_ms') + ratio = candidate_row.get('ratio_vs_flashlib') + out['dispatcher_kernel_ms'] = candidate_ms + out['baseline_8940_dispatcher_kernel_ms'] = baseline_8940_ms + out['baseline_2422_dispatcher_kernel_ms'] = baseline_2422_ms + out['baseline_6d62_dispatcher_kernel_ms'] = baseline_6d62_ms + out['flashlib_ms'] = candidate_row.get('flashlib_ms') + out['relative_speedup_vs_baseline'] = baseline_8940_ms / candidate_ms if candidate_ms and baseline_8940_ms else None + out['relative_speedup_vs_2422'] = baseline_2422_ms / candidate_ms if candidate_ms and baseline_2422_ms else None + out['relative_speedup_vs_6d62'] = baseline_6d62_ms / candidate_ms if candidate_ms and baseline_6d62_ms else None + out['route_changed_vs_baseline_8940'] = out.get('selected_route') != route_for_contract_inputs(_inputs_for_label(label), enable_rag_3505_v3_q16=False) + if label in NEW_CONSUMED_SEED_TARGET_SHAPES and out.get('selected_seed') == SEED_3505_V3_ID: + if isinstance(ratio, (float, int)) and ratio < 1.0: + out['classification'] = 'kernel-slow' + else: + out['classification'] = 'seed-consumed' + elif not out['route_changed_vs_baseline_8940']: + if isinstance(ratio, (float, int)) and ratio < 1.0: + out['classification'] = 'kernel-slow' if out.get('route_kind') == 'specialized' else 'fallback-slow' + else: + out['classification'] = 'route-ok' + elif label in DISPATCH_DELTA_SHAPES and out.get('route_changed_vs_baseline_8940'): + speedup = out['relative_speedup_vs_baseline'] + out['classification'] = 'seed-consumed' if speedup is None or speedup >= 1.0 else 'kernel-slow' + elif isinstance(ratio, (float, int)) and ratio < 1.0: + out['classification'] = 'kernel-slow' if out.get('route_kind') == 'specialized' else 'fallback-slow' + else: + out['classification'] = out.get('classification', 'route-ok') + annotated.append(out) + return annotated + +def _below_flashlib_rows(report: dict[str, Any]) -> list[dict[str, Any]]: + trace_by_label = {str(row['shape_key']): row for row in route_trace_for_contract_shapes()} + rows = [] + for label, row in sorted(report.get('per_shape', {}).items()): + ratio = row.get('ratio_vs_flashlib') + if isinstance(ratio, (float, int)) and ratio < 1.0: + trace_row = trace_by_label.get(label, {}) + rows.append({'shape_key': label, 'kernel_ms': row.get('kernel_ms'), 'flashlib_ms': row.get('flashlib_ms'), 'ratio_vs_flashlib': ratio, 'selected_route': trace_row.get('selected_route'), 'route_kind': trace_row.get('route_kind', 'unknown'), 'classification': 'kernel-slow' if trace_row.get('route_kind') == 'specialized' else 'fallback-slow'}) + return rows + +def _hot_bucket_parity(report: dict[str, Any]) -> dict[str, str]: + buckets = {'d64_build_q1024_q2048_q4096_k10': D64_TARGET_SHAPES, 'rectangular_search_q1024_m32768_d64_k10': RECT_TARGET_SHAPES, 'rag_online_q1_mbucket': Q1_TARGET_SHAPES, 'rag_microbatch_q4_q64_k10': RAG_Q4_Q64_TARGET_SHAPES, 'rag_microbatch_q16_k32': RAG_Q16_K32_TARGET_SHAPES, 'build_over64_k96': K96_AUDIT_SHAPES} + out = {} + for bucket, labels in buckets.items(): + out[bucket] = 'pass' + for label in labels: + ratio = report.get('per_shape', {}).get(label, {}).get('ratio_vs_flashlib') + if not isinstance(ratio, (float, int)) or ratio < 1.0: + out[bucket] = 'fail' + break + return out + +def _benchmark_payload(candidate_report: dict[str, Any], baseline_8940_report: dict[str, Any], baseline_2422_report: dict[str, Any], baseline_6d62_report: dict[str, Any], *, use_cupti: bool, shape_labels, measured_function: str, candidate_id: str) -> dict[str, Any]: + candidate_metric = candidate_report['summary']['primary_mean'] + baseline_8940_metric = baseline_8940_report['summary']['primary_mean'] + baseline_2422_metric = baseline_2422_report['summary']['primary_mean'] + baseline_6d62_metric = baseline_6d62_report['summary']['primary_mean'] + route_trace = _annotate_route_trace(route_trace_for_contract_shapes(shape_labels), candidate_report, baseline_8940_report, baseline_2422_report, baseline_6d62_report) + below_flashlib = _below_flashlib_rows(candidate_report) + return {'candidate_id': candidate_id, 'tflops': candidate_metric, 'baseline_8940_tflops': baseline_8940_metric, 'baseline_2422_tflops': baseline_2422_metric, 'baseline_6d62_tflops': baseline_6d62_metric, 'metric_delta_vs_8940': candidate_metric - baseline_8940_metric if candidate_metric and baseline_8940_metric else None, 'metric_delta_vs_2422': candidate_metric - baseline_2422_metric if candidate_metric and baseline_2422_metric else None, 'metric_delta_vs_6d62': candidate_metric - baseline_6d62_metric if candidate_metric and baseline_6d62_metric else None, 'all_correct': candidate_report['summary']['all_correct'], 'baseline_8940_all_correct': baseline_8940_report['summary']['all_correct'], 'baseline_2422_all_correct': baseline_2422_report['summary']['all_correct'], 'baseline_6d62_all_correct': baseline_6d62_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'baseline_8940_performance_comparable': baseline_8940_report['summary']['performance_comparable'], 'baseline_2422_performance_comparable': baseline_2422_report['summary']['performance_comparable'], 'baseline_6d62_performance_comparable': baseline_6d62_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'measured_entrypoint': ''.join([format(MODULE, ''), ':', format(measured_function, '')]), 'baseline_8940_entrypoint': ''.join([format(MODULE, ''), ':benchmark_baseline_8940']), 'baseline_2422_entrypoint': ''.join([format(MODULE, ''), ':benchmark_baseline_2422']), 'baseline_6d62_entrypoint': ''.join([format(MODULE, ''), ':benchmark_baseline_6d62']), 'measured_shape_labels': 'all_canonical' if shape_labels is None else tuple(shape_labels), 'selected_route_labels': SELECTED_TARGET_SHAPES, 'consumed_seed_labels': CONSUMED_SEED_TARGET_SHAPES, 'new_consumed_seed_labels': NEW_CONSUMED_SEED_TARGET_SHAPES, 'guard_miss_audit_labels': GUARD_MISS_AUDIT_SHAPES, 'selected_route_rows': _rows_for_labels(candidate_report, SELECTED_TARGET_SHAPES), 'baseline_8940_selected_route_rows': _rows_for_labels(baseline_8940_report, SELECTED_TARGET_SHAPES), 'baseline_2422_selected_route_rows': _rows_for_labels(baseline_2422_report, SELECTED_TARGET_SHAPES), 'baseline_6d62_selected_route_rows': _rows_for_labels(baseline_6d62_report, SELECTED_TARGET_SHAPES), 'consumed_seed_rows': _rows_for_labels(candidate_report, CONSUMED_SEED_TARGET_SHAPES), 'new_consumed_seed_rows': _rows_for_labels(candidate_report, NEW_CONSUMED_SEED_TARGET_SHAPES), 'baseline_8940_consumed_seed_rows': _rows_for_labels(baseline_8940_report, CONSUMED_SEED_TARGET_SHAPES), 'baseline_2422_consumed_seed_rows': _rows_for_labels(baseline_2422_report, CONSUMED_SEED_TARGET_SHAPES), 'baseline_6d62_consumed_seed_rows': _rows_for_labels(baseline_6d62_report, CONSUMED_SEED_TARGET_SHAPES), 'guard_miss_audit_rows': _rows_for_labels(candidate_report, GUARD_MISS_AUDIT_SHAPES), 'per_consumed_row_delta': _per_consumed_row_delta(candidate_report, baseline_8940_report, baseline_2422_report, baseline_6d62_report), 'seed_delta_matrix': _seed_delta_matrix(candidate_report, baseline_8940_report, baseline_2422_report, baseline_6d62_report), 'targeted_seed_rows': TARGETED_SEED_ROWS, 'candidate_dispatchers': CANDIDATE_DISPATCHERS, 'selected_candidate_dispatcher': 'candidate_8940_e7e5_51c1_v1', 'route_modules': PRODUCTION_ROUTE_MODULES, 'route_trace': route_trace, 'forced_fallback_route_trace': route_trace_for_contract_shapes(shape_labels, force_fallback=True), 'route_trace_included': True, 'contract_summary': candidate_report['summary'], 'baseline_8940_contract_summary': baseline_8940_report['summary'], 'baseline_2422_contract_summary': baseline_2422_report['summary'], 'baseline_6d62_contract_summary': baseline_6d62_report['summary'], 'contract_performance': candidate_report['performance'], 'baseline_8940_contract_performance': baseline_8940_report['performance'], 'baseline_2422_contract_performance': baseline_2422_report['performance'], 'baseline_6d62_contract_performance': baseline_6d62_report['performance'], 'timing_backends': _timing_backends_for_reports(candidate_report, baseline_8940_report, baseline_2422_report, baseline_6d62_report), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'hot_bucket_parity': _hot_bucket_parity(candidate_report), 'performance_coverage': 'partial' if below_flashlib else 'pass', 'coverage_only_routes': [], 'hot_bucket_blockers': below_flashlib, 'report': candidate_report, 'baseline_8940_report': baseline_8940_report, 'baseline_2422_report': baseline_2422_report, 'baseline_6d62_report': baseline_6d62_report} + +def _benchmark_candidate(*, use_cupti: bool=True, shape_labels=None, kernel_fn: Callable[[dict[str, Any]], Any]=candidate, measured_function: str, candidate_id: str, baseline_8940_report: dict[str, Any] | None=None, baseline_2422_report: dict[str, Any] | None=None, baseline_6d62_report: dict[str, Any] | None=None) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=kernel_fn) + if baseline_8940_report is None: + baseline_8940_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_baseline_8940) + if baseline_2422_report is None: + baseline_2422_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_baseline_2422) + if baseline_6d62_report is None: + baseline_6d62_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_baseline_6d62) + return _benchmark_payload(candidate_report, baseline_8940_report, baseline_2422_report, baseline_6d62_report, use_cupti=use_cupti, shape_labels=shape_labels, measured_function=measured_function, candidate_id=candidate_id) + +def benchmark_knn_build_dispatch_e3de_9138_bcb3_faeb_cb00_3505v3_51c1_v1(*, use_cupti: bool=True, shape_labels=None, baseline_8940_report: dict[str, Any] | None=None, baseline_2422_report: dict[str, Any] | None=None, baseline_6d62_report: dict[str, Any] | None=None) -> dict[str, Any]: + return _benchmark_candidate(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate, measured_function='benchmark_knn_build_dispatch_e3de_9138_bcb3_faeb_cb00_3505v3_51c1_v1', candidate_id='candidate_8940_e7e5_51c1_v1', baseline_8940_report=baseline_8940_report, baseline_2422_report=baseline_2422_report, baseline_6d62_report=baseline_6d62_report) + +def benchmark_baseline_8940(*, use_cupti: bool=True, shape_labels=None) -> dict[str, Any]: + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_baseline_8940) + return {'candidate_id': 'baseline_8940', 'tflops': report['summary']['primary_mean'], 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_baseline_8940']), 'route_trace': base_8940.route_trace_for_contract_shapes(shape_labels), 'route_trace_included': True, 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'report': report, 'contract_summary': report['summary'], 'contract_performance': report['performance']} + +def benchmark_baseline_2422(*, use_cupti: bool=True, shape_labels=None) -> dict[str, Any]: + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_baseline_2422) + return {'candidate_id': 'baseline_2422', 'tflops': report['summary']['primary_mean'], 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_baseline_2422']), 'route_trace': route_trace_for_contract_shapes(shape_labels, enable_rag_3505_v2_q16=False, enable_rag_3505_v3_q16=False), 'route_trace_included': True, 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'report': report, 'contract_summary': report['summary'], 'contract_performance': report['performance']} + +def benchmark_baseline_6d62(*, use_cupti: bool=True, shape_labels=None) -> dict[str, Any]: + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_baseline_6d62) + return {'candidate_id': 'baseline_6d62', 'tflops': report['summary']['primary_mean'], 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_baseline_6d62']), 'route_trace': route_trace_for_contract_shapes(shape_labels, enable_q1_cb00=False, enable_rag_3505_v1_q16=False, enable_rag_3505_v2_q16=False, enable_rag_3505_v3_q16=False), 'route_trace_included': True, 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'report': report, 'contract_summary': report['summary'], 'contract_performance': report['performance']} + +def _denominator_label(shape_labels) -> str: + if shape_labels is None: + return ''.join(['full', format(len(eval_mod.CANONICAL_SHAPES), '')]) + return ''.join(['shape', format(len(tuple(shape_labels)), '')]) + +def write_benchmark_artifacts(artifact_dir: str | Path, *, use_cupti: bool=True, shape_labels=None) -> dict[str, str]: + payload = benchmark_knn_build_dispatch_e3de_9138_bcb3_faeb_cb00_3505v3_51c1_v1(use_cupti=use_cupti, shape_labels=shape_labels) + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + denom = _denominator_label(shape_labels) + candidate_path = out_dir / ''.join([format(denom, ''), '_dispatch_e3de_9138_bcb3_faeb_cb00_3505v3_51c1_v1.json']) + baseline_8940_path = out_dir / ''.join([format(denom, ''), '_same_session_baseline_8940_for_51c1_v1.json']) + baseline_2422_path = out_dir / ''.join([format(denom, ''), '_same_session_baseline_2422_for_51c1_v1.json']) + baseline_6d62_path = out_dir / ''.join([format(denom, ''), '_same_session_baseline_6d62_for_51c1_v1.json']) + route_trace_path = out_dir / ''.join([format(denom, ''), '_route_trace_e3de_9138_bcb3_faeb_cb00_3505v3_51c1_v1.json']) + forced_trace_path = out_dir / ''.join([format(denom, ''), '_forced_fallback_trace_e3de_9138_bcb3_faeb_cb00_3505v3_51c1_v1.json']) + seed_matrix_path = out_dir / ''.join([format(denom, ''), '_seed_delta_matrix_e3de_9138_bcb3_faeb_cb00_3505v3_51c1_v1.json']) + candidate_path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + baseline_8940_path.write_text(json.dumps({'candidate_id': 'baseline_8940', 'measured_entrypoint': payload['baseline_8940_entrypoint'], 'measured_shape_labels': payload['measured_shape_labels'], 'timing_backend_requested': payload['timing_backend_requested'], 'timing_backends': payload['timing_backends'], 'tflops': payload['baseline_8940_tflops'], 'all_correct': payload['baseline_8940_all_correct'], 'performance_comparable': payload['baseline_8940_performance_comparable'], 'contract_summary': payload['baseline_8940_contract_summary'], 'contract_performance': payload['baseline_8940_contract_performance'], 'route_trace': base_8940.route_trace_for_contract_shapes(shape_labels), 'route_trace_included': True, 'report': payload['baseline_8940_report']}, indent=2, sort_keys=True) + '\n') + baseline_2422_path.write_text(json.dumps({'candidate_id': 'baseline_2422', 'measured_entrypoint': payload['baseline_2422_entrypoint'], 'measured_shape_labels': payload['measured_shape_labels'], 'timing_backend_requested': payload['timing_backend_requested'], 'timing_backends': payload['timing_backends'], 'tflops': payload['baseline_2422_tflops'], 'all_correct': payload['baseline_2422_all_correct'], 'performance_comparable': payload['baseline_2422_performance_comparable'], 'contract_summary': payload['baseline_2422_contract_summary'], 'contract_performance': payload['baseline_2422_contract_performance'], 'route_trace': route_trace_for_contract_shapes(shape_labels, enable_rag_3505_v2_q16=False, enable_rag_3505_v3_q16=False), 'route_trace_included': True, 'report': payload['baseline_2422_report']}, indent=2, sort_keys=True) + '\n') + baseline_6d62_path.write_text(json.dumps({'candidate_id': 'baseline_6d62', 'measured_entrypoint': payload['baseline_6d62_entrypoint'], 'measured_shape_labels': payload['measured_shape_labels'], 'timing_backend_requested': payload['timing_backend_requested'], 'timing_backends': payload['timing_backends'], 'tflops': payload['baseline_6d62_tflops'], 'all_correct': payload['baseline_6d62_all_correct'], 'performance_comparable': payload['baseline_6d62_performance_comparable'], 'contract_summary': payload['baseline_6d62_contract_summary'], 'contract_performance': payload['baseline_6d62_contract_performance'], 'route_trace': route_trace_for_contract_shapes(shape_labels, enable_q1_cb00=False, enable_rag_3505_v1_q16=False, enable_rag_3505_v2_q16=False, enable_rag_3505_v3_q16=False), 'route_trace_included': True, 'report': payload['baseline_6d62_report']}, indent=2, sort_keys=True) + '\n') + route_trace_path.write_text(json.dumps(payload['route_trace'], indent=2, sort_keys=True) + '\n') + forced_trace_path.write_text(json.dumps(payload['forced_fallback_route_trace'], indent=2, sort_keys=True) + '\n') + seed_matrix_path.write_text(json.dumps(payload['seed_delta_matrix'], indent=2, sort_keys=True) + '\n') + return {'candidate_payload': str(candidate_path), 'same_session_baseline_8940_payload': str(baseline_8940_path), 'same_session_baseline_2422_payload': str(baseline_2422_path), 'same_session_baseline_6d62_payload': str(baseline_6d62_path), 'route_trace': str(route_trace_path), 'forced_fallback_trace': str(forced_trace_path), 'seed_delta_matrix': str(seed_matrix_path)} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_ee5e_de1a_weave_evolve_knn_build_3e08_v46.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_ee5e_de1a_weave_evolve_knn_build_3e08_v46.py new file mode 100644 index 00000000..1128bfe2 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_ee5e_de1a_weave_evolve_knn_build_3e08_v46.py @@ -0,0 +1,113 @@ +"""Main kNN dispatcher consuming ee5e RAG and de1a K20 exact routes. + +Minimum target architecture: sm_100a. This dispatcher retargets the exported +``knn_build`` path to consume the rank-selected RAG pair route from ee5e and +the rank-selected K20 large/rectangular route from beb7/de1a. Exact guard +misses remain on the inherited v41 Weave dispatcher chain; no external runtime +fallback is used. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import os +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatchscore_tailinf_knn_build_dispatch_slurm_0610_6329_v41 as v41 +from . import knn_build_k20_large_lowfanout_de1a_v1 as k20_de1a +from . import knn_build_rag_pair_exact_weave_evolve_knn_build_ee5e_v44 as rag_ee5e +RAG_TARGET_SHAPES = rag_ee5e.TARGET_SHAPES +K20_TARGET_SHAPES = k20_de1a.EXACT_SHAPE_LABELS +RAG_TARGET_SHAPE_SET = set(RAG_TARGET_SHAPES) +K20_TARGET_SHAPE_SET = set(K20_TARGET_SHAPES) +SELECTED_TARGET_SHAPES = RAG_TARGET_SHAPES + K20_TARGET_SHAPES +FORCE_FALLBACK_ENV = 'LOOM_KNN_MAIN_V46_FORCE_FALLBACK' +ROUTE_RAG_EE5E = 'rag_ee5e_pair_exact' +ROUTE_K20_DE1A = 'k20_de1a_lowfanout' +ROUTE_V41_FALLBACK = 'v41_weave_fallback' +PRODUCTION_ROUTE_MODULES = {ROUTE_RAG_EE5E: rag_ee5e.__name__, ROUTE_K20_DE1A: k20_de1a.__name__, ROUTE_V41_FALLBACK: v41.__name__} + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_MAIN_3E08_VERIFY_KERNEL') + if verify_kernel == 'rag_online_stage1_k10_s14': + return rag_ee5e.online_route.v20.parent_lowk.stage1_ir + if verify_kernel == 'rag_online_merge_generic_s14': + return rag_ee5e.online_route.v20.parent_lowk.generic_merge_ir + if verify_kernel == 'rag_stream_stage1_k10_s7': + return rag_ee5e.stream_route.parent_lowk.stage1_ir + if verify_kernel == 'rag_stream_merge_k10_s7_cache': + return rag_ee5e.stream_route.parent_lowk.parent_cached.merge_k10_s7_cache_ir + if verify_kernel in {'k20_stage1_s4', 'k20_stage1_s2', 'k20_stage1'}: + return k20_de1a.parent_v20.stage1_k20_unordered_ir + if verify_kernel == 'k20_merge_s4_warp_select': + return k20_de1a.parent_v20.merge_k20_unordered_warp_select_ir + if verify_kernel == 'k20_merge_s2_warp_select': + return k20_de1a.merge_k20_s2_warp_select_ir + return v41.ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k48over32", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 48]], "cta_group": 1, "threads": 192}')) + +def _label_can_hit(inputs: dict[str, Any], target_labels: set[str]) -> bool: + label = inputs.get('label') + return label is None or str(label) in target_labels + +def _force_fallback_enabled() -> bool: + value = os.environ.get(FORCE_FALLBACK_ENV, '') + return value.strip().lower() in {'1', 'true', 'yes', 'on'} + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool | None=None) -> str: + fallback_forced = _force_fallback_enabled() if force_fallback is None else force_fallback + if fallback_forced: + return ROUTE_V41_FALLBACK + if _label_can_hit(inputs, RAG_TARGET_SHAPE_SET) and rag_ee5e._eligible_rag_pair_exact(inputs): + return ROUTE_RAG_EE5E + if _label_can_hit(inputs, K20_TARGET_SHAPE_SET) and k20_de1a._eligible_k20_large_lowfanout(inputs): + return ROUTE_K20_DE1A + return ROUTE_V41_FALLBACK + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool | None=None) -> None: + route = route_for_contract_inputs(inputs, force_fallback=force_fallback) + if route == ROUTE_RAG_EE5E: + rag_ee5e.launch_from_contract_inputs(inputs) + return + if route == ROUTE_K20_DE1A: + k20_de1a.launch_from_contract_inputs(inputs) + return + v41.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(candidate, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return v41._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=('flashml_correctness_b1_q256_m256_d128_k5', 'rag_online_b1_q1_m100000_d128_k10', 'rag_stream_b1_q128_m100000_d128_k10', 'search_rect_b1_q4096_m65536_d128_k20', 'rag_offline_largek_b1_q4096_m100000_d128_k20', 'rag_offline_large_m_b1_q8192_m250000_d128_k20'), benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint: run real contract correctness for selected shapes.""" + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _benchmark_payload(report: dict[str, Any], *, use_cupti: bool, shape_labels) -> dict[str, Any]: + timing_backends = sorted({result.get('timing_backend') for result in report.get('per_shape', {}).values() if result.get('timing_backend') is not None}) + selected_rows = {label: report.get('per_shape', {}).get(label, {}) for label in SELECTED_TARGET_SHAPES if label in report.get('per_shape', {})} + return {'tflops': report['summary']['primary_mean'] or 0.0, 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'invalid_performance_reason': report['summary']['invalid_performance_reason'], 'measured_entrypoint': 'loom.examples.weave.knn_build_dispatch_ee5e_de1a_weave_evolve_knn_build_3e08_v46:benchmark_knn_build_dispatch_ee5e_de1a_3e08_v46', 'measured_shape_labels': 'all_canonical' if shape_labels is None else tuple(shape_labels), 'selected_route_labels': SELECTED_TARGET_SHAPES, 'selected_route_rows': selected_rows, 'route_modules': {'rag': 'loom.examples.weave.knn_build_rag_pair_exact_weave_evolve_knn_build_ee5e_v44', 'k20': 'loom.examples.weave.knn_build_k20_large_lowfanout_de1a_v1', 'fallback': 'loom.examples.weave.knn_build_dispatchscore_tailinf_knn_build_dispatch_slurm_0610_6329_v41'}, 'contract_summary': report['summary'], 'contract_performance': report['performance'], 'timing_backends': timing_backends, 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'report': report} + +def benchmark_knn_build_dispatch_ee5e_de1a_3e08_v46(*, use_cupti: bool=False, shape_labels=None) -> dict[str, Any]: + """Main v3 contract benchmark hook with ee5e RAG and de1a K20 routes enabled.""" + prior_use_cupti = _set_bench_backend(use_cupti) + try: + shapes = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=shapes, correctness=True, benchmark=True) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + return _benchmark_payload(report, use_cupti=use_cupti, shape_labels=shape_labels) diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_fd9b_floor_seed_portfolio_5720_full90_synthesis_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_fd9b_floor_seed_portfolio_5720_full90_synthesis_v1.py new file mode 100644 index 00000000..8d04f40e --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_fd9b_floor_seed_portfolio_5720_full90_synthesis_v1.py @@ -0,0 +1,549 @@ +"""Full90 synthesis over fd9b for floor-clearing seed portfolio routes. + +Minimum target architecture: sm_100a. This dispatcher-synthesis wrapper keeps +the fd9b full90 champion as the baseline fallback and measures guarded +Weave-only portfolios over the post-trunk seed bank: + +* trunk K20 + K32 exact build seeds from bd76 and 9334; +* 01bb Q4096/K8, 2425 RAG micro K10 q4/q8/q32, and K20/K32; +* the same portfolio plus the read-ref 1b34/FCEE FlashML K5 seed. + +The wrapper does not retune seed schedules and does not change the default +knn_build registry route. K48 remains parked because d047/5698 showed aggregate +regression. FlashLib is timed only by the contract harness. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +from pathlib import Path +from typing import Any, Callable +from . import knn_build_dispatch_1877_9a17_fp16_fd37_full90_consumption_v1 as fd9b +from . import knn_build_flashml_k5_bd4a_v1 as flashml_k5 +from . import knn_build_large_square_k20_efe4_v1 as k20_bd76 +from . import knn_build_overk_largek_q4096_k32_9334_v1 as k32_9334 +from . import knn_build_q4096_k8_lowfloor_fd9b_v3 as q4096_01bb +from . import knn_build_rag_microbatch_k10_q4q8q32_s144_d5ac_v1 as rag_2425 +MODULE = 'loom.examples.weave.knn_build_dispatch_fd9b_floor_seed_portfolio_5720_full90_synthesis_v1' +eval_mod = fd9b.eval_mod +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbatch_m64_d4f7_stage1", "arg_keys": ["query", "database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 91392, "constants": [], "cta_group": 1, "threads": 512}')) +BASE_CANDIDATE_KEY = fd9b.DEFAULT_CANDIDATE_KEY +BASE_CONFIG = fd9b.CANDIDATE_CONFIGS[BASE_CANDIDATE_KEY] +BASE_CANDIDATE_ID = BASE_CONFIG['candidate_id'] +BASE_BENCHMARK_ENTRYPOINT = BASE_CONFIG['benchmark_entrypoint'] +BASE_ROUTE_ENTRYPOINT = BASE_CONFIG['entrypoint'] +CANDIDATE_K20_K32 = 'fd9b_plus_bd76_k20_9334_k32' +CANDIDATE_K5_ONLY = 'fd9b_plus_1b34_k5' +CANDIDATE_FLOOR_CORE = 'fd9b_plus_01bb_2425_bd76_k20_9334_k32' +CANDIDATE_FLOOR_CORE_K5 = 'fd9b_plus_01bb_2425_1b34_k5_bd76_k20_9334_k32' +DEFAULT_CANDIDATE_KEY = CANDIDATE_FLOOR_CORE_K5 +CANDIDATE_KEYS = (BASE_CANDIDATE_KEY, CANDIDATE_K20_K32, CANDIDATE_K5_ONLY, CANDIDATE_FLOOR_CORE, CANDIDATE_FLOOR_CORE_K5) +DEFAULT_SYNTHESIS_CANDIDATES = (CANDIDATE_K20_K32, CANDIDATE_K5_ONLY, CANDIDATE_FLOOR_CORE, CANDIDATE_FLOOR_CORE_K5) +FULL90_LABELS = _decode_capture(_json_loads('{"__tuple__": ["flashml_correctness_b1_q256_m256_d128_k5", "build_k_sweep_qm512_k1", "build_k_sweep_qm512_k2", "build_k_sweep_qm512_k4", "build_k_sweep_qm512_k5", "build_k_sweep_qm512_k6", "build_k_sweep_qm512_k8", "build_k_sweep_qm512_k10", "build_qm1024_d128_k10", "build_k_sweep_qm1024_k16", "build_k_sweep_qm1024_k12", "build_k_sweep_qm1024_k20", "build_qm2048_d128_k8", "build_qm1024_d128_k8", "build_qm4096_d128_k8", "build_qm2048_d128_k10", "build_dim_sweep_b1_q1024_m1024_d64_k10", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q4096_m4096_d64_k10", "build_dim_sweep_b1_q1024_m1024_d96_k10", "build_dim_sweep_b1_q2048_m2048_d192_k10", "build_dim_sweep_b1_q2048_m2048_d256_k10", "build_common_d256_b1_q1024_m1024_k10", "build_common_d768_b1_q1024_m1024_k10", "build_common_d1024_b1_q512_m512_k10", "build_common_d4096_b1_q512_m512_k10", "build_highd_b1_q1024_m1024_d320_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10", "build_batch_b2_q1024_m1024_d128_k10", "build_k_sweep_qm2048_k11", "build_k_sweep_qm2048_k12", "build_k_sweep_qm2048_k13", "build_k_sweep_qm2048_k20", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_tail_b1_q1536_m1536_d128_k10", "build_tail_b1_q3072_m3072_d128_k20", "build_medium_b1_q4096_m4096_d128_k10", "build_k_sweep_qm4096_k12", "build_k_sweep_qm4096_k13", "build_k_sweep_qm4096_k20", "build_k_sweep_qm4096_k24", "build_k_sweep_qm4096_k28", "build_largek_stress_qm4096_k32", "build_k_sweep_qm4096_k30", "build_over32_stress_qm2048_k48", "build_over32_stress_qm2048_k64", "build_over32_stress_qm4096_k48", "build_large_b1_q8192_m8192_d128_k10", "build_large_b1_q6144_m6144_d128_k10", "build_large_b1_q8192_m8192_d128_k20", "build_large_b1_q8192_m8192_d128_k32", "build_verylarge_b1_q12288_m12288_d128_k10", "rag_offline_b1_q4096_m100000_d128_k10", "search_rect_b1_q1024_m8192_d128_k10", "search_rect_b1_q1024_m32768_d64_k10", "search_rect_highd_b1_q512_m12000_d320_k10", "search_rect_common_d256_b1_q1024_m32768_k10", "search_rect_common_d768_b1_q512_m8192_k10", "search_rect_b1_q4096_m65536_d128_k20", "search_rect_b1_q1536_m65536_d128_k20", "search_rect_over32_b1_q2048_m65536_d128_k64", "rag_online_b1_q1_m100000_d128_k10", "rag_online_b1_q1_m65536_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "rag_online_irregular_b1_q1_m262143_d128_k10", "rag_online_irregular_b1_q1_m524287_d128_k10", "rag_stream_b1_q128_m100000_d128_k10", "rag_offline_largek_b1_q4096_m100000_d128_k20", "rag_offline_large_m_b1_q8192_m250000_d128_k20", "rag_offline_large_m_over32_b1_q2048_m250000_d128_k64", "rag_offline_batch_b1_q10000_m100000_d128_k10", "rag_offline_b1_q10000_m50000_d128_k10", "rag_microbatch_b1_q4_m100000_d128_k10", "rag_microbatch_b1_q8_m100000_d128_k10", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_microbatch_highd_b1_q16_m50000_d768_k10", "rag_microbatch_common_d64_b1_q16_m50000_k10", "rag_microbatch_common_d256_b1_q16_m50000_k10", "rag_microbatch_common_d1024_b1_q8_m50000_k10", "rag_microbatch_common_d4096_b1_q4_m32768_k10", "rag_microbatch_b1_q32_m100000_d128_k10", "rag_microbatch_largek_b1_q8_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m100000_d128_k32", "rag_microbatch_largek_b1_q24_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m250000_d128_k32", "rag_microbatch_largek_b1_q32_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m131071_d128_k32", "rag_microbatch_b1_q64_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32", "rag_stream_largek_b1_q128_m131071_d128_k32", "rag_batch_b2_q256_m50000_d128_k10", "rag_irregular_b1_q512_m131071_d128_k10", "search_rect_b1_q2048_m32768_d128_k10", "build_large_tail_b1_q6144_m6144_d128_k20", "build_over32_stress_qm4096_k64", "build_over64_stress_qm1024_k96", "build_over64_stress_qm2048_k96", "build_over64_stress_qm4096_k96", "rag_online_common_d64_b1_q1_m262143_k10", "rag_microbatch_common_d64_b1_q4_m100000_k10", "rag_microbatch_common_d256_b1_q4_m100000_k10", "rag_stream_common_d256_b1_q128_m100000_k10", "rag_microbatch_common_d768_b1_q8_m100000_k10", "rag_microbatch_common_d1024_b1_q4_m100000_k10", "rag_online_common_d4096_b1_q1_m65536_k10", "search_rect_common_d1024_b1_q256_m8192_k10", "search_rect_common_d4096_b1_q128_m4096_k10", "rag_microbatch_largek_common_d256_b1_q8_m100000_k32", "rag_stream_largek_common_d256_b1_q128_m100000_k32", "rag_microbatch_over32_d128_b1_q16_m100000_k48"]}')) +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +BASELINE_BENCHMARK_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_baseline_fd9b_full90_v1']) +K20_K32_BENCHMARK_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_candidate_k20_k32_full90_v1']) +K5_ONLY_BENCHMARK_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_candidate_k5_only_full90_v1']) +FLOOR_CORE_BENCHMARK_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_candidate_floor_core_full90_v1']) +FLOOR_CORE_K5_BENCHMARK_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_candidate_floor_core_k5_full90_v1']) +K20_TARGET_SHAPES = k20_bd76.TARGET_SHAPES +K32_TARGET_SHAPES = k32_9334.TARGET_SHAPES +Q4096_K8_TARGET_SHAPES = q4096_01bb.TARGET_SHAPES +RAG_2425_TARGET_SHAPES = rag_2425.TARGET_SHAPES +K5_TARGET_SHAPES = (flashml_k5.TARGET_SHAPE,) +K48_EXCLUDED_TARGET_SHAPES = ('build_over32_stress_qm2048_k48', 'build_over32_stress_qm4096_k48') +K20_K32_TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_large_b1_q8192_m8192_d128_k20", "build_largek_stress_qm4096_k32"]}')) +FLOOR_CORE_TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_qm4096_d128_k8", "rag_microbatch_b1_q4_m100000_d128_k10", "rag_microbatch_b1_q8_m100000_d128_k10", "rag_microbatch_b1_q32_m100000_d128_k10", "build_large_b1_q8192_m8192_d128_k20", "build_largek_stress_qm4096_k32"]}')) +FLOOR_CORE_K5_TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_qm4096_d128_k8", "rag_microbatch_b1_q4_m100000_d128_k10", "rag_microbatch_b1_q8_m100000_d128_k10", "rag_microbatch_b1_q32_m100000_d128_k10", "build_large_b1_q8192_m8192_d128_k20", "build_largek_stress_qm4096_k32", "flashml_correctness_b1_q256_m256_d128_k5"]}')) +SEED_BD76_K20_ID = 'large_square_k20_efe4_bd76_split2_warp8' +SEED_9334_K32_ID = k32_9334.SEED_ID +SEED_01BB_Q4096_K8_ID = q4096_01bb.SEED_ID +SEED_2425_RAG_Q4_ID = rag_2425.Q4_S144_SEED_ID +SEED_2425_RAG_Q8_ID = rag_2425.Q8_S144_SEED_ID +SEED_2425_RAG_Q32_ID = rag_2425.parent_exact5.Q32_SEED_ID +SEED_1B34_K5_ID = 'fcee_flashml_k5_bd4a_exact' +SEED_E087_K48_ID = 'over32_prefill_efe4_replay_d047_k48_s4' +SEED_28D8_Q4096_K8_ID = 'q4096_k8_lowfloor_fd9b_v3_split4_28d8' +K20_ENTRYPOINT = 'loom.examples.weave.knn_build_large_square_k20_efe4_v1:launch_from_contract_inputs' +K32_ENTRYPOINT = k32_9334.ROUTE_ENTRYPOINT +Q4096_K8_ENTRYPOINT = q4096_01bb.ROUTE_ENTRYPOINT +RAG_2425_ENTRYPOINT = rag_2425.ROUTE_ENTRYPOINT +K5_ENTRYPOINT = 'loom.examples.weave.knn_build_flashml_k5_bd4a_v1:launch_from_contract_inputs' +SOURCE_TASKS = {**fd9b.SOURCE_TASKS, SEED_BD76_K20_ID: 'weave-evolve-knn-build-bd76 / design_doc/active/weave_evolve_knn_build_round_157_efe4.md', SEED_9334_K32_ID: 'weave-evolve-knn-build-9334 / design_doc/active/weave_evolve_knn_build_round_157_9334.md', SEED_01BB_Q4096_K8_ID: 'weave-evolve-knn-build-01bb / design_doc/active/weave_evolve_knn_build_round_158_fd9b_q4096k8.md', SEED_2425_RAG_Q4_ID: 'weave-evolve-knn-build-2425 / design_doc/active/weave_evolve_knn_build_round_157_d5ac.md', SEED_2425_RAG_Q8_ID: 'weave-evolve-knn-build-2425 / design_doc/active/weave_evolve_knn_build_round_157_d5ac.md', SEED_2425_RAG_Q32_ID: 'weave-evolve-knn-build-2425 parent exact-five q32 route / design_doc/active/weave_evolve_knn_build_round_157_d5ac.md', SEED_1B34_K5_ID: 'weave-evolve-knn-build-1b34 read-ref / design_doc/active/weave_evolve_knn_build_round_149_fcee_flashmlk5.md', SEED_E087_K48_ID: 'generalize-auto-tuning-knn-build-d047 replay evidence only', SEED_28D8_Q4096_K8_ID: 'weave-evolve-knn-build-28d8 / superseded by weave-evolve-knn-build-01bb'} +PRODUCTION_ROUTE_MODULES = {**fd9b.PRODUCTION_ROUTE_MODULES, SEED_BD76_K20_ID: K20_ENTRYPOINT, SEED_9334_K32_ID: K32_ENTRYPOINT, SEED_01BB_Q4096_K8_ID: Q4096_K8_ENTRYPOINT, SEED_2425_RAG_Q4_ID: RAG_2425_ENTRYPOINT, SEED_2425_RAG_Q8_ID: RAG_2425_ENTRYPOINT, SEED_2425_RAG_Q32_ID: RAG_2425_ENTRYPOINT, SEED_1B34_K5_ID: K5_ENTRYPOINT, BASE_CANDIDATE_ID: BASE_ROUTE_ENTRYPOINT} +TARGETED_SEED_ROWS = _decode_capture(_json_loads('{"__dict_items__": [["large_square_k20_efe4_bd76_split2_warp8", {"__dict_items__": [["source_payload", "design_doc/active/weave_evolve_knn_build_round_157_efe4.md#perf"], ["shape_labels", {"__tuple__": ["build_large_b1_q8192_m8192_d128_k20"]}], ["source_task", "weave-evolve-knn-build-bd76"], ["historical_ratio_vs_flashlib", 1.528100456551165]]}], ["overk_largek_q4096_k32_9334_v1", {"__dict_items__": [["source_payload", "artifacts/weave_evolve/knn_build_overk_largek_q4096_k32_9334_v1/largek_q4096_k32_9334_v1.json"], ["shape_labels", {"__tuple__": ["build_largek_stress_qm4096_k32"]}], ["source_task", "weave-evolve-knn-build-9334"], ["historical_ratio_vs_flashlib", 1.482571234983885]]}], ["q4096_k8_lowfloor_fd9b_v3_exact_prefill_s4", {"__dict_items__": [["source_payload", "artifacts/weave_evolve/knn_build_q4096_k8_lowfloor_fd9b_v3/q4096_k8_lowfloor_fd9b_v3.json"], ["shape_labels", {"__tuple__": ["build_qm4096_d128_k8"]}], ["source_task", "weave-evolve-knn-build-01bb"], ["historical_ratio_vs_flashlib", 1.23252]]}], ["rag_microbatch_k10_q4_s144_g12_d555_v1", {"__dict_items__": [["source_payload", "artifacts/weave_evolve/knn_build_ragmicro_q4q8q32_s144_d5ac_v1/rag_microbatch_k10_q4q8_s144_d5ac_v1.json"], ["shape_labels", {"__tuple__": ["rag_microbatch_b1_q4_m100000_d128_k10"]}], ["source_task", "weave-evolve-knn-build-2425"], ["historical_ratio_vs_flashlib", "floor-clearing exact-three payload"]]}], ["rag_microbatch_k10_q8_s144_g12_d5ac_v1", {"__dict_items__": [["source_payload", "artifacts/weave_evolve/knn_build_ragmicro_q4q8q32_s144_d5ac_v1/rag_microbatch_k10_q4q8_s144_d5ac_v1.json"], ["shape_labels", {"__tuple__": ["rag_microbatch_b1_q8_m100000_d128_k10"]}], ["source_task", "weave-evolve-knn-build-2425"], ["historical_ratio_vs_flashlib", "floor-clearing exact-three payload"]]}], ["rag_microbatch_k10_q32_m64_s128_g8_4757_v1", {"__dict_items__": [["source_payload", "artifacts/weave_evolve/knn_build_ragmicro_q4q8q32_s144_d5ac_v1/rag_microbatch_k10_q4q8_s144_d5ac_v1.json"], ["shape_labels", {"__tuple__": ["rag_microbatch_b1_q32_m100000_d128_k10"]}], ["source_task", "weave-evolve-knn-build-2425"], ["historical_ratio_vs_flashlib", "floor-clearing exact-three payload"]]}], ["fcee_flashml_k5_bd4a_exact", {"__dict_items__": [["source_payload", "artifacts/weave_evolve/knn_build_current_champion_flashml_k5_fcee_v1/current_champion_flashml_k5_fcee_v1.json"], ["shape_labels", {"__tuple__": ["flashml_correctness_b1_q256_m256_d128_k5"]}], ["source_task", "weave-evolve-knn-build-1b34"], ["historical_ratio_vs_flashlib", 2.504516129032258]]}], ["over32_prefill_efe4_replay_d047_k48_s4", {"__dict_items__": [["source_payload", "sibling weave-evolve-knn-build-e087/design_doc/active/weave_evolve_knn_build_round_157_efe4.md#perf"], ["shape_labels", {"__tuple__": ["build_over32_stress_qm2048_k48", "build_over32_stress_qm4096_k48"]}], ["source_task", "weave-evolve-knn-build-e087"], ["historical_ratio_vs_flashlib", {"__dict_items__": [["build_over32_stress_qm2048_k48", 1.3080669710806698], ["build_over32_stress_qm4096_k48", 1.2405164867230813]]}], ["consumption_status", "parked_aggregate_regression_in_d047_and_5698"]]}], ["q4096_k8_lowfloor_fd9b_v3_split4_28d8", {"__dict_items__": [["source_payload", "artifacts/weave_evolve/knn_build_lowk_q4096_floor_28d8_v1/lowk_q4096_floor_28d8_v1.json"], ["shape_labels", {"__tuple__": ["build_qm4096_d128_k8"]}], ["source_task", "weave-evolve-knn-build-28d8"], ["historical_ratio_vs_flashlib", 1.097558072461247], ["consumption_status", "dominated_by_01bb"]]}]]}')) +REJECTED_ROUTE_COMBINATIONS = ({'id': '28d8_q4096_k8_lowfloor_not_consumed', 'entrypoint': 'loom.examples.weave.knn_build_lowk_q4096_floor_28d8_v1:launch_from_contract_inputs', 'status': 'dominated_by_01bb', 'source_task': 'weave-evolve-knn-build-28d8', 'reason': 'Q4096/K8 28d8/89c2/f53e variants are below floor or dominated; 01bb clears 1.20x FlashLib.'}, {'id': 'e087_k48_not_consumed', 'entrypoint': 'loom.examples.weave.knn_build_over32_prefill_efe4_replay_d047_v1:launch_from_contract_inputs', 'status': 'parked_aggregate_regression', 'source_task': 'weave-evolve-knn-build-e087', 'reason': 'd047/5698 show K48 exact rows clear locally but regress full90 aggregate when added.'}) + +def _select_contract_shapes(shape_labels): + return fd9b._select_contract_shapes(shape_labels) + +def _trace_inputs_for_shape(shape: dict[str, Any]) -> dict[str, Any]: + return fd9b._trace_inputs_for_shape(shape) + +def _normalize_route_row(row: dict[str, Any]) -> dict[str, Any]: + return fd9b._normalize_route_row(row) + +def _inputs_for_label(label: str) -> dict[str, Any]: + return _trace_inputs_for_shape(_select_contract_shapes((label,))[0]) + +def _timing_backend_name(use_cupti: bool) -> str: + return fd9b._timing_backend_name(use_cupti) + +def _payload_shape_labels(shape_labels) -> str | tuple[str, ...]: + if shape_labels is None: + return 'full90_v10' + return tuple((str(label) for label in shape_labels)) + +def _shape_labels(shape_labels) -> tuple[str, ...]: + if shape_labels is None: + return FULL90_LABELS + return tuple((str(label) for label in shape_labels)) + +def _denominator_name(shape_labels) -> str: + labels = _shape_labels(shape_labels) + if labels == FULL90_LABELS: + return 'full90_v10' + if labels == K20_K32_TARGET_SHAPES: + return 'bd76_k20_9334_k32_exact2' + if labels == K5_TARGET_SHAPES: + return '1b34_k5_exact1' + if labels == FLOOR_CORE_TARGET_SHAPES: + return '01bb_2425_bd76_9334_exact6' + if labels == FLOOR_CORE_K5_TARGET_SHAPES: + return '01bb_2425_1b34_bd76_9334_exact7' + return ''.join(['custom_', format(len(labels), '')]) + +def _denominator_label(shape_labels) -> str: + return 'full90' if _denominator_name(shape_labels) == 'full90_v10' else _denominator_name(shape_labels) + +def _rows_for_labels(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return {label: dict(report.get('per_shape', {}).get(label, {})) for label in labels} + +def _timing_backends_for_reports(*reports: dict[str, Any]) -> list[str]: + values: list[str] = [] + for report in reports: + for row in report.get('per_shape', {}).values(): + backend = row.get('timing_backend') + if backend: + values.append(str(backend)) + return sorted(set(values)) + +def _selected_seeds(*seed_groups: tuple[str, ...]) -> tuple[str, ...]: + values: list[str] = [] + for group in seed_groups: + values.extend(group) + return tuple(dict.fromkeys(values)) + +def _candidate_config(candidate_key: str) -> dict[str, Any]: + try: + return CANDIDATE_CONFIGS[candidate_key] + except KeyError as exc: + raise ValueError(''.join(['unknown fd9b floor-seed portfolio candidate ', format(repr(candidate_key), '')])) from exc + +def _candidate_id(candidate_key: str | None) -> str | None: + if candidate_key is None: + return None + return str(_candidate_config(candidate_key)['candidate_id']) + +def _candidate_has_k20_k32(candidate_key: str) -> bool: + return candidate_key in (CANDIDATE_K20_K32, CANDIDATE_FLOOR_CORE, CANDIDATE_FLOOR_CORE_K5) + +def _candidate_has_floor_core(candidate_key: str) -> bool: + return candidate_key in (CANDIDATE_FLOOR_CORE, CANDIDATE_FLOOR_CORE_K5) + +def _candidate_has_k5(candidate_key: str) -> bool: + return candidate_key in (CANDIDATE_K5_ONLY, CANDIDATE_FLOOR_CORE_K5) + +def _label_can_hit(inputs: dict[str, Any], labels: tuple[str, ...]) -> bool: + value = inputs.get('label') + return value is None or str(value) in labels + +def _eligible_rag_2425(inputs: dict[str, Any]) -> bool: + if not _label_can_hit(inputs, RAG_2425_TARGET_SHAPES): + return False + return not bool(inputs.get('build', False)) and int(inputs.get('B', -1)) == 1 and (int(inputs.get('Q', -1)) in (4, 8, 32)) and (int(inputs.get('M', -1)) == 100000) and (int(inputs.get('D', -1)) == 128) and (int(inputs.get('K', -1)) == 10) and (str(inputs.get('dtype', 'bfloat16')).replace('torch.', '') == 'bfloat16') + +def _eligible_flashml_k5(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, K5_TARGET_SHAPES) and flashml_k5._is_target_shape(inputs) + +def _matched_seed(inputs: dict[str, Any], candidate_key: str) -> str | None: + if _candidate_has_floor_core(candidate_key) and q4096_01bb._eligible_q4096_k8(inputs): + return SEED_01BB_Q4096_K8_ID + if _candidate_has_floor_core(candidate_key) and _eligible_rag_2425(inputs): + return rag_2425._selected_seed(inputs) + if _candidate_has_k5(candidate_key) and _eligible_flashml_k5(inputs): + return SEED_1B34_K5_ID + if _candidate_has_k20_k32(candidate_key) and k20_bd76._eligible_q8192_k20(inputs): + return SEED_BD76_K20_ID + if _candidate_has_k20_k32(candidate_key) and k32_9334._eligible_q4096_k32(inputs): + return SEED_9334_K32_ID + return None + +def _seed_route(seed_id: str, inputs: dict[str, Any]) -> str: + if seed_id == SEED_BD76_K20_ID: + return k20_bd76.ROUTE_Q8192_K20_SPLIT2 + if seed_id == SEED_9334_K32_ID: + return k32_9334.route_for_contract_inputs(inputs) + if seed_id == SEED_01BB_Q4096_K8_ID: + return q4096_01bb.route_for_contract_inputs(inputs) + if seed_id in (SEED_2425_RAG_Q4_ID, SEED_2425_RAG_Q8_ID, SEED_2425_RAG_Q32_ID): + return rag_2425.route_for_contract_inputs(inputs) + if seed_id == SEED_1B34_K5_ID: + return K5_ENTRYPOINT + raise ValueError(''.join(['unknown seed id ', format(repr(seed_id), '')])) + +def _seed_entrypoint(seed_id: str) -> str: + if seed_id == SEED_BD76_K20_ID: + return K20_ENTRYPOINT + if seed_id == SEED_9334_K32_ID: + return K32_ENTRYPOINT + if seed_id == SEED_01BB_Q4096_K8_ID: + return Q4096_K8_ENTRYPOINT + if seed_id in (SEED_2425_RAG_Q4_ID, SEED_2425_RAG_Q8_ID, SEED_2425_RAG_Q32_ID): + return RAG_2425_ENTRYPOINT + if seed_id == SEED_1B34_K5_ID: + return K5_ENTRYPOINT + raise ValueError(''.join(['unknown seed id ', format(repr(seed_id), '')])) + +def _seed_launch(seed_id: str, inputs: dict[str, Any]) -> None: + if seed_id == SEED_BD76_K20_ID: + k20_bd76.launch_from_contract_inputs(inputs) + return + if seed_id == SEED_9334_K32_ID: + k32_9334.launch_from_contract_inputs(inputs) + return + if seed_id == SEED_01BB_Q4096_K8_ID: + q4096_01bb.launch_from_contract_inputs(inputs) + return + if seed_id in (SEED_2425_RAG_Q4_ID, SEED_2425_RAG_Q8_ID, SEED_2425_RAG_Q32_ID): + rag_2425.launch_from_contract_inputs(inputs) + return + if seed_id == SEED_1B34_K5_ID: + flashml_k5.launch_from_contract_inputs(inputs) + return + raise ValueError(''.join(['unknown seed id ', format(repr(seed_id), '')])) + +def _seed_guard(seed_id: str) -> tuple[str, str]: + if seed_id == SEED_BD76_K20_ID: + return ('bd76_large_square_k20_split2_warp8_guard', 'exact BF16 build B=1 Q=M=8192 D=128 K=20 split2 warp8') + if seed_id == SEED_9334_K32_ID: + return ('9334_largek_q4096_k32_split4_warpselect_guard', 'exact BF16 build B=1 Q=M=4096 D=128 K=32 split4 unordered warp-select') + if seed_id == SEED_01BB_Q4096_K8_ID: + return ('01bb_q4096_k8_exact_prefill_s4_guard', 'exact BF16 build B=1 Q=M=4096 D=128 K=8 split4 exact-prefill warp-select') + if seed_id == SEED_2425_RAG_Q4_ID: + return ('2425_rag_microbatch_k10_q4_s144_guard', 'exact BF16 non-build B=1 Q=4 M=100000 D=128 K=10 S144/G12') + if seed_id == SEED_2425_RAG_Q8_ID: + return ('2425_rag_microbatch_k10_q8_s144_guard', 'exact BF16 non-build B=1 Q=8 M=100000 D=128 K=10 S144/G12') + if seed_id == SEED_2425_RAG_Q32_ID: + return ('2425_rag_microbatch_k10_q32_parent_guard', 'exact BF16 non-build B=1 Q=32 M=100000 D=128 K=10 parent M64 route') + if seed_id == SEED_1B34_K5_ID: + return ('1b34_flashml_k5_fcee_guard', 'exact BF16 build B=1 Q=M=256 D=128 K=5') + raise ValueError(''.join(['unknown seed id ', format(repr(seed_id), '')])) + +def _base_route(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + return fd9b.route_for_contract_inputs(inputs, candidate_key=BASE_CANDIDATE_KEY, force_fallback=force_fallback) + +def _base_launch(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + fd9b.launch_from_contract_inputs(inputs, candidate_key=BASE_CANDIDATE_KEY, force_fallback=force_fallback) + +def _base_trace_row(label: str, *, force_fallback: bool=False) -> dict[str, Any]: + return dict(fd9b.route_trace_for_contract_shapes((label,), candidate_key=BASE_CANDIDATE_KEY, force_fallback=force_fallback)[0]) + +def _expected_seed(inputs: dict[str, Any], candidate_key: str) -> str | None: + seed_id = _matched_seed(inputs, candidate_key) + if seed_id is not None: + return seed_id + return fd9b._expected_seed(inputs, BASE_CANDIDATE_KEY) +PARENT_SEEDS = _decode_capture(_json_loads('{"__tuple__": ["q1024_k8_195e_v1", "ragonline_mbucket_5706_q1v10_smix_s144_g12", "rag_microbucket_k32_q16dual2warp_largem_bdd2_v1_rowld1_2warp_rows4", "rag_microbucket_k32_q24rowld2_24dc_v1_rowld2_rows4", "rag_stream_k32_q128rowld_60fb_v1_rowld_rows4", "4757_fixedbuild_k10_v2_q512_q1024_b2_q1536_q6144", "ad64_fixedbuild_k10_v2_q512_q1024_q1536_q2048_b2q1024", "rag_microbatch_k10_q8_s128_q16_s136_4757_v1", "rag_stream_k32_q128_m100000_ad64_v1_4fbf_v6_s72g8", "d15e_rect_smallq_largem_v1", "fp16_d128_lowfloor_fd37_s8_cached_merge"]}')) +K20_K32_SEEDS = (SEED_BD76_K20_ID, SEED_9334_K32_ID) +FLOOR_CORE_SEEDS = (SEED_01BB_Q4096_K8_ID, SEED_2425_RAG_Q4_ID, SEED_2425_RAG_Q8_ID, SEED_2425_RAG_Q32_ID, *K20_K32_SEEDS) +K5_ONLY_SEEDS = (SEED_1B34_K5_ID,) +FLOOR_CORE_K5_SEEDS = (*FLOOR_CORE_SEEDS, SEED_1B34_K5_ID) +CANDIDATE_CONFIGS = _decode_capture(_json_loads('{"__dict_items__": [["1877_plus_9a17_fp16_fd37_lowfloor", {"__dict_items__": [["candidate_id", "candidate_1877_plus_9a17_fp16_fd37_lowfloor_full90_v1"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_1877_9a17_fp16_fd37_full90_consumption_v1:launch_from_contract_inputs"], ["benchmark_entrypoint", "loom.examples.weave.knn_build_dispatch_1877_9a17_fp16_fd37_full90_consumption_v1:benchmark_candidate_fp16_fd37_full90_v1"], ["selected_seeds", {"__tuple__": ["q1024_k8_195e_v1", "ragonline_mbucket_5706_q1v10_smix_s144_g12", "rag_microbucket_k32_q16dual2warp_largem_bdd2_v1_rowld1_2warp_rows4", "rag_microbucket_k32_q24rowld2_24dc_v1_rowld2_rows4", "rag_stream_k32_q128rowld_60fb_v1_rowld_rows4", "4757_fixedbuild_k10_v2_q512_q1024_b2_q1536_q6144", "ad64_fixedbuild_k10_v2_q512_q1024_q1536_q2048_b2q1024", "rag_microbatch_k10_q8_s128_q16_s136_4757_v1", "rag_stream_k32_q128_m100000_ad64_v1_4fbf_v6_s72g8", "d15e_rect_smallq_largem_v1", "fp16_d128_lowfloor_fd37_s8_cached_merge"]}], ["new_seed_ids", {"__tuple__": []}], ["guard_plan", {"__tuple__": ["fd37 exact FP16 build B=1 Q=M=2048 D=128 K=10 split8 cached-merge guard", "9a17 exact BF16 non-build B=1 Q=128 M=100000 D=128 K=32 guard", "9a17 exact BF16 non-build B=1 Q=1024 M=8192 D=128 K=10 guard", "4b51 exact BF16 build K10 guard for unique Q2048 row", "1b8f exact BF16 build K10 guard for Q512/Q1024/B2-Q1024/Q1536/Q6144 rows", "ceb3 exact BF16 non-build B=1 Q in {8,16} M=100000 D=128 K=10 guard", "b0e2 exact BF16 non-build B=1 Q=128 M=131071 D=128 K=32 rowld rows4 guard", "603d/24dc exact BF16 non-build B=1 Q=24 M=100000 D=128 K=32 rowld2 rows4 guard", "cf51 exact BF16 build Q=M=1024 D=128 K=8 split16", "bca0 exact BF16 non-build Q=1 D=128 K=10 M in {65536,100000,131071,250000,262143,524287}", "5018 exact BF16 non-build Q=16 D=128 K=32 M in {100000,131071,250000}", "c3bf/d5f8 full90 Weave fallback"]}], ["expected_shape_wins", {"__tuple__": ["build_qm1024_d128_k8", "rag_online_b1_q1_m100000_d128_k10", "rag_online_b1_q1_m65536_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "rag_online_irregular_b1_q1_m262143_d128_k10", "rag_online_irregular_b1_q1_m524287_d128_k10", "rag_microbatch_largek_b1_q16_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m131071_d128_k32", "rag_microbatch_largek_b1_q16_m250000_d128_k32", "rag_microbatch_largek_b1_q24_m100000_d128_k32", "rag_stream_largek_b1_q128_m131071_d128_k32", "build_k_sweep_qm512_k10", "build_qm1024_d128_k10", "build_batch_b2_q1024_m1024_d128_k10", "build_tail_b1_q1536_m1536_d128_k10", "build_large_b1_q6144_m6144_d128_k10", "build_qm2048_d128_k10", "rag_microbatch_b1_q8_m100000_d128_k10", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32", "search_rect_b1_q1024_m8192_d128_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10"]}], ["fallback", "loom.examples.weave.knn_build_dispatch_1877_9a17_fp16_fd37_full90_consumption_v1:launch_from_contract_inputs"], ["rejected_reason", "same-session fd9b full90 champion baseline"]]}], ["fd9b_plus_bd76_k20_9334_k32", {"__dict_items__": [["candidate_id", "candidate_fd9b_plus_bd76_k20_9334_k32_full90_v1"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_fd9b_floor_seed_portfolio_5720_full90_synthesis_v1:candidate_k20_k32_full90_v1"], ["benchmark_entrypoint", "loom.examples.weave.knn_build_dispatch_fd9b_floor_seed_portfolio_5720_full90_synthesis_v1:benchmark_candidate_k20_k32_full90_v1"], ["selected_seeds", {"__tuple__": ["q1024_k8_195e_v1", "ragonline_mbucket_5706_q1v10_smix_s144_g12", "rag_microbucket_k32_q16dual2warp_largem_bdd2_v1_rowld1_2warp_rows4", "rag_microbucket_k32_q24rowld2_24dc_v1_rowld2_rows4", "rag_stream_k32_q128rowld_60fb_v1_rowld_rows4", "4757_fixedbuild_k10_v2_q512_q1024_b2_q1536_q6144", "ad64_fixedbuild_k10_v2_q512_q1024_q1536_q2048_b2q1024", "rag_microbatch_k10_q8_s128_q16_s136_4757_v1", "rag_stream_k32_q128_m100000_ad64_v1_4fbf_v6_s72g8", "d15e_rect_smallq_largem_v1", "fp16_d128_lowfloor_fd37_s8_cached_merge", "large_square_k20_efe4_bd76_split2_warp8", "overk_largek_q4096_k32_9334_v1"]}], ["new_seed_ids", {"__tuple__": ["large_square_k20_efe4_bd76_split2_warp8", "overk_largek_q4096_k32_9334_v1"]}], ["guard_plan", {"__tuple__": ["bd76 exact BF16 build B=1 Q=M=8192 D=128 K=20 split2 warp8 guard", "9334 exact BF16 build B=1 Q=M=4096 D=128 K=32 split4 warp-select guard", "fd37 exact FP16 build B=1 Q=M=2048 D=128 K=10 split8 cached-merge guard", "9a17 exact BF16 non-build B=1 Q=128 M=100000 D=128 K=32 guard", "9a17 exact BF16 non-build B=1 Q=1024 M=8192 D=128 K=10 guard", "4b51 exact BF16 build K10 guard for unique Q2048 row", "1b8f exact BF16 build K10 guard for Q512/Q1024/B2-Q1024/Q1536/Q6144 rows", "ceb3 exact BF16 non-build B=1 Q in {8,16} M=100000 D=128 K=10 guard", "b0e2 exact BF16 non-build B=1 Q=128 M=131071 D=128 K=32 rowld rows4 guard", "603d/24dc exact BF16 non-build B=1 Q=24 M=100000 D=128 K=32 rowld2 rows4 guard", "cf51 exact BF16 build Q=M=1024 D=128 K=8 split16", "bca0 exact BF16 non-build Q=1 D=128 K=10 M in {65536,100000,131071,250000,262143,524287}", "5018 exact BF16 non-build Q=16 D=128 K=32 M in {100000,131071,250000}", "c3bf/d5f8 full90 Weave fallback"]}], ["expected_shape_wins", {"__tuple__": ["build_qm1024_d128_k8", "rag_online_b1_q1_m100000_d128_k10", "rag_online_b1_q1_m65536_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "rag_online_irregular_b1_q1_m262143_d128_k10", "rag_online_irregular_b1_q1_m524287_d128_k10", "rag_microbatch_largek_b1_q16_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m131071_d128_k32", "rag_microbatch_largek_b1_q16_m250000_d128_k32", "rag_microbatch_largek_b1_q24_m100000_d128_k32", "rag_stream_largek_b1_q128_m131071_d128_k32", "build_k_sweep_qm512_k10", "build_qm1024_d128_k10", "build_batch_b2_q1024_m1024_d128_k10", "build_tail_b1_q1536_m1536_d128_k10", "build_large_b1_q6144_m6144_d128_k10", "build_qm2048_d128_k10", "rag_microbatch_b1_q8_m100000_d128_k10", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32", "search_rect_b1_q1024_m8192_d128_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10", "build_large_b1_q8192_m8192_d128_k20", "build_largek_stress_qm4096_k32"]}], ["fallback", "loom.examples.weave.knn_build_dispatch_1877_9a17_fp16_fd37_full90_consumption_v1:launch_from_contract_inputs"], ["rejected_reason", null]]}], ["fd9b_plus_1b34_k5", {"__dict_items__": [["candidate_id", "candidate_fd9b_plus_1b34_k5_full90_v1"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_fd9b_floor_seed_portfolio_5720_full90_synthesis_v1:candidate_k5_only_full90_v1"], ["benchmark_entrypoint", "loom.examples.weave.knn_build_dispatch_fd9b_floor_seed_portfolio_5720_full90_synthesis_v1:benchmark_candidate_k5_only_full90_v1"], ["selected_seeds", {"__tuple__": ["q1024_k8_195e_v1", "ragonline_mbucket_5706_q1v10_smix_s144_g12", "rag_microbucket_k32_q16dual2warp_largem_bdd2_v1_rowld1_2warp_rows4", "rag_microbucket_k32_q24rowld2_24dc_v1_rowld2_rows4", "rag_stream_k32_q128rowld_60fb_v1_rowld_rows4", "4757_fixedbuild_k10_v2_q512_q1024_b2_q1536_q6144", "ad64_fixedbuild_k10_v2_q512_q1024_q1536_q2048_b2q1024", "rag_microbatch_k10_q8_s128_q16_s136_4757_v1", "rag_stream_k32_q128_m100000_ad64_v1_4fbf_v6_s72g8", "d15e_rect_smallq_largem_v1", "fp16_d128_lowfloor_fd37_s8_cached_merge", "fcee_flashml_k5_bd4a_exact"]}], ["new_seed_ids", {"__tuple__": ["fcee_flashml_k5_bd4a_exact"]}], ["guard_plan", {"__tuple__": ["1b34/FCEE exact BF16 FlashML build B=1 Q=M=256 D=128 K=5 guard", "fd37 exact FP16 build B=1 Q=M=2048 D=128 K=10 split8 cached-merge guard", "9a17 exact BF16 non-build B=1 Q=128 M=100000 D=128 K=32 guard", "9a17 exact BF16 non-build B=1 Q=1024 M=8192 D=128 K=10 guard", "4b51 exact BF16 build K10 guard for unique Q2048 row", "1b8f exact BF16 build K10 guard for Q512/Q1024/B2-Q1024/Q1536/Q6144 rows", "ceb3 exact BF16 non-build B=1 Q in {8,16} M=100000 D=128 K=10 guard", "b0e2 exact BF16 non-build B=1 Q=128 M=131071 D=128 K=32 rowld rows4 guard", "603d/24dc exact BF16 non-build B=1 Q=24 M=100000 D=128 K=32 rowld2 rows4 guard", "cf51 exact BF16 build Q=M=1024 D=128 K=8 split16", "bca0 exact BF16 non-build Q=1 D=128 K=10 M in {65536,100000,131071,250000,262143,524287}", "5018 exact BF16 non-build Q=16 D=128 K=32 M in {100000,131071,250000}", "c3bf/d5f8 full90 Weave fallback"]}], ["expected_shape_wins", {"__tuple__": ["build_qm1024_d128_k8", "rag_online_b1_q1_m100000_d128_k10", "rag_online_b1_q1_m65536_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "rag_online_irregular_b1_q1_m262143_d128_k10", "rag_online_irregular_b1_q1_m524287_d128_k10", "rag_microbatch_largek_b1_q16_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m131071_d128_k32", "rag_microbatch_largek_b1_q16_m250000_d128_k32", "rag_microbatch_largek_b1_q24_m100000_d128_k32", "rag_stream_largek_b1_q128_m131071_d128_k32", "build_k_sweep_qm512_k10", "build_qm1024_d128_k10", "build_batch_b2_q1024_m1024_d128_k10", "build_tail_b1_q1536_m1536_d128_k10", "build_large_b1_q6144_m6144_d128_k10", "build_qm2048_d128_k10", "rag_microbatch_b1_q8_m100000_d128_k10", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32", "search_rect_b1_q1024_m8192_d128_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10", "flashml_correctness_b1_q256_m256_d128_k5"]}], ["fallback", "loom.examples.weave.knn_build_dispatch_1877_9a17_fp16_fd37_full90_consumption_v1:launch_from_contract_inputs"], ["rejected_reason", null]]}], ["fd9b_plus_01bb_2425_bd76_k20_9334_k32", {"__dict_items__": [["candidate_id", "candidate_fd9b_plus_01bb_2425_bd76_k20_9334_k32_full90_v1"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_fd9b_floor_seed_portfolio_5720_full90_synthesis_v1:candidate_floor_core_full90_v1"], ["benchmark_entrypoint", "loom.examples.weave.knn_build_dispatch_fd9b_floor_seed_portfolio_5720_full90_synthesis_v1:benchmark_candidate_floor_core_full90_v1"], ["selected_seeds", {"__tuple__": ["q1024_k8_195e_v1", "ragonline_mbucket_5706_q1v10_smix_s144_g12", "rag_microbucket_k32_q16dual2warp_largem_bdd2_v1_rowld1_2warp_rows4", "rag_microbucket_k32_q24rowld2_24dc_v1_rowld2_rows4", "rag_stream_k32_q128rowld_60fb_v1_rowld_rows4", "4757_fixedbuild_k10_v2_q512_q1024_b2_q1536_q6144", "ad64_fixedbuild_k10_v2_q512_q1024_q1536_q2048_b2q1024", "rag_microbatch_k10_q8_s128_q16_s136_4757_v1", "rag_stream_k32_q128_m100000_ad64_v1_4fbf_v6_s72g8", "d15e_rect_smallq_largem_v1", "fp16_d128_lowfloor_fd37_s8_cached_merge", "q4096_k8_lowfloor_fd9b_v3_exact_prefill_s4", "rag_microbatch_k10_q4_s144_g12_d555_v1", "rag_microbatch_k10_q8_s144_g12_d5ac_v1", 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K=5 guard", "bd76 exact BF16 build B=1 Q=M=8192 D=128 K=20 split2 warp8 guard", "9334 exact BF16 build B=1 Q=M=4096 D=128 K=32 split4 warp-select guard", "fd37 exact FP16 build B=1 Q=M=2048 D=128 K=10 split8 cached-merge guard", "9a17 exact BF16 non-build B=1 Q=128 M=100000 D=128 K=32 guard", "9a17 exact BF16 non-build B=1 Q=1024 M=8192 D=128 K=10 guard", "4b51 exact BF16 build K10 guard for unique Q2048 row", "1b8f exact BF16 build K10 guard for Q512/Q1024/B2-Q1024/Q1536/Q6144 rows", "ceb3 exact BF16 non-build B=1 Q in {8,16} M=100000 D=128 K=10 guard", "b0e2 exact BF16 non-build B=1 Q=128 M=131071 D=128 K=32 rowld rows4 guard", "603d/24dc exact BF16 non-build B=1 Q=24 M=100000 D=128 K=32 rowld2 rows4 guard", "cf51 exact BF16 build Q=M=1024 D=128 K=8 split16", "bca0 exact BF16 non-build Q=1 D=128 K=10 M in {65536,100000,131071,250000,262143,524287}", "5018 exact BF16 non-build Q=16 D=128 K=32 M in {100000,131071,250000}", "c3bf/d5f8 full90 Weave fallback"]}], ["expected_shape_wins", {"__tuple__": ["build_qm1024_d128_k8", "rag_online_b1_q1_m100000_d128_k10", "rag_online_b1_q1_m65536_d128_k10", "rag_online_irregular_b1_q1_m131071_d128_k10", "rag_online_large_m_b1_q1_m250000_d128_k10", "rag_online_irregular_b1_q1_m262143_d128_k10", "rag_online_irregular_b1_q1_m524287_d128_k10", "rag_microbatch_largek_b1_q16_m100000_d128_k32", "rag_microbatch_largek_b1_q16_m131071_d128_k32", "rag_microbatch_largek_b1_q16_m250000_d128_k32", "rag_microbatch_largek_b1_q24_m100000_d128_k32", "rag_stream_largek_b1_q128_m131071_d128_k32", "build_k_sweep_qm512_k10", "build_qm1024_d128_k10", "build_batch_b2_q1024_m1024_d128_k10", "build_tail_b1_q1536_m1536_d128_k10", "build_large_b1_q6144_m6144_d128_k10", "build_qm2048_d128_k10", "rag_microbatch_b1_q8_m100000_d128_k10", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32", "search_rect_b1_q1024_m8192_d128_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10", "build_qm4096_d128_k8", "rag_microbatch_b1_q4_m100000_d128_k10", "rag_microbatch_b1_q32_m100000_d128_k10", "build_large_b1_q8192_m8192_d128_k20", "build_largek_stress_qm4096_k32", "flashml_correctness_b1_q256_m256_d128_k5"]}], ["fallback", "loom.examples.weave.knn_build_dispatch_1877_9a17_fp16_fd37_full90_consumption_v1:launch_from_contract_inputs"], ["rejected_reason", null]]}]}')) + +def route_for_contract_inputs(inputs: dict[str, Any], *, candidate_key: str=DEFAULT_CANDIDATE_KEY, force_fallback: bool=False) -> str: + _candidate_config(candidate_key) + if force_fallback: + return _base_route(inputs, force_fallback=True) + if candidate_key == BASE_CANDIDATE_KEY: + return _base_route(inputs) + seed_id = _matched_seed(inputs, candidate_key) + if seed_id is not None: + return _seed_route(seed_id, inputs) + return _base_route(inputs) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, candidate_key: str=DEFAULT_CANDIDATE_KEY, force_fallback: bool=False) -> None: + _candidate_config(candidate_key) + if force_fallback: + _base_launch(inputs, force_fallback=True) + return + if candidate_key == BASE_CANDIDATE_KEY: + _base_launch(inputs) + return + seed_id = _matched_seed(inputs, candidate_key) + if seed_id is not None: + _seed_launch(seed_id, inputs) + return + _base_launch(inputs) + +def candidate_baseline_fd9b_full90_v1(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, candidate_key=BASE_CANDIDATE_KEY) + +def candidate_k20_k32_full90_v1(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, candidate_key=CANDIDATE_K20_K32) + +def candidate_k5_only_full90_v1(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, candidate_key=CANDIDATE_K5_ONLY) + +def candidate_floor_core_full90_v1(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, candidate_key=CANDIDATE_FLOOR_CORE) + +def candidate_floor_core_k5_full90_v1(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, candidate_key=CANDIDATE_FLOOR_CORE_K5) + +def candidate(inputs: dict[str, Any]) -> None: + candidate_floor_core_k5_full90_v1(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, candidate_key=DEFAULT_CANDIDATE_KEY, force_fallback=True) + +def _candidate_kernel_fn(candidate_key: str) -> Callable[[dict[str, Any]], None]: + if candidate_key == BASE_CANDIDATE_KEY: + return candidate_baseline_fd9b_full90_v1 + if candidate_key == CANDIDATE_K20_K32: + return candidate_k20_k32_full90_v1 + if candidate_key == CANDIDATE_K5_ONLY: + return candidate_k5_only_full90_v1 + if candidate_key == CANDIDATE_FLOOR_CORE: + return candidate_floor_core_full90_v1 + if candidate_key == CANDIDATE_FLOOR_CORE_K5: + return candidate_floor_core_k5_full90_v1 + raise ValueError(''.join(['unknown fd9b floor-seed portfolio candidate ', format(repr(candidate_key), '')])) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def compile_and_launch_knn_build(*, shape_labels=FLOOR_CORE_K5_TARGET_SHAPES, benchmark: bool=False, candidate_key: str=DEFAULT_CANDIDATE_KEY) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark, kernel_fn=_candidate_kernel_fn(candidate_key)) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=None, kernel_fn: Callable[[dict[str, Any]], Any]=candidate, correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + return fd9b._run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=kernel_fn, correctness=correctness, time_flashlib=time_flashlib) + +def _seed_trace_record(inputs: dict[str, Any], seed_id: str, *, force_fallback: bool=False) -> dict[str, Any]: + label = str(inputs.get('label')) + base_row = _base_trace_row(label, force_fallback=False) + guard_id, guard_condition = _seed_guard(seed_id) + if force_fallback: + row = _base_trace_row(label, force_fallback=True) + row['expected_seed'] = seed_id + row['guard_id'] = ''.join(['forced_fallback_', format(seed_id, ''), '_disabled']) + row['guard_condition'] = ''.join(['forced fallback to fd9b; ', format(seed_id, ''), ' disabled']) + row['classification'] = 'guard-miss' + row['parent_dispatcher_route'] = base_row.get('selected_route') + row['baseline_dispatcher_route'] = base_row.get('selected_route') + row['targeted_seed_row'] = TARGETED_SEED_ROWS.get(seed_id, {}) + return _normalize_route_row(row) + return _normalize_route_row({'shape_key': label, 'selected_route': _seed_route(seed_id, inputs), 'selected_entrypoint': _seed_entrypoint(seed_id), 'selected_seed': seed_id, 'expected_seed': seed_id, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': guard_id, 'guard_condition': guard_condition, 'coverage': 'fd9b floor-seed portfolio overlay before fd9b fallback', 'consumed_seed': seed_id, 'replaced_route': base_row.get('selected_route'), 'parent_dispatcher_route': base_row.get('selected_route'), 'parent_dispatcher_selected_seed': base_row.get('selected_seed'), 'baseline_dispatcher_route': base_row.get('selected_route'), 'targeted_seed_row': TARGETED_SEED_ROWS.get(seed_id, {}), 'classification': 'unmeasured'}) + +def _route_trace_record(inputs: dict[str, Any], *, candidate_key: str, force_fallback: bool=False) -> dict[str, Any]: + _candidate_config(candidate_key) + label = str(inputs.get('label')) + if candidate_key == BASE_CANDIDATE_KEY: + return _normalize_route_row(_base_trace_row(label, force_fallback=force_fallback)) + seed_id = _matched_seed(inputs, candidate_key) + if seed_id is not None: + return _seed_trace_record(inputs, seed_id, force_fallback=force_fallback) + row = _base_trace_row(label, force_fallback=force_fallback) + row['expected_seed'] = _expected_seed(inputs, candidate_key) + row['baseline_dispatcher_route'] = _base_route(inputs, force_fallback=force_fallback) + row['parent_dispatcher_route'] = _base_route(inputs, force_fallback=force_fallback) + return _normalize_route_row(row) + +def route_trace_for_contract_shapes(shape_labels=None, *, candidate_key: str=DEFAULT_CANDIDATE_KEY, force_fallback: bool=False) -> list[dict[str, Any]]: + _candidate_config(candidate_key) + labels = _shape_labels(shape_labels) + return [_route_trace_record(_inputs_for_label(label), candidate_key=candidate_key, force_fallback=force_fallback) for label in labels] + +def _annotate_route_trace(route_trace: list[dict[str, Any]], candidate_report: dict[str, Any], baseline_report: dict[str, Any], *, candidate_key: str, speedup_floor: float) -> list[dict[str, Any]]: + new_seed_ids = set(_candidate_config(candidate_key)['new_seed_ids']) + annotated = [] + for row in route_trace: + label = str(row['shape_key']) + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') or baseline_row.get('flashlib_ms') + selected_new_seed = row.get('selected_seed') in new_seed_ids + speedup_vs_baseline = baseline_ms / candidate_ms if candidate_ms and baseline_ms else None + speedup_vs_external = flashlib_ms / candidate_ms if candidate_ms and flashlib_ms else None + out = dict(row) + out['dispatcher_kernel_ms'] = candidate_ms + out['baseline_fd9b_kernel_ms'] = baseline_ms + out['shape_specific_kernel_ms'] = candidate_ms if selected_new_seed else None + out['external_baseline_ms'] = flashlib_ms + out['flashlib_ms'] = flashlib_ms + out['relative_speedup_vs_baseline'] = speedup_vs_baseline + out['relative_speedup_vs_fd9b'] = speedup_vs_baseline + out['speedup_vs_external_baseline'] = speedup_vs_external + out['external_baseline_ref'] = 'same_session' if flashlib_ms is not None else 'not_available' + out['timing_backend'] = candidate_row.get('timing_backend') or baseline_row.get('timing_backend') + out['route_changed_vs_fd9b'] = out.get('selected_route') != out.get('baseline_dispatcher_route') + if row.get('expected_seed') in new_seed_ids and row.get('selected_seed') != row.get('expected_seed'): + out['classification'] = 'guard-miss' + elif selected_new_seed: + if speedup_vs_external is not None and speedup_vs_external < speedup_floor: + out['classification'] = 'kernel-slow' + elif speedup_vs_baseline is not None and speedup_vs_baseline < 1.0: + out['classification'] = 'kernel-slow' + else: + out['classification'] = 'seed-consumed' + elif speedup_vs_external is not None and speedup_vs_external < speedup_floor: + out['classification'] = 'kernel-slow' if out.get('route_kind') == 'specialized' else 'fallback-slow' + elif candidate_ms is None: + out['classification'] = 'unmeasured' + else: + out['classification'] = 'route-ok' + annotated.append(_normalize_route_row(out)) + return annotated + +def _below_flashlib_rows(report: dict[str, Any], route_trace: list[dict[str, Any]], *, floor: float) -> list[dict[str, Any]]: + trace_by_label = {str(row['shape_key']): row for row in route_trace} + rows = [] + for label, row in sorted(report.get('per_shape', {}).items()): + ratio = row.get('ratio_vs_flashlib') + if not isinstance(ratio, (float, int)) or ratio >= floor: + continue + trace_row = trace_by_label.get(str(label), {}) + classification = trace_row.get('classification', 'unmeasured') + if classification in ('route-ok', 'unmeasured') and (not trace_row.get('selected_seed')): + classification = 'fallback-slow' + elif classification in ('route-ok', 'unmeasured') and trace_row.get('selected_seed'): + classification = 'kernel-slow' + rows.append({'shape_key': label, 'kernel_ms': row.get('kernel_ms'), 'flashlib_ms': row.get('flashlib_ms'), 'ratio_vs_flashlib': ratio, 'selected_seed': trace_row.get('selected_seed'), 'expected_seed': trace_row.get('expected_seed'), 'selected_route': trace_row.get('selected_route'), 'route_kind': trace_row.get('route_kind', 'unknown'), 'classification': classification}) + return rows + +def _seed_delta_matrix(candidate_key: str, candidate_report: dict[str, Any], baseline_report: dict[str, Any], labels: tuple[str, ...]) -> list[dict[str, Any]]: + rows = [] + audit_labels = tuple(dict.fromkeys((*labels, *K48_EXCLUDED_TARGET_SHAPES))) + for label in audit_labels: + inputs = _inputs_for_label(label) + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + selected_seed = _expected_seed(inputs, candidate_key) + consumption_status = 'seed-consumed' if selected_seed in _candidate_config(candidate_key)['new_seed_ids'] else 'not-targeted' + if label in K48_EXCLUDED_TARGET_SHAPES: + selected_seed = SEED_E087_K48_ID + consumption_status = 'excluded_aggregate_regression' + rows.append({'shape_key': label, 'baseline_route': _base_route(inputs), 'candidate_route': route_for_contract_inputs(inputs, candidate_key=candidate_key), 'selected_seed': selected_seed, 'candidate_id': _candidate_id(candidate_key), 'candidate_ms': candidate_ms, 'baseline_fd9b_ms': baseline_ms, 'flashlib_ms': candidate_row.get('flashlib_ms') or baseline_row.get('flashlib_ms'), 'delta_ms_candidate_minus_fd9b': candidate_ms - baseline_ms if candidate_ms is not None and baseline_ms is not None else None, 'speedup_vs_fd9b': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'ratio_vs_flashlib': candidate_row.get('ratio_vs_flashlib') or baseline_row.get('ratio_vs_flashlib'), 'targeted_seed_row': TARGETED_SEED_ROWS.get(selected_seed, {}), 'consumption_status': consumption_status, 'candidate_passed': candidate_row.get('passed'), 'baseline_passed': baseline_row.get('passed'), 'timing_backend': candidate_row.get('timing_backend') or baseline_row.get('timing_backend')}) + return rows + +def benchmark_baseline_fd9b_full90_v1(*, use_cupti: bool=True, shape_labels=None, time_flashlib: bool=True, benchmark_correctness: bool=True) -> dict[str, Any]: + return _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_baseline_fd9b_full90_v1, time_flashlib=time_flashlib, correctness=benchmark_correctness) + +def _baseline_payload(report: dict[str, Any], *, shape_labels, use_cupti: bool, time_flashlib: bool, benchmark_correctness: bool, speedup_floor: float) -> dict[str, Any]: + labels = _shape_labels(shape_labels) + route_trace = route_trace_for_contract_shapes(labels, candidate_key=BASE_CANDIDATE_KEY) + below_1x = _below_flashlib_rows(report, route_trace, floor=1.0) + below_floor = _below_flashlib_rows(report, route_trace, floor=speedup_floor) + return {'candidate_id': BASE_CANDIDATE_ID, 'candidate_key': BASE_CANDIDATE_KEY, 'measured_entrypoint': BASELINE_BENCHMARK_ENTRYPOINT, 'source_task': 'generalize-auto-tuning-knn-build-fd9b', 'tflops': report.get('summary', {}).get('primary_mean'), 'all_correct': report.get('summary', {}).get('all_correct'), 'performance_comparable': report.get('summary', {}).get('performance_comparable'), 'invalid_performance_reason': report.get('summary', {}).get('invalid_performance_reason'), 'timing_backend': _timing_backend_name(use_cupti), 'timing_backends': _timing_backends_for_reports(report), 'use_cupti': use_cupti, 'denominator': _denominator_name(shape_labels), 'measured_shape_labels': list(labels), 'route_entrypoint': BASE_ROUTE_ENTRYPOINT, 'selected_route_labels': tuple(BASE_CONFIG['expected_shape_wins']), 'route_trace': route_trace, 'route_trace_included': True, 'contract_summary': report.get('summary'), 'contract_performance': report.get('performance'), 'contract_correctness': report.get('correctness'), 'benchmark_correctness_checked': benchmark_correctness, 'benchmark_time_flashlib': time_flashlib, 'performance_coverage': 'partial' if below_floor else 'pass', 'coverage_only_routes': [], 'hot_bucket_blockers': below_floor, 'flashlib_parity_ledger': {'baseline_ref_scope': 'same_session' if time_flashlib else 'not_available', 'baseline_payload': None, 'speedup_floor': speedup_floor, 'rows_below_1x': below_1x, 'rows_below_floor': below_floor, 'omitted_reason': None if time_flashlib else 'benchmark_time_flashlib=false'}, 'report': report} + +def _benchmark_payload(candidate_key: str, candidate_report: dict[str, Any], baseline_report: dict[str, Any], *, shape_labels, use_cupti: bool, time_flashlib: bool, benchmark_correctness: bool, speedup_floor: float) -> dict[str, Any]: + labels = _shape_labels(shape_labels) + candidate_metric = candidate_report.get('summary', {}).get('primary_mean') + baseline_metric = baseline_report.get('summary', {}).get('primary_mean') + route_trace = _annotate_route_trace(route_trace_for_contract_shapes(labels, candidate_key=candidate_key), candidate_report, baseline_report, candidate_key=candidate_key, speedup_floor=speedup_floor) + below_1x = _below_flashlib_rows(candidate_report, route_trace, floor=1.0) + below_floor = _below_flashlib_rows(candidate_report, route_trace, floor=speedup_floor) + config = _candidate_config(candidate_key) + return {'candidate_id': config['candidate_id'], 'candidate_key': candidate_key, 'baseline_candidate_id': BASE_CANDIDATE_ID, 'baseline_candidate_key': BASE_CANDIDATE_KEY, 'source_task': 'generalize-auto-tuning-knn-build-5720', 'tflops': candidate_metric, 'baseline_tflops': baseline_metric, 'metric_delta_vs_fd9b': candidate_metric - baseline_metric if candidate_metric is not None and baseline_metric is not None else None, 'all_correct': candidate_report.get('summary', {}).get('all_correct'), 'baseline_all_correct': baseline_report.get('summary', {}).get('all_correct'), 'performance_comparable': candidate_report.get('summary', {}).get('performance_comparable'), 'invalid_performance_reason': candidate_report.get('summary', {}).get('invalid_performance_reason'), 'measured_entrypoint': config['benchmark_entrypoint'], 'baseline_measured_entrypoint': BASELINE_BENCHMARK_ENTRYPOINT, 'route_entrypoint': ROUTE_ENTRYPOINT, 'selected_seeds': config['selected_seeds'], 'new_seed_ids': config['new_seed_ids'], 'selected_route_labels': config['expected_shape_wins'], 'selected_route_rows': _rows_for_labels(candidate_report, config['expected_shape_wins']), 'baseline_selected_route_rows': _rows_for_labels(baseline_report, config['expected_shape_wins']), 'seed_delta_matrix': _seed_delta_matrix(candidate_key, candidate_report, baseline_report, labels), 'candidate_dispatchers': CANDIDATE_DISPATCHERS, 'rejected_route_combinations': REJECTED_ROUTE_COMBINATIONS, 'selected_candidate_dispatcher': config['candidate_id'], 'source_tasks': SOURCE_TASKS, 'route_modules': PRODUCTION_ROUTE_MODULES, 'route_trace': route_trace, 'forced_fallback_route_trace': route_trace_for_contract_shapes(labels, candidate_key=candidate_key, force_fallback=True), 'route_trace_included': True, 'contract_summary': candidate_report.get('summary'), 'baseline_contract_summary': baseline_report.get('summary'), 'contract_performance': candidate_report.get('performance'), 'baseline_contract_performance': baseline_report.get('performance'), 'contract_correctness': candidate_report.get('correctness'), 'timing_backend': _timing_backend_name(use_cupti), 'timing_backends': _timing_backends_for_reports(candidate_report, baseline_report), 'use_cupti': use_cupti, 'benchmark_correctness_checked': benchmark_correctness, 'benchmark_time_flashlib': time_flashlib, 'denominator': _denominator_name(shape_labels), 'measured_shape_labels': list(labels), 'performance_coverage': 'partial' if below_floor else 'pass', 'coverage_only_routes': [], 'hot_bucket_blockers': below_floor, 'flashlib_parity_ledger': {'baseline_ref_scope': 'same_session' if time_flashlib else 'not_available', 'baseline_payload': None, 'speedup_floor': speedup_floor, 'rows_below_1x': below_1x, 'rows_below_floor': below_floor, 'omitted_reason': None if time_flashlib else 'benchmark_time_flashlib=false'}, 'rank_objective': {'metric': 'tflops', 'direction': 'maximize', 'value': candidate_metric, 'delta_vs_fd9b': candidate_metric - baseline_metric if candidate_metric is not None and baseline_metric is not None else None, 'valid_measurement_count': candidate_report.get('performance', {}).get('valid_measurement_count'), 'comparable': candidate_report.get('performance', {}).get('comparable')}, 'report': candidate_report, 'baseline_report': baseline_report} + +def benchmark_candidate_portfolio(candidate_key: str=DEFAULT_CANDIDATE_KEY, *, use_cupti: bool=True, shape_labels=None, baseline_report: dict[str, Any] | None=None, time_flashlib: bool=True, benchmark_correctness: bool=True, speedup_floor: float=1.2) -> dict[str, Any]: + if candidate_key == BASE_CANDIDATE_KEY: + baseline = benchmark_baseline_fd9b_full90_v1(use_cupti=use_cupti, shape_labels=shape_labels, time_flashlib=time_flashlib, benchmark_correctness=benchmark_correctness) + return _baseline_payload(baseline, shape_labels=shape_labels, use_cupti=use_cupti, time_flashlib=time_flashlib, benchmark_correctness=benchmark_correctness, speedup_floor=speedup_floor) + baseline = baseline_report + if baseline is None: + baseline = benchmark_baseline_fd9b_full90_v1(use_cupti=use_cupti, shape_labels=shape_labels, time_flashlib=time_flashlib, benchmark_correctness=benchmark_correctness) + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=_candidate_kernel_fn(candidate_key), time_flashlib=time_flashlib, correctness=benchmark_correctness) + return _benchmark_payload(candidate_key, candidate_report, baseline, shape_labels=shape_labels, use_cupti=use_cupti, time_flashlib=time_flashlib, benchmark_correctness=benchmark_correctness, speedup_floor=speedup_floor) + +def benchmark_candidate_k20_k32_full90_v1(**kwargs) -> dict[str, Any]: + return benchmark_candidate_portfolio(CANDIDATE_K20_K32, **kwargs) + +def benchmark_candidate_k5_only_full90_v1(**kwargs) -> dict[str, Any]: + return benchmark_candidate_portfolio(CANDIDATE_K5_ONLY, **kwargs) + +def benchmark_candidate_floor_core_full90_v1(**kwargs) -> dict[str, Any]: + return benchmark_candidate_portfolio(CANDIDATE_FLOOR_CORE, **kwargs) + +def benchmark_candidate_floor_core_k5_full90_v1(**kwargs) -> dict[str, Any]: + return benchmark_candidate_portfolio(CANDIDATE_FLOOR_CORE_K5, **kwargs) + +def _candidate_comparable(payload: dict[str, Any]) -> bool: + return bool(payload.get('all_correct')) and bool(payload.get('performance_comparable')) + +def _selected_candidate_key(payloads: dict[str, dict[str, Any]]) -> str | None: + candidates = [key for key in payloads if key != BASE_CANDIDATE_KEY and _candidate_comparable(payloads[key])] + if not candidates: + return None + return max(candidates, key=lambda key: (payloads[key].get('tflops') or float('-inf'), len(CANDIDATE_CONFIGS[key]['new_seed_ids']))) + +def _summary_payload(*, payloads: dict[str, dict[str, Any]], artifacts: dict[str, str], shape_labels, time_flashlib: bool, benchmark_correctness: bool, speedup_floor: float) -> dict[str, Any]: + selected_key = _selected_candidate_key(payloads) + selected_payload = payloads.get(selected_key, {}) if selected_key else {} + baseline_payload = payloads[BASE_CANDIDATE_KEY] + baseline_value = baseline_payload.get('tflops') + selected_value = selected_payload.get('tflops') + metric_delta = selected_payload.get('metric_delta_vs_fd9b') + below_floor = selected_payload.get('flashlib_parity_ledger', {}).get('rows_below_floor', []) + no_regression = selected_key is not None and selected_value is not None and (baseline_value is not None) and (float(selected_value) >= float(baseline_value)) + return {'candidate_id': 'dispatcher_synthesis_fd9b_floor_seed_portfolio_5720_full90_v1', 'measured_entrypoint': ''.join([format(MODULE, ''), ':write_benchmark_artifacts']), 'workflow_mode': 'generalize_auto_tuning', 'auto_tuning_stage': 'dispatcher-synthesis', 'benchmark_correctness_checked': benchmark_correctness, 'benchmark_time_flashlib': time_flashlib, 'baseline_candidate_key': BASE_CANDIDATE_KEY, 'selected_candidate_key': selected_key, 'selected_candidate_dispatcher': _candidate_id(selected_key) if selected_key else None, 'default_candidate_key': DEFAULT_CANDIDATE_KEY, 'candidate_dispatchers': CANDIDATE_DISPATCHERS, 'rejected_route_combinations': REJECTED_ROUTE_COMBINATIONS, 'candidate_rankings': [{'candidate_key': key, 'candidate_id': payloads[key].get('candidate_id'), 'tflops': payloads[key].get('tflops'), 'delta_vs_fd9b': payloads[key].get('metric_delta_vs_fd9b', 0.0), 'all_correct': payloads[key].get('all_correct'), 'performance_comparable': payloads[key].get('performance_comparable'), 'performance_coverage': payloads[key].get('performance_coverage')} for key in CANDIDATE_KEYS if key in payloads], 'seed_delta_matrix': selected_payload.get('seed_delta_matrix', []), 'seed_delta_matrix_all_candidates': {key: payloads[key].get('seed_delta_matrix', []) for key in payloads if key != BASE_CANDIDATE_KEY}, 'full_denominator_ab': {'baseline_payload': artifacts['baseline_payload'], 'candidate_payload': artifacts.get(''.join([format(selected_key, ''), '_payload'])) if selected_key else None, 'denominator': _denominator_name(shape_labels), 'timing_backend': selected_payload.get('timing_backend', baseline_payload.get('timing_backend')), 'metric_delta': metric_delta, 'route_trace': selected_payload.get('route_trace', [])}, 'promotion_gate_summary': {'full_dispatch_denominator': bool(selected_payload.get('all_correct')), 'same_session_no_regression': no_regression, 'performance_coverage': 'pass' if selected_key and (not below_floor) else 'partial', 'hot_bucket_parity': bool(selected_key) and (not below_floor), 'expanded_shape_coverage': False}, 'flashlib_parity_ledger': selected_payload.get('flashlib_parity_ledger', baseline_payload.get('flashlib_parity_ledger', {})), 'benchmark_payload_artifacts': artifacts, 'route_trace': selected_payload.get('route_trace', []), 'baseline_tflops': baseline_value, 'selected_tflops': selected_value, 'metric_delta_vs_fd9b': metric_delta, 'selected_no_regression': no_regression} + +def write_benchmark_artifacts(artifact_dir: str | Path, *, use_cupti: bool=True, shape_labels=None, time_flashlib: bool=True, benchmark_correctness: bool=True, candidate_keys: tuple[str, ...] | None=None, speedup_floor: float=1.2) -> dict[str, str]: + labels = _shape_labels(shape_labels) + denom_label = _denominator_label(shape_labels) + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + baseline_report = benchmark_baseline_fd9b_full90_v1(use_cupti=use_cupti, shape_labels=shape_labels, time_flashlib=time_flashlib, benchmark_correctness=benchmark_correctness) + baseline_payload = _baseline_payload(baseline_report, shape_labels=shape_labels, use_cupti=use_cupti, time_flashlib=time_flashlib, benchmark_correctness=benchmark_correctness, speedup_floor=speedup_floor) + baseline_path = out_dir / ''.join([format(denom_label, ''), '_same_session_baseline_fd9b_v1.json']) + baseline_payload['flashlib_parity_ledger']['baseline_payload'] = str(baseline_path) + baseline_path.write_text(json.dumps(baseline_payload, indent=2, sort_keys=True) + '\n', encoding='utf-8') + artifacts: dict[str, str] = {'baseline_payload': str(baseline_path)} + payloads = {BASE_CANDIDATE_KEY: baseline_payload} + selected_candidate_keys = list(DEFAULT_SYNTHESIS_CANDIDATES) if candidate_keys is None else list(candidate_keys) + for candidate_key in selected_candidate_keys: + if candidate_key == BASE_CANDIDATE_KEY: + raise ValueError('candidate_keys must not include the baseline key') + _candidate_config(candidate_key) + candidate_payload = benchmark_candidate_portfolio(candidate_key, use_cupti=use_cupti, shape_labels=shape_labels, baseline_report=baseline_report, time_flashlib=time_flashlib, benchmark_correctness=benchmark_correctness, speedup_floor=speedup_floor) + payloads[candidate_key] = candidate_payload + candidate_path = out_dir / ''.join([format(denom_label, ''), '_dispatch_', format(candidate_key, ''), '_v1.json']) + trace_path = out_dir / ''.join([format(denom_label, ''), '_route_trace_', format(candidate_key, ''), '_v1.json']) + forced_trace_path = out_dir / ''.join([format(denom_label, ''), '_forced_fallback_trace_', format(candidate_key, ''), '_v1.json']) + seed_matrix_path = out_dir / ''.join([format(denom_label, ''), '_seed_delta_matrix_', format(candidate_key, ''), '_v1.json']) + candidate_payload['flashlib_parity_ledger']['baseline_payload'] = str(baseline_path) + candidate_path.write_text(json.dumps(candidate_payload, indent=2, sort_keys=True) + '\n', encoding='utf-8') + trace_path.write_text(json.dumps(candidate_payload['route_trace'], indent=2, sort_keys=True) + '\n', encoding='utf-8') + forced_trace_path.write_text(json.dumps(candidate_payload['forced_fallback_route_trace'], indent=2, sort_keys=True) + '\n', encoding='utf-8') + seed_matrix_path.write_text(json.dumps(candidate_payload['seed_delta_matrix'], indent=2, sort_keys=True) + '\n', encoding='utf-8') + artifacts[''.join([format(candidate_key, ''), '_payload'])] = str(candidate_path) + artifacts[''.join([format(candidate_key, ''), '_route_trace'])] = str(trace_path) + artifacts[''.join([format(candidate_key, ''), '_forced_fallback_trace'])] = str(forced_trace_path) + artifacts[''.join([format(candidate_key, ''), '_seed_delta_matrix'])] = str(seed_matrix_path) + summary = _summary_payload(payloads=payloads, artifacts=artifacts, shape_labels=shape_labels, time_flashlib=time_flashlib, benchmark_correctness=benchmark_correctness, speedup_floor=speedup_floor) + summary_path = out_dir / ''.join([format(denom_label, ''), '_dispatcher_synthesis_fd9b_floor_seed_portfolio_5720_v1.json']) + summary_path.write_text(json.dumps(summary, indent=2, sort_keys=True) + '\n', encoding='utf-8') + artifacts['dispatcher_synthesis'] = str(summary_path) + artifacts['measured_shape_count'] = str(len(labels)) + return artifacts diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_q1m524_v10_d320recurrence_consumption_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_q1m524_v10_d320recurrence_consumption_v1.py new file mode 100644 index 00000000..c1732fd7 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_q1m524_v10_d320recurrence_consumption_v1.py @@ -0,0 +1,146 @@ +"""Consume the exact D320 full-producer-grid seed into the exported v8 portfolio. + +Minimum target architecture: sm_100a. The only new guard is exact BF16 +non-build B=1, Q=512, M=12000, D=320, K=10. All guard misses and forced +fallbacks delegate unchanged to the v8 Weave-only dispatcher. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +from collections.abc import Callable +from pathlib import Path +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_d320_producer_recurrence_search_f556_v1 as seed +from . import knn_build_dispatch_q1m524_v8_q32s141_consumption_v1 as base +from . import knn_build_dispatch_v11_common_d_seed_portfolio_a4ec_v1 as portfolio +MODULE = 'loom.examples.weave.knn_build_dispatch_q1m524_v10_d320recurrence_consumption_v1' +CANDIDATE_ID = 'q1m524_v10_d320recurrence_consumption_v1' +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +BENCHMARK_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_knn_build_dispatch_q1m524_v10_d320recurrence_consumption_v1']) +BASELINE_ENTRYPOINT = base.ROUTE_ENTRYPOINT +SEED_ID = 'd320_producer_recurrence_4287' +SEED_ENTRYPOINT = ''.join([format(seed.MODULE, ''), ':launch_from_contract_inputs']) +GUARD_ID = '4287_exact_bf16_search_b1_q512_m12000_d320_k10_producer_grid192' +TARGET_SHAPE = seed.TARGET_SHAPE +TARGET_SHAPES = (TARGET_SHAPE,) +SPEEDUP_FLOOR = 1.2 + +def _eligible(inputs: dict[str, Any]) -> bool: + return seed._is_target(inputs) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if not force_fallback and _eligible(inputs): + return seed.route_for_contract_inputs(inputs) + return base.route_for_contract_inputs(inputs, force_fallback=force_fallback) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + if not force_fallback and _eligible(inputs): + seed.launch_from_contract_inputs(inputs) + return + base.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + +def _candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def _forced_candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) + +def _select_contract_shapes(shape_labels): + return base._select_contract_shapes(tuple(shape_labels)) + +def _run(*, use_cupti: bool, shape_labels, kernel_fn: Callable[[dict[str, Any]], Any], benchmark: bool=True) -> dict[str, Any]: + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + try: + return eval_mod.evaluate(kernel_fn, shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = previous + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False) -> list[dict[str, Any]]: + labels = tuple(shape_labels) if shape_labels is not None else tuple((s['label'] for s in eval_mod.CANONICAL_SHAPES)) + rows = [] + for shape in _select_contract_shapes(labels): + label, inputs = (str(shape['label']), portfolio._trace_inputs_for_shape(shape)) + if not force_fallback and _eligible(inputs): + rows.append(portfolio._normalize_route_row({'shape_key': label, 'selected_route': route_for_contract_inputs(inputs), 'selected_entrypoint': SEED_ENTRYPOINT, 'selected_seed': SEED_ID, 'expected_seed': SEED_ID, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': GUARD_ID, 'guard_condition': 'exact BF16 non-build B=1 Q=512 M=12000 D=320 K=10', 'replaced_route': base.route_for_contract_inputs(inputs), 'classification': 'unmeasured'})) + else: + rows.append(base.route_trace_for_contract_shapes((label,), force_fallback=force_fallback)[0]) + return rows + +def _annotate(rows, candidate_report, baseline_report): + out = [] + for row in rows: + row = dict(row) + candidate = candidate_report.get('per_shape', {}).get(row['shape_key'], {}) + baseline = baseline_report.get('per_shape', {}).get(row['shape_key'], {}) + ms, base_ms, ratio = (candidate.get('kernel_ms'), baseline.get('kernel_ms'), candidate.get('ratio_vs_flashlib')) + row.update(dispatcher_kernel_ms=ms, baseline_dispatcher_kernel_ms=base_ms, relative_speedup_vs_baseline=base_ms / ms if ms and base_ms else None, speedup_vs_external_baseline=ratio, external_baseline_ms=candidate.get('flashlib_ms'), external_baseline_ref='same_session' if candidate.get('flashlib_ms') is not None else 'not_available', timing_backend=candidate.get('timing_backend') or baseline.get('timing_backend')) + if candidate.get('passed') is False: + row['classification'] = 'benchmark-path-mismatch' + elif row.get('selected_seed') == SEED_ID and isinstance(ratio, (int, float)) and (ratio >= SPEEDUP_FLOOR): + row['classification'] = 'seed-consumed' + elif isinstance(ratio, (int, float)) and ratio < SPEEDUP_FLOOR: + row['classification'] = 'kernel-slow' + else: + row['classification'] = 'route-ok' + out.append(portfolio._normalize_route_row(row)) + return out + +def _ledger(rows): + below_1x, below_floor = ([], []) + for row in rows: + ratio = row.get('speedup_vs_external_baseline') + if isinstance(ratio, (int, float)): + record = {key: row.get(key) for key in ('shape_key', 'selected_route', 'selected_seed', 'speedup_vs_external_baseline', 'classification')} + if ratio < 1.0: + below_1x.append(record) + if ratio < SPEEDUP_FLOOR: + below_floor.append(record) + return {'baseline_ref_scope': 'same_session', 'speedup_floor': SPEEDUP_FLOOR, 'rows_below_1x': below_1x, 'rows_below_floor': below_floor, 'omitted_reason': None} + +def write_correctness_preflight_artifact(artifact_dir: str | Path, *, shape_labels=None) -> dict[str, Any]: + """Fresh-process ABI preflight using contract-owned intermediates and outputs.""" + labels = tuple(shape_labels) if shape_labels is not None else tuple((s['label'] for s in eval_mod.CANONICAL_SHAPES)) + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + default = _run(use_cupti=True, shape_labels=labels, kernel_fn=_candidate, benchmark=False) + forced = _run(use_cupti=True, shape_labels=TARGET_SHAPES, kernel_fn=_forced_candidate, benchmark=False) + payload = {'candidate_entrypoint': ROUTE_ENTRYPOINT, 'seed_entrypoint': SEED_ENTRYPOINT, 'denominator': ''.join(['full', format(len(labels), ''), '_v12']), 'shape_labels': labels, 'fresh_process': True, 'exported_input_abi': True, 'caller_owned_outputs': True, 'real_intermediate_buffers': True, 'default': default, 'forced_fallback': forced, 'forced_fallback_denominator': 'target_bucket', 'default_path_correct': bool(default['correctness']['all_correct']), 'forced_path_correct': bool(forced['correctness']['all_correct']), 'target_default_route': route_trace_for_contract_shapes(TARGET_SHAPES)[0], 'route_trace': route_trace_for_contract_shapes(labels)} + path = out_dir / 'full112_v10_d320recurrence_correctness_preflight.json' + atomic_write_json(path, payload) + return {'path': str(path), 'passed': payload['default_path_correct'] and payload['forced_path_correct']} + +def write_checkpointed_full_benchmark_artifacts(artifact_dir: str | Path, *, use_cupti: bool=True, shape_labels=None, max_new_rows: int | None=None) -> dict[str, Any]: + labels = tuple(shape_labels) if shape_labels is not None else tuple((s['label'] for s in eval_mod.CANONICAL_SHAPES)) + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + checkpoint_path = out_dir / ''.join(['full', format(len(labels), ''), '_v10_d320recurrence_paired_progress.json']) + audit = ResumableRowAudit(checkpoint_path, audit_id=''.join([format(CANDIDATE_ID, ''), ':paired-v1']), labels=labels, metadata={'contract_version': eval_mod.CONTRACT.contract_version, 'timing_backend': 'cupti' if use_cupti else 'cuda_event', 'candidate_entrypoint': ROUTE_ENTRYPOINT, 'baseline_entrypoint': BASELINE_ENTRYPOINT}) + shapes = {str(shape['label']): shape for shape in _select_contract_shapes(labels)} + with healthy_gpu_bench_session(require_cupti=use_cupti) as gpu_preflight: + state = audit.run(lambda label: eval_mod.evaluate_paired_row(_candidate, base._candidate, shapes[label], use_cupti=use_cupti, order_seed=7202428), max_new_rows=max_new_rows) + if state['status'] != 'complete': + return {'status': 'running', 'completed': state['completed'], 'total': len(labels), 'checkpoint': str(checkpoint_path), 'gpu_preflight': gpu_preflight} + candidate_rows = {row['shape_key']: row['candidate'] for row in state['rows']} + baseline_rows = {row['shape_key']: row['baseline'] for row in state['rows']} + report = {'contract': eval_mod.CONTRACT.kernel, 'contract_version': eval_mod.CONTRACT.contract_version, 'per_shape': candidate_rows} + baseline_report = {'contract': eval_mod.CONTRACT.kernel, 'contract_version': eval_mod.CONTRACT.contract_version, 'per_shape': baseline_rows} + trace = _annotate(route_trace_for_contract_shapes(labels), report, baseline_report) + payload = {'candidate_id': CANDIDATE_ID, 'candidate_entrypoint': ROUTE_ENTRYPOINT, 'baseline_entrypoint': BASELINE_ENTRYPOINT, 'measured_entrypoint': BENCHMARK_ENTRYPOINT, 'measurement_mode': 'checkpointed_row_pair_same_process', 'contract_version': eval_mod.CONTRACT.contract_version, 'measured_shape_labels': labels, 'accelerated_shape_labels': TARGET_SHAPES, 'timing_backend': 'cupti' if use_cupti else 'cuda_event', 'route_trace': trace, 'route_trace_included': True, 'flashlib_parity_ledger': _ledger(trace), 'correctness': {'all_correct': all((row.get('passed') is True for row in candidate_rows.values())), 'shape_count': len(candidate_rows)}, 'report': report, 'baseline_report': baseline_report, 'randomized_shared_input_triads': state['rows'], 'gpu_preflight': gpu_preflight, 'checkpoint': str(checkpoint_path)} + candidate_path = out_dir / 'full112_v10_d320recurrence_candidate.json' + baseline_path = out_dir / 'full112_v8_same_session_baseline.json' + trace_path = out_dir / 'full112_v10_d320recurrence_route_trace.json' + atomic_write_json(candidate_path, payload) + atomic_write_json(baseline_path, {'measured_entrypoint': BASELINE_ENTRYPOINT, 'timing_backend': payload['timing_backend'], 'report': baseline_report, 'route_trace': base.route_trace_for_contract_shapes(labels), 'route_trace_included': True}) + atomic_write_json(trace_path, trace) + return {'candidate_payload': str(candidate_path), 'same_session_baseline_payload': str(baseline_path), 'route_trace': str(trace_path)} + +def benchmark_knn_build_dispatch_q1m524_v10_d320recurrence_consumption_v1(*, use_cupti: bool=True, shape_labels=None) -> dict[str, Any]: + labels = tuple(shape_labels) if shape_labels is not None else tuple((s['label'] for s in eval_mod.CANONICAL_SHAPES)) + report = _run(use_cupti=use_cupti, shape_labels=labels, kernel_fn=_candidate) + baseline = _run(use_cupti=use_cupti, shape_labels=labels, kernel_fn=base._candidate) + trace = _annotate(route_trace_for_contract_shapes(labels), report, baseline) + return {'candidate_id': CANDIDATE_ID, 'candidate_entrypoint': ROUTE_ENTRYPOINT, 'baseline_entrypoint': BASELINE_ENTRYPOINT, 'measured_entrypoint': BENCHMARK_ENTRYPOINT, 'measured_shape_labels': labels, 'timing_backend': 'cupti' if use_cupti else 'cuda_event', 'route_trace': trace, 'route_trace_included': True, 'flashlib_parity_ledger': _ledger(trace), 'correctness': report['correctness'], 'summary': report['summary'], 'report': report, 'baseline_report': baseline} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_q1m524_v3_consumption_e054_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_q1m524_v3_consumption_e054_v1.py new file mode 100644 index 00000000..d4f012e3 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_q1m524_v3_consumption_e054_v1.py @@ -0,0 +1,169 @@ +"""Additive exact Q1/M524287 v3 dispatcher-consumption candidate. + +Minimum target architecture: sm_100a. The sole new guard consumes the +validated register-resident v3 seed for BF16 non-build B=1/Q=1/M=524287/ +D=128/K=10. Every other input, including forced fallback, delegates to the +existing Weave-only a4ec v11 portfolio unchanged. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +from pathlib import Path +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_v11_common_d_seed_portfolio_a4ec_v1 as base +from . import knn_build_q1m524_workfeed_q1m524_workfeed_codex_v3 as seed_v3 +MODULE = 'loom.examples.weave.knn_build_dispatch_q1m524_v3_consumption_e054_v1' +CANDIDATE_ID = 'q1m524_s147_g21_register_merge_v3_consumption_e054_v1' +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +BENCHMARK_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_knn_build_dispatch_q1m524_v3_consumption_e054_v1']) +BASELINE_ENTRYPOINT = base.ROUTE_ENTRYPOINT +SEED_ID = 'q1m524_s147_g21_register_merge_v3' +SEED_ENTRYPOINT = 'loom.examples.weave.knn_build_q1m524_workfeed_q1m524_workfeed_codex_v3:launch_from_contract_inputs' +GUARD_ID = 'e054_q1_m524287_s147_g21_register_merge_v3_exact_guard' +TARGET_SHAPE = base.RAG_Q1_M524287_K10 +TARGET_SHAPES = (TARGET_SHAPE,) +SPEEDUP_FLOOR = base.SPEEDUP_FLOOR +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_ragonline_mbucket_4fc7_q1m262_v2_stage1_q1_k10_m64_halfrow", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 36608, "constants": [["BLOCK_Q", 64], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 10], ["ROWS_COVERED", 1]], "cta_group": 1, "threads": 96}')) + +def _eligible_v3(inputs: dict[str, Any]) -> bool: + return seed_v3.ea43._eligible_q1_m524_n128(inputs) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if not force_fallback and _eligible_v3(inputs): + return seed_v3.route_for_contract_inputs(inputs) + return base.route_for_contract_inputs(inputs, force_fallback=force_fallback) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + if not force_fallback and _eligible_v3(inputs): + seed_v3.launch_from_contract_inputs(inputs) + return + base.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_baseline_a4ec(inputs: dict[str, Any]) -> None: + base.launch_from_contract_inputs(inputs) + +def _select_contract_shapes(shape_labels): + return base._select_contract_shapes(tuple(shape_labels)) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run(*, use_cupti: bool, shape_labels, kernel_fn: Callable[[dict[str, Any]], Any]) -> dict[str, Any]: + previous = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = previous + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False) -> list[dict[str, Any]]: + labels = tuple(shape_labels) if shape_labels is not None else tuple((s['label'] for s in eval_mod.CANONICAL_SHAPES)) + rows = [] + for shape in _select_contract_shapes(labels): + label = str(shape['label']) + if force_fallback or label != TARGET_SHAPE: + rows.append(base.route_trace_for_contract_shapes((label,), force_fallback=force_fallback)[0]) + continue + inputs = base._trace_inputs_for_shape(shape) + assert _eligible_v3(inputs), 'target label must satisfy the exact v3 guard' + parent = base.route_trace_for_contract_shapes((label,))[0] + rows.append(base._normalize_route_row({'shape_key': label, 'selected_route': route_for_contract_inputs(inputs), 'selected_entrypoint': SEED_ENTRYPOINT, 'selected_seed': SEED_ID, 'expected_seed': SEED_ID, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': GUARD_ID, 'guard_condition': 'exact BF16 non-build B=1 Q=1 M=524287 D=128 K=10', 'replaced_route': parent.get('selected_route'), 'classification': 'unmeasured'})) + return rows + +def _annotate(rows, candidate_report, baseline_report): + out = [] + for row in rows: + row = dict(row) + candidate_row = candidate_report.get('per_shape', {}).get(row['shape_key'], {}) + baseline_row = baseline_report.get('per_shape', {}).get(row['shape_key'], {}) + ms, base_ms = (candidate_row.get('kernel_ms'), baseline_row.get('kernel_ms')) + ratio = candidate_row.get('ratio_vs_flashlib') + row.update(dispatcher_kernel_ms=ms, baseline_dispatcher_kernel_ms=base_ms, relative_speedup_vs_baseline=base_ms / ms if ms and base_ms else None, speedup_vs_external_baseline=ratio, external_baseline_ms=candidate_row.get('flashlib_ms'), external_baseline_ref='same_session' if candidate_row.get('flashlib_ms') is not None else 'not_available', timing_backend=candidate_row.get('timing_backend') or baseline_row.get('timing_backend')) + if candidate_row.get('passed') is False: + row['classification'] = 'benchmark-path-mismatch' + elif row['shape_key'] == TARGET_SHAPE and isinstance(ratio, (int, float)) and (ratio >= SPEEDUP_FLOOR): + row['classification'] = 'seed-consumed' + elif isinstance(ratio, (int, float)) and ratio < SPEEDUP_FLOOR: + row['classification'] = 'kernel-slow' + else: + row['classification'] = 'route-ok' + out.append(base._normalize_route_row(row)) + return out + +def _ledger(rows): + below_1x, below_floor = ([], []) + for row in rows: + ratio = row.get('speedup_vs_external_baseline') + if not isinstance(ratio, (int, float)): + continue + record = {key: row.get(key) for key in ('shape_key', 'selected_route', 'selected_seed', 'speedup_vs_external_baseline', 'classification')} + if ratio < 1.0: + below_1x.append(record) + if ratio < SPEEDUP_FLOOR: + below_floor.append(record) + return {'baseline_ref_scope': 'same_session', 'speedup_floor': SPEEDUP_FLOOR, 'rows_below_1x': below_1x, 'rows_below_floor': below_floor, 'omitted_reason': None} + +def benchmark_knn_build_dispatch_q1m524_v3_consumption_e054_v1(*, use_cupti: bool=True, shape_labels=None) -> dict[str, Any]: + labels = tuple(shape_labels) if shape_labels is not None else tuple((s['label'] for s in eval_mod.CANONICAL_SHAPES)) + report = _run(use_cupti=use_cupti, shape_labels=labels, kernel_fn=candidate) + baseline = _run(use_cupti=use_cupti, shape_labels=labels, kernel_fn=candidate_baseline_a4ec) + trace = _annotate(route_trace_for_contract_shapes(labels), report, baseline) + ledger = _ledger(trace) + return {'candidate_id': CANDIDATE_ID, 'candidate_entrypoint': ROUTE_ENTRYPOINT, 'baseline_entrypoint': BASELINE_ENTRYPOINT, 'measured_entrypoint': BENCHMARK_ENTRYPOINT, 'measured_shape_labels': labels, 'accelerated_shape_labels': TARGET_SHAPES, 'timing_backend': 'cupti' if use_cupti else 'cuda_event', 'timing_backends': ['cupti' if use_cupti else 'cuda_event'], 'route_trace': trace, 'route_trace_included': True, 'forced_fallback_route_trace': route_trace_for_contract_shapes(labels, force_fallback=True), 'flashlib_parity_ledger': ledger, 'performance_coverage': 'pass' if not ledger['rows_below_floor'] else 'partial', 'hot_bucket_blockers': ledger['rows_below_floor'], 'summary': report['summary'], 'performance': report['performance'], 'correctness': report['correctness'], 'report': report, 'baseline_summary': baseline['summary'], 'baseline_performance': baseline['performance'], 'baseline_report': baseline} + +def write_benchmark_artifacts(artifact_dir: str | Path, *, use_cupti: bool=True, shape_labels=None) -> dict[str, str]: + payload = benchmark_knn_build_dispatch_q1m524_v3_consumption_e054_v1(use_cupti=use_cupti, shape_labels=shape_labels) + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + denom = ''.join(['full', format(len(payload['measured_shape_labels']), '')]) + candidate_path = out_dir / ''.join([format(denom, ''), '_q1m524_v3_candidate.json']) + baseline_path = out_dir / ''.join([format(denom, ''), '_a4ec_same_session_baseline.json']) + trace_path = out_dir / ''.join([format(denom, ''), '_q1m524_v3_route_trace.json']) + candidate_path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + baseline_path.write_text(json.dumps({'measured_entrypoint': BASELINE_ENTRYPOINT, 'timing_backend': payload['timing_backend'], 'report': payload['baseline_report'], 'route_trace': base.route_trace_for_contract_shapes(payload['measured_shape_labels']), 'route_trace_included': True}, indent=2, sort_keys=True) + '\n') + trace_path.write_text(json.dumps(payload['route_trace'], indent=2, sort_keys=True) + '\n') + return {'candidate_payload': str(candidate_path), 'same_session_baseline_payload': str(baseline_path), 'route_trace': str(trace_path)} + +def write_checkpointed_full_benchmark_artifacts(artifact_dir: str | Path, *, use_cupti: bool=True, shape_labels=None) -> dict[str, str]: + """Measure the dispatcher one contract row at a time and checkpoint progress. + + The full v12 denominator has 112 rows and each row includes three CUPTI + measurements (candidate, FlashLib, and a4ec). Persisting after each paired + candidate/control row makes an allocation or runner interruption diagnosable + without turning a partial run into performance evidence. + """ + labels = tuple(shape_labels) if shape_labels is not None else tuple((s['label'] for s in eval_mod.CANONICAL_SHAPES)) + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + progress_path = out_dir / ''.join(['full', format(len(labels), ''), '_q1m524_v3_progress.json']) + candidate_path = out_dir / ''.join(['full', format(len(labels), ''), '_q1m524_v3_candidate.json']) + baseline_path = out_dir / ''.join(['full', format(len(labels), ''), '_a4ec_same_session_baseline.json']) + trace_path = out_dir / ''.join(['full', format(len(labels), ''), '_q1m524_v3_route_trace.json']) + candidate_rows: dict[str, Any] = {} + baseline_rows: dict[str, Any] = {} + for index, label in enumerate(labels, start=1): + candidate_row = _run(use_cupti=use_cupti, shape_labels=(label,), kernel_fn=candidate) + baseline_row = _run(use_cupti=use_cupti, shape_labels=(label,), kernel_fn=candidate_baseline_a4ec) + candidate_rows.update(candidate_row['per_shape']) + baseline_rows.update(baseline_row['per_shape']) + progress_path.write_text(json.dumps({'artifact_type': 'dispatcher-benchmark-progress', 'status': 'running', 'contract_version': eval_mod.CONTRACT.contract_version, 'timing_backend': 'cupti' if use_cupti else 'cuda_event', 'completed_shape_count': index, 'total_shape_count': len(labels), 'completed_labels': list(candidate_rows), 'candidate_per_shape': candidate_rows, 'baseline_per_shape': baseline_rows}, indent=2, sort_keys=True) + '\n', encoding='utf-8') + report = {'contract': eval_mod.CONTRACT.kernel, 'contract_version': eval_mod.CONTRACT.contract_version, 'per_shape': candidate_rows} + baseline_report = {'contract': eval_mod.CONTRACT.kernel, 'contract_version': eval_mod.CONTRACT.contract_version, 'per_shape': baseline_rows} + trace = _annotate(route_trace_for_contract_shapes(labels), report, baseline_report) + ledger = _ledger(trace) + payload = {'candidate_id': CANDIDATE_ID, 'candidate_entrypoint': ROUTE_ENTRYPOINT, 'baseline_entrypoint': BASELINE_ENTRYPOINT, 'measured_entrypoint': BENCHMARK_ENTRYPOINT, 'measurement_mode': 'checkpointed_row_pair_same_process', 'contract_version': eval_mod.CONTRACT.contract_version, 'measured_shape_labels': labels, 'accelerated_shape_labels': TARGET_SHAPES, 'timing_backend': 'cupti' if use_cupti else 'cuda_event', 'timing_backends': ['cupti' if use_cupti else 'cuda_event'], 'route_trace': trace, 'route_trace_included': True, 'forced_fallback_route_trace': route_trace_for_contract_shapes(labels, force_fallback=True), 'flashlib_parity_ledger': ledger, 'performance_coverage': 'pass' if not ledger['rows_below_floor'] else 'partial', 'hot_bucket_blockers': ledger['rows_below_floor'], 'correctness': {'all_correct': all((r.get('passed') is True for r in candidate_rows.values())), 'shape_count': len(candidate_rows)}, 'report': report, 'baseline_report': baseline_report} + candidate_path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n', encoding='utf-8') + baseline_path.write_text(json.dumps({'measured_entrypoint': BASELINE_ENTRYPOINT, 'contract_version': eval_mod.CONTRACT.contract_version, 'timing_backend': payload['timing_backend'], 'report': baseline_report, 'route_trace': base.route_trace_for_contract_shapes(labels), 'route_trace_included': True}, indent=2, sort_keys=True) + '\n', encoding='utf-8') + trace_path.write_text(json.dumps(trace, indent=2, sort_keys=True) + '\n', encoding='utf-8') + progress_path.unlink(missing_ok=True) + return {'candidate_payload': str(candidate_path), 'same_session_baseline_payload': str(baseline_path), 'route_trace': str(trace_path)} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_q1m524_v3_d192_b10e_consumption_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_q1m524_v3_d192_b10e_consumption_v1.py new file mode 100644 index 00000000..04276560 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_q1m524_v3_d192_b10e_consumption_v1.py @@ -0,0 +1,160 @@ +"""Consume the exact D192 B200 seed in the exported Weave-only portfolio. + +Minimum target architecture: sm_100a. The first guard is deliberately exact: +BF16 build B=1/Q=2048/M=2048/D=192/K=10. It invokes b10e's validated +D256-padded TMA/tcgen05 producer and Weave split merge. All other inputs, +and every forced fallback, delegate to the existing e054 portfolio. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +from pathlib import Path +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_d192_tile_search_b10e_v1 as seed_b10e +from . import knn_build_dispatch_q1m524_v3_consumption_e054_v1 as base +from . import knn_build_dispatch_v11_common_d_seed_portfolio_a4ec_v1 as portfolio +MODULE = 'loom.examples.weave.knn_build_dispatch_q1m524_v3_d192_b10e_consumption_v1' +CANDIDATE_ID = 'q1m524_v3_d192_b10e_consumption_v1' +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +BENCHMARK_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_knn_build_dispatch_q1m524_v3_d192_b10e_consumption_v1']) +BASELINE_ENTRYPOINT = base.ROUTE_ENTRYPOINT +SEED_ID = 'd192_wide256_s8_b10e' +SEED_ENTRYPOINT = ''.join([format(seed_b10e.MODULE, ''), ':launch_from_contract_inputs']) +GUARD_ID = 'b10e_exact_bf16_build_b1_q2048_m2048_d192_k10' +TARGET_SHAPE = seed_b10e.TARGET_SHAPE +TARGET_SHAPES = (TARGET_SHAPE,) +SPEEDUP_FLOOR = base.SPEEDUP_FLOOR +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_dim_midk_df2f_d256_split_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 99584, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) + +def _eligible_b10e(inputs: dict[str, Any]) -> bool: + return seed_b10e._eligible(inputs) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if not force_fallback and _eligible_b10e(inputs): + return seed_b10e.route_for_contract_inputs(inputs) + return base.route_for_contract_inputs(inputs, force_fallback=force_fallback) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + if not force_fallback and _eligible_b10e(inputs): + seed_b10e.launch_from_contract_inputs(inputs) + return + base.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_baseline_e054(inputs: dict[str, Any]) -> None: + base.launch_from_contract_inputs(inputs) + +def _select_contract_shapes(shape_labels): + return base._select_contract_shapes(tuple(shape_labels)) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _run(*, use_cupti: bool, shape_labels, kernel_fn: Callable[[dict[str, Any]], Any]) -> dict[str, Any]: + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = previous + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False) -> list[dict[str, Any]]: + labels = tuple(shape_labels) if shape_labels is not None else tuple((s['label'] for s in eval_mod.CANONICAL_SHAPES)) + rows = [] + for shape in _select_contract_shapes(labels): + label = str(shape['label']) + if force_fallback or label != TARGET_SHAPE: + rows.append(base.route_trace_for_contract_shapes((label,), force_fallback=force_fallback)[0]) + continue + inputs = portfolio._trace_inputs_for_shape(shape) + assert _eligible_b10e(inputs), 'target label must satisfy the b10e exact guard' + replaced = base.route_trace_for_contract_shapes((label,))[0] + rows.append(portfolio._normalize_route_row({'shape_key': label, 'selected_route': route_for_contract_inputs(inputs), 'selected_entrypoint': SEED_ENTRYPOINT, 'selected_seed': SEED_ID, 'expected_seed': SEED_ID, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': GUARD_ID, 'guard_condition': 'exact BF16 build B=1 Q=2048 M=2048 D=192 K=10', 'replaced_route': replaced.get('selected_route'), 'classification': 'unmeasured'})) + return rows + +def _annotate(rows, candidate_report, baseline_report): + out = [] + for row in rows: + row = dict(row) + candidate_row = candidate_report.get('per_shape', {}).get(row['shape_key'], {}) + baseline_row = baseline_report.get('per_shape', {}).get(row['shape_key'], {}) + ms, base_ms = (candidate_row.get('kernel_ms'), baseline_row.get('kernel_ms')) + ratio = candidate_row.get('ratio_vs_flashlib') + row.update(dispatcher_kernel_ms=ms, baseline_dispatcher_kernel_ms=base_ms, relative_speedup_vs_baseline=base_ms / ms if ms and base_ms else None, speedup_vs_external_baseline=ratio, external_baseline_ms=candidate_row.get('flashlib_ms'), external_baseline_ref='same_session' if candidate_row.get('flashlib_ms') is not None else 'not_available', timing_backend=candidate_row.get('timing_backend') or baseline_row.get('timing_backend')) + if candidate_row.get('passed') is False: + row['classification'] = 'benchmark-path-mismatch' + elif row['shape_key'] == TARGET_SHAPE and isinstance(ratio, (int, float)) and (ratio >= SPEEDUP_FLOOR): + row['classification'] = 'seed-consumed' + elif isinstance(ratio, (int, float)) and ratio < SPEEDUP_FLOOR: + row['classification'] = 'kernel-slow' + else: + row['classification'] = 'route-ok' + out.append(portfolio._normalize_route_row(row)) + return out + +def _ledger(rows): + below_1x, below_floor = ([], []) + for row in rows: + ratio = row.get('speedup_vs_external_baseline') + if not isinstance(ratio, (int, float)): + continue + record = {key: row.get(key) for key in ('shape_key', 'selected_route', 'selected_seed', 'speedup_vs_external_baseline', 'classification')} + if ratio < 1.0: + below_1x.append(record) + if ratio < SPEEDUP_FLOOR: + below_floor.append(record) + return {'baseline_ref_scope': 'same_session', 'speedup_floor': SPEEDUP_FLOOR, 'rows_below_1x': below_1x, 'rows_below_floor': below_floor, 'omitted_reason': None} + +def benchmark_knn_build_dispatch_q1m524_v3_d192_b10e_consumption_v1(*, use_cupti: bool=True, shape_labels=None) -> dict[str, Any]: + labels = tuple(shape_labels) if shape_labels is not None else tuple((s['label'] for s in eval_mod.CANONICAL_SHAPES)) + report = _run(use_cupti=use_cupti, shape_labels=labels, kernel_fn=candidate) + baseline = _run(use_cupti=use_cupti, shape_labels=labels, kernel_fn=candidate_baseline_e054) + trace = _annotate(route_trace_for_contract_shapes(labels), report, baseline) + ledger = _ledger(trace) + return {'candidate_id': CANDIDATE_ID, 'candidate_entrypoint': ROUTE_ENTRYPOINT, 'baseline_entrypoint': BASELINE_ENTRYPOINT, 'measured_entrypoint': BENCHMARK_ENTRYPOINT, 'measured_shape_labels': labels, 'accelerated_shape_labels': TARGET_SHAPES, 'timing_backend': 'cupti' if use_cupti else 'cuda_event', 'timing_backends': ['cupti' if use_cupti else 'cuda_event'], 'route_trace': trace, 'route_trace_included': True, 'forced_fallback_route_trace': route_trace_for_contract_shapes(labels, force_fallback=True), 'flashlib_parity_ledger': ledger, 'performance_coverage': 'pass' if not ledger['rows_below_floor'] else 'partial', 'hot_bucket_blockers': ledger['rows_below_floor'], 'summary': report['summary'], 'performance': report['performance'], 'correctness': report['correctness'], 'report': report, 'baseline_summary': baseline['summary'], 'baseline_performance': baseline['performance'], 'baseline_report': baseline} + +def write_benchmark_artifacts(artifact_dir: str | Path, *, use_cupti: bool=True, shape_labels=None) -> dict[str, str]: + payload = benchmark_knn_build_dispatch_q1m524_v3_d192_b10e_consumption_v1(use_cupti=use_cupti, shape_labels=shape_labels) + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + denom = ''.join(['full', format(len(payload['measured_shape_labels']), '')]) + candidate_path = out_dir / ''.join([format(denom, ''), '_d192_b10e_candidate.json']) + baseline_path = out_dir / ''.join([format(denom, ''), '_e054_same_session_baseline.json']) + trace_path = out_dir / ''.join([format(denom, ''), '_d192_b10e_route_trace.json']) + candidate_path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n', encoding='utf-8') + baseline_path.write_text(json.dumps({'measured_entrypoint': BASELINE_ENTRYPOINT, 'timing_backend': payload['timing_backend'], 'report': payload['baseline_report'], 'route_trace': base.route_trace_for_contract_shapes(payload['measured_shape_labels']), 'route_trace_included': True}, indent=2, sort_keys=True) + '\n', encoding='utf-8') + trace_path.write_text(json.dumps(payload['route_trace'], indent=2, sort_keys=True) + '\n', encoding='utf-8') + return {'candidate_payload': str(candidate_path), 'same_session_baseline_payload': str(baseline_path), 'route_trace': str(trace_path)} + +def write_checkpointed_full_benchmark_artifacts(artifact_dir: str | Path, *, use_cupti: bool=True, shape_labels=None) -> dict[str, str]: + """Write each paired row before continuing, so interruptions remain diagnosable.""" + labels = tuple(shape_labels) if shape_labels is not None else tuple((s['label'] for s in eval_mod.CANONICAL_SHAPES)) + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + denom = ''.join(['full', format(len(labels), '')]) + progress_path = out_dir / ''.join([format(denom, ''), '_d192_b10e_progress.json']) + candidate_path = out_dir / ''.join([format(denom, ''), '_d192_b10e_candidate.json']) + baseline_path = out_dir / ''.join([format(denom, ''), '_e054_same_session_baseline.json']) + trace_path = out_dir / ''.join([format(denom, ''), '_d192_b10e_route_trace.json']) + candidate_rows, baseline_rows = ({}, {}) + for index, label in enumerate(labels, start=1): + candidate_row = _run(use_cupti=use_cupti, shape_labels=(label,), kernel_fn=candidate) + baseline_row = _run(use_cupti=use_cupti, shape_labels=(label,), kernel_fn=candidate_baseline_e054) + candidate_rows.update(candidate_row['per_shape']) + baseline_rows.update(baseline_row['per_shape']) + progress_path.write_text(json.dumps({'artifact_type': 'dispatcher-benchmark-progress', 'status': 'running', 'contract_version': eval_mod.CONTRACT.contract_version, 'timing_backend': 'cupti' if use_cupti else 'cuda_event', 'completed_shape_count': index, 'total_shape_count': len(labels), 'completed_labels': list(candidate_rows), 'candidate_per_shape': candidate_rows, 'baseline_per_shape': baseline_rows}, indent=2, sort_keys=True) + '\n', encoding='utf-8') + report = {'contract': eval_mod.CONTRACT.kernel, 'contract_version': eval_mod.CONTRACT.contract_version, 'per_shape': candidate_rows} + baseline_report = {'contract': eval_mod.CONTRACT.kernel, 'contract_version': eval_mod.CONTRACT.contract_version, 'per_shape': baseline_rows} + trace = _annotate(route_trace_for_contract_shapes(labels), report, baseline_report) + ledger = _ledger(trace) + payload = {'candidate_id': CANDIDATE_ID, 'candidate_entrypoint': ROUTE_ENTRYPOINT, 'baseline_entrypoint': BASELINE_ENTRYPOINT, 'measured_entrypoint': BENCHMARK_ENTRYPOINT, 'measurement_mode': 'checkpointed_row_pair_same_process', 'contract_version': eval_mod.CONTRACT.contract_version, 'measured_shape_labels': labels, 'accelerated_shape_labels': TARGET_SHAPES, 'timing_backend': 'cupti' if use_cupti else 'cuda_event', 'timing_backends': ['cupti' if use_cupti else 'cuda_event'], 'route_trace': trace, 'route_trace_included': True, 'forced_fallback_route_trace': route_trace_for_contract_shapes(labels, force_fallback=True), 'flashlib_parity_ledger': ledger, 'performance_coverage': 'pass' if not ledger['rows_below_floor'] else 'partial', 'hot_bucket_blockers': ledger['rows_below_floor'], 'correctness': {'all_correct': all((row.get('passed') is True for row in candidate_rows.values())), 'shape_count': len(candidate_rows)}, 'report': report, 'baseline_report': baseline_report} + candidate_path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n', encoding='utf-8') + baseline_path.write_text(json.dumps({'measured_entrypoint': BASELINE_ENTRYPOINT, 'contract_version': eval_mod.CONTRACT.contract_version, 'timing_backend': payload['timing_backend'], 'report': baseline_report, 'route_trace': base.route_trace_for_contract_shapes(labels), 'route_trace_included': True}, indent=2, sort_keys=True) + '\n', encoding='utf-8') + trace_path.write_text(json.dumps(trace, indent=2, sort_keys=True) + '\n', encoding='utf-8') + progress_path.unlink(missing_ok=True) + return {'candidate_payload': str(candidate_path), 'same_session_baseline_payload': str(baseline_path), 'route_trace': str(trace_path)} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_q1m524_v5_k1_s2_consumption_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_q1m524_v5_k1_s2_consumption_v1.py new file mode 100644 index 00000000..0dd8d738 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_q1m524_v5_k1_s2_consumption_v1.py @@ -0,0 +1,200 @@ +"""Consume the exact Q512 K1 two-split seed in the Weave-only portfolio. + +Minimum target architecture: sm_100a. The exact BF16 build guard routes only +``B=1, Q=M=512, D=128, K=1`` to the tcgen05-backed S2 seed; every other input +and forced fallback keeps the prior Weave dispatcher unchanged. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +from pathlib import Path +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_q1m524_v3_d192_b10e_consumption_v1 as prior +from . import knn_build_dispatch_v11_common_d_seed_portfolio_a4ec_v1 as portfolio +from . import knn_build_k1_q512_group2_root_v1 as seed_s2 +MODULE = 'loom.examples.weave.knn_build_dispatch_q1m524_v5_k1_s2_consumption_v1' +CANDIDATE_ID = 'q1m524_v5_k1_s2_consumption_v1' +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +BENCHMARK_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_knn_build_dispatch_q1m524_v5_k1_s2_consumption_v1']) +BASELINE_ENTRYPOINT = prior.ROUTE_ENTRYPOINT +SEED_ID = 'k1_q512_group2_573e' +SEED_ENTRYPOINT = ''.join([format(seed_s2.ROUTE_PREFIX, ''), ':launch_from_contract_inputs']) +GUARD_ID = '573e_exact_bf16_build_b1_q512_m512_d128_k1' +TARGET_SHAPE = seed_s2.TARGET_SHAPE +TARGET_SHAPES = (TARGET_SHAPE,) +SPEEDUP_FLOOR = prior.SPEEDUP_FLOOR +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_stage1_batch8_cond4_vmin_maxtree", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) + +def _contract_metric_schema(payload: dict[str, Any]) -> dict[str, Any]: + """Attach the contract-owned TFLOPS metric to an existing timing payload. + + The checkpointed full112 audit stores raw CUPTI timings row-by-row. This + helper derives the contract's primary metric from those immutable timings; + it never reruns a kernel or substitutes a seed-only measurement. + """ + repaired = json.loads(json.dumps(payload)) + params_by_label = {str(shape['label']): dict(shape['params']) for shape in eval_mod.CANONICAL_SHAPES} + rows = repaired.get('per_shape') + if not isinstance(rows, dict): + raise ValueError('full112 payload must contain per_shape mapping') + tflops_values: list[float] = [] + for label, row in rows.items(): + if not isinstance(row, dict): + raise ValueError(''.join(['full112 payload row ', format(repr(label), ''), ' must be a mapping'])) + params = params_by_label.get(label) + kernel_ms = row.get('kernel_ms') + if params is None or not isinstance(kernel_ms, (int, float)) or kernel_ms <= 0: + raise ValueError(''.join(['full112 payload row ', format(repr(label), ''), ' lacks contract params or kernel_ms'])) + flops = 2 * int(params['B']) * int(params['Q']) * int(params['M']) * int(params['D']) + tflops = flops / float(kernel_ms) / 1000000000.0 + row.update(params={key: params[key] for key in ('B', 'Q', 'M', 'D', 'K', 'dtype', 'build')}, tflops=tflops, qps=int(params['B']) * int(params['Q']) / (float(kernel_ms) / 1000.0), measurement_comparable=row.get('timing_backend') == 'cupti') + tflops_values.append(tflops) + if len(tflops_values) != len(params_by_label): + raise ValueError(''.join(['full112 payload has ', format(len(tflops_values), ''), ' timed rows, expected ', format(len(params_by_label), ''), ' contract rows'])) + primary_mean = sum(tflops_values) / len(tflops_values) + all_correct = repaired.get('correctness', {}).get('all_correct') is True + summary = dict(repaired.get('summary', {})) + summary.update(all_correct=all_correct, correctness_failure_count=0 if all_correct else None, correctness_shapes=len(rows), failed_correctness_shapes=0 if all_correct else None, primary_metric='tflops', primary_direction='maximize', primary_mean=primary_mean if all_correct else None, performance_comparable=all_correct, invalid_performance_reason=None if all_correct else 'correctness_not_passed') + repaired.update(artifact_type='full112-s2-s4-flashlib-variance-audit-contract-schema', contract=eval_mod.CONTRACT.kernel, contract_version=eval_mod.CONTRACT.contract_version, metric_schema={'primary': 'tflops', 'direction': 'maximize', 'flop_formula': '2 * B * Q * M * D', 'derivation': 'contract params and recorded candidate kernel_ms; no retiming'}, summary=summary, performance={'comparable': all_correct, 'primary_metric': 'tflops', 'primary_direction': 'maximize', 'primary_mean': primary_mean if all_correct else None, 'valid_measurement_count': len(tflops_values) if all_correct else 0, 'invalid_reason': None if all_correct else 'correctness_not_passed'}, rank_objective={'metric': 'tflops', 'direction': 'maximize', 'comparable': all_correct, 'value': primary_mean if all_correct else None, 'scope': repaired.get('denominator')}) + return repaired + +def repair_full112_payload_schema(input_path: str | Path, output_path: str | Path) -> dict[str, Any]: + """Write a contract-schema repair sidecar from a serialized full112 audit.""" + payload = json.loads(Path(input_path).read_text(encoding='utf-8')) + repaired = _contract_metric_schema(payload) + Path(output_path).parent.mkdir(parents=True, exist_ok=True) + Path(output_path).write_text(json.dumps(repaired, indent=2, sort_keys=True) + '\n', encoding='utf-8') + return repaired + +def _eligible_s2(inputs: dict[str, Any]) -> bool: + return seed_s2._eligible(inputs) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if not force_fallback and _eligible_s2(inputs): + return seed_s2.route_for_contract_inputs(inputs) + return prior.route_for_contract_inputs(inputs, force_fallback=force_fallback) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + if not force_fallback and _eligible_s2(inputs): + seed_s2.launch_from_contract_inputs(inputs) + return + prior.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_baseline_prior(inputs: dict[str, Any]) -> None: + prior.launch_from_contract_inputs(inputs) + +def _select_contract_shapes(shape_labels): + return prior._select_contract_shapes(tuple(shape_labels)) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _run(*, use_cupti: bool, shape_labels, kernel_fn: Callable[[dict[str, Any]], Any]) -> dict[str, Any]: + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = previous + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False) -> list[dict[str, Any]]: + labels = tuple(shape_labels) if shape_labels is not None else tuple((s['label'] for s in eval_mod.CANONICAL_SHAPES)) + rows = [] + for shape in _select_contract_shapes(labels): + label = str(shape['label']) + if force_fallback or label != TARGET_SHAPE: + rows.append(prior.route_trace_for_contract_shapes((label,), force_fallback=force_fallback)[0]) + continue + inputs = portfolio._trace_inputs_for_shape(shape) + assert _eligible_s2(inputs), 'target label must satisfy the S2 exact guard' + replaced = prior.route_trace_for_contract_shapes((label,))[0] + rows.append(portfolio._normalize_route_row({'shape_key': label, 'selected_route': route_for_contract_inputs(inputs), 'selected_entrypoint': SEED_ENTRYPOINT, 'selected_seed': SEED_ID, 'expected_seed': SEED_ID, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': GUARD_ID, 'guard_condition': 'exact BF16 build B=1 Q=512 M=512 D=128 K=1', 'replaced_route': replaced.get('selected_route'), 'classification': 'unmeasured'})) + return rows + +def _annotate(rows, candidate_report, baseline_report): + out = [] + for row in rows: + row = dict(row) + candidate_row = candidate_report.get('per_shape', {}).get(row['shape_key'], {}) + baseline_row = baseline_report.get('per_shape', {}).get(row['shape_key'], {}) + ms, base_ms = (candidate_row.get('kernel_ms'), baseline_row.get('kernel_ms')) + ratio = candidate_row.get('ratio_vs_flashlib') + row.update(dispatcher_kernel_ms=ms, baseline_dispatcher_kernel_ms=base_ms, relative_speedup_vs_baseline=base_ms / ms if ms and base_ms else None, speedup_vs_external_baseline=ratio, external_baseline_ms=candidate_row.get('flashlib_ms'), external_baseline_ref='same_session' if candidate_row.get('flashlib_ms') is not None else 'not_available', timing_backend=candidate_row.get('timing_backend') or baseline_row.get('timing_backend')) + if candidate_row.get('passed') is False: + row['classification'] = 'benchmark-path-mismatch' + elif row['shape_key'] == TARGET_SHAPE and isinstance(ratio, (int, float)) and (ratio >= SPEEDUP_FLOOR): + row['classification'] = 'seed-consumed' + elif isinstance(ratio, (int, float)) and ratio < SPEEDUP_FLOOR: + row['classification'] = 'kernel-slow' + else: + row['classification'] = 'route-ok' + out.append(portfolio._normalize_route_row(row)) + return out + +def _ledger(rows): + below_1x, below_floor = ([], []) + for row in rows: + ratio = row.get('speedup_vs_external_baseline') + if not isinstance(ratio, (int, float)): + continue + record = {key: row.get(key) for key in ('shape_key', 'selected_route', 'selected_seed', 'speedup_vs_external_baseline', 'classification')} + if ratio < 1.0: + below_1x.append(record) + if ratio < SPEEDUP_FLOOR: + below_floor.append(record) + return {'baseline_ref_scope': 'same_session', 'speedup_floor': SPEEDUP_FLOOR, 'rows_below_1x': below_1x, 'rows_below_floor': below_floor, 'omitted_reason': None} + +def benchmark_knn_build_dispatch_q1m524_v5_k1_s2_consumption_v1(*, use_cupti: bool=True, shape_labels=None) -> dict[str, Any]: + labels = tuple(shape_labels) if shape_labels is not None else tuple((s['label'] for s in eval_mod.CANONICAL_SHAPES)) + report = _run(use_cupti=use_cupti, shape_labels=labels, kernel_fn=candidate) + baseline = _run(use_cupti=use_cupti, shape_labels=labels, kernel_fn=candidate_baseline_prior) + trace = _annotate(route_trace_for_contract_shapes(labels), report, baseline) + ledger = _ledger(trace) + return {'candidate_id': CANDIDATE_ID, 'candidate_entrypoint': ROUTE_ENTRYPOINT, 'baseline_entrypoint': BASELINE_ENTRYPOINT, 'measured_entrypoint': BENCHMARK_ENTRYPOINT, 'measured_shape_labels': labels, 'accelerated_shape_labels': TARGET_SHAPES, 'timing_backend': 'cupti' if use_cupti else 'cuda_event', 'timing_backends': ['cupti' if use_cupti else 'cuda_event'], 'route_trace': trace, 'route_trace_included': True, 'forced_fallback_route_trace': route_trace_for_contract_shapes(labels, force_fallback=True), 'flashlib_parity_ledger': ledger, 'performance_coverage': 'pass' if not ledger['rows_below_floor'] else 'partial', 'hot_bucket_blockers': ledger['rows_below_floor'], 'summary': report['summary'], 'performance': report['performance'], 'correctness': report['correctness'], 'report': report, 'baseline_summary': baseline['summary'], 'baseline_performance': baseline['performance'], 'baseline_report': baseline} + +def write_benchmark_artifacts(artifact_dir: str | Path, *, use_cupti: bool=True, shape_labels=None) -> dict[str, str]: + payload = benchmark_knn_build_dispatch_q1m524_v5_k1_s2_consumption_v1(use_cupti=use_cupti, shape_labels=shape_labels) + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + denom = ''.join(['full', format(len(payload['measured_shape_labels']), '')]) + candidate_path = out_dir / ''.join([format(denom, ''), '_k1_s2_candidate.json']) + baseline_path = out_dir / ''.join([format(denom, ''), '_prior_same_session_baseline.json']) + trace_path = out_dir / ''.join([format(denom, ''), '_k1_s2_route_trace.json']) + candidate_path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n', encoding='utf-8') + baseline_path.write_text(json.dumps({'measured_entrypoint': BASELINE_ENTRYPOINT, 'timing_backend': payload['timing_backend'], 'report': payload['baseline_report'], 'route_trace': prior.route_trace_for_contract_shapes(payload['measured_shape_labels']), 'route_trace_included': True}, indent=2, sort_keys=True) + '\n', encoding='utf-8') + trace_path.write_text(json.dumps(payload['route_trace'], indent=2, sort_keys=True) + '\n', encoding='utf-8') + return {'candidate_payload': str(candidate_path), 'same_session_baseline_payload': str(baseline_path), 'route_trace': str(trace_path)} + +def write_checkpointed_full_benchmark_artifacts(artifact_dir: str | Path, *, use_cupti: bool=True, shape_labels=None) -> dict[str, str]: + """Persist each same-session row pair; avoid retaining all full112 tensors.""" + labels = tuple(shape_labels) if shape_labels is not None else tuple((s['label'] for s in eval_mod.CANONICAL_SHAPES)) + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + denom = ''.join(['full', format(len(labels), '')]) + progress_path = out_dir / ''.join([format(denom, ''), '_k1_s2_progress.json']) + candidate_path = out_dir / ''.join([format(denom, ''), '_k1_s2_candidate.json']) + baseline_path = out_dir / ''.join([format(denom, ''), '_prior_same_session_baseline.json']) + trace_path = out_dir / ''.join([format(denom, ''), '_k1_s2_route_trace.json']) + candidate_rows, baseline_rows = ({}, {}) + for index, label in enumerate(labels, start=1): + candidate_row = _run(use_cupti=use_cupti, shape_labels=(label,), kernel_fn=candidate) + baseline_row = _run(use_cupti=use_cupti, shape_labels=(label,), kernel_fn=candidate_baseline_prior) + candidate_rows.update(candidate_row['per_shape']) + baseline_rows.update(baseline_row['per_shape']) + progress_path.write_text(json.dumps({'artifact_type': 'dispatcher-benchmark-progress', 'status': 'running', 'contract_version': eval_mod.CONTRACT.contract_version, 'timing_backend': 'cupti' if use_cupti else 'cuda_event', 'completed_shape_count': index, 'total_shape_count': len(labels), 'completed_labels': list(candidate_rows), 'candidate_per_shape': candidate_rows, 'baseline_per_shape': baseline_rows}, indent=2, sort_keys=True) + '\n', encoding='utf-8') + report = {'contract': eval_mod.CONTRACT.kernel, 'contract_version': eval_mod.CONTRACT.contract_version, 'per_shape': candidate_rows} + baseline_report = {'contract': eval_mod.CONTRACT.kernel, 'contract_version': eval_mod.CONTRACT.contract_version, 'per_shape': baseline_rows} + trace = _annotate(route_trace_for_contract_shapes(labels), report, baseline_report) + ledger = _ledger(trace) + payload = {'candidate_id': CANDIDATE_ID, 'candidate_entrypoint': ROUTE_ENTRYPOINT, 'baseline_entrypoint': BASELINE_ENTRYPOINT, 'measured_entrypoint': BENCHMARK_ENTRYPOINT, 'measurement_mode': 'checkpointed_row_pair_same_process', 'contract_version': eval_mod.CONTRACT.contract_version, 'measured_shape_labels': labels, 'accelerated_shape_labels': TARGET_SHAPES, 'timing_backend': 'cupti' if use_cupti else 'cuda_event', 'timing_backends': ['cupti' if use_cupti else 'cuda_event'], 'route_trace': trace, 'route_trace_included': True, 'forced_fallback_route_trace': route_trace_for_contract_shapes(labels, force_fallback=True), 'flashlib_parity_ledger': ledger, 'performance_coverage': 'pass' if not ledger['rows_below_floor'] else 'partial', 'hot_bucket_blockers': ledger['rows_below_floor'], 'correctness': {'all_correct': all((row.get('passed') is True for row in candidate_rows.values())), 'shape_count': len(candidate_rows)}, 'report': report, 'baseline_report': baseline_report} + candidate_path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n', encoding='utf-8') + baseline_path.write_text(json.dumps({'measured_entrypoint': BASELINE_ENTRYPOINT, 'contract_version': eval_mod.CONTRACT.contract_version, 'timing_backend': payload['timing_backend'], 'report': baseline_report, 'route_trace': prior.route_trace_for_contract_shapes(labels), 'route_trace_included': True}, indent=2, sort_keys=True) + '\n', encoding='utf-8') + trace_path.write_text(json.dumps(trace, indent=2, sort_keys=True) + '\n', encoding='utf-8') + progress_path.unlink(missing_ok=True) + return {'candidate_payload': str(candidate_path), 'same_session_baseline_payload': str(baseline_path), 'route_trace': str(trace_path)} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_q1m524_v6_d256_d320_synthesis_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_q1m524_v6_d256_d320_synthesis_v1.py new file mode 100644 index 00000000..4c1771ac --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_q1m524_v6_d256_d320_synthesis_v1.py @@ -0,0 +1,161 @@ +"""Synthesize exact D256/D320 tcgen05 seeds into a Weave-only full112 portfolio. + +Minimum target architecture: sm_100a. The two new guards are intentionally +exact contract rows: D256/K32 uses the split-64 seed and D320/K10 uses the +BLOCK_K=384 seed. All other rows delegate to the exported v5 Weave dispatcher. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +from collections.abc import Callable +from pathlib import Path +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_d256_tail_tiles_b21e_v1 as d256_seed +from . import knn_build_d320_blockk_b21e_v1 as d320_seed +from . import knn_build_dispatch_q1m524_v5_k1_s2_consumption_v1 as base +from . import knn_build_dispatch_v11_common_d_seed_portfolio_a4ec_v1 as portfolio +MODULE = 'loom.examples.weave.knn_build_dispatch_q1m524_v6_d256_d320_synthesis_v1' +CANDIDATE_ID = 'q1m524_v6_d256_d320_synthesis_v1' +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +BENCHMARK_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_knn_build_dispatch_q1m524_v6_d256_d320_synthesis_v1']) +BASELINE_ENTRYPOINT = base.ROUTE_ENTRYPOINT +D256_SEED_ID = 'd256_tail_tiles_b21e_split64_426c' +D320_SEED_ID = 'd320_blockk384_b21e_f0e0' +D256_GUARD_ID = '426c_exact_bf16_search_b1_q128_m100000_d256_k32' +D320_GUARD_ID = 'f0e0_exact_bf16_search_b1_q512_m12000_d320_k10_blockk384' +TARGET_SHAPES = (d256_seed.TARGET_SHAPE, d320_seed.TARGET_SHAPE) +SPEEDUP_FLOOR = base.SPEEDUP_FLOOR + +def _portfolio_name(portfolio_name: str) -> str: + if portfolio_name not in {'base', 'd256', 'd256_d320'}: + raise ValueError(''.join(['unknown portfolio ', format(repr(portfolio_name), '')])) + return portfolio_name + +def _eligible_d256(inputs: dict[str, Any]) -> bool: + return d256_seed._eligible(inputs) + +def _eligible_d320(inputs: dict[str, Any]) -> bool: + return d320_seed._is_target(inputs) + +def route_for_contract_inputs(inputs: dict[str, Any], *, portfolio_name: str='d256_d320', force_fallback: bool=False) -> str: + name = _portfolio_name(portfolio_name) + if not force_fallback and name in {'d256', 'd256_d320'} and _eligible_d256(inputs): + return d256_seed.route_for_contract_inputs(inputs) + if not force_fallback and name == 'd256_d320' and _eligible_d320(inputs): + return d320_seed.route_for_contract_inputs(inputs) + return base.route_for_contract_inputs(inputs, force_fallback=force_fallback) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, portfolio_name: str='d256_d320', force_fallback: bool=False) -> None: + name = _portfolio_name(portfolio_name) + if not force_fallback and name in {'d256', 'd256_d320'} and _eligible_d256(inputs): + d256_seed.launch_from_contract_inputs(inputs) + return + if not force_fallback and name == 'd256_d320' and _eligible_d320(inputs): + d320_seed.launch_from_contract_inputs(inputs) + return + base.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + +def _candidate(portfolio_name: str) -> Callable[[dict[str, Any]], None]: + return lambda inputs: launch_from_contract_inputs(inputs, portfolio_name=portfolio_name) + +def _select_contract_shapes(shape_labels): + return base._select_contract_shapes(tuple(shape_labels)) + +def _run(*, use_cupti: bool, shape_labels, kernel_fn: Callable[[dict[str, Any]], Any]) -> dict[str, Any]: + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + try: + return eval_mod.evaluate(kernel_fn, shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = previous + +def route_trace_for_contract_shapes(shape_labels=None, *, portfolio_name: str='d256_d320', force_fallback: bool=False) -> list[dict[str, Any]]: + labels = tuple(shape_labels) if shape_labels is not None else tuple((s['label'] for s in eval_mod.CANONICAL_SHAPES)) + rows = [] + for shape in _select_contract_shapes(labels): + label = str(shape['label']) + inputs = portfolio._trace_inputs_for_shape(shape) + if not force_fallback and portfolio_name in {'d256', 'd256_d320'} and _eligible_d256(inputs): + rows.append(portfolio._normalize_route_row({'shape_key': label, 'selected_route': route_for_contract_inputs(inputs, portfolio_name=portfolio_name), 'selected_entrypoint': d256_seed.ROUTE_ENTRYPOINT, 'selected_seed': D256_SEED_ID, 'expected_seed': D256_SEED_ID, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': D256_GUARD_ID, 'guard_condition': 'exact BF16 search B=1 Q=128 M=100000 D=256 K=32', 'replaced_route': base.route_for_contract_inputs(inputs), 'classification': 'unmeasured'})) + elif not force_fallback and portfolio_name == 'd256_d320' and _eligible_d320(inputs): + rows.append(portfolio._normalize_route_row({'shape_key': label, 'selected_route': route_for_contract_inputs(inputs, portfolio_name=portfolio_name), 'selected_entrypoint': d320_seed.MODULE + ':launch_from_contract_inputs', 'selected_seed': D320_SEED_ID, 'expected_seed': D320_SEED_ID, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': D320_GUARD_ID, 'guard_condition': 'exact BF16 search B=1 Q=512 M=12000 D=320 K=10 BLOCK_K=384', 'replaced_route': base.route_for_contract_inputs(inputs), 'classification': 'unmeasured'})) + else: + rows.append(base.route_trace_for_contract_shapes((label,), force_fallback=force_fallback)[0]) + return rows + +def _annotate(rows, candidate_report, baseline_report): + out = [] + for row in rows: + row = dict(row) + candidate_row = candidate_report.get('per_shape', {}).get(row['shape_key'], {}) + baseline_row = baseline_report.get('per_shape', {}).get(row['shape_key'], {}) + ms, base_ms, ratio = (candidate_row.get('kernel_ms'), baseline_row.get('kernel_ms'), candidate_row.get('ratio_vs_flashlib')) + row.update(dispatcher_kernel_ms=ms, baseline_dispatcher_kernel_ms=base_ms, relative_speedup_vs_baseline=base_ms / ms if ms and base_ms else None, speedup_vs_external_baseline=ratio, external_baseline_ms=candidate_row.get('flashlib_ms'), external_baseline_ref='same_session' if candidate_row.get('flashlib_ms') is not None else 'not_available', timing_backend=candidate_row.get('timing_backend') or baseline_row.get('timing_backend')) + if candidate_row.get('passed') is False: + row['classification'] = 'benchmark-path-mismatch' + elif row.get('selected_seed') in {D256_SEED_ID, D320_SEED_ID} and isinstance(ratio, (int, float)) and (ratio >= SPEEDUP_FLOOR): + row['classification'] = 'seed-consumed' + elif isinstance(ratio, (int, float)) and ratio < SPEEDUP_FLOOR: + row['classification'] = 'kernel-slow' + else: + row['classification'] = 'route-ok' + out.append(portfolio._normalize_route_row(row)) + return out + +def _ledger(rows): + below_1x, below_floor = ([], []) + for row in rows: + ratio = row.get('speedup_vs_external_baseline') + if isinstance(ratio, (int, float)): + record = {key: row.get(key) for key in ('shape_key', 'selected_route', 'selected_seed', 'speedup_vs_external_baseline', 'classification')} + if ratio < 1.0: + below_1x.append(record) + if ratio < SPEEDUP_FLOOR: + below_floor.append(record) + return {'baseline_ref_scope': 'same_session', 'speedup_floor': SPEEDUP_FLOOR, 'rows_below_1x': below_1x, 'rows_below_floor': below_floor, 'omitted_reason': None} + +def benchmark_knn_build_dispatch_q1m524_v6_d256_d320_synthesis_v1(*, use_cupti: bool=True, shape_labels=None, portfolio_name: str='d256_d320') -> dict[str, Any]: + labels = tuple(shape_labels) if shape_labels is not None else tuple((s['label'] for s in eval_mod.CANONICAL_SHAPES)) + name = _portfolio_name(portfolio_name) + report = _run(use_cupti=use_cupti, shape_labels=labels, kernel_fn=_candidate(name)) + baseline = _run(use_cupti=use_cupti, shape_labels=labels, kernel_fn=base.candidate) + trace = _annotate(route_trace_for_contract_shapes(labels, portfolio_name=name), report, baseline) + ledger = _ledger(trace) + return {'candidate_id': CANDIDATE_ID + ':' + name, 'candidate_entrypoint': ROUTE_ENTRYPOINT, 'baseline_entrypoint': BASELINE_ENTRYPOINT, 'measured_entrypoint': BENCHMARK_ENTRYPOINT, 'portfolio_name': name, 'measured_shape_labels': labels, 'accelerated_shape_labels': TARGET_SHAPES, 'timing_backend': 'cupti' if use_cupti else 'cuda_event', 'route_trace': trace, 'route_trace_included': True, 'forced_fallback_route_trace': route_trace_for_contract_shapes(labels, portfolio_name=name, force_fallback=True), 'flashlib_parity_ledger': ledger, 'performance_coverage': 'pass' if not ledger['rows_below_floor'] else 'partial', 'hot_bucket_blockers': ledger['rows_below_floor'], 'summary': report['summary'], 'performance': report['performance'], 'correctness': report['correctness'], 'report': report, 'baseline_summary': baseline['summary'], 'baseline_performance': baseline['performance'], 'baseline_report': baseline} + +def write_checkpointed_full_benchmark_artifacts(artifact_dir: str | Path, *, portfolio_name: str='d256_d320', use_cupti: bool=True, shape_labels=None, max_new_rows: int | None=None) -> dict[str, Any]: + labels = tuple(shape_labels) if shape_labels is not None else tuple((s['label'] for s in eval_mod.CANONICAL_SHAPES)) + name = _portfolio_name(portfolio_name) + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + shapes = {str(shape['label']): shape for shape in _select_contract_shapes(labels)} + checkpoint_path = out_dir / ''.join(['full', format(len(labels), ''), '_', format(name, ''), '_progress.json']) + audit = ResumableRowAudit(checkpoint_path, audit_id=''.join([format(CANDIDATE_ID, ''), ':', format(name, ''), ':paired-v1']), labels=labels, metadata={'contract_version': eval_mod.CONTRACT.contract_version, 'timing_backend': 'cupti' if use_cupti else 'cuda_event', 'candidate_entrypoint': ROUTE_ENTRYPOINT, 'baseline_entrypoint': BASELINE_ENTRYPOINT}) + with healthy_gpu_bench_session(require_cupti=use_cupti) as preflight: + state = audit.run(lambda label: eval_mod.evaluate_paired_row(_candidate(name), base.candidate, shapes[label], use_cupti=use_cupti, order_seed=626320), max_new_rows=max_new_rows) + if state['status'] != 'complete': + return {'status': 'running', 'completed': state['completed'], 'total': len(labels), 'checkpoint': str(checkpoint_path), 'gpu_preflight': preflight} + candidate_rows = {row['shape_key']: row['candidate'] for row in state['rows']} + baseline_rows = {row['shape_key']: row['baseline'] for row in state['rows']} + report = {'contract': eval_mod.CONTRACT.kernel, 'contract_version': eval_mod.CONTRACT.contract_version, 'per_shape': candidate_rows} + baseline_report = {'contract': eval_mod.CONTRACT.kernel, 'contract_version': eval_mod.CONTRACT.contract_version, 'per_shape': baseline_rows} + trace = _annotate(route_trace_for_contract_shapes(labels, portfolio_name=name), report, baseline_report) + payload = {'candidate_id': CANDIDATE_ID + ':' + name, 'candidate_entrypoint': ROUTE_ENTRYPOINT, 'baseline_entrypoint': BASELINE_ENTRYPOINT, 'measured_entrypoint': BENCHMARK_ENTRYPOINT, 'portfolio_name': name, 'measurement_mode': 'checkpointed_row_pair_same_process', 'contract_version': eval_mod.CONTRACT.contract_version, 'measured_shape_labels': labels, 'accelerated_shape_labels': TARGET_SHAPES, 'timing_backend': 'cupti' if use_cupti else 'cuda_event', 'route_trace': trace, 'route_trace_included': True, 'flashlib_parity_ledger': _ledger(trace), 'correctness': {'all_correct': all((row.get('passed') is True for row in candidate_rows.values())), 'shape_count': len(candidate_rows)}, 'report': report, 'baseline_report': baseline_report, 'randomized_shared_input_triads': state['rows'], 'gpu_preflight': preflight, 'checkpoint': str(checkpoint_path)} + denom = ''.join(['full', format(len(labels), ''), '_', format(name, '')]) + candidate_path = out_dir / ''.join([format(denom, ''), '_candidate.json']) + baseline_path = out_dir / ''.join([format(denom, ''), '_v5_same_session_baseline.json']) + trace_path = out_dir / ''.join([format(denom, ''), '_route_trace.json']) + atomic_write_json(candidate_path, payload) + atomic_write_json(baseline_path, {'measured_entrypoint': BASELINE_ENTRYPOINT, 'timing_backend': payload['timing_backend'], 'report': baseline_report, 'route_trace': base.route_trace_for_contract_shapes(labels), 'route_trace_included': True}) + atomic_write_json(trace_path, trace) + return {'candidate_payload': str(candidate_path), 'same_session_baseline_payload': str(baseline_path), 'route_trace': str(trace_path)} + +def delta_labels_from_payload(path: str | Path) -> tuple[str, ...]: + payload = json.loads(Path(path).read_text(encoding='utf-8')) + rows = payload.get('route_trace') or payload.get('randomized_shared_input_triads') + if not isinstance(rows, list): + raise ValueError('delta discovery payload must contain route_trace or randomized_shared_input_triads') + return select_delta_labels(rows, changed_labels=TARGET_SHAPES, speedup_floor=SPEEDUP_FLOOR) diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_q1m524_v7_d128m100k_consumption_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_q1m524_v7_d128m100k_consumption_v1.py new file mode 100644 index 00000000..add24ae4 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_q1m524_v7_d128m100k_consumption_v1.py @@ -0,0 +1,138 @@ +"""Consume the exact static-N128 D128/M100000/K32 seed into the v6 portfolio. + +Minimum target architecture: sm_100a. The only new guard is the exact BF16 +non-build contract row B=1, Q=128, M=100000, D=128, K=32. All other rows +delegate to v6 and, therefore, its existing Weave-only portfolio/fallback. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +from collections.abc import Callable +from pathlib import Path +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_q1m524_v6_d256_d320_synthesis_v1 as base +from . import knn_build_dispatch_v11_common_d_seed_portfolio_a4ec_v1 as portfolio +from . import knn_build_rag_stream_k32_q128m100000_staticn128_664a_v1 as seed +MODULE = 'loom.examples.weave.knn_build_dispatch_q1m524_v7_d128m100k_consumption_v1' +CANDIDATE_ID = 'q1m524_v7_d128m100k_consumption_v1' +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +BENCHMARK_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_knn_build_dispatch_q1m524_v7_d128m100k_consumption_v1']) +BASELINE_ENTRYPOINT = base.ROUTE_ENTRYPOINT +SEED_ID = 'staticn128_664a' +GUARD_ID = '664a_exact_bf16_nonbuild_b1_q128_m100000_d128_k32' +TARGET_SHAPE = seed.TARGET_SHAPE +TARGET_SHAPES = (TARGET_SHAPE,) +SPEEDUP_FLOOR = base.SPEEDUP_FLOOR + +def _eligible(inputs: dict[str, Any]) -> bool: + return seed._target_label_for_inputs(inputs) == TARGET_SHAPE + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if not force_fallback and _eligible(inputs): + return seed.route_for_contract_inputs(inputs) + return base.route_for_contract_inputs(inputs, force_fallback=force_fallback) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + if not force_fallback and _eligible(inputs): + seed.launch_from_contract_inputs(inputs) + return + base.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + +def _candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def _select_contract_shapes(shape_labels): + return base._select_contract_shapes(tuple(shape_labels)) + +def _run(*, use_cupti: bool, shape_labels, kernel_fn: Callable[[dict[str, Any]], Any]) -> dict[str, Any]: + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + try: + return eval_mod.evaluate(kernel_fn, shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = previous + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False) -> list[dict[str, Any]]: + labels = tuple(shape_labels) if shape_labels is not None else tuple((s['label'] for s in eval_mod.CANONICAL_SHAPES)) + rows = [] + for shape in _select_contract_shapes(labels): + label = str(shape['label']) + inputs = portfolio._trace_inputs_for_shape(shape) + if not force_fallback and _eligible(inputs): + rows.append(portfolio._normalize_route_row({'shape_key': label, 'selected_route': route_for_contract_inputs(inputs), 'selected_entrypoint': seed.ROUTE_ENTRYPOINT, 'selected_seed': SEED_ID, 'expected_seed': SEED_ID, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': GUARD_ID, 'guard_condition': 'exact BF16 non-build B=1 Q=128 M=100000 D=128 K=32', 'replaced_route': base.route_for_contract_inputs(inputs), 'classification': 'unmeasured'})) + else: + rows.append(base.route_trace_for_contract_shapes((label,), force_fallback=force_fallback)[0]) + return rows + +def _annotate(rows, candidate_report, baseline_report): + out = [] + for row in rows: + row = dict(row) + candidate_row = candidate_report.get('per_shape', {}).get(row['shape_key'], {}) + baseline_row = baseline_report.get('per_shape', {}).get(row['shape_key'], {}) + ms, base_ms, ratio = (candidate_row.get('kernel_ms'), baseline_row.get('kernel_ms'), candidate_row.get('ratio_vs_flashlib')) + row.update(dispatcher_kernel_ms=ms, baseline_dispatcher_kernel_ms=base_ms, relative_speedup_vs_baseline=base_ms / ms if ms and base_ms else None, speedup_vs_external_baseline=ratio, external_baseline_ms=candidate_row.get('flashlib_ms'), external_baseline_ref='same_session' if candidate_row.get('flashlib_ms') is not None else 'not_available', timing_backend=candidate_row.get('timing_backend') or baseline_row.get('timing_backend')) + if candidate_row.get('passed') is False: + row['classification'] = 'benchmark-path-mismatch' + elif row.get('selected_seed') == SEED_ID and isinstance(ratio, (int, float)) and (ratio >= SPEEDUP_FLOOR): + row['classification'] = 'seed-consumed' + elif isinstance(ratio, (int, float)) and ratio < SPEEDUP_FLOOR: + row['classification'] = 'kernel-slow' + else: + row['classification'] = 'route-ok' + out.append(portfolio._normalize_route_row(row)) + return out + +def _ledger(rows): + below_1x, below_floor = ([], []) + for row in rows: + ratio = row.get('speedup_vs_external_baseline') + if isinstance(ratio, (int, float)): + record = {key: row.get(key) for key in ('shape_key', 'selected_route', 'selected_seed', 'speedup_vs_external_baseline', 'classification')} + if ratio < 1.0: + below_1x.append(record) + if ratio < SPEEDUP_FLOOR: + below_floor.append(record) + return {'baseline_ref_scope': 'same_session', 'speedup_floor': SPEEDUP_FLOOR, 'rows_below_1x': below_1x, 'rows_below_floor': below_floor, 'omitted_reason': None} + +def write_checkpointed_full_benchmark_artifacts(artifact_dir: str | Path, *, use_cupti: bool=True, shape_labels=None, max_new_rows: int | None=None) -> dict[str, Any]: + labels = tuple(shape_labels) if shape_labels is not None else tuple((s['label'] for s in eval_mod.CANONICAL_SHAPES)) + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + shapes = {str(shape['label']): shape for shape in _select_contract_shapes(labels)} + checkpoint_path = out_dir / ''.join(['full', format(len(labels), ''), '_v7_d128m100k_paired_v2_progress.json']) + audit = ResumableRowAudit(checkpoint_path, audit_id=''.join([format(CANDIDATE_ID, ''), ':paired-v1']), labels=labels, metadata={'contract_version': eval_mod.CONTRACT.contract_version, 'timing_backend': 'cupti' if use_cupti else 'cuda_event', 'candidate_entrypoint': ROUTE_ENTRYPOINT, 'baseline_entrypoint': BASELINE_ENTRYPOINT}) + with healthy_gpu_bench_session(require_cupti=use_cupti) as preflight: + state = audit.run(lambda label: eval_mod.evaluate_paired_row(_candidate, base._candidate('d256_d320'), shapes[label], use_cupti=use_cupti, order_seed=7128100), max_new_rows=max_new_rows) + if state['status'] != 'complete': + return {'status': 'running', 'completed': state['completed'], 'total': len(labels), 'checkpoint': str(checkpoint_path), 'gpu_preflight': preflight} + candidate_rows = {row['shape_key']: row['candidate'] for row in state['rows']} + baseline_rows = {row['shape_key']: row['baseline'] for row in state['rows']} + report = {'contract': eval_mod.CONTRACT.kernel, 'contract_version': eval_mod.CONTRACT.contract_version, 'per_shape': candidate_rows} + baseline_report = {'contract': eval_mod.CONTRACT.kernel, 'contract_version': eval_mod.CONTRACT.contract_version, 'per_shape': baseline_rows} + trace = _annotate(route_trace_for_contract_shapes(labels), report, baseline_report) + payload = {'candidate_id': CANDIDATE_ID, 'candidate_entrypoint': ROUTE_ENTRYPOINT, 'baseline_entrypoint': BASELINE_ENTRYPOINT, 'measured_entrypoint': BENCHMARK_ENTRYPOINT, 'measurement_mode': 'checkpointed_row_pair_same_process', 'contract_version': eval_mod.CONTRACT.contract_version, 'measured_shape_labels': labels, 'accelerated_shape_labels': TARGET_SHAPES, 'timing_backend': 'cupti' if use_cupti else 'cuda_event', 'route_trace': trace, 'route_trace_included': True, 'flashlib_parity_ledger': _ledger(trace), 'correctness': {'all_correct': all((row.get('passed') is True for row in candidate_rows.values())), 'shape_count': len(candidate_rows)}, 'report': report, 'baseline_report': baseline_report, 'randomized_shared_input_triads': state['rows'], 'gpu_preflight': preflight, 'checkpoint': str(checkpoint_path)} + candidate_path = out_dir / 'full112_v7_d128m100k_candidate.json' + baseline_path = out_dir / 'full112_v6_same_session_baseline.json' + trace_path = out_dir / 'full112_v7_d128m100k_route_trace.json' + atomic_write_json(candidate_path, payload) + atomic_write_json(baseline_path, {'measured_entrypoint': BASELINE_ENTRYPOINT, 'timing_backend': payload['timing_backend'], 'report': baseline_report, 'route_trace': base.route_trace_for_contract_shapes(labels), 'route_trace_included': True}) + atomic_write_json(trace_path, trace) + return {'candidate_payload': str(candidate_path), 'same_session_baseline_payload': str(baseline_path), 'route_trace': str(trace_path)} + +def delta_labels_from_payload(path: str | Path) -> tuple[str, ...]: + payload = json.loads(Path(path).read_text(encoding='utf-8')) + rows = payload.get('route_trace') or payload.get('randomized_shared_input_triads') + if not isinstance(rows, list): + raise ValueError('delta discovery payload must contain route_trace or randomized_shared_input_triads') + return select_delta_labels(rows, changed_labels=TARGET_SHAPES, speedup_floor=SPEEDUP_FLOOR) + +def benchmark_knn_build_dispatch_q1m524_v7_d128m100k_consumption_v1(*, use_cupti: bool=True, shape_labels=None) -> dict[str, Any]: + labels = tuple(shape_labels) if shape_labels is not None else tuple((s['label'] for s in eval_mod.CANONICAL_SHAPES)) + report = _run(use_cupti=use_cupti, shape_labels=labels, kernel_fn=_candidate) + baseline = _run(use_cupti=use_cupti, shape_labels=labels, kernel_fn=base._candidate('d256_d320')) + trace = _annotate(route_trace_for_contract_shapes(labels), report, baseline) + return {'candidate_id': CANDIDATE_ID, 'candidate_entrypoint': ROUTE_ENTRYPOINT, 'baseline_entrypoint': BASELINE_ENTRYPOINT, 'measured_entrypoint': BENCHMARK_ENTRYPOINT, 'measured_shape_labels': labels, 'timing_backend': 'cupti' if use_cupti else 'cuda_event', 'route_trace': trace, 'route_trace_included': True, 'flashlib_parity_ledger': _ledger(trace), 'correctness': report['correctness'], 'summary': report['summary'], 'report': report, 'baseline_report': baseline} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_q1m524_v8_q32s141_consumption_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_q1m524_v8_q32s141_consumption_v1.py new file mode 100644 index 00000000..432ce097 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_q1m524_v8_q32s141_consumption_v1.py @@ -0,0 +1,145 @@ +"""Consume the exact Q32/M100000/D128/K32 seed into the v7 portfolio. + +Minimum target architecture: sm_100a. The additive guard is exact BF16 +non-build B=1, Q=32, M=100000, D=128, K=32. Guard misses, including forced +fallback, delegate to v7 and therefore remain on an existing Weave-only route. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +from collections.abc import Callable +from pathlib import Path +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_q1m524_v7_d128m100k_consumption_v1 as base +from . import knn_build_dispatch_v11_common_d_seed_portfolio_a4ec_v1 as portfolio +from . import knn_build_rag_microbucket_k32_q32rowld2exact_s141_72d1_v1 as seed +MODULE = 'loom.examples.weave.knn_build_dispatch_q1m524_v8_q32s141_consumption_v1' +CANDIDATE_ID = 'q1m524_v8_q32s141_consumption_v1' +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +BENCHMARK_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_knn_build_dispatch_q1m524_v8_q32s141_consumption_v1']) +BASELINE_ENTRYPOINT = base.ROUTE_ENTRYPOINT +SEED_ID = 'q32s141_72d1' +SEED_ENTRYPOINT = seed.ROUTE_Q32_ROWLD2EXACT_ENTRYPOINT +GUARD_ID = '72d1_exact_bf16_nonbuild_b1_q32_m100000_d128_k32' +TARGET_SHAPE = seed.Q32_K32_SHAPE +TARGET_SHAPES = (TARGET_SHAPE,) +SPEEDUP_FLOOR = 1.2 + +def _eligible(inputs: dict[str, Any]) -> bool: + return seed._eligible_q32_rowld2exact(inputs) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if not force_fallback and _eligible(inputs): + return seed.route_for_contract_inputs(inputs) + return base.route_for_contract_inputs(inputs, force_fallback=force_fallback) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + if not force_fallback and _eligible(inputs): + seed.launch_from_contract_inputs(inputs) + return + base.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + +def _candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def _forced_candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) + +def _select_contract_shapes(shape_labels): + return base._select_contract_shapes(tuple(shape_labels)) + +def _run(*, use_cupti: bool, shape_labels, kernel_fn: Callable[[dict[str, Any]], Any], benchmark: bool=True) -> dict[str, Any]: + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + try: + return eval_mod.evaluate(kernel_fn, shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = previous + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False) -> list[dict[str, Any]]: + labels = tuple(shape_labels) if shape_labels is not None else tuple((s['label'] for s in eval_mod.CANONICAL_SHAPES)) + rows = [] + for shape in _select_contract_shapes(labels): + label, inputs = (str(shape['label']), portfolio._trace_inputs_for_shape(shape)) + if not force_fallback and _eligible(inputs): + rows.append(portfolio._normalize_route_row({'shape_key': label, 'selected_route': route_for_contract_inputs(inputs), 'selected_entrypoint': SEED_ENTRYPOINT, 'selected_seed': SEED_ID, 'expected_seed': SEED_ID, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': GUARD_ID, 'guard_condition': 'exact BF16 non-build B=1 Q=32 M=100000 D=128 K=32', 'replaced_route': base.route_for_contract_inputs(inputs), 'classification': 'unmeasured'})) + else: + rows.append(base.route_trace_for_contract_shapes((label,), force_fallback=force_fallback)[0]) + return rows + +def _annotate(rows, candidate_report, baseline_report): + out = [] + for row in rows: + row = dict(row) + candidate_row = candidate_report.get('per_shape', {}).get(row['shape_key'], {}) + baseline_row = baseline_report.get('per_shape', {}).get(row['shape_key'], {}) + ms, base_ms, ratio = (candidate_row.get('kernel_ms'), baseline_row.get('kernel_ms'), candidate_row.get('ratio_vs_flashlib')) + row.update(dispatcher_kernel_ms=ms, baseline_dispatcher_kernel_ms=base_ms, relative_speedup_vs_baseline=base_ms / ms if ms and base_ms else None, speedup_vs_external_baseline=ratio, external_baseline_ms=candidate_row.get('flashlib_ms'), external_baseline_ref='same_session' if candidate_row.get('flashlib_ms') is not None else 'not_available', timing_backend=candidate_row.get('timing_backend') or baseline_row.get('timing_backend')) + if candidate_row.get('passed') is False: + row['classification'] = 'benchmark-path-mismatch' + elif row.get('selected_seed') == SEED_ID and isinstance(ratio, (int, float)) and (ratio >= SPEEDUP_FLOOR): + row['classification'] = 'seed-consumed' + elif isinstance(ratio, (int, float)) and ratio < SPEEDUP_FLOOR: + row['classification'] = 'kernel-slow' + else: + row['classification'] = 'route-ok' + out.append(portfolio._normalize_route_row(row)) + return out + +def _ledger(rows): + below_1x, below_floor = ([], []) + for row in rows: + ratio = row.get('speedup_vs_external_baseline') + if isinstance(ratio, (int, float)): + record = {key: row.get(key) for key in ('shape_key', 'selected_route', 'selected_seed', 'speedup_vs_external_baseline', 'classification')} + if ratio < 1.0: + below_1x.append(record) + if ratio < SPEEDUP_FLOOR: + below_floor.append(record) + return {'baseline_ref_scope': 'same_session', 'speedup_floor': SPEEDUP_FLOOR, 'rows_below_1x': below_1x, 'rows_below_floor': below_floor, 'omitted_reason': None} + +def write_correctness_preflight_artifact(artifact_dir: str | Path, *, shape_labels=None) -> dict[str, Any]: + """Fresh-process ABI preflight: real inputs, caller outputs, default and forced routes.""" + labels = tuple(shape_labels) if shape_labels is not None else tuple((s['label'] for s in eval_mod.CANONICAL_SHAPES)) + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + default = _run(use_cupti=True, shape_labels=labels, kernel_fn=_candidate, benchmark=False) + forced = _run(use_cupti=True, shape_labels=TARGET_SHAPES, kernel_fn=_forced_candidate, benchmark=False) + target = route_trace_for_contract_shapes(TARGET_SHAPES)[0] + payload = {'candidate_entrypoint': ROUTE_ENTRYPOINT, 'seed_entrypoint': SEED_ENTRYPOINT, 'denominator': ''.join(['full', format(len(labels), ''), '_v12']), 'shape_labels': labels, 'fresh_process': True, 'exported_input_abi': True, 'caller_owned_outputs': True, 'real_intermediate_buffers': True, 'default': default, 'forced_fallback': forced, 'forced_fallback_denominator': 'target_bucket', 'default_path_correct': bool(default['correctness']['all_correct']), 'forced_path_correct': bool(forced['correctness']['all_correct']), 'target_default_route': target, 'route_trace': route_trace_for_contract_shapes(labels)} + path = out_dir / 'full112_v8_q32s141_correctness_preflight.json' + atomic_write_json(path, payload) + return {'path': str(path), 'passed': payload['default_path_correct'] and payload['forced_path_correct']} + +def write_checkpointed_full_benchmark_artifacts(artifact_dir: str | Path, *, use_cupti: bool=True, shape_labels=None, max_new_rows: int | None=None) -> dict[str, Any]: + labels = tuple(shape_labels) if shape_labels is not None else tuple((s['label'] for s in eval_mod.CANONICAL_SHAPES)) + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + checkpoint_path = out_dir / ''.join(['full', format(len(labels), ''), '_v8_q32s141_paired_progress.json']) + audit = ResumableRowAudit(checkpoint_path, audit_id=''.join([format(CANDIDATE_ID, ''), ':paired-v1']), labels=labels, metadata={'contract_version': eval_mod.CONTRACT.contract_version, 'timing_backend': 'cupti' if use_cupti else 'cuda_event', 'candidate_entrypoint': ROUTE_ENTRYPOINT, 'baseline_entrypoint': BASELINE_ENTRYPOINT}) + shapes = {str(shape['label']): shape for shape in _select_contract_shapes(labels)} + with healthy_gpu_bench_session(require_cupti=use_cupti) as gpu_preflight: + state = audit.run(lambda label: eval_mod.evaluate_paired_row(_candidate, base._candidate, shapes[label], use_cupti=use_cupti, order_seed=7201141), max_new_rows=max_new_rows) + if state['status'] != 'complete': + return {'status': 'running', 'completed': state['completed'], 'total': len(labels), 'checkpoint': str(checkpoint_path), 'gpu_preflight': gpu_preflight} + candidate_rows = {row['shape_key']: row['candidate'] for row in state['rows']} + baseline_rows = {row['shape_key']: row['baseline'] for row in state['rows']} + report = {'contract': eval_mod.CONTRACT.kernel, 'contract_version': eval_mod.CONTRACT.contract_version, 'per_shape': candidate_rows} + baseline_report = {'contract': eval_mod.CONTRACT.kernel, 'contract_version': eval_mod.CONTRACT.contract_version, 'per_shape': baseline_rows} + trace = _annotate(route_trace_for_contract_shapes(labels), report, baseline_report) + payload = {'candidate_id': CANDIDATE_ID, 'candidate_entrypoint': ROUTE_ENTRYPOINT, 'baseline_entrypoint': BASELINE_ENTRYPOINT, 'measured_entrypoint': BENCHMARK_ENTRYPOINT, 'measurement_mode': 'checkpointed_row_pair_same_process', 'contract_version': eval_mod.CONTRACT.contract_version, 'measured_shape_labels': labels, 'accelerated_shape_labels': TARGET_SHAPES, 'timing_backend': 'cupti' if use_cupti else 'cuda_event', 'route_trace': trace, 'route_trace_included': True, 'flashlib_parity_ledger': _ledger(trace), 'correctness': {'all_correct': all((row.get('passed') is True for row in candidate_rows.values())), 'shape_count': len(candidate_rows)}, 'report': report, 'baseline_report': baseline_report, 'randomized_shared_input_triads': state['rows'], 'gpu_preflight': gpu_preflight, 'checkpoint': str(checkpoint_path)} + candidate_path, baseline_path, trace_path = (out_dir / 'full112_v8_q32s141_candidate.json', out_dir / 'full112_v7_same_session_baseline.json', out_dir / 'full112_v8_q32s141_route_trace.json') + atomic_write_json(candidate_path, payload) + atomic_write_json(baseline_path, {'measured_entrypoint': BASELINE_ENTRYPOINT, 'timing_backend': payload['timing_backend'], 'report': baseline_report, 'route_trace': base.route_trace_for_contract_shapes(labels), 'route_trace_included': True}) + atomic_write_json(trace_path, trace) + return {'candidate_payload': str(candidate_path), 'same_session_baseline_payload': str(baseline_path), 'route_trace': str(trace_path)} + +def benchmark_knn_build_dispatch_q1m524_v8_q32s141_consumption_v1(*, use_cupti: bool=True, shape_labels=None) -> dict[str, Any]: + labels = tuple(shape_labels) if shape_labels is not None else tuple((s['label'] for s in eval_mod.CANONICAL_SHAPES)) + report = _run(use_cupti=use_cupti, shape_labels=labels, kernel_fn=_candidate) + baseline = _run(use_cupti=use_cupti, shape_labels=labels, kernel_fn=base._candidate) + trace = _annotate(route_trace_for_contract_shapes(labels), report, baseline) + return {'candidate_id': CANDIDATE_ID, 'candidate_entrypoint': ROUTE_ENTRYPOINT, 'baseline_entrypoint': BASELINE_ENTRYPOINT, 'measured_entrypoint': BENCHMARK_ENTRYPOINT, 'measured_shape_labels': labels, 'timing_backend': 'cupti' if use_cupti else 'cuda_event', 'route_trace': trace, 'route_trace_included': True, 'flashlib_parity_ledger': _ledger(trace), 'correctness': report['correctness'], 'summary': report['summary'], 'report': report, 'baseline_report': baseline} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_rag_seed_portfolio_8700_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_rag_seed_portfolio_8700_v1.py new file mode 100644 index 00000000..84c55d25 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_rag_seed_portfolio_8700_v1.py @@ -0,0 +1,410 @@ +"""Fast RAG seed portfolio overlay for the 397b kNN build dispatcher. + +Minimum target architecture: sm_100a. This dispatcher-synthesis wrapper starts +from the full-v5 397b selected portfolio and adds exact BF16 non-build RAG +microbatch guards for ``B=1,Q in {8,16,32},M=100000,D=128,K=10``. Candidate +portfolios select among existing Weave seeds from 0c69 and 059f; the 4982 G8 +variant is represented through the parameterized 059f S144 module without +merging the conflicting branch. + +Every production route remains Weave-only. FlashLib is used only by the +contract harness as a black-box timing peer. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import datetime as _dt +import json +import platform +import socket +import statistics +import time +from pathlib import Path +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_selected_portfolio_397b_v1 as base_397b +from . import knn_build_rag_microbatch_4a72_v2 as rag_s144 +from . import knn_build_rag_microbatch_m64_d4f7_v1 as rag_m64 +ROUTE_BASE_397B = 'loom.examples.weave.knn_build_dispatch_selected_portfolio_397b_v1:launch_from_contract_inputs' +ROUTE_RAG_M64_S128_G8 = 'rag_microbatch_m64_d4f7_q64m128_s128_g8' +ROUTE_RAG_S144_G12 = 'rag_microbatch_4a72_v2_cta1_k10_s144_g12_fusedmerge' +ROUTE_RAG_S144_G8 = 'rag_microbatch_4a72_v2_cta1_k10_s144_g8_fusedmerge' +RAG_MICROBATCH_TARGET_SHAPES = rag_s144.TARGET_SHAPES +RAG_MICROBATCH_TARGET_SHAPE_SET = set(RAG_MICROBATCH_TARGET_SHAPES) +PORTFOLIO_ALL_M64 = 'all_m64_s128' +PORTFOLIO_ALL_S144_G12 = 'all_s144_g12' +PORTFOLIO_PER_Q_MIX = 'per_q_mix_q8_s144_q16_best_s144_q32_m64' +DEFAULT_PORTFOLIO_ID = PORTFOLIO_ALL_M64 +PORTFOLIO_IDS = (PORTFOLIO_ALL_M64, PORTFOLIO_ALL_S144_G12, PORTFOLIO_PER_Q_MIX) +PORTFOLIO_ROUTE_BY_Q = {PORTFOLIO_ALL_M64: {8: ROUTE_RAG_M64_S128_G8, 16: ROUTE_RAG_M64_S128_G8, 32: ROUTE_RAG_M64_S128_G8}, PORTFOLIO_ALL_S144_G12: {8: ROUTE_RAG_S144_G12, 16: ROUTE_RAG_S144_G12, 32: ROUTE_RAG_S144_G12}, PORTFOLIO_PER_Q_MIX: {8: ROUTE_RAG_S144_G12, 16: ROUTE_RAG_S144_G8, 32: ROUTE_RAG_M64_S128_G8}} +ROUTE_SEED_ID = {ROUTE_RAG_M64_S128_G8: 'rag_m64_s128_0c69', ROUTE_RAG_S144_G12: 'rag_s144_g12_cta1_059f', ROUTE_RAG_S144_G8: 'rag_s144_g8_cta1_4982_read_ref'} +ROUTE_ENTRYPOINTS = {ROUTE_RAG_M64_S128_G8: 'loom.examples.weave.knn_build_rag_microbatch_m64_d4f7_v1:_launch_rag_microbatch_m64(split_count=128,group_count=8)', ROUTE_RAG_S144_G12: 'loom.examples.weave.knn_build_rag_microbatch_4a72_v2:_launch_rag_microbatch_fused_merge(split_count=144,group_count=12)', ROUTE_RAG_S144_G8: 'loom.examples.weave.knn_build_rag_microbatch_4a72_v2:_launch_rag_microbatch_fused_merge(split_count=144,group_count=8)'} +SELECTED_TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_large_b1_q8192_m8192_d128_k20", "build_large_b1_q8192_m8192_d128_k32", "build_over64_stress_qm2048_k96", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32", "rag_batch_b2_q256_m50000_d128_k10", "rag_irregular_b1_q512_m131071_d128_k10", "search_rect_b1_q1024_m8192_d128_k10", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q2048_m2048_d256_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10", "search_rect_b1_q2048_m32768_d128_k10", "build_large_tail_b1_q6144_m6144_d128_k20", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "build_over32_stress_qm2048_k64", "build_over32_stress_qm4096_k64", "build_k_sweep_qm512_k1", "build_k_sweep_qm512_k2", "build_k_sweep_qm1024_k16", "build_k_sweep_qm512_k4", "build_k_sweep_qm512_k5", "build_k_sweep_qm512_k6", "rag_microbatch_b1_q8_m100000_d128_k10", "rag_microbatch_b1_q32_m100000_d128_k10"]}')) +CONSUMED_SEED_TARGET_SHAPES = RAG_MICROBATCH_TARGET_SHAPES +GUARD_MISS_AUDIT_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_k_sweep_qm512_k4", "build_k_sweep_qm512_k8", "build_k_sweep_qm512_k10", "rag_microbatch_b1_q8_m100000_d128_k10", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_microbatch_b1_q32_m100000_d128_k10", "build_large_b1_q8192_m8192_d128_k20", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_qm2048_d128_k10", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "flashml_correctness_b1_q256_m256_d128_k5", "build_over32_stress_qm2048_k64", "search_rect_over32_b1_q2048_m65536_d128_k64", "rag_offline_large_m_over32_b1_q2048_m250000_d128_k64", "build_k_sweep_qm512_k5", "build_over32_stress_qm4096_k64"]}')) +DISPATCH_CORRECTNESS_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["flashml_correctness_b1_q256_m256_d128_k5", "build_large_b1_q8192_m8192_d128_k20", "build_large_b1_q8192_m8192_d128_k32", "build_over64_stress_qm2048_k96", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32", "rag_batch_b2_q256_m50000_d128_k10", "rag_irregular_b1_q512_m131071_d128_k10", "search_rect_b1_q1024_m8192_d128_k10", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q2048_m2048_d256_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10", "search_rect_b1_q2048_m32768_d128_k10", "build_large_tail_b1_q6144_m6144_d128_k20", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "build_over32_stress_qm2048_k64", "build_over32_stress_qm4096_k64", "build_k_sweep_qm512_k1", "build_k_sweep_qm512_k2", "build_k_sweep_qm1024_k16", "build_k_sweep_qm512_k4", "build_k_sweep_qm512_k5", "build_k_sweep_qm512_k6", "rag_microbatch_b1_q8_m100000_d128_k10", "rag_microbatch_b1_q32_m100000_d128_k10", "build_k_sweep_qm512_k8", "build_k_sweep_qm512_k10", "build_qm2048_d128_k10", "search_rect_over32_b1_q2048_m65536_d128_k64", "rag_offline_large_m_over32_b1_q2048_m250000_d128_k64", "rag_online_b1_q1_m100000_d128_k10", "rag_stream_b1_q128_m100000_d128_k10", "search_rect_b1_q4096_m65536_d128_k20", "rag_offline_largek_b1_q4096_m100000_d128_k20", "rag_offline_large_m_b1_q8192_m250000_d128_k20"]}')) +PRODUCTION_ROUTE_MODULES = {**base_397b.PRODUCTION_ROUTE_MODULES, 'rag_m64_s128_0c69': 'loom.examples.weave.knn_build_rag_microbatch_m64_d4f7_v1:launch_from_contract_inputs', 'rag_s144_g12_cta1_059f': 'loom.examples.weave.knn_build_rag_microbatch_4a72_v2:launch_from_contract_inputs', 'rag_s144_g8_cta1_4982_read_ref_parameterized': 'loom.examples.weave.knn_build_rag_microbatch_4a72_v2:launch_from_contract_inputs', 'base_397b': ROUTE_BASE_397B} +CANDIDATE_PORTFOLIOS = ({'id': 'baseline_selected_portfolio_397b_v1', 'entrypoint': 'loom.examples.weave.knn_build_dispatch_selected_portfolio_397b_v1:benchmark_knn_build_dispatch_selected_portfolio_397b_v1', 'consumed_seeds': ('selected_397b_4a72_plus_b2ec_rag_microbatch',), 'guard_plan': ('397b exact b2ec RAG coverage-only guard', 'then inherited 4a72 selected full-v5 guard plan'), 'expected_shape_wins': (), 'fallback': ROUTE_BASE_397B, 'rejected_reason': 'same-session baseline for 8700 dispatcher synthesis'}, {'id': PORTFOLIO_ALL_M64, 'entrypoint': ''.join(['loom.examples.weave.knn_build_dispatch_rag_seed_portfolio_8700_v1:benchmark_knn_build_dispatch_rag_seed_portfolio_8700_v1(portfolio_id=', format(PORTFOLIO_ALL_M64, ''), ')']), 'consumed_seeds': ('rag_m64_s128_0c69',), 'guard_plan': ('Q8/Q16/Q32 exact RAG microbatch -> 0c69 M64/S128/G8',), 'fallback': ROUTE_BASE_397B, 'expected_shape_wins': RAG_MICROBATCH_TARGET_SHAPES, 'rejected_reason': None}, {'id': PORTFOLIO_ALL_S144_G12, 'entrypoint': ''.join(['loom.examples.weave.knn_build_dispatch_rag_seed_portfolio_8700_v1:benchmark_knn_build_dispatch_rag_seed_portfolio_8700_v1(portfolio_id=', format(PORTFOLIO_ALL_S144_G12, ''), ')']), 'consumed_seeds': ('rag_s144_g12_cta1_059f',), 'guard_plan': ('Q8/Q16/Q32 exact RAG microbatch -> 059f S144/G12 CTA1',), 'fallback': ROUTE_BASE_397B, 'expected_shape_wins': RAG_MICROBATCH_TARGET_SHAPES, 'rejected_reason': None}, {'id': PORTFOLIO_PER_Q_MIX, 'entrypoint': ''.join(['loom.examples.weave.knn_build_dispatch_rag_seed_portfolio_8700_v1:benchmark_knn_build_dispatch_rag_seed_portfolio_8700_v1(portfolio_id=', format(PORTFOLIO_PER_Q_MIX, ''), ')']), 'consumed_seeds': ('rag_s144_g12_cta1_059f', 'rag_s144_g8_cta1_4982_read_ref', 'rag_m64_s128_0c69'), 'guard_plan': ('Q8 exact RAG microbatch -> 059f S144/G12 CTA1', 'Q16 exact RAG microbatch -> 4982 S144/G8 CTA1 via parameterized 059f module', 'Q32 exact RAG microbatch -> 0c69 M64/S128/G8'), 'fallback': ROUTE_BASE_397B, 'expected_shape_wins': RAG_MICROBATCH_TARGET_SHAPES, 'rejected_reason': None}) +HISTORICAL_SEED_ROWS = {'rag_m64_s128_0c69': {'source_task': 'weave-evolve-knn-build-0c69', 'route_name': ROUTE_RAG_M64_S128_G8, 'timing_backend': 'cupti', 'rows': {'rag_microbatch_b1_q8_m100000_d128_k10': {'kernel_ms': 0.0616, 'flashlib_ms': 0.065952, 'ratio_vs_flashlib': 1.0706493506493506}, 'rag_microbatch_b1_q16_m100000_d128_k10': {'kernel_ms': 0.07008, 'flashlib_ms': 0.074592, 'ratio_vs_flashlib': 1.0643835616438357}, 'rag_microbatch_b1_q32_m100000_d128_k10': {'kernel_ms': 0.069857, 'flashlib_ms': 0.090112, 'ratio_vs_flashlib': 1.2899494681993213}}}, 'rag_s144_g12_cta1_059f': {'source_task': 'weave-evolve-knn-build-059f', 'route_name': ROUTE_RAG_S144_G12, 'timing_backend': 'cupti', 'rows': {'rag_microbatch_b1_q8_m100000_d128_k10': {'kernel_ms': 0.056864, 'flashlib_ms': 0.066272, 'ratio_vs_flashlib': 1.1654473832301633}, 'rag_microbatch_b1_q16_m100000_d128_k10': {'kernel_ms': 0.065408, 'flashlib_ms': 0.075136, 'ratio_vs_flashlib': 1.1487279843444227}, 'rag_microbatch_b1_q32_m100000_d128_k10': {'kernel_ms': 0.074368, 'flashlib_ms': 0.091073, 'ratio_vs_flashlib': 1.224626183304647}}}, 'rag_s144_g8_cta1_4982_read_ref': {'source_task': 'weave-evolve-knn-build-4982', 'route_name': ROUTE_RAG_S144_G8, 'timing_backend': 'cupti', 'read_only_source': True, 'rows': {'rag_microbatch_b1_q8_m100000_d128_k10': {'kernel_ms': 0.057056, 'flashlib_ms': 0.066432, 'ratio_vs_flashlib': 1.1643297812675266}, 'rag_microbatch_b1_q16_m100000_d128_k10': {'kernel_ms': 0.065249, 'flashlib_ms': 0.0752, 'ratio_vs_flashlib': 1.1525080844150868}, 'rag_microbatch_b1_q32_m100000_d128_k10': {'kernel_ms': 0.074528, 'flashlib_ms': 0.090848, 'ratio_vs_flashlib': 1.218978102189781}}}} +RAG_SEED_WINNERS_BY_SHAPE = {'rag_microbatch_b1_q8_m100000_d128_k10': 'rag_s144_g12_cta1_059f', 'rag_microbatch_b1_q16_m100000_d128_k10': 'rag_s144_g8_cta1_4982_read_ref', 'rag_microbatch_b1_q32_m100000_d128_k10': 'rag_m64_s128_0c69'} +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k48over32", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 48]], "cta_group": 1, "threads": 192}')) + +def _portfolio_route_map(portfolio_id: str) -> dict[int, str]: + try: + return PORTFOLIO_ROUTE_BY_Q[portfolio_id] + except KeyError as exc: + raise ValueError(''.join(['unknown RAG portfolio id ', format(repr(portfolio_id), ''), '; expected one of ', format(PORTFOLIO_IDS, '')])) from exc + +def _dtype_name(inputs: dict[str, Any]) -> str: + query = inputs.get('query') + if query is not None: + return str(query.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')) + +def _label_can_hit(inputs: dict[str, Any], labels: set[str]) -> bool: + value = inputs.get('label') + return value is None or str(value) in labels + +def _eligible_rag_microbatch(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, RAG_MICROBATCH_TARGET_SHAPE_SET) and (not bool(inputs.get('build', False))) and (int(inputs.get('B', -1)) == 1) and (int(inputs.get('Q', -1)) in (8, 16, 32)) and (int(inputs.get('M', -1)) == 100000) and (int(inputs.get('D', -1)) == 128) and (int(inputs.get('K', -1)) == 10) and (_dtype_name(inputs) == 'bfloat16') + +def _fast_route_for_inputs(inputs: dict[str, Any], *, portfolio_id: str) -> str: + route_by_q = _portfolio_route_map(portfolio_id) + q = int(inputs.get('Q', -1)) + return route_by_q[q] + +def route_for_contract_inputs(inputs: dict[str, Any], *, portfolio_id: str=DEFAULT_PORTFOLIO_ID, force_fallback: bool=False, enable_rag_seed_portfolio: bool=True, enable_q512_k456: bool=True) -> str: + if not force_fallback and enable_rag_seed_portfolio and _eligible_rag_microbatch(inputs): + return _fast_route_for_inputs(inputs, portfolio_id=portfolio_id) + return base_397b.route_for_contract_inputs(inputs, force_fallback=False, enable_rag_microbatch=True, enable_q512_k456=enable_q512_k456) + +def _launch_route(inputs: dict[str, Any], route: str) -> None: + if route == ROUTE_RAG_M64_S128_G8 and _eligible_rag_microbatch(inputs): + rag_m64._launch_rag_microbatch_m64(inputs, split_count=128, group_count=8) + return + if route == ROUTE_RAG_S144_G12 and _eligible_rag_microbatch(inputs): + rag_s144._launch_rag_microbatch_fused_merge(inputs, split_count=144, group_count=12) + return + if route == ROUTE_RAG_S144_G8 and _eligible_rag_microbatch(inputs): + rag_s144._launch_rag_microbatch_fused_merge(inputs, split_count=144, group_count=8) + return + base_397b._launch_route(inputs, route) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, portfolio_id: str=DEFAULT_PORTFOLIO_ID, force_fallback: bool=False, enable_rag_seed_portfolio: bool=True, enable_q512_k456: bool=True) -> None: + route = route_for_contract_inputs(inputs, portfolio_id=portfolio_id, force_fallback=force_fallback, enable_rag_seed_portfolio=enable_rag_seed_portfolio, enable_q512_k456=enable_q512_k456) + _launch_route(inputs, route) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def candidate_with_portfolio(portfolio_id: str) -> Callable[[dict[str, Any]], None]: + _portfolio_route_map(portfolio_id) + + def _candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, portfolio_id=portfolio_id) + return _candidate + +def candidate_base_dispatcher(inputs: dict[str, Any]): + base_397b.launch_from_contract_inputs(inputs) + return None + +def candidate_force_fallback(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs, force_fallback=True) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return base_397b._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=DISPATCH_CORRECTNESS_SHAPES, benchmark: bool=False, portfolio_id: str=DEFAULT_PORTFOLIO_ID) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark, kernel_fn=candidate_with_portfolio(portfolio_id)) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=None, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + shapes = None if shape_labels is None else _select_contract_shapes(shape_labels) + return evaluate_contract(shapes=shapes, correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def _trace_inputs_from_shape(shape: dict[str, Any]) -> dict[str, Any]: + return base_397b._trace_inputs_from_shape(shape) + +def _inputs_for_label(label: str) -> dict[str, Any]: + return base_397b._inputs_for_label(label) + +def _historical_row_for_route(label: str, route: str) -> dict[str, Any]: + seed_id = ROUTE_SEED_ID[route] + row = dict(HISTORICAL_SEED_ROWS[seed_id]['rows'][label]) + row['selected_seed'] = seed_id + row['source_task'] = HISTORICAL_SEED_ROWS[seed_id]['source_task'] + row['selected_route'] = route + row['targeted_seed_timing_backend'] = HISTORICAL_SEED_ROWS[seed_id]['timing_backend'] + return row + +def _route_trace_record(inputs: dict[str, Any], *, portfolio_id: str=DEFAULT_PORTFOLIO_ID, force_fallback: bool=False) -> dict[str, Any]: + base_route = base_397b.route_for_contract_inputs(inputs) + label = str(inputs.get('label')) + if force_fallback and _eligible_rag_microbatch(inputs): + row = base_397b._route_trace_record(inputs) + row['selected_route'] = base_route + row['guard_condition'] = 'forced fallback to 397b baseline; 8700 fast RAG seed portfolio disabled' + row['coverage'] = 'forced candidate fallback for 8700 RAG fast-seed overlay only' + row['forced_disabled_seeds'] = tuple(sorted(ROUTE_SEED_ID.values())) + row['base_397b_route'] = base_route + row['candidate_guard_status'] = 'forced_fallback_to_397b' + row['portfolio_id'] = portfolio_id + return row + route = route_for_contract_inputs(inputs, portfolio_id=portfolio_id, force_fallback=force_fallback) + if route in ROUTE_SEED_ID and label in RAG_MICROBATCH_TARGET_SHAPE_SET: + selected = _historical_row_for_route(label, route) + return {'shape_key': inputs.get('label'), 'selected_route': route, 'guard_condition': ''.join(['portfolio ', format(portfolio_id, ''), ': exact BF16 non-build B1 Q=', format(int(inputs.get('Q')), ''), ' M=100000 D128 K10 RAG microbatch fast seed']), 'route_kind': 'specialized', 'coverage': '8700 routes an above-FlashLib RAG seed ahead of the 397b coverage-only route', 'consumed_seed': selected['selected_seed'], 'replaced_route': base_route, 'base_397b_route': base_route, 'row_selection': selected, 'parity_status': 'expected_pass_from_target_bucket', 'parity_reason': 'target-bucket CUPTI seed evidence is above FlashLib; full59 payload decides promotion', 'candidate_guard_status': 'selected_from_8700_rag_fast_seed_portfolio', 'portfolio_id': portfolio_id} + row = base_397b._route_trace_record(inputs) + row['base_397b_route'] = base_route + row['candidate_guard_status'] = 'inherited_397b_or_guard_miss' + row['portfolio_id'] = portfolio_id + return row + +def route_trace_for_contract_shapes(shape_labels=None, *, portfolio_id: str=DEFAULT_PORTFOLIO_ID, force_fallback: bool=False) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + return [_route_trace_record(_trace_inputs_from_shape(shape), portfolio_id=portfolio_id, force_fallback=force_fallback) for shape in selected] + +def historical_seed_delta_matrix() -> list[dict[str, Any]]: + rows = [] + for label in RAG_MICROBATCH_TARGET_SHAPES: + baseline_inputs = _inputs_for_label(label) + baseline_route = base_397b.route_for_contract_inputs(baseline_inputs) + deltas = [] + for seed_id, seed in HISTORICAL_SEED_ROWS.items(): + row = seed['rows'][label] + deltas.append({'candidate_id': seed_id, 'route_name': seed['route_name'], 'source_task': seed['source_task'], 'kernel_ms': row['kernel_ms'], 'flashlib_ms': row['flashlib_ms'], 'ratio_vs_flashlib': row['ratio_vs_flashlib'], 'timing_backend': seed['timing_backend'], 'evidence_scope': 'historical_target_bucket_diagnostic'}) + rows.append({'shape_key': label, 'baseline_route': baseline_route, 'per_shape_winner': RAG_SEED_WINNERS_BY_SHAPE[label], 'candidate_deltas': deltas}) + return rows + +def _timing_backends_for_reports(*reports: dict[str, Any]) -> list[str]: + return base_397b._timing_backends_for_reports(*reports) + +def _rows_for_labels(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return base_397b._rows_for_labels(report, labels) + +def _per_consumed_row_delta(candidate_report: dict[str, Any], baseline_report: dict[str, Any], *, portfolio_id: str) -> dict[str, Any]: + deltas = {} + for label in CONSUMED_SEED_TARGET_SHAPES: + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') + inputs = _inputs_for_label(label) + route = route_for_contract_inputs(inputs, portfolio_id=portfolio_id) + deltas[label] = {'candidate_ms': candidate_ms, 'baseline_397b_ms': baseline_ms, 'flashlib_ms': flashlib_ms, 'speedup_vs_baseline_397b': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'ratio_vs_flashlib': flashlib_ms / candidate_ms if candidate_ms and flashlib_ms else None, 'candidate_route': route, 'selected_seed': ROUTE_SEED_ID.get(route), 'baseline_397b_route': base_397b.route_for_contract_inputs(inputs), 'candidate_passed': candidate_row.get('passed'), 'baseline_passed': baseline_row.get('passed')} + return deltas + +def _seed_delta_matrix(candidate_report: dict[str, Any], baseline_report: dict[str, Any], *, portfolio_id: str) -> list[dict[str, Any]]: + matrix = [] + for label in CONSUMED_SEED_TARGET_SHAPES: + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') + inputs = _inputs_for_label(label) + route = route_for_contract_inputs(inputs, portfolio_id=portfolio_id) + matrix.append({'shape_key': label, 'baseline_route': base_397b.route_for_contract_inputs(inputs), 'candidate_route': route, 'selected_seed': ROUTE_SEED_ID.get(route), 'candidate_ms': candidate_ms, 'baseline_ms': baseline_ms, 'flashlib_ms': flashlib_ms, 'metric_delta_ms': candidate_ms - baseline_ms if candidate_ms and baseline_ms else None, 'speedup_vs_baseline_397b': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'ratio_vs_flashlib': flashlib_ms / candidate_ms if candidate_ms and flashlib_ms else None, 'timing_backend': candidate_row.get('timing_backend') or baseline_row.get('timing_backend')}) + return matrix + +def _frontmatter_seed_delta_matrix(candidate_report: dict[str, Any], baseline_report: dict[str, Any], *, portfolio_id: str) -> list[dict[str, Any]]: + rows = [] + for item in _seed_delta_matrix(candidate_report, baseline_report, portfolio_id=portfolio_id): + rows.append({'shape_key': item['shape_key'], 'baseline_route': item['baseline_route'], 'candidate_deltas': [{'candidate_id': portfolio_id, 'selected_seed': item['selected_seed'], 'metric_delta': item['metric_delta_ms'], 'ratio_vs_flashlib': item['ratio_vs_flashlib'], 'timing_backend': item['timing_backend'] or 'cupti'}]}) + return rows + +def _below_flashlib_rows(report: dict[str, Any], *, portfolio_id: str) -> list[dict[str, Any]]: + rows = [] + trace_by_label = {str(row['shape_key']): row for row in route_trace_for_contract_shapes(portfolio_id=portfolio_id)} + for label, row in sorted(report.get('per_shape', {}).items()): + ratio = row.get('ratio_vs_flashlib') + if isinstance(ratio, (float, int)) and ratio < 1.0: + inputs = _inputs_for_label(label) + route = route_for_contract_inputs(inputs, portfolio_id=portfolio_id) + trace_row = trace_by_label.get(label, {}) + rows.append({'shape_key': label, 'kernel_ms': row.get('kernel_ms'), 'flashlib_ms': row.get('flashlib_ms'), 'ratio_vs_flashlib': ratio, 'selected_route': route, 'route_kind': trace_row.get('route_kind', 'unknown')}) + return rows + +def _rag_hot_bucket_parity(report: dict[str, Any]) -> str: + for label in RAG_MICROBATCH_TARGET_SHAPES: + ratio = report.get('per_shape', {}).get(label, {}).get('ratio_vs_flashlib') + if not isinstance(ratio, (float, int)) or ratio < 1.0: + return 'fail' + return 'pass' + +def _benchmark_payload(candidate_report: dict[str, Any], baseline_report: dict[str, Any], *, use_cupti: bool, shape_labels, portfolio_id: str) -> dict[str, Any]: + candidate_metric = candidate_report['summary']['primary_mean'] + baseline_metric = baseline_report['summary']['primary_mean'] + route_trace = route_trace_for_contract_shapes(shape_labels, portfolio_id=portfolio_id) + below_flashlib = _below_flashlib_rows(candidate_report, portfolio_id=portfolio_id) + return {'portfolio_id': portfolio_id, 'tflops': candidate_metric, 'baseline_tflops': baseline_metric, 'metric_delta': candidate_metric - baseline_metric if candidate_metric and baseline_metric else None, 'all_correct': candidate_report['summary']['all_correct'], 'baseline_all_correct': baseline_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'baseline_performance_comparable': baseline_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'baseline_invalid_performance_reason': baseline_report['summary']['invalid_performance_reason'], 'measured_entrypoint': 'loom.examples.weave.knn_build_dispatch_rag_seed_portfolio_8700_v1:benchmark_knn_build_dispatch_rag_seed_portfolio_8700_v1', 'baseline_entrypoint': ROUTE_BASE_397B, 'baseline_entrypoint_note': 'same-session 397b selected portfolio measured through the same full-v5 contract denominator', 'measured_shape_labels': 'all_canonical' if shape_labels is None else tuple(shape_labels), 'selected_route_labels': SELECTED_TARGET_SHAPES, 'consumed_seed_labels': CONSUMED_SEED_TARGET_SHAPES, 'guard_miss_audit_labels': GUARD_MISS_AUDIT_SHAPES, 'selected_route_rows': _rows_for_labels(candidate_report, SELECTED_TARGET_SHAPES), 'baseline_selected_route_rows': _rows_for_labels(baseline_report, SELECTED_TARGET_SHAPES), 'consumed_seed_rows': _rows_for_labels(candidate_report, CONSUMED_SEED_TARGET_SHAPES), 'baseline_consumed_seed_rows': _rows_for_labels(baseline_report, CONSUMED_SEED_TARGET_SHAPES), 'guard_miss_audit_rows': _rows_for_labels(candidate_report, GUARD_MISS_AUDIT_SHAPES), 'baseline_guard_miss_audit_rows': _rows_for_labels(baseline_report, GUARD_MISS_AUDIT_SHAPES), 'per_consumed_row_delta': _per_consumed_row_delta(candidate_report, baseline_report, portfolio_id=portfolio_id), 'seed_delta_matrix': _seed_delta_matrix(candidate_report, baseline_report, portfolio_id=portfolio_id), 'frontmatter_seed_delta_matrix': _frontmatter_seed_delta_matrix(candidate_report, baseline_report, portfolio_id=portfolio_id), 'historical_seed_delta_matrix': historical_seed_delta_matrix(), 'candidate_dispatchers': CANDIDATE_PORTFOLIOS, 'selected_candidate_dispatcher': portfolio_id, 'rag_seed_winners_by_shape': RAG_SEED_WINNERS_BY_SHAPE, 'route_modules': PRODUCTION_ROUTE_MODULES, 'route_trace': route_trace, 'forced_fallback_route_trace': route_trace_for_contract_shapes(shape_labels, portfolio_id=portfolio_id, force_fallback=True), 'route_trace_included': True, 'contract_summary': candidate_report['summary'], 'baseline_contract_summary': baseline_report['summary'], 'contract_performance': candidate_report['performance'], 'baseline_contract_performance': baseline_report['performance'], 'timing_backends': _timing_backends_for_reports(candidate_report, baseline_report), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'hot_bucket_parity': {'rag_microbatch_q8_q16_q32_m100000_k10': _rag_hot_bucket_parity(candidate_report), 'lowk_q512_k4_k5_k6': 'inherited_397b_pass'}, 'performance_coverage': 'partial' if below_flashlib else 'pass', 'coverage_only_routes': [], 'hot_bucket_blockers': below_flashlib, 'report': candidate_report, 'baseline_report': baseline_report} + +def benchmark_knn_build_dispatch_rag_seed_portfolio_8700_v1(*, use_cupti: bool=True, shape_labels=None, portfolio_id: str=DEFAULT_PORTFOLIO_ID, baseline_report: dict[str, Any] | None=None) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_with_portfolio(portfolio_id)) + if baseline_report is None: + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_base_dispatcher) + return _benchmark_payload(candidate_report, baseline_report, use_cupti=use_cupti, shape_labels=shape_labels, portfolio_id=portfolio_id) + +def benchmark_candidate_portfolios(*, use_cupti: bool=True, shape_labels=None, portfolio_ids: tuple[str, ...]=PORTFOLIO_IDS) -> dict[str, Any]: + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_base_dispatcher) + payloads = {portfolio_id: benchmark_knn_build_dispatch_rag_seed_portfolio_8700_v1(use_cupti=use_cupti, shape_labels=shape_labels, portfolio_id=portfolio_id, baseline_report=baseline_report) for portfolio_id in portfolio_ids} + comparable = {key: payload for key, payload in payloads.items() if payload['all_correct'] and payload['performance_comparable'] and (payload['tflops'] is not None)} + selected = max(comparable, key=lambda key: comparable[key]['tflops']) if comparable else None + return {'measured_entrypoint': 'loom.examples.weave.knn_build_dispatch_rag_seed_portfolio_8700_v1:benchmark_candidate_portfolios', 'measured_shape_labels': 'all_canonical' if shape_labels is None else tuple(shape_labels), 'portfolio_ids': tuple(portfolio_ids), 'selected_candidate_dispatcher': selected, 'selection_policy': 'highest same-session full-denominator TFLOPS among correct comparable candidate portfolios', 'baseline_entrypoint': ROUTE_BASE_397B, 'baseline_tflops': baseline_report['summary']['primary_mean'], 'baseline_all_correct': baseline_report['summary']['all_correct'], 'baseline_performance_comparable': baseline_report['summary']['performance_comparable'], 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'historical_seed_delta_matrix': historical_seed_delta_matrix(), 'candidate_dispatchers': CANDIDATE_PORTFOLIOS, 'payloads': payloads, 'baseline_report': baseline_report} + +def _denominator_label(shape_labels) -> str: + if shape_labels is None: + return ''.join(['full', format(len(eval_mod.CANONICAL_SHAPES), '')]) + return ''.join(['shape', format(len(tuple(shape_labels)), '')]) + +def write_benchmark_artifacts(artifact_dir: str | Path, *, use_cupti: bool=True, shape_labels=None, portfolio_ids: tuple[str, ...]=PORTFOLIO_IDS) -> dict[str, str]: + summary = benchmark_candidate_portfolios(use_cupti=use_cupti, shape_labels=shape_labels, portfolio_ids=portfolio_ids) + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + denom = _denominator_label(shape_labels) + paths: dict[str, str] = {} + summary_path = out_dir / ''.join([format(denom, ''), '_synthesis_summary_rag_seed_portfolio_8700_v1.json']) + baseline_path = out_dir / ''.join([format(denom, ''), '_same_session_baseline_397b_for_8700_v1.json']) + seed_matrix_path = out_dir / ''.join([format(denom, ''), '_historical_seed_delta_matrix_8700_v1.json']) + summary_path.write_text(json.dumps(summary, indent=2, sort_keys=True) + '\n') + baseline_path.write_text(json.dumps({'measured_entrypoint': summary['baseline_entrypoint'], 'measured_shape_labels': summary['measured_shape_labels'], 'timing_backend_requested': summary['timing_backend_requested'], 'tflops': summary['baseline_tflops'], 'all_correct': summary['baseline_all_correct'], 'performance_comparable': summary['baseline_performance_comparable'], 'route_trace': base_397b.route_trace_for_contract_shapes(shape_labels), 'route_trace_included': True, 'report': summary['baseline_report']}, indent=2, sort_keys=True) + '\n') + seed_matrix_path.write_text(json.dumps(summary['historical_seed_delta_matrix'], indent=2, sort_keys=True) + '\n') + paths['synthesis_summary'] = str(summary_path) + paths['baseline_397b_payload'] = str(baseline_path) + paths['historical_seed_delta_matrix'] = str(seed_matrix_path) + for portfolio_id, payload in summary['payloads'].items(): + candidate_path = out_dir / ''.join([format(denom, ''), '_dispatch_rag_seed_portfolio_8700_v1_', format(portfolio_id, ''), '.json']) + route_trace_path = out_dir / ''.join([format(denom, ''), '_route_trace_rag_seed_portfolio_8700_v1_', format(portfolio_id, ''), '.json']) + forced_trace_path = out_dir / ''.join([format(denom, ''), '_forced_fallback_trace_rag_seed_portfolio_8700_v1_', format(portfolio_id, ''), '.json']) + candidate_path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + route_trace_path.write_text(json.dumps(payload['route_trace'], indent=2, sort_keys=True) + '\n') + forced_trace_path.write_text(json.dumps(payload['forced_fallback_route_trace'], indent=2, sort_keys=True) + '\n') + paths[''.join(['candidate_', format(portfolio_id, ''), '_payload'])] = str(candidate_path) + paths[''.join(['candidate_', format(portfolio_id, ''), '_route_trace'])] = str(route_trace_path) + paths[''.join(['candidate_', format(portfolio_id, ''), '_forced_fallback_trace'])] = str(forced_trace_path) + return paths + +def _utc_now() -> str: + return _dt.datetime.now(_dt.timezone.utc).replace(microsecond=0).isoformat().replace('+00:00', 'Z') + +def _gpu_identity() -> dict[str, Any]: + try: + import torch + if not torch.cuda.is_available(): + return {'cuda_available': False} + device = torch.cuda.current_device() + capability = torch.cuda.get_device_capability(device) + props = torch.cuda.get_device_properties(device) + return {'cuda_available': True, 'device_index': device, 'device_name': torch.cuda.get_device_name(device), 'capability': ''.join(['sm_', format(capability[0], ''), format(capability[1], '')]), 'multi_processor_count': props.multi_processor_count, 'total_memory_bytes': props.total_memory} + except Exception as exc: + return {'cuda_available': 'unknown', 'error': repr(exc)} + +def _checked_shape_count(report: dict[str, Any]) -> int | None: + summary = report.get('summary', {}) + if 'checked_shape_count' in summary: + return summary.get('checked_shape_count') + correctness = report.get('correctness', {}) + if 'checked_shape_count' in correctness: + return correctness.get('checked_shape_count') + per_shape = report.get('per_shape') + return len(per_shape) if isinstance(per_shape, dict) else None + +def _report_record(*, candidate_id: str, pair_index: int, path: str, report: dict[str, Any], route_trace_count: int) -> dict[str, Any]: + summary = report.get('summary', {}) + return {'candidate_id': candidate_id, 'pair_index': pair_index, 'path': path, 'tflops': summary.get('primary_mean'), 'all_correct': summary.get('all_correct'), 'performance_comparable': summary.get('performance_comparable'), 'checked_shape_count': _checked_shape_count(report), 'timing_backend': summary.get('timing_backend') or _timing_backends_for_reports(report)[0], 'route_trace_included': True, 'route_trace_count': route_trace_count} + +def _write_json(path: Path, payload: Any) -> str: + path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + return str(path) + +def _baseline_397b_payload(report: dict[str, Any], *, use_cupti: bool, shape_labels, route_trace: list[dict[str, Any]]) -> dict[str, Any]: + return {'candidate_id': 'baseline_397b', 'measured_entrypoint': ROUTE_BASE_397B, 'measured_shape_labels': 'all_canonical' if shape_labels is None else tuple(shape_labels), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'tflops': report['summary']['primary_mean'], 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'route_trace': route_trace, 'route_trace_included': True, 'report': report} + +def write_variance_audit_artifacts(artifact_dir: str | Path, *, pair_count: int=3, use_cupti: bool=True, shape_labels=None, portfolio_id: str=DEFAULT_PORTFOLIO_ID) -> dict[str, str]: + if pair_count <= 0: + raise ValueError('pair_count must be positive') + _portfolio_route_map(portfolio_id) + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + denom = _denominator_label(shape_labels) + started_at = _utc_now() + audit_start = time.perf_counter() + candidate_route_trace = route_trace_for_contract_shapes(shape_labels, portfolio_id=portfolio_id) + forced_fallback_trace = route_trace_for_contract_shapes(shape_labels, portfolio_id=portfolio_id, force_fallback=True) + baseline_route_trace = base_397b.route_trace_for_contract_shapes(shape_labels) + route_trace_paths = {'candidate': _write_json(out_dir / ''.join([format(denom, ''), '_route_trace_rag_seed_portfolio_8700_v1_', format(portfolio_id, ''), '.json']), candidate_route_trace), 'forced_fallback': _write_json(out_dir / ''.join([format(denom, ''), '_forced_fallback_trace_rag_seed_portfolio_8700_v1_', format(portfolio_id, ''), '.json']), forced_fallback_trace), 'baseline': _write_json(out_dir / ''.join([format(denom, ''), '_route_trace_selected_portfolio_397b_v1.json']), baseline_route_trace)} + pairs: list[dict[str, Any]] = [] + candidate_tflops: list[float] = [] + baseline_tflops: list[float] = [] + deltas: list[float] = [] + timing_backends_seen: set[str] = set() + + def _measure(side: str) -> dict[str, Any]: + if side == 'candidate': + return _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_with_portfolio(portfolio_id)) + if side == 'baseline': + return _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_base_dispatcher) + raise ValueError(side) + for pair_index in range(1, pair_count + 1): + pair_started = _utc_now() + pair_start = time.perf_counter() + order = ('candidate', 'baseline') if pair_index % 2 else ('baseline', 'candidate') + reports: dict[str, dict[str, Any]] = {} + for side in order: + reports[side] = _measure(side) + candidate_report = reports['candidate'] + baseline_report = reports['baseline'] + candidate_metric = candidate_report['summary']['primary_mean'] + baseline_metric = baseline_report['summary']['primary_mean'] + delta = candidate_metric - baseline_metric + candidate_tflops.append(candidate_metric) + baseline_tflops.append(baseline_metric) + deltas.append(delta) + timing_backends_seen.update(_timing_backends_for_reports(candidate_report, baseline_report)) + candidate_payload = _benchmark_payload(candidate_report, baseline_report, use_cupti=use_cupti, shape_labels=shape_labels, portfolio_id=portfolio_id) + candidate_payload['audit_candidate_id'] = ''.join(['rag_seed_portfolio_8700_v1_', format(portfolio_id, '')]) + candidate_payload['pair_index'] = pair_index + baseline_payload = _baseline_397b_payload(baseline_report, use_cupti=use_cupti, shape_labels=shape_labels, route_trace=baseline_route_trace) + baseline_payload['pair_index'] = pair_index + candidate_path = _write_json(out_dir / ''.join(['pair', format(pair_index, ''), '_rag_seed_portfolio_8700_v1_', format(portfolio_id, ''), '_', format(denom, ''), '.json']), candidate_payload) + baseline_path = _write_json(out_dir / ''.join(['pair', format(pair_index, ''), '_baseline_397b_', format(denom, ''), '.json']), baseline_payload) + candidate_record = _report_record(candidate_id=''.join(['rag_seed_portfolio_8700_v1_', format(portfolio_id, '')]), pair_index=pair_index, path=candidate_path, report=candidate_report, route_trace_count=len(candidate_route_trace)) + baseline_record = _report_record(candidate_id='baseline_397b', pair_index=pair_index, path=baseline_path, report=baseline_report, route_trace_count=len(baseline_route_trace)) + pairs.append({'pair_index': pair_index, 'started_at': pair_started, 'finished_at': _utc_now(), 'elapsed_s': time.perf_counter() - pair_start, 'order': [''.join(['rag_seed_portfolio_8700_v1_', format(portfolio_id, '')]) if side == 'candidate' else 'baseline_397b' for side in order], 'candidate_path': candidate_path, 'baseline_path': baseline_path, 'candidate_tflops': candidate_metric, 'baseline_tflops': baseline_metric, 'delta_tflops_candidate_minus_base': delta, 'candidate_all_correct': candidate_report['summary']['all_correct'], 'baseline_all_correct': baseline_report['summary']['all_correct'], 'candidate_checked_shape_count': _checked_shape_count(candidate_report), 'baseline_checked_shape_count': _checked_shape_count(baseline_report), 'candidate_record': candidate_record, 'baseline_record': baseline_record}) + all_payloads_correct = all((bool(pair['candidate_all_correct']) and bool(pair['baseline_all_correct']) for pair in pairs)) + route_trace_count = len(candidate_route_trace) + expected_shape_count = len(eval_mod.CANONICAL_SHAPES) if shape_labels is None else len(tuple(shape_labels)) + all_route_traces_present = len(candidate_route_trace) == expected_shape_count and len(forced_fallback_trace) == expected_shape_count and (len(baseline_route_trace) == expected_shape_count) + median_delta = statistics.median(deltas) + mean_delta = statistics.fmean(deltas) + delta_range = [min(deltas), max(deltas)] + status = 'pass' if all_payloads_correct and all_route_traces_present and (median_delta >= 0) else 'fail' + decision = 'promotion_gate_candidate' if status == 'pass' else 'route_guard_order_or_launch_overhead_repair' + confidence_interval = ''.join(['range [', format(delta_range[0], ''.join(['.6f'])), ', ', format(delta_range[1], ''.join(['.6f'])), '] TFLOPS across ', format(pair_count, ''), ' paired deltas']) + summary = {'audit': ''.join(['8700_', format(portfolio_id, ''), '_vs_397b_repeated_', format(denom, '')]), 'started_at': started_at, 'finished_at': _utc_now(), 'elapsed_s': time.perf_counter() - audit_start, 'host': socket.gethostname(), 'platform': platform.platform(), 'gpu': _gpu_identity(), 'denominator': ''.join([format(denom, ''), ' v5 knn_build dispatcher']), 'pair_count': pair_count, 'order_policy': 'interleaved alternating order; odd pairs 8700->397b, even pairs 397b->8700', 'randomized_or_interleaved_order': True, 'candidate_entrypoint': ''.join(['loom.examples.weave.knn_build_dispatch_rag_seed_portfolio_8700_v1:launch_from_contract_inputs(portfolio_id=', format(portfolio_id, ''), ')']), 'baseline_entrypoint': ROUTE_BASE_397B, 'same_recorded_entrypoints': True, 'same_entrypoint': True, 'same_entrypoint_note': 'candidate and baseline use the recorded 8700 and 397b dispatcher entrypoints through the same full contract harness', 'timing_backend': 'cupti' if use_cupti else 'cuda_event', 'timing_backends_observed': sorted(timing_backends_seen), 'same_backend': len(timing_backends_seen) == 1, 'same_node': True, 'same_gpu_class': True, 'candidate_tflops': candidate_tflops, 'baseline_tflops': baseline_tflops, 'delta_tflops_candidate_minus_base': deltas, 'median_candidate_tflops': statistics.median(candidate_tflops), 'median_baseline_tflops': statistics.median(baseline_tflops), 'median_delta_tflops_candidate_minus_base': median_delta, 'mean_delta_tflops_candidate_minus_base': mean_delta, 'delta_range_tflops_candidate_minus_base': delta_range, 'confidence_interval': confidence_interval, 'all_payloads_correct': all_payloads_correct, 'all_route_traces_present': all_route_traces_present, 'route_trace_count': route_trace_count, 'forced_fallback_route_trace_count': len(forced_fallback_trace), 'baseline_route_trace_count': len(baseline_route_trace), 'route_trace_paths': route_trace_paths, 'pairs': pairs, 'historical_baseline_classification': 'stale_until_reconciled_by_this_same-session_audit', 'stale_historical_gate': True, 'decision': decision, 'variance_audit_frontmatter': {'status': status, 'repeated_pair_count': pair_count, 'same_node': True, 'same_gpu_class': True, 'same_backend': len(timing_backends_seen) == 1, 'same_entrypoint': True, 'randomized_or_interleaved_order': True, 'median_delta': median_delta, 'confidence_interval': confidence_interval, 'stale_historical_gate': True}} + summary_path = out_dir / ''.join([format(denom, ''), '_variance_summary_8700_', format(portfolio_id, ''), '_vs_397b.json']) + paths: dict[str, str] = {'variance_summary': _write_json(summary_path, summary), **route_trace_paths} + for pair in pairs: + paths[''.join(['pair', format(pair['pair_index'], ''), '_candidate_payload'])] = pair['candidate_path'] + paths[''.join(['pair', format(pair['pair_index'], ''), '_baseline_payload'])] = pair['baseline_path'] + return paths diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_selected_portfolio_397b_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_selected_portfolio_397b_v1.py new file mode 100644 index 00000000..58a790de --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_selected_portfolio_397b_v1.py @@ -0,0 +1,200 @@ +"""Coverage-only RAG microbatch overlay for the 4a72 kNN build dispatcher. + +Minimum target architecture: sm_100a. This wrapper starts from the 4a72 full-v5 +selected portfolio and adds one exact guard for BF16 non-build RAG microbatch +rows ``B=1,Q in {8,16,32},M=100000,D=128,K=10``. The guarded route reuses the +b2ec fused S72/G8 Weave sidecar. This is a coverage/latency A/B only: the route +remains below FlashLib and must not be treated as a production promotion. + +Every production route remains Weave-only. FlashLib is used only by the +contract harness as a black-box timing peer. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +from pathlib import Path +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_selected_portfolio_4a72_v1 as base_4a72 +from . import knn_build_rag_microbatch_4a72_v1 as rag_seed +ROUTE_BASE_4A72 = 'loom.examples.weave.knn_build_dispatch_selected_portfolio_4a72_v1:launch_from_contract_inputs' +ROUTE_RAG_MICROBATCH_S72_G8 = ''.join(['loom.examples.weave.knn_build_rag_microbatch_4a72_v1:rag_microbatch_4a72_k10_s', format(rag_seed.K10_SPLIT_COUNT, ''), '_g', format(rag_seed.K10_GROUP_COUNT, ''), '_fusedmerge']) +RAG_MICROBATCH_TARGET_SHAPES = rag_seed.TARGET_SHAPES +RAG_MICROBATCH_TARGET_SHAPE_SET = set(RAG_MICROBATCH_TARGET_SHAPES) +SELECTED_TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_large_b1_q8192_m8192_d128_k20", "build_large_b1_q8192_m8192_d128_k32", "build_over64_stress_qm2048_k96", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32", "rag_batch_b2_q256_m50000_d128_k10", "rag_irregular_b1_q512_m131071_d128_k10", "search_rect_b1_q1024_m8192_d128_k10", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q2048_m2048_d256_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10", "search_rect_b1_q2048_m32768_d128_k10", "build_large_tail_b1_q6144_m6144_d128_k20", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "build_over32_stress_qm2048_k64", "build_over32_stress_qm4096_k64", "build_k_sweep_qm512_k1", "build_k_sweep_qm512_k2", "build_k_sweep_qm1024_k16", "build_k_sweep_qm512_k4", "build_k_sweep_qm512_k5", "build_k_sweep_qm512_k6", "rag_microbatch_b1_q8_m100000_d128_k10", "rag_microbatch_b1_q32_m100000_d128_k10"]}')) +CONSUMED_SEED_TARGET_SHAPES = RAG_MICROBATCH_TARGET_SHAPES +GUARD_MISS_AUDIT_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_k_sweep_qm512_k4", "build_k_sweep_qm512_k8", "build_k_sweep_qm512_k10", "rag_microbatch_b1_q8_m100000_d128_k10", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_microbatch_b1_q32_m100000_d128_k10", "build_large_b1_q8192_m8192_d128_k20", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_qm2048_d128_k10", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "flashml_correctness_b1_q256_m256_d128_k5", "build_over32_stress_qm2048_k64", "search_rect_over32_b1_q2048_m65536_d128_k64", "rag_offline_large_m_over32_b1_q2048_m250000_d128_k64", "build_k_sweep_qm512_k5", "build_over32_stress_qm4096_k64"]}')) +DISPATCH_CORRECTNESS_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["flashml_correctness_b1_q256_m256_d128_k5", "build_large_b1_q8192_m8192_d128_k20", "build_large_b1_q8192_m8192_d128_k32", "build_over64_stress_qm2048_k96", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32", "rag_batch_b2_q256_m50000_d128_k10", "rag_irregular_b1_q512_m131071_d128_k10", "search_rect_b1_q1024_m8192_d128_k10", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q2048_m2048_d256_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10", "search_rect_b1_q2048_m32768_d128_k10", "build_large_tail_b1_q6144_m6144_d128_k20", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "build_over32_stress_qm2048_k64", "build_over32_stress_qm4096_k64", "build_k_sweep_qm512_k1", "build_k_sweep_qm512_k2", "build_k_sweep_qm1024_k16", "build_k_sweep_qm512_k4", "build_k_sweep_qm512_k5", "build_k_sweep_qm512_k6", "rag_microbatch_b1_q8_m100000_d128_k10", "rag_microbatch_b1_q32_m100000_d128_k10", "build_k_sweep_qm512_k8", "build_k_sweep_qm512_k10", "build_qm2048_d128_k10", "search_rect_over32_b1_q2048_m65536_d128_k64", "rag_offline_large_m_over32_b1_q2048_m250000_d128_k64", "rag_online_b1_q1_m100000_d128_k10", "rag_stream_b1_q128_m100000_d128_k10", "search_rect_b1_q4096_m65536_d128_k20", "rag_offline_largek_b1_q4096_m100000_d128_k20", "rag_offline_large_m_b1_q8192_m250000_d128_k20"]}')) +PRODUCTION_ROUTE_MODULES = {**base_4a72.PRODUCTION_ROUTE_MODULES, 'rag_microbatch_b2ec_s72_g8': 'loom.examples.weave.knn_build_rag_microbatch_4a72_v1:launch_from_contract_inputs', 'base_4a72': ROUTE_BASE_4A72} +CANDIDATE_PORTFOLIOS = ({'id': 'baseline_selected_portfolio_4a72_v1', 'entrypoint': 'loom.examples.weave.knn_build_dispatch_selected_portfolio_4a72_v1:benchmark_knn_build_dispatch_selected_portfolio_4a72_v1', 'consumed_seeds': base_4a72.CANDIDATE_PORTFOLIOS[-1]['consumed_seeds'], 'guard_plan': base_4a72.CANDIDATE_PORTFOLIOS[-1]['guard_plan'], 'expected_shape_wins': base_4a72.SELECTED_TARGET_SHAPES, 'rejected_reason': 'same-session baseline for the 397b RAG coverage-only overlay'}, {'id': 'selected_397b_4a72_plus_b2ec_rag_microbatch', 'entrypoint': 'loom.examples.weave.knn_build_dispatch_selected_portfolio_397b_v1:benchmark_knn_build_dispatch_selected_portfolio_397b_v1', 'consumed_seeds': ('selected_4a72_f16b_plus_q512_k4_k5_k6', 'rag_microbatch_4a72_v1_s72_g8_b2ec_coverage_sidecar'), 'guard_plan': ('exact b2ec RAG microbatch BF16 non-build B1 Q in {8,16,32} M100000 D128 K10 guard', 'then 4a72 selected full-v5 guard plan'), 'expected_shape_wins': SELECTED_TARGET_SHAPES, 'rejected_reason': None}) +RAG_MICROBATCH_ROW_SELECTION = {'rag_microbatch_b1_q8_m100000_d128_k10': {'selected_seed': 'rag_microbatch_4a72_v1_s72_g8_b2ec_coverage_sidecar', 'selected_route': ROUTE_RAG_MICROBATCH_S72_G8, 'targeted_seed_ms': 0.078304, 'targeted_seed_tflops': None, 'targeted_seed_timing_backend': 'cupti', 'targeted_ratio_vs_flashlib': 0.8917041275030649, 'targeted_speedup_vs_current_4a72': 121.5089267470372, 'targeted_current_4a72_ms': 9.514635, 'targeted_flashlib_ms': 0.069824, 'reason': 'b2ec repairs the inherited Q8 guard miss by routing to a correct Weave sidecar, but remains below FlashLib.'}, 'rag_microbatch_b1_q16_m100000_d128_k10': {'selected_seed': 'rag_microbatch_4a72_v1_s72_g8_b2ec_coverage_sidecar', 'selected_route': ROUTE_RAG_MICROBATCH_S72_G8, 'targeted_seed_ms': 0.091552, 'targeted_seed_tflops': None, 'targeted_seed_timing_backend': 'cupti', 'targeted_ratio_vs_flashlib': 0.8130133694512409, 'targeted_speedup_vs_current_4a72': 1.0304089479203076, 'targeted_current_4a72_ms': 0.094336, 'targeted_flashlib_ms': 0.074433, 'reason': 'b2ec is modestly faster than the inherited Q16 7399 route, but remains below FlashLib.'}, 'rag_microbatch_b1_q32_m100000_d128_k10': {'selected_seed': 'rag_microbatch_4a72_v1_s72_g8_b2ec_coverage_sidecar', 'selected_route': ROUTE_RAG_MICROBATCH_S72_G8, 'targeted_seed_ms': 0.107393, 'targeted_seed_tflops': None, 'targeted_seed_timing_backend': 'cupti', 'targeted_ratio_vs_flashlib': 0.8423547158567132, 'targeted_speedup_vs_current_4a72': 80.77571620272737, 'targeted_current_4a72_ms': 8.6747465, 'targeted_flashlib_ms': 0.090463, 'reason': 'b2ec repairs the inherited Q32 guard miss by routing to a correct Weave sidecar, but remains below FlashLib.'}} +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k48over32", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 48]], "cta_group": 1, "threads": 192}')) + +def _eligible_rag_microbatch(inputs: dict[str, Any]) -> bool: + return rag_seed._eligible_rag_microbatch(inputs) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False, enable_rag_microbatch: bool=True, enable_q512_k456: bool=True) -> str: + if not force_fallback and enable_rag_microbatch and _eligible_rag_microbatch(inputs): + return ROUTE_RAG_MICROBATCH_S72_G8 + return base_4a72.route_for_contract_inputs(inputs, force_fallback=False, enable_q512_k456=enable_q512_k456) + +def _launch_route(inputs: dict[str, Any], route: str) -> None: + if route == ROUTE_RAG_MICROBATCH_S72_G8 and _eligible_rag_microbatch(inputs): + rag_seed._launch_rag_microbatch_fused_merge(inputs, split_count=rag_seed.K10_SPLIT_COUNT, group_count=rag_seed.K10_GROUP_COUNT) + return + base_4a72._launch_route(inputs, route) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False, enable_rag_microbatch: bool=True, enable_q512_k456: bool=True) -> None: + route = route_for_contract_inputs(inputs, force_fallback=force_fallback, enable_rag_microbatch=enable_rag_microbatch, enable_q512_k456=enable_q512_k456) + _launch_route(inputs, route) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def candidate_base_dispatcher(inputs: dict[str, Any]): + base_4a72.launch_from_contract_inputs(inputs) + return None + +def candidate_force_fallback(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs, force_fallback=True) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return base_4a72._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=DISPATCH_CORRECTNESS_SHAPES, benchmark: bool=False) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=None, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + shapes = None if shape_labels is None else _select_contract_shapes(shape_labels) + return evaluate_contract(shapes=shapes, correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def _trace_inputs_from_shape(shape: dict[str, Any]) -> dict[str, Any]: + return base_4a72._trace_inputs_from_shape(shape) + +def _inputs_for_label(label: str) -> dict[str, Any]: + return base_4a72._inputs_for_label(label) + +def _route_trace_record(inputs: dict[str, Any], *, force_fallback: bool=False) -> dict[str, Any]: + base_route = base_4a72.route_for_contract_inputs(inputs) + label = str(inputs.get('label')) + if force_fallback and _eligible_rag_microbatch(inputs): + row = base_4a72._route_trace_record(inputs) + row['selected_route'] = base_route + row['guard_condition'] = 'forced fallback to 4a72 base; b2ec RAG coverage overlay disabled' + row['coverage'] = 'forced candidate fallback for 397b RAG coverage overlay only' + row['forced_disabled_seeds'] = ('rag_microbatch_4a72_v1_s72_g8_b2ec_coverage_sidecar',) + row['base_4a72_route'] = base_route + row['candidate_guard_status'] = 'forced_fallback_to_4a72' + return row + route = route_for_contract_inputs(inputs, force_fallback=force_fallback) + if route == ROUTE_RAG_MICROBATCH_S72_G8 and label in RAG_MICROBATCH_ROW_SELECTION: + selected = RAG_MICROBATCH_ROW_SELECTION[label] + return {'shape_key': inputs.get('label'), 'selected_route': route, 'guard_condition': 'exact BF16 non-build B1 Q in {8,16,32} M=100000 D128 K10 RAG microbatch coverage route', 'route_kind': 'coverage-only', 'coverage': '397b routes the b2ec RAG sidecar ahead of the inherited 4a72 route', 'consumed_seed': selected['selected_seed'], 'replaced_route': base_route, 'base_4a72_route': base_route, 'row_selection': selected, 'parity_status': 'fail', 'parity_reason': selected['reason'], 'candidate_guard_status': 'selected_from_b2ec_rag_microbatch_s72_g8'} + row = base_4a72._route_trace_record(inputs) + row['base_4a72_route'] = base_route + row['candidate_guard_status'] = 'inherited_4a72_or_guard_miss' + return row + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + return [_route_trace_record(_trace_inputs_from_shape(shape), force_fallback=force_fallback) for shape in selected] + +def _timing_backends_for_reports(*reports: dict[str, Any]) -> list[str]: + return base_4a72._timing_backends_for_reports(*reports) + +def _rows_for_labels(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return base_4a72._rows_for_labels(report, labels) + +def _per_consumed_row_delta(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> dict[str, Any]: + deltas = {} + for label in CONSUMED_SEED_TARGET_SHAPES: + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') + inputs = _inputs_for_label(label) + deltas[label] = {'candidate_ms': candidate_ms, 'baseline_4a72_ms': baseline_ms, 'flashlib_ms': flashlib_ms, 'speedup_vs_baseline_4a72': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'ratio_vs_flashlib': flashlib_ms / candidate_ms if candidate_ms and flashlib_ms else None, 'candidate_route': route_for_contract_inputs(inputs), 'baseline_4a72_route': base_4a72.route_for_contract_inputs(inputs), 'candidate_passed': candidate_row.get('passed'), 'baseline_passed': baseline_row.get('passed')} + return deltas + +def _seed_delta_matrix(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> list[dict[str, Any]]: + matrix = [] + for label in CONSUMED_SEED_TARGET_SHAPES: + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + inputs = _inputs_for_label(label) + matrix.append({'shape_key': label, 'baseline_route': base_4a72.route_for_contract_inputs(inputs), 'candidate_route': route_for_contract_inputs(inputs), 'candidate_ms': candidate_ms, 'baseline_ms': baseline_ms, 'metric_delta_ms': candidate_ms - baseline_ms if candidate_ms and baseline_ms else None, 'speedup_vs_baseline_4a72': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'timing_backend': candidate_row.get('timing_backend') or baseline_row.get('timing_backend')}) + return matrix + +def _frontmatter_seed_delta_matrix(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> list[dict[str, Any]]: + rows = [] + for item in _seed_delta_matrix(candidate_report, baseline_report): + rows.append({'shape_key': item['shape_key'], 'baseline_route': item['baseline_route'], 'candidate_deltas': [{'candidate_id': 'selected_397b_4a72_plus_b2ec_rag_microbatch', 'metric_delta': item['metric_delta_ms'], 'timing_backend': item['timing_backend'] or 'cupti'}]}) + return rows + +def _below_flashlib_rows(report: dict[str, Any]) -> list[dict[str, Any]]: + rows = [] + trace_by_label = {str(row['shape_key']): row for row in route_trace_for_contract_shapes()} + for label, row in sorted(report.get('per_shape', {}).items()): + ratio = row.get('ratio_vs_flashlib') + if isinstance(ratio, (float, int)) and ratio < 1.0: + inputs = _inputs_for_label(label) + route = route_for_contract_inputs(inputs) + trace_row = trace_by_label.get(label, {}) + rows.append({'shape_key': label, 'kernel_ms': row.get('kernel_ms'), 'flashlib_ms': row.get('flashlib_ms'), 'ratio_vs_flashlib': ratio, 'selected_route': route, 'route_kind': trace_row.get('route_kind', 'unknown')}) + return rows + +def _benchmark_payload(candidate_report: dict[str, Any], baseline_report: dict[str, Any], *, use_cupti: bool, shape_labels) -> dict[str, Any]: + candidate_metric = candidate_report['summary']['primary_mean'] + baseline_metric = baseline_report['summary']['primary_mean'] + route_trace = route_trace_for_contract_shapes(shape_labels) + below_flashlib = _below_flashlib_rows(candidate_report) + return {'tflops': candidate_metric, 'baseline_tflops': baseline_metric, 'metric_delta': candidate_metric - baseline_metric if candidate_metric and baseline_metric else None, 'all_correct': candidate_report['summary']['all_correct'], 'baseline_all_correct': baseline_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'baseline_performance_comparable': baseline_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'baseline_invalid_performance_reason': baseline_report['summary']['invalid_performance_reason'], 'measured_entrypoint': 'loom.examples.weave.knn_build_dispatch_selected_portfolio_397b_v1:benchmark_knn_build_dispatch_selected_portfolio_397b_v1', 'baseline_entrypoint': ROUTE_BASE_4A72, 'baseline_entrypoint_note': 'same-session 4a72 selected portfolio measured through the same contract denominator', 'measured_shape_labels': 'all_canonical' if shape_labels is None else tuple(shape_labels), 'selected_route_labels': SELECTED_TARGET_SHAPES, 'consumed_seed_labels': CONSUMED_SEED_TARGET_SHAPES, 'guard_miss_audit_labels': GUARD_MISS_AUDIT_SHAPES, 'selected_route_rows': _rows_for_labels(candidate_report, SELECTED_TARGET_SHAPES), 'baseline_selected_route_rows': _rows_for_labels(baseline_report, SELECTED_TARGET_SHAPES), 'consumed_seed_rows': _rows_for_labels(candidate_report, CONSUMED_SEED_TARGET_SHAPES), 'baseline_consumed_seed_rows': _rows_for_labels(baseline_report, CONSUMED_SEED_TARGET_SHAPES), 'guard_miss_audit_rows': _rows_for_labels(candidate_report, GUARD_MISS_AUDIT_SHAPES), 'baseline_guard_miss_audit_rows': _rows_for_labels(baseline_report, GUARD_MISS_AUDIT_SHAPES), 'per_consumed_row_delta': _per_consumed_row_delta(candidate_report, baseline_report), 'seed_delta_matrix': _seed_delta_matrix(candidate_report, baseline_report), 'frontmatter_seed_delta_matrix': _frontmatter_seed_delta_matrix(candidate_report, baseline_report), 'candidate_dispatchers': CANDIDATE_PORTFOLIOS, 'selected_candidate_dispatcher': 'selected_397b_4a72_plus_b2ec_rag_microbatch', 'rag_microbatch_row_selection': RAG_MICROBATCH_ROW_SELECTION, 'route_modules': PRODUCTION_ROUTE_MODULES, 'route_trace': route_trace, 'forced_fallback_route_trace': route_trace_for_contract_shapes(shape_labels, force_fallback=True), 'route_trace_included': True, 'contract_summary': candidate_report['summary'], 'baseline_contract_summary': baseline_report['summary'], 'contract_performance': candidate_report['performance'], 'baseline_contract_performance': baseline_report['performance'], 'timing_backends': _timing_backends_for_reports(candidate_report, baseline_report), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'hot_bucket_parity': {'rag_microbatch_q8_q16_q32_m100000_k10': 'fail_below_flashlib_coverage_only', 'lowk_q512_k4_k5_k6': 'inherited_4a72_pass'}, 'performance_coverage': 'partial' if below_flashlib else 'pass', 'coverage_only_routes': list(CONSUMED_SEED_TARGET_SHAPES), 'hot_bucket_blockers': below_flashlib, 'report': candidate_report, 'baseline_report': baseline_report} + +def benchmark_knn_build_dispatch_selected_portfolio_397b_v1(*, use_cupti: bool=True, shape_labels=None) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels) + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_base_dispatcher) + return _benchmark_payload(candidate_report, baseline_report, use_cupti=use_cupti, shape_labels=shape_labels) + +def _denominator_label(shape_labels) -> str: + if shape_labels is None: + return ''.join(['full', format(len(eval_mod.CANONICAL_SHAPES), '')]) + return ''.join(['shape', format(len(tuple(shape_labels)), '')]) + +def write_benchmark_artifacts(artifact_dir: str | Path, *, use_cupti: bool=True, shape_labels=None) -> dict[str, str]: + payload = benchmark_knn_build_dispatch_selected_portfolio_397b_v1(use_cupti=use_cupti, shape_labels=shape_labels) + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + denom = _denominator_label(shape_labels) + candidate_path = out_dir / ''.join([format(denom, ''), '_dispatch_selected_portfolio_397b_v1.json']) + baseline_path = out_dir / ''.join([format(denom, ''), '_same_session_baseline_4a72_for_397b_v1.json']) + route_trace_path = out_dir / ''.join([format(denom, ''), '_route_trace_selected_portfolio_397b_v1.json']) + forced_trace_path = out_dir / ''.join([format(denom, ''), '_forced_fallback_trace_selected_portfolio_397b_v1.json']) + candidate_path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + baseline_path.write_text(json.dumps({'measured_entrypoint': payload['baseline_entrypoint'], 'measured_shape_labels': payload['measured_shape_labels'], 'timing_backend_requested': payload['timing_backend_requested'], 'timing_backends': payload['timing_backends'], 'tflops': payload['baseline_tflops'], 'all_correct': payload['baseline_all_correct'], 'performance_comparable': payload['baseline_performance_comparable'], 'contract_summary': payload['baseline_contract_summary'], 'contract_performance': payload['baseline_contract_performance'], 'route_trace': base_4a72.route_trace_for_contract_shapes(shape_labels), 'route_trace_included': True, 'report': payload['baseline_report']}, indent=2, sort_keys=True) + '\n') + route_trace_path.write_text(json.dumps(payload['route_trace'], indent=2, sort_keys=True) + '\n') + forced_trace_path.write_text(json.dumps(payload['forced_fallback_route_trace'], indent=2, sort_keys=True) + '\n') + return {'candidate_payload': str(candidate_path), 'baseline_4a72_payload': str(baseline_path), 'route_trace': str(route_trace_path), 'forced_fallback_trace': str(forced_trace_path)} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_selected_portfolio_4a72_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_selected_portfolio_4a72_v1.py new file mode 100644 index 00000000..b04a457c --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_selected_portfolio_4a72_v1.py @@ -0,0 +1,215 @@ +"""v5 kNN build dispatcher overlay for the Q512 K4/K5/K6 blind spot. + +Minimum target architecture: sm_100a. This wrapper starts from the f16b +selected portfolio and adds one exact guard for BF16 build rows +``B=1,Q=M=512,D=128,K in {4,5,6}``. The guarded route reuses the existing +low-K split4 Weave seed; all other shapes delegate to f16b unchanged. + +Every production route remains Weave-only. FlashLib is used only by the +contract harness as a black-box timing peer. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +from pathlib import Path +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_selected_portfolio_f16b_v1 as base_f16b +from . import knn_build_lowk_f8c3_q512_q1024_v1 as lowk_seed +ROUTE_BASE_F16B = 'loom.examples.weave.knn_build_dispatch_selected_portfolio_f16b_v1:launch_from_contract_inputs' +ROUTE_LOWK_Q512_K456_S4 = _decode_capture(_json_loads('"loom.examples.weave.knn_build_lowk_f8c3_q512_q1024_v1:q512_lowk_s4"')) +Q512_K456_TARGET_SHAPES = ('build_k_sweep_qm512_k4', 'build_k_sweep_qm512_k5', 'build_k_sweep_qm512_k6') +Q512_K456_TARGET_SHAPE_SET = set(Q512_K456_TARGET_SHAPES) +SELECTED_TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_large_b1_q8192_m8192_d128_k20", "build_large_b1_q8192_m8192_d128_k32", "build_over64_stress_qm2048_k96", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32", "rag_batch_b2_q256_m50000_d128_k10", "rag_irregular_b1_q512_m131071_d128_k10", "search_rect_b1_q1024_m8192_d128_k10", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q2048_m2048_d256_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10", "search_rect_b1_q2048_m32768_d128_k10", "build_large_tail_b1_q6144_m6144_d128_k20", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "build_over32_stress_qm2048_k64", "build_over32_stress_qm4096_k64", "build_k_sweep_qm512_k1", "build_k_sweep_qm512_k2", "build_k_sweep_qm1024_k16", "build_k_sweep_qm512_k4", "build_k_sweep_qm512_k5", "build_k_sweep_qm512_k6"]}')) +CONSUMED_SEED_TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["rag_stream_largek_b1_q128_m100000_d128_k32", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q2048_m2048_d256_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10", "search_rect_b1_q2048_m32768_d128_k10", "build_over64_stress_qm2048_k96", "build_large_tail_b1_q6144_m6144_d128_k20", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "build_over32_stress_qm2048_k64", "build_over32_stress_qm4096_k64", "build_k_sweep_qm512_k1", "build_k_sweep_qm512_k2", "build_k_sweep_qm1024_k16", "build_large_b1_q8192_m8192_d128_k32", "build_k_sweep_qm512_k4", "build_k_sweep_qm512_k5", "build_k_sweep_qm512_k6"]}')) +GUARD_MISS_AUDIT_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_k_sweep_qm512_k8", "build_k_sweep_qm512_k10", "build_large_b1_q8192_m8192_d128_k20", "rag_microbatch_b1_q8_m100000_d128_k10", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_microbatch_b1_q32_m100000_d128_k10", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_qm2048_d128_k10", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "flashml_correctness_b1_q256_m256_d128_k5", "build_over32_stress_qm2048_k64", "search_rect_over32_b1_q2048_m65536_d128_k64", "rag_offline_large_m_over32_b1_q2048_m250000_d128_k64", "build_k_sweep_qm512_k5", "build_over32_stress_qm4096_k64"]}')) +DISPATCH_CORRECTNESS_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["flashml_correctness_b1_q256_m256_d128_k5", "build_large_b1_q8192_m8192_d128_k20", "build_large_b1_q8192_m8192_d128_k32", "build_over64_stress_qm2048_k96", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32", "rag_batch_b2_q256_m50000_d128_k10", "rag_irregular_b1_q512_m131071_d128_k10", "search_rect_b1_q1024_m8192_d128_k10", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q2048_m2048_d256_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10", "search_rect_b1_q2048_m32768_d128_k10", "build_large_tail_b1_q6144_m6144_d128_k20", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "build_over32_stress_qm2048_k64", "build_over32_stress_qm4096_k64", "build_k_sweep_qm512_k1", "build_k_sweep_qm512_k2", "build_k_sweep_qm1024_k16", "build_k_sweep_qm512_k4", "build_k_sweep_qm512_k5", "build_k_sweep_qm512_k6", "build_k_sweep_qm512_k8", "build_k_sweep_qm512_k10", "rag_microbatch_b1_q8_m100000_d128_k10", "rag_microbatch_b1_q32_m100000_d128_k10", "build_qm2048_d128_k10", "search_rect_over32_b1_q2048_m65536_d128_k64", "rag_offline_large_m_over32_b1_q2048_m250000_d128_k64", "rag_online_b1_q1_m100000_d128_k10", "rag_stream_b1_q128_m100000_d128_k10", "search_rect_b1_q4096_m65536_d128_k20", "rag_offline_largek_b1_q4096_m100000_d128_k20", "rag_offline_large_m_b1_q8192_m250000_d128_k20"]}')) +PRODUCTION_ROUTE_MODULES = {**base_f16b.PRODUCTION_ROUTE_MODULES, 'lowk_b193_q512_k4_k5_k6_s4': ROUTE_LOWK_Q512_K456_S4, 'base_f16b': ROUTE_BASE_F16B} +CANDIDATE_PORTFOLIOS = ({'id': 'baseline_selected_portfolio_f16b_v1', 'entrypoint': 'loom.examples.weave.knn_build_dispatch_selected_portfolio_f16b_v1:benchmark_knn_build_dispatch_selected_portfolio_f16b_v1', 'consumed_seeds': base_f16b.CANDIDATE_PORTFOLIOS[-1]['consumed_seeds'], 'guard_plan': base_f16b.CANDIDATE_PORTFOLIOS[-1]['guard_plan'], 'expected_shape_wins': base_f16b.SELECTED_TARGET_SHAPES, 'rejected_reason': 'same-session baseline for the v5 Q512 K4/K5/K6 overlay'}, {'id': 'selected_4a72_f16b_plus_q512_k4_k5_k6', 'entrypoint': 'loom.examples.weave.knn_build_dispatch_selected_portfolio_4a72_v1:benchmark_knn_build_dispatch_selected_portfolio_4a72_v1', 'consumed_seeds': ('selected_f16b_f853_plus_b193_lowk_plus_5407_q8192', 'lowk_f8c3_q512_q1024_b193_q512_k4_k5_k6_adapter'), 'guard_plan': ('exact b193-derived Q512 K4/K5/K6 BF16 build guard', 'then f16b selected full-v5 guard plan'), 'expected_shape_wins': SELECTED_TARGET_SHAPES, 'rejected_reason': None}) +Q512_K456_ROW_SELECTION = {'build_k_sweep_qm512_k4': {'selected_seed': 'lowk_f8c3_q512_q1024_b193_q512_k4_k5_k6_adapter', 'selected_route': ROUTE_LOWK_Q512_K456_S4, 'targeted_seed_ms': 0.030496, 'targeted_seed_tflops': 2.2002606905823714, 'targeted_seed_timing_backend': 'cupti', 'targeted_ratio_vs_flashlib': 1.9979013641133265, 'targeted_baseline_f16b_correctness': 'fail', 'reason': 'The split4 low-K route is correct for Q512 K4 and beats FlashLib in the 4a72 CUPTI bucket probe.'}, 'build_k_sweep_qm512_k5': {'selected_seed': 'lowk_f8c3_q512_q1024_b193_q512_k4_k5_k6_adapter', 'selected_route': ROUTE_LOWK_Q512_K456_S4, 'targeted_seed_ms': 0.031424, 'targeted_seed_tflops': 2.135282586557408, 'targeted_seed_timing_backend': 'cupti', 'targeted_ratio_vs_flashlib': 1.9490994144602851, 'targeted_baseline_f16b_correctness': 'pass', 'reason': 'The split4 low-K route is correct for Q512 K5 and beats FlashLib in the 4a72 CUPTI bucket probe.'}, 'build_k_sweep_qm512_k6': {'selected_seed': 'lowk_f8c3_q512_q1024_b193_q512_k4_k5_k6_adapter', 'selected_route': ROUTE_LOWK_Q512_K456_S4, 'targeted_seed_ms': 0.035328, 'targeted_seed_tflops': 1.8992972141072246, 'targeted_seed_timing_backend': 'cupti', 'targeted_ratio_vs_flashlib': 2.556159420289855, 'targeted_baseline_f16b_correctness': 'fail', 'reason': 'The split4 low-K route is correct for Q512 K6 and beats FlashLib in the 4a72 CUPTI bucket probe.'}} +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k48over32", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 48]], "cta_group": 1, "threads": 192}')) + +def _dtype_name(inputs: dict[str, Any]) -> str: + query = inputs.get('query') + if query is not None: + return str(query.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')) + +def _label_can_hit(inputs: dict[str, Any], labels: set[str]) -> bool: + value = inputs.get('label') + return value is None or str(value) in labels + +def _eligible_q512_k456(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, Q512_K456_TARGET_SHAPE_SET) and bool(inputs.get('build', False)) and (int(inputs.get('B', -1)) == 1) and (int(inputs.get('Q', -1)) == 512) and (int(inputs.get('M', -2)) == 512) and (int(inputs.get('D', -1)) == 128) and (int(inputs.get('K', -1)) in (4, 5, 6)) and (_dtype_name(inputs) == 'bfloat16') + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False, enable_q512_k456: bool=True) -> str: + if not force_fallback and enable_q512_k456 and _eligible_q512_k456(inputs): + return ROUTE_LOWK_Q512_K456_S4 + return base_f16b.route_for_contract_inputs(inputs, force_fallback=force_fallback) + +def _launch_route(inputs: dict[str, Any], route: str) -> None: + if route == ROUTE_LOWK_Q512_K456_S4 and _eligible_q512_k456(inputs): + lowk_seed._launch_q512_lowk_split(inputs, split_count=lowk_seed.DEFAULT_Q512_SPLITS) + return + base_f16b._launch_route(inputs, route) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False, enable_q512_k456: bool=True) -> None: + route = route_for_contract_inputs(inputs, force_fallback=force_fallback, enable_q512_k456=enable_q512_k456) + _launch_route(inputs, route) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def candidate_base_dispatcher(inputs: dict[str, Any]): + base_f16b.launch_from_contract_inputs(inputs) + return None + +def candidate_force_fallback(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs, force_fallback=True) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return base_f16b._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=DISPATCH_CORRECTNESS_SHAPES, benchmark: bool=False) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=None, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + shapes = None if shape_labels is None else _select_contract_shapes(shape_labels) + return evaluate_contract(shapes=shapes, correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def _trace_inputs_from_shape(shape: dict[str, Any]) -> dict[str, Any]: + return base_f16b._trace_inputs_from_shape(shape) + +def _inputs_for_label(label: str) -> dict[str, Any]: + return base_f16b._inputs_for_label(label) + +def _route_trace_record(inputs: dict[str, Any], *, force_fallback: bool=False) -> dict[str, Any]: + base_route = base_f16b.route_for_contract_inputs(inputs) + if force_fallback: + row = base_f16b._route_trace_record(inputs, force_fallback=True) + row['guard_condition'] = 'forced fallback to f16b baseline; 4a72 Q512 K4/K5/K6 overlay disabled' + row['coverage'] = 'forced candidate fallback for 4a72 Q512 K4/K5/K6 overlay only' + row['forced_disabled_seeds'] = ('lowk_f8c3_q512_q1024_b193_q512_k4_k5_k6_adapter',) + row['base_f16b_route'] = base_route + row['candidate_guard_status'] = 'forced_fallback_to_f16b' + return row + route = route_for_contract_inputs(inputs) + label = str(inputs.get('label')) + if route == ROUTE_LOWK_Q512_K456_S4 and label in Q512_K456_ROW_SELECTION: + selected = Q512_K456_ROW_SELECTION[label] + return {'shape_key': inputs.get('label'), 'selected_route': route, 'guard_condition': 'exact BF16 build B1 Q=M=512 D128 K in {4,5,6} low-K split4 route', 'route_kind': 'specialized', 'coverage': 'exact 4a72 Q512 K4/K5/K6 adapter selected ahead of f16b inherited route', 'consumed_seed': selected['selected_seed'], 'replaced_route': base_route, 'base_f16b_route': base_route, 'row_selection': selected, 'parity_status': 'pass', 'parity_reason': selected['reason'], 'candidate_guard_status': 'selected_from_b193_lowk_q512_k456'} + row = base_f16b._route_trace_record(inputs) + row['base_f16b_route'] = base_route + row['candidate_guard_status'] = 'inherited_f16b_or_guard_miss' + return row + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + return [_route_trace_record(_trace_inputs_from_shape(shape), force_fallback=force_fallback) for shape in selected] + +def _timing_backends_for_reports(*reports: dict[str, Any]) -> list[str]: + return base_f16b._timing_backends_for_reports(*reports) + +def _rows_for_labels(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return base_f16b._rows_for_labels(report, labels) + +def _per_consumed_row_delta(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> dict[str, Any]: + deltas = {} + for label in CONSUMED_SEED_TARGET_SHAPES: + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') + inputs = _inputs_for_label(label) + deltas[label] = {'candidate_ms': candidate_ms, 'baseline_f16b_ms': baseline_ms, 'flashlib_ms': flashlib_ms, 'speedup_vs_baseline_f16b': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'ratio_vs_flashlib': flashlib_ms / candidate_ms if candidate_ms and flashlib_ms else None, 'candidate_route': route_for_contract_inputs(inputs), 'baseline_f16b_route': base_f16b.route_for_contract_inputs(inputs), 'candidate_passed': candidate_row.get('passed'), 'baseline_passed': baseline_row.get('passed')} + return deltas + +def _seed_delta_matrix(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> list[dict[str, Any]]: + matrix = [] + for label in SELECTED_TARGET_SHAPES: + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + inputs = _inputs_for_label(label) + matrix.append({'shape_key': label, 'baseline_route': base_f16b.route_for_contract_inputs(inputs), 'candidate_route': route_for_contract_inputs(inputs), 'candidate_ms': candidate_ms, 'baseline_ms': baseline_ms, 'metric_delta_ms': candidate_ms - baseline_ms if candidate_ms and baseline_ms else None, 'speedup_vs_baseline_f16b': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'timing_backend': candidate_row.get('timing_backend') or baseline_row.get('timing_backend')}) + return matrix + +def _frontmatter_seed_delta_matrix(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> list[dict[str, Any]]: + rows = [] + for item in _seed_delta_matrix(candidate_report, baseline_report): + rows.append({'shape_key': item['shape_key'], 'baseline_route': item['baseline_route'], 'candidate_deltas': [{'candidate_id': 'selected_4a72_f16b_plus_q512_k4_k5_k6', 'metric_delta': item['metric_delta_ms'], 'timing_backend': item['timing_backend'] or 'cupti'}]}) + return rows + +def _below_flashlib_rows(report: dict[str, Any]) -> list[dict[str, Any]]: + rows = [] + trace_by_label = {str(row['shape_key']): row for row in route_trace_for_contract_shapes()} + for label, row in sorted(report.get('per_shape', {}).items()): + ratio = row.get('ratio_vs_flashlib') + if isinstance(ratio, (float, int)) and ratio < 1.0: + inputs = _inputs_for_label(label) + route = route_for_contract_inputs(inputs) + trace_row = trace_by_label.get(label, {}) + rows.append({'shape_key': label, 'kernel_ms': row.get('kernel_ms'), 'flashlib_ms': row.get('flashlib_ms'), 'ratio_vs_flashlib': ratio, 'selected_route': route, 'route_kind': trace_row.get('route_kind', 'unknown')}) + return rows + +def _benchmark_payload(candidate_report: dict[str, Any], baseline_report: dict[str, Any], *, use_cupti: bool, shape_labels) -> dict[str, Any]: + candidate_metric = candidate_report['summary']['primary_mean'] + baseline_metric = baseline_report['summary']['primary_mean'] + route_trace = route_trace_for_contract_shapes(shape_labels) + below_flashlib = _below_flashlib_rows(candidate_report) + return {'tflops': candidate_metric, 'baseline_tflops': baseline_metric, 'metric_delta': candidate_metric - baseline_metric if candidate_metric and baseline_metric else None, 'all_correct': candidate_report['summary']['all_correct'], 'baseline_all_correct': baseline_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'baseline_performance_comparable': baseline_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'baseline_invalid_performance_reason': baseline_report['summary']['invalid_performance_reason'], 'measured_entrypoint': 'loom.examples.weave.knn_build_dispatch_selected_portfolio_4a72_v1:benchmark_knn_build_dispatch_selected_portfolio_4a72_v1', 'baseline_entrypoint': ROUTE_BASE_F16B, 'baseline_entrypoint_note': 'same-session f16b selected portfolio measured through the same contract denominator', 'measured_shape_labels': 'all_canonical' if shape_labels is None else tuple(shape_labels), 'selected_route_labels': SELECTED_TARGET_SHAPES, 'consumed_seed_labels': CONSUMED_SEED_TARGET_SHAPES, 'guard_miss_audit_labels': GUARD_MISS_AUDIT_SHAPES, 'selected_route_rows': _rows_for_labels(candidate_report, SELECTED_TARGET_SHAPES), 'baseline_selected_route_rows': _rows_for_labels(baseline_report, SELECTED_TARGET_SHAPES), 'consumed_seed_rows': _rows_for_labels(candidate_report, CONSUMED_SEED_TARGET_SHAPES), 'baseline_consumed_seed_rows': _rows_for_labels(baseline_report, CONSUMED_SEED_TARGET_SHAPES), 'guard_miss_audit_rows': _rows_for_labels(candidate_report, GUARD_MISS_AUDIT_SHAPES), 'baseline_guard_miss_audit_rows': _rows_for_labels(baseline_report, GUARD_MISS_AUDIT_SHAPES), 'per_consumed_row_delta': _per_consumed_row_delta(candidate_report, baseline_report), 'seed_delta_matrix': _seed_delta_matrix(candidate_report, baseline_report), 'frontmatter_seed_delta_matrix': _frontmatter_seed_delta_matrix(candidate_report, baseline_report), 'candidate_dispatchers': CANDIDATE_PORTFOLIOS, 'selected_candidate_dispatcher': 'selected_4a72_f16b_plus_q512_k4_k5_k6', 'q512_k456_row_selection': Q512_K456_ROW_SELECTION, 'route_modules': PRODUCTION_ROUTE_MODULES, 'route_trace': route_trace, 'forced_fallback_route_trace': route_trace_for_contract_shapes(shape_labels, force_fallback=True), 'route_trace_included': True, 'contract_summary': candidate_report['summary'], 'baseline_contract_summary': baseline_report['summary'], 'contract_performance': candidate_report['performance'], 'baseline_contract_performance': baseline_report['performance'], 'timing_backends': _timing_backends_for_reports(candidate_report, baseline_report), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'hot_bucket_parity': {'lowk_q512_k4': 'selected_b193_lowk_q512_k456', 'lowk_q512_k5': 'selected_b193_lowk_q512_k456', 'lowk_q512_k6': 'selected_b193_lowk_q512_k456', 'rag_microbatch_q8_q16_q32_m100000_k10': 'not_repaired_existing_routes'}, 'performance_coverage': 'partial' if below_flashlib else 'pass', 'coverage_only_routes': [], 'hot_bucket_blockers': below_flashlib, 'report': candidate_report, 'baseline_report': baseline_report} + +def _baseline_exception_report(exc: BaseException) -> dict[str, Any]: + message = ''.join([format(type(exc).__name__, ''), ': ', format(exc, '')]) + return {'contract': eval_mod.CONTRACT.kernel, 'contract_version': eval_mod.CONTRACT.contract_version, 'summary': {'all_correct': False, 'correctness_failure_count': 1, 'correctness_shapes': 0, 'failed_correctness_shapes': 1, 'first_correctness_failure': {'failure_kind': 'baseline_exception', 'message': message}, 'invalid_performance_reason': ''.join(['baseline_exception: ', format(message, '')]), 'performance_comparable': False, 'primary_mean': None, 'primary_metric': 'tflops'}, 'correctness': {'all_correct': False, 'checked_shape_count': 0, 'failed_shape_count': 1, 'failures': [{'failure_kind': 'baseline_exception', 'message': message}], 'first_failure': {'failure_kind': 'baseline_exception', 'message': message}}, 'performance': {'comparable': False, 'debug_measurements_present': False, 'invalid_reason': ''.join(['baseline_exception: ', format(message, '')]), 'primary_mean': None, 'primary_metric': 'tflops', 'valid_measurement_count': 0}, 'per_shape': {}, 'baseline_exception': message} + +def benchmark_knn_build_dispatch_selected_portfolio_4a72_v1(*, use_cupti: bool=True, shape_labels=None) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels) + try: + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_base_dispatcher) + except Exception as exc: + baseline_report = _baseline_exception_report(exc) + return _benchmark_payload(candidate_report, baseline_report, use_cupti=use_cupti, shape_labels=shape_labels) + +def _denominator_label(shape_labels) -> str: + if shape_labels is None: + return ''.join(['full', format(len(eval_mod.CANONICAL_SHAPES), '')]) + return ''.join(['shape', format(len(tuple(shape_labels)), '')]) + +def write_benchmark_artifacts(artifact_dir: str | Path, *, use_cupti: bool=True, shape_labels=None) -> dict[str, str]: + payload = benchmark_knn_build_dispatch_selected_portfolio_4a72_v1(use_cupti=use_cupti, shape_labels=shape_labels) + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + denom = _denominator_label(shape_labels) + candidate_path = out_dir / ''.join([format(denom, ''), '_dispatch_selected_portfolio_4a72_v1.json']) + baseline_path = out_dir / ''.join([format(denom, ''), '_same_session_baseline_f16b_for_4a72_v1.json']) + route_trace_path = out_dir / ''.join([format(denom, ''), '_route_trace_selected_portfolio_4a72_v1.json']) + forced_trace_path = out_dir / ''.join([format(denom, ''), '_forced_fallback_trace_selected_portfolio_4a72_v1.json']) + candidate_path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + baseline_path.write_text(json.dumps({'measured_entrypoint': payload['baseline_entrypoint'], 'measured_shape_labels': payload['measured_shape_labels'], 'timing_backend_requested': payload['timing_backend_requested'], 'timing_backends': payload['timing_backends'], 'tflops': payload['baseline_tflops'], 'all_correct': payload['baseline_all_correct'], 'performance_comparable': payload['baseline_performance_comparable'], 'contract_summary': payload['baseline_contract_summary'], 'contract_performance': payload['baseline_contract_performance'], 'route_trace': base_f16b.route_trace_for_contract_shapes(shape_labels), 'route_trace_included': True, 'report': payload['baseline_report']}, indent=2, sort_keys=True) + '\n') + route_trace_path.write_text(json.dumps(payload['route_trace'], indent=2, sort_keys=True) + '\n') + forced_trace_path.write_text(json.dumps(payload['forced_fallback_route_trace'], indent=2, sort_keys=True) + '\n') + return {'candidate_payload': str(candidate_path), 'baseline_f16b_payload': str(baseline_path), 'route_trace': str(route_trace_path), 'forced_fallback_trace': str(forced_trace_path)} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_selected_portfolio_77db_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_selected_portfolio_77db_v1.py new file mode 100644 index 00000000..92400e15 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_selected_portfolio_77db_v1.py @@ -0,0 +1,223 @@ +"""Opt-in kNN build dispatcher consuming the a194 q4096 K64 split8 seed. + +Minimum target architecture: sm_100a. This dispatcher-consumption candidate is +wrapper-only. It starts from the f8c3 selected full55 portfolio and adds one +exact guard for the BF16 build ``B=1,Q=M=4096,D=128,K=64`` row, routing that +row to the validated a194 split8 seed. + +Every production route remains Weave-only; PyTorch and FlashLib are references +only through the contract harness. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from pathlib import Path +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dim_midk_f8c3_q4096k64split_v1 as q4096_k64_split8 +from . import knn_build_dispatch_selected_portfolio_f8c3_v1 as base_f8c3 +ROUTE_Q4096_K64_SPLIT8 = q4096_k64_split8.ROUTE_Q4096_K64 +ROUTE_BASE_F8C3 = 'loom.examples.weave.knn_build_dispatch_selected_portfolio_f8c3_v1:launch_from_contract_inputs' +ROUTE_REFERENCE_E51C = 'loom.examples.weave.knn_build_dispatch_selected_portfolio_e51c_v1:launch_from_contract_inputs' +Q4096_K64_TARGET_SHAPES = ('build_over32_stress_qm4096_k64',) +Q4096_K64_TARGET_SHAPE_SET = set(Q4096_K64_TARGET_SHAPES) +CONSUMED_SEED_TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["rag_stream_largek_b1_q128_m100000_d128_k32", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q2048_m2048_d256_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10", "search_rect_b1_q2048_m32768_d128_k10", "build_over64_stress_qm2048_k96", "build_large_tail_b1_q6144_m6144_d128_k20", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "build_over32_stress_qm2048_k64", "build_over32_stress_qm4096_k64"]}')) +SELECTED_TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_large_b1_q8192_m8192_d128_k20", "build_large_b1_q8192_m8192_d128_k32", "build_over64_stress_qm2048_k96", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32", "rag_batch_b2_q256_m50000_d128_k10", "rag_irregular_b1_q512_m131071_d128_k10", "search_rect_b1_q1024_m8192_d128_k10", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q2048_m2048_d256_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10", "search_rect_b1_q2048_m32768_d128_k10", "build_large_tail_b1_q6144_m6144_d128_k20", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "build_over32_stress_qm2048_k64", "build_over32_stress_qm4096_k64"]}')) +GUARD_MISS_AUDIT_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_dim_sweep_b1_q2048_m2048_d64_k10", "build_qm2048_d128_k10", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "flashml_correctness_b1_q256_m256_d128_k5", "build_k_sweep_qm1024_k16"]}')) +DISPATCH_CORRECTNESS_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["flashml_correctness_b1_q256_m256_d128_k5", "build_large_b1_q8192_m8192_d128_k20", "build_large_b1_q8192_m8192_d128_k32", "build_over64_stress_qm2048_k96", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32", "rag_batch_b2_q256_m50000_d128_k10", "rag_irregular_b1_q512_m131071_d128_k10", "search_rect_b1_q1024_m8192_d128_k10", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q2048_m2048_d256_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10", "search_rect_b1_q2048_m32768_d128_k10", "build_large_tail_b1_q6144_m6144_d128_k20", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "build_over32_stress_qm2048_k64", "build_over32_stress_qm4096_k64", "build_qm2048_d128_k10", "build_k_sweep_qm1024_k16", "rag_online_b1_q1_m100000_d128_k10", "rag_stream_b1_q128_m100000_d128_k10", "search_rect_b1_q4096_m65536_d128_k20", "rag_offline_largek_b1_q4096_m100000_d128_k20", "rag_offline_large_m_b1_q8192_m250000_d128_k20", "search_rect_over32_b1_q2048_m65536_d128_k64", "rag_offline_large_m_over32_b1_q2048_m250000_d128_k64"]}')) +PRODUCTION_ROUTE_MODULES = {**base_f8c3.PRODUCTION_ROUTE_MODULES, 'midk_f8c3_q4096k64split8_a194': ROUTE_Q4096_K64_SPLIT8, 'base_f8c3': ROUTE_BASE_F8C3} +CANDIDATE_PORTFOLIOS = ({'id': 'baseline_f8c3_selected_portfolio', 'entrypoint': 'loom.examples.weave.knn_build_dispatch_selected_portfolio_f8c3_v1:benchmark_knn_build_dispatch_selected_portfolio_f8c3_v1', 'consumed_seeds': ('selected_e51c_f552_a330_a4f6_row_level_midk', 'midk_bad5_k64split8_q2048'), 'guard_plan': ('f8c3 selected full55 guard plan',), 'expected_shape_wins': base_f8c3.CONSUMED_SEED_TARGET_SHAPES, 'rejected_reason': 'same-session baseline for 77db q4096 K64 dispatcher-consumption lane'}, {'id': 'selected_77db_f8c3_plus_q4096_k64split8', 'entrypoint': 'loom.examples.weave.knn_build_dispatch_selected_portfolio_77db_v1:benchmark_knn_build_dispatch_selected_portfolio_77db_v1', 'consumed_seeds': ('selected_f8c3_e51c_plus_k64split8', 'midk_f8c3_q4096k64split8_a194'), 'guard_plan': ('exact a194 K64 split8 BF16 build B1 Q=M=4096 D128 K64 guard', 'then f8c3 selected full55 guard plan'), 'expected_shape_wins': CONSUMED_SEED_TARGET_SHAPES, 'rejected_reason': None}) +Q4096_K64_ROW_SELECTION = {'build_over32_stress_qm4096_k64': {'selected_seed': 'midk_f8c3_q4096k64split8_a194', 'selected_route': ROUTE_Q4096_K64_SPLIT8, 'targeted_seed_ms': 0.265698, 'targeted_seed_tflops': 16.164846163689603, 'targeted_seed_timing_backend': 'cupti', 'targeted_ratio_vs_flashlib': 2.087192978494381, 'targeted_baseline_f8c3_ms': 0.744356, 'targeted_baseline_f8c3_tflops': 5.77004457007131, 'targeted_speedup_vs_f8c3': 2.8015114904892022, 'reason': 'a194 exact q4096 K64 split8 seed is correct and 2.80x faster than the f8c3 inherited route on same-session CUPTI exact-row timing'}} + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_DISPATCH_SELECTED_77DB_VERIFY_KERNEL') + if verify_kernel == 'q4096_k64_stage1_tailinf': + os.environ['LOOM_KNN_DIMMIDK_F8C3_Q4096K64_VERIFY_KERNEL'] = 'stage1_k64_tailinf' + return q4096_k64_split8._verify_export_ir() + if verify_kernel == 'q4096_k64_merge_s8': + os.environ['LOOM_KNN_DIMMIDK_F8C3_Q4096K64_VERIFY_KERNEL'] = 'merge_k64_s8' + return q4096_k64_split8._verify_export_ir() + return base_f8c3.ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k48over32", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 48]], "cta_group": 1, "threads": 192}')) + +def _eligible_q4096_k64_split8(inputs: dict[str, Any]) -> bool: + return q4096_k64_split8._eligible_q4096_k64(inputs) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if force_fallback: + return base_f8c3.route_for_contract_inputs(inputs) + if _eligible_q4096_k64_split8(inputs): + return ROUTE_Q4096_K64_SPLIT8 + return base_f8c3.route_for_contract_inputs(inputs) + +def _launch_route(inputs: dict[str, Any], route: str) -> None: + if route == ROUTE_Q4096_K64_SPLIT8: + q4096_k64_split8._launch_q4096_k64_split(inputs) + return + base_f8c3._launch_route(inputs, route) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + _launch_route(inputs, route_for_contract_inputs(inputs, force_fallback=force_fallback)) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def candidate_base_dispatcher(inputs: dict[str, Any]): + base_f8c3.launch_from_contract_inputs(inputs) + return None + +def candidate_reference_e51c(inputs: dict[str, Any]): + base_f8c3.base_e51c.launch_from_contract_inputs(inputs) + return None + +def candidate_force_fallback(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs, force_fallback=True) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return base_f8c3._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=DISPATCH_CORRECTNESS_SHAPES, benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint: run real contract correctness for selected shapes.""" + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=None, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + shapes = None if shape_labels is None else _select_contract_shapes(shape_labels) + return evaluate_contract(shapes=shapes, correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def _trace_inputs_from_shape(shape: dict[str, Any]) -> dict[str, Any]: + return base_f8c3._trace_inputs_from_shape(shape) + +def _inputs_for_label(label: str) -> dict[str, Any]: + return base_f8c3._inputs_for_label(label) + +def _base_f8c3_route(inputs: dict[str, Any]) -> str: + return base_f8c3.route_for_contract_inputs(inputs) + +def _route_trace_record(inputs: dict[str, Any], *, force_fallback: bool=False) -> dict[str, Any]: + base_route = _base_f8c3_route(inputs) + if force_fallback: + row = base_f8c3._route_trace_record(inputs) + row['guard_condition'] = 'forced fallback to f8c3 baseline; 77db q4096 K64 overlay disabled' + row['coverage'] = 'forced candidate fallback for 77db q4096 K64 overlay' + row['forced_disabled_seeds'] = ('midk_f8c3_q4096k64split8_a194',) + row['base_f8c3_route'] = base_route + row['candidate_guard_status'] = 'forced_fallback_to_f8c3' + return row + route = route_for_contract_inputs(inputs) + label = str(inputs.get('label')) + if route == ROUTE_Q4096_K64_SPLIT8 and label in Q4096_K64_ROW_SELECTION: + selected = Q4096_K64_ROW_SELECTION[label] + return {'shape_key': inputs.get('label'), 'selected_route': route, 'guard_condition': 'exact BF16 build B1 Q=M=4096 D128 K64 split8 tail-infinity route', 'route_kind': 'specialized', 'coverage': 'exact a194 q4096 K64 split8 seed selected ahead of f8c3 inherited fallback', 'consumed_seed': selected['selected_seed'], 'replaced_route': base_route, 'base_f8c3_route': base_route, 'row_selection': selected, 'parity_status': 'pass', 'parity_reason': selected['reason'], 'candidate_guard_status': 'selected_from_q4096_k64split8'} + row = base_f8c3._route_trace_record(inputs) + row['base_f8c3_route'] = base_route + row['candidate_guard_status'] = 'inherited_f8c3_or_guard_miss' + return row + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + return [_route_trace_record(_trace_inputs_from_shape(shape), force_fallback=force_fallback) for shape in selected] + +def _timing_backends_for_reports(*reports: dict[str, Any]) -> list[str]: + return base_f8c3._timing_backends_for_reports(*reports) + +def _rows_for_labels(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return base_f8c3._rows_for_labels(report, labels) + +def _per_consumed_row_delta(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> dict[str, Any]: + deltas = {} + for label in CONSUMED_SEED_TARGET_SHAPES: + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') + inputs = _inputs_for_label(label) + deltas[label] = {'candidate_ms': candidate_ms, 'baseline_f8c3_ms': baseline_ms, 'flashlib_ms': flashlib_ms, 'speedup_vs_baseline_f8c3': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'ratio_vs_flashlib': flashlib_ms / candidate_ms if candidate_ms and flashlib_ms else None, 'candidate_route': route_for_contract_inputs(inputs), 'baseline_f8c3_route': _base_f8c3_route(inputs)} + return deltas + +def _seed_delta_matrix(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> list[dict[str, Any]]: + matrix = [] + for label in SELECTED_TARGET_SHAPES: + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + inputs = _inputs_for_label(label) + matrix.append({'shape_key': label, 'baseline_route': _base_f8c3_route(inputs), 'candidate_route': route_for_contract_inputs(inputs), 'candidate_ms': candidate_ms, 'baseline_ms': baseline_ms, 'metric_delta_ms': candidate_ms - baseline_ms if candidate_ms and baseline_ms else None, 'speedup_vs_baseline_f8c3': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'timing_backend': candidate_row.get('timing_backend') or baseline_row.get('timing_backend')}) + return matrix + +def _frontmatter_seed_delta_matrix(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> list[dict[str, Any]]: + rows = [] + for item in _seed_delta_matrix(candidate_report, baseline_report): + delta = item['metric_delta_ms'] + rows.append({'shape_key': item['shape_key'], 'baseline_route': item['baseline_route'], 'candidate_deltas': [{'candidate_id': 'selected_77db_f8c3_plus_q4096_k64split8', 'metric_delta': 0.0 if delta is None else float(delta), 'timing_backend': item['timing_backend'] or 'cuda_event'}]}) + return rows + +def _below_flashlib_rows(report: dict[str, Any]) -> list[dict[str, Any]]: + rows = [] + production_routes = set(PRODUCTION_ROUTE_MODULES.values()) + for label, row in sorted(report.get('per_shape', {}).items()): + ratio = row.get('ratio_vs_flashlib') + if isinstance(ratio, (float, int)) and ratio < 1.0: + inputs = _inputs_for_label(label) + selected_route = route_for_contract_inputs(inputs) + rows.append({'shape_key': label, 'kernel_ms': row.get('kernel_ms'), 'flashlib_ms': row.get('flashlib_ms'), 'ratio_vs_flashlib': ratio, 'selected_route': selected_route, 'route_kind': 'specialized' if selected_route in production_routes else 'general'}) + return rows + +def _reference_payload(report: dict[str, Any], *, shape_labels, route_trace) -> dict[str, Any]: + return {'measured_entrypoint': ROUTE_REFERENCE_E51C, 'measured_shape_labels': 'all_canonical' if shape_labels is None else tuple(shape_labels), 'tflops': report['summary']['primary_mean'], 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'contract_summary': report['summary'], 'contract_performance': report['performance'], 'route_trace': route_trace, 'route_trace_included': True, 'report': report} + +def _benchmark_payload(candidate_report: dict[str, Any], baseline_report: dict[str, Any], reference_e51c_report: dict[str, Any], *, use_cupti: bool, shape_labels, measured_function: str) -> dict[str, Any]: + candidate_metric = candidate_report['summary']['primary_mean'] or 0.0 + baseline_metric = baseline_report['summary']['primary_mean'] or 0.0 + reference_metric = reference_e51c_report['summary']['primary_mean'] or 0.0 + route_trace = route_trace_for_contract_shapes(shape_labels) + forced_trace = route_trace_for_contract_shapes(shape_labels, force_fallback=True) + below_flashlib = _below_flashlib_rows(candidate_report) + return {'tflops': candidate_metric, 'baseline_f8c3_tflops': baseline_metric, 'reference_e51c_tflops': reference_metric, 'metric_delta_vs_f8c3': candidate_metric - baseline_metric, 'metric_delta_vs_e51c': candidate_metric - reference_metric, 'all_correct': candidate_report['summary']['all_correct'], 'baseline_f8c3_all_correct': baseline_report['summary']['all_correct'], 'reference_e51c_all_correct': reference_e51c_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'baseline_f8c3_performance_comparable': baseline_report['summary']['performance_comparable'], 'reference_e51c_performance_comparable': reference_e51c_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'baseline_f8c3_invalid_performance_reason': baseline_report['summary']['invalid_performance_reason'], 'reference_e51c_invalid_performance_reason': reference_e51c_report['summary']['invalid_performance_reason'], 'measured_entrypoint': ''.join(['loom.examples.weave.knn_build_dispatch_selected_portfolio_77db_v1:', format(measured_function, '')]), 'baseline_entrypoint': ROUTE_BASE_F8C3, 'reference_entrypoint': ROUTE_REFERENCE_E51C, 'baseline_entrypoint_note': 'same-session f8c3 selected portfolio measured through the same contract denominator', 'reference_entrypoint_note': 'same-session e51c reference measured through the same contract denominator', 'measured_shape_labels': 'all_canonical' if shape_labels is None else tuple(shape_labels), 'selected_route_labels': SELECTED_TARGET_SHAPES, 'consumed_seed_labels': CONSUMED_SEED_TARGET_SHAPES, 'guard_miss_audit_labels': GUARD_MISS_AUDIT_SHAPES, 'selected_route_rows': _rows_for_labels(candidate_report, SELECTED_TARGET_SHAPES), 'baseline_selected_route_rows': _rows_for_labels(baseline_report, SELECTED_TARGET_SHAPES), 'reference_selected_route_rows': _rows_for_labels(reference_e51c_report, SELECTED_TARGET_SHAPES), 'consumed_seed_rows': _rows_for_labels(candidate_report, CONSUMED_SEED_TARGET_SHAPES), 'baseline_consumed_seed_rows': _rows_for_labels(baseline_report, CONSUMED_SEED_TARGET_SHAPES), 'guard_miss_audit_rows': _rows_for_labels(candidate_report, GUARD_MISS_AUDIT_SHAPES), 'baseline_guard_miss_audit_rows': _rows_for_labels(baseline_report, GUARD_MISS_AUDIT_SHAPES), 'per_consumed_row_delta': _per_consumed_row_delta(candidate_report, baseline_report), 'seed_delta_matrix': _seed_delta_matrix(candidate_report, baseline_report), 'frontmatter_seed_delta_matrix': _frontmatter_seed_delta_matrix(candidate_report, baseline_report), 'candidate_dispatchers': CANDIDATE_PORTFOLIOS, 'selected_candidate_dispatcher': 'selected_77db_f8c3_plus_q4096_k64split8', 'q4096_k64_row_selection': Q4096_K64_ROW_SELECTION, 'route_modules': PRODUCTION_ROUTE_MODULES, 'route_trace': route_trace, 'forced_fallback_route_trace': forced_trace, 'route_trace_included': True, 'contract_summary': candidate_report['summary'], 'baseline_f8c3_contract_summary': baseline_report['summary'], 'reference_e51c_contract_summary': reference_e51c_report['summary'], 'contract_performance': candidate_report['performance'], 'baseline_f8c3_contract_performance': baseline_report['performance'], 'reference_e51c_contract_performance': reference_e51c_report['performance'], 'timing_backends': _timing_backends_for_reports(candidate_report, baseline_report, reference_e51c_report), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'hot_bucket_parity': {'q2048_k64': 'inherited_f8c3', 'q4096_k64': 'selected_q4096_k64split8_a194', 'rag_k32': 'inherited_f8c3', 'dim_sweep_qm2048_d64_k10': 'inherited_f8c3', 'dim_sweep_qm2048_d256_k10': 'inherited_f8c3', 'dim_sweep_qm2048_fp16_d128_k10': 'inherited_f8c3', 'rect_q2048_m32768_k10': 'inherited_f8c3', 'default_k96_registry_gate': 'inherited_f8c3', 'large_tail_k20_q6144': 'inherited_f8c3', 'midk_k24_k28': 'inherited_f8c3'}, 'performance_coverage': 'partial' if below_flashlib else 'pass', 'coverage_only_routes': [], 'hot_bucket_blockers': below_flashlib, 'report': candidate_report, 'baseline_f8c3_report': baseline_report, 'reference_e51c_report': reference_e51c_report} + +def benchmark_knn_build_dispatch_selected_portfolio_77db_v1(*, use_cupti: bool=False, shape_labels=None) -> dict[str, Any]: + """Full-denominator A/B against f8c3 plus same-session e51c reference.""" + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels) + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_base_dispatcher) + reference_e51c_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_reference_e51c) + return _benchmark_payload(candidate_report, baseline_report, reference_e51c_report, use_cupti=use_cupti, shape_labels=shape_labels, measured_function='benchmark_knn_build_dispatch_selected_portfolio_77db_v1') + +def write_benchmark_artifacts(artifact_dir: str | os.PathLike[str], *, use_cupti: bool=False, shape_labels=None) -> dict[str, str]: + payload = benchmark_knn_build_dispatch_selected_portfolio_77db_v1(use_cupti=use_cupti, shape_labels=shape_labels) + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + candidate_path = out_dir / 'full55_dispatch_selected_portfolio_77db_v1.json' + baseline_path = out_dir / 'full55_same_session_baseline_f8c3_for_77db_v1.json' + reference_path = out_dir / 'full55_same_session_reference_e51c_for_77db_v1.json' + route_trace_path = out_dir / 'full55_route_trace_selected_portfolio_77db_v1.json' + forced_trace_path = out_dir / 'full55_forced_fallback_trace_selected_portfolio_77db_v1.json' + candidate_path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + baseline_path.write_text(json.dumps({'measured_entrypoint': payload['baseline_entrypoint'], 'measured_shape_labels': payload['measured_shape_labels'], 'timing_backend_requested': payload['timing_backend_requested'], 'timing_backends': payload['timing_backends'], 'tflops': payload['baseline_f8c3_tflops'], 'all_correct': payload['baseline_f8c3_all_correct'], 'performance_comparable': payload['baseline_f8c3_performance_comparable'], 'contract_summary': payload['baseline_f8c3_contract_summary'], 'contract_performance': payload['baseline_f8c3_contract_performance'], 'route_trace': base_f8c3.route_trace_for_contract_shapes(shape_labels), 'route_trace_included': True, 'report': payload['baseline_f8c3_report']}, indent=2, sort_keys=True) + '\n') + reference_path.write_text(json.dumps(_reference_payload(payload['reference_e51c_report'], shape_labels=shape_labels, route_trace=base_f8c3.base_e51c.route_trace_for_contract_shapes(shape_labels)), indent=2, sort_keys=True) + '\n') + route_trace_path.write_text(json.dumps(payload['route_trace'], indent=2, sort_keys=True) + '\n') + forced_trace_path.write_text(json.dumps(payload['forced_fallback_route_trace'], indent=2, sort_keys=True) + '\n') + return {'candidate_payload': str(candidate_path), 'baseline_payload': str(baseline_path), 'reference_e51c_payload': str(reference_path), 'route_trace': str(route_trace_path), 'forced_fallback_trace': str(forced_trace_path)} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_selected_portfolio_99f2_q4096k64_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_selected_portfolio_99f2_q4096k64_v1.py new file mode 100644 index 00000000..dc58cf64 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_selected_portfolio_99f2_q4096k64_v1.py @@ -0,0 +1,216 @@ +"""Opt-in kNN build dispatcher consuming the q4096 K64 split8 seed. + +Minimum target architecture: sm_100a. This dispatcher-consumption candidate is +wrapper-only. It starts from the f8c3 selected full55 portfolio and adds one +exact guard for the BF16 build ``B=1,Q=M=4096,D=128,K=64`` row, routing that +row to the validated q4096 K64 split8 seed. + +Every production route remains Weave-only; PyTorch and FlashLib are references +only through the contract harness. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from pathlib import Path +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dim_midk_f8c3_q4096k64split_v1 as q4096_k64 +from . import knn_build_dispatch_selected_portfolio_f8c3_v1 as parent_f8c3 +ROUTE_Q4096_K64 = q4096_k64.ROUTE_Q4096_K64 +ROUTE_PARENT_F8C3 = 'loom.examples.weave.knn_build_dispatch_selected_portfolio_f8c3_v1:launch_from_contract_inputs' +Q4096_TARGET_SHAPES = q4096_k64.TARGET_SHAPES +Q4096_TARGET_SHAPE_SET = set(Q4096_TARGET_SHAPES) +CONSUMED_SEED_TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["rag_stream_largek_b1_q128_m100000_d128_k32", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q2048_m2048_d256_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10", "search_rect_b1_q2048_m32768_d128_k10", "build_over64_stress_qm2048_k96", "build_large_tail_b1_q6144_m6144_d128_k20", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "build_over32_stress_qm2048_k64", "build_over32_stress_qm4096_k64"]}')) +SELECTED_TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_large_b1_q8192_m8192_d128_k20", "build_large_b1_q8192_m8192_d128_k32", "build_over64_stress_qm2048_k96", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32", "rag_batch_b2_q256_m50000_d128_k10", "rag_irregular_b1_q512_m131071_d128_k10", "search_rect_b1_q1024_m8192_d128_k10", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q2048_m2048_d256_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10", "search_rect_b1_q2048_m32768_d128_k10", "build_large_tail_b1_q6144_m6144_d128_k20", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "build_over32_stress_qm2048_k64", "build_over32_stress_qm4096_k64"]}')) +GUARD_MISS_AUDIT_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_dim_sweep_b1_q2048_m2048_d64_k10", "build_qm2048_d128_k10", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "flashml_correctness_b1_q256_m256_d128_k5", "build_k_sweep_qm1024_k16"]}')) +DISPATCH_CORRECTNESS_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["flashml_correctness_b1_q256_m256_d128_k5", "build_large_b1_q8192_m8192_d128_k20", "build_large_b1_q8192_m8192_d128_k32", "build_over64_stress_qm2048_k96", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32", "rag_batch_b2_q256_m50000_d128_k10", "rag_irregular_b1_q512_m131071_d128_k10", "search_rect_b1_q1024_m8192_d128_k10", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q2048_m2048_d256_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10", "search_rect_b1_q2048_m32768_d128_k10", "build_large_tail_b1_q6144_m6144_d128_k20", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "build_over32_stress_qm2048_k64", "build_over32_stress_qm4096_k64", "build_qm2048_d128_k10", "build_k_sweep_qm1024_k16", "rag_online_b1_q1_m100000_d128_k10", "rag_stream_b1_q128_m100000_d128_k10", "search_rect_b1_q4096_m65536_d128_k20", "rag_offline_largek_b1_q4096_m100000_d128_k20", "rag_offline_large_m_b1_q8192_m250000_d128_k20", "search_rect_over32_b1_q2048_m65536_d128_k64", "rag_offline_large_m_over32_b1_q2048_m250000_d128_k64"]}')) +PRODUCTION_ROUTE_MODULES = {**parent_f8c3.PRODUCTION_ROUTE_MODULES, 'midk_f8c3_q4096k64_split8': ROUTE_Q4096_K64, 'parent_f8c3': ROUTE_PARENT_F8C3} +CANDIDATE_PORTFOLIOS = ({'id': 'baseline_f8c3_selected_portfolio', 'entrypoint': 'loom.examples.weave.knn_build_dispatch_selected_portfolio_f8c3_v1:benchmark_knn_build_dispatch_selected_portfolio_f8c3_v1', 'consumed_seeds': ('selected_e51c_f552_a330_a4f6_row_level_midk', 'midk_bad5_k64split8_q2048'), 'guard_plan': parent_f8c3.CANDIDATE_PORTFOLIOS[1]['guard_plan'], 'expected_shape_wins': parent_f8c3.CONSUMED_SEED_TARGET_SHAPES, 'rejected_reason': 'same-session baseline for q4096 K64 dispatcher-consumption lane'}, {'id': 'selected_99f2_f8c3_plus_q4096k64', 'entrypoint': 'loom.examples.weave.knn_build_dispatch_selected_portfolio_99f2_q4096k64_v1:benchmark_knn_build_dispatch_selected_portfolio_99f2_q4096k64_v1', 'consumed_seeds': ('selected_f8c3_e51c_plus_k64split8', 'midk_f8c3_q4096k64_split8'), 'guard_plan': ('exact q4096 K64 BF16 build B1 Q=M=4096 D128 K64 split8 guard', 'then f8c3 selected full55 guard plan'), 'expected_shape_wins': CONSUMED_SEED_TARGET_SHAPES, 'rejected_reason': None}) +Q4096_ROW_SELECTION = {'build_over32_stress_qm4096_k64': {'selected_seed': 'midk_f8c3_q4096k64_split8', 'selected_route': ROUTE_Q4096_K64, 'candidate_ms': 0.265698, 'candidate_tflops': 16.164846163689603, 'ratio_vs_flashlib': 2.087192978494381, 'baseline_seed_ms': 0.744356, 'speedup_vs_f8c3': 2.8015114904892022, 'reason': 'q4096 K64 split8 seed is correct and 2.80x faster than f8c3 on same-session CUPTI.'}} + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_DISPATCH_99F2_Q4096K64_VERIFY_KERNEL') + if verify_kernel == 'q4096_stage1': + os.environ['LOOM_KNN_DIMMIDK_F8C3_Q4096K64_VERIFY_KERNEL'] = 'stage1_k64_tailinf' + return q4096_k64._verify_export_ir() + if verify_kernel == 'q4096_merge_s8': + os.environ['LOOM_KNN_DIMMIDK_F8C3_Q4096K64_VERIFY_KERNEL'] = 'merge_k64_s8' + return q4096_k64._verify_export_ir() + if verify_kernel == 'q2048_stage1': + os.environ['LOOM_KNN_DISPATCH_SELECTED_F8C3_VERIFY_KERNEL'] = 'k64_stage1_s8_tailinf' + return parent_f8c3._verify_export_ir() + if verify_kernel == 'q2048_merge_s8': + os.environ['LOOM_KNN_DISPATCH_SELECTED_F8C3_VERIFY_KERNEL'] = 'k64_merge_s8_warp_select' + return parent_f8c3._verify_export_ir() + return parent_f8c3.ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k48over32", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 48]], "cta_group": 1, "threads": 192}')) + +def _eligible_q4096_k64(inputs: dict[str, Any]) -> bool: + return q4096_k64._eligible_q4096_k64(inputs) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if force_fallback: + return parent_f8c3.route_for_contract_inputs(inputs) + if _eligible_q4096_k64(inputs): + return ROUTE_Q4096_K64 + return parent_f8c3.route_for_contract_inputs(inputs) + +def _launch_route(inputs: dict[str, Any], route: str) -> None: + if route == ROUTE_Q4096_K64: + q4096_k64._launch_q4096_k64_split(inputs) + return + parent_f8c3._launch_route(inputs, route) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + _launch_route(inputs, route_for_contract_inputs(inputs, force_fallback=force_fallback)) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def candidate_base_dispatcher(inputs: dict[str, Any]): + parent_f8c3.launch_from_contract_inputs(inputs) + return None + +def candidate_force_fallback(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs, force_fallback=True) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return parent_f8c3._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=DISPATCH_CORRECTNESS_SHAPES, benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint: run real contract correctness for selected shapes.""" + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=None, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + shapes = None if shape_labels is None else _select_contract_shapes(shape_labels) + return evaluate_contract(shapes=shapes, correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def _trace_inputs_from_shape(shape: dict[str, Any]) -> dict[str, Any]: + return parent_f8c3._trace_inputs_from_shape(shape) + +def _inputs_for_label(label: str) -> dict[str, Any]: + return parent_f8c3._inputs_for_label(label) + +def _base_f8c3_route(inputs: dict[str, Any]) -> str: + return parent_f8c3.route_for_contract_inputs(inputs) + +def _route_trace_record(inputs: dict[str, Any], *, force_fallback: bool=False) -> dict[str, Any]: + base_route = _base_f8c3_route(inputs) + if force_fallback: + row = parent_f8c3._route_trace_record(inputs) + row['guard_condition'] = 'forced fallback to f8c3 baseline; 99f2 q4096 K64 overlay disabled' + row['coverage'] = 'forced candidate fallback for 99f2 q4096 K64 overlay' + row['forced_disabled_seeds'] = ('midk_f8c3_q4096k64_split8',) + row['base_f8c3_route'] = base_route + row['candidate_guard_status'] = 'forced_fallback_to_f8c3' + return row + route = route_for_contract_inputs(inputs) + label = str(inputs.get('label')) + if route == ROUTE_Q4096_K64 and label in Q4096_ROW_SELECTION: + selected = Q4096_ROW_SELECTION[label] + return {'shape_key': inputs.get('label'), 'selected_route': route, 'guard_condition': 'exact BF16 build B1 Q=M=4096 D128 K64 split8 tail-infinity route', 'route_kind': 'specialized', 'coverage': 'exact q4096 K64 split8 seed selected ahead of f8c3 inherited fallback', 'consumed_seed': selected['selected_seed'], 'replaced_route': base_route, 'base_f8c3_route': base_route, 'row_selection': selected, 'parity_status': 'pass', 'parity_reason': selected['reason'], 'candidate_guard_status': 'selected_from_q4096k64'} + row = parent_f8c3._route_trace_record(inputs) + row['base_f8c3_route'] = base_route + row['candidate_guard_status'] = 'inherited_f8c3_or_guard_miss' + return row + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + return [_route_trace_record(_trace_inputs_from_shape(shape), force_fallback=force_fallback) for shape in selected] + +def _timing_backends_for_reports(*reports: dict[str, Any]) -> list[str]: + return parent_f8c3._timing_backends_for_reports(*reports) + +def _rows_for_labels(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return parent_f8c3._rows_for_labels(report, labels) + +def _per_consumed_row_delta(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> dict[str, Any]: + deltas = {} + for label in CONSUMED_SEED_TARGET_SHAPES: + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') + inputs = _inputs_for_label(label) + deltas[label] = {'candidate_ms': candidate_ms, 'baseline_f8c3_ms': baseline_ms, 'flashlib_ms': flashlib_ms, 'speedup_vs_baseline_f8c3': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'ratio_vs_flashlib': flashlib_ms / candidate_ms if candidate_ms and flashlib_ms else None, 'candidate_route': route_for_contract_inputs(inputs), 'baseline_f8c3_route': _base_f8c3_route(inputs)} + return deltas + +def _seed_delta_matrix(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> list[dict[str, Any]]: + matrix = [] + for label in SELECTED_TARGET_SHAPES: + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + inputs = _inputs_for_label(label) + matrix.append({'shape_key': label, 'baseline_route': _base_f8c3_route(inputs), 'candidate_route': route_for_contract_inputs(inputs), 'candidate_ms': candidate_ms, 'baseline_ms': baseline_ms, 'metric_delta_ms': candidate_ms - baseline_ms if candidate_ms and baseline_ms else None, 'speedup_vs_baseline_f8c3': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'timing_backend': candidate_row.get('timing_backend') or baseline_row.get('timing_backend')}) + return matrix + +def _frontmatter_seed_delta_matrix(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> list[dict[str, Any]]: + rows = [] + for item in _seed_delta_matrix(candidate_report, baseline_report): + delta = item['metric_delta_ms'] + rows.append({'shape_key': item['shape_key'], 'baseline_route': item['baseline_route'], 'candidate_deltas': [{'candidate_id': 'selected_99f2_f8c3_plus_q4096k64', 'metric_delta': 0.0 if delta is None else float(delta), 'timing_backend': item['timing_backend'] or 'cuda_event'}]}) + return rows + +def _below_flashlib_rows(report: dict[str, Any]) -> list[dict[str, Any]]: + rows = [] + q4096_route_prefix = ROUTE_Q4096_K64.rsplit(':', 1)[0] + for label, row in sorted(report.get('per_shape', {}).items()): + ratio = row.get('ratio_vs_flashlib') + if isinstance(ratio, (float, int)) and ratio < 1.0: + inputs = _inputs_for_label(label) + route = route_for_contract_inputs(inputs) + rows.append({'shape_key': label, 'kernel_ms': row.get('kernel_ms'), 'flashlib_ms': row.get('flashlib_ms'), 'ratio_vs_flashlib': ratio, 'selected_route': route, 'route_kind': 'specialized' if route.startswith(q4096_route_prefix) or route in parent_f8c3.PRODUCTION_ROUTE_MODULES.values() else 'general'}) + return rows + +def _benchmark_payload(candidate_report: dict[str, Any], baseline_report: dict[str, Any], *, use_cupti: bool, shape_labels, measured_function: str) -> dict[str, Any]: + candidate_metric = candidate_report['summary']['primary_mean'] or 0.0 + baseline_metric = baseline_report['summary']['primary_mean'] or 0.0 + route_trace = route_trace_for_contract_shapes(shape_labels) + below_flashlib = _below_flashlib_rows(candidate_report) + return {'tflops': candidate_metric, 'baseline_tflops': baseline_metric, 'metric_delta': candidate_metric - baseline_metric, 'all_correct': candidate_report['summary']['all_correct'], 'baseline_all_correct': baseline_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'baseline_performance_comparable': baseline_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'baseline_invalid_performance_reason': baseline_report['summary']['invalid_performance_reason'], 'measured_entrypoint': ''.join(['loom.examples.weave.knn_build_dispatch_selected_portfolio_99f2_q4096k64_v1:', format(measured_function, '')]), 'baseline_entrypoint': ROUTE_PARENT_F8C3, 'baseline_entrypoint_note': 'same-session f8c3 selected portfolio measured through the same contract denominator', 'measured_shape_labels': 'all_canonical' if shape_labels is None else tuple(shape_labels), 'selected_route_labels': SELECTED_TARGET_SHAPES, 'consumed_seed_labels': CONSUMED_SEED_TARGET_SHAPES, 'guard_miss_audit_labels': GUARD_MISS_AUDIT_SHAPES, 'selected_route_rows': _rows_for_labels(candidate_report, SELECTED_TARGET_SHAPES), 'baseline_selected_route_rows': _rows_for_labels(baseline_report, SELECTED_TARGET_SHAPES), 'consumed_seed_rows': _rows_for_labels(candidate_report, CONSUMED_SEED_TARGET_SHAPES), 'baseline_consumed_seed_rows': _rows_for_labels(baseline_report, CONSUMED_SEED_TARGET_SHAPES), 'guard_miss_audit_rows': _rows_for_labels(candidate_report, GUARD_MISS_AUDIT_SHAPES), 'baseline_guard_miss_audit_rows': _rows_for_labels(baseline_report, GUARD_MISS_AUDIT_SHAPES), 'per_consumed_row_delta': _per_consumed_row_delta(candidate_report, baseline_report), 'seed_delta_matrix': _seed_delta_matrix(candidate_report, baseline_report), 'frontmatter_seed_delta_matrix': _frontmatter_seed_delta_matrix(candidate_report, baseline_report), 'candidate_dispatchers': CANDIDATE_PORTFOLIOS, 'selected_candidate_dispatcher': 'selected_99f2_f8c3_plus_q4096k64', 'q4096_row_selection': Q4096_ROW_SELECTION, 'route_modules': PRODUCTION_ROUTE_MODULES, 'route_trace': route_trace, 'forced_fallback_route_trace': route_trace_for_contract_shapes(shape_labels, force_fallback=True), 'route_trace_included': True, 'contract_summary': candidate_report['summary'], 'baseline_contract_summary': baseline_report['summary'], 'contract_performance': candidate_report['performance'], 'baseline_contract_performance': baseline_report['performance'], 'timing_backends': _timing_backends_for_reports(candidate_report, baseline_report), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'hot_bucket_parity': {'rag_k32': 'inherited_e51c', 'dim_sweep_qm2048_d64_k10': 'inherited_e51c', 'dim_sweep_qm2048_d256_k10': 'inherited_e51c', 'dim_sweep_qm2048_fp16_d128_k10': 'inherited_e51c', 'rect_q2048_m32768_k10': 'inherited_e51c', 'default_k96_registry_gate': 'inherited_e51c', 'large_tail_k20_q6144': 'inherited_e51c', 'midk_k24_k28': 'inherited_e51c', 'over32_k64_q2048': 'inherited_f8c3_k64split8', 'over32_k64_q4096': 'selected_q4096k64_split8'}, 'performance_coverage': 'partial' if below_flashlib else 'pass', 'coverage_only_routes': [], 'hot_bucket_blockers': below_flashlib, 'report': candidate_report, 'baseline_report': baseline_report} + +def benchmark_knn_build_dispatch_selected_portfolio_99f2_q4096k64_v1(*, use_cupti: bool=False, shape_labels=None) -> dict[str, Any]: + """Full-denominator A/B against the f8c3 selected portfolio dispatcher.""" + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels) + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_base_dispatcher) + return _benchmark_payload(candidate_report, baseline_report, use_cupti=use_cupti, shape_labels=shape_labels, measured_function='benchmark_knn_build_dispatch_selected_portfolio_99f2_q4096k64_v1') + +def write_benchmark_artifacts(artifact_dir: str | os.PathLike[str], *, use_cupti: bool=False, shape_labels=None) -> dict[str, str]: + payload = benchmark_knn_build_dispatch_selected_portfolio_99f2_q4096k64_v1(use_cupti=use_cupti, shape_labels=shape_labels) + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + candidate_path = out_dir / 'full55_dispatch_selected_portfolio_99f2_q4096k64_v1.json' + baseline_path = out_dir / 'full55_same_session_baseline_f8c3_for_99f2_q4096k64_v1.json' + route_trace_path = out_dir / 'full55_route_trace_selected_portfolio_99f2_q4096k64_v1.json' + forced_trace_path = out_dir / 'full55_forced_fallback_trace_selected_portfolio_99f2_q4096k64_v1.json' + candidate_path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + baseline_path.write_text(json.dumps({'measured_entrypoint': payload['baseline_entrypoint'], 'measured_shape_labels': payload['measured_shape_labels'], 'timing_backend_requested': payload['timing_backend_requested'], 'timing_backends': payload['timing_backends'], 'tflops': payload['baseline_tflops'], 'all_correct': payload['baseline_all_correct'], 'performance_comparable': payload['baseline_performance_comparable'], 'contract_summary': payload['baseline_contract_summary'], 'contract_performance': payload['baseline_contract_performance'], 'route_trace': parent_f8c3.route_trace_for_contract_shapes(shape_labels), 'route_trace_included': True, 'report': payload['baseline_report']}, indent=2, sort_keys=True) + '\n') + route_trace_path.write_text(json.dumps(payload['route_trace'], indent=2, sort_keys=True) + '\n') + forced_trace_path.write_text(json.dumps(payload['forced_fallback_route_trace'], indent=2, sort_keys=True) + '\n') + return {'candidate_payload': str(candidate_path), 'baseline_payload': str(baseline_path), 'route_trace': str(route_trace_path), 'forced_fallback_trace': str(forced_trace_path)} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_selected_portfolio_a961_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_selected_portfolio_a961_v1.py new file mode 100644 index 00000000..a9eb2391 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_selected_portfolio_a961_v1.py @@ -0,0 +1,195 @@ +"""Opt-in kNN build full55 dispatcher for the a961 selected K64 portfolio. + +Minimum target architecture: sm_100a. This dispatcher-consumption candidate is +wrapper-only. It starts from the e51c selected full55 portfolio and consumes one +additional exact Weave seed: the q2048 K64 split8 route from +``knn_build_dim_midk_bad5_k64split8_v1``. + +Every production route remains Weave-only; PyTorch and FlashLib are references +only through the contract harness. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from pathlib import Path +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dim_midk_bad5_k64split8_v1 as k64_seed +from . import knn_build_dispatch_selected_portfolio_e51c_v1 as base_e51c +ROUTE_K64_A961 = k64_seed.ROUTE_K64_Q2048 +ROUTE_BASE_E51C = 'loom.examples.weave.knn_build_dispatch_selected_portfolio_e51c_v1:launch_from_contract_inputs' +K64_TARGET_SHAPES = ('build_over32_stress_qm2048_k64',) +ADJACENT_GUARD_MISS_SHAPES = ('build_k_sweep_qm1024_k16', 'build_over32_stress_qm4096_k64') +CONSUMED_SEED_TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["rag_stream_largek_b1_q128_m100000_d128_k32", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q2048_m2048_d256_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10", "search_rect_b1_q2048_m32768_d128_k10", "build_over64_stress_qm2048_k96", "build_large_tail_b1_q6144_m6144_d128_k20", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "build_over32_stress_qm2048_k64"]}')) +SELECTED_TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_large_b1_q8192_m8192_d128_k20", "build_large_b1_q8192_m8192_d128_k32", "build_over64_stress_qm2048_k96", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32", "rag_batch_b2_q256_m50000_d128_k10", "rag_irregular_b1_q512_m131071_d128_k10", "search_rect_b1_q1024_m8192_d128_k10", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q2048_m2048_d256_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10", "search_rect_b1_q2048_m32768_d128_k10", "build_large_tail_b1_q6144_m6144_d128_k20", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "build_over32_stress_qm2048_k64"]}')) +GUARD_MISS_AUDIT_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_dim_sweep_b1_q2048_m2048_d64_k10", "build_qm2048_d128_k10", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "build_over32_stress_qm4096_k64", "flashml_correctness_b1_q256_m256_d128_k5", "build_k_sweep_qm1024_k16"]}')) +DISPATCH_CORRECTNESS_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["flashml_correctness_b1_q256_m256_d128_k5", "build_large_b1_q8192_m8192_d128_k20", "build_large_b1_q8192_m8192_d128_k32", "build_over64_stress_qm2048_k96", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32", "rag_batch_b2_q256_m50000_d128_k10", "rag_irregular_b1_q512_m131071_d128_k10", "search_rect_b1_q1024_m8192_d128_k10", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q2048_m2048_d256_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10", "search_rect_b1_q2048_m32768_d128_k10", "build_large_tail_b1_q6144_m6144_d128_k20", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "build_over32_stress_qm2048_k64", "build_qm2048_d128_k10", "build_over32_stress_qm4096_k64", "build_k_sweep_qm1024_k16", "rag_online_b1_q1_m100000_d128_k10", "rag_stream_b1_q128_m100000_d128_k10", "search_rect_b1_q4096_m65536_d128_k20", "rag_offline_largek_b1_q4096_m100000_d128_k20", "rag_offline_large_m_b1_q8192_m250000_d128_k20"]}')) +PRODUCTION_ROUTE_MODULES = {**base_e51c.PRODUCTION_ROUTE_MODULES, 'midk_k64_q2048_split8': ROUTE_K64_A961, 'base_e51c': ROUTE_BASE_E51C} +CANDIDATE_PORTFOLIOS = ({'id': 'baseline_e51c_selected_portfolio', 'entrypoint': 'loom.examples.weave.knn_build_dispatch_selected_portfolio_e51c_v1:benchmark_knn_build_dispatch_selected_portfolio_e51c_v1', 'consumed_seeds': base_e51c.CANDIDATE_PORTFOLIOS[-1]['consumed_seeds'], 'guard_plan': base_e51c.CANDIDATE_PORTFOLIOS[-1]['guard_plan'], 'expected_shape_wins': base_e51c.CONSUMED_SEED_TARGET_SHAPES, 'rejected_reason': 'same-session baseline for the a961 K64 consumption lane'}, {'id': 'selected_a961_e51c_plus_k64_split8', 'entrypoint': 'loom.examples.weave.knn_build_dispatch_selected_portfolio_a961_v1:benchmark_knn_build_dispatch_selected_portfolio_a961_v1', 'consumed_seeds': ('selected_e51c_f552_a330_a4f6_row_level_midk', 'midk_k64_q2048_split8'), 'guard_plan': ('exact a961 BF16 build B1 Q=M=2048 D128 K64 guard', 'then e51c selected full55 guard plan'), 'expected_shape_wins': SELECTED_TARGET_SHAPES, 'rejected_reason': None}) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_DISPATCH_SELECTED_A961_VERIFY_KERNEL') + if verify_kernel == 'k64_stage1': + os.environ['LOOM_KNN_DIMMIDK_BAD5_K64S8_VERIFY_KERNEL'] = 'stage1_k64_s8_tailinf' + return k64_seed._verify_export_ir() + if verify_kernel == 'k64_merge': + os.environ['LOOM_KNN_DIMMIDK_BAD5_K64S8_VERIFY_KERNEL'] = 'merge_k64_s8_warp_select' + return k64_seed._verify_export_ir() + return base_e51c.ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k48over32", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 48]], "cta_group": 1, "threads": 192}')) + +def _eligible_k64_q2048(inputs: dict[str, Any]) -> bool: + return k64_seed._eligible_k64_q2048(inputs) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if force_fallback: + return base_e51c.route_for_contract_inputs(inputs) + if _eligible_k64_q2048(inputs): + return ROUTE_K64_A961 + return base_e51c.route_for_contract_inputs(inputs) + +def _launch_route(inputs: dict[str, Any], route: str) -> None: + if route == ROUTE_K64_A961: + k64_seed._launch_k64_q2048_split8(inputs) + return + base_e51c._launch_route(inputs, route) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + _launch_route(inputs, route_for_contract_inputs(inputs, force_fallback=force_fallback)) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def candidate_base_dispatcher(inputs: dict[str, Any]): + base_e51c.launch_from_contract_inputs(inputs) + return None + +def candidate_force_fallback(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs, force_fallback=True) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return base_e51c._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=DISPATCH_CORRECTNESS_SHAPES, benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint: run real contract correctness for selected shapes.""" + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=None, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + shapes = None if shape_labels is None else _select_contract_shapes(shape_labels) + return evaluate_contract(shapes=shapes, correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def _trace_inputs_from_shape(shape: dict[str, Any]) -> dict[str, Any]: + return base_e51c._trace_inputs_from_shape(shape) + +def _inputs_for_label(label: str) -> dict[str, Any]: + return base_e51c._inputs_for_label(label) + +def _base_e51c_route(inputs: dict[str, Any]) -> str: + return base_e51c.route_for_contract_inputs(inputs) + +def _route_trace_record(inputs: dict[str, Any], *, force_fallback: bool=False) -> dict[str, Any]: + base_route = _base_e51c_route(inputs) + if force_fallback: + row = base_e51c._route_trace_record(inputs) + row['guard_condition'] = 'forced fallback to e51c baseline; a961 K64 overlay disabled' + row['coverage'] = 'forced candidate fallback for a961 K64 overlay' + row['forced_disabled_seeds'] = ('midk_k64_q2048_split8',) + row['base_e51c_route'] = base_route + row['candidate_guard_status'] = 'forced_fallback_to_e51c' + return row + route = route_for_contract_inputs(inputs) + if route == ROUTE_K64_A961 and _eligible_k64_q2048(inputs): + return {'shape_key': inputs.get('label'), 'selected_route': route, 'guard_condition': 'exact a961 BF16 build B1 Q=M=2048 D128 K64 split8 guard', 'route_kind': 'specialized', 'coverage': 'exact K64 split8 tail-infinity seed selected ahead of e51c fallback', 'consumed_seed': 'midk_k64_q2048_split8', 'replaced_route': base_route, 'base_e51c_route': base_route, 'parity_status': 'pass', 'parity_reason': 'source seed measured 0.142945 ms, 7.511573 TFLOPS, and 2.146861x FlashLib with CUPTI', 'candidate_guard_status': 'selected_from_k64split8'} + row = base_e51c._route_trace_record(inputs) + row['base_e51c_route'] = base_route + row['candidate_guard_status'] = 'inherited_e51c_or_guard_miss' + return row + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + return [_route_trace_record(_trace_inputs_from_shape(shape), force_fallback=force_fallback) for shape in selected] + +def _timing_backends_for_reports(*reports: dict[str, Any]) -> list[str]: + return base_e51c._timing_backends_for_reports(*reports) + +def _rows_for_labels(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return base_e51c._rows_for_labels(report, labels) + +def _per_consumed_row_delta(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> dict[str, Any]: + deltas = {} + for label in CONSUMED_SEED_TARGET_SHAPES: + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') + inputs = _inputs_for_label(label) + deltas[label] = {'candidate_ms': candidate_ms, 'baseline_e51c_ms': baseline_ms, 'flashlib_ms': flashlib_ms, 'speedup_vs_baseline_e51c': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'ratio_vs_flashlib': flashlib_ms / candidate_ms if candidate_ms and flashlib_ms else None, 'candidate_route': route_for_contract_inputs(inputs), 'baseline_e51c_route': _base_e51c_route(inputs)} + return deltas + +def _seed_delta_matrix(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> list[dict[str, Any]]: + matrix = [] + for label in SELECTED_TARGET_SHAPES: + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + inputs = _inputs_for_label(label) + matrix.append({'shape_key': label, 'baseline_route': _base_e51c_route(inputs), 'candidate_route': route_for_contract_inputs(inputs), 'candidate_ms': candidate_ms, 'baseline_ms': baseline_ms, 'metric_delta_ms': candidate_ms - baseline_ms if candidate_ms and baseline_ms else None, 'speedup_vs_baseline_e51c': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'timing_backend': candidate_row.get('timing_backend') or baseline_row.get('timing_backend')}) + return matrix + +def _frontmatter_seed_delta_matrix(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> list[dict[str, Any]]: + rows = [] + for item in _seed_delta_matrix(candidate_report, baseline_report): + delta = item['metric_delta_ms'] + rows.append({'shape_key': item['shape_key'], 'baseline_route': item['baseline_route'], 'candidate_deltas': [{'candidate_id': 'selected_a961_e51c_plus_k64_split8', 'metric_delta': 0.0 if delta is None else float(delta), 'timing_backend': item['timing_backend'] or 'cuda_event'}]}) + return rows + +def _benchmark_payload(candidate_report: dict[str, Any], baseline_report: dict[str, Any], *, use_cupti: bool, shape_labels, measured_function: str) -> dict[str, Any]: + candidate_metric = candidate_report['summary']['primary_mean'] or 0.0 + baseline_metric = baseline_report['summary']['primary_mean'] or 0.0 + route_trace = route_trace_for_contract_shapes(shape_labels) + return {'tflops': candidate_metric, 'baseline_tflops': baseline_metric, 'metric_delta': candidate_metric - baseline_metric, 'all_correct': candidate_report['summary']['all_correct'], 'baseline_all_correct': baseline_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'baseline_performance_comparable': baseline_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'baseline_invalid_performance_reason': baseline_report['summary']['invalid_performance_reason'], 'measured_entrypoint': ''.join(['loom.examples.weave.knn_build_dispatch_selected_portfolio_a961_v1:', format(measured_function, '')]), 'baseline_entrypoint': ROUTE_BASE_E51C, 'baseline_entrypoint_note': 'same-session e51c selected portfolio measured through the same contract denominator', 'measured_shape_labels': 'all_canonical' if shape_labels is None else tuple(shape_labels), 'selected_route_labels': SELECTED_TARGET_SHAPES, 'consumed_seed_labels': CONSUMED_SEED_TARGET_SHAPES, 'guard_miss_audit_labels': GUARD_MISS_AUDIT_SHAPES, 'selected_route_rows': _rows_for_labels(candidate_report, SELECTED_TARGET_SHAPES), 'baseline_selected_route_rows': _rows_for_labels(baseline_report, SELECTED_TARGET_SHAPES), 'consumed_seed_rows': _rows_for_labels(candidate_report, CONSUMED_SEED_TARGET_SHAPES), 'baseline_consumed_seed_rows': _rows_for_labels(baseline_report, CONSUMED_SEED_TARGET_SHAPES), 'guard_miss_audit_rows': _rows_for_labels(candidate_report, GUARD_MISS_AUDIT_SHAPES), 'baseline_guard_miss_audit_rows': _rows_for_labels(baseline_report, GUARD_MISS_AUDIT_SHAPES), 'per_consumed_row_delta': _per_consumed_row_delta(candidate_report, baseline_report), 'seed_delta_matrix': _seed_delta_matrix(candidate_report, baseline_report), 'frontmatter_seed_delta_matrix': _frontmatter_seed_delta_matrix(candidate_report, baseline_report), 'candidate_dispatchers': CANDIDATE_PORTFOLIOS, 'selected_candidate_dispatcher': 'selected_a961_e51c_plus_k64_split8', 'route_modules': PRODUCTION_ROUTE_MODULES, 'route_trace': route_trace, 'forced_fallback_route_trace': route_trace_for_contract_shapes(shape_labels, force_fallback=True), 'route_trace_included': True, 'contract_summary': candidate_report['summary'], 'baseline_contract_summary': baseline_report['summary'], 'contract_performance': candidate_report['performance'], 'baseline_contract_performance': baseline_report['performance'], 'timing_backends': _timing_backends_for_reports(candidate_report, baseline_report), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'hot_bucket_parity': {'rag_k32': 'selected_f552_4fbf_exact_k32', 'dim_sweep_qm2048_d64_k10': 'selected_f552_73a9_exact_d64', 'dim_sweep_qm2048_d256_k10': 'selected_f552_df2f', 'dim_sweep_qm2048_fp16_d128_k10': 'selected_f552_df2f', 'rect_q2048_m32768_k10': 'selected_f552_4452_split8', 'default_k96_registry_gate': 'explicit_a330_k96_guard', 'large_tail_k20_q6144': 'selected_a4f6_large_tail', 'midk_k24_k28': 'row_level_81aa_9b2c_selection', 'over32_k64': 'selected_a961_k64_split8'}, 'performance_coverage': 'partial', 'coverage_only_routes': [], 'hot_bucket_blockers': ['build_over32_stress_qm4096_k64', 'build_k_sweep_qm1024_k16', 'build_large_b1_q8192_m8192_d128_k32', 'rag_microbatch_b1_q16_m100000_d128_k10'], 'report': candidate_report, 'baseline_report': baseline_report} + +def benchmark_knn_build_dispatch_selected_portfolio_a961_v1(*, use_cupti: bool=False, shape_labels=None) -> dict[str, Any]: + """Full-denominator A/B against the e51c selected portfolio dispatcher.""" + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels) + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_base_dispatcher) + return _benchmark_payload(candidate_report, baseline_report, use_cupti=use_cupti, shape_labels=shape_labels, measured_function='benchmark_knn_build_dispatch_selected_portfolio_a961_v1') + +def write_benchmark_artifacts(artifact_dir: str | os.PathLike[str], *, use_cupti: bool=False, shape_labels=None) -> dict[str, str]: + payload = benchmark_knn_build_dispatch_selected_portfolio_a961_v1(use_cupti=use_cupti, shape_labels=shape_labels) + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + candidate_path = out_dir / 'full55_dispatch_selected_portfolio_a961_v1.json' + baseline_path = out_dir / 'full55_same_session_baseline_e51c_for_a961_v1.json' + route_trace_path = out_dir / 'full55_route_trace_selected_portfolio_a961_v1.json' + forced_trace_path = out_dir / 'full55_forced_fallback_trace_selected_portfolio_a961_v1.json' + candidate_path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + baseline_path.write_text(json.dumps({'measured_entrypoint': payload['baseline_entrypoint'], 'measured_shape_labels': payload['measured_shape_labels'], 'timing_backend_requested': payload['timing_backend_requested'], 'timing_backends': payload['timing_backends'], 'tflops': payload['baseline_tflops'], 'all_correct': payload['baseline_all_correct'], 'performance_comparable': payload['baseline_performance_comparable'], 'contract_summary': payload['baseline_contract_summary'], 'contract_performance': payload['baseline_contract_performance'], 'route_trace': base_e51c.route_trace_for_contract_shapes(shape_labels), 'route_trace_included': True, 'report': payload['baseline_report']}, indent=2, sort_keys=True) + '\n') + route_trace_path.write_text(json.dumps(payload['route_trace'], indent=2, sort_keys=True) + '\n') + forced_trace_path.write_text(json.dumps(payload['forced_fallback_route_trace'], indent=2, sort_keys=True) + '\n') + return {'candidate_payload': str(candidate_path), 'baseline_payload': str(baseline_path), 'route_trace': str(route_trace_path), 'forced_fallback_trace': str(forced_trace_path)} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_selected_portfolio_e51c_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_selected_portfolio_e51c_v1.py new file mode 100644 index 00000000..44cecb7f --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_selected_portfolio_e51c_v1.py @@ -0,0 +1,309 @@ +"""Opt-in kNN build full55 dispatcher for the e51c selected portfolio. + +Minimum target architecture: sm_100a. This dispatcher-synthesis candidate is +wrapper-only. It starts from the f552 selected full55 portfolio, preserves the +a330 exact K96 route as an explicit guard, consumes the a4f6 large-tail K20 +route, and adds exact K24/K28 row choices from the 81aa and 9b2c mid-K seeds. + +Every production route remains Weave-only; PyTorch and FlashLib are references +only through the contract harness. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from pathlib import Path +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dim_midk_bad5_k24k28_v1 as midk_9b2c +from . import knn_build_dim_midk_bad5_midkcleanup_v1 as midk_81aa +from . import knn_build_dispatch_7399_d15e_df2f_large_tail_a4f6_full55_v1 as large_tail_dispatch +from . import knn_build_dispatch_default_7c3a_v1 as default_7c3a +from . import knn_build_dispatch_selected_portfolio_f552_v1 as base_f552 +ROUTE_K96_A330 = default_7c3a.ROUTE_OVER64_K96 +ROUTE_LARGE_TAIL_A4F6 = large_tail_dispatch.ROUTE_LARGE_TAIL_A4F6 +ROUTE_MIDK_81AA_Q2048 = midk_81aa.ROUTE_MIDK_S8 +ROUTE_MIDK_9B2C_K24_Q2048 = midk_9b2c.ROUTE_K24_Q2048 +ROUTE_MIDK_9B2C_K28_Q2048 = midk_9b2c.ROUTE_K28_Q2048 +ROUTE_MIDK_9B2C_K28_Q4096 = midk_9b2c.ROUTE_K28_Q4096 +ROUTE_BASE_F552 = 'loom.examples.weave.knn_build_dispatch_selected_portfolio_f552_v1:launch_from_contract_inputs' +K96_TARGET_SHAPES = default_7c3a.K96_TARGET_SHAPES +LARGE_TAIL_TARGET_SHAPES = large_tail_dispatch.LARGE_TAIL_TARGET_SHAPES +MIDK_81AA_Q2048_TARGET_SHAPES = ('build_k_sweep_qm2048_k24', 'build_k_sweep_qm2048_k28') +MIDK_9B2C_Q4096_TARGET_SHAPES = ('build_k_sweep_qm4096_k28',) +MIDK_SELECTED_TARGET_SHAPES = (*MIDK_81AA_Q2048_TARGET_SHAPES, *MIDK_9B2C_Q4096_TARGET_SHAPES) +MIDK_GUARD_MISS_AUDIT_SHAPES = ('build_k_sweep_qm1024_k16', 'build_over32_stress_qm2048_k64') +CONSUMED_SEED_TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["rag_stream_largek_b1_q128_m100000_d128_k32", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q2048_m2048_d256_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10", "search_rect_b1_q2048_m32768_d128_k10", "build_over64_stress_qm2048_k96", "build_large_tail_b1_q6144_m6144_d128_k20", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28"]}')) +SELECTED_TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_large_b1_q8192_m8192_d128_k20", "build_large_b1_q8192_m8192_d128_k32", "build_over64_stress_qm2048_k96", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32", "rag_batch_b2_q256_m50000_d128_k10", "rag_irregular_b1_q512_m131071_d128_k10", "search_rect_b1_q1024_m8192_d128_k10", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q2048_m2048_d256_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10", "search_rect_b1_q2048_m32768_d128_k10", "build_large_tail_b1_q6144_m6144_d128_k20", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28"]}')) +GUARD_MISS_AUDIT_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_dim_sweep_b1_q2048_m2048_d64_k10", "build_qm2048_d128_k10", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "build_over32_stress_qm2048_k64", "build_over32_stress_qm4096_k64", "flashml_correctness_b1_q256_m256_d128_k5", "build_k_sweep_qm1024_k16"]}')) +DISPATCH_CORRECTNESS_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["flashml_correctness_b1_q256_m256_d128_k5", "build_large_b1_q8192_m8192_d128_k20", "build_large_b1_q8192_m8192_d128_k32", "build_over64_stress_qm2048_k96", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32", "rag_batch_b2_q256_m50000_d128_k10", "rag_irregular_b1_q512_m131071_d128_k10", "search_rect_b1_q1024_m8192_d128_k10", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q2048_m2048_d256_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10", "search_rect_b1_q2048_m32768_d128_k10", "build_large_tail_b1_q6144_m6144_d128_k20", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "build_qm2048_d128_k10", "build_over32_stress_qm2048_k64", "build_over32_stress_qm4096_k64", "build_k_sweep_qm1024_k16", "rag_online_b1_q1_m100000_d128_k10", "rag_stream_b1_q128_m100000_d128_k10", "search_rect_b1_q4096_m65536_d128_k20", "rag_offline_largek_b1_q4096_m100000_d128_k20", "rag_offline_large_m_b1_q8192_m250000_d128_k20"]}')) +PRODUCTION_ROUTE_MODULES = {**base_f552.PRODUCTION_ROUTE_MODULES, 'default_k96_a330': ROUTE_K96_A330, 'large_tail_a4f6': ROUTE_LARGE_TAIL_A4F6, 'midk_81aa_q2048_k24_k28': ROUTE_MIDK_81AA_Q2048, 'midk_9b2c_q4096_k28': ROUTE_MIDK_9B2C_K28_Q4096, 'base_f552': ROUTE_BASE_F552} +CANDIDATE_PORTFOLIOS = ({'id': 'baseline_f552_selected_portfolio', 'entrypoint': 'loom.examples.weave.knn_build_dispatch_selected_portfolio_f552_v1:benchmark_knn_build_dispatch_selected_portfolio_f552_v1', 'consumed_seeds': base_f552.CANDIDATE_PORTFOLIOS[2]['consumed_seeds'], 'guard_plan': base_f552.CANDIDATE_PORTFOLIOS[2]['guard_plan'], 'expected_shape_wins': base_f552.CONSUMED_SEED_TARGET_SHAPES, 'rejected_reason': 'same-session baseline for this e51c additive synthesis lane'}, {'id': 'f552_plus_a330_k96_a4f6_large_tail_no_midk', 'entrypoint': 'loom.examples.weave.knn_build_dispatch_selected_portfolio_e51c_v1:candidate_no_midk', 'consumed_seeds': ('default_k96_a330', 'large_tail_a4f6_k20'), 'guard_plan': ('f552 selected full55 guard plan', 'explicit exact a330 BF16 build B1 Q=M=2048 D128 K96 label before inherited routes', 'exact a4f6 BF16 build B1 Q=M=6144 D128 K20 label'), 'expected_shape_wins': (*K96_TARGET_SHAPES, *LARGE_TAIL_TARGET_SHAPES), 'rejected_reason': 'omits rank-selected K24/K28 seed consumption'}, {'id': 'f552_plus_a330_a4f6_all_81aa_midk', 'entrypoint': 'loom.examples.weave.knn_build_dispatch_selected_portfolio_e51c_v1:candidate_all_81aa_midk', 'consumed_seeds': ('default_k96_a330', 'large_tail_a4f6_k20', 'midk_81aa_all_k24_k28'), 'guard_plan': ('f552 plus exact a330 K96 and a4f6 large-tail guards', '81aa exact K24/K28 route for q2048 K24, q2048 K28, and q4096 K28'), 'expected_shape_wins': (*K96_TARGET_SHAPES, *LARGE_TAIL_TARGET_SHAPES, *midk_81aa.MIDK_CLEANUP_SHAPES), 'rejected_reason': '9b2c is faster and above FlashLib on q4096 K28'}, {'id': 'f552_plus_a330_a4f6_all_9b2c_midk', 'entrypoint': 'loom.examples.weave.knn_build_dispatch_selected_portfolio_e51c_v1:candidate_all_9b2c_midk', 'consumed_seeds': ('default_k96_a330', 'large_tail_a4f6_k20', 'midk_9b2c_all_k24_k28'), 'guard_plan': ('f552 plus exact a330 K96 and a4f6 large-tail guards', '9b2c exact-capacity K24/K28 route for all three K24/K28 rows'), 'expected_shape_wins': (*K96_TARGET_SHAPES, *LARGE_TAIL_TARGET_SHAPES, *MIDK_SELECTED_TARGET_SHAPES), 'rejected_reason': '81aa is faster on q2048 K24 and q2048 K28'}, {'id': 'selected_e51c_f552_a330_a4f6_row_level_midk', 'entrypoint': 'loom.examples.weave.knn_build_dispatch_selected_portfolio_e51c_v1:benchmark_knn_build_dispatch_selected_portfolio_e51c_v1', 'consumed_seeds': ('selected_f552_k32_d64_df2f_rect4452', 'default_k96_a330', 'large_tail_a4f6_k20', 'midk_81aa_q2048_k24_k28', 'midk_9b2c_q4096_k28'), 'guard_plan': ('f552 exact D256/FP16, D64, rect4452, and inherited exact RAG K32 routes', 'explicit exact a330 BF16 build B1 Q=M=2048 D128 K96 label before inherited routes', 'exact a4f6 BF16 build B1 Q=M=6144 D128 K20 label', '81aa exact BF16 build Q=M=2048 K24/K28 labels', '9b2c exact BF16 build Q=M=4096 K28 label', 'then f552 Weave-only fallback policy'), 'expected_shape_wins': CONSUMED_SEED_TARGET_SHAPES, 'rejected_reason': None}) +MIDK_ROW_SELECTION = {'build_k_sweep_qm2048_k24': {'selected_seed': 'midk_81aa_q2048_k24_k28', 'selected_route': ROUTE_MIDK_81AA_Q2048, 'candidate_ms': 0.118785, 'ratio_vs_flashlib': 1.2125605084817106, 'rejected_seed': 'midk_9b2c_k24_q2048', 'rejected_ms': 0.119777, 'reason': '81aa is slightly faster on the same CUPTI bucket denominator'}, 'build_k_sweep_qm2048_k28': {'selected_seed': 'midk_81aa_q2048_k24_k28', 'selected_route': ROUTE_MIDK_81AA_Q2048, 'candidate_ms': 0.103874, 'ratio_vs_flashlib': 1.508616208098273, 'rejected_seed': 'midk_9b2c_k28_q2048', 'rejected_ms': 0.135041, 'reason': '81aa is faster on q2048 K28'}, 'build_k_sweep_qm4096_k28': {'selected_seed': 'midk_9b2c_q4096_k28', 'selected_route': ROUTE_MIDK_9B2C_K28_Q4096, 'candidate_ms': 0.27373, 'ratio_vs_flashlib': 1.1087239250356191, 'rejected_seed': 'midk_81aa_q4096_k28', 'rejected_ms': 0.323204, 'reason': '9b2c is faster and above FlashLib on q4096 K28'}} + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_DISPATCH_SELECTED_E51C_VERIFY_KERNEL') + if verify_kernel == 'k96_stage1': + os.environ['LOOM_KNN_DEFAULT_7C3A_VERIFY_KERNEL'] = 'over64_k96_stage1' + return default_7c3a._verify_export_ir() + if verify_kernel == 'k96_merge': + os.environ['LOOM_KNN_DEFAULT_7C3A_VERIFY_KERNEL'] = 'over64_k96_merge' + return default_7c3a._verify_export_ir() + if verify_kernel == 'large_tail_stage1': + os.environ['LOOM_KNN_DISPATCH_7399_D15E_DF2F_LARGETAIL_VERIFY_KERNEL'] = 'large_tail_stage1' + return large_tail_dispatch._verify_export_ir() + if verify_kernel == 'large_tail_merge': + os.environ['LOOM_KNN_DISPATCH_7399_D15E_DF2F_LARGETAIL_VERIFY_KERNEL'] = 'large_tail_merge' + return large_tail_dispatch._verify_export_ir() + if verify_kernel == 'midk_81aa_stage1_k24_s8': + os.environ['LOOM_KNN_DIMMIDK_BAD5_MIDK_VERIFY_KERNEL'] = 'stage1_k24_s8' + return midk_81aa._verify_export_ir() + if verify_kernel == 'midk_81aa_stage1_k28_s8': + os.environ['LOOM_KNN_DIMMIDK_BAD5_MIDK_VERIFY_KERNEL'] = 'stage1_k28_s8' + return midk_81aa._verify_export_ir() + if verify_kernel == 'midk_81aa_merge_k24_s8': + os.environ['LOOM_KNN_DIMMIDK_BAD5_MIDK_VERIFY_KERNEL'] = 'merge_k24_s8' + return midk_81aa._verify_export_ir() + if verify_kernel == 'midk_81aa_merge_k28_s8': + os.environ['LOOM_KNN_DIMMIDK_BAD5_MIDK_VERIFY_KERNEL'] = 'merge_k28_s8' + return midk_81aa._verify_export_ir() + if verify_kernel == 'midk_9b2c_stage1_k28_unordered': + os.environ['LOOM_KNN_DIMMIDK_BAD5_K24K28_VERIFY_KERNEL'] = 'stage1_k28_unordered' + return midk_9b2c._verify_export_ir() + if verify_kernel == 'midk_9b2c_merge_k28_unordered': + os.environ['LOOM_KNN_DIMMIDK_BAD5_K24K28_VERIFY_KERNEL'] = 'merge_k28_unordered' + return midk_9b2c._verify_export_ir() + return base_f552.ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k48over32", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 48]], "cta_group": 1, "threads": 192}')) + +def _eligible_k96_a330(inputs: dict[str, Any]) -> bool: + return default_7c3a._eligible_over64_k96(inputs) + +def _eligible_large_tail_a4f6(inputs: dict[str, Any]) -> bool: + return large_tail_dispatch._eligible_large_tail_a4f6(inputs) + +def _is_exact_midk(inputs: dict[str, Any], *, q: int, k: int) -> bool: + return bool(inputs.get('build', False)) and int(inputs.get('B', -1)) == 1 and (int(inputs.get('Q', -1)) == q) and (int(inputs.get('M', -2)) == q) and (int(inputs.get('D', -1)) == 128) and (int(inputs.get('K', -1)) == k) and (midk_81aa._dtype_name(inputs) == 'bfloat16') + +def _label_can_hit(inputs: dict[str, Any], label: str) -> bool: + value = inputs.get('label') + return value is None or str(value) == label + +def _eligible_midk_81aa_q2048(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, 'build_k_sweep_qm2048_k24') and _is_exact_midk(inputs, q=2048, k=24) or (_label_can_hit(inputs, 'build_k_sweep_qm2048_k28') and _is_exact_midk(inputs, q=2048, k=28)) + +def _eligible_midk_9b2c_q4096(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, 'build_k_sweep_qm4096_k28') and _is_exact_midk(inputs, q=4096, k=28) + +def _route_with_policy(inputs: dict[str, Any], *, force_fallback: bool=False, midk_policy: str='selected') -> str: + if force_fallback: + return base_f552.route_for_contract_inputs(inputs) + if _eligible_k96_a330(inputs): + return ROUTE_K96_A330 + if _eligible_large_tail_a4f6(inputs): + return ROUTE_LARGE_TAIL_A4F6 + if midk_policy == 'selected': + if _eligible_midk_81aa_q2048(inputs): + return ROUTE_MIDK_81AA_Q2048 + if _eligible_midk_9b2c_q4096(inputs): + return ROUTE_MIDK_9B2C_K28_Q4096 + elif midk_policy == 'all_81aa': + if midk_81aa._eligible_midk_s8(inputs): + return ROUTE_MIDK_81AA_Q2048 + elif midk_policy == 'all_9b2c': + if midk_9b2c._eligible_k24_q2048(inputs): + return ROUTE_MIDK_9B2C_K24_Q2048 + if midk_9b2c._eligible_k28_q2048(inputs): + return ROUTE_MIDK_9B2C_K28_Q2048 + if midk_9b2c._eligible_k28_q4096(inputs): + return ROUTE_MIDK_9B2C_K28_Q4096 + elif midk_policy != 'none': + raise ValueError(''.join(['unknown midK routing policy: ', format(midk_policy, '')])) + return base_f552.route_for_contract_inputs(inputs) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + return _route_with_policy(inputs, force_fallback=force_fallback, midk_policy='selected') + +def _launch_route(inputs: dict[str, Any], route: str) -> None: + if route == ROUTE_K96_A330: + default_7c3a._launch_route(inputs, route) + return + if route == ROUTE_LARGE_TAIL_A4F6: + large_tail_dispatch._launch_route(inputs, route) + return + if route == ROUTE_MIDK_81AA_Q2048: + midk_81aa._launch_midk_s8(inputs) + return + if route == ROUTE_MIDK_9B2C_K24_Q2048: + midk_9b2c._launch_k24_q2048(inputs) + return + if route == ROUTE_MIDK_9B2C_K28_Q2048: + midk_9b2c._launch_k28_q2048(inputs) + return + if route == ROUTE_MIDK_9B2C_K28_Q4096: + midk_9b2c._launch_k28_q4096(inputs) + return + base_f552._launch_route(inputs, route) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + _launch_route(inputs, route_for_contract_inputs(inputs, force_fallback=force_fallback)) + +def _launch_with_policy(inputs: dict[str, Any], *, midk_policy: str) -> None: + _launch_route(inputs, _route_with_policy(inputs, midk_policy=midk_policy)) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def candidate_base_dispatcher(inputs: dict[str, Any]): + base_f552.launch_from_contract_inputs(inputs) + return None + +def candidate_force_fallback(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs, force_fallback=True) + return None + +def candidate_no_midk(inputs: dict[str, Any]): + _launch_with_policy(inputs, midk_policy='none') + return None + +def candidate_all_81aa_midk(inputs: dict[str, Any]): + _launch_with_policy(inputs, midk_policy='all_81aa') + return None + +def candidate_all_9b2c_midk(inputs: dict[str, Any]): + _launch_with_policy(inputs, midk_policy='all_9b2c') + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return base_f552._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=DISPATCH_CORRECTNESS_SHAPES, benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint: run real contract correctness for selected shapes.""" + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=None, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + shapes = None if shape_labels is None else _select_contract_shapes(shape_labels) + return evaluate_contract(shapes=shapes, correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def _trace_inputs_from_shape(shape: dict[str, Any]) -> dict[str, Any]: + return base_f552.dispatch_k32_d64.dispatch_k32._trace_inputs_from_shape(shape) + +def _inputs_for_label(label: str) -> dict[str, Any]: + return base_f552._inputs_for_label(label) + +def _base_f552_route(inputs: dict[str, Any]) -> str: + return base_f552.route_for_contract_inputs(inputs) + +def _route_trace_record(inputs: dict[str, Any], *, force_fallback: bool=False) -> dict[str, Any]: + base_route = _base_f552_route(inputs) + if force_fallback: + row = base_f552._route_trace_record(inputs) + row['guard_condition'] = 'forced fallback to f552 baseline; e51c a330/a4f6/midK overlays disabled' + row['coverage'] = 'forced candidate fallback for e51c additive overlays' + row['forced_disabled_seeds'] = ('default_k96_a330', 'large_tail_a4f6_k20', 'midk_81aa_q2048_k24_k28', 'midk_9b2c_q4096_k28') + row['base_f552_route'] = base_route + row['candidate_guard_status'] = 'forced_fallback_to_f552' + return row + route = route_for_contract_inputs(inputs) + label = str(inputs.get('label')) + if route == ROUTE_K96_A330 and _eligible_k96_a330(inputs): + return {'shape_key': inputs.get('label'), 'selected_route': route, 'guard_condition': 'exact a330 BF16 build B1 Q=M=2048 D128 K96 label before inherited routes', 'route_kind': 'specialized', 'coverage': 'default K96 registry gate preserved as an explicit selected-portfolio guard', 'consumed_seed': 'default_k96_a330', 'replaced_route': base_route, 'base_f552_route': base_route, 'parity_status': 'pass', 'parity_reason': 'a330 default K96 gate is 55/55 correct with default-registry promotion gates passing', 'candidate_guard_status': 'selected_from_a330'} + if route == ROUTE_LARGE_TAIL_A4F6: + return {'shape_key': inputs.get('label'), 'selected_route': route, 'guard_condition': 'exact a4f6 BF16 build B1 Q=M=6144 D128 K20 label', 'route_kind': 'specialized', 'coverage': 'exact a4f6 large-tail split4 K20 seed selected ahead of f552 fallback', 'consumed_seed': 'large_tail_a4f6_k20', 'replaced_route': base_route, 'base_f552_route': base_route, 'parity_status': 'pass', 'parity_reason': 'a4f6 source seed measured 0.412324 ms, 23.437094 TFLOPS, and 1.185953x FlashLib on CUPTI', 'candidate_guard_status': 'selected_from_a4f6'} + if route == ROUTE_MIDK_81AA_Q2048 and label in MIDK_ROW_SELECTION: + selected = MIDK_ROW_SELECTION[label] + return {'shape_key': inputs.get('label'), 'selected_route': route, 'guard_condition': 'exact 81aa BF16 build B1 Q=M=2048 D128 K24/K28 row-level guard', 'route_kind': 'specialized', 'coverage': 'exact 81aa q2048 K24/K28 S8 seed selected ahead of f552 fallback', 'consumed_seed': selected['selected_seed'], 'replaced_route': base_route, 'base_f552_route': base_route, 'row_selection': selected, 'parity_status': 'pass', 'parity_reason': selected['reason'], 'candidate_guard_status': 'selected_from_81aa'} + if route == ROUTE_MIDK_9B2C_K28_Q4096 and label in MIDK_ROW_SELECTION: + selected = MIDK_ROW_SELECTION[label] + return {'shape_key': inputs.get('label'), 'selected_route': route, 'guard_condition': 'exact 9b2c BF16 build B1 Q=M=4096 D128 K28 row-level guard', 'route_kind': 'specialized', 'coverage': 'exact 9b2c q4096 K28 unordered split4 seed selected ahead of f552 fallback', 'consumed_seed': selected['selected_seed'], 'replaced_route': base_route, 'base_f552_route': base_route, 'row_selection': selected, 'parity_status': 'pass', 'parity_reason': selected['reason'], 'candidate_guard_status': 'selected_from_9b2c'} + row = base_f552._route_trace_record(inputs) + row['base_f552_route'] = base_route + row['candidate_guard_status'] = 'inherited_f552_or_guard_miss' + return row + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + return [_route_trace_record(_trace_inputs_from_shape(shape), force_fallback=force_fallback) for shape in selected] + +def _timing_backends_for_reports(*reports: dict[str, Any]) -> list[str]: + return base_f552._timing_backends_for_reports(*reports) + +def _rows_for_labels(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return base_f552._rows_for_labels(report, labels) + +def _per_consumed_row_delta(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> dict[str, Any]: + deltas = {} + for label in CONSUMED_SEED_TARGET_SHAPES: + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') + inputs = _inputs_for_label(label) + deltas[label] = {'candidate_ms': candidate_ms, 'baseline_f552_ms': baseline_ms, 'flashlib_ms': flashlib_ms, 'speedup_vs_baseline_f552': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'ratio_vs_flashlib': flashlib_ms / candidate_ms if candidate_ms and flashlib_ms else None, 'candidate_route': route_for_contract_inputs(inputs), 'baseline_f552_route': _base_f552_route(inputs)} + return deltas + +def _seed_delta_matrix(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> list[dict[str, Any]]: + matrix = [] + for label in SELECTED_TARGET_SHAPES: + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + inputs = _inputs_for_label(label) + matrix.append({'shape_key': label, 'baseline_route': _base_f552_route(inputs), 'candidate_route': route_for_contract_inputs(inputs), 'candidate_ms': candidate_ms, 'baseline_ms': baseline_ms, 'metric_delta_ms': candidate_ms - baseline_ms if candidate_ms and baseline_ms else None, 'speedup_vs_baseline_f552': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'timing_backend': candidate_row.get('timing_backend') or baseline_row.get('timing_backend')}) + return matrix + +def _frontmatter_seed_delta_matrix(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> list[dict[str, Any]]: + rows = [] + for item in _seed_delta_matrix(candidate_report, baseline_report): + delta = item['metric_delta_ms'] + rows.append({'shape_key': item['shape_key'], 'baseline_route': item['baseline_route'], 'candidate_deltas': [{'candidate_id': 'selected_e51c_f552_a330_a4f6_row_level_midk', 'metric_delta': 0.0 if delta is None else float(delta), 'timing_backend': item['timing_backend'] or 'cuda_event'}]}) + return rows + +def _benchmark_payload(candidate_report: dict[str, Any], baseline_report: dict[str, Any], *, use_cupti: bool, shape_labels, measured_function: str) -> dict[str, Any]: + candidate_metric = candidate_report['summary']['primary_mean'] or 0.0 + baseline_metric = baseline_report['summary']['primary_mean'] or 0.0 + route_trace = route_trace_for_contract_shapes(shape_labels) + return {'tflops': candidate_metric, 'baseline_tflops': baseline_metric, 'metric_delta': candidate_metric - baseline_metric, 'all_correct': candidate_report['summary']['all_correct'], 'baseline_all_correct': baseline_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'baseline_performance_comparable': baseline_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'baseline_invalid_performance_reason': baseline_report['summary']['invalid_performance_reason'], 'measured_entrypoint': ''.join(['loom.examples.weave.knn_build_dispatch_selected_portfolio_e51c_v1:', format(measured_function, '')]), 'baseline_entrypoint': ROUTE_BASE_F552, 'baseline_entrypoint_note': 'same-session f552 full55 champion measured through the same contract denominator', 'measured_shape_labels': 'all_canonical' if shape_labels is None else tuple(shape_labels), 'selected_route_labels': SELECTED_TARGET_SHAPES, 'consumed_seed_labels': CONSUMED_SEED_TARGET_SHAPES, 'guard_miss_audit_labels': GUARD_MISS_AUDIT_SHAPES, 'selected_route_rows': _rows_for_labels(candidate_report, SELECTED_TARGET_SHAPES), 'baseline_selected_route_rows': _rows_for_labels(baseline_report, SELECTED_TARGET_SHAPES), 'consumed_seed_rows': _rows_for_labels(candidate_report, CONSUMED_SEED_TARGET_SHAPES), 'baseline_consumed_seed_rows': _rows_for_labels(baseline_report, CONSUMED_SEED_TARGET_SHAPES), 'guard_miss_audit_rows': _rows_for_labels(candidate_report, GUARD_MISS_AUDIT_SHAPES), 'baseline_guard_miss_audit_rows': _rows_for_labels(baseline_report, GUARD_MISS_AUDIT_SHAPES), 'per_consumed_row_delta': _per_consumed_row_delta(candidate_report, baseline_report), 'seed_delta_matrix': _seed_delta_matrix(candidate_report, baseline_report), 'frontmatter_seed_delta_matrix': _frontmatter_seed_delta_matrix(candidate_report, baseline_report), 'candidate_dispatchers': CANDIDATE_PORTFOLIOS, 'selected_candidate_dispatcher': 'selected_e51c_f552_a330_a4f6_row_level_midk', 'midk_row_selection': MIDK_ROW_SELECTION, 'route_modules': PRODUCTION_ROUTE_MODULES, 'route_trace': route_trace, 'forced_fallback_route_trace': route_trace_for_contract_shapes(shape_labels, force_fallback=True), 'route_trace_included': True, 'contract_summary': candidate_report['summary'], 'baseline_contract_summary': baseline_report['summary'], 'contract_performance': candidate_report['performance'], 'baseline_contract_performance': baseline_report['performance'], 'timing_backends': _timing_backends_for_reports(candidate_report, baseline_report), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'hot_bucket_parity': {'rag_k32': 'selected_f552_4fbf_exact_k32', 'dim_sweep_qm2048_d64_k10': 'selected_f552_73a9_exact_d64', 'dim_sweep_qm2048_d256_k10': 'selected_f552_df2f', 'dim_sweep_qm2048_fp16_d128_k10': 'selected_f552_df2f', 'rect_q2048_m32768_k10': 'selected_f552_4452_split8', 'default_k96_registry_gate': 'explicit_a330_k96_guard', 'large_tail_k20_q6144': 'selected_a4f6_large_tail', 'midk_k24_k28': 'row_level_81aa_9b2c_selection', 'over32_k64': 'inherited_fail'}, 'performance_coverage': 'partial', 'coverage_only_routes': [], 'hot_bucket_blockers': ['over32_k64'], 'report': candidate_report, 'baseline_report': baseline_report} + +def benchmark_knn_build_dispatch_selected_portfolio_e51c_v1(*, use_cupti: bool=False, shape_labels=None) -> dict[str, Any]: + """Full-denominator A/B against the f552 selected portfolio dispatcher.""" + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels) + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_base_dispatcher) + return _benchmark_payload(candidate_report, baseline_report, use_cupti=use_cupti, shape_labels=shape_labels, measured_function='benchmark_knn_build_dispatch_selected_portfolio_e51c_v1') + +def write_benchmark_artifacts(artifact_dir: str | os.PathLike[str], *, use_cupti: bool=False, shape_labels=None) -> dict[str, str]: + payload = benchmark_knn_build_dispatch_selected_portfolio_e51c_v1(use_cupti=use_cupti, shape_labels=shape_labels) + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + candidate_path = out_dir / 'full55_dispatch_selected_portfolio_e51c_v1.json' + baseline_path = out_dir / 'full55_same_session_baseline_f552_for_e51c_v1.json' + route_trace_path = out_dir / 'full55_route_trace_selected_portfolio_e51c_v1.json' + forced_trace_path = out_dir / 'full55_forced_fallback_trace_selected_portfolio_e51c_v1.json' + candidate_path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + baseline_path.write_text(json.dumps({'measured_entrypoint': payload['baseline_entrypoint'], 'measured_shape_labels': payload['measured_shape_labels'], 'timing_backend_requested': payload['timing_backend_requested'], 'timing_backends': payload['timing_backends'], 'tflops': payload['baseline_tflops'], 'all_correct': payload['baseline_all_correct'], 'performance_comparable': payload['baseline_performance_comparable'], 'contract_summary': payload['baseline_contract_summary'], 'contract_performance': payload['baseline_contract_performance'], 'route_trace': base_f552.route_trace_for_contract_shapes(shape_labels), 'route_trace_included': True, 'report': payload['baseline_report']}, indent=2, sort_keys=True) + '\n') + route_trace_path.write_text(json.dumps(payload['route_trace'], indent=2, sort_keys=True) + '\n') + forced_trace_path.write_text(json.dumps(payload['forced_fallback_route_trace'], indent=2, sort_keys=True) + '\n') + return {'candidate_payload': str(candidate_path), 'baseline_payload': str(baseline_path), 'route_trace': str(route_trace_path), 'forced_fallback_trace': str(forced_trace_path)} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_selected_portfolio_f16b_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_selected_portfolio_f16b_v1.py new file mode 100644 index 00000000..5ca92084 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_selected_portfolio_f16b_v1.py @@ -0,0 +1,281 @@ +"""Synthesized kNN build dispatcher consuming f853 plus b193 and 5407 seeds. + +Minimum target architecture: sm_100a. This dispatcher-synthesis candidate is +wrapper-only. It starts from the f853 selected full55 portfolio, adds exact +guards for the b193 low-K rows ``Q=M=512,K in {1,2}`` and ``Q=M=1024,K=16``, +then adds the 5407 exact ``Q=M=8192,K=32`` split2 route. Guard misses delegate +to f853, so the inherited a194 q4096 K64 route remains active. + +Every production route remains Weave-only; PyTorch and FlashLib are references +only through the contract harness. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from pathlib import Path +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_selected_portfolio_77db_v1 as compare_77db +from . import knn_build_dispatch_selected_portfolio_99f2_q4096k64_v1 as compare_99f2 +from . import knn_build_dispatch_selected_portfolio_f8c3_v1 as compare_f8c3 +from . import knn_build_dispatch_selected_portfolio_f853_v1 as base_f853 +from . import knn_build_large_square_k32_8a83_v1 as q8192_k32_seed +from . import knn_build_lowk_f8c3_q512_q1024_v1 as lowk_seed +ROUTE_BASE_F853 = 'loom.examples.weave.knn_build_dispatch_selected_portfolio_f853_v1:launch_from_contract_inputs' +ROUTE_LOWK_Q512_S4 = _decode_capture(_json_loads('"loom.examples.weave.knn_build_lowk_f8c3_q512_q1024_v1:q512_lowk_s4"')) +ROUTE_LOWK_Q1024_K16_S16 = _decode_capture(_json_loads('"loom.examples.weave.knn_build_lowk_f8c3_q512_q1024_v1:q1024_k16_s16"')) +ROUTE_Q8192_K32_SPLIT2 = q8192_k32_seed.ROUTE_Q8192_K32_SPLIT2 +LOWK_TARGET_SHAPES = lowk_seed.TARGET_SHAPES +Q8192_K32_TARGET_SHAPES = q8192_k32_seed.TARGET_SHAPES +NEW_TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_k_sweep_qm512_k1", "build_k_sweep_qm512_k2", "build_k_sweep_qm1024_k16", "build_large_b1_q8192_m8192_d128_k32"]}')) +NEW_TARGET_SHAPE_SET = set(NEW_TARGET_SHAPES) +ADJACENT_GUARD_MISS_SHAPES = ('build_k_sweep_qm512_k5', 'build_large_b1_q8192_m8192_d128_k20', 'build_over32_stress_qm4096_k64', 'rag_microbatch_b1_q16_m100000_d128_k10') +CONSUMED_SEED_TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["rag_stream_largek_b1_q128_m100000_d128_k32", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q2048_m2048_d256_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10", "search_rect_b1_q2048_m32768_d128_k10", "build_over64_stress_qm2048_k96", "build_large_tail_b1_q6144_m6144_d128_k20", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "build_over32_stress_qm2048_k64", "build_over32_stress_qm4096_k64", "build_k_sweep_qm512_k1", "build_k_sweep_qm512_k2", "build_k_sweep_qm1024_k16", "build_large_b1_q8192_m8192_d128_k32"]}')) +SELECTED_TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_large_b1_q8192_m8192_d128_k20", "build_large_b1_q8192_m8192_d128_k32", "build_over64_stress_qm2048_k96", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32", "rag_batch_b2_q256_m50000_d128_k10", "rag_irregular_b1_q512_m131071_d128_k10", "search_rect_b1_q1024_m8192_d128_k10", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q2048_m2048_d256_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10", "search_rect_b1_q2048_m32768_d128_k10", "build_large_tail_b1_q6144_m6144_d128_k20", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "build_over32_stress_qm2048_k64", "build_over32_stress_qm4096_k64", "build_k_sweep_qm512_k1", "build_k_sweep_qm512_k2", "build_k_sweep_qm1024_k16"]}')) +GUARD_MISS_AUDIT_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_dim_sweep_b1_q2048_m2048_d64_k10", "build_qm2048_d128_k10", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "flashml_correctness_b1_q256_m256_d128_k5", "build_over32_stress_qm2048_k64", "search_rect_over32_b1_q2048_m65536_d128_k64", "rag_offline_large_m_over32_b1_q2048_m250000_d128_k64", "build_k_sweep_qm512_k5", "build_large_b1_q8192_m8192_d128_k20", "build_over32_stress_qm4096_k64", "rag_microbatch_b1_q16_m100000_d128_k10"]}')) +DISPATCH_CORRECTNESS_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["flashml_correctness_b1_q256_m256_d128_k5", "build_large_b1_q8192_m8192_d128_k20", "build_large_b1_q8192_m8192_d128_k32", "build_over64_stress_qm2048_k96", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32", "rag_batch_b2_q256_m50000_d128_k10", "rag_irregular_b1_q512_m131071_d128_k10", "search_rect_b1_q1024_m8192_d128_k10", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q2048_m2048_d256_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10", "search_rect_b1_q2048_m32768_d128_k10", "build_large_tail_b1_q6144_m6144_d128_k20", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "build_over32_stress_qm2048_k64", "build_over32_stress_qm4096_k64", "build_k_sweep_qm512_k1", "build_k_sweep_qm512_k2", "build_k_sweep_qm1024_k16", "build_qm2048_d128_k10", "search_rect_over32_b1_q2048_m65536_d128_k64", "rag_offline_large_m_over32_b1_q2048_m250000_d128_k64", "build_k_sweep_qm512_k5", "rag_online_b1_q1_m100000_d128_k10", "rag_stream_b1_q128_m100000_d128_k10", "search_rect_b1_q4096_m65536_d128_k20", "rag_offline_largek_b1_q4096_m100000_d128_k20", "rag_offline_large_m_b1_q8192_m250000_d128_k20"]}')) +PRODUCTION_ROUTE_MODULES = {**base_f853.PRODUCTION_ROUTE_MODULES, 'lowk_b193_q512_s4': ROUTE_LOWK_Q512_S4, 'lowk_b193_q1024_k16_s16': ROUTE_LOWK_Q1024_K16_S16, 'large_square_5407_q8192_k32_s2': ROUTE_Q8192_K32_SPLIT2, 'base_f853': ROUTE_BASE_F853} +CANDIDATE_PORTFOLIOS = _decode_capture(_json_loads('{"__tuple__": [{"__dict_items__": [["id", "baseline_f853_selected_portfolio"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_selected_portfolio_f853_v1:benchmark_knn_build_dispatch_selected_portfolio_f853_v1"], ["consumed_seeds", {"__tuple__": ["selected_f8c3_e51c_plus_q2048_k64split8", "midk_f8c3_q4096_k64_split8_a194"]}], ["guard_plan", {"__tuple__": ["exact a194 BF16 build B1 Q=M=4096 D128 K64 split8 guard", "then f8c3 selected full55 guard plan"]}], ["expected_shape_wins", {"__tuple__": ["build_large_b1_q8192_m8192_d128_k20", "build_large_b1_q8192_m8192_d128_k32", "build_over64_stress_qm2048_k96", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32", "rag_batch_b2_q256_m50000_d128_k10", "rag_irregular_b1_q512_m131071_d128_k10", "search_rect_b1_q1024_m8192_d128_k10", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q2048_m2048_d256_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10", "search_rect_b1_q2048_m32768_d128_k10", "build_large_tail_b1_q6144_m6144_d128_k20", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "build_over32_stress_qm2048_k64", "build_over32_stress_qm4096_k64"]}], ["rejected_reason", "same-session baseline for the f16b combined dispatcher-synthesis lane"]]}, {"__dict_items__": [["id", "candidate_f853_plus_b193_lowk_only"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_selected_portfolio_f16b_v1:candidate_lowk_only"], ["consumed_seeds", {"__tuple__": ["selected_f853_f8c3_plus_q4096_k64_split8", "lowk_f8c3_q512_q1024_b193"]}], ["guard_plan", {"__tuple__": ["exact b193 Q512 K1/K2 and Q1024 K16 guards", "then f853 selected full55 guard plan"]}], ["expected_shape_wins", {"__tuple__": ["build_large_b1_q8192_m8192_d128_k20", "build_large_b1_q8192_m8192_d128_k32", "build_over64_stress_qm2048_k96", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32", "rag_batch_b2_q256_m50000_d128_k10", "rag_irregular_b1_q512_m131071_d128_k10", "search_rect_b1_q1024_m8192_d128_k10", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q2048_m2048_d256_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10", "search_rect_b1_q2048_m32768_d128_k10", "build_large_tail_b1_q6144_m6144_d128_k20", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "build_over32_stress_qm2048_k64", "build_over32_stress_qm4096_k64", "build_k_sweep_qm512_k1", "build_k_sweep_qm512_k2", "build_k_sweep_qm1024_k16"]}], ["rejected_reason", "valid but leaves the available 5407 Q8192 K32 seed unconsumed"]]}, {"__dict_items__": [["id", "candidate_f853_plus_5407_q8192_only"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_selected_portfolio_f16b_v1:candidate_q8192_only"], ["consumed_seeds", {"__tuple__": ["selected_f853_f8c3_plus_q4096_k64_split8", "large_square_k32_8a83_5407"]}], ["guard_plan", {"__tuple__": ["exact 5407 Q8192 K32 guard", "then f853 selected full55 guard plan"]}], ["expected_shape_wins", {"__tuple__": ["build_large_b1_q8192_m8192_d128_k20", "build_large_b1_q8192_m8192_d128_k32", "build_over64_stress_qm2048_k96", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32", "rag_batch_b2_q256_m50000_d128_k10", "rag_irregular_b1_q512_m131071_d128_k10", "search_rect_b1_q1024_m8192_d128_k10", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q2048_m2048_d256_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10", "search_rect_b1_q2048_m32768_d128_k10", "build_large_tail_b1_q6144_m6144_d128_k20", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "build_over32_stress_qm2048_k64", "build_over32_stress_qm4096_k64"]}], ["rejected_reason", "valid but leaves the available b193 low-K seed unconsumed"]]}, {"__dict_items__": [["id", "selected_f16b_f853_plus_b193_lowk_plus_5407_q8192"], ["entrypoint", "loom.examples.weave.knn_build_dispatch_selected_portfolio_f16b_v1:benchmark_knn_build_dispatch_selected_portfolio_f16b_v1"], ["consumed_seeds", {"__tuple__": ["selected_f853_f8c3_plus_q4096_k64_split8", "lowk_f8c3_q512_q1024_b193", "large_square_k32_8a83_5407"]}], ["guard_plan", {"__tuple__": ["exact b193 low-K BF16 build guards", "exact 5407 BF16 build B1 Q=M=8192 D128 K32 split2 guard", "then f853 selected full55 guard plan"]}], ["expected_shape_wins", {"__tuple__": ["build_large_b1_q8192_m8192_d128_k20", "build_large_b1_q8192_m8192_d128_k32", "build_over64_stress_qm2048_k96", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32", "rag_batch_b2_q256_m50000_d128_k10", "rag_irregular_b1_q512_m131071_d128_k10", "search_rect_b1_q1024_m8192_d128_k10", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q2048_m2048_d256_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10", "search_rect_b1_q2048_m32768_d128_k10", "build_large_tail_b1_q6144_m6144_d128_k20", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "build_over32_stress_qm2048_k64", "build_over32_stress_qm4096_k64", "build_k_sweep_qm512_k1", "build_k_sweep_qm512_k2", "build_k_sweep_qm1024_k16"]}], ["rejected_reason", null]]}]}')) +LOWK_ROW_SELECTION = {'build_k_sweep_qm512_k1': {'selected_seed': 'lowk_f8c3_q512_q1024_b193', 'selected_route': ROUTE_LOWK_Q512_S4, 'targeted_seed_ms': 0.029728, 'targeted_seed_tflops': 2.2574294940796555, 'targeted_seed_timing_backend': 'cupti', 'targeted_ratio_vs_flashlib': 1.609812298170075, 'targeted_baseline_f8c3_ms': 0.049696, 'targeted_speedup_vs_f8c3': 1.6716899892357373, 'reason': 'b193 Q512 K1 split4 route is correct and 1.61x FlashLib in target-bucket CUPTI timing.'}, 'build_k_sweep_qm512_k2': {'selected_seed': 'lowk_f8c3_q512_q1024_b193', 'selected_route': ROUTE_LOWK_Q512_S4, 'targeted_seed_ms': 0.035584, 'targeted_seed_tflops': 1.885928057553957, 'targeted_seed_timing_backend': 'cupti', 'targeted_ratio_vs_flashlib': 1.8920863309352518, 'targeted_baseline_f8c3_ms': 0.051072, 'targeted_speedup_vs_f8c3': 1.4352517985611513, 'reason': 'b193 Q512 K2 split4 route is correct and 1.89x FlashLib in target-bucket CUPTI timing.'}, 'build_k_sweep_qm1024_k16': {'selected_seed': 'lowk_f8c3_q512_q1024_b193', 'selected_route': ROUTE_LOWK_Q1024_K16_S16, 'targeted_seed_ms': 0.031744, 'targeted_seed_tflops': 8.45625806451613, 'targeted_seed_timing_backend': 'cupti', 'targeted_ratio_vs_flashlib': 2.130071824596774, 'targeted_baseline_f8c3_ms': 0.075937, 'targeted_speedup_vs_f8c3': 2.3921685987903225, 'reason': 'b193 Q1024 K16 split16 route is correct and 2.13x FlashLib in target-bucket CUPTI timing.'}} +Q8192_K32_ROW_SELECTION = {'build_large_b1_q8192_m8192_d128_k32': {'selected_seed': 'large_square_k32_8a83_5407', 'selected_route': ROUTE_Q8192_K32_SPLIT2, 'targeted_seed_ms': 0.539043, 'targeted_seed_tflops': 31.871055155154593, 'targeted_seed_timing_backend': 'cupti', 'targeted_ratio_vs_flashlib': 1.1481755629884813, 'targeted_baseline_a989_ms': 0.661348, 'targeted_speedup_vs_a989': 1.2268928452832149, 'reason': '5407 Q8192 K32 split2 route is correct and 1.148x FlashLib in target-bucket CUPTI timing.'}} + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_DISPATCH_SELECTED_F16B_VERIFY_KERNEL') + if verify_kernel == 'lowk_q512_stage1': + os.environ['LOOM_KNN_LOWK_F8C3_VERIFY_KERNEL'] = 'q512_stage1' + return lowk_seed._verify_export_ir() + if verify_kernel == 'lowk_q512_merge': + os.environ['LOOM_KNN_LOWK_F8C3_VERIFY_KERNEL'] = 'q512_merge_generic' + return lowk_seed._verify_export_ir() + if verify_kernel == 'lowk_q1024_k16_stage1': + os.environ['LOOM_KNN_LOWK_F8C3_VERIFY_KERNEL'] = 'q1024_k16_stage1' + return lowk_seed._verify_export_ir() + if verify_kernel == 'lowk_q1024_k16_merge': + os.environ['LOOM_KNN_LOWK_F8C3_VERIFY_KERNEL'] = 'q1024_k16_merge_s16' + return lowk_seed._verify_export_ir() + if verify_kernel == 'q8192_k32_stage1': + os.environ['LOOM_KNN_LARGE_SQUARE_K32_8A83_VERIFY_KERNEL'] = 'stage1' + return q8192_k32_seed._verify_export_ir() + if verify_kernel == 'q8192_k32_merge': + os.environ['LOOM_KNN_LARGE_SQUARE_K32_8A83_VERIFY_KERNEL'] = 'merge_k32_s2_warp_select' + return q8192_k32_seed._verify_export_ir() + return base_f853.ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k48over32", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 48]], "cta_group": 1, "threads": 192}')) + +def _eligible_lowk(inputs: dict[str, Any]) -> bool: + return lowk_seed._eligible_q512_lowk(inputs) or lowk_seed._eligible_q1024_k16(inputs) + +def _lowk_route(inputs: dict[str, Any]) -> str: + return lowk_seed.route_for_contract_inputs(inputs) + +def _eligible_q8192_k32(inputs: dict[str, Any]) -> bool: + return q8192_k32_seed._eligible_q8192_k32(inputs) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False, enable_lowk: bool=True, enable_q8192: bool=True) -> str: + if not force_fallback and enable_lowk and _eligible_lowk(inputs): + return _lowk_route(inputs) + if not force_fallback and enable_q8192 and _eligible_q8192_k32(inputs): + return ROUTE_Q8192_K32_SPLIT2 + return base_f853.route_for_contract_inputs(inputs) + +def _launch_route(inputs: dict[str, Any], route: str) -> None: + if route == ROUTE_LOWK_Q512_S4: + lowk_seed._launch_q512_lowk_split(inputs, split_count=lowk_seed.DEFAULT_Q512_SPLITS) + return + if route == ROUTE_LOWK_Q1024_K16_S16: + lowk_seed._launch_q1024_k16_split(inputs, split_count=lowk_seed.DEFAULT_Q1024_K16_SPLITS) + return + if route == ROUTE_Q8192_K32_SPLIT2: + q8192_k32_seed._launch_q8192_k32_split2(inputs) + return + base_f853._launch_route(inputs, route) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False, enable_lowk: bool=True, enable_q8192: bool=True) -> None: + route = route_for_contract_inputs(inputs, force_fallback=force_fallback, enable_lowk=enable_lowk, enable_q8192=enable_q8192) + _launch_route(inputs, route) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def candidate_base_dispatcher(inputs: dict[str, Any]): + base_f853.launch_from_contract_inputs(inputs) + return None + +def candidate_lowk_only(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs, enable_lowk=True, enable_q8192=False) + return None + +def candidate_q8192_only(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs, enable_lowk=False, enable_q8192=True) + return None + +def candidate_force_fallback(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs, force_fallback=True) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return base_f853._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=DISPATCH_CORRECTNESS_SHAPES, benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint: run real contract correctness for selected shapes.""" + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=None, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + shapes = None if shape_labels is None else _select_contract_shapes(shape_labels) + return evaluate_contract(shapes=shapes, correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def _comparison_report(module: Any, *, use_cupti: bool, shape_labels=None) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + shapes = None if shape_labels is None else _select_contract_shapes(shape_labels) + return module.evaluate_contract(shapes=shapes, correctness=True, benchmark=True, kernel_fn=module.candidate) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def _trace_inputs_from_shape(shape: dict[str, Any]) -> dict[str, Any]: + return base_f853._trace_inputs_from_shape(shape) + +def _inputs_for_label(label: str) -> dict[str, Any]: + return base_f853._inputs_for_label(label) + +def _base_f853_route(inputs: dict[str, Any]) -> str: + return base_f853.route_for_contract_inputs(inputs) + +def _route_trace_record(inputs: dict[str, Any], *, force_fallback: bool=False) -> dict[str, Any]: + base_route = _base_f853_route(inputs) + if force_fallback: + row = base_f853._route_trace_record(inputs) + row['guard_condition'] = 'forced fallback to f853 baseline; f16b low-K and Q8192 overlays disabled' + row['coverage'] = 'forced candidate fallback for f16b overlays only' + row['forced_disabled_seeds'] = ('lowk_f8c3_q512_q1024_b193', 'large_square_k32_8a83_5407') + row['base_f853_route'] = base_route + row['candidate_guard_status'] = 'forced_fallback_to_f853' + return row + route = route_for_contract_inputs(inputs) + label = str(inputs.get('label')) + if route in {ROUTE_LOWK_Q512_S4, ROUTE_LOWK_Q1024_K16_S16} and label in LOWK_ROW_SELECTION: + selected = LOWK_ROW_SELECTION[label] + return {'shape_key': inputs.get('label'), 'selected_route': route, 'guard_condition': lowk_seed._guard_description(route), 'route_kind': 'specialized', 'coverage': 'exact b193 low-K seed selected ahead of f853 inherited fallback', 'consumed_seed': selected['selected_seed'], 'replaced_route': base_route, 'base_f853_route': base_route, 'row_selection': selected, 'parity_status': 'pass', 'parity_reason': selected['reason'], 'candidate_guard_status': 'selected_from_b193_lowk'} + if route == ROUTE_Q8192_K32_SPLIT2 and label in Q8192_K32_ROW_SELECTION: + selected = Q8192_K32_ROW_SELECTION[label] + return {'shape_key': inputs.get('label'), 'selected_route': route, 'guard_condition': q8192_k32_seed._guard_description(route), 'route_kind': 'specialized', 'coverage': 'exact 5407 Q8192 K32 seed selected ahead of f853 inherited fallback', 'consumed_seed': selected['selected_seed'], 'replaced_route': base_route, 'base_f853_route': base_route, 'row_selection': selected, 'parity_status': 'pass', 'parity_reason': selected['reason'], 'candidate_guard_status': 'selected_from_5407_q8192_k32'} + row = base_f853._route_trace_record(inputs) + row['base_f853_route'] = base_route + row['candidate_guard_status'] = 'inherited_f853_or_guard_miss' + return row + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + return [_route_trace_record(_trace_inputs_from_shape(shape), force_fallback=force_fallback) for shape in selected] + +def _timing_backends_for_reports(*reports: dict[str, Any]) -> list[str]: + return base_f853._timing_backends_for_reports(*reports) + +def _rows_for_labels(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return base_f853._rows_for_labels(report, labels) + +def _per_consumed_row_delta(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> dict[str, Any]: + deltas = {} + for label in CONSUMED_SEED_TARGET_SHAPES: + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') + inputs = _inputs_for_label(label) + deltas[label] = {'candidate_ms': candidate_ms, 'baseline_f853_ms': baseline_ms, 'flashlib_ms': flashlib_ms, 'speedup_vs_baseline_f853': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'ratio_vs_flashlib': flashlib_ms / candidate_ms if candidate_ms and flashlib_ms else None, 'candidate_route': route_for_contract_inputs(inputs), 'baseline_f853_route': _base_f853_route(inputs)} + return deltas + +def _seed_delta_matrix(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> list[dict[str, Any]]: + matrix = [] + for label in SELECTED_TARGET_SHAPES: + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + inputs = _inputs_for_label(label) + matrix.append({'shape_key': label, 'baseline_route': _base_f853_route(inputs), 'candidate_route': route_for_contract_inputs(inputs), 'candidate_ms': candidate_ms, 'baseline_ms': baseline_ms, 'metric_delta_ms': candidate_ms - baseline_ms if candidate_ms and baseline_ms else None, 'speedup_vs_baseline_f853': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'timing_backend': candidate_row.get('timing_backend') or baseline_row.get('timing_backend')}) + return matrix + +def _frontmatter_seed_delta_matrix(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> list[dict[str, Any]]: + rows = [] + for item in _seed_delta_matrix(candidate_report, baseline_report): + delta = item['metric_delta_ms'] + rows.append({'shape_key': item['shape_key'], 'baseline_route': item['baseline_route'], 'candidate_deltas': [{'candidate_id': 'selected_f16b_f853_plus_b193_lowk_plus_5407_q8192', 'metric_delta': 0.0 if delta is None else float(delta), 'timing_backend': item['timing_backend'] or 'cuda_event'}]}) + return rows + +def _below_flashlib_rows(report: dict[str, Any]) -> list[dict[str, Any]]: + rows = [] + specialized_routes = set(PRODUCTION_ROUTE_MODULES.values()) + for label, row in sorted(report.get('per_shape', {}).items()): + ratio = row.get('ratio_vs_flashlib') + if isinstance(ratio, (float, int)) and ratio < 1.0: + inputs = _inputs_for_label(label) + route = route_for_contract_inputs(inputs) + rows.append({'shape_key': label, 'kernel_ms': row.get('kernel_ms'), 'flashlib_ms': row.get('flashlib_ms'), 'ratio_vs_flashlib': ratio, 'selected_route': route, 'route_kind': 'specialized' if route in specialized_routes else 'general'}) + return rows + +def _comparison_summary(name: str, report: dict[str, Any], route_trace: list[dict[str, Any]]) -> dict[str, Any]: + return {'id': name, 'tflops': report['summary']['primary_mean'] or 0.0, 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'contract_summary': report['summary'], 'contract_performance': report['performance'], 'timing_backends': _timing_backends_for_reports(report), 'route_trace': route_trace, 'route_trace_included': True, 'report': report} + +def _benchmark_payload(candidate_report: dict[str, Any], baseline_report: dict[str, Any], *, use_cupti: bool, shape_labels, measured_function: str, comparisons: dict[str, Any] | None=None) -> dict[str, Any]: + candidate_metric = candidate_report['summary']['primary_mean'] or 0.0 + baseline_metric = baseline_report['summary']['primary_mean'] or 0.0 + route_trace = route_trace_for_contract_shapes(shape_labels) + below_flashlib = _below_flashlib_rows(candidate_report) + return {'tflops': candidate_metric, 'baseline_tflops': baseline_metric, 'metric_delta': candidate_metric - baseline_metric, 'all_correct': candidate_report['summary']['all_correct'], 'baseline_all_correct': baseline_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'baseline_performance_comparable': baseline_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'baseline_invalid_performance_reason': baseline_report['summary']['invalid_performance_reason'], 'measured_entrypoint': ''.join(['loom.examples.weave.knn_build_dispatch_selected_portfolio_f16b_v1:', format(measured_function, '')]), 'baseline_entrypoint': ROUTE_BASE_F853, 'baseline_entrypoint_note': 'same-session f853 selected portfolio measured through the same full55 contract denominator', 'measured_shape_labels': 'all_canonical' if shape_labels is None else tuple(shape_labels), 'selected_route_labels': SELECTED_TARGET_SHAPES, 'consumed_seed_labels': CONSUMED_SEED_TARGET_SHAPES, 'guard_miss_audit_labels': GUARD_MISS_AUDIT_SHAPES, 'selected_route_rows': _rows_for_labels(candidate_report, SELECTED_TARGET_SHAPES), 'baseline_selected_route_rows': _rows_for_labels(baseline_report, SELECTED_TARGET_SHAPES), 'consumed_seed_rows': _rows_for_labels(candidate_report, CONSUMED_SEED_TARGET_SHAPES), 'baseline_consumed_seed_rows': _rows_for_labels(baseline_report, CONSUMED_SEED_TARGET_SHAPES), 'guard_miss_audit_rows': _rows_for_labels(candidate_report, GUARD_MISS_AUDIT_SHAPES), 'baseline_guard_miss_audit_rows': _rows_for_labels(baseline_report, GUARD_MISS_AUDIT_SHAPES), 'per_consumed_row_delta': _per_consumed_row_delta(candidate_report, baseline_report), 'seed_delta_matrix': _seed_delta_matrix(candidate_report, baseline_report), 'frontmatter_seed_delta_matrix': _frontmatter_seed_delta_matrix(candidate_report, baseline_report), 'candidate_dispatchers': CANDIDATE_PORTFOLIOS, 'selected_candidate_dispatcher': 'selected_f16b_f853_plus_b193_lowk_plus_5407_q8192', 'lowk_row_selection': LOWK_ROW_SELECTION, 'q8192_k32_row_selection': Q8192_K32_ROW_SELECTION, 'route_modules': PRODUCTION_ROUTE_MODULES, 'route_trace': route_trace, 'forced_fallback_route_trace': route_trace_for_contract_shapes(shape_labels, force_fallback=True), 'route_trace_included': True, 'contract_summary': candidate_report['summary'], 'baseline_contract_summary': baseline_report['summary'], 'contract_performance': candidate_report['performance'], 'baseline_contract_performance': baseline_report['performance'], 'timing_backends': _timing_backends_for_reports(candidate_report, baseline_report), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'same_session_comparisons': comparisons or {}, 'hot_bucket_parity': {'lowk_q512_k1': 'selected_b193_lowk', 'lowk_q512_k2': 'selected_b193_lowk', 'lowk_q512_k5': 'inherited_f853_blocker', 'lowk_q1024_k16': 'selected_b193_lowk', 'large_square_q8192_k32': 'selected_5407_q8192_k32_split2', 'over32_k64_q4096': 'inherited_f853_selected_a194_q4096_k64_split8', 'rag_microbatch_q16_m100000_k10': 'inherited_f853_blocker'}, 'performance_coverage': 'partial' if below_flashlib else 'pass', 'coverage_only_routes': [], 'hot_bucket_blockers': below_flashlib, 'report': candidate_report, 'baseline_report': baseline_report} + +def benchmark_knn_build_dispatch_selected_portfolio_f16b_v1(*, use_cupti: bool=False, shape_labels=None, include_comparisons: bool=False) -> dict[str, Any]: + """Full-denominator A/B against f853, optionally with 77db/99f2/f8c3 comparisons.""" + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels) + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_base_dispatcher) + comparisons = {} + if include_comparisons: + f8c3_report = _comparison_report(compare_f8c3, use_cupti=use_cupti, shape_labels=shape_labels) + comparisons['f8c3'] = _comparison_summary('f8c3', f8c3_report, compare_f8c3.route_trace_for_contract_shapes(shape_labels)) + r77db_report = _comparison_report(compare_77db, use_cupti=use_cupti, shape_labels=shape_labels) + comparisons['77db'] = _comparison_summary('77db', r77db_report, compare_77db.route_trace_for_contract_shapes(shape_labels)) + q99f2_report = _comparison_report(compare_99f2, use_cupti=use_cupti, shape_labels=shape_labels) + comparisons['99f2_q4096k64'] = _comparison_summary('99f2_q4096k64', q99f2_report, compare_99f2.route_trace_for_contract_shapes(shape_labels)) + return _benchmark_payload(candidate_report, baseline_report, use_cupti=use_cupti, shape_labels=shape_labels, measured_function='benchmark_knn_build_dispatch_selected_portfolio_f16b_v1', comparisons=comparisons) + +def write_benchmark_artifacts(artifact_dir: str | os.PathLike[str], *, use_cupti: bool=False, shape_labels=None, include_comparisons: bool=False) -> dict[str, str]: + payload = benchmark_knn_build_dispatch_selected_portfolio_f16b_v1(use_cupti=use_cupti, shape_labels=shape_labels, include_comparisons=include_comparisons) + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + candidate_path = out_dir / 'full55_dispatch_selected_portfolio_f16b_v1.json' + baseline_path = out_dir / 'full55_same_session_baseline_f853_for_f16b_v1.json' + route_trace_path = out_dir / 'full55_route_trace_selected_portfolio_f16b_v1.json' + forced_trace_path = out_dir / 'full55_forced_fallback_trace_selected_portfolio_f16b_v1.json' + candidate_path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + baseline_path.write_text(json.dumps({'measured_entrypoint': payload['baseline_entrypoint'], 'measured_shape_labels': payload['measured_shape_labels'], 'timing_backend_requested': payload['timing_backend_requested'], 'timing_backends': payload['timing_backends'], 'tflops': payload['baseline_tflops'], 'all_correct': payload['baseline_all_correct'], 'performance_comparable': payload['baseline_performance_comparable'], 'contract_summary': payload['baseline_contract_summary'], 'contract_performance': payload['baseline_contract_performance'], 'route_trace': base_f853.route_trace_for_contract_shapes(shape_labels), 'route_trace_included': True, 'report': payload['baseline_report']}, indent=2, sort_keys=True) + '\n') + route_trace_path.write_text(json.dumps(payload['route_trace'], indent=2, sort_keys=True) + '\n') + forced_trace_path.write_text(json.dumps(payload['forced_fallback_route_trace'], indent=2, sort_keys=True) + '\n') + paths = {'candidate_payload': str(candidate_path), 'baseline_f853_payload': str(baseline_path), 'route_trace': str(route_trace_path), 'forced_fallback_trace': str(forced_trace_path)} + for name, comparison in payload.get('same_session_comparisons', {}).items(): + comparison_path = out_dir / ''.join(['full55_same_session_', format(name, ''), '_for_f16b_v1.json']) + comparison_path.write_text(json.dumps(comparison, indent=2, sort_keys=True) + '\n') + paths[''.join(['comparison_', format(name, ''), '_payload'])] = str(comparison_path) + return paths diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_selected_portfolio_f552_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_selected_portfolio_f552_v1.py new file mode 100644 index 00000000..ccfcc43a --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_selected_portfolio_f552_v1.py @@ -0,0 +1,257 @@ +"""Opt-in kNN build full55 dispatcher for the selected f552 portfolio. + +Minimum target architecture: sm_100a. This dispatcher-synthesis candidate is +wrapper-only. It combines the selected full55 components from rank 39cc: +6b59 D256/FP16, 62b1 exact K32+D64, and the 4452 rectangular split8 seed. +Every production route remains Weave-only; PyTorch and FlashLib are references +only through the contract harness. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from pathlib import Path +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_4fbf_7399_d15e_73a9_full55_v1 as dispatch_k32_d64 +from . import knn_build_dispatch_7399_d15e_df2f_full55_v1 as dispatch_df2f +from . import knn_build_rect_intermediate_frontier_6a73_4452_v2 as rect_4452 +ROUTE_DIM_D256_DF2F = dispatch_df2f.ROUTE_DIM_D256_DF2F +ROUTE_DIM_FP16_DF2F = dispatch_df2f.ROUTE_DIM_FP16_DF2F +ROUTE_DIM_D64_73A9 = dispatch_k32_d64.ROUTE_DIM_D64_73A9 +ROUTE_RECT_4452 = 'loom.examples.weave.knn_build_rect_intermediate_frontier_6a73_4452_v2:rect_s8_k10_cached' +ROUTE_BASE_CHAMPION_6B59 = 'loom.examples.weave.knn_build_dispatch_7399_d15e_df2f_full55_v1:launch_from_contract_inputs' +ROUTE_BASE_K32_D64_62B1 = 'loom.examples.weave.knn_build_dispatch_4fbf_7399_d15e_73a9_full55_v1:launch_from_contract_inputs' +RAG_K32_TARGET_SHAPES = dispatch_k32_d64.RAG_K32_TARGET_SHAPES +DIM_D64_TARGET_SHAPES = dispatch_k32_d64.DIM_D64_TARGET_SHAPES +DIM_D256_TARGET_SHAPES = dispatch_df2f.DIM_D256_TARGET_SHAPES +DIM_FP16_TARGET_SHAPES = dispatch_df2f.DIM_FP16_TARGET_SHAPES +RECT_4452_TARGET_SHAPES = rect_4452.TARGET_SHAPES +CONSUMED_SEED_TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["rag_stream_largek_b1_q128_m100000_d128_k32", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q2048_m2048_d256_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10", "search_rect_b1_q2048_m32768_d128_k10"]}')) +CONSUMED_SEED_TARGET_SHAPE_SET = set(CONSUMED_SEED_TARGET_SHAPES) +SELECTED_TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_large_b1_q8192_m8192_d128_k20", "build_large_b1_q8192_m8192_d128_k32", "build_over64_stress_qm2048_k96", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32", "rag_batch_b2_q256_m50000_d128_k10", "rag_irregular_b1_q512_m131071_d128_k10", "search_rect_b1_q1024_m8192_d128_k10", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q2048_m2048_d256_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10", "search_rect_b1_q2048_m32768_d128_k10"]}')) +GUARD_MISS_AUDIT_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_dim_sweep_b1_q2048_m2048_d64_k10", "build_qm2048_d128_k10", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "build_over32_stress_qm2048_k64", "build_over32_stress_qm4096_k64", "flashml_correctness_b1_q256_m256_d128_k5"]}')) +DISPATCH_CORRECTNESS_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["flashml_correctness_b1_q256_m256_d128_k5", "build_large_b1_q8192_m8192_d128_k20", "build_large_b1_q8192_m8192_d128_k32", "build_over64_stress_qm2048_k96", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32", "rag_batch_b2_q256_m50000_d128_k10", "rag_irregular_b1_q512_m131071_d128_k10", "search_rect_b1_q1024_m8192_d128_k10", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q2048_m2048_d256_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10", "search_rect_b1_q2048_m32768_d128_k10", "build_qm2048_d128_k10", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "build_over32_stress_qm2048_k64", "build_over32_stress_qm4096_k64", "rag_online_b1_q1_m100000_d128_k10", "rag_stream_b1_q128_m100000_d128_k10", "search_rect_b1_q4096_m65536_d128_k20", "rag_offline_largek_b1_q4096_m100000_d128_k20", "rag_offline_large_m_b1_q8192_m250000_d128_k20"]}')) +PRODUCTION_ROUTE_MODULES = {**dispatch_k32_d64.PRODUCTION_ROUTE_MODULES, **dispatch_df2f.PRODUCTION_ROUTE_MODULES, 'rect_intermediate_4452_s8': ROUTE_RECT_4452, 'base_champion_6b59': ROUTE_BASE_CHAMPION_6B59, 'base_k32_d64_62b1': ROUTE_BASE_K32_D64_62B1} +CANDIDATE_PORTFOLIOS = ({'id': 'baseline_6b59_df2f', 'entrypoint': 'loom.examples.weave.knn_build_dispatch_7399_d15e_df2f_full55_v1:benchmark_knn_build_dispatch_7399_d15e_df2f_full55_v1', 'consumed_seeds': ('dim_midk_df2f_d256', 'dim_midk_df2f_fp16_d128'), 'guard_plan': ('exact 099f BF16 build B1 Q=M=2048 D256 K10 label', 'exact 099f FP16 build B1 Q=M=2048 D128 K10 label', 'then unchanged 7399+d15e full55 guard plan'), 'expected_shape_wins': (*DIM_D256_TARGET_SHAPES, *DIM_FP16_TARGET_SHAPES), 'rejected_reason': 'same-session baseline champion for selected-portfolio synthesis'}, {'id': 'df2f_plus_k32_d64_keep_d15e_rect', 'entrypoint': 'loom.examples.weave.knn_build_dispatch_selected_portfolio_f552_v1:candidate_no_rect_4452', 'consumed_seeds': ('rag_frontier_4fbf_v7_exact_k32', 'dim_midk_73a9_d64', 'dim_midk_df2f_d256', 'dim_midk_df2f_fp16_d128'), 'guard_plan': ('6b59 D256/FP16 exact guards', '62b1 exact K32+D64 guard policy', 'inherited d15e rectangular route'), 'expected_shape_wins': (*RAG_K32_TARGET_SHAPES, *DIM_D64_TARGET_SHAPES, *DIM_D256_TARGET_SHAPES, *DIM_FP16_TARGET_SHAPES), 'rejected_reason': 'does not consume the rank-selected 4452 rect split8 seed'}, {'id': 'selected_f552_k32_d64_df2f_rect4452', 'entrypoint': 'loom.examples.weave.knn_build_dispatch_selected_portfolio_f552_v1:benchmark_knn_build_dispatch_selected_portfolio_f552_v1', 'consumed_seeds': ('rag_frontier_4fbf_v7_exact_k32', 'dim_midk_73a9_d64', 'dim_midk_df2f_d256', 'dim_midk_df2f_fp16_d128', 'rect_intermediate_4452_s8'), 'guard_plan': ('exact 099f BF16 build B1 Q=M=2048 D256 K10 label', 'exact 099f FP16 build B1 Q=M=2048 D128 K10 label', 'exact 73a9 BF16 build B1 Q=M=2048 D64 K10 label', 'exact 4452 BF16 non-build B1 Q2048 M32768 D128 K10 label', 'then 62b1 exact K32 / inherited K96, K10, rect, and fallback policy'), 'expected_shape_wins': CONSUMED_SEED_TARGET_SHAPES, 'rejected_reason': None}) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_DISPATCH_SELECTED_F552_VERIFY_KERNEL') + if verify_kernel == 'd256_stage1': + os.environ['LOOM_KNN_DISPATCH_7399_D15E_DF2F_VERIFY_KERNEL'] = 'd256_stage1' + return dispatch_df2f._verify_export_ir() + if verify_kernel == 'fp16_stage1': + os.environ['LOOM_KNN_DISPATCH_7399_D15E_DF2F_VERIFY_KERNEL'] = 'fp16_stage1' + return dispatch_df2f._verify_export_ir() + if verify_kernel == 'd64_stage1': + os.environ['LOOM_KNN_DISPATCH_4FBF_73A9_VERIFY_KERNEL'] = 'd64_stage1' + return dispatch_k32_d64._verify_export_ir() + if verify_kernel == 'rect_stage1': + return rect_4452.parent_lowk.stage1_ir + if verify_kernel == 'rect_merge_s8': + os.environ['LOOM_KNN_RECT_INTERMEDIATE_4452_VERIFY_KERNEL'] = 'merge_s8' + return rect_4452._verify_export_ir() + return dispatch_k32_d64.ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k48over32", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 48]], "cta_group": 1, "threads": 192}')) + +def _label_can_hit(inputs: dict[str, Any], target_labels: set[str]) -> bool: + label = inputs.get('label') + return label is None or str(label) in target_labels + +def _eligible_rect_4452(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, set(RECT_4452_TARGET_SHAPES)) and rect_4452._eligible_rect_intermediate(inputs) + +def _route_without_rect_4452(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if force_fallback: + return dispatch_df2f.route_for_contract_inputs(inputs) + if dispatch_df2f._eligible_dim_d256_df2f(inputs): + return ROUTE_DIM_D256_DF2F + if dispatch_df2f._eligible_dim_fp16_df2f(inputs): + return ROUTE_DIM_FP16_DF2F + return dispatch_k32_d64.route_for_contract_inputs(inputs) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if force_fallback: + return dispatch_df2f.route_for_contract_inputs(inputs) + if dispatch_df2f._eligible_dim_d256_df2f(inputs): + return ROUTE_DIM_D256_DF2F + if dispatch_df2f._eligible_dim_fp16_df2f(inputs): + return ROUTE_DIM_FP16_DF2F + if dispatch_k32_d64._eligible_dim_d64_73a9(inputs): + return ROUTE_DIM_D64_73A9 + if _eligible_rect_4452(inputs): + return ROUTE_RECT_4452 + return dispatch_k32_d64.route_for_contract_inputs(inputs) + +def _launch_route(inputs: dict[str, Any], route: str) -> None: + if route == ROUTE_DIM_D256_DF2F: + dispatch_df2f.dim_df2f._launch_d256_split(inputs) + return + if route == ROUTE_DIM_FP16_DF2F: + dispatch_df2f.dim_df2f._launch_fp16_split(inputs) + return + if route == ROUTE_DIM_D64_73A9: + dispatch_k32_d64.dim_73a9._launch_d64_split(inputs) + return + if route == ROUTE_RECT_4452: + rect_4452._launch_rect_intermediate(inputs) + return + dispatch_k32_d64._launch_route(inputs, route) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + _launch_route(inputs, route_for_contract_inputs(inputs, force_fallback=force_fallback)) + +def launch_no_rect_4452(inputs: dict[str, Any]) -> None: + _launch_route(inputs, _route_without_rect_4452(inputs)) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def candidate_no_rect_4452(inputs: dict[str, Any]): + launch_no_rect_4452(inputs) + return None + +def candidate_base_dispatcher(inputs: dict[str, Any]): + dispatch_df2f.launch_from_contract_inputs(inputs) + return None + +def candidate_force_fallback(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs, force_fallback=True) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return dispatch_k32_d64._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=DISPATCH_CORRECTNESS_SHAPES, benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint: run real contract correctness for selected shapes.""" + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=None, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + shapes = None if shape_labels is None else _select_contract_shapes(shape_labels) + return evaluate_contract(shapes=shapes, correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def _inputs_for_label(label: str) -> dict[str, Any]: + return dispatch_k32_d64._inputs_for_label(label) + +def _baseline_6b59_route(inputs: dict[str, Any]) -> str: + return dispatch_df2f.route_for_contract_inputs(inputs) + +def _k32_d64_route(inputs: dict[str, Any]) -> str: + return dispatch_k32_d64.route_for_contract_inputs(inputs) + +def _route_trace_record(inputs: dict[str, Any], *, force_fallback: bool=False) -> dict[str, Any]: + baseline_route = _baseline_6b59_route(inputs) + if force_fallback: + row = dispatch_df2f._route_trace_record(inputs) + row['guard_condition'] = 'forced fallback to 6b59 champion; selected K32/D64/4452 overlays disabled' + row['coverage'] = 'forced candidate fallback for selected-portfolio overlay routes' + row['forced_disabled_seeds'] = ('rag_frontier_4fbf_v7_exact_k32', 'dim_midk_73a9_d64', 'rect_intermediate_4452_s8') + row['baseline_6b59_route'] = baseline_route + return row + route = route_for_contract_inputs(inputs) + if route in (ROUTE_DIM_D256_DF2F, ROUTE_DIM_FP16_DF2F): + row = dispatch_df2f._route_trace_record(inputs) + row['baseline_6b59_route'] = baseline_route + row['k32_d64_component_route'] = _k32_d64_route(inputs) + row['candidate_guard_status'] = 'selected_from_6b59' + return row + if route == ROUTE_DIM_D64_73A9: + row = dispatch_k32_d64._route_trace_record(inputs) + row['baseline_6b59_route'] = baseline_route + row['replaced_route'] = baseline_route + row['candidate_guard_status'] = 'selected_from_62b1' + return row + if route == ROUTE_RECT_4452: + inherited_route = _k32_d64_route(inputs) + return {'shape_key': inputs.get('label'), 'selected_route': route, 'guard_condition': 'exact 4452 BF16 non-build B1 Q2048 M32768 D128 K10 label', 'route_kind': 'specialized', 'coverage': 'exact 4452 rectangular Q2048/M32768 K10 split8 cached seed selected ahead of inherited d15e rect route', 'consumed_seed': 'rect_intermediate_4452_s8', 'replaced_route': baseline_route, 'baseline_6b59_route': baseline_route, 'k32_d64_component_route': inherited_route, 'baseline_7c3a_route': dispatch_k32_d64.dispatch_k32._base_7c3a_route_for_contract_inputs(inputs), 'inherited_route': dispatch_k32_d64.dispatch_k32._baseline_inherited_route(inputs), 'parity_status': 'pass', 'parity_reason': '4452 CUPTI target-bucket primary mean is 66.74930427113323 TFLOPS', 'candidate_guard_status': 'selected_from_4452'} + row = dispatch_k32_d64._route_trace_record(inputs) + row['baseline_6b59_route'] = baseline_route + row['candidate_guard_status'] = 'inherited_or_guard_miss' + return row + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + return [_route_trace_record(dispatch_k32_d64.dispatch_k32._trace_inputs_from_shape(shape), force_fallback=force_fallback) for shape in selected] + +def _timing_backends_for_reports(*reports: dict[str, Any]) -> list[str]: + return dispatch_k32_d64._timing_backends_for_reports(*reports) + +def _rows_for_labels(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return dispatch_k32_d64._rows_for_labels(report, labels) + +def _per_consumed_row_delta(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> dict[str, Any]: + deltas = {} + for label in CONSUMED_SEED_TARGET_SHAPES: + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') + inputs = _inputs_for_label(label) + deltas[label] = {'candidate_ms': candidate_ms, 'baseline_6b59_ms': baseline_ms, 'flashlib_ms': flashlib_ms, 'speedup_vs_baseline_6b59': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'ratio_vs_flashlib': flashlib_ms / candidate_ms if candidate_ms and flashlib_ms else None, 'candidate_route': route_for_contract_inputs(inputs), 'baseline_6b59_route': _baseline_6b59_route(inputs), 'k32_d64_component_route': _k32_d64_route(inputs)} + return deltas + +def _seed_delta_matrix(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> list[dict[str, Any]]: + matrix = [] + for label in SELECTED_TARGET_SHAPES: + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + inputs = _inputs_for_label(label) + matrix.append({'shape_key': label, 'baseline_route': _baseline_6b59_route(inputs), 'candidate_route': route_for_contract_inputs(inputs), 'candidate_ms': candidate_ms, 'baseline_ms': baseline_ms, 'metric_delta_ms': candidate_ms - baseline_ms if candidate_ms and baseline_ms else None, 'speedup_vs_baseline_6b59': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'timing_backend': candidate_row.get('timing_backend') or baseline_row.get('timing_backend')}) + return matrix + +def _frontmatter_seed_delta_matrix(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> list[dict[str, Any]]: + rows = [] + for item in _seed_delta_matrix(candidate_report, baseline_report): + delta = item['metric_delta_ms'] + rows.append({'shape_key': item['shape_key'], 'baseline_route': item['baseline_route'], 'candidate_deltas': [{'candidate_id': 'selected_f552_k32_d64_df2f_rect4452', 'metric_delta': 0.0 if delta is None else float(delta), 'timing_backend': item['timing_backend'] or 'cuda_event'}]}) + return rows + +def _benchmark_payload(candidate_report: dict[str, Any], baseline_report: dict[str, Any], *, use_cupti: bool, shape_labels, measured_function: str) -> dict[str, Any]: + candidate_metric = candidate_report['summary']['primary_mean'] or 0.0 + baseline_metric = baseline_report['summary']['primary_mean'] or 0.0 + route_trace = route_trace_for_contract_shapes(shape_labels) + return {'tflops': candidate_metric, 'baseline_tflops': baseline_metric, 'metric_delta': candidate_metric - baseline_metric, 'all_correct': candidate_report['summary']['all_correct'], 'baseline_all_correct': baseline_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'baseline_performance_comparable': baseline_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'baseline_invalid_performance_reason': baseline_report['summary']['invalid_performance_reason'], 'measured_entrypoint': ''.join(['loom.examples.weave.knn_build_dispatch_selected_portfolio_f552_v1:', format(measured_function, '')]), 'baseline_entrypoint': 'loom.examples.weave.knn_build_dispatch_7399_d15e_df2f_full55_v1:launch_from_contract_inputs', 'baseline_entrypoint_note': 'same-session 6b59 full55 champion measured through the same contract denominator', 'measured_shape_labels': 'all_canonical' if shape_labels is None else tuple(shape_labels), 'selected_route_labels': SELECTED_TARGET_SHAPES, 'consumed_seed_labels': CONSUMED_SEED_TARGET_SHAPES, 'guard_miss_audit_labels': GUARD_MISS_AUDIT_SHAPES, 'selected_route_rows': _rows_for_labels(candidate_report, SELECTED_TARGET_SHAPES), 'baseline_selected_route_rows': _rows_for_labels(baseline_report, SELECTED_TARGET_SHAPES), 'consumed_seed_rows': _rows_for_labels(candidate_report, CONSUMED_SEED_TARGET_SHAPES), 'baseline_consumed_seed_rows': _rows_for_labels(baseline_report, CONSUMED_SEED_TARGET_SHAPES), 'guard_miss_audit_rows': _rows_for_labels(candidate_report, GUARD_MISS_AUDIT_SHAPES), 'baseline_guard_miss_audit_rows': _rows_for_labels(baseline_report, GUARD_MISS_AUDIT_SHAPES), 'per_consumed_row_delta': _per_consumed_row_delta(candidate_report, baseline_report), 'seed_delta_matrix': _seed_delta_matrix(candidate_report, baseline_report), 'frontmatter_seed_delta_matrix': _frontmatter_seed_delta_matrix(candidate_report, baseline_report), 'candidate_dispatchers': CANDIDATE_PORTFOLIOS, 'selected_candidate_dispatcher': 'selected_f552_k32_d64_df2f_rect4452', 'route_modules': PRODUCTION_ROUTE_MODULES, 'route_trace': route_trace, 'forced_fallback_route_trace': route_trace_for_contract_shapes(shape_labels, force_fallback=True), 'route_trace_included': True, 'contract_summary': candidate_report['summary'], 'baseline_contract_summary': baseline_report['summary'], 'contract_performance': candidate_report['performance'], 'baseline_contract_performance': baseline_report['performance'], 'timing_backends': _timing_backends_for_reports(candidate_report, baseline_report), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'hot_bucket_parity': {'rag_k32': 'selected_62b1_exact_k32', 'dim_sweep_qm2048_d64_k10': 'selected_62b1_exact_d64', 'dim_sweep_qm2048_d256_k10': 'selected_6b59_df2f', 'dim_sweep_qm2048_fp16_d128_k10': 'selected_6b59_df2f', 'rect_q2048_m32768_k10': 'selected_4452_split8', 'midk_k24_k28_over32_k64': 'inherited_fail', 'default_k96_registry_gate': 'inherited_open_gate'}, 'performance_coverage': 'partial', 'coverage_only_routes': [], 'hot_bucket_blockers': ['midk_k24_k28_over32_k64', 'default_k96_registry_gate'], 'report': candidate_report, 'baseline_report': baseline_report} + +def benchmark_knn_build_dispatch_selected_portfolio_f552_v1(*, use_cupti: bool=False, shape_labels=None) -> dict[str, Any]: + """Full-denominator A/B against the 6b59 champion dispatcher.""" + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels) + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_base_dispatcher) + return _benchmark_payload(candidate_report, baseline_report, use_cupti=use_cupti, shape_labels=shape_labels, measured_function='benchmark_knn_build_dispatch_selected_portfolio_f552_v1') + +def write_benchmark_artifacts(artifact_dir: str | os.PathLike[str], *, use_cupti: bool=False, shape_labels=None) -> dict[str, str]: + payload = benchmark_knn_build_dispatch_selected_portfolio_f552_v1(use_cupti=use_cupti, shape_labels=shape_labels) + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + candidate_path = out_dir / 'full55_dispatch_selected_portfolio_f552_v1.json' + baseline_path = out_dir / 'full55_same_session_baseline_6b59_for_f552_v1.json' + route_trace_path = out_dir / 'full55_route_trace_selected_portfolio_f552_v1.json' + forced_trace_path = out_dir / 'full55_forced_fallback_trace_selected_portfolio_f552_v1.json' + candidate_path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + baseline_path.write_text(json.dumps({'measured_entrypoint': payload['baseline_entrypoint'], 'measured_shape_labels': payload['measured_shape_labels'], 'timing_backend_requested': payload['timing_backend_requested'], 'timing_backends': payload['timing_backends'], 'tflops': payload['baseline_tflops'], 'all_correct': payload['baseline_all_correct'], 'performance_comparable': payload['baseline_performance_comparable'], 'contract_summary': payload['baseline_contract_summary'], 'contract_performance': payload['baseline_contract_performance'], 'route_trace': dispatch_df2f.route_trace_for_contract_shapes(shape_labels), 'route_trace_included': True, 'report': payload['baseline_report']}, indent=2, sort_keys=True) + '\n') + route_trace_path.write_text(json.dumps(payload['route_trace'], indent=2, sort_keys=True) + '\n') + forced_trace_path.write_text(json.dumps(payload['forced_fallback_route_trace'], indent=2, sort_keys=True) + '\n') + return {'candidate_payload': str(candidate_path), 'baseline_payload': str(baseline_path), 'route_trace': str(route_trace_path), 'forced_fallback_trace': str(forced_trace_path)} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_selected_portfolio_f853_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_selected_portfolio_f853_v1.py new file mode 100644 index 00000000..9e8fa95e --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_selected_portfolio_f853_v1.py @@ -0,0 +1,236 @@ +"""Opt-in kNN build dispatcher consuming the a194 q4096 K64 split8 seed. + +Minimum target architecture: sm_100a. This dispatcher-consumption candidate is +wrapper-only. It starts from the f8c3 selected full55 portfolio and adds one +exact guard for the BF16 build ``B=1,Q=M=4096,D=128,K=64`` row, routing that +row to the validated a194 q4096 K64 split8 seed. + +Every production route remains Weave-only; PyTorch and FlashLib are references +only through the contract harness. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from pathlib import Path +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dim_midk_f8c3_q4096k64split_v1 as q4096_seed +from . import knn_build_dispatch_selected_portfolio_a961_v1 as compare_a961 +from . import knn_build_dispatch_selected_portfolio_f8c3_v1 as base_f8c3 +ROUTE_Q4096_K64_A194 = q4096_seed.ROUTE_Q4096_K64 +ROUTE_BASE_F8C3 = 'loom.examples.weave.knn_build_dispatch_selected_portfolio_f8c3_v1:launch_from_contract_inputs' +Q4096_K64_TARGET_SHAPES = ('build_over32_stress_qm4096_k64',) +Q4096_K64_TARGET_SHAPE_SET = set(Q4096_K64_TARGET_SHAPES) +ADJACENT_GUARD_MISS_SHAPES = ('build_over32_stress_qm2048_k64', 'build_k_sweep_qm1024_k16', 'search_rect_over32_b1_q2048_m65536_d128_k64', 'rag_offline_large_m_over32_b1_q2048_m250000_d128_k64') +CONSUMED_SEED_TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["rag_stream_largek_b1_q128_m100000_d128_k32", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q2048_m2048_d256_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10", "search_rect_b1_q2048_m32768_d128_k10", "build_over64_stress_qm2048_k96", "build_large_tail_b1_q6144_m6144_d128_k20", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "build_over32_stress_qm2048_k64", "build_over32_stress_qm4096_k64"]}')) +SELECTED_TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_large_b1_q8192_m8192_d128_k20", "build_large_b1_q8192_m8192_d128_k32", "build_over64_stress_qm2048_k96", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32", "rag_batch_b2_q256_m50000_d128_k10", "rag_irregular_b1_q512_m131071_d128_k10", "search_rect_b1_q1024_m8192_d128_k10", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q2048_m2048_d256_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10", "search_rect_b1_q2048_m32768_d128_k10", "build_large_tail_b1_q6144_m6144_d128_k20", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "build_over32_stress_qm2048_k64", "build_over32_stress_qm4096_k64"]}')) +GUARD_MISS_AUDIT_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_dim_sweep_b1_q2048_m2048_d64_k10", "build_qm2048_d128_k10", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "flashml_correctness_b1_q256_m256_d128_k5", "build_k_sweep_qm1024_k16", "build_over32_stress_qm2048_k64", "search_rect_over32_b1_q2048_m65536_d128_k64", "rag_offline_large_m_over32_b1_q2048_m250000_d128_k64"]}')) +DISPATCH_CORRECTNESS_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["flashml_correctness_b1_q256_m256_d128_k5", "build_large_b1_q8192_m8192_d128_k20", "build_large_b1_q8192_m8192_d128_k32", "build_over64_stress_qm2048_k96", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32", "rag_batch_b2_q256_m50000_d128_k10", "rag_irregular_b1_q512_m131071_d128_k10", "search_rect_b1_q1024_m8192_d128_k10", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q2048_m2048_d256_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10", "search_rect_b1_q2048_m32768_d128_k10", "build_large_tail_b1_q6144_m6144_d128_k20", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "build_over32_stress_qm2048_k64", "build_over32_stress_qm4096_k64", "build_qm2048_d128_k10", "build_k_sweep_qm1024_k16", "search_rect_over32_b1_q2048_m65536_d128_k64", "rag_offline_large_m_over32_b1_q2048_m250000_d128_k64", "rag_online_b1_q1_m100000_d128_k10", "rag_stream_b1_q128_m100000_d128_k10", "search_rect_b1_q4096_m65536_d128_k20", "rag_offline_largek_b1_q4096_m100000_d128_k20", "rag_offline_large_m_b1_q8192_m250000_d128_k20"]}')) +PRODUCTION_ROUTE_MODULES = {**base_f8c3.PRODUCTION_ROUTE_MODULES, 'midk_f8c3_q4096_k64_split8_a194': ROUTE_Q4096_K64_A194, 'base_f8c3': ROUTE_BASE_F8C3} +CANDIDATE_PORTFOLIOS = ({'id': 'baseline_f8c3_selected_portfolio', 'entrypoint': 'loom.examples.weave.knn_build_dispatch_selected_portfolio_f8c3_v1:benchmark_knn_build_dispatch_selected_portfolio_f8c3_v1', 'consumed_seeds': base_f8c3.CANDIDATE_PORTFOLIOS[-1]['consumed_seeds'], 'guard_plan': base_f8c3.CANDIDATE_PORTFOLIOS[-1]['guard_plan'], 'expected_shape_wins': base_f8c3.SELECTED_TARGET_SHAPES, 'rejected_reason': 'same-session baseline for the f853 q4096 K64 consumption lane'}, {'id': 'selected_f853_f8c3_plus_q4096_k64_split8', 'entrypoint': 'loom.examples.weave.knn_build_dispatch_selected_portfolio_f853_v1:benchmark_knn_build_dispatch_selected_portfolio_f853_v1', 'consumed_seeds': ('selected_f8c3_e51c_plus_q2048_k64split8', 'midk_f8c3_q4096_k64_split8_a194'), 'guard_plan': ('exact a194 BF16 build B1 Q=M=4096 D128 K64 split8 guard', 'then f8c3 selected full55 guard plan'), 'expected_shape_wins': SELECTED_TARGET_SHAPES, 'rejected_reason': None}) +Q4096_K64_ROW_SELECTION = {'build_over32_stress_qm4096_k64': {'selected_seed': 'midk_f8c3_q4096_k64_split8_a194', 'selected_route': ROUTE_Q4096_K64_A194, 'candidate_ms': 0.265698, 'candidate_tflops': 16.164846163689603, 'ratio_vs_flashlib': 2.087192978494381, 'baseline_seed_ms': 0.744356, 'speedup_vs_f8c3': 2.8015114904892022, 'reason': 'a194 split8 seed is 2.80x faster than f8c3 and 2.09x FlashLib on same-session CUPTI exact-row timing.'}} + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_DISPATCH_SELECTED_F853_VERIFY_KERNEL') + if verify_kernel == 'q4096_k64_stage1': + os.environ['LOOM_KNN_DIMMIDK_F8C3_Q4096K64_VERIFY_KERNEL'] = 'stage1_k64_tailinf' + return q4096_seed._verify_export_ir() + if verify_kernel == 'q4096_k64_merge': + os.environ['LOOM_KNN_DIMMIDK_F8C3_Q4096K64_VERIFY_KERNEL'] = 'merge_k64_s8' + return q4096_seed._verify_export_ir() + return base_f8c3.ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k48over32", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 48]], "cta_group": 1, "threads": 192}')) + +def _eligible_q4096_k64(inputs: dict[str, Any]) -> bool: + return q4096_seed._eligible_q4096_k64(inputs) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if force_fallback: + return base_f8c3.route_for_contract_inputs(inputs) + if _eligible_q4096_k64(inputs): + return ROUTE_Q4096_K64_A194 + return base_f8c3.route_for_contract_inputs(inputs) + +def _launch_route(inputs: dict[str, Any], route: str) -> None: + if route == ROUTE_Q4096_K64_A194: + q4096_seed._launch_q4096_k64_split(inputs, split_count=q4096_seed.DEFAULT_Q4096_K64_SPLITS) + return + base_f8c3._launch_route(inputs, route) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + _launch_route(inputs, route_for_contract_inputs(inputs, force_fallback=force_fallback)) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def candidate_base_dispatcher(inputs: dict[str, Any]): + base_f8c3.launch_from_contract_inputs(inputs) + return None + +def candidate_force_fallback(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs, force_fallback=True) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return base_f8c3._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=DISPATCH_CORRECTNESS_SHAPES, benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint: run real contract correctness for selected shapes.""" + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=None, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + shapes = None if shape_labels is None else _select_contract_shapes(shape_labels) + return evaluate_contract(shapes=shapes, correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def _trace_inputs_from_shape(shape: dict[str, Any]) -> dict[str, Any]: + return base_f8c3._trace_inputs_from_shape(shape) + +def _inputs_for_label(label: str) -> dict[str, Any]: + return base_f8c3._inputs_for_label(label) + +def _base_f8c3_route(inputs: dict[str, Any]) -> str: + return base_f8c3.route_for_contract_inputs(inputs) + +def _route_trace_record(inputs: dict[str, Any], *, force_fallback: bool=False) -> dict[str, Any]: + base_route = _base_f8c3_route(inputs) + if force_fallback: + row = base_f8c3._route_trace_record(inputs) + row['guard_condition'] = 'forced fallback to f8c3 baseline; f853 q4096 K64 overlay disabled' + row['coverage'] = 'forced candidate fallback for f853 q4096 K64 overlay' + row['forced_disabled_seeds'] = ('midk_f8c3_q4096_k64_split8_a194',) + row['base_f8c3_route'] = base_route + row['candidate_guard_status'] = 'forced_fallback_to_f8c3' + return row + route = route_for_contract_inputs(inputs) + label = str(inputs.get('label')) + if route == ROUTE_Q4096_K64_A194 and label in Q4096_K64_ROW_SELECTION: + selected = Q4096_K64_ROW_SELECTION[label] + return {'shape_key': inputs.get('label'), 'selected_route': route, 'guard_condition': 'exact a194 BF16 build B1 Q=M=4096 D128 K64 split8 tail-infinity route', 'route_kind': 'specialized', 'coverage': 'exact q4096 K64 split8 seed selected ahead of f8c3 inherited fallback', 'consumed_seed': selected['selected_seed'], 'replaced_route': base_route, 'base_f8c3_route': base_route, 'row_selection': selected, 'parity_status': 'pass', 'parity_reason': selected['reason'], 'candidate_guard_status': 'selected_from_q4096_k64_split8'} + row = base_f8c3._route_trace_record(inputs) + row['base_f8c3_route'] = base_route + row['candidate_guard_status'] = 'inherited_f8c3_or_guard_miss' + return row + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + return [_route_trace_record(_trace_inputs_from_shape(shape), force_fallback=force_fallback) for shape in selected] + +def _timing_backends_for_reports(*reports: dict[str, Any]) -> list[str]: + return base_f8c3._timing_backends_for_reports(*reports) + +def _rows_for_labels(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return base_f8c3._rows_for_labels(report, labels) + +def _per_consumed_row_delta(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> dict[str, Any]: + deltas = {} + for label in CONSUMED_SEED_TARGET_SHAPES: + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') + inputs = _inputs_for_label(label) + deltas[label] = {'candidate_ms': candidate_ms, 'baseline_f8c3_ms': baseline_ms, 'flashlib_ms': flashlib_ms, 'speedup_vs_baseline_f8c3': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'ratio_vs_flashlib': flashlib_ms / candidate_ms if candidate_ms and flashlib_ms else None, 'candidate_route': route_for_contract_inputs(inputs), 'baseline_f8c3_route': _base_f8c3_route(inputs)} + return deltas + +def _seed_delta_matrix(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> list[dict[str, Any]]: + matrix = [] + for label in SELECTED_TARGET_SHAPES: + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + inputs = _inputs_for_label(label) + matrix.append({'shape_key': label, 'baseline_route': _base_f8c3_route(inputs), 'candidate_route': route_for_contract_inputs(inputs), 'candidate_ms': candidate_ms, 'baseline_ms': baseline_ms, 'metric_delta_ms': candidate_ms - baseline_ms if candidate_ms and baseline_ms else None, 'speedup_vs_baseline_f8c3': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'timing_backend': candidate_row.get('timing_backend') or baseline_row.get('timing_backend')}) + return matrix + +def _frontmatter_seed_delta_matrix(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> list[dict[str, Any]]: + rows = [] + for item in _seed_delta_matrix(candidate_report, baseline_report): + delta = item['metric_delta_ms'] + rows.append({'shape_key': item['shape_key'], 'baseline_route': item['baseline_route'], 'candidate_deltas': [{'candidate_id': 'selected_f853_f8c3_plus_q4096_k64_split8', 'metric_delta': 0.0 if delta is None else float(delta), 'timing_backend': item['timing_backend'] or 'cuda_event'}]}) + return rows + +def _below_flashlib_rows(report: dict[str, Any]) -> list[dict[str, Any]]: + rows = [] + specialized_routes = set(PRODUCTION_ROUTE_MODULES.values()) + for label, row in sorted(report.get('per_shape', {}).items()): + ratio = row.get('ratio_vs_flashlib') + if isinstance(ratio, (float, int)) and ratio < 1.0: + inputs = _inputs_for_label(label) + route = route_for_contract_inputs(inputs) + rows.append({'shape_key': label, 'kernel_ms': row.get('kernel_ms'), 'flashlib_ms': row.get('flashlib_ms'), 'ratio_vs_flashlib': ratio, 'selected_route': route, 'route_kind': 'specialized' if route in specialized_routes else 'general'}) + return rows + +def _benchmark_payload(candidate_report: dict[str, Any], baseline_report: dict[str, Any], *, use_cupti: bool, shape_labels, measured_function: str) -> dict[str, Any]: + candidate_metric = candidate_report['summary']['primary_mean'] or 0.0 + baseline_metric = baseline_report['summary']['primary_mean'] or 0.0 + route_trace = route_trace_for_contract_shapes(shape_labels) + below_flashlib = _below_flashlib_rows(candidate_report) + return {'tflops': candidate_metric, 'baseline_tflops': baseline_metric, 'metric_delta': candidate_metric - baseline_metric, 'all_correct': candidate_report['summary']['all_correct'], 'baseline_all_correct': baseline_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'baseline_performance_comparable': baseline_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'baseline_invalid_performance_reason': baseline_report['summary']['invalid_performance_reason'], 'measured_entrypoint': ''.join(['loom.examples.weave.knn_build_dispatch_selected_portfolio_f853_v1:', format(measured_function, '')]), 'baseline_entrypoint': 'loom.examples.weave.knn_build_dispatch_selected_portfolio_f8c3_v1:launch_from_contract_inputs', 'baseline_entrypoint_note': 'same-session f8c3 selected portfolio measured through the same full55 contract denominator', 'measured_shape_labels': 'all_canonical' if shape_labels is None else tuple(shape_labels), 'selected_route_labels': SELECTED_TARGET_SHAPES, 'consumed_seed_labels': CONSUMED_SEED_TARGET_SHAPES, 'guard_miss_audit_labels': GUARD_MISS_AUDIT_SHAPES, 'selected_route_rows': _rows_for_labels(candidate_report, SELECTED_TARGET_SHAPES), 'baseline_selected_route_rows': _rows_for_labels(baseline_report, SELECTED_TARGET_SHAPES), 'consumed_seed_rows': _rows_for_labels(candidate_report, CONSUMED_SEED_TARGET_SHAPES), 'baseline_consumed_seed_rows': _rows_for_labels(baseline_report, CONSUMED_SEED_TARGET_SHAPES), 'guard_miss_audit_rows': _rows_for_labels(candidate_report, GUARD_MISS_AUDIT_SHAPES), 'baseline_guard_miss_audit_rows': _rows_for_labels(baseline_report, GUARD_MISS_AUDIT_SHAPES), 'per_consumed_row_delta': _per_consumed_row_delta(candidate_report, baseline_report), 'seed_delta_matrix': _seed_delta_matrix(candidate_report, baseline_report), 'frontmatter_seed_delta_matrix': _frontmatter_seed_delta_matrix(candidate_report, baseline_report), 'candidate_dispatchers': CANDIDATE_PORTFOLIOS, 'selected_candidate_dispatcher': 'selected_f853_f8c3_plus_q4096_k64_split8', 'q4096_k64_row_selection': Q4096_K64_ROW_SELECTION, 'route_modules': PRODUCTION_ROUTE_MODULES, 'route_trace': route_trace, 'forced_fallback_route_trace': route_trace_for_contract_shapes(shape_labels, force_fallback=True), 'route_trace_included': True, 'contract_summary': candidate_report['summary'], 'baseline_contract_summary': baseline_report['summary'], 'contract_performance': candidate_report['performance'], 'baseline_contract_performance': baseline_report['performance'], 'timing_backends': _timing_backends_for_reports(candidate_report, baseline_report), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'hot_bucket_parity': {'rag_k32': 'inherited_f8c3', 'dim_sweep_qm2048_d64_k10': 'inherited_f8c3', 'dim_sweep_qm2048_d256_k10': 'inherited_f8c3', 'dim_sweep_qm2048_fp16_d128_k10': 'inherited_f8c3', 'rect_q2048_m32768_k10': 'inherited_f8c3', 'default_k96_registry_gate': 'inherited_f8c3', 'large_tail_k20_q6144': 'inherited_f8c3', 'midk_k24_k28': 'inherited_f8c3', 'over32_k64_q2048': 'inherited_f8c3_selected_k64split8', 'over32_k64_q4096': 'selected_a194_q4096_k64_split8'}, 'performance_coverage': 'partial' if below_flashlib else 'pass', 'coverage_only_routes': [], 'hot_bucket_blockers': below_flashlib, 'report': candidate_report, 'baseline_report': baseline_report} + +def benchmark_knn_build_dispatch_selected_portfolio_f853_v1(*, use_cupti: bool=False, shape_labels=None) -> dict[str, Any]: + """Full-denominator A/B against the f8c3 selected portfolio dispatcher.""" + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels) + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_base_dispatcher) + return _benchmark_payload(candidate_report, baseline_report, use_cupti=use_cupti, shape_labels=shape_labels, measured_function='benchmark_knn_build_dispatch_selected_portfolio_f853_v1') + +def _a961_report(*, use_cupti: bool, shape_labels=None) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + shapes = None if shape_labels is None else _select_contract_shapes(shape_labels) + return compare_a961.evaluate_contract(shapes=shapes, correctness=True, benchmark=True, kernel_fn=compare_a961.candidate) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def write_benchmark_artifacts(artifact_dir: str | os.PathLike[str], *, use_cupti: bool=False, shape_labels=None, include_a961: bool=False) -> dict[str, str]: + payload = benchmark_knn_build_dispatch_selected_portfolio_f853_v1(use_cupti=use_cupti, shape_labels=shape_labels) + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + comparison_path = None + if include_a961: + comparison_report = _a961_report(use_cupti=use_cupti, shape_labels=shape_labels) + payload['comparison_a961_entrypoint'] = 'loom.examples.weave.knn_build_dispatch_selected_portfolio_a961_v1:launch_from_contract_inputs' + payload['comparison_a961_tflops'] = comparison_report['summary']['primary_mean'] or 0.0 + payload['comparison_a961_all_correct'] = comparison_report['summary']['all_correct'] + payload['comparison_a961_contract_summary'] = comparison_report['summary'] + payload['comparison_a961_contract_performance'] = comparison_report['performance'] + payload['comparison_a961_timing_backends'] = _timing_backends_for_reports(comparison_report) + payload['comparison_a961_report'] = comparison_report + candidate_path = out_dir / 'full55_dispatch_selected_portfolio_f853_v1.json' + baseline_path = out_dir / 'full55_same_session_baseline_f8c3_for_f853_v1.json' + route_trace_path = out_dir / 'full55_route_trace_selected_portfolio_f853_v1.json' + forced_trace_path = out_dir / 'full55_forced_fallback_trace_selected_portfolio_f853_v1.json' + candidate_path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + baseline_path.write_text(json.dumps({'measured_entrypoint': payload['baseline_entrypoint'], 'measured_shape_labels': payload['measured_shape_labels'], 'timing_backend_requested': payload['timing_backend_requested'], 'timing_backends': payload['timing_backends'], 'tflops': payload['baseline_tflops'], 'all_correct': payload['baseline_all_correct'], 'performance_comparable': payload['baseline_performance_comparable'], 'contract_summary': payload['baseline_contract_summary'], 'contract_performance': payload['baseline_contract_performance'], 'route_trace': base_f8c3.route_trace_for_contract_shapes(shape_labels), 'route_trace_included': True, 'report': payload['baseline_report']}, indent=2, sort_keys=True) + '\n') + route_trace_path.write_text(json.dumps(payload['route_trace'], indent=2, sort_keys=True) + '\n') + forced_trace_path.write_text(json.dumps(payload['forced_fallback_route_trace'], indent=2, sort_keys=True) + '\n') + if include_a961: + comparison_path = out_dir / 'full55_same_session_a961_for_f853_v1.json' + comparison_path.write_text(json.dumps({'measured_entrypoint': payload['comparison_a961_entrypoint'], 'measured_shape_labels': payload['measured_shape_labels'], 'timing_backend_requested': payload['timing_backend_requested'], 'timing_backends': payload['comparison_a961_timing_backends'], 'tflops': payload['comparison_a961_tflops'], 'all_correct': payload['comparison_a961_all_correct'], 'performance_comparable': payload['comparison_a961_contract_summary']['performance_comparable'], 'contract_summary': payload['comparison_a961_contract_summary'], 'contract_performance': payload['comparison_a961_contract_performance'], 'route_trace': compare_a961.route_trace_for_contract_shapes(shape_labels), 'route_trace_included': True, 'report': payload['comparison_a961_report']}, indent=2, sort_keys=True) + '\n') + paths = {'candidate_payload': str(candidate_path), 'baseline_payload': str(baseline_path), 'route_trace': str(route_trace_path), 'forced_fallback_trace': str(forced_trace_path)} + if comparison_path is not None: + paths['comparison_a961_payload'] = str(comparison_path) + return paths diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_selected_portfolio_f8c3_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_selected_portfolio_f8c3_v1.py new file mode 100644 index 00000000..59070e3e --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_selected_portfolio_f8c3_v1.py @@ -0,0 +1,208 @@ +"""Opt-in kNN build dispatcher consuming the f8c3 K64 split8 seed. + +Minimum target architecture: sm_100a. This dispatcher-consumption candidate is +wrapper-only. It starts from the e51c selected full55 portfolio and adds one +exact guard for the BF16 build ``B=1,Q=M=2048,D=128,K=64`` row, routing that +row to the validated bad5 K64 split8 seed. + +Every production route remains Weave-only; PyTorch and FlashLib are references +only through the contract harness. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from pathlib import Path +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dim_midk_bad5_k64split8_v1 as k64_split8 +from . import knn_build_dispatch_selected_portfolio_e51c_v1 as base_e51c +ROUTE_K64_SPLIT8 = k64_split8.ROUTE_K64_Q2048 +ROUTE_BASE_E51C = 'loom.examples.weave.knn_build_dispatch_selected_portfolio_e51c_v1:launch_from_contract_inputs' +K64_TARGET_SHAPES = ('build_over32_stress_qm2048_k64',) +K64_TARGET_SHAPE_SET = set(K64_TARGET_SHAPES) +CONSUMED_SEED_TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["rag_stream_largek_b1_q128_m100000_d128_k32", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q2048_m2048_d256_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10", "search_rect_b1_q2048_m32768_d128_k10", "build_over64_stress_qm2048_k96", "build_large_tail_b1_q6144_m6144_d128_k20", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "build_over32_stress_qm2048_k64"]}')) +SELECTED_TARGET_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_large_b1_q8192_m8192_d128_k20", "build_large_b1_q8192_m8192_d128_k32", "build_over64_stress_qm2048_k96", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32", "rag_batch_b2_q256_m50000_d128_k10", "rag_irregular_b1_q512_m131071_d128_k10", "search_rect_b1_q1024_m8192_d128_k10", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q2048_m2048_d256_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10", "search_rect_b1_q2048_m32768_d128_k10", "build_large_tail_b1_q6144_m6144_d128_k20", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "build_over32_stress_qm2048_k64"]}')) +GUARD_MISS_AUDIT_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["build_dim_sweep_b1_q2048_m2048_d64_k10", "build_qm2048_d128_k10", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "build_over32_stress_qm4096_k64", "flashml_correctness_b1_q256_m256_d128_k5", "build_k_sweep_qm1024_k16"]}')) +DISPATCH_CORRECTNESS_SHAPES = _decode_capture(_json_loads('{"__tuple__": ["flashml_correctness_b1_q256_m256_d128_k5", "build_large_b1_q8192_m8192_d128_k20", "build_large_b1_q8192_m8192_d128_k32", "build_over64_stress_qm2048_k96", "rag_microbatch_b1_q16_m100000_d128_k10", "rag_stream_largek_b1_q128_m100000_d128_k32", "rag_batch_b2_q256_m50000_d128_k10", "rag_irregular_b1_q512_m131071_d128_k10", "search_rect_b1_q1024_m8192_d128_k10", "build_dim_sweep_b1_q2048_m2048_d64_k10", "build_dim_sweep_b1_q2048_m2048_d256_k10", "build_dtype_fp16_b1_q2048_m2048_d128_k10", "search_rect_b1_q2048_m32768_d128_k10", "build_large_tail_b1_q6144_m6144_d128_k20", "build_k_sweep_qm2048_k24", "build_k_sweep_qm2048_k28", "build_k_sweep_qm4096_k28", "build_over32_stress_qm2048_k64", "build_qm2048_d128_k10", "build_over32_stress_qm4096_k64", "build_k_sweep_qm1024_k16", "rag_online_b1_q1_m100000_d128_k10", "rag_stream_b1_q128_m100000_d128_k10", "search_rect_b1_q4096_m65536_d128_k20", "rag_offline_largek_b1_q4096_m100000_d128_k20", "rag_offline_large_m_b1_q8192_m250000_d128_k20", "search_rect_over32_b1_q2048_m65536_d128_k64", "rag_offline_large_m_over32_b1_q2048_m250000_d128_k64"]}')) +PRODUCTION_ROUTE_MODULES = {**base_e51c.PRODUCTION_ROUTE_MODULES, 'midk_bad5_k64split8': ROUTE_K64_SPLIT8, 'base_e51c': ROUTE_BASE_E51C} +CANDIDATE_PORTFOLIOS = ({'id': 'baseline_e51c_selected_portfolio', 'entrypoint': 'loom.examples.weave.knn_build_dispatch_selected_portfolio_e51c_v1:benchmark_knn_build_dispatch_selected_portfolio_e51c_v1', 'consumed_seeds': ('selected_f552_k32_d64_df2f_rect4452', 'default_k96_a330', 'large_tail_a4f6_k20', 'midk_81aa_q2048_k24_k28', 'midk_9b2c_q4096_k28'), 'guard_plan': ('e51c selected full55 guard plan',), 'expected_shape_wins': base_e51c.CONSUMED_SEED_TARGET_SHAPES, 'rejected_reason': 'same-session baseline for f8c3 K64 dispatcher-consumption lane'}, {'id': 'selected_f8c3_e51c_plus_k64split8', 'entrypoint': 'loom.examples.weave.knn_build_dispatch_selected_portfolio_f8c3_v1:benchmark_knn_build_dispatch_selected_portfolio_f8c3_v1', 'consumed_seeds': ('selected_e51c_f552_a330_a4f6_row_level_midk', 'midk_bad5_k64split8_q2048'), 'guard_plan': ('exact bad5 K64 split8 BF16 build B1 Q=M=2048 D128 K64 guard', 'then e51c selected full55 guard plan'), 'expected_shape_wins': CONSUMED_SEED_TARGET_SHAPES, 'rejected_reason': None}) +K64_ROW_SELECTION = {'build_over32_stress_qm2048_k64': {'selected_seed': 'midk_bad5_k64split8_q2048', 'selected_route': ROUTE_K64_SPLIT8, 'candidate_ms': 0.142945, 'candidate_tflops': 7.511573150512436, 'ratio_vs_flashlib': 2.1468606806813813, 'baseline_seed_ms': 0.613798, 'speedup_vs_bad5_k24k28': 4.293945223687432, 'reason': 'K64 split8 seed is 4.29x faster than the parent route and 2.15x FlashLib on same-session CUPTI.'}} + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_DISPATCH_SELECTED_F8C3_VERIFY_KERNEL') + if verify_kernel == 'k64_stage1_s8_tailinf': + os.environ['LOOM_KNN_DIMMIDK_BAD5_K64S8_VERIFY_KERNEL'] = 'stage1_k64_s8_tailinf' + return k64_split8._verify_export_ir() + if verify_kernel == 'k64_merge_s8_warp_select': + os.environ['LOOM_KNN_DIMMIDK_BAD5_K64S8_VERIFY_KERNEL'] = 'merge_k64_s8_warp_select' + return k64_split8._verify_export_ir() + return base_e51c.ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k48over32", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 48]], "cta_group": 1, "threads": 192}')) + +def _eligible_k64_split8(inputs: dict[str, Any]) -> bool: + return k64_split8._eligible_k64_q2048(inputs) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if force_fallback: + return base_e51c.route_for_contract_inputs(inputs) + if _eligible_k64_split8(inputs): + return ROUTE_K64_SPLIT8 + return base_e51c.route_for_contract_inputs(inputs) + +def _launch_route(inputs: dict[str, Any], route: str) -> None: + if route == ROUTE_K64_SPLIT8: + k64_split8._launch_k64_q2048_split8(inputs) + return + base_e51c._launch_route(inputs, route) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + _launch_route(inputs, route_for_contract_inputs(inputs, force_fallback=force_fallback)) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def candidate_base_dispatcher(inputs: dict[str, Any]): + base_e51c.launch_from_contract_inputs(inputs) + return None + +def candidate_force_fallback(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs, force_fallback=True) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return base_e51c._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=DISPATCH_CORRECTNESS_SHAPES, benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint: run real contract correctness for selected shapes.""" + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=None, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + shapes = None if shape_labels is None else _select_contract_shapes(shape_labels) + return evaluate_contract(shapes=shapes, correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def _trace_inputs_from_shape(shape: dict[str, Any]) -> dict[str, Any]: + return base_e51c._trace_inputs_from_shape(shape) + +def _inputs_for_label(label: str) -> dict[str, Any]: + return base_e51c._inputs_for_label(label) + +def _base_e51c_route(inputs: dict[str, Any]) -> str: + return base_e51c.route_for_contract_inputs(inputs) + +def _route_trace_record(inputs: dict[str, Any], *, force_fallback: bool=False) -> dict[str, Any]: + base_route = _base_e51c_route(inputs) + if force_fallback: + row = base_e51c._route_trace_record(inputs) + row['guard_condition'] = 'forced fallback to e51c baseline; f8c3 K64 overlay disabled' + row['coverage'] = 'forced candidate fallback for f8c3 K64 overlay' + row['forced_disabled_seeds'] = ('midk_bad5_k64split8_q2048',) + row['base_e51c_route'] = base_route + row['candidate_guard_status'] = 'forced_fallback_to_e51c' + return row + route = route_for_contract_inputs(inputs) + label = str(inputs.get('label')) + if route == ROUTE_K64_SPLIT8 and label in K64_ROW_SELECTION: + selected = K64_ROW_SELECTION[label] + return {'shape_key': inputs.get('label'), 'selected_route': route, 'guard_condition': 'exact BF16 build B1 Q=M=2048 D128 K64 split8 tail-infinity route', 'route_kind': 'specialized', 'coverage': 'exact bad5 K64 split8 seed selected ahead of e51c inherited fallback', 'consumed_seed': selected['selected_seed'], 'replaced_route': base_route, 'base_e51c_route': base_route, 'row_selection': selected, 'parity_status': 'pass', 'parity_reason': selected['reason'], 'candidate_guard_status': 'selected_from_k64split8'} + row = base_e51c._route_trace_record(inputs) + row['base_e51c_route'] = base_route + row['candidate_guard_status'] = 'inherited_e51c_or_guard_miss' + return row + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + return [_route_trace_record(_trace_inputs_from_shape(shape), force_fallback=force_fallback) for shape in selected] + +def _timing_backends_for_reports(*reports: dict[str, Any]) -> list[str]: + return base_e51c._timing_backends_for_reports(*reports) + +def _rows_for_labels(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return base_e51c._rows_for_labels(report, labels) + +def _per_consumed_row_delta(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> dict[str, Any]: + deltas = {} + for label in CONSUMED_SEED_TARGET_SHAPES: + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') + inputs = _inputs_for_label(label) + deltas[label] = {'candidate_ms': candidate_ms, 'baseline_e51c_ms': baseline_ms, 'flashlib_ms': flashlib_ms, 'speedup_vs_baseline_e51c': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'ratio_vs_flashlib': flashlib_ms / candidate_ms if candidate_ms and flashlib_ms else None, 'candidate_route': route_for_contract_inputs(inputs), 'baseline_e51c_route': _base_e51c_route(inputs)} + return deltas + +def _seed_delta_matrix(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> list[dict[str, Any]]: + matrix = [] + for label in SELECTED_TARGET_SHAPES: + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + inputs = _inputs_for_label(label) + matrix.append({'shape_key': label, 'baseline_route': _base_e51c_route(inputs), 'candidate_route': route_for_contract_inputs(inputs), 'candidate_ms': candidate_ms, 'baseline_ms': baseline_ms, 'metric_delta_ms': candidate_ms - baseline_ms if candidate_ms and baseline_ms else None, 'speedup_vs_baseline_e51c': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'timing_backend': candidate_row.get('timing_backend') or baseline_row.get('timing_backend')}) + return matrix + +def _frontmatter_seed_delta_matrix(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> list[dict[str, Any]]: + rows = [] + for item in _seed_delta_matrix(candidate_report, baseline_report): + delta = item['metric_delta_ms'] + rows.append({'shape_key': item['shape_key'], 'baseline_route': item['baseline_route'], 'candidate_deltas': [{'candidate_id': 'selected_f8c3_e51c_plus_k64split8', 'metric_delta': 0.0 if delta is None else float(delta), 'timing_backend': item['timing_backend'] or 'cuda_event'}]}) + return rows + +def _below_flashlib_rows(report: dict[str, Any]) -> list[dict[str, Any]]: + rows = [] + for label, row in sorted(report.get('per_shape', {}).items()): + ratio = row.get('ratio_vs_flashlib') + if isinstance(ratio, (float, int)) and ratio < 1.0: + inputs = _inputs_for_label(label) + rows.append({'shape_key': label, 'kernel_ms': row.get('kernel_ms'), 'flashlib_ms': row.get('flashlib_ms'), 'ratio_vs_flashlib': ratio, 'selected_route': route_for_contract_inputs(inputs), 'route_kind': 'specialized' if route_for_contract_inputs(inputs) in {ROUTE_K64_SPLIT8, *base_e51c.PRODUCTION_ROUTE_MODULES.values()} else 'general'}) + return rows + +def _benchmark_payload(candidate_report: dict[str, Any], baseline_report: dict[str, Any], *, use_cupti: bool, shape_labels, measured_function: str) -> dict[str, Any]: + candidate_metric = candidate_report['summary']['primary_mean'] or 0.0 + baseline_metric = baseline_report['summary']['primary_mean'] or 0.0 + route_trace = route_trace_for_contract_shapes(shape_labels) + below_flashlib = _below_flashlib_rows(candidate_report) + return {'tflops': candidate_metric, 'baseline_tflops': baseline_metric, 'metric_delta': candidate_metric - baseline_metric, 'all_correct': candidate_report['summary']['all_correct'], 'baseline_all_correct': baseline_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'baseline_performance_comparable': baseline_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'baseline_invalid_performance_reason': baseline_report['summary']['invalid_performance_reason'], 'measured_entrypoint': ''.join(['loom.examples.weave.knn_build_dispatch_selected_portfolio_f8c3_v1:', format(measured_function, '')]), 'baseline_entrypoint': ROUTE_BASE_E51C, 'baseline_entrypoint_note': 'same-session e51c selected portfolio measured through the same contract denominator', 'measured_shape_labels': 'all_canonical' if shape_labels is None else tuple(shape_labels), 'selected_route_labels': SELECTED_TARGET_SHAPES, 'consumed_seed_labels': CONSUMED_SEED_TARGET_SHAPES, 'guard_miss_audit_labels': GUARD_MISS_AUDIT_SHAPES, 'selected_route_rows': _rows_for_labels(candidate_report, SELECTED_TARGET_SHAPES), 'baseline_selected_route_rows': _rows_for_labels(baseline_report, SELECTED_TARGET_SHAPES), 'consumed_seed_rows': _rows_for_labels(candidate_report, CONSUMED_SEED_TARGET_SHAPES), 'baseline_consumed_seed_rows': _rows_for_labels(baseline_report, CONSUMED_SEED_TARGET_SHAPES), 'guard_miss_audit_rows': _rows_for_labels(candidate_report, GUARD_MISS_AUDIT_SHAPES), 'baseline_guard_miss_audit_rows': _rows_for_labels(baseline_report, GUARD_MISS_AUDIT_SHAPES), 'per_consumed_row_delta': _per_consumed_row_delta(candidate_report, baseline_report), 'seed_delta_matrix': _seed_delta_matrix(candidate_report, baseline_report), 'frontmatter_seed_delta_matrix': _frontmatter_seed_delta_matrix(candidate_report, baseline_report), 'candidate_dispatchers': CANDIDATE_PORTFOLIOS, 'selected_candidate_dispatcher': 'selected_f8c3_e51c_plus_k64split8', 'k64_row_selection': K64_ROW_SELECTION, 'route_modules': PRODUCTION_ROUTE_MODULES, 'route_trace': route_trace, 'forced_fallback_route_trace': route_trace_for_contract_shapes(shape_labels, force_fallback=True), 'route_trace_included': True, 'contract_summary': candidate_report['summary'], 'baseline_contract_summary': baseline_report['summary'], 'contract_performance': candidate_report['performance'], 'baseline_contract_performance': baseline_report['performance'], 'timing_backends': _timing_backends_for_reports(candidate_report, baseline_report), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'hot_bucket_parity': {'rag_k32': 'inherited_e51c', 'dim_sweep_qm2048_d64_k10': 'inherited_e51c', 'dim_sweep_qm2048_d256_k10': 'inherited_e51c', 'dim_sweep_qm2048_fp16_d128_k10': 'inherited_e51c', 'rect_q2048_m32768_k10': 'inherited_e51c', 'default_k96_registry_gate': 'inherited_e51c', 'large_tail_k20_q6144': 'inherited_e51c', 'midk_k24_k28': 'inherited_e51c', 'over32_k64_q2048': 'selected_k64split8'}, 'performance_coverage': 'partial' if below_flashlib else 'pass', 'coverage_only_routes': [], 'hot_bucket_blockers': below_flashlib, 'report': candidate_report, 'baseline_report': baseline_report} + +def benchmark_knn_build_dispatch_selected_portfolio_f8c3_v1(*, use_cupti: bool=False, shape_labels=None) -> dict[str, Any]: + """Full-denominator A/B against the e51c selected portfolio dispatcher.""" + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels) + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_base_dispatcher) + return _benchmark_payload(candidate_report, baseline_report, use_cupti=use_cupti, shape_labels=shape_labels, measured_function='benchmark_knn_build_dispatch_selected_portfolio_f8c3_v1') + +def write_benchmark_artifacts(artifact_dir: str | os.PathLike[str], *, use_cupti: bool=False, shape_labels=None) -> dict[str, str]: + payload = benchmark_knn_build_dispatch_selected_portfolio_f8c3_v1(use_cupti=use_cupti, shape_labels=shape_labels) + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + candidate_path = out_dir / 'full55_dispatch_selected_portfolio_f8c3_v1.json' + baseline_path = out_dir / 'full55_same_session_baseline_e51c_for_f8c3_v1.json' + route_trace_path = out_dir / 'full55_route_trace_selected_portfolio_f8c3_v1.json' + forced_trace_path = out_dir / 'full55_forced_fallback_trace_selected_portfolio_f8c3_v1.json' + candidate_path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + baseline_path.write_text(json.dumps({'measured_entrypoint': payload['baseline_entrypoint'], 'measured_shape_labels': payload['measured_shape_labels'], 'timing_backend_requested': payload['timing_backend_requested'], 'timing_backends': payload['timing_backends'], 'tflops': payload['baseline_tflops'], 'all_correct': payload['baseline_all_correct'], 'performance_comparable': payload['baseline_performance_comparable'], 'contract_summary': payload['baseline_contract_summary'], 'contract_performance': payload['baseline_contract_performance'], 'route_trace': base_e51c.route_trace_for_contract_shapes(shape_labels), 'route_trace_included': True, 'report': payload['baseline_report']}, indent=2, sort_keys=True) + '\n') + route_trace_path.write_text(json.dumps(payload['route_trace'], indent=2, sort_keys=True) + '\n') + forced_trace_path.write_text(json.dumps(payload['forced_fallback_route_trace'], indent=2, sort_keys=True) + '\n') + return {'candidate_payload': str(candidate_path), 'baseline_payload': str(baseline_path), 'route_trace': str(route_trace_path), 'forced_fallback_trace': str(forced_trace_path)} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_split72_4e09_de1a_3dc7_v48.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_split72_4e09_de1a_3dc7_v48.py new file mode 100644 index 00000000..44ee0e30 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_split72_4e09_de1a_3dc7_v48.py @@ -0,0 +1,196 @@ +"""Main kNN dispatcher consuming split72 RAG, de1a K20, and exact K96 routes. + +Minimum target architecture: sm_100a. This dispatcher consumes the 4e09 +split-72 RAG online/stream K10 seed for exactly its two measured labels, keeps +the rank-selected cce5/v46 de1a K20 route for the three measured K20 labels, +adds the exact over64 K96 Weave coverage route for the v4 frontier row, and +delegates all other guard misses to the restored v46 Weave dispatcher chain. A +named 08ec K20 comparison hook is included for same-denominator A/B timing, but +the production route remains de1a by default. No external runtime fallback is +used. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import os +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_ee5e_de1a_weave_evolve_knn_build_3e08_v46 as previous_main +from . import knn_build_k20_large_lowfanout_de1a_v1 as k20_de1a +from . import knn_build_k20_mergeown_08ec_v3 as k20_08ec +from . import knn_build_over64_k96_a989_v1 as over64_k96 +from . import knn_build_rag_online_stream_split72_4e09_v1 as rag_split72 +ROUTE_RAG_SPLIT72 = 'loom.examples.weave.knn_build_rag_online_stream_split72_4e09_v1' +ROUTE_K20_DE1A = 'loom.examples.weave.knn_build_k20_large_lowfanout_de1a_v1' +ROUTE_K20_08EC = 'loom.examples.weave.knn_build_k20_mergeown_08ec_v3' +ROUTE_OVER64_K96 = 'loom.examples.weave.knn_build_over64_k96_a989_v1' +ROUTE_PREVIOUS_MAIN = 'loom.examples.weave.knn_build_dispatch_ee5e_de1a_weave_evolve_knn_build_3e08_v46' +PRODUCTION_ROUTE_MODULES = {'rag': ROUTE_RAG_SPLIT72, 'k20': ROUTE_K20_DE1A, 'over64_k96': ROUTE_OVER64_K96, 'fallback': ROUTE_PREVIOUS_MAIN} +COMPARISON_ROUTE_MODULES = {'rag': ROUTE_RAG_SPLIT72, 'k20': ROUTE_K20_08EC, 'over64_k96': ROUTE_OVER64_K96, 'fallback': ROUTE_PREVIOUS_MAIN} +RAG_TARGET_SHAPES = rag_split72.TARGET_SHAPES +K20_TARGET_SHAPES = k20_de1a.EXACT_SHAPE_LABELS +K96_TARGET_SHAPES = ('build_over64_stress_qm2048_k96',) +RAG_TARGET_SHAPE_SET = set(RAG_TARGET_SHAPES) +K20_TARGET_SHAPE_SET = set(K20_TARGET_SHAPES) +K96_TARGET_SHAPE_SET = set(K96_TARGET_SHAPES) +SELECTED_TARGET_SHAPES = RAG_TARGET_SHAPES + K20_TARGET_SHAPES + K96_TARGET_SHAPES +DISPATCH_CORRECTNESS_SHAPES = ('flashml_correctness_b1_q256_m256_d128_k5', *SELECTED_TARGET_SHAPES) + +class _TraceTensor: + + def __init__(self, dtype: str) -> None: + self.dtype = dtype if dtype.startswith('torch.') else ''.join(['torch.', format(dtype, '')]) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_MAIN_3DC7_VERIFY_KERNEL') + if verify_kernel == 'rag_split72_stage1_k10': + return rag_split72.parent_lowk.stage1_ir + if verify_kernel == 'rag_split72_merge_k10_s72_cache': + return rag_split72.merge_k10_s72_cache_ir + if verify_kernel in {'k20_de1a_stage1', 'k20_stage1'}: + return k20_de1a.parent_v20.stage1_k20_unordered_ir + if verify_kernel == 'k20_de1a_merge_s4': + return k20_de1a.parent_v20.merge_k20_unordered_warp_select_ir + if verify_kernel == 'k20_de1a_merge_s2': + return k20_de1a.merge_k20_s2_warp_select_ir + if verify_kernel == 'k20_08ec_merge_s4': + return k20_08ec.parent_v20.merge_k20_unordered_warp_select_ir + if verify_kernel == 'k20_08ec_merge_s2_warp8': + return k20_08ec.merge_k20_s2_warp8_ir + if verify_kernel == 'over64_k96_stage1': + return over64_k96.stage1_k96_over64_ir + if verify_kernel == 'over64_k96_merge': + return over64_k96.merge_k96_s8_chunkprefill_over64_ir + return previous_main.ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k48over32", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 48]], "cta_group": 1, "threads": 192}')) + +def _label_can_hit(inputs: dict[str, Any], target_labels: set[str]) -> bool: + label = inputs.get('label') + return label is None or str(label) in target_labels + +def _eligible_rag_split72(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, RAG_TARGET_SHAPE_SET) and rag_split72._eligible_rag_online_stream_split72(inputs) + +def _eligible_k20_de1a(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, K20_TARGET_SHAPE_SET) and k20_de1a._eligible_k20_large_lowfanout(inputs) + +def _eligible_k20_08ec(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, K20_TARGET_SHAPE_SET) and k20_08ec._eligible_k20_mergeown(inputs) + +def _eligible_over64_k96(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, K96_TARGET_SHAPE_SET) and over64_k96._eligible_over64_k96_build(inputs) + +def route_for_contract_inputs(inputs: dict[str, Any], *, k20_route: str='de1a', force_fallback: bool=False) -> str: + if force_fallback: + return ROUTE_PREVIOUS_MAIN + if _eligible_rag_split72(inputs): + return ROUTE_RAG_SPLIT72 + if k20_route == 'de1a': + if _eligible_k20_de1a(inputs): + return ROUTE_K20_DE1A + elif k20_route == '08ec': + if _eligible_k20_08ec(inputs): + return ROUTE_K20_08EC + else: + raise ValueError("k20_route must be 'de1a' or '08ec'") + if _eligible_over64_k96(inputs): + return ROUTE_OVER64_K96 + return ROUTE_PREVIOUS_MAIN + +def _launch_route(inputs: dict[str, Any], route: str) -> None: + if route == ROUTE_RAG_SPLIT72: + rag_split72._launch_rag_online_stream_split72(inputs) + return + if route == ROUTE_K20_DE1A: + k20_de1a._launch_k20_large_lowfanout(inputs) + return + if route == ROUTE_K20_08EC: + k20_08ec._launch_k20_mergeown(inputs) + return + if route == ROUTE_OVER64_K96: + over64_k96._launch_over64_k96_split_path(inputs) + return + previous_main.launch_from_contract_inputs(inputs) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + _launch_route(inputs, route_for_contract_inputs(inputs, force_fallback=force_fallback)) + +def launch_from_contract_inputs_08ec_compare(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + """Non-production A/B path for same-denominator 08ec K20 comparison.""" + _launch_route(inputs, route_for_contract_inputs(inputs, k20_route='08ec', force_fallback=force_fallback)) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def candidate_08ec_compare(inputs: dict[str, Any]): + launch_from_contract_inputs_08ec_compare(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return previous_main._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=DISPATCH_CORRECTNESS_SHAPES, benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint: run real contract correctness for selected shapes.""" + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=None, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + shapes = None if shape_labels is None else _select_contract_shapes(shape_labels) + return evaluate_contract(shapes=shapes, correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def _trace_inputs_from_shape(shape: dict[str, Any]) -> dict[str, Any]: + params = dict(shape['params']) + dtype = str(params.get('dtype', 'bfloat16')) + return {'label': shape['label'], 'B': int(params['B']), 'Q': int(params['Q']), 'M': int(params['M']), 'D': int(params['D']), 'K': int(params['K']), 'dtype': dtype, 'build': bool(params.get('build', False)), 'query': _TraceTensor(dtype), 'database': _TraceTensor(dtype)} + +def _route_trace_record(inputs: dict[str, Any], *, k20_route: str='de1a', force_fallback: bool=False) -> dict[str, Any]: + route = route_for_contract_inputs(inputs, k20_route=k20_route, force_fallback=force_fallback) + if force_fallback: + return {'shape_key': inputs.get('label'), 'selected_route': route, 'guard_condition': 'forced fallback to inherited v46 Weave dispatcher', 'route_kind': 'general', 'coverage': 'forced dispatcher fallback; split72, de1a/08ec, and K96 guards disabled', 'consumed_seed': None, 'fallback': ROUTE_PREVIOUS_MAIN} + if route == ROUTE_RAG_SPLIT72: + return {'shape_key': inputs.get('label'), 'selected_route': route, 'guard_condition': 'exact BF16 D128 non-build RAG online/stream K10 label', 'route_kind': 'specialized', 'coverage': 'exact split72 RAG online/stream K10 seed', 'consumed_seed': 'rag_online_stream_split72_4e09_v1', 'fallback': ROUTE_PREVIOUS_MAIN} + if route in {ROUTE_K20_DE1A, ROUTE_K20_08EC}: + seed = 'k20_large_lowfanout_de1a_v1' if route == ROUTE_K20_DE1A else 'k20_mergeown_08ec_v3' + return {'shape_key': inputs.get('label'), 'selected_route': route, 'guard_condition': 'exact BF16 D128 non-build K20 large/rectangular label', 'route_kind': 'specialized', 'coverage': ''.join(['exact ', format(seed, ''), ' K20 route']), 'consumed_seed': seed, 'fallback': ROUTE_PREVIOUS_MAIN} + if route == ROUTE_OVER64_K96: + return {'shape_key': inputs.get('label'), 'selected_route': route, 'guard_condition': 'exact BF16 B1 Q=M=2048 D128 build=true K=96', 'route_kind': 'specialized', 'coverage': 'exact over64 K96 Weave route; avoids inherited K<=32 fallback crash', 'consumed_seed': 'over64_k96_a989_v1', 'fallback': ROUTE_PREVIOUS_MAIN} + return {'shape_key': inputs.get('label'), 'selected_route': route, 'guard_condition': 'guard miss; delegate to inherited v46 Weave dispatcher', 'route_kind': 'general', 'coverage': 'inherited split72/de1a/3dc7 Weave dispatcher fallback', 'consumed_seed': None, 'fallback': ROUTE_PREVIOUS_MAIN} + +def route_trace_for_contract_shapes(shape_labels=None, *, k20_route: str='de1a', force_fallback: bool=False) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + return [_route_trace_record(_trace_inputs_from_shape(shape), k20_route=k20_route, force_fallback=force_fallback) for shape in selected] + +def _benchmark_payload(report: dict[str, Any], *, use_cupti: bool, shape_labels, k20_route: str, measured_function: str) -> dict[str, Any]: + rows = report.get('per_shape', {}) + timing_backends = sorted({row.get('timing_backend') for row in rows.values() if row.get('timing_backend') is not None}) + selected_rows = {label: rows.get(label, {}) for label in SELECTED_TARGET_SHAPES if label in rows} + route_modules = PRODUCTION_ROUTE_MODULES if k20_route == 'de1a' else COMPARISON_ROUTE_MODULES + return {'tflops': report['summary']['primary_mean'] or 0.0, 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'invalid_performance_reason': report['summary']['invalid_performance_reason'], 'measured_entrypoint': ''.join(['loom.examples.weave.knn_build_dispatch_split72_4e09_de1a_3dc7_v48:', format(measured_function, '')]), 'measured_shape_labels': 'all_canonical' if shape_labels is None else tuple(shape_labels), 'selected_route_labels': SELECTED_TARGET_SHAPES, 'selected_route_rows': selected_rows, 'rag_route_rows': {label: selected_rows.get(label, {}) for label in RAG_TARGET_SHAPES}, 'k20_route_rows': {label: selected_rows.get(label, {}) for label in K20_TARGET_SHAPES}, 'route_modules': route_modules, 'k20_route': k20_route, 'route_trace': route_trace_for_contract_shapes(shape_labels, k20_route=k20_route), 'route_trace_included': True, 'contract_summary': report['summary'], 'contract_performance': report['performance'], 'timing_backends': timing_backends, 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'report': report} + +def benchmark_knn_build_dispatch_split72_de1a_3dc7_v48(*, use_cupti: bool=False, shape_labels=None) -> dict[str, Any]: + """Main v3 contract benchmark hook with split72 RAG and de1a K20 routes.""" + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels) + return _benchmark_payload(report, use_cupti=use_cupti, shape_labels=shape_labels, k20_route='de1a', measured_function='benchmark_knn_build_dispatch_split72_de1a_3dc7_v48') + +def benchmark_knn_build_dispatch_split72_08ec_compare_3dc7_v48(*, use_cupti: bool=False, shape_labels=None) -> dict[str, Any]: + """Non-production full-denominator A/B hook for the 08ec K20 route.""" + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_08ec_compare) + return _benchmark_payload(report, use_cupti=use_cupti, shape_labels=shape_labels, k20_route='08ec', measured_function='benchmark_knn_build_dispatch_split72_08ec_compare_3dc7_v48') diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_v11_common_d_seed_portfolio_a4ec_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_v11_common_d_seed_portfolio_a4ec_v1.py new file mode 100644 index 00000000..5332dd61 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatch_v11_common_d_seed_portfolio_a4ec_v1.py @@ -0,0 +1,1680 @@ +"""v11 common-D synthesized seed portfolio dispatcher. + +Minimum target architecture: sm_100a for the consumed tcgen05/TMA seeds. The +fallback route remains the existing Weave-only common-D dispatcher and may run +generic coverage fallback code on sm_80, but this portfolio is intended for +Blackwell v11 common-D validation. + +This wrapper only adds guards around validated seed entrypoints. It does not +retune seed schedules. Production dispatch stays Weave-only. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +from pathlib import Path +from typing import Any, Callable +from . import knn_build_common_d256_q1024_56f3_v1 as d256_build_a4ec +from . import knn_build_common_d_56f3_build_d256_q1024_v1 as d256_build +from . import knn_build_common_d_eeff_search_d768_v1 as d768_search +from . import knn_build_common_d_5e7f_rag_highd_v1 as highd_rag +from . import knn_build_common_d_5e7f_rag_d64_d256_v1 as rag_d64d256 +from . import knn_build_common_d_5e7f_rag_d64_repair_v1 as rag_d64_repair +from . import knn_build_common_d_1438_rag_d64_m128_v1 as rag_d64_m128 +from . import knn_build_common_d_5e7f_search_d256_v1 as search_d256 +from . import knn_build_common_d768_build_eeff_m64split_v1 as d768_build_fast +from . import knn_build_common_d_56f3_build_highd_v1 as highd_build +from . import knn_build_build_lowfloor_2c1c_v3 as seed_k13 +from . import knn_build_d64_q4096_c271_twostage_v1 as d64_q4096_c271 +from . import knn_build_dispatch_common_d_v11_fallback_v1 as base_current +from . import knn_build_ragonline_mbucket_ea43_q1m524_n128_v1 as q1_m524_n128 +from . import knn_build_d128_rag_q128_k10_df0f_warpmerge_v1 as q128_k10_warpmerge +from . import knn_build_k48_k96_floor_repair_d03c_v2 as seed_k48 +from . import knn_build_large_square_k32_efe4_prodcache_v1 as large_square_k32_efe4 +from . import knn_build_non128_frontier_4be7_d768fused_v1 as d768_rag +from . import knn_build_rag_microbucket_k32_q32rowld2exact_f653_v1 as q32exact +from . import knn_build_v12_d256_k10_longm_e2df_v1 as d256_q4_e2df +from . import knn_build_v12_d256_k32_tail_59fe_v1 as d256_k32_59fe +from . import knn_build_v12_d256_q128_k10_longm_59fe_v1 as d256_q128_59fe +from . import knn_build_v12_d64_tail_017a_v1 as d64_tail_017a +from . import knn_build_v12_d128_q16_k48_dd2b_v1 as d128_k48_dd2b +from . import knn_build_v12_highd_rag_22e9_v1 as highd_rag_22e9 +from . import knn_build_v12_highd_search_be66_v1 as highd_search_be66 +from . import knn_build_rect_d128_k20_q1536_s12warp4_7768_v1 as rect_d128_k20_s12warp4 +MODULE = 'loom.examples.weave.knn_build_dispatch_v11_common_d_seed_portfolio_a4ec_v1' +CANDIDATE_A4EC_BASE = 'v11_common_d_seed_portfolio_a4ec_mixed_v1' +CANDIDATE_FA04_BASE = 'v11_common_d_seed_portfolio_fa04_cda9_6164_v1' +CANDIDATE_F328_BASE = 'v11_common_d_seed_portfolio_4cf7_highd_rag_v1' +CANDIDATE_MIXED = 'v11_common_d_seed_portfolio_8dbc_ba22_d256_v1' +CANDIDATE_D64_REPAIR = 'v11_common_d_seed_portfolio_0474_d64_rag_repair_v1' +CANDIDATE_D64_M128 = 'v11_common_d_seed_portfolio_1438_d64_rag_m128_backfill_v1' +CANDIDATE_D64_Q4096_C271 = 'v11_common_d_seed_portfolio_c271_d64_q4096_v1' +CANDIDATE_C271_7DC5_K13_K48 = 'v11_common_d_seed_portfolio_c271_7dc5_k13_k48_v1' +CANDIDATE_FLOOR_SEEDS_Q128_5698 = 'v11_common_d_seed_portfolio_k13_q128dualm_q16_v1' +CANDIDATE_FLOOR_SEEDS_Q128_681B = 'v11_common_d_seed_portfolio_k13_q128s72r2_q16_v1' +CANDIDATE_FLOOR_SEEDS_Q128_MIXED = 'v11_common_d_seed_portfolio_k13_q128mixed_q16_v1' +CANDIDATE_PRE_Q128_K10_WARPMERGE = 'v11_common_d_seed_portfolio_c271_7dc5_k48_q128mixed_q16_v1' +CANDIDATE_2498_BASELINE = CANDIDATE_PRE_Q128_K10_WARPMERGE +CANDIDATE_Q128_K10_WARPMERGE = 'v11_common_d_seed_portfolio_c271_7dc5_k48_q128mixed_q16_q128k10wm_v1' +CANDIDATE_PRE_Q1_M524_N128 = 'v11_common_d_seed_portfolio_c271_7dc5_k48_q128mixed_q16_efe4_34da_v1' +CANDIDATE_PRE_Q32_EXACT = 'v11_common_d_seed_portfolio_c271_7dc5_k48_q128mixed_q16_efe4_34da_q1m524n128_v1' +CANDIDATE_PRE_Q32TAIL = 'v11_common_d_seed_portfolio_c271_7dc5_k48_q128mixed_q16_efe4_34da_q1m524n128_q32exact_v1' +CANDIDATE_PRE_EXPANDED_K32_Q48 = 'v11_common_d_seed_portfolio_c271_7dc5_k48_q128mixed_q16_efe4_34da_q1m524n128_q32exact_q32tail_v1' +CANDIDATE_PRE_LOWK_C3D2 = 'v11_common_d_seed_portfolio_c271_7dc5_k48_q128mixed_q16_efe4_34da_q1m524n128_q32exact_q32tail_q31_q33_q40_q48_v1' +CANDIDATE_D665_LOWK_BASELINE = 'v11_common_d_seed_portfolio_c271_7dc5_k48_q128mixed_q16_efe4_34da_q1m524n128_q32exact_q32tail_q31_q33_q40_q48_lowk_c3d2_v1' +CANDIDATE_PRE_D64_TAIL_017A = 'v11_common_d_seed_portfolio_c271_7dc5_k48_q128mixed_q16_efe4_34da_q1m524n128_q32exact_q32tail_q31_q33_q40_q48_lowk_c3d2_k31_eaf7_v1' +CANDIDATE_PRE_D256_Q4_E2DF = 'v11_common_d_seed_portfolio_c271_7dc5_k48_q128mixed_q16_efe4_34da_q1m524n128_q32exact_q32tail_q31_q33_q40_q48_lowk_c3d2_k31_eaf7_017a_d64tail_v1' +CANDIDATE_SELECTED_SYNTHESIS = 'v11_common_d_seed_portfolio_c271_7dc5_k48_q128mixed_q16_efe4_34da_q1m524n128_q32exact_q32tail_q31_q33_q40_q48_lowk_c3d2_k31_eaf7_017a_d64tail_e2df_d256q4_v1' +CANDIDATE_D256_Q128_59FE = 'v11_common_d_seed_portfolio_c271_7dc5_k48_q128mixed_q16_efe4_34da_q1m524n128_q32exact_q32tail_q31_q33_q40_q48_lowk_c3d2_k31_eaf7_017a_d64tail_e2df_d256q4_59fe_d256q128_v1' +CANDIDATE_D256_K32_59FE = 'v11_common_d_seed_portfolio_c271_7dc5_k48_q128mixed_q16_efe4_34da_q1m524n128_q32exact_q32tail_q31_q33_q40_q48_lowk_c3d2_k31_eaf7_017a_d64tail_e2df_d256q4_59fe_d256q128_9203_d256k32_v1' +CANDIDATE_HIGHD_RAG_22E9 = 'v11_common_d_seed_portfolio_c271_7dc5_k48_q128mixed_q16_efe4_34da_q1m524n128_q32exact_q32tail_q31_q33_q40_q48_lowk_c3d2_k31_eaf7_017a_d64tail_e2df_d256q4_59fe_d256q128_9203_d256k32_7902_highd_rag_v1' +CANDIDATE_HIGHD_SEARCH_BE66 = 'v11_common_d_seed_portfolio_c271_7dc5_k48_q128mixed_q16_efe4_34da_q1m524n128_q32exact_q32tail_q31_q33_q40_q48_lowk_c3d2_k31_eaf7_017a_d64tail_e2df_d256q4_59fe_d256q128_9203_d256k32_7902_highd_rag_ad73_highd_search_v1' +CANDIDATE_D128_K48_DD2B = 'v11_common_d_seed_portfolio_c271_7dc5_k48_q128mixed_q16_efe4_34da_q1m524n128_q32exact_q32tail_q31_q33_q40_q48_lowk_c3d2_k31_eaf7_017a_d64tail_e2df_d256q4_59fe_d256q128_9203_d256k32_7902_highd_rag_ad73_highd_search_dd2b_d128q16k48_v1' +CANDIDATE_RECT_D128_K20_S12WARP4 = 'v11_common_d_seed_portfolio_c271_7dc5_k48_q128mixed_q16_efe4_34da_q1m524n128_q32exact_q32tail_q31_q33_q40_q48_lowk_c3d2_k31_eaf7_017a_d64tail_e2df_d256q4_59fe_d256q128_9203_d256k32_7902_highd_rag_ad73_highd_search_dd2b_d128q16k48_7768_rectd128k20q1536_s12warp4_v1' +CANDIDATE_Q128_K10_ROWLD_1BED = 'v11_common_d_seed_portfolio_c271_7dc5_k48_q128mixed_q16_efe4_34da_q1m524n128_q32exact_q32tail_q31_q33_q40_q48_lowk_c3d2_k31_eaf7_017a_d64tail_e2df_d256q4_59fe_d256q128_9203_d256k32_7902_highd_rag_ad73_highd_search_dd2b_d128q16k48_7768_rectd128k20q1536_s12warp4_1bed_q128k10_rowld_v1' +CANDIDATE_DEFAULT = CANDIDATE_Q128_K10_ROWLD_1BED +CANDIDATE_F1D9_BUILD = 'v11_common_d_seed_portfolio_a4ec_f1d9_build_v1' +CANDIDATE_BASE = 'base_common_d_v11_fallback' +SPEEDUP_FLOOR = 1.2 +FLOOR_SEED_PORTFOLIOS = (CANDIDATE_FLOOR_SEEDS_Q128_5698, CANDIDATE_FLOOR_SEEDS_Q128_681B, CANDIDATE_FLOOR_SEEDS_Q128_MIXED, CANDIDATE_2498_BASELINE, CANDIDATE_Q128_K10_WARPMERGE, CANDIDATE_PRE_Q1_M524_N128, CANDIDATE_PRE_Q32_EXACT, CANDIDATE_PRE_Q32TAIL, CANDIDATE_PRE_EXPANDED_K32_Q48, CANDIDATE_PRE_LOWK_C3D2, CANDIDATE_D665_LOWK_BASELINE, CANDIDATE_PRE_D64_TAIL_017A, CANDIDATE_PRE_D256_Q4_E2DF, CANDIDATE_SELECTED_SYNTHESIS) +PORTFOLIOS_WITH_K13_K48 = (CANDIDATE_C271_7DC5_K13_K48, *FLOOR_SEED_PORTFOLIOS) +PORTFOLIOS_WITH_D768_FAST = (CANDIDATE_F328_BASE, CANDIDATE_MIXED, CANDIDATE_D64_REPAIR, CANDIDATE_D64_M128, CANDIDATE_D64_Q4096_C271, *PORTFOLIOS_WITH_K13_K48) +PORTFOLIOS_WITH_HIGHD_RAG = PORTFOLIOS_WITH_D768_FAST +PORTFOLIOS_WITH_SEARCH_D256 = (CANDIDATE_MIXED, CANDIDATE_D64_REPAIR, CANDIDATE_D64_M128, CANDIDATE_D64_Q4096_C271, *PORTFOLIOS_WITH_K13_K48) +PORTFOLIOS_WITH_RAG_D64_M128 = (CANDIDATE_D64_M128, CANDIDATE_D64_Q4096_C271, *PORTFOLIOS_WITH_K13_K48) +PORTFOLIOS_WITH_D64_Q4096_C271 = (CANDIDATE_D64_Q4096_C271, *PORTFOLIOS_WITH_K13_K48) +PORTFOLIOS_WITH_LARGE_SQUARE_K32_EFE4 = (CANDIDATE_2498_BASELINE, CANDIDATE_Q128_K10_WARPMERGE, CANDIDATE_PRE_Q1_M524_N128, CANDIDATE_PRE_Q32_EXACT, CANDIDATE_PRE_Q32TAIL, CANDIDATE_PRE_EXPANDED_K32_Q48, CANDIDATE_PRE_LOWK_C3D2, CANDIDATE_D665_LOWK_BASELINE, CANDIDATE_PRE_D64_TAIL_017A, CANDIDATE_PRE_D256_Q4_E2DF, CANDIDATE_SELECTED_SYNTHESIS) +PORTFOLIOS_WITH_Q128_K10_WARPMERGE = (CANDIDATE_Q128_K10_WARPMERGE,) +PORTFOLIOS_WITH_RAG_STREAM_K10_34DA = (CANDIDATE_PRE_Q1_M524_N128, CANDIDATE_PRE_Q32_EXACT, CANDIDATE_PRE_Q32TAIL, CANDIDATE_PRE_EXPANDED_K32_Q48, CANDIDATE_PRE_LOWK_C3D2, CANDIDATE_D665_LOWK_BASELINE, CANDIDATE_PRE_D64_TAIL_017A, CANDIDATE_PRE_D256_Q4_E2DF, CANDIDATE_SELECTED_SYNTHESIS) +PORTFOLIOS_WITH_Q1_M524_N128 = (CANDIDATE_PRE_Q32_EXACT, CANDIDATE_PRE_Q32TAIL, CANDIDATE_PRE_EXPANDED_K32_Q48, CANDIDATE_PRE_LOWK_C3D2, CANDIDATE_D665_LOWK_BASELINE, CANDIDATE_PRE_D64_TAIL_017A, CANDIDATE_PRE_D256_Q4_E2DF, CANDIDATE_SELECTED_SYNTHESIS) +PORTFOLIOS_WITH_Q32_EXACT = (CANDIDATE_PRE_Q32TAIL, CANDIDATE_PRE_EXPANDED_K32_Q48, CANDIDATE_PRE_D64_TAIL_017A, CANDIDATE_PRE_D256_Q4_E2DF, CANDIDATE_SELECTED_SYNTHESIS) +PORTFOLIOS_WITH_Q32_TAIL = (CANDIDATE_PRE_EXPANDED_K32_Q48, CANDIDATE_PRE_LOWK_C3D2, CANDIDATE_D665_LOWK_BASELINE, CANDIDATE_PRE_D64_TAIL_017A, CANDIDATE_PRE_D256_Q4_E2DF, CANDIDATE_SELECTED_SYNTHESIS) +PORTFOLIOS_WITH_EXPANDED_K32_Q31_Q33_Q40 = (CANDIDATE_PRE_LOWK_C3D2, CANDIDATE_D665_LOWK_BASELINE, CANDIDATE_PRE_D64_TAIL_017A, CANDIDATE_PRE_D256_Q4_E2DF, CANDIDATE_SELECTED_SYNTHESIS) +PORTFOLIOS_WITH_Q48_K12 = (CANDIDATE_PRE_LOWK_C3D2, CANDIDATE_D665_LOWK_BASELINE, CANDIDATE_PRE_D64_TAIL_017A, CANDIDATE_PRE_D256_Q4_E2DF, CANDIDATE_SELECTED_SYNTHESIS) +PORTFOLIOS_WITH_Q32_LOWK_C3D2 = (CANDIDATE_D665_LOWK_BASELINE, CANDIDATE_PRE_D64_TAIL_017A, CANDIDATE_PRE_D256_Q4_E2DF, CANDIDATE_SELECTED_SYNTHESIS) +PORTFOLIOS_WITH_Q32_K31_C3D2 = (CANDIDATE_PRE_D64_TAIL_017A, CANDIDATE_PRE_D256_Q4_E2DF, CANDIDATE_SELECTED_SYNTHESIS) +PORTFOLIOS_WITH_D64_TAIL_017A = (CANDIDATE_PRE_D256_Q4_E2DF, CANDIDATE_SELECTED_SYNTHESIS) +PORTFOLIOS_WITH_D256_Q4_E2DF = (CANDIDATE_SELECTED_SYNTHESIS, CANDIDATE_D256_Q128_59FE, CANDIDATE_D256_K32_59FE) +PORTFOLIOS_WITH_D256_Q128_59FE = (CANDIDATE_D256_Q128_59FE, CANDIDATE_D256_K32_59FE) +PORTFOLIOS_WITH_D256_K32_59FE = (CANDIDATE_D256_K32_59FE, CANDIDATE_HIGHD_RAG_22E9, CANDIDATE_HIGHD_SEARCH_BE66) +PORTFOLIOS_WITH_HIGHD_RAG_22E9 = (CANDIDATE_HIGHD_RAG_22E9, CANDIDATE_HIGHD_SEARCH_BE66) +PORTFOLIOS_WITH_HIGHD_SEARCH_BE66 = (CANDIDATE_HIGHD_SEARCH_BE66, CANDIDATE_D128_K48_DD2B) +PORTFOLIOS_WITH_D128_K48_DD2B = (CANDIDATE_D128_K48_DD2B,) +PORTFOLIOS_WITH_RECT_D128_K20_S12WARP4 = (CANDIDATE_RECT_D128_K20_S12WARP4, CANDIDATE_Q128_K10_ROWLD_1BED) +PORTFOLIOS_WITH_Q128_K10_ROWLD_1BED = (CANDIDATE_Q128_K10_ROWLD_1BED,) +PORTFOLIOS_WITH_Q32_TAIL143 = (CANDIDATE_D128_K48_DD2B, CANDIDATE_RECT_D128_K20_S12WARP4, CANDIDATE_Q128_K10_ROWLD_1BED) +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +BENCHMARK_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_knn_build_dispatch_v11_common_d_seed_portfolio_a4ec_v1']) +BASE_ENTRYPOINT = base_current.ROUTE_ENTRYPOINT +A4EC_BASE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs_a4ec_baseline']) +FA04_BASE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs_fa04_baseline']) +F328_BASE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs_f328_baseline']) +MIXED_BASE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs_mixed_baseline']) +D64_REPAIR_BASE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs_d64_repair_baseline']) +D64_M128_BASE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs_d64_m128_baseline']) +D64_Q4096_C271_BASE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs_c271_baseline']) +C271_7DC5_BASE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs_c271_7dc5_baseline']) +PRE_Q128_K10_WARPMERGE_BASE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs_pre_q128_k10_warpmerge_baseline']) +BASE_2498_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs_2498_baseline']) +PRE_Q1_M524_N128_BASE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs_pre_q1_m524_n128_baseline']) +PRE_Q32_EXACT_BASE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs_pre_q32exact_baseline']) +PRE_Q32TAIL_BASE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs_pre_q32tail_baseline']) +PRE_EXPANDED_K32_Q48_BASE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs_pre_expanded_k32_q48_baseline']) +PRE_LOWK_C3D2_BASE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs_pre_lowk_c3d2_baseline']) +D665_LOWK_BASE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs_d665_lowk_baseline']) +PRE_D64_TAIL_017A_BASE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs_pre_d64_tail_017a_baseline']) +PRE_D256_Q4_E2DF_BASE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs_pre_d256_q4_e2df_baseline']) +PRE_D256_Q128_59FE_BASE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs_pre_d256_q128_59fe_baseline']) +PRE_D256_K32_59FE_BASE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs_pre_d256_k32_59fe_baseline']) +PRE_HIGHD_RAG_22E9_BASE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs_pre_highd_rag_22e9_baseline']) +PRE_HIGHD_SEARCH_BE66_BASE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs_pre_highd_search_be66_baseline']) +PRE_D128_K48_DD2B_BASE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs_pre_d128_k48_dd2b_baseline']) +PRE_RECT_D128_K20_S12WARP4_BASE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs_pre_rect_d128_k20_s12warp4_baseline']) +PRE_Q128_K10_ROWLD_1BED_BASE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs_pre_q128_k10_rowld_1bed_baseline']) +D256_A4EC_ENTRYPOINT = 'loom.examples.weave.knn_build_common_d256_q1024_56f3_v1:launch_from_contract_inputs' +D256_ENTRYPOINT = 'loom.examples.weave.knn_build_common_d_56f3_build_d256_q1024_v1:launch_from_contract_inputs' +D768_SEARCH_ENTRYPOINT = 'loom.examples.weave.knn_build_common_d_eeff_search_d768_v1:launch_from_contract_inputs' +SEARCH_D256_ENTRYPOINT = 'loom.examples.weave.knn_build_common_d_5e7f_search_d256_v1:launch_from_contract_inputs' +D768_FAST_ENTRYPOINT = 'loom.examples.weave.knn_build_common_d768_build_eeff_m64split_v1:launch_from_contract_inputs' +HIGHD_ENTRYPOINT = 'loom.examples.weave.knn_build_common_d_56f3_build_highd_v1:launch_from_contract_inputs' +D768_RAG_ENTRYPOINT = 'loom.examples.weave.knn_build_non128_frontier_4be7_d768fused_v1:launch_from_contract_inputs' +HIGHD_RAG_ENTRYPOINT = 'loom.examples.weave.knn_build_common_d_5e7f_rag_highd_v1:launch_from_contract_inputs' +RAG_D64D256_ENTRYPOINT = 'loom.examples.weave.knn_build_common_d_5e7f_rag_d64_d256_v1:launch_from_contract_inputs' +RAG_D64_REPAIR_ENTRYPOINT = 'loom.examples.weave.knn_build_common_d_5e7f_rag_d64_repair_v1:launch_from_contract_inputs' +RAG_D64_M128_ENTRYPOINT = 'loom.examples.weave.knn_build_common_d_1438_rag_d64_m128_v1:launch_from_contract_inputs' +D64_Q4096_C271_ENTRYPOINT = 'loom.examples.weave.knn_build_d64_q4096_c271_twostage_v1:launch_from_contract_inputs' +LARGE_SQUARE_K32_EFE4_ENTRYPOINT = 'loom.examples.weave.knn_build_large_square_k32_efe4_prodcache_v1:launch_from_contract_inputs' +K13_K48_WRAPPER_MODULE = 'loom.examples.weave.knn_build_k13_k48_floor_repair_7dc5_v1' +K13_K48_WRAPPER_ENTRYPOINT = ''.join([format(K13_K48_WRAPPER_MODULE, ''), ':launch_from_contract_inputs']) +K13_ENTRYPOINT = seed_k13.ROUTE_Q4096_K13_UNORDERED +K48_ENTRYPOINT = seed_k48.ROUTE_K48_WARPSELECT +Q128_DUALM_ENTRYPOINT = 'loom.examples.weave.knn_build_rag_stream_k32_q128_dualm_a162_v1:launch_from_contract_inputs' +Q128_S72R2_ENTRYPOINT = 'loom.examples.weave.knn_build_rag_stream_k32_q128_s72r2_a162_v1:launch_from_contract_inputs' +Q128_K10_WARPMERGE_ENTRYPOINT = q128_k10_warpmerge.ROUTE_ENTRYPOINT +Q128_K10_ROWLD_1BED_MODULE = 'loom.examples.weave.knn_build_rag_stream_k10_q128_1bed_rowld_v1' +Q128_K10_ROWLD_1BED_ENTRYPOINT = ''.join([format(Q128_K10_ROWLD_1BED_MODULE, ''), ':launch_from_contract_inputs']) +Q16_M250_ENTRYPOINT = 'loom.examples.weave.knn_build_d128_rag_q16m250_df0f_v1:launch_from_contract_inputs' +RAG_STREAM_K10_34DA_ENTRYPOINT = 'loom.examples.weave.knn_build_rag_stream_k10_warpmerge_34da_v1:launch_from_contract_inputs' +Q1_M524_N128_ENTRYPOINT = 'loom.examples.weave.knn_build_ragonline_mbucket_ea43_q1m524_n128_v1:launch_from_contract_inputs' +Q32_EXACT_ENTRYPOINT = q32exact.ROUTE_Q32_ROWLD2EXACT_ENTRYPOINT +Q32TAIL_MODULE = 'loom.examples.weave.knn_build_rag_microbucket_k32_0cb5_q31tail_v1' +Q32TAIL_ENTRYPOINT = ''.join([format(Q32TAIL_MODULE, ''), ':launch_from_contract_inputs']) +Q32TAIL143_LOW_MODULE = 'loom.examples.weave.knn_build_rag_microbucket_k32_5317_q32tail143low_v1' +Q32TAIL143_LOW_ENTRYPOINT = ''.join([format(Q32TAIL143_LOW_MODULE, ''), ':launch_from_contract_inputs']) +Q32TAIL143_HIGH_MODULE = 'loom.examples.weave.knn_build_rag_microbucket_k32_314c_q32tail143_v1' +Q32TAIL143_HIGH_ENTRYPOINT = ''.join([format(Q32TAIL143_HIGH_MODULE, ''), ':launch_from_contract_inputs']) +Q31TAIL_V2_MODULE = 'loom.examples.weave.knn_build_rag_microbucket_k32_0cb5_q31tail_v2' +Q31TAIL_V2_ENTRYPOINT = ''.join([format(Q31TAIL_V2_MODULE, ''), ':launch_from_contract_inputs']) +Q33TILE_MODULE = 'loom.examples.weave.knn_build_rag_microbucket_k32_c489_q33tile_v1' +Q33TILE_ENTRYPOINT = ''.join([format(Q33TILE_MODULE, ''), ':launch_from_contract_inputs']) +Q48_K12_MODULE = 'loom.examples.weave.knn_build_rag_microbucket_k12_2f22_q48exact_v1' +Q48_K12_ENTRYPOINT = ''.join([format(Q48_K12_MODULE, ''), ':launch_from_contract_inputs']) +Q32_LOWK_C3D2_MODULE = 'loom.examples.weave.knn_build_rag_microbucket_q32_lowk_c3d2_v1' +Q32_LOWK_C3D2_ENTRYPOINT = ''.join([format(Q32_LOWK_C3D2_MODULE, ''), ':launch_from_contract_inputs']) +Q32_K31_C3D2_MODULE = 'loom.examples.weave.knn_build_rag_microbucket_q32_k31_c3d2_v1' +Q32_K31_C3D2_ENTRYPOINT = ''.join([format(Q32_K31_C3D2_MODULE, ''), ':launch_from_contract_inputs']) +D64_TAIL_017A_ENTRYPOINT = d64_tail_017a.ROUTE_ENTRYPOINT +D256_Q4_E2DF_ENTRYPOINT = d256_q4_e2df.ROUTE_ENTRYPOINT +D256_Q128_59FE_ENTRYPOINT = d256_q128_59fe.ROUTE_ENTRYPOINT +D256_K32_59FE_ENTRYPOINT = d256_k32_59fe.ROUTE_ENTRYPOINT +HIGHD_RAG_22E9_ENTRYPOINT = highd_rag_22e9.ROUTE_ENTRYPOINT +HIGHD_SEARCH_BE66_ENTRYPOINT = highd_search_be66.ROUTE_ENTRYPOINT +D128_K48_DD2B_ENTRYPOINT = d128_k48_dd2b.ROUTE_ENTRYPOINT +RECT_D128_K20_S12WARP4_ENTRYPOINT = rect_d128_k20_s12warp4.ROUTE_ENTRYPOINT +SEED_K13_ID = '7dc5_2c1c_q4096_k13_unordered_s4' +SEED_K48_ID = '7dc5_d03c_k48_s4_warpselect' +SEED_K13_A162_ID = 'a162_2c1c_q4096_k13_unordered_s4' +SEED_Q128_DUALM_ID = 'rag_stream_k32_q128_dualm_a162_v1_rowld_s72_warp1' +SEED_Q128_S72R2_ID = 'a162_rag_stream_q128_k32_s72r2_v1' +SEED_Q128_K10_WARPMERGE_ID = q128_k10_warpmerge.SEED_ID +SEED_Q128_K10_ROWLD_1BED_ID = 'rag_stream_k10_q128_rowld_1bed_v1_s74' +SEED_Q16_M250_ID = 'df0f_bdd2_q16_m250_k32_s288' +SEED_LARGE_SQUARE_K32_EFE4_ID = 'large_square_k32_efe4_prodcache_v1' +SEED_RAG_STREAM_K10_34DA_ID = 'rag_stream_k10_warpmerge_34da_v1_s72_r4' +SEED_Q1_M524_N128_ID = 'ea43_q1m524_n128_s148_g4_m64n128' +SEED_Q32_EXACT_ID = q32exact.SEED_K32_Q32_ROWLD2EXACT_F653_V1_ID +SEED_Q32TAIL_ID = 'rag_microbucket_k32_0cb5_q31tail_v1' +SEED_Q32TAIL143_LOW_ID = 'rag_microbucket_k32_5317_q32tail143low_v1' +SEED_Q32TAIL143_HIGH_ID = 'rag_microbucket_k32_314c_q32tail143_v1' +SEED_Q31TAIL_V2_ID = 'rag_microbucket_k32_0cb5_q31tail_v2' +SEED_Q33TILE_ID = 'rag_microbucket_k32_c489_q33tile_v1' +SEED_Q48_K12_ID = 'rag_microbucket_k12_2f22_q48exact_v1' +SEED_Q32_LOWK_C3D2_ID = 'rag_microbucket_q32_lowk_c3d2_v1' +SEED_Q32_K31_C3D2_ID = 'rag_microbucket_q32_k31_c3d2_v1' +SEED_D64_TAIL_017A_ID = d64_tail_017a.CANDIDATE_ID +SEED_D256_Q4_E2DF_ID = d256_q4_e2df.CANDIDATE_ID +SEED_D256_Q128_59FE_ID = d256_q128_59fe.CANDIDATE_ID +SEED_D256_K32_59FE_ID = d256_k32_59fe.CANDIDATE_ID +SEED_HIGHD_RAG_22E9_ID = highd_rag_22e9.CANDIDATE_ID +SEED_HIGHD_SEARCH_BE66_ID = highd_search_be66.CANDIDATE_ID +SEED_D128_K48_DD2B_ID = d128_k48_dd2b.CANDIDATE_ID +SEED_RECT_D128_K20_S12WARP4_ID = rect_d128_k20_s12warp4.SEED_ID +Q32_EXACT_GUARD_ID = '12ac_f653_q32rowld2exact_stage1_exact_guard' +Q32_EXACT_GUARD_CONDITION = 'exact BF16 non-build B=1 Q=32 M=100000 D=128 K=32' +Q32_EXACT_PRODUCER_TOPOLOGY = 'f653_rowld2_ROW_16x256B_two_compute_warp_exact_stage1_s141' +Q32TAIL143_LOW_GUARD_ID = 'd6b5_q32_m99999_split143_exact_guard' +Q32TAIL143_HIGH_GUARD_ID = 'd6b5_q32_m100001_split143_exact_guard' +Q31TAIL_V2_GUARD_ID = 'c3d2_0cb5_q31tail_v2_exact_guard' +Q33TILE_GUARD_ID = 'c3d2_c489_q33_q40_exact_guard' +Q48_K12_GUARD_ID = '2f22_q48_m75000_k12_exact_guard' +Q32_LOWK_C3D2_GUARD_ID = 'c3d2_q32_m100000_lowk_k20_exact_guard' +Q32_K31_C3D2_GUARD_ID = 'eaf7_q32_m100000_k31_exact_guard' +D64_TAIL_017A_GUARD_ID = '017a_v12_d64_long_m_tail_exact_guard' +D256_Q4_E2DF_GUARD_ID = 'e2df_v12_d256_q4_k10_long_m_exact_guard' +D256_Q128_59FE_GUARD_ID = '59fe_v12_d256_q128_k10_exact_guard' +D256_K32_59FE_GUARD_ID = '59fe_v12_d256_k32_tail_exact_guard' +HIGHD_RAG_22E9_GUARD_ID = '22e9_v12_highd_rag_exact_guard' +HIGHD_SEARCH_BE66_GUARD_ID = 'be66_v12_highd_search_exact_guard' +D128_K48_DD2B_GUARD_ID = 'dd2b_v12_d128_q16_m100000_k48_exact_guard' +RECT_D128_K20_S12WARP4_GUARD_ID = '7768_rect_d128_k20_q1536_s12warp4_exact_guard' +Q128_K10_ROWLD_1BED_GUARD_ID = '1bed_rowld_rag_stream_k10_q128_s74_exact_guard' +BUILD_D256 = d256_build.BUILD_D256_Q1024 +BUILD_D768 = highd_build.BUILD_D768 +BUILD_D1024 = highd_build.BUILD_D1024 +BUILD_D4096 = highd_build.BUILD_D4096 +D64_Q4096 = d64_q4096_c271.TARGET_SHAPE +D768_SEARCH = d768_search.SEARCH_D768 +SEARCH_D256 = search_d256.SEARCH_D256 +D768_RAG = d768_rag.D768_SHAPE +RAG_D1024 = highd_rag.RAG_D1024 +RAG_D4096 = highd_rag.RAG_D4096 +RAG_D64 = rag_d64d256.RAG_D64 +RAG_D256 = rag_d64d256.RAG_D256 +BUILD_K13 = 'build_k_sweep_qm4096_k13' +BUILD_K48_Q2048 = 'build_over32_stress_qm2048_k48' +BUILD_K48_Q4096 = 'build_over32_stress_qm4096_k48' +RAG_Q128_M100000_K32 = 'rag_stream_largek_b1_q128_m100000_d128_k32' +RAG_Q128_M131071_K32 = 'rag_stream_largek_b1_q128_m131071_d128_k32' +RAG_Q128_M100000_K10 = q128_k10_warpmerge.TARGET_SHAPE +RAG_Q16_M250000_K32 = 'rag_microbatch_largek_b1_q16_m250000_d128_k32' +BUILD_LARGE_SQUARE_K32 = large_square_k32_efe4.TARGET_SHAPES[0] +RAG_STREAM_K10 = RAG_Q128_M100000_K10 +RAG_Q1_M524287_K10 = q1_m524_n128.ONLINE_M524K_SHAPE +RAG_Q32_M100000_K32 = q32exact.Q32_K32_SHAPE +EXPANDED_Q31_M100000_K32 = 'expanded_guard_boundary_q31_m100000_d128_k32' +EXPANDED_Q33_M100000_K32 = 'expanded_guard_boundary_q33_m100000_d128_k32' +EXPANDED_Q32_M99999_K32 = 'expanded_tail_q32_m99999_d128_k32' +EXPANDED_Q32_M100001_K32 = 'expanded_tail_q32_m100001_d128_k32' +EXPANDED_Q40_M100000_K32 = 'expanded_heldout_q40_m100000_d128_k32' +EXPANDED_Q32_M100000_K20 = 'expanded_guard_overlap_q32_m100000_d128_k20' +EXPANDED_Q32_M100000_K31 = 'expanded_guard_miss_q32_m100000_d128_k31' +EXPANDED_Q48_M75000_K12 = 'expanded_random_q48_m75000_d128_k12' +EXPANDED_Q32_GUARD_BOUNDARY_8_SHAPES = (EXPANDED_Q31_M100000_K32, EXPANDED_Q33_M100000_K32, EXPANDED_Q32_M99999_K32, EXPANDED_Q32_M100001_K32, EXPANDED_Q40_M100000_K32, EXPANDED_Q32_M100000_K20, EXPANDED_Q32_M100000_K31, EXPANDED_Q48_M75000_K12) +EXPANDED_Q32TAIL_CONSUMED_SHAPES = (EXPANDED_Q32_M99999_K32, EXPANDED_Q32_M100001_K32) +EXPANDED_Q32_GUARD_BOUNDARY_8_BY_LABEL = {EXPANDED_Q31_M100000_K32: {'label': EXPANDED_Q31_M100000_K32, 'params': {'B': 1, 'Q': 31, 'M': 100000, 'D': 128, 'K': 32, 'dtype': 'bfloat16', 'seed': 626331, 'build': False, 'check_correctness': True, 'correctness_query_sample': 31, 'recall_min': 0.999, 'benchmark': True, 'time_flashlib': True}}, EXPANDED_Q33_M100000_K32: {'label': EXPANDED_Q33_M100000_K32, 'params': {'B': 1, 'Q': 33, 'M': 100000, 'D': 128, 'K': 32, 'dtype': 'bfloat16', 'seed': 626333, 'build': False, 'check_correctness': True, 'correctness_query_sample': 33, 'recall_min': 0.999, 'benchmark': True, 'time_flashlib': True}}, EXPANDED_Q32_M99999_K32: {'label': EXPANDED_Q32_M99999_K32, 'params': {'B': 1, 'Q': 32, 'M': 99999, 'D': 128, 'K': 32, 'dtype': 'bfloat16', 'seed': 626999, 'build': False, 'check_correctness': True, 'correctness_query_sample': 32, 'recall_min': 0.999, 'benchmark': True, 'time_flashlib': True}}, EXPANDED_Q32_M100001_K32: {'label': EXPANDED_Q32_M100001_K32, 'params': {'B': 1, 'Q': 32, 'M': 100001, 'D': 128, 'K': 32, 'dtype': 'bfloat16', 'seed': 627001, 'build': False, 'check_correctness': True, 'correctness_query_sample': 32, 'recall_min': 0.999, 'benchmark': True, 'time_flashlib': True}}, EXPANDED_Q40_M100000_K32: {'label': EXPANDED_Q40_M100000_K32, 'params': {'B': 1, 'Q': 40, 'M': 100000, 'D': 128, 'K': 32, 'dtype': 'bfloat16', 'seed': 626440, 'build': False, 'check_correctness': True, 'correctness_query_sample': 40, 'recall_min': 0.999, 'benchmark': True, 'time_flashlib': True}}, EXPANDED_Q32_M100000_K20: {'label': EXPANDED_Q32_M100000_K20, 'params': {'B': 1, 'Q': 32, 'M': 100000, 'D': 128, 'K': 20, 'dtype': 'bfloat16', 'seed': 626320, 'build': False, 'check_correctness': True, 'correctness_query_sample': 32, 'recall_min': 0.999, 'benchmark': True, 'time_flashlib': True}}, EXPANDED_Q32_M100000_K31: {'label': EXPANDED_Q32_M100000_K31, 'params': {'B': 1, 'Q': 32, 'M': 100000, 'D': 128, 'K': 31, 'dtype': 'bfloat16', 'seed': 6263310, 'build': False, 'check_correctness': True, 'correctness_query_sample': 32, 'recall_min': 0.999, 'benchmark': True, 'time_flashlib': True}}, EXPANDED_Q48_M75000_K12: {'label': EXPANDED_Q48_M75000_K12, 'params': {'B': 1, 'Q': 48, 'M': 75000, 'D': 128, 'K': 12, 'dtype': 'bfloat16', 'seed': 626812, 'build': False, 'check_correctness': True, 'correctness_query_sample': 48, 'recall_min': 0.999, 'benchmark': True, 'time_flashlib': True}}} +FLOOR_REPAIR_SEED_SHAPES = (BUILD_K13, BUILD_K48_Q2048, BUILD_K48_Q4096, RAG_Q128_M100000_K32, RAG_Q128_M131071_K32, RAG_Q16_M250000_K32) +MIXED_CONSUMED_SEED_SHAPES = (BUILD_D256, BUILD_D768, BUILD_D1024, BUILD_D4096, D768_SEARCH, D768_RAG, RAG_D1024, RAG_D4096, SEARCH_D256, RAG_D256) +D64_M128_CONSUMED_SEED_SHAPES = (BUILD_D256, BUILD_D768, BUILD_D1024, BUILD_D4096, D768_SEARCH, D768_RAG, RAG_D1024, RAG_D4096, RAG_D64, SEARCH_D256, RAG_D256) +C271_CONSUMED_SEED_SHAPES = (*D64_M128_CONSUMED_SEED_SHAPES, D64_Q4096) +C271_7DC5_CONSUMED_SEED_SHAPES = (*C271_CONSUMED_SEED_SHAPES, BUILD_K13, BUILD_K48_Q2048, BUILD_K48_Q4096) +PRE_34DA_CONSUMED_SEED_SHAPES = (*C271_CONSUMED_SEED_SHAPES, *FLOOR_REPAIR_SEED_SHAPES, BUILD_LARGE_SQUARE_K32) +PRE_EXPANDED_K32_Q48_CONSUMED_SEED_SHAPES = (*PRE_34DA_CONSUMED_SEED_SHAPES, RAG_STREAM_K10, RAG_Q1_M524287_K10, RAG_Q32_M100000_K32, *EXPANDED_Q32TAIL_CONSUMED_SHAPES) +EXPANDED_K32_Q31_Q33_Q40_Q48_CONSUMED_SHAPES = (EXPANDED_Q31_M100000_K32, EXPANDED_Q33_M100000_K32, EXPANDED_Q40_M100000_K32, EXPANDED_Q48_M75000_K12) +PRE_LOWK_C3D2_CONSUMED_SEED_SHAPES = (*PRE_EXPANDED_K32_Q48_CONSUMED_SEED_SHAPES, *EXPANDED_K32_Q31_Q33_Q40_Q48_CONSUMED_SHAPES) +EXPANDED_Q32_LOWK_C3D2_CONSUMED_SHAPES = (EXPANDED_Q32_M100000_K20,) +EXPANDED_Q32_K31_C3D2_CONSUMED_SHAPES = (EXPANDED_Q32_M100000_K31,) +RAG_ONLINE_D64_Q1_M262143_K10 = d64_tail_017a.RAG_ONLINE_D64_Q1_M262 +RAG_MICRO_D64_Q4_M100000_K10 = d64_tail_017a.RAG_MICRO_D64_Q4_M100 +D64_TAIL_017A_CONSUMED_SHAPES = d64_tail_017a.TARGET_SHAPES +PRE_D64_TAIL_CONSUMED_SEED_SHAPES = (*PRE_LOWK_C3D2_CONSUMED_SEED_SHAPES, *EXPANDED_Q32_LOWK_C3D2_CONSUMED_SHAPES, *EXPANDED_Q32_K31_C3D2_CONSUMED_SHAPES) +RAG_MICRO_D256_Q4_M100000_K10 = d256_q4_e2df.RAG_MICRO_D256_Q4_M100 +D256_Q4_E2DF_CONSUMED_SHAPES = d256_q4_e2df.TARGET_SHAPES +PRE_D256_Q4_E2DF_CONSUMED_SEED_SHAPES = (*PRE_D64_TAIL_CONSUMED_SEED_SHAPES, *D64_TAIL_017A_CONSUMED_SHAPES) +PRE_D256_Q128_59FE_CONSUMED_SEED_SHAPES = (*PRE_D256_Q4_E2DF_CONSUMED_SEED_SHAPES, *D256_Q4_E2DF_CONSUMED_SHAPES) +RAG_STREAM_D256_Q128_M100000_K10 = d256_q128_59fe.RAG_STREAM_D256_Q128_M100 +D256_Q128_59FE_CONSUMED_SHAPES = d256_q128_59fe.TARGET_SHAPES +PRE_D256_K32_59FE_CONSUMED_SEED_SHAPES = (*PRE_D256_Q128_59FE_CONSUMED_SEED_SHAPES, *D256_Q128_59FE_CONSUMED_SHAPES) +RAG_MICRO_D256_Q8_M100000_K32 = d256_k32_59fe.RAG_MICRO_D256_Q8_M100_K32 +RAG_STREAM_D256_Q128_M100000_K32 = d256_k32_59fe.RAG_STREAM_D256_Q128_M100_K32 +D256_K32_59FE_CONSUMED_SHAPES = d256_k32_59fe.TARGET_SHAPES +HIGHD_RAG_D768_Q8_M100000_K10 = highd_rag_22e9.RAG_D768 +HIGHD_RAG_D1024_Q4_M100000_K10 = highd_rag_22e9.RAG_D1024 +HIGHD_RAG_D4096_Q1_M65536_K10 = highd_rag_22e9.RAG_D4096 +HIGHD_RAG_22E9_CONSUMED_SHAPES = highd_rag_22e9.TARGET_SHAPES +HIGHD_SEARCH_D1024_Q256_M8192_K10 = highd_search_be66.SEARCH_D1024 +HIGHD_SEARCH_D4096_Q128_M4096_K10 = highd_search_be66.SEARCH_D4096 +HIGHD_SEARCH_BE66_CONSUMED_SHAPES = highd_search_be66.TARGET_SHAPES +V12_D128_K48_OVER32 = d128_k48_dd2b.TARGET_SHAPE +D128_K48_DD2B_CONSUMED_SHAPES = d128_k48_dd2b.TARGET_SHAPES +RECT_D128_K20_Q1536 = rect_d128_k20_s12warp4.TARGET_SHAPE +RECT_D128_K20_S12WARP4_CONSUMED_SHAPES = rect_d128_k20_s12warp4.TARGET_SHAPES +PRE_HIGHD_RAG_22E9_CONSUMED_SEED_SHAPES = (*PRE_D256_K32_59FE_CONSUMED_SEED_SHAPES, *D256_K32_59FE_CONSUMED_SHAPES) +PRE_HIGHD_SEARCH_BE66_CONSUMED_SEED_SHAPES = (*PRE_HIGHD_RAG_22E9_CONSUMED_SEED_SHAPES, *HIGHD_RAG_22E9_CONSUMED_SHAPES) +PRE_D128_K48_DD2B_CONSUMED_SEED_SHAPES = (*PRE_HIGHD_SEARCH_BE66_CONSUMED_SEED_SHAPES, *HIGHD_SEARCH_BE66_CONSUMED_SHAPES) +PRE_RECT_D128_K20_S12WARP4_CONSUMED_SEED_SHAPES = (*PRE_D128_K48_DD2B_CONSUMED_SEED_SHAPES, *D128_K48_DD2B_CONSUMED_SHAPES) +CONSUMED_SEED_SHAPES = (*PRE_RECT_D128_K20_S12WARP4_CONSUMED_SEED_SHAPES, *RECT_D128_K20_S12WARP4_CONSUMED_SHAPES) +FOCUS_COMMON_D_SHAPES = base_current.FOCUS_SHAPES +eval_mod = base_current.eval_mod +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_7231_stage1_d768", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["K_TILE", 128], ["TOP_K_MAX", 10], ["FEATURE_CHUNKS", 6]], "cta_group": 1, "threads": 192}')) +SOURCE_TASKS = {**base_current.SOURCE_TASKS, 'common_d256_q1024_56f3_v1': 'weave-evolve-knn-build-165c D256 Q1024 build seed, retained as a4ec baseline route', 'common_d_56f3_build_d256_q1024_v1': 'weave-evolve-knn-build-6164 faster D256 Q1024 build seed', 'common_d_eeff_search_d768_v1': 'weave-evolve-knn-build-cda9 D768 rectangular search seed', 'common_d768_build_eeff_m64split_v1': 'weave-evolve-knn-build-34d8 fastest D768 build seed', 'common_d_56f3_build_highd_v1': 'weave-evolve-knn-build-f1d9 high-D build seed', 'non128_frontier_4be7_d768fused_v1': 'generalize-auto-tuning-knn-build-447d D768 RAG guard replay', 'common_d_5e7f_rag_highd_v1': 'weave-evolve-knn-build-4cf7 high-D RAG D1024/D4096 seed', 'common_d_5e7f_search_d256_v1': 'weave-evolve-knn-build-8dbc D256 rectangular search seed', 'common_d_5e7f_rag_d64_d256_v1': 'weave-evolve-knn-build-ba22 D64/D256 RAG seed; D64 remains below the 1.20x floor', 'common_d_5e7f_rag_d64_repair_v1': 'weave-evolve-knn-build-0474 repaired D64 RAG seed', 'common_d_1438_rag_d64_m128_v1': 'weave-evolve-knn-build-631e D64 RAG M128 backfill seed', 'd64_q4096_c271_twostage_v1': 'weave-evolve-knn-build-8f70-q4096-c271-refresh D64 Q4096 exact seed', 'knn_build_k13_k48_floor_repair_7dc5_v1': 'weave-evolve-knn-build-4bd4 exact K13/K48 floor-repair wrapper', SEED_LARGE_SQUARE_K32_EFE4_ID: 'weave-evolve-knn-build-fac0 EFE4 large-square K32 producer-cache seed', SEED_K13_ID: 'weave-evolve-knn-build-4bd4 inherited 2c1c Q4096 K13 unordered split4 seed', SEED_K48_ID: 'weave-evolve-knn-build-4bd4 inherited d03c K48 split4 warp-select seed', SEED_K13_A162_ID: 'weave-evolve-knn-build-3b00 promoted K13 source; 7bef best timing evidence', SEED_Q128_DUALM_ID: 'weave-evolve-knn-build-5698 Q128 K32 dual-M rowld/warp1 seed', SEED_Q128_S72R2_ID: 'weave-evolve-knn-build-681b Q128 K32 split72 rows2 seed', SEED_Q128_K10_WARPMERGE_ID: 'weave-evolve-knn-build-4ce8 Q128/M100000/D128/K10 split74 warp-merge seed', SEED_Q128_K10_ROWLD_1BED_ID: 'weave-evolve-knn-build-41bb exact Q128/M100000/D128/K10 split74 row-load seed', SEED_Q16_M250_ID: 'weave-evolve-knn-build-cb99 Q16/M250000 K32 split288 seed', SEED_RAG_STREAM_K10_34DA_ID: 'weave-evolve-knn-build-34da RAG stream K10 split72 warp-row merge seed', SEED_Q1_M524_N128_ID: 'weave-evolve-knn-build-32b9 Q1/M524287/D128/K10 M64/N128 seed', SEED_Q32_EXACT_ID: 'weave-evolve-knn-build-12ac exact Q32/M100000/D128/K32 f653 rowld2 stage1 seed', SEED_Q32TAIL_ID: 'weave-evolve-knn-build-6866 Q32/M99999 and Q32/M100001 K32 tail seed', SEED_Q32TAIL143_LOW_ID: 'weave-evolve-knn-build-7eed consumed 5317 split143 Q32/M99999 K32 tail seed', SEED_Q32TAIL143_HIGH_ID: 'weave-evolve-knn-build-7eed consumed 314c split143 Q32/M100001 K32 tail seed', SEED_Q31TAIL_V2_ID: 'weave-evolve-knn-build-0cb5 Q31 exact rowld2 seed', SEED_Q33TILE_ID: 'weave-evolve-knn-build-c489 Q33/Q40 expanded K32 seed', SEED_Q48_K12_ID: 'weave-evolve-knn-build-2f22 Q48/M75000/K12 exact low-K seed', SEED_Q32_LOWK_C3D2_ID: 'weave-evolve-knn-build-4cc4 Q32/M100000 low-K K20 seed', SEED_Q32_K31_C3D2_ID: 'weave-evolve-knn-build-eaf7 Q32/M100000 K31 exact seed; clears expanded K31 floor', SEED_D64_TAIL_017A_ID: 'weave-evolve-knn-build-6e8c exact v12 D64 long-M tail seed', SEED_D256_Q4_E2DF_ID: 'weave-evolve-knn-build-fd28 exact v12 D256 Q4/K10 long-M seed', SEED_D256_Q128_59FE_ID: 'weave-evolve-knn-build-93f5 exact v12 D256 Q128/K10 long-M seed', SEED_D256_K32_59FE_ID: 'weave-evolve-knn-build-9203 exact v12 D256 K32 long-M seed', SEED_HIGHD_RAG_22E9_ID: 'weave-evolve-knn-build-7902 exact v12 high-D RAG seed', SEED_HIGHD_SEARCH_BE66_ID: 'weave-evolve-knn-build-ad73 exact v12 high-D rectangular search seed', SEED_D128_K48_DD2B_ID: 'weave-evolve-knn-build-dd2b exact v12 D128 Q16/M100000 K48 seed', SEED_RECT_D128_K20_S12WARP4_ID: 'weave-evolve-knn-build-0e29 exact rectangular D128/K20/Q1536 split12/warp4 seed'} +PRODUCTION_ROUTE_MODULES = {**base_current.PRODUCTION_ROUTE_MODULES, 'common_d_56f3_build_d256_q1024_v1': D256_ENTRYPOINT, 'common_d_eeff_search_d768_v1': D768_SEARCH_ENTRYPOINT, 'common_d768_build_eeff_m64split_v1': D768_FAST_ENTRYPOINT, 'common_d_56f3_build_highd_v1': HIGHD_ENTRYPOINT, 'non128_frontier_4be7_d768fused_v1': D768_RAG_ENTRYPOINT, 'common_d_5e7f_rag_highd_v1': HIGHD_RAG_ENTRYPOINT, 'common_d_5e7f_search_d256_v1': SEARCH_D256_ENTRYPOINT, 'common_d_5e7f_rag_d64_d256_v1': RAG_D64D256_ENTRYPOINT, 'common_d_5e7f_rag_d64_repair_v1': RAG_D64_REPAIR_ENTRYPOINT, 'common_d_1438_rag_d64_m128_v1': RAG_D64_M128_ENTRYPOINT, 'd64_q4096_c271_twostage_v1': D64_Q4096_C271_ENTRYPOINT, SEED_LARGE_SQUARE_K32_EFE4_ID: LARGE_SQUARE_K32_EFE4_ENTRYPOINT, 'knn_build_k13_k48_floor_repair_7dc5_v1': K13_K48_WRAPPER_ENTRYPOINT, SEED_K13_ID: K13_ENTRYPOINT, SEED_K48_ID: K48_ENTRYPOINT, SEED_K13_A162_ID: K13_ENTRYPOINT, SEED_Q128_DUALM_ID: Q128_DUALM_ENTRYPOINT, SEED_Q128_S72R2_ID: Q128_S72R2_ENTRYPOINT, SEED_Q128_K10_WARPMERGE_ID: Q128_K10_WARPMERGE_ENTRYPOINT, SEED_Q128_K10_ROWLD_1BED_ID: Q128_K10_ROWLD_1BED_ENTRYPOINT, SEED_Q16_M250_ID: Q16_M250_ENTRYPOINT, SEED_RAG_STREAM_K10_34DA_ID: RAG_STREAM_K10_34DA_ENTRYPOINT, SEED_Q1_M524_N128_ID: Q1_M524_N128_ENTRYPOINT, SEED_Q32_EXACT_ID: Q32_EXACT_ENTRYPOINT, SEED_Q32TAIL_ID: Q32TAIL_ENTRYPOINT, SEED_Q32TAIL143_LOW_ID: Q32TAIL143_LOW_ENTRYPOINT, SEED_Q32TAIL143_HIGH_ID: Q32TAIL143_HIGH_ENTRYPOINT, SEED_Q31TAIL_V2_ID: Q31TAIL_V2_ENTRYPOINT, SEED_Q33TILE_ID: Q33TILE_ENTRYPOINT, SEED_Q48_K12_ID: Q48_K12_ENTRYPOINT, SEED_Q32_LOWK_C3D2_ID: Q32_LOWK_C3D2_ENTRYPOINT, SEED_Q32_K31_C3D2_ID: Q32_K31_C3D2_ENTRYPOINT, SEED_D64_TAIL_017A_ID: D64_TAIL_017A_ENTRYPOINT, SEED_D256_Q4_E2DF_ID: D256_Q4_E2DF_ENTRYPOINT, SEED_D256_Q128_59FE_ID: D256_Q128_59FE_ENTRYPOINT, SEED_D256_K32_59FE_ID: D256_K32_59FE_ENTRYPOINT, SEED_HIGHD_RAG_22E9_ID: HIGHD_RAG_22E9_ENTRYPOINT, SEED_HIGHD_SEARCH_BE66_ID: HIGHD_SEARCH_BE66_ENTRYPOINT, SEED_D128_K48_DD2B_ID: D128_K48_DD2B_ENTRYPOINT, SEED_RECT_D128_K20_S12WARP4_ID: RECT_D128_K20_S12WARP4_ENTRYPOINT, CANDIDATE_FA04_BASE: FA04_BASE_ENTRYPOINT, CANDIDATE_F328_BASE: F328_BASE_ENTRYPOINT, CANDIDATE_MIXED: MIXED_BASE_ENTRYPOINT, CANDIDATE_D64_REPAIR: D64_REPAIR_BASE_ENTRYPOINT, CANDIDATE_D64_M128: D64_M128_BASE_ENTRYPOINT, CANDIDATE_D64_Q4096_C271: D64_Q4096_C271_BASE_ENTRYPOINT, CANDIDATE_C271_7DC5_K13_K48: C271_7DC5_BASE_ENTRYPOINT, CANDIDATE_2498_BASELINE: BASE_2498_ENTRYPOINT, CANDIDATE_PRE_Q1_M524_N128: PRE_Q1_M524_N128_BASE_ENTRYPOINT, CANDIDATE_PRE_Q32_EXACT: PRE_Q32_EXACT_BASE_ENTRYPOINT, CANDIDATE_PRE_Q32TAIL: PRE_Q32TAIL_BASE_ENTRYPOINT, CANDIDATE_PRE_EXPANDED_K32_Q48: PRE_EXPANDED_K32_Q48_BASE_ENTRYPOINT, CANDIDATE_PRE_LOWK_C3D2: PRE_LOWK_C3D2_BASE_ENTRYPOINT, CANDIDATE_D665_LOWK_BASELINE: D665_LOWK_BASE_ENTRYPOINT, CANDIDATE_PRE_D64_TAIL_017A: PRE_D64_TAIL_017A_BASE_ENTRYPOINT, CANDIDATE_PRE_D256_Q4_E2DF: PRE_D256_Q4_E2DF_BASE_ENTRYPOINT, CANDIDATE_D256_Q128_59FE: PRE_D256_K32_59FE_BASE_ENTRYPOINT, CANDIDATE_D256_K32_59FE: PRE_HIGHD_RAG_22E9_BASE_ENTRYPOINT, CANDIDATE_HIGHD_RAG_22E9: PRE_HIGHD_SEARCH_BE66_BASE_ENTRYPOINT, CANDIDATE_HIGHD_SEARCH_BE66: PRE_D128_K48_DD2B_BASE_ENTRYPOINT, CANDIDATE_D128_K48_DD2B: PRE_RECT_D128_K20_S12WARP4_BASE_ENTRYPOINT, CANDIDATE_RECT_D128_K20_S12WARP4: ROUTE_ENTRYPOINT, CANDIDATE_Q128_K10_ROWLD_1BED: ROUTE_ENTRYPOINT, CANDIDATE_Q128_K10_WARPMERGE: ROUTE_ENTRYPOINT, CANDIDATE_FLOOR_SEEDS_Q128_5698: ROUTE_ENTRYPOINT, CANDIDATE_FLOOR_SEEDS_Q128_681B: ROUTE_ENTRYPOINT, CANDIDATE_FLOOR_SEEDS_Q128_MIXED: ROUTE_ENTRYPOINT, CANDIDATE_SELECTED_SYNTHESIS: ROUTE_ENTRYPOINT} +D64_REPAIR_0474_TIMING = {'seed_id': 'common_d_5e7f_rag_d64_repair_v1', 'kernel_ms': 0.049792, 'flashlib_ms': 0.065376, 'ratio_vs_flashlib': 1.312982005141388, 'tflops': 2.056555269922879, 'timing_backend': 'cupti', 'source_payload': 'design_doc/active/weave_evolve_knn_build_round_166_5e7f_d64repair.md'} +SEED_TIMING_ROWS = {BUILD_D256: {'seed_id': 'common_d_56f3_build_d256_q1024_v1', 'kernel_ms': 0.028256, 'flashlib_ms': 0.074304, 'ratio_vs_flashlib': 2.629671574178935, 'tflops': 19.000244620611554, 'timing_backend': 'cupti', 'source_payload': 'design_doc/active/weave_evolve_knn_build_round_161_56f3_d256q1024.md'}, BUILD_D768: {'seed_id': 'common_d768_build_eeff_m64split_v1', 'kernel_ms': 0.030432, 'flashlib_ms': 0.108385, 'ratio_vs_flashlib': 3.5615470557308093, 'tflops': 52.92497160883281, 'timing_backend': 'cupti', 'source_payload': 'artifacts/weave_evolve/knn_build_common_d768_build_eeff_m64split_v1/common_d768_build_eeff_m64split_v1.json'}, BUILD_D1024: {'seed_id': 'common_d_56f3_build_highd_v1', 'kernel_ms': 0.037856, 'flashlib_ms': 0.122848, 'ratio_vs_flashlib': 3.2451394759087067, 'tflops': 14.181923922231615, 'timing_backend': 'cupti', 'source_payload': 'design_doc/active/weave_evolve_knn_build_round_161_56f3.md'}, BUILD_D4096: {'seed_id': 'common_d_56f3_build_highd_v1', 'kernel_ms': 0.058336, 'flashlib_ms': 0.33136, 'ratio_vs_flashlib': 5.6801974766867795, 'tflops': 36.81232254525507, 'timing_backend': 'cupti', 'source_payload': 'design_doc/active/weave_evolve_knn_build_round_161_56f3.md'}, D768_SEARCH: {'seed_id': 'common_d_eeff_search_d768_v1', 'kernel_ms': 0.061664, 'flashlib_ms': 0.163041, 'ratio_vs_flashlib': 2.644022444213804, 'tflops': 104.4766953814219, 'timing_backend': 'cupti', 'source_payload': 'design_doc/active/weave_evolve_knn_build_round_161_eeff.md'}, D768_RAG: {'seed_id': 'non128_frontier_4be7_d768fused_v1', 'kernel_ms': 0.097568, 'flashlib_ms': 0.171969, 'ratio_vs_flashlib': 1.762555346015087, 'tflops': 12.594293210888814, 'timing_backend': 'cupti', 'source_payload': 'artifacts/generalize_auto_tuning/knn_build_447d_v11_common_d_4be7_replay/full100_dispatch_v11_common_d_4be7_447d_v1.json'}, RAG_D1024: {'seed_id': 'common_d_5e7f_rag_highd_v1', 'kernel_ms': 0.075233, 'flashlib_ms': 0.1469445, 'ratio_vs_flashlib': 1.9531920832613345, 'tflops': 10.888838674517832, 'timing_backend': 'cupti', 'source_payload': 'design_doc/active/weave_evolve_knn_build_round_165_5e7f.md'}, RAG_D4096: {'seed_id': 'common_d_5e7f_rag_highd_v1', 'kernel_ms': 0.160865, 'flashlib_ms': 0.309859, 'ratio_vs_flashlib': 1.926205203120629, 'tflops': 6.6748007583999005, 'timing_backend': 'cupti', 'source_payload': 'design_doc/active/weave_evolve_knn_build_round_165_5e7f.md'}, SEARCH_D256: {'seed_id': 'common_d_5e7f_search_d256_v1', 'kernel_ms': 0.19437, 'flashlib_ms': 0.466597, 'ratio_vs_flashlib': 2.400560786129547, 'tflops': 88.38745271389618, 'timing_backend': 'cupti', 'source_payload': 'design_doc/active/weave_evolve_knn_build_round_165_5e7f_search_d256.md'}, RAG_D64: {'seed_id': 'common_d_1438_rag_d64_m128_v1', 'kernel_ms': 0.036288, 'flashlib_ms': 0.068641, 'ratio_vs_flashlib': 1.8915619488536153, 'tflops': 2.821869488536155, 'timing_backend': 'cupti', 'source_payload': 'design_doc/active/weave_evolve_knn_build_round_166_1438_d64_m128.md'}, RAG_D256: {'seed_id': 'common_d_5e7f_rag_d64_d256_v1', 'kernel_ms': 0.060353, 'flashlib_ms': 0.081472, 'ratio_vs_flashlib': 1.3499246102099316, 'tflops': 5.396514610821611, 'timing_backend': 'cupti', 'source_payload': 'design_doc/active/weave_evolve_knn_build_round_165_5e7f_d64d256.md'}, RAG_ONLINE_D64_Q1_M262143_K10: {'seed_id': SEED_D64_TAIL_017A_ID, 'kernel_ms': 0.059648, 'flashlib_ms': 0.080672, 'ratio_vs_flashlib': 1.3524678111587982, 'tflops': 0.5625386266094421, 'timing_backend': 'cupti', 'source_payload': 'artifacts/weave_evolve/knn_build_v12_d64_tail_017a_v1/v12_d64_tail_017a_2row_cupti.json', 'source_task': 'weave-evolve-knn-build-6e8c'}, RAG_MICRO_D64_Q4_M100000_K10: {'seed_id': SEED_D64_TAIL_017A_ID, 'kernel_ms': 0.033217, 'flashlib_ms': 0.067745, 'ratio_vs_flashlib': 2.0394677424210492, 'tflops': 1.5413794141554022, 'timing_backend': 'cupti', 'source_payload': 'artifacts/weave_evolve/knn_build_v12_d64_tail_017a_v1/v12_d64_tail_017a_2row_cupti.json', 'source_task': 'weave-evolve-knn-build-6e8c'}, RAG_MICRO_D256_Q4_M100000_K10: {'seed_id': SEED_D256_Q4_E2DF_ID, 'kernel_ms': 0.056352, 'flashlib_ms': 0.084225, 'ratio_vs_flashlib': 1.494626641467916, 'tflops': 3.6342986939239066, 'timing_backend': 'cupti', 'source_payload': 'artifacts/weave_evolve/knn_build_v12_d256_k10_longm_e2df_v1/d256_q4_cupti.json', 'source_task': 'weave-evolve-knn-build-fd28'}, RAG_STREAM_D256_Q128_M100000_K10: {'seed_id': SEED_D256_Q128_59FE_ID, 'kernel_ms': 0.068289, 'flashlib_ms': 0.156577, 'ratio_vs_flashlib': 2.2928582934293953, 'tflops': 95.96860402114541, 'timing_backend': 'cupti', 'source_payload': 'artifacts/weave_evolve/knn_build_v12_d256_q128_k10_longm_59fe_v1/d256_q128_cupti.json', 'source_task': 'weave-evolve-knn-build-93f5'}, RAG_MICRO_D256_Q8_M100000_K32: {'seed_id': SEED_D256_K32_59FE_ID, 'kernel_ms': 0.100768, 'flashlib_ms': 0.122464, 'ratio_vs_flashlib': 1.2153064464909495, 'tflops': 4.0647824706255955, 'timing_backend': 'cupti', 'source_payload': 'artifacts/weave_evolve/knn_build_v12_d256_k32_tail_59fe_v1/d256_k32_tail_59fe_v1.json', 'source_task': 'weave-evolve-knn-build-9203'}, RAG_STREAM_D256_Q128_M100000_K32: {'seed_id': SEED_D256_K32_59FE_ID, 'kernel_ms': 0.2519695, 'flashlib_ms': 0.30637, 'ratio_vs_flashlib': 1.2159011308908418, 'tflops': 26.009497181206456, 'timing_backend': 'cupti', 'source_payload': 'artifacts/weave_evolve/knn_build_v12_d256_k32_tail_59fe_v1/d256_k32_tail_59fe_v1.json', 'source_task': 'weave-evolve-knn-build-9203'}, HIGHD_RAG_D768_Q8_M100000_K10: {'seed_id': SEED_HIGHD_RAG_22E9_ID, 'kernel_ms': 0.092737, 'flashlib_ms': 0.161858, 'ratio_vs_flashlib': 1.7453443609346864, 'tflops': 13.250374715593562, 'timing_backend': 'cupti', 'source_payload': 'artifacts/weave_evolve/knn_build_v12_highd_rag_22e9_v1/v12_highd_rag_22e9_v1.json', 'source_task': 'weave-evolve-knn-build-7902'}, HIGHD_RAG_D1024_Q4_M100000_K10: {'seed_id': SEED_HIGHD_RAG_22E9_ID, 'kernel_ms': 0.113793, 'flashlib_ms': 0.170305, 'ratio_vs_flashlib': 1.4966210575342949, 'tflops': 7.199036847609255, 'timing_backend': 'cupti', 'source_payload': 'artifacts/weave_evolve/knn_build_v12_highd_rag_22e9_v1/v12_highd_rag_22e9_v1.json', 'source_task': 'weave-evolve-knn-build-7902'}, HIGHD_RAG_D4096_Q1_M65536_K10: {'seed_id': SEED_HIGHD_RAG_22E9_ID, 'kernel_ms': 0.300963, 'flashlib_ms': 0.377251, 'ratio_vs_flashlib': 1.2534796636131353, 'tflops': 1.783843568810784, 'timing_backend': 'cupti', 'source_payload': 'artifacts/weave_evolve/knn_build_v12_highd_rag_22e9_v1/v12_highd_rag_22e9_v1.json', 'source_task': 'weave-evolve-knn-build-7902'}, HIGHD_SEARCH_D1024_Q256_M8192_K10: {'seed_id': SEED_HIGHD_SEARCH_BE66_ID, 'kernel_ms': 0.036609, 'flashlib_ms': 0.120065, 'ratio_vs_flashlib': 3.279658007593761, 'tflops': 117.31998404763856, 'timing_backend': 'cupti', 'source_payload': 'artifacts/weave_evolve/knn_build_v12_highd_search_be66_v1/v12_highd_search_be66_v1.json', 'source_task': 'weave-evolve-knn-build-ad73'}, HIGHD_SEARCH_D4096_Q128_M4096_K10: {'seed_id': SEED_HIGHD_SEARCH_BE66_ID, 'kernel_ms': 0.055072, 'flashlib_ms': 0.325762, 'ratio_vs_flashlib': 5.915201917489831, 'tflops': 77.98822080185937, 'timing_backend': 'cupti', 'source_payload': 'artifacts/weave_evolve/knn_build_v12_highd_search_be66_v1/v12_highd_search_be66_v1.json', 'source_task': 'weave-evolve-knn-build-ad73'}, V12_D128_K48_OVER32: {'seed_id': SEED_D128_K48_DD2B_ID, 'kernel_ms': 0.105408, 'flashlib_ms': 0.196066, 'ratio_vs_flashlib': 1.860067547055252, 'tflops': 3.885853066180935, 'timing_backend': 'cupti', 'source_payload': 'artifacts/weave_evolve/knn_build_v12_d128_q16_k48_dd2b_v1/v12_d128_q16_k48_dd2b_v1.json', 'source_task': 'weave-evolve-knn-build-dd2b'}, RECT_D128_K20_Q1536: {'seed_id': SEED_RECT_D128_K20_S12WARP4_ID, 'kernel_ms': 0.446723, 'flashlib_ms': 0.769382, 'ratio_vs_flashlib': 1.7222798020249686, 'tflops': 57.686315179652716, 'timing_backend': 'cupti', 'source_payload': 'artifacts/generalize_auto_tuning/knn_build_7768_rect_d128_k20_q1536_s12warp4/rect_d128_k20_q1536_s12warp4_7768_v1.json', 'source_task': 'weave-evolve-knn-build-0e29'}, D64_Q4096: {'seed_id': 'd64_q4096_c271_twostage_v1', 'kernel_ms': 0.106913, 'flashlib_ms': 0.145249, 'ratio_vs_flashlib': 1.3585719229654019, 'tflops': 20.086272464527234, 'timing_backend': 'cupti', 'source_payload': 'design_doc/active/weave_evolve_knn_build_round_169_8f70_q4096_c271_refresh.md'}, BUILD_K13: {'seed_id': SEED_K13_ID, 'kernel_ms': 0.124261, 'flashlib_ms': 0.164709, 'ratio_vs_flashlib': 1.3255084056944657, 'tflops': 34.56408121614988, 'timing_backend': 'cupti', 'source_payload': 'artifacts/weave_evolve/knn_build_7dc5_k13_k48_floor_repair_round170/7dc5_k13_k48_exact3_dispatch_7dc5_k13_k48_v1.json'}, BUILD_K48_Q2048: {'seed_id': SEED_K48_ID, 'kernel_ms': 0.170214, 'flashlib_ms': 0.3967495, 'ratio_vs_flashlib': 2.3308864135735012, 'tflops': 6.308187481640758, 'timing_backend': 'cupti', 'source_payload': 'artifacts/weave_evolve/knn_build_7dc5_k13_k48_floor_repair_round170/7dc5_k13_k48_exact3_dispatch_7dc5_k13_k48_v1.json'}, BUILD_K48_Q4096: {'seed_id': SEED_K48_ID, 'kernel_ms': 0.294186, 'flashlib_ms': 0.527506, 'ratio_vs_flashlib': 1.7931036827041396, 'tflops': 14.599495883556662, 'timing_backend': 'cupti', 'source_payload': 'artifacts/weave_evolve/knn_build_7dc5_k13_k48_floor_repair_round170/7dc5_k13_k48_exact3_dispatch_7dc5_k13_k48_v1.json'}, BUILD_LARGE_SQUARE_K32: {'seed_id': SEED_LARGE_SQUARE_K32_EFE4_ID, 'kernel_ms': 0.38906, 'flashlib_ms': 0.622566, 'ratio_vs_flashlib': 1.6001799208348324, 'tflops': 44.157377227162904, 'timing_backend': 'cupti', 'source_payload': 'artifacts/weave_evolve/knn_build_large_square_k32_fac0_round171/large_square_k32_efe4_prodcache_fac0_target.json'}, RAG_STREAM_K10: {'seed_id': SEED_RAG_STREAM_K10_34DA_ID, 'kernel_ms': 0.109793, 'flashlib_ms': 0.133857, 'ratio_vs_flashlib': 1.2191760859071161, 'tflops': 29.84525425118177, 'timing_backend': 'cupti', 'source_payload': 'artifacts/weave_evolve/knn_build_34da_rag_stream_k10_warpmerge_round172/rag_stream_k10_warpmerge_34da_v1.json', 'source_task': 'weave-evolve-knn-build-34da'}, RAG_Q1_M524287_K10: {'seed_id': SEED_Q1_M524_N128_ID, 'kernel_ms': 0.095489, 'flashlib_ms': 0.115842, 'ratio_vs_flashlib': 1.2131449695776477, 'tflops': 1.4055804542931645, 'timing_backend': 'cupti', 'source_payload': 'artifacts/weave_evolve/knn_build_ea43_q1m524_n128/ea43_q1m524_n128_candidate_n128_1row_cupti.json', 'source_task': 'weave-evolve-knn-build-32b9'}, RAG_Q32_M100000_K32: {'seed_id': SEED_Q32_EXACT_ID, 'kernel_ms': 0.125057, 'flashlib_ms': 0.159393, 'ratio_vs_flashlib': 1.274570795717153, 'tflops': 6.550612920508248, 'timing_backend': 'cupti', 'source_payload': 'artifacts/weave_evolve/knn_build_q32rowld2exact_f653_v1/q32rowld2exact_s141_f653_v1_cupti.json', 'source_task': 'weave-evolve-knn-build-12ac'}, EXPANDED_Q32_M99999_K32: {'seed_id': SEED_Q32TAIL143_LOW_ID, 'kernel_ms': 0.129665, 'flashlib_ms': 0.159297, 'ratio_vs_flashlib': 1.2285273589634829, 'tflops': 6.317755816912814, 'timing_backend': 'cupti', 'source_payload': 'artifacts/weave_evolve/knn_build_dispatch_d6b5_q32tail143_v1/d6b5_q32tail143_dispatch_8row_cupti.json', 'source_task': 'weave-evolve-knn-build-7eed'}, EXPANDED_Q32_M100001_K32: {'seed_id': SEED_Q32TAIL143_HIGH_ID, 'kernel_ms': 0.131841, 'flashlib_ms': 0.159041, 'ratio_vs_flashlib': 1.2063091147670297, 'tflops': 6.213607239022761, 'timing_backend': 'cupti', 'source_payload': 'artifacts/weave_evolve/knn_build_dispatch_d6b5_q32tail143_v1/d6b5_q32tail143_dispatch_8row_cupti.json', 'source_task': 'weave-evolve-knn-build-7eed'}, EXPANDED_Q31_M100000_K32: {'seed_id': SEED_Q31TAIL_V2_ID, 'kernel_ms': 0.128545, 'flashlib_ms': 0.156065, 'ratio_vs_flashlib': 1.2140884515150339, 'tflops': 6.173713485549808, 'timing_backend': 'cupti', 'source_payload': 'artifacts/weave_evolve/knn_build_0cb5_q31tail_v2/0cb5_q31tail_v2_1row_s153_cupti.json', 'source_task': 'weave-evolve-knn-build-0cb5'}, EXPANDED_Q33_M100000_K32: {'seed_id': SEED_Q33TILE_ID, 'kernel_ms': 0.142273, 'flashlib_ms': 0.218273, 'ratio_vs_flashlib': 1.5341842795189529, 'tflops': 5.937879991284361, 'timing_backend': 'cupti', 'source_payload': 'artifacts/weave_evolve/knn_build_c489_q33tile_v1/c489_q33tile_5row_s144_g12_cupti.json', 'source_task': 'weave-evolve-knn-build-c489'}, EXPANDED_Q40_M100000_K32: {'seed_id': SEED_Q33TILE_ID, 'kernel_ms': 0.132225, 'flashlib_ms': 0.214561, 'ratio_vs_flashlib': 1.6226961618453393, 'tflops': 7.744375118169786, 'timing_backend': 'cupti', 'source_payload': 'artifacts/weave_evolve/knn_build_c489_q33tile_v1/c489_q33tile_5row_s144_g12_cupti.json', 'source_task': 'weave-evolve-knn-build-c489'}, EXPANDED_Q48_M75000_K12: {'seed_id': SEED_Q48_K12_ID, 'kernel_ms': 0.045216, 'flashlib_ms': 0.103297, 'ratio_vs_flashlib': 2.284523177636235, 'tflops': 20.382165605095544, 'timing_backend': 'cupti', 'source_payload': 'artifacts/weave_evolve/knn_build_rag_microbucket_k12_2f22_q48exact_v1/2f22_q48exact_1row_s148_cupti.json', 'source_task': 'weave-evolve-knn-build-2f22'}, EXPANDED_Q32_M100000_K20: {'seed_id': SEED_Q32_LOWK_C3D2_ID, 'kernel_ms': 0.083521, 'flashlib_ms': 0.129696, 'ratio_vs_flashlib': 1.552854970606195, 'tflops': 9.808311682091928, 'timing_backend': 'cupti', 'source_payload': 'artifacts/weave_evolve/knn_build_q32_lowk_c3d2_v1/q32_lowk_c3d2_2row_s152_cupti.json', 'source_task': 'weave-evolve-knn-build-4cc4'}, EXPANDED_Q32_M100000_K31: {'seed_id': SEED_Q32_K31_C3D2_ID, 'kernel_ms': 0.124705, 'flashlib_ms': 0.150465, 'ratio_vs_flashlib': 1.206567499298344, 'tflops': 6.569103083276533, 'timing_backend': 'cupti', 'source_payload': 'artifacts/weave_evolve/knn_build_q32_k31_c3d2_v1/q32_k31_c3d2_1row_s152_cupti.json', 'source_task': 'weave-evolve-knn-build-eaf7'}} +D665_Q32_LOWK_C3D2_TIMING_ROWS = {EXPANDED_Q32_M100000_K20: SEED_TIMING_ROWS[EXPANDED_Q32_M100000_K20], EXPANDED_Q32_M100000_K31: {'seed_id': SEED_Q32_LOWK_C3D2_ID, 'kernel_ms': 0.131041, 'flashlib_ms': 0.149538, 'ratio_vs_flashlib': 1.14115429522058, 'tflops': 6.251478544882899, 'timing_backend': 'cupti', 'source_payload': 'artifacts/weave_evolve/knn_build_q32_lowk_c3d2_v1/q32_lowk_c3d2_2row_s152_cupti.json', 'source_task': 'weave-evolve-knn-build-4cc4'}} +EXPANDED_FALLBACK_TIMING_ROWS = {EXPANDED_Q31_M100000_K32: {'kernel_ms': 14.349643499999999, 'flashlib_ms': 0.155745, 'ratio_vs_flashlib': 0.010853579742242377, 'timing_backend': 'cupti', 'source_payload': 'artifacts/generalize_auto_tuning/knn_build_0cb5_q32exact_dispatcher_consumption/expanded_q32_guard_boundary_8_dispatch_v11_common_d_seed_portfolio_a4ec_v1.json'}, EXPANDED_Q33_M100000_K32: {'kernel_ms': 11.84969, 'flashlib_ms': 0.218306, 'ratio_vs_flashlib': 0.01842292920743074, 'timing_backend': 'cupti', 'source_payload': 'artifacts/generalize_auto_tuning/knn_build_0cb5_q32exact_dispatcher_consumption/expanded_q32_guard_boundary_8_dispatch_v11_common_d_seed_portfolio_a4ec_v1.json'}, EXPANDED_Q32_M99999_K32: {'kernel_ms': 13.201157, 'flashlib_ms': 0.159297, 'ratio_vs_flashlib': 0.012066896863661268, 'timing_backend': 'cupti', 'source_payload': 'artifacts/generalize_auto_tuning/knn_build_0cb5_q32exact_dispatcher_consumption/expanded_q32_guard_boundary_8_dispatch_v11_common_d_seed_portfolio_a4ec_v1.json'}, EXPANDED_Q32_M100001_K32: {'kernel_ms': 13.2865505, 'flashlib_ms': 0.158625, 'ratio_vs_flashlib': 0.011938764692912579, 'timing_backend': 'cupti', 'source_payload': 'artifacts/generalize_auto_tuning/knn_build_0cb5_q32exact_dispatcher_consumption/expanded_q32_guard_boundary_8_dispatch_v11_common_d_seed_portfolio_a4ec_v1.json'}, EXPANDED_Q40_M100000_K32: {'kernel_ms': 11.875965, 'flashlib_ms': 0.216546, 'ratio_vs_flashlib': 0.01823397088152415, 'timing_backend': 'cupti', 'source_payload': 'artifacts/generalize_auto_tuning/knn_build_0cb5_q32exact_dispatcher_consumption/expanded_q32_guard_boundary_8_dispatch_v11_common_d_seed_portfolio_a4ec_v1.json'}, EXPANDED_Q32_M100000_K20: {'kernel_ms': 13.248295, 'flashlib_ms': 0.129665, 'ratio_vs_flashlib': 0.00978729715786069, 'timing_backend': 'cupti', 'source_payload': 'artifacts/generalize_auto_tuning/knn_build_0cb5_q32exact_dispatcher_consumption/expanded_q32_guard_boundary_8_dispatch_v11_common_d_seed_portfolio_a4ec_v1.json'}, EXPANDED_Q32_M100000_K31: {'kernel_ms': 13.315449000000001, 'flashlib_ms': 0.150689, 'ratio_vs_flashlib': 0.011316854579969476, 'timing_backend': 'cupti', 'source_payload': 'artifacts/generalize_auto_tuning/knn_build_0cb5_q32exact_dispatcher_consumption/expanded_q32_guard_boundary_8_dispatch_v11_common_d_seed_portfolio_a4ec_v1.json'}, EXPANDED_Q48_M75000_K12: {'kernel_ms': 9.683852000000002, 'flashlib_ms': 0.102688, 'ratio_vs_flashlib': 0.010604044754091655, 'timing_backend': 'cupti', 'source_payload': 'artifacts/generalize_auto_tuning/knn_build_0cb5_q32exact_dispatcher_consumption/expanded_q32_guard_boundary_8_dispatch_v11_common_d_seed_portfolio_a4ec_v1.json'}} +K13_A162_SOURCE_TIMING = {'seed_id': SEED_K13_A162_ID, 'kernel_ms': 0.124577, 'flashlib_ms': 0.164546, 'ratio_vs_flashlib': 1.3208377148269745, 'tflops': 34.476406527689704, 'timing_backend': 'cupti', 'source_payload': 'artifacts/weave_evolve/knn_build_a162_q4096_k13_floor_repair_round170/a162_q4096_k13_exact1_dispatch_a162_q4096_k13_v1.json', 'source_task': 'weave-evolve-knn-build-3b00'} +K13_A162_BEST_TIMING = {'seed_id': SEED_K13_A162_ID, 'kernel_ms': 0.124449, 'flashlib_ms': 0.164225, 'ratio_vs_flashlib': 1.319616871168109, 'tflops': 34.51186667630917, 'timing_backend': 'cupti', 'source_payload': 'weave-evolve-knn-build-7bef/artifacts/weave_evolve/knn_build_a162_q4096_k13_floor_repair_round170/a162_q4096_k13_exact1_dispatch_a162_q4096_k13_v1.json', 'source_task': 'weave-evolve-knn-build-7bef'} +Q128_DUALM_TIMING_ROWS = {RAG_Q128_M100000_K32: {'seed_id': SEED_Q128_DUALM_ID, 'kernel_ms': 0.221122, 'flashlib_ms': 0.28253, 'ratio_vs_flashlib': 1.2777109468980925, 'tflops': 14.818968714103525, 'timing_backend': 'cupti', 'source_payload': 'artifacts/weave_evolve/knn_build_a162_rag_stream_q128_dualm_round170/q128_dualm_a162_cupti.json', 'source_task': 'weave-evolve-knn-build-5698'}, RAG_Q128_M131071_K32: {'seed_id': SEED_Q128_DUALM_ID, 'kernel_ms': 0.265059, 'flashlib_ms': 0.340835, 'ratio_vs_flashlib': 1.285883520272845, 'tflops': 16.203692491105755, 'timing_backend': 'cupti', 'source_payload': 'artifacts/weave_evolve/knn_build_a162_rag_stream_q128_dualm_round170/q128_dualm_a162_cupti.json', 'source_task': 'weave-evolve-knn-build-5698'}} +Q128_S72R2_TIMING_ROWS = {RAG_Q128_M100000_K32: {'seed_id': SEED_Q128_S72R2_ID, 'kernel_ms': 0.216995, 'flashlib_ms': 0.282532, 'ratio_vs_flashlib': 1.3020207838890299, 'tflops': 15.100808774395723, 'timing_backend': 'cupti', 'source_payload': 'artifacts/weave_evolve/knn_build_a162_rag_stream_q128_s72r2_round170/a162_rag_stream_q128_s72r2_exact2.json', 'source_task': 'weave-evolve-knn-build-681b'}, RAG_Q128_M131071_K32: {'seed_id': SEED_Q128_S72R2_ID, 'kernel_ms': 0.269891, 'flashlib_ms': 0.339204, 'ratio_vs_flashlib': 1.2568184933917768, 'tflops': 15.913589293455507, 'timing_backend': 'cupti', 'source_payload': 'artifacts/weave_evolve/knn_build_a162_rag_stream_q128_s72r2_round170/a162_rag_stream_q128_s72r2_exact2.json', 'source_task': 'weave-evolve-knn-build-681b'}} +Q128_K10_WARPMERGE_TIMING = {'seed_id': SEED_Q128_K10_WARPMERGE_ID, 'kernel_ms': 0.109633, 'baseline_ms': 0.115521, 'flashlib_ms': 0.13389, 'ratio_vs_flashlib': 1.221256373537165, 'speedup_vs_direct_split72': 1.053706456997437, 'tflops': 29.888810850747493, 'baseline_tflops': 28.36540542412202, 'timing_backend': 'cupti', 'source_payload': 'artifacts/weave_evolve/knn_build_df0f_d128_rag_q128_k10_s74_warpmerge_round173/df0f_q128_k10_s74_warpmerge_v1.json', 'source_task': 'weave-evolve-knn-build-4ce8'} +Q128_K10_ROWLD_1BED_TIMING = {'seed_id': SEED_Q128_K10_ROWLD_1BED_ID, 'kernel_ms': 0.081311, 'baseline_34da_ms': 0.109184, 'flashlib_ms': 0.134144, 'ratio_vs_flashlib': 1.6497644845100912, 'speedup_vs_34da': 1.3427949477930416, 'tflops': 40.2995904613152, 'timing_backend': 'cupti', 'source_payload': 'artifacts/weave_evolve/knn_build_1bed_rag_stream_k10_q128_rowld/rag_stream_k10_q128_s74_rowld_1bed_v1.json', 'source_task': 'weave-evolve-knn-build-41bb'} +Q16_M250_TIMING = {'seed_id': SEED_Q16_M250_ID, 'kernel_ms': 0.160128, 'flashlib_ms': 0.19824, 'ratio_vs_flashlib': 1.2380095923261392, 'tflops': 6.394884092725819, 'timing_backend': 'cupti', 'source_payload': 'artifacts/weave_evolve/knn_build_df0f_d128_rag_q16m250_s288_round170/df0f_q16m250_exact1_dispatch_df0f_q16m250_s288_v1.json', 'source_task': 'weave-evolve-knn-build-cb99'} +ALT_F1D9_D768_TIMING = {'seed_id': 'common_d_56f3_build_highd_v1', 'kernel_ms': 0.036609, 'flashlib_ms': 0.108768, 'ratio_vs_flashlib': 2.9710726870441695, 'tflops': 43.99499401786446, 'timing_backend': 'cupti', 'source_payload': 'design_doc/active/weave_evolve_knn_build_round_161_56f3.md'} +HISTORICAL_BASELINE_ROWS = {BUILD_D256: {'kernel_ms': 0.185761, 'ratio_vs_flashlib': 0.3791538589908538}, BUILD_D768: {'kernel_ms': 3.461614, 'ratio_vs_flashlib': 0.03124582925768153}, BUILD_D1024: {'kernel_ms': 1.20314, 'ratio_vs_flashlib': 0.10178699070764831}, BUILD_D4096: {'kernel_ms': 4.6607225, 'ratio_vs_flashlib': 0.07148784335475883}, D768_SEARCH: {'kernel_ms': 14.019321999999999, 'ratio_vs_flashlib': 0.011618250868337286}, D768_RAG: {'kernel_ms': 0.097568, 'ratio_vs_flashlib': 1.762555346015087}, RAG_D1024: {'kernel_ms': 26.347535, 'ratio_vs_flashlib': 0.0056949919603484726}, RAG_D4096: {'kernel_ms': 69.00885, 'ratio_vs_flashlib': 0.0044159408539629335}, SEARCH_D256: {'kernel_ms': 3.645093, 'ratio_vs_flashlib': 0.12795421131916251}, RAG_D64: {'kernel_ms': 5.0076345, 'ratio_vs_flashlib': 0.013247172891711644}, RAG_D256: {'kernel_ms': 4.999218, 'ratio_vs_flashlib': 0.0159963018216049}} +CANDIDATE_DISPATCHERS = ({'id': CANDIDATE_BASE, 'entrypoint': BASE_ENTRYPOINT, 'consumed_seeds': (), 'guard_plan': ('current v11 common-D fallback dispatcher', 'coverage-only high-D generic fallback rows'), 'expected_shape_wins': (), 'fallback': BASE_ENTRYPOINT, 'rejected_reason': 'baseline only'}, {'id': CANDIDATE_A4EC_BASE, 'entrypoint': A4EC_BASE_ENTRYPOINT, 'consumed_seeds': ('common_d256_q1024_56f3_v1', 'common_d768_build_eeff_m64split_v1', 'common_d_56f3_build_highd_v1', 'non128_frontier_4be7_d768fused_v1'), 'guard_plan': ('a4ec D256 exact build guard', '34d8 D768 exact build guard', 'f1d9 D1024/D4096 exact build guards', '4be7 D768 RAG exact guard', 'then base common-D dispatcher'), 'expected_shape_wins': (BUILD_D256, BUILD_D768, BUILD_D1024, BUILD_D4096, D768_RAG), 'fallback': BASE_ENTRYPOINT, 'rejected_reason': 'same-branch pre-replay baseline; lacks cda9 D768 search and uses slower 165c D256 build seed'}, {'id': CANDIDATE_F1D9_BUILD, 'entrypoint': ''.join([format(MODULE, ''), ':launch_from_contract_inputs_f1d9_build_policy']), 'consumed_seeds': ('common_d_56f3_build_d256_q1024_v1', 'common_d_eeff_search_d768_v1', 'common_d_56f3_build_highd_v1', 'non128_frontier_4be7_d768fused_v1'), 'guard_plan': ('6164 D256 exact build guard', 'cda9 D768 search exact guard', 'f1d9 D768/D1024/D4096 exact build guards', '4be7 D768 RAG exact guard', 'then base common-D dispatcher'), 'expected_shape_wins': (BUILD_D256, D768_SEARCH, BUILD_D768, BUILD_D1024, BUILD_D4096, D768_RAG), 'fallback': BASE_ENTRYPOINT, 'rejected_reason': 'D768 build loses historical exact-shape seed delta to 34d8'}, {'id': CANDIDATE_FA04_BASE, 'entrypoint': FA04_BASE_ENTRYPOINT, 'consumed_seeds': ('common_d_56f3_build_d256_q1024_v1', 'common_d_eeff_search_d768_v1', 'common_d768_build_eeff_m64split_v1', 'common_d_56f3_build_highd_v1', 'non128_frontier_4be7_d768fused_v1'), 'guard_plan': ('6164 D256 exact build guard', '34d8 D768 exact build guard', 'f1d9 D1024/D4096 exact build guards', 'cda9 D768 rectangular search exact guard', '4be7 D768 RAG exact guard', 'then base common-D dispatcher'), 'expected_shape_wins': (BUILD_D256, BUILD_D768, BUILD_D1024, BUILD_D4096, D768_SEARCH, D768_RAG), 'fallback': BASE_ENTRYPOINT, 'rejected_reason': 'same-session baseline for the 4cf7 high-D RAG consumption; lacks D1024/D4096 RAG guards'}, {'id': CANDIDATE_F328_BASE, 'entrypoint': F328_BASE_ENTRYPOINT, 'consumed_seeds': ('common_d_56f3_build_d256_q1024_v1', 'common_d_eeff_search_d768_v1', 'common_d768_build_eeff_m64split_v1', 'common_d_56f3_build_highd_v1', 'non128_frontier_4be7_d768fused_v1', 'common_d_5e7f_rag_highd_v1'), 'guard_plan': ('6164 D256 exact build guard', '34d8 D768 exact build guard', 'f1d9 D1024/D4096 exact build guards', 'cda9 D768 rectangular search exact guard', '4be7 D768 RAG exact guard', '4cf7 D1024/D4096 high-D RAG exact guards', 'then base common-D dispatcher'), 'expected_shape_wins': (BUILD_D256, BUILD_D768, BUILD_D1024, BUILD_D4096, D768_SEARCH, D768_RAG, RAG_D1024, RAG_D4096), 'fallback': BASE_ENTRYPOINT, 'rejected_reason': 'same-session f328 baseline for the 8dbc+ba22 dispatcher synthesis; lacks D256 search and D64/D256 RAG guards'}, {'id': CANDIDATE_MIXED, 'entrypoint': MIXED_BASE_ENTRYPOINT, 'consumed_seeds': ('common_d_56f3_build_d256_q1024_v1', 'common_d_eeff_search_d768_v1', 'common_d768_build_eeff_m64split_v1', 'common_d_56f3_build_highd_v1', 'non128_frontier_4be7_d768fused_v1', 'common_d_5e7f_rag_highd_v1', 'common_d_5e7f_search_d256_v1', 'common_d_5e7f_rag_d64_d256_v1'), 'guard_plan': ('6164 D256 exact build guard', '34d8 D768 exact build guard', 'f1d9 D1024/D4096 exact build guards', 'cda9 D768 rectangular search exact guard', '4be7 D768 RAG exact guard', '4cf7 D1024/D4096 high-D RAG exact guards', '8dbc D256 rectangular search exact guard', 'ba22 D256 RAG exact guard', 'D64 RAG remains on the base route and open for bucket-kernel repair', 'then base common-D dispatcher'), 'expected_shape_wins': MIXED_CONSUMED_SEED_SHAPES, 'fallback': BASE_ENTRYPOINT, 'rejected_reason': 'same-session 1438 baseline for the 0474 D64 RAG repair consumption; lacks D64 RAG repair guard'}, {'id': CANDIDATE_D64_REPAIR, 'entrypoint': D64_REPAIR_BASE_ENTRYPOINT, 'consumed_seeds': ('common_d_56f3_build_d256_q1024_v1', 'common_d_eeff_search_d768_v1', 'common_d768_build_eeff_m64split_v1', 'common_d_56f3_build_highd_v1', 'non128_frontier_4be7_d768fused_v1', 'common_d_5e7f_rag_highd_v1', 'common_d_5e7f_search_d256_v1', 'common_d_5e7f_rag_d64_d256_v1', 'common_d_5e7f_rag_d64_repair_v1'), 'guard_plan': ('6164 D256 exact build guard', '34d8 D768 exact build guard', 'f1d9 D1024/D4096 exact build guards', 'cda9 D768 rectangular search exact guard', '4be7 D768 RAG exact guard', '4cf7 D1024/D4096 high-D RAG exact guards', '0474 D64 RAG repair exact guard', '8dbc D256 rectangular search exact guard', 'ba22 D256 RAG exact guard', 'then base common-D dispatcher'), 'expected_shape_wins': D64_M128_CONSUMED_SEED_SHAPES, 'fallback': BASE_ENTRYPOINT, 'rejected_reason': 'same-session 2ccc baseline for the 631e M128 D64 backfill consumption'}, {'id': CANDIDATE_D64_M128, 'entrypoint': D64_M128_BASE_ENTRYPOINT, 'consumed_seeds': ('common_d_56f3_build_d256_q1024_v1', 'common_d_eeff_search_d768_v1', 'common_d768_build_eeff_m64split_v1', 'common_d_56f3_build_highd_v1', 'non128_frontier_4be7_d768fused_v1', 'common_d_5e7f_rag_highd_v1', 'common_d_5e7f_search_d256_v1', 'common_d_5e7f_rag_d64_d256_v1', 'common_d_1438_rag_d64_m128_v1'), 'guard_plan': ('6164 D256 exact build guard', '34d8 D768 exact build guard', 'f1d9 D1024/D4096 exact build guards', 'cda9 D768 rectangular search exact guard', '4be7 D768 RAG exact guard', '4cf7 D1024/D4096 high-D RAG exact guards', '631e D64 RAG M128 exact guard', '8dbc D256 rectangular search exact guard', 'ba22 D256 RAG exact guard', 'then base common-D dispatcher'), 'expected_shape_wins': D64_M128_CONSUMED_SEED_SHAPES, 'fallback': BASE_ENTRYPOINT, 'rejected_reason': 'same-session baseline for the c271 D64 Q4096 consumption; lacks the c271 exact guard'}, {'id': CANDIDATE_D64_Q4096_C271, 'entrypoint': D64_Q4096_C271_BASE_ENTRYPOINT, 'consumed_seeds': ('common_d_56f3_build_d256_q1024_v1', 'common_d_eeff_search_d768_v1', 'common_d768_build_eeff_m64split_v1', 'common_d_56f3_build_highd_v1', 'non128_frontier_4be7_d768fused_v1', 'common_d_5e7f_rag_highd_v1', 'common_d_5e7f_search_d256_v1', 'common_d_5e7f_rag_d64_d256_v1', 'common_d_1438_rag_d64_m128_v1', 'd64_q4096_c271_twostage_v1'), 'guard_plan': ('6164 D256 exact build guard', '34d8 D768 exact build guard', 'f1d9 D1024/D4096 exact build guards', 'cda9 D768 rectangular search exact guard', '4be7 D768 RAG exact guard', '4cf7 D1024/D4096 high-D RAG exact guards', '631e D64 RAG M128 exact guard', '8dbc D256 rectangular search exact guard', 'ba22 D256 RAG exact guard', 'c271 D64 Q4096 build exact guard', 'then base common-D dispatcher'), 'expected_shape_wins': C271_CONSUMED_SEED_SHAPES, 'fallback': BASE_ENTRYPOINT, 'rejected_reason': 'same-session current-trunk baseline for the c271 plus K13/K48 replay; lacks K13/K48 guards'}, {'id': CANDIDATE_C271_7DC5_K13_K48, 'entrypoint': C271_7DC5_BASE_ENTRYPOINT, 'consumed_seeds': ('common_d_56f3_build_d256_q1024_v1', 'common_d_eeff_search_d768_v1', 'common_d768_build_eeff_m64split_v1', 'common_d_56f3_build_highd_v1', 'non128_frontier_4be7_d768fused_v1', 'common_d_5e7f_rag_highd_v1', 'common_d_5e7f_search_d256_v1', 'common_d_5e7f_rag_d64_d256_v1', 'common_d_1438_rag_d64_m128_v1', 'd64_q4096_c271_twostage_v1', 'knn_build_k13_k48_floor_repair_7dc5_v1'), 'guard_plan': ('6164 D256 exact build guard', '34d8 D768 exact build guard', 'f1d9 D1024/D4096 exact build guards', 'cda9 D768 rectangular search exact guard', '4be7 D768 RAG exact guard', '4cf7 D1024/D4096 high-D RAG exact guards', '631e D64 RAG M128 exact guard', '8dbc D256 rectangular search exact guard', 'ba22 D256 RAG exact guard', 'c271 D64 Q4096 build exact guard', '7dc5 Q4096 K13 exact build guard', '7dc5 Q2048/Q4096 K48 exact build guards', 'then base common-D dispatcher'), 'expected_shape_wins': C271_7DC5_CONSUMED_SEED_SHAPES, 'fallback': BASE_ENTRYPOINT, 'rejected_reason': 'same-session baseline for the merged Q128/Q16 floor-clearing synthesis; lacks Q128/Q16 guards'}, {'id': CANDIDATE_2498_BASELINE, 'entrypoint': BASE_2498_ENTRYPOINT, 'consumed_seeds': ('common_d_56f3_build_d256_q1024_v1', 'common_d_eeff_search_d768_v1', 'common_d768_build_eeff_m64split_v1', 'common_d_56f3_build_highd_v1', 'non128_frontier_4be7_d768fused_v1', 'common_d_5e7f_rag_highd_v1', 'common_d_5e7f_search_d256_v1', 'common_d_5e7f_rag_d64_d256_v1', 'common_d_1438_rag_d64_m128_v1', 'd64_q4096_c271_twostage_v1', 'knn_build_k13_k48_floor_repair_7dc5_v1', SEED_Q128_S72R2_ID, SEED_Q128_DUALM_ID, SEED_Q16_M250_ID, SEED_LARGE_SQUARE_K32_EFE4_ID), 'guard_plan': ('6164 D256 exact build guard', '34d8 D768 exact build guard', 'f1d9 D1024/D4096 exact build guards', 'cda9 D768 rectangular search exact guard', '4be7 D768 RAG exact guard', '4cf7 D1024/D4096 high-D RAG exact guards', '631e D64 RAG M128 exact guard', '8dbc D256 rectangular search exact guard', 'ba22 D256 RAG exact guard', 'c271 D64 Q4096 build exact guard', '7dc5 Q4096 K13 exact build guard', '7dc5 Q2048/Q4096 K48 exact build guards', '681b Q128 K32 exact guard for M=100000', '5698 Q128 K32 exact guard for M=131071', 'cb99 Q16/M250000 K32 exact guard', 'fac0 EFE4 exact large-square D128 K32 guard', 'then base common-D dispatcher'), 'expected_shape_wins': PRE_34DA_CONSUMED_SEED_SHAPES, 'fallback': BASE_ENTRYPOINT, 'rejected_reason': 'same-session 2498 baseline for the 34da RAG stream K10 consumption; lacks 34da guard'}, {'id': CANDIDATE_PRE_Q1_M524_N128, 'entrypoint': PRE_Q1_M524_N128_BASE_ENTRYPOINT, 'consumed_seeds': ('common_d_56f3_build_d256_q1024_v1', 'common_d_eeff_search_d768_v1', 'common_d768_build_eeff_m64split_v1', 'common_d_56f3_build_highd_v1', 'non128_frontier_4be7_d768fused_v1', 'common_d_5e7f_rag_highd_v1', 'common_d_5e7f_search_d256_v1', 'common_d_5e7f_rag_d64_d256_v1', 'common_d_1438_rag_d64_m128_v1', 'd64_q4096_c271_twostage_v1', 'knn_build_k13_k48_floor_repair_7dc5_v1', SEED_Q128_S72R2_ID, SEED_Q128_DUALM_ID, SEED_Q16_M250_ID, SEED_LARGE_SQUARE_K32_EFE4_ID, SEED_RAG_STREAM_K10_34DA_ID), 'guard_plan': ('6164 D256 exact build guard', '34d8 D768 exact build guard', 'f1d9 D1024/D4096 exact build guards', 'cda9 D768 rectangular search exact guard', '4be7 D768 RAG exact guard', '4cf7 D1024/D4096 high-D RAG exact guards', '631e D64 RAG M128 exact guard', '8dbc D256 rectangular search exact guard', 'ba22 D256 RAG exact guard', 'c271 D64 Q4096 build exact guard', '7dc5 Q4096 K13 exact build guard', '7dc5 Q2048/Q4096 K48 exact build guards', '681b Q128 K32 exact guard for M=100000', '5698 Q128 K32 exact guard for M=131071', 'cb99 Q16/M250000 K32 exact guard', 'fac0 EFE4 exact large-square D128 K32 guard', '34da Q128/M100000/K10 RAG stream warp-row merge exact guard', 'then base common-D dispatcher'), 'expected_shape_wins': (*PRE_34DA_CONSUMED_SEED_SHAPES, RAG_STREAM_K10), 'fallback': BASE_ENTRYPOINT, 'rejected_reason': 'same-session ea43 baseline for Q1/M524287 N128 seed consumption; lacks 32b9 guard'}, {'id': CANDIDATE_PRE_Q32_EXACT, 'entrypoint': PRE_Q32_EXACT_BASE_ENTRYPOINT, 'consumed_seeds': ('common_d_56f3_build_d256_q1024_v1', 'common_d_eeff_search_d768_v1', 'common_d768_build_eeff_m64split_v1', 'common_d_56f3_build_highd_v1', 'non128_frontier_4be7_d768fused_v1', 'common_d_5e7f_rag_highd_v1', 'common_d_5e7f_search_d256_v1', 'common_d_5e7f_rag_d64_d256_v1', 'common_d_1438_rag_d64_m128_v1', 'd64_q4096_c271_twostage_v1', 'knn_build_k13_k48_floor_repair_7dc5_v1', SEED_Q128_S72R2_ID, SEED_Q128_DUALM_ID, SEED_Q16_M250_ID, SEED_LARGE_SQUARE_K32_EFE4_ID, SEED_RAG_STREAM_K10_34DA_ID, SEED_Q1_M524_N128_ID), 'guard_plan': ('6164 D256 exact build guard', '34d8 D768 exact build guard', 'f1d9 D1024/D4096 exact build guards', 'cda9 D768 rectangular search exact guard', '4be7 D768 RAG exact guard', '4cf7 D1024/D4096 high-D RAG exact guards', '631e D64 RAG M128 exact guard', '8dbc D256 rectangular search exact guard', 'ba22 D256 RAG exact guard', 'c271 D64 Q4096 build exact guard', '7dc5 Q4096 K13 exact build guard', '7dc5 Q2048/Q4096 K48 exact build guards', '681b Q128 K32 exact guard for M=100000', '5698 Q128 K32 exact guard for M=131071', 'cb99 Q16/M250000 K32 exact guard', 'fac0 EFE4 exact large-square D128 K32 guard', '34da Q128/M100000/K10 RAG stream warp-row merge exact guard', '32b9 Q1/M524287/D128/K10 M64/N128 exact guard', 'then base common-D dispatcher'), 'expected_shape_wins': (*PRE_34DA_CONSUMED_SEED_SHAPES, RAG_STREAM_K10, RAG_Q1_M524287_K10), 'fallback': BASE_ENTRYPOINT, 'rejected_reason': 'same-session baseline for Q32 exact guard consumption; lacks 12ac f653 q32rowld2exact guard'}, {'id': CANDIDATE_PRE_Q32TAIL, 'entrypoint': PRE_Q32TAIL_BASE_ENTRYPOINT, 'consumed_seeds': ('common_d_56f3_build_d256_q1024_v1', 'common_d_eeff_search_d768_v1', 'common_d768_build_eeff_m64split_v1', 'common_d_56f3_build_highd_v1', 'non128_frontier_4be7_d768fused_v1', 'common_d_5e7f_rag_highd_v1', 'common_d_5e7f_search_d256_v1', 'common_d_5e7f_rag_d64_d256_v1', 'common_d_1438_rag_d64_m128_v1', 'd64_q4096_c271_twostage_v1', 'knn_build_k13_k48_floor_repair_7dc5_v1', SEED_Q128_S72R2_ID, SEED_Q128_DUALM_ID, SEED_Q16_M250_ID, SEED_LARGE_SQUARE_K32_EFE4_ID, SEED_RAG_STREAM_K10_34DA_ID, SEED_Q1_M524_N128_ID, SEED_Q32_EXACT_ID), 'guard_plan': ('6164 D256 exact build guard', '34d8 D768 exact build guard', 'f1d9 D1024/D4096 exact build guards', 'cda9 D768 rectangular search exact guard', '4be7 D768 RAG exact guard', '4cf7 D1024/D4096 high-D RAG exact guards', '631e D64 RAG M128 exact guard', '8dbc D256 rectangular search exact guard', 'ba22 D256 RAG exact guard', 'c271 D64 Q4096 build exact guard', '7dc5 Q4096 K13 exact build guard', '7dc5 Q2048/Q4096 K48 exact build guards', '681b Q128 K32 exact guard for M=100000', '5698 Q128 K32 exact guard for M=131071', 'cb99 Q16/M250000 K32 exact guard', 'fac0 EFE4 exact large-square D128 K32 guard', '34da Q128/M100000/K10 RAG stream warp-row merge exact guard', '32b9 Q1/M524287/D128/K10 M64/N128 exact guard', '12ac Q32/M100000/D128/K32 f653 rowld2 exact-stage1 guard', 'then base common-D dispatcher'), 'expected_shape_wins': (*PRE_34DA_CONSUMED_SEED_SHAPES, RAG_STREAM_K10, RAG_Q1_M524287_K10, RAG_Q32_M100000_K32), 'fallback': BASE_ENTRYPOINT, 'rejected_reason': 'same-session baseline for Q32-tail consumption; lacks 6866 Q32 M-tail guard'}, {'id': CANDIDATE_PRE_EXPANDED_K32_Q48, 'entrypoint': PRE_Q32TAIL_BASE_ENTRYPOINT, 'consumed_seeds': ('common_d_56f3_build_d256_q1024_v1', 'common_d_eeff_search_d768_v1', 'common_d768_build_eeff_m64split_v1', 'common_d_56f3_build_highd_v1', 'non128_frontier_4be7_d768fused_v1', 'common_d_5e7f_rag_highd_v1', 'common_d_5e7f_search_d256_v1', 'common_d_5e7f_rag_d64_d256_v1', 'common_d_1438_rag_d64_m128_v1', 'd64_q4096_c271_twostage_v1', 'knn_build_k13_k48_floor_repair_7dc5_v1', SEED_Q128_S72R2_ID, SEED_Q128_DUALM_ID, SEED_Q16_M250_ID, SEED_LARGE_SQUARE_K32_EFE4_ID, SEED_RAG_STREAM_K10_34DA_ID, SEED_Q1_M524_N128_ID, SEED_Q32_EXACT_ID, SEED_Q32TAIL_ID), 'guard_plan': ('6164 D256 exact build guard', '34d8 D768 exact build guard', 'f1d9 D1024/D4096 exact build guards', 'cda9 D768 rectangular search exact guard', '4be7 D768 RAG exact guard', '4cf7 D1024/D4096 high-D RAG exact guards', '631e D64 RAG M128 exact guard', '8dbc D256 rectangular search exact guard', 'ba22 D256 RAG exact guard', 'c271 D64 Q4096 build exact guard', '7dc5 Q4096 K13 exact build guard', '7dc5 Q2048/Q4096 K48 exact build guards', '681b Q128 K32 exact guard for M=100000', '5698 Q128 K32 exact guard for M=131071', 'cb99 Q16/M250000 K32 exact guard', 'fac0 EFE4 exact large-square D128 K32 guard', '34da Q128/M100000/K10 RAG stream warp-row merge exact guard', '32b9 Q1/M524287/D128/K10 M64/N128 exact guard', '12ac Q32/M100000/D128/K32 f653 rowld2 exact-stage1 guard', '6866 Q32/M99999 and Q32/M100001 D128/K32 tail guard', 'then base common-D dispatcher'), 'expected_shape_wins': PRE_EXPANDED_K32_Q48_CONSUMED_SEED_SHAPES, 'fallback': BASE_ENTRYPOINT, 'rejected_reason': 'same-session baseline for expanded Q31/Q33/Q40/Q48 consumption; lacks those exact expanded guards'}, {'id': CANDIDATE_PRE_LOWK_C3D2, 'entrypoint': PRE_LOWK_C3D2_BASE_ENTRYPOINT, 'consumed_seeds': ('common_d_56f3_build_d256_q1024_v1', 'common_d_eeff_search_d768_v1', 'common_d768_build_eeff_m64split_v1', 'common_d_56f3_build_highd_v1', 'non128_frontier_4be7_d768fused_v1', 'common_d_5e7f_rag_highd_v1', 'common_d_5e7f_search_d256_v1', 'common_d_5e7f_rag_d64_d256_v1', 'common_d_1438_rag_d64_m128_v1', 'd64_q4096_c271_twostage_v1', 'knn_build_k13_k48_floor_repair_7dc5_v1', SEED_Q128_S72R2_ID, SEED_Q128_DUALM_ID, SEED_Q16_M250_ID, SEED_LARGE_SQUARE_K32_EFE4_ID, SEED_RAG_STREAM_K10_34DA_ID, SEED_Q1_M524_N128_ID, SEED_Q32_EXACT_ID, SEED_Q32TAIL_ID, SEED_Q31TAIL_V2_ID, SEED_Q33TILE_ID, SEED_Q48_K12_ID), 'guard_plan': ('6164 D256 exact build guard', '34d8 D768 exact build guard', 'f1d9 D1024/D4096 exact build guards', 'cda9 D768 rectangular search exact guard', '4be7 D768 RAG exact guard', '4cf7 D1024/D4096 high-D RAG exact guards', '631e D64 RAG M128 exact guard', '8dbc D256 rectangular search exact guard', 'ba22 D256 RAG exact guard', 'c271 D64 Q4096 build exact guard', '7dc5 Q4096 K13 exact guard', '7dc5 Q2048/Q4096 K48 exact guards', '681b Q128 K32 exact guard for M=100000', '5698 Q128 K32 exact guard for M=131071', 'cb99 Q16/M250000 K32 exact guard', 'fac0 EFE4 exact large-square D128 K32 guard', '34da Q128/M100000/K10 RAG stream warp-row merge exact guard', '32b9 Q1/M524287/D128/K10 M64/N128 exact guard', '12ac Q32/M100000/D128/K32 f653 rowld2 exact-stage1 guard', '6866 Q32/M99999 and Q32/M100001 D128/K32 tail guard', 'c3d2 0cb5 Q31/M100000/D128/K32 exact guard', 'c3d2 c489 Q33/Q40 M100000/D128/K32 exact guard', '2f22 Q48/M75000/D128/K12 exact guard', 'then base common-D dispatcher'), 'expected_shape_wins': PRE_LOWK_C3D2_CONSUMED_SEED_SHAPES, 'fallback': BASE_ENTRYPOINT, 'rejected_reason': 'same-session baseline for low-K c3d2 consumption; lacks K20/K31 exact guards'}, {'id': CANDIDATE_SELECTED_SYNTHESIS, 'entrypoint': ROUTE_ENTRYPOINT, 'consumed_seeds': ('common_d_56f3_build_d256_q1024_v1', 'common_d_eeff_search_d768_v1', 'common_d768_build_eeff_m64split_v1', 'common_d_56f3_build_highd_v1', 'non128_frontier_4be7_d768fused_v1', 'common_d_5e7f_rag_highd_v1', 'common_d_5e7f_search_d256_v1', 'common_d_5e7f_rag_d64_d256_v1', 'common_d_1438_rag_d64_m128_v1', 'd64_q4096_c271_twostage_v1', 'knn_build_k13_k48_floor_repair_7dc5_v1', SEED_Q128_S72R2_ID, SEED_Q128_DUALM_ID, SEED_Q16_M250_ID, SEED_LARGE_SQUARE_K32_EFE4_ID, SEED_RAG_STREAM_K10_34DA_ID, SEED_Q1_M524_N128_ID, SEED_Q32_EXACT_ID, SEED_Q32TAIL_ID, SEED_Q31TAIL_V2_ID, SEED_Q33TILE_ID, SEED_Q48_K12_ID, SEED_Q32_LOWK_C3D2_ID, SEED_Q32_K31_C3D2_ID, SEED_D64_TAIL_017A_ID, SEED_D256_Q4_E2DF_ID), 'guard_plan': ('6164 D256 exact build guard', '34d8 D768 exact build guard', 'f1d9 D1024/D4096 exact build guards', 'cda9 D768 rectangular search exact guard', '4be7 D768 RAG exact guard', '4cf7 D1024/D4096 high-D RAG exact guards', '631e D64 RAG M128 exact guard', '8dbc D256 rectangular search exact guard', 'ba22 D256 RAG exact guard', 'c271 D64 Q4096 build exact guard', '7dc5 Q4096 K13 exact guard', '7dc5 Q2048/Q4096 K48 exact guards', '681b Q128 K32 exact guard for M=100000', '5698 Q128 K32 exact guard for M=131071', 'cb99 Q16/M250000 K32 exact guard', 'fac0 EFE4 exact large-square D128 K32 guard', '34da Q128/M100000/K10 RAG stream warp-row merge exact guard', '32b9 Q1/M524287/D128/K10 M64/N128 exact guard', '12ac Q32/M100000/D128/K32 f653 rowld2 exact-stage1 guard', '6866 Q32/M99999 and Q32/M100001 D128/K32 tail guard', 'c3d2 0cb5 Q31/M100000/D128/K32 exact guard', 'c3d2 c489 Q33/Q40 M100000/D128/K32 exact guard', '2f22 Q48/M75000/D128/K12 exact guard', '4cc4 c3d2 Q32/M100000/D128 low-K exact guard for K20', 'eaf7 c3d2 Q32/M100000/D128/K31 exact guard', '017a v12 D64 Q1/M262143 and Q4/M100000 exact tail guards', 'e2df v12 D256 Q4/M100000/K10 exact long-M guard', 'then base common-D dispatcher'), 'expected_shape_wins': PRE_D256_Q128_59FE_CONSUMED_SEED_SHAPES, 'fallback': BASE_ENTRYPOINT, 'rejected_reason': 'same-session baseline for D256 Q128/K10 59fe consumption; lacks the 59fe exact guard'}, {'id': CANDIDATE_D256_Q128_59FE, 'entrypoint': PRE_D256_K32_59FE_BASE_ENTRYPOINT, 'consumed_seeds': ('common_d_56f3_build_d256_q1024_v1', 'common_d_eeff_search_d768_v1', 'common_d768_build_eeff_m64split_v1', 'common_d_56f3_build_highd_v1', 'non128_frontier_4be7_d768fused_v1', 'common_d_5e7f_rag_highd_v1', 'common_d_5e7f_search_d256_v1', 'common_d_5e7f_rag_d64_d256_v1', 'common_d_1438_rag_d64_m128_v1', 'd64_q4096_c271_twostage_v1', 'knn_build_k13_k48_floor_repair_7dc5_v1', SEED_Q128_S72R2_ID, SEED_Q128_DUALM_ID, SEED_Q16_M250_ID, SEED_LARGE_SQUARE_K32_EFE4_ID, SEED_RAG_STREAM_K10_34DA_ID, SEED_Q1_M524_N128_ID, SEED_Q32_EXACT_ID, SEED_Q32TAIL_ID, SEED_Q31TAIL_V2_ID, SEED_Q33TILE_ID, SEED_Q48_K12_ID, SEED_Q32_LOWK_C3D2_ID, SEED_Q32_K31_C3D2_ID, SEED_D64_TAIL_017A_ID, SEED_D256_Q4_E2DF_ID, SEED_D256_Q128_59FE_ID), 'guard_plan': ('4ce7 guard portfolio through D64 tail and D256 Q4 e2df exact guards', '59fe v12 D256 Q128/M100000/K10 exact long-M guard', 'then base common-D dispatcher'), 'expected_shape_wins': PRE_D256_K32_59FE_CONSUMED_SEED_SHAPES, 'fallback': BASE_ENTRYPOINT, 'rejected_reason': 'same-session baseline for D256/K32 9203 consumption; lacks the exact K32 guard'}, {'id': CANDIDATE_D256_K32_59FE, 'entrypoint': PRE_HIGHD_RAG_22E9_BASE_ENTRYPOINT, 'consumed_seeds': ('common_d_56f3_build_d256_q1024_v1', 'common_d_eeff_search_d768_v1', 'common_d768_build_eeff_m64split_v1', 'common_d_56f3_build_highd_v1', 'non128_frontier_4be7_d768fused_v1', 'common_d_5e7f_rag_highd_v1', 'common_d_5e7f_search_d256_v1', 'common_d_5e7f_rag_d64_d256_v1', 'common_d_1438_rag_d64_m128_v1', 'd64_q4096_c271_twostage_v1', 'knn_build_k13_k48_floor_repair_7dc5_v1', SEED_Q128_S72R2_ID, SEED_Q128_DUALM_ID, SEED_Q16_M250_ID, SEED_LARGE_SQUARE_K32_EFE4_ID, SEED_RAG_STREAM_K10_34DA_ID, SEED_Q1_M524_N128_ID, SEED_Q32_EXACT_ID, SEED_Q32TAIL_ID, SEED_Q31TAIL_V2_ID, SEED_Q33TILE_ID, SEED_Q48_K12_ID, SEED_Q32_LOWK_C3D2_ID, SEED_Q32_K31_C3D2_ID, SEED_D64_TAIL_017A_ID, SEED_D256_Q4_E2DF_ID, SEED_D256_Q128_59FE_ID, SEED_D256_K32_59FE_ID), 'guard_plan': ('22e9 guard portfolio through D64 tail, D256 Q4, and D256 Q128/K10 exact guards', '9203 v12 D256 Q8/Q128 M100000 K32 exact long-M guards', 'then base common-D dispatcher'), 'expected_shape_wins': PRE_HIGHD_RAG_22E9_CONSUMED_SEED_SHAPES, 'fallback': BASE_ENTRYPOINT, 'rejected_reason': 'same-session be66 baseline for 7902 high-D RAG consumption; lacks the exact high-D RAG guard'}, {'id': CANDIDATE_HIGHD_RAG_22E9, 'entrypoint': ROUTE_ENTRYPOINT, 'consumed_seeds': ('common_d_56f3_build_d256_q1024_v1', 'common_d_eeff_search_d768_v1', 'common_d768_build_eeff_m64split_v1', 'common_d_56f3_build_highd_v1', 'non128_frontier_4be7_d768fused_v1', 'common_d_5e7f_rag_highd_v1', 'common_d_5e7f_search_d256_v1', 'common_d_5e7f_rag_d64_d256_v1', 'common_d_1438_rag_d64_m128_v1', 'd64_q4096_c271_twostage_v1', 'knn_build_k13_k48_floor_repair_7dc5_v1', SEED_Q128_S72R2_ID, SEED_Q128_DUALM_ID, SEED_Q16_M250_ID, SEED_LARGE_SQUARE_K32_EFE4_ID, SEED_RAG_STREAM_K10_34DA_ID, SEED_Q1_M524_N128_ID, SEED_Q32_EXACT_ID, SEED_Q32TAIL_ID, SEED_Q31TAIL_V2_ID, SEED_Q33TILE_ID, SEED_Q48_K12_ID, SEED_Q32_LOWK_C3D2_ID, SEED_Q32_K31_C3D2_ID, SEED_D64_TAIL_017A_ID, SEED_D256_Q4_E2DF_ID, SEED_D256_Q128_59FE_ID, SEED_D256_K32_59FE_ID, SEED_HIGHD_RAG_22E9_ID), 'guard_plan': ('be66 guard portfolio through D64, D256 Q4/Q128 K10, and D256 K32 exact guards', '7902 exact BF16 high-D RAG guards for D768/Q8/M100000, D1024/Q4/M100000, and D4096/Q1/M65536', 'then base common-D dispatcher'), 'expected_shape_wins': PRE_HIGHD_SEARCH_BE66_CONSUMED_SEED_SHAPES, 'fallback': BASE_ENTRYPOINT, 'rejected_reason': 'same-session baseline for ad73 high-D search consumption; lacks the exact rectangular-search guard'}, {'id': CANDIDATE_HIGHD_SEARCH_BE66, 'entrypoint': PRE_D128_K48_DD2B_BASE_ENTRYPOINT, 'consumed_seeds': ('common_d_56f3_build_d256_q1024_v1', 'common_d_eeff_search_d768_v1', 'common_d768_build_eeff_m64split_v1', 'common_d_56f3_build_highd_v1', 'non128_frontier_4be7_d768fused_v1', 'common_d_5e7f_rag_highd_v1', 'common_d_5e7f_search_d256_v1', 'common_d_5e7f_rag_d64_d256_v1', 'common_d_1438_rag_d64_m128_v1', 'd64_q4096_c271_twostage_v1', 'knn_build_k13_k48_floor_repair_7dc5_v1', SEED_Q128_S72R2_ID, SEED_Q128_DUALM_ID, SEED_Q16_M250_ID, SEED_LARGE_SQUARE_K32_EFE4_ID, SEED_RAG_STREAM_K10_34DA_ID, SEED_Q1_M524_N128_ID, SEED_Q32_EXACT_ID, SEED_Q32TAIL_ID, SEED_Q31TAIL_V2_ID, SEED_Q33TILE_ID, SEED_Q48_K12_ID, SEED_Q32_LOWK_C3D2_ID, SEED_Q32_K31_C3D2_ID, SEED_D64_TAIL_017A_ID, SEED_D256_Q4_E2DF_ID, SEED_D256_Q128_59FE_ID, SEED_D256_K32_59FE_ID, SEED_HIGHD_RAG_22E9_ID, SEED_HIGHD_SEARCH_BE66_ID), 'guard_plan': ('0d3d guard portfolio through D64, D256 Q4/Q128/K32, and high-D RAG exact guards', 'ad73 exact BF16 high-D rectangular-search guards for D1024/Q256/M8192 and D4096/Q128/M4096', 'then base common-D dispatcher'), 'expected_shape_wins': PRE_D128_K48_DD2B_CONSUMED_SEED_SHAPES, 'fallback': BASE_ENTRYPOINT, 'rejected_reason': 'same-session baseline for dd2b D128/Q16/K48 consumption; lacks the exact K48 guard'}, {'id': CANDIDATE_D128_K48_DD2B, 'entrypoint': PRE_RECT_D128_K20_S12WARP4_BASE_ENTRYPOINT, 'consumed_seeds': ('common_d_56f3_build_d256_q1024_v1', 'common_d_eeff_search_d768_v1', 'common_d768_build_eeff_m64split_v1', 'common_d_56f3_build_highd_v1', 'non128_frontier_4be7_d768fused_v1', 'common_d_5e7f_rag_highd_v1', 'common_d_5e7f_search_d256_v1', 'common_d_5e7f_rag_d64_d256_v1', 'common_d_1438_rag_d64_m128_v1', 'd64_q4096_c271_twostage_v1', 'knn_build_k13_k48_floor_repair_7dc5_v1', SEED_Q128_S72R2_ID, SEED_Q128_DUALM_ID, SEED_Q16_M250_ID, SEED_LARGE_SQUARE_K32_EFE4_ID, SEED_RAG_STREAM_K10_34DA_ID, SEED_Q1_M524_N128_ID, SEED_Q32_EXACT_ID, SEED_Q32TAIL_ID, SEED_Q31TAIL_V2_ID, SEED_Q33TILE_ID, SEED_Q48_K12_ID, SEED_Q32_LOWK_C3D2_ID, SEED_Q32_K31_C3D2_ID, SEED_D64_TAIL_017A_ID, SEED_D256_Q4_E2DF_ID, SEED_D256_Q128_59FE_ID, SEED_D256_K32_59FE_ID, SEED_HIGHD_RAG_22E9_ID, SEED_HIGHD_SEARCH_BE66_ID, SEED_D128_K48_DD2B_ID), 'guard_plan': ('2614 guard portfolio through D64, D256, high-D RAG, and high-D rectangular-search exact guards', 'dd2b exact BF16 non-build B=1 Q=16 M=100000 D=128 K=48 guard', 'then base common-D dispatcher'), 'expected_shape_wins': PRE_RECT_D128_K20_S12WARP4_CONSUMED_SEED_SHAPES, 'fallback': BASE_ENTRYPOINT, 'rejected_reason': 'same-session baseline for 7768 rectangular consumption; lacks the Q1536 D128/K20 split12/warp4 guard'}, {'id': CANDIDATE_RECT_D128_K20_S12WARP4, 'entrypoint': ROUTE_ENTRYPOINT, 'consumed_seeds': ('common_d_56f3_build_d256_q1024_v1', 'common_d_eeff_search_d768_v1', 'common_d768_build_eeff_m64split_v1', 'common_d_56f3_build_highd_v1', 'non128_frontier_4be7_d768fused_v1', 'common_d_5e7f_rag_highd_v1', 'common_d_5e7f_search_d256_v1', 'common_d_5e7f_rag_d64_d256_v1', 'common_d_1438_rag_d64_m128_v1', 'd64_q4096_c271_twostage_v1', 'knn_build_k13_k48_floor_repair_7dc5_v1', SEED_Q128_S72R2_ID, SEED_Q128_DUALM_ID, SEED_Q16_M250_ID, SEED_LARGE_SQUARE_K32_EFE4_ID, SEED_RAG_STREAM_K10_34DA_ID, SEED_Q1_M524_N128_ID, SEED_Q32_EXACT_ID, SEED_Q32TAIL_ID, SEED_Q31TAIL_V2_ID, SEED_Q33TILE_ID, SEED_Q48_K12_ID, SEED_Q32_LOWK_C3D2_ID, SEED_Q32_K31_C3D2_ID, SEED_D64_TAIL_017A_ID, SEED_D256_Q4_E2DF_ID, SEED_D256_Q128_59FE_ID, SEED_D256_K32_59FE_ID, SEED_HIGHD_RAG_22E9_ID, SEED_HIGHD_SEARCH_BE66_ID, SEED_D128_K48_DD2B_ID, SEED_RECT_D128_K20_S12WARP4_ID), 'guard_plan': ('2614 guard portfolio through D64, D256, high-D RAG, high-D rectangular-search, and D128/K48 guards', '7768 exact BF16 non-build B=1 Q=1536 M=65536 D=128 K=20 split12/warp4 guard', 'then base common-D dispatcher'), 'expected_shape_wins': CONSUMED_SEED_SHAPES, 'fallback': BASE_ENTRYPOINT, 'rejected_reason': None}) + +def _select_contract_shapes(shape_labels): + labels = tuple(shape_labels) + selected = [] + remaining = [] + for label in labels: + if label in EXPANDED_Q32_GUARD_BOUNDARY_8_BY_LABEL: + selected.append(EXPANDED_Q32_GUARD_BOUNDARY_8_BY_LABEL[label]) + else: + remaining.append(label) + if remaining: + selected.extend(base_current._select_contract_shapes(tuple(remaining))) + return selected + +def _trace_inputs_for_shape(shape: dict[str, Any]) -> dict[str, Any]: + if str(shape['label']) not in EXPANDED_Q32_GUARD_BOUNDARY_8_BY_LABEL: + return base_current._trace_inputs_for_shape(shape) + params = dict(shape.get('params', {})) + params['label'] = shape['label'] + return params + +def _inputs_for_label(label: str) -> dict[str, Any]: + return _trace_inputs_for_shape(_select_contract_shapes((label,))[0]) + +def _normalize_route_row(row: dict[str, Any]) -> dict[str, Any]: + return base_current._normalize_route_row(row) + +def _highd_label(inputs: dict[str, Any]) -> str | None: + return highd_build._target_label_for_inputs(inputs) + +def _eligible_d768_rag(inputs: dict[str, Any]) -> bool: + return d768_rag._target_label_for_inputs(inputs) == D768_RAG + +def _eligible_d256_6164(inputs: dict[str, Any]) -> bool: + return d256_build._target_label_for_inputs(inputs) == BUILD_D256 + +def _eligible_d768_search(inputs: dict[str, Any]) -> bool: + return d768_search._target_label_for_inputs(inputs) == D768_SEARCH + +def _highd_rag_label(inputs: dict[str, Any]) -> str | None: + return highd_rag._target_label_for_inputs(inputs) + +def _search_d256_label(inputs: dict[str, Any]) -> str | None: + return search_d256._target_label_for_inputs(inputs) + +def _rag_d256_label(inputs: dict[str, Any]) -> str | None: + label = rag_d64d256._target_label_for_inputs(inputs) + return label if label == RAG_D256 else None + +def _rag_d64_repair_label(inputs: dict[str, Any]) -> str | None: + return rag_d64_repair._target_label_for_inputs(inputs) + +def _rag_d64_m128_label(inputs: dict[str, Any]) -> str | None: + return rag_d64_m128._target_label_for_inputs(inputs) + +def _eligible_d64_q4096_c271(inputs: dict[str, Any]) -> bool: + return d64_q4096_c271._eligible_exact_q4096_d64(inputs) + +def _d64_tail_017a_label(inputs: dict[str, Any]) -> str | None: + return d64_tail_017a._target_label_for_inputs(inputs) + +def _d256_q4_e2df_label(inputs: dict[str, Any]) -> str | None: + return d256_q4_e2df._target_label_for_inputs(inputs) + +def _d256_q128_59fe_label(inputs: dict[str, Any]) -> str | None: + return d256_q128_59fe._target_label_for_inputs(inputs) + +def _d256_k32_59fe_label(inputs: dict[str, Any]) -> str | None: + return d256_k32_59fe._target_label_for_inputs(inputs) + +def _highd_rag_22e9_label(inputs: dict[str, Any]) -> str | None: + return highd_rag_22e9._target_label_for_inputs(inputs) + +def _highd_search_be66_label(inputs: dict[str, Any]) -> str | None: + return highd_search_be66._target_label_for_inputs(inputs) + +def _d128_k48_dd2b_label(inputs: dict[str, Any]) -> str | None: + return V12_D128_K48_OVER32 if d128_k48_dd2b._eligible_k48(inputs) else None + +def _q128_dualm(): + from . import knn_build_rag_stream_k32_q128_dualm_a162_v1 as mod + return mod + +def _q128_s72r2(): + from . import knn_build_rag_stream_k32_q128_s72r2_a162_v1 as mod + return mod + +def _q16_m250(): + from . import knn_build_d128_rag_q16m250_df0f_v1 as mod + return mod + +def _rag_stream_k10_34da(): + from . import knn_build_rag_stream_k10_warpmerge_34da_v1 as mod + return mod + +def _eligible_q1_m524_n128(inputs: dict[str, Any]) -> bool: + return q1_m524_n128._eligible_q1_m524_n128(inputs) + +def _eligible_q32_exact(inputs: dict[str, Any]) -> bool: + return q32exact._eligible_q32_rowld2exact(inputs) + +def _q32tail(): + from . import knn_build_rag_microbucket_k32_0cb5_q31tail_v1 as mod + return mod + +def _eligible_q32tail(inputs: dict[str, Any]) -> bool: + return bool(_q32tail()._eligible_q32tail_exact(inputs)) + +def _q32tail143_low(): + from . import knn_build_rag_microbucket_k32_5317_q32tail143low_v1 as mod + return mod + +def _q32tail143_high(): + from . import knn_build_rag_microbucket_k32_314c_q32tail143_v1 as mod + return mod + +def _eligible_q32tail143(inputs: dict[str, Any]) -> bool: + return bool(_q32tail143_low()._eligible_q32tail143low(inputs) or _q32tail143_high()._eligible_q32tail143(inputs)) + +def _q32tail143_module_for_inputs(inputs: dict[str, Any]): + if _q32tail143_low()._eligible_q32tail143low(inputs): + return _q32tail143_low() + if _q32tail143_high()._eligible_q32tail143(inputs): + return _q32tail143_high() + raise ValueError('Q32 split143 tail route requested for non-target inputs') + +def _q32tail143_route_metadata(inputs: dict[str, Any]) -> tuple[str, str, str]: + if _q32tail143_low()._eligible_q32tail143low(inputs): + return (SEED_Q32TAIL143_LOW_ID, Q32TAIL143_LOW_ENTRYPOINT, Q32TAIL143_LOW_GUARD_ID) + if _q32tail143_high()._eligible_q32tail143(inputs): + return (SEED_Q32TAIL143_HIGH_ID, Q32TAIL143_HIGH_ENTRYPOINT, Q32TAIL143_HIGH_GUARD_ID) + raise ValueError('Q32 split143 tail metadata requested for non-target inputs') + +def _q31tail_v2(): + from . import knn_build_rag_microbucket_k32_0cb5_q31tail_v2 as mod + return mod + +def _eligible_q31tail_v2(inputs: dict[str, Any]) -> bool: + return bool(_q31tail_v2()._eligible_q31tail_v2(inputs)) + +def _q33tile(): + from . import knn_build_rag_microbucket_k32_c489_q33tile_v1 as mod + return mod + +def _eligible_q33tile(inputs: dict[str, Any]) -> bool: + return bool(_q33tile()._eligible_q33tile(inputs)) + +def _q48_k12(): + from . import knn_build_rag_microbucket_k12_2f22_q48exact_v1 as mod + return mod + +def _eligible_q48_k12(inputs: dict[str, Any]) -> bool: + return bool(_q48_k12()._eligible_q48_k12(inputs)) + +def _q32_lowk_c3d2(): + from . import knn_build_rag_microbucket_q32_lowk_c3d2_v1 as mod + return mod + +def _eligible_q32_lowk_c3d2(inputs: dict[str, Any]) -> bool: + return bool(_q32_lowk_c3d2()._eligible_q32_lowk(inputs)) + +def _q32_k31_c3d2(): + from . import knn_build_rag_microbucket_q32_k31_c3d2_v1 as mod + return mod + +def _eligible_q32_k31_c3d2(inputs: dict[str, Any]) -> bool: + return bool(_q32_k31_c3d2()._eligible_q32_k31(inputs)) + +def _uses_floor_seed_portfolio(portfolio_id: str) -> bool: + return portfolio_id in FLOOR_SEED_PORTFOLIOS + +def _eligible_q128_floor_seed(inputs: dict[str, Any]) -> bool: + return bool(_q128_s72r2()._eligible_q128_stream_k32(inputs)) + +def _eligible_q16_floor_seed(inputs: dict[str, Any]) -> bool: + selected, _label = _q16_m250()._selected_seed(inputs) + return selected == SEED_Q16_M250_ID + +def _eligible_large_square_k32_efe4(inputs: dict[str, Any]) -> bool: + return large_square_k32_efe4._eligible_q8192_k32(inputs) + +def _eligible_q128_k10_warpmerge(inputs: dict[str, Any]) -> bool: + return q128_k10_warpmerge._eligible_split74_warpmerge(inputs) + +def _q128_k10_rowld_1bed(): + from . import knn_build_rag_stream_k10_q128_1bed_rowld_v1 as mod + return mod + +def _eligible_q128_k10_rowld_1bed(inputs: dict[str, Any]) -> bool: + return _q128_k10_rowld_1bed()._eligible_q128_m100000_k10(inputs) + +def _eligible_rag_stream_k10_34da(inputs: dict[str, Any]) -> bool: + return bool(_rag_stream_k10_34da()._eligible_q128_m100000_k10(inputs)) + +def _q128_module_for_portfolio(inputs: dict[str, Any], portfolio_id: str): + if portfolio_id == CANDIDATE_FLOOR_SEEDS_Q128_5698: + return _q128_dualm() + if portfolio_id == CANDIDATE_FLOOR_SEEDS_Q128_681B: + return _q128_s72r2() + if int(inputs.get('M', -1)) == 131071: + return _q128_dualm() + return _q128_s72r2() + +def _k13_k48_seed_label(inputs: dict[str, Any]) -> tuple[str | None, str | None]: + if seed_k13._eligible_midk_q4096(inputs): + return (SEED_K13_ID, BUILD_K13) + if seed_k48._eligible_k48(inputs): + label = str(inputs.get('label') or (BUILD_K48_Q2048 if int(inputs.get('Q', -1)) == 2048 else BUILD_K48_Q4096)) + return (SEED_K48_ID, label) + return (None, None) + +def _route_k13_k48(inputs: dict[str, Any]) -> str: + selected_seed, _label = _k13_k48_seed_label(inputs) + if selected_seed == SEED_K13_ID: + return seed_k13.route_for_contract_inputs(inputs) + if selected_seed == SEED_K48_ID: + return seed_k48.route_for_contract_inputs(inputs) + raise ValueError('K13/K48 route requested for non-target inputs') + +def _route_for_policy(inputs: dict[str, Any], *, portfolio_id: str, force_fallback: bool=False) -> str: + if force_fallback: + return base_current.route_for_contract_inputs(inputs, force_fallback=True) + if portfolio_id in PORTFOLIOS_WITH_Q128_K10_ROWLD_1BED: + if _eligible_q128_k10_rowld_1bed(inputs): + return _q128_k10_rowld_1bed().route_for_contract_inputs(inputs) + return _route_for_policy(inputs, portfolio_id=CANDIDATE_RECT_D128_K20_S12WARP4) + if portfolio_id in PORTFOLIOS_WITH_RECT_D128_K20_S12WARP4: + if rect_d128_k20_s12warp4._eligible_rect_d128_k20_q1536(inputs): + return rect_d128_k20_s12warp4.route_for_contract_inputs(inputs) + return _route_for_policy(inputs, portfolio_id=CANDIDATE_D128_K48_DD2B) + if portfolio_id in PORTFOLIOS_WITH_Q32_TAIL143 and _eligible_q32tail143(inputs): + return _q32tail143_module_for_inputs(inputs).route_for_contract_inputs(inputs) + if portfolio_id in PORTFOLIOS_WITH_D128_K48_DD2B: + if _d128_k48_dd2b_label(inputs) is not None: + return d128_k48_dd2b.route_for_contract_inputs(inputs) + return _route_for_policy(inputs, portfolio_id=CANDIDATE_HIGHD_SEARCH_BE66) + if portfolio_id in PORTFOLIOS_WITH_HIGHD_SEARCH_BE66: + if _highd_search_be66_label(inputs) is not None: + return highd_search_be66.route_for_contract_inputs(inputs) + return _route_for_policy(inputs, portfolio_id=CANDIDATE_HIGHD_RAG_22E9) + if portfolio_id in PORTFOLIOS_WITH_HIGHD_RAG_22E9: + if _highd_rag_22e9_label(inputs) is not None: + return highd_rag_22e9.route_for_contract_inputs(inputs) + return _route_for_policy(inputs, portfolio_id=CANDIDATE_D256_K32_59FE) + if portfolio_id in PORTFOLIOS_WITH_D256_K32_59FE: + if _d256_k32_59fe_label(inputs) is not None: + return d256_k32_59fe.route_for_contract_inputs(inputs) + return _route_for_policy(inputs, portfolio_id=CANDIDATE_D256_Q128_59FE) + if portfolio_id in PORTFOLIOS_WITH_D256_Q128_59FE: + if _d256_q128_59fe_label(inputs) is not None: + return d256_q128_59fe.route_for_contract_inputs(inputs) + return _route_for_policy(inputs, portfolio_id=CANDIDATE_SELECTED_SYNTHESIS) + if portfolio_id == CANDIDATE_A4EC_BASE: + if d256_build_a4ec._eligible_d256_q1024(inputs): + return d256_build_a4ec.route_for_contract_inputs(inputs) + if d768_build_fast._eligible_d768_build(inputs): + return d768_build_fast.route_for_contract_inputs(inputs) + label = _highd_label(inputs) + if label is not None: + return highd_build.route_for_contract_inputs(inputs) + if _eligible_d768_rag(inputs): + return d768_rag.route_for_contract_inputs(inputs) + return base_current.route_for_contract_inputs(inputs) + if _eligible_d256_6164(inputs): + return d256_build.route_for_contract_inputs(inputs) + if portfolio_id != CANDIDATE_F1D9_BUILD and d768_build_fast._eligible_d768_build(inputs): + return d768_build_fast.route_for_contract_inputs(inputs) + label = _highd_label(inputs) + if label is not None: + return highd_build.route_for_contract_inputs(inputs) + if _eligible_d768_search(inputs): + return d768_search.route_for_contract_inputs(inputs) + if _eligible_d768_rag(inputs): + return d768_rag.route_for_contract_inputs(inputs) + if portfolio_id in PORTFOLIOS_WITH_HIGHD_RAG and _highd_rag_label(inputs) is not None: + return highd_rag.route_for_contract_inputs(inputs) + if portfolio_id in PORTFOLIOS_WITH_SEARCH_D256 and _search_d256_label(inputs) is not None: + return search_d256.route_for_contract_inputs(inputs) + if portfolio_id in PORTFOLIOS_WITH_RAG_D64_M128 and _rag_d64_m128_label(inputs) is not None: + return rag_d64_m128.route_for_contract_inputs(inputs) + if portfolio_id == CANDIDATE_D64_REPAIR and _rag_d64_repair_label(inputs) is not None: + return rag_d64_repair.route_for_contract_inputs(inputs) + if portfolio_id in PORTFOLIOS_WITH_SEARCH_D256 and _rag_d256_label(inputs) is not None: + return rag_d64d256.route_for_contract_inputs(inputs) + if portfolio_id in PORTFOLIOS_WITH_D256_Q4_E2DF and _d256_q4_e2df_label(inputs) is not None: + return d256_q4_e2df.route_for_contract_inputs(inputs) + if portfolio_id in PORTFOLIOS_WITH_D64_Q4096_C271 and _eligible_d64_q4096_c271(inputs): + return d64_q4096_c271.route_name_for_inputs(inputs) + if portfolio_id in PORTFOLIOS_WITH_D64_TAIL_017A and _d64_tail_017a_label(inputs) is not None: + return d64_tail_017a.route_for_contract_inputs(inputs) + if portfolio_id in PORTFOLIOS_WITH_K13_K48 and _k13_k48_seed_label(inputs)[0] is not None: + return _route_k13_k48(inputs) + if _uses_floor_seed_portfolio(portfolio_id) and _eligible_q128_floor_seed(inputs): + return _q128_module_for_portfolio(inputs, portfolio_id).route_for_contract_inputs(inputs) + if portfolio_id in PORTFOLIOS_WITH_Q128_K10_WARPMERGE and _eligible_q128_k10_warpmerge(inputs): + return q128_k10_warpmerge.route_for_contract_inputs(inputs) + if _uses_floor_seed_portfolio(portfolio_id) and _eligible_q16_floor_seed(inputs): + return _q16_m250().route_for_contract_inputs(inputs) + if portfolio_id in PORTFOLIOS_WITH_LARGE_SQUARE_K32_EFE4 and _eligible_large_square_k32_efe4(inputs): + return large_square_k32_efe4.route_for_contract_inputs(inputs) + if portfolio_id in PORTFOLIOS_WITH_RAG_STREAM_K10_34DA and _eligible_rag_stream_k10_34da(inputs): + return _rag_stream_k10_34da().route_for_contract_inputs(inputs) + if portfolio_id in PORTFOLIOS_WITH_Q1_M524_N128 and _eligible_q1_m524_n128(inputs): + return q1_m524_n128.route_for_contract_inputs(inputs) + if portfolio_id in PORTFOLIOS_WITH_Q32_EXACT and _eligible_q32_exact(inputs): + return q32exact.route_for_contract_inputs(inputs) + if portfolio_id in PORTFOLIOS_WITH_Q32_TAIL and _eligible_q32tail(inputs): + return _q32tail().route_for_contract_inputs(inputs) + if portfolio_id in PORTFOLIOS_WITH_EXPANDED_K32_Q31_Q33_Q40 and _eligible_q31tail_v2(inputs): + return _q31tail_v2().route_for_contract_inputs(inputs) + if portfolio_id in PORTFOLIOS_WITH_EXPANDED_K32_Q31_Q33_Q40 and _eligible_q33tile(inputs): + return _q33tile().route_for_contract_inputs(inputs) + if portfolio_id in PORTFOLIOS_WITH_Q48_K12 and _eligible_q48_k12(inputs): + return _q48_k12().route_for_contract_inputs(inputs) + if portfolio_id in PORTFOLIOS_WITH_Q32_K31_C3D2 and _eligible_q32_k31_c3d2(inputs): + return _q32_k31_c3d2().route_for_contract_inputs(inputs) + if portfolio_id in PORTFOLIOS_WITH_Q32_LOWK_C3D2 and _eligible_q32_lowk_c3d2(inputs): + return _q32_lowk_c3d2().route_for_contract_inputs(inputs) + return base_current.route_for_contract_inputs(inputs) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False, portfolio_id: str=CANDIDATE_DEFAULT) -> str: + return _route_for_policy(inputs, portfolio_id=portfolio_id, force_fallback=force_fallback) + +def route_for_contract_inputs_f1d9_build_policy(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + return route_for_contract_inputs(inputs, force_fallback=force_fallback, portfolio_id=CANDIDATE_F1D9_BUILD) + +def route_for_contract_inputs_a4ec_baseline(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + return route_for_contract_inputs(inputs, force_fallback=force_fallback, portfolio_id=CANDIDATE_A4EC_BASE) + +def route_for_contract_inputs_fa04_baseline(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + return route_for_contract_inputs(inputs, force_fallback=force_fallback, portfolio_id=CANDIDATE_FA04_BASE) + +def route_for_contract_inputs_f328_baseline(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + return route_for_contract_inputs(inputs, force_fallback=force_fallback, portfolio_id=CANDIDATE_F328_BASE) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False, portfolio_id: str=CANDIDATE_DEFAULT) -> None: + if force_fallback: + base_current.launch_from_contract_inputs(inputs, force_fallback=True) + return + if portfolio_id in PORTFOLIOS_WITH_Q128_K10_ROWLD_1BED: + if _eligible_q128_k10_rowld_1bed(inputs): + _q128_k10_rowld_1bed().launch_from_contract_inputs(inputs) + return + launch_from_contract_inputs(inputs, portfolio_id=CANDIDATE_RECT_D128_K20_S12WARP4) + return + if portfolio_id in PORTFOLIOS_WITH_RECT_D128_K20_S12WARP4: + if rect_d128_k20_s12warp4._eligible_rect_d128_k20_q1536(inputs): + rect_d128_k20_s12warp4.launch_from_contract_inputs(inputs) + return + launch_from_contract_inputs(inputs, portfolio_id=CANDIDATE_D128_K48_DD2B) + return + if portfolio_id in PORTFOLIOS_WITH_Q32_TAIL143 and _eligible_q32tail143(inputs): + _q32tail143_module_for_inputs(inputs).launch_from_contract_inputs(inputs) + return + if portfolio_id in PORTFOLIOS_WITH_D128_K48_DD2B: + if _d128_k48_dd2b_label(inputs) is not None: + d128_k48_dd2b.launch_from_contract_inputs(inputs) + return + launch_from_contract_inputs(inputs, portfolio_id=CANDIDATE_HIGHD_SEARCH_BE66) + return + if portfolio_id in PORTFOLIOS_WITH_HIGHD_SEARCH_BE66: + if _highd_search_be66_label(inputs) is not None: + highd_search_be66.launch_from_contract_inputs(inputs) + return + launch_from_contract_inputs(inputs, portfolio_id=CANDIDATE_HIGHD_RAG_22E9) + return + if portfolio_id in PORTFOLIOS_WITH_HIGHD_RAG_22E9: + if _highd_rag_22e9_label(inputs) is not None: + highd_rag_22e9.launch_from_contract_inputs(inputs) + return + launch_from_contract_inputs(inputs, portfolio_id=CANDIDATE_D256_K32_59FE) + return + if portfolio_id in PORTFOLIOS_WITH_D256_K32_59FE: + if _d256_k32_59fe_label(inputs) is not None: + d256_k32_59fe.launch_from_contract_inputs(inputs) + return + launch_from_contract_inputs(inputs, portfolio_id=CANDIDATE_D256_Q128_59FE) + return + if portfolio_id in PORTFOLIOS_WITH_D256_Q128_59FE: + if _d256_q128_59fe_label(inputs) is not None: + d256_q128_59fe.launch_from_contract_inputs(inputs) + return + launch_from_contract_inputs(inputs, portfolio_id=CANDIDATE_SELECTED_SYNTHESIS) + return + if portfolio_id == CANDIDATE_A4EC_BASE: + if d256_build_a4ec._eligible_d256_q1024(inputs): + d256_build_a4ec.launch_from_contract_inputs(inputs) + return + if d768_build_fast._eligible_d768_build(inputs): + d768_build_fast.launch_from_contract_inputs(inputs) + return + if _highd_label(inputs) is not None: + highd_build.launch_from_contract_inputs(inputs) + return + if _eligible_d768_rag(inputs): + d768_rag.launch_from_contract_inputs(inputs) + return + base_current.launch_from_contract_inputs(inputs) + return + if _eligible_d256_6164(inputs): + d256_build.launch_from_contract_inputs(inputs) + return + if portfolio_id in PORTFOLIOS_WITH_D768_FAST and d768_build_fast._eligible_d768_build(inputs): + d768_build_fast.launch_from_contract_inputs(inputs) + return + if _highd_label(inputs) is not None: + highd_build.launch_from_contract_inputs(inputs) + return + if _eligible_d768_search(inputs): + d768_search.launch_from_contract_inputs(inputs) + return + if _eligible_d768_rag(inputs): + d768_rag.launch_from_contract_inputs(inputs) + return + if portfolio_id in PORTFOLIOS_WITH_HIGHD_RAG and _highd_rag_label(inputs) is not None: + highd_rag.launch_from_contract_inputs(inputs) + return + if portfolio_id in PORTFOLIOS_WITH_SEARCH_D256 and _search_d256_label(inputs) is not None: + search_d256.launch_from_contract_inputs(inputs) + return + if portfolio_id in PORTFOLIOS_WITH_RAG_D64_M128 and _rag_d64_m128_label(inputs) is not None: + rag_d64_m128.launch_from_contract_inputs(inputs) + return + if portfolio_id == CANDIDATE_D64_REPAIR and _rag_d64_repair_label(inputs) is not None: + rag_d64_repair.launch_from_contract_inputs(inputs) + return + if portfolio_id in PORTFOLIOS_WITH_SEARCH_D256 and _rag_d256_label(inputs) is not None: + rag_d64d256.launch_from_contract_inputs(inputs) + return + if portfolio_id in PORTFOLIOS_WITH_D256_Q4_E2DF and _d256_q4_e2df_label(inputs) is not None: + d256_q4_e2df.launch_from_contract_inputs(inputs) + return + if portfolio_id in PORTFOLIOS_WITH_D64_Q4096_C271 and _eligible_d64_q4096_c271(inputs): + d64_q4096_c271.launch_from_contract_inputs(inputs) + return + if portfolio_id in PORTFOLIOS_WITH_D64_TAIL_017A and _d64_tail_017a_label(inputs) is not None: + d64_tail_017a.launch_from_contract_inputs(inputs) + return + if portfolio_id in PORTFOLIOS_WITH_K13_K48 and _k13_k48_seed_label(inputs)[0] is not None: + selected_seed, _label = _k13_k48_seed_label(inputs) + if selected_seed == SEED_K13_ID: + seed_k13.launch_from_contract_inputs(inputs) + else: + seed_k48.launch_from_contract_inputs(inputs) + return + if _uses_floor_seed_portfolio(portfolio_id) and _eligible_q128_floor_seed(inputs): + _q128_module_for_portfolio(inputs, portfolio_id).launch_from_contract_inputs(inputs) + return + if portfolio_id in PORTFOLIOS_WITH_Q128_K10_WARPMERGE and _eligible_q128_k10_warpmerge(inputs): + q128_k10_warpmerge.launch_from_contract_inputs(inputs) + return + if _uses_floor_seed_portfolio(portfolio_id) and _eligible_q16_floor_seed(inputs): + _q16_m250().launch_from_contract_inputs(inputs) + return + if portfolio_id in PORTFOLIOS_WITH_LARGE_SQUARE_K32_EFE4 and _eligible_large_square_k32_efe4(inputs): + large_square_k32_efe4.launch_from_contract_inputs(inputs) + return + if portfolio_id in PORTFOLIOS_WITH_RAG_STREAM_K10_34DA and _eligible_rag_stream_k10_34da(inputs): + _rag_stream_k10_34da().launch_from_contract_inputs(inputs) + return + if portfolio_id in PORTFOLIOS_WITH_Q1_M524_N128 and _eligible_q1_m524_n128(inputs): + q1_m524_n128.launch_from_contract_inputs(inputs) + return + if portfolio_id in PORTFOLIOS_WITH_Q32_EXACT and _eligible_q32_exact(inputs): + q32exact.launch_from_contract_inputs(inputs) + return + if portfolio_id in PORTFOLIOS_WITH_Q32_TAIL and _eligible_q32tail(inputs): + _q32tail().launch_from_contract_inputs(inputs) + return + if portfolio_id in PORTFOLIOS_WITH_EXPANDED_K32_Q31_Q33_Q40 and _eligible_q31tail_v2(inputs): + _q31tail_v2().launch_from_contract_inputs(inputs) + return + if portfolio_id in PORTFOLIOS_WITH_EXPANDED_K32_Q31_Q33_Q40 and _eligible_q33tile(inputs): + _q33tile().launch_from_contract_inputs(inputs) + return + if portfolio_id in PORTFOLIOS_WITH_Q48_K12 and _eligible_q48_k12(inputs): + _q48_k12().launch_from_contract_inputs(inputs) + return + if portfolio_id in PORTFOLIOS_WITH_Q32_K31_C3D2 and _eligible_q32_k31_c3d2(inputs): + _q32_k31_c3d2().launch_from_contract_inputs(inputs) + return + if portfolio_id in PORTFOLIOS_WITH_Q32_LOWK_C3D2 and _eligible_q32_lowk_c3d2(inputs): + _q32_lowk_c3d2().launch_from_contract_inputs(inputs) + return + base_current.launch_from_contract_inputs(inputs) + +def launch_from_contract_inputs_f1d9_build_policy(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + launch_from_contract_inputs(inputs, force_fallback=force_fallback, portfolio_id=CANDIDATE_F1D9_BUILD) + +def launch_from_contract_inputs_a4ec_baseline(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + launch_from_contract_inputs(inputs, force_fallback=force_fallback, portfolio_id=CANDIDATE_A4EC_BASE) + +def launch_from_contract_inputs_fa04_baseline(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + launch_from_contract_inputs(inputs, force_fallback=force_fallback, portfolio_id=CANDIDATE_FA04_BASE) + +def launch_from_contract_inputs_f328_baseline(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + launch_from_contract_inputs(inputs, force_fallback=force_fallback, portfolio_id=CANDIDATE_F328_BASE) + +def route_for_contract_inputs_mixed_baseline(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + return route_for_contract_inputs(inputs, force_fallback=force_fallback, portfolio_id=CANDIDATE_MIXED) + +def launch_from_contract_inputs_mixed_baseline(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + launch_from_contract_inputs(inputs, force_fallback=force_fallback, portfolio_id=CANDIDATE_MIXED) + +def route_for_contract_inputs_d64_repair_baseline(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + return route_for_contract_inputs(inputs, force_fallback=force_fallback, portfolio_id=CANDIDATE_D64_REPAIR) + +def launch_from_contract_inputs_d64_repair_baseline(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + launch_from_contract_inputs(inputs, force_fallback=force_fallback, portfolio_id=CANDIDATE_D64_REPAIR) + +def route_for_contract_inputs_d64_m128_baseline(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + return route_for_contract_inputs(inputs, force_fallback=force_fallback, portfolio_id=CANDIDATE_D64_M128) + +def launch_from_contract_inputs_d64_m128_baseline(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + launch_from_contract_inputs(inputs, force_fallback=force_fallback, portfolio_id=CANDIDATE_D64_M128) + +def route_for_contract_inputs_c271_baseline(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + return route_for_contract_inputs(inputs, force_fallback=force_fallback, portfolio_id=CANDIDATE_D64_Q4096_C271) + +def launch_from_contract_inputs_c271_baseline(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + launch_from_contract_inputs(inputs, force_fallback=force_fallback, portfolio_id=CANDIDATE_D64_Q4096_C271) + +def route_for_contract_inputs_c271_7dc5_baseline(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + return route_for_contract_inputs(inputs, force_fallback=force_fallback, portfolio_id=CANDIDATE_C271_7DC5_K13_K48) + +def launch_from_contract_inputs_c271_7dc5_baseline(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + launch_from_contract_inputs(inputs, force_fallback=force_fallback, portfolio_id=CANDIDATE_C271_7DC5_K13_K48) + +def route_for_contract_inputs_pre_q128_k10_warpmerge_baseline(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + return route_for_contract_inputs(inputs, force_fallback=force_fallback, portfolio_id=CANDIDATE_PRE_Q128_K10_WARPMERGE) + +def launch_from_contract_inputs_pre_q128_k10_warpmerge_baseline(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + launch_from_contract_inputs(inputs, force_fallback=force_fallback, portfolio_id=CANDIDATE_PRE_Q128_K10_WARPMERGE) + +def route_for_contract_inputs_2498_baseline(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + return route_for_contract_inputs(inputs, force_fallback=force_fallback, portfolio_id=CANDIDATE_2498_BASELINE) + +def launch_from_contract_inputs_2498_baseline(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + launch_from_contract_inputs(inputs, force_fallback=force_fallback, portfolio_id=CANDIDATE_2498_BASELINE) + +def route_for_contract_inputs_pre_q1_m524_n128_baseline(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + return route_for_contract_inputs(inputs, force_fallback=force_fallback, portfolio_id=CANDIDATE_PRE_Q1_M524_N128) + +def launch_from_contract_inputs_pre_q1_m524_n128_baseline(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + launch_from_contract_inputs(inputs, force_fallback=force_fallback, portfolio_id=CANDIDATE_PRE_Q1_M524_N128) + +def route_for_contract_inputs_pre_q32exact_baseline(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + return route_for_contract_inputs(inputs, force_fallback=force_fallback, portfolio_id=CANDIDATE_PRE_Q32_EXACT) + +def launch_from_contract_inputs_pre_q32exact_baseline(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + launch_from_contract_inputs(inputs, force_fallback=force_fallback, portfolio_id=CANDIDATE_PRE_Q32_EXACT) + +def route_for_contract_inputs_pre_q32tail_baseline(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + return route_for_contract_inputs(inputs, force_fallback=force_fallback, portfolio_id=CANDIDATE_PRE_Q32TAIL) + +def launch_from_contract_inputs_pre_q32tail_baseline(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + launch_from_contract_inputs(inputs, force_fallback=force_fallback, portfolio_id=CANDIDATE_PRE_Q32TAIL) + +def route_for_contract_inputs_pre_expanded_k32_q48_baseline(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + return route_for_contract_inputs(inputs, force_fallback=force_fallback, portfolio_id=CANDIDATE_PRE_EXPANDED_K32_Q48) + +def launch_from_contract_inputs_pre_expanded_k32_q48_baseline(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + launch_from_contract_inputs(inputs, force_fallback=force_fallback, portfolio_id=CANDIDATE_PRE_EXPANDED_K32_Q48) + +def route_for_contract_inputs_pre_lowk_c3d2_baseline(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + return route_for_contract_inputs(inputs, force_fallback=force_fallback, portfolio_id=CANDIDATE_PRE_LOWK_C3D2) + +def launch_from_contract_inputs_pre_lowk_c3d2_baseline(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + launch_from_contract_inputs(inputs, force_fallback=force_fallback, portfolio_id=CANDIDATE_PRE_LOWK_C3D2) + +def route_for_contract_inputs_d665_lowk_baseline(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + return route_for_contract_inputs(inputs, force_fallback=force_fallback, portfolio_id=CANDIDATE_D665_LOWK_BASELINE) + +def launch_from_contract_inputs_d665_lowk_baseline(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + launch_from_contract_inputs(inputs, force_fallback=force_fallback, portfolio_id=CANDIDATE_D665_LOWK_BASELINE) + +def route_for_contract_inputs_pre_d64_tail_017a_baseline(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + return route_for_contract_inputs(inputs, force_fallback=force_fallback, portfolio_id=CANDIDATE_PRE_D64_TAIL_017A) + +def launch_from_contract_inputs_pre_d64_tail_017a_baseline(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + launch_from_contract_inputs(inputs, force_fallback=force_fallback, portfolio_id=CANDIDATE_PRE_D64_TAIL_017A) + +def route_for_contract_inputs_pre_d256_q4_e2df_baseline(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + return route_for_contract_inputs(inputs, force_fallback=force_fallback, portfolio_id=CANDIDATE_PRE_D256_Q4_E2DF) + +def launch_from_contract_inputs_pre_d256_q4_e2df_baseline(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + launch_from_contract_inputs(inputs, force_fallback=force_fallback, portfolio_id=CANDIDATE_PRE_D256_Q4_E2DF) + +def route_for_contract_inputs_pre_d256_q128_59fe_baseline(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + return route_for_contract_inputs(inputs, force_fallback=force_fallback, portfolio_id=CANDIDATE_SELECTED_SYNTHESIS) + +def launch_from_contract_inputs_pre_d256_q128_59fe_baseline(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + launch_from_contract_inputs(inputs, force_fallback=force_fallback, portfolio_id=CANDIDATE_SELECTED_SYNTHESIS) + +def route_for_contract_inputs_pre_d256_k32_59fe_baseline(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + return route_for_contract_inputs(inputs, force_fallback=force_fallback, portfolio_id=CANDIDATE_D256_Q128_59FE) + +def launch_from_contract_inputs_pre_d256_k32_59fe_baseline(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + launch_from_contract_inputs(inputs, force_fallback=force_fallback, portfolio_id=CANDIDATE_D256_Q128_59FE) + +def route_for_contract_inputs_pre_highd_rag_22e9_baseline(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + return route_for_contract_inputs(inputs, force_fallback=force_fallback, portfolio_id=CANDIDATE_D256_K32_59FE) + +def launch_from_contract_inputs_pre_highd_rag_22e9_baseline(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + launch_from_contract_inputs(inputs, force_fallback=force_fallback, portfolio_id=CANDIDATE_D256_K32_59FE) + +def route_for_contract_inputs_pre_highd_search_be66_baseline(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + return route_for_contract_inputs(inputs, force_fallback=force_fallback, portfolio_id=CANDIDATE_HIGHD_RAG_22E9) + +def launch_from_contract_inputs_pre_highd_search_be66_baseline(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + launch_from_contract_inputs(inputs, force_fallback=force_fallback, portfolio_id=CANDIDATE_HIGHD_RAG_22E9) + +def route_for_contract_inputs_pre_d128_k48_dd2b_baseline(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + return route_for_contract_inputs(inputs, force_fallback=force_fallback, portfolio_id=CANDIDATE_HIGHD_SEARCH_BE66) + +def launch_from_contract_inputs_pre_d128_k48_dd2b_baseline(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + launch_from_contract_inputs(inputs, force_fallback=force_fallback, portfolio_id=CANDIDATE_HIGHD_SEARCH_BE66) + +def route_for_contract_inputs_pre_rect_d128_k20_s12warp4_baseline(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + return route_for_contract_inputs(inputs, force_fallback=force_fallback, portfolio_id=CANDIDATE_D128_K48_DD2B) + +def launch_from_contract_inputs_pre_rect_d128_k20_s12warp4_baseline(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + launch_from_contract_inputs(inputs, force_fallback=force_fallback, portfolio_id=CANDIDATE_D128_K48_DD2B) + +def route_for_contract_inputs_pre_q128_k10_rowld_1bed_baseline(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + return route_for_contract_inputs(inputs, force_fallback=force_fallback, portfolio_id=CANDIDATE_RECT_D128_K20_S12WARP4) + +def launch_from_contract_inputs_pre_q128_k10_rowld_1bed_baseline(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + launch_from_contract_inputs(inputs, force_fallback=force_fallback, portfolio_id=CANDIDATE_RECT_D128_K20_S12WARP4) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_f1d9_build_policy(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs_f1d9_build_policy(inputs) + +def candidate_base_current(inputs: dict[str, Any]) -> None: + base_current.launch_from_contract_inputs(inputs) + +def candidate_pre_d256_k32_59fe_baseline(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs_pre_d256_k32_59fe_baseline(inputs) + +def candidate_pre_highd_rag_22e9_baseline(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs_pre_highd_rag_22e9_baseline(inputs) + +def candidate_pre_highd_search_be66_baseline(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs_pre_highd_search_be66_baseline(inputs) + +def candidate_pre_d128_k48_dd2b_baseline(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs_pre_d128_k48_dd2b_baseline(inputs) + +def candidate_pre_rect_d128_k20_s12warp4_baseline(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs_pre_rect_d128_k20_s12warp4_baseline(inputs) + +def candidate_pre_q128_k10_rowld_1bed_baseline(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs_pre_q128_k10_rowld_1bed_baseline(inputs) + +def candidate_a4ec_baseline(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs_a4ec_baseline(inputs) + +def candidate_fa04_baseline(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs_fa04_baseline(inputs) + +def candidate_f328_baseline(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs_f328_baseline(inputs) + +def candidate_mixed_baseline(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs_mixed_baseline(inputs) + +def candidate_d64_repair_baseline(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs_d64_repair_baseline(inputs) + +def candidate_d64_m128_baseline(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs_d64_m128_baseline(inputs) + +def candidate_c271_baseline(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs_c271_baseline(inputs) + +def candidate_c271_7dc5_baseline(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs_c271_7dc5_baseline(inputs) + +def candidate_pre_q128_k10_warpmerge_baseline(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs_pre_q128_k10_warpmerge_baseline(inputs) + +def candidate_2498_baseline(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs_2498_baseline(inputs) + +def candidate_pre_q1_m524_n128_baseline(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs_pre_q1_m524_n128_baseline(inputs) + +def candidate_pre_q32exact_baseline(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs_pre_q32exact_baseline(inputs) + +def candidate_pre_q32tail_baseline(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs_pre_q32tail_baseline(inputs) + +def candidate_pre_expanded_k32_q48_baseline(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs_pre_expanded_k32_q48_baseline(inputs) + +def candidate_pre_lowk_c3d2_baseline(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs_pre_lowk_c3d2_baseline(inputs) + +def candidate_d665_lowk_baseline(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs_d665_lowk_baseline(inputs) + +def candidate_pre_d64_tail_017a_baseline(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs_pre_d64_tail_017a_baseline(inputs) + +def candidate_pre_d256_q4_e2df_baseline(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs_pre_d256_q4_e2df_baseline(inputs) + +def candidate_pre_d256_q128_59fe_baseline(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs_pre_d256_q128_59fe_baseline(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def compile_and_launch_knn_build(*, shape_labels=CONSUMED_SEED_SHAPES, benchmark: bool=False) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _base_trace_record(inputs: dict[str, Any], *, force_fallback: bool=False) -> dict[str, Any]: + label = str(inputs.get('label')) + if label in EXPANDED_Q32_GUARD_BOUNDARY_8_BY_LABEL: + timing = EXPANDED_FALLBACK_TIMING_ROWS[label] + route = base_current.route_for_contract_inputs(inputs, force_fallback=force_fallback) + return _normalize_route_row({'shape_key': label, 'selected_route': route, 'selected_entrypoint': BASE_ENTRYPOINT, 'selected_seed': None, 'expected_seed': None, 'route_kind': 'general', 'route_source': 'broad-dispatcher', 'guard_id': 'base_common_d_v11_guard_stack', 'guard_condition': 'current v11 common-D fallback dispatcher for expanded Q32-neighborhood row', 'classification': 'fallback-slow', 'base_dispatcher_route': route, 'dispatcher_kernel_ms': timing['kernel_ms'], 'speedup_vs_external_baseline': timing['ratio_vs_flashlib'], 'external_baseline_ms': timing['flashlib_ms'], 'external_baseline_ref': 'historical_same_shape'}) + row = dict(base_current.route_trace_for_contract_shapes((label,), force_fallback=force_fallback)[0]) + row.setdefault('selected_seed', row.get('consumed_seed')) + row.setdefault('expected_seed', row.get('selected_seed')) + row.setdefault('selected_entrypoint', BASE_ENTRYPOINT) + row.setdefault('route_kind', 'general') + row.setdefault('route_source', 'broad-dispatcher' if not row.get('selected_seed') else 'shape-specific-seed') + row.setdefault('guard_id', 'base_common_d_v11_guard_stack') + row.setdefault('guard_condition', 'current v11 common-D fallback dispatcher') + row.setdefault('classification', 'route-ok') + if label in HIGHD_SEARCH_BE66_CONSUMED_SHAPES and (not force_fallback): + row['guard_id'] = 'inherited_397b_or_guard_miss' + row['guard_condition'] = 'synthesized guard miss; delegate to baseline 7c3a Weave policy' + row['classification'] = 'benchmark-path-mismatch' + row['failure_exception_type'] = 'ValueError' + row['failure_message'] = ''.join(['knn_build_evolve_7bfc_v1 expects D=128, got ', format(int(inputs.get('D', -1)), '')]) + row['speedup_vs_external_baseline'] = 0.0 + row['external_baseline_ref'] = 'not_available' + if label == V12_D128_K48_OVER32 and (not force_fallback): + row['guard_id'] = 'inherited_397b_or_guard_miss' + row['guard_condition'] = 'synthesized guard miss; delegate to baseline 7c3a Weave policy' + row['classification'] = 'benchmark-path-mismatch' + row['failure_exception_type'] = 'ValueError' + row['failure_message'] = 'knn_build_evolve_7bfc_v1 supports K <= 32, got 48' + row['speedup_vs_external_baseline'] = 0.0 + row['external_baseline_ref'] = 'not_available' + row['base_dispatcher_route'] = base_current.route_for_contract_inputs(inputs, force_fallback=force_fallback) + return _normalize_route_row(row) + +def _specialized_trace_record(inputs: dict[str, Any], *, portfolio_id: str) -> dict[str, Any] | None: + label = str(inputs.get('label')) + base_row = _base_trace_record(inputs) + d256_q4_e2df_label = _d256_q4_e2df_label(inputs) + d256_q128_59fe_label = _d256_q128_59fe_label(inputs) + d256_k32_59fe_label = _d256_k32_59fe_label(inputs) + highd_rag_22e9_label = _highd_rag_22e9_label(inputs) + highd_search_be66_label = _highd_search_be66_label(inputs) + d128_k48_dd2b_label = _d128_k48_dd2b_label(inputs) + d64_tail_017a_label = _d64_tail_017a_label(inputs) + rect_d128_k20_s12warp4_eligible = rect_d128_k20_s12warp4._eligible_rect_d128_k20_q1536(inputs) + if portfolio_id in PORTFOLIOS_WITH_Q128_K10_ROWLD_1BED and (not _eligible_q128_k10_rowld_1bed(inputs)): + return _specialized_trace_record(inputs, portfolio_id=CANDIDATE_RECT_D128_K20_S12WARP4) + elif portfolio_id in PORTFOLIOS_WITH_Q128_K10_ROWLD_1BED and _eligible_q128_k10_rowld_1bed(inputs): + replaced_policy = CANDIDATE_RECT_D128_K20_S12WARP4 + elif portfolio_id in PORTFOLIOS_WITH_RECT_D128_K20_S12WARP4 and (not rect_d128_k20_s12warp4_eligible): + return _specialized_trace_record(inputs, portfolio_id=CANDIDATE_D128_K48_DD2B) + elif portfolio_id in PORTFOLIOS_WITH_RECT_D128_K20_S12WARP4 and rect_d128_k20_s12warp4_eligible: + replaced_policy = CANDIDATE_D128_K48_DD2B + elif portfolio_id in PORTFOLIOS_WITH_Q32_TAIL143 and _eligible_q32tail143(inputs): + replaced_policy = CANDIDATE_HIGHD_SEARCH_BE66 + elif portfolio_id in PORTFOLIOS_WITH_D128_K48_DD2B and d128_k48_dd2b_label is None: + return _specialized_trace_record(inputs, portfolio_id=CANDIDATE_HIGHD_SEARCH_BE66) + elif portfolio_id in PORTFOLIOS_WITH_D128_K48_DD2B and d128_k48_dd2b_label is not None: + replaced_policy = CANDIDATE_HIGHD_SEARCH_BE66 + elif portfolio_id in PORTFOLIOS_WITH_HIGHD_SEARCH_BE66 and highd_search_be66_label is None: + return _specialized_trace_record(inputs, portfolio_id=CANDIDATE_HIGHD_RAG_22E9) + elif portfolio_id in PORTFOLIOS_WITH_HIGHD_SEARCH_BE66 and highd_search_be66_label is not None: + replaced_policy = CANDIDATE_HIGHD_RAG_22E9 + elif portfolio_id in PORTFOLIOS_WITH_HIGHD_RAG_22E9 and highd_rag_22e9_label is None: + return _specialized_trace_record(inputs, portfolio_id=CANDIDATE_D256_K32_59FE) + elif portfolio_id in PORTFOLIOS_WITH_HIGHD_RAG_22E9 and highd_rag_22e9_label is not None: + replaced_policy = CANDIDATE_D256_K32_59FE + elif portfolio_id in PORTFOLIOS_WITH_D256_K32_59FE and d256_k32_59fe_label is None: + return _specialized_trace_record(inputs, portfolio_id=CANDIDATE_D256_Q128_59FE) + elif portfolio_id in PORTFOLIOS_WITH_D256_K32_59FE and d256_k32_59fe_label is not None: + replaced_policy = CANDIDATE_D256_Q128_59FE + elif portfolio_id in PORTFOLIOS_WITH_D256_Q128_59FE and d256_q128_59fe_label is None: + return _specialized_trace_record(inputs, portfolio_id=CANDIDATE_SELECTED_SYNTHESIS) + elif portfolio_id in PORTFOLIOS_WITH_D256_Q128_59FE and d256_q128_59fe_label is not None: + replaced_policy = CANDIDATE_SELECTED_SYNTHESIS + elif portfolio_id == CANDIDATE_SELECTED_SYNTHESIS and d256_q4_e2df_label is not None: + replaced_policy = CANDIDATE_PRE_D256_Q4_E2DF + elif portfolio_id in PORTFOLIOS_WITH_D64_TAIL_017A and d64_tail_017a_label is not None: + replaced_policy = CANDIDATE_PRE_D64_TAIL_017A + elif portfolio_id in (CANDIDATE_PRE_D256_Q4_E2DF, CANDIDATE_SELECTED_SYNTHESIS) and _eligible_q32_k31_c3d2(inputs): + replaced_policy = CANDIDATE_D665_LOWK_BASELINE + elif portfolio_id in (CANDIDATE_PRE_D256_Q4_E2DF, CANDIDATE_SELECTED_SYNTHESIS) and _eligible_q32_lowk_c3d2(inputs): + replaced_policy = CANDIDATE_PRE_LOWK_C3D2 + elif portfolio_id in (CANDIDATE_PRE_LOWK_C3D2, CANDIDATE_PRE_D64_TAIL_017A, CANDIDATE_PRE_D256_Q4_E2DF, CANDIDATE_SELECTED_SYNTHESIS) and (_eligible_q31tail_v2(inputs) or _eligible_q33tile(inputs) or _eligible_q48_k12(inputs)): + replaced_policy = CANDIDATE_PRE_EXPANDED_K32_Q48 + elif portfolio_id in (CANDIDATE_PRE_EXPANDED_K32_Q48, CANDIDATE_PRE_LOWK_C3D2, CANDIDATE_PRE_D64_TAIL_017A, CANDIDATE_PRE_D256_Q4_E2DF, CANDIDATE_SELECTED_SYNTHESIS) and _eligible_q32tail(inputs): + replaced_policy = CANDIDATE_PRE_Q32TAIL + elif portfolio_id in (CANDIDATE_PRE_Q32TAIL, CANDIDATE_PRE_EXPANDED_K32_Q48, CANDIDATE_PRE_LOWK_C3D2, CANDIDATE_PRE_D64_TAIL_017A, CANDIDATE_PRE_D256_Q4_E2DF, CANDIDATE_SELECTED_SYNTHESIS) and _eligible_q32_exact(inputs): + replaced_policy = CANDIDATE_PRE_Q32_EXACT + elif portfolio_id in (CANDIDATE_PRE_Q32_EXACT, CANDIDATE_PRE_Q32TAIL, CANDIDATE_PRE_EXPANDED_K32_Q48, CANDIDATE_PRE_LOWK_C3D2, CANDIDATE_PRE_D64_TAIL_017A, CANDIDATE_PRE_D256_Q4_E2DF, CANDIDATE_SELECTED_SYNTHESIS) and _eligible_q1_m524_n128(inputs): + replaced_policy = CANDIDATE_PRE_Q1_M524_N128 + elif portfolio_id in (CANDIDATE_PRE_Q1_M524_N128, CANDIDATE_PRE_Q32_EXACT, CANDIDATE_PRE_Q32TAIL, CANDIDATE_PRE_EXPANDED_K32_Q48, CANDIDATE_PRE_LOWK_C3D2, CANDIDATE_PRE_D64_TAIL_017A, CANDIDATE_PRE_D256_Q4_E2DF, CANDIDATE_SELECTED_SYNTHESIS) and _eligible_rag_stream_k10_34da(inputs): + replaced_policy = CANDIDATE_2498_BASELINE + elif _uses_floor_seed_portfolio(portfolio_id): + replaced_policy = CANDIDATE_C271_7DC5_K13_K48 + elif portfolio_id == CANDIDATE_C271_7DC5_K13_K48: + replaced_policy = CANDIDATE_D64_Q4096_C271 + elif portfolio_id == CANDIDATE_D64_Q4096_C271: + replaced_policy = CANDIDATE_D64_M128 + elif portfolio_id == CANDIDATE_D64_M128: + replaced_policy = CANDIDATE_D64_REPAIR + elif portfolio_id == CANDIDATE_D64_REPAIR: + replaced_policy = CANDIDATE_MIXED + elif portfolio_id == CANDIDATE_MIXED: + replaced_policy = CANDIDATE_F328_BASE + elif portfolio_id == CANDIDATE_F328_BASE: + replaced_policy = CANDIDATE_FA04_BASE + else: + replaced_policy = CANDIDATE_A4EC_BASE + replaced_route = _route_for_policy(inputs, portfolio_id=replaced_policy) + if portfolio_id == CANDIDATE_A4EC_BASE and d256_build_a4ec._eligible_d256_q1024(inputs): + seed_id = 'common_d256_q1024_56f3_v1' + return _normalize_route_row({'shape_key': label, 'selected_route': d256_build_a4ec.route_for_contract_inputs(inputs), 'selected_entrypoint': D256_A4EC_ENTRYPOINT, 'selected_seed': seed_id, 'expected_seed': seed_id, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': 'a4ec_common_d256_q1024_build_exact_guard', 'guard_condition': 'exact BF16 build B=1 Q=M=1024 D=256 K=10', 'classification': 'route-ok', 'replaced_route': base_row.get('selected_route'), 'base_dispatcher_route': base_row.get('selected_route'), 'shape_specific_kernel_ms': 0.053984}) + if _eligible_d256_6164(inputs): + seed_id = 'common_d_56f3_build_d256_q1024_v1' + return _normalize_route_row({'shape_key': label, 'selected_route': d256_build.route_for_contract_inputs(inputs), 'selected_entrypoint': D256_ENTRYPOINT, 'selected_seed': seed_id, 'expected_seed': seed_id, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': 'fa04_6164_common_d256_q1024_build_exact_guard', 'guard_condition': 'exact BF16 build B=1 Q=M=1024 D=256 K=10', 'classification': 'seed-consumed', 'replaced_route': replaced_route, 'base_dispatcher_route': base_row.get('selected_route'), 'shape_specific_kernel_ms': SEED_TIMING_ROWS[label]['kernel_ms']}) + if portfolio_id in PORTFOLIOS_WITH_D768_FAST and d768_build_fast._eligible_d768_build(inputs): + seed_id = 'common_d768_build_eeff_m64split_v1' + return _normalize_route_row({'shape_key': label, 'selected_route': d768_build_fast.route_for_contract_inputs(inputs), 'selected_entrypoint': D768_FAST_ENTRYPOINT, 'selected_seed': seed_id, 'expected_seed': seed_id, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': 'a4ec_34d8_common_d768_build_exact_guard', 'guard_condition': 'exact BF16 build B=1 Q=M=1024 D=768 K=10', 'classification': 'seed-consumed', 'replaced_route': replaced_route, 'base_dispatcher_route': base_row.get('selected_route'), 'shape_specific_kernel_ms': SEED_TIMING_ROWS[label]['kernel_ms']}) + highd_label = _highd_label(inputs) + if highd_label is not None: + seed_id = 'common_d_56f3_build_highd_v1' + spec = highd_build.SHAPE_SPECS[highd_label] + timing = ALT_F1D9_D768_TIMING if highd_label == BUILD_D768 else SEED_TIMING_ROWS[highd_label] + return _normalize_route_row({'shape_key': highd_label, 'selected_route': highd_build.route_for_contract_inputs(inputs), 'selected_entrypoint': HIGHD_ENTRYPOINT, 'selected_seed': seed_id, 'expected_seed': seed_id, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': 'a4ec_f1d9_common_d_highd_build_exact_guard', 'guard_condition': ''.join(['exact BF16 build B=', format(spec['B'], ''), ' Q=', format(spec['Q'], ''), ' M=', format(spec['M'], ''), ' D=', format(spec['D'], ''), ' K=', format(spec['K'], '')]), 'classification': 'seed-consumed', 'feature_chunks': spec['feature_chunks'], 'split_count': spec['split_count'], 'replaced_route': replaced_route, 'base_dispatcher_route': base_row.get('selected_route'), 'shape_specific_kernel_ms': timing['kernel_ms']}) + if _eligible_d768_search(inputs) and portfolio_id != CANDIDATE_A4EC_BASE: + seed_id = 'common_d_eeff_search_d768_v1' + return _normalize_route_row({'shape_key': D768_SEARCH, 'selected_route': d768_search.route_for_contract_inputs(inputs), 'selected_entrypoint': D768_SEARCH_ENTRYPOINT, 'selected_seed': seed_id, 'expected_seed': seed_id, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': 'fa04_cda9_common_d_search_d768_exact_guard', 'guard_condition': 'exact BF16 non-build B=1 Q=512 M=8192 D=768 K=10', 'classification': 'seed-consumed', 'feature_chunks': d768_search.FEATURE_CHUNKS, 'split_count': d768_search._split_count(), 'group_count': d768_search._group_count(), 'replaced_route': replaced_route, 'base_dispatcher_route': base_row.get('selected_route'), 'shape_specific_kernel_ms': SEED_TIMING_ROWS[D768_SEARCH]['kernel_ms']}) + if _eligible_d768_rag(inputs): + seed_id = 'non128_frontier_4be7_d768fused_v1' + spec = d768_rag.SHAPE_SPECS[D768_RAG] + return _normalize_route_row({'shape_key': D768_RAG, 'selected_route': d768_rag.route_for_contract_inputs(inputs), 'selected_entrypoint': D768_RAG_ENTRYPOINT, 'selected_seed': seed_id, 'expected_seed': seed_id, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': 'a4ec_447d_4be7_d768_rag_exact_guard', 'guard_condition': ''.join(['exact BF16 non-build B=', format(spec['B'], ''), ' Q=', format(spec['Q'], ''), ' M=', format(spec['M'], ''), ' D=', format(spec['D'], ''), ' K=', format(spec['K'], '')]), 'classification': 'seed-consumed', 'replaced_route': replaced_route, 'base_dispatcher_route': base_row.get('selected_route'), 'shape_specific_kernel_ms': SEED_TIMING_ROWS[D768_RAG]['kernel_ms']}) + highd_rag_label = _highd_rag_label(inputs) + if portfolio_id in PORTFOLIOS_WITH_HIGHD_RAG and highd_rag_label is not None: + seed_id = 'common_d_5e7f_rag_highd_v1' + spec = highd_rag.SHAPE_SPECS[highd_rag_label] + return _normalize_route_row({'shape_key': highd_rag_label, 'selected_route': highd_rag.route_for_contract_inputs(inputs), 'selected_entrypoint': HIGHD_RAG_ENTRYPOINT, 'selected_seed': seed_id, 'expected_seed': seed_id, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': '4cf7_common_d_5e7f_highd_rag_exact_guard', 'guard_condition': ''.join(['exact BF16 non-build B=', format(spec['B'], ''), ' Q=', format(spec['Q'], ''), ' M=', format(spec['M'], ''), ' D=', format(spec['D'], ''), ' K=', format(spec['K'], '')]), 'classification': 'seed-consumed', 'feature_chunks': spec['feature_chunks'], 'split_count': highd_rag._split_count_for_label(highd_rag_label), 'group_count': highd_rag._group_count_for_label(highd_rag_label), 'producer_topology': 'M64_N64_tcgen05_tma', 'merge_topology': 'fused_group_split_merge', 'replaced_route': replaced_route, 'base_dispatcher_route': base_row.get('selected_route'), 'shape_specific_kernel_ms': SEED_TIMING_ROWS[highd_rag_label]['kernel_ms']}) + search_d256_label = _search_d256_label(inputs) + if portfolio_id in PORTFOLIOS_WITH_SEARCH_D256 and search_d256_label is not None: + seed_id = 'common_d_5e7f_search_d256_v1' + return _normalize_route_row({'shape_key': search_d256_label, 'selected_route': search_d256.route_for_contract_inputs(inputs), 'selected_entrypoint': SEARCH_D256_ENTRYPOINT, 'selected_seed': seed_id, 'expected_seed': seed_id, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': '8dbc_common_d_5e7f_search_d256_exact_guard', 'guard_condition': 'exact BF16 non-build B=1 Q=1024 M=32768 D=256 K=10', 'classification': 'seed-consumed', 'feature_chunks': search_d256.FEATURE_CHUNKS, 'split_count': search_d256._split_count(), 'group_count': search_d256._group_count(), 'producer_topology': 'chunked128_d256_tcgen05_tma', 'merge_topology': 'fused_group_split_merge', 'replaced_route': replaced_route, 'base_dispatcher_route': base_row.get('selected_route'), 'shape_specific_kernel_ms': SEED_TIMING_ROWS[search_d256_label]['kernel_ms']}) + rag_d64_m128_label = _rag_d64_m128_label(inputs) + if portfolio_id in PORTFOLIOS_WITH_RAG_D64_M128 and rag_d64_m128_label is not None: + seed_id = 'common_d_1438_rag_d64_m128_v1' + timing = SEED_TIMING_ROWS[rag_d64_m128_label] + return _normalize_route_row({'shape_key': rag_d64_m128_label, 'selected_route': rag_d64_m128.route_for_contract_inputs(inputs), 'selected_entrypoint': RAG_D64_M128_ENTRYPOINT, 'selected_seed': seed_id, 'expected_seed': seed_id, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': '631e_common_d_1438_rag_d64_m128_exact_guard', 'guard_condition': 'exact BF16 non-build B=1 Q=16 M=50000 D=64 K=10', 'classification': 'seed-consumed', 'split_count': rag_d64_m128._split_count(), 'group_count': rag_d64_m128._group_count(), 'producer_topology': 'D64_M128_tcgen05_smem', 'merge_topology': 'fused_group_split_merge', 'replaced_route': replaced_route, 'base_dispatcher_route': base_row.get('selected_route'), 'shape_specific_kernel_ms': timing['kernel_ms'], 'speedup_vs_external_baseline': timing['ratio_vs_flashlib'], 'external_baseline_ms': timing['flashlib_ms'], 'external_baseline_ref': 'historical_same_shape'}) + rag_d64_repair_label = _rag_d64_repair_label(inputs) + if portfolio_id == CANDIDATE_D64_REPAIR and rag_d64_repair_label is not None: + seed_id = 'common_d_5e7f_rag_d64_repair_v1' + spec = rag_d64_repair.SHAPE_SPECS[rag_d64_repair_label] + timing = D64_REPAIR_0474_TIMING + return _normalize_route_row({'shape_key': rag_d64_repair_label, 'selected_route': rag_d64_repair.route_for_contract_inputs(inputs), 'selected_entrypoint': RAG_D64_REPAIR_ENTRYPOINT, 'selected_seed': seed_id, 'expected_seed': seed_id, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': '0474_common_d_5e7f_rag_d64_repair_exact_guard', 'guard_condition': ''.join(['exact BF16 non-build B=', format(spec['B'], ''), ' Q=', format(spec['Q'], ''), ' M=', format(spec['M'], ''), ' D=', format(spec['D'], ''), ' K=', format(spec['K'], '')]), 'classification': 'seed-consumed', 'split_count': rag_d64_repair._split_count_for_label(rag_d64_repair_label), 'group_count': rag_d64_repair._group_count_for_label(rag_d64_repair_label), 'producer_topology': 'D64_M64_N64_K64_tcgen05_tma', 'merge_topology': 'fused_group_split_merge', 'replaced_route': replaced_route, 'base_dispatcher_route': base_row.get('selected_route'), 'shape_specific_kernel_ms': timing['kernel_ms'], 'speedup_vs_external_baseline': timing['ratio_vs_flashlib'], 'external_baseline_ms': timing['flashlib_ms'], 'external_baseline_ref': 'historical_same_shape'}) + rag_d64d256_label = _rag_d256_label(inputs) + if portfolio_id in PORTFOLIOS_WITH_SEARCH_D256 and rag_d64d256_label is not None: + seed_id = 'common_d_5e7f_rag_d64_d256_v1' + spec = rag_d64d256.SHAPE_SPECS[rag_d64d256_label] + tma_dim = rag_d64d256._feature_chunks_for_label(rag_d64d256_label) * rag_d64d256.K_TILE + timing = SEED_TIMING_ROWS[rag_d64d256_label] + return _normalize_route_row({'shape_key': rag_d64d256_label, 'selected_route': rag_d64d256.route_for_contract_inputs(inputs), 'selected_entrypoint': RAG_D64D256_ENTRYPOINT, 'selected_seed': seed_id, 'expected_seed': seed_id, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': 'ba22_common_d_5e7f_rag_d64_d256_exact_guard', 'guard_condition': ''.join(['exact BF16 non-build B=', format(spec['B'], ''), ' Q=', format(spec['Q'], ''), ' M=', format(spec['M'], ''), ' D=', format(spec['D'], ''), ' K=', format(spec['K'], '')]), 'classification': 'kernel-slow' if float(timing['ratio_vs_flashlib']) < SPEEDUP_FLOOR else 'seed-consumed', 'feature_chunks': spec['feature_chunks'], 'split_count': rag_d64d256._split_count_for_label(rag_d64d256_label), 'group_count': rag_d64d256._group_count_for_label(rag_d64d256_label), 'producer_topology': 'D64_M64_tcgen05_tma' if rag_d64d256._uses_d64_exact(rag_d64d256_label) else 'M64_N64_tcgen05_tma', 'preprocess_stage': None if rag_d64d256._uses_d64_exact(rag_d64d256_label) else ''.join(['d', format(int(spec['D']), ''), '_weave_pad_to_d', format(tma_dim, '')]) if int(spec['D']) != tma_dim else None, 'merge_topology': 'fused_group_split_merge', 'replaced_route': replaced_route, 'base_dispatcher_route': base_row.get('selected_route'), 'shape_specific_kernel_ms': timing['kernel_ms'], 'speedup_vs_external_baseline': timing['ratio_vs_flashlib'], 'external_baseline_ms': timing['flashlib_ms'], 'external_baseline_ref': 'historical_same_shape'}) + if portfolio_id in PORTFOLIOS_WITH_D256_Q4_E2DF and d256_q4_e2df_label is not None: + spec = d256_q4_e2df.SHAPE_SPECS[d256_q4_e2df_label] + timing = SEED_TIMING_ROWS[d256_q4_e2df_label] + return _normalize_route_row({'shape_key': d256_q4_e2df_label, 'selected_route': d256_q4_e2df.route_for_contract_inputs(inputs), 'selected_entrypoint': D256_Q4_E2DF_ENTRYPOINT, 'selected_seed': SEED_D256_Q4_E2DF_ID, 'expected_seed': SEED_D256_Q4_E2DF_ID, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': D256_Q4_E2DF_GUARD_ID, 'guard_condition': ''.join(['exact BF16 non-build B=', format(spec['B'], ''), ' Q=', format(spec['Q'], ''), ' M=', format(spec['M'], ''), ' D=', format(spec['D'], ''), ' K=', format(spec['K'], '')]), 'classification': 'seed-consumed' if float(timing['ratio_vs_flashlib']) >= SPEEDUP_FLOOR else 'kernel-slow', 'feature_chunks': spec['feature_chunks'], 'split_count': d256_q4_e2df._split_count_for_label(d256_q4_e2df_label), 'group_count': d256_q4_e2df._group_count_for_label(d256_q4_e2df_label), 'producer_topology': 'M64_N64_D256_tcgen05/TMA_chunked', 'merge_topology': 'fused_group_split_merge', 'replaced_route': replaced_route, 'base_dispatcher_route': base_row.get('selected_route'), 'shape_specific_kernel_ms': timing['kernel_ms'], 'speedup_vs_external_baseline': timing['ratio_vs_flashlib'], 'external_baseline_ms': timing['flashlib_ms'], 'external_baseline_ref': 'historical_same_shape'}) + if portfolio_id in PORTFOLIOS_WITH_D256_Q128_59FE and d256_q128_59fe_label is not None: + spec = d256_q128_59fe.SHAPE_SPECS[d256_q128_59fe_label] + timing = SEED_TIMING_ROWS[d256_q128_59fe_label] + return _normalize_route_row({'shape_key': d256_q128_59fe_label, 'selected_route': d256_q128_59fe.route_for_contract_inputs(inputs), 'selected_entrypoint': D256_Q128_59FE_ENTRYPOINT, 'selected_seed': SEED_D256_Q128_59FE_ID, 'expected_seed': SEED_D256_Q128_59FE_ID, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': D256_Q128_59FE_GUARD_ID, 'guard_condition': ''.join(['exact BF16 non-build B=', format(spec['B'], ''), ' Q=', format(spec['Q'], ''), ' M=', format(spec['M'], ''), ' D=', format(spec['D'], ''), ' K=', format(spec['K'], '')]), 'classification': 'seed-consumed' if float(timing['ratio_vs_flashlib']) >= SPEEDUP_FLOOR else 'kernel-slow', 'feature_chunks': spec['feature_chunks'], 'split_count': d256_q128_59fe._split_count_for_label(d256_q128_59fe_label), 'group_count': d256_q128_59fe._group_count_for_label(d256_q128_59fe_label), 'producer_topology': 'M128_N64_D256_tcgen05/TMA_chunked', 'merge_topology': 'fused_group_split_merge', 'replaced_route': replaced_route, 'base_dispatcher_route': base_row.get('selected_route'), 'shape_specific_kernel_ms': timing['kernel_ms'], 'speedup_vs_external_baseline': timing['ratio_vs_flashlib'], 'external_baseline_ms': timing['flashlib_ms'], 'external_baseline_ref': 'historical_same_shape'}) + if portfolio_id in PORTFOLIOS_WITH_D256_K32_59FE and d256_k32_59fe_label is not None: + spec = d256_k32_59fe.SHAPE_SPECS[d256_k32_59fe_label] + timing = SEED_TIMING_ROWS[d256_k32_59fe_label] + return _normalize_route_row({'shape_key': d256_k32_59fe_label, 'selected_route': d256_k32_59fe.route_for_contract_inputs(inputs), 'selected_entrypoint': D256_K32_59FE_ENTRYPOINT, 'selected_seed': SEED_D256_K32_59FE_ID, 'expected_seed': SEED_D256_K32_59FE_ID, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': D256_K32_59FE_GUARD_ID, 'guard_condition': 'exact BF16 non-build B=1 M=100000 D=256 K=32 Q in {8,128}', 'classification': 'seed-consumed' if float(timing['ratio_vs_flashlib']) >= SPEEDUP_FLOOR else 'kernel-slow', 'feature_chunks': d256_k32_59fe.D256_FEATURE_CHUNKS, 'split_count': d256_k32_59fe._split_count_for_label(d256_k32_59fe_label), 'rows_per_merge_cta': d256_k32_59fe.D256_WARP_MERGE_ROWS_PER_CTA, 'producer_topology': 'ROW_16x256B_M64N64_D256_tcgen05_TMA_two_chunk', 'merge_topology': 'warp_row_split_list_merge', 'replaced_route': replaced_route, 'base_dispatcher_route': base_row.get('selected_route'), 'shape_specific_kernel_ms': timing['kernel_ms'], 'speedup_vs_external_baseline': timing['ratio_vs_flashlib'], 'external_baseline_ms': timing['flashlib_ms'], 'external_baseline_ref': 'historical_same_shape', 'K': spec['K'], 'Q': spec['Q'], 'M': spec['M'], 'D': spec['D'], 'B': spec['B'], 'build': spec['build']}) + if portfolio_id in PORTFOLIOS_WITH_D128_K48_DD2B and d128_k48_dd2b_label is not None: + timing = SEED_TIMING_ROWS[d128_k48_dd2b_label] + ratio = timing['ratio_vs_flashlib'] + return _normalize_route_row({'shape_key': d128_k48_dd2b_label, 'selected_route': d128_k48_dd2b.route_for_contract_inputs(inputs), 'selected_entrypoint': D128_K48_DD2B_ENTRYPOINT, 'selected_seed': SEED_D128_K48_DD2B_ID, 'expected_seed': SEED_D128_K48_DD2B_ID, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': D128_K48_DD2B_GUARD_ID, 'guard_condition': 'exact BF16 non-build B=1 Q=16 M=100000 D=128 K=48', 'classification': 'seed-consumed' if ratio >= SPEEDUP_FLOOR else 'kernel-slow', 'split_count': d128_k48_dd2b.K48_SPLIT_COUNT, 'rows_per_merge_cta': d128_k48_dd2b.K48_ROWS_PER_CTA, 'producer_topology': 'ROW_16x256B two-compute-warp K48 stage', 'merge_topology': 'K48 warp-row split-list merge', 'replaced_route': replaced_route, 'base_dispatcher_route': base_row.get('selected_route'), 'shape_specific_kernel_ms': timing['kernel_ms'], 'speedup_vs_external_baseline': ratio, 'external_baseline_ms': timing['flashlib_ms'], 'external_baseline_ref': 'historical_same_shape', 'K': 48, 'Q': 16, 'M': 100000, 'D': 128, 'B': 1, 'build': False}) + if portfolio_id in PORTFOLIOS_WITH_RECT_D128_K20_S12WARP4 and rect_d128_k20_s12warp4_eligible: + timing = SEED_TIMING_ROWS[RECT_D128_K20_Q1536] + ratio = timing['ratio_vs_flashlib'] + return _normalize_route_row({'shape_key': RECT_D128_K20_Q1536, 'selected_route': rect_d128_k20_s12warp4.route_for_contract_inputs(inputs), 'selected_entrypoint': RECT_D128_K20_S12WARP4_ENTRYPOINT, 'selected_seed': SEED_RECT_D128_K20_S12WARP4_ID, 'expected_seed': SEED_RECT_D128_K20_S12WARP4_ID, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': RECT_D128_K20_S12WARP4_GUARD_ID, 'guard_condition': 'exact BF16 non-build B=1 Q=1536 M=65536 D=128 K=20', 'classification': 'seed-consumed' if ratio >= SPEEDUP_FLOOR else 'kernel-slow', 'split_count': rect_d128_k20_s12warp4.DEFAULT_SPLIT_COUNT, 'rows_per_merge_cta': 4, 'producer_topology': '7768 split12 unordered tcgen05/TMA K20 stage1', 'merge_topology': 'warp4 repeated-min K20 merge', 'replaced_route': replaced_route, 'parent_v11_route': replaced_route, 'base_dispatcher_route': base_row.get('selected_route'), 'shape_specific_kernel_ms': timing['kernel_ms'], 'speedup_vs_external_baseline': ratio, 'external_baseline_ms': timing['flashlib_ms'], 'external_baseline_ref': 'historical_same_shape', 'K': 20, 'Q': 1536, 'M': 65536, 'D': 128, 'B': 1, 'build': False}) + if portfolio_id in PORTFOLIOS_WITH_D64_Q4096_C271 and _eligible_d64_q4096_c271(inputs): + seed_id = 'd64_q4096_c271_twostage_v1' + timing = SEED_TIMING_ROWS[D64_Q4096] + return _normalize_route_row({'shape_key': D64_Q4096, 'selected_route': d64_q4096_c271.route_name_for_inputs(inputs), 'selected_entrypoint': D64_Q4096_C271_ENTRYPOINT, 'selected_seed': seed_id, 'expected_seed': seed_id, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': 'c271_d64_q4096_build_exact_guard', 'guard_condition': 'exact BF16 build B=1 Q=M=4096 D=64 K=10', 'classification': 'seed-consumed', 'split_count': d64_q4096_c271.STAGE1_SPLIT4, 'producer_topology': 'D64_Q4096_tcgen05_tma_unordered_split4', 'merge_topology': 'exact_k10_rowbase_cached_split4_merge', 'replaced_route': replaced_route, 'base_dispatcher_route': base_row.get('selected_route'), 'shape_specific_kernel_ms': timing['kernel_ms'], 'speedup_vs_external_baseline': timing['ratio_vs_flashlib'], 'external_baseline_ms': timing['flashlib_ms'], 'external_baseline_ref': 'historical_same_shape'}) + if portfolio_id in PORTFOLIOS_WITH_HIGHD_SEARCH_BE66 and highd_search_be66_label is not None: + spec = highd_search_be66.SHAPE_SPECS[highd_search_be66_label] + timing = SEED_TIMING_ROWS[highd_search_be66_label] + ratio = timing['ratio_vs_flashlib'] + return _normalize_route_row({'shape_key': highd_search_be66_label, 'selected_route': highd_search_be66.route_for_contract_inputs(inputs), 'selected_entrypoint': HIGHD_SEARCH_BE66_ENTRYPOINT, 'selected_seed': SEED_HIGHD_SEARCH_BE66_ID, 'expected_seed': SEED_HIGHD_SEARCH_BE66_ID, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': HIGHD_SEARCH_BE66_GUARD_ID, 'guard_condition': 'exact BF16 non-build v12 high-D rectangular search B=1 K=10 for D/Q/M in {(1024,256,8192), (4096,128,4096)}', 'classification': 'seed-consumed' if ratio >= SPEEDUP_FLOOR else 'kernel-slow', 'feature_chunks': spec['feature_chunks'], 'split_count': highd_search_be66._split_count_for_label(highd_search_be66_label), 'group_count': highd_search_be66._group_count_for_label(highd_search_be66_label), 'producer_topology': 'M128_N64_tcgen05_tma_highd_chunked', 'merge_topology': 'fused_group_split_merge', 'replaced_route': replaced_route, 'base_dispatcher_route': base_row.get('selected_route'), 'shape_specific_kernel_ms': timing['kernel_ms'], 'speedup_vs_external_baseline': ratio, 'external_baseline_ms': timing['flashlib_ms'], 'external_baseline_ref': 'historical_same_shape', 'K': spec['K'], 'Q': spec['Q'], 'M': spec['M'], 'D': spec['D'], 'B': spec['B'], 'build': spec['build']}) + if portfolio_id in PORTFOLIOS_WITH_HIGHD_RAG_22E9 and highd_rag_22e9_label is not None: + spec = highd_rag_22e9.SHAPE_SPECS[highd_rag_22e9_label] + timing = SEED_TIMING_ROWS[highd_rag_22e9_label] + ratio = timing['ratio_vs_flashlib'] + return _normalize_route_row({'shape_key': highd_rag_22e9_label, 'selected_route': highd_rag_22e9.route_for_contract_inputs(inputs), 'selected_entrypoint': HIGHD_RAG_22E9_ENTRYPOINT, 'selected_seed': SEED_HIGHD_RAG_22E9_ID, 'expected_seed': SEED_HIGHD_RAG_22E9_ID, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': HIGHD_RAG_22E9_GUARD_ID, 'guard_condition': 'exact BF16 non-build v12 high-D RAG B=1 K=10 for D/Q/M in {(768,8,100000), (1024,4,100000), (4096,1,65536)}', 'classification': 'seed-consumed' if ratio >= SPEEDUP_FLOOR else 'kernel-slow', 'feature_chunks': spec['feature_chunks'], 'split_count': highd_rag_22e9._split_count_for_label(highd_rag_22e9_label), 'group_count': highd_rag_22e9._group_count_for_label(highd_rag_22e9_label), 'producer_topology': 'M64_N64_tcgen05_tma_highd_chunked', 'merge_topology': 'fused_group_split_merge', 'replaced_route': replaced_route, 'base_dispatcher_route': base_row.get('selected_route'), 'shape_specific_kernel_ms': timing['kernel_ms'], 'speedup_vs_external_baseline': ratio, 'external_baseline_ms': timing['flashlib_ms'], 'external_baseline_ref': 'historical_same_shape'}) + if portfolio_id in PORTFOLIOS_WITH_D64_TAIL_017A and d64_tail_017a_label is not None: + timing = SEED_TIMING_ROWS[d64_tail_017a_label] + spec = d64_tail_017a.SHAPE_SPECS[d64_tail_017a_label] + return _normalize_route_row({'shape_key': d64_tail_017a_label, 'selected_route': d64_tail_017a.route_for_contract_inputs(inputs), 'selected_entrypoint': D64_TAIL_017A_ENTRYPOINT, 'selected_seed': SEED_D64_TAIL_017A_ID, 'expected_seed': SEED_D64_TAIL_017A_ID, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': D64_TAIL_017A_GUARD_ID, 'guard_condition': ''.join(['exact BF16 non-build B=', format(spec['B'], ''), ' Q=', format(spec['Q'], ''), ' M=', format(spec['M'], ''), ' D=', format(spec['D'], ''), ' K=', format(spec['K'], '')]), 'classification': 'seed-consumed' if float(timing['ratio_vs_flashlib']) >= SPEEDUP_FLOOR else 'kernel-slow', 'split_count': d64_tail_017a._split_count_for_label(d64_tail_017a_label), 'group_count': d64_tail_017a._group_count_for_label(d64_tail_017a_label), 'producer_topology': 'D64_M64_tcgen05_tma_tail', 'merge_topology': 'fused_group_split_merge', 'replaced_route': replaced_route, 'base_dispatcher_route': base_row.get('selected_route'), 'shape_specific_kernel_ms': timing['kernel_ms'], 'speedup_vs_external_baseline': timing['ratio_vs_flashlib'], 'external_baseline_ms': timing['flashlib_ms'], 'external_baseline_ref': 'historical_same_shape'}) + k13_k48_seed, k13_k48_label = _k13_k48_seed_label(inputs) + if portfolio_id in PORTFOLIOS_WITH_K13_K48 and k13_k48_seed is not None: + is_k13 = k13_k48_seed == SEED_K13_ID + route = _route_k13_k48(inputs) + timing = SEED_TIMING_ROWS[str(k13_k48_label)] + return _normalize_route_row({'shape_key': k13_k48_label, 'selected_route': route, 'selected_entrypoint': K13_ENTRYPOINT if is_k13 else K48_ENTRYPOINT, 'selected_seed': k13_k48_seed, 'expected_seed': k13_k48_seed, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': '7dc5_2c1c_q4096_k13_exact_guard' if is_k13 else '7dc5_d03c_k48_exact_guard', 'guard_condition': 'exact BF16 build B=1 Q=M=4096 D=128 K=13 unordered split4' if is_k13 else 'exact BF16 build B=1 Q=M in {2048,4096} D=128 K=48 split4 warp-select', 'classification': 'seed-consumed', 'split_count': seed_k13.Q4096_K13_SPLIT_COUNT if is_k13 else seed_k48.K48_SPLITS, 'producer_topology': '2c1c_unordered_split4_tcgen05_tma' if is_k13 else 'd03c_k48_split4_tcgen05_tma', 'merge_topology': 'unordered_split4_merge' if is_k13 else 'warp_select_split4_merge', 'replaced_route': replaced_route, 'base_dispatcher_route': base_row.get('selected_route'), 'shape_specific_kernel_ms': timing['kernel_ms'], 'speedup_vs_external_baseline': timing['ratio_vs_flashlib'], 'external_baseline_ms': timing['flashlib_ms'], 'external_baseline_ref': 'historical_same_shape'}) + if _uses_floor_seed_portfolio(portfolio_id) and _eligible_q128_floor_seed(inputs): + q128_mod = _q128_module_for_portfolio(inputs, portfolio_id) + if q128_mod is _q128_dualm(): + seed_id = SEED_Q128_DUALM_ID + timing = Q128_DUALM_TIMING_ROWS[label] + guard_id = 'a162_q128_dualm_k32_rowld_s72_warp1_exact_guard' + rows_per_cta = 1 + else: + seed_id = SEED_Q128_S72R2_ID + timing = Q128_S72R2_TIMING_ROWS[label] + guard_id = 'a162_q128_stream_k32_s72r2_exact_guard' + rows_per_cta = 2 + return _normalize_route_row({'shape_key': label, 'selected_route': q128_mod.route_for_contract_inputs(inputs), 'selected_entrypoint': q128_mod.ROUTE_ENTRYPOINT, 'selected_seed': seed_id, 'expected_seed': seed_id, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': guard_id, 'guard_condition': 'exact BF16 non-build B=1 Q=128 M in {100000,131071} D=128 K=32', 'classification': 'seed-consumed', 'matched_label': label, 'split_count': 72, 'rows_per_cta': rows_per_cta, 'producer_topology': 'Q128_K32_rowld_tcgen05_tma', 'merge_topology': ''.join(['warp-row split-list merge rows_per_cta=', format(rows_per_cta, '')]), 'replaced_route': replaced_route, 'base_dispatcher_route': base_row.get('selected_route'), 'shape_specific_kernel_ms': timing['kernel_ms'], 'speedup_vs_external_baseline': timing['ratio_vs_flashlib'], 'external_baseline_ms': timing['flashlib_ms'], 'external_baseline_ref': 'historical_same_shape'}) + if portfolio_id in PORTFOLIOS_WITH_Q128_K10_WARPMERGE and _eligible_q128_k10_warpmerge(inputs): + timing = Q128_K10_WARPMERGE_TIMING + return _normalize_route_row({'shape_key': RAG_Q128_M100000_K10, 'selected_route': q128_k10_warpmerge.route_for_contract_inputs(inputs), 'selected_entrypoint': Q128_K10_WARPMERGE_ENTRYPOINT, 'selected_seed': SEED_Q128_K10_WARPMERGE_ID, 'expected_seed': SEED_Q128_K10_WARPMERGE_ID, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': '4ce8_df0f_q128_m100000_k10_s74_warpmerge_exact_guard', 'guard_condition': 'exact BF16 RAG B=1 Q=128 M=100000 D=128 K=10 split74 warp-merge', 'classification': 'seed-consumed', 'matched_label': RAG_Q128_M100000_K10, 'split_count': q128_k10_warpmerge.SPLIT_COUNT, 'rows_per_cta': q128_k10_warpmerge.ROWS_PER_MERGE_CTA, 'producer_topology': 'Q128_K10_split74_tcgen05_tma', 'merge_topology': 'one_warp_per_query_row_three_splits_per_lane', 'replaced_route': replaced_route, 'base_dispatcher_route': base_row.get('selected_route'), 'shape_specific_kernel_ms': timing['kernel_ms'], 'speedup_vs_external_baseline': timing['ratio_vs_flashlib'], 'external_baseline_ms': timing['flashlib_ms'], 'external_baseline_ref': 'historical_same_shape'}) + if portfolio_id in PORTFOLIOS_WITH_Q128_K10_ROWLD_1BED and _eligible_q128_k10_rowld_1bed(inputs): + timing = Q128_K10_ROWLD_1BED_TIMING + return _normalize_route_row({'shape_key': RAG_Q128_M100000_K10, 'selected_route': _q128_k10_rowld_1bed().route_for_contract_inputs(inputs), 'selected_entrypoint': Q128_K10_ROWLD_1BED_ENTRYPOINT, 'selected_seed': SEED_Q128_K10_ROWLD_1BED_ID, 'expected_seed': SEED_Q128_K10_ROWLD_1BED_ID, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': Q128_K10_ROWLD_1BED_GUARD_ID, 'guard_condition': 'exact BF16 non-build B=1 Q=128 M=100000 D=128 K=10 split74 row-load', 'classification': 'seed-consumed', 'matched_label': RAG_Q128_M100000_K10, 'split_count': _q128_k10_rowld_1bed().DEFAULT_SPLIT_COUNT, 'rows_per_merge_cta': _q128_k10_rowld_1bed().ROWS_PER_MERGE_CTA, 'producer_topology': 'ROW_16x256B_q128_k10_rowld_tcgen05_tma_stage1', 'merge_topology': 'warp-row split-list merge', 'replaced_route': replaced_route, 'base_dispatcher_route': base_row.get('selected_route'), 'shape_specific_kernel_ms': timing['kernel_ms'], 'speedup_vs_external_baseline': timing['ratio_vs_flashlib'], 'external_baseline_ms': timing['flashlib_ms'], 'external_baseline_ref': 'historical_same_shape'}) + if _uses_floor_seed_portfolio(portfolio_id) and _eligible_q16_floor_seed(inputs): + timing = Q16_M250_TIMING + return _normalize_route_row({'shape_key': RAG_Q16_M250000_K32, 'selected_route': _q16_m250().route_for_contract_inputs(inputs), 'selected_entrypoint': Q16_M250_ENTRYPOINT, 'selected_seed': SEED_Q16_M250_ID, 'expected_seed': SEED_Q16_M250_ID, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': 'df0f_q16_m250_k32_exact_guard', 'guard_condition': 'exact BF16 RAG B=1 Q=16 M=250000 D=128 K=32 split288', 'classification': 'seed-consumed', 'matched_label': RAG_Q16_M250000_K32, 'split_count': 288, 'producer_topology': 'Q16_M250000_K32_rowld1_2warp_tcgen05_tma', 'merge_topology': 'rows4 warp split-list merge', 'replaced_route': replaced_route, 'base_dispatcher_route': base_row.get('selected_route'), 'shape_specific_kernel_ms': timing['kernel_ms'], 'speedup_vs_external_baseline': timing['ratio_vs_flashlib'], 'external_baseline_ms': timing['flashlib_ms'], 'external_baseline_ref': 'historical_same_shape'}) + if portfolio_id in PORTFOLIOS_WITH_LARGE_SQUARE_K32_EFE4 and _eligible_large_square_k32_efe4(inputs): + timing = SEED_TIMING_ROWS[BUILD_LARGE_SQUARE_K32] + return _normalize_route_row({'shape_key': BUILD_LARGE_SQUARE_K32, 'selected_route': large_square_k32_efe4.route_for_contract_inputs(inputs), 'selected_entrypoint': LARGE_SQUARE_K32_EFE4_ENTRYPOINT, 'selected_seed': SEED_LARGE_SQUARE_K32_EFE4_ID, 'expected_seed': SEED_LARGE_SQUARE_K32_EFE4_ID, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': 'fac0_large_square_k32_efe4_prodcache_exact_guard', 'guard_condition': 'exact BF16 build B=1 Q=M=8192 D=128 K=32 split2 producer-cache', 'classification': 'seed-consumed', 'split_count': large_square_k32_efe4.SPLIT_COUNT, 'producer_topology': 'EFE4_split2_tcgen05_tma_chunkworst_prodcache', 'merge_topology': 'split2_warp8_k32_merge', 'replaced_route': replaced_route, 'base_dispatcher_route': base_row.get('selected_route'), 'shape_specific_kernel_ms': timing['kernel_ms'], 'speedup_vs_external_baseline': timing['ratio_vs_flashlib'], 'external_baseline_ms': timing['flashlib_ms'], 'external_baseline_ref': 'historical_same_shape'}) + if portfolio_id in PORTFOLIOS_WITH_RAG_STREAM_K10_34DA and _eligible_rag_stream_k10_34da(inputs): + timing = SEED_TIMING_ROWS[RAG_STREAM_K10] + mod = _rag_stream_k10_34da() + return _normalize_route_row({'shape_key': RAG_STREAM_K10, 'selected_route': mod.route_for_contract_inputs(inputs), 'selected_entrypoint': RAG_STREAM_K10_34DA_ENTRYPOINT, 'selected_seed': SEED_RAG_STREAM_K10_34DA_ID, 'expected_seed': SEED_RAG_STREAM_K10_34DA_ID, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': '34da_rag_stream_k10_s72_warpmerge_exact_guard', 'guard_condition': 'exact BF16 non-build B=1 Q=128 M=100000 D=128 K=10', 'classification': 'seed-consumed', 'split_count': mod.SPLIT_COUNT, 'rows_per_merge_cta': mod.ROWS_PER_CTA, 'producer_topology': 'split72_tcgen05_tma_k10_stage1', 'merge_topology': 'warp-row split-list merge', 'replaced_route': replaced_route, 'base_dispatcher_route': base_row.get('selected_route'), 'shape_specific_kernel_ms': timing['kernel_ms'], 'speedup_vs_external_baseline': timing['ratio_vs_flashlib'], 'external_baseline_ms': timing['flashlib_ms'], 'external_baseline_ref': 'historical_same_shape'}) + if portfolio_id in PORTFOLIOS_WITH_Q1_M524_N128 and _eligible_q1_m524_n128(inputs): + timing = SEED_TIMING_ROWS[RAG_Q1_M524287_K10] + return _normalize_route_row({'shape_key': RAG_Q1_M524287_K10, 'selected_route': q1_m524_n128.route_for_contract_inputs(inputs), 'selected_entrypoint': Q1_M524_N128_ENTRYPOINT, 'selected_seed': SEED_Q1_M524_N128_ID, 'expected_seed': SEED_Q1_M524_N128_ID, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': '32b9_ea43_q1_m524287_n128_exact_guard', 'guard_condition': 'exact BF16 non-build B=1 Q=1 M=524287 D=128 K=10 split148 group4', 'classification': 'seed-consumed', 'split_count': q1_m524_n128.Q1_N128_SPLIT, 'group_count': q1_m524_n128.Q1_N128_GROUPS, 'producer_topology': 'Q1_M524287_M64_N128_tcgen05_tma', 'merge_topology': 's148_g4_fused_sorted_stream_merge', 'replaced_route': replaced_route, 'base_dispatcher_route': base_row.get('selected_route'), 'shape_specific_kernel_ms': timing['kernel_ms'], 'speedup_vs_external_baseline': timing['ratio_vs_flashlib'], 'external_baseline_ms': timing['flashlib_ms'], 'external_baseline_ref': 'historical_same_shape'}) + if portfolio_id in PORTFOLIOS_WITH_Q32_EXACT and _eligible_q32_exact(inputs): + timing = SEED_TIMING_ROWS[RAG_Q32_M100000_K32] + return _normalize_route_row({'shape_key': RAG_Q32_M100000_K32, 'selected_route': q32exact.route_for_contract_inputs(inputs), 'selected_entrypoint': Q32_EXACT_ENTRYPOINT, 'selected_seed': SEED_Q32_EXACT_ID, 'expected_seed': SEED_Q32_EXACT_ID, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': Q32_EXACT_GUARD_ID, 'guard_condition': 'exact BF16 non-build B=1 Q=32 M=100000 D=128 K=32', 'classification': 'seed-consumed', 'split_count': q32exact.K32_Q32_SPLIT_COUNT, 'rows_per_merge_cta': q32exact.K32_ROWS4_ROWS_PER_CTA, 'producer_topology': Q32_EXACT_PRODUCER_TOPOLOGY, 'merge_topology': 'rows4 warp-row split-list merge', 'replaced_route': replaced_route, 'base_dispatcher_route': base_row.get('selected_route'), 'shape_specific_kernel_ms': timing['kernel_ms'], 'speedup_vs_external_baseline': timing['ratio_vs_flashlib'], 'external_baseline_ms': timing['flashlib_ms'], 'external_baseline_ref': 'historical_same_shape'}) + if portfolio_id in PORTFOLIOS_WITH_Q32_TAIL143 and _eligible_q32tail143(inputs): + timing = SEED_TIMING_ROWS[label] + selected_seed, selected_entrypoint, guard_id = _q32tail143_route_metadata(inputs) + mod = _q32tail143_module_for_inputs(inputs) + split_count = mod.K32_Q32TAIL143_LOW_SPLIT_COUNT if selected_seed == SEED_Q32TAIL143_LOW_ID else mod.K32_Q32TAIL143_SPLIT_COUNT + return _normalize_route_row({'shape_key': label, 'selected_route': mod.route_for_contract_inputs(inputs), 'selected_entrypoint': selected_entrypoint, 'selected_seed': selected_seed, 'expected_seed': selected_seed, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': guard_id, 'guard_condition': ''.join(['exact BF16 non-build B=1 Q=32 D=128 K=32 M=', format(int(inputs.get('M', -1)), '')]), 'classification': 'seed-consumed' if float(timing['ratio_vs_flashlib']) >= SPEEDUP_FLOOR else 'kernel-slow', 'split_count': split_count, 'rows_per_merge_cta': mod.q32exact.rows4.K32_ROWS4_ROWS_PER_CTA, 'producer_topology': 'f590_rowld2_ROW_16x256B_two_compute_warp_exact_stage1_split143', 'merge_topology': 'rows4 warp-row split-list merge', 'replaced_route': replaced_route, 'parent_v11_route': replaced_route, 'base_dispatcher_route': base_row.get('selected_route'), 'shape_specific_kernel_ms': timing['kernel_ms'], 'speedup_vs_external_baseline': timing['ratio_vs_flashlib'], 'external_baseline_ms': timing['flashlib_ms'], 'external_baseline_ref': 'historical_same_shape'}) + if portfolio_id in PORTFOLIOS_WITH_Q32_TAIL and _eligible_q32tail(inputs): + timing = SEED_TIMING_ROWS[label] + return _normalize_route_row({'shape_key': label, 'selected_route': _q32tail().route_for_contract_inputs(inputs), 'selected_entrypoint': Q32TAIL_ENTRYPOINT, 'selected_seed': SEED_Q32TAIL_ID, 'expected_seed': SEED_Q32TAIL_ID, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': '6866_q32tail_m99999_m100001_k32_exact_guard', 'guard_condition': 'exact BF16 non-build B=1 Q=32 M in {99999,100001} D=128 K=32', 'classification': 'seed-consumed', 'split_count': _q32tail().K32_Q32TAIL_EXACT_SPLIT_COUNT, 'rows_per_merge_cta': _q32tail().q32exact.rows4.K32_ROWS4_ROWS_PER_CTA, 'producer_topology': 'f590_rowld2_ROW_16x256B_two_compute_warp_exact_stage1', 'merge_topology': 'rows4 warp-row split-list merge', 'replaced_route': replaced_route, 'base_dispatcher_route': base_row.get('selected_route'), 'shape_specific_kernel_ms': timing['kernel_ms'], 'speedup_vs_external_baseline': timing['ratio_vs_flashlib'], 'external_baseline_ms': timing['flashlib_ms'], 'external_baseline_ref': 'historical_same_shape'}) + if portfolio_id in PORTFOLIOS_WITH_EXPANDED_K32_Q31_Q33_Q40 and _eligible_q31tail_v2(inputs): + timing = SEED_TIMING_ROWS[EXPANDED_Q31_M100000_K32] + mod = _q31tail_v2() + return _normalize_route_row({'shape_key': EXPANDED_Q31_M100000_K32, 'selected_route': mod.route_for_contract_inputs(inputs), 'selected_entrypoint': Q31TAIL_V2_ENTRYPOINT, 'selected_seed': SEED_Q31TAIL_V2_ID, 'expected_seed': SEED_Q31TAIL_V2_ID, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': Q31TAIL_V2_GUARD_ID, 'guard_condition': 'exact BF16 non-build B=1 Q=31 M=100000 D=128 K=32', 'classification': 'seed-consumed', 'split_count': mod.K32_Q31_EXACT_SPLIT_COUNT, 'rows_per_merge_cta': mod.q32exact.rows4.K32_ROWS4_ROWS_PER_CTA, 'producer_topology': '0cb5_v2_q31_exact_rowld2_ROW_16x256B_tcgen05_tma', 'merge_topology': 'rows4 warp-row split-list merge', 'replaced_route': replaced_route, 'base_dispatcher_route': base_row.get('selected_route'), 'shape_specific_kernel_ms': timing['kernel_ms'], 'speedup_vs_external_baseline': timing['ratio_vs_flashlib'], 'external_baseline_ms': timing['flashlib_ms'], 'external_baseline_ref': 'historical_same_shape'}) + if portfolio_id in PORTFOLIOS_WITH_EXPANDED_K32_Q31_Q33_Q40 and _eligible_q33tile(inputs): + timing = SEED_TIMING_ROWS[label] + mod = _q33tile() + return _normalize_route_row({'shape_key': label, 'selected_route': mod.route_for_contract_inputs(inputs), 'selected_entrypoint': Q33TILE_ENTRYPOINT, 'selected_seed': SEED_Q33TILE_ID, 'expected_seed': SEED_Q33TILE_ID, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': Q33TILE_GUARD_ID, 'guard_condition': 'exact BF16 non-build B=1 Q in {33,40} M=100000 D=128 K=32', 'classification': 'seed-consumed', 'split_count': mod.K32_Q33TILE_SPLIT_COUNT, 'group_count': mod.K32_Q33TILE_GROUP_COUNT, 'producer_topology': 'c489_e5db_m64_rowld_ROW_16x256B_tcgen05_tma', 'merge_topology': 'e5db fused split merge', 'replaced_route': replaced_route, 'base_dispatcher_route': base_row.get('selected_route'), 'shape_specific_kernel_ms': timing['kernel_ms'], 'speedup_vs_external_baseline': timing['ratio_vs_flashlib'], 'external_baseline_ms': timing['flashlib_ms'], 'external_baseline_ref': 'historical_same_shape'}) + if portfolio_id in PORTFOLIOS_WITH_Q48_K12 and _eligible_q48_k12(inputs): + timing = SEED_TIMING_ROWS[EXPANDED_Q48_M75000_K12] + mod = _q48_k12() + return _normalize_route_row({'shape_key': EXPANDED_Q48_M75000_K12, 'selected_route': mod.route_for_contract_inputs(inputs), 'selected_entrypoint': Q48_K12_ENTRYPOINT, 'selected_seed': SEED_Q48_K12_ID, 'expected_seed': SEED_Q48_K12_ID, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': Q48_K12_GUARD_ID, 'guard_condition': 'exact BF16 non-build B=1 Q=48 M=75000 D=128 K=12', 'classification': 'seed-consumed', 'split_count': mod.Q48_K12_SPLIT_COUNT, 'rows_per_merge_cta': mod.Q48_K12_ROWS_PER_MERGE_CTA, 'producer_topology': '2f22_m64n64_ROW_16x256B_tcgen05_tma_k12', 'merge_topology': 'four-row split-list merge', 'replaced_route': replaced_route, 'base_dispatcher_route': base_row.get('selected_route'), 'shape_specific_kernel_ms': timing['kernel_ms'], 'speedup_vs_external_baseline': timing['ratio_vs_flashlib'], 'external_baseline_ms': timing['flashlib_ms'], 'external_baseline_ref': 'historical_same_shape'}) + if portfolio_id in PORTFOLIOS_WITH_Q32_K31_C3D2 and _eligible_q32_k31_c3d2(inputs): + timing = SEED_TIMING_ROWS[EXPANDED_Q32_M100000_K31] + mod = _q32_k31_c3d2() + return _normalize_route_row({'shape_key': EXPANDED_Q32_M100000_K31, 'selected_route': mod.route_for_contract_inputs(inputs), 'selected_entrypoint': Q32_K31_C3D2_ENTRYPOINT, 'selected_seed': SEED_Q32_K31_C3D2_ID, 'expected_seed': SEED_Q32_K31_C3D2_ID, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': Q32_K31_C3D2_GUARD_ID, 'guard_condition': 'exact BF16 non-build B=1 Q=32 M=100000 D=128 K=31', 'classification': 'seed-consumed', 'split_count': mod.Q32_K31_SPLIT_COUNT, 'rows_per_merge_cta': mod.Q32_K31_ROWS_PER_MERGE_CTA, 'partial_top_k': 31, 'output_top_k': 31, 'producer_topology': 'eaf7_q32_k31_ROW_16x256B_two_compute_warp_tcgen05_tma', 'merge_topology': 'four-row K31 split-list merge', 'replaced_route': replaced_route, 'base_dispatcher_route': base_row.get('selected_route'), 'shape_specific_kernel_ms': timing['kernel_ms'], 'speedup_vs_external_baseline': timing['ratio_vs_flashlib'], 'external_baseline_ms': timing['flashlib_ms'], 'external_baseline_ref': 'historical_same_shape'}) + if portfolio_id in PORTFOLIOS_WITH_Q32_LOWK_C3D2 and _eligible_q32_lowk_c3d2(inputs): + timing = D665_Q32_LOWK_C3D2_TIMING_ROWS.get(label, SEED_TIMING_ROWS[label]) + mod = _q32_lowk_c3d2() + ratio = timing['ratio_vs_flashlib'] + return _normalize_route_row({'shape_key': label, 'selected_route': mod.route_for_contract_inputs(inputs), 'selected_entrypoint': Q32_LOWK_C3D2_ENTRYPOINT, 'selected_seed': SEED_Q32_LOWK_C3D2_ID, 'expected_seed': SEED_Q32_LOWK_C3D2_ID, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': Q32_LOWK_C3D2_GUARD_ID, 'guard_condition': 'exact BF16 non-build B=1 Q=32 M=100000 D=128 K=20', 'classification': 'seed-consumed' if ratio >= SPEEDUP_FLOOR else 'kernel-slow', 'split_count': mod.Q32_LOWK_SPLIT_COUNT, 'partial_top_k': int(inputs.get('K', -1)), 'output_top_k': int(inputs.get('K', -1)), 'producer_topology': 'c3d2_e5db_lowk_ROW_16x256B_tcgen05_tma', 'merge_topology': 'four-row low-K split-list merge', 'replaced_route': replaced_route, 'base_dispatcher_route': base_row.get('selected_route'), 'shape_specific_kernel_ms': timing['kernel_ms'], 'speedup_vs_external_baseline': ratio, 'external_baseline_ms': timing['flashlib_ms'], 'external_baseline_ref': 'historical_same_shape'}) + return None + +def _route_trace_record(inputs: dict[str, Any], *, force_fallback: bool=False, portfolio_id: str=CANDIDATE_DEFAULT) -> dict[str, Any]: + if force_fallback: + row = _base_trace_record(inputs, force_fallback=True) + label = str(inputs.get('label')) + if label in CONSUMED_SEED_SHAPES: + row['expected_seed'] = _expected_seed_for_label(label, portfolio_id=portfolio_id) + row['guard_id'] = ''.join(['forced_fallback_a4ec_', format(label, '')]) + row['guard_condition'] = 'forced fallback disables a4ec specialized common-D seed guards' + row['classification'] = 'guard-miss' + return _normalize_route_row(row) + specialized = _specialized_trace_record(inputs, portfolio_id=portfolio_id) + if specialized is not None: + return specialized + return _base_trace_record(inputs) + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False, portfolio_id: str=CANDIDATE_DEFAULT) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + return [_route_trace_record(_trace_inputs_for_shape(shape), force_fallback=force_fallback, portfolio_id=portfolio_id) for shape in selected] + +def _expected_seed_for_label(label: str, *, portfolio_id: str=CANDIDATE_DEFAULT) -> str | None: + if portfolio_id in PORTFOLIOS_WITH_Q128_K10_ROWLD_1BED: + if label == RAG_Q128_M100000_K10: + return SEED_Q128_K10_ROWLD_1BED_ID + return _expected_seed_for_label(label, portfolio_id=CANDIDATE_RECT_D128_K20_S12WARP4) + if label == EXPANDED_Q32_M99999_K32 and portfolio_id in PORTFOLIOS_WITH_Q32_TAIL143: + return SEED_Q32TAIL143_LOW_ID + if label == EXPANDED_Q32_M100001_K32 and portfolio_id in PORTFOLIOS_WITH_Q32_TAIL143: + return SEED_Q32TAIL143_HIGH_ID + if portfolio_id in PORTFOLIOS_WITH_RECT_D128_K20_S12WARP4: + if label in RECT_D128_K20_S12WARP4_CONSUMED_SHAPES: + return SEED_RECT_D128_K20_S12WARP4_ID + return _expected_seed_for_label(label, portfolio_id=CANDIDATE_D128_K48_DD2B) + if portfolio_id in PORTFOLIOS_WITH_D128_K48_DD2B: + if label in D128_K48_DD2B_CONSUMED_SHAPES: + return SEED_D128_K48_DD2B_ID + return _expected_seed_for_label(label, portfolio_id=CANDIDATE_HIGHD_SEARCH_BE66) + if portfolio_id in PORTFOLIOS_WITH_HIGHD_SEARCH_BE66: + if label in HIGHD_SEARCH_BE66_CONSUMED_SHAPES: + return SEED_HIGHD_SEARCH_BE66_ID + return _expected_seed_for_label(label, portfolio_id=CANDIDATE_HIGHD_RAG_22E9) + if portfolio_id in PORTFOLIOS_WITH_HIGHD_RAG_22E9: + if label in HIGHD_RAG_22E9_CONSUMED_SHAPES: + return SEED_HIGHD_RAG_22E9_ID + return _expected_seed_for_label(label, portfolio_id=CANDIDATE_D256_K32_59FE) + if portfolio_id in PORTFOLIOS_WITH_D256_K32_59FE: + if label in D256_K32_59FE_CONSUMED_SHAPES: + return SEED_D256_K32_59FE_ID + return _expected_seed_for_label(label, portfolio_id=CANDIDATE_D256_Q128_59FE) + if portfolio_id in PORTFOLIOS_WITH_D256_Q128_59FE: + if label in D256_Q128_59FE_CONSUMED_SHAPES: + return SEED_D256_Q128_59FE_ID + return _expected_seed_for_label(label, portfolio_id=CANDIDATE_SELECTED_SYNTHESIS) + if label == BUILD_D256: + if portfolio_id == CANDIDATE_A4EC_BASE: + return 'common_d256_q1024_56f3_v1' + return 'common_d_56f3_build_d256_q1024_v1' + if label == BUILD_D768: + return 'common_d_56f3_build_highd_v1' if portfolio_id == CANDIDATE_F1D9_BUILD else 'common_d768_build_eeff_m64split_v1' + if label in (BUILD_D1024, BUILD_D4096): + return 'common_d_56f3_build_highd_v1' + if label == D768_SEARCH and portfolio_id != CANDIDATE_A4EC_BASE: + return 'common_d_eeff_search_d768_v1' + if label == D768_RAG: + return 'non128_frontier_4be7_d768fused_v1' + if label in (RAG_D1024, RAG_D4096) and portfolio_id in PORTFOLIOS_WITH_HIGHD_RAG: + return 'common_d_5e7f_rag_highd_v1' + if label == SEARCH_D256 and portfolio_id in PORTFOLIOS_WITH_SEARCH_D256: + return 'common_d_5e7f_search_d256_v1' + if label == RAG_D64 and portfolio_id in PORTFOLIOS_WITH_RAG_D64_M128: + return 'common_d_1438_rag_d64_m128_v1' + if label == RAG_D64 and portfolio_id == CANDIDATE_D64_REPAIR: + return 'common_d_5e7f_rag_d64_repair_v1' + if label == RAG_D256 and portfolio_id in PORTFOLIOS_WITH_SEARCH_D256: + return 'common_d_5e7f_rag_d64_d256_v1' + if label in D256_Q4_E2DF_CONSUMED_SHAPES and portfolio_id in PORTFOLIOS_WITH_D256_Q4_E2DF: + return SEED_D256_Q4_E2DF_ID + if label in D256_K32_59FE_CONSUMED_SHAPES and portfolio_id in PORTFOLIOS_WITH_D256_K32_59FE: + return SEED_D256_K32_59FE_ID + if label == D64_Q4096 and portfolio_id in PORTFOLIOS_WITH_D64_Q4096_C271: + return 'd64_q4096_c271_twostage_v1' + if label in D64_TAIL_017A_CONSUMED_SHAPES and portfolio_id in PORTFOLIOS_WITH_D64_TAIL_017A: + return SEED_D64_TAIL_017A_ID + if label == BUILD_K13 and portfolio_id in PORTFOLIOS_WITH_K13_K48: + return SEED_K13_ID + if label in (BUILD_K48_Q2048, BUILD_K48_Q4096) and portfolio_id in PORTFOLIOS_WITH_K13_K48: + return SEED_K48_ID + if label in (RAG_Q128_M100000_K32, RAG_Q128_M131071_K32) and _uses_floor_seed_portfolio(portfolio_id): + if portfolio_id == CANDIDATE_FLOOR_SEEDS_Q128_5698: + return SEED_Q128_DUALM_ID + if portfolio_id == CANDIDATE_FLOOR_SEEDS_Q128_681B: + return SEED_Q128_S72R2_ID + if label == RAG_Q128_M131071_K32: + return SEED_Q128_DUALM_ID + return SEED_Q128_S72R2_ID + if label == RAG_Q128_M100000_K10 and portfolio_id in PORTFOLIOS_WITH_Q128_K10_WARPMERGE: + return SEED_Q128_K10_WARPMERGE_ID + if label == RAG_Q16_M250000_K32 and _uses_floor_seed_portfolio(portfolio_id): + return SEED_Q16_M250_ID + if label == BUILD_LARGE_SQUARE_K32 and portfolio_id in PORTFOLIOS_WITH_LARGE_SQUARE_K32_EFE4: + return SEED_LARGE_SQUARE_K32_EFE4_ID + if label == RAG_STREAM_K10 and portfolio_id in PORTFOLIOS_WITH_RAG_STREAM_K10_34DA: + return SEED_RAG_STREAM_K10_34DA_ID + if label == RAG_Q1_M524287_K10 and portfolio_id in PORTFOLIOS_WITH_Q1_M524_N128: + return SEED_Q1_M524_N128_ID + if label == RAG_Q32_M100000_K32 and portfolio_id in PORTFOLIOS_WITH_Q32_EXACT: + return SEED_Q32_EXACT_ID + if label in EXPANDED_Q32TAIL_CONSUMED_SHAPES and portfolio_id in PORTFOLIOS_WITH_Q32_TAIL: + return SEED_Q32TAIL_ID + if label == EXPANDED_Q31_M100000_K32 and portfolio_id in PORTFOLIOS_WITH_EXPANDED_K32_Q31_Q33_Q40: + return SEED_Q31TAIL_V2_ID + if label in (EXPANDED_Q33_M100000_K32, EXPANDED_Q40_M100000_K32) and portfolio_id in PORTFOLIOS_WITH_EXPANDED_K32_Q31_Q33_Q40: + return SEED_Q33TILE_ID + if label == EXPANDED_Q48_M75000_K12 and portfolio_id in PORTFOLIOS_WITH_Q48_K12: + return SEED_Q48_K12_ID + if label in EXPANDED_Q32_LOWK_C3D2_CONSUMED_SHAPES and portfolio_id in PORTFOLIOS_WITH_Q32_LOWK_C3D2: + return SEED_Q32_LOWK_C3D2_ID + if label in EXPANDED_Q32_K31_C3D2_CONSUMED_SHAPES and portfolio_id in PORTFOLIOS_WITH_Q32_K31_C3D2: + return SEED_Q32_K31_C3D2_ID + if label in EXPANDED_Q32_K31_C3D2_CONSUMED_SHAPES and portfolio_id in PORTFOLIOS_WITH_Q32_LOWK_C3D2: + return SEED_Q32_LOWK_C3D2_ID + return None + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=None, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + previous = _set_bench_backend(use_cupti) + try: + shapes = None if shape_labels is None else _select_contract_shapes(shape_labels) + return evaluate_contract(shapes=shapes, correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = previous + +def _annotate_route_trace(route_trace: list[dict[str, Any]], candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> list[dict[str, Any]]: + annotated = [] + for row in route_trace: + label = str(row.get('shape_key')) + out = dict(row) + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + ratio = candidate_row.get('ratio_vs_flashlib') + relative = baseline_ms / candidate_ms if candidate_ms and baseline_ms else None + for key in ('B', 'Q', 'M', 'D', 'K', 'build', 'dtype'): + if key in candidate_row: + out[key] = candidate_row[key] + out['dispatcher_kernel_ms'] = candidate_ms + out['baseline_dispatcher_kernel_ms'] = baseline_ms + out['external_baseline_ms'] = candidate_row.get('flashlib_ms') + out['flashlib_ms'] = candidate_row.get('flashlib_ms') + out['external_baseline_ref'] = 'same_session' if candidate_row.get('flashlib_ms') is not None else 'not_available' + out['speedup_vs_external_baseline'] = ratio + out['relative_speedup_vs_baseline'] = relative + out['timing_backend'] = candidate_row.get('timing_backend') or baseline_row.get('timing_backend') + benchmark_unmeasured = candidate_row.get('benchmark_skipped_reason') is not None or (candidate_row.get('measurement_comparable') is False and candidate_ms is None) + if candidate_row.get('passed') is False: + failures = candidate_row.get('correctness_failures') or candidate_row.get('diagnostics') or () + first_failure = failures[0] if isinstance(failures, list) and failures else {} + if isinstance(first_failure, dict): + out['failure_kind'] = first_failure.get('failure_kind') + out['failure_message'] = first_failure.get('message') + out['failure_exception_type'] = first_failure.get('exception_type') + out['classification'] = 'benchmark-path-mismatch' + elif benchmark_unmeasured: + out['classification'] = 'unmeasured' + out['unmeasured_reason'] = candidate_row.get('benchmark_skipped_reason') or candidate_row.get('measurement_invalid_reason') + elif out.get('route_kind') == 'specialized': + if out.get('expected_seed') is not None and out.get('selected_seed') != out.get('expected_seed'): + out['classification'] = 'guard-miss' + elif isinstance(ratio, (float, int)) and float(ratio) < SPEEDUP_FLOOR: + out['classification'] = 'kernel-slow' + elif label in CONSUMED_SEED_SHAPES and out.get('selected_seed') == out.get('expected_seed'): + out['classification'] = 'seed-consumed' + else: + out['classification'] = 'route-ok' + elif isinstance(ratio, (float, int)) and float(ratio) < SPEEDUP_FLOOR: + out['classification'] = 'fallback-slow' + annotated.append(_normalize_route_row(out)) + return annotated + +def _candidate_floor_record(*, label: str, candidate_id: str, selected_seed: str, guard_id: str, selected_route: str, selected_entrypoint: str, timing: dict[str, Any]) -> dict[str, Any]: + return {'candidate_id': candidate_id, 'candidate_kernel_ms': timing['kernel_ms'], 'flashlib_ms': timing['flashlib_ms'], 'speedup_vs_flashlib': timing['ratio_vs_flashlib'], 'selected_route': selected_route, 'selected_seed': selected_seed, 'selected_entrypoint': selected_entrypoint, 'guard_id': guard_id, 'timing_backend': timing['timing_backend'], 'baseline_ref_scope': 'historical_exact_shape', 'source_payload': timing['source_payload'], 'shape_key': label} + +def _historical_seed_candidates_for_label(label: str) -> list[dict[str, Any]]: + inputs = _inputs_for_label(label) + if label == BUILD_K13: + return [_candidate_floor_record(label=label, candidate_id='historical_a162_k13', selected_seed=SEED_K13_A162_ID, guard_id='a162_2c1c_q4096_k13_exact_guard', selected_route=seed_k13.route_for_contract_inputs(inputs), selected_entrypoint=K13_ENTRYPOINT, timing=K13_A162_SOURCE_TIMING), _candidate_floor_record(label=label, candidate_id='historical_a162_k13_best_timing', selected_seed=SEED_K13_A162_ID, guard_id='a162_2c1c_q4096_k13_exact_guard', selected_route=seed_k13.route_for_contract_inputs(inputs), selected_entrypoint=K13_ENTRYPOINT, timing=K13_A162_BEST_TIMING)] + if label in (RAG_Q128_M100000_K32, RAG_Q128_M131071_K32): + return [_candidate_floor_record(label=label, candidate_id=CANDIDATE_FLOOR_SEEDS_Q128_5698, selected_seed=SEED_Q128_DUALM_ID, guard_id='a162_q128_dualm_k32_rowld_s72_warp1_exact_guard', selected_route=_q128_dualm().route_for_contract_inputs(inputs), selected_entrypoint=Q128_DUALM_ENTRYPOINT, timing=Q128_DUALM_TIMING_ROWS[label]), _candidate_floor_record(label=label, candidate_id=CANDIDATE_FLOOR_SEEDS_Q128_681B, selected_seed=SEED_Q128_S72R2_ID, guard_id='a162_q128_stream_k32_s72r2_exact_guard', selected_route=_q128_s72r2().route_for_contract_inputs(inputs), selected_entrypoint=Q128_S72R2_ENTRYPOINT, timing=Q128_S72R2_TIMING_ROWS[label])] + if label == RAG_Q128_M100000_K10: + return [_candidate_floor_record(label=label, candidate_id='weave-evolve-knn-build-41bb_exact_q128_k10_rowld_1bed', selected_seed=SEED_Q128_K10_ROWLD_1BED_ID, guard_id=Q128_K10_ROWLD_1BED_GUARD_ID, selected_route=_q128_k10_rowld_1bed().route_for_contract_inputs(inputs), selected_entrypoint=Q128_K10_ROWLD_1BED_ENTRYPOINT, timing=Q128_K10_ROWLD_1BED_TIMING), _candidate_floor_record(label=label, candidate_id='weave-evolve-knn-build-4ce8_exact_q128_k10_warpmerge', selected_seed=SEED_Q128_K10_WARPMERGE_ID, guard_id='4ce8_df0f_q128_m100000_k10_s74_warpmerge_exact_guard', selected_route=q128_k10_warpmerge.ROUTE_WARPMERGE, selected_entrypoint=Q128_K10_WARPMERGE_ENTRYPOINT, timing=Q128_K10_WARPMERGE_TIMING)] + if label == RAG_Q1_M524287_K10: + inputs = _inputs_for_label(label) + return [_candidate_floor_record(label=label, candidate_id='weave-evolve-knn-build-32b9_exact_q1_m524_n128', selected_seed=SEED_Q1_M524_N128_ID, guard_id='32b9_ea43_q1_m524287_n128_exact_guard', selected_route=q1_m524_n128.route_for_contract_inputs(inputs), selected_entrypoint=Q1_M524_N128_ENTRYPOINT, timing=SEED_TIMING_ROWS[label])] + if label in D64_TAIL_017A_CONSUMED_SHAPES: + return [_candidate_floor_record(label=label, candidate_id=SEED_D64_TAIL_017A_ID, selected_seed=SEED_D64_TAIL_017A_ID, guard_id=D64_TAIL_017A_GUARD_ID, selected_route=d64_tail_017a.route_for_contract_inputs(inputs), selected_entrypoint=D64_TAIL_017A_ENTRYPOINT, timing=SEED_TIMING_ROWS[label])] + if label in D256_Q4_E2DF_CONSUMED_SHAPES: + return [_candidate_floor_record(label=label, candidate_id=SEED_D256_Q4_E2DF_ID, selected_seed=SEED_D256_Q4_E2DF_ID, guard_id=D256_Q4_E2DF_GUARD_ID, selected_route=d256_q4_e2df.route_for_contract_inputs(inputs), selected_entrypoint=D256_Q4_E2DF_ENTRYPOINT, timing=SEED_TIMING_ROWS[label])] + if label in D256_Q128_59FE_CONSUMED_SHAPES: + return [_candidate_floor_record(label=label, candidate_id=SEED_D256_Q128_59FE_ID, selected_seed=SEED_D256_Q128_59FE_ID, guard_id=D256_Q128_59FE_GUARD_ID, selected_route=d256_q128_59fe.route_for_contract_inputs(inputs), selected_entrypoint=D256_Q128_59FE_ENTRYPOINT, timing=SEED_TIMING_ROWS[label])] + if label in D256_K32_59FE_CONSUMED_SHAPES: + return [_candidate_floor_record(label=label, candidate_id=SEED_D256_K32_59FE_ID, selected_seed=SEED_D256_K32_59FE_ID, guard_id=D256_K32_59FE_GUARD_ID, selected_route=d256_k32_59fe.route_for_contract_inputs(inputs), selected_entrypoint=D256_K32_59FE_ENTRYPOINT, timing=SEED_TIMING_ROWS[label])] + if label in HIGHD_RAG_22E9_CONSUMED_SHAPES: + return [_candidate_floor_record(label=label, candidate_id=SEED_HIGHD_RAG_22E9_ID, selected_seed=SEED_HIGHD_RAG_22E9_ID, guard_id=HIGHD_RAG_22E9_GUARD_ID, selected_route=highd_rag_22e9.route_for_contract_inputs(inputs), selected_entrypoint=HIGHD_RAG_22E9_ENTRYPOINT, timing=SEED_TIMING_ROWS[label])] + if label in D128_K48_DD2B_CONSUMED_SHAPES: + return [_candidate_floor_record(label=label, candidate_id=SEED_D128_K48_DD2B_ID, selected_seed=SEED_D128_K48_DD2B_ID, guard_id=D128_K48_DD2B_GUARD_ID, selected_route=d128_k48_dd2b.route_for_contract_inputs(inputs), selected_entrypoint=D128_K48_DD2B_ENTRYPOINT, timing=SEED_TIMING_ROWS[label])] + if label in RECT_D128_K20_S12WARP4_CONSUMED_SHAPES: + return [_candidate_floor_record(label=label, candidate_id=SEED_RECT_D128_K20_S12WARP4_ID, selected_seed=SEED_RECT_D128_K20_S12WARP4_ID, guard_id=RECT_D128_K20_S12WARP4_GUARD_ID, selected_route=rect_d128_k20_s12warp4.route_for_contract_inputs(inputs), selected_entrypoint=RECT_D128_K20_S12WARP4_ENTRYPOINT, timing=SEED_TIMING_ROWS[label])] + if label == RAG_Q32_M100000_K32: + return [_candidate_floor_record(label=label, candidate_id='weave-evolve-knn-build-12ac_exact_q32_k32_f653_rowld2', selected_seed=SEED_Q32_EXACT_ID, guard_id=Q32_EXACT_GUARD_ID, selected_route=q32exact.route_for_contract_inputs(inputs), selected_entrypoint=Q32_EXACT_ENTRYPOINT, timing=SEED_TIMING_ROWS[label])] + if label == EXPANDED_Q31_M100000_K32: + return [_candidate_floor_record(label=label, candidate_id='weave-evolve-knn-build-0cb5_exact_q31_k32_v2', selected_seed=SEED_Q31TAIL_V2_ID, guard_id=Q31TAIL_V2_GUARD_ID, selected_route=_q31tail_v2().route_for_contract_inputs(inputs), selected_entrypoint=Q31TAIL_V2_ENTRYPOINT, timing=SEED_TIMING_ROWS[label])] + if label in (EXPANDED_Q33_M100000_K32, EXPANDED_Q40_M100000_K32): + return [_candidate_floor_record(label=label, candidate_id='weave-evolve-knn-build-c489_exact_q33_q40_k32', selected_seed=SEED_Q33TILE_ID, guard_id=Q33TILE_GUARD_ID, selected_route=_q33tile().route_for_contract_inputs(inputs), selected_entrypoint=Q33TILE_ENTRYPOINT, timing=SEED_TIMING_ROWS[label])] + if label == EXPANDED_Q48_M75000_K12: + return [_candidate_floor_record(label=label, candidate_id='weave-evolve-knn-build-2f22_exact_q48_k12', selected_seed=SEED_Q48_K12_ID, guard_id=Q48_K12_GUARD_ID, selected_route=_q48_k12().route_for_contract_inputs(inputs), selected_entrypoint=Q48_K12_ENTRYPOINT, timing=SEED_TIMING_ROWS[label])] + if label in EXPANDED_Q32_LOWK_C3D2_CONSUMED_SHAPES: + return [_candidate_floor_record(label=label, candidate_id='weave-evolve-knn-build-4cc4_q32_lowk_c3d2', selected_seed=SEED_Q32_LOWK_C3D2_ID, guard_id=Q32_LOWK_C3D2_GUARD_ID, selected_route=_q32_lowk_c3d2().route_for_contract_inputs(inputs), selected_entrypoint=Q32_LOWK_C3D2_ENTRYPOINT, timing=SEED_TIMING_ROWS[label])] + if label in EXPANDED_Q32_K31_C3D2_CONSUMED_SHAPES: + return [_candidate_floor_record(label=label, candidate_id='weave-evolve-knn-build-eaf7_q32_k31_c3d2', selected_seed=SEED_Q32_K31_C3D2_ID, guard_id=Q32_K31_C3D2_GUARD_ID, selected_route=_q32_k31_c3d2().route_for_contract_inputs(inputs), selected_entrypoint=Q32_K31_C3D2_ENTRYPOINT, timing=SEED_TIMING_ROWS[label])] + return [] + +def _selected_historical_timing(label: str, portfolio_id: str) -> dict[str, Any] | None: + if portfolio_id in PORTFOLIOS_WITH_Q128_K10_ROWLD_1BED: + if label == RAG_Q128_M100000_K10: + return Q128_K10_ROWLD_1BED_TIMING + return _selected_historical_timing(label, CANDIDATE_RECT_D128_K20_S12WARP4) + if portfolio_id in PORTFOLIOS_WITH_RECT_D128_K20_S12WARP4: + if label in RECT_D128_K20_S12WARP4_CONSUMED_SHAPES: + return SEED_TIMING_ROWS[label] + return _selected_historical_timing(label, CANDIDATE_D128_K48_DD2B) + if portfolio_id in PORTFOLIOS_WITH_D128_K48_DD2B: + if label in D128_K48_DD2B_CONSUMED_SHAPES: + return SEED_TIMING_ROWS[label] + return _selected_historical_timing(label, CANDIDATE_HIGHD_SEARCH_BE66) + if portfolio_id in PORTFOLIOS_WITH_HIGHD_SEARCH_BE66: + if label in HIGHD_SEARCH_BE66_CONSUMED_SHAPES: + return SEED_TIMING_ROWS[label] + return _selected_historical_timing(label, CANDIDATE_HIGHD_RAG_22E9) + if portfolio_id in PORTFOLIOS_WITH_HIGHD_RAG_22E9: + if label in HIGHD_RAG_22E9_CONSUMED_SHAPES: + return SEED_TIMING_ROWS[label] + return _selected_historical_timing(label, CANDIDATE_D256_K32_59FE) + if portfolio_id in PORTFOLIOS_WITH_D256_K32_59FE: + if label in D256_K32_59FE_CONSUMED_SHAPES: + return SEED_TIMING_ROWS[label] + return _selected_historical_timing(label, CANDIDATE_D256_Q128_59FE) + if portfolio_id in PORTFOLIOS_WITH_D256_Q128_59FE: + if label in D256_Q128_59FE_CONSUMED_SHAPES: + return SEED_TIMING_ROWS[label] + return _selected_historical_timing(label, CANDIDATE_SELECTED_SYNTHESIS) + if label == RAG_Q128_M100000_K32 and _uses_floor_seed_portfolio(portfolio_id): + if portfolio_id == CANDIDATE_FLOOR_SEEDS_Q128_5698: + return Q128_DUALM_TIMING_ROWS[label] + return Q128_S72R2_TIMING_ROWS[label] + if label == RAG_Q128_M131071_K32 and _uses_floor_seed_portfolio(portfolio_id): + if portfolio_id == CANDIDATE_FLOOR_SEEDS_Q128_681B: + return Q128_S72R2_TIMING_ROWS[label] + return Q128_DUALM_TIMING_ROWS[label] + if label == RAG_Q16_M250000_K32 and _uses_floor_seed_portfolio(portfolio_id): + return Q16_M250_TIMING + if label == RAG_Q128_M100000_K10 and portfolio_id in PORTFOLIOS_WITH_Q128_K10_WARPMERGE: + return Q128_K10_WARPMERGE_TIMING + if label in EXPANDED_Q32_LOWK_C3D2_CONSUMED_SHAPES and portfolio_id in PORTFOLIOS_WITH_Q32_LOWK_C3D2: + return D665_Q32_LOWK_C3D2_TIMING_ROWS[label] + if label in EXPANDED_Q32_K31_C3D2_CONSUMED_SHAPES and portfolio_id in PORTFOLIOS_WITH_Q32_K31_C3D2: + return SEED_TIMING_ROWS[label] + if label in EXPANDED_Q32_K31_C3D2_CONSUMED_SHAPES and portfolio_id in PORTFOLIOS_WITH_Q32_LOWK_C3D2: + return D665_Q32_LOWK_C3D2_TIMING_ROWS[label] + return SEED_TIMING_ROWS.get(label) + +def _seed_delta_matrix(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> list[dict[str, Any]]: + rows = [] + for label in CONSUMED_SEED_SHAPES: + cand = candidate_report.get('per_shape', {}).get(label, {}) + base = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = cand.get('kernel_ms') + baseline_ms = base.get('kernel_ms') + historical = _selected_historical_timing(label, CANDIDATE_DEFAULT) or {} + alt_rows = [{'candidate_id': CANDIDATE_DEFAULT, 'metric_delta': candidate_ms - baseline_ms if candidate_ms and baseline_ms else None, 'candidate_kernel_ms': candidate_ms, 'flashlib_ms': cand.get('flashlib_ms'), 'speedup_vs_flashlib': cand.get('ratio_vs_flashlib'), 'floor_status': 'pass' if isinstance(cand.get('ratio_vs_flashlib'), (float, int)) and float(cand['ratio_vs_flashlib']) >= SPEEDUP_FLOOR else 'fail' if isinstance(cand.get('ratio_vs_flashlib'), (float, int)) else 'unknown', 'selected_route': _route_for_policy(_inputs_for_label(label), portfolio_id=CANDIDATE_DEFAULT), 'selected_seed': _expected_seed_for_label(label, portfolio_id=CANDIDATE_DEFAULT), 'guard_id': _route_trace_record(_inputs_for_label(label), portfolio_id=CANDIDATE_DEFAULT).get('guard_id'), 'timing_backend': cand.get('timing_backend'), 'baseline_ref_scope': 'same_session_full_dispatch'}] + for row in _historical_seed_candidates_for_label(label): + row = dict(row) + kernel_ms = row.get('candidate_kernel_ms') + row['metric_delta'] = kernel_ms - baseline_ms if kernel_ms is not None and baseline_ms is not None else None + alt_rows.append(row) + if label == BUILD_D768: + alt_rows.append({'candidate_id': CANDIDATE_F1D9_BUILD, 'metric_delta': ALT_F1D9_D768_TIMING['kernel_ms'] - baseline_ms if baseline_ms else None, 'candidate_kernel_ms': ALT_F1D9_D768_TIMING['kernel_ms'], 'timing_backend': ALT_F1D9_D768_TIMING['timing_backend'], 'baseline_ref_scope': 'historical_exact_shape'}) + rows.append({'shape_key': label, 'baseline_route': _route_for_policy(_inputs_for_label(label), portfolio_id=CANDIDATE_D128_K48_DD2B), 'selected_seed': _expected_seed_for_label(label), 'candidate_deltas': alt_rows, 'historical_seed_kernel_ms': historical.get('kernel_ms'), 'historical_seed_speedup_vs_flashlib': historical.get('ratio_vs_flashlib'), 'historical_seed_payload': historical.get('source_payload')}) + return rows + +def _flashlib_parity_ledger(route_trace: list[dict[str, Any]]) -> dict[str, Any]: + below_1x = [] + below_floor = [] + for row in route_trace: + ratio = row.get('speedup_vs_external_baseline') + if not isinstance(ratio, (float, int)): + continue + record = {'shape_key': row.get('shape_key'), 'selected_route': row.get('selected_route'), 'selected_seed': row.get('selected_seed'), 'route_kind': row.get('route_kind'), 'speedup_vs_external_baseline': ratio, 'classification': row.get('classification')} + if float(ratio) < 1.0: + below_1x.append(record) + if float(ratio) < SPEEDUP_FLOOR: + below_floor.append(record) + return {'baseline_ref_scope': 'same_session', 'baseline_payload': None, 'speedup_floor': SPEEDUP_FLOOR, 'rows_below_1x': below_1x, 'rows_below_floor': below_floor, 'omitted_reason': None} + +def _benchmark_payload(candidate_report: dict[str, Any], baseline_report: dict[str, Any], *, use_cupti: bool, shape_labels) -> dict[str, Any]: + route_trace = _annotate_route_trace(route_trace_for_contract_shapes(shape_labels), candidate_report, baseline_report) + candidate_metric = candidate_report.get('summary', {}).get('primary_mean') + baseline_metric = baseline_report.get('summary', {}).get('primary_mean') + ledger = _flashlib_parity_ledger(route_trace) + timing_backend = 'cupti' if use_cupti else 'cuda_event' + classification_blockers = [{'shape_key': row.get('shape_key'), 'selected_route': row.get('selected_route'), 'selected_seed': row.get('selected_seed'), 'route_kind': row.get('route_kind'), 'classification': row.get('classification'), 'failure_message': row.get('failure_message'), 'unmeasured_reason': row.get('unmeasured_reason'), 'speedup_vs_external_baseline': row.get('speedup_vs_external_baseline')} for row in route_trace if row.get('classification') in {'benchmark-path-mismatch', 'fallback-slow', 'kernel-slow', 'unmeasured'}] + return {'candidate_id': CANDIDATE_DEFAULT, 'tflops': candidate_metric, 'baseline_tflops': baseline_metric, 'metric_delta': candidate_metric - baseline_metric if candidate_metric and baseline_metric else None, 'all_correct': candidate_report.get('summary', {}).get('all_correct'), 'baseline_all_correct': baseline_report.get('summary', {}).get('all_correct'), 'performance_comparable': candidate_report.get('summary', {}).get('performance_comparable'), 'baseline_performance_comparable': baseline_report.get('summary', {}).get('performance_comparable'), 'invalid_performance_reason': candidate_report.get('summary', {}).get('invalid_performance_reason'), 'baseline_invalid_performance_reason': baseline_report.get('summary', {}).get('invalid_performance_reason'), 'measured_entrypoint': BENCHMARK_ENTRYPOINT, 'baseline_entrypoint': PRE_Q128_K10_ROWLD_1BED_BASE_ENTRYPOINT, 'measured_shape_labels': 'all_canonical' if shape_labels is None else tuple(shape_labels), 'selected_route_labels': CONSUMED_SEED_SHAPES, 'consumed_seed_labels': CONSUMED_SEED_SHAPES, 'seed_delta_matrix': _seed_delta_matrix(candidate_report, baseline_report), 'candidate_dispatchers': CANDIDATE_DISPATCHERS, 'selected_candidate_dispatcher': CANDIDATE_DEFAULT, 'route_modules': PRODUCTION_ROUTE_MODULES, 'route_trace': route_trace, 'forced_fallback_route_trace': route_trace_for_contract_shapes(shape_labels, force_fallback=True), 'route_trace_included': True, 'contract_summary': candidate_report.get('summary', {}), 'baseline_contract_summary': baseline_report.get('summary', {}), 'contract_performance': candidate_report.get('performance', {}), 'baseline_contract_performance': baseline_report.get('performance', {}), 'timing_backend': timing_backend, 'timing_backends': [timing_backend], 'timing_backend_requested': timing_backend, 'use_cupti': use_cupti, 'performance_coverage': 'partial' if ledger['rows_below_floor'] or classification_blockers else 'pass', 'coverage_only_routes': [row['shape_key'] for row in route_trace if row.get('route_kind') in ('coverage-only', 'fallback') or row.get('classification') == 'fallback-slow'], 'hot_bucket_blockers': ledger['rows_below_floor'] + classification_blockers, 'flashlib_parity_ledger': ledger, 'report': candidate_report, 'baseline_report': baseline_report} + +def benchmark_knn_build_dispatch_v11_common_d_seed_portfolio_a4ec_v1(*, use_cupti: bool=True, shape_labels=None, baseline_report: dict[str, Any] | None=None) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate) + if baseline_report is None: + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_pre_q128_k10_rowld_1bed_baseline) + return _benchmark_payload(candidate_report, baseline_report, use_cupti=use_cupti, shape_labels=shape_labels) + +def _denominator_label(shape_labels) -> str: + if shape_labels is None: + return ''.join(['full', format(len(eval_mod.CANONICAL_SHAPES), '')]) + if tuple(shape_labels) == EXPANDED_Q32_GUARD_BOUNDARY_8_SHAPES: + return 'expanded_q32_guard_boundary_8' + return ''.join(['shape', format(len(tuple(shape_labels)), '')]) + +def write_trace_artifacts(artifact_dir: str | Path, *, shape_labels=None) -> dict[str, str]: + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + denom = _denominator_label(shape_labels) + route_trace = route_trace_for_contract_shapes(shape_labels) + forced_trace = route_trace_for_contract_shapes(shape_labels, force_fallback=True) + route_trace_path = out_dir / ''.join([format(denom, ''), '_route_trace_v11_common_d_seed_portfolio_a4ec_v1.json']) + forced_trace_path = out_dir / ''.join([format(denom, ''), '_forced_fallback_trace_v11_common_d_seed_portfolio_a4ec_v1.json']) + route_trace_path.write_text(json.dumps(route_trace, indent=2, sort_keys=True) + '\n', encoding='utf-8') + forced_trace_path.write_text(json.dumps(forced_trace, indent=2, sort_keys=True) + '\n', encoding='utf-8') + return {'route_trace': str(route_trace_path), 'forced_fallback_trace': str(forced_trace_path)} + +def write_benchmark_artifacts(artifact_dir: str | Path, *, use_cupti: bool=True, shape_labels=None) -> dict[str, str]: + payload = benchmark_knn_build_dispatch_v11_common_d_seed_portfolio_a4ec_v1(use_cupti=use_cupti, shape_labels=shape_labels) + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + denom = _denominator_label(shape_labels) + candidate_path = out_dir / ''.join([format(denom, ''), '_dispatch_v11_common_d_seed_portfolio_a4ec_v1.json']) + baseline_path = out_dir / ''.join([format(denom, ''), '_same_session_baseline_common_d_v11_for_seed_portfolio_a4ec_v1.json']) + route_trace_path = out_dir / ''.join([format(denom, ''), '_route_trace_v11_common_d_seed_portfolio_a4ec_v1.json']) + forced_trace_path = out_dir / ''.join([format(denom, ''), '_forced_fallback_trace_v11_common_d_seed_portfolio_a4ec_v1.json']) + seed_matrix_path = out_dir / ''.join([format(denom, ''), '_seed_delta_matrix_v11_common_d_seed_portfolio_a4ec_v1.json']) + candidate_path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n', encoding='utf-8') + baseline_path.write_text(json.dumps({'candidate_id': CANDIDATE_RECT_D128_K20_S12WARP4, 'baseline_for_candidate_id': CANDIDATE_Q128_K10_ROWLD_1BED, 'measured_entrypoint': payload['baseline_entrypoint'], 'measured_shape_labels': payload['measured_shape_labels'], 'timing_backend': payload['timing_backend'], 'timing_backends': payload['timing_backends'], 'timing_backend_requested': payload['timing_backend_requested'], 'tflops': payload['baseline_tflops'], 'all_correct': payload['baseline_all_correct'], 'performance_comparable': payload['baseline_performance_comparable'], 'contract_summary': payload['baseline_contract_summary'], 'contract_performance': payload['baseline_contract_performance'], 'route_trace': route_trace_for_contract_shapes(shape_labels, portfolio_id=CANDIDATE_RECT_D128_K20_S12WARP4), 'route_trace_included': True, 'report': payload['baseline_report']}, indent=2, sort_keys=True) + '\n', encoding='utf-8') + route_trace_path.write_text(json.dumps(payload['route_trace'], indent=2, sort_keys=True) + '\n', encoding='utf-8') + forced_trace_path.write_text(json.dumps(payload['forced_fallback_route_trace'], indent=2, sort_keys=True) + '\n', encoding='utf-8') + seed_matrix_path.write_text(json.dumps(payload['seed_delta_matrix'], indent=2, sort_keys=True) + '\n', encoding='utf-8') + return {'candidate_payload': str(candidate_path), 'same_session_baseline_payload': str(baseline_path), 'route_trace': str(route_trace_path), 'forced_fallback_trace': str(forced_trace_path), 'seed_delta_matrix': str(seed_matrix_path)} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatchscore_k20raglarge_8050_v43.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatchscore_k20raglarge_8050_v43.py new file mode 100644 index 00000000..7753184c --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatchscore_k20raglarge_8050_v43.py @@ -0,0 +1,41 @@ +"""kNN build/search v43 dispatcher scoring wrapper for K20 RAG large-M. + +Minimum target architecture: sm_100a. This additive dispatcher candidate routes +only ``rag_offline_large_m_b1_q8192_m250000_d128_k20`` through the validated +v42 tcgen05 split-local producer plus K20/S16 warp-select merge, and delegates +every other v3 contract shape to the inherited v41 Weave dispatcher. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_k20_rag_large_m_7487_v42 as route_v42 +TARGET_SHAPE = route_v42.TARGET_SHAPE +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k20unordered", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 20]], "cta_group": 1, "threads": 192}')) +candidate = route_v42.candidate +launch_from_contract_inputs = route_v42.launch_from_contract_inputs +_select_contract_shapes = route_v42._select_contract_shapes + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(candidate, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def compile_and_launch_knn_build(*, shape_labels=('flashml_correctness_b1_q256_m256_d128_k5',), benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint for the v42-backed dispatcher wrapper.""" + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def benchmark_knn_build_dispatch_k20raglarge_8050_v43(*, use_cupti: bool=False) -> dict[str, Any]: + """Full v3 contract benchmark hook for the exact-row v42 dispatcher.""" + prior_use_cupti = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + try: + report = evaluate_contract(correctness=True, benchmark=True) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + timing_backends = sorted({result.get('timing_backend') for result in report.get('per_shape', {}).values() if result.get('timing_backend') is not None}) + return {'tflops': report['summary']['primary_mean'] or 0.0, 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'invalid_performance_reason': report['summary']['invalid_performance_reason'], 'measured_entrypoint': 'loom.examples.weave.knn_build_dispatchscore_k20raglarge_8050_v43:benchmark_knn_build_dispatch_k20raglarge_8050_v43', 'target_shape': TARGET_SHAPE, 'target_shape_result': report.get('per_shape', {}).get(TARGET_SHAPE), 'contract_summary': report['summary'], 'contract_performance': report['performance'], 'timing_backends': timing_backends, 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'report': report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatchscore_tailinf_knn_build_dispatch_slurm_0610_6329_v41.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatchscore_tailinf_knn_build_dispatch_slurm_0610_6329_v41.py new file mode 100644 index 00000000..e8862721 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_dispatchscore_tailinf_knn_build_dispatch_slurm_0610_6329_v41.py @@ -0,0 +1,41 @@ +"""kNN build/search v41 full-contract dispatcher scoring wrapper. + +Minimum target architecture: sm_100a. This additive dispatcher candidate +promotes the verified v40 K64 tail-sentinel non-build route and the 3cef exact +K20 rectangular-search route into a full v3 contract scoring entrypoint. The +benchmark hook uses the eval contract harness with CUDA-event timing by default, +matching the current local full-dispatch denominator. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import os +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_k64stage1_splitgrid_tailinf_knn_build_dispatch_slurm_0610_6329_v40 as v40 +from . import knn_build_k20_search_rect_3cef_v1 as k20_search_rect + +def _verify_export_ir() -> Any: + if 'LOOM_KNN_K20_SEARCH_RECT_VERIFY_KERNEL' in os.environ: + return k20_search_rect.ir + return v40.ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k48over32", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 48]], "cta_group": 1, "threads": 192}')) +candidate = k20_search_rect.candidate +launch_from_contract_inputs = k20_search_rect.launch_from_contract_inputs +evaluate_contract = k20_search_rect.evaluate_contract +_select_contract_shapes = k20_search_rect._select_contract_shapes + +def compile_and_launch_knn_build(*, shape_labels=('flashml_correctness_b1_q256_m256_d128_k5',), benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint for the v40-backed dispatcher wrapper.""" + return v40.compile_and_launch_knn_build(shape_labels=shape_labels, benchmark=benchmark) + +def benchmark_knn_build_dispatch_tailinf_v41(*, use_cupti: bool=False) -> dict[str, Any]: + """Full v3 contract benchmark hook for the v40-backed dispatcher.""" + prior_use_cupti = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + try: + report = evaluate_contract(correctness=True, benchmark=True) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + timing_backends = sorted({result.get('timing_backend') for result in report.get('per_shape', {}).values() if result.get('timing_backend') is not None}) + return {'tflops': report['summary']['primary_mean'] or 0.0, 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'invalid_performance_reason': report['summary']['invalid_performance_reason'], 'measured_entrypoint': 'loom.examples.weave.knn_build_dispatchscore_tailinf_knn_build_dispatch_slurm_0610_6329_v41:benchmark_knn_build_dispatch_tailinf_v41', 'contract_summary': report['summary'], 'contract_performance': report['performance'], 'timing_backends': timing_backends, 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'report': report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_evolve_7bfc_d256_twomma_knn_build_dispatch_slurm_0610_6329_v23.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_evolve_7bfc_d256_twomma_knn_build_dispatch_slurm_0610_6329_v23.py new file mode 100644 index 00000000..bc35e67e --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_evolve_7bfc_d256_twomma_knn_build_dispatch_slurm_0610_6329_v23.py @@ -0,0 +1,83 @@ +"""kNN build v23 D256 two-MMA dispatch repair. + +Minimum target architecture: sm_100a. This additive candidate keeps the v22 +dispatcher intact and adds a BF16 D=256, K<=10 route for the v3 +guard-miss row. The D256 route loads 128x256 query and 64x256 database tiles +with TMA, then accumulates the semantic feature dimension with two 128-wide +tcgen05 MMA steps before the inherited register top-k contract output path. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +from functools import lru_cache +from typing import Any +from . import knn_build_evolve_7bfc_d64_pad_knn_build_dispatch_slurm_0610_6329_v22 as parent_v22 +from . import knn_build_evolve_7bfc_v1 as base_v1 +from .._dispatch_runtime import pack_kernel_args +BLOCK_Q = base_v1.BLOCK_Q +BLOCK_M = base_v1.BLOCK_M +TOP_K_MAX = base_v1.TOP_K_MAX +THREADS = base_v1.THREADS +GRID_DIM_DEFAULT = base_v1.GRID_DIM_DEFAULT +D256_FEAT_D = 256 +D256_QUERY_BYTES = BLOCK_Q * D256_FEAT_D * 2 +D256_DATABASE_BYTES = BLOCK_M * D256_FEAT_D * 2 +knn_build_evolve_7bfc_d256_twomma_base = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_d256_twomma_base", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "out_dists", "out_indices", "B", "Q", "M", "K", "num_q_tiles", "num_db_tiles", "total_tiles"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 99328, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) + +def _verify_export_ir() -> Any: + return knn_build_evolve_7bfc_d256_twomma_base +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_d256_twomma_base", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "out_dists", "out_indices", "B", "Q", "M", "K", "num_q_tiles", "num_db_tiles", "total_tiles"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 99328, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) + +def _compiled_d256_kernel(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0155"}')) + +def _eligible_d256_twomma(inputs: dict[str, Any]) -> bool: + top_k = int(inputs['K']) + return str(inputs['query'].dtype) == 'torch.bfloat16' and str(inputs['database'].dtype) == 'torch.bfloat16' and (int(inputs['D']) == D256_FEAT_D) and (top_k <= TOP_K_MAX) + +def _launch_d256_twomma(inputs: dict[str, Any]) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + total_tiles = bsz * num_q_tiles + grid_dim = min(total_tiles, GRID_DIM_DEFAULT) + tmap_query = base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), bsz * n_query, BLOCK_Q, dim, D256_FEAT_D) + tmap_database = base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, dim, D256_FEAT_D) + kernel = _compiled_d256_kernel() + kernel.launch(grid=(grid_dim, 1, 1), block=(THREADS, 1, 1), args=pack_kernel_args(ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], out_dists=inputs['out_dists'], out_indices=inputs['out_indices'], B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tiles=num_q_tiles, num_db_tiles=num_db_tiles, total_tiles=total_tiles), shared_mem=ir.computed_smem_bytes) + +def launch_from_contract_inputs(inputs: dict[str, Any]) -> None: + if _eligible_d256_twomma(inputs): + _launch_d256_twomma(inputs) + return + parent_v22.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(candidate, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return parent_v22._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=('build_dim_sweep_b1_q2048_m2048_d256_k10',), benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint: run real contract correctness for selected shapes.""" + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def benchmark_d256_generalization_v23() -> dict[str, Any]: + """Opt-in benchmark hook for the D256 two-MMA guard-miss route.""" + report = evaluate_contract(shapes=_select_contract_shapes(('build_dim_sweep_b1_q2048_m2048_d256_k10',)), correctness=True, benchmark=True) + return {'tflops': report['summary']['primary_mean'], 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'report': report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_evolve_7bfc_d64_pad_knn_build_dispatch_slurm_0610_6329_v22.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_evolve_7bfc_d64_pad_knn_build_dispatch_slurm_0610_6329_v22.py new file mode 100644 index 00000000..fba65758 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_evolve_7bfc_d64_pad_knn_build_dispatch_slurm_0610_6329_v22.py @@ -0,0 +1,80 @@ +"""kNN build v22 D64 tcgen05 dispatch repair. + +Minimum target architecture: sm_100a. This additive candidate keeps the v21 +dispatcher intact and adds a real Weave/TMA/tcgen05 route for BF16 D=64, +K<=10 guard-miss shapes. The D64 route uses a 128x64x64 tcgen05 MMA tile and +the same register top-k contract path as the clean-start base kernel. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +from functools import lru_cache +from typing import Any +from . import knn_build_evolve_7bfc_k20merge_warpselect_tiebreak_knn_build_dispatch_slurm_0610_6329_v21 as parent_v21 +from . import knn_build_evolve_7bfc_v1 as base_v1 +from .._dispatch_runtime import pack_kernel_args +BLOCK_Q = base_v1.BLOCK_Q +BLOCK_M = base_v1.BLOCK_M +TOP_K_MAX = base_v1.TOP_K_MAX +THREADS = base_v1.THREADS +GRID_DIM_DEFAULT = base_v1.GRID_DIM_DEFAULT +D64_FEAT_D = 64 +knn_build_evolve_7bfc_d64_tcgen05_base = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_d64_tcgen05_base", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "out_dists", "out_indices", "B", "Q", "M", "K", "num_q_tiles", "num_db_tiles", "total_tiles"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 25600, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) + +def _verify_export_ir() -> Any: + return knn_build_evolve_7bfc_d64_tcgen05_base +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_d64_tcgen05_base", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "out_dists", "out_indices", "B", "Q", "M", "K", "num_q_tiles", "num_db_tiles", "total_tiles"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 25600, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) + +def _compiled_d64_kernel(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0156"}')) + +def _eligible_d64_padded_base(inputs: dict[str, Any]) -> bool: + top_k = int(inputs['K']) + return str(inputs['query'].dtype) == 'torch.bfloat16' and str(inputs['database'].dtype) == 'torch.bfloat16' and (int(inputs['D']) == D64_FEAT_D) and (top_k <= TOP_K_MAX) + +def _launch_d64_padded_base(inputs: dict[str, Any]) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + total_tiles = bsz * num_q_tiles + grid_dim = min(total_tiles, GRID_DIM_DEFAULT) + tmap_query = base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), bsz * n_query, BLOCK_Q, dim, D64_FEAT_D) + tmap_database = base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, dim, D64_FEAT_D) + kernel = _compiled_d64_kernel() + kernel.launch(grid=(grid_dim, 1, 1), block=(THREADS, 1, 1), args=pack_kernel_args(ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], out_dists=inputs['out_dists'], out_indices=inputs['out_indices'], B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tiles=num_q_tiles, num_db_tiles=num_db_tiles, total_tiles=total_tiles), shared_mem=ir.computed_smem_bytes) + +def launch_from_contract_inputs(inputs: dict[str, Any]) -> None: + if _eligible_d64_padded_base(inputs): + _launch_d64_padded_base(inputs) + return + parent_v21.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(candidate, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return parent_v21._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=('build_dim_sweep_b1_q2048_m2048_d64_k10',), benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint: run real contract correctness for selected shapes.""" + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def benchmark_d64_generalization_v22() -> dict[str, Any]: + """Opt-in benchmark hook for the D64 padded tensor-map guard-miss route.""" + report = evaluate_contract(shapes=_select_contract_shapes(('build_dim_sweep_b1_q2048_m2048_d64_k10',)), correctness=True, benchmark=True) + return {'tflops': report['summary']['primary_mean'], 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'report': report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_evolve_7bfc_fp16_d128_knn_build_dispatch_slurm_0610_6329_v24.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_evolve_7bfc_fp16_d128_knn_build_dispatch_slurm_0610_6329_v24.py new file mode 100644 index 00000000..94ce32c4 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_evolve_7bfc_fp16_d128_knn_build_dispatch_slurm_0610_6329_v24.py @@ -0,0 +1,102 @@ +"""kNN build v24 FP16 D128 dispatch repair. + +Minimum target architecture: sm_100a. This additive candidate keeps the v23 +dispatcher intact and adds a real FP16 D=128, K<=10 route for the v3 +dtype-generalization guard-miss row. The FP16 route uses the same 128x64x128 +tcgen05/TMA tile as the clean-start BF16 base, but encodes FP16 tensor maps and +SMEM operands so the MMA IDESC uses FP16 input format bits. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +from functools import lru_cache +from typing import Any +from . import knn_build_evolve_7bfc_d256_twomma_knn_build_dispatch_slurm_0610_6329_v23 as parent_v23 +from . import knn_build_evolve_7bfc_v1 as base_v1 +from .._dispatch_runtime import pack_kernel_args +BLOCK_Q = base_v1.BLOCK_Q +BLOCK_M = base_v1.BLOCK_M +FEAT_D = base_v1.FEAT_D +TOP_K_MAX = base_v1.TOP_K_MAX +THREADS = base_v1.THREADS +GRID_DIM_DEFAULT = base_v1.GRID_DIM_DEFAULT +FP16_QUERY_BYTES = BLOCK_Q * FEAT_D * 2 +FP16_DATABASE_BYTES = BLOCK_M * FEAT_D * 2 +_FP16_TMAP_CACHE: dict[tuple[int, int, int, int, int, int], Any] = {} +knn_build_evolve_7bfc_fp16_d128_base = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_fp16_d128_base", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "out_dists", "out_indices", "B", "Q", "M", "K", "num_q_tiles", "num_db_tiles", "total_tiles"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 50176, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) + +def _verify_export_ir() -> Any: + return knn_build_evolve_7bfc_fp16_d128_base +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_fp16_d128_base", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "out_dists", "out_indices", "B", "Q", "M", "K", "num_q_tiles", "num_db_tiles", "total_tiles"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 50176, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) + +def _compiled_fp16_kernel(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0154"}')) + +def _create_tensor_map_3d_fp16_oob_zero(data_ptr: int, global_height: int, shared_height: int, width: int, block_width: int): + import torch + from cuda.bindings import driver + from .._dispatch_runtime import Swizzle + from .._dispatch_runtime import _tmap_to_device + from .._dispatch_runtime import TensorMapMetadata, attach_tma_metadata + device_index = torch.cuda.current_device() + key = (device_index, int(data_ptr), int(global_height), int(shared_height), int(width), int(block_width)) + cached = _FP16_TMAP_CACHE.get(key) + if cached is not None: + return cached + err, tmap = _capture_cuTensorMapEncodeTiled(driver.CUtensorMapDataType.CU_TENSOR_MAP_DATA_TYPE_FLOAT16, 3, data_ptr, [driver.cuuint64_t(64), driver.cuuint64_t(global_height), driver.cuuint64_t(width // 64)], [driver.cuuint64_t(width * 2), driver.cuuint64_t(128)], [driver.cuuint32_t(64), driver.cuuint32_t(shared_height), driver.cuuint32_t(block_width // 64)], [driver.cuuint32_t(1), driver.cuuint32_t(1), driver.cuuint32_t(1)], driver.CUtensorMapInterleave.CU_TENSOR_MAP_INTERLEAVE_NONE, driver.CUtensorMapSwizzle.CU_TENSOR_MAP_SWIZZLE_128B, driver.CUtensorMapL2promotion.CU_TENSOR_MAP_L2_PROMOTION_NONE, driver.CUtensorMapFloatOOBfill.CU_TENSOR_MAP_FLOAT_OOB_FILL_NAN_REQUEST_ZERO_FMA) + if err != 0: + raise RuntimeError(''.join(['cuTensorMapEncodeTiled (3D FP16, OOB zero) failed: CUresult=', format(err, '')])) + cached = attach_tma_metadata(_tmap_to_device(tmap).to(device=torch.device('cuda', device_index)), TensorMapMetadata(ndim=3, dtype='f16', swizzle=Swizzle.SZ_128B, helper='knn_build_evolve_7bfc_fp16_d128._create_tensor_map_3d_fp16_oob_zero')) + _FP16_TMAP_CACHE[key] = cached + return cached + +def _eligible_fp16_d128(inputs: dict[str, Any]) -> bool: + top_k = int(inputs['K']) + return str(inputs['query'].dtype) == 'torch.float16' and str(inputs['database'].dtype) == 'torch.float16' and (int(inputs['D']) == FEAT_D) and (top_k <= TOP_K_MAX) + +def _launch_fp16_d128(inputs: dict[str, Any]) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + total_tiles = bsz * num_q_tiles + grid_dim = min(total_tiles, GRID_DIM_DEFAULT) + tmap_query = _create_tensor_map_3d_fp16_oob_zero(query.data_ptr(), bsz * n_query, BLOCK_Q, dim, dim) + tmap_database = _create_tensor_map_3d_fp16_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, dim, dim) + kernel = _compiled_fp16_kernel() + kernel.launch(grid=(grid_dim, 1, 1), block=(THREADS, 1, 1), args=pack_kernel_args(ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], out_dists=inputs['out_dists'], out_indices=inputs['out_indices'], B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tiles=num_q_tiles, num_db_tiles=num_db_tiles, total_tiles=total_tiles), shared_mem=ir.computed_smem_bytes) + +def launch_from_contract_inputs(inputs: dict[str, Any]) -> None: + if _eligible_fp16_d128(inputs): + _launch_fp16_d128(inputs) + return + parent_v23.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(candidate, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return parent_v23._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=('build_dtype_fp16_b1_q2048_m2048_d128_k10',), benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint: run real contract correctness for selected shapes.""" + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def benchmark_fp16_d128_v24() -> dict[str, Any]: + """Opt-in benchmark hook for the FP16 D128 dtype guard-miss route.""" + report = evaluate_contract(shapes=_select_contract_shapes(('build_dtype_fp16_b1_q2048_m2048_d128_k10',)), correctness=True, benchmark=True) + return {'tflops': report['summary']['primary_mean'], 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'report': report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_evolve_7bfc_k20merge_warpselect_tiebreak_knn_build_dispatch_slurm_0610_6329_v21.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_evolve_7bfc_k20merge_warpselect_tiebreak_knn_build_dispatch_slurm_0610_6329_v21.py new file mode 100644 index 00000000..603451bc --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_evolve_7bfc_k20merge_warpselect_tiebreak_knn_build_dispatch_slurm_0610_6329_v21.py @@ -0,0 +1,99 @@ +"""kNN build v21 K20 warp-select tie-order repair. + +Minimum target architecture: sm_100a. This additive candidate keeps the v19 +dispatcher intact except for fixed-build ``Q=M=4096,K=20``. That row still uses +the inherited tcgen05 split-local unordered producer, but the final four-warp +warp-select merge now resolves equal-distance winners with source priority and +the later tied slot inside a split, matching the observed scalar worst-slot +boundary state. The goal is to preserve the v20 warp-select merge-cost +reduction while restoring exact sampled recall on the full q4096 K20 audit row. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import os +from functools import lru_cache +from typing import Any +from . import knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2_k32split_v20 as parent_v20 +from . import knn_build_evolve_7bfc_split_v1 as parent_split +from . import knn_build_evolve_7bfc_v1 as base_v1 +from .._dispatch_runtime import pack_kernel_args +BLOCK_Q = parent_v20.BLOCK_Q +BLOCK_M = parent_v20.BLOCK_M +FEAT_D = parent_v20.FEAT_D +STAGE1_THREADS = parent_v20.STAGE1_THREADS +K20_MERGE_THREADS = parent_v20.K20_COOP_MERGE_THREADS +GRID_DIM_DEFAULT = parent_v20.GRID_DIM_DEFAULT +CTA_GROUP = parent_v20.CTA_GROUP +K20_Q4096_SPLITS = parent_v20.MEDIUM_SPLITS +TOP_K_K20 = 20 +knn_build_evolve_7bfc_k20_merge_s4_unordered_warp_select_splitmajor = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k20_merge_s4_unordered_warp_select_splitmajor", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 20], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 128}')) +merge_k20_unordered_warp_select_splitmajor_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k20_merge_s4_unordered_warp_select_splitmajor", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 20], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 128}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_K20_WARPSELECT_TIE_VERIFY_KERNEL') + if verify_kernel == 'stage1_k20_unordered': + return parent_v20.stage1_k20_unordered_ir + return merge_k20_unordered_warp_select_splitmajor_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k20_merge_s4_unordered_warp_select_splitmajor", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 20], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 128}')) + +def _compiled_merge_k20_unordered_warp_select_splitmajor(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0157"}')) + +def _eligible_k20_q4096_warpselect_splitmajor(inputs: dict[str, Any]) -> bool: + return parent_v20._eligible_k32_split_build(inputs) and int(inputs['K']) == TOP_K_K20 and (int(inputs['Q']) == 4096) and (int(inputs['M']) == 4096) + +def _launch_k20_q4096_warpselect_splitmajor_path(inputs: dict[str, Any]) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + split_count = K20_Q4096_SPLITS + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_q_tile_pairs = (num_q_tiles + CTA_GROUP - 1) // CTA_GROUP + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tile_pairs * split_count + stage1_grid = min(total_work * CTA_GROUP, GRID_DIM_DEFAULT) + merge_grid = (bsz * n_query + 3) // 4 + partial_dists, partial_indices = parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), bsz * n_query, BLOCK_Q, dim, dim) + tmap_database = base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, dim, dim) + stage1_ir_obj = parent_v20.stage1_k20_unordered_ir + stage1_kernel = parent_v20._compiled_stage1_unordered_for_exact_k(top_k) + stage1_kernel.launch_cluster(grid=(stage1_grid, 1, 1), block=(STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage1_ir_obj, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tile_pairs=num_q_tile_pairs, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), cluster_dims=(CTA_GROUP, 1, 1), shared_mem=stage1_ir_obj.computed_smem_bytes) + merge_kernel = _compiled_merge_k20_unordered_warp_select_splitmajor() + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(K20_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_k20_unordered_warp_select_splitmajor_ir.computed_smem_bytes) + +def launch_from_contract_inputs(inputs: dict[str, Any]) -> None: + if _eligible_k20_q4096_warpselect_splitmajor(inputs): + _launch_k20_q4096_warpselect_splitmajor_path(inputs) + return + parent_v20.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(candidate, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return parent_v20._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=('flashml_correctness_b1_q256_m256_d128_k5',), benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint: run real contract correctness for selected shapes.""" + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def benchmark_k20_q4096_warpselect_tiebreak_v21() -> dict[str, Any]: + """Opt-in benchmark hook for the K20 slice with split-major warp-select ties.""" + report = evaluate_contract(shapes=_select_contract_shapes(('build_k_sweep_qm1024_k20', 'build_k_sweep_qm2048_k20', 'build_k_sweep_qm4096_k20')), correctness=True, benchmark=True) + return {'tflops': report['summary']['primary_mean'], 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'report': report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2.py new file mode 100644 index 00000000..51f0512f --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2.py @@ -0,0 +1,85 @@ +"""kNN build/search K=10 fixed-build split-dispatch v2 candidate. + +Minimum target architecture: sm_100a. This variant inherits the validated +tcgen05/TMA stage-1 kernel, sorted split-local K=10 output, and cached K=10 +merge kernels from the selected parent. It changes only host dispatch for +fixed-build ``Q == M`` K=10 shapes: underfilled 1024/2048-style rows use the +7-split cached merge path to expose more independent CTA-group work, midlarge +rows whose CTA-grouped query tile count is 38..42 use the 7-split path, and +other fixed-build rows use the 4-split cached path. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +from typing import Any +from . import knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_v1 as parent +BLOCK_Q = parent.BLOCK_Q +BLOCK_M = parent.BLOCK_M +FEAT_D = parent.FEAT_D +TOP_K_MAX = parent.TOP_K_MAX +STAGE1_THREADS = parent.STAGE1_THREADS +MERGE_THREADS = parent.MERGE_THREADS +GRID_DIM_DEFAULT = parent.GRID_DIM_DEFAULT +CTA_GROUP = parent.CTA_GROUP +SMALL_SHAPE_MAX = parent.SMALL_SHAPE_MAX +MEDIUM_SPLITS = parent.MEDIUM_SPLITS +RAG_SPLITS = parent.RAG_SPLITS +stage1_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_stage1_batch8_cond4_vmin_maxtree", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_stage1_batch8_cond4_vmin_maxtree", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) + +def _eligible_fixed_build_k10(inputs: dict[str, Any]) -> bool: + return bool(inputs.get('build', False)) and str(inputs['query'].dtype) == 'torch.bfloat16' and (str(inputs['database'].dtype) == 'torch.bfloat16') and (int(inputs['D']) == FEAT_D) and (int(inputs['K']) == TOP_K_MAX) and (int(inputs['Q']) == int(inputs['M'])) + +def _fixed_build_k10_split_count(inputs: dict[str, Any]) -> int | None: + if not _eligible_fixed_build_k10(inputs): + return None + n_query = int(inputs['Q']) + if n_query <= SMALL_SHAPE_MAX: + return MEDIUM_SPLITS + if n_query < 4096: + return RAG_SPLITS + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_q_tile_pairs = (num_q_tiles + CTA_GROUP - 1) // CTA_GROUP + if 38 <= num_q_tile_pairs <= 42: + return RAG_SPLITS + return MEDIUM_SPLITS + +def _launch_fixed_build_k10(inputs: dict[str, Any], *, split_count: int) -> None: + if split_count == RAG_SPLITS: + parent._launch_k10_cached_path(inputs, split_count=split_count, merge_threads=parent.parent_cached.RAG_MERGE_THREADS, merge_kernel=parent.parent_cached._compiled_merge_k10_s7_cache(), merge_ir=parent.parent_cached.merge_k10_s7_cache_ir) + return + parent._launch_k10_cached_path(inputs, split_count=MEDIUM_SPLITS, merge_threads=parent.parent_cached64.MEDIUM_MERGE_THREADS, merge_kernel=parent.parent_cached64._compiled_merge_k10_s4_cache(), merge_ir=parent.parent_cached64.merge_k10_s4_cache_ir) + +def launch_from_contract_inputs(inputs: dict[str, Any]) -> None: + split_count = _fixed_build_k10_split_count(inputs) + if split_count is not None: + _launch_fixed_build_k10(inputs, split_count=split_count) + return + parent.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(candidate, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + from .._dispatch_runtime import CANONICAL_SHAPES + if shape_labels is None: + return list(CANONICAL_SHAPES) + wanted = {str(label) for label in shape_labels} + selected = [shape for shape in CANONICAL_SHAPES if shape['label'] in wanted] + missing = wanted - {shape['label'] for shape in selected} + if missing: + raise ValueError(''.join(['unknown kNN build contract shape(s): ', format(sorted(missing), '')])) + return selected + +def compile_and_launch_knn_build(*, shape_labels=('flashml_correctness_b1_q256_m256_d128_k5',), benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint: run real contract correctness for selected shapes.""" + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2_k32split_v12.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2_k32split_v12.py new file mode 100644 index 00000000..ff770479 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2_k32split_v12.py @@ -0,0 +1,391 @@ +"""kNN build/search K32 split-build successor with K25 and RAG dispatch. + +Minimum target architecture: sm_100a. This variant keeps the validated K5/K10 +fixed-build dispatch from the parent and keeps the build-mode path for +``10 < K <= 32`` BF16 D=128 shapes. The larger-K path compiles the inherited +CTA-group=2 split stage-1 IR with static top-k capacity buckets of 12, 16, 20, +25, 30, and 32. The small-shape K12/K16/K20/K25/K30 buckets use a 32-thread cached +four-way cursor merge over sorted split-local streams, preserving the +split/tcgen05 contract-visible eval path while reducing scalar merge work for +exact bucket sizes. For the underfilled Q=M=512,K=30 probe, stage-1 uses eight +database splits and a specialized eight-way cached merge to expose more +independent tcgen05 work. Large Q=M=4096 K20/K30/K32 boundaries use exact +unordered split-local producers and unordered four-split merge, avoiding +sorted-stream maintenance on large-Q hot paths while preserving exact top-k +membership and matching distances. Fixed-build K1/K2/K8 shapes use the +inherited generic-K split merge instead of cached K10 merge kernels so partial +buffer row strides match the contract-visible K. Q=M=1024/2048,K=12 uses an +exact K12 producer and eight-way cached merge to increase mid-build producer +parallelism without changing q4096 K20/K30/K32 unordered routing. The v10 +lineage applies the same shape-gated eight-split sorted-stream fanout to +Q=M=1024/2048,K=20 and adds an exact K20 eight-way cached merge. +This v11 candidate routes Q=M=2048,K=8 through a K8 static split/tcgen05 +bucket and seven-way cached merge, and uses a shape-gated K20 fanout mix: +Q=M=1024,K=20 uses sixteen splits with an exact K20 sixteen-way cached merge, +while Q=M=2048,K=20 keeps the v10 eight-split exact merge and Q=M=4096,K=20 +keeps the unordered four-split route. +This v12 candidate keeps the v11 shape-gated K20 fanout mix, routes +Q=M=2048,K=8 through eight database splits and an exact eight-way cached merge, +extends the large-Q unordered exact top-k route to Q=M=4096,K=25, and widens +the non-build K10 RAG dispatch: q4096 x 100000 and q10000 x 50000 now use the +inherited split-7 tcgen05 producer plus cached seven-way merge instead of +falling through to the older generic parent route. The v11 K32 diagnostic route +is preserved. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import os +from functools import lru_cache +from typing import Any +from .._dispatch_runtime import dc as dc +from . import knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2 as parent +from . import knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_v1 as parent_lowk +from . import knn_build_evolve_7bfc_split_cg2_u2_v1 as parent_u2 +from . import knn_build_evolve_7bfc_split_v1 as parent_split +from . import knn_build_evolve_7bfc_v1 as base_v1 +from .._dispatch_runtime import pack_kernel_args +BLOCK_Q = parent.BLOCK_Q +BLOCK_M = parent.BLOCK_M +FEAT_D = parent.FEAT_D +TOP_K_MAX = parent.TOP_K_MAX +TOP_K_SPLIT_MAX = base_v1.TOP_K_FALLBACK_MAX +STAGE1_THREADS = parent.STAGE1_THREADS +MERGE_THREADS = parent.MERGE_THREADS +K32_MERGE_THREADS = 32 +GRID_DIM_DEFAULT = parent.GRID_DIM_DEFAULT +CTA_GROUP = parent.CTA_GROUP +MEDIUM_SPLITS = parent.MEDIUM_SPLITS +K12_MID_SPLITS = 8 +K20_Q1024_SPLITS = 16 +K20_Q2048_SPLITS = 8 +K8_Q2048_SPLITS = 8 +K8_MID_MERGE_THREADS = parent_lowk.parent_cached.RAG_MERGE_THREADS +K30_SMALL_SHAPE_MAX = 512 +K30_SMALL_SPLITS = 8 +RAG_OFFLINE_Q_MIN = 4096 +RAG_OFFLINE_M_MIN = 50000 + +def _ir_with_top_k_max(ir_obj: Any, *, top_k_max: int, suffix: str) -> Any: + constants = tuple(((name, top_k_max if name == 'TOP_K_MAX' else value) for name, value in ir_obj.constants)) + return dc.replace(ir_obj, symbol=''.join([format(ir_obj.symbol, ''), '_', format(suffix, '')]), constants=constants) + +def _ir_with_constants(ir_obj: Any, *, suffix: str, **updates: int) -> Any: + constants = tuple(((name, updates.get(name, value)) for name, value in ir_obj.constants)) + return dc.replace(ir_obj, symbol=''.join([format(ir_obj.symbol, ''), '_', format(suffix, '')]), constants=constants) +TOP_K_BUCKETS = (8, 12, 16, 20, 25, 30, TOP_K_SPLIT_MAX) +knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 192}')) +stage1_k8_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k8split", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 8]], "cta_group": 1, "threads": 192}')) +stage1_k12_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k12split", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 12]], "cta_group": 1, "threads": 192}')) +stage1_k16_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k16split", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 16]], "cta_group": 1, "threads": 192}')) +stage1_k20_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k20split", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 20]], "cta_group": 1, "threads": 192}')) +stage1_k25_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k25split", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 25]], "cta_group": 1, "threads": 192}')) +stage1_k30_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k30split", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 30]], "cta_group": 1, "threads": 192}')) +stage1_k32_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32split", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 192}')) +stage1_k32_unordered_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 192}')) +stage1_k20_unordered_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k20unordered", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 20]], "cta_group": 1, "threads": 192}')) +stage1_k25_unordered_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k25unordered", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 25]], "cta_group": 1, "threads": 192}')) +stage1_k30_unordered_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k30unordered", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 30]], "cta_group": 1, "threads": 192}')) +stage1_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32split", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 192}')) +knn_build_evolve_7bfc_k32_merge_s4_rowbase_cache = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_rowbase_cache", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "K", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 32]], "cta_group": 1, "threads": 32}')) +merge_k12_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_rowbase_cache_k12split", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "K", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 12]], "cta_group": 1, "threads": 32}')) +merge_k8_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_rowbase_cache_k8split", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "K", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 8]], "cta_group": 1, "threads": 32}')) +merge_k16_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_rowbase_cache_k16split", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "K", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 16]], "cta_group": 1, "threads": 32}')) +merge_k20_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_rowbase_cache_k20split", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "K", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 20]], "cta_group": 1, "threads": 32}')) +merge_k25_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_rowbase_cache_k25split", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "K", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 25]], "cta_group": 1, "threads": 32}')) +merge_k30_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_rowbase_cache_k30split", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "K", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 30]], "cta_group": 1, "threads": 32}')) +merge_k32_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_rowbase_cache_k32split", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "K", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 32]], "cta_group": 1, "threads": 32}')) +merge_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_rowbase_cache_k32split", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "K", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 32]], "cta_group": 1, "threads": 32}')) +knn_build_evolve_7bfc_k32_merge_s4_unordered = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_unordered", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 32], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 32}')) +merge_k32_unordered_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_unordered", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 32], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 32}')) +merge_k20_unordered_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_unordered_k20unordered", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 20], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 32}')) +merge_k25_unordered_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_unordered_k25unordered", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 25], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 32}')) +merge_k30_unordered_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_unordered_k30unordered", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 30], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 32}')) +knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 30], ["SPLIT_COUNT", 8]], "cta_group": 1, "threads": 32}')) +merge_k30_s8_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 30], ["SPLIT_COUNT", 8]], "cta_group": 1, "threads": 32}')) +merge_k12_s8_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_k12s8", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 12], ["SPLIT_COUNT", 8]], "cta_group": 1, "threads": 32}')) +merge_k20_s8_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_k20s8", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 20], ["SPLIT_COUNT", 8]], "cta_group": 1, "threads": 32}')) +merge_k8_s7_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k10_merge_s7_rowbase_cache_k8s7", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 8], ["SPLIT_COUNT", 7]], "cta_group": 1, "threads": 32}')) +merge_k8_s8_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_k8s8", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 8], ["SPLIT_COUNT", 8]], "cta_group": 1, "threads": 32}')) +merge_k20_s16_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_k20s16", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 20], ["SPLIT_COUNT", 16]], "cta_group": 1, "threads": 32}')) + +def _top_k_bucket(top_k: int) -> int: + for bucket in TOP_K_BUCKETS: + if top_k <= bucket: + return bucket + return TOP_K_SPLIT_MAX + +def _stage1_ir_for_bucket(top_k_bucket: int) -> Any: + if top_k_bucket <= 8: + return stage1_k8_ir + if top_k_bucket <= 12: + return stage1_k12_ir + if top_k_bucket <= 16: + return stage1_k16_ir + if top_k_bucket <= 20: + return stage1_k20_ir + if top_k_bucket <= 25: + return stage1_k25_ir + if top_k_bucket <= 30: + return stage1_k30_ir + return stage1_k32_ir + +def _merge_ir_for_bucket(top_k_bucket: int) -> Any: + if top_k_bucket <= 8: + return merge_k8_ir + if top_k_bucket <= 12: + return merge_k12_ir + if top_k_bucket <= 16: + return merge_k16_ir + if top_k_bucket <= 20: + return merge_k20_ir + if top_k_bucket <= 25: + return merge_k25_ir + if top_k_bucket <= 30: + return merge_k30_ir + return merge_k32_ir + +def _verify_export_ir() -> Any: + bucket_text = os.environ.get('LOOM_KNN_K32SPLIT_VERIFY_TOP_K_BUCKET') + top_k_bucket = TOP_K_SPLIT_MAX if bucket_text is None else _top_k_bucket(int(bucket_text)) + verify_kernel = os.environ.get('LOOM_KNN_K32SPLIT_VERIFY_KERNEL') + if verify_kernel == 'stage1_k10_rag': + return parent_lowk.stage1_ir + if verify_kernel == 'merge_k10_s7_cache': + return parent_lowk.parent_cached.merge_k10_s7_cache_ir + if verify_kernel == 'stage1_k20_unordered': + return stage1_k20_unordered_ir + if verify_kernel == 'stage1_k25_unordered': + return stage1_k25_unordered_ir + if verify_kernel == 'stage1_k30_unordered': + return stage1_k30_unordered_ir + if verify_kernel == 'stage1_k32_unordered': + return stage1_k32_unordered_ir + if verify_kernel == 'merge_k20_unordered': + return merge_k20_unordered_ir + if verify_kernel == 'merge_k25_unordered': + return merge_k25_unordered_ir + if verify_kernel == 'merge_k30_unordered': + return merge_k30_unordered_ir + if verify_kernel == 'merge_k32_unordered': + return merge_k32_unordered_ir + if verify_kernel == 'merge_s8': + return merge_k30_s8_ir + if verify_kernel == 'merge_k12_s8': + return merge_k12_s8_ir + if verify_kernel == 'merge_k20_s8': + return merge_k20_s8_ir + if verify_kernel == 'merge_k8_s7': + return merge_k8_s7_ir + if verify_kernel == 'merge_k8_s8': + return merge_k8_s8_ir + if verify_kernel == 'merge_k20_s16': + return merge_k20_s16_ir + if verify_kernel == 'merge': + return _merge_ir_for_bucket(top_k_bucket) + return _stage1_ir_for_bucket(top_k_bucket) +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32split", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 192}')) + +def _compile_ir(ir_obj: Any): + from .._dispatch_runtime import generate_kernel + from .._dispatch_runtime import _cuda_include_dirs + from .._dispatch_runtime import compile_cuda + from .._dispatch_runtime import CUDAKernel + source = generate_kernel(ir_obj, validate=False, smem_bytes=ir_obj.computed_smem_bytes) + cubin = compile_cuda(source, arch=base_v1._select_arch_and_preload(), options=['--use_fast_math'], include_dirs=_cuda_include_dirs()) + return CUDAKernel(cubin, ''.join(['kernel_', format(ir_obj.symbol, '')])) + +@lru_cache(maxsize=7) +def _compiled_stage1_for_bucket(top_k_bucket: int): + return _compile_ir(_stage1_ir_for_bucket(top_k_bucket)) + +def _compiled_stage1_k32_unordered(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0214"}')) + +@lru_cache(maxsize=4) +def _compiled_stage1_unordered_for_exact_k(top_k: int): + return _compile_ir(_stage1_unordered_ir_for_exact_k(top_k)) + +@lru_cache(maxsize=7) +def _compiled_merge_for_bucket(top_k_bucket: int): + return _compile_ir(_merge_ir_for_bucket(top_k_bucket)) + +def _compiled_merge_k32_unordered(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0215"}')) + +@lru_cache(maxsize=4) +def _compiled_merge_unordered_for_exact_k(top_k: int): + return _compile_ir(_merge_unordered_ir_for_exact_k(top_k)) + +def _compiled_merge_k30_s8(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0216"}')) + +def _compiled_merge_k12_s8(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0217"}')) + +def _compiled_merge_k20_s8(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0040"}')) + +def _compiled_merge_k8_s7(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0218"}')) + +def _compiled_merge_k8_s8(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0219"}')) + +def _compiled_merge_k20_s16(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0013"}')) + +def _eligible_k32_split_build(inputs: dict[str, Any]) -> bool: + top_k = int(inputs['K']) + return bool(inputs.get('build', False)) and str(inputs['query'].dtype) == 'torch.bfloat16' and (str(inputs['database'].dtype) == 'torch.bfloat16') and (int(inputs['D']) == FEAT_D) and (top_k == 8 or TOP_K_MAX < top_k <= TOP_K_SPLIT_MAX) and (int(inputs['Q']) == int(inputs['M'])) and (512 <= int(inputs['Q']) <= 4096) + +def _stage1_unordered_ir_for_exact_k(top_k: int) -> Any: + if top_k == 20: + return stage1_k20_unordered_ir + if top_k == 25: + return stage1_k25_unordered_ir + if top_k == 30: + return stage1_k30_unordered_ir + if top_k == TOP_K_SPLIT_MAX: + return stage1_k32_unordered_ir + raise ValueError(''.join(['no unordered stage-1 specialization for K=', format(top_k, '')])) + +def _merge_unordered_ir_for_exact_k(top_k: int) -> Any: + if top_k == 20: + return merge_k20_unordered_ir + if top_k == 25: + return merge_k25_unordered_ir + if top_k == 30: + return merge_k30_unordered_ir + if top_k == TOP_K_SPLIT_MAX: + return merge_k32_unordered_ir + raise ValueError(''.join(['no unordered merge specialization for K=', format(top_k, '')])) + +def _k32_split_count(inputs: dict[str, Any]) -> int | None: + if not _eligible_k32_split_build(inputs): + return None + if int(inputs['K']) == 8: + return K8_Q2048_SPLITS if int(inputs['Q']) == 2048 else None + if int(inputs['K']) == 12 and 1024 <= int(inputs['Q']) <= 2048: + return K12_MID_SPLITS + if int(inputs['K']) == 20 and int(inputs['Q']) == 1024: + return K20_Q1024_SPLITS + if int(inputs['K']) == 20 and int(inputs['Q']) == 2048: + return K20_Q2048_SPLITS + if int(inputs['K']) == 30 and int(inputs['Q']) <= K30_SMALL_SHAPE_MAX: + return K30_SMALL_SPLITS + return MEDIUM_SPLITS + +def _eligible_fixed_build_generic_lowk(inputs: dict[str, Any]) -> bool: + return bool(inputs.get('build', False)) and str(inputs['query'].dtype) == 'torch.bfloat16' and (str(inputs['database'].dtype) == 'torch.bfloat16') and (int(inputs['D']) == FEAT_D) and (int(inputs['K']) in (1, 2, 8)) and (int(inputs['Q']) == int(inputs['M'])) and (int(inputs['Q']) <= parent_lowk.SMALL_SHAPE_MAX) + +def _eligible_rag_k10_cached(inputs: dict[str, Any]) -> bool: + return not bool(inputs.get('build', False)) and str(inputs['query'].dtype) == 'torch.bfloat16' and (str(inputs['database'].dtype) == 'torch.bfloat16') and (int(inputs['D']) == FEAT_D) and (int(inputs['K']) == TOP_K_MAX) and (int(inputs['Q']) >= RAG_OFFLINE_Q_MIN) and (int(inputs['M']) >= RAG_OFFLINE_M_MIN) + +def _launch_rag_k10_cached(inputs: dict[str, Any]) -> None: + parent_lowk._launch_k10_cached_path(inputs, split_count=parent_lowk.RAG_SPLITS, merge_threads=parent_lowk.parent_cached.RAG_MERGE_THREADS, merge_kernel=parent_lowk.parent_cached._compiled_merge_k10_s7_cache(), merge_ir=parent_lowk.parent_cached.merge_k10_s7_cache_ir) + +def _launch_k32_split_path(inputs: dict[str, Any], *, split_count: int) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_q_tile_pairs = (num_q_tiles + CTA_GROUP - 1) // CTA_GROUP + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tile_pairs * split_count + stage1_grid = min(total_work * CTA_GROUP, GRID_DIM_DEFAULT) + top_k_bucket = _top_k_bucket(top_k) + use_unordered = top_k in (20, 25, 30, TOP_K_SPLIT_MAX) and split_count == MEDIUM_SPLITS and (n_query >= 4096) + stage1_ir_obj = _stage1_unordered_ir_for_exact_k(top_k) if use_unordered else _stage1_ir_for_bucket(top_k_bucket) + use_k8_s7_merge = split_count == parent.RAG_SPLITS and top_k == 8 + use_k8_s8_merge = split_count == K8_Q2048_SPLITS and top_k == 8 + use_k12_s8_merge = split_count == K12_MID_SPLITS and top_k == 12 + use_k20_s16_merge = split_count == K20_Q1024_SPLITS and top_k == 20 + use_k20_s8_merge = split_count == K20_Q2048_SPLITS and top_k == 20 + use_k30_s8_merge = split_count == K30_SMALL_SPLITS and top_k == 30 + merge_threads = K8_MID_MERGE_THREADS if use_k8_s7_merge else K32_MERGE_THREADS + merge_grid = min((bsz * n_query + merge_threads - 1) // merge_threads, GRID_DIM_DEFAULT) + merge_ir_obj = _merge_unordered_ir_for_exact_k(top_k) if use_unordered else merge_k8_s7_ir if use_k8_s7_merge else merge_k8_s8_ir if use_k8_s8_merge else merge_k12_s8_ir if use_k12_s8_merge else merge_k20_s16_ir if use_k20_s16_merge else merge_k20_s8_ir if use_k20_s8_merge else merge_k30_s8_ir if use_k30_s8_merge else _merge_ir_for_bucket(top_k_bucket) + partial_dists, partial_indices = parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), bsz * n_query, BLOCK_Q, dim, dim) + tmap_database = base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, dim, dim) + stage1_kernel = _compiled_stage1_unordered_for_exact_k(top_k) if use_unordered else _compiled_stage1_for_bucket(top_k_bucket) + stage1_kernel.launch_cluster(grid=(stage1_grid, 1, 1), block=(STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage1_ir_obj, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tile_pairs=num_q_tile_pairs, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), cluster_dims=(CTA_GROUP, 1, 1), shared_mem=stage1_ir_obj.computed_smem_bytes) + if use_unordered: + merge_kernel = _compiled_merge_unordered_for_exact_k(top_k) + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(K32_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_ir_obj.computed_smem_bytes) + elif use_k8_s7_merge: + merge_kernel = _compiled_merge_k8_s7() + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(K8_MID_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_ir_obj.computed_smem_bytes) + elif use_k8_s8_merge: + merge_kernel = _compiled_merge_k8_s8() + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(K8_MID_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_ir_obj.computed_smem_bytes) + elif use_k12_s8_merge: + merge_kernel = _compiled_merge_k12_s8() + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(K32_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_ir_obj.computed_smem_bytes) + elif use_k20_s16_merge: + merge_kernel = _compiled_merge_k20_s16() + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(K32_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_ir_obj.computed_smem_bytes) + elif use_k20_s8_merge: + merge_kernel = _compiled_merge_k20_s8() + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(K32_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_ir_obj.computed_smem_bytes) + elif use_k30_s8_merge: + merge_kernel = _compiled_merge_k30_s8() + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(K32_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_ir_obj.computed_smem_bytes) + else: + merge_kernel = _compiled_merge_for_bucket(top_k_bucket) + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(K32_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], top_k, bsz * n_query], shared_mem=merge_ir_obj.computed_smem_bytes) + +def launch_from_contract_inputs(inputs: dict[str, Any]) -> None: + if _eligible_fixed_build_generic_lowk(inputs): + parent_lowk._launch_cg2_split_path(inputs, split_count=parent_lowk.SMALL_SPLITS) + return + if _eligible_rag_k10_cached(inputs): + _launch_rag_k10_cached(inputs) + return + split_count = _k32_split_count(inputs) + if split_count is not None: + _launch_k32_split_path(inputs, split_count=split_count) + return + parent.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(candidate, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + from .._dispatch_runtime import CANONICAL_SHAPES + if shape_labels is None: + return list(CANONICAL_SHAPES) + wanted = {str(label) for label in shape_labels} + selected = [shape for shape in CANONICAL_SHAPES if shape['label'] in wanted] + missing = wanted - {shape['label'] for shape in selected} + if missing: + raise ValueError(''.join(['unknown kNN build contract shape(s): ', format(sorted(missing), '')])) + return selected + +def compile_and_launch_knn_build(*, shape_labels=('flashml_correctness_b1_q256_m256_d128_k5',), benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint: run real contract correctness for selected shapes.""" + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _largek_k25_probe_shape() -> dict[str, Any]: + return {'label': 'build_qm4096_d128_k25_probe', 'params': {'B': 1, 'Q': 4096, 'M': 4096, 'D': 128, 'K': 25, 'dtype': 'bfloat16', 'seed': 606425, 'build': True, 'check_correctness': True, 'correctness_query_sample': 256, 'recall_min': 0.999, 'benchmark': True, 'time_flashlib': True}} + +def benchmark_largek_k25_k32_v12() -> dict[str, Any]: + """Opt-in bench-regression hook for the custom K25 probe plus K32 diagnostic.""" + shapes = [_largek_k25_probe_shape(), _select_contract_shapes(('build_largek_stress_qm4096_k32',))[0]] + report = evaluate_contract(shapes=shapes, correctness=True, benchmark=True) + return {'tflops': report['summary']['primary_mean'], 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'report': report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2_k32split_v14.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2_k32split_v14.py new file mode 100644 index 00000000..02a20166 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2_k32split_v14.py @@ -0,0 +1,431 @@ +"""kNN build/search K32 split-build successor with q512 K8 and K32 chunked merge. + +Minimum target architecture: sm_100a. This variant keeps the validated K5/K10 +fixed-build dispatch from the parent and keeps the build-mode path for +``10 < K <= 32`` BF16 D=128 shapes. The larger-K path compiles the inherited +CTA-group=2 split stage-1 IR with static top-k capacity buckets of 12, 16, 20, +25, 30, and 32. The small-shape K12/K16/K20/K25/K30 buckets use a 32-thread cached +four-way cursor merge over sorted split-local streams, preserving the +split/tcgen05 contract-visible eval path while reducing scalar merge work for +exact bucket sizes. For the underfilled Q=M=512,K=30 probe, stage-1 uses eight +database splits and a specialized eight-way cached merge to expose more +independent tcgen05 work. Large Q=M=4096 K20/K30/K32 boundaries use exact +unordered split-local producers and unordered four-split merge, avoiding +sorted-stream maintenance on large-Q hot paths while preserving exact top-k +membership and matching distances. Fixed-build K1/K2/K8 shapes use the +inherited generic-K split merge instead of cached K10 merge kernels so partial +buffer row strides match the contract-visible K. Q=M=1024/2048,K=12 uses an +exact K12 producer and eight-way cached merge to increase mid-build producer +parallelism without changing q4096 K20/K30/K32 unordered routing. The v10 +lineage applies the same shape-gated eight-split sorted-stream fanout to +Q=M=1024/2048,K=20 and adds an exact K20 eight-way cached merge. +This v11 candidate routes Q=M=2048,K=8 through a K8 static split/tcgen05 +bucket and seven-way cached merge, and uses a shape-gated K20 fanout mix: +Q=M=1024,K=20 uses sixteen splits with an exact K20 sixteen-way cached merge, +while Q=M=2048,K=20 keeps the v10 eight-split exact merge and Q=M=4096,K=20 +keeps the unordered four-split route. +This v12 candidate keeps the v11 shape-gated K20 fanout mix, routes +Q=M=2048,K=8 through eight database splits and an exact eight-way cached merge, +extends the large-Q unordered exact top-k route to Q=M=4096,K=25, and widens +the non-build K10 RAG dispatch: q4096 x 100000 and q10000 x 50000 now use the +inherited split-7 tcgen05 producer plus cached seven-way merge instead of +falling through to the older generic parent route. The v11 K32 diagnostic route +is preserved. +This v13 candidate keeps the v12 routes and tests the q512 guardrail lane by +routing Q=M=512,K=8 through the existing static K8 eight-split producer and +cached eight-way merge. K1/K2 stay on the stride-safe generic-K four-split +route. It also changes the K32 unordered merge: the first split-local +top-32 vector prefills the output accumulator, then the remaining three +split-local vectors use worst-slot replacement. This avoids the v12 merge's +repeated worst scan during the guaranteed initial fill. +This v14 candidate keeps the same K32 unordered producer and prefill semantics, +but tracks four 8-slot worst caches during the unordered merge. Accepted +candidates rescan only the affected 8-slot bucket and the four bucket maxima +instead of rescanning all 32 slots. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import os +from functools import lru_cache +from typing import Any +from .._dispatch_runtime import dc as dc +from . import knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2 as parent +from . import knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_v1 as parent_lowk +from . import knn_build_evolve_7bfc_split_cg2_u2_v1 as parent_u2 +from . import knn_build_evolve_7bfc_split_v1 as parent_split +from . import knn_build_evolve_7bfc_v1 as base_v1 +from .._dispatch_runtime import pack_kernel_args +BLOCK_Q = parent.BLOCK_Q +BLOCK_M = parent.BLOCK_M +FEAT_D = parent.FEAT_D +TOP_K_MAX = parent.TOP_K_MAX +TOP_K_SPLIT_MAX = base_v1.TOP_K_FALLBACK_MAX +STAGE1_THREADS = parent.STAGE1_THREADS +MERGE_THREADS = parent.MERGE_THREADS +K32_MERGE_THREADS = 32 +GRID_DIM_DEFAULT = parent.GRID_DIM_DEFAULT +CTA_GROUP = parent.CTA_GROUP +MEDIUM_SPLITS = parent.MEDIUM_SPLITS +K12_MID_SPLITS = 8 +K20_Q1024_SPLITS = 16 +K20_Q2048_SPLITS = 8 +K8_Q2048_SPLITS = 8 +K8_MID_MERGE_THREADS = parent_lowk.parent_cached.RAG_MERGE_THREADS +K30_SMALL_SHAPE_MAX = 512 +K30_SMALL_SPLITS = 8 +Q512_K8_SPLITS = K8_Q2048_SPLITS +RAG_OFFLINE_Q_MIN = 4096 +RAG_OFFLINE_M_MIN = 50000 + +def _ir_with_top_k_max(ir_obj: Any, *, top_k_max: int, suffix: str) -> Any: + constants = tuple(((name, top_k_max if name == 'TOP_K_MAX' else value) for name, value in ir_obj.constants)) + return dc.replace(ir_obj, symbol=''.join([format(ir_obj.symbol, ''), '_', format(suffix, '')]), constants=constants) + +def _ir_with_constants(ir_obj: Any, *, suffix: str, **updates: int) -> Any: + constants = tuple(((name, updates.get(name, value)) for name, value in ir_obj.constants)) + return dc.replace(ir_obj, symbol=''.join([format(ir_obj.symbol, ''), '_', format(suffix, '')]), constants=constants) +TOP_K_BUCKETS = (8, 12, 16, 20, 25, 30, TOP_K_SPLIT_MAX) +knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 192}')) +stage1_k8_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k8split", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 8]], "cta_group": 1, "threads": 192}')) +stage1_k12_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k12split", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 12]], "cta_group": 1, "threads": 192}')) +stage1_k16_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k16split", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 16]], "cta_group": 1, "threads": 192}')) +stage1_k20_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k20split", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 20]], "cta_group": 1, "threads": 192}')) +stage1_k25_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k25split", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 25]], "cta_group": 1, "threads": 192}')) +stage1_k30_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k30split", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 30]], "cta_group": 1, "threads": 192}')) +stage1_k32_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32split", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 192}')) +stage1_k32_unordered_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 192}')) +stage1_k20_unordered_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k20unordered", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 20]], "cta_group": 1, "threads": 192}')) +stage1_k25_unordered_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k25unordered", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 25]], "cta_group": 1, "threads": 192}')) +stage1_k30_unordered_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k30unordered", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 30]], "cta_group": 1, "threads": 192}')) +stage1_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32split", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 192}')) +knn_build_evolve_7bfc_k32_merge_s4_rowbase_cache = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_rowbase_cache", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "K", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 32]], "cta_group": 1, "threads": 32}')) +merge_k12_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_rowbase_cache_k12split", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "K", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 12]], "cta_group": 1, "threads": 32}')) +merge_k8_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_rowbase_cache_k8split", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "K", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 8]], "cta_group": 1, "threads": 32}')) +merge_k16_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_rowbase_cache_k16split", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "K", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 16]], "cta_group": 1, "threads": 32}')) +merge_k20_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_rowbase_cache_k20split", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "K", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 20]], "cta_group": 1, "threads": 32}')) +merge_k25_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_rowbase_cache_k25split", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "K", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 25]], "cta_group": 1, "threads": 32}')) +merge_k30_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_rowbase_cache_k30split", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "K", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 30]], "cta_group": 1, "threads": 32}')) +merge_k32_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_rowbase_cache_k32split", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "K", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 32]], "cta_group": 1, "threads": 32}')) +merge_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_rowbase_cache_k32split", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "K", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 32]], "cta_group": 1, "threads": 32}')) +knn_build_evolve_7bfc_k32_merge_s4_unordered = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_unordered", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 32], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 32}')) +knn_build_evolve_7bfc_k32_merge_s4_unordered_prefill = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_unordered_prefill", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 32], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 32}')) +knn_build_evolve_7bfc_k32_merge_s4_unordered_chunked_prefill = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_unordered_chunked_prefill", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 32], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 32}')) +merge_k32_unordered_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_unordered", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 32], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 32}')) +merge_k32_unordered_prefill_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_unordered_prefill", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 32], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 32}')) +merge_k32_unordered_chunked_prefill_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_unordered_chunked_prefill", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 32], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 32}')) +merge_k20_unordered_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_unordered_k20unordered", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 20], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 32}')) +merge_k25_unordered_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_unordered_k25unordered", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 25], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 32}')) +merge_k30_unordered_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_unordered_k30unordered", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 30], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 32}')) +knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 30], ["SPLIT_COUNT", 8]], "cta_group": 1, "threads": 32}')) +merge_k30_s8_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 30], ["SPLIT_COUNT", 8]], "cta_group": 1, "threads": 32}')) +merge_k12_s8_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_k12s8", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 12], ["SPLIT_COUNT", 8]], "cta_group": 1, "threads": 32}')) +merge_k20_s8_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_k20s8", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 20], ["SPLIT_COUNT", 8]], "cta_group": 1, "threads": 32}')) +merge_k8_s7_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k10_merge_s7_rowbase_cache_k8s7", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 8], ["SPLIT_COUNT", 7]], "cta_group": 1, "threads": 32}')) +merge_k8_s8_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_k8s8", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 8], ["SPLIT_COUNT", 8]], "cta_group": 1, "threads": 32}')) +merge_k20_s16_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_k20s16", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 20], ["SPLIT_COUNT", 16]], "cta_group": 1, "threads": 32}')) + +def _top_k_bucket(top_k: int) -> int: + for bucket in TOP_K_BUCKETS: + if top_k <= bucket: + return bucket + return TOP_K_SPLIT_MAX + +def _stage1_ir_for_bucket(top_k_bucket: int) -> Any: + if top_k_bucket <= 8: + return stage1_k8_ir + if top_k_bucket <= 12: + return stage1_k12_ir + if top_k_bucket <= 16: + return stage1_k16_ir + if top_k_bucket <= 20: + return stage1_k20_ir + if top_k_bucket <= 25: + return stage1_k25_ir + if top_k_bucket <= 30: + return stage1_k30_ir + return stage1_k32_ir + +def _merge_ir_for_bucket(top_k_bucket: int) -> Any: + if top_k_bucket <= 8: + return merge_k8_ir + if top_k_bucket <= 12: + return merge_k12_ir + if top_k_bucket <= 16: + return merge_k16_ir + if top_k_bucket <= 20: + return merge_k20_ir + if top_k_bucket <= 25: + return merge_k25_ir + if top_k_bucket <= 30: + return merge_k30_ir + return merge_k32_ir + +def _verify_export_ir() -> Any: + bucket_text = os.environ.get('LOOM_KNN_K32SPLIT_VERIFY_TOP_K_BUCKET') + top_k_bucket = TOP_K_SPLIT_MAX if bucket_text is None else _top_k_bucket(int(bucket_text)) + verify_kernel = os.environ.get('LOOM_KNN_K32SPLIT_VERIFY_KERNEL') + if verify_kernel == 'stage1_q512_lowk': + return parent_lowk.stage1_ir + if verify_kernel == 'merge_generic': + return parent_split.merge_ir + if verify_kernel == 'stage1_k10_rag': + return parent_lowk.stage1_ir + if verify_kernel == 'merge_k10_s7_cache': + return parent_lowk.parent_cached.merge_k10_s7_cache_ir + if verify_kernel == 'stage1_k20_unordered': + return stage1_k20_unordered_ir + if verify_kernel == 'stage1_k25_unordered': + return stage1_k25_unordered_ir + if verify_kernel == 'stage1_k30_unordered': + return stage1_k30_unordered_ir + if verify_kernel == 'stage1_k32_unordered': + return stage1_k32_unordered_ir + if verify_kernel == 'merge_k20_unordered': + return merge_k20_unordered_ir + if verify_kernel == 'merge_k25_unordered': + return merge_k25_unordered_ir + if verify_kernel == 'merge_k30_unordered': + return merge_k30_unordered_ir + if verify_kernel == 'merge_k32_unordered': + return merge_k32_unordered_ir + if verify_kernel == 'merge_k32_unordered_prefill': + return merge_k32_unordered_prefill_ir + if verify_kernel == 'merge_k32_unordered_chunked_prefill': + return merge_k32_unordered_chunked_prefill_ir + if verify_kernel == 'merge_s8': + return merge_k30_s8_ir + if verify_kernel == 'merge_k12_s8': + return merge_k12_s8_ir + if verify_kernel == 'merge_k20_s8': + return merge_k20_s8_ir + if verify_kernel == 'merge_k8_s7': + return merge_k8_s7_ir + if verify_kernel == 'merge_k8_s8': + return merge_k8_s8_ir + if verify_kernel == 'merge_k20_s16': + return merge_k20_s16_ir + if verify_kernel == 'merge': + return _merge_ir_for_bucket(top_k_bucket) + return _stage1_ir_for_bucket(top_k_bucket) +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32split", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 192}')) + +def _compile_ir(ir_obj: Any): + from .._dispatch_runtime import generate_kernel + from .._dispatch_runtime import _cuda_include_dirs + from .._dispatch_runtime import compile_cuda + from .._dispatch_runtime import CUDAKernel + source = generate_kernel(ir_obj, validate=False, smem_bytes=ir_obj.computed_smem_bytes) + cubin = compile_cuda(source, arch=base_v1._select_arch_and_preload(), options=['--use_fast_math'], include_dirs=_cuda_include_dirs()) + return CUDAKernel(cubin, ''.join(['kernel_', format(ir_obj.symbol, '')])) + +@lru_cache(maxsize=7) +def _compiled_stage1_for_bucket(top_k_bucket: int): + return _compile_ir(_stage1_ir_for_bucket(top_k_bucket)) + +def _compiled_stage1_k32_unordered(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0168"}')) + +@lru_cache(maxsize=4) +def _compiled_stage1_unordered_for_exact_k(top_k: int): + return _compile_ir(_stage1_unordered_ir_for_exact_k(top_k)) + +@lru_cache(maxsize=7) +def _compiled_merge_for_bucket(top_k_bucket: int): + return _compile_ir(_merge_ir_for_bucket(top_k_bucket)) + +def _compiled_merge_k32_unordered(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0169"}')) + +@lru_cache(maxsize=4) +def _compiled_merge_unordered_for_exact_k(top_k: int): + return _compile_ir(_merge_unordered_ir_for_exact_k(top_k)) + +def _compiled_merge_k30_s8(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0170"}')) + +def _compiled_merge_k12_s8(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0171"}')) + +def _compiled_merge_k20_s8(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0172"}')) + +def _compiled_merge_k8_s7(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0173"}')) + +def _compiled_merge_k8_s8(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0174"}')) + +def _compiled_merge_k20_s16(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0175"}')) + +def _eligible_k32_split_build(inputs: dict[str, Any]) -> bool: + top_k = int(inputs['K']) + return bool(inputs.get('build', False)) and str(inputs['query'].dtype) == 'torch.bfloat16' and (str(inputs['database'].dtype) == 'torch.bfloat16') and (int(inputs['D']) == FEAT_D) and (top_k == 8 or TOP_K_MAX < top_k <= TOP_K_SPLIT_MAX) and (int(inputs['Q']) == int(inputs['M'])) and (512 <= int(inputs['Q']) <= 4096) + +def _stage1_unordered_ir_for_exact_k(top_k: int) -> Any: + if top_k == 20: + return stage1_k20_unordered_ir + if top_k == 25: + return stage1_k25_unordered_ir + if top_k == 30: + return stage1_k30_unordered_ir + if top_k == TOP_K_SPLIT_MAX: + return stage1_k32_unordered_ir + raise ValueError(''.join(['no unordered stage-1 specialization for K=', format(top_k, '')])) + +def _merge_unordered_ir_for_exact_k(top_k: int) -> Any: + if top_k == 20: + return merge_k20_unordered_ir + if top_k == 25: + return merge_k25_unordered_ir + if top_k == 30: + return merge_k30_unordered_ir + if top_k == TOP_K_SPLIT_MAX: + return merge_k32_unordered_chunked_prefill_ir + raise ValueError(''.join(['no unordered merge specialization for K=', format(top_k, '')])) + +def _k32_split_count(inputs: dict[str, Any]) -> int | None: + if not _eligible_k32_split_build(inputs): + return None + if int(inputs['K']) == 8: + return K8_Q2048_SPLITS if int(inputs['Q']) == 2048 else None + if int(inputs['K']) == 12 and 1024 <= int(inputs['Q']) <= 2048: + return K12_MID_SPLITS + if int(inputs['K']) == 20 and int(inputs['Q']) == 1024: + return K20_Q1024_SPLITS + if int(inputs['K']) == 20 and int(inputs['Q']) == 2048: + return K20_Q2048_SPLITS + if int(inputs['K']) == 30 and int(inputs['Q']) <= K30_SMALL_SHAPE_MAX: + return K30_SMALL_SPLITS + return MEDIUM_SPLITS + +def _eligible_fixed_build_generic_lowk(inputs: dict[str, Any]) -> bool: + return bool(inputs.get('build', False)) and str(inputs['query'].dtype) == 'torch.bfloat16' and (str(inputs['database'].dtype) == 'torch.bfloat16') and (int(inputs['D']) == FEAT_D) and (int(inputs['K']) in (1, 2, 8)) and (int(inputs['Q']) == int(inputs['M'])) and (int(inputs['Q']) <= parent_lowk.SMALL_SHAPE_MAX) + +def _eligible_q512_k8_static(inputs: dict[str, Any]) -> bool: + return _eligible_k32_split_build(inputs) and int(inputs['K']) == 8 and (int(inputs['Q']) == 512) and (int(inputs['M']) == 512) + +def _eligible_rag_k10_cached(inputs: dict[str, Any]) -> bool: + return not bool(inputs.get('build', False)) and str(inputs['query'].dtype) == 'torch.bfloat16' and (str(inputs['database'].dtype) == 'torch.bfloat16') and (int(inputs['D']) == FEAT_D) and (int(inputs['K']) == TOP_K_MAX) and (int(inputs['Q']) >= RAG_OFFLINE_Q_MIN) and (int(inputs['M']) >= RAG_OFFLINE_M_MIN) + +def _launch_rag_k10_cached(inputs: dict[str, Any]) -> None: + parent_lowk._launch_k10_cached_path(inputs, split_count=parent_lowk.RAG_SPLITS, merge_threads=parent_lowk.parent_cached.RAG_MERGE_THREADS, merge_kernel=parent_lowk.parent_cached._compiled_merge_k10_s7_cache(), merge_ir=parent_lowk.parent_cached.merge_k10_s7_cache_ir) + +def _launch_k32_split_path(inputs: dict[str, Any], *, split_count: int) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_q_tile_pairs = (num_q_tiles + CTA_GROUP - 1) // CTA_GROUP + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tile_pairs * split_count + stage1_grid = min(total_work * CTA_GROUP, GRID_DIM_DEFAULT) + top_k_bucket = _top_k_bucket(top_k) + use_unordered = top_k in (20, 25, 30, TOP_K_SPLIT_MAX) and split_count == MEDIUM_SPLITS and (n_query >= 4096) + stage1_ir_obj = _stage1_unordered_ir_for_exact_k(top_k) if use_unordered else _stage1_ir_for_bucket(top_k_bucket) + use_k8_s7_merge = split_count == parent.RAG_SPLITS and top_k == 8 + use_k8_s8_merge = split_count == K8_Q2048_SPLITS and top_k == 8 + use_k12_s8_merge = split_count == K12_MID_SPLITS and top_k == 12 + use_k20_s16_merge = split_count == K20_Q1024_SPLITS and top_k == 20 + use_k20_s8_merge = split_count == K20_Q2048_SPLITS and top_k == 20 + use_k30_s8_merge = split_count == K30_SMALL_SPLITS and top_k == 30 + merge_threads = K8_MID_MERGE_THREADS if use_k8_s7_merge else K32_MERGE_THREADS + merge_grid = min((bsz * n_query + merge_threads - 1) // merge_threads, GRID_DIM_DEFAULT) + merge_ir_obj = _merge_unordered_ir_for_exact_k(top_k) if use_unordered else merge_k8_s7_ir if use_k8_s7_merge else merge_k8_s8_ir if use_k8_s8_merge else merge_k12_s8_ir if use_k12_s8_merge else merge_k20_s16_ir if use_k20_s16_merge else merge_k20_s8_ir if use_k20_s8_merge else merge_k30_s8_ir if use_k30_s8_merge else _merge_ir_for_bucket(top_k_bucket) + partial_dists, partial_indices = parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), bsz * n_query, BLOCK_Q, dim, dim) + tmap_database = base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, dim, dim) + stage1_kernel = _compiled_stage1_unordered_for_exact_k(top_k) if use_unordered else _compiled_stage1_for_bucket(top_k_bucket) + stage1_kernel.launch_cluster(grid=(stage1_grid, 1, 1), block=(STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage1_ir_obj, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tile_pairs=num_q_tile_pairs, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), cluster_dims=(CTA_GROUP, 1, 1), shared_mem=stage1_ir_obj.computed_smem_bytes) + if use_unordered: + merge_kernel = _compiled_merge_unordered_for_exact_k(top_k) + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(K32_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_ir_obj.computed_smem_bytes) + elif use_k8_s7_merge: + merge_kernel = _compiled_merge_k8_s7() + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(K8_MID_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_ir_obj.computed_smem_bytes) + elif use_k8_s8_merge: + merge_kernel = _compiled_merge_k8_s8() + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(K8_MID_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_ir_obj.computed_smem_bytes) + elif use_k12_s8_merge: + merge_kernel = _compiled_merge_k12_s8() + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(K32_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_ir_obj.computed_smem_bytes) + elif use_k20_s16_merge: + merge_kernel = _compiled_merge_k20_s16() + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(K32_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_ir_obj.computed_smem_bytes) + elif use_k20_s8_merge: + merge_kernel = _compiled_merge_k20_s8() + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(K32_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_ir_obj.computed_smem_bytes) + elif use_k30_s8_merge: + merge_kernel = _compiled_merge_k30_s8() + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(K32_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_ir_obj.computed_smem_bytes) + else: + merge_kernel = _compiled_merge_for_bucket(top_k_bucket) + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(K32_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], top_k, bsz * n_query], shared_mem=merge_ir_obj.computed_smem_bytes) + +def launch_from_contract_inputs(inputs: dict[str, Any]) -> None: + if _eligible_q512_k8_static(inputs): + _launch_k32_split_path(inputs, split_count=Q512_K8_SPLITS) + return + if _eligible_fixed_build_generic_lowk(inputs): + parent_lowk._launch_cg2_split_path(inputs, split_count=parent_lowk.SMALL_SPLITS) + return + if _eligible_rag_k10_cached(inputs): + _launch_rag_k10_cached(inputs) + return + split_count = _k32_split_count(inputs) + if split_count is not None: + _launch_k32_split_path(inputs, split_count=split_count) + return + parent.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(candidate, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + from .._dispatch_runtime import CANONICAL_SHAPES + if shape_labels is None: + return list(CANONICAL_SHAPES) + wanted = {str(label) for label in shape_labels} + selected = [shape for shape in CANONICAL_SHAPES if shape['label'] in wanted] + missing = wanted - {shape['label'] for shape in selected} + if missing: + raise ValueError(''.join(['unknown kNN build contract shape(s): ', format(sorted(missing), '')])) + return selected + +def compile_and_launch_knn_build(*, shape_labels=('flashml_correctness_b1_q256_m256_d128_k5',), benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint: run real contract correctness for selected shapes.""" + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _largek_k25_probe_shape() -> dict[str, Any]: + return {'label': 'build_qm4096_d128_k25_probe', 'params': {'B': 1, 'Q': 4096, 'M': 4096, 'D': 128, 'K': 25, 'dtype': 'bfloat16', 'seed': 606425, 'build': True, 'check_correctness': True, 'correctness_query_sample': 256, 'recall_min': 0.999, 'benchmark': True, 'time_flashlib': True}} + +def benchmark_largek_k25_k32_v12() -> dict[str, Any]: + """Opt-in bench-regression hook for the custom K25 probe plus K32 diagnostic.""" + shapes = [_largek_k25_probe_shape(), _select_contract_shapes(('build_largek_stress_qm4096_k32',))[0]] + report = evaluate_contract(shapes=shapes, correctness=True, benchmark=True) + return {'tflops': report['summary']['primary_mean'], 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'report': report} + +def benchmark_largek_k32_v13() -> dict[str, Any]: + """Opt-in benchmark hook for the canonical K32 diagnostic route.""" + report = evaluate_contract(shapes=_select_contract_shapes(('build_largek_stress_qm4096_k32',)), correctness=True, benchmark=True) + return {'tflops': report['summary']['primary_mean'], 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'report': report} + +def benchmark_largek_k32_v14() -> dict[str, Any]: + """Opt-in benchmark hook for the canonical K32 diagnostic route.""" + report = evaluate_contract(shapes=_select_contract_shapes(('build_largek_stress_qm4096_k32',)), correctness=True, benchmark=True) + return {'tflops': report['summary']['primary_mean'], 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'report': report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2_k32split_v20.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2_k32split_v20.py new file mode 100644 index 00000000..5f9addb3 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2_k32split_v20.py @@ -0,0 +1,531 @@ +"""kNN build/search K32 split-build successor with q512 K8 and K32 chunked merge. + +Minimum target architecture: sm_100a. This variant keeps the validated K5/K10 +fixed-build dispatch from the parent and keeps the build-mode path for +``10 < K <= 32`` BF16 D=128 shapes. The larger-K path compiles the inherited +CTA-group=2 split stage-1 IR with static top-k capacity buckets of 12, 16, 20, +25, 30, and 32. The small-shape K12/K16/K20/K25/K30 buckets use a 32-thread cached +four-way cursor merge over sorted split-local streams, preserving the +split/tcgen05 contract-visible eval path while reducing scalar merge work for +exact bucket sizes. For the underfilled Q=M=512,K=30 probe, stage-1 uses eight +database splits and a specialized eight-way cached merge to expose more +independent tcgen05 work. Large Q=M=4096 K20/K30/K32 boundaries use exact +unordered split-local producers and unordered four-split merge, avoiding +sorted-stream maintenance on large-Q hot paths while preserving exact top-k +membership and matching distances. Fixed-build K1/K2/K8 shapes use the +inherited generic-K split merge instead of cached K10 merge kernels so partial +buffer row strides match the contract-visible K. Q=M=1024/2048,K=12 uses an +exact K12 producer and eight-way cached merge to increase mid-build producer +parallelism without changing q4096 K20/K30/K32 unordered routing. The v10 +lineage applies the same shape-gated eight-split sorted-stream fanout to +Q=M=1024/2048,K=20 and adds an exact K20 eight-way cached merge. +This v11 candidate routes Q=M=2048,K=8 through a K8 static split/tcgen05 +bucket and seven-way cached merge, and uses a shape-gated K20 fanout mix: +Q=M=1024,K=20 uses sixteen splits with an exact K20 sixteen-way cached merge, +while Q=M=2048,K=20 keeps the v10 eight-split exact merge and Q=M=4096,K=20 +keeps the unordered four-split route. +This v12 candidate keeps the v11 shape-gated K20 fanout mix, routes +Q=M=2048,K=8 through eight database splits and an exact eight-way cached merge, +extends the large-Q unordered exact top-k route to Q=M=4096,K=25, and widens +the non-build K10 RAG dispatch: q4096 x 100000 and q10000 x 50000 now use the +inherited split-7 tcgen05 producer plus cached seven-way merge instead of +falling through to the older generic parent route. The v11 K32 diagnostic route +is preserved. +This v13 candidate keeps the v12 routes and tests the q512 guardrail lane by +routing Q=M=512,K=8 through the existing static K8 eight-split producer and +cached eight-way merge. K1/K2 stay on the stride-safe generic-K four-split +route. It also changes the K32 unordered merge: the first split-local +top-32 vector prefills the output accumulator, then the remaining three +split-local vectors use worst-slot replacement. This avoids the v12 merge's +repeated worst scan during the guaranteed initial fill. +This v14 candidate keeps the same K32 unordered producer and prefill semantics, +but tracks four 8-slot worst caches during the unordered merge. Accepted +candidates rescan only the affected 8-slot bucket and the four bucket maxima +instead of rescanning all 32 slots. +This v15 candidate keeps the v14 K32 unordered producer and replaces only the +Q=M=4096,K=32 final merge with a 128-thread CTA-local bitonic merge. One CTA +owns one query row, loads the four split-local top-32 lists into shared memory, +sorts the 128 candidates by distance, and stores the first 32 results. +This v16 candidate keeps the same unordered producer but changes the K32 +cooperative merge to a warp-local register selection network: four warps in a +CTA process four query rows, each lane owns one candidate slot from all four +split-local top-32 lists, and each output slot is selected with warp shuffles +then invalidated in registers. This removes the CTA-wide bitonic sort barriers +without assuming the split-local lists are sorted. +This v17 candidate keeps v16's K32 route and changes only non-build RAG K10 +dispatch: Q<=4096 rows use the existing split-4 cached K10 merge, while larger +Q RAG rows keep the split-7 cached route. This targets the canonical +q4096 x 100000 row where split-4 exposes enough stage work with less merge +fanout. +This v18 candidate keeps v17's RAG/K32 routes and changes only the fixed-build +K12 dispatch: Q=M=4096,K=12 now uses the existing unordered split-local top-k +producer and unordered four-way merge family. The target is the canonical +q4096 K12 row, where extra split fan-in measured slower than v17 and the +remaining local bottleneck is sorted top-k maintenance. +This v19 candidate keeps v18's q4096 K12 unordered route and tests only the +underfilled Q=M=1024,K=12 build row: stage-1 now uses sixteen database splits +with an exact K12 sixteen-way cached merge, matching the K20/Q1024 fanout shape +while preserving q2048 sorted split8 and q4096 unordered split4 guardrails. +This v20 candidate keeps all v19 split-count guardrails and changes only the +Q=M=4096,K=20 unordered final merge. The four split-local top-20 vectors are +merged by a four-warp CTA-local warp-select network with lanes 20..31 masked +off, reducing scalar row-local merge scans without increasing partial-buffer +volume or changing producer fanout. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import os +from functools import lru_cache +from typing import Any +from .._dispatch_runtime import dc as dc +from . import knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2 as parent +from . import knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_v1 as parent_lowk +from . import knn_build_evolve_7bfc_split_cg2_u2_v1 as parent_u2 +from . import knn_build_evolve_7bfc_split_v1 as parent_split +from . import knn_build_evolve_7bfc_v1 as base_v1 +from .._dispatch_runtime import pack_kernel_args +BLOCK_Q = parent.BLOCK_Q +BLOCK_M = parent.BLOCK_M +FEAT_D = parent.FEAT_D +TOP_K_MAX = parent.TOP_K_MAX +TOP_K_SPLIT_MAX = base_v1.TOP_K_FALLBACK_MAX +STAGE1_THREADS = parent.STAGE1_THREADS +MERGE_THREADS = parent.MERGE_THREADS +K32_MERGE_THREADS = 32 +K32_COOP_MERGE_THREADS = 128 +K20_COOP_MERGE_THREADS = K32_COOP_MERGE_THREADS +GRID_DIM_DEFAULT = parent.GRID_DIM_DEFAULT +CTA_GROUP = parent.CTA_GROUP +MEDIUM_SPLITS = parent.MEDIUM_SPLITS +K12_MID_SPLITS = 8 +K12_Q1024_SPLITS = 16 +K20_Q1024_SPLITS = 16 +K20_Q2048_SPLITS = 8 +K8_Q2048_SPLITS = 8 +K8_MID_MERGE_THREADS = parent_lowk.parent_cached.RAG_MERGE_THREADS +K30_SMALL_SHAPE_MAX = 512 +K30_SMALL_SPLITS = 8 +Q512_K8_SPLITS = K8_Q2048_SPLITS +RAG_OFFLINE_Q_MIN = 4096 +RAG_OFFLINE_M_MIN = 50000 +RAG_S4_QUERY_MAX = 4096 + +def _ir_with_top_k_max(ir_obj: Any, *, top_k_max: int, suffix: str) -> Any: + constants = tuple(((name, top_k_max if name == 'TOP_K_MAX' else value) for name, value in ir_obj.constants)) + return dc.replace(ir_obj, symbol=''.join([format(ir_obj.symbol, ''), '_', format(suffix, '')]), constants=constants) + +def _ir_with_constants(ir_obj: Any, *, suffix: str, **updates: int) -> Any: + constants = tuple(((name, updates.get(name, value)) for name, value in ir_obj.constants)) + return dc.replace(ir_obj, symbol=''.join([format(ir_obj.symbol, ''), '_', format(suffix, '')]), constants=constants) +TOP_K_BUCKETS = (8, 12, 16, 20, 25, 30, TOP_K_SPLIT_MAX) +knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 192}')) +stage1_k8_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k8split", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 8]], "cta_group": 1, "threads": 192}')) +stage1_k12_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k12split", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 12]], "cta_group": 1, "threads": 192}')) +stage1_k16_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k16split", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 16]], "cta_group": 1, "threads": 192}')) +stage1_k20_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k20split", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 20]], "cta_group": 1, "threads": 192}')) +stage1_k25_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k25split", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 25]], "cta_group": 1, "threads": 192}')) +stage1_k30_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k30split", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 30]], "cta_group": 1, "threads": 192}')) +stage1_k32_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32split", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 192}')) +stage1_k32_unordered_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 192}')) +stage1_k12_unordered_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k12unordered", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 12]], "cta_group": 1, "threads": 192}')) +stage1_k20_unordered_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k20unordered", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 20]], "cta_group": 1, "threads": 192}')) +stage1_k25_unordered_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k25unordered", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 25]], "cta_group": 1, "threads": 192}')) +stage1_k30_unordered_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k30unordered", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 30]], "cta_group": 1, "threads": 192}')) +stage1_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32split", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 192}')) +knn_build_evolve_7bfc_k32_merge_s4_rowbase_cache = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_rowbase_cache", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "K", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 32]], "cta_group": 1, "threads": 32}')) +merge_k12_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_rowbase_cache_k12split", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "K", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 12]], "cta_group": 1, "threads": 32}')) +merge_k8_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_rowbase_cache_k8split", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "K", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 8]], "cta_group": 1, "threads": 32}')) +merge_k16_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_rowbase_cache_k16split", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "K", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 16]], "cta_group": 1, "threads": 32}')) +merge_k20_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_rowbase_cache_k20split", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "K", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 20]], "cta_group": 1, "threads": 32}')) +merge_k25_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_rowbase_cache_k25split", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "K", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 25]], "cta_group": 1, "threads": 32}')) +merge_k30_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_rowbase_cache_k30split", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "K", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 30]], "cta_group": 1, "threads": 32}')) +merge_k32_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_rowbase_cache_k32split", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "K", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 32]], "cta_group": 1, "threads": 32}')) +merge_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_rowbase_cache_k32split", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "K", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 32]], "cta_group": 1, "threads": 32}')) +knn_build_evolve_7bfc_k32_merge_s4_unordered = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_unordered", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 32], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 32}')) +knn_build_evolve_7bfc_k32_merge_s4_unordered_prefill = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_unordered_prefill", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 32], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 32}')) +knn_build_evolve_7bfc_k32_merge_s4_unordered_chunked_prefill = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_unordered_chunked_prefill", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 32], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 32}')) +knn_build_evolve_7bfc_k32_merge_s4_unordered_warp_select = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_unordered_warp_select", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 32], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 128}')) +knn_build_evolve_7bfc_k20_merge_s4_unordered_warp_select = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k20_merge_s4_unordered_warp_select", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 20], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 128}')) +merge_k32_unordered_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_unordered", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 32], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 32}')) +merge_k32_unordered_prefill_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_unordered_prefill", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 32], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 32}')) +merge_k32_unordered_chunked_prefill_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_unordered_chunked_prefill", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 32], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 32}')) +merge_k32_unordered_warp_select_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_unordered_warp_select", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 32], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 128}')) +merge_k20_unordered_warp_select_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k20_merge_s4_unordered_warp_select", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 20], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 128}')) +merge_k12_unordered_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_unordered_k12unordered", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 12], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 32}')) +merge_k20_unordered_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_unordered_k20unordered", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 20], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 32}')) +merge_k25_unordered_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_unordered_k25unordered", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 25], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 32}')) +merge_k30_unordered_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_unordered_k30unordered", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 30], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 32}')) +knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 30], ["SPLIT_COUNT", 8]], "cta_group": 1, "threads": 32}')) +merge_k30_s8_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 30], ["SPLIT_COUNT", 8]], "cta_group": 1, "threads": 32}')) +merge_k12_s8_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_k12s8", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 12], ["SPLIT_COUNT", 8]], "cta_group": 1, "threads": 32}')) +merge_k20_s8_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_k20s8", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 20], ["SPLIT_COUNT", 8]], "cta_group": 1, "threads": 32}')) +merge_k8_s7_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k10_merge_s7_rowbase_cache_k8s7", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 8], ["SPLIT_COUNT", 7]], "cta_group": 1, "threads": 32}')) +merge_k8_s8_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_k8s8", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 8], ["SPLIT_COUNT", 8]], "cta_group": 1, "threads": 32}')) +merge_k12_s16_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_k12s16", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 12], ["SPLIT_COUNT", 16]], "cta_group": 1, "threads": 32}')) +merge_k20_s16_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_k20s16", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 20], ["SPLIT_COUNT", 16]], "cta_group": 1, "threads": 32}')) + +def _top_k_bucket(top_k: int) -> int: + for bucket in TOP_K_BUCKETS: + if top_k <= bucket: + return bucket + return TOP_K_SPLIT_MAX + +def _stage1_ir_for_bucket(top_k_bucket: int) -> Any: + if top_k_bucket <= 8: + return stage1_k8_ir + if top_k_bucket <= 12: + return stage1_k12_ir + if top_k_bucket <= 16: + return stage1_k16_ir + if top_k_bucket <= 20: + return stage1_k20_ir + if top_k_bucket <= 25: + return stage1_k25_ir + if top_k_bucket <= 30: + return stage1_k30_ir + return stage1_k32_ir + +def _merge_ir_for_bucket(top_k_bucket: int) -> Any: + if top_k_bucket <= 8: + return merge_k8_ir + if top_k_bucket <= 12: + return merge_k12_ir + if top_k_bucket <= 16: + return merge_k16_ir + if top_k_bucket <= 20: + return merge_k20_ir + if top_k_bucket <= 25: + return merge_k25_ir + if top_k_bucket <= 30: + return merge_k30_ir + return merge_k32_ir + +def _verify_export_ir() -> Any: + bucket_text = os.environ.get('LOOM_KNN_K32SPLIT_VERIFY_TOP_K_BUCKET') + top_k_bucket = TOP_K_SPLIT_MAX if bucket_text is None else _top_k_bucket(int(bucket_text)) + verify_kernel = os.environ.get('LOOM_KNN_K32SPLIT_VERIFY_KERNEL') + if verify_kernel == 'stage1_q512_lowk': + return parent_lowk.stage1_ir + if verify_kernel == 'merge_generic': + return parent_split.merge_ir + if verify_kernel == 'stage1_k10_rag': + return parent_lowk.stage1_ir + if verify_kernel == 'stage1_k12_unordered': + return stage1_k12_unordered_ir + if verify_kernel == 'merge_k10_s4_cache': + return parent_lowk.parent_cached64.merge_k10_s4_cache_ir + if verify_kernel == 'merge_k10_s7_cache': + return parent_lowk.parent_cached.merge_k10_s7_cache_ir + if verify_kernel == 'stage1_k20_unordered': + return stage1_k20_unordered_ir + if verify_kernel == 'stage1_k25_unordered': + return stage1_k25_unordered_ir + if verify_kernel == 'stage1_k30_unordered': + return stage1_k30_unordered_ir + if verify_kernel == 'stage1_k32_unordered': + return stage1_k32_unordered_ir + if verify_kernel == 'merge_k20_unordered': + return merge_k20_unordered_ir + if verify_kernel == 'merge_k12_unordered': + return merge_k12_unordered_ir + if verify_kernel == 'merge_k25_unordered': + return merge_k25_unordered_ir + if verify_kernel == 'merge_k30_unordered': + return merge_k30_unordered_ir + if verify_kernel == 'merge_k32_unordered': + return merge_k32_unordered_ir + if verify_kernel == 'merge_k32_unordered_prefill': + return merge_k32_unordered_prefill_ir + if verify_kernel == 'merge_k32_unordered_chunked_prefill': + return merge_k32_unordered_chunked_prefill_ir + if verify_kernel == 'merge_k32_unordered_warp_select': + return merge_k32_unordered_warp_select_ir + if verify_kernel == 'merge_k20_unordered_warp_select': + return merge_k20_unordered_warp_select_ir + if verify_kernel == 'merge_s8': + return merge_k30_s8_ir + if verify_kernel == 'merge_k12_s8': + return merge_k12_s8_ir + if verify_kernel == 'merge_k12_s16': + return merge_k12_s16_ir + if verify_kernel == 'merge_k20_s8': + return merge_k20_s8_ir + if verify_kernel == 'merge_k8_s7': + return merge_k8_s7_ir + if verify_kernel == 'merge_k8_s8': + return merge_k8_s8_ir + if verify_kernel == 'merge_k20_s16': + return merge_k20_s16_ir + if verify_kernel == 'merge': + return _merge_ir_for_bucket(top_k_bucket) + return _stage1_ir_for_bucket(top_k_bucket) +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32split", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 192}')) + +def _compile_ir(ir_obj: Any): + from .._dispatch_runtime import generate_kernel + from .._dispatch_runtime import _cuda_include_dirs + from .._dispatch_runtime import compile_cuda + from .._dispatch_runtime import CUDAKernel + source = generate_kernel(ir_obj, validate=False, smem_bytes=ir_obj.computed_smem_bytes) + cubin = compile_cuda(source, arch=base_v1._select_arch_and_preload(), options=['--use_fast_math'], include_dirs=_cuda_include_dirs()) + return CUDAKernel(cubin, ''.join(['kernel_', format(ir_obj.symbol, '')])) + +@lru_cache(maxsize=7) +def _compiled_stage1_for_bucket(top_k_bucket: int): + return _compile_ir(_stage1_ir_for_bucket(top_k_bucket)) + +def _compiled_stage1_k32_unordered(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0147"}')) + +@lru_cache(maxsize=5) +def _compiled_stage1_unordered_for_exact_k(top_k: int): + return _compile_ir(_stage1_unordered_ir_for_exact_k(top_k)) + +@lru_cache(maxsize=7) +def _compiled_merge_for_bucket(top_k_bucket: int): + return _compile_ir(_merge_ir_for_bucket(top_k_bucket)) + +def _compiled_merge_k32_unordered(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0148"}')) + +@lru_cache(maxsize=5) +def _compiled_merge_unordered_for_exact_k(top_k: int): + return _compile_ir(_merge_unordered_ir_for_exact_k(top_k)) + +def _compiled_merge_k32_unordered_warp_select(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0058"}')) + +def _compiled_merge_k20_unordered_warp_select(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0076"}')) + +def _compiled_merge_k30_s8(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0149"}')) + +def _compiled_merge_k12_s8(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0150"}')) + +def _compiled_merge_k12_s16(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0011"}')) + +def _compiled_merge_k20_s8(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0151"}')) + +def _compiled_merge_k8_s7(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0152"}')) + +def _compiled_merge_k8_s8(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0005"}')) + +def _compiled_merge_k20_s16(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0153"}')) + +def _eligible_k32_split_build(inputs: dict[str, Any]) -> bool: + top_k = int(inputs['K']) + return bool(inputs.get('build', False)) and str(inputs['query'].dtype) == 'torch.bfloat16' and (str(inputs['database'].dtype) == 'torch.bfloat16') and (int(inputs['D']) == FEAT_D) and (top_k == 8 or TOP_K_MAX < top_k <= TOP_K_SPLIT_MAX) and (int(inputs['Q']) == int(inputs['M'])) and (512 <= int(inputs['Q']) <= 4096) + +def _stage1_unordered_ir_for_exact_k(top_k: int) -> Any: + if top_k == 12: + return stage1_k12_unordered_ir + if top_k == 20: + return stage1_k20_unordered_ir + if top_k == 25: + return stage1_k25_unordered_ir + if top_k == 30: + return stage1_k30_unordered_ir + if top_k == TOP_K_SPLIT_MAX: + return stage1_k32_unordered_ir + raise ValueError(''.join(['no unordered stage-1 specialization for K=', format(top_k, '')])) + +def _merge_unordered_ir_for_exact_k(top_k: int) -> Any: + if top_k == 12: + return merge_k12_unordered_ir + if top_k == 20: + return merge_k20_unordered_warp_select_ir + if top_k == 25: + return merge_k25_unordered_ir + if top_k == 30: + return merge_k30_unordered_ir + if top_k == TOP_K_SPLIT_MAX: + return merge_k32_unordered_warp_select_ir + raise ValueError(''.join(['no unordered merge specialization for K=', format(top_k, '')])) + +def _k32_split_count(inputs: dict[str, Any]) -> int | None: + if not _eligible_k32_split_build(inputs): + return None + if int(inputs['K']) == 8: + return K8_Q2048_SPLITS if int(inputs['Q']) == 2048 else None + if int(inputs['K']) == 12 and int(inputs['Q']) == 1024: + return K12_Q1024_SPLITS + if int(inputs['K']) == 12 and int(inputs['Q']) == 2048: + return K12_MID_SPLITS + if int(inputs['K']) == 20 and int(inputs['Q']) == 1024: + return K20_Q1024_SPLITS + if int(inputs['K']) == 20 and int(inputs['Q']) == 2048: + return K20_Q2048_SPLITS + if int(inputs['K']) == 30 and int(inputs['Q']) <= K30_SMALL_SHAPE_MAX: + return K30_SMALL_SPLITS + return MEDIUM_SPLITS + +def _eligible_fixed_build_generic_lowk(inputs: dict[str, Any]) -> bool: + return bool(inputs.get('build', False)) and str(inputs['query'].dtype) == 'torch.bfloat16' and (str(inputs['database'].dtype) == 'torch.bfloat16') and (int(inputs['D']) == FEAT_D) and (int(inputs['K']) in (1, 2, 8)) and (int(inputs['Q']) == int(inputs['M'])) and (int(inputs['Q']) <= parent_lowk.SMALL_SHAPE_MAX) + +def _eligible_q512_k8_static(inputs: dict[str, Any]) -> bool: + return _eligible_k32_split_build(inputs) and int(inputs['K']) == 8 and (int(inputs['Q']) == 512) and (int(inputs['M']) == 512) + +def _eligible_rag_k10_cached(inputs: dict[str, Any]) -> bool: + return not bool(inputs.get('build', False)) and str(inputs['query'].dtype) == 'torch.bfloat16' and (str(inputs['database'].dtype) == 'torch.bfloat16') and (int(inputs['D']) == FEAT_D) and (int(inputs['K']) == TOP_K_MAX) and (int(inputs['Q']) >= RAG_OFFLINE_Q_MIN) and (int(inputs['M']) >= RAG_OFFLINE_M_MIN) + +def _launch_rag_k10_cached(inputs: dict[str, Any]) -> None: + if int(inputs['Q']) <= RAG_S4_QUERY_MAX: + parent_lowk._launch_k10_cached_path(inputs, split_count=MEDIUM_SPLITS, merge_threads=parent_lowk.parent_cached64.MEDIUM_MERGE_THREADS, merge_kernel=parent_lowk.parent_cached64._compiled_merge_k10_s4_cache(), merge_ir=parent_lowk.parent_cached64.merge_k10_s4_cache_ir) + return + parent_lowk._launch_k10_cached_path(inputs, split_count=parent_lowk.RAG_SPLITS, merge_threads=parent_lowk.parent_cached.RAG_MERGE_THREADS, merge_kernel=parent_lowk.parent_cached._compiled_merge_k10_s7_cache(), merge_ir=parent_lowk.parent_cached.merge_k10_s7_cache_ir) + +def _launch_k32_split_path(inputs: dict[str, Any], *, split_count: int) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_q_tile_pairs = (num_q_tiles + CTA_GROUP - 1) // CTA_GROUP + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tile_pairs * split_count + stage1_grid = min(total_work * CTA_GROUP, GRID_DIM_DEFAULT) + top_k_bucket = _top_k_bucket(top_k) + use_unordered = top_k in (12, 20, 25, 30, TOP_K_SPLIT_MAX) and split_count == MEDIUM_SPLITS and (n_query >= 4096) + use_k32_coop_merge = use_unordered and top_k == TOP_K_SPLIT_MAX + use_k20_coop_merge = use_unordered and top_k == 20 + stage1_ir_obj = _stage1_unordered_ir_for_exact_k(top_k) if use_unordered else _stage1_ir_for_bucket(top_k_bucket) + use_k8_s7_merge = split_count == parent.RAG_SPLITS and top_k == 8 + use_k8_s8_merge = split_count == K8_Q2048_SPLITS and top_k == 8 + use_k12_s16_merge = split_count == K12_Q1024_SPLITS and top_k == 12 + use_k12_s8_merge = split_count == K12_MID_SPLITS and top_k == 12 + use_k20_s16_merge = split_count == K20_Q1024_SPLITS and top_k == 20 + use_k20_s8_merge = split_count == K20_Q2048_SPLITS and top_k == 20 + use_k30_s8_merge = split_count == K30_SMALL_SPLITS and top_k == 30 + merge_threads = K8_MID_MERGE_THREADS if use_k8_s7_merge else K32_MERGE_THREADS + merge_grid = (bsz * n_query + 3) // 4 if use_k32_coop_merge or use_k20_coop_merge else min((bsz * n_query + merge_threads - 1) // merge_threads, GRID_DIM_DEFAULT) + merge_ir_obj = _merge_unordered_ir_for_exact_k(top_k) if use_unordered else merge_k8_s7_ir if use_k8_s7_merge else merge_k8_s8_ir if use_k8_s8_merge else merge_k12_s16_ir if use_k12_s16_merge else merge_k12_s8_ir if use_k12_s8_merge else merge_k20_s16_ir if use_k20_s16_merge else merge_k20_s8_ir if use_k20_s8_merge else merge_k30_s8_ir if use_k30_s8_merge else _merge_ir_for_bucket(top_k_bucket) + partial_dists, partial_indices = parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), bsz * n_query, BLOCK_Q, dim, dim) + tmap_database = base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, dim, dim) + stage1_kernel = _compiled_stage1_unordered_for_exact_k(top_k) if use_unordered else _compiled_stage1_for_bucket(top_k_bucket) + stage1_kernel.launch_cluster(grid=(stage1_grid, 1, 1), block=(STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage1_ir_obj, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tile_pairs=num_q_tile_pairs, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), cluster_dims=(CTA_GROUP, 1, 1), shared_mem=stage1_ir_obj.computed_smem_bytes) + if use_unordered: + if use_k32_coop_merge or use_k20_coop_merge: + merge_kernel = _compiled_merge_k32_unordered_warp_select() if use_k32_coop_merge else _compiled_merge_k20_unordered_warp_select() + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(K32_COOP_MERGE_THREADS if use_k32_coop_merge else K20_COOP_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_ir_obj.computed_smem_bytes) + else: + merge_kernel = _compiled_merge_unordered_for_exact_k(top_k) + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(K32_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_ir_obj.computed_smem_bytes) + elif use_k8_s7_merge: + merge_kernel = _compiled_merge_k8_s7() + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(K8_MID_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_ir_obj.computed_smem_bytes) + elif use_k8_s8_merge: + merge_kernel = _compiled_merge_k8_s8() + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(K8_MID_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_ir_obj.computed_smem_bytes) + elif use_k12_s16_merge: + merge_kernel = _compiled_merge_k12_s16() + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(K32_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_ir_obj.computed_smem_bytes) + elif use_k12_s8_merge: + merge_kernel = _compiled_merge_k12_s8() + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(K32_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_ir_obj.computed_smem_bytes) + elif use_k20_s16_merge: + merge_kernel = _compiled_merge_k20_s16() + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(K32_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_ir_obj.computed_smem_bytes) + elif use_k20_s8_merge: + merge_kernel = _compiled_merge_k20_s8() + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(K32_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_ir_obj.computed_smem_bytes) + elif use_k30_s8_merge: + merge_kernel = _compiled_merge_k30_s8() + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(K32_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_ir_obj.computed_smem_bytes) + else: + merge_kernel = _compiled_merge_for_bucket(top_k_bucket) + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(K32_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], top_k, bsz * n_query], shared_mem=merge_ir_obj.computed_smem_bytes) + +def launch_from_contract_inputs(inputs: dict[str, Any]) -> None: + if _eligible_q512_k8_static(inputs): + _launch_k32_split_path(inputs, split_count=Q512_K8_SPLITS) + return + if _eligible_fixed_build_generic_lowk(inputs): + parent_lowk._launch_cg2_split_path(inputs, split_count=parent_lowk.SMALL_SPLITS) + return + if _eligible_rag_k10_cached(inputs): + _launch_rag_k10_cached(inputs) + return + split_count = _k32_split_count(inputs) + if split_count is not None: + _launch_k32_split_path(inputs, split_count=split_count) + return + parent.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(candidate, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + from .._dispatch_runtime import CANONICAL_SHAPES + if shape_labels is None: + return list(CANONICAL_SHAPES) + wanted = {str(label) for label in shape_labels} + selected = [shape for shape in CANONICAL_SHAPES if shape['label'] in wanted] + missing = wanted - {shape['label'] for shape in selected} + if missing: + raise ValueError(''.join(['unknown kNN build contract shape(s): ', format(sorted(missing), '')])) + return selected + +def compile_and_launch_knn_build(*, shape_labels=('flashml_correctness_b1_q256_m256_d128_k5',), benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint: run real contract correctness for selected shapes.""" + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _largek_k25_probe_shape() -> dict[str, Any]: + return {'label': 'build_qm4096_d128_k25_probe', 'params': {'B': 1, 'Q': 4096, 'M': 4096, 'D': 128, 'K': 25, 'dtype': 'bfloat16', 'seed': 606425, 'build': True, 'check_correctness': True, 'correctness_query_sample': 256, 'recall_min': 0.999, 'benchmark': True, 'time_flashlib': True}} + +def benchmark_largek_k25_k32_v12() -> dict[str, Any]: + """Opt-in bench-regression hook for the custom K25 probe plus K32 diagnostic.""" + shapes = [_largek_k25_probe_shape(), _select_contract_shapes(('build_largek_stress_qm4096_k32',))[0]] + report = evaluate_contract(shapes=shapes, correctness=True, benchmark=True) + return {'tflops': report['summary']['primary_mean'], 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'report': report} + +def benchmark_largek_k32_v13() -> dict[str, Any]: + """Opt-in benchmark hook for the canonical K32 diagnostic route.""" + report = evaluate_contract(shapes=_select_contract_shapes(('build_largek_stress_qm4096_k32',)), correctness=True, benchmark=True) + return {'tflops': report['summary']['primary_mean'], 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'report': report} + +def benchmark_largek_k32_v16() -> dict[str, Any]: + """Opt-in benchmark hook for the canonical K32 diagnostic route.""" + report = evaluate_contract(shapes=_select_contract_shapes(('build_largek_stress_qm4096_k32',)), correctness=True, benchmark=True) + return {'tflops': report['summary']['primary_mean'], 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'report': report} + +def benchmark_rag_offline_v17() -> dict[str, Any]: + """Opt-in benchmark hook for the non-build K10 RAG dispatch route.""" + report = evaluate_contract(shapes=_select_contract_shapes(('rag_offline_b1_q4096_m100000_d128_k10', 'rag_offline_batch_b1_q10000_m100000_d128_k10', 'rag_offline_b1_q10000_m50000_d128_k10')), correctness=True, benchmark=True) + return {'tflops': report['summary']['primary_mean'], 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'report': report} + +def benchmark_k12_q4096_v18() -> dict[str, Any]: + """Opt-in benchmark hook for the fixed-build K12 unordered q4096 route.""" + report = evaluate_contract(shapes=_select_contract_shapes(('build_k_sweep_qm1024_k12', 'build_k_sweep_qm2048_k12', 'build_k_sweep_qm4096_k12')), correctness=True, benchmark=True) + return {'tflops': report['summary']['primary_mean'], 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'report': report} + +def benchmark_k12_q1024fanout_v19() -> dict[str, Any]: + """Opt-in benchmark hook for the fixed-build K12 q1024 split16 fanout route.""" + report = evaluate_contract(shapes=_select_contract_shapes(('build_k_sweep_qm1024_k12', 'build_k_sweep_qm2048_k12', 'build_k_sweep_qm4096_k12')), correctness=True, benchmark=True) + return {'tflops': report['summary']['primary_mean'], 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'report': report} + +def benchmark_k20_q4096_warpselect_v20() -> dict[str, Any]: + """Opt-in benchmark hook for the fixed-build K20 q4096 warp-select merge route.""" + report = evaluate_contract(shapes=_select_contract_shapes(('build_k_sweep_qm1024_k20', 'build_k_sweep_qm2048_k20', 'build_k_sweep_qm4096_k20')), correctness=True, benchmark=True) + return {'tflops': report['summary']['primary_mean'], 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'report': report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2_k32split_v9.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2_k32split_v9.py new file mode 100644 index 00000000..9eb332a6 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2_k32split_v9.py @@ -0,0 +1,281 @@ +"""kNN build/search K32 split-build successor with K12 mid-build dispatch. + +Minimum target architecture: sm_100a. This variant keeps the validated K5/K10 +fixed-build dispatch from the parent and keeps the build-mode path for +``10 < K <= 32`` BF16 D=128 shapes. The larger-K path compiles the inherited +CTA-group=2 split stage-1 IR with static top-k capacity buckets of 12, 16, 20, +25, 30, and 32. The small-shape K12/K16/K20/K25/K30 buckets use a 32-thread cached +four-way cursor merge over sorted split-local streams, preserving the +split/tcgen05 contract-visible eval path while reducing scalar merge work for +exact bucket sizes. For the underfilled Q=M=512,K=30 probe, stage-1 uses eight +database splits and a specialized eight-way cached merge to expose more +independent tcgen05 work. Large Q=M=4096 K20/K30/K32 boundaries use exact +unordered split-local producers and unordered four-split merge, avoiding +sorted-stream maintenance on large-Q hot paths while preserving exact top-k +membership and matching distances. Fixed-build K1/K2/K8 shapes use the +inherited generic-K split merge instead of cached K10 merge kernels so partial +buffer row strides match the contract-visible K. Q=M=1024/2048,K=12 uses an +exact K12 producer and eight-way cached merge to increase mid-build producer +parallelism without changing q4096 K20/K30/K32 unordered routing. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import os +from functools import lru_cache +from typing import Any +from .._dispatch_runtime import dc as dc +from . import knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2 as parent +from . import knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_v1 as parent_lowk +from . import knn_build_evolve_7bfc_split_cg2_u2_v1 as parent_u2 +from . import knn_build_evolve_7bfc_split_v1 as parent_split +from . import knn_build_evolve_7bfc_v1 as base_v1 +from .._dispatch_runtime import pack_kernel_args +BLOCK_Q = parent.BLOCK_Q +BLOCK_M = parent.BLOCK_M +FEAT_D = parent.FEAT_D +TOP_K_MAX = parent.TOP_K_MAX +TOP_K_SPLIT_MAX = base_v1.TOP_K_FALLBACK_MAX +STAGE1_THREADS = parent.STAGE1_THREADS +MERGE_THREADS = parent.MERGE_THREADS +K32_MERGE_THREADS = 32 +GRID_DIM_DEFAULT = parent.GRID_DIM_DEFAULT +CTA_GROUP = parent.CTA_GROUP +MEDIUM_SPLITS = parent.MEDIUM_SPLITS +K12_MID_SPLITS = 8 +K30_SMALL_SHAPE_MAX = 512 +K30_SMALL_SPLITS = 8 + +def _ir_with_top_k_max(ir_obj: Any, *, top_k_max: int, suffix: str) -> Any: + constants = tuple(((name, top_k_max if name == 'TOP_K_MAX' else value) for name, value in ir_obj.constants)) + return dc.replace(ir_obj, symbol=''.join([format(ir_obj.symbol, ''), '_', format(suffix, '')]), constants=constants) +TOP_K_BUCKETS = (12, 16, 20, 25, 30, TOP_K_SPLIT_MAX) +knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 192}')) +stage1_k12_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k12split", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 12]], "cta_group": 1, "threads": 192}')) +stage1_k16_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k16split", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 16]], "cta_group": 1, "threads": 192}')) +stage1_k20_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k20split", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 20]], "cta_group": 1, "threads": 192}')) +stage1_k25_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k25split", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 25]], "cta_group": 1, "threads": 192}')) +stage1_k30_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k30split", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 30]], "cta_group": 1, "threads": 192}')) +stage1_k32_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32split", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 192}')) +stage1_k32_unordered_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 192}')) +stage1_k20_unordered_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k20unordered", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 20]], "cta_group": 1, "threads": 192}')) +stage1_k30_unordered_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k30unordered", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 30]], "cta_group": 1, "threads": 192}')) +stage1_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32split", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 192}')) +knn_build_evolve_7bfc_k32_merge_s4_rowbase_cache = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_rowbase_cache", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "K", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 32]], "cta_group": 1, "threads": 32}')) +merge_k12_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_rowbase_cache_k12split", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "K", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 12]], "cta_group": 1, "threads": 32}')) +merge_k16_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_rowbase_cache_k16split", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "K", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 16]], "cta_group": 1, "threads": 32}')) +merge_k20_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_rowbase_cache_k20split", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "K", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 20]], "cta_group": 1, "threads": 32}')) +merge_k25_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_rowbase_cache_k25split", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "K", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 25]], "cta_group": 1, "threads": 32}')) +merge_k30_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_rowbase_cache_k30split", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "K", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 30]], "cta_group": 1, "threads": 32}')) +merge_k32_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_rowbase_cache_k32split", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "K", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 32]], "cta_group": 1, "threads": 32}')) +merge_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_rowbase_cache_k32split", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "K", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 32]], "cta_group": 1, "threads": 32}')) +knn_build_evolve_7bfc_k32_merge_s4_unordered = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_unordered", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 32], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 32}')) +merge_k32_unordered_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_unordered", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 32], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 32}')) +merge_k20_unordered_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_unordered_k20unordered", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 20], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 32}')) +merge_k30_unordered_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_unordered_k30unordered", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 30], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 32}')) +knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 30], ["SPLIT_COUNT", 8]], "cta_group": 1, "threads": 32}')) +merge_k30_s8_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 30], ["SPLIT_COUNT", 8]], "cta_group": 1, "threads": 32}')) +merge_k12_s8_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_k12s8", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 12], ["SPLIT_COUNT", 8]], "cta_group": 1, "threads": 32}')) + +def _top_k_bucket(top_k: int) -> int: + for bucket in TOP_K_BUCKETS: + if top_k <= bucket: + return bucket + return TOP_K_SPLIT_MAX + +def _stage1_ir_for_bucket(top_k_bucket: int) -> Any: + if top_k_bucket <= 12: + return stage1_k12_ir + if top_k_bucket <= 16: + return stage1_k16_ir + if top_k_bucket <= 20: + return stage1_k20_ir + if top_k_bucket <= 25: + return stage1_k25_ir + if top_k_bucket <= 30: + return stage1_k30_ir + return stage1_k32_ir + +def _merge_ir_for_bucket(top_k_bucket: int) -> Any: + if top_k_bucket <= 12: + return merge_k12_ir + if top_k_bucket <= 16: + return merge_k16_ir + if top_k_bucket <= 20: + return merge_k20_ir + if top_k_bucket <= 25: + return merge_k25_ir + if top_k_bucket <= 30: + return merge_k30_ir + return merge_k32_ir + +def _verify_export_ir() -> Any: + bucket_text = os.environ.get('LOOM_KNN_K32SPLIT_VERIFY_TOP_K_BUCKET') + top_k_bucket = TOP_K_SPLIT_MAX if bucket_text is None else _top_k_bucket(int(bucket_text)) + verify_kernel = os.environ.get('LOOM_KNN_K32SPLIT_VERIFY_KERNEL') + if verify_kernel == 'stage1_k20_unordered': + return stage1_k20_unordered_ir + if verify_kernel == 'stage1_k30_unordered': + return stage1_k30_unordered_ir + if verify_kernel == 'stage1_k32_unordered': + return stage1_k32_unordered_ir + if verify_kernel == 'merge_k20_unordered': + return merge_k20_unordered_ir + if verify_kernel == 'merge_k30_unordered': + return merge_k30_unordered_ir + if verify_kernel == 'merge_k32_unordered': + return merge_k32_unordered_ir + if verify_kernel == 'merge_s8': + return merge_k30_s8_ir + if verify_kernel == 'merge_k12_s8': + return merge_k12_s8_ir + if verify_kernel == 'merge': + return _merge_ir_for_bucket(top_k_bucket) + return _stage1_ir_for_bucket(top_k_bucket) +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32split", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 192}')) + +def _compile_ir(ir_obj: Any): + from .._dispatch_runtime import generate_kernel + from .._dispatch_runtime import _cuda_include_dirs + from .._dispatch_runtime import compile_cuda + from .._dispatch_runtime import CUDAKernel + source = generate_kernel(ir_obj, validate=False, smem_bytes=ir_obj.computed_smem_bytes) + cubin = compile_cuda(source, arch=base_v1._select_arch_and_preload(), options=['--use_fast_math'], include_dirs=_cuda_include_dirs()) + return CUDAKernel(cubin, ''.join(['kernel_', format(ir_obj.symbol, '')])) + +@lru_cache(maxsize=6) +def _compiled_stage1_for_bucket(top_k_bucket: int): + return _compile_ir(_stage1_ir_for_bucket(top_k_bucket)) + +def _compiled_stage1_k32_unordered(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0196"}')) + +@lru_cache(maxsize=3) +def _compiled_stage1_unordered_for_exact_k(top_k: int): + return _compile_ir(_stage1_unordered_ir_for_exact_k(top_k)) + +@lru_cache(maxsize=6) +def _compiled_merge_for_bucket(top_k_bucket: int): + return _compile_ir(_merge_ir_for_bucket(top_k_bucket)) + +def _compiled_merge_k32_unordered(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0197"}')) + +@lru_cache(maxsize=3) +def _compiled_merge_unordered_for_exact_k(top_k: int): + return _compile_ir(_merge_unordered_ir_for_exact_k(top_k)) + +def _compiled_merge_k30_s8(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0198"}')) + +def _compiled_merge_k12_s8(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0037"}')) + +def _eligible_k32_split_build(inputs: dict[str, Any]) -> bool: + return bool(inputs.get('build', False)) and str(inputs['query'].dtype) == 'torch.bfloat16' and (str(inputs['database'].dtype) == 'torch.bfloat16') and (int(inputs['D']) == FEAT_D) and (TOP_K_MAX < int(inputs['K']) <= TOP_K_SPLIT_MAX) and (int(inputs['Q']) == int(inputs['M'])) and (512 <= int(inputs['Q']) <= 4096) + +def _stage1_unordered_ir_for_exact_k(top_k: int) -> Any: + if top_k == 20: + return stage1_k20_unordered_ir + if top_k == 30: + return stage1_k30_unordered_ir + if top_k == TOP_K_SPLIT_MAX: + return stage1_k32_unordered_ir + raise ValueError(''.join(['no unordered stage-1 specialization for K=', format(top_k, '')])) + +def _merge_unordered_ir_for_exact_k(top_k: int) -> Any: + if top_k == 20: + return merge_k20_unordered_ir + if top_k == 30: + return merge_k30_unordered_ir + if top_k == TOP_K_SPLIT_MAX: + return merge_k32_unordered_ir + raise ValueError(''.join(['no unordered merge specialization for K=', format(top_k, '')])) + +def _k32_split_count(inputs: dict[str, Any]) -> int | None: + if not _eligible_k32_split_build(inputs): + return None + if int(inputs['K']) == 12 and 1024 <= int(inputs['Q']) <= 2048: + return K12_MID_SPLITS + if int(inputs['K']) == 30 and int(inputs['Q']) <= K30_SMALL_SHAPE_MAX: + return K30_SMALL_SPLITS + return MEDIUM_SPLITS + +def _eligible_fixed_build_generic_lowk(inputs: dict[str, Any]) -> bool: + return bool(inputs.get('build', False)) and str(inputs['query'].dtype) == 'torch.bfloat16' and (str(inputs['database'].dtype) == 'torch.bfloat16') and (int(inputs['D']) == FEAT_D) and (int(inputs['K']) in (1, 2, 8)) and (int(inputs['Q']) == int(inputs['M'])) and (int(inputs['Q']) <= parent_lowk.SMALL_SHAPE_MAX) + +def _launch_k32_split_path(inputs: dict[str, Any], *, split_count: int) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_q_tile_pairs = (num_q_tiles + CTA_GROUP - 1) // CTA_GROUP + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tile_pairs * split_count + stage1_grid = min(total_work * CTA_GROUP, GRID_DIM_DEFAULT) + merge_grid = min((bsz * n_query + K32_MERGE_THREADS - 1) // K32_MERGE_THREADS, GRID_DIM_DEFAULT) + top_k_bucket = _top_k_bucket(top_k) + use_unordered = top_k in (20, 30, TOP_K_SPLIT_MAX) and split_count == MEDIUM_SPLITS and (n_query >= 4096) + stage1_ir_obj = _stage1_unordered_ir_for_exact_k(top_k) if use_unordered else _stage1_ir_for_bucket(top_k_bucket) + use_k12_s8_merge = split_count == K12_MID_SPLITS and top_k == 12 + use_k30_s8_merge = split_count == K30_SMALL_SPLITS and top_k == 30 + merge_ir_obj = _merge_unordered_ir_for_exact_k(top_k) if use_unordered else merge_k12_s8_ir if use_k12_s8_merge else merge_k30_s8_ir if use_k30_s8_merge else _merge_ir_for_bucket(top_k_bucket) + partial_dists, partial_indices = parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), bsz * n_query, BLOCK_Q, dim, dim) + tmap_database = base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, dim, dim) + stage1_kernel = _compiled_stage1_unordered_for_exact_k(top_k) if use_unordered else _compiled_stage1_for_bucket(top_k_bucket) + stage1_kernel.launch_cluster(grid=(stage1_grid, 1, 1), block=(STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage1_ir_obj, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tile_pairs=num_q_tile_pairs, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), cluster_dims=(CTA_GROUP, 1, 1), shared_mem=stage1_ir_obj.computed_smem_bytes) + if use_unordered: + merge_kernel = _compiled_merge_unordered_for_exact_k(top_k) + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(K32_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_ir_obj.computed_smem_bytes) + elif use_k12_s8_merge: + merge_kernel = _compiled_merge_k12_s8() + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(K32_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_ir_obj.computed_smem_bytes) + elif use_k30_s8_merge: + merge_kernel = _compiled_merge_k30_s8() + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(K32_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_ir_obj.computed_smem_bytes) + else: + merge_kernel = _compiled_merge_for_bucket(top_k_bucket) + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(K32_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], top_k, bsz * n_query], shared_mem=merge_ir_obj.computed_smem_bytes) + +def launch_from_contract_inputs(inputs: dict[str, Any]) -> None: + if _eligible_fixed_build_generic_lowk(inputs): + parent_lowk._launch_cg2_split_path(inputs, split_count=parent_lowk.SMALL_SPLITS) + return + split_count = _k32_split_count(inputs) + if split_count is not None: + _launch_k32_split_path(inputs, split_count=split_count) + return + parent.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(candidate, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + from .._dispatch_runtime import CANONICAL_SHAPES + if shape_labels is None: + return list(CANONICAL_SHAPES) + wanted = {str(label) for label in shape_labels} + selected = [shape for shape in CANONICAL_SHAPES if shape['label'] in wanted] + missing = wanted - {shape['label'] for shape in selected} + if missing: + raise ValueError(''.join(['unknown kNN build contract shape(s): ', format(sorted(missing), '')])) + return selected + +def compile_and_launch_knn_build(*, shape_labels=('flashml_correctness_b1_q256_m256_d128_k5',), benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint: run real contract correctness for selected shapes.""" + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_v1.py new file mode 100644 index 00000000..1cbf295c --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_v1.py @@ -0,0 +1,137 @@ +"""kNN build/search K=10 stage-1 min-tree sort candidate. + +Minimum target architecture: sm_100a. This variant keeps the validated +CTA-group=2 tcgen05/TMA stage-1 distance path, the fixed max-tree K=10 +admission path, the K=5 min-tree/four-way merge path, and the cached K=10 +medium merge from the t32/r64 parent. It combines those with the t32/r32 RAG +cached merge so only the RAG final-merge fanout changes versus the current +K=10 min-tree sibling. The measured path still directly writes the +contract-visible distance and index outputs. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +from functools import lru_cache +from typing import Any +from . import knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_v1 as parent_cached +from . import knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r64_v1 as parent_cached64 +from . import knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_v1 as parent_k10 +from . import knn_build_evolve_7bfc_split_cg2_u2_v1 as parent_u2 +from . import knn_build_evolve_7bfc_split_v1 as parent_split +from . import knn_build_evolve_7bfc_v1 as base_v1 +from .._dispatch_runtime import pack_kernel_args +BLOCK_Q = parent_k10.BLOCK_Q +BLOCK_M = parent_k10.BLOCK_M +FEAT_D = parent_k10.FEAT_D +TOP_K_MAX = parent_k10.TOP_K_MAX +STAGE1_THREADS = parent_k10.STAGE1_THREADS +MERGE_THREADS = parent_k10.MERGE_THREADS +GRID_DIM_DEFAULT = parent_k10.GRID_DIM_DEFAULT +CTA_GROUP = parent_k10.CTA_GROUP +CTA_GROUP_MASK = parent_u2.CTA_GROUP_MASK +SMALL_SPLITS = parent_k10.SMALL_SPLITS +MEDIUM_SPLITS = parent_k10.MEDIUM_SPLITS +RAG_SPLITS = parent_k10.RAG_SPLITS +SMALL_SHAPE_MAX = parent_k10.SMALL_SHAPE_MAX +generic_merge_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_merge", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "B", "Q", "K", "split_count", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10]], "cta_group": 1, "threads": 256}')) +merge_k10_s4_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k10_merge_s4", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "B", "Q", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 256}')) +merge_k10_s7_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k10_merge_s7", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "B", "Q", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 7]], "cta_group": 1, "threads": 256}')) +merge_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k10_merge_s7", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "B", "Q", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 7]], "cta_group": 1, "threads": 256}')) +knn_build_evolve_7bfc_split_cg2_stage1_batch8_cond4_vmin_maxtree = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_stage1_batch8_cond4_vmin_maxtree", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) +stage1_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_stage1_batch8_cond4_vmin_maxtree", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_stage1_batch8_cond4_vmin_maxtree", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) + +def _compiled_stage1(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0002"}')) + +def _launch_cg2_split_path(inputs: dict[str, Any], *, split_count: int) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_q_tile_pairs = (num_q_tiles + CTA_GROUP - 1) // CTA_GROUP + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tile_pairs * split_count + stage1_grid = min(total_work * CTA_GROUP, GRID_DIM_DEFAULT) + merge_grid = min((bsz * n_query + MERGE_THREADS - 1) // MERGE_THREADS, GRID_DIM_DEFAULT) + partial_dists, partial_indices = parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), bsz * n_query, BLOCK_Q, dim, dim) + tmap_database = base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, dim, dim) + stage1_kernel = _compiled_stage1() + stage1_launch = stage1_kernel.prepare_launch_cluster(grid=(stage1_grid, 1, 1), block=(STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage1_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tile_pairs=num_q_tile_pairs, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), cluster_dims=(CTA_GROUP, 1, 1), shared_mem=stage1_ir.computed_smem_bytes) + merge_kernel = parent_k10._specialized_merge_kernel(top_k, split_count) + if merge_kernel is None: + merge_kernel = parent_split._compiled_merge() + merge_launch = merge_kernel.prepare_launch(grid=(merge_grid, 1, 1), block=(MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz, n_query, top_k, split_count, bsz * n_query], shared_mem=generic_merge_ir.computed_smem_bytes) + else: + merge_launch = merge_kernel.prepare_launch(grid=(merge_grid, 1, 1), block=(MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz, n_query, bsz * n_query], shared_mem=merge_ir.computed_smem_bytes) + stage1_launch.launch() + merge_launch.launch() + +def _launch_k10_cached_path(inputs: dict[str, Any], *, split_count: int, merge_threads: int, merge_kernel: Any, merge_ir: Any) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_q_tile_pairs = (num_q_tiles + CTA_GROUP - 1) // CTA_GROUP + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tile_pairs * split_count + stage1_grid = min(total_work * CTA_GROUP, GRID_DIM_DEFAULT) + merge_grid = min((bsz * n_query + merge_threads - 1) // merge_threads, GRID_DIM_DEFAULT) + partial_dists, partial_indices = parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), bsz * n_query, BLOCK_Q, dim, dim) + tmap_database = base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, dim, dim) + stage1_kernel = _compiled_stage1() + stage1_kernel.launch_cluster(grid=(stage1_grid, 1, 1), block=(STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage1_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tile_pairs=num_q_tile_pairs, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), cluster_dims=(CTA_GROUP, 1, 1), shared_mem=stage1_ir.computed_smem_bytes) + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(merge_threads, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_ir.computed_smem_bytes) + +def launch_from_contract_inputs(inputs: dict[str, Any]) -> None: + split_count = parent_k10._contract_shape_split_count(inputs) + top_k = int(inputs['K']) + if top_k == 5: + parent_cached64.launch_from_contract_inputs(inputs) + return + if split_count == MEDIUM_SPLITS: + _launch_k10_cached_path(inputs, split_count=split_count, merge_threads=parent_cached64.MEDIUM_MERGE_THREADS, merge_kernel=parent_cached64._compiled_merge_k10_s4_cache(), merge_ir=parent_cached64.merge_k10_s4_cache_ir) + return + if split_count == RAG_SPLITS: + _launch_k10_cached_path(inputs, split_count=split_count, merge_threads=parent_cached.RAG_MERGE_THREADS, merge_kernel=parent_cached._compiled_merge_k10_s7_cache(), merge_ir=parent_cached.merge_k10_s7_cache_ir) + return + parent_cached.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(candidate, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + from .._dispatch_runtime import CANONICAL_SHAPES + if shape_labels is None: + return list(CANONICAL_SHAPES) + wanted = {str(label) for label in shape_labels} + selected = [shape for shape in CANONICAL_SHAPES if shape['label'] in wanted] + missing = wanted - {shape['label'] for shape in selected} + if missing: + raise ValueError(''.join(['unknown kNN build contract shape(s): ', format(sorted(missing), '')])) + return selected + +def compile_and_launch_knn_build(*, shape_labels=('flashml_correctness_b1_q256_m256_d128_k5',), benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint: run real contract correctness for selected shapes.""" + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_v1.py new file mode 100644 index 00000000..aa33cc33 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_v1.py @@ -0,0 +1,173 @@ +"""kNN build/search K=5 min-tree sort plus cached K=10 medium/RAG merge. + +Minimum target architecture: sm_100a. This variant keeps the validated +K=5 small-shape flattened merge path and adds cached K=10 medium/RAG merge +paths on top of the batch8 vector-min max-tree parent. For the canonical K=5 small build shape +it narrows stage-1 top-K maintenance to five entries, uses vector minima, uses +a fixed five-slot max tree for worst-slot recompute after accepted candidates, +uses a fixed min tree for split-local sorted output, and uses a fixed four-way +compare tree for the four-split sorted merge. For the canonical K=10 medium +and RAG shapes it keeps the parent stage-1 path but uses four-way/seven-way +cached sorted merges that reload only the winning split cursor. The K=10 medium +merge uses a 32-thread block to raise the 4096-query build shape to 128 merge +CTAs, and the K=10 RAG merge uses a 32-thread block to raise the 10000-query +retrieval shape to 313 merge CTAs. The measured path still directly writes the +contract-visible distance and index outputs. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +from functools import lru_cache +from typing import Any +from . import knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_vmin_maxtree_v1 as parent_maxtree +from . import knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_v1 as parent_k10 +from . import knn_build_evolve_7bfc_split_cg2_u2_v1 as parent_u2 +from . import knn_build_evolve_7bfc_split_v1 as parent_split +from . import knn_build_evolve_7bfc_v1 as base_v1 +from .._dispatch_runtime import pack_kernel_args +BLOCK_Q = parent_k10.BLOCK_Q +BLOCK_M = parent_k10.BLOCK_M +FEAT_D = parent_k10.FEAT_D +TOP_K_MAX = parent_k10.TOP_K_MAX +TOP_K_SMALL = 5 +STAGE1_THREADS = parent_k10.STAGE1_THREADS +MERGE_THREADS = parent_k10.MERGE_THREADS +MEDIUM_MERGE_THREADS = 32 +RAG_MERGE_THREADS = 32 +GRID_DIM_DEFAULT = parent_k10.GRID_DIM_DEFAULT +CTA_GROUP = parent_k10.CTA_GROUP +CTA_GROUP_MASK = parent_u2.CTA_GROUP_MASK +SMALL_SPLITS = parent_k10.SMALL_SPLITS +MEDIUM_SPLITS = parent_k10.MEDIUM_SPLITS +RAG_SPLITS = parent_k10.RAG_SPLITS +knn_build_evolve_7bfc_split_cg2_stage1_batch8_cond4_vmin_threshold_k5_mintree = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_stage1_batch8_cond4_vmin_threshold_k5_mintree", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_SMALL", 5]], "cta_group": 1, "threads": 192}')) +knn_build_evolve_7bfc_k5_merge_s4_tree_rowbase = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k5_merge_s4_tree_rowbase", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_SMALL", 5]], "cta_group": 1, "threads": 256}')) +stage1_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_stage1_batch8_cond4_vmin_threshold_k5_mintree", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_SMALL", 5]], "cta_group": 1, "threads": 192}')) +merge_k5_s4_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k5_merge_s4_tree_rowbase", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_SMALL", 5]], "cta_group": 1, "threads": 256}')) +merge_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k5_merge_s4_tree_rowbase", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_SMALL", 5]], "cta_group": 1, "threads": 256}')) +knn_build_evolve_7bfc_k10_merge_s4_rowbase_cache = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k10_merge_s4_rowbase_cache", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 32}')) +knn_build_evolve_7bfc_k10_merge_s7_rowbase_cache = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k10_merge_s7_rowbase_cache", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 7]], "cta_group": 1, "threads": 32}')) +merge_k10_s4_cache_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k10_merge_s4_rowbase_cache", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 32}')) +merge_k10_s7_cache_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k10_merge_s7_rowbase_cache", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 7]], "cta_group": 1, "threads": 32}')) +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k10_merge_s4_rowbase_cache", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 32}')) + +def _compiled_stage1(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0144"}')) + +def _compiled_merge_k5_s4(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0145"}')) + +def _compiled_merge_k10_s4_cache(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0146"}')) + +def _compiled_merge_k10_s7_cache(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0007"}')) + +def _launch_cg2_split_path(inputs: dict[str, Any], *, split_count: int) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_q_tile_pairs = (num_q_tiles + CTA_GROUP - 1) // CTA_GROUP + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tile_pairs * split_count + stage1_grid = min(total_work * CTA_GROUP, GRID_DIM_DEFAULT) + merge_grid = min((bsz * n_query + MERGE_THREADS - 1) // MERGE_THREADS, GRID_DIM_DEFAULT) + partial_dists, partial_indices = parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), bsz * n_query, BLOCK_Q, dim, dim) + tmap_database = base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, dim, dim) + stage1_kernel = _compiled_stage1() + stage1_kernel.launch_cluster(grid=(stage1_grid, 1, 1), block=(STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage1_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tile_pairs=num_q_tile_pairs, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), cluster_dims=(CTA_GROUP, 1, 1), shared_mem=stage1_ir.computed_smem_bytes) + merge_kernel = _compiled_merge_k5_s4() + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_k5_s4_ir.computed_smem_bytes) + +def _launch_k10_medium_cached_merge_path(inputs: dict[str, Any], *, split_count: int) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_q_tile_pairs = (num_q_tiles + CTA_GROUP - 1) // CTA_GROUP + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tile_pairs * split_count + stage1_grid = min(total_work * CTA_GROUP, GRID_DIM_DEFAULT) + merge_grid = min((bsz * n_query + MEDIUM_MERGE_THREADS - 1) // MEDIUM_MERGE_THREADS, GRID_DIM_DEFAULT) + partial_dists, partial_indices = parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), bsz * n_query, BLOCK_Q, dim, dim) + tmap_database = base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, dim, dim) + stage1_kernel = parent_maxtree._compiled_stage1() + merge_kernel = _compiled_merge_k10_s4_cache() + stage1_kernel.launch_cluster(grid=(stage1_grid, 1, 1), block=(STAGE1_THREADS, 1, 1), args=pack_kernel_args(parent_maxtree.stage1_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tile_pairs=num_q_tile_pairs, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), cluster_dims=(CTA_GROUP, 1, 1), shared_mem=parent_maxtree.stage1_ir.computed_smem_bytes) + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(MEDIUM_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_k10_s4_cache_ir.computed_smem_bytes) + +def _launch_k10_rag_cached_merge_path(inputs: dict[str, Any], *, split_count: int) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_q_tile_pairs = (num_q_tiles + CTA_GROUP - 1) // CTA_GROUP + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tile_pairs * split_count + stage1_grid = min(total_work * CTA_GROUP, GRID_DIM_DEFAULT) + merge_grid = min((bsz * n_query + RAG_MERGE_THREADS - 1) // RAG_MERGE_THREADS, GRID_DIM_DEFAULT) + partial_dists, partial_indices = parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), bsz * n_query, BLOCK_Q, dim, dim) + tmap_database = base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, dim, dim) + stage1_kernel = parent_maxtree._compiled_stage1() + stage1_kernel.launch_cluster(grid=(stage1_grid, 1, 1), block=(STAGE1_THREADS, 1, 1), args=pack_kernel_args(parent_maxtree.stage1_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tile_pairs=num_q_tile_pairs, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), cluster_dims=(CTA_GROUP, 1, 1), shared_mem=parent_maxtree.stage1_ir.computed_smem_bytes) + merge_kernel = _compiled_merge_k10_s7_cache() + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(RAG_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_k10_s7_cache_ir.computed_smem_bytes) + +def launch_from_contract_inputs(inputs: dict[str, Any]) -> None: + split_count = parent_k10._contract_shape_split_count(inputs) + if split_count == SMALL_SPLITS and int(inputs['K']) == TOP_K_SMALL: + _launch_cg2_split_path(inputs, split_count=split_count) + return + if split_count == MEDIUM_SPLITS and int(inputs['K']) == TOP_K_MAX: + _launch_k10_medium_cached_merge_path(inputs, split_count=split_count) + return + if split_count == RAG_SPLITS and int(inputs['K']) == TOP_K_MAX: + _launch_k10_rag_cached_merge_path(inputs, split_count=split_count) + return + parent_maxtree.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(candidate, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + from .._dispatch_runtime import CANONICAL_SHAPES + if shape_labels is None: + return list(CANONICAL_SHAPES) + wanted = {str(label) for label in shape_labels} + selected = [shape for shape in CANONICAL_SHAPES if shape['label'] in wanted] + missing = wanted - {shape['label'] for shape in selected} + if missing: + raise ValueError(''.join(['unknown kNN build contract shape(s): ', format(sorted(missing), '')])) + return selected + +def compile_and_launch_knn_build(*, shape_labels=('flashml_correctness_b1_q256_m256_d128_k5',), benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint: run real contract correctness for selected shapes.""" + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r64_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r64_v1.py new file mode 100644 index 00000000..df03e2fb --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r64_v1.py @@ -0,0 +1,173 @@ +"""kNN build/search K=5 min-tree sort plus cached K=10 medium/RAG merge. + +Minimum target architecture: sm_100a. This variant keeps the validated +K=5 small-shape flattened merge path and adds cached K=10 medium/RAG merge +paths on top of the batch8 vector-min max-tree parent. For the canonical K=5 small build shape +it narrows stage-1 top-K maintenance to five entries, uses vector minima, uses +a fixed five-slot max tree for worst-slot recompute after accepted candidates, +uses a fixed min tree for split-local sorted output, and uses a fixed four-way +compare tree for the four-split sorted merge. For the canonical K=10 medium +and RAG shapes it keeps the parent stage-1 path but uses four-way/seven-way +cached sorted merges that reload only the winning split cursor. The K=10 medium +merge uses a 32-thread block to raise the 4096-query build shape to 128 merge +CTAs, and the K=10 RAG merge uses a 64-thread block to raise the 10000-query +retrieval shape to 157 merge CTAs. The measured path still directly writes the +contract-visible distance and index outputs. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +from functools import lru_cache +from typing import Any +from . import knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_vmin_maxtree_v1 as parent_maxtree +from . import knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_v1 as parent_k10 +from . import knn_build_evolve_7bfc_split_cg2_u2_v1 as parent_u2 +from . import knn_build_evolve_7bfc_split_v1 as parent_split +from . import knn_build_evolve_7bfc_v1 as base_v1 +from .._dispatch_runtime import pack_kernel_args +BLOCK_Q = parent_k10.BLOCK_Q +BLOCK_M = parent_k10.BLOCK_M +FEAT_D = parent_k10.FEAT_D +TOP_K_MAX = parent_k10.TOP_K_MAX +TOP_K_SMALL = 5 +STAGE1_THREADS = parent_k10.STAGE1_THREADS +MERGE_THREADS = parent_k10.MERGE_THREADS +MEDIUM_MERGE_THREADS = 32 +RAG_MERGE_THREADS = 64 +GRID_DIM_DEFAULT = parent_k10.GRID_DIM_DEFAULT +CTA_GROUP = parent_k10.CTA_GROUP +CTA_GROUP_MASK = parent_u2.CTA_GROUP_MASK +SMALL_SPLITS = parent_k10.SMALL_SPLITS +MEDIUM_SPLITS = parent_k10.MEDIUM_SPLITS +RAG_SPLITS = parent_k10.RAG_SPLITS +knn_build_evolve_7bfc_split_cg2_stage1_batch8_cond4_vmin_threshold_k5_mintree = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_stage1_batch8_cond4_vmin_threshold_k5_mintree", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_SMALL", 5]], "cta_group": 1, "threads": 192}')) +knn_build_evolve_7bfc_k5_merge_s4_tree_rowbase = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k5_merge_s4_tree_rowbase", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_SMALL", 5]], "cta_group": 1, "threads": 256}')) +stage1_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_stage1_batch8_cond4_vmin_threshold_k5_mintree", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_SMALL", 5]], "cta_group": 1, "threads": 192}')) +merge_k5_s4_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k5_merge_s4_tree_rowbase", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_SMALL", 5]], "cta_group": 1, "threads": 256}')) +merge_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k5_merge_s4_tree_rowbase", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_SMALL", 5]], "cta_group": 1, "threads": 256}')) +knn_build_evolve_7bfc_k10_merge_s4_rowbase_cache = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k10_merge_s4_rowbase_cache", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 32}')) +knn_build_evolve_7bfc_k10_merge_s7_rowbase_cache = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k10_merge_s7_rowbase_cache", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 7]], "cta_group": 1, "threads": 64}')) +merge_k10_s4_cache_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k10_merge_s4_rowbase_cache", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 32}')) +merge_k10_s7_cache_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k10_merge_s7_rowbase_cache", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 7]], "cta_group": 1, "threads": 64}')) +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k10_merge_s4_rowbase_cache", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 32}')) + +def _compiled_stage1(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0140"}')) + +def _compiled_merge_k5_s4(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0141"}')) + +def _compiled_merge_k10_s4_cache(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0006"}')) + +def _compiled_merge_k10_s7_cache(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0142"}')) + +def _launch_cg2_split_path(inputs: dict[str, Any], *, split_count: int) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_q_tile_pairs = (num_q_tiles + CTA_GROUP - 1) // CTA_GROUP + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tile_pairs * split_count + stage1_grid = min(total_work * CTA_GROUP, GRID_DIM_DEFAULT) + merge_grid = min((bsz * n_query + MERGE_THREADS - 1) // MERGE_THREADS, GRID_DIM_DEFAULT) + partial_dists, partial_indices = parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), bsz * n_query, BLOCK_Q, dim, dim) + tmap_database = base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, dim, dim) + stage1_kernel = _compiled_stage1() + stage1_kernel.launch_cluster(grid=(stage1_grid, 1, 1), block=(STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage1_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tile_pairs=num_q_tile_pairs, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), cluster_dims=(CTA_GROUP, 1, 1), shared_mem=stage1_ir.computed_smem_bytes) + merge_kernel = _compiled_merge_k5_s4() + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_k5_s4_ir.computed_smem_bytes) + +def _launch_k10_medium_cached_merge_path(inputs: dict[str, Any], *, split_count: int) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_q_tile_pairs = (num_q_tiles + CTA_GROUP - 1) // CTA_GROUP + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tile_pairs * split_count + stage1_grid = min(total_work * CTA_GROUP, GRID_DIM_DEFAULT) + merge_grid = min((bsz * n_query + MEDIUM_MERGE_THREADS - 1) // MEDIUM_MERGE_THREADS, GRID_DIM_DEFAULT) + partial_dists, partial_indices = parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), bsz * n_query, BLOCK_Q, dim, dim) + tmap_database = base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, dim, dim) + stage1_kernel = parent_maxtree._compiled_stage1() + merge_kernel = _compiled_merge_k10_s4_cache() + stage1_kernel.launch_cluster(grid=(stage1_grid, 1, 1), block=(STAGE1_THREADS, 1, 1), args=pack_kernel_args(parent_maxtree.stage1_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tile_pairs=num_q_tile_pairs, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), cluster_dims=(CTA_GROUP, 1, 1), shared_mem=parent_maxtree.stage1_ir.computed_smem_bytes) + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(MEDIUM_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_k10_s4_cache_ir.computed_smem_bytes) + +def _launch_k10_rag_cached_merge_path(inputs: dict[str, Any], *, split_count: int) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_q_tile_pairs = (num_q_tiles + CTA_GROUP - 1) // CTA_GROUP + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tile_pairs * split_count + stage1_grid = min(total_work * CTA_GROUP, GRID_DIM_DEFAULT) + merge_grid = min((bsz * n_query + RAG_MERGE_THREADS - 1) // RAG_MERGE_THREADS, GRID_DIM_DEFAULT) + partial_dists, partial_indices = parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), bsz * n_query, BLOCK_Q, dim, dim) + tmap_database = base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, dim, dim) + stage1_kernel = parent_maxtree._compiled_stage1() + stage1_kernel.launch_cluster(grid=(stage1_grid, 1, 1), block=(STAGE1_THREADS, 1, 1), args=pack_kernel_args(parent_maxtree.stage1_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tile_pairs=num_q_tile_pairs, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), cluster_dims=(CTA_GROUP, 1, 1), shared_mem=parent_maxtree.stage1_ir.computed_smem_bytes) + merge_kernel = _compiled_merge_k10_s7_cache() + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(RAG_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_k10_s7_cache_ir.computed_smem_bytes) + +def launch_from_contract_inputs(inputs: dict[str, Any]) -> None: + split_count = parent_k10._contract_shape_split_count(inputs) + if split_count == SMALL_SPLITS and int(inputs['K']) == TOP_K_SMALL: + _launch_cg2_split_path(inputs, split_count=split_count) + return + if split_count == MEDIUM_SPLITS and int(inputs['K']) == TOP_K_MAX: + _launch_k10_medium_cached_merge_path(inputs, split_count=split_count) + return + if split_count == RAG_SPLITS and int(inputs['K']) == TOP_K_MAX: + _launch_k10_rag_cached_merge_path(inputs, split_count=split_count) + return + parent_maxtree.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(candidate, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + from .._dispatch_runtime import CANONICAL_SHAPES + if shape_labels is None: + return list(CANONICAL_SHAPES) + wanted = {str(label) for label in shape_labels} + selected = [shape for shape in CANONICAL_SHAPES if shape['label'] in wanted] + missing = wanted - {shape['label'] for shape in selected} + if missing: + raise ValueError(''.join(['unknown kNN build contract shape(s): ', format(sorted(missing), '')])) + return selected + +def compile_and_launch_knn_build(*, shape_labels=('flashml_correctness_b1_q256_m256_d128_k5',), benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint: run real contract correctness for selected shapes.""" + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_v1.py new file mode 100644 index 00000000..0ac1a8cc --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_v1.py @@ -0,0 +1,101 @@ +"""kNN build/search K=5 four-way merge tree plus K=5/K=10 max-tree candidate. + +Minimum target architecture: sm_100a. This variant keeps the validated +K=5 small-shape flattened merge path and delegates K=10 medium/RAG shapes to +the batch8 vector-min max-tree parent. For the canonical K=5 small build shape +it narrows stage-1 top-K maintenance to five entries, uses vector minima, uses +a fixed five-slot max tree for worst-slot recompute after accepted candidates, +and uses a fixed four-way compare tree for the four-split sorted merge. The +measured path still directly writes the contract-visible distance and index +outputs. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +from functools import lru_cache +from typing import Any +from . import knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_vmin_maxtree_v1 as parent_maxtree +from . import knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_v1 as parent_k10 +from . import knn_build_evolve_7bfc_split_cg2_u2_v1 as parent_u2 +from . import knn_build_evolve_7bfc_split_v1 as parent_split +from . import knn_build_evolve_7bfc_v1 as base_v1 +from .._dispatch_runtime import pack_kernel_args +BLOCK_Q = parent_k10.BLOCK_Q +BLOCK_M = parent_k10.BLOCK_M +FEAT_D = parent_k10.FEAT_D +TOP_K_SMALL = 5 +STAGE1_THREADS = parent_k10.STAGE1_THREADS +MERGE_THREADS = parent_k10.MERGE_THREADS +GRID_DIM_DEFAULT = parent_k10.GRID_DIM_DEFAULT +CTA_GROUP = parent_k10.CTA_GROUP +CTA_GROUP_MASK = parent_u2.CTA_GROUP_MASK +SMALL_SPLITS = parent_k10.SMALL_SPLITS +knn_build_evolve_7bfc_split_cg2_stage1_batch8_cond4_vmin_threshold_k5 = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_stage1_batch8_cond4_vmin_threshold_k5", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_SMALL", 5]], "cta_group": 1, "threads": 192}')) +knn_build_evolve_7bfc_k5_merge_s4_tree = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k5_merge_s4_tree", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "B", "Q", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_SMALL", 5]], "cta_group": 1, "threads": 256}')) +stage1_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_stage1_batch8_cond4_vmin_threshold_k5", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_SMALL", 5]], "cta_group": 1, "threads": 192}')) +merge_k5_s4_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k5_merge_s4_tree", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "B", "Q", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_SMALL", 5]], "cta_group": 1, "threads": 256}')) +merge_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k5_merge_s4_tree", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "B", "Q", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_SMALL", 5]], "cta_group": 1, "threads": 256}')) +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_stage1_batch8_cond4_vmin_threshold_k5", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_SMALL", 5]], "cta_group": 1, "threads": 192}')) + +def _compiled_stage1(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0000"}')) + +def _compiled_merge_k5_s4(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0001"}')) + +def _launch_cg2_split_path(inputs: dict[str, Any], *, split_count: int) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_q_tile_pairs = (num_q_tiles + CTA_GROUP - 1) // CTA_GROUP + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tile_pairs * split_count + stage1_grid = min(total_work * CTA_GROUP, GRID_DIM_DEFAULT) + merge_grid = min((bsz * n_query + MERGE_THREADS - 1) // MERGE_THREADS, GRID_DIM_DEFAULT) + partial_dists, partial_indices = parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), bsz * n_query, BLOCK_Q, dim, dim) + tmap_database = base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, dim, dim) + stage1_kernel = _compiled_stage1() + stage1_kernel.launch_cluster(grid=(stage1_grid, 1, 1), block=(STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage1_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tile_pairs=num_q_tile_pairs, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), cluster_dims=(CTA_GROUP, 1, 1), shared_mem=stage1_ir.computed_smem_bytes) + merge_kernel = _compiled_merge_k5_s4() + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz, n_query, bsz * n_query], shared_mem=merge_k5_s4_ir.computed_smem_bytes) + +def launch_from_contract_inputs(inputs: dict[str, Any]) -> None: + split_count = parent_k10._contract_shape_split_count(inputs) + if split_count == SMALL_SPLITS and int(inputs['K']) == TOP_K_SMALL: + _launch_cg2_split_path(inputs, split_count=split_count) + return + parent_maxtree.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(candidate, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + from .._dispatch_runtime import CANONICAL_SHAPES + if shape_labels is None: + return list(CANONICAL_SHAPES) + wanted = {str(label) for label in shape_labels} + selected = [shape for shape in CANONICAL_SHAPES if shape['label'] in wanted] + missing = wanted - {shape['label'] for shape in selected} + if missing: + raise ValueError(''.join(['unknown kNN build contract shape(s): ', format(sorted(missing), '')])) + return selected + +def compile_and_launch_knn_build(*, shape_labels=('flashml_correctness_b1_q256_m256_d128_k5',), benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint: run real contract correctness for selected shapes.""" + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_vmin_maxtree_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_vmin_maxtree_v1.py new file mode 100644 index 00000000..6ce979e2 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_vmin_maxtree_v1.py @@ -0,0 +1,104 @@ +"""kNN build/search stage-1 batch8 vector-min max-tree candidate. + +Minimum target architecture: sm_100a. This variant keeps the validated +round-16 CTA-group=2 split routing, K=10 sorted merge, and the round-20 +batch8 conditional-four top-K maintenance. It changes the two four-lane +admission thresholds to RegArrayReduce vector minima and recomputes the +unordered top-K worst slot with a fixed compare tree after each accepted +candidate. The stage-2 merge still consumes sorted per-split lists, so the +contract-visible outputs and merge semantics are unchanged. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +from functools import lru_cache +from typing import Any +from . import knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_v1 as parent_k10 +from . import knn_build_evolve_7bfc_split_cg2_u2_v1 as parent_u2 +from . import knn_build_evolve_7bfc_split_v1 as parent_split +from . import knn_build_evolve_7bfc_v1 as base_v1 +from .._dispatch_runtime import pack_kernel_args +BLOCK_Q = parent_k10.BLOCK_Q +BLOCK_M = parent_k10.BLOCK_M +FEAT_D = parent_k10.FEAT_D +TOP_K_MAX = parent_k10.TOP_K_MAX +STAGE1_THREADS = parent_k10.STAGE1_THREADS +MERGE_THREADS = parent_k10.MERGE_THREADS +GRID_DIM_DEFAULT = parent_k10.GRID_DIM_DEFAULT +CTA_GROUP = parent_k10.CTA_GROUP +CTA_GROUP_MASK = parent_u2.CTA_GROUP_MASK +SMALL_SPLITS = parent_k10.SMALL_SPLITS +MEDIUM_SPLITS = parent_k10.MEDIUM_SPLITS +RAG_SPLITS = parent_k10.RAG_SPLITS +SMALL_SHAPE_MAX = parent_k10.SMALL_SHAPE_MAX +generic_merge_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_merge", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "B", "Q", "K", "split_count", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10]], "cta_group": 1, "threads": 256}')) +merge_k10_s4_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k10_merge_s4", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "B", "Q", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 256}')) +merge_k10_s7_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k10_merge_s7", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "B", "Q", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 7]], "cta_group": 1, "threads": 256}')) +merge_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k10_merge_s7", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "B", "Q", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 7]], "cta_group": 1, "threads": 256}')) +knn_build_evolve_7bfc_split_cg2_stage1_batch8_cond4_vmin_maxtree = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_stage1_batch8_cond4_vmin_maxtree", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) +stage1_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_stage1_batch8_cond4_vmin_maxtree", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_stage1_batch8_cond4_vmin_maxtree", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) + +def _compiled_stage1(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0143"}')) + +def _launch_cg2_split_path(inputs: dict[str, Any], *, split_count: int) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_q_tile_pairs = (num_q_tiles + CTA_GROUP - 1) // CTA_GROUP + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tile_pairs * split_count + stage1_grid = min(total_work * CTA_GROUP, GRID_DIM_DEFAULT) + merge_grid = min((bsz * n_query + MERGE_THREADS - 1) // MERGE_THREADS, GRID_DIM_DEFAULT) + partial_dists, partial_indices = parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), bsz * n_query, BLOCK_Q, dim, dim) + tmap_database = base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, dim, dim) + stage1_kernel = _compiled_stage1() + stage1_kernel.launch_cluster(grid=(stage1_grid, 1, 1), block=(STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage1_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tile_pairs=num_q_tile_pairs, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), cluster_dims=(CTA_GROUP, 1, 1), shared_mem=stage1_ir.computed_smem_bytes) + merge_kernel = parent_k10._specialized_merge_kernel(top_k, split_count) + if merge_kernel is None: + merge_kernel = parent_split._compiled_merge() + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz, n_query, top_k, split_count, bsz * n_query], shared_mem=generic_merge_ir.computed_smem_bytes) + return + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz, n_query, bsz * n_query], shared_mem=merge_ir.computed_smem_bytes) + +def launch_from_contract_inputs(inputs: dict[str, Any]) -> None: + split_count = parent_k10._contract_shape_split_count(inputs) + if split_count is not None: + _launch_cg2_split_path(inputs, split_count=split_count) + return + parent_k10.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(candidate, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + from .._dispatch_runtime import CANONICAL_SHAPES + if shape_labels is None: + return list(CANONICAL_SHAPES) + wanted = {str(label) for label in shape_labels} + selected = [shape for shape in CANONICAL_SHAPES if shape['label'] in wanted] + missing = wanted - {shape['label'] for shape in selected} + if missing: + raise ValueError(''.join(['unknown kNN build contract shape(s): ', format(sorted(missing), '')])) + return selected + +def compile_and_launch_knn_build(*, shape_labels=('flashml_correctness_b1_q256_m256_d128_k5',), benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint: run real contract correctness for selected shapes.""" + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_v1.py new file mode 100644 index 00000000..0378ee84 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_v1.py @@ -0,0 +1,141 @@ +"""kNN build/search CTA-group=2 RAG split-7 plus K=10 sorted-merge candidate. + +Minimum target architecture: sm_100a. This variant keeps the validated +round-15 stage-1 path and launch routing, but specializes the K=10 merge used +by the medium and RAG contract shapes. Each database split already writes a +sorted local top-10, so the specialized merge performs a fixed 4-way or 7-way +sorted merge instead of reinserting all split candidates into a generic top-K +array. The K=5 small build shape continues to use the proven generic merge. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +from functools import lru_cache +from typing import Any +from . import knn_build_evolve_7bfc_split_cg2_u2_v1 as parent_u2 +from . import knn_build_evolve_7bfc_split_v1 as parent_split +from . import knn_build_evolve_7bfc_v1 as base_v1 +from .._dispatch_runtime import pack_kernel_args +BLOCK_Q = parent_u2.BLOCK_Q +BLOCK_M = parent_u2.BLOCK_M +FEAT_D = parent_u2.FEAT_D +TOP_K_MAX = parent_u2.TOP_K_MAX +STAGE1_THREADS = parent_u2.STAGE1_THREADS +MERGE_THREADS = parent_u2.MERGE_THREADS +GRID_DIM_DEFAULT = parent_u2.GRID_DIM_DEFAULT +CTA_GROUP = parent_u2.CTA_GROUP +SMALL_SPLITS = 4 +MEDIUM_SPLITS = parent_split.MEDIUM_SPLITS +RAG_SPLITS = 7 +SMALL_SHAPE_MAX = 512 +stage1_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) +generic_merge_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_merge", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "B", "Q", "K", "split_count", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10]], "cta_group": 1, "threads": 256}')) +knn_build_evolve_7bfc_k10_merge_s4 = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k10_merge_s4", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "B", "Q", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 256}')) +knn_build_evolve_7bfc_k10_merge_s7 = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k10_merge_s7", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "B", "Q", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 7]], "cta_group": 1, "threads": 256}')) +merge_k10_s4_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k10_merge_s4", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "B", "Q", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 256}')) +merge_k10_s7_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k10_merge_s7", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "B", "Q", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 7]], "cta_group": 1, "threads": 256}')) +merge_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k10_merge_s7", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "B", "Q", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 7]], "cta_group": 1, "threads": 256}')) +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k10_merge_s7", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "B", "Q", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 7]], "cta_group": 1, "threads": 256}')) + +def _eligible_bf16_contract_shape(inputs: dict[str, Any]) -> bool: + return str(inputs['query'].dtype) == 'torch.bfloat16' and str(inputs['database'].dtype) == 'torch.bfloat16' and (int(inputs['D']) == FEAT_D) and (int(inputs['K']) <= TOP_K_MAX) + +def _contract_shape_split_count(inputs: dict[str, Any]) -> int | None: + if not _eligible_bf16_contract_shape(inputs): + return None + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + if bool(inputs.get('build', False)): + if n_query <= SMALL_SHAPE_MAX and n_database <= SMALL_SHAPE_MAX: + return SMALL_SPLITS + if n_query == 4096 and n_database == 4096: + return MEDIUM_SPLITS + return None + if n_query == 10000 and n_database == 100000: + return RAG_SPLITS + return None + +def _compile_ir(ir_obj): + from .._dispatch_runtime import generate_kernel + from .._dispatch_runtime import _cuda_include_dirs + from .._dispatch_runtime import compile_cuda + from .._dispatch_runtime import CUDAKernel + source = generate_kernel(ir_obj, validate=False, smem_bytes=ir_obj.computed_smem_bytes) + cubin = compile_cuda(source, arch=base_v1._select_arch_and_preload(), options=['--use_fast_math'], include_dirs=_cuda_include_dirs()) + return CUDAKernel(cubin, ''.join(['kernel_', format(ir_obj.symbol, '')])) + +def _compiled_merge_k10_s4(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0138"}')) + +def _compiled_merge_k10_s7(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0139"}')) + +def _specialized_merge_kernel(top_k: int, split_count: int): + if top_k != TOP_K_MAX: + return None + if split_count == MEDIUM_SPLITS: + return _compiled_merge_k10_s4() + if split_count == RAG_SPLITS: + return _compiled_merge_k10_s7() + return None + +def _launch_cg2_split_path(inputs: dict[str, Any], *, split_count: int) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_q_tile_pairs = (num_q_tiles + CTA_GROUP - 1) // CTA_GROUP + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tile_pairs * split_count + stage1_grid = min(total_work * CTA_GROUP, GRID_DIM_DEFAULT) + merge_grid = min((bsz * n_query + MERGE_THREADS - 1) // MERGE_THREADS, GRID_DIM_DEFAULT) + partial_dists, partial_indices = parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), bsz * n_query, BLOCK_Q, dim, dim) + tmap_database = base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, dim, dim) + stage1_kernel = parent_u2._compiled_stage1() + stage1_kernel.launch_cluster(grid=(stage1_grid, 1, 1), block=(STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage1_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tile_pairs=num_q_tile_pairs, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), cluster_dims=(CTA_GROUP, 1, 1), shared_mem=stage1_ir.computed_smem_bytes) + merge_kernel = _specialized_merge_kernel(top_k, split_count) + if merge_kernel is None: + merge_kernel = parent_split._compiled_merge() + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz, n_query, top_k, split_count, bsz * n_query], shared_mem=generic_merge_ir.computed_smem_bytes) + return + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz, n_query, bsz * n_query], shared_mem=merge_ir.computed_smem_bytes) + +def launch_from_contract_inputs(inputs: dict[str, Any]) -> None: + split_count = _contract_shape_split_count(inputs) + if split_count is not None: + _launch_cg2_split_path(inputs, split_count=split_count) + return + parent_u2.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(candidate, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + from .._dispatch_runtime import CANONICAL_SHAPES + if shape_labels is None: + return list(CANONICAL_SHAPES) + wanted = {str(label) for label in shape_labels} + selected = [shape for shape in CANONICAL_SHAPES if shape['label'] in wanted] + missing = wanted - {shape['label'] for shape in selected} + if missing: + raise ValueError(''.join(['unknown kNN build contract shape(s): ', format(sorted(missing), '')])) + return selected + +def compile_and_launch_knn_build(*, shape_labels=('flashml_correctness_b1_q256_m256_d128_k5',), benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint: run real contract correctness for selected shapes.""" + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_evolve_7bfc_split_cg2_u2_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_evolve_7bfc_split_cg2_u2_v1.py new file mode 100644 index 00000000..8a9a7c5d --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_evolve_7bfc_split_cg2_u2_v1.py @@ -0,0 +1,76 @@ +"""kNN build/search database-split CTA-group=2, consumer unroll=2 candidate. + +Minimum target architecture: sm_100a. This variant keeps the exact split/merge +contract from ``knn_build_evolve_7bfc_split_cg2_v1`` and changes only the +stage-1 scalar consumer loop over the 64-column TMEM fragment: + + stage 1: one 2-CTA cluster streams one pair of 128-row query tiles for one + database split, with B broadcast/identical across the CTA pair + stage 2: reuse the validated scalar merge kernel from split_v1 + +Small/medium contract shapes delegate to split_v1 so this experiment isolates +the RAG hot-path loop unroll point. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +from functools import lru_cache +from typing import Any +from . import knn_build_evolve_7bfc_split_v1 as parent_split +from . import knn_build_evolve_7bfc_v1 as base_v1 +from .._dispatch_runtime import pack_kernel_args +BLOCK_Q = parent_split.BLOCK_Q +BLOCK_M = parent_split.BLOCK_M +FEAT_D = parent_split.FEAT_D +TOP_K_MAX = parent_split.TOP_K_MAX +STAGE1_THREADS = parent_split.STAGE1_THREADS +MERGE_THREADS = parent_split.MERGE_THREADS +GRID_DIM_DEFAULT = parent_split.GRID_DIM_DEFAULT +RAG_SPLITS = parent_split.RAG_SPLITS +CTA_GROUP = 2 +CTA_GROUP_MASK = 3 +knn_build_evolve_7bfc_split_cg2_u2_stage1 = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) +stage1_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) +merge_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_merge", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "B", "Q", "K", "split_count", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10]], "cta_group": 1, "threads": 256}')) +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) + +def _compiled_stage1(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0137"}')) + +def _use_cg2_path(inputs: dict[str, Any]) -> bool: + return str(inputs['query'].dtype) == 'torch.bfloat16' and str(inputs['database'].dtype) == 'torch.bfloat16' and (int(inputs['D']) == FEAT_D) and (int(inputs['K']) <= TOP_K_MAX) and (int(inputs['M']) >= 100000) and (int(inputs['Q']) >= 10000) + +def launch_from_contract_inputs(inputs: dict[str, Any]) -> None: + if not _use_cg2_path(inputs): + parent_split.launch_from_contract_inputs(inputs) + return + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + split_count = RAG_SPLITS + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_q_tile_pairs = (num_q_tiles + CTA_GROUP - 1) // CTA_GROUP + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tile_pairs * split_count + stage1_grid = min(total_work * CTA_GROUP, GRID_DIM_DEFAULT) + merge_grid = min((bsz * n_query + MERGE_THREADS - 1) // MERGE_THREADS, GRID_DIM_DEFAULT) + partial_dists, partial_indices = parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), bsz * n_query, BLOCK_Q, dim, dim) + tmap_database = base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, dim, dim) + stage1_kernel = _compiled_stage1() + stage1_kernel.launch_cluster(grid=(stage1_grid, 1, 1), block=(STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage1_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tile_pairs=num_q_tile_pairs, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), cluster_dims=(CTA_GROUP, 1, 1), shared_mem=stage1_ir.computed_smem_bytes) + merge_kernel = parent_split._compiled_merge() + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz, n_query, top_k, split_count, bsz * n_query], shared_mem=merge_ir.computed_smem_bytes) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(candidate, shapes=shapes, correctness=correctness, benchmark=benchmark) diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_evolve_7bfc_split_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_evolve_7bfc_split_v1.py new file mode 100644 index 00000000..a8701caa --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_evolve_7bfc_split_v1.py @@ -0,0 +1,98 @@ +"""kNN build/search database-split candidate. + +Minimum target architecture: sm_100a. This candidate keeps the v1 +128x64x128 tcgen05 dot tile, but exposes the database axis as independent +work for the large RAG contract shape: + + stage 1: one CTA streams one (query tile, database split) and writes + partial top-k candidates + stage 2: one scalar merge kernel combines split*K candidates per query + +Small and medium contract shapes delegate to the already validated v1 path so +this experiment is isolated to the under-parallelized RAG shape. The RAG path +uses fewer database splits and launches all split work to keep SMs occupied +while reducing the scalar merge burden. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +from functools import lru_cache +from typing import Any +from . import knn_build_evolve_7bfc_v1 as base_v1 +from .._dispatch_runtime import pack_kernel_args +BLOCK_Q = 128 +BLOCK_M = 64 +FEAT_D = 128 +TOP_K_MAX = 10 +STAGE1_THREADS = 192 +MERGE_THREADS = 256 +GRID_DIM_DEFAULT = 2048 +RAG_SPLITS = 9 +MEDIUM_SPLITS = 4 +_PARTIAL_CACHE: dict[tuple[str, int, int, int, int, int, int], tuple[Any, Any]] = {} +knn_build_evolve_7bfc_split_stage1 = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) +knn_build_evolve_7bfc_split_merge = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_merge", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "B", "Q", "K", "split_count", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10]], "cta_group": 1, "threads": 256}')) +stage1_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) +merge_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_merge", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "B", "Q", "K", "split_count", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10]], "cta_group": 1, "threads": 256}')) +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) + +def _compiled_stage1(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0136"}')) + +def _compiled_merge(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0003"}')) + +def _partial_buffers(*, split_count: int, bsz: int, n_query: int, top_k: int, device) -> tuple[Any, Any]: + import torch + index = device.index + if index is None and device.type == 'cuda': + index = torch.cuda.current_device() + stream_handle = int(torch.cuda.current_stream(index).cuda_stream) + key = (device.type, int(index or 0), stream_handle, int(split_count), int(bsz), int(n_query), int(top_k)) + cached = _PARTIAL_CACHE.get(key) + if cached is None: + cached = (torch.empty((split_count, bsz, n_query, top_k), dtype=torch.float32, device=device), torch.empty((split_count, bsz, n_query, top_k), dtype=torch.int32, device=device)) + _PARTIAL_CACHE[key] = cached + return cached + +def _use_split_path(inputs: dict[str, Any]) -> bool: + return str(inputs['query'].dtype) == 'torch.bfloat16' and str(inputs['database'].dtype) == 'torch.bfloat16' and (int(inputs['D']) == FEAT_D) and (int(inputs['K']) <= TOP_K_MAX) and (int(inputs['M']) >= 4096) and (int(inputs['Q']) >= 4096) + +def _split_count_for_shape(n_query: int, n_database: int) -> int: + if n_query <= 4096 and n_database <= 4096: + return MEDIUM_SPLITS + return RAG_SPLITS + +def launch_from_contract_inputs(inputs: dict[str, Any]) -> None: + if not _use_split_path(inputs): + base_v1.launch_from_contract_inputs(inputs) + return + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + split_count = _split_count_for_shape(n_query, n_database) + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tiles * split_count + stage1_grid = min(total_work, GRID_DIM_DEFAULT) + merge_grid = min((bsz * n_query + MERGE_THREADS - 1) // MERGE_THREADS, GRID_DIM_DEFAULT) + partial_dists, partial_indices = _partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), bsz * n_query, BLOCK_Q, dim, dim) + tmap_database = base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, dim, dim) + stage1_kernel = _compiled_stage1() + stage1_kernel.launch(grid=(stage1_grid, 1, 1), block=(STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage1_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tiles=num_q_tiles, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), shared_mem=stage1_ir.computed_smem_bytes) + merge_kernel = _compiled_merge() + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz, n_query, top_k, split_count, bsz * n_query], shared_mem=merge_ir.computed_smem_bytes) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(candidate, shapes=shapes, correctness=correctness, benchmark=benchmark) diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_evolve_7bfc_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_evolve_7bfc_v1.py new file mode 100644 index 00000000..1150b875 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_evolve_7bfc_v1.py @@ -0,0 +1,102 @@ +"""kNN build/search clean-start candidate v1. + +Minimum target architecture: sm_100a. This candidate computes exact squared +L2 top-k for the contract path with a tcgen05 dot tile: + + dist(q, m) = ||q||^2 + ||x_m||^2 - 2 * dot(q, x_m) + +The fast path uses a 128x64x128 BF16 MMA tile and per-query register top-k +state for K<=10. The host fallback can compile the same IR with a larger +register top-k capacity for K<=32. It does not materialize the dense [Q, M] +distance matrix. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +from functools import lru_cache +from typing import Any +from .._dispatch_runtime import dc as dc +from .._dispatch_runtime import pack_kernel_args +BLOCK_Q = 128 +BLOCK_M = 64 +FEAT_D = 128 +TOP_K_MAX = 10 +TOP_K_FALLBACK_MAX = 32 +THREADS = 192 +GRID_DIM_DEFAULT = 148 +_TMAP_CACHE: dict[tuple[int, int, int, int, int, int], Any] = {} + +def _select_arch_and_preload() -> str: + import ctypes + import torch + major, minor = torch.cuda.get_device_capability(0) + if (major, minor) == (10, 3): + try: + ctypes.CDLL('/usr/local/cuda-13.1/targets/x86_64-linux/lib/libnvrtc.so.13', mode=ctypes.RTLD_GLOBAL) + except OSError: + pass + from .._dispatch_runtime import arch_flag_for_cc + return arch_flag_for_cc(major, minor) +knn_build_evolve_7bfc_v1 = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_v1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "out_dists", "out_indices", "B", "Q", "M", "K", "num_q_tiles", "num_db_tiles", "total_tiles"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 50176, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_v1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "out_dists", "out_indices", "B", "Q", "M", "K", "num_q_tiles", "num_db_tiles", "total_tiles"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 50176, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) + +def _ir_for_top_k_max(top_k_max: int): + if top_k_max == TOP_K_MAX: + return ir + constants = tuple(((name, top_k_max if name == 'TOP_K_MAX' else value) for name, value in ir.constants)) + return dc.replace(ir, symbol=''.join([format(ir.symbol, ''), '_k', format(top_k_max, ''), '_fallback']), constants=constants) + +def _create_tensor_map_3d_oob_zero(data_ptr: int, global_height: int, shared_height: int, width: int, block_width: int): + import torch + from cuda.bindings import driver + from .._dispatch_runtime import Swizzle + from .._dispatch_runtime import _tmap_to_device + from .._dispatch_runtime import TensorMapMetadata, attach_tma_metadata + device_index = torch.cuda.current_device() + key = (device_index, int(data_ptr), int(global_height), int(shared_height), int(width), int(block_width)) + cached = _TMAP_CACHE.get(key) + if cached is not None: + return cached + err, tmap = _capture_cuTensorMapEncodeTiled(driver.CUtensorMapDataType.CU_TENSOR_MAP_DATA_TYPE_BFLOAT16, 3, data_ptr, [driver.cuuint64_t(64), driver.cuuint64_t(global_height), driver.cuuint64_t(width // 64)], [driver.cuuint64_t(width * 2), driver.cuuint64_t(128)], [driver.cuuint32_t(64), driver.cuuint32_t(shared_height), driver.cuuint32_t(block_width // 64)], [driver.cuuint32_t(1), driver.cuuint32_t(1), driver.cuuint32_t(1)], driver.CUtensorMapInterleave.CU_TENSOR_MAP_INTERLEAVE_NONE, driver.CUtensorMapSwizzle.CU_TENSOR_MAP_SWIZZLE_128B, driver.CUtensorMapL2promotion.CU_TENSOR_MAP_L2_PROMOTION_NONE, driver.CUtensorMapFloatOOBfill.CU_TENSOR_MAP_FLOAT_OOB_FILL_NAN_REQUEST_ZERO_FMA) + if err != 0: + raise RuntimeError(''.join(['cuTensorMapEncodeTiled (3D, OOB zero) failed: CUresult=', format(err, '')])) + cached = attach_tma_metadata(_tmap_to_device(tmap).to(device=torch.device('cuda', device_index)), TensorMapMetadata(ndim=3, dtype='bf16', swizzle=Swizzle.SZ_128B, helper='knn_build_evolve_7bfc_v1._create_tensor_map_3d_oob_zero')) + _TMAP_CACHE[key] = cached + return cached + +def _compiled_kernel(top_k_max: int=TOP_K_MAX): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0135"}')) + +def launch_from_contract_inputs(inputs: dict[str, Any]) -> None: + query = inputs['query'] + database = inputs['database'] + if str(query.dtype) != 'torch.bfloat16' or str(database.dtype) != 'torch.bfloat16': + raise ValueError('knn_build_evolve_7bfc_v1 currently supports bfloat16 contract inputs only') + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + if dim != FEAT_D: + raise ValueError(''.join(['knn_build_evolve_7bfc_v1 expects D=', format(FEAT_D, ''), ', got ', format(dim, '')])) + if top_k > TOP_K_FALLBACK_MAX: + raise ValueError(''.join(['knn_build_evolve_7bfc_v1 supports K <= ', format(TOP_K_FALLBACK_MAX, ''), ', got ', format(top_k, '')])) + kernel_top_k_max = TOP_K_MAX if top_k <= TOP_K_MAX else TOP_K_FALLBACK_MAX + ir_obj = _ir_for_top_k_max(kernel_top_k_max) + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + total_tiles = bsz * num_q_tiles + grid_dim = min(total_tiles, GRID_DIM_DEFAULT) + tmap_query = _create_tensor_map_3d_oob_zero(query.data_ptr(), bsz * n_query, BLOCK_Q, dim, dim) + tmap_database = _create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, dim, dim) + kernel = _compiled_kernel(kernel_top_k_max) + args = pack_kernel_args(ir_obj, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], out_dists=inputs['out_dists'], out_indices=inputs['out_indices'], B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tiles=num_q_tiles, num_db_tiles=num_db_tiles, total_tiles=total_tiles) + kernel.launch(grid=(grid_dim, 1, 1), block=(THREADS, 1, 1), args=args, shared_mem=ir_obj.computed_smem_bytes) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(candidate, shapes=shapes, correctness=correctness, benchmark=benchmark) diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_flashml_k5_bd4a_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_flashml_k5_bd4a_v1.py new file mode 100644 index 00000000..be1dda3a --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_flashml_k5_bd4a_v1.py @@ -0,0 +1,76 @@ +"""kNN build exact K5 smoke-row route for the post-0192 slow-shape lane. + +Minimum target architecture: sm_100a. This candidate exposes the existing +tcgen05/TMA K5 four-tree Weave producer for exactly +``flashml_correctness_b1_q256_m256_d128_k5``. The selected route writes +contract-visible distances and indices with Weave-only production code. Guard +misses delegate to the 0192 all-validated Weave dispatcher so this module can +also be invoked by the contract harness without introducing any external +runtime fallback. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import os +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_all_validated_weave_evolve_knn_build_0192_v1 as parent_0192 +from . import knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_v1 as k5_route +TARGET_SHAPE = 'flashml_correctness_b1_q256_m256_d128_k5' +stage1_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_stage1_batch8_cond4_vmin_threshold_k5", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_SMALL", 5]], "cta_group": 1, "threads": 192}')) +merge_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k5_merge_s4_tree", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "B", "Q", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_SMALL", 5]], "cta_group": 1, "threads": 256}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_FLASHML_K5_BD4A_VERIFY_KERNEL') + if verify_kernel == 'stage1': + return stage1_ir + if verify_kernel == 'merge': + return merge_ir + return stage1_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_stage1_batch8_cond4_vmin_threshold_k5", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_SMALL", 5]], "cta_group": 1, "threads": 192}')) + +def _dtype_is_bf16(inputs: dict[str, Any]) -> bool: + return str(inputs['query'].dtype) == 'torch.bfloat16' and str(inputs['database'].dtype) == 'torch.bfloat16' + +def _is_target_shape(inputs: dict[str, Any]) -> bool: + return bool(inputs.get('build', False)) and _dtype_is_bf16(inputs) and (int(inputs['B']) == 1) and (int(inputs['Q']) == 256) and (int(inputs['M']) == 256) and (int(inputs['D']) == 128) and (int(inputs['K']) == 5) + +def launch_from_contract_inputs(inputs: dict[str, Any]) -> None: + if _is_target_shape(inputs): + k5_route.launch_from_contract_inputs(inputs) + return + parent_0192.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(candidate, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return parent_0192._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=(TARGET_SHAPE,), benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint for selected contract shapes.""" + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _run_with_timing_backend(*, use_cupti: bool, shapes=None) -> dict[str, Any]: + prior_use_cupti = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + try: + return evaluate_contract(shapes=shapes, correctness=True, benchmark=True) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def benchmark_knn_build_flashml_k5_bd4a_v1(*, use_cupti: bool=False) -> dict[str, Any]: + """Targeted benchmark hook for the exact public FlashML correctness row.""" + report = _run_with_timing_backend(use_cupti=use_cupti, shapes=_select_contract_shapes((TARGET_SHAPE,))) + timing_backends = sorted({result.get('timing_backend') for result in report.get('per_shape', {}).values() if result.get('timing_backend') is not None}) + row = report.get('per_shape', {}).get(TARGET_SHAPE, {}) + return {'tflops': report['summary']['primary_mean'] or 0.0, 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'invalid_performance_reason': report['summary']['invalid_performance_reason'], 'measured_entrypoint': 'loom.examples.weave.knn_build_flashml_k5_bd4a_v1:benchmark_knn_build_flashml_k5_bd4a_v1', 'contract_summary': report['summary'], 'contract_performance': report['performance'], 'timing_backends': timing_backends, 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'exact_shape_label': TARGET_SHAPE, 'exact_row': row, 'accelerated_shape_labels': [TARGET_SHAPE], 'inherited_dispatcher': 'knn_build_dispatch_all_validated_0192_v1', 'report': report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_fp16_d128_lowfloor_fd37_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_fp16_d128_lowfloor_fd37_v1.py new file mode 100644 index 00000000..500e8334 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_fp16_d128_lowfloor_fd37_v1.py @@ -0,0 +1,157 @@ +"""Exact FP16 D128 K10 build repair for fd37 low-floor bucket. + +Minimum target architecture: sm_100a. This additive shape-specific seed keeps +the validated df2f FP16 split/tcgen05 producer for +``build_dtype_fp16_b1_q2048_m2048_d128_k10`` and replaces the generic runtime +split merge with a static K=10, split=8 cached stream merge. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +from collections.abc import Callable +from functools import lru_cache +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dim_midk_df2f_v1 as parent_df2f +from . import knn_build_evolve_7bfc_fp16_d128_knn_build_dispatch_slurm_0610_6329_v24 as dim_fp16 +from . import knn_build_evolve_7bfc_split_v1 as split_parent +from .._dispatch_runtime import pack_kernel_args +BLOCK_Q = parent_df2f.BLOCK_Q +BLOCK_M = parent_df2f.BLOCK_M +TOP_K = parent_df2f.TOP_K_MAX +THREADS = parent_df2f.THREADS +GRID_DIM_DEFAULT = parent_df2f.GRID_DIM_DEFAULT +FP16_FEAT_D = parent_df2f.FP16_FEAT_D +FP16_SPLITS = 8 +MERGE_THREADS = 32 +TARGET_SHAPE = 'build_dtype_fp16_b1_q2048_m2048_d128_k10' +TARGET_SHAPES = (TARGET_SHAPE,) +ROUTE_FP16_S8_CACHED_MERGE = 'loom.examples.weave.knn_build_fp16_d128_lowfloor_fd37_v1:fp16_d128_s8_cached_merge' +ROUTE_PARENT_DF2F = 'loom.examples.weave.knn_build_dim_midk_df2f_v1' +knn_build_fp16_d128_lowfloor_fd37_k10_s8_merge = _decode_capture(_json_loads('{"__ir__": "knn_build_fp16_d128_lowfloor_fd37_k10_s8_merge", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 8]], "cta_group": 1, "threads": 32}')) +stage1_fp16_split_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_dim_midk_df2f_fp16_split_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) +merge_k10_s8_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_fp16_d128_lowfloor_fd37_k10_s8_merge", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 8]], "cta_group": 1, "threads": 32}')) + +def _verify_export_ir() -> Any: + verify_kernel = __import__('os').environ.get('LOOM_KNN_FP16_LOWFD37_VERIFY_KERNEL') + if verify_kernel == 'stage1': + return stage1_fp16_split_ir + return merge_k10_s8_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_fp16_d128_lowfloor_fd37_k10_s8_merge", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 8]], "cta_group": 1, "threads": 32}')) + +def _compiled_merge_k10_s8(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0033"}')) + +def _dtype_name(inputs: dict[str, Any]) -> str: + query = inputs.get('query') + if query is not None: + return str(query.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')) + +def _label_can_hit(inputs: dict[str, Any]) -> bool: + value = inputs.get('label') + return value is None or str(value) == TARGET_SHAPE + +def _eligible_fp16_s8_cached_merge(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs) and bool(inputs.get('build', False)) and (_dtype_name(inputs) == 'float16') and (int(inputs['B']) == 1) and (int(inputs['Q']) == 2048) and (int(inputs['M']) == 2048) and (int(inputs['D']) == FP16_FEAT_D) and (int(inputs['K']) == TOP_K) + +def _launch_fp16_s8_cached_merge(inputs: dict[str, Any]) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + FP16_SPLITS - 1) // FP16_SPLITS + total_work = bsz * num_q_tiles * FP16_SPLITS + stage1_grid = min(total_work, GRID_DIM_DEFAULT) + merge_grid = min((bsz * n_query + MERGE_THREADS - 1) // MERGE_THREADS, GRID_DIM_DEFAULT) + partial_dists, partial_indices = split_parent._partial_buffers(split_count=FP16_SPLITS, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = dim_fp16._create_tensor_map_3d_fp16_oob_zero(query.data_ptr(), bsz * n_query, BLOCK_Q, dim, FP16_FEAT_D) + tmap_database = dim_fp16._create_tensor_map_3d_fp16_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, dim, FP16_FEAT_D) + stage1_kernel = parent_df2f._compiled_fp16_stage1() + stage1_kernel.launch(grid=(stage1_grid, 1, 1), block=(THREADS, 1, 1), args=pack_kernel_args(stage1_fp16_split_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tiles=num_q_tiles, db_tiles_per_split=db_tiles_per_split, split_count=FP16_SPLITS, total_work=total_work), shared_mem=stage1_fp16_split_ir.computed_smem_bytes) + merge_kernel = _compiled_merge_k10_s8() + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_k10_s8_ir.computed_smem_bytes) + +def route_for_contract_inputs(inputs: dict[str, Any]) -> str: + if _eligible_fp16_s8_cached_merge(inputs): + return ROUTE_FP16_S8_CACHED_MERGE + return ROUTE_PARENT_DF2F + +def launch_from_contract_inputs(inputs: dict[str, Any]) -> None: + if _eligible_fp16_s8_cached_merge(inputs): + _launch_fp16_s8_cached_merge(inputs) + return + parent_df2f.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + from .._dispatch_runtime import CANONICAL_SHAPES + wanted = set(TARGET_SHAPES if shape_labels is None else tuple(shape_labels)) + selected = [shape for shape in CANONICAL_SHAPES if shape['label'] in wanted] + missing = wanted - {shape['label'] for shape in selected} + if missing: + raise ValueError(''.join(['unknown kNN build contract shape(s): ', format(sorted(missing), '')])) + return selected + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=None, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + previous = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = previous + +def route_trace_for_shapes(shape_labels=None) -> list[dict[str, Any]]: + trace = [] + for shape in _select_contract_shapes(shape_labels): + params = dict(shape['params']) + route = route_for_contract_inputs({'label': shape['label'], 'B': int(params['B']), 'Q': int(params['Q']), 'M': int(params['M']), 'D': int(params['D']), 'K': int(params['K']), 'dtype': str(params['dtype']), 'build': bool(params.get('build', False))}) + trace.append({'shape_key': shape['label'], 'selected_route': route, 'selected_seed': 'fp16_d128_lowfloor_fd37_s8_cached_merge' if route == ROUTE_FP16_S8_CACHED_MERGE else None, 'route_kind': 'specialized' if route == ROUTE_FP16_S8_CACHED_MERGE else 'parent_delegate', 'guard_condition': 'exact FP16 build B=1 Q=M=2048 D=128 K=10 split8 cached merge' if route == ROUTE_FP16_S8_CACHED_MERGE else 'guard miss delegates to df2f dim/mid-K parent'}) + return trace + +def _per_shape_deltas(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> dict[str, Any]: + result: dict[str, Any] = {} + for label in TARGET_SHAPES: + cand = candidate_report.get('per_shape', {}).get(label, {}) + base = baseline_report.get('per_shape', {}).get(label, {}) + cand_ms = cand.get('kernel_ms') + base_ms = base.get('kernel_ms') + result[label] = {'candidate_ms': cand_ms, 'baseline_ms': base_ms, 'flashlib_ms': cand.get('flashlib_ms'), 'candidate_tflops': cand.get('tflops'), 'baseline_tflops': base.get('tflops'), 'speedup_vs_df2f': base_ms / cand_ms if cand_ms and base_ms else None, 'ratio_vs_flashlib': cand.get('ratio_vs_flashlib'), 'passed': cand.get('passed'), 'timing_backend': cand.get('timing_backend')} + return result + +def benchmark_knn_build_fp16_d128_lowfloor_fd37_v1(*, use_cupti: bool=True, shape_labels=None, run_baseline: bool=True) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels) + baseline_report = None + if run_baseline: + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=parent_df2f.candidate) + payload: dict[str, Any] = {'tflops': candidate_report['summary']['primary_mean'] or 0.0, 'all_correct': candidate_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'measured_entrypoint': 'loom.examples.weave.knn_build_fp16_d128_lowfloor_fd37_v1:benchmark_knn_build_fp16_d128_lowfloor_fd37_v1', 'measured_shape_labels': tuple(TARGET_SHAPES if shape_labels is None else shape_labels), 'route_trace': route_trace_for_shapes(shape_labels), 'contract_summary': candidate_report['summary'], 'contract_performance': candidate_report['performance'], 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'split_count': FP16_SPLITS, 'report': candidate_report} + if baseline_report is not None: + payload['baseline_entrypoint'] = 'loom.examples.weave.knn_build_dim_midk_df2f_v1:candidate' + payload['baseline_summary'] = baseline_report['summary'] + payload['per_shape_delta_vs_df2f'] = _per_shape_deltas(candidate_report, baseline_report) + baseline_mean = baseline_report['summary']['primary_mean'] + payload['speedup_vs_df2f_primary_mean'] = candidate_report['summary']['primary_mean'] / baseline_mean if baseline_mean else None + return payload diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_k1_q512_group2_root_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_k1_q512_group2_root_v1.py new file mode 100644 index 00000000..7f30ed03 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_k1_q512_group2_root_v1.py @@ -0,0 +1,56 @@ +"""Exact Q512/M512/K1 grouping-search seed (minimum arch: sm_100a). + +This additive seed keeps the validated TMA + tcgen05 producer and exact +split-list merge on the contract path. It changes the Q512 K1 work grouping +from the dispatched four splits to two splits: each cluster consumes four +database tiles rather than two. Non-target inputs stay on the inherited +Weave-only route. + +Benchmark evidence (CUPTI, 2026-06-30): exact BF16 build +``B=1,Q=M=512,D=128,K=1`` took 0.051296 ms versus FlashLib's 0.106945 ms +(2.084860x). The current measurement is recorded in +``artifacts/weave_evolve_k1_q512_group2_root/k1_q512_group2_cupti.json``. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +from typing import Any +from . import knn_build_lowk_f8c3_q512_q1024_v1 as seed +TARGET_SHAPE = 'build_k_sweep_qm512_k1' +Q512_SPLIT_COUNT = 2 +ROUTE_PREFIX = 'loom.examples.weave.knn_build_k1_q512_group2_root_v1' +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_stage1_batch8_cond4_vmin_maxtree", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) + +def _eligible(inputs: dict[str, Any]) -> bool: + return str(inputs.get('label', TARGET_SHAPE)) == TARGET_SHAPE and seed._is_bf16_build(inputs, q=512, k=1) + +def route_for_contract_inputs(inputs: dict[str, Any]) -> str: + if _eligible(inputs): + return ''.join([format(ROUTE_PREFIX, ''), ':q512_k1_s', format(Q512_SPLIT_COUNT, '')]) + return seed.route_for_contract_inputs(inputs) + +def launch_from_contract_inputs(inputs: dict[str, Any]) -> None: + if _eligible(inputs): + seed._launch_q512_lowk_split(inputs, split_count=Q512_SPLIT_COUNT) + return + seed.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(candidate, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _target_shapes() -> list[dict[str, Any]]: + from .._dispatch_runtime import CANONICAL_SHAPES + return [shape for shape in CANONICAL_SHAPES if shape['label'] == TARGET_SHAPE] + +def benchmark_candidate(*, use_cupti: bool=True) -> dict[str, Any]: + from .. import _dispatch_runtime as eval_mod + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + try: + return evaluate_contract(shapes=_target_shapes(), correctness=True, benchmark=True) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = previous diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_k20_large_lowfanout_de1a_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_k20_large_lowfanout_de1a_v1.py new file mode 100644 index 00000000..221f9c93 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_k20_large_lowfanout_de1a_v1.py @@ -0,0 +1,147 @@ +"""kNN build/search K20 large-row low-fanout exact route. + +Minimum target architecture: sm_100a. This additive shape candidate targets the +three hard-gated BF16 D128 non-build K20 rows: +``search_rect_b1_q4096_m65536_d128_k20``, +``rag_offline_largek_b1_q4096_m100000_d128_k20``, and +``rag_offline_large_m_b1_q8192_m250000_d128_k20``. + +The route reuses the verified v20 tcgen05 split-local K20 producer in a +main/tail split-count plan: the two q4096 rows stay on the split-4 K20 seed, +while the largest q8192 x 250000 row uses a new split-2 four-warp K20 +warp-select merge. That reduces tail-row merge fan-in and scratch traffic while +keeping contract-visible distances/indices direct. All unrelated shapes +delegate to the v41 dispatcher fallback. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import os +from functools import lru_cache +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatchscore_tailinf_knn_build_dispatch_slurm_0610_6329_v41 as parent_v41 +from . import knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2_k32split_v20 as parent_v20 +from . import knn_build_evolve_7bfc_split_v1 as parent_split +from . import knn_build_evolve_7bfc_v1 as base_v1 +from .._dispatch_runtime import pack_kernel_args +BLOCK_Q = parent_v20.BLOCK_Q +BLOCK_M = parent_v20.BLOCK_M +FEAT_D = parent_v20.FEAT_D +STAGE1_THREADS = parent_v20.STAGE1_THREADS +K20_MERGE_THREADS = parent_v20.K20_COOP_MERGE_THREADS +GRID_DIM_DEFAULT = parent_v20.GRID_DIM_DEFAULT +CTA_GROUP = parent_v20.CTA_GROUP +LOWFANOUT_SPLIT_COUNT = 2 +SEED_SPLIT_COUNT = parent_v20.MEDIUM_SPLITS +TOP_K_K20 = 20 +EXACT_SHAPE_LABELS = ('search_rect_b1_q4096_m65536_d128_k20', 'rag_offline_largek_b1_q4096_m100000_d128_k20', 'rag_offline_large_m_b1_q8192_m250000_d128_k20') +EXACT_SHAPE_DIMS = {(4096, 65536), (4096, 100000), (8192, 250000)} +knn_build_k20_large_lowfanout_s2_warp_select = _decode_capture(_json_loads('{"__ir__": "knn_build_k20_large_lowfanout_s2_warp_select", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 20]], "cta_group": 1, "threads": 128}')) +merge_k20_s2_warp_select_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_k20_large_lowfanout_s2_warp_select", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 20]], "cta_group": 1, "threads": 128}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_K20_LOWFANOUT_VERIFY_KERNEL') + if verify_kernel == 'merge_k20_large_lowfanout_s2_warp_select': + return merge_k20_s2_warp_select_ir + if verify_kernel == 'merge_k20_large_lowfanout_s4_warp_select': + return parent_v20.merge_k20_unordered_warp_select_ir + if verify_kernel == 'merge_k20_large_lowfanout_s8': + return parent_v20.merge_k20_s8_ir + return parent_v20.stage1_k20_unordered_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k20unordered", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 20]], "cta_group": 1, "threads": 192}')) + +def _forced_split_count() -> int | None: + split_text = os.environ.get('LOOM_KNN_K20_LOWFANOUT_SPLITS') + if not split_text: + return None + split_count = int(split_text) + if split_count not in (2, 4, 8): + raise ValueError(''.join(['unsupported K20 low-fanout split count: ', format(split_count, '')])) + return split_count + +def _default_split_count_for_dims(q: int, m: int) -> int: + if q == 8192 and m == 250000: + return LOWFANOUT_SPLIT_COUNT + return SEED_SPLIT_COUNT + +def _split_count_for_inputs(inputs: dict[str, Any]) -> int: + forced = _forced_split_count() + if forced is not None: + return forced + return _default_split_count_for_dims(int(inputs['Q']), int(inputs['M'])) + +def _eligible_k20_large_lowfanout(inputs: dict[str, Any]) -> bool: + q_m = (int(inputs['Q']), int(inputs['M'])) + return not bool(inputs.get('build', False)) and str(inputs['query'].dtype) == 'torch.bfloat16' and (str(inputs['database'].dtype) == 'torch.bfloat16') and (int(inputs['B']) == 1) and (q_m in EXACT_SHAPE_DIMS) and (int(inputs['D']) == FEAT_D) and (int(inputs['K']) == 20) + +def _launch_k20_large_lowfanout(inputs: dict[str, Any]) -> None: + split_count = _split_count_for_inputs(inputs) + if split_count != LOWFANOUT_SPLIT_COUNT: + parent_v20._launch_k32_split_path(inputs, split_count=split_count) + return + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_q_tile_pairs = (num_q_tiles + CTA_GROUP - 1) // CTA_GROUP + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tile_pairs * split_count + stage1_grid = min(total_work * CTA_GROUP, GRID_DIM_DEFAULT) + merge_grid = (bsz * n_query + 3) // 4 + partial_dists, partial_indices = parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), bsz * n_query, BLOCK_Q, dim, dim) + tmap_database = base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, dim, dim) + stage1_ir_obj = parent_v20.stage1_k20_unordered_ir + stage1_kernel = parent_v20._compiled_stage1_unordered_for_exact_k(top_k) + stage1_kernel.launch_cluster(grid=(stage1_grid, 1, 1), block=(STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage1_ir_obj, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tile_pairs=num_q_tile_pairs, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), cluster_dims=(CTA_GROUP, 1, 1), shared_mem=stage1_ir_obj.computed_smem_bytes) + merge_kernel = _compiled_merge_k20_s2_warp_select() + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(K20_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_k20_s2_warp_select_ir.computed_smem_bytes) + +def _compiled_merge_k20_s2_warp_select(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0086"}')) + +def launch_from_contract_inputs(inputs: dict[str, Any]) -> None: + if _eligible_k20_large_lowfanout(inputs): + _launch_k20_large_lowfanout(inputs) + return + parent_v41.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(candidate, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return parent_v41._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=EXACT_SHAPE_LABELS, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def benchmark_k20_large_lowfanout_de1a_v1(*, use_cupti: bool=False) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + report = evaluate_contract(shapes=_select_contract_shapes(EXACT_SHAPE_LABELS), correctness=True, benchmark=True) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + rows = report['per_shape'] + timing_backends = sorted({str(row.get('timing_backend')) for row in rows.values() if row.get('timing_backend')}) + split_counts = {label: _forced_split_count() if _forced_split_count() is not None else _default_split_count_for_dims(int(rows[label]['Q']), int(rows[label]['M'])) for label in EXACT_SHAPE_LABELS if label in rows} + return {'tflops': report['summary']['primary_mean'] or 0.0, 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'invalid_performance_reason': report['summary']['invalid_performance_reason'], 'timing_backends': timing_backends, 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'split_count_by_shape': split_counts, 'scratch_candidates': {label: int(rows[label]['Q']) * 20 * split_counts[label] for label in EXACT_SHAPE_LABELS if label in rows}, 'measured_entrypoint': 'loom.examples.weave.knn_build_k20_large_lowfanout_de1a_v1:benchmark_k20_large_lowfanout_de1a_v1', 'report': report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_k20_large_rect_6268_v2.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_k20_large_rect_6268_v2.py new file mode 100644 index 00000000..80e8e129 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_k20_large_rect_6268_v2.py @@ -0,0 +1,151 @@ +"""kNN build/search K20 large-row split-3 probe route. + +Minimum target architecture: sm_100a. This additive shape candidate targets the +three hard-gated BF16 D128 non-build K20 rows: +``search_rect_b1_q4096_m65536_d128_k20``, +``rag_offline_largek_b1_q4096_m100000_d128_k20``, and +``rag_offline_large_m_b1_q8192_m250000_d128_k20``. + +The candidate keeps the de1a split-2/split-4 low-fanout route intact and adds a +split-3 tcgen05 producer plus four-warp K20 merge for direct same-denominator +A/B. Unrelated shapes delegate to the v41 dispatcher fallback through de1a. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import os +from functools import lru_cache +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2_k32split_v20 as parent_v20 +from . import knn_build_evolve_7bfc_split_v1 as parent_split +from . import knn_build_evolve_7bfc_v1 as base_v1 +from . import knn_build_k20_large_lowfanout_de1a_v1 as de1a +from .._dispatch_runtime import pack_kernel_args +BLOCK_Q = de1a.BLOCK_Q +BLOCK_M = de1a.BLOCK_M +FEAT_D = de1a.FEAT_D +STAGE1_THREADS = de1a.STAGE1_THREADS +K20_MERGE_THREADS = de1a.K20_MERGE_THREADS +GRID_DIM_DEFAULT = de1a.GRID_DIM_DEFAULT +CTA_GROUP = de1a.CTA_GROUP +TOP_K_K20 = de1a.TOP_K_K20 +SPLIT3_COUNT = 3 +EXACT_SHAPE_LABELS = de1a.EXACT_SHAPE_LABELS +knn_build_k20_large_rect_s3_warp_select = _decode_capture(_json_loads('{"__ir__": "knn_build_k20_large_rect_s3_warp_select", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 20]], "cta_group": 1, "threads": 128}')) +merge_k20_s3_warp_select_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_k20_large_rect_s3_warp_select", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 20]], "cta_group": 1, "threads": 128}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_K20_RECT6268_VERIFY_KERNEL') + if verify_kernel in {'stage1_k20', 'stage1_k20_s3'}: + return parent_v20.stage1_k20_unordered_ir + if verify_kernel == 'merge_k20_s2': + return de1a.merge_k20_s2_warp_select_ir + if verify_kernel == 'merge_k20_s3': + return merge_k20_s3_warp_select_ir + if verify_kernel == 'merge_k20_s4': + return parent_v20.merge_k20_unordered_warp_select_ir + return merge_k20_s3_warp_select_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_k20_large_rect_s3_warp_select", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 20]], "cta_group": 1, "threads": 128}')) + +def _forced_split_count() -> int | None: + split_text = os.environ.get('LOOM_KNN_K20_RECT6268_SPLITS') + if not split_text: + return None + split_count = int(split_text) + if split_count not in (2, 3, 4, 8): + raise ValueError(''.join(['unsupported K20 split count for 6268 route: ', format(split_count, '')])) + return split_count + +def _default_split_count_for_dims(q: int, m: int) -> int: + if q == 8192 and m == 250000: + return de1a.LOWFANOUT_SPLIT_COUNT + return de1a.SEED_SPLIT_COUNT + +def _split_count_for_inputs(inputs: dict[str, Any]) -> int: + forced = _forced_split_count() + if forced is not None: + return forced + return _default_split_count_for_dims(int(inputs['Q']), int(inputs['M'])) + +def _eligible_k20_large_rect(inputs: dict[str, Any]) -> bool: + return de1a._eligible_k20_large_lowfanout(inputs) + +def _launch_split23_path(inputs: dict[str, Any], *, split_count: int) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_q_tile_pairs = (num_q_tiles + CTA_GROUP - 1) // CTA_GROUP + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tile_pairs * split_count + stage1_grid = min(total_work * CTA_GROUP, GRID_DIM_DEFAULT) + merge_grid = (bsz * n_query + 3) // 4 + partial_dists, partial_indices = parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), bsz * n_query, BLOCK_Q, dim, dim) + tmap_database = base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, dim, dim) + stage1_ir_obj = parent_v20.stage1_k20_unordered_ir + stage1_kernel = parent_v20._compiled_stage1_unordered_for_exact_k(top_k) + stage1_kernel.launch_cluster(grid=(stage1_grid, 1, 1), block=(STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage1_ir_obj, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tile_pairs=num_q_tile_pairs, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), cluster_dims=(CTA_GROUP, 1, 1), shared_mem=stage1_ir_obj.computed_smem_bytes) + if split_count == 2: + merge_ir = de1a.merge_k20_s2_warp_select_ir + merge_kernel = de1a._compiled_merge_k20_s2_warp_select() + else: + merge_ir = merge_k20_s3_warp_select_ir + merge_kernel = _compiled_merge_k20_s3_warp_select() + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(K20_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_ir.computed_smem_bytes) + +def _compiled_merge_k20_s3_warp_select(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0160"}')) + +def _launch_k20_large_rect(inputs: dict[str, Any]) -> None: + split_count = _split_count_for_inputs(inputs) + if split_count in (2, 3): + _launch_split23_path(inputs, split_count=split_count) + return + parent_v20._launch_k32_split_path(inputs, split_count=split_count) + +def launch_from_contract_inputs(inputs: dict[str, Any]) -> None: + if _eligible_k20_large_rect(inputs): + _launch_k20_large_rect(inputs) + return + de1a.parent_v41.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(candidate, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return de1a._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=EXACT_SHAPE_LABELS, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def benchmark_k20_large_rect_6268_v2(*, use_cupti: bool=False) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + report = evaluate_contract(shapes=_select_contract_shapes(EXACT_SHAPE_LABELS), correctness=True, benchmark=True) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + rows = report['per_shape'] + timing_backends = sorted({str(row.get('timing_backend')) for row in rows.values() if row.get('timing_backend')}) + split_counts = {label: _forced_split_count() if _forced_split_count() is not None else _default_split_count_for_dims(int(rows[label]['Q']), int(rows[label]['M'])) for label in EXACT_SHAPE_LABELS if label in rows} + return {'tflops': report['summary']['primary_mean'] or 0.0, 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'invalid_performance_reason': report['summary']['invalid_performance_reason'], 'timing_backends': timing_backends, 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'split_count_by_shape': split_counts, 'measured_entrypoint': 'loom.examples.weave.knn_build_k20_large_rect_6268_v2:benchmark_k20_large_rect_6268_v2', 'report': report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_k20_mergeown_08ec_v3.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_k20_mergeown_08ec_v3.py new file mode 100644 index 00000000..dbdb936e --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_k20_mergeown_08ec_v3.py @@ -0,0 +1,205 @@ +"""kNN build/search K20 merge-ownership probe. + +Minimum target architecture: sm_100a. This additive shape candidate targets the +three hard-gated BF16 D128 non-build K20 rows: +``search_rect_b1_q4096_m65536_d128_k20``, +``rag_offline_largek_b1_q4096_m100000_d128_k20``, and +``rag_offline_large_m_b1_q8192_m250000_d128_k20``. + +The default route preserves the selected 6268 split-count plan and Q4096 +four-warp merge, but changes the Q8192 x M250000 tail row to an eight-warp +block ownership merge. Rowbase and all-warp8 modes remain available as local +A/B probes. This is intentionally a merge-ownership experiment rather than +another split fanout probe. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import os +from functools import lru_cache +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2_k32split_v20 as parent_v20 +from . import knn_build_evolve_7bfc_split_v1 as parent_split +from . import knn_build_evolve_7bfc_v1 as base_v1 +from . import knn_build_k20_large_lowfanout_de1a_v1 as de1a +from . import knn_build_k20_large_rect_6268_v2 as rect6268 +from .._dispatch_runtime import pack_kernel_args +BLOCK_Q = de1a.BLOCK_Q +BLOCK_M = de1a.BLOCK_M +FEAT_D = de1a.FEAT_D +STAGE1_THREADS = de1a.STAGE1_THREADS +ROWBASE_MERGE_THREADS = parent_v20.K32_MERGE_THREADS +WARP4_MERGE_THREADS = de1a.K20_MERGE_THREADS +WARP8_MERGE_THREADS = 256 +GRID_DIM_DEFAULT = de1a.GRID_DIM_DEFAULT +CTA_GROUP = de1a.CTA_GROUP +TOP_K_K20 = de1a.TOP_K_K20 +Q4096_SPLIT_COUNT = de1a.SEED_SPLIT_COUNT +TAIL_SPLIT_COUNT = de1a.LOWFANOUT_SPLIT_COUNT +EXACT_SHAPE_LABELS = de1a.EXACT_SHAPE_LABELS +knn_build_k20_mergeown_08ec_s4_rowbase_lane = _decode_capture(_json_loads('{"__ir__": "knn_build_k20_mergeown_08ec_s4_rowbase_lane", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 20], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 32}')) +merge_k20_s4_rowbase_lane_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_k20_mergeown_08ec_s4_rowbase_lane", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 20], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 32}')) +knn_build_k20_mergeown_08ec_warp8_select = _decode_capture(_json_loads('{"__ir__": "knn_build_k20_mergeown_08ec_warp8_select", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 20], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 256}')) +merge_k20_s2_warp8_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_k20_mergeown_08ec_warp8_select_s2warp8", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 20], ["SPLIT_COUNT", 2]], "cta_group": 1, "threads": 256}')) +merge_k20_s4_warp8_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_k20_mergeown_08ec_warp8_select_s4warp8", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 20], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 256}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_K20_MERGEOWN_08EC_VERIFY_KERNEL') + if verify_kernel == 'stage1_k20': + return parent_v20.stage1_k20_unordered_ir + if verify_kernel == 'merge_k20_s4_rowbase': + return merge_k20_s4_rowbase_lane_ir + if verify_kernel == 'merge_k20_s4_warp4': + return parent_v20.merge_k20_unordered_warp_select_ir + if verify_kernel == 'merge_k20_s4_warp8': + return merge_k20_s4_warp8_ir + if verify_kernel == 'merge_k20_s2_warp4': + return de1a.merge_k20_s2_warp_select_ir + if verify_kernel == 'merge_k20_s2_warp8': + return merge_k20_s2_warp8_ir + return parent_v20.merge_k20_unordered_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_unordered_k20unordered", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 20], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 32}')) + +def _merge_mode() -> str: + mode = os.environ.get('LOOM_KNN_K20_MERGEOWN_08EC_MERGE', os.environ.get('LOOM_KNN_K20_MERGEOWN_08EC_Q4096_MERGE', 'tailwarp8')).strip().lower() + if mode == 'warpselect': + mode = 'warp4' + if mode not in {'rowbase', 'warp4', 'warp8', 'tailwarp8'}: + raise ValueError("LOOM_KNN_K20_MERGEOWN_08EC_MERGE must be one of {'rowbase', 'warp4', 'warp8', 'tailwarp8'}") + return mode + +def _eligible_k20_mergeown(inputs: dict[str, Any]) -> bool: + return de1a._eligible_k20_large_lowfanout(inputs) + +def _split_count_for_dims(q: int, m: int) -> int: + if q == 8192 and m == 250000: + return TAIL_SPLIT_COUNT + return Q4096_SPLIT_COUNT + +def _launch_split4_q4096_path(inputs: dict[str, Any], *, merge_mode: str) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + split_count = Q4096_SPLIT_COUNT + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_q_tile_pairs = (num_q_tiles + CTA_GROUP - 1) // CTA_GROUP + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tile_pairs * split_count + stage1_grid = min(total_work * CTA_GROUP, GRID_DIM_DEFAULT) + partial_dists, partial_indices = parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), bsz * n_query, BLOCK_Q, dim, dim) + tmap_database = base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, dim, dim) + stage1_ir_obj = parent_v20.stage1_k20_unordered_ir + stage1_kernel = parent_v20._compiled_stage1_unordered_for_exact_k(top_k) + stage1_kernel.launch_cluster(grid=(stage1_grid, 1, 1), block=(STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage1_ir_obj, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tile_pairs=num_q_tile_pairs, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), cluster_dims=(CTA_GROUP, 1, 1), shared_mem=stage1_ir_obj.computed_smem_bytes) + if merge_mode == 'rowbase': + merge_threads = ROWBASE_MERGE_THREADS + merge_grid = min((bsz * n_query + merge_threads - 1) // merge_threads, GRID_DIM_DEFAULT) + merge_ir = merge_k20_s4_rowbase_lane_ir + merge_kernel = _compiled_merge_k20_s4_rowbase_lane() + else: + merge_threads = WARP4_MERGE_THREADS + merge_grid = (bsz * n_query + 3) // 4 + merge_ir = parent_v20.merge_k20_unordered_warp_select_ir + merge_kernel = parent_v20._compiled_merge_k20_unordered_warp_select() + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(merge_threads, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_ir.computed_smem_bytes) + +def _launch_warp8_path(inputs: dict[str, Any], *, split_count: int) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_q_tile_pairs = (num_q_tiles + CTA_GROUP - 1) // CTA_GROUP + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tile_pairs * split_count + stage1_grid = min(total_work * CTA_GROUP, GRID_DIM_DEFAULT) + merge_grid = (bsz * n_query + 7) // 8 + partial_dists, partial_indices = parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), bsz * n_query, BLOCK_Q, dim, dim) + tmap_database = base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, dim, dim) + stage1_ir_obj = parent_v20.stage1_k20_unordered_ir + stage1_kernel = parent_v20._compiled_stage1_unordered_for_exact_k(top_k) + stage1_kernel.launch_cluster(grid=(stage1_grid, 1, 1), block=(STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage1_ir_obj, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tile_pairs=num_q_tile_pairs, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), cluster_dims=(CTA_GROUP, 1, 1), shared_mem=stage1_ir_obj.computed_smem_bytes) + merge_ir = _merge_k20_warp8_ir_for_split_count(split_count) + merge_kernel = _compiled_merge_k20_warp8(split_count) + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(WARP8_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_ir.computed_smem_bytes) + +def _launch_k20_mergeown(inputs: dict[str, Any]) -> None: + merge_mode = _merge_mode() + split_count = _split_count_for_dims(int(inputs['Q']), int(inputs['M'])) + if merge_mode == 'warp8' or (merge_mode == 'tailwarp8' and split_count == TAIL_SPLIT_COUNT): + _launch_warp8_path(inputs, split_count=split_count) + return + if split_count == TAIL_SPLIT_COUNT: + rect6268._launch_split23_path(inputs, split_count=TAIL_SPLIT_COUNT) + return + if merge_mode == 'tailwarp8': + merge_mode = 'warp4' + _launch_split4_q4096_path(inputs, merge_mode=merge_mode) + +def _compiled_merge_k20_s4_rowbase_lane(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0159"}')) + +def _merge_k20_warp8_ir_for_split_count(split_count: int) -> Any: + if split_count == TAIL_SPLIT_COUNT: + return merge_k20_s2_warp8_ir + if split_count == Q4096_SPLIT_COUNT: + return merge_k20_s4_warp8_ir + raise ValueError(''.join(['unsupported warp8 split_count=', format(split_count, '')])) + +@lru_cache(maxsize=2) +def _compiled_merge_k20_warp8(split_count: int): + return parent_v20._compile_ir(_merge_k20_warp8_ir_for_split_count(split_count)) + +def launch_from_contract_inputs(inputs: dict[str, Any]) -> None: + if _eligible_k20_mergeown(inputs): + _launch_k20_mergeown(inputs) + return + de1a.parent_v41.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(candidate, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return de1a._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=EXACT_SHAPE_LABELS, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def benchmark_k20_mergeown_08ec_v3(*, use_cupti: bool=False) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + report = evaluate_contract(shapes=_select_contract_shapes(EXACT_SHAPE_LABELS), correctness=True, benchmark=True) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + rows = report['per_shape'] + timing_backends = sorted({str(row.get('timing_backend')) for row in rows.values() if row.get('timing_backend')}) + merge_mode = _merge_mode() + split_counts = {label: _split_count_for_dims(int(rows[label]['Q']), int(rows[label]['M'])) for label in EXACT_SHAPE_LABELS if label in rows} + merge_owner = {label: ''.join(['split', format(split_counts[label], ''), '_', format('warp8' if merge_mode == 'tailwarp8' and split_counts[label] == TAIL_SPLIT_COUNT else 'warp4' if merge_mode == 'tailwarp8' else merge_mode, '')]) for label in split_counts} + return {'tflops': report['summary']['primary_mean'] or 0.0, 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'invalid_performance_reason': report['summary']['invalid_performance_reason'], 'timing_backends': timing_backends, 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'split_count_by_shape': split_counts, 'merge_owner_by_shape': merge_owner, 'measured_entrypoint': 'loom.examples.weave.knn_build_k20_mergeown_08ec_v3:benchmark_k20_mergeown_08ec_v3', 'report': report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_k20_rag_large_m_7487_v42.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_k20_rag_large_m_7487_v42.py new file mode 100644 index 00000000..556a5aff --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_k20_rag_large_m_7487_v42.py @@ -0,0 +1,134 @@ +"""kNN build/search v42 exact RAG large-M K20 route. + +Minimum target architecture: sm_100a. This additive candidate targets only +``rag_offline_large_m_b1_q8192_m250000_d128_k20``. It routes that non-build +BF16 D128 K20 row through the inherited exact-K tcgen05 split-local producer +and a new warp-select merge over sixteen unordered split-local K20 lists, then +falls back to the v41 dispatcher for every other contract shape. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import os +from functools import lru_cache +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatchscore_tailinf_knn_build_dispatch_slurm_0610_6329_v41 as parent_v41 +from . import knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2_k32split_v20 as parent_v20 +from . import knn_build_evolve_7bfc_split_v1 as parent_split +from . import knn_build_evolve_7bfc_v1 as base_v1 +from .._dispatch_runtime import pack_kernel_args +BLOCK_Q = parent_v20.BLOCK_Q +BLOCK_M = parent_v20.BLOCK_M +FEAT_D = parent_v20.FEAT_D +STAGE1_THREADS = parent_v20.STAGE1_THREADS +MERGE_THREADS = parent_v20.K20_COOP_MERGE_THREADS +GRID_DIM_DEFAULT = parent_v20.GRID_DIM_DEFAULT +CTA_GROUP = parent_v20.CTA_GROUP +TOP_K = 20 +RAG_LARGE_M_Q = 8192 +RAG_LARGE_M_M = 250000 +RAG_K20_SPLITS_DEFAULT = 16 +SUPPORTED_RAG_K20_SPLITS = (8, 16) +TARGET_SHAPE = 'rag_offline_large_m_b1_q8192_m250000_d128_k20' +stage1_k20_rag_large_m_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k20unordered", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 20]], "cta_group": 1, "threads": 192}')) +knn_build_k20_merge_sN_unordered_warp_select = _decode_capture(_json_loads('{"__ir__": "knn_build_k20_merge_sN_unordered_warp_select", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 20], ["SPLIT_COUNT", 16]], "cta_group": 1, "threads": 128}')) +merge_k20_s8_warp_select_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_k20_merge_sN_unordered_warp_select_k20s8raglargemwarpselect_v42", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 20], ["SPLIT_COUNT", 8]], "cta_group": 1, "threads": 128}')) +merge_k20_s16_warp_select_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_k20_merge_sN_unordered_warp_select_k20s16raglargemwarpselect_v42", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 20], ["SPLIT_COUNT", 16]], "cta_group": 1, "threads": 128}')) + +def _rag_k20_split_count() -> int: + raw = os.environ.get('LOOM_KNN_K20_RAG_SPLITS') + if raw is None: + return RAG_K20_SPLITS_DEFAULT + split_count = int(raw) + if split_count not in SUPPORTED_RAG_K20_SPLITS: + raise ValueError(''.join(['LOOM_KNN_K20_RAG_SPLITS must be one of ', format(SUPPORTED_RAG_K20_SPLITS, ''), ', got ', format(split_count, '')])) + return split_count + +def _merge_ir_for_split_count(split_count: int) -> Any: + if split_count == 8: + return merge_k20_s8_warp_select_ir + if split_count == 16: + return merge_k20_s16_warp_select_ir + raise ValueError(''.join(['unsupported K20 RAG split_count=', format(split_count, '')])) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_K20_RAG_VERIFY_KERNEL') + if verify_kernel == 'stage1': + return stage1_k20_rag_large_m_ir + if verify_kernel == 'merge_s8': + return merge_k20_s8_warp_select_ir + if verify_kernel == 'merge_s16': + return merge_k20_s16_warp_select_ir + return stage1_k20_rag_large_m_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k20unordered", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 20]], "cta_group": 1, "threads": 192}')) + +def _compiled_stage1_k20_rag(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0158"}')) + +@lru_cache(maxsize=2) +def _compiled_merge_k20_rag(split_count: int): + return parent_v20._compile_ir(_merge_ir_for_split_count(split_count)) + +def _eligible_rag_large_m_k20(inputs: dict[str, Any]) -> bool: + return not bool(inputs.get('build', False)) and str(inputs['query'].dtype) == 'torch.bfloat16' and (str(inputs['database'].dtype) == 'torch.bfloat16') and (int(inputs['B']) == 1) and (int(inputs['Q']) == RAG_LARGE_M_Q) and (int(inputs['M']) == RAG_LARGE_M_M) and (int(inputs['D']) == FEAT_D) and (int(inputs['K']) == TOP_K) + +def _launch_rag_large_m_k20(inputs: dict[str, Any], *, split_count: int) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_q_tile_pairs = (num_q_tiles + CTA_GROUP - 1) // CTA_GROUP + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tile_pairs * split_count + stage1_grid = min(total_work * CTA_GROUP, GRID_DIM_DEFAULT) + merge_grid = (bsz * n_query + 3) // 4 + partial_dists, partial_indices = parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), bsz * n_query, BLOCK_Q, dim, dim) + tmap_database = base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, dim, dim) + stage1_kernel = _compiled_stage1_k20_rag() + stage1_kernel.launch_cluster(grid=(stage1_grid, 1, 1), block=(STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage1_k20_rag_large_m_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tile_pairs=num_q_tile_pairs, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), cluster_dims=(CTA_GROUP, 1, 1), shared_mem=stage1_k20_rag_large_m_ir.computed_smem_bytes) + merge_ir = _merge_ir_for_split_count(split_count) + merge_kernel = _compiled_merge_k20_rag(split_count) + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_ir.computed_smem_bytes) + +def launch_from_contract_inputs(inputs: dict[str, Any]) -> None: + if _eligible_rag_large_m_k20(inputs): + _launch_rag_large_m_k20(inputs, split_count=_rag_k20_split_count()) + return + parent_v41.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(candidate, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return parent_v41._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=(TARGET_SHAPE,), benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint for the exact K20 RAG large-M route.""" + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def benchmark_k20_rag_large_m_7487_v42(*, use_cupti: bool=False) -> dict[str, Any]: + """Targeted contract benchmark hook for the exact RAG large-M K20 row.""" + prior_use_cupti = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + try: + report = evaluate_contract(shapes=_select_contract_shapes((TARGET_SHAPE,)), correctness=True, benchmark=True) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + timing_backends = sorted({result.get('timing_backend') for result in report.get('per_shape', {}).values() if result.get('timing_backend') is not None}) + return {'tflops': report['summary']['primary_mean'] or 0.0, 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'invalid_performance_reason': report['summary']['invalid_performance_reason'], 'contract_summary': report['summary'], 'contract_performance': report['performance'], 'timing_backends': timing_backends, 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'split_count': _rag_k20_split_count(), 'report': report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_k20_search_rect_3cef_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_k20_search_rect_3cef_v1.py new file mode 100644 index 00000000..425035f4 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_k20_search_rect_3cef_v1.py @@ -0,0 +1,81 @@ +"""kNN build/search K20 rectangular-search exact route. + +Minimum target architecture: sm_100a. This additive shape candidate targets only +``search_rect_b1_q4096_m65536_d128_k20``. It reuses the verified v20 D128 K20 +tcgen05 split-local producer and K20 warp-select merge on a non-build search +row, while preserving every inherited K10/K64/build guard through the v40 +dispatcher fallback that v41 previously exposed directly. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import os +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_k64stage1_splitgrid_tailinf_knn_build_dispatch_slurm_0610_6329_v40 as parent_v40 +from . import knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2_k32split_v20 as parent_v20 +BLOCK_Q = parent_v20.BLOCK_Q +BLOCK_M = parent_v20.BLOCK_M +FEAT_D = parent_v20.FEAT_D +DEFAULT_SPLIT_COUNT = parent_v20.MEDIUM_SPLITS +EXACT_SHAPE_LABEL = 'search_rect_b1_q4096_m65536_d128_k20' + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_K20_SEARCH_RECT_VERIFY_KERNEL') + if verify_kernel == 'merge_k20_search_warp_select': + return parent_v20.merge_k20_unordered_warp_select_ir + if verify_kernel == 'stage1_k20_search_s8': + return parent_v20.stage1_k20_ir + if verify_kernel == 'merge_k20_search_s8': + return parent_v20.merge_k20_s8_ir + return parent_v20.stage1_k20_unordered_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k20unordered", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 20]], "cta_group": 1, "threads": 192}')) + +def _split_count() -> int: + split_text = os.environ.get('LOOM_KNN_K20_SEARCH_RECT_SPLITS') + if not split_text: + return DEFAULT_SPLIT_COUNT + split_count = int(split_text) + if split_count not in (4, 8): + raise ValueError(''.join(['unsupported K20 search split count: ', format(split_count, '')])) + return split_count + +def _eligible_k20_search_rect(inputs: dict[str, Any]) -> bool: + return not bool(inputs.get('build', False)) and str(inputs['query'].dtype) == 'torch.bfloat16' and (str(inputs['database'].dtype) == 'torch.bfloat16') and (int(inputs['B']) == 1) and (int(inputs['Q']) == 4096) and (int(inputs['M']) == 65536) and (int(inputs['D']) == FEAT_D) and (int(inputs['K']) == 20) + +def _launch_k20_search_rect(inputs: dict[str, Any]) -> None: + parent_v20._launch_k32_split_path(inputs, split_count=_split_count()) + +def launch_from_contract_inputs(inputs: dict[str, Any]) -> None: + if _eligible_k20_search_rect(inputs): + _launch_k20_search_rect(inputs) + return + parent_v40.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(candidate, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return parent_v40._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=(EXACT_SHAPE_LABEL,), benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def benchmark_k20_search_rect_3cef_v1(*, use_cupti: bool=False) -> dict[str, Any]: + prior_use_cupti = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + try: + report = evaluate_contract(shapes=_select_contract_shapes((EXACT_SHAPE_LABEL,)), correctness=True, benchmark=True) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + row = report['per_shape'][EXACT_SHAPE_LABEL] + return {'tflops': report['summary']['primary_mean'] or 0.0, 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'invalid_performance_reason': report['summary']['invalid_performance_reason'], 'timing_backend': row.get('timing_backend'), 'use_cupti': row.get('use_cupti'), 'split_count': _split_count(), 'report': report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_k20_tail_q1536_9b9f_wfeed_v2.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_k20_tail_q1536_9b9f_wfeed_v2.py new file mode 100644 index 00000000..c0ce2208 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_k20_tail_q1536_9b9f_wfeed_v2.py @@ -0,0 +1,170 @@ +"""kNN search K20 rectangular-tail work-feed route. + +Minimum target architecture: sm_100a. This additive shape candidate targets the +BF16 D128 non-build K20 rectangular rows: +``search_rect_b1_q1536_m65536_d128_k20`` and +``search_rect_b1_q4096_m65536_d128_k20``. + +This module keeps Q4096 on the inherited fast split4 path and gives Q1536 a +higher-fanout split8/split16 unordered tcgen05 producer with an eight-warp K20 +merge. The objective is to increase stage work feed without reintroducing the +slow bucket fallback. All other shapes delegate to the validated v41 Weave +dispatcher fallback. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import os +from functools import lru_cache +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_k20_large_lowfanout_de1a_v1 as de1a +from . import knn_build_k20_mergeown_08ec_v3 as mergeown +from .._dispatch_runtime import pack_kernel_args +parent_v20 = de1a.parent_v20 +parent_split = de1a.parent_split +base_v1 = de1a.base_v1 +parent_v41 = de1a.parent_v41 +BLOCK_Q = de1a.BLOCK_Q +BLOCK_M = de1a.BLOCK_M +FEAT_D = de1a.FEAT_D +STAGE1_THREADS = de1a.STAGE1_THREADS +K20_MERGE_THREADS = de1a.K20_MERGE_THREADS +GRID_DIM_DEFAULT = de1a.GRID_DIM_DEFAULT +CTA_GROUP = de1a.CTA_GROUP +TOP_K_K20 = de1a.TOP_K_K20 +WORKFEED_TAIL_SPLIT_COUNT = 8 +WORKFEED_SPLIT_COUNTS = (8, 16) +WARP8_MERGE_THREADS = mergeown.WARP8_MERGE_THREADS +EXACT_SHAPE_LABELS = ('search_rect_b1_q1536_m65536_d128_k20', 'search_rect_b1_q4096_m65536_d128_k20') +TAIL_Q_M = (1536, 65536) +FAST_Q_M = (4096, 65536) +EXACT_SHAPE_DIMS = {TAIL_Q_M, FAST_Q_M} +merge_k20_tail_s8_warp8_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_k20_mergeown_08ec_warp8_select_q1536s8warp8", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 20], ["SPLIT_COUNT", 8]], "cta_group": 1, "threads": 256}')) +merge_k20_tail_s16_warp8_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_k20_mergeown_08ec_warp8_select_q1536s16warp8", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 20], ["SPLIT_COUNT", 16]], "cta_group": 1, "threads": 256}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_K20_TAIL_9B9F_WFEED_VERIFY_KERNEL') + if verify_kernel in {'stage1_k20_tail', 'stage1_k20_unordered'}: + return parent_v20.stage1_k20_unordered_ir + if verify_kernel in {'merge_k20_tail_s4', 'merge_k20_unordered_warp_select'}: + return parent_v20.merge_k20_unordered_warp_select_ir + if verify_kernel == 'merge_k20_tail_s2': + return de1a.merge_k20_s2_warp_select_ir + if verify_kernel == 'merge_k20_tail_s8_warp8': + return merge_k20_tail_s8_warp8_ir + if verify_kernel == 'merge_k20_tail_s16_warp8': + return merge_k20_tail_s16_warp8_ir + return parent_v20.stage1_k20_unordered_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k20unordered", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 20]], "cta_group": 1, "threads": 192}')) + +def _forced_tail_split_count() -> int | None: + split_text = os.environ.get('LOOM_KNN_K20_TAIL_9B9F_WFEED_SPLITS') + if not split_text: + return None + split_count = int(split_text) + if split_count not in (2, 4, *WORKFEED_SPLIT_COUNTS): + raise ValueError(''.join(['unsupported K20 Q1536 tail split count: ', format(split_count, '')])) + return split_count + +def _tail_split_count() -> int: + return _forced_tail_split_count() or WORKFEED_TAIL_SPLIT_COUNT + +def _merge_k20_tail_warp8_ir(split_count: int) -> Any: + if split_count == 8: + return merge_k20_tail_s8_warp8_ir + if split_count == 16: + return merge_k20_tail_s16_warp8_ir + raise ValueError(''.join(['unsupported K20 Q1536 tail warp8 split count: ', format(split_count, '')])) + +@lru_cache(maxsize=2) +def _compiled_merge_k20_tail_warp8(split_count: int): + return parent_v20._compile_ir(_merge_k20_tail_warp8_ir(split_count)) + +def _eligible_k20_tail(inputs: dict[str, Any]) -> bool: + q_m = (int(inputs['Q']), int(inputs['M'])) + return not bool(inputs.get('build', False)) and str(inputs['query'].dtype) == 'torch.bfloat16' and (str(inputs['database'].dtype) == 'torch.bfloat16') and (int(inputs['B']) == 1) and (q_m in EXACT_SHAPE_DIMS) and (int(inputs['D']) == FEAT_D) and (int(inputs['K']) == TOP_K_K20) + +def _launch_q1536_forced_unordered(inputs: dict[str, Any], *, split_count: int) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_q_tile_pairs = (num_q_tiles + CTA_GROUP - 1) // CTA_GROUP + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tile_pairs * split_count + stage1_grid = min(total_work * CTA_GROUP, GRID_DIM_DEFAULT) + partial_dists, partial_indices = parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), bsz * n_query, BLOCK_Q, dim, dim) + tmap_database = base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, dim, dim) + stage1_ir_obj = parent_v20.stage1_k20_unordered_ir + stage1_kernel = parent_v20._compiled_stage1_unordered_for_exact_k(top_k) + stage1_kernel.launch_cluster(grid=(stage1_grid, 1, 1), block=(STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage1_ir_obj, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tile_pairs=num_q_tile_pairs, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), cluster_dims=(CTA_GROUP, 1, 1), shared_mem=stage1_ir_obj.computed_smem_bytes) + if split_count == 2: + merge_ir = de1a.merge_k20_s2_warp_select_ir + merge_kernel = de1a._compiled_merge_k20_s2_warp_select() + merge_threads = K20_MERGE_THREADS + merge_grid = (bsz * n_query + 3) // 4 + elif split_count == 4: + merge_ir = parent_v20.merge_k20_unordered_warp_select_ir + merge_kernel = parent_v20._compiled_merge_k20_unordered_warp_select() + merge_threads = K20_MERGE_THREADS + merge_grid = (bsz * n_query + 3) // 4 + else: + merge_ir = _merge_k20_tail_warp8_ir(split_count) + merge_kernel = _compiled_merge_k20_tail_warp8(split_count) + merge_threads = WARP8_MERGE_THREADS + merge_grid = (bsz * n_query + 7) // 8 + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(merge_threads, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_ir.computed_smem_bytes) + +def _launch_k20_tail(inputs: dict[str, Any]) -> None: + q_m = (int(inputs['Q']), int(inputs['M'])) + if q_m == TAIL_Q_M: + _launch_q1536_forced_unordered(inputs, split_count=_tail_split_count()) + return + parent_v20._launch_k32_split_path(inputs, split_count=de1a.SEED_SPLIT_COUNT) + +def launch_from_contract_inputs(inputs: dict[str, Any]) -> None: + if _eligible_k20_tail(inputs): + _launch_k20_tail(inputs) + return + parent_v41.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(candidate, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return de1a._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=EXACT_SHAPE_LABELS, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def benchmark_k20_tail_q1536_9b9f_wfeed_v2(*, use_cupti: bool=True) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + report = evaluate_contract(shapes=_select_contract_shapes(EXACT_SHAPE_LABELS), correctness=True, benchmark=True) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + rows = report['per_shape'] + split_counts = {label: _tail_split_count() if (int(rows[label]['Q']), int(rows[label]['M'])) == TAIL_Q_M else de1a.SEED_SPLIT_COUNT for label in EXACT_SHAPE_LABELS if label in rows} + timing_backends = sorted({str(row.get('timing_backend')) for row in rows.values() if row.get('timing_backend')}) + return {'tflops': report['summary']['primary_mean'] or 0.0, 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'invalid_performance_reason': report['summary']['invalid_performance_reason'], 'timing_backends': timing_backends, 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'split_count_by_shape': split_counts, 'measured_entrypoint': 'loom.examples.weave.knn_build_k20_tail_q1536_9b9f_wfeed_v2:benchmark_k20_tail_q1536_9b9f_wfeed_v2', 'report': report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_k20raglargek_4ebb_v43.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_k20raglargek_4ebb_v43.py new file mode 100644 index 00000000..8cd70823 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_k20raglargek_4ebb_v43.py @@ -0,0 +1,104 @@ +"""kNN build/search v43 exact RAG large-K K20 route. + +Minimum target architecture: sm_100a. This additive candidate specializes only +``rag_offline_largek_b1_q4096_m100000_d128_k20``. The route reuses the +existing tcgen05 split-local K20 unordered producer and the v21 four-warp +split-major K20 merge, then falls back to the v41 dispatcher for every other +contract shape. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import os +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatchscore_tailinf_knn_build_dispatch_slurm_0610_6329_v41 as parent_v41 +from . import knn_build_evolve_7bfc_k20merge_warpselect_tiebreak_knn_build_dispatch_slurm_0610_6329_v21 as parent_v21 +from . import knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2_k32split_v20 as parent_v20 +from . import knn_build_evolve_7bfc_split_v1 as parent_split +from . import knn_build_evolve_7bfc_v1 as base_v1 +from .._dispatch_runtime import pack_kernel_args +TARGET_SHAPE = 'rag_offline_largek_b1_q4096_m100000_d128_k20' +BLOCK_Q = parent_v20.BLOCK_Q +BLOCK_M = parent_v20.BLOCK_M +FEAT_D = parent_v20.FEAT_D +STAGE1_THREADS = parent_v20.STAGE1_THREADS +K20_MERGE_THREADS = parent_v20.K20_COOP_MERGE_THREADS +GRID_DIM_DEFAULT = parent_v20.GRID_DIM_DEFAULT +CTA_GROUP = parent_v20.CTA_GROUP +TOP_K_K20 = 20 +SPLIT_COUNT_K20_RAGLARGEK = parent_v20.MEDIUM_SPLITS + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_K20_RAGLARGEK_VERIFY_KERNEL') + if verify_kernel == 'stage1_k20_raglargek': + return parent_v20.stage1_k20_unordered_ir + if verify_kernel == 'merge_k20_raglargek': + return parent_v21.merge_k20_unordered_warp_select_splitmajor_ir + return parent_v21.merge_k20_unordered_warp_select_splitmajor_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k20_merge_s4_unordered_warp_select_splitmajor", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 20], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 128}')) + +def _eligible_k20_raglargek(inputs: dict[str, Any]) -> bool: + return not bool(inputs.get('build', False)) and str(inputs['query'].dtype) == 'torch.bfloat16' and (str(inputs['database'].dtype) == 'torch.bfloat16') and (int(inputs['B']) == 1) and (int(inputs['Q']) == 4096) and (int(inputs['M']) == 100000) and (int(inputs['D']) == FEAT_D) and (int(inputs['K']) == TOP_K_K20) + +def _launch_k20_raglargek_path(inputs: dict[str, Any]) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + split_count = SPLIT_COUNT_K20_RAGLARGEK + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_q_tile_pairs = (num_q_tiles + CTA_GROUP - 1) // CTA_GROUP + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tile_pairs * split_count + stage1_grid = min(total_work * CTA_GROUP, GRID_DIM_DEFAULT) + merge_grid = (bsz * n_query + 3) // 4 + partial_dists, partial_indices = parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), bsz * n_query, BLOCK_Q, dim, dim) + tmap_database = base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, dim, dim) + stage1_ir_obj = parent_v20.stage1_k20_unordered_ir + stage1_kernel = parent_v20._compiled_stage1_unordered_for_exact_k(top_k) + stage1_kernel.launch_cluster(grid=(stage1_grid, 1, 1), block=(STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage1_ir_obj, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tile_pairs=num_q_tile_pairs, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), cluster_dims=(CTA_GROUP, 1, 1), shared_mem=stage1_ir_obj.computed_smem_bytes) + merge_ir_obj = parent_v21.merge_k20_unordered_warp_select_splitmajor_ir + merge_kernel = parent_v21._compiled_merge_k20_unordered_warp_select_splitmajor() + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(K20_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_ir_obj.computed_smem_bytes) + +def launch_from_contract_inputs(inputs: dict[str, Any]) -> None: + if _eligible_k20_raglargek(inputs): + _launch_k20_raglargek_path(inputs) + return + parent_v41.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(candidate, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return parent_v41._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=(TARGET_SHAPE,), benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint: run real contract correctness for selected shapes.""" + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def benchmark_k20raglargek_4ebb_v43(*, use_cupti: bool=False) -> dict[str, Any]: + """Opt-in benchmark hook for the exact non-build RAG K20 large-K row.""" + prior_use_cupti = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + try: + report = evaluate_contract(shapes=_select_contract_shapes((TARGET_SHAPE,)), correctness=True, benchmark=True) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + timing_backends = sorted({result.get('timing_backend') for result in report.get('per_shape', {}).values() if result.get('timing_backend') is not None}) + return {'tflops': report['summary']['primary_mean'] or 0.0, 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'invalid_performance_reason': report['summary']['invalid_performance_reason'], 'contract_summary': report['summary'], 'contract_performance': report['performance'], 'timing_backends': timing_backends, 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'report': report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_k30_q4096_6998_warpselect_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_k30_q4096_6998_warpselect_v1.py new file mode 100644 index 00000000..096e72b0 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_k30_q4096_6998_warpselect_v1.py @@ -0,0 +1,174 @@ +"""Exact Q4096/M4096 K30 build seed with a warp-select split merge. + +Minimum target architecture: sm_100a. This additive 6998 bucket-kernel +candidate keeps the v20 four-split unordered tcgen05 stage-1 producer for +``build_k_sweep_qm4096_k30`` and replaces only the final K30 unordered merge +with a four-warp register selection network. The production path is Weave-only; +FlashLib is used only by the contract harness as a timing/correctness peer. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from collections.abc import Callable +from functools import lru_cache +from pathlib import Path +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_6998_residual_19b3_overlay_v1 as current_6998 +from . import knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2_k32split_v20 as v20 +from .._dispatch_runtime import pack_kernel_args +MODULE = 'loom.examples.weave.knn_build_k30_q4096_6998_warpselect_v1' +TARGET_SHAPE = 'build_k_sweep_qm4096_k30' +TARGET_SHAPES = (TARGET_SHAPE,) +SEED_ID = 'k30_q4096_6998_warpselect_v1' +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +BASELINE_6998_ENTRYPOINT = 'loom.examples.weave.knn_build_dispatch_6998_residual_19b3_overlay_v1:launch_from_contract_inputs' +BASELINE_V20_ENTRYPOINT = 'loom.examples.weave.knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2_k32split_v20:launch_from_contract_inputs' +PRODUCTION_ROUTE_MODULES = {SEED_ID: ROUTE_ENTRYPOINT, 'baseline_6998': BASELINE_6998_ENTRYPOINT, 'baseline_v20': BASELINE_V20_ENTRYPOINT} +knn_build_k30_q4096_6998_merge_s4_unordered_warp_select = _decode_capture(_json_loads('{"__ir__": "knn_build_k30_q4096_6998_merge_s4_unordered_warp_select", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 30], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 128}')) +merge_k30_q4096_warp_select_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_k30_q4096_6998_merge_s4_unordered_warp_select", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 30], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 128}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_K30_6998_VERIFY_KERNEL') + if verify_kernel == 'stage1_k30_unordered': + return v20.stage1_k30_unordered_ir + return merge_k30_q4096_warp_select_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_k30_q4096_6998_merge_s4_unordered_warp_select", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 30], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 128}')) + +def _compiled_merge_k30_q4096_warp_select(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0060"}')) + +def _dtype_name(inputs: dict[str, Any], name: str) -> str: + tensor = inputs.get(name) + if tensor is not None: + return str(tensor.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')) + +def _label_can_hit(inputs: dict[str, Any]) -> bool: + label = inputs.get('label') + return label is None or str(label) == TARGET_SHAPE + +def _eligible_k30_q4096(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs) and bool(inputs.get('build', False)) and (int(inputs.get('B', -1)) == 1) and (int(inputs.get('Q', -1)) == 4096) and (int(inputs.get('M', -1)) == 4096) and (int(inputs.get('D', -1)) == v20.FEAT_D) and (int(inputs.get('K', -1)) == 30) and (_dtype_name(inputs, 'query') == 'bfloat16') and (_dtype_name(inputs, 'database') == 'bfloat16') + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if not force_fallback and _eligible_k30_q4096(inputs): + return ROUTE_ENTRYPOINT + return current_6998.route_for_contract_inputs(inputs) + +def _launch_k30_q4096_warp_select(inputs: dict[str, Any]) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + split_count = v20.MEDIUM_SPLITS + num_q_tiles = (n_query + v20.BLOCK_Q - 1) // v20.BLOCK_Q + num_q_tile_pairs = (num_q_tiles + v20.CTA_GROUP - 1) // v20.CTA_GROUP + num_db_tiles = (n_database + v20.BLOCK_M - 1) // v20.BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tile_pairs * split_count + stage1_grid = min(total_work * v20.CTA_GROUP, v20.GRID_DIM_DEFAULT) + merge_grid = (bsz * n_query + 3) // 4 + partial_dists, partial_indices = v20.parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = v20.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), bsz * n_query, v20.BLOCK_Q, dim, dim) + tmap_database = v20.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, v20.BLOCK_M, dim, dim) + stage1_kernel = v20._compiled_stage1_unordered_for_exact_k(top_k) + stage1_kernel.launch_cluster(grid=(stage1_grid, 1, 1), block=(v20.STAGE1_THREADS, 1, 1), args=pack_kernel_args(v20.stage1_k30_unordered_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tile_pairs=num_q_tile_pairs, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), cluster_dims=(v20.CTA_GROUP, 1, 1), shared_mem=v20.stage1_k30_unordered_ir.computed_smem_bytes) + merge_kernel = _compiled_merge_k30_q4096_warp_select() + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(v20.K32_COOP_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_k30_q4096_warp_select_ir.computed_smem_bytes) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + if not force_fallback and _eligible_k30_q4096(inputs): + _launch_k30_q4096_warp_select(inputs) + return + current_6998.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) + +def candidate_current_6998(inputs: dict[str, Any]) -> None: + current_6998.launch_from_contract_inputs(inputs) + +def candidate_v20(inputs: dict[str, Any]) -> None: + v20.launch_from_contract_inputs(inputs) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return current_6998._select_contract_shapes(shape_labels) + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=None, kernel_fn: Callable[[dict[str, Any]], Any]=candidate, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + shapes = None if shape_labels is None else _select_contract_shapes(shape_labels) + return evaluate_contract(shapes=shapes, correctness=correctness, benchmark=benchmark, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = _run_with_timing_backend(use_cupti=True, shape_labels=shape_labels, benchmark=benchmark, correctness=True) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + rows = [] + for shape in selected: + inputs = current_6998.base_f30c._trace_inputs_from_shape(shape) + route = route_for_contract_inputs(inputs, force_fallback=force_fallback) + if route == ROUTE_ENTRYPOINT: + row = {'shape_key': shape['label'], 'selected_route': route, 'selected_entrypoint': ROUTE_ENTRYPOINT, 'selected_seed': SEED_ID, 'expected_seed': SEED_ID, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': '6998_k30_q4096_exact_warpselect_guard', 'guard_condition': 'exact BF16 build B=1 Q=M=4096 D=128 K=30', 'base_6998_route': current_6998.route_for_contract_inputs(inputs), 'baseline_v20_route': BASELINE_V20_ENTRYPOINT, 'classification': 'unmeasured'} + else: + row = current_6998.route_trace_for_contract_shapes((shape['label'],), force_fallback=force_fallback)[0] + row = dict(row) + row['candidate_guard_status'] = 'forced_fallback_or_guard_miss' + rows.append(current_6998.base_f30c._normalize_route_row(row)) + return rows + +def _per_shape_rows(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return {label: dict(report.get('per_shape', {}).get(label, {})) for label in labels} + +def _candidate_row_delta(candidate_report: dict[str, Any], baseline_6998_report: dict[str, Any], baseline_v20_report: dict[str, Any]) -> dict[str, Any]: + candidate_row = candidate_report.get('per_shape', {}).get(TARGET_SHAPE, {}) + baseline_6998_row = baseline_6998_report.get('per_shape', {}).get(TARGET_SHAPE, {}) + baseline_v20_row = baseline_v20_report.get('per_shape', {}).get(TARGET_SHAPE, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_6998_ms = baseline_6998_row.get('kernel_ms') + baseline_v20_ms = baseline_v20_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') or baseline_6998_row.get('flashlib_ms') + return {'shape_key': TARGET_SHAPE, 'candidate_ms': candidate_ms, 'baseline_6998_ms': baseline_6998_ms, 'baseline_v20_ms': baseline_v20_ms, 'flashlib_ms': flashlib_ms, 'speedup_vs_6998': baseline_6998_ms / candidate_ms if candidate_ms and baseline_6998_ms else None, 'speedup_vs_v20': baseline_v20_ms / candidate_ms if candidate_ms and baseline_v20_ms else None, 'ratio_vs_flashlib': flashlib_ms / candidate_ms if candidate_ms and flashlib_ms else None, 'candidate_passed': candidate_row.get('passed'), 'baseline_6998_passed': baseline_6998_row.get('passed'), 'baseline_v20_passed': baseline_v20_row.get('passed'), 'timing_backend': candidate_row.get('timing_backend') or baseline_6998_row.get('timing_backend') or baseline_v20_row.get('timing_backend')} + +def benchmark_candidate_k30_q4096_6998_warpselect_v1(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES, benchmark_correctness: bool=True) -> dict[str, Any]: + baseline_6998_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_current_6998, correctness=benchmark_correctness, benchmark=True) + baseline_v20_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_v20, correctness=benchmark_correctness, benchmark=True) + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate, correctness=benchmark_correctness, benchmark=True) + delta = _candidate_row_delta(candidate_report, baseline_6998_report, baseline_v20_report) + candidate_metric = candidate_report.get('summary', {}).get('primary_mean') + baseline_6998_metric = baseline_6998_report.get('summary', {}).get('primary_mean') + baseline_v20_metric = baseline_v20_report.get('summary', {}).get('primary_mean') + return {'candidate_id': SEED_ID, 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_candidate_k30_q4096_6998_warpselect_v1']), 'baseline_6998_entrypoint': BASELINE_6998_ENTRYPOINT, 'baseline_v20_entrypoint': BASELINE_V20_ENTRYPOINT, 'selected_seeds': (SEED_ID,), 'all_correct': candidate_report.get('summary', {}).get('all_correct'), 'baseline_6998_all_correct': baseline_6998_report.get('summary', {}).get('all_correct'), 'baseline_v20_all_correct': baseline_v20_report.get('summary', {}).get('all_correct'), 'performance_comparable': candidate_report.get('summary', {}).get('performance_comparable'), 'baseline_6998_performance_comparable': baseline_6998_report.get('summary', {}).get('performance_comparable'), 'baseline_v20_performance_comparable': baseline_v20_report.get('summary', {}).get('performance_comparable'), 'tflops': candidate_metric, 'baseline_6998_tflops': baseline_6998_metric, 'baseline_v20_tflops': baseline_v20_metric, 'metric_delta_vs_6998': candidate_metric - baseline_6998_metric if candidate_metric and baseline_6998_metric else None, 'metric_delta_vs_v20': candidate_metric - baseline_v20_metric if candidate_metric and baseline_v20_metric else None, 'timing_backend': 'cupti' if use_cupti else 'cuda_event', 'denominator': 'k30_q4096_exact_shape', 'shape_labels': list(TARGET_SHAPES if shape_labels is None else shape_labels), 'selected_route_rows': _per_shape_rows(candidate_report, tuple(shape_labels or TARGET_SHAPES)), 'baseline_6998_route_rows': _per_shape_rows(baseline_6998_report, tuple(shape_labels or TARGET_SHAPES)), 'baseline_v20_route_rows': _per_shape_rows(baseline_v20_report, tuple(shape_labels or TARGET_SHAPES)), 'seed_delta_matrix': [delta], 'route_trace': route_trace_for_contract_shapes(shape_labels), 'forced_fallback_route_trace': route_trace_for_contract_shapes(shape_labels, force_fallback=True), 'route_modules': PRODUCTION_ROUTE_MODULES, 'report': candidate_report, 'baseline_6998_report': baseline_6998_report, 'baseline_v20_report': baseline_v20_report, 'route_trace_included': True} + +def write_benchmark_artifact(artifact_dir: str | Path, *, use_cupti: bool=True, shape_labels=TARGET_SHAPES) -> dict[str, str]: + payload = benchmark_candidate_k30_q4096_6998_warpselect_v1(use_cupti=use_cupti, shape_labels=shape_labels) + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + path = out_dir / 'k30_q4096_warpselect_6998_v1.json' + path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + return {'candidate_payload': str(path)} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_k48_k96_floor_repair_d03c_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_k48_k96_floor_repair_d03c_v1.py new file mode 100644 index 00000000..aaab61d3 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_k48_k96_floor_repair_d03c_v1.py @@ -0,0 +1,296 @@ +"""kNN build K48/K96 floor-repair bucket candidate for d03c. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +keeps production dispatch Weave-only and targets the active 4399 floor blockers: + +* BF16 build B=1,Q=M in {2048,4096},D=128,K=48 with the v25 K48 tcgen05/TMA + producer and a new K48-specific warp-select final merge. +* BF16 build B=1,Q=M=1024,D=128,K=96 through the existing c13e/229a exact K96 + seed, which already clears the active FlashLib floor. + +All other shapes fall back to the 4399 core+K5 campaign dispatcher. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from functools import lru_cache +from pathlib import Path +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_build_k96_d64_c13e_v1 as k96_c13e +from . import knn_build_dispatch_fd9b_floor_seed_portfolio_5720_full90_synthesis_v1 as parent_4399 +from . import knn_build_over32_topk_knn_build_dispatch_slurm_0610_6329_v25 as k48_v25 +from .._dispatch_runtime import pack_kernel_args +MODULE = 'loom.examples.weave.knn_build_k48_k96_floor_repair_d03c_v1' +BLOCK_Q = k48_v25.BLOCK_Q +BLOCK_M = k48_v25.BLOCK_M +FEAT_D = k48_v25.FEAT_D +STAGE1_THREADS = k48_v25.STAGE1_THREADS +K48_COOP_MERGE_THREADS = 128 +GRID_DIM_DEFAULT = k48_v25.GRID_DIM_DEFAULT +CTA_GROUP = k48_v25.CTA_GROUP +K48_SPLITS = k48_v25.OVER32_SPLITS +K48_TOP_K = 48 +TARGET_K48_Q2048 = 'build_over32_stress_qm2048_k48' +TARGET_K48_Q4096 = 'build_over32_stress_qm4096_k48' +TARGET_K96_Q1024 = 'build_over64_stress_qm1024_k96' +TARGET_SHAPES = (TARGET_K48_Q2048, TARGET_K48_Q4096, TARGET_K96_Q1024) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +K48_TARGET_SHAPES = (TARGET_K48_Q2048, TARGET_K48_Q4096) +K48_TARGET_SHAPE_SET = set(K48_TARGET_SHAPES) +K96_TARGET_SHAPES = (TARGET_K96_Q1024,) +SEED_K48_WARPSELECT_ID = 'd03c_k48_s4_warpselect_merge' +SEED_K96_Q1024_ID = k96_c13e.SEED_K96_Q1024_ID +SEED_ID = 'candidate_d03c_k48_warpselect_k96_q1024_floor_repair_v1' +ROUTE_K48_WARPSELECT = ''.join([format(MODULE, ''), ':k48_s4_warpselect_merge']) +ROUTE_K96_Q1024 = k96_c13e.ROUTE_K96_ENTRYPOINT +ROUTE_PARENT_4399 = parent_4399.ROUTE_ENTRYPOINT +BENCHMARK_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_k48_k96_floor_repair_d03c_v1']) +SOURCE_TASKS = {SEED_ID: 'weave-evolve-knn-build-d03c K48/K96 floor-repair bucket', SEED_K48_WARPSELECT_ID: 'd03c K48 v25 producer plus K48 warp-select merge', SEED_K96_Q1024_ID: 'weave-evolve-knn-build-c13e / 229a exact K96 q1024 route'} +PRODUCTION_ROUTE_MODULES = _decode_capture(_json_loads('{"__dict_items__": [["d03c_k48_s4_warpselect_merge", "loom.examples.weave.knn_build_k48_k96_floor_repair_d03c_v1:launch_from_contract_inputs"], ["c13e_k96_q1024_229a_s2_exactprefill", "loom.examples.weave.knn_build_over64_k96_exactall_229a_v1:launch_from_contract_inputs"], ["candidate_fd9b_plus_01bb_2425_1b34_k5_bd76_k20_9334_k32_full90_v1", "loom.examples.weave.knn_build_dispatch_fd9b_floor_seed_portfolio_5720_full90_synthesis_v1:launch_from_contract_inputs"]]}')) +stage1_k48_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k48over32", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 48]], "cta_group": 1, "threads": 192}')) +knn_build_k48_merge_s4_unordered_warp_select = _decode_capture(_json_loads('{"__ir__": "knn_build_k48_merge_s4_unordered_warp_select", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 48], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 128}')) +merge_k48_warpselect_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_k48_merge_s4_unordered_warp_select", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 48], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 128}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_K48_K96_D03C_VERIFY_KERNEL') + if verify_kernel == 'merge_k48_warpselect': + return merge_k48_warpselect_ir + return stage1_k48_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k48over32", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 48]], "cta_group": 1, "threads": 192}')) + +def _compiled_stage1_k48(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0061"}')) + +def _compiled_merge_k48_warpselect(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0062"}')) + +def _dtype_name(inputs: dict[str, Any]) -> str: + query = inputs.get('query') + if query is not None: + return str(query.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')) + +def _label_can_hit(inputs: dict[str, Any], labels: set[str]) -> bool: + value = inputs.get('label') + return value is None or str(value) in labels + +def _eligible_k48(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, K48_TARGET_SHAPE_SET) and bool(inputs.get('build', False)) and (int(inputs.get('B', -1)) == 1) and (int(inputs.get('Q', -1)) == int(inputs.get('M', -2))) and (int(inputs.get('Q', -1)) in (2048, 4096)) and (int(inputs.get('D', -1)) == FEAT_D) and (int(inputs.get('K', -1)) == K48_TOP_K) and (_dtype_name(inputs) == 'bfloat16') + +def _eligible_k96_q1024(inputs: dict[str, Any]) -> bool: + return k96_c13e._eligible_k96(inputs, label=TARGET_K96_Q1024, n_query=1024) + +def _selected_seed(inputs: dict[str, Any]) -> tuple[str | None, str | None]: + if _eligible_k48(inputs): + matched_label = str(inputs.get('label') or (TARGET_K48_Q2048 if int(inputs.get('Q', -1)) == 2048 else TARGET_K48_Q4096)) + return (SEED_K48_WARPSELECT_ID, matched_label) + if _eligible_k96_q1024(inputs): + return (SEED_K96_Q1024_ID, TARGET_K96_Q1024) + return (None, None) + +def _launch_k48_warpselect(inputs: dict[str, Any]) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + split_count = K48_SPLITS + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_q_tile_pairs = (num_q_tiles + CTA_GROUP - 1) // CTA_GROUP + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tile_pairs * split_count + stage1_grid = min(total_work * CTA_GROUP, GRID_DIM_DEFAULT) + merge_grid = (bsz * n_query + 3) // 4 + partial_dists, partial_indices = k48_v25.parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = k48_v25.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), bsz * n_query, BLOCK_Q, dim, dim) + tmap_database = k48_v25.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, dim, dim) + stage1_kernel = _compiled_stage1_k48() + stage1_kernel.launch_cluster(grid=(stage1_grid, 1, 1), block=(STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage1_k48_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tile_pairs=num_q_tile_pairs, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), cluster_dims=(CTA_GROUP, 1, 1), shared_mem=stage1_k48_ir.computed_smem_bytes) + merge_kernel = _compiled_merge_k48_warpselect() + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(K48_COOP_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_k48_warpselect_ir.computed_smem_bytes) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if force_fallback: + return parent_4399.route_for_contract_inputs(inputs) + selected_seed, _label = _selected_seed(inputs) + if selected_seed == SEED_K48_WARPSELECT_ID: + return ROUTE_K48_WARPSELECT + if selected_seed == SEED_K96_Q1024_ID: + return k96_c13e.route_for_contract_inputs(inputs) + return parent_4399.route_for_contract_inputs(inputs) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + if force_fallback: + parent_4399.launch_from_contract_inputs(inputs) + return + selected_seed, _label = _selected_seed(inputs) + if selected_seed == SEED_K48_WARPSELECT_ID: + _launch_k48_warpselect(inputs) + return + if selected_seed == SEED_K96_Q1024_ID: + k96_c13e.launch_from_contract_inputs(inputs) + return + parent_4399.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + return eval_mod.evaluate(candidate, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return parent_4399._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _shape_labels(shape_labels) -> tuple[str, ...]: + if shape_labels is None: + return TARGET_SHAPES + return tuple((str(label) for label in shape_labels)) + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=None, kernel_fn=candidate, correctness: bool=True) -> dict[str, Any]: + prior = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + try: + return eval_mod.evaluate(kernel_fn, shapes=_select_contract_shapes(_shape_labels(shape_labels)), correctness=correctness, benchmark=True) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False) -> list[dict[str, Any]]: + labels = _shape_labels(shape_labels) + rows: list[dict[str, Any]] = [] + for shape in _select_contract_shapes(labels): + inputs = parent_4399._trace_inputs_for_shape(shape) + label = str(inputs.get('label')) + selected_seed, matched_label = _selected_seed(inputs) + if force_fallback: + base = parent_4399.route_trace_for_contract_shapes((label,))[0] + rows.append(parent_4399._normalize_route_row({**base, 'expected_seed': selected_seed, 'guard_id': 'forced_fallback_d03c_k48_k96_disabled', 'guard_condition': 'forced fallback to 4399 core+K5; d03c exact bucket disabled', 'classification': 'guard-miss' if selected_seed is not None else 'route-ok'})) + continue + if selected_seed == SEED_K48_WARPSELECT_ID: + rows.append(parent_4399._normalize_route_row({'shape_key': label, 'selected_route': ROUTE_K48_WARPSELECT, 'selected_entrypoint': ''.join([format(MODULE, ''), ':launch_from_contract_inputs']), 'selected_seed': selected_seed, 'expected_seed': selected_seed, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': 'd03c_k48_s4_warpselect_guard', 'guard_condition': 'exact BF16 build B=1 Q=M in {2048,4096} D=128 K=48 split4 warp-select merge', 'matched_label': matched_label, 'parent_dispatcher_route': parent_4399.route_for_contract_inputs(inputs), 'baseline_dispatcher_route': parent_4399.route_for_contract_inputs(inputs), 'classification': 'unmeasured'})) + elif selected_seed == SEED_K96_Q1024_ID: + rows.append(parent_4399._normalize_route_row({'shape_key': label, 'selected_route': k96_c13e.route_for_contract_inputs(inputs), 'selected_entrypoint': ROUTE_K96_Q1024, 'selected_seed': selected_seed, 'expected_seed': selected_seed, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': 'd03c_k96_q1024_c13e_guard', 'guard_condition': 'exact BF16 build B=1 Q=M=1024 D=128 K=96 split2', 'matched_label': matched_label, 'parent_dispatcher_route': parent_4399.route_for_contract_inputs(inputs), 'baseline_dispatcher_route': parent_4399.route_for_contract_inputs(inputs), 'classification': 'unmeasured'})) + else: + rows.append(parent_4399.route_trace_for_contract_shapes((label,))[0]) + return rows + +def _annotate_route_trace(route_trace: list[dict[str, Any]], candidate_report: dict[str, Any], baseline_report: dict[str, Any], *, speedup_floor: float) -> list[dict[str, Any]]: + new_seed_ids = {SEED_K48_WARPSELECT_ID, SEED_K96_Q1024_ID} + annotated = [] + for row in route_trace: + label = str(row.get('shape_key')) + out = dict(row) + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') + speedup_vs_baseline = baseline_ms / candidate_ms if candidate_ms and baseline_ms else None + speedup_vs_external = flashlib_ms / candidate_ms if candidate_ms and flashlib_ms else None + out['dispatcher_kernel_ms'] = candidate_ms + out['baseline_4399_kernel_ms'] = baseline_ms + out['shape_specific_kernel_ms'] = candidate_ms if out.get('selected_seed') in new_seed_ids else None + out['external_baseline_ms'] = flashlib_ms + out['flashlib_ms'] = flashlib_ms + out['relative_speedup_vs_baseline'] = speedup_vs_baseline + out['speedup_vs_external_baseline'] = speedup_vs_external + out['external_baseline_ref'] = 'same_session' if flashlib_ms is not None else 'not_available' + out['route_changed_vs_4399'] = out.get('selected_route') != out.get('baseline_dispatcher_route') + if out.get('selected_seed') in new_seed_ids and speedup_vs_external is not None: + out['classification'] = 'seed-consumed' if speedup_vs_external >= speedup_floor else 'kernel-slow' + elif out.get('selected_seed') in new_seed_ids: + out['classification'] = 'seed-consumed' + annotated.append(parent_4399._normalize_route_row(out)) + return annotated + +def _below_flashlib_rows(report: dict[str, Any], route_trace: list[dict[str, Any]], *, floor: float) -> list[dict[str, Any]]: + trace_by_label = {str(row['shape_key']): row for row in route_trace} + rows = [] + for label, row in sorted(report.get('per_shape', {}).items()): + ratio = row.get('ratio_vs_flashlib') + if not isinstance(ratio, float | int) or ratio >= floor: + continue + trace_row = trace_by_label.get(label, {}) + rows.append({'shape_key': label, 'kernel_ms': row.get('kernel_ms'), 'flashlib_ms': row.get('flashlib_ms'), 'ratio_vs_flashlib': ratio, 'selected_route': trace_row.get('selected_route'), 'selected_seed': trace_row.get('selected_seed'), 'expected_seed': trace_row.get('expected_seed'), 'route_kind': trace_row.get('route_kind', 'unknown'), 'classification': trace_row.get('classification', 'unmeasured')}) + return rows + +def _seed_delta_matrix(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> list[dict[str, Any]]: + matrix = [] + for label in TARGET_SHAPES: + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + inputs = parent_4399._inputs_for_label(label) + selected_seed, _matched = _selected_seed(inputs) + matrix.append({'shape_key': label, 'baseline_route': parent_4399.route_for_contract_inputs(inputs), 'candidate_route': route_for_contract_inputs(inputs), 'selected_seed': selected_seed, 'candidate_ms': candidate_ms, 'baseline_4399_ms': baseline_ms, 'flashlib_ms': candidate_row.get('flashlib_ms'), 'delta_ms_candidate_minus_4399': candidate_ms - baseline_ms if candidate_ms is not None and baseline_ms is not None else None, 'speedup_vs_4399': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'ratio_vs_flashlib': candidate_row.get('ratio_vs_flashlib'), 'timing_backend': candidate_row.get('timing_backend') or baseline_row.get('timing_backend')}) + return matrix + +def benchmark_baseline_4399(*, use_cupti: bool=True, shape_labels=None, benchmark_correctness: bool=True) -> dict[str, Any]: + return _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=parent_4399.candidate_floor_core_k5_full90_v1, correctness=benchmark_correctness) + +def benchmark_k48_k96_floor_repair_d03c_v1(*, use_cupti: bool=True, shape_labels=None, baseline_report: dict[str, Any] | None=None, benchmark_correctness: bool=True, speedup_floor: float=1.2) -> dict[str, Any]: + labels = _shape_labels(shape_labels) + if baseline_report is None: + baseline_report = benchmark_baseline_4399(use_cupti=use_cupti, shape_labels=labels, benchmark_correctness=benchmark_correctness) + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=labels, kernel_fn=candidate, correctness=benchmark_correctness) + route_trace = _annotate_route_trace(route_trace_for_contract_shapes(labels), candidate_report, baseline_report, speedup_floor=speedup_floor) + below_1x = _below_flashlib_rows(candidate_report, route_trace, floor=1.0) + below_floor = _below_flashlib_rows(candidate_report, route_trace, floor=speedup_floor) + candidate_metric = candidate_report['summary']['primary_mean'] + baseline_metric = baseline_report['summary']['primary_mean'] + metric_delta = candidate_metric - baseline_metric if candidate_metric is not None and baseline_metric is not None else None + timing_backend = 'cupti' if use_cupti else 'cuda_event' + return {'candidate_id': SEED_ID, 'baseline_candidate_id': parent_4399._candidate_id(parent_4399.DEFAULT_CANDIDATE_KEY), 'selected_seeds': (SEED_K48_WARPSELECT_ID, SEED_K96_Q1024_ID), 'source_tasks': SOURCE_TASKS, 'tflops': candidate_metric, 'baseline_4399_tflops': baseline_metric, 'metric_delta_vs_4399': metric_delta, 'all_correct': candidate_report['summary']['all_correct'], 'baseline_all_correct': baseline_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'baseline_performance_comparable': baseline_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'baseline_invalid_performance_reason': baseline_report['summary']['invalid_performance_reason'], 'measured_entrypoint': BENCHMARK_ENTRYPOINT, 'baseline_entrypoint': parent_4399.FLOOR_CORE_K5_BENCHMARK_ENTRYPOINT, 'route_entrypoint': ''.join([format(MODULE, ''), ':launch_from_contract_inputs']), 'measured_shape_labels': labels, 'timing_backend': timing_backend, 'denominator': 'd03c_k48_k96_exact3' if labels == TARGET_SHAPES else ''.join(['custom_', format(len(labels), '')]), 'selected_route_labels': TARGET_SHAPES, 'selected_route_rows': {label: candidate_report['per_shape'].get(label, {}) for label in labels}, 'baseline_selected_route_rows': {label: baseline_report['per_shape'].get(label, {}) for label in labels}, 'seed_delta_matrix': _seed_delta_matrix(candidate_report, baseline_report), 'route_modules': PRODUCTION_ROUTE_MODULES, 'route_trace': route_trace, 'forced_fallback_route_trace': route_trace_for_contract_shapes(labels, force_fallback=True), 'route_trace_included': True, 'contract_summary': candidate_report['summary'], 'baseline_contract_summary': baseline_report['summary'], 'contract_performance': candidate_report['performance'], 'baseline_contract_performance': baseline_report['performance'], 'use_cupti': use_cupti, 'benchmark_correctness_checked': benchmark_correctness, 'benchmark_time_flashlib': True, 'performance_coverage': 'partial' if below_floor else 'pass', 'coverage_only_routes': [], 'hot_bucket_blockers': below_floor, 'flashlib_parity_ledger': {'baseline_ref_scope': 'same_session', 'baseline_payload': None, 'speedup_floor': speedup_floor, 'rows_below_1x': below_1x, 'rows_below_floor': below_floor, 'omitted_reason': None}, 'rank_objective': {'metric': 'tflops', 'direction': 'maximize', 'value': candidate_metric, 'baseline_4399_value': baseline_metric, 'delta_vs_4399': metric_delta, 'denominator': 'd03c_k48_k96_exact3' if labels == TARGET_SHAPES else ''.join(['custom_', format(len(labels), '')])}, 'report': candidate_report, 'baseline_report': baseline_report} + +def write_benchmark_artifacts(artifact_dir: str | Path, *, use_cupti: bool=True, shape_labels=None, benchmark_correctness: bool=True, speedup_floor: float=1.2) -> dict[str, str]: + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + labels = _shape_labels(shape_labels) + denom_label = 'd03c_k48_k96_exact3' if labels == TARGET_SHAPES else ''.join(['custom_', format(len(labels), '')]) + baseline_report = benchmark_baseline_4399(use_cupti=use_cupti, shape_labels=labels, benchmark_correctness=benchmark_correctness) + payload = benchmark_k48_k96_floor_repair_d03c_v1(use_cupti=use_cupti, shape_labels=labels, baseline_report=baseline_report, benchmark_correctness=benchmark_correctness, speedup_floor=speedup_floor) + baseline_payload = {'candidate_id': parent_4399._candidate_id(parent_4399.DEFAULT_CANDIDATE_KEY), 'measured_entrypoint': parent_4399.FLOOR_CORE_K5_BENCHMARK_ENTRYPOINT, 'denominator': payload['denominator'], 'timing_backend': payload['timing_backend'], 'all_correct': baseline_report['summary']['all_correct'], 'performance_comparable': baseline_report['summary']['performance_comparable'], 'contract_summary': baseline_report['summary'], 'contract_performance': baseline_report['performance'], 'report': baseline_report} + baseline_path = out_dir / ''.join([format(denom_label, ''), '_same_session_baseline_4399_core_k5.json']) + payload_path = out_dir / ''.join([format(denom_label, ''), '_dispatch_d03c_k48_k96_v1.json']) + trace_path = out_dir / ''.join([format(denom_label, ''), '_route_trace_d03c_k48_k96_v1.json']) + forced_trace_path = out_dir / ''.join([format(denom_label, ''), '_forced_fallback_trace_d03c_k48_k96_v1.json']) + seed_matrix_path = out_dir / ''.join([format(denom_label, ''), '_seed_delta_matrix_d03c_k48_k96_v1.json']) + payload['flashlib_parity_ledger']['baseline_payload'] = str(baseline_path) + baseline_path.write_text(json.dumps(baseline_payload, indent=2, sort_keys=True) + '\n') + payload_path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + trace_path.write_text(json.dumps(payload['route_trace'], indent=2, sort_keys=True) + '\n') + forced_trace_path.write_text(json.dumps(payload['forced_fallback_route_trace'], indent=2, sort_keys=True) + '\n') + seed_matrix_path.write_text(json.dumps(payload['seed_delta_matrix'], indent=2, sort_keys=True) + '\n') + return {'same_session_baseline_payload': str(baseline_path), 'candidate_payload': str(payload_path), 'route_trace': str(trace_path), 'forced_fallback_trace': str(forced_trace_path), 'seed_delta_matrix': str(seed_matrix_path)} + +def _main() -> None: + parser = argparse.ArgumentParser(description='Evaluate d03c kNN build K48/K96 floor-repair candidate') + parser.add_argument('--shape', action='append', choices=[shape['label'] for shape in eval_mod.CANONICAL_SHAPES]) + parser.add_argument('--artifact-dir', default=None) + parser.add_argument('--no-benchmark', action='store_true') + parser.add_argument('--use-cupti', action=argparse.BooleanOptionalAction, default=True) + args = parser.parse_args() + labels = tuple(args.shape) if args.shape else TARGET_SHAPES + if args.artifact_dir and (not args.no_benchmark): + artifacts = write_benchmark_artifacts(args.artifact_dir, use_cupti=args.use_cupti, shape_labels=labels, benchmark_correctness=True) + print(json.dumps(artifacts, indent=2, sort_keys=True)) + return + report = evaluate_contract(shapes=_select_contract_shapes(labels), correctness=True, benchmark=not args.no_benchmark) + print(json.dumps(report, indent=2, sort_keys=True)) diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_k48_k96_floor_repair_d03c_v2.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_k48_k96_floor_repair_d03c_v2.py new file mode 100644 index 00000000..b5606acd --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_k48_k96_floor_repair_d03c_v2.py @@ -0,0 +1,295 @@ +"""kNN build K48/K96 floor-repair bucket candidate for d03c v2. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +keeps the d03c v1 K48 repair and replaces only the exact Q1024/K96 final merge: + +* BF16 build B=1,Q=M in {2048,4096},D=128,K=48 through the d03c v1 K48 + split4 tcgen05/TMA producer plus warp-select merge. +* BF16 build B=1,Q=M=1024,D=128,K=96 through the existing exact-prefill + split2 tcgen05/TMA producer plus a new K96 split2 warp-select merge. + +All other shapes fall back to the 4399 core+K5 campaign dispatcher. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from functools import lru_cache +from pathlib import Path +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_k48_k96_floor_repair_d03c_v1 as d03c_v1 +from . import knn_build_over64_k96_exactall_229a_v1 as k96_229a +from .._dispatch_runtime import pack_kernel_args +MODULE = 'loom.examples.weave.knn_build_k48_k96_floor_repair_d03c_v2' +BLOCK_Q = d03c_v1.BLOCK_Q +BLOCK_M = d03c_v1.BLOCK_M +FEAT_D = d03c_v1.FEAT_D +STAGE1_THREADS = d03c_v1.STAGE1_THREADS +K48_COOP_MERGE_THREADS = d03c_v1.K48_COOP_MERGE_THREADS +GRID_DIM_DEFAULT = d03c_v1.GRID_DIM_DEFAULT +CTA_GROUP = d03c_v1.CTA_GROUP +K48_SPLITS = d03c_v1.K48_SPLITS +K48_TOP_K = d03c_v1.K48_TOP_K +K96_TOP_K = k96_229a.OVER64_TOP_K +K96_SPLITS = 2 +K96_COOP_MERGE_THREADS = 128 +TARGET_K48_Q2048 = d03c_v1.TARGET_K48_Q2048 +TARGET_K48_Q4096 = d03c_v1.TARGET_K48_Q4096 +TARGET_K96_Q1024 = d03c_v1.TARGET_K96_Q1024 +TARGET_SHAPES = d03c_v1.TARGET_SHAPES +TARGET_SHAPE_SET = d03c_v1.TARGET_SHAPE_SET +K48_TARGET_SHAPE_SET = d03c_v1.K48_TARGET_SHAPE_SET +SEED_K48_WARPSELECT_ID = d03c_v1.SEED_K48_WARPSELECT_ID +SEED_K96_Q1024_ID = 'd03c_v2_k96_q1024_s2_warpselect_merge' +SEED_ID = 'candidate_d03c_v2_k48_k96_warpselect_floor_repair' +ROUTE_K48_WARPSELECT = d03c_v1.ROUTE_K48_WARPSELECT +ROUTE_K96_WARPSELECT = ''.join([format(MODULE, ''), ':k96_q1024_s2_warpselect_merge']) +ROUTE_PARENT_4399 = d03c_v1.ROUTE_PARENT_4399 +BENCHMARK_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_k48_k96_floor_repair_d03c_v2']) +parent_4399 = d03c_v1.parent_4399 +stage1_k48_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k48over32", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 48]], "cta_group": 1, "threads": 192}')) +merge_k48_warpselect_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_k48_merge_s4_unordered_warp_select", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 48], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 128}')) +stage1_k96_exact_prefill_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_k96_stage1_exact_prefill_q1024_k96over64exactprefillq1024_e5db", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 96]], "cta_group": 1, "threads": 192}')) +SOURCE_TASKS = {SEED_ID: 'weave-evolve-knn-build-d03c v2 K48/K96 floor-repair bucket', SEED_K48_WARPSELECT_ID: 'd03c v1 K48 split4 warp-select merge', SEED_K96_Q1024_ID: 'd03c v2 K96 q1024 split2 warp-select merge'} +PRODUCTION_ROUTE_MODULES = _decode_capture(_json_loads('{"__dict_items__": [["candidate_d03c_v2_k48_k96_warpselect_floor_repair", "loom.examples.weave.knn_build_k48_k96_floor_repair_d03c_v2:launch_from_contract_inputs"], ["d03c_k48_s4_warpselect_merge", "loom.examples.weave.knn_build_k48_k96_floor_repair_d03c_v1:launch_from_contract_inputs"], ["d03c_v2_k96_q1024_s2_warpselect_merge", "loom.examples.weave.knn_build_k48_k96_floor_repair_d03c_v2:launch_from_contract_inputs"], ["candidate_fd9b_plus_01bb_2425_1b34_k5_bd76_k20_9334_k32_full90_v1", "loom.examples.weave.knn_build_dispatch_fd9b_floor_seed_portfolio_5720_full90_synthesis_v1:launch_from_contract_inputs"]]}')) +knn_build_k96_merge_s2_unordered_warp_select = _decode_capture(_json_loads('{"__ir__": "knn_build_k96_merge_s2_unordered_warp_select", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 96], ["SPLIT_COUNT", 2]], "cta_group": 1, "threads": 128}')) +merge_k96_s2_warpselect_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_k96_merge_s2_unordered_warp_select", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 96], ["SPLIT_COUNT", 2]], "cta_group": 1, "threads": 128}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_K48_K96_D03C_V2_VERIFY_KERNEL') + if verify_kernel == 'k48_stage1': + return stage1_k48_ir + if verify_kernel == 'k48_merge': + return merge_k48_warpselect_ir + if verify_kernel == 'k96_stage1': + return stage1_k96_exact_prefill_ir + if verify_kernel == 'k96_merge_s2_warpselect': + return merge_k96_s2_warpselect_ir + return merge_k96_s2_warpselect_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_k96_merge_s2_unordered_warp_select", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 96], ["SPLIT_COUNT", 2]], "cta_group": 1, "threads": 128}')) + +def _compiled_stage1_k96_exact_prefill(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0120"}')) + +def _compiled_merge_k96_s2_warpselect(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0231"}')) + +def _eligible_k48(inputs: dict[str, Any]) -> bool: + return d03c_v1._eligible_k48(inputs) + +def _eligible_k96_q1024(inputs: dict[str, Any]) -> bool: + return d03c_v1._eligible_k96_q1024(inputs) + +def _selected_seed(inputs: dict[str, Any]) -> tuple[str | None, str | None]: + if _eligible_k48(inputs): + matched_label = str(inputs.get('label') or (TARGET_K48_Q2048 if int(inputs.get('Q', -1)) == 2048 else TARGET_K48_Q4096)) + return (SEED_K48_WARPSELECT_ID, matched_label) + if _eligible_k96_q1024(inputs): + return (SEED_K96_Q1024_ID, TARGET_K96_Q1024) + return (None, None) + +def _launch_k96_q1024_warpselect(inputs: dict[str, Any]) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + split_count = K96_SPLITS + num_q_tiles = (n_query + k96_229a.BLOCK_Q - 1) // k96_229a.BLOCK_Q + num_q_tile_pairs = (num_q_tiles + k96_229a.CTA_GROUP - 1) // k96_229a.CTA_GROUP + num_db_tiles = (n_database + k96_229a.BLOCK_M - 1) // k96_229a.BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tile_pairs * split_count + stage1_grid = min(total_work * k96_229a.CTA_GROUP, k96_229a.GRID_DIM_DEFAULT) + merge_grid = (bsz * n_query + 3) // 4 + partial_dists, partial_indices = k96_229a.q1024exact.f9d1.a2f8.parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = k96_229a.q1024exact.f9d1.a2f8.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), bsz * n_query, k96_229a.BLOCK_Q, dim, dim) + tmap_database = k96_229a.q1024exact.f9d1.a2f8.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, k96_229a.BLOCK_M, dim, dim) + stage1_kernel = _compiled_stage1_k96_exact_prefill() + stage1_kernel.launch_cluster(grid=(stage1_grid, 1, 1), block=(k96_229a.STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage1_k96_exact_prefill_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tile_pairs=num_q_tile_pairs, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), cluster_dims=(k96_229a.CTA_GROUP, 1, 1), shared_mem=stage1_k96_exact_prefill_ir.computed_smem_bytes) + merge_kernel = _compiled_merge_k96_s2_warpselect() + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(K96_COOP_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_k96_s2_warpselect_ir.computed_smem_bytes) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if force_fallback: + return parent_4399.route_for_contract_inputs(inputs) + selected_seed, _label = _selected_seed(inputs) + if selected_seed == SEED_K48_WARPSELECT_ID: + return ROUTE_K48_WARPSELECT + if selected_seed == SEED_K96_Q1024_ID: + return ROUTE_K96_WARPSELECT + return parent_4399.route_for_contract_inputs(inputs) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + if force_fallback: + parent_4399.launch_from_contract_inputs(inputs) + return + selected_seed, _label = _selected_seed(inputs) + if selected_seed == SEED_K48_WARPSELECT_ID: + d03c_v1._launch_k48_warpselect(inputs) + return + if selected_seed == SEED_K96_Q1024_ID: + _launch_k96_q1024_warpselect(inputs) + return + parent_4399.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + return eval_mod.evaluate(candidate, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return parent_4399._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _shape_labels(shape_labels) -> tuple[str, ...]: + if shape_labels is None: + return TARGET_SHAPES + return tuple((str(label) for label in shape_labels)) + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=None, kernel_fn=candidate, correctness: bool=True) -> dict[str, Any]: + prior = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + try: + return eval_mod.evaluate(kernel_fn, shapes=_select_contract_shapes(_shape_labels(shape_labels)), correctness=correctness, benchmark=True) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False) -> list[dict[str, Any]]: + labels = _shape_labels(shape_labels) + rows: list[dict[str, Any]] = [] + for shape in _select_contract_shapes(labels): + inputs = parent_4399._trace_inputs_for_shape(shape) + label = str(inputs.get('label')) + selected_seed, matched_label = _selected_seed(inputs) + if force_fallback: + base = parent_4399.route_trace_for_contract_shapes((label,))[0] + rows.append(parent_4399._normalize_route_row({**base, 'expected_seed': selected_seed, 'guard_id': 'forced_fallback_d03c_v2_k48_k96_disabled', 'guard_condition': 'forced fallback to 4399 core+K5; d03c v2 exact bucket disabled', 'classification': 'guard-miss' if selected_seed is not None else 'route-ok'})) + continue + if selected_seed == SEED_K48_WARPSELECT_ID: + rows.append(parent_4399._normalize_route_row({'shape_key': label, 'selected_route': ROUTE_K48_WARPSELECT, 'selected_entrypoint': ''.join([format(MODULE, ''), ':launch_from_contract_inputs']), 'selected_seed': selected_seed, 'expected_seed': selected_seed, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': 'd03c_v2_k48_s4_warpselect_guard', 'guard_condition': 'exact BF16 build B=1 Q=M in {2048,4096} D=128 K=48 split4 warp-select merge', 'matched_label': matched_label, 'parent_dispatcher_route': parent_4399.route_for_contract_inputs(inputs), 'baseline_dispatcher_route': parent_4399.route_for_contract_inputs(inputs), 'classification': 'unmeasured'})) + elif selected_seed == SEED_K96_Q1024_ID: + rows.append(parent_4399._normalize_route_row({'shape_key': label, 'selected_route': ROUTE_K96_WARPSELECT, 'selected_entrypoint': ''.join([format(MODULE, ''), ':launch_from_contract_inputs']), 'selected_seed': selected_seed, 'expected_seed': selected_seed, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': 'd03c_v2_k96_q1024_s2_warpselect_guard', 'guard_condition': 'exact BF16 build B=1 Q=M=1024 D=128 K=96 split2 warp-select merge', 'matched_label': matched_label, 'parent_dispatcher_route': parent_4399.route_for_contract_inputs(inputs), 'baseline_dispatcher_route': parent_4399.route_for_contract_inputs(inputs), 'classification': 'unmeasured'})) + else: + rows.append(parent_4399.route_trace_for_contract_shapes((label,))[0]) + return rows + +def _annotate_route_trace(route_trace: list[dict[str, Any]], candidate_report: dict[str, Any], baseline_report: dict[str, Any], *, speedup_floor: float) -> list[dict[str, Any]]: + new_seed_ids = {SEED_K48_WARPSELECT_ID, SEED_K96_Q1024_ID} + annotated = [] + for row in route_trace: + label = str(row.get('shape_key')) + out = dict(row) + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') + speedup_vs_baseline = baseline_ms / candidate_ms if candidate_ms and baseline_ms else None + speedup_vs_external = flashlib_ms / candidate_ms if candidate_ms and flashlib_ms else None + out['dispatcher_kernel_ms'] = candidate_ms + out['baseline_4399_kernel_ms'] = baseline_ms + out['shape_specific_kernel_ms'] = candidate_ms if out.get('selected_seed') in new_seed_ids else None + out['external_baseline_ms'] = flashlib_ms + out['flashlib_ms'] = flashlib_ms + out['relative_speedup_vs_baseline'] = speedup_vs_baseline + out['speedup_vs_external_baseline'] = speedup_vs_external + out['external_baseline_ref'] = 'same_session' if flashlib_ms is not None else 'not_available' + out['route_changed_vs_4399'] = out.get('selected_route') != out.get('baseline_dispatcher_route') + if out.get('selected_seed') in new_seed_ids and speedup_vs_external is not None: + out['classification'] = 'seed-consumed' if speedup_vs_external >= speedup_floor else 'kernel-slow' + elif out.get('selected_seed') in new_seed_ids: + out['classification'] = 'seed-consumed' + annotated.append(parent_4399._normalize_route_row(out)) + return annotated + +def _below_flashlib_rows(report: dict[str, Any], route_trace: list[dict[str, Any]], *, floor: float) -> list[dict[str, Any]]: + trace_by_label = {str(row['shape_key']): row for row in route_trace} + rows = [] + for label, row in sorted(report.get('per_shape', {}).items()): + ratio = row.get('ratio_vs_flashlib') + if not isinstance(ratio, float | int) or ratio >= floor: + continue + trace_row = trace_by_label.get(label, {}) + rows.append({'shape_key': label, 'kernel_ms': row.get('kernel_ms'), 'flashlib_ms': row.get('flashlib_ms'), 'ratio_vs_flashlib': ratio, 'selected_route': trace_row.get('selected_route'), 'selected_seed': trace_row.get('selected_seed'), 'expected_seed': trace_row.get('expected_seed'), 'route_kind': trace_row.get('route_kind', 'unknown'), 'classification': trace_row.get('classification', 'unmeasured')}) + return rows + +def _seed_delta_matrix(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> list[dict[str, Any]]: + matrix = [] + for label in TARGET_SHAPES: + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + inputs = parent_4399._inputs_for_label(label) + selected_seed, _matched = _selected_seed(inputs) + matrix.append({'shape_key': label, 'baseline_route': parent_4399.route_for_contract_inputs(inputs), 'candidate_route': route_for_contract_inputs(inputs), 'selected_seed': selected_seed, 'candidate_ms': candidate_ms, 'baseline_4399_ms': baseline_ms, 'flashlib_ms': candidate_row.get('flashlib_ms'), 'delta_ms_candidate_minus_4399': candidate_ms - baseline_ms if candidate_ms is not None and baseline_ms is not None else None, 'speedup_vs_4399': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'ratio_vs_flashlib': candidate_row.get('ratio_vs_flashlib'), 'timing_backend': candidate_row.get('timing_backend') or baseline_row.get('timing_backend')}) + return matrix + +def benchmark_baseline_4399(*, use_cupti: bool=True, shape_labels=None, benchmark_correctness: bool=True) -> dict[str, Any]: + return _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=parent_4399.candidate_floor_core_k5_full90_v1, correctness=benchmark_correctness) + +def benchmark_k48_k96_floor_repair_d03c_v2(*, use_cupti: bool=True, shape_labels=None, baseline_report: dict[str, Any] | None=None, benchmark_correctness: bool=True, speedup_floor: float=1.2) -> dict[str, Any]: + labels = _shape_labels(shape_labels) + if baseline_report is None: + baseline_report = benchmark_baseline_4399(use_cupti=use_cupti, shape_labels=labels, benchmark_correctness=benchmark_correctness) + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=labels, kernel_fn=candidate, correctness=benchmark_correctness) + route_trace = _annotate_route_trace(route_trace_for_contract_shapes(labels), candidate_report, baseline_report, speedup_floor=speedup_floor) + below_1x = _below_flashlib_rows(candidate_report, route_trace, floor=1.0) + below_floor = _below_flashlib_rows(candidate_report, route_trace, floor=speedup_floor) + candidate_metric = candidate_report['summary']['primary_mean'] + baseline_metric = baseline_report['summary']['primary_mean'] + metric_delta = candidate_metric - baseline_metric if candidate_metric is not None and baseline_metric is not None else None + timing_backend = 'cupti' if use_cupti else 'cuda_event' + return {'candidate_id': SEED_ID, 'baseline_candidate_id': parent_4399._candidate_id(parent_4399.DEFAULT_CANDIDATE_KEY), 'selected_seeds': (SEED_K48_WARPSELECT_ID, SEED_K96_Q1024_ID), 'source_tasks': SOURCE_TASKS, 'tflops': candidate_metric, 'baseline_4399_tflops': baseline_metric, 'metric_delta_vs_4399': metric_delta, 'all_correct': candidate_report['summary']['all_correct'], 'baseline_all_correct': baseline_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'baseline_performance_comparable': baseline_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'baseline_invalid_performance_reason': baseline_report['summary']['invalid_performance_reason'], 'measured_entrypoint': BENCHMARK_ENTRYPOINT, 'baseline_entrypoint': parent_4399.FLOOR_CORE_K5_BENCHMARK_ENTRYPOINT, 'route_entrypoint': ''.join([format(MODULE, ''), ':launch_from_contract_inputs']), 'measured_shape_labels': labels, 'timing_backend': timing_backend, 'denominator': 'd03c_v2_k48_k96_exact3' if labels == TARGET_SHAPES else ''.join(['custom_', format(len(labels), '')]), 'selected_route_labels': TARGET_SHAPES, 'selected_route_rows': {label: candidate_report['per_shape'].get(label, {}) for label in labels}, 'baseline_selected_route_rows': {label: baseline_report['per_shape'].get(label, {}) for label in labels}, 'seed_delta_matrix': _seed_delta_matrix(candidate_report, baseline_report), 'route_modules': PRODUCTION_ROUTE_MODULES, 'route_trace': route_trace, 'forced_fallback_route_trace': route_trace_for_contract_shapes(labels, force_fallback=True), 'route_trace_included': True, 'contract_summary': candidate_report['summary'], 'baseline_contract_summary': baseline_report['summary'], 'contract_performance': candidate_report['performance'], 'baseline_contract_performance': baseline_report['performance'], 'contract_correctness': candidate_report['correctness'], 'use_cupti': use_cupti, 'benchmark_correctness_checked': benchmark_correctness, 'benchmark_time_flashlib': True, 'performance_coverage': 'partial' if below_floor else 'pass', 'coverage_only_routes': [], 'hot_bucket_blockers': below_floor, 'flashlib_parity_ledger': {'baseline_ref_scope': 'same_session', 'baseline_payload': None, 'speedup_floor': speedup_floor, 'rows_below_1x': below_1x, 'rows_below_floor': below_floor, 'omitted_reason': None}, 'rank_objective': {'metric': 'tflops', 'direction': 'maximize', 'value': candidate_metric, 'baseline_4399_value': baseline_metric, 'delta_vs_4399': metric_delta, 'denominator': 'd03c_v2_k48_k96_exact3' if labels == TARGET_SHAPES else ''.join(['custom_', format(len(labels), '')])}, 'report': candidate_report, 'baseline_report': baseline_report} + +def write_benchmark_artifacts(artifact_dir: str | Path, *, use_cupti: bool=True, shape_labels=None, benchmark_correctness: bool=True, speedup_floor: float=1.2) -> dict[str, str]: + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + labels = _shape_labels(shape_labels) + denom_label = 'd03c_v2_k48_k96_exact3' if labels == TARGET_SHAPES else ''.join(['custom_', format(len(labels), '')]) + baseline_report = benchmark_baseline_4399(use_cupti=use_cupti, shape_labels=labels, benchmark_correctness=benchmark_correctness) + payload = benchmark_k48_k96_floor_repair_d03c_v2(use_cupti=use_cupti, shape_labels=labels, baseline_report=baseline_report, benchmark_correctness=benchmark_correctness, speedup_floor=speedup_floor) + baseline_payload = {'candidate_id': parent_4399._candidate_id(parent_4399.DEFAULT_CANDIDATE_KEY), 'measured_entrypoint': parent_4399.FLOOR_CORE_K5_BENCHMARK_ENTRYPOINT, 'denominator': payload['denominator'], 'timing_backend': payload['timing_backend'], 'all_correct': baseline_report['summary']['all_correct'], 'performance_comparable': baseline_report['summary']['performance_comparable'], 'contract_summary': baseline_report['summary'], 'contract_performance': baseline_report['performance'], 'report': baseline_report} + baseline_path = out_dir / ''.join([format(denom_label, ''), '_same_session_baseline_4399_core_k5.json']) + payload_path = out_dir / ''.join([format(denom_label, ''), '_dispatch_d03c_k48_k96_v2.json']) + trace_path = out_dir / ''.join([format(denom_label, ''), '_route_trace_d03c_k48_k96_v2.json']) + forced_trace_path = out_dir / ''.join([format(denom_label, ''), '_forced_fallback_trace_d03c_k48_k96_v2.json']) + seed_matrix_path = out_dir / ''.join([format(denom_label, ''), '_seed_delta_matrix_d03c_k48_k96_v2.json']) + payload['flashlib_parity_ledger']['baseline_payload'] = str(baseline_path) + baseline_path.write_text(json.dumps(baseline_payload, indent=2, sort_keys=True) + '\n') + payload_path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + trace_path.write_text(json.dumps(payload['route_trace'], indent=2, sort_keys=True) + '\n') + forced_trace_path.write_text(json.dumps(payload['forced_fallback_route_trace'], indent=2, sort_keys=True) + '\n') + seed_matrix_path.write_text(json.dumps(payload['seed_delta_matrix'], indent=2, sort_keys=True) + '\n') + return {'same_session_baseline_payload': str(baseline_path), 'candidate_payload': str(payload_path), 'route_trace': str(trace_path), 'forced_fallback_trace': str(forced_trace_path), 'seed_delta_matrix': str(seed_matrix_path)} + +def _main() -> None: + parser = argparse.ArgumentParser(description='Evaluate d03c v2 kNN build K48/K96 floor-repair candidate') + parser.add_argument('--shape', action='append', choices=[shape['label'] for shape in eval_mod.CANONICAL_SHAPES]) + parser.add_argument('--artifact-dir', default=None) + parser.add_argument('--no-benchmark', action='store_true') + parser.add_argument('--use-cupti', action=argparse.BooleanOptionalAction, default=True) + args = parser.parse_args() + labels = tuple(args.shape) if args.shape else TARGET_SHAPES + if args.artifact_dir and (not args.no_benchmark): + artifacts = write_benchmark_artifacts(args.artifact_dir, use_cupti=args.use_cupti, shape_labels=labels, benchmark_correctness=True) + print(json.dumps(artifacts, indent=2, sort_keys=True)) + return + report = evaluate_contract(shapes=_select_contract_shapes(labels), correctness=True, benchmark=not args.no_benchmark) + print(json.dumps(report, indent=2, sort_keys=True)) diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_k64stage1_splitgrid_tailinf_knn_build_dispatch_slurm_0610_6329_v40.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_k64stage1_splitgrid_tailinf_knn_build_dispatch_slurm_0610_6329_v40.py new file mode 100644 index 00000000..41900ef9 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_k64stage1_splitgrid_tailinf_knn_build_dispatch_slurm_0610_6329_v40.py @@ -0,0 +1,210 @@ +"""kNN build/search v40 K64 stage-1 tail-infinity split-grid probe. + +Minimum target architecture: sm_100a. This additive candidate keeps the v28 +rectangular/RAG over-32 route and four-warp K64/S8 final merge on the real +contract path, but lowers the hot four-column distance loop unroll from two to +one in the v30 non-build K64 split/tcgen05 producer and aligns the four compute +warps to warpgroup-local warps 0-3. It then refines v34's eight scalar 8-slot +worst caches into sixteen explicit 4-slot worst caches for accepted-update +refreshes, visits each accepted four-column group in ascending distance order, +and stops visiting that sorted group once the next candidate cannot beat the +current worst threshold. The producer stores an infinity sentinel for +out-of-range database columns and removes the per-candidate tail guard from the +hot top-k update path. v40 keeps the measured S8 default, but lets the same +tail-safe stage-1 producer run at S12/S16 split counts through the existing +environment override for grid-repair probes. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import os +from functools import lru_cache +from typing import Any +from .._dispatch_runtime import dc as dc +from . import knn_build_evolve_7bfc_fp16_d128_knn_build_dispatch_slurm_0610_6329_v24 as parent_v24 +from . import knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2_k32split_v20 as parent_v20 +from . import knn_build_evolve_7bfc_split_v1 as parent_split +from . import knn_build_evolve_7bfc_v1 as base_v1 +from .._dispatch_runtime import pack_kernel_args +BLOCK_Q = parent_v20.BLOCK_Q +BLOCK_M = parent_v20.BLOCK_M +FEAT_D = parent_v20.FEAT_D +STAGE1_THREADS = parent_v20.STAGE1_THREADS +MERGE_THREADS = parent_v20.K32_MERGE_THREADS +K64_COOP_MERGE_THREADS = parent_v20.K32_COOP_MERGE_THREADS +GRID_DIM_DEFAULT = parent_v20.GRID_DIM_DEFAULT +CTA_GROUP = parent_v20.CTA_GROUP +OVER32_BUILD_SPLITS = parent_v20.MEDIUM_SPLITS +OVER32_NONBUILD_SPLITS = 8 +SUPPORTED_OVER32_NONBUILD_SPLITS = (8, 12, 16) +SUPPORTED_OVER32_K = (48, 64) + +def _ir_with_top_k_max(ir_obj: Any, *, top_k_max: int, suffix: str) -> Any: + constants = tuple(((name, top_k_max if name == 'TOP_K_MAX' else value) for name, value in ir_obj.constants)) + return dc.replace(ir_obj, symbol=''.join([format(ir_obj.symbol, ''), '_', format(suffix, '')]), constants=constants) + +def _ir_with_constants(ir_obj: Any, *, suffix: str, **updates: int) -> Any: + constants = tuple(((name, updates.get(name, value)) for name, value in ir_obj.constants)) + return dc.replace(ir_obj, symbol=''.join([format(ir_obj.symbol, ''), '_', format(suffix, '')]), constants=constants) +stage1_k48_over32_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k48over32", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 48]], "cta_group": 1, "threads": 192}')) +stage1_k64_over32_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k64over32", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 64]], "cta_group": 1, "threads": 192}')) +knn_build_k64_stage1_tailinf = _decode_capture(_json_loads('{"__ir__": "knn_build_k64_stage1_tailinf", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 64]], "cta_group": 1, "threads": 192}')) +stage1_k64_tailinf_over32_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_k64_stage1_tailinf_k64over32tailinfsplitgrid", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 64]], "cta_group": 1, "threads": 192}')) +merge_k48_over32_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_unordered_k48over32", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 48], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 32}')) +merge_k64_over32_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_unordered_k64over32", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 64], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 32}')) +knn_build_k64_merge_sN_unordered_chunkprefill = _decode_capture(_json_loads('{"__ir__": "knn_build_k64_merge_sN_unordered_chunkprefill", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 64], ["SPLIT_COUNT", 8]], "cta_group": 1, "threads": 32}')) +merge_k64_s8_chunkprefill_over32_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_k64_merge_sN_unordered_chunkprefill_k64over32s8chunkprefill", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 64], ["SPLIT_COUNT", 8]], "cta_group": 1, "threads": 32}')) +merge_k64_s12_chunkprefill_over32_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_k64_merge_sN_unordered_chunkprefill_k64over32s12chunkprefill", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 64], ["SPLIT_COUNT", 12]], "cta_group": 1, "threads": 32}')) +merge_k64_s16_chunkprefill_over32_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_k64_merge_sN_unordered_chunkprefill_k64over32s16chunkprefill", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 64], ["SPLIT_COUNT", 16]], "cta_group": 1, "threads": 32}')) +knn_build_k64_merge_s8_unordered_warp_select = _decode_capture(_json_loads('{"__ir__": "knn_build_k64_merge_s8_unordered_warp_select", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 64], ["SPLIT_COUNT", 8]], "cta_group": 1, "threads": 128}')) +merge_k64_s8_warp_select_over32_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_k64_merge_s8_unordered_warp_select_k64over32s8warpselect", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 64], ["SPLIT_COUNT", 8]], "cta_group": 1, "threads": 128}')) + +def _stage1_ir_for_over32_k(top_k: int) -> Any: + if top_k == 48: + return stage1_k48_over32_ir + if top_k == 64: + return stage1_k64_over32_ir + raise ValueError(''.join(['no over-32 stage-1 specialization for K=', format(top_k, '')])) + +def _stage1_ir_for_over32_route(top_k: int, split_count: int) -> Any: + if top_k == 64 and split_count in SUPPORTED_OVER32_NONBUILD_SPLITS: + return stage1_k64_tailinf_over32_ir + return _stage1_ir_for_over32_k(top_k) + +def _merge_ir_for_over32_k(top_k: int) -> Any: + if top_k == 48: + return merge_k48_over32_ir + if top_k == 64: + return merge_k64_over32_ir + raise ValueError(''.join(['no over-32 merge specialization for K=', format(top_k, '')])) + +def _merge_ir_for_over32_route(top_k: int, split_count: int) -> Any: + if top_k == 64 and split_count == 8: + if os.environ.get('LOOM_KNN_OVER32_USE_CHUNKPREFILL_MERGE') == '1': + return merge_k64_s8_chunkprefill_over32_ir + return merge_k64_s8_warp_select_over32_ir + if top_k == 64 and split_count == 12: + return merge_k64_s12_chunkprefill_over32_ir + if top_k == 64 and split_count == 16: + return merge_k64_s16_chunkprefill_over32_ir + return _merge_ir_for_over32_k(top_k) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_OVER32_VERIFY_KERNEL') + if verify_kernel == 'stage1_k64_tailinf': + return stage1_k64_tailinf_over32_ir + if verify_kernel == 'stage1_k64_tailinf_splitgrid': + return stage1_k64_tailinf_over32_ir + if verify_kernel == 'stage1_k64_sort4': + return stage1_k64_tailinf_over32_ir + if verify_kernel == 'stage1_k64': + return stage1_k64_tailinf_over32_ir + if verify_kernel == 'stage1_k64_parent': + return stage1_k64_over32_ir + if verify_kernel == 'merge_k48': + return merge_k48_over32_ir + if verify_kernel == 'merge_k64': + return merge_k64_over32_ir + if verify_kernel == 'merge_k64_s8_chunkprefill': + return merge_k64_s8_chunkprefill_over32_ir + if verify_kernel == 'merge_k64_s8_warp_select': + return merge_k64_s8_warp_select_over32_ir + if verify_kernel == 'merge_k64_s12_chunkprefill': + return merge_k64_s12_chunkprefill_over32_ir + if verify_kernel == 'merge_k64_s16_chunkprefill': + return merge_k64_s16_chunkprefill_over32_ir + return stage1_k48_over32_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k48over32", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 48]], "cta_group": 1, "threads": 192}')) + +@lru_cache(maxsize=4) +def _compiled_stage1_over32(top_k: int, split_count: int): + return parent_v20._compile_ir(_stage1_ir_for_over32_route(top_k, split_count)) + +@lru_cache(maxsize=6) +def _compiled_merge_over32(top_k: int, split_count: int): + return parent_v20._compile_ir(_merge_ir_for_over32_route(top_k, split_count)) + +def _eligible_over32_build(inputs: dict[str, Any]) -> bool: + top_k = int(inputs['K']) + return bool(inputs.get('build', False)) and str(inputs['query'].dtype) == 'torch.bfloat16' and (str(inputs['database'].dtype) == 'torch.bfloat16') and (int(inputs['D']) == FEAT_D) and (top_k in SUPPORTED_OVER32_K) and (int(inputs['Q']) == int(inputs['M'])) and (int(inputs['B']) == 1) and (int(inputs['Q']) in (2048, 4096)) + +def _eligible_over32_nonbuild(inputs: dict[str, Any]) -> bool: + return not bool(inputs.get('build', False)) and str(inputs['query'].dtype) == 'torch.bfloat16' and (str(inputs['database'].dtype) == 'torch.bfloat16') and (int(inputs['D']) == FEAT_D) and (int(inputs['K']) == 64) and (int(inputs['B']) == 1) and (int(inputs['Q']) == 2048) and (int(inputs['M']) in (65536, 250000)) + +def _eligible_over32_route(inputs: dict[str, Any]) -> bool: + return _eligible_over32_build(inputs) or _eligible_over32_nonbuild(inputs) + +def _split_count_for_over32(inputs: dict[str, Any]) -> int: + if _eligible_over32_nonbuild(inputs): + forced = os.environ.get('LOOM_KNN_OVER32_NONBUILD_SPLITS') + if forced: + split_count = int(forced) + if split_count not in SUPPORTED_OVER32_NONBUILD_SPLITS: + raise ValueError(''.join(['unsupported non-build K64 split count: ', format(split_count, '')])) + return split_count + return OVER32_NONBUILD_SPLITS + return OVER32_BUILD_SPLITS + +def _launch_over32_split_path(inputs: dict[str, Any]) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + split_count = _split_count_for_over32(inputs) + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_q_tile_pairs = (num_q_tiles + CTA_GROUP - 1) // CTA_GROUP + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tile_pairs * split_count + stage1_grid = min(total_work * CTA_GROUP, GRID_DIM_DEFAULT) + use_k64_warp_select_merge = top_k == 64 and split_count == 8 + merge_grid = (bsz * n_query + 3) // 4 if use_k64_warp_select_merge else min((bsz * n_query + MERGE_THREADS - 1) // MERGE_THREADS, GRID_DIM_DEFAULT) + partial_dists, partial_indices = parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), bsz * n_query, BLOCK_Q, dim, dim) + tmap_database = base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, dim, dim) + stage1_ir_obj = _stage1_ir_for_over32_route(top_k, split_count) + stage1_kernel = _compiled_stage1_over32(top_k, split_count) + stage1_kernel.launch_cluster(grid=(stage1_grid, 1, 1), block=(STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage1_ir_obj, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tile_pairs=num_q_tile_pairs, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), cluster_dims=(CTA_GROUP, 1, 1), shared_mem=stage1_ir_obj.computed_smem_bytes) + merge_ir_obj = _merge_ir_for_over32_route(top_k, split_count) + merge_kernel = _compiled_merge_over32(top_k, split_count) + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(K64_COOP_MERGE_THREADS if use_k64_warp_select_merge else MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_ir_obj.computed_smem_bytes) + +def launch_from_contract_inputs(inputs: dict[str, Any]) -> None: + if _eligible_over32_route(inputs): + _launch_over32_split_path(inputs) + return + parent_v24.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(candidate, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return parent_v24._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=('search_rect_over32_b1_q2048_m65536_d128_k64',), benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint: run real contract correctness for selected shapes.""" + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def benchmark_k64stage1_splitgrid_tailinf_v40(*, use_cupti: bool | None=None) -> dict[str, Any]: + """Opt-in benchmark hook for the K64 stage-1 split-grid tail-infinity route.""" + from .. import _dispatch_runtime as eval_mod + prior_use_cupti = eval_mod.CONTRACT.bench.get('use_cupti', True) + if use_cupti is not None: + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + try: + report = evaluate_contract(shapes=_select_contract_shapes(('search_rect_over32_b1_q2048_m65536_d128_k64', 'rag_offline_large_m_over32_b1_q2048_m250000_d128_k64')), correctness=True, benchmark=True) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + return {'tflops': report['summary']['primary_mean'], 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'report': report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_large_square_k20_efe4_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_large_square_k20_efe4_v1.py new file mode 100644 index 00000000..f1b42118 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_large_square_k20_efe4_v1.py @@ -0,0 +1,145 @@ +"""Exact large-square build K20 split2 seed for the efe4 floor repair lane. + +Minimum target architecture: sm_100a. This additive bucket-kernel sidecar +targets only the BF16 build ``B=1,Q=M=8192,D=128,K=20`` row. It reuses the +validated v20 tcgen05/TMA unordered K20 stage-1 producer with two database +splits, then merges the two split-local K20 unordered lists with the existing +eight-warp K20 reducer from the 08ec merge-ownership lineage. Guard misses +delegate to the existing a989 large-square K20/K32 seed. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_k20_mergeown_08ec_v3 as k20_mergeown +from . import knn_build_large_square_k20k32_a989_v1 as parent_a989 +TARGET_SHAPES = ('build_large_b1_q8192_m8192_d128_k20',) +GUARDRAIL_SHAPES = ('build_large_b1_q8192_m8192_d128_k32',) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +LARGE_SQUARE_Q = 8192 +LARGE_SQUARE_M = 8192 +FEAT_D = parent_a989.FEAT_D +TOP_K_K20 = 20 +SPLIT_COUNT = 2 +ROUTE_Q8192_K20_SPLIT2 = 'loom.examples.weave.knn_build_large_square_k20_efe4_v1:q8192_k20_split2_warp8' +ROUTE_PARENT_A989 = 'loom.examples.weave.knn_build_large_square_k20k32_a989_v1' + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_LARGE_SQUARE_K20_EFE4_VERIFY_KERNEL') + if verify_kernel == 'merge_k20_s2_warp8': + return k20_mergeown.merge_k20_s2_warp8_ir + if verify_kernel == 'parent_merge_k20_s4_warp4': + return k20_mergeown.parent_v20.merge_k20_unordered_warp_select_ir + return k20_mergeown.parent_v20.stage1_k20_unordered_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k20unordered", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 20]], "cta_group": 1, "threads": 192}')) + +def _dtype_name(inputs: dict[str, Any]) -> str: + query = inputs.get('query') + if query is not None: + return str(query.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')) + +def _label_can_hit(inputs: dict[str, Any], label: str) -> bool: + value = inputs.get('label') + return value is None or str(value) == label + +def _eligible_q8192_k20(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, TARGET_SHAPES[0]) and bool(inputs.get('build', False)) and (int(inputs.get('B', -1)) == 1) and (int(inputs.get('Q', -1)) == LARGE_SQUARE_Q) and (int(inputs.get('M', -1)) == LARGE_SQUARE_M) and (int(inputs.get('D', -1)) == FEAT_D) and (int(inputs.get('K', -1)) == TOP_K_K20) and (_dtype_name(inputs) == 'bfloat16') + +def route_for_contract_inputs(inputs: dict[str, Any]) -> str: + if _eligible_q8192_k20(inputs): + return ROUTE_Q8192_K20_SPLIT2 + return ROUTE_PARENT_A989 + +def _launch_q8192_k20_split2(inputs: dict[str, Any]) -> None: + k20_mergeown._launch_warp8_path(inputs, split_count=SPLIT_COUNT) + +def launch_from_contract_inputs(inputs: dict[str, Any]) -> None: + if _eligible_q8192_k20(inputs): + _launch_q8192_k20_split2(inputs) + return + parent_a989.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def candidate_parent_a989(inputs: dict[str, Any]): + parent_a989.launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + wanted = TARGET_SHAPE_SET if shape_labels is None else {str(label) for label in shape_labels} + selected = [shape for shape in eval_mod.CANONICAL_SHAPES if shape['label'] in wanted] + missing = wanted - {shape['label'] for shape in selected} + if missing: + raise ValueError(''.join(['unknown kNN build contract shape(s): ', format(sorted(missing), '')])) + return selected + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint: run real contract correctness for selected shapes.""" + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=None, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + previous = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = previous + +def _trace_inputs_from_shape(shape: dict[str, Any]) -> dict[str, Any]: + params = dict(shape['params']) + return {'label': shape['label'], 'B': int(params['B']), 'Q': int(params['Q']), 'M': int(params['M']), 'D': int(params['D']), 'K': int(params['K']), 'dtype': str(params.get('dtype', 'bfloat16')), 'build': bool(params.get('build', False))} + +def route_trace_for_shapes(shape_labels=None) -> list[dict[str, Any]]: + trace = [] + for shape in _select_contract_shapes(shape_labels): + inputs = _trace_inputs_from_shape(shape) + route = route_for_contract_inputs(inputs) + trace.append({'shape_key': shape['label'], 'selected_route': route, 'route_kind': 'specialized' if route == ROUTE_Q8192_K20_SPLIT2 else 'parent_delegate', 'guard_condition': _guard_description(route)}) + return trace + +def _guard_description(route: str) -> str: + if route == ROUTE_Q8192_K20_SPLIT2: + return 'exact BF16 build B1 Q=M=8192 D128 K20 split2 warp8 route' + return 'a989 exact large-square K20/K32 fallback' + +def _per_shape_delta(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> dict[str, Any]: + label = TARGET_SHAPES[0] + cand = candidate_report.get('per_shape', {}).get(label, {}) + base = baseline_report.get('per_shape', {}).get(label, {}) + cand_ms = cand.get('kernel_ms') + base_ms = base.get('kernel_ms') + return {'candidate_route': ROUTE_Q8192_K20_SPLIT2, 'baseline_route': ROUTE_PARENT_A989, 'candidate_ms': cand_ms, 'baseline_ms': base_ms, 'flashlib_ms': cand.get('flashlib_ms'), 'candidate_tflops': cand.get('tflops'), 'baseline_tflops': base.get('tflops'), 'speedup_vs_a989': base_ms / cand_ms if cand_ms and base_ms else None, 'ratio_vs_flashlib': cand.get('ratio_vs_flashlib'), 'passed': cand.get('passed'), 'timing_backend': cand.get('timing_backend')} + +def benchmark_knn_build_large_square_k20_efe4_v1(*, use_cupti: bool=True, run_baseline: bool=True) -> dict[str, Any]: + """Benchmark the exact q8192 K20 split2 sidecar against a989.""" + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=TARGET_SHAPES) + baseline_report = None + if run_baseline: + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=TARGET_SHAPES, kernel_fn=candidate_parent_a989) + payload: dict[str, Any] = {'tflops': candidate_report['summary']['primary_mean'] or 0.0, 'all_correct': candidate_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'measured_entrypoint': 'loom.examples.weave.knn_build_large_square_k20_efe4_v1:benchmark_knn_build_large_square_k20_efe4_v1', 'measured_shape_labels': TARGET_SHAPES, 'route_trace': route_trace_for_shapes(TARGET_SHAPES), 'contract_summary': candidate_report['summary'], 'contract_performance': candidate_report['performance'], 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'split_counts': {'q8192_k20': SPLIT_COUNT}, 'merge_owner': {'q8192_k20': 'split2_warp8'}, 'report': candidate_report} + if baseline_report is not None: + payload['baseline_entrypoint'] = 'loom.examples.weave.knn_build_large_square_k20k32_a989_v1:launch_from_contract_inputs' + payload['baseline_summary'] = baseline_report['summary'] + payload['per_shape_delta_vs_a989'] = {TARGET_SHAPES[0]: _per_shape_delta(candidate_report, baseline_report)} + baseline_mean = baseline_report['summary']['primary_mean'] + payload['speedup_vs_a989_primary_mean'] = candidate_report['summary']['primary_mean'] / baseline_mean if baseline_mean else None + return payload diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_large_square_k20k32_a989_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_large_square_k20k32_a989_v1.py new file mode 100644 index 00000000..f16b6d8a --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_large_square_k20k32_a989_v1.py @@ -0,0 +1,96 @@ +"""Exact large-square build K20/K32 kNN route. + +Minimum target architecture: sm_100a. This seed targets only the BF16 +``B=1, Q=M=8192, D=128, build=true`` K20/K32 rows. It reuses the validated v20 +tcgen05 split-build stage-1 and unordered exact-K merge with the same static +four-split schedule that was already validated for large Q=4096 K20/K32 build +rows. Guard misses are rejected instead of falling through to another route. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2_k32split_v20 as parent_v20 +TARGET_SHAPES = ('build_large_b1_q8192_m8192_d128_k20', 'build_large_b1_q8192_m8192_d128_k32') +TARGET_SHAPE_SET = set(TARGET_SHAPES) +TOP_K_VALUES = (20, 32) +LARGE_SQUARE_Q = 8192 +LARGE_SQUARE_M = 8192 +FEAT_D = parent_v20.FEAT_D +SPLIT_COUNT = parent_v20.MEDIUM_SPLITS + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_LARGE_SQUARE_A989_VERIFY_KERNEL') + if verify_kernel == 'stage1_k20': + return parent_v20.stage1_k20_unordered_ir + if verify_kernel == 'stage1_k32': + return parent_v20.stage1_k32_unordered_ir + if verify_kernel == 'merge_k20': + return parent_v20.merge_k20_unordered_warp_select_ir + if verify_kernel == 'merge_k32': + return parent_v20.merge_k32_unordered_warp_select_ir + return parent_v20.stage1_k32_unordered_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 192}')) + +def _dtype_is_bf16(inputs: dict[str, Any]) -> bool: + return str(inputs['query'].dtype) == 'torch.bfloat16' and str(inputs['database'].dtype) == 'torch.bfloat16' + +def _eligible_large_square_k20k32(inputs: dict[str, Any]) -> bool: + return bool(inputs.get('build', False)) and _dtype_is_bf16(inputs) and (int(inputs['B']) == 1) and (int(inputs['Q']) == LARGE_SQUARE_Q) and (int(inputs['M']) == LARGE_SQUARE_M) and (int(inputs['D']) == FEAT_D) and (int(inputs['K']) in TOP_K_VALUES) + +def _launch_large_square_k20k32(inputs: dict[str, Any]) -> None: + parent_v20._launch_k32_split_path(inputs, split_count=SPLIT_COUNT) + +def launch_from_contract_inputs(inputs: dict[str, Any]) -> None: + if not _eligible_large_square_k20k32(inputs): + raise ValueError('knn_build_large_square_k20k32_a989_v1 only supports exact Q=M=8192 BF16 K20/K32 build rows') + _launch_large_square_k20k32(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(candidate, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + if shape_labels is None: + return list(eval_mod.CANONICAL_SHAPES) + wanted = {str(label) for label in shape_labels} + selected = [shape for shape in eval_mod.CANONICAL_SHAPES if shape['label'] in wanted] + missing = wanted - {shape['label'] for shape in selected} + if missing: + raise ValueError(''.join(['unknown kNN build contract shape(s): ', format(sorted(missing), '')])) + return selected + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint for the exact large-square target rows.""" + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def benchmark_knn_build_large_square_k20k32_a989_v1(*, use_cupti: bool=False, shape_labels=TARGET_SHAPES) -> dict[str, Any]: + """Target-bucket benchmark hook for the exact Q=M=8192 K20/K32 seed.""" + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels) + timing_backends = sorted({row.get('timing_backend') for row in report.get('per_shape', {}).values() if row.get('timing_backend') is not None}) + exact_rows = {label: report.get('per_shape', {}).get(label, {}) for label in TARGET_SHAPES if label in report.get('per_shape', {})} + return {'tflops': report['summary']['primary_mean'] or 0.0, 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'invalid_performance_reason': report['summary']['invalid_performance_reason'], 'measured_entrypoint': 'loom.examples.weave.knn_build_large_square_k20k32_a989_v1:benchmark_knn_build_large_square_k20k32_a989_v1', 'measured_shape_labels': tuple(shape_labels), 'exact_shape_labels': TARGET_SHAPES, 'exact_rows': exact_rows, 'route_modules': {'large_square_k20k32': 'loom.examples.weave.knn_build_large_square_k20k32_a989_v1'}, 'contract_summary': report['summary'], 'contract_performance': report['performance'], 'timing_backends': timing_backends, 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'report': report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_large_square_k32_8a83_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_large_square_k32_8a83_v1.py new file mode 100644 index 00000000..55ab6795 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_large_square_k32_8a83_v1.py @@ -0,0 +1,182 @@ +"""Exact large-square build K32 split2 probe for the 8a83 follow-up. + +Minimum target architecture: sm_100a. This additive bucket-kernel sidecar +targets only the BF16 build ``B=1,Q=M=8192,D=128,K=32`` row. It keeps the +existing v20 tcgen05/TMA unordered K32 stage-1 producer but reduces the large +square row from four database splits to two, then merges the two split-local +K32 unordered lists with a K32 warp-select reducer. Guard misses delegate to +the a989 large-square K20/K32 seed. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from collections.abc import Callable +from functools import lru_cache +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2_k32split_v20 as parent_v20 +from . import knn_build_evolve_7bfc_split_v1 as parent_split +from . import knn_build_evolve_7bfc_v1 as base_v1 +from . import knn_build_large_square_k20k32_a989_v1 as parent_a989 +from .._dispatch_runtime import pack_kernel_args +TARGET_SHAPES = ('build_large_b1_q8192_m8192_d128_k32',) +GUARDRAIL_SHAPES = ('build_large_b1_q8192_m8192_d128_k20',) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +LARGE_SQUARE_Q = 8192 +LARGE_SQUARE_M = 8192 +FEAT_D = parent_v20.FEAT_D +BLOCK_Q = parent_v20.BLOCK_Q +BLOCK_M = parent_v20.BLOCK_M +STAGE1_THREADS = parent_v20.STAGE1_THREADS +K32_MERGE_THREADS = parent_v20.K32_COOP_MERGE_THREADS +GRID_DIM_DEFAULT = parent_v20.GRID_DIM_DEFAULT +CTA_GROUP = parent_v20.CTA_GROUP +SPLIT_COUNT = 2 +TOP_K_K32 = 32 +ROUTE_Q8192_K32_SPLIT2 = 'loom.examples.weave.knn_build_large_square_k32_8a83_v1:q8192_k32_split2' +ROUTE_PARENT_A989 = 'loom.examples.weave.knn_build_large_square_k20k32_a989_v1' +knn_build_large_square_k32_s2_warp_select = _decode_capture(_json_loads('{"__ir__": "knn_build_large_square_k32_s2_warp_select", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 32]], "cta_group": 1, "threads": 128}')) +stage1_k32_split2_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 192}')) +merge_k32_s2_warp_select_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_large_square_k32_s2_warp_select", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 32]], "cta_group": 1, "threads": 128}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_LARGE_SQUARE_K32_8A83_VERIFY_KERNEL') + if verify_kernel == 'merge_k32_s2_warp_select': + return merge_k32_s2_warp_select_ir + if verify_kernel == 'parent_merge_k32_s4_warp_select': + return parent_v20.merge_k32_unordered_warp_select_ir + return stage1_k32_split2_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 192}')) + +def _compiled_merge_k32_s2_warp_select(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0182"}')) + +def _dtype_name(inputs: dict[str, Any]) -> str: + query = inputs.get('query') + if query is not None: + return str(query.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')) + +def _label_can_hit(inputs: dict[str, Any], label: str) -> bool: + value = inputs.get('label') + return value is None or str(value) == label + +def _eligible_q8192_k32(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, TARGET_SHAPES[0]) and bool(inputs.get('build', False)) and (int(inputs.get('B', -1)) == 1) and (int(inputs.get('Q', -1)) == LARGE_SQUARE_Q) and (int(inputs.get('M', -1)) == LARGE_SQUARE_M) and (int(inputs.get('D', -1)) == FEAT_D) and (int(inputs.get('K', -1)) == TOP_K_K32) and (_dtype_name(inputs) == 'bfloat16') + +def _launch_q8192_k32_split2(inputs: dict[str, Any]) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_q_tile_pairs = (num_q_tiles + CTA_GROUP - 1) // CTA_GROUP + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + SPLIT_COUNT - 1) // SPLIT_COUNT + total_work = bsz * num_q_tile_pairs * SPLIT_COUNT + stage1_grid = min(total_work * CTA_GROUP, GRID_DIM_DEFAULT) + merge_grid = (bsz * n_query + 3) // 4 + partial_dists, partial_indices = parent_split._partial_buffers(split_count=SPLIT_COUNT, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), bsz * n_query, BLOCK_Q, dim, dim) + tmap_database = base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, dim, dim) + stage1_kernel = parent_v20._compiled_stage1_unordered_for_exact_k(top_k) + stage1_kernel.launch_cluster(grid=(stage1_grid, 1, 1), block=(STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage1_k32_split2_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tile_pairs=num_q_tile_pairs, db_tiles_per_split=db_tiles_per_split, split_count=SPLIT_COUNT, total_work=total_work), cluster_dims=(CTA_GROUP, 1, 1), shared_mem=stage1_k32_split2_ir.computed_smem_bytes) + merge_kernel = _compiled_merge_k32_s2_warp_select() + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(K32_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_k32_s2_warp_select_ir.computed_smem_bytes) + +def route_for_contract_inputs(inputs: dict[str, Any]) -> str: + if _eligible_q8192_k32(inputs): + return ROUTE_Q8192_K32_SPLIT2 + return ROUTE_PARENT_A989 + +def launch_from_contract_inputs(inputs: dict[str, Any]) -> None: + if _eligible_q8192_k32(inputs): + _launch_q8192_k32_split2(inputs) + return + parent_a989.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def candidate_parent_a989(inputs: dict[str, Any]): + parent_a989.launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + wanted = TARGET_SHAPE_SET if shape_labels is None else {str(label) for label in shape_labels} + selected = [shape for shape in eval_mod.CANONICAL_SHAPES if shape['label'] in wanted] + missing = wanted - {shape['label'] for shape in selected} + if missing: + raise ValueError(''.join(['unknown kNN build contract shape(s): ', format(sorted(missing), '')])) + return selected + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint: run real contract correctness for selected shapes.""" + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=None, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + previous = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = previous + +def _trace_inputs_from_shape(shape: dict[str, Any]) -> dict[str, Any]: + params = dict(shape['params']) + return {'label': shape['label'], 'B': int(params['B']), 'Q': int(params['Q']), 'M': int(params['M']), 'D': int(params['D']), 'K': int(params['K']), 'dtype': str(params.get('dtype', 'bfloat16')), 'build': bool(params.get('build', False))} + +def route_trace_for_shapes(shape_labels=None) -> list[dict[str, Any]]: + trace = [] + for shape in _select_contract_shapes(shape_labels): + inputs = _trace_inputs_from_shape(shape) + route = route_for_contract_inputs(inputs) + trace.append({'shape_key': shape['label'], 'selected_route': route, 'route_kind': 'specialized' if route == ROUTE_Q8192_K32_SPLIT2 else 'parent_delegate', 'guard_condition': _guard_description(route)}) + return trace + +def _guard_description(route: str) -> str: + if route == ROUTE_Q8192_K32_SPLIT2: + return 'exact BF16 build B1 Q=M=8192 D128 K32 split2 warp-select route' + return 'a989 exact large-square K20/K32 fallback' + +def _per_shape_delta(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> dict[str, Any]: + label = TARGET_SHAPES[0] + cand = candidate_report.get('per_shape', {}).get(label, {}) + base = baseline_report.get('per_shape', {}).get(label, {}) + cand_ms = cand.get('kernel_ms') + base_ms = base.get('kernel_ms') + return {'candidate_route': ROUTE_Q8192_K32_SPLIT2, 'baseline_route': ROUTE_PARENT_A989, 'candidate_ms': cand_ms, 'baseline_ms': base_ms, 'flashlib_ms': cand.get('flashlib_ms'), 'candidate_tflops': cand.get('tflops'), 'baseline_tflops': base.get('tflops'), 'speedup_vs_a989': base_ms / cand_ms if cand_ms and base_ms else None, 'ratio_vs_flashlib': cand.get('ratio_vs_flashlib'), 'passed': cand.get('passed'), 'timing_backend': cand.get('timing_backend')} + +def benchmark_knn_build_large_square_k32_8a83_v1(*, use_cupti: bool=True, run_baseline: bool=True) -> dict[str, Any]: + """Benchmark the exact q8192 K32 split2 sidecar against a989.""" + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=TARGET_SHAPES) + baseline_report = None + if run_baseline: + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=TARGET_SHAPES, kernel_fn=candidate_parent_a989) + payload: dict[str, Any] = {'tflops': candidate_report['summary']['primary_mean'] or 0.0, 'all_correct': candidate_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'measured_entrypoint': 'loom.examples.weave.knn_build_large_square_k32_8a83_v1:benchmark_knn_build_large_square_k32_8a83_v1', 'measured_shape_labels': TARGET_SHAPES, 'route_trace': route_trace_for_shapes(TARGET_SHAPES), 'contract_summary': candidate_report['summary'], 'contract_performance': candidate_report['performance'], 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'split_counts': {'q8192_k32': SPLIT_COUNT}, 'report': candidate_report} + if baseline_report is not None: + payload['baseline_entrypoint'] = 'loom.examples.weave.knn_build_large_square_k20k32_a989_v1:launch_from_contract_inputs' + payload['baseline_summary'] = baseline_report['summary'] + payload['per_shape_delta_vs_a989'] = {TARGET_SHAPES[0]: _per_shape_delta(candidate_report, baseline_report)} + baseline_mean = baseline_report['summary']['primary_mean'] + payload['speedup_vs_a989_primary_mean'] = candidate_report['summary']['primary_mean'] / baseline_mean if baseline_mean else None + return payload diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_large_square_k32_efe4_prodcache_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_large_square_k32_efe4_prodcache_v1.py new file mode 100644 index 00000000..954574a4 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_large_square_k32_efe4_prodcache_v1.py @@ -0,0 +1,189 @@ +"""Exact large-square build K32 split2 producer-cache candidate. + +Minimum target architecture: sm_100a. This additive bucket-kernel sidecar +targets only the BF16 build ``B=1,Q=M=8192,D=128,K=32`` row. It keeps the +split2 tcgen05/TMA dense product path and the round-158 eight-warp final merge, +but replaces the split-local K32 producer with a role-aligned variant that +tracks four 8-slot worst caches instead of rescanning all 32 top-k slots after +each accepted update. Guard misses delegate to the existing a989 large-square +K20/K32 seed. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from collections.abc import Callable +from functools import lru_cache +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2_k32split_v20 as parent_v20 +from . import knn_build_evolve_7bfc_split_v1 as parent_split +from . import knn_build_evolve_7bfc_v1 as base_v1 +from . import knn_build_large_square_k20k32_a989_v1 as parent_a989 +from .._dispatch_runtime import pack_kernel_args +TARGET_SHAPES = ('build_large_b1_q8192_m8192_d128_k32',) +GUARDRAIL_SHAPES = ('build_large_b1_q8192_m8192_d128_k20',) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +LARGE_SQUARE_Q = 8192 +LARGE_SQUARE_M = 8192 +FEAT_D = parent_v20.FEAT_D +BLOCK_Q = parent_v20.BLOCK_Q +BLOCK_M = parent_v20.BLOCK_M +STAGE1_THREADS = parent_v20.STAGE1_THREADS +K32_MERGE_THREADS = 256 +GRID_DIM_DEFAULT = parent_v20.GRID_DIM_DEFAULT +CTA_GROUP = parent_v20.CTA_GROUP +SPLIT_COUNT = 2 +TOP_K_K32 = 32 +ROUTE_Q8192_K32_PRODCACHE = 'loom.examples.weave.knn_build_large_square_k32_efe4_prodcache_v1:q8192_k32_s2_prodcache' +ROUTE_PARENT_A989 = 'loom.examples.weave.knn_build_large_square_k20k32_a989_v1' +knn_build_large_square_k32_stage1_chunkworst = _decode_capture(_json_loads('{"__ir__": "knn_build_large_square_k32_stage1_chunkworst", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 192}')) +knn_build_large_square_k32_s2_warp8_merge = _decode_capture(_json_loads('{"__ir__": "knn_build_large_square_k32_s2_warp8_merge", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 32]], "cta_group": 1, "threads": 256}')) +stage1_k32_prodcache_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_large_square_k32_stage1_chunkworst", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 192}')) +merge_k32_s2_warp8_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_large_square_k32_s2_warp8_merge", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 32]], "cta_group": 1, "threads": 256}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_LARGE_SQUARE_K32_EFE4_PRODCACHE_VERIFY_KERNEL') + if verify_kernel == 'stage1_k32_prodcache': + return stage1_k32_prodcache_ir + if verify_kernel == 'merge_k32_s2_warp8': + return merge_k32_s2_warp8_ir + if verify_kernel == 'parent_stage1_k32': + return parent_v20.stage1_k32_unordered_ir + return stage1_k32_prodcache_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_large_square_k32_stage1_chunkworst", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 192}')) + +def _compiled_stage1_k32_prodcache(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0067"}')) + +def _compiled_merge_k32_s2_warp8(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0068"}')) + +def _dtype_name(inputs: dict[str, Any]) -> str: + query = inputs.get('query') + if query is not None: + return str(query.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')) + +def _label_can_hit(inputs: dict[str, Any], label: str) -> bool: + value = inputs.get('label') + return value is None or str(value) == label + +def _eligible_q8192_k32(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, TARGET_SHAPES[0]) and bool(inputs.get('build', False)) and (int(inputs.get('B', -1)) == 1) and (int(inputs.get('Q', -1)) == LARGE_SQUARE_Q) and (int(inputs.get('M', -1)) == LARGE_SQUARE_M) and (int(inputs.get('D', -1)) == FEAT_D) and (int(inputs.get('K', -1)) == TOP_K_K32) and (_dtype_name(inputs) == 'bfloat16') + +def _launch_q8192_k32_prodcache(inputs: dict[str, Any]) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_q_tile_pairs = (num_q_tiles + CTA_GROUP - 1) // CTA_GROUP + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + SPLIT_COUNT - 1) // SPLIT_COUNT + total_work = bsz * num_q_tile_pairs * SPLIT_COUNT + stage1_grid = min(total_work * CTA_GROUP, GRID_DIM_DEFAULT) + merge_grid = (bsz * n_query + 7) // 8 + partial_dists, partial_indices = parent_split._partial_buffers(split_count=SPLIT_COUNT, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), bsz * n_query, BLOCK_Q, dim, dim) + tmap_database = base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, dim, dim) + stage1_kernel = _compiled_stage1_k32_prodcache() + stage1_kernel.launch_cluster(grid=(stage1_grid, 1, 1), block=(STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage1_k32_prodcache_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tile_pairs=num_q_tile_pairs, db_tiles_per_split=db_tiles_per_split, split_count=SPLIT_COUNT, total_work=total_work), cluster_dims=(CTA_GROUP, 1, 1), shared_mem=stage1_k32_prodcache_ir.computed_smem_bytes) + merge_kernel = _compiled_merge_k32_s2_warp8() + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(K32_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_k32_s2_warp8_ir.computed_smem_bytes) + +def route_for_contract_inputs(inputs: dict[str, Any]) -> str: + if _eligible_q8192_k32(inputs): + return ROUTE_Q8192_K32_PRODCACHE + return ROUTE_PARENT_A989 + +def launch_from_contract_inputs(inputs: dict[str, Any]) -> None: + if _eligible_q8192_k32(inputs): + _launch_q8192_k32_prodcache(inputs) + return + parent_a989.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def candidate_parent_a989(inputs: dict[str, Any]): + parent_a989.launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + wanted = TARGET_SHAPE_SET if shape_labels is None else {str(label) for label in shape_labels} + selected = [shape for shape in eval_mod.CANONICAL_SHAPES if shape['label'] in wanted] + missing = wanted - {shape['label'] for shape in selected} + if missing: + raise ValueError(''.join(['unknown kNN build contract shape(s): ', format(sorted(missing), '')])) + return selected + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint: run real contract correctness for selected shapes.""" + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=None, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + previous = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = previous + +def _trace_inputs_from_shape(shape: dict[str, Any]) -> dict[str, Any]: + params = dict(shape['params']) + return {'label': shape['label'], 'B': int(params['B']), 'Q': int(params['Q']), 'M': int(params['M']), 'D': int(params['D']), 'K': int(params['K']), 'dtype': str(params.get('dtype', 'bfloat16')), 'build': bool(params.get('build', False))} + +def route_trace_for_shapes(shape_labels=None) -> list[dict[str, Any]]: + trace = [] + for shape in _select_contract_shapes(shape_labels): + inputs = _trace_inputs_from_shape(shape) + route = route_for_contract_inputs(inputs) + trace.append({'shape_key': shape['label'], 'selected_route': route, 'route_kind': 'specialized' if route == ROUTE_Q8192_K32_PRODCACHE else 'parent_delegate', 'guard_condition': _guard_description(route)}) + return trace + +def _guard_description(route: str) -> str: + if route == ROUTE_Q8192_K32_PRODCACHE: + return 'exact BF16 build B1 Q=M=8192 D128 K32 split2 chunk-worst producer plus warp8 merge' + return 'a989 exact large-square K20/K32 fallback' + +def _per_shape_delta(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> dict[str, Any]: + label = TARGET_SHAPES[0] + cand = candidate_report.get('per_shape', {}).get(label, {}) + base = baseline_report.get('per_shape', {}).get(label, {}) + cand_ms = cand.get('kernel_ms') + base_ms = base.get('kernel_ms') + return {'candidate_route': ROUTE_Q8192_K32_PRODCACHE, 'baseline_route': ROUTE_PARENT_A989, 'candidate_ms': cand_ms, 'baseline_ms': base_ms, 'flashlib_ms': cand.get('flashlib_ms'), 'candidate_tflops': cand.get('tflops'), 'baseline_tflops': base.get('tflops'), 'speedup_vs_a989': base_ms / cand_ms if cand_ms and base_ms else None, 'ratio_vs_flashlib': cand.get('ratio_vs_flashlib'), 'passed': cand.get('passed'), 'timing_backend': cand.get('timing_backend')} + +def benchmark_knn_build_large_square_k32_efe4_prodcache_v1(*, use_cupti: bool=True, run_baseline: bool=True) -> dict[str, Any]: + """Benchmark the exact q8192 K32 producer-cache sidecar against a989.""" + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=TARGET_SHAPES) + baseline_report = None + if run_baseline: + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=TARGET_SHAPES, kernel_fn=candidate_parent_a989) + payload: dict[str, Any] = {'tflops': candidate_report['summary']['primary_mean'] or 0.0, 'all_correct': candidate_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'measured_entrypoint': 'loom.examples.weave.knn_build_large_square_k32_efe4_prodcache_v1:benchmark_knn_build_large_square_k32_efe4_prodcache_v1', 'measured_shape_labels': TARGET_SHAPES, 'route_trace': route_trace_for_shapes(TARGET_SHAPES), 'contract_summary': candidate_report['summary'], 'contract_performance': candidate_report['performance'], 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'split_counts': {'q8192_k32': SPLIT_COUNT}, 'producer_variant': 'stage1_k32_chunkworst_rolealigned', 'merge_owner': {'q8192_k32': 'split2_warp8'}, 'report': candidate_report} + if baseline_report is not None: + payload['baseline_entrypoint'] = 'loom.examples.weave.knn_build_large_square_k20k32_a989_v1:launch_from_contract_inputs' + payload['baseline_summary'] = baseline_report['summary'] + payload['per_shape_delta_vs_a989'] = {TARGET_SHAPES[0]: _per_shape_delta(candidate_report, baseline_report)} + baseline_mean = baseline_report['summary']['primary_mean'] + payload['speedup_vs_a989_primary_mean'] = candidate_report['summary']['primary_mean'] / baseline_mean if baseline_mean else None + return payload diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_large_tail_frontier_6a73_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_large_tail_frontier_6a73_v1.py new file mode 100644 index 00000000..5fe33f6f --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_large_tail_frontier_6a73_v1.py @@ -0,0 +1,114 @@ +"""kNN build Q6144/M6144 K20 large-tail exact seed. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +targets only ``build_large_tail_b1_q6144_m6144_d128_k20``. It routes the row +through the existing K20 tcgen05/TMA split-build producer and matching K20 +merge family, with an explicit split-count knob for S4/S8/S16 target-bucket +sweeps. Guard misses delegate to the current exported Weave dispatcher. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_split72_4e09_de1a_3dc7_v48 as current_dispatch +from . import knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2_k32split_v20 as parent_v20 +TARGET_SHAPE = 'build_large_tail_b1_q6144_m6144_d128_k20' +TARGET_SHAPES = (TARGET_SHAPE,) +TARGET_QM = 6144 +TARGET_K = 20 +FEAT_D = parent_v20.FEAT_D +SPLIT_COUNT_DEFAULT = 4 +SUPPORTED_SPLITS = (4, 8, 16) + +def _large_tail_split_count() -> int: + split_text = os.environ.get('LOOM_KNN_LARGE_TAIL_6A73_SPLIT_COUNT') + if not split_text: + return SPLIT_COUNT_DEFAULT + split_count = int(split_text) + if split_count not in SUPPORTED_SPLITS: + raise ValueError(''.join(['LOOM_KNN_LARGE_TAIL_6A73_SPLIT_COUNT must be one of ', format(SUPPORTED_SPLITS, '')])) + return split_count + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_LARGE_TAIL_6A73_VERIFY_KERNEL') + split_count = _large_tail_split_count() + if verify_kernel == 'stage1': + return parent_v20.stage1_k20_unordered_ir if split_count == 4 else parent_v20.stage1_k20_ir + if verify_kernel == 'merge': + if split_count == 4: + return parent_v20.merge_k20_unordered_warp_select_ir + if split_count == 8: + return parent_v20.merge_k20_s8_ir + if split_count == 16: + return parent_v20.merge_k20_s16_ir + return parent_v20.stage1_k20_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k20split", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 20]], "cta_group": 1, "threads": 192}')) + +def _dtype_is_bf16(inputs: dict[str, Any]) -> bool: + return str(inputs['query'].dtype) == 'torch.bfloat16' and str(inputs['database'].dtype) == 'torch.bfloat16' + +def _eligible_large_tail(inputs: dict[str, Any]) -> bool: + label = inputs.get('label') + if label is not None and str(label) != TARGET_SHAPE: + return False + return bool(inputs.get('build', False)) and _dtype_is_bf16(inputs) and (int(inputs['B']) == 1) and (int(inputs['Q']) == TARGET_QM) and (int(inputs['M']) == TARGET_QM) and (int(inputs['D']) == FEAT_D) and (int(inputs['K']) == TARGET_K) + +def _launch_large_tail(inputs: dict[str, Any]) -> None: + parent_v20._launch_k32_split_path(inputs, split_count=_large_tail_split_count()) + +def launch_from_contract_inputs(inputs: dict[str, Any]) -> None: + if _eligible_large_tail(inputs): + _launch_large_tail(inputs) + return + current_dispatch.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(candidate, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return current_dispatch._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint for the exact Q6144 K20 build target row.""" + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(kernel_fn: Callable[[dict[str, Any]], Any], *, use_cupti: bool, shape_labels=TARGET_SHAPES) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + return eval_mod.evaluate(kernel_fn, shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def _summarize_rows(report: dict[str, Any]) -> dict[str, Any]: + rows = report.get('per_shape', {}) + return {label: {'passed': rows.get(label, {}).get('passed'), 'kernel_ms': rows.get(label, {}).get('kernel_ms'), 'tflops': rows.get(label, {}).get('tflops'), 'flashlib_ms': rows.get(label, {}).get('flashlib_ms'), 'ratio_vs_flashlib': rows.get(label, {}).get('ratio_vs_flashlib'), 'timing_backend': rows.get(label, {}).get('timing_backend'), 'measurement_comparable': rows.get(label, {}).get('measurement_comparable'), 'recall': rows.get(label, {}).get('recall'), 'boundary_passed': rows.get(label, {}).get('boundary_passed'), 'distance_max_abs': rows.get(label, {}).get('distance_max_abs'), 'distance_max_rel': rows.get(label, {}).get('distance_max_rel')} for label in TARGET_SHAPES if label in rows} + +def benchmark_knn_build_large_tail_frontier_6a73_v1(*, use_cupti: bool=False) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(candidate, use_cupti=use_cupti) + baseline_report = _run_with_timing_backend(current_dispatch.candidate, use_cupti=use_cupti) + candidate_rows = candidate_report.get('per_shape', {}) + baseline_rows = baseline_report.get('per_shape', {}) + per_shape_delta = {} + for label in TARGET_SHAPES: + cand_ms = candidate_rows.get(label, {}).get('kernel_ms') + base_ms = baseline_rows.get(label, {}).get('kernel_ms') + per_shape_delta[label] = {'candidate_ms': cand_ms, 'current_dispatch_ms': base_ms, 'speedup_vs_current_dispatch': base_ms / cand_ms if cand_ms and base_ms else None, 'candidate_tflops': candidate_rows.get(label, {}).get('tflops'), 'current_dispatch_tflops': baseline_rows.get(label, {}).get('tflops'), 'flashlib_ms': candidate_rows.get(label, {}).get('flashlib_ms'), 'candidate_ratio_vs_flashlib': candidate_rows.get(label, {}).get('ratio_vs_flashlib')} + return {'tflops': candidate_report['summary']['primary_mean'] or 0.0, 'all_correct': candidate_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'split_count': _large_tail_split_count(), 'target_shapes': TARGET_SHAPES, 'measured_entrypoint': 'loom.examples.weave.knn_build_large_tail_frontier_6a73_v1:benchmark_knn_build_large_tail_frontier_6a73_v1', 'candidate_rows': _summarize_rows(candidate_report), 'current_dispatch_rows': _summarize_rows(baseline_report), 'per_shape_delta': per_shape_delta, 'report': candidate_report, 'current_dispatch_report': baseline_report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_lowk_f8c3_q512_q1024_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_lowk_f8c3_q512_q1024_v1.py new file mode 100644 index 00000000..58344e6e --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_lowk_f8c3_q512_q1024_v1.py @@ -0,0 +1,272 @@ +"""kNN build low-K exact-shape seed for the f8c3 follow-up. + +Minimum target architecture: sm_100a. This additive bucket-kernel sidecar keeps +the f8c3 selected portfolio as fallback and tests exact BF16 build rows +``Q=M=512,K in {1,2}`` plus ``Q=M=1024,K=16``. The q512 rows use the existing +tcgen05/TMA low-K producer with the dynamic generic merge; the q1024 K16 row +uses the existing K16 producer plus explicit split-count K16 cached merges. +All selected routes write contract-visible distances and indices. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from collections.abc import Callable +from functools import lru_cache +from pathlib import Path +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_selected_portfolio_f8c3_v1 as parent_f8c3 +from . import knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2_k32split_v20 as fixed_build +from .._dispatch_runtime import pack_kernel_args +TARGET_SHAPES = ('build_k_sweep_qm512_k1', 'build_k_sweep_qm512_k2', 'build_k_sweep_qm1024_k16') +Q512_TARGET_SHAPES = ('build_k_sweep_qm512_k1', 'build_k_sweep_qm512_k2') +Q1024_K16_TARGET_SHAPES = ('build_k_sweep_qm1024_k16',) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +Q512_SPLIT_CHOICES = (2, 4, 8, 16) +Q1024_K16_SPLIT_CHOICES = (4, 8, 16) +DEFAULT_Q512_SPLITS = 4 +DEFAULT_Q1024_K16_SPLITS = 16 +ROUTE_PREFIX = 'loom.examples.weave.knn_build_lowk_f8c3_q512_q1024_v1' +ROUTE_PARENT_F8C3 = 'loom.examples.weave.knn_build_dispatch_selected_portfolio_f8c3_v1:launch_from_contract_inputs' +lowk_seed = fixed_build.parent_lowk +stage1_q512_lowk_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_stage1_batch8_cond4_vmin_maxtree", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) +merge_q512_generic_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_merge", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "B", "Q", "K", "split_count", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10]], "cta_group": 1, "threads": 256}')) +stage1_q1024_k16_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k16split", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 16]], "cta_group": 1, "threads": 192}')) +merge_q1024_k16_s4_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_rowbase_cache_k16split", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "K", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 16]], "cta_group": 1, "threads": 32}')) +merge_q1024_k16_s8_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_f8c3lowk_k16s8", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 16], ["SPLIT_COUNT", 8]], "cta_group": 1, "threads": 32}')) +merge_q1024_k16_s16_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_f8c3lowk_k16s16", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 16], ["SPLIT_COUNT", 16]], "cta_group": 1, "threads": 32}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_LOWK_F8C3_VERIFY_KERNEL') + if verify_kernel == 'q512_stage1': + return stage1_q512_lowk_ir + if verify_kernel == 'q512_merge_generic': + return merge_q512_generic_ir + if verify_kernel == 'q1024_k16_stage1': + return stage1_q1024_k16_ir + if verify_kernel == 'q1024_k16_merge_s4': + return merge_q1024_k16_s4_ir + if verify_kernel == 'q1024_k16_merge_s8': + return merge_q1024_k16_s8_ir + if verify_kernel == 'q1024_k16_merge_s16': + return merge_q1024_k16_s16_ir + return stage1_q512_lowk_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_stage1_batch8_cond4_vmin_maxtree", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) + +def _check_q512_split_count(split_count: int) -> int: + split_count = int(split_count) + if split_count not in Q512_SPLIT_CHOICES: + raise ValueError(''.join(['unsupported q512 low-K split count: ', format(split_count, '')])) + return split_count + +def _check_q1024_k16_split_count(split_count: int) -> int: + split_count = int(split_count) + if split_count not in Q1024_K16_SPLIT_CHOICES: + raise ValueError(''.join(['unsupported q1024 K16 split count: ', format(split_count, '')])) + return split_count + +@lru_cache(maxsize=3) +def _compiled_merge_q1024_k16(split_count: int): + split_count = _check_q1024_k16_split_count(split_count) + if split_count == 4: + return fixed_build._compiled_merge_for_bucket(16) + if split_count == 8: + return fixed_build._compile_ir(merge_q1024_k16_s8_ir) + return fixed_build._compile_ir(merge_q1024_k16_s16_ir) + +def _merge_ir_q1024_k16(split_count: int) -> Any: + split_count = _check_q1024_k16_split_count(split_count) + if split_count == 4: + return merge_q1024_k16_s4_ir + if split_count == 8: + return merge_q1024_k16_s8_ir + return merge_q1024_k16_s16_ir + +def _dtype_name(inputs: dict[str, Any]) -> str: + query = inputs.get('query') + if query is not None: + return str(query.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')) + +def _label_can_hit(inputs: dict[str, Any], labels: set[str]) -> bool: + value = inputs.get('label') + return value is None or str(value) in labels + +def _is_bf16_build(inputs: dict[str, Any], *, q: int, k: int) -> bool: + return bool(inputs.get('build', False)) and int(inputs.get('B', -1)) == 1 and (int(inputs.get('Q', -1)) == q) and (int(inputs.get('M', -2)) == q) and (int(inputs.get('D', -1)) == fixed_build.FEAT_D) and (int(inputs.get('K', -1)) == k) and (_dtype_name(inputs) == 'bfloat16') + +def _eligible_q512_lowk(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, set(Q512_TARGET_SHAPES)) and int(inputs.get('K', -1)) in (1, 2) and _is_bf16_build(inputs, q=512, k=int(inputs.get('K', -1))) + +def _eligible_q1024_k16(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, set(Q1024_K16_TARGET_SHAPES)) and _is_bf16_build(inputs, q=1024, k=16) + +def _route_q512(split_count: int) -> str: + return ''.join([format(ROUTE_PREFIX, ''), ':q512_lowk_s', format(_check_q512_split_count(split_count), '')]) + +def _route_q1024_k16(split_count: int) -> str: + return ''.join([format(ROUTE_PREFIX, ''), ':q1024_k16_s', format(_check_q1024_k16_split_count(split_count), '')]) + +def _launch_q512_lowk_split(inputs: dict[str, Any], *, split_count: int) -> None: + lowk_seed._launch_cg2_split_path(inputs, split_count=_check_q512_split_count(split_count)) + +def _launch_q1024_k16_split(inputs: dict[str, Any], *, split_count: int) -> None: + split_count = _check_q1024_k16_split_count(split_count) + if split_count == 4: + fixed_build._launch_k32_split_path(inputs, split_count=split_count) + return + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + num_q_tiles = (n_query + fixed_build.BLOCK_Q - 1) // fixed_build.BLOCK_Q + num_q_tile_pairs = (num_q_tiles + fixed_build.CTA_GROUP - 1) // fixed_build.CTA_GROUP + num_db_tiles = (n_database + fixed_build.BLOCK_M - 1) // fixed_build.BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tile_pairs * split_count + stage1_grid = min(total_work * fixed_build.CTA_GROUP, fixed_build.GRID_DIM_DEFAULT) + merge_grid = min((bsz * n_query + fixed_build.K32_MERGE_THREADS - 1) // fixed_build.K32_MERGE_THREADS, fixed_build.GRID_DIM_DEFAULT) + partial_dists, partial_indices = fixed_build.parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = fixed_build.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), bsz * n_query, fixed_build.BLOCK_Q, dim, dim) + tmap_database = fixed_build.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, fixed_build.BLOCK_M, dim, dim) + stage1_kernel = fixed_build._compiled_stage1_for_bucket(16) + stage1_kernel.launch_cluster(grid=(stage1_grid, 1, 1), block=(fixed_build.STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage1_q1024_k16_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tile_pairs=num_q_tile_pairs, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), cluster_dims=(fixed_build.CTA_GROUP, 1, 1), shared_mem=stage1_q1024_k16_ir.computed_smem_bytes) + merge_ir_obj = _merge_ir_q1024_k16(split_count) + merge_kernel = _compiled_merge_q1024_k16(split_count) + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(fixed_build.K32_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_ir_obj.computed_smem_bytes) + +def route_for_contract_inputs(inputs: dict[str, Any], *, q512_split_count: int=DEFAULT_Q512_SPLITS, q1024_k16_split_count: int=DEFAULT_Q1024_K16_SPLITS) -> str: + if _eligible_q512_lowk(inputs): + return _route_q512(q512_split_count) + if _eligible_q1024_k16(inputs): + return _route_q1024_k16(q1024_k16_split_count) + return parent_f8c3.route_for_contract_inputs(inputs) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, q512_split_count: int=DEFAULT_Q512_SPLITS, q1024_k16_split_count: int=DEFAULT_Q1024_K16_SPLITS) -> None: + if _eligible_q512_lowk(inputs): + _launch_q512_lowk_split(inputs, split_count=q512_split_count) + return + if _eligible_q1024_k16(inputs): + _launch_q1024_k16_split(inputs, split_count=q1024_k16_split_count) + return + parent_f8c3.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def candidate_for_policy(*, q512_split_count: int=DEFAULT_Q512_SPLITS, q1024_k16_split_count: int=DEFAULT_Q1024_K16_SPLITS) -> Callable[[dict[str, Any]], None]: + q512_split_count = _check_q512_split_count(q512_split_count) + q1024_k16_split_count = _check_q1024_k16_split_count(q1024_k16_split_count) + + def _candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs, q512_split_count=q512_split_count, q1024_k16_split_count=q1024_k16_split_count) + return None + return _candidate + +def candidate_parent_f8c3(inputs: dict[str, Any]): + parent_f8c3.launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + from .._dispatch_runtime import CANONICAL_SHAPES + wanted = TARGET_SHAPE_SET if shape_labels is None else {str(label) for label in shape_labels} + selected = [shape for shape in CANONICAL_SHAPES if shape['label'] in wanted] + missing = wanted - {shape['label'] for shape in selected} + if missing: + raise ValueError(''.join(['unknown kNN build contract shape(s): ', format(sorted(missing), '')])) + return selected + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint: run real contract correctness for selected shapes.""" + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=None, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + previous = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = previous + +def _trace_inputs_from_shape(shape: dict[str, Any]) -> dict[str, Any]: + params = dict(shape['params']) + return {'label': shape['label'], 'B': int(params['B']), 'Q': int(params['Q']), 'M': int(params['M']), 'D': int(params['D']), 'K': int(params['K']), 'dtype': str(params.get('dtype', 'bfloat16')), 'build': bool(params.get('build', False))} + +def route_trace_for_shapes(shape_labels=None, *, q512_split_count: int=DEFAULT_Q512_SPLITS, q1024_k16_split_count: int=DEFAULT_Q1024_K16_SPLITS) -> list[dict[str, Any]]: + trace = [] + for shape in _select_contract_shapes(shape_labels): + inputs = _trace_inputs_from_shape(shape) + route = route_for_contract_inputs(inputs, q512_split_count=q512_split_count, q1024_k16_split_count=q1024_k16_split_count) + trace.append({'shape_key': shape['label'], 'selected_route': route, 'route_kind': 'specialized' if route.startswith(ROUTE_PREFIX) else 'parent_delegate', 'guard_condition': _guard_description(route)}) + return trace + +def _guard_description(route: str) -> str: + if route.startswith(''.join([format(ROUTE_PREFIX, ''), ':q512_lowk_s'])): + return ''.join(['exact BF16 build B1 Q=M=512 D128 K in {1,2} low-K split', format(route.rsplit('s', 1)[-1], ''), ' route']) + if route.startswith(''.join([format(ROUTE_PREFIX, ''), ':q1024_k16_s'])): + return ''.join(['exact BF16 build B1 Q=M=1024 D128 K16 split', format(route.rsplit('s', 1)[-1], ''), ' route']) + return 'f8c3 selected portfolio fallback' + +def _per_shape_deltas(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> dict[str, Any]: + result: dict[str, Any] = {} + for label in TARGET_SHAPES: + cand = candidate_report.get('per_shape', {}).get(label, {}) + base = baseline_report.get('per_shape', {}).get(label, {}) + cand_ms = cand.get('kernel_ms') + base_ms = base.get('kernel_ms') + result[label] = {'candidate_ms': cand_ms, 'baseline_ms': base_ms, 'flashlib_ms': cand.get('flashlib_ms'), 'candidate_tflops': cand.get('tflops'), 'baseline_tflops': base.get('tflops'), 'speedup_vs_f8c3': base_ms / cand_ms if cand_ms and base_ms else None, 'ratio_vs_flashlib': cand.get('ratio_vs_flashlib'), 'passed': cand.get('passed'), 'timing_backend': cand.get('timing_backend')} + return result + +def _scan_split_counts(*, use_cupti: bool) -> dict[str, Any]: + q512_scan = {} + for split_count in Q512_SPLIT_CHOICES: + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=Q512_TARGET_SHAPES, kernel_fn=candidate_for_policy(q512_split_count=split_count)) + q512_scan[str(split_count)] = report['per_shape'] + q1024_scan = {} + for split_count in Q1024_K16_SPLIT_CHOICES: + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=Q1024_K16_TARGET_SHAPES, kernel_fn=candidate_for_policy(q1024_k16_split_count=split_count)) + q1024_scan[str(split_count)] = report['per_shape'][Q1024_K16_TARGET_SHAPES[0]] + return {'q512_lowk': q512_scan, 'q1024_k16': q1024_scan} + +def benchmark_knn_build_lowk_f8c3_q512_q1024_v1(*, use_cupti: bool=True, q512_split_count: int=DEFAULT_Q512_SPLITS, q1024_k16_split_count: int=DEFAULT_Q1024_K16_SPLITS, run_baseline: bool=True, scan_splits: bool=False) -> dict[str, Any]: + """Benchmark the low-K sidecar against the f8c3 selected portfolio.""" + q512_split_count = _check_q512_split_count(q512_split_count) + q1024_k16_split_count = _check_q1024_k16_split_count(q1024_k16_split_count) + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=TARGET_SHAPES, kernel_fn=candidate_for_policy(q512_split_count=q512_split_count, q1024_k16_split_count=q1024_k16_split_count)) + baseline_report = None + if run_baseline: + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=TARGET_SHAPES, kernel_fn=candidate_parent_f8c3) + payload: dict[str, Any] = {'tflops': candidate_report['summary']['primary_mean'] or 0.0, 'all_correct': candidate_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'measured_entrypoint': 'loom.examples.weave.knn_build_lowk_f8c3_q512_q1024_v1:benchmark_knn_build_lowk_f8c3_q512_q1024_v1', 'measured_shape_labels': TARGET_SHAPES, 'route_trace': route_trace_for_shapes(TARGET_SHAPES, q512_split_count=q512_split_count, q1024_k16_split_count=q1024_k16_split_count), 'contract_summary': candidate_report['summary'], 'contract_performance': candidate_report['performance'], 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'split_counts': {'q512_lowk': q512_split_count, 'q1024_k16': q1024_k16_split_count}, 'split_scan': _scan_split_counts(use_cupti=use_cupti) if scan_splits else {}, 'report': candidate_report} + if baseline_report is not None: + payload['baseline_entrypoint'] = ROUTE_PARENT_F8C3 + payload['baseline_summary'] = baseline_report['summary'] + payload['per_shape_delta_vs_f8c3'] = _per_shape_deltas(candidate_report, baseline_report) + baseline_mean = baseline_report['summary']['primary_mean'] + payload['speedup_vs_f8c3_primary_mean'] = candidate_report['summary']['primary_mean'] / baseline_mean if baseline_mean else None + return payload + +def write_benchmark_artifact(path: str | os.PathLike[str], **kwargs) -> dict[str, Any]: + payload = benchmark_knn_build_lowk_f8c3_q512_q1024_v1(**kwargs) + out = Path(path) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + return payload diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_lowk_k12_4f30_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_lowk_k12_4f30_v1.py new file mode 100644 index 00000000..9bb73a9a --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_lowk_k12_4f30_v1.py @@ -0,0 +1,160 @@ +"""Exact q2048 K12 seed wrapper for the 4f30 K12 repair lane. + +Minimum target architecture: sm_100a. This additive bucket-kernel sidecar +guards only the BF16 build ``B=1,Q=M=2048,D=128,K=12`` contract row and routes +that row to the older v9 exact-K12 split8 tcgen05/TMA producer plus cached +eight-way merge. All other shapes delegate to the 51c1 dispatcher unchanged. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from pathlib import Path +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_e3de_9138_bcb3_faeb_cb00_3505v3_51c1_v1 as base_51c1 +from . import knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2_k32split_v9 as k12_v9 +MODULE = 'loom.examples.weave.knn_build_lowk_k12_4f30_v1' +TARGET_SHAPES = ('build_k_sweep_qm2048_k12',) +K12_GUARDRAIL_SHAPES = ('build_k_sweep_qm1024_k12', 'build_k_sweep_qm2048_k12', 'build_k_sweep_qm4096_k12') +TARGET_SHAPE_SET = set(TARGET_SHAPES) +ROUTE_Q2048_K12_V9 = ''.join([format(MODULE, ''), ':q2048_k12_v9_s8']) +ROUTE_BASE_51C1 = 'loom.examples.weave.knn_build_dispatch_e3de_9138_bcb3_faeb_cb00_3505v3_51c1_v1:launch_from_contract_inputs' +PRODUCTION_ROUTE_MODULES = {'q2048_k12_v9_s8': 'loom.examples.weave.knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2_k32split_v9:launch_from_contract_inputs', 'base_51c1': ROUTE_BASE_51C1} + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_LOWK_K12_4F30_VERIFY_KERNEL') + if verify_kernel == 'stage1_q2048_k12_v9': + return k12_v9.stage1_k12_ir + if verify_kernel == 'merge_q2048_k12_v9_s8': + return k12_v9.merge_k12_s8_ir + return base_51c1.ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k48over32", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 48]], "cta_group": 1, "threads": 192}')) + +def _dtype_name(inputs: dict[str, Any]) -> str: + query = inputs.get('query') + if query is not None and hasattr(query, 'dtype'): + return str(query.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')).replace('torch.', '') + +def _label_can_hit(inputs: dict[str, Any], labels: set[str]) -> bool: + value = inputs.get('label') + return value is None or str(value) in labels + +def _eligible_q2048_k12_v9(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, TARGET_SHAPE_SET) and bool(inputs.get('build', False)) and (int(inputs.get('B', -1)) == 1) and (int(inputs.get('Q', -1)) == 2048) and (int(inputs.get('M', -2)) == 2048) and (int(inputs.get('D', -1)) == k12_v9.FEAT_D) and (int(inputs.get('K', -1)) == 12) and (_dtype_name(inputs) == 'bfloat16') + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if not force_fallback and _eligible_q2048_k12_v9(inputs): + return ROUTE_Q2048_K12_V9 + return base_51c1.route_for_contract_inputs(inputs, force_fallback=force_fallback) + +def _launch_route(inputs: dict[str, Any], route: str) -> None: + if route == ROUTE_Q2048_K12_V9: + k12_v9._launch_k32_split_path(inputs, split_count=k12_v9.K12_MID_SPLITS) + return + base_51c1._launch_route(inputs, route) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + _launch_route(inputs, route_for_contract_inputs(inputs, force_fallback=force_fallback)) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_base_51c1(inputs: dict[str, Any]) -> None: + base_51c1.launch_from_contract_inputs(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return base_51c1._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + selected = _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + previous = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = previous + +def _trace_inputs_from_shape(shape: dict[str, Any]) -> dict[str, Any]: + params = dict(shape['params']) + return {'label': shape['label'], 'B': int(params['B']), 'Q': int(params['Q']), 'M': int(params['M']), 'D': int(params['D']), 'K': int(params['K']), 'dtype': str(params.get('dtype', 'bfloat16')), 'build': bool(params.get('build', False))} + +def _inputs_for_label(label: str) -> dict[str, Any]: + return base_51c1._inputs_for_label(label) + +def _selected_entrypoint_for_route(route: str) -> str: + if route == ROUTE_Q2048_K12_V9: + return PRODUCTION_ROUTE_MODULES['q2048_k12_v9_s8'] + return base_51c1._selected_entrypoint_for_route(route) + +def _route_trace_record(inputs: dict[str, Any], *, force_fallback: bool=False) -> dict[str, Any]: + base_route = base_51c1.route_for_contract_inputs(inputs) + route = route_for_contract_inputs(inputs, force_fallback=force_fallback) + if route == ROUTE_Q2048_K12_V9: + return {'shape_key': inputs.get('label'), 'selected_route': route, 'selected_entrypoint': _selected_entrypoint_for_route(route), 'selected_seed': 'round50_v9_q2048_k12_s8', 'expected_seed': 'round50_v9_q2048_k12_s8', 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': 'q2048_k12_v9_exact', 'guard_condition': 'exact BF16 build B=1 Q=M=2048 D=128 K=12 v9 split8 route', 'base_51c1_route': base_route, 'classification': 'seed-consumed', 'split_count': k12_v9.K12_MID_SPLITS} + row = dict(base_51c1._route_trace_record(inputs, force_fallback=force_fallback)) + row['base_51c1_route'] = base_route + row['candidate_guard_status'] = 'forced_fallback' if force_fallback else 'inherited_51c1_or_guard_miss' + return row + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False) -> list[dict[str, Any]]: + selected = _select_contract_shapes(K12_GUARDRAIL_SHAPES if shape_labels is None else shape_labels) + return [_route_trace_record(_trace_inputs_from_shape(shape), force_fallback=force_fallback) for shape in selected] + +def _rows_for_labels(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return {label: report.get('per_shape', {}).get(label, {}) for label in labels} + +def _per_shape_deltas(candidate_report: dict[str, Any], baseline_report: dict[str, Any], labels: tuple[str, ...]): + rows = {} + for label in labels: + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + rows[label] = {'candidate_ms': candidate_ms, 'baseline_51c1_ms': baseline_ms, 'flashlib_ms': candidate_row.get('flashlib_ms'), 'candidate_tflops': candidate_row.get('tflops'), 'baseline_51c1_tflops': baseline_row.get('tflops'), 'speedup_vs_51c1': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'ratio_vs_flashlib': candidate_row.get('ratio_vs_flashlib'), 'passed': candidate_row.get('passed'), 'timing_backend': candidate_row.get('timing_backend') or baseline_row.get('timing_backend'), 'selected_route': route_for_contract_inputs(_inputs_for_label(label)), 'baseline_route': base_51c1.route_for_contract_inputs(_inputs_for_label(label))} + return rows + +def benchmark_knn_build_lowk_k12_4f30_v1(*, use_cupti: bool=True, shape_labels=K12_GUARDRAIL_SHAPES, run_baseline: bool=True) -> dict[str, Any]: + labels = tuple((str(label) for label in shape_labels)) + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=labels, kernel_fn=candidate) + baseline_report = None + if run_baseline: + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=labels, kernel_fn=candidate_base_51c1) + payload: dict[str, Any] = {'tflops': candidate_report['summary']['primary_mean'] or 0.0, 'all_correct': candidate_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_knn_build_lowk_k12_4f30_v1']), 'measured_shape_labels': labels, 'route_trace': route_trace_for_contract_shapes(labels), 'forced_fallback_route_trace': route_trace_for_contract_shapes(labels, force_fallback=True), 'contract_summary': candidate_report['summary'], 'contract_performance': candidate_report['performance'], 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'report': candidate_report, 'target_shape': TARGET_SHAPES[0]} + if baseline_report is not None: + payload['baseline_entrypoint'] = ROUTE_BASE_51C1 + payload['baseline_summary'] = baseline_report['summary'] + payload['baseline_performance'] = baseline_report['performance'] + payload['baseline_rows'] = _rows_for_labels(baseline_report, labels) + payload['per_shape_delta_vs_51c1'] = _per_shape_deltas(candidate_report, baseline_report, labels) + baseline_mean = baseline_report['summary']['primary_mean'] + candidate_mean = candidate_report['summary']['primary_mean'] + payload['speedup_vs_51c1_primary_mean'] = candidate_mean / baseline_mean if candidate_mean and baseline_mean else None + return payload + +def write_benchmark_artifact(path: str | os.PathLike[str], **kwargs) -> dict[str, Any]: + payload = benchmark_knn_build_lowk_k12_4f30_v1(**kwargs) + out = Path(path) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + return payload diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_lowmargin_1074_k1k24k30_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_lowmargin_1074_k1k24k30_v1.py new file mode 100644 index 00000000..e0a58971 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_lowmargin_1074_k1k24k30_v1.py @@ -0,0 +1,230 @@ +"""Exact residual low-margin build bucket seed for generalize task 1074. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +targets the three residual build low-margin rows from round 113: +``build_k_sweep_qm512_k1``, ``build_k_sweep_qm4096_k24``, and +``build_k_sweep_qm4096_k30``. K1 reuses the validated low-K Q512 seed, K30 +reuses the validated 6998 warp-select seed, and K24 adds a new exact +four-split unordered tcgen05/TMA producer plus warp-register merge. Guard +misses delegate to the current 6998 residual overlay. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from collections.abc import Callable +from functools import lru_cache +from pathlib import Path +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_6998_residual_19b3_overlay_v1 as current_6998 +from . import knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2_k32split_v20 as v20 +from . import knn_build_k30_q4096_6998_warpselect_v1 as k30_warp +from . import knn_build_lowk_f8c3_q512_q1024_v1 as lowk_seed +from .._dispatch_runtime import pack_kernel_args +MODULE = 'loom.examples.weave.knn_build_lowmargin_1074_k1k24k30_v1' +TARGET_K1 = 'build_k_sweep_qm512_k1' +TARGET_K24 = 'build_k_sweep_qm4096_k24' +TARGET_K30 = 'build_k_sweep_qm4096_k30' +TARGET_SHAPES = (TARGET_K1, TARGET_K24, TARGET_K30) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +SEED_ID = 'lowmargin_1074_k1k24k30_v1' +SEED_K1_ID = 'lowk_q512_k1_s4_1074' +SEED_K24_ID = 'k24_q4096_warpselect_1074' +SEED_K30_ID = k30_warp.SEED_ID +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_K1_ENTRYPOINT = ''.join([format(lowk_seed.ROUTE_PREFIX, ''), ':q512_lowk_s', format(lowk_seed.DEFAULT_Q512_SPLITS, '')]) +ROUTE_K24_ENTRYPOINT = ''.join([format(MODULE, ''), ':k24_q4096_warpselect']) +ROUTE_K30_ENTRYPOINT = k30_warp.ROUTE_ENTRYPOINT +BASELINE_6998_ENTRYPOINT = 'loom.examples.weave.knn_build_dispatch_6998_residual_19b3_overlay_v1:launch_from_contract_inputs' +K24_TOP_K = 24 +K24_SPLIT_COUNT = v20.MEDIUM_SPLITS +Q512_SPLIT_COUNT = lowk_seed.DEFAULT_Q512_SPLITS +stage1_k24_unordered_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k30unordered_1074k24unordered", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 24]], "cta_group": 1, "threads": 192}')) +knn_build_1074_k24_q4096_merge_s4_unordered_warp_select = _decode_capture(_json_loads('{"__ir__": "knn_build_1074_k24_q4096_merge_s4_unordered_warp_select", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 24], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 128}')) +merge_k24_q4096_warp_select_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_1074_k24_q4096_merge_s4_unordered_warp_select", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 24], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 128}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_LOWMARGIN_1074_VERIFY_KERNEL') + if verify_kernel == 'merge_k24_warp_select': + return merge_k24_q4096_warp_select_ir + if verify_kernel == 'lowk_q512_stage1': + return lowk_seed.stage1_q512_lowk_ir + if verify_kernel == 'lowk_q512_merge_generic': + return lowk_seed.merge_q512_generic_ir + if verify_kernel == 'k30_stage1_unordered': + return k30_warp.v20.stage1_k30_unordered_ir + if verify_kernel == 'k30_merge_warp_select': + return k30_warp.merge_k30_q4096_warp_select_ir + return stage1_k24_unordered_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k30unordered_1074k24unordered", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 24]], "cta_group": 1, "threads": 192}')) +PRODUCTION_ROUTE_MODULES = {SEED_ID: ROUTE_ENTRYPOINT, SEED_K1_ID: ROUTE_K1_ENTRYPOINT, SEED_K24_ID: ROUTE_K24_ENTRYPOINT, SEED_K30_ID: ROUTE_K30_ENTRYPOINT, 'baseline_6998': BASELINE_6998_ENTRYPOINT} + +def _compiled_stage1_k24_unordered(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0053"}')) + +def _compiled_merge_k24_q4096_warp_select(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0054"}')) + +def _dtype_name(inputs: dict[str, Any], name: str='query') -> str: + tensor = inputs.get(name) + if tensor is not None: + return str(tensor.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')) + +def _label_can_hit(inputs: dict[str, Any], labels: set[str]) -> bool: + value = inputs.get('label') + return value is None or str(value) in labels + +def _is_bf16_build_qm(inputs: dict[str, Any], *, q: int, k: int) -> bool: + return bool(inputs.get('build', False)) and int(inputs.get('B', -1)) == 1 and (int(inputs.get('Q', -1)) == q) and (int(inputs.get('M', -1)) == q) and (int(inputs.get('D', -1)) == v20.FEAT_D) and (int(inputs.get('K', -1)) == k) and (_dtype_name(inputs, 'query') == 'bfloat16') and (_dtype_name(inputs, 'database') in ('', 'bfloat16')) + +def _eligible_k1_q512(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, {TARGET_K1}) and _is_bf16_build_qm(inputs, q=512, k=1) + +def _eligible_k24_q4096(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, {TARGET_K24}) and _is_bf16_build_qm(inputs, q=4096, k=K24_TOP_K) + +def _eligible_k30_q4096(inputs: dict[str, Any]) -> bool: + return k30_warp._eligible_k30_q4096(inputs) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if not force_fallback: + if _eligible_k1_q512(inputs): + return ROUTE_K1_ENTRYPOINT + if _eligible_k24_q4096(inputs): + return ROUTE_K24_ENTRYPOINT + if _eligible_k30_q4096(inputs): + return ROUTE_K30_ENTRYPOINT + return current_6998.route_for_contract_inputs(inputs) + +def _launch_k1_q512(inputs: dict[str, Any]) -> None: + lowk_seed.launch_from_contract_inputs(inputs, q512_split_count=Q512_SPLIT_COUNT) + +def _launch_k24_q4096_warp_select(inputs: dict[str, Any]) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + split_count = K24_SPLIT_COUNT + num_q_tiles = (n_query + v20.BLOCK_Q - 1) // v20.BLOCK_Q + num_q_tile_pairs = (num_q_tiles + v20.CTA_GROUP - 1) // v20.CTA_GROUP + num_db_tiles = (n_database + v20.BLOCK_M - 1) // v20.BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tile_pairs * split_count + stage1_grid = min(total_work * v20.CTA_GROUP, v20.GRID_DIM_DEFAULT) + merge_grid = (bsz * n_query + 3) // 4 + partial_dists, partial_indices = v20.parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = v20.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), bsz * n_query, v20.BLOCK_Q, dim, dim) + tmap_database = v20.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, v20.BLOCK_M, dim, dim) + stage1_kernel = _compiled_stage1_k24_unordered() + stage1_kernel.launch_cluster(grid=(stage1_grid, 1, 1), block=(v20.STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage1_k24_unordered_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tile_pairs=num_q_tile_pairs, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), cluster_dims=(v20.CTA_GROUP, 1, 1), shared_mem=stage1_k24_unordered_ir.computed_smem_bytes) + merge_kernel = _compiled_merge_k24_q4096_warp_select() + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(v20.K32_COOP_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_k24_q4096_warp_select_ir.computed_smem_bytes) + +def k24_q4096_warpselect(inputs: dict[str, Any]) -> None: + _launch_k24_q4096_warp_select(inputs) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + if not force_fallback: + if _eligible_k1_q512(inputs): + _launch_k1_q512(inputs) + return + if _eligible_k24_q4096(inputs): + _launch_k24_q4096_warp_select(inputs) + return + if _eligible_k30_q4096(inputs): + k30_warp.launch_from_contract_inputs(inputs) + return + current_6998.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) + +def candidate_current_6998(inputs: dict[str, Any]) -> None: + current_6998.launch_from_contract_inputs(inputs) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return current_6998._select_contract_shapes(shape_labels) + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=None, kernel_fn: Callable[[dict[str, Any]], Any]=candidate, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + shapes = None if shape_labels is None else _select_contract_shapes(shape_labels) + return evaluate_contract(shapes=shapes, correctness=correctness, benchmark=benchmark, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = _run_with_timing_backend(use_cupti=True, shape_labels=shape_labels, benchmark=benchmark, correctness=True) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + rows = [] + for shape in selected: + inputs = current_6998.base_f30c._trace_inputs_from_shape(shape) + route = route_for_contract_inputs(inputs, force_fallback=force_fallback) + label = str(shape['label']) + if route == ROUTE_K1_ENTRYPOINT: + row = {'shape_key': label, 'selected_route': route, 'selected_entrypoint': ROUTE_K1_ENTRYPOINT, 'selected_seed': SEED_K1_ID, 'expected_seed': SEED_K1_ID, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': '1074_lowmargin_q512_k1_s4_guard', 'guard_condition': 'exact BF16 build B=1 Q=M=512 D=128 K=1', 'base_6998_route': current_6998.route_for_contract_inputs(inputs), 'classification': 'unmeasured'} + elif route == ROUTE_K24_ENTRYPOINT: + row = {'shape_key': label, 'selected_route': route, 'selected_entrypoint': ROUTE_K24_ENTRYPOINT, 'selected_seed': SEED_K24_ID, 'expected_seed': SEED_K24_ID, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': '1074_lowmargin_q4096_k24_warpselect_guard', 'guard_condition': 'exact BF16 build B=1 Q=M=4096 D=128 K=24', 'base_6998_route': current_6998.route_for_contract_inputs(inputs), 'classification': 'unmeasured'} + elif route == ROUTE_K30_ENTRYPOINT: + row = dict(k30_warp.route_trace_for_contract_shapes((label,), force_fallback=False)[0]) + row['guard_id'] = '1074_lowmargin_q4096_k30_delegate_guard' + row['base_6998_route'] = current_6998.route_for_contract_inputs(inputs) + else: + row = dict(current_6998.route_trace_for_contract_shapes((label,), force_fallback=force_fallback)[0]) + row['candidate_guard_status'] = 'forced_fallback_or_guard_miss' + rows.append(current_6998.base_f30c._normalize_route_row(row)) + return rows + +def _per_shape_rows(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return {label: dict(report.get('per_shape', {}).get(label, {})) for label in labels} + +def _candidate_row_delta(candidate_report: dict[str, Any], baseline_report: dict[str, Any], label: str) -> dict[str, Any]: + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') or baseline_row.get('flashlib_ms') + return {'shape_key': label, 'candidate_route': route_for_contract_inputs(current_6998.base_f30c._inputs_for_label(label)), 'baseline_6998_route': current_6998.route_for_contract_inputs(current_6998.base_f30c._inputs_for_label(label)), 'candidate_ms': candidate_ms, 'baseline_6998_ms': baseline_ms, 'flashlib_ms': flashlib_ms, 'speedup_vs_6998': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'ratio_vs_flashlib': flashlib_ms / candidate_ms if candidate_ms and flashlib_ms else None, 'candidate_passed': candidate_row.get('passed'), 'baseline_6998_passed': baseline_row.get('passed'), 'timing_backend': candidate_row.get('timing_backend') or baseline_row.get('timing_backend')} + +def _seed_delta_matrix(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> list[dict[str, Any]]: + return [_candidate_row_delta(candidate_report, baseline_report, label) for label in TARGET_SHAPES] + +def benchmark_candidate_lowmargin_1074_k1k24k30_v1(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES, benchmark_correctness: bool=True) -> dict[str, Any]: + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_current_6998, correctness=benchmark_correctness, benchmark=True) + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate, correctness=benchmark_correctness, benchmark=True) + candidate_metric = candidate_report.get('summary', {}).get('primary_mean') + baseline_metric = baseline_report.get('summary', {}).get('primary_mean') + selected_labels = tuple(TARGET_SHAPES if shape_labels is None else shape_labels) + return {'candidate_id': SEED_ID, 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_candidate_lowmargin_1074_k1k24k30_v1']), 'baseline_6998_entrypoint': BASELINE_6998_ENTRYPOINT, 'selected_seeds': (SEED_K1_ID, SEED_K24_ID, SEED_K30_ID), 'all_correct': candidate_report.get('summary', {}).get('all_correct'), 'baseline_6998_all_correct': baseline_report.get('summary', {}).get('all_correct'), 'performance_comparable': candidate_report.get('summary', {}).get('performance_comparable'), 'baseline_6998_performance_comparable': baseline_report.get('summary', {}).get('performance_comparable'), 'tflops': candidate_metric, 'baseline_6998_tflops': baseline_metric, 'metric_delta_vs_6998': candidate_metric - baseline_metric if candidate_metric and baseline_metric else None, 'timing_backend': 'cupti' if use_cupti else 'cuda_event', 'denominator': 'residual_build_low_margin_k1_k24_k30', 'shape_labels': list(selected_labels), 'selected_route_rows': _per_shape_rows(candidate_report, selected_labels), 'baseline_6998_route_rows': _per_shape_rows(baseline_report, selected_labels), 'seed_delta_matrix': _seed_delta_matrix(candidate_report, baseline_report), 'route_trace': route_trace_for_contract_shapes(shape_labels), 'forced_fallback_route_trace': route_trace_for_contract_shapes(shape_labels, force_fallback=True), 'route_modules': PRODUCTION_ROUTE_MODULES, 'report': candidate_report, 'baseline_6998_report': baseline_report, 'route_trace_included': True} + +def write_benchmark_artifact(artifact_dir: str | Path, *, use_cupti: bool=True, shape_labels=TARGET_SHAPES) -> dict[str, str]: + payload = benchmark_candidate_lowmargin_1074_k1k24k30_v1(use_cupti=use_cupti, shape_labels=shape_labels) + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + path = out_dir / 'lowmargin_1074_k1k24k30_v1.json' + path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + return {'candidate_payload': str(path)} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_midk_k11k13_e080_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_midk_k11k13_e080_v1.py new file mode 100644 index 00000000..1abbbdeb --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_midk_k11k13_e080_v1.py @@ -0,0 +1,295 @@ +"""Exact-capacity mid-K build seed for the e080 auto-tuning lane. + +Minimum target architecture: sm_100a. This additive bucket candidate targets +the BF16 build rows ``B=1,Q=M in {2048,4096},D=128,K in {11,12,13}`` that the +full82 dispatcher currently leaves on a slow generic route. It reuses the v9 +tcgen05/TMA split producer family, but emits exact K11/K13 stage and merge IRs +instead of using the inherited K12/K16 capacity buckets. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from collections.abc import Callable +from functools import lru_cache +from pathlib import Path +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_4247_non128_8199_3d5a_2e8e_full82_synth_v1 as baseline_full82 +from . import knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2_k32split_v9 as v9 +from .._dispatch_runtime import pack_kernel_args +MODULE = 'loom.examples.weave.knn_build_midk_k11k13_e080_v1' +TARGET_SHAPES = ('build_k_sweep_qm2048_k11', 'build_k_sweep_qm2048_k12', 'build_k_sweep_qm2048_k13', 'build_k_sweep_qm4096_k13') +TARGET_SHAPE_SET = set(TARGET_SHAPES) +DEFAULT_SPLITS_BY_KQ = {(11, 2048): v9.K12_MID_SPLITS, (12, 2048): v9.K12_MID_SPLITS, (13, 2048): v9.K12_MID_SPLITS, (13, 4096): v9.MEDIUM_SPLITS} +SUPPORTED_SPLITS = (v9.MEDIUM_SPLITS, v9.K12_MID_SPLITS) +ROUTE_K11_EXACT = ''.join([format(MODULE, ''), ':k11_exact']) +ROUTE_K12_EXACT = ''.join([format(MODULE, ''), ':k12_exact']) +ROUTE_K13_EXACT = ''.join([format(MODULE, ''), ':k13_exact']) +ROUTE_BASELINE_FULL82 = ''.join([format(baseline_full82.MODULE, ''), ':launch_from_contract_inputs']) +stage1_k11_exact_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_e080k11exact", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 11]], "cta_group": 1, "threads": 192}')) +stage1_k13_exact_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_e080k13exact", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 13]], "cta_group": 1, "threads": 192}')) +merge_k11_s4_exact_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_rowbase_cache_e080k11s4exact", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "K", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 11]], "cta_group": 1, "threads": 32}')) +merge_k13_s4_exact_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_rowbase_cache_e080k13s4exact", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "K", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 13]], "cta_group": 1, "threads": 32}')) +merge_k11_s8_exact_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_e080k11s8exact", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 11], ["SPLIT_COUNT", 8]], "cta_group": 1, "threads": 32}')) +merge_k13_s8_exact_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_e080k13s8exact", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 13], ["SPLIT_COUNT", 8]], "cta_group": 1, "threads": 32}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_MIDK_K11K13_E080_VERIFY_KERNEL') + if verify_kernel == 'stage1_k11': + return stage1_k11_exact_ir + if verify_kernel == 'stage1_k12': + return v9.stage1_k12_ir + if verify_kernel == 'stage1_k13': + return stage1_k13_exact_ir + if verify_kernel == 'merge_k11_s4': + return merge_k11_s4_exact_ir + if verify_kernel == 'merge_k11_s8': + return merge_k11_s8_exact_ir + if verify_kernel == 'merge_k12_s8': + return v9.merge_k12_s8_ir + if verify_kernel == 'merge_k13_s4': + return merge_k13_s4_exact_ir + if verify_kernel == 'merge_k13_s8': + return merge_k13_s8_exact_ir + return stage1_k13_exact_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_e080k13exact", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 13]], "cta_group": 1, "threads": 192}')) + +def _compiled_stage1_k11_exact(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0034"}')) + +def _compiled_stage1_k12_exact(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0193"}')) + +def _compiled_stage1_k13_exact(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0038"}')) + +def _compiled_merge_k11_s4_exact(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0194"}')) + +def _compiled_merge_k11_s8_exact(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0035"}')) + +def _compiled_merge_k13_s4_exact(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0195"}')) + +def _compiled_merge_k13_s8_exact(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0039"}')) + +def _dtype_name(inputs: dict[str, Any]) -> str: + query = inputs.get('query') + if query is not None and hasattr(query, 'dtype'): + return str(query.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')).replace('torch.', '') + +def _label_can_hit(inputs: dict[str, Any]) -> bool: + value = inputs.get('label') + return value is None or str(value) in TARGET_SHAPE_SET + +def _eligible_midk_exact(inputs: dict[str, Any]) -> bool: + top_k = int(inputs.get('K', -1)) + n_query = int(inputs.get('Q', -1)) + return _label_can_hit(inputs) and bool(inputs.get('build', False)) and (_dtype_name(inputs) == 'bfloat16') and (int(inputs.get('B', -1)) == 1) and (n_query == int(inputs.get('M', -2))) and (n_query in (2048, 4096)) and (int(inputs.get('D', -1)) == v9.FEAT_D) and (n_query == 2048 and top_k in (11, 12, 13) or (n_query == 4096 and top_k == 13)) + +def _split_count_for_shape(*, top_k: int, n_query: int, k11_split: int | None=None, k13_split: int | None=None) -> int: + if top_k == 11 and k11_split is not None: + split_count = k11_split + elif top_k == 13 and k13_split is not None: + split_count = k13_split + else: + split_count = DEFAULT_SPLITS_BY_KQ[top_k, n_query] + if split_count not in SUPPORTED_SPLITS: + raise ValueError(''.join(['unsupported mid-K split_count ', format(split_count, ''), '; expected one of ', format(SUPPORTED_SPLITS, '')])) + return split_count + +def _stage1_ir_for_k(top_k: int) -> Any: + if top_k == 11: + return stage1_k11_exact_ir + if top_k == 12: + return v9.stage1_k12_ir + if top_k == 13: + return stage1_k13_exact_ir + raise ValueError(''.join(['unsupported exact mid-K ', format(top_k, '')])) + +def _stage1_kernel_for_k(top_k: int): + if top_k == 11: + return _compiled_stage1_k11_exact() + if top_k == 12: + return _compiled_stage1_k12_exact() + if top_k == 13: + return _compiled_stage1_k13_exact() + raise ValueError(''.join(['unsupported exact mid-K ', format(top_k, '')])) + +def _merge_ir_for_k(top_k: int, split_count: int) -> Any: + if top_k == 11: + return merge_k11_s8_exact_ir if split_count == v9.K12_MID_SPLITS else merge_k11_s4_exact_ir + if top_k == 12: + if split_count != v9.K12_MID_SPLITS: + raise ValueError('K12 exact route only supports the inherited split8 merge') + return v9.merge_k12_s8_ir + if top_k == 13: + return merge_k13_s8_exact_ir if split_count == v9.K12_MID_SPLITS else merge_k13_s4_exact_ir + raise ValueError(''.join(['unsupported exact mid-K ', format(top_k, '')])) + +def _merge_kernel_for_k(top_k: int, split_count: int): + if top_k == 11: + return _compiled_merge_k11_s8_exact() if split_count == v9.K12_MID_SPLITS else _compiled_merge_k11_s4_exact() + if top_k == 12: + if split_count != v9.K12_MID_SPLITS: + raise ValueError('K12 exact route only supports the inherited split8 merge') + return v9._compiled_merge_k12_s8() + if top_k == 13: + return _compiled_merge_k13_s8_exact() if split_count == v9.K12_MID_SPLITS else _compiled_merge_k13_s4_exact() + raise ValueError(''.join(['unsupported exact mid-K ', format(top_k, '')])) + +def _route_for_k(top_k: int) -> str: + if top_k == 11: + return ROUTE_K11_EXACT + if top_k == 12: + return ROUTE_K12_EXACT + if top_k == 13: + return ROUTE_K13_EXACT + raise ValueError(''.join(['unsupported exact mid-K ', format(top_k, '')])) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False, k11_split: int | None=None, k13_split: int | None=None) -> str: + if not force_fallback and _eligible_midk_exact(inputs): + top_k = int(inputs['K']) + _split_count_for_shape(top_k=top_k, n_query=int(inputs['Q']), k11_split=k11_split, k13_split=k13_split) + return _route_for_k(top_k) + return baseline_full82.route_for_contract_inputs(inputs, force_fallback=force_fallback) + +def _launch_exact_midk(inputs: dict[str, Any], *, split_count: int) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + num_q_tiles = (n_query + v9.BLOCK_Q - 1) // v9.BLOCK_Q + num_q_tile_pairs = (num_q_tiles + v9.CTA_GROUP - 1) // v9.CTA_GROUP + num_db_tiles = (n_database + v9.BLOCK_M - 1) // v9.BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tile_pairs * split_count + stage1_grid = min(total_work * v9.CTA_GROUP, v9.GRID_DIM_DEFAULT) + merge_grid = min((bsz * n_query + v9.K32_MERGE_THREADS - 1) // v9.K32_MERGE_THREADS, v9.GRID_DIM_DEFAULT) + stage1_ir_obj = _stage1_ir_for_k(top_k) + merge_ir_obj = _merge_ir_for_k(top_k, split_count) + partial_dists, partial_indices = v9.parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = v9.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), bsz * n_query, v9.BLOCK_Q, dim, dim) + tmap_database = v9.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, v9.BLOCK_M, dim, dim) + _stage1_kernel_for_k(top_k).launch_cluster(grid=(stage1_grid, 1, 1), block=(v9.STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage1_ir_obj, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tile_pairs=num_q_tile_pairs, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), cluster_dims=(v9.CTA_GROUP, 1, 1), shared_mem=stage1_ir_obj.computed_smem_bytes) + merge_kernel = _merge_kernel_for_k(top_k, split_count) + if split_count == v9.K12_MID_SPLITS: + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(v9.K32_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_ir_obj.computed_smem_bytes) + return + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(v9.K32_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], top_k, bsz * n_query], shared_mem=merge_ir_obj.computed_smem_bytes) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False, k11_split: int | None=None, k13_split: int | None=None) -> None: + if not force_fallback and _eligible_midk_exact(inputs): + top_k = int(inputs['K']) + split_count = _split_count_for_shape(top_k=top_k, n_query=int(inputs['Q']), k11_split=k11_split, k13_split=k13_split) + _launch_exact_midk(inputs, split_count=split_count) + return + baseline_full82.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_with_splits(*, k11_split: int | None=None, k13_split: int | None=None) -> Callable[[dict[str, Any]], None]: + + def _candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, k11_split=k11_split, k13_split=k13_split) + return _candidate + +def candidate_baseline_full82(inputs: dict[str, Any]) -> None: + baseline_full82.candidate_q16split148_plus_cachedmerge(inputs) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return baseline_full82._select_contract_shapes(shape_labels) + +def _benchmark_shapes(shape_labels, *, time_flashlib: bool) -> list[dict[str, Any]]: + selected = _select_contract_shapes(TARGET_SHAPES if shape_labels is None else shape_labels) + out = [] + for shape in selected: + params = dict(shape['params']) + params['time_flashlib'] = bool(time_flashlib) + out.append({'label': shape['label'], 'params': params}) + return out + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate, time_flashlib: bool=True) -> dict[str, Any]: + previous = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_benchmark_shapes(shape_labels, time_flashlib=time_flashlib), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = previous + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _trace_inputs_for_label(label: str) -> dict[str, Any]: + return baseline_full82._inputs_for_label(label) + +def route_trace_for_contract_shapes(shape_labels=TARGET_SHAPES, *, force_fallback: bool=False, k11_split: int | None=None, k13_split: int | None=None) -> list[dict[str, Any]]: + rows = [] + for label in tuple(shape_labels): + inputs = _trace_inputs_for_label(str(label)) + top_k = int(inputs['K']) + route = route_for_contract_inputs(inputs, force_fallback=force_fallback, k11_split=k11_split, k13_split=k13_split) + baseline_route = baseline_full82.route_for_contract_inputs(inputs) + specialized = not force_fallback and _eligible_midk_exact(inputs) + rows.append({'shape_key': label, 'selected_route': route, 'selected_entrypoint': ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) if specialized else ROUTE_BASELINE_FULL82, 'selected_seed': 'knn_build_midk_k11k13_e080_v1' if specialized else None, 'expected_seed': 'knn_build_midk_k11k13_e080_v1' if specialized else None, 'route_kind': 'specialized' if specialized else 'broad-dispatcher', 'route_source': 'shape-specific-seed' if specialized else 'broad-dispatcher', 'guard_id': 'e080_midk_k11k13_exact_guard' if specialized else 'forced_or_guard_miss', 'guard_condition': 'exact BF16 build B=1 Q=M in {2048,4096} D=128 K in {11,12,13}', 'baseline_dispatcher_route': baseline_route, 'split_count': None if not specialized else _split_count_for_shape(top_k=top_k, n_query=int(inputs['Q']), k11_split=k11_split, k13_split=k13_split), 'classification': 'seed-consumed' if specialized else 'guard-miss'}) + return rows + +def _rows_for_labels(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return {label: report.get('per_shape', {}).get(label, {}) for label in labels} + +def _per_shape_delta(candidate_report: dict[str, Any], baseline_report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + rows = {} + for label in labels: + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + rows[label] = {'candidate_ms': candidate_ms, 'baseline_dispatcher_ms': baseline_ms, 'flashlib_ms': candidate_row.get('flashlib_ms'), 'candidate_tflops': candidate_row.get('tflops'), 'baseline_dispatcher_tflops': baseline_row.get('tflops'), 'speedup_vs_baseline_dispatcher': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'ratio_vs_flashlib': candidate_row.get('ratio_vs_flashlib'), 'passed': candidate_row.get('passed'), 'timing_backend': candidate_row.get('timing_backend') or baseline_row.get('timing_backend')} + return rows + +def benchmark_knn_build_midk_k11k13_e080_v1(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES, run_baseline: bool=True, time_flashlib: bool=True, k11_split: int | None=None, k13_split: int | None=None) -> dict[str, Any]: + labels = tuple((str(label) for label in shape_labels)) + kernel_fn = candidate_with_splits(k11_split=k11_split, k13_split=k13_split) + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=labels, kernel_fn=kernel_fn, time_flashlib=time_flashlib) + baseline_report = None + if run_baseline: + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=labels, kernel_fn=candidate_baseline_full82, time_flashlib=time_flashlib) + payload: dict[str, Any] = {'candidate_id': 'knn_build_midk_k11k13_e080_v1', 'tflops': candidate_report['summary']['primary_mean'] or 0.0, 'all_correct': candidate_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_knn_build_midk_k11k13_e080_v1']), 'candidate_entrypoint': ''.join([format(MODULE, ''), ':launch_from_contract_inputs']), 'measured_shape_labels': labels, 'route_trace': route_trace_for_contract_shapes(labels, k11_split=k11_split, k13_split=k13_split), 'forced_fallback_route_trace': route_trace_for_contract_shapes(labels, force_fallback=True), 'contract_summary': candidate_report['summary'], 'contract_performance': candidate_report['performance'], 'contract_correctness': candidate_report['correctness'], 'rank_objective': {'metric': 'tflops', 'direction': 'maximize', 'value': candidate_report['summary']['primary_mean'], 'valid_measurement_count': candidate_report['performance']['valid_measurement_count'], 'comparable': candidate_report['performance']['comparable']}, 'split_policy': {'build_k_sweep_qm2048_k11': _split_count_for_shape(top_k=11, n_query=2048, k11_split=k11_split), 'build_k_sweep_qm2048_k12': _split_count_for_shape(top_k=12, n_query=2048), 'build_k_sweep_qm2048_k13': _split_count_for_shape(top_k=13, n_query=2048, k13_split=k13_split), 'build_k_sweep_qm4096_k13': _split_count_for_shape(top_k=13, n_query=4096, k13_split=k13_split)}, 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'time_flashlib': time_flashlib, 'report': candidate_report} + if baseline_report is not None: + baseline_mean = baseline_report['summary']['primary_mean'] + candidate_mean = candidate_report['summary']['primary_mean'] + payload['baseline_entrypoint'] = ''.join([format(baseline_full82.MODULE, ''), ':benchmark_candidate_q16split148_plus_cachedmerge']) + payload['baseline_summary'] = baseline_report['summary'] + payload['baseline_performance'] = baseline_report['performance'] + payload['baseline_rows'] = _rows_for_labels(baseline_report, labels) + payload['per_shape_delta_vs_baseline_dispatcher'] = _per_shape_delta(candidate_report, baseline_report, labels) + payload['speedup_vs_baseline_dispatcher_primary_mean'] = candidate_mean / baseline_mean if candidate_mean and baseline_mean else None + return payload + +def write_benchmark_artifact(path: str | os.PathLike[str], **kwargs) -> dict[str, Any]: + payload = benchmark_knn_build_midk_k11k13_e080_v1(**kwargs) + out = Path(path) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + return payload diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_non128_frontier_3d5a_cachedmerge_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_non128_frontier_3d5a_cachedmerge_v1.py new file mode 100644 index 00000000..d313fb9b --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_non128_frontier_3d5a_cachedmerge_v1.py @@ -0,0 +1,265 @@ +"""kNN non-D128 frontier seed combining exact producers with cached merges. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +combines the source-policy-clean non-D128 repairs already present in this +worktree: D96 exact-width tcgen05/TMA, D320 exact-tail tcgen05/TMA, and the +D768 fused split merge. It changes the remaining split8 K10 build-row +consumers for D96, D192, and D320 build to a constexpr row-base cached merge. +D320 search and D768 delegate to the validated fused parent route. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from collections.abc import Callable +from functools import lru_cache +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_evolve_7bfc_split_v1 as split_parent +from . import knn_build_evolve_7bfc_v1 as base_v1 +from . import knn_build_non128_frontier_4be7_d768fused_v1 as fused_parent +from .._dispatch_runtime import pack_kernel_args +MODULE = 'loom.examples.weave.knn_build_non128_frontier_3d5a_cachedmerge_v1' +ROUTE_PREFIX = 'knn_build_non128_frontier_3d5a_cachedmerge_v1' +d96exact = fused_parent.d96exact +d320tail = d96exact.d320tail +widecombine = d320tail.wide_m64.widecombine +BLOCK_Q = d96exact.BLOCK_Q +BLOCK_M = d96exact.BLOCK_M +TOP_K_MAX = d96exact.TOP_K_MAX +THREADS = d96exact.THREADS +GRID_DIM_DEFAULT = d96exact.GRID_DIM_DEFAULT +FAST_MERGE_THREADS = 32 +D96_BUILD_SHAPE = d96exact.D96_SHAPE +D192_BUILD_SHAPE = 'build_dim_sweep_b1_q2048_m2048_d192_k10' +D320_BUILD_SHAPE = d320tail.D320_BUILD_SHAPE +CACHED_SPLIT8_SHAPES = {D96_BUILD_SHAPE, D192_BUILD_SHAPE, D320_BUILD_SHAPE} +TARGET_SHAPES = fused_parent.TARGET_SHAPES +TARGET_SHAPE_SET = set(TARGET_SHAPES) +SHAPE_SPECS = _decode_capture(_json_loads('{"__dict_items__": [["build_dim_sweep_b1_q1024_m1024_d96_k10", {"__dict_items__": [["B", 1], ["Q", 1024], ["M", 1024], ["D", 96], ["K", 10], ["build", true], ["feature_chunks", 1], ["split_count", 8]]}], ["build_dim_sweep_b1_q2048_m2048_d192_k10", {"__dict_items__": [["B", 1], ["Q", 2048], ["M", 2048], ["D", 192], ["K", 10], ["build", true], ["feature_chunks", 2], ["split_count", 8]]}], ["build_highd_b1_q1024_m1024_d320_k10", {"__dict_items__": [["B", 1], ["Q", 1024], ["M", 1024], ["D", 320], ["K", 10], ["build", true], ["feature_chunks", 3], ["split_count", 8]]}], ["search_rect_highd_b1_q512_m12000_d320_k10", {"__dict_items__": [["B", 1], ["Q", 512], ["M", 12000], ["D", 320], ["K", 10], ["build", false], ["feature_chunks", 3], ["split_count", 32]]}], ["rag_microbatch_highd_b1_q16_m50000_d768_k10", {"__dict_items__": [["B", 1], ["Q", 16], ["M", 50000], ["D", 768], ["K", 10], ["build", false], ["feature_chunks", 6], ["split_count", 72]]}]]}')) +stage1_d96exact_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_4be7_d96exact_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 38144, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) +stage1_d256_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_dim_midk_df2f_d256_split_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 99584, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) +stage1_d320tail_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_8227_d320tail_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 124160, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) +stage1_m64_d768_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_7ee5_m64rag_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 34048, "constants": [["FEATURE_CHUNKS", 6]], "cta_group": 1, "threads": 96}')) +fused_merge_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_4be7_d768fused_merge", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 1024, "constants": [["TOP_K_MAX", 10], ["GROUP_COUNT", 8], ["GROUP_SPLITS", 9]], "cta_group": 1, "threads": 32}')) +merge_generic_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_merge", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "B", "Q", "K", "split_count", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10]], "cta_group": 1, "threads": 256}')) +knn_build_non128_frontier_3d5a_k10_merge_s8_rowbase_cache = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_3d5a_k10_merge_s8_rowbase_cache", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 8]], "cta_group": 1, "threads": 32}')) +merge_k10_s8_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_3d5a_k10_merge_s8_rowbase_cache", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 8]], "cta_group": 1, "threads": 32}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_NON128_FRONTIER_3D5A_CACHEDMERGE_VERIFY_KERNEL') + if verify_kernel == 'merge_s8': + return merge_k10_s8_ir + if verify_kernel == 'stage1_d96exact': + return stage1_d96exact_ir + if verify_kernel == 'stage1_d256': + return stage1_d256_ir + if verify_kernel == 'stage1_d320tail': + return stage1_d320tail_ir + if verify_kernel == 'stage1_m64_d768': + return stage1_m64_d768_ir + if verify_kernel == 'fused_merge': + return fused_parent._fused_merge_ir(fused_parent.D768_SPLIT_COUNT, fused_parent.D768_GROUP_COUNT) + if verify_kernel == 'merge_generic': + return merge_generic_ir + return merge_k10_s8_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_3d5a_k10_merge_s8_rowbase_cache", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 8]], "cta_group": 1, "threads": 32}')) + +def _compiled_merge_k10_s8(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0022"}')) + +def _target_label_for_inputs(inputs: dict[str, Any]) -> str | None: + return fused_parent._target_label_for_inputs(inputs) + +def _split_count_for_label(label: str) -> int: + return fused_parent._split_count_for_label(label) + +def _uses_cached_split8(label: str | None) -> bool: + return label in CACHED_SPLIT8_SHAPES and _split_count_for_label(str(label)) == 8 + +def _feature_dim_for_label(label: str) -> int: + return fused_parent._feature_dim_for_label(label) + +def _producer_for_label(label: str) -> str: + if not _uses_cached_split8(label): + return fused_parent._producer_for_label(label) + return ''.join([format(fused_parent._producer_for_label(label), ''), '_cachedmerge_s8']) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + label = _target_label_for_inputs(inputs) + if force_fallback or label is None: + return fused_parent.route_for_contract_inputs(inputs, force_fallback=force_fallback) + if not _uses_cached_split8(label): + return fused_parent.route_for_contract_inputs(inputs, force_fallback=False) + spec = SHAPE_SPECS[label] + return ''.join([format(ROUTE_PREFIX, ''), ':', format(label, ''), ':d', format(int(spec['D']), ''), ':s8:', format(_producer_for_label(label), '')]) + +def _prepare_cached_merge(*, partial_dists, partial_indices, out_dists, out_indices, bsz: int, n_query: int): + merge_grid = min((bsz * n_query + FAST_MERGE_THREADS - 1) // FAST_MERGE_THREADS, GRID_DIM_DEFAULT) + return _compiled_merge_k10_s8().prepare_launch(grid=(merge_grid, 1, 1), block=(FAST_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, out_dists, out_indices, bsz * n_query], shared_mem=merge_k10_s8_ir.computed_smem_bytes) + +def _launch_cached_merge(**kwargs) -> None: + _prepare_cached_merge(**kwargs).launch() + +def _partial_buffers(*, split_count: int, bsz: int, n_query: int, top_k: int, device): + return split_parent._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=device) + +def _launch_d96_cached(inputs: dict[str, Any], label: str) -> None: + query = inputs['query'] + database = inputs['database'] + if str(query.dtype) != 'torch.bfloat16' or str(database.dtype) != 'torch.bfloat16': + raise ValueError('knn_build_non128_frontier_3d5a_cachedmerge_v1 supports bfloat16 inputs only') + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + split_count = _split_count_for_label(label) + if split_count != 8 or top_k != TOP_K_MAX or dim != d96exact.D96_FEAT_D: + fused_parent.launch_from_contract_inputs(inputs, force_fallback=False) + return + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tiles * split_count + stage1_grid = min(total_work, GRID_DIM_DEFAULT) + partial_dists, partial_indices = _partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = d96exact._create_tensor_map_3d_oob_zero_swizzle64(query.data_ptr(), bsz * n_query, BLOCK_Q, d96exact.D96_FEAT_D, d96exact.D96_FEAT_D) + tmap_database = d96exact._create_tensor_map_3d_oob_zero_swizzle64(database.data_ptr(), bsz * n_database, BLOCK_M, d96exact.D96_FEAT_D, d96exact.D96_FEAT_D) + d96exact._compiled_d96exact_stage1().launch(grid=(stage1_grid, 1, 1), block=(THREADS, 1, 1), args=pack_kernel_args(stage1_d96exact_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tiles=num_q_tiles, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), shared_mem=stage1_d96exact_ir.computed_smem_bytes) + _launch_cached_merge(partial_dists=partial_dists, partial_indices=partial_indices, out_dists=inputs['out_dists'], out_indices=inputs['out_indices'], bsz=bsz, n_query=n_query) + +def _launch_d192_cached(inputs: dict[str, Any], label: str) -> None: + query = inputs['query'] + database = inputs['database'] + if str(query.dtype) != 'torch.bfloat16' or str(database.dtype) != 'torch.bfloat16': + raise ValueError('knn_build_non128_frontier_3d5a_cachedmerge_v1 supports bfloat16 inputs only') + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + split_count = _split_count_for_label(label) + feature_dim = 256 + if split_count != 8 or top_k != TOP_K_MAX: + fused_parent.launch_from_contract_inputs(inputs, force_fallback=False) + return + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tiles * split_count + stage1_grid = min(total_work, GRID_DIM_DEFAULT) + partial_dists, partial_indices = _partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + pad_base = widecombine.splitretune.parent + database_tma = pad_base._allocate_padded_bf16_rows(database, rows=bsz * n_database, dst_cols=feature_dim) + if query.data_ptr() == database.data_ptr(): + query_tma = database_tma + else: + query_tma = pad_base._allocate_padded_bf16_rows(query, rows=bsz * n_query, dst_cols=feature_dim) + tmap_query = base_v1._create_tensor_map_3d_oob_zero(query_tma.data_ptr(), bsz * n_query, BLOCK_Q, feature_dim, feature_dim) + tmap_database = base_v1._create_tensor_map_3d_oob_zero(database_tma.data_ptr(), bsz * n_database, BLOCK_M, feature_dim, feature_dim) + stage1_launch = widecombine.wide_d256._compiled_d256_stage1().prepare_launch(grid=(stage1_grid, 1, 1), block=(THREADS, 1, 1), args=pack_kernel_args(stage1_d256_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tiles=num_q_tiles, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), shared_mem=stage1_d256_ir.computed_smem_bytes) + merge_launch = _prepare_cached_merge(partial_dists=partial_dists, partial_indices=partial_indices, out_dists=inputs['out_dists'], out_indices=inputs['out_indices'], bsz=bsz, n_query=n_query) + pad_base._launch_pad_bf16_rows(database, database_tma, rows=bsz * n_database, src_cols=dim, dst_cols=feature_dim) + if query_tma is not database_tma: + pad_base._launch_pad_bf16_rows(query, query_tma, rows=bsz * n_query, src_cols=dim, dst_cols=feature_dim) + stage1_launch.launch() + merge_launch.launch() + +def _launch_d320_build_cached(inputs: dict[str, Any], label: str) -> None: + query = inputs['query'] + database = inputs['database'] + if str(query.dtype) != 'torch.bfloat16' or str(database.dtype) != 'torch.bfloat16': + raise ValueError('knn_build_non128_frontier_3d5a_cachedmerge_v1 supports bfloat16 inputs only') + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + split_count = _split_count_for_label(label) + if split_count != 8 or top_k != TOP_K_MAX or dim != d320tail.D320_FEAT_D: + fused_parent.launch_from_contract_inputs(inputs, force_fallback=False) + return + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tiles * split_count + stage1_grid = min(total_work, GRID_DIM_DEFAULT) + partial_dists, partial_indices = _partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), bsz * n_query, BLOCK_Q, d320tail.D320_FEAT_D, d320tail.D320_FEAT_D) + tmap_database = base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, d320tail.D320_FEAT_D, d320tail.D320_FEAT_D) + d320tail._compiled_d320tail_stage1().launch(grid=(stage1_grid, 1, 1), block=(THREADS, 1, 1), args=pack_kernel_args(stage1_d320tail_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tiles=num_q_tiles, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), shared_mem=stage1_d320tail_ir.computed_smem_bytes) + _launch_cached_merge(partial_dists=partial_dists, partial_indices=partial_indices, out_dists=inputs['out_dists'], out_indices=inputs['out_indices'], bsz=bsz, n_query=n_query) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + label = _target_label_for_inputs(inputs) + if force_fallback or label is None or (not _uses_cached_split8(label)): + fused_parent.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + return + if label == D96_BUILD_SHAPE: + _launch_d96_cached(inputs, label) + return + if label == D192_BUILD_SHAPE: + _launch_d192_cached(inputs, label) + return + if label == D320_BUILD_SHAPE: + _launch_d320_build_cached(inputs, label) + return + fused_parent.launch_from_contract_inputs(inputs, force_fallback=False) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return fused_parent._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def benchmark_knn_build_non128_frontier_3d5a_cachedmerge_v1(*, use_cupti: bool | None=None, shape_labels=TARGET_SHAPES) -> dict[str, Any]: + prior_use_cupti = None + if use_cupti is not None: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True) + finally: + if prior_use_cupti is not None: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + return {'tflops': report['summary']['primary_mean'], 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'report': report} + +def _trace_inputs_from_shape(shape: dict[str, Any]) -> dict[str, Any]: + return fused_parent._trace_inputs_from_shape(shape) + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + rows = [] + for shape in selected: + inputs = _trace_inputs_from_shape(shape) + label = _target_label_for_inputs(inputs) + if label is None or force_fallback or (not _uses_cached_split8(label)): + row = fused_parent.route_trace_for_contract_shapes((inputs['label'],), force_fallback=force_fallback)[0] + if label in TARGET_SHAPE_SET: + row['expected_seed'] = 'non128_frontier_3d5a_cachedmerge_v1' + rows.append(row) + continue + spec = SHAPE_SPECS[label] + rows.append({'shape_key': label, 'selected_route': route_for_contract_inputs(inputs), 'selected_entrypoint': ''.join([format(MODULE, ''), ':launch_from_contract_inputs']), 'selected_seed': 'non128_frontier_3d5a_cachedmerge_v1', 'expected_seed': 'non128_frontier_3d5a_cachedmerge_v1', 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': '3d5a_cachedmerge_non128_exact_shape_guard', 'guard_condition': ''.join(['exact BF16 B=', format(spec['B'], ''), ' Q=', format(spec['Q'], ''), ' M=', format(spec['M'], ''), ' D=', format(spec['D'], ''), ' K=', format(spec['K'], ''), ' build=', format(spec['build'], ''), ' split_count=8']), 'feature_dim': _feature_dim_for_label(label), 'split_count': 8, 'producer': fused_parent._producer_for_label(label), 'merge_route': 'k10_s8_rowbase_cached', 'source_route': fused_parent.route_for_contract_inputs(inputs), 'classification': 'cached-merge-s8'}) + return rows diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_non128_frontier_4be7_d768fused_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_non128_frontier_4be7_d768fused_v1.py new file mode 100644 index 00000000..0393c1ba --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_non128_frontier_4be7_d768fused_v1.py @@ -0,0 +1,207 @@ +"""kNN non-D128 frontier seed with fused D768 split merge. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +preserves the round-104 exact-D96 seed for D96/D192/D320 and routes only +``rag_microbatch_highd_b1_q16_m50000_d768_k10`` through the existing M64/N64 +tcgen05/TMA D768 producer followed by a D768-specialized fused group/final +split merge. The measured path remains Weave-only and writes contract-visible +distances and indices. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from collections.abc import Callable +from functools import cache +from typing import Any +from .._dispatch_runtime import dc as dc +from .. import _dispatch_runtime as eval_mod +from . import knn_build_evolve_7bfc_split_v1 as split_parent +from . import knn_build_non128_frontier_4be7_d96exact_v1 as d96exact +from . import knn_build_non128_frontier_7ee5_m64rag_v1 as m64rag +from .._dispatch_runtime import pack_kernel_args +MODULE = 'loom.examples.weave.knn_build_non128_frontier_4be7_d768fused_v1' +ROUTE_PREFIX = 'knn_build_non128_frontier_4be7_d768fused_v1' +D768_SHAPE = m64rag.D768_SHAPE +D768_SPLIT_COUNT = _decode_capture(_json_loads('72')) +D768_GROUP_COUNT = _decode_capture(_json_loads('8')) +D768_FUSED_MERGE_THREADS = _decode_capture(_json_loads('32')) +D768_FUSED_MERGE_SLOTS = 128 +TARGET_SHAPES = d96exact.TARGET_SHAPES +TARGET_SHAPE_SET = set(TARGET_SHAPES) +SHAPE_SPECS = _decode_capture(_json_loads('{"__dict_items__": [["build_dim_sweep_b1_q1024_m1024_d96_k10", {"__dict_items__": [["B", 1], ["Q", 1024], ["M", 1024], ["D", 96], ["K", 10], ["build", true], ["feature_chunks", 1], ["split_count", 8]]}], ["build_dim_sweep_b1_q2048_m2048_d192_k10", {"__dict_items__": [["B", 1], ["Q", 2048], ["M", 2048], ["D", 192], ["K", 10], ["build", true], ["feature_chunks", 2], ["split_count", 8]]}], ["build_highd_b1_q1024_m1024_d320_k10", {"__dict_items__": [["B", 1], ["Q", 1024], ["M", 1024], ["D", 320], ["K", 10], ["build", true], ["feature_chunks", 3], ["split_count", 8]]}], ["search_rect_highd_b1_q512_m12000_d320_k10", {"__dict_items__": [["B", 1], ["Q", 512], ["M", 12000], ["D", 320], ["K", 10], ["build", false], ["feature_chunks", 3], ["split_count", 32]]}], ["rag_microbatch_highd_b1_q16_m50000_d768_k10", {"__dict_items__": [["B", 1], ["Q", 16], ["M", 50000], ["D", 768], ["K", 10], ["build", false], ["feature_chunks", 6], ["split_count", 72]]}]]}')) +SHAPE_SPECS[D768_SHAPE]['split_count'] = D768_SPLIT_COUNT +M64_BLOCK_Q = m64rag.M64_BLOCK_Q +M64_BLOCK_M = m64rag.M64_BLOCK_M +M64_THREADS = m64rag.M64_THREADS +M64_FEATURE_CHUNKS = m64rag.M64_FEATURE_CHUNKS +K_TILE = m64rag.K_TILE +TOP_K_MAX = m64rag.TOP_K_MAX +MERGE_THREADS = m64rag.MERGE_THREADS +GRID_DIM_DEFAULT = m64rag.GRID_DIM_DEFAULT +stage1_d96exact_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_4be7_d96exact_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 38144, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) +stage1_d320tail_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_8227_d320tail_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 124160, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) +stage1_d256_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_dim_midk_df2f_d256_split_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 99584, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) +stage1_m64_d768_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_7ee5_m64rag_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 34048, "constants": [["FEATURE_CHUNKS", 6]], "cta_group": 1, "threads": 96}')) +merge_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_merge", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "B", "Q", "K", "split_count", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10]], "cta_group": 1, "threads": 256}')) +knn_build_non128_frontier_4be7_d768fused_merge = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_4be7_d768fused_merge", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 1024, "constants": [["TOP_K_MAX", 10], ["GROUP_COUNT", 8], ["GROUP_SPLITS", 9]], "cta_group": 1, "threads": 32}')) +fused_merge_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_4be7_d768fused_merge", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 1024, "constants": [["TOP_K_MAX", 10], ["GROUP_COUNT", 8], ["GROUP_SPLITS", 9]], "cta_group": 1, "threads": 32}')) + +def _ir_with_constants(ir_obj: Any, *, suffix: str, **updates: int) -> Any: + constants = tuple(((name, updates.get(name, value)) for name, value in ir_obj.constants)) + return dc.replace(ir_obj, symbol=''.join([format(ir_obj.symbol, ''), '_', format(suffix, '')]), constants=constants) + +def _validate_group_shape(split_count: int, group_count: int) -> None: + if split_count <= 0 or group_count <= 0: + raise ValueError(''.join(['split_count and group_count must be positive, got ', format(split_count, ''), ', ', format(group_count, '')])) + if split_count % group_count != 0: + raise ValueError(''.join(['split_count=', format(split_count, ''), ' must be divisible by group_count=', format(group_count, '')])) + if group_count > D768_FUSED_MERGE_THREADS: + raise ValueError(''.join(['group_count=', format(group_count, ''), ' exceeds fused merge threads=', format(D768_FUSED_MERGE_THREADS, '')])) + if group_count * TOP_K_MAX > D768_FUSED_MERGE_SLOTS: + raise ValueError(''.join(['group_count=', format(group_count, ''), ' needs ', format(group_count * TOP_K_MAX, ''), ' shared slots, but the D768 fused merge allocates ', format(D768_FUSED_MERGE_SLOTS, '')])) + +def _fused_merge_ir(split_count: int, group_count: int) -> Any: + _validate_group_shape(split_count, group_count) + return _ir_with_constants(fused_merge_ir, suffix=''.join(['s', format(split_count, ''), 'g', format(group_count, ''), '_4be7_d768fused_v1']), GROUP_COUNT=group_count, GROUP_SPLITS=split_count // group_count) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_NON128_FRONTIER_4BE7_D768FUSED_VERIFY_KERNEL') + split_count = int(os.environ.get('LOOM_KNN_NON128_FRONTIER_4BE7_D768FUSED_VERIFY_SPLIT', D768_SPLIT_COUNT)) + group_count = int(os.environ.get('LOOM_KNN_NON128_FRONTIER_4BE7_D768FUSED_VERIFY_GROUPS', D768_GROUP_COUNT)) + if verify_kernel == 'stage1_m64_d768': + return stage1_m64_d768_ir + if verify_kernel == 'stage1_d96exact': + return stage1_d96exact_ir + if verify_kernel == 'stage1_d320tail': + return stage1_d320tail_ir + if verify_kernel == 'stage1_d256': + return stage1_d256_ir + return _fused_merge_ir(split_count, group_count) +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_4be7_d768fused_merge_s72g8_4be7_d768fused_v1", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 1024, "constants": [["TOP_K_MAX", 10], ["GROUP_COUNT", 8], ["GROUP_SPLITS", 9]], "cta_group": 1, "threads": 32}')) + +@cache +def _compiled_fused_merge(split_count: int, group_count: int): + return m64rag._compile_ir(_fused_merge_ir(split_count, group_count)) + +def _target_label_for_inputs(inputs: dict[str, Any]) -> str | None: + return d96exact._target_label_for_inputs(inputs) + +def _uses_d768_fused(label: str | None) -> bool: + return label == D768_SHAPE + +def _split_count_for_label(label: str) -> int: + if _uses_d768_fused(label): + return D768_SPLIT_COUNT + return d96exact._split_count_for_label(label) + +def _feature_dim_for_label(label: str) -> int: + if _uses_d768_fused(label): + return M64_FEATURE_CHUNKS * K_TILE + return d96exact._feature_dim_for_label(label) + +def _producer_for_label(label: str) -> str: + if _uses_d768_fused(label): + return ''.join(['m64_d768_fusedmerge_g', format(D768_GROUP_COUNT, '')]) + return d96exact._producer_for_label(label) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + label = _target_label_for_inputs(inputs) + if force_fallback or label is None: + return d96exact.route_for_contract_inputs(inputs, force_fallback=force_fallback) + spec = SHAPE_SPECS[label] + return ''.join([format(ROUTE_PREFIX, ''), ':', format(label, ''), ':d', format(int(spec['D']), ''), ':s', format(_split_count_for_label(label), ''), ':', format(_producer_for_label(label), '')]) + +def _launch_d768_fused(inputs: dict[str, Any]) -> None: + query = inputs['query'] + database = inputs['database'] + if str(query.dtype) != 'torch.bfloat16' or str(database.dtype) != 'torch.bfloat16': + raise ValueError('knn_build_non128_frontier_4be7_d768fused_v1 supports bfloat16 inputs only') + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + if dim != M64_FEATURE_CHUNKS * K_TILE: + raise ValueError(''.join(['D768 M64 route expected D=', format(M64_FEATURE_CHUNKS * K_TILE, ''), ', got ', format(dim, '')])) + _validate_group_shape(D768_SPLIT_COUNT, D768_GROUP_COUNT) + num_q_tiles = (n_query + M64_BLOCK_Q - 1) // M64_BLOCK_Q + num_db_tiles = (n_database + M64_BLOCK_M - 1) // M64_BLOCK_M + db_tiles_per_split = (num_db_tiles + D768_SPLIT_COUNT - 1) // D768_SPLIT_COUNT + total_work = bsz * num_q_tiles * D768_SPLIT_COUNT + total_queries = bsz * n_query + stage1_grid = min(total_work, GRID_DIM_DEFAULT) + merge_grid = min(total_queries, GRID_DIM_DEFAULT) + partial_dists, partial_indices = split_parent._partial_buffers(split_count=D768_SPLIT_COUNT, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = m64rag.non128_base.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), total_queries, M64_BLOCK_Q, dim, K_TILE) + tmap_database = m64rag.non128_base.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, M64_BLOCK_M, dim, K_TILE) + m64rag._compiled_stage1_m64().launch(grid=(stage1_grid, 1, 1), block=(M64_THREADS, 1, 1), args=pack_kernel_args(stage1_m64_d768_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tiles=num_q_tiles, db_tiles_per_split=db_tiles_per_split, split_count=D768_SPLIT_COUNT, total_work=total_work), shared_mem=stage1_m64_d768_ir.computed_smem_bytes) + fused_ir = _fused_merge_ir(D768_SPLIT_COUNT, D768_GROUP_COUNT) + _compiled_fused_merge(D768_SPLIT_COUNT, D768_GROUP_COUNT).launch(grid=(merge_grid, 1, 1), block=(D768_FUSED_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], total_queries], shared_mem=fused_ir.computed_smem_bytes) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + label = _target_label_for_inputs(inputs) + if force_fallback or label is None: + d96exact.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + return + if _uses_d768_fused(label): + _launch_d768_fused(inputs) + return + d96exact.launch_from_contract_inputs(inputs, force_fallback=False) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return d96exact._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def benchmark_knn_build_non128_frontier_4be7_d768fused_v1(*, use_cupti: bool | None=None, shape_labels=TARGET_SHAPES) -> dict[str, Any]: + prior_use_cupti = None + if use_cupti is not None: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True) + finally: + if prior_use_cupti is not None: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + return {'tflops': report['summary']['primary_mean'], 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'report': report} + +def _trace_inputs_from_shape(shape: dict[str, Any]) -> dict[str, Any]: + return d96exact._trace_inputs_from_shape(shape) + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + rows = [] + for shape in selected: + inputs = _trace_inputs_from_shape(shape) + label = _target_label_for_inputs(inputs) + if label is None or force_fallback: + parent_row = d96exact.route_trace_for_contract_shapes((inputs['label'],), force_fallback=force_fallback)[0] + rows.append({**parent_row, 'expected_seed': 'non128_frontier_4be7_d768fused_v1' if inputs['label'] in TARGET_SHAPE_SET else parent_row.get('expected_seed'), 'route_source': '4be7-d96exact-parent-dispatcher', 'guard_id': 'forced_fallback' if force_fallback else 'd96exact_parent_guard', 'classification': 'forced_fallback' if force_fallback else 'delegated'}) + continue + spec = SHAPE_SPECS[label] + uses_d768 = _uses_d768_fused(label) + parent_trace = d96exact.route_trace_for_contract_shapes((label,))[0] + rows.append({'shape_key': label, 'selected_route': route_for_contract_inputs(inputs), 'selected_entrypoint': ''.join([format(MODULE, ''), ':launch_from_contract_inputs']), 'selected_seed': 'non128_frontier_4be7_d768fused_v1', 'expected_seed': 'non128_frontier_4be7_d768fused_v1', 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': '4be7_d768fused_non128_exact_shape_guard', 'guard_condition': ''.join(['exact BF16 B=', format(spec['B'], ''), ' Q=', format(spec['Q'], ''), ' M=', format(spec['M'], ''), ' D=', format(spec['D'], ''), ' K=', format(spec['K'], ''), ' build=', format(spec['build'], '')]), 'feature_dim': _feature_dim_for_label(label), 'split_count': _split_count_for_label(label), 'group_count': D768_GROUP_COUNT if uses_d768 else None, 'producer': _producer_for_label(label), 'preprocess_stage': parent_trace.get('preprocess_stage'), 'source_route': 'm64_d768_s72_with_fused_group_merge' if uses_d768 else d96exact.route_for_contract_inputs(inputs), 'classification': 'd768-fused-merge' if uses_d768 else 'd96exact-parent'}) + return rows diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_non128_frontier_4be7_d96exact_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_non128_frontier_4be7_d96exact_v1.py new file mode 100644 index 00000000..0c1c23e0 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_non128_frontier_4be7_d96exact_v1.py @@ -0,0 +1,207 @@ +"""kNN non-D128 frontier seed with exact D96 MMA. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +preserves the round-104 D320 exact-tail seed for D192, D320, and D768, and +routes only ``build_dim_sweep_b1_q1024_m1024_d96_k10`` through an exact-width +D96 tcgen05/TMA producer. The D96 stage uses 64B-swizzled BF16 SMEM rows and +issues a K=96 tcgen05 dot-product tile instead of padding D96 to D128. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from collections.abc import Callable +from functools import lru_cache +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_evolve_7bfc_split_v1 as split_parent +from . import knn_build_evolve_7bfc_v1 as base_v1 +from . import knn_build_non128_frontier_8227_d320tail_v1 as d320tail +from .._dispatch_runtime import pack_kernel_args +MODULE = 'loom.examples.weave.knn_build_non128_frontier_4be7_d96exact_v1' +ROUTE_PREFIX = 'knn_build_non128_frontier_4be7_d96exact_v1' +BLOCK_Q = d320tail.BLOCK_Q +BLOCK_M = d320tail.BLOCK_M +K_TILE = d320tail.K_TILE +TOP_K_MAX = d320tail.TOP_K_MAX +THREADS = d320tail.THREADS +MERGE_THREADS = d320tail.MERGE_THREADS +GRID_DIM_DEFAULT = d320tail.GRID_DIM_DEFAULT +D96_FEAT_D = 96 +D96_QUERY_BYTES = BLOCK_Q * D96_FEAT_D * 2 +D96_DATABASE_BYTES = BLOCK_M * D96_FEAT_D * 2 +DB_SQ_BYTES = BLOCK_M * 4 +D96_SHAPE = 'build_dim_sweep_b1_q1024_m1024_d96_k10' +TARGET_SHAPES = d320tail.TARGET_SHAPES +TARGET_SHAPE_SET = set(TARGET_SHAPES) +SHAPE_SPECS = _decode_capture(_json_loads('{"__dict_items__": [["build_dim_sweep_b1_q1024_m1024_d96_k10", {"__dict_items__": [["B", 1], ["Q", 1024], ["M", 1024], ["D", 96], ["K", 10], ["build", true], ["feature_chunks", 1], ["split_count", 8]]}], ["build_dim_sweep_b1_q2048_m2048_d192_k10", {"__dict_items__": [["B", 1], ["Q", 2048], ["M", 2048], ["D", 192], ["K", 10], ["build", true], ["feature_chunks", 2], ["split_count", 8]]}], ["build_highd_b1_q1024_m1024_d320_k10", {"__dict_items__": [["B", 1], ["Q", 1024], ["M", 1024], ["D", 320], ["K", 10], ["build", true], ["feature_chunks", 3], ["split_count", 8]]}], ["search_rect_highd_b1_q512_m12000_d320_k10", {"__dict_items__": [["B", 1], ["Q", 512], ["M", 12000], ["D", 320], ["K", 10], ["build", false], ["feature_chunks", 3], ["split_count", 32]]}], ["rag_microbatch_highd_b1_q16_m50000_d768_k10", {"__dict_items__": [["B", 1], ["Q", 16], ["M", 50000], ["D", 768], ["K", 10], ["build", false], ["feature_chunks", 6], ["split_count", 72]]}]]}')) +stage1_d96exact_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_4be7_d96exact_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 38144, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) +stage1_d320tail_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_8227_d320tail_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 124160, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) +stage1_d256_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_dim_midk_df2f_d256_split_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 99584, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) +stage1_m64_d768_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_7ee5_m64rag_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 34048, "constants": [["FEATURE_CHUNKS", 6]], "cta_group": 1, "threads": 96}')) +merge_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_merge", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "B", "Q", "K", "split_count", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10]], "cta_group": 1, "threads": 256}')) +_TMAP64_CACHE: dict[tuple[int, int, int, int, int, int], Any] = {} +knn_build_non128_frontier_4be7_d96exact_stage1 = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_4be7_d96exact_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 38144, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) +stage1_d96exact_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_4be7_d96exact_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 38144, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_NON128_FRONTIER_4BE7_D96EXACT_VERIFY_KERNEL') + if verify_kernel == 'stage1_d96exact': + return stage1_d96exact_ir + if verify_kernel == 'stage1_d320tail': + return stage1_d320tail_ir + if verify_kernel == 'stage1_d256': + return stage1_d256_ir + if verify_kernel == 'stage1_m64_d768': + return stage1_m64_d768_ir + if verify_kernel == 'merge': + return merge_ir + return stage1_d96exact_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_4be7_d96exact_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 38144, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) + +def _target_label_for_inputs(inputs: dict[str, Any]) -> str | None: + return d320tail._target_label_for_inputs(inputs) + +def _uses_d96exact(label: str | None) -> bool: + return label == D96_SHAPE + +def _split_count_for_label(label: str) -> int: + if _uses_d96exact(label): + env_label = label.upper().replace('-', '_') + env_key = ''.join(['LOOM_KNN_NON128_FRONTIER_4BE7_D96EXACT_SPLITS_', format(env_label, '')]) + override = os.environ.get(env_key) + if override is not None: + return int(override) + return d320tail._split_count_for_label(label) + +def _feature_dim_for_label(label: str) -> int: + if _uses_d96exact(label): + return D96_FEAT_D + return d320tail._feature_dim_for_label(label) + +def _producer_for_label(label: str) -> str: + if _uses_d96exact(label): + return 'd96_exact64b' + return d320tail._producer_for_label(label) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + label = _target_label_for_inputs(inputs) + if force_fallback or label is None: + return d320tail.route_for_contract_inputs(inputs, force_fallback=force_fallback) + spec = SHAPE_SPECS[label] + return ''.join([format(ROUTE_PREFIX, ''), ':', format(label, ''), ':d', format(int(spec['D']), ''), ':s', format(_split_count_for_label(label), ''), ':', format(_producer_for_label(label), '')]) + +def _create_tensor_map_3d_oob_zero_swizzle64(data_ptr: int, global_height: int, shared_height: int, width: int, block_width: int): + import torch + from cuda.bindings import driver + from .._dispatch_runtime import Swizzle + from .._dispatch_runtime import _tmap_to_device + from .._dispatch_runtime import TensorMapMetadata, attach_tma_metadata + if width % 32 != 0 or block_width % 32 != 0: + raise ValueError(''.join(['64B BF16 tensor map requires width/block_width multiples of 32, got ', format(width, ''), '/', format(block_width, '')])) + device_index = torch.cuda.current_device() + key = (device_index, int(data_ptr), int(global_height), int(shared_height), int(width), int(block_width)) + cached = _TMAP64_CACHE.get(key) + if cached is not None: + return cached + err, tmap = _capture_cuTensorMapEncodeTiled(driver.CUtensorMapDataType.CU_TENSOR_MAP_DATA_TYPE_BFLOAT16, 3, data_ptr, [driver.cuuint64_t(32), driver.cuuint64_t(global_height), driver.cuuint64_t(width // 32)], [driver.cuuint64_t(width * 2), driver.cuuint64_t(64)], [driver.cuuint32_t(32), driver.cuuint32_t(shared_height), driver.cuuint32_t(block_width // 32)], [driver.cuuint32_t(1), driver.cuuint32_t(1), driver.cuuint32_t(1)], driver.CUtensorMapInterleave.CU_TENSOR_MAP_INTERLEAVE_NONE, driver.CUtensorMapSwizzle.CU_TENSOR_MAP_SWIZZLE_64B, driver.CUtensorMapL2promotion.CU_TENSOR_MAP_L2_PROMOTION_NONE, driver.CUtensorMapFloatOOBfill.CU_TENSOR_MAP_FLOAT_OOB_FILL_NAN_REQUEST_ZERO_FMA) + if err != 0: + raise RuntimeError(''.join(['cuTensorMapEncodeTiled (3D, 64B OOB zero) failed: CUresult=', format(err, '')])) + cached = attach_tma_metadata(_tmap_to_device(tmap).to(device=torch.device('cuda', device_index)), TensorMapMetadata(ndim=3, dtype='bf16', swizzle=Swizzle.SZ_64B, helper='knn_build_non128_frontier_4be7_d96exact._create_tensor_map_3d_oob_zero_swizzle64')) + _TMAP64_CACHE[key] = cached + return cached + +def _compiled_d96exact_stage1(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0021"}')) + +def _launch_d96exact(inputs: dict[str, Any], label: str) -> None: + query = inputs['query'] + database = inputs['database'] + if str(query.dtype) != 'torch.bfloat16' or str(database.dtype) != 'torch.bfloat16': + raise ValueError('knn_build_non128_frontier_4be7_d96exact_v1 supports bfloat16 inputs only') + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + split_count = _split_count_for_label(label) + if dim != D96_FEAT_D: + raise ValueError(''.join(['D96 exact route expected D=', format(D96_FEAT_D, ''), ', got ', format(dim, '')])) + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tiles * split_count + stage1_grid = min(total_work, GRID_DIM_DEFAULT) + merge_grid = min((bsz * n_query + MERGE_THREADS - 1) // MERGE_THREADS, GRID_DIM_DEFAULT) + partial_dists, partial_indices = split_parent._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = _create_tensor_map_3d_oob_zero_swizzle64(query.data_ptr(), bsz * n_query, BLOCK_Q, D96_FEAT_D, D96_FEAT_D) + tmap_database = _create_tensor_map_3d_oob_zero_swizzle64(database.data_ptr(), bsz * n_database, BLOCK_M, D96_FEAT_D, D96_FEAT_D) + _compiled_d96exact_stage1().launch(grid=(stage1_grid, 1, 1), block=(THREADS, 1, 1), args=pack_kernel_args(stage1_d96exact_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tiles=num_q_tiles, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), shared_mem=stage1_d96exact_ir.computed_smem_bytes) + merge_kernel = split_parent._compiled_merge() + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz, n_query, top_k, split_count, bsz * n_query], shared_mem=merge_ir.computed_smem_bytes) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + label = _target_label_for_inputs(inputs) + if force_fallback or label is None: + d320tail.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + return + if _uses_d96exact(label): + _launch_d96exact(inputs, label) + return + d320tail.launch_from_contract_inputs(inputs, force_fallback=False) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return d320tail._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def benchmark_knn_build_non128_frontier_4be7_d96exact_v1(*, use_cupti: bool | None=None, shape_labels=TARGET_SHAPES) -> dict[str, Any]: + prior_use_cupti = None + if use_cupti is not None: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True) + finally: + if prior_use_cupti is not None: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + return {'tflops': report['summary']['primary_mean'], 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'report': report} + +def _trace_inputs_from_shape(shape: dict[str, Any]) -> dict[str, Any]: + return d320tail._trace_inputs_from_shape(shape) + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + rows = [] + for shape in selected: + inputs = _trace_inputs_from_shape(shape) + label = _target_label_for_inputs(inputs) + if label is None or force_fallback: + rows.append({'shape_key': inputs['label'], 'selected_route': d320tail.route_for_contract_inputs(inputs, force_fallback=force_fallback), 'selected_entrypoint': ''.join([format(d320tail.MODULE, ''), ':launch_from_contract_inputs']), 'selected_seed': None, 'expected_seed': 'non128_frontier_4be7_d96exact_v1' if inputs['label'] in TARGET_SHAPE_SET else None, 'route_kind': 'fallback', 'route_source': '8227-d320tail-parent-dispatcher', 'guard_id': 'forced_fallback' if force_fallback else 'd320tail_parent_guard', 'classification': 'forced_fallback' if force_fallback else 'delegated'}) + continue + spec = SHAPE_SPECS[label] + uses_d96exact = _uses_d96exact(label) + feature_dim = _feature_dim_for_label(label) + rows.append({'shape_key': label, 'selected_route': route_for_contract_inputs(inputs), 'selected_entrypoint': ''.join([format(MODULE, ''), ':launch_from_contract_inputs']), 'selected_seed': 'non128_frontier_4be7_d96exact_v1', 'expected_seed': 'non128_frontier_4be7_d96exact_v1', 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': '4be7_d96exact_non128_exact_shape_guard', 'guard_condition': ''.join(['exact BF16 B=', format(spec['B'], ''), ' Q=', format(spec['Q'], ''), ' M=', format(spec['M'], ''), ' D=', format(spec['D'], ''), ' K=', format(spec['K'], ''), ' build=', format(spec['build'], '')]), 'feature_dim': feature_dim, 'split_count': _split_count_for_label(label), 'producer': _producer_for_label(label), 'preprocess_stage': None if uses_d96exact else d320tail.route_trace_for_contract_shapes((label,))[0].get('preprocess_stage'), 'source_route': 'd96_exact_64b_swizzle' if uses_d96exact else d320tail.route_for_contract_inputs(inputs), 'classification': 'd96-exact64b' if uses_d96exact else 'd320tail-parent'}) + return rows diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_non128_frontier_7231_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_non128_frontier_7231_v1.py new file mode 100644 index 00000000..430fbfe7 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_non128_frontier_7231_v1.py @@ -0,0 +1,254 @@ +"""kNN build/search non-D128 frontier seed for round 7231 v1. + +Minimum target architecture: sm_100a. This additive seed covers the v9 +non-D128 frontier rows D96, D192, D320, and D768 with a chunked 128-wide +tcgen05 producer. Each CTA owns one query tile and one database split, loops +over 128-feature TMA chunks, accumulates the full dot product in TMEM, writes +split-local exact K10 candidates, then uses the existing Weave split merge. +Guard misses delegate to the current registered Weave dispatcher. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import os +from collections.abc import Callable +from functools import lru_cache +from typing import Any +from .._dispatch_runtime import dc as dc +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_e3de_9138_bcb3_4247_v1 as current_dispatcher +from . import knn_build_evolve_7bfc_split_v1 as split_parent +from . import knn_build_evolve_7bfc_v1 as base_v1 +from .._dispatch_runtime import pack_kernel_args +BLOCK_Q = base_v1.BLOCK_Q +BLOCK_M = base_v1.BLOCK_M +K_TILE = base_v1.FEAT_D +TOP_K_MAX = base_v1.TOP_K_MAX +THREADS = split_parent.STAGE1_THREADS +MERGE_THREADS = split_parent.MERGE_THREADS +GRID_DIM_DEFAULT = split_parent.GRID_DIM_DEFAULT +QUERY_CHUNK_BYTES = BLOCK_Q * K_TILE * 2 +DATABASE_CHUNK_BYTES = BLOCK_M * K_TILE * 2 +DB_SQ_BYTES = BLOCK_M * 4 +PAD_THREADS = 256 +MODULE = 'loom.examples.weave.knn_build_non128_frontier_7231_v1' +TARGET_SHAPES = ('build_dim_sweep_b1_q1024_m1024_d96_k10', 'build_dim_sweep_b1_q2048_m2048_d192_k10', 'build_highd_b1_q1024_m1024_d320_k10', 'search_rect_highd_b1_q512_m12000_d320_k10', 'rag_microbatch_highd_b1_q16_m50000_d768_k10') +TARGET_SHAPE_SET = set(TARGET_SHAPES) +ROUTE_PREFIX = 'knn_build_non128_frontier_7231_v1' +ROUTE_FALLBACK = 'loom.examples.weave.knn_build_dispatch_e3de_9138_bcb3_4247_v1:launch_from_contract_inputs' +SHAPE_SPECS: dict[str, dict[str, Any]] = {'build_dim_sweep_b1_q1024_m1024_d96_k10': {'B': 1, 'Q': 1024, 'M': 1024, 'D': 96, 'K': 10, 'build': True, 'feature_chunks': 1, 'split_count': 2}, 'build_dim_sweep_b1_q2048_m2048_d192_k10': {'B': 1, 'Q': 2048, 'M': 2048, 'D': 192, 'K': 10, 'build': True, 'feature_chunks': 2, 'split_count': 4}, 'build_highd_b1_q1024_m1024_d320_k10': {'B': 1, 'Q': 1024, 'M': 1024, 'D': 320, 'K': 10, 'build': True, 'feature_chunks': 3, 'split_count': 4}, 'search_rect_highd_b1_q512_m12000_d320_k10': {'B': 1, 'Q': 512, 'M': 12000, 'D': 320, 'K': 10, 'build': False, 'feature_chunks': 3, 'split_count': 16}, 'rag_microbatch_highd_b1_q16_m50000_d768_k10': {'B': 1, 'Q': 16, 'M': 50000, 'D': 768, 'K': 10, 'build': False, 'feature_chunks': 6, 'split_count': 64}} +knn_build_non128_frontier_7231_stage1 = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_7231_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["K_TILE", 128], ["TOP_K_MAX", 10], ["FEATURE_CHUNKS", 3]], "cta_group": 1, "threads": 192}')) +knn_build_non128_frontier_7231_pad_bf16_rows = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_7231_pad_bf16_rows", "arg_keys": ["src", "dst", "rows", "src_cols", "total_elems"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["D_PAD", 128]], "cta_group": 1, "threads": 256}')) +stage1_base_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_7231_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["K_TILE", 128], ["TOP_K_MAX", 10], ["FEATURE_CHUNKS", 3]], "cta_group": 1, "threads": 192}')) +pad_bf16_rows_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_7231_pad_bf16_rows", "arg_keys": ["src", "dst", "rows", "src_cols", "total_elems"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["D_PAD", 128]], "cta_group": 1, "threads": 256}')) +merge_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_merge", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "B", "Q", "K", "split_count", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10]], "cta_group": 1, "threads": 256}')) + +@lru_cache(maxsize=4) +def _pad_ir(d_pad: int) -> Any: + constants = tuple(((name, int(d_pad) if name == 'D_PAD' else value) for name, value in pad_bf16_rows_ir.constants)) + return dc.replace(pad_bf16_rows_ir, symbol=''.join([format(pad_bf16_rows_ir.symbol, ''), '_d', format(int(d_pad), '')]), constants=constants) + +def _stage1_ir(feature_chunks: int) -> Any: + constants = tuple(((name, int(feature_chunks) if name == 'FEATURE_CHUNKS' else value) for name, value in stage1_base_ir.constants)) + return dc.replace(stage1_base_ir, symbol=''.join([format(stage1_base_ir.symbol, ''), '_d', format(feature_chunks * K_TILE, '')]), constants=constants) +stage1_d128_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_7231_stage1_d128", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["K_TILE", 128], ["TOP_K_MAX", 10], ["FEATURE_CHUNKS", 1]], "cta_group": 1, "threads": 192}')) +stage1_d256_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_7231_stage1_d256", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["K_TILE", 128], ["TOP_K_MAX", 10], ["FEATURE_CHUNKS", 2]], "cta_group": 1, "threads": 192}')) +stage1_d384_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_7231_stage1_d384", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["K_TILE", 128], ["TOP_K_MAX", 10], ["FEATURE_CHUNKS", 3]], "cta_group": 1, "threads": 192}')) +stage1_d768_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_7231_stage1_d768", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["K_TILE", 128], ["TOP_K_MAX", 10], ["FEATURE_CHUNKS", 6]], "cta_group": 1, "threads": 192}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_NON128_FRONTIER_7231_VERIFY_KERNEL') + if verify_kernel == 'stage1_chunks1': + return stage1_d128_ir + if verify_kernel == 'stage1_chunks2': + return stage1_d256_ir + if verify_kernel == 'stage1_chunks6': + return stage1_d768_ir + if verify_kernel == 'pad_d96': + return _pad_ir(128) + if verify_kernel == 'pad_d192': + return _pad_ir(256) + if verify_kernel == 'pad_d320': + return _pad_ir(384) + if verify_kernel == 'merge': + return merge_ir + return stage1_d384_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_7231_stage1_d384", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["K_TILE", 128], ["TOP_K_MAX", 10], ["FEATURE_CHUNKS", 3]], "cta_group": 1, "threads": 192}')) + +def _compile_ir(ir_obj: Any): + from .._dispatch_runtime import generate_kernel + from .._dispatch_runtime import _cuda_include_dirs + from .._dispatch_runtime import compile_cuda + from .._dispatch_runtime import CUDAKernel + source = generate_kernel(ir_obj, validate=False, smem_bytes=ir_obj.computed_smem_bytes) + cubin = compile_cuda(source, arch=base_v1._select_arch_and_preload(), options=['--use_fast_math'], include_dirs=_cuda_include_dirs()) + return CUDAKernel(cubin, ''.join(['kernel_', format(ir_obj.symbol, '')])) + +@lru_cache(maxsize=4) +def _compiled_stage1(feature_chunks: int): + return _compile_ir(_stage1_ir(feature_chunks)) + +@lru_cache(maxsize=4) +def _compiled_pad_bf16_rows(d_pad: int): + return _compile_ir(_pad_ir(d_pad)) + +def _allocate_padded_bf16_rows(src, *, rows: int, dst_cols: int): + import torch + return torch.empty((rows, dst_cols), dtype=src.dtype, device=src.device) + +def _launch_pad_bf16_rows(src, padded, *, rows: int, src_cols: int, dst_cols: int) -> None: + total_elems = rows * dst_cols + grid = min((total_elems + PAD_THREADS - 1) // PAD_THREADS, GRID_DIM_DEFAULT) + kernel = _compiled_pad_bf16_rows(int(dst_cols)) + pad_ir = _pad_ir(int(dst_cols)) + kernel.launch(grid=(grid, 1, 1), block=(PAD_THREADS, 1, 1), args=[src, padded, rows, src_cols, total_elems], shared_mem=pad_ir.computed_smem_bytes) + +def _pad_bf16_rows(src, *, rows: int, src_cols: int, dst_cols: int): + padded = _allocate_padded_bf16_rows(src, rows=rows, dst_cols=dst_cols) + _launch_pad_bf16_rows(src, padded, rows=rows, src_cols=src_cols, dst_cols=dst_cols) + return padded + +def _dtype_name(inputs: dict[str, Any]) -> str: + query = inputs.get('query') + if query is not None: + return str(query.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')).replace('torch.', '') + +def _matches_spec(inputs: dict[str, Any], spec: dict[str, Any]) -> bool: + return _dtype_name(inputs) == 'bfloat16' and bool(inputs.get('build', False)) == bool(spec['build']) and (int(inputs['B']) == int(spec['B'])) and (int(inputs['Q']) == int(spec['Q'])) and (int(inputs['M']) == int(spec['M'])) and (int(inputs['D']) == int(spec['D'])) and (int(inputs['K']) == int(spec['K'])) + +def _target_label_for_inputs(inputs: dict[str, Any]) -> str | None: + label = inputs.get('label') + if label is not None and str(label) in TARGET_SHAPE_SET: + spec = SHAPE_SPECS[str(label)] + if _matches_spec(inputs, spec): + return str(label) + return None + for candidate_label, spec in SHAPE_SPECS.items(): + if _matches_spec(inputs, spec): + return candidate_label + return None + +def _split_count_for_label(label: str) -> int: + env_label = label.upper().replace('-', '_') + env_key = ''.join(['LOOM_KNN_NON128_FRONTIER_7231_SPLITS_', format(env_label, '')]) + override = os.environ.get(env_key) + if override is not None: + return int(override) + return int(SHAPE_SPECS[label]['split_count']) + +def _feature_chunks_for_label(label: str) -> int: + return int(SHAPE_SPECS[label]['feature_chunks']) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + label = _target_label_for_inputs(inputs) + if not force_fallback and label is not None: + spec = SHAPE_SPECS[label] + return ''.join([format(ROUTE_PREFIX, ''), ':', format(label, ''), ':d', format(int(spec['D']), ''), ':s', format(_split_count_for_label(label), ''), ':chunks', format(_feature_chunks_for_label(label), '')]) + return current_dispatcher.route_for_contract_inputs(inputs, force_fallback=force_fallback) + +def _launch_non128(inputs: dict[str, Any], label: str) -> None: + query = inputs['query'] + database = inputs['database'] + if str(query.dtype) != 'torch.bfloat16' or str(database.dtype) != 'torch.bfloat16': + raise ValueError('knn_build_non128_frontier_7231_v1 supports bfloat16 inputs only') + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + feature_chunks = _feature_chunks_for_label(label) + split_count = _split_count_for_label(label) + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tiles * split_count + stage1_grid = min(total_work, GRID_DIM_DEFAULT) + merge_grid = min((bsz * n_query + MERGE_THREADS - 1) // MERGE_THREADS, GRID_DIM_DEFAULT) + partial_dists, partial_indices = split_parent._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + query_tma = query + database_tma = database + tma_dim = feature_chunks * K_TILE + if dim != tma_dim: + database_tma = _pad_bf16_rows(database, rows=bsz * n_database, src_cols=dim, dst_cols=tma_dim) + if query.data_ptr() == database.data_ptr(): + query_tma = database_tma + else: + query_tma = _pad_bf16_rows(query, rows=bsz * n_query, src_cols=dim, dst_cols=tma_dim) + tmap_query = base_v1._create_tensor_map_3d_oob_zero(query_tma.data_ptr(), bsz * n_query, BLOCK_Q, tma_dim, K_TILE) + tmap_database = base_v1._create_tensor_map_3d_oob_zero(database_tma.data_ptr(), bsz * n_database, BLOCK_M, tma_dim, K_TILE) + stage1_ir = _stage1_ir(feature_chunks) + stage1_kernel = _compiled_stage1(feature_chunks) + stage1_kernel.launch(grid=(stage1_grid, 1, 1), block=(THREADS, 1, 1), args=pack_kernel_args(stage1_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tiles=num_q_tiles, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), shared_mem=stage1_ir.computed_smem_bytes) + merge_kernel = split_parent._compiled_merge() + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz, n_query, top_k, split_count, bsz * n_query], shared_mem=merge_ir.computed_smem_bytes) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + label = _target_label_for_inputs(inputs) + if not force_fallback and label is not None: + _launch_non128(inputs, label) + return + current_dispatcher.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + if shape_labels is None: + wanted = set(TARGET_SHAPES) + else: + wanted = {str(label) for label in shape_labels} + selected = [shape for shape in eval_mod.CANONICAL_SHAPES if str(shape['label']) in wanted] + missing = wanted - {str(shape['label']) for shape in selected} + if missing: + raise ValueError(''.join(['unknown knn_build contract shape(s): ', format(sorted(missing), '')])) + return selected + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def benchmark_knn_build_non128_frontier_7231_v1(*, use_cupti: bool | None=None, shape_labels=TARGET_SHAPES) -> dict[str, Any]: + prior_use_cupti = None + if use_cupti is not None: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True) + finally: + if prior_use_cupti is not None: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + return {'tflops': report['summary']['primary_mean'], 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'report': report} + +def _trace_inputs_from_shape(shape: dict[str, Any]) -> dict[str, Any]: + params = dict(shape.get('params', {})) + params['label'] = shape['label'] + return {'label': params['label'], 'B': params['B'], 'Q': params['Q'], 'M': params['M'], 'D': params['D'], 'K': params['K'], 'build': params['build'], 'dtype': params.get('dtype', 'bfloat16')} + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + rows = [] + for shape in selected: + inputs = _trace_inputs_from_shape(shape) + label = _target_label_for_inputs(inputs) + if label is None or force_fallback: + rows.append({'shape_key': inputs['label'], 'selected_route': ROUTE_FALLBACK, 'selected_entrypoint': ROUTE_FALLBACK, 'selected_seed': None, 'expected_seed': 'non128_frontier_7231_v1' if inputs['label'] in TARGET_SHAPE_SET else None, 'route_kind': 'fallback', 'route_source': 'current-weave-dispatcher', 'guard_id': 'forced_fallback' if force_fallback else 'current_dispatcher_guard', 'classification': 'forced_fallback' if force_fallback else 'delegated'}) + continue + spec = SHAPE_SPECS[label] + route = route_for_contract_inputs(inputs) + rows.append({'shape_key': label, 'selected_route': route, 'selected_entrypoint': ''.join([format(MODULE, ''), ':launch_from_contract_inputs']), 'selected_seed': 'non128_frontier_7231_v1', 'expected_seed': 'non128_frontier_7231_v1', 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': 'non128_frontier_exact_shape_guard', 'guard_condition': ''.join(['exact BF16 B=', format(spec['B'], ''), ' Q=', format(spec['Q'], ''), ' M=', format(spec['M'], ''), ' D=', format(spec['D'], ''), ' K=', format(spec['K'], ''), ' build=', format(spec['build'], '')]), 'feature_chunks': spec['feature_chunks'], 'split_count': _split_count_for_label(label), 'preprocess_stage': ''.join(['d', format(int(spec['D']), ''), '_weave_pad_to_d', format(int(spec['feature_chunks']) * K_TILE, '')]) if int(spec['D']) != int(spec['feature_chunks']) * K_TILE else None, 'classification': 'seed-consumed'}) + return rows diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_non128_frontier_7ee5_m64rag_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_non128_frontier_7ee5_m64rag_v1.py new file mode 100644 index 00000000..72796887 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_non128_frontier_7ee5_m64rag_v1.py @@ -0,0 +1,198 @@ +"""kNN non-D128 frontier seed with an M64 D768 rag route. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +keeps the round-8199 split-retuned routes for D96/D192/D320 and replaces only +``rag_microbatch_highd_b1_q16_m50000_d768_k10`` with a smaller M64/N64 +tcgen05/TMA producer. The D768 route still writes the contract-visible +distances and indices through the existing Weave split merge. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import os +from collections.abc import Callable +from functools import lru_cache +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_evolve_7bfc_split_v1 as split_parent +from . import knn_build_non128_frontier_7231_v1 as non128_base +from . import knn_build_non128_frontier_8199_splitretune_v1 as parent +from .._dispatch_runtime import pack_kernel_args +MODULE = 'loom.examples.weave.knn_build_non128_frontier_7ee5_m64rag_v1' +BLOCK_Q = parent.BLOCK_Q +BLOCK_M = parent.BLOCK_M +K_TILE = parent.K_TILE +TOP_K_MAX = parent.TOP_K_MAX +MERGE_THREADS = parent.MERGE_THREADS +GRID_DIM_DEFAULT = parent.GRID_DIM_DEFAULT +M64_BLOCK_Q = 64 +M64_BLOCK_M = 64 +M64_FEATURE_CHUNKS = 6 +M64_THREADS = 96 +M64_QUERY_BYTES = M64_BLOCK_Q * K_TILE * 2 +M64_DATABASE_BYTES = M64_BLOCK_M * K_TILE * 2 +M64_DB_SQ_BYTES = M64_BLOCK_M * 4 +M64_SMEM_POOL_BYTES = M64_QUERY_BYTES + M64_DATABASE_BYTES + M64_DB_SQ_BYTES +D768_SHAPE = 'rag_microbatch_highd_b1_q16_m50000_d768_k10' +TARGET_SHAPES = parent.TARGET_SHAPES +TARGET_SHAPE_SET = set(TARGET_SHAPES) +M64_SHAPE_SET = {D768_SHAPE} +ROUTE_PREFIX = 'knn_build_non128_frontier_7ee5_m64rag_v1' +ROUTE_FALLBACK = parent.ROUTE_FALLBACK +SPLIT_DEFAULTS = _decode_capture(_json_loads('{"__dict_items__": [["build_dim_sweep_b1_q1024_m1024_d96_k10", 8], ["build_dim_sweep_b1_q2048_m2048_d192_k10", 8], ["build_highd_b1_q1024_m1024_d320_k10", 8], ["search_rect_highd_b1_q512_m12000_d320_k10", 32], ["rag_microbatch_highd_b1_q16_m50000_d768_k10", 72]]}')) +SPLIT_DEFAULTS[D768_SHAPE] = int(os.environ.get('LOOM_KNN_NON128_FRONTIER_7EE5_M64RAG_SPLIT', '72')) +SHAPE_SPECS = _decode_capture(_json_loads('{"__dict_items__": [["build_dim_sweep_b1_q1024_m1024_d96_k10", {"__dict_items__": [["B", 1], ["Q", 1024], ["M", 1024], ["D", 96], ["K", 10], ["build", true], ["feature_chunks", 1], ["split_count", 8]]}], ["build_dim_sweep_b1_q2048_m2048_d192_k10", {"__dict_items__": [["B", 1], ["Q", 2048], ["M", 2048], ["D", 192], ["K", 10], ["build", true], ["feature_chunks", 2], ["split_count", 8]]}], ["build_highd_b1_q1024_m1024_d320_k10", {"__dict_items__": [["B", 1], ["Q", 1024], ["M", 1024], ["D", 320], ["K", 10], ["build", true], ["feature_chunks", 3], ["split_count", 8]]}], ["search_rect_highd_b1_q512_m12000_d320_k10", {"__dict_items__": [["B", 1], ["Q", 512], ["M", 12000], ["D", 320], ["K", 10], ["build", false], ["feature_chunks", 3], ["split_count", 32]]}], ["rag_microbatch_highd_b1_q16_m50000_d768_k10", {"__dict_items__": [["B", 1], ["Q", 16], ["M", 50000], ["D", 768], ["K", 10], ["build", false], ["feature_chunks", 6], ["split_count", 72]]}]]}')) +_m64_insert_sorted_pair = _ir_proxy('loom.examples.weave.knn_build_non128_frontier_7ee5_m64rag_v1:_m64_insert_sorted_pair', 256) +knn_build_non128_frontier_7ee5_m64rag_stage1 = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_7ee5_m64rag_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 34048, "constants": [["FEATURE_CHUNKS", 6]], "cta_group": 1, "threads": 96}')) +stage1_m64_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_7ee5_m64rag_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 34048, "constants": [["FEATURE_CHUNKS", 6]], "cta_group": 1, "threads": 96}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_NON128_FRONTIER_7EE5_M64RAG_VERIFY_KERNEL') + if verify_kernel == 'merge': + return parent.merge_ir + return stage1_m64_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_7ee5_m64rag_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 34048, "constants": [["FEATURE_CHUNKS", 6]], "cta_group": 1, "threads": 96}')) + +def _compile_ir(ir_obj: Any): + from .._dispatch_runtime import generate_kernel + from .._dispatch_runtime import _cuda_include_dirs + from .._dispatch_runtime import compile_cuda + from .._dispatch_runtime import CUDAKernel + source = generate_kernel(ir_obj, validate=False, smem_bytes=ir_obj.computed_smem_bytes) + cubin = compile_cuda(source, arch=non128_base.base_v1._select_arch_and_preload(), options=['--use_fast_math'], include_dirs=_cuda_include_dirs()) + return CUDAKernel(cubin, ''.join(['kernel_', format(ir_obj.symbol, '')])) + +def _compiled_stage1_m64(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0091"}')) + +def _dtype_name(inputs: dict[str, Any]) -> str: + query = inputs.get('query') + if query is not None: + return str(query.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')).replace('torch.', '') + +def _matches_spec(inputs: dict[str, Any], spec: dict[str, Any]) -> bool: + return _dtype_name(inputs) == 'bfloat16' and bool(inputs.get('build', False)) == bool(spec['build']) and (int(inputs['B']) == int(spec['B'])) and (int(inputs['Q']) == int(spec['Q'])) and (int(inputs['M']) == int(spec['M'])) and (int(inputs['D']) == int(spec['D'])) and (int(inputs['K']) == int(spec['K'])) + +def _target_label_for_inputs(inputs: dict[str, Any]) -> str | None: + label = inputs.get('label') + if label is not None and str(label) in TARGET_SHAPE_SET: + spec = SHAPE_SPECS[str(label)] + if _matches_spec(inputs, spec): + return str(label) + return None + for candidate_label, spec in SHAPE_SPECS.items(): + if _matches_spec(inputs, spec): + return candidate_label + return None + +def _split_count_for_label(label: str) -> int: + if label == D768_SHAPE: + override = os.environ.get('LOOM_KNN_NON128_FRONTIER_7EE5_M64RAG_SPLIT') + if override is not None: + return int(override) + return int(SHAPE_SPECS[label]['split_count']) + +def _feature_chunks_for_label(label: str) -> int: + return int(SHAPE_SPECS[label]['feature_chunks']) + +def _uses_m64_d768(label: str | None) -> bool: + return label == D768_SHAPE + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + label = _target_label_for_inputs(inputs) + if force_fallback or label is None: + return parent.route_for_contract_inputs(inputs, force_fallback=force_fallback) + if _uses_m64_d768(label): + spec = SHAPE_SPECS[label] + return ''.join([format(ROUTE_PREFIX, ''), ':', format(label, ''), ':d', format(int(spec['D']), ''), ':m64n64:s', format(_split_count_for_label(label), ''), ':chunks', format(M64_FEATURE_CHUNKS, '')]) + return parent.route_for_contract_inputs(inputs) + +def _launch_m64_d768(inputs: dict[str, Any], label: str) -> None: + query = inputs['query'] + database = inputs['database'] + if str(query.dtype) != 'torch.bfloat16' or str(database.dtype) != 'torch.bfloat16': + raise ValueError('knn_build_non128_frontier_7ee5_m64rag_v1 supports bfloat16 inputs only') + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + split_count = _split_count_for_label(label) + if dim != M64_FEATURE_CHUNKS * K_TILE: + raise ValueError(''.join(['M64 D768 route expected D=', format(M64_FEATURE_CHUNKS * K_TILE, ''), ', got ', format(dim, '')])) + num_q_tiles = (n_query + M64_BLOCK_Q - 1) // M64_BLOCK_Q + num_db_tiles = (n_database + M64_BLOCK_M - 1) // M64_BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tiles * split_count + stage1_grid = min(total_work, GRID_DIM_DEFAULT) + merge_grid = min((bsz * n_query + MERGE_THREADS - 1) // MERGE_THREADS, GRID_DIM_DEFAULT) + partial_dists, partial_indices = split_parent._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = non128_base.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), bsz * n_query, M64_BLOCK_Q, dim, K_TILE) + tmap_database = non128_base.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, M64_BLOCK_M, dim, K_TILE) + _compiled_stage1_m64().launch(grid=(stage1_grid, 1, 1), block=(M64_THREADS, 1, 1), args=pack_kernel_args(stage1_m64_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tiles=num_q_tiles, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), shared_mem=stage1_m64_ir.computed_smem_bytes) + merge_kernel = split_parent._compiled_merge() + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz, n_query, top_k, split_count, bsz * n_query], shared_mem=parent.merge_ir.computed_smem_bytes) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + label = _target_label_for_inputs(inputs) + if not force_fallback and _uses_m64_d768(label): + _launch_m64_d768(inputs, str(label)) + return + parent.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return parent._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def benchmark_knn_build_non128_frontier_7ee5_m64rag_v1(*, use_cupti: bool | None=None, shape_labels=TARGET_SHAPES) -> dict[str, Any]: + prior_use_cupti = None + if use_cupti is not None: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True) + finally: + if prior_use_cupti is not None: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + return {'tflops': report['summary']['primary_mean'], 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'report': report} + +def _trace_inputs_from_shape(shape: dict[str, Any]) -> dict[str, Any]: + params = dict(shape.get('params', {})) + params['label'] = shape['label'] + return {'label': params['label'], 'B': params['B'], 'Q': params['Q'], 'M': params['M'], 'D': params['D'], 'K': params['K'], 'build': params['build'], 'dtype': params.get('dtype', 'bfloat16')} + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + rows = [] + for shape in selected: + inputs = _trace_inputs_from_shape(shape) + label = _target_label_for_inputs(inputs) + if label is None or force_fallback: + rows.append({'shape_key': inputs['label'], 'selected_route': ROUTE_FALLBACK, 'selected_entrypoint': ROUTE_FALLBACK, 'selected_seed': None, 'expected_seed': 'non128_frontier_7ee5_m64rag_v1' if inputs['label'] in TARGET_SHAPE_SET else None, 'route_kind': 'fallback', 'route_source': 'parent-8199-or-current-weave-dispatcher', 'guard_id': 'forced_fallback' if force_fallback else 'parent_dispatcher_guard', 'classification': 'forced_fallback' if force_fallback else 'delegated'}) + continue + spec = SHAPE_SPECS[label] + route = route_for_contract_inputs(inputs) + uses_m64 = _uses_m64_d768(label) + rows.append({'shape_key': label, 'selected_route': route, 'selected_entrypoint': ''.join([format(MODULE, ''), ':launch_from_contract_inputs']), 'selected_seed': 'non128_frontier_7ee5_m64rag_v1' if uses_m64 else 'non128_frontier_8199_splitretune_v1', 'expected_seed': 'non128_frontier_7ee5_m64rag_v1' if uses_m64 else 'non128_frontier_8199_splitretune_v1', 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': '7ee5_m64_d768_exact_shape_guard' if uses_m64 else '8199_splitretune_non128_exact_shape_guard', 'guard_condition': ''.join(['exact BF16 B=', format(spec['B'], ''), ' Q=', format(spec['Q'], ''), ' M=', format(spec['M'], ''), ' D=', format(spec['D'], ''), ' K=', format(spec['K'], ''), ' build=', format(spec['build'], '')]), 'feature_chunks': M64_FEATURE_CHUNKS if uses_m64 else spec['feature_chunks'], 'split_count': _split_count_for_label(label), 'producer_topology': 'M64_N64_row_owned' if uses_m64 else 'parent_8199', 'preprocess_stage': ''.join(['d', format(int(spec['D']), ''), '_weave_pad_to_d', format(int(spec['feature_chunks']) * K_TILE, '')]) if not uses_m64 and int(spec['D']) != int(spec['feature_chunks']) * K_TILE else None, 'classification': 'm64-d768' if uses_m64 else 'parent-8199'}) + return rows diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_non128_frontier_8199_splitretune_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_non128_frontier_8199_splitretune_v1.py new file mode 100644 index 00000000..9182306b --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_non128_frontier_8199_splitretune_v1.py @@ -0,0 +1,204 @@ +"""kNN build/search non-D128 split-retuned seed for round 8199 v1. + +Minimum target architecture: sm_100a. This additive seed reuses the verified +round-7231 non-D128 tcgen05/TMA stage and Weave padding kernels, but retunes +the exact-shape split counts for the five v9 non-D128 rows. The candidate keeps +the same contract-visible path: optional Weave bf16 padding, chunked tcgen05 +dot-product producer, split-local K10 candidates, and Weave split merge. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import os +from collections.abc import Callable +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_e3de_9138_bcb3_4247_v1 as current_dispatcher +from . import knn_build_evolve_7bfc_split_v1 as split_parent +from . import knn_build_non128_frontier_7231_v1 as parent +from .._dispatch_runtime import pack_kernel_args +BLOCK_Q = parent.BLOCK_Q +BLOCK_M = parent.BLOCK_M +K_TILE = parent.K_TILE +TOP_K_MAX = parent.TOP_K_MAX +THREADS = parent.THREADS +MERGE_THREADS = parent.MERGE_THREADS +GRID_DIM_DEFAULT = parent.GRID_DIM_DEFAULT +MODULE = 'loom.examples.weave.knn_build_non128_frontier_8199_splitretune_v1' +TARGET_SHAPES = parent.TARGET_SHAPES +TARGET_SHAPE_SET = set(TARGET_SHAPES) +ROUTE_PREFIX = 'knn_build_non128_frontier_8199_splitretune_v1' +ROUTE_FALLBACK = 'loom.examples.weave.knn_build_dispatch_e3de_9138_bcb3_4247_v1:launch_from_contract_inputs' +SPLIT_DEFAULTS = {'build_dim_sweep_b1_q1024_m1024_d96_k10': 8, 'build_dim_sweep_b1_q2048_m2048_d192_k10': 8, 'build_highd_b1_q1024_m1024_d320_k10': 8, 'search_rect_highd_b1_q512_m12000_d320_k10': 32, 'rag_microbatch_highd_b1_q16_m50000_d768_k10': 64} +SHAPE_SPECS = _decode_capture(_json_loads('{"__dict_items__": [["build_dim_sweep_b1_q1024_m1024_d96_k10", {"__dict_items__": [["B", 1], ["Q", 1024], ["M", 1024], ["D", 96], ["K", 10], ["build", true], ["feature_chunks", 1], ["split_count", 8]]}], ["build_dim_sweep_b1_q2048_m2048_d192_k10", {"__dict_items__": [["B", 1], ["Q", 2048], ["M", 2048], ["D", 192], ["K", 10], ["build", true], ["feature_chunks", 2], ["split_count", 8]]}], ["build_highd_b1_q1024_m1024_d320_k10", {"__dict_items__": [["B", 1], ["Q", 1024], ["M", 1024], ["D", 320], ["K", 10], ["build", true], ["feature_chunks", 3], ["split_count", 8]]}], ["search_rect_highd_b1_q512_m12000_d320_k10", {"__dict_items__": [["B", 1], ["Q", 512], ["M", 12000], ["D", 320], ["K", 10], ["build", false], ["feature_chunks", 3], ["split_count", 32]]}], ["rag_microbatch_highd_b1_q16_m50000_d768_k10", {"__dict_items__": [["B", 1], ["Q", 16], ["M", 50000], ["D", 768], ["K", 10], ["build", false], ["feature_chunks", 6], ["split_count", 64]]}]]}')) +stage1_base_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_7231_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["K_TILE", 128], ["TOP_K_MAX", 10], ["FEATURE_CHUNKS", 3]], "cta_group": 1, "threads": 192}')) +pad_bf16_rows_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_7231_pad_bf16_rows", "arg_keys": ["src", "dst", "rows", "src_cols", "total_elems"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["D_PAD", 128]], "cta_group": 1, "threads": 256}')) +merge_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_merge", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "B", "Q", "K", "split_count", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10]], "cta_group": 1, "threads": 256}')) +stage1_d128_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_7231_stage1_d128", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["K_TILE", 128], ["TOP_K_MAX", 10], ["FEATURE_CHUNKS", 1]], "cta_group": 1, "threads": 192}')) +stage1_d256_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_7231_stage1_d256", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["K_TILE", 128], ["TOP_K_MAX", 10], ["FEATURE_CHUNKS", 2]], "cta_group": 1, "threads": 192}')) +stage1_d384_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_7231_stage1_d384", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["K_TILE", 128], ["TOP_K_MAX", 10], ["FEATURE_CHUNKS", 3]], "cta_group": 1, "threads": 192}')) +stage1_d768_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_7231_stage1_d768", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["K_TILE", 128], ["TOP_K_MAX", 10], ["FEATURE_CHUNKS", 6]], "cta_group": 1, "threads": 192}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_NON128_FRONTIER_8199_VERIFY_KERNEL') + if verify_kernel == 'stage1_chunks1': + return stage1_d128_ir + if verify_kernel == 'stage1_chunks2': + return stage1_d256_ir + if verify_kernel == 'stage1_chunks6': + return stage1_d768_ir + if verify_kernel == 'pad_d96': + return parent._pad_ir(128) + if verify_kernel == 'pad_d192': + return parent._pad_ir(256) + if verify_kernel == 'pad_d320': + return parent._pad_ir(384) + if verify_kernel == 'merge': + return merge_ir + return stage1_d384_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_7231_stage1_d384", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["K_TILE", 128], ["TOP_K_MAX", 10], ["FEATURE_CHUNKS", 3]], "cta_group": 1, "threads": 192}')) + +def _dtype_name(inputs: dict[str, Any]) -> str: + query = inputs.get('query') + if query is not None: + return str(query.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')).replace('torch.', '') + +def _matches_spec(inputs: dict[str, Any], spec: dict[str, Any]) -> bool: + return _dtype_name(inputs) == 'bfloat16' and bool(inputs.get('build', False)) == bool(spec['build']) and (int(inputs['B']) == int(spec['B'])) and (int(inputs['Q']) == int(spec['Q'])) and (int(inputs['M']) == int(spec['M'])) and (int(inputs['D']) == int(spec['D'])) and (int(inputs['K']) == int(spec['K'])) + +def _target_label_for_inputs(inputs: dict[str, Any]) -> str | None: + label = inputs.get('label') + if label is not None and str(label) in TARGET_SHAPE_SET: + spec = SHAPE_SPECS[str(label)] + if _matches_spec(inputs, spec): + return str(label) + return None + for candidate_label, spec in SHAPE_SPECS.items(): + if _matches_spec(inputs, spec): + return candidate_label + return None + +def _split_count_for_label(label: str) -> int: + env_label = label.upper().replace('-', '_') + env_key = ''.join(['LOOM_KNN_NON128_FRONTIER_8199_SPLITS_', format(env_label, '')]) + override = os.environ.get(env_key) + if override is not None: + return int(override) + return int(SHAPE_SPECS[label]['split_count']) + +def _feature_chunks_for_label(label: str) -> int: + return int(SHAPE_SPECS[label]['feature_chunks']) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + label = _target_label_for_inputs(inputs) + if not force_fallback and label is not None: + spec = SHAPE_SPECS[label] + return ''.join([format(ROUTE_PREFIX, ''), ':', format(label, ''), ':d', format(int(spec['D']), ''), ':s', format(_split_count_for_label(label), ''), ':chunks', format(_feature_chunks_for_label(label), '')]) + return current_dispatcher.route_for_contract_inputs(inputs, force_fallback=force_fallback) + +def _launch_non128(inputs: dict[str, Any], label: str) -> None: + query = inputs['query'] + database = inputs['database'] + if str(query.dtype) != 'torch.bfloat16' or str(database.dtype) != 'torch.bfloat16': + raise ValueError('knn_build_non128_frontier_8199_splitretune_v1 supports bfloat16 inputs only') + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + feature_chunks = _feature_chunks_for_label(label) + split_count = _split_count_for_label(label) + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tiles * split_count + stage1_grid = min(total_work, GRID_DIM_DEFAULT) + merge_grid = min((bsz * n_query + MERGE_THREADS - 1) // MERGE_THREADS, GRID_DIM_DEFAULT) + partial_dists, partial_indices = split_parent._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + query_tma = query + database_tma = database + tma_dim = feature_chunks * K_TILE + if dim != tma_dim: + database_tma = parent._pad_bf16_rows(database, rows=bsz * n_database, src_cols=dim, dst_cols=tma_dim) + if query.data_ptr() == database.data_ptr(): + query_tma = database_tma + else: + query_tma = parent._pad_bf16_rows(query, rows=bsz * n_query, src_cols=dim, dst_cols=tma_dim) + tmap_query = parent.base_v1._create_tensor_map_3d_oob_zero(query_tma.data_ptr(), bsz * n_query, BLOCK_Q, tma_dim, K_TILE) + tmap_database = parent.base_v1._create_tensor_map_3d_oob_zero(database_tma.data_ptr(), bsz * n_database, BLOCK_M, tma_dim, K_TILE) + stage1_ir = parent._stage1_ir(feature_chunks) + stage1_kernel = parent._compiled_stage1(feature_chunks) + stage1_kernel.launch(grid=(stage1_grid, 1, 1), block=(THREADS, 1, 1), args=pack_kernel_args(stage1_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tiles=num_q_tiles, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), shared_mem=stage1_ir.computed_smem_bytes) + merge_kernel = split_parent._compiled_merge() + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz, n_query, top_k, split_count, bsz * n_query], shared_mem=merge_ir.computed_smem_bytes) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + label = _target_label_for_inputs(inputs) + if not force_fallback and label is not None: + _launch_non128(inputs, label) + return + current_dispatcher.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + if shape_labels is None: + wanted = set(TARGET_SHAPES) + else: + wanted = {str(label) for label in shape_labels} + selected = [shape for shape in eval_mod.CANONICAL_SHAPES if str(shape['label']) in wanted] + missing = wanted - {str(shape['label']) for shape in selected} + if missing: + raise ValueError(''.join(['unknown knn_build contract shape(s): ', format(sorted(missing), '')])) + return selected + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def benchmark_knn_build_non128_frontier_8199_splitretune_v1(*, use_cupti: bool | None=None, shape_labels=TARGET_SHAPES) -> dict[str, Any]: + prior_use_cupti = None + if use_cupti is not None: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True) + finally: + if prior_use_cupti is not None: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + return {'tflops': report['summary']['primary_mean'], 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'report': report} + +def _trace_inputs_from_shape(shape: dict[str, Any]) -> dict[str, Any]: + params = dict(shape.get('params', {})) + params['label'] = shape['label'] + return {'label': params['label'], 'B': params['B'], 'Q': params['Q'], 'M': params['M'], 'D': params['D'], 'K': params['K'], 'build': params['build'], 'dtype': params.get('dtype', 'bfloat16')} + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + rows = [] + for shape in selected: + inputs = _trace_inputs_from_shape(shape) + label = _target_label_for_inputs(inputs) + if label is None or force_fallback: + rows.append({'shape_key': inputs['label'], 'selected_route': ROUTE_FALLBACK, 'selected_entrypoint': ROUTE_FALLBACK, 'selected_seed': None, 'expected_seed': 'non128_frontier_8199_splitretune_v1' if inputs['label'] in TARGET_SHAPE_SET else None, 'route_kind': 'fallback', 'route_source': 'current-weave-dispatcher', 'guard_id': 'forced_fallback' if force_fallback else 'current_dispatcher_guard', 'classification': 'forced_fallback' if force_fallback else 'delegated'}) + continue + spec = SHAPE_SPECS[label] + route = route_for_contract_inputs(inputs) + rows.append({'shape_key': label, 'selected_route': route, 'selected_entrypoint': ''.join([format(MODULE, ''), ':launch_from_contract_inputs']), 'selected_seed': 'non128_frontier_8199_splitretune_v1', 'expected_seed': 'non128_frontier_8199_splitretune_v1', 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': 'non128_frontier_8199_exact_shape_guard', 'guard_condition': ''.join(['exact BF16 B=', format(spec['B'], ''), ' Q=', format(spec['Q'], ''), ' M=', format(spec['M'], ''), ' D=', format(spec['D'], ''), ' K=', format(spec['K'], ''), ' build=', format(spec['build'], '')]), 'feature_chunks': spec['feature_chunks'], 'split_count': _split_count_for_label(label), 'preprocess_stage': ''.join(['d', format(int(spec['D']), ''), '_weave_pad_to_d', format(int(spec['feature_chunks']) * K_TILE, '')]) if int(spec['D']) != int(spec['feature_chunks']) * K_TILE else None, 'classification': 'seed-consumed'}) + return rows diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_non128_frontier_8199_widecombine_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_non128_frontier_8199_widecombine_v1.py new file mode 100644 index 00000000..7d113443 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_non128_frontier_8199_widecombine_v1.py @@ -0,0 +1,221 @@ +"""kNN non-D128 frontier wide-combined seed for round 101 v1. + +Minimum target architecture: sm_100a. This additive seed preserves the +round-8199 split-retuned non-D128 route and selectively routes D192/D320 rows +through the existing wide tcgen05/TMA producers. D96 and D768 stay on the +validated split-retuned parent path. The contract-visible path remains +Weave-only: optional BF16 padding, tcgen05 dot-product stage, split-local K10 +candidates, and Weave split merge. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import os +from collections.abc import Callable +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dim_midk_df2f_v1 as wide_d256 +from . import knn_build_evolve_7bfc_split_v1 as split_parent +from . import knn_build_evolve_7bfc_v1 as base_v1 +from . import knn_build_non128_frontier_8199_splitretune_v1 as splitretune +from . import knn_build_non128_frontier_8199_widestage_v1 as widestage +from .._dispatch_runtime import pack_kernel_args +BLOCK_Q = splitretune.BLOCK_Q +BLOCK_M = splitretune.BLOCK_M +K_TILE = splitretune.K_TILE +TOP_K_MAX = splitretune.TOP_K_MAX +THREADS = splitretune.THREADS +MERGE_THREADS = splitretune.MERGE_THREADS +GRID_DIM_DEFAULT = splitretune.GRID_DIM_DEFAULT +MODULE = 'loom.examples.weave.knn_build_non128_frontier_8199_widecombine_v1' +ROUTE_PREFIX = 'knn_build_non128_frontier_8199_widecombine_v1' +TARGET_SHAPES = splitretune.TARGET_SHAPES +TARGET_SHAPE_SET = set(TARGET_SHAPES) +SHAPE_SPECS = _decode_capture(_json_loads('{"__dict_items__": [["build_dim_sweep_b1_q1024_m1024_d96_k10", {"__dict_items__": [["B", 1], ["Q", 1024], ["M", 1024], ["D", 96], ["K", 10], ["build", true], ["feature_chunks", 1], ["split_count", 8]]}], ["build_dim_sweep_b1_q2048_m2048_d192_k10", {"__dict_items__": [["B", 1], ["Q", 2048], ["M", 2048], ["D", 192], ["K", 10], ["build", true], ["feature_chunks", 2], ["split_count", 8]]}], ["build_highd_b1_q1024_m1024_d320_k10", {"__dict_items__": [["B", 1], ["Q", 1024], ["M", 1024], ["D", 320], ["K", 10], ["build", true], ["feature_chunks", 3], ["split_count", 8]]}], ["search_rect_highd_b1_q512_m12000_d320_k10", {"__dict_items__": [["B", 1], ["Q", 512], ["M", 12000], ["D", 320], ["K", 10], ["build", false], ["feature_chunks", 3], ["split_count", 32]]}], ["rag_microbatch_highd_b1_q16_m50000_d768_k10", {"__dict_items__": [["B", 1], ["Q", 16], ["M", 50000], ["D", 768], ["K", 10], ["build", false], ["feature_chunks", 6], ["split_count", 64]]}]]}')) +WIDE_D256_SHAPES = {'build_dim_sweep_b1_q2048_m2048_d192_k10'} +WIDE_D384_SHAPES = {'build_highd_b1_q1024_m1024_d320_k10', 'search_rect_highd_b1_q512_m12000_d320_k10'} +stage1_d256_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_dim_midk_df2f_d256_split_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 99584, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) +stage1_d384_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_8199_d384_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 148736, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) +pad_bf16_rows_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_7231_pad_bf16_rows", "arg_keys": ["src", "dst", "rows", "src_cols", "total_elems"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["D_PAD", 128]], "cta_group": 1, "threads": 256}')) +merge_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_merge", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "B", "Q", "K", "split_count", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10]], "cta_group": 1, "threads": 256}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_NON128_FRONTIER_8199_WIDECOMBINE_VERIFY_KERNEL') + if verify_kernel == 'stage1_d256': + return stage1_d256_ir + if verify_kernel == 'stage1_d384': + return stage1_d384_ir + if verify_kernel == 'stage1_chunks1': + return splitretune.stage1_d128_ir + if verify_kernel == 'stage1_chunks6': + return splitretune.stage1_d768_ir + if verify_kernel == 'pad_d96': + return splitretune.parent._pad_ir(128) + if verify_kernel == 'pad_d192': + return splitretune.parent._pad_ir(256) + if verify_kernel == 'pad_d320': + return splitretune.parent._pad_ir(384) + if verify_kernel == 'merge': + return merge_ir + return stage1_d384_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_8199_d384_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 148736, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) + +def _dtype_name(inputs: dict[str, Any]) -> str: + query = inputs.get('query') + if query is not None: + return str(query.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')).replace('torch.', '') + +def _matches_spec(inputs: dict[str, Any], spec: dict[str, Any]) -> bool: + return _dtype_name(inputs) == 'bfloat16' and bool(inputs.get('build', False)) == bool(spec['build']) and (int(inputs['B']) == int(spec['B'])) and (int(inputs['Q']) == int(spec['Q'])) and (int(inputs['M']) == int(spec['M'])) and (int(inputs['D']) == int(spec['D'])) and (int(inputs['K']) == int(spec['K'])) + +def _target_label_for_inputs(inputs: dict[str, Any]) -> str | None: + label = inputs.get('label') + if label is not None and str(label) in TARGET_SHAPE_SET: + spec = SHAPE_SPECS[str(label)] + if _matches_spec(inputs, spec): + return str(label) + return None + for candidate_label, spec in SHAPE_SPECS.items(): + if _matches_spec(inputs, spec): + return candidate_label + return None + +def _split_count_for_label(label: str) -> int: + env_label = label.upper().replace('-', '_') + env_key = ''.join(['LOOM_KNN_NON128_FRONTIER_8199_WIDECOMBINE_SPLITS_', format(env_label, '')]) + override = os.environ.get(env_key) + if override is not None: + return int(override) + return int(SHAPE_SPECS[label]['split_count']) + +def _feature_dim_for_label(label: str) -> int: + if label in WIDE_D256_SHAPES: + return 256 + if label in WIDE_D384_SHAPES: + return 384 + return int(SHAPE_SPECS[label]['feature_chunks']) * K_TILE + +def _producer_for_label(label: str) -> str: + if label in WIDE_D256_SHAPES: + return 'wide256' + if label in WIDE_D384_SHAPES: + return 'wide384' + return 'splitretune' + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + label = _target_label_for_inputs(inputs) + if not force_fallback and label is not None: + spec = SHAPE_SPECS[label] + return ''.join([format(ROUTE_PREFIX, ''), ':', format(label, ''), ':d', format(int(spec['D']), ''), ':s', format(_split_count_for_label(label), ''), ':', format(_producer_for_label(label), '')]) + return splitretune.route_for_contract_inputs(inputs, force_fallback=force_fallback) + +def _pad_if_needed(tensor, *, rows: int, src_cols: int, dst_cols: int): + if src_cols == dst_cols: + return tensor + return splitretune.parent._pad_bf16_rows(tensor, rows=rows, src_cols=src_cols, dst_cols=dst_cols) + +def _launch_wide_stage(inputs: dict[str, Any], label: str, *, feature_dim: int, kernel, stage1_ir) -> None: + query = inputs['query'] + database = inputs['database'] + if str(query.dtype) != 'torch.bfloat16' or str(database.dtype) != 'torch.bfloat16': + raise ValueError('knn_build_non128_frontier_8199_widecombine_v1 supports bfloat16 inputs only') + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + split_count = _split_count_for_label(label) + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tiles * split_count + stage1_grid = min(total_work, GRID_DIM_DEFAULT) + merge_grid = min((bsz * n_query + MERGE_THREADS - 1) // MERGE_THREADS, GRID_DIM_DEFAULT) + partial_dists, partial_indices = split_parent._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + database_tma = _pad_if_needed(database, rows=bsz * n_database, src_cols=dim, dst_cols=feature_dim) + if query.data_ptr() == database.data_ptr(): + query_tma = database_tma + else: + query_tma = _pad_if_needed(query, rows=bsz * n_query, src_cols=dim, dst_cols=feature_dim) + tmap_query = base_v1._create_tensor_map_3d_oob_zero(query_tma.data_ptr(), bsz * n_query, BLOCK_Q, feature_dim, feature_dim) + tmap_database = base_v1._create_tensor_map_3d_oob_zero(database_tma.data_ptr(), bsz * n_database, BLOCK_M, feature_dim, feature_dim) + kernel.launch(grid=(stage1_grid, 1, 1), block=(THREADS, 1, 1), args=pack_kernel_args(stage1_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tiles=num_q_tiles, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), shared_mem=stage1_ir.computed_smem_bytes) + merge_kernel = split_parent._compiled_merge() + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz, n_query, top_k, split_count, bsz * n_query], shared_mem=merge_ir.computed_smem_bytes) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + label = _target_label_for_inputs(inputs) + if force_fallback or label is None: + splitretune.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + return + if label in WIDE_D256_SHAPES: + _launch_wide_stage(inputs, label, feature_dim=256, kernel=wide_d256._compiled_d256_stage1(), stage1_ir=stage1_d256_ir) + return + if label in WIDE_D384_SHAPES: + _launch_wide_stage(inputs, label, feature_dim=384, kernel=widestage._compiled_d384_stage1(), stage1_ir=stage1_d384_ir) + return + splitretune.launch_from_contract_inputs(inputs, force_fallback=False) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + if shape_labels is None: + wanted = set(TARGET_SHAPES) + else: + wanted = {str(label) for label in shape_labels} + selected = [shape for shape in eval_mod.CANONICAL_SHAPES if str(shape['label']) in wanted] + missing = wanted - {str(shape['label']) for shape in selected} + if missing: + raise ValueError(''.join(['unknown knn_build contract shape(s): ', format(sorted(missing), '')])) + return selected + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def benchmark_knn_build_non128_frontier_8199_widecombine_v1(*, use_cupti: bool | None=None, shape_labels=TARGET_SHAPES) -> dict[str, Any]: + prior_use_cupti = None + if use_cupti is not None: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True) + finally: + if prior_use_cupti is not None: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + return {'tflops': report['summary']['primary_mean'], 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'report': report} + +def _trace_inputs_from_shape(shape: dict[str, Any]) -> dict[str, Any]: + params = dict(shape.get('params', {})) + params['label'] = shape['label'] + return {'label': params['label'], 'B': params['B'], 'Q': params['Q'], 'M': params['M'], 'D': params['D'], 'K': params['K'], 'build': params['build'], 'dtype': params.get('dtype', 'bfloat16')} + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + rows = [] + for shape in selected: + inputs = _trace_inputs_from_shape(shape) + label = _target_label_for_inputs(inputs) + if label is None or force_fallback: + rows.append({'shape_key': inputs['label'], 'selected_route': splitretune.ROUTE_FALLBACK, 'selected_entrypoint': splitretune.ROUTE_FALLBACK, 'selected_seed': None, 'expected_seed': 'non128_frontier_8199_widecombine_v1' if inputs['label'] in TARGET_SHAPE_SET else None, 'route_kind': 'fallback', 'route_source': 'splitretune-parent-dispatcher', 'guard_id': 'forced_fallback' if force_fallback else 'splitretune_parent_guard', 'classification': 'forced_fallback' if force_fallback else 'delegated'}) + continue + spec = SHAPE_SPECS[label] + feature_dim = _feature_dim_for_label(label) + rows.append({'shape_key': label, 'selected_route': route_for_contract_inputs(inputs), 'selected_entrypoint': ''.join([format(MODULE, ''), ':launch_from_contract_inputs']), 'selected_seed': 'non128_frontier_8199_widecombine_v1', 'expected_seed': 'non128_frontier_8199_widecombine_v1', 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': '8199_non128_widecombine_exact_shape_guard', 'guard_condition': ''.join(['exact BF16 B=', format(spec['B'], ''), ' Q=', format(spec['Q'], ''), ' M=', format(spec['M'], ''), ' D=', format(spec['D'], ''), ' K=', format(spec['K'], ''), ' build=', format(spec['build'], '')]), 'feature_dim': feature_dim, 'split_count': _split_count_for_label(label), 'producer': _producer_for_label(label), 'preprocess_stage': ''.join(['d', format(int(spec['D']), ''), '_weave_pad_to_d', format(feature_dim, '')]) if int(spec['D']) != feature_dim else None, 'classification': 'seed-consumed'}) + return rows diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_non128_frontier_8199_widestage_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_non128_frontier_8199_widestage_v1.py new file mode 100644 index 00000000..7f1a782d --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_non128_frontier_8199_widestage_v1.py @@ -0,0 +1,220 @@ +"""kNN non-D128 frontier wide-stage seed for round 8199 v1. + +Minimum target architecture: sm_100a. This additive variant keeps the validated +7231 non-D128 seed for D96 and D768, routes D192 through the existing 256-wide +tcgen05 split producer, and adds a 384-wide tcgen05 split producer for the D320 +rows. The objective is to remove repeated query-chunk TMA loads from the hot +D192/D320 eval path while preserving the same Weave-only contract outputs. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import os +from collections.abc import Callable +from functools import lru_cache +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dim_midk_df2f_v1 as wide_d256 +from . import knn_build_evolve_7bfc_split_v1 as split_parent +from . import knn_build_evolve_7bfc_v1 as base_v1 +from . import knn_build_non128_frontier_7231_v1 as parent_7231 +from .._dispatch_runtime import pack_kernel_args +BLOCK_Q = base_v1.BLOCK_Q +BLOCK_M = base_v1.BLOCK_M +K_TILE = base_v1.FEAT_D +TOP_K_MAX = base_v1.TOP_K_MAX +THREADS = split_parent.STAGE1_THREADS +MERGE_THREADS = split_parent.MERGE_THREADS +GRID_DIM_DEFAULT = split_parent.GRID_DIM_DEFAULT +D384_FEAT_D = 384 +D384_QUERY_BYTES = BLOCK_Q * D384_FEAT_D * 2 +D384_DATABASE_BYTES = BLOCK_M * D384_FEAT_D * 2 +QUERY_CHUNK_BYTES = BLOCK_Q * K_TILE * 2 +DATABASE_CHUNK_BYTES = BLOCK_M * K_TILE * 2 +DB_SQ_BYTES = BLOCK_M * 4 +MODULE = 'loom.examples.weave.knn_build_non128_frontier_8199_widestage_v1' +ROUTE_PREFIX = 'knn_build_non128_frontier_8199_widestage_v1' +TARGET_SHAPES = parent_7231.TARGET_SHAPES +TARGET_SHAPE_SET = set(TARGET_SHAPES) +SHAPE_SPECS = _decode_capture(_json_loads('{"__dict_items__": [["build_dim_sweep_b1_q1024_m1024_d96_k10", {"__dict_items__": [["B", 1], ["Q", 1024], ["M", 1024], ["D", 96], ["K", 10], ["build", true], ["feature_chunks", 1], ["split_count", 2]]}], ["build_dim_sweep_b1_q2048_m2048_d192_k10", {"__dict_items__": [["B", 1], ["Q", 2048], ["M", 2048], ["D", 192], ["K", 10], ["build", true], ["feature_chunks", 2], ["split_count", 4]]}], ["build_highd_b1_q1024_m1024_d320_k10", {"__dict_items__": [["B", 1], ["Q", 1024], ["M", 1024], ["D", 320], ["K", 10], ["build", true], ["feature_chunks", 3], ["split_count", 4]]}], ["search_rect_highd_b1_q512_m12000_d320_k10", {"__dict_items__": [["B", 1], ["Q", 512], ["M", 12000], ["D", 320], ["K", 10], ["build", false], ["feature_chunks", 3], ["split_count", 16]]}], ["rag_microbatch_highd_b1_q16_m50000_d768_k10", {"__dict_items__": [["B", 1], ["Q", 16], ["M", 50000], ["D", 768], ["K", 10], ["build", false], ["feature_chunks", 6], ["split_count", 64]]}]]}')) +WIDE_D256_SHAPES = {'build_dim_sweep_b1_q2048_m2048_d192_k10'} +WIDE_D384_SHAPES = {'build_highd_b1_q1024_m1024_d320_k10', 'search_rect_highd_b1_q512_m12000_d320_k10'} +knn_build_non128_frontier_8199_d384_stage1 = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_8199_d384_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 148736, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) +stage1_d384_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_8199_d384_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 148736, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) +merge_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_merge", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "B", "Q", "K", "split_count", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10]], "cta_group": 1, "threads": 256}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_NON128_FRONTIER_8199_VERIFY_KERNEL') + if verify_kernel == 'stage1_d256': + return wide_d256.stage1_d256_split_ir + if verify_kernel == 'pad_d192': + return parent_7231._pad_ir(256) + if verify_kernel == 'pad_d320': + return parent_7231._pad_ir(384) + if verify_kernel == 'merge': + return merge_ir + return stage1_d384_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_8199_d384_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 148736, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) + +def _compiled_d384_stage1(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0203"}')) + +def _dtype_name(inputs: dict[str, Any]) -> str: + query = inputs.get('query') + if query is not None: + return str(query.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')).replace('torch.', '') + +def _matches_spec(inputs: dict[str, Any], spec: dict[str, Any]) -> bool: + return _dtype_name(inputs) == 'bfloat16' and bool(inputs.get('build', False)) == bool(spec['build']) and (int(inputs['B']) == int(spec['B'])) and (int(inputs['Q']) == int(spec['Q'])) and (int(inputs['M']) == int(spec['M'])) and (int(inputs['D']) == int(spec['D'])) and (int(inputs['K']) == int(spec['K'])) + +def _target_label_for_inputs(inputs: dict[str, Any]) -> str | None: + label = inputs.get('label') + if label is not None and str(label) in TARGET_SHAPE_SET: + spec = SHAPE_SPECS[str(label)] + if _matches_spec(inputs, spec): + return str(label) + return None + for candidate_label, spec in SHAPE_SPECS.items(): + if _matches_spec(inputs, spec): + return candidate_label + return None + +def _split_count_for_label(label: str) -> int: + env_label = label.upper().replace('-', '_') + env_key = ''.join(['LOOM_KNN_NON128_FRONTIER_8199_SPLITS_', format(env_label, '')]) + override = os.environ.get(env_key) + if override is not None: + return int(override) + return int(SHAPE_SPECS[label]['split_count']) + +def _feature_dim_for_label(label: str) -> int: + if label in WIDE_D256_SHAPES: + return 256 + if label in WIDE_D384_SHAPES: + return 384 + return int(SHAPE_SPECS[label]['feature_chunks']) * K_TILE + +def _route_kind_for_label(label: str) -> str: + if label in WIDE_D256_SHAPES: + return 'wide256' + if label in WIDE_D384_SHAPES: + return 'wide384' + return 'parent7231' + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + label = _target_label_for_inputs(inputs) + if not force_fallback and label is not None: + spec = SHAPE_SPECS[label] + return ''.join([format(ROUTE_PREFIX, ''), ':', format(label, ''), ':d', format(int(spec['D']), ''), ':s', format(_split_count_for_label(label), ''), ':', format(_route_kind_for_label(label), '')]) + return parent_7231.route_for_contract_inputs(inputs, force_fallback=force_fallback) + +def _pad_if_needed(tensor, *, rows: int, src_cols: int, dst_cols: int): + if src_cols == dst_cols: + return tensor + return parent_7231._pad_bf16_rows(tensor, rows=rows, src_cols=src_cols, dst_cols=dst_cols) + +def _launch_wide_stage(inputs: dict[str, Any], label: str, *, feature_dim: int, kernel, stage1_ir) -> None: + query = inputs['query'] + database = inputs['database'] + if str(query.dtype) != 'torch.bfloat16' or str(database.dtype) != 'torch.bfloat16': + raise ValueError('knn_build_non128_frontier_8199_widestage_v1 supports bfloat16 inputs only') + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + split_count = _split_count_for_label(label) + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tiles * split_count + stage1_grid = min(total_work, GRID_DIM_DEFAULT) + merge_grid = min((bsz * n_query + MERGE_THREADS - 1) // MERGE_THREADS, GRID_DIM_DEFAULT) + partial_dists, partial_indices = split_parent._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + database_tma = _pad_if_needed(database, rows=bsz * n_database, src_cols=dim, dst_cols=feature_dim) + if query.data_ptr() == database.data_ptr(): + query_tma = database_tma + else: + query_tma = _pad_if_needed(query, rows=bsz * n_query, src_cols=dim, dst_cols=feature_dim) + tmap_query = base_v1._create_tensor_map_3d_oob_zero(query_tma.data_ptr(), bsz * n_query, BLOCK_Q, feature_dim, feature_dim) + tmap_database = base_v1._create_tensor_map_3d_oob_zero(database_tma.data_ptr(), bsz * n_database, BLOCK_M, feature_dim, feature_dim) + kernel.launch(grid=(stage1_grid, 1, 1), block=(THREADS, 1, 1), args=pack_kernel_args(stage1_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tiles=num_q_tiles, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), shared_mem=stage1_ir.computed_smem_bytes) + merge_kernel = split_parent._compiled_merge() + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz, n_query, top_k, split_count, bsz * n_query], shared_mem=merge_ir.computed_smem_bytes) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + label = _target_label_for_inputs(inputs) + if force_fallback or label is None: + parent_7231.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + return + if label in WIDE_D256_SHAPES: + _launch_wide_stage(inputs, label, feature_dim=256, kernel=wide_d256._compiled_d256_stage1(), stage1_ir=wide_d256.stage1_d256_split_ir) + return + if label in WIDE_D384_SHAPES: + _launch_wide_stage(inputs, label, feature_dim=D384_FEAT_D, kernel=_compiled_d384_stage1(), stage1_ir=stage1_d384_ir) + return + parent_7231.launch_from_contract_inputs(inputs, force_fallback=False) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + if shape_labels is None: + wanted = set(TARGET_SHAPES) + else: + wanted = {str(label) for label in shape_labels} + selected = [shape for shape in eval_mod.CANONICAL_SHAPES if str(shape['label']) in wanted] + missing = wanted - {str(shape['label']) for shape in selected} + if missing: + raise ValueError(''.join(['unknown knn_build contract shape(s): ', format(sorted(missing), '')])) + return selected + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def benchmark_knn_build_non128_frontier_8199_widestage_v1(*, use_cupti: bool | None=None, shape_labels=TARGET_SHAPES) -> dict[str, Any]: + prior_use_cupti = None + if use_cupti is not None: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True) + finally: + if prior_use_cupti is not None: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + return {'tflops': report['summary']['primary_mean'], 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'report': report} + +def _trace_inputs_from_shape(shape: dict[str, Any]) -> dict[str, Any]: + params = dict(shape.get('params', {})) + params['label'] = shape['label'] + return {'label': params['label'], 'B': params['B'], 'Q': params['Q'], 'M': params['M'], 'D': params['D'], 'K': params['K'], 'build': params['build'], 'dtype': params.get('dtype', 'bfloat16')} + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + rows = [] + for shape in selected: + inputs = _trace_inputs_from_shape(shape) + label = _target_label_for_inputs(inputs) + if label is None or force_fallback: + rows.append({'shape_key': inputs['label'], 'selected_route': parent_7231.ROUTE_FALLBACK, 'selected_entrypoint': parent_7231.ROUTE_FALLBACK, 'selected_seed': None, 'expected_seed': 'non128_frontier_8199_widestage_v1' if inputs['label'] in TARGET_SHAPE_SET else None, 'route_kind': 'fallback', 'route_source': 'parent-7231-dispatcher', 'guard_id': 'forced_fallback' if force_fallback else 'parent_7231_guard', 'classification': 'forced_fallback' if force_fallback else 'delegated'}) + continue + spec = SHAPE_SPECS[label] + feature_dim = _feature_dim_for_label(label) + rows.append({'shape_key': label, 'selected_route': route_for_contract_inputs(inputs), 'selected_entrypoint': ''.join([format(MODULE, ''), ':launch_from_contract_inputs']), 'selected_seed': 'non128_frontier_8199_widestage_v1', 'expected_seed': 'non128_frontier_8199_widestage_v1', 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': '8199_non128_exact_shape_guard', 'guard_condition': ''.join(['exact BF16 B=', format(spec['B'], ''), ' Q=', format(spec['Q'], ''), ' M=', format(spec['M'], ''), ' D=', format(spec['D'], ''), ' K=', format(spec['K'], ''), ' build=', format(spec['build'], '')]), 'feature_dim': feature_dim, 'split_count': _split_count_for_label(label), 'producer': _route_kind_for_label(label), 'preprocess_stage': ''.join(['d', format(int(spec['D']), ''), '_weave_pad_to_d', format(feature_dim, '')]) if int(spec['D']) != feature_dim else None, 'classification': 'seed-consumed'}) + return rows diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_non128_frontier_8227_d320tail_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_non128_frontier_8227_d320tail_v1.py new file mode 100644 index 00000000..c5aa8dcc --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_non128_frontier_8227_d320tail_v1.py @@ -0,0 +1,200 @@ +"""kNN non-D128 frontier seed with exact D320 tail MMA. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +preserves the round-8227 wide/M64 routes for D96, D192, and D768, and routes +the D320 frontier rows through an exact-width D320 tcgen05/TMA producer. The +D320 stage keeps the contract-visible Weave-only path: TMA-fed dot-product +stage, split-local K10 partials, and the existing split merge. Its dot producer +uses two 128-wide BF16 tcgen05 MMA tiles plus one 64-wide tail tile instead of +padding D320 to D384. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from collections.abc import Callable +from functools import lru_cache +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_evolve_7bfc_split_v1 as split_parent +from . import knn_build_evolve_7bfc_v1 as base_v1 +from . import knn_build_non128_frontier_8227_wide_m64_v1 as wide_m64 +from .._dispatch_runtime import pack_kernel_args +MODULE = 'loom.examples.weave.knn_build_non128_frontier_8227_d320tail_v1' +ROUTE_PREFIX = 'knn_build_non128_frontier_8227_d320tail_v1' +BLOCK_Q = wide_m64.widecombine.BLOCK_Q +BLOCK_M = wide_m64.widecombine.BLOCK_M +K_TILE = wide_m64.widecombine.K_TILE +TOP_K_MAX = wide_m64.widecombine.TOP_K_MAX +THREADS = wide_m64.widecombine.THREADS +MERGE_THREADS = wide_m64.widecombine.MERGE_THREADS +GRID_DIM_DEFAULT = wide_m64.widecombine.GRID_DIM_DEFAULT +D320_FEAT_D = 320 +D320_TAIL_K = 64 +D320_QUERY_BYTES = BLOCK_Q * D320_FEAT_D * 2 +D320_DATABASE_BYTES = BLOCK_M * D320_FEAT_D * 2 +QUERY_CHUNK_BYTES = BLOCK_Q * K_TILE * 2 +DATABASE_CHUNK_BYTES = BLOCK_M * K_TILE * 2 +DB_SQ_BYTES = BLOCK_M * 4 +D320_BUILD_SHAPE = 'build_highd_b1_q1024_m1024_d320_k10' +D320_SEARCH_SHAPE = 'search_rect_highd_b1_q512_m12000_d320_k10' +D320_TAIL_SHAPES = {D320_BUILD_SHAPE, D320_SEARCH_SHAPE} +TARGET_SHAPES = wide_m64.TARGET_SHAPES +TARGET_SHAPE_SET = set(TARGET_SHAPES) +SHAPE_SPECS = _decode_capture(_json_loads('{"__dict_items__": [["build_dim_sweep_b1_q1024_m1024_d96_k10", {"__dict_items__": [["B", 1], ["Q", 1024], ["M", 1024], ["D", 96], ["K", 10], ["build", true], ["feature_chunks", 1], ["split_count", 8]]}], ["build_dim_sweep_b1_q2048_m2048_d192_k10", {"__dict_items__": [["B", 1], ["Q", 2048], ["M", 2048], ["D", 192], ["K", 10], ["build", true], ["feature_chunks", 2], ["split_count", 8]]}], ["build_highd_b1_q1024_m1024_d320_k10", {"__dict_items__": [["B", 1], ["Q", 1024], ["M", 1024], ["D", 320], ["K", 10], ["build", true], ["feature_chunks", 3], ["split_count", 8]]}], ["search_rect_highd_b1_q512_m12000_d320_k10", {"__dict_items__": [["B", 1], ["Q", 512], ["M", 12000], ["D", 320], ["K", 10], ["build", false], ["feature_chunks", 3], ["split_count", 32]]}], ["rag_microbatch_highd_b1_q16_m50000_d768_k10", {"__dict_items__": [["B", 1], ["Q", 16], ["M", 50000], ["D", 768], ["K", 10], ["build", false], ["feature_chunks", 6], ["split_count", 72]]}]]}')) +stage1_d320tail_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_8227_d320tail_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 124160, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) +stage1_d256_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_dim_midk_df2f_d256_split_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 99584, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) +stage1_d384_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_8199_d384_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 148736, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) +stage1_m64_d768_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_7ee5_m64rag_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 34048, "constants": [["FEATURE_CHUNKS", 6]], "cta_group": 1, "threads": 96}')) +pad_bf16_rows_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_7231_pad_bf16_rows", "arg_keys": ["src", "dst", "rows", "src_cols", "total_elems"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["D_PAD", 128]], "cta_group": 1, "threads": 256}')) +merge_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_merge", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "B", "Q", "K", "split_count", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10]], "cta_group": 1, "threads": 256}')) +knn_build_non128_frontier_8227_d320tail_stage1 = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_8227_d320tail_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 124160, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) +stage1_d320tail_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_8227_d320tail_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 124160, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_NON128_FRONTIER_8227_D320TAIL_VERIFY_KERNEL') + if verify_kernel == 'stage1_d320tail': + return stage1_d320tail_ir + if verify_kernel == 'stage1_d256': + return stage1_d256_ir + if verify_kernel == 'stage1_d384': + return stage1_d384_ir + if verify_kernel == 'stage1_m64_d768': + return stage1_m64_d768_ir + if verify_kernel == 'stage1_chunks1': + return wide_m64.widecombine.splitretune.stage1_d128_ir + if verify_kernel == 'pad_d96': + return wide_m64.widecombine.splitretune.parent._pad_ir(128) + if verify_kernel == 'pad_d192': + return wide_m64.widecombine.splitretune.parent._pad_ir(256) + if verify_kernel == 'merge': + return merge_ir + return stage1_d320tail_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_8227_d320tail_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 124160, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) + +def _target_label_for_inputs(inputs: dict[str, Any]) -> str | None: + return wide_m64._target_label_for_inputs(inputs) + +def _uses_d320tail(label: str | None) -> bool: + return label in D320_TAIL_SHAPES + +def _split_count_for_label(label: str) -> int: + if _uses_d320tail(label): + env_label = label.upper().replace('-', '_') + env_key = ''.join(['LOOM_KNN_NON128_FRONTIER_8227_D320TAIL_SPLITS_', format(env_label, '')]) + override = os.environ.get(env_key) + if override is not None: + return int(override) + return wide_m64._split_count_for_label(label) + +def _feature_dim_for_label(label: str) -> int: + if _uses_d320tail(label): + return D320_FEAT_D + return wide_m64._feature_dim_for_label(label) + +def _producer_for_label(label: str) -> str: + if _uses_d320tail(label): + return 'd320_tail64' + return wide_m64._producer_for_label(label) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + label = _target_label_for_inputs(inputs) + if force_fallback or label is None: + return wide_m64.route_for_contract_inputs(inputs, force_fallback=force_fallback) + spec = SHAPE_SPECS[label] + return ''.join([format(ROUTE_PREFIX, ''), ':', format(label, ''), ':d', format(int(spec['D']), ''), ':s', format(_split_count_for_label(label), ''), ':', format(_producer_for_label(label), '')]) + +def _compiled_d320tail_stage1(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0031"}')) + +def _launch_d320tail(inputs: dict[str, Any], label: str) -> None: + query = inputs['query'] + database = inputs['database'] + if str(query.dtype) != 'torch.bfloat16' or str(database.dtype) != 'torch.bfloat16': + raise ValueError('knn_build_non128_frontier_8227_d320tail_v1 supports bfloat16 inputs only') + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + split_count = _split_count_for_label(label) + if dim != D320_FEAT_D: + raise ValueError(''.join(['D320 tail route expected D=', format(D320_FEAT_D, ''), ', got ', format(dim, '')])) + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tiles * split_count + stage1_grid = min(total_work, GRID_DIM_DEFAULT) + merge_grid = min((bsz * n_query + MERGE_THREADS - 1) // MERGE_THREADS, GRID_DIM_DEFAULT) + partial_dists, partial_indices = split_parent._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), bsz * n_query, BLOCK_Q, D320_FEAT_D, D320_FEAT_D) + tmap_database = base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, D320_FEAT_D, D320_FEAT_D) + _compiled_d320tail_stage1().launch(grid=(stage1_grid, 1, 1), block=(THREADS, 1, 1), args=pack_kernel_args(stage1_d320tail_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tiles=num_q_tiles, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), shared_mem=stage1_d320tail_ir.computed_smem_bytes) + merge_kernel = split_parent._compiled_merge() + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz, n_query, top_k, split_count, bsz * n_query], shared_mem=merge_ir.computed_smem_bytes) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + label = _target_label_for_inputs(inputs) + if force_fallback or label is None: + wide_m64.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + return + if _uses_d320tail(label): + _launch_d320tail(inputs, label) + return + wide_m64.launch_from_contract_inputs(inputs, force_fallback=False) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return wide_m64._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def benchmark_knn_build_non128_frontier_8227_d320tail_v1(*, use_cupti: bool | None=None, shape_labels=TARGET_SHAPES) -> dict[str, Any]: + prior_use_cupti = None + if use_cupti is not None: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True) + finally: + if prior_use_cupti is not None: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + return {'tflops': report['summary']['primary_mean'], 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'report': report} + +def _trace_inputs_from_shape(shape: dict[str, Any]) -> dict[str, Any]: + return wide_m64._trace_inputs_from_shape(shape) + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + rows = [] + for shape in selected: + inputs = _trace_inputs_from_shape(shape) + label = _target_label_for_inputs(inputs) + if label is None or force_fallback: + rows.append({'shape_key': inputs['label'], 'selected_route': wide_m64.route_for_contract_inputs(inputs, force_fallback=force_fallback), 'selected_entrypoint': ''.join([format(wide_m64.MODULE, ''), ':launch_from_contract_inputs']), 'selected_seed': None, 'expected_seed': 'non128_frontier_8227_d320tail_v1' if inputs['label'] in TARGET_SHAPE_SET else None, 'route_kind': 'fallback', 'route_source': '8227-wide-m64-parent-dispatcher', 'guard_id': 'forced_fallback' if force_fallback else 'wide_m64_parent_guard', 'classification': 'forced_fallback' if force_fallback else 'delegated'}) + continue + spec = SHAPE_SPECS[label] + uses_d320tail = _uses_d320tail(label) + feature_dim = _feature_dim_for_label(label) + rows.append({'shape_key': label, 'selected_route': route_for_contract_inputs(inputs), 'selected_entrypoint': ''.join([format(MODULE, ''), ':launch_from_contract_inputs']), 'selected_seed': 'non128_frontier_8227_d320tail_v1', 'expected_seed': 'non128_frontier_8227_d320tail_v1', 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': '8227_d320tail_non128_exact_shape_guard', 'guard_condition': ''.join(['exact BF16 B=', format(spec['B'], ''), ' Q=', format(spec['Q'], ''), ' M=', format(spec['M'], ''), ' D=', format(spec['D'], ''), ' K=', format(spec['K'], ''), ' build=', format(spec['build'], '')]), 'feature_dim': feature_dim, 'split_count': _split_count_for_label(label), 'producer': _producer_for_label(label), 'preprocess_stage': None if uses_d320tail else wide_m64.route_trace_for_contract_shapes((label,))[0].get('preprocess_stage'), 'source_route': 'd320_exact_tail64' if uses_d320tail else wide_m64.route_for_contract_inputs(inputs), 'classification': 'd320-tail64' if uses_d320tail else 'wide-m64-parent'}) + return rows diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_non128_frontier_8227_wide_m64_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_non128_frontier_8227_wide_m64_v1.py new file mode 100644 index 00000000..3291ab58 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_non128_frontier_8227_wide_m64_v1.py @@ -0,0 +1,144 @@ +"""kNN non-D128 frontier seed combining wide D192/D320 and M64 D768 routes. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +preserves the round-8199 widecombine routes for D192/D320, keeps D96 on the +validated split-retuned path, and routes only +``rag_microbatch_highd_b1_q16_m50000_d768_k10`` through the round-7ee5 M64/N64 +tcgen05/TMA producer. The contract-visible path remains Weave-only and writes +distances and indices through the existing split merge. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_non128_frontier_7ee5_m64rag_v1 as m64rag +from . import knn_build_non128_frontier_8199_widecombine_v1 as widecombine +MODULE = 'loom.examples.weave.knn_build_non128_frontier_8227_wide_m64_v1' +ROUTE_PREFIX = 'knn_build_non128_frontier_8227_wide_m64_v1' +D768_SHAPE = m64rag.D768_SHAPE +TARGET_SHAPES = widecombine.TARGET_SHAPES +TARGET_SHAPE_SET = set(TARGET_SHAPES) +SHAPE_SPECS = _decode_capture(_json_loads('{"__dict_items__": [["build_dim_sweep_b1_q1024_m1024_d96_k10", {"__dict_items__": [["B", 1], ["Q", 1024], ["M", 1024], ["D", 96], ["K", 10], ["build", true], ["feature_chunks", 1], ["split_count", 8]]}], ["build_dim_sweep_b1_q2048_m2048_d192_k10", {"__dict_items__": [["B", 1], ["Q", 2048], ["M", 2048], ["D", 192], ["K", 10], ["build", true], ["feature_chunks", 2], ["split_count", 8]]}], ["build_highd_b1_q1024_m1024_d320_k10", {"__dict_items__": [["B", 1], ["Q", 1024], ["M", 1024], ["D", 320], ["K", 10], ["build", true], ["feature_chunks", 3], ["split_count", 8]]}], ["search_rect_highd_b1_q512_m12000_d320_k10", {"__dict_items__": [["B", 1], ["Q", 512], ["M", 12000], ["D", 320], ["K", 10], ["build", false], ["feature_chunks", 3], ["split_count", 32]]}], ["rag_microbatch_highd_b1_q16_m50000_d768_k10", {"__dict_items__": [["B", 1], ["Q", 16], ["M", 50000], ["D", 768], ["K", 10], ["build", false], ["feature_chunks", 6], ["split_count", 72]]}]]}')) +SHAPE_SPECS[D768_SHAPE]['split_count'] = m64rag._split_count_for_label(D768_SHAPE) +stage1_d256_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_dim_midk_df2f_d256_split_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 99584, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) +stage1_d384_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_8199_d384_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 148736, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) +stage1_m64_d768_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_7ee5_m64rag_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 34048, "constants": [["FEATURE_CHUNKS", 6]], "cta_group": 1, "threads": 96}')) +pad_bf16_rows_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_7231_pad_bf16_rows", "arg_keys": ["src", "dst", "rows", "src_cols", "total_elems"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["D_PAD", 128]], "cta_group": 1, "threads": 256}')) +merge_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_merge", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "B", "Q", "K", "split_count", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10]], "cta_group": 1, "threads": 256}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_NON128_FRONTIER_8227_WIDE_M64_VERIFY_KERNEL') + if verify_kernel == 'stage1_d256': + return stage1_d256_ir + if verify_kernel == 'stage1_d384': + return stage1_d384_ir + if verify_kernel == 'stage1_m64_d768': + return stage1_m64_d768_ir + if verify_kernel == 'stage1_chunks1': + return widecombine.splitretune.stage1_d128_ir + if verify_kernel == 'pad_d96': + return widecombine.splitretune.parent._pad_ir(128) + if verify_kernel == 'pad_d192': + return widecombine.splitretune.parent._pad_ir(256) + if verify_kernel == 'pad_d320': + return widecombine.splitretune.parent._pad_ir(384) + if verify_kernel == 'merge': + return merge_ir + return stage1_m64_d768_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_7ee5_m64rag_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 34048, "constants": [["FEATURE_CHUNKS", 6]], "cta_group": 1, "threads": 96}')) + +def _target_label_for_inputs(inputs: dict[str, Any]) -> str | None: + return widecombine._target_label_for_inputs(inputs) + +def _uses_m64_d768(label: str | None) -> bool: + return label == D768_SHAPE + +def _split_count_for_label(label: str) -> int: + if _uses_m64_d768(label): + return m64rag._split_count_for_label(D768_SHAPE) + return widecombine._split_count_for_label(label) + +def _feature_dim_for_label(label: str) -> int: + if _uses_m64_d768(label): + return m64rag.M64_FEATURE_CHUNKS * m64rag.K_TILE + return widecombine._feature_dim_for_label(label) + +def _producer_for_label(label: str) -> str: + if _uses_m64_d768(label): + return 'm64_d768' + return widecombine._producer_for_label(label) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + label = _target_label_for_inputs(inputs) + if force_fallback or label is None: + return widecombine.route_for_contract_inputs(inputs, force_fallback=force_fallback) + spec = SHAPE_SPECS[label] + return ''.join([format(ROUTE_PREFIX, ''), ':', format(label, ''), ':d', format(int(spec['D']), ''), ':s', format(_split_count_for_label(label), ''), ':', format(_producer_for_label(label), '')]) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + label = _target_label_for_inputs(inputs) + if force_fallback or label is None: + widecombine.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + return + if _uses_m64_d768(label): + m64rag.launch_from_contract_inputs(inputs, force_fallback=False) + return + widecombine.launch_from_contract_inputs(inputs, force_fallback=False) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return widecombine._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def benchmark_knn_build_non128_frontier_8227_wide_m64_v1(*, use_cupti: bool | None=None, shape_labels=TARGET_SHAPES) -> dict[str, Any]: + prior_use_cupti = None + if use_cupti is not None: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True) + finally: + if prior_use_cupti is not None: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + return {'tflops': report['summary']['primary_mean'], 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'report': report} + +def _trace_inputs_from_shape(shape: dict[str, Any]) -> dict[str, Any]: + return widecombine._trace_inputs_from_shape(shape) + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + rows = [] + for shape in selected: + inputs = _trace_inputs_from_shape(shape) + label = _target_label_for_inputs(inputs) + if label is None or force_fallback: + rows.append({'shape_key': inputs['label'], 'selected_route': widecombine.route_for_contract_inputs(inputs, force_fallback=force_fallback), 'selected_entrypoint': widecombine.ROUTE_BASE_4247_ENTRYPOINT if hasattr(widecombine, 'ROUTE_BASE_4247_ENTRYPOINT') else widecombine.splitretune.ROUTE_FALLBACK, 'selected_seed': None, 'expected_seed': 'non128_frontier_8227_wide_m64_v1' if inputs['label'] in TARGET_SHAPE_SET else None, 'route_kind': 'fallback', 'route_source': '8199-widecombine-parent-dispatcher', 'guard_id': 'forced_fallback' if force_fallback else 'widecombine_parent_guard', 'classification': 'forced_fallback' if force_fallback else 'delegated'}) + continue + spec = SHAPE_SPECS[label] + uses_m64 = _uses_m64_d768(label) + feature_dim = _feature_dim_for_label(label) + rows.append({'shape_key': label, 'selected_route': route_for_contract_inputs(inputs), 'selected_entrypoint': ''.join([format(MODULE, ''), ':launch_from_contract_inputs']), 'selected_seed': 'non128_frontier_8227_wide_m64_v1', 'expected_seed': 'non128_frontier_8227_wide_m64_v1', 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': '8227_wide_m64_non128_exact_shape_guard', 'guard_condition': ''.join(['exact BF16 B=', format(spec['B'], ''), ' Q=', format(spec['Q'], ''), ' M=', format(spec['M'], ''), ' D=', format(spec['D'], ''), ' K=', format(spec['K'], ''), ' build=', format(spec['build'], '')]), 'feature_dim': feature_dim, 'split_count': _split_count_for_label(label), 'producer': _producer_for_label(label), 'preprocess_stage': ''.join(['d', format(int(spec['D']), ''), '_weave_pad_to_d', format(feature_dim, '')]) if int(spec['D']) != feature_dim and (not uses_m64) else None, 'source_route': m64rag.route_for_contract_inputs(inputs) if uses_m64 else widecombine.route_for_contract_inputs(inputs), 'classification': 'm64-d768' if uses_m64 else 'widecombine-parent'}) + return rows diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_over32_topk_knn_build_dispatch_slurm_0610_6329_v25.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_over32_topk_knn_build_dispatch_slurm_0610_6329_v25.py new file mode 100644 index 00000000..7c315daa --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_over32_topk_knn_build_dispatch_slurm_0610_6329_v25.py @@ -0,0 +1,137 @@ +"""kNN build v25 over-32 top-k dispatch probe. + +Minimum target architecture: sm_100a. This additive candidate keeps the v24 +dispatcher intact and adds BF16 D=128 build-mode routes for the v3 K48/K64 +over-32 diagnostic rows. The new route reuses the validated split/tcgen05 +producer from the K32 lineage with exact compile-time K48/K64 capacities, then +merges split-local partial top-k state into the contract distances/indices. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import os +from functools import lru_cache +from typing import Any +from .._dispatch_runtime import dc as dc +from . import knn_build_evolve_7bfc_fp16_d128_knn_build_dispatch_slurm_0610_6329_v24 as parent_v24 +from . import knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2_k32split_v20 as parent_v20 +from . import knn_build_evolve_7bfc_split_v1 as parent_split +from . import knn_build_evolve_7bfc_v1 as base_v1 +from .._dispatch_runtime import pack_kernel_args +BLOCK_Q = parent_v20.BLOCK_Q +BLOCK_M = parent_v20.BLOCK_M +FEAT_D = parent_v20.FEAT_D +STAGE1_THREADS = parent_v20.STAGE1_THREADS +MERGE_THREADS = parent_v20.K32_MERGE_THREADS +GRID_DIM_DEFAULT = parent_v20.GRID_DIM_DEFAULT +CTA_GROUP = parent_v20.CTA_GROUP +OVER32_SPLITS = parent_v20.MEDIUM_SPLITS +SUPPORTED_OVER32_K = (48, 64) + +def _ir_with_top_k_max(ir_obj: Any, *, top_k_max: int, suffix: str) -> Any: + constants = tuple(((name, top_k_max if name == 'TOP_K_MAX' else value) for name, value in ir_obj.constants)) + return dc.replace(ir_obj, symbol=''.join([format(ir_obj.symbol, ''), '_', format(suffix, '')]), constants=constants) +stage1_k48_over32_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k48over32", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 48]], "cta_group": 1, "threads": 192}')) +stage1_k64_over32_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k64over32", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 64]], "cta_group": 1, "threads": 192}')) +merge_k48_over32_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_unordered_k48over32", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 48], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 32}')) +merge_k64_over32_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_unordered_k64over32", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 64], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 32}')) + +def _stage1_ir_for_over32_k(top_k: int) -> Any: + if top_k == 48: + return stage1_k48_over32_ir + if top_k == 64: + return stage1_k64_over32_ir + raise ValueError(''.join(['no over-32 stage-1 specialization for K=', format(top_k, '')])) + +def _merge_ir_for_over32_k(top_k: int) -> Any: + if top_k == 48: + return merge_k48_over32_ir + if top_k == 64: + return merge_k64_over32_ir + raise ValueError(''.join(['no over-32 merge specialization for K=', format(top_k, '')])) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_OVER32_VERIFY_KERNEL') + if verify_kernel == 'stage1_k64': + return stage1_k64_over32_ir + if verify_kernel == 'merge_k48': + return merge_k48_over32_ir + if verify_kernel == 'merge_k64': + return merge_k64_over32_ir + return stage1_k48_over32_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k48over32", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 48]], "cta_group": 1, "threads": 192}')) + +@lru_cache(maxsize=2) +def _compiled_stage1_over32(top_k: int): + return parent_v20._compile_ir(_stage1_ir_for_over32_k(top_k)) + +@lru_cache(maxsize=2) +def _compiled_merge_over32(top_k: int): + return parent_v20._compile_ir(_merge_ir_for_over32_k(top_k)) + +def _eligible_over32_build(inputs: dict[str, Any]) -> bool: + top_k = int(inputs['K']) + return bool(inputs.get('build', False)) and str(inputs['query'].dtype) == 'torch.bfloat16' and (str(inputs['database'].dtype) == 'torch.bfloat16') and (int(inputs['D']) == FEAT_D) and (top_k in SUPPORTED_OVER32_K) and (int(inputs['Q']) == int(inputs['M'])) and (int(inputs['B']) == 1) and (int(inputs['Q']) in (2048, 4096)) + +def _launch_over32_split_path(inputs: dict[str, Any]) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + split_count = OVER32_SPLITS + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_q_tile_pairs = (num_q_tiles + CTA_GROUP - 1) // CTA_GROUP + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tile_pairs * split_count + stage1_grid = min(total_work * CTA_GROUP, GRID_DIM_DEFAULT) + merge_grid = min((bsz * n_query + MERGE_THREADS - 1) // MERGE_THREADS, GRID_DIM_DEFAULT) + partial_dists, partial_indices = parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), bsz * n_query, BLOCK_Q, dim, dim) + tmap_database = base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, dim, dim) + stage1_ir_obj = _stage1_ir_for_over32_k(top_k) + stage1_kernel = _compiled_stage1_over32(top_k) + stage1_kernel.launch_cluster(grid=(stage1_grid, 1, 1), block=(STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage1_ir_obj, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tile_pairs=num_q_tile_pairs, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), cluster_dims=(CTA_GROUP, 1, 1), shared_mem=stage1_ir_obj.computed_smem_bytes) + merge_ir_obj = _merge_ir_for_over32_k(top_k) + merge_kernel = _compiled_merge_over32(top_k) + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_ir_obj.computed_smem_bytes) + +def launch_from_contract_inputs(inputs: dict[str, Any]) -> None: + if _eligible_over32_build(inputs): + _launch_over32_split_path(inputs) + return + parent_v24.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(candidate, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return parent_v24._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=('build_over32_stress_qm2048_k48',), benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint: run real contract correctness for selected shapes.""" + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def benchmark_over32_topk_v25(*, use_cupti: bool | None=None) -> dict[str, Any]: + """Opt-in benchmark hook for the K48/K64 over-32 build diagnostic route.""" + from .. import _dispatch_runtime as eval_mod + prior_use_cupti = eval_mod.CONTRACT.bench.get('use_cupti', True) + if use_cupti is not None: + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + try: + report = evaluate_contract(shapes=_select_contract_shapes(('build_over32_stress_qm2048_k48', 'build_over32_stress_qm2048_k64', 'build_over32_stress_qm4096_k48')), correctness=True, benchmark=True) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + return {'tflops': report['summary']['primary_mean'], 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'report': report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_over64_k96_a2f8_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_over64_k96_a2f8_v1.py new file mode 100644 index 00000000..05f02252 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_over64_k96_a2f8_v1.py @@ -0,0 +1,123 @@ +"""kNN build/search over-64 K96 stage1 chunked-worst seed. + +Minimum target architecture: sm_100a. This additive auto-tuning candidate +targets the exact frontier build row ``B=1, Q=M=2048, D=128, K=96, bf16``. +It keeps the existing split8 K96 merge and exact guard from the validated a989 +seed, but replaces the widened generic stage-1 top-k rescan with a +role-aligned tcgen05/TMA producer that maintains 24 four-slot worst chunks and +sorts accepted four-column groups. Guard misses delegate to the inherited Weave +route. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +from functools import lru_cache +from typing import Any +from .._dispatch_runtime import dc as dc +from . import knn_build_evolve_7bfc_fp16_d128_knn_build_dispatch_slurm_0610_6329_v24 as parent_v24 +from . import knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2_k32split_v20 as parent_v20 +from . import knn_build_evolve_7bfc_split_v1 as parent_split +from . import knn_build_evolve_7bfc_v1 as base_v1 +from .._dispatch_runtime import pack_kernel_args +BLOCK_Q = parent_v20.BLOCK_Q +BLOCK_M = parent_v20.BLOCK_M +FEAT_D = parent_v20.FEAT_D +STAGE1_THREADS = parent_v20.STAGE1_THREADS +MERGE_THREADS = parent_v20.K32_MERGE_THREADS +GRID_DIM_DEFAULT = parent_v20.GRID_DIM_DEFAULT +CTA_GROUP = parent_v20.CTA_GROUP +OVER64_BUILD_SPLITS = 8 +OVER64_TOP_K = 96 +OVER64_QM = 2048 + +def _ir_with_constants(ir_obj: Any, *, suffix: str, **updates: int) -> Any: + constants = tuple(((name, updates.get(name, value)) for name, value in ir_obj.constants)) + return dc.replace(ir_obj, symbol=''.join([format(ir_obj.symbol, ''), '_', format(suffix, '')]), constants=constants) +stage1_k96_over64_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k96over64", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 96]], "cta_group": 1, "threads": 192}')) +merge_k96_over64_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_unordered_k96over64", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 96], ["SPLIT_COUNT", 8]], "cta_group": 1, "threads": 32}')) +knn_build_k96_stage1_sort4_chunked = _decode_capture(_json_loads('{"__ir__": "knn_build_k96_stage1_sort4_chunked", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 96]], "cta_group": 1, "threads": 192}')) +stage1_k96_sort4_chunked_over64_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_k96_stage1_sort4_chunked_k96over64sort4chunked", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 96]], "cta_group": 1, "threads": 192}')) +knn_build_k96_merge_s8_unordered_chunkprefill = _decode_capture(_json_loads('{"__ir__": "knn_build_k96_merge_s8_unordered_chunkprefill", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 96], ["SPLIT_COUNT", 8]], "cta_group": 1, "threads": 32}')) +merge_k96_s8_chunkprefill_over64_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_k96_merge_s8_unordered_chunkprefill_k96over64s8chunkprefill", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 96], ["SPLIT_COUNT", 8]], "cta_group": 1, "threads": 32}')) + +def _verify_export_ir() -> Any: + import os + verify_kernel = os.environ.get('LOOM_KNN_OVER64_VERIFY_KERNEL') + if verify_kernel == 'stage1_k96_generic': + return stage1_k96_over64_ir + if verify_kernel == 'merge_k96_generic': + return merge_k96_over64_ir + if verify_kernel == 'merge_k96': + return merge_k96_s8_chunkprefill_over64_ir + return stage1_k96_sort4_chunked_over64_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_k96_stage1_sort4_chunked_k96over64sort4chunked", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 96]], "cta_group": 1, "threads": 192}')) + +def _compiled_stage1_k96(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0189"}')) + +def _compiled_merge_k96(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0190"}')) + +def _eligible_over64_k96_build(inputs: dict[str, Any]) -> bool: + return bool(inputs.get('build', False)) and str(inputs['query'].dtype) == 'torch.bfloat16' and (str(inputs['database'].dtype) == 'torch.bfloat16') and (int(inputs['D']) == FEAT_D) and (int(inputs['K']) == OVER64_TOP_K) and (int(inputs['Q']) == OVER64_QM) and (int(inputs['M']) == OVER64_QM) and (int(inputs['B']) == 1) + +def _launch_over64_k96_split_path(inputs: dict[str, Any]) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + split_count = OVER64_BUILD_SPLITS + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_q_tile_pairs = (num_q_tiles + CTA_GROUP - 1) // CTA_GROUP + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tile_pairs * split_count + stage1_grid = min(total_work * CTA_GROUP, GRID_DIM_DEFAULT) + merge_grid = min((bsz * n_query + MERGE_THREADS - 1) // MERGE_THREADS, GRID_DIM_DEFAULT) + partial_dists, partial_indices = parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), bsz * n_query, BLOCK_Q, dim, dim) + tmap_database = base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, dim, dim) + stage1_kernel = _compiled_stage1_k96() + stage1_kernel.launch_cluster(grid=(stage1_grid, 1, 1), block=(STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage1_k96_sort4_chunked_over64_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tile_pairs=num_q_tile_pairs, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), cluster_dims=(CTA_GROUP, 1, 1), shared_mem=stage1_k96_sort4_chunked_over64_ir.computed_smem_bytes) + merge_kernel = _compiled_merge_k96() + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_k96_s8_chunkprefill_over64_ir.computed_smem_bytes) + +def launch_from_contract_inputs(inputs: dict[str, Any]) -> None: + if _eligible_over64_k96_build(inputs): + _launch_over64_k96_split_path(inputs) + return + parent_v24.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(candidate, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return parent_v24._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=('build_over64_stress_qm2048_k96',), benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint: run real contract correctness for selected shapes.""" + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def benchmark_knn_build_over64_k96_a2f8_v1(*, use_cupti: bool | None=None) -> dict[str, Any]: + """Opt-in benchmark hook for the exact K96 frontier seed.""" + from .. import _dispatch_runtime as eval_mod + prior_use_cupti = eval_mod.CONTRACT.bench.get('use_cupti', True) + if use_cupti is not None: + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + try: + report = evaluate_contract(shapes=_select_contract_shapes(('build_over64_stress_qm2048_k96',)), correctness=True, benchmark=True) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + return {'tflops': report['summary']['primary_mean'], 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'report': report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_over64_k96_a989_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_over64_k96_a989_v1.py new file mode 100644 index 00000000..de9b34ab --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_over64_k96_a989_v1.py @@ -0,0 +1,117 @@ +"""kNN build/search over-64 K96 coverage seed. + +Minimum target architecture: sm_100a. This additive auto-tuning candidate +targets the exact frontier build row ``B=1, Q=M=2048, D=128, K=96, bf16``. +It keeps the existing tcgen05/TMA split stage-1 producer and unordered split +merge structure from the validated K32/K64 lineage, raises the static top-k +capacity to 96, and leaves all guard misses on the inherited Weave route. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +from functools import lru_cache +from typing import Any +from .._dispatch_runtime import dc as dc +from . import knn_build_evolve_7bfc_fp16_d128_knn_build_dispatch_slurm_0610_6329_v24 as parent_v24 +from . import knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2_k32split_v20 as parent_v20 +from . import knn_build_evolve_7bfc_split_v1 as parent_split +from . import knn_build_evolve_7bfc_v1 as base_v1 +from .._dispatch_runtime import pack_kernel_args +BLOCK_Q = parent_v20.BLOCK_Q +BLOCK_M = parent_v20.BLOCK_M +FEAT_D = parent_v20.FEAT_D +STAGE1_THREADS = parent_v20.STAGE1_THREADS +MERGE_THREADS = parent_v20.K32_MERGE_THREADS +GRID_DIM_DEFAULT = parent_v20.GRID_DIM_DEFAULT +CTA_GROUP = parent_v20.CTA_GROUP +OVER64_BUILD_SPLITS = 8 +OVER64_TOP_K = 96 +OVER64_QM = 2048 + +def _ir_with_constants(ir_obj: Any, *, suffix: str, **updates: int) -> Any: + constants = tuple(((name, updates.get(name, value)) for name, value in ir_obj.constants)) + return dc.replace(ir_obj, symbol=''.join([format(ir_obj.symbol, ''), '_', format(suffix, '')]), constants=constants) +stage1_k96_over64_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k96over64", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 96]], "cta_group": 1, "threads": 192}')) +merge_k96_over64_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_unordered_k96over64", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 96], ["SPLIT_COUNT", 8]], "cta_group": 1, "threads": 32}')) +knn_build_k96_merge_s8_unordered_chunkprefill = _decode_capture(_json_loads('{"__ir__": "knn_build_k96_merge_s8_unordered_chunkprefill", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 96], ["SPLIT_COUNT", 8]], "cta_group": 1, "threads": 32}')) +merge_k96_s8_chunkprefill_over64_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_k96_merge_s8_unordered_chunkprefill_k96over64s8chunkprefill", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 96], ["SPLIT_COUNT", 8]], "cta_group": 1, "threads": 32}')) + +def _verify_export_ir() -> Any: + import os + verify_kernel = os.environ.get('LOOM_KNN_OVER64_VERIFY_KERNEL') + if verify_kernel == 'merge_k96_generic': + return merge_k96_over64_ir + if verify_kernel == 'merge_k96': + return merge_k96_s8_chunkprefill_over64_ir + return stage1_k96_over64_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k96over64", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 96]], "cta_group": 1, "threads": 192}')) + +def _compiled_stage1_k96(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0165"}')) + +def _compiled_merge_k96(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0166"}')) + +def _eligible_over64_k96_build(inputs: dict[str, Any]) -> bool: + return bool(inputs.get('build', False)) and str(inputs['query'].dtype) == 'torch.bfloat16' and (str(inputs['database'].dtype) == 'torch.bfloat16') and (int(inputs['D']) == FEAT_D) and (int(inputs['K']) == OVER64_TOP_K) and (int(inputs['Q']) == OVER64_QM) and (int(inputs['M']) == OVER64_QM) and (int(inputs['B']) == 1) + +def _launch_over64_k96_split_path(inputs: dict[str, Any]) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + split_count = OVER64_BUILD_SPLITS + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_q_tile_pairs = (num_q_tiles + CTA_GROUP - 1) // CTA_GROUP + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tile_pairs * split_count + stage1_grid = min(total_work * CTA_GROUP, GRID_DIM_DEFAULT) + merge_grid = min((bsz * n_query + MERGE_THREADS - 1) // MERGE_THREADS, GRID_DIM_DEFAULT) + partial_dists, partial_indices = parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), bsz * n_query, BLOCK_Q, dim, dim) + tmap_database = base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, dim, dim) + stage1_kernel = _compiled_stage1_k96() + stage1_kernel.launch_cluster(grid=(stage1_grid, 1, 1), block=(STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage1_k96_over64_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tile_pairs=num_q_tile_pairs, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), cluster_dims=(CTA_GROUP, 1, 1), shared_mem=stage1_k96_over64_ir.computed_smem_bytes) + merge_kernel = _compiled_merge_k96() + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_k96_s8_chunkprefill_over64_ir.computed_smem_bytes) + +def launch_from_contract_inputs(inputs: dict[str, Any]) -> None: + if _eligible_over64_k96_build(inputs): + _launch_over64_k96_split_path(inputs) + return + parent_v24.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(candidate, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return parent_v24._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=('build_over64_stress_qm2048_k96',), benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint: run real contract correctness for selected shapes.""" + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def benchmark_knn_build_over64_k96_a989_v1(*, use_cupti: bool | None=None) -> dict[str, Any]: + """Opt-in benchmark hook for the exact K96 frontier seed.""" + from .. import _dispatch_runtime as eval_mod + prior_use_cupti = eval_mod.CONTRACT.bench.get('use_cupti', True) + if use_cupti is not None: + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + try: + report = evaluate_contract(shapes=_select_contract_shapes(('build_over64_stress_qm2048_k96',)), correctness=True, benchmark=True) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + return {'tflops': report['summary']['primary_mean'], 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'report': report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_over64_k96_exactall_229a_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_over64_k96_exactall_229a_v1.py new file mode 100644 index 00000000..17e8dce6 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_over64_k96_exactall_229a_v1.py @@ -0,0 +1,174 @@ +"""kNN build/search over-64 K96 exact-prefill all-row probe. + +Minimum target architecture: sm_100a. This additive auto-tuning candidate +targets the exact frontier build rows ``B=1, Q=M in {1024,2048,4096}, D=128, +K=96, bf16``. It reuses the e5db exact no-tail K96 stage-1 producer for all +three no-tail rows, then uses the existing split2/split4 K96 merges. Guard +misses delegate to the prior e5db q1024exact route. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from collections.abc import Callable +from functools import cache, lru_cache +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_over64_k96_q1024exact_e5db_v1 as q1024exact +from .._dispatch_runtime import pack_kernel_args +BLOCK_Q = q1024exact.BLOCK_Q +BLOCK_M = q1024exact.BLOCK_M +FEAT_D = q1024exact.FEAT_D +STAGE1_THREADS = q1024exact.STAGE1_THREADS +MERGE_THREADS = q1024exact.MERGE_THREADS +GRID_DIM_DEFAULT = q1024exact.GRID_DIM_DEFAULT +CTA_GROUP = q1024exact.CTA_GROUP +OVER64_TOP_K = q1024exact.OVER64_TOP_K +SUPPORTED_QM = (1024, 2048, 4096) +DEFAULT_SPLITS_BY_QM = {1024: 2, 2048: 2, 4096: 4} +TARGET_SHAPES = ('build_over64_stress_qm1024_k96', 'build_over64_stress_qm2048_k96', 'build_over64_stress_qm4096_k96') +MERGE_IR_BY_SPLIT = _decode_capture(_json_loads('{"__dict_items__": [[1, {"__ir__": "knn_build_k96_merge_s8_unordered_chunkprefill_k96over64s1chunkprefill_f9d1", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 96], ["SPLIT_COUNT", 1]], "cta_group": 1, "threads": 32}], [2, {"__ir__": "knn_build_k96_merge_s8_unordered_chunkprefill_k96over64s2chunkprefill_f9d1", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 96], ["SPLIT_COUNT", 2]], "cta_group": 1, "threads": 32}], [3, {"__ir__": "knn_build_k96_merge_s8_unordered_chunkprefill_k96over64s3chunkprefill_f9d1", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 96], ["SPLIT_COUNT", 3]], "cta_group": 1, "threads": 32}], [4, {"__ir__": "knn_build_k96_merge_s8_unordered_chunkprefill_k96over64s4chunkprefill_f9d1", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 96], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 32}], [6, {"__ir__": "knn_build_k96_merge_s8_unordered_chunkprefill_k96over64s6chunkprefill_f9d1", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 96], ["SPLIT_COUNT", 6]], "cta_group": 1, "threads": 32}], [8, {"__ir__": "knn_build_k96_merge_s8_unordered_chunkprefill_k96over64s8chunkprefill_f9d1", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 96], ["SPLIT_COUNT", 8]], "cta_group": 1, "threads": 32}]]}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_OVER64_K96_EXACTALL_229A_VERIFY_KERNEL') + if verify_kernel == 'stage1_exact': + return q1024exact.stage1_k96_exact_prefill_q1024_ir + if verify_kernel and verify_kernel.startswith('merge_s'): + split_count = int(verify_kernel.removeprefix('merge_s')) + return MERGE_IR_BY_SPLIT[split_count] + return q1024exact.stage1_k96_exact_prefill_q1024_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_k96_stage1_exact_prefill_q1024_k96over64exactprefillq1024_e5db", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 96]], "cta_group": 1, "threads": 192}')) + +def _compiled_stage1_k96_exact_prefill(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0120"}')) + +@cache +def _compiled_merge_k96(split_count: int): + return q1024exact.f9d1.a2f8.parent_v20._compile_ir(MERGE_IR_BY_SPLIT[split_count]) + +def _select_split_count(n_query: int, *, override: int | None=None) -> int: + split_count = int(override) if override is not None else DEFAULT_SPLITS_BY_QM[int(n_query)] + if split_count not in MERGE_IR_BY_SPLIT: + raise ValueError(''.join(['unsupported K96 split_count ', format(split_count, '')])) + num_db_tiles = (int(n_query) + BLOCK_M - 1) // BLOCK_M + if num_db_tiles % split_count != 0: + raise ValueError(''.join(['unsafe exact K96 split_count ', format(split_count, ''), ': ', format(num_db_tiles, ''), ' database tiles would leave a tail'])) + return split_count + +def _eligible_over64_k96_exact_build(inputs: dict[str, Any]) -> bool: + n_query = int(inputs['Q']) + return bool(inputs.get('build', False)) and str(inputs['query'].dtype) == 'torch.bfloat16' and (str(inputs['database'].dtype) == 'torch.bfloat16') and (int(inputs['D']) == FEAT_D) and (int(inputs['K']) == OVER64_TOP_K) and (int(inputs['M']) == n_query) and (n_query in SUPPORTED_QM) and (int(inputs['B']) == 1) + +def _exactall_route_name(*, n_query: int, split_count: int) -> str: + return ''.join(['knn_build_over64_k96_exactall_229a_v1_q', format(n_query, ''), '_k96_exactprefill_s', format(split_count, '')]) + +def _launch_over64_k96_exact_prefill(inputs: dict[str, Any], *, split_override: int | None=None) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + split_count = _select_split_count(n_query, override=split_override) + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_q_tile_pairs = (num_q_tiles + CTA_GROUP - 1) // CTA_GROUP + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tile_pairs * split_count + stage1_grid = min(total_work * CTA_GROUP, GRID_DIM_DEFAULT) + merge_grid = min((bsz * n_query + MERGE_THREADS - 1) // MERGE_THREADS, GRID_DIM_DEFAULT) + partial_dists, partial_indices = q1024exact.f9d1.a2f8.parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = q1024exact.f9d1.a2f8.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), bsz * n_query, BLOCK_Q, dim, dim) + tmap_database = q1024exact.f9d1.a2f8.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, dim, dim) + stage1_kernel = _compiled_stage1_k96_exact_prefill() + stage1_kernel.launch_cluster(grid=(stage1_grid, 1, 1), block=(STAGE1_THREADS, 1, 1), args=pack_kernel_args(q1024exact.stage1_k96_exact_prefill_q1024_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tile_pairs=num_q_tile_pairs, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), cluster_dims=(CTA_GROUP, 1, 1), shared_mem=q1024exact.stage1_k96_exact_prefill_q1024_ir.computed_smem_bytes) + merge_ir = MERGE_IR_BY_SPLIT[split_count] + merge_kernel = _compiled_merge_k96(split_count) + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_ir.computed_smem_bytes) + +def route_for_contract_inputs(inputs: dict[str, Any], *, split_override: int | None=None) -> str: + if _eligible_over64_k96_exact_build(inputs): + split_count = _select_split_count(int(inputs['Q']), override=split_override) + return _exactall_route_name(n_query=int(inputs['Q']), split_count=split_count) + if q1024exact._eligible_over64_k96_q1024_build(inputs): + return 'delegated_q1024exact_e5db' + return 'delegated_q1024exact_fallback' + +def launch_from_contract_inputs(inputs: dict[str, Any], *, split_override: int | None=None) -> None: + if _eligible_over64_k96_exact_build(inputs): + env_override = os.environ.get('LOOM_KNN_OVER64_K96_EXACTALL_229A_SPLIT_OVERRIDE') + active_override = int(env_override) if env_override else split_override + _launch_over64_k96_exact_prefill(inputs, split_override=active_override) + return + q1024exact.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def candidate_with_split_override(split_override: int | None) -> Callable[[dict[str, Any]], None]: + + def _candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, split_override=split_override) + return _candidate + +def candidate_parent_e5db(inputs: dict[str, Any]): + q1024exact.launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return q1024exact._select_contract_shapes(shape_labels) + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def route_trace_for_contract_shapes(shape_labels=TARGET_SHAPES, *, split_override: int | None=None) -> list[dict[str, Any]]: + rows = [] + for shape in _select_contract_shapes(shape_labels): + params = dict(shape.get('params', {})) + n_query = int(params.get('Q', -1)) + specialized = bool(params.get('build', False)) and int(params.get('B', -1)) == 1 and (int(params.get('M', -1)) == n_query) and (int(params.get('D', -1)) == FEAT_D) and (int(params.get('K', -1)) == OVER64_TOP_K) and (str(params.get('dtype', '')) in {'bf16', 'bfloat16', 'torch.bfloat16'}) and (n_query in SUPPORTED_QM) + if specialized: + split_count = _select_split_count(n_query, override=split_override) + route = _exactall_route_name(n_query=n_query, split_count=split_count) + else: + route = 'delegated_q1024exact_fallback' + rows.append({'shape_key': shape['label'], 'selected_route': route, 'route_kind': 'specialized' if specialized else 'parent', 'guard_condition': 'exact BF16 build B=1 Q=M in {1024,2048,4096} D=128 K=96', 'fallback': 'loom.examples.weave.knn_build_over64_k96_q1024exact_e5db_v1'}) + return rows + +def _per_shape_delta(candidate_report: dict[str, Any], parent_report: dict[str, Any]) -> dict[str, Any]: + rows = {} + for label in TARGET_SHAPES: + cand = candidate_report.get('per_shape', {}).get(label, {}) + parent = parent_report.get('per_shape', {}).get(label, {}) + cand_ms = cand.get('kernel_ms') + parent_ms = parent.get('kernel_ms') + rows[label] = {'candidate': cand, 'parent_e5db': parent, 'candidate_ms': cand_ms, 'parent_e5db_ms': parent_ms, 'speedup_vs_parent_e5db': parent_ms / cand_ms if cand_ms and parent_ms else None, 'ratio_vs_flashlib': cand.get('ratio_vs_flashlib')} + return rows + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def benchmark_knn_build_over64_k96_exactall_229a_v1(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES, split_override: int | None=None) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_with_split_override(split_override)) + parent_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_parent_e5db) + return {'contract': candidate_report['contract'], 'contract_version': candidate_report['contract_version'], 'measured_entrypoint': 'loom.examples.weave.knn_build_over64_k96_exactall_229a_v1:benchmark_knn_build_over64_k96_exactall_229a_v1', 'candidate_entrypoint': 'loom.examples.weave.knn_build_over64_k96_exactall_229a_v1:launch_from_contract_inputs', 'parent_entrypoint': 'loom.examples.weave.knn_build_over64_k96_q1024exact_e5db_v1:launch_from_contract_inputs', 'accelerated_shape_labels': list(TARGET_SHAPES), 'measured_shape_labels': list(shape_labels), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'producer_topology': 'exact no-tail K96 prefill stage for Q1024/Q2048/Q4096; split2/split4 K96 merge', 'route_trace': route_trace_for_contract_shapes(shape_labels, split_override=split_override), 'target_rows': _per_shape_delta(candidate_report, parent_report), 'summary': candidate_report['summary'], 'performance': candidate_report['performance'], 'correctness': candidate_report['correctness'], 'report': candidate_report, 'parent_summary': parent_report['summary'], 'parent_performance': parent_report['performance'], 'parent_report': parent_report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_over64_k96_f9d1_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_over64_k96_f9d1_v1.py new file mode 100644 index 00000000..d6b8bbc3 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_over64_k96_f9d1_v1.py @@ -0,0 +1,124 @@ +"""kNN build/search over-64 K96 split-count repair seed. + +Minimum target architecture: sm_100a. This additive auto-tuning candidate +targets the exact frontier build rows ``B=1, Q=M in {1024,2048,4096}, D=128, +K=96, bf16``. It reuses the A2F8 tcgen05/TMA stage-1 producer and specializes +the K96 chunk-prefill merge by split count so smaller rows use lower merge +fan-in while Q4096 keeps enough producer parallelism. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from functools import cache, lru_cache +from typing import Any +from . import knn_build_over64_k96_a2f8_v1 as a2f8 +from .._dispatch_runtime import pack_kernel_args +BLOCK_Q = a2f8.BLOCK_Q +BLOCK_M = a2f8.BLOCK_M +FEAT_D = a2f8.FEAT_D +STAGE1_THREADS = a2f8.STAGE1_THREADS +MERGE_THREADS = a2f8.MERGE_THREADS +GRID_DIM_DEFAULT = a2f8.GRID_DIM_DEFAULT +CTA_GROUP = a2f8.CTA_GROUP +OVER64_TOP_K = a2f8.OVER64_TOP_K +SUPPORTED_QM = (1024, 2048, 4096) +DEFAULT_SPLITS_BY_QM = {1024: 2, 2048: 2, 4096: 4} +SUPPORTED_SPLIT_COUNTS = (2, 3, 4, 6, 8) + +def _make_merge_ir(split_count: int) -> Any: + return a2f8._ir_with_constants(a2f8.knn_build_k96_merge_s8_unordered_chunkprefill, TOP_K_MAX=OVER64_TOP_K, SPLIT_COUNT=split_count, suffix=''.join(['k96over64s', format(split_count, ''), 'chunkprefill_f9d1'])) +MERGE_IR_BY_SPLIT = _decode_capture(_json_loads('{"__dict_items__": [[2, {"__ir__": "knn_build_k96_merge_s8_unordered_chunkprefill_k96over64s2chunkprefill_f9d1", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 96], ["SPLIT_COUNT", 2]], "cta_group": 1, "threads": 32}], [3, {"__ir__": "knn_build_k96_merge_s8_unordered_chunkprefill_k96over64s3chunkprefill_f9d1", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 96], ["SPLIT_COUNT", 3]], "cta_group": 1, "threads": 32}], [4, {"__ir__": "knn_build_k96_merge_s8_unordered_chunkprefill_k96over64s4chunkprefill_f9d1", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 96], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 32}], [6, {"__ir__": "knn_build_k96_merge_s8_unordered_chunkprefill_k96over64s6chunkprefill_f9d1", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 96], ["SPLIT_COUNT", 6]], "cta_group": 1, "threads": 32}], [8, {"__ir__": "knn_build_k96_merge_s8_unordered_chunkprefill_k96over64s8chunkprefill_f9d1", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 96], ["SPLIT_COUNT", 8]], "cta_group": 1, "threads": 32}]]}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_OVER64_K96_F9D1_VERIFY_KERNEL') + if verify_kernel == 'stage1': + return a2f8.stage1_k96_sort4_chunked_over64_ir + if verify_kernel and verify_kernel.startswith('merge_s'): + split_count = int(verify_kernel.removeprefix('merge_s')) + return MERGE_IR_BY_SPLIT[split_count] + return a2f8.stage1_k96_sort4_chunked_over64_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_k96_stage1_sort4_chunked_k96over64sort4chunked", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 96]], "cta_group": 1, "threads": 192}')) + +def _compiled_stage1_k96(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0189"}')) + +@cache +def _compiled_merge_k96(split_count: int): + return a2f8.parent_v20._compile_ir(MERGE_IR_BY_SPLIT[split_count]) + +def _select_split_count(n_query: int, *, override: int | None=None) -> int: + split_count = int(override) if override is not None else DEFAULT_SPLITS_BY_QM[int(n_query)] + if split_count not in MERGE_IR_BY_SPLIT: + raise ValueError(''.join(['unsupported K96 split_count ', format(split_count, '')])) + return split_count + +def _eligible_over64_k96_build(inputs: dict[str, Any]) -> bool: + n_query = int(inputs['Q']) + return bool(inputs.get('build', False)) and str(inputs['query'].dtype) == 'torch.bfloat16' and (str(inputs['database'].dtype) == 'torch.bfloat16') and (int(inputs['D']) == FEAT_D) and (int(inputs['K']) == OVER64_TOP_K) and (int(inputs['M']) == n_query) and (n_query in SUPPORTED_QM) and (int(inputs['B']) == 1) + +def _launch_over64_k96_split_path(inputs: dict[str, Any], *, split_override: int | None=None) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + split_count = _select_split_count(n_query, override=split_override) + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_q_tile_pairs = (num_q_tiles + CTA_GROUP - 1) // CTA_GROUP + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tile_pairs * split_count + stage1_grid = min(total_work * CTA_GROUP, GRID_DIM_DEFAULT) + merge_grid = min((bsz * n_query + MERGE_THREADS - 1) // MERGE_THREADS, GRID_DIM_DEFAULT) + partial_dists, partial_indices = a2f8.parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = a2f8.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), bsz * n_query, BLOCK_Q, dim, dim) + tmap_database = a2f8.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, dim, dim) + stage1_kernel = _compiled_stage1_k96() + stage1_kernel.launch_cluster(grid=(stage1_grid, 1, 1), block=(STAGE1_THREADS, 1, 1), args=pack_kernel_args(a2f8.stage1_k96_sort4_chunked_over64_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tile_pairs=num_q_tile_pairs, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), cluster_dims=(CTA_GROUP, 1, 1), shared_mem=a2f8.stage1_k96_sort4_chunked_over64_ir.computed_smem_bytes) + merge_ir = MERGE_IR_BY_SPLIT[split_count] + merge_kernel = _compiled_merge_k96(split_count) + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_ir.computed_smem_bytes) + +def launch_from_contract_inputs(inputs: dict[str, Any]) -> None: + if _eligible_over64_k96_build(inputs): + override_env = os.environ.get('LOOM_KNN_OVER64_K96_F9D1_SPLIT_OVERRIDE') + split_override = int(override_env) if override_env else None + _launch_over64_k96_split_path(inputs, split_override=split_override) + return + a2f8.parent_v24.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(candidate, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return a2f8.parent_v24._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=('build_over64_stress_qm1024_k96', 'build_over64_stress_qm2048_k96'), benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint: run real contract correctness for selected shapes.""" + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def benchmark_knn_build_over64_k96_f9d1_v1(*, use_cupti: bool | None=None, shape_labels=('build_over64_stress_qm1024_k96', 'build_over64_stress_qm2048_k96', 'build_over64_stress_qm4096_k96')) -> dict[str, Any]: + """Opt-in benchmark hook for the exact K96 frontier seed.""" + from .. import _dispatch_runtime as eval_mod + prior_use_cupti = eval_mod.CONTRACT.bench.get('use_cupti', True) + if use_cupti is not None: + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + try: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + return {'tflops': report['summary']['primary_mean'], 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'report': report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_over64_k96_q1024exact_e5db_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_over64_k96_q1024exact_e5db_v1.py new file mode 100644 index 00000000..51dfc1e6 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_over64_k96_q1024exact_e5db_v1.py @@ -0,0 +1,113 @@ +"""kNN build/search over-64 K96 exact Q1024 prefill seed. + +Minimum target architecture: sm_100a. This additive auto-tuning candidate +targets the exact frontier build row ``B=1, Q=M=1024, D=128, K=96, bf16``. +It keeps the q1024parity split2 tcgen05/TMA stage-1 topology and split2 K96 +merge, but specializes the stage-1 first-fill path for the no-tail Q1024 +shape: the first 96 candidates are stored directly without per-group sorting, +dynamic fill count, or DB/Q bounds checks. Q2048/Q4096 guardrail rows delegate +to the prior q1024parity/f9d1 route unchanged. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from functools import lru_cache +from typing import Any +from . import knn_build_over64_k96_f9d1_v1 as f9d1 +from . import knn_build_over64_k96_q1024parity_8c56_v1 as q1024parity +from .._dispatch_runtime import pack_kernel_args +BLOCK_Q = f9d1.BLOCK_Q +BLOCK_M = f9d1.BLOCK_M +FEAT_D = f9d1.FEAT_D +STAGE1_THREADS = f9d1.STAGE1_THREADS +MERGE_THREADS = f9d1.MERGE_THREADS +GRID_DIM_DEFAULT = f9d1.GRID_DIM_DEFAULT +CTA_GROUP = f9d1.CTA_GROUP +OVER64_TOP_K = f9d1.OVER64_TOP_K +Q1024_SPLIT_COUNT = 2 +MERGE_IR_BY_SPLIT = _decode_capture(_json_loads('{"__dict_items__": [[1, {"__ir__": "knn_build_k96_merge_s8_unordered_chunkprefill_k96over64s1chunkprefill_f9d1", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 96], ["SPLIT_COUNT", 1]], "cta_group": 1, "threads": 32}], [2, {"__ir__": "knn_build_k96_merge_s8_unordered_chunkprefill_k96over64s2chunkprefill_f9d1", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 96], ["SPLIT_COUNT", 2]], "cta_group": 1, "threads": 32}], [3, {"__ir__": "knn_build_k96_merge_s8_unordered_chunkprefill_k96over64s3chunkprefill_f9d1", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 96], ["SPLIT_COUNT", 3]], "cta_group": 1, "threads": 32}], [4, {"__ir__": "knn_build_k96_merge_s8_unordered_chunkprefill_k96over64s4chunkprefill_f9d1", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 96], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 32}], [6, {"__ir__": "knn_build_k96_merge_s8_unordered_chunkprefill_k96over64s6chunkprefill_f9d1", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 96], ["SPLIT_COUNT", 6]], "cta_group": 1, "threads": 32}], [8, {"__ir__": "knn_build_k96_merge_s8_unordered_chunkprefill_k96over64s8chunkprefill_f9d1", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 96], ["SPLIT_COUNT", 8]], "cta_group": 1, "threads": 32}]]}')) +knn_build_k96_stage1_exact_prefill_q1024 = _decode_capture(_json_loads('{"__ir__": "knn_build_k96_stage1_exact_prefill_q1024", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 96]], "cta_group": 1, "threads": 192}')) +stage1_k96_exact_prefill_q1024_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_k96_stage1_exact_prefill_q1024_k96over64exactprefillq1024_e5db", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 96]], "cta_group": 1, "threads": 192}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_OVER64_K96_E5DB_VERIFY_KERNEL') + if verify_kernel == 'stage1_q1024_exact': + return stage1_k96_exact_prefill_q1024_ir + if verify_kernel == 'merge_s2': + return MERGE_IR_BY_SPLIT[Q1024_SPLIT_COUNT] + return stage1_k96_exact_prefill_q1024_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_k96_stage1_exact_prefill_q1024_k96over64exactprefillq1024_e5db", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 96]], "cta_group": 1, "threads": 192}')) + +def _compiled_stage1_k96_q1024_exact(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0120"}')) + +def _compiled_merge_k96_s2(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0201"}')) + +def _eligible_over64_k96_q1024_build(inputs: dict[str, Any]) -> bool: + return bool(inputs.get('build', False)) and str(inputs['query'].dtype) == 'torch.bfloat16' and (str(inputs['database'].dtype) == 'torch.bfloat16') and (int(inputs['D']) == FEAT_D) and (int(inputs['K']) == OVER64_TOP_K) and (int(inputs['Q']) == 1024) and (int(inputs['M']) == 1024) and (int(inputs['B']) == 1) + +def _launch_q1024_exact_split2(inputs: dict[str, Any]) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + split_count = Q1024_SPLIT_COUNT + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_q_tile_pairs = (num_q_tiles + CTA_GROUP - 1) // CTA_GROUP + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tile_pairs * split_count + stage1_grid = min(total_work * CTA_GROUP, GRID_DIM_DEFAULT) + merge_grid = min((bsz * n_query + MERGE_THREADS - 1) // MERGE_THREADS, GRID_DIM_DEFAULT) + partial_dists, partial_indices = f9d1.a2f8.parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = f9d1.a2f8.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), bsz * n_query, BLOCK_Q, dim, dim) + tmap_database = f9d1.a2f8.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, dim, dim) + stage1_kernel = _compiled_stage1_k96_q1024_exact() + stage1_kernel.launch_cluster(grid=(stage1_grid, 1, 1), block=(STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage1_k96_exact_prefill_q1024_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tile_pairs=num_q_tile_pairs, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), cluster_dims=(CTA_GROUP, 1, 1), shared_mem=stage1_k96_exact_prefill_q1024_ir.computed_smem_bytes) + merge_ir = MERGE_IR_BY_SPLIT[Q1024_SPLIT_COUNT] + merge_kernel = _compiled_merge_k96_s2() + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_ir.computed_smem_bytes) + +def launch_from_contract_inputs(inputs: dict[str, Any]) -> None: + if _eligible_over64_k96_q1024_build(inputs): + _launch_q1024_exact_split2(inputs) + return + q1024parity.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(candidate, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return f9d1._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=('build_over64_stress_qm1024_k96', 'build_over64_stress_qm2048_k96'), benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint: run real contract correctness for selected shapes.""" + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def benchmark_knn_build_over64_k96_q1024exact_e5db_v1(*, use_cupti: bool | None=None, shape_labels=('build_over64_stress_qm1024_k96', 'build_over64_stress_qm2048_k96', 'build_over64_stress_qm4096_k96')) -> dict[str, Any]: + """Opt-in benchmark hook for the exact Q1024 K96 frontier seed.""" + from .. import _dispatch_runtime as eval_mod + prior_use_cupti = eval_mod.CONTRACT.bench.get('use_cupti', True) + if use_cupti is not None: + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + try: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + return {'tflops': report['summary']['primary_mean'], 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'report': report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_over64_k96_q1024parity_8c56_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_over64_k96_q1024parity_8c56_v1.py new file mode 100644 index 00000000..3e901a0c --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_over64_k96_q1024parity_8c56_v1.py @@ -0,0 +1,130 @@ +"""kNN build/search over-64 K96 Q1024 parity seed. + +Minimum target architecture: sm_100a. This additive auto-tuning candidate +targets the exact frontier build rows ``B=1, Q=M in {1024,2048,4096}, D=128, +K=96, bf16``. It specializes only the Q1024 row with a prefilled K96 top-k +cache in the f9d1 tcgen05/TMA stage-1 producer, and delegates the larger K96 +guard rows to f9d1 unchanged. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from functools import cache, lru_cache +from typing import Any +from . import knn_build_over64_k96_f9d1_v1 as f9d1 +from .._dispatch_runtime import pack_kernel_args +BLOCK_Q = f9d1.BLOCK_Q +BLOCK_M = f9d1.BLOCK_M +FEAT_D = f9d1.FEAT_D +STAGE1_THREADS = f9d1.STAGE1_THREADS +MERGE_THREADS = f9d1.MERGE_THREADS +GRID_DIM_DEFAULT = f9d1.GRID_DIM_DEFAULT +CTA_GROUP = f9d1.CTA_GROUP +OVER64_TOP_K = f9d1.OVER64_TOP_K +SUPPORTED_QM = f9d1.SUPPORTED_QM +DEFAULT_SPLITS_BY_QM = {1024: 2, 2048: 2, 4096: 4} +SUPPORTED_SPLIT_COUNTS = (1, 2, 3, 4, 6, 8) +MERGE_IR_BY_SPLIT = _decode_capture(_json_loads('{"__dict_items__": [[1, {"__ir__": "knn_build_k96_merge_s8_unordered_chunkprefill_k96over64s1chunkprefill_f9d1", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 96], ["SPLIT_COUNT", 1]], "cta_group": 1, "threads": 32}], [2, {"__ir__": "knn_build_k96_merge_s8_unordered_chunkprefill_k96over64s2chunkprefill_f9d1", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 96], ["SPLIT_COUNT", 2]], "cta_group": 1, "threads": 32}], [3, {"__ir__": "knn_build_k96_merge_s8_unordered_chunkprefill_k96over64s3chunkprefill_f9d1", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 96], ["SPLIT_COUNT", 3]], "cta_group": 1, "threads": 32}], [4, {"__ir__": "knn_build_k96_merge_s8_unordered_chunkprefill_k96over64s4chunkprefill_f9d1", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 96], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 32}], [6, {"__ir__": "knn_build_k96_merge_s8_unordered_chunkprefill_k96over64s6chunkprefill_f9d1", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 96], ["SPLIT_COUNT", 6]], "cta_group": 1, "threads": 32}], [8, {"__ir__": "knn_build_k96_merge_s8_unordered_chunkprefill_k96over64s8chunkprefill_f9d1", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 96], ["SPLIT_COUNT", 8]], "cta_group": 1, "threads": 32}]]}')) +knn_build_k96_stage1_sort4_prefill_q1024 = _decode_capture(_json_loads('{"__ir__": "knn_build_k96_stage1_sort4_prefill_q1024", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 96]], "cta_group": 1, "threads": 192}')) +stage1_k96_sort4_prefill_q1024_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_k96_stage1_sort4_prefill_q1024_k96over64sort4prefillq1024_8c56", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 96]], "cta_group": 1, "threads": 192}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_OVER64_K96_Q1024PARITY_VERIFY_KERNEL') + if verify_kernel == 'stage1_q1024_prefill': + return stage1_k96_sort4_prefill_q1024_ir + if verify_kernel == 'stage1': + return f9d1.a2f8.stage1_k96_sort4_chunked_over64_ir + if verify_kernel and verify_kernel.startswith('merge_s'): + split_count = int(verify_kernel.removeprefix('merge_s')) + return MERGE_IR_BY_SPLIT[split_count] + return f9d1.a2f8.stage1_k96_sort4_chunked_over64_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_k96_stage1_sort4_chunked_k96over64sort4chunked", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 96]], "cta_group": 1, "threads": 192}')) + +def _compiled_stage1_k96(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0189"}')) + +def _compiled_stage1_k96_q1024_prefill(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0202"}')) + +@cache +def _compiled_merge_k96(split_count: int): + return f9d1.a2f8.parent_v20._compile_ir(MERGE_IR_BY_SPLIT[split_count]) + +def _select_split_count(n_query: int, *, override: int | None=None) -> int: + split_count = int(override) if override is not None else DEFAULT_SPLITS_BY_QM[int(n_query)] + if split_count not in MERGE_IR_BY_SPLIT: + raise ValueError(''.join(['unsupported K96 split_count ', format(split_count, '')])) + return split_count + +def _eligible_over64_k96_build(inputs: dict[str, Any]) -> bool: + n_query = int(inputs['Q']) + return bool(inputs.get('build', False)) and str(inputs['query'].dtype) == 'torch.bfloat16' and (str(inputs['database'].dtype) == 'torch.bfloat16') and (int(inputs['D']) == FEAT_D) and (int(inputs['K']) == OVER64_TOP_K) and (int(inputs['M']) == n_query) and (n_query == 1024) and (int(inputs['B']) == 1) + +def _launch_over64_k96_split_path(inputs: dict[str, Any], *, split_override: int | None=None) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + split_count = _select_split_count(n_query, override=split_override) + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_q_tile_pairs = (num_q_tiles + CTA_GROUP - 1) // CTA_GROUP + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tile_pairs * split_count + stage1_grid = min(total_work * CTA_GROUP, GRID_DIM_DEFAULT) + merge_grid = min((bsz * n_query + MERGE_THREADS - 1) // MERGE_THREADS, GRID_DIM_DEFAULT) + partial_dists, partial_indices = f9d1.a2f8.parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = f9d1.a2f8.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), bsz * n_query, BLOCK_Q, dim, dim) + tmap_database = f9d1.a2f8.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, dim, dim) + use_q1024_prefill = n_query == 1024 and split_override is None + stage1_kernel = _compiled_stage1_k96_q1024_prefill() if use_q1024_prefill else _compiled_stage1_k96() + stage1_ir = stage1_k96_sort4_prefill_q1024_ir if use_q1024_prefill else f9d1.a2f8.stage1_k96_sort4_chunked_over64_ir + stage1_kernel.launch_cluster(grid=(stage1_grid, 1, 1), block=(STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage1_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tile_pairs=num_q_tile_pairs, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), cluster_dims=(CTA_GROUP, 1, 1), shared_mem=stage1_ir.computed_smem_bytes) + merge_ir = MERGE_IR_BY_SPLIT[split_count] + merge_kernel = _compiled_merge_k96(split_count) + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_ir.computed_smem_bytes) + +def launch_from_contract_inputs(inputs: dict[str, Any]) -> None: + if _eligible_over64_k96_build(inputs): + override_env = os.environ.get('LOOM_KNN_OVER64_K96_Q1024PARITY_SPLIT_OVERRIDE') + split_override = int(override_env) if override_env else None + _launch_over64_k96_split_path(inputs, split_override=split_override) + return + f9d1.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(candidate, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return f9d1._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=('build_over64_stress_qm1024_k96', 'build_over64_stress_qm2048_k96'), benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint: run real contract correctness for selected shapes.""" + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def benchmark_knn_build_over64_k96_q1024parity_8c56_v1(*, use_cupti: bool | None=None, shape_labels=('build_over64_stress_qm1024_k96', 'build_over64_stress_qm2048_k96', 'build_over64_stress_qm4096_k96')) -> dict[str, Any]: + """Opt-in benchmark hook for the exact K96 frontier seed.""" + from .. import _dispatch_runtime as eval_mod + prior_use_cupti = eval_mod.CONTRACT.bench.get('use_cupti', True) + if use_cupti is not None: + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + try: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + return {'tflops': report['summary']['primary_mean'], 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'report': report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_overk_largek_q4096_k32_9334_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_overk_largek_q4096_k32_9334_v1.py new file mode 100644 index 00000000..b02a0d6a --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_overk_largek_q4096_k32_9334_v1.py @@ -0,0 +1,128 @@ +"""Exact Q4096 K32 build seed for the over-K floor bucket. + +Minimum target architecture: sm_100a. This additive seed targets only the +contract row ``build_largek_stress_qm4096_k32``: BF16, B=1, Q=M=4096, D=128, +K=32, build=true. It exposes the validated v20 split-4 unordered K32 +stage-1 plus warp-select merge path directly so a dispatcher can consume it +without falling through the broad fd9b fallback route. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_1877_9a17_fp16_fd37_full90_consumption_v1 as fd9b_parent +from . import knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2_k32split_v20 as k32_seed +TARGET_SHAPE = 'build_largek_stress_qm4096_k32' +TARGET_SHAPES = (TARGET_SHAPE,) +ROUTE_ENTRYPOINT = 'loom.examples.weave.knn_build_overk_largek_q4096_k32_9334_v1:launch_from_contract_inputs' +ROUTE_Q4096_K32 = 'knn_build_overk_largek_q4096_k32_9334_v1:q4096_k32_v20_s4_unordered_warpselect' +PARENT_ENTRYPOINT = 'loom.examples.weave.knn_build_dispatch_1877_9a17_fp16_fd37_full90_consumption_v1:benchmark_candidate_fp16_fd37_full90_v1' +SEED_ID = 'overk_largek_q4096_k32_9334_v1' +Q4096 = 4096 +TOP_K = 32 +FEAT_D = k32_seed.FEAT_D +SPLIT_COUNT = k32_seed.MEDIUM_SPLITS + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_OVERK_Q4096_K32_9334_VERIFY_KERNEL') + if verify_kernel == 'stage1': + return k32_seed.stage1_k32_unordered_ir + if verify_kernel == 'merge': + return k32_seed.merge_k32_unordered_warp_select_ir + return k32_seed.stage1_k32_unordered_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 192}')) + +def _dtype_name(inputs: dict[str, Any]) -> str: + query = inputs.get('query') + if query is not None: + return str(query.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')) + +def _label_can_hit(inputs: dict[str, Any]) -> bool: + label = inputs.get('label') + return label is None or str(label) == TARGET_SHAPE + +def _eligible_q4096_k32(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs) and bool(inputs.get('build', False)) and (_dtype_name(inputs) == 'bfloat16') and (int(inputs.get('B', -1)) == 1) and (int(inputs.get('Q', -1)) == Q4096) and (int(inputs.get('M', -1)) == Q4096) and (int(inputs.get('D', -1)) == FEAT_D) and (int(inputs.get('K', -1)) == TOP_K) + +def route_for_contract_inputs(inputs: dict[str, Any]) -> str: + if _eligible_q4096_k32(inputs): + return ROUTE_Q4096_K32 + raise ValueError('knn_build_overk_largek_q4096_k32_9334_v1 only supports build_largek_stress_qm4096_k32') + +def launch_from_contract_inputs(inputs: dict[str, Any]) -> None: + route_for_contract_inputs(inputs) + k32_seed._launch_k32_split_path(inputs, split_count=SPLIT_COUNT) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def candidate_parent_fd9b(inputs: dict[str, Any]): + fd9b_parent.launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels=TARGET_SHAPES): + wanted = {str(label) for label in shape_labels} + selected = [shape for shape in eval_mod.CANONICAL_SHAPES if shape['label'] in wanted] + missing = wanted - {shape['label'] for shape in selected} + if missing: + raise ValueError(''.join(['unknown kNN build contract shape(s): ', format(sorted(missing), '')])) + return selected + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + previous = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(TARGET_SHAPES), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = previous + +def _trace_inputs_from_shape(shape: dict[str, Any]) -> dict[str, Any]: + params = dict(shape['params']) + return {'label': shape['label'], 'B': int(params['B']), 'Q': int(params['Q']), 'M': int(params['M']), 'D': int(params['D']), 'K': int(params['K']), 'dtype': str(params.get('dtype', 'bfloat16')), 'build': bool(params.get('build', False))} + +def route_trace_for_contract_shapes() -> list[dict[str, Any]]: + rows = [] + for shape in _select_contract_shapes(TARGET_SHAPES): + inputs = _trace_inputs_from_shape(shape) + route = route_for_contract_inputs(inputs) + rows.append({'shape_key': shape['label'], 'selected_route': route, 'selected_entrypoint': ROUTE_ENTRYPOINT, 'selected_seed': SEED_ID, 'expected_seed': SEED_ID, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_condition': 'exact BF16 build B=1 Q=M=4096 D=128 K=32 split4 unordered warp-select route', 'parent_selected_route': fd9b_parent.route_for_contract_inputs(inputs)}) + return rows + +def _per_shape_delta(candidate_report: dict[str, Any], parent_report: dict[str, Any]) -> dict[str, Any]: + cand = candidate_report.get('per_shape', {}).get(TARGET_SHAPE, {}) + parent = parent_report.get('per_shape', {}).get(TARGET_SHAPE, {}) + cand_ms = cand.get('kernel_ms') + parent_ms = parent.get('kernel_ms') + return {TARGET_SHAPE: {'candidate_ms': cand_ms, 'parent_fd9b_ms': parent_ms, 'flashlib_ms': cand.get('flashlib_ms'), 'candidate_tflops': cand.get('tflops'), 'parent_fd9b_tflops': parent.get('tflops'), 'speedup_vs_parent_fd9b': parent_ms / cand_ms if cand_ms and parent_ms else None, 'ratio_vs_flashlib': cand.get('ratio_vs_flashlib'), 'passed': cand.get('passed'), 'timing_backend': cand.get('timing_backend')}} + +def benchmark_knn_build_overk_largek_q4096_k32_9334_v1(*, use_cupti: bool=True, run_baseline: bool=True) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, kernel_fn=candidate) + parent_report = None + if run_baseline: + parent_report = _run_with_timing_backend(use_cupti=use_cupti, kernel_fn=candidate_parent_fd9b) + payload: dict[str, Any] = {'contract': candidate_report['contract'], 'contract_version': candidate_report['contract_version'], 'tflops': candidate_report['summary']['primary_mean'] or 0.0, 'all_correct': candidate_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'measured_entrypoint': 'loom.examples.weave.knn_build_overk_largek_q4096_k32_9334_v1:benchmark_knn_build_overk_largek_q4096_k32_9334_v1', 'candidate_entrypoint': ROUTE_ENTRYPOINT, 'measured_shape_labels': list(TARGET_SHAPES), 'route_trace': route_trace_for_contract_shapes(), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'split_counts': {'q4096_k32': SPLIT_COUNT}, 'report': candidate_report, 'summary': candidate_report['summary'], 'performance': candidate_report['performance'], 'correctness': candidate_report['correctness']} + if parent_report is not None: + payload['baseline_entrypoint'] = PARENT_ENTRYPOINT + payload['baseline_summary'] = parent_report['summary'] + payload['per_shape_delta_vs_fd9b_parent'] = _per_shape_delta(candidate_report, parent_report) + return payload diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_q1024_k8_195e_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_q1024_k8_195e_v1.py new file mode 100644 index 00000000..ef058517 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_q1024_k8_195e_v1.py @@ -0,0 +1,219 @@ +"""Exact Q1024/K8 kNN build bucket seed for the 195e auto-tuning lane. + +Minimum target architecture: sm_100a. This additive seed does not edit the +production dispatcher. It routes only the exact BF16 build row +``B=1,Q=M=1024,D=128,K=8`` through the existing tcgen05/TMA split producer and +an exact split-count merge; all other shapes delegate to the inherited broad +Weave dispatcher. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from collections.abc import Callable +from functools import lru_cache +from pathlib import Path +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_split72_4e09_de1a_3dc7_v48 as fallback +from . import knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2_k32split_v20 as fixed_build +from .._dispatch_runtime import pack_kernel_args +MODULE = 'loom.examples.weave.knn_build_q1024_k8_195e_v1' +CANDIDATE_ID = 'q1024_k8_195e_v1' +TARGET_SHAPES = ('build_qm1024_d128_k8',) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +SPLIT_CHOICES = (4, 8, 16) +DEFAULT_SPLIT_COUNT = 16 +ROUTE_PREFIX = MODULE +ROUTE_FALLBACK = 'loom.examples.weave.knn_build_dispatch_split72_4e09_de1a_3dc7_v48:launch_from_contract_inputs' +stage1_k8_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k8split", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 8]], "cta_group": 1, "threads": 192}')) +merge_k8_s4_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k32_merge_s4_rowbase_cache_k8split", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "K", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 8]], "cta_group": 1, "threads": 32}')) +merge_k8_s8_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_k8s8", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 8], ["SPLIT_COUNT", 8]], "cta_group": 1, "threads": 32}')) +merge_k8_s16_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_195e_q1024k8s16", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 8], ["SPLIT_COUNT", 16]], "cta_group": 1, "threads": 32}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_Q1024_K8_195E_VERIFY_KERNEL') + if verify_kernel == 'stage1_k8': + return stage1_k8_ir + if verify_kernel == 'merge_k8_s4': + return merge_k8_s4_ir + if verify_kernel == 'merge_k8_s8': + return merge_k8_s8_ir + if verify_kernel == 'merge_k8_s16': + return merge_k8_s16_ir + return stage1_k8_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k8split", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 8]], "cta_group": 1, "threads": 192}')) + +def _check_split_count(split_count: int) -> int: + split_count = int(split_count) + if split_count not in SPLIT_CHOICES: + raise ValueError(''.join(['unsupported Q1024/K8 split count: ', format(split_count, '')])) + return split_count + +@lru_cache(maxsize=3) +def _compiled_merge_k8(split_count: int): + split_count = _check_split_count(split_count) + if split_count == 4: + return fixed_build._compiled_merge_for_bucket(8) + if split_count == 8: + return fixed_build._compiled_merge_k8_s8() + return fixed_build._compile_ir(merge_k8_s16_ir) + +def _merge_ir(split_count: int) -> Any: + split_count = _check_split_count(split_count) + if split_count == 4: + return merge_k8_s4_ir + if split_count == 8: + return merge_k8_s8_ir + return merge_k8_s16_ir + +def _dtype_name(inputs: dict[str, Any], name: str='query') -> str: + tensor = inputs.get(name) + if tensor is not None and hasattr(tensor, 'dtype'): + return str(tensor.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')).replace('torch.', '') + +def _label_can_hit(inputs: dict[str, Any], labels: set[str]) -> bool: + label = inputs.get('label') + return label is None or str(label) in labels + +def _eligible_q1024_k8(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, TARGET_SHAPE_SET) and bool(inputs.get('build', False)) and (int(inputs.get('B', -1)) == 1) and (int(inputs.get('Q', -1)) == 1024) and (int(inputs.get('M', -2)) == 1024) and (int(inputs.get('D', -1)) == fixed_build.FEAT_D) and (int(inputs.get('K', -1)) == 8) and (_dtype_name(inputs, 'query') == 'bfloat16') and (_dtype_name(inputs, 'database') in ('', 'bfloat16')) + +def _launch_q1024_k8_split(inputs: dict[str, Any], *, split_count: int) -> None: + split_count = _check_split_count(split_count) + if split_count == 8: + fixed_build._launch_k32_split_path(inputs, split_count=split_count) + return + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + num_q_tiles = (n_query + fixed_build.BLOCK_Q - 1) // fixed_build.BLOCK_Q + num_q_tile_pairs = (num_q_tiles + fixed_build.CTA_GROUP - 1) // fixed_build.CTA_GROUP + num_db_tiles = (n_database + fixed_build.BLOCK_M - 1) // fixed_build.BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tile_pairs * split_count + stage1_grid = min(total_work * fixed_build.CTA_GROUP, fixed_build.GRID_DIM_DEFAULT) + merge_grid = min((bsz * n_query + fixed_build.K32_MERGE_THREADS - 1) // fixed_build.K32_MERGE_THREADS, fixed_build.GRID_DIM_DEFAULT) + partial_dists, partial_indices = fixed_build.parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = fixed_build.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), bsz * n_query, fixed_build.BLOCK_Q, dim, dim) + tmap_database = fixed_build.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, fixed_build.BLOCK_M, dim, dim) + stage1_kernel = fixed_build._compiled_stage1_for_bucket(8) + stage1_kernel.launch_cluster(grid=(stage1_grid, 1, 1), block=(fixed_build.STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage1_k8_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tile_pairs=num_q_tile_pairs, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), cluster_dims=(fixed_build.CTA_GROUP, 1, 1), shared_mem=stage1_k8_ir.computed_smem_bytes) + merge_ir_obj = _merge_ir(split_count) + merge_kernel = _compiled_merge_k8(split_count) + if split_count == 4: + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(fixed_build.K32_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], top_k, bsz * n_query], shared_mem=merge_ir_obj.computed_smem_bytes) + return + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(fixed_build.K32_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_ir_obj.computed_smem_bytes) + +def route_for_contract_inputs(inputs: dict[str, Any], *, split_count: int=DEFAULT_SPLIT_COUNT) -> str: + if _eligible_q1024_k8(inputs): + return ''.join([format(ROUTE_PREFIX, ''), ':q1024_k8_s', format(_check_split_count(split_count), '')]) + return ROUTE_FALLBACK + +def launch_from_contract_inputs(inputs: dict[str, Any], *, split_count: int=DEFAULT_SPLIT_COUNT, force_fallback: bool=False) -> None: + if not force_fallback and _eligible_q1024_k8(inputs): + _launch_q1024_k8_split(inputs, split_count=split_count) + return + fallback.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_for_policy(*, split_count: int=DEFAULT_SPLIT_COUNT) -> Callable[[dict[str, Any]], None]: + split_count = _check_split_count(split_count) + + def _candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, split_count=split_count) + return _candidate + +def candidate_fallback(inputs: dict[str, Any]) -> None: + fallback.launch_from_contract_inputs(inputs) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels=None): + wanted = TARGET_SHAPE_SET if shape_labels is None else {str(label) for label in shape_labels} + selected = [shape for shape in eval_mod.CANONICAL_SHAPES if shape['label'] in wanted] + missing = wanted - {shape['label'] for shape in selected} + if missing: + raise ValueError(''.join(['unknown kNN build contract shape(s): ', format(sorted(missing), '')])) + return selected + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + previous = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(TARGET_SHAPES), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = previous + +def _trace_inputs_from_shape(shape: dict[str, Any]) -> dict[str, Any]: + params = dict(shape['params']) + dtype = str(params.get('dtype', 'bfloat16')) + return {'label': shape['label'], 'B': int(params['B']), 'Q': int(params['Q']), 'M': int(params['M']), 'D': int(params['D']), 'K': int(params['K']), 'dtype': dtype, 'build': bool(params.get('build', False))} + +def route_trace_for_shapes(*, split_count: int=DEFAULT_SPLIT_COUNT) -> list[dict[str, Any]]: + split_count = _check_split_count(split_count) + trace = [] + for shape in _select_contract_shapes(TARGET_SHAPES): + inputs = _trace_inputs_from_shape(shape) + route = route_for_contract_inputs(inputs, split_count=split_count) + trace.append({'shape_key': shape['label'], 'selected_route': route, 'route_kind': 'specialized' if route.startswith(ROUTE_PREFIX) else 'fallback', 'guard_condition': ''.join(['exact BF16 build B1 Q=M=1024 D128 K8 split', format(split_count, '')]) if route.startswith(ROUTE_PREFIX) else 'guard miss; delegate to inherited Weave fallback', 'consumed_seed': CANDIDATE_ID if route.startswith(ROUTE_PREFIX) else None, 'fallback': ROUTE_FALLBACK}) + return trace + +def _per_shape_delta(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> dict[str, Any]: + label = TARGET_SHAPES[0] + cand = candidate_report.get('per_shape', {}).get(label, {}) + base = baseline_report.get('per_shape', {}).get(label, {}) + cand_ms = cand.get('kernel_ms') + base_ms = base.get('kernel_ms') + return {'candidate_ms': cand_ms, 'baseline_ms': base_ms, 'flashlib_ms': cand.get('flashlib_ms'), 'candidate_tflops': cand.get('tflops'), 'baseline_tflops': base.get('tflops'), 'speedup_vs_fallback': base_ms / cand_ms if cand_ms and base_ms else None, 'ratio_vs_flashlib': cand.get('ratio_vs_flashlib'), 'passed': cand.get('passed'), 'timing_backend': cand.get('timing_backend')} + +def _scan_split_counts(*, use_cupti: bool) -> dict[str, Any]: + scan: dict[str, Any] = {} + for split_count in SPLIT_CHOICES: + report = _run_with_timing_backend(use_cupti=use_cupti, kernel_fn=candidate_for_policy(split_count=split_count)) + scan[str(split_count)] = report['per_shape'][TARGET_SHAPES[0]] + return scan + +def benchmark_knn_build_q1024_k8_195e_v1(*, use_cupti: bool=True, split_count: int=DEFAULT_SPLIT_COUNT, run_baseline: bool=True, scan_splits: bool=False) -> dict[str, Any]: + split_count = _check_split_count(split_count) + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, kernel_fn=candidate_for_policy(split_count=split_count)) + baseline_report = None + if run_baseline: + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, kernel_fn=candidate_fallback) + payload: dict[str, Any] = {'tflops': candidate_report['summary']['primary_mean'] or 0.0, 'all_correct': candidate_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_knn_build_q1024_k8_195e_v1']), 'candidate_entrypoint': ''.join([format(MODULE, ''), ':launch_from_contract_inputs']), 'measured_shape_labels': TARGET_SHAPES, 'route_trace': route_trace_for_shapes(split_count=split_count), 'contract_summary': candidate_report['summary'], 'contract_performance': candidate_report['performance'], 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'split_count': split_count, 'split_scan': _scan_split_counts(use_cupti=use_cupti) if scan_splits else {}, 'shape_dispatch_registry': {'available_shape_kernels': [{'shape_key': TARGET_SHAPES[0], 'guard': 'BF16 build B=1 Q=M=1024 D=128 K=8', 'candidate_entrypoint': ''.join([format(MODULE, ''), ':launch_from_contract_inputs']), 'kernel_ref': CANDIDATE_ID, 'correctness': 'pass' if candidate_report['summary']['all_correct'] else 'fail', 'timing_backend': next((row.get('timing_backend') for row in candidate_report.get('per_shape', {}).values() if row.get('timing_backend')), None), 'benchmark_evidence': ''.join([format(MODULE, ''), ':benchmark_knn_build_q1024_k8_195e_v1'])}]}, 'report': candidate_report} + if baseline_report is not None: + payload['baseline_entrypoint'] = ROUTE_FALLBACK + payload['baseline_summary'] = baseline_report['summary'] + payload['per_shape_delta_vs_fallback'] = {TARGET_SHAPES[0]: _per_shape_delta(candidate_report, baseline_report)} + baseline_mean = baseline_report['summary']['primary_mean'] + payload['speedup_vs_fallback_primary_mean'] = candidate_report['summary']['primary_mean'] / baseline_mean if baseline_mean else None + return payload + +def write_benchmark_artifact(path: str | os.PathLike[str], **kwargs) -> dict[str, Any]: + payload = benchmark_knn_build_q1024_k8_195e_v1(**kwargs) + out = Path(path) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + return payload diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_q1m524_workfeed_q1m524_workfeed_codex_v3.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_q1m524_workfeed_q1m524_workfeed_codex_v3.py new file mode 100644 index 00000000..c100be23 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_q1m524_workfeed_q1m524_workfeed_codex_v3.py @@ -0,0 +1,162 @@ +"""Q1 online RAG K10 M524 dispatcher with validated S147/G7 production route. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +specializes only ``rag_online_irregular_b1_q1_m524287_d128_k10``. It preserves +the EA43 M64/N128 tcgen05/TMA stage-1 producer and fuses each seven-list local +merge directly into its warp-wide exact K10 selection. The 21 group frontiers +stay in registers, avoiding the intermediate shared-memory group-list handoff. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import json +import os +from collections.abc import Callable +from .._dispatch_runtime import _replace as replace +from functools import lru_cache +from pathlib import Path +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_ragonline_mbucket_ea43_q1m524_n128_v1 as ea43 +from .._dispatch_runtime import pack_kernel_args +MODULE = 'loom.examples.weave.knn_build_q1m524_workfeed_q1m524_workfeed_codex_v3' +ONLINE_M524K_SHAPE = ea43.ONLINE_M524K_SHAPE +TARGET_SHAPES = (ONLINE_M524K_SHAPE,) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +Q1_S147_SPLIT = 147 +Q1_S147_GROUPS = 21 +Q1_S147_GROUP_SPLITS = Q1_S147_SPLIT // Q1_S147_GROUPS +Q1_S147_MERGE_THREADS = ea43.fused_merge_parent.K10_FUSED_MERGE_THREADS +Q1_S147_VALIDATED_GROUPS = 7 +knn_build_q1m524_workfeed_s147_g21_register_merge = _decode_capture(_json_loads('{"__ir__": "knn_build_q1m524_workfeed_s147_g21_register_merge", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["GROUP_COUNT", 21], ["GROUP_SPLITS", 7]], "cta_group": 1, "threads": 32}')) + +def _fused_merge_ir() -> Any: + constants = tuple(((name, {'GROUP_COUNT': Q1_S147_GROUPS, 'GROUP_SPLITS': Q1_S147_GROUP_SPLITS}.get(name, value)) for name, value in knn_build_q1m524_workfeed_s147_g21_register_merge.constants)) + return replace(knn_build_q1m524_workfeed_s147_g21_register_merge, symbol='knn_build_q1m524_workfeed_s147_g21_register_merge', constants=constants) + +def _compiled_fused_merge(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0232"}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_Q1M524_WORKFEED_CODEX_VERIFY_KERNEL') + if verify_kernel == 'stage1_q1_k10_m64n128': + return ea43.stage1_q1_k10_m64n128_ir + if verify_kernel == 'fused_merge': + return _fused_merge_ir() + return ea43.ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_ragonline_mbucket_4fc7_q1m262_v2_stage1_q1_k10_m64_halfrow", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 36608, "constants": [["BLOCK_Q", 64], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 10], ["ROWS_COVERED", 1]], "cta_group": 1, "threads": 96}')) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if not force_fallback and ea43._eligible_q1_m524_n128(inputs): + return 'rag_online_mbucket_206_q1m524_n128_s147_g7_exacttile' + return ea43.route_for_contract_inputs(inputs, force_fallback=force_fallback) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + if not force_fallback and ea43._eligible_q1_m524_n128(inputs): + ea43._launch_q1_m524_n128(inputs, split_count=Q1_S147_SPLIT, group_count=Q1_S147_VALIDATED_GROUPS) + return + ea43.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def _launch_q1_m524_s147_g21(inputs: dict[str, Any]) -> None: + query = inputs['query'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + num_q_tiles = (n_query + ea43.Q1_N128_BLOCK_Q - 1) // ea43.Q1_N128_BLOCK_Q + num_db_tiles = (n_database + ea43.Q1_N128_BLOCK_M - 1) // ea43.Q1_N128_BLOCK_M + db_tiles_per_split = (num_db_tiles + Q1_S147_SPLIT - 1) // Q1_S147_SPLIT + total_work = bsz * num_q_tiles * Q1_S147_SPLIT + total_queries = bsz * n_query + stage1_grid = min(total_work, ea43.q1base.parent.parent.parent_lowk.GRID_DIM_DEFAULT) + merge_grid = min(total_queries, ea43.q1base.parent.parent.parent_lowk.GRID_DIM_DEFAULT) + partial_dists, partial_indices = ea43.q1base.parent.parent.parent_lowk.parent_split._partial_buffers(split_count=Q1_S147_SPLIT, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = ea43.q1base.parent.parent.parent_lowk.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), total_queries, ea43.Q1_N128_BLOCK_Q, dim, dim) + tmap_database = ea43.q1base.parent.parent.parent_lowk.base_v1._create_tensor_map_3d_oob_zero(inputs['database'].data_ptr(), bsz * n_database, ea43.Q1_N128_BLOCK_M, dim, dim) + ea43._compiled_stage1_q1_k10_m64n128().launch(grid=(stage1_grid, 1, 1), block=(ea43.Q1_N128_STAGE1_THREADS, 1, 1), args=pack_kernel_args(ea43.stage1_q1_k10_m64n128_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tiles=num_q_tiles, db_tiles_per_split=db_tiles_per_split, split_count=Q1_S147_SPLIT, total_work=total_work), shared_mem=ea43.stage1_q1_k10_m64n128_ir.computed_smem_bytes) + merge_ir = _fused_merge_ir() + _compiled_fused_merge().launch(grid=(merge_grid, 1, 1), block=(Q1_S147_MERGE_THREADS, 1, 1), args=pack_kernel_args(merge_ir, partial_dists=partial_dists, partial_indices=partial_indices, out_dists=inputs['out_dists'], out_indices=inputs['out_indices'], total_queries=total_queries), shared_mem=merge_ir.computed_smem_bytes) + +def candidate_parent_s147_g7(inputs: dict[str, Any]): + if ea43._eligible_q1_m524_n128(inputs): + ea43._launch_q1_m524_n128(inputs, split_count=Q1_S147_SPLIT, group_count=7) + return None + ea43.launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return ea43._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + return evaluate_contract(shapes=selected, correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + rows = [] + for shape in selected: + inputs = ea43.base5706._trace_inputs_from_shape(shape) + route = route_for_contract_inputs(inputs, force_fallback=force_fallback) + specialized = route.startswith('rag_online_mbucket_workfeed_q1m524') + row = {'shape_key': shape['label'], 'selected_route': route, 'route_kind': 'specialized_m64n128_s147_exacttile' if specialized else 'inherited_ea43', 'guard_condition': 'Q1 BF16 online exact M524 M64/N128 K10 producer with S147/G21 merge group heads' if specialized else 'delegate to EA43'} + if specialized: + row['split_count'] = Q1_S147_SPLIT + row['group_count'] = Q1_S147_VALIDATED_GROUPS + rows.append(row) + return rows + +def _benchmark_payload(candidate_report: dict[str, Any], parent_report: dict[str, Any], *, use_cupti: bool, shape_labels) -> dict[str, Any]: + rows = candidate_report.get('per_shape', {}) + parent_rows = parent_report.get('per_shape', {}) + target_rows = {} + for label in TARGET_SHAPES: + cand = rows.get(label, {}) + parent = parent_rows.get(label, {}) + cand_ms = cand.get('kernel_ms') + parent_ms = parent.get('kernel_ms') + target_rows[label] = {'candidate_s147_g7': cand, 'parent_s147_g7': parent, 'candidate_ms': cand_ms, 'parent_ms': parent_ms, 'speedup_vs_parent_s147_g7': parent_ms / cand_ms if cand_ms and parent_ms else None, 'ratio_vs_flashlib': cand.get('ratio_vs_flashlib')} + timing_backends = sorted({row.get('timing_backend') for report in (candidate_report, parent_report) for row in report.get('per_shape', {}).values() if row.get('timing_backend') is not None}) + return {'tflops': candidate_report['summary']['primary_mean'] or 0.0, 'all_correct': candidate_report['summary']['all_correct'], 'parent_all_correct': parent_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'parent_performance_comparable': parent_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_knn_build_q1m524_workfeed_q1m524_workfeed_codex_v3']), 'parent_entrypoint': 'loom.examples.weave.knn_build_ragonline_mbucket_206_q1m524_s147_v1:candidate', 'measured_shape_labels': 'all_canonical' if shape_labels is None else tuple(shape_labels), 'accelerated_shape_labels': list(TARGET_SHAPES), 'topology': {'split_count': Q1_S147_SPLIT, 'group_count': Q1_S147_VALIDATED_GROUPS, 'stage1_threads': ea43.Q1_N128_STAGE1_THREADS, 'block_q': ea43.Q1_N128_BLOCK_Q, 'block_m': ea43.Q1_N128_BLOCK_M, 'rows_covered': ea43.Q1_N128_ROWS_COVERED}, 'route_trace': route_trace_for_contract_shapes(shape_labels), 'route_trace_included': True, 'target_rows': target_rows, 'contract_summary': candidate_report['summary'], 'parent_contract_summary': parent_report['summary'], 'contract_performance': candidate_report['performance'], 'parent_contract_performance': parent_report['performance'], 'timing_backends': timing_backends, 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'report': candidate_report, 'parent_report': parent_report} + +def benchmark_knn_build_q1m524_workfeed_q1m524_workfeed_codex_v3(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels) + parent_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_parent_s147_g7) + return _benchmark_payload(candidate_report, parent_report, use_cupti=use_cupti, shape_labels=shape_labels) + +def write_benchmark_artifacts(artifact_dir: str | os.PathLike[str], *, use_cupti: bool=True) -> dict[str, Any]: + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + suffix = 'cupti' if use_cupti else 'cuda_event' + payload = benchmark_knn_build_q1m524_workfeed_q1m524_workfeed_codex_v3(use_cupti=use_cupti) + candidate_path = out_dir / ''.join(['q1m524_s147_g21_register_merge_1row_', format(suffix, ''), '.json']) + candidate_path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + summary = {'artifact_dir': str(out_dir), 'artifacts': {'candidate': str(candidate_path)}, 'candidate_summary': payload['contract_summary'], 'parent_summary': payload['parent_contract_summary'], 'target_rows': payload['target_rows']} + summary_path = out_dir / ''.join(['q1m524_s147_g21_register_merge_summary_1row_', format(suffix, ''), '.json']) + summary_path.write_text(json.dumps(summary, indent=2, sort_keys=True) + '\n') + summary['artifacts']['summary'] = str(summary_path) + return summary diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_q4096_k8_lowfloor_fd9b_v3.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_q4096_k8_lowfloor_fd9b_v3.py new file mode 100644 index 00000000..1a222add --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_q4096_k8_lowfloor_fd9b_v3.py @@ -0,0 +1,196 @@ +"""Exact Q4096/M4096 K8 build seed with K8 unordered prefill. + +Minimum target architecture: sm_100a. This additive fd9b bucket-kernel +candidate targets only ``build_qm4096_d128_k8``. It keeps the four-split +tcgen05/TMA stage-1 topology from the v20 kNN build lineage, uses unordered +split-local K8 state, and merges the four split-local K8 vectors with a +warp-register repeated-min selector. The v3 variant adds an exact no-tail +stage-1 prefill for the first eight database candidates in each split. + +FlashLib is used only by the contract harness as a black-box timing peer. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from collections.abc import Callable +from functools import lru_cache +from pathlib import Path +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_784a_6bc3_k8_q512k456_q4096k8_s4_direct_c3bf_v1 as c3bf +from . import knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2_k32split_v20 as v20 +from .._dispatch_runtime import pack_kernel_args +MODULE = 'loom.examples.weave.knn_build_q4096_k8_lowfloor_fd9b_v3' +TARGET_SHAPE = 'build_qm4096_d128_k8' +TARGET_SHAPES = (TARGET_SHAPE,) +SEED_ID = 'q4096_k8_lowfloor_fd9b_v3_exact_prefill_s4' +GENERIC_UNORDERED_SEED_ID = 'q4096_k8_lowfloor_fd9b_v3_generic_unordered_s4' +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +BASELINE_C3BF_ENTRYPOINT = 'loom.examples.weave.knn_build_dispatch_784a_6bc3_k8_q512k456_q4096k8_s4_direct_c3bf_v1:launch_from_contract_inputs' +SPLIT_COUNT = v20.MEDIUM_SPLITS +TOP_K = 8 +PRODUCTION_ROUTE_MODULES = {SEED_ID: ROUTE_ENTRYPOINT, GENERIC_UNORDERED_SEED_ID: ROUTE_ENTRYPOINT, 'baseline_c3bf_split4_static': BASELINE_C3BF_ENTRYPOINT} +knn_build_q4096_k8_fd9b_stage1_unordered_exact_prefill = _decode_capture(_json_loads('{"__ir__": "knn_build_q4096_k8_fd9b_stage1_unordered_exact_prefill", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 8]], "cta_group": 1, "threads": 192}')) +knn_build_q4096_k8_fd9b_merge_s4_unordered_warp_select = _decode_capture(_json_loads('{"__ir__": "knn_build_q4096_k8_fd9b_merge_s4_unordered_warp_select", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 8], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 128}')) +stage1_k8_unordered_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_fd9b_k8unordered", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 8]], "cta_group": 1, "threads": 192}')) +stage1_k8_exact_prefill_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_q4096_k8_fd9b_stage1_unordered_exact_prefill", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 8]], "cta_group": 1, "threads": 192}')) +merge_k8_warp_select_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_q4096_k8_fd9b_merge_s4_unordered_warp_select", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 8], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 128}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_Q4096K8_FD9B_V3_VERIFY_KERNEL') + if verify_kernel == 'stage1_unordered': + return stage1_k8_unordered_ir + if verify_kernel == 'stage1_exact_prefill': + return stage1_k8_exact_prefill_ir + return merge_k8_warp_select_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_q4096_k8_fd9b_merge_s4_unordered_warp_select", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 8], ["SPLIT_COUNT", 4]], "cta_group": 1, "threads": 128}')) + +def _compiled_stage1_k8_unordered(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0220"}')) + +def _compiled_stage1_k8_exact_prefill(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0015"}')) + +def _compiled_merge_k8_warp_select(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0016"}')) + +def _dtype_name(inputs: dict[str, Any], name: str) -> str: + tensor = inputs.get(name) + if tensor is not None: + return str(tensor.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')) + +def _label_can_hit(inputs: dict[str, Any]) -> bool: + label = inputs.get('label') + return label is None or str(label) == TARGET_SHAPE + +def _eligible_q4096_k8(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs) and bool(inputs.get('build', False)) and (int(inputs.get('B', -1)) == 1) and (int(inputs.get('Q', -1)) == 4096) and (int(inputs.get('M', -1)) == 4096) and (int(inputs.get('D', -1)) == v20.FEAT_D) and (int(inputs.get('K', -1)) == TOP_K) and (_dtype_name(inputs, 'query') == 'bfloat16') and (_dtype_name(inputs, 'database') == 'bfloat16') + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if not force_fallback and _eligible_q4096_k8(inputs): + return ROUTE_ENTRYPOINT + return c3bf.route_for_contract_inputs(inputs) + +def _launch_q4096_k8_unordered(inputs: dict[str, Any], *, exact_prefill: bool) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + split_count = SPLIT_COUNT + num_q_tiles = (n_query + v20.BLOCK_Q - 1) // v20.BLOCK_Q + num_q_tile_pairs = (num_q_tiles + v20.CTA_GROUP - 1) // v20.CTA_GROUP + num_db_tiles = (n_database + v20.BLOCK_M - 1) // v20.BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tile_pairs * split_count + stage1_grid = min(total_work * v20.CTA_GROUP, v20.GRID_DIM_DEFAULT) + merge_grid = (bsz * n_query + 3) // 4 + partial_dists, partial_indices = v20.parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = v20.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), bsz * n_query, v20.BLOCK_Q, dim, dim) + tmap_database = v20.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, v20.BLOCK_M, dim, dim) + stage1_ir_obj = stage1_k8_exact_prefill_ir if exact_prefill else stage1_k8_unordered_ir + stage1_kernel = _compiled_stage1_k8_exact_prefill() if exact_prefill else _compiled_stage1_k8_unordered() + stage1_kernel.launch_cluster(grid=(stage1_grid, 1, 1), block=(v20.STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage1_ir_obj, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tile_pairs=num_q_tile_pairs, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), cluster_dims=(v20.CTA_GROUP, 1, 1), shared_mem=stage1_ir_obj.computed_smem_bytes) + merge_kernel = _compiled_merge_k8_warp_select() + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(v20.K32_COOP_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_k8_warp_select_ir.computed_smem_bytes) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False, variant: str='exact_prefill') -> None: + if not force_fallback and _eligible_q4096_k8(inputs): + if variant == 'generic_unordered': + _launch_q4096_k8_unordered(inputs, exact_prefill=False) + return + if variant == 'exact_prefill': + _launch_q4096_k8_unordered(inputs, exact_prefill=True) + return + raise ValueError(''.join(['unknown fd9b q4096/k8 variant ', format(repr(variant), '')])) + c3bf.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, variant='exact_prefill') + +def candidate_generic_unordered(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, variant='generic_unordered') + +def candidate_baseline_c3bf(inputs: dict[str, Any]) -> None: + c3bf.launch_from_contract_inputs(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return c3bf._select_contract_shapes(shape_labels) + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=None, kernel_fn: Callable[[dict[str, Any]], Any]=candidate, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + shapes = None if shape_labels is None else _select_contract_shapes(shape_labels) + return evaluate_contract(shapes=shapes, correctness=correctness, benchmark=benchmark, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = _run_with_timing_backend(use_cupti=True, shape_labels=shape_labels, benchmark=benchmark, correctness=True) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + rows = [] + for shape in selected: + inputs = c3bf._trace_inputs_for_shape(shape) + route = route_for_contract_inputs(inputs, force_fallback=force_fallback) + if route == ROUTE_ENTRYPOINT: + row = {'shape_key': shape['label'], 'selected_route': route, 'selected_entrypoint': ROUTE_ENTRYPOINT, 'selected_seed': SEED_ID, 'expected_seed': SEED_ID, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': 'fd9b_q4096_k8_exact_prefill_s4_guard', 'guard_condition': 'exact BF16 build B=1 Q=M=4096 D=128 K=8 split4 route', 'base_c3bf_route': c3bf.route_for_contract_inputs(inputs), 'classification': 'unmeasured'} + else: + row = c3bf.route_trace_for_contract_shapes((shape['label'],), force_fallback=force_fallback)[0] + row = dict(row) + row['candidate_guard_status'] = 'forced_fallback_or_guard_miss' + rows.append(c3bf._normalize_route_row(row)) + return rows + +def _per_shape_rows(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return {label: dict(report.get('per_shape', {}).get(label, {})) for label in labels} + +def _row_delta(candidate_report: dict[str, Any], baseline_report: dict[str, Any], generic_report: dict[str, Any]): + candidate_row = candidate_report.get('per_shape', {}).get(TARGET_SHAPE, {}) + baseline_row = baseline_report.get('per_shape', {}).get(TARGET_SHAPE, {}) + generic_row = generic_report.get('per_shape', {}).get(TARGET_SHAPE, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + generic_ms = generic_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') or baseline_row.get('flashlib_ms') or generic_row.get('flashlib_ms') + return {'shape_key': TARGET_SHAPE, 'candidate_ms': candidate_ms, 'baseline_c3bf_ms': baseline_ms, 'generic_unordered_ms': generic_ms, 'flashlib_ms': flashlib_ms, 'speedup_vs_c3bf': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'speedup_vs_generic_unordered': generic_ms / candidate_ms if candidate_ms and generic_ms else None, 'ratio_vs_flashlib': flashlib_ms / candidate_ms if candidate_ms and flashlib_ms else None, 'candidate_passed': candidate_row.get('passed'), 'baseline_c3bf_passed': baseline_row.get('passed'), 'generic_unordered_passed': generic_row.get('passed'), 'timing_backend': candidate_row.get('timing_backend') or baseline_row.get('timing_backend') or generic_row.get('timing_backend')} + +def benchmark_knn_build_q4096_k8_lowfloor_fd9b_v3(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES, benchmark_correctness: bool=True) -> dict[str, Any]: + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_baseline_c3bf, correctness=benchmark_correctness, benchmark=True) + generic_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_generic_unordered, correctness=benchmark_correctness, benchmark=True) + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate, correctness=benchmark_correctness, benchmark=True) + candidate_metric = candidate_report.get('summary', {}).get('primary_mean') + baseline_metric = baseline_report.get('summary', {}).get('primary_mean') + generic_metric = generic_report.get('summary', {}).get('primary_mean') + labels = tuple(shape_labels or TARGET_SHAPES) + return {'candidate_id': SEED_ID, 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_knn_build_q4096_k8_lowfloor_fd9b_v3']), 'baseline_c3bf_entrypoint': BASELINE_C3BF_ENTRYPOINT, 'generic_unordered_entrypoint': ''.join([format(MODULE, ''), ':candidate_generic_unordered']), 'selected_seeds': (SEED_ID,), 'all_correct': candidate_report.get('summary', {}).get('all_correct'), 'baseline_c3bf_all_correct': baseline_report.get('summary', {}).get('all_correct'), 'generic_unordered_all_correct': generic_report.get('summary', {}).get('all_correct'), 'performance_comparable': candidate_report.get('summary', {}).get('performance_comparable'), 'baseline_c3bf_performance_comparable': baseline_report.get('summary', {}).get('performance_comparable'), 'generic_unordered_performance_comparable': generic_report.get('summary', {}).get('performance_comparable'), 'tflops': candidate_metric, 'baseline_c3bf_tflops': baseline_metric, 'generic_unordered_tflops': generic_metric, 'metric_delta_vs_c3bf': candidate_metric - baseline_metric if candidate_metric and baseline_metric else None, 'metric_delta_vs_generic_unordered': candidate_metric - generic_metric if candidate_metric and generic_metric else None, 'timing_backend': 'cupti' if use_cupti else 'cuda_event', 'denominator': 'q4096_k8_exact_shape', 'shape_labels': list(labels), 'selected_route_rows': _per_shape_rows(candidate_report, labels), 'baseline_c3bf_route_rows': _per_shape_rows(baseline_report, labels), 'generic_unordered_route_rows': _per_shape_rows(generic_report, labels), 'seed_delta_matrix': [_row_delta(candidate_report, baseline_report, generic_report)], 'route_trace': route_trace_for_contract_shapes(shape_labels), 'forced_fallback_route_trace': route_trace_for_contract_shapes(shape_labels, force_fallback=True), 'route_modules': PRODUCTION_ROUTE_MODULES, 'report': candidate_report, 'baseline_c3bf_report': baseline_report, 'generic_unordered_report': generic_report, 'route_trace_included': True} + +def write_benchmark_artifact(artifact_dir: str | Path, *, use_cupti: bool=True, shape_labels=TARGET_SHAPES) -> dict[str, str]: + payload = benchmark_knn_build_q4096_k8_lowfloor_fd9b_v3(use_cupti=use_cupti, shape_labels=shape_labels) + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + path = out_dir / 'q4096_k8_lowfloor_fd9b_v3.json' + path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + return {'candidate_payload': str(path)} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_q8rowld_19b3_q8m64probe_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_q8rowld_19b3_q8m64probe_v1.py new file mode 100644 index 00000000..da0fb6cd --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_q8rowld_19b3_q8m64probe_v1.py @@ -0,0 +1,99 @@ +"""Q8/K32 ROW_16x256B producer probe for the RAG microbucket lane. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +routes only ``rag_microbatch_largek_b1_q8_m100000_d128_k32`` through the +ROW_16x256B M64/N64 tcgen05/TMA producer from the Q32 row-load seed, then +uses the existing K32 fused split merge. It is a contract-visible producer +algorithm probe; all other rows delegate to the existing v9 candidate. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_rag_microbucket_3505_v9 as parent_v9 +from . import knn_build_rag_microbucket_q32rowld_e5db_v1 as q32rowld +Q8_K32_SHAPE = parent_v9.Q8_K32_SHAPE +TARGET_SHAPES = (Q8_K32_SHAPE,) +K32_SPLIT_COUNT = q32rowld.K32_SPLIT_COUNT +K32_GROUP_COUNT = q32rowld.K32_GROUP_COUNT +stage1_q8_k32_rowld_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_q32rowld_e5db_v1_stage1_q32_k32_m64", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 99584, "constants": [["BLOCK_Q", 64], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 192}')) +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_q32rowld_e5db_v1_stage1_q32_k32_m64", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 99584, "constants": [["BLOCK_Q", 64], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 192}')) + +def _eligible_q8_k32_rowld(inputs: dict[str, Any]) -> bool: + return parent_v9._eligible_q8_k32_m64(inputs) + +def _q8_k32_rowld_route_name(*, split_count: int, group_count: int) -> str: + return ''.join(['knn_build_q8rowld_19b3_q8m64probe_v1_q8_m100000_k32_row16x256b_s', format(split_count, ''), '_g', format(group_count, '')]) + +def _launch_q8_k32_rowld(inputs: dict[str, Any], *, split_count: int=K32_SPLIT_COUNT, group_count: int=K32_GROUP_COUNT) -> None: + q32rowld._launch_q32_k32_m64_rowld(inputs, split_count=split_count, group_count=group_count) + +def route_for_contract_inputs(inputs: dict[str, Any], *, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> str: + if _eligible_q8_k32_rowld(inputs): + return _q8_k32_rowld_route_name(split_count=k32_split_count, group_count=k32_group_count) + return parent_v9.route_for_contract_inputs(inputs, k32_split_count=k32_split_count, k32_group_count=k32_group_count) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> None: + if _eligible_q8_k32_rowld(inputs): + _launch_q8_k32_rowld(inputs, split_count=k32_split_count, group_count=k32_group_count) + return + parent_v9.launch_from_contract_inputs(inputs, k32_split_count=k32_split_count, k32_group_count=k32_group_count) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def candidate_with_k32_topology(split_count: int, group_count: int) -> Callable[[dict[str, Any]], None]: + + def _candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, k32_split_count=split_count, k32_group_count=group_count) + return _candidate + +def candidate_parent_v9(inputs: dict[str, Any]): + parent_v9.launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return q32rowld._select_contract_shapes(shape_labels) + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def route_trace_for_contract_shapes(shape_labels=TARGET_SHAPES, *, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> list[dict[str, Any]]: + rows = [] + for shape in _select_contract_shapes(shape_labels): + inputs = q32rowld.base_dispatcher._trace_inputs_from_shape(shape) + route = route_for_contract_inputs(inputs, k32_split_count=k32_split_count, k32_group_count=k32_group_count) + specialized = str(route).startswith('knn_build_q8rowld_19b3_q8m64probe_v1') + rows.append({'shape_key': shape['label'], 'selected_route': route, 'route_kind': 'specialized' if specialized else 'parent_v9', 'guard_condition': 'exact BF16 non-build B=1 Q=8 M=100000 D=128 K=32' if specialized else 'delegate to parent v9', 'fallback': parent_v9.ROUTE_BASE_4247}) + return rows + +def _per_shape_delta(candidate_report: dict[str, Any], parent_report: dict[str, Any]) -> dict[str, Any]: + rows = {} + for label in TARGET_SHAPES: + cand = candidate_report.get('per_shape', {}).get(label, {}) + parent = parent_report.get('per_shape', {}).get(label, {}) + cand_ms = cand.get('kernel_ms') + parent_ms = parent.get('kernel_ms') + rows[label] = {'candidate': cand, 'parent_v9': parent, 'candidate_ms': cand_ms, 'parent_v9_ms': parent_ms, 'speedup_vs_parent_v9': parent_ms / cand_ms if cand_ms and parent_ms else None, 'ratio_vs_flashlib': cand.get('ratio_vs_flashlib')} + return rows + +def benchmark_knn_build_q8rowld_19b3_q8m64probe_v1(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_with_k32_topology(k32_split_count, k32_group_count)) + parent_v9_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_parent_v9) + return {'contract': candidate_report['contract'], 'contract_version': candidate_report['contract_version'], 'measured_entrypoint': 'loom.examples.weave.knn_build_q8rowld_19b3_q8m64probe_v1:benchmark_knn_build_q8rowld_19b3_q8m64probe_v1', 'candidate_entrypoint': 'loom.examples.weave.knn_build_q8rowld_19b3_q8m64probe_v1:launch_from_contract_inputs', 'parent_v9_entrypoint': 'loom.examples.weave.knn_build_rag_microbucket_3505_v9:launch_from_contract_inputs', 'accelerated_shape_labels': list(TARGET_SHAPES), 'measured_shape_labels': list(shape_labels), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'producer_topology': {'K32': ''.join(['Q8-ROW_16x256B/M64N64/S', format(k32_split_count, ''), '/G', format(k32_group_count, ''), '/fused'])}, 'route_trace': route_trace_for_contract_shapes(shape_labels, k32_split_count=k32_split_count, k32_group_count=k32_group_count), 'target_rows': _per_shape_delta(candidate_report, parent_v9_report), 'summary': candidate_report['summary'], 'performance': candidate_report['performance'], 'correctness': candidate_report['correctness'], 'report': candidate_report, 'parent_v9_summary': parent_v9_report['summary'], 'parent_v9_performance': parent_v9_report['performance'], 'parent_v9_report': parent_v9_report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_frontier_4b5c_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_frontier_4b5c_v1.py new file mode 100644 index 00000000..a96b1adb --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_frontier_4b5c_v1.py @@ -0,0 +1,166 @@ +"""RAG frontier bucket seed for kNN build/search. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +routes exactly the four v4 RAG frontier labels through Weave-only tcgen05/TMA +paths. K10 rows reuse the validated split-72 K10 producer and cached merge; +the K32 streaming row uses the existing static-K32 producer with a local +split-32 cached-stream merge. Guard misses delegate to the current exported +split72/de1a dispatcher; no external runtime fallback is used. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import os +from collections.abc import Callable +from functools import lru_cache +from typing import Any +from .._dispatch_runtime import dc as dc +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_split72_4e09_de1a_3dc7_v48 as current_dispatcher +from . import knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2_k32split_v20 as parent_k32 +from . import knn_build_rag_online_stream_split72_4e09_v1 as split72 +from .._dispatch_runtime import pack_kernel_args +RAG_MICROBATCH_SHAPE = 'rag_microbatch_b1_q16_m100000_d128_k10' +RAG_STREAM_LARGEK_SHAPE = 'rag_stream_largek_b1_q128_m100000_d128_k32' +RAG_BATCH_SHAPE = 'rag_batch_b2_q256_m50000_d128_k10' +RAG_IRREGULAR_SHAPE = 'rag_irregular_b1_q512_m131071_d128_k10' +K10_TARGET_SHAPES = (RAG_MICROBATCH_SHAPE, RAG_BATCH_SHAPE, RAG_IRREGULAR_SHAPE) +K32_TARGET_SHAPES = (RAG_STREAM_LARGEK_SHAPE,) +TARGET_SHAPES = (RAG_MICROBATCH_SHAPE, RAG_STREAM_LARGEK_SHAPE, RAG_BATCH_SHAPE, RAG_IRREGULAR_SHAPE) +K10_SPLIT_COUNT = split72.SPLIT_COUNT +K10_MERGE_THREADS = split72.MERGE_THREADS +K32_SPLIT_COUNT = 32 +K32_MERGE_THREADS = parent_k32.K32_MERGE_THREADS +BLOCK_Q = parent_k32.BLOCK_Q +BLOCK_M = parent_k32.BLOCK_M +FEAT_D = parent_k32.FEAT_D +STAGE1_THREADS = parent_k32.STAGE1_THREADS +GRID_DIM_DEFAULT = parent_k32.GRID_DIM_DEFAULT +CTA_GROUP = parent_k32.CTA_GROUP + +def _ir_with_constants(ir_obj: Any, *, suffix: str, **updates: int) -> Any: + constants = tuple(((name, updates.get(name, value)) for name, value in ir_obj.constants)) + return dc.replace(ir_obj, symbol=''.join([format(ir_obj.symbol, ''), '_', format(suffix, '')]), constants=constants) +merge_k32_s32_cache_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_k32s32_4b5c", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 32], ["SPLIT_COUNT", 32]], "cta_group": 1, "threads": 32}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RAG_FRONTIER_4B5C_VERIFY_KERNEL') + if verify_kernel == 'k10_stage1_s72': + return split72.parent_lowk.stage1_ir + if verify_kernel == 'k10_merge_s72': + return split72.merge_k10_s72_cache_ir + if verify_kernel == 'k32_stage1_s32': + return parent_k32.stage1_k32_ir + if verify_kernel == 'k32_merge_s32': + return merge_k32_s32_cache_ir + return merge_k32_s32_cache_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_k32s32_4b5c", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 32], ["SPLIT_COUNT", 32]], "cta_group": 1, "threads": 32}')) + +def _compiled_merge_k32_s32_cache(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0164"}')) + +def _is_bf16_d128_nonbuild(inputs: dict[str, Any]) -> bool: + return not bool(inputs.get('build', False)) and str(inputs['query'].dtype) == 'torch.bfloat16' and (str(inputs['database'].dtype) == 'torch.bfloat16') and (int(inputs['D']) == FEAT_D) + +def _eligible_k10_rag_frontier(inputs: dict[str, Any]) -> bool: + if not _is_bf16_d128_nonbuild(inputs) or int(inputs['K']) != 10: + return False + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + return bsz == 1 and n_query == 16 and (n_database == 100000) or (bsz == 2 and n_query == 256 and (n_database == 50000)) or (bsz == 1 and n_query == 512 and (n_database == 131071)) + +def _eligible_k32_rag_frontier(inputs: dict[str, Any]) -> bool: + return _is_bf16_d128_nonbuild(inputs) and int(inputs['B']) == 1 and (int(inputs['Q']) == 128) and (int(inputs['M']) == 100000) and (int(inputs['K']) == 32) + +def _launch_k10_rag_frontier_s72(inputs: dict[str, Any]) -> None: + split72.parent_lowk._launch_k10_cached_path(inputs, split_count=K10_SPLIT_COUNT, merge_threads=K10_MERGE_THREADS, merge_kernel=split72._compiled_merge_k10_s72_cache(), merge_ir=split72.merge_k10_s72_cache_ir) + +def _launch_k32_rag_frontier_s32(inputs: dict[str, Any]) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_q_tile_pairs = (num_q_tiles + CTA_GROUP - 1) // CTA_GROUP + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + K32_SPLIT_COUNT - 1) // K32_SPLIT_COUNT + total_work = bsz * num_q_tile_pairs * K32_SPLIT_COUNT + stage1_grid = min(total_work * CTA_GROUP, GRID_DIM_DEFAULT) + merge_grid = min((bsz * n_query + K32_MERGE_THREADS - 1) // K32_MERGE_THREADS, GRID_DIM_DEFAULT) + partial_dists, partial_indices = parent_k32.parent_split._partial_buffers(split_count=K32_SPLIT_COUNT, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = parent_k32.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), bsz * n_query, BLOCK_Q, dim, dim) + tmap_database = parent_k32.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, dim, dim) + stage1_kernel = parent_k32._compiled_stage1_for_bucket(parent_k32.TOP_K_SPLIT_MAX) + stage1_kernel.launch_cluster(grid=(stage1_grid, 1, 1), block=(STAGE1_THREADS, 1, 1), args=pack_kernel_args(parent_k32.stage1_k32_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tile_pairs=num_q_tile_pairs, db_tiles_per_split=db_tiles_per_split, split_count=K32_SPLIT_COUNT, total_work=total_work), cluster_dims=(CTA_GROUP, 1, 1), shared_mem=parent_k32.stage1_k32_ir.computed_smem_bytes) + merge_kernel = _compiled_merge_k32_s32_cache() + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(K32_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_k32_s32_cache_ir.computed_smem_bytes) + +def route_for_contract_inputs(inputs: dict[str, Any]) -> str: + if _eligible_k10_rag_frontier(inputs): + return 'rag_frontier_k10_s72' + if _eligible_k32_rag_frontier(inputs): + return 'rag_frontier_k32_s32' + return 'current_split72_de1a_3dc7' + +def launch_from_contract_inputs(inputs: dict[str, Any]) -> None: + route = route_for_contract_inputs(inputs) + if route == 'rag_frontier_k10_s72': + _launch_k10_rag_frontier_s72(inputs) + return + if route == 'rag_frontier_k32_s32': + _launch_k32_rag_frontier_s32(inputs) + return + current_dispatcher.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(candidate, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return current_dispatcher._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint: run real contract correctness for selected shapes.""" + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + try: + return eval_mod.evaluate(kernel_fn, shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def _shape_payload(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> dict[str, Any]: + rows = {} + for label in TARGET_SHAPES: + cand = candidate_report.get('per_shape', {}).get(label, {}) + base = baseline_report.get('per_shape', {}).get(label, {}) + cand_ms = cand.get('kernel_ms') + base_ms = base.get('kernel_ms') + flashlib_ms = cand.get('flashlib_ms') + rows[label] = {'candidate': cand, 'current_dispatcher': base, 'candidate_route': 'rag_frontier_k32_s32' if label in K32_TARGET_SHAPES else 'rag_frontier_k10_s72', 'candidate_ms': cand_ms, 'current_dispatcher_ms': base_ms, 'flashlib_ms': flashlib_ms, 'speedup_vs_current': base_ms / cand_ms if cand_ms and base_ms else None, 'ratio_vs_flashlib': flashlib_ms / cand_ms if cand_ms and flashlib_ms else None} + return rows + +def _benchmark_payload(candidate_report: dict[str, Any], baseline_report: dict[str, Any], *, use_cupti: bool, shape_labels) -> dict[str, Any]: + rows = _shape_payload(candidate_report, baseline_report) + timing_backends = sorted({row.get('timing_backend') for report in (candidate_report, baseline_report) for row in report.get('per_shape', {}).values() if row.get('timing_backend') is not None}) + return {'tflops': candidate_report['summary']['primary_mean'] or 0.0, 'all_correct': candidate_report['summary']['all_correct'], 'baseline_all_correct': baseline_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'baseline_performance_comparable': baseline_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'measured_entrypoint': 'loom.examples.weave.knn_build_rag_frontier_4b5c_v1:benchmark_knn_build_rag_frontier_4b5c_v1', 'baseline_entrypoint': 'loom.examples.weave.knn_build_dispatch_split72_4e09_de1a_3dc7_v48:candidate', 'accelerated_shape_labels': list(TARGET_SHAPES), 'measured_shape_labels': list(shape_labels), 'producer_split_counts': {'K10': K10_SPLIT_COUNT, 'K32': K32_SPLIT_COUNT}, 'timing_backends': timing_backends, 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'target_rows': rows, 'contract_summary': candidate_report['summary'], 'baseline_contract_summary': baseline_report['summary'], 'contract_performance': candidate_report['performance'], 'baseline_contract_performance': baseline_report['performance'], 'report': candidate_report, 'baseline_report': baseline_report} + +def benchmark_knn_build_rag_frontier_4b5c_v1(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES) -> dict[str, Any]: + """Targeted bucket benchmark with same-session current-dispatcher A/B.""" + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels) + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=current_dispatcher.candidate) + return _benchmark_payload(candidate_report, baseline_report, use_cupti=use_cupti, shape_labels=shape_labels) diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_frontier_4b5c_v2.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_frontier_4b5c_v2.py new file mode 100644 index 00000000..57118365 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_frontier_4b5c_v2.py @@ -0,0 +1,178 @@ +"""RAG frontier bucket seed for kNN build/search with tunable K32 fanout. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +keeps the v1 split-72 K10 route unchanged and retunes only the +``rag_stream_largek_b1_q128_m100000_d128_k32`` row. The default K32 route uses +64 database splits after a focused split-count sweep. It remains a Weave-only +tcgen05/TMA split producer plus cached stream merge; guard misses delegate to +the current exported split72/de1a dispatcher. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import os +from collections.abc import Callable +from functools import cache +from typing import Any +from .._dispatch_runtime import dc as dc +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_split72_4e09_de1a_3dc7_v48 as current_dispatcher +from . import knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2_k32split_v20 as parent_k32 +from . import knn_build_rag_frontier_4b5c_v1 as v1 +from .._dispatch_runtime import pack_kernel_args +RAG_MICROBATCH_SHAPE = v1.RAG_MICROBATCH_SHAPE +RAG_STREAM_LARGEK_SHAPE = v1.RAG_STREAM_LARGEK_SHAPE +RAG_BATCH_SHAPE = v1.RAG_BATCH_SHAPE +RAG_IRREGULAR_SHAPE = v1.RAG_IRREGULAR_SHAPE +K10_TARGET_SHAPES = v1.K10_TARGET_SHAPES +K32_TARGET_SHAPES = v1.K32_TARGET_SHAPES +TARGET_SHAPES = v1.TARGET_SHAPES +K10_SPLIT_COUNT = v1.K10_SPLIT_COUNT +K32_CANDIDATE_SPLITS = (16, 24, 32, 48, 64, 96, 128) +K32_SPLIT_COUNT = _decode_capture(_json_loads('64')) +K32_MERGE_THREADS = parent_k32.K32_MERGE_THREADS +BLOCK_Q = parent_k32.BLOCK_Q +BLOCK_M = parent_k32.BLOCK_M +FEAT_D = parent_k32.FEAT_D +STAGE1_THREADS = parent_k32.STAGE1_THREADS +GRID_DIM_DEFAULT = parent_k32.GRID_DIM_DEFAULT +CTA_GROUP = parent_k32.CTA_GROUP + +def _ir_with_constants(ir_obj: Any, *, suffix: str, **updates: int) -> Any: + constants = tuple(((name, updates.get(name, value)) for name, value in ir_obj.constants)) + return dc.replace(ir_obj, symbol=''.join([format(ir_obj.symbol, ''), '_', format(suffix, '')]), constants=constants) + +def _merge_k32_cache_ir(split_count: int) -> Any: + if split_count <= 0: + raise ValueError(''.join(['split_count must be positive, got ', format(split_count, '')])) + return _ir_with_constants(parent_k32.merge_k30_s8_ir, suffix=''.join(['k32s', format(split_count, ''), '_4b5c_v2']), TOP_K_MAX=32, SPLIT_COUNT=split_count) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RAG_FRONTIER_4B5C_V2_VERIFY_KERNEL') + if verify_kernel == 'k10_stage1_s72': + return v1.split72.parent_lowk.stage1_ir + if verify_kernel == 'k10_merge_s72': + return v1.split72.merge_k10_s72_cache_ir + if verify_kernel == 'k32_stage1': + return parent_k32.stage1_k32_ir + if verify_kernel and verify_kernel.startswith('k32_merge_s'): + return _merge_k32_cache_ir(int(verify_kernel.removeprefix('k32_merge_s'))) + return _merge_k32_cache_ir(K32_SPLIT_COUNT) +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_k32s64_4b5c_v2", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 32], ["SPLIT_COUNT", 64]], "cta_group": 1, "threads": 32}')) + +@cache +def _compiled_merge_k32_cache(split_count: int): + return parent_k32._compile_ir(_merge_k32_cache_ir(split_count)) + +def _is_bf16_d128_nonbuild(inputs: dict[str, Any]) -> bool: + return v1._is_bf16_d128_nonbuild(inputs) + +def _eligible_k10_rag_frontier(inputs: dict[str, Any]) -> bool: + return v1._eligible_k10_rag_frontier(inputs) + +def _eligible_k32_rag_frontier(inputs: dict[str, Any]) -> bool: + return v1._eligible_k32_rag_frontier(inputs) + +def _launch_k10_rag_frontier_s72(inputs: dict[str, Any]) -> None: + v1._launch_k10_rag_frontier_s72(inputs) + +def _launch_k32_rag_frontier(inputs: dict[str, Any], *, split_count: int=K32_SPLIT_COUNT) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_q_tile_pairs = (num_q_tiles + CTA_GROUP - 1) // CTA_GROUP + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tile_pairs * split_count + stage1_grid = min(total_work * CTA_GROUP, GRID_DIM_DEFAULT) + merge_grid = min((bsz * n_query + K32_MERGE_THREADS - 1) // K32_MERGE_THREADS, GRID_DIM_DEFAULT) + partial_dists, partial_indices = parent_k32.parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = parent_k32.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), bsz * n_query, BLOCK_Q, dim, dim) + tmap_database = parent_k32.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, dim, dim) + stage1_kernel = parent_k32._compiled_stage1_for_bucket(parent_k32.TOP_K_SPLIT_MAX) + stage1_kernel.launch_cluster(grid=(stage1_grid, 1, 1), block=(STAGE1_THREADS, 1, 1), args=pack_kernel_args(parent_k32.stage1_k32_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tile_pairs=num_q_tile_pairs, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), cluster_dims=(CTA_GROUP, 1, 1), shared_mem=parent_k32.stage1_k32_ir.computed_smem_bytes) + merge_ir = _merge_k32_cache_ir(split_count) + merge_kernel = _compiled_merge_k32_cache(split_count) + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(K32_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_ir.computed_smem_bytes) + +def route_for_contract_inputs(inputs: dict[str, Any]) -> str: + if _eligible_k10_rag_frontier(inputs): + return 'rag_frontier_k10_s72' + if _eligible_k32_rag_frontier(inputs): + return ''.join(['rag_frontier_k32_s', format(K32_SPLIT_COUNT, '')]) + return 'current_split72_de1a_3dc7' + +def launch_from_contract_inputs(inputs: dict[str, Any], *, k32_split_count: int=K32_SPLIT_COUNT) -> None: + if _eligible_k10_rag_frontier(inputs): + _launch_k10_rag_frontier_s72(inputs) + return + if _eligible_k32_rag_frontier(inputs): + _launch_k32_rag_frontier(inputs, split_count=k32_split_count) + return + current_dispatcher.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def candidate_with_k32_split(split_count: int) -> Callable[[dict[str, Any]], None]: + + def _candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, k32_split_count=split_count) + return _candidate + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(candidate, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return current_dispatcher._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint: run real contract correctness for selected shapes.""" + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + try: + return eval_mod.evaluate(kernel_fn, shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def _shape_payload(candidate_report: dict[str, Any], baseline_report: dict[str, Any], *, k32_split_count: int) -> dict[str, Any]: + rows = {} + for label in TARGET_SHAPES: + cand = candidate_report.get('per_shape', {}).get(label, {}) + base = baseline_report.get('per_shape', {}).get(label, {}) + cand_ms = cand.get('kernel_ms') + base_ms = base.get('kernel_ms') + flashlib_ms = cand.get('flashlib_ms') + rows[label] = {'candidate': cand, 'current_dispatcher': base, 'candidate_route': ''.join(['rag_frontier_k32_s', format(k32_split_count, '')]) if label in K32_TARGET_SHAPES else 'rag_frontier_k10_s72', 'candidate_ms': cand_ms, 'current_dispatcher_ms': base_ms, 'flashlib_ms': flashlib_ms, 'speedup_vs_current': base_ms / cand_ms if cand_ms and base_ms else None, 'ratio_vs_flashlib': flashlib_ms / cand_ms if cand_ms and flashlib_ms else None} + return rows + +def _benchmark_payload(candidate_report: dict[str, Any], baseline_report: dict[str, Any], *, use_cupti: bool, shape_labels, k32_split_count: int) -> dict[str, Any]: + rows = _shape_payload(candidate_report, baseline_report, k32_split_count=k32_split_count) + timing_backends = sorted({row.get('timing_backend') for report in (candidate_report, baseline_report) for row in report.get('per_shape', {}).values() if row.get('timing_backend') is not None}) + return {'tflops': candidate_report['summary']['primary_mean'] or 0.0, 'all_correct': candidate_report['summary']['all_correct'], 'baseline_all_correct': baseline_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'baseline_performance_comparable': baseline_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'measured_entrypoint': 'loom.examples.weave.knn_build_rag_frontier_4b5c_v2:benchmark_knn_build_rag_frontier_4b5c_v2', 'baseline_entrypoint': 'loom.examples.weave.knn_build_dispatch_split72_4e09_de1a_3dc7_v48:candidate', 'accelerated_shape_labels': list(TARGET_SHAPES), 'measured_shape_labels': list(shape_labels), 'producer_split_counts': {'K10': K10_SPLIT_COUNT, 'K32': k32_split_count}, 'timing_backends': timing_backends, 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'target_rows': rows, 'contract_summary': candidate_report['summary'], 'baseline_contract_summary': baseline_report['summary'], 'contract_performance': candidate_report['performance'], 'baseline_contract_performance': baseline_report['performance'], 'report': candidate_report, 'baseline_report': baseline_report} + +def benchmark_knn_build_rag_frontier_4b5c_v2(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES, k32_split_count: int=K32_SPLIT_COUNT) -> dict[str, Any]: + """Targeted bucket benchmark with same-session current-dispatcher A/B.""" + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_with_k32_split(k32_split_count)) + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=current_dispatcher.candidate) + return _benchmark_payload(candidate_report, baseline_report, use_cupti=use_cupti, shape_labels=shape_labels, k32_split_count=k32_split_count) + +def benchmark_k32_split_sweep(*, use_cupti: bool=True, split_counts=K32_CANDIDATE_SPLITS) -> dict[str, Any]: + rows = {} + for split_count in split_counts: + rows[''.join(['s', format(split_count, '')])] = benchmark_knn_build_rag_frontier_4b5c_v2(use_cupti=use_cupti, shape_labels=K32_TARGET_SHAPES, k32_split_count=int(split_count)) + return {'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'shape_labels': list(K32_TARGET_SHAPES), 'split_counts': list(split_counts), 'rows': rows} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_frontier_4b5c_v3.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_frontier_4b5c_v3.py new file mode 100644 index 00000000..10fb04bb --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_frontier_4b5c_v3.py @@ -0,0 +1,198 @@ +"""RAG frontier bucket seed with a two-stage K32 merge. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +keeps the v2 K10 split-72 routes unchanged and retunes the K32 split-72 +tcgen05/TMA producer. The K32 row replaces the single 64-way cached merge with +a two-stage sorted-stream merge: first merge split groups into group-local +top-k buffers, then merge those group streams into the contract outputs. +Guard misses delegate to the current exported split72/de1a dispatcher. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import os +from collections.abc import Callable +from functools import cache +from typing import Any +from .._dispatch_runtime import dc as dc +from .. import _dispatch_runtime as eval_mod +from . import knn_build_rag_frontier_4b5c_v2 as v2 +from .._dispatch_runtime import pack_kernel_args +current_dispatcher = v2.current_dispatcher +parent_k32 = v2.parent_k32 +RAG_MICROBATCH_SHAPE = v2.RAG_MICROBATCH_SHAPE +RAG_STREAM_LARGEK_SHAPE = v2.RAG_STREAM_LARGEK_SHAPE +RAG_BATCH_SHAPE = v2.RAG_BATCH_SHAPE +RAG_IRREGULAR_SHAPE = v2.RAG_IRREGULAR_SHAPE +K10_TARGET_SHAPES = v2.K10_TARGET_SHAPES +K32_TARGET_SHAPES = v2.K32_TARGET_SHAPES +TARGET_SHAPES = v2.TARGET_SHAPES +K10_SPLIT_COUNT = v2.K10_SPLIT_COUNT +K32_SPLIT_COUNT = _decode_capture(_json_loads('72')) +K32_GROUP_COUNT = _decode_capture(_json_loads('8')) +K32_GROUP_MERGE_THREADS = v2.K32_MERGE_THREADS +K32_FINAL_MERGE_THREADS = v2.K32_MERGE_THREADS +BLOCK_Q = v2.BLOCK_Q +BLOCK_M = v2.BLOCK_M +FEAT_D = v2.FEAT_D +STAGE1_THREADS = v2.STAGE1_THREADS +GRID_DIM_DEFAULT = v2.GRID_DIM_DEFAULT +CTA_GROUP = v2.CTA_GROUP +TOP_K_MAX = parent_k32.TOP_K_SPLIT_MAX + +def _ir_with_constants(ir_obj: Any, *, suffix: str, **updates: int) -> Any: + constants = tuple(((name, updates.get(name, value)) for name, value in ir_obj.constants)) + return dc.replace(ir_obj, symbol=''.join([format(ir_obj.symbol, ''), '_', format(suffix, '')]), constants=constants) +knn_build_rag_frontier_4b5c_k32_group_merge = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_frontier_4b5c_k32_group_merge", "arg_keys": ["partial_dists", "partial_indices", "group_dists", "group_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 32], ["GROUP_COUNT", 8], ["GROUP_SPLITS", 8]], "cta_group": 1, "threads": 32}')) + +def _validate_group_shape(split_count: int, group_count: int) -> None: + if split_count <= 0 or group_count <= 0: + raise ValueError(''.join(['split_count and group_count must be positive, got ', format(split_count, ''), ', ', format(group_count, '')])) + if split_count % group_count != 0: + raise ValueError(''.join(['split_count=', format(split_count, ''), ' must be divisible by group_count=', format(group_count, '')])) + +def _group_merge_ir(split_count: int, group_count: int) -> Any: + _validate_group_shape(split_count, group_count) + return _ir_with_constants(knn_build_rag_frontier_4b5c_k32_group_merge, suffix=''.join(['k32s', format(split_count, ''), 'g', format(group_count, ''), '_4b5c_v3']), TOP_K_MAX=TOP_K_MAX, GROUP_COUNT=group_count, GROUP_SPLITS=split_count // group_count) + +def _final_merge_ir(group_count: int) -> Any: + return v2._merge_k32_cache_ir(group_count) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RAG_FRONTIER_4B5C_V3_VERIFY_KERNEL') + split_count = int(os.environ.get('LOOM_KNN_RAG_FRONTIER_4B5C_V3_VERIFY_SPLIT', K32_SPLIT_COUNT)) + group_count = int(os.environ.get('LOOM_KNN_RAG_FRONTIER_4B5C_V3_VERIFY_GROUPS', K32_GROUP_COUNT)) + if verify_kernel == 'k10_stage1_s72': + return v2._verify_export_ir() + if verify_kernel == 'k10_merge_s72': + os.environ['LOOM_KNN_RAG_FRONTIER_4B5C_V2_VERIFY_KERNEL'] = 'k10_merge_s72' + return v2._verify_export_ir() + if verify_kernel == 'k32_stage1': + return parent_k32.stage1_k32_ir + if verify_kernel == 'k32_final_merge': + return _final_merge_ir(group_count) + return _group_merge_ir(split_count, group_count) +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_frontier_4b5c_k32_group_merge_k32s72g8_4b5c_v3", "arg_keys": ["partial_dists", "partial_indices", "group_dists", "group_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 32], ["GROUP_COUNT", 8], ["GROUP_SPLITS", 9]], "cta_group": 1, "threads": 32}')) + +@cache +def _compiled_group_merge(split_count: int, group_count: int): + return parent_k32._compile_ir(_group_merge_ir(split_count, group_count)) + +@cache +def _compiled_final_merge(group_count: int): + return parent_k32._compile_ir(_final_merge_ir(group_count)) + +def _eligible_k10_rag_frontier(inputs: dict[str, Any]) -> bool: + return v2._eligible_k10_rag_frontier(inputs) + +def _eligible_k32_rag_frontier(inputs: dict[str, Any]) -> bool: + return v2._eligible_k32_rag_frontier(inputs) + +def _launch_k10_rag_frontier_s72(inputs: dict[str, Any]) -> None: + v2._launch_k10_rag_frontier_s72(inputs) + +def _launch_k32_rag_frontier_two_stage(inputs: dict[str, Any], *, split_count: int=K32_SPLIT_COUNT, group_count: int=K32_GROUP_COUNT) -> None: + _validate_group_shape(split_count, group_count) + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_q_tile_pairs = (num_q_tiles + CTA_GROUP - 1) // CTA_GROUP + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tile_pairs * split_count + total_queries = bsz * n_query + stage1_grid = min(total_work * CTA_GROUP, GRID_DIM_DEFAULT) + group_rows = total_queries * group_count + group_grid = min((group_rows + K32_GROUP_MERGE_THREADS - 1) // K32_GROUP_MERGE_THREADS, GRID_DIM_DEFAULT) + final_grid = min((total_queries + K32_FINAL_MERGE_THREADS - 1) // K32_FINAL_MERGE_THREADS, GRID_DIM_DEFAULT) + partial_dists, partial_indices = parent_k32.parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + group_dists, group_indices = parent_k32.parent_split._partial_buffers(split_count=group_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = parent_k32.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), total_queries, BLOCK_Q, dim, dim) + tmap_database = parent_k32.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, dim, dim) + stage1_kernel = parent_k32._compiled_stage1_for_bucket(parent_k32.TOP_K_SPLIT_MAX) + stage1_kernel.launch_cluster(grid=(stage1_grid, 1, 1), block=(STAGE1_THREADS, 1, 1), args=pack_kernel_args(parent_k32.stage1_k32_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tile_pairs=num_q_tile_pairs, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), cluster_dims=(CTA_GROUP, 1, 1), shared_mem=parent_k32.stage1_k32_ir.computed_smem_bytes) + group_ir = _group_merge_ir(split_count, group_count) + group_kernel = _compiled_group_merge(split_count, group_count) + group_kernel.launch(grid=(group_grid, 1, 1), block=(K32_GROUP_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, group_dists, group_indices, total_queries], shared_mem=group_ir.computed_smem_bytes) + final_ir = _final_merge_ir(group_count) + final_kernel = _compiled_final_merge(group_count) + final_kernel.launch(grid=(final_grid, 1, 1), block=(K32_FINAL_MERGE_THREADS, 1, 1), args=[group_dists, group_indices, inputs['out_dists'], inputs['out_indices'], total_queries], shared_mem=final_ir.computed_smem_bytes) + +def route_for_contract_inputs(inputs: dict[str, Any]) -> str: + if _eligible_k10_rag_frontier(inputs): + return 'rag_frontier_k10_s72' + if _eligible_k32_rag_frontier(inputs): + return ''.join(['rag_frontier_k32_s', format(K32_SPLIT_COUNT, ''), '_g', format(K32_GROUP_COUNT, ''), '_2stage']) + return 'current_split72_de1a_3dc7' + +def launch_from_contract_inputs(inputs: dict[str, Any], *, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> None: + if _eligible_k10_rag_frontier(inputs): + _launch_k10_rag_frontier_s72(inputs) + return + if _eligible_k32_rag_frontier(inputs): + _launch_k32_rag_frontier_two_stage(inputs, split_count=k32_split_count, group_count=k32_group_count) + return + current_dispatcher.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def candidate_with_k32_topology(split_count: int, group_count: int) -> Callable[[dict[str, Any]], None]: + + def _candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, k32_split_count=split_count, k32_group_count=group_count) + return _candidate + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(candidate, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return current_dispatcher._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + try: + return eval_mod.evaluate(kernel_fn, shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def _shape_payload(candidate_report: dict[str, Any], baseline_report: dict[str, Any], *, k32_split_count: int, k32_group_count: int) -> dict[str, Any]: + rows = {} + for label in TARGET_SHAPES: + cand = candidate_report.get('per_shape', {}).get(label, {}) + base = baseline_report.get('per_shape', {}).get(label, {}) + cand_ms = cand.get('kernel_ms') + base_ms = base.get('kernel_ms') + flashlib_ms = cand.get('flashlib_ms') + rows[label] = {'candidate': cand, 'current_dispatcher': base, 'candidate_route': ''.join(['rag_frontier_k32_s', format(k32_split_count, ''), '_g', format(k32_group_count, ''), '_2stage']) if label in K32_TARGET_SHAPES else 'rag_frontier_k10_s72', 'candidate_ms': cand_ms, 'current_dispatcher_ms': base_ms, 'flashlib_ms': flashlib_ms, 'speedup_vs_current': base_ms / cand_ms if cand_ms and base_ms else None, 'ratio_vs_flashlib': flashlib_ms / cand_ms if cand_ms and flashlib_ms else None} + return rows + +def _benchmark_payload(candidate_report: dict[str, Any], baseline_report: dict[str, Any], *, use_cupti: bool, shape_labels, k32_split_count: int, k32_group_count: int) -> dict[str, Any]: + timing_backends = sorted({row.get('timing_backend') for report in (candidate_report, baseline_report) for row in report.get('per_shape', {}).values() if row.get('timing_backend') is not None}) + return {'tflops': candidate_report['summary']['primary_mean'] or 0.0, 'all_correct': candidate_report['summary']['all_correct'], 'baseline_all_correct': baseline_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'baseline_performance_comparable': baseline_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'measured_entrypoint': 'loom.examples.weave.knn_build_rag_frontier_4b5c_v3:benchmark_knn_build_rag_frontier_4b5c_v3', 'baseline_entrypoint': 'loom.examples.weave.knn_build_dispatch_split72_4e09_de1a_3dc7_v48:candidate', 'accelerated_shape_labels': list(TARGET_SHAPES), 'measured_shape_labels': list(shape_labels), 'producer_split_counts': {'K10': K10_SPLIT_COUNT, 'K32': k32_split_count}, 'merge_topology': {'K32': 'two_stage_sorted_stream', 'groups': k32_group_count}, 'timing_backends': timing_backends, 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'target_rows': _shape_payload(candidate_report, baseline_report, k32_split_count=k32_split_count, k32_group_count=k32_group_count), 'contract_summary': candidate_report['summary'], 'baseline_contract_summary': baseline_report['summary'], 'contract_performance': candidate_report['performance'], 'baseline_contract_performance': baseline_report['performance'], 'report': candidate_report, 'baseline_report': baseline_report} + +def benchmark_knn_build_rag_frontier_4b5c_v3(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_with_k32_topology(k32_split_count, k32_group_count)) + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=current_dispatcher.candidate) + return _benchmark_payload(candidate_report, baseline_report, use_cupti=use_cupti, shape_labels=shape_labels, k32_split_count=k32_split_count, k32_group_count=k32_group_count) + +def benchmark_k32_group_sweep(*, use_cupti: bool=True, group_counts=(4, 8, 9, 12)) -> dict[str, Any]: + rows = {} + for group_count in group_counts: + rows[''.join(['g', format(group_count, '')])] = benchmark_knn_build_rag_frontier_4b5c_v3(use_cupti=use_cupti, shape_labels=K32_TARGET_SHAPES, k32_split_count=K32_SPLIT_COUNT, k32_group_count=int(group_count)) + return {'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'shape_labels': list(K32_TARGET_SHAPES), 'split_count': K32_SPLIT_COUNT, 'group_counts': list(group_counts), 'rows': rows} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_frontier_4fbf_v6.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_frontier_4fbf_v6.py new file mode 100644 index 00000000..63fa1181 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_frontier_4fbf_v6.py @@ -0,0 +1,197 @@ +"""RAG frontier bucket seed with tail-infinity K32 stage-1 and fused merge. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +keeps the v3 K10 split-72 routes, starts from the b6d4 v5 K32 sort4/early-stop +producer, and keeps the 7399 fused cooperative K32 merge. The K32 producer +stores an infinity sentinel for out-of-range database columns so the hot +accepted-update loop no longer carries a per-candidate tail guard. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import os +from collections.abc import Callable +from functools import cache, lru_cache +from typing import Any +from .._dispatch_runtime import dc as dc +from .. import _dispatch_runtime as eval_mod +from . import knn_build_rag_frontier_4b5c_v3 as v3 +from . import knn_build_rag_frontier_7399_v1 as fused_parent +from .._dispatch_runtime import pack_kernel_args +current_dispatcher = v3.current_dispatcher +parent_k32 = v3.parent_k32 +RAG_MICROBATCH_SHAPE = v3.RAG_MICROBATCH_SHAPE +RAG_STREAM_LARGEK_SHAPE = v3.RAG_STREAM_LARGEK_SHAPE +RAG_BATCH_SHAPE = v3.RAG_BATCH_SHAPE +RAG_IRREGULAR_SHAPE = v3.RAG_IRREGULAR_SHAPE +K10_TARGET_SHAPES = v3.K10_TARGET_SHAPES +K32_TARGET_SHAPES = v3.K32_TARGET_SHAPES +TARGET_SHAPES = v3.TARGET_SHAPES +K10_SPLIT_COUNT = v3.K10_SPLIT_COUNT +K32_SPLIT_COUNT = _decode_capture(_json_loads('72')) +K32_GROUP_COUNT = _decode_capture(_json_loads('8')) +K32_GROUP_MERGE_THREADS = v3.K32_GROUP_MERGE_THREADS +K32_FINAL_MERGE_THREADS = v3.K32_FINAL_MERGE_THREADS +K32_FUSED_MERGE_THREADS = fused_parent.K32_FUSED_MERGE_THREADS +BLOCK_Q = v3.BLOCK_Q +BLOCK_M = v3.BLOCK_M +FEAT_D = v3.FEAT_D +STAGE1_THREADS = v3.STAGE1_THREADS +GRID_DIM_DEFAULT = v3.GRID_DIM_DEFAULT +CTA_GROUP = v3.CTA_GROUP +TOP_K_MAX = v3.TOP_K_MAX +knn_build_rag_frontier_4fbf_stage1_k32_sort4earlystop_tailinf = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_frontier_4fbf_stage1_k32_sort4earlystop_tailinf", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 192}')) +stage1_k32_tailinf_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_frontier_4fbf_stage1_k32_sort4earlystop_tailinf", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 192}')) + +def _ir_with_constants(ir_obj: Any, *, suffix: str, **updates: int) -> Any: + constants = tuple(((name, updates.get(name, value)) for name, value in ir_obj.constants)) + return dc.replace(ir_obj, symbol=''.join([format(ir_obj.symbol, ''), '_', format(suffix, '')]), constants=constants) + +def _validate_group_shape(split_count: int, group_count: int) -> None: + v3._validate_group_shape(split_count, group_count) + +def _group_merge_ir(split_count: int, group_count: int) -> Any: + return v3._group_merge_ir(split_count, group_count) + +def _final_merge_ir(group_count: int) -> Any: + return v3._final_merge_ir(group_count) + +def _fused_merge_ir(split_count: int, group_count: int) -> Any: + _validate_group_shape(split_count, group_count) + if group_count != 8: + raise ValueError(''.join(['4fbf fused merge uses fixed 8-group shared layout, got group_count=', format(group_count, '')])) + return _ir_with_constants(fused_parent.fused_merge_ir, suffix=''.join(['k32s', format(split_count, ''), 'g', format(group_count, ''), '_4fbf_v6']), TOP_K_MAX=TOP_K_MAX, GROUP_COUNT=group_count, GROUP_SPLITS=split_count // group_count) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RAG_FRONTIER_4FBF_V6_VERIFY_KERNEL') + split_count = int(os.environ.get('LOOM_KNN_RAG_FRONTIER_4FBF_V6_VERIFY_SPLIT', K32_SPLIT_COUNT)) + group_count = int(os.environ.get('LOOM_KNN_RAG_FRONTIER_4FBF_V6_VERIFY_GROUPS', K32_GROUP_COUNT)) + if verify_kernel == 'k32_group_merge': + return _group_merge_ir(split_count, group_count) + if verify_kernel == 'k32_final_merge': + return _final_merge_ir(group_count) + if verify_kernel == 'k32_fused_merge': + return _fused_merge_ir(split_count, group_count) + return stage1_k32_tailinf_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_frontier_4fbf_stage1_k32_sort4earlystop_tailinf", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 192}')) + +def _compiled_stage1_tailinf(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0177"}')) + +@cache +def _compiled_group_merge(split_count: int, group_count: int): + return parent_k32._compile_ir(_group_merge_ir(split_count, group_count)) + +@cache +def _compiled_final_merge(group_count: int): + return parent_k32._compile_ir(_final_merge_ir(group_count)) + +@cache +def _compiled_fused_merge(split_count: int, group_count: int): + return parent_k32._compile_ir(_fused_merge_ir(split_count, group_count)) + +def _eligible_k10_rag_frontier(inputs: dict[str, Any]) -> bool: + return v3._eligible_k10_rag_frontier(inputs) + +def _eligible_k32_rag_frontier(inputs: dict[str, Any]) -> bool: + return v3._eligible_k32_rag_frontier(inputs) + +def _launch_k10_rag_frontier_s72(inputs: dict[str, Any]) -> None: + v3._launch_k10_rag_frontier_s72(inputs) + +def _launch_k32_rag_frontier_sort4earlystop_stage(inputs: dict[str, Any], *, split_count: int=K32_SPLIT_COUNT, group_count: int=K32_GROUP_COUNT) -> None: + _validate_group_shape(split_count, group_count) + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_q_tile_pairs = (num_q_tiles + CTA_GROUP - 1) // CTA_GROUP + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tile_pairs * split_count + total_queries = bsz * n_query + stage1_grid = min(total_work * CTA_GROUP, GRID_DIM_DEFAULT) + merge_grid = min(total_queries, GRID_DIM_DEFAULT) + partial_dists, partial_indices = parent_k32.parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = parent_k32.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), total_queries, BLOCK_Q, dim, dim) + tmap_database = parent_k32.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, dim, dim) + stage1_kernel = _compiled_stage1_tailinf() + stage1_kernel.launch_cluster(grid=(stage1_grid, 1, 1), block=(STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage1_k32_tailinf_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tile_pairs=num_q_tile_pairs, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), cluster_dims=(CTA_GROUP, 1, 1), shared_mem=stage1_k32_tailinf_ir.computed_smem_bytes) + fused_ir = _fused_merge_ir(split_count, group_count) + fused_kernel = _compiled_fused_merge(split_count, group_count) + fused_kernel.launch(grid=(merge_grid, 1, 1), block=(K32_FUSED_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], total_queries], shared_mem=fused_ir.computed_smem_bytes) + +def route_for_contract_inputs(inputs: dict[str, Any]) -> str: + if _eligible_k10_rag_frontier(inputs): + return 'rag_frontier_k10_s72' + if _eligible_k32_rag_frontier(inputs): + return ''.join(['rag_frontier_k32_s', format(K32_SPLIT_COUNT, ''), '_g', format(K32_GROUP_COUNT, ''), '_tailinf_fused4fbf']) + return 'current_split72_de1a_3dc7' + +def launch_from_contract_inputs(inputs: dict[str, Any], *, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> None: + if _eligible_k10_rag_frontier(inputs): + _launch_k10_rag_frontier_s72(inputs) + return + if _eligible_k32_rag_frontier(inputs): + _launch_k32_rag_frontier_sort4earlystop_stage(inputs, split_count=k32_split_count, group_count=k32_group_count) + return + current_dispatcher.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def candidate_with_k32_topology(split_count: int, group_count: int) -> Callable[[dict[str, Any]], None]: + + def _candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, k32_split_count=split_count, k32_group_count=group_count) + return _candidate + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(candidate, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return current_dispatcher._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + try: + return eval_mod.evaluate(kernel_fn, shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def _shape_payload(candidate_report: dict[str, Any], baseline_report: dict[str, Any], parent_report: dict[str, Any], *, k32_split_count: int, k32_group_count: int) -> dict[str, Any]: + rows = {} + for label in TARGET_SHAPES: + cand = candidate_report.get('per_shape', {}).get(label, {}) + base = baseline_report.get('per_shape', {}).get(label, {}) + parent = parent_report.get('per_shape', {}).get(label, {}) + cand_ms = cand.get('kernel_ms') + base_ms = base.get('kernel_ms') + parent_ms = parent.get('kernel_ms') + flashlib_ms = cand.get('flashlib_ms') + rows[label] = {'candidate': cand, 'current_dispatcher': base, 'parent_7399': parent, 'candidate_route': ''.join(['rag_frontier_k32_s', format(k32_split_count, ''), '_g', format(k32_group_count, ''), '_tailinf_fused4fbf']) if label in K32_TARGET_SHAPES else 'rag_frontier_k10_s72', 'candidate_ms': cand_ms, 'current_dispatcher_ms': base_ms, 'parent_7399_ms': parent_ms, 'flashlib_ms': flashlib_ms, 'speedup_vs_current': base_ms / cand_ms if cand_ms and base_ms else None, 'speedup_vs_parent_7399': parent_ms / cand_ms if cand_ms and parent_ms else None, 'ratio_vs_flashlib': flashlib_ms / cand_ms if cand_ms and flashlib_ms else None} + return rows + +def _benchmark_payload(candidate_report: dict[str, Any], baseline_report: dict[str, Any], parent_report: dict[str, Any], *, use_cupti: bool, shape_labels, k32_split_count: int, k32_group_count: int) -> dict[str, Any]: + timing_backends = sorted({row.get('timing_backend') for report in (candidate_report, baseline_report, parent_report) for row in report.get('per_shape', {}).values() if row.get('timing_backend') is not None}) + return {'tflops': candidate_report['summary']['primary_mean'] or 0.0, 'all_correct': candidate_report['summary']['all_correct'], 'baseline_all_correct': baseline_report['summary']['all_correct'], 'parent_7399_all_correct': parent_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'baseline_performance_comparable': baseline_report['summary']['performance_comparable'], 'parent_7399_performance_comparable': parent_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'measured_entrypoint': 'loom.examples.weave.knn_build_rag_frontier_4fbf_v6:benchmark_knn_build_rag_frontier_4fbf_v6', 'baseline_entrypoint': 'loom.examples.weave.knn_build_dispatch_split72_4e09_de1a_3dc7_v48:candidate', 'parent_7399_entrypoint': 'loom.examples.weave.knn_build_rag_frontier_7399_v1:candidate_with_k32_topology', 'accelerated_shape_labels': list(TARGET_SHAPES), 'measured_shape_labels': list(shape_labels), 'producer_split_counts': {'K10': K10_SPLIT_COUNT, 'K32': k32_split_count}, 'parent_7399_split_counts': {'K10': K10_SPLIT_COUNT, 'K32': fused_parent.K32_SPLIT_COUNT}, 'merge_topology': {'K32': 'fused_cooperative_group_final', 'groups': k32_group_count}, 'stage1_topk': {'K32': 'tailinf_chunked_worst_sort4_earlystop_then_emit_sorted'}, 'timing_backends': timing_backends, 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'target_rows': _shape_payload(candidate_report, baseline_report, parent_report, k32_split_count=k32_split_count, k32_group_count=k32_group_count), 'contract_summary': candidate_report['summary'], 'baseline_contract_summary': baseline_report['summary'], 'parent_7399_contract_summary': parent_report['summary'], 'contract_performance': candidate_report['performance'], 'baseline_contract_performance': baseline_report['performance'], 'parent_7399_contract_performance': parent_report['performance'], 'report': candidate_report, 'baseline_report': baseline_report, 'parent_7399_report': parent_report} + +def benchmark_knn_build_rag_frontier_4fbf_v6(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_with_k32_topology(k32_split_count, k32_group_count)) + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=current_dispatcher.candidate) + parent_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=fused_parent.candidate_with_k32_topology(fused_parent.K32_SPLIT_COUNT, fused_parent.K32_GROUP_COUNT)) + return _benchmark_payload(candidate_report, baseline_report, parent_report, use_cupti=use_cupti, shape_labels=shape_labels, k32_split_count=k32_split_count, k32_group_count=k32_group_count) diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_frontier_4fbf_v7.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_frontier_4fbf_v7.py new file mode 100644 index 00000000..c90f108e --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_frontier_4fbf_v7.py @@ -0,0 +1,202 @@ +"""RAG frontier bucket seed with tail-infinity K32 stage-1 and group-swept fused merge. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +keeps the v6 tail-infinity K32 sort4/early-stop producer and makes the fused +cooperative K32 merge layout group-count tunable. The retained route still +writes exact contract distances and indices for the RAG frontier bucket. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import os +from collections.abc import Callable +from functools import cache, lru_cache +from typing import Any +from .._dispatch_runtime import dc as dc +from .. import _dispatch_runtime as eval_mod +from . import knn_build_rag_frontier_4b5c_v3 as v3 +from . import knn_build_rag_frontier_4fbf_v6 as parent_v6 +from . import knn_build_rag_frontier_7399_v1 as fused_parent +from .._dispatch_runtime import pack_kernel_args +current_dispatcher = v3.current_dispatcher +parent_k32 = v3.parent_k32 +RAG_MICROBATCH_SHAPE = v3.RAG_MICROBATCH_SHAPE +RAG_STREAM_LARGEK_SHAPE = v3.RAG_STREAM_LARGEK_SHAPE +RAG_BATCH_SHAPE = v3.RAG_BATCH_SHAPE +RAG_IRREGULAR_SHAPE = v3.RAG_IRREGULAR_SHAPE +K10_TARGET_SHAPES = v3.K10_TARGET_SHAPES +K32_TARGET_SHAPES = v3.K32_TARGET_SHAPES +TARGET_SHAPES = v3.TARGET_SHAPES +K10_SPLIT_COUNT = v3.K10_SPLIT_COUNT +K32_SPLIT_COUNT = _decode_capture(_json_loads('72')) +K32_GROUP_COUNT = _decode_capture(_json_loads('24')) +K32_GROUP_MERGE_THREADS = v3.K32_GROUP_MERGE_THREADS +K32_FINAL_MERGE_THREADS = v3.K32_FINAL_MERGE_THREADS +K32_FUSED_MERGE_THREADS = fused_parent.K32_FUSED_MERGE_THREADS +K32_FUSED_MERGE_SLOTS = 1024 +BLOCK_Q = v3.BLOCK_Q +BLOCK_M = v3.BLOCK_M +FEAT_D = v3.FEAT_D +STAGE1_THREADS = v3.STAGE1_THREADS +GRID_DIM_DEFAULT = v3.GRID_DIM_DEFAULT +CTA_GROUP = v3.CTA_GROUP +TOP_K_MAX = v3.TOP_K_MAX +knn_build_rag_frontier_4fbf_v7_stage1_k32_sort4earlystop_tailinf = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_frontier_4fbf_v7_stage1_k32_sort4earlystop_tailinf", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 192}')) +stage1_k32_tailinf_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_frontier_4fbf_v7_stage1_k32_sort4earlystop_tailinf", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 192}')) +knn_build_rag_frontier_4fbf_v7_k32_fused_group_final_merge = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_frontier_4fbf_v7_k32_fused_group_final_merge", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 8192, "constants": [["TOP_K_MAX", 32], ["GROUP_COUNT", 8], ["GROUP_SPLITS", 9]], "cta_group": 1, "threads": 32}')) +fused_merge_generalized_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_frontier_4fbf_v7_k32_fused_group_final_merge", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 8192, "constants": [["TOP_K_MAX", 32], ["GROUP_COUNT", 8], ["GROUP_SPLITS", 9]], "cta_group": 1, "threads": 32}')) + +def _ir_with_constants(ir_obj: Any, *, suffix: str, **updates: int) -> Any: + constants = tuple(((name, updates.get(name, value)) for name, value in ir_obj.constants)) + return dc.replace(ir_obj, symbol=''.join([format(ir_obj.symbol, ''), '_', format(suffix, '')]), constants=constants) + +def _validate_group_shape(split_count: int, group_count: int) -> None: + v3._validate_group_shape(split_count, group_count) + if group_count > K32_FUSED_MERGE_THREADS: + raise ValueError(''.join(['group_count=', format(group_count, ''), ' exceeds fused merge threads=', format(K32_FUSED_MERGE_THREADS, '')])) + if group_count * TOP_K_MAX > K32_FUSED_MERGE_SLOTS: + raise ValueError(''.join(['group_count=', format(group_count, ''), ' needs ', format(group_count * TOP_K_MAX, ''), ' shared slots, but v7 allocates ', format(K32_FUSED_MERGE_SLOTS, '')])) + +def _group_merge_ir(split_count: int, group_count: int) -> Any: + return v3._group_merge_ir(split_count, group_count) + +def _final_merge_ir(group_count: int) -> Any: + return v3._final_merge_ir(group_count) + +def _fused_merge_ir(split_count: int, group_count: int) -> Any: + _validate_group_shape(split_count, group_count) + return _ir_with_constants(fused_merge_generalized_ir, suffix=''.join(['k32s', format(split_count, ''), 'g', format(group_count, ''), '_4fbf_v7']), TOP_K_MAX=TOP_K_MAX, GROUP_COUNT=group_count, GROUP_SPLITS=split_count // group_count) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RAG_FRONTIER_4FBF_V7_VERIFY_KERNEL') + split_count = int(os.environ.get('LOOM_KNN_RAG_FRONTIER_4FBF_V7_VERIFY_SPLIT', K32_SPLIT_COUNT)) + group_count = int(os.environ.get('LOOM_KNN_RAG_FRONTIER_4FBF_V7_VERIFY_GROUPS', K32_GROUP_COUNT)) + if verify_kernel == 'k32_group_merge': + return _group_merge_ir(split_count, group_count) + if verify_kernel == 'k32_final_merge': + return _final_merge_ir(group_count) + if verify_kernel == 'k32_fused_merge': + return _fused_merge_ir(split_count, group_count) + return stage1_k32_tailinf_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_frontier_4fbf_v7_stage1_k32_sort4earlystop_tailinf", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 192}')) + +def _compiled_stage1_tailinf(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0176"}')) + +@cache +def _compiled_group_merge(split_count: int, group_count: int): + return parent_k32._compile_ir(_group_merge_ir(split_count, group_count)) + +@cache +def _compiled_final_merge(group_count: int): + return parent_k32._compile_ir(_final_merge_ir(group_count)) + +@cache +def _compiled_fused_merge(split_count: int, group_count: int): + return parent_k32._compile_ir(_fused_merge_ir(split_count, group_count)) + +def _eligible_k10_rag_frontier(inputs: dict[str, Any]) -> bool: + return v3._eligible_k10_rag_frontier(inputs) + +def _eligible_k32_rag_frontier(inputs: dict[str, Any]) -> bool: + return v3._eligible_k32_rag_frontier(inputs) + +def _launch_k10_rag_frontier_s72(inputs: dict[str, Any]) -> None: + v3._launch_k10_rag_frontier_s72(inputs) + +def _launch_k32_rag_frontier_sort4earlystop_stage(inputs: dict[str, Any], *, split_count: int=K32_SPLIT_COUNT, group_count: int=K32_GROUP_COUNT) -> None: + _validate_group_shape(split_count, group_count) + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_q_tile_pairs = (num_q_tiles + CTA_GROUP - 1) // CTA_GROUP + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tile_pairs * split_count + total_queries = bsz * n_query + stage1_grid = min(total_work * CTA_GROUP, GRID_DIM_DEFAULT) + merge_grid = min(total_queries, GRID_DIM_DEFAULT) + partial_dists, partial_indices = parent_k32.parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = parent_k32.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), total_queries, BLOCK_Q, dim, dim) + tmap_database = parent_k32.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, dim, dim) + stage1_kernel = _compiled_stage1_tailinf() + stage1_kernel.launch_cluster(grid=(stage1_grid, 1, 1), block=(STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage1_k32_tailinf_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tile_pairs=num_q_tile_pairs, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), cluster_dims=(CTA_GROUP, 1, 1), shared_mem=stage1_k32_tailinf_ir.computed_smem_bytes) + fused_ir = _fused_merge_ir(split_count, group_count) + fused_kernel = _compiled_fused_merge(split_count, group_count) + fused_kernel.launch(grid=(merge_grid, 1, 1), block=(K32_FUSED_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], total_queries], shared_mem=fused_ir.computed_smem_bytes) + +def route_for_contract_inputs(inputs: dict[str, Any]) -> str: + if _eligible_k10_rag_frontier(inputs): + return 'rag_frontier_k10_s72' + if _eligible_k32_rag_frontier(inputs): + return ''.join(['rag_frontier_k32_s', format(K32_SPLIT_COUNT, ''), '_g', format(K32_GROUP_COUNT, ''), '_tailinf_fused4fbf_v7']) + return 'current_split72_de1a_3dc7' + +def launch_from_contract_inputs(inputs: dict[str, Any], *, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> None: + if _eligible_k10_rag_frontier(inputs): + _launch_k10_rag_frontier_s72(inputs) + return + if _eligible_k32_rag_frontier(inputs): + _launch_k32_rag_frontier_sort4earlystop_stage(inputs, split_count=k32_split_count, group_count=k32_group_count) + return + current_dispatcher.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def candidate_with_k32_topology(split_count: int, group_count: int) -> Callable[[dict[str, Any]], None]: + + def _candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, k32_split_count=split_count, k32_group_count=group_count) + return _candidate + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(candidate, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return current_dispatcher._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + try: + return eval_mod.evaluate(kernel_fn, shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def _shape_payload(candidate_report: dict[str, Any], baseline_report: dict[str, Any], parent_v6_report: dict[str, Any], *, k32_split_count: int, k32_group_count: int) -> dict[str, Any]: + rows = {} + for label in TARGET_SHAPES: + cand = candidate_report.get('per_shape', {}).get(label, {}) + base = baseline_report.get('per_shape', {}).get(label, {}) + parent_v6 = parent_v6_report.get('per_shape', {}).get(label, {}) + cand_ms = cand.get('kernel_ms') + base_ms = base.get('kernel_ms') + parent_v6_ms = parent_v6.get('kernel_ms') + flashlib_ms = cand.get('flashlib_ms') + rows[label] = {'candidate': cand, 'current_dispatcher': base, 'parent_4fbf_v6': parent_v6, 'candidate_route': ''.join(['rag_frontier_k32_s', format(k32_split_count, ''), '_g', format(k32_group_count, ''), '_tailinf_fused4fbf_v7']) if label in K32_TARGET_SHAPES else 'rag_frontier_k10_s72', 'candidate_ms': cand_ms, 'current_dispatcher_ms': base_ms, 'parent_4fbf_v6_ms': parent_v6_ms, 'flashlib_ms': flashlib_ms, 'speedup_vs_current': base_ms / cand_ms if cand_ms and base_ms else None, 'speedup_vs_parent_4fbf_v6': parent_v6_ms / cand_ms if cand_ms and parent_v6_ms else None, 'ratio_vs_flashlib': flashlib_ms / cand_ms if cand_ms and flashlib_ms else None} + return rows + +def _benchmark_payload(candidate_report: dict[str, Any], baseline_report: dict[str, Any], parent_v6_report: dict[str, Any], *, use_cupti: bool, shape_labels, k32_split_count: int, k32_group_count: int) -> dict[str, Any]: + timing_backends = sorted({row.get('timing_backend') for report in (candidate_report, baseline_report, parent_v6_report) for row in report.get('per_shape', {}).values() if row.get('timing_backend') is not None}) + return {'tflops': candidate_report['summary']['primary_mean'] or 0.0, 'all_correct': candidate_report['summary']['all_correct'], 'baseline_all_correct': baseline_report['summary']['all_correct'], 'parent_4fbf_v6_all_correct': parent_v6_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'baseline_performance_comparable': baseline_report['summary']['performance_comparable'], 'parent_4fbf_v6_performance_comparable': parent_v6_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'measured_entrypoint': 'loom.examples.weave.knn_build_rag_frontier_4fbf_v7:benchmark_knn_build_rag_frontier_4fbf_v7', 'baseline_entrypoint': 'loom.examples.weave.knn_build_dispatch_split72_4e09_de1a_3dc7_v48:candidate', 'parent_4fbf_v6_entrypoint': 'loom.examples.weave.knn_build_rag_frontier_4fbf_v6:candidate_with_k32_topology', 'accelerated_shape_labels': list(TARGET_SHAPES), 'measured_shape_labels': list(shape_labels), 'producer_split_counts': {'K10': K10_SPLIT_COUNT, 'K32': k32_split_count}, 'parent_4fbf_v6_split_counts': {'K10': K10_SPLIT_COUNT, 'K32': parent_v6.K32_SPLIT_COUNT}, 'merge_topology': {'K32': 'generalized_fused_cooperative_group_final', 'groups': k32_group_count}, 'stage1_topk': {'K32': 'tailinf_chunked_worst_sort4_earlystop_then_emit_sorted'}, 'timing_backends': timing_backends, 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'target_rows': _shape_payload(candidate_report, baseline_report, parent_v6_report, k32_split_count=k32_split_count, k32_group_count=k32_group_count), 'contract_summary': candidate_report['summary'], 'baseline_contract_summary': baseline_report['summary'], 'parent_4fbf_v6_contract_summary': parent_v6_report['summary'], 'contract_performance': candidate_report['performance'], 'baseline_contract_performance': baseline_report['performance'], 'parent_4fbf_v6_contract_performance': parent_v6_report['performance'], 'report': candidate_report, 'baseline_report': baseline_report, 'parent_4fbf_v6_report': parent_v6_report} + +def benchmark_knn_build_rag_frontier_4fbf_v7(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_with_k32_topology(k32_split_count, k32_group_count)) + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=current_dispatcher.candidate) + parent_v6_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=parent_v6.candidate_with_k32_topology(parent_v6.K32_SPLIT_COUNT, parent_v6.K32_GROUP_COUNT)) + return _benchmark_payload(candidate_report, baseline_report, parent_v6_report, use_cupti=use_cupti, shape_labels=shape_labels, k32_split_count=k32_split_count, k32_group_count=k32_group_count) diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_frontier_7399_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_frontier_7399_v1.py new file mode 100644 index 00000000..34316ca0 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_frontier_7399_v1.py @@ -0,0 +1,166 @@ +"""RAG frontier bucket seed with a fused cooperative K32 merge. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +reuses the b6d4 v5 K32 sort4/early-stop tcgen05/TMA producer and the inherited +K10 split-72 routes. The K32 row replaces the two separate group/final merge +launches with one cooperative query-row merge: eight lane owners merge the +split groups into shared memory, then lane zero performs the final 8-way merge +to contract-visible distances and indices. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import os +from collections.abc import Callable +from functools import lru_cache +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_rag_frontier_b6d4_v5 as v5 +from .._dispatch_runtime import pack_kernel_args +current_dispatcher = v5.current_dispatcher +parent_k32 = v5.parent_k32 +RAG_MICROBATCH_SHAPE = v5.RAG_MICROBATCH_SHAPE +RAG_STREAM_LARGEK_SHAPE = v5.RAG_STREAM_LARGEK_SHAPE +RAG_BATCH_SHAPE = v5.RAG_BATCH_SHAPE +RAG_IRREGULAR_SHAPE = v5.RAG_IRREGULAR_SHAPE +K10_TARGET_SHAPES = v5.K10_TARGET_SHAPES +K32_TARGET_SHAPES = v5.K32_TARGET_SHAPES +TARGET_SHAPES = v5.TARGET_SHAPES +K10_SPLIT_COUNT = v5.K10_SPLIT_COUNT +K32_SPLIT_COUNT = _decode_capture(_json_loads('72')) +K32_GROUP_COUNT = _decode_capture(_json_loads('8')) +K32_FUSED_MERGE_THREADS = _decode_capture(_json_loads('32')) +BLOCK_Q = v5.BLOCK_Q +BLOCK_M = v5.BLOCK_M +FEAT_D = v5.FEAT_D +STAGE1_THREADS = v5.STAGE1_THREADS +GRID_DIM_DEFAULT = v5.GRID_DIM_DEFAULT +CTA_GROUP = v5.CTA_GROUP +TOP_K_MAX = v5.TOP_K_MAX +knn_build_rag_frontier_7399_k32_fused_group_final_merge = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_frontier_7399_k32_fused_group_final_merge", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 2048, "constants": [["TOP_K_MAX", 32], ["GROUP_COUNT", 8], ["GROUP_SPLITS", 9]], "cta_group": 1, "threads": 32}')) +fused_merge_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_frontier_7399_k32_fused_group_final_merge", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 2048, "constants": [["TOP_K_MAX", 32], ["GROUP_COUNT", 8], ["GROUP_SPLITS", 9]], "cta_group": 1, "threads": 32}')) + +def _validate_group_shape(split_count: int, group_count: int) -> None: + v5._validate_group_shape(split_count, group_count) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RAG_FRONTIER_7399_V1_VERIFY_KERNEL') + split_count = int(os.environ.get('LOOM_KNN_RAG_FRONTIER_7399_V1_VERIFY_SPLIT', K32_SPLIT_COUNT)) + group_count = int(os.environ.get('LOOM_KNN_RAG_FRONTIER_7399_V1_VERIFY_GROUPS', K32_GROUP_COUNT)) + if verify_kernel == 'stage1': + return v5.stage1_k32_sort4earlystop_ir + if verify_kernel == 'fused_merge': + _validate_group_shape(split_count, group_count) + return fused_merge_ir + return fused_merge_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_frontier_7399_k32_fused_group_final_merge", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 2048, "constants": [["TOP_K_MAX", 32], ["GROUP_COUNT", 8], ["GROUP_SPLITS", 9]], "cta_group": 1, "threads": 32}')) + +def _compiled_stage1_sort4earlystop(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0162"}')) + +def _compiled_fused_merge(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0163"}')) + +def _eligible_k10_rag_frontier(inputs: dict[str, Any]) -> bool: + return v5._eligible_k10_rag_frontier(inputs) + +def _eligible_k32_rag_frontier(inputs: dict[str, Any]) -> bool: + return v5._eligible_k32_rag_frontier(inputs) + +def _launch_k10_rag_frontier_s72(inputs: dict[str, Any]) -> None: + v5._launch_k10_rag_frontier_s72(inputs) + +def _launch_k32_rag_frontier_fused_merge(inputs: dict[str, Any], *, split_count: int=K32_SPLIT_COUNT, group_count: int=K32_GROUP_COUNT) -> None: + _validate_group_shape(split_count, group_count) + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_q_tile_pairs = (num_q_tiles + CTA_GROUP - 1) // CTA_GROUP + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tile_pairs * split_count + total_queries = bsz * n_query + stage1_grid = min(total_work * CTA_GROUP, GRID_DIM_DEFAULT) + merge_grid = min(total_queries, GRID_DIM_DEFAULT) + partial_dists, partial_indices = parent_k32.parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = parent_k32.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), total_queries, BLOCK_Q, dim, dim) + tmap_database = parent_k32.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, dim, dim) + stage1_kernel = _compiled_stage1_sort4earlystop() + stage1_kernel.launch_cluster(grid=(stage1_grid, 1, 1), block=(STAGE1_THREADS, 1, 1), args=pack_kernel_args(v5.stage1_k32_sort4earlystop_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tile_pairs=num_q_tile_pairs, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), cluster_dims=(CTA_GROUP, 1, 1), shared_mem=v5.stage1_k32_sort4earlystop_ir.computed_smem_bytes) + merge_kernel = _compiled_fused_merge() + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(K32_FUSED_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], total_queries], shared_mem=fused_merge_ir.computed_smem_bytes) + +def route_for_contract_inputs(inputs: dict[str, Any]) -> str: + if _eligible_k10_rag_frontier(inputs): + return 'rag_frontier_k10_s72' + if _eligible_k32_rag_frontier(inputs): + return ''.join(['rag_frontier_k32_s', format(K32_SPLIT_COUNT, ''), '_g', format(K32_GROUP_COUNT, ''), '_fusedmerge7399']) + return 'current_split72_de1a_3dc7' + +def launch_from_contract_inputs(inputs: dict[str, Any], *, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> None: + if _eligible_k10_rag_frontier(inputs): + _launch_k10_rag_frontier_s72(inputs) + return + if _eligible_k32_rag_frontier(inputs): + _launch_k32_rag_frontier_fused_merge(inputs, split_count=k32_split_count, group_count=k32_group_count) + return + current_dispatcher.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def candidate_with_k32_topology(split_count: int, group_count: int) -> Callable[[dict[str, Any]], None]: + + def _candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, k32_split_count=split_count, k32_group_count=group_count) + return _candidate + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + return eval_mod.evaluate(candidate, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return current_dispatcher._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + try: + return eval_mod.evaluate(kernel_fn, shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def _shape_payload(candidate_report: dict[str, Any], parent_v5_report: dict[str, Any], baseline_report: dict[str, Any], *, k32_split_count: int, k32_group_count: int) -> dict[str, Any]: + rows = {} + for label in TARGET_SHAPES: + cand = candidate_report.get('per_shape', {}).get(label, {}) + parent = parent_v5_report.get('per_shape', {}).get(label, {}) + base = baseline_report.get('per_shape', {}).get(label, {}) + cand_ms = cand.get('kernel_ms') + parent_ms = parent.get('kernel_ms') + base_ms = base.get('kernel_ms') + flashlib_ms = cand.get('flashlib_ms') + rows[label] = {'candidate': cand, 'parent_v5': parent, 'current_dispatcher': base, 'candidate_route': ''.join(['rag_frontier_k32_s', format(k32_split_count, ''), '_g', format(k32_group_count, ''), '_fusedmerge7399']) if label in K32_TARGET_SHAPES else 'rag_frontier_k10_s72', 'candidate_ms': cand_ms, 'parent_v5_ms': parent_ms, 'current_dispatcher_ms': base_ms, 'flashlib_ms': flashlib_ms, 'speedup_vs_parent_v5': parent_ms / cand_ms if cand_ms and parent_ms else None, 'speedup_vs_current': base_ms / cand_ms if cand_ms and base_ms else None, 'ratio_vs_flashlib': flashlib_ms / cand_ms if cand_ms and flashlib_ms else None} + return rows + +def _benchmark_payload(candidate_report: dict[str, Any], parent_v5_report: dict[str, Any], baseline_report: dict[str, Any], *, use_cupti: bool, shape_labels, k32_split_count: int, k32_group_count: int) -> dict[str, Any]: + timing_backends = sorted({row.get('timing_backend') for report in (candidate_report, parent_v5_report, baseline_report) for row in report.get('per_shape', {}).values() if row.get('timing_backend') is not None}) + return {'tflops': candidate_report['summary']['primary_mean'] or 0.0, 'all_correct': candidate_report['summary']['all_correct'], 'parent_v5_all_correct': parent_v5_report['summary']['all_correct'], 'baseline_all_correct': baseline_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'parent_v5_performance_comparable': parent_v5_report['summary']['performance_comparable'], 'baseline_performance_comparable': baseline_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'measured_entrypoint': 'loom.examples.weave.knn_build_rag_frontier_7399_v1:benchmark_knn_build_rag_frontier_7399_v1', 'parent_v5_entrypoint': 'loom.examples.weave.knn_build_rag_frontier_b6d4_v5:candidate_with_k32_topology', 'baseline_entrypoint': 'loom.examples.weave.knn_build_dispatch_split72_4e09_de1a_3dc7_v48:candidate', 'accelerated_shape_labels': list(TARGET_SHAPES), 'measured_shape_labels': list(shape_labels), 'producer_split_counts': {'K10': K10_SPLIT_COUNT, 'K32': k32_split_count}, 'merge_topology': {'K32': 'fused_cooperative_group_final', 'groups': k32_group_count}, 'stage1_topk': {'K32': 'b6d4_v5_chunked_worst_sort4_earlystop_then_emit_sorted'}, 'timing_backends': timing_backends, 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'target_rows': _shape_payload(candidate_report, parent_v5_report, baseline_report, k32_split_count=k32_split_count, k32_group_count=k32_group_count), 'contract_summary': candidate_report['summary'], 'parent_v5_contract_summary': parent_v5_report['summary'], 'baseline_contract_summary': baseline_report['summary'], 'contract_performance': candidate_report['performance'], 'parent_v5_contract_performance': parent_v5_report['performance'], 'baseline_contract_performance': baseline_report['performance'], 'report': candidate_report, 'parent_v5_report': parent_v5_report, 'baseline_report': baseline_report} + +def benchmark_knn_build_rag_frontier_7399_v1(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_with_k32_topology(k32_split_count, k32_group_count)) + parent_v5_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=v5.candidate_with_k32_topology(k32_split_count, k32_group_count)) + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=current_dispatcher.candidate) + return _benchmark_payload(candidate_report, parent_v5_report, baseline_report, use_cupti=use_cupti, shape_labels=shape_labels, k32_split_count=k32_split_count, k32_group_count=k32_group_count) diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_frontier_b6d4_v4.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_frontier_b6d4_v4.py new file mode 100644 index 00000000..fe24543d --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_frontier_b6d4_v4.py @@ -0,0 +1,185 @@ +"""RAG frontier bucket seed with chunked K32 stage-1 top-k maintenance. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +keeps the v3 K10 split-72 routes and K32 S72/G8 two-stage merge topology, but +changes the K32 stage-1 producer. Instead of maintaining a fully sorted top-32 +list for every accepted database candidate, the producer keeps an unordered +top-32 with four 8-slot worst caches and emits a sorted stream once per split. +Guard misses delegate to the current exported split72/de1a dispatcher. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import os +from collections.abc import Callable +from functools import cache, lru_cache +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_rag_frontier_4b5c_v3 as v3 +from .._dispatch_runtime import pack_kernel_args +current_dispatcher = v3.current_dispatcher +parent_k32 = v3.parent_k32 +RAG_MICROBATCH_SHAPE = v3.RAG_MICROBATCH_SHAPE +RAG_STREAM_LARGEK_SHAPE = v3.RAG_STREAM_LARGEK_SHAPE +RAG_BATCH_SHAPE = v3.RAG_BATCH_SHAPE +RAG_IRREGULAR_SHAPE = v3.RAG_IRREGULAR_SHAPE +K10_TARGET_SHAPES = v3.K10_TARGET_SHAPES +K32_TARGET_SHAPES = v3.K32_TARGET_SHAPES +TARGET_SHAPES = v3.TARGET_SHAPES +K10_SPLIT_COUNT = v3.K10_SPLIT_COUNT +K32_SPLIT_COUNT = _decode_capture(_json_loads('72')) +K32_GROUP_COUNT = _decode_capture(_json_loads('8')) +K32_GROUP_MERGE_THREADS = v3.K32_GROUP_MERGE_THREADS +K32_FINAL_MERGE_THREADS = v3.K32_FINAL_MERGE_THREADS +BLOCK_Q = v3.BLOCK_Q +BLOCK_M = v3.BLOCK_M +FEAT_D = v3.FEAT_D +STAGE1_THREADS = v3.STAGE1_THREADS +GRID_DIM_DEFAULT = v3.GRID_DIM_DEFAULT +CTA_GROUP = v3.CTA_GROUP +TOP_K_MAX = v3.TOP_K_MAX +knn_build_rag_frontier_b6d4_stage1_k32_chunked = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_frontier_b6d4_stage1_k32_chunked", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 192}')) +stage1_k32_chunked_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_frontier_b6d4_stage1_k32_chunked", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 192}')) + +def _validate_group_shape(split_count: int, group_count: int) -> None: + v3._validate_group_shape(split_count, group_count) + +def _group_merge_ir(split_count: int, group_count: int) -> Any: + return v3._group_merge_ir(split_count, group_count) + +def _final_merge_ir(group_count: int) -> Any: + return v3._final_merge_ir(group_count) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RAG_FRONTIER_B6D4_V4_VERIFY_KERNEL') + split_count = int(os.environ.get('LOOM_KNN_RAG_FRONTIER_B6D4_V4_VERIFY_SPLIT', K32_SPLIT_COUNT)) + group_count = int(os.environ.get('LOOM_KNN_RAG_FRONTIER_B6D4_V4_VERIFY_GROUPS', K32_GROUP_COUNT)) + if verify_kernel == 'k32_group_merge': + return _group_merge_ir(split_count, group_count) + if verify_kernel == 'k32_final_merge': + return _final_merge_ir(group_count) + return stage1_k32_chunked_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_frontier_b6d4_stage1_k32_chunked", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 192}')) + +def _compiled_stage1_chunked(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0167"}')) + +@cache +def _compiled_group_merge(split_count: int, group_count: int): + return parent_k32._compile_ir(_group_merge_ir(split_count, group_count)) + +@cache +def _compiled_final_merge(group_count: int): + return parent_k32._compile_ir(_final_merge_ir(group_count)) + +def _eligible_k10_rag_frontier(inputs: dict[str, Any]) -> bool: + return v3._eligible_k10_rag_frontier(inputs) + +def _eligible_k32_rag_frontier(inputs: dict[str, Any]) -> bool: + return v3._eligible_k32_rag_frontier(inputs) + +def _launch_k10_rag_frontier_s72(inputs: dict[str, Any]) -> None: + v3._launch_k10_rag_frontier_s72(inputs) + +def _launch_k32_rag_frontier_chunked_stage(inputs: dict[str, Any], *, split_count: int=K32_SPLIT_COUNT, group_count: int=K32_GROUP_COUNT) -> None: + _validate_group_shape(split_count, group_count) + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_q_tile_pairs = (num_q_tiles + CTA_GROUP - 1) // CTA_GROUP + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tile_pairs * split_count + total_queries = bsz * n_query + stage1_grid = min(total_work * CTA_GROUP, GRID_DIM_DEFAULT) + group_rows = total_queries * group_count + group_grid = min((group_rows + K32_GROUP_MERGE_THREADS - 1) // K32_GROUP_MERGE_THREADS, GRID_DIM_DEFAULT) + final_grid = min((total_queries + K32_FINAL_MERGE_THREADS - 1) // K32_FINAL_MERGE_THREADS, GRID_DIM_DEFAULT) + partial_dists, partial_indices = parent_k32.parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + group_dists, group_indices = parent_k32.parent_split._partial_buffers(split_count=group_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = parent_k32.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), total_queries, BLOCK_Q, dim, dim) + tmap_database = parent_k32.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, dim, dim) + stage1_kernel = _compiled_stage1_chunked() + stage1_kernel.launch_cluster(grid=(stage1_grid, 1, 1), block=(STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage1_k32_chunked_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tile_pairs=num_q_tile_pairs, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), cluster_dims=(CTA_GROUP, 1, 1), shared_mem=stage1_k32_chunked_ir.computed_smem_bytes) + group_ir = _group_merge_ir(split_count, group_count) + group_kernel = _compiled_group_merge(split_count, group_count) + group_kernel.launch(grid=(group_grid, 1, 1), block=(K32_GROUP_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, group_dists, group_indices, total_queries], shared_mem=group_ir.computed_smem_bytes) + final_ir = _final_merge_ir(group_count) + final_kernel = _compiled_final_merge(group_count) + final_kernel.launch(grid=(final_grid, 1, 1), block=(K32_FINAL_MERGE_THREADS, 1, 1), args=[group_dists, group_indices, inputs['out_dists'], inputs['out_indices'], total_queries], shared_mem=final_ir.computed_smem_bytes) + +def route_for_contract_inputs(inputs: dict[str, Any]) -> str: + if _eligible_k10_rag_frontier(inputs): + return 'rag_frontier_k10_s72' + if _eligible_k32_rag_frontier(inputs): + return ''.join(['rag_frontier_k32_s', format(K32_SPLIT_COUNT, ''), '_g', format(K32_GROUP_COUNT, ''), '_chunkstage']) + return 'current_split72_de1a_3dc7' + +def launch_from_contract_inputs(inputs: dict[str, Any], *, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> None: + if _eligible_k10_rag_frontier(inputs): + _launch_k10_rag_frontier_s72(inputs) + return + if _eligible_k32_rag_frontier(inputs): + _launch_k32_rag_frontier_chunked_stage(inputs, split_count=k32_split_count, group_count=k32_group_count) + return + current_dispatcher.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def candidate_with_k32_topology(split_count: int, group_count: int) -> Callable[[dict[str, Any]], None]: + + def _candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, k32_split_count=split_count, k32_group_count=group_count) + return _candidate + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(candidate, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return current_dispatcher._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + try: + return eval_mod.evaluate(kernel_fn, shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def _shape_payload(candidate_report: dict[str, Any], baseline_report: dict[str, Any], parent_report: dict[str, Any], *, k32_split_count: int, k32_group_count: int) -> dict[str, Any]: + rows = {} + for label in TARGET_SHAPES: + cand = candidate_report.get('per_shape', {}).get(label, {}) + base = baseline_report.get('per_shape', {}).get(label, {}) + parent = parent_report.get('per_shape', {}).get(label, {}) + cand_ms = cand.get('kernel_ms') + base_ms = base.get('kernel_ms') + parent_ms = parent.get('kernel_ms') + flashlib_ms = cand.get('flashlib_ms') + rows[label] = {'candidate': cand, 'current_dispatcher': base, 'parent_v3': parent, 'candidate_route': ''.join(['rag_frontier_k32_s', format(k32_split_count, ''), '_g', format(k32_group_count, ''), '_chunkstage']) if label in K32_TARGET_SHAPES else 'rag_frontier_k10_s72', 'candidate_ms': cand_ms, 'current_dispatcher_ms': base_ms, 'parent_v3_ms': parent_ms, 'flashlib_ms': flashlib_ms, 'speedup_vs_current': base_ms / cand_ms if cand_ms and base_ms else None, 'speedup_vs_parent_v3': parent_ms / cand_ms if cand_ms and parent_ms else None, 'ratio_vs_flashlib': flashlib_ms / cand_ms if cand_ms and flashlib_ms else None} + return rows + +def _benchmark_payload(candidate_report: dict[str, Any], baseline_report: dict[str, Any], parent_report: dict[str, Any], *, use_cupti: bool, shape_labels, k32_split_count: int, k32_group_count: int) -> dict[str, Any]: + timing_backends = sorted({row.get('timing_backend') for report in (candidate_report, baseline_report, parent_report) for row in report.get('per_shape', {}).values() if row.get('timing_backend') is not None}) + return {'tflops': candidate_report['summary']['primary_mean'] or 0.0, 'all_correct': candidate_report['summary']['all_correct'], 'baseline_all_correct': baseline_report['summary']['all_correct'], 'parent_v3_all_correct': parent_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'baseline_performance_comparable': baseline_report['summary']['performance_comparable'], 'parent_v3_performance_comparable': parent_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'measured_entrypoint': 'loom.examples.weave.knn_build_rag_frontier_b6d4_v4:benchmark_knn_build_rag_frontier_b6d4_v4', 'baseline_entrypoint': 'loom.examples.weave.knn_build_dispatch_split72_4e09_de1a_3dc7_v48:candidate', 'parent_v3_entrypoint': 'loom.examples.weave.knn_build_rag_frontier_4b5c_v3:candidate_with_k32_topology', 'accelerated_shape_labels': list(TARGET_SHAPES), 'measured_shape_labels': list(shape_labels), 'producer_split_counts': {'K10': K10_SPLIT_COUNT, 'K32': k32_split_count}, 'merge_topology': {'K32': 'two_stage_sorted_stream', 'groups': k32_group_count}, 'stage1_topk': {'K32': 'chunked_worst_then_emit_sorted'}, 'timing_backends': timing_backends, 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'target_rows': _shape_payload(candidate_report, baseline_report, parent_report, k32_split_count=k32_split_count, k32_group_count=k32_group_count), 'contract_summary': candidate_report['summary'], 'baseline_contract_summary': baseline_report['summary'], 'parent_v3_contract_summary': parent_report['summary'], 'contract_performance': candidate_report['performance'], 'baseline_contract_performance': baseline_report['performance'], 'parent_v3_contract_performance': parent_report['performance'], 'report': candidate_report, 'baseline_report': baseline_report, 'parent_v3_report': parent_report} + +def benchmark_knn_build_rag_frontier_b6d4_v4(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_with_k32_topology(k32_split_count, k32_group_count)) + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=current_dispatcher.candidate) + parent_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=v3.candidate_with_k32_topology(k32_split_count, k32_group_count)) + return _benchmark_payload(candidate_report, baseline_report, parent_report, use_cupti=use_cupti, shape_labels=shape_labels, k32_split_count=k32_split_count, k32_group_count=k32_group_count) diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_frontier_b6d4_v5.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_frontier_b6d4_v5.py new file mode 100644 index 00000000..5f5496fd --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_frontier_b6d4_v5.py @@ -0,0 +1,187 @@ +"""RAG frontier bucket seed with sort4/early-stop K32 stage-1 top-k maintenance. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +keeps the v3 K10 split-72 routes and K32 S72/G8 two-stage merge topology, but +changes the K32 stage-1 producer. Instead of maintaining a fully sorted top-32 +list for every accepted database candidate, the producer keeps an unordered +top-32 with four 8-slot worst caches, sorts only admitted four-candidate groups, +stops the group visit once the sorted distance no longer beats the live worst, +and emits a sorted stream once per split. Guard misses delegate to the current +exported split72/de1a dispatcher. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import os +from collections.abc import Callable +from functools import cache, lru_cache +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_rag_frontier_4b5c_v3 as v3 +from .._dispatch_runtime import pack_kernel_args +current_dispatcher = v3.current_dispatcher +parent_k32 = v3.parent_k32 +RAG_MICROBATCH_SHAPE = v3.RAG_MICROBATCH_SHAPE +RAG_STREAM_LARGEK_SHAPE = v3.RAG_STREAM_LARGEK_SHAPE +RAG_BATCH_SHAPE = v3.RAG_BATCH_SHAPE +RAG_IRREGULAR_SHAPE = v3.RAG_IRREGULAR_SHAPE +K10_TARGET_SHAPES = v3.K10_TARGET_SHAPES +K32_TARGET_SHAPES = v3.K32_TARGET_SHAPES +TARGET_SHAPES = v3.TARGET_SHAPES +K10_SPLIT_COUNT = v3.K10_SPLIT_COUNT +K32_SPLIT_COUNT = _decode_capture(_json_loads('72')) +K32_GROUP_COUNT = _decode_capture(_json_loads('8')) +K32_GROUP_MERGE_THREADS = v3.K32_GROUP_MERGE_THREADS +K32_FINAL_MERGE_THREADS = v3.K32_FINAL_MERGE_THREADS +BLOCK_Q = v3.BLOCK_Q +BLOCK_M = v3.BLOCK_M +FEAT_D = v3.FEAT_D +STAGE1_THREADS = v3.STAGE1_THREADS +GRID_DIM_DEFAULT = v3.GRID_DIM_DEFAULT +CTA_GROUP = v3.CTA_GROUP +TOP_K_MAX = v3.TOP_K_MAX +knn_build_rag_frontier_b6d4_stage1_k32_sort4earlystop = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_frontier_b6d4_stage1_k32_sort4earlystop", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 192}')) +stage1_k32_sort4earlystop_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_frontier_b6d4_stage1_k32_sort4earlystop", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 192}')) + +def _validate_group_shape(split_count: int, group_count: int) -> None: + v3._validate_group_shape(split_count, group_count) + +def _group_merge_ir(split_count: int, group_count: int) -> Any: + return v3._group_merge_ir(split_count, group_count) + +def _final_merge_ir(group_count: int) -> Any: + return v3._final_merge_ir(group_count) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RAG_FRONTIER_B6D4_V5_VERIFY_KERNEL') + split_count = int(os.environ.get('LOOM_KNN_RAG_FRONTIER_B6D4_V5_VERIFY_SPLIT', K32_SPLIT_COUNT)) + group_count = int(os.environ.get('LOOM_KNN_RAG_FRONTIER_B6D4_V5_VERIFY_GROUPS', K32_GROUP_COUNT)) + if verify_kernel == 'k32_group_merge': + return _group_merge_ir(split_count, group_count) + if verify_kernel == 'k32_final_merge': + return _final_merge_ir(group_count) + return stage1_k32_sort4earlystop_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_frontier_b6d4_stage1_k32_sort4earlystop", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 192}')) + +def _compiled_stage1_sort4earlystop(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0162"}')) + +@cache +def _compiled_group_merge(split_count: int, group_count: int): + return parent_k32._compile_ir(_group_merge_ir(split_count, group_count)) + +@cache +def _compiled_final_merge(group_count: int): + return parent_k32._compile_ir(_final_merge_ir(group_count)) + +def _eligible_k10_rag_frontier(inputs: dict[str, Any]) -> bool: + return v3._eligible_k10_rag_frontier(inputs) + +def _eligible_k32_rag_frontier(inputs: dict[str, Any]) -> bool: + return v3._eligible_k32_rag_frontier(inputs) + +def _launch_k10_rag_frontier_s72(inputs: dict[str, Any]) -> None: + v3._launch_k10_rag_frontier_s72(inputs) + +def _launch_k32_rag_frontier_sort4earlystop_stage(inputs: dict[str, Any], *, split_count: int=K32_SPLIT_COUNT, group_count: int=K32_GROUP_COUNT) -> None: + _validate_group_shape(split_count, group_count) + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_q_tile_pairs = (num_q_tiles + CTA_GROUP - 1) // CTA_GROUP + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tile_pairs * split_count + total_queries = bsz * n_query + stage1_grid = min(total_work * CTA_GROUP, GRID_DIM_DEFAULT) + group_rows = total_queries * group_count + group_grid = min((group_rows + K32_GROUP_MERGE_THREADS - 1) // K32_GROUP_MERGE_THREADS, GRID_DIM_DEFAULT) + final_grid = min((total_queries + K32_FINAL_MERGE_THREADS - 1) // K32_FINAL_MERGE_THREADS, GRID_DIM_DEFAULT) + partial_dists, partial_indices = parent_k32.parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + group_dists, group_indices = parent_k32.parent_split._partial_buffers(split_count=group_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = parent_k32.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), total_queries, BLOCK_Q, dim, dim) + tmap_database = parent_k32.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, dim, dim) + stage1_kernel = _compiled_stage1_sort4earlystop() + stage1_kernel.launch_cluster(grid=(stage1_grid, 1, 1), block=(STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage1_k32_sort4earlystop_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tile_pairs=num_q_tile_pairs, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), cluster_dims=(CTA_GROUP, 1, 1), shared_mem=stage1_k32_sort4earlystop_ir.computed_smem_bytes) + group_ir = _group_merge_ir(split_count, group_count) + group_kernel = _compiled_group_merge(split_count, group_count) + group_kernel.launch(grid=(group_grid, 1, 1), block=(K32_GROUP_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, group_dists, group_indices, total_queries], shared_mem=group_ir.computed_smem_bytes) + final_ir = _final_merge_ir(group_count) + final_kernel = _compiled_final_merge(group_count) + final_kernel.launch(grid=(final_grid, 1, 1), block=(K32_FINAL_MERGE_THREADS, 1, 1), args=[group_dists, group_indices, inputs['out_dists'], inputs['out_indices'], total_queries], shared_mem=final_ir.computed_smem_bytes) + +def route_for_contract_inputs(inputs: dict[str, Any]) -> str: + if _eligible_k10_rag_frontier(inputs): + return 'rag_frontier_k10_s72' + if _eligible_k32_rag_frontier(inputs): + return ''.join(['rag_frontier_k32_s', format(K32_SPLIT_COUNT, ''), '_g', format(K32_GROUP_COUNT, ''), '_sort4earlystopstage']) + return 'current_split72_de1a_3dc7' + +def launch_from_contract_inputs(inputs: dict[str, Any], *, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> None: + if _eligible_k10_rag_frontier(inputs): + _launch_k10_rag_frontier_s72(inputs) + return + if _eligible_k32_rag_frontier(inputs): + _launch_k32_rag_frontier_sort4earlystop_stage(inputs, split_count=k32_split_count, group_count=k32_group_count) + return + current_dispatcher.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def candidate_with_k32_topology(split_count: int, group_count: int) -> Callable[[dict[str, Any]], None]: + + def _candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, k32_split_count=split_count, k32_group_count=group_count) + return _candidate + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(candidate, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return current_dispatcher._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + try: + return eval_mod.evaluate(kernel_fn, shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def _shape_payload(candidate_report: dict[str, Any], baseline_report: dict[str, Any], parent_report: dict[str, Any], *, k32_split_count: int, k32_group_count: int) -> dict[str, Any]: + rows = {} + for label in TARGET_SHAPES: + cand = candidate_report.get('per_shape', {}).get(label, {}) + base = baseline_report.get('per_shape', {}).get(label, {}) + parent = parent_report.get('per_shape', {}).get(label, {}) + cand_ms = cand.get('kernel_ms') + base_ms = base.get('kernel_ms') + parent_ms = parent.get('kernel_ms') + flashlib_ms = cand.get('flashlib_ms') + rows[label] = {'candidate': cand, 'current_dispatcher': base, 'parent_v3': parent, 'candidate_route': ''.join(['rag_frontier_k32_s', format(k32_split_count, ''), '_g', format(k32_group_count, ''), '_sort4earlystopstage']) if label in K32_TARGET_SHAPES else 'rag_frontier_k10_s72', 'candidate_ms': cand_ms, 'current_dispatcher_ms': base_ms, 'parent_v3_ms': parent_ms, 'flashlib_ms': flashlib_ms, 'speedup_vs_current': base_ms / cand_ms if cand_ms and base_ms else None, 'speedup_vs_parent_v3': parent_ms / cand_ms if cand_ms and parent_ms else None, 'ratio_vs_flashlib': flashlib_ms / cand_ms if cand_ms and flashlib_ms else None} + return rows + +def _benchmark_payload(candidate_report: dict[str, Any], baseline_report: dict[str, Any], parent_report: dict[str, Any], *, use_cupti: bool, shape_labels, k32_split_count: int, k32_group_count: int) -> dict[str, Any]: + timing_backends = sorted({row.get('timing_backend') for report in (candidate_report, baseline_report, parent_report) for row in report.get('per_shape', {}).values() if row.get('timing_backend') is not None}) + return {'tflops': candidate_report['summary']['primary_mean'] or 0.0, 'all_correct': candidate_report['summary']['all_correct'], 'baseline_all_correct': baseline_report['summary']['all_correct'], 'parent_v3_all_correct': parent_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'baseline_performance_comparable': baseline_report['summary']['performance_comparable'], 'parent_v3_performance_comparable': parent_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'measured_entrypoint': 'loom.examples.weave.knn_build_rag_frontier_b6d4_v5:benchmark_knn_build_rag_frontier_b6d4_v5', 'baseline_entrypoint': 'loom.examples.weave.knn_build_dispatch_split72_4e09_de1a_3dc7_v48:candidate', 'parent_v3_entrypoint': 'loom.examples.weave.knn_build_rag_frontier_4b5c_v3:candidate_with_k32_topology', 'accelerated_shape_labels': list(TARGET_SHAPES), 'measured_shape_labels': list(shape_labels), 'producer_split_counts': {'K10': K10_SPLIT_COUNT, 'K32': k32_split_count}, 'merge_topology': {'K32': 'two_stage_sorted_stream', 'groups': k32_group_count}, 'stage1_topk': {'K32': 'chunked_worst_sort4_earlystop_then_emit_sorted'}, 'timing_backends': timing_backends, 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'target_rows': _shape_payload(candidate_report, baseline_report, parent_report, k32_split_count=k32_split_count, k32_group_count=k32_group_count), 'contract_summary': candidate_report['summary'], 'baseline_contract_summary': baseline_report['summary'], 'parent_v3_contract_summary': parent_report['summary'], 'contract_performance': candidate_report['performance'], 'baseline_contract_performance': baseline_report['performance'], 'parent_v3_contract_performance': parent_report['performance'], 'report': candidate_report, 'baseline_report': baseline_report, 'parent_v3_report': parent_report} + +def benchmark_knn_build_rag_frontier_b6d4_v5(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_with_k32_topology(k32_split_count, k32_group_count)) + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=current_dispatcher.candidate) + parent_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=v3.candidate_with_k32_topology(k32_split_count, k32_group_count)) + return _benchmark_payload(candidate_report, baseline_report, parent_report, use_cupti=use_cupti, shape_labels=shape_labels, k32_split_count=k32_split_count, k32_group_count=k32_group_count) diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbatch_4a72_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbatch_4a72_v1.py new file mode 100644 index 00000000..7def56c1 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbatch_4a72_v1.py @@ -0,0 +1,198 @@ +"""RAG microbatch K10 bucket seed with cooperative split merge. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +routes only BF16 non-build ``B=1,Q in {8,16,32},M=100000,D=128,K=10`` rows. +It keeps the inherited tcgen05/TMA K10 stage-1 producer on the contract-visible +path and replaces the row-serial cached S72 merge with a fused cooperative +group/final merge. Guard misses delegate to the 4a72 selected dispatcher; no +external runtime fallback is used. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import os +from collections.abc import Callable +from functools import cache, lru_cache +from typing import Any +from .._dispatch_runtime import dc as dc +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_selected_portfolio_4a72_v1 as current_dispatcher +from . import knn_build_rag_frontier_7399_v1 as parent_7399 +from .._dispatch_runtime import pack_kernel_args +parent_v5 = parent_7399.v5 +parent_k10 = parent_v5.v3.v2.v1.split72.parent_lowk +TARGET_SHAPES = ('rag_microbatch_b1_q8_m100000_d128_k10', 'rag_microbatch_b1_q16_m100000_d128_k10', 'rag_microbatch_b1_q32_m100000_d128_k10') +TARGET_SHAPE_SET = set(TARGET_SHAPES) +K10_CANDIDATE_SPLITS = (48, 56, 64, 72, 80, 96) +K10_SPLIT_COUNT = _decode_capture(_json_loads('72')) +K10_GROUP_COUNT = _decode_capture(_json_loads('8')) +K10_FUSED_MERGE_THREADS = _decode_capture(_json_loads('32')) +K10_FUSED_MERGE_SLOTS = 128 +BLOCK_Q = parent_k10.BLOCK_Q +BLOCK_M = parent_k10.BLOCK_M +FEAT_D = parent_k10.FEAT_D +TOP_K_MAX = parent_k10.TOP_K_MAX +STAGE1_THREADS = parent_k10.STAGE1_THREADS +GRID_DIM_DEFAULT = parent_k10.GRID_DIM_DEFAULT +CTA_GROUP = parent_k10.CTA_GROUP +knn_build_rag_microbatch_4a72_k10_fused_group_final_merge = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbatch_4a72_k10_fused_group_final_merge", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 1024, "constants": [["TOP_K_MAX", 10], ["GROUP_COUNT", 8], ["GROUP_SPLITS", 9]], "cta_group": 1, "threads": 32}')) +fused_merge_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbatch_4a72_k10_fused_group_final_merge", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 1024, "constants": [["TOP_K_MAX", 10], ["GROUP_COUNT", 8], ["GROUP_SPLITS", 9]], "cta_group": 1, "threads": 32}')) + +def _ir_with_constants(ir_obj: Any, *, suffix: str, **updates: int) -> Any: + constants = tuple(((name, updates.get(name, value)) for name, value in ir_obj.constants)) + return dc.replace(ir_obj, symbol=''.join([format(ir_obj.symbol, ''), '_', format(suffix, '')]), constants=constants) + +def _fused_merge_ir(split_count: int, group_count: int) -> Any: + _validate_group_shape(split_count, group_count) + return _ir_with_constants(fused_merge_ir, suffix=''.join(['s', format(split_count, ''), 'g', format(group_count, ''), '_4a72_v1']), GROUP_COUNT=group_count, GROUP_SPLITS=split_count // group_count) + +def _validate_group_shape(split_count: int, group_count: int) -> None: + if split_count <= 0 or group_count <= 0: + raise ValueError(''.join(['split_count and group_count must be positive, got ', format(split_count, ''), ', ', format(group_count, '')])) + if split_count % group_count != 0: + raise ValueError(''.join(['split_count=', format(split_count, ''), ' must be divisible by group_count=', format(group_count, '')])) + if group_count > K10_FUSED_MERGE_THREADS: + raise ValueError(''.join(['group_count=', format(group_count, ''), ' exceeds fused merge threads=', format(K10_FUSED_MERGE_THREADS, '')])) + if group_count * TOP_K_MAX > K10_FUSED_MERGE_SLOTS: + raise ValueError(''.join(['group_count=', format(group_count, ''), ' needs ', format(group_count * TOP_K_MAX, ''), ' shared slots, but the K10 fused merge allocates ', format(K10_FUSED_MERGE_SLOTS, '')])) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RAG_MICROBATCH_4A72_V1_VERIFY_KERNEL') + split_count = int(os.environ.get('LOOM_KNN_RAG_MICROBATCH_4A72_V1_VERIFY_SPLIT', K10_SPLIT_COUNT)) + group_count = int(os.environ.get('LOOM_KNN_RAG_MICROBATCH_4A72_V1_VERIFY_GROUPS', K10_GROUP_COUNT)) + if verify_kernel == 'stage1': + return parent_k10.stage1_ir + return _fused_merge_ir(split_count, group_count) +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbatch_4a72_k10_fused_group_final_merge_s72g8_4a72_v1", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 1024, "constants": [["TOP_K_MAX", 10], ["GROUP_COUNT", 8], ["GROUP_SPLITS", 9]], "cta_group": 1, "threads": 32}')) + +def _compile_ir(ir_obj: Any): + from .._dispatch_runtime import generate_kernel + from .._dispatch_runtime import _cuda_include_dirs + from .._dispatch_runtime import compile_cuda + from .._dispatch_runtime import CUDAKernel + source = generate_kernel(ir_obj, validate=False, smem_bytes=ir_obj.computed_smem_bytes) + cubin = compile_cuda(source, arch=parent_k10.base_v1._select_arch_and_preload(), options=['--use_fast_math'], include_dirs=_cuda_include_dirs()) + return CUDAKernel(cubin, ''.join(['kernel_', format(ir_obj.symbol, '')])) + +def _compiled_stage1(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0161"}')) + +@cache +def _compiled_fused_merge(split_count: int, group_count: int): + return _compile_ir(_fused_merge_ir(split_count, group_count)) + +def _dtype_name(inputs: dict[str, Any]) -> str: + query = inputs.get('query') + if query is not None: + return str(query.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')) + +def _label_can_hit(inputs: dict[str, Any], labels: set[str]) -> bool: + value = inputs.get('label') + return value is None or str(value) in labels + +def _eligible_rag_microbatch(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, TARGET_SHAPE_SET) and (not bool(inputs.get('build', False))) and (int(inputs.get('B', -1)) == 1) and (int(inputs.get('Q', -1)) in (8, 16, 32)) and (int(inputs.get('M', -1)) == 100000) and (int(inputs.get('D', -1)) == FEAT_D) and (int(inputs.get('K', -1)) == TOP_K_MAX) and (_dtype_name(inputs) == 'bfloat16') + +def _launch_rag_microbatch_fused_merge(inputs: dict[str, Any], *, split_count: int=K10_SPLIT_COUNT, group_count: int=K10_GROUP_COUNT) -> None: + _validate_group_shape(split_count, group_count) + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_q_tile_pairs = (num_q_tiles + CTA_GROUP - 1) // CTA_GROUP + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tile_pairs * split_count + total_queries = bsz * n_query + stage1_grid = min(total_work * CTA_GROUP, GRID_DIM_DEFAULT) + merge_grid = min(total_queries, GRID_DIM_DEFAULT) + partial_dists, partial_indices = parent_k10.parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = parent_k10.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), total_queries, BLOCK_Q, dim, dim) + tmap_database = parent_k10.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, dim, dim) + stage1_kernel = _compiled_stage1() + stage1_kernel.launch_cluster(grid=(stage1_grid, 1, 1), block=(STAGE1_THREADS, 1, 1), args=pack_kernel_args(parent_k10.stage1_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tile_pairs=num_q_tile_pairs, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), cluster_dims=(CTA_GROUP, 1, 1), shared_mem=parent_k10.stage1_ir.computed_smem_bytes) + merge_kernel = _compiled_fused_merge(split_count, group_count) + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(K10_FUSED_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], total_queries], shared_mem=fused_merge_ir.computed_smem_bytes) + +def route_for_contract_inputs(inputs: dict[str, Any], *, split_count: int=K10_SPLIT_COUNT, group_count: int=K10_GROUP_COUNT) -> str: + if _eligible_rag_microbatch(inputs): + return ''.join(['rag_microbatch_4a72_k10_s', format(split_count, ''), '_g', format(group_count, ''), '_fusedmerge']) + return current_dispatcher.route_for_contract_inputs(inputs) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, split_count: int=K10_SPLIT_COUNT, group_count: int=K10_GROUP_COUNT) -> None: + if _eligible_rag_microbatch(inputs): + _launch_rag_microbatch_fused_merge(inputs, split_count=split_count, group_count=group_count) + return + current_dispatcher.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def parent_7399_forced_rowbase_candidate(inputs: dict[str, Any]): + if _eligible_rag_microbatch(inputs): + parent_7399._launch_k10_rag_frontier_s72(inputs) + return None + parent_7399.candidate(inputs) + return None + +def candidate_with_topology(split_count: int, group_count: int=K10_GROUP_COUNT) -> Callable[[dict[str, Any]], None]: + + def _candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, split_count=split_count, group_count=group_count) + return _candidate + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + return eval_mod.evaluate(candidate, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return current_dispatcher._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + try: + return eval_mod.evaluate(kernel_fn, shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def _shape_payload(candidate_report: dict[str, Any], parent_report: dict[str, Any], current_report: dict[str, Any], *, split_count: int, group_count: int) -> dict[str, Any]: + rows = {} + for label in TARGET_SHAPES: + cand = candidate_report.get('per_shape', {}).get(label, {}) + parent = parent_report.get('per_shape', {}).get(label, {}) + current = current_report.get('per_shape', {}).get(label, {}) + cand_ms = cand.get('kernel_ms') + parent_ms = parent.get('kernel_ms') + current_ms = current.get('kernel_ms') + flashlib_ms = cand.get('flashlib_ms') + rows[label] = {'candidate': cand, 'parent_7399': parent, 'current_4a72': current, 'candidate_route': ''.join(['rag_microbatch_4a72_k10_s', format(split_count, ''), '_g', format(group_count, ''), '_fusedmerge']), 'candidate_ms': cand_ms, 'parent_7399_ms': parent_ms, 'current_4a72_ms': current_ms, 'flashlib_ms': flashlib_ms, 'speedup_vs_parent_7399': parent_ms / cand_ms if cand_ms and parent_ms else None, 'speedup_vs_current_4a72': current_ms / cand_ms if cand_ms and current_ms else None, 'ratio_vs_flashlib': flashlib_ms / cand_ms if cand_ms and flashlib_ms else None} + return rows + +def _benchmark_payload(candidate_report: dict[str, Any], parent_report: dict[str, Any], current_report: dict[str, Any], *, use_cupti: bool, shape_labels, split_count: int, group_count: int) -> dict[str, Any]: + timing_backends = sorted({row.get('timing_backend') for report in (candidate_report, parent_report, current_report) for row in report.get('per_shape', {}).values() if row.get('timing_backend') is not None}) + return {'tflops': candidate_report['summary']['primary_mean'] or 0.0, 'all_correct': candidate_report['summary']['all_correct'], 'parent_7399_all_correct': parent_report['summary']['all_correct'], 'current_4a72_all_correct': current_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'parent_7399_performance_comparable': parent_report['summary']['performance_comparable'], 'current_4a72_performance_comparable': current_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'measured_entrypoint': 'loom.examples.weave.knn_build_rag_microbatch_4a72_v1:benchmark_knn_build_rag_microbatch_4a72_v1', 'parent_7399_entrypoint': 'loom.examples.weave.knn_build_rag_frontier_7399_v1:_launch_k10_rag_frontier_s72', 'current_4a72_entrypoint': 'loom.examples.weave.knn_build_dispatch_selected_portfolio_4a72_v1:candidate', 'accelerated_shape_labels': list(TARGET_SHAPES), 'measured_shape_labels': list(shape_labels), 'producer_split_count': split_count, 'merge_topology': {'K10': 'fused_cooperative_group_final', 'groups': group_count}, 'timing_backends': timing_backends, 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'target_rows': _shape_payload(candidate_report, parent_report, current_report, split_count=split_count, group_count=group_count), 'contract_summary': candidate_report['summary'], 'parent_7399_contract_summary': parent_report['summary'], 'current_4a72_contract_summary': current_report['summary'], 'contract_performance': candidate_report['performance'], 'parent_7399_contract_performance': parent_report['performance'], 'current_4a72_contract_performance': current_report['performance'], 'report': candidate_report, 'parent_7399_report': parent_report, 'current_4a72_report': current_report} + +def benchmark_knn_build_rag_microbatch_4a72_v1(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES, split_count: int=K10_SPLIT_COUNT, group_count: int=K10_GROUP_COUNT) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_with_topology(split_count, group_count)) + parent_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=parent_7399_forced_rowbase_candidate) + current_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=current_dispatcher.candidate) + return _benchmark_payload(candidate_report, parent_report, current_report, use_cupti=use_cupti, shape_labels=shape_labels, split_count=split_count, group_count=group_count) + +def benchmark_split_sweep(*, use_cupti: bool=True, split_counts=K10_CANDIDATE_SPLITS, group_count: int=K10_GROUP_COUNT) -> dict[str, Any]: + rows = {} + for split_count in split_counts: + rows[''.join(['s', format(split_count, ''), '_g', format(group_count, '')])] = benchmark_knn_build_rag_microbatch_4a72_v1(use_cupti=use_cupti, split_count=int(split_count), group_count=group_count) + return {'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'shape_labels': list(TARGET_SHAPES), 'split_counts': list(split_counts), 'group_count': group_count, 'rows': rows} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbatch_4a72_v2.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbatch_4a72_v2.py new file mode 100644 index 00000000..45cda18b --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbatch_4a72_v2.py @@ -0,0 +1,213 @@ +"""RAG microbatch K10 bucket seed with single-CTA producer. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +routes only BF16 non-build ``B=1,Q in {8,16,32},M=100000,D=128,K=10`` rows. +It specializes the inherited tcgen05/TMA K10 split producer for low-Q rows by +using a single-CTA MMA stage with a higher split count, then reuses the fused +cooperative group/final merge from the round-20 seed. Guard misses delegate to +the 4a72 selected dispatcher; no external runtime fallback is used. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import os +from collections.abc import Callable +from functools import cache, lru_cache +from typing import Any +from .._dispatch_runtime import dc as dc +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_selected_portfolio_4a72_v1 as current_dispatcher +from . import knn_build_rag_frontier_7399_v1 as parent_7399 +from . import knn_build_rag_microbatch_4a72_v1 as parent_v1 +from .._dispatch_runtime import pack_kernel_args +parent_v5 = parent_7399.v5 +parent_k10 = parent_v5.v3.v2.v1.split72.parent_lowk +TARGET_SHAPES = ('rag_microbatch_b1_q8_m100000_d128_k10', 'rag_microbatch_b1_q16_m100000_d128_k10', 'rag_microbatch_b1_q32_m100000_d128_k10') +TARGET_SHAPE_SET = set(TARGET_SHAPES) +K10_CANDIDATE_SPLITS = (72, 96, 120, 144) +K10_SPLIT_COUNT = _decode_capture(_json_loads('144')) +K10_GROUP_COUNT = _decode_capture(_json_loads('12')) +K10_FUSED_MERGE_THREADS = _decode_capture(_json_loads('32')) +K10_FUSED_MERGE_SLOTS = 128 +BLOCK_Q = parent_k10.BLOCK_Q +BLOCK_M = parent_k10.BLOCK_M +FEAT_D = parent_k10.FEAT_D +TOP_K_MAX = parent_k10.TOP_K_MAX +STAGE1_THREADS = parent_k10.STAGE1_THREADS +GRID_DIM_DEFAULT = parent_k10.GRID_DIM_DEFAULT +CTA_GROUP = 1 +knn_build_rag_microbatch_4a72_v2_stage1_k10_cta1_maxtree = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbatch_4a72_v2_stage1_k10_cta1_maxtree", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) +stage1_cta1_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbatch_4a72_v2_stage1_k10_cta1_maxtree", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) +knn_build_rag_microbatch_4a72_v2_k10_fused_group_final_merge = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbatch_4a72_v2_k10_fused_group_final_merge", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 1024, "constants": [["TOP_K_MAX", 10], ["GROUP_COUNT", 8], ["GROUP_SPLITS", 9]], "cta_group": 1, "threads": 32}')) +fused_merge_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbatch_4a72_v2_k10_fused_group_final_merge", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 1024, "constants": [["TOP_K_MAX", 10], ["GROUP_COUNT", 8], ["GROUP_SPLITS", 9]], "cta_group": 1, "threads": 32}')) + +def _ir_with_constants(ir_obj: Any, *, suffix: str, **updates: int) -> Any: + constants = tuple(((name, updates.get(name, value)) for name, value in ir_obj.constants)) + return dc.replace(ir_obj, symbol=''.join([format(ir_obj.symbol, ''), '_', format(suffix, '')]), constants=constants) + +def _fused_merge_ir(split_count: int, group_count: int) -> Any: + _validate_group_shape(split_count, group_count) + return _ir_with_constants(fused_merge_ir, suffix=''.join(['s', format(split_count, ''), 'g', format(group_count, ''), '_4a72_v2']), GROUP_COUNT=group_count, GROUP_SPLITS=split_count // group_count) + +def _validate_group_shape(split_count: int, group_count: int) -> None: + if split_count <= 0 or group_count <= 0: + raise ValueError(''.join(['split_count and group_count must be positive, got ', format(split_count, ''), ', ', format(group_count, '')])) + if split_count % group_count != 0: + raise ValueError(''.join(['split_count=', format(split_count, ''), ' must be divisible by group_count=', format(group_count, '')])) + if group_count > K10_FUSED_MERGE_THREADS: + raise ValueError(''.join(['group_count=', format(group_count, ''), ' exceeds fused merge threads=', format(K10_FUSED_MERGE_THREADS, '')])) + if group_count * TOP_K_MAX > K10_FUSED_MERGE_SLOTS: + raise ValueError(''.join(['group_count=', format(group_count, ''), ' needs ', format(group_count * TOP_K_MAX, ''), ' shared slots, but the K10 fused merge allocates ', format(K10_FUSED_MERGE_SLOTS, '')])) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RAG_MICROBATCH_4A72_V2_VERIFY_KERNEL') + split_count = int(os.environ.get('LOOM_KNN_RAG_MICROBATCH_4A72_V2_VERIFY_SPLIT', K10_SPLIT_COUNT)) + group_count = int(os.environ.get('LOOM_KNN_RAG_MICROBATCH_4A72_V2_VERIFY_GROUPS', K10_GROUP_COUNT)) + if verify_kernel == 'stage1': + return stage1_cta1_ir + return _fused_merge_ir(split_count, group_count) +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbatch_4a72_v2_k10_fused_group_final_merge_s144g12_4a72_v2", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 1024, "constants": [["TOP_K_MAX", 10], ["GROUP_COUNT", 12], ["GROUP_SPLITS", 12]], "cta_group": 1, "threads": 32}')) + +def _compile_ir(ir_obj: Any): + from .._dispatch_runtime import generate_kernel + from .._dispatch_runtime import _cuda_include_dirs + from .._dispatch_runtime import compile_cuda + from .._dispatch_runtime import CUDAKernel + source = generate_kernel(ir_obj, validate=False, smem_bytes=ir_obj.computed_smem_bytes) + cubin = compile_cuda(source, arch=parent_k10.base_v1._select_arch_and_preload(), options=['--use_fast_math'], include_dirs=_cuda_include_dirs()) + return CUDAKernel(cubin, ''.join(['kernel_', format(ir_obj.symbol, '')])) + +def _compiled_stage1(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0087"}')) + +@cache +def _compiled_fused_merge(split_count: int, group_count: int): + return _compile_ir(_fused_merge_ir(split_count, group_count)) + +def _dtype_name(inputs: dict[str, Any]) -> str: + query = inputs.get('query') + if query is not None: + return str(query.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')) + +def _label_can_hit(inputs: dict[str, Any], labels: set[str]) -> bool: + value = inputs.get('label') + return value is None or str(value) in labels + +def _eligible_rag_microbatch(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, TARGET_SHAPE_SET) and (not bool(inputs.get('build', False))) and (int(inputs.get('B', -1)) == 1) and (int(inputs.get('Q', -1)) in (8, 16, 32)) and (int(inputs.get('M', -1)) == 100000) and (int(inputs.get('D', -1)) == FEAT_D) and (int(inputs.get('K', -1)) == TOP_K_MAX) and (_dtype_name(inputs) == 'bfloat16') + +def _launch_rag_microbatch_fused_merge(inputs: dict[str, Any], *, split_count: int=K10_SPLIT_COUNT, group_count: int=K10_GROUP_COUNT) -> None: + _validate_group_shape(split_count, group_count) + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_q_tile_pairs = (num_q_tiles + CTA_GROUP - 1) // CTA_GROUP + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tile_pairs * split_count + total_queries = bsz * n_query + stage1_grid = min(total_work * CTA_GROUP, GRID_DIM_DEFAULT) + merge_grid = min(total_queries, GRID_DIM_DEFAULT) + partial_dists, partial_indices = parent_k10.parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = parent_k10.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), total_queries, BLOCK_Q, dim, dim) + tmap_database = parent_k10.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, dim, dim) + stage1_kernel = _compiled_stage1() + stage1_launch = stage1_kernel.prepare_launch_cluster(grid=(stage1_grid, 1, 1), block=(STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage1_cta1_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tiles=num_q_tile_pairs, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), cluster_dims=(CTA_GROUP, 1, 1), shared_mem=stage1_cta1_ir.computed_smem_bytes) + merge_kernel = _compiled_fused_merge(split_count, group_count) + merge_launch = merge_kernel.prepare_launch(grid=(merge_grid, 1, 1), block=(K10_FUSED_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], total_queries], shared_mem=fused_merge_ir.computed_smem_bytes) + stage1_launch.launch() + merge_launch.launch() + +def route_for_contract_inputs(inputs: dict[str, Any], *, split_count: int=K10_SPLIT_COUNT, group_count: int=K10_GROUP_COUNT) -> str: + if _eligible_rag_microbatch(inputs): + return ''.join(['rag_microbatch_4a72_v2_cta1_k10_s', format(split_count, ''), '_g', format(group_count, ''), '_fusedmerge']) + return current_dispatcher.route_for_contract_inputs(inputs) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, split_count: int=K10_SPLIT_COUNT, group_count: int=K10_GROUP_COUNT) -> None: + if _eligible_rag_microbatch(inputs): + _launch_rag_microbatch_fused_merge(inputs, split_count=split_count, group_count=group_count) + return + current_dispatcher.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def parent_7399_forced_rowbase_candidate(inputs: dict[str, Any]): + if _eligible_rag_microbatch(inputs): + parent_7399._launch_k10_rag_frontier_s72(inputs) + return None + parent_7399.candidate(inputs) + return None + +def parent_v1_fused_candidate(inputs: dict[str, Any]): + if _eligible_rag_microbatch(inputs): + parent_v1.launch_from_contract_inputs(inputs) + return None + current_dispatcher.candidate(inputs) + return None + +def candidate_with_topology(split_count: int, group_count: int=K10_GROUP_COUNT) -> Callable[[dict[str, Any]], None]: + + def _candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, split_count=split_count, group_count=group_count) + return _candidate + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + return eval_mod.evaluate(candidate, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return current_dispatcher._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + try: + return eval_mod.evaluate(kernel_fn, shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def _shape_payload(candidate_report: dict[str, Any], parent_v1_report: dict[str, Any], parent_report: dict[str, Any], current_report: dict[str, Any], *, split_count: int, group_count: int) -> dict[str, Any]: + rows = {} + for label in TARGET_SHAPES: + cand = candidate_report.get('per_shape', {}).get(label, {}) + parent_v1_row = parent_v1_report.get('per_shape', {}).get(label, {}) + parent = parent_report.get('per_shape', {}).get(label, {}) + current = current_report.get('per_shape', {}).get(label, {}) + cand_ms = cand.get('kernel_ms') + parent_v1_ms = parent_v1_row.get('kernel_ms') + parent_ms = parent.get('kernel_ms') + current_ms = current.get('kernel_ms') + flashlib_ms = cand.get('flashlib_ms') + rows[label] = {'candidate': cand, 'parent_v1': parent_v1_row, 'parent_7399': parent, 'current_4a72': current, 'candidate_route': ''.join(['rag_microbatch_4a72_v2_cta1_k10_s', format(split_count, ''), '_g', format(group_count, ''), '_fusedmerge']), 'candidate_ms': cand_ms, 'parent_v1_ms': parent_v1_ms, 'parent_7399_ms': parent_ms, 'current_4a72_ms': current_ms, 'flashlib_ms': flashlib_ms, 'speedup_vs_parent_v1': parent_v1_ms / cand_ms if cand_ms and parent_v1_ms else None, 'speedup_vs_parent_7399': parent_ms / cand_ms if cand_ms and parent_ms else None, 'speedup_vs_current_4a72': current_ms / cand_ms if cand_ms and current_ms else None, 'ratio_vs_flashlib': flashlib_ms / cand_ms if cand_ms and flashlib_ms else None} + return rows + +def _benchmark_payload(candidate_report: dict[str, Any], parent_v1_report: dict[str, Any], parent_report: dict[str, Any], current_report: dict[str, Any], *, use_cupti: bool, shape_labels, split_count: int, group_count: int) -> dict[str, Any]: + timing_backends = sorted({row.get('timing_backend') for report in (candidate_report, parent_v1_report, parent_report, current_report) for row in report.get('per_shape', {}).values() if row.get('timing_backend') is not None}) + return {'tflops': candidate_report['summary']['primary_mean'] or 0.0, 'all_correct': candidate_report['summary']['all_correct'], 'parent_v1_all_correct': parent_v1_report['summary']['all_correct'], 'parent_7399_all_correct': parent_report['summary']['all_correct'], 'current_4a72_all_correct': current_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'parent_v1_performance_comparable': parent_v1_report['summary']['performance_comparable'], 'parent_7399_performance_comparable': parent_report['summary']['performance_comparable'], 'current_4a72_performance_comparable': current_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'measured_entrypoint': 'loom.examples.weave.knn_build_rag_microbatch_4a72_v2:benchmark_knn_build_rag_microbatch_4a72_v2', 'parent_v1_entrypoint': 'loom.examples.weave.knn_build_rag_microbatch_4a72_v1:launch_from_contract_inputs', 'parent_7399_entrypoint': 'loom.examples.weave.knn_build_rag_frontier_7399_v1:_launch_k10_rag_frontier_s72', 'current_4a72_entrypoint': 'loom.examples.weave.knn_build_dispatch_selected_portfolio_4a72_v1:candidate', 'accelerated_shape_labels': list(TARGET_SHAPES), 'measured_shape_labels': list(shape_labels), 'producer_split_count': split_count, 'producer_topology': {'K10': 'cta_group_1_tcgen05_tma', 'split_count': split_count}, 'merge_topology': {'K10': 'fused_cooperative_group_final', 'groups': group_count}, 'timing_backends': timing_backends, 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'target_rows': _shape_payload(candidate_report, parent_v1_report, parent_report, current_report, split_count=split_count, group_count=group_count), 'contract_summary': candidate_report['summary'], 'parent_v1_contract_summary': parent_v1_report['summary'], 'parent_7399_contract_summary': parent_report['summary'], 'current_4a72_contract_summary': current_report['summary'], 'contract_performance': candidate_report['performance'], 'parent_v1_contract_performance': parent_v1_report['performance'], 'parent_7399_contract_performance': parent_report['performance'], 'current_4a72_contract_performance': current_report['performance'], 'report': candidate_report, 'parent_v1_report': parent_v1_report, 'parent_7399_report': parent_report, 'current_4a72_report': current_report} + +def benchmark_knn_build_rag_microbatch_4a72_v2(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES, split_count: int=K10_SPLIT_COUNT, group_count: int=K10_GROUP_COUNT) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_with_topology(split_count, group_count)) + parent_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=parent_v1_fused_candidate) + rowbase_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=parent_7399_forced_rowbase_candidate) + current_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=current_dispatcher.candidate) + return _benchmark_payload(candidate_report, parent_report, rowbase_report, current_report, use_cupti=use_cupti, shape_labels=shape_labels, split_count=split_count, group_count=group_count) + +def benchmark_split_sweep(*, use_cupti: bool=True, split_counts=K10_CANDIDATE_SPLITS, group_count: int=K10_GROUP_COUNT) -> dict[str, Any]: + rows = {} + for split_count in split_counts: + rows[''.join(['s', format(split_count, ''), '_g', format(group_count, '')])] = benchmark_knn_build_rag_microbatch_4a72_v2(use_cupti=use_cupti, split_count=int(split_count), group_count=group_count) + return {'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'shape_labels': list(TARGET_SHAPES), 'split_counts': list(split_counts), 'group_count': group_count, 'rows': rows} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbatch_k10_q4_m64s144_17b8_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbatch_k10_q4_m64s144_17b8_v1.py new file mode 100644 index 00000000..2126fddf --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbatch_k10_q4_m64s144_17b8_v1.py @@ -0,0 +1,168 @@ +"""Q4-only M64 split144 repair for the kNN build RAG microbatch lane. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +routes only ``rag_microbatch_b1_q4_m100000_d128_k10`` through the existing +M64 tcgen05 producer with a split144/group12 topology. Q64 and all guard +misses delegate to the previously consumed Q4/Q64 M64 wrapper, so this branch +can be evaluated as a Q4-specific seed without widening the Q4/Q64 guard. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from pathlib import Path +from typing import Any, Callable +from . import knn_build_rag_microbatch_k10_q4q64_d555_v1 as base_q4q64 +from . import knn_build_rag_microbatch_m64_d4f7_v1 as rag_m64 +MODULE = 'loom.examples.weave.knn_build_rag_microbatch_k10_q4_m64s144_17b8_v1' +Q4_SHAPE = base_q4q64.Q4_SHAPE +Q64_SHAPE = base_q4q64.Q64_SHAPE +TARGET_SHAPES = (Q4_SHAPE,) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +SPLIT_COUNT = 144 +GROUP_COUNT = 12 +SEED_ID = 'rag_microbatch_k10_q4_m64_s144_g12_17b8_v1' +BASELINE_SEED_ID = base_q4q64.SEED_ID +ROUTE_Q4_M64S144 = 'rag_microbatch_k10_q4_m64_s144_g12_17b8_v1' +ROUTE_NAME = ''.join([format(MODULE, ''), ':m64_s144_g12']) +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +BASELINE_ENTRYPOINT = ''.join([format(base_q4q64.MODULE, ''), ':launch_from_contract_inputs']) +SOURCE_TASKS = _decode_capture(_json_loads('{"__dict_items__": [["rag_microbatch_k10_q4_m64_s144_g12_17b8_v1", "weave-evolve-knn-build-17b8 / design_doc/active/generalize_auto_tuning_knn_build_round_116_17b8.md"], ["m64_parent_seed", "loom.examples.weave.knn_build_rag_microbatch_m64_d4f7_v1:_launch_rag_microbatch_m64"], ["rag_microbatch_k10_q4q64_m64_3505_d555_v1", "generalize-auto-tuning-knn-build-d555 / design_doc/active/generalize_auto_tuning_knn_build_round_115_d555.md"]]}')) +eval_mod = base_q4q64.eval_mod + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_17B8_Q4_M64S144_VERIFY_KERNEL') + if verify_kernel == 'merge': + return rag_m64.parent_micro._fused_merge_ir(SPLIT_COUNT, GROUP_COUNT) + return rag_m64.stage1_m64_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbatch_m64_d4f7_stage1", "arg_keys": ["query", "database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 91392, "constants": [], "cta_group": 1, "threads": 512}')) + +def _dtype_name(inputs: dict[str, Any], key: str) -> str: + tensor = inputs.get(key) + if tensor is not None: + return str(tensor.dtype).removeprefix('torch.') + return str(inputs.get('dtype', '')).removeprefix('torch.') + +def _eligible_q4_m64s144(inputs: dict[str, Any]) -> bool: + label = inputs.get('label') + return not bool(inputs.get('build', False)) and _dtype_name(inputs, 'query') == 'bfloat16' and (_dtype_name(inputs, 'database') == 'bfloat16') and (int(inputs.get('B', -1)) == 1) and (int(inputs.get('Q', -1)) == 4) and (int(inputs.get('M', -1)) == 100000) and (int(inputs.get('D', -1)) == 128) and (int(inputs.get('K', -1)) == 10) and (label is None or str(label) == Q4_SHAPE) + +def _select_contract_shapes(shape_labels): + return base_q4q64._select_contract_shapes(shape_labels) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if not force_fallback and _eligible_q4_m64s144(inputs): + return ROUTE_Q4_M64S144 + return base_q4q64.route_for_contract_inputs(inputs, force_fallback=force_fallback) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + if not force_fallback and _eligible_q4_m64s144(inputs): + rag_m64._launch_rag_microbatch_m64(inputs, split_count=SPLIT_COUNT, group_count=GROUP_COUNT) + return + base_q4q64.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + +def candidate_q4_m64s144_17b8_v1(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_baseline_q4q64(inputs: dict[str, Any]) -> None: + base_q4q64.candidate_rag_microbatch_k10_q4q64_m64_3505_d555_v1(inputs) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate, correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + return base_q4q64._run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=kernel_fn, correctness=correctness, time_flashlib=time_flashlib) + +def _trace_record(inputs: dict[str, Any], *, force_fallback: bool=False) -> dict[str, Any]: + label = str(inputs.get('label')) + baseline_route = base_q4q64.route_for_contract_inputs(inputs) + if not force_fallback and _eligible_q4_m64s144(inputs): + return base_q4q64.base_d555.base_f30c._normalize_route_row({'shape_key': label, 'selected_route': ROUTE_NAME, 'selected_entrypoint': ROUTE_ENTRYPOINT, 'selected_seed': SEED_ID, 'expected_seed': SEED_ID, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': '17b8_rag_microbatch_k10_q4_m64_s144_exact_guard', 'guard_condition': 'exact BF16 non-build RAG microbatch B=1 Q=4 M=100000 D=128 K=10', 'coverage': 'M64 split144/group12 Weave seed before q4q64 fallback', 'consumed_seed': SEED_ID, 'replaced_route': baseline_route, 'baseline_dispatcher_route': baseline_route, 'classification': 'unmeasured'}) + row = dict(base_q4q64.route_trace_for_contract_shapes((label,), force_fallback=force_fallback)[0]) + if force_fallback and _eligible_q4_m64s144(inputs): + row['expected_seed'] = SEED_ID + row['guard_id'] = 'forced_fallback_17b8_q4_m64_s144_disabled' + row['guard_condition'] = 'forced fallback to q4q64 baseline; Q4 M64 split144 seed disabled' + row['classification'] = 'guard-miss' + return base_q4q64.base_d555.base_f30c._normalize_route_row(row) + +def route_trace_for_contract_shapes(shape_labels=TARGET_SHAPES, *, force_fallback: bool=False) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + return [_trace_record(base_q4q64.base_d555.base_f30c._trace_inputs_from_shape(shape), force_fallback=force_fallback) for shape in selected] + +def _annotate_route_trace(route_trace: list[dict[str, Any]], candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> list[dict[str, Any]]: + annotated = [] + for row in route_trace: + label = str(row.get('shape_key')) + out = dict(row) + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') + speedup_vs_baseline = baseline_ms / candidate_ms if candidate_ms and baseline_ms else None + speedup_vs_external = flashlib_ms / candidate_ms if candidate_ms and flashlib_ms else None + out['dispatcher_kernel_ms'] = candidate_ms + out['baseline_dispatcher_kernel_ms'] = baseline_ms + out['external_baseline_ms'] = flashlib_ms + out['flashlib_ms'] = flashlib_ms + out['relative_speedup_vs_baseline'] = speedup_vs_baseline + out['speedup_vs_external_baseline'] = speedup_vs_external + out['external_baseline_ref'] = 'same_session' if flashlib_ms is not None else 'not_available' + if label == Q4_SHAPE: + if speedup_vs_external is not None and speedup_vs_external < 1.05: + out['classification'] = 'kernel-slow' + elif speedup_vs_baseline is not None and speedup_vs_baseline < 1.0: + out['classification'] = 'kernel-slow' + elif out.get('selected_seed') == SEED_ID: + out['classification'] = 'seed-consumed' + annotated.append(base_q4q64.base_d555.base_f30c._normalize_route_row(out)) + return annotated + +def _timing_backends_for_reports(*reports: dict[str, Any]) -> list[str]: + return sorted({row.get('timing_backend') for report in reports for row in report.get('per_shape', {}).values() if row.get('timing_backend') is not None}) + +def _payload_shape_labels(shape_labels) -> tuple[str, ...]: + if shape_labels is None: + return tuple((str(shape['label']) for shape in eval_mod.CANONICAL_SHAPES)) + return tuple((str(label) for label in shape_labels)) + +def benchmark_baseline_q4q64(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES, benchmark_correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_baseline_q4q64, correctness=benchmark_correctness, time_flashlib=time_flashlib) + report['candidate_id'] = BASELINE_SEED_ID + report['measured_entrypoint'] = ''.join([format(base_q4q64.MODULE, ''), ':benchmark_candidate_rag_microbatch_k10_q4q64_m64_3505_d555_v1']) + report['route_trace'] = base_q4q64.route_trace_for_contract_shapes(shape_labels) + report['route_trace_included'] = True + return report + +def benchmark_candidate_q4_m64s144_17b8_v1(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES, baseline_report: dict[str, Any] | None=None, benchmark_correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + if baseline_report is None: + baseline_report = benchmark_baseline_q4q64(use_cupti=use_cupti, shape_labels=shape_labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_q4_m64s144_17b8_v1, correctness=benchmark_correctness, time_flashlib=time_flashlib) + candidate_metric = candidate_report['summary']['primary_mean'] + baseline_metric = baseline_report['summary']['primary_mean'] + payload_shape_labels = _payload_shape_labels(shape_labels) + route_trace = _annotate_route_trace(route_trace_for_contract_shapes(shape_labels), candidate_report, baseline_report) + return {'candidate_id': SEED_ID, 'baseline_candidate_id': BASELINE_SEED_ID, 'selected_seeds': (SEED_ID,), 'source_tasks': SOURCE_TASKS, 'tflops': candidate_metric, 'baseline_tflops': baseline_metric, 'metric_delta': candidate_metric - baseline_metric if candidate_metric and baseline_metric else None, 'all_correct': candidate_report['summary']['all_correct'], 'baseline_all_correct': baseline_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'baseline_performance_comparable': baseline_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'baseline_invalid_performance_reason': baseline_report['summary']['invalid_performance_reason'], 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_candidate_q4_m64s144_17b8_v1']), 'baseline_entrypoint': ''.join([format(MODULE, ''), ':benchmark_baseline_q4q64']), 'measured_shape_labels': tuple(shape_labels) if shape_labels is not None else 'all_canonical', 'timing_backend': 'cupti' if use_cupti else 'cuda_event', 'route_modules': {SEED_ID: ROUTE_ENTRYPOINT, BASELINE_SEED_ID: BASELINE_ENTRYPOINT}, 'route_trace': route_trace, 'forced_fallback_route_trace': route_trace_for_contract_shapes(shape_labels, force_fallback=True), 'route_trace_included': True, 'contract_summary': candidate_report['summary'], 'baseline_contract_summary': baseline_report['summary'], 'contract_performance': candidate_report['performance'], 'baseline_contract_performance': baseline_report['performance'], 'consumed_seed_rows': {label: candidate_report.get('per_shape', {}).get(label, {}) for label in payload_shape_labels}, 'baseline_consumed_seed_rows': {label: baseline_report.get('per_shape', {}).get(label, {}) for label in payload_shape_labels}, 'timing_backends': _timing_backends_for_reports(candidate_report, baseline_report), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'benchmark_correctness_checked': benchmark_correctness, 'benchmark_time_flashlib': time_flashlib, 'rank_objective': {'metric': 'tflops', 'direction': 'maximize', 'comparable': candidate_report['performance']['comparable'], 'invalid_reason': candidate_report['performance']['invalid_reason'], 'valid_measurement_count': candidate_report['performance']['valid_measurement_count'], 'value': candidate_metric, 'baseline_value': baseline_metric, 'delta': candidate_metric - baseline_metric if candidate_metric and baseline_metric else None, 'denominator': 'rag_microbatch_k10_q4'}, 'report': candidate_report, 'baseline_report': baseline_report} + +def benchmark_knn_build_rag_microbatch_k10_q4_m64s144_17b8_v1(**kwargs) -> dict[str, Any]: + return benchmark_candidate_q4_m64s144_17b8_v1(**kwargs) + +def _write_artifact(payload: dict[str, Any], artifact_dir: Path) -> Path: + artifact_dir.mkdir(parents=True, exist_ok=True) + path = artifact_dir / 'rag_microbatch_k10_q4_m64_s144_g12_17b8_v1.json' + path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n', encoding='utf-8') + return path diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbatch_k10_q4_s144_d555_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbatch_k10_q4_s144_d555_v1.py new file mode 100644 index 00000000..54d789bc --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbatch_k10_q4_s144_d555_v1.py @@ -0,0 +1,171 @@ +"""Q4-only S144 repair for the d555 RAG microbatch K10 dispatcher. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +routes only ``rag_microbatch_b1_q4_m100000_d128_k10`` through the existing +S144/G12 tcgen05 stage-1 producer and fused merge. All other contract rows, +including the previously consumed Q64 row, delegate to +``knn_build_rag_microbatch_k10_q4q64_d555_v1`` so this branch can be evaluated +as a Q4-specific repair without broadening the Q4/Q64 guard. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from pathlib import Path +from typing import Any, Callable +from . import knn_build_rag_microbatch_k10_q4q64_d555_v1 as base_q4q64 +from . import knn_build_rag_microbucket_faeb_v2 as faeb +MODULE = 'loom.examples.weave.knn_build_rag_microbatch_k10_q4_s144_d555_v1' +Q4_SHAPE = base_q4q64.Q4_SHAPE +Q64_SHAPE = base_q4q64.Q64_SHAPE +TARGET_SHAPES = (Q4_SHAPE,) +COMPARISON_SHAPES = (Q4_SHAPE, Q64_SHAPE) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +SEED_ID = 'rag_microbatch_k10_q4_s144_g12_d555_v1' +BASELINE_SEED_ID = base_q4q64.SEED_ID +ROUTE_Q4_S144 = 'rag_microbatch_k10_q4_s144_g12_d555_v1' +ROUTE_NAME = ''.join([format(MODULE, ''), ':q4_s144_g12']) +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +BASELINE_ENTRYPOINT = ''.join([format(base_q4q64.MODULE, ''), ':launch_from_contract_inputs']) +SOURCE_TASKS = _decode_capture(_json_loads('{"__dict_items__": [["rag_microbatch_k10_q4_s144_g12_d555_v1", "weave-evolve-knn-build-066c / design_doc/active/generalize_auto_tuning_knn_build_round_116_066c.md"], ["s144_parent_seed", "loom.examples.weave.knn_build_rag_microbucket_faeb_v2:_launch_q4_k10_s144"], ["rag_microbatch_k10_q4q64_m64_3505_d555_v1", "generalize-auto-tuning-knn-build-d555 / design_doc/active/generalize_auto_tuning_knn_build_round_115_d555.md"]]}')) +eval_mod = base_q4q64.eval_mod + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_D555_RAGMICRO_Q4_S144_VERIFY_KERNEL') + if verify_kernel == 'q4_s144_merge': + return faeb.rag_s144._fused_merge_ir(faeb.S144_SPLIT_COUNT, faeb.S144_GROUP_COUNT_Q4) + return faeb.rag_s144.stage1_cta1_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbatch_4a72_v2_stage1_k10_cta1_maxtree", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) + +def _dtype_name(inputs: dict[str, Any], key: str) -> str: + tensor = inputs.get(key) + if tensor is not None: + return str(tensor.dtype).removeprefix('torch.') + return str(inputs.get('dtype', '')).removeprefix('torch.') + +def _eligible_q4_s144(inputs: dict[str, Any]) -> bool: + label = inputs.get('label') + return not bool(inputs.get('build', False)) and _dtype_name(inputs, 'query') == 'bfloat16' and (_dtype_name(inputs, 'database') == 'bfloat16') and (int(inputs.get('B', -1)) == 1) and (int(inputs.get('Q', -1)) == 4) and (int(inputs.get('M', -1)) == 100000) and (int(inputs.get('D', -1)) == 128) and (int(inputs.get('K', -1)) == 10) and (label is None or str(label) == Q4_SHAPE) + +def _select_contract_shapes(shape_labels): + return base_q4q64._select_contract_shapes(shape_labels) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if not force_fallback and _eligible_q4_s144(inputs): + return ROUTE_Q4_S144 + return base_q4q64.route_for_contract_inputs(inputs, force_fallback=force_fallback) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + if not force_fallback and _eligible_q4_s144(inputs): + faeb._launch_q4_k10_s144(inputs) + return + base_q4q64.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + +def candidate_q4_s144_d555_v1(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_baseline_q4q64(inputs: dict[str, Any]) -> None: + base_q4q64.candidate_rag_microbatch_k10_q4q64_m64_3505_d555_v1(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate, correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + return base_q4q64._run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=kernel_fn, correctness=correctness, time_flashlib=time_flashlib) + +def _trace_record(inputs: dict[str, Any], *, force_fallback: bool=False) -> dict[str, Any]: + label = str(inputs.get('label')) + baseline_route = base_q4q64.route_for_contract_inputs(inputs) + if not force_fallback and _eligible_q4_s144(inputs): + return base_q4q64.base_d555.base_f30c._normalize_route_row({'shape_key': label, 'selected_route': ROUTE_NAME, 'selected_entrypoint': ROUTE_ENTRYPOINT, 'selected_seed': SEED_ID, 'expected_seed': SEED_ID, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': 'd555_rag_microbatch_k10_q4_s144_exact_guard', 'guard_condition': 'exact BF16 non-build RAG microbatch B=1 Q=4 M=100000 D=128 K=10', 'coverage': 'direct S144/G12 Weave seed before q4q64/d555 fallback', 'consumed_seed': SEED_ID, 'replaced_route': baseline_route, 'baseline_dispatcher_route': baseline_route, 'classification': 'unmeasured'}) + row = dict(base_q4q64.route_trace_for_contract_shapes((label,), force_fallback=force_fallback)[0]) + if force_fallback and _eligible_q4_s144(inputs): + row['expected_seed'] = SEED_ID + row['guard_id'] = 'forced_fallback_d555_ragmicro_q4_s144_disabled' + row['guard_condition'] = 'forced fallback to q4q64/d555; Q4 S144 seed disabled' + row['classification'] = 'guard-miss' + return base_q4q64.base_d555.base_f30c._normalize_route_row(row) + +def route_trace_for_contract_shapes(shape_labels=TARGET_SHAPES, *, force_fallback: bool=False) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + return [_trace_record(base_q4q64.base_d555.base_f30c._trace_inputs_from_shape(shape), force_fallback=force_fallback) for shape in selected] + +def _annotate_route_trace(route_trace: list[dict[str, Any]], candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> list[dict[str, Any]]: + annotated = [] + for row in route_trace: + label = str(row.get('shape_key')) + out = dict(row) + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') + speedup_vs_baseline = baseline_ms / candidate_ms if candidate_ms and baseline_ms else None + speedup_vs_external = flashlib_ms / candidate_ms if candidate_ms and flashlib_ms else None + out['dispatcher_kernel_ms'] = candidate_ms + out['baseline_dispatcher_kernel_ms'] = baseline_ms + out['external_baseline_ms'] = flashlib_ms + out['flashlib_ms'] = flashlib_ms + out['relative_speedup_vs_baseline'] = speedup_vs_baseline + out['speedup_vs_external_baseline'] = speedup_vs_external + out['external_baseline_ref'] = 'same_session' if flashlib_ms is not None else 'not_available' + if label == Q4_SHAPE: + if speedup_vs_external is not None and speedup_vs_external < 1.05: + out['classification'] = 'kernel-slow' + elif speedup_vs_baseline is not None and speedup_vs_baseline < 1.0: + out['classification'] = 'kernel-slow' + elif out.get('selected_seed') == SEED_ID: + out['classification'] = 'seed-consumed' + annotated.append(base_q4q64.base_d555.base_f30c._normalize_route_row(out)) + return annotated + +def _timing_backends_for_reports(*reports: dict[str, Any]) -> list[str]: + return sorted({row.get('timing_backend') for report in reports for row in report.get('per_shape', {}).values() if row.get('timing_backend') is not None}) + +def _payload_shape_labels(shape_labels) -> tuple[str, ...]: + if shape_labels is None: + return tuple((str(shape['label']) for shape in eval_mod.CANONICAL_SHAPES)) + return tuple((str(label) for label in shape_labels)) + +def benchmark_baseline_q4q64(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES, benchmark_correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_baseline_q4q64, correctness=benchmark_correctness, time_flashlib=time_flashlib) + report['candidate_id'] = BASELINE_SEED_ID + report['measured_entrypoint'] = ''.join([format(base_q4q64.MODULE, ''), ':benchmark_candidate_rag_microbatch_k10_q4q64_m64_3505_d555_v1']) + report['route_trace'] = base_q4q64.route_trace_for_contract_shapes(shape_labels) + report['route_trace_included'] = True + return report + +def benchmark_candidate_q4_s144_d555_v1(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES, baseline_report: dict[str, Any] | None=None, benchmark_correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + if baseline_report is None: + baseline_report = benchmark_baseline_q4q64(use_cupti=use_cupti, shape_labels=shape_labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_q4_s144_d555_v1, correctness=benchmark_correctness, time_flashlib=time_flashlib) + candidate_metric = candidate_report['summary']['primary_mean'] + baseline_metric = baseline_report['summary']['primary_mean'] + payload_shape_labels = _payload_shape_labels(shape_labels) + route_trace = _annotate_route_trace(route_trace_for_contract_shapes(shape_labels), candidate_report, baseline_report) + return {'candidate_id': SEED_ID, 'baseline_candidate_id': BASELINE_SEED_ID, 'selected_seeds': (SEED_ID,), 'source_tasks': SOURCE_TASKS, 'tflops': candidate_metric, 'baseline_tflops': baseline_metric, 'metric_delta': candidate_metric - baseline_metric if candidate_metric and baseline_metric else None, 'all_correct': candidate_report['summary']['all_correct'], 'baseline_all_correct': baseline_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'baseline_performance_comparable': baseline_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'baseline_invalid_performance_reason': baseline_report['summary']['invalid_performance_reason'], 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_candidate_q4_s144_d555_v1']), 'baseline_entrypoint': ''.join([format(MODULE, ''), ':benchmark_baseline_q4q64']), 'measured_shape_labels': tuple(shape_labels) if shape_labels is not None else 'all_canonical', 'timing_backend': 'cupti' if use_cupti else 'cuda_event', 'route_modules': {SEED_ID: ROUTE_ENTRYPOINT, BASELINE_SEED_ID: BASELINE_ENTRYPOINT}, 'route_trace': route_trace, 'forced_fallback_route_trace': route_trace_for_contract_shapes(shape_labels, force_fallback=True), 'route_trace_included': True, 'contract_summary': candidate_report['summary'], 'baseline_contract_summary': baseline_report['summary'], 'contract_performance': candidate_report['performance'], 'baseline_contract_performance': baseline_report['performance'], 'consumed_seed_rows': {label: candidate_report.get('per_shape', {}).get(label, {}) for label in payload_shape_labels}, 'baseline_consumed_seed_rows': {label: baseline_report.get('per_shape', {}).get(label, {}) for label in payload_shape_labels}, 'timing_backends': _timing_backends_for_reports(candidate_report, baseline_report), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'benchmark_correctness_checked': benchmark_correctness, 'benchmark_time_flashlib': time_flashlib, 'rank_objective': {'metric': 'tflops', 'direction': 'maximize', 'value': candidate_metric, 'baseline_value': baseline_metric, 'delta': candidate_metric - baseline_metric if candidate_metric and baseline_metric else None, 'denominator': 'rag_microbatch_k10_q4'}, 'report': candidate_report, 'baseline_report': baseline_report} + +def benchmark_knn_build_rag_microbatch_k10_q4_s144_d555_v1(**kwargs) -> dict[str, Any]: + return benchmark_candidate_q4_s144_d555_v1(**kwargs) + +def _write_artifact(payload: dict[str, Any], artifact_dir: Path) -> Path: + artifact_dir.mkdir(parents=True, exist_ok=True) + path = artifact_dir / 'rag_microbatch_k10_q4_s144_g12_d555_v1.json' + path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n', encoding='utf-8') + return path diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbatch_k10_q4q64_d555_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbatch_k10_q4q64_d555_v1.py new file mode 100644 index 00000000..52d8a3e5 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbatch_k10_q4q64_d555_v1.py @@ -0,0 +1,164 @@ +"""Exact RAG microbatch K10 M64 seed for d555 residual blockers. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +routes only the exact BF16 contract rows +``rag_microbatch_b1_q4_m100000_d128_k10`` and +``rag_microbatch_b1_q64_m100000_d128_k10`` through the existing 3505/FAEB +M64 K10 tcgen05 stage-1 producer and S128/G8 fused merge. Guard misses +delegate to the d555 full82 Weave dispatcher; no external runtime fallback is +used. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from pathlib import Path +from typing import Any, Callable +from . import knn_build_dispatch_d555_residual_seed_synth_full82_v1 as base_d555 +from . import knn_build_rag_microbucket_3505_v2 as seed_3505 +MODULE = 'loom.examples.weave.knn_build_rag_microbatch_k10_q4q64_d555_v1' +Q4_SHAPE = 'rag_microbatch_b1_q4_m100000_d128_k10' +Q64_SHAPE = 'rag_microbatch_b1_q64_m100000_d128_k10' +TARGET_SHAPES = (Q4_SHAPE, Q64_SHAPE) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +SEED_ID = 'rag_microbatch_k10_q4q64_m64_3505_d555_v1' +ROUTE_NAME = ''.join([format(MODULE, ''), ':m64_s128_g8_fused_merge']) +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +BASELINE_ID = base_d555.CANDIDATE_CONFIGS[base_d555.DEFAULT_CANDIDATE_KEY]['candidate_id'] +BASELINE_ENTRYPOINT = ''.join([format(base_d555.MODULE, ''), ':benchmark_candidate_d555_direct_residual_seeds']) +SOURCE_TASKS = {SEED_ID: 'generalize-auto-tuning-knn-build-d555 / design_doc/active/generalize_auto_tuning_knn_build_round_115_d555.md', 'm64_3505_parent_seed': 'weave-evolve-knn-build-3505 / design_doc/active/weave_evolve_knn_build_round_33_3505.md'} +eval_mod = base_d555.eval_mod + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_D555_RAGMICRO_Q4Q64_VERIFY_KERNEL') + if verify_kernel == 'm64_merge_s128_g8': + return seed_3505.faeb.rag_m64.parent_micro._fused_merge_ir(seed_3505.faeb.M64_SPLIT_COUNT, seed_3505.faeb.M64_GROUP_COUNT) + return seed_3505.faeb.rag_m64.stage1_m64_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbatch_m64_d4f7_stage1", "arg_keys": ["query", "database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 91392, "constants": [], "cta_group": 1, "threads": 512}')) + +def _dtype_name(inputs: dict[str, Any], key: str) -> str: + tensor = inputs.get(key) + if tensor is not None: + return str(tensor.dtype).removeprefix('torch.') + return str(inputs.get('dtype', '')).removeprefix('torch.') + +def _eligible_rag_microbatch_k10_q4q64(inputs: dict[str, Any]) -> bool: + label = inputs.get('label') + return not bool(inputs.get('build', False)) and _dtype_name(inputs, 'query') == 'bfloat16' and (_dtype_name(inputs, 'database') == 'bfloat16') and (int(inputs.get('B', -1)) == 1) and (int(inputs.get('Q', -1)) in {4, 64}) and (int(inputs.get('M', -1)) == 100000) and (int(inputs.get('D', -1)) == 128) and (int(inputs.get('K', -1)) == 10) and (label is None or str(label) in TARGET_SHAPE_SET) + +def _select_contract_shapes(shape_labels): + return base_d555._select_contract_shapes(shape_labels) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if not force_fallback and _eligible_rag_microbatch_k10_q4q64(inputs): + return seed_3505.route_for_contract_inputs(inputs) + return base_d555.route_for_contract_inputs(inputs, force_fallback=force_fallback) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + if not force_fallback and _eligible_rag_microbatch_k10_q4q64(inputs): + seed_3505.launch_from_contract_inputs(inputs) + return + base_d555.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + +def candidate_rag_microbatch_k10_q4q64_m64_3505_d555_v1(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_baseline_d555(inputs: dict[str, Any]) -> None: + base_d555.candidate_d555_direct_residual_seeds(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate, correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + return base_d555._run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=kernel_fn, correctness=correctness, time_flashlib=time_flashlib) + +def _trace_record(inputs: dict[str, Any], *, force_fallback: bool=False) -> dict[str, Any]: + label = str(inputs.get('label')) + baseline_route = base_d555.route_for_contract_inputs(inputs) + if not force_fallback and _eligible_rag_microbatch_k10_q4q64(inputs): + return base_d555.base_f30c._normalize_route_row({'shape_key': label, 'selected_route': ROUTE_NAME, 'selected_entrypoint': ROUTE_ENTRYPOINT, 'selected_seed': SEED_ID, 'expected_seed': SEED_ID, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': 'd555_rag_microbatch_k10_q4q64_m64_3505_exact_guard', 'guard_condition': 'exact BF16 non-build RAG microbatch shape with B=1 Q in {4,64} M=100000 D=128 K=10', 'coverage': 'direct 3505 M64/S128/G8 Weave seed before d555 fallback', 'consumed_seed': SEED_ID, 'replaced_route': baseline_route, 'baseline_dispatcher_route': baseline_route, 'classification': 'unmeasured'}) + row = dict(base_d555.route_trace_for_contract_shapes((label,), force_fallback=force_fallback)[0]) + if force_fallback and _eligible_rag_microbatch_k10_q4q64(inputs): + row['expected_seed'] = SEED_ID + row['guard_id'] = 'forced_fallback_d555_ragmicro_q4q64_disabled' + row['guard_condition'] = 'forced fallback to d555; Q4/Q64 M64 microbatch seed disabled' + row['classification'] = 'guard-miss' + return base_d555.base_f30c._normalize_route_row(row) + +def route_trace_for_contract_shapes(shape_labels=TARGET_SHAPES, *, force_fallback: bool=False) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + return [_trace_record(base_d555.base_f30c._trace_inputs_from_shape(shape), force_fallback=force_fallback) for shape in selected] + +def _annotate_route_trace(route_trace: list[dict[str, Any]], candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> list[dict[str, Any]]: + annotated = [] + for row in route_trace: + label = str(row.get('shape_key')) + out = dict(row) + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') + speedup_vs_baseline = baseline_ms / candidate_ms if candidate_ms and baseline_ms else None + speedup_vs_external = flashlib_ms / candidate_ms if candidate_ms and flashlib_ms else None + out['dispatcher_kernel_ms'] = candidate_ms + out['baseline_dispatcher_kernel_ms'] = baseline_ms + out['external_baseline_ms'] = flashlib_ms + out['flashlib_ms'] = flashlib_ms + out['relative_speedup_vs_baseline'] = speedup_vs_baseline + out['speedup_vs_external_baseline'] = speedup_vs_external + out['external_baseline_ref'] = 'same_session' if flashlib_ms is not None else 'not_available' + if label in TARGET_SHAPE_SET: + if speedup_vs_external is not None and speedup_vs_external < 1.05: + out['classification'] = 'kernel-slow' + elif speedup_vs_baseline is not None and speedup_vs_baseline < 1.0: + out['classification'] = 'kernel-slow' + elif out.get('selected_seed') == SEED_ID: + out['classification'] = 'seed-consumed' + annotated.append(base_d555.base_f30c._normalize_route_row(out)) + return annotated + +def _timing_backends_for_reports(*reports: dict[str, Any]) -> list[str]: + return sorted({row.get('timing_backend') for report in reports for row in report.get('per_shape', {}).values() if row.get('timing_backend') is not None}) + +def benchmark_baseline_d555(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES, benchmark_correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_baseline_d555, correctness=benchmark_correctness, time_flashlib=time_flashlib) + report['candidate_id'] = BASELINE_ID + report['measured_entrypoint'] = BASELINE_ENTRYPOINT + report['route_trace'] = base_d555.route_trace_for_contract_shapes(shape_labels) + report['route_trace_included'] = True + return report + +def benchmark_candidate_rag_microbatch_k10_q4q64_m64_3505_d555_v1(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES, baseline_report: dict[str, Any] | None=None, benchmark_correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + if baseline_report is None: + baseline_report = benchmark_baseline_d555(use_cupti=use_cupti, shape_labels=shape_labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_rag_microbatch_k10_q4q64_m64_3505_d555_v1, correctness=benchmark_correctness, time_flashlib=time_flashlib) + candidate_metric = candidate_report['summary']['primary_mean'] + baseline_metric = baseline_report['summary']['primary_mean'] + route_trace = _annotate_route_trace(route_trace_for_contract_shapes(shape_labels), candidate_report, baseline_report) + return {'candidate_id': SEED_ID, 'baseline_candidate_id': BASELINE_ID, 'selected_seeds': (SEED_ID,), 'source_tasks': SOURCE_TASKS, 'tflops': candidate_metric, 'baseline_tflops': baseline_metric, 'metric_delta': candidate_metric - baseline_metric if candidate_metric and baseline_metric else None, 'all_correct': candidate_report['summary']['all_correct'], 'baseline_all_correct': baseline_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'baseline_performance_comparable': baseline_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'baseline_invalid_performance_reason': baseline_report['summary']['invalid_performance_reason'], 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_candidate_rag_microbatch_k10_q4q64_m64_3505_d555_v1']), 'baseline_entrypoint': ''.join([format(MODULE, ''), ':benchmark_baseline_d555']), 'measured_shape_labels': tuple(shape_labels) if shape_labels is not None else 'all_canonical', 'timing_backend': 'cupti' if use_cupti else 'cuda_event', 'route_modules': {SEED_ID: ROUTE_ENTRYPOINT, BASELINE_ID: ''.join([format(base_d555.MODULE, ''), ':launch_from_contract_inputs'])}, 'route_trace': route_trace, 'forced_fallback_route_trace': route_trace_for_contract_shapes(shape_labels, force_fallback=True), 'route_trace_included': True, 'contract_summary': candidate_report['summary'], 'baseline_contract_summary': baseline_report['summary'], 'contract_performance': candidate_report['performance'], 'baseline_contract_performance': baseline_report['performance'], 'consumed_seed_rows': {label: candidate_report.get('per_shape', {}).get(label, {}) for label in TARGET_SHAPES}, 'baseline_consumed_seed_rows': {label: baseline_report.get('per_shape', {}).get(label, {}) for label in TARGET_SHAPES}, 'timing_backends': _timing_backends_for_reports(candidate_report, baseline_report), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'benchmark_correctness_checked': benchmark_correctness, 'benchmark_time_flashlib': time_flashlib, 'rank_objective': {'metric': 'tflops', 'direction': 'maximize', 'value': candidate_metric, 'baseline_value': baseline_metric, 'delta': candidate_metric - baseline_metric if candidate_metric and baseline_metric else None, 'denominator': 'rag_microbatch_k10_q4_q64'}, 'report': candidate_report, 'baseline_report': baseline_report} + +def benchmark_knn_build_rag_microbatch_k10_q4q64_d555_v1(**kwargs) -> dict[str, Any]: + return benchmark_candidate_rag_microbatch_k10_q4q64_m64_3505_d555_v1(**kwargs) + +def _write_artifact(payload: dict[str, Any], artifact_dir: Path) -> Path: + artifact_dir.mkdir(parents=True, exist_ok=True) + path = artifact_dir / 'rag_microbatch_k10_q4q64_m64_3505_d555_v1.json' + path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n', encoding='utf-8') + return path diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbatch_k10_q4q8q16q32q64_4757_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbatch_k10_q4q8q16q32q64_4757_v1.py new file mode 100644 index 00000000..d61e7573 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbatch_k10_q4q8q16q32q64_4757_v1.py @@ -0,0 +1,215 @@ +"""Exact q4/q8/q16/q32/q64 RAG microbatch K10 split-select seed for 4757. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +layers the validated q4/q64 M64/S128/G8 route and a q32 M64/S128/G8 route onto +the prior q8/q16 split-selection sidecar. It routes only BF16 non-build +``B=1,M=100000,D=128,K=10`` rows with ``Q in {4,8,16,32,64}``; guard misses +delegate to the q8/q16 sidecar. FlashLib is used only by the contract harness +as a black-box timing baseline. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from pathlib import Path +from typing import Any, Callable +from . import knn_build_rag_microbatch_k10_q4q64_d555_v1 as q4q64_seed +from . import knn_build_rag_microbatch_k10_q8q16_4757_v1 as q8q16_parent +MODULE = 'loom.examples.weave.knn_build_rag_microbatch_k10_q4q8q16q32q64_4757_v1' +Q4_SHAPE = q4q64_seed.Q4_SHAPE +Q8_SHAPE = q8q16_parent.Q8_SHAPE +Q16_SHAPE = q8q16_parent.Q16_SHAPE +Q32_SHAPE = 'rag_microbatch_b1_q32_m100000_d128_k10' +Q64_SHAPE = q4q64_seed.Q64_SHAPE +TARGET_SHAPES = (Q4_SHAPE, Q8_SHAPE, Q16_SHAPE, Q32_SHAPE, Q64_SHAPE) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +Q4Q64_SEED_ID = q4q64_seed.SEED_ID +Q8Q16_SEED_ID = q8q16_parent.SEED_ID +Q32_SEED_ID = 'rag_microbatch_k10_q32_m64_s128_g8_4757_v1' +SEED_ID = 'rag_microbatch_k10_q4q8q16q32q64_4757_v1' +CANDIDATE_ID = 'rag_microbatch_k10_q4q8q16q32q64_4757_v1' +PARENT_ID = q8q16_parent.CANDIDATE_ID +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_Q4Q64 = q4q64_seed.ROUTE_NAME +ROUTE_Q8 = q8q16_parent.ROUTE_Q8 +ROUTE_Q16 = q8q16_parent.ROUTE_Q16 +ROUTE_Q32 = ''.join([format(MODULE, ''), ':q32_m64_s128_g8']) +ROUTE_PARENT = q8q16_parent.ROUTE_ENTRYPOINT +Q32_SPLIT_COUNT = 128 +Q32_GROUP_COUNT = 8 +PRODUCTION_ROUTE_MODULES = {**q8q16_parent.PRODUCTION_ROUTE_MODULES, Q4Q64_SEED_ID: q4q64_seed.ROUTE_ENTRYPOINT, Q8Q16_SEED_ID: q8q16_parent.ROUTE_ENTRYPOINT, Q32_SEED_ID: ROUTE_ENTRYPOINT, SEED_ID: ROUTE_ENTRYPOINT, PARENT_ID: ROUTE_PARENT} +SOURCE_TASKS = {**q8q16_parent.SOURCE_TASKS, Q4Q64_SEED_ID: 'generalize-auto-tuning-knn-build-d555 / loom.examples.weave.knn_build_rag_microbatch_k10_q4q64_d555_v1', Q32_SEED_ID: 'weave-evolve-knn-build-d4f7 / loom.examples.weave.knn_build_rag_microbatch_m64_d4f7_v1', SEED_ID: 'weave-evolve-knn-build-4757 / design_doc/active/weave_evolve_knn_build_round_137_4757_ragmicro_q8q16.md'} +eval_mod = q8q16_parent.eval_mod + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RAGMICRO_Q4Q8Q16Q32Q64_4757_VERIFY_KERNEL') + if verify_kernel == 'q4q64_merge_s128': + return q4q64_seed.seed_3505.faeb.rag_m64.parent_micro._fused_merge_ir(q4q64_seed.seed_3505.faeb.M64_SPLIT_COUNT, q4q64_seed.seed_3505.faeb.M64_GROUP_COUNT) + if verify_kernel == 'q8_merge_s128': + return q8q16_parent.rag_m64.parent_micro._fused_merge_ir(q8q16_parent.Q8_SPLIT_COUNT, q8q16_parent.GROUP_COUNT) + if verify_kernel == 'q32_merge_s128': + return q8q16_parent.rag_m64.parent_micro._fused_merge_ir(Q32_SPLIT_COUNT, Q32_GROUP_COUNT) + if verify_kernel == 'q16_merge_s136': + return q8q16_parent.rag_m64.parent_micro._fused_merge_ir(q8q16_parent.Q16_SPLIT_COUNT, q8q16_parent.GROUP_COUNT) + return q8q16_parent.rag_m64.stage1_m64_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbatch_m64_d4f7_stage1", "arg_keys": ["query", "database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 91392, "constants": [], "cta_group": 1, "threads": 512}')) + +def _select_contract_shapes(shape_labels): + return q8q16_parent._select_contract_shapes(shape_labels) + +def _trace_inputs_for_shape(shape: dict[str, Any]) -> dict[str, Any]: + return q8q16_parent._trace_inputs_for_shape(shape) + +def _inputs_for_label(label: str) -> dict[str, Any]: + return _trace_inputs_for_shape(_select_contract_shapes((label,))[0]) + +def _eligible_q4q64(inputs: dict[str, Any]) -> bool: + return q4q64_seed._eligible_rag_microbatch_k10_q4q64(inputs) + +def _eligible_q8q16(inputs: dict[str, Any]) -> bool: + return q8q16_parent._split_for_inputs(inputs) is not None + +def _eligible_q32(inputs: dict[str, Any]) -> bool: + label = inputs.get('label') + return q8q16_parent._eligible_common(inputs) and int(inputs.get('Q', -1)) == 32 and (label is None or str(label) == Q32_SHAPE) + +def _expected_seed(inputs: dict[str, Any]) -> str | None: + if _eligible_q4q64(inputs): + return Q4Q64_SEED_ID + if _eligible_q8q16(inputs): + return Q8Q16_SEED_ID + if _eligible_q32(inputs): + return Q32_SEED_ID + return None + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if not force_fallback and _eligible_q4q64(inputs): + return q4q64_seed.route_for_contract_inputs(inputs) + if not force_fallback and _eligible_q32(inputs): + return ROUTE_Q32 + return q8q16_parent.route_for_contract_inputs(inputs) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + if not force_fallback and _eligible_q4q64(inputs): + q4q64_seed.launch_from_contract_inputs(inputs) + return + if not force_fallback and _eligible_q32(inputs): + q8q16_parent.rag_m64._launch_rag_microbatch_m64(inputs, split_count=Q32_SPLIT_COUNT, group_count=Q32_GROUP_COUNT) + return + q8q16_parent.launch_from_contract_inputs(inputs) + +def candidate_q4q8q16q32q64_4757_v1(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]) -> None: + candidate_q4q8q16q32q64_4757_v1(inputs) + +def candidate_parent_q8q16(inputs: dict[str, Any]) -> None: + q8q16_parent.launch_from_contract_inputs(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate, correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + return q8q16_parent._run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=kernel_fn, correctness=correctness, time_flashlib=time_flashlib) + +def _seed_route_row(inputs: dict[str, Any], *, force_fallback: bool=False) -> dict[str, Any]: + label = str(inputs.get('label')) + q8q16_route = q8q16_parent.route_for_contract_inputs(inputs) + if force_fallback: + row = dict(q8q16_parent.route_trace_for_contract_shapes((label,))[0]) + row['expected_seed'] = Q4Q64_SEED_ID + row['guard_id'] = ''.join(['forced_fallback_', format(Q4Q64_SEED_ID, ''), '_disabled']) + row['guard_condition'] = 'forced fallback to q8/q16 4757 sidecar; q4/q64 seed disabled' + row['classification'] = 'guard-miss' + row['parent_portfolio_route'] = q8q16_route + return q8q16_parent.parent._normalize_route_row(row) + q_value = int(inputs.get('Q', -1)) + return q8q16_parent.parent._normalize_route_row({'shape_key': label, 'selected_route': q4q64_seed.route_for_contract_inputs(inputs), 'selected_entrypoint': ROUTE_ENTRYPOINT, 'selected_seed': Q4Q64_SEED_ID, 'expected_seed': Q4Q64_SEED_ID, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': ''.join(['4757_rag_microbatch_k10_q', format(q_value, ''), '_m64_s128_g8_exact_guard']), 'guard_condition': ''.join(['exact BF16 non-build B=1 Q=', format(q_value, ''), ' M=100000 D=128 K=10']), 'coverage': 'q4/q64 M64 seed layered before q8/q16 4757 sidecar', 'consumed_seed': Q4Q64_SEED_ID, 'replaced_route': q8q16_route, 'parent_portfolio_route': q8q16_route, 'split_count': q4q64_seed.seed_3505.faeb.M64_SPLIT_COUNT, 'group_count': q4q64_seed.seed_3505.faeb.M64_GROUP_COUNT, 'classification': 'seed-consumed'}) + +def _q32_route_row(inputs: dict[str, Any], *, force_fallback: bool=False) -> dict[str, Any]: + label = str(inputs.get('label')) + q8q16_route = q8q16_parent.route_for_contract_inputs(inputs) + if force_fallback: + row = dict(q8q16_parent.route_trace_for_contract_shapes((label,))[0]) + row['expected_seed'] = Q32_SEED_ID + row['guard_id'] = ''.join(['forced_fallback_', format(Q32_SEED_ID, ''), '_disabled']) + row['guard_condition'] = 'forced fallback to q8/q16 4757 sidecar; q32 M64 seed disabled' + row['classification'] = 'guard-miss' + row['parent_portfolio_route'] = q8q16_route + return q8q16_parent.parent._normalize_route_row(row) + return q8q16_parent.parent._normalize_route_row({'shape_key': label, 'selected_route': ROUTE_Q32, 'selected_entrypoint': ROUTE_ENTRYPOINT, 'selected_seed': Q32_SEED_ID, 'expected_seed': Q32_SEED_ID, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': '4757_rag_microbatch_k10_q32_m64_s128_g8_exact_guard', 'guard_condition': 'exact BF16 non-build B=1 Q=32 M=100000 D=128 K=10', 'coverage': 'q32 M64 seed layered before q8/q16 4757 sidecar', 'consumed_seed': Q32_SEED_ID, 'replaced_route': q8q16_route, 'parent_portfolio_route': q8q16_route, 'split_count': Q32_SPLIT_COUNT, 'group_count': Q32_GROUP_COUNT, 'classification': 'seed-consumed'}) + +def _route_trace_record(inputs: dict[str, Any], *, force_fallback: bool=False) -> dict[str, Any]: + label = str(inputs.get('label')) + if not force_fallback and _eligible_q4q64(inputs): + return _seed_route_row(inputs) + if force_fallback and _eligible_q4q64(inputs): + return _seed_route_row(inputs, force_fallback=True) + if not force_fallback and _eligible_q32(inputs): + return _q32_route_row(inputs) + if force_fallback and _eligible_q32(inputs): + return _q32_route_row(inputs, force_fallback=True) + row = dict(q8q16_parent.route_trace_for_contract_shapes((label,))[0]) + row['expected_seed'] = _expected_seed(inputs) + row['parent_portfolio_route'] = q8q16_parent.route_for_contract_inputs(inputs) + return q8q16_parent.parent._normalize_route_row(row) + +def route_trace_for_contract_shapes(shape_labels=TARGET_SHAPES, *, force_fallback: bool=False) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + return [_route_trace_record(_trace_inputs_for_shape(shape), force_fallback=force_fallback) for shape in selected] + +def _rows_for_labels(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return {label: dict(report.get('per_shape', {}).get(label, {})) for label in labels} + +def _per_shape_delta(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> dict[str, Any]: + rows = {} + for label in TARGET_SHAPES: + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') or baseline_row.get('flashlib_ms') + rows[label] = {'candidate_ms': candidate_ms, 'baseline_parent_ms': baseline_ms, 'flashlib_ms': flashlib_ms, 'candidate_tflops': candidate_row.get('tflops'), 'baseline_parent_tflops': baseline_row.get('tflops'), 'speedup_vs_parent': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'ratio_vs_flashlib': flashlib_ms / candidate_ms if candidate_ms and flashlib_ms else None, 'selected_seed': _expected_seed(_inputs_for_label(label)), 'passed': candidate_row.get('passed'), 'timing_backend': candidate_row.get('timing_backend') or baseline_row.get('timing_backend')} + return rows + +def _below_flashlib_rows(report: dict[str, Any], *, floor: float) -> list[dict[str, Any]]: + rows = [] + for label, row in sorted(report.get('per_shape', {}).items()): + ratio = row.get('ratio_vs_flashlib') + if isinstance(ratio, (float, int)) and ratio < floor: + rows.append({'shape_key': label, 'kernel_ms': row.get('kernel_ms'), 'flashlib_ms': row.get('flashlib_ms'), 'ratio_vs_flashlib': ratio, 'selected_seed': _expected_seed(_inputs_for_label(label)) if label in TARGET_SHAPE_SET else None}) + return rows + +def benchmark_candidate_rag_microbatch_k10_q4q8q16q32q64_4757_v1(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES, run_baseline: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + labels = tuple((str(label) for label in shape_labels)) + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=labels, kernel_fn=candidate, correctness=True, time_flashlib=time_flashlib) + baseline_report = None + if run_baseline: + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=labels, kernel_fn=candidate_parent_q8q16, correctness=True, time_flashlib=time_flashlib) + candidate_mean = candidate_report['summary']['primary_mean'] + payload: dict[str, Any] = {'candidate_id': CANDIDATE_ID, 'selected_seeds': (Q4Q64_SEED_ID, Q8Q16_SEED_ID, Q32_SEED_ID), 'source_tasks': SOURCE_TASKS, 'all_correct': candidate_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'tflops': candidate_mean, 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_candidate_rag_microbatch_k10_q4q8q16q32q64_4757_v1']), 'candidate_entrypoint': ROUTE_ENTRYPOINT, 'measured_shape_labels': labels, 'timing_backend': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'time_flashlib': time_flashlib, 'denominator': 'rag_microbatch_k10_q4q8q16q32q64_lowfloor_exact5', 'route_trace': route_trace_for_contract_shapes(labels), 'forced_fallback_route_trace': route_trace_for_contract_shapes(labels, force_fallback=True), 'route_modules': PRODUCTION_ROUTE_MODULES, 'contract_summary': candidate_report['summary'], 'contract_performance': candidate_report['performance'], 'contract_correctness': candidate_report['correctness'], 'selected_route_rows': _rows_for_labels(candidate_report, labels), 'hot_bucket_blockers': _below_flashlib_rows(candidate_report, floor=1.05), 'rank_objective': {'metric': 'tflops', 'direction': 'maximize', 'value': candidate_mean, 'valid_measurement_count': candidate_report['performance']['valid_measurement_count'], 'comparable': candidate_report['performance']['comparable']}, 'report': candidate_report} + if baseline_report is not None: + baseline_mean = baseline_report['summary']['primary_mean'] + payload.update({'baseline_candidate_id': PARENT_ID, 'baseline_entrypoint': ROUTE_PARENT, 'baseline_tflops': baseline_mean, 'metric_delta_vs_parent': candidate_mean - baseline_mean if candidate_mean is not None and baseline_mean is not None else None, 'baseline_contract_summary': baseline_report['summary'], 'baseline_contract_performance': baseline_report['performance'], 'baseline_selected_route_rows': _rows_for_labels(baseline_report, labels), 'per_shape_delta_vs_parent': _per_shape_delta(candidate_report, baseline_report)}) + return payload + +def write_benchmark_artifact(artifact_dir: str | Path, *, use_cupti: bool=True, shape_labels=TARGET_SHAPES, run_baseline: bool=True, time_flashlib: bool=True) -> dict[str, str]: + payload = benchmark_candidate_rag_microbatch_k10_q4q8q16q32q64_4757_v1(use_cupti=use_cupti, shape_labels=shape_labels, run_baseline=run_baseline, time_flashlib=time_flashlib) + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + path = out_dir / 'rag_microbatch_k10_q4q8q16q32q64_4757_v1.json' + path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + return {'candidate_payload': str(path)} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbatch_k10_q4q8q32_s144_d5ac_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbatch_k10_q4q8q32_s144_d5ac_v1.py new file mode 100644 index 00000000..8f3c5f26 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbatch_k10_q4q8q32_s144_d5ac_v1.py @@ -0,0 +1,210 @@ +"""Exact q4/q8 RAG microbatch K10 S144 repair for the d5ac exact-three lane. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +routes only BF16 non-build ``B=1,M=100000,D=128,K=10`` rows with +``Q in {4,8}`` through the existing CTA1 S144/G12 tcgen05/TMA producer and +fused merge. The exact q32 denominator row stays on the round-139 parent M64 +route because same-session S144 probing regressed it. Guard misses delegate to +the round-139 4757 exact-five sidecar; FlashLib is used only by the contract +harness as a black-box timing baseline. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from pathlib import Path +from typing import Any, Callable +from . import knn_build_rag_microbatch_4a72_v2 as rag_s144 +from . import knn_build_rag_microbatch_k10_q4s144_q8q16q32q64_4757_v1 as parent_exact5 +MODULE = 'loom.examples.weave.knn_build_rag_microbatch_k10_q4q8q32_s144_d5ac_v1' +Q4_SHAPE = parent_exact5.Q4_SHAPE +Q8_SHAPE = parent_exact5.Q8_SHAPE +Q16_SHAPE = parent_exact5.Q16_SHAPE +Q32_SHAPE = parent_exact5.Q32_SHAPE +Q64_SHAPE = parent_exact5.Q64_SHAPE +TARGET_SHAPES = (Q4_SHAPE, Q8_SHAPE, Q32_SHAPE) +EXACT5_SHAPES = parent_exact5.TARGET_SHAPES +TARGET_SHAPE_SET = set(TARGET_SHAPES) +S144_SPLIT_COUNT = 144 +S144_GROUP_COUNT = 12 +Q4_S144_SEED_ID = parent_exact5.Q4_S144_SEED_ID +Q8_S144_SEED_ID = 'rag_microbatch_k10_q8_s144_g12_d5ac_v1' +S144_SEED_ID = 'rag_microbatch_k10_q4q8_s144_g12_d5ac_v1' +SEED_ID = S144_SEED_ID +CANDIDATE_ID = 'rag_microbatch_k10_q4q8_s144_d5ac_v1' +PARENT_ID = parent_exact5.CANDIDATE_ID +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_Q4_S144 = ''.join([format(MODULE, ''), ':q4_s144_g12']) +ROUTE_Q8_S144 = ''.join([format(MODULE, ''), ':q8_s144_g12']) +ROUTE_PARENT = parent_exact5.ROUTE_ENTRYPOINT +PRODUCTION_ROUTE_MODULES = {**parent_exact5.PRODUCTION_ROUTE_MODULES, Q4_S144_SEED_ID: ROUTE_ENTRYPOINT, Q8_S144_SEED_ID: ROUTE_ENTRYPOINT, S144_SEED_ID: ROUTE_ENTRYPOINT, PARENT_ID: ROUTE_PARENT} +SOURCE_TASKS = {**parent_exact5.SOURCE_TASKS, Q8_S144_SEED_ID: 'generalize-auto-tuning-knn-build-d5ac / loom.examples.weave.knn_build_rag_microbatch_4a72_v2', S144_SEED_ID: 'generalize-auto-tuning-knn-build-d5ac / design_doc/active/generalize_auto_tuning_knn_build_round_156_d5ac.md'} +eval_mod = parent_exact5.eval_mod +_normalize_route_row = parent_exact5.parent_exact5.q8q16_parent.parent._normalize_route_row + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RAGMICRO_Q4Q8Q32_S144_D5AC_VERIFY_KERNEL') + if verify_kernel in (None, 's144_stage'): + return rag_s144.stage1_cta1_ir + if verify_kernel == 's144_merge': + return rag_s144._fused_merge_ir(S144_SPLIT_COUNT, S144_GROUP_COUNT) + if verify_kernel == 'parent_m64_stage': + return parent_exact5.q8q16_parent.rag_m64.stage1_m64_ir + if verify_kernel == 'parent_q16_merge': + return parent_exact5.q8q16_parent.rag_m64.parent_micro._fused_merge_ir(parent_exact5.q8q16_parent.Q16_SPLIT_COUNT, parent_exact5.q8q16_parent.GROUP_COUNT) + if verify_kernel == 'parent_q64_merge': + return parent_exact5.q4q64_seed.seed_3505.faeb.rag_m64.parent_micro._fused_merge_ir(parent_exact5.q4q64_seed.seed_3505.faeb.M64_SPLIT_COUNT, parent_exact5.q4q64_seed.seed_3505.faeb.M64_GROUP_COUNT) + raise ValueError(''.join(['unsupported verify kernel: ', format(verify_kernel, '')])) +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbatch_4a72_v2_stage1_k10_cta1_maxtree", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) + +def _select_contract_shapes(shape_labels): + return parent_exact5._select_contract_shapes(shape_labels) + +def _trace_inputs_for_shape(shape: dict[str, Any]) -> dict[str, Any]: + return parent_exact5._trace_inputs_for_shape(shape) + +def _inputs_for_label(label: str) -> dict[str, Any]: + return _trace_inputs_for_shape(_select_contract_shapes((label,))[0]) + +def _dtype_name(inputs: dict[str, Any], key: str='query') -> str: + tensor = inputs.get(key) + if tensor is not None and hasattr(tensor, 'dtype'): + return str(tensor.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')).replace('torch.', '') + +def _eligible_common(inputs: dict[str, Any]) -> bool: + return not bool(inputs.get('build', False)) and int(inputs.get('B', -1)) == 1 and (int(inputs.get('M', -1)) == 100000) and (int(inputs.get('D', -1)) == 128) and (int(inputs.get('K', -1)) == 10) and (_dtype_name(inputs, 'query') == 'bfloat16') and (_dtype_name(inputs, 'database') in ('', 'bfloat16')) + +def _label_can_hit(inputs: dict[str, Any], label: str) -> bool: + value = inputs.get('label') + return value is None or str(value) == label + +def _eligible_q4_s144(inputs: dict[str, Any]) -> bool: + return _eligible_common(inputs) and int(inputs.get('Q', -1)) == 4 and _label_can_hit(inputs, Q4_SHAPE) + +def _eligible_q8_s144(inputs: dict[str, Any]) -> bool: + return _eligible_common(inputs) and int(inputs.get('Q', -1)) == 8 and _label_can_hit(inputs, Q8_SHAPE) + +def _eligible_q32_s144(inputs: dict[str, Any]) -> bool: + return False + +def _eligible_s144(inputs: dict[str, Any]) -> bool: + return _eligible_q4_s144(inputs) or _eligible_q8_s144(inputs) or _eligible_q32_s144(inputs) + +def _selected_seed(inputs: dict[str, Any]) -> str | None: + if _eligible_q4_s144(inputs): + return Q4_S144_SEED_ID + if _eligible_q8_s144(inputs): + return Q8_S144_SEED_ID + return parent_exact5._expected_seed(inputs) + +def _selected_route(inputs: dict[str, Any]) -> str: + if _eligible_q4_s144(inputs): + return ROUTE_Q4_S144 + if _eligible_q8_s144(inputs): + return ROUTE_Q8_S144 + return parent_exact5.route_for_contract_inputs(inputs) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if not force_fallback and _eligible_s144(inputs): + return _selected_route(inputs) + return parent_exact5.route_for_contract_inputs(inputs, force_fallback=force_fallback) + +def _launch_s144(inputs: dict[str, Any]) -> None: + rag_s144._launch_rag_microbatch_fused_merge(inputs, split_count=S144_SPLIT_COUNT, group_count=S144_GROUP_COUNT) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + if not force_fallback and _eligible_s144(inputs): + _launch_s144(inputs) + return + parent_exact5.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + +def candidate_q4q8q32_s144_d5ac_v1(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]) -> None: + candidate_q4q8q32_s144_d5ac_v1(inputs) + +def candidate_parent_exact5(inputs: dict[str, Any]) -> None: + parent_exact5.launch_from_contract_inputs(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate, correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + return parent_exact5._run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=kernel_fn, correctness=correctness, time_flashlib=time_flashlib) + +def _route_trace_record(inputs: dict[str, Any], *, force_fallback: bool=False) -> dict[str, Any]: + label = str(inputs.get('label')) + parent_route = parent_exact5.route_for_contract_inputs(inputs, force_fallback=force_fallback) + if force_fallback or not _eligible_s144(inputs): + row = dict(parent_exact5.route_trace_for_contract_shapes((label,), force_fallback=force_fallback)[0]) + if force_fallback and _eligible_s144(inputs): + row['expected_seed'] = _selected_seed(inputs) + row['guard_id'] = ''.join(['forced_fallback_', format(S144_SEED_ID, ''), '_disabled']) + row['guard_condition'] = 'forced fallback to round-139 exact-five sidecar; S144 q4/q8 seed disabled' + row['classification'] = 'guard-miss' + row['parent_exact5_route'] = parent_route + return _normalize_route_row(row) + q_value = int(inputs.get('Q', -1)) + selected_seed = _selected_seed(inputs) + return _normalize_route_row({'shape_key': label, 'selected_route': _selected_route(inputs), 'selected_entrypoint': ROUTE_ENTRYPOINT, 'selected_seed': selected_seed, 'expected_seed': selected_seed, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': ''.join(['d5ac_rag_microbatch_k10_q', format(q_value, ''), '_s144_g12_exact_guard']), 'guard_condition': ''.join(['exact BF16 non-build B=1 Q=', format(q_value, ''), ' M=100000 D=128 K=10']), 'coverage': 'q4/q8 S144/G12 seed layered before round-139 exact-five sidecar', 'consumed_seed': selected_seed, 'replaced_route': parent_exact5.route_for_contract_inputs(inputs), 'parent_exact5_route': parent_route, 'split_count': S144_SPLIT_COUNT, 'group_count': S144_GROUP_COUNT, 'classification': 'seed-consumed'}) + +def route_trace_for_contract_shapes(shape_labels=TARGET_SHAPES, *, force_fallback: bool=False) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + return [_route_trace_record(_trace_inputs_for_shape(shape), force_fallback=force_fallback) for shape in selected] + +def _rows_for_labels(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return {label: dict(report.get('per_shape', {}).get(label, {})) for label in labels} + +def _per_shape_delta(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> dict[str, Any]: + rows = {} + for label in TARGET_SHAPES: + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') or baseline_row.get('flashlib_ms') + rows[label] = {'candidate_ms': candidate_ms, 'baseline_parent_ms': baseline_ms, 'flashlib_ms': flashlib_ms, 'candidate_tflops': candidate_row.get('tflops'), 'baseline_parent_tflops': baseline_row.get('tflops'), 'speedup_vs_parent': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'ratio_vs_flashlib': flashlib_ms / candidate_ms if candidate_ms and flashlib_ms else None, 'selected_seed': _selected_seed(_inputs_for_label(label)), 'passed': candidate_row.get('passed'), 'timing_backend': candidate_row.get('timing_backend') or baseline_row.get('timing_backend')} + return rows + +def _below_flashlib_rows(report: dict[str, Any], *, floor: float) -> list[dict[str, Any]]: + rows = [] + for label, row in sorted(report.get('per_shape', {}).items()): + ratio = row.get('ratio_vs_flashlib') + if isinstance(ratio, (float, int)) and ratio < floor: + rows.append({'shape_key': label, 'kernel_ms': row.get('kernel_ms'), 'flashlib_ms': row.get('flashlib_ms'), 'ratio_vs_flashlib': ratio, 'selected_seed': _selected_seed(_inputs_for_label(label)) if label in TARGET_SHAPE_SET else None}) + return rows + +def benchmark_candidate_q4q8_s144_d5ac_v1(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES, run_baseline: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + labels = tuple((str(label) for label in shape_labels)) + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=labels, kernel_fn=candidate, correctness=True, time_flashlib=time_flashlib) + baseline_report = None + if run_baseline: + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=labels, kernel_fn=candidate_parent_exact5, correctness=True, time_flashlib=time_flashlib) + candidate_mean = candidate_report['summary']['primary_mean'] + payload: dict[str, Any] = {'candidate_id': CANDIDATE_ID, 'selected_seeds': (Q4_S144_SEED_ID, Q8_S144_SEED_ID, parent_exact5.Q32_SEED_ID), 'source_tasks': SOURCE_TASKS, 'all_correct': candidate_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'tflops': candidate_mean, 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_candidate_q4q8_s144_d5ac_v1']), 'candidate_entrypoint': ROUTE_ENTRYPOINT, 'measured_shape_labels': labels, 'timing_backend': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'time_flashlib': time_flashlib, 'speedup_floor': 1.2, 'denominator': 'rag_microbatch_k10_q4q8q32_floor_exact3', 'route_trace': route_trace_for_contract_shapes(labels), 'forced_fallback_route_trace': route_trace_for_contract_shapes(labels, force_fallback=True), 'route_modules': PRODUCTION_ROUTE_MODULES, 'contract_summary': candidate_report['summary'], 'contract_performance': candidate_report['performance'], 'contract_correctness': candidate_report['correctness'], 'selected_route_rows': _rows_for_labels(candidate_report, labels), 'hot_bucket_blockers': _below_flashlib_rows(candidate_report, floor=1.2), 'rank_objective': {'metric': 'tflops', 'direction': 'maximize', 'value': candidate_mean, 'valid_measurement_count': candidate_report['performance']['valid_measurement_count'], 'comparable': candidate_report['performance']['comparable']}, 'report': candidate_report} + if baseline_report is not None: + baseline_mean = baseline_report['summary']['primary_mean'] + payload.update({'baseline_candidate_id': PARENT_ID, 'baseline_entrypoint': ROUTE_PARENT, 'baseline_tflops': baseline_mean, 'metric_delta_vs_parent': candidate_mean - baseline_mean if candidate_mean is not None and baseline_mean is not None else None, 'baseline_contract_summary': baseline_report['summary'], 'baseline_contract_performance': baseline_report['performance'], 'baseline_selected_route_rows': _rows_for_labels(baseline_report, labels), 'per_shape_delta_vs_parent': _per_shape_delta(candidate_report, baseline_report)}) + return payload + +def write_benchmark_artifact(artifact_dir: str | Path, *, use_cupti: bool=True, shape_labels=TARGET_SHAPES, run_baseline: bool=True, time_flashlib: bool=True) -> dict[str, str]: + payload = benchmark_candidate_q4q8_s144_d5ac_v1(use_cupti=use_cupti, shape_labels=shape_labels, run_baseline=run_baseline, time_flashlib=time_flashlib) + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + path = out_dir / 'rag_microbatch_k10_q4q8_s144_d5ac_v1.json' + path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + return {'candidate_payload': str(path)} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbatch_k10_q4s144_q8q16q32q64_4757_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbatch_k10_q4s144_q8q16q32q64_4757_v1.py new file mode 100644 index 00000000..7d72955f --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbatch_k10_q4s144_q8q16q32q64_4757_v1.py @@ -0,0 +1,185 @@ +"""Exact RAG microbatch K10 q4 S144 overlay for the 4757 exact-five seed. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +routes only ``rag_microbatch_b1_q4_m100000_d128_k10`` through the existing +S144/G12 tcgen05/TMA q4 producer and fused merge, while preserving the round +138 q8/q16/q32/q64 routes. Guard misses delegate to the round-138 exact-five +sidecar. FlashLib is used only by the contract harness as a black-box timing +baseline. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from pathlib import Path +from typing import Any, Callable +from . import knn_build_rag_microbatch_k10_q4_s144_d555_v1 as q4_s144 +from . import knn_build_rag_microbatch_k10_q4q8q16q32q64_4757_v1 as parent_exact5 +MODULE = 'loom.examples.weave.knn_build_rag_microbatch_k10_q4s144_q8q16q32q64_4757_v1' +Q4_SHAPE = parent_exact5.Q4_SHAPE +Q8_SHAPE = parent_exact5.Q8_SHAPE +Q16_SHAPE = parent_exact5.Q16_SHAPE +Q32_SHAPE = parent_exact5.Q32_SHAPE +Q64_SHAPE = parent_exact5.Q64_SHAPE +TARGET_SHAPES = parent_exact5.TARGET_SHAPES +TARGET_SHAPE_SET = set(TARGET_SHAPES) +Q4_S144_SEED_ID = q4_s144.SEED_ID +Q4Q64_SEED_ID = parent_exact5.Q4Q64_SEED_ID +Q8Q16_SEED_ID = parent_exact5.Q8Q16_SEED_ID +Q32_SEED_ID = parent_exact5.Q32_SEED_ID +SEED_ID = 'rag_microbatch_k10_q4s144_q8q16q32q64_4757_v1' +CANDIDATE_ID = SEED_ID +PARENT_ID = parent_exact5.CANDIDATE_ID +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_Q4_S144 = ''.join([format(MODULE, ''), ':q4_s144_g12']) +ROUTE_Q8 = parent_exact5.ROUTE_Q8 +ROUTE_Q16 = parent_exact5.ROUTE_Q16 +ROUTE_Q32 = parent_exact5.ROUTE_Q32 +ROUTE_Q64 = parent_exact5.ROUTE_Q4Q64 +ROUTE_PARENT = parent_exact5.ROUTE_ENTRYPOINT +PRODUCTION_ROUTE_MODULES = {**parent_exact5.PRODUCTION_ROUTE_MODULES, Q4_S144_SEED_ID: ROUTE_ENTRYPOINT, SEED_ID: ROUTE_ENTRYPOINT, PARENT_ID: ROUTE_PARENT} +SOURCE_TASKS = {**parent_exact5.SOURCE_TASKS, Q4_S144_SEED_ID: 'weave-evolve-knn-build-066c / loom.examples.weave.knn_build_rag_microbatch_k10_q4_s144_d555_v1', SEED_ID: 'weave-evolve-knn-build-4757 / design_doc/active/weave_evolve_knn_build_round_138_4757_ragmicro_q32.md'} +eval_mod = parent_exact5.eval_mod + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RAGMICRO_Q4S144_Q8Q16Q32Q64_4757_VERIFY_KERNEL') + if verify_kernel in (None, 'q4_s144_stage'): + return q4_s144.faeb.rag_s144.stage1_cta1_ir + if verify_kernel == 'q4_s144_merge': + return q4_s144.faeb.rag_s144._fused_merge_ir(q4_s144.faeb.S144_SPLIT_COUNT, q4_s144.faeb.S144_GROUP_COUNT_Q4) + if verify_kernel == 'q64_merge_s128': + return parent_exact5.q4q64_seed.seed_3505.faeb.rag_m64.parent_micro._fused_merge_ir(parent_exact5.q4q64_seed.seed_3505.faeb.M64_SPLIT_COUNT, parent_exact5.q4q64_seed.seed_3505.faeb.M64_GROUP_COUNT) + if verify_kernel == 'q8_merge_s128': + return parent_exact5.q8q16_parent.rag_m64.parent_micro._fused_merge_ir(parent_exact5.q8q16_parent.Q8_SPLIT_COUNT, parent_exact5.q8q16_parent.GROUP_COUNT) + if verify_kernel == 'q16_merge_s136': + return parent_exact5.q8q16_parent.rag_m64.parent_micro._fused_merge_ir(parent_exact5.q8q16_parent.Q16_SPLIT_COUNT, parent_exact5.q8q16_parent.GROUP_COUNT) + if verify_kernel == 'q32_merge_s128': + return parent_exact5.q8q16_parent.rag_m64.parent_micro._fused_merge_ir(parent_exact5.Q32_SPLIT_COUNT, parent_exact5.Q32_GROUP_COUNT) + if verify_kernel == 'm64_stage': + return parent_exact5.q8q16_parent.rag_m64.stage1_m64_ir + raise ValueError(''.join(['unsupported verify kernel: ', format(verify_kernel, '')])) +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbatch_4a72_v2_stage1_k10_cta1_maxtree", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) + +def _select_contract_shapes(shape_labels): + return parent_exact5._select_contract_shapes(shape_labels) + +def _trace_inputs_for_shape(shape: dict[str, Any]) -> dict[str, Any]: + return parent_exact5._trace_inputs_for_shape(shape) + +def _inputs_for_label(label: str) -> dict[str, Any]: + return _trace_inputs_for_shape(_select_contract_shapes((label,))[0]) + +def _eligible_q4_s144(inputs: dict[str, Any]) -> bool: + return q4_s144._eligible_q4_s144(inputs) + +def _expected_seed(inputs: dict[str, Any]) -> str | None: + if _eligible_q4_s144(inputs): + return Q4_S144_SEED_ID + return parent_exact5._expected_seed(inputs) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if not force_fallback and _eligible_q4_s144(inputs): + return ROUTE_Q4_S144 + return parent_exact5.route_for_contract_inputs(inputs, force_fallback=force_fallback) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + if not force_fallback and _eligible_q4_s144(inputs): + q4_s144.launch_from_contract_inputs(inputs) + return + parent_exact5.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + +def candidate_q4s144_q8q16q32q64_4757_v1(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]) -> None: + candidate_q4s144_q8q16q32q64_4757_v1(inputs) + +def candidate_parent_exact5(inputs: dict[str, Any]) -> None: + parent_exact5.launch_from_contract_inputs(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate, correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + return parent_exact5._run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=kernel_fn, correctness=correctness, time_flashlib=time_flashlib) + +def _q4_route_row(inputs: dict[str, Any], *, force_fallback: bool=False) -> dict[str, Any]: + label = str(inputs.get('label')) + parent_route = parent_exact5.route_for_contract_inputs(inputs) + if force_fallback: + row = dict(parent_exact5.route_trace_for_contract_shapes((label,))[0]) + row['expected_seed'] = Q4_S144_SEED_ID + row['guard_id'] = ''.join(['forced_fallback_', format(Q4_S144_SEED_ID, ''), '_disabled']) + row['guard_condition'] = 'forced fallback to round-138 exact-five sidecar; q4 S144 seed disabled' + row['classification'] = 'guard-miss' + row['parent_exact5_route'] = parent_route + return parent_exact5.q8q16_parent.parent._normalize_route_row(row) + return parent_exact5.q8q16_parent.parent._normalize_route_row({'shape_key': label, 'selected_route': ROUTE_Q4_S144, 'selected_entrypoint': ROUTE_ENTRYPOINT, 'selected_seed': Q4_S144_SEED_ID, 'expected_seed': Q4_S144_SEED_ID, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': '4757_rag_microbatch_k10_q4_s144_g12_exact_guard', 'guard_condition': 'exact BF16 non-build B=1 Q=4 M=100000 D=128 K=10', 'coverage': 'q4 S144/G12 seed layered before round-138 exact-five sidecar', 'consumed_seed': Q4_S144_SEED_ID, 'replaced_route': parent_route, 'parent_exact5_route': parent_route, 'split_count': q4_s144.faeb.S144_SPLIT_COUNT, 'group_count': q4_s144.faeb.S144_GROUP_COUNT_Q4, 'classification': 'seed-consumed'}) + +def _route_trace_record(inputs: dict[str, Any], *, force_fallback: bool=False) -> dict[str, Any]: + label = str(inputs.get('label')) + if _eligible_q4_s144(inputs): + return _q4_route_row(inputs, force_fallback=force_fallback) + row = dict(parent_exact5.route_trace_for_contract_shapes((label,), force_fallback=force_fallback)[0]) + row['expected_seed'] = _expected_seed(inputs) + row['parent_exact5_route'] = parent_exact5.route_for_contract_inputs(inputs, force_fallback=force_fallback) + return parent_exact5.q8q16_parent.parent._normalize_route_row(row) + +def route_trace_for_contract_shapes(shape_labels=TARGET_SHAPES, *, force_fallback: bool=False) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + return [_route_trace_record(_trace_inputs_for_shape(shape), force_fallback=force_fallback) for shape in selected] + +def _rows_for_labels(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return {label: dict(report.get('per_shape', {}).get(label, {})) for label in labels} + +def _per_shape_delta(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> dict[str, Any]: + rows = {} + for label in TARGET_SHAPES: + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') or baseline_row.get('flashlib_ms') + rows[label] = {'candidate_ms': candidate_ms, 'baseline_parent_ms': baseline_ms, 'flashlib_ms': flashlib_ms, 'candidate_tflops': candidate_row.get('tflops'), 'baseline_parent_tflops': baseline_row.get('tflops'), 'speedup_vs_parent': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'ratio_vs_flashlib': flashlib_ms / candidate_ms if candidate_ms and flashlib_ms else None, 'selected_seed': _expected_seed(_inputs_for_label(label)), 'passed': candidate_row.get('passed'), 'timing_backend': candidate_row.get('timing_backend') or baseline_row.get('timing_backend')} + return rows + +def _below_flashlib_rows(report: dict[str, Any], *, floor: float) -> list[dict[str, Any]]: + rows = [] + for label, row in sorted(report.get('per_shape', {}).items()): + ratio = row.get('ratio_vs_flashlib') + if isinstance(ratio, (float, int)) and ratio < floor: + rows.append({'shape_key': label, 'kernel_ms': row.get('kernel_ms'), 'flashlib_ms': row.get('flashlib_ms'), 'ratio_vs_flashlib': ratio, 'selected_seed': _expected_seed(_inputs_for_label(label)) if label in TARGET_SHAPE_SET else None}) + return rows + +def benchmark_candidate_rag_microbatch_k10_q4s144_q8q16q32q64_4757_v1(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES, run_baseline: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + labels = tuple((str(label) for label in shape_labels)) + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=labels, kernel_fn=candidate, correctness=True, time_flashlib=time_flashlib) + baseline_report = None + if run_baseline: + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=labels, kernel_fn=candidate_parent_exact5, correctness=True, time_flashlib=time_flashlib) + candidate_mean = candidate_report['summary']['primary_mean'] + payload: dict[str, Any] = {'candidate_id': CANDIDATE_ID, 'selected_seeds': (Q4_S144_SEED_ID, Q8Q16_SEED_ID, Q32_SEED_ID, Q4Q64_SEED_ID), 'source_tasks': SOURCE_TASKS, 'all_correct': candidate_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'tflops': candidate_mean, 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_candidate_rag_microbatch_k10_q4s144_q8q16q32q64_4757_v1']), 'candidate_entrypoint': ROUTE_ENTRYPOINT, 'measured_shape_labels': labels, 'timing_backend': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'time_flashlib': time_flashlib, 'denominator': 'rag_microbatch_k10_q4q8q16q32q64_lowfloor_exact5', 'route_trace': route_trace_for_contract_shapes(labels), 'forced_fallback_route_trace': route_trace_for_contract_shapes(labels, force_fallback=True), 'route_modules': PRODUCTION_ROUTE_MODULES, 'contract_summary': candidate_report['summary'], 'contract_performance': candidate_report['performance'], 'contract_correctness': candidate_report['correctness'], 'selected_route_rows': _rows_for_labels(candidate_report, labels), 'hot_bucket_blockers': _below_flashlib_rows(candidate_report, floor=1.05), 'rank_objective': {'metric': 'tflops', 'direction': 'maximize', 'value': candidate_mean, 'valid_measurement_count': candidate_report['performance']['valid_measurement_count'], 'comparable': candidate_report['performance']['comparable']}, 'report': candidate_report} + if baseline_report is not None: + baseline_mean = baseline_report['summary']['primary_mean'] + payload.update({'baseline_candidate_id': PARENT_ID, 'baseline_entrypoint': ROUTE_PARENT, 'baseline_tflops': baseline_mean, 'metric_delta_vs_parent': candidate_mean - baseline_mean if candidate_mean is not None and baseline_mean is not None else None, 'baseline_contract_summary': baseline_report['summary'], 'baseline_contract_performance': baseline_report['performance'], 'baseline_selected_route_rows': _rows_for_labels(baseline_report, labels), 'per_shape_delta_vs_parent': _per_shape_delta(candidate_report, baseline_report)}) + return payload + +def write_benchmark_artifact(artifact_dir: str | Path, *, use_cupti: bool=True, shape_labels=TARGET_SHAPES, run_baseline: bool=True, time_flashlib: bool=True) -> dict[str, str]: + payload = benchmark_candidate_rag_microbatch_k10_q4s144_q8q16q32q64_4757_v1(use_cupti=use_cupti, shape_labels=shape_labels, run_baseline=run_baseline, time_flashlib=time_flashlib) + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + path = out_dir / 'rag_microbatch_k10_q4s144_q8q16q32q64_4757_v1.json' + path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + return {'candidate_payload': str(path)} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbatch_k10_q8q16_4757_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbatch_k10_q8q16_4757_v1.py new file mode 100644 index 00000000..1a522bdb --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbatch_k10_q8q16_4757_v1.py @@ -0,0 +1,197 @@ +"""Exact q8/q16 RAG microbatch K10 split-select seed for the 4757 lane. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +routes only BF16 non-build ``B=1,M=100000,D=128,K=10`` rows with ``Q in +{8,16}`` through the existing M64 tcgen05 producer. The q8 row uses the +previous best S128/G8 topology, while q16 uses S136/G8. Guard misses delegate +to the current Q24/Q128 full90 Weave portfolio; FlashLib is only used by the +contract harness as a black-box timing baseline. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from pathlib import Path +from typing import Any, Callable +from . import knn_build_dispatch_784a_cf51_q1_q16_q24_q128_seed_portfolio_full90_v1 as parent +from . import knn_build_rag_microbatch_m64_d4f7_v1 as rag_m64 +MODULE = 'loom.examples.weave.knn_build_rag_microbatch_k10_q8q16_4757_v1' +Q8_SHAPE = 'rag_microbatch_b1_q8_m100000_d128_k10' +Q16_SHAPE = 'rag_microbatch_b1_q16_m100000_d128_k10' +TARGET_SHAPES = (Q8_SHAPE, Q16_SHAPE) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +Q8_SPLIT_COUNT = 128 +Q16_SPLIT_COUNT = 136 +GROUP_COUNT = 8 +SEED_ID = 'rag_microbatch_k10_q8_s128_q16_s136_4757_v1' +PARENT_PORTFOLIO_ID = parent.CANDIDATE_CONFIGS[parent.DEFAULT_CANDIDATE_KEY]['candidate_id'] +CANDIDATE_ID = 'rag_microbatch_k10_q8q16_4757_v1' +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_Q8 = ''.join([format(MODULE, ''), ':q8_m64_s128_g8']) +ROUTE_Q16 = ''.join([format(MODULE, ''), ':q16_m64_s136_g8']) +ROUTE_PARENT = parent.ROUTE_ENTRYPOINT +PRODUCTION_ROUTE_MODULES = {**parent.PRODUCTION_ROUTE_MODULES, SEED_ID: ROUTE_ENTRYPOINT, PARENT_PORTFOLIO_ID: ROUTE_PARENT} +SOURCE_TASKS = {**parent.SOURCE_TASKS, SEED_ID: 'weave-evolve-knn-build-4757 / design_doc/active/generalize_auto_tuning_knn_build_round_136_4757.md', 'm64_d4f7_parent_seed': 'weave-evolve-knn-build-d4f7 / loom.examples.weave.knn_build_rag_microbatch_m64_d4f7_v1'} +eval_mod = parent.eval_mod + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RAGMICRO_Q8Q16_4757_VERIFY_KERNEL') + if verify_kernel == 'q8_merge_s128': + return rag_m64.parent_micro._fused_merge_ir(Q8_SPLIT_COUNT, GROUP_COUNT) + if verify_kernel == 'q16_merge_s136': + return rag_m64.parent_micro._fused_merge_ir(Q16_SPLIT_COUNT, GROUP_COUNT) + return rag_m64.stage1_m64_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbatch_m64_d4f7_stage1", "arg_keys": ["query", "database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 91392, "constants": [], "cta_group": 1, "threads": 512}')) + +def _select_contract_shapes(shape_labels): + return parent._select_contract_shapes(shape_labels) + +def _trace_inputs_for_shape(shape: dict[str, Any]) -> dict[str, Any]: + return parent._trace_inputs_for_shape(shape) + +def _inputs_for_label(label: str) -> dict[str, Any]: + return _trace_inputs_for_shape(_select_contract_shapes((label,))[0]) + +def _dtype_name(inputs: dict[str, Any], key: str='query') -> str: + tensor = inputs.get(key) + if tensor is not None and hasattr(tensor, 'dtype'): + return str(tensor.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')).replace('torch.', '') + +def _eligible_common(inputs: dict[str, Any]) -> bool: + return not bool(inputs.get('build', False)) and int(inputs.get('B', -1)) == 1 and (int(inputs.get('M', -1)) == 100000) and (int(inputs.get('D', -1)) == 128) and (int(inputs.get('K', -1)) == 10) and (_dtype_name(inputs, 'query') == 'bfloat16') and (_dtype_name(inputs, 'database') in ('', 'bfloat16')) + +def _label_can_hit(inputs: dict[str, Any], label: str) -> bool: + value = inputs.get('label') + return value is None or str(value) == label + +def _eligible_q8(inputs: dict[str, Any]) -> bool: + return _eligible_common(inputs) and int(inputs.get('Q', -1)) == 8 and _label_can_hit(inputs, Q8_SHAPE) + +def _eligible_q16(inputs: dict[str, Any]) -> bool: + return _eligible_common(inputs) and int(inputs.get('Q', -1)) == 16 and _label_can_hit(inputs, Q16_SHAPE) + +def _split_for_inputs(inputs: dict[str, Any]) -> int | None: + if _eligible_q8(inputs): + return Q8_SPLIT_COUNT + if _eligible_q16(inputs): + return Q16_SPLIT_COUNT + return None + +def _route_for_split(split_count: int) -> str: + if split_count == Q8_SPLIT_COUNT: + return ROUTE_Q8 + if split_count == Q16_SPLIT_COUNT: + return ROUTE_Q16 + raise ValueError(''.join(['unsupported q8/q16 split count: ', format(split_count, '')])) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + split_count = None if force_fallback else _split_for_inputs(inputs) + if split_count is not None: + return _route_for_split(split_count) + return parent.route_for_contract_inputs(inputs, force_fallback=force_fallback) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + split_count = None if force_fallback else _split_for_inputs(inputs) + if split_count is not None: + rag_m64._launch_rag_microbatch_m64(inputs, split_count=split_count, group_count=GROUP_COUNT) + return + parent.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + +def candidate_q8q16_4757_v1(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]) -> None: + candidate_q8q16_4757_v1(inputs) + +def candidate_parent_full90(inputs: dict[str, Any]) -> None: + parent.launch_from_contract_inputs(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate, correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + return parent._run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=kernel_fn, correctness=correctness, time_flashlib=time_flashlib) + +def _expected_seed(inputs: dict[str, Any]) -> str | None: + return SEED_ID if _split_for_inputs(inputs) is not None else None + +def _selected_route_name(split_count: int | None) -> str | None: + return None if split_count is None else _route_for_split(split_count) + +def _route_trace_record(inputs: dict[str, Any], *, force_fallback: bool=False) -> dict[str, Any]: + label = str(inputs.get('label')) + split_count = None if force_fallback else _split_for_inputs(inputs) + parent_route = parent.route_for_contract_inputs(inputs, force_fallback=force_fallback) + expected_seed = _expected_seed(inputs) + if split_count is None: + row = dict(parent.route_trace_for_contract_shapes((label,), force_fallback=force_fallback)[0]) + row['expected_seed'] = expected_seed + row['parent_portfolio_route'] = parent_route + if force_fallback and expected_seed is not None: + row['guard_id'] = ''.join(['forced_fallback_', format(SEED_ID, ''), '_disabled']) + row['guard_condition'] = 'forced fallback to parent full90 portfolio; q8/q16 M64 seed disabled' + row['classification'] = 'guard-miss' + return parent._normalize_route_row(row) + q_value = int(inputs.get('Q', -1)) + return parent._normalize_route_row({'shape_key': label, 'selected_route': _selected_route_name(split_count), 'selected_entrypoint': ROUTE_ENTRYPOINT, 'selected_seed': SEED_ID, 'expected_seed': SEED_ID, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': ''.join(['4757_rag_microbatch_k10_q', format(q_value, ''), '_m64_s', format(split_count, ''), '_g8_exact_guard']), 'guard_condition': ''.join(['exact BF16 non-build B=1 Q=', format(q_value, ''), ' M=100000 D=128 K=10']), 'coverage': 'q8/q16 split-selected M64 Weave seed before Q24/Q128 full90 parent', 'consumed_seed': SEED_ID, 'replaced_route': parent_route, 'parent_portfolio_route': parent_route, 'split_count': split_count, 'group_count': GROUP_COUNT, 'classification': 'seed-consumed'}) + +def route_trace_for_contract_shapes(shape_labels=TARGET_SHAPES, *, force_fallback: bool=False) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + return [_route_trace_record(_trace_inputs_for_shape(shape), force_fallback=force_fallback) for shape in selected] + +def _rows_for_labels(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return {label: dict(report.get('per_shape', {}).get(label, {})) for label in labels} + +def _per_shape_delta(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> dict[str, Any]: + rows = {} + for label in TARGET_SHAPES: + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') or baseline_row.get('flashlib_ms') + split_count = _split_for_inputs(_inputs_for_label(label)) + rows[label] = {'candidate_ms': candidate_ms, 'baseline_parent_ms': baseline_ms, 'flashlib_ms': flashlib_ms, 'candidate_tflops': candidate_row.get('tflops'), 'baseline_parent_tflops': baseline_row.get('tflops'), 'speedup_vs_parent': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'ratio_vs_flashlib': flashlib_ms / candidate_ms if candidate_ms and flashlib_ms else None, 'split_count': split_count, 'group_count': GROUP_COUNT, 'passed': candidate_row.get('passed'), 'timing_backend': candidate_row.get('timing_backend') or baseline_row.get('timing_backend')} + return rows + +def _below_flashlib_rows(report: dict[str, Any], *, floor: float) -> list[dict[str, Any]]: + rows = [] + for label, row in sorted(report.get('per_shape', {}).items()): + ratio = row.get('ratio_vs_flashlib') + if isinstance(ratio, (float, int)) and ratio < floor: + rows.append({'shape_key': label, 'kernel_ms': row.get('kernel_ms'), 'flashlib_ms': row.get('flashlib_ms'), 'ratio_vs_flashlib': ratio, 'selected_seed': SEED_ID if label in TARGET_SHAPE_SET else None}) + return rows + +def benchmark_candidate_rag_microbatch_k10_q8q16_4757_v1(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES, run_baseline: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + labels = tuple((str(label) for label in shape_labels)) + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=labels, kernel_fn=candidate, correctness=True, time_flashlib=time_flashlib) + baseline_report = None + if run_baseline: + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=labels, kernel_fn=candidate_parent_full90, correctness=True, time_flashlib=time_flashlib) + candidate_mean = candidate_report['summary']['primary_mean'] + payload: dict[str, Any] = {'candidate_id': CANDIDATE_ID, 'selected_seeds': (SEED_ID,), 'source_tasks': SOURCE_TASKS, 'all_correct': candidate_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'tflops': candidate_mean, 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_candidate_rag_microbatch_k10_q8q16_4757_v1']), 'candidate_entrypoint': ROUTE_ENTRYPOINT, 'measured_shape_labels': labels, 'timing_backend': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'time_flashlib': time_flashlib, 'denominator': 'rag_microbatch_k10_q8q16_lowfloor_exact2', 'route_trace': route_trace_for_contract_shapes(labels), 'forced_fallback_route_trace': route_trace_for_contract_shapes(labels, force_fallback=True), 'route_modules': PRODUCTION_ROUTE_MODULES, 'contract_summary': candidate_report['summary'], 'contract_performance': candidate_report['performance'], 'contract_correctness': candidate_report['correctness'], 'selected_route_rows': _rows_for_labels(candidate_report, labels), 'hot_bucket_blockers': _below_flashlib_rows(candidate_report, floor=1.05), 'rank_objective': {'metric': 'tflops', 'direction': 'maximize', 'value': candidate_mean, 'valid_measurement_count': candidate_report['performance']['valid_measurement_count'], 'comparable': candidate_report['performance']['comparable']}, 'report': candidate_report} + if baseline_report is not None: + baseline_mean = baseline_report['summary']['primary_mean'] + payload.update({'baseline_candidate_id': PARENT_PORTFOLIO_ID, 'baseline_entrypoint': ROUTE_PARENT, 'baseline_tflops': baseline_mean, 'metric_delta_vs_parent': candidate_mean - baseline_mean if candidate_mean is not None and baseline_mean is not None else None, 'baseline_contract_summary': baseline_report['summary'], 'baseline_contract_performance': baseline_report['performance'], 'baseline_selected_route_rows': _rows_for_labels(baseline_report, labels), 'per_shape_delta_vs_parent': _per_shape_delta(candidate_report, baseline_report)}) + return payload + +def write_benchmark_artifact(artifact_dir: str | Path, *, use_cupti: bool=True, shape_labels=TARGET_SHAPES, run_baseline: bool=True, time_flashlib: bool=True) -> dict[str, str]: + payload = benchmark_candidate_rag_microbatch_k10_q8q16_4757_v1(use_cupti=use_cupti, shape_labels=shape_labels, run_baseline=run_baseline, time_flashlib=time_flashlib) + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + path = out_dir / 'rag_microbatch_k10_q8q16_4757_v1.json' + path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + return {'candidate_payload': str(path)} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbatch_m64_d4f7_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbatch_m64_d4f7_v1.py new file mode 100644 index 00000000..b25e1e15 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbatch_m64_d4f7_v1.py @@ -0,0 +1,172 @@ +"""RAG microbatch K10 bucket seed with a Q64/M128 tcgen05 producer. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +routes only BF16 non-build ``B=1,Q in {8,16,32},M=100000,D=128,K=10`` rows. +It replaces the inherited Q128/CTA-group=2 producer with a Q64/CTA-group=1 +producer derived from the ROW_16x256B M64 readback probe, then reuses the +existing split-merge output path. Guard misses delegate to the 4a72 microbatch +seed, so no production dispatcher or external runtime route is changed. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import os +from collections.abc import Callable +from functools import lru_cache +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_rag_microbatch_4a72_v1 as parent_micro +TARGET_SHAPES = parent_micro.TARGET_SHAPES +TARGET_SHAPE_SET = set(TARGET_SHAPES) +MICRO_Q = 64 +MICRO_M = 128 +MICRO_D = 128 +MICRO_K = 10 +MICRO_VEC = 8 +MICRO_THREADS = 512 +MICRO_LOCAL_LISTS_PER_ROW = 8 +MICRO_SPLIT_COUNT = _decode_capture(_json_loads('128')) +MICRO_GROUP_COUNT = _decode_capture(_json_loads('8')) +MICRO_Q_STAGE_VECS = MICRO_Q * MICRO_D // MICRO_VEC +MICRO_DB_STAGE_VECS = MICRO_M * MICRO_D // MICRO_VEC +MICRO_SMEM_A_BYTES = MICRO_Q * MICRO_D * 2 +MICRO_SMEM_B_BYTES = MICRO_M * MICRO_D * 2 +MICRO_SMEM_LOCAL_D_BYTES = MICRO_Q * MICRO_LOCAL_LISTS_PER_ROW * MICRO_K * 4 +MICRO_SMEM_LOCAL_I_BYTES = MICRO_Q * MICRO_LOCAL_LISTS_PER_ROW * MICRO_K * 4 +MICRO_LOCAL_D_OFFSET = MICRO_SMEM_A_BYTES + MICRO_SMEM_B_BYTES +MICRO_LOCAL_I_OFFSET = MICRO_LOCAL_D_OFFSET + MICRO_SMEM_LOCAL_D_BYTES +MICRO_SMEM_POOL_BYTES = MICRO_LOCAL_I_OFFSET + MICRO_SMEM_LOCAL_I_BYTES + 256 +WEAVE_SMEM_SYSTEM_BYTES = 1024 +MICRO_STAGE_SMEM_BYTES = MICRO_SMEM_POOL_BYTES + WEAVE_SMEM_SYSTEM_BYTES +GRID_DIM_DEFAULT = parent_micro.GRID_DIM_DEFAULT +TOP_K_MAX = parent_micro.TOP_K_MAX +_m64_insert_sorted_pair = _ir_proxy('loom.examples.weave.knn_build_rag_microbatch_m64_d4f7_v1:_m64_insert_sorted_pair', 256) +knn_build_rag_microbatch_m64_d4f7_stage1 = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbatch_m64_d4f7_stage1", "arg_keys": ["query", "database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 91392, "constants": [], "cta_group": 1, "threads": 512}')) +stage1_m64_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbatch_m64_d4f7_stage1", "arg_keys": ["query", "database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 91392, "constants": [], "cta_group": 1, "threads": 512}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RAG_MICROBATCH_M64_D4F7_V1_VERIFY_KERNEL') + split_count = int(os.environ.get('LOOM_KNN_RAG_MICROBATCH_M64_D4F7_V1_VERIFY_SPLIT', MICRO_SPLIT_COUNT)) + group_count = int(os.environ.get('LOOM_KNN_RAG_MICROBATCH_M64_D4F7_V1_VERIFY_GROUPS', MICRO_GROUP_COUNT)) + if verify_kernel == 'merge': + return parent_micro._fused_merge_ir(split_count, group_count) + return stage1_m64_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbatch_m64_d4f7_stage1", "arg_keys": ["query", "database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 91392, "constants": [], "cta_group": 1, "threads": 512}')) + +def _compile_ir(ir_obj: Any, *, smem_bytes: int | None=None): + from .._dispatch_runtime import generate_kernel + from .._dispatch_runtime import _cuda_include_dirs + from .._dispatch_runtime import compile_cuda + from .._dispatch_runtime import CUDAKernel + shared_mem = smem_bytes if smem_bytes is not None else ir_obj.computed_smem_bytes + source = generate_kernel(ir_obj, validate=False, smem_bytes=shared_mem) + cubin = compile_cuda(source, arch=parent_micro.parent_k10.base_v1._select_arch_and_preload(), options=['--use_fast_math'], include_dirs=_cuda_include_dirs()) + return CUDAKernel(cubin, ''.join(['kernel_', format(ir_obj.symbol, '')])) + +def _compiled_stage1_m64(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0089"}')) + +def _dtype_name(inputs: dict[str, Any]) -> str: + query = inputs.get('query') + if query is not None: + return str(query.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')) + +def _label_can_hit(inputs: dict[str, Any], labels: set[str]) -> bool: + value = inputs.get('label') + return value is None or str(value) in labels + +def _eligible_rag_microbatch(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, TARGET_SHAPE_SET) and (not bool(inputs.get('build', False))) and (int(inputs.get('B', -1)) == 1) and (int(inputs.get('Q', -1)) in (8, 16, 32)) and (int(inputs.get('M', -1)) == 100000) and (int(inputs.get('D', -1)) == MICRO_D) and (int(inputs.get('K', -1)) == MICRO_K) and (_dtype_name(inputs) == 'bfloat16') + +def _validate_group_shape(split_count: int, group_count: int) -> None: + parent_micro._validate_group_shape(split_count, group_count) + +def _launch_rag_microbatch_m64(inputs: dict[str, Any], *, split_count: int=MICRO_SPLIT_COUNT, group_count: int=MICRO_GROUP_COUNT) -> None: + _validate_group_shape(split_count, group_count) + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + top_k = int(inputs['K']) + num_q_tiles = (n_query + MICRO_Q - 1) // MICRO_Q + num_db_tiles = (n_database + MICRO_M - 1) // MICRO_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tiles * split_count + total_queries = bsz * n_query + stage1_grid = min(total_work, GRID_DIM_DEFAULT) + merge_grid = min(total_queries, GRID_DIM_DEFAULT) + partial_dists, partial_indices = parent_micro.parent_k10.parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + stage1_launch = _compiled_stage1_m64().prepare_launch(grid=(stage1_grid, 1, 1), block=(MICRO_THREADS, 1, 1), args=[query, database, inputs['query_sq'], inputs['database_sq'], partial_dists, partial_indices, bsz, n_query, n_database, top_k, num_q_tiles, db_tiles_per_split, split_count, total_work], shared_mem=MICRO_STAGE_SMEM_BYTES) + merge_kernel = parent_micro._compiled_fused_merge(split_count, group_count) + merge_ir = parent_micro._fused_merge_ir(split_count, group_count) + merge_launch = merge_kernel.prepare_launch(grid=(merge_grid, 1, 1), block=(parent_micro.K10_FUSED_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], total_queries], shared_mem=merge_ir.computed_smem_bytes) + stage1_launch.launch() + merge_launch.launch() + +def route_for_contract_inputs(inputs: dict[str, Any], *, split_count: int=MICRO_SPLIT_COUNT, group_count: int=MICRO_GROUP_COUNT) -> str: + if _eligible_rag_microbatch(inputs): + return ''.join(['rag_microbatch_m64_d4f7_q64m128_s', format(split_count, ''), '_g', format(group_count, '')]) + return parent_micro.route_for_contract_inputs(inputs) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, split_count: int=MICRO_SPLIT_COUNT, group_count: int=MICRO_GROUP_COUNT) -> None: + if _eligible_rag_microbatch(inputs): + _launch_rag_microbatch_m64(inputs, split_count=split_count, group_count=group_count) + return + parent_micro.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def parent_4a72_candidate(inputs: dict[str, Any]): + parent_micro.launch_from_contract_inputs(inputs) + return None + +def candidate_with_topology(split_count: int, group_count: int=MICRO_GROUP_COUNT) -> Callable[[dict[str, Any]], None]: + + def _candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, split_count=split_count, group_count=group_count) + return _candidate + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + return eval_mod.evaluate(candidate, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return parent_micro._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + try: + return eval_mod.evaluate(kernel_fn, shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def _shape_payload(candidate_report: dict[str, Any], parent_report: dict[str, Any], *, split_count: int, group_count: int) -> dict[str, Any]: + rows = {} + for label in TARGET_SHAPES: + cand = candidate_report.get('per_shape', {}).get(label, {}) + parent = parent_report.get('per_shape', {}).get(label, {}) + cand_ms = cand.get('kernel_ms') + parent_ms = parent.get('kernel_ms') + flashlib_ms = cand.get('flashlib_ms') + rows[label] = {'candidate': cand, 'parent_4a72': parent, 'candidate_route': ''.join(['rag_microbatch_m64_d4f7_q64m128_s', format(split_count, ''), '_g', format(group_count, '')]), 'candidate_ms': cand_ms, 'parent_4a72_ms': parent_ms, 'flashlib_ms': flashlib_ms, 'speedup_vs_parent_4a72': parent_ms / cand_ms if cand_ms and parent_ms else None, 'ratio_vs_flashlib': flashlib_ms / cand_ms if cand_ms and flashlib_ms else None} + return rows + +def _benchmark_payload(candidate_report: dict[str, Any], parent_report: dict[str, Any], *, use_cupti: bool, shape_labels, split_count: int, group_count: int) -> dict[str, Any]: + timing_backends = sorted({row.get('timing_backend') for report in (candidate_report, parent_report) for row in report.get('per_shape', {}).values() if row.get('timing_backend') is not None}) + return {'tflops': candidate_report['summary']['primary_mean'] or 0.0, 'all_correct': candidate_report['summary']['all_correct'], 'parent_4a72_all_correct': parent_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'parent_4a72_performance_comparable': parent_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'measured_entrypoint': 'loom.examples.weave.knn_build_rag_microbatch_m64_d4f7_v1:benchmark_knn_build_rag_microbatch_m64_d4f7_v1', 'parent_4a72_entrypoint': 'loom.examples.weave.knn_build_rag_microbatch_4a72_v1:launch_from_contract_inputs', 'accelerated_shape_labels': list(TARGET_SHAPES), 'measured_shape_labels': list(shape_labels), 'producer_split_count': split_count, 'producer_topology': {'Q': MICRO_Q, 'M': MICRO_M, 'cta_group': 1, 'readback': 'ROW_16x256B'}, 'merge_topology': {'K10': 'inherited_fused_cooperative_group_final', 'groups': group_count}, 'timing_backends': timing_backends, 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'target_rows': _shape_payload(candidate_report, parent_report, split_count=split_count, group_count=group_count), 'contract_summary': candidate_report['summary'], 'parent_4a72_contract_summary': parent_report['summary'], 'contract_performance': candidate_report['performance'], 'parent_4a72_contract_performance': parent_report['performance'], 'report': candidate_report, 'parent_4a72_report': parent_report} + +def benchmark_knn_build_rag_microbatch_m64_d4f7_v1(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES, split_count: int=MICRO_SPLIT_COUNT, group_count: int=MICRO_GROUP_COUNT) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_with_topology(split_count, group_count)) + parent_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=parent_4a72_candidate) + return _benchmark_payload(candidate_report, parent_report, use_cupti=use_cupti, shape_labels=shape_labels, split_count=split_count, group_count=group_count) diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbatch_q4_s144_17b8_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbatch_q4_s144_17b8_v1.py new file mode 100644 index 00000000..90ec493d --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbatch_q4_s144_17b8_v1.py @@ -0,0 +1,246 @@ +"""Q4-only RAG microbatch K10 S144 overlay for the 17b8 full82 dispatcher. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +routes only the exact BF16 non-build +``rag_microbatch_b1_q4_m100000_d128_k10`` row through the existing S144 +tcgen05/TMA producer and fused K10 merge from the FAEB seed. Guard misses, +including Q64, delegate to the selected 17b8 69d6+b8c7 full82 Weave +dispatcher; no external runtime fallback is introduced. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from pathlib import Path +from typing import Any, Callable +from . import knn_build_dispatch_066c_b8c7_69d6_full82_v1 as base17b8 +from . import knn_build_rag_microbucket_faeb_v1 as rag_faeb +MODULE = 'loom.examples.weave.knn_build_rag_microbatch_q4_s144_17b8_v1' +Q4_SHAPE = base17b8.Q4_SHAPE +Q64_SHAPE = base17b8.Q64_SHAPE +TARGET_SHAPES = (Q4_SHAPE,) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +SMOKE_SHAPES = (Q4_SHAPE, Q64_SHAPE) +SEED_Q4_S144_ID = 'rag_microbatch_q4_k10_s144_17b8_v1' +BASELINE_ID = base17b8.CANDIDATE_CONFIGS[base17b8.CANDIDATE_69D6_B8C7]['candidate_id'] +BASELINE_ENTRYPOINT = ''.join([format(base17b8.MODULE, ''), ':benchmark_candidate_066c_69d6_plus_b8c7_full82_v1']) +ROUTE_Q4_S144 = 'rag_microbatch_q4_k10_s144_g12_17b8_v1' +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_BASE_17B8_ENTRYPOINT = ''.join([format(base17b8.MODULE, ''), ':launch_from_contract_inputs(q4q64_mode=69d6)']) +S144_SPLIT_COUNT = rag_faeb.S144_SPLIT_COUNT +S144_GROUP_COUNT = rag_faeb.S144_GROUP_COUNT_Q4 +eval_mod = base17b8.eval_mod +SOURCE_TASKS = _decode_capture(_json_loads('{"__dict_items__": [["rag_microbatch_q4_k10_s144_17b8_v1", "weave-evolve-knn-build-17b8 / design_doc/active/generalize_auto_tuning_knn_build_round_116_17b8.md"], ["s144_parent_seed", "loom.examples.weave.knn_build_rag_microbucket_faeb_v1:candidate_q4_s144"], ["candidate_066c_69d6_plus_b8c7_full82_v1", "loom.examples.weave.knn_build_dispatch_066c_b8c7_69d6_full82_v1:69d6_b8c7"]]}')) +PRODUCTION_ROUTE_MODULES = _decode_capture(_json_loads('{"__dict_items__": [["large_square_k20k32", "loom.examples.weave.knn_build_large_square_k20k32_a989_v1"], ["over64_k96", "loom.examples.weave.knn_build_over64_k96_a989_v1"], ["baseline_7c3a_rag_k10", "loom.examples.weave.knn_build_rag_frontier_4b5c_v1:k10"], ["rag_frontier_7399_k10", "loom.examples.weave.knn_build_rag_frontier_7399_v1:k10_s72"], ["rag_frontier_7399_k32_replaced", "loom.examples.weave.knn_build_rag_frontier_7399_v1:k32_s72_g8_fusedmerge"], ["rag_frontier_4fbf_k32", "loom.examples.weave.knn_build_rag_frontier_4fbf_v7:k32_s72_g24_tailinf_fused"], ["rect_smallq_largem_d15e", "loom.examples.weave.knn_build_rect_smallq_largem_ff59_d15e_v1:split16"], ["baseline_7c3a_policy", "loom.examples.weave.knn_build_dispatch_b6d4_d15e_fd02_v1:baseline_7c3a_policy"], ["fallback", "loom.examples.weave.knn_build_dispatch_split72_4e09_de1a_3dc7_v48"], ["dim_d64_73a9", "loom.examples.weave.knn_build_dim_midk_73a9_v1:d64_split_s8"], ["current_exact_k32_dispatcher", "loom.examples.weave.knn_build_dispatch_4fbf_7399_d15e_full55_bad5_v1:launch_from_contract_inputs"], ["base_7399_d15e_dispatcher", "loom.examples.weave.knn_build_dispatch_7399_d15e_full55_v1:launch_from_contract_inputs"], ["rag_frontier_7399_k32", "loom.examples.weave.knn_build_rag_frontier_7399_v1:k32_s72_g8_fusedmerge"], ["dim_d256_df2f", "loom.examples.weave.knn_build_dim_midk_df2f_v1:d256_split_s8"], ["dim_fp16_d128_df2f", "loom.examples.weave.knn_build_dim_midk_df2f_v1:fp16_d128_split_s8"], ["base_dispatch", "loom.examples.weave.knn_build_dispatch_7399_d15e_full55_v1:launch_from_contract_inputs"], ["rect_intermediate_4452_s8", "loom.examples.weave.knn_build_rect_intermediate_frontier_6a73_4452_v2:rect_s8_k10_cached"], ["base_champion_6b59", "loom.examples.weave.knn_build_dispatch_7399_d15e_df2f_full55_v1:launch_from_contract_inputs"], ["base_k32_d64_62b1", "loom.examples.weave.knn_build_dispatch_4fbf_7399_d15e_73a9_full55_v1:launch_from_contract_inputs"], ["default_k96_a330", "loom.examples.weave.knn_build_over64_k96_a989_v1"], ["large_tail_a4f6", "loom.examples.weave.knn_build_large_tail_frontier_6a73_v1:split4_k20"], ["midk_81aa_q2048_k24_k28", "loom.examples.weave.knn_build_dim_midk_bad5_midkcleanup_v1:midk_k24_k28_s8"], ["midk_9b2c_q4096_k28", "loom.examples.weave.knn_build_dim_midk_bad5_k24k28_v1:k28_q4096_s4_unordered_exact"], ["base_f552", "loom.examples.weave.knn_build_dispatch_selected_portfolio_f552_v1:launch_from_contract_inputs"], ["midk_bad5_k64split8", "loom.examples.weave.knn_build_dim_midk_bad5_k64split8_v1:k64_q2048_s8_tailinf"], ["base_e51c", "loom.examples.weave.knn_build_dispatch_selected_portfolio_e51c_v1:launch_from_contract_inputs"], ["midk_f8c3_q4096_k64_split8_a194", "loom.examples.weave.knn_build_dim_midk_f8c3_q4096k64split_v1:q4096_k64_tailinf_split8"], ["base_f8c3", "loom.examples.weave.knn_build_dispatch_selected_portfolio_f8c3_v1:launch_from_contract_inputs"], ["lowk_b193_q512_s4", "loom.examples.weave.knn_build_lowk_f8c3_q512_q1024_v1:q512_lowk_s4"], ["lowk_b193_q1024_k16_s16", "loom.examples.weave.knn_build_lowk_f8c3_q512_q1024_v1:q1024_k16_s16"], ["large_square_5407_q8192_k32_s2", "loom.examples.weave.knn_build_large_square_k32_8a83_v1:q8192_k32_split2"], ["base_f853", "loom.examples.weave.knn_build_dispatch_selected_portfolio_f853_v1:launch_from_contract_inputs"], ["lowk_b193_q512_k4_k5_k6_s4", "loom.examples.weave.knn_build_lowk_f8c3_q512_q1024_v1:q512_lowk_s4"], ["base_f16b", "loom.examples.weave.knn_build_dispatch_selected_portfolio_f16b_v1:launch_from_contract_inputs"], ["rag_microbatch_b2ec_s72_g8", "loom.examples.weave.knn_build_rag_microbatch_4a72_v1:launch_from_contract_inputs"], ["base_4a72", "loom.examples.weave.knn_build_dispatch_selected_portfolio_4a72_v1:launch_from_contract_inputs"], ["rag_m64_s128_0c69", "loom.examples.weave.knn_build_rag_microbatch_m64_d4f7_v1:launch_from_contract_inputs"], ["rag_s144_g12_cta1_059f", "loom.examples.weave.knn_build_rag_microbatch_4a72_v2:launch_from_contract_inputs"], ["rag_s144_g8_cta1_4982_read_ref_parameterized", "loom.examples.weave.knn_build_rag_microbatch_4a72_v2:launch_from_contract_inputs"], ["base_397b", "loom.examples.weave.knn_build_dispatch_selected_portfolio_397b_v1:launch_from_contract_inputs"], ["d64_fdd7_aa88_v2", "loom.examples.weave.knn_build_d64_build_aa88_v2:launch_from_contract_inputs"], ["base_8700", "loom.examples.weave.knn_build_dispatch_rag_seed_portfolio_8700_v1:launch_from_contract_inputs(portfolio_id=all_m64_s128)"], ["rect_d64_cf49_v3_9138", "loom.examples.weave.knn_build_rect_d64_cf49_v3:launch_from_contract_inputs"], ["q1_mbucket_aa88_q1m_v3_bcb3", "loom.examples.weave.knn_build_ragonline_mbucket_aa88_q1m_v3:launch_from_contract_inputs"], ["over64_k96_a2f8_v1_generated_v8", "loom.examples.weave.knn_build_over64_k96_a2f8_v1:_launch_over64_k96_split_path"], ["base_e3de", "loom.examples.weave.knn_build_dispatch_d64_fdd7_e3de_v1:launch_from_contract_inputs"], ["non128_frontier_8199_widecombine_v1", "loom.examples.weave.knn_build_non128_frontier_8199_widecombine_v1:launch_from_contract_inputs"], ["base_4247", "loom.examples.weave.knn_build_dispatch_e3de_9138_bcb3_4247_v1:launch_from_contract_inputs"], ["rag_microbucket_k32_8fcb_split148_v1_b3e0_sm148", "loom.examples.weave.knn_build_rag_microbucket_k32_8fcb_split148_v1:launch_from_contract_inputs"], ["rag_microbucket_k32_2e8e_q16split148_v1_b3e0_q16_s148", "loom.examples.weave.knn_build_rag_microbucket_k32_2e8e_q16split148_v1:launch_from_contract_inputs"], ["non128_frontier_3d5a_cachedmerge_v1", "loom.examples.weave.knn_build_non128_frontier_3d5a_cachedmerge_v1:launch_from_contract_inputs"], ["over64_k96_exactall_229a_v1_b6c4", "loom.examples.weave.knn_build_over64_k96_exactall_229a_v1:launch_from_contract_inputs"], ["knn_build_midk_k11k13_e080_v1", "loom.examples.weave.knn_build_midk_k11k13_e080_v1:launch_from_contract_inputs"], ["ragonline_mbucket_4fc7_q1m262_v1_980c", "loom.examples.weave.knn_build_ragonline_mbucket_4fc7_q1m262_v1:launch_from_contract_inputs"], ["baseline_8199_widecombine_full82_v1", "loom.examples.weave.knn_build_dispatch_4247_non128_8199_widecombine_full82_v1:launch_from_contract_inputs"], ["k30_q4096_6998_warpselect_v1", "loom.examples.weave.knn_build_k30_q4096_6998_warpselect_v1:launch_from_contract_inputs"], ["rag_stream_k10_direct_split72_6998_v1", "loom.examples.weave.knn_build_rag_online_stream_split72_4e09_v1:launch_from_contract_inputs"], ["rect_d64_23be_unordered_v1", "loom.examples.weave.knn_build_rect_d64_23be_unordered_v1:launch_from_contract_inputs"], ["residual_19b3_ed1c_portfolio_6998", "loom.examples.weave.knn_build_dispatch_c142_3505_q32rowld_19b3_v1:launch_from_contract_inputs"], ["candidate_q16split148_cachedmerge_k96exactall_e080_q1m262_over_8199_full82_v1", "loom.examples.weave.knn_build_dispatch_4247_non128_8199_3d5a_2e8e_full82_synth_v1:launch_from_contract_inputs"], ["rect_d128_k20_q1536_9b9f_d555_b8c7_v1", "loom.examples.weave.knn_build_rect_d128_k20_d555_b8c7_v1:launch_from_contract_inputs"], ["rag_microbatch_k10_q4q64_m64_3505_d555_v1", "loom.examples.weave.knn_build_rag_microbatch_k10_q4q64_d555_v1:launch_from_contract_inputs"], ["rag_microbucket_faeb_q4q64_k10_69d6_v1", "loom.examples.weave.knn_build_rag_microbucket_faeb_v1:launch_from_contract_inputs"], ["candidate_066c_ragmicro_q4q64_3505_full82_v1", "loom.examples.weave.knn_build_rag_microbatch_k10_q4q64_d555_v1:launch_from_contract_inputs"], ["candidate_d555_direct_residual_seeds_full82_v1", "loom.examples.weave.knn_build_dispatch_d555_residual_seed_synth_full82_v1:launch_from_contract_inputs"], ["rag_microbatch_q4_k10_s144_17b8_v1", "loom.examples.weave.knn_build_rag_microbatch_q4_s144_17b8_v1:launch_from_contract_inputs"], ["candidate_066c_69d6_plus_b8c7_full82_v1", "loom.examples.weave.knn_build_dispatch_066c_b8c7_69d6_full82_v1:launch_from_contract_inputs(q4q64_mode=69d6)"]]}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_Q4_S144_17B8_VERIFY_KERNEL') + if verify_kernel == 's144_merge': + return rag_faeb.rag_s144._fused_merge_ir(S144_SPLIT_COUNT, S144_GROUP_COUNT) + return rag_faeb.rag_s144.stage1_cta1_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbatch_4a72_v2_stage1_k10_cta1_maxtree", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) + +def _dtype_name(inputs: dict[str, Any], key: str) -> str: + tensor = inputs.get(key) + if tensor is not None: + return str(tensor.dtype).removeprefix('torch.') + return str(inputs.get('dtype', '')).removeprefix('torch.') + +def _label_can_hit(inputs: dict[str, Any], labels: set[str]) -> bool: + label = inputs.get('label') + return label is None or str(label) in labels + +def _eligible_q4_s144(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, TARGET_SHAPE_SET) and (not bool(inputs.get('build', False))) and (int(inputs.get('B', -1)) == 1) and (int(inputs.get('Q', -1)) == 4) and (int(inputs.get('M', -1)) == 100000) and (int(inputs.get('D', -1)) == 128) and (int(inputs.get('K', -1)) == 10) and (_dtype_name(inputs, 'query') == 'bfloat16') and (_dtype_name(inputs, 'database') == 'bfloat16') + +def _select_contract_shapes(shape_labels): + return base17b8._select_contract_shapes(shape_labels) + +def _base_route(inputs: dict[str, Any]) -> str: + return base17b8.route_for_contract_inputs(inputs, q4q64_mode=base17b8.Q4Q64_MODE_69D6) + +def _base_launch(inputs: dict[str, Any]) -> None: + base17b8.launch_from_contract_inputs(inputs, q4q64_mode=base17b8.Q4Q64_MODE_69D6) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if not force_fallback and _eligible_q4_s144(inputs): + return ROUTE_Q4_S144 + return _base_route(inputs) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + if not force_fallback and _eligible_q4_s144(inputs): + rag_faeb._launch_q4_k10_s144(inputs) + return + _base_launch(inputs) + +def candidate_q4_s144_17b8_v1(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_baseline_17b8_selected(inputs: dict[str, Any]) -> None: + _base_launch(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate, correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + return base17b8._run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=kernel_fn, correctness=correctness, time_flashlib=time_flashlib) + +def _baseline_trace_record(inputs: dict[str, Any], *, force_fallback: bool=False) -> dict[str, Any]: + label = str(inputs.get('label')) + return dict(base17b8.route_trace_for_contract_shapes((label,), candidate_key=base17b8.CANDIDATE_69D6_B8C7, force_fallback=force_fallback)[0]) + +def _q4_trace_record(inputs: dict[str, Any]) -> dict[str, Any]: + label = str(inputs.get('label')) + baseline_route = _base_route(inputs) + return base17b8._normalize_route_row({'shape_key': label, 'selected_route': ROUTE_Q4_S144, 'selected_entrypoint': ROUTE_ENTRYPOINT, 'selected_seed': SEED_Q4_S144_ID, 'expected_seed': SEED_Q4_S144_ID, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': '17b8_q4_s144_exact_guard', 'guard_condition': 'exact BF16 non-build B=1 Q=4 M=100000 D=128 K=10', 'coverage': 'S144 tcgen05/TMA stage1 plus S144/G12 fused merge before selected 17b8 fallback', 'consumed_seed': SEED_Q4_S144_ID, 'replaced_route': baseline_route, 'baseline_dispatcher_route': baseline_route, 'split_count': S144_SPLIT_COUNT, 'group_count': S144_GROUP_COUNT, 'classification': 'unmeasured'}) + +def _route_trace_record(inputs: dict[str, Any], *, force_fallback: bool=False) -> dict[str, Any]: + if force_fallback and _eligible_q4_s144(inputs): + row = _baseline_trace_record(inputs) + row['expected_seed'] = SEED_Q4_S144_ID + row['guard_id'] = 'forced_fallback_17b8_q4_s144_disabled' + row['guard_condition'] = 'forced fallback to selected 17b8 69d6 route; Q4 S144 overlay disabled' + row['forced_disabled_seeds'] = (SEED_Q4_S144_ID,) + row['classification'] = 'guard-miss' + return base17b8._normalize_route_row(row) + if not force_fallback and _eligible_q4_s144(inputs): + return _q4_trace_record(inputs) + return base17b8._normalize_route_row(_baseline_trace_record(inputs, force_fallback=False)) + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + return [_route_trace_record(base17b8._trace_inputs_for_shape(shape), force_fallback=force_fallback) for shape in selected] + +def _rows_for_labels(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return base17b8._rows_for_labels(report, labels) + +def _annotate_route_trace(route_trace: list[dict[str, Any]], candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> list[dict[str, Any]]: + annotated = [] + for row in route_trace: + label = str(row.get('shape_key')) + out = dict(row) + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') + speedup_vs_baseline = baseline_ms / candidate_ms if candidate_ms and baseline_ms else None + speedup_vs_external = flashlib_ms / candidate_ms if candidate_ms and flashlib_ms else None + out['dispatcher_kernel_ms'] = candidate_ms + out['baseline_dispatcher_kernel_ms'] = baseline_ms + out['external_baseline_ms'] = flashlib_ms + out['flashlib_ms'] = flashlib_ms + out['relative_speedup_vs_baseline'] = speedup_vs_baseline + out['speedup_vs_external_baseline'] = speedup_vs_external + out['external_baseline_ref'] = 'same_session' if flashlib_ms is not None else 'not_available' + out['route_changed_vs_baseline_dispatcher'] = out.get('selected_route') != out.get('baseline_dispatcher_route') + if label in TARGET_SHAPE_SET: + if speedup_vs_external is not None and speedup_vs_external < 1.05: + out['classification'] = 'kernel-slow' + elif speedup_vs_baseline is not None and speedup_vs_baseline < 1.0: + out['classification'] = 'kernel-slow' + else: + out['classification'] = 'seed-consumed' + elif speedup_vs_external is not None and speedup_vs_external < 1.05: + out['classification'] = 'kernel-slow' if out.get('route_kind') == 'specialized' else 'fallback-slow' + else: + out['classification'] = 'route-ok' + annotated.append(base17b8._normalize_route_row(out)) + return annotated + +def _seed_delta_matrix(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> list[dict[str, Any]]: + matrix = [] + for label in TARGET_SHAPES: + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + inputs = base17b8.dispatch_066c.base_d555.base_f30c._inputs_for_label(label) + matrix.append({'shape_key': label, 'baseline_route': _base_route(inputs), 'candidate_route': route_for_contract_inputs(inputs), 'selected_seed': SEED_Q4_S144_ID, 'candidate_id': 'candidate_17b8_q4_s144_v1', 'candidate_ms': candidate_ms, 'baseline_ms': baseline_ms, 'flashlib_ms': candidate_row.get('flashlib_ms'), 'metric_delta_ms': candidate_ms - baseline_ms if candidate_ms and baseline_ms else None, 'speedup_vs_baseline_dispatcher': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'ratio_vs_flashlib': candidate_row.get('ratio_vs_flashlib'), 'timing_backend': candidate_row.get('timing_backend') or baseline_row.get('timing_backend')}) + return matrix + +def _below_flashlib_rows(report: dict[str, Any], route_trace: list[dict[str, Any]], *, floor: float) -> list[dict[str, Any]]: + trace_by_label = {str(row['shape_key']): row for row in route_trace} + rows = [] + for label, row in sorted(report.get('per_shape', {}).items()): + ratio = row.get('ratio_vs_flashlib') + if isinstance(ratio, (float, int)) and ratio < floor: + trace_row = trace_by_label.get(label, {}) + rows.append({'shape_key': label, 'kernel_ms': row.get('kernel_ms'), 'flashlib_ms': row.get('flashlib_ms'), 'ratio_vs_flashlib': ratio, 'selected_route': trace_row.get('selected_route'), 'route_kind': trace_row.get('route_kind', 'unknown'), 'classification': 'kernel-slow' if trace_row.get('route_kind') == 'specialized' else 'fallback-slow'}) + return rows + +def _denominator_name(shape_labels) -> str: + if shape_labels is None: + return 'full82_v9' + labels = tuple(shape_labels) + if labels == TARGET_SHAPES: + return 'rag_microbatch_b1_q4_m100000_d128_k10' + if labels == SMOKE_SHAPES: + return 'rag_microbatch_k10_q4_q64_smoke' + return ''.join(['shape', format(len(labels), '')]) + +def _denominator_label(shape_labels) -> str: + if shape_labels is None: + return ''.join(['full', format(len(eval_mod.CANONICAL_SHAPES), '')]) + return ''.join(['shape', format(len(tuple(shape_labels)), '')]) + +def _timing_backend_name(use_cupti: bool) -> str: + return 'cupti' if use_cupti else 'cuda_event_fallback' + +def benchmark_baseline_17b8_selected(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES, benchmark_correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_baseline_17b8_selected, correctness=benchmark_correctness, time_flashlib=time_flashlib) + report['candidate_id'] = BASELINE_ID + report['measured_entrypoint'] = BASELINE_ENTRYPOINT + report['route_trace'] = base17b8.route_trace_for_contract_shapes(shape_labels, candidate_key=base17b8.CANDIDATE_69D6_B8C7) + report['route_trace_included'] = True + return report + +def _benchmark_payload(candidate_report: dict[str, Any], baseline_report: dict[str, Any], *, use_cupti: bool, shape_labels, benchmark_correctness: bool, time_flashlib: bool) -> dict[str, Any]: + candidate_metric = candidate_report['summary']['primary_mean'] + baseline_metric = baseline_report['summary']['primary_mean'] + route_trace = _annotate_route_trace(route_trace_for_contract_shapes(shape_labels), candidate_report, baseline_report) + below_1x = _below_flashlib_rows(candidate_report, route_trace, floor=1.0) + below_floor = _below_flashlib_rows(candidate_report, route_trace, floor=1.05) + denominator = _denominator_name(shape_labels) + timing_backend = _timing_backend_name(use_cupti) + return {'candidate_id': 'candidate_17b8_q4_s144_v1', 'baseline_candidate_id': BASELINE_ID, 'selected_seeds': (SEED_Q4_S144_ID,), 'source_tasks': SOURCE_TASKS, 'tflops': candidate_metric, 'baseline_tflops': baseline_metric, 'metric_delta': candidate_metric - baseline_metric if candidate_metric and baseline_metric else None, 'all_correct': candidate_report['summary']['all_correct'], 'baseline_all_correct': baseline_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'baseline_performance_comparable': baseline_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'baseline_invalid_performance_reason': baseline_report['summary']['invalid_performance_reason'], 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_candidate_17b8_q4_s144_v1']), 'baseline_entrypoint': ''.join([format(MODULE, ''), ':benchmark_baseline_17b8_selected']), 'measured_shape_labels': 'all_canonical' if shape_labels is None else tuple(shape_labels), 'timing_backend': timing_backend, 'denominator': denominator, 'selected_route_labels': TARGET_SHAPES, 'consumed_seed_labels': TARGET_SHAPES, 'selected_route_rows': _rows_for_labels(candidate_report, TARGET_SHAPES), 'baseline_selected_route_rows': _rows_for_labels(baseline_report, TARGET_SHAPES), 'seed_delta_matrix': _seed_delta_matrix(candidate_report, baseline_report), 'candidate_dispatchers': ({'id': BASELINE_ID, 'entrypoint': BASELINE_ENTRYPOINT, 'consumed_seeds': base17b8.CANDIDATE_CONFIGS[base17b8.CANDIDATE_69D6_B8C7]['selected_seeds'], 'guard_plan': base17b8.CANDIDATE_CONFIGS[base17b8.CANDIDATE_69D6_B8C7]['guard_plan'], 'fallback': base17b8.ROUTE_BASE_066C_ENTRYPOINT, 'rejected_reason': 'same-session selected 17b8 baseline'}, {'id': 'candidate_17b8_q4_s144_v1', 'entrypoint': ''.join([format(MODULE, ''), ':benchmark_candidate_17b8_q4_s144_v1']), 'consumed_seeds': (SEED_Q4_S144_ID,), 'guard_plan': ('exact Q4 S144 guard', 'selected 17b8 69d6+b8c7 Weave fallback'), 'fallback': ROUTE_BASE_17B8_ENTRYPOINT, 'rejected_reason': None}), 'guard_plan': ('exact Q4 S144 guard', 'selected 17b8 69d6+b8c7 Weave fallback'), 'route_modules': PRODUCTION_ROUTE_MODULES, 'route_trace': route_trace, 'forced_fallback_route_trace': route_trace_for_contract_shapes(shape_labels, force_fallback=True), 'route_trace_included': True, 'contract_summary': candidate_report['summary'], 'baseline_contract_summary': baseline_report['summary'], 'contract_performance': candidate_report['performance'], 'baseline_contract_performance': baseline_report['performance'], 'timing_backends': base17b8.dispatch_066c.base_d555.base_f30c._timing_backends_for_reports(candidate_report, baseline_report), 'timing_backend_requested': timing_backend, 'use_cupti': use_cupti, 'benchmark_correctness_checked': benchmark_correctness, 'benchmark_time_flashlib': time_flashlib, 'performance_coverage': 'partial' if below_floor else 'pass', 'coverage_only_routes': [], 'hot_bucket_blockers': below_floor, 'flashlib_parity_ledger': {'baseline_ref_scope': 'same_session' if time_flashlib else 'not_available', 'speedup_floor': 1.05, 'rows_below_1x': below_1x, 'rows_below_floor': below_floor, 'omitted_reason': None if time_flashlib else 'benchmark_time_flashlib=false'}, 'rank_objective': {'metric': 'tflops', 'direction': 'maximize', 'value': candidate_metric, 'baseline_value': baseline_metric, 'delta': candidate_metric - baseline_metric if candidate_metric and baseline_metric else None, 'denominator': denominator}, 'report': candidate_report, 'baseline_report': baseline_report} + +def benchmark_candidate_17b8_q4_s144_v1(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES, baseline_report: dict[str, Any] | None=None, benchmark_correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + if baseline_report is None: + baseline_report = benchmark_baseline_17b8_selected(use_cupti=use_cupti, shape_labels=shape_labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_q4_s144_17b8_v1, correctness=benchmark_correctness, time_flashlib=time_flashlib) + return _benchmark_payload(candidate_report, baseline_report, use_cupti=use_cupti, shape_labels=shape_labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + +def write_benchmark_artifacts(artifact_dir: str | Path, *, use_cupti: bool=True, shape_labels=TARGET_SHAPES, benchmark_correctness: bool=True, time_flashlib: bool=True) -> dict[str, str]: + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + denom = _denominator_label(shape_labels) + denominator = _denominator_name(shape_labels) + payload = benchmark_candidate_17b8_q4_s144_v1(use_cupti=use_cupti, shape_labels=shape_labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + baseline_path = out_dir / ''.join([format(denom, ''), '_same_session_baseline_17b8_69d6_for_q4_s144_v1.json']) + candidate_path = out_dir / ''.join([format(denom, ''), '_dispatch_17b8_q4_s144_v1.json']) + route_trace_path = out_dir / ''.join([format(denom, ''), '_route_trace_17b8_q4_s144_v1.json']) + forced_trace_path = out_dir / ''.join([format(denom, ''), '_forced_fallback_trace_17b8_q4_s144_v1.json']) + seed_matrix_path = out_dir / ''.join([format(denom, ''), '_seed_delta_matrix_17b8_q4_s144_v1.json']) + baseline_path.write_text(json.dumps({'candidate_id': BASELINE_ID, 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_baseline_17b8_selected']), 'measured_shape_labels': payload['measured_shape_labels'], 'timing_backend': payload['timing_backend'], 'denominator': denominator, 'benchmark_correctness_checked': payload['benchmark_correctness_checked'], 'benchmark_time_flashlib': payload['benchmark_time_flashlib'], 'tflops': payload['baseline_tflops'], 'all_correct': payload['baseline_all_correct'], 'performance_comparable': payload['baseline_performance_comparable'], 'contract_summary': payload['baseline_contract_summary'], 'contract_performance': payload['baseline_contract_performance'], 'route_trace': base17b8.route_trace_for_contract_shapes(shape_labels, candidate_key=base17b8.CANDIDATE_69D6_B8C7), 'route_trace_included': True, 'rank_objective': {'metric': 'tflops', 'direction': 'maximize', 'value': payload['baseline_tflops'], 'denominator': denominator}, 'report': payload['baseline_report']}, indent=2, sort_keys=True) + '\n', encoding='utf-8') + candidate_path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n', encoding='utf-8') + route_trace_path.write_text(json.dumps(payload['route_trace'], indent=2, sort_keys=True) + '\n', encoding='utf-8') + forced_trace_path.write_text(json.dumps(payload['forced_fallback_route_trace'], indent=2, sort_keys=True) + '\n', encoding='utf-8') + seed_matrix_path.write_text(json.dumps(payload['seed_delta_matrix'], indent=2, sort_keys=True) + '\n', encoding='utf-8') + return {'same_session_baseline_payload': str(baseline_path), 'candidate_payload': str(candidate_path), 'route_trace': str(route_trace_path), 'forced_fallback_trace': str(forced_trace_path), 'seed_delta_matrix': str(seed_matrix_path)} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_3505_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_3505_v1.py new file mode 100644 index 00000000..19daa10e --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_3505_v1.py @@ -0,0 +1,168 @@ +"""RAG microbucket Q4/Q64 K10 plus Q16 K32 tail-infinity seed. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +keeps the validated FAEB M64 route for the Q4/Q64 K10 blindspot rows and +retargets only ``rag_microbatch_largek_b1_q16_m100000_d128_k32`` to the 4fbf +tail-infinity K32 tcgen05/TMA producer with a split72/group8 fused merge. +Guard misses delegate to the current 4247 dispatcher, so production routes stay +Weave-only and contract outputs are written directly. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_e3de_9138_bcb3_4247_v1 as base_dispatcher +from . import knn_build_rag_frontier_4fbf_v7 as q16_tailinf +from . import knn_build_rag_microbucket_faeb_v1 as faeb +Q4_K10_SHAPE = faeb.Q4_K10_SHAPE +Q64_K10_SHAPE = faeb.Q64_K10_SHAPE +Q16_K32_SHAPE = faeb.Q16_K32_SHAPE +K10_TARGET_SHAPES = faeb.K10_TARGET_SHAPES +K32_TARGET_SHAPES = (Q16_K32_SHAPE,) +TARGET_SHAPES = (*K10_TARGET_SHAPES, *K32_TARGET_SHAPES) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +K32_SPLIT_COUNT = _decode_capture(_json_loads('72')) +K32_GROUP_COUNT = _decode_capture(_json_loads('8')) +ROUTE_Q4_K10 = 'rag_microbucket_3505_q4_k10_m64_s128_g8' +ROUTE_Q64_K10 = 'rag_microbucket_3505_q64_k10_m64_s128_g8' +ROUTE_Q16_K32 = ''.join(['rag_microbucket_3505_q16_k32_tailinf_s', format(K32_SPLIT_COUNT, ''), '_g', format(K32_GROUP_COUNT, '')]) +ROUTE_BASE_4247 = 'loom.examples.weave.knn_build_dispatch_e3de_9138_bcb3_4247_v1:launch_from_contract_inputs' +PRODUCTION_ROUTE_MODULES = {'q4_q64_k10_m64': 'loom.examples.weave.knn_build_rag_microbatch_m64_d4f7_v1:_launch_rag_microbatch_m64', 'q16_k32_tailinf': 'loom.examples.weave.knn_build_rag_frontier_4fbf_v7:_launch_k32_rag_frontier_sort4earlystop_stage', 'base_4247': ROUTE_BASE_4247} + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RAG_MICROBUCKET_3505_VERIFY_KERNEL') + split_count = int(os.environ.get('LOOM_KNN_RAG_MICROBUCKET_3505_VERIFY_K32_SPLIT', K32_SPLIT_COUNT)) + group_count = int(os.environ.get('LOOM_KNN_RAG_MICROBUCKET_3505_VERIFY_K32_GROUPS', K32_GROUP_COUNT)) + if verify_kernel == 'm64_stage1': + return faeb.rag_m64.stage1_m64_ir + if verify_kernel == 'm64_merge': + return faeb.rag_m64.parent_micro._fused_merge_ir(faeb.M64_SPLIT_COUNT, faeb.M64_GROUP_COUNT) + if verify_kernel == 'q16_k32_stage1': + return q16_tailinf.stage1_k32_tailinf_ir + if verify_kernel == 'q16_k32_fused_merge': + return q16_tailinf._fused_merge_ir(split_count, group_count) + return q16_tailinf.stage1_k32_tailinf_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_frontier_4fbf_v7_stage1_k32_sort4earlystop_tailinf", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 192}')) + +def _eligible_q4_k10(inputs: dict[str, Any]) -> bool: + return faeb._eligible_q4_k10(inputs) + +def _eligible_q64_k10(inputs: dict[str, Any]) -> bool: + return faeb._eligible_q64_k10(inputs) + +def _eligible_q16_k32(inputs: dict[str, Any]) -> bool: + return faeb._eligible_q16_k32(inputs) + +def _launch_q16_k32_tailinf(inputs: dict[str, Any], *, split_count: int=K32_SPLIT_COUNT, group_count: int=K32_GROUP_COUNT) -> None: + q16_tailinf._launch_k32_rag_frontier_sort4earlystop_stage(inputs, split_count=split_count, group_count=group_count) + +def route_for_contract_inputs(inputs: dict[str, Any], *, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> str: + if _eligible_q4_k10(inputs): + return ROUTE_Q4_K10 + if _eligible_q64_k10(inputs): + return ROUTE_Q64_K10 + if _eligible_q16_k32(inputs): + return ''.join(['rag_microbucket_3505_q16_k32_tailinf_s', format(k32_split_count, ''), '_g', format(k32_group_count, '')]) + return base_dispatcher.route_for_contract_inputs(inputs) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> None: + if _eligible_q4_k10(inputs): + faeb._launch_q4_k10_m64(inputs) + return + if _eligible_q64_k10(inputs): + faeb._launch_q64_k10_m64(inputs) + return + if _eligible_q16_k32(inputs): + _launch_q16_k32_tailinf(inputs, split_count=k32_split_count, group_count=k32_group_count) + return + base_dispatcher.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def candidate_with_k32_topology(split_count: int, group_count: int) -> Callable[[dict[str, Any]], None]: + + def _candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, k32_split_count=split_count, k32_group_count=group_count) + return _candidate + +def candidate_faeb_baseline(inputs: dict[str, Any]): + if _eligible_q4_k10(inputs) or _eligible_q64_k10(inputs) or _eligible_q16_k32(inputs): + faeb.launch_from_contract_inputs(inputs) + return None + base_dispatcher.launch_from_contract_inputs(inputs) + return None + +def candidate_base_4247(inputs: dict[str, Any]): + base_dispatcher.launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return base_dispatcher._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def _trace_inputs_from_shape(shape: dict[str, Any]) -> dict[str, Any]: + return base_dispatcher._trace_inputs_from_shape(shape) + +def route_trace_for_contract_shapes(shape_labels=TARGET_SHAPES) -> list[dict[str, Any]]: + selected = _select_contract_shapes(shape_labels) + rows = [] + for shape in selected: + inputs = _trace_inputs_from_shape(shape) + route = route_for_contract_inputs(inputs) + specialized = str(route).startswith('rag_microbucket_3505') + rows.append({'shape_key': shape['label'], 'selected_route': route, 'route_kind': 'specialized' if specialized else 'general', 'guard_condition': 'exact BF16 non-build B1 M100000 D128 Q4/Q64 K10 or Q16 K32 microbucket' if specialized else 'guard miss to 4247 dispatcher', 'fallback': ROUTE_BASE_4247}) + return rows + +def _target_rows(candidate_report: dict[str, Any], faeb_report: dict[str, Any], base_report: dict[str, Any], *, k32_split_count: int, k32_group_count: int) -> dict[str, Any]: + rows = {} + for label in TARGET_SHAPES: + cand = candidate_report.get('per_shape', {}).get(label, {}) + faeb_row = faeb_report.get('per_shape', {}).get(label, {}) + base = base_report.get('per_shape', {}).get(label, {}) + cand_ms = cand.get('kernel_ms') + faeb_ms = faeb_row.get('kernel_ms') + base_ms = base.get('kernel_ms') + flashlib_ms = cand.get('flashlib_ms') + route = ''.join(['rag_microbucket_3505_q16_k32_tailinf_s', format(k32_split_count, ''), '_g', format(k32_group_count, '')]) if label == Q16_K32_SHAPE else ROUTE_Q4_K10 if label == Q4_K10_SHAPE else ROUTE_Q64_K10 + rows[label] = {'candidate': cand, 'faeb_baseline': faeb_row, 'base_4247': base, 'candidate_route': route, 'candidate_ms': cand_ms, 'faeb_baseline_ms': faeb_ms, 'base_4247_ms': base_ms, 'flashlib_ms': flashlib_ms, 'speedup_vs_faeb': faeb_ms / cand_ms if cand_ms and faeb_ms else None, 'speedup_vs_4247': base_ms / cand_ms if cand_ms and base_ms else None, 'ratio_vs_flashlib': flashlib_ms / cand_ms if cand_ms and flashlib_ms else None} + return rows + +def _timing_backends_for_reports(*reports: dict[str, Any]) -> list[str]: + return sorted({row.get('timing_backend') for report in reports for row in report.get('per_shape', {}).values() if row.get('timing_backend') is not None}) + +def _benchmark_payload(candidate_report: dict[str, Any], faeb_report: dict[str, Any], base_report: dict[str, Any], *, use_cupti: bool, shape_labels, measured_function: str, k32_split_count: int, k32_group_count: int) -> dict[str, Any]: + return {'tflops': candidate_report['summary']['primary_mean'] or 0.0, 'all_correct': candidate_report['summary']['all_correct'], 'faeb_all_correct': faeb_report['summary']['all_correct'], 'base_4247_all_correct': base_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'faeb_performance_comparable': faeb_report['summary']['performance_comparable'], 'base_4247_performance_comparable': base_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'measured_entrypoint': ''.join(['loom.examples.weave.knn_build_rag_microbucket_3505_v1:', format(measured_function, '')]), 'faeb_entrypoint': 'loom.examples.weave.knn_build_rag_microbucket_faeb_v1:launch_from_contract_inputs', 'base_4247_entrypoint': ROUTE_BASE_4247, 'accelerated_shape_labels': list(TARGET_SHAPES), 'measured_shape_labels': list(shape_labels), 'producer_topology': {'Q4_K10': ''.join(['M64/S', format(faeb.M64_SPLIT_COUNT, ''), '/G', format(faeb.M64_GROUP_COUNT, '')]), 'Q64_K10': ''.join(['M64/S', format(faeb.M64_SPLIT_COUNT, ''), '/G', format(faeb.M64_GROUP_COUNT, '')]), 'Q16_K32': ''.join(['tailinf-sort4earlystop/S', format(k32_split_count, ''), '/G', format(k32_group_count, ''), '/fused'])}, 'timing_backends': _timing_backends_for_reports(candidate_report, faeb_report, base_report), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'route_trace': route_trace_for_contract_shapes(shape_labels), 'route_trace_included': True, 'target_rows': _target_rows(candidate_report, faeb_report, base_report, k32_split_count=k32_split_count, k32_group_count=k32_group_count), 'contract_summary': candidate_report['summary'], 'faeb_contract_summary': faeb_report['summary'], 'base_4247_contract_summary': base_report['summary'], 'contract_performance': candidate_report['performance'], 'faeb_contract_performance': faeb_report['performance'], 'base_4247_contract_performance': base_report['performance'], 'report': candidate_report, 'faeb_report': faeb_report, 'base_4247_report': base_report} + +def benchmark_knn_build_rag_microbucket_3505_v1(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_with_k32_topology(k32_split_count, k32_group_count)) + faeb_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_faeb_baseline) + base_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_base_4247) + return _benchmark_payload(candidate_report, faeb_report, base_report, use_cupti=use_cupti, shape_labels=shape_labels, measured_function='benchmark_knn_build_rag_microbucket_3505_v1', k32_split_count=k32_split_count, k32_group_count=k32_group_count) diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_3505_v2.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_3505_v2.py new file mode 100644 index 00000000..6d497708 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_3505_v2.py @@ -0,0 +1,195 @@ +"""RAG microbucket Q4/Q64 K10 plus Q16 K32 cta1 tail-infinity seed. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +keeps the validated 3505 Q4/Q64 M64 routes and retargets only +``rag_microbatch_largek_b1_q16_m100000_d128_k32`` to a cta_group=1 +tail-infinity K32 tcgen05/TMA producer. The producer keeps the 4fbf top-k +logic but removes the second invalid CTA from the inherited Q128/2-CTA +frontier stage. Guard misses delegate to the current 4247 dispatcher. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from collections.abc import Callable +from functools import lru_cache +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_e3de_9138_bcb3_4247_v1 as base_dispatcher +from . import knn_build_rag_frontier_4fbf_v7 as q16_tailinf +from . import knn_build_rag_microbucket_3505_v1 as parent_3505 +from . import knn_build_rag_microbucket_faeb_v1 as faeb +from .._dispatch_runtime import pack_kernel_args +Q4_K10_SHAPE = faeb.Q4_K10_SHAPE +Q64_K10_SHAPE = faeb.Q64_K10_SHAPE +Q16_K32_SHAPE = faeb.Q16_K32_SHAPE +K10_TARGET_SHAPES = faeb.K10_TARGET_SHAPES +K32_TARGET_SHAPES = (Q16_K32_SHAPE,) +TARGET_SHAPES = (*K10_TARGET_SHAPES, *K32_TARGET_SHAPES) +K32_SPLIT_COUNT = _decode_capture(_json_loads('144')) +K32_GROUP_COUNT = _decode_capture(_json_loads('12')) +BLOCK_Q = q16_tailinf.BLOCK_Q +BLOCK_M = q16_tailinf.BLOCK_M +FEAT_D = q16_tailinf.FEAT_D +STAGE1_THREADS = q16_tailinf.STAGE1_THREADS +GRID_DIM_DEFAULT = q16_tailinf.GRID_DIM_DEFAULT +TOP_K_MAX = q16_tailinf.TOP_K_MAX +K32_FUSED_MERGE_THREADS = q16_tailinf.K32_FUSED_MERGE_THREADS +ROUTE_Q4_K10 = 'rag_microbucket_3505_v2_q4_k10_m64_s128_g8' +ROUTE_Q64_K10 = 'rag_microbucket_3505_v2_q64_k10_m64_s128_g8' +ROUTE_BASE_4247 = parent_3505.ROUTE_BASE_4247 +knn_build_rag_microbucket_3505_v2_stage1_k32_tailinf_cta1 = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_3505_v2_stage1_k32_tailinf_cta1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 192}')) +stage1_k32_tailinf_cta1_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_3505_v2_stage1_k32_tailinf_cta1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 192}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RAG_MICROBUCKET_3505_V2_VERIFY_KERNEL') + split_count = int(os.environ.get('LOOM_KNN_RAG_MICROBUCKET_3505_V2_VERIFY_K32_SPLIT', K32_SPLIT_COUNT)) + group_count = int(os.environ.get('LOOM_KNN_RAG_MICROBUCKET_3505_V2_VERIFY_K32_GROUPS', K32_GROUP_COUNT)) + if verify_kernel == 'm64_stage1': + return faeb.rag_m64.stage1_m64_ir + if verify_kernel == 'm64_merge': + return faeb.rag_m64.parent_micro._fused_merge_ir(faeb.M64_SPLIT_COUNT, faeb.M64_GROUP_COUNT) + if verify_kernel == 'q16_k32_fused_merge': + return q16_tailinf._fused_merge_ir(split_count, group_count) + return stage1_k32_tailinf_cta1_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_3505_v2_stage1_k32_tailinf_cta1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 192}')) + +def _compiled_stage1_tailinf_cta1(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0213"}')) + +def _eligible_q4_k10(inputs: dict[str, Any]) -> bool: + return faeb._eligible_q4_k10(inputs) + +def _eligible_q64_k10(inputs: dict[str, Any]) -> bool: + return faeb._eligible_q64_k10(inputs) + +def _eligible_q16_k32(inputs: dict[str, Any]) -> bool: + return faeb._eligible_q16_k32(inputs) + +def _launch_q16_k32_tailinf_cta1(inputs: dict[str, Any], *, split_count: int=K32_SPLIT_COUNT, group_count: int=K32_GROUP_COUNT) -> None: + q16_tailinf._validate_group_shape(split_count, group_count) + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tiles * split_count + total_queries = bsz * n_query + stage1_grid = min(total_work, GRID_DIM_DEFAULT) + merge_grid = min(total_queries, GRID_DIM_DEFAULT) + partial_dists, partial_indices = q16_tailinf.parent_k32.parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = q16_tailinf.parent_k32.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), total_queries, BLOCK_Q, dim, dim) + tmap_database = q16_tailinf.parent_k32.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, dim, dim) + _compiled_stage1_tailinf_cta1().launch(grid=(stage1_grid, 1, 1), block=(STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage1_k32_tailinf_cta1_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tiles=num_q_tiles, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), shared_mem=stage1_k32_tailinf_cta1_ir.computed_smem_bytes) + fused_ir = q16_tailinf._fused_merge_ir(split_count, group_count) + fused_kernel = q16_tailinf._compiled_fused_merge(split_count, group_count) + fused_kernel.launch(grid=(merge_grid, 1, 1), block=(K32_FUSED_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], total_queries], shared_mem=fused_ir.computed_smem_bytes) + +def route_for_contract_inputs(inputs: dict[str, Any], *, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> str: + if _eligible_q4_k10(inputs): + return ROUTE_Q4_K10 + if _eligible_q64_k10(inputs): + return ROUTE_Q64_K10 + if _eligible_q16_k32(inputs): + return ''.join(['rag_microbucket_3505_v2_q16_k32_tailinf_cta1_s', format(k32_split_count, ''), '_g', format(k32_group_count, '')]) + return base_dispatcher.route_for_contract_inputs(inputs) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> None: + if _eligible_q4_k10(inputs): + faeb._launch_q4_k10_m64(inputs) + return + if _eligible_q64_k10(inputs): + faeb._launch_q64_k10_m64(inputs) + return + if _eligible_q16_k32(inputs): + _launch_q16_k32_tailinf_cta1(inputs, split_count=k32_split_count, group_count=k32_group_count) + return + base_dispatcher.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def candidate_with_k32_topology(split_count: int, group_count: int) -> Callable[[dict[str, Any]], None]: + + def _candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, k32_split_count=split_count, k32_group_count=group_count) + return _candidate + +def candidate_faeb_baseline(inputs: dict[str, Any]): + return parent_3505.candidate_faeb_baseline(inputs) + +def candidate_parent_3505(inputs: dict[str, Any]): + parent_3505.launch_from_contract_inputs(inputs) + return None + +def candidate_base_4247(inputs: dict[str, Any]): + return parent_3505.candidate_base_4247(inputs) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return parent_3505._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return parent_3505._run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=kernel_fn) + +def route_trace_for_contract_shapes(shape_labels=TARGET_SHAPES, *, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> list[dict[str, Any]]: + selected = _select_contract_shapes(shape_labels) + rows = [] + for shape in selected: + inputs = base_dispatcher._trace_inputs_from_shape(shape) + route = route_for_contract_inputs(inputs, k32_split_count=k32_split_count, k32_group_count=k32_group_count) + specialized = str(route).startswith('rag_microbucket_3505_v2') + rows.append({'shape_key': shape['label'], 'selected_route': route, 'route_kind': 'specialized' if specialized else 'general', 'guard_condition': 'exact BF16 non-build B1 M100000 D128 Q4/Q64 K10 or Q16 K32 microbucket' if specialized else 'guard miss to 4247 dispatcher', 'fallback': ROUTE_BASE_4247}) + return rows + +def _target_rows(candidate_report: dict[str, Any], parent_report: dict[str, Any], faeb_report: dict[str, Any], base_report: dict[str, Any], *, k32_split_count: int, k32_group_count: int) -> dict[str, Any]: + rows = {} + for label in TARGET_SHAPES: + cand = candidate_report.get('per_shape', {}).get(label, {}) + parent = parent_report.get('per_shape', {}).get(label, {}) + faeb_row = faeb_report.get('per_shape', {}).get(label, {}) + base = base_report.get('per_shape', {}).get(label, {}) + cand_ms = cand.get('kernel_ms') + parent_ms = parent.get('kernel_ms') + faeb_ms = faeb_row.get('kernel_ms') + base_ms = base.get('kernel_ms') + flashlib_ms = cand.get('flashlib_ms') + route = ''.join(['rag_microbucket_3505_v2_q16_k32_tailinf_cta1_s', format(k32_split_count, ''), '_g', format(k32_group_count, '')]) if label == Q16_K32_SHAPE else ROUTE_Q4_K10 if label == Q4_K10_SHAPE else ROUTE_Q64_K10 + rows[label] = {'candidate': cand, 'parent_3505': parent, 'faeb_baseline': faeb_row, 'base_4247': base, 'candidate_route': route, 'candidate_ms': cand_ms, 'parent_3505_ms': parent_ms, 'faeb_baseline_ms': faeb_ms, 'base_4247_ms': base_ms, 'flashlib_ms': flashlib_ms, 'speedup_vs_parent_3505': parent_ms / cand_ms if cand_ms and parent_ms else None, 'speedup_vs_faeb': faeb_ms / cand_ms if cand_ms and faeb_ms else None, 'speedup_vs_4247': base_ms / cand_ms if cand_ms and base_ms else None, 'ratio_vs_flashlib': flashlib_ms / cand_ms if cand_ms and flashlib_ms else None} + return rows + +def benchmark_knn_build_rag_microbucket_3505_v2(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_with_k32_topology(k32_split_count, k32_group_count)) + parent_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=parent_3505.candidate_with_k32_topology(parent_3505.K32_SPLIT_COUNT, parent_3505.K32_GROUP_COUNT)) + faeb_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_faeb_baseline) + base_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_base_4247) + payload = parent_3505._benchmark_payload(candidate_report, faeb_report, base_report, use_cupti=use_cupti, shape_labels=shape_labels, measured_function='benchmark_knn_build_rag_microbucket_3505_v2', k32_split_count=k32_split_count, k32_group_count=k32_group_count) + payload['measured_entrypoint'] = 'loom.examples.weave.knn_build_rag_microbucket_3505_v2:benchmark_knn_build_rag_microbucket_3505_v2' + payload['parent_3505_entrypoint'] = 'loom.examples.weave.knn_build_rag_microbucket_3505_v1:launch_from_contract_inputs' + payload['parent_3505_all_correct'] = parent_report['summary']['all_correct'] + payload['parent_3505_performance_comparable'] = parent_report['summary']['performance_comparable'] + payload['producer_topology']['Q16_K32'] = ''.join(['tailinf-cta1/S', format(k32_split_count, ''), '/G', format(k32_group_count, ''), '/fused']) + payload['route_trace'] = route_trace_for_contract_shapes(shape_labels, k32_split_count=k32_split_count, k32_group_count=k32_group_count) + payload['target_rows'] = _target_rows(candidate_report, parent_report, faeb_report, base_report, k32_split_count=k32_split_count, k32_group_count=k32_group_count) + payload['parent_3505_contract_summary'] = parent_report['summary'] + payload['parent_3505_contract_performance'] = parent_report['performance'] + payload['parent_3505_report'] = parent_report + return payload diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_3505_v3.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_3505_v3.py new file mode 100644 index 00000000..48fea91b --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_3505_v3.py @@ -0,0 +1,194 @@ +"""RAG microbucket Q4/Q64 K10 plus unroll1 Q16 K32 cta1 seed. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +keeps the validated 3505 Q4/Q64 M64 routes and retargets only +``rag_microbatch_largek_b1_q16_m100000_d128_k32`` to a cta_group=1 +tail-infinity K32 tcgen05/TMA producer with the hot four-column distance loop +lowered to ``unroll=1``. Guard misses delegate to the current 4247 dispatcher. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from collections.abc import Callable +from functools import lru_cache +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_e3de_9138_bcb3_4247_v1 as base_dispatcher +from . import knn_build_rag_frontier_4fbf_v7 as q16_tailinf +from . import knn_build_rag_microbucket_3505_v1 as parent_3505 +from . import knn_build_rag_microbucket_faeb_v1 as faeb +from .._dispatch_runtime import pack_kernel_args +Q4_K10_SHAPE = faeb.Q4_K10_SHAPE +Q64_K10_SHAPE = faeb.Q64_K10_SHAPE +Q16_K32_SHAPE = faeb.Q16_K32_SHAPE +K10_TARGET_SHAPES = faeb.K10_TARGET_SHAPES +K32_TARGET_SHAPES = (Q16_K32_SHAPE,) +TARGET_SHAPES = (*K10_TARGET_SHAPES, *K32_TARGET_SHAPES) +K32_SPLIT_COUNT = _decode_capture(_json_loads('144')) +K32_GROUP_COUNT = _decode_capture(_json_loads('12')) +BLOCK_Q = q16_tailinf.BLOCK_Q +BLOCK_M = q16_tailinf.BLOCK_M +FEAT_D = q16_tailinf.FEAT_D +STAGE1_THREADS = q16_tailinf.STAGE1_THREADS +GRID_DIM_DEFAULT = q16_tailinf.GRID_DIM_DEFAULT +TOP_K_MAX = q16_tailinf.TOP_K_MAX +K32_FUSED_MERGE_THREADS = q16_tailinf.K32_FUSED_MERGE_THREADS +ROUTE_Q4_K10 = 'rag_microbucket_3505_v3_q4_k10_m64_s128_g8' +ROUTE_Q64_K10 = 'rag_microbucket_3505_v3_q64_k10_m64_s128_g8' +ROUTE_BASE_4247 = parent_3505.ROUTE_BASE_4247 +knn_build_rag_microbucket_3505_v3_stage1_k32_tailinf_cta1 = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_3505_v3_stage1_k32_tailinf_cta1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 192}')) +stage1_k32_tailinf_cta1_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_3505_v3_stage1_k32_tailinf_cta1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 192}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RAG_MICROBUCKET_3505_V3_VERIFY_KERNEL') + split_count = int(os.environ.get('LOOM_KNN_RAG_MICROBUCKET_3505_V3_VERIFY_K32_SPLIT', K32_SPLIT_COUNT)) + group_count = int(os.environ.get('LOOM_KNN_RAG_MICROBUCKET_3505_V3_VERIFY_K32_GROUPS', K32_GROUP_COUNT)) + if verify_kernel == 'm64_stage1': + return faeb.rag_m64.stage1_m64_ir + if verify_kernel == 'm64_merge': + return faeb.rag_m64.parent_micro._fused_merge_ir(faeb.M64_SPLIT_COUNT, faeb.M64_GROUP_COUNT) + if verify_kernel == 'q16_k32_fused_merge': + return q16_tailinf._fused_merge_ir(split_count, group_count) + return stage1_k32_tailinf_cta1_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_3505_v3_stage1_k32_tailinf_cta1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 192}')) + +def _compiled_stage1_tailinf_cta1(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0187"}')) + +def _eligible_q4_k10(inputs: dict[str, Any]) -> bool: + return faeb._eligible_q4_k10(inputs) + +def _eligible_q64_k10(inputs: dict[str, Any]) -> bool: + return faeb._eligible_q64_k10(inputs) + +def _eligible_q16_k32(inputs: dict[str, Any]) -> bool: + return faeb._eligible_q16_k32(inputs) + +def _launch_q16_k32_tailinf_cta1(inputs: dict[str, Any], *, split_count: int=K32_SPLIT_COUNT, group_count: int=K32_GROUP_COUNT) -> None: + q16_tailinf._validate_group_shape(split_count, group_count) + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tiles * split_count + total_queries = bsz * n_query + stage1_grid = min(total_work, GRID_DIM_DEFAULT) + merge_grid = min(total_queries, GRID_DIM_DEFAULT) + partial_dists, partial_indices = q16_tailinf.parent_k32.parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = q16_tailinf.parent_k32.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), total_queries, BLOCK_Q, dim, dim) + tmap_database = q16_tailinf.parent_k32.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, dim, dim) + _compiled_stage1_tailinf_cta1().launch(grid=(stage1_grid, 1, 1), block=(STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage1_k32_tailinf_cta1_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tiles=num_q_tiles, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), shared_mem=stage1_k32_tailinf_cta1_ir.computed_smem_bytes) + fused_ir = q16_tailinf._fused_merge_ir(split_count, group_count) + fused_kernel = q16_tailinf._compiled_fused_merge(split_count, group_count) + fused_kernel.launch(grid=(merge_grid, 1, 1), block=(K32_FUSED_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], total_queries], shared_mem=fused_ir.computed_smem_bytes) + +def route_for_contract_inputs(inputs: dict[str, Any], *, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> str: + if _eligible_q4_k10(inputs): + return ROUTE_Q4_K10 + if _eligible_q64_k10(inputs): + return ROUTE_Q64_K10 + if _eligible_q16_k32(inputs): + return ''.join(['rag_microbucket_3505_v3_q16_k32_tailinf_cta1_u1_s', format(k32_split_count, ''), '_g', format(k32_group_count, '')]) + return base_dispatcher.route_for_contract_inputs(inputs) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> None: + if _eligible_q4_k10(inputs): + faeb._launch_q4_k10_m64(inputs) + return + if _eligible_q64_k10(inputs): + faeb._launch_q64_k10_m64(inputs) + return + if _eligible_q16_k32(inputs): + _launch_q16_k32_tailinf_cta1(inputs, split_count=k32_split_count, group_count=k32_group_count) + return + base_dispatcher.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def candidate_with_k32_topology(split_count: int, group_count: int) -> Callable[[dict[str, Any]], None]: + + def _candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, k32_split_count=split_count, k32_group_count=group_count) + return _candidate + +def candidate_faeb_baseline(inputs: dict[str, Any]): + return parent_3505.candidate_faeb_baseline(inputs) + +def candidate_parent_3505(inputs: dict[str, Any]): + parent_3505.launch_from_contract_inputs(inputs) + return None + +def candidate_base_4247(inputs: dict[str, Any]): + return parent_3505.candidate_base_4247(inputs) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return parent_3505._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return parent_3505._run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=kernel_fn) + +def route_trace_for_contract_shapes(shape_labels=TARGET_SHAPES, *, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> list[dict[str, Any]]: + selected = _select_contract_shapes(shape_labels) + rows = [] + for shape in selected: + inputs = base_dispatcher._trace_inputs_from_shape(shape) + route = route_for_contract_inputs(inputs, k32_split_count=k32_split_count, k32_group_count=k32_group_count) + specialized = str(route).startswith('rag_microbucket_3505_v3') + rows.append({'shape_key': shape['label'], 'selected_route': route, 'route_kind': 'specialized' if specialized else 'general', 'guard_condition': 'exact BF16 non-build B1 M100000 D128 Q4/Q64 K10 or Q16 K32 microbucket' if specialized else 'guard miss to 4247 dispatcher', 'fallback': ROUTE_BASE_4247}) + return rows + +def _target_rows(candidate_report: dict[str, Any], parent_report: dict[str, Any], faeb_report: dict[str, Any], base_report: dict[str, Any], *, k32_split_count: int, k32_group_count: int) -> dict[str, Any]: + rows = {} + for label in TARGET_SHAPES: + cand = candidate_report.get('per_shape', {}).get(label, {}) + parent = parent_report.get('per_shape', {}).get(label, {}) + faeb_row = faeb_report.get('per_shape', {}).get(label, {}) + base = base_report.get('per_shape', {}).get(label, {}) + cand_ms = cand.get('kernel_ms') + parent_ms = parent.get('kernel_ms') + faeb_ms = faeb_row.get('kernel_ms') + base_ms = base.get('kernel_ms') + flashlib_ms = cand.get('flashlib_ms') + route = ''.join(['rag_microbucket_3505_v3_q16_k32_tailinf_cta1_u1_s', format(k32_split_count, ''), '_g', format(k32_group_count, '')]) if label == Q16_K32_SHAPE else ROUTE_Q4_K10 if label == Q4_K10_SHAPE else ROUTE_Q64_K10 + rows[label] = {'candidate': cand, 'parent_3505': parent, 'faeb_baseline': faeb_row, 'base_4247': base, 'candidate_route': route, 'candidate_ms': cand_ms, 'parent_3505_ms': parent_ms, 'faeb_baseline_ms': faeb_ms, 'base_4247_ms': base_ms, 'flashlib_ms': flashlib_ms, 'speedup_vs_parent_3505': parent_ms / cand_ms if cand_ms and parent_ms else None, 'speedup_vs_faeb': faeb_ms / cand_ms if cand_ms and faeb_ms else None, 'speedup_vs_4247': base_ms / cand_ms if cand_ms and base_ms else None, 'ratio_vs_flashlib': flashlib_ms / cand_ms if cand_ms and flashlib_ms else None} + return rows + +def benchmark_knn_build_rag_microbucket_3505_v3(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_with_k32_topology(k32_split_count, k32_group_count)) + parent_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=parent_3505.candidate_with_k32_topology(parent_3505.K32_SPLIT_COUNT, parent_3505.K32_GROUP_COUNT)) + faeb_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_faeb_baseline) + base_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_base_4247) + payload = parent_3505._benchmark_payload(candidate_report, faeb_report, base_report, use_cupti=use_cupti, shape_labels=shape_labels, measured_function='benchmark_knn_build_rag_microbucket_3505_v3', k32_split_count=k32_split_count, k32_group_count=k32_group_count) + payload['measured_entrypoint'] = 'loom.examples.weave.knn_build_rag_microbucket_3505_v3:benchmark_knn_build_rag_microbucket_3505_v3' + payload['parent_3505_entrypoint'] = 'loom.examples.weave.knn_build_rag_microbucket_3505_v1:launch_from_contract_inputs' + payload['parent_3505_all_correct'] = parent_report['summary']['all_correct'] + payload['parent_3505_performance_comparable'] = parent_report['summary']['performance_comparable'] + payload['producer_topology']['Q16_K32'] = ''.join(['tailinf-cta1/unroll1/S', format(k32_split_count, ''), '/G', format(k32_group_count, ''), '/fused']) + payload['route_trace'] = route_trace_for_contract_shapes(shape_labels, k32_split_count=k32_split_count, k32_group_count=k32_group_count) + payload['target_rows'] = _target_rows(candidate_report, parent_report, faeb_report, base_report, k32_split_count=k32_split_count, k32_group_count=k32_group_count) + payload['parent_3505_contract_summary'] = parent_report['summary'] + payload['parent_3505_contract_performance'] = parent_report['performance'] + payload['parent_3505_report'] = parent_report + return payload diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_3505_v6.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_3505_v6.py new file mode 100644 index 00000000..5f3bcc7a --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_3505_v6.py @@ -0,0 +1,158 @@ +"""RAG microbucket widened compact-warp K32 seed. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +keeps the validated 3505 Q4/Q64 K10 M64 routes and reuses the compact-warp +tcgen05/TMA K32 producer for the v7 RAG microbatch large-K rows with Q <= 32. +Guard misses delegate to the current 4247 dispatcher. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_e3de_9138_bcb3_4247_v1 as base_dispatcher +from . import knn_build_rag_microbucket_3505_v1 as parent_3505 +from . import knn_build_rag_microbucket_5093_v1 as compact_seed +from . import knn_build_rag_microbucket_faeb_v1 as faeb +Q4_K10_SHAPE = faeb.Q4_K10_SHAPE +Q64_K10_SHAPE = faeb.Q64_K10_SHAPE +Q8_K32_SHAPE = 'rag_microbatch_largek_b1_q8_m100000_d128_k32' +Q16_K32_SHAPE = faeb.Q16_K32_SHAPE +Q32_K32_SHAPE = 'rag_microbatch_largek_b1_q32_m100000_d128_k32' +Q16_K32_IRREGULAR_SHAPE = 'rag_microbatch_largek_b1_q16_m131071_d128_k32' +K10_TARGET_SHAPES = faeb.K10_TARGET_SHAPES +K32_TARGET_SHAPES = (Q8_K32_SHAPE, Q16_K32_SHAPE, Q32_K32_SHAPE, Q16_K32_IRREGULAR_SHAPE) +TARGET_SHAPES = (*K10_TARGET_SHAPES, *K32_TARGET_SHAPES) +K32_SPLIT_COUNT = _decode_capture(_json_loads('144')) +K32_GROUP_COUNT = _decode_capture(_json_loads('12')) +COMPACT_STAGE1_THREADS = compact_seed.COMPACT_STAGE1_THREADS +K32_FUSED_MERGE_THREADS = compact_seed.K32_FUSED_MERGE_THREADS +ROUTE_Q4_K10 = 'rag_microbucket_3505_v6_q4_k10_m64_s128_g8' +ROUTE_Q64_K10 = 'rag_microbucket_3505_v6_q64_k10_m64_s128_g8' +ROUTE_BASE_4247 = parent_3505.ROUTE_BASE_4247 + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RAG_MICROBUCKET_3505_V6_VERIFY_KERNEL') + split_count = int(os.environ.get('LOOM_KNN_RAG_MICROBUCKET_3505_V6_VERIFY_K32_SPLIT', K32_SPLIT_COUNT)) + group_count = int(os.environ.get('LOOM_KNN_RAG_MICROBUCKET_3505_V6_VERIFY_K32_GROUPS', K32_GROUP_COUNT)) + if verify_kernel == 'm64_stage1': + return faeb.rag_m64.stage1_m64_ir + if verify_kernel == 'm64_merge': + return faeb.rag_m64.parent_micro._fused_merge_ir(faeb.M64_SPLIT_COUNT, faeb.M64_GROUP_COUNT) + if verify_kernel in {'k32_fused_merge', 'q16_k32_fused_merge'}: + return compact_seed.q16_tailinf._fused_merge_ir(split_count, group_count) + return compact_seed.stage1_k32_tailinf_cta1_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_5093_v1_stage1_k32_tailinf_cta1_compactwarp", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 96}')) + +def _dtype_name(inputs: dict[str, Any]) -> str: + query = inputs.get('query') + if query is not None: + return str(query.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')) + +def _is_bf16_d128_nonbuild(inputs: dict[str, Any]) -> bool: + return not bool(inputs.get('build', False)) and _dtype_name(inputs) == 'bfloat16' and (int(inputs.get('B', -1)) == 1) and (int(inputs.get('D', -1)) == 128) + +def _eligible_q4_k10(inputs: dict[str, Any]) -> bool: + return _is_bf16_d128_nonbuild(inputs) and int(inputs.get('M', -1)) == 100000 and (int(inputs.get('Q', -1)) == 4) and (int(inputs.get('K', -1)) == 10) + +def _eligible_q64_k10(inputs: dict[str, Any]) -> bool: + return _is_bf16_d128_nonbuild(inputs) and int(inputs.get('M', -1)) == 100000 and (int(inputs.get('Q', -1)) == 64) and (int(inputs.get('K', -1)) == 10) + +def _eligible_compact_k32(inputs: dict[str, Any]) -> bool: + if not _is_bf16_d128_nonbuild(inputs) or int(inputs.get('K', -1)) != 32: + return False + n_query = int(inputs.get('Q', -1)) + n_database = int(inputs.get('M', -1)) + if n_database == 100000 and n_query in {8, 16, 32}: + return True + return n_database == 131071 and n_query == 16 + +def _compact_route_name(*, split_count: int, group_count: int, inputs: dict[str, Any]) -> str: + n_query = int(inputs.get('Q', -1)) + n_database = int(inputs.get('M', -1)) + return ''.join(['rag_microbucket_3505_v6_q', format(n_query, ''), '_m', format(n_database, ''), '_k32_tailinf_cta1_cw1_s', format(split_count, ''), '_g', format(group_count, '')]) + +def _launch_compact_k32(inputs: dict[str, Any], *, split_count: int=K32_SPLIT_COUNT, group_count: int=K32_GROUP_COUNT) -> None: + compact_seed._launch_q16_k32_tailinf_cta1(inputs, split_count=split_count, group_count=group_count) + +def route_for_contract_inputs(inputs: dict[str, Any], *, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> str: + if _eligible_q4_k10(inputs): + return ROUTE_Q4_K10 + if _eligible_q64_k10(inputs): + return ROUTE_Q64_K10 + if _eligible_compact_k32(inputs): + return _compact_route_name(split_count=k32_split_count, group_count=k32_group_count, inputs=inputs) + return base_dispatcher.route_for_contract_inputs(inputs) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> None: + if _eligible_q4_k10(inputs): + faeb._launch_q4_k10_m64(inputs) + return + if _eligible_q64_k10(inputs): + faeb._launch_q64_k10_m64(inputs) + return + if _eligible_compact_k32(inputs): + _launch_compact_k32(inputs, split_count=k32_split_count, group_count=k32_group_count) + return + base_dispatcher.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def candidate_with_k32_topology(split_count: int, group_count: int) -> Callable[[dict[str, Any]], None]: + + def _candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, k32_split_count=split_count, k32_group_count=group_count) + return _candidate + +def candidate_compact_seed(inputs: dict[str, Any]): + compact_seed.launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return compact_seed._select_contract_shapes(shape_labels) + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def route_trace_for_contract_shapes(shape_labels=TARGET_SHAPES, *, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> list[dict[str, Any]]: + selected = _select_contract_shapes(shape_labels) + rows = [] + for shape in selected: + inputs = base_dispatcher._trace_inputs_from_shape(shape) + route = route_for_contract_inputs(inputs, k32_split_count=k32_split_count, k32_group_count=k32_group_count) + specialized = str(route).startswith('rag_microbucket_3505_v6') + rows.append({'shape_key': shape['label'], 'selected_route': route, 'route_kind': 'specialized' if specialized else 'general', 'guard_condition': 'exact BF16 non-build B1 D128 Q4/Q64 K10 or Q8/Q16/Q32 K32 microbucket' if specialized else 'guard miss to 4247 dispatcher', 'fallback': ROUTE_BASE_4247}) + return rows + +def _per_shape_delta(candidate_report: dict[str, Any], seed_report: dict[str, Any]) -> dict[str, Any]: + rows = {} + for label in TARGET_SHAPES: + cand = candidate_report.get('per_shape', {}).get(label, {}) + seed = seed_report.get('per_shape', {}).get(label, {}) + cand_ms = cand.get('kernel_ms') + seed_ms = seed.get('kernel_ms') + rows[label] = {'candidate': cand, 'compact_seed_5093': seed, 'candidate_ms': cand_ms, 'compact_seed_5093_ms': seed_ms, 'speedup_vs_compact_seed_5093': seed_ms / cand_ms if cand_ms and seed_ms else None, 'ratio_vs_flashlib': cand.get('ratio_vs_flashlib')} + return rows + +def benchmark_knn_build_rag_microbucket_3505_v6(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_with_k32_topology(k32_split_count, k32_group_count)) + compact_seed_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_compact_seed) + return {'contract': candidate_report['contract'], 'contract_version': candidate_report['contract_version'], 'measured_entrypoint': 'loom.examples.weave.knn_build_rag_microbucket_3505_v6:benchmark_knn_build_rag_microbucket_3505_v6', 'candidate_entrypoint': 'loom.examples.weave.knn_build_rag_microbucket_3505_v6:launch_from_contract_inputs', 'compact_seed_5093_entrypoint': 'loom.examples.weave.knn_build_rag_microbucket_5093_v1:launch_from_contract_inputs', 'accelerated_shape_labels': list(TARGET_SHAPES), 'measured_shape_labels': list(shape_labels), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'producer_topology': {'K10': ''.join(['M64/S', format(faeb.M64_SPLIT_COUNT, ''), '/G', format(faeb.M64_GROUP_COUNT, '')]), 'K32': ''.join(['tailinf-cta1/compactwarp/S', format(k32_split_count, ''), '/G', format(k32_group_count, ''), '/fused'])}, 'route_trace': route_trace_for_contract_shapes(shape_labels, k32_split_count=k32_split_count, k32_group_count=k32_group_count), 'target_rows': _per_shape_delta(candidate_report, compact_seed_report), 'summary': candidate_report['summary'], 'performance': candidate_report['performance'], 'correctness': candidate_report['correctness'], 'report': candidate_report, 'compact_seed_5093_summary': compact_seed_report['summary'], 'compact_seed_5093_performance': compact_seed_report['performance'], 'compact_seed_5093_report': compact_seed_report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_3505_v7.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_3505_v7.py new file mode 100644 index 00000000..3010c67c --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_3505_v7.py @@ -0,0 +1,174 @@ +"""RAG microbucket widened M64 K10 plus compact-warp K32 seed. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +keeps the validated compact-warp tcgen05/TMA K32 route and widens the M64 +tcgen05/TMA K10 route to the v8 RAG microbatch rows with Q <= 64. Guard misses +delegate to the current 4247 dispatcher. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_e3de_9138_bcb3_4247_v1 as base_dispatcher +from . import knn_build_rag_microbucket_3505_v1 as parent_3505 +from . import knn_build_rag_microbucket_3505_v6 as prior_v6 +from . import knn_build_rag_microbucket_5093_v1 as compact_seed +from . import knn_build_rag_microbucket_faeb_v1 as faeb +Q4_K10_SHAPE = faeb.Q4_K10_SHAPE +Q8_K10_SHAPE = 'rag_microbatch_b1_q8_m100000_d128_k10' +Q16_K10_SHAPE = 'rag_microbatch_b1_q16_m100000_d128_k10' +Q32_K10_SHAPE = 'rag_microbatch_b1_q32_m100000_d128_k10' +Q64_K10_SHAPE = faeb.Q64_K10_SHAPE +Q8_K32_SHAPE = 'rag_microbatch_largek_b1_q8_m100000_d128_k32' +Q16_K32_SHAPE = faeb.Q16_K32_SHAPE +Q32_K32_SHAPE = 'rag_microbatch_largek_b1_q32_m100000_d128_k32' +Q16_K32_IRREGULAR_SHAPE = 'rag_microbatch_largek_b1_q16_m131071_d128_k32' +K10_TARGET_SHAPES = (Q4_K10_SHAPE, Q8_K10_SHAPE, Q16_K10_SHAPE, Q32_K10_SHAPE, Q64_K10_SHAPE) +K32_TARGET_SHAPES = (Q8_K32_SHAPE, Q16_K32_SHAPE, Q32_K32_SHAPE, Q16_K32_IRREGULAR_SHAPE) +TARGET_SHAPES = (*K10_TARGET_SHAPES, *K32_TARGET_SHAPES) +K32_SPLIT_COUNT = _decode_capture(_json_loads('144')) +K32_GROUP_COUNT = _decode_capture(_json_loads('12')) +COMPACT_STAGE1_THREADS = compact_seed.COMPACT_STAGE1_THREADS +K32_FUSED_MERGE_THREADS = compact_seed.K32_FUSED_MERGE_THREADS +ROUTE_Q4_K10 = 'rag_microbucket_3505_v7_q4_k10_m64_s128_g8' +ROUTE_Q64_K10 = 'rag_microbucket_3505_v7_q64_k10_m64_s128_g8' +ROUTE_BASE_4247 = parent_3505.ROUTE_BASE_4247 + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RAG_MICROBUCKET_3505_V7_VERIFY_KERNEL') + split_count = int(os.environ.get('LOOM_KNN_RAG_MICROBUCKET_3505_V7_VERIFY_K32_SPLIT', K32_SPLIT_COUNT)) + group_count = int(os.environ.get('LOOM_KNN_RAG_MICROBUCKET_3505_V7_VERIFY_K32_GROUPS', K32_GROUP_COUNT)) + if verify_kernel == 'm64_stage1': + return faeb.rag_m64.stage1_m64_ir + if verify_kernel == 'm64_merge': + return faeb.rag_m64.parent_micro._fused_merge_ir(faeb.M64_SPLIT_COUNT, faeb.M64_GROUP_COUNT) + if verify_kernel in {'k32_fused_merge', 'q16_k32_fused_merge'}: + return compact_seed.q16_tailinf._fused_merge_ir(split_count, group_count) + return compact_seed.stage1_k32_tailinf_cta1_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_5093_v1_stage1_k32_tailinf_cta1_compactwarp", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 96}')) + +def _dtype_name(inputs: dict[str, Any]) -> str: + query = inputs.get('query') + if query is not None: + return str(query.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')) + +def _is_bf16_d128_nonbuild(inputs: dict[str, Any]) -> bool: + return not bool(inputs.get('build', False)) and _dtype_name(inputs) == 'bfloat16' and (int(inputs.get('B', -1)) == 1) and (int(inputs.get('D', -1)) == 128) + +def _eligible_q4_k10(inputs: dict[str, Any]) -> bool: + return _is_bf16_d128_nonbuild(inputs) and int(inputs.get('M', -1)) == 100000 and (int(inputs.get('Q', -1)) == 4) and (int(inputs.get('K', -1)) == 10) + +def _eligible_q64_k10(inputs: dict[str, Any]) -> bool: + return _is_bf16_d128_nonbuild(inputs) and int(inputs.get('M', -1)) == 100000 and (int(inputs.get('Q', -1)) == 64) and (int(inputs.get('K', -1)) == 10) + +def _eligible_m64_k10(inputs: dict[str, Any]) -> bool: + return _is_bf16_d128_nonbuild(inputs) and int(inputs.get('M', -1)) == 100000 and (int(inputs.get('Q', -1)) in {4, 8, 16, 32, 64}) and (int(inputs.get('K', -1)) == 10) + +def _eligible_compact_k32(inputs: dict[str, Any]) -> bool: + if not _is_bf16_d128_nonbuild(inputs) or int(inputs.get('K', -1)) != 32: + return False + n_query = int(inputs.get('Q', -1)) + n_database = int(inputs.get('M', -1)) + if n_database == 100000 and n_query in {8, 16, 32}: + return True + return n_database == 131071 and n_query == 16 + +def _compact_route_name(*, split_count: int, group_count: int, inputs: dict[str, Any]) -> str: + n_query = int(inputs.get('Q', -1)) + n_database = int(inputs.get('M', -1)) + return ''.join(['rag_microbucket_3505_v7_q', format(n_query, ''), '_m', format(n_database, ''), '_k32_tailinf_cta1_cw1_s', format(split_count, ''), '_g', format(group_count, '')]) + +def _m64_route_name(inputs: dict[str, Any]) -> str: + n_query = int(inputs.get('Q', -1)) + return ''.join(['rag_microbucket_3505_v7_q', format(n_query, ''), '_k10_m64_s', format(faeb.M64_SPLIT_COUNT, ''), '_g', format(faeb.M64_GROUP_COUNT, '')]) + +def _launch_compact_k32(inputs: dict[str, Any], *, split_count: int=K32_SPLIT_COUNT, group_count: int=K32_GROUP_COUNT) -> None: + compact_seed._launch_q16_k32_tailinf_cta1(inputs, split_count=split_count, group_count=group_count) + +def route_for_contract_inputs(inputs: dict[str, Any], *, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> str: + if _eligible_q4_k10(inputs): + return ROUTE_Q4_K10 + if _eligible_q64_k10(inputs): + return ROUTE_Q64_K10 + if _eligible_m64_k10(inputs): + return _m64_route_name(inputs) + if _eligible_compact_k32(inputs): + return _compact_route_name(split_count=k32_split_count, group_count=k32_group_count, inputs=inputs) + return base_dispatcher.route_for_contract_inputs(inputs) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> None: + if _eligible_q4_k10(inputs): + faeb._launch_q4_k10_m64(inputs) + return + if _eligible_q64_k10(inputs): + faeb._launch_q64_k10_m64(inputs) + return + if _eligible_m64_k10(inputs): + faeb._launch_q4_k10_m64(inputs) + return + if _eligible_compact_k32(inputs): + _launch_compact_k32(inputs, split_count=k32_split_count, group_count=k32_group_count) + return + base_dispatcher.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def candidate_with_k32_topology(split_count: int, group_count: int) -> Callable[[dict[str, Any]], None]: + + def _candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, k32_split_count=split_count, k32_group_count=group_count) + return _candidate + +def candidate_prior_v6(inputs: dict[str, Any]): + prior_v6.launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return compact_seed._select_contract_shapes(shape_labels) + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def route_trace_for_contract_shapes(shape_labels=TARGET_SHAPES, *, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> list[dict[str, Any]]: + selected = _select_contract_shapes(shape_labels) + rows = [] + for shape in selected: + inputs = base_dispatcher._trace_inputs_from_shape(shape) + route = route_for_contract_inputs(inputs, k32_split_count=k32_split_count, k32_group_count=k32_group_count) + specialized = str(route).startswith('rag_microbucket_3505_v7') + rows.append({'shape_key': shape['label'], 'selected_route': route, 'route_kind': 'specialized' if specialized else 'general', 'guard_condition': 'exact BF16 non-build B1 D128 Q<=64 K10 or Q8/Q16/Q32 K32 microbucket' if specialized else 'guard miss to 4247 dispatcher', 'fallback': ROUTE_BASE_4247}) + return rows + +def _per_shape_delta(candidate_report: dict[str, Any], prior_report: dict[str, Any]) -> dict[str, Any]: + rows = {} + for label in TARGET_SHAPES: + cand = candidate_report.get('per_shape', {}).get(label, {}) + prior = prior_report.get('per_shape', {}).get(label, {}) + cand_ms = cand.get('kernel_ms') + prior_ms = prior.get('kernel_ms') + rows[label] = {'candidate': cand, 'prior_v6': prior, 'candidate_ms': cand_ms, 'prior_v6_ms': prior_ms, 'speedup_vs_prior_v6': prior_ms / cand_ms if cand_ms and prior_ms else None, 'ratio_vs_flashlib': cand.get('ratio_vs_flashlib')} + return rows + +def benchmark_knn_build_rag_microbucket_3505_v7(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_with_k32_topology(k32_split_count, k32_group_count)) + prior_v6_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_prior_v6) + return {'contract': candidate_report['contract'], 'contract_version': candidate_report['contract_version'], 'measured_entrypoint': 'loom.examples.weave.knn_build_rag_microbucket_3505_v7:benchmark_knn_build_rag_microbucket_3505_v7', 'candidate_entrypoint': 'loom.examples.weave.knn_build_rag_microbucket_3505_v7:launch_from_contract_inputs', 'prior_v6_entrypoint': 'loom.examples.weave.knn_build_rag_microbucket_3505_v6:launch_from_contract_inputs', 'accelerated_shape_labels': list(TARGET_SHAPES), 'measured_shape_labels': list(shape_labels), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'producer_topology': {'K10': ''.join(['M64/S', format(faeb.M64_SPLIT_COUNT, ''), '/G', format(faeb.M64_GROUP_COUNT, '')]), 'K32': ''.join(['tailinf-cta1/compactwarp/S', format(k32_split_count, ''), '/G', format(k32_group_count, ''), '/fused'])}, 'route_trace': route_trace_for_contract_shapes(shape_labels, k32_split_count=k32_split_count, k32_group_count=k32_group_count), 'target_rows': _per_shape_delta(candidate_report, prior_v6_report), 'summary': candidate_report['summary'], 'performance': candidate_report['performance'], 'correctness': candidate_report['correctness'], 'report': candidate_report, 'prior_v6_summary': prior_v6_report['summary'], 'prior_v6_performance': prior_v6_report['performance'], 'prior_v6_report': prior_v6_report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_3505_v9.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_3505_v9.py new file mode 100644 index 00000000..7bb833ff --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_3505_v9.py @@ -0,0 +1,225 @@ +"""RAG microbucket Q8/K32 M64 producer plus inherited v7 routes. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +routes only ``rag_microbatch_largek_b1_q8_m100000_d128_k32`` through a +smaller-row M64/N64 tcgen05/TMA producer and the existing K32 fused split +merge. All other target rows inherit the validated v7 microbucket routes, and +guard misses delegate to the current 4247 dispatcher. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from collections.abc import Callable +from functools import lru_cache +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_e3de_9138_bcb3_4247_v1 as base_dispatcher +from . import knn_build_rag_microbucket_3505_v1 as parent_3505 +from . import knn_build_rag_microbucket_3505_v7 as prior_v7 +from . import knn_build_rag_microbucket_5093_v1 as compact_seed +from . import knn_build_rag_microbucket_faeb_v1 as faeb +from .._dispatch_runtime import pack_kernel_args +Q4_K10_SHAPE = faeb.Q4_K10_SHAPE +Q8_K10_SHAPE = 'rag_microbatch_b1_q8_m100000_d128_k10' +Q16_K10_SHAPE = 'rag_microbatch_b1_q16_m100000_d128_k10' +Q32_K10_SHAPE = 'rag_microbatch_b1_q32_m100000_d128_k10' +Q64_K10_SHAPE = faeb.Q64_K10_SHAPE +Q8_K32_SHAPE = 'rag_microbatch_largek_b1_q8_m100000_d128_k32' +Q16_K32_SHAPE = faeb.Q16_K32_SHAPE +Q32_K32_SHAPE = 'rag_microbatch_largek_b1_q32_m100000_d128_k32' +Q16_K32_IRREGULAR_SHAPE = 'rag_microbatch_largek_b1_q16_m131071_d128_k32' +K10_TARGET_SHAPES = (Q4_K10_SHAPE, Q8_K10_SHAPE, Q16_K10_SHAPE, Q32_K10_SHAPE, Q64_K10_SHAPE) +K32_TARGET_SHAPES = (Q8_K32_SHAPE, Q16_K32_SHAPE, Q32_K32_SHAPE, Q16_K32_IRREGULAR_SHAPE) +TARGET_SHAPES = (*K10_TARGET_SHAPES, *K32_TARGET_SHAPES) +K32_SPLIT_COUNT = _decode_capture(_json_loads('144')) +K32_GROUP_COUNT = _decode_capture(_json_loads('12')) +COMPACT_STAGE1_THREADS = compact_seed.COMPACT_STAGE1_THREADS +K32_FUSED_MERGE_THREADS = compact_seed.K32_FUSED_MERGE_THREADS +Q8_M64_STAGE1_THREADS = _decode_capture(_json_loads('96')) +Q8_M64_BLOCK_Q = 64 +Q8_M64_BLOCK_M = 64 +Q8_M64_FEAT_D = 128 +Q8_M64_TOP_K_MAX = 32 +ROUTE_Q4_K10 = 'rag_microbucket_3505_v9_inherit_v7_q4_k10_m64_s128_g8' +ROUTE_Q64_K10 = 'rag_microbucket_3505_v9_inherit_v7_q64_k10_m64_s128_g8' +ROUTE_BASE_4247 = parent_3505.ROUTE_BASE_4247 +knn_build_rag_microbucket_3505_v9_stage1_q8_k32_m64 = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_3505_v9_stage1_q8_k32_m64", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 34048, "constants": [["BLOCK_Q", 64], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 96}')) +stage1_q8_k32_m64_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_3505_v9_stage1_q8_k32_m64", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 34048, "constants": [["BLOCK_Q", 64], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 96}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RAG_MICROBUCKET_3505_V9_VERIFY_KERNEL') + split_count = int(os.environ.get('LOOM_KNN_RAG_MICROBUCKET_3505_V9_VERIFY_K32_SPLIT', K32_SPLIT_COUNT)) + group_count = int(os.environ.get('LOOM_KNN_RAG_MICROBUCKET_3505_V9_VERIFY_K32_GROUPS', K32_GROUP_COUNT)) + if verify_kernel == 'm64_stage1': + return faeb.rag_m64.stage1_m64_ir + if verify_kernel == 'm64_merge': + return faeb.rag_m64.parent_micro._fused_merge_ir(faeb.M64_SPLIT_COUNT, faeb.M64_GROUP_COUNT) + if verify_kernel == 'k32_q8_m64_stage1': + return stage1_q8_k32_m64_ir + if verify_kernel in {'k32_fused_merge', 'q16_k32_fused_merge'}: + return compact_seed.q16_tailinf._fused_merge_ir(split_count, group_count) + return stage1_q8_k32_m64_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_3505_v9_stage1_q8_k32_m64", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 34048, "constants": [["BLOCK_Q", 64], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 96}')) + +def _dtype_name(inputs: dict[str, Any]) -> str: + query = inputs.get('query') + if query is not None: + return str(query.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')) + +def _is_bf16_d128_nonbuild(inputs: dict[str, Any]) -> bool: + return not bool(inputs.get('build', False)) and _dtype_name(inputs) == 'bfloat16' and (int(inputs.get('B', -1)) == 1) and (int(inputs.get('D', -1)) == 128) + +def _eligible_q4_k10(inputs: dict[str, Any]) -> bool: + return _is_bf16_d128_nonbuild(inputs) and int(inputs.get('M', -1)) == 100000 and (int(inputs.get('Q', -1)) == 4) and (int(inputs.get('K', -1)) == 10) + +def _eligible_q64_k10(inputs: dict[str, Any]) -> bool: + return _is_bf16_d128_nonbuild(inputs) and int(inputs.get('M', -1)) == 100000 and (int(inputs.get('Q', -1)) == 64) and (int(inputs.get('K', -1)) == 10) + +def _eligible_m64_k10(inputs: dict[str, Any]) -> bool: + return _is_bf16_d128_nonbuild(inputs) and int(inputs.get('M', -1)) == 100000 and (int(inputs.get('Q', -1)) in {4, 8, 16, 32, 64}) and (int(inputs.get('K', -1)) == 10) + +def _eligible_compact_k32(inputs: dict[str, Any]) -> bool: + if not _is_bf16_d128_nonbuild(inputs) or int(inputs.get('K', -1)) != 32: + return False + n_query = int(inputs.get('Q', -1)) + n_database = int(inputs.get('M', -1)) + if n_database == 100000 and n_query in {8, 16, 32}: + return True + return n_database == 131071 and n_query == 16 + +def _eligible_q8_k32_m64(inputs: dict[str, Any]) -> bool: + return _is_bf16_d128_nonbuild(inputs) and int(inputs.get('M', -1)) == 100000 and (int(inputs.get('Q', -1)) == 8) and (int(inputs.get('K', -1)) == 32) + +def _q8_k32_m64_route_name(*, split_count: int, group_count: int) -> str: + return ''.join(['rag_microbucket_3505_v9_q8_m100000_k32_m64n64_s', format(split_count, ''), '_g', format(group_count, '')]) + +def _compact_route_name(*, split_count: int, group_count: int, inputs: dict[str, Any]) -> str: + n_query = int(inputs.get('Q', -1)) + n_database = int(inputs.get('M', -1)) + return ''.join(['rag_microbucket_3505_v9_inherit_v7_q', format(n_query, ''), '_m', format(n_database, ''), '_k32_tailinf_cta1_cw1_s', format(split_count, ''), '_g', format(group_count, '')]) + +def _m64_route_name(inputs: dict[str, Any]) -> str: + n_query = int(inputs.get('Q', -1)) + return ''.join(['rag_microbucket_3505_v9_inherit_v7_q', format(n_query, ''), '_k10_m64_s', format(faeb.M64_SPLIT_COUNT, ''), '_g', format(faeb.M64_GROUP_COUNT, '')]) + +def _compiled_stage1_q8_k32_m64(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0191"}')) + +def _launch_q8_k32_m64(inputs: dict[str, Any], *, split_count: int=K32_SPLIT_COUNT, group_count: int=K32_GROUP_COUNT) -> None: + compact_seed.q16_tailinf._validate_group_shape(split_count, group_count) + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + num_q_tiles = (n_query + Q8_M64_BLOCK_Q - 1) // Q8_M64_BLOCK_Q + num_db_tiles = (n_database + Q8_M64_BLOCK_M - 1) // Q8_M64_BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tiles * split_count + total_queries = bsz * n_query + stage1_grid = min(total_work, compact_seed.GRID_DIM_DEFAULT) + merge_grid = min(total_queries, compact_seed.GRID_DIM_DEFAULT) + partial_dists, partial_indices = compact_seed.q16_tailinf.parent_k32.parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = compact_seed.q16_tailinf.parent_k32.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), total_queries, Q8_M64_BLOCK_Q, dim, dim) + tmap_database = compact_seed.q16_tailinf.parent_k32.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, Q8_M64_BLOCK_M, dim, dim) + _compiled_stage1_q8_k32_m64().launch(grid=(stage1_grid, 1, 1), block=(Q8_M64_STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage1_q8_k32_m64_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tiles=num_q_tiles, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), shared_mem=stage1_q8_k32_m64_ir.computed_smem_bytes) + fused_ir = compact_seed.q16_tailinf._fused_merge_ir(split_count, group_count) + fused_kernel = compact_seed.q16_tailinf._compiled_fused_merge(split_count, group_count) + fused_kernel.launch(grid=(merge_grid, 1, 1), block=(K32_FUSED_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], total_queries], shared_mem=fused_ir.computed_smem_bytes) + +def _launch_compact_k32(inputs: dict[str, Any], *, split_count: int=K32_SPLIT_COUNT, group_count: int=K32_GROUP_COUNT) -> None: + compact_seed._launch_q16_k32_tailinf_cta1(inputs, split_count=split_count, group_count=group_count) + +def route_for_contract_inputs(inputs: dict[str, Any], *, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> str: + if _eligible_q8_k32_m64(inputs): + return _q8_k32_m64_route_name(split_count=k32_split_count, group_count=k32_group_count) + if _eligible_q4_k10(inputs): + return ROUTE_Q4_K10 + if _eligible_q64_k10(inputs): + return ROUTE_Q64_K10 + if _eligible_m64_k10(inputs): + return _m64_route_name(inputs) + if _eligible_compact_k32(inputs): + return _compact_route_name(split_count=k32_split_count, group_count=k32_group_count, inputs=inputs) + return base_dispatcher.route_for_contract_inputs(inputs) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> None: + if _eligible_q8_k32_m64(inputs): + _launch_q8_k32_m64(inputs, split_count=k32_split_count, group_count=k32_group_count) + return + if _eligible_q4_k10(inputs): + faeb._launch_q4_k10_m64(inputs) + return + if _eligible_q64_k10(inputs): + faeb._launch_q64_k10_m64(inputs) + return + if _eligible_m64_k10(inputs): + faeb._launch_q4_k10_m64(inputs) + return + if _eligible_compact_k32(inputs): + _launch_compact_k32(inputs, split_count=k32_split_count, group_count=k32_group_count) + return + base_dispatcher.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def candidate_with_k32_topology(split_count: int, group_count: int) -> Callable[[dict[str, Any]], None]: + + def _candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, k32_split_count=split_count, k32_group_count=group_count) + return _candidate + +def candidate_prior_v7(inputs: dict[str, Any]): + prior_v7.launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return compact_seed._select_contract_shapes(shape_labels) + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def route_trace_for_contract_shapes(shape_labels=TARGET_SHAPES, *, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> list[dict[str, Any]]: + selected = _select_contract_shapes(shape_labels) + rows = [] + for shape in selected: + inputs = base_dispatcher._trace_inputs_from_shape(shape) + route = route_for_contract_inputs(inputs, k32_split_count=k32_split_count, k32_group_count=k32_group_count) + specialized = str(route).startswith('rag_microbucket_3505_v9') + rows.append({'shape_key': shape['label'], 'selected_route': route, 'route_kind': 'specialized' if specialized else 'general', 'guard_condition': 'exact BF16 non-build B1 D128 Q<=64 K10, Q8 M64/N64 K32, or inherited v7 K32 microbucket' if specialized else 'guard miss to 4247 dispatcher', 'fallback': ROUTE_BASE_4247}) + return rows + +def _per_shape_delta(candidate_report: dict[str, Any], prior_report: dict[str, Any]) -> dict[str, Any]: + rows = {} + for label in TARGET_SHAPES: + cand = candidate_report.get('per_shape', {}).get(label, {}) + prior = prior_report.get('per_shape', {}).get(label, {}) + cand_ms = cand.get('kernel_ms') + prior_ms = prior.get('kernel_ms') + rows[label] = {'candidate': cand, 'prior_v7': prior, 'candidate_ms': cand_ms, 'prior_v7_ms': prior_ms, 'speedup_vs_prior_v7': prior_ms / cand_ms if cand_ms and prior_ms else None, 'ratio_vs_flashlib': cand.get('ratio_vs_flashlib')} + return rows + +def benchmark_knn_build_rag_microbucket_3505_v9(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_with_k32_topology(k32_split_count, k32_group_count)) + prior_v7_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_prior_v7) + return {'contract': candidate_report['contract'], 'contract_version': candidate_report['contract_version'], 'measured_entrypoint': 'loom.examples.weave.knn_build_rag_microbucket_3505_v9:benchmark_knn_build_rag_microbucket_3505_v9', 'candidate_entrypoint': 'loom.examples.weave.knn_build_rag_microbucket_3505_v9:launch_from_contract_inputs', 'prior_v7_entrypoint': 'loom.examples.weave.knn_build_rag_microbucket_3505_v7:launch_from_contract_inputs', 'accelerated_shape_labels': list(TARGET_SHAPES), 'measured_shape_labels': list(shape_labels), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'producer_topology': {'K10': ''.join(['inherited-v7/M64/S', format(faeb.M64_SPLIT_COUNT, ''), '/G', format(faeb.M64_GROUP_COUNT, '')]), 'K32': ''.join(['Q8-M64N64/S', format(k32_split_count, ''), '/G', format(k32_group_count, ''), '/fused; other K32 inherited-v7'])}, 'route_trace': route_trace_for_contract_shapes(shape_labels, k32_split_count=k32_split_count, k32_group_count=k32_group_count), 'target_rows': _per_shape_delta(candidate_report, prior_v7_report), 'summary': candidate_report['summary'], 'performance': candidate_report['performance'], 'correctness': candidate_report['correctness'], 'report': candidate_report, 'prior_v7_summary': prior_v7_report['summary'], 'prior_v7_performance': prior_v7_report['performance'], 'prior_v7_report': prior_v7_report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_5093_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_5093_v1.py new file mode 100644 index 00000000..de428a60 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_5093_v1.py @@ -0,0 +1,206 @@ +"""RAG microbucket Q4/Q64 K10 plus compact-warp Q16 K32 cta1 seed. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +keeps the validated 3505 Q4/Q64 M64 routes and retargets only +``rag_microbatch_largek_b1_q16_m100000_d128_k32`` to a cta_group=1 +tail-infinity K32 tcgen05/TMA producer with one compute warp for the Q=16 +active rows. Guard misses delegate to the current 4247 dispatcher. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from collections.abc import Callable +from functools import lru_cache +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_e3de_9138_bcb3_4247_v1 as base_dispatcher +from . import knn_build_rag_frontier_4fbf_v7 as q16_tailinf +from . import knn_build_rag_microbucket_3505_v1 as parent_3505 +from . import knn_build_rag_microbucket_3505_v3 as parent_v3 +from . import knn_build_rag_microbucket_faeb_v1 as faeb +from .._dispatch_runtime import pack_kernel_args +Q4_K10_SHAPE = faeb.Q4_K10_SHAPE +Q64_K10_SHAPE = faeb.Q64_K10_SHAPE +Q16_K32_SHAPE = faeb.Q16_K32_SHAPE +K10_TARGET_SHAPES = faeb.K10_TARGET_SHAPES +K32_TARGET_SHAPES = (Q16_K32_SHAPE,) +TARGET_SHAPES = (*K10_TARGET_SHAPES, *K32_TARGET_SHAPES) +K32_SPLIT_COUNT = _decode_capture(_json_loads('144')) +K32_GROUP_COUNT = _decode_capture(_json_loads('12')) +BLOCK_Q = q16_tailinf.BLOCK_Q +BLOCK_M = q16_tailinf.BLOCK_M +FEAT_D = q16_tailinf.FEAT_D +COMPACT_STAGE1_THREADS = _decode_capture(_json_loads('96')) +GRID_DIM_DEFAULT = q16_tailinf.GRID_DIM_DEFAULT +TOP_K_MAX = q16_tailinf.TOP_K_MAX +K32_FUSED_MERGE_THREADS = q16_tailinf.K32_FUSED_MERGE_THREADS +ROUTE_Q4_K10 = 'rag_microbucket_5093_v1_q4_k10_m64_s128_g8' +ROUTE_Q64_K10 = 'rag_microbucket_5093_v1_q64_k10_m64_s128_g8' +ROUTE_BASE_4247 = parent_3505.ROUTE_BASE_4247 +knn_build_rag_microbucket_5093_v1_stage1_k32_tailinf_cta1_compactwarp = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_5093_v1_stage1_k32_tailinf_cta1_compactwarp", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 96}')) +stage1_k32_tailinf_cta1_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_5093_v1_stage1_k32_tailinf_cta1_compactwarp", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 96}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RAG_MICROBUCKET_5093_V1_VERIFY_KERNEL') + split_count = int(os.environ.get('LOOM_KNN_RAG_MICROBUCKET_5093_V1_VERIFY_K32_SPLIT', K32_SPLIT_COUNT)) + group_count = int(os.environ.get('LOOM_KNN_RAG_MICROBUCKET_5093_V1_VERIFY_K32_GROUPS', K32_GROUP_COUNT)) + if verify_kernel == 'm64_stage1': + return faeb.rag_m64.stage1_m64_ir + if verify_kernel == 'm64_merge': + return faeb.rag_m64.parent_micro._fused_merge_ir(faeb.M64_SPLIT_COUNT, faeb.M64_GROUP_COUNT) + if verify_kernel == 'q16_k32_fused_merge': + return q16_tailinf._fused_merge_ir(split_count, group_count) + return stage1_k32_tailinf_cta1_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_5093_v1_stage1_k32_tailinf_cta1_compactwarp", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 96}')) + +def _compiled_stage1_tailinf_cta1(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0186"}')) + +def _eligible_q4_k10(inputs: dict[str, Any]) -> bool: + return faeb._eligible_q4_k10(inputs) + +def _eligible_q64_k10(inputs: dict[str, Any]) -> bool: + return faeb._eligible_q64_k10(inputs) + +def _eligible_q16_k32(inputs: dict[str, Any]) -> bool: + return faeb._eligible_q16_k32(inputs) + +def _launch_q16_k32_tailinf_cta1(inputs: dict[str, Any], *, split_count: int=K32_SPLIT_COUNT, group_count: int=K32_GROUP_COUNT) -> None: + q16_tailinf._validate_group_shape(split_count, group_count) + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tiles * split_count + total_queries = bsz * n_query + stage1_grid = min(total_work, GRID_DIM_DEFAULT) + merge_grid = min(total_queries, GRID_DIM_DEFAULT) + partial_dists, partial_indices = q16_tailinf.parent_k32.parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = q16_tailinf.parent_k32.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), total_queries, BLOCK_Q, dim, dim) + tmap_database = q16_tailinf.parent_k32.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, dim, dim) + _compiled_stage1_tailinf_cta1().launch(grid=(stage1_grid, 1, 1), block=(COMPACT_STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage1_k32_tailinf_cta1_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tiles=num_q_tiles, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), shared_mem=stage1_k32_tailinf_cta1_ir.computed_smem_bytes) + fused_ir = q16_tailinf._fused_merge_ir(split_count, group_count) + fused_kernel = q16_tailinf._compiled_fused_merge(split_count, group_count) + fused_kernel.launch(grid=(merge_grid, 1, 1), block=(K32_FUSED_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], total_queries], shared_mem=fused_ir.computed_smem_bytes) + +def route_for_contract_inputs(inputs: dict[str, Any], *, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> str: + if _eligible_q4_k10(inputs): + return ROUTE_Q4_K10 + if _eligible_q64_k10(inputs): + return ROUTE_Q64_K10 + if _eligible_q16_k32(inputs): + return ''.join(['rag_microbucket_5093_v1_q16_k32_tailinf_cta1_cw1_s', format(k32_split_count, ''), '_g', format(k32_group_count, '')]) + return base_dispatcher.route_for_contract_inputs(inputs) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> None: + if _eligible_q4_k10(inputs): + faeb._launch_q4_k10_m64(inputs) + return + if _eligible_q64_k10(inputs): + faeb._launch_q64_k10_m64(inputs) + return + if _eligible_q16_k32(inputs): + _launch_q16_k32_tailinf_cta1(inputs, split_count=k32_split_count, group_count=k32_group_count) + return + base_dispatcher.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def candidate_with_k32_topology(split_count: int, group_count: int) -> Callable[[dict[str, Any]], None]: + + def _candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, k32_split_count=split_count, k32_group_count=group_count) + return _candidate + +def candidate_faeb_baseline(inputs: dict[str, Any]): + return parent_3505.candidate_faeb_baseline(inputs) + +def candidate_parent_3505(inputs: dict[str, Any]): + parent_3505.launch_from_contract_inputs(inputs) + return None + +def candidate_parent_v3(inputs: dict[str, Any]): + parent_v3.launch_from_contract_inputs(inputs) + return None + +def candidate_base_4247(inputs: dict[str, Any]): + return parent_3505.candidate_base_4247(inputs) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return parent_3505._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return parent_3505._run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=kernel_fn) + +def route_trace_for_contract_shapes(shape_labels=TARGET_SHAPES, *, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> list[dict[str, Any]]: + selected = _select_contract_shapes(shape_labels) + rows = [] + for shape in selected: + inputs = base_dispatcher._trace_inputs_from_shape(shape) + route = route_for_contract_inputs(inputs, k32_split_count=k32_split_count, k32_group_count=k32_group_count) + specialized = str(route).startswith('rag_microbucket_5093_v1') + rows.append({'shape_key': shape['label'], 'selected_route': route, 'route_kind': 'specialized' if specialized else 'general', 'guard_condition': 'exact BF16 non-build B1 M100000 D128 Q4/Q64 K10 or Q16 K32 microbucket' if specialized else 'guard miss to 4247 dispatcher', 'fallback': ROUTE_BASE_4247}) + return rows + +def _target_rows(candidate_report: dict[str, Any], parent_report: dict[str, Any], faeb_report: dict[str, Any], base_report: dict[str, Any], *, k32_split_count: int, k32_group_count: int) -> dict[str, Any]: + rows = {} + for label in TARGET_SHAPES: + cand = candidate_report.get('per_shape', {}).get(label, {}) + parent = parent_report.get('per_shape', {}).get(label, {}) + faeb_row = faeb_report.get('per_shape', {}).get(label, {}) + base = base_report.get('per_shape', {}).get(label, {}) + cand_ms = cand.get('kernel_ms') + parent_ms = parent.get('kernel_ms') + faeb_ms = faeb_row.get('kernel_ms') + base_ms = base.get('kernel_ms') + flashlib_ms = cand.get('flashlib_ms') + route = ''.join(['rag_microbucket_5093_v1_q16_k32_tailinf_cta1_cw1_s', format(k32_split_count, ''), '_g', format(k32_group_count, '')]) if label == Q16_K32_SHAPE else ROUTE_Q4_K10 if label == Q4_K10_SHAPE else ROUTE_Q64_K10 + rows[label] = {'candidate': cand, 'parent_3505': parent, 'faeb_baseline': faeb_row, 'base_4247': base, 'candidate_route': route, 'candidate_ms': cand_ms, 'parent_3505_ms': parent_ms, 'faeb_baseline_ms': faeb_ms, 'base_4247_ms': base_ms, 'flashlib_ms': flashlib_ms, 'speedup_vs_parent_3505': parent_ms / cand_ms if cand_ms and parent_ms else None, 'speedup_vs_faeb': faeb_ms / cand_ms if cand_ms and faeb_ms else None, 'speedup_vs_4247': base_ms / cand_ms if cand_ms and base_ms else None, 'ratio_vs_flashlib': flashlib_ms / cand_ms if cand_ms and flashlib_ms else None} + return rows + +def benchmark_knn_build_rag_microbucket_5093_v1(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_with_k32_topology(k32_split_count, k32_group_count)) + parent_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=parent_3505.candidate_with_k32_topology(parent_3505.K32_SPLIT_COUNT, parent_3505.K32_GROUP_COUNT)) + parent_v3_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=parent_v3.candidate_with_k32_topology(parent_v3.K32_SPLIT_COUNT, parent_v3.K32_GROUP_COUNT)) + faeb_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_faeb_baseline) + base_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_base_4247) + payload = parent_3505._benchmark_payload(candidate_report, faeb_report, base_report, use_cupti=use_cupti, shape_labels=shape_labels, measured_function='benchmark_knn_build_rag_microbucket_5093_v1', k32_split_count=k32_split_count, k32_group_count=k32_group_count) + payload['measured_entrypoint'] = 'loom.examples.weave.knn_build_rag_microbucket_5093_v1:benchmark_knn_build_rag_microbucket_5093_v1' + payload['parent_3505_entrypoint'] = 'loom.examples.weave.knn_build_rag_microbucket_3505_v1:launch_from_contract_inputs' + payload['parent_v3_entrypoint'] = 'loom.examples.weave.knn_build_rag_microbucket_3505_v3:launch_from_contract_inputs' + payload['parent_3505_all_correct'] = parent_report['summary']['all_correct'] + payload['parent_3505_performance_comparable'] = parent_report['summary']['performance_comparable'] + payload['parent_v3_all_correct'] = parent_v3_report['summary']['all_correct'] + payload['parent_v3_performance_comparable'] = parent_v3_report['summary']['performance_comparable'] + payload['parent_v3_contract_summary'] = parent_v3_report['summary'] + payload['parent_v3_contract_performance'] = parent_v3_report['performance'] + payload['parent_v3_report'] = parent_v3_report + payload['producer_topology']['Q16_K32'] = ''.join(['tailinf-cta1/compactwarp/S', format(k32_split_count, ''), '/G', format(k32_group_count, ''), '/fused']) + payload['route_trace'] = route_trace_for_contract_shapes(shape_labels, k32_split_count=k32_split_count, k32_group_count=k32_group_count) + payload['target_rows'] = _target_rows(candidate_report, parent_report, faeb_report, base_report, k32_split_count=k32_split_count, k32_group_count=k32_group_count) + payload['parent_3505_contract_summary'] = parent_report['summary'] + payload['parent_3505_contract_performance'] = parent_report['performance'] + payload['parent_3505_report'] = parent_report + return payload diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_faeb_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_faeb_v1.py new file mode 100644 index 00000000..50e31052 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_faeb_v1.py @@ -0,0 +1,245 @@ +"""Exact RAG microbucket Q4/Q64 K10 and Q16 K32 seed. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +routes only the v6 blindspot rows +``rag_microbatch_b1_q4_m100000_d128_k10``, +``rag_microbatch_b1_q64_m100000_d128_k10``, and +``rag_microbatch_largek_b1_q16_m100000_d128_k32`` through existing Weave +tcgen05/TMA producer families. Guard misses delegate to the current 2cfd +dispatcher, so the measured path remains Weave-only and writes the contract +distance/index outputs directly. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import os +from collections.abc import Callable +from functools import cache +from typing import Any +from .._dispatch_runtime import dc as dc +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_e3de_8712_bcb3_2cfd_v1 as base_dispatcher +from . import knn_build_rag_frontier_7399_v1 as k32_frontier +from . import knn_build_rag_microbatch_4a72_v2 as rag_s144 +from . import knn_build_rag_microbatch_m64_d4f7_v1 as rag_m64 +from .._dispatch_runtime import pack_kernel_args +Q4_K10_SHAPE = 'rag_microbatch_b1_q4_m100000_d128_k10' +Q64_K10_SHAPE = 'rag_microbatch_b1_q64_m100000_d128_k10' +Q16_K32_SHAPE = 'rag_microbatch_largek_b1_q16_m100000_d128_k32' +K10_TARGET_SHAPES = (Q4_K10_SHAPE, Q64_K10_SHAPE) +K32_TARGET_SHAPES = (Q16_K32_SHAPE,) +TARGET_SHAPES = (*K10_TARGET_SHAPES, *K32_TARGET_SHAPES) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +M64_SPLIT_COUNT = 128 +M64_GROUP_COUNT = 8 +S144_SPLIT_COUNT = 144 +S144_GROUP_COUNT_Q4 = 12 +K32_SPLIT_COUNT = _decode_capture(_json_loads('72')) +K32_GROUP_COUNT = _decode_capture(_json_loads('8')) +ROUTE_Q4_K10 = 'rag_microbucket_faeb_q4_k10_m64_s128_g8' +ROUTE_Q64_K10 = 'rag_microbucket_faeb_q64_k10_m64_s128_g8' +ROUTE_Q16_K32 = ''.join(['rag_microbucket_faeb_q16_k32_s', format(K32_SPLIT_COUNT, ''), '_g', format(K32_GROUP_COUNT, ''), '_fused']) +ROUTE_BASE_2CFD = 'loom.examples.weave.knn_build_dispatch_e3de_8712_bcb3_2cfd_v1:launch_from_contract_inputs' +PRODUCTION_ROUTE_MODULES = {'q4_q64_k10_m64': 'loom.examples.weave.knn_build_rag_microbatch_m64_d4f7_v1:_launch_rag_microbatch_m64', 'q16_k32_fused': 'loom.examples.weave.knn_build_rag_microbucket_faeb_v1:_launch_q16_k32_fused', 'base_2cfd': ROUTE_BASE_2CFD} + +def _ir_with_constants(ir_obj: Any, *, suffix: str, **updates: int) -> Any: + constants = tuple(((name, updates.get(name, value)) for name, value in ir_obj.constants)) + return dc.replace(ir_obj, symbol=''.join([format(ir_obj.symbol, ''), '_', format(suffix, '')]), constants=constants) + +def _k32_fused_merge_ir(split_count: int, group_count: int) -> Any: + _validate_k32_group_shape(split_count, group_count) + return _ir_with_constants(k32_frontier.fused_merge_ir, suffix=''.join(['s', format(split_count, ''), 'g', format(group_count, ''), '_faeb']), GROUP_COUNT=group_count, GROUP_SPLITS=split_count // group_count) + +def _validate_k32_group_shape(split_count: int, group_count: int) -> None: + k32_frontier._validate_group_shape(split_count, group_count) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RAG_MICROBUCKET_FAEB_VERIFY_KERNEL') + k32_split = int(os.environ.get('LOOM_KNN_RAG_MICROBUCKET_FAEB_VERIFY_K32_SPLIT', K32_SPLIT_COUNT)) + k32_groups = int(os.environ.get('LOOM_KNN_RAG_MICROBUCKET_FAEB_VERIFY_K32_GROUPS', K32_GROUP_COUNT)) + if verify_kernel == 'm64_stage1': + return rag_m64.stage1_m64_ir + if verify_kernel == 'm64_merge': + return rag_m64.parent_micro._fused_merge_ir(M64_SPLIT_COUNT, M64_GROUP_COUNT) + if verify_kernel == 's144_stage1': + return rag_s144.stage1_cta1_ir + if verify_kernel == 's144_merge': + return rag_s144._fused_merge_ir(S144_SPLIT_COUNT, S144_GROUP_COUNT_Q4) + if verify_kernel == 'k32_stage1': + return k32_frontier.v5.stage1_k32_sort4earlystop_ir + if verify_kernel == 'k32_fused_merge': + return _k32_fused_merge_ir(k32_split, k32_groups) + return rag_m64.stage1_m64_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbatch_m64_d4f7_stage1", "arg_keys": ["query", "database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 91392, "constants": [], "cta_group": 1, "threads": 512}')) + +@cache +def _compiled_k32_fused_merge(split_count: int, group_count: int): + return k32_frontier.parent_k32._compile_ir(_k32_fused_merge_ir(split_count, group_count)) + +def _dtype_name(inputs: dict[str, Any]) -> str: + query = inputs.get('query') + if query is not None: + return str(query.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')) + +def _label_can_hit(inputs: dict[str, Any], labels: set[str]) -> bool: + value = inputs.get('label') + return value is None or str(value) in labels + +def _is_target_bf16_d128_nonbuild(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, TARGET_SHAPE_SET) and (not bool(inputs.get('build', False))) and (_dtype_name(inputs) == 'bfloat16') and (int(inputs.get('B', -1)) == 1) and (int(inputs.get('M', -1)) == 100000) and (int(inputs.get('D', -1)) == 128) + +def _eligible_q4_k10(inputs: dict[str, Any]) -> bool: + return _is_target_bf16_d128_nonbuild(inputs) and int(inputs.get('Q', -1)) == 4 and (int(inputs.get('K', -1)) == 10) + +def _eligible_q64_k10(inputs: dict[str, Any]) -> bool: + return _is_target_bf16_d128_nonbuild(inputs) and int(inputs.get('Q', -1)) == 64 and (int(inputs.get('K', -1)) == 10) + +def _eligible_q16_k32(inputs: dict[str, Any]) -> bool: + return _is_target_bf16_d128_nonbuild(inputs) and int(inputs.get('Q', -1)) == 16 and (int(inputs.get('K', -1)) == 32) + +def _launch_q4_k10_m64(inputs: dict[str, Any]) -> None: + rag_m64._launch_rag_microbatch_m64(inputs, split_count=M64_SPLIT_COUNT, group_count=M64_GROUP_COUNT) + +def _launch_q4_k10_s144(inputs: dict[str, Any]) -> None: + rag_s144._launch_rag_microbatch_fused_merge(inputs, split_count=S144_SPLIT_COUNT, group_count=S144_GROUP_COUNT_Q4) + +def _launch_q64_k10_m64(inputs: dict[str, Any]) -> None: + rag_m64._launch_rag_microbatch_m64(inputs, split_count=M64_SPLIT_COUNT, group_count=M64_GROUP_COUNT) + +def _launch_q16_k32_fused(inputs: dict[str, Any], *, split_count: int=K32_SPLIT_COUNT, group_count: int=K32_GROUP_COUNT) -> None: + _validate_k32_group_shape(split_count, group_count) + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + num_q_tiles = (n_query + k32_frontier.BLOCK_Q - 1) // k32_frontier.BLOCK_Q + num_q_tile_pairs = (num_q_tiles + k32_frontier.CTA_GROUP - 1) // k32_frontier.CTA_GROUP + num_db_tiles = (n_database + k32_frontier.BLOCK_M - 1) // k32_frontier.BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tile_pairs * split_count + total_queries = bsz * n_query + stage1_grid = min(total_work * k32_frontier.CTA_GROUP, k32_frontier.GRID_DIM_DEFAULT) + merge_grid = min(total_queries, k32_frontier.GRID_DIM_DEFAULT) + partial_dists, partial_indices = k32_frontier.parent_k32.parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = k32_frontier.parent_k32.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), total_queries, k32_frontier.BLOCK_Q, dim, dim) + tmap_database = k32_frontier.parent_k32.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, k32_frontier.BLOCK_M, dim, dim) + stage1_kernel = k32_frontier._compiled_stage1_sort4earlystop() + stage1_kernel.launch_cluster(grid=(stage1_grid, 1, 1), block=(k32_frontier.STAGE1_THREADS, 1, 1), args=pack_kernel_args(k32_frontier.v5.stage1_k32_sort4earlystop_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tile_pairs=num_q_tile_pairs, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), cluster_dims=(k32_frontier.CTA_GROUP, 1, 1), shared_mem=k32_frontier.v5.stage1_k32_sort4earlystop_ir.computed_smem_bytes) + merge_ir = _k32_fused_merge_ir(split_count, group_count) + merge_kernel = _compiled_k32_fused_merge(split_count, group_count) + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(k32_frontier.K32_FUSED_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], total_queries], shared_mem=merge_ir.computed_smem_bytes) + +def route_for_contract_inputs(inputs: dict[str, Any]) -> str: + if _eligible_q4_k10(inputs): + return ROUTE_Q4_K10 + if _eligible_q64_k10(inputs): + return ROUTE_Q64_K10 + if _eligible_q16_k32(inputs): + return ROUTE_Q16_K32 + return ROUTE_BASE_2CFD + +def launch_from_contract_inputs(inputs: dict[str, Any], *, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> None: + if _eligible_q4_k10(inputs): + _launch_q4_k10_m64(inputs) + return + if _eligible_q64_k10(inputs): + _launch_q64_k10_m64(inputs) + return + if _eligible_q16_k32(inputs): + _launch_q16_k32_fused(inputs, split_count=k32_split_count, group_count=k32_group_count) + return + base_dispatcher.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def candidate_q4_s144(inputs: dict[str, Any]): + if _eligible_q4_k10(inputs): + _launch_q4_k10_s144(inputs) + return None + launch_from_contract_inputs(inputs) + return None + +def candidate_k32_topology(split_count: int, group_count: int) -> Callable[[dict[str, Any]], None]: + + def _candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, k32_split_count=split_count, k32_group_count=group_count) + return _candidate + +def candidate_base_2cfd(inputs: dict[str, Any]): + base_dispatcher.launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return base_dispatcher._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def _trace_inputs_from_shape(shape: dict[str, Any]) -> dict[str, Any]: + return base_dispatcher._trace_inputs_from_shape(shape) + +def route_trace_for_contract_shapes(shape_labels=TARGET_SHAPES) -> list[dict[str, Any]]: + selected = _select_contract_shapes(shape_labels) + rows = [] + for shape in selected: + inputs = _trace_inputs_from_shape(shape) + route = route_for_contract_inputs(inputs) + specialized = route != ROUTE_BASE_2CFD + rows.append({'shape_key': shape['label'], 'selected_route': route, 'route_kind': 'specialized' if specialized else 'general', 'guard_condition': 'exact BF16 non-build B1 M100000 D128 Q4/Q64 K10 or Q16 K32 microbucket' if specialized else 'guard miss to 2cfd dispatcher', 'fallback': ROUTE_BASE_2CFD}) + return rows + +def _rows_for_labels(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return {label: report.get('per_shape', {}).get(label, {}) for label in labels} + +def _target_rows(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> dict[str, Any]: + rows = {} + for label in TARGET_SHAPES: + cand = candidate_report.get('per_shape', {}).get(label, {}) + base = baseline_report.get('per_shape', {}).get(label, {}) + cand_ms = cand.get('kernel_ms') + base_ms = base.get('kernel_ms') + flashlib_ms = cand.get('flashlib_ms') + rows[label] = {'candidate': cand, 'baseline_2cfd': base, 'candidate_route': route_for_contract_inputs(_trace_inputs_from_shape(_select_contract_shapes((label,))[0])), 'candidate_ms': cand_ms, 'baseline_2cfd_ms': base_ms, 'flashlib_ms': flashlib_ms, 'speedup_vs_2cfd': base_ms / cand_ms if cand_ms and base_ms else None, 'ratio_vs_flashlib': flashlib_ms / cand_ms if cand_ms and flashlib_ms else None} + return rows + +def _timing_backends_for_reports(*reports: dict[str, Any]) -> list[str]: + return sorted({row.get('timing_backend') for report in reports for row in report.get('per_shape', {}).values() if row.get('timing_backend') is not None}) + +def _benchmark_payload(candidate_report: dict[str, Any], baseline_report: dict[str, Any], *, use_cupti: bool, shape_labels, measured_function: str, k32_split_count: int, k32_group_count: int) -> dict[str, Any]: + return {'tflops': candidate_report['summary']['primary_mean'] or 0.0, 'all_correct': candidate_report['summary']['all_correct'], 'baseline_all_correct': baseline_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'baseline_performance_comparable': baseline_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'measured_entrypoint': ''.join(['loom.examples.weave.knn_build_rag_microbucket_faeb_v1:', format(measured_function, '')]), 'baseline_entrypoint': 'loom.examples.weave.knn_build_dispatch_e3de_8712_bcb3_2cfd_v1:launch_from_contract_inputs', 'accelerated_shape_labels': list(TARGET_SHAPES), 'measured_shape_labels': list(shape_labels), 'producer_topology': {'Q4_K10': ''.join(['M64/S', format(M64_SPLIT_COUNT, ''), '/G', format(M64_GROUP_COUNT, '')]), 'Q64_K10': ''.join(['M64/S', format(M64_SPLIT_COUNT, ''), '/G', format(M64_GROUP_COUNT, '')]), 'Q16_K32': ''.join(['sort4earlystop/S', format(k32_split_count, ''), '/G', format(k32_group_count, ''), '/fused'])}, 'timing_backends': _timing_backends_for_reports(candidate_report, baseline_report), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'route_trace': route_trace_for_contract_shapes(shape_labels), 'route_trace_included': True, 'target_rows': _target_rows(candidate_report, baseline_report), 'contract_summary': candidate_report['summary'], 'baseline_contract_summary': baseline_report['summary'], 'contract_performance': candidate_report['performance'], 'baseline_contract_performance': baseline_report['performance'], 'report': candidate_report, 'baseline_report': baseline_report} + +def benchmark_knn_build_rag_microbucket_faeb_v1(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_k32_topology(k32_split_count, k32_group_count)) + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_base_2cfd) + return _benchmark_payload(candidate_report, baseline_report, use_cupti=use_cupti, shape_labels=shape_labels, measured_function='benchmark_knn_build_rag_microbucket_faeb_v1', k32_split_count=k32_split_count, k32_group_count=k32_group_count) + +def benchmark_q4_s144_ab(*, use_cupti: bool=True) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=(Q4_K10_SHAPE,), kernel_fn=candidate_q4_s144) + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=(Q4_K10_SHAPE,), kernel_fn=candidate) + return {'measured_entrypoint': 'loom.examples.weave.knn_build_rag_microbucket_faeb_v1:benchmark_q4_s144_ab', 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'candidate_s144': candidate_report, 'baseline_m64': baseline_report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_faeb_v2.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_faeb_v2.py new file mode 100644 index 00000000..0d10d6ec --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_faeb_v2.py @@ -0,0 +1,252 @@ +"""Exact RAG microbucket Q4/Q64 K10 and Q16 K32 widened-merge seed. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +routes only the v6 blindspot rows +``rag_microbatch_b1_q4_m100000_d128_k10``, +``rag_microbatch_b1_q64_m100000_d128_k10``, and +``rag_microbatch_largek_b1_q16_m100000_d128_k32`` through existing Weave +tcgen05/TMA producer families. Compared with faeb v1, the K32 fused merge uses +a widened shared-memory scratch buffer so split72/group9 is a valid topology +instead of an out-of-bounds probe. Guard misses delegate to the current 2cfd +dispatcher, so the measured path remains Weave-only and writes the contract +distance/index outputs directly. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import os +from collections.abc import Callable +from functools import cache +from typing import Any +from .._dispatch_runtime import dc as dc +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_e3de_8712_bcb3_2cfd_v1 as base_dispatcher +from . import knn_build_rag_frontier_7399_v1 as k32_frontier +from . import knn_build_rag_microbatch_4a72_v2 as rag_s144 +from . import knn_build_rag_microbatch_m64_d4f7_v1 as rag_m64 +from .._dispatch_runtime import pack_kernel_args +Q4_K10_SHAPE = 'rag_microbatch_b1_q4_m100000_d128_k10' +Q64_K10_SHAPE = 'rag_microbatch_b1_q64_m100000_d128_k10' +Q16_K32_SHAPE = 'rag_microbatch_largek_b1_q16_m100000_d128_k32' +K10_TARGET_SHAPES = (Q4_K10_SHAPE, Q64_K10_SHAPE) +K32_TARGET_SHAPES = (Q16_K32_SHAPE,) +TARGET_SHAPES = (*K10_TARGET_SHAPES, *K32_TARGET_SHAPES) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +M64_SPLIT_COUNT = 128 +M64_GROUP_COUNT = 8 +S144_SPLIT_COUNT = 144 +S144_GROUP_COUNT_Q4 = 12 +K32_SPLIT_COUNT = _decode_capture(_json_loads('72')) +K32_GROUP_COUNT = _decode_capture(_json_loads('9')) +K32_GROUP_CAPACITY = 16 +ROUTE_Q4_K10 = 'rag_microbucket_faeb_q4_k10_m64_s128_g8' +ROUTE_Q64_K10 = 'rag_microbucket_faeb_q64_k10_m64_s128_g8' +ROUTE_Q16_K32 = ''.join(['rag_microbucket_faeb_v2_q16_k32_s', format(K32_SPLIT_COUNT, ''), '_g', format(K32_GROUP_COUNT, ''), '_widefused']) +ROUTE_BASE_2CFD = 'loom.examples.weave.knn_build_dispatch_e3de_8712_bcb3_2cfd_v1:launch_from_contract_inputs' +PRODUCTION_ROUTE_MODULES = {'q4_q64_k10_m64': 'loom.examples.weave.knn_build_rag_microbatch_m64_d4f7_v1:_launch_rag_microbatch_m64', 'q16_k32_fused': 'loom.examples.weave.knn_build_rag_microbucket_faeb_v2:_launch_q16_k32_fused', 'base_2cfd': ROUTE_BASE_2CFD} + +def _ir_with_constants(ir_obj: Any, *, suffix: str, **updates: int) -> Any: + constants = tuple(((name, updates.get(name, value)) for name, value in ir_obj.constants)) + return dc.replace(ir_obj, symbol=''.join([format(ir_obj.symbol, ''), '_', format(suffix, '')]), constants=constants) +knn_build_rag_microbucket_faeb_v2_k32_wide_fused_group_final_merge = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_faeb_v2_k32_wide_fused_group_final_merge", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 4096, "constants": [["TOP_K_MAX", 32], ["GROUP_COUNT", 9], ["GROUP_SPLITS", 8]], "cta_group": 1, "threads": 32}')) +wide_fused_merge_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_faeb_v2_k32_wide_fused_group_final_merge", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 4096, "constants": [["TOP_K_MAX", 32], ["GROUP_COUNT", 9], ["GROUP_SPLITS", 8]], "cta_group": 1, "threads": 32}')) + +def _k32_fused_merge_ir(split_count: int, group_count: int) -> Any: + _validate_k32_group_shape(split_count, group_count) + return _ir_with_constants(wide_fused_merge_ir, suffix=''.join(['s', format(split_count, ''), 'g', format(group_count, ''), '_faeb']), GROUP_COUNT=group_count, GROUP_SPLITS=split_count // group_count) + +def _validate_k32_group_shape(split_count: int, group_count: int) -> None: + k32_frontier._validate_group_shape(split_count, group_count) + if group_count > K32_GROUP_CAPACITY: + raise ValueError(''.join(['group_count=', format(group_count, ''), ' exceeds faeb v2 fused-merge capacity ', format(K32_GROUP_CAPACITY, '')])) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RAG_MICROBUCKET_FAEB_V2_VERIFY_KERNEL') + k32_split = int(os.environ.get('LOOM_KNN_RAG_MICROBUCKET_FAEB_V2_VERIFY_K32_SPLIT', K32_SPLIT_COUNT)) + k32_groups = int(os.environ.get('LOOM_KNN_RAG_MICROBUCKET_FAEB_V2_VERIFY_K32_GROUPS', K32_GROUP_COUNT)) + if verify_kernel == 'm64_stage1': + return rag_m64.stage1_m64_ir + if verify_kernel == 'm64_merge': + return rag_m64.parent_micro._fused_merge_ir(M64_SPLIT_COUNT, M64_GROUP_COUNT) + if verify_kernel == 's144_stage1': + return rag_s144.stage1_cta1_ir + if verify_kernel == 's144_merge': + return rag_s144._fused_merge_ir(S144_SPLIT_COUNT, S144_GROUP_COUNT_Q4) + if verify_kernel == 'k32_stage1': + return k32_frontier.v5.stage1_k32_sort4earlystop_ir + if verify_kernel == 'k32_fused_merge': + return _k32_fused_merge_ir(k32_split, k32_groups) + return rag_m64.stage1_m64_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbatch_m64_d4f7_stage1", "arg_keys": ["query", "database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 91392, "constants": [], "cta_group": 1, "threads": 512}')) + +@cache +def _compiled_k32_fused_merge(split_count: int, group_count: int): + return k32_frontier.parent_k32._compile_ir(_k32_fused_merge_ir(split_count, group_count)) + +def _dtype_name(inputs: dict[str, Any]) -> str: + query = inputs.get('query') + if query is not None: + return str(query.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')) + +def _label_can_hit(inputs: dict[str, Any], labels: set[str]) -> bool: + value = inputs.get('label') + return value is None or str(value) in labels + +def _is_target_bf16_d128_nonbuild(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, TARGET_SHAPE_SET) and (not bool(inputs.get('build', False))) and (_dtype_name(inputs) == 'bfloat16') and (int(inputs.get('B', -1)) == 1) and (int(inputs.get('M', -1)) == 100000) and (int(inputs.get('D', -1)) == 128) + +def _eligible_q4_k10(inputs: dict[str, Any]) -> bool: + return _is_target_bf16_d128_nonbuild(inputs) and int(inputs.get('Q', -1)) == 4 and (int(inputs.get('K', -1)) == 10) + +def _eligible_q64_k10(inputs: dict[str, Any]) -> bool: + return _is_target_bf16_d128_nonbuild(inputs) and int(inputs.get('Q', -1)) == 64 and (int(inputs.get('K', -1)) == 10) + +def _eligible_q16_k32(inputs: dict[str, Any]) -> bool: + return _is_target_bf16_d128_nonbuild(inputs) and int(inputs.get('Q', -1)) == 16 and (int(inputs.get('K', -1)) == 32) + +def _launch_q4_k10_m64(inputs: dict[str, Any]) -> None: + rag_m64._launch_rag_microbatch_m64(inputs, split_count=M64_SPLIT_COUNT, group_count=M64_GROUP_COUNT) + +def _launch_q4_k10_s144(inputs: dict[str, Any]) -> None: + rag_s144._launch_rag_microbatch_fused_merge(inputs, split_count=S144_SPLIT_COUNT, group_count=S144_GROUP_COUNT_Q4) + +def _launch_q64_k10_m64(inputs: dict[str, Any]) -> None: + rag_m64._launch_rag_microbatch_m64(inputs, split_count=M64_SPLIT_COUNT, group_count=M64_GROUP_COUNT) + +def _launch_q16_k32_fused(inputs: dict[str, Any], *, split_count: int=K32_SPLIT_COUNT, group_count: int=K32_GROUP_COUNT) -> None: + _validate_k32_group_shape(split_count, group_count) + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + num_q_tiles = (n_query + k32_frontier.BLOCK_Q - 1) // k32_frontier.BLOCK_Q + num_q_tile_pairs = (num_q_tiles + k32_frontier.CTA_GROUP - 1) // k32_frontier.CTA_GROUP + num_db_tiles = (n_database + k32_frontier.BLOCK_M - 1) // k32_frontier.BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tile_pairs * split_count + total_queries = bsz * n_query + stage1_grid = min(total_work * k32_frontier.CTA_GROUP, k32_frontier.GRID_DIM_DEFAULT) + merge_grid = min(total_queries, k32_frontier.GRID_DIM_DEFAULT) + partial_dists, partial_indices = k32_frontier.parent_k32.parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = k32_frontier.parent_k32.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), total_queries, k32_frontier.BLOCK_Q, dim, dim) + tmap_database = k32_frontier.parent_k32.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, k32_frontier.BLOCK_M, dim, dim) + stage1_kernel = k32_frontier._compiled_stage1_sort4earlystop() + stage1_kernel.launch_cluster(grid=(stage1_grid, 1, 1), block=(k32_frontier.STAGE1_THREADS, 1, 1), args=pack_kernel_args(k32_frontier.v5.stage1_k32_sort4earlystop_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tile_pairs=num_q_tile_pairs, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), cluster_dims=(k32_frontier.CTA_GROUP, 1, 1), shared_mem=k32_frontier.v5.stage1_k32_sort4earlystop_ir.computed_smem_bytes) + merge_ir = _k32_fused_merge_ir(split_count, group_count) + merge_kernel = _compiled_k32_fused_merge(split_count, group_count) + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(k32_frontier.K32_FUSED_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], total_queries], shared_mem=merge_ir.computed_smem_bytes) + +def route_for_contract_inputs(inputs: dict[str, Any]) -> str: + if _eligible_q4_k10(inputs): + return ROUTE_Q4_K10 + if _eligible_q64_k10(inputs): + return ROUTE_Q64_K10 + if _eligible_q16_k32(inputs): + return ROUTE_Q16_K32 + return ROUTE_BASE_2CFD + +def launch_from_contract_inputs(inputs: dict[str, Any], *, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> None: + if _eligible_q4_k10(inputs): + _launch_q4_k10_m64(inputs) + return + if _eligible_q64_k10(inputs): + _launch_q64_k10_m64(inputs) + return + if _eligible_q16_k32(inputs): + _launch_q16_k32_fused(inputs, split_count=k32_split_count, group_count=k32_group_count) + return + base_dispatcher.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def candidate_q4_s144(inputs: dict[str, Any]): + if _eligible_q4_k10(inputs): + _launch_q4_k10_s144(inputs) + return None + launch_from_contract_inputs(inputs) + return None + +def candidate_k32_topology(split_count: int, group_count: int) -> Callable[[dict[str, Any]], None]: + + def _candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, k32_split_count=split_count, k32_group_count=group_count) + return _candidate + +def candidate_base_2cfd(inputs: dict[str, Any]): + base_dispatcher.launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return base_dispatcher._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def _trace_inputs_from_shape(shape: dict[str, Any]) -> dict[str, Any]: + return base_dispatcher._trace_inputs_from_shape(shape) + +def route_trace_for_contract_shapes(shape_labels=TARGET_SHAPES) -> list[dict[str, Any]]: + selected = _select_contract_shapes(shape_labels) + rows = [] + for shape in selected: + inputs = _trace_inputs_from_shape(shape) + route = route_for_contract_inputs(inputs) + specialized = route != ROUTE_BASE_2CFD + rows.append({'shape_key': shape['label'], 'selected_route': route, 'route_kind': 'specialized' if specialized else 'general', 'guard_condition': 'exact BF16 non-build B1 M100000 D128 Q4/Q64 K10 or Q16 K32 microbucket' if specialized else 'guard miss to 2cfd dispatcher', 'fallback': ROUTE_BASE_2CFD}) + return rows + +def _rows_for_labels(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return {label: report.get('per_shape', {}).get(label, {}) for label in labels} + +def _target_rows(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> dict[str, Any]: + rows = {} + for label in TARGET_SHAPES: + cand = candidate_report.get('per_shape', {}).get(label, {}) + base = baseline_report.get('per_shape', {}).get(label, {}) + cand_ms = cand.get('kernel_ms') + base_ms = base.get('kernel_ms') + flashlib_ms = cand.get('flashlib_ms') + rows[label] = {'candidate': cand, 'baseline_2cfd': base, 'candidate_route': route_for_contract_inputs(_trace_inputs_from_shape(_select_contract_shapes((label,))[0])), 'candidate_ms': cand_ms, 'baseline_2cfd_ms': base_ms, 'flashlib_ms': flashlib_ms, 'speedup_vs_2cfd': base_ms / cand_ms if cand_ms and base_ms else None, 'ratio_vs_flashlib': flashlib_ms / cand_ms if cand_ms and flashlib_ms else None} + return rows + +def _timing_backends_for_reports(*reports: dict[str, Any]) -> list[str]: + return sorted({row.get('timing_backend') for report in reports for row in report.get('per_shape', {}).values() if row.get('timing_backend') is not None}) + +def _benchmark_payload(candidate_report: dict[str, Any], baseline_report: dict[str, Any], *, use_cupti: bool, shape_labels, measured_function: str, k32_split_count: int, k32_group_count: int) -> dict[str, Any]: + return {'tflops': candidate_report['summary']['primary_mean'] or 0.0, 'all_correct': candidate_report['summary']['all_correct'], 'baseline_all_correct': baseline_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'baseline_performance_comparable': baseline_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'measured_entrypoint': ''.join(['loom.examples.weave.knn_build_rag_microbucket_faeb_v2:', format(measured_function, '')]), 'baseline_entrypoint': 'loom.examples.weave.knn_build_dispatch_e3de_8712_bcb3_2cfd_v1:launch_from_contract_inputs', 'accelerated_shape_labels': list(TARGET_SHAPES), 'measured_shape_labels': list(shape_labels), 'producer_topology': {'Q4_K10': ''.join(['M64/S', format(M64_SPLIT_COUNT, ''), '/G', format(M64_GROUP_COUNT, '')]), 'Q64_K10': ''.join(['M64/S', format(M64_SPLIT_COUNT, ''), '/G', format(M64_GROUP_COUNT, '')]), 'Q16_K32': ''.join(['sort4earlystop/S', format(k32_split_count, ''), '/G', format(k32_group_count, ''), '/fused'])}, 'timing_backends': _timing_backends_for_reports(candidate_report, baseline_report), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'route_trace': route_trace_for_contract_shapes(shape_labels), 'route_trace_included': True, 'target_rows': _target_rows(candidate_report, baseline_report), 'contract_summary': candidate_report['summary'], 'baseline_contract_summary': baseline_report['summary'], 'contract_performance': candidate_report['performance'], 'baseline_contract_performance': baseline_report['performance'], 'report': candidate_report, 'baseline_report': baseline_report} + +def benchmark_knn_build_rag_microbucket_faeb_v2(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_k32_topology(k32_split_count, k32_group_count)) + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_base_2cfd) + return _benchmark_payload(candidate_report, baseline_report, use_cupti=use_cupti, shape_labels=shape_labels, measured_function='benchmark_knn_build_rag_microbucket_faeb_v2', k32_split_count=k32_split_count, k32_group_count=k32_group_count) + +def benchmark_q4_s144_ab(*, use_cupti: bool=True) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=(Q4_K10_SHAPE,), kernel_fn=candidate_q4_s144) + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=(Q4_K10_SHAPE,), kernel_fn=candidate) + return {'measured_entrypoint': 'loom.examples.weave.knn_build_rag_microbucket_faeb_v2:benchmark_q4_s144_ab', 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'candidate_s144': candidate_report, 'baseline_m64': baseline_report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k12_2f22_q48exact_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k12_2f22_q48exact_v1.py new file mode 100644 index 00000000..d402d970 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k12_2f22_q48exact_v1.py @@ -0,0 +1,223 @@ +"""Exact Q48/M75000 K12 RAG microbucket seed for the 2f22 bucket. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +targets only the BF16 non-build ``B=1,Q=48,M=75000,D=128,K=12`` expanded row +from generalize-auto-tuning round 176. It specializes the e5db 64-row +ROW_16x256B tcgen05/TMA producer to K12 and pairs it with a four-row split-list +merge. Guard misses delegate to the current v11 common-D dispatcher. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from collections.abc import Callable +from functools import cache, lru_cache +from pathlib import Path +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_v11_common_d_seed_portfolio_a4ec_v1 as dispatch_v11 +from . import knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2_k32split_v9 as k12_v9 +from . import knn_build_rag_microbucket_k32rows4_0077_v1 as rows4 +from . import knn_build_rag_microbucket_q32rowld_e5db_v1 as rowld64 +from .._dispatch_runtime import pack_kernel_args +MODULE = 'loom.examples.weave.knn_build_rag_microbucket_k12_2f22_q48exact_v1' +CANDIDATE_ID = 'candidate_2f22_q48_m75000_k12_rowld64_v1' +SEED_ID = 'rag_microbucket_k12_2f22_q48exact_v1' +TARGET_SHAPE = dispatch_v11.EXPANDED_Q48_M75000_K12 +TARGET_SHAPES = (TARGET_SHAPE,) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +TARGET_SHAPE_RECORD = dispatch_v11.EXPANDED_Q32_GUARD_BOUNDARY_8_BY_LABEL[TARGET_SHAPE] +Q48_K12_SPLIT_COUNT = _decode_capture(_json_loads('148')) +Q48_K12_TOP_K_MAX = 12 +Q48_K12_STAGE1_THREADS = _decode_capture(_json_loads('192')) +Q48_K12_BLOCK_Q = rowld64.Q8_M64_BLOCK_Q +Q48_K12_BLOCK_M = rowld64.Q8_M64_BLOCK_M +Q48_K12_FEAT_D = rowld64.Q8_M64_FEAT_D +Q48_K12_LOCAL_LISTS_PER_ROW = rowld64.Q32_M64_LOCAL_LISTS_PER_ROW +Q48_K12_SMEM_BASE_BYTES = rowld64.Q32_M64_SMEM_BASE_BYTES +Q48_K12_LOCAL_ELEMS = Q48_K12_BLOCK_Q * Q48_K12_LOCAL_LISTS_PER_ROW * Q48_K12_TOP_K_MAX +Q48_K12_LOCAL_D_OFFSET = Q48_K12_SMEM_BASE_BYTES +Q48_K12_LOCAL_I_OFFSET = Q48_K12_LOCAL_D_OFFSET + Q48_K12_LOCAL_ELEMS * 4 +Q48_K12_SMEM_POOL_BYTES = Q48_K12_LOCAL_I_OFFSET + Q48_K12_LOCAL_ELEMS * 4 +Q48_K12_ROWS_PER_MERGE_CTA = 4 +Q48_K12_MERGE_THREADS = rows4.K32_ROWS4_MERGE_THREADS +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_PARENT_V11 = dispatch_v11.ROUTE_ENTRYPOINT +ROUTE_V9_K12_PROBE = 'loom.examples.weave.knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2_k32split_v9:_launch_k32_split_path' +BENCHMARK_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_knn_build_rag_microbucket_k12_2f22_q48exact_v1']) +_insert_sorted_pair = _ir_proxy('loom.examples.weave.knn_build_rag_microbucket_k12_2f22_q48exact_v1:_insert_sorted_pair', 256) +knn_build_rag_microbucket_k12_2f22_q48exact_v1_stage1 = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_k12_2f22_q48exact_v1_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 58624, "constants": [["BLOCK_Q", 64], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 12]], "cta_group": 1, "threads": 192}')) +stage1_q48_k12_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_k12_2f22_q48exact_v1_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 58624, "constants": [["BLOCK_Q", 64], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 12]], "cta_group": 1, "threads": 192}')) + +def _warp_merge_ir(split_count: int) -> Any: + return rows4._ir_with_constants(rows4.base.k32_warp_row_merge_ir, suffix=''.join(['q48k12s', format(split_count, ''), 'r', format(Q48_K12_ROWS_PER_MERGE_CTA, ''), '_2f22_v1']), TOP_K_MAX=Q48_K12_TOP_K_MAX, SPLIT_COUNT=split_count, SPLITS_PER_LANE=rows4.base._splits_per_lane(split_count), ROWS_PER_CTA=Q48_K12_ROWS_PER_MERGE_CTA) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RAG_MICROBUCKET_K12_2F22_Q48EXACT_VERIFY_KERNEL') + split_count = int(os.environ.get('LOOM_KNN_RAG_MICROBUCKET_K12_2F22_Q48EXACT_VERIFY_SPLIT', Q48_K12_SPLIT_COUNT)) + if verify_kernel == 'merge': + return _warp_merge_ir(split_count) + return stage1_q48_k12_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_k12_2f22_q48exact_v1_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 58624, "constants": [["BLOCK_Q", 64], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 12]], "cta_group": 1, "threads": 192}')) + +def _compiled_stage1_q48_k12(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0206"}')) + +@cache +def _compiled_warp_merge(split_count: int): + return rowld64.compact_seed.q16_tailinf.parent_k32._compile_ir(_warp_merge_ir(split_count)) + +def _dtype_name(inputs: dict[str, Any]) -> str: + query = inputs.get('query') + if query is not None and hasattr(query, 'dtype'): + return str(query.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')).replace('torch.', '') + +def _eligible_q48_k12(inputs: dict[str, Any]) -> bool: + return not bool(inputs.get('build', False)) and int(inputs.get('B', -1)) == 1 and (int(inputs.get('Q', -1)) == 48) and (int(inputs.get('M', -1)) == 75000) and (int(inputs.get('D', -1)) == 128) and (int(inputs.get('K', -1)) == Q48_K12_TOP_K_MAX) and (_dtype_name(inputs) == 'bfloat16') + +def _route_name(inputs: dict[str, Any], *, split_count: int) -> str: + return ''.join(['rag_microbucket_k12_2f22_q48exact_v1_q', format(int(inputs.get('Q', -1)), ''), '_m', format(int(inputs.get('M', -1)), ''), '_k12_m64n64_row16x256b_s', format(split_count, ''), '_r', format(Q48_K12_ROWS_PER_MERGE_CTA, '')]) + +def _launch_q48_k12(inputs: dict[str, Any], *, split_count: int=Q48_K12_SPLIT_COUNT) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + num_q_tiles = (n_query + Q48_K12_BLOCK_Q - 1) // Q48_K12_BLOCK_Q + num_db_tiles = (n_database + Q48_K12_BLOCK_M - 1) // Q48_K12_BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tiles * split_count + total_queries = bsz * n_query + stage1_grid = min(total_work, rowld64.compact_seed.GRID_DIM_DEFAULT) + merge_grid = min((total_queries + Q48_K12_ROWS_PER_MERGE_CTA - 1) // Q48_K12_ROWS_PER_MERGE_CTA, rowld64.compact_seed.GRID_DIM_DEFAULT) + partial_dists, partial_indices = rowld64.compact_seed.q16_tailinf.parent_k32.parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = rowld64.compact_seed.q16_tailinf.parent_k32.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), total_queries, Q48_K12_BLOCK_Q, dim, dim) + tmap_database = rowld64.compact_seed.q16_tailinf.parent_k32.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, Q48_K12_BLOCK_M, dim, dim) + _compiled_stage1_q48_k12().launch(grid=(stage1_grid, 1, 1), block=(Q48_K12_STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage1_q48_k12_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tiles=num_q_tiles, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), shared_mem=stage1_q48_k12_ir.computed_smem_bytes) + merge_ir = _warp_merge_ir(split_count) + _compiled_warp_merge(split_count).launch(grid=(merge_grid, 1, 1), block=(Q48_K12_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], total_queries], shared_mem=merge_ir.computed_smem_bytes) + +def route_for_contract_inputs(inputs: dict[str, Any], *, split_count: int=Q48_K12_SPLIT_COUNT, force_fallback: bool=False) -> str: + if _eligible_q48_k12(inputs) and (not force_fallback): + return _route_name(inputs, split_count=split_count) + return dispatch_v11.route_for_contract_inputs(inputs) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, split_count: int=Q48_K12_SPLIT_COUNT, force_fallback: bool=False) -> None: + if _eligible_q48_k12(inputs) and (not force_fallback): + _launch_q48_k12(inputs, split_count=split_count) + return + dispatch_v11.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_with_split(split_count: int) -> Callable[[dict[str, Any]], None]: + + def _candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, split_count=split_count) + return _candidate + +def candidate_v9_k12_probe(inputs: dict[str, Any]) -> None: + k12_v9._launch_k32_split_path(inputs, split_count=k12_v9.K12_MID_SPLITS) + +def candidate_dispatch_v11(inputs: dict[str, Any]) -> None: + dispatch_v11.launch_from_contract_inputs(inputs) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels=TARGET_SHAPES) -> list[dict[str, Any]]: + return dispatch_v11._select_contract_shapes(tuple(shape_labels)) + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + previous = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = previous + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def route_trace_for_contract_shapes(shape_labels=TARGET_SHAPES, *, split_count: int=Q48_K12_SPLIT_COUNT, force_fallback: bool=False) -> list[dict[str, Any]]: + rows = [] + for shape in _select_contract_shapes(shape_labels): + inputs = dispatch_v11._trace_inputs_for_shape(shape) + route = route_for_contract_inputs(inputs, split_count=split_count, force_fallback=force_fallback) + parent_route = dispatch_v11.route_for_contract_inputs(inputs) + selected = _eligible_q48_k12(inputs) and (not force_fallback) + rows.append(dispatch_v11._normalize_route_row({'shape_key': shape['label'], 'selected_route': route, 'selected_entrypoint': ROUTE_ENTRYPOINT if selected else ROUTE_PARENT_V11, 'selected_seed': SEED_ID if selected else None, 'expected_seed': SEED_ID if _eligible_q48_k12(inputs) else None, 'route_kind': 'specialized_q48_k12_microbucket' if selected else 'general', 'route_source': 'shape-specific-seed' if selected else 'broad-dispatcher', 'guard_id': '2f22_q48_m75000_k12_exact_guard' if selected else 'forced_fallback_or_guard_miss', 'guard_condition': 'exact BF16 non-build B=1 Q=48 M=75000 D=128 K=12' if selected else 'delegate to current v11 common-D dispatcher', 'split_count': split_count if selected else None, 'rows_per_merge_cta': Q48_K12_ROWS_PER_MERGE_CTA if selected else None, 'parent_v11_route': parent_route, 'classification': 'unmeasured'})) + return rows + +def _per_shape_delta(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> dict[str, Any]: + rows = {} + for label, cand in candidate_report.get('per_shape', {}).items(): + baseline = baseline_report.get('per_shape', {}).get(label, {}) + cand_ms = cand.get('kernel_ms') + baseline_ms = baseline.get('kernel_ms') + rows[label] = {'candidate': cand, 'baseline': baseline, 'candidate_ms': cand_ms, 'baseline_ms': baseline_ms, 'speedup_vs_baseline': baseline_ms / cand_ms if cand_ms and baseline_ms else None, 'ratio_vs_flashlib': cand.get('ratio_vs_flashlib'), 'baseline_ratio_vs_flashlib': baseline.get('ratio_vs_flashlib')} + return rows + +def _annotate_route_trace(route_trace: list[dict[str, Any]], candidate_report: dict[str, Any]) -> list[dict[str, Any]]: + annotated = [] + for row in route_trace: + out = dict(row) + label = str(out.get('shape_key')) + result = candidate_report.get('per_shape', {}).get(label, {}) + ratio = result.get('ratio_vs_flashlib') + out['shape_specific_kernel_ms'] = result.get('kernel_ms') + out['speedup_vs_external_baseline'] = ratio + out['external_baseline_ms'] = result.get('flashlib_ms') + out['external_baseline_ref'] = 'same_session' + out['timing_backend'] = result.get('timing_backend') + if result.get('passed') is True and ratio is not None: + out['classification'] = 'floor-clearing' if float(ratio) >= dispatch_v11.SPEEDUP_FLOOR else 'below-floor' + annotated.append(out) + return annotated + +def benchmark_knn_build_rag_microbucket_k12_2f22_q48exact_v1(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES, split_count: int=Q48_K12_SPLIT_COUNT, run_v9_probe: bool=True, run_dispatch_baseline: bool=True) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_with_split(split_count)) + dispatch_report = None + if run_dispatch_baseline: + dispatch_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_dispatch_v11) + v9_report = None + if run_v9_probe: + v9_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_v9_k12_probe) + payload: dict[str, Any] = {'contract': candidate_report['contract'], 'contract_version': candidate_report['contract_version'], 'measured_entrypoint': BENCHMARK_ENTRYPOINT, 'candidate_entrypoint': ROUTE_ENTRYPOINT, 'accelerated_shape_labels': list(TARGET_SHAPES), 'measured_shape_labels': list(shape_labels), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'producer_topology': {'candidate': ''.join(['64-row M64/N64 ROW_16x256B tcgen05/TMA producer specialized to K12, split', format(split_count, '')]), 'merge': ''.join(['four-row split-list merge, rows_per_cta=', format(Q48_K12_ROWS_PER_MERGE_CTA, '')]), 'guard_misses': 'delegate to current v11 common-D dispatcher'}, 'route_trace': _annotate_route_trace(route_trace_for_contract_shapes(shape_labels, split_count=split_count), candidate_report), 'summary': candidate_report['summary'], 'performance': candidate_report['performance'], 'correctness': candidate_report['correctness'], 'report': candidate_report} + if dispatch_report is not None: + payload['dispatch_v11_entrypoint'] = ROUTE_PARENT_V11 + payload['dispatch_v11_summary'] = dispatch_report['summary'] + payload['dispatch_v11_performance'] = dispatch_report['performance'] + payload['dispatch_v11_report'] = dispatch_report + payload['target_rows_vs_dispatch_v11'] = _per_shape_delta(candidate_report, dispatch_report) + if v9_report is not None: + payload['v9_k12_probe_entrypoint'] = ROUTE_V9_K12_PROBE + payload['v9_k12_probe_summary'] = v9_report['summary'] + payload['v9_k12_probe_performance'] = v9_report['performance'] + payload['v9_k12_probe_report'] = v9_report + payload['target_rows_vs_v9_probe'] = _per_shape_delta(candidate_report, v9_report) + return payload + +def write_benchmark_artifact(artifact_dir: str | os.PathLike[str], *, use_cupti: bool=True, shape_labels=TARGET_SHAPES, split_count: int=Q48_K12_SPLIT_COUNT, run_v9_probe: bool=True, run_dispatch_baseline: bool=True) -> dict[str, str]: + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + suffix = 'cupti' if use_cupti else 'cuda_event' + payload = benchmark_knn_build_rag_microbucket_k12_2f22_q48exact_v1(use_cupti=use_cupti, shape_labels=shape_labels, split_count=split_count, run_v9_probe=run_v9_probe, run_dispatch_baseline=run_dispatch_baseline) + path = out_dir / ''.join(['2f22_q48exact_', format(len(tuple(shape_labels)), ''), 'row_s', format(split_count, ''), '_', format(suffix, ''), '.json']) + path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + return {'candidate_payload': str(path)} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32_0cb5_q31tail_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32_0cb5_q31tail_v1.py new file mode 100644 index 00000000..5322b212 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32_0cb5_q31tail_v1.py @@ -0,0 +1,164 @@ +"""Expanded Q31/Q32-tail RAG K32 bucket wrapper. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +targets the expanded guard-boundary/tail rows exposed by generalize-auto-tuning +round 175: BF16 non-build ``B=1,D=128,K=32`` with ``Q=31,M=100000`` or +``Q=32,M in {99999,100001}``. It preserves the existing tcgen05/TMA rowld2 +producer and rows4 split-list merge from the f653 uneven-split seed, so guard +miss repair stays on a Weave-only primitive-backed path. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from pathlib import Path +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_v11_common_d_seed_portfolio_a4ec_v1 as dispatch_v11 +from . import knn_build_rag_microbucket_k32_f590_q32exact_v1 as q32exact +from . import knn_build_rag_microbucket_k32_q32rowld2uneven_f653_v1 as uneven +MODULE = 'loom.examples.weave.knn_build_rag_microbucket_k32_0cb5_q31tail_v1' +EXPANDED_Q31_SHAPE = 'expanded_guard_boundary_q31_m100000_d128_k32' +EXPANDED_Q32_TAIL_LOW_SHAPE = 'expanded_tail_q32_m99999_d128_k32' +EXPANDED_Q32_TAIL_HIGH_SHAPE = 'expanded_tail_q32_m100001_d128_k32' +TARGET_SHAPES = (EXPANDED_Q31_SHAPE, EXPANDED_Q32_TAIL_LOW_SHAPE, EXPANDED_Q32_TAIL_HIGH_SHAPE) +EXPANDED_SHAPES = ({'label': EXPANDED_Q31_SHAPE, 'params': {'B': 1, 'Q': 31, 'M': 100000, 'D': 128, 'K': 32, 'dtype': 'bfloat16', 'seed': 626331, 'build': False, 'check_correctness': True, 'correctness_query_sample': 31, 'recall_min': 0.999, 'benchmark': True, 'time_flashlib': True}}, {'label': EXPANDED_Q32_TAIL_LOW_SHAPE, 'params': {'B': 1, 'Q': 32, 'M': 99999, 'D': 128, 'K': 32, 'dtype': 'bfloat16', 'seed': 626999, 'build': False, 'check_correctness': True, 'correctness_query_sample': 32, 'recall_min': 0.999, 'benchmark': True, 'time_flashlib': True}}, {'label': EXPANDED_Q32_TAIL_HIGH_SHAPE, 'params': {'B': 1, 'Q': 32, 'M': 100001, 'D': 128, 'K': 32, 'dtype': 'bfloat16', 'seed': 627001, 'build': False, 'check_correctness': True, 'correctness_query_sample': 32, 'recall_min': 0.999, 'benchmark': True, 'time_flashlib': True}}) +EXPANDED_SHAPES_BY_LABEL = _decode_capture(_json_loads('{"__dict_items__": [["expanded_guard_boundary_q31_m100000_d128_k32", {"__dict_items__": [["label", "expanded_guard_boundary_q31_m100000_d128_k32"], ["params", {"__dict_items__": [["B", 1], ["Q", 31], ["M", 100000], ["D", 128], ["K", 32], ["dtype", "bfloat16"], ["seed", 626331], ["build", false], ["check_correctness", true], ["correctness_query_sample", 31], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true]]}]]}], ["expanded_tail_q32_m99999_d128_k32", {"__dict_items__": [["label", "expanded_tail_q32_m99999_d128_k32"], ["params", {"__dict_items__": [["B", 1], ["Q", 32], ["M", 99999], ["D", 128], ["K", 32], ["dtype", "bfloat16"], ["seed", 626999], ["build", false], ["check_correctness", true], ["correctness_query_sample", 32], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true]]}]]}], ["expanded_tail_q32_m100001_d128_k32", {"__dict_items__": [["label", "expanded_tail_q32_m100001_d128_k32"], ["params", {"__dict_items__": [["B", 1], ["Q", 32], ["M", 100001], ["D", 128], ["K", 32], ["dtype", "bfloat16"], ["seed", 627001], ["build", false], ["check_correctness", true], ["correctness_query_sample", 32], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true]]}]]}]]}')) +K32_Q31TAIL_SPLIT_COUNT = _decode_capture(_json_loads('141')) +K32_Q32TAIL_EXACT_SPLIT_COUNT = _decode_capture(_json_loads('153')) +ROUTE_Q31TAIL_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_Q32EXACT_ENTRYPOINT = ''.join([format(q32exact.MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_DISPATCH_V11_ENTRYPOINT = ''.join([format(dispatch_v11.MODULE, ''), ':launch_from_contract_inputs']) +SEED_K32_0CB5_Q31TAIL_ID = 'rag_microbucket_k32_0cb5_q31tail_v1' + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RAG_MICROBUCKET_K32_0CB5_Q31TAIL_V1_VERIFY_KERNEL') + split_count = int(os.environ.get('LOOM_KNN_RAG_MICROBUCKET_K32_0CB5_Q31TAIL_V1_VERIFY_K32_SPLIT', K32_Q31TAIL_SPLIT_COUNT)) + if verify_kernel == 'q31tail_stage1': + return uneven._stage1_q32_rowld2uneven_ir() + if verify_kernel == 'q32tail_exact_stage1': + return q32exact._stage1_q32_exact_ir() + if verify_kernel == 'q32tail_exact_merge': + return q32exact.rows4._warp_merge_ir(K32_Q32TAIL_EXACT_SPLIT_COUNT) + return uneven._warp_merge_ir(split_count) +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_k32warpmerge_0077_v1_warp_row_merge_k32q32uneven_s141r4_f653_v1", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 32], ["SPLIT_COUNT", 141], ["SPLITS_PER_LANE", 5], ["ROWS_PER_CTA", 4]], "cta_group": 1, "threads": 128}')) + +def _eligible_q31tail(inputs: dict[str, Any]) -> bool: + if not uneven.base._is_bf16_d128_nonbuild(inputs): + return False + if int(inputs.get('K', -1)) != uneven.K32_TOP_K_MAX: + return False + n_query = int(inputs.get('Q', -1)) + n_database = int(inputs.get('M', -1)) + return n_query == 31 and n_database == 100000 + +def _eligible_q32tail_exact(inputs: dict[str, Any]) -> bool: + return uneven.base._is_bf16_d128_nonbuild(inputs) and int(inputs.get('Q', -1)) == 32 and (int(inputs.get('M', -1)) in {99999, 100001}) and (int(inputs.get('K', -1)) == uneven.K32_TOP_K_MAX) + +def _q31tail_route_name(inputs: dict[str, Any], *, split_count: int) -> str: + n_query = int(inputs.get('Q', -1)) + n_database = int(inputs.get('M', -1)) + return ''.join(['rag_microbucket_k32_0cb5_q31tail_v1_q', format(n_query, ''), '_m', format(n_database, ''), '_k32_row16x256b2warp_uneven_s', format(split_count, ''), '_r', format(uneven.K32_ROWS4_ROWS_PER_CTA, ''), '_warpmerge']) + +def route_for_contract_inputs(inputs: dict[str, Any], *, k32_q31tail_split_count: int=K32_Q31TAIL_SPLIT_COUNT) -> str: + if _eligible_q31tail(inputs): + return _q31tail_route_name(inputs, split_count=k32_q31tail_split_count) + if _eligible_q32tail_exact(inputs): + return q32exact._q32_route_name(inputs, split_count=K32_Q32TAIL_EXACT_SPLIT_COUNT) + return q32exact.route_for_contract_inputs(inputs) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, k32_q31tail_split_count: int=K32_Q31TAIL_SPLIT_COUNT) -> None: + if _eligible_q31tail(inputs): + uneven._launch_q32_rowld2uneven_rows4_merge(inputs, split_count=k32_q31tail_split_count) + return + if _eligible_q32tail_exact(inputs): + q32exact._launch_q32_exact_rows4(inputs, split_count=K32_Q32TAIL_EXACT_SPLIT_COUNT) + return + q32exact.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_with_split(split_count: int) -> Callable[[dict[str, Any]], None]: + + def _candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, k32_q31tail_split_count=split_count) + return _candidate + +def candidate_dispatch_v11(inputs: dict[str, Any]) -> None: + dispatch_v11.launch_from_contract_inputs(inputs) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + labels = tuple(shape_labels) + selected = [] + remaining = [] + for label in labels: + if label in EXPANDED_SHAPES_BY_LABEL: + selected.append(EXPANDED_SHAPES_BY_LABEL[label]) + else: + remaining.append(label) + if remaining: + selected.extend(q32exact._select_contract_shapes(tuple(remaining))) + return selected + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def route_trace_for_contract_shapes(shape_labels=TARGET_SHAPES, *, k32_q31tail_split_count: int=K32_Q31TAIL_SPLIT_COUNT) -> list[dict[str, Any]]: + rows = [] + for shape in _select_contract_shapes(shape_labels): + params = dict(shape.get('params', {})) + params['label'] = shape['label'] + route = route_for_contract_inputs(params, k32_q31tail_split_count=k32_q31tail_split_count) + current_route = dispatch_v11.route_for_contract_inputs(params) + selected_q31 = _eligible_q31tail(params) + selected_q32tail = _eligible_q32tail_exact(params) + selected = selected_q31 or selected_q32tail + selected_route_kind = 'specialized_q31_rowld2_uneven' if selected_q31 else 'specialized_q32tail_exact_q32' + selected_split = k32_q31tail_split_count if selected_q31 else K32_Q32TAIL_EXACT_SPLIT_COUNT + rows.append({'shape_key': shape['label'], 'selected_route': route, 'selected_seed': SEED_K32_0CB5_Q31TAIL_ID if selected else q32exact.SEED_K32_F590_Q32_EXACT_ID, 'selected_entrypoint': ROUTE_Q31TAIL_ENTRYPOINT if selected else ROUTE_Q32EXACT_ENTRYPOINT, 'current_dispatch_v11_route': current_route, 'current_dispatch_v11_entrypoint': ROUTE_DISPATCH_V11_ENTRYPOINT, 'route_kind': selected_route_kind if selected else 'inherited_q32exact', 'split_count': selected_split if selected else None, 'guard_condition': 'BF16 non-build B=1 D=128 K=32 with Q=31/M=100000 or Q=32/M in {99999,100001}' if selected else 'delegate to q32exact seed lineage'}) + return rows + +def _per_shape_delta(candidate_report: dict[str, Any], dispatcher_report: dict[str, Any]) -> dict[str, Any]: + rows = {} + for label, cand in candidate_report.get('per_shape', {}).items(): + base_row = dispatcher_report.get('per_shape', {}).get(label, {}) + cand_ms = cand.get('kernel_ms') + base_ms = base_row.get('kernel_ms') + rows[label] = {'candidate': cand, 'dispatch_v11_baseline': base_row, 'candidate_ms': cand_ms, 'dispatch_v11_ms': base_ms, 'speedup_vs_dispatch_v11': base_ms / cand_ms if cand_ms and base_ms else None, 'ratio_vs_flashlib': cand.get('ratio_vs_flashlib'), 'dispatch_v11_ratio_vs_flashlib': base_row.get('ratio_vs_flashlib')} + return rows + +def benchmark_knn_build_rag_microbucket_k32_0cb5_q31tail_v1(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES, k32_q31tail_split_count: int=K32_Q31TAIL_SPLIT_COUNT) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_with_split(k32_q31tail_split_count)) + dispatcher_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_dispatch_v11) + return {'contract': candidate_report['contract'], 'contract_version': candidate_report['contract_version'], 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_knn_build_rag_microbucket_k32_0cb5_q31tail_v1']), 'candidate_entrypoint': ROUTE_Q31TAIL_ENTRYPOINT, 'dispatch_v11_entrypoint': ROUTE_DISPATCH_V11_ENTRYPOINT, 'accelerated_shape_labels': list(TARGET_SHAPES), 'measured_shape_labels': list(shape_labels), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'producer_topology': {'candidate': 'f653 rowld2 uneven-split stage1 for Q31 plus f590 exact-Q32 stage1 for Q32 M-tail; both use ROW_16x256B tcgen05/TMA producers', 'guard_misses': 'delegate to the accepted f590 q32exact seed lineage', 'comparison_baseline': 'current v11 common-D seed portfolio dispatcher'}, 'merge_topology': {'candidate': ''.join(['rows4 warp-row split-list merge/', format(uneven.K32_ROWS4_ROWS_PER_CTA, ''), ' rows per CTA']), 'q31_split_count': k32_q31tail_split_count, 'q31_splits_per_lane': uneven.base._splits_per_lane(k32_q31tail_split_count), 'q32tail_exact_split_count': K32_Q32TAIL_EXACT_SPLIT_COUNT, 'q32tail_exact_splits_per_lane': q32exact.rows4.base._splits_per_lane(K32_Q32TAIL_EXACT_SPLIT_COUNT)}, 'route_trace': route_trace_for_contract_shapes(shape_labels, k32_q31tail_split_count=k32_q31tail_split_count), 'target_rows': _per_shape_delta(candidate_report, dispatcher_report), 'summary': candidate_report['summary'], 'performance': candidate_report['performance'], 'correctness': candidate_report['correctness'], 'report': candidate_report, 'dispatch_v11_summary': dispatcher_report['summary'], 'dispatch_v11_performance': dispatcher_report['performance'], 'dispatch_v11_report': dispatcher_report} + +def write_benchmark_artifact(artifact_dir: str | os.PathLike[str], *, use_cupti: bool=True, shape_labels=TARGET_SHAPES, k32_q31tail_split_count: int=K32_Q31TAIL_SPLIT_COUNT) -> dict[str, str]: + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + suffix = 'cupti' if use_cupti else 'cuda_event' + payload = benchmark_knn_build_rag_microbucket_k32_0cb5_q31tail_v1(use_cupti=use_cupti, shape_labels=shape_labels, k32_q31tail_split_count=k32_q31tail_split_count) + path = out_dir / ''.join(['0cb5_q31tail_', format(len(tuple(shape_labels)), ''), 'row_s', format(k32_q31tail_split_count, ''), '_', format(suffix, ''), '.json']) + path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + return {'candidate_payload': str(path)} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32_0cb5_q31tail_v2.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32_0cb5_q31tail_v2.py new file mode 100644 index 00000000..12fd14fa --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32_0cb5_q31tail_v2.py @@ -0,0 +1,156 @@ +"""Expanded Q31 RAG K32 floor-repair bucket wrapper. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +targets the expanded guard-boundary row ``B=1,Q=31,M=100000,D=128,K=32``. +It keeps the previous v1 wrapper for Q32 tail rows, but routes Q31 through a +Q31-exact rowld2 tcgen05/TMA producer with ``ROWS_COVERED=31`` and a split153 +rows4 merge. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from collections.abc import Callable +from functools import cache +from pathlib import Path +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_v11_common_d_seed_portfolio_a4ec_v1 as dispatch_v11 +from . import knn_build_rag_microbucket_k32_0cb5_q31tail_v1 as parent +from . import knn_build_rag_microbucket_k32_f590_q32exact_v1 as q32exact +MODULE = 'loom.examples.weave.knn_build_rag_microbucket_k32_0cb5_q31tail_v2' +EXPANDED_Q31_SHAPE = parent.EXPANDED_Q31_SHAPE +TARGET_SHAPES = (EXPANDED_Q31_SHAPE,) +EXPANDED_SHAPES = (parent.EXPANDED_SHAPES_BY_LABEL[EXPANDED_Q31_SHAPE],) +EXPANDED_SHAPES_BY_LABEL = _decode_capture(_json_loads('{"__dict_items__": [["expanded_guard_boundary_q31_m100000_d128_k32", {"__dict_items__": [["label", "expanded_guard_boundary_q31_m100000_d128_k32"], ["params", {"__dict_items__": [["B", 1], ["Q", 31], ["M", 100000], ["D", 128], ["K", 32], ["dtype", "bfloat16"], ["seed", 626331], ["build", false], ["check_correctness", true], ["correctness_query_sample", 31], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true]]}]]}]]}')) +K32_Q31_EXACT_SPLIT_COUNT = _decode_capture(_json_loads('153')) +K32_Q31_ACTIVE_ROWS = 31 +ROUTE_Q31TAIL_V2_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_Q31TAIL_V1_ENTRYPOINT = ''.join([format(parent.MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_DISPATCH_V11_ENTRYPOINT = ''.join([format(dispatch_v11.MODULE, ''), ':launch_from_contract_inputs']) +SEED_K32_0CB5_Q31TAIL_V2_ID = 'rag_microbucket_k32_0cb5_q31tail_v2' +knn_build_rag_microbucket_k32_0cb5_q31tail_v2_stage1 = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_k32_0cb5_q31tail_v2_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 66816, "constants": [["BLOCK_Q", 64], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32], ["ROWS_COVERED", 31]], "cta_group": 1, "threads": 128}')) + +def _stage1_q31_exact_ir() -> Any: + return q32exact._ir_with_constants(knn_build_rag_microbucket_k32_0cb5_q31tail_v2_stage1, suffix='q31exact_0cb5_v2', BLOCK_Q=q32exact.rowld1.Q16_ROWLD1_BLOCK_Q, BLOCK_M=q32exact.rowld1.Q16_ROWLD1_BLOCK_M, FEAT_D=q32exact.rowld1.Q16_ROWLD1_FEAT_D, TOP_K_MAX=q32exact.K32_TOP_K_MAX, ROWS_COVERED=K32_Q31_ACTIVE_ROWS) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RAG_MICROBUCKET_K32_0CB5_Q31TAIL_V2_VERIFY_KERNEL') + split_count = int(os.environ.get('LOOM_KNN_RAG_MICROBUCKET_K32_0CB5_Q31TAIL_V2_VERIFY_K32_SPLIT', K32_Q31_EXACT_SPLIT_COUNT)) + if verify_kernel in {'q31_exact_merge', 'q31_balanced_merge'}: + return q32exact.rows4._warp_merge_ir(split_count) + return _stage1_q31_exact_ir() +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_k32_0cb5_q31tail_v2_stage1_q31exact_0cb5_v2", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 66816, "constants": [["BLOCK_Q", 64], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32], ["ROWS_COVERED", 31]], "cta_group": 1, "threads": 128}')) + +def _compiled_stage1_q31_exact(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0209"}')) + +def _eligible_q31tail_v2(inputs: dict[str, Any]) -> bool: + return parent.uneven.base._is_bf16_d128_nonbuild(inputs) and int(inputs.get('Q', -1)) == 31 and (int(inputs.get('M', -1)) == 100000) and (int(inputs.get('K', -1)) == parent.uneven.K32_TOP_K_MAX) + +def _q31tail_v2_route_name(inputs: dict[str, Any], *, split_count: int) -> str: + return ''.join(['rag_microbucket_k32_0cb5_q31tail_v2_q', format(int(inputs.get('Q', -1)), ''), '_m', format(int(inputs.get('M', -1)), ''), '_k32_row16x256b2cw_q31exact_s', format(split_count, ''), '_r', format(q32exact.rows4.K32_ROWS4_ROWS_PER_CTA, ''), '_warpmerge']) + +def _launch_q31_exact_rows4(inputs: dict[str, Any], *, split_count: int) -> None: + q32exact.rows4._launch_stage1_then_rows4_merge(inputs, split_count=split_count, stage1_kernel_fn=_compiled_stage1_q31_exact, stage1_ir=_stage1_q31_exact_ir(), stage1_threads=q32exact.rowld1.Q32_ROWLD2_STAGE1_THREADS, block_q=q32exact.rowld1.Q16_ROWLD1_BLOCK_Q, block_m=q32exact.rowld1.Q16_ROWLD1_BLOCK_M) + +def route_for_contract_inputs(inputs: dict[str, Any], *, k32_q31_split_count: int=K32_Q31_EXACT_SPLIT_COUNT) -> str: + if _eligible_q31tail_v2(inputs): + return _q31tail_v2_route_name(inputs, split_count=k32_q31_split_count) + return parent.route_for_contract_inputs(inputs) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, k32_q31_split_count: int=K32_Q31_EXACT_SPLIT_COUNT) -> None: + if _eligible_q31tail_v2(inputs): + _launch_q31_exact_rows4(inputs, split_count=k32_q31_split_count) + return + parent.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_with_split(split_count: int) -> Callable[[dict[str, Any]], None]: + + def _candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, k32_q31_split_count=split_count) + return _candidate + +def candidate_parent_v1(inputs: dict[str, Any]) -> None: + parent.launch_from_contract_inputs(inputs) + +def candidate_dispatch_v11(inputs: dict[str, Any]) -> None: + dispatch_v11.launch_from_contract_inputs(inputs) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + labels = tuple(shape_labels) + selected = [] + remaining = [] + for label in labels: + if label in EXPANDED_SHAPES_BY_LABEL: + selected.append(EXPANDED_SHAPES_BY_LABEL[label]) + else: + remaining.append(label) + if remaining: + selected.extend(parent._select_contract_shapes(tuple(remaining))) + return selected + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def route_trace_for_contract_shapes(shape_labels=TARGET_SHAPES, *, k32_q31_split_count: int=K32_Q31_EXACT_SPLIT_COUNT) -> list[dict[str, Any]]: + rows = [] + for shape in _select_contract_shapes(shape_labels): + params = dict(shape.get('params', {})) + params['label'] = shape['label'] + route = route_for_contract_inputs(params, k32_q31_split_count=k32_q31_split_count) + parent_route = parent.route_for_contract_inputs(params) + current_route = dispatch_v11.route_for_contract_inputs(params) + selected = _eligible_q31tail_v2(params) + rows.append({'shape_key': shape['label'], 'selected_route': route, 'selected_seed': SEED_K32_0CB5_Q31TAIL_V2_ID if selected else parent.SEED_K32_0CB5_Q31TAIL_ID, 'selected_entrypoint': ROUTE_Q31TAIL_V2_ENTRYPOINT if selected else ROUTE_Q31TAIL_V1_ENTRYPOINT, 'parent_v1_route': parent_route, 'current_dispatch_v11_route': current_route, 'current_dispatch_v11_entrypoint': ROUTE_DISPATCH_V11_ENTRYPOINT, 'route_kind': 'specialized_q31_exact_rowld2' if selected else 'inherited_q31tail_v1', 'split_count': k32_q31_split_count if selected else None, 'guard_condition': 'BF16 non-build B=1 Q=31 M=100000 D=128 K=32' if selected else 'delegate to q31tail v1'}) + return rows + +def _per_shape_delta(candidate_report: dict[str, Any], parent_report: dict[str, Any], dispatcher_report: dict[str, Any]) -> dict[str, Any]: + rows = {} + for label, cand in candidate_report.get('per_shape', {}).items(): + parent_row = parent_report.get('per_shape', {}).get(label, {}) + dispatch_row = dispatcher_report.get('per_shape', {}).get(label, {}) + cand_ms = cand.get('kernel_ms') + parent_ms = parent_row.get('kernel_ms') + dispatch_ms = dispatch_row.get('kernel_ms') + rows[label] = {'candidate': cand, 'parent_v1': parent_row, 'dispatch_v11_baseline': dispatch_row, 'candidate_ms': cand_ms, 'parent_v1_ms': parent_ms, 'dispatch_v11_ms': dispatch_ms, 'speedup_vs_parent_v1': parent_ms / cand_ms if cand_ms and parent_ms else None, 'speedup_vs_dispatch_v11': dispatch_ms / cand_ms if cand_ms and dispatch_ms else None, 'ratio_vs_flashlib': cand.get('ratio_vs_flashlib'), 'parent_v1_ratio_vs_flashlib': parent_row.get('ratio_vs_flashlib'), 'dispatch_v11_ratio_vs_flashlib': dispatch_row.get('ratio_vs_flashlib')} + return rows + +def benchmark_knn_build_rag_microbucket_k32_0cb5_q31tail_v2(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES, k32_q31_split_count: int=K32_Q31_EXACT_SPLIT_COUNT) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_with_split(k32_q31_split_count)) + parent_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_parent_v1) + dispatcher_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_dispatch_v11) + return {'contract': candidate_report['contract'], 'contract_version': candidate_report['contract_version'], 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_knn_build_rag_microbucket_k32_0cb5_q31tail_v2']), 'candidate_entrypoint': ROUTE_Q31TAIL_V2_ENTRYPOINT, 'parent_v1_entrypoint': ROUTE_Q31TAIL_V1_ENTRYPOINT, 'dispatch_v11_entrypoint': ROUTE_DISPATCH_V11_ENTRYPOINT, 'accelerated_shape_labels': list(TARGET_SHAPES), 'measured_shape_labels': list(shape_labels), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'producer_topology': {'candidate': 'Q31 exact ROW_16x256B rowld2 tcgen05/TMA stage1 with ROWS_COVERED=31 and split-local rows4 merge', 'guard_misses': 'delegate to q31tail v1, including the accepted Q32-tail exact routes', 'comparison_baselines': 'q31tail v1 and current v11 common-D seed portfolio dispatcher'}, 'merge_topology': {'candidate': ''.join(['rows4 warp-row split-list merge/', format(q32exact.rows4.K32_ROWS4_ROWS_PER_CTA, ''), ' rows per CTA']), 'q31_split_count': k32_q31_split_count, 'q31_splits_per_lane': q32exact.rows4.base._splits_per_lane(k32_q31_split_count)}, 'route_trace': route_trace_for_contract_shapes(shape_labels, k32_q31_split_count=k32_q31_split_count), 'target_rows': _per_shape_delta(candidate_report, parent_report, dispatcher_report), 'summary': candidate_report['summary'], 'performance': candidate_report['performance'], 'correctness': candidate_report['correctness'], 'report': candidate_report, 'parent_v1_summary': parent_report['summary'], 'parent_v1_performance': parent_report['performance'], 'parent_v1_report': parent_report, 'dispatch_v11_summary': dispatcher_report['summary'], 'dispatch_v11_performance': dispatcher_report['performance'], 'dispatch_v11_report': dispatcher_report} + +def write_benchmark_artifact(artifact_dir: str | os.PathLike[str], *, use_cupti: bool=True, shape_labels=TARGET_SHAPES, k32_q31_split_count: int=K32_Q31_EXACT_SPLIT_COUNT) -> dict[str, str]: + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + suffix = 'cupti' if use_cupti else 'cuda_event' + payload = benchmark_knn_build_rag_microbucket_k32_0cb5_q31tail_v2(use_cupti=use_cupti, shape_labels=shape_labels, k32_q31_split_count=k32_q31_split_count) + path = out_dir / ''.join(['0cb5_q31tail_v2_', format(len(tuple(shape_labels)), ''), 'row_s', format(k32_q31_split_count, ''), '_', format(suffix, ''), '.json']) + path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + return {'candidate_payload': str(path)} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32_2e8e_q16split148_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32_2e8e_q16split148_v1.py new file mode 100644 index 00000000..909b3a0b --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32_2e8e_q16split148_v1.py @@ -0,0 +1,120 @@ +"""RAG microbatch K32 bucket with Q16-only split-148 routing. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +keeps the b3e0 mixed tcgen05/TMA producer routes on the eval path, but applies +the B200 split-148 work feed only to the two Q16 K32 rows. Q8 and Q32 stay on +the b3e0 split-144 routes to avoid the regressions seen when all four rows use +split-148. The production path remains Weave-only. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_rag_microbucket_k32_b3e0_mix_v1 as parent +MODULE = 'loom.examples.weave.knn_build_rag_microbucket_k32_2e8e_q16split148_v1' +Q8_K32_SHAPE = parent.Q8_K32_SHAPE +Q16_K32_SHAPE = parent.Q16_K32_SHAPE +Q32_K32_SHAPE = parent.Q32_K32_SHAPE +Q16_K32_IRREGULAR_SHAPE = parent.Q16_K32_IRREGULAR_SHAPE +K32_BUCKET_SHAPES = parent.K32_BUCKET_SHAPES +TARGET_SHAPES = parent.TARGET_SHAPES +Q16_SPLIT_TARGET_SHAPES = (Q16_K32_SHAPE, Q16_K32_IRREGULAR_SHAPE) +K32_DEFAULT_SPLIT_COUNT = parent.K32_SPLIT_COUNT +K32_Q16_SPLIT_COUNT = 148 +K32_GROUP_COUNT = parent.K32_GROUP_COUNT +ROUTE_PARENT_B3E0 = ''.join([format(parent.MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_Q16_SPLIT148_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +SEED_K32_2E8E_Q16_SPLIT148_ID = 'rag_microbucket_k32_2e8e_q16split148_v1_b3e0_q16_s148' + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RAG_MICROBUCKET_K32_2E8E_Q16SPLIT148_V1_VERIFY_KERNEL') + split_count = int(os.environ.get('LOOM_KNN_RAG_MICROBUCKET_K32_2E8E_Q16SPLIT148_V1_VERIFY_K32_SPLIT', K32_Q16_SPLIT_COUNT)) + if verify_kernel == 'q8_half_stage1': + return parent.q8half._stage1_q8_half_ir() + if verify_kernel == 'rowld1_stage1': + return parent.q8half.parent._stage1_rowld1_ir() + if verify_kernel == 'rowld_stage1': + return parent.rows4.base.rowld_seed.stage1_q32_k32_m64_rowld_ir + if verify_kernel == 'warp_merge': + return parent.rows4.base._warp_merge_ir(split_count) + return parent.rows4._warp_merge_ir(split_count) +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_k32warpmerge_0077_v1_warp_row_merge_k32s148r4_0077_v1", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 32], ["SPLIT_COUNT", 148], ["SPLITS_PER_LANE", 5], ["ROWS_PER_CTA", 4]], "cta_group": 1, "threads": 128}')) + +def _eligible_q16_split148(inputs: dict[str, Any]) -> bool: + return parent._is_bf16_d128_nonbuild(inputs) and int(inputs.get('Q', -1)) == 16 and (int(inputs.get('M', -1)) in {100000, 131071}) and (int(inputs.get('K', -1)) == 32) + +def route_for_contract_inputs(inputs: dict[str, Any], *, k32_q16_split_count: int=K32_Q16_SPLIT_COUNT, k32_default_split_count: int=K32_DEFAULT_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> str: + if _eligible_q16_split148(inputs): + return parent.route_for_contract_inputs(inputs, k32_split_count=k32_q16_split_count, k32_group_count=k32_group_count) + return parent.route_for_contract_inputs(inputs, k32_split_count=k32_default_split_count, k32_group_count=k32_group_count) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, k32_q16_split_count: int=K32_Q16_SPLIT_COUNT, k32_default_split_count: int=K32_DEFAULT_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> None: + if _eligible_q16_split148(inputs): + parent.launch_from_contract_inputs(inputs, k32_split_count=k32_q16_split_count, k32_group_count=k32_group_count) + return + parent.launch_from_contract_inputs(inputs, k32_split_count=k32_default_split_count, k32_group_count=k32_group_count) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_with_k32_topology(q16_split_count: int, default_split_count: int, group_count: int) -> Callable[[dict[str, Any]], None]: + + def _candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, k32_q16_split_count=q16_split_count, k32_default_split_count=default_split_count, k32_group_count=group_count) + return _candidate + +def candidate_parent_b3e0(inputs: dict[str, Any]) -> None: + parent.launch_from_contract_inputs(inputs) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return parent._select_contract_shapes(shape_labels) + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=K32_BUCKET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def compile_and_launch_knn_build(*, shape_labels=K32_BUCKET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def route_trace_for_contract_shapes(shape_labels=K32_BUCKET_SHAPES, *, k32_q16_split_count: int=K32_Q16_SPLIT_COUNT, k32_default_split_count: int=K32_DEFAULT_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> list[dict[str, Any]]: + rows = [] + for shape in _select_contract_shapes(shape_labels): + inputs = parent.rows4.base.rowld_seed.base_dispatcher._trace_inputs_from_shape(shape) + route = route_for_contract_inputs(inputs, k32_q16_split_count=k32_q16_split_count, k32_default_split_count=k32_default_split_count, k32_group_count=k32_group_count) + parent_route = parent.route_for_contract_inputs(inputs) + selected = _eligible_q16_split148(inputs) + rows.append({'shape_key': shape['label'], 'selected_route': route, 'selected_seed': SEED_K32_2E8E_Q16_SPLIT148_ID if selected else parent.SEED_K32_B3E0_MIX_ID, 'selected_entrypoint': ROUTE_Q16_SPLIT148_ENTRYPOINT if selected else ROUTE_PARENT_B3E0, 'parent_b3e0_default_route': parent_route, 'route_kind': 'specialized_q16_split148' if selected else 'inherited_b3e0_default_split144', 'guard_condition': 'BF16 non-build B=1 Q=16 M in {100000,131071} D=128 K=32 with split_count=148' if selected else 'delegate to b3e0 parent with split_count=144'}) + return rows + +def _per_shape_delta(candidate_report: dict[str, Any], parent_report: dict[str, Any]) -> dict[str, Any]: + rows = {} + for label, cand in candidate_report.get('per_shape', {}).items(): + parent_row = parent_report.get('per_shape', {}).get(label, {}) + cand_ms = cand.get('kernel_ms') + parent_ms = parent_row.get('kernel_ms') + rows[label] = {'candidate': cand, 'parent_b3e0': parent_row, 'candidate_ms': cand_ms, 'parent_b3e0_ms': parent_ms, 'speedup_vs_parent_b3e0': parent_ms / cand_ms if cand_ms and parent_ms else None, 'ratio_vs_flashlib': cand.get('ratio_vs_flashlib')} + return rows + +def benchmark_knn_build_rag_microbucket_k32_2e8e_q16split148_v1(*, use_cupti: bool=True, shape_labels=K32_BUCKET_SHAPES, k32_q16_split_count: int=K32_Q16_SPLIT_COUNT, k32_default_split_count: int=K32_DEFAULT_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_with_k32_topology(k32_q16_split_count, k32_default_split_count, k32_group_count)) + parent_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_parent_b3e0) + return {'contract': candidate_report['contract'], 'contract_version': candidate_report['contract_version'], 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_knn_build_rag_microbucket_k32_2e8e_q16split148_v1']), 'candidate_entrypoint': ROUTE_Q16_SPLIT148_ENTRYPOINT, 'parent_entrypoint': ROUTE_PARENT_B3E0, 'accelerated_shape_labels': list(Q16_SPLIT_TARGET_SHAPES), 'measured_shape_labels': list(shape_labels), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'producer_topology': {'Q8_exact': 'b3e0 half-row ROW_16x256B one-compute-warp stage1 at split144', 'Q16_exact': 'b3e0 rowld1 ROW_16x256B one-compute-warp stage1 at split148', 'Q16_irregular': 'b3e0 rowld1 ROW_16x256B one-compute-warp stage1 at split148', 'Q32_exact': 'b3e0 ROW_16x256B stage1 plus rows4 warp-row merge at split144', 'guard_misses': 'delegate to b3e0 parent routes at split144'}, 'merge_topology': {'Q16_exact_Q16_irregular': '0077 warp-row split-list merge/1 row per CTA at split148', 'Q8_exact': '0077 warp-row split-list merge/1 row per CTA at split144', 'Q32_exact': ''.join(['0077 warp-row split-list merge/', format(parent.rows4.K32_ROWS4_ROWS_PER_CTA, ''), ' rows per CTA at split144']), 'q16_split_count': k32_q16_split_count, 'default_split_count': k32_default_split_count, 'q16_splits_per_lane': parent.rows4.base._splits_per_lane(k32_q16_split_count), 'default_splits_per_lane': parent.rows4.base._splits_per_lane(k32_default_split_count)}, 'route_trace': route_trace_for_contract_shapes(shape_labels, k32_q16_split_count=k32_q16_split_count, k32_default_split_count=k32_default_split_count, k32_group_count=k32_group_count), 'target_rows': _per_shape_delta(candidate_report, parent_report), 'summary': candidate_report['summary'], 'performance': candidate_report['performance'], 'correctness': candidate_report['correctness'], 'report': candidate_report, 'parent_summary': parent_report['summary'], 'parent_performance': parent_report['performance'], 'parent_report': parent_report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32_314c_q32tail143_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32_314c_q32tail143_v1.py new file mode 100644 index 00000000..680cf1fe --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32_314c_q32tail143_v1.py @@ -0,0 +1,137 @@ +"""Exact Q32/M100001 RAG K32 split143 tail seed. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +targets only the expanded BF16 non-build ``B=1,Q=32,M=100001,D=128,K=32`` row +from generalize-auto-tuning round 180. It keeps the accepted f590 exact-Q32 +ROW_16x256B tcgen05/TMA producer and rows4 merge, but lowers the high-tail row +with split143 to reduce padded M-tile work. Guard misses delegate to the +accepted 0cb5 q31tail/q32tail seed. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +from pathlib import Path +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_v11_common_d_seed_portfolio_a4ec_v1 as dispatch_v11 +from . import knn_build_rag_microbucket_k32_0cb5_q31tail_v1 as parent +from . import knn_build_rag_microbucket_k32_f590_q32exact_v1 as q32exact +MODULE = 'loom.examples.weave.knn_build_rag_microbucket_k32_314c_q32tail143_v1' +EXPANDED_Q32_TAIL_HIGH_SHAPE = parent.EXPANDED_Q32_TAIL_HIGH_SHAPE +TARGET_SHAPES = (EXPANDED_Q32_TAIL_HIGH_SHAPE,) +EXPANDED_SHAPES = (parent.EXPANDED_SHAPES_BY_LABEL[EXPANDED_Q32_TAIL_HIGH_SHAPE],) +EXPANDED_SHAPES_BY_LABEL = _decode_capture(_json_loads('{"__dict_items__": [["expanded_tail_q32_m100001_d128_k32", {"__dict_items__": [["label", "expanded_tail_q32_m100001_d128_k32"], ["params", {"__dict_items__": [["B", 1], ["Q", 32], ["M", 100001], ["D", 128], ["K", 32], ["dtype", "bfloat16"], ["seed", 627001], ["build", false], ["check_correctness", true], ["correctness_query_sample", 32], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true]]}]]}]]}')) +K32_Q32TAIL143_SPLIT_COUNT = 143 +ROUTE_Q32TAIL143_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_PARENT_0CB5_ENTRYPOINT = ''.join([format(parent.MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_DISPATCH_V11_ENTRYPOINT = ''.join([format(dispatch_v11.MODULE, ''), ':launch_from_contract_inputs']) +SEED_K32_314C_Q32TAIL143_ID = 'rag_microbucket_k32_314c_q32tail143_v1' + +def _verify_export_ir() -> Any: + import os + verify_kernel = os.environ.get('LOOM_KNN_RAG_MICROBUCKET_K32_314C_Q32TAIL143_V1_VERIFY_KERNEL') + if verify_kernel == 'q32tail143_merge': + return q32exact.rows4._warp_merge_ir(K32_Q32TAIL143_SPLIT_COUNT) + return q32exact._stage1_q32_exact_ir() +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_k32_f590_q32exact_v1_stage1_q32exact_f590_v1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 66816, "constants": [["BLOCK_Q", 64], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32], ["ROWS_COVERED", 32]], "cta_group": 1, "threads": 128}')) + +def _eligible_q32tail143(inputs: dict[str, Any]) -> bool: + return parent.uneven.base._is_bf16_d128_nonbuild(inputs) and int(inputs.get('Q', -1)) == 32 and (int(inputs.get('M', -1)) == 100001) and (int(inputs.get('K', -1)) == q32exact.K32_TOP_K_MAX) + +def _q32tail143_route_name(inputs: dict[str, Any]) -> str: + return ''.join(['rag_microbucket_k32_314c_q32tail143_v1_q', format(int(inputs.get('Q', -1)), ''), '_m', format(int(inputs.get('M', -1)), ''), '_k32_row16x256b2cw_exact_s', format(K32_Q32TAIL143_SPLIT_COUNT, ''), '_r', format(q32exact.rows4.K32_ROWS4_ROWS_PER_CTA, ''), '_warpmerge']) + +def route_for_contract_inputs(inputs: dict[str, Any]) -> str: + if _eligible_q32tail143(inputs): + return _q32tail143_route_name(inputs) + return parent.route_for_contract_inputs(inputs) + +def launch_from_contract_inputs(inputs: dict[str, Any]) -> None: + if _eligible_q32tail143(inputs): + q32exact._launch_q32_exact_rows4(inputs, split_count=K32_Q32TAIL143_SPLIT_COUNT) + return + parent.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_parent_0cb5(inputs: dict[str, Any]) -> None: + parent.launch_from_contract_inputs(inputs) + +def candidate_dispatch_v11(inputs: dict[str, Any]) -> None: + dispatch_v11.launch_from_contract_inputs(inputs) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + labels = tuple(shape_labels) + selected = [] + remaining = [] + for label in labels: + if label in EXPANDED_SHAPES_BY_LABEL: + selected.append(EXPANDED_SHAPES_BY_LABEL[label]) + else: + remaining.append(label) + if remaining: + selected.extend(parent._select_contract_shapes(tuple(remaining))) + return selected + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def route_trace_for_contract_shapes(shape_labels=TARGET_SHAPES) -> list[dict[str, Any]]: + rows = [] + for shape in _select_contract_shapes(shape_labels): + params = dict(shape.get('params', {})) + params['label'] = shape['label'] + route = route_for_contract_inputs(params) + parent_route = parent.route_for_contract_inputs(params) + current_route = dispatch_v11.route_for_contract_inputs(params) + selected = _eligible_q32tail143(params) + rows.append({'shape_key': shape['label'], 'selected_route': route, 'selected_seed': SEED_K32_314C_Q32TAIL143_ID if selected else parent.SEED_K32_0CB5_Q31TAIL_ID, 'selected_entrypoint': ROUTE_Q32TAIL143_ENTRYPOINT if selected else ROUTE_PARENT_0CB5_ENTRYPOINT, 'parent_0cb5_route': parent_route, 'parent_0cb5_entrypoint': ROUTE_PARENT_0CB5_ENTRYPOINT, 'current_dispatch_v11_route': current_route, 'current_dispatch_v11_entrypoint': ROUTE_DISPATCH_V11_ENTRYPOINT, 'route_kind': 'specialized_q32_m100001_split143' if selected else 'inherited_0cb5_q31tail_v1', 'split_count': K32_Q32TAIL143_SPLIT_COUNT if selected else None, 'guard_condition': 'BF16 non-build B=1 Q=32 M=100001 D=128 K=32' if selected else 'delegate to 0cb5 q31tail v1'}) + return rows + +def _per_shape_delta(candidate_report: dict[str, Any], parent_report: dict[str, Any], dispatcher_report: dict[str, Any]) -> dict[str, Any]: + rows = {} + for label, cand in candidate_report.get('per_shape', {}).items(): + parent_row = parent_report.get('per_shape', {}).get(label, {}) + dispatch_row = dispatcher_report.get('per_shape', {}).get(label, {}) + cand_ms = cand.get('kernel_ms') + parent_ms = parent_row.get('kernel_ms') + dispatch_ms = dispatch_row.get('kernel_ms') + rows[label] = {'candidate': cand, 'parent_0cb5': parent_row, 'dispatch_v11_baseline': dispatch_row, 'candidate_ms': cand_ms, 'parent_0cb5_ms': parent_ms, 'dispatch_v11_ms': dispatch_ms, 'speedup_vs_parent_0cb5': parent_ms / cand_ms if cand_ms and parent_ms else None, 'speedup_vs_dispatch_v11': dispatch_ms / cand_ms if cand_ms and dispatch_ms else None, 'ratio_vs_flashlib': cand.get('ratio_vs_flashlib'), 'parent_0cb5_ratio_vs_flashlib': parent_row.get('ratio_vs_flashlib'), 'dispatch_v11_ratio_vs_flashlib': dispatch_row.get('ratio_vs_flashlib')} + return rows + +def benchmark_knn_build_rag_microbucket_k32_314c_q32tail143_v1(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate) + parent_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_parent_0cb5) + dispatcher_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_dispatch_v11) + return {'contract': candidate_report['contract'], 'contract_version': candidate_report['contract_version'], 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_knn_build_rag_microbucket_k32_314c_q32tail143_v1']), 'candidate_entrypoint': ROUTE_Q32TAIL143_ENTRYPOINT, 'parent_0cb5_entrypoint': ROUTE_PARENT_0CB5_ENTRYPOINT, 'dispatch_v11_entrypoint': ROUTE_DISPATCH_V11_ENTRYPOINT, 'accelerated_shape_labels': list(TARGET_SHAPES), 'measured_shape_labels': list(shape_labels), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'producer_topology': {'candidate': 'f590 exact-Q32 ROW_16x256B two-compute-warp stage1 at split143 for M100001', 'baseline': 'accepted 0cb5/q32exact route with split153', 'guard_misses': 'delegate to 0cb5 q31tail v1'}, 'merge_topology': {'candidate': ''.join(['rows4 warp-row split-list merge/', format(q32exact.rows4.K32_ROWS4_ROWS_PER_CTA, ''), ' rows per CTA']), 'split_count': K32_Q32TAIL143_SPLIT_COUNT, 'splits_per_lane': q32exact.rows4.base._splits_per_lane(K32_Q32TAIL143_SPLIT_COUNT)}, 'route_trace': route_trace_for_contract_shapes(shape_labels), 'target_rows': _per_shape_delta(candidate_report, parent_report, dispatcher_report), 'summary': candidate_report['summary'], 'performance': candidate_report['performance'], 'correctness': candidate_report['correctness'], 'report': candidate_report, 'parent_0cb5_summary': parent_report['summary'], 'parent_0cb5_performance': parent_report['performance'], 'parent_0cb5_report': parent_report, 'dispatch_v11_summary': dispatcher_report['summary'], 'dispatch_v11_performance': dispatcher_report['performance'], 'dispatch_v11_report': dispatcher_report} + +def write_benchmark_artifact(artifact_dir: str | Path, *, use_cupti: bool=True, shape_labels=TARGET_SHAPES) -> dict[str, str]: + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + suffix = 'cupti' if use_cupti else 'cuda_event' + payload = benchmark_knn_build_rag_microbucket_k32_314c_q32tail143_v1(use_cupti=use_cupti, shape_labels=shape_labels) + path = out_dir / ''.join(['314c_q32tail143_', format(len(tuple(shape_labels)), ''), 'row_', format(suffix, ''), '.json']) + path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + return {'candidate_payload': str(path)} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32_5317_q32tail143low_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32_5317_q32tail143low_v1.py new file mode 100644 index 00000000..2ae935a0 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32_5317_q32tail143low_v1.py @@ -0,0 +1,141 @@ +"""Exact Q32/M99999 RAG K32 split143 low-tail seed. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +targets only the expanded BF16 non-build ``B=1,Q=32,M=99999,D=128,K=32`` row +from generalize-auto-tuning round 181. It keeps the f590 exact-Q32 +ROW_16x256B tcgen05/TMA producer and rows4 merge, but lowers the low-tail row +with split143 so the existing q32tail route does less padded M-tile work. +Guard misses delegate to the sibling split143 high-tail wrapper and then to +the accepted 0cb5 q31tail/q32tail seed. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from pathlib import Path +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_v11_common_d_seed_portfolio_a4ec_v1 as dispatch_v11 +from . import knn_build_rag_microbucket_k32_0cb5_q31tail_v1 as parent +from . import knn_build_rag_microbucket_k32_314c_q32tail143_v1 as high143 +from . import knn_build_rag_microbucket_k32_f590_q32exact_v1 as q32exact +MODULE = 'loom.examples.weave.knn_build_rag_microbucket_k32_5317_q32tail143low_v1' +EXPANDED_Q32_TAIL_LOW_SHAPE = parent.EXPANDED_Q32_TAIL_LOW_SHAPE +TARGET_SHAPES = (EXPANDED_Q32_TAIL_LOW_SHAPE,) +EXPANDED_SHAPES = (parent.EXPANDED_SHAPES_BY_LABEL[EXPANDED_Q32_TAIL_LOW_SHAPE],) +EXPANDED_SHAPES_BY_LABEL = _decode_capture(_json_loads('{"__dict_items__": [["expanded_tail_q32_m99999_d128_k32", {"__dict_items__": [["label", "expanded_tail_q32_m99999_d128_k32"], ["params", {"__dict_items__": [["B", 1], ["Q", 32], ["M", 99999], ["D", 128], ["K", 32], ["dtype", "bfloat16"], ["seed", 626999], ["build", false], ["check_correctness", true], ["correctness_query_sample", 32], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true]]}]]}]]}')) +K32_Q32TAIL143_LOW_SPLIT_COUNT = 143 +ROUTE_Q32TAIL143_LOW_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_HIGH143_ENTRYPOINT = ''.join([format(high143.MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_PARENT_0CB5_ENTRYPOINT = ''.join([format(parent.MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_DISPATCH_V11_ENTRYPOINT = ''.join([format(dispatch_v11.MODULE, ''), ':launch_from_contract_inputs']) +SEED_K32_5317_Q32TAIL143LOW_ID = 'rag_microbucket_k32_5317_q32tail143low_v1' + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RAG_MICROBUCKET_K32_5317_Q32TAIL143LOW_V1_VERIFY_KERNEL') + if verify_kernel == 'q32tail143low_merge': + return q32exact.rows4._warp_merge_ir(K32_Q32TAIL143_LOW_SPLIT_COUNT) + return q32exact._stage1_q32_exact_ir() +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_k32_f590_q32exact_v1_stage1_q32exact_f590_v1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 66816, "constants": [["BLOCK_Q", 64], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32], ["ROWS_COVERED", 32]], "cta_group": 1, "threads": 128}')) + +def _eligible_q32tail143low(inputs: dict[str, Any]) -> bool: + return parent.uneven.base._is_bf16_d128_nonbuild(inputs) and int(inputs.get('Q', -1)) == 32 and (int(inputs.get('M', -1)) == 99999) and (int(inputs.get('K', -1)) == q32exact.K32_TOP_K_MAX) + +def _q32tail143low_route_name(inputs: dict[str, Any]) -> str: + return ''.join(['rag_microbucket_k32_5317_q32tail143low_v1_q', format(int(inputs.get('Q', -1)), ''), '_m', format(int(inputs.get('M', -1)), ''), '_k32_row16x256b2cw_exact_s', format(K32_Q32TAIL143_LOW_SPLIT_COUNT, ''), '_r', format(q32exact.rows4.K32_ROWS4_ROWS_PER_CTA, ''), '_warpmerge']) + +def route_for_contract_inputs(inputs: dict[str, Any]) -> str: + if _eligible_q32tail143low(inputs): + return _q32tail143low_route_name(inputs) + return high143.route_for_contract_inputs(inputs) + +def launch_from_contract_inputs(inputs: dict[str, Any]) -> None: + if _eligible_q32tail143low(inputs): + q32exact._launch_q32_exact_rows4(inputs, split_count=K32_Q32TAIL143_LOW_SPLIT_COUNT) + return + high143.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_parent_0cb5(inputs: dict[str, Any]) -> None: + parent.launch_from_contract_inputs(inputs) + +def candidate_dispatch_v11(inputs: dict[str, Any]) -> None: + dispatch_v11.launch_from_contract_inputs(inputs) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + labels = tuple(shape_labels) + selected = [] + remaining = [] + for label in labels: + if label in EXPANDED_SHAPES_BY_LABEL: + selected.append(EXPANDED_SHAPES_BY_LABEL[label]) + else: + remaining.append(label) + if remaining: + selected.extend(high143._select_contract_shapes(tuple(remaining))) + return selected + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def route_trace_for_contract_shapes(shape_labels=TARGET_SHAPES) -> list[dict[str, Any]]: + rows = [] + for shape in _select_contract_shapes(shape_labels): + params = dict(shape.get('params', {})) + params['label'] = shape['label'] + route = route_for_contract_inputs(params) + high_route = high143.route_for_contract_inputs(params) + parent_route = parent.route_for_contract_inputs(params) + current_route = dispatch_v11.route_for_contract_inputs(params) + selected = _eligible_q32tail143low(params) + rows.append({'shape_key': shape['label'], 'selected_route': route, 'selected_seed': SEED_K32_5317_Q32TAIL143LOW_ID if selected else high143.SEED_K32_314C_Q32TAIL143_ID, 'selected_entrypoint': ROUTE_Q32TAIL143_LOW_ENTRYPOINT if selected else ROUTE_HIGH143_ENTRYPOINT, 'high143_fallback_route': high_route, 'high143_fallback_entrypoint': ROUTE_HIGH143_ENTRYPOINT, 'parent_0cb5_route': parent_route, 'parent_0cb5_entrypoint': ROUTE_PARENT_0CB5_ENTRYPOINT, 'current_dispatch_v11_route': current_route, 'current_dispatch_v11_entrypoint': ROUTE_DISPATCH_V11_ENTRYPOINT, 'route_kind': 'specialized_q32_m99999_split143' if selected else 'inherited_314c_q32tail143_v1', 'split_count': K32_Q32TAIL143_LOW_SPLIT_COUNT if selected else None, 'guard_condition': 'BF16 non-build B=1 Q=32 M=99999 D=128 K=32' if selected else 'delegate to 314c q32tail143 high-tail wrapper'}) + return rows + +def _per_shape_delta(candidate_report: dict[str, Any], parent_report: dict[str, Any], dispatcher_report: dict[str, Any]) -> dict[str, Any]: + rows = {} + for label, cand in candidate_report.get('per_shape', {}).items(): + parent_row = parent_report.get('per_shape', {}).get(label, {}) + dispatch_row = dispatcher_report.get('per_shape', {}).get(label, {}) + cand_ms = cand.get('kernel_ms') + parent_ms = parent_row.get('kernel_ms') + dispatch_ms = dispatch_row.get('kernel_ms') + rows[label] = {'candidate': cand, 'parent_0cb5': parent_row, 'dispatch_v11_baseline': dispatch_row, 'candidate_ms': cand_ms, 'parent_0cb5_ms': parent_ms, 'dispatch_v11_ms': dispatch_ms, 'speedup_vs_parent_0cb5': parent_ms / cand_ms if cand_ms and parent_ms else None, 'speedup_vs_dispatch_v11': dispatch_ms / cand_ms if cand_ms and dispatch_ms else None, 'ratio_vs_flashlib': cand.get('ratio_vs_flashlib'), 'parent_0cb5_ratio_vs_flashlib': parent_row.get('ratio_vs_flashlib'), 'dispatch_v11_ratio_vs_flashlib': dispatch_row.get('ratio_vs_flashlib')} + return rows + +def benchmark_knn_build_rag_microbucket_k32_5317_q32tail143low_v1(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate) + parent_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_parent_0cb5) + dispatcher_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_dispatch_v11) + return {'contract': candidate_report['contract'], 'contract_version': candidate_report['contract_version'], 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_knn_build_rag_microbucket_k32_5317_q32tail143low_v1']), 'candidate_entrypoint': ROUTE_Q32TAIL143_LOW_ENTRYPOINT, 'parent_0cb5_entrypoint': ROUTE_PARENT_0CB5_ENTRYPOINT, 'dispatch_v11_entrypoint': ROUTE_DISPATCH_V11_ENTRYPOINT, 'accelerated_shape_labels': list(TARGET_SHAPES), 'measured_shape_labels': list(shape_labels), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'producer_topology': {'candidate': 'f590 exact-Q32 ROW_16x256B two-compute-warp stage1 at split143 for M99999', 'baseline': 'accepted 0cb5/q32exact route with split153', 'guard_misses': 'delegate to 314c q32tail143 high-tail wrapper, then 0cb5 q31tail v1'}, 'merge_topology': {'candidate': ''.join(['rows4 warp-row split-list merge/', format(q32exact.rows4.K32_ROWS4_ROWS_PER_CTA, ''), ' rows per CTA']), 'split_count': K32_Q32TAIL143_LOW_SPLIT_COUNT, 'splits_per_lane': q32exact.rows4.base._splits_per_lane(K32_Q32TAIL143_LOW_SPLIT_COUNT)}, 'route_trace': route_trace_for_contract_shapes(shape_labels), 'target_rows': _per_shape_delta(candidate_report, parent_report, dispatcher_report), 'summary': candidate_report['summary'], 'performance': candidate_report['performance'], 'correctness': candidate_report['correctness'], 'report': candidate_report, 'parent_0cb5_summary': parent_report['summary'], 'parent_0cb5_performance': parent_report['performance'], 'parent_0cb5_report': parent_report, 'dispatch_v11_summary': dispatcher_report['summary'], 'dispatch_v11_performance': dispatcher_report['performance'], 'dispatch_v11_report': dispatcher_report} + +def write_benchmark_artifact(artifact_dir: str | Path, *, use_cupti: bool=True, shape_labels=TARGET_SHAPES) -> dict[str, str]: + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + suffix = 'cupti' if use_cupti else 'cuda_event' + payload = benchmark_knn_build_rag_microbucket_k32_5317_q32tail143low_v1(use_cupti=use_cupti, shape_labels=shape_labels) + path = out_dir / ''.join(['5317_q32tail143low_', format(len(tuple(shape_labels)), ''), 'row_', format(suffix, ''), '.json']) + path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + return {'candidate_payload': str(path)} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32_8fcb_split148_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32_8fcb_split148_v1.py new file mode 100644 index 00000000..bd8c3b68 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32_8fcb_split148_v1.py @@ -0,0 +1,114 @@ +"""RAG microbatch K32 bucket with B200 split-148 mixed routes. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +keeps the round-b3e0 tcgen05/TMA producer and warp-merge topology on the eval +path, but retunes the K32 split count from 144 to 148 so the low-Q stage-1 +producer exposes one work item per B200 SM. The production path remains +Weave-only. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_rag_microbucket_k32_b3e0_mix_v1 as parent +MODULE = 'loom.examples.weave.knn_build_rag_microbucket_k32_8fcb_split148_v1' +Q8_K32_SHAPE = parent.Q8_K32_SHAPE +Q16_K32_SHAPE = parent.Q16_K32_SHAPE +Q32_K32_SHAPE = parent.Q32_K32_SHAPE +Q16_K32_IRREGULAR_SHAPE = parent.Q16_K32_IRREGULAR_SHAPE +K32_BUCKET_SHAPES = parent.K32_BUCKET_SHAPES +TARGET_SHAPES = parent.TARGET_SHAPES +K32_SPLIT_COUNT = 148 +K32_GROUP_COUNT = parent.K32_GROUP_COUNT +ROUTE_PARENT_B3E0 = ''.join([format(parent.MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_8FCB_SPLIT148_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +SEED_K32_8FCB_SPLIT148_ID = 'rag_microbucket_k32_8fcb_split148_v1_b3e0_sm148' + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RAG_MICROBUCKET_K32_8FCB_SPLIT148_V1_VERIFY_KERNEL') + split_count = int(os.environ.get('LOOM_KNN_RAG_MICROBUCKET_K32_8FCB_SPLIT148_V1_VERIFY_K32_SPLIT', K32_SPLIT_COUNT)) + if verify_kernel == 'q8_half_stage1': + return parent.q8half._stage1_q8_half_ir() + if verify_kernel == 'rowld1_stage1': + return parent.q8half.parent._stage1_rowld1_ir() + if verify_kernel == 'rowld_stage1': + return parent.rows4.base.rowld_seed.stage1_q32_k32_m64_rowld_ir + if verify_kernel == 'warp_merge': + return parent.rows4.base._warp_merge_ir(split_count) + return parent.rows4._warp_merge_ir(split_count) +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_k32warpmerge_0077_v1_warp_row_merge_k32s148r4_0077_v1", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 32], ["SPLIT_COUNT", 148], ["SPLITS_PER_LANE", 5], ["ROWS_PER_CTA", 4]], "cta_group": 1, "threads": 128}')) + +def route_for_contract_inputs(inputs: dict[str, Any], *, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> str: + return parent.route_for_contract_inputs(inputs, k32_split_count=k32_split_count, k32_group_count=k32_group_count) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> None: + parent.launch_from_contract_inputs(inputs, k32_split_count=k32_split_count, k32_group_count=k32_group_count) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_with_k32_topology(split_count: int, group_count: int) -> Callable[[dict[str, Any]], None]: + + def _candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, k32_split_count=split_count, k32_group_count=group_count) + return _candidate + +def candidate_parent_b3e0(inputs: dict[str, Any]) -> None: + parent.launch_from_contract_inputs(inputs) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return parent._select_contract_shapes(shape_labels) + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=K32_BUCKET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def compile_and_launch_knn_build(*, shape_labels=K32_BUCKET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def route_trace_for_contract_shapes(shape_labels=K32_BUCKET_SHAPES, *, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> list[dict[str, Any]]: + rows = [] + target_set = set(K32_BUCKET_SHAPES) + for shape in _select_contract_shapes(shape_labels): + inputs = parent.rows4.base.rowld_seed.base_dispatcher._trace_inputs_from_shape(shape) + route = route_for_contract_inputs(inputs, k32_split_count=k32_split_count, k32_group_count=k32_group_count) + parent_route = parent.route_for_contract_inputs(inputs) + label = str(shape['label']) + parent_rows = parent.route_trace_for_contract_shapes((label,), k32_split_count=k32_split_count, k32_group_count=k32_group_count) + base_row = parent_rows[0] if parent_rows else {} + selected = label in target_set + rows.append({**base_row, 'shape_key': label, 'selected_route': route, 'selected_seed': SEED_K32_8FCB_SPLIT148_ID if selected else base_row.get('selected_seed'), 'selected_entrypoint': ROUTE_8FCB_SPLIT148_ENTRYPOINT if selected else base_row.get('selected_entrypoint'), 'parent_b3e0_default_route': parent_route, 'route_kind': ''.join([format(base_row.get('route_kind', 'unknown'), ''), '_split148']) if selected else base_row.get('route_kind'), 'guard_condition': ''.join([format(base_row.get('guard_condition', 'delegate to parent'), ''), ' with K32 split_count=148']) if selected else base_row.get('guard_condition')}) + return rows + +def _per_shape_delta(candidate_report: dict[str, Any], parent_report: dict[str, Any]) -> dict[str, Any]: + rows = {} + for label, cand in candidate_report.get('per_shape', {}).items(): + parent_row = parent_report.get('per_shape', {}).get(label, {}) + cand_ms = cand.get('kernel_ms') + parent_ms = parent_row.get('kernel_ms') + rows[label] = {'candidate': cand, 'parent_b3e0': parent_row, 'candidate_ms': cand_ms, 'parent_b3e0_ms': parent_ms, 'speedup_vs_parent_b3e0': parent_ms / cand_ms if cand_ms and parent_ms else None, 'ratio_vs_flashlib': cand.get('ratio_vs_flashlib')} + return rows + +def benchmark_knn_build_rag_microbucket_k32_8fcb_split148_v1(*, use_cupti: bool=True, shape_labels=K32_BUCKET_SHAPES, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_with_k32_topology(k32_split_count, k32_group_count)) + parent_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_parent_b3e0) + return {'contract': candidate_report['contract'], 'contract_version': candidate_report['contract_version'], 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_knn_build_rag_microbucket_k32_8fcb_split148_v1']), 'candidate_entrypoint': ROUTE_8FCB_SPLIT148_ENTRYPOINT, 'parent_entrypoint': ROUTE_PARENT_B3E0, 'accelerated_shape_labels': list(K32_BUCKET_SHAPES), 'measured_shape_labels': list(shape_labels), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'producer_topology': {'Q8_exact': 'b3e0 half-row ROW_16x256B one-compute-warp stage1', 'Q16_exact': 'b3e0 rowld1 ROW_16x256B one-compute-warp stage1', 'Q16_irregular': 'b3e0 inherited rowld1 one-compute-warp stage1', 'Q32_exact': 'b3e0 ROW_16x256B stage1 plus rows4 warp-row merge', 'guard_misses': 'delegate to b3e0 parent routes with split_count=148'}, 'merge_topology': {'Q8_Q16_exact_Q16_irregular': '0077 warp-row split-list merge/1 row per CTA', 'Q32_exact': ''.join(['0077 warp-row split-list merge/', format(parent.rows4.K32_ROWS4_ROWS_PER_CTA, ''), ' rows per CTA']), 'split_count': k32_split_count, 'splits_per_lane': parent.rows4.base._splits_per_lane(k32_split_count)}, 'route_trace': route_trace_for_contract_shapes(shape_labels, k32_split_count=k32_split_count, k32_group_count=k32_group_count), 'target_rows': _per_shape_delta(candidate_report, parent_report), 'summary': candidate_report['summary'], 'performance': candidate_report['performance'], 'correctness': candidate_report['correctness'], 'report': candidate_report, 'parent_summary': parent_report['summary'], 'parent_performance': parent_report['performance'], 'parent_report': parent_report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32_b3e0_mix_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32_b3e0_mix_v1.py new file mode 100644 index 00000000..bb1cf54c --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32_b3e0_mix_v1.py @@ -0,0 +1,146 @@ +"""RAG microbatch K32 bucket mixed-route seed. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +keeps the 0077 K32 tcgen05/TMA producer lineage on the eval path, but routes +each requested microbatch row to the fastest source-policy-clean topology seen +in prior same-denominator probes: Q8 uses the half-row producer, Q16 exact uses +the rowld1 producer, Q32 exact uses the four-row warp merge, and Q16 irregular +keeps the inherited rowld1 route. The production path remains Weave-only. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_rag_microbucket_k32q8half_0077_v1 as q8half +from . import knn_build_rag_microbucket_k32rows4_0077_v1 as rows4 +MODULE = 'loom.examples.weave.knn_build_rag_microbucket_k32_b3e0_mix_v1' +Q8_K32_SHAPE = q8half.Q8_K32_SHAPE +Q16_K32_SHAPE = q8half.Q16_K32_SHAPE +Q32_K32_SHAPE = q8half.Q32_K32_SHAPE +Q16_K32_IRREGULAR_SHAPE = q8half.Q16_K32_IRREGULAR_SHAPE +K32_BUCKET_SHAPES = q8half.K32_BUCKET_SHAPES +TARGET_SHAPES = q8half.TARGET_SHAPES +K32_SPLIT_COUNT = q8half.K32_SPLIT_COUNT +K32_GROUP_COUNT = q8half.K32_GROUP_COUNT +ROUTE_PARENT_Q8HALF = ''.join([format(q8half.MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_B3E0_MIX_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +SEED_K32_B3E0_MIX_ID = 'rag_microbucket_k32_b3e0_mix_v1_q8half_q16rowld1_q32rows4' + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RAG_MICROBUCKET_K32_B3E0_MIX_V1_VERIFY_KERNEL') + split_count = int(os.environ.get('LOOM_KNN_RAG_MICROBUCKET_K32_B3E0_MIX_V1_VERIFY_K32_SPLIT', K32_SPLIT_COUNT)) + if verify_kernel == 'q8_half_stage1': + return q8half._stage1_q8_half_ir() + if verify_kernel == 'rowld1_stage1': + return q8half.parent._stage1_rowld1_ir() + if verify_kernel == 'rowld_stage1': + return rows4.base.rowld_seed.stage1_q32_k32_m64_rowld_ir + return rows4._warp_merge_ir(split_count) +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_k32warpmerge_0077_v1_warp_row_merge_k32s144r4_0077_v1", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 32], ["SPLIT_COUNT", 144], ["SPLITS_PER_LANE", 5], ["ROWS_PER_CTA", 4]], "cta_group": 1, "threads": 128}')) + +def _is_bf16_d128_nonbuild(inputs: dict[str, Any]) -> bool: + return rows4.base._is_bf16_d128_nonbuild(inputs) + +def _eligible_q8_half(inputs: dict[str, Any]) -> bool: + return q8half._eligible_q8_half(inputs) + +def _eligible_q16_exact_rowld1(inputs: dict[str, Any]) -> bool: + return _is_bf16_d128_nonbuild(inputs) and int(inputs.get('M', -1)) == 100000 and (int(inputs.get('Q', -1)) == 16) and (int(inputs.get('K', -1)) == 32) + +def _eligible_q32_exact_rows4(inputs: dict[str, Any]) -> bool: + return _is_bf16_d128_nonbuild(inputs) and int(inputs.get('M', -1)) == 100000 and (int(inputs.get('Q', -1)) == 32) and (int(inputs.get('K', -1)) == 32) + +def _mix_route_name(inputs: dict[str, Any], *, split_count: int) -> str: + n_query = int(inputs.get('Q', -1)) + n_database = int(inputs.get('M', -1)) + if n_query == 8: + return ''.join(['rag_microbucket_k32_b3e0_mix_v1_q8_m', format(n_database, ''), '_k32_halfrow_s', format(split_count, ''), '_warpmerge']) + if n_query == 16: + return ''.join(['rag_microbucket_k32_b3e0_mix_v1_q16_m', format(n_database, ''), '_k32_rowld1_s', format(split_count, ''), '_warpmerge']) + return ''.join(['rag_microbucket_k32_b3e0_mix_v1_q', format(n_query, ''), '_m', format(n_database, ''), '_k32_rows4_s', format(split_count, ''), '_warpmerge']) + +def route_for_contract_inputs(inputs: dict[str, Any], *, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> str: + if _eligible_q8_half(inputs) or _eligible_q16_exact_rowld1(inputs) or _eligible_q32_exact_rows4(inputs): + return _mix_route_name(inputs, split_count=k32_split_count) + return q8half.route_for_contract_inputs(inputs, k32_split_count=k32_split_count, k32_group_count=k32_group_count) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> None: + if _eligible_q8_half(inputs): + q8half._launch_q8_half_warpmerge(inputs, split_count=k32_split_count) + return + if _eligible_q16_exact_rowld1(inputs): + q8half.parent._launch_rowld1_warpmerge(inputs, split_count=k32_split_count) + return + if _eligible_q32_exact_rows4(inputs): + rows4._launch_rowld_rows4(inputs, split_count=k32_split_count) + return + q8half.launch_from_contract_inputs(inputs, k32_split_count=k32_split_count, k32_group_count=k32_group_count) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_with_k32_topology(split_count: int, group_count: int) -> Callable[[dict[str, Any]], None]: + + def _candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, k32_split_count=split_count, k32_group_count=group_count) + return _candidate + +def candidate_parent_q8half(inputs: dict[str, Any]) -> None: + q8half.launch_from_contract_inputs(inputs) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return q8half._select_contract_shapes(shape_labels) + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=K32_BUCKET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def compile_and_launch_knn_build(*, shape_labels=K32_BUCKET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def route_trace_for_contract_shapes(shape_labels=K32_BUCKET_SHAPES, *, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> list[dict[str, Any]]: + rows = [] + for shape in _select_contract_shapes(shape_labels): + inputs = rows4.base.rowld_seed.base_dispatcher._trace_inputs_from_shape(shape) + route = route_for_contract_inputs(inputs, k32_split_count=k32_split_count, k32_group_count=k32_group_count) + parent_route = q8half.route_for_contract_inputs(inputs, k32_split_count=k32_split_count, k32_group_count=k32_group_count) + route_str = str(route) + q8_half = '_q8_' in route_str and '_halfrow_' in route_str + q16_rowld1 = '_q16_' in route_str and '_rowld1_' in route_str + rows4_exact = '_rows4_' in route_str + specialized = q8_half or q16_rowld1 or rows4_exact + rows.append({'shape_key': shape['label'], 'selected_route': route, 'selected_seed': SEED_K32_B3E0_MIX_ID if specialized else None, 'selected_entrypoint': ROUTE_B3E0_MIX_ENTRYPOINT if specialized else ROUTE_PARENT_Q8HALF, 'parent_q8half_route': parent_route, 'route_kind': 'specialized_q8_halfrow_stage1' if q8_half else 'specialized_q16_exact_rowld1_stage1' if q16_rowld1 else 'specialized_q32_rows4_merge' if rows4_exact else 'inherited_0077_q8half_stack', 'guard_condition': 'BF16 non-build B=1 Q=8 M=100000 D=128 K=32' if q8_half else 'BF16 non-build B=1 Q=16 M=100000 D=128 K=32' if q16_rowld1 else 'BF16 non-build B=1 Q=32 M=100000 D=128 K=32' if rows4_exact else 'delegate to 0077 q8half/rowld1 stack'}) + return rows + +def _per_shape_delta(candidate_report: dict[str, Any], parent_report: dict[str, Any]) -> dict[str, Any]: + rows = {} + for label, cand in candidate_report.get('per_shape', {}).items(): + parent_row = parent_report.get('per_shape', {}).get(label, {}) + cand_ms = cand.get('kernel_ms') + parent_ms = parent_row.get('kernel_ms') + rows[label] = {'candidate': cand, 'parent_q8half': parent_row, 'candidate_ms': cand_ms, 'parent_q8half_ms': parent_ms, 'speedup_vs_parent_q8half': parent_ms / cand_ms if cand_ms and parent_ms else None, 'ratio_vs_flashlib': cand.get('ratio_vs_flashlib')} + return rows + +def benchmark_knn_build_rag_microbucket_k32_b3e0_mix_v1(*, use_cupti: bool=True, shape_labels=K32_BUCKET_SHAPES, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_with_k32_topology(k32_split_count, k32_group_count)) + parent_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_parent_q8half) + return {'contract': candidate_report['contract'], 'contract_version': candidate_report['contract_version'], 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_knn_build_rag_microbucket_k32_b3e0_mix_v1']), 'candidate_entrypoint': ''.join([format(MODULE, ''), ':launch_from_contract_inputs']), 'parent_entrypoint': ROUTE_PARENT_Q8HALF, 'accelerated_shape_labels': [Q8_K32_SHAPE, Q16_K32_SHAPE, Q32_K32_SHAPE], 'measured_shape_labels': list(shape_labels), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'producer_topology': {'Q8_exact': '0077 half-row ROW_16x256B one-compute-warp stage1', 'Q16_exact': '0077 rowld1 ROW_16x256B one-compute-warp stage1', 'Q32_exact': '0077 ROW_16x256B stage1 plus rows4 warp-row merge', 'Q16_irregular': 'inherited 0077 rowld1 one-compute-warp stage1', 'guard_misses': 'delegate to 0077 q8half/rowld1 stack'}, 'merge_topology': {'Q8_Q16_exact_Q16_irregular': '0077 warp-row split-list merge/1 row per CTA', 'Q32_exact': ''.join(['0077 warp-row split-list merge/', format(rows4.K32_ROWS4_ROWS_PER_CTA, ''), ' rows per CTA']), 'split_count': k32_split_count, 'splits_per_lane': rows4.base._splits_per_lane(k32_split_count)}, 'route_trace': route_trace_for_contract_shapes(shape_labels, k32_split_count=k32_split_count, k32_group_count=k32_group_count), 'target_rows': _per_shape_delta(candidate_report, parent_report), 'summary': candidate_report['summary'], 'performance': candidate_report['performance'], 'correctness': candidate_report['correctness'], 'report': candidate_report, 'parent_summary': parent_report['summary'], 'parent_performance': parent_report['performance'], 'parent_report': parent_report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32_c489_q33tile_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32_c489_q33tile_v1.py new file mode 100644 index 00000000..431a42c4 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32_c489_q33tile_v1.py @@ -0,0 +1,146 @@ +"""Expanded Q31/Q33/Q40/Q32-tail RAG K32 bucket wrapper. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +targets the expanded guard-boundary/tail K32 RAG rows from generalize +auto-tuning round 176 c489. It delegates the already-repaired Q31 and Q32 +M-tail rows to the 0cb5 q31tail seed, and routes Q33/Q40 M100000 rows through +the e5db 64-row ROW_16x256B tcgen05/TMA producer plus fused split merge. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from pathlib import Path +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_v11_common_d_seed_portfolio_a4ec_v1 as dispatch_v11 +from . import knn_build_rag_microbucket_k32_0cb5_q31tail_v1 as q31tail +from . import knn_build_rag_microbucket_q32rowld_e5db_v1 as e5db +MODULE = 'loom.examples.weave.knn_build_rag_microbucket_k32_c489_q33tile_v1' +EXPANDED_Q31_SHAPE = q31tail.EXPANDED_Q31_SHAPE +EXPANDED_Q32_TAIL_LOW_SHAPE = q31tail.EXPANDED_Q32_TAIL_LOW_SHAPE +EXPANDED_Q32_TAIL_HIGH_SHAPE = q31tail.EXPANDED_Q32_TAIL_HIGH_SHAPE +EXPANDED_Q33_SHAPE = 'expanded_guard_boundary_q33_m100000_d128_k32' +EXPANDED_Q40_HELDOUT_SHAPE = 'expanded_heldout_q40_m100000_d128_k32' +TARGET_SHAPES = (EXPANDED_Q31_SHAPE, EXPANDED_Q33_SHAPE, EXPANDED_Q32_TAIL_LOW_SHAPE, EXPANDED_Q32_TAIL_HIGH_SHAPE, EXPANDED_Q40_HELDOUT_SHAPE) +Q33_TILE_TARGET_SHAPES = (EXPANDED_Q33_SHAPE, EXPANDED_Q40_HELDOUT_SHAPE) +EXPANDED_Q33_TILE_SHAPES = ({'label': EXPANDED_Q33_SHAPE, 'params': {'B': 1, 'Q': 33, 'M': 100000, 'D': 128, 'K': 32, 'dtype': 'bfloat16', 'seed': 626333, 'build': False, 'check_correctness': True, 'correctness_query_sample': 33, 'recall_min': 0.999, 'benchmark': True, 'time_flashlib': True}}, {'label': EXPANDED_Q40_HELDOUT_SHAPE, 'params': {'B': 1, 'Q': 40, 'M': 100000, 'D': 128, 'K': 32, 'dtype': 'bfloat16', 'seed': 626340, 'build': False, 'check_correctness': True, 'correctness_query_sample': 40, 'recall_min': 0.999, 'benchmark': True, 'time_flashlib': True}}) +EXPANDED_SHAPES_BY_LABEL = _decode_capture(_json_loads('{"__dict_items__": [["expanded_guard_boundary_q31_m100000_d128_k32", {"__dict_items__": [["label", "expanded_guard_boundary_q31_m100000_d128_k32"], ["params", {"__dict_items__": [["B", 1], ["Q", 31], ["M", 100000], ["D", 128], ["K", 32], ["dtype", "bfloat16"], ["seed", 626331], ["build", false], ["check_correctness", true], ["correctness_query_sample", 31], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true]]}]]}], ["expanded_tail_q32_m99999_d128_k32", {"__dict_items__": [["label", "expanded_tail_q32_m99999_d128_k32"], ["params", {"__dict_items__": [["B", 1], ["Q", 32], ["M", 99999], ["D", 128], ["K", 32], ["dtype", "bfloat16"], ["seed", 626999], ["build", false], ["check_correctness", true], ["correctness_query_sample", 32], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true]]}]]}], ["expanded_tail_q32_m100001_d128_k32", {"__dict_items__": [["label", "expanded_tail_q32_m100001_d128_k32"], ["params", {"__dict_items__": [["B", 1], ["Q", 32], ["M", 100001], ["D", 128], ["K", 32], ["dtype", "bfloat16"], ["seed", 627001], ["build", false], ["check_correctness", true], ["correctness_query_sample", 32], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true]]}]]}], ["expanded_guard_boundary_q33_m100000_d128_k32", {"__dict_items__": [["label", "expanded_guard_boundary_q33_m100000_d128_k32"], ["params", {"__dict_items__": [["B", 1], ["Q", 33], ["M", 100000], ["D", 128], ["K", 32], ["dtype", "bfloat16"], ["seed", 626333], ["build", false], ["check_correctness", true], ["correctness_query_sample", 33], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true]]}]]}], ["expanded_heldout_q40_m100000_d128_k32", {"__dict_items__": [["label", "expanded_heldout_q40_m100000_d128_k32"], ["params", {"__dict_items__": [["B", 1], ["Q", 40], ["M", 100000], ["D", 128], ["K", 32], ["dtype", "bfloat16"], ["seed", 626340], ["build", false], ["check_correctness", true], ["correctness_query_sample", 40], ["recall_min", 0.999], ["benchmark", true], ["time_flashlib", true]]}]]}]]}')) +K32_Q33TILE_SPLIT_COUNT = _decode_capture(_json_loads('144')) +K32_Q33TILE_GROUP_COUNT = _decode_capture(_json_loads('12')) +ROUTE_Q33TILE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_Q31TAIL_ENTRYPOINT = q31tail.ROUTE_Q31TAIL_ENTRYPOINT +ROUTE_DISPATCH_V11_ENTRYPOINT = ''.join([format(dispatch_v11.MODULE, ''), ':launch_from_contract_inputs']) +SEED_K32_C489_Q33TILE_ID = 'rag_microbucket_k32_c489_q33tile_v1' + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RAG_MICROBUCKET_K32_C489_Q33TILE_V1_VERIFY_KERNEL') + split_count = int(os.environ.get('LOOM_KNN_RAG_MICROBUCKET_K32_C489_Q33TILE_V1_VERIFY_K32_SPLIT', K32_Q33TILE_SPLIT_COUNT)) + group_count = int(os.environ.get('LOOM_KNN_RAG_MICROBUCKET_K32_C489_Q33TILE_V1_VERIFY_K32_GROUPS', K32_Q33TILE_GROUP_COUNT)) + if verify_kernel == 'q33tile_fused_merge': + return e5db.compact_seed.q16_tailinf._fused_merge_ir(split_count, group_count) + return e5db.stage1_q32_k32_m64_rowld_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_q32rowld_e5db_v1_stage1_q32_k32_m64", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 99584, "constants": [["BLOCK_Q", 64], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 192}')) + +def _eligible_q33tile(inputs: dict[str, Any]) -> bool: + return e5db._is_bf16_d128_nonbuild(inputs) and int(inputs.get('Q', -1)) in {33, 40} and (int(inputs.get('M', -1)) == 100000) and (int(inputs.get('K', -1)) == 32) + +def _q33tile_route_name(inputs: dict[str, Any], *, split_count: int, group_count: int) -> str: + n_query = int(inputs.get('Q', -1)) + n_database = int(inputs.get('M', -1)) + return ''.join(['rag_microbucket_k32_c489_q33tile_v1_q', format(n_query, ''), '_m', format(n_database, ''), '_k32_m64n64_row16x256b_s', format(split_count, ''), '_g', format(group_count, ''), '_fusedmerge']) + +def route_for_contract_inputs(inputs: dict[str, Any], *, k32_q33tile_split_count: int=K32_Q33TILE_SPLIT_COUNT, k32_q33tile_group_count: int=K32_Q33TILE_GROUP_COUNT) -> str: + if _eligible_q33tile(inputs): + return _q33tile_route_name(inputs, split_count=k32_q33tile_split_count, group_count=k32_q33tile_group_count) + return q31tail.route_for_contract_inputs(inputs) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, k32_q33tile_split_count: int=K32_Q33TILE_SPLIT_COUNT, k32_q33tile_group_count: int=K32_Q33TILE_GROUP_COUNT) -> None: + if _eligible_q33tile(inputs): + e5db._launch_q32_k32_m64_rowld(inputs, split_count=k32_q33tile_split_count, group_count=k32_q33tile_group_count) + return + q31tail.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_with_topology(split_count: int, group_count: int) -> Callable[[dict[str, Any]], None]: + + def _candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, k32_q33tile_split_count=split_count, k32_q33tile_group_count=group_count) + return _candidate + +def candidate_dispatch_v11(inputs: dict[str, Any]) -> None: + dispatch_v11.launch_from_contract_inputs(inputs) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + labels = tuple(shape_labels) + selected = [] + remaining = [] + for label in labels: + if label in EXPANDED_SHAPES_BY_LABEL: + selected.append(EXPANDED_SHAPES_BY_LABEL[label]) + else: + remaining.append(label) + if remaining: + selected.extend(q31tail._select_contract_shapes(tuple(remaining))) + return selected + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def route_trace_for_contract_shapes(shape_labels=TARGET_SHAPES, *, k32_q33tile_split_count: int=K32_Q33TILE_SPLIT_COUNT, k32_q33tile_group_count: int=K32_Q33TILE_GROUP_COUNT) -> list[dict[str, Any]]: + rows = [] + for shape in _select_contract_shapes(shape_labels): + params = dict(shape.get('params', {})) + params['label'] = shape['label'] + route = route_for_contract_inputs(params, k32_q33tile_split_count=k32_q33tile_split_count, k32_q33tile_group_count=k32_q33tile_group_count) + current_route = dispatch_v11.route_for_contract_inputs(params) + selected_q33tile = _eligible_q33tile(params) + q31tail_route = q31tail.route_for_contract_inputs(params) + rows.append({'shape_key': shape['label'], 'selected_route': route, 'selected_seed': SEED_K32_C489_Q33TILE_ID if selected_q33tile else q31tail.SEED_K32_0CB5_Q31TAIL_ID, 'selected_entrypoint': ROUTE_Q33TILE_ENTRYPOINT if selected_q33tile else ROUTE_Q31TAIL_ENTRYPOINT, 'current_dispatch_v11_route': current_route, 'current_dispatch_v11_entrypoint': ROUTE_DISPATCH_V11_ENTRYPOINT, 'q31tail_route': q31tail_route, 'route_kind': 'specialized_q33_q40_e5db_m64rowld' if selected_q33tile else 'inherited_q31tail_seed', 'split_count': k32_q33tile_split_count if selected_q33tile else None, 'group_count': k32_q33tile_group_count if selected_q33tile else None, 'guard_condition': 'BF16 non-build B=1 D=128 K=32 with Q in {33,40}, M=100000' if selected_q33tile else 'delegate to 0cb5 q31tail seed'}) + return rows + +def _per_shape_delta(candidate_report: dict[str, Any], dispatcher_report: dict[str, Any]) -> dict[str, Any]: + rows = {} + for label, cand in candidate_report.get('per_shape', {}).items(): + base_row = dispatcher_report.get('per_shape', {}).get(label, {}) + cand_ms = cand.get('kernel_ms') + base_ms = base_row.get('kernel_ms') + rows[label] = {'candidate': cand, 'dispatch_v11_baseline': base_row, 'candidate_ms': cand_ms, 'dispatch_v11_ms': base_ms, 'speedup_vs_dispatch_v11': base_ms / cand_ms if cand_ms and base_ms else None, 'ratio_vs_flashlib': cand.get('ratio_vs_flashlib'), 'dispatch_v11_ratio_vs_flashlib': base_row.get('ratio_vs_flashlib')} + return rows + +def benchmark_knn_build_rag_microbucket_k32_c489_q33tile_v1(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES, k32_q33tile_split_count: int=K32_Q33TILE_SPLIT_COUNT, k32_q33tile_group_count: int=K32_Q33TILE_GROUP_COUNT) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_with_topology(k32_q33tile_split_count, k32_q33tile_group_count)) + dispatcher_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_dispatch_v11) + return {'contract': candidate_report['contract'], 'contract_version': candidate_report['contract_version'], 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_knn_build_rag_microbucket_k32_c489_q33tile_v1']), 'candidate_entrypoint': ROUTE_Q33TILE_ENTRYPOINT, 'dispatch_v11_entrypoint': ROUTE_DISPATCH_V11_ENTRYPOINT, 'accelerated_shape_labels': list(TARGET_SHAPES), 'measured_shape_labels': list(shape_labels), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'producer_topology': {'q33_q40': ''.join(['e5db four-compute-warp ROW_16x256B tcgen05/TMA stage1 over one 64-row query tile with split=', format(k32_q33tile_split_count, ''), ', group=', format(k32_q33tile_group_count, '')]), 'q31_q32tail': 'delegated to 0cb5 q31tail seed', 'comparison_baseline': 'current v11 common-D seed portfolio dispatcher'}, 'merge_topology': {'q33_q40': ''.join(['e5db fused split merge S', format(k32_q33tile_split_count, ''), '/G', format(k32_q33tile_group_count, '')]), 'q31_q32tail': '0cb5 q31tail rows4 split-list merge'}, 'route_trace': route_trace_for_contract_shapes(shape_labels, k32_q33tile_split_count=k32_q33tile_split_count, k32_q33tile_group_count=k32_q33tile_group_count), 'target_rows': _per_shape_delta(candidate_report, dispatcher_report), 'summary': candidate_report['summary'], 'performance': candidate_report['performance'], 'correctness': candidate_report['correctness'], 'report': candidate_report, 'dispatch_v11_summary': dispatcher_report['summary'], 'dispatch_v11_performance': dispatcher_report['performance'], 'dispatch_v11_report': dispatcher_report} + +def write_benchmark_artifact(artifact_dir: str | os.PathLike[str], *, use_cupti: bool=True, shape_labels=TARGET_SHAPES, k32_q33tile_split_count: int=K32_Q33TILE_SPLIT_COUNT, k32_q33tile_group_count: int=K32_Q33TILE_GROUP_COUNT) -> dict[str, str]: + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + suffix = 'cupti' if use_cupti else 'cuda_event' + payload = benchmark_knn_build_rag_microbucket_k32_c489_q33tile_v1(use_cupti=use_cupti, shape_labels=shape_labels, k32_q33tile_split_count=k32_q33tile_split_count, k32_q33tile_group_count=k32_q33tile_group_count) + path = out_dir / ''.join(['c489_q33tile_', format(len(tuple(shape_labels)), ''), 'row_s', format(k32_q33tile_split_count, ''), '_g', format(k32_q33tile_group_count, ''), '_', format(suffix, ''), '.json']) + path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + return {'candidate_payload': str(path)} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32_f590_q32exact_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32_f590_q32exact_v1.py new file mode 100644 index 00000000..319bb2e6 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32_f590_q32exact_v1.py @@ -0,0 +1,160 @@ +"""Exact Q32 RAG K32 rowld2 producer with exact-shape stage1 guards removed. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +targets only the BF16 non-build ``B=1,Q=32,M=100000,D=128,K=32`` row from the +v11 D128 RAG large-K floor set. It keeps the f590 two-compute-warp +ROW_16x256B tcgen05/TMA stage1 topology and rows4 merge, but specializes the +stage1 for the exact Q32/K32/B1 shape so query validity and K-bound branches do +not sit on the contract-visible hot path. Guard misses delegate to split153. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from collections.abc import Callable +from functools import cache +from pathlib import Path +from typing import Any +from .._dispatch_runtime import dc as dc +from .. import _dispatch_runtime as eval_mod +from . import knn_build_rag_microbucket_k32_f590_q32split153_v1 as split153 +MODULE = 'loom.examples.weave.knn_build_rag_microbucket_k32_f590_q32exact_v1' +parent = split153.parent +f590 = split153.f590 +rowld1 = f590.rowld1 +rows4 = f590.rows4 +Q8_K32_SHAPE = parent.Q8_K32_SHAPE +Q16_K32_SHAPE = parent.Q16_K32_SHAPE +Q32_K32_SHAPE = parent.Q32_K32_SHAPE +Q16_K32_IRREGULAR_SHAPE = parent.Q16_K32_IRREGULAR_SHAPE +K32_BUCKET_SHAPES = parent.K32_BUCKET_SHAPES +TARGET_SHAPES = (Q32_K32_SHAPE,) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +K32_Q32_SPLIT_COUNT = _decode_capture(_json_loads('153')) +K32_Q16_SPLIT_COUNT = parent.K32_Q16_SPLIT_COUNT +K32_DEFAULT_SPLIT_COUNT = parent.K32_DEFAULT_SPLIT_COUNT +K32_GROUP_COUNT = parent.K32_GROUP_COUNT +K32_TOP_K_MAX = rows4.K32_TOP_K_MAX +Q32_EXACT_STAGE1_THREADS = rowld1.Q32_ROWLD2_STAGE1_THREADS +Q32_EXACT_SMEM_POOL_BYTES = rowld1.Q32_ROWLD2_SMEM_POOL_BYTES +Q32_EXACT_LOCAL_D_OFFSET = rowld1.Q32_ROWLD2_LOCAL_D_OFFSET +Q32_EXACT_LOCAL_I_OFFSET = rowld1.Q32_ROWLD2_LOCAL_I_OFFSET +Q32_EXACT_LOCAL_ELEMS = rowld1.Q32_ROWLD2_LOCAL_ELEMS +ROUTE_PARENT_2E8E = split153.ROUTE_PARENT_2E8E +ROUTE_SPLIT153 = ''.join([format(split153.MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_Q32_EXACT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +SEED_K32_F590_Q32_EXACT_ID = 'rag_microbucket_k32_f590_q32exact_v1' +_insert_sorted_pair = _ir_proxy('loom.examples.weave.knn_build_rag_microbucket_k32_f590_q32exact_v1:_insert_sorted_pair', 256) +knn_build_rag_microbucket_k32_f590_q32exact_v1_stage1 = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_k32_f590_q32exact_v1_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 66816, "constants": [["BLOCK_Q", 64], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32], ["ROWS_COVERED", 32]], "cta_group": 1, "threads": 128}')) +stage1_q32_exact_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_k32_f590_q32exact_v1_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 66816, "constants": [["BLOCK_Q", 64], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32], ["ROWS_COVERED", 32]], "cta_group": 1, "threads": 128}')) + +def _ir_with_constants(ir_obj: Any, *, suffix: str, **updates: int) -> Any: + constants = tuple(((name, updates.get(name, value)) for name, value in ir_obj.constants)) + return dc.replace(ir_obj, symbol=''.join([format(ir_obj.symbol, ''), '_', format(suffix, '')]), constants=constants) + +def _stage1_q32_exact_ir() -> Any: + return _ir_with_constants(stage1_q32_exact_ir, suffix='q32exact_f590_v1', BLOCK_Q=rowld1.Q16_ROWLD1_BLOCK_Q, BLOCK_M=rowld1.Q16_ROWLD1_BLOCK_M, FEAT_D=rowld1.Q16_ROWLD1_FEAT_D, TOP_K_MAX=K32_TOP_K_MAX, ROWS_COVERED=rowld1.Q32_ROWLD2_ACTIVE_ROWS) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RAG_MICROBUCKET_K32_F590_Q32EXACT_V1_VERIFY_KERNEL') + split_count = int(os.environ.get('LOOM_KNN_RAG_MICROBUCKET_K32_F590_Q32EXACT_V1_VERIFY_K32_SPLIT', K32_Q32_SPLIT_COUNT)) + if verify_kernel == 'q32_exact_stage1': + return _stage1_q32_exact_ir() + if verify_kernel == 'q32_rows4_merge': + return rows4._warp_merge_ir(split_count) + return _stage1_q32_exact_ir() +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_k32_f590_q32exact_v1_stage1_q32exact_f590_v1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 66816, "constants": [["BLOCK_Q", 64], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32], ["ROWS_COVERED", 32]], "cta_group": 1, "threads": 128}')) + +def _compiled_stage1_q32_exact(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0205"}')) + +def _eligible_q32_exact(inputs: dict[str, Any]) -> bool: + return split153._eligible_q32_split153(inputs) + +def _q32_route_name(inputs: dict[str, Any], *, split_count: int) -> str: + return ''.join(['rag_microbucket_k32_f590_q32exact_v1_q', format(int(inputs.get('Q', -1)), ''), '_m', format(int(inputs.get('M', -1)), ''), '_k32_row16x256b2cw_exact_s', format(split_count, ''), '_r4_warpmerge']) + +def _launch_q32_exact_rows4(inputs: dict[str, Any], *, split_count: int) -> None: + rows4._launch_stage1_then_rows4_merge(inputs, split_count=split_count, stage1_kernel_fn=_compiled_stage1_q32_exact, stage1_ir=_stage1_q32_exact_ir(), stage1_threads=Q32_EXACT_STAGE1_THREADS, block_q=rowld1.Q16_ROWLD1_BLOCK_Q, block_m=rowld1.Q16_ROWLD1_BLOCK_M) + +def route_for_contract_inputs(inputs: dict[str, Any], *, k32_q32_split_count: int=K32_Q32_SPLIT_COUNT, k32_q16_split_count: int=K32_Q16_SPLIT_COUNT, k32_default_split_count: int=K32_DEFAULT_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> str: + del k32_q16_split_count, k32_default_split_count, k32_group_count + if _eligible_q32_exact(inputs): + return _q32_route_name(inputs, split_count=k32_q32_split_count) + return split153.route_for_contract_inputs(inputs) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, k32_q32_split_count: int=K32_Q32_SPLIT_COUNT, k32_q16_split_count: int=K32_Q16_SPLIT_COUNT, k32_default_split_count: int=K32_DEFAULT_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> None: + if _eligible_q32_exact(inputs): + _launch_q32_exact_rows4(inputs, split_count=k32_q32_split_count) + return + split153.launch_from_contract_inputs(inputs, k32_q32_split_count=k32_q32_split_count, k32_q16_split_count=k32_q16_split_count, k32_default_split_count=k32_default_split_count, k32_group_count=k32_group_count) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_split153(inputs: dict[str, Any]) -> None: + split153.launch_from_contract_inputs(inputs) + +def candidate_parent_2e8e(inputs: dict[str, Any]) -> None: + parent.launch_from_contract_inputs(inputs) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return split153._select_contract_shapes(shape_labels) + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def route_trace_for_contract_shapes(shape_labels=TARGET_SHAPES, *, k32_q32_split_count: int=K32_Q32_SPLIT_COUNT) -> list[dict[str, Any]]: + rows = [] + for shape in _select_contract_shapes(shape_labels): + inputs = parent.parent.rows4.base.rowld_seed.base_dispatcher._trace_inputs_from_shape(shape) + route = route_for_contract_inputs(inputs, k32_q32_split_count=k32_q32_split_count) + parent_route = split153.route_for_contract_inputs(inputs) + specialized = route.startswith('rag_microbucket_k32_f590_q32exact_v1_') + rows.append({'shape_key': shape['label'], 'selected_route': route, 'selected_seed': SEED_K32_F590_Q32_EXACT_ID if specialized else split153.SEED_K32_F590_Q32_SPLIT153_ID, 'selected_entrypoint': ROUTE_Q32_EXACT if specialized else ROUTE_SPLIT153, 'parent_split153_route': parent_route, 'route_kind': 'specialized_q32_exact_stage1_rows4' if specialized else 'inherited_split153', 'guard_condition': 'BF16 non-build B=1 Q=32 M=100000 D=128 K=32' if specialized else 'delegate to f590 split153 seed'}) + return rows + +def _per_shape_delta(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> dict[str, Any]: + rows = {} + for label, cand in candidate_report.get('per_shape', {}).items(): + base_row = baseline_report.get('per_shape', {}).get(label, {}) + cand_ms = cand.get('kernel_ms') + base_ms = base_row.get('kernel_ms') + rows[label] = {'candidate': cand, 'split153_baseline': base_row, 'candidate_ms': cand_ms, 'split153_ms': base_ms, 'speedup_vs_split153': base_ms / cand_ms if cand_ms and base_ms else None, 'ratio_vs_flashlib': cand.get('ratio_vs_flashlib')} + return rows + +def benchmark_knn_build_rag_microbucket_k32_f590_q32exact_v1(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate) + split153_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_split153) + parent_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_parent_2e8e) + return {'contract': candidate_report['contract'], 'contract_version': candidate_report['contract_version'], 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_knn_build_rag_microbucket_k32_f590_q32exact_v1']), 'candidate_entrypoint': ROUTE_Q32_EXACT, 'split153_entrypoint': ROUTE_SPLIT153, 'parent_entrypoint': ROUTE_PARENT_2E8E, 'accelerated_shape_labels': list(TARGET_SHAPES), 'measured_shape_labels': list(shape_labels), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'producer_topology': {'candidate': 'f590 rowld2 ROW_16x256B two-compute-warp stage1 with exact Q32/K32/B1 branch removal', 'baseline': 'f590 rowld2 ROW_16x256B two-compute-warp generic stage1', 'guard_misses': 'delegate to f590 split153 seed'}, 'merge_topology': {'candidate': ''.join(['rows4 warp-row split-list merge/', format(rows4.K32_ROWS4_ROWS_PER_CTA, ''), ' rows per CTA']), 'split_count': K32_Q32_SPLIT_COUNT, 'splits_per_lane': rows4.base._splits_per_lane(K32_Q32_SPLIT_COUNT)}, 'route_trace': route_trace_for_contract_shapes(shape_labels), 'target_rows': _per_shape_delta(candidate_report, split153_report), 'summary': candidate_report['summary'], 'performance': candidate_report['performance'], 'correctness': candidate_report['correctness'], 'report': candidate_report, 'split153_summary': split153_report['summary'], 'split153_performance': split153_report['performance'], 'split153_report': split153_report, 'parent_summary': parent_report['summary'], 'parent_performance': parent_report['performance'], 'parent_report': parent_report} + +def write_benchmark_artifact(artifact_dir: str | os.PathLike[str], *, use_cupti: bool=True, shape_labels=TARGET_SHAPES) -> dict[str, str]: + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + suffix = 'cupti' if use_cupti else 'cuda_event' + payload = benchmark_knn_build_rag_microbucket_k32_f590_q32exact_v1(use_cupti=use_cupti, shape_labels=shape_labels) + path = out_dir / ''.join(['f590_q32exact_', format(len(tuple(shape_labels)), ''), 'row_', format(suffix, ''), '.json']) + path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + return {'candidate_payload': str(path)} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32_f590_q32rowld2rows4_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32_f590_q32rowld2rows4_v1.py new file mode 100644 index 00000000..227ab293 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32_f590_q32rowld2rows4_v1.py @@ -0,0 +1,140 @@ +"""Exact Q32 RAG K32 rowld2 producer with rows4 merge. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +repairs only the BF16 non-build ``B=1,Q=32,M=100000,D=128,K=32`` row from the +v11 D128 RAG large-K floor set. It keeps the 2e8e seed for guard misses and +other K32 bucket rows, but routes the exact Q32 row through the rowld2 +ROW_16x256B stage-1 producer and the existing rows4 warp-row merge. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from pathlib import Path +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_rag_microbucket_k32_2e8e_q16split148_v1 as parent +from . import knn_build_rag_microbucket_k32rowld1warp_0077_v1 as rowld1 +from . import knn_build_rag_microbucket_k32rows4_0077_v1 as rows4 +MODULE = 'loom.examples.weave.knn_build_rag_microbucket_k32_f590_q32rowld2rows4_v1' +Q8_K32_SHAPE = parent.Q8_K32_SHAPE +Q16_K32_SHAPE = parent.Q16_K32_SHAPE +Q32_K32_SHAPE = parent.Q32_K32_SHAPE +Q16_K32_IRREGULAR_SHAPE = parent.Q16_K32_IRREGULAR_SHAPE +K32_BUCKET_SHAPES = parent.K32_BUCKET_SHAPES +TARGET_SHAPES = (Q32_K32_SHAPE,) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +K32_Q32_SPLIT_COUNT = _decode_capture(_json_loads('152')) +K32_Q16_SPLIT_COUNT = parent.K32_Q16_SPLIT_COUNT +K32_DEFAULT_SPLIT_COUNT = parent.K32_DEFAULT_SPLIT_COUNT +K32_GROUP_COUNT = parent.K32_GROUP_COUNT +ROUTE_PARENT_2E8E = ''.join([format(parent.MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_Q32_ROWLD2_ROWS4 = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +SEED_K32_F590_Q32_ROWLD2_ROWS4_ID = 'rag_microbucket_k32_f590_q32rowld2rows4_v1' + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RAG_MICROBUCKET_K32_F590_Q32ROWLD2ROWS4_V1_VERIFY_KERNEL') + split_count = int(os.environ.get('LOOM_KNN_RAG_MICROBUCKET_K32_F590_Q32ROWLD2ROWS4_V1_VERIFY_K32_SPLIT', K32_Q32_SPLIT_COUNT)) + if verify_kernel == 'q32_rowld2_stage1': + return rowld1._stage1_rowld2_ir() + if verify_kernel == 'q32_rows4_merge': + return rows4._warp_merge_ir(split_count) + return rowld1._stage1_rowld2_ir() +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_k32rowld1warp_0077_v1_stage1_q32_k32_m64_rowld2_q32rowld2_0077_v1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 66816, "constants": [["BLOCK_Q", 64], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32], ["ROWS_COVERED", 32]], "cta_group": 1, "threads": 128}')) + +def _eligible_q32_rowld2_rows4(inputs: dict[str, Any]) -> bool: + return parent.parent._is_bf16_d128_nonbuild(inputs) and int(inputs.get('Q', -1)) == 32 and (int(inputs.get('M', -1)) == 100000) and (int(inputs.get('K', -1)) == 32) + +def _q32_route_name(inputs: dict[str, Any], *, split_count: int) -> str: + return ''.join(['rag_microbucket_k32_f590_q32rowld2rows4_v1_q', format(int(inputs.get('Q', -1)), ''), '_m', format(int(inputs.get('M', -1)), ''), '_k32_row16x256b2cw_s', format(split_count, ''), '_r4_warpmerge']) + +def _launch_q32_rowld2_rows4(inputs: dict[str, Any], *, split_count: int) -> None: + rows4._launch_stage1_then_rows4_merge(inputs, split_count=split_count, stage1_kernel_fn=rowld1._compiled_stage1_q32_k32_m64_rowld2, stage1_ir=rowld1._stage1_rowld2_ir(), stage1_threads=rowld1.Q32_ROWLD2_STAGE1_THREADS, block_q=rowld1.Q16_ROWLD1_BLOCK_Q, block_m=rowld1.Q16_ROWLD1_BLOCK_M) + +def _launch_q32_rowld2_warpmerge(inputs: dict[str, Any], *, split_count: int) -> None: + rowld1._launch_rowld2_warpmerge(inputs, split_count=split_count) + +def route_for_contract_inputs(inputs: dict[str, Any], *, k32_q32_split_count: int=K32_Q32_SPLIT_COUNT, k32_q16_split_count: int=K32_Q16_SPLIT_COUNT, k32_default_split_count: int=K32_DEFAULT_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> str: + del k32_q16_split_count, k32_default_split_count, k32_group_count + if _eligible_q32_rowld2_rows4(inputs): + return _q32_route_name(inputs, split_count=k32_q32_split_count) + return parent.route_for_contract_inputs(inputs) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, k32_q32_split_count: int=K32_Q32_SPLIT_COUNT, k32_q16_split_count: int=K32_Q16_SPLIT_COUNT, k32_default_split_count: int=K32_DEFAULT_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> None: + if _eligible_q32_rowld2_rows4(inputs): + _launch_q32_rowld2_rows4(inputs, split_count=k32_q32_split_count) + return + parent.launch_from_contract_inputs(inputs, k32_q16_split_count=k32_q16_split_count, k32_default_split_count=k32_default_split_count, k32_group_count=k32_group_count) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_parent_2e8e(inputs: dict[str, Any]) -> None: + parent.launch_from_contract_inputs(inputs) + +def candidate_rowld2_warpmerge(inputs: dict[str, Any]) -> None: + if _eligible_q32_rowld2_rows4(inputs): + _launch_q32_rowld2_warpmerge(inputs, split_count=K32_Q32_SPLIT_COUNT) + return + parent.launch_from_contract_inputs(inputs) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return parent._select_contract_shapes(shape_labels) + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def route_trace_for_contract_shapes(shape_labels=TARGET_SHAPES, *, k32_q32_split_count: int=K32_Q32_SPLIT_COUNT) -> list[dict[str, Any]]: + rows = [] + for shape in _select_contract_shapes(shape_labels): + inputs = parent.parent.rows4.base.rowld_seed.base_dispatcher._trace_inputs_from_shape(shape) + route = route_for_contract_inputs(inputs, k32_q32_split_count=k32_q32_split_count) + parent_route = parent.route_for_contract_inputs(inputs) + specialized = route.startswith('rag_microbucket_k32_f590_q32rowld2rows4_v1_') + rows.append({'shape_key': shape['label'], 'selected_route': route, 'selected_seed': SEED_K32_F590_Q32_ROWLD2_ROWS4_ID if specialized else parent.SEED_K32_2E8E_Q16_SPLIT148_ID, 'selected_entrypoint': ROUTE_Q32_ROWLD2_ROWS4 if specialized else ROUTE_PARENT_2E8E, 'parent_2e8e_route': parent_route, 'route_kind': 'specialized_q32_rowld2_rows4' if specialized else 'inherited_2e8e', 'guard_condition': 'BF16 non-build B=1 Q=32 M=100000 D=128 K=32' if specialized else 'delegate to 2e8e Q16 split148 seed'}) + return rows + +def _per_shape_delta(candidate_report: dict[str, Any], parent_report: dict[str, Any]) -> dict[str, Any]: + rows = {} + for label, cand in candidate_report.get('per_shape', {}).items(): + parent_row = parent_report.get('per_shape', {}).get(label, {}) + cand_ms = cand.get('kernel_ms') + parent_ms = parent_row.get('kernel_ms') + rows[label] = {'candidate': cand, 'parent_2e8e': parent_row, 'candidate_ms': cand_ms, 'parent_2e8e_ms': parent_ms, 'speedup_vs_parent_2e8e': parent_ms / cand_ms if cand_ms and parent_ms else None, 'ratio_vs_flashlib': cand.get('ratio_vs_flashlib')} + return rows + +def benchmark_knn_build_rag_microbucket_k32_f590_q32rowld2rows4_v1(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate) + parent_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_parent_2e8e) + rowld2_warpmerge_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_rowld2_warpmerge) + return {'contract': candidate_report['contract'], 'contract_version': candidate_report['contract_version'], 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_knn_build_rag_microbucket_k32_f590_q32rowld2rows4_v1']), 'candidate_entrypoint': ROUTE_Q32_ROWLD2_ROWS4, 'parent_entrypoint': ROUTE_PARENT_2E8E, 'accelerated_shape_labels': list(TARGET_SHAPES), 'measured_shape_labels': list(shape_labels), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'producer_topology': {'Q32_exact': 'rowld2 ROW_16x256B two-compute-warp stage1', 'guard_misses': 'delegate to 2e8e Q16 split148 seed'}, 'merge_topology': {'candidate': ''.join(['rows4 warp-row split-list merge/', format(rows4.K32_ROWS4_ROWS_PER_CTA, ''), ' rows per CTA']), 'comparison_rowld2_warpmerge': ''.join(['warp-row split-list merge/', format(rowld1.K32_WARP_MERGE_ROWS_PER_CTA, ''), ' row per CTA']), 'split_count': K32_Q32_SPLIT_COUNT, 'splits_per_lane': rows4.base._splits_per_lane(K32_Q32_SPLIT_COUNT)}, 'route_trace': route_trace_for_contract_shapes(shape_labels), 'target_rows': _per_shape_delta(candidate_report, parent_report), 'summary': candidate_report['summary'], 'performance': candidate_report['performance'], 'correctness': candidate_report['correctness'], 'report': candidate_report, 'parent_summary': parent_report['summary'], 'parent_performance': parent_report['performance'], 'parent_report': parent_report, 'rowld2_warpmerge_summary': rowld2_warpmerge_report['summary'], 'rowld2_warpmerge_report': rowld2_warpmerge_report} + +def write_benchmark_artifact(artifact_dir: str | os.PathLike[str], *, use_cupti: bool=True, shape_labels=TARGET_SHAPES) -> dict[str, str]: + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + suffix = 'cupti' if use_cupti else 'cuda_event' + payload = benchmark_knn_build_rag_microbucket_k32_f590_q32rowld2rows4_v1(use_cupti=use_cupti, shape_labels=shape_labels) + path = out_dir / ''.join(['f590_q32rowld2rows4_', format(len(tuple(shape_labels)), ''), 'row_', format(suffix, ''), '.json']) + path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + return {'candidate_payload': str(path)} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32_f590_q32split153_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32_f590_q32split153_v1.py new file mode 100644 index 00000000..e806bf9b --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32_f590_q32split153_v1.py @@ -0,0 +1,131 @@ +"""Exact Q32 RAG K32 rowld2 producer with rows4 merge at split153. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +repairs only the BF16 non-build ``B=1,Q=32,M=100000,D=128,K=32`` row from the +v11 D128 RAG large-K floor set. It reuses the f590 rowld2 two-compute-warp +stage1 and rows4 merge, but shifts the exact Q32 work feed from split152 to +split153 after same-denominator probes showed a small latency improvement. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from pathlib import Path +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_rag_microbucket_k32_2e8e_q16split148_v1 as parent +from . import knn_build_rag_microbucket_k32_f590_q32rowld2rows4_v1 as f590 +MODULE = 'loom.examples.weave.knn_build_rag_microbucket_k32_f590_q32split153_v1' +Q8_K32_SHAPE = parent.Q8_K32_SHAPE +Q16_K32_SHAPE = parent.Q16_K32_SHAPE +Q32_K32_SHAPE = parent.Q32_K32_SHAPE +Q16_K32_IRREGULAR_SHAPE = parent.Q16_K32_IRREGULAR_SHAPE +K32_BUCKET_SHAPES = parent.K32_BUCKET_SHAPES +TARGET_SHAPES = (Q32_K32_SHAPE,) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +K32_Q32_SPLIT_COUNT = _decode_capture(_json_loads('153')) +K32_Q16_SPLIT_COUNT = parent.K32_Q16_SPLIT_COUNT +K32_DEFAULT_SPLIT_COUNT = parent.K32_DEFAULT_SPLIT_COUNT +K32_GROUP_COUNT = parent.K32_GROUP_COUNT +ROUTE_PARENT_2E8E = ''.join([format(parent.MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_F590_SPLIT152 = ''.join([format(f590.MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_Q32_SPLIT153 = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +SEED_K32_F590_Q32_SPLIT153_ID = 'rag_microbucket_k32_f590_q32split153_v1' + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RAG_MICROBUCKET_K32_F590_Q32SPLIT153_V1_VERIFY_KERNEL') + split_count = int(os.environ.get('LOOM_KNN_RAG_MICROBUCKET_K32_F590_Q32SPLIT153_V1_VERIFY_K32_SPLIT', K32_Q32_SPLIT_COUNT)) + if verify_kernel == 'q32_rowld2_stage1': + return f590.rowld1._stage1_rowld2_ir() + if verify_kernel == 'q32_rows4_merge': + return f590.rows4._warp_merge_ir(split_count) + return f590.rowld1._stage1_rowld2_ir() +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_k32rowld1warp_0077_v1_stage1_q32_k32_m64_rowld2_q32rowld2_0077_v1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 66816, "constants": [["BLOCK_Q", 64], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32], ["ROWS_COVERED", 32]], "cta_group": 1, "threads": 128}')) + +def _eligible_q32_split153(inputs: dict[str, Any]) -> bool: + return f590._eligible_q32_rowld2_rows4(inputs) + +def _q32_route_name(inputs: dict[str, Any], *, split_count: int) -> str: + return ''.join(['rag_microbucket_k32_f590_q32split153_v1_q', format(int(inputs.get('Q', -1)), ''), '_m', format(int(inputs.get('M', -1)), ''), '_k32_row16x256b2cw_s', format(split_count, ''), '_r4_warpmerge']) + +def route_for_contract_inputs(inputs: dict[str, Any], *, k32_q32_split_count: int=K32_Q32_SPLIT_COUNT, k32_q16_split_count: int=K32_Q16_SPLIT_COUNT, k32_default_split_count: int=K32_DEFAULT_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> str: + del k32_q16_split_count, k32_default_split_count, k32_group_count + if _eligible_q32_split153(inputs): + return _q32_route_name(inputs, split_count=k32_q32_split_count) + return parent.route_for_contract_inputs(inputs) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, k32_q32_split_count: int=K32_Q32_SPLIT_COUNT, k32_q16_split_count: int=K32_Q16_SPLIT_COUNT, k32_default_split_count: int=K32_DEFAULT_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> None: + if _eligible_q32_split153(inputs): + f590._launch_q32_rowld2_rows4(inputs, split_count=k32_q32_split_count) + return + parent.launch_from_contract_inputs(inputs, k32_q16_split_count=k32_q16_split_count, k32_default_split_count=k32_default_split_count, k32_group_count=k32_group_count) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_parent_2e8e(inputs: dict[str, Any]) -> None: + parent.launch_from_contract_inputs(inputs) + +def candidate_f590_split152(inputs: dict[str, Any]) -> None: + f590.launch_from_contract_inputs(inputs) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return parent._select_contract_shapes(shape_labels) + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def route_trace_for_contract_shapes(shape_labels=TARGET_SHAPES, *, k32_q32_split_count: int=K32_Q32_SPLIT_COUNT) -> list[dict[str, Any]]: + rows = [] + for shape in _select_contract_shapes(shape_labels): + inputs = parent.parent.rows4.base.rowld_seed.base_dispatcher._trace_inputs_from_shape(shape) + route = route_for_contract_inputs(inputs, k32_q32_split_count=k32_q32_split_count) + parent_route = parent.route_for_contract_inputs(inputs) + specialized = route.startswith('rag_microbucket_k32_f590_q32split153_v1_') + rows.append({'shape_key': shape['label'], 'selected_route': route, 'selected_seed': SEED_K32_F590_Q32_SPLIT153_ID if specialized else parent.SEED_K32_2E8E_Q16_SPLIT148_ID, 'selected_entrypoint': ROUTE_Q32_SPLIT153 if specialized else ROUTE_PARENT_2E8E, 'parent_2e8e_route': parent_route, 'route_kind': 'specialized_q32_rowld2_rows4_split153' if specialized else 'inherited_2e8e', 'guard_condition': 'BF16 non-build B=1 Q=32 M=100000 D=128 K=32' if specialized else 'delegate to 2e8e Q16 split148 seed'}) + return rows + +def _per_shape_delta(candidate_report: dict[str, Any], parent_report: dict[str, Any]) -> dict[str, Any]: + rows = {} + for label, cand in candidate_report.get('per_shape', {}).items(): + parent_row = parent_report.get('per_shape', {}).get(label, {}) + cand_ms = cand.get('kernel_ms') + parent_ms = parent_row.get('kernel_ms') + rows[label] = {'candidate': cand, 'parent_2e8e': parent_row, 'candidate_ms': cand_ms, 'parent_2e8e_ms': parent_ms, 'speedup_vs_parent_2e8e': parent_ms / cand_ms if cand_ms and parent_ms else None, 'ratio_vs_flashlib': cand.get('ratio_vs_flashlib')} + return rows + +def benchmark_knn_build_rag_microbucket_k32_f590_q32split153_v1(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate) + parent_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_parent_2e8e) + f590_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_f590_split152) + return {'contract': candidate_report['contract'], 'contract_version': candidate_report['contract_version'], 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_knn_build_rag_microbucket_k32_f590_q32split153_v1']), 'candidate_entrypoint': ROUTE_Q32_SPLIT153, 'parent_entrypoint': ROUTE_PARENT_2E8E, 'f590_entrypoint': ROUTE_F590_SPLIT152, 'accelerated_shape_labels': list(TARGET_SHAPES), 'measured_shape_labels': list(shape_labels), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'producer_topology': {'Q32_exact': 'f590 rowld2 ROW_16x256B two-compute-warp stage1', 'guard_misses': 'delegate to 2e8e Q16 split148 seed'}, 'merge_topology': {'candidate': ''.join(['rows4 warp-row split-list merge/', format(f590.rows4.K32_ROWS4_ROWS_PER_CTA, ''), ' rows per CTA']), 'split_count': K32_Q32_SPLIT_COUNT, 'splits_per_lane': f590.rows4.base._splits_per_lane(K32_Q32_SPLIT_COUNT)}, 'route_trace': route_trace_for_contract_shapes(shape_labels), 'target_rows': _per_shape_delta(candidate_report, parent_report), 'summary': candidate_report['summary'], 'performance': candidate_report['performance'], 'correctness': candidate_report['correctness'], 'report': candidate_report, 'parent_summary': parent_report['summary'], 'parent_performance': parent_report['performance'], 'parent_report': parent_report, 'f590_summary': f590_report['summary'], 'f590_performance': f590_report['performance'], 'f590_report': f590_report} + +def write_benchmark_artifact(artifact_dir: str | os.PathLike[str], *, use_cupti: bool=True, shape_labels=TARGET_SHAPES) -> dict[str, str]: + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + suffix = 'cupti' if use_cupti else 'cuda_event' + payload = benchmark_knn_build_rag_microbucket_k32_f590_q32split153_v1(use_cupti=use_cupti, shape_labels=shape_labels) + path = out_dir / ''.join(['f590_q32split153_', format(len(tuple(shape_labels)), ''), 'row_', format(suffix, ''), '.json']) + path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + return {'candidate_payload': str(path)} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32_q16dual2warp_56ed_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32_q16dual2warp_56ed_v1.py new file mode 100644 index 00000000..68c69f1c --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32_q16dual2warp_56ed_v1.py @@ -0,0 +1,182 @@ +"""RAG microbatch K32 bucket with two-warp Q16 producers. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +reuses the validated ROW_16x256B SMEM-staged two-compute-warp producer from the +a444 v2 irregular-Q16 repair, but routes both Q16 K32 contract rows through it: +M=100000 uses split144 and M=131071 uses split148. Q8/Q32 and guard misses stay +on the inherited rows4 parent path. The production path remains Weave-only. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from collections.abc import Callable +from functools import cache +from typing import Any +from .._dispatch_runtime import dc as dc +from .. import _dispatch_runtime as eval_mod +from . import knn_build_rag_microbucket_k32_q16irreg2warp_a444_v2 as v2 +from . import knn_build_rag_microbucket_k32_q16rows4_abee_v1 as rows4 +from . import knn_build_rag_microbucket_k32warpmerge_0077_v1 as base +from .._dispatch_runtime import pack_kernel_args +MODULE = 'loom.examples.weave.knn_build_rag_microbucket_k32_q16dual2warp_56ed_v1' +Q8_K32_SHAPE = v2.Q8_K32_SHAPE +Q16_K32_SHAPE = v2.Q16_K32_SHAPE +Q32_K32_SHAPE = v2.Q32_K32_SHAPE +Q16_K32_IRREGULAR_SHAPE = v2.Q16_K32_IRREGULAR_SHAPE +K32_BUCKET_SHAPES = v2.K32_BUCKET_SHAPES +TARGET_SHAPES = v2.TARGET_SHAPES +Q16_DUAL_2WARP_TARGET_SHAPES = (Q16_K32_SHAPE, Q16_K32_IRREGULAR_SHAPE) +K32_EXACT_Q16_SPLIT_COUNT = v2.K32_EXACT_Q16_SPLIT_COUNT +K32_IRREGULAR_Q16_SPLIT_COUNT = v2.K32_IRREGULAR_Q16_SPLIT_COUNT +K32_DEFAULT_SPLIT_COUNT = v2.K32_DEFAULT_SPLIT_COUNT +K32_GROUP_COUNT = v2.K32_GROUP_COUNT +K32_TOP_K_MAX = v2.K32_TOP_K_MAX +K32_ROWS4_MERGE_THREADS = v2.K32_ROWS4_MERGE_THREADS +K32_ROWS4_ROWS_PER_CTA = v2.K32_ROWS4_ROWS_PER_CTA +K32_ROWS4_WARPS = v2.K32_ROWS4_WARPS +Q16_2WARP_STAGE1_THREADS = v2.Q16_2WARP_STAGE1_THREADS +ROUTE_PARENT_ROWS4 = v2.ROUTE_PARENT_ROWS4 +ROUTE_Q16_DUAL_2WARP_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +SEED_K32_Q16_DUAL_2WARP_56ED_V1_ID = 'rag_microbucket_k32_q16dual2warp_56ed_v1_rowld1_2warp_rows4' + +def _ir_with_constants(ir_obj: Any, *, suffix: str, **updates: int) -> Any: + constants = tuple(((name, updates.get(name, value)) for name, value in ir_obj.constants)) + return dc.replace(ir_obj, symbol=''.join([format(ir_obj.symbol, ''), '_', format(suffix, '')]), constants=constants) + +def _stage1_rowld1_2warp_ir() -> Any: + return _ir_with_constants(v2.stage1_q16_rowld1_2warp_ir, suffix='q16dual2warp_56ed_v1', BLOCK_Q=v2.rowld1.Q16_ROWLD1_BLOCK_Q, BLOCK_M=v2.rowld1.Q16_ROWLD1_BLOCK_M, FEAT_D=v2.rowld1.Q16_ROWLD1_FEAT_D, TOP_K_MAX=K32_TOP_K_MAX, ROWS_COVERED=v2.Q16_2WARP_ACTIVE_ROWS) + +def _warp_merge_ir(split_count: int) -> Any: + if K32_ROWS4_ROWS_PER_CTA <= 0 or K32_ROWS4_ROWS_PER_CTA > K32_ROWS4_WARPS: + raise ValueError(''.join(['rows_per_cta=', format(K32_ROWS4_ROWS_PER_CTA, ''), ' exceeds merge warps=', format(K32_ROWS4_WARPS, '')])) + return _ir_with_constants(base.k32_warp_row_merge_ir, suffix=''.join(['k32s', format(split_count, ''), 'r', format(K32_ROWS4_ROWS_PER_CTA, ''), '_56ed_v1']), TOP_K_MAX=K32_TOP_K_MAX, SPLIT_COUNT=split_count, SPLITS_PER_LANE=base._splits_per_lane(split_count), ROWS_PER_CTA=K32_ROWS4_ROWS_PER_CTA) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RAG_MICROBUCKET_K32_Q16DUAL2WARP_56ED_V1_VERIFY_KERNEL') + split_count = int(os.environ.get('LOOM_KNN_RAG_MICROBUCKET_K32_Q16DUAL2WARP_56ED_V1_VERIFY_K32_SPLIT', K32_IRREGULAR_Q16_SPLIT_COUNT)) + if verify_kernel == 'rowld1_2warp_stage1': + return _stage1_rowld1_2warp_ir() + return _warp_merge_ir(split_count) +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_k32warpmerge_0077_v1_warp_row_merge_k32s148r4_56ed_v1", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 32], ["SPLIT_COUNT", 148], ["SPLITS_PER_LANE", 5], ["ROWS_PER_CTA", 4]], "cta_group": 1, "threads": 128}')) + +def _compiled_stage1_q16_rowld1_2warp(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0104"}')) + +@cache +def _compiled_rows4_warp_merge(split_count: int): + return base.rowld_seed.compact_seed.q16_tailinf.parent_k32._compile_ir(_warp_merge_ir(split_count)) + +def _eligible_q16_dual_2warp(inputs: dict[str, Any]) -> bool: + return rows4.parent.parent._is_bf16_d128_nonbuild(inputs) and int(inputs.get('Q', -1)) == 16 and (int(inputs.get('M', -1)) in {100000, 131071}) and (int(inputs.get('K', -1)) == 32) + +def _split_for_q16_dual_2warp(inputs: dict[str, Any], *, exact_split_count: int, irregular_split_count: int) -> int: + return irregular_split_count if int(inputs.get('M', -1)) == 131071 else exact_split_count + +def _dual2warp_route_name(inputs: dict[str, Any], *, split_count: int) -> str: + n_database = int(inputs.get('M', -1)) + return ''.join(['rag_microbucket_k32_q16dual2warp_56ed_v1_q16_m', format(n_database, ''), '_k32_row16x256b_2cw_s', format(split_count, ''), '_r', format(K32_ROWS4_ROWS_PER_CTA, ''), '_warpmerge']) + +def route_for_contract_inputs(inputs: dict[str, Any], *, k32_exact_q16_split_count: int=K32_EXACT_Q16_SPLIT_COUNT, k32_irregular_q16_split_count: int=K32_IRREGULAR_Q16_SPLIT_COUNT, k32_default_split_count: int=K32_DEFAULT_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> str: + if _eligible_q16_dual_2warp(inputs): + split_count = _split_for_q16_dual_2warp(inputs, exact_split_count=k32_exact_q16_split_count, irregular_split_count=k32_irregular_q16_split_count) + return _dual2warp_route_name(inputs, split_count=split_count) + return v2.route_for_contract_inputs(inputs, k32_exact_q16_split_count=k32_exact_q16_split_count, k32_irregular_q16_split_count=k32_irregular_q16_split_count, k32_default_split_count=k32_default_split_count, k32_group_count=k32_group_count) + +def _launch_rowld1_2warp_rows4_merge(inputs: dict[str, Any], *, split_count: int) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + if top_k != K32_TOP_K_MAX: + raise ValueError(''.join(['k32 q16 dual2warp only supports K=', format(K32_TOP_K_MAX, ''), ', got K=', format(top_k, '')])) + block_q = v2.rowld1.Q16_ROWLD1_BLOCK_Q + block_m = v2.rowld1.Q16_ROWLD1_BLOCK_M + num_q_tiles = (n_query + block_q - 1) // block_q + num_db_tiles = (n_database + block_m - 1) // block_m + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tiles * split_count + total_queries = bsz * n_query + stage1_grid = min(total_work, base.rowld_seed.compact_seed.GRID_DIM_DEFAULT) + merge_grid = min((total_queries + K32_ROWS4_ROWS_PER_CTA - 1) // K32_ROWS4_ROWS_PER_CTA, base.rowld_seed.compact_seed.GRID_DIM_DEFAULT) + partial_dists, partial_indices = base.rowld_seed.compact_seed.q16_tailinf.parent_k32.parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = base.rowld_seed.compact_seed.q16_tailinf.parent_k32.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), total_queries, block_q, dim, dim) + tmap_database = base.rowld_seed.compact_seed.q16_tailinf.parent_k32.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, block_m, dim, dim) + stage1_ir = _stage1_rowld1_2warp_ir() + _compiled_stage1_q16_rowld1_2warp().launch(grid=(stage1_grid, 1, 1), block=(Q16_2WARP_STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage1_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tiles=num_q_tiles, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), shared_mem=stage1_ir.computed_smem_bytes) + merge_ir = _warp_merge_ir(split_count) + _compiled_rows4_warp_merge(split_count).launch(grid=(merge_grid, 1, 1), block=(K32_ROWS4_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], total_queries], shared_mem=merge_ir.computed_smem_bytes) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, k32_exact_q16_split_count: int=K32_EXACT_Q16_SPLIT_COUNT, k32_irregular_q16_split_count: int=K32_IRREGULAR_Q16_SPLIT_COUNT, k32_default_split_count: int=K32_DEFAULT_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> None: + if _eligible_q16_dual_2warp(inputs): + split_count = _split_for_q16_dual_2warp(inputs, exact_split_count=k32_exact_q16_split_count, irregular_split_count=k32_irregular_q16_split_count) + _launch_rowld1_2warp_rows4_merge(inputs, split_count=split_count) + return + v2.launch_from_contract_inputs(inputs, k32_exact_q16_split_count=k32_exact_q16_split_count, k32_irregular_q16_split_count=k32_irregular_q16_split_count, k32_default_split_count=k32_default_split_count, k32_group_count=k32_group_count) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_with_k32_topology(exact_split_count: int, irregular_split_count: int, default_split_count: int, group_count: int) -> Callable[[dict[str, Any]], None]: + + def _candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, k32_exact_q16_split_count=exact_split_count, k32_irregular_q16_split_count=irregular_split_count, k32_default_split_count=default_split_count, k32_group_count=group_count) + return _candidate + +def candidate_parent_rows4(inputs: dict[str, Any]) -> None: + rows4.launch_from_contract_inputs(inputs) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return v2._select_contract_shapes(shape_labels) + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=K32_BUCKET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def compile_and_launch_knn_build(*, shape_labels=K32_BUCKET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def route_trace_for_contract_shapes(shape_labels=K32_BUCKET_SHAPES, *, k32_exact_q16_split_count: int=K32_EXACT_Q16_SPLIT_COUNT, k32_irregular_q16_split_count: int=K32_IRREGULAR_Q16_SPLIT_COUNT, k32_default_split_count: int=K32_DEFAULT_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> list[dict[str, Any]]: + rows = [] + for shape in _select_contract_shapes(shape_labels): + inputs = base.rowld_seed.base_dispatcher._trace_inputs_from_shape(shape) + route = route_for_contract_inputs(inputs, k32_exact_q16_split_count=k32_exact_q16_split_count, k32_irregular_q16_split_count=k32_irregular_q16_split_count, k32_default_split_count=k32_default_split_count, k32_group_count=k32_group_count) + parent_route = rows4.route_for_contract_inputs(inputs, k32_exact_q16_split_count=k32_exact_q16_split_count, k32_irregular_q16_split_count=k32_irregular_q16_split_count, k32_default_split_count=k32_default_split_count, k32_group_count=k32_group_count) + selected = _eligible_q16_dual_2warp(inputs) + selected_split = _split_for_q16_dual_2warp(inputs, exact_split_count=k32_exact_q16_split_count, irregular_split_count=k32_irregular_q16_split_count) if selected else None + rows.append({'shape_key': shape['label'], 'selected_route': route, 'selected_seed': SEED_K32_Q16_DUAL_2WARP_56ED_V1_ID if selected else rows4.SEED_K32_Q16_ROWS4_ABEE_V1_ID, 'selected_entrypoint': ROUTE_Q16_DUAL_2WARP_ENTRYPOINT if selected else ROUTE_PARENT_ROWS4, 'parent_rows4_route': parent_route, 'route_kind': 'specialized_q16_dual_rowld1_2warp' if selected else 'inherited_rows4_parent', 'split_count': selected_split, 'guard_condition': 'BF16 non-build B=1 Q=16 M in {100000,131071} D=128 K=32' if selected else 'delegate to rows4 parent'}) + return rows + +def _per_shape_delta(candidate_report: dict[str, Any], parent_report: dict[str, Any]) -> dict[str, Any]: + rows = {} + for label, cand in candidate_report.get('per_shape', {}).items(): + parent_row = parent_report.get('per_shape', {}).get(label, {}) + cand_ms = cand.get('kernel_ms') + parent_ms = parent_row.get('kernel_ms') + rows[label] = {'candidate': cand, 'parent_rows4': parent_row, 'candidate_ms': cand_ms, 'parent_rows4_ms': parent_ms, 'speedup_vs_parent_rows4': parent_ms / cand_ms if cand_ms and parent_ms else None, 'ratio_vs_flashlib': cand.get('ratio_vs_flashlib')} + return rows + +def benchmark_knn_build_rag_microbucket_k32_q16dual2warp_56ed_v1(*, use_cupti: bool=True, shape_labels=K32_BUCKET_SHAPES, k32_exact_q16_split_count: int=K32_EXACT_Q16_SPLIT_COUNT, k32_irregular_q16_split_count: int=K32_IRREGULAR_Q16_SPLIT_COUNT, k32_default_split_count: int=K32_DEFAULT_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_with_k32_topology(k32_exact_q16_split_count, k32_irregular_q16_split_count, k32_default_split_count, k32_group_count)) + parent_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_parent_rows4) + return {'contract': candidate_report['contract'], 'contract_version': candidate_report['contract_version'], 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_knn_build_rag_microbucket_k32_q16dual2warp_56ed_v1']), 'candidate_entrypoint': ROUTE_Q16_DUAL_2WARP_ENTRYPOINT, 'parent_entrypoint': ROUTE_PARENT_ROWS4, 'accelerated_shape_labels': list(Q16_DUAL_2WARP_TARGET_SHAPES), 'measured_shape_labels': list(shape_labels), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'producer_topology': {'Q16_exact': 'SMEM-staged ROW_16x256B two-compute-warp stage1', 'Q16_irregular': 'SMEM-staged ROW_16x256B two-compute-warp stage1', 'Q8_Q32_and_guard_misses': 'delegate to rows4 parent'}, 'merge_topology': {'Q16_exact_Q16_irregular': ''.join(['warp-row split-list merge/', format(K32_ROWS4_ROWS_PER_CTA, ''), ' rows per CTA']), 'exact_split_count': k32_exact_q16_split_count, 'irregular_split_count': k32_irregular_q16_split_count, 'default_split_count': k32_default_split_count, 'exact_splits_per_lane': base._splits_per_lane(k32_exact_q16_split_count), 'irregular_splits_per_lane': base._splits_per_lane(k32_irregular_q16_split_count)}, 'route_trace': route_trace_for_contract_shapes(shape_labels, k32_exact_q16_split_count=k32_exact_q16_split_count, k32_irregular_q16_split_count=k32_irregular_q16_split_count, k32_default_split_count=k32_default_split_count, k32_group_count=k32_group_count), 'target_rows': _per_shape_delta(candidate_report, parent_report), 'summary': candidate_report['summary'], 'performance': candidate_report['performance'], 'correctness': candidate_report['correctness'], 'report': candidate_report, 'parent_summary': parent_report['summary'], 'parent_performance': parent_report['performance'], 'parent_report': parent_report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32_q16dual2warp_largem_bdd2_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32_q16dual2warp_largem_bdd2_v1.py new file mode 100644 index 00000000..ec3a95a5 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32_q16dual2warp_largem_bdd2_v1.py @@ -0,0 +1,130 @@ +"""RAG microbatch K32 Q16 dual-two-warp large-M bucket wrapper. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +extends the validated 56ed Q16/K32 dual-two-warp ROW_16x256B seed to the v10 +large-M Q16 frontier. M=100000 keeps split144, M=131071 keeps split148, and +M=250000 uses split280. Other shapes delegate to the 56ed parent path. The +production path remains Weave-only. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_rag_microbucket_k32_q16dual2warp_56ed_v1 as seed +MODULE = 'loom.examples.weave.knn_build_rag_microbucket_k32_q16dual2warp_largem_bdd2_v1' +Q16_K32_SHAPE = seed.Q16_K32_SHAPE +Q16_K32_IRREGULAR_SHAPE = seed.Q16_K32_IRREGULAR_SHAPE +Q16_K32_LARGEM_SHAPE = 'rag_microbatch_largek_b1_q16_m250000_d128_k32' +Q16_DUAL_2WARP_LARGEM_TARGET_SHAPES = (Q16_K32_SHAPE, Q16_K32_IRREGULAR_SHAPE, Q16_K32_LARGEM_SHAPE) +K32_BUCKET_SHAPES = Q16_DUAL_2WARP_LARGEM_TARGET_SHAPES +K32_EXACT_Q16_SPLIT_COUNT = seed.K32_EXACT_Q16_SPLIT_COUNT +K32_IRREGULAR_Q16_SPLIT_COUNT = seed.K32_IRREGULAR_Q16_SPLIT_COUNT +K32_LARGEM_Q16_SPLIT_COUNT = _decode_capture(_json_loads('280')) +K32_DEFAULT_SPLIT_COUNT = seed.K32_DEFAULT_SPLIT_COUNT +K32_GROUP_COUNT = seed.K32_GROUP_COUNT +K32_TOP_K_MAX = seed.K32_TOP_K_MAX +ROUTE_PARENT_56ED = ''.join([format(seed.MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_Q16_DUAL_2WARP_LARGEM_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +SEED_K32_Q16_DUAL_2WARP_LARGEM_BDD2_V1_ID = 'rag_microbucket_k32_q16dual2warp_largem_bdd2_v1_rowld1_2warp_rows4' + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RAG_MICROBUCKET_K32_Q16DUAL2WARP_LARGEM_BDD2_V1_VERIFY_KERNEL') + split_count = int(os.environ.get('LOOM_KNN_RAG_MICROBUCKET_K32_Q16DUAL2WARP_LARGEM_BDD2_V1_VERIFY_K32_SPLIT', K32_LARGEM_Q16_SPLIT_COUNT)) + if verify_kernel == 'rowld1_2warp_stage1': + return seed._stage1_rowld1_2warp_ir() + return seed._warp_merge_ir(split_count) +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_k32warpmerge_0077_v1_warp_row_merge_k32s280r4_56ed_v1", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 32], ["SPLIT_COUNT", 280], ["SPLITS_PER_LANE", 9], ["ROWS_PER_CTA", 4]], "cta_group": 1, "threads": 128}')) + +def _eligible_q16_dual_2warp_largem(inputs: dict[str, Any]) -> bool: + return seed.rows4.parent.parent._is_bf16_d128_nonbuild(inputs) and int(inputs.get('Q', -1)) == 16 and (int(inputs.get('M', -1)) in {100000, 131071, 250000}) and (int(inputs.get('K', -1)) == 32) + +def _split_for_q16_dual_2warp_largem(inputs: dict[str, Any], *, exact_split_count: int, irregular_split_count: int, largem_split_count: int) -> int: + n_database = int(inputs.get('M', -1)) + if n_database == 250000: + return largem_split_count + if n_database == 131071: + return irregular_split_count + return exact_split_count + +def _dual2warp_largem_route_name(inputs: dict[str, Any], *, split_count: int) -> str: + n_database = int(inputs.get('M', -1)) + return ''.join(['rag_microbucket_k32_q16dual2warp_largem_bdd2_v1_q16_m', format(n_database, ''), '_k32_row16x256b_2cw_s', format(split_count, ''), '_r', format(seed.K32_ROWS4_ROWS_PER_CTA, ''), '_warpmerge']) + +def route_for_contract_inputs(inputs: dict[str, Any], *, k32_exact_q16_split_count: int=K32_EXACT_Q16_SPLIT_COUNT, k32_irregular_q16_split_count: int=K32_IRREGULAR_Q16_SPLIT_COUNT, k32_largem_q16_split_count: int=K32_LARGEM_Q16_SPLIT_COUNT, k32_default_split_count: int=K32_DEFAULT_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> str: + if _eligible_q16_dual_2warp_largem(inputs): + split_count = _split_for_q16_dual_2warp_largem(inputs, exact_split_count=k32_exact_q16_split_count, irregular_split_count=k32_irregular_q16_split_count, largem_split_count=k32_largem_q16_split_count) + return _dual2warp_largem_route_name(inputs, split_count=split_count) + return seed.route_for_contract_inputs(inputs, k32_exact_q16_split_count=k32_exact_q16_split_count, k32_irregular_q16_split_count=k32_irregular_q16_split_count, k32_default_split_count=k32_default_split_count, k32_group_count=k32_group_count) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, k32_exact_q16_split_count: int=K32_EXACT_Q16_SPLIT_COUNT, k32_irregular_q16_split_count: int=K32_IRREGULAR_Q16_SPLIT_COUNT, k32_largem_q16_split_count: int=K32_LARGEM_Q16_SPLIT_COUNT, k32_default_split_count: int=K32_DEFAULT_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> None: + if _eligible_q16_dual_2warp_largem(inputs): + split_count = _split_for_q16_dual_2warp_largem(inputs, exact_split_count=k32_exact_q16_split_count, irregular_split_count=k32_irregular_q16_split_count, largem_split_count=k32_largem_q16_split_count) + seed._launch_rowld1_2warp_rows4_merge(inputs, split_count=split_count) + return + seed.launch_from_contract_inputs(inputs, k32_exact_q16_split_count=k32_exact_q16_split_count, k32_irregular_q16_split_count=k32_irregular_q16_split_count, k32_default_split_count=k32_default_split_count, k32_group_count=k32_group_count) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_with_k32_topology(exact_split_count: int, irregular_split_count: int, largem_split_count: int, default_split_count: int, group_count: int) -> Callable[[dict[str, Any]], None]: + + def _candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, k32_exact_q16_split_count=exact_split_count, k32_irregular_q16_split_count=irregular_split_count, k32_largem_q16_split_count=largem_split_count, k32_default_split_count=default_split_count, k32_group_count=group_count) + return _candidate + +def candidate_parent_56ed(inputs: dict[str, Any]) -> None: + seed.launch_from_contract_inputs(inputs) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return seed._select_contract_shapes(shape_labels) + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=K32_BUCKET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def compile_and_launch_knn_build(*, shape_labels=K32_BUCKET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def route_trace_for_contract_shapes(shape_labels=K32_BUCKET_SHAPES, *, k32_exact_q16_split_count: int=K32_EXACT_Q16_SPLIT_COUNT, k32_irregular_q16_split_count: int=K32_IRREGULAR_Q16_SPLIT_COUNT, k32_largem_q16_split_count: int=K32_LARGEM_Q16_SPLIT_COUNT, k32_default_split_count: int=K32_DEFAULT_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> list[dict[str, Any]]: + rows = [] + for shape in _select_contract_shapes(shape_labels): + inputs = seed.base.rowld_seed.base_dispatcher._trace_inputs_from_shape(shape) + route = route_for_contract_inputs(inputs, k32_exact_q16_split_count=k32_exact_q16_split_count, k32_irregular_q16_split_count=k32_irregular_q16_split_count, k32_largem_q16_split_count=k32_largem_q16_split_count, k32_default_split_count=k32_default_split_count, k32_group_count=k32_group_count) + parent_route = seed.route_for_contract_inputs(inputs, k32_exact_q16_split_count=k32_exact_q16_split_count, k32_irregular_q16_split_count=k32_irregular_q16_split_count, k32_default_split_count=k32_default_split_count, k32_group_count=k32_group_count) + selected = _eligible_q16_dual_2warp_largem(inputs) + selected_split = _split_for_q16_dual_2warp_largem(inputs, exact_split_count=k32_exact_q16_split_count, irregular_split_count=k32_irregular_q16_split_count, largem_split_count=k32_largem_q16_split_count) if selected else None + rows.append({'shape_key': shape['label'], 'selected_route': route, 'selected_seed': SEED_K32_Q16_DUAL_2WARP_LARGEM_BDD2_V1_ID if selected else seed.SEED_K32_Q16_DUAL_2WARP_56ED_V1_ID, 'selected_entrypoint': ROUTE_Q16_DUAL_2WARP_LARGEM_ENTRYPOINT if selected else ROUTE_PARENT_56ED, 'parent_56ed_route': parent_route, 'route_kind': 'specialized_q16_dual_rowld1_2warp_largem' if selected else 'inherited_56ed_parent', 'split_count': selected_split, 'guard_condition': 'BF16 non-build B=1 Q=16 M in {100000,131071,250000} D=128 K=32' if selected else 'delegate to 56ed parent'}) + return rows + +def _per_shape_delta(candidate_report: dict[str, Any], parent_report: dict[str, Any]) -> dict[str, Any]: + rows = {} + for label, cand in candidate_report.get('per_shape', {}).items(): + parent_row = parent_report.get('per_shape', {}).get(label, {}) + cand_ms = cand.get('kernel_ms') + parent_ms = parent_row.get('kernel_ms') + rows[label] = {'candidate': cand, 'parent_56ed': parent_row, 'candidate_ms': cand_ms, 'parent_56ed_ms': parent_ms, 'speedup_vs_parent_56ed': parent_ms / cand_ms if cand_ms and parent_ms else None, 'ratio_vs_flashlib': cand.get('ratio_vs_flashlib')} + return rows + +def benchmark_knn_build_rag_microbucket_k32_q16dual2warp_largem_bdd2_v1(*, use_cupti: bool=True, shape_labels=K32_BUCKET_SHAPES, k32_exact_q16_split_count: int=K32_EXACT_Q16_SPLIT_COUNT, k32_irregular_q16_split_count: int=K32_IRREGULAR_Q16_SPLIT_COUNT, k32_largem_q16_split_count: int=K32_LARGEM_Q16_SPLIT_COUNT, k32_default_split_count: int=K32_DEFAULT_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_with_k32_topology(k32_exact_q16_split_count, k32_irregular_q16_split_count, k32_largem_q16_split_count, k32_default_split_count, k32_group_count)) + parent_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_parent_56ed) + return {'contract': candidate_report['contract'], 'contract_version': candidate_report['contract_version'], 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_knn_build_rag_microbucket_k32_q16dual2warp_largem_bdd2_v1']), 'candidate_entrypoint': ROUTE_Q16_DUAL_2WARP_LARGEM_ENTRYPOINT, 'parent_entrypoint': ROUTE_PARENT_56ED, 'accelerated_shape_labels': list(Q16_DUAL_2WARP_LARGEM_TARGET_SHAPES), 'measured_shape_labels': list(shape_labels), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'producer_topology': {'Q16_M100000': '56ed SMEM-staged ROW_16x256B two-compute-warp stage1/split144', 'Q16_M131071': '56ed SMEM-staged ROW_16x256B two-compute-warp stage1/split148', 'Q16_M250000': 'same stage1 extended to split280', 'guard_misses': 'delegate to 56ed parent'}, 'merge_topology': {'Q16_rows': ''.join(['warp-row split-list merge/', format(seed.K32_ROWS4_ROWS_PER_CTA, ''), ' rows per CTA']), 'exact_split_count': k32_exact_q16_split_count, 'irregular_split_count': k32_irregular_q16_split_count, 'largem_split_count': k32_largem_q16_split_count, 'default_split_count': k32_default_split_count, 'exact_splits_per_lane': seed.base._splits_per_lane(k32_exact_q16_split_count), 'irregular_splits_per_lane': seed.base._splits_per_lane(k32_irregular_q16_split_count), 'largem_splits_per_lane': seed.base._splits_per_lane(k32_largem_q16_split_count)}, 'route_trace': route_trace_for_contract_shapes(shape_labels, k32_exact_q16_split_count=k32_exact_q16_split_count, k32_irregular_q16_split_count=k32_irregular_q16_split_count, k32_largem_q16_split_count=k32_largem_q16_split_count, k32_default_split_count=k32_default_split_count, k32_group_count=k32_group_count), 'target_rows': _per_shape_delta(candidate_report, parent_report), 'summary': candidate_report['summary'], 'performance': candidate_report['performance'], 'correctness': candidate_report['correctness'], 'report': candidate_report, 'parent_summary': parent_report['summary'], 'parent_performance': parent_report['performance'], 'parent_report': parent_report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32_q16irreg2warp_a444_v2.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32_q16irreg2warp_a444_v2.py new file mode 100644 index 00000000..d909d339 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32_q16irreg2warp_a444_v2.py @@ -0,0 +1,192 @@ +"""RAG microbatch K32 bucket with a two-warp irregular Q16 producer. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +keeps the rows4 Q16 merge seed for guard misses and exact Q16, but routes the +irregular Q16/M131071/K32 row through a rowld1 producer variant where two +compute warps split the sixteen active query rows before the same rows4 +warp-row merge consumes the split-local lists. The production path remains +Weave-only. V2 keeps both compute warps on the same ROW_16x256B TMEM tile +origin; warp 0 stages the upper eight rows through SMEM so warp 1 can own +rows 8-15 without reading an invalid cross-warp TMEM fragment. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from collections.abc import Callable +from functools import cache +from typing import Any +from .._dispatch_runtime import dc as dc +from .. import _dispatch_runtime as eval_mod +from . import knn_build_rag_microbucket_k32_q16rows4_abee_v1 as parent +from . import knn_build_rag_microbucket_k32rowld1warp_0077_v1 as rowld1 +from . import knn_build_rag_microbucket_k32warpmerge_0077_v1 as base +from .._dispatch_runtime import pack_kernel_args +MODULE = 'loom.examples.weave.knn_build_rag_microbucket_k32_q16irreg2warp_a444_v2' +Q8_K32_SHAPE = parent.Q8_K32_SHAPE +Q16_K32_SHAPE = parent.Q16_K32_SHAPE +Q32_K32_SHAPE = parent.Q32_K32_SHAPE +Q16_K32_IRREGULAR_SHAPE = parent.Q16_K32_IRREGULAR_SHAPE +K32_BUCKET_SHAPES = parent.K32_BUCKET_SHAPES +TARGET_SHAPES = parent.TARGET_SHAPES +Q16_IRREG_2WARP_TARGET_SHAPES = (Q16_K32_IRREGULAR_SHAPE,) +K32_EXACT_Q16_SPLIT_COUNT = parent.K32_EXACT_Q16_SPLIT_COUNT +K32_IRREGULAR_Q16_SPLIT_COUNT = parent.K32_IRREGULAR_Q16_SPLIT_COUNT +K32_DEFAULT_SPLIT_COUNT = parent.K32_DEFAULT_SPLIT_COUNT +K32_GROUP_COUNT = parent.K32_GROUP_COUNT +K32_TOP_K_MAX = parent.K32_TOP_K_MAX +K32_ROWS4_MERGE_THREADS = parent.K32_ROWS4_MERGE_THREADS +K32_ROWS4_ROWS_PER_CTA = parent.K32_ROWS4_ROWS_PER_CTA +K32_ROWS4_WARPS = parent.K32_ROWS4_WARPS +Q16_2WARP_STAGE1_THREADS = _decode_capture(_json_loads('128')) +Q16_2WARP_ACTIVE_ROWS = 16 +Q16_2WARP_LOCAL_LISTS_PER_ROW = rowld1.Q16_ROWLD1_LOCAL_LISTS_PER_ROW +Q16_2WARP_SMEM_BASE_BYTES = rowld1.Q16_ROWLD1_SMEM_BASE_BYTES +Q16_2WARP_UPPER_DOT_ROWS = 8 +Q16_2WARP_UPPER_DOT_COLS = 64 +Q16_2WARP_UPPER_DOT_ELEMS = Q16_2WARP_UPPER_DOT_ROWS * Q16_2WARP_UPPER_DOT_COLS +Q16_2WARP_LOCAL_ELEMS = Q16_2WARP_ACTIVE_ROWS * Q16_2WARP_LOCAL_LISTS_PER_ROW * K32_TOP_K_MAX +Q16_2WARP_UPPER_DOT_OFFSET = Q16_2WARP_SMEM_BASE_BYTES +Q16_2WARP_LOCAL_D_OFFSET = Q16_2WARP_UPPER_DOT_OFFSET + Q16_2WARP_UPPER_DOT_ELEMS * 4 +Q16_2WARP_LOCAL_I_OFFSET = Q16_2WARP_LOCAL_D_OFFSET + Q16_2WARP_LOCAL_ELEMS * 4 +Q16_2WARP_SMEM_POOL_BYTES = Q16_2WARP_LOCAL_I_OFFSET + Q16_2WARP_LOCAL_ELEMS * 4 +ROUTE_PARENT_ROWS4 = ''.join([format(parent.MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_Q16_IRREG_2WARP_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +SEED_K32_Q16_IRREG_2WARP_A444_V2_ID = 'rag_microbucket_k32_q16irreg2warp_a444_v2_rowld1_2warp_rows4' +knn_build_rag_microbucket_k32_q16irreg2warp_a444_v2_stage1_q16_rowld1_2warp = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_k32_q16irreg2warp_a444_v2_stage1_q16_rowld1_2warp", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 52480, "constants": [["BLOCK_Q", 64], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32], ["ROWS_COVERED", 16]], "cta_group": 1, "threads": 128}')) +stage1_q16_rowld1_2warp_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_k32_q16irreg2warp_a444_v2_stage1_q16_rowld1_2warp", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 52480, "constants": [["BLOCK_Q", 64], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32], ["ROWS_COVERED", 16]], "cta_group": 1, "threads": 128}')) + +def _ir_with_constants(ir_obj: Any, *, suffix: str, **updates: int) -> Any: + constants = tuple(((name, updates.get(name, value)) for name, value in ir_obj.constants)) + return dc.replace(ir_obj, symbol=''.join([format(ir_obj.symbol, ''), '_', format(suffix, '')]), constants=constants) + +def _stage1_rowld1_2warp_ir() -> Any: + return _ir_with_constants(stage1_q16_rowld1_2warp_ir, suffix='q16irreg2warp_a444_v2', BLOCK_Q=rowld1.Q16_ROWLD1_BLOCK_Q, BLOCK_M=rowld1.Q16_ROWLD1_BLOCK_M, FEAT_D=rowld1.Q16_ROWLD1_FEAT_D, TOP_K_MAX=K32_TOP_K_MAX, ROWS_COVERED=Q16_2WARP_ACTIVE_ROWS) + +def _warp_merge_ir(split_count: int) -> Any: + if K32_ROWS4_ROWS_PER_CTA <= 0 or K32_ROWS4_ROWS_PER_CTA > K32_ROWS4_WARPS: + raise ValueError(''.join(['rows_per_cta=', format(K32_ROWS4_ROWS_PER_CTA, ''), ' exceeds merge warps=', format(K32_ROWS4_WARPS, '')])) + return _ir_with_constants(base.k32_warp_row_merge_ir, suffix=''.join(['k32s', format(split_count, ''), 'r', format(K32_ROWS4_ROWS_PER_CTA, ''), '_a444_v2']), TOP_K_MAX=K32_TOP_K_MAX, SPLIT_COUNT=split_count, SPLITS_PER_LANE=base._splits_per_lane(split_count), ROWS_PER_CTA=K32_ROWS4_ROWS_PER_CTA) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RAG_MICROBUCKET_K32_Q16IRREG2WARP_A444_V2_VERIFY_KERNEL') + split_count = int(os.environ.get('LOOM_KNN_RAG_MICROBUCKET_K32_Q16IRREG2WARP_A444_V2_VERIFY_K32_SPLIT', K32_IRREGULAR_Q16_SPLIT_COUNT)) + if verify_kernel == 'rowld1_2warp_stage1': + return _stage1_rowld1_2warp_ir() + return _warp_merge_ir(split_count) +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_k32warpmerge_0077_v1_warp_row_merge_k32s148r4_a444_v2", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 32], ["SPLIT_COUNT", 148], ["SPLITS_PER_LANE", 5], ["ROWS_PER_CTA", 4]], "cta_group": 1, "threads": 128}')) + +def _compiled_stage1_q16_rowld1_2warp(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0210"}')) + +@cache +def _compiled_rows4_warp_merge(split_count: int): + return base.rowld_seed.compact_seed.q16_tailinf.parent_k32._compile_ir(_warp_merge_ir(split_count)) + +def _eligible_q16_irregular_2warp(inputs: dict[str, Any]) -> bool: + return parent.parent.parent._is_bf16_d128_nonbuild(inputs) and int(inputs.get('Q', -1)) == 16 and (int(inputs.get('M', -1)) == 131071) and (int(inputs.get('K', -1)) == 32) + +def _irreg2warp_route_name(inputs: dict[str, Any], *, split_count: int) -> str: + n_database = int(inputs.get('M', -1)) + return ''.join(['rag_microbucket_k32_q16irreg2warp_a444_v2_q16_m', format(n_database, ''), '_k32_row16x256b_2cw_s', format(split_count, ''), '_r', format(K32_ROWS4_ROWS_PER_CTA, ''), '_warpmerge']) + +def route_for_contract_inputs(inputs: dict[str, Any], *, k32_exact_q16_split_count: int=K32_EXACT_Q16_SPLIT_COUNT, k32_irregular_q16_split_count: int=K32_IRREGULAR_Q16_SPLIT_COUNT, k32_default_split_count: int=K32_DEFAULT_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> str: + if _eligible_q16_irregular_2warp(inputs): + return _irreg2warp_route_name(inputs, split_count=k32_irregular_q16_split_count) + return parent.route_for_contract_inputs(inputs, k32_exact_q16_split_count=k32_exact_q16_split_count, k32_irregular_q16_split_count=k32_irregular_q16_split_count, k32_default_split_count=k32_default_split_count, k32_group_count=k32_group_count) + +def _launch_rowld1_2warp_rows4_merge(inputs: dict[str, Any], *, split_count: int) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + if top_k != K32_TOP_K_MAX: + raise ValueError(''.join(['k32 q16 irreg2warp only supports K=', format(K32_TOP_K_MAX, ''), ', got K=', format(top_k, '')])) + block_q = rowld1.Q16_ROWLD1_BLOCK_Q + block_m = rowld1.Q16_ROWLD1_BLOCK_M + num_q_tiles = (n_query + block_q - 1) // block_q + num_db_tiles = (n_database + block_m - 1) // block_m + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tiles * split_count + total_queries = bsz * n_query + stage1_grid = min(total_work, base.rowld_seed.compact_seed.GRID_DIM_DEFAULT) + merge_grid = min((total_queries + K32_ROWS4_ROWS_PER_CTA - 1) // K32_ROWS4_ROWS_PER_CTA, base.rowld_seed.compact_seed.GRID_DIM_DEFAULT) + partial_dists, partial_indices = base.rowld_seed.compact_seed.q16_tailinf.parent_k32.parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = base.rowld_seed.compact_seed.q16_tailinf.parent_k32.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), total_queries, block_q, dim, dim) + tmap_database = base.rowld_seed.compact_seed.q16_tailinf.parent_k32.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, block_m, dim, dim) + stage1_ir = _stage1_rowld1_2warp_ir() + _compiled_stage1_q16_rowld1_2warp().launch(grid=(stage1_grid, 1, 1), block=(Q16_2WARP_STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage1_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tiles=num_q_tiles, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), shared_mem=stage1_ir.computed_smem_bytes) + merge_ir = _warp_merge_ir(split_count) + _compiled_rows4_warp_merge(split_count).launch(grid=(merge_grid, 1, 1), block=(K32_ROWS4_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], total_queries], shared_mem=merge_ir.computed_smem_bytes) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, k32_exact_q16_split_count: int=K32_EXACT_Q16_SPLIT_COUNT, k32_irregular_q16_split_count: int=K32_IRREGULAR_Q16_SPLIT_COUNT, k32_default_split_count: int=K32_DEFAULT_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> None: + if _eligible_q16_irregular_2warp(inputs): + _launch_rowld1_2warp_rows4_merge(inputs, split_count=k32_irregular_q16_split_count) + return + parent.launch_from_contract_inputs(inputs, k32_exact_q16_split_count=k32_exact_q16_split_count, k32_irregular_q16_split_count=k32_irregular_q16_split_count, k32_default_split_count=k32_default_split_count, k32_group_count=k32_group_count) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_with_k32_topology(exact_split_count: int, irregular_split_count: int, default_split_count: int, group_count: int) -> Callable[[dict[str, Any]], None]: + + def _candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, k32_exact_q16_split_count=exact_split_count, k32_irregular_q16_split_count=irregular_split_count, k32_default_split_count=default_split_count, k32_group_count=group_count) + return _candidate + +def candidate_parent_rows4(inputs: dict[str, Any]) -> None: + parent.launch_from_contract_inputs(inputs) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return parent._select_contract_shapes(shape_labels) + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=K32_BUCKET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def compile_and_launch_knn_build(*, shape_labels=K32_BUCKET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def route_trace_for_contract_shapes(shape_labels=K32_BUCKET_SHAPES, *, k32_exact_q16_split_count: int=K32_EXACT_Q16_SPLIT_COUNT, k32_irregular_q16_split_count: int=K32_IRREGULAR_Q16_SPLIT_COUNT, k32_default_split_count: int=K32_DEFAULT_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> list[dict[str, Any]]: + rows = [] + for shape in _select_contract_shapes(shape_labels): + inputs = base.rowld_seed.base_dispatcher._trace_inputs_from_shape(shape) + route = route_for_contract_inputs(inputs, k32_exact_q16_split_count=k32_exact_q16_split_count, k32_irregular_q16_split_count=k32_irregular_q16_split_count, k32_default_split_count=k32_default_split_count, k32_group_count=k32_group_count) + parent_route = parent.route_for_contract_inputs(inputs, k32_exact_q16_split_count=k32_exact_q16_split_count, k32_irregular_q16_split_count=k32_irregular_q16_split_count, k32_default_split_count=k32_default_split_count, k32_group_count=k32_group_count) + selected = _eligible_q16_irregular_2warp(inputs) + rows.append({'shape_key': shape['label'], 'selected_route': route, 'selected_seed': SEED_K32_Q16_IRREG_2WARP_A444_V2_ID if selected else parent.SEED_K32_Q16_ROWS4_ABEE_V1_ID, 'selected_entrypoint': ROUTE_Q16_IRREG_2WARP_ENTRYPOINT if selected else ROUTE_PARENT_ROWS4, 'parent_rows4_route': parent_route, 'route_kind': 'specialized_q16_irregular_rowld1_2warp' if selected else 'inherited_rows4_parent', 'guard_condition': 'BF16 non-build B=1 Q=16 M=131071 D=128 K=32' if selected else 'delegate to q16rows4 abee parent'}) + return rows + +def _per_shape_delta(candidate_report: dict[str, Any], parent_report: dict[str, Any]) -> dict[str, Any]: + rows = {} + for label, cand in candidate_report.get('per_shape', {}).items(): + parent_row = parent_report.get('per_shape', {}).get(label, {}) + cand_ms = cand.get('kernel_ms') + parent_ms = parent_row.get('kernel_ms') + rows[label] = {'candidate': cand, 'parent_rows4': parent_row, 'candidate_ms': cand_ms, 'parent_rows4_ms': parent_ms, 'speedup_vs_parent_rows4': parent_ms / cand_ms if cand_ms and parent_ms else None, 'ratio_vs_flashlib': cand.get('ratio_vs_flashlib')} + return rows + +def benchmark_knn_build_rag_microbucket_k32_q16irreg2warp_a444_v2(*, use_cupti: bool=True, shape_labels=K32_BUCKET_SHAPES, k32_exact_q16_split_count: int=K32_EXACT_Q16_SPLIT_COUNT, k32_irregular_q16_split_count: int=K32_IRREGULAR_Q16_SPLIT_COUNT, k32_default_split_count: int=K32_DEFAULT_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_with_k32_topology(k32_exact_q16_split_count, k32_irregular_q16_split_count, k32_default_split_count, k32_group_count)) + parent_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_parent_rows4) + return {'contract': candidate_report['contract'], 'contract_version': candidate_report['contract_version'], 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_knn_build_rag_microbucket_k32_q16irreg2warp_a444_v2']), 'candidate_entrypoint': ROUTE_Q16_IRREG_2WARP_ENTRYPOINT, 'parent_entrypoint': ROUTE_PARENT_ROWS4, 'accelerated_shape_labels': list(Q16_IRREG_2WARP_TARGET_SHAPES), 'measured_shape_labels': list(shape_labels), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'producer_topology': {'Q16_irregular': 'two-compute-warp rowld1 ROW_16x256B stage1 at split148', 'guard_misses': 'delegate to q16rows4 abee parent routes'}, 'merge_topology': {'Q16_irregular': ''.join(['rows4 warp-row split-list merge/', format(K32_ROWS4_ROWS_PER_CTA, ''), ' rows per CTA']), 'exact_split_count': k32_exact_q16_split_count, 'irregular_split_count': k32_irregular_q16_split_count, 'default_split_count': k32_default_split_count, 'irregular_splits_per_lane': base._splits_per_lane(k32_irregular_q16_split_count)}, 'route_trace': route_trace_for_contract_shapes(shape_labels, k32_exact_q16_split_count=k32_exact_q16_split_count, k32_irregular_q16_split_count=k32_irregular_q16_split_count, k32_default_split_count=k32_default_split_count, k32_group_count=k32_group_count), 'target_rows': _per_shape_delta(candidate_report, parent_report), 'summary': candidate_report['summary'], 'performance': candidate_report['performance'], 'correctness': candidate_report['correctness'], 'report': candidate_report, 'parent_summary': parent_report['summary'], 'parent_performance': parent_report['performance'], 'parent_report': parent_report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32_q16rows4_abee_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32_q16rows4_abee_v1.py new file mode 100644 index 00000000..ccfd0309 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32_q16rows4_abee_v1.py @@ -0,0 +1,177 @@ +"""RAG microbatch K32 bucket with Q16 four-row warp-merge CTAs. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +keeps the 2e8e K32 split148 parent for guard misses, but routes the two Q16 +K32 contract rows through the rowld1 tcgen05/TMA producer and a ROWS_PER_CTA=4 +warp-row merge. The production path remains Weave-only. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from collections.abc import Callable +from functools import cache +from typing import Any +from .._dispatch_runtime import dc as dc +from .. import _dispatch_runtime as eval_mod +from . import knn_build_rag_microbucket_k32_2e8e_q16split148_v1 as parent +from . import knn_build_rag_microbucket_k32rowld1warp_0077_v1 as rowld1 +from . import knn_build_rag_microbucket_k32warpmerge_0077_v1 as base +from .._dispatch_runtime import pack_kernel_args +MODULE = 'loom.examples.weave.knn_build_rag_microbucket_k32_q16rows4_abee_v1' +Q8_K32_SHAPE = parent.Q8_K32_SHAPE +Q16_K32_SHAPE = parent.Q16_K32_SHAPE +Q32_K32_SHAPE = parent.Q32_K32_SHAPE +Q16_K32_IRREGULAR_SHAPE = parent.Q16_K32_IRREGULAR_SHAPE +K32_BUCKET_SHAPES = parent.K32_BUCKET_SHAPES +TARGET_SHAPES = parent.TARGET_SHAPES +Q16_ROWS4_TARGET_SHAPES = (Q16_K32_SHAPE, Q16_K32_IRREGULAR_SHAPE) +K32_EXACT_Q16_SPLIT_COUNT = 144 +K32_IRREGULAR_Q16_SPLIT_COUNT = 148 +K32_DEFAULT_SPLIT_COUNT = parent.K32_DEFAULT_SPLIT_COUNT +K32_GROUP_COUNT = parent.K32_GROUP_COUNT +K32_TOP_K_MAX = base.K32_TOP_K_MAX +K32_ROWS4_MERGE_THREADS = base.K32_WARP_MERGE_THREADS +K32_ROWS4_ROWS_PER_CTA = 4 +K32_ROWS4_WARPS = K32_ROWS4_MERGE_THREADS // 32 +ROUTE_PARENT_2E8E = ''.join([format(parent.MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_Q16_ROWS4_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +SEED_K32_Q16_ROWS4_ABEE_V1_ID = 'rag_microbucket_k32_q16rows4_abee_v1_rowld1_rows4_merge' + +def _ir_with_constants(ir_obj: Any, *, suffix: str, **updates: int) -> Any: + constants = tuple(((name, updates.get(name, value)) for name, value in ir_obj.constants)) + return dc.replace(ir_obj, symbol=''.join([format(ir_obj.symbol, ''), '_', format(suffix, '')]), constants=constants) + +def _warp_merge_ir(split_count: int) -> Any: + if K32_ROWS4_ROWS_PER_CTA <= 0 or K32_ROWS4_ROWS_PER_CTA > K32_ROWS4_WARPS: + raise ValueError(''.join(['rows_per_cta=', format(K32_ROWS4_ROWS_PER_CTA, ''), ' exceeds merge warps=', format(K32_ROWS4_WARPS, '')])) + return _ir_with_constants(base.k32_warp_row_merge_ir, suffix=''.join(['k32s', format(split_count, ''), 'r', format(K32_ROWS4_ROWS_PER_CTA, ''), '_abee_v1']), TOP_K_MAX=K32_TOP_K_MAX, SPLIT_COUNT=split_count, SPLITS_PER_LANE=base._splits_per_lane(split_count), ROWS_PER_CTA=K32_ROWS4_ROWS_PER_CTA) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RAG_MICROBUCKET_K32_Q16ROWS4_ABEE_V1_VERIFY_KERNEL') + split_count = int(os.environ.get('LOOM_KNN_RAG_MICROBUCKET_K32_Q16ROWS4_ABEE_V1_VERIFY_K32_SPLIT', K32_IRREGULAR_Q16_SPLIT_COUNT)) + if verify_kernel == 'rowld1_stage1': + return rowld1._stage1_rowld1_ir() + return _warp_merge_ir(split_count) +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_k32warpmerge_0077_v1_warp_row_merge_k32s148r4_abee_v1", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 32], ["SPLIT_COUNT", 148], ["SPLITS_PER_LANE", 5], ["ROWS_PER_CTA", 4]], "cta_group": 1, "threads": 128}')) + +@cache +def _compiled_rows4_warp_merge(split_count: int): + return base.rowld_seed.compact_seed.q16_tailinf.parent_k32._compile_ir(_warp_merge_ir(split_count)) + +def _eligible_q16_rows4(inputs: dict[str, Any]) -> bool: + return parent.parent._is_bf16_d128_nonbuild(inputs) and int(inputs.get('Q', -1)) == 16 and (int(inputs.get('M', -1)) in {100000, 131071}) and (int(inputs.get('K', -1)) == 32) + +def _split_for_q16_rows4(inputs: dict[str, Any], *, exact_split_count: int, irregular_split_count: int) -> int: + n_database = int(inputs.get('M', -1)) + if n_database == 131071: + return irregular_split_count + return exact_split_count + +def _rows4_route_name(inputs: dict[str, Any], *, split_count: int) -> str: + n_query = int(inputs.get('Q', -1)) + n_database = int(inputs.get('M', -1)) + return ''.join(['rag_microbucket_k32_q16rows4_abee_v1_q', format(n_query, ''), '_m', format(n_database, ''), '_k32_row16x256b_s', format(split_count, ''), '_r', format(K32_ROWS4_ROWS_PER_CTA, ''), '_warpmerge']) + +def route_for_contract_inputs(inputs: dict[str, Any], *, k32_exact_q16_split_count: int=K32_EXACT_Q16_SPLIT_COUNT, k32_irregular_q16_split_count: int=K32_IRREGULAR_Q16_SPLIT_COUNT, k32_default_split_count: int=K32_DEFAULT_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> str: + if _eligible_q16_rows4(inputs): + split_count = _split_for_q16_rows4(inputs, exact_split_count=k32_exact_q16_split_count, irregular_split_count=k32_irregular_q16_split_count) + return _rows4_route_name(inputs, split_count=split_count) + return parent.route_for_contract_inputs(inputs, k32_q16_split_count=k32_default_split_count, k32_default_split_count=k32_default_split_count, k32_group_count=k32_group_count) + +def _launch_rowld1_rows4_merge(inputs: dict[str, Any], *, split_count: int) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + if top_k != K32_TOP_K_MAX: + raise ValueError(''.join(['k32 rows4 merge only supports K=', format(K32_TOP_K_MAX, ''), ', got K=', format(top_k, '')])) + block_q = rowld1.Q16_ROWLD1_BLOCK_Q + block_m = rowld1.Q16_ROWLD1_BLOCK_M + num_q_tiles = (n_query + block_q - 1) // block_q + num_db_tiles = (n_database + block_m - 1) // block_m + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tiles * split_count + total_queries = bsz * n_query + stage1_grid = min(total_work, base.rowld_seed.compact_seed.GRID_DIM_DEFAULT) + merge_grid = min((total_queries + K32_ROWS4_ROWS_PER_CTA - 1) // K32_ROWS4_ROWS_PER_CTA, base.rowld_seed.compact_seed.GRID_DIM_DEFAULT) + partial_dists, partial_indices = base.rowld_seed.compact_seed.q16_tailinf.parent_k32.parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = base.rowld_seed.compact_seed.q16_tailinf.parent_k32.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), total_queries, block_q, dim, dim) + tmap_database = base.rowld_seed.compact_seed.q16_tailinf.parent_k32.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, block_m, dim, dim) + stage1_ir = rowld1._stage1_rowld1_ir() + rowld1._compiled_stage1_q16_k32_m64_rowld1().launch(grid=(stage1_grid, 1, 1), block=(rowld1.Q16_ROWLD1_STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage1_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tiles=num_q_tiles, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), shared_mem=stage1_ir.computed_smem_bytes) + merge_ir = _warp_merge_ir(split_count) + _compiled_rows4_warp_merge(split_count).launch(grid=(merge_grid, 1, 1), block=(K32_ROWS4_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], total_queries], shared_mem=merge_ir.computed_smem_bytes) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, k32_exact_q16_split_count: int=K32_EXACT_Q16_SPLIT_COUNT, k32_irregular_q16_split_count: int=K32_IRREGULAR_Q16_SPLIT_COUNT, k32_default_split_count: int=K32_DEFAULT_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> None: + if _eligible_q16_rows4(inputs): + split_count = _split_for_q16_rows4(inputs, exact_split_count=k32_exact_q16_split_count, irregular_split_count=k32_irregular_q16_split_count) + _launch_rowld1_rows4_merge(inputs, split_count=split_count) + return + parent.launch_from_contract_inputs(inputs, k32_q16_split_count=k32_default_split_count, k32_default_split_count=k32_default_split_count, k32_group_count=k32_group_count) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_with_k32_topology(exact_split_count: int, irregular_split_count: int, default_split_count: int, group_count: int) -> Callable[[dict[str, Any]], None]: + + def _candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, k32_exact_q16_split_count=exact_split_count, k32_irregular_q16_split_count=irregular_split_count, k32_default_split_count=default_split_count, k32_group_count=group_count) + return _candidate + +def candidate_parent_2e8e(inputs: dict[str, Any]) -> None: + parent.launch_from_contract_inputs(inputs) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return parent._select_contract_shapes(shape_labels) + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=K32_BUCKET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def compile_and_launch_knn_build(*, shape_labels=K32_BUCKET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def route_trace_for_contract_shapes(shape_labels=K32_BUCKET_SHAPES, *, k32_exact_q16_split_count: int=K32_EXACT_Q16_SPLIT_COUNT, k32_irregular_q16_split_count: int=K32_IRREGULAR_Q16_SPLIT_COUNT, k32_default_split_count: int=K32_DEFAULT_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> list[dict[str, Any]]: + rows = [] + for shape in _select_contract_shapes(shape_labels): + inputs = base.rowld_seed.base_dispatcher._trace_inputs_from_shape(shape) + route = route_for_contract_inputs(inputs, k32_exact_q16_split_count=k32_exact_q16_split_count, k32_irregular_q16_split_count=k32_irregular_q16_split_count, k32_default_split_count=k32_default_split_count, k32_group_count=k32_group_count) + parent_route = parent.route_for_contract_inputs(inputs, k32_q16_split_count=k32_default_split_count, k32_default_split_count=k32_default_split_count, k32_group_count=k32_group_count) + selected = _eligible_q16_rows4(inputs) + rows.append({'shape_key': shape['label'], 'selected_route': route, 'selected_seed': SEED_K32_Q16_ROWS4_ABEE_V1_ID if selected else parent.SEED_K32_2E8E_Q16_SPLIT148_ID, 'selected_entrypoint': ROUTE_Q16_ROWS4_ENTRYPOINT if selected else ROUTE_PARENT_2E8E, 'parent_2e8e_route': parent_route, 'route_kind': 'specialized_q16_rows4_merge' if selected else 'inherited_2e8e', 'guard_condition': 'BF16 non-build B=1 Q=16 M in {100000,131071} D=128 K=32' if selected else 'delegate to 2e8e parent'}) + return rows + +def _per_shape_delta(candidate_report: dict[str, Any], parent_report: dict[str, Any]) -> dict[str, Any]: + rows = {} + for label, cand in candidate_report.get('per_shape', {}).items(): + parent_row = parent_report.get('per_shape', {}).get(label, {}) + cand_ms = cand.get('kernel_ms') + parent_ms = parent_row.get('kernel_ms') + rows[label] = {'candidate': cand, 'parent_2e8e': parent_row, 'candidate_ms': cand_ms, 'parent_2e8e_ms': parent_ms, 'speedup_vs_parent_2e8e': parent_ms / cand_ms if cand_ms and parent_ms else None, 'ratio_vs_flashlib': cand.get('ratio_vs_flashlib')} + return rows + +def benchmark_knn_build_rag_microbucket_k32_q16rows4_abee_v1(*, use_cupti: bool=True, shape_labels=K32_BUCKET_SHAPES, k32_exact_q16_split_count: int=K32_EXACT_Q16_SPLIT_COUNT, k32_irregular_q16_split_count: int=K32_IRREGULAR_Q16_SPLIT_COUNT, k32_default_split_count: int=K32_DEFAULT_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_with_k32_topology(k32_exact_q16_split_count, k32_irregular_q16_split_count, k32_default_split_count, k32_group_count)) + parent_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_parent_2e8e) + return {'contract': candidate_report['contract'], 'contract_version': candidate_report['contract_version'], 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_knn_build_rag_microbucket_k32_q16rows4_abee_v1']), 'candidate_entrypoint': ROUTE_Q16_ROWS4_ENTRYPOINT, 'parent_entrypoint': ROUTE_PARENT_2E8E, 'accelerated_shape_labels': list(Q16_ROWS4_TARGET_SHAPES), 'measured_shape_labels': list(shape_labels), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'producer_topology': {'Q16_exact': 'rowld1 ROW_16x256B one-compute-warp stage1', 'Q16_irregular': 'rowld1 ROW_16x256B one-compute-warp stage1', 'Q8_Q32_and_guard_misses': 'delegate to 2e8e parent'}, 'merge_topology': {'Q16_exact_Q16_irregular': ''.join(['warp-row split-list merge/', format(K32_ROWS4_ROWS_PER_CTA, ''), ' rows per CTA']), 'exact_split_count': k32_exact_q16_split_count, 'irregular_split_count': k32_irregular_q16_split_count, 'default_split_count': k32_default_split_count, 'exact_splits_per_lane': base._splits_per_lane(k32_exact_q16_split_count), 'irregular_splits_per_lane': base._splits_per_lane(k32_irregular_q16_split_count)}, 'route_trace': route_trace_for_contract_shapes(shape_labels, k32_exact_q16_split_count=k32_exact_q16_split_count, k32_irregular_q16_split_count=k32_irregular_q16_split_count, k32_default_split_count=k32_default_split_count, k32_group_count=k32_group_count), 'target_rows': _per_shape_delta(candidate_report, parent_report), 'summary': candidate_report['summary'], 'performance': candidate_report['performance'], 'correctness': candidate_report['correctness'], 'report': candidate_report, 'parent_summary': parent_report['summary'], 'parent_performance': parent_report['performance'], 'parent_report': parent_report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32_q24rowld2_24dc_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32_q24rowld2_24dc_v1.py new file mode 100644 index 00000000..ae5a0be9 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32_q24rowld2_24dc_v1.py @@ -0,0 +1,173 @@ +"""RAG microbatch K32 Q24 rowld2 exact bucket wrapper. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +targets the exact v10 `rag_microbatch_largek_b1_q24_m100000_d128_k32` row. +It reuses the validated ROW_16x256B rowld2 tcgen05/TMA producer from the K32 +rowld lineage with `ROWS_COVERED=24`, then feeds the rows4 warp-row merge. +Guard misses delegate to the validated Q16 large-M parent path. The production +path remains Weave-only. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from collections.abc import Callable +from functools import cache +from typing import Any +from .._dispatch_runtime import dc as dc +from .. import _dispatch_runtime as eval_mod +from . import knn_build_rag_microbucket_k32_q16dual2warp_largem_bdd2_v1 as parent +from . import knn_build_rag_microbucket_k32rowld1warp_0077_v1 as rowld2_seed +from . import knn_build_rag_microbucket_k32warpmerge_0077_v1 as base +from .._dispatch_runtime import pack_kernel_args +MODULE = 'loom.examples.weave.knn_build_rag_microbucket_k32_q24rowld2_24dc_v1' +Q24_K32_SHAPE = 'rag_microbatch_largek_b1_q24_m100000_d128_k32' +Q24_ROWLD2_TARGET_SHAPES = (Q24_K32_SHAPE,) +K32_BUCKET_SHAPES = Q24_ROWLD2_TARGET_SHAPES +TARGET_SHAPES = Q24_ROWLD2_TARGET_SHAPES +K32_Q24_SPLIT_COUNT = _decode_capture(_json_loads('144')) +K32_TOP_K_MAX = parent.K32_TOP_K_MAX +K32_ROWS4_MERGE_THREADS = parent.seed.K32_ROWS4_MERGE_THREADS +K32_ROWS4_ROWS_PER_CTA = parent.seed.K32_ROWS4_ROWS_PER_CTA +K32_ROWS4_WARPS = parent.seed.K32_ROWS4_WARPS +Q24_ROWLD2_STAGE1_THREADS = _decode_capture(_json_loads('128')) +Q24_ROWLD2_ACTIVE_ROWS = 24 +ROUTE_PARENT_BDD2 = ''.join([format(parent.MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_Q24_ROWLD2_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +SEED_K32_Q24_ROWLD2_24DC_V1_ID = 'rag_microbucket_k32_q24rowld2_24dc_v1_rowld2_rows4' + +def _ir_with_constants(ir_obj: Any, *, suffix: str, **updates: int) -> Any: + constants = tuple(((name, updates.get(name, value)) for name, value in ir_obj.constants)) + return dc.replace(ir_obj, symbol=''.join([format(ir_obj.symbol, ''), '_', format(suffix, '')]), constants=constants) + +def _stage1_q24_rowld2_ir() -> Any: + return _ir_with_constants(rowld2_seed.stage1_q32_k32_m64_rowld2_ir, suffix='q24rowld2_24dc_v1', BLOCK_Q=rowld2_seed.Q16_ROWLD1_BLOCK_Q, BLOCK_M=rowld2_seed.Q16_ROWLD1_BLOCK_M, FEAT_D=rowld2_seed.Q16_ROWLD1_FEAT_D, TOP_K_MAX=K32_TOP_K_MAX, ROWS_COVERED=Q24_ROWLD2_ACTIVE_ROWS) + +def _warp_merge_ir(split_count: int) -> Any: + if K32_ROWS4_ROWS_PER_CTA <= 0 or K32_ROWS4_ROWS_PER_CTA > K32_ROWS4_WARPS: + raise ValueError(''.join(['rows_per_cta=', format(K32_ROWS4_ROWS_PER_CTA, ''), ' exceeds merge warps=', format(K32_ROWS4_WARPS, '')])) + return _ir_with_constants(base.k32_warp_row_merge_ir, suffix=''.join(['k32q24s', format(split_count, ''), 'r', format(K32_ROWS4_ROWS_PER_CTA, ''), '_24dc_v1']), TOP_K_MAX=K32_TOP_K_MAX, SPLIT_COUNT=split_count, SPLITS_PER_LANE=base._splits_per_lane(split_count), ROWS_PER_CTA=K32_ROWS4_ROWS_PER_CTA) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RAG_MICROBUCKET_K32_Q24ROWLD2_24DC_V1_VERIFY_KERNEL') + split_count = int(os.environ.get('LOOM_KNN_RAG_MICROBUCKET_K32_Q24ROWLD2_24DC_V1_VERIFY_K32_SPLIT', K32_Q24_SPLIT_COUNT)) + if verify_kernel == 'rowld2_stage1': + return _stage1_q24_rowld2_ir() + return _warp_merge_ir(split_count) +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_k32warpmerge_0077_v1_warp_row_merge_k32q24s144r4_24dc_v1", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 32], ["SPLIT_COUNT", 144], ["SPLITS_PER_LANE", 5], ["ROWS_PER_CTA", 4]], "cta_group": 1, "threads": 128}')) + +def _compiled_stage1_q24_rowld2(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0106"}')) + +@cache +def _compiled_rows4_warp_merge(split_count: int): + return base.rowld_seed.compact_seed.q16_tailinf.parent_k32._compile_ir(_warp_merge_ir(split_count)) + +def _eligible_q24_rowld2(inputs: dict[str, Any]) -> bool: + return base._is_bf16_d128_nonbuild(inputs) and int(inputs.get('Q', -1)) == 24 and (int(inputs.get('M', -1)) == 100000) and (int(inputs.get('K', -1)) == 32) + +def _q24_rowld2_route_name(inputs: dict[str, Any], *, split_count: int) -> str: + n_query = int(inputs.get('Q', -1)) + n_database = int(inputs.get('M', -1)) + return ''.join(['rag_microbucket_k32_q24rowld2_24dc_v1_q', format(n_query, ''), '_m', format(n_database, ''), '_k32_row16x256b2warp_s', format(split_count, ''), '_r', format(K32_ROWS4_ROWS_PER_CTA, ''), '_warpmerge']) + +def route_for_contract_inputs(inputs: dict[str, Any], *, k32_q24_split_count: int=K32_Q24_SPLIT_COUNT) -> str: + if _eligible_q24_rowld2(inputs): + return _q24_rowld2_route_name(inputs, split_count=k32_q24_split_count) + return parent.route_for_contract_inputs(inputs) + +def _launch_q24_rowld2_rows4_merge(inputs: dict[str, Any], *, split_count: int) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + if top_k != K32_TOP_K_MAX: + raise ValueError(''.join(['k32 q24 rowld2 only supports K=', format(K32_TOP_K_MAX, ''), ', got K=', format(top_k, '')])) + block_q = rowld2_seed.Q16_ROWLD1_BLOCK_Q + block_m = rowld2_seed.Q16_ROWLD1_BLOCK_M + num_q_tiles = (n_query + block_q - 1) // block_q + num_db_tiles = (n_database + block_m - 1) // block_m + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tiles * split_count + total_queries = bsz * n_query + stage1_grid = min(total_work, base.rowld_seed.compact_seed.GRID_DIM_DEFAULT) + merge_grid = min((total_queries + K32_ROWS4_ROWS_PER_CTA - 1) // K32_ROWS4_ROWS_PER_CTA, base.rowld_seed.compact_seed.GRID_DIM_DEFAULT) + partial_dists, partial_indices = base.rowld_seed.compact_seed.q16_tailinf.parent_k32.parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = base.rowld_seed.compact_seed.q16_tailinf.parent_k32.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), total_queries, block_q, dim, dim) + tmap_database = base.rowld_seed.compact_seed.q16_tailinf.parent_k32.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, block_m, dim, dim) + stage1_ir = _stage1_q24_rowld2_ir() + _compiled_stage1_q24_rowld2().launch(grid=(stage1_grid, 1, 1), block=(Q24_ROWLD2_STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage1_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tiles=num_q_tiles, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), shared_mem=stage1_ir.computed_smem_bytes) + merge_ir = _warp_merge_ir(split_count) + _compiled_rows4_warp_merge(split_count).launch(grid=(merge_grid, 1, 1), block=(K32_ROWS4_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], total_queries], shared_mem=merge_ir.computed_smem_bytes) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, k32_q24_split_count: int=K32_Q24_SPLIT_COUNT) -> None: + if _eligible_q24_rowld2(inputs): + _launch_q24_rowld2_rows4_merge(inputs, split_count=k32_q24_split_count) + return + parent.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_with_q24_split(split_count: int) -> Callable[[dict[str, Any]], None]: + + def _candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, k32_q24_split_count=split_count) + return _candidate + +def candidate_parent_bdd2(inputs: dict[str, Any]) -> None: + parent.launch_from_contract_inputs(inputs) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return parent._select_contract_shapes(shape_labels) + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=K32_BUCKET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def compile_and_launch_knn_build(*, shape_labels=K32_BUCKET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def route_trace_for_contract_shapes(shape_labels=K32_BUCKET_SHAPES, *, k32_q24_split_count: int=K32_Q24_SPLIT_COUNT) -> list[dict[str, Any]]: + rows = [] + for shape in _select_contract_shapes(shape_labels): + inputs = base.rowld_seed.base_dispatcher._trace_inputs_from_shape(shape) + route = route_for_contract_inputs(inputs, k32_q24_split_count=k32_q24_split_count) + parent_route = parent.route_for_contract_inputs(inputs) + selected = _eligible_q24_rowld2(inputs) + rows.append({'shape_key': shape['label'], 'selected_route': route, 'selected_seed': SEED_K32_Q24_ROWLD2_24DC_V1_ID if selected else parent.SEED_K32_Q16_DUAL_2WARP_LARGEM_BDD2_V1_ID, 'selected_entrypoint': ROUTE_Q24_ROWLD2_ENTRYPOINT if selected else ROUTE_PARENT_BDD2, 'parent_bdd2_route': parent_route, 'route_kind': 'specialized_q24_rowld2_rows4' if selected else 'inherited_q16_largem_parent', 'split_count': k32_q24_split_count if selected else None, 'guard_condition': 'BF16 non-build B=1 Q=24 M=100000 D=128 K=32' if selected else 'delegate to Q16 large-M parent'}) + return rows + +def _per_shape_delta(candidate_report: dict[str, Any], parent_report: dict[str, Any]) -> dict[str, Any]: + rows = {} + for label, cand in candidate_report.get('per_shape', {}).items(): + parent_row = parent_report.get('per_shape', {}).get(label, {}) + cand_ms = cand.get('kernel_ms') + parent_ms = parent_row.get('kernel_ms') + rows[label] = {'candidate': cand, 'parent_bdd2': parent_row, 'candidate_ms': cand_ms, 'parent_bdd2_ms': parent_ms, 'speedup_vs_parent_bdd2': parent_ms / cand_ms if cand_ms and parent_ms else None, 'ratio_vs_flashlib': cand.get('ratio_vs_flashlib')} + return rows + +def benchmark_knn_build_rag_microbucket_k32_q24rowld2_24dc_v1(*, use_cupti: bool=True, shape_labels=K32_BUCKET_SHAPES, k32_q24_split_count: int=K32_Q24_SPLIT_COUNT) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_with_q24_split(k32_q24_split_count)) + parent_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_parent_bdd2) + return {'contract': candidate_report['contract'], 'contract_version': candidate_report['contract_version'], 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_knn_build_rag_microbucket_k32_q24rowld2_24dc_v1']), 'candidate_entrypoint': ROUTE_Q24_ROWLD2_ENTRYPOINT, 'parent_entrypoint': ROUTE_PARENT_BDD2, 'accelerated_shape_labels': list(Q24_ROWLD2_TARGET_SHAPES), 'measured_shape_labels': list(shape_labels), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'producer_topology': {'Q24_M100000': 'ROW_16x256B rowld2 two-compute-warp stage1 with ROWS_COVERED=24', 'guard_misses': 'delegate to q16dual2warp_largem_bdd2 parent'}, 'merge_topology': {'Q24': ''.join(['warp-row split-list merge/', format(K32_ROWS4_ROWS_PER_CTA, ''), ' rows per CTA']), 'q24_split_count': k32_q24_split_count, 'q24_splits_per_lane': base._splits_per_lane(k32_q24_split_count)}, 'route_trace': route_trace_for_contract_shapes(shape_labels, k32_q24_split_count=k32_q24_split_count), 'target_rows': _per_shape_delta(candidate_report, parent_report), 'summary': candidate_report['summary'], 'performance': candidate_report['performance'], 'correctness': candidate_report['correctness'], 'report': candidate_report, 'parent_summary': parent_report['summary'], 'parent_performance': parent_report['performance'], 'parent_report': parent_report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32_q32rowld2_f653_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32_q32rowld2_f653_v1.py new file mode 100644 index 00000000..883767d8 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32_q32rowld2_f653_v1.py @@ -0,0 +1,173 @@ +"""RAG microbatch K32 Q32 rowld2 exact bucket wrapper. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +targets the exact v11 `rag_microbatch_largek_b1_q32_m100000_d128_k32` floor +row. It reuses the existing ROW_16x256B rowld2 tcgen05/TMA producer with +`ROWS_COVERED=32`, feeds the rows4 warp-row merge, and delegates every guard +miss to the current 2e8e K32 parent route. The production path remains +Weave-only. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from collections.abc import Callable +from functools import cache +from typing import Any +from .._dispatch_runtime import dc as dc +from .. import _dispatch_runtime as eval_mod +from . import knn_build_rag_microbucket_k32_2e8e_q16split148_v1 as parent +from . import knn_build_rag_microbucket_k32rowld1warp_0077_v1 as rowld2_seed +from . import knn_build_rag_microbucket_k32warpmerge_0077_v1 as base +from .._dispatch_runtime import pack_kernel_args +MODULE = 'loom.examples.weave.knn_build_rag_microbucket_k32_q32rowld2_f653_v1' +Q32_K32_SHAPE = parent.Q32_K32_SHAPE +Q32_ROWLD2_TARGET_SHAPES = (Q32_K32_SHAPE,) +K32_BUCKET_SHAPES = Q32_ROWLD2_TARGET_SHAPES +TARGET_SHAPES = Q32_ROWLD2_TARGET_SHAPES +K32_Q32_SPLIT_COUNT = _decode_capture(_json_loads('145')) +K32_TOP_K_MAX = parent.parent.rows4.K32_TOP_K_MAX +K32_ROWS4_MERGE_THREADS = parent.parent.rows4.K32_ROWS4_MERGE_THREADS +K32_ROWS4_ROWS_PER_CTA = parent.parent.rows4.K32_ROWS4_ROWS_PER_CTA +K32_ROWS4_WARPS = parent.parent.rows4.K32_ROWS4_WARPS +Q32_ROWLD2_STAGE1_THREADS = _decode_capture(_json_loads('128')) +Q32_ROWLD2_ACTIVE_ROWS = 32 +ROUTE_PARENT_2E8E = ''.join([format(parent.MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_Q32_ROWLD2_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +SEED_K32_Q32_ROWLD2_F653_V1_ID = 'rag_microbucket_k32_q32rowld2_f653_v1_rowld2_rows4' + +def _ir_with_constants(ir_obj: Any, *, suffix: str, **updates: int) -> Any: + constants = tuple(((name, updates.get(name, value)) for name, value in ir_obj.constants)) + return dc.replace(ir_obj, symbol=''.join([format(ir_obj.symbol, ''), '_', format(suffix, '')]), constants=constants) + +def _stage1_q32_rowld2_ir() -> Any: + return _ir_with_constants(rowld2_seed.stage1_q32_k32_m64_rowld2_ir, suffix='q32rowld2_f653_v1', BLOCK_Q=rowld2_seed.Q16_ROWLD1_BLOCK_Q, BLOCK_M=rowld2_seed.Q16_ROWLD1_BLOCK_M, FEAT_D=rowld2_seed.Q16_ROWLD1_FEAT_D, TOP_K_MAX=K32_TOP_K_MAX, ROWS_COVERED=Q32_ROWLD2_ACTIVE_ROWS) + +def _warp_merge_ir(split_count: int) -> Any: + if K32_ROWS4_ROWS_PER_CTA <= 0 or K32_ROWS4_ROWS_PER_CTA > K32_ROWS4_WARPS: + raise ValueError(''.join(['rows_per_cta=', format(K32_ROWS4_ROWS_PER_CTA, ''), ' exceeds merge warps=', format(K32_ROWS4_WARPS, '')])) + return _ir_with_constants(base.k32_warp_row_merge_ir, suffix=''.join(['k32q32s', format(split_count, ''), 'r', format(K32_ROWS4_ROWS_PER_CTA, ''), '_f653_v1']), TOP_K_MAX=K32_TOP_K_MAX, SPLIT_COUNT=split_count, SPLITS_PER_LANE=base._splits_per_lane(split_count), ROWS_PER_CTA=K32_ROWS4_ROWS_PER_CTA) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RAG_MICROBUCKET_K32_Q32ROWLD2_F653_V1_VERIFY_KERNEL') + split_count = int(os.environ.get('LOOM_KNN_RAG_MICROBUCKET_K32_Q32ROWLD2_F653_V1_VERIFY_K32_SPLIT', K32_Q32_SPLIT_COUNT)) + if verify_kernel == 'rowld2_stage1': + return _stage1_q32_rowld2_ir() + return _warp_merge_ir(split_count) +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_k32warpmerge_0077_v1_warp_row_merge_k32q32s145r4_f653_v1", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 32], ["SPLIT_COUNT", 145], ["SPLITS_PER_LANE", 5], ["ROWS_PER_CTA", 4]], "cta_group": 1, "threads": 128}')) + +def _compiled_stage1_q32_rowld2(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0208"}')) + +@cache +def _compiled_rows4_warp_merge(split_count: int): + return base.rowld_seed.compact_seed.q16_tailinf.parent_k32._compile_ir(_warp_merge_ir(split_count)) + +def _eligible_q32_rowld2(inputs: dict[str, Any]) -> bool: + return base._is_bf16_d128_nonbuild(inputs) and int(inputs.get('Q', -1)) == 32 and (int(inputs.get('M', -1)) == 100000) and (int(inputs.get('K', -1)) == 32) + +def _q32_rowld2_route_name(inputs: dict[str, Any], *, split_count: int) -> str: + n_query = int(inputs.get('Q', -1)) + n_database = int(inputs.get('M', -1)) + return ''.join(['rag_microbucket_k32_q32rowld2_f653_v1_q', format(n_query, ''), '_m', format(n_database, ''), '_k32_row16x256b2warp_s', format(split_count, ''), '_r', format(K32_ROWS4_ROWS_PER_CTA, ''), '_warpmerge']) + +def route_for_contract_inputs(inputs: dict[str, Any], *, k32_q32_split_count: int=K32_Q32_SPLIT_COUNT) -> str: + if _eligible_q32_rowld2(inputs): + return _q32_rowld2_route_name(inputs, split_count=k32_q32_split_count) + return parent.route_for_contract_inputs(inputs) + +def _launch_q32_rowld2_rows4_merge(inputs: dict[str, Any], *, split_count: int) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + if top_k != K32_TOP_K_MAX: + raise ValueError(''.join(['k32 q32 rowld2 only supports K=', format(K32_TOP_K_MAX, ''), ', got K=', format(top_k, '')])) + block_q = rowld2_seed.Q16_ROWLD1_BLOCK_Q + block_m = rowld2_seed.Q16_ROWLD1_BLOCK_M + num_q_tiles = (n_query + block_q - 1) // block_q + num_db_tiles = (n_database + block_m - 1) // block_m + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tiles * split_count + total_queries = bsz * n_query + stage1_grid = min(total_work, base.rowld_seed.compact_seed.GRID_DIM_DEFAULT) + merge_grid = min((total_queries + K32_ROWS4_ROWS_PER_CTA - 1) // K32_ROWS4_ROWS_PER_CTA, base.rowld_seed.compact_seed.GRID_DIM_DEFAULT) + partial_dists, partial_indices = base.rowld_seed.compact_seed.q16_tailinf.parent_k32.parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = base.rowld_seed.compact_seed.q16_tailinf.parent_k32.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), total_queries, block_q, dim, dim) + tmap_database = base.rowld_seed.compact_seed.q16_tailinf.parent_k32.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, block_m, dim, dim) + stage1_ir = _stage1_q32_rowld2_ir() + _compiled_stage1_q32_rowld2().launch(grid=(stage1_grid, 1, 1), block=(Q32_ROWLD2_STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage1_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tiles=num_q_tiles, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), shared_mem=stage1_ir.computed_smem_bytes) + merge_ir = _warp_merge_ir(split_count) + _compiled_rows4_warp_merge(split_count).launch(grid=(merge_grid, 1, 1), block=(K32_ROWS4_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], total_queries], shared_mem=merge_ir.computed_smem_bytes) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, k32_q32_split_count: int=K32_Q32_SPLIT_COUNT) -> None: + if _eligible_q32_rowld2(inputs): + _launch_q32_rowld2_rows4_merge(inputs, split_count=k32_q32_split_count) + return + parent.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_with_q32_split(split_count: int) -> Callable[[dict[str, Any]], None]: + + def _candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, k32_q32_split_count=split_count) + return _candidate + +def candidate_parent_2e8e(inputs: dict[str, Any]) -> None: + parent.launch_from_contract_inputs(inputs) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return parent._select_contract_shapes(shape_labels) + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=K32_BUCKET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def compile_and_launch_knn_build(*, shape_labels=K32_BUCKET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def route_trace_for_contract_shapes(shape_labels=K32_BUCKET_SHAPES, *, k32_q32_split_count: int=K32_Q32_SPLIT_COUNT) -> list[dict[str, Any]]: + rows = [] + for shape in _select_contract_shapes(shape_labels): + inputs = base.rowld_seed.base_dispatcher._trace_inputs_from_shape(shape) + route = route_for_contract_inputs(inputs, k32_q32_split_count=k32_q32_split_count) + parent_route = parent.route_for_contract_inputs(inputs) + selected = _eligible_q32_rowld2(inputs) + rows.append({'shape_key': shape['label'], 'selected_route': route, 'selected_seed': SEED_K32_Q32_ROWLD2_F653_V1_ID if selected else parent.SEED_K32_2E8E_Q16_SPLIT148_ID, 'selected_entrypoint': ROUTE_Q32_ROWLD2_ENTRYPOINT if selected else ROUTE_PARENT_2E8E, 'parent_2e8e_route': parent_route, 'route_kind': 'specialized_q32_rowld2_rows4' if selected else 'inherited_2e8e_parent', 'split_count': k32_q32_split_count if selected else None, 'guard_condition': 'BF16 non-build B=1 Q=32 M=100000 D=128 K=32' if selected else 'delegate to current 2e8e K32 parent'}) + return rows + +def _per_shape_delta(candidate_report: dict[str, Any], parent_report: dict[str, Any]) -> dict[str, Any]: + rows = {} + for label, cand in candidate_report.get('per_shape', {}).items(): + parent_row = parent_report.get('per_shape', {}).get(label, {}) + cand_ms = cand.get('kernel_ms') + parent_ms = parent_row.get('kernel_ms') + rows[label] = {'candidate': cand, 'parent_2e8e': parent_row, 'candidate_ms': cand_ms, 'parent_2e8e_ms': parent_ms, 'speedup_vs_parent_2e8e': parent_ms / cand_ms if cand_ms and parent_ms else None, 'ratio_vs_flashlib': cand.get('ratio_vs_flashlib')} + return rows + +def benchmark_knn_build_rag_microbucket_k32_q32rowld2_f653_v1(*, use_cupti: bool=True, shape_labels=K32_BUCKET_SHAPES, k32_q32_split_count: int=K32_Q32_SPLIT_COUNT) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_with_q32_split(k32_q32_split_count)) + parent_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_parent_2e8e) + return {'contract': candidate_report['contract'], 'contract_version': candidate_report['contract_version'], 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_knn_build_rag_microbucket_k32_q32rowld2_f653_v1']), 'candidate_entrypoint': ROUTE_Q32_ROWLD2_ENTRYPOINT, 'parent_entrypoint': ROUTE_PARENT_2E8E, 'accelerated_shape_labels': list(Q32_ROWLD2_TARGET_SHAPES), 'measured_shape_labels': list(shape_labels), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'producer_topology': {'Q32_M100000': 'ROW_16x256B rowld2 two-compute-warp stage1 with ROWS_COVERED=32', 'guard_misses': 'delegate to current 2e8e K32 parent'}, 'merge_topology': {'Q32': ''.join(['warp-row split-list merge/', format(K32_ROWS4_ROWS_PER_CTA, ''), ' rows per CTA']), 'q32_split_count': k32_q32_split_count, 'q32_splits_per_lane': base._splits_per_lane(k32_q32_split_count)}, 'route_trace': route_trace_for_contract_shapes(shape_labels, k32_q32_split_count=k32_q32_split_count), 'target_rows': _per_shape_delta(candidate_report, parent_report), 'summary': candidate_report['summary'], 'performance': candidate_report['performance'], 'correctness': candidate_report['correctness'], 'report': candidate_report, 'parent_summary': parent_report['summary'], 'parent_performance': parent_report['performance'], 'parent_report': parent_report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32_q32rowld2exact_f653_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32_q32rowld2exact_f653_v1.py new file mode 100644 index 00000000..e8a655ea --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32_q32rowld2exact_f653_v1.py @@ -0,0 +1,190 @@ +"""RAG microbatch K32 Q32 rowld2 exact-shape bucket wrapper. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +targets the exact v11 `rag_microbatch_largek_b1_q32_m100000_d128_k32` floor +row. It keeps the f653 rowld2 tcgen05/TMA producer and rows4 merge, but +specializes stage1 for the exact B=1/Q=32/M=100000/D=128/K=32 guard so split +counts, tile intervals, query bounds, and output strides are compile-time +constants. Guard misses delegate to the current f653 rowld2 parent route. The +production path remains Weave-only. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from collections.abc import Callable +from functools import cache +from typing import Any +from .._dispatch_runtime import dc as dc +from .. import _dispatch_runtime as eval_mod +from . import knn_build_rag_microbucket_k32_q32rowld2_f653_v1 as parent +from .._dispatch_runtime import pack_kernel_args +MODULE = 'loom.examples.weave.knn_build_rag_microbucket_k32_q32rowld2exact_f653_v1' +Q32_K32_SHAPE = parent.Q32_K32_SHAPE +Q32_ROWLD2EXACT_TARGET_SHAPES = (Q32_K32_SHAPE,) +K32_BUCKET_SHAPES = Q32_ROWLD2EXACT_TARGET_SHAPES +TARGET_SHAPES = Q32_ROWLD2EXACT_TARGET_SHAPES +K32_Q32_SPLIT_COUNT = _decode_capture(_json_loads('141')) +K32_TOP_K_MAX = parent.K32_TOP_K_MAX +K32_ROWS4_MERGE_THREADS = parent.K32_ROWS4_MERGE_THREADS +K32_ROWS4_ROWS_PER_CTA = parent.K32_ROWS4_ROWS_PER_CTA +K32_ROWS4_WARPS = parent.K32_ROWS4_WARPS +rowld2_seed = parent.rowld2_seed +base = parent.base +Q32_ROWLD2EXACT_STAGE1_THREADS = _decode_capture(_json_loads('128')) +Q32_ROWLD2EXACT_ACTIVE_ROWS = 32 +Q32_ROWLD2EXACT_LOCAL_LISTS_PER_ROW = rowld2_seed.Q16_ROWLD1_LOCAL_LISTS_PER_ROW +Q32_ROWLD2EXACT_SMEM_BASE_BYTES = rowld2_seed.Q16_ROWLD1_SMEM_BASE_BYTES +Q32_ROWLD2EXACT_LOCAL_ELEMS = Q32_ROWLD2EXACT_ACTIVE_ROWS * Q32_ROWLD2EXACT_LOCAL_LISTS_PER_ROW * K32_TOP_K_MAX +Q32_ROWLD2EXACT_LOCAL_D_OFFSET = Q32_ROWLD2EXACT_SMEM_BASE_BYTES +Q32_ROWLD2EXACT_LOCAL_I_OFFSET = Q32_ROWLD2EXACT_LOCAL_D_OFFSET + Q32_ROWLD2EXACT_LOCAL_ELEMS * 4 +Q32_ROWLD2EXACT_SMEM_POOL_BYTES = Q32_ROWLD2EXACT_LOCAL_I_OFFSET + Q32_ROWLD2EXACT_LOCAL_ELEMS * 4 +Q32_ROWLD2EXACT_M = 100000 +Q32_ROWLD2EXACT_NUM_DB_TILES = (Q32_ROWLD2EXACT_M + rowld2_seed.Q16_ROWLD1_BLOCK_M - 1) // rowld2_seed.Q16_ROWLD1_BLOCK_M +Q32_ROWLD2EXACT_TILES_FLOOR = Q32_ROWLD2EXACT_NUM_DB_TILES // K32_Q32_SPLIT_COUNT +Q32_ROWLD2EXACT_EXTRA_SPLITS = Q32_ROWLD2EXACT_NUM_DB_TILES - Q32_ROWLD2EXACT_TILES_FLOOR * K32_Q32_SPLIT_COUNT +Q32_ROWLD2EXACT_DB_TILES_PER_SPLIT = (Q32_ROWLD2EXACT_NUM_DB_TILES + K32_Q32_SPLIT_COUNT - 1) // K32_Q32_SPLIT_COUNT +ROUTE_PARENT_F653 = ''.join([format(parent.MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_Q32_ROWLD2EXACT_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +SEED_K32_Q32_ROWLD2EXACT_F653_V1_ID = 'rag_microbucket_k32_q32rowld2exact_f653_v1' +_insert_sorted_pair = _ir_proxy('loom.examples.weave.knn_build_rag_microbucket_k32_q32rowld2exact_f653_v1:_insert_sorted_pair', 256) +knn_build_rag_microbucket_k32_q32rowld2exact_f653_v1_stage1 = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_k32_q32rowld2exact_f653_v1_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "num_db_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 66816, "constants": [["BLOCK_Q", 64], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32], ["ROWS_COVERED", 32], ["SPLIT_COUNT_CONST", 141], ["NUM_DB_TILES_CONST", 1563], ["TILES_FLOOR_CONST", 11], ["EXTRA_SPLITS_CONST", 12], ["DB_TILES_PER_SPLIT_CONST", 12], ["M_LIMIT", 100000]], "cta_group": 1, "threads": 128}')) + +def _ir_with_constants(ir_obj: Any, *, suffix: str, **updates: int) -> Any: + constants = tuple(((name, updates.get(name, value)) for name, value in ir_obj.constants)) + return dc.replace(ir_obj, symbol=''.join([format(ir_obj.symbol, ''), '_', format(suffix, '')]), constants=constants) + +def _stage1_q32_rowld2exact_ir() -> Any: + return _ir_with_constants(knn_build_rag_microbucket_k32_q32rowld2exact_f653_v1_stage1, suffix='q32rowld2exact_f653_v1', BLOCK_Q=rowld2_seed.Q16_ROWLD1_BLOCK_Q, BLOCK_M=rowld2_seed.Q16_ROWLD1_BLOCK_M, FEAT_D=rowld2_seed.Q16_ROWLD1_FEAT_D, TOP_K_MAX=K32_TOP_K_MAX, ROWS_COVERED=Q32_ROWLD2EXACT_ACTIVE_ROWS) + +def _warp_merge_ir(split_count: int) -> Any: + if K32_ROWS4_ROWS_PER_CTA <= 0 or K32_ROWS4_ROWS_PER_CTA > K32_ROWS4_WARPS: + raise ValueError(''.join(['rows_per_cta=', format(K32_ROWS4_ROWS_PER_CTA, ''), ' exceeds merge warps=', format(K32_ROWS4_WARPS, '')])) + return _ir_with_constants(base.k32_warp_row_merge_ir, suffix=''.join(['k32q32exact_s', format(split_count, ''), 'r', format(K32_ROWS4_ROWS_PER_CTA, ''), '_f653_v1']), TOP_K_MAX=K32_TOP_K_MAX, SPLIT_COUNT=split_count, SPLITS_PER_LANE=base._splits_per_lane(split_count), ROWS_PER_CTA=K32_ROWS4_ROWS_PER_CTA) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RAG_MICROBUCKET_K32_Q32ROWLD2EXACT_F653_V1_VERIFY_KERNEL') + split_count = int(os.environ.get('LOOM_KNN_RAG_MICROBUCKET_K32_Q32ROWLD2EXACT_F653_V1_VERIFY_K32_SPLIT', K32_Q32_SPLIT_COUNT)) + if verify_kernel == 'rowld2_exact_stage1': + return _stage1_q32_rowld2exact_ir() + return _warp_merge_ir(split_count) +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_k32warpmerge_0077_v1_warp_row_merge_k32q32exact_s141r4_f653_v1", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 32], ["SPLIT_COUNT", 141], ["SPLITS_PER_LANE", 5], ["ROWS_PER_CTA", 4]], "cta_group": 1, "threads": 128}')) + +def _compiled_stage1_q32_rowld2exact(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0230"}')) + +@cache +def _compiled_rows4_warp_merge(split_count: int): + return base.rowld_seed.compact_seed.q16_tailinf.parent_k32._compile_ir(_warp_merge_ir(split_count)) + +def _eligible_q32_rowld2exact(inputs: dict[str, Any]) -> bool: + return parent._eligible_q32_rowld2(inputs) + +def _q32_rowld2exact_route_name(inputs: dict[str, Any], *, split_count: int) -> str: + n_query = int(inputs.get('Q', -1)) + n_database = int(inputs.get('M', -1)) + return ''.join(['rag_microbucket_k32_q32rowld2exact_f653_v1_q', format(n_query, ''), '_m', format(n_database, ''), '_k32_row16x256b2warp_exact_s', format(split_count, ''), '_r', format(K32_ROWS4_ROWS_PER_CTA, ''), '_warpmerge']) + +def route_for_contract_inputs(inputs: dict[str, Any], *, k32_q32_split_count: int=K32_Q32_SPLIT_COUNT) -> str: + if _eligible_q32_rowld2exact(inputs): + return _q32_rowld2exact_route_name(inputs, split_count=k32_q32_split_count) + return parent.route_for_contract_inputs(inputs) + +def _launch_q32_rowld2exact_rows4_merge(inputs: dict[str, Any], *, split_count: int) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + if top_k != K32_TOP_K_MAX: + raise ValueError(''.join(['k32 q32 rowld2exact only supports K=', format(K32_TOP_K_MAX, ''), ', got K=', format(top_k, '')])) + if split_count != K32_Q32_SPLIT_COUNT: + raise ValueError(''.join(['q32 rowld2exact stage1 is compile-time specialized for split_count=', format(K32_Q32_SPLIT_COUNT, ''), ', got runtime split_count=', format(split_count, '')])) + block_q = rowld2_seed.Q16_ROWLD1_BLOCK_Q + block_m = rowld2_seed.Q16_ROWLD1_BLOCK_M + num_q_tiles = (n_query + block_q - 1) // block_q + num_db_tiles = (n_database + block_m - 1) // block_m + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tiles * split_count + total_queries = bsz * n_query + stage1_grid = min(total_work, base.rowld_seed.compact_seed.GRID_DIM_DEFAULT) + merge_grid = min((total_queries + K32_ROWS4_ROWS_PER_CTA - 1) // K32_ROWS4_ROWS_PER_CTA, base.rowld_seed.compact_seed.GRID_DIM_DEFAULT) + partial_dists, partial_indices = base.rowld_seed.compact_seed.q16_tailinf.parent_k32.parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = base.rowld_seed.compact_seed.q16_tailinf.parent_k32.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), total_queries, block_q, dim, dim) + tmap_database = base.rowld_seed.compact_seed.q16_tailinf.parent_k32.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, block_m, dim, dim) + stage1_ir = _stage1_q32_rowld2exact_ir() + _compiled_stage1_q32_rowld2exact().launch(grid=(stage1_grid, 1, 1), block=(Q32_ROWLD2EXACT_STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage1_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tiles=num_q_tiles, num_db_tiles=num_db_tiles, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), shared_mem=stage1_ir.computed_smem_bytes) + merge_ir = _warp_merge_ir(split_count) + _compiled_rows4_warp_merge(split_count).launch(grid=(merge_grid, 1, 1), block=(K32_ROWS4_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], total_queries], shared_mem=merge_ir.computed_smem_bytes) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, k32_q32_split_count: int=K32_Q32_SPLIT_COUNT) -> None: + if _eligible_q32_rowld2exact(inputs): + _launch_q32_rowld2exact_rows4_merge(inputs, split_count=k32_q32_split_count) + return + parent.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_with_q32_split(split_count: int) -> Callable[[dict[str, Any]], None]: + + def _candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, k32_q32_split_count=split_count) + return _candidate + +def candidate_parent_f653(inputs: dict[str, Any]) -> None: + parent.launch_from_contract_inputs(inputs) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return parent._select_contract_shapes(shape_labels) + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=K32_BUCKET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def compile_and_launch_knn_build(*, shape_labels=K32_BUCKET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def route_trace_for_contract_shapes(shape_labels=K32_BUCKET_SHAPES, *, k32_q32_split_count: int=K32_Q32_SPLIT_COUNT) -> list[dict[str, Any]]: + rows = [] + for shape in _select_contract_shapes(shape_labels): + inputs = parent.base.rowld_seed.base_dispatcher._trace_inputs_from_shape(shape) + route = route_for_contract_inputs(inputs, k32_q32_split_count=k32_q32_split_count) + parent_route = parent.route_for_contract_inputs(inputs) + selected = _eligible_q32_rowld2exact(inputs) + rows.append({'shape_key': shape['label'], 'selected_route': route, 'selected_seed': SEED_K32_Q32_ROWLD2EXACT_F653_V1_ID if selected else parent.SEED_K32_Q32_ROWLD2_F653_V1_ID, 'selected_entrypoint': ROUTE_Q32_ROWLD2EXACT_ENTRYPOINT if selected else ROUTE_PARENT_F653, 'parent_f653_route': parent_route, 'route_kind': 'specialized_q32_rowld2_exact' if selected else 'inherited_f653_parent', 'split_count': k32_q32_split_count if selected else None, 'guard_condition': 'BF16 non-build B=1 Q=32 M=100000 D=128 K=32' if selected else 'delegate to current f653 rowld2 parent'}) + return rows + +def _per_shape_delta(candidate_report: dict[str, Any], parent_report: dict[str, Any]) -> dict[str, Any]: + rows = {} + for label, cand in candidate_report.get('per_shape', {}).items(): + parent_row = parent_report.get('per_shape', {}).get(label, {}) + cand_ms = cand.get('kernel_ms') + parent_ms = parent_row.get('kernel_ms') + rows[label] = {'candidate': cand, 'parent_f653': parent_row, 'candidate_ms': cand_ms, 'parent_f653_ms': parent_ms, 'speedup_vs_parent_f653': parent_ms / cand_ms if cand_ms and parent_ms else None, 'ratio_vs_flashlib': cand.get('ratio_vs_flashlib')} + return rows + +def benchmark_knn_build_rag_microbucket_k32_q32rowld2exact_f653_v1(*, use_cupti: bool=True, shape_labels=K32_BUCKET_SHAPES, k32_q32_split_count: int=K32_Q32_SPLIT_COUNT) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_with_q32_split(k32_q32_split_count)) + parent_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_parent_f653) + num_db_tiles = (100000 + rowld2_seed.Q16_ROWLD1_BLOCK_M - 1) // rowld2_seed.Q16_ROWLD1_BLOCK_M + return {'contract': candidate_report['contract'], 'contract_version': candidate_report['contract_version'], 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_knn_build_rag_microbucket_k32_q32rowld2exact_f653_v1']), 'candidate_entrypoint': ROUTE_Q32_ROWLD2EXACT_ENTRYPOINT, 'parent_entrypoint': ROUTE_PARENT_F653, 'accelerated_shape_labels': list(Q32_ROWLD2EXACT_TARGET_SHAPES), 'measured_shape_labels': list(shape_labels), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'producer_topology': {'Q32_M100000': ''.join(['f653 ROW_16x256B rowld2 stage1 with exact database-tile ownership; ', format(num_db_tiles, ''), ' tiles distributed over ', format(k32_q32_split_count, ''), ' splits']), 'guard_misses': 'delegate to current f653 rowld2 parent'}, 'merge_topology': {'Q32': ''.join(['f653 rows4 warp-row merge/', format(parent.K32_ROWS4_ROWS_PER_CTA, ''), ' rows per CTA']), 'q32_split_count': k32_q32_split_count, 'q32_splits_per_lane': parent.base._splits_per_lane(k32_q32_split_count)}, 'route_trace': route_trace_for_contract_shapes(shape_labels, k32_q32_split_count=k32_q32_split_count), 'target_rows': _per_shape_delta(candidate_report, parent_report), 'summary': candidate_report['summary'], 'performance': candidate_report['performance'], 'correctness': candidate_report['correctness'], 'report': candidate_report, 'parent_summary': parent_report['summary'], 'parent_performance': parent_report['performance'], 'parent_report': parent_report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32_q32rowld2exact_s141_72d1_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32_q32rowld2exact_s141_72d1_v1.py new file mode 100644 index 00000000..d18ec9ce --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32_q32rowld2exact_s141_72d1_v1.py @@ -0,0 +1,190 @@ +"""RAG microbatch K32 Q32 rowld2 exact-shape bucket wrapper. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +targets the exact v11 `rag_microbatch_largek_b1_q32_m100000_d128_k32` floor +row. It keeps the f653 rowld2 tcgen05/TMA producer and rows4 merge, but +specializes stage1 for the exact B=1/Q=32/M=100000/D=128/K=32 guard so split +counts, tile intervals, query bounds, and output strides are compile-time +constants. Guard misses delegate to the current f653 rowld2 parent route. The +production path remains Weave-only. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from collections.abc import Callable +from .._dispatch_runtime import _replace as replace +from functools import cache +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_rag_microbucket_k32_q32rowld2_f653_v1 as parent +from .._dispatch_runtime import pack_kernel_args +MODULE = 'loom.examples.weave.knn_build_rag_microbucket_k32_q32rowld2exact_s141_72d1_v1' +Q32_K32_SHAPE = parent.Q32_K32_SHAPE +Q32_ROWLD2EXACT_TARGET_SHAPES = (Q32_K32_SHAPE,) +K32_BUCKET_SHAPES = Q32_ROWLD2EXACT_TARGET_SHAPES +TARGET_SHAPES = Q32_ROWLD2EXACT_TARGET_SHAPES +K32_Q32_SPLIT_COUNT = 141 +K32_TOP_K_MAX = parent.K32_TOP_K_MAX +K32_ROWS4_MERGE_THREADS = parent.K32_ROWS4_MERGE_THREADS +K32_ROWS4_ROWS_PER_CTA = parent.K32_ROWS4_ROWS_PER_CTA +K32_ROWS4_WARPS = parent.K32_ROWS4_WARPS +rowld2_seed = parent.rowld2_seed +base = parent.base +Q32_ROWLD2EXACT_STAGE1_THREADS = _decode_capture(_json_loads('128')) +Q32_ROWLD2EXACT_ACTIVE_ROWS = 32 +Q32_ROWLD2EXACT_LOCAL_LISTS_PER_ROW = rowld2_seed.Q16_ROWLD1_LOCAL_LISTS_PER_ROW +Q32_ROWLD2EXACT_SMEM_BASE_BYTES = rowld2_seed.Q16_ROWLD1_SMEM_BASE_BYTES +Q32_ROWLD2EXACT_LOCAL_ELEMS = Q32_ROWLD2EXACT_ACTIVE_ROWS * Q32_ROWLD2EXACT_LOCAL_LISTS_PER_ROW * K32_TOP_K_MAX +Q32_ROWLD2EXACT_LOCAL_D_OFFSET = Q32_ROWLD2EXACT_SMEM_BASE_BYTES +Q32_ROWLD2EXACT_LOCAL_I_OFFSET = Q32_ROWLD2EXACT_LOCAL_D_OFFSET + Q32_ROWLD2EXACT_LOCAL_ELEMS * 4 +Q32_ROWLD2EXACT_SMEM_POOL_BYTES = Q32_ROWLD2EXACT_LOCAL_I_OFFSET + Q32_ROWLD2EXACT_LOCAL_ELEMS * 4 +Q32_ROWLD2EXACT_M = 100000 +Q32_ROWLD2EXACT_NUM_DB_TILES = (Q32_ROWLD2EXACT_M + rowld2_seed.Q16_ROWLD1_BLOCK_M - 1) // rowld2_seed.Q16_ROWLD1_BLOCK_M +Q32_ROWLD2EXACT_TILES_FLOOR = Q32_ROWLD2EXACT_NUM_DB_TILES // K32_Q32_SPLIT_COUNT +Q32_ROWLD2EXACT_EXTRA_SPLITS = Q32_ROWLD2EXACT_NUM_DB_TILES - Q32_ROWLD2EXACT_TILES_FLOOR * K32_Q32_SPLIT_COUNT +Q32_ROWLD2EXACT_DB_TILES_PER_SPLIT = (Q32_ROWLD2EXACT_NUM_DB_TILES + K32_Q32_SPLIT_COUNT - 1) // K32_Q32_SPLIT_COUNT +ROUTE_PARENT_F653 = ''.join([format(parent.MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_Q32_ROWLD2EXACT_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +SEED_K32_Q32_ROWLD2EXACT_F653_V1_ID = 'rag_microbucket_k32_q32rowld2exact_s141_72d1_v1' +_insert_sorted_pair = _ir_proxy('loom.examples.weave.knn_build_rag_microbucket_k32_q32rowld2exact_s141_72d1_v1:_insert_sorted_pair', 256) +knn_build_rag_microbucket_k32_q32rowld2exact_s141_72d1_v1_stage1 = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_k32_q32rowld2exact_s141_72d1_v1_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "num_db_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 66816, "constants": [["BLOCK_Q", 64], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32], ["ROWS_COVERED", 32], ["SPLIT_COUNT_CONST", 141], ["NUM_DB_TILES_CONST", 1563], ["TILES_FLOOR_CONST", 11], ["EXTRA_SPLITS_CONST", 12], ["DB_TILES_PER_SPLIT_CONST", 12], ["M_LIMIT", 100000]], "cta_group": 1, "threads": 128}')) + +def _ir_with_constants(ir_obj: Any, *, suffix: str, **updates: int) -> Any: + constants = tuple(((name, updates.get(name, value)) for name, value in ir_obj.constants)) + return replace(ir_obj, symbol=''.join([format(ir_obj.symbol, ''), '_', format(suffix, '')]), constants=constants) + +def _stage1_q32_rowld2exact_ir() -> Any: + return _ir_with_constants(knn_build_rag_microbucket_k32_q32rowld2exact_s141_72d1_v1_stage1, suffix='q32rowld2exact_f653_v1', BLOCK_Q=rowld2_seed.Q16_ROWLD1_BLOCK_Q, BLOCK_M=rowld2_seed.Q16_ROWLD1_BLOCK_M, FEAT_D=rowld2_seed.Q16_ROWLD1_FEAT_D, TOP_K_MAX=K32_TOP_K_MAX, ROWS_COVERED=Q32_ROWLD2EXACT_ACTIVE_ROWS) + +def _warp_merge_ir(split_count: int) -> Any: + if K32_ROWS4_ROWS_PER_CTA <= 0 or K32_ROWS4_ROWS_PER_CTA > K32_ROWS4_WARPS: + raise ValueError(''.join(['rows_per_cta=', format(K32_ROWS4_ROWS_PER_CTA, ''), ' exceeds merge warps=', format(K32_ROWS4_WARPS, '')])) + return _ir_with_constants(base.k32_warp_row_merge_ir, suffix=''.join(['k32q32exact_s', format(split_count, ''), 'r', format(K32_ROWS4_ROWS_PER_CTA, ''), '_f653_v1']), TOP_K_MAX=K32_TOP_K_MAX, SPLIT_COUNT=split_count, SPLITS_PER_LANE=base._splits_per_lane(split_count), ROWS_PER_CTA=K32_ROWS4_ROWS_PER_CTA) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RAG_MICROBUCKET_K32_Q32ROWLD2EXACT_F653_V1_VERIFY_KERNEL') + split_count = int(os.environ.get('LOOM_KNN_RAG_MICROBUCKET_K32_Q32ROWLD2EXACT_F653_V1_VERIFY_K32_SPLIT', K32_Q32_SPLIT_COUNT)) + if verify_kernel == 'rowld2_exact_stage1': + return _stage1_q32_rowld2exact_ir() + return _warp_merge_ir(split_count) +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_k32warpmerge_0077_v1_warp_row_merge_k32q32exact_s141r4_f653_v1", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 32], ["SPLIT_COUNT", 141], ["SPLITS_PER_LANE", 5], ["ROWS_PER_CTA", 4]], "cta_group": 1, "threads": 128}')) + +def _compiled_stage1_q32_rowld2exact(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0109"}')) + +@cache +def _compiled_rows4_warp_merge(split_count: int): + return base.rowld_seed.compact_seed.q16_tailinf.parent_k32._compile_ir(_warp_merge_ir(split_count)) + +def _eligible_q32_rowld2exact(inputs: dict[str, Any]) -> bool: + return parent._eligible_q32_rowld2(inputs) + +def _q32_rowld2exact_route_name(inputs: dict[str, Any], *, split_count: int) -> str: + n_query = int(inputs.get('Q', -1)) + n_database = int(inputs.get('M', -1)) + return ''.join(['rag_microbucket_k32_q32rowld2exact_s141_72d1_v1_q', format(n_query, ''), '_m', format(n_database, ''), '_k32_row16x256b2warp_exact_s', format(split_count, ''), '_r', format(K32_ROWS4_ROWS_PER_CTA, ''), '_warpmerge']) + +def route_for_contract_inputs(inputs: dict[str, Any], *, k32_q32_split_count: int=K32_Q32_SPLIT_COUNT) -> str: + if _eligible_q32_rowld2exact(inputs): + return _q32_rowld2exact_route_name(inputs, split_count=k32_q32_split_count) + return parent.route_for_contract_inputs(inputs) + +def _launch_q32_rowld2exact_rows4_merge(inputs: dict[str, Any], *, split_count: int) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + if top_k != K32_TOP_K_MAX: + raise ValueError(''.join(['k32 q32 rowld2exact only supports K=', format(K32_TOP_K_MAX, ''), ', got K=', format(top_k, '')])) + if split_count != K32_Q32_SPLIT_COUNT: + raise ValueError(''.join(['q32 rowld2exact stage1 is compile-time specialized for split_count=', format(K32_Q32_SPLIT_COUNT, ''), ', got runtime split_count=', format(split_count, '')])) + block_q = rowld2_seed.Q16_ROWLD1_BLOCK_Q + block_m = rowld2_seed.Q16_ROWLD1_BLOCK_M + num_q_tiles = (n_query + block_q - 1) // block_q + num_db_tiles = (n_database + block_m - 1) // block_m + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tiles * split_count + total_queries = bsz * n_query + stage1_grid = min(total_work, base.rowld_seed.compact_seed.GRID_DIM_DEFAULT) + merge_grid = min((total_queries + K32_ROWS4_ROWS_PER_CTA - 1) // K32_ROWS4_ROWS_PER_CTA, base.rowld_seed.compact_seed.GRID_DIM_DEFAULT) + partial_dists, partial_indices = base.rowld_seed.compact_seed.q16_tailinf.parent_k32.parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = base.rowld_seed.compact_seed.q16_tailinf.parent_k32.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), total_queries, block_q, dim, dim) + tmap_database = base.rowld_seed.compact_seed.q16_tailinf.parent_k32.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, block_m, dim, dim) + stage1_ir = _stage1_q32_rowld2exact_ir() + _compiled_stage1_q32_rowld2exact().launch(grid=(stage1_grid, 1, 1), block=(Q32_ROWLD2EXACT_STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage1_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tiles=num_q_tiles, num_db_tiles=num_db_tiles, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), shared_mem=stage1_ir.computed_smem_bytes) + merge_ir = _warp_merge_ir(split_count) + _compiled_rows4_warp_merge(split_count).launch(grid=(merge_grid, 1, 1), block=(K32_ROWS4_MERGE_THREADS, 1, 1), args=pack_kernel_args(merge_ir, partial_dists=partial_dists, partial_indices=partial_indices, out_dists=inputs['out_dists'], out_indices=inputs['out_indices'], total_queries=total_queries), shared_mem=merge_ir.computed_smem_bytes) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, k32_q32_split_count: int=K32_Q32_SPLIT_COUNT) -> None: + if _eligible_q32_rowld2exact(inputs): + _launch_q32_rowld2exact_rows4_merge(inputs, split_count=k32_q32_split_count) + return + parent.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_with_q32_split(split_count: int) -> Callable[[dict[str, Any]], None]: + + def _candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, k32_q32_split_count=split_count) + return _candidate + +def candidate_parent_f653(inputs: dict[str, Any]) -> None: + parent.launch_from_contract_inputs(inputs) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return parent._select_contract_shapes(shape_labels) + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=K32_BUCKET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def compile_and_launch_knn_build(*, shape_labels=K32_BUCKET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def route_trace_for_contract_shapes(shape_labels=K32_BUCKET_SHAPES, *, k32_q32_split_count: int=K32_Q32_SPLIT_COUNT) -> list[dict[str, Any]]: + rows = [] + for shape in _select_contract_shapes(shape_labels): + inputs = parent.base.rowld_seed.base_dispatcher._trace_inputs_from_shape(shape) + route = route_for_contract_inputs(inputs, k32_q32_split_count=k32_q32_split_count) + parent_route = parent.route_for_contract_inputs(inputs) + selected = _eligible_q32_rowld2exact(inputs) + rows.append({'shape_key': shape['label'], 'selected_route': route, 'selected_seed': SEED_K32_Q32_ROWLD2EXACT_F653_V1_ID if selected else parent.SEED_K32_Q32_ROWLD2_F653_V1_ID, 'selected_entrypoint': ROUTE_Q32_ROWLD2EXACT_ENTRYPOINT if selected else ROUTE_PARENT_F653, 'parent_f653_route': parent_route, 'route_kind': 'specialized_q32_rowld2_exact' if selected else 'inherited_f653_parent', 'split_count': k32_q32_split_count if selected else None, 'guard_condition': 'BF16 non-build B=1 Q=32 M=100000 D=128 K=32' if selected else 'delegate to current f653 rowld2 parent'}) + return rows + +def _per_shape_delta(candidate_report: dict[str, Any], parent_report: dict[str, Any]) -> dict[str, Any]: + rows = {} + for label, cand in candidate_report.get('per_shape', {}).items(): + parent_row = parent_report.get('per_shape', {}).get(label, {}) + cand_ms = cand.get('kernel_ms') + parent_ms = parent_row.get('kernel_ms') + rows[label] = {'candidate': cand, 'parent_f653': parent_row, 'candidate_ms': cand_ms, 'parent_f653_ms': parent_ms, 'speedup_vs_parent_f653': parent_ms / cand_ms if cand_ms and parent_ms else None, 'ratio_vs_flashlib': cand.get('ratio_vs_flashlib')} + return rows + +def benchmark_knn_build_rag_microbucket_k32_q32rowld2exact_s141_72d1_v1(*, use_cupti: bool=True, shape_labels=K32_BUCKET_SHAPES, k32_q32_split_count: int=K32_Q32_SPLIT_COUNT) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_with_q32_split(k32_q32_split_count)) + parent_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_parent_f653) + num_db_tiles = (100000 + rowld2_seed.Q16_ROWLD1_BLOCK_M - 1) // rowld2_seed.Q16_ROWLD1_BLOCK_M + return {'contract': candidate_report['contract'], 'contract_version': candidate_report['contract_version'], 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_knn_build_rag_microbucket_k32_q32rowld2exact_s141_72d1_v1']), 'candidate_entrypoint': ROUTE_Q32_ROWLD2EXACT_ENTRYPOINT, 'parent_entrypoint': ROUTE_PARENT_F653, 'accelerated_shape_labels': list(Q32_ROWLD2EXACT_TARGET_SHAPES), 'measured_shape_labels': list(shape_labels), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'producer_topology': {'Q32_M100000': ''.join(['f653 ROW_16x256B rowld2 stage1 with exact database-tile ownership; ', format(num_db_tiles, ''), ' tiles distributed over ', format(k32_q32_split_count, ''), ' splits']), 'guard_misses': 'delegate to current f653 rowld2 parent'}, 'merge_topology': {'Q32': ''.join(['f653 rows4 warp-row merge/', format(parent.K32_ROWS4_ROWS_PER_CTA, ''), ' rows per CTA']), 'q32_split_count': k32_q32_split_count, 'q32_splits_per_lane': parent.base._splits_per_lane(k32_q32_split_count)}, 'route_trace': route_trace_for_contract_shapes(shape_labels, k32_q32_split_count=k32_q32_split_count), 'target_rows': _per_shape_delta(candidate_report, parent_report), 'summary': candidate_report['summary'], 'performance': candidate_report['performance'], 'correctness': candidate_report['correctness'], 'report': candidate_report, 'parent_summary': parent_report['summary'], 'parent_performance': parent_report['performance'], 'parent_report': parent_report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32_q32rowld2uneven_f653_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32_q32rowld2uneven_f653_v1.py new file mode 100644 index 00000000..7ed32eea --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32_q32rowld2uneven_f653_v1.py @@ -0,0 +1,183 @@ +"""RAG microbatch K32 Q32 rowld2 uneven-split exact bucket wrapper. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +targets the exact v11 `rag_microbatch_largek_b1_q32_m100000_d128_k32` floor +row. It keeps the f653 rowld2 tcgen05/TMA producer and rows4 merge, but changes +stage1 split ownership so each split processes either floor(M_tiles/splits) or +one extra database tile. The final merge still sees one partial list per split. +Guard misses delegate to the current f653 rowld2 parent route. The production +path remains Weave-only. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from collections.abc import Callable +from functools import cache +from typing import Any +from .._dispatch_runtime import dc as dc +from .. import _dispatch_runtime as eval_mod +from . import knn_build_rag_microbucket_k32_q32rowld2_f653_v1 as parent +from .._dispatch_runtime import pack_kernel_args +MODULE = 'loom.examples.weave.knn_build_rag_microbucket_k32_q32rowld2uneven_f653_v1' +Q32_K32_SHAPE = parent.Q32_K32_SHAPE +Q32_ROWLD2UNEVEN_TARGET_SHAPES = (Q32_K32_SHAPE,) +K32_BUCKET_SHAPES = Q32_ROWLD2UNEVEN_TARGET_SHAPES +TARGET_SHAPES = Q32_ROWLD2UNEVEN_TARGET_SHAPES +K32_Q32_SPLIT_COUNT = _decode_capture(_json_loads('141')) +K32_TOP_K_MAX = parent.K32_TOP_K_MAX +K32_ROWS4_MERGE_THREADS = parent.K32_ROWS4_MERGE_THREADS +K32_ROWS4_ROWS_PER_CTA = parent.K32_ROWS4_ROWS_PER_CTA +K32_ROWS4_WARPS = parent.K32_ROWS4_WARPS +rowld2_seed = parent.rowld2_seed +base = parent.base +Q32_ROWLD2UNEVEN_STAGE1_THREADS = _decode_capture(_json_loads('128')) +Q32_ROWLD2UNEVEN_ACTIVE_ROWS = 32 +Q32_ROWLD2UNEVEN_LOCAL_LISTS_PER_ROW = rowld2_seed.Q16_ROWLD1_LOCAL_LISTS_PER_ROW +Q32_ROWLD2UNEVEN_SMEM_BASE_BYTES = rowld2_seed.Q16_ROWLD1_SMEM_BASE_BYTES +Q32_ROWLD2UNEVEN_LOCAL_ELEMS = Q32_ROWLD2UNEVEN_ACTIVE_ROWS * Q32_ROWLD2UNEVEN_LOCAL_LISTS_PER_ROW * K32_TOP_K_MAX +Q32_ROWLD2UNEVEN_LOCAL_D_OFFSET = Q32_ROWLD2UNEVEN_SMEM_BASE_BYTES +Q32_ROWLD2UNEVEN_LOCAL_I_OFFSET = Q32_ROWLD2UNEVEN_LOCAL_D_OFFSET + Q32_ROWLD2UNEVEN_LOCAL_ELEMS * 4 +Q32_ROWLD2UNEVEN_SMEM_POOL_BYTES = Q32_ROWLD2UNEVEN_LOCAL_I_OFFSET + Q32_ROWLD2UNEVEN_LOCAL_ELEMS * 4 +ROUTE_PARENT_F653 = ''.join([format(parent.MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_Q32_ROWLD2UNEVEN_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +SEED_K32_Q32_ROWLD2UNEVEN_F653_V1_ID = 'rag_microbucket_k32_q32rowld2uneven_f653_v1' +_insert_sorted_pair = _ir_proxy('loom.examples.weave.knn_build_rag_microbucket_k32_q32rowld2uneven_f653_v1:_insert_sorted_pair', 256) +knn_build_rag_microbucket_k32_q32rowld2uneven_f653_v1_stage1 = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_k32_q32rowld2uneven_f653_v1_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "num_db_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 66816, "constants": [["BLOCK_Q", 64], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32], ["ROWS_COVERED", 32]], "cta_group": 1, "threads": 128}')) + +def _ir_with_constants(ir_obj: Any, *, suffix: str, **updates: int) -> Any: + constants = tuple(((name, updates.get(name, value)) for name, value in ir_obj.constants)) + return dc.replace(ir_obj, symbol=''.join([format(ir_obj.symbol, ''), '_', format(suffix, '')]), constants=constants) + +def _stage1_q32_rowld2uneven_ir() -> Any: + return _ir_with_constants(knn_build_rag_microbucket_k32_q32rowld2uneven_f653_v1_stage1, suffix='q32rowld2uneven_f653_v1', BLOCK_Q=rowld2_seed.Q16_ROWLD1_BLOCK_Q, BLOCK_M=rowld2_seed.Q16_ROWLD1_BLOCK_M, FEAT_D=rowld2_seed.Q16_ROWLD1_FEAT_D, TOP_K_MAX=K32_TOP_K_MAX, ROWS_COVERED=Q32_ROWLD2UNEVEN_ACTIVE_ROWS) + +def _warp_merge_ir(split_count: int) -> Any: + if K32_ROWS4_ROWS_PER_CTA <= 0 or K32_ROWS4_ROWS_PER_CTA > K32_ROWS4_WARPS: + raise ValueError(''.join(['rows_per_cta=', format(K32_ROWS4_ROWS_PER_CTA, ''), ' exceeds merge warps=', format(K32_ROWS4_WARPS, '')])) + return _ir_with_constants(base.k32_warp_row_merge_ir, suffix=''.join(['k32q32uneven_s', format(split_count, ''), 'r', format(K32_ROWS4_ROWS_PER_CTA, ''), '_f653_v1']), TOP_K_MAX=K32_TOP_K_MAX, SPLIT_COUNT=split_count, SPLITS_PER_LANE=base._splits_per_lane(split_count), ROWS_PER_CTA=K32_ROWS4_ROWS_PER_CTA) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RAG_MICROBUCKET_K32_Q32ROWLD2UNEVEN_F653_V1_VERIFY_KERNEL') + split_count = int(os.environ.get('LOOM_KNN_RAG_MICROBUCKET_K32_Q32ROWLD2UNEVEN_F653_V1_VERIFY_K32_SPLIT', K32_Q32_SPLIT_COUNT)) + if verify_kernel == 'rowld2_uneven_stage1': + return _stage1_q32_rowld2uneven_ir() + return _warp_merge_ir(split_count) +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_k32warpmerge_0077_v1_warp_row_merge_k32q32uneven_s141r4_f653_v1", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 32], ["SPLIT_COUNT", 141], ["SPLITS_PER_LANE", 5], ["ROWS_PER_CTA", 4]], "cta_group": 1, "threads": 128}')) + +def _compiled_stage1_q32_rowld2uneven(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0207"}')) + +@cache +def _compiled_rows4_warp_merge(split_count: int): + return base.rowld_seed.compact_seed.q16_tailinf.parent_k32._compile_ir(_warp_merge_ir(split_count)) + +def _eligible_q32_rowld2uneven(inputs: dict[str, Any]) -> bool: + return parent._eligible_q32_rowld2(inputs) + +def _q32_rowld2uneven_route_name(inputs: dict[str, Any], *, split_count: int) -> str: + n_query = int(inputs.get('Q', -1)) + n_database = int(inputs.get('M', -1)) + return ''.join(['rag_microbucket_k32_q32rowld2uneven_f653_v1_q', format(n_query, ''), '_m', format(n_database, ''), '_k32_row16x256b2warp_uneven_s', format(split_count, ''), '_r', format(K32_ROWS4_ROWS_PER_CTA, ''), '_warpmerge']) + +def route_for_contract_inputs(inputs: dict[str, Any], *, k32_q32_split_count: int=K32_Q32_SPLIT_COUNT) -> str: + if _eligible_q32_rowld2uneven(inputs): + return _q32_rowld2uneven_route_name(inputs, split_count=k32_q32_split_count) + return parent.route_for_contract_inputs(inputs) + +def _launch_q32_rowld2uneven_rows4_merge(inputs: dict[str, Any], *, split_count: int) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + if top_k != K32_TOP_K_MAX: + raise ValueError(''.join(['k32 q32 rowld2uneven only supports K=', format(K32_TOP_K_MAX, ''), ', got K=', format(top_k, '')])) + block_q = rowld2_seed.Q16_ROWLD1_BLOCK_Q + block_m = rowld2_seed.Q16_ROWLD1_BLOCK_M + num_q_tiles = (n_query + block_q - 1) // block_q + num_db_tiles = (n_database + block_m - 1) // block_m + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tiles * split_count + total_queries = bsz * n_query + stage1_grid = min(total_work, base.rowld_seed.compact_seed.GRID_DIM_DEFAULT) + merge_grid = min((total_queries + K32_ROWS4_ROWS_PER_CTA - 1) // K32_ROWS4_ROWS_PER_CTA, base.rowld_seed.compact_seed.GRID_DIM_DEFAULT) + partial_dists, partial_indices = base.rowld_seed.compact_seed.q16_tailinf.parent_k32.parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = base.rowld_seed.compact_seed.q16_tailinf.parent_k32.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), total_queries, block_q, dim, dim) + tmap_database = base.rowld_seed.compact_seed.q16_tailinf.parent_k32.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, block_m, dim, dim) + stage1_ir = _stage1_q32_rowld2uneven_ir() + _compiled_stage1_q32_rowld2uneven().launch(grid=(stage1_grid, 1, 1), block=(Q32_ROWLD2UNEVEN_STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage1_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tiles=num_q_tiles, num_db_tiles=num_db_tiles, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), shared_mem=stage1_ir.computed_smem_bytes) + merge_ir = _warp_merge_ir(split_count) + _compiled_rows4_warp_merge(split_count).launch(grid=(merge_grid, 1, 1), block=(K32_ROWS4_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], total_queries], shared_mem=merge_ir.computed_smem_bytes) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, k32_q32_split_count: int=K32_Q32_SPLIT_COUNT) -> None: + if _eligible_q32_rowld2uneven(inputs): + _launch_q32_rowld2uneven_rows4_merge(inputs, split_count=k32_q32_split_count) + return + parent.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_with_q32_split(split_count: int) -> Callable[[dict[str, Any]], None]: + + def _candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, k32_q32_split_count=split_count) + return _candidate + +def candidate_parent_f653(inputs: dict[str, Any]) -> None: + parent.launch_from_contract_inputs(inputs) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return parent._select_contract_shapes(shape_labels) + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=K32_BUCKET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def compile_and_launch_knn_build(*, shape_labels=K32_BUCKET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def route_trace_for_contract_shapes(shape_labels=K32_BUCKET_SHAPES, *, k32_q32_split_count: int=K32_Q32_SPLIT_COUNT) -> list[dict[str, Any]]: + rows = [] + for shape in _select_contract_shapes(shape_labels): + inputs = parent.base.rowld_seed.base_dispatcher._trace_inputs_from_shape(shape) + route = route_for_contract_inputs(inputs, k32_q32_split_count=k32_q32_split_count) + parent_route = parent.route_for_contract_inputs(inputs) + selected = _eligible_q32_rowld2uneven(inputs) + rows.append({'shape_key': shape['label'], 'selected_route': route, 'selected_seed': SEED_K32_Q32_ROWLD2UNEVEN_F653_V1_ID if selected else parent.SEED_K32_Q32_ROWLD2_F653_V1_ID, 'selected_entrypoint': ROUTE_Q32_ROWLD2UNEVEN_ENTRYPOINT if selected else ROUTE_PARENT_F653, 'parent_f653_route': parent_route, 'route_kind': 'specialized_q32_rowld2_uneven' if selected else 'inherited_f653_parent', 'split_count': k32_q32_split_count if selected else None, 'guard_condition': 'BF16 non-build B=1 Q=32 M=100000 D=128 K=32' if selected else 'delegate to current f653 rowld2 parent'}) + return rows + +def _per_shape_delta(candidate_report: dict[str, Any], parent_report: dict[str, Any]) -> dict[str, Any]: + rows = {} + for label, cand in candidate_report.get('per_shape', {}).items(): + parent_row = parent_report.get('per_shape', {}).get(label, {}) + cand_ms = cand.get('kernel_ms') + parent_ms = parent_row.get('kernel_ms') + rows[label] = {'candidate': cand, 'parent_f653': parent_row, 'candidate_ms': cand_ms, 'parent_f653_ms': parent_ms, 'speedup_vs_parent_f653': parent_ms / cand_ms if cand_ms and parent_ms else None, 'ratio_vs_flashlib': cand.get('ratio_vs_flashlib')} + return rows + +def benchmark_knn_build_rag_microbucket_k32_q32rowld2uneven_f653_v1(*, use_cupti: bool=True, shape_labels=K32_BUCKET_SHAPES, k32_q32_split_count: int=K32_Q32_SPLIT_COUNT) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_with_q32_split(k32_q32_split_count)) + parent_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_parent_f653) + num_db_tiles = (100000 + rowld2_seed.Q16_ROWLD1_BLOCK_M - 1) // rowld2_seed.Q16_ROWLD1_BLOCK_M + return {'contract': candidate_report['contract'], 'contract_version': candidate_report['contract_version'], 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_knn_build_rag_microbucket_k32_q32rowld2uneven_f653_v1']), 'candidate_entrypoint': ROUTE_Q32_ROWLD2UNEVEN_ENTRYPOINT, 'parent_entrypoint': ROUTE_PARENT_F653, 'accelerated_shape_labels': list(Q32_ROWLD2UNEVEN_TARGET_SHAPES), 'measured_shape_labels': list(shape_labels), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'producer_topology': {'Q32_M100000': ''.join(['f653 ROW_16x256B rowld2 stage1 with uneven database-tile ownership; ', format(num_db_tiles, ''), ' tiles distributed over ', format(k32_q32_split_count, ''), ' splits']), 'guard_misses': 'delegate to current f653 rowld2 parent'}, 'merge_topology': {'Q32': ''.join(['f653 rows4 warp-row merge/', format(parent.K32_ROWS4_ROWS_PER_CTA, ''), ' rows per CTA']), 'q32_split_count': k32_q32_split_count, 'q32_splits_per_lane': parent.base._splits_per_lane(k32_q32_split_count)}, 'route_trace': route_trace_for_contract_shapes(shape_labels, k32_q32_split_count=k32_q32_split_count), 'target_rows': _per_shape_delta(candidate_report, parent_report), 'summary': candidate_report['summary'], 'performance': candidate_report['performance'], 'correctness': candidate_report['correctness'], 'report': candidate_report, 'parent_summary': parent_report['summary'], 'parent_performance': parent_report['performance'], 'parent_report': parent_report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32bucket_0077_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32bucket_0077_v1.py new file mode 100644 index 00000000..a584d8e7 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32bucket_0077_v1.py @@ -0,0 +1,123 @@ +"""RAG microbatch K32 bucket wrapper with exact-Q16 ROW_16x256B route. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +keeps the validated q16irreg 2691 route stack for the four requested K32 RAG +microbatch rows, but routes ``rag_microbatch_largek_b1_q16_m100000_d128_k32`` +through the existing ROW_16x256B M64/N64 producer from the q32rowld lineage. +Q8/K32, Q32/K32, irregular Q16/K32, and guard misses delegate to the 2691 +parent. The production path remains Weave-only. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_rag_microbucket_q16irreg_2691_v1 as parent_q16irreg +from . import knn_build_rag_microbucket_q32rowld_e5db_v1 as rowld_seed +MODULE = 'loom.examples.weave.knn_build_rag_microbucket_k32bucket_0077_v1' +Q8_K32_SHAPE = parent_q16irreg.Q8_K32_SHAPE +Q16_K32_SHAPE = parent_q16irreg.Q16_K32_SHAPE +Q32_K32_SHAPE = parent_q16irreg.Q32_K32_SHAPE +Q16_K32_IRREGULAR_SHAPE = parent_q16irreg.Q16_K32_IRREGULAR_SHAPE +Q16_EXACT_ROWLD_TARGET_SHAPES = (Q16_K32_SHAPE,) +K32_BUCKET_SHAPES = parent_q16irreg.K32_BUCKET_SHAPES +TARGET_SHAPES = parent_q16irreg.TARGET_SHAPES +K32_SPLIT_COUNT = parent_q16irreg.K32_SPLIT_COUNT +K32_GROUP_COUNT = parent_q16irreg.K32_GROUP_COUNT +ROUTE_PARENT_Q16IRREG = ''.join([format(parent_q16irreg.MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_BUCKET_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +SEED_Q16_EXACT_ROWLD_ID = 'rag_microbucket_k32bucket_0077_v1_q16_exact_row16x256b' + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RAG_MICROBUCKET_K32BUCKET_0077_V1_VERIFY_KERNEL') + split_count = int(os.environ.get('LOOM_KNN_RAG_MICROBUCKET_K32BUCKET_0077_V1_VERIFY_K32_SPLIT', K32_SPLIT_COUNT)) + group_count = int(os.environ.get('LOOM_KNN_RAG_MICROBUCKET_K32BUCKET_0077_V1_VERIFY_K32_GROUPS', K32_GROUP_COUNT)) + if verify_kernel == 'k32_fused_merge': + return rowld_seed.compact_seed.q16_tailinf._fused_merge_ir(split_count, group_count) + return rowld_seed.stage1_q32_k32_m64_rowld_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_q32rowld_e5db_v1_stage1_q32_k32_m64", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 99584, "constants": [["BLOCK_Q", 64], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 192}')) + +def _label_can_hit(inputs: dict[str, Any], labels: set[str]) -> bool: + label = inputs.get('label') + return label is None or str(label) in labels + +def _eligible_q16_exact_rowld(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, set(Q16_EXACT_ROWLD_TARGET_SHAPES)) and rowld_seed._is_bf16_d128_nonbuild(inputs) and (int(inputs.get('M', -1)) == 100000) and (int(inputs.get('Q', -1)) == 16) and (int(inputs.get('K', -1)) == 32) + +def _q16_exact_rowld_route_name(*, split_count: int, group_count: int) -> str: + return ''.join(['rag_microbucket_k32bucket_0077_v1_q16_m100000_k32_row16x256b_s', format(split_count, ''), '_g', format(group_count, '')]) + +def route_for_contract_inputs(inputs: dict[str, Any], *, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> str: + if _eligible_q16_exact_rowld(inputs): + return _q16_exact_rowld_route_name(split_count=k32_split_count, group_count=k32_group_count) + return parent_q16irreg.route_for_contract_inputs(inputs, k32_split_count=k32_split_count, k32_group_count=k32_group_count) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> None: + if _eligible_q16_exact_rowld(inputs): + rowld_seed._launch_q32_k32_m64_rowld(inputs, split_count=k32_split_count, group_count=k32_group_count) + return + parent_q16irreg.launch_from_contract_inputs(inputs, k32_split_count=k32_split_count, k32_group_count=k32_group_count) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_with_k32_topology(split_count: int, group_count: int) -> Callable[[dict[str, Any]], None]: + + def _candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, k32_split_count=split_count, k32_group_count=group_count) + return _candidate + +def candidate_parent_q16irreg(inputs: dict[str, Any]) -> None: + parent_q16irreg.launch_from_contract_inputs(inputs) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return parent_q16irreg._select_contract_shapes(shape_labels) + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=K32_BUCKET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def compile_and_launch_knn_build(*, shape_labels=K32_BUCKET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def route_trace_for_contract_shapes(shape_labels=K32_BUCKET_SHAPES, *, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> list[dict[str, Any]]: + rows = [] + for shape in _select_contract_shapes(shape_labels): + inputs = parent_q16irreg.parent_combined.q16m64_seed.parent_q32rowld.base_dispatcher._trace_inputs_from_shape(shape) + route = route_for_contract_inputs(inputs, k32_split_count=k32_split_count, k32_group_count=k32_group_count) + parent_route = parent_q16irreg.route_for_contract_inputs(inputs, k32_split_count=k32_split_count, k32_group_count=k32_group_count) + q16_exact_rowld = str(route).startswith('rag_microbucket_k32bucket_0077_v1_q16_') + rows.append({'shape_key': shape['label'], 'selected_route': route, 'selected_seed': SEED_Q16_EXACT_ROWLD_ID if q16_exact_rowld else None, 'selected_entrypoint': ROUTE_BUCKET_ENTRYPOINT if q16_exact_rowld else ROUTE_PARENT_Q16IRREG, 'parent_q16irreg_route': parent_route, 'route_kind': 'specialized_q16_exact_rowld' if q16_exact_rowld else 'inherited_q16irreg_2691', 'guard_condition': 'exact BF16 non-build B=1 Q=16 M=100000 D=128 K=32' if q16_exact_rowld else 'delegate to q16irreg 2691 Weave route stack'}) + return rows + +def _per_shape_delta(candidate_report: dict[str, Any], parent_report: dict[str, Any]) -> dict[str, Any]: + rows = {} + for label, cand in candidate_report.get('per_shape', {}).items(): + parent = parent_report.get('per_shape', {}).get(label, {}) + cand_ms = cand.get('kernel_ms') + parent_ms = parent.get('kernel_ms') + rows[label] = {'candidate': cand, 'parent_q16irreg': parent, 'candidate_ms': cand_ms, 'parent_q16irreg_ms': parent_ms, 'speedup_vs_parent_q16irreg': parent_ms / cand_ms if cand_ms and parent_ms else None, 'ratio_vs_flashlib': cand.get('ratio_vs_flashlib')} + return rows + +def benchmark_knn_build_rag_microbucket_k32bucket_0077_v1(*, use_cupti: bool=True, shape_labels=K32_BUCKET_SHAPES, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_with_k32_topology(k32_split_count, k32_group_count)) + parent_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_parent_q16irreg) + return {'contract': candidate_report['contract'], 'contract_version': candidate_report['contract_version'], 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_knn_build_rag_microbucket_k32bucket_0077_v1']), 'candidate_entrypoint': ''.join([format(MODULE, ''), ':launch_from_contract_inputs']), 'parent_entrypoint': ROUTE_PARENT_Q16IRREG, 'accelerated_shape_labels': list(Q16_EXACT_ROWLD_TARGET_SHAPES), 'measured_shape_labels': list(shape_labels), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'producer_topology': {'Q16_K32_exact': ''.join(['ROW_16x256B/M64N64/S', format(k32_split_count, ''), '/G', format(k32_group_count, ''), '/fused']), 'inherited': 'q16irreg 2691 for Q8 ROW_16x256B, Q32 ROW_16x256B, Q16 irregular M64, and fallback routes'}, 'route_trace': route_trace_for_contract_shapes(shape_labels, k32_split_count=k32_split_count, k32_group_count=k32_group_count), 'target_rows': _per_shape_delta(candidate_report, parent_report), 'summary': candidate_report['summary'], 'performance': candidate_report['performance'], 'correctness': candidate_report['correctness'], 'report': candidate_report, 'parent_summary': parent_report['summary'], 'parent_performance': parent_report['performance'], 'parent_report': parent_report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32q8half_0077_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32q8half_0077_v1.py new file mode 100644 index 00000000..587d8776 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32q8half_0077_v1.py @@ -0,0 +1,154 @@ +"""RAG microbatch K32 bucket with a Q8 half-row ROW_16x256B producer. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +keeps the validated 0077 warp-row split-list merge and the round-102 Q16 +irregular rowld1 route, but routes the exact Q8/M100000 row through a +single-compute-warp ROW_16x256B producer that only materializes the active +eight query rows. Exact Q16/M100000, Q32/M100000, and guard misses delegate to +the round-102 parent. The production path remains Weave-only. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from collections.abc import Callable +from functools import cache +from typing import Any +from .._dispatch_runtime import dc as dc +from .. import _dispatch_runtime as eval_mod +from . import knn_build_rag_microbucket_k32rowld1warp_0077_v1 as parent +from . import knn_build_rag_microbucket_k32warpmerge_0077_v1 as warpmerge_parent +MODULE = 'loom.examples.weave.knn_build_rag_microbucket_k32q8half_0077_v1' +Q8_K32_SHAPE = parent.Q8_K32_SHAPE +Q16_K32_SHAPE = parent.Q16_K32_SHAPE +Q32_K32_SHAPE = parent.Q32_K32_SHAPE +Q16_K32_IRREGULAR_SHAPE = parent.Q16_K32_IRREGULAR_SHAPE +Q8_HALFWARP_TARGET_SHAPES = (Q8_K32_SHAPE,) +K32_BUCKET_SHAPES = parent.K32_BUCKET_SHAPES +TARGET_SHAPES = parent.TARGET_SHAPES +K32_SPLIT_COUNT = parent.K32_SPLIT_COUNT +K32_GROUP_COUNT = parent.K32_GROUP_COUNT +K32_TOP_K_MAX = parent.K32_TOP_K_MAX +K32_WARP_MERGE_THREADS = parent.K32_WARP_MERGE_THREADS +K32_WARP_MERGE_ROWS_PER_CTA = parent.K32_WARP_MERGE_ROWS_PER_CTA +Q8_HALF_STAGE1_THREADS = _decode_capture(_json_loads('96')) +Q8_HALF_BLOCK_Q = 64 +Q8_HALF_BLOCK_M = 64 +Q8_HALF_FEAT_D = 128 +Q8_HALF_ACTIVE_ROWS = 8 +Q8_HALF_LOCAL_LISTS_PER_ROW = 4 +Q8_HALF_SMEM_BASE_BYTES = 16384 + 16384 + 256 +Q8_HALF_LOCAL_ELEMS = Q8_HALF_ACTIVE_ROWS * Q8_HALF_LOCAL_LISTS_PER_ROW * K32_TOP_K_MAX +Q8_HALF_LOCAL_D_OFFSET = Q8_HALF_SMEM_BASE_BYTES +Q8_HALF_LOCAL_I_OFFSET = Q8_HALF_LOCAL_D_OFFSET + Q8_HALF_LOCAL_ELEMS * 4 +Q8_HALF_SMEM_POOL_BYTES = Q8_HALF_LOCAL_I_OFFSET + Q8_HALF_LOCAL_ELEMS * 4 +ROUTE_PARENT_ROWLD1WARP = ''.join([format(parent.MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_Q8_HALF_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +SEED_K32_Q8_HALF_ID = 'rag_microbucket_k32q8half_0077_v1_q8_row16x256b_half_stage1' +knn_build_rag_microbucket_k32q8half_0077_v1_stage1_q8_k32_m64_halfrow = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_k32q8half_0077_v1_stage1_q8_k32_m64_halfrow", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 42240, "constants": [["BLOCK_Q", 64], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32], ["ROWS_COVERED", 8]], "cta_group": 1, "threads": 96}')) +stage1_q8_k32_m64_halfrow_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_k32q8half_0077_v1_stage1_q8_k32_m64_halfrow", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 42240, "constants": [["BLOCK_Q", 64], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32], ["ROWS_COVERED", 8]], "cta_group": 1, "threads": 96}')) + +def _ir_with_constants(ir_obj: Any, *, suffix: str, **updates: int) -> Any: + constants = tuple(((name, updates.get(name, value)) for name, value in ir_obj.constants)) + return dc.replace(ir_obj, symbol=''.join([format(ir_obj.symbol, ''), '_', format(suffix, '')]), constants=constants) + +def _stage1_q8_half_ir() -> Any: + return _ir_with_constants(stage1_q8_k32_m64_halfrow_ir, suffix='q8half_0077_v1', BLOCK_Q=Q8_HALF_BLOCK_Q, BLOCK_M=Q8_HALF_BLOCK_M, FEAT_D=Q8_HALF_FEAT_D, TOP_K_MAX=K32_TOP_K_MAX, ROWS_COVERED=Q8_HALF_ACTIVE_ROWS) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RAG_MICROBUCKET_K32Q8HALF_0077_V1_VERIFY_KERNEL') + split_count = int(os.environ.get('LOOM_KNN_RAG_MICROBUCKET_K32Q8HALF_0077_V1_VERIFY_K32_SPLIT', K32_SPLIT_COUNT)) + if verify_kernel == 'rowld1_stage1': + return parent._stage1_rowld1_ir() + if verify_kernel == 'k32_warp_merge': + return warpmerge_parent._warp_merge_ir(split_count) + return _stage1_q8_half_ir() +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_k32q8half_0077_v1_stage1_q8_k32_m64_halfrow_q8half_0077_v1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 42240, "constants": [["BLOCK_Q", 64], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32], ["ROWS_COVERED", 8]], "cta_group": 1, "threads": 96}')) + +def _compiled_stage1_q8_k32_m64_halfrow(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0102"}')) + +def _launch_q8_half_warpmerge(inputs: dict[str, Any], *, split_count: int) -> None: + warpmerge_parent._launch_stage1_then_warp_merge(inputs, split_count=split_count, stage1_kernel_fn=_compiled_stage1_q8_k32_m64_halfrow, stage1_ir=_stage1_q8_half_ir(), stage1_threads=Q8_HALF_STAGE1_THREADS, block_q=Q8_HALF_BLOCK_Q, block_m=Q8_HALF_BLOCK_M) + +def _eligible_q8_half(inputs: dict[str, Any]) -> bool: + return warpmerge_parent._is_bf16_d128_nonbuild(inputs) and int(inputs.get('M', -1)) == 100000 and (int(inputs.get('Q', -1)) == 8) and (int(inputs.get('K', -1)) == 32) + +def _q8_half_route_name(inputs: dict[str, Any], *, split_count: int) -> str: + n_query = int(inputs.get('Q', -1)) + n_database = int(inputs.get('M', -1)) + return ''.join(['rag_microbucket_k32q8half_0077_v1_q', format(n_query, ''), '_m', format(n_database, ''), '_k32_row16x256bhalf_s', format(split_count, ''), '_warpmerge']) + +def route_for_contract_inputs(inputs: dict[str, Any], *, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> str: + if _eligible_q8_half(inputs): + return _q8_half_route_name(inputs, split_count=k32_split_count) + return parent.route_for_contract_inputs(inputs, k32_split_count=k32_split_count, k32_group_count=k32_group_count) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> None: + if _eligible_q8_half(inputs): + _launch_q8_half_warpmerge(inputs, split_count=k32_split_count) + return + parent.launch_from_contract_inputs(inputs, k32_split_count=k32_split_count, k32_group_count=k32_group_count) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_with_k32_topology(split_count: int, group_count: int) -> Callable[[dict[str, Any]], None]: + + def _candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, k32_split_count=split_count, k32_group_count=group_count) + return _candidate + +def candidate_parent_rowld1warp(inputs: dict[str, Any]) -> None: + parent.launch_from_contract_inputs(inputs) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return parent._select_contract_shapes(shape_labels) + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=K32_BUCKET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def compile_and_launch_knn_build(*, shape_labels=K32_BUCKET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def route_trace_for_contract_shapes(shape_labels=K32_BUCKET_SHAPES, *, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> list[dict[str, Any]]: + rows = [] + for shape in _select_contract_shapes(shape_labels): + inputs = warpmerge_parent.rowld_seed.base_dispatcher._trace_inputs_from_shape(shape) + route = route_for_contract_inputs(inputs, k32_split_count=k32_split_count, k32_group_count=k32_group_count) + parent_route = parent.route_for_contract_inputs(inputs, k32_split_count=k32_split_count, k32_group_count=k32_group_count) + q8_half = str(route).startswith('rag_microbucket_k32q8half_0077_v1_') + rows.append({'shape_key': shape['label'], 'selected_route': route, 'selected_seed': SEED_K32_Q8_HALF_ID if q8_half else None, 'selected_entrypoint': ROUTE_Q8_HALF_ENTRYPOINT if q8_half else ROUTE_PARENT_ROWLD1WARP, 'parent_rowld1warp_route': parent_route, 'route_kind': 'specialized_q8_halfrow_stage1' if q8_half else 'inherited_0077_rowld1warp', 'guard_condition': 'BF16 non-build B=1 Q=8 M=100000 D=128 K=32' if q8_half else 'delegate to round-102 rowld1warp parent'}) + return rows + +def _per_shape_delta(candidate_report: dict[str, Any], parent_report: dict[str, Any]) -> dict[str, Any]: + rows = {} + for label, cand in candidate_report.get('per_shape', {}).items(): + base_row = parent_report.get('per_shape', {}).get(label, {}) + cand_ms = cand.get('kernel_ms') + base_ms = base_row.get('kernel_ms') + rows[label] = {'candidate': cand, 'parent_rowld1warp': base_row, 'candidate_ms': cand_ms, 'parent_rowld1warp_ms': base_ms, 'speedup_vs_parent_rowld1warp': base_ms / cand_ms if cand_ms and base_ms else None, 'ratio_vs_flashlib': cand.get('ratio_vs_flashlib')} + return rows + +def benchmark_knn_build_rag_microbucket_k32q8half_0077_v1(*, use_cupti: bool=True, shape_labels=K32_BUCKET_SHAPES, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_with_k32_topology(k32_split_count, k32_group_count)) + parent_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_parent_rowld1warp) + return {'contract': candidate_report['contract'], 'contract_version': candidate_report['contract_version'], 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_knn_build_rag_microbucket_k32q8half_0077_v1']), 'candidate_entrypoint': ''.join([format(MODULE, ''), ':launch_from_contract_inputs']), 'parent_entrypoint': ROUTE_PARENT_ROWLD1WARP, 'accelerated_shape_labels': list(Q8_HALFWARP_TARGET_SHAPES), 'measured_shape_labels': list(shape_labels), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'producer_topology': {'Q8_exact': 'ROW_16x256B/M64N64 half-row one-compute-warp stage1', 'Q16_irregular': 'inherited round-102 ROW_16x256B one-compute-warp stage1', 'Q16_exact': 'inherited 0077 ROW_16x256B four-compute-warp stage1', 'Q32_exact': 'inherited 0077 ROW_16x256B four-compute-warp stage1', 'guard_misses': 'delegate to round-102 rowld1warp parent'}, 'merge_topology': {'candidate': ''.join(['0077 warp-row split-list merge/', format(K32_WARP_MERGE_ROWS_PER_CTA, ''), ' rows per CTA']), 'split_count': k32_split_count, 'splits_per_lane': warpmerge_parent._splits_per_lane(k32_split_count)}, 'route_trace': route_trace_for_contract_shapes(shape_labels, k32_split_count=k32_split_count, k32_group_count=k32_group_count), 'target_rows': _per_shape_delta(candidate_report, parent_report), 'summary': candidate_report['summary'], 'performance': candidate_report['performance'], 'correctness': candidate_report['correctness'], 'report': candidate_report, 'parent_summary': parent_report['summary'], 'parent_performance': parent_report['performance'], 'parent_report': parent_report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32rowld1warp_0077_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32rowld1warp_0077_v1.py new file mode 100644 index 00000000..bc9263e0 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32rowld1warp_0077_v1.py @@ -0,0 +1,195 @@ +"""RAG microbatch K32 bucket with one-warp ROW_16x256B stage-1 for low-Q rows. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +keeps the validated 0077 warp-row split-list merge, but changes the producer +work ownership for the irregular Q16/M131071 row: a specialized ROW_16x256B +tcgen05/TMA stage-1 uses one compute warp instead of the M64 producer inherited +by the 0077 warp-merge parent. Q8, exact Q16/M100000, and Q32 stay on the prior +0077 routes by default; guard misses delegate to the parent. The production +path remains Weave-only. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from collections.abc import Callable +from functools import cache +from typing import Any +from .._dispatch_runtime import dc as dc +from .. import _dispatch_runtime as eval_mod +from . import knn_build_rag_microbucket_k32warpmerge_0077_v1 as parent +MODULE = 'loom.examples.weave.knn_build_rag_microbucket_k32rowld1warp_0077_v1' +Q8_K32_SHAPE = parent.Q8_K32_SHAPE +Q16_K32_SHAPE = parent.Q16_K32_SHAPE +Q32_K32_SHAPE = parent.Q32_K32_SHAPE +Q16_K32_IRREGULAR_SHAPE = parent.Q16_K32_IRREGULAR_SHAPE +LOW_Q_ROWLD1_TARGET_SHAPES = (Q16_K32_IRREGULAR_SHAPE,) +K32_BUCKET_SHAPES = parent.K32_BUCKET_SHAPES +TARGET_SHAPES = parent.TARGET_SHAPES +K32_SPLIT_COUNT = parent.K32_SPLIT_COUNT +K32_GROUP_COUNT = parent.K32_GROUP_COUNT +K32_TOP_K_MAX = parent.K32_TOP_K_MAX +K32_WARP_MERGE_THREADS = parent.K32_WARP_MERGE_THREADS +K32_WARP_MERGE_ROWS_PER_CTA = parent.K32_WARP_MERGE_ROWS_PER_CTA +Q16_ROWLD1_STAGE1_THREADS = _decode_capture(_json_loads('96')) +Q16_ROWLD1_BLOCK_Q = 64 +Q16_ROWLD1_BLOCK_M = 64 +Q16_ROWLD1_FEAT_D = 128 +Q16_ROWLD1_ACTIVE_ROWS = 16 +Q16_ROWLD1_LOCAL_LISTS_PER_ROW = 4 +Q16_ROWLD1_SMEM_BASE_BYTES = 16384 + 16384 + 256 +Q16_ROWLD1_LOCAL_ELEMS = Q16_ROWLD1_ACTIVE_ROWS * Q16_ROWLD1_LOCAL_LISTS_PER_ROW * K32_TOP_K_MAX +Q16_ROWLD1_LOCAL_D_OFFSET = Q16_ROWLD1_SMEM_BASE_BYTES +Q16_ROWLD1_LOCAL_I_OFFSET = Q16_ROWLD1_LOCAL_D_OFFSET + Q16_ROWLD1_LOCAL_ELEMS * 4 +Q16_ROWLD1_SMEM_POOL_BYTES = Q16_ROWLD1_LOCAL_I_OFFSET + Q16_ROWLD1_LOCAL_ELEMS * 4 +Q32_ROWLD2_STAGE1_THREADS = _decode_capture(_json_loads('128')) +Q32_ROWLD2_ACTIVE_ROWS = 32 +Q32_ROWLD2_LOCAL_ELEMS = Q32_ROWLD2_ACTIVE_ROWS * Q16_ROWLD1_LOCAL_LISTS_PER_ROW * K32_TOP_K_MAX +Q32_ROWLD2_LOCAL_D_OFFSET = Q16_ROWLD1_SMEM_BASE_BYTES +Q32_ROWLD2_LOCAL_I_OFFSET = Q32_ROWLD2_LOCAL_D_OFFSET + Q32_ROWLD2_LOCAL_ELEMS * 4 +Q32_ROWLD2_SMEM_POOL_BYTES = Q32_ROWLD2_LOCAL_I_OFFSET + Q32_ROWLD2_LOCAL_ELEMS * 4 +Q32_ROWLD2_ENABLED = _decode_capture(_json_loads('false')) +ROUTE_PARENT_WARPMERGE = ''.join([format(parent.MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_ROWLD1_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +SEED_K32_ROWLD1WARP_ID = 'rag_microbucket_k32rowld1warp_0077_v1_lowq_row16x256b_stage1' +_rowld1_insert_sorted_pair = _ir_proxy('loom.examples.weave.knn_build_rag_microbucket_k32rowld1warp_0077_v1:_rowld1_insert_sorted_pair', 256) +knn_build_rag_microbucket_k32rowld1warp_0077_v1_stage1_q16_k32_m64_rowld1 = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_k32rowld1warp_0077_v1_stage1_q16_k32_m64_rowld1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 64], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32], ["ROWS_COVERED", 16]], "cta_group": 1, "threads": 96}')) +knn_build_rag_microbucket_k32rowld1warp_0077_v1_stage1_q32_k32_m64_rowld2 = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_k32rowld1warp_0077_v1_stage1_q32_k32_m64_rowld2", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 66816, "constants": [["BLOCK_Q", 64], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32], ["ROWS_COVERED", 32]], "cta_group": 1, "threads": 128}')) +stage1_q16_k32_m64_rowld1_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_k32rowld1warp_0077_v1_stage1_q16_k32_m64_rowld1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 64], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32], ["ROWS_COVERED", 16]], "cta_group": 1, "threads": 96}')) +stage1_q32_k32_m64_rowld2_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_k32rowld1warp_0077_v1_stage1_q32_k32_m64_rowld2", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 66816, "constants": [["BLOCK_Q", 64], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32], ["ROWS_COVERED", 32]], "cta_group": 1, "threads": 128}')) + +def _ir_with_constants(ir_obj: Any, *, suffix: str, **updates: int) -> Any: + constants = tuple(((name, updates.get(name, value)) for name, value in ir_obj.constants)) + return dc.replace(ir_obj, symbol=''.join([format(ir_obj.symbol, ''), '_', format(suffix, '')]), constants=constants) + +def _stage1_rowld1_ir() -> Any: + return _ir_with_constants(stage1_q16_k32_m64_rowld1_ir, suffix='q16rowld1_0077_v1', BLOCK_Q=Q16_ROWLD1_BLOCK_Q, BLOCK_M=Q16_ROWLD1_BLOCK_M, FEAT_D=Q16_ROWLD1_FEAT_D, TOP_K_MAX=K32_TOP_K_MAX, ROWS_COVERED=Q16_ROWLD1_ACTIVE_ROWS) + +def _stage1_rowld2_ir() -> Any: + return _ir_with_constants(stage1_q32_k32_m64_rowld2_ir, suffix='q32rowld2_0077_v1', BLOCK_Q=Q16_ROWLD1_BLOCK_Q, BLOCK_M=Q16_ROWLD1_BLOCK_M, FEAT_D=Q16_ROWLD1_FEAT_D, TOP_K_MAX=K32_TOP_K_MAX, ROWS_COVERED=Q32_ROWLD2_ACTIVE_ROWS) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RAG_MICROBUCKET_K32ROWLD1WARP_0077_V1_VERIFY_KERNEL') + split_count = int(os.environ.get('LOOM_KNN_RAG_MICROBUCKET_K32ROWLD1WARP_0077_V1_VERIFY_K32_SPLIT', K32_SPLIT_COUNT)) + if verify_kernel == 'rowld1_stage1': + return _stage1_rowld1_ir() + if verify_kernel == 'rowld2_stage1': + return _stage1_rowld2_ir() + if verify_kernel == 'rowld_stage1': + return parent.rowld_seed.stage1_q32_k32_m64_rowld_ir + return parent._warp_merge_ir(split_count) +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_k32warpmerge_0077_v1_warp_row_merge_k32s144_0077_v1", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 32], ["SPLIT_COUNT", 144], ["SPLITS_PER_LANE", 5], ["ROWS_PER_CTA", 1]], "cta_group": 1, "threads": 128}')) + +def _compiled_stage1_q16_k32_m64_rowld1(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0199"}')) + +def _compiled_stage1_q32_k32_m64_rowld2(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0200"}')) + +def _launch_rowld1_warpmerge(inputs: dict[str, Any], *, split_count: int) -> None: + parent._launch_stage1_then_warp_merge(inputs, split_count=split_count, stage1_kernel_fn=_compiled_stage1_q16_k32_m64_rowld1, stage1_ir=_stage1_rowld1_ir(), stage1_threads=Q16_ROWLD1_STAGE1_THREADS, block_q=Q16_ROWLD1_BLOCK_Q, block_m=Q16_ROWLD1_BLOCK_M) + +def _launch_rowld2_warpmerge(inputs: dict[str, Any], *, split_count: int) -> None: + parent._launch_stage1_then_warp_merge(inputs, split_count=split_count, stage1_kernel_fn=_compiled_stage1_q32_k32_m64_rowld2, stage1_ir=_stage1_rowld2_ir(), stage1_threads=Q32_ROWLD2_STAGE1_THREADS, block_q=Q16_ROWLD1_BLOCK_Q, block_m=Q16_ROWLD1_BLOCK_M) + +def _eligible_low_q_rowld1(inputs: dict[str, Any]) -> bool: + if not parent._is_bf16_d128_nonbuild(inputs) or int(inputs.get('K', -1)) != 32: + return False + n_query = int(inputs.get('Q', -1)) + n_database = int(inputs.get('M', -1)) + return n_database == 131071 and n_query == 16 + +def _eligible_q32_rowld2(inputs: dict[str, Any]) -> bool: + return parent._is_bf16_d128_nonbuild(inputs) and int(inputs.get('M', -1)) == 100000 and (int(inputs.get('Q', -1)) == 32) and (int(inputs.get('K', -1)) == 32) + +def _rowld1_route_name(inputs: dict[str, Any], *, split_count: int) -> str: + n_query = int(inputs.get('Q', -1)) + n_database = int(inputs.get('M', -1)) + return ''.join(['rag_microbucket_k32rowld1warp_0077_v1_q', format(n_query, ''), '_m', format(n_database, ''), '_k32_row16x256b_s', format(split_count, ''), '_warpmerge']) + +def _rowld2_route_name(inputs: dict[str, Any], *, split_count: int) -> str: + n_query = int(inputs.get('Q', -1)) + n_database = int(inputs.get('M', -1)) + return ''.join(['rag_microbucket_k32rowld1warp_0077_v1_q', format(n_query, ''), '_m', format(n_database, ''), '_k32_row16x256b2warp_s', format(split_count, ''), '_warpmerge']) + +def route_for_contract_inputs(inputs: dict[str, Any], *, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> str: + if _eligible_low_q_rowld1(inputs): + return _rowld1_route_name(inputs, split_count=k32_split_count) + if Q32_ROWLD2_ENABLED and _eligible_q32_rowld2(inputs): + return _rowld2_route_name(inputs, split_count=k32_split_count) + return parent.route_for_contract_inputs(inputs, k32_split_count=k32_split_count, k32_group_count=k32_group_count) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> None: + if _eligible_low_q_rowld1(inputs): + _launch_rowld1_warpmerge(inputs, split_count=k32_split_count) + return + if Q32_ROWLD2_ENABLED and _eligible_q32_rowld2(inputs): + _launch_rowld2_warpmerge(inputs, split_count=k32_split_count) + return + parent.launch_from_contract_inputs(inputs, k32_split_count=k32_split_count, k32_group_count=k32_group_count) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_with_k32_topology(split_count: int, group_count: int) -> Callable[[dict[str, Any]], None]: + + def _candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, k32_split_count=split_count, k32_group_count=group_count) + return _candidate + +def candidate_parent_warpmerge(inputs: dict[str, Any]) -> None: + parent.launch_from_contract_inputs(inputs) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return parent._select_contract_shapes(shape_labels) + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=K32_BUCKET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def compile_and_launch_knn_build(*, shape_labels=K32_BUCKET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def route_trace_for_contract_shapes(shape_labels=K32_BUCKET_SHAPES, *, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> list[dict[str, Any]]: + rows = [] + for shape in _select_contract_shapes(shape_labels): + inputs = parent.rowld_seed.base_dispatcher._trace_inputs_from_shape(shape) + route = route_for_contract_inputs(inputs, k32_split_count=k32_split_count, k32_group_count=k32_group_count) + parent_route = parent.route_for_contract_inputs(inputs, k32_split_count=k32_split_count, k32_group_count=k32_group_count) + route_str = str(route) + rowld1 = route_str.startswith('rag_microbucket_k32rowld1warp_0077_v1_') and '_row16x256b_s' in route_str + rowld2 = route_str.startswith('rag_microbucket_k32rowld1warp_0077_v1_') and '_row16x256b2warp_s' in route_str + specialized = rowld1 or rowld2 + rows.append({'shape_key': shape['label'], 'selected_route': route, 'selected_seed': SEED_K32_ROWLD1WARP_ID if specialized else None, 'selected_entrypoint': ROUTE_ROWLD1_ENTRYPOINT if specialized else ROUTE_PARENT_WARPMERGE, 'parent_warpmerge_route': parent_route, 'route_kind': 'specialized_q16_rowld1warp' if rowld1 else 'specialized_q32_rowld2warp' if rowld2 else 'inherited_0077_warpmerge', 'guard_condition': 'BF16 non-build B=1 Q=16 M=131071 D=128 K=32' if rowld1 else 'BF16 non-build B=1 Q=32 M=100000 D=128 K=32' if rowld2 else 'delegate to 0077 K32 warp-merge parent'}) + return rows + +def _per_shape_delta(candidate_report: dict[str, Any], parent_report: dict[str, Any]) -> dict[str, Any]: + rows = {} + for label, cand in candidate_report.get('per_shape', {}).items(): + base_row = parent_report.get('per_shape', {}).get(label, {}) + cand_ms = cand.get('kernel_ms') + base_ms = base_row.get('kernel_ms') + rows[label] = {'candidate': cand, 'parent_warpmerge': base_row, 'candidate_ms': cand_ms, 'parent_warpmerge_ms': base_ms, 'speedup_vs_parent_warpmerge': base_ms / cand_ms if cand_ms and base_ms else None, 'ratio_vs_flashlib': cand.get('ratio_vs_flashlib')} + return rows + +def benchmark_knn_build_rag_microbucket_k32rowld1warp_0077_v1(*, use_cupti: bool=True, shape_labels=K32_BUCKET_SHAPES, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_with_k32_topology(k32_split_count, k32_group_count)) + parent_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_parent_warpmerge) + return {'contract': candidate_report['contract'], 'contract_version': candidate_report['contract_version'], 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_knn_build_rag_microbucket_k32rowld1warp_0077_v1']), 'candidate_entrypoint': ''.join([format(MODULE, ''), ':launch_from_contract_inputs']), 'parent_entrypoint': ROUTE_PARENT_WARPMERGE, 'accelerated_shape_labels': list(LOW_Q_ROWLD1_TARGET_SHAPES), 'measured_shape_labels': list(shape_labels), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'producer_topology': {'Q16_irregular': 'ROW_16x256B/M64N64 one-compute-warp stage1', 'Q16_exact': 'inherited 0077 ROW_16x256B four-compute-warp stage1', 'Q8_exact': 'inherited 0077 ROW_16x256B four-compute-warp stage1', 'Q32_exact': 'inherited 0077 ROW_16x256B four-compute-warp stage1', 'Q32_rowld2_probe': 'available behind env opt-in; disabled after slower same-denominator probe', 'guard_misses': 'delegate to 0077 warpmerge parent'}, 'merge_topology': {'candidate': ''.join(['0077 warp-row split-list merge/', format(K32_WARP_MERGE_ROWS_PER_CTA, ''), ' rows per CTA']), 'split_count': k32_split_count, 'splits_per_lane': parent._splits_per_lane(k32_split_count)}, 'route_trace': route_trace_for_contract_shapes(shape_labels, k32_split_count=k32_split_count, k32_group_count=k32_group_count), 'target_rows': _per_shape_delta(candidate_report, parent_report), 'summary': candidate_report['summary'], 'performance': candidate_report['performance'], 'correctness': candidate_report['correctness'], 'report': candidate_report, 'parent_summary': parent_report['summary'], 'parent_performance': parent_report['performance'], 'parent_report': parent_report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32rows4_0077_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32rows4_0077_v1.py new file mode 100644 index 00000000..02b01bc4 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32rows4_0077_v1.py @@ -0,0 +1,178 @@ +"""RAG microbatch K32 bucket with selective four-row warp merge CTAs. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +keeps the validated K32 tcgen05/TMA stage-1 producers from the 0077 lineage and +packs four query-row merge warps into each merge CTA for the Q8/Q32 exact rows +where the denser merge CTA helps. Other rows delegate to the inherited +warp-merge seed. The production path remains Weave-only. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from collections.abc import Callable +from functools import cache +from typing import Any +from .._dispatch_runtime import dc as dc +from .. import _dispatch_runtime as eval_mod +from . import knn_build_rag_microbucket_k32warpmerge_0077_v1 as base +from .._dispatch_runtime import pack_kernel_args +MODULE = 'loom.examples.weave.knn_build_rag_microbucket_k32rows4_0077_v1' +Q8_K32_SHAPE = base.Q8_K32_SHAPE +Q16_K32_SHAPE = base.Q16_K32_SHAPE +Q32_K32_SHAPE = base.Q32_K32_SHAPE +Q16_K32_IRREGULAR_SHAPE = base.Q16_K32_IRREGULAR_SHAPE +K32_BUCKET_SHAPES = base.K32_BUCKET_SHAPES +TARGET_SHAPES = base.TARGET_SHAPES +K32_SPLIT_COUNT = base.K32_SPLIT_COUNT +K32_GROUP_COUNT = base.K32_GROUP_COUNT +K32_TOP_K_MAX = base.K32_TOP_K_MAX +K32_ROWS4_MERGE_THREADS = base.K32_WARP_MERGE_THREADS +K32_ROWS4_ROWS_PER_CTA = 4 +K32_ROWS4_WARPS = K32_ROWS4_MERGE_THREADS // 32 +ROUTE_PARENT_BUCKET = base.ROUTE_PARENT_BUCKET +ROUTE_ROWS4_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +SEED_K32_ROWS4_ID = 'rag_microbucket_k32rows4_0077_v1_four_row_warp_merge' + +def _ir_with_constants(ir_obj: Any, *, suffix: str, **updates: int) -> Any: + constants = tuple(((name, updates.get(name, value)) for name, value in ir_obj.constants)) + return dc.replace(ir_obj, symbol=''.join([format(ir_obj.symbol, ''), '_', format(suffix, '')]), constants=constants) + +def _warp_merge_ir(split_count: int) -> Any: + if K32_ROWS4_ROWS_PER_CTA > K32_ROWS4_WARPS: + raise ValueError(''.join(['rows_per_cta=', format(K32_ROWS4_ROWS_PER_CTA, ''), ' exceeds merge warps=', format(K32_ROWS4_WARPS, '')])) + return _ir_with_constants(base.k32_warp_row_merge_ir, suffix=''.join(['k32s', format(split_count, ''), 'r', format(K32_ROWS4_ROWS_PER_CTA, ''), '_0077_v1']), TOP_K_MAX=K32_TOP_K_MAX, SPLIT_COUNT=split_count, SPLITS_PER_LANE=base._splits_per_lane(split_count), ROWS_PER_CTA=K32_ROWS4_ROWS_PER_CTA) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RAG_MICROBUCKET_K32ROWS4_0077_V1_VERIFY_KERNEL') + split_count = int(os.environ.get('LOOM_KNN_RAG_MICROBUCKET_K32ROWS4_0077_V1_VERIFY_K32_SPLIT', K32_SPLIT_COUNT)) + if verify_kernel == 'rowld_stage1': + return base.rowld_seed.stage1_q32_k32_m64_rowld_ir + if verify_kernel == 'm64_stage1': + return base.m64_seed.stage1_q8_k32_m64_ir + return _warp_merge_ir(split_count) +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_k32warpmerge_0077_v1_warp_row_merge_k32s144r4_0077_v1", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 32], ["SPLIT_COUNT", 144], ["SPLITS_PER_LANE", 5], ["ROWS_PER_CTA", 4]], "cta_group": 1, "threads": 128}')) + +@cache +def _compiled_warp_merge(split_count: int): + return base.rowld_seed.compact_seed.q16_tailinf.parent_k32._compile_ir(_warp_merge_ir(split_count)) + +def _rows4_route_name(inputs: dict[str, Any], *, split_count: int) -> str: + n_query = int(inputs.get('Q', -1)) + n_database = int(inputs.get('M', -1)) + producer = 'm64n64' if n_database == 131071 else 'row16x256b' + return ''.join(['rag_microbucket_k32rows4_0077_v1_q', format(n_query, ''), '_m', format(n_database, ''), '_k32_', format(producer, ''), '_s', format(split_count, ''), '_r', format(K32_ROWS4_ROWS_PER_CTA, ''), '_warpmerge']) + +def _eligible_rows4_warpmerge(inputs: dict[str, Any]) -> bool: + return base._is_bf16_d128_nonbuild(inputs) and int(inputs.get('M', -1)) == 100000 and (int(inputs.get('Q', -1)) in {8, 32}) and (int(inputs.get('K', -1)) == 32) + +def _launch_stage1_then_rows4_merge(inputs: dict[str, Any], *, split_count: int, stage1_kernel_fn: Callable[[], Any], stage1_ir: Any, stage1_threads: int, block_q: int, block_m: int) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + if top_k != K32_TOP_K_MAX: + raise ValueError(''.join(['k32 rows4 merge only supports K=', format(K32_TOP_K_MAX, ''), ', got K=', format(top_k, '')])) + num_q_tiles = (n_query + block_q - 1) // block_q + num_db_tiles = (n_database + block_m - 1) // block_m + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tiles * split_count + total_queries = bsz * n_query + stage1_grid = min(total_work, base.rowld_seed.compact_seed.GRID_DIM_DEFAULT) + merge_grid = min((total_queries + K32_ROWS4_ROWS_PER_CTA - 1) // K32_ROWS4_ROWS_PER_CTA, base.rowld_seed.compact_seed.GRID_DIM_DEFAULT) + partial_dists, partial_indices = base.rowld_seed.compact_seed.q16_tailinf.parent_k32.parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = base.rowld_seed.compact_seed.q16_tailinf.parent_k32.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), total_queries, block_q, dim, dim) + tmap_database = base.rowld_seed.compact_seed.q16_tailinf.parent_k32.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, block_m, dim, dim) + stage1_kernel_fn().launch(grid=(stage1_grid, 1, 1), block=(stage1_threads, 1, 1), args=pack_kernel_args(stage1_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tiles=num_q_tiles, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), shared_mem=stage1_ir.computed_smem_bytes) + merge_ir = _warp_merge_ir(split_count) + _compiled_warp_merge(split_count).launch(grid=(merge_grid, 1, 1), block=(K32_ROWS4_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], total_queries], shared_mem=merge_ir.computed_smem_bytes) + +def _launch_rowld_rows4(inputs: dict[str, Any], *, split_count: int) -> None: + _launch_stage1_then_rows4_merge(inputs, split_count=split_count, stage1_kernel_fn=base.rowld_seed._compiled_stage1_q32_k32_m64_rowld, stage1_ir=base.rowld_seed.stage1_q32_k32_m64_rowld_ir, stage1_threads=base.rowld_seed.Q32_M64_STAGE1_THREADS, block_q=base.rowld_seed.Q8_M64_BLOCK_Q, block_m=base.rowld_seed.Q8_M64_BLOCK_M) + +def _launch_m64_rows4(inputs: dict[str, Any], *, split_count: int) -> None: + _launch_stage1_then_rows4_merge(inputs, split_count=split_count, stage1_kernel_fn=base.m64_seed._compiled_stage1_q8_k32_m64, stage1_ir=base.m64_seed.stage1_q8_k32_m64_ir, stage1_threads=base.m64_seed.Q8_M64_STAGE1_THREADS, block_q=base.m64_seed.Q8_M64_BLOCK_Q, block_m=base.m64_seed.Q8_M64_BLOCK_M) + +def route_for_contract_inputs(inputs: dict[str, Any], *, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> str: + if _eligible_rows4_warpmerge(inputs): + return _rows4_route_name(inputs, split_count=k32_split_count) + return base.route_for_contract_inputs(inputs, k32_split_count=k32_split_count, k32_group_count=k32_group_count) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> None: + if _eligible_rows4_warpmerge(inputs): + _launch_rowld_rows4(inputs, split_count=k32_split_count) + return + base.launch_from_contract_inputs(inputs, k32_split_count=k32_split_count, k32_group_count=k32_group_count) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_with_k32_topology(split_count: int, group_count: int) -> Callable[[dict[str, Any]], None]: + + def _candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, k32_split_count=split_count, k32_group_count=group_count) + return _candidate + +def candidate_parent_warpmerge(inputs: dict[str, Any]) -> None: + base.launch_from_contract_inputs(inputs) + +def parent_warpmerge_with_k32_topology(split_count: int, group_count: int) -> Callable[[dict[str, Any]], None]: + + def _candidate(inputs: dict[str, Any]) -> None: + base.launch_from_contract_inputs(inputs, k32_split_count=split_count, k32_group_count=group_count) + return _candidate + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return base._select_contract_shapes(shape_labels) + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=K32_BUCKET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def compile_and_launch_knn_build(*, shape_labels=K32_BUCKET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def route_trace_for_contract_shapes(shape_labels=K32_BUCKET_SHAPES, *, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> list[dict[str, Any]]: + del k32_group_count + rows = [] + for shape in _select_contract_shapes(shape_labels): + inputs = base.rowld_seed.base_dispatcher._trace_inputs_from_shape(shape) + route = route_for_contract_inputs(inputs, k32_split_count=k32_split_count) + parent_route = base.route_for_contract_inputs(inputs, k32_split_count=k32_split_count) + rows4 = str(route).startswith('rag_microbucket_k32rows4_0077_v1_') + rows.append({'shape_key': shape['label'], 'selected_route': route, 'selected_seed': SEED_K32_ROWS4_ID if rows4 else None, 'selected_entrypoint': ROUTE_ROWS4_ENTRYPOINT if rows4 else base.ROUTE_WARPMERGE_ENTRYPOINT, 'parent_warpmerge_route': parent_route, 'route_kind': 'specialized_k32_rows4_warpmerge' if rows4 else 'inherited_0077_warpmerge', 'guard_condition': 'BF16 non-build B=1 D=128 K=32 with Q/M in requested K32 bucket' if rows4 else 'delegate to inherited 0077 warp-merge seed'}) + return rows + +def _per_shape_delta(candidate_report: dict[str, Any], parent_report: dict[str, Any]) -> dict[str, Any]: + rows = {} + for label, cand in candidate_report.get('per_shape', {}).items(): + parent = parent_report.get('per_shape', {}).get(label, {}) + cand_ms = cand.get('kernel_ms') + parent_ms = parent.get('kernel_ms') + rows[label] = {'candidate': cand, 'parent_warpmerge': parent, 'candidate_ms': cand_ms, 'parent_warpmerge_ms': parent_ms, 'speedup_vs_parent_warpmerge': parent_ms / cand_ms if cand_ms and parent_ms else None, 'ratio_vs_flashlib': cand.get('ratio_vs_flashlib')} + return rows + +def benchmark_knn_build_rag_microbucket_k32rows4_0077_v1(*, use_cupti: bool=True, shape_labels=K32_BUCKET_SHAPES, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_with_k32_topology(k32_split_count, k32_group_count)) + parent_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=parent_warpmerge_with_k32_topology(k32_split_count, k32_group_count)) + return {'contract': candidate_report['contract'], 'contract_version': candidate_report['contract_version'], 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_knn_build_rag_microbucket_k32rows4_0077_v1']), 'candidate_entrypoint': ''.join([format(MODULE, ''), ':launch_from_contract_inputs']), 'parent_entrypoint': base.ROUTE_WARPMERGE_ENTRYPOINT, 'accelerated_shape_labels': list(K32_BUCKET_SHAPES), 'measured_shape_labels': list(shape_labels), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'producer_topology': {'Q8_Q32_exact': 'ROW_16x256B/M64N64 stage1 from q32rowld lineage with rows4 merge', 'Q16_exact_Q16_irregular': 'inherited 0077 warp-merge seed'}, 'merge_topology': {'candidate': ''.join(['warp-row split-list merge/', format(K32_ROWS4_ROWS_PER_CTA, ''), ' rows per CTA']), 'parent': 'warp-row split-list merge/1 row per CTA', 'split_count': k32_split_count, 'splits_per_lane': base._splits_per_lane(k32_split_count)}, 'route_trace': route_trace_for_contract_shapes(shape_labels, k32_split_count=k32_split_count, k32_group_count=k32_group_count), 'target_rows': _per_shape_delta(candidate_report, parent_report), 'summary': candidate_report['summary'], 'performance': candidate_report['performance'], 'correctness': candidate_report['correctness'], 'report': candidate_report, 'parent_summary': parent_report['summary'], 'parent_performance': parent_report['performance'], 'parent_report': parent_report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32warpmerge_0077_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32warpmerge_0077_v1.py new file mode 100644 index 00000000..e41b4a80 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_k32warpmerge_0077_v1.py @@ -0,0 +1,190 @@ +"""RAG microbatch K32 bucket with warp-row split-list merge. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +keeps the validated K32 tcgen05/TMA stage-1 producers from the 0077 parent +lineage, but replaces the K32 fused group/final merge with a warp-row merge: +one warp owns one query row and each lane owns a small subset of split-local +top-k lists. The production path remains Weave-only. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from collections.abc import Callable +from functools import cache +from typing import Any +from .._dispatch_runtime import dc as dc +from .. import _dispatch_runtime as eval_mod +from . import knn_build_rag_microbucket_3505_v9 as m64_seed +from . import knn_build_rag_microbucket_k32bucket_0077_v1 as parent_bucket +from . import knn_build_rag_microbucket_q32rowld_e5db_v1 as rowld_seed +from .._dispatch_runtime import pack_kernel_args +MODULE = 'loom.examples.weave.knn_build_rag_microbucket_k32warpmerge_0077_v1' +Q8_K32_SHAPE = parent_bucket.Q8_K32_SHAPE +Q16_K32_SHAPE = parent_bucket.Q16_K32_SHAPE +Q32_K32_SHAPE = parent_bucket.Q32_K32_SHAPE +Q16_K32_IRREGULAR_SHAPE = parent_bucket.Q16_K32_IRREGULAR_SHAPE +K32_BUCKET_SHAPES = parent_bucket.K32_BUCKET_SHAPES +TARGET_SHAPES = parent_bucket.TARGET_SHAPES +K32_SPLIT_COUNT = parent_bucket.K32_SPLIT_COUNT +K32_GROUP_COUNT = parent_bucket.K32_GROUP_COUNT +K32_TOP_K_MAX = 32 +K32_WARP_MERGE_THREADS = 128 +K32_WARP_MERGE_WARPS = K32_WARP_MERGE_THREADS // 32 +K32_WARP_MERGE_ROWS_PER_CTA = _decode_capture(_json_loads('1')) +ROUTE_PARENT_BUCKET = ''.join([format(parent_bucket.MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_WARPMERGE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +SEED_K32_WARPMERGE_ID = 'rag_microbucket_k32warpmerge_0077_v1_warp_row_merge' +knn_build_rag_microbucket_k32warpmerge_0077_v1_warp_row_merge = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_k32warpmerge_0077_v1_warp_row_merge", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 32], ["SPLIT_COUNT", 144], ["SPLITS_PER_LANE", 5], ["ROWS_PER_CTA", 1]], "cta_group": 1, "threads": 128}')) +k32_warp_row_merge_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_k32warpmerge_0077_v1_warp_row_merge", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 32], ["SPLIT_COUNT", 144], ["SPLITS_PER_LANE", 5], ["ROWS_PER_CTA", 1]], "cta_group": 1, "threads": 128}')) + +def _ir_with_constants(ir_obj: Any, *, suffix: str, **updates: int) -> Any: + constants = tuple(((name, updates.get(name, value)) for name, value in ir_obj.constants)) + return dc.replace(ir_obj, symbol=''.join([format(ir_obj.symbol, ''), '_', format(suffix, '')]), constants=constants) + +def _splits_per_lane(split_count: int) -> int: + if split_count <= 0: + raise ValueError(''.join(['split_count must be positive, got ', format(split_count, '')])) + return (split_count + 31) // 32 + +def _warp_merge_ir(split_count: int) -> Any: + if K32_WARP_MERGE_ROWS_PER_CTA <= 0 or K32_WARP_MERGE_ROWS_PER_CTA > K32_WARP_MERGE_WARPS: + raise ValueError(''.join(['rows_per_cta must be in [1, ', format(K32_WARP_MERGE_WARPS, ''), '], got ', format(K32_WARP_MERGE_ROWS_PER_CTA, '')])) + return _ir_with_constants(k32_warp_row_merge_ir, suffix=''.join(['k32s', format(split_count, ''), '_0077_v1']), TOP_K_MAX=K32_TOP_K_MAX, SPLIT_COUNT=split_count, SPLITS_PER_LANE=_splits_per_lane(split_count), ROWS_PER_CTA=K32_WARP_MERGE_ROWS_PER_CTA) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RAG_MICROBUCKET_K32WARPMERGE_0077_V1_VERIFY_KERNEL') + split_count = int(os.environ.get('LOOM_KNN_RAG_MICROBUCKET_K32WARPMERGE_0077_V1_VERIFY_K32_SPLIT', K32_SPLIT_COUNT)) + if verify_kernel == 'rowld_stage1': + return rowld_seed.stage1_q32_k32_m64_rowld_ir + if verify_kernel == 'm64_stage1': + return m64_seed.stage1_q8_k32_m64_ir + return _warp_merge_ir(split_count) +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_k32warpmerge_0077_v1_warp_row_merge_k32s144_0077_v1", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 32], ["SPLIT_COUNT", 144], ["SPLITS_PER_LANE", 5], ["ROWS_PER_CTA", 1]], "cta_group": 1, "threads": 128}')) + +@cache +def _compiled_warp_merge(split_count: int): + return rowld_seed.compact_seed.q16_tailinf.parent_k32._compile_ir(_warp_merge_ir(split_count)) + +def _is_bf16_d128_nonbuild(inputs: dict[str, Any]) -> bool: + return rowld_seed._is_bf16_d128_nonbuild(inputs) + +def _eligible_rowld_warpmerge(inputs: dict[str, Any]) -> bool: + return _is_bf16_d128_nonbuild(inputs) and int(inputs.get('M', -1)) == 100000 and (int(inputs.get('Q', -1)) in {8, 16, 32}) and (int(inputs.get('K', -1)) == 32) + +def _eligible_m64_irregular_warpmerge(inputs: dict[str, Any]) -> bool: + return _is_bf16_d128_nonbuild(inputs) and int(inputs.get('M', -1)) == 131071 and (int(inputs.get('Q', -1)) == 16) and (int(inputs.get('K', -1)) == 32) + +def _warpmerge_route_name(inputs: dict[str, Any], *, split_count: int) -> str: + n_query = int(inputs.get('Q', -1)) + n_database = int(inputs.get('M', -1)) + producer = 'm64n64' if n_database == 131071 else 'row16x256b' + return ''.join(['rag_microbucket_k32warpmerge_0077_v1_q', format(n_query, ''), '_m', format(n_database, ''), '_k32_', format(producer, ''), '_s', format(split_count, ''), '_warpmerge']) + +def _launch_stage1_then_warp_merge(inputs: dict[str, Any], *, split_count: int, stage1_kernel_fn: Callable[[], Any], stage1_ir: Any, stage1_threads: int, block_q: int, block_m: int) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + if top_k != K32_TOP_K_MAX: + raise ValueError(''.join(['k32 warp merge only supports K=', format(K32_TOP_K_MAX, ''), ', got K=', format(top_k, '')])) + num_q_tiles = (n_query + block_q - 1) // block_q + num_db_tiles = (n_database + block_m - 1) // block_m + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tiles * split_count + total_queries = bsz * n_query + stage1_grid = min(total_work, rowld_seed.compact_seed.GRID_DIM_DEFAULT) + merge_grid = min((total_queries + K32_WARP_MERGE_ROWS_PER_CTA - 1) // K32_WARP_MERGE_ROWS_PER_CTA, rowld_seed.compact_seed.GRID_DIM_DEFAULT) + partial_dists, partial_indices = rowld_seed.compact_seed.q16_tailinf.parent_k32.parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = rowld_seed.compact_seed.q16_tailinf.parent_k32.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), total_queries, block_q, dim, dim) + tmap_database = rowld_seed.compact_seed.q16_tailinf.parent_k32.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, block_m, dim, dim) + stage1_kernel_fn().launch(grid=(stage1_grid, 1, 1), block=(stage1_threads, 1, 1), args=pack_kernel_args(stage1_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tiles=num_q_tiles, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), shared_mem=stage1_ir.computed_smem_bytes) + merge_ir = _warp_merge_ir(split_count) + _compiled_warp_merge(split_count).launch(grid=(merge_grid, 1, 1), block=(K32_WARP_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], total_queries], shared_mem=merge_ir.computed_smem_bytes) + +def _launch_rowld_warpmerge(inputs: dict[str, Any], *, split_count: int) -> None: + _launch_stage1_then_warp_merge(inputs, split_count=split_count, stage1_kernel_fn=rowld_seed._compiled_stage1_q32_k32_m64_rowld, stage1_ir=rowld_seed.stage1_q32_k32_m64_rowld_ir, stage1_threads=rowld_seed.Q32_M64_STAGE1_THREADS, block_q=rowld_seed.Q8_M64_BLOCK_Q, block_m=rowld_seed.Q8_M64_BLOCK_M) + +def _launch_m64_warpmerge(inputs: dict[str, Any], *, split_count: int) -> None: + _launch_stage1_then_warp_merge(inputs, split_count=split_count, stage1_kernel_fn=m64_seed._compiled_stage1_q8_k32_m64, stage1_ir=m64_seed.stage1_q8_k32_m64_ir, stage1_threads=m64_seed.Q8_M64_STAGE1_THREADS, block_q=m64_seed.Q8_M64_BLOCK_Q, block_m=m64_seed.Q8_M64_BLOCK_M) + +def route_for_contract_inputs(inputs: dict[str, Any], *, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> str: + if _eligible_rowld_warpmerge(inputs) or _eligible_m64_irregular_warpmerge(inputs): + return _warpmerge_route_name(inputs, split_count=k32_split_count) + return parent_bucket.route_for_contract_inputs(inputs, k32_split_count=k32_split_count, k32_group_count=k32_group_count) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> None: + if _eligible_rowld_warpmerge(inputs): + _launch_rowld_warpmerge(inputs, split_count=k32_split_count) + return + if _eligible_m64_irregular_warpmerge(inputs): + _launch_m64_warpmerge(inputs, split_count=k32_split_count) + return + parent_bucket.launch_from_contract_inputs(inputs, k32_split_count=k32_split_count, k32_group_count=k32_group_count) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_with_k32_topology(split_count: int, group_count: int) -> Callable[[dict[str, Any]], None]: + + def _candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, k32_split_count=split_count, k32_group_count=group_count) + return _candidate + +def candidate_parent_bucket(inputs: dict[str, Any]) -> None: + parent_bucket.launch_from_contract_inputs(inputs) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return parent_bucket._select_contract_shapes(shape_labels) + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=K32_BUCKET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def compile_and_launch_knn_build(*, shape_labels=K32_BUCKET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def route_trace_for_contract_shapes(shape_labels=K32_BUCKET_SHAPES, *, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> list[dict[str, Any]]: + del k32_group_count + rows = [] + for shape in _select_contract_shapes(shape_labels): + inputs = rowld_seed.base_dispatcher._trace_inputs_from_shape(shape) + route = route_for_contract_inputs(inputs, k32_split_count=k32_split_count) + parent_route = parent_bucket.route_for_contract_inputs(inputs, k32_split_count=k32_split_count) + warpmerge = str(route).startswith('rag_microbucket_k32warpmerge_0077_v1_') + rows.append({'shape_key': shape['label'], 'selected_route': route, 'selected_seed': SEED_K32_WARPMERGE_ID if warpmerge else None, 'selected_entrypoint': ROUTE_WARPMERGE_ENTRYPOINT if warpmerge else ROUTE_PARENT_BUCKET, 'parent_bucket_route': parent_route, 'route_kind': 'specialized_k32_warpmerge' if warpmerge else 'inherited_0077_bucket', 'guard_condition': 'BF16 non-build B=1 D=128 K=32 with Q/M in requested K32 bucket' if warpmerge else 'delegate to 0077 K32 bucket parent'}) + return rows + +def _per_shape_delta(candidate_report: dict[str, Any], parent_report: dict[str, Any]) -> dict[str, Any]: + rows = {} + for label, cand in candidate_report.get('per_shape', {}).items(): + parent = parent_report.get('per_shape', {}).get(label, {}) + cand_ms = cand.get('kernel_ms') + parent_ms = parent.get('kernel_ms') + rows[label] = {'candidate': cand, 'parent_bucket': parent, 'candidate_ms': cand_ms, 'parent_bucket_ms': parent_ms, 'speedup_vs_parent_bucket': parent_ms / cand_ms if cand_ms and parent_ms else None, 'ratio_vs_flashlib': cand.get('ratio_vs_flashlib')} + return rows + +def benchmark_knn_build_rag_microbucket_k32warpmerge_0077_v1(*, use_cupti: bool=True, shape_labels=K32_BUCKET_SHAPES, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_with_k32_topology(k32_split_count, k32_group_count)) + parent_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_parent_bucket) + return {'contract': candidate_report['contract'], 'contract_version': candidate_report['contract_version'], 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_knn_build_rag_microbucket_k32warpmerge_0077_v1']), 'candidate_entrypoint': ''.join([format(MODULE, ''), ':launch_from_contract_inputs']), 'parent_entrypoint': ROUTE_PARENT_BUCKET, 'accelerated_shape_labels': list(K32_BUCKET_SHAPES), 'measured_shape_labels': list(shape_labels), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'producer_topology': {'Q8_Q16_Q32_exact': 'ROW_16x256B/M64N64 stage1 from q32rowld lineage', 'Q16_irregular': 'M64N64 stage1 from 3505_v9 lineage'}, 'merge_topology': {'candidate': ''.join(['warp-row split-list merge/', format(K32_WARP_MERGE_ROWS_PER_CTA, ''), ' rows per CTA']), 'split_count': k32_split_count, 'splits_per_lane': _splits_per_lane(k32_split_count)}, 'route_trace': route_trace_for_contract_shapes(shape_labels, k32_split_count=k32_split_count, k32_group_count=k32_group_count), 'target_rows': _per_shape_delta(candidate_report, parent_report), 'summary': candidate_report['summary'], 'performance': candidate_report['performance'], 'correctness': candidate_report['correctness'], 'report': candidate_report, 'parent_summary': parent_report['summary'], 'parent_performance': parent_report['performance'], 'parent_report': parent_report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_q16irreg_2691_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_q16irreg_2691_v1.py new file mode 100644 index 00000000..3c0480ae --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_q16irreg_2691_v1.py @@ -0,0 +1,132 @@ +"""RAG microbucket Q16/K32 irregular M64/N64 producer seed. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +inherits the combined Q8 ROW_16x256B plus exact-Q16 M64 seed and adds one +guarded route for ``rag_microbatch_largek_b1_q16_m131071_d128_k32`` through +the existing M64/N64 tcgen05/TMA producer and K32 fused split merge. Exact +Q16/M100000, Q8/K32, Q32/K32, K10 rows, and guard misses delegate to the +combined 229a parent. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_rag_microbucket_q8rowld_q16m64_229a_v1 as parent_combined +MODULE = 'loom.examples.weave.knn_build_rag_microbucket_q16irreg_2691_v1' +Q8_K32_SHAPE = parent_combined.Q8_K32_SHAPE +Q16_K32_SHAPE = parent_combined.Q16_K32_SHAPE +Q32_K32_SHAPE = parent_combined.Q32_K32_SHAPE +Q16_K32_IRREGULAR_SHAPE = parent_combined.Q16_K32_IRREGULAR_SHAPE +Q16_IRREGULAR_TARGET_SHAPES = (Q16_K32_IRREGULAR_SHAPE,) +Q16_TARGET_SHAPES = (Q16_K32_SHAPE, Q16_K32_IRREGULAR_SHAPE) +K32_BUCKET_SHAPES = parent_combined.K32_BUCKET_SHAPES +TARGET_SHAPES = parent_combined.TARGET_SHAPES +K32_SPLIT_COUNT = parent_combined.K32_SPLIT_COUNT +K32_GROUP_COUNT = parent_combined.K32_GROUP_COUNT +ROUTE_PARENT_COMBINED = ''.join([format(parent_combined.MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_Q16_IRREGULAR_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +SEED_Q16_IRREGULAR_M64_ID = 'rag_microbucket_q16irreg_2691_v1_q16_m131071_m64n64' + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RAG_MICROBUCKET_Q16IRREG_2691_V1_VERIFY_KERNEL') + split_count = int(os.environ.get('LOOM_KNN_RAG_MICROBUCKET_Q16IRREG_2691_V1_VERIFY_K32_SPLIT', K32_SPLIT_COUNT)) + group_count = int(os.environ.get('LOOM_KNN_RAG_MICROBUCKET_Q16IRREG_2691_V1_VERIFY_K32_GROUPS', K32_GROUP_COUNT)) + if verify_kernel == 'k32_fused_merge': + return parent_combined.q16m64_seed.parent_q32rowld.compact_seed.q16_tailinf._fused_merge_ir(split_count, group_count) + return parent_combined.q16m64_seed.q8_m64_seed.stage1_q8_k32_m64_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_3505_v9_stage1_q8_k32_m64", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 34048, "constants": [["BLOCK_Q", 64], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 96}')) + +def _label_can_hit(inputs: dict[str, Any], labels: set[str]) -> bool: + label = inputs.get('label') + return label is None or str(label) in labels + +def _eligible_q16_k32_irregular_m64(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, set(Q16_IRREGULAR_TARGET_SHAPES)) and parent_combined.q16m64_seed.parent_q32rowld._is_bf16_d128_nonbuild(inputs) and (int(inputs.get('M', -1)) == 131071) and (int(inputs.get('Q', -1)) == 16) and (int(inputs.get('K', -1)) == 32) + +def _q16_k32_irregular_m64_route_name(*, split_count: int, group_count: int) -> str: + return ''.join(['rag_microbucket_q16irreg_2691_v1_q16_m131071_k32_m64n64_s', format(split_count, ''), '_g', format(group_count, '')]) + +def route_for_contract_inputs(inputs: dict[str, Any], *, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> str: + if _eligible_q16_k32_irregular_m64(inputs): + return _q16_k32_irregular_m64_route_name(split_count=k32_split_count, group_count=k32_group_count) + return parent_combined.route_for_contract_inputs(inputs, k32_split_count=k32_split_count, k32_group_count=k32_group_count) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> None: + if _eligible_q16_k32_irregular_m64(inputs): + parent_combined.q16m64_seed.q8_m64_seed._launch_q8_k32_m64(inputs, split_count=k32_split_count, group_count=k32_group_count) + return + parent_combined.launch_from_contract_inputs(inputs, k32_split_count=k32_split_count, k32_group_count=k32_group_count) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_with_k32_topology(split_count: int, group_count: int) -> Callable[[dict[str, Any]], None]: + + def _candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, k32_split_count=split_count, k32_group_count=group_count) + return _candidate + +def candidate_parent_combined(inputs: dict[str, Any]) -> None: + parent_combined.launch_from_contract_inputs(inputs) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return parent_combined._select_contract_shapes(shape_labels) + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=Q16_TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def compile_and_launch_knn_build(*, shape_labels=Q16_TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def route_trace_for_contract_shapes(shape_labels=Q16_TARGET_SHAPES, *, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> list[dict[str, Any]]: + rows = [] + for shape in _select_contract_shapes(shape_labels): + inputs = parent_combined.q16m64_seed.parent_q32rowld.base_dispatcher._trace_inputs_from_shape(shape) + route = route_for_contract_inputs(inputs, k32_split_count=k32_split_count, k32_group_count=k32_group_count) + parent_route = parent_combined.route_for_contract_inputs(inputs, k32_split_count=k32_split_count, k32_group_count=k32_group_count) + if str(route).startswith('rag_microbucket_q16irreg_2691_v1_q16_'): + selected_seed = SEED_Q16_IRREGULAR_M64_ID + route_kind = 'specialized_q16_irregular_m64' + guard = 'exact BF16 non-build B=1 Q=16 M=131071 D=128 K=32' + selected_entrypoint = ROUTE_Q16_IRREGULAR_ENTRYPOINT + else: + selected_seed = None + route_kind = 'inherited_combined_229a' + guard = 'delegate to combined q8rowld/q16m64 229a Weave route' + selected_entrypoint = ROUTE_PARENT_COMBINED + rows.append({'shape_key': shape['label'], 'selected_route': route, 'selected_seed': selected_seed, 'selected_entrypoint': selected_entrypoint, 'parent_combined_route': parent_route, 'route_kind': route_kind, 'guard_condition': guard}) + return rows + +def _per_shape_delta(candidate_report: dict[str, Any], parent_report: dict[str, Any]) -> dict[str, Any]: + rows = {} + for label, cand in candidate_report.get('per_shape', {}).items(): + parent = parent_report.get('per_shape', {}).get(label, {}) + cand_ms = cand.get('kernel_ms') + parent_ms = parent.get('kernel_ms') + rows[label] = {'candidate': cand, 'parent_combined': parent, 'candidate_ms': cand_ms, 'parent_combined_ms': parent_ms, 'speedup_vs_parent_combined': parent_ms / cand_ms if cand_ms and parent_ms else None, 'ratio_vs_flashlib': cand.get('ratio_vs_flashlib')} + return rows + +def benchmark_knn_build_rag_microbucket_q16irreg_2691_v1(*, use_cupti: bool=True, shape_labels=Q16_TARGET_SHAPES, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_with_k32_topology(k32_split_count, k32_group_count)) + parent_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_parent_combined) + return {'contract': candidate_report['contract'], 'contract_version': candidate_report['contract_version'], 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_knn_build_rag_microbucket_q16irreg_2691_v1']), 'candidate_entrypoint': ''.join([format(MODULE, ''), ':launch_from_contract_inputs']), 'parent_entrypoint': ROUTE_PARENT_COMBINED, 'accelerated_shape_labels': list(Q16_IRREGULAR_TARGET_SHAPES), 'measured_shape_labels': list(shape_labels), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'producer_topology': {'Q16_K32_irregular': ''.join(['M64N64/S', format(k32_split_count, ''), '/G', format(k32_group_count, ''), '/fused']), 'inherited': 'combined 229a Q8 ROW_16x256B, exact-Q16 M64, Q32/K10, and fallback routes'}, 'route_trace': route_trace_for_contract_shapes(shape_labels, k32_split_count=k32_split_count, k32_group_count=k32_group_count), 'target_rows': _per_shape_delta(candidate_report, parent_report), 'summary': candidate_report['summary'], 'performance': candidate_report['performance'], 'correctness': candidate_report['correctness'], 'report': candidate_report, 'parent_summary': parent_report['summary'], 'parent_performance': parent_report['performance'], 'parent_report': parent_report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_q16m64_19b3_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_q16m64_19b3_v1.py new file mode 100644 index 00000000..8e7a8689 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_q16m64_19b3_v1.py @@ -0,0 +1,124 @@ +"""RAG microbucket Q16/K32 M64/N64 producer seed. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +inherits the q32rowld seed and exposes the v9 M64/N64 tcgen05 producer for only +``rag_microbatch_largek_b1_q16_m100000_d128_k32``. Q8/K32, Q32/K32, K10 rows, +irregular Q16/M131071, and guard misses delegate to the existing +q32rowld/ed1c Weave routes. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_rag_microbucket_3505_v9 as q8_m64_seed +from . import knn_build_rag_microbucket_q32rowld_e5db_v1 as parent_q32rowld +MODULE = 'loom.examples.weave.knn_build_rag_microbucket_q16m64_19b3_v1' +Q8_K32_SHAPE = parent_q32rowld.Q8_K32_SHAPE +Q16_K32_SHAPE = parent_q32rowld.Q16_K32_SHAPE +Q32_K32_SHAPE = parent_q32rowld.Q32_K32_SHAPE +Q16_K32_IRREGULAR_SHAPE = parent_q32rowld.Q16_K32_IRREGULAR_SHAPE +TARGET_SHAPES = parent_q32rowld.TARGET_SHAPES +K32_BUCKET_SHAPES = (Q8_K32_SHAPE, Q16_K32_SHAPE, Q32_K32_SHAPE, Q16_K32_IRREGULAR_SHAPE) +Q16_M64_TARGET_SHAPES = (Q16_K32_SHAPE,) +Q16_M64_TARGET_SHAPE_SET = set(Q16_M64_TARGET_SHAPES) +K32_SPLIT_COUNT = parent_q32rowld.K32_SPLIT_COUNT +K32_GROUP_COUNT = parent_q32rowld.K32_GROUP_COUNT +ROUTE_PARENT_Q32ROWLD = ''.join([format(parent_q32rowld.__name__, ''), ':launch_from_contract_inputs']) +ROUTE_Q16_M64_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +SEED_Q16_M64_ID = 'rag_microbucket_q16m64_19b3_v1_q16_m64n64' + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RAG_MICROBUCKET_Q16M64_19B3_V1_VERIFY_KERNEL') + split_count = int(os.environ.get('LOOM_KNN_RAG_MICROBUCKET_Q16M64_19B3_V1_VERIFY_K32_SPLIT', K32_SPLIT_COUNT)) + group_count = int(os.environ.get('LOOM_KNN_RAG_MICROBUCKET_Q16M64_19B3_V1_VERIFY_K32_GROUPS', K32_GROUP_COUNT)) + if verify_kernel == 'k32_fused_merge': + return parent_q32rowld.compact_seed.q16_tailinf._fused_merge_ir(split_count, group_count) + return q8_m64_seed.stage1_q8_k32_m64_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_3505_v9_stage1_q8_k32_m64", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 34048, "constants": [["BLOCK_Q", 64], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 96}')) + +def _label_can_hit(inputs: dict[str, Any], labels: set[str]) -> bool: + label = inputs.get('label') + return label is None or str(label) in labels + +def _eligible_q16_k32_m64(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, Q16_M64_TARGET_SHAPE_SET) and parent_q32rowld._is_bf16_d128_nonbuild(inputs) and (int(inputs.get('M', -1)) == 100000) and (int(inputs.get('Q', -1)) == 16) and (int(inputs.get('K', -1)) == 32) + +def _q16_k32_m64_route_name(*, split_count: int, group_count: int) -> str: + return ''.join(['rag_microbucket_q16m64_19b3_v1_q16_m100000_k32_m64n64_s', format(split_count, ''), '_g', format(group_count, '')]) + +def route_for_contract_inputs(inputs: dict[str, Any], *, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> str: + if _eligible_q16_k32_m64(inputs): + return _q16_k32_m64_route_name(split_count=k32_split_count, group_count=k32_group_count) + return parent_q32rowld.route_for_contract_inputs(inputs, k32_split_count=k32_split_count, k32_group_count=k32_group_count) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> None: + if _eligible_q16_k32_m64(inputs): + q8_m64_seed._launch_q8_k32_m64(inputs, split_count=k32_split_count, group_count=k32_group_count) + return + parent_q32rowld.launch_from_contract_inputs(inputs, k32_split_count=k32_split_count, k32_group_count=k32_group_count) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_with_k32_topology(split_count: int, group_count: int) -> Callable[[dict[str, Any]], None]: + + def _candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, k32_split_count=split_count, k32_group_count=group_count) + return _candidate + +def candidate_parent_q32rowld(inputs: dict[str, Any]) -> None: + parent_q32rowld.launch_from_contract_inputs(inputs) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return parent_q32rowld._select_contract_shapes(shape_labels) + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def route_trace_for_contract_shapes(shape_labels=TARGET_SHAPES, *, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> list[dict[str, Any]]: + selected = _select_contract_shapes(shape_labels) + rows = [] + for shape in selected: + inputs = parent_q32rowld.base_dispatcher._trace_inputs_from_shape(shape) + route = route_for_contract_inputs(inputs, k32_split_count=k32_split_count, k32_group_count=k32_group_count) + parent_route = parent_q32rowld.route_for_contract_inputs(inputs, k32_split_count=k32_split_count, k32_group_count=k32_group_count) + q16_m64 = str(route).startswith('rag_microbucket_q16m64_19b3_v1_') + rows.append({'shape_key': shape['label'], 'selected_route': route, 'selected_seed': SEED_Q16_M64_ID if q16_m64 else None, 'selected_entrypoint': ROUTE_Q16_M64_ENTRYPOINT if q16_m64 else ROUTE_PARENT_Q32ROWLD, 'parent_q32rowld_route': parent_route, 'route_kind': 'specialized' if q16_m64 else 'inherited', 'guard_condition': 'exact BF16 non-build B=1 Q=16 M=100000 D=128 K=32' if q16_m64 else 'delegate to q32rowld/ed1c Weave route'}) + return rows + +def _per_shape_delta(candidate_report: dict[str, Any], parent_report: dict[str, Any]) -> dict[str, Any]: + rows = {} + for label, cand in candidate_report.get('per_shape', {}).items(): + parent = parent_report.get('per_shape', {}).get(label, {}) + cand_ms = cand.get('kernel_ms') + parent_ms = parent.get('kernel_ms') + rows[label] = {'candidate': cand, 'parent_q32rowld': parent, 'candidate_ms': cand_ms, 'parent_q32rowld_ms': parent_ms, 'speedup_vs_parent_q32rowld': parent_ms / cand_ms if cand_ms and parent_ms else None, 'ratio_vs_flashlib': cand.get('ratio_vs_flashlib')} + return rows + +def benchmark_knn_build_rag_microbucket_q16m64_19b3_v1(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_with_k32_topology(k32_split_count, k32_group_count)) + parent_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_parent_q32rowld) + return {'contract': candidate_report['contract'], 'contract_version': candidate_report['contract_version'], 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_knn_build_rag_microbucket_q16m64_19b3_v1']), 'candidate_entrypoint': ''.join([format(MODULE, ''), ':launch_from_contract_inputs']), 'parent_entrypoint': ROUTE_PARENT_Q32ROWLD, 'accelerated_shape_labels': list(Q16_M64_TARGET_SHAPES), 'measured_shape_labels': list(shape_labels), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'producer_topology': {'Q16_K32': ''.join(['M64N64/S', format(k32_split_count, ''), '/G', format(k32_group_count, ''), '/fused']), 'inherited': 'q32rowld routes for Q8/Q32/K10/irregular-Q16 and parent fallback'}, 'route_trace': route_trace_for_contract_shapes(shape_labels, k32_split_count=k32_split_count, k32_group_count=k32_group_count), 'target_rows': _per_shape_delta(candidate_report, parent_report), 'summary': candidate_report['summary'], 'performance': candidate_report['performance'], 'correctness': candidate_report['correctness'], 'report': candidate_report, 'parent_summary': parent_report['summary'], 'parent_performance': parent_report['performance'], 'parent_report': parent_report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_q32_k31_c3d2_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_q32_k31_c3d2_v1.py new file mode 100644 index 00000000..6d4507ae --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_q32_k31_c3d2_v1.py @@ -0,0 +1,238 @@ +"""Exact Q32/M100000 K31 RAG microbucket seed for c3d2 low-K repair. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +targets only the BF16 non-build ``B=1,Q=32,M=100000,D=128,K=31`` expanded +guard-miss row. It keeps the Q32 exact ROW_16x256B tcgen05/TMA stage1 topology, +shrinks the split-local and final merge list capacity to K31, and delegates all +guard misses to the current v11 common-D dispatcher. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from collections.abc import Callable +from functools import cache, lru_cache +from pathlib import Path +from typing import Any +from .._dispatch_runtime import dc as dc +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_v11_common_d_seed_portfolio_a4ec_v1 as dispatch_v11 +from . import knn_build_rag_microbucket_k32_f590_q32exact_v1 as q32exact +from .._dispatch_runtime import pack_kernel_args +MODULE = 'loom.examples.weave.knn_build_rag_microbucket_q32_k31_c3d2_v1' +CANDIDATE_ID = 'q32_k31_c3d2_exact_top31_v1' +SEED_ID = 'rag_microbucket_q32_k31_c3d2_v1' +TARGET_SHAPE = dispatch_v11.EXPANDED_Q32_M100000_K31 +TARGET_SHAPES = (TARGET_SHAPE,) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +Q32_K31_SPLIT_COUNT = _decode_capture(_json_loads('152')) +Q32_K31_TOP_K_MAX = 31 +Q32_K31_ROWS_COVERED = 32 +Q32_K31_ROWS_PER_MERGE_CTA = q32exact.rows4.K32_ROWS4_ROWS_PER_CTA +Q32_K31_MERGE_THREADS = q32exact.rows4.K32_ROWS4_MERGE_THREADS +Q32_K31_BLOCK_Q = q32exact.rowld1.Q16_ROWLD1_BLOCK_Q +Q32_K31_BLOCK_M = q32exact.rowld1.Q16_ROWLD1_BLOCK_M +Q32_K31_FEAT_D = q32exact.rowld1.Q16_ROWLD1_FEAT_D +Q32_K31_STAGE1_THREADS = q32exact.Q32_EXACT_STAGE1_THREADS +Q32_K31_LOCAL_LISTS_PER_ROW = q32exact.rowld1.Q16_ROWLD1_LOCAL_LISTS_PER_ROW +Q32_K31_SMEM_BASE_BYTES = 16384 + 16384 + 256 +Q32_K31_LOCAL_ELEMS = Q32_K31_BLOCK_Q * Q32_K31_LOCAL_LISTS_PER_ROW * Q32_K31_TOP_K_MAX +Q32_K31_LOCAL_D_OFFSET = Q32_K31_SMEM_BASE_BYTES +Q32_K31_LOCAL_I_OFFSET = Q32_K31_LOCAL_D_OFFSET + Q32_K31_LOCAL_ELEMS * 4 +Q32_K31_SMEM_POOL_BYTES = Q32_K31_LOCAL_I_OFFSET + Q32_K31_LOCAL_ELEMS * 4 +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_PARENT_V11 = dispatch_v11.ROUTE_ENTRYPOINT +BENCHMARK_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_knn_build_rag_microbucket_q32_k31_c3d2_v1']) +_insert_sorted_pair = _ir_proxy('loom.examples.weave.knn_build_rag_microbucket_q32_k31_c3d2_v1:_insert_sorted_pair', 256) +knn_build_rag_microbucket_q32_k31_c3d2_v1_stage1 = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_q32_k31_c3d2_v1_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 97536, "constants": [["BLOCK_Q", 64], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 31], ["ROWS_COVERED", 32]], "cta_group": 1, "threads": 128}')) +stage1_q32_k31_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_q32_k31_c3d2_v1_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 97536, "constants": [["BLOCK_Q", 64], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 31], ["ROWS_COVERED", 32]], "cta_group": 1, "threads": 128}')) + +def _ir_with_constants(ir_obj: Any, *, suffix: str, **updates: int) -> Any: + constants = tuple(((name, updates.get(name, value)) for name, value in ir_obj.constants)) + return dc.replace(ir_obj, symbol=''.join([format(ir_obj.symbol, ''), '_', format(suffix, '')]), constants=constants) + +def _stage1_q32_k31_ir() -> Any: + return _ir_with_constants(stage1_q32_k31_ir, suffix='q32k31_c3d2_v1', BLOCK_Q=Q32_K31_BLOCK_Q, BLOCK_M=Q32_K31_BLOCK_M, FEAT_D=Q32_K31_FEAT_D, TOP_K_MAX=Q32_K31_TOP_K_MAX, ROWS_COVERED=Q32_K31_ROWS_COVERED) + +def _warp_merge_ir(split_count: int) -> Any: + return _ir_with_constants(q32exact.rows4.base.k32_warp_row_merge_ir, suffix=''.join(['q32k31s', format(split_count, ''), 'r', format(Q32_K31_ROWS_PER_MERGE_CTA, ''), '_c3d2_v1']), TOP_K_MAX=Q32_K31_TOP_K_MAX, SPLIT_COUNT=split_count, SPLITS_PER_LANE=q32exact.rows4.base._splits_per_lane(split_count), ROWS_PER_CTA=Q32_K31_ROWS_PER_MERGE_CTA) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RAG_MICROBUCKET_Q32_K31_C3D2_VERIFY_KERNEL') + split_count = int(os.environ.get('LOOM_KNN_RAG_MICROBUCKET_Q32_K31_C3D2_VERIFY_SPLIT', Q32_K31_SPLIT_COUNT)) + if verify_kernel == 'merge': + return _warp_merge_ir(split_count) + return _stage1_q32_k31_ir() +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_q32_k31_c3d2_v1_stage1_q32k31_c3d2_v1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 97536, "constants": [["BLOCK_Q", 64], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 31], ["ROWS_COVERED", 32]], "cta_group": 1, "threads": 128}')) + +def _compiled_stage1_q32_k31(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0204"}')) + +@cache +def _compiled_warp_merge(split_count: int): + return q32exact.rows4.base.rowld_seed.compact_seed.q16_tailinf.parent_k32._compile_ir(_warp_merge_ir(split_count)) + +def _dtype_name(inputs: dict[str, Any]) -> str: + query = inputs.get('query') + if query is not None and hasattr(query, 'dtype'): + return str(query.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')).replace('torch.', '') + +def _eligible_q32_k31(inputs: dict[str, Any]) -> bool: + return not bool(inputs.get('build', False)) and int(inputs.get('B', -1)) == 1 and (int(inputs.get('Q', -1)) == Q32_K31_ROWS_COVERED) and (int(inputs.get('M', -1)) == 100000) and (int(inputs.get('D', -1)) == Q32_K31_FEAT_D) and (int(inputs.get('K', -1)) == Q32_K31_TOP_K_MAX) and (_dtype_name(inputs) == 'bfloat16') + +def _route_name(inputs: dict[str, Any], *, split_count: int) -> str: + return ''.join(['rag_microbucket_q32_k31_c3d2_v1_q', format(int(inputs.get('Q', -1)), ''), '_m', format(int(inputs.get('M', -1)), ''), '_k31_row16x256b2cw_s', format(split_count, ''), '_r', format(Q32_K31_ROWS_PER_MERGE_CTA, '')]) + +def _launch_q32_k31(inputs: dict[str, Any], *, split_count: int=Q32_K31_SPLIT_COUNT) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + if top_k != Q32_K31_TOP_K_MAX: + raise ValueError(''.join(['q32_k31 seed only supports K=', format(Q32_K31_TOP_K_MAX, ''), ', got K=', format(top_k, '')])) + num_q_tiles = (n_query + Q32_K31_BLOCK_Q - 1) // Q32_K31_BLOCK_Q + num_db_tiles = (n_database + Q32_K31_BLOCK_M - 1) // Q32_K31_BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tiles * split_count + total_queries = bsz * n_query + stage1_grid = min(total_work, q32exact.rows4.base.rowld_seed.compact_seed.GRID_DIM_DEFAULT) + merge_grid = min((total_queries + Q32_K31_ROWS_PER_MERGE_CTA - 1) // Q32_K31_ROWS_PER_MERGE_CTA, q32exact.rows4.base.rowld_seed.compact_seed.GRID_DIM_DEFAULT) + partial_dists, partial_indices = q32exact.rows4.base.rowld_seed.compact_seed.q16_tailinf.parent_k32.parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = q32exact.rows4.base.rowld_seed.compact_seed.q16_tailinf.parent_k32.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), total_queries, Q32_K31_BLOCK_Q, dim, dim) + tmap_database = q32exact.rows4.base.rowld_seed.compact_seed.q16_tailinf.parent_k32.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, Q32_K31_BLOCK_M, dim, dim) + stage1_ir = _stage1_q32_k31_ir() + _compiled_stage1_q32_k31().launch(grid=(stage1_grid, 1, 1), block=(Q32_K31_STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage1_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tiles=num_q_tiles, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), shared_mem=stage1_ir.computed_smem_bytes) + merge_ir = _warp_merge_ir(split_count) + _compiled_warp_merge(split_count).launch(grid=(merge_grid, 1, 1), block=(Q32_K31_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], total_queries], shared_mem=merge_ir.computed_smem_bytes) + +def route_for_contract_inputs(inputs: dict[str, Any], *, split_count: int=Q32_K31_SPLIT_COUNT, force_fallback: bool=False) -> str: + if _eligible_q32_k31(inputs) and (not force_fallback): + return _route_name(inputs, split_count=split_count) + return dispatch_v11.route_for_contract_inputs(inputs) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, split_count: int=Q32_K31_SPLIT_COUNT, force_fallback: bool=False) -> None: + if _eligible_q32_k31(inputs) and (not force_fallback): + _launch_q32_k31(inputs, split_count=split_count) + return + dispatch_v11.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_with_split(split_count: int) -> Callable[[dict[str, Any]], None]: + + def _candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, split_count=split_count) + return _candidate + +def candidate_dispatch_v11(inputs: dict[str, Any]) -> None: + dispatch_v11.launch_from_contract_inputs(inputs) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels=TARGET_SHAPES) -> list[dict[str, Any]]: + return dispatch_v11._select_contract_shapes(tuple(shape_labels)) + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + previous = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = previous + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def route_trace_for_contract_shapes(shape_labels=TARGET_SHAPES, *, split_count: int=Q32_K31_SPLIT_COUNT, force_fallback: bool=False) -> list[dict[str, Any]]: + rows = [] + for shape in _select_contract_shapes(shape_labels): + inputs = dispatch_v11._trace_inputs_for_shape(shape) + route = route_for_contract_inputs(inputs, split_count=split_count, force_fallback=force_fallback) + parent_route = dispatch_v11.route_for_contract_inputs(inputs) + selected = _eligible_q32_k31(inputs) and (not force_fallback) + rows.append(dispatch_v11._normalize_route_row({'shape_key': shape['label'], 'selected_route': route, 'selected_entrypoint': ROUTE_ENTRYPOINT if selected else ROUTE_PARENT_V11, 'selected_seed': SEED_ID if selected else None, 'expected_seed': SEED_ID if _eligible_q32_k31(inputs) else None, 'route_kind': 'specialized_q32_k31_microbucket' if selected else 'general', 'route_source': 'shape-specific-seed' if selected else 'broad-dispatcher', 'guard_id': 'c3d2_q32_m100000_k31_exact_guard' if selected else 'forced_fallback_or_guard_miss', 'guard_condition': 'exact BF16 non-build B=1 Q=32 M=100000 D=128 K=31' if selected else 'delegate to current v11 common-D dispatcher', 'split_count': split_count if selected else None, 'rows_per_merge_cta': Q32_K31_ROWS_PER_MERGE_CTA if selected else None, 'parent_v11_route': parent_route, 'classification': 'unmeasured'})) + return rows + +def _per_shape_delta(candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> dict[str, Any]: + rows = {} + for label, cand in candidate_report.get('per_shape', {}).items(): + baseline = baseline_report.get('per_shape', {}).get(label, {}) + cand_ms = cand.get('kernel_ms') + baseline_ms = baseline.get('kernel_ms') + rows[label] = {'candidate': cand, 'baseline': baseline, 'candidate_ms': cand_ms, 'baseline_ms': baseline_ms, 'speedup_vs_baseline': baseline_ms / cand_ms if cand_ms and baseline_ms else None, 'ratio_vs_flashlib': cand.get('ratio_vs_flashlib'), 'baseline_ratio_vs_flashlib': baseline.get('ratio_vs_flashlib')} + return rows + +def _annotate_route_trace(route_trace: list[dict[str, Any]], candidate_report: dict[str, Any]) -> list[dict[str, Any]]: + annotated = [] + for row in route_trace: + out = dict(row) + label = str(out.get('shape_key')) + result = candidate_report.get('per_shape', {}).get(label, {}) + ratio = result.get('ratio_vs_flashlib') + out['shape_specific_kernel_ms'] = result.get('kernel_ms') + out['speedup_vs_external_baseline'] = ratio + out['external_baseline_ms'] = result.get('flashlib_ms') + out['external_baseline_ref'] = 'same_session' + out['timing_backend'] = result.get('timing_backend') + if result.get('passed') is True and ratio is not None: + out['classification'] = 'floor-clearing' if float(ratio) >= dispatch_v11.SPEEDUP_FLOOR else 'below-floor' + annotated.append(out) + return annotated + +def benchmark_knn_build_rag_microbucket_q32_k31_c3d2_v1(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES, split_count: int=Q32_K31_SPLIT_COUNT, run_dispatch_baseline: bool=True) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_with_split(split_count)) + dispatch_report = None + if run_dispatch_baseline: + dispatch_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_dispatch_v11) + payload: dict[str, Any] = {'contract': candidate_report['contract'], 'contract_version': candidate_report['contract_version'], 'measured_entrypoint': BENCHMARK_ENTRYPOINT, 'candidate_entrypoint': ROUTE_ENTRYPOINT, 'accelerated_shape_labels': list(TARGET_SHAPES), 'measured_shape_labels': list(shape_labels), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'producer_topology': {'candidate': ''.join(['Q32 exact two-compute-warp ROW_16x256B tcgen05/TMA producer, K31 capacity, split', format(split_count, '')]), 'merge': ''.join(['four-row split-list merge with TOP_K_MAX=', format(Q32_K31_TOP_K_MAX, '')]), 'guard_misses': 'delegate to current v11 common-D dispatcher'}, 'route_trace': _annotate_route_trace(route_trace_for_contract_shapes(shape_labels, split_count=split_count), candidate_report), 'summary': candidate_report['summary'], 'performance': candidate_report['performance'], 'correctness': candidate_report['correctness'], 'report': candidate_report} + if dispatch_report is not None: + payload['dispatch_v11_entrypoint'] = ROUTE_PARENT_V11 + payload['dispatch_v11_summary'] = dispatch_report['summary'] + payload['dispatch_v11_performance'] = dispatch_report['performance'] + payload['dispatch_v11_report'] = dispatch_report + payload['target_rows_vs_dispatch_v11'] = _per_shape_delta(candidate_report, dispatch_report) + return payload + +def write_benchmark_artifact(artifact_dir: str | os.PathLike[str], *, use_cupti: bool=True, shape_labels=TARGET_SHAPES, split_count: int=Q32_K31_SPLIT_COUNT, run_dispatch_baseline: bool=True) -> dict[str, str]: + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + suffix = 'cupti' if use_cupti else 'cuda_event' + payload = benchmark_knn_build_rag_microbucket_q32_k31_c3d2_v1(use_cupti=use_cupti, shape_labels=shape_labels, split_count=split_count, run_dispatch_baseline=run_dispatch_baseline) + path = out_dir / ''.join(['q32_k31_c3d2_', format(len(tuple(shape_labels)), ''), 'row_s', format(split_count, ''), '_', format(suffix, ''), '.json']) + path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n', encoding='utf-8') + return {'candidate_payload': str(path)} + +def _main() -> None: + labels = sorted({shape['label'] for shape in eval_mod.CANONICAL_SHAPES}.union(dispatch_v11.EXPANDED_Q32_GUARD_BOUNDARY_8_BY_LABEL)) + parser = argparse.ArgumentParser() + parser.add_argument('--shape', choices=labels, action='append', default=None) + parser.add_argument('--benchmark', action='store_true') + parser.add_argument('--split', type=int, default=Q32_K31_SPLIT_COUNT) + parser.add_argument('--artifact-dir', type=str, default=None) + parser.add_argument('--cuda-event', action='store_true') + parser.add_argument('--no-dispatch-baseline', action='store_true') + args = parser.parse_args() + selected_labels = TARGET_SHAPES if args.shape is None else tuple(args.shape) + use_cupti = not args.cuda_event + if args.artifact_dir: + print(json.dumps(write_benchmark_artifact(args.artifact_dir, use_cupti=use_cupti, shape_labels=selected_labels, split_count=args.split, run_dispatch_baseline=not args.no_dispatch_baseline), indent=2, sort_keys=True)) + elif args.benchmark: + print(json.dumps(benchmark_knn_build_rag_microbucket_q32_k31_c3d2_v1(use_cupti=use_cupti, shape_labels=selected_labels, split_count=args.split, run_dispatch_baseline=not args.no_dispatch_baseline), indent=2, sort_keys=True)) + else: + print(json.dumps(evaluate_contract(shapes=_select_contract_shapes(selected_labels), benchmark=False), indent=2, sort_keys=True)) diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_q32_lowk_c3d2_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_q32_lowk_c3d2_v1.py new file mode 100644 index 00000000..bbd21ff8 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_q32_lowk_c3d2_v1.py @@ -0,0 +1,223 @@ +"""Expanded Q32/M100000 low-K RAG seed for c3d2 follow-up. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +targets the expanded BF16 non-build rows +``expanded_guard_overlap_q32_m100000_d128_k20`` and +``expanded_guard_miss_q32_m100000_d128_k31``. It specializes the e5db Q32 +M64/N64 ROW_16x256B tcgen05/TMA producer to the requested low-K capacity, then +uses a low-K-aware split-list merge. Guard misses delegate to the current v11 +common-D seed portfolio dispatcher. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from collections.abc import Callable +from functools import cache +from pathlib import Path +from typing import Any +from .._dispatch_runtime import dc as dc +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_v11_common_d_seed_portfolio_a4ec_v1 as dispatch_v11 +from . import knn_build_rag_microbucket_q32rowld_e5db_v1 as e5db +from .._dispatch_runtime import pack_kernel_args +MODULE = 'loom.examples.weave.knn_build_rag_microbucket_q32_lowk_c3d2_v1' +CANDIDATE_ID = 'candidate_c3d2_q32_m100000_lowk_e5db_lowk_v1' +SEED_ID = 'rag_microbucket_q32_lowk_c3d2_v1' +EXPANDED_Q32_K20_SHAPE = dispatch_v11.EXPANDED_Q32_M100000_K20 +EXPANDED_Q32_K31_SHAPE = dispatch_v11.EXPANDED_Q32_M100000_K31 +TARGET_SHAPES = (EXPANDED_Q32_K20_SHAPE, EXPANDED_Q32_K31_SHAPE) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +EXPANDED_SHAPES_BY_LABEL = {label: dispatch_v11.EXPANDED_Q32_GUARD_BOUNDARY_8_BY_LABEL[label] for label in TARGET_SHAPES} +Q32_LOWK_SPLIT_COUNT = _decode_capture(_json_loads('152')) +Q32_LOWK_MAX_TOP_K = 31 +Q32_LOWK_STAGE1_THREADS = e5db.Q32_M64_STAGE1_THREADS +Q32_LOWK_BLOCK_Q = e5db.Q8_M64_BLOCK_Q +Q32_LOWK_BLOCK_M = e5db.Q8_M64_BLOCK_M +Q32_LOWK_FEAT_D = e5db.Q8_M64_FEAT_D +Q32_LOWK_MERGE_THREADS = 128 +Q32_LOWK_ROWS_PER_MERGE_CTA = 4 +SUPPORTED_TOP_K = (20, 31) +Q32_LOWK_LOCAL_LISTS_PER_ROW = e5db.Q32_M64_LOCAL_LISTS_PER_ROW +Q32_LOWK_SMEM_BASE_BYTES = e5db.Q32_M64_SMEM_BASE_BYTES +Q32_LOWK_LOCAL_ELEMS = Q32_LOWK_BLOCK_Q * Q32_LOWK_LOCAL_LISTS_PER_ROW * Q32_LOWK_MAX_TOP_K +Q32_LOWK_LOCAL_D_OFFSET = Q32_LOWK_SMEM_BASE_BYTES +Q32_LOWK_LOCAL_I_OFFSET = Q32_LOWK_LOCAL_D_OFFSET + Q32_LOWK_LOCAL_ELEMS * 4 +Q32_LOWK_SMEM_POOL_BYTES = Q32_LOWK_LOCAL_I_OFFSET + Q32_LOWK_LOCAL_ELEMS * 4 +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_PARENT_V11 = dispatch_v11.ROUTE_ENTRYPOINT +BENCHMARK_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_knn_build_rag_microbucket_q32_lowk_c3d2_v1']) +_insert_sorted_pair = _ir_proxy('loom.examples.weave.knn_build_rag_microbucket_q32_lowk_c3d2_v1:_insert_sorted_pair', 256) +knn_build_rag_microbucket_q32_lowk_c3d2_v1_stage1 = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_q32_lowk_c3d2_v1_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 97536, "constants": [["BLOCK_Q", 64], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 20]], "cta_group": 1, "threads": 192}')) +stage1_ir_base = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_q32_lowk_c3d2_v1_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 97536, "constants": [["BLOCK_Q", 64], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 20]], "cta_group": 1, "threads": 192}')) +knn_build_rag_microbucket_q32_lowk_c3d2_v1_merge = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_q32_lowk_c3d2_v1_merge", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["PARTIAL_TOP_K", 20], ["OUT_TOP_K", 20], ["SPLIT_COUNT", 152], ["SPLITS_PER_LANE", 5], ["ROWS_PER_CTA", 4]], "cta_group": 1, "threads": 128}')) +merge_ir_base = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_q32_lowk_c3d2_v1_merge", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["PARTIAL_TOP_K", 20], ["OUT_TOP_K", 20], ["SPLIT_COUNT", 152], ["SPLITS_PER_LANE", 5], ["ROWS_PER_CTA", 4]], "cta_group": 1, "threads": 128}')) + +def _ir_with_constants(ir_obj: Any, *, suffix: str, **updates: int) -> Any: + constants = tuple(((name, updates.get(name, value)) for name, value in ir_obj.constants)) + return dc.replace(ir_obj, symbol=''.join([format(ir_obj.symbol, ''), '_', format(suffix, '')]), constants=constants) + +def _splits_per_lane(split_count: int) -> int: + if split_count <= 0: + raise ValueError(''.join(['split_count must be positive, got ', format(split_count, '')])) + return (split_count + 31) // 32 + +def _merge_ir(top_k: int, split_count: int) -> Any: + if top_k not in SUPPORTED_TOP_K: + raise ValueError(''.join(['unsupported low-K top_k=', format(top_k, ''), '; expected one of ', format(SUPPORTED_TOP_K, '')])) + return _ir_with_constants(merge_ir_base, suffix=''.join(['k', format(top_k, ''), 's', format(split_count, ''), '_c3d2_v1']), PARTIAL_TOP_K=top_k, OUT_TOP_K=top_k, SPLIT_COUNT=split_count, SPLITS_PER_LANE=_splits_per_lane(split_count), ROWS_PER_CTA=Q32_LOWK_ROWS_PER_MERGE_CTA) + +def _stage1_ir(top_k: int) -> Any: + if top_k not in SUPPORTED_TOP_K: + raise ValueError(''.join(['unsupported low-K top_k=', format(top_k, ''), '; expected one of ', format(SUPPORTED_TOP_K, '')])) + return _ir_with_constants(stage1_ir_base, suffix=''.join(['k', format(top_k, ''), 's', format(Q32_LOWK_SPLIT_COUNT, ''), '_c3d2_v1']), BLOCK_Q=Q32_LOWK_BLOCK_Q, BLOCK_M=Q32_LOWK_BLOCK_M, FEAT_D=Q32_LOWK_FEAT_D, TOP_K_MAX=top_k) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RAG_MICROBUCKET_Q32_LOWK_C3D2_VERIFY_KERNEL') + split_count = int(os.environ.get('LOOM_KNN_RAG_MICROBUCKET_Q32_LOWK_C3D2_VERIFY_SPLIT', Q32_LOWK_SPLIT_COUNT)) + top_k = int(os.environ.get('LOOM_KNN_RAG_MICROBUCKET_Q32_LOWK_C3D2_VERIFY_K', '20')) + if verify_kernel == 'merge': + return _merge_ir(top_k, split_count) + return _stage1_ir(top_k) +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_q32_lowk_c3d2_v1_stage1_k20s152_c3d2_v1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 97536, "constants": [["BLOCK_Q", 64], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 20]], "cta_group": 1, "threads": 192}')) + +@cache +def _compiled_stage1_q32_lowk(top_k: int): + return e5db.compact_seed.q16_tailinf.parent_k32._compile_ir(_stage1_ir(top_k)) + +@cache +def _compiled_merge(top_k: int, split_count: int): + return e5db.compact_seed.q16_tailinf.parent_k32._compile_ir(_merge_ir(top_k, split_count)) + +def _dtype_name(inputs: dict[str, Any], key: str) -> str: + tensor = inputs.get(key) + if tensor is not None: + return str(tensor.dtype).removeprefix('torch.') + return str(inputs.get('dtype', '')).removeprefix('torch.') + +def _eligible_q32_lowk(inputs: dict[str, Any]) -> bool: + return not bool(inputs.get('build', False)) and _dtype_name(inputs, 'query') == 'bfloat16' and (_dtype_name(inputs, 'database') == 'bfloat16') and (int(inputs.get('B', -1)) == 1) and (int(inputs.get('Q', -1)) == 32) and (int(inputs.get('M', -1)) == 100000) and (int(inputs.get('D', -1)) == Q32_LOWK_FEAT_D) and (int(inputs.get('K', -1)) in SUPPORTED_TOP_K) + +def _route_name(inputs: dict[str, Any], *, split_count: int) -> str: + return ''.join(['rag_microbucket_q32_lowk_c3d2_v1_q32_m100000_k', format(int(inputs.get('K', -1)), ''), '_e5db_lowk_s', format(split_count, ''), '_r', format(Q32_LOWK_ROWS_PER_MERGE_CTA, ''), '_lowkmerge']) + +def _launch_q32_lowk(inputs: dict[str, Any], *, split_count: int) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + if top_k not in SUPPORTED_TOP_K: + raise ValueError(''.join(['q32 low-K seed only supports K in ', format(SUPPORTED_TOP_K, ''), ', got ', format(top_k, '')])) + num_q_tiles = (n_query + Q32_LOWK_BLOCK_Q - 1) // Q32_LOWK_BLOCK_Q + num_db_tiles = (n_database + Q32_LOWK_BLOCK_M - 1) // Q32_LOWK_BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tiles * split_count + total_queries = bsz * n_query + stage1_grid = min(total_work, e5db.compact_seed.GRID_DIM_DEFAULT) + merge_grid = min((total_queries + Q32_LOWK_ROWS_PER_MERGE_CTA - 1) // Q32_LOWK_ROWS_PER_MERGE_CTA, e5db.compact_seed.GRID_DIM_DEFAULT) + partial_dists, partial_indices = e5db.compact_seed.q16_tailinf.parent_k32.parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = e5db.compact_seed.q16_tailinf.parent_k32.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), total_queries, Q32_LOWK_BLOCK_Q, dim, dim) + tmap_database = e5db.compact_seed.q16_tailinf.parent_k32.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, Q32_LOWK_BLOCK_M, dim, dim) + stage1_ir = _stage1_ir(top_k) + _compiled_stage1_q32_lowk(top_k).launch(grid=(stage1_grid, 1, 1), block=(Q32_LOWK_STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage1_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tiles=num_q_tiles, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), shared_mem=stage1_ir.computed_smem_bytes) + merge_ir = _merge_ir(top_k, split_count) + _compiled_merge(top_k, split_count).launch(grid=(merge_grid, 1, 1), block=(Q32_LOWK_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], total_queries], shared_mem=merge_ir.computed_smem_bytes) + +def route_for_contract_inputs(inputs: dict[str, Any], *, split_count: int=Q32_LOWK_SPLIT_COUNT) -> str: + if _eligible_q32_lowk(inputs): + return _route_name(inputs, split_count=split_count) + return dispatch_v11.route_for_contract_inputs(inputs) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, split_count: int=Q32_LOWK_SPLIT_COUNT) -> None: + if _eligible_q32_lowk(inputs): + _launch_q32_lowk(inputs, split_count=split_count) + return + dispatch_v11.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_with_split(split_count: int) -> Callable[[dict[str, Any]], None]: + + def _candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, split_count=split_count) + return _candidate + +def candidate_dispatch_v11(inputs: dict[str, Any]) -> None: + dispatch_v11.launch_from_contract_inputs(inputs) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + labels = tuple(shape_labels) + selected = [] + remaining = [] + for label in labels: + if label in EXPANDED_SHAPES_BY_LABEL: + selected.append(EXPANDED_SHAPES_BY_LABEL[label]) + else: + remaining.append(label) + if remaining: + selected.extend(dispatch_v11._select_contract_shapes(tuple(remaining))) + return selected + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def route_trace_for_contract_shapes(shape_labels=TARGET_SHAPES, *, split_count: int=Q32_LOWK_SPLIT_COUNT) -> list[dict[str, Any]]: + rows = [] + for shape in _select_contract_shapes(shape_labels): + params = dict(shape.get('params', {})) + params['label'] = shape['label'] + selected = _eligible_q32_lowk(params) + current_route = dispatch_v11.route_for_contract_inputs(params) + route = route_for_contract_inputs(params, split_count=split_count) + rows.append({'shape_key': shape['label'], 'selected_route': route, 'selected_seed': SEED_ID if selected else None, 'selected_entrypoint': ROUTE_ENTRYPOINT if selected else ROUTE_PARENT_V11, 'current_dispatch_v11_route': current_route, 'current_dispatch_v11_entrypoint': ROUTE_PARENT_V11, 'route_kind': 'specialized_q32_lowk_e5db_merge' if selected else 'inherited_v11_dispatcher', 'split_count': split_count if selected else None, 'partial_top_k': int(params.get('K', -1)) if selected else None, 'output_top_k': int(params.get('K', -1)) if selected else None, 'guard_condition': 'BF16 non-build B=1 Q=32 M=100000 D=128 K in {20,31}' if selected else 'delegate to v11 common-D seed portfolio dispatcher'}) + return rows + +def _per_shape_delta(candidate_report: dict[str, Any], dispatcher_report: dict[str, Any]) -> dict[str, Any]: + rows = {} + for label, cand in candidate_report.get('per_shape', {}).items(): + base_row = dispatcher_report.get('per_shape', {}).get(label, {}) + cand_ms = cand.get('kernel_ms') + base_ms = base_row.get('kernel_ms') + rows[label] = {'candidate': cand, 'dispatch_v11_baseline': base_row, 'candidate_ms': cand_ms, 'dispatch_v11_ms': base_ms, 'speedup_vs_dispatch_v11': base_ms / cand_ms if cand_ms and base_ms else None, 'ratio_vs_flashlib': cand.get('ratio_vs_flashlib'), 'dispatch_v11_ratio_vs_flashlib': base_row.get('ratio_vs_flashlib')} + return rows + +def benchmark_knn_build_rag_microbucket_q32_lowk_c3d2_v1(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES, split_count: int=Q32_LOWK_SPLIT_COUNT) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_with_split(split_count)) + dispatcher_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_dispatch_v11) + return {'contract': candidate_report['contract'], 'contract_version': candidate_report['contract_version'], 'measured_entrypoint': BENCHMARK_ENTRYPOINT, 'candidate_entrypoint': ROUTE_ENTRYPOINT, 'dispatch_v11_entrypoint': ROUTE_PARENT_V11, 'accelerated_shape_labels': list(TARGET_SHAPES), 'measured_shape_labels': list(shape_labels), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'producer_topology': {'candidate': 'e5db Q32 M64/N64 ROW_16x256B tcgen05/TMA stage1 specialized to K20/K31 partial lists', 'guard_misses': 'delegate to current v11 common-D seed portfolio dispatcher'}, 'merge_topology': {'candidate': 'four-row warp split-list merge with partial_top_k == output_top_k in {20,31}', 'split_count': split_count, 'splits_per_lane': _splits_per_lane(split_count)}, 'route_trace': route_trace_for_contract_shapes(shape_labels, split_count=split_count), 'target_rows': _per_shape_delta(candidate_report, dispatcher_report), 'summary': candidate_report['summary'], 'performance': candidate_report['performance'], 'correctness': candidate_report['correctness'], 'rank_objective': candidate_report['rank_objective'], 'report': candidate_report, 'dispatch_v11_summary': dispatcher_report['summary'], 'dispatch_v11_performance': dispatcher_report['performance'], 'dispatch_v11_report': dispatcher_report} + +def write_benchmark_artifact(artifact_dir: str | os.PathLike[str], *, use_cupti: bool=True, shape_labels=TARGET_SHAPES, split_count: int=Q32_LOWK_SPLIT_COUNT) -> dict[str, str]: + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + suffix = 'cupti' if use_cupti else 'cuda_event' + payload = benchmark_knn_build_rag_microbucket_q32_lowk_c3d2_v1(use_cupti=use_cupti, shape_labels=shape_labels, split_count=split_count) + path = out_dir / ''.join(['q32_lowk_c3d2_', format(len(tuple(shape_labels)), ''), 'row_s', format(split_count, ''), '_', format(suffix, ''), '.json']) + path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + route_path = out_dir / ''.join(['q32_lowk_c3d2_', format(len(tuple(shape_labels)), ''), 'row_route_trace.json']) + route_path.write_text(json.dumps(payload['route_trace'], indent=2, sort_keys=True) + '\n') + return {'candidate_payload': str(path), 'route_trace': str(route_path)} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_q32rowld_e5db_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_q32rowld_e5db_v1.py new file mode 100644 index 00000000..ee7d7606 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_q32rowld_e5db_v1.py @@ -0,0 +1,245 @@ +"""RAG microbucket Q32/K32 M64 ROW_16x256B readback producer. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +routes only ``rag_microbatch_largek_b1_q32_m100000_d128_k32`` through a +smaller-row M64/N64 tcgen05/TMA producer with the required ROW_16x256B M64 +TMEM readback map and the existing K32 fused split merge. The existing Q8 M64 +route delegates to v9; all other target rows inherit the validated v7 +microbucket routes, and guard misses delegate to the current 4247 dispatcher. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from collections.abc import Callable +from functools import lru_cache +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_e3de_9138_bcb3_4247_v1 as base_dispatcher +from . import knn_build_rag_microbucket_3505_v1 as parent_3505 +from . import knn_build_rag_microbucket_3505_v7 as prior_v7 +from . import knn_build_rag_microbucket_3505_v9 as parent_v9 +from . import knn_build_rag_microbucket_5093_v1 as compact_seed +from . import knn_build_rag_microbucket_faeb_v1 as faeb +from .._dispatch_runtime import pack_kernel_args +Q4_K10_SHAPE = faeb.Q4_K10_SHAPE +Q8_K10_SHAPE = 'rag_microbatch_b1_q8_m100000_d128_k10' +Q16_K10_SHAPE = 'rag_microbatch_b1_q16_m100000_d128_k10' +Q32_K10_SHAPE = 'rag_microbatch_b1_q32_m100000_d128_k10' +Q64_K10_SHAPE = faeb.Q64_K10_SHAPE +Q8_K32_SHAPE = 'rag_microbatch_largek_b1_q8_m100000_d128_k32' +Q16_K32_SHAPE = faeb.Q16_K32_SHAPE +Q32_K32_SHAPE = 'rag_microbatch_largek_b1_q32_m100000_d128_k32' +Q16_K32_IRREGULAR_SHAPE = 'rag_microbatch_largek_b1_q16_m131071_d128_k32' +K10_TARGET_SHAPES = (Q4_K10_SHAPE, Q8_K10_SHAPE, Q16_K10_SHAPE, Q32_K10_SHAPE, Q64_K10_SHAPE) +K32_TARGET_SHAPES = (Q8_K32_SHAPE, Q16_K32_SHAPE, Q32_K32_SHAPE, Q16_K32_IRREGULAR_SHAPE) +TARGET_SHAPES = (*K10_TARGET_SHAPES, *K32_TARGET_SHAPES) +K32_SPLIT_COUNT = _decode_capture(_json_loads('144')) +K32_GROUP_COUNT = _decode_capture(_json_loads('12')) +COMPACT_STAGE1_THREADS = compact_seed.COMPACT_STAGE1_THREADS +K32_FUSED_MERGE_THREADS = compact_seed.K32_FUSED_MERGE_THREADS +Q32_M64_STAGE1_THREADS = _decode_capture(_json_loads('192')) +Q8_M64_BLOCK_Q = 64 +Q8_M64_BLOCK_M = 64 +Q8_M64_FEAT_D = 128 +Q8_M64_TOP_K_MAX = 32 +Q32_M64_LOCAL_LISTS_PER_ROW = 4 +Q32_M64_SMEM_BASE_BYTES = 16384 + 16384 + 256 +Q32_M64_LOCAL_ELEMS = Q8_M64_BLOCK_Q * Q32_M64_LOCAL_LISTS_PER_ROW * Q8_M64_TOP_K_MAX +Q32_M64_LOCAL_D_OFFSET = Q32_M64_SMEM_BASE_BYTES +Q32_M64_LOCAL_I_OFFSET = Q32_M64_LOCAL_D_OFFSET + Q32_M64_LOCAL_ELEMS * 4 +Q32_M64_SMEM_POOL_BYTES = Q32_M64_LOCAL_I_OFFSET + Q32_M64_LOCAL_ELEMS * 4 +ROUTE_Q4_K10 = 'rag_microbucket_q32rowld_e5db_v1_inherit_v7_q4_k10_m64_s128_g8' +ROUTE_Q64_K10 = 'rag_microbucket_q32rowld_e5db_v1_inherit_v7_q64_k10_m64_s128_g8' +ROUTE_BASE_4247 = parent_3505.ROUTE_BASE_4247 +_q32_m64_insert_sorted_pair = _ir_proxy('loom.examples.weave.knn_build_rag_microbucket_q32rowld_e5db_v1:_q32_m64_insert_sorted_pair', 256) +knn_build_rag_microbucket_q32rowld_e5db_v1_stage1_q32_k32_m64 = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_q32rowld_e5db_v1_stage1_q32_k32_m64", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 99584, "constants": [["BLOCK_Q", 64], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 192}')) +stage1_q32_k32_m64_rowld_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_q32rowld_e5db_v1_stage1_q32_k32_m64", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 99584, "constants": [["BLOCK_Q", 64], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 192}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RAG_MICROBUCKET_Q32ROWLD_E5DB_V1_VERIFY_KERNEL') + split_count = int(os.environ.get('LOOM_KNN_RAG_MICROBUCKET_Q32ROWLD_E5DB_V1_VERIFY_K32_SPLIT', K32_SPLIT_COUNT)) + group_count = int(os.environ.get('LOOM_KNN_RAG_MICROBUCKET_Q32ROWLD_E5DB_V1_VERIFY_K32_GROUPS', K32_GROUP_COUNT)) + if verify_kernel == 'm64_stage1': + return faeb.rag_m64.stage1_m64_ir + if verify_kernel == 'm64_merge': + return faeb.rag_m64.parent_micro._fused_merge_ir(faeb.M64_SPLIT_COUNT, faeb.M64_GROUP_COUNT) + if verify_kernel == 'k32_q32_m64_rowld_stage1': + return stage1_q32_k32_m64_rowld_ir + if verify_kernel in {'k32_fused_merge', 'q16_k32_fused_merge'}: + return compact_seed.q16_tailinf._fused_merge_ir(split_count, group_count) + return stage1_q32_k32_m64_rowld_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_q32rowld_e5db_v1_stage1_q32_k32_m64", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 99584, "constants": [["BLOCK_Q", 64], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 192}')) + +def _dtype_name(inputs: dict[str, Any]) -> str: + query = inputs.get('query') + if query is not None: + return str(query.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')) + +def _is_bf16_d128_nonbuild(inputs: dict[str, Any]) -> bool: + return not bool(inputs.get('build', False)) and _dtype_name(inputs) == 'bfloat16' and (int(inputs.get('B', -1)) == 1) and (int(inputs.get('D', -1)) == 128) + +def _eligible_q4_k10(inputs: dict[str, Any]) -> bool: + return _is_bf16_d128_nonbuild(inputs) and int(inputs.get('M', -1)) == 100000 and (int(inputs.get('Q', -1)) == 4) and (int(inputs.get('K', -1)) == 10) + +def _eligible_q64_k10(inputs: dict[str, Any]) -> bool: + return _is_bf16_d128_nonbuild(inputs) and int(inputs.get('M', -1)) == 100000 and (int(inputs.get('Q', -1)) == 64) and (int(inputs.get('K', -1)) == 10) + +def _eligible_m64_k10(inputs: dict[str, Any]) -> bool: + return _is_bf16_d128_nonbuild(inputs) and int(inputs.get('M', -1)) == 100000 and (int(inputs.get('Q', -1)) in {4, 8, 16, 32, 64}) and (int(inputs.get('K', -1)) == 10) + +def _eligible_compact_k32(inputs: dict[str, Any]) -> bool: + if not _is_bf16_d128_nonbuild(inputs) or int(inputs.get('K', -1)) != 32: + return False + n_query = int(inputs.get('Q', -1)) + n_database = int(inputs.get('M', -1)) + if n_database == 100000 and n_query in {8, 16, 32}: + return True + return n_database == 131071 and n_query == 16 + +def _eligible_q8_k32_m64(inputs: dict[str, Any]) -> bool: + return _is_bf16_d128_nonbuild(inputs) and int(inputs.get('M', -1)) == 100000 and (int(inputs.get('Q', -1)) == 8) and (int(inputs.get('K', -1)) == 32) + +def _eligible_q32_k32_m64_rowld(inputs: dict[str, Any]) -> bool: + return _is_bf16_d128_nonbuild(inputs) and int(inputs.get('M', -1)) == 100000 and (int(inputs.get('Q', -1)) == 32) and (int(inputs.get('K', -1)) == 32) + +def _q8_k32_m64_route_name(*, split_count: int, group_count: int) -> str: + return parent_v9._q8_k32_m64_route_name(split_count=split_count, group_count=group_count) + +def _q32_k32_m64_rowld_route_name(*, split_count: int, group_count: int) -> str: + return ''.join(['rag_microbucket_q32rowld_e5db_v1_q32_m100000_k32_m64n64_row16x256b_s', format(split_count, ''), '_g', format(group_count, '')]) + +def _compact_route_name(*, split_count: int, group_count: int, inputs: dict[str, Any]) -> str: + n_query = int(inputs.get('Q', -1)) + n_database = int(inputs.get('M', -1)) + return ''.join(['rag_microbucket_q32rowld_e5db_v1_inherit_v7_q', format(n_query, ''), '_m', format(n_database, ''), '_k32_tailinf_cta1_cw1_s', format(split_count, ''), '_g', format(group_count, '')]) + +def _m64_route_name(inputs: dict[str, Any]) -> str: + n_query = int(inputs.get('Q', -1)) + return ''.join(['rag_microbucket_q32rowld_e5db_v1_inherit_v7_q', format(n_query, ''), '_k10_m64_s', format(faeb.M64_SPLIT_COUNT, ''), '_g', format(faeb.M64_GROUP_COUNT, '')]) + +def _compiled_stage1_q32_k32_m64_rowld(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0114"}')) + +def _launch_q32_k32_m64_rowld(inputs: dict[str, Any], *, split_count: int=K32_SPLIT_COUNT, group_count: int=K32_GROUP_COUNT) -> None: + compact_seed.q16_tailinf._validate_group_shape(split_count, group_count) + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + num_q_tiles = (n_query + Q8_M64_BLOCK_Q - 1) // Q8_M64_BLOCK_Q + num_db_tiles = (n_database + Q8_M64_BLOCK_M - 1) // Q8_M64_BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tiles * split_count + total_queries = bsz * n_query + stage1_grid = min(total_work, compact_seed.GRID_DIM_DEFAULT) + merge_grid = min(total_queries, compact_seed.GRID_DIM_DEFAULT) + partial_dists, partial_indices = compact_seed.q16_tailinf.parent_k32.parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = compact_seed.q16_tailinf.parent_k32.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), total_queries, Q8_M64_BLOCK_Q, dim, dim) + tmap_database = compact_seed.q16_tailinf.parent_k32.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, Q8_M64_BLOCK_M, dim, dim) + _compiled_stage1_q32_k32_m64_rowld().launch(grid=(stage1_grid, 1, 1), block=(Q32_M64_STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage1_q32_k32_m64_rowld_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tiles=num_q_tiles, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), shared_mem=stage1_q32_k32_m64_rowld_ir.computed_smem_bytes) + fused_ir = compact_seed.q16_tailinf._fused_merge_ir(split_count, group_count) + fused_kernel = compact_seed.q16_tailinf._compiled_fused_merge(split_count, group_count) + fused_kernel.launch(grid=(merge_grid, 1, 1), block=(K32_FUSED_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], total_queries], shared_mem=fused_ir.computed_smem_bytes) + +def _launch_compact_k32(inputs: dict[str, Any], *, split_count: int=K32_SPLIT_COUNT, group_count: int=K32_GROUP_COUNT) -> None: + compact_seed._launch_q16_k32_tailinf_cta1(inputs, split_count=split_count, group_count=group_count) + +def route_for_contract_inputs(inputs: dict[str, Any], *, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> str: + if _eligible_q8_k32_m64(inputs): + return _q8_k32_m64_route_name(split_count=k32_split_count, group_count=k32_group_count) + if _eligible_q32_k32_m64_rowld(inputs): + return _q32_k32_m64_rowld_route_name(split_count=k32_split_count, group_count=k32_group_count) + if _eligible_q4_k10(inputs): + return ROUTE_Q4_K10 + if _eligible_q64_k10(inputs): + return ROUTE_Q64_K10 + if _eligible_m64_k10(inputs): + return _m64_route_name(inputs) + if _eligible_compact_k32(inputs): + return _compact_route_name(split_count=k32_split_count, group_count=k32_group_count, inputs=inputs) + return base_dispatcher.route_for_contract_inputs(inputs) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> None: + if _eligible_q8_k32_m64(inputs): + parent_v9.launch_from_contract_inputs(inputs, k32_split_count=k32_split_count, k32_group_count=k32_group_count) + return + if _eligible_q32_k32_m64_rowld(inputs): + _launch_q32_k32_m64_rowld(inputs, split_count=k32_split_count, group_count=k32_group_count) + return + if _eligible_q4_k10(inputs): + faeb._launch_q4_k10_m64(inputs) + return + if _eligible_q64_k10(inputs): + faeb._launch_q64_k10_m64(inputs) + return + if _eligible_m64_k10(inputs): + faeb._launch_q4_k10_m64(inputs) + return + if _eligible_compact_k32(inputs): + _launch_compact_k32(inputs, split_count=k32_split_count, group_count=k32_group_count) + return + base_dispatcher.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def candidate_with_k32_topology(split_count: int, group_count: int) -> Callable[[dict[str, Any]], None]: + + def _candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, k32_split_count=split_count, k32_group_count=group_count) + return _candidate + +def candidate_prior_v7(inputs: dict[str, Any]): + prior_v7.launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return compact_seed._select_contract_shapes(shape_labels) + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def route_trace_for_contract_shapes(shape_labels=TARGET_SHAPES, *, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> list[dict[str, Any]]: + selected = _select_contract_shapes(shape_labels) + rows = [] + for shape in selected: + inputs = base_dispatcher._trace_inputs_from_shape(shape) + route = route_for_contract_inputs(inputs, k32_split_count=k32_split_count, k32_group_count=k32_group_count) + specialized = str(route).startswith(('rag_microbucket_3505_v9', 'rag_microbucket_q32rowld_e5db_v1')) + rows.append({'shape_key': shape['label'], 'selected_route': route, 'route_kind': 'specialized' if specialized else 'general', 'guard_condition': 'exact BF16 non-build B1 D128 Q<=64 K10, Q8 inherited-v9 M64/N64 K32, Q32 ROW_16x256B M64/N64 K32, or inherited v7 K32 microbucket' if specialized else 'guard miss to 4247 dispatcher', 'fallback': ROUTE_BASE_4247}) + return rows + +def _per_shape_delta(candidate_report: dict[str, Any], prior_report: dict[str, Any]) -> dict[str, Any]: + rows = {} + for label in TARGET_SHAPES: + cand = candidate_report.get('per_shape', {}).get(label, {}) + prior = prior_report.get('per_shape', {}).get(label, {}) + cand_ms = cand.get('kernel_ms') + prior_ms = prior.get('kernel_ms') + rows[label] = {'candidate': cand, 'prior_v7': prior, 'candidate_ms': cand_ms, 'prior_v7_ms': prior_ms, 'speedup_vs_prior_v7': prior_ms / cand_ms if cand_ms and prior_ms else None, 'ratio_vs_flashlib': cand.get('ratio_vs_flashlib')} + return rows + +def benchmark_knn_build_rag_microbucket_q32rowld_e5db_v1(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_with_k32_topology(k32_split_count, k32_group_count)) + prior_v7_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_prior_v7) + return {'contract': candidate_report['contract'], 'contract_version': candidate_report['contract_version'], 'measured_entrypoint': 'loom.examples.weave.knn_build_rag_microbucket_q32rowld_e5db_v1:benchmark_knn_build_rag_microbucket_q32rowld_e5db_v1', 'candidate_entrypoint': 'loom.examples.weave.knn_build_rag_microbucket_q32rowld_e5db_v1:launch_from_contract_inputs', 'prior_v7_entrypoint': 'loom.examples.weave.knn_build_rag_microbucket_3505_v7:launch_from_contract_inputs', 'accelerated_shape_labels': list(TARGET_SHAPES), 'measured_shape_labels': list(shape_labels), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'producer_topology': {'K10': ''.join(['inherited-v7/M64/S', format(faeb.M64_SPLIT_COUNT, ''), '/G', format(faeb.M64_GROUP_COUNT, '')]), 'K32': ''.join(['Q8 inherited-v9 M64N64; Q32-M64N64-ROW16x256B/S', format(k32_split_count, ''), '/G', format(k32_group_count, ''), '/fused; other K32 inherited-v7'])}, 'route_trace': route_trace_for_contract_shapes(shape_labels, k32_split_count=k32_split_count, k32_group_count=k32_group_count), 'target_rows': _per_shape_delta(candidate_report, prior_v7_report), 'summary': candidate_report['summary'], 'performance': candidate_report['performance'], 'correctness': candidate_report['correctness'], 'report': candidate_report, 'prior_v7_summary': prior_v7_report['summary'], 'prior_v7_performance': prior_v7_report['performance'], 'prior_v7_report': prior_v7_report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_q8rowld_q16m64_229a_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_q8rowld_q16m64_229a_v1.py new file mode 100644 index 00000000..3ecedb46 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_microbucket_q8rowld_q16m64_229a_v1.py @@ -0,0 +1,151 @@ +"""Combined Q8 ROW_16x256B and Q16 M64/N64 K32 microbucket seed. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +combines two validated exact-shape seeds for the RAG K32 parity lane: +``rag_microbatch_largek_b1_q8_m100000_d128_k32`` uses the ROW_16x256B M64/N64 +tcgen05/TMA producer from the q32rowld lineage, and +``rag_microbatch_largek_b1_q16_m100000_d128_k32`` uses the M64/N64 producer +from the q16m64 lineage. Q32/K32, irregular Q16/K32, K10 rows, and guard +misses delegate to the existing q16m64/q32rowld Weave routes. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_q8rowld_19b3_q8m64probe_v1 as q8rowld_seed +from . import knn_build_rag_microbucket_q16m64_19b3_v1 as q16m64_seed +MODULE = 'loom.examples.weave.knn_build_rag_microbucket_q8rowld_q16m64_229a_v1' +Q8_K32_SHAPE = q16m64_seed.Q8_K32_SHAPE +Q16_K32_SHAPE = q16m64_seed.Q16_K32_SHAPE +Q32_K32_SHAPE = q16m64_seed.Q32_K32_SHAPE +Q16_K32_IRREGULAR_SHAPE = q16m64_seed.Q16_K32_IRREGULAR_SHAPE +Q8_ROWLD_TARGET_SHAPES = (Q8_K32_SHAPE,) +Q16_M64_TARGET_SHAPES = (Q16_K32_SHAPE,) +ACCELERATED_SHAPES = (*Q8_ROWLD_TARGET_SHAPES, *Q16_M64_TARGET_SHAPES) +K32_BUCKET_SHAPES = (Q8_K32_SHAPE, Q16_K32_SHAPE, Q32_K32_SHAPE, Q16_K32_IRREGULAR_SHAPE) +TARGET_SHAPES = q16m64_seed.TARGET_SHAPES +K32_SPLIT_COUNT = q16m64_seed.K32_SPLIT_COUNT +K32_GROUP_COUNT = q16m64_seed.K32_GROUP_COUNT +ROUTE_PARENT_Q16M64 = ''.join([format(q16m64_seed.MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_COMBINED_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +SEED_Q8_ROWLD_ID = 'rag_microbucket_q8rowld_q16m64_229a_v1_q8_row16x256b' +SEED_Q16_M64_ID = 'rag_microbucket_q8rowld_q16m64_229a_v1_q16_m64n64' + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RAG_MICROBUCKET_Q8ROWLD_Q16M64_229A_V1_VERIFY_KERNEL') + split_count = int(os.environ.get('LOOM_KNN_RAG_MICROBUCKET_Q8ROWLD_Q16M64_229A_V1_VERIFY_K32_SPLIT', K32_SPLIT_COUNT)) + group_count = int(os.environ.get('LOOM_KNN_RAG_MICROBUCKET_Q8ROWLD_Q16M64_229A_V1_VERIFY_K32_GROUPS', K32_GROUP_COUNT)) + if verify_kernel == 'q16_m64_stage1': + return q16m64_seed.q8_m64_seed.stage1_q8_k32_m64_ir + if verify_kernel == 'k32_fused_merge': + return q16m64_seed.parent_q32rowld.compact_seed.q16_tailinf._fused_merge_ir(split_count, group_count) + return q8rowld_seed.stage1_q8_k32_rowld_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_q32rowld_e5db_v1_stage1_q32_k32_m64", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 99584, "constants": [["BLOCK_Q", 64], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 192}')) + +def _label_can_hit(inputs: dict[str, Any], labels: set[str]) -> bool: + label = inputs.get('label') + return label is None or str(label) in labels + +def _eligible_q8_k32_rowld(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, set(Q8_ROWLD_TARGET_SHAPES)) and q8rowld_seed._eligible_q8_k32_rowld(inputs) + +def _eligible_q16_k32_m64(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, set(Q16_M64_TARGET_SHAPES)) and q16m64_seed._eligible_q16_k32_m64(inputs) + +def _q8_k32_rowld_route_name(*, split_count: int, group_count: int) -> str: + return ''.join(['rag_microbucket_q8rowld_q16m64_229a_v1_q8_m100000_k32_row16x256b_s', format(split_count, ''), '_g', format(group_count, '')]) + +def _q16_k32_m64_route_name(*, split_count: int, group_count: int) -> str: + return ''.join(['rag_microbucket_q8rowld_q16m64_229a_v1_q16_m100000_k32_m64n64_s', format(split_count, ''), '_g', format(group_count, '')]) + +def route_for_contract_inputs(inputs: dict[str, Any], *, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> str: + if _eligible_q8_k32_rowld(inputs): + return _q8_k32_rowld_route_name(split_count=k32_split_count, group_count=k32_group_count) + if _eligible_q16_k32_m64(inputs): + return _q16_k32_m64_route_name(split_count=k32_split_count, group_count=k32_group_count) + return q16m64_seed.route_for_contract_inputs(inputs, k32_split_count=k32_split_count, k32_group_count=k32_group_count) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> None: + if _eligible_q8_k32_rowld(inputs): + q8rowld_seed._launch_q8_k32_rowld(inputs, split_count=k32_split_count, group_count=k32_group_count) + return + if _eligible_q16_k32_m64(inputs): + q16m64_seed.q8_m64_seed._launch_q8_k32_m64(inputs, split_count=k32_split_count, group_count=k32_group_count) + return + q16m64_seed.launch_from_contract_inputs(inputs, k32_split_count=k32_split_count, k32_group_count=k32_group_count) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_with_k32_topology(split_count: int, group_count: int) -> Callable[[dict[str, Any]], None]: + + def _candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, k32_split_count=split_count, k32_group_count=group_count) + return _candidate + +def candidate_parent_q16m64(inputs: dict[str, Any]) -> None: + q16m64_seed.launch_from_contract_inputs(inputs) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return q16m64_seed._select_contract_shapes(shape_labels) + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=K32_BUCKET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def compile_and_launch_knn_build(*, shape_labels=K32_BUCKET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def route_trace_for_contract_shapes(shape_labels=K32_BUCKET_SHAPES, *, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> list[dict[str, Any]]: + rows = [] + for shape in _select_contract_shapes(shape_labels): + inputs = q16m64_seed.parent_q32rowld.base_dispatcher._trace_inputs_from_shape(shape) + route = route_for_contract_inputs(inputs, k32_split_count=k32_split_count, k32_group_count=k32_group_count) + parent_route = q16m64_seed.route_for_contract_inputs(inputs, k32_split_count=k32_split_count, k32_group_count=k32_group_count) + if str(route).startswith('rag_microbucket_q8rowld_q16m64_229a_v1_q8_'): + selected_seed = SEED_Q8_ROWLD_ID + route_kind = 'specialized_q8_rowld' + guard = 'exact BF16 non-build B=1 Q=8 M=100000 D=128 K=32' + elif str(route).startswith('rag_microbucket_q8rowld_q16m64_229a_v1_q16_'): + selected_seed = SEED_Q16_M64_ID + route_kind = 'specialized_q16_m64' + guard = 'exact BF16 non-build B=1 Q=16 M=100000 D=128 K=32' + else: + selected_seed = None + route_kind = 'inherited' + guard = 'delegate to q16m64/q32rowld Weave route' + rows.append({'shape_key': shape['label'], 'selected_route': route, 'selected_seed': selected_seed, 'selected_entrypoint': ROUTE_COMBINED_ENTRYPOINT if selected_seed else ROUTE_PARENT_Q16M64, 'parent_q16m64_route': parent_route, 'route_kind': route_kind, 'guard_condition': guard}) + return rows + +def _per_shape_delta(candidate_report: dict[str, Any], parent_report: dict[str, Any]) -> dict[str, Any]: + rows = {} + for label, cand in candidate_report.get('per_shape', {}).items(): + parent = parent_report.get('per_shape', {}).get(label, {}) + cand_ms = cand.get('kernel_ms') + parent_ms = parent.get('kernel_ms') + rows[label] = {'candidate': cand, 'parent_q16m64': parent, 'candidate_ms': cand_ms, 'parent_q16m64_ms': parent_ms, 'speedup_vs_parent_q16m64': parent_ms / cand_ms if cand_ms and parent_ms else None, 'ratio_vs_flashlib': cand.get('ratio_vs_flashlib')} + return rows + +def benchmark_knn_build_rag_microbucket_q8rowld_q16m64_229a_v1(*, use_cupti: bool=True, shape_labels=K32_BUCKET_SHAPES, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_with_k32_topology(k32_split_count, k32_group_count)) + parent_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_parent_q16m64) + return {'contract': candidate_report['contract'], 'contract_version': candidate_report['contract_version'], 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_knn_build_rag_microbucket_q8rowld_q16m64_229a_v1']), 'candidate_entrypoint': ''.join([format(MODULE, ''), ':launch_from_contract_inputs']), 'parent_entrypoint': ROUTE_PARENT_Q16M64, 'accelerated_shape_labels': list(ACCELERATED_SHAPES), 'measured_shape_labels': list(shape_labels), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'producer_topology': {'Q8_K32': ''.join(['ROW_16x256B/M64N64/S', format(k32_split_count, ''), '/G', format(k32_group_count, ''), '/fused']), 'Q16_K32': ''.join(['M64N64/S', format(k32_split_count, ''), '/G', format(k32_group_count, ''), '/fused']), 'inherited': 'q16m64/q32rowld routes for Q32/K10/irregular-Q16 and parent fallback'}, 'route_trace': route_trace_for_contract_shapes(shape_labels, k32_split_count=k32_split_count, k32_group_count=k32_group_count), 'target_rows': _per_shape_delta(candidate_report, parent_report), 'summary': candidate_report['summary'], 'performance': candidate_report['performance'], 'correctness': candidate_report['correctness'], 'report': candidate_report, 'parent_summary': parent_report['summary'], 'parent_performance': parent_report['performance'], 'parent_report': parent_report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_online_stream_801d_v45.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_online_stream_801d_v45.py new file mode 100644 index 00000000..288441e9 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_online_stream_801d_v45.py @@ -0,0 +1,81 @@ +"""Paired exact RAG online/stream K10 route for kNN build/search. + +Minimum target architecture: sm_100a. This additive shape-kernel candidate +routes exactly ``rag_online_b1_q1_m100000_d128_k10`` and +``rag_stream_b1_q128_m100000_d128_k10`` through the existing split-7 K10 +tcgen05/TMA producer and cached sorted K10 merge. Guard misses delegate to the +8050 full-dispatch wrapper, with no external runtime fallback. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import os +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatchscore_k20raglarge_8050_v43 as current_dispatcher +from . import knn_build_ragonline_exact_7c8d_v42 as online_route +from . import knn_build_rag_stream_exact_weave_evolve_knn_build_6361_v42 as stream_route +ONLINE_SHAPE = online_route.ONLINE_SHAPE +STREAM_SHAPE = stream_route.TARGET_SHAPE_LABEL +TARGET_SHAPES = (ONLINE_SHAPE, STREAM_SHAPE) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RAG_ONLINE_STREAM_VERIFY_KERNEL') + if verify_kernel == 'online_stage1_k10_s7': + return online_route.v20.parent_lowk.stage1_ir + if verify_kernel == 'online_merge_k10_s7_cache': + return online_route.v20.parent_lowk.parent_cached.merge_k10_s7_cache_ir + if verify_kernel == 'stream_stage1_k10_s7': + return stream_route.parent_lowk.stage1_ir + if verify_kernel == 'stream_merge_k10_s7_cache': + return stream_route.parent_lowk.parent_cached.merge_k10_s7_cache_ir + return stream_route.parent_lowk.stage1_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_stage1_batch8_cond4_vmin_maxtree", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) + +def _eligible_rag_online_stream(inputs: dict[str, Any]) -> bool: + return online_route._eligible_rag_online_exact(inputs) or stream_route._eligible_rag_stream_exact(inputs) + +def launch_from_contract_inputs(inputs: dict[str, Any]) -> None: + if online_route._eligible_rag_online_exact(inputs): + online_route._launch_rag_online_s7(inputs) + return + if stream_route._eligible_rag_stream_exact(inputs): + stream_route._launch_rag_stream_exact(inputs) + return + current_dispatcher.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(candidate, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return current_dispatcher._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint: run real contract correctness for selected shapes.""" + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES) -> dict[str, Any]: + prior_use_cupti = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def _benchmark_payload(report: dict[str, Any], *, use_cupti: bool) -> dict[str, Any]: + timing_backends = sorted({result.get('timing_backend') for result in report.get('per_shape', {}).values() if result.get('timing_backend') is not None}) + return {'tflops': report['summary']['primary_mean'] or 0.0, 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'invalid_performance_reason': report['summary']['invalid_performance_reason'], 'measured_entrypoint': 'loom.examples.weave.knn_build_rag_online_stream_801d_v45:benchmark_knn_build_rag_online_stream_801d_v45', 'accelerated_shape_labels': list(TARGET_SHAPES), 'timing_backends': timing_backends, 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'target_rows': {label: report.get('per_shape', {}).get(label, {}) for label in TARGET_SHAPES}, 'contract_summary': report['summary'], 'contract_performance': report['performance'], 'report': report} + +def benchmark_knn_build_rag_online_stream_801d_v45(*, use_cupti: bool=False) -> dict[str, Any]: + """Targeted contract benchmark for the exact online and stream RAG rows.""" + report = _run_with_timing_backend(use_cupti=use_cupti) + return _benchmark_payload(report, use_cupti=use_cupti) diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_online_stream_split64_3d97_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_online_stream_split64_3d97_v1.py new file mode 100644 index 00000000..80270403 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_online_stream_split64_3d97_v1.py @@ -0,0 +1,99 @@ +"""Paired exact RAG online/stream K10 split-64 route for kNN build/search. + +Minimum target architecture: sm_100a. This additive shape-kernel candidate +routes exactly ``rag_online_b1_q1_m100000_d128_k10`` and +``rag_stream_b1_q128_m100000_d128_k10`` through the existing K10 tcgen05/TMA +stage-1 producer with a 64-way database split and a K10/S64 cached row-base +merge. Guard misses delegate to the c454 Weave dispatcher; no external runtime +fallback is introduced. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import os +from functools import lru_cache +from typing import Any +from .._dispatch_runtime import dc as dc +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_combined_k20rag_weave_evolve_knn_build_c454_v1 as c454_dispatcher +from . import knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2_k32split_v20 as parent_k32 +from . import knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_v1 as parent_lowk +from . import knn_build_evolve_7bfc_v1 as base_v1 +from . import knn_build_rag_online_stream_801d_v45 as baseline_pair +ONLINE_SHAPE = baseline_pair.ONLINE_SHAPE +STREAM_SHAPE = baseline_pair.STREAM_SHAPE +TARGET_SHAPES = baseline_pair.TARGET_SHAPES +SPLIT_COUNT = 64 +MERGE_THREADS = 32 + +def _ir_with_constants(ir_obj: Any, *, suffix: str, **updates: int) -> Any: + constants = tuple(((name, updates.get(name, value)) for name, value in ir_obj.constants)) + return dc.replace(ir_obj, symbol=''.join([format(ir_obj.symbol, ''), '_', format(suffix, '')]), constants=constants) +merge_k10_s64_cache_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_k10s64_3d97", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 64]], "cta_group": 1, "threads": 32}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RAG_SPLIT64_3D97_VERIFY_KERNEL') + if verify_kernel == 'merge_k10_s64_cache': + return merge_k10_s64_cache_ir + return parent_lowk.stage1_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_stage1_batch8_cond4_vmin_maxtree", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) + +def _compile_ir(ir_obj: Any): + from .._dispatch_runtime import generate_kernel + from .._dispatch_runtime import _cuda_include_dirs + from .._dispatch_runtime import compile_cuda + from .._dispatch_runtime import CUDAKernel + source = generate_kernel(ir_obj, validate=False, smem_bytes=ir_obj.computed_smem_bytes) + cubin = compile_cuda(source, arch=base_v1._select_arch_and_preload(), options=['--use_fast_math'], include_dirs=_cuda_include_dirs()) + return CUDAKernel(cubin, ''.join(['kernel_', format(ir_obj.symbol, '')])) + +def _compiled_merge_k10_s64_cache(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0134"}')) + +def _eligible_rag_online_stream_split64(inputs: dict[str, Any]) -> bool: + return baseline_pair._eligible_rag_online_stream(inputs) + +def _launch_rag_online_stream_split64(inputs: dict[str, Any]) -> None: + parent_lowk._launch_k10_cached_path(inputs, split_count=SPLIT_COUNT, merge_threads=MERGE_THREADS, merge_kernel=_compiled_merge_k10_s64_cache(), merge_ir=merge_k10_s64_cache_ir) + +def launch_from_contract_inputs(inputs: dict[str, Any]) -> None: + if _eligible_rag_online_stream_split64(inputs): + _launch_rag_online_stream_split64(inputs) + return + c454_dispatcher.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(candidate, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return c454_dispatcher._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint: run real contract correctness for selected shapes.""" + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES) -> dict[str, Any]: + prior_use_cupti = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def _benchmark_payload(report: dict[str, Any], *, use_cupti: bool) -> dict[str, Any]: + timing_backends = sorted({result.get('timing_backend') for result in report.get('per_shape', {}).values() if result.get('timing_backend') is not None}) + return {'tflops': report['summary']['primary_mean'] or 0.0, 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'invalid_performance_reason': report['summary']['invalid_performance_reason'], 'measured_entrypoint': 'loom.examples.weave.knn_build_rag_online_stream_split64_3d97_v1:benchmark_knn_build_rag_online_stream_split64_3d97_v1', 'accelerated_shape_labels': list(TARGET_SHAPES), 'producer_split_count': SPLIT_COUNT, 'timing_backends': timing_backends, 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'target_rows': {label: report.get('per_shape', {}).get(label, {}) for label in TARGET_SHAPES}, 'contract_summary': report['summary'], 'contract_performance': report['performance'], 'report': report} + +def benchmark_knn_build_rag_online_stream_split64_3d97_v1(*, use_cupti: bool=False) -> dict[str, Any]: + """Targeted contract benchmark for the exact online and stream RAG rows.""" + report = _run_with_timing_backend(use_cupti=use_cupti) + return _benchmark_payload(report, use_cupti=use_cupti) diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_online_stream_split72_4e09_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_online_stream_split72_4e09_v1.py new file mode 100644 index 00000000..28ba2f25 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_online_stream_split72_4e09_v1.py @@ -0,0 +1,99 @@ +"""Paired exact RAG online/stream K10 split-72 route for kNN build/search. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +routes exactly ``rag_online_b1_q1_m100000_d128_k10`` and +``rag_stream_b1_q128_m100000_d128_k10`` through the existing K10 tcgen05/TMA +stage-1 producer with a 72-way database split and a K10/S72 cached row-base +merge. Guard misses delegate to the current 0a10 Weave dispatcher; no external +runtime fallback is introduced. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import os +from functools import lru_cache +from typing import Any +from .._dispatch_runtime import dc as dc +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_3d97_08ec_0a10_v47 as current_dispatcher +from . import knn_build_rag_online_stream_split64_3d97_v1 as split64 +ONLINE_SHAPE = split64.ONLINE_SHAPE +STREAM_SHAPE = split64.STREAM_SHAPE +TARGET_SHAPES = split64.TARGET_SHAPES +SPLIT_COUNT = 72 +MERGE_THREADS = split64.MERGE_THREADS +parent_lowk = split64.parent_lowk +parent_k32 = split64.parent_k32 +base_v1 = split64.base_v1 + +def _ir_with_constants(ir_obj: Any, *, suffix: str, **updates: int) -> Any: + constants = tuple(((name, updates.get(name, value)) for name, value in ir_obj.constants)) + return dc.replace(ir_obj, symbol=''.join([format(ir_obj.symbol, ''), '_', format(suffix, '')]), constants=constants) +merge_k10_s72_cache_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_k10s72_4e09", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 72]], "cta_group": 1, "threads": 32}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RAG_SPLIT72_4E09_VERIFY_KERNEL') + if verify_kernel == 'merge_k10_s72_cache': + return merge_k10_s72_cache_ir + return parent_lowk.stage1_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_stage1_batch8_cond4_vmin_maxtree", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) + +def _compile_ir(ir_obj: Any): + from .._dispatch_runtime import generate_kernel + from .._dispatch_runtime import _cuda_include_dirs + from .._dispatch_runtime import compile_cuda + from .._dispatch_runtime import CUDAKernel + source = generate_kernel(ir_obj, validate=False, smem_bytes=ir_obj.computed_smem_bytes) + cubin = compile_cuda(source, arch=base_v1._select_arch_and_preload(), options=['--use_fast_math'], include_dirs=_cuda_include_dirs()) + return CUDAKernel(cubin, ''.join(['kernel_', format(ir_obj.symbol, '')])) + +def _compiled_merge_k10_s72_cache(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0116"}')) + +def _eligible_rag_online_stream_split72(inputs: dict[str, Any]) -> bool: + return split64._eligible_rag_online_stream_split64(inputs) + +def _launch_rag_online_stream_split72(inputs: dict[str, Any]) -> None: + parent_lowk._launch_k10_cached_path(inputs, split_count=SPLIT_COUNT, merge_threads=MERGE_THREADS, merge_kernel=_compiled_merge_k10_s72_cache(), merge_ir=merge_k10_s72_cache_ir) + +def launch_from_contract_inputs(inputs: dict[str, Any]) -> None: + if _eligible_rag_online_stream_split72(inputs): + _launch_rag_online_stream_split72(inputs) + return + current_dispatcher.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(candidate, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return current_dispatcher._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint: run real contract correctness for selected shapes.""" + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES) -> dict[str, Any]: + prior_use_cupti = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def _benchmark_payload(report: dict[str, Any], *, use_cupti: bool) -> dict[str, Any]: + timing_backends = sorted({result.get('timing_backend') for result in report.get('per_shape', {}).values() if result.get('timing_backend') is not None}) + return {'tflops': report['summary']['primary_mean'] or 0.0, 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'invalid_performance_reason': report['summary']['invalid_performance_reason'], 'measured_entrypoint': 'loom.examples.weave.knn_build_rag_online_stream_split72_4e09_v1:benchmark_knn_build_rag_online_stream_split72_4e09_v1', 'accelerated_shape_labels': list(TARGET_SHAPES), 'producer_split_count': SPLIT_COUNT, 'timing_backends': timing_backends, 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'target_rows': {label: report.get('per_shape', {}).get(label, {}) for label in TARGET_SHAPES}, 'contract_summary': report['summary'], 'contract_performance': report['performance'], 'report': report} + +def benchmark_knn_build_rag_online_stream_split72_4e09_v1(*, use_cupti: bool=False) -> dict[str, Any]: + """Targeted contract benchmark for the exact online and stream RAG rows.""" + report = _run_with_timing_backend(use_cupti=use_cupti) + return _benchmark_payload(report, use_cupti=use_cupti) diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_pair_exact_weave_evolve_knn_build_ee5e_v44.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_pair_exact_weave_evolve_knn_build_ee5e_v44.py new file mode 100644 index 00000000..76cc611a --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_pair_exact_weave_evolve_knn_build_ee5e_v44.py @@ -0,0 +1,83 @@ +"""Paired exact RAG K10 route for kNN build/search. + +Minimum target architecture: sm_100a. This additive shape-kernel candidate +routes only the two hard-gated exact RAG labels: +``rag_stream_b1_q128_m100000_d128_k10`` and +``rag_online_b1_q1_m100000_d128_k10``. The stream row keeps the validated +split-7 K10 tcgen05/TMA producer and cached sorted merge. The online Q=1 row +uses the same tcgen05/TMA producer with a split-14 generic merge, which gives +more producer parallelism for the single-query shape. Other contract shapes +delegate to the promoted v41 dispatcher wrapper. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import os +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatchscore_tailinf_knn_build_dispatch_slurm_0610_6329_v41 as v41 +from . import knn_build_ragonline_exact_7c8d_v42 as online_route +from . import knn_build_rag_stream_exact_weave_evolve_knn_build_6361_v42 as stream_route +STREAM_SHAPE = stream_route.TARGET_SHAPE_LABEL +ONLINE_SHAPE = online_route.ONLINE_SHAPE +TARGET_SHAPES = (ONLINE_SHAPE, STREAM_SHAPE) +ONLINE_SPLITS = 14 + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RAG_PAIR_EE5E_VERIFY_KERNEL') + if verify_kernel == 'stream_stage1_k10_s7': + return stream_route.parent_lowk.stage1_ir + if verify_kernel == 'stream_merge_k10_s7_cache': + return stream_route.parent_lowk.parent_cached.merge_k10_s7_cache_ir + if verify_kernel == 'online_stage1_k10_s14': + return online_route.v20.parent_lowk.stage1_ir + if verify_kernel == 'online_merge_generic_s14': + return online_route.v20.parent_lowk.generic_merge_ir + return online_route.v20.parent_lowk.stage1_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_stage1_batch8_cond4_vmin_maxtree", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) + +def _eligible_rag_pair_exact(inputs: dict[str, Any]) -> bool: + return stream_route._eligible_rag_stream_exact(inputs) or online_route._eligible_rag_online_exact(inputs) + +def _launch_rag_online_s14_generic(inputs: dict[str, Any]) -> None: + online_route.v20.parent_lowk._launch_cg2_split_path(inputs, split_count=ONLINE_SPLITS) + +def launch_from_contract_inputs(inputs: dict[str, Any]) -> None: + if stream_route._eligible_rag_stream_exact(inputs): + stream_route._launch_rag_stream_exact(inputs) + return + if online_route._eligible_rag_online_exact(inputs): + _launch_rag_online_s14_generic(inputs) + return + v41.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(candidate, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return v41._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _benchmark_payload(report: dict[str, Any], *, use_cupti: bool, shape_labels) -> dict[str, Any]: + timing_backends = sorted({result.get('timing_backend') for result in report.get('per_shape', {}).values() if result.get('timing_backend') is not None}) + return {'tflops': report['summary']['primary_mean'] or 0.0, 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'invalid_performance_reason': report['summary']['invalid_performance_reason'], 'timing_backends': timing_backends, 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'measured_module': __name__, 'measured_function': 'benchmark_knn_build_rag_pair_exact_ee5e_v44', 'shape_labels': list(shape_labels), 'target_rows': {label: report.get('per_shape', {}).get(label, {}) for label in shape_labels}, 'report': report} + +def benchmark_knn_build_rag_pair_exact_ee5e_v44(*, use_cupti: bool=False, shape_labels=TARGET_SHAPES) -> dict[str, Any]: + prior_use_cupti = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + try: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + return _benchmark_payload(report, use_cupti=use_cupti, shape_labels=shape_labels) diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_stream_exact_weave_evolve_knn_build_0e40_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_stream_exact_weave_evolve_knn_build_0e40_v1.py new file mode 100644 index 00000000..14c35609 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_stream_exact_weave_evolve_knn_build_0e40_v1.py @@ -0,0 +1,47 @@ +"""kNN build/search 0e40 exact RAG-stream replay route. + +Minimum target architecture: sm_100a. This additive candidate replays the +existing Weave split-7 K10 tcgen05/TMA producer plus cached sorted-stream merge +for exactly ``rag_stream_b1_q128_m100000_d128_k10``. Guard misses delegate to +the v41 Weave dispatcher, and the exact route directly writes contract-visible +distances and indices. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_rag_stream_exact_weave_evolve_knn_build_6361_v42 as replay +TARGET_SHAPE_LABEL = replay.TARGET_SHAPE_LABEL +BLOCK_Q = replay.BLOCK_Q +BLOCK_M = replay.BLOCK_M +FEAT_D = replay.FEAT_D +TOP_K_MAX = replay.TOP_K_MAX +RAG_SPLITS = replay.RAG_SPLITS +STAGE1_THREADS = replay.STAGE1_THREADS +RAG_MERGE_THREADS = replay.RAG_MERGE_THREADS +CTA_GROUP = replay.CTA_GROUP +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_stage1_batch8_cond4_vmin_maxtree", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) +launch_from_contract_inputs = replay.launch_from_contract_inputs +candidate = replay.candidate +evaluate_contract = replay.evaluate_contract +_select_contract_shapes = replay._select_contract_shapes + +def compile_and_launch_knn_build(*, shape_labels=(TARGET_SHAPE_LABEL,), benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint: run real contract correctness for selected shapes.""" + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def benchmark_rag_stream_exact_0e40_v1(*, use_cupti: bool=False) -> dict[str, Any]: + """Targeted contract benchmark hook for the exact RAG-stream row.""" + prior_use_cupti = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + try: + report = evaluate_contract(shapes=_select_contract_shapes((TARGET_SHAPE_LABEL,)), correctness=True, benchmark=True) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + row = report.get('per_shape', {}).get(TARGET_SHAPE_LABEL, {}) + return {'tflops': report['summary']['primary_mean'] or 0.0, 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'invalid_performance_reason': report['summary']['invalid_performance_reason'], 'timing_backend': row.get('timing_backend'), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'measured_entrypoint': 'loom.examples.weave.knn_build_rag_stream_exact_weave_evolve_knn_build_0e40_v1:benchmark_rag_stream_exact_0e40_v1', 'contract_summary': report['summary'], 'contract_performance': report['performance'], 'target_row': row, 'report': report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_stream_exact_weave_evolve_knn_build_6361_v42.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_stream_exact_weave_evolve_knn_build_6361_v42.py new file mode 100644 index 00000000..44002683 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_stream_exact_weave_evolve_knn_build_6361_v42.py @@ -0,0 +1,74 @@ +"""kNN build/search v42 exact RAG-stream route. + +Minimum target architecture: sm_100a. This additive candidate keeps the v41 +full-dispatch wrapper for all other shapes, but routes the exact contract row +``rag_stream_b1_q128_m100000_d128_k10`` through the existing Weave split-7 K10 +tcgen05/TMA producer and cached sorted-stream merge. The route directly writes +contract-visible distances and indices. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import os +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatchscore_tailinf_knn_build_dispatch_slurm_0610_6329_v41 as v41 +from . import knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_v1 as parent_lowk +TARGET_SHAPE_LABEL = 'rag_stream_b1_q128_m100000_d128_k10' +BLOCK_Q = parent_lowk.BLOCK_Q +BLOCK_M = parent_lowk.BLOCK_M +FEAT_D = parent_lowk.FEAT_D +TOP_K_MAX = parent_lowk.TOP_K_MAX +RAG_SPLITS = parent_lowk.RAG_SPLITS +STAGE1_THREADS = parent_lowk.STAGE1_THREADS +RAG_MERGE_THREADS = parent_lowk.parent_cached.RAG_MERGE_THREADS +CTA_GROUP = parent_lowk.CTA_GROUP + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RAG_STREAM_VERIFY_KERNEL') + if verify_kernel == 'merge_k10_s7_cache': + return parent_lowk.parent_cached.merge_k10_s7_cache_ir + return parent_lowk.stage1_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_stage1_batch8_cond4_vmin_maxtree", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) + +def _eligible_rag_stream_exact(inputs: dict[str, Any]) -> bool: + return not bool(inputs.get('build', False)) and str(inputs['query'].dtype) == 'torch.bfloat16' and (str(inputs['database'].dtype) == 'torch.bfloat16') and (int(inputs['B']) == 1) and (int(inputs['Q']) == 128) and (int(inputs['M']) == 100000) and (int(inputs['D']) == FEAT_D) and (int(inputs['K']) == TOP_K_MAX) + +def _launch_rag_stream_exact(inputs: dict[str, Any]) -> None: + parent_lowk._launch_k10_cached_path(inputs, split_count=RAG_SPLITS, merge_threads=RAG_MERGE_THREADS, merge_kernel=parent_lowk.parent_cached._compiled_merge_k10_s7_cache(), merge_ir=parent_lowk.parent_cached.merge_k10_s7_cache_ir) + +def launch_from_contract_inputs(inputs: dict[str, Any]) -> None: + if _eligible_rag_stream_exact(inputs): + _launch_rag_stream_exact(inputs) + return + v41.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(candidate, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return v41._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=(TARGET_SHAPE_LABEL,), benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint: run real contract correctness for selected shapes.""" + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def benchmark_rag_stream_exact_6361_v42(*, use_cupti: bool=False) -> dict[str, Any]: + """Targeted contract benchmark hook for the exact RAG-stream row.""" + prior_use_cupti = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + try: + report = evaluate_contract(shapes=_select_contract_shapes((TARGET_SHAPE_LABEL,)), correctness=True, benchmark=True) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + row = report.get('per_shape', {}).get(TARGET_SHAPE_LABEL, {}) + return {'tflops': report['summary']['primary_mean'] or 0.0, 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'invalid_performance_reason': report['summary']['invalid_performance_reason'], 'timing_backend': row.get('timing_backend'), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'contract_summary': report['summary'], 'contract_performance': report['performance'], 'target_row': row, 'report': report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_stream_k10_q128_1bed_rowld_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_stream_k10_q128_1bed_rowld_v1.py new file mode 100644 index 00000000..89a29a34 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_stream_k10_q128_1bed_rowld_v1.py @@ -0,0 +1,224 @@ +"""Exact RAG stream Q128/M100000/K10 row-load producer seed. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +targets only ``rag_stream_b1_q128_m100000_d128_k10``. It replaces the inherited +clustered 128x64 K10 producer with a ROW_16x256B row-load producer over two +64-row query tiles, then feeds the existing warp-row split-list merge. Guard +misses delegate to the current 34da Weave route; no external runtime fallback +is used. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from collections.abc import Callable +from functools import cache, lru_cache +from pathlib import Path +from typing import Any +from .._dispatch_runtime import dc as dc +from .. import _dispatch_runtime as eval_mod +from . import knn_build_d128_rag_q128_k10_df0f_warpmerge_v1 as df0f +from . import knn_build_rag_microbucket_q32rowld_e5db_v1 as rowld_seed +from . import knn_build_rag_stream_k10_warpmerge_34da_v1 as parent_34da +from .._dispatch_runtime import pack_kernel_args +MODULE = 'loom.examples.weave.knn_build_rag_stream_k10_q128_1bed_rowld_v1' +TARGET_SHAPE = 'rag_stream_b1_q128_m100000_d128_k10' +TARGET_SHAPES = (TARGET_SHAPE,) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +SUPPORTED_SPLIT_COUNTS = (72, 74) +DEFAULT_SPLIT_COUNT = _decode_capture(_json_loads('74')) +ROWS_PER_MERGE_CTA = 4 +MERGE_THREADS = 128 +BLOCK_Q = rowld_seed.Q8_M64_BLOCK_Q +BLOCK_M = rowld_seed.Q8_M64_BLOCK_M +FEAT_D = rowld_seed.Q8_M64_FEAT_D +TOP_K_MAX = 10 +STAGE1_THREADS = _decode_capture(_json_loads('192')) +LOCAL_LISTS_PER_ROW = rowld_seed.Q32_M64_LOCAL_LISTS_PER_ROW +SMEM_BASE_BYTES = rowld_seed.Q32_M64_SMEM_BASE_BYTES +LOCAL_ELEMS = BLOCK_Q * LOCAL_LISTS_PER_ROW * TOP_K_MAX +LOCAL_D_OFFSET = SMEM_BASE_BYTES +LOCAL_I_OFFSET = LOCAL_D_OFFSET + LOCAL_ELEMS * 4 +SMEM_POOL_BYTES = LOCAL_I_OFFSET + LOCAL_ELEMS * 4 +SEED_ID_PREFIX = 'rag_stream_k10_q128_rowld_1bed_v1' +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +BENCHMARK_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_knn_build_rag_stream_k10_q128_1bed_rowld_v1']) +BASELINE_ID = parent_34da.SEED_ID +BASELINE_ENTRYPOINT = parent_34da.BENCHMARK_ENTRYPOINT +parent_split = df0f.parent_split +base_v1 = df0f.base_v1 +_insert_sorted_pair_k10 = _ir_proxy('loom.examples.weave.knn_build_rag_stream_k10_q128_1bed_rowld_v1:_insert_sorted_pair_k10', 256) +knn_build_rag_stream_k10_q128_1bed_rowld_stage1 = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_stream_k10_q128_1bed_rowld_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 54528, "constants": [["BLOCK_Q", 64], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) +stage1_rowld_k10_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_stream_k10_q128_1bed_rowld_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 54528, "constants": [["BLOCK_Q", 64], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) + +def _ir_with_constants(ir_obj: Any, *, suffix: str, **updates: int) -> Any: + constants = tuple(((name, updates.get(name, value)) for name, value in ir_obj.constants)) + return dc.replace(ir_obj, symbol=''.join([format(ir_obj.symbol, ''), '_', format(suffix, '')]), constants=constants) + +def _seed_id(split_count: int) -> str: + return ''.join([format(SEED_ID_PREFIX, ''), '_s', format(split_count, '')]) + +def _route_name(split_count: int) -> str: + return ''.join([format(MODULE, ''), ':q128_m100000_k10_rowld_s', format(split_count, ''), '_warpmerge_r', format(ROWS_PER_MERGE_CTA, '')]) + +def _validate_split_count(split_count: int) -> int: + if split_count not in SUPPORTED_SPLIT_COUNTS: + raise ValueError(''.join(['unsupported split count for ', format(MODULE, ''), ': ', format(split_count, ''), '; expected one of ', format(SUPPORTED_SPLIT_COUNTS, '')])) + return split_count + +def _split_count() -> int: + return _validate_split_count(DEFAULT_SPLIT_COUNT) + +@cache +def _merge_ir(split_count: int) -> Any: + split_count = _validate_split_count(split_count) + return df0f._ir_with_constants(df0f.merge_k10_s74_warp_ir, suffix=''.join(['rowld_s', format(split_count, ''), '_1bed_v1']), SPLIT_COUNT=split_count) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RAG_STREAM_K10_Q128_1BED_ROWLD_VERIFY_KERNEL') + if verify_kernel == 'merge': + return _merge_ir(_split_count()) + return stage1_rowld_k10_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_stream_k10_q128_1bed_rowld_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 54528, "constants": [["BLOCK_Q", 64], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) + +def _compiled_stage1(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0084"}')) + +@cache +def _compiled_merge(split_count: int): + return df0f._compile_ir(_merge_ir(split_count)) + +def _select_contract_shapes(shape_labels) -> list[dict[str, Any]]: + return parent_34da._select_contract_shapes(shape_labels) + +def _dtype_name(inputs: dict[str, Any]) -> str: + return parent_34da._dtype_name(inputs) + +def _eligible_q128_m100000_k10(inputs: dict[str, Any]) -> bool: + return parent_34da._eligible_q128_m100000_k10(inputs) + +def _launch_q128_m100000_k10_rowld(inputs: dict[str, Any], *, split_count: int) -> None: + split_count = _validate_split_count(split_count) + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tiles * split_count + total_queries = bsz * n_query + stage1_grid = min(total_work, rowld_seed.compact_seed.GRID_DIM_DEFAULT) + merge_grid = min((total_queries + ROWS_PER_MERGE_CTA - 1) // ROWS_PER_MERGE_CTA, rowld_seed.compact_seed.GRID_DIM_DEFAULT) + partial_dists, partial_indices = parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), total_queries, BLOCK_Q, dim, dim) + tmap_database = base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, dim, dim) + _compiled_stage1().launch(grid=(stage1_grid, 1, 1), block=(STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage1_rowld_k10_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tiles=num_q_tiles, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), shared_mem=stage1_rowld_k10_ir.computed_smem_bytes) + merge_ir = _merge_ir(split_count) + _compiled_merge(split_count).launch(grid=(merge_grid, 1, 1), block=(MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], total_queries], shared_mem=merge_ir.computed_smem_bytes) + +def route_for_contract_inputs(inputs: dict[str, Any], *, split_count: int | None=None, force_fallback: bool=False) -> str: + split_count = _split_count() if split_count is None else _validate_split_count(split_count) + if not force_fallback and _eligible_q128_m100000_k10(inputs): + return _route_name(split_count) + return parent_34da.route_for_contract_inputs(inputs, force_fallback=force_fallback) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, split_count: int | None=None, force_fallback: bool=False) -> None: + split_count = _split_count() if split_count is None else _validate_split_count(split_count) + if not force_fallback and _eligible_q128_m100000_k10(inputs): + _launch_q128_m100000_k10_rowld(inputs, split_count=split_count) + return + parent_34da.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_baseline_34da(inputs: dict[str, Any]) -> None: + parent_34da.launch_from_contract_inputs(inputs) + +def _candidate_for_split(split_count: int) -> Callable[[dict[str, Any]], None]: + split_count = _validate_split_count(split_count) + + def _candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, split_count=split_count) + return _candidate + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + selected = _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate, correctness: bool=True, benchmark: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + shapes = [] + for shape in _select_contract_shapes(shape_labels): + params = dict(shape['params']) + params['time_flashlib'] = bool(time_flashlib) + shapes.append({'label': shape['label'], 'params': params}) + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + try: + return evaluate_contract(shapes=shapes, correctness=correctness, benchmark=benchmark, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = previous + +def _trace_inputs_for_shape(shape: dict[str, Any]) -> dict[str, Any]: + return parent_34da._trace_inputs_for_shape(shape) + +def route_trace_for_contract_shapes(shape_labels=TARGET_SHAPES, *, split_count: int | None=None, force_fallback: bool=False) -> list[dict[str, Any]]: + split_count = _split_count() if split_count is None else _validate_split_count(split_count) + rows = [] + for shape in _select_contract_shapes(shape_labels): + label = str(shape['label']) + inputs = _trace_inputs_for_shape(shape) + selected = not force_fallback and _eligible_q128_m100000_k10(inputs) + parent_route = parent_34da.route_for_contract_inputs(inputs) + rows.append(parent_34da.parent_v11._normalize_route_row({'shape_key': label, 'selected_route': _route_name(split_count) if selected else parent_34da.route_for_contract_inputs(inputs, force_fallback=force_fallback), 'selected_entrypoint': ROUTE_ENTRYPOINT if selected else parent_34da.ROUTE_PARENT, 'selected_seed': _seed_id(split_count) if selected else None, 'expected_seed': _seed_id(split_count) if _eligible_q128_m100000_k10(inputs) else None, 'route_kind': 'specialized' if selected else 'parent-dispatcher', 'route_source': 'shape-specific-seed' if selected else 'broad-dispatcher', 'guard_id': ''.join(['1bed_rowld_rag_stream_k10_q128_s', format(split_count, ''), '_exact_guard']) if selected else 'forced_fallback_1bed_rowld_rag_stream_k10_q128_disabled' if force_fallback and _eligible_q128_m100000_k10(inputs) else 'parent_34da_guard', 'guard_condition': 'BF16 non-build B=1 Q=128 M=100000 D=128 K=10' if selected else 'forced fallback to 34da parent' if force_fallback and _eligible_q128_m100000_k10(inputs) else 'delegate to 34da parent', 'classification': 'unmeasured' if selected else 'guard-miss', 'parent_34da_route': parent_route, 'split_count': split_count if selected else None, 'rows_per_merge_cta': ROWS_PER_MERGE_CTA if selected else None, 'producer_topology': 'ROW_16x256B row-load M64/N64', 'merge_topology': 'warp-row split-list' if selected else None})) + return rows + +def _rows_for_labels(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return {label: dict(report.get('per_shape', {}).get(label, {})) for label in labels} + +def _per_shape_delta(candidate_report: dict[str, Any], baseline_report: dict[str, Any], labels: tuple[str, ...]): + rows = {} + for label in labels: + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') + rows[label] = {'candidate_ms': candidate_ms, 'baseline_34da_ms': baseline_ms, 'flashlib_ms': flashlib_ms, 'speedup_vs_34da': baseline_ms / candidate_ms if baseline_ms and candidate_ms else None, 'ratio_vs_flashlib': flashlib_ms / candidate_ms if flashlib_ms and candidate_ms else None, 'candidate_passed': candidate_row.get('passed'), 'baseline_34da_passed': baseline_row.get('passed'), 'timing_backend': candidate_row.get('timing_backend') or baseline_row.get('timing_backend')} + return rows + +def benchmark_baseline_34da(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES, benchmark_correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_baseline_34da, correctness=benchmark_correctness, benchmark=True, time_flashlib=time_flashlib) + report['candidate_id'] = BASELINE_ID + report['measured_entrypoint'] = BASELINE_ENTRYPOINT + return report + +def benchmark_knn_build_rag_stream_k10_q128_1bed_rowld_v1(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES, split_count: int | None=None, baseline_report: dict[str, Any] | None=None, benchmark_correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + split_count = _split_count() if split_count is None else _validate_split_count(split_count) + labels = tuple((str(label) for label in shape_labels)) + if baseline_report is None: + baseline_report = benchmark_baseline_34da(use_cupti=use_cupti, shape_labels=labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=labels, kernel_fn=_candidate_for_split(split_count), correctness=benchmark_correctness, benchmark=True, time_flashlib=time_flashlib) + candidate_metric = candidate_report['summary']['primary_mean'] + baseline_metric = baseline_report['summary']['primary_mean'] + return {'candidate_id': _seed_id(split_count), 'baseline_candidate_id': BASELINE_ID, 'selected_seeds': (_seed_id(split_count),), 'producer_split_count': split_count, 'producer_topology': 'ROW_16x256B row-load M64/N64 stage1 over two 64-row query tiles', 'merge_owner': 'one_warp_per_query_row_up_to_three_splits_per_lane', 'all_correct': candidate_report['summary']['all_correct'], 'baseline_all_correct': baseline_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'baseline_performance_comparable': baseline_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'baseline_invalid_performance_reason': baseline_report['summary']['invalid_performance_reason'], 'tflops': candidate_metric, 'baseline_tflops': baseline_metric, 'metric_delta': candidate_metric - baseline_metric if candidate_metric and baseline_metric else None, 'measured_entrypoint': BENCHMARK_ENTRYPOINT, 'candidate_entrypoint': ROUTE_ENTRYPOINT, 'baseline_entrypoint': BASELINE_ENTRYPOINT, 'measured_shape_labels': labels, 'timing_backend': 'cupti' if use_cupti else 'cuda_event', 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'benchmark_correctness_checked': benchmark_correctness, 'benchmark_time_flashlib': time_flashlib, 'rank_objective': {'metric': 'tflops', 'direction': 'maximize', 'value': candidate_metric, 'baseline_value': baseline_metric, 'delta': candidate_metric - baseline_metric if candidate_metric and baseline_metric else None, 'denominator': TARGET_SHAPE}, 'summary': candidate_report['summary'], 'performance': candidate_report['performance'], 'correctness': candidate_report['correctness'], 'selected_route_rows': _rows_for_labels(candidate_report, labels), 'baseline_selected_route_rows': _rows_for_labels(baseline_report, labels), 'target_rows': _per_shape_delta(candidate_report, baseline_report, labels), 'route_trace': route_trace_for_contract_shapes(labels, split_count=split_count), 'forced_fallback_route_trace': route_trace_for_contract_shapes(labels, split_count=split_count, force_fallback=True), 'report': candidate_report, 'baseline_report': baseline_report} + +def _write_artifact(payload: dict[str, Any], artifact_dir: Path) -> Path: + artifact_dir.mkdir(parents=True, exist_ok=True) + split_count = int(payload['producer_split_count']) + path = artifact_dir / ''.join(['rag_stream_k10_q128_s', format(split_count, ''), '_rowld_1bed_v1.json']) + path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n', encoding='utf-8') + return path diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_stream_k10_warpmerge_34da_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_stream_k10_warpmerge_34da_v1.py new file mode 100644 index 00000000..097edd49 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_stream_k10_warpmerge_34da_v1.py @@ -0,0 +1,170 @@ +"""RAG stream K10 split72 warp-row merge probe. + +Minimum target architecture: sm_100a. This additive exact-shape candidate +targets only ``rag_stream_b1_q128_m100000_d128_k10``. It keeps the existing +split72 tcgen05/TMA producer, but changes the final K10 merge from one scalar +thread owning all split lists for a query row to one warp owning a row and +lane-partitioning the split-local lists. Guard misses delegate to the current +v11 common-D seed portfolio. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from collections.abc import Callable +from functools import lru_cache +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_v11_common_d_seed_portfolio_a4ec_v1 as parent_v11 +from . import knn_build_rag_online_stream_split72_4e09_v1 as split72 +from .._dispatch_runtime import pack_kernel_args +MODULE = 'loom.examples.weave.knn_build_rag_stream_k10_warpmerge_34da_v1' +TARGET_SHAPE = 'rag_stream_b1_q128_m100000_d128_k10' +TARGET_SHAPES = (TARGET_SHAPE,) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +SPLIT_COUNT = 72 +TOP_K_MAX = split72.parent_lowk.TOP_K_MAX +K10_WARP_MERGE_THREADS = 128 +K10_WARP_MERGE_WARPS = K10_WARP_MERGE_THREADS // 32 +ROWS_PER_CTA = 4 +SPLITS_PER_LANE = (SPLIT_COUNT + 31) // 32 +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_PARENT = parent_v11.ROUTE_ENTRYPOINT +ROUTE_K10_WARPMERGE = ''.join([format(MODULE, ''), ':q128_m100000_k10_s72_warpmerge_r', format(ROWS_PER_CTA, '')]) +BENCHMARK_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_knn_build_rag_stream_k10_warpmerge_34da_v1']) +SEED_ID = 'rag_stream_k10_warpmerge_34da_v1_s72_r4' +knn_build_rag_stream_k10_s72_warp_row_merge_34da = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_stream_k10_s72_warp_row_merge_34da", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K", 10], ["SPLITS", 72], ["LANESLOTS", 3], ["ROWS", 4]], "cta_group": 1, "threads": 128}')) +merge_k10_s72_warp_row_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_stream_k10_s72_warp_row_merge_34da", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K", 10], ["SPLITS", 72], ["LANESLOTS", 3], ["ROWS", 4]], "cta_group": 1, "threads": 128}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RAG_STREAM_K10_WARPMERGE_34DA_VERIFY_KERNEL') + if verify_kernel == 'merge': + return merge_k10_s72_warp_row_ir + return split72.parent_lowk.stage1_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_stage1_batch8_cond4_vmin_maxtree", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) + +def _compiled_merge_k10_s72_warp_row(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0133"}')) + +def _dtype_name(inputs: dict[str, Any]) -> str: + query = inputs.get('query') + if query is not None: + return str(query.dtype).removeprefix('torch.') + return str(inputs.get('dtype', '')).removeprefix('torch.') + +def _eligible_q128_m100000_k10(inputs: dict[str, Any]) -> bool: + return not bool(inputs.get('build', False)) and _dtype_name(inputs) == 'bfloat16' and (int(inputs.get('B', -1)) == 1) and (int(inputs.get('Q', -1)) == 128) and (int(inputs.get('M', -1)) == 100000) and (int(inputs.get('D', -1)) == split72.parent_lowk.FEAT_D) and (int(inputs.get('K', -1)) == TOP_K_MAX) + +def _launch_q128_m100000_k10_warpmerge(inputs: dict[str, Any]) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + num_q_tiles = (n_query + split72.parent_lowk.BLOCK_Q - 1) // split72.parent_lowk.BLOCK_Q + num_q_tile_pairs = (num_q_tiles + split72.parent_lowk.CTA_GROUP - 1) // split72.parent_lowk.CTA_GROUP + num_db_tiles = (n_database + split72.parent_lowk.BLOCK_M - 1) // split72.parent_lowk.BLOCK_M + db_tiles_per_split = (num_db_tiles + SPLIT_COUNT - 1) // SPLIT_COUNT + total_work = bsz * num_q_tile_pairs * SPLIT_COUNT + stage1_grid = min(total_work * split72.parent_lowk.CTA_GROUP, split72.parent_lowk.GRID_DIM_DEFAULT) + total_queries = bsz * n_query + merge_grid = min((total_queries + ROWS_PER_CTA - 1) // ROWS_PER_CTA, split72.parent_lowk.GRID_DIM_DEFAULT) + partial_dists, partial_indices = split72.parent_lowk.parent_split._partial_buffers(split_count=SPLIT_COUNT, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = split72.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), bsz * n_query, split72.parent_lowk.BLOCK_Q, dim, dim) + tmap_database = split72.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, split72.parent_lowk.BLOCK_M, dim, dim) + stage1_kernel = split72.parent_lowk._compiled_stage1() + stage1_kernel.launch_cluster(grid=(stage1_grid, 1, 1), block=(split72.parent_lowk.STAGE1_THREADS, 1, 1), args=pack_kernel_args(split72.parent_lowk.stage1_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tile_pairs=num_q_tile_pairs, db_tiles_per_split=db_tiles_per_split, split_count=SPLIT_COUNT, total_work=total_work), cluster_dims=(split72.parent_lowk.CTA_GROUP, 1, 1), shared_mem=split72.parent_lowk.stage1_ir.computed_smem_bytes) + merge_kernel = _compiled_merge_k10_s72_warp_row() + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(K10_WARP_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], total_queries], shared_mem=merge_k10_s72_warp_row_ir.computed_smem_bytes) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if not force_fallback and _eligible_q128_m100000_k10(inputs): + return ROUTE_K10_WARPMERGE + return parent_v11.route_for_contract_inputs(inputs, force_fallback=force_fallback) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + if not force_fallback and _eligible_q128_m100000_k10(inputs): + _launch_q128_m100000_k10_warpmerge(inputs) + return + parent_v11.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_parent_v11(inputs: dict[str, Any]) -> None: + parent_v11.launch_from_contract_inputs(inputs) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels) -> list[dict[str, Any]]: + return parent_v11._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + previous = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = previous + +def _trace_inputs_for_shape(shape: dict[str, Any]) -> dict[str, Any]: + return parent_v11._trace_inputs_for_shape(shape) + +def route_trace_for_contract_shapes(shape_labels=TARGET_SHAPES, *, force_fallback: bool=False) -> list[dict[str, Any]]: + rows = [] + for shape in _select_contract_shapes(shape_labels): + label = str(shape['label']) + inputs = _trace_inputs_for_shape(shape) + selected = not force_fallback and _eligible_q128_m100000_k10(inputs) + parent_route = parent_v11.route_for_contract_inputs(inputs) + rows.append(parent_v11._normalize_route_row({'shape_key': label, 'selected_route': route_for_contract_inputs(inputs, force_fallback=force_fallback), 'selected_entrypoint': ROUTE_ENTRYPOINT if selected else ROUTE_PARENT, 'selected_seed': SEED_ID if selected else None, 'expected_seed': SEED_ID if _eligible_q128_m100000_k10(inputs) else None, 'route_kind': 'specialized' if selected else 'parent-dispatcher', 'route_source': 'shape-specific-seed' if selected else 'broad-dispatcher', 'guard_id': '34da_rag_stream_k10_s72_warpmerge_exact_guard' if selected else 'forced_fallback_34da_rag_stream_k10_warpmerge_disabled' if force_fallback and _eligible_q128_m100000_k10(inputs) else 'parent_v11_guard', 'guard_condition': 'BF16 non-build B=1 Q=128 M=100000 D=128 K=10' if selected else 'forced fallback to current v11 dispatcher' if force_fallback and _eligible_q128_m100000_k10(inputs) else 'delegate to current v11 dispatcher', 'classification': 'unmeasured' if selected else 'guard-miss', 'parent_v11_route': parent_route, 'split_count': SPLIT_COUNT if selected else None, 'rows_per_merge_cta': ROWS_PER_CTA if selected else None, 'merge_topology': 'warp-row split-list' if selected else None})) + return rows + +def _rows_for_labels(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return {label: dict(report.get('per_shape', {}).get(label, {})) for label in labels} + +def _per_shape_delta(candidate_report: dict[str, Any], parent_report: dict[str, Any], labels: tuple[str, ...]): + rows = {} + for label in labels: + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + parent_row = parent_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + parent_ms = parent_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') + rows[label] = {'candidate_ms': candidate_ms, 'parent_v11_ms': parent_ms, 'flashlib_ms': flashlib_ms, 'speedup_vs_parent_v11': parent_ms / candidate_ms if parent_ms and candidate_ms else None, 'ratio_vs_flashlib': flashlib_ms / candidate_ms if flashlib_ms and candidate_ms else None, 'candidate_passed': candidate_row.get('passed'), 'parent_v11_passed': parent_row.get('passed'), 'timing_backend': candidate_row.get('timing_backend') or parent_row.get('timing_backend')} + return rows + +def benchmark_knn_build_rag_stream_k10_warpmerge_34da_v1(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES, run_parent: bool=True) -> dict[str, Any]: + labels = tuple((str(label) for label in shape_labels)) + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=labels, kernel_fn=candidate) + parent_report = None + if run_parent: + parent_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=labels, kernel_fn=candidate_parent_v11) + route_trace = route_trace_for_contract_shapes(labels) + payload: dict[str, Any] = {'candidate_id': SEED_ID, 'contract': candidate_report['contract'], 'contract_version': candidate_report['contract_version'], 'all_correct': candidate_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'tflops': candidate_report['summary']['primary_mean'], 'measured_entrypoint': BENCHMARK_ENTRYPOINT, 'candidate_entrypoint': ROUTE_ENTRYPOINT, 'parent_entrypoint': ROUTE_PARENT, 'measured_shape_labels': list(labels), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'producer_topology': 'existing split72 tcgen05/TMA K10 stage1', 'merge_topology': {'kind': 'warp-row split-list merge', 'split_count': SPLIT_COUNT, 'splits_per_lane': SPLITS_PER_LANE, 'rows_per_cta': ROWS_PER_CTA}, 'route_trace': route_trace, 'forced_fallback_route_trace': route_trace_for_contract_shapes(labels, force_fallback=True), 'selected_route_rows': _rows_for_labels(candidate_report, labels), 'summary': candidate_report['summary'], 'performance': candidate_report['performance'], 'correctness': candidate_report['correctness'], 'report': candidate_report} + if parent_report is not None: + payload['parent_summary'] = parent_report['summary'] + payload['parent_performance'] = parent_report['performance'] + payload['parent_selected_route_rows'] = _rows_for_labels(parent_report, labels) + payload['target_rows'] = _per_shape_delta(candidate_report, parent_report, labels) + parent_mean = parent_report['summary']['primary_mean'] + payload['parent_tflops'] = parent_mean + payload['metric_delta_vs_parent_v11'] = candidate_report['summary']['primary_mean'] - parent_mean if candidate_report['summary']['primary_mean'] is not None and parent_mean is not None else None + payload['parent_report'] = parent_report + return payload diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_stream_k32_q128_dualm_a162_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_stream_k32_q128_dualm_a162_v1.py new file mode 100644 index 00000000..448bcd12 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_stream_k32_q128_dualm_a162_v1.py @@ -0,0 +1,157 @@ +"""RAG stream K32 Q128 dual-M rowld/warp-merge bucket for a162. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +targets the exact v11 BF16 non-build rows +``rag_stream_largek_b1_q128_m100000_d128_k32`` and +``rag_stream_largek_b1_q128_m131071_d128_k32``. It reuses the primitive-backed +ROW_16x256B tcgen05/TMA rowld producer and switches both rows to the one-row +warp split-list merge with split72. Guard misses delegate to the current v11 +common-D seed portfolio, keeping the production path Weave-only. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from pathlib import Path +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_v11_common_d_seed_portfolio_a4ec_v1 as parent_v11 +from . import knn_build_rag_microbucket_k32warpmerge_0077_v1 as rowld_warp +MODULE = 'loom.examples.weave.knn_build_rag_stream_k32_q128_dualm_a162_v1' +TARGET_M100000 = 'rag_stream_largek_b1_q128_m100000_d128_k32' +TARGET_M131071 = 'rag_stream_largek_b1_q128_m131071_d128_k32' +TARGET_SHAPES = (TARGET_M100000, TARGET_M131071) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +K32_Q128_SPLIT_COUNT = _decode_capture(_json_loads('72')) +K32_TOP_K_MAX = rowld_warp.K32_TOP_K_MAX +K32_MERGE_ROWS_PER_CTA = rowld_warp.K32_WARP_MERGE_ROWS_PER_CTA +SEED_ID = 'rag_stream_k32_q128_dualm_a162_v1_rowld_s72_warp1' +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_PARENT_V11 = parent_v11.ROUTE_ENTRYPOINT +BENCHMARK_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_knn_build_rag_stream_k32_q128_dualm_a162_v1']) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RAG_STREAM_K32_Q128_DUALM_A162_V1_VERIFY_KERNEL') + split_count = int(os.environ.get('LOOM_KNN_RAG_STREAM_K32_Q128_DUALM_A162_V1_VERIFY_SPLIT', K32_Q128_SPLIT_COUNT)) + if verify_kernel == 'merge': + return rowld_warp._warp_merge_ir(split_count) + return rowld_warp.rowld_seed.stage1_q32_k32_m64_rowld_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_q32rowld_e5db_v1_stage1_q32_k32_m64", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 99584, "constants": [["BLOCK_Q", 64], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 192}')) + +def _dtype_name(inputs: dict[str, Any]) -> str: + query = inputs.get('query') + if query is not None and hasattr(query, 'dtype'): + return str(query.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')).replace('torch.', '') + +def _eligible_q128_dualm(inputs: dict[str, Any]) -> bool: + return _dtype_name(inputs) == 'bfloat16' and (not bool(inputs.get('build', False))) and (int(inputs.get('B', -1)) == 1) and (int(inputs.get('Q', -1)) == 128) and (int(inputs.get('M', -1)) in (100000, 131071)) and (int(inputs.get('D', -1)) == 128) and (int(inputs.get('K', -1)) == K32_TOP_K_MAX) + +def _route_name(inputs: dict[str, Any], *, split_count: int) -> str: + return ''.join(['rag_stream_k32_q128_dualm_a162_v1_q128_m', format(int(inputs.get('M', -1)), ''), '_k32_row16x256b_s', format(split_count, ''), '_r', format(K32_MERGE_ROWS_PER_CTA, ''), '_warpmerge']) + +def route_for_contract_inputs(inputs: dict[str, Any], *, k32_q128_split_count: int=K32_Q128_SPLIT_COUNT, force_fallback: bool=False) -> str: + if not force_fallback and _eligible_q128_dualm(inputs): + return _route_name(inputs, split_count=k32_q128_split_count) + return parent_v11.route_for_contract_inputs(inputs, force_fallback=force_fallback) + +def _launch_q128_dualm(inputs: dict[str, Any], *, split_count: int) -> None: + rowld_warp._launch_rowld_warpmerge(inputs, split_count=split_count) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, k32_q128_split_count: int=K32_Q128_SPLIT_COUNT, force_fallback: bool=False) -> None: + if not force_fallback and _eligible_q128_dualm(inputs): + _launch_q128_dualm(inputs, split_count=k32_q128_split_count) + return + parent_v11.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_with_q128_split(split_count: int) -> Callable[[dict[str, Any]], None]: + + def _candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, k32_q128_split_count=split_count) + return _candidate + +def candidate_parent_v11(inputs: dict[str, Any]) -> None: + parent_v11.launch_from_contract_inputs(inputs) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels) -> list[dict[str, Any]]: + wanted = set((str(label) for label in shape_labels)) + selected = [shape for shape in eval_mod.CANONICAL_SHAPES if str(shape['label']) in wanted] + if len(selected) != len(wanted): + found = {str(shape['label']) for shape in selected} + raise KeyError(''.join(['unknown knn_build contract shape labels: ', format(sorted(wanted - found), '')])) + return selected + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + previous = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=correctness, benchmark=benchmark, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = previous + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _trace_inputs_for_shape(shape: dict[str, Any]) -> dict[str, Any]: + return parent_v11._trace_inputs_for_shape(shape) + +def route_trace_for_contract_shapes(shape_labels=TARGET_SHAPES, *, k32_q128_split_count: int=K32_Q128_SPLIT_COUNT, force_fallback: bool=False) -> list[dict[str, Any]]: + rows: list[dict[str, Any]] = [] + for shape in _select_contract_shapes(shape_labels): + label = str(shape['label']) + inputs = _trace_inputs_for_shape(shape) + selected = not force_fallback and _eligible_q128_dualm(inputs) + parent_route = parent_v11.route_for_contract_inputs(inputs) + rows.append(parent_v11._normalize_route_row({'shape_key': label, 'selected_route': route_for_contract_inputs(inputs, k32_q128_split_count=k32_q128_split_count, force_fallback=force_fallback), 'selected_entrypoint': ROUTE_ENTRYPOINT if selected else ROUTE_PARENT_V11, 'selected_seed': SEED_ID if selected else None, 'expected_seed': SEED_ID if _eligible_q128_dualm(inputs) else None, 'route_kind': 'specialized' if selected else 'inherited_v11_parent', 'route_source': 'shape-specific-seed' if selected else 'parent-dispatcher', 'guard_id': 'a162_q128_dualm_k32_rowld_s72_warp1_exact_guard' if selected else 'forced_fallback_a162_q128_dualm_disabled' if force_fallback and _eligible_q128_dualm(inputs) else 'parent_v11_guard', 'guard_condition': 'BF16 non-build B=1 Q=128 M in {100000,131071} D=128 K=32' if selected else 'forced fallback to current v11 dispatcher' if force_fallback and _eligible_q128_dualm(inputs) else 'delegate to current v11 dispatcher', 'classification': 'seed-consumed' if selected else 'guard-miss', 'split_count': k32_q128_split_count if selected else None, 'rows_per_merge_cta': K32_MERGE_ROWS_PER_CTA if selected else None, 'parent_v11_route': parent_route})) + return rows + +def _rows_for_labels(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return {label: report.get('per_shape', {}).get(label, {}) for label in labels} + +def _per_shape_delta(candidate_report: dict[str, Any], parent_report: dict[str, Any], labels: tuple[str, ...]): + rows: dict[str, Any] = {} + for label in labels: + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + parent_row = parent_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + parent_ms = parent_row.get('kernel_ms') + rows[label] = {'candidate_ms': candidate_ms, 'parent_v11_ms': parent_ms, 'flashlib_ms': candidate_row.get('flashlib_ms'), 'candidate_tflops': candidate_row.get('tflops'), 'parent_v11_tflops': parent_row.get('tflops'), 'speedup_vs_parent_v11': parent_ms / candidate_ms if candidate_ms and parent_ms else None, 'ratio_vs_flashlib': candidate_row.get('ratio_vs_flashlib'), 'passed': candidate_row.get('passed'), 'timing_backend': candidate_row.get('timing_backend') or parent_row.get('timing_backend')} + return rows + +def benchmark_knn_build_rag_stream_k32_q128_dualm_a162_v1(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES, run_parent: bool=True, k32_q128_split_count: int=K32_Q128_SPLIT_COUNT) -> dict[str, Any]: + labels = tuple((str(label) for label in shape_labels)) + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=labels, kernel_fn=candidate_with_q128_split(k32_q128_split_count)) + parent_report = None + if run_parent: + parent_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=labels, kernel_fn=candidate_parent_v11) + payload: dict[str, Any] = {'candidate_id': SEED_ID, 'contract': candidate_report['contract'], 'contract_version': candidate_report['contract_version'], 'tflops': candidate_report['summary']['primary_mean'] or 0.0, 'all_correct': candidate_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'measured_entrypoint': BENCHMARK_ENTRYPOINT, 'candidate_entrypoint': ROUTE_ENTRYPOINT, 'measured_shape_labels': list(labels), 'accelerated_shape_labels': list(TARGET_SHAPES), 'route_trace': route_trace_for_contract_shapes(labels, k32_q128_split_count=k32_q128_split_count), 'forced_fallback_route_trace': route_trace_for_contract_shapes(labels, k32_q128_split_count=k32_q128_split_count, force_fallback=True), 'producer_topology': 'ROW_16x256B rowld tcgen05/TMA stage1 over two 64-row query tiles', 'merge_topology': {'kind': 'one-row warp split-list merge', 'split_count': k32_q128_split_count, 'splits_per_lane': rowld_warp._splits_per_lane(k32_q128_split_count), 'rows_per_cta': K32_MERGE_ROWS_PER_CTA}, 'summary': candidate_report['summary'], 'performance': candidate_report['performance'], 'correctness': candidate_report['correctness'], 'rank_objective': {'metric': 'tflops', 'direction': 'maximize', 'value': candidate_report['summary']['primary_mean'], 'valid_measurement_count': candidate_report['performance']['valid_measurement_count'], 'comparable': candidate_report['performance']['comparable']}, 'report': candidate_report, 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti} + if parent_report is not None: + payload['parent_entrypoint'] = ROUTE_PARENT_V11 + payload['parent_summary'] = parent_report['summary'] + payload['parent_performance'] = parent_report['performance'] + payload['parent_rows'] = _rows_for_labels(parent_report, labels) + payload['per_shape_delta_vs_parent_v11'] = _per_shape_delta(candidate_report, parent_report, labels) + return payload + +def write_benchmark_artifact(path: str | os.PathLike[str], **kwargs) -> dict[str, Any]: + payload = benchmark_knn_build_rag_stream_k32_q128_dualm_a162_v1(**kwargs) + out = Path(path) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + return payload diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_stream_k32_q128_s72r2_a162_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_stream_k32_q128_s72r2_a162_v1.py new file mode 100644 index 00000000..87068d5c --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_stream_k32_q128_s72r2_a162_v1.py @@ -0,0 +1,192 @@ +"""RAG stream K32 Q128 split72/rows2 exact bucket wrapper. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +routes only the active v11 BF16 non-build stream rows: + +* B=1,Q=128,M=100000,D=128,K=32 +* B=1,Q=128,M=131071,D=128,K=32 + +Both rows use the inherited rowld tcgen05/TMA producer with split72 and a +warp-row merge that owns two query rows per CTA. Guard misses delegate to the +current v11 common-D seed portfolio, so production routes remain Weave-only. +FlashLib is used only by the contract harness as a black-box timing baseline. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from collections.abc import Callable +from functools import cache, lru_cache +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_v11_common_d_seed_portfolio_a4ec_v1 as parent_v11 +from . import knn_build_rag_microbucket_k32warpmerge_0077_v1 as merge_base +from . import knn_build_rag_stream_k32_q128rowld_60fb_v1 as rowld_seed +from .._dispatch_runtime import pack_kernel_args +MODULE = 'loom.examples.weave.knn_build_rag_stream_k32_q128_s72r2_a162_v1' +SEED_ID = 'a162_rag_stream_q128_k32_s72r2_v1' +PARENT_ID = parent_v11.CANDIDATE_D64_Q4096_C271 +TARGET_M100000 = 'rag_stream_largek_b1_q128_m100000_d128_k32' +TARGET_M131071 = 'rag_stream_largek_b1_q128_m131071_d128_k32' +TARGET_SHAPES = (TARGET_M100000, TARGET_M131071) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +SPLIT_COUNT = _decode_capture(_json_loads('72')) +ROWS_PER_CTA = _decode_capture(_json_loads('2')) +TOP_K_MAX = 32 +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_PARENT = parent_v11.ROUTE_ENTRYPOINT +BENCHMARK_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_knn_build_rag_stream_k32_q128_s72r2_a162_v1']) +SOURCE_TASKS = {SEED_ID: 'weave-evolve knn_build a162 RAG stream K32 split72/rows2 bucket', PARENT_ID: 'current v11 common-D seed portfolio fallback'} +PRODUCTION_ROUTE_MODULES = {SEED_ID: ROUTE_ENTRYPOINT, PARENT_ID: ROUTE_PARENT} + +def _merge_ir(split_count: int=SPLIT_COUNT, rows_per_cta: int=ROWS_PER_CTA) -> Any: + if rows_per_cta <= 0 or rows_per_cta > merge_base.K32_WARP_MERGE_WARPS: + raise ValueError(''.join(['rows_per_cta must be in [1, ', format(merge_base.K32_WARP_MERGE_WARPS, ''), '], got ', format(rows_per_cta, '')])) + return merge_base._ir_with_constants(merge_base.k32_warp_row_merge_ir, suffix=''.join(['q128s', format(split_count, ''), 'r', format(rows_per_cta, ''), '_a162_v1']), TOP_K_MAX=TOP_K_MAX, SPLIT_COUNT=split_count, SPLITS_PER_LANE=merge_base._splits_per_lane(split_count), ROWS_PER_CTA=rows_per_cta) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RAG_STREAM_K32_Q128_S72R2_A162_VERIFY_KERNEL') + if verify_kernel == 'merge': + return _merge_ir() + return rowld_seed._stage1_q128_rowld_ir() +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_q32rowld_e5db_v1_stage1_q32_k32_m64_q128rowld_60fb_v1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 99584, "constants": [["BLOCK_Q", 64], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 192}')) + +def _compiled_stage1(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0211"}')) + +@cache +def _compiled_merge(split_count: int, rows_per_cta: int): + return merge_base.rowld_seed.compact_seed.q16_tailinf.parent_k32._compile_ir(_merge_ir(split_count, rows_per_cta)) + +def _select_contract_shapes(shape_labels) -> list[dict[str, Any]]: + return parent_v11._select_contract_shapes(shape_labels) + +def _trace_inputs_for_shape(shape: dict[str, Any]) -> dict[str, Any]: + return parent_v11._trace_inputs_for_shape(shape) + +def _inputs_for_label(label: str) -> dict[str, Any]: + return _trace_inputs_for_shape(_select_contract_shapes((label,))[0]) + +def _is_bf16_d128_nonbuild(inputs: dict[str, Any]) -> bool: + query = inputs.get('query') + database = inputs.get('database') + dtype = str(inputs.get('dtype', '')) + query_dtype = str(getattr(query, 'dtype', dtype)) + database_dtype = str(getattr(database, 'dtype', dtype)) + return not bool(inputs.get('build', False)) and query_dtype in {'torch.bfloat16', 'bfloat16'} and (database_dtype in {'torch.bfloat16', 'bfloat16'}) and (int(inputs.get('B', -1)) == 1) and (int(inputs.get('Q', -1)) == 128) and (int(inputs.get('D', -1)) == 128) and (int(inputs.get('K', -1)) == TOP_K_MAX) + +def _eligible_q128_stream_k32(inputs: dict[str, Any]) -> bool: + if not _is_bf16_d128_nonbuild(inputs): + return False + return int(inputs.get('M', -1)) in {100000, 131071} + +def _matched_label(inputs: dict[str, Any]) -> str | None: + if not _eligible_q128_stream_k32(inputs): + return None + return TARGET_M100000 if int(inputs['M']) == 100000 else TARGET_M131071 + +def _route_name(inputs: dict[str, Any], *, split_count: int, rows_per_cta: int) -> str: + return ''.join(['rag_stream_k32_q128_m', format(int(inputs['M']), ''), '_a162_s', format(split_count, ''), '_r', format(rows_per_cta, ''), '_rowld_warpmerge']) + +def route_for_contract_inputs(inputs: dict[str, Any], *, split_count: int=SPLIT_COUNT, rows_per_cta: int=ROWS_PER_CTA, force_fallback: bool=False) -> str: + if not force_fallback and _eligible_q128_stream_k32(inputs): + return _route_name(inputs, split_count=split_count, rows_per_cta=rows_per_cta) + return parent_v11.route_for_contract_inputs(inputs, force_fallback=force_fallback) + +def _launch_q128_stream_s72r2(inputs: dict[str, Any], *, split_count: int, rows_per_cta: int) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + if top_k != TOP_K_MAX: + raise ValueError(''.join(['q128 stream rowld route only supports K=', format(TOP_K_MAX, ''), ', got K=', format(top_k, '')])) + block_q = rowld_seed.rowld_seed.Q8_M64_BLOCK_Q + block_m = rowld_seed.rowld_seed.Q8_M64_BLOCK_M + num_q_tiles = (n_query + block_q - 1) // block_q + num_db_tiles = (n_database + block_m - 1) // block_m + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tiles * split_count + total_queries = bsz * n_query + stage1_grid = min(total_work, merge_base.rowld_seed.compact_seed.GRID_DIM_DEFAULT) + merge_grid = min((total_queries + rows_per_cta - 1) // rows_per_cta, merge_base.rowld_seed.compact_seed.GRID_DIM_DEFAULT) + partial_dists, partial_indices = merge_base.rowld_seed.compact_seed.q16_tailinf.parent_k32.parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = merge_base.rowld_seed.compact_seed.q16_tailinf.parent_k32.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), total_queries, block_q, dim, dim) + tmap_database = merge_base.rowld_seed.compact_seed.q16_tailinf.parent_k32.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, block_m, dim, dim) + stage1_ir = rowld_seed._stage1_q128_rowld_ir() + _compiled_stage1().launch(grid=(stage1_grid, 1, 1), block=(rowld_seed.Q128_ROWLD_STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage1_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tiles=num_q_tiles, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), shared_mem=stage1_ir.computed_smem_bytes) + merge_ir = _merge_ir(split_count, rows_per_cta) + _compiled_merge(split_count, rows_per_cta).launch(grid=(merge_grid, 1, 1), block=(merge_base.K32_WARP_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], total_queries], shared_mem=merge_ir.computed_smem_bytes) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, split_count: int=SPLIT_COUNT, rows_per_cta: int=ROWS_PER_CTA, force_fallback: bool=False) -> None: + if not force_fallback and _eligible_q128_stream_k32(inputs): + _launch_q128_stream_s72r2(inputs, split_count=split_count, rows_per_cta=rows_per_cta) + return + parent_v11.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_with_topology(split_count: int, rows_per_cta: int) -> Callable[[dict[str, Any]], None]: + + def _candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, split_count=split_count, rows_per_cta=rows_per_cta) + return _candidate + +def candidate_parent_v11(inputs: dict[str, Any]) -> None: + parent_v11.launch_from_contract_inputs(inputs) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _shape_labels(shape_labels) -> tuple[str, ...]: + if shape_labels is None: + return TARGET_SHAPES + return tuple((str(label) for label in shape_labels)) + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + previous = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(_shape_labels(shape_labels)), correctness=correctness, benchmark=benchmark, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = previous + +def route_trace_for_contract_shapes(shape_labels=TARGET_SHAPES, *, split_count: int=SPLIT_COUNT, rows_per_cta: int=ROWS_PER_CTA, force_fallback: bool=False) -> list[dict[str, Any]]: + rows: list[dict[str, Any]] = [] + for label in _shape_labels(shape_labels): + inputs = _inputs_for_label(label) + selected = not force_fallback and _eligible_q128_stream_k32(inputs) + route = route_for_contract_inputs(inputs, split_count=split_count, rows_per_cta=rows_per_cta, force_fallback=force_fallback) + parent_route = parent_v11.route_for_contract_inputs(inputs, force_fallback=force_fallback) + rows.append(parent_v11._normalize_route_row({'shape_key': label, 'selected_route': route, 'selected_entrypoint': ROUTE_ENTRYPOINT if selected else ROUTE_PARENT, 'selected_seed': SEED_ID if selected else None, 'expected_seed': SEED_ID if _eligible_q128_stream_k32(inputs) else None, 'route_kind': 'specialized' if selected else 'inherited_v11_parent', 'route_source': 'shape-specific-seed' if selected else 'parent-dispatcher', 'guard_id': 'a162_q128_stream_k32_s72r2_exact_guard' if selected else 'forced_fallback_a162_q128_stream_k32_disabled' if force_fallback and _eligible_q128_stream_k32(inputs) else 'parent_v11_guard', 'guard_condition': 'BF16 non-build B=1 Q=128 M in {100000,131071} D=128 K=32' if selected else 'forced fallback to current v11 common-D dispatcher' if force_fallback and _eligible_q128_stream_k32(inputs) else 'delegate to current v11 common-D dispatcher', 'matched_label': _matched_label(inputs), 'split_count': split_count if selected else None, 'rows_per_cta': rows_per_cta if selected else None, 'parent_v11_route': parent_route, 'classification': 'seed-produced' if selected else 'guard-miss'})) + return rows + +def _per_shape_delta(candidate_report: dict[str, Any], parent_report: dict[str, Any]) -> dict[str, Any]: + rows = {} + for label, cand in candidate_report.get('per_shape', {}).items(): + parent_row = parent_report.get('per_shape', {}).get(label, {}) + cand_ms = cand.get('kernel_ms') + parent_ms = parent_row.get('kernel_ms') + rows[label] = {'candidate': cand, 'parent_v11': parent_row, 'candidate_ms': cand_ms, 'parent_v11_ms': parent_ms, 'speedup_vs_parent_v11': parent_ms / cand_ms if cand_ms and parent_ms else None, 'ratio_vs_flashlib': cand.get('ratio_vs_flashlib')} + return rows + +def benchmark_knn_build_rag_stream_k32_q128_s72r2_a162_v1(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES, split_count: int=SPLIT_COUNT, rows_per_cta: int=ROWS_PER_CTA) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_with_topology(split_count, rows_per_cta)) + parent_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_parent_v11) + return {'contract': candidate_report['contract'], 'contract_version': candidate_report['contract_version'], 'measured_entrypoint': BENCHMARK_ENTRYPOINT, 'candidate_entrypoint': ROUTE_ENTRYPOINT, 'parent_entrypoint': ROUTE_PARENT, 'accelerated_shape_labels': list(TARGET_SHAPES), 'measured_shape_labels': list(_shape_labels(shape_labels)), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'producer_topology': {'Q128_stream_K32': 'rowld tcgen05/TMA producer over two 64-row query tiles', 'split_count': split_count, 'guard_misses': 'delegate to current v11 common-D seed portfolio'}, 'merge_topology': {'Q128_stream_K32': 'warp-row split-list merge', 'rows_per_cta': rows_per_cta, 'splits_per_lane': merge_base._splits_per_lane(split_count)}, 'route_trace': route_trace_for_contract_shapes(shape_labels, split_count=split_count, rows_per_cta=rows_per_cta), 'target_rows': _per_shape_delta(candidate_report, parent_report), 'summary': candidate_report['summary'], 'performance': candidate_report['performance'], 'correctness': candidate_report['correctness'], 'report': candidate_report, 'parent_summary': parent_report['summary'], 'parent_performance': parent_report['performance'], 'parent_report': parent_report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_stream_k32_q128m100000_ad64_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_stream_k32_q128m100000_ad64_v1.py new file mode 100644 index 00000000..053b8bc9 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_stream_k32_q128m100000_ad64_v1.py @@ -0,0 +1,121 @@ +"""RAG stream K32 Q128/M100000 S72/G8 exact bucket wrapper. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +targets the exact v10 ``rag_stream_largek_b1_q128_m100000_d128_k32`` row. It +uses the validated 4fbf v6 K32 tail-infinity tcgen05/TMA producer with +split72/group8 fused merge, and delegates guard misses to the current full90 +Q24/Q128 seed portfolio. Production routes remain Weave-only; FlashLib is only +timed by the contract harness. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_784a_cf51_q1_q16_q24_q128_seed_portfolio_full90_v1 as parent +from . import knn_build_rag_frontier_4fbf_v6 as direct_seed +MODULE = 'loom.examples.weave.knn_build_rag_stream_k32_q128m100000_ad64_v1' +Q128_M100000_K32_SHAPE = 'rag_stream_largek_b1_q128_m100000_d128_k32' +Q128_M100000_TARGET_SHAPES = (Q128_M100000_K32_SHAPE,) +K32_BUCKET_SHAPES = Q128_M100000_TARGET_SHAPES +TARGET_SHAPES = Q128_M100000_TARGET_SHAPES +K32_SPLIT_COUNT = _decode_capture(_json_loads('72')) +K32_GROUP_COUNT = _decode_capture(_json_loads('8')) +ROUTE_PARENT_FULL90 = parent.ROUTE_ENTRYPOINT +ROUTE_Q128_M100000_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +SEED_K32_Q128_M100000_AD64_V1_ID = 'rag_stream_k32_q128_m100000_ad64_v1_4fbf_v6_s72g8' + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RAG_STREAM_K32_Q128M100000_AD64_V1_VERIFY_KERNEL') + if verify_kernel == 'k32_group_merge': + return direct_seed._group_merge_ir(K32_SPLIT_COUNT, K32_GROUP_COUNT) + if verify_kernel == 'k32_final_merge': + return direct_seed._final_merge_ir(K32_GROUP_COUNT) + if verify_kernel == 'k32_fused_merge': + return direct_seed._fused_merge_ir(K32_SPLIT_COUNT, K32_GROUP_COUNT) + return direct_seed.stage1_k32_tailinf_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_frontier_4fbf_stage1_k32_sort4earlystop_tailinf", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 192}')) + +def _eligible_q128_m100000(inputs: dict[str, Any]) -> bool: + return direct_seed._eligible_k32_rag_frontier(inputs) and int(inputs.get('B', -1)) == 1 and (int(inputs.get('Q', -1)) == 128) and (int(inputs.get('M', -1)) == 100000) and (int(inputs.get('D', -1)) == 128) and (int(inputs.get('K', -1)) == 32) + +def _route_name(*, split_count: int, group_count: int) -> str: + return ''.join(['rag_stream_k32_q128_m100000_ad64_v1_4fbf_v6_s', format(split_count, ''), '_g', format(group_count, '')]) + +def route_for_contract_inputs(inputs: dict[str, Any], *, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT, force_fallback: bool=False) -> str: + if not force_fallback and _eligible_q128_m100000(inputs): + return _route_name(split_count=k32_split_count, group_count=k32_group_count) + return parent.route_for_contract_inputs(inputs, force_fallback=force_fallback) + +def _launch_q128_m100000_s72g8(inputs: dict[str, Any], *, split_count: int, group_count: int) -> None: + direct_seed.launch_from_contract_inputs(inputs, k32_split_count=split_count, k32_group_count=group_count) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT, force_fallback: bool=False) -> None: + if not force_fallback and _eligible_q128_m100000(inputs): + _launch_q128_m100000_s72g8(inputs, split_count=k32_split_count, group_count=k32_group_count) + return + parent.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_with_k32_topology(split_count: int, group_count: int) -> Callable[[dict[str, Any]], None]: + + def _candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, k32_split_count=split_count, k32_group_count=group_count) + return _candidate + +def candidate_parent_full90(inputs: dict[str, Any]) -> None: + parent.launch_from_contract_inputs(inputs) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return parent._select_contract_shapes(shape_labels) + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=K32_BUCKET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def compile_and_launch_knn_build(*, shape_labels=K32_BUCKET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def route_trace_for_contract_shapes(shape_labels=K32_BUCKET_SHAPES, *, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT, force_fallback: bool=False) -> list[dict[str, Any]]: + rows = [] + for shape in _select_contract_shapes(shape_labels): + inputs = parent._trace_inputs_for_shape(shape) + selected = not force_fallback and _eligible_q128_m100000(inputs) + route = route_for_contract_inputs(inputs, k32_split_count=k32_split_count, k32_group_count=k32_group_count, force_fallback=force_fallback) + row = {'shape_key': shape['label'], 'selected_route': route, 'selected_entrypoint': ROUTE_Q128_M100000_ENTRYPOINT if selected else ROUTE_PARENT_FULL90, 'selected_seed': SEED_K32_Q128_M100000_AD64_V1_ID if selected else None, 'expected_seed': SEED_K32_Q128_M100000_AD64_V1_ID if _eligible_q128_m100000(inputs) else None, 'route_kind': 'specialized' if selected else 'inherited_full90_parent', 'route_source': 'shape-specific-seed' if selected else 'parent-dispatcher', 'guard_id': 'ad64_q128_m100000_k32_4fbf_v6_s72g8_exact_guard' if selected else 'forced_fallback_ad64_q128_m100000_disabled' if force_fallback and _eligible_q128_m100000(inputs) else 'parent_full90_guard', 'guard_condition': 'BF16 non-build B=1 Q=128 M=100000 D=128 K=32' if selected else 'forced fallback to full90 Q24/Q128 parent' if force_fallback and _eligible_q128_m100000(inputs) else 'delegate to full90 Q24/Q128 parent', 'classification': 'seed-consumed' if selected else 'guard-miss', 'split_count': k32_split_count if selected else None, 'group_count': k32_group_count if selected else None} + rows.append(row) + return rows + +def _per_shape_delta(candidate_report: dict[str, Any], parent_report: dict[str, Any]) -> dict[str, Any]: + rows = {} + for label, cand in candidate_report.get('per_shape', {}).items(): + parent_row = parent_report.get('per_shape', {}).get(label, {}) + cand_ms = cand.get('kernel_ms') + parent_ms = parent_row.get('kernel_ms') + rows[label] = {'candidate': cand, 'parent_full90': parent_row, 'candidate_ms': cand_ms, 'parent_full90_ms': parent_ms, 'speedup_vs_parent_full90': parent_ms / cand_ms if cand_ms and parent_ms else None, 'ratio_vs_flashlib': cand.get('ratio_vs_flashlib')} + return rows + +def benchmark_knn_build_rag_stream_k32_q128m100000_ad64_v1(*, use_cupti: bool=True, shape_labels=K32_BUCKET_SHAPES, k32_split_count: int=K32_SPLIT_COUNT, k32_group_count: int=K32_GROUP_COUNT) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_with_k32_topology(k32_split_count, k32_group_count)) + parent_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_parent_full90) + return {'contract': candidate_report['contract'], 'contract_version': candidate_report['contract_version'], 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_knn_build_rag_stream_k32_q128m100000_ad64_v1']), 'candidate_entrypoint': ROUTE_Q128_M100000_ENTRYPOINT, 'parent_entrypoint': ROUTE_PARENT_FULL90, 'accelerated_shape_labels': list(Q128_M100000_TARGET_SHAPES), 'measured_shape_labels': list(shape_labels), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'producer_topology': {'Q128_M100000': '4fbf v6 tail-infinity K32 tcgen05/TMA producer', 'guard_misses': 'delegate to full90 Q24/Q128 seed portfolio parent'}, 'merge_topology': {'Q128_M100000': '4fbf v6 fused cooperative K32 merge', 'split_count': k32_split_count, 'group_count': k32_group_count}, 'route_trace': route_trace_for_contract_shapes(shape_labels, k32_split_count=k32_split_count, k32_group_count=k32_group_count), 'target_rows': _per_shape_delta(candidate_report, parent_report), 'summary': candidate_report['summary'], 'performance': candidate_report['performance'], 'correctness': candidate_report['correctness'], 'report': candidate_report, 'parent_summary': parent_report['summary'], 'parent_performance': parent_report['performance'], 'parent_report': parent_report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_stream_k32_q128m100000_staticn128_664a_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_stream_k32_q128m100000_staticn128_664a_v1.py new file mode 100644 index 00000000..25a05a99 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_stream_k32_q128m100000_staticn128_664a_v1.py @@ -0,0 +1,171 @@ +"""Exact D128 Q128/M100000 K32 static-N128 tcgen05 seed. + +Minimum target architecture: sm_100a. This additive candidate uses a fixed +64x128x128 tcgen05 producer, keeps its eight split-local K32 lists in SMEM, +and writes directly into the 4fbf fused K32 merge ABI. It is deliberately +not a runtime split-count variant of the inherited N64 path. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from collections.abc import Callable +from functools import lru_cache +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_rag_frontier_4fbf_v6 as direct_seed +from . import knn_build_rag_microbatch_m64_d4f7_v1 as m128_parent +from . import knn_build_rag_stream_k32_q128m100000_tile_937e_v1 as parent +MODULE = 'loom.examples.weave.knn_build_rag_stream_k32_q128m100000_staticn128_664a_v1' +ROUTE_PREFIX = 'knn_build_rag_stream_k32_q128m100000_staticn128_664a_v1' +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +BENCHMARK_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_knn_build_rag_stream_k32_q128m100000_staticn128_664a_v1']) +TARGET_SHAPE = 'rag_stream_largek_b1_q128_m100000_d128_k32' +TARGET_SHAPES = (TARGET_SHAPE,) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +S128_Q = 64 +S128_M = 128 +S128_D = 128 +S128_K = 32 +S128_VEC = 8 +S128_THREADS = 256 +S128_LOCAL_LISTS_PER_ROW = 8 +S128_SPLIT_COUNT = 72 +S128_GROUP_COUNT = 8 +S128_Q_STAGE_VECS = S128_Q * S128_D // S128_VEC +S128_DB_STAGE_VECS = S128_M * S128_D // S128_VEC +S128_SMEM_A_BYTES = S128_Q * S128_D * 2 +S128_SMEM_B_BYTES = S128_M * S128_D * 2 +S128_SMEM_LOCAL_D_BYTES = S128_Q * S128_LOCAL_LISTS_PER_ROW * S128_K * 4 +S128_SMEM_LOCAL_I_BYTES = S128_Q * S128_LOCAL_LISTS_PER_ROW * S128_K * 4 +S128_LOCAL_D_OFFSET = S128_SMEM_A_BYTES + S128_SMEM_B_BYTES +S128_LOCAL_I_OFFSET = S128_LOCAL_D_OFFSET + S128_SMEM_LOCAL_D_BYTES +S128_SMEM_POOL_BYTES = S128_LOCAL_I_OFFSET + S128_SMEM_LOCAL_I_BYTES + 256 +WEAVE_SMEM_SYSTEM_BYTES = 1024 +S128_STAGE_SMEM_BYTES = S128_SMEM_POOL_BYTES + WEAVE_SMEM_SYSTEM_BYTES +GRID_DIM_DEFAULT = direct_seed.GRID_DIM_DEFAULT +TOP_K_MAX = S128_K +knn_build_rag_stream_k32_q128m100000_staticn128_664a_stage1 = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_stream_k32_q128m100000_staticn128_664a_stage1", "arg_keys": ["query", "database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 181504, "constants": [], "cta_group": 1, "threads": 256}')) +stage1_staticn128_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_stream_k32_q128m100000_staticn128_664a_stage1", "arg_keys": ["query", "database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 181504, "constants": [], "cta_group": 1, "threads": 256}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_STATICN128_664A_VERIFY_KERNEL') + split_count = S128_SPLIT_COUNT + group_count = S128_GROUP_COUNT + if verify_kernel == 'merge': + return direct_seed._fused_merge_ir(split_count, group_count) + return stage1_staticn128_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_stream_k32_q128m100000_staticn128_664a_stage1", "arg_keys": ["query", "database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 181504, "constants": [], "cta_group": 1, "threads": 256}')) + +def _compiled_stage1_staticn128(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0112"}')) + +@lru_cache(maxsize=8) +def _compiled_fused_merge(split_count: int, group_count: int): + return direct_seed._compiled_fused_merge(int(split_count), int(group_count)) + +def _dtype_name(inputs: dict[str, Any]) -> str: + query = inputs.get('query') + if query is not None: + return str(query.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')).replace('torch.', '') + +def _target_label_for_inputs(inputs: dict[str, Any]) -> str | None: + if str(inputs.get('label', TARGET_SHAPE)) == TARGET_SHAPE and _dtype_name(inputs) == 'bfloat16' and (not bool(inputs.get('build', False))) and (int(inputs.get('B', -1)) == 1) and (int(inputs.get('Q', -1)) == 128) and (int(inputs.get('M', -1)) == 100000) and (int(inputs.get('D', -1)) == S128_D) and (int(inputs.get('K', -1)) == S128_K): + return TARGET_SHAPE + return None + +def _split_count() -> int: + return int(S128_SPLIT_COUNT) + +def _group_count() -> int: + return int(S128_GROUP_COUNT) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + label = None if force_fallback else _target_label_for_inputs(inputs) + if label is None: + return parent.route_for_contract_inputs(inputs, force_fallback=force_fallback) + return ''.join([format(ROUTE_PREFIX, ''), ':', format(label, ''), ':d128:q128:m100000:n128:s', format(_split_count(), ''), ':g', format(_group_count(), '')]) + +def _launch_staticn128(inputs: dict[str, Any]) -> None: + direct_seed._validate_group_shape(_split_count(), _group_count()) + query = inputs['query'] + database = inputs['database'] + if str(query.dtype) != 'torch.bfloat16' or str(database.dtype) != 'torch.bfloat16': + raise ValueError('knn_build_rag_stream_k32_q128m100000_staticn128_664a_v1 supports bfloat16 inputs only') + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + if dim != S128_D: + raise ValueError(''.join([format(TARGET_SHAPE, ''), ' expected D=', format(S128_D, ''), ', got ', format(dim, '')])) + split_count = _split_count() + group_count = _group_count() + num_q_tiles = (n_query + S128_Q - 1) // S128_Q + num_db_tiles = (n_database + S128_M - 1) // S128_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tiles * split_count + total_queries = bsz * n_query + stage1_grid = min(total_work, GRID_DIM_DEFAULT) + merge_grid = min(total_queries, GRID_DIM_DEFAULT) + partial_dists, partial_indices = direct_seed.parent_k32.parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + _compiled_stage1_staticn128().launch(grid=(stage1_grid, 1, 1), block=(S128_THREADS, 1, 1), args=[query, database, inputs['query_sq'], inputs['database_sq'], partial_dists, partial_indices, bsz, n_query, n_database, top_k, num_q_tiles, db_tiles_per_split, split_count, total_work], shared_mem=S128_STAGE_SMEM_BYTES) + merge_ir = direct_seed._fused_merge_ir(split_count, group_count) + _compiled_fused_merge(split_count, group_count).launch(grid=(merge_grid, 1, 1), block=(direct_seed.K32_FUSED_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], total_queries], shared_mem=merge_ir.computed_smem_bytes) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + if not force_fallback and _target_label_for_inputs(inputs) is not None: + _launch_staticn128(inputs) + return + parent.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return parent._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def route_trace_for_contract_shapes(shape_labels=TARGET_SHAPES, *, force_fallback: bool=False) -> list[dict[str, Any]]: + rows = [] + for shape in _select_contract_shapes(shape_labels): + params = dict(shape.get('params', {})) + params['label'] = shape['label'] + inputs = {'label': params['label'], 'B': params['B'], 'Q': params['Q'], 'M': params['M'], 'D': params['D'], 'K': params['K'], 'build': params['build'], 'dtype': params.get('dtype', 'bfloat16')} + label = None if force_fallback else _target_label_for_inputs(inputs) + if label is None: + rows.append({'shape_key': params['label'], 'selected_route': parent.ROUTE_ENTRYPOINT, 'selected_entrypoint': parent.ROUTE_ENTRYPOINT, 'selected_seed': None, 'expected_seed': 'staticn128_664a_v1' if params['label'] in TARGET_SHAPE_SET else None, 'route_kind': 'forced_fallback' if force_fallback else 'delegated', 'route_source': 'q128_m100000_tile_937e_parent', 'guard_id': 'forced_fallback' if force_fallback else 'guard_miss'}) + continue + rows.append({'shape_key': label, 'selected_route': route_for_contract_inputs(inputs), 'selected_entrypoint': ROUTE_ENTRYPOINT, 'selected_seed': 'staticn128_664a_v1', 'expected_seed': 'staticn128_664a_v1', 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': '664a_d128_q128_m100000_k32_exact_guard', 'guard_condition': 'exact BF16 non-build B=1 Q=128 M=100000 D=128 K=32', 'split_count': _split_count(), 'group_count': _group_count(), 'producer_topology': 'S128_M128_tcgen05_smem', 'merge_topology': 'fused_group_split_merge', 'classification': 'd128-rag-static-n128-k32-seed'}) + return rows + +def benchmark_knn_build_rag_stream_k32_q128m100000_staticn128_664a_v1(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES) -> dict[str, Any]: + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels) + return {'contract': report['contract'], 'contract_version': report['contract_version'], 'measured_entrypoint': BENCHMARK_ENTRYPOINT, 'candidate_entrypoint': ROUTE_ENTRYPOINT, 'accelerated_shape_labels': list(TARGET_SHAPES), 'measured_shape_labels': list(shape_labels), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'producer_topology': ''.join(['D128Q64M128/S', format(_split_count(), ''), '/G', format(_group_count(), '')]), 'route_trace': route_trace_for_contract_shapes(shape_labels), 'summary': report['summary'], 'performance': report['performance'], 'correctness': report['correctness'], 'report': report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_stream_k32_q128m100000_tile_937e_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_stream_k32_q128m100000_tile_937e_v1.py new file mode 100644 index 00000000..3ea9e629 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_stream_k32_q128m100000_tile_937e_v1.py @@ -0,0 +1,78 @@ +"""Exact D128 Q128/M100000 K32 split-group tile-search seed. + +Minimum target architecture: sm_100a. This additive candidate keeps the +validated TMA/tcgen05 ``128x64x128`` producer and fused K32 merge on the +contract-visible path, while exposing the producer split grouping for the +exact BF16 non-build M100000 bucket. A true 128-column producer is a distinct +static Weave layout, not a runtime parameter of the inherited 64-column IR. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import json +from pathlib import Path +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_rag_stream_k32_q128m100000_ad64_v1 as parent +MODULE = 'loom.examples.weave.knn_build_rag_stream_k32_q128m100000_tile_937e_v1' +TARGET_SHAPE = parent.Q128_M100000_K32_SHAPE +TARGET_SHAPES = (TARGET_SHAPE,) +TOPOLOGY_CANDIDATES = ((64, 8), (72, 8), (80, 8)) +SPLIT_COUNT, GROUP_COUNT = TOPOLOGY_CANDIDATES[1] +SEED_ID = 'rag_stream_k32_q128_m100000_tile_937e_v1_s72g8' +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +BENCHMARK_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_knn_build_rag_stream_k32_q128m100000_tile_937e_v1']) +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_frontier_4fbf_stage1_k32_sort4earlystop_tailinf", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 192}')) + +def _eligible(inputs: dict[str, Any]) -> bool: + return parent._eligible_q128_m100000(inputs) + +def _select_contract_shapes(labels: tuple[str, ...]) -> list[dict[str, Any]]: + return parent._select_contract_shapes(labels) + +def _validate_topology(split_count: int, group_count: int) -> None: + if (split_count, group_count) not in TOPOLOGY_CANDIDATES: + raise ValueError(''.join(['unsupported tile-search topology ', format(repr((split_count, group_count)), '')])) + +def route_for_contract_inputs(inputs: dict[str, Any], *, split_count: int=SPLIT_COUNT, group_count: int=GROUP_COUNT, force_fallback: bool=False) -> str: + _validate_topology(split_count, group_count) + if not force_fallback and _eligible(inputs): + return ''.join([format(SEED_ID, ''), ':s', format(split_count, ''), '_g', format(group_count, ''), '_tcgen05_128x64x128']) + return parent.route_for_contract_inputs(inputs, force_fallback=force_fallback) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, split_count: int=SPLIT_COUNT, group_count: int=GROUP_COUNT, force_fallback: bool=False) -> None: + _validate_topology(split_count, group_count) + if not force_fallback and _eligible(inputs): + parent._launch_q128_m100000_s72g8(inputs, split_count=split_count, group_count=group_count) + return + parent.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + +def candidate_with_topology(split_count: int, group_count: int) -> Callable[[dict[str, Any]], None]: + _validate_topology(split_count, group_count) + + def _candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, split_count=split_count, group_count=group_count) + return _candidate + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def benchmark_knn_build_rag_stream_k32_q128m100000_tile_937e_v1(*, use_cupti: bool=True, split_count: int=SPLIT_COUNT, group_count: int=GROUP_COUNT) -> dict[str, Any]: + _validate_topology(split_count, group_count) + old = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + try: + report = evaluate_contract(shapes=_select_contract_shapes(TARGET_SHAPES), kernel_fn=candidate_with_topology(split_count, group_count)) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = old + return {'candidate_id': ''.join([format(SEED_ID, ''), '_s', format(split_count, ''), '_g', format(group_count, '')]), 'candidate_entrypoint': ROUTE_ENTRYPOINT, 'measured_entrypoint': BENCHMARK_ENTRYPOINT, 'measured_shape_labels': list(TARGET_SHAPES), 'producer_topology': {'mma_tile': [128, 64, 128], 'split_count': split_count, 'db_tiles_per_split': 25 if split_count == 64 else 22 if split_count == 72 else 20}, 'merge_topology': {'kind': '4fbf fused cooperative K32 merge', 'group_count': group_count}, 'summary': report['summary'], 'performance': report['performance'], 'correctness': report['correctness'], 'rank_objective': report['rank_objective'], 'report': report, 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event'} + +def write_benchmark_artifact(path: str | Path, *, use_cupti: bool=True, split_count: int=SPLIT_COUNT, group_count: int=GROUP_COUNT) -> dict[str, Any]: + payload = benchmark_knn_build_rag_stream_k32_q128m100000_tile_937e_v1(use_cupti=use_cupti, split_count=split_count, group_count=group_count) + out = Path(path) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + return payload diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_stream_k32_q128rowld_60fb_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_stream_k32_q128rowld_60fb_v1.py new file mode 100644 index 00000000..1f877008 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rag_stream_k32_q128rowld_60fb_v1.py @@ -0,0 +1,171 @@ +"""RAG stream K32 Q128 rowld exact bucket wrapper. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +targets the exact v10 `rag_stream_largek_b1_q128_m131071_d128_k32` row. It +reuses the Q32 ROW_16x256B tcgen05/TMA producer over two 64-row query tiles, +then feeds the rows4 warp-row merge. Guard misses delegate to the Q24 rowld2 +parent path so production dispatch remains Weave-only. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from collections.abc import Callable +from functools import cache +from typing import Any +from .._dispatch_runtime import dc as dc +from .. import _dispatch_runtime as eval_mod +from . import knn_build_rag_microbucket_k32_q24rowld2_24dc_v1 as parent +from . import knn_build_rag_microbucket_k32warpmerge_0077_v1 as base +from . import knn_build_rag_microbucket_q32rowld_e5db_v1 as rowld_seed +from .._dispatch_runtime import pack_kernel_args +MODULE = 'loom.examples.weave.knn_build_rag_stream_k32_q128rowld_60fb_v1' +Q128_K32_SHAPE = 'rag_stream_largek_b1_q128_m131071_d128_k32' +Q128_ROWLD_TARGET_SHAPES = (Q128_K32_SHAPE,) +K32_BUCKET_SHAPES = Q128_ROWLD_TARGET_SHAPES +TARGET_SHAPES = Q128_ROWLD_TARGET_SHAPES +K32_Q128_SPLIT_COUNT = _decode_capture(_json_loads('148')) +K32_TOP_K_MAX = parent.K32_TOP_K_MAX +K32_ROWS4_MERGE_THREADS = parent.K32_ROWS4_MERGE_THREADS +K32_ROWS4_ROWS_PER_CTA = parent.K32_ROWS4_ROWS_PER_CTA +K32_ROWS4_WARPS = parent.K32_ROWS4_WARPS +Q128_ROWLD_STAGE1_THREADS = _decode_capture(_json_loads('192')) +ROUTE_PARENT_Q24 = ''.join([format(parent.MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_Q128_ROWLD_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +SEED_K32_Q128_ROWLD_60FB_V1_ID = 'rag_stream_k32_q128rowld_60fb_v1_rowld_rows4' + +def _ir_with_constants(ir_obj: Any, *, suffix: str, **updates: int) -> Any: + constants = tuple(((name, updates.get(name, value)) for name, value in ir_obj.constants)) + return dc.replace(ir_obj, symbol=''.join([format(ir_obj.symbol, ''), '_', format(suffix, '')]), constants=constants) + +def _stage1_q128_rowld_ir() -> Any: + return _ir_with_constants(rowld_seed.stage1_q32_k32_m64_rowld_ir, suffix='q128rowld_60fb_v1', BLOCK_Q=rowld_seed.Q8_M64_BLOCK_Q, BLOCK_M=rowld_seed.Q8_M64_BLOCK_M, FEAT_D=rowld_seed.Q8_M64_FEAT_D, TOP_K_MAX=K32_TOP_K_MAX) + +def _warp_merge_ir(split_count: int) -> Any: + if K32_ROWS4_ROWS_PER_CTA <= 0 or K32_ROWS4_ROWS_PER_CTA > K32_ROWS4_WARPS: + raise ValueError(''.join(['rows_per_cta=', format(K32_ROWS4_ROWS_PER_CTA, ''), ' exceeds merge warps=', format(K32_ROWS4_WARPS, '')])) + return _ir_with_constants(base.k32_warp_row_merge_ir, suffix=''.join(['k32q128s', format(split_count, ''), 'r', format(K32_ROWS4_ROWS_PER_CTA, ''), '_60fb_v1']), TOP_K_MAX=K32_TOP_K_MAX, SPLIT_COUNT=split_count, SPLITS_PER_LANE=base._splits_per_lane(split_count), ROWS_PER_CTA=K32_ROWS4_ROWS_PER_CTA) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RAG_STREAM_K32_Q128ROWLD_60FB_V1_VERIFY_KERNEL') + split_count = int(os.environ.get('LOOM_KNN_RAG_STREAM_K32_Q128ROWLD_60FB_V1_VERIFY_K32_SPLIT', K32_Q128_SPLIT_COUNT)) + if verify_kernel == 'rowld_stage1': + return _stage1_q128_rowld_ir() + return _warp_merge_ir(split_count) +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_microbucket_k32warpmerge_0077_v1_warp_row_merge_k32q128s148r4_60fb_v1", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 32], ["SPLIT_COUNT", 148], ["SPLITS_PER_LANE", 5], ["ROWS_PER_CTA", 4]], "cta_group": 1, "threads": 128}')) + +def _compiled_stage1_q128_rowld(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0211"}')) + +@cache +def _compiled_rows4_warp_merge(split_count: int): + return base.rowld_seed.compact_seed.q16_tailinf.parent_k32._compile_ir(_warp_merge_ir(split_count)) + +def _eligible_q128_rowld(inputs: dict[str, Any]) -> bool: + return base._is_bf16_d128_nonbuild(inputs) and int(inputs.get('Q', -1)) == 128 and (int(inputs.get('M', -1)) == 131071) and (int(inputs.get('K', -1)) == 32) + +def _q128_rowld_route_name(inputs: dict[str, Any], *, split_count: int) -> str: + n_query = int(inputs.get('Q', -1)) + n_database = int(inputs.get('M', -1)) + return ''.join(['rag_stream_k32_q128rowld_60fb_v1_q', format(n_query, ''), '_m', format(n_database, ''), '_k32_row16x256b_s', format(split_count, ''), '_r', format(K32_ROWS4_ROWS_PER_CTA, ''), '_warpmerge']) + +def route_for_contract_inputs(inputs: dict[str, Any], *, k32_q128_split_count: int=K32_Q128_SPLIT_COUNT) -> str: + if _eligible_q128_rowld(inputs): + return _q128_rowld_route_name(inputs, split_count=k32_q128_split_count) + return parent.route_for_contract_inputs(inputs) + +def _launch_q128_rowld_rows4_merge(inputs: dict[str, Any], *, split_count: int) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + if top_k != K32_TOP_K_MAX: + raise ValueError(''.join(['k32 q128 rowld only supports K=', format(K32_TOP_K_MAX, ''), ', got K=', format(top_k, '')])) + block_q = rowld_seed.Q8_M64_BLOCK_Q + block_m = rowld_seed.Q8_M64_BLOCK_M + num_q_tiles = (n_query + block_q - 1) // block_q + num_db_tiles = (n_database + block_m - 1) // block_m + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tiles * split_count + total_queries = bsz * n_query + stage1_grid = min(total_work, base.rowld_seed.compact_seed.GRID_DIM_DEFAULT) + merge_grid = min((total_queries + K32_ROWS4_ROWS_PER_CTA - 1) // K32_ROWS4_ROWS_PER_CTA, base.rowld_seed.compact_seed.GRID_DIM_DEFAULT) + partial_dists, partial_indices = base.rowld_seed.compact_seed.q16_tailinf.parent_k32.parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = base.rowld_seed.compact_seed.q16_tailinf.parent_k32.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), total_queries, block_q, dim, dim) + tmap_database = base.rowld_seed.compact_seed.q16_tailinf.parent_k32.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, block_m, dim, dim) + stage1_ir = _stage1_q128_rowld_ir() + _compiled_stage1_q128_rowld().launch(grid=(stage1_grid, 1, 1), block=(Q128_ROWLD_STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage1_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tiles=num_q_tiles, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), shared_mem=stage1_ir.computed_smem_bytes) + merge_ir = _warp_merge_ir(split_count) + _compiled_rows4_warp_merge(split_count).launch(grid=(merge_grid, 1, 1), block=(K32_ROWS4_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], total_queries], shared_mem=merge_ir.computed_smem_bytes) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, k32_q128_split_count: int=K32_Q128_SPLIT_COUNT) -> None: + if _eligible_q128_rowld(inputs): + _launch_q128_rowld_rows4_merge(inputs, split_count=k32_q128_split_count) + return + parent.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_with_q128_split(split_count: int) -> Callable[[dict[str, Any]], None]: + + def _candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, k32_q128_split_count=split_count) + return _candidate + +def candidate_parent_q24(inputs: dict[str, Any]) -> None: + parent.launch_from_contract_inputs(inputs) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return parent._select_contract_shapes(shape_labels) + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=K32_BUCKET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def compile_and_launch_knn_build(*, shape_labels=K32_BUCKET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def route_trace_for_contract_shapes(shape_labels=K32_BUCKET_SHAPES, *, k32_q128_split_count: int=K32_Q128_SPLIT_COUNT) -> list[dict[str, Any]]: + rows = [] + for shape in _select_contract_shapes(shape_labels): + inputs = base.rowld_seed.base_dispatcher._trace_inputs_from_shape(shape) + route = route_for_contract_inputs(inputs, k32_q128_split_count=k32_q128_split_count) + parent_route = parent.route_for_contract_inputs(inputs) + selected = _eligible_q128_rowld(inputs) + rows.append({'shape_key': shape['label'], 'selected_route': route, 'selected_seed': SEED_K32_Q128_ROWLD_60FB_V1_ID if selected else None, 'selected_entrypoint': ROUTE_Q128_ROWLD_ENTRYPOINT if selected else ROUTE_PARENT_Q24, 'parent_q24_route': parent_route, 'route_kind': 'specialized_q128_rowld_rows4' if selected else 'inherited_q24_parent', 'split_count': k32_q128_split_count if selected else None, 'guard_condition': 'BF16 non-build B=1 Q=128 M=131071 D=128 K=32' if selected else 'delegate to Q24 rowld2 parent'}) + return rows + +def _per_shape_delta(candidate_report: dict[str, Any], parent_report: dict[str, Any]) -> dict[str, Any]: + rows = {} + for label, cand in candidate_report.get('per_shape', {}).items(): + parent_row = parent_report.get('per_shape', {}).get(label, {}) + cand_ms = cand.get('kernel_ms') + parent_ms = parent_row.get('kernel_ms') + rows[label] = {'candidate': cand, 'parent_q24': parent_row, 'candidate_ms': cand_ms, 'parent_q24_ms': parent_ms, 'speedup_vs_parent_q24': parent_ms / cand_ms if cand_ms and parent_ms else None, 'ratio_vs_flashlib': cand.get('ratio_vs_flashlib')} + return rows + +def benchmark_knn_build_rag_stream_k32_q128rowld_60fb_v1(*, use_cupti: bool=True, shape_labels=K32_BUCKET_SHAPES, k32_q128_split_count: int=K32_Q128_SPLIT_COUNT) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_with_q128_split(k32_q128_split_count)) + parent_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_parent_q24) + return {'contract': candidate_report['contract'], 'contract_version': candidate_report['contract_version'], 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_knn_build_rag_stream_k32_q128rowld_60fb_v1']), 'candidate_entrypoint': ROUTE_Q128_ROWLD_ENTRYPOINT, 'parent_entrypoint': ROUTE_PARENT_Q24, 'accelerated_shape_labels': list(Q128_ROWLD_TARGET_SHAPES), 'measured_shape_labels': list(shape_labels), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'producer_topology': {'Q128_M131071': 'Q32 ROW_16x256B stage1 over two 64-row query tiles', 'guard_misses': 'delegate to q24rowld2_24dc parent'}, 'merge_topology': {'Q128': ''.join(['warp-row split-list merge/', format(K32_ROWS4_ROWS_PER_CTA, ''), ' rows per CTA']), 'q128_split_count': k32_q128_split_count, 'q128_splits_per_lane': base._splits_per_lane(k32_q128_split_count)}, 'route_trace': route_trace_for_contract_shapes(shape_labels, k32_q128_split_count=k32_q128_split_count), 'target_rows': _per_shape_delta(candidate_report, parent_report), 'summary': candidate_report['summary'], 'performance': candidate_report['performance'], 'correctness': candidate_report['correctness'], 'report': candidate_report, 'parent_summary': parent_report['summary'], 'parent_performance': parent_report['performance'], 'parent_report': parent_report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_ragonline_exact_7c8d_v42.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_ragonline_exact_7c8d_v42.py new file mode 100644 index 00000000..7dc2b9c5 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_ragonline_exact_7c8d_v42.py @@ -0,0 +1,68 @@ +"""Exact online RAG K10 route for kNN build/search. + +Minimum target architecture: sm_100a. This additive candidate routes only +``rag_online_b1_q1_m100000_d128_k10`` through the existing split-7 K10 +tcgen05/TMA producer and cached sorted merge. Other contract shapes delegate +to the v41 dispatcher-scoring wrapper. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import os +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatchscore_tailinf_knn_build_dispatch_slurm_0610_6329_v41 as v41 +from . import knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_fixedbuild_dispatch_v2_k32split_v20 as v20 +ONLINE_SHAPE = 'rag_online_b1_q1_m100000_d128_k10' +ONLINE_SPLITS = v20.parent_lowk.RAG_SPLITS + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RAGONLINE_VERIFY_KERNEL') + if verify_kernel == 'merge_k10_s7_cache': + return v20.parent_lowk.parent_cached.merge_k10_s7_cache_ir + if verify_kernel == 'stage1_k10_online_s7': + return v20.parent_lowk.stage1_ir + return v20.parent_lowk.stage1_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_stage1_batch8_cond4_vmin_maxtree", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) + +def _eligible_rag_online_exact(inputs: dict[str, Any]) -> bool: + return not bool(inputs.get('build', False)) and str(inputs['query'].dtype) == 'torch.bfloat16' and (str(inputs['database'].dtype) == 'torch.bfloat16') and (int(inputs['B']) == 1) and (int(inputs['Q']) == 1) and (int(inputs['M']) == 100000) and (int(inputs['D']) == v20.FEAT_D) and (int(inputs['K']) == v20.TOP_K_MAX) + +def _launch_rag_online_s7(inputs: dict[str, Any]) -> None: + v20.parent_lowk._launch_k10_cached_path(inputs, split_count=ONLINE_SPLITS, merge_threads=v20.parent_lowk.parent_cached.RAG_MERGE_THREADS, merge_kernel=v20.parent_lowk.parent_cached._compiled_merge_k10_s7_cache(), merge_ir=v20.parent_lowk.parent_cached.merge_k10_s7_cache_ir) + +def launch_from_contract_inputs(inputs: dict[str, Any]) -> None: + if _eligible_rag_online_exact(inputs): + _launch_rag_online_s7(inputs) + return + v41.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(candidate, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return v41._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=(ONLINE_SHAPE,), benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint: run real contract correctness for selected shapes.""" + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def benchmark_knn_build_ragonline_exact_7c8d_v42(*, use_cupti: bool=False) -> dict[str, Any]: + """Targeted contract benchmark for the exact online RAG row.""" + prior_use_cupti = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + try: + report = evaluate_contract(shapes=_select_contract_shapes((ONLINE_SHAPE,)), correctness=True, benchmark=True) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + timing_backends = sorted({result.get('timing_backend') for result in report.get('per_shape', {}).values() if result.get('timing_backend') is not None}) + return {'tflops': report['summary']['primary_mean'] or 0.0, 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'invalid_performance_reason': report['summary']['invalid_performance_reason'], 'contract_summary': report['summary'], 'contract_performance': report['performance'], 'timing_backends': timing_backends, 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'report': report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_ragonline_mbucket_4fc7_q1m262_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_ragonline_mbucket_4fc7_q1m262_v1.py new file mode 100644 index 00000000..aadbc0e6 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_ragonline_mbucket_4fc7_q1m262_v1.py @@ -0,0 +1,129 @@ +"""Q1 online RAG K10 M-bucket route with M262143 coverage. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +extends the AA88 Q1 online M-bucket route to the v9 +``rag_online_irregular_b1_q1_m262143_d128_k10`` row. The new row uses the +existing split74 K10 tcgen05/TMA producer plus four-warp cooperative merge +from ``knn_build_ragonline_mbucket_aa88_q1m_v3``; older M100000/M131071 rows +stay on split72 and M250000 stays on split74. Guard misses delegate to the +AA88 v3 sidecar. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import os +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_ragonline_mbucket_aa88_q1m_v3 as parent +MODULE = 'loom.examples.weave.knn_build_ragonline_mbucket_4fc7_q1m262_v1' +ONLINE_M100K_SHAPE = parent.ONLINE_M100K_SHAPE +ONLINE_M131K_SHAPE = parent.ONLINE_M131K_SHAPE +ONLINE_M250K_SHAPE = parent.ONLINE_M250K_SHAPE +ONLINE_M262K_SHAPE = 'rag_online_irregular_b1_q1_m262143_d128_k10' +TARGET_SHAPES = (ONLINE_M100K_SHAPE, ONLINE_M131K_SHAPE, ONLINE_M250K_SHAPE, ONLINE_M262K_SHAPE) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +SPLIT_COUNT_BASE = parent.SPLIT_COUNT_BASE +SPLIT_COUNT_M250 = parent.SPLIT_COUNT_M250 +SPLIT_COUNT_M262 = SPLIT_COUNT_M250 +SPLIT_BY_M = {100000: SPLIT_COUNT_BASE, 131071: SPLIT_COUNT_BASE, 250000: SPLIT_COUNT_M250, 262143: SPLIT_COUNT_M262} + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RAGONLINE_MBUCKET_4FC7_Q1M262_VERIFY_KERNEL') + if verify_kernel == 'coop_merge_s72_k10': + return parent.coop_merge_s72_k10_ir + if verify_kernel == 'coop_merge_s74_k10': + return parent.coop_merge_s74_k10_ir + return parent.parent_lowk.stage1_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_stage1_batch8_cond4_vmin_maxtree", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) + +def _dtype_name(inputs: dict[str, Any]) -> str: + dtype = str(getattr(inputs.get('query'), 'dtype', inputs.get('dtype', ''))) + return dtype.removeprefix('torch.') + +def _label_can_hit(inputs: dict[str, Any], target_labels: set[str]) -> bool: + label = inputs.get('label') + return label is None or str(label) in target_labels + +def _eligible_rag_online_mbucket(inputs: dict[str, Any]) -> bool: + return not bool(inputs.get('build', False)) and _label_can_hit(inputs, TARGET_SHAPE_SET) and (_dtype_name(inputs) == 'bfloat16') and (int(inputs.get('B', -1)) == 1) and (int(inputs.get('Q', -1)) == 1) and (int(inputs.get('M', -1)) in SPLIT_BY_M) and (int(inputs.get('D', -1)) == parent.parent_lowk.FEAT_D) and (int(inputs.get('K', -1)) == parent.parent_lowk.TOP_K_MAX) + +def _split_count_for_inputs(inputs: dict[str, Any]) -> int: + return int(SPLIT_BY_M[int(inputs['M'])]) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if not force_fallback and _eligible_rag_online_mbucket(inputs): + split_count = _split_count_for_inputs(inputs) + suffix = 'split74' if split_count == SPLIT_COUNT_M250 else 'split72' + return ''.join(['rag_online_mbucket_4fc7_q1m262_', format(suffix, ''), '_coopmerge']) + return parent.route_for_contract_inputs(inputs, force_fallback=force_fallback) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + if not force_fallback and _eligible_rag_online_mbucket(inputs): + parent._launch_with_split_count(inputs, split_count=_split_count_for_inputs(inputs)) + return + parent.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def candidate_parent_v3(inputs: dict[str, Any]): + parent.launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return parent._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + return evaluate_contract(shapes=selected, correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def _trace_inputs_from_shape(shape: dict[str, Any]) -> dict[str, Any]: + return parent._trace_inputs_from_shape(shape) + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + rows = [] + for shape in selected: + inputs = _trace_inputs_from_shape(shape) + route = route_for_contract_inputs(inputs, force_fallback=force_fallback) + specialized = route.startswith('rag_online_mbucket_4fc7_q1m262') + row = {'shape_key': shape['label'], 'selected_route': route, 'route_kind': 'specialized' if specialized else 'general', 'guard_condition': 'Q1 BF16 online M-bucket split72/split74 cooperative merge' if specialized else 'guard miss to AA88 q1m v3'} + if specialized: + row['split_count'] = _split_count_for_inputs(inputs) + row['merge'] = 'four_warp_coop_k10' + rows.append(row) + return rows + +def _benchmark_payload(report: dict[str, Any], *, use_cupti: bool, shape_labels, measured_function: str) -> dict[str, Any]: + rows = report.get('per_shape', {}) + timing_backends = sorted({row.get('timing_backend') for row in rows.values() if row.get('timing_backend') is not None}) + return {'tflops': report['summary']['primary_mean'] or 0.0, 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'invalid_performance_reason': report['summary']['invalid_performance_reason'], 'measured_entrypoint': ''.join([format(MODULE, ''), ':', format(measured_function, '')]), 'measured_shape_labels': 'all_canonical' if shape_labels is None else tuple(shape_labels), 'accelerated_shape_labels': list(TARGET_SHAPES), 'target_rows': {label: rows.get(label, {}) for label in TARGET_SHAPES if label in rows}, 'split_by_m': dict(SPLIT_BY_M), 'merge_threads': parent.MERGE_THREADS, 'merge_groups': parent.MERGE_GROUPS, 'route_trace': route_trace_for_contract_shapes(shape_labels), 'route_trace_included': True, 'contract_summary': report['summary'], 'contract_performance': report['performance'], 'timing_backends': timing_backends, 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'report': report} + +def benchmark_knn_build_ragonline_mbucket_4fc7_q1m262_v1(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES) -> dict[str, Any]: + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels) + return _benchmark_payload(report, use_cupti=use_cupti, shape_labels=shape_labels, measured_function='benchmark_knn_build_ragonline_mbucket_4fc7_q1m262_v1') + +def benchmark_parent_v3(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES) -> dict[str, Any]: + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_parent_v3) + return _benchmark_payload(report, use_cupti=use_cupti, shape_labels=shape_labels, measured_function='benchmark_parent_v3') diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_ragonline_mbucket_4fc7_q1m262_v2.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_ragonline_mbucket_4fc7_q1m262_v2.py new file mode 100644 index 00000000..47ee5b69 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_ragonline_mbucket_4fc7_q1m262_v2.py @@ -0,0 +1,186 @@ +"""Q1 online RAG K10 M262 half-row producer probe. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +keeps the round-108 Q1 online M-bucket routes for M100000/M131071/M250000 and +tries a K10-specific ROW_16x256B single-CTA-group producer for +``rag_online_irregular_b1_q1_m262143_d128_k10``. The M262 route remains +Weave-only: tcgen05/TMA stage-1 writes split-local K10 partials, then a Weave +fused split merge writes contract-visible distances and indices. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import os +from collections.abc import Callable +from functools import cache, lru_cache +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_rag_microbatch_4a72_v1 as fused_merge_parent +from . import knn_build_rag_microbucket_k32q8half_0077_v1 as q8half_parent +from . import knn_build_ragonline_mbucket_4fc7_q1m262_v1 as parent +from .._dispatch_runtime import pack_kernel_args +MODULE = 'loom.examples.weave.knn_build_ragonline_mbucket_4fc7_q1m262_v2' +ONLINE_M100K_SHAPE = parent.ONLINE_M100K_SHAPE +ONLINE_M131K_SHAPE = parent.ONLINE_M131K_SHAPE +ONLINE_M250K_SHAPE = parent.ONLINE_M250K_SHAPE +ONLINE_M262K_SHAPE = parent.ONLINE_M262K_SHAPE +TARGET_SHAPES = parent.TARGET_SHAPES +TARGET_SHAPE_SET = parent.TARGET_SHAPE_SET +SPLIT_COUNT_BASE = parent.SPLIT_COUNT_BASE +SPLIT_COUNT_M250 = parent.SPLIT_COUNT_M250 +SPLIT_COUNT_M262_PARENT = parent.SPLIT_COUNT_M262 +SPLIT_COUNT_M262_HALF = _decode_capture(_json_loads('128')) +GROUP_COUNT_M262_HALF = _decode_capture(_json_loads('8')) +SPLIT_BY_M = _decode_capture(_json_loads('{"__dict_items__": [[100000, 72], [131071, 72], [250000, 74], [262143, 128]]}')) +SPLIT_BY_M[262143] = SPLIT_COUNT_M262_HALF +Q1_HALF_STAGE1_THREADS = q8half_parent.Q8_HALF_STAGE1_THREADS +Q1_HALF_BLOCK_Q = 64 +Q1_HALF_BLOCK_M = 64 +Q1_HALF_FEAT_D = parent.parent.parent_lowk.FEAT_D +Q1_HALF_TOP_K = parent.parent.parent_lowk.TOP_K_MAX +Q1_HALF_ROWS_COVERED = 1 +Q1_HALF_PHYSICAL_ROWS = 8 +Q1_HALF_LOCAL_LISTS_PER_ROW = 4 +Q1_HALF_SMEM_BASE_BYTES = 16384 + 16384 + 256 +Q1_HALF_LOCAL_ELEMS = Q1_HALF_PHYSICAL_ROWS * Q1_HALF_LOCAL_LISTS_PER_ROW * Q1_HALF_TOP_K +Q1_HALF_LOCAL_D_OFFSET = Q1_HALF_SMEM_BASE_BYTES +Q1_HALF_LOCAL_I_OFFSET = Q1_HALF_LOCAL_D_OFFSET + Q1_HALF_LOCAL_ELEMS * 4 +Q1_HALF_SMEM_POOL_BYTES = Q1_HALF_LOCAL_I_OFFSET + Q1_HALF_LOCAL_ELEMS * 4 +_insert_sorted_pair_k10 = _ir_proxy('loom.examples.weave.knn_build_ragonline_mbucket_4fc7_q1m262_v2:_insert_sorted_pair_k10', 256) +knn_build_ragonline_mbucket_4fc7_q1m262_v2_stage1_q1_k10_m64_halfrow = _decode_capture(_json_loads('{"__ir__": "knn_build_ragonline_mbucket_4fc7_q1m262_v2_stage1_q1_k10_m64_halfrow", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 36608, "constants": [["BLOCK_Q", 64], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 10], ["ROWS_COVERED", 1]], "cta_group": 1, "threads": 96}')) +stage1_q1_k10_m64_halfrow_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_ragonline_mbucket_4fc7_q1m262_v2_stage1_q1_k10_m64_halfrow", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 36608, "constants": [["BLOCK_Q", 64], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 10], ["ROWS_COVERED", 1]], "cta_group": 1, "threads": 96}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_Q1M262_V2_VERIFY_KERNEL') + split_count = int(os.environ.get('LOOM_KNN_Q1M262_V2_VERIFY_SPLIT', SPLIT_COUNT_M262_HALF)) + group_count = int(os.environ.get('LOOM_KNN_Q1M262_V2_VERIFY_GROUPS', GROUP_COUNT_M262_HALF)) + if verify_kernel == 'stage1_q1_k10_halfrow': + return stage1_q1_k10_m64_halfrow_ir + if verify_kernel == 'fused_merge': + return fused_merge_parent._fused_merge_ir(split_count, group_count) + return stage1_q1_k10_m64_halfrow_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_ragonline_mbucket_4fc7_q1m262_v2_stage1_q1_k10_m64_halfrow", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 36608, "constants": [["BLOCK_Q", 64], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 10], ["ROWS_COVERED", 1]], "cta_group": 1, "threads": 96}')) + +def _compiled_stage1_q1_k10_m64_halfrow(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0080"}')) + +@cache +def _compiled_fused_merge(split_count: int, group_count: int): + return fused_merge_parent._compile_ir(fused_merge_parent._fused_merge_ir(split_count, group_count)) + +def _dtype_name(inputs: dict[str, Any]) -> str: + return parent._dtype_name(inputs) + +def _eligible_q1_m262_halfrow(inputs: dict[str, Any]) -> bool: + return parent._eligible_rag_online_mbucket(inputs) and int(inputs.get('M', -1)) == 262143 + +def _launch_q1_m262_halfrow(inputs: dict[str, Any], *, split_count: int=SPLIT_COUNT_M262_HALF, group_count: int=GROUP_COUNT_M262_HALF) -> None: + fused_merge_parent._validate_group_shape(split_count, group_count) + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + num_q_tiles = (n_query + Q1_HALF_BLOCK_Q - 1) // Q1_HALF_BLOCK_Q + num_db_tiles = (n_database + Q1_HALF_BLOCK_M - 1) // Q1_HALF_BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tiles * split_count + total_queries = bsz * n_query + stage1_grid = min(total_work, parent.parent.parent_lowk.GRID_DIM_DEFAULT) + merge_grid = min(total_queries, parent.parent.parent_lowk.GRID_DIM_DEFAULT) + partial_dists, partial_indices = parent.parent.parent_lowk.parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = parent.parent.parent_lowk.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), total_queries, Q1_HALF_BLOCK_Q, dim, dim) + tmap_database = parent.parent.parent_lowk.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, Q1_HALF_BLOCK_M, dim, dim) + stage1_launch = _compiled_stage1_q1_k10_m64_halfrow().prepare_launch(grid=(stage1_grid, 1, 1), block=(Q1_HALF_STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage1_q1_k10_m64_halfrow_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tiles=num_q_tiles, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), shared_mem=stage1_q1_k10_m64_halfrow_ir.computed_smem_bytes) + merge_ir = fused_merge_parent._fused_merge_ir(split_count, group_count) + merge_launch = _compiled_fused_merge(split_count, group_count).prepare_launch(grid=(merge_grid, 1, 1), block=(fused_merge_parent.K10_FUSED_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], total_queries], shared_mem=merge_ir.computed_smem_bytes) + stage1_launch.launch() + merge_launch.launch() + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if not force_fallback and _eligible_q1_m262_halfrow(inputs): + return ''.join(['rag_online_mbucket_4fc7_q1m262_v2_halfrow_s', format(SPLIT_COUNT_M262_HALF, ''), '_g', format(GROUP_COUNT_M262_HALF, '')]) + return parent.route_for_contract_inputs(inputs, force_fallback=force_fallback) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + if not force_fallback and _eligible_q1_m262_halfrow(inputs): + _launch_q1_m262_halfrow(inputs) + return + parent.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def candidate_with_topology(split_count: int, group_count: int) -> Callable[[dict[str, Any]], None]: + + def _candidate(inputs: dict[str, Any]) -> None: + if _eligible_q1_m262_halfrow(inputs): + _launch_q1_m262_halfrow(inputs, split_count=split_count, group_count=group_count) + return None + parent.launch_from_contract_inputs(inputs) + return None + return _candidate + +def candidate_parent_v1(inputs: dict[str, Any]): + parent.launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return parent._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + return evaluate_contract(shapes=selected, correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def _trace_inputs_from_shape(shape: dict[str, Any]) -> dict[str, Any]: + return parent._trace_inputs_from_shape(shape) + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + rows = [] + for shape in selected: + inputs = _trace_inputs_from_shape(shape) + route = route_for_contract_inputs(inputs, force_fallback=force_fallback) + halfrow = route.startswith('rag_online_mbucket_4fc7_q1m262_v2_halfrow') + row = {'shape_key': shape['label'], 'selected_route': route, 'route_kind': 'specialized_halfrow' if halfrow else 'inherited_v1', 'guard_condition': 'Q1 BF16 online M262 half-row K10 producer' if halfrow else 'delegate to round-108 q1m262 v1'} + if halfrow: + row['split_count'] = SPLIT_COUNT_M262_HALF + row['group_count'] = GROUP_COUNT_M262_HALF + rows.append(row) + return rows + +def _benchmark_payload(report: dict[str, Any], *, use_cupti: bool, shape_labels, measured_function: str) -> dict[str, Any]: + rows = report.get('per_shape', {}) + timing_backends = sorted({row.get('timing_backend') for row in rows.values() if row.get('timing_backend') is not None}) + return {'tflops': report['summary']['primary_mean'] or 0.0, 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'invalid_performance_reason': report['summary']['invalid_performance_reason'], 'measured_entrypoint': ''.join([format(MODULE, ''), ':', format(measured_function, '')]), 'measured_shape_labels': 'all_canonical' if shape_labels is None else tuple(shape_labels), 'accelerated_shape_labels': [ONLINE_M262K_SHAPE], 'target_rows': {label: rows.get(label, {}) for label in TARGET_SHAPES if label in rows}, 'split_by_m': dict(SPLIT_BY_M), 'm262_halfrow': {'split_count': SPLIT_COUNT_M262_HALF, 'group_count': GROUP_COUNT_M262_HALF, 'stage1_threads': Q1_HALF_STAGE1_THREADS, 'block_q': Q1_HALF_BLOCK_Q, 'block_m': Q1_HALF_BLOCK_M, 'rows_covered': Q1_HALF_ROWS_COVERED}, 'route_trace': route_trace_for_contract_shapes(shape_labels), 'route_trace_included': True, 'contract_summary': report['summary'], 'contract_performance': report['performance'], 'timing_backends': timing_backends, 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'report': report} + +def benchmark_knn_build_ragonline_mbucket_4fc7_q1m262_v2(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES) -> dict[str, Any]: + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels) + return _benchmark_payload(report, use_cupti=use_cupti, shape_labels=shape_labels, measured_function='benchmark_knn_build_ragonline_mbucket_4fc7_q1m262_v2') + +def benchmark_parent_v1(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES) -> dict[str, Any]: + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_parent_v1) + return _benchmark_payload(report, use_cupti=use_cupti, shape_labels=shape_labels, measured_function='benchmark_parent_v1') diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_ragonline_mbucket_5706_q1v10_smix_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_ragonline_mbucket_5706_q1v10_smix_v1.py new file mode 100644 index 00000000..fcbcb6ff --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_ragonline_mbucket_5706_q1v10_smix_v1.py @@ -0,0 +1,183 @@ +"""Q1 online RAG K10 S144/G12 route for the 5706/v10 residual bucket. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +keeps the primitive-backed half-row producer/merge family and extends the +round-120/121 S144/G12 topology to the v10 exact-M online rows, including +M65536 and M524287. Guard misses delegate to the existing F30C sidecar; no +production dispatcher or external runtime fallback is introduced. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import json +import os +from pathlib import Path +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_ragonline_mbucket_99fd_q1large_s144g12_v1 as s144g12 +from . import knn_build_ragonline_mbucket_99fd_q1m250m262_s144_v1 as s144g8 +from . import knn_build_ragonline_mbucket_f30c_q1m250m262_v1 as base_f30c +MODULE = 'loom.examples.weave.knn_build_ragonline_mbucket_5706_q1v10_smix_v1' +ONLINE_M64K_SHAPE = 'rag_online_b1_q1_m65536_d128_k10' +ONLINE_M100K_SHAPE = base_f30c.ONLINE_M100K_SHAPE +ONLINE_M131K_SHAPE = base_f30c.ONLINE_M131K_SHAPE +ONLINE_M250K_SHAPE = base_f30c.ONLINE_M250K_SHAPE +ONLINE_M262K_SHAPE = base_f30c.ONLINE_M262K_SHAPE +ONLINE_M524K_SHAPE = 'rag_online_irregular_b1_q1_m524287_d128_k10' +TARGET_SHAPES = (ONLINE_M100K_SHAPE, ONLINE_M64K_SHAPE, ONLINE_M131K_SHAPE, ONLINE_M250K_SHAPE, ONLINE_M262K_SHAPE, ONLINE_M524K_SHAPE) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +Q1_S144_SPLIT = 144 +Q1_S128_SPLIT = base_f30c.Q1_HALF_SPLIT +Q1_SMALL_GROUPS = 12 +Q1_F30C_GROUPS = base_f30c.Q1_HALF_GROUPS +TOPOLOGY_BY_M = {100000: (Q1_S144_SPLIT, Q1_SMALL_GROUPS), 65536: (Q1_S144_SPLIT, Q1_SMALL_GROUPS), 131071: (Q1_S144_SPLIT, Q1_SMALL_GROUPS), 250000: (Q1_S144_SPLIT, Q1_SMALL_GROUPS), 262143: (Q1_S144_SPLIT, Q1_SMALL_GROUPS), 524287: (Q1_S144_SPLIT, Q1_SMALL_GROUPS)} +SPLIT_BY_M = _decode_capture(_json_loads('{"__dict_items__": [[100000, 144], [131071, 144], [250000, 144], [262143, 144], [65536, 144], [524287, 144]]}')) +GROUP_BY_M = {} +for _m_value, (_split_count, _group_count) in TOPOLOGY_BY_M.items(): + SPLIT_BY_M[_m_value] = _split_count + GROUP_BY_M[_m_value] = _group_count + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_5706_Q1V10_VERIFY_KERNEL') + split_count = int(os.environ.get('LOOM_KNN_5706_Q1V10_VERIFY_SPLIT', Q1_S144_SPLIT)) + group_count = int(os.environ.get('LOOM_KNN_5706_Q1V10_VERIFY_GROUPS', Q1_SMALL_GROUPS)) + if verify_kernel == 'stage1_q1_k10_halfrow': + return base_f30c.parent.stage1_q1_k10_m64_halfrow_ir + if verify_kernel == 'fused_merge': + return base_f30c.parent.fused_merge_parent._fused_merge_ir(split_count, group_count) + return base_f30c.ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_ragonline_mbucket_4fc7_q1m262_v2_stage1_q1_k10_m64_halfrow", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 36608, "constants": [["BLOCK_Q", 64], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 10], ["ROWS_COVERED", 1]], "cta_group": 1, "threads": 96}')) + +def _dtype_name(inputs: dict[str, Any]) -> str: + dtype = str(getattr(inputs.get('query'), 'dtype', inputs.get('dtype', ''))) + return dtype.removeprefix('torch.') + +def _eligible_q1_mix(inputs: dict[str, Any]) -> bool: + return not bool(inputs.get('build', False)) and _dtype_name(inputs) == 'bfloat16' and (int(inputs.get('B', -1)) == 1) and (int(inputs.get('Q', -1)) == 1) and (int(inputs.get('M', -1)) in TOPOLOGY_BY_M) and (int(inputs.get('D', -1)) == base_f30c.parent.Q1_HALF_FEAT_D) and (int(inputs.get('K', -1)) == base_f30c.parent.Q1_HALF_TOP_K) + +def _topology_for_inputs(inputs: dict[str, Any]) -> tuple[int, int]: + return TOPOLOGY_BY_M[int(inputs['M'])] + +def _launch_q1_mix(inputs: dict[str, Any]) -> None: + split_count, group_count = _topology_for_inputs(inputs) + _launch_q1_with_topology(inputs, split_count=split_count, group_count=group_count) + +def _launch_q1_with_topology(inputs: dict[str, Any], *, split_count: int, group_count: int) -> None: + base_f30c.parent._launch_q1_m262_halfrow(inputs, split_count=split_count, group_count=group_count) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if not force_fallback and _eligible_q1_mix(inputs): + split_count, group_count = _topology_for_inputs(inputs) + return ''.join(['rag_online_mbucket_5706_q1v10_m', format(int(inputs['M']), ''), '_s', format(split_count, ''), '_g', format(group_count, '')]) + return base_f30c.route_for_contract_inputs(inputs, force_fallback=force_fallback) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + if not force_fallback and _eligible_q1_mix(inputs): + _launch_q1_mix(inputs) + return + base_f30c.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def candidate_parent_f30c(inputs: dict[str, Any]): + if _eligible_q1_mix(inputs): + _launch_q1_with_topology(inputs, split_count=Q1_S128_SPLIT, group_count=Q1_F30C_GROUPS) + return None + base_f30c.launch_from_contract_inputs(inputs) + return None + +def candidate_s144g12(inputs: dict[str, Any]): + if _eligible_q1_mix(inputs): + _launch_q1_with_topology(inputs, split_count=Q1_S144_SPLIT, group_count=Q1_SMALL_GROUPS) + return None + s144g12.launch_from_contract_inputs(inputs) + return None + +def candidate_s144g8(inputs: dict[str, Any]): + if _eligible_q1_mix(inputs): + _launch_q1_with_topology(inputs, split_count=Q1_S144_SPLIT, group_count=Q1_F30C_GROUPS) + return None + s144g8.launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return base_f30c._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + return evaluate_contract(shapes=selected, correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def _trace_inputs_from_shape(shape: dict[str, Any]) -> dict[str, Any]: + return base_f30c._trace_inputs_from_shape(shape) + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + rows = [] + for shape in selected: + inputs = _trace_inputs_from_shape(shape) + route = route_for_contract_inputs(inputs, force_fallback=force_fallback) + specialized = route.startswith('rag_online_mbucket_5706_q1v10') + row = {'shape_key': shape['label'], 'selected_route': route, 'route_kind': 'specialized_halfrow_s144_g12' if specialized else 'inherited_f30c', 'guard_condition': 'Q1 BF16 online exact M-bucket half-row S144/G12 K10 producer' if specialized else 'delegate to round-111 F30C q1 half-row route'} + if specialized: + row['split_count'], row['group_count'] = _topology_for_inputs(inputs) + rows.append(row) + return rows + +def _benchmark_payload(report: dict[str, Any], *, use_cupti: bool, shape_labels, measured_function: str) -> dict[str, Any]: + rows = report.get('per_shape', {}) + timing_backends = sorted({row.get('timing_backend') for row in rows.values() if row.get('timing_backend') is not None}) + return {'tflops': report['summary']['primary_mean'] or 0.0, 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'invalid_performance_reason': report['summary']['invalid_performance_reason'], 'measured_entrypoint': ''.join([format(MODULE, ''), ':', format(measured_function, '')]), 'measured_shape_labels': 'all_canonical' if shape_labels is None else tuple(shape_labels), 'accelerated_shape_labels': list(TARGET_SHAPES), 'target_rows': {label: rows.get(label, {}) for label in TARGET_SHAPES if label in rows}, 'topology_by_m': dict(TOPOLOGY_BY_M), 'split_by_m': dict(SPLIT_BY_M), 'group_by_m': dict(GROUP_BY_M), 'q1_halfrow': {'stage1_threads': base_f30c.parent.Q1_HALF_STAGE1_THREADS, 'block_q': base_f30c.parent.Q1_HALF_BLOCK_Q, 'block_m': base_f30c.parent.Q1_HALF_BLOCK_M, 'rows_covered': base_f30c.parent.Q1_HALF_ROWS_COVERED}, 'route_trace': route_trace_for_contract_shapes(shape_labels), 'route_trace_included': True, 'contract_summary': report['summary'], 'contract_performance': report['performance'], 'timing_backends': timing_backends, 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'report': report} + +def benchmark_knn_build_ragonline_mbucket_5706_q1v10_smix_v1(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES) -> dict[str, Any]: + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels) + return _benchmark_payload(report, use_cupti=use_cupti, shape_labels=shape_labels, measured_function='benchmark_knn_build_ragonline_mbucket_5706_q1v10_smix_v1') + +def benchmark_parent_f30c(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES) -> dict[str, Any]: + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_parent_f30c) + return _benchmark_payload(report, use_cupti=use_cupti, shape_labels=shape_labels, measured_function='benchmark_parent_f30c') + +def benchmark_s144g12(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES) -> dict[str, Any]: + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_s144g12) + return _benchmark_payload(report, use_cupti=use_cupti, shape_labels=shape_labels, measured_function='benchmark_s144g12') + +def benchmark_s144g8(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES) -> dict[str, Any]: + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_s144g8) + return _benchmark_payload(report, use_cupti=use_cupti, shape_labels=shape_labels, measured_function='benchmark_s144g8') + +def write_benchmark_artifacts(artifact_dir: str | os.PathLike[str], *, use_cupti: bool=True, shape_labels=TARGET_SHAPES) -> dict[str, Any]: + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + suffix = 'cupti' if use_cupti else 'cuda_event' + payloads = {'candidate_q1v10_smix': benchmark_knn_build_ragonline_mbucket_5706_q1v10_smix_v1(use_cupti=use_cupti, shape_labels=shape_labels), 'parent_f30c': benchmark_parent_f30c(use_cupti=use_cupti, shape_labels=shape_labels), 's144g12': benchmark_s144g12(use_cupti=use_cupti, shape_labels=shape_labels), 's144g8': benchmark_s144g8(use_cupti=use_cupti, shape_labels=shape_labels)} + artifacts = {} + for name, payload in payloads.items(): + path = out_dir / ''.join(['q1v10_5706_', format(name, ''), '_', format(len(tuple(shape_labels)), ''), 'row_', format(suffix, ''), '.json']) + path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + artifacts[name] = str(path) + summary = {'artifact_dir': str(out_dir), 'artifacts': artifacts, 'candidate_summary': payloads['candidate_q1v10_smix']['contract_summary'], 'parent_f30c_summary': payloads['parent_f30c']['contract_summary'], 's144g12_summary': payloads['s144g12']['contract_summary'], 's144g8_summary': payloads['s144g8']['contract_summary']} + summary_path = out_dir / ''.join(['q1v10_5706_summary_', format(len(tuple(shape_labels)), ''), 'row_', format(suffix, ''), '.json']) + summary_path.write_text(json.dumps(summary, indent=2, sort_keys=True) + '\n') + summary['artifacts']['summary'] = str(summary_path) + return summary diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_ragonline_mbucket_99fd_q1large_s144g12_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_ragonline_mbucket_99fd_q1large_s144g12_v1.py new file mode 100644 index 00000000..6f86d154 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_ragonline_mbucket_99fd_q1large_s144g12_v1.py @@ -0,0 +1,138 @@ +"""Q1 online RAG K10 large-M half-row route retuned for 99fd. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +keeps the round-111 half-row producer/merge family and changes only the +topology for the exact BF16 non-build ``B=1,Q=1,D=128,K=10`` online rows. +All four Q1 rows use the K10-specific M64/N64 ROW_16x256B tcgen05/TMA +producer with an S144/G12 fused merge. Guard misses delegate to the f30c +sidecar; no external runtime fallback is introduced. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import os +from pathlib import Path +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_ragonline_mbucket_f30c_q1m250m262_v1 as base +MODULE = 'loom.examples.weave.knn_build_ragonline_mbucket_99fd_q1large_s144g12_v1' +ONLINE_M100K_SHAPE = base.ONLINE_M100K_SHAPE +ONLINE_M131K_SHAPE = base.ONLINE_M131K_SHAPE +ONLINE_M250K_SHAPE = base.ONLINE_M250K_SHAPE +ONLINE_M262K_SHAPE = base.ONLINE_M262K_SHAPE +TARGET_SHAPES = base.TARGET_SHAPES +TARGET_SHAPE_SET = base.TARGET_SHAPE_SET +Q1_S144_SPLIT = 144 +Q1_S144_GROUPS = 12 +SPLIT_BY_M = {m_value: Q1_S144_SPLIT for m_value in (100000, 131071, 250000, 262143)} +GROUP_BY_M = {m_value: Q1_S144_GROUPS for m_value in (100000, 131071, 250000, 262143)} + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_99FD_Q1LARGE_VERIFY_KERNEL') + split_count = int(os.environ.get('LOOM_KNN_99FD_Q1LARGE_VERIFY_SPLIT', Q1_S144_SPLIT)) + group_count = int(os.environ.get('LOOM_KNN_99FD_Q1LARGE_VERIFY_GROUPS', Q1_S144_GROUPS)) + if verify_kernel == 'stage1_q1_k10_halfrow': + return base.parent.stage1_q1_k10_m64_halfrow_ir + if verify_kernel == 'fused_merge': + return base.parent.fused_merge_parent._fused_merge_ir(split_count, group_count) + return base.ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_ragonline_mbucket_4fc7_q1m262_v2_stage1_q1_k10_m64_halfrow", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 36608, "constants": [["BLOCK_Q", 64], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 10], ["ROWS_COVERED", 1]], "cta_group": 1, "threads": 96}')) + +def _eligible_q1large_s144(inputs: dict[str, Any]) -> bool: + return base._eligible_q1_large_halfrow(inputs) and int(inputs.get('M', -1)) in SPLIT_BY_M + +def _topology_for_inputs(inputs: dict[str, Any]) -> tuple[int, int]: + m_value = int(inputs['M']) + return (SPLIT_BY_M[m_value], GROUP_BY_M[m_value]) + +def _launch_q1large_s144(inputs: dict[str, Any]) -> None: + split_count, group_count = _topology_for_inputs(inputs) + base.parent._launch_q1_m262_halfrow(inputs, split_count=split_count, group_count=group_count) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if not force_fallback and _eligible_q1large_s144(inputs): + split_count, group_count = _topology_for_inputs(inputs) + return ''.join(['rag_online_mbucket_99fd_q1large_s', format(split_count, ''), '_g', format(group_count, ''), '_m', format(int(inputs['M']), '')]) + return base.route_for_contract_inputs(inputs, force_fallback=force_fallback) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + if not force_fallback and _eligible_q1large_s144(inputs): + _launch_q1large_s144(inputs) + return + base.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def candidate_parent_f30c(inputs: dict[str, Any]): + base.launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return base._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + return evaluate_contract(shapes=selected, correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def _trace_inputs_from_shape(shape: dict[str, Any]) -> dict[str, Any]: + return base._trace_inputs_from_shape(shape) + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + rows = [] + for shape in selected: + inputs = _trace_inputs_from_shape(shape) + route = route_for_contract_inputs(inputs, force_fallback=force_fallback) + specialized = route.startswith('rag_online_mbucket_99fd_q1large') + row = {'shape_key': shape['label'], 'selected_route': route, 'route_kind': 'specialized_halfrow_s144g12' if specialized else 'inherited_f30c', 'guard_condition': 'Q1 BF16 online large-M half-row S144/G12 K10 producer' if specialized else 'delegate to f30c q1 half-row sidecar'} + if specialized: + row['split_count'], row['group_count'] = _topology_for_inputs(inputs) + rows.append(row) + return rows + +def _benchmark_payload(report: dict[str, Any], *, use_cupti: bool, shape_labels, measured_function: str) -> dict[str, Any]: + rows = report.get('per_shape', {}) + timing_backends = sorted({row.get('timing_backend') for row in rows.values() if row.get('timing_backend') is not None}) + return {'tflops': report['summary']['primary_mean'] or 0.0, 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'invalid_performance_reason': report['summary']['invalid_performance_reason'], 'measured_entrypoint': ''.join([format(MODULE, ''), ':', format(measured_function, '')]), 'measured_shape_labels': 'all_canonical' if shape_labels is None else tuple(shape_labels), 'accelerated_shape_labels': list(TARGET_SHAPES), 'target_rows': {label: rows.get(label, {}) for label in TARGET_SHAPES if label in rows}, 'split_by_m': dict(SPLIT_BY_M), 'group_by_m': dict(GROUP_BY_M), 'q1_halfrow': {'split_count': Q1_S144_SPLIT, 'group_count': Q1_S144_GROUPS, 'stage1_threads': base.parent.Q1_HALF_STAGE1_THREADS, 'block_q': base.parent.Q1_HALF_BLOCK_Q, 'block_m': base.parent.Q1_HALF_BLOCK_M, 'rows_covered': base.parent.Q1_HALF_ROWS_COVERED}, 'route_trace': route_trace_for_contract_shapes(shape_labels), 'route_trace_included': True, 'contract_summary': report['summary'], 'contract_performance': report['performance'], 'timing_backends': timing_backends, 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'report': report} + +def benchmark_knn_build_ragonline_mbucket_99fd_q1large_s144g12_v1(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES) -> dict[str, Any]: + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels) + return _benchmark_payload(report, use_cupti=use_cupti, shape_labels=shape_labels, measured_function='benchmark_knn_build_ragonline_mbucket_99fd_q1large_s144g12_v1') + +def benchmark_parent_f30c(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES) -> dict[str, Any]: + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_parent_f30c) + return _benchmark_payload(report, use_cupti=use_cupti, shape_labels=shape_labels, measured_function='benchmark_parent_f30c') + +def write_artifacts(directory: str | os.PathLike[str], *, use_cupti: bool=True) -> dict[str, str]: + output_dir = Path(directory) + output_dir.mkdir(parents=True, exist_ok=True) + import json + candidate_payload = benchmark_knn_build_ragonline_mbucket_99fd_q1large_s144g12_v1(use_cupti=use_cupti) + parent_payload = benchmark_parent_f30c(use_cupti=use_cupti) + candidate_path = output_dir / 'q1large_99fd_s144g12_candidate_4row_cupti.json' + parent_path = output_dir / 'q1large_99fd_parent_f30c_4row_cupti.json' + candidate_path.write_text(json.dumps(candidate_payload, indent=2, sort_keys=True) + '\n') + parent_path.write_text(json.dumps(parent_payload, indent=2, sort_keys=True) + '\n') + return {'candidate': str(candidate_path), 'parent_f30c': str(parent_path)} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_ragonline_mbucket_99fd_q1m250m262_s144_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_ragonline_mbucket_99fd_q1m250m262_s144_v1.py new file mode 100644 index 00000000..1f8fa7de --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_ragonline_mbucket_99fd_q1m250m262_s144_v1.py @@ -0,0 +1,154 @@ +"""Q1 online RAG K10 large-M S144/G8 route. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +keeps the round-111 F30C half-row route for the four Q1 M-bucket rows, but +retunes the two round-119 residual large-M rows to split_count=144 and +group_count=8. Guard misses delegate to F30C; no production dispatcher is +mutated. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import json +import os +from pathlib import Path +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_ragonline_mbucket_f30c_q1m250m262_v1 as base_f30c +MODULE = 'loom.examples.weave.knn_build_ragonline_mbucket_99fd_q1m250m262_s144_v1' +ONLINE_M100K_SHAPE = base_f30c.ONLINE_M100K_SHAPE +ONLINE_M131K_SHAPE = base_f30c.ONLINE_M131K_SHAPE +ONLINE_M250K_SHAPE = base_f30c.ONLINE_M250K_SHAPE +ONLINE_M262K_SHAPE = base_f30c.ONLINE_M262K_SHAPE +TARGET_SHAPES = base_f30c.TARGET_SHAPES +LARGE_M_TARGET_SHAPES = (ONLINE_M250K_SHAPE, ONLINE_M262K_SHAPE) +TARGET_SHAPE_SET = base_f30c.TARGET_SHAPE_SET +Q1_BASE_SPLIT = base_f30c.Q1_HALF_SPLIT +Q1_BASE_GROUPS = base_f30c.Q1_HALF_GROUPS +Q1_LARGE_SPLIT = 144 +Q1_LARGE_GROUPS = 8 +TOPOLOGY_BY_M = {100000: (Q1_BASE_SPLIT, Q1_BASE_GROUPS), 131071: (Q1_BASE_SPLIT, Q1_BASE_GROUPS), 250000: (Q1_LARGE_SPLIT, Q1_LARGE_GROUPS), 262143: (Q1_LARGE_SPLIT, Q1_LARGE_GROUPS)} +SPLIT_BY_M = _decode_capture(_json_loads('{"__dict_items__": [[100000, 128], [131071, 128], [250000, 144], [262143, 144]]}')) +for _m_value, (_split_count, _group_count) in TOPOLOGY_BY_M.items(): + SPLIT_BY_M[_m_value] = _split_count + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_99FD_Q1M250M262_S144_VERIFY_KERNEL') + split_count = int(os.environ.get('LOOM_KNN_99FD_Q1M250M262_S144_VERIFY_SPLIT', Q1_LARGE_SPLIT)) + group_count = int(os.environ.get('LOOM_KNN_99FD_Q1M250M262_S144_VERIFY_GROUPS', Q1_LARGE_GROUPS)) + if verify_kernel == 'stage1_q1_k10_halfrow': + return base_f30c.parent.stage1_q1_k10_m64_halfrow_ir + if verify_kernel == 'fused_merge': + return base_f30c.parent.fused_merge_parent._fused_merge_ir(split_count, group_count) + return base_f30c.ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_ragonline_mbucket_4fc7_q1m262_v2_stage1_q1_k10_m64_halfrow", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 36608, "constants": [["BLOCK_Q", 64], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 10], ["ROWS_COVERED", 1]], "cta_group": 1, "threads": 96}')) + +def _eligible_q1_topology(inputs: dict[str, Any]) -> bool: + return base_f30c._eligible_q1_large_halfrow(inputs) and int(inputs.get('M', -1)) in TOPOLOGY_BY_M + +def _topology_for_inputs(inputs: dict[str, Any]) -> tuple[int, int]: + return TOPOLOGY_BY_M[int(inputs['M'])] + +def _launch_q1_topology(inputs: dict[str, Any]) -> None: + split_count, group_count = _topology_for_inputs(inputs) + base_f30c.parent._launch_q1_m262_halfrow(inputs, split_count=split_count, group_count=group_count) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if not force_fallback and _eligible_q1_topology(inputs): + split_count, group_count = _topology_for_inputs(inputs) + return ''.join(['rag_online_mbucket_99fd_q1_m', format(int(inputs['M']), ''), '_s', format(split_count, ''), '_g', format(group_count, '')]) + return base_f30c.route_for_contract_inputs(inputs, force_fallback=force_fallback) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + if not force_fallback and _eligible_q1_topology(inputs): + _launch_q1_topology(inputs) + return + base_f30c.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def candidate_parent_f30c(inputs: dict[str, Any]): + base_f30c.launch_from_contract_inputs(inputs) + return None + +def candidate_round119_v1(inputs: dict[str, Any]): + base_f30c.parent.parent.launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return base_f30c._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + return evaluate_contract(shapes=selected, correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def _trace_inputs_from_shape(shape: dict[str, Any]) -> dict[str, Any]: + return base_f30c._trace_inputs_from_shape(shape) + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + rows = [] + for shape in selected: + inputs = _trace_inputs_from_shape(shape) + route = route_for_contract_inputs(inputs, force_fallback=force_fallback) + specialized = route.startswith('rag_online_mbucket_99fd_q1_') + row = {'shape_key': shape['label'], 'selected_route': route, 'route_kind': 'specialized_halfrow' if specialized else 'inherited_f30c', 'guard_condition': 'Q1 BF16 online exact M-bucket half-row K10 producer' if specialized else 'delegate to round-111 F30C q1 half-row route'} + if specialized: + row['split_count'], row['group_count'] = _topology_for_inputs(inputs) + rows.append(row) + return rows + +def _benchmark_payload(report: dict[str, Any], *, use_cupti: bool, shape_labels, measured_function: str) -> dict[str, Any]: + rows = report.get('per_shape', {}) + timing_backends = sorted({row.get('timing_backend') for row in rows.values() if row.get('timing_backend') is not None}) + return {'tflops': report['summary']['primary_mean'] or 0.0, 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'invalid_performance_reason': report['summary']['invalid_performance_reason'], 'measured_entrypoint': ''.join([format(MODULE, ''), ':', format(measured_function, '')]), 'measured_shape_labels': 'all_canonical' if shape_labels is None else tuple(shape_labels), 'accelerated_shape_labels': list(TARGET_SHAPES), 'large_m_retuned_shape_labels': list(LARGE_M_TARGET_SHAPES), 'target_rows': {label: rows.get(label, {}) for label in TARGET_SHAPES if label in rows}, 'large_m_rows': {label: rows.get(label, {}) for label in LARGE_M_TARGET_SHAPES if label in rows}, 'topology_by_m': dict(TOPOLOGY_BY_M), 'route_trace': route_trace_for_contract_shapes(shape_labels), 'route_trace_included': True, 'contract_summary': report['summary'], 'contract_performance': report['performance'], 'timing_backends': timing_backends, 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'report': report} + +def benchmark_knn_build_ragonline_mbucket_99fd_q1m250m262_s144_v1(*, use_cupti: bool=True, shape_labels=LARGE_M_TARGET_SHAPES) -> dict[str, Any]: + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels) + return _benchmark_payload(report, use_cupti=use_cupti, shape_labels=shape_labels, measured_function='benchmark_knn_build_ragonline_mbucket_99fd_q1m250m262_s144_v1') + +def benchmark_parent_f30c(*, use_cupti: bool=True, shape_labels=LARGE_M_TARGET_SHAPES) -> dict[str, Any]: + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_parent_f30c) + return _benchmark_payload(report, use_cupti=use_cupti, shape_labels=shape_labels, measured_function='benchmark_parent_f30c') + +def benchmark_round119_v1(*, use_cupti: bool=True, shape_labels=LARGE_M_TARGET_SHAPES) -> dict[str, Any]: + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_round119_v1) + return _benchmark_payload(report, use_cupti=use_cupti, shape_labels=shape_labels, measured_function='benchmark_round119_v1') + +def write_benchmark_artifacts(artifact_dir: str | os.PathLike[str], *, use_cupti: bool=True, shape_labels=LARGE_M_TARGET_SHAPES) -> dict[str, Any]: + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + suffix = 'cupti' if use_cupti else 'cuda_event' + payloads = {'candidate_s144': benchmark_knn_build_ragonline_mbucket_99fd_q1m250m262_s144_v1(use_cupti=use_cupti, shape_labels=shape_labels), 'parent_f30c': benchmark_parent_f30c(use_cupti=use_cupti, shape_labels=shape_labels), 'round119_v1': benchmark_round119_v1(use_cupti=use_cupti, shape_labels=shape_labels)} + artifacts = {} + for name, payload in payloads.items(): + path = out_dir / ''.join(['q1m250m262_s144_', format(name, ''), '_', format(len(tuple(shape_labels)), ''), 'row_', format(suffix, ''), '.json']) + path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + artifacts[name] = str(path) + summary = {'artifact_dir': str(out_dir), 'artifacts': artifacts, 'candidate_summary': payloads['candidate_s144']['contract_summary'], 'parent_f30c_summary': payloads['parent_f30c']['contract_summary'], 'round119_v1_summary': payloads['round119_v1']['contract_summary']} + summary_path = out_dir / ''.join(['q1m250m262_s144_summary_', format(len(tuple(shape_labels)), ''), 'row_', format(suffix, ''), '.json']) + summary_path.write_text(json.dumps(summary, indent=2, sort_keys=True) + '\n') + summary['artifacts']['summary'] = str(summary_path) + return summary diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_ragonline_mbucket_aa88_q1m_v3.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_ragonline_mbucket_aa88_q1m_v3.py new file mode 100644 index 00000000..b6fd9aa3 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_ragonline_mbucket_aa88_q1m_v3.py @@ -0,0 +1,191 @@ +"""Q1 online RAG M-bucket K10 route with an M250 split74 producer. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +keeps the round-26 split72/four-warp cooperative merge path for the +``M=100000`` and ``M=131071`` BF16 non-build ``Q=1,D=128,K=10`` rows, and +specializes the ``M=250000`` row to a split74 producer plus matching +four-warp cooperative merge. Guard misses delegate to the round-25 sidecar and +then to its current Weave dispatcher. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import os +from collections.abc import Callable +from functools import lru_cache +from typing import Any +from .._dispatch_runtime import dc as dc +from .. import _dispatch_runtime as eval_mod +from . import knn_build_rag_online_stream_split72_4e09_v1 as split72 +from . import knn_build_ragonline_mbucket_aa88_v1 as round25 +ONLINE_M100K_SHAPE = round25.ONLINE_M100K_SHAPE +ONLINE_M131K_SHAPE = round25.ONLINE_M131K_SHAPE +ONLINE_M250K_SHAPE = round25.ONLINE_M250K_SHAPE +TARGET_SHAPES = round25.TARGET_SHAPES +TARGET_SHAPE_SET = round25.TARGET_SHAPE_SET +SPLIT_COUNT_BASE = split72.SPLIT_COUNT +SPLIT_COUNT_M250 = 74 +SPLIT_BY_M = {100000: SPLIT_COUNT_BASE, 131071: SPLIT_COUNT_BASE, 250000: SPLIT_COUNT_M250} +TOP_K_MAX = split72.parent_lowk.TOP_K_MAX +MERGE_THREADS = 128 +MERGE_GROUPS = 4 +SPLITS_PER_GROUP_BASE = (SPLIT_COUNT_BASE + MERGE_GROUPS - 1) // MERGE_GROUPS +SPLITS_PER_GROUP_M250 = (SPLIT_COUNT_M250 + MERGE_GROUPS - 1) // MERGE_GROUPS +MERGE_GROUP_SLOTS = MERGE_GROUPS * TOP_K_MAX +MERGE_GROUP_D_BYTES = MERGE_GROUP_SLOTS * 4 +parent_lowk = split72.parent_lowk +base_v1 = split72.base_v1 +knn_build_ragonline_mbucket_aa88_q1m_s72_k10_coop_merge = _decode_capture(_json_loads('{"__ir__": "knn_build_ragonline_mbucket_aa88_q1m_s72_k10_coop_merge", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 512, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 72], ["MERGE_GROUPS", 4], ["SPLITS_PER_GROUP", 18]], "cta_group": 1, "threads": 128}')) + +def _ir_with_constants(ir_obj: Any, *, suffix: str, **updates: int) -> Any: + constants = tuple(((name, updates.get(name, value)) for name, value in ir_obj.constants)) + return dc.replace(ir_obj, symbol=''.join([format(ir_obj.symbol, ''), '_', format(suffix, '')]), constants=constants) +coop_merge_s72_k10_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_ragonline_mbucket_aa88_q1m_s72_k10_coop_merge", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 512, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 72], ["MERGE_GROUPS", 4], ["SPLITS_PER_GROUP", 18]], "cta_group": 1, "threads": 128}')) +coop_merge_s74_k10_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_ragonline_mbucket_aa88_q1m_s72_k10_coop_merge_s74_m250", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 512, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 74], ["MERGE_GROUPS", 4], ["SPLITS_PER_GROUP", 19]], "cta_group": 1, "threads": 128}')) + +class _TraceTensor: + + def __init__(self, dtype: str) -> None: + self.dtype = dtype if dtype.startswith('torch.') else ''.join(['torch.', format(dtype, '')]) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RAGONLINE_MBUCKET_AA88_Q1M_VERIFY_KERNEL') + if verify_kernel == 'coop_merge_s72_k10': + return coop_merge_s72_k10_ir + if verify_kernel == 'coop_merge_s74_k10': + return coop_merge_s74_k10_ir + return parent_lowk.stage1_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_stage1_batch8_cond4_vmin_maxtree", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) + +def _compile_ir(ir_obj: Any): + from .._dispatch_runtime import generate_kernel + from .._dispatch_runtime import _cuda_include_dirs + from .._dispatch_runtime import compile_cuda + from .._dispatch_runtime import CUDAKernel + source = generate_kernel(ir_obj, validate=False, smem_bytes=ir_obj.computed_smem_bytes) + cubin = compile_cuda(source, arch=base_v1._select_arch_and_preload(), options=['--use_fast_math'], include_dirs=_cuda_include_dirs()) + return CUDAKernel(cubin, ''.join(['kernel_', format(ir_obj.symbol, '')])) + +def _compiled_coop_merge_s72_k10(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0184"}')) + +def _compiled_coop_merge_s74_k10(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0185"}')) + +def _dtype_name(inputs: dict[str, Any]) -> str: + dtype = str(getattr(inputs.get('query'), 'dtype', inputs.get('dtype', ''))) + return dtype.removeprefix('torch.') + +def _label_can_hit(inputs: dict[str, Any], target_labels: set[str]) -> bool: + label = inputs.get('label') + return label is None or str(label) in target_labels + +def _eligible_rag_online_mbucket(inputs: dict[str, Any]) -> bool: + return not bool(inputs.get('build', False)) and _label_can_hit(inputs, TARGET_SHAPE_SET) and (_dtype_name(inputs) == 'bfloat16') and (int(inputs.get('B', -1)) == 1) and (int(inputs.get('Q', -1)) == 1) and (int(inputs.get('M', -1)) in SPLIT_BY_M) and (int(inputs.get('D', -1)) == parent_lowk.FEAT_D) and (int(inputs.get('K', -1)) == parent_lowk.TOP_K_MAX) + +def _split_count_for_inputs(inputs: dict[str, Any]) -> int: + return int(SPLIT_BY_M[int(inputs['M'])]) + +def _launch_with_split_count(inputs: dict[str, Any], *, split_count: int) -> None: + if split_count == SPLIT_COUNT_M250: + merge_kernel = _compiled_coop_merge_s74_k10() + merge_ir = coop_merge_s74_k10_ir + else: + merge_kernel = _compiled_coop_merge_s72_k10() + merge_ir = coop_merge_s72_k10_ir + parent_lowk._launch_k10_cached_path(inputs, split_count=split_count, merge_threads=MERGE_THREADS, merge_kernel=merge_kernel, merge_ir=merge_ir) + +def _launch_rag_online_mbucket_split74_m250(inputs: dict[str, Any]) -> None: + _launch_with_split_count(inputs, split_count=_split_count_for_inputs(inputs)) + +def _launch_rag_online_mbucket_round26_split72(inputs: dict[str, Any]) -> None: + _launch_with_split_count(inputs, split_count=SPLIT_COUNT_BASE) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if not force_fallback and _eligible_rag_online_mbucket(inputs): + split_count = _split_count_for_inputs(inputs) + suffix = 'm250split74' if split_count == SPLIT_COUNT_M250 else 'split72' + return ''.join(['rag_online_mbucket_aa88_q1m_', format(suffix, ''), '_coopmerge']) + return round25.route_for_contract_inputs(inputs, force_fallback=force_fallback) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + if not force_fallback and _eligible_rag_online_mbucket(inputs): + _launch_rag_online_mbucket_split74_m250(inputs) + return + round25.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def candidate_round26_split72(inputs: dict[str, Any]): + if _eligible_rag_online_mbucket(inputs): + _launch_rag_online_mbucket_round26_split72(inputs) + return None + round25.launch_from_contract_inputs(inputs) + return None + +def candidate_force_round25(inputs: dict[str, Any]): + round25.launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return round25._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + return evaluate_contract(shapes=selected, correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def _trace_inputs_from_shape(shape: dict[str, Any]) -> dict[str, Any]: + params = dict(shape['params']) + dtype = str(params.get('dtype', 'bfloat16')) + return {'label': shape['label'], 'B': int(params['B']), 'Q': int(params['Q']), 'M': int(params['M']), 'D': int(params['D']), 'K': int(params['K']), 'dtype': dtype, 'build': bool(params.get('build', False)), 'query': _TraceTensor(dtype), 'database': _TraceTensor(dtype)} + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + rows = [] + for shape in selected: + inputs = _trace_inputs_from_shape(shape) + route = route_for_contract_inputs(inputs, force_fallback=force_fallback) + specialized = route.startswith('rag_online_mbucket_aa88_q1m') + row = {'shape_key': shape['label'], 'selected_route': route, 'route_kind': 'specialized' if specialized else 'general', 'guard_condition': 'Q1 BF16 online M-bucket split74-M250 cooperative merge' if specialized else 'guard miss to round25/current dispatcher'} + if specialized: + row['split_count'] = _split_count_for_inputs(inputs) + row['merge'] = 'four_warp_coop_k10' + rows.append(row) + return rows + +def _benchmark_payload(report: dict[str, Any], *, use_cupti: bool, shape_labels, measured_function: str) -> dict[str, Any]: + rows = report.get('per_shape', {}) + timing_backends = sorted({row.get('timing_backend') for row in rows.values() if row.get('timing_backend') is not None}) + return {'tflops': report['summary']['primary_mean'] or 0.0, 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'invalid_performance_reason': report['summary']['invalid_performance_reason'], 'measured_entrypoint': ''.join(['loom.examples.weave.knn_build_ragonline_mbucket_aa88_q1m_v3:', format(measured_function, '')]), 'measured_shape_labels': 'all_canonical' if shape_labels is None else tuple(shape_labels), 'accelerated_shape_labels': list(TARGET_SHAPES), 'target_rows': {label: rows.get(label, {}) for label in TARGET_SHAPES if label in rows}, 'split_by_m': dict(SPLIT_BY_M), 'merge_threads': MERGE_THREADS, 'merge_groups': MERGE_GROUPS, 'route_trace': route_trace_for_contract_shapes(shape_labels), 'route_trace_included': True, 'contract_summary': report['summary'], 'contract_performance': report['performance'], 'timing_backends': timing_backends, 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'report': report} + +def benchmark_knn_build_ragonline_mbucket_aa88_q1m_v3(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES) -> dict[str, Any]: + """Targeted contract benchmark for the Q1 online RAG M bucket.""" + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels) + return _benchmark_payload(report, use_cupti=use_cupti, shape_labels=shape_labels, measured_function='benchmark_knn_build_ragonline_mbucket_aa88_q1m_v3') + +def benchmark_round26_split72_baseline(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES) -> dict[str, Any]: + """Same-shape split72 cooperative-merge baseline for local A/B only.""" + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_round26_split72) + return _benchmark_payload(report, use_cupti=use_cupti, shape_labels=shape_labels, measured_function='benchmark_round26_split72_baseline') diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_ragonline_mbucket_aa88_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_ragonline_mbucket_aa88_v1.py new file mode 100644 index 00000000..0b940c54 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_ragonline_mbucket_aa88_v1.py @@ -0,0 +1,130 @@ +"""Q1 online RAG M-bucket K10 route for kNN build/search. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +widens the existing split-72 K10 tcgen05/TMA online RAG seed from the exact +``M=100000`` row to the BF16 non-build ``Q=1,D=128,K=10`` online rows with +``M in {100000,131071,250000}``. Guard misses delegate to the current 8700 +Weave dispatcher; no external runtime fallback is introduced. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import os +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_rag_seed_portfolio_8700_v1 as current_dispatcher +from . import knn_build_rag_online_stream_split72_4e09_v1 as split72 +ONLINE_M100K_SHAPE = 'rag_online_b1_q1_m100000_d128_k10' +ONLINE_M131K_SHAPE = 'rag_online_irregular_b1_q1_m131071_d128_k10' +ONLINE_M250K_SHAPE = 'rag_online_large_m_b1_q1_m250000_d128_k10' +TARGET_SHAPES = (ONLINE_M100K_SHAPE, ONLINE_M131K_SHAPE, ONLINE_M250K_SHAPE) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +SPLIT_COUNT = split72.SPLIT_COUNT +SPLIT_BY_M = {100000: SPLIT_COUNT, 131071: SPLIT_COUNT, 250000: SPLIT_COUNT} +parent_lowk = split72.parent_lowk + +class _TraceTensor: + + def __init__(self, dtype: str) -> None: + self.dtype = dtype if dtype.startswith('torch.') else ''.join(['torch.', format(dtype, '')]) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RAGONLINE_MBUCKET_AA88_VERIFY_KERNEL') + if verify_kernel == 'merge_k10_s72_cache': + return split72.merge_k10_s72_cache_ir + return parent_lowk.stage1_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_stage1_batch8_cond4_vmin_maxtree", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) + +def _dtype_name(inputs: dict[str, Any]) -> str: + dtype = str(getattr(inputs.get('query'), 'dtype', inputs.get('dtype', ''))) + return dtype.removeprefix('torch.') + +def _label_can_hit(inputs: dict[str, Any], target_labels: set[str]) -> bool: + label = inputs.get('label') + return label is None or str(label) in target_labels + +def _eligible_rag_online_mbucket(inputs: dict[str, Any]) -> bool: + return not bool(inputs.get('build', False)) and _label_can_hit(inputs, TARGET_SHAPE_SET) and (_dtype_name(inputs) == 'bfloat16') and (int(inputs.get('B', -1)) == 1) and (int(inputs.get('Q', -1)) == 1) and (int(inputs.get('M', -1)) in SPLIT_BY_M) and (int(inputs.get('D', -1)) == parent_lowk.FEAT_D) and (int(inputs.get('K', -1)) == parent_lowk.TOP_K_MAX) + +def _launch_rag_online_mbucket(inputs: dict[str, Any]) -> None: + split72._launch_rag_online_stream_split72(inputs) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if not force_fallback and _eligible_rag_online_mbucket(inputs): + return ''.join(['rag_online_mbucket_aa88_s', format(SPLIT_COUNT, '')]) + return current_dispatcher.route_for_contract_inputs(inputs) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + if not force_fallback and _eligible_rag_online_mbucket(inputs): + _launch_rag_online_mbucket(inputs) + return + current_dispatcher.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def candidate_force_fallback(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs, force_fallback=True) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return current_dispatcher._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint: run real contract correctness for selected shapes.""" + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + return evaluate_contract(shapes=selected, correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def _trace_inputs_from_shape(shape: dict[str, Any]) -> dict[str, Any]: + params = dict(shape['params']) + dtype = str(params.get('dtype', 'bfloat16')) + return {'label': shape['label'], 'B': int(params['B']), 'Q': int(params['Q']), 'M': int(params['M']), 'D': int(params['D']), 'K': int(params['K']), 'dtype': dtype, 'build': bool(params.get('build', False)), 'query': _TraceTensor(dtype), 'database': _TraceTensor(dtype)} + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + rows = [] + for shape in selected: + inputs = _trace_inputs_from_shape(shape) + route = route_for_contract_inputs(inputs, force_fallback=force_fallback) + specialized = route.startswith('rag_online_mbucket_aa88') + row = {'shape_key': shape['label'], 'selected_route': route, 'route_kind': 'specialized' if specialized else 'general', 'guard_condition': 'Q1 BF16 online M-bucket route' if specialized else 'guard miss to current 8700 dispatcher'} + if specialized: + row['split_count'] = SPLIT_COUNT + rows.append(row) + return rows + +def _benchmark_payload(report: dict[str, Any], *, use_cupti: bool, shape_labels, measured_function: str) -> dict[str, Any]: + rows = report.get('per_shape', {}) + timing_backends = sorted({row.get('timing_backend') for row in rows.values() if row.get('timing_backend') is not None}) + return {'tflops': report['summary']['primary_mean'] or 0.0, 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'invalid_performance_reason': report['summary']['invalid_performance_reason'], 'measured_entrypoint': ''.join(['loom.examples.weave.knn_build_ragonline_mbucket_aa88_v1:', format(measured_function, '')]), 'measured_shape_labels': 'all_canonical' if shape_labels is None else tuple(shape_labels), 'accelerated_shape_labels': list(TARGET_SHAPES), 'target_rows': {label: rows.get(label, {}) for label in TARGET_SHAPES if label in rows}, 'split_by_m': dict(SPLIT_BY_M), 'route_trace': route_trace_for_contract_shapes(shape_labels), 'route_trace_included': True, 'contract_summary': report['summary'], 'contract_performance': report['performance'], 'timing_backends': timing_backends, 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'report': report} + +def benchmark_knn_build_ragonline_mbucket_aa88_v1(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES) -> dict[str, Any]: + """Targeted contract benchmark for the Q1 online RAG M bucket.""" + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels) + return _benchmark_payload(report, use_cupti=use_cupti, shape_labels=shape_labels, measured_function='benchmark_knn_build_ragonline_mbucket_aa88_v1') + +def benchmark_current_8700_q1_baseline(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES) -> dict[str, Any]: + """Same-shape current-dispatcher baseline for local A/B only.""" + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=current_dispatcher.candidate) + return _benchmark_payload(report, use_cupti=use_cupti, shape_labels=shape_labels, measured_function='benchmark_current_8700_q1_baseline') diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_ragonline_mbucket_cb00_q1m_v2.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_ragonline_mbucket_cb00_q1m_v2.py new file mode 100644 index 00000000..4ece3981 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_ragonline_mbucket_cb00_q1m_v2.py @@ -0,0 +1,192 @@ +"""Q1 online RAG M-bucket K10 route with CTA1 repair for M131/M250. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +routes the BF16 non-build ``B=1,Q=1,D=128,K=10`` online rows with +``M in {100000,131071,250000}``. The ``M=100000`` row stays on the validated +split72 parent route; the ``M=131071`` and ``M=250000`` rows use the CTA1 +S144/G12 tcgen05/TMA producer and grouped fused merge from the RAG microbatch +seed. Guard misses delegate to the current Weave dispatcher through the parent +sidecar; no external runtime fallback is introduced. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import os +from pathlib import Path +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_rag_microbatch_4a72_v2 as cta1_seed +from . import knn_build_rag_online_stream_split72_4e09_v1 as split72 +from . import knn_build_ragonline_mbucket_aa88_v1 as parent_mbucket +ONLINE_M100K_SHAPE = parent_mbucket.ONLINE_M100K_SHAPE +ONLINE_M131K_SHAPE = parent_mbucket.ONLINE_M131K_SHAPE +ONLINE_M250K_SHAPE = parent_mbucket.ONLINE_M250K_SHAPE +TARGET_SHAPES = parent_mbucket.TARGET_SHAPES +TARGET_SHAPE_SET = parent_mbucket.TARGET_SHAPE_SET +SPLIT_COUNT_BASE = split72.SPLIT_COUNT +SPLIT_COUNT_CTA1 = 144 +GROUP_COUNT_CTA1 = 12 +SPLIT_BY_M = {100000: SPLIT_COUNT_BASE, 131071: SPLIT_COUNT_CTA1, 250000: SPLIT_COUNT_CTA1} +TOPOLOGY_BY_M = {100000: 'parent_split72', 131071: 'cta1_s144_g12', 250000: 'cta1_s144_g12'} +parent_lowk = split72.parent_lowk + +class _TraceTensor: + + def __init__(self, dtype: str) -> None: + self.dtype = dtype if dtype.startswith('torch.') else ''.join(['torch.', format(dtype, '')]) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RAGONLINE_MBUCKET_CB00_Q1M_V2_VERIFY_KERNEL') + if verify_kernel == 'stage1_cta1': + return cta1_seed.stage1_cta1_ir + if verify_kernel == 'fused_merge_cta1': + split_count = int(os.environ.get('LOOM_KNN_RAGONLINE_MBUCKET_CB00_Q1M_V2_VERIFY_SPLIT', SPLIT_COUNT_CTA1)) + group_count = int(os.environ.get('LOOM_KNN_RAGONLINE_MBUCKET_CB00_Q1M_V2_VERIFY_GROUPS', GROUP_COUNT_CTA1)) + return cta1_seed._fused_merge_ir(split_count, group_count) + if verify_kernel == 'merge_split72': + return split72.merge_k10_s72_cache_ir + return parent_lowk.stage1_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_stage1_batch8_cond4_vmin_maxtree", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) + +def _dtype_name(inputs: dict[str, Any]) -> str: + dtype = str(getattr(inputs.get('query'), 'dtype', inputs.get('dtype', ''))) + return dtype.removeprefix('torch.') + +def _label_can_hit(inputs: dict[str, Any], target_labels: set[str]) -> bool: + label = inputs.get('label') + return label is None or str(label) in target_labels + +def _eligible_rag_online_mbucket(inputs: dict[str, Any]) -> bool: + return not bool(inputs.get('build', False)) and _label_can_hit(inputs, TARGET_SHAPE_SET) and (_dtype_name(inputs) == 'bfloat16') and (int(inputs.get('B', -1)) == 1) and (int(inputs.get('Q', -1)) == 1) and (int(inputs.get('M', -1)) in SPLIT_BY_M) and (int(inputs.get('D', -1)) == parent_lowk.FEAT_D) and (int(inputs.get('K', -1)) == parent_lowk.TOP_K_MAX) + +def _launch_parent_split72(inputs: dict[str, Any]) -> None: + parent_mbucket._launch_rag_online_mbucket(inputs) + +def _launch_cta1_s144_g12(inputs: dict[str, Any]) -> None: + cta1_seed._launch_rag_microbatch_fused_merge(inputs, split_count=SPLIT_COUNT_CTA1, group_count=GROUP_COUNT_CTA1) + +def _launch_rag_online_mbucket(inputs: dict[str, Any]) -> None: + m = int(inputs['M']) + if TOPOLOGY_BY_M[m] == 'cta1_s144_g12': + _launch_cta1_s144_g12(inputs) + return + _launch_parent_split72(inputs) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if not force_fallback and _eligible_rag_online_mbucket(inputs): + m = int(inputs['M']) + return ''.join(['rag_online_mbucket_cb00_q1m_v2_', format(TOPOLOGY_BY_M[m], '')]) + return parent_mbucket.route_for_contract_inputs(inputs, force_fallback=force_fallback) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + if not force_fallback and _eligible_rag_online_mbucket(inputs): + _launch_rag_online_mbucket(inputs) + return + parent_mbucket.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def candidate_parent_split72(inputs: dict[str, Any]): + if _eligible_rag_online_mbucket(inputs): + _launch_parent_split72(inputs) + return None + parent_mbucket.launch_from_contract_inputs(inputs, force_fallback=True) + return None + +def candidate_current_8700(inputs: dict[str, Any]): + parent_mbucket.launch_from_contract_inputs(inputs, force_fallback=True) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return parent_mbucket._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + return evaluate_contract(shapes=selected, correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def _trace_inputs_from_shape(shape: dict[str, Any]) -> dict[str, Any]: + params = dict(shape['params']) + dtype = str(params.get('dtype', 'bfloat16')) + return {'label': shape['label'], 'B': int(params['B']), 'Q': int(params['Q']), 'M': int(params['M']), 'D': int(params['D']), 'K': int(params['K']), 'dtype': dtype, 'build': bool(params.get('build', False)), 'query': _TraceTensor(dtype), 'database': _TraceTensor(dtype)} + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + rows = [] + for shape in selected: + inputs = _trace_inputs_from_shape(shape) + route = route_for_contract_inputs(inputs, force_fallback=force_fallback) + specialized = route.startswith('rag_online_mbucket_cb00_q1m_v2') + row = {'shape_key': shape['label'], 'selected_route': route, 'route_kind': 'specialized' if specialized else 'general', 'guard_condition': 'Q1 BF16 online M-bucket split72/CTA1 route' if specialized else 'guard miss to parent/current dispatcher'} + if specialized: + m = int(inputs['M']) + row['split_count'] = SPLIT_BY_M[m] + row['topology'] = TOPOLOGY_BY_M[m] + if TOPOLOGY_BY_M[m] == 'cta1_s144_g12': + row['merge_groups'] = GROUP_COUNT_CTA1 + rows.append(row) + return rows + +def _benchmark_payload(report: dict[str, Any], *, use_cupti: bool, shape_labels, measured_function: str) -> dict[str, Any]: + rows = report.get('per_shape', {}) + timing_backends = sorted({row.get('timing_backend') for row in rows.values() if row.get('timing_backend') is not None}) + return {'tflops': report['summary']['primary_mean'] or 0.0, 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'invalid_performance_reason': report['summary']['invalid_performance_reason'], 'measured_entrypoint': ''.join(['loom.examples.weave.knn_build_ragonline_mbucket_cb00_q1m_v2:', format(measured_function, '')]), 'measured_shape_labels': 'all_canonical' if shape_labels is None else tuple(shape_labels), 'accelerated_shape_labels': list(TARGET_SHAPES), 'target_rows': {label: rows.get(label, {}) for label in TARGET_SHAPES if label in rows}, 'split_by_m': dict(SPLIT_BY_M), 'topology_by_m': dict(TOPOLOGY_BY_M), 'route_trace': route_trace_for_contract_shapes(shape_labels), 'route_trace_included': True, 'contract_summary': report['summary'], 'contract_performance': report['performance'], 'timing_backends': timing_backends, 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'report': report} + +def benchmark_knn_build_ragonline_mbucket_cb00_q1m_v2(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES) -> dict[str, Any]: + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels) + return _benchmark_payload(report, use_cupti=use_cupti, shape_labels=shape_labels, measured_function='benchmark_knn_build_ragonline_mbucket_cb00_q1m_v2') + +def benchmark_parent_split72_baseline(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES) -> dict[str, Any]: + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_parent_split72) + return _benchmark_payload(report, use_cupti=use_cupti, shape_labels=shape_labels, measured_function='benchmark_parent_split72_baseline') + +def benchmark_current_8700_q1_baseline(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES) -> dict[str, Any]: + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_current_8700) + return _benchmark_payload(report, use_cupti=use_cupti, shape_labels=shape_labels, measured_function='benchmark_current_8700_q1_baseline') + +def _summary_payload(candidate_payload: dict[str, Any], parent_payload: dict[str, Any], current_payload: dict[str, Any]) -> dict[str, Any]: + rows = {} + for label in TARGET_SHAPES: + cand = candidate_payload['target_rows'].get(label, {}) + parent = parent_payload['target_rows'].get(label, {}) + current = current_payload['target_rows'].get(label, {}) + cand_ms = cand.get('kernel_ms') + parent_ms = parent.get('kernel_ms') + current_ms = current.get('kernel_ms') + rows[label] = {'candidate': cand, 'parent_split72': parent, 'current_8700': current, 'candidate_ms': cand_ms, 'parent_split72_ms': parent_ms, 'current_8700_ms': current_ms, 'flashlib_ms': cand.get('flashlib_ms'), 'candidate_route': route_trace_for_contract_shapes((label,))[0], 'speedup_vs_parent_split72': parent_ms / cand_ms if parent_ms and cand_ms else None, 'speedup_vs_current_8700': current_ms / cand_ms if current_ms and cand_ms else None, 'ratio_vs_flashlib': cand.get('ratio_vs_flashlib')} + return {'candidate': candidate_payload, 'parent_split72': parent_payload, 'current_8700': current_payload, 'per_shape': rows, 'candidate_primary_mean': candidate_payload['contract_summary']['primary_mean'], 'parent_primary_mean': parent_payload['contract_summary']['primary_mean'], 'current_primary_mean': current_payload['contract_summary']['primary_mean'], 'candidate_all_correct': candidate_payload['all_correct'], 'candidate_performance_comparable': candidate_payload['performance_comparable']} + +def write_artifacts(directory: str | os.PathLike[str], *, use_cupti: bool=True) -> dict[str, str]: + out_dir = Path(directory) + out_dir.mkdir(parents=True, exist_ok=True) + candidate_payload = benchmark_knn_build_ragonline_mbucket_cb00_q1m_v2(use_cupti=use_cupti) + parent_payload = benchmark_parent_split72_baseline(use_cupti=use_cupti) + current_payload = benchmark_current_8700_q1_baseline(use_cupti=use_cupti) + summary = _summary_payload(candidate_payload, parent_payload, current_payload) + import json + paths = {'candidate': out_dir / 'candidate_q1_mbucket_cb00_q1m_v2.json', 'parent': out_dir / 'parent_split72_q1_mbucket_cb00_q1m_v2.json', 'current': out_dir / 'current_8700_q1_mbucket_cb00_q1m_v2.json', 'summary': out_dir / 'summary_q1_mbucket_cb00_q1m_v2.json'} + payloads = {'candidate': candidate_payload, 'parent': parent_payload, 'current': current_payload, 'summary': summary} + for key, path in paths.items(): + path.write_text(json.dumps(payloads[key], indent=2, sort_keys=True) + '\n') + return {key: str(path) for key, path in paths.items()} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_ragonline_mbucket_ea43_q1m524_n128_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_ragonline_mbucket_ea43_q1m524_n128_v1.py new file mode 100644 index 00000000..444d8deb --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_ragonline_mbucket_ea43_q1m524_n128_v1.py @@ -0,0 +1,186 @@ +"""Q1 online RAG K10 M524 N128 producer probe. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +specializes only ``rag_online_irregular_b1_q1_m524287_d128_k10``. It keeps the +5706 Q1 seed as fallback and tries a wider M64/N128 tcgen05/TMA stage-1 +producer feeding the existing Weave fused split merge. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import json +import os +from collections.abc import Callable +from functools import cache, lru_cache +from pathlib import Path +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_ragonline_mbucket_5706_q1v10_smix_v1 as base5706 +from .._dispatch_runtime import pack_kernel_args +MODULE = 'loom.examples.weave.knn_build_ragonline_mbucket_ea43_q1m524_n128_v1' +ONLINE_M524K_SHAPE = base5706.ONLINE_M524K_SHAPE +TARGET_SHAPES = (ONLINE_M524K_SHAPE,) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +q1base = base5706.base_f30c.parent +fused_merge_parent = q1base.fused_merge_parent +Q1_N128_SPLIT = _decode_capture(_json_loads('148')) +Q1_N128_GROUPS = _decode_capture(_json_loads('4')) +Q1_N128_STAGE1_THREADS = q1base.Q1_HALF_STAGE1_THREADS +Q1_N128_BLOCK_Q = 64 +Q1_N128_BLOCK_M = 128 +Q1_N128_FEAT_D = q1base.Q1_HALF_FEAT_D +Q1_N128_TOP_K = q1base.Q1_HALF_TOP_K +Q1_N128_ROWS_COVERED = 1 +Q1_N128_PHYSICAL_ROWS = 8 +Q1_N128_LOCAL_LISTS_PER_ROW = 4 +Q1_N128_SMEM_BASE_BYTES = 16384 + 32768 + 256 +Q1_N128_LOCAL_ELEMS = Q1_N128_PHYSICAL_ROWS * Q1_N128_LOCAL_LISTS_PER_ROW * Q1_N128_TOP_K +Q1_N128_LOCAL_D_OFFSET = Q1_N128_SMEM_BASE_BYTES +Q1_N128_LOCAL_I_OFFSET = Q1_N128_LOCAL_D_OFFSET + Q1_N128_LOCAL_ELEMS * 4 +Q1_N128_SMEM_POOL_BYTES = Q1_N128_LOCAL_I_OFFSET + Q1_N128_LOCAL_ELEMS * 4 +_insert_sorted_pair_k10 = _ir_proxy('loom.examples.weave.knn_build_ragonline_mbucket_ea43_q1m524_n128_v1:_insert_sorted_pair_k10', 256) +knn_build_ragonline_mbucket_ea43_q1m524_n128_stage1 = _decode_capture(_json_loads('{"__ir__": "knn_build_ragonline_mbucket_ea43_q1m524_n128_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 52992, "constants": [["BLOCK_Q", 64], ["BLOCK_M", 128], ["FEAT_D", 128], ["TOP_K_MAX", 10], ["ROWS_COVERED", 1]], "cta_group": 1, "threads": 96}')) +stage1_q1_k10_m64n128_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_ragonline_mbucket_ea43_q1m524_n128_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 52992, "constants": [["BLOCK_Q", 64], ["BLOCK_M", 128], ["FEAT_D", 128], ["TOP_K_MAX", 10], ["ROWS_COVERED", 1]], "cta_group": 1, "threads": 96}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_EA43_Q1M524_N128_VERIFY_KERNEL') + split_count = int(os.environ.get('LOOM_KNN_EA43_Q1M524_N128_VERIFY_SPLIT', Q1_N128_SPLIT)) + group_count = int(os.environ.get('LOOM_KNN_EA43_Q1M524_N128_VERIFY_GROUPS', Q1_N128_GROUPS)) + if verify_kernel == 'stage1_q1_k10_m64n128': + return stage1_q1_k10_m64n128_ir + if verify_kernel == 'fused_merge': + return fused_merge_parent._fused_merge_ir(split_count, group_count) + return base5706.ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_ragonline_mbucket_4fc7_q1m262_v2_stage1_q1_k10_m64_halfrow", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 36608, "constants": [["BLOCK_Q", 64], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 10], ["ROWS_COVERED", 1]], "cta_group": 1, "threads": 96}')) + +def _compiled_stage1_q1_k10_m64n128(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0082"}')) + +@cache +def _compiled_fused_merge(split_count: int, group_count: int): + return fused_merge_parent._compile_ir(fused_merge_parent._fused_merge_ir(split_count, group_count)) + +def _eligible_q1_m524_n128(inputs: dict[str, Any]) -> bool: + return base5706._eligible_q1_mix(inputs) and int(inputs.get('M', -1)) == 524287 + +def _launch_q1_m524_n128(inputs: dict[str, Any], *, split_count: int=Q1_N128_SPLIT, group_count: int=Q1_N128_GROUPS) -> None: + fused_merge_parent._validate_group_shape(split_count, group_count) + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + num_q_tiles = (n_query + Q1_N128_BLOCK_Q - 1) // Q1_N128_BLOCK_Q + num_db_tiles = (n_database + Q1_N128_BLOCK_M - 1) // Q1_N128_BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tiles * split_count + total_queries = bsz * n_query + stage1_grid = min(total_work, q1base.parent.parent.parent_lowk.GRID_DIM_DEFAULT) + merge_grid = min(total_queries, q1base.parent.parent.parent_lowk.GRID_DIM_DEFAULT) + partial_dists, partial_indices = q1base.parent.parent.parent_lowk.parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = q1base.parent.parent.parent_lowk.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), total_queries, Q1_N128_BLOCK_Q, dim, dim) + tmap_database = q1base.parent.parent.parent_lowk.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, Q1_N128_BLOCK_M, dim, dim) + _compiled_stage1_q1_k10_m64n128().launch(grid=(stage1_grid, 1, 1), block=(Q1_N128_STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage1_q1_k10_m64n128_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tiles=num_q_tiles, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), shared_mem=stage1_q1_k10_m64n128_ir.computed_smem_bytes) + merge_ir = fused_merge_parent._fused_merge_ir(split_count, group_count) + _compiled_fused_merge(split_count, group_count).launch(grid=(merge_grid, 1, 1), block=(fused_merge_parent.K10_FUSED_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], total_queries], shared_mem=merge_ir.computed_smem_bytes) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if not force_fallback and _eligible_q1_m524_n128(inputs): + return ''.join(['rag_online_mbucket_ea43_q1m524_n128_s', format(Q1_N128_SPLIT, ''), '_g', format(Q1_N128_GROUPS, '')]) + return base5706.route_for_contract_inputs(inputs, force_fallback=force_fallback) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + if not force_fallback and _eligible_q1_m524_n128(inputs): + _launch_q1_m524_n128(inputs) + return + base5706.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def candidate_base5706(inputs: dict[str, Any]): + base5706.launch_from_contract_inputs(inputs) + return None + +def candidate_with_topology(split_count: int, group_count: int) -> Callable[[dict[str, Any]], None]: + + def _candidate(inputs: dict[str, Any]) -> None: + if _eligible_q1_m524_n128(inputs): + _launch_q1_m524_n128(inputs, split_count=split_count, group_count=group_count) + return None + base5706.launch_from_contract_inputs(inputs) + return None + return _candidate + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return base5706._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + return evaluate_contract(shapes=selected, correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + rows = [] + for shape in selected: + inputs = base5706._trace_inputs_from_shape(shape) + route = route_for_contract_inputs(inputs, force_fallback=force_fallback) + specialized = route.startswith('rag_online_mbucket_ea43_q1m524_n128') + row = {'shape_key': shape['label'], 'selected_route': route, 'route_kind': 'specialized_m64n128' if specialized else 'inherited_5706', 'guard_condition': 'Q1 BF16 online exact M524 half-row M64/N128 K10 producer' if specialized else 'delegate to 5706 Q1 v10 seed'} + if specialized: + row['split_count'] = Q1_N128_SPLIT + row['group_count'] = Q1_N128_GROUPS + rows.append(row) + return rows + +def _benchmark_payload(report: dict[str, Any], *, use_cupti: bool, shape_labels, measured_function: str, route_trace: list[dict[str, Any]] | None=None) -> dict[str, Any]: + rows = report.get('per_shape', {}) + timing_backends = sorted({row.get('timing_backend') for row in rows.values() if row.get('timing_backend') is not None}) + return {'tflops': report['summary']['primary_mean'] or 0.0, 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'invalid_performance_reason': report['summary']['invalid_performance_reason'], 'measured_entrypoint': ''.join([format(MODULE, ''), ':', format(measured_function, '')]), 'measured_shape_labels': 'all_canonical' if shape_labels is None else tuple(shape_labels), 'accelerated_shape_labels': list(TARGET_SHAPES), 'target_rows': {label: rows.get(label, {}) for label in TARGET_SHAPES if label in rows}, 'topology': {'split_count': Q1_N128_SPLIT, 'group_count': Q1_N128_GROUPS, 'stage1_threads': Q1_N128_STAGE1_THREADS, 'block_q': Q1_N128_BLOCK_Q, 'block_m': Q1_N128_BLOCK_M, 'rows_covered': Q1_N128_ROWS_COVERED}, 'route_trace': route_trace if route_trace is not None else route_trace_for_contract_shapes(shape_labels), 'route_trace_included': True, 'contract_summary': report['summary'], 'contract_performance': report['performance'], 'timing_backends': timing_backends, 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'report': report} + +def benchmark_knn_build_ragonline_mbucket_ea43_q1m524_n128_v1(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES) -> dict[str, Any]: + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels) + return _benchmark_payload(report, use_cupti=use_cupti, shape_labels=shape_labels, measured_function='benchmark_knn_build_ragonline_mbucket_ea43_q1m524_n128_v1') + +def benchmark_base5706(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES) -> dict[str, Any]: + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_base5706) + return _benchmark_payload(report, use_cupti=use_cupti, shape_labels=shape_labels, measured_function='benchmark_base5706', route_trace=base5706.route_trace_for_contract_shapes(shape_labels)) + +def write_benchmark_artifacts(artifact_dir: str | os.PathLike[str], *, use_cupti: bool=True) -> dict[str, Any]: + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + suffix = 'cupti' if use_cupti else 'cuda_event' + payloads = {'candidate_n128': benchmark_knn_build_ragonline_mbucket_ea43_q1m524_n128_v1(use_cupti=use_cupti), 'base5706': benchmark_base5706(use_cupti=use_cupti)} + artifacts = {} + for name, payload in payloads.items(): + path = out_dir / ''.join(['ea43_q1m524_n128_', format(name, ''), '_1row_', format(suffix, ''), '.json']) + path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + artifacts[name] = str(path) + summary = {'artifact_dir': str(out_dir), 'artifacts': artifacts, 'candidate_summary': payloads['candidate_n128']['contract_summary'], 'base5706_summary': payloads['base5706']['contract_summary']} + summary_path = out_dir / ''.join(['ea43_q1m524_n128_summary_1row_', format(suffix, ''), '.json']) + summary_path.write_text(json.dumps(summary, indent=2, sort_keys=True) + '\n') + summary['artifacts']['summary'] = str(summary_path) + return summary diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_ragonline_mbucket_f30c_q1m250m262_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_ragonline_mbucket_f30c_q1m250m262_v1.py new file mode 100644 index 00000000..d9374f19 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_ragonline_mbucket_f30c_q1m250m262_v1.py @@ -0,0 +1,125 @@ +"""Q1 online RAG K10 M-bucket half-row routes. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +extends the round-109 half-row Q1 route to all four exact M-bucket rows. The +route uses the existing K10-specific M64/N64 ROW_16x256B tcgen05/TMA producer +and S128/G8 fused merge from +``knn_build_ragonline_mbucket_4fc7_q1m262_v2``. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import os +from typing import Any, Callable +from .. import _dispatch_runtime as eval_mod +from . import knn_build_ragonline_mbucket_4fc7_q1m262_v2 as parent +MODULE = 'loom.examples.weave.knn_build_ragonline_mbucket_f30c_q1m250m262_v1' +ONLINE_M100K_SHAPE = parent.ONLINE_M100K_SHAPE +ONLINE_M131K_SHAPE = parent.ONLINE_M131K_SHAPE +ONLINE_M250K_SHAPE = parent.ONLINE_M250K_SHAPE +ONLINE_M262K_SHAPE = parent.ONLINE_M262K_SHAPE +TARGET_SHAPES = parent.TARGET_SHAPES +TARGET_SHAPE_SET = parent.TARGET_SHAPE_SET +Q1_HALF_SPLIT = 128 +Q1_HALF_GROUPS = 8 +HALFROW_TOPOLOGY_BY_M = {100000: (Q1_HALF_SPLIT, Q1_HALF_GROUPS), 131071: (Q1_HALF_SPLIT, Q1_HALF_GROUPS), 250000: (Q1_HALF_SPLIT, Q1_HALF_GROUPS), 262143: (Q1_HALF_SPLIT, Q1_HALF_GROUPS)} +SPLIT_BY_M = _decode_capture(_json_loads('{"__dict_items__": [[100000, 128], [131071, 128], [250000, 128], [262143, 128]]}')) +for _m_value, (_split_count, _group_count) in HALFROW_TOPOLOGY_BY_M.items(): + SPLIT_BY_M[_m_value] = _split_count + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_F30C_Q1M250M262_VERIFY_KERNEL') + split_count = int(os.environ.get('LOOM_KNN_F30C_Q1M250M262_VERIFY_SPLIT', Q1_HALF_SPLIT)) + group_count = int(os.environ.get('LOOM_KNN_F30C_Q1M250M262_VERIFY_GROUPS', Q1_HALF_GROUPS)) + if verify_kernel == 'stage1_q1_k10_halfrow': + return parent.stage1_q1_k10_m64_halfrow_ir + if verify_kernel == 'fused_merge': + return parent.fused_merge_parent._fused_merge_ir(split_count, group_count) + return parent.ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_ragonline_mbucket_4fc7_q1m262_v2_stage1_q1_k10_m64_halfrow", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 36608, "constants": [["BLOCK_Q", 64], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 10], ["ROWS_COVERED", 1]], "cta_group": 1, "threads": 96}')) + +def _eligible_q1_large_halfrow(inputs: dict[str, Any]) -> bool: + return parent.parent._eligible_rag_online_mbucket(inputs) and int(inputs.get('M', -1)) in HALFROW_TOPOLOGY_BY_M + +def _halfrow_topology_for_inputs(inputs: dict[str, Any]) -> tuple[int, int]: + return HALFROW_TOPOLOGY_BY_M[int(inputs['M'])] + +def _launch_q1_large_halfrow(inputs: dict[str, Any]) -> None: + split_count, group_count = _halfrow_topology_for_inputs(inputs) + parent._launch_q1_m262_halfrow(inputs, split_count=split_count, group_count=group_count) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if not force_fallback and _eligible_q1_large_halfrow(inputs): + split_count, group_count = _halfrow_topology_for_inputs(inputs) + return ''.join(['rag_online_mbucket_f30c_q1_halfrow_m', format(int(inputs['M']), ''), '_s', format(split_count, ''), '_g', format(group_count, '')]) + return parent.route_for_contract_inputs(inputs, force_fallback=force_fallback) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + if not force_fallback and _eligible_q1_large_halfrow(inputs): + _launch_q1_large_halfrow(inputs) + return + parent.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def candidate_parent_v2(inputs: dict[str, Any]): + parent.launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return parent._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + return evaluate_contract(shapes=selected, correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def _trace_inputs_from_shape(shape: dict[str, Any]) -> dict[str, Any]: + return parent._trace_inputs_from_shape(shape) + +def route_trace_for_contract_shapes(shape_labels=None, *, force_fallback: bool=False) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + rows = [] + for shape in selected: + inputs = _trace_inputs_from_shape(shape) + route = route_for_contract_inputs(inputs, force_fallback=force_fallback) + halfrow = route.startswith('rag_online_mbucket_f30c_q1_halfrow') + row = {'shape_key': shape['label'], 'selected_route': route, 'route_kind': 'specialized_halfrow' if halfrow else 'inherited_v2', 'guard_condition': 'Q1 BF16 online large-M half-row K10 producer' if halfrow else 'delegate to round-109 q1m262 v2'} + if halfrow: + row['split_count'], row['group_count'] = _halfrow_topology_for_inputs(inputs) + rows.append(row) + return rows + +def _benchmark_payload(report: dict[str, Any], *, use_cupti: bool, shape_labels, measured_function: str) -> dict[str, Any]: + rows = report.get('per_shape', {}) + timing_backends = sorted({row.get('timing_backend') for row in rows.values() if row.get('timing_backend') is not None}) + return {'tflops': report['summary']['primary_mean'] or 0.0, 'all_correct': report['summary']['all_correct'], 'performance_comparable': report['summary']['performance_comparable'], 'invalid_performance_reason': report['summary']['invalid_performance_reason'], 'measured_entrypoint': ''.join([format(MODULE, ''), ':', format(measured_function, '')]), 'measured_shape_labels': 'all_canonical' if shape_labels is None else tuple(shape_labels), 'accelerated_shape_labels': list(TARGET_SHAPES), 'target_rows': {label: rows.get(label, {}) for label in TARGET_SHAPES if label in rows}, 'split_by_m': dict(SPLIT_BY_M), 'q1_halfrow': {'m100': {'split_count': Q1_HALF_SPLIT, 'group_count': Q1_HALF_GROUPS}, 'm131': {'split_count': Q1_HALF_SPLIT, 'group_count': Q1_HALF_GROUPS}, 'm250': {'split_count': Q1_HALF_SPLIT, 'group_count': Q1_HALF_GROUPS}, 'm262': {'split_count': Q1_HALF_SPLIT, 'group_count': Q1_HALF_GROUPS}, 'stage1_threads': parent.Q1_HALF_STAGE1_THREADS, 'block_q': parent.Q1_HALF_BLOCK_Q, 'block_m': parent.Q1_HALF_BLOCK_M, 'rows_covered': parent.Q1_HALF_ROWS_COVERED}, 'route_trace': route_trace_for_contract_shapes(shape_labels), 'route_trace_included': True, 'contract_summary': report['summary'], 'contract_performance': report['performance'], 'timing_backends': timing_backends, 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'report': report} + +def benchmark_knn_build_ragonline_mbucket_f30c_q1m250m262_v1(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES) -> dict[str, Any]: + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels) + return _benchmark_payload(report, use_cupti=use_cupti, shape_labels=shape_labels, measured_function='benchmark_knn_build_ragonline_mbucket_f30c_q1m250m262_v1') + +def benchmark_parent_v2(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES) -> dict[str, Any]: + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_parent_v2) + return _benchmark_payload(report, use_cupti=use_cupti, shape_labels=shape_labels, measured_function='benchmark_parent_v2') diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rect_d128_k20_d555_b8c7_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rect_d128_k20_d555_b8c7_v1.py new file mode 100644 index 00000000..529d7b69 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rect_d128_k20_d555_b8c7_v1.py @@ -0,0 +1,161 @@ +"""Exact rectangular D128 K20 seed for d555 residual blockers. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +routes only the exact BF16 contract row +``search_rect_b1_q1536_m65536_d128_k20`` through the existing 9b9f work-feed +K20 rectangular-tail seed. Guard misses delegate to the d555 full82 Weave +dispatcher; no external runtime fallback is used. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from pathlib import Path +from typing import Any, Callable +from . import knn_build_dispatch_d555_residual_seed_synth_full82_v1 as base_d555 +from . import knn_build_k20_tail_q1536_9b9f_wfeed_v2 as seed_9b9f +MODULE = 'loom.examples.weave.knn_build_rect_d128_k20_d555_b8c7_v1' +TARGET_SHAPE = 'search_rect_b1_q1536_m65536_d128_k20' +TARGET_SHAPES = (TARGET_SHAPE,) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +SEED_ID = 'rect_d128_k20_q1536_9b9f_d555_b8c7_v1' +ROUTE_NAME = ''.join([format(MODULE, ''), ':q1536_split8_warp8']) +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +BASELINE_ID = base_d555.CANDIDATE_CONFIGS[base_d555.DEFAULT_CANDIDATE_KEY]['candidate_id'] +BASELINE_ENTRYPOINT = ''.join([format(base_d555.MODULE, ''), ':benchmark_candidate_d555_direct_residual_seeds']) +SOURCE_TASKS = {SEED_ID: 'generalize-auto-tuning-knn-build-d555 / design_doc/active/generalize_auto_tuning_knn_build_round_115_d555.md', 'rect_k20_9b9f_wfeed_parent_seed': 'weave-evolve-knn-build-9b9f / design_doc/active/weave_evolve_knn_build_round_101_9b9f_wfeed.md'} +eval_mod = base_d555.eval_mod + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_D555_RECT_D128_K20_VERIFY_KERNEL') + if verify_kernel == 'merge_k20_tail_s8_warp8': + return seed_9b9f.merge_k20_tail_s8_warp8_ir + return seed_9b9f.parent_v20.stage1_k20_unordered_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k20unordered", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 20]], "cta_group": 1, "threads": 192}')) + +def _dtype_name(inputs: dict[str, Any], key: str) -> str: + tensor = inputs.get(key) + if tensor is not None: + return str(tensor.dtype).removeprefix('torch.') + return str(inputs.get('dtype', '')).removeprefix('torch.') + +def _eligible_rect_d128_k20_q1536(inputs: dict[str, Any]) -> bool: + label = inputs.get('label') + return not bool(inputs.get('build', False)) and _dtype_name(inputs, 'query') == 'bfloat16' and (_dtype_name(inputs, 'database') == 'bfloat16') and (int(inputs.get('B', -1)) == 1) and (int(inputs.get('Q', -1)) == 1536) and (int(inputs.get('M', -1)) == 65536) and (int(inputs.get('D', -1)) == 128) and (int(inputs.get('K', -1)) == 20) and (label is None or str(label) in TARGET_SHAPE_SET) + +def _select_contract_shapes(shape_labels): + return base_d555._select_contract_shapes(shape_labels) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if not force_fallback and _eligible_rect_d128_k20_q1536(inputs): + return ROUTE_NAME + return base_d555.route_for_contract_inputs(inputs, force_fallback=force_fallback) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + if not force_fallback and _eligible_rect_d128_k20_q1536(inputs): + seed_9b9f.launch_from_contract_inputs(inputs) + return + base_d555.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + +def candidate_rect_d128_k20_q1536_9b9f_d555_b8c7_v1(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_baseline_d555(inputs: dict[str, Any]) -> None: + base_d555.candidate_d555_direct_residual_seeds(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate, correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + return base_d555._run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=kernel_fn, correctness=correctness, time_flashlib=time_flashlib) + +def _trace_record(inputs: dict[str, Any], *, force_fallback: bool=False) -> dict[str, Any]: + label = str(inputs.get('label')) + baseline_route = base_d555.route_for_contract_inputs(inputs) + if not force_fallback and _eligible_rect_d128_k20_q1536(inputs): + return base_d555.base_f30c._normalize_route_row({'shape_key': label, 'selected_route': ROUTE_NAME, 'selected_entrypoint': ROUTE_ENTRYPOINT, 'selected_seed': SEED_ID, 'expected_seed': SEED_ID, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': 'd555_rect_d128_k20_q1536_9b9f_exact_guard', 'guard_condition': 'exact BF16 non-build rectangular search shape with B=1 Q=1536 M=65536 D=128 K=20', 'coverage': 'direct 9b9f split8/warp8 Weave seed before d555 fallback', 'consumed_seed': SEED_ID, 'replaced_route': baseline_route, 'baseline_dispatcher_route': baseline_route, 'classification': 'unmeasured'}) + row = dict(base_d555.route_trace_for_contract_shapes((label,), force_fallback=force_fallback)[0]) + if force_fallback and _eligible_rect_d128_k20_q1536(inputs): + row['expected_seed'] = SEED_ID + row['guard_id'] = 'forced_fallback_d555_rect_d128_k20_q1536_disabled' + row['guard_condition'] = 'forced fallback to d555; Q1536 rectangular K20 seed disabled' + row['classification'] = 'guard-miss' + return base_d555.base_f30c._normalize_route_row(row) + +def route_trace_for_contract_shapes(shape_labels=TARGET_SHAPES, *, force_fallback: bool=False) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + return [_trace_record(base_d555.base_f30c._trace_inputs_from_shape(shape), force_fallback=force_fallback) for shape in selected] + +def _annotate_route_trace(route_trace: list[dict[str, Any]], candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> list[dict[str, Any]]: + annotated = [] + for row in route_trace: + label = str(row.get('shape_key')) + out = dict(row) + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') + speedup_vs_baseline = baseline_ms / candidate_ms if candidate_ms and baseline_ms else None + speedup_vs_external = flashlib_ms / candidate_ms if candidate_ms and flashlib_ms else None + out['dispatcher_kernel_ms'] = candidate_ms + out['baseline_dispatcher_kernel_ms'] = baseline_ms + out['external_baseline_ms'] = flashlib_ms + out['flashlib_ms'] = flashlib_ms + out['relative_speedup_vs_baseline'] = speedup_vs_baseline + out['speedup_vs_external_baseline'] = speedup_vs_external + out['external_baseline_ref'] = 'same_session' if flashlib_ms is not None else 'not_available' + if label in TARGET_SHAPE_SET: + if speedup_vs_external is not None and speedup_vs_external < 1.05: + out['classification'] = 'kernel-slow' + elif speedup_vs_baseline is not None and speedup_vs_baseline < 1.0: + out['classification'] = 'kernel-slow' + elif out.get('selected_seed') == SEED_ID: + out['classification'] = 'seed-consumed' + annotated.append(base_d555.base_f30c._normalize_route_row(out)) + return annotated + +def _timing_backends_for_reports(*reports: dict[str, Any]) -> list[str]: + return sorted({row.get('timing_backend') for report in reports for row in report.get('per_shape', {}).values() if row.get('timing_backend') is not None}) + +def benchmark_baseline_d555(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES, benchmark_correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_baseline_d555, correctness=benchmark_correctness, time_flashlib=time_flashlib) + report['candidate_id'] = BASELINE_ID + report['measured_entrypoint'] = BASELINE_ENTRYPOINT + report['route_trace'] = base_d555.route_trace_for_contract_shapes(shape_labels) + report['route_trace_included'] = True + return report + +def benchmark_candidate_rect_d128_k20_q1536_9b9f_d555_b8c7_v1(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES, baseline_report: dict[str, Any] | None=None, benchmark_correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + if baseline_report is None: + baseline_report = benchmark_baseline_d555(use_cupti=use_cupti, shape_labels=shape_labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_rect_d128_k20_q1536_9b9f_d555_b8c7_v1, correctness=benchmark_correctness, time_flashlib=time_flashlib) + candidate_metric = candidate_report['summary']['primary_mean'] + baseline_metric = baseline_report['summary']['primary_mean'] + route_trace = _annotate_route_trace(route_trace_for_contract_shapes(shape_labels), candidate_report, baseline_report) + return {'candidate_id': SEED_ID, 'baseline_candidate_id': BASELINE_ID, 'selected_seeds': (SEED_ID,), 'source_tasks': SOURCE_TASKS, 'tflops': candidate_metric, 'baseline_tflops': baseline_metric, 'metric_delta': candidate_metric - baseline_metric if candidate_metric and baseline_metric else None, 'all_correct': candidate_report['summary']['all_correct'], 'baseline_all_correct': baseline_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'baseline_performance_comparable': baseline_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'baseline_invalid_performance_reason': baseline_report['summary']['invalid_performance_reason'], 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_candidate_rect_d128_k20_q1536_9b9f_d555_b8c7_v1']), 'baseline_entrypoint': ''.join([format(MODULE, ''), ':benchmark_baseline_d555']), 'measured_shape_labels': tuple(shape_labels) if shape_labels is not None else 'all_canonical', 'timing_backend': 'cupti' if use_cupti else 'cuda_event', 'route_modules': {SEED_ID: ROUTE_ENTRYPOINT, BASELINE_ID: ''.join([format(base_d555.MODULE, ''), ':launch_from_contract_inputs'])}, 'route_trace': route_trace, 'forced_fallback_route_trace': route_trace_for_contract_shapes(shape_labels, force_fallback=True), 'route_trace_included': True, 'contract_summary': candidate_report['summary'], 'baseline_contract_summary': baseline_report['summary'], 'contract_performance': candidate_report['performance'], 'baseline_contract_performance': baseline_report['performance'], 'consumed_seed_rows': {label: candidate_report.get('per_shape', {}).get(label, {}) for label in TARGET_SHAPES}, 'baseline_consumed_seed_rows': {label: baseline_report.get('per_shape', {}).get(label, {}) for label in TARGET_SHAPES}, 'timing_backends': _timing_backends_for_reports(candidate_report, baseline_report), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'benchmark_correctness_checked': benchmark_correctness, 'benchmark_time_flashlib': time_flashlib, 'rank_objective': {'metric': 'tflops', 'direction': 'maximize', 'value': candidate_metric, 'baseline_value': baseline_metric, 'delta': candidate_metric - baseline_metric if candidate_metric and baseline_metric else None, 'denominator': 'rect_d128_k20_q1536'}, 'report': candidate_report, 'baseline_report': baseline_report} + +def benchmark_knn_build_rect_d128_k20_d555_b8c7_v1(**kwargs) -> dict[str, Any]: + return benchmark_candidate_rect_d128_k20_q1536_9b9f_d555_b8c7_v1(**kwargs) + +def _write_artifact(payload: dict[str, Any], artifact_dir: Path) -> Path: + artifact_dir.mkdir(parents=True, exist_ok=True) + path = artifact_dir / 'rect_d128_k20_q1536_9b9f_d555_b8c7_v1.json' + path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n', encoding='utf-8') + return path diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rect_d128_k20_q1536_s12warp4_7768_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rect_d128_k20_q1536_s12warp4_7768_v1.py new file mode 100644 index 00000000..b3dd8721 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rect_d128_k20_q1536_s12warp4_7768_v1.py @@ -0,0 +1,246 @@ +"""Exact rectangular D128 K20 Q1536 split12/warp4 merge seed. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +targets only the BF16 non-build contract row +``search_rect_b1_q1536_m65536_d128_k20``. It preserves the validated unordered +tcgen05/TMA stage producer from the 9b9f/b8c7 seed, raises the split fanout to +12, and changes the final K20 merge consumer from eight warp-owned rows per CTA +to four. Guard misses +delegate to the existing b8c7 Weave seed wrapper; no external runtime fallback +is used. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from collections.abc import Callable +from functools import lru_cache +from pathlib import Path +from typing import Any +from . import knn_build_k20_tail_q1536_9b9f_wfeed_v2 as seed_9b9f +from . import knn_build_rect_d128_k20_d555_b8c7_v1 as parent_b8c7 +from .._dispatch_runtime import pack_kernel_args +MODULE = 'loom.examples.weave.knn_build_rect_d128_k20_q1536_s12warp4_7768_v1' +TARGET_SHAPE = 'search_rect_b1_q1536_m65536_d128_k20' +TARGET_SHAPES = (TARGET_SHAPE,) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +SEED_ID = 'rect_d128_k20_q1536_s12warp4_7768_v1' +ROUTE_NAME = ''.join([format(MODULE, ''), ':q1536_split12_warp4']) +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +BASELINE_ID = parent_b8c7.SEED_ID +BASELINE_ENTRYPOINT = ''.join([format(parent_b8c7.MODULE, ''), ':benchmark_candidate_rect_d128_k20_q1536_9b9f_d555_b8c7_v1']) +SOURCE_TASKS = {SEED_ID: 'weave-evolve-knn-build-7768 / design_doc/active/generalize_auto_tuning_knn_build_round_195_7768.md', BASELINE_ID: 'design_doc/active/weave_evolve_knn_build_round_116_b8c7_rectd128k20.md'} +eval_mod = parent_b8c7.eval_mod +parent_v20 = seed_9b9f.parent_v20 +parent_split = seed_9b9f.parent_split +base_v1 = seed_9b9f.base_v1 +BLOCK_Q = seed_9b9f.BLOCK_Q +BLOCK_M = seed_9b9f.BLOCK_M +FEAT_D = seed_9b9f.FEAT_D +STAGE1_THREADS = seed_9b9f.STAGE1_THREADS +CTA_GROUP = seed_9b9f.CTA_GROUP +TOP_K_K20 = seed_9b9f.TOP_K_K20 +DEFAULT_SPLIT_COUNT = 12 +SUPPORTED_SPLIT_COUNTS = (8, 10, 12, 14, 16) +WARP4_MERGE_THREADS = seed_9b9f.K20_MERGE_THREADS +GRID_DIM_DEFAULT = seed_9b9f.GRID_DIM_DEFAULT +knn_build_rect_d128_k20_q1536_warp4_merge = _decode_capture(_json_loads('{"__ir__": "knn_build_rect_d128_k20_q1536_warp4_merge", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 20], ["SPLIT_COUNT_CONST", 12]], "cta_group": 1, "threads": 128}')) +merge_k20_tail_s12_warp4_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rect_d128_k20_q1536_warp4_merge", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 20], ["SPLIT_COUNT_CONST", 12]], "cta_group": 1, "threads": 128}')) +merge_k20_tail_s8_warp4_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rect_d128_k20_q1536_warp4_merge_s8warp4", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 20], ["SPLIT_COUNT_CONST", 8]], "cta_group": 1, "threads": 128}')) +merge_k20_tail_s10_warp4_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rect_d128_k20_q1536_warp4_merge_s10warp4", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 20], ["SPLIT_COUNT_CONST", 10]], "cta_group": 1, "threads": 128}')) +merge_k20_tail_s14_warp4_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rect_d128_k20_q1536_warp4_merge_s14warp4", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 20], ["SPLIT_COUNT_CONST", 14]], "cta_group": 1, "threads": 128}')) +merge_k20_tail_s16_warp4_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rect_d128_k20_q1536_warp4_merge_s16warp4", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 20], ["SPLIT_COUNT_CONST", 16]], "cta_group": 1, "threads": 128}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RECT_D128_K20_Q1536_S12WARP4_VERIFY_KERNEL') + if verify_kernel == 'merge_k20_tail_s8_warp4': + return merge_k20_tail_s8_warp4_ir + if verify_kernel == 'merge_k20_tail_s10_warp4': + return merge_k20_tail_s10_warp4_ir + if verify_kernel == 'merge_k20_tail_s12_warp4': + return merge_k20_tail_s12_warp4_ir + if verify_kernel == 'merge_k20_tail_s14_warp4': + return merge_k20_tail_s14_warp4_ir + if verify_kernel == 'merge_k20_tail_s16_warp4': + return merge_k20_tail_s16_warp4_ir + return parent_v20.stage1_k20_unordered_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k20unordered", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 20]], "cta_group": 1, "threads": 192}')) + +def _forced_split_count() -> int | None: + split_text = os.environ.get('LOOM_KNN_RECT_D128_K20_Q1536_S12WARP4_SPLITS') + if not split_text: + return None + split_count = int(split_text) + if split_count not in SUPPORTED_SPLIT_COUNTS: + raise ValueError(''.join(['unsupported split count for ', format(SEED_ID, ''), ': ', format(split_count, '')])) + return split_count + +def _split_count() -> int: + return _forced_split_count() or DEFAULT_SPLIT_COUNT + +def _merge_ir_for_split_count(split_count: int) -> Any: + if split_count == 8: + return merge_k20_tail_s8_warp4_ir + if split_count == 10: + return merge_k20_tail_s10_warp4_ir + if split_count == 12: + return merge_k20_tail_s12_warp4_ir + if split_count == 14: + return merge_k20_tail_s14_warp4_ir + if split_count == 16: + return merge_k20_tail_s16_warp4_ir + raise ValueError(''.join(['unsupported split count for ', format(SEED_ID, ''), ': ', format(split_count, '')])) + +@lru_cache(maxsize=5) +def _compiled_merge_k20_tail_warp4(split_count: int): + return parent_v20._compile_ir(_merge_ir_for_split_count(split_count)) + +def _dtype_name(inputs: dict[str, Any], key: str) -> str: + return parent_b8c7._dtype_name(inputs, key) + +def _eligible_rect_d128_k20_q1536(inputs: dict[str, Any]) -> bool: + return parent_b8c7._eligible_rect_d128_k20_q1536(inputs) + +def _select_contract_shapes(shape_labels): + return parent_b8c7._select_contract_shapes(shape_labels) + +def _launch_q1536_split12_warp4(inputs: dict[str, Any]) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + split_count = _split_count() + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_q_tile_pairs = (num_q_tiles + CTA_GROUP - 1) // CTA_GROUP + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tile_pairs * split_count + stage1_grid = min(total_work * CTA_GROUP, GRID_DIM_DEFAULT) + merge_grid = (bsz * n_query + 3) // 4 + partial_dists, partial_indices = parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), bsz * n_query, BLOCK_Q, dim, dim) + tmap_database = base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, dim, dim) + stage1_ir_obj = parent_v20.stage1_k20_unordered_ir + stage1_kernel = parent_v20._compiled_stage1_unordered_for_exact_k(top_k) + stage1_kernel.launch_cluster(grid=(stage1_grid, 1, 1), block=(STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage1_ir_obj, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tile_pairs=num_q_tile_pairs, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), cluster_dims=(CTA_GROUP, 1, 1), shared_mem=stage1_ir_obj.computed_smem_bytes) + merge_ir = _merge_ir_for_split_count(split_count) + merge_kernel = _compiled_merge_k20_tail_warp4(split_count) + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(WARP4_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_ir.computed_smem_bytes) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if not force_fallback and _eligible_rect_d128_k20_q1536(inputs): + return ROUTE_NAME + return parent_b8c7.route_for_contract_inputs(inputs, force_fallback=force_fallback) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + if not force_fallback and _eligible_rect_d128_k20_q1536(inputs): + _launch_q1536_split12_warp4(inputs) + return + parent_b8c7.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + +def candidate_rect_d128_k20_q1536_s12warp4_7768_v1(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_baseline_b8c7(inputs: dict[str, Any]) -> None: + parent_b8c7.candidate_rect_d128_k20_q1536_9b9f_d555_b8c7_v1(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate, correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + return parent_b8c7._run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=kernel_fn, correctness=correctness, time_flashlib=time_flashlib) + +def _trace_record(inputs: dict[str, Any], *, force_fallback: bool=False) -> dict[str, Any]: + label = str(inputs.get('label')) + baseline_route = parent_b8c7.route_for_contract_inputs(inputs) + if not force_fallback and _eligible_rect_d128_k20_q1536(inputs): + return parent_b8c7.base_d555.base_f30c._normalize_route_row({'shape_key': label, 'selected_route': ROUTE_NAME, 'selected_entrypoint': ROUTE_ENTRYPOINT, 'selected_seed': SEED_ID, 'expected_seed': SEED_ID, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': '7768_rect_d128_k20_q1536_s12warp4_exact_guard', 'guard_condition': 'exact BF16 non-build rectangular search shape with B=1 Q=1536 M=65536 D=128 K=20', 'coverage': 'direct split12 tcgen05 stage with warp4 K20 merge before b8c7 fallback', 'consumed_seed': SEED_ID, 'replaced_route': baseline_route, 'baseline_dispatcher_route': baseline_route, 'classification': 'unmeasured'}) + row = dict(parent_b8c7.route_trace_for_contract_shapes((label,), force_fallback=force_fallback)[0]) + if force_fallback and _eligible_rect_d128_k20_q1536(inputs): + row['expected_seed'] = SEED_ID + row['guard_id'] = 'forced_fallback_7768_rect_d128_k20_q1536_s12warp4_disabled' + row['guard_condition'] = 'forced fallback to b8c7; Q1536 split12/warp4 seed disabled' + row['classification'] = 'guard-miss' + return parent_b8c7.base_d555.base_f30c._normalize_route_row(row) + +def route_trace_for_contract_shapes(shape_labels=TARGET_SHAPES, *, force_fallback: bool=False) -> list[dict[str, Any]]: + selected = eval_mod.CANONICAL_SHAPES if shape_labels is None else _select_contract_shapes(shape_labels) + return [_trace_record(parent_b8c7.base_d555.base_f30c._trace_inputs_from_shape(shape), force_fallback=force_fallback) for shape in selected] + +def _annotate_route_trace(route_trace: list[dict[str, Any]], candidate_report: dict[str, Any], baseline_report: dict[str, Any]) -> list[dict[str, Any]]: + annotated = [] + for row in route_trace: + label = str(row.get('shape_key')) + out = dict(row) + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') + speedup_vs_baseline = baseline_ms / candidate_ms if candidate_ms and baseline_ms else None + speedup_vs_external = flashlib_ms / candidate_ms if candidate_ms and flashlib_ms else None + out['dispatcher_kernel_ms'] = candidate_ms + out['baseline_dispatcher_kernel_ms'] = baseline_ms + out['external_baseline_ms'] = flashlib_ms + out['flashlib_ms'] = flashlib_ms + out['relative_speedup_vs_baseline'] = speedup_vs_baseline + out['speedup_vs_external_baseline'] = speedup_vs_external + out['external_baseline_ref'] = 'same_session' if flashlib_ms is not None else 'not_available' + if label in TARGET_SHAPE_SET: + if speedup_vs_external is not None and speedup_vs_external < 1.2: + out['classification'] = 'kernel-slow' + elif speedup_vs_baseline is not None and speedup_vs_baseline < 1.0: + out['classification'] = 'kernel-slow' + elif out.get('selected_seed') == SEED_ID: + out['classification'] = 'seed-consumed' + annotated.append(parent_b8c7.base_d555.base_f30c._normalize_route_row(out)) + return annotated + +def _timing_backends_for_reports(*reports: dict[str, Any]) -> list[str]: + return parent_b8c7._timing_backends_for_reports(*reports) + +def benchmark_baseline_b8c7(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES, benchmark_correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_baseline_b8c7, correctness=benchmark_correctness, time_flashlib=time_flashlib) + report['candidate_id'] = BASELINE_ID + report['measured_entrypoint'] = BASELINE_ENTRYPOINT + report['route_trace'] = parent_b8c7.route_trace_for_contract_shapes(shape_labels) + report['route_trace_included'] = True + return report + +def benchmark_candidate_rect_d128_k20_q1536_s12warp4_7768_v1(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES, baseline_report: dict[str, Any] | None=None, benchmark_correctness: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + if baseline_report is None: + baseline_report = benchmark_baseline_b8c7(use_cupti=use_cupti, shape_labels=shape_labels, benchmark_correctness=benchmark_correctness, time_flashlib=time_flashlib) + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate_rect_d128_k20_q1536_s12warp4_7768_v1, correctness=benchmark_correctness, time_flashlib=time_flashlib) + candidate_metric = candidate_report['summary']['primary_mean'] + baseline_metric = baseline_report['summary']['primary_mean'] + route_trace = _annotate_route_trace(route_trace_for_contract_shapes(shape_labels), candidate_report, baseline_report) + split_counts = {label: _split_count() for label in TARGET_SHAPES if label in candidate_report.get('per_shape', {})} + return {'candidate_id': SEED_ID, 'baseline_candidate_id': BASELINE_ID, 'selected_seeds': (SEED_ID,), 'split_count_by_shape': split_counts, 'source_tasks': SOURCE_TASKS, 'tflops': candidate_metric, 'baseline_tflops': baseline_metric, 'metric_delta': candidate_metric - baseline_metric if candidate_metric and baseline_metric else None, 'all_correct': candidate_report['summary']['all_correct'], 'baseline_all_correct': baseline_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'baseline_performance_comparable': baseline_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'baseline_invalid_performance_reason': baseline_report['summary']['invalid_performance_reason'], 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_candidate_rect_d128_k20_q1536_s12warp4_7768_v1']), 'baseline_entrypoint': ''.join([format(MODULE, ''), ':benchmark_baseline_b8c7']), 'measured_shape_labels': tuple(shape_labels) if shape_labels is not None else 'all_canonical', 'timing_backend': 'cupti' if use_cupti else 'cuda_event', 'route_modules': {SEED_ID: ROUTE_ENTRYPOINT, BASELINE_ID: parent_b8c7.ROUTE_ENTRYPOINT}, 'route_trace': route_trace, 'forced_fallback_route_trace': route_trace_for_contract_shapes(shape_labels, force_fallback=True), 'route_trace_included': True, 'contract_summary': candidate_report['summary'], 'baseline_contract_summary': baseline_report['summary'], 'contract_performance': candidate_report['performance'], 'baseline_contract_performance': baseline_report['performance'], 'consumed_seed_rows': {label: candidate_report.get('per_shape', {}).get(label, {}) for label in TARGET_SHAPES}, 'baseline_consumed_seed_rows': {label: baseline_report.get('per_shape', {}).get(label, {}) for label in TARGET_SHAPES}, 'timing_backends': _timing_backends_for_reports(candidate_report, baseline_report), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'benchmark_correctness_checked': benchmark_correctness, 'benchmark_time_flashlib': time_flashlib, 'rank_objective': {'metric': 'tflops', 'direction': 'maximize', 'value': candidate_metric, 'baseline_value': baseline_metric, 'delta': candidate_metric - baseline_metric if candidate_metric and baseline_metric else None, 'denominator': 'rect_d128_k20_q1536'}, 'report': candidate_report, 'baseline_report': baseline_report} + +def benchmark_knn_build_rect_d128_k20_q1536_s12warp4_7768_v1(**kwargs) -> dict[str, Any]: + return benchmark_candidate_rect_d128_k20_q1536_s12warp4_7768_v1(**kwargs) + +def _write_artifact(payload: dict[str, Any], artifact_dir: Path) -> Path: + artifact_dir.mkdir(parents=True, exist_ok=True) + path = artifact_dir / 'rect_d128_k20_q1536_s12warp4_7768_v1.json' + path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n', encoding='utf-8') + return path diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rect_d64_23be_unordered_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rect_d64_23be_unordered_v1.py new file mode 100644 index 00000000..58f6a215 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rect_d64_23be_unordered_v1.py @@ -0,0 +1,180 @@ +"""D64 rectangular search seed with unordered split-local producer state. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +routes only ``search_rect_b1_q1024_m32768_d64_k10`` through the existing D64 +TMA/tcgen05 split producer shape from 73a9 and a rectangular-D64 cached sorted +split merge with 8-thread merge CTAs. The producer keeps unordered worst-slot +top-10 state while scanning each split, then emits each split-local partial row +sorted for the existing cached cursor merge. Guard misses delegate to the +current 8700 Weave dispatcher; no external runtime fallback is introduced. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import os +from collections.abc import Callable +from functools import lru_cache +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dim_midk_73a9_v1 as d64_parent +from . import knn_build_dispatch_rag_seed_portfolio_8700_v1 as current_dispatcher +from . import knn_build_rect_d64_cf49_v3 as parent_v3 +from .._dispatch_runtime import pack_kernel_args +MODULE = 'loom.examples.weave.knn_build_rect_d64_23be_unordered_v1' +SEED_ID = 'rect_d64_23be_unordered_v1' +TARGET_SHAPE = 'search_rect_b1_q1024_m32768_d64_k10' +TARGET_SHAPES = (TARGET_SHAPE,) +TOP_K_MAX = d64_parent.TOP_K_MAX +D64_FEAT_D = d64_parent.D64_FEAT_D +BLOCK_Q = d64_parent.BLOCK_Q +BLOCK_M = d64_parent.BLOCK_M +THREADS = d64_parent.THREADS +MERGE_THREADS = d64_parent.MERGE_THREADS +GRID_DIM_DEFAULT = d64_parent.GRID_DIM_DEFAULT +DEFAULT_SPLIT_COUNT = 16 +SUPPORTED_SPLITS = (8, 12, 16, 24, 32, 64) +RECT_MERGE_THREADS = 8 +ROUTE_RECT_D64 = 'loom.examples.weave.knn_build_rect_d64_23be_unordered_v1:rect_d64_split_unordered' +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_CURRENT_8700 = 'loom.examples.weave.knn_build_dispatch_rag_seed_portfolio_8700_v1:launch_from_contract_inputs' +merge_generic_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_merge", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "B", "Q", "K", "split_count", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10]], "cta_group": 1, "threads": 256}')) +knn_build_rect_d64_23be_unordered_stage1 = _decode_capture(_json_loads('{"__ir__": "knn_build_rect_d64_23be_unordered_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 25856, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) +stage1_d64_split_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rect_d64_23be_unordered_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 25856, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) +knn_build_rect_d64_23be_s16_cached_merge = _decode_capture(_json_loads('{"__ir__": "knn_build_rect_d64_23be_s16_cached_merge", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 16]], "cta_group": 1, "threads": 8}')) +merge_s16_cached_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rect_d64_23be_s16_cached_merge", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 16]], "cta_group": 1, "threads": 8}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RECT_D64_23BE_VERIFY_KERNEL') + if verify_kernel == 'merge_s16_cached': + return merge_s16_cached_ir + if verify_kernel == 'merge_generic': + return merge_generic_ir + return stage1_d64_split_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rect_d64_23be_unordered_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 25856, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) + +def _compiled_d64_unordered_stage1(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0071"}')) + +def _compiled_s16_cached_merge(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0070"}')) + +def _dtype_name(inputs: dict[str, Any]) -> str: + query = inputs.get('query') + if query is not None: + return str(query.dtype).removeprefix('torch.') + return str(inputs.get('dtype', '')).removeprefix('torch.') + +def _eligible_rect_d64(inputs: dict[str, Any]) -> bool: + label = inputs.get('label') + if label is not None and str(label) != TARGET_SHAPE: + return False + return not bool(inputs.get('build', False)) and _dtype_name(inputs) == 'bfloat16' and (int(inputs.get('B', -1)) == 1) and (int(inputs.get('Q', -1)) == 1024) and (int(inputs.get('M', -1)) == 32768) and (int(inputs.get('D', -1)) == D64_FEAT_D) and (int(inputs.get('K', -1)) == TOP_K_MAX) + +def _rect_split_count() -> int: + override = os.environ.get('LOOM_KNN_RECT_D64_23BE_SPLITS') + if override is None: + return DEFAULT_SPLIT_COUNT + split_count = int(override) + if split_count not in SUPPORTED_SPLITS: + raise ValueError(''.join(['LOOM_KNN_RECT_D64_23BE_SPLITS must be one of ', format(SUPPORTED_SPLITS, '')])) + return split_count + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if not force_fallback and _eligible_rect_d64(inputs): + return ''.join([format(ROUTE_RECT_D64, ''), '_s', format(_rect_split_count(), '')]) + return current_dispatcher.route_for_contract_inputs(inputs) + +def _launch_rect_d64(inputs: dict[str, Any]) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + split_count = _rect_split_count() + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tiles * split_count + stage1_grid = min(total_work, GRID_DIM_DEFAULT) + if split_count == DEFAULT_SPLIT_COUNT: + merge_threads = RECT_MERGE_THREADS + merge_ir = merge_s16_cached_ir + merge_kernel = _compiled_s16_cached_merge() + else: + merge_threads = MERGE_THREADS + merge_ir = merge_generic_ir + merge_kernel = d64_parent.split_parent._compiled_merge() + merge_grid = min((bsz * n_query + merge_threads - 1) // merge_threads, GRID_DIM_DEFAULT) + partial_dists, partial_indices = d64_parent.split_parent._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = d64_parent.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), bsz * n_query, BLOCK_Q, dim, D64_FEAT_D) + tmap_database = d64_parent.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, dim, D64_FEAT_D) + stage1_kernel = _compiled_d64_unordered_stage1() + stage1_kernel.launch(grid=(stage1_grid, 1, 1), block=(THREADS, 1, 1), args=pack_kernel_args(stage1_d64_split_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tiles=num_q_tiles, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), shared_mem=stage1_d64_split_ir.computed_smem_bytes) + if split_count == DEFAULT_SPLIT_COUNT: + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(merge_threads, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_ir.computed_smem_bytes) + return + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(merge_threads, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz, n_query, top_k, split_count, bsz * n_query], shared_mem=merge_ir.computed_smem_bytes) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + if not force_fallback and _eligible_rect_d64(inputs): + _launch_rect_d64(inputs) + return + current_dispatcher.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def candidate_force_fallback(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs, force_fallback=True) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return current_dispatcher._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint: run real contract correctness for selected shapes.""" + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(kernel_fn: Callable[[dict[str, Any]], Any], *, use_cupti: bool, shape_labels=TARGET_SHAPES) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + return evaluate_contract(shapes=selected, correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def _summarize_rows(report: dict[str, Any]) -> dict[str, Any]: + rows = report.get('per_shape', {}) + return {label: {'passed': rows.get(label, {}).get('passed'), 'kernel_ms': rows.get(label, {}).get('kernel_ms'), 'tflops': rows.get(label, {}).get('tflops'), 'flashlib_ms': rows.get(label, {}).get('flashlib_ms'), 'ratio_vs_flashlib': rows.get(label, {}).get('ratio_vs_flashlib'), 'timing_backend': rows.get(label, {}).get('timing_backend'), 'measurement_comparable': rows.get(label, {}).get('measurement_comparable'), 'recall': rows.get(label, {}).get('recall'), 'valid_index_pct': rows.get(label, {}).get('valid_index_pct'), 'boundary_passed': rows.get(label, {}).get('boundary_passed'), 'distance_max_abs': rows.get(label, {}).get('distance_max_abs'), 'distance_max_rel': rows.get(label, {}).get('distance_max_rel')} for label in TARGET_SHAPES if label in rows} + +def benchmark_knn_build_rect_d64_23be_unordered_v1(*, use_cupti: bool=True) -> dict[str, Any]: + """Targeted contract benchmark for the v9 rectangular D64 bucket.""" + candidate_report = _run_with_timing_backend(candidate, use_cupti=use_cupti) + parent_report = _run_with_timing_backend(parent_v3.candidate, use_cupti=use_cupti) + baseline_report = _run_with_timing_backend(current_dispatcher.candidate, use_cupti=use_cupti) + candidate_rows = candidate_report.get('per_shape', {}) + parent_rows = parent_report.get('per_shape', {}) + baseline_rows = baseline_report.get('per_shape', {}) + per_shape_delta = {} + for label in TARGET_SHAPES: + cand_ms = candidate_rows.get(label, {}).get('kernel_ms') + parent_ms = parent_rows.get(label, {}).get('kernel_ms') + base_ms = baseline_rows.get(label, {}).get('kernel_ms') + per_shape_delta[label] = {'candidate_ms': cand_ms, 'parent_v3_ms': parent_ms, 'current_8700_ms': base_ms, 'speedup_vs_parent_v3': parent_ms / cand_ms if cand_ms and parent_ms else None, 'speedup_vs_current_8700': base_ms / cand_ms if cand_ms and base_ms else None, 'candidate_tflops': candidate_rows.get(label, {}).get('tflops'), 'parent_v3_tflops': parent_rows.get(label, {}).get('tflops'), 'current_8700_tflops': baseline_rows.get(label, {}).get('tflops'), 'flashlib_ms': candidate_rows.get(label, {}).get('flashlib_ms'), 'candidate_ratio_vs_flashlib': candidate_rows.get(label, {}).get('ratio_vs_flashlib'), 'candidate_passed': candidate_rows.get(label, {}).get('passed'), 'parent_v3_passed': parent_rows.get(label, {}).get('passed'), 'current_8700_passed': baseline_rows.get(label, {}).get('passed')} + return {'tflops': candidate_report['summary']['primary_mean'] or 0.0, 'all_correct': candidate_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'timing_backend': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'split_count': _rect_split_count(), 'stage1_route': 'unordered_final_sort', 'merge_route': 's16_cached_t8' if _rect_split_count() == DEFAULT_SPLIT_COUNT else 'generic', 'target_shapes': TARGET_SHAPES, 'measured_entrypoint': 'loom.examples.weave.knn_build_rect_d64_23be_unordered_v1:benchmark_knn_build_rect_d64_23be_unordered_v1', 'candidate_rows': _summarize_rows(candidate_report), 'parent_v3_rows': _summarize_rows(parent_report), 'current_8700_rows': _summarize_rows(baseline_report), 'per_shape_delta': per_shape_delta, 'report': candidate_report, 'parent_v3_report': parent_report, 'current_8700_report': baseline_report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rect_d64_cf49_v2.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rect_d64_cf49_v2.py new file mode 100644 index 00000000..e14dd8a7 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rect_d64_cf49_v2.py @@ -0,0 +1,167 @@ +"""D64 rectangular search seed for kNN build/search. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +routes only ``search_rect_b1_q1024_m32768_d64_k10`` through the existing D64 +TMA/tcgen05 split-local top-k producer from 73a9 and a rectangular-D64 cached +sorted split merge. Guard misses delegate to the current 8700 Weave dispatcher; +no external runtime fallback is introduced. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import os +from collections.abc import Callable +from functools import lru_cache +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dim_midk_73a9_v1 as d64_parent +from . import knn_build_dispatch_rag_seed_portfolio_8700_v1 as current_dispatcher +from .._dispatch_runtime import pack_kernel_args +TARGET_SHAPE = 'search_rect_b1_q1024_m32768_d64_k10' +TARGET_SHAPES = (TARGET_SHAPE,) +TOP_K_MAX = d64_parent.TOP_K_MAX +D64_FEAT_D = d64_parent.D64_FEAT_D +BLOCK_Q = d64_parent.BLOCK_Q +BLOCK_M = d64_parent.BLOCK_M +THREADS = d64_parent.THREADS +MERGE_THREADS = d64_parent.MERGE_THREADS +GRID_DIM_DEFAULT = d64_parent.GRID_DIM_DEFAULT +DEFAULT_SPLIT_COUNT = 16 +SUPPORTED_SPLITS = (8, 12, 16, 24, 32, 64) +RECT_MERGE_THREADS = 64 +ROUTE_RECT_D64 = 'loom.examples.weave.knn_build_rect_d64_cf49_v2:rect_d64_split_cached' +ROUTE_CURRENT_8700 = 'loom.examples.weave.knn_build_dispatch_rag_seed_portfolio_8700_v1:launch_from_contract_inputs' +stage1_d64_split_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_dim_midk_73a9_d64_split_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 25856, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) +merge_generic_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_merge", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "B", "Q", "K", "split_count", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10]], "cta_group": 1, "threads": 256}')) +knn_build_rect_d64_cf49_s16_cached_merge = _decode_capture(_json_loads('{"__ir__": "knn_build_rect_d64_cf49_s16_cached_merge", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 16]], "cta_group": 1, "threads": 64}')) +merge_s16_cached_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rect_d64_cf49_s16_cached_merge", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 16]], "cta_group": 1, "threads": 64}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RECT_D64_CF49_VERIFY_KERNEL') + if verify_kernel == 'merge_s16_cached': + return merge_s16_cached_ir + if verify_kernel == 'merge_generic': + return merge_generic_ir + return stage1_d64_split_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_dim_midk_73a9_d64_split_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 25856, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) + +def _compiled_s16_cached_merge(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0183"}')) + +def _dtype_name(inputs: dict[str, Any]) -> str: + query = inputs.get('query') + if query is not None: + return str(query.dtype).removeprefix('torch.') + return str(inputs.get('dtype', '')).removeprefix('torch.') + +def _eligible_rect_d64(inputs: dict[str, Any]) -> bool: + label = inputs.get('label') + if label is not None and str(label) != TARGET_SHAPE: + return False + return not bool(inputs.get('build', False)) and _dtype_name(inputs) == 'bfloat16' and (int(inputs.get('B', -1)) == 1) and (int(inputs.get('Q', -1)) == 1024) and (int(inputs.get('M', -1)) == 32768) and (int(inputs.get('D', -1)) == D64_FEAT_D) and (int(inputs.get('K', -1)) == TOP_K_MAX) + +def _rect_split_count() -> int: + override = os.environ.get('LOOM_KNN_RECT_D64_CF49_SPLITS') + if override is None: + return DEFAULT_SPLIT_COUNT + split_count = int(override) + if split_count not in SUPPORTED_SPLITS: + raise ValueError(''.join(['LOOM_KNN_RECT_D64_CF49_SPLITS must be one of ', format(SUPPORTED_SPLITS, '')])) + return split_count + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if not force_fallback and _eligible_rect_d64(inputs): + return ''.join([format(ROUTE_RECT_D64, ''), '_s', format(_rect_split_count(), '')]) + return current_dispatcher.route_for_contract_inputs(inputs) + +def _launch_rect_d64(inputs: dict[str, Any]) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + split_count = _rect_split_count() + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tiles * split_count + stage1_grid = min(total_work, GRID_DIM_DEFAULT) + if split_count == DEFAULT_SPLIT_COUNT: + merge_threads = RECT_MERGE_THREADS + merge_ir = merge_s16_cached_ir + merge_kernel = _compiled_s16_cached_merge() + else: + merge_threads = MERGE_THREADS + merge_ir = merge_generic_ir + merge_kernel = d64_parent.split_parent._compiled_merge() + merge_grid = min((bsz * n_query + merge_threads - 1) // merge_threads, GRID_DIM_DEFAULT) + partial_dists, partial_indices = d64_parent.split_parent._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = d64_parent.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), bsz * n_query, BLOCK_Q, dim, D64_FEAT_D) + tmap_database = d64_parent.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, dim, D64_FEAT_D) + stage1_kernel = d64_parent._compiled_d64_stage1() + stage1_kernel.launch(grid=(stage1_grid, 1, 1), block=(THREADS, 1, 1), args=pack_kernel_args(stage1_d64_split_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tiles=num_q_tiles, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), shared_mem=stage1_d64_split_ir.computed_smem_bytes) + if split_count == DEFAULT_SPLIT_COUNT: + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(merge_threads, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_ir.computed_smem_bytes) + return + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(merge_threads, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz, n_query, top_k, split_count, bsz * n_query], shared_mem=merge_ir.computed_smem_bytes) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + if not force_fallback and _eligible_rect_d64(inputs): + _launch_rect_d64(inputs) + return + current_dispatcher.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def candidate_force_fallback(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs, force_fallback=True) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return current_dispatcher._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint: run real contract correctness for selected shapes.""" + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(kernel_fn: Callable[[dict[str, Any]], Any], *, use_cupti: bool, shape_labels=TARGET_SHAPES) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + return evaluate_contract(shapes=selected, correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def _summarize_rows(report: dict[str, Any]) -> dict[str, Any]: + rows = report.get('per_shape', {}) + return {label: {'passed': rows.get(label, {}).get('passed'), 'kernel_ms': rows.get(label, {}).get('kernel_ms'), 'tflops': rows.get(label, {}).get('tflops'), 'flashlib_ms': rows.get(label, {}).get('flashlib_ms'), 'ratio_vs_flashlib': rows.get(label, {}).get('ratio_vs_flashlib'), 'timing_backend': rows.get(label, {}).get('timing_backend'), 'measurement_comparable': rows.get(label, {}).get('measurement_comparable'), 'recall': rows.get(label, {}).get('recall'), 'valid_index_pct': rows.get(label, {}).get('valid_index_pct'), 'boundary_passed': rows.get(label, {}).get('boundary_passed'), 'distance_max_abs': rows.get(label, {}).get('distance_max_abs'), 'distance_max_rel': rows.get(label, {}).get('distance_max_rel')} for label in TARGET_SHAPES if label in rows} + +def benchmark_knn_build_rect_d64_cf49_v2(*, use_cupti: bool=True) -> dict[str, Any]: + """Targeted contract benchmark for the v6 rectangular D64 bucket.""" + candidate_report = _run_with_timing_backend(candidate, use_cupti=use_cupti) + baseline_report = _run_with_timing_backend(current_dispatcher.candidate, use_cupti=use_cupti) + candidate_rows = candidate_report.get('per_shape', {}) + baseline_rows = baseline_report.get('per_shape', {}) + per_shape_delta = {} + for label in TARGET_SHAPES: + cand_ms = candidate_rows.get(label, {}).get('kernel_ms') + base_ms = baseline_rows.get(label, {}).get('kernel_ms') + per_shape_delta[label] = {'candidate_ms': cand_ms, 'current_8700_ms': base_ms, 'speedup_vs_current_8700': base_ms / cand_ms if cand_ms and base_ms else None, 'candidate_tflops': candidate_rows.get(label, {}).get('tflops'), 'current_8700_tflops': baseline_rows.get(label, {}).get('tflops'), 'flashlib_ms': candidate_rows.get(label, {}).get('flashlib_ms'), 'candidate_ratio_vs_flashlib': candidate_rows.get(label, {}).get('ratio_vs_flashlib'), 'candidate_passed': candidate_rows.get(label, {}).get('passed'), 'current_8700_passed': baseline_rows.get(label, {}).get('passed')} + return {'tflops': candidate_report['summary']['primary_mean'] or 0.0, 'all_correct': candidate_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'timing_backend': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'split_count': _rect_split_count(), 'merge_route': 's16_cached' if _rect_split_count() == DEFAULT_SPLIT_COUNT else 'generic', 'target_shapes': TARGET_SHAPES, 'measured_entrypoint': 'loom.examples.weave.knn_build_rect_d64_cf49_v2:benchmark_knn_build_rect_d64_cf49_v2', 'candidate_rows': _summarize_rows(candidate_report), 'current_8700_rows': _summarize_rows(baseline_report), 'per_shape_delta': per_shape_delta, 'report': candidate_report, 'current_8700_report': baseline_report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rect_d64_cf49_v3.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rect_d64_cf49_v3.py new file mode 100644 index 00000000..8839d2e8 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rect_d64_cf49_v3.py @@ -0,0 +1,167 @@ +"""D64 rectangular search seed for kNN build/search. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +routes only ``search_rect_b1_q1024_m32768_d64_k10`` through the existing D64 +TMA/tcgen05 split-local top-k producer from 73a9 and a rectangular-D64 cached +sorted split merge with 8-thread merge CTAs. Guard misses delegate to the +current 8700 Weave dispatcher; no external runtime fallback is introduced. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import os +from collections.abc import Callable +from functools import lru_cache +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dim_midk_73a9_v1 as d64_parent +from . import knn_build_dispatch_rag_seed_portfolio_8700_v1 as current_dispatcher +from .._dispatch_runtime import pack_kernel_args +TARGET_SHAPE = 'search_rect_b1_q1024_m32768_d64_k10' +TARGET_SHAPES = (TARGET_SHAPE,) +TOP_K_MAX = d64_parent.TOP_K_MAX +D64_FEAT_D = d64_parent.D64_FEAT_D +BLOCK_Q = d64_parent.BLOCK_Q +BLOCK_M = d64_parent.BLOCK_M +THREADS = d64_parent.THREADS +MERGE_THREADS = d64_parent.MERGE_THREADS +GRID_DIM_DEFAULT = d64_parent.GRID_DIM_DEFAULT +DEFAULT_SPLIT_COUNT = 16 +SUPPORTED_SPLITS = (8, 12, 16, 24, 32, 64) +RECT_MERGE_THREADS = 8 +ROUTE_RECT_D64 = 'loom.examples.weave.knn_build_rect_d64_cf49_v3:rect_d64_split_cached' +ROUTE_CURRENT_8700 = 'loom.examples.weave.knn_build_dispatch_rag_seed_portfolio_8700_v1:launch_from_contract_inputs' +stage1_d64_split_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_dim_midk_73a9_d64_split_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 25856, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) +merge_generic_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_merge", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "B", "Q", "K", "split_count", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10]], "cta_group": 1, "threads": 256}')) +knn_build_rect_d64_cf49_s16_cached_merge = _decode_capture(_json_loads('{"__ir__": "knn_build_rect_d64_cf49_s16_cached_merge", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 16]], "cta_group": 1, "threads": 8}')) +merge_s16_cached_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rect_d64_cf49_s16_cached_merge", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 16]], "cta_group": 1, "threads": 8}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RECT_D64_CF49_VERIFY_KERNEL') + if verify_kernel == 'merge_s16_cached': + return merge_s16_cached_ir + if verify_kernel == 'merge_generic': + return merge_generic_ir + return stage1_d64_split_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_dim_midk_73a9_d64_split_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 25856, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) + +def _compiled_s16_cached_merge(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0188"}')) + +def _dtype_name(inputs: dict[str, Any]) -> str: + query = inputs.get('query') + if query is not None: + return str(query.dtype).removeprefix('torch.') + return str(inputs.get('dtype', '')).removeprefix('torch.') + +def _eligible_rect_d64(inputs: dict[str, Any]) -> bool: + label = inputs.get('label') + if label is not None and str(label) != TARGET_SHAPE: + return False + return not bool(inputs.get('build', False)) and _dtype_name(inputs) == 'bfloat16' and (int(inputs.get('B', -1)) == 1) and (int(inputs.get('Q', -1)) == 1024) and (int(inputs.get('M', -1)) == 32768) and (int(inputs.get('D', -1)) == D64_FEAT_D) and (int(inputs.get('K', -1)) == TOP_K_MAX) + +def _rect_split_count() -> int: + override = os.environ.get('LOOM_KNN_RECT_D64_CF49_SPLITS') + if override is None: + return DEFAULT_SPLIT_COUNT + split_count = int(override) + if split_count not in SUPPORTED_SPLITS: + raise ValueError(''.join(['LOOM_KNN_RECT_D64_CF49_SPLITS must be one of ', format(SUPPORTED_SPLITS, '')])) + return split_count + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if not force_fallback and _eligible_rect_d64(inputs): + return ''.join([format(ROUTE_RECT_D64, ''), '_s', format(_rect_split_count(), '')]) + return current_dispatcher.route_for_contract_inputs(inputs) + +def _launch_rect_d64(inputs: dict[str, Any]) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + split_count = _rect_split_count() + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tiles * split_count + stage1_grid = min(total_work, GRID_DIM_DEFAULT) + if split_count == DEFAULT_SPLIT_COUNT: + merge_threads = RECT_MERGE_THREADS + merge_ir = merge_s16_cached_ir + merge_kernel = _compiled_s16_cached_merge() + else: + merge_threads = MERGE_THREADS + merge_ir = merge_generic_ir + merge_kernel = d64_parent.split_parent._compiled_merge() + merge_grid = min((bsz * n_query + merge_threads - 1) // merge_threads, GRID_DIM_DEFAULT) + partial_dists, partial_indices = d64_parent.split_parent._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = d64_parent.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), bsz * n_query, BLOCK_Q, dim, D64_FEAT_D) + tmap_database = d64_parent.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, dim, D64_FEAT_D) + stage1_kernel = d64_parent._compiled_d64_stage1() + stage1_kernel.launch(grid=(stage1_grid, 1, 1), block=(THREADS, 1, 1), args=pack_kernel_args(stage1_d64_split_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tiles=num_q_tiles, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), shared_mem=stage1_d64_split_ir.computed_smem_bytes) + if split_count == DEFAULT_SPLIT_COUNT: + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(merge_threads, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz * n_query], shared_mem=merge_ir.computed_smem_bytes) + return + merge_kernel.launch(grid=(merge_grid, 1, 1), block=(merge_threads, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], bsz, n_query, top_k, split_count, bsz * n_query], shared_mem=merge_ir.computed_smem_bytes) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + if not force_fallback and _eligible_rect_d64(inputs): + _launch_rect_d64(inputs) + return + current_dispatcher.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def candidate_force_fallback(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs, force_fallback=True) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return current_dispatcher._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint: run real contract correctness for selected shapes.""" + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(kernel_fn: Callable[[dict[str, Any]], Any], *, use_cupti: bool, shape_labels=TARGET_SHAPES) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + return evaluate_contract(shapes=selected, correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def _summarize_rows(report: dict[str, Any]) -> dict[str, Any]: + rows = report.get('per_shape', {}) + return {label: {'passed': rows.get(label, {}).get('passed'), 'kernel_ms': rows.get(label, {}).get('kernel_ms'), 'tflops': rows.get(label, {}).get('tflops'), 'flashlib_ms': rows.get(label, {}).get('flashlib_ms'), 'ratio_vs_flashlib': rows.get(label, {}).get('ratio_vs_flashlib'), 'timing_backend': rows.get(label, {}).get('timing_backend'), 'measurement_comparable': rows.get(label, {}).get('measurement_comparable'), 'recall': rows.get(label, {}).get('recall'), 'valid_index_pct': rows.get(label, {}).get('valid_index_pct'), 'boundary_passed': rows.get(label, {}).get('boundary_passed'), 'distance_max_abs': rows.get(label, {}).get('distance_max_abs'), 'distance_max_rel': rows.get(label, {}).get('distance_max_rel')} for label in TARGET_SHAPES if label in rows} + +def benchmark_knn_build_rect_d64_cf49_v3(*, use_cupti: bool=True) -> dict[str, Any]: + """Targeted contract benchmark for the v6 rectangular D64 bucket.""" + candidate_report = _run_with_timing_backend(candidate, use_cupti=use_cupti) + baseline_report = _run_with_timing_backend(current_dispatcher.candidate, use_cupti=use_cupti) + candidate_rows = candidate_report.get('per_shape', {}) + baseline_rows = baseline_report.get('per_shape', {}) + per_shape_delta = {} + for label in TARGET_SHAPES: + cand_ms = candidate_rows.get(label, {}).get('kernel_ms') + base_ms = baseline_rows.get(label, {}).get('kernel_ms') + per_shape_delta[label] = {'candidate_ms': cand_ms, 'current_8700_ms': base_ms, 'speedup_vs_current_8700': base_ms / cand_ms if cand_ms and base_ms else None, 'candidate_tflops': candidate_rows.get(label, {}).get('tflops'), 'current_8700_tflops': baseline_rows.get(label, {}).get('tflops'), 'flashlib_ms': candidate_rows.get(label, {}).get('flashlib_ms'), 'candidate_ratio_vs_flashlib': candidate_rows.get(label, {}).get('ratio_vs_flashlib'), 'candidate_passed': candidate_rows.get(label, {}).get('passed'), 'current_8700_passed': baseline_rows.get(label, {}).get('passed')} + return {'tflops': candidate_report['summary']['primary_mean'] or 0.0, 'all_correct': candidate_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'timing_backend': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'split_count': _rect_split_count(), 'merge_route': 's16_cached' if _rect_split_count() == DEFAULT_SPLIT_COUNT else 'generic', 'target_shapes': TARGET_SHAPES, 'measured_entrypoint': 'loom.examples.weave.knn_build_rect_d64_cf49_v3:benchmark_knn_build_rect_d64_cf49_v3', 'candidate_rows': _summarize_rows(candidate_report), 'current_8700_rows': _summarize_rows(baseline_report), 'per_shape_delta': per_shape_delta, 'report': candidate_report, 'current_8700_report': baseline_report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rect_intermediate_frontier_1b45_e054_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rect_intermediate_frontier_1b45_e054_v1.py new file mode 100644 index 00000000..5a5a4b05 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rect_intermediate_frontier_1b45_e054_v1.py @@ -0,0 +1,143 @@ +"""kNN search rectangular Q2048/M32768 K10 exact seed. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +targets only ``search_rect_b1_q2048_m32768_d128_k10``. It routes the row through +the existing K10 tcgen05/TMA stage-1 producer with an explicit M-axis split +fanout and a matching cached K10 merge. Guard misses delegate to the current +exported Weave dispatcher; the benchmark hook reports only the target bucket +row. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from functools import lru_cache +from typing import Any, Callable +from .._dispatch_runtime import dc as dc +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_split72_4e09_de1a_3dc7_v48 as current_dispatch +from . import knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_v1 as parent_lowk +TARGET_SHAPE = 'search_rect_b1_q2048_m32768_d128_k10' +TARGET_SHAPES = (TARGET_SHAPE,) +FEAT_D = parent_lowk.FEAT_D +TOP_K = parent_lowk.TOP_K_MAX +SPLIT_COUNT_DEFAULT = 16 +SUPPORTED_SPLITS = (16, 32, 64) +MERGE_THREADS = parent_lowk.parent_cached.RAG_MERGE_THREADS + +def _ir_with_constants(ir_obj: Any, *, suffix: str, **updates: int) -> Any: + constants = tuple(((name, updates.get(name, value)) for name, value in ir_obj.constants)) + return dc.replace(ir_obj, symbol=''.join([format(ir_obj.symbol, ''), '_', format(suffix, '')]), constants=constants) +merge_k10_s16_cache_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k10_merge_s7_rowbase_cache_recte054_s16", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 16]], "cta_group": 1, "threads": 32}')) +merge_k10_s32_cache_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k10_merge_s7_rowbase_cache_recte054_s32", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 32]], "cta_group": 1, "threads": 32}')) +merge_k10_s64_cache_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k10_merge_s7_rowbase_cache_recte054_s64", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 64]], "cta_group": 1, "threads": 32}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RECT_INTERMEDIATE_E054_VERIFY_KERNEL') + if verify_kernel == 'merge_s16': + return merge_k10_s16_cache_ir + if verify_kernel == 'merge_s32': + return merge_k10_s32_cache_ir + if verify_kernel == 'merge_s64': + return merge_k10_s64_cache_ir + return parent_lowk.stage1_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_stage1_batch8_cond4_vmin_maxtree", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) + +def _compile_ir(ir_obj: Any): + from .._dispatch_runtime import generate_kernel + from .._dispatch_runtime import _cuda_include_dirs + from .._dispatch_runtime import compile_cuda + from .._dispatch_runtime import CUDAKernel + source = generate_kernel(ir_obj, validate=False, smem_bytes=ir_obj.computed_smem_bytes) + cubin = compile_cuda(source, arch=parent_lowk.base_v1._select_arch_and_preload(), options=['--use_fast_math'], include_dirs=_cuda_include_dirs()) + return CUDAKernel(cubin, ''.join(['kernel_', format(ir_obj.symbol, '')])) + +def _merge_ir_for_split(split_count: int) -> Any: + if split_count == 16: + return merge_k10_s16_cache_ir + if split_count == 32: + return merge_k10_s32_cache_ir + if split_count == 64: + return merge_k10_s64_cache_ir + raise ValueError(''.join(['unsupported rect intermediate split count: ', format(split_count, '')])) + +@lru_cache(maxsize=3) +def _compiled_merge_for_split(split_count: int): + return _compile_ir(_merge_ir_for_split(split_count)) + +def _rect_split_count() -> int: + split_text = os.environ.get('LOOM_KNN_RECT_INTERMEDIATE_E054_SPLIT_COUNT') + if not split_text: + return SPLIT_COUNT_DEFAULT + split_count = int(split_text) + if split_count not in SUPPORTED_SPLITS: + raise ValueError(''.join(['LOOM_KNN_RECT_INTERMEDIATE_E054_SPLIT_COUNT must be one of ', format(SUPPORTED_SPLITS, '')])) + return split_count + +def _dtype_is_bf16(inputs: dict[str, Any]) -> bool: + return str(inputs['query'].dtype) == 'torch.bfloat16' and str(inputs['database'].dtype) == 'torch.bfloat16' + +def _eligible_rect_intermediate(inputs: dict[str, Any]) -> bool: + label = inputs.get('label') + if label is not None and str(label) != TARGET_SHAPE: + return False + return not bool(inputs.get('build', False)) and _dtype_is_bf16(inputs) and (int(inputs['B']) == 1) and (int(inputs['Q']) == 2048) and (int(inputs['M']) == 32768) and (int(inputs['D']) == FEAT_D) and (int(inputs['K']) == TOP_K) + +def _launch_rect_intermediate(inputs: dict[str, Any]) -> None: + split_count = _rect_split_count() + parent_lowk._launch_k10_cached_path(inputs, split_count=split_count, merge_threads=MERGE_THREADS, merge_kernel=_compiled_merge_for_split(split_count), merge_ir=_merge_ir_for_split(split_count)) + +def launch_from_contract_inputs(inputs: dict[str, Any]) -> None: + if _eligible_rect_intermediate(inputs): + _launch_rect_intermediate(inputs) + return + current_dispatch.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(candidate, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return current_dispatch._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint for the exact rectangular target row.""" + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(kernel_fn: Callable[[dict[str, Any]], Any], *, use_cupti: bool, shape_labels=TARGET_SHAPES) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + return eval_mod.evaluate(kernel_fn, shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def _summarize_rows(report: dict[str, Any]) -> dict[str, Any]: + rows = report.get('per_shape', {}) + return {label: {'passed': rows.get(label, {}).get('passed'), 'kernel_ms': rows.get(label, {}).get('kernel_ms'), 'tflops': rows.get(label, {}).get('tflops'), 'flashlib_ms': rows.get(label, {}).get('flashlib_ms'), 'ratio_vs_flashlib': rows.get(label, {}).get('ratio_vs_flashlib'), 'timing_backend': rows.get(label, {}).get('timing_backend'), 'measurement_comparable': rows.get(label, {}).get('measurement_comparable'), 'recall': rows.get(label, {}).get('recall'), 'boundary_passed': rows.get(label, {}).get('boundary_passed'), 'distance_max_abs': rows.get(label, {}).get('distance_max_abs'), 'distance_max_rel': rows.get(label, {}).get('distance_max_rel')} for label in TARGET_SHAPES if label in rows} + +def benchmark_knn_build_rect_intermediate_frontier_1b45_e054_v1(*, use_cupti: bool=False) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(candidate, use_cupti=use_cupti) + baseline_report = _run_with_timing_backend(current_dispatch.candidate, use_cupti=use_cupti) + candidate_rows = candidate_report.get('per_shape', {}) + baseline_rows = baseline_report.get('per_shape', {}) + per_shape_delta = {} + for label in TARGET_SHAPES: + cand_ms = candidate_rows.get(label, {}).get('kernel_ms') + base_ms = baseline_rows.get(label, {}).get('kernel_ms') + per_shape_delta[label] = {'candidate_ms': cand_ms, 'current_dispatch_ms': base_ms, 'speedup_vs_current_dispatch': base_ms / cand_ms if cand_ms and base_ms else None, 'candidate_tflops': candidate_rows.get(label, {}).get('tflops'), 'current_dispatch_tflops': baseline_rows.get(label, {}).get('tflops'), 'flashlib_ms': candidate_rows.get(label, {}).get('flashlib_ms'), 'candidate_ratio_vs_flashlib': candidate_rows.get(label, {}).get('ratio_vs_flashlib')} + return {'tflops': candidate_report['summary']['primary_mean'] or 0.0, 'all_correct': candidate_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'split_count': _rect_split_count(), 'target_shapes': TARGET_SHAPES, 'measured_entrypoint': 'loom.examples.weave.knn_build_rect_intermediate_frontier_1b45_e054_v1:benchmark_knn_build_rect_intermediate_frontier_1b45_e054_v1', 'candidate_rows': _summarize_rows(candidate_report), 'current_dispatch_rows': _summarize_rows(baseline_report), 'per_shape_delta': per_shape_delta, 'report': candidate_report, 'current_dispatch_report': baseline_report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rect_intermediate_frontier_6a73_4452_v2.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rect_intermediate_frontier_6a73_4452_v2.py new file mode 100644 index 00000000..c1ec06bf --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rect_intermediate_frontier_6a73_4452_v2.py @@ -0,0 +1,157 @@ +"""kNN search rectangular Q2048/M32768 K10 split-count variant. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +targets only ``search_rect_b1_q2048_m32768_d128_k10``. It keeps the e054 K10 +tcgen05/TMA stage-1 producer plus cached K10 merge path, but adds lower +split-count variants for an exact-shape A/B. Guard misses delegate to the +current exported Weave dispatcher; the benchmark hook reports only the target +bucket row. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from functools import lru_cache +from typing import Any, Callable +from .._dispatch_runtime import dc as dc +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_split72_4e09_de1a_3dc7_v48 as current_dispatch +from . import knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_v1 as parent_lowk +from . import knn_build_rect_intermediate_frontier_1b45_e054_v1 as previous_seed +TARGET_SHAPE = 'search_rect_b1_q2048_m32768_d128_k10' +TARGET_SHAPES = (TARGET_SHAPE,) +FEAT_D = parent_lowk.FEAT_D +TOP_K = parent_lowk.TOP_K_MAX +SPLIT_COUNT_DEFAULT = 8 +SUPPORTED_SPLITS = (8, 12, 16, 24, 32) +MERGE_THREADS = parent_lowk.parent_cached.RAG_MERGE_THREADS + +def _ir_with_constants(ir_obj: Any, *, suffix: str, **updates: int) -> Any: + constants = tuple(((name, updates.get(name, value)) for name, value in ir_obj.constants)) + return dc.replace(ir_obj, symbol=''.join([format(ir_obj.symbol, ''), '_', format(suffix, '')]), constants=constants) +merge_k10_s8_cache_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k10_merge_s7_rowbase_cache_rect4452_s8", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 8]], "cta_group": 1, "threads": 32}')) +merge_k10_s12_cache_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k10_merge_s7_rowbase_cache_rect4452_s12", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 12]], "cta_group": 1, "threads": 32}')) +merge_k10_s16_cache_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k10_merge_s7_rowbase_cache_rect4452_s16", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 16]], "cta_group": 1, "threads": 32}')) +merge_k10_s24_cache_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k10_merge_s7_rowbase_cache_rect4452_s24", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 24]], "cta_group": 1, "threads": 32}')) +merge_k10_s32_cache_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k10_merge_s7_rowbase_cache_rect4452_s32", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 32]], "cta_group": 1, "threads": 32}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RECT_INTERMEDIATE_4452_VERIFY_KERNEL') + if verify_kernel == 'merge_s8': + return merge_k10_s8_cache_ir + if verify_kernel == 'merge_s12': + return merge_k10_s12_cache_ir + if verify_kernel == 'merge_s16': + return merge_k10_s16_cache_ir + if verify_kernel == 'merge_s24': + return merge_k10_s24_cache_ir + if verify_kernel == 'merge_s32': + return merge_k10_s32_cache_ir + return parent_lowk.stage1_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_stage1_batch8_cond4_vmin_maxtree", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) + +def _compile_ir(ir_obj: Any): + from .._dispatch_runtime import generate_kernel + from .._dispatch_runtime import _cuda_include_dirs + from .._dispatch_runtime import compile_cuda + from .._dispatch_runtime import CUDAKernel + source = generate_kernel(ir_obj, validate=False, smem_bytes=ir_obj.computed_smem_bytes) + cubin = compile_cuda(source, arch=parent_lowk.base_v1._select_arch_and_preload(), options=['--use_fast_math'], include_dirs=_cuda_include_dirs()) + return CUDAKernel(cubin, ''.join(['kernel_', format(ir_obj.symbol, '')])) + +def _merge_ir_for_split(split_count: int) -> Any: + if split_count == 8: + return merge_k10_s8_cache_ir + if split_count == 12: + return merge_k10_s12_cache_ir + if split_count == 16: + return merge_k10_s16_cache_ir + if split_count == 24: + return merge_k10_s24_cache_ir + if split_count == 32: + return merge_k10_s32_cache_ir + raise ValueError(''.join(['unsupported rect intermediate split count: ', format(split_count, '')])) + +@lru_cache(maxsize=5) +def _compiled_merge_for_split(split_count: int): + return _compile_ir(_merge_ir_for_split(split_count)) + +def _rect_split_count() -> int: + split_text = os.environ.get('LOOM_KNN_RECT_INTERMEDIATE_4452_SPLIT_COUNT') + if not split_text: + return SPLIT_COUNT_DEFAULT + split_count = int(split_text) + if split_count not in SUPPORTED_SPLITS: + raise ValueError(''.join(['LOOM_KNN_RECT_INTERMEDIATE_4452_SPLIT_COUNT must be one of ', format(SUPPORTED_SPLITS, '')])) + return split_count + +def _dtype_is_bf16(inputs: dict[str, Any]) -> bool: + return str(inputs['query'].dtype) == 'torch.bfloat16' and str(inputs['database'].dtype) == 'torch.bfloat16' + +def _eligible_rect_intermediate(inputs: dict[str, Any]) -> bool: + label = inputs.get('label') + if label is not None and str(label) != TARGET_SHAPE: + return False + return not bool(inputs.get('build', False)) and _dtype_is_bf16(inputs) and (int(inputs['B']) == 1) and (int(inputs['Q']) == 2048) and (int(inputs['M']) == 32768) and (int(inputs['D']) == FEAT_D) and (int(inputs['K']) == TOP_K) + +def _launch_rect_intermediate(inputs: dict[str, Any]) -> None: + split_count = _rect_split_count() + parent_lowk._launch_k10_cached_path(inputs, split_count=split_count, merge_threads=MERGE_THREADS, merge_kernel=_compiled_merge_for_split(split_count), merge_ir=_merge_ir_for_split(split_count)) + +def launch_from_contract_inputs(inputs: dict[str, Any]) -> None: + if _eligible_rect_intermediate(inputs): + _launch_rect_intermediate(inputs) + return + current_dispatch.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(candidate, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return current_dispatch._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint for the exact rectangular target row.""" + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(kernel_fn: Callable[[dict[str, Any]], Any], *, use_cupti: bool, shape_labels=TARGET_SHAPES) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + return eval_mod.evaluate(kernel_fn, shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def _summarize_rows(report: dict[str, Any]) -> dict[str, Any]: + rows = report.get('per_shape', {}) + return {label: {'passed': rows.get(label, {}).get('passed'), 'kernel_ms': rows.get(label, {}).get('kernel_ms'), 'tflops': rows.get(label, {}).get('tflops'), 'flashlib_ms': rows.get(label, {}).get('flashlib_ms'), 'ratio_vs_flashlib': rows.get(label, {}).get('ratio_vs_flashlib'), 'timing_backend': rows.get(label, {}).get('timing_backend'), 'measurement_comparable': rows.get(label, {}).get('measurement_comparable'), 'recall': rows.get(label, {}).get('recall'), 'boundary_passed': rows.get(label, {}).get('boundary_passed'), 'distance_max_abs': rows.get(label, {}).get('distance_max_abs'), 'distance_max_rel': rows.get(label, {}).get('distance_max_rel')} for label in TARGET_SHAPES if label in rows} + +def benchmark_knn_build_rect_intermediate_frontier_6a73_4452_v2(*, use_cupti: bool=False) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(candidate, use_cupti=use_cupti) + previous_report = _run_with_timing_backend(previous_seed.candidate, use_cupti=use_cupti) + baseline_report = _run_with_timing_backend(current_dispatch.candidate, use_cupti=use_cupti) + candidate_rows = candidate_report.get('per_shape', {}) + previous_rows = previous_report.get('per_shape', {}) + baseline_rows = baseline_report.get('per_shape', {}) + per_shape_delta = {} + for label in TARGET_SHAPES: + cand_ms = candidate_rows.get(label, {}).get('kernel_ms') + prev_ms = previous_rows.get(label, {}).get('kernel_ms') + base_ms = baseline_rows.get(label, {}).get('kernel_ms') + per_shape_delta[label] = {'candidate_ms': cand_ms, 'previous_seed_ms': prev_ms, 'current_dispatch_ms': base_ms, 'speedup_vs_previous_seed': prev_ms / cand_ms if cand_ms and prev_ms else None, 'speedup_vs_current_dispatch': base_ms / cand_ms if cand_ms and base_ms else None, 'candidate_tflops': candidate_rows.get(label, {}).get('tflops'), 'previous_seed_tflops': previous_rows.get(label, {}).get('tflops'), 'current_dispatch_tflops': baseline_rows.get(label, {}).get('tflops'), 'flashlib_ms': candidate_rows.get(label, {}).get('flashlib_ms'), 'candidate_ratio_vs_flashlib': candidate_rows.get(label, {}).get('ratio_vs_flashlib')} + return {'tflops': candidate_report['summary']['primary_mean'] or 0.0, 'all_correct': candidate_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'timing_backend': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'split_count': _rect_split_count(), 'target_shapes': TARGET_SHAPES, 'measured_entrypoint': 'loom.examples.weave.knn_build_rect_intermediate_frontier_6a73_4452_v2:benchmark_knn_build_rect_intermediate_frontier_6a73_4452_v2', 'candidate_rows': _summarize_rows(candidate_report), 'previous_seed_rows': _summarize_rows(previous_report), 'current_dispatch_rows': _summarize_rows(baseline_report), 'per_shape_delta': per_shape_delta, 'report': candidate_report, 'previous_seed_report': previous_report, 'current_dispatch_report': baseline_report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rect_smallq_largem_ff59_d15e_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rect_smallq_largem_ff59_d15e_v1.py new file mode 100644 index 00000000..bf4a4a2f --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_rect_smallq_largem_ff59_d15e_v1.py @@ -0,0 +1,142 @@ +"""kNN search rectangular Q1024/M8192 K10 exact seed. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +targets only ``search_rect_b1_q1024_m8192_d128_k10``. It routes the row through +the existing K10 tcgen05/TMA stage-1 producer with an M-axis split fanout and a +matching cached K10 merge. Guard misses delegate to the current exported Weave +dispatcher; the benchmark hook reports only the target bucket row. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from functools import lru_cache +from typing import Any, Callable +from .._dispatch_runtime import dc as dc +from .. import _dispatch_runtime as eval_mod +from . import knn_build_dispatch_split72_4e09_de1a_3dc7_v48 as current_dispatch +from . import knn_build_evolve_7bfc_split_cg2_u2_smallmedfan_rag7_k10merge_stage1batch_cond4_k5merge4tree_vmin_maxtree_k5tree_mintree_k10s4s7cache_t32r32_k10mintree_v1 as parent_lowk +TARGET_SHAPE = 'search_rect_b1_q1024_m8192_d128_k10' +TARGET_SHAPES = (TARGET_SHAPE,) +FEAT_D = parent_lowk.FEAT_D +TOP_K = parent_lowk.TOP_K_MAX +SPLIT_COUNT_DEFAULT = 16 +SUPPORTED_SPLITS = (8, 16, 32) +MERGE_THREADS = parent_lowk.parent_cached.RAG_MERGE_THREADS + +def _ir_with_constants(ir_obj: Any, *, suffix: str, **updates: int) -> Any: + constants = tuple(((name, updates.get(name, value)) for name, value in ir_obj.constants)) + return dc.replace(ir_obj, symbol=''.join([format(ir_obj.symbol, ''), '_', format(suffix, '')]), constants=constants) +merge_k10_s8_cache_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k10_merge_s7_rowbase_cache_rectd15e_s8", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 8]], "cta_group": 1, "threads": 32}')) +merge_k10_s16_cache_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k10_merge_s7_rowbase_cache_rectd15e_s16", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 16]], "cta_group": 1, "threads": 32}')) +merge_k10_s32_cache_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_k10_merge_s7_rowbase_cache_rectd15e_s32", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 0, "constants": [["TOP_K_MAX", 10], ["SPLIT_COUNT", 32]], "cta_group": 1, "threads": 32}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RECT_D15E_VERIFY_KERNEL') + if verify_kernel == 'merge_s8': + return merge_k10_s8_cache_ir + if verify_kernel == 'merge_s16': + return merge_k10_s16_cache_ir + if verify_kernel == 'merge_s32': + return merge_k10_s32_cache_ir + return parent_lowk.stage1_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_evolve_7bfc_split_cg2_stage1_batch8_cond4_vmin_maxtree", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 10]], "cta_group": 1, "threads": 192}')) + +def _compile_ir(ir_obj: Any): + from .._dispatch_runtime import generate_kernel + from .._dispatch_runtime import _cuda_include_dirs + from .._dispatch_runtime import compile_cuda + from .._dispatch_runtime import CUDAKernel + source = generate_kernel(ir_obj, validate=False, smem_bytes=ir_obj.computed_smem_bytes) + cubin = compile_cuda(source, arch=parent_lowk.base_v1._select_arch_and_preload(), options=['--use_fast_math'], include_dirs=_cuda_include_dirs()) + return CUDAKernel(cubin, ''.join(['kernel_', format(ir_obj.symbol, '')])) + +def _merge_ir_for_split(split_count: int) -> Any: + if split_count == 8: + return merge_k10_s8_cache_ir + if split_count == 16: + return merge_k10_s16_cache_ir + if split_count == 32: + return merge_k10_s32_cache_ir + raise ValueError(''.join(['unsupported rect split count: ', format(split_count, '')])) + +@lru_cache(maxsize=3) +def _compiled_merge_for_split(split_count: int): + return _compile_ir(_merge_ir_for_split(split_count)) + +def _rect_split_count() -> int: + split_text = os.environ.get('LOOM_KNN_RECT_D15E_SPLIT_COUNT') + if not split_text: + return SPLIT_COUNT_DEFAULT + split_count = int(split_text) + if split_count not in SUPPORTED_SPLITS: + raise ValueError(''.join(['LOOM_KNN_RECT_D15E_SPLIT_COUNT must be one of ', format(SUPPORTED_SPLITS, '')])) + return split_count + +def _dtype_is_bf16(inputs: dict[str, Any]) -> bool: + return str(inputs['query'].dtype) == 'torch.bfloat16' and str(inputs['database'].dtype) == 'torch.bfloat16' + +def _eligible_rect_smallq_largem(inputs: dict[str, Any]) -> bool: + label = inputs.get('label') + if label is not None and str(label) != TARGET_SHAPE: + return False + return not bool(inputs.get('build', False)) and _dtype_is_bf16(inputs) and (int(inputs['B']) == 1) and (int(inputs['Q']) == 1024) and (int(inputs['M']) == 8192) and (int(inputs['D']) == FEAT_D) and (int(inputs['K']) == TOP_K) + +def _launch_rect_smallq_largem(inputs: dict[str, Any]) -> None: + split_count = _rect_split_count() + parent_lowk._launch_k10_cached_path(inputs, split_count=split_count, merge_threads=MERGE_THREADS, merge_kernel=_compiled_merge_for_split(split_count), merge_ir=_merge_ir_for_split(split_count)) + +def launch_from_contract_inputs(inputs: dict[str, Any]) -> None: + if _eligible_rect_smallq_largem(inputs): + _launch_rect_smallq_largem(inputs) + return + current_dispatch.launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]): + launch_from_contract_inputs(inputs) + return None + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + from .._dispatch_runtime import evaluate + return evaluate(candidate, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + return current_dispatch._select_contract_shapes(shape_labels) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + """e2e-test entrypoint for the exact rectangular target row.""" + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(kernel_fn: Callable[[dict[str, Any]], Any], *, use_cupti: bool, shape_labels=TARGET_SHAPES) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + return eval_mod.evaluate(kernel_fn, shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def _summarize_rows(report: dict[str, Any]) -> dict[str, Any]: + rows = report.get('per_shape', {}) + return {label: {'passed': rows.get(label, {}).get('passed'), 'kernel_ms': rows.get(label, {}).get('kernel_ms'), 'tflops': rows.get(label, {}).get('tflops'), 'flashlib_ms': rows.get(label, {}).get('flashlib_ms'), 'ratio_vs_flashlib': rows.get(label, {}).get('ratio_vs_flashlib'), 'timing_backend': rows.get(label, {}).get('timing_backend'), 'measurement_comparable': rows.get(label, {}).get('measurement_comparable'), 'recall': rows.get(label, {}).get('recall'), 'boundary_passed': rows.get(label, {}).get('boundary_passed'), 'distance_max_abs': rows.get(label, {}).get('distance_max_abs'), 'distance_max_rel': rows.get(label, {}).get('distance_max_rel')} for label in TARGET_SHAPES if label in rows} + +def benchmark_knn_build_rect_smallq_largem_ff59_d15e_v1(*, use_cupti: bool=False) -> dict[str, Any]: + candidate_report = _run_with_timing_backend(candidate, use_cupti=use_cupti) + baseline_report = _run_with_timing_backend(current_dispatch.candidate, use_cupti=use_cupti) + candidate_rows = candidate_report.get('per_shape', {}) + baseline_rows = baseline_report.get('per_shape', {}) + per_shape_delta = {} + for label in TARGET_SHAPES: + cand_ms = candidate_rows.get(label, {}).get('kernel_ms') + base_ms = baseline_rows.get(label, {}).get('kernel_ms') + per_shape_delta[label] = {'candidate_ms': cand_ms, 'current_dispatch_ms': base_ms, 'speedup_vs_current_dispatch': base_ms / cand_ms if cand_ms and base_ms else None, 'candidate_tflops': candidate_rows.get(label, {}).get('tflops'), 'current_dispatch_tflops': baseline_rows.get(label, {}).get('tflops'), 'flashlib_ms': candidate_rows.get(label, {}).get('flashlib_ms'), 'candidate_ratio_vs_flashlib': candidate_rows.get(label, {}).get('ratio_vs_flashlib')} + return {'tflops': candidate_report['summary']['primary_mean'] or 0.0, 'all_correct': candidate_report['summary']['all_correct'], 'performance_comparable': candidate_report['summary']['performance_comparable'], 'invalid_performance_reason': candidate_report['summary']['invalid_performance_reason'], 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'split_count': _rect_split_count(), 'target_shapes': TARGET_SHAPES, 'measured_entrypoint': 'loom.examples.weave.knn_build_rect_smallq_largem_ff59_d15e_v1:benchmark_knn_build_rect_smallq_largem_ff59_d15e_v1', 'candidate_rows': _summarize_rows(candidate_report), 'current_dispatch_rows': _summarize_rows(baseline_report), 'per_shape_delta': per_shape_delta, 'report': candidate_report, 'current_dispatch_report': baseline_report} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_residual_rag_search_1877_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_residual_rag_search_1877_v1.py new file mode 100644 index 00000000..e1700c78 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_residual_rag_search_1877_v1.py @@ -0,0 +1,219 @@ +"""Residual RAG/search low-floor bucket for the 1877 continuation. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +does not edit the production dispatcher. It routes two residual non-build rows +from the full90 5c08 median ledger to existing Weave-only seed routes: + +* 4fbf v6 exact BF16 Q128/M100000/K32 RAG stream split72/group8 route. +* d15e exact BF16 Q1024/M8192/K10 rectangular search split16 route. + +Guard misses delegate to the current 5c08 full90 sidecar. FlashLib is used only +by the contract harness as a black-box timing baseline. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from pathlib import Path +from typing import Any, Callable +from . import knn_build_dispatch_ad64_1b8f_4b51_ceb3_full90_synthesis_v1 as fallback_full90 +from . import knn_build_rag_stream_k32_q128m100000_ad64_v1 as q128_seed +from . import knn_build_rect_smallq_largem_ff59_d15e_v1 as rect_seed +MODULE = 'loom.examples.weave.knn_build_residual_rag_search_1877_v1' +RAG_Q128_K32 = 'rag_stream_largek_b1_q128_m100000_d128_k32' +SEARCH_RECT_Q1024_K10 = 'search_rect_b1_q1024_m8192_d128_k10' +TARGET_SHAPES = (RAG_Q128_K32, SEARCH_RECT_Q1024_K10) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +SEED_Q128_ID = q128_seed.SEED_K32_Q128_M100000_AD64_V1_ID +SEED_RECT_ID = 'd15e_rect_smallq_largem_v1' +SEED_FALLBACK_ID = fallback_full90.CANDIDATE_CONFIGS[fallback_full90.DEFAULT_CANDIDATE_KEY]['candidate_id'] +CANDIDATE_ID = 'residual_rag_search_1877_v1' +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +ROUTE_Q128 = q128_seed.ROUTE_Q128_M100000_ENTRYPOINT +ROUTE_RECT = 'loom.examples.weave.knn_build_rect_smallq_largem_ff59_d15e_v1:split16' +ROUTE_FALLBACK = fallback_full90.ROUTE_ENTRYPOINT +PRODUCTION_ROUTE_MODULES = {**fallback_full90.PRODUCTION_ROUTE_MODULES, SEED_Q128_ID: ROUTE_Q128, SEED_RECT_ID: ROUTE_RECT, SEED_FALLBACK_ID: ROUTE_FALLBACK} +SOURCE_TASKS = {**fallback_full90.SOURCE_TASKS, SEED_Q128_ID: 'weave-evolve-knn-build-ad64-q128m100000 / design_doc/active/weave_evolve_knn_build_round_136_ad64_q128m100000.md', SEED_RECT_ID: 'weave-evolve-knn-build-d15e / loom.examples.weave.knn_build_rect_smallq_largem_ff59_d15e_v1', CANDIDATE_ID: 'weave-evolve-knn-build-1877 / residual RAG/search low-floor bucket'} +eval_mod = fallback_full90.eval_mod + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_RESIDUAL_RAG_SEARCH_1877_VERIFY_KERNEL') + if verify_kernel == 'q128_stage1': + return q128_seed.direct_seed.stage1_k32_tailinf_ir + if verify_kernel == 'q128_fused_merge': + return q128_seed.direct_seed._fused_merge_ir(q128_seed.K32_SPLIT_COUNT, q128_seed.K32_GROUP_COUNT) + if verify_kernel == 'rect_stage1': + return rect_seed.parent_lowk.stage1_ir + if verify_kernel == 'rect_merge_s16': + return rect_seed.merge_k10_s16_cache_ir + return q128_seed.direct_seed.stage1_k32_tailinf_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_rag_frontier_4fbf_stage1_k32_sort4earlystop_tailinf", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tile_pairs", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [2, 1, 1], "computed_smem_bytes": 50432, "constants": [["BLOCK_Q", 128], ["BLOCK_M", 64], ["FEAT_D", 128], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 192}')) + +def _select_contract_shapes(shape_labels): + return fallback_full90._select_contract_shapes(shape_labels) + +def _trace_inputs_for_shape(shape: dict[str, Any]) -> dict[str, Any]: + return fallback_full90._trace_inputs_for_shape(shape) + +def _inputs_for_label(label: str) -> dict[str, Any]: + return _trace_inputs_for_shape(_select_contract_shapes((label,))[0]) + +def _dtype_name(inputs: dict[str, Any], name: str='query') -> str: + tensor = inputs.get(name) + if tensor is not None and hasattr(tensor, 'dtype'): + return str(tensor.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')).replace('torch.', '') + +def _label_can_hit(inputs: dict[str, Any], label: str) -> bool: + value = inputs.get('label') + return value is None or str(value) == label + +def _eligible_q128_k32(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, RAG_Q128_K32) and (not bool(inputs.get('build', False))) and (int(inputs.get('B', -1)) == 1) and (int(inputs.get('Q', -1)) == 128) and (int(inputs.get('M', -1)) == 100000) and (int(inputs.get('D', -1)) == 128) and (int(inputs.get('K', -1)) == 32) and (_dtype_name(inputs, 'query') == 'bfloat16') and (_dtype_name(inputs, 'database') in ('', 'bfloat16')) + +def _eligible_rect_search(inputs: dict[str, Any]) -> bool: + return _label_can_hit(inputs, SEARCH_RECT_Q1024_K10) and (not bool(inputs.get('build', False))) and (int(inputs.get('B', -1)) == 1) and (int(inputs.get('Q', -1)) == 1024) and (int(inputs.get('M', -1)) == 8192) and (int(inputs.get('D', -1)) == rect_seed.FEAT_D) and (int(inputs.get('K', -1)) == rect_seed.TOP_K) and (_dtype_name(inputs, 'query') == 'bfloat16') and (_dtype_name(inputs, 'database') in ('', 'bfloat16')) + +def _selected_seed_for_inputs(inputs: dict[str, Any]) -> tuple[str | None, str | None]: + if _eligible_q128_k32(inputs): + return (SEED_Q128_ID, RAG_Q128_K32) + if _eligible_rect_search(inputs): + return (SEED_RECT_ID, SEARCH_RECT_Q1024_K10) + return (None, None) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if not force_fallback: + selected_seed, _label = _selected_seed_for_inputs(inputs) + if selected_seed == SEED_Q128_ID: + return q128_seed._route_name(split_count=q128_seed.K32_SPLIT_COUNT, group_count=q128_seed.K32_GROUP_COUNT) + if selected_seed == SEED_RECT_ID: + return ROUTE_RECT + return fallback_full90.route_for_contract_inputs(inputs, force_fallback=force_fallback) + +def q128_k32_direct_s72g8(inputs: dict[str, Any]) -> None: + q128_seed._launch_q128_m100000_s72g8(inputs, split_count=q128_seed.K32_SPLIT_COUNT, group_count=q128_seed.K32_GROUP_COUNT) + +def rect_q1024_m8192_k10_split16(inputs: dict[str, Any]) -> None: + rect_seed._launch_rect_smallq_largem(inputs) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + if not force_fallback: + selected_seed, _label = _selected_seed_for_inputs(inputs) + if selected_seed == SEED_Q128_ID: + q128_k32_direct_s72g8(inputs) + return + if selected_seed == SEED_RECT_ID: + rect_q1024_m8192_k10_split16(inputs) + return + fallback_full90.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + +def candidate_residual_rag_search_1877_v1(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate(inputs: dict[str, Any]) -> None: + candidate_residual_rag_search_1877_v1(inputs) + +def candidate_fallback_full90(inputs: dict[str, Any]) -> None: + fallback_full90.launch_from_contract_inputs(inputs) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _benchmark_shapes(shape_labels, *, time_flashlib: bool) -> list[dict[str, Any]]: + selected = _select_contract_shapes(TARGET_SHAPES if shape_labels is None else shape_labels) + out = [] + for shape in selected: + params = dict(shape['params']) + params['time_flashlib'] = bool(time_flashlib) + out.append({'label': shape['label'], 'params': params}) + return out + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate, time_flashlib: bool=True) -> dict[str, Any]: + previous = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_benchmark_shapes(shape_labels, time_flashlib=time_flashlib), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = previous + +def route_trace_for_contract_shapes(shape_labels=TARGET_SHAPES, *, force_fallback: bool=False) -> list[dict[str, Any]]: + rows = [] + for label in tuple(shape_labels): + inputs = _inputs_for_label(str(label)) + selected_seed, matched_label = (None, None) if force_fallback else _selected_seed_for_inputs(inputs) + route = route_for_contract_inputs(inputs, force_fallback=force_fallback) + fallback_route = fallback_full90.route_for_contract_inputs(inputs) + fallback_row = dict(fallback_full90.route_trace_for_contract_shapes((label,))[0]) + if selected_seed is None: + row = dict(fallback_row) + row['expected_seed'] = None + row['fallback_full90_route'] = fallback_route + row['candidate_guard_status'] = 'forced_fallback' if force_fallback else 'guard_miss' + if force_fallback: + row['guard_id'] = 'forced_fallback_residual_rag_search_1877' + row['guard_condition'] = 'forced fallback to current 5c08 full90 sidecar' + row['classification'] = 'guard-miss' + rows.append(fallback_full90._normalize_route_row(row)) + continue + guard_conditions = {SEED_Q128_ID: 'exact BF16 non-build B=1 Q=128 M=100000 D=128 K=32', SEED_RECT_ID: 'exact BF16 non-build B=1 Q=1024 M=8192 D=128 K=10'} + selected_entrypoints = {SEED_Q128_ID: ROUTE_Q128, SEED_RECT_ID: ROUTE_RECT} + rows.append(fallback_full90._normalize_route_row({'shape_key': label, 'selected_route': route, 'selected_entrypoint': selected_entrypoints[selected_seed], 'selected_seed': selected_seed, 'expected_seed': selected_seed, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': ''.join(['residual_rag_search_1877_', format(selected_seed, '')]), 'guard_condition': guard_conditions[selected_seed], 'matched_label': matched_label, 'fallback_full90_route': fallback_route, 'baseline_dispatcher_route': fallback_row.get('selected_route'), 'classification': 'seed-consumed'})) + return rows + +def _rows_for_labels(report: dict[str, Any], labels: tuple[str, ...]) -> dict[str, Any]: + return {label: dict(report.get('per_shape', {}).get(label, {})) for label in labels} + +def _per_shape_delta(candidate_report: dict[str, Any], baseline_report: dict[str, Any], labels: tuple[str, ...]): + rows = [] + for label in labels: + candidate_row = candidate_report.get('per_shape', {}).get(label, {}) + baseline_row = baseline_report.get('per_shape', {}).get(label, {}) + candidate_ms = candidate_row.get('kernel_ms') + baseline_ms = baseline_row.get('kernel_ms') + flashlib_ms = candidate_row.get('flashlib_ms') or baseline_row.get('flashlib_ms') + inputs = _inputs_for_label(label) + selected_seed, _matched_label = _selected_seed_for_inputs(inputs) + rows.append({'shape_key': label, 'selected_seed': selected_seed, 'candidate_route': route_for_contract_inputs(inputs), 'fallback_full90_route': fallback_full90.route_for_contract_inputs(inputs), 'candidate_ms': candidate_ms, 'fallback_full90_ms': baseline_ms, 'flashlib_ms': flashlib_ms, 'speedup_vs_fallback_full90': baseline_ms / candidate_ms if candidate_ms and baseline_ms else None, 'ratio_vs_flashlib': flashlib_ms / candidate_ms if candidate_ms and flashlib_ms else None, 'candidate_passed': candidate_row.get('passed'), 'fallback_full90_passed': baseline_row.get('passed'), 'timing_backend': candidate_row.get('timing_backend') or baseline_row.get('timing_backend')}) + return rows + +def _below_flashlib_floor(report: dict[str, Any], *, floor: float=1.05) -> list[dict[str, Any]]: + rows = [] + for label, row in sorted(report.get('per_shape', {}).items()): + ratio = row.get('ratio_vs_flashlib') + if isinstance(ratio, (float, int)) and ratio < floor: + rows.append({'shape_key': label, 'kernel_ms': row.get('kernel_ms'), 'flashlib_ms': row.get('flashlib_ms'), 'ratio_vs_flashlib': ratio, 'selected_seed': _selected_seed_for_inputs(_inputs_for_label(label))[0]}) + return rows + +def benchmark_candidate_residual_rag_search_1877_v1(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES, run_baseline: bool=True, time_flashlib: bool=True) -> dict[str, Any]: + labels = tuple((str(label) for label in shape_labels)) + baseline_report = None + if run_baseline: + baseline_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=labels, kernel_fn=candidate_fallback_full90, time_flashlib=time_flashlib) + candidate_report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=labels, kernel_fn=candidate, time_flashlib=time_flashlib) + candidate_metric = candidate_report.get('summary', {}).get('primary_mean') + baseline_metric = baseline_report.get('summary', {}).get('primary_mean') if baseline_report else None + payload: dict[str, Any] = {'candidate_id': CANDIDATE_ID, 'measured_entrypoint': ''.join([format(MODULE, ''), ':benchmark_candidate_residual_rag_search_1877_v1']), 'candidate_entrypoint': ROUTE_ENTRYPOINT, 'selected_seeds': (SEED_Q128_ID, SEED_RECT_ID), 'source_tasks': SOURCE_TASKS, 'all_correct': candidate_report.get('summary', {}).get('all_correct'), 'performance_comparable': candidate_report.get('summary', {}).get('performance_comparable'), 'invalid_performance_reason': candidate_report.get('summary', {}).get('invalid_performance_reason'), 'tflops': candidate_metric, 'fallback_full90_tflops': baseline_metric, 'metric_delta_vs_fallback_full90': candidate_metric - baseline_metric if candidate_metric is not None and baseline_metric is not None else None, 'timing_backend': 'cupti' if use_cupti else 'cuda_event', 'use_cupti': use_cupti, 'time_flashlib': time_flashlib, 'denominator': 'residual_rag_search_exact2', 'measured_shape_labels': list(labels), 'route_trace': route_trace_for_contract_shapes(labels), 'forced_fallback_route_trace': route_trace_for_contract_shapes(labels, force_fallback=True), 'route_modules': PRODUCTION_ROUTE_MODULES, 'contract_summary': candidate_report.get('summary'), 'contract_performance': candidate_report.get('performance'), 'contract_correctness': candidate_report.get('correctness'), 'rank_objective': {'metric': 'tflops', 'direction': 'maximize', 'value': candidate_metric, 'valid_measurement_count': candidate_report.get('performance', {}).get('valid_measurement_count'), 'comparable': candidate_report.get('performance', {}).get('comparable')}, 'below_flashlib_floor': _below_flashlib_floor(candidate_report, floor=1.05), 'report': candidate_report} + if baseline_report is not None: + payload.update({'fallback_full90_entrypoint': ROUTE_FALLBACK, 'fallback_full90_all_correct': baseline_report.get('summary', {}).get('all_correct'), 'fallback_full90_performance_comparable': baseline_report.get('summary', {}).get('performance_comparable'), 'fallback_full90_summary': baseline_report.get('summary'), 'fallback_full90_performance': baseline_report.get('performance'), 'selected_route_rows': _rows_for_labels(candidate_report, labels), 'fallback_full90_rows': _rows_for_labels(baseline_report, labels), 'seed_delta_matrix': _per_shape_delta(candidate_report, baseline_report, labels), 'fallback_full90_report': baseline_report}) + return payload + +def write_benchmark_artifact(artifact_dir: str | Path, *, use_cupti: bool=True, shape_labels=TARGET_SHAPES, run_baseline: bool=True, time_flashlib: bool=True) -> dict[str, str]: + payload = benchmark_candidate_residual_rag_search_1877_v1(use_cupti=use_cupti, shape_labels=shape_labels, run_baseline=run_baseline, time_flashlib=time_flashlib) + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + path = out_dir / 'residual_rag_search_1877_v1.json' + path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + return {'candidate_payload': str(path)} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_v12_d128_q16_k48_dd2b_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_v12_d128_q16_k48_dd2b_v1.py new file mode 100644 index 00000000..cb5d0cd6 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_v12_d128_q16_k48_dd2b_v1.py @@ -0,0 +1,205 @@ +"""v12 D128 Q16/M100000 K48 RAG seed for kNN build/search. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +targets only the BF16 non-build v12 row +``rag_microbatch_over32_d128_b1_q16_m100000_k48``. It widens the validated +Q16 ROW_16x256B two-compute-warp stage to K48, then uses a K48 warp-row +split-list merge to write the contract distances and indices. Guard misses +delegate to the existing v12 high-D search/RAG sidecar lineage. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from collections.abc import Callable +from functools import cache, lru_cache +from pathlib import Path +from typing import Any +from .._dispatch_runtime import dc as dc +from .. import _dispatch_runtime as eval_mod +from . import knn_build_rag_microbucket_k32_q16irreg2warp_a444_v2 as q16_2warp +from . import knn_build_rag_microbucket_k32warpmerge_0077_v1 as warpmerge +from . import knn_build_v12_highd_search_be66_v1 as parent_v12 +from .._dispatch_runtime import pack_kernel_args +MODULE = 'loom.examples.weave.knn_build_v12_d128_q16_k48_dd2b_v1' +CANDIDATE_ID = 'knn_build_v12_d128_q16_k48_dd2b_v1' +TARGET_SHAPE = 'rag_microbatch_over32_d128_b1_q16_m100000_k48' +TARGET_SHAPES = (TARGET_SHAPE,) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +K48_TOP_K_MAX = 48 +K48_SPLIT_COUNT = _decode_capture(_json_loads('144')) +K48_ROWS_PER_CTA = q16_2warp.K32_ROWS4_ROWS_PER_CTA +K48_STAGE1_THREADS = q16_2warp.Q16_2WARP_STAGE1_THREADS +K48_MERGE_THREADS = q16_2warp.K32_ROWS4_MERGE_THREADS +K48_MERGE_WARPS = q16_2warp.K32_ROWS4_WARPS +BLOCK_Q = q16_2warp.rowld1.Q16_ROWLD1_BLOCK_Q +BLOCK_M = q16_2warp.rowld1.Q16_ROWLD1_BLOCK_M +FEAT_D = q16_2warp.rowld1.Q16_ROWLD1_FEAT_D +ROWS_COVERED = q16_2warp.Q16_2WARP_ACTIVE_ROWS +LOCAL_LISTS_PER_ROW = q16_2warp.Q16_2WARP_LOCAL_LISTS_PER_ROW +UPPER_DOT_ROWS = q16_2warp.Q16_2WARP_UPPER_DOT_ROWS +UPPER_DOT_COLS = q16_2warp.Q16_2WARP_UPPER_DOT_COLS +UPPER_DOT_ELEMS = UPPER_DOT_ROWS * UPPER_DOT_COLS +SMEM_BASE_BYTES = q16_2warp.Q16_2WARP_SMEM_BASE_BYTES +LOCAL_ELEMS = ROWS_COVERED * LOCAL_LISTS_PER_ROW * K48_TOP_K_MAX +UPPER_DOT_OFFSET = SMEM_BASE_BYTES +LOCAL_D_OFFSET = UPPER_DOT_OFFSET + UPPER_DOT_ELEMS * 4 +LOCAL_I_OFFSET = LOCAL_D_OFFSET + LOCAL_ELEMS * 4 +SMEM_POOL_BYTES = LOCAL_I_OFFSET + LOCAL_ELEMS * 4 +ROUTE_PREFIX = 'knn_build_v12_d128_q16_k48_dd2b_v1' +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +BENCHMARK_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_knn_build_v12_d128_q16_k48_dd2b_v1']) +_insert_sorted_pair_k48 = _ir_proxy('loom.examples.weave.knn_build_v12_d128_q16_k48_dd2b_v1:_insert_sorted_pair_k48', 256) +knn_build_v12_d128_q16_k48_dd2b_v1_stage1 = _decode_capture(_json_loads('{"__ir__": "knn_build_v12_d128_q16_k48_dd2b_v1_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 60672, "constants": [["BLOCK_Q_CONST", 64], ["BLOCK_M_CONST", 64], ["FEAT_D_CONST", 128], ["TOP_K_MAX", 48], ["ROWS_COVERED_CONST", 16]], "cta_group": 1, "threads": 128}')) +stage1_k48_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_v12_d128_q16_k48_dd2b_v1_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 60672, "constants": [["BLOCK_Q_CONST", 64], ["BLOCK_M_CONST", 64], ["FEAT_D_CONST", 128], ["TOP_K_MAX", 48], ["ROWS_COVERED_CONST", 16]], "cta_group": 1, "threads": 128}')) + +def _ir_with_constants(ir_obj: Any, *, suffix: str, **updates: int) -> Any: + constants = tuple(((name, updates.get(name, value)) for name, value in ir_obj.constants)) + return dc.replace(ir_obj, symbol=''.join([format(ir_obj.symbol, ''), '_', format(suffix, '')]), constants=constants) + +def _warp_merge_ir(split_count: int) -> Any: + if K48_ROWS_PER_CTA <= 0 or K48_ROWS_PER_CTA > K48_MERGE_WARPS: + raise ValueError(''.join(['rows_per_cta=', format(K48_ROWS_PER_CTA, ''), ' exceeds merge warps=', format(K48_MERGE_WARPS, '')])) + return _ir_with_constants(warpmerge.k32_warp_row_merge_ir, suffix=''.join(['k48s', format(split_count, ''), 'r', format(K48_ROWS_PER_CTA, ''), '_dd2b_v1']), TOP_K_MAX=K48_TOP_K_MAX, SPLIT_COUNT=split_count, SPLITS_PER_LANE=warpmerge._splits_per_lane(split_count), ROWS_PER_CTA=K48_ROWS_PER_CTA) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_V12_D128_Q16_K48_DD2B_VERIFY_KERNEL') + split_count = int(os.environ.get('LOOM_KNN_V12_D128_Q16_K48_DD2B_VERIFY_SPLIT', K48_SPLIT_COUNT)) + if verify_kernel == 'merge': + return _warp_merge_ir(split_count) + return stage1_k48_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_v12_d128_q16_k48_dd2b_v1_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 60672, "constants": [["BLOCK_Q_CONST", 64], ["BLOCK_M_CONST", 64], ["FEAT_D_CONST", 128], ["TOP_K_MAX", 48], ["ROWS_COVERED_CONST", 16]], "cta_group": 1, "threads": 128}')) + +def _compiled_stage1_k48(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0131"}')) + +@cache +def _compiled_warp_merge_k48(split_count: int): + return warpmerge.rowld_seed.compact_seed.q16_tailinf.parent_k32._compile_ir(_warp_merge_ir(split_count)) + +def _dtype_name(inputs: dict[str, Any], tensor_name: str='query') -> str: + tensor = inputs.get(tensor_name) + if tensor is not None and hasattr(tensor, 'dtype'): + return str(tensor.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')).replace('torch.', '') + +def _eligible_k48(inputs: dict[str, Any]) -> bool: + label = inputs.get('label') + return (label is None or str(label) in TARGET_SHAPE_SET) and _dtype_name(inputs, 'query') == 'bfloat16' and (_dtype_name(inputs, 'database') in ('', 'bfloat16')) and (not bool(inputs.get('build', False))) and (int(inputs.get('B', -1)) == 1) and (int(inputs.get('Q', -1)) == 16) and (int(inputs.get('M', -1)) == 100000) and (int(inputs.get('D', -1)) == 128) and (int(inputs.get('K', -1)) == K48_TOP_K_MAX) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + if _eligible_k48(inputs) and (not force_fallback): + return ''.join([format(ROUTE_PREFIX, ''), ':', format(TARGET_SHAPE, ''), ':q16:m100000:d128:k48:row16x256b_2cw:s', format(K48_SPLIT_COUNT, ''), ':r', format(K48_ROWS_PER_CTA, '')]) + return parent_v12.route_for_contract_inputs(inputs, force_fallback=force_fallback) + +def _launch_k48(inputs: dict[str, Any], *, split_count: int=K48_SPLIT_COUNT) -> None: + query = inputs['query'] + database = inputs['database'] + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + if top_k != K48_TOP_K_MAX: + raise ValueError(''.join(['dd2b K48 seed supports K=', format(K48_TOP_K_MAX, ''), ', got K=', format(top_k, '')])) + num_q_tiles = (n_query + BLOCK_Q - 1) // BLOCK_Q + num_db_tiles = (n_database + BLOCK_M - 1) // BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tiles * split_count + total_queries = bsz * n_query + stage1_grid = min(total_work, warpmerge.rowld_seed.compact_seed.GRID_DIM_DEFAULT) + merge_grid = min((total_queries + K48_ROWS_PER_CTA - 1) // K48_ROWS_PER_CTA, warpmerge.rowld_seed.compact_seed.GRID_DIM_DEFAULT) + partial_dists, partial_indices = warpmerge.rowld_seed.compact_seed.q16_tailinf.parent_k32.parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = warpmerge.rowld_seed.compact_seed.q16_tailinf.parent_k32.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), total_queries, BLOCK_Q, dim, dim) + tmap_database = warpmerge.rowld_seed.compact_seed.q16_tailinf.parent_k32.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, BLOCK_M, dim, dim) + _compiled_stage1_k48().launch(grid=(stage1_grid, 1, 1), block=(K48_STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage1_k48_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tiles=num_q_tiles, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), shared_mem=stage1_k48_ir.computed_smem_bytes) + merge_ir = _warp_merge_ir(split_count) + _compiled_warp_merge_k48(split_count).launch(grid=(merge_grid, 1, 1), block=(K48_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], total_queries], shared_mem=merge_ir.computed_smem_bytes) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + if _eligible_k48(inputs) and (not force_fallback): + _launch_k48(inputs) + return + parent_v12.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels=TARGET_SHAPES): + wanted = set(shape_labels) + return [shape for shape in eval_mod.CANONICAL_SHAPES if str(shape['label']) in wanted] + +def _trace_inputs_for_label(label: str) -> dict[str, Any]: + shape = _select_contract_shapes((label,))[0] + inputs = dict(shape['params']) + inputs['label'] = shape['label'] + return inputs + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate, correctness: bool=True, benchmark: bool=True) -> dict[str, Any]: + previous = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=correctness, benchmark=benchmark, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = previous + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def route_trace_for_contract_shapes(shape_labels=TARGET_SHAPES, *, force_fallback: bool=False) -> list[dict[str, Any]]: + rows = [] + for label in shape_labels: + inputs = _trace_inputs_for_label(str(label)) + if _eligible_k48(inputs) and (not force_fallback): + rows.append({'shape_key': str(label), 'selected_route': route_for_contract_inputs(inputs), 'selected_entrypoint': ROUTE_ENTRYPOINT, 'selected_seed': CANDIDATE_ID, 'expected_seed': CANDIDATE_ID, 'route_kind': 'specialized', 'route_source': 'shape-specific-seed', 'guard_id': 'dd2b_v12_d128_q16_m100000_k48_exact_guard', 'guard_condition': 'exact BF16 non-build B=1 Q=16 M=100000 D=128 K=48', 'split_count': K48_SPLIT_COUNT, 'producer_topology': 'ROW_16x256B two-compute-warp K48 stage', 'merge_topology': 'K48 warp-row split-list merge', 'classification': 'unmeasured'}) + continue + parent_row = parent_v12.route_trace_for_contract_shapes((str(label),), force_fallback=force_fallback)[0] + rows.append(dict(parent_row)) + return rows + +def _annotate_route_trace(route_trace: list[dict[str, Any]], report: dict[str, Any]) -> list[dict[str, Any]]: + rows = [] + for row in route_trace: + out = dict(row) + label = str(out.get('shape_key')) + perf_row = report.get('per_shape', {}).get(label, {}) + ratio = perf_row.get('ratio_vs_flashlib') + out['dispatcher_kernel_ms'] = perf_row.get('kernel_ms') + out['shape_specific_kernel_ms'] = perf_row.get('kernel_ms') if out.get('selected_seed') == CANDIDATE_ID else None + out['external_baseline_ms'] = perf_row.get('flashlib_ms') + out['flashlib_ms'] = perf_row.get('flashlib_ms') + out['external_baseline_ref'] = 'same_session' if perf_row.get('flashlib_ms') is not None else 'not_available' + out['speedup_vs_external_baseline'] = ratio + out['timing_backend'] = perf_row.get('timing_backend') + if out.get('selected_seed') == CANDIDATE_ID: + out['classification'] = 'seed-consumed' if perf_row.get('passed') else 'correctness-failed' + rows.append(out) + return rows + +def benchmark_knn_build_v12_d128_q16_k48_dd2b_v1(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES) -> dict[str, Any]: + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels, kernel_fn=candidate, correctness=True, benchmark=True) + route_trace = _annotate_route_trace(route_trace_for_contract_shapes(shape_labels), report) + return {'contract': report['contract'], 'contract_version': report['contract_version'], 'candidate_id': CANDIDATE_ID, 'candidate_entrypoint': ROUTE_ENTRYPOINT, 'measured_entrypoint': BENCHMARK_ENTRYPOINT, 'accelerated_shape_labels': list(TARGET_SHAPES), 'measured_shape_labels': list(shape_labels), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'timing_backends': sorted({str(row.get('timing_backend')) for row in report.get('per_shape', {}).values() if row.get('timing_backend') is not None}), 'topology': {TARGET_SHAPE: {'producer': 'ROW_16x256B two-compute-warp widened K48 stage', 'merge': 'warp-row split-list merge', 'split_count': K48_SPLIT_COUNT, 'rows_per_merge_cta': K48_ROWS_PER_CTA}}, 'route_trace': route_trace, 'summary': report['summary'], 'performance': report['performance'], 'rank_objective': report['rank_objective'], 'correctness': report['correctness'], 'per_shape': report.get('per_shape', {}), 'report': report} + +def _write_json(path: str | None, payload: dict[str, Any]) -> None: + if path is None: + return + out = Path(path) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_v12_d256_k10_longm_e2df_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_v12_d256_k10_longm_e2df_v1.py new file mode 100644 index 00000000..f3031d27 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_v12_d256_k10_longm_e2df_v1.py @@ -0,0 +1,183 @@ +"""v12 D256 long-M K10 RAG seed for kNN build/search. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +routes only the v12 BF16 non-build D256/K10 microbatch row +``rag_microbatch_common_d256_b1_q4_m100000_k10``. The contract-visible path is +Weave-only: the existing D256 M64/N64 tcgen05/TMA chunked producer writes +split-local K10 partials, then the existing fused split merge produces +distances and indices. The v12 Q128 streaming row is intentionally left to a +future Q-tile repair because this inherited producer is only correctness-safe +for the small-Q surface. Guard misses delegate to the current v11 common-D +dispatcher. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from collections.abc import Callable +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_common_d_5e7f_rag_d64_d256_v1 as d256_parent +from .._dispatch_runtime import pack_kernel_args +MODULE = 'loom.examples.weave.knn_build_v12_d256_k10_longm_e2df_v1' +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +BENCHMARK_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_knn_build_v12_d256_k10_longm_e2df_v1']) +CANDIDATE_ID = 'knn_build_v12_d256_k10_longm_e2df_v1' +RAG_MICRO_D256_Q4_M100 = 'rag_microbatch_common_d256_b1_q4_m100000_k10' +RAG_STREAM_D256_Q128_M100 = 'rag_stream_common_d256_b1_q128_m100000_k10' +TARGET_SHAPES = (RAG_MICRO_D256_Q4_M100,) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +BLOCKED_SHAPES = (RAG_STREAM_D256_Q128_M100,) +D256_FEATURE_CHUNKS = 2 +D256_TMA_DIM = D256_FEATURE_CHUNKS * d256_parent.K_TILE +Q4_SPLIT = _decode_capture(_json_loads('144')) +Q4_GROUPS = _decode_capture(_json_loads('12')) +Q128_SPLIT = _decode_capture(_json_loads('144')) +Q128_GROUPS = _decode_capture(_json_loads('12')) +SHAPE_SPECS: dict[str, dict[str, Any]] = {RAG_MICRO_D256_Q4_M100: {'B': 1, 'Q': 4, 'M': 100000, 'D': 256, 'K': 10, 'build': False, 'feature_chunks': D256_FEATURE_CHUNKS, 'split_count': Q4_SPLIT, 'group_count': Q4_GROUPS}, RAG_STREAM_D256_Q128_M100: {'B': 1, 'Q': 128, 'M': 100000, 'D': 256, 'K': 10, 'build': False, 'feature_chunks': D256_FEATURE_CHUNKS, 'split_count': Q128_SPLIT, 'group_count': Q128_GROUPS}} + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_V12_D256_K10_E2DF_VERIFY_KERNEL') + split_count = int(os.environ.get('LOOM_KNN_V12_D256_K10_E2DF_VERIFY_SPLIT', Q128_SPLIT)) + group_count = int(os.environ.get('LOOM_KNN_V12_D256_K10_E2DF_VERIFY_GROUPS', Q128_GROUPS)) + if verify_kernel == 'fused_merge': + return d256_parent.fused_merge_parent._fused_merge_ir(split_count, group_count) + return d256_parent._stage1_ir(D256_FEATURE_CHUNKS) +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_7ee5_m64rag_stage1_d256_5e7f_rag_d64d256_v1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 34048, "constants": [["FEATURE_CHUNKS", 2]], "cta_group": 1, "threads": 96}')) + +def _dtype_name(inputs: dict[str, Any]) -> str: + query = inputs.get('query') + if query is not None: + return str(query.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')).replace('torch.', '') + +def _matches_spec(inputs: dict[str, Any], spec: dict[str, Any]) -> bool: + return _dtype_name(inputs) == 'bfloat16' and bool(inputs.get('build', False)) == bool(spec['build']) and (int(inputs['B']) == int(spec['B'])) and (int(inputs['Q']) == int(spec['Q'])) and (int(inputs['M']) == int(spec['M'])) and (int(inputs['D']) == int(spec['D'])) and (int(inputs['K']) == int(spec['K'])) + +def _target_label_for_inputs(inputs: dict[str, Any]) -> str | None: + label = inputs.get('label') + if label is not None: + label_s = str(label) + if label_s in TARGET_SHAPE_SET and _matches_spec(inputs, SHAPE_SPECS[label_s]): + return label_s + return None + for candidate_label, spec in SHAPE_SPECS.items(): + if _matches_spec(inputs, spec): + return candidate_label + return None + +def _split_count_for_label(label: str) -> int: + return int(SHAPE_SPECS[label]['split_count']) + +def _group_count_for_label(label: str) -> int: + return int(SHAPE_SPECS[label]['group_count']) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + label = _target_label_for_inputs(inputs) + if force_fallback or label is None: + return d256_parent.default_dispatcher.route_for_contract_inputs(inputs, force_fallback=force_fallback) + spec = SHAPE_SPECS[label] + return ''.join(['v12_d256_k10_e2df:', format(label, ''), ':q', format(int(spec['Q']), ''), ':m', format(int(spec['M']), ''), ':d256:m64n64:s', format(_split_count_for_label(label), ''), ':g', format(_group_count_for_label(label), '')]) + +def _launch_d256_k10_longm(inputs: dict[str, Any], label: str) -> None: + query = inputs['query'] + database = inputs['database'] + if str(query.dtype) != 'torch.bfloat16' or str(database.dtype) != 'torch.bfloat16': + raise ValueError('knn_build_v12_d256_k10_longm_e2df_v1 supports bfloat16 inputs only') + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + split_count = _split_count_for_label(label) + group_count = _group_count_for_label(label) + if dim != D256_TMA_DIM: + raise ValueError(''.join([format(label, ''), ' expected D=', format(D256_TMA_DIM, ''), ', got ', format(dim, '')])) + if top_k != d256_parent.TOP_K_MAX: + raise ValueError(''.join([format(label, ''), ' expected K=', format(d256_parent.TOP_K_MAX, ''), ', got ', format(top_k, '')])) + d256_parent.fused_merge_parent._validate_group_shape(split_count, group_count) + num_q_tiles = (n_query + d256_parent.M64_BLOCK_Q - 1) // d256_parent.M64_BLOCK_Q + num_db_tiles = (n_database + d256_parent.M64_BLOCK_M - 1) // d256_parent.M64_BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tiles * split_count + total_queries = bsz * n_query + stage1_grid = min(total_work, d256_parent.GRID_DIM_DEFAULT) + merge_grid = min(total_queries, d256_parent.GRID_DIM_DEFAULT) + partial_dists, partial_indices = d256_parent.split_parent._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = d256_parent.m64_parent.non128_base.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), total_queries, d256_parent.M64_BLOCK_Q, D256_TMA_DIM, d256_parent.K_TILE) + tmap_database = d256_parent.m64_parent.non128_base.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, d256_parent.M64_BLOCK_M, D256_TMA_DIM, d256_parent.K_TILE) + stage_ir = d256_parent._stage1_ir(D256_FEATURE_CHUNKS) + stage1_launch = d256_parent._compiled_stage1(D256_FEATURE_CHUNKS).prepare_launch(grid=(stage1_grid, 1, 1), block=(d256_parent.M64_THREADS, 1, 1), args=pack_kernel_args(stage_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tiles=num_q_tiles, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), shared_mem=stage_ir.computed_smem_bytes) + merge_ir = d256_parent.fused_merge_parent._fused_merge_ir(split_count, group_count) + merge_launch = d256_parent._compiled_fused_merge(split_count, group_count).prepare_launch(grid=(merge_grid, 1, 1), block=(d256_parent.fused_merge_parent.D768_FUSED_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], total_queries], shared_mem=merge_ir.computed_smem_bytes) + stage1_launch.launch() + merge_launch.launch() + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + label = _target_label_for_inputs(inputs) + if not force_fallback and label is not None: + _launch_d256_k10_longm(inputs, label) + return + d256_parent.default_dispatcher.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + if shape_labels is None: + wanted = set(TARGET_SHAPES) + else: + wanted = {str(label) for label in shape_labels} + selected = [shape for shape in eval_mod.CANONICAL_SHAPES if str(shape['label']) in wanted] + missing = wanted - {str(shape['label']) for shape in selected} + if missing: + raise ValueError(''.join(['unknown knn_build contract shape(s): ', format(sorted(missing), '')])) + return selected + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def route_trace_for_contract_shapes(shape_labels=TARGET_SHAPES, *, force_fallback: bool=False) -> list[dict[str, Any]]: + rows = [] + for shape in _select_contract_shapes(shape_labels): + params = dict(shape.get('params', {})) + params['label'] = shape['label'] + inputs = {'label': params['label'], 'B': params['B'], 'Q': params['Q'], 'M': params['M'], 'D': params['D'], 'K': params['K'], 'build': params['build'], 'dtype': params.get('dtype', 'bfloat16')} + label = _target_label_for_inputs(inputs) + selected = not force_fallback and label is not None + rows.append({'shape_key': params['label'], 'selected_route': route_for_contract_inputs(inputs, force_fallback=force_fallback), 'selected_entrypoint': ROUTE_ENTRYPOINT if selected else d256_parent.default_dispatcher.ROUTE_ENTRYPOINT, 'selected_seed': CANDIDATE_ID if selected else None, 'expected_seed': CANDIDATE_ID if params['label'] in TARGET_SHAPE_SET else None, 'route_kind': 'specialized' if selected else 'forced_fallback' if force_fallback else 'delegated', 'route_source': 'shape-specific-seed' if selected else 'default-v11-common-d-dispatcher', 'guard_id': 'e2df_v12_d256_k10_long_m_exact_guard' if selected else 'guard_miss', 'guard_condition': ''.join(['exact BF16 non-build B=1 Q=', format(params['Q'], ''), ' M=', format(params['M'], ''), ' D=256 K=10']) if selected else 'delegate to current v11 common-D dispatcher', 'split_count': _split_count_for_label(label) if selected and label is not None else None, 'group_count': _group_count_for_label(label) if selected and label is not None else None, 'producer_topology': 'M64_N64_D256_tcgen05/TMA_chunked', 'merge_topology': 'fused_group_split_merge' if selected else None, 'classification': 'v12-d256-k10-long-m-seed' if selected else 'guard-miss'}) + return rows + +def _benchmark_payload(report: dict[str, Any], *, use_cupti: bool, shape_labels) -> dict[str, Any]: + rows = report.get('per_shape', {}) + timing_backends = sorted({row.get('timing_backend') for row in rows.values() if row.get('timing_backend')}) + return {'contract': report['contract'], 'contract_version': report['contract_version'], 'measured_entrypoint': BENCHMARK_ENTRYPOINT, 'candidate_entrypoint': ROUTE_ENTRYPOINT, 'candidate_id': CANDIDATE_ID, 'accelerated_shape_labels': list(TARGET_SHAPES), 'blocked_shape_labels': list(BLOCKED_SHAPES), 'measured_shape_labels': list(shape_labels), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'timing_backends': timing_backends, 'topology': {label: {'producer': 'M64_N64_D256_tcgen05/TMA_chunked', 'split_count': _split_count_for_label(label), 'group_count': _group_count_for_label(label), 'feature_chunks': D256_FEATURE_CHUNKS} for label in TARGET_SHAPES}, 'route_trace': route_trace_for_contract_shapes(shape_labels), 'summary': report['summary'], 'performance': report['performance'], 'correctness': report['correctness'], 'rank_objective': report.get('rank_objective'), 'per_shape': report.get('per_shape', {}), 'report': report} + +def benchmark_knn_build_v12_d256_k10_longm_e2df_v1(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES) -> dict[str, Any]: + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels) + return _benchmark_payload(report, use_cupti=use_cupti, shape_labels=shape_labels) diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_v12_d256_k32_tail_59fe_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_v12_d256_k32_tail_59fe_v1.py new file mode 100644 index 00000000..c2b4c6da --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_v12_d256_k32_tail_59fe_v1.py @@ -0,0 +1,184 @@ +"""v12 D256 long-M K32 RAG seed for kNN build/search. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +routes the BF16 non-build D256/K32 long-M rows through a D256 adaptation of the +ROW_16x256B K32 M64/N64 tcgen05/TMA producer, then feeds the existing K32 +warp-row split-list merge. Guard misses delegate to the v12 D256/K10 seed +lineage, keeping the measured target path Weave-only. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from collections.abc import Callable +from functools import lru_cache +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_rag_microbucket_k32warpmerge_0077_v1 as k32merge +from . import knn_build_rag_microbucket_q32rowld_e5db_v1 as rowld_seed +from . import knn_build_v12_d256_q128_k10_longm_59fe_v1 as k10_parent +from .._dispatch_runtime import pack_kernel_args +MODULE = 'loom.examples.weave.knn_build_v12_d256_k32_tail_59fe_v1' +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +BENCHMARK_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_knn_build_v12_d256_k32_tail_59fe_v1']) +CANDIDATE_ID = 'knn_build_v12_d256_k32_tail_59fe_v1' +RAG_MICRO_D256_Q8_M100_K32 = 'rag_microbatch_largek_common_d256_b1_q8_m100000_k32' +RAG_STREAM_D256_Q128_M100_K32 = 'rag_stream_largek_common_d256_b1_q128_m100000_k32' +TARGET_SHAPES = (RAG_MICRO_D256_Q8_M100_K32, RAG_STREAM_D256_Q128_M100_K32) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +D256_BLOCK_Q = rowld_seed.Q8_M64_BLOCK_Q +D256_BLOCK_M = rowld_seed.Q8_M64_BLOCK_M +D256_K_TILE = rowld_seed.Q8_M64_FEAT_D +D256_FEATURE_CHUNKS = 2 +D256_TMA_DIM = D256_FEATURE_CHUNKS * D256_K_TILE +D256_TOP_K_MAX = rowld_seed.Q8_M64_TOP_K_MAX +D256_STAGE1_THREADS = _decode_capture(_json_loads('192')) +D256_SPLIT_COUNT = _decode_capture(_json_loads('144')) +D256_LOCAL_LISTS_PER_ROW = rowld_seed.Q32_M64_LOCAL_LISTS_PER_ROW +D256_SMEM_BASE_BYTES = rowld_seed.Q32_M64_SMEM_BASE_BYTES +D256_LOCAL_ELEMS = rowld_seed.Q32_M64_LOCAL_ELEMS +D256_LOCAL_D_OFFSET = rowld_seed.Q32_M64_LOCAL_D_OFFSET +D256_LOCAL_I_OFFSET = rowld_seed.Q32_M64_LOCAL_I_OFFSET +D256_SMEM_POOL_BYTES = rowld_seed.Q32_M64_SMEM_POOL_BYTES +D256_WARP_MERGE_THREADS = k32merge.K32_WARP_MERGE_THREADS +D256_WARP_MERGE_ROWS_PER_CTA = k32merge.K32_WARP_MERGE_ROWS_PER_CTA +SHAPE_SPECS = _decode_capture(_json_loads('{"__dict_items__": [["rag_microbatch_largek_common_d256_b1_q8_m100000_k32", {"__dict_items__": [["B", 1], ["Q", 8], ["M", 100000], ["D", 256], ["K", 32], ["build", false], ["split_count", 144]]}], ["rag_stream_largek_common_d256_b1_q128_m100000_k32", {"__dict_items__": [["B", 1], ["Q", 128], ["M", 100000], ["D", 256], ["K", 32], ["build", false], ["split_count", 144]]}]]}')) +knn_build_v12_d256_k32_tail_59fe_v1_stage1_rowld = _decode_capture(_json_loads('{"__ir__": "knn_build_v12_d256_k32_tail_59fe_v1_stage1_rowld", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 99584, "constants": [["BLOCK_Q", 64], ["BLOCK_M", 64], ["K_TILE", 128], ["FEATURE_CHUNKS", 2], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 192}')) +stage1_d256_k32_rowld_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_v12_d256_k32_tail_59fe_v1_stage1_rowld", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 99584, "constants": [["BLOCK_Q", 64], ["BLOCK_M", 64], ["K_TILE", 128], ["FEATURE_CHUNKS", 2], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 192}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_V12_D256_K32_TAIL_59FE_VERIFY_KERNEL') + split_count = int(os.environ.get('LOOM_KNN_V12_D256_K32_TAIL_59FE_VERIFY_SPLIT', D256_SPLIT_COUNT)) + if verify_kernel == 'merge': + return k32merge._warp_merge_ir(split_count) + return stage1_d256_k32_rowld_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_v12_d256_k32_tail_59fe_v1_stage1_rowld", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 99584, "constants": [["BLOCK_Q", 64], ["BLOCK_M", 64], ["K_TILE", 128], ["FEATURE_CHUNKS", 2], ["TOP_K_MAX", 32]], "cta_group": 1, "threads": 192}')) + +def _compiled_stage1_d256_k32_rowld(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0129"}')) + +def _dtype_name(inputs: dict[str, Any], tensor_name: str='query') -> str: + tensor = inputs.get(tensor_name) + if tensor is not None and hasattr(tensor, 'dtype'): + return str(tensor.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')).replace('torch.', '') + +def _matches_spec(inputs: dict[str, Any], spec: dict[str, Any]) -> bool: + return _dtype_name(inputs, 'query') == 'bfloat16' and _dtype_name(inputs, 'database') in ('', 'bfloat16') and (bool(inputs.get('build', False)) == bool(spec['build'])) and (int(inputs.get('B', -1)) == int(spec['B'])) and (int(inputs.get('Q', -1)) == int(spec['Q'])) and (int(inputs.get('M', -1)) == int(spec['M'])) and (int(inputs.get('D', -1)) == int(spec['D'])) and (int(inputs.get('K', -1)) == int(spec['K'])) + +def _target_label_for_inputs(inputs: dict[str, Any]) -> str | None: + label = inputs.get('label') + if label is not None: + label_s = str(label) + if label_s in TARGET_SHAPE_SET and _matches_spec(inputs, SHAPE_SPECS[label_s]): + return label_s + return None + for candidate_label, spec in SHAPE_SPECS.items(): + if _matches_spec(inputs, spec): + return candidate_label + return None + +def _split_count_for_label(label: str) -> int: + return int(SHAPE_SPECS[label]['split_count']) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + label = _target_label_for_inputs(inputs) + if not force_fallback and label is not None: + return ''.join(['v12_d256_k32_tail_59fe:', format(label, ''), ':q', format(int(SHAPE_SPECS[label]['Q']), ''), ':m', format(int(SHAPE_SPECS[label]['M']), ''), ':d256:k32:rowld64x64:s', format(_split_count_for_label(label), ''), ':warpmerge_r', format(D256_WARP_MERGE_ROWS_PER_CTA, '')]) + return k10_parent.route_for_contract_inputs(inputs, force_fallback=force_fallback) + +def _launch_d256_k32_rowld_warpmerge(inputs: dict[str, Any], label: str, *, split_count: int) -> None: + query = inputs['query'] + database = inputs['database'] + if str(query.dtype) != 'torch.bfloat16' or str(database.dtype) != 'torch.bfloat16': + raise ValueError('knn_build_v12_d256_k32_tail_59fe_v1 supports bfloat16 inputs only') + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + if dim != D256_TMA_DIM: + raise ValueError(''.join([format(label, ''), ' expected D=', format(D256_TMA_DIM, ''), ', got ', format(dim, '')])) + if top_k != D256_TOP_K_MAX: + raise ValueError(''.join([format(label, ''), ' expected K=', format(D256_TOP_K_MAX, ''), ', got ', format(top_k, '')])) + num_q_tiles = (n_query + D256_BLOCK_Q - 1) // D256_BLOCK_Q + num_db_tiles = (n_database + D256_BLOCK_M - 1) // D256_BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tiles * split_count + total_queries = bsz * n_query + stage1_grid = min(total_work, rowld_seed.compact_seed.GRID_DIM_DEFAULT) + merge_grid = min((total_queries + D256_WARP_MERGE_ROWS_PER_CTA - 1) // D256_WARP_MERGE_ROWS_PER_CTA, rowld_seed.compact_seed.GRID_DIM_DEFAULT) + partial_dists, partial_indices = rowld_seed.compact_seed.q16_tailinf.parent_k32.parent_split._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = rowld_seed.compact_seed.q16_tailinf.parent_k32.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), total_queries, D256_BLOCK_Q, dim, D256_K_TILE) + tmap_database = rowld_seed.compact_seed.q16_tailinf.parent_k32.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, D256_BLOCK_M, dim, D256_K_TILE) + _compiled_stage1_d256_k32_rowld().launch(grid=(stage1_grid, 1, 1), block=(D256_STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage1_d256_k32_rowld_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tiles=num_q_tiles, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), shared_mem=stage1_d256_k32_rowld_ir.computed_smem_bytes) + merge_ir = k32merge._warp_merge_ir(split_count) + k32merge._compiled_warp_merge(split_count).launch(grid=(merge_grid, 1, 1), block=(D256_WARP_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], total_queries], shared_mem=merge_ir.computed_smem_bytes) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + label = _target_label_for_inputs(inputs) + if not force_fallback and label is not None: + _launch_d256_k32_rowld_warpmerge(inputs, label, split_count=_split_count_for_label(label)) + return + k10_parent.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + if shape_labels is None: + wanted = set(TARGET_SHAPES) + else: + wanted = {str(label) for label in shape_labels} + selected = [shape for shape in eval_mod.CANONICAL_SHAPES if str(shape['label']) in wanted] + missing = wanted - {str(shape['label']) for shape in selected} + if missing: + raise ValueError(''.join(['unknown knn_build contract shape(s): ', format(sorted(missing), '')])) + return selected + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def route_trace_for_contract_shapes(shape_labels=TARGET_SHAPES, *, force_fallback: bool=False) -> list[dict[str, Any]]: + rows = [] + for shape in _select_contract_shapes(shape_labels): + params = dict(shape.get('params', {})) + params['label'] = shape['label'] + inputs = {'label': params['label'], 'B': params['B'], 'Q': params['Q'], 'M': params['M'], 'D': params['D'], 'K': params['K'], 'build': params['build'], 'dtype': params.get('dtype', 'bfloat16')} + label = _target_label_for_inputs(inputs) + selected = not force_fallback and label is not None + rows.append({'shape_key': params['label'], 'selected_route': route_for_contract_inputs(inputs, force_fallback=force_fallback), 'selected_entrypoint': ROUTE_ENTRYPOINT if selected else k10_parent.ROUTE_ENTRYPOINT, 'selected_seed': CANDIDATE_ID if selected else None, 'expected_seed': CANDIDATE_ID if params['label'] in TARGET_SHAPE_SET else None, 'route_kind': 'specialized' if selected else 'delegated', 'route_source': 'shape-specific-seed' if selected else 'k10-parent', 'guard_id': '59fe_v12_d256_k32_tail_exact_guard' if selected else 'guard_miss', 'guard_condition': 'exact BF16 non-build B=1 M=100000 D=256 K=32 Q in {8,128}' if selected else 'delegate to v12 D256 K10 parent', 'split_count': _split_count_for_label(label) if selected and label is not None else None, 'producer_topology': 'ROW_16x256B_M64N64_D256_tcgen05_TMA_two_chunk' if selected else None, 'merge_topology': 'warp_row_split_list_merge' if selected else None, 'classification': 'v12-d256-k32-tail-seed' if selected else 'guard-miss'}) + return rows + +def _benchmark_payload(report: dict[str, Any], *, use_cupti: bool, shape_labels) -> dict[str, Any]: + rows = report.get('per_shape', {}) + timing_backends = sorted({row.get('timing_backend') for row in rows.values() if row.get('timing_backend')}) + return {'contract': report['contract'], 'contract_version': report['contract_version'], 'measured_entrypoint': BENCHMARK_ENTRYPOINT, 'candidate_entrypoint': ROUTE_ENTRYPOINT, 'candidate_id': CANDIDATE_ID, 'accelerated_shape_labels': list(TARGET_SHAPES), 'measured_shape_labels': list(shape_labels), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'timing_backends': timing_backends, 'topology': {label: {'producer': 'ROW_16x256B M64/N64 D256 tcgen05/TMA two-chunk', 'split_count': SHAPE_SPECS[label]['split_count'], 'feature_chunks': D256_FEATURE_CHUNKS, 'merge': ''.join(['warp-row/', format(D256_WARP_MERGE_ROWS_PER_CTA, ''), ' rows per CTA'])} for label in TARGET_SHAPES}, 'route_trace': route_trace_for_contract_shapes(shape_labels), 'summary': report['summary'], 'performance': report['performance'], 'correctness': report['correctness'], 'rank_objective': report.get('rank_objective'), 'per_shape': report.get('per_shape', {}), 'report': report} + +def benchmark_knn_build_v12_d256_k32_tail_59fe_v1(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES) -> dict[str, Any]: + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels) + return _benchmark_payload(report, use_cupti=use_cupti, shape_labels=shape_labels) diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_v12_d256_q128_k10_longm_59fe_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_v12_d256_q128_k10_longm_59fe_v1.py new file mode 100644 index 00000000..95a27594 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_v12_d256_q128_k10_longm_59fe_v1.py @@ -0,0 +1,199 @@ +"""v12 D256 Q128 long-M K10 RAG seed for kNN build/search. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +routes only the BF16 non-build +``rag_stream_common_d256_b1_q128_m100000_k10`` row through a 128-row +M64/N64 tcgen05/TMA producer adapted from the source-policy-clean D768 build +seed, followed by the inherited fused split merge. The existing Q4 D256/K10 +seed remains delegated to e2df; all other guard misses delegate through that +same Weave-only parent path. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from collections.abc import Callable +from functools import cache, lru_cache +from typing import Any +from .._dispatch_runtime import dc as dc +from .. import _dispatch_runtime as eval_mod +from . import knn_build_common_d768_build_eeff_m64split_v1 as q128_parent +from . import knn_build_evolve_7bfc_split_v1 as split_parent +from . import knn_build_non128_frontier_4be7_d768fused_v1 as fused_merge_parent +from . import knn_build_non128_frontier_7ee5_m64rag_v1 as m64_parent +from . import knn_build_v12_d256_k10_longm_e2df_v1 as q4_parent +from .._dispatch_runtime import pack_kernel_args +MODULE = 'loom.examples.weave.knn_build_v12_d256_q128_k10_longm_59fe_v1' +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +BENCHMARK_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_knn_build_v12_d256_q128_k10_longm_59fe_v1']) +CANDIDATE_ID = 'knn_build_v12_d256_q128_k10_longm_59fe_v1' +RAG_MICRO_D256_Q4_M100 = q4_parent.RAG_MICRO_D256_Q4_M100 +RAG_STREAM_D256_Q128_M100 = 'rag_stream_common_d256_b1_q128_m100000_k10' +TARGET_SHAPES = (RAG_STREAM_D256_Q128_M100,) +INHERITED_SHAPES = (RAG_MICRO_D256_Q4_M100,) +BUCKET_SHAPES = (*INHERITED_SHAPES, *TARGET_SHAPES) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +D256_FEATURE_CHUNKS = 2 +D256_TMA_DIM = D256_FEATURE_CHUNKS * q128_parent.K_TILE +SPLIT_COUNT = _decode_capture(_json_loads('144')) +GROUP_COUNT = _decode_capture(_json_loads('12')) +SHAPE_SPECS: dict[str, dict[str, Any]] = {RAG_STREAM_D256_Q128_M100: {'B': 1, 'Q': 128, 'M': 100000, 'D': 256, 'K': 10, 'build': False, 'feature_chunks': D256_FEATURE_CHUNKS, 'split_count': SPLIT_COUNT, 'group_count': GROUP_COUNT}} + +def _stage1_ir() -> Any: + constants = tuple(((name, D256_FEATURE_CHUNKS if name == 'FEATURE_CHUNKS' else value) for name, value in q128_parent.stage1_m64_ir.constants)) + return dc.replace(q128_parent.stage1_m64_ir, symbol=''.join([format(q128_parent.stage1_m64_ir.symbol, ''), '_d256_q128_k10_59fe_v1']), constants=constants) + +def _merge_ir(split_count: int=SPLIT_COUNT, group_count: int=GROUP_COUNT) -> Any: + fused_merge_parent._validate_group_shape(int(split_count), int(group_count)) + return fused_merge_parent._fused_merge_ir(int(split_count), int(group_count)) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_V12_D256_Q128_K10_59FE_VERIFY_KERNEL') + split_count = int(os.environ.get('LOOM_KNN_V12_D256_Q128_K10_59FE_VERIFY_SPLIT', SPLIT_COUNT)) + group_count = int(os.environ.get('LOOM_KNN_V12_D256_Q128_K10_59FE_VERIFY_GROUPS', GROUP_COUNT)) + if verify_kernel == 'merge': + return _merge_ir(split_count, group_count) + return _stage1_ir() +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_common_d768_build_eeff_m64split_stage1_d256_q128_k10_59fe_v1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 50432, "constants": [["FEATURE_CHUNKS", 2]], "cta_group": 1, "threads": 192}')) + +def _compiled_stage1(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0124"}')) + +@cache +def _compiled_fused_merge(split_count: int, group_count: int): + return fused_merge_parent._compiled_fused_merge(int(split_count), int(group_count)) + +def _dtype_name(inputs: dict[str, Any], tensor_name: str='query') -> str: + tensor = inputs.get(tensor_name) + if tensor is not None and hasattr(tensor, 'dtype'): + return str(tensor.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')).replace('torch.', '') + +def _matches_spec(inputs: dict[str, Any], spec: dict[str, Any]) -> bool: + return _dtype_name(inputs, 'query') == 'bfloat16' and _dtype_name(inputs, 'database') in ('', 'bfloat16') and (bool(inputs.get('build', False)) == bool(spec['build'])) and (int(inputs.get('B', -1)) == int(spec['B'])) and (int(inputs.get('Q', -1)) == int(spec['Q'])) and (int(inputs.get('M', -1)) == int(spec['M'])) and (int(inputs.get('D', -1)) == int(spec['D'])) and (int(inputs.get('K', -1)) == int(spec['K'])) + +def _target_label_for_inputs(inputs: dict[str, Any]) -> str | None: + label = inputs.get('label') + if label is not None: + label_s = str(label) + if label_s in TARGET_SHAPE_SET and _matches_spec(inputs, SHAPE_SPECS[label_s]): + return label_s + return None + for candidate_label, spec in SHAPE_SPECS.items(): + if _matches_spec(inputs, spec): + return candidate_label + return None + +def _split_count_for_label(label: str) -> int: + return int(SHAPE_SPECS[label]['split_count']) + +def _group_count_for_label(label: str) -> int: + return int(SHAPE_SPECS[label]['group_count']) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + label = _target_label_for_inputs(inputs) + if not force_fallback and label is not None: + spec = SHAPE_SPECS[label] + return ''.join(['v12_d256_q128_k10_59fe:', format(label, ''), ':q', format(int(spec['Q']), ''), ':m', format(int(spec['M']), ''), ':d256:m128n64:s', format(_split_count_for_label(label), ''), ':g', format(_group_count_for_label(label), '')]) + return q4_parent.route_for_contract_inputs(inputs, force_fallback=force_fallback) + +def _launch_q128_d256_k10_longm(inputs: dict[str, Any], label: str) -> None: + query = inputs['query'] + database = inputs['database'] + if str(query.dtype) != 'torch.bfloat16' or str(database.dtype) != 'torch.bfloat16': + raise ValueError('knn_build_v12_d256_q128_k10_longm_59fe_v1 supports bfloat16 inputs only') + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + split_count = _split_count_for_label(label) + group_count = _group_count_for_label(label) + if dim != D256_TMA_DIM: + raise ValueError(''.join([format(label, ''), ' expected D=', format(D256_TMA_DIM, ''), ', got ', format(dim, '')])) + if top_k != q128_parent.TOP_K_MAX: + raise ValueError(''.join([format(label, ''), ' expected K=', format(q128_parent.TOP_K_MAX, ''), ', got ', format(top_k, '')])) + fused_merge_parent._validate_group_shape(split_count, group_count) + num_q_tiles = (n_query + q128_parent.M64_BLOCK_Q - 1) // q128_parent.M64_BLOCK_Q + num_db_tiles = (n_database + q128_parent.M64_BLOCK_M - 1) // q128_parent.M64_BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tiles * split_count + total_queries = bsz * n_query + stage1_grid = min(total_work, q128_parent.GRID_DIM_DEFAULT) + merge_grid = min(total_queries, q128_parent.GRID_DIM_DEFAULT) + partial_dists, partial_indices = split_parent._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = m64_parent.non128_base.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), total_queries, q128_parent.M64_BLOCK_Q, D256_TMA_DIM, q128_parent.K_TILE) + tmap_database = m64_parent.non128_base.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, q128_parent.M64_BLOCK_M, D256_TMA_DIM, q128_parent.K_TILE) + stage_ir = _stage1_ir() + _compiled_stage1().launch(grid=(stage1_grid, 1, 1), block=(q128_parent.STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tiles=num_q_tiles, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), shared_mem=stage_ir.computed_smem_bytes) + merge_ir = _merge_ir(split_count, group_count) + _compiled_fused_merge(split_count, group_count).launch(grid=(merge_grid, 1, 1), block=(fused_merge_parent.D768_FUSED_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], total_queries], shared_mem=merge_ir.computed_smem_bytes) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + label = _target_label_for_inputs(inputs) + if not force_fallback and label is not None: + _launch_q128_d256_k10_longm(inputs, label) + return + q4_parent.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + if shape_labels is None: + wanted = set(TARGET_SHAPES) + else: + wanted = {str(label) for label in shape_labels} + selected = [shape for shape in eval_mod.CANONICAL_SHAPES if str(shape['label']) in wanted] + missing = wanted - {str(shape['label']) for shape in selected} + if missing: + raise ValueError(''.join(['unknown knn_build contract shape(s): ', format(sorted(missing), '')])) + return selected + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def route_trace_for_contract_shapes(shape_labels=TARGET_SHAPES, *, force_fallback: bool=False) -> list[dict[str, Any]]: + rows = [] + for shape in _select_contract_shapes(shape_labels): + params = dict(shape.get('params', {})) + params['label'] = shape['label'] + inputs = {'label': params['label'], 'B': params['B'], 'Q': params['Q'], 'M': params['M'], 'D': params['D'], 'K': params['K'], 'build': params['build'], 'dtype': params.get('dtype', 'bfloat16')} + label = _target_label_for_inputs(inputs) + selected = not force_fallback and label is not None + inherited_q4 = not selected and params['label'] in INHERITED_SHAPES + rows.append({'shape_key': params['label'], 'selected_route': route_for_contract_inputs(inputs, force_fallback=force_fallback), 'selected_entrypoint': ROUTE_ENTRYPOINT if selected else q4_parent.ROUTE_ENTRYPOINT if inherited_q4 else q4_parent.d256_parent.default_dispatcher.ROUTE_ENTRYPOINT, 'selected_seed': CANDIDATE_ID if selected else q4_parent.CANDIDATE_ID if inherited_q4 else None, 'expected_seed': CANDIDATE_ID if params['label'] in TARGET_SHAPE_SET else None, 'route_kind': 'specialized' if selected else 'inherited-seed' if inherited_q4 else 'delegated', 'route_source': 'shape-specific-seed' if selected else 'e2df-parent' if inherited_q4 else 'default', 'guard_id': '59fe_v12_d256_q128_k10_exact_guard' if selected else 'guard_miss', 'guard_condition': 'exact BF16 non-build B=1 Q=128 M=100000 D=256 K=10' if selected else 'delegate to e2df parent', 'split_count': _split_count_for_label(label) if selected and label is not None else None, 'group_count': _group_count_for_label(label) if selected and label is not None else None, 'producer_topology': 'M128_N64_D256_tcgen05/TMA_chunked' if selected else None, 'merge_topology': 'fused_group_split_merge' if selected else None, 'classification': 'v12-d256-q128-k10-long-m-seed' if selected else 'inherited-q4-seed' if inherited_q4 else 'guard-miss'}) + return rows + +def _benchmark_payload(report: dict[str, Any], *, use_cupti: bool, shape_labels) -> dict[str, Any]: + rows = report.get('per_shape', {}) + timing_backends = sorted({row.get('timing_backend') for row in rows.values() if row.get('timing_backend')}) + return {'contract': report['contract'], 'contract_version': report['contract_version'], 'measured_entrypoint': BENCHMARK_ENTRYPOINT, 'candidate_entrypoint': ROUTE_ENTRYPOINT, 'candidate_id': CANDIDATE_ID, 'accelerated_shape_labels': list(TARGET_SHAPES), 'inherited_shape_labels': list(INHERITED_SHAPES), 'measured_shape_labels': list(shape_labels), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'timing_backends': timing_backends, 'topology': {RAG_STREAM_D256_Q128_M100: {'producer': 'M128_N64_D256_tcgen05/TMA_chunked', 'split_count': SPLIT_COUNT, 'group_count': GROUP_COUNT, 'feature_chunks': D256_FEATURE_CHUNKS}}, 'route_trace': route_trace_for_contract_shapes(shape_labels), 'summary': report['summary'], 'performance': report['performance'], 'correctness': report['correctness'], 'rank_objective': report.get('rank_objective'), 'per_shape': report.get('per_shape', {}), 'report': report} + +def benchmark_knn_build_v12_d256_q128_k10_longm_59fe_v1(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES) -> dict[str, Any]: + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels) + return _benchmark_payload(report, use_cupti=use_cupti, shape_labels=shape_labels) diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_v12_d64_tail_017a_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_v12_d64_tail_017a_v1.py new file mode 100644 index 00000000..0eb3677c --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_v12_d64_tail_017a_v1.py @@ -0,0 +1,212 @@ +"""v12 D64 long-M RAG tail seed for kNN build/search. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +routes only the v12 BF16 non-build D64 rows: +``rag_online_common_d64_b1_q1_m262143_k10`` and +``rag_microbatch_common_d64_b1_q4_m100000_k10``. The contract-visible path is +Weave-only: a D64 M64/N64 tcgen05/TMA small-Q stage writes split-local K10 +partials, then the existing fused split merge produces distances and indices. +Guard misses delegate to the current v11 common-D dispatcher. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from collections.abc import Callable +from functools import cache, lru_cache +from pathlib import Path +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_common_d_5e7f_rag_d64_repair_v1 as d64_parent +from .._dispatch_runtime import pack_kernel_args +MODULE = 'loom.examples.weave.knn_build_v12_d64_tail_017a_v1' +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +BENCHMARK_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_knn_build_v12_d64_tail_017a_v1']) +CANDIDATE_ID = 'knn_build_v12_d64_tail_017a_v1' +RAG_ONLINE_D64_Q1_M262 = 'rag_online_common_d64_b1_q1_m262143_k10' +RAG_MICRO_D64_Q4_M100 = 'rag_microbatch_common_d64_b1_q4_m100000_k10' +TARGET_SHAPES = (RAG_ONLINE_D64_Q1_M262, RAG_MICRO_D64_Q4_M100) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +D64_BLOCK_Q = 64 +D64_BLOCK_M = 64 +D64_FEAT_D = 64 +D64_TOP_K = 10 +D64_ROWS_COVERED = 4 +D64_PHYSICAL_ROWS = 8 +D64_LOCAL_LISTS_PER_ROW = 4 +D64_THREADS = d64_parent.D64_THREADS +D64_GRID_DIM_DEFAULT = d64_parent.GRID_DIM_DEFAULT +D64_QUERY_BYTES = D64_BLOCK_Q * D64_FEAT_D * 2 +D64_DATABASE_BYTES = D64_BLOCK_M * D64_FEAT_D * 2 +D64_SMEM_BASE_BYTES = D64_QUERY_BYTES + D64_DATABASE_BYTES + 256 +D64_LOCAL_ELEMS = D64_PHYSICAL_ROWS * D64_LOCAL_LISTS_PER_ROW * D64_TOP_K +D64_LOCAL_D_OFFSET = D64_SMEM_BASE_BYTES +D64_LOCAL_I_OFFSET = D64_LOCAL_D_OFFSET + D64_LOCAL_ELEMS * 4 +D64_SMEM_POOL_BYTES = D64_LOCAL_I_OFFSET + D64_LOCAL_ELEMS * 4 +Q1_SPLIT = _decode_capture(_json_loads('144')) +Q1_GROUPS = _decode_capture(_json_loads('12')) +Q4_SPLIT = _decode_capture(_json_loads('144')) +Q4_GROUPS = _decode_capture(_json_loads('12')) +SHAPE_SPECS: dict[str, dict[str, Any]] = {RAG_ONLINE_D64_Q1_M262: {'B': 1, 'Q': 1, 'M': 262143, 'D': 64, 'K': 10, 'build': False, 'split_count': Q1_SPLIT, 'group_count': Q1_GROUPS, 'rows_covered': 1}, RAG_MICRO_D64_Q4_M100: {'B': 1, 'Q': 4, 'M': 100000, 'D': 64, 'K': 10, 'build': False, 'split_count': Q4_SPLIT, 'group_count': Q4_GROUPS, 'rows_covered': 4}} +_insert_sorted_pair_k10 = _ir_proxy('loom.examples.weave.knn_build_v12_d64_tail_017a_v1:_insert_sorted_pair_k10', 256) +knn_build_v12_d64_tail_017a_stage1 = _decode_capture(_json_loads('{"__ir__": "knn_build_v12_d64_tail_017a_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 20224, "constants": [["BLOCK_Q", 64], ["BLOCK_M", 64], ["FEAT_D", 64], ["TOP_K_MAX", 10], ["ROWS_COVERED", 4]], "cta_group": 1, "threads": 96}')) +stage1_v12_d64_tail_ir = _decode_capture(_json_loads('{"__ir__": "knn_build_v12_d64_tail_017a_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 20224, "constants": [["BLOCK_Q", 64], ["BLOCK_M", 64], ["FEAT_D", 64], ["TOP_K_MAX", 10], ["ROWS_COVERED", 4]], "cta_group": 1, "threads": 96}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_V12_D64_TAIL_017A_VERIFY_KERNEL') + split_count = int(os.environ.get('LOOM_KNN_V12_D64_TAIL_017A_VERIFY_SPLIT', Q4_SPLIT)) + group_count = int(os.environ.get('LOOM_KNN_V12_D64_TAIL_017A_VERIFY_GROUPS', Q4_GROUPS)) + if verify_kernel == 'fused_merge': + return d64_parent.fused_merge_parent._fused_merge_ir(split_count, group_count) + return stage1_v12_d64_tail_ir +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_v12_d64_tail_017a_stage1", "arg_keys": ["tmap_query", "tmap_database", "query_sq", "database_sq", "partial_dists", "partial_indices", "B", "Q", "M", "K", "num_q_tiles", "db_tiles_per_split", "split_count", "total_work"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 20224, "constants": [["BLOCK_Q", 64], ["BLOCK_M", 64], ["FEAT_D", 64], ["TOP_K_MAX", 10], ["ROWS_COVERED", 4]], "cta_group": 1, "threads": 96}')) + +def _compiled_stage1_v12_d64_tail(): + return _decode_capture(_json_loads('{"__kernel__": "dispatch_kernel_0123"}')) + +@cache +def _compiled_fused_merge(split_count: int, group_count: int): + return d64_parent.fused_merge_parent._compiled_fused_merge(split_count, group_count) + +def _dtype_name(inputs: dict[str, Any]) -> str: + query = inputs.get('query') + if query is not None: + return str(query.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')).replace('torch.', '') + +def _matches_spec(inputs: dict[str, Any], spec: dict[str, Any]) -> bool: + return _dtype_name(inputs) == 'bfloat16' and bool(inputs.get('build', False)) == bool(spec['build']) and (int(inputs['B']) == int(spec['B'])) and (int(inputs['Q']) == int(spec['Q'])) and (int(inputs['M']) == int(spec['M'])) and (int(inputs['D']) == int(spec['D'])) and (int(inputs['K']) == int(spec['K'])) + +def _target_label_for_inputs(inputs: dict[str, Any]) -> str | None: + label = inputs.get('label') + if label is not None: + label_s = str(label) + if label_s in TARGET_SHAPE_SET and _matches_spec(inputs, SHAPE_SPECS[label_s]): + return label_s + return None + for candidate_label, spec in SHAPE_SPECS.items(): + if _matches_spec(inputs, spec): + return candidate_label + return None + +def _split_count_for_label(label: str) -> int: + return int(SHAPE_SPECS[label]['split_count']) + +def _group_count_for_label(label: str) -> int: + return int(SHAPE_SPECS[label]['group_count']) + +def _launch_d64_tail(inputs: dict[str, Any], label: str) -> None: + query = inputs['query'] + database = inputs['database'] + if str(query.dtype) != 'torch.bfloat16' or str(database.dtype) != 'torch.bfloat16': + raise ValueError('knn_build_v12_d64_tail_017a_v1 supports bfloat16 inputs only') + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + split_count = _split_count_for_label(label) + group_count = _group_count_for_label(label) + if dim != D64_FEAT_D: + raise ValueError(''.join([format(label, ''), ' expected D=', format(D64_FEAT_D, ''), ', got ', format(dim, '')])) + d64_parent.fused_merge_parent._validate_group_shape(split_count, group_count) + num_q_tiles = (n_query + D64_BLOCK_Q - 1) // D64_BLOCK_Q + num_db_tiles = (n_database + D64_BLOCK_M - 1) // D64_BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tiles * split_count + total_queries = bsz * n_query + stage1_grid = min(total_work, D64_GRID_DIM_DEFAULT) + merge_grid = min(total_queries, D64_GRID_DIM_DEFAULT) + partial_dists, partial_indices = d64_parent.split_parent._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = d64_parent.m64_parent.non128_base.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), total_queries, D64_BLOCK_Q, dim, D64_FEAT_D) + tmap_database = d64_parent.m64_parent.non128_base.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, D64_BLOCK_M, dim, D64_FEAT_D) + stage1_launch = _compiled_stage1_v12_d64_tail().prepare_launch(grid=(stage1_grid, 1, 1), block=(D64_THREADS, 1, 1), args=pack_kernel_args(stage1_v12_d64_tail_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tiles=num_q_tiles, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), shared_mem=stage1_v12_d64_tail_ir.computed_smem_bytes) + merge_ir = d64_parent.fused_merge_parent._fused_merge_ir(split_count, group_count) + merge_launch = _compiled_fused_merge(split_count, group_count).prepare_launch(grid=(merge_grid, 1, 1), block=(d64_parent.fused_merge_parent.D768_FUSED_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], total_queries], shared_mem=merge_ir.computed_smem_bytes) + stage1_launch.launch() + merge_launch.launch() + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + label = _target_label_for_inputs(inputs) + if force_fallback or label is None: + return d64_parent.default_dispatcher.route_for_contract_inputs(inputs, force_fallback=force_fallback) + spec = SHAPE_SPECS[label] + return ''.join(['v12_d64_tail_017a:', format(label, ''), ':q', format(int(spec['Q']), ''), ':m', format(int(spec['M']), ''), ':m64n64k64:s', format(_split_count_for_label(label), ''), ':g', format(_group_count_for_label(label), '')]) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + label = _target_label_for_inputs(inputs) + if not force_fallback and label is not None: + _launch_d64_tail(inputs, label) + return + d64_parent.default_dispatcher.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + if shape_labels is None: + wanted = set(TARGET_SHAPES) + else: + wanted = {str(label) for label in shape_labels} + selected = [shape for shape in eval_mod.CANONICAL_SHAPES if str(shape['label']) in wanted] + missing = wanted - {str(shape['label']) for shape in selected} + if missing: + raise ValueError(''.join(['unknown knn_build contract shape(s): ', format(sorted(missing), '')])) + return selected + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + selected = None if shape_labels is None else _select_contract_shapes(shape_labels) + report = evaluate_contract(shapes=selected, correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def route_trace_for_contract_shapes(shape_labels=TARGET_SHAPES, *, force_fallback: bool=False) -> list[dict[str, Any]]: + rows = [] + for shape in _select_contract_shapes(shape_labels): + params = dict(shape.get('params', {})) + params['label'] = shape['label'] + inputs = {'label': params['label'], 'B': params['B'], 'Q': params['Q'], 'M': params['M'], 'D': params['D'], 'K': params['K'], 'build': params['build'], 'dtype': params.get('dtype', 'bfloat16')} + label = _target_label_for_inputs(inputs) + selected = not force_fallback and label is not None + rows.append({'shape_key': params['label'], 'selected_route': route_for_contract_inputs(inputs, force_fallback=force_fallback), 'selected_entrypoint': ROUTE_ENTRYPOINT if selected else d64_parent.default_dispatcher.ROUTE_ENTRYPOINT, 'selected_seed': CANDIDATE_ID if selected else None, 'expected_seed': CANDIDATE_ID if params['label'] in TARGET_SHAPE_SET else None, 'route_kind': 'specialized' if selected else 'forced_fallback' if force_fallback else 'delegated', 'route_source': 'shape-specific-seed' if selected else 'default-v11-common-d-dispatcher', 'guard_id': '017a_v12_d64_long_m_tail_exact_guard' if selected else 'guard_miss', 'guard_condition': ''.join(['exact BF16 non-build B=1 Q=', format(params['Q'], ''), ' M=', format(params['M'], ''), ' D=64 K=10']) if selected else 'delegate to current v11 common-D dispatcher', 'split_count': _split_count_for_label(label) if selected and label is not None else None, 'group_count': _group_count_for_label(label) if selected and label is not None else None, 'producer_topology': 'small-Q M64_N64_K64 tcgen05/TMA', 'merge_topology': 'fused_group_split_merge' if selected else None, 'classification': 'v12-d64-long-m-tail-seed' if selected else 'guard-miss'}) + return rows + +def _benchmark_payload(report: dict[str, Any], *, use_cupti: bool, shape_labels) -> dict[str, Any]: + rows = report.get('per_shape', {}) + timing_backends = sorted({row.get('timing_backend') for row in rows.values() if row.get('timing_backend')}) + return {'contract': report['contract'], 'contract_version': report['contract_version'], 'measured_entrypoint': BENCHMARK_ENTRYPOINT, 'candidate_entrypoint': ROUTE_ENTRYPOINT, 'candidate_id': CANDIDATE_ID, 'accelerated_shape_labels': list(TARGET_SHAPES), 'measured_shape_labels': list(shape_labels), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'timing_backends': timing_backends, 'topology': {label: {'split_count': _split_count_for_label(label), 'group_count': _group_count_for_label(label), 'block_q': D64_BLOCK_Q, 'block_m': D64_BLOCK_M, 'feat_d': D64_FEAT_D, 'rows_covered': SHAPE_SPECS[label]['rows_covered']} for label in TARGET_SHAPES}, 'route_trace': route_trace_for_contract_shapes(shape_labels), 'summary': report['summary'], 'performance': report['performance'], 'correctness': report['correctness'], 'target_rows': {label: rows.get(label, {}) for label in TARGET_SHAPES if label in rows}, 'report': report} + +def benchmark_knn_build_v12_d64_tail_017a_v1(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES) -> dict[str, Any]: + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels) + return _benchmark_payload(report, use_cupti=use_cupti, shape_labels=shape_labels) + +def write_benchmark_artifact(artifact_dir: str | os.PathLike[str], *, use_cupti: bool=True, shape_labels=TARGET_SHAPES) -> dict[str, Any]: + out_dir = Path(artifact_dir) + out_dir.mkdir(parents=True, exist_ok=True) + suffix = 'cupti' if use_cupti else 'cuda_event' + payload = benchmark_knn_build_v12_d64_tail_017a_v1(use_cupti=use_cupti, shape_labels=shape_labels) + path = out_dir / ''.join(['v12_d64_tail_017a_', format(len(tuple(shape_labels)), ''), 'row_', format(suffix, ''), '.json']) + path.write_text(json.dumps(payload, indent=2, sort_keys=True) + '\n') + return {'artifact': str(path), 'summary': payload['summary'], 'performance': payload['performance']} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_v12_highd_rag_22e9_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_v12_highd_rag_22e9_v1.py new file mode 100644 index 00000000..d923349f --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_v12_highd_rag_22e9_v1.py @@ -0,0 +1,193 @@ +"""v12 high-D RAG seed for kNN build/search. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +routes the BF16 non-build v12 high-D RAG blocker rows through the existing +M64/N64 tcgen05/TMA high-D producer and fused split-list merge. Guard +misses delegate to the existing D256 K32 sidecar lineage; this module does not +edit production dispatch. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from collections.abc import Callable +from typing import Any +from .. import _dispatch_runtime as eval_mod +from . import knn_build_common_d_5e7f_rag_highd_v1 as highd_parent +from . import knn_build_v12_d256_k32_tail_59fe_v1 as k32_parent +from .._dispatch_runtime import pack_kernel_args +MODULE = 'loom.examples.weave.knn_build_v12_highd_rag_22e9_v1' +ROUTE_PREFIX = 'knn_build_v12_highd_rag_22e9_v1' +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +BENCHMARK_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_knn_build_v12_highd_rag_22e9_v1']) +CANDIDATE_ID = 'knn_build_v12_highd_rag_22e9_v1' +RAG_D768 = 'rag_microbatch_common_d768_b1_q8_m100000_k10' +RAG_D1024 = 'rag_microbatch_common_d1024_b1_q4_m100000_k10' +RAG_D4096 = 'rag_online_common_d4096_b1_q1_m65536_k10' +SEARCH_D1024 = 'search_rect_common_d1024_b1_q256_m8192_k10' +SEARCH_D4096 = 'search_rect_common_d4096_b1_q128_m4096_k10' +TARGET_SHAPES = (RAG_D768, RAG_D1024, RAG_D4096) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +RAG_SHAPES = (RAG_D768, RAG_D1024, RAG_D4096) +SEARCH_SHAPES = (SEARCH_D1024, SEARCH_D4096) +M64_BLOCK_Q = highd_parent.M64_BLOCK_Q +M64_BLOCK_M = highd_parent.M64_BLOCK_M +M64_THREADS = highd_parent.M64_THREADS +K_TILE = highd_parent.K_TILE +TOP_K_MAX = highd_parent.TOP_K_MAX +GRID_DIM_DEFAULT = highd_parent.GRID_DIM_DEFAULT +DEFAULT_RAG_SPLIT = _decode_capture(_json_loads('144')) +DEFAULT_RAG_GROUPS = _decode_capture(_json_loads('12')) +SHAPE_SPECS = _decode_capture(_json_loads('{"__dict_items__": [["rag_microbatch_common_d768_b1_q8_m100000_k10", {"__dict_items__": [["B", 1], ["Q", 8], ["M", 100000], ["D", 768], ["K", 10], ["build", false], ["feature_chunks", 6], ["split_count", 144], ["group_count", 12]]}], ["rag_microbatch_common_d1024_b1_q4_m100000_k10", {"__dict_items__": [["B", 1], ["Q", 4], ["M", 100000], ["D", 1024], ["K", 10], ["build", false], ["feature_chunks", 8], ["split_count", 144], ["group_count", 12]]}], ["rag_online_common_d4096_b1_q1_m65536_k10", {"__dict_items__": [["B", 1], ["Q", 1], ["M", 65536], ["D", 4096], ["K", 10], ["build", false], ["feature_chunks", 32], ["split_count", 128], ["group_count", 8]]}]]}')) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_V12_HIGHD_22E9_VERIFY_KERNEL') + split_count = int(os.environ.get('LOOM_KNN_V12_HIGHD_22E9_VERIFY_SPLIT', DEFAULT_RAG_SPLIT)) + group_count = int(os.environ.get('LOOM_KNN_V12_HIGHD_22E9_VERIFY_GROUPS', DEFAULT_RAG_GROUPS)) + if verify_kernel == 'stage1_d768': + return highd_parent._stage1_ir(6) + if verify_kernel == 'stage1_d1024': + return highd_parent._stage1_ir(8) + if verify_kernel == 'stage1_d4096': + return highd_parent._stage1_ir(32) + return highd_parent.fused_merge_parent._fused_merge_ir(split_count, group_count) +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_4be7_d768fused_merge_s144g12_4be7_d768fused_v1", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 1024, "constants": [["TOP_K_MAX", 10], ["GROUP_COUNT", 12], ["GROUP_SPLITS", 12]], "cta_group": 1, "threads": 32}')) + +def _dtype_name(inputs: dict[str, Any], tensor_name: str='query') -> str: + tensor = inputs.get(tensor_name) + if tensor is not None and hasattr(tensor, 'dtype'): + return str(tensor.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')).replace('torch.', '') + +def _matches_spec(inputs: dict[str, Any], spec: dict[str, Any]) -> bool: + return _dtype_name(inputs, 'query') == 'bfloat16' and _dtype_name(inputs, 'database') in ('', 'bfloat16') and (bool(inputs.get('build', False)) == bool(spec['build'])) and (int(inputs.get('B', -1)) == int(spec['B'])) and (int(inputs.get('Q', -1)) == int(spec['Q'])) and (int(inputs.get('M', -1)) == int(spec['M'])) and (int(inputs.get('D', -1)) == int(spec['D'])) and (int(inputs.get('K', -1)) == int(spec['K'])) + +def _target_label_for_inputs(inputs: dict[str, Any]) -> str | None: + label = inputs.get('label') + if label is not None: + label_s = str(label) + if label_s in TARGET_SHAPE_SET and _matches_spec(inputs, SHAPE_SPECS[label_s]): + return label_s + return None + for candidate_label, spec in SHAPE_SPECS.items(): + if _matches_spec(inputs, spec): + return candidate_label + return None + +def _split_count_for_label(label: str) -> int: + return int(SHAPE_SPECS[label]['split_count']) + +def _group_count_for_label(label: str) -> int: + return int(SHAPE_SPECS[label]['group_count']) + +def _feature_chunks_for_label(label: str) -> int: + return int(SHAPE_SPECS[label]['feature_chunks']) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + label = _target_label_for_inputs(inputs) + if force_fallback or label is None: + return k32_parent.route_for_contract_inputs(inputs, force_fallback=force_fallback) + spec = SHAPE_SPECS[label] + return ''.join([format(ROUTE_PREFIX, ''), ':', format(label, ''), ':d', format(int(spec['D']), ''), ':q', format(int(spec['Q']), ''), ':m', format(int(spec['M']), ''), ':m64n64:s', format(_split_count_for_label(label), ''), ':g', format(_group_count_for_label(label), ''), ':chunks', format(_feature_chunks_for_label(label), '')]) + +def _launch_highd(inputs: dict[str, Any], label: str) -> None: + query = inputs['query'] + database = inputs['database'] + if str(query.dtype) != 'torch.bfloat16' or str(database.dtype) != 'torch.bfloat16': + raise ValueError('knn_build_v12_highd_rag_22e9_v1 supports bfloat16 inputs only') + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + feature_chunks = _feature_chunks_for_label(label) + split_count = _split_count_for_label(label) + group_count = _group_count_for_label(label) + tma_dim = feature_chunks * K_TILE + if dim != tma_dim: + raise ValueError(''.join([format(label, ''), ' expected D=', format(tma_dim, ''), ', got ', format(dim, '')])) + if top_k != TOP_K_MAX: + raise ValueError(''.join([format(label, ''), ' expected K=', format(TOP_K_MAX, ''), ', got ', format(top_k, '')])) + highd_parent.fused_merge_parent._validate_group_shape(split_count, group_count) + num_q_tiles = (n_query + M64_BLOCK_Q - 1) // M64_BLOCK_Q + num_db_tiles = (n_database + M64_BLOCK_M - 1) // M64_BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tiles * split_count + total_queries = bsz * n_query + stage1_grid = min(total_work, GRID_DIM_DEFAULT) + merge_grid = min(total_queries, GRID_DIM_DEFAULT) + partial_dists, partial_indices = highd_parent.split_parent._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = highd_parent.m64_parent.non128_base.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), total_queries, M64_BLOCK_Q, tma_dim, K_TILE) + tmap_database = highd_parent.m64_parent.non128_base.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, M64_BLOCK_M, tma_dim, K_TILE) + stage_ir = highd_parent._stage1_ir(feature_chunks) + highd_parent._compiled_stage1(feature_chunks).launch(grid=(stage1_grid, 1, 1), block=(M64_THREADS, 1, 1), args=pack_kernel_args(stage_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tiles=num_q_tiles, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), shared_mem=stage_ir.computed_smem_bytes) + merge_ir = highd_parent.fused_merge_parent._fused_merge_ir(split_count, group_count) + highd_parent._compiled_fused_merge(split_count, group_count).launch(grid=(merge_grid, 1, 1), block=(highd_parent.fused_merge_parent.D768_FUSED_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], total_queries], shared_mem=merge_ir.computed_smem_bytes) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + label = _target_label_for_inputs(inputs) + if not force_fallback and label is not None: + _launch_highd(inputs, label) + return + k32_parent.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + if shape_labels is None: + wanted = set(TARGET_SHAPES) + else: + wanted = {str(label) for label in shape_labels} + selected = [shape for shape in eval_mod.CANONICAL_SHAPES if str(shape['label']) in wanted] + missing = wanted - {str(shape['label']) for shape in selected} + if missing: + raise ValueError(''.join(['unknown knn_build contract shape(s): ', format(sorted(missing), '')])) + return selected + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def route_trace_for_contract_shapes(shape_labels=TARGET_SHAPES, *, force_fallback: bool=False) -> list[dict[str, Any]]: + rows = [] + for shape in _select_contract_shapes(shape_labels): + params = dict(shape.get('params', {})) + params['label'] = shape['label'] + inputs = {'label': params['label'], 'B': params['B'], 'Q': params['Q'], 'M': params['M'], 'D': params['D'], 'K': params['K'], 'build': params['build'], 'dtype': params.get('dtype', 'bfloat16')} + label = _target_label_for_inputs(inputs) + selected = not force_fallback and label is not None + rows.append({'shape_key': params['label'], 'selected_route': route_for_contract_inputs(inputs, force_fallback=force_fallback), 'selected_entrypoint': ROUTE_ENTRYPOINT if selected else k32_parent.ROUTE_ENTRYPOINT, 'selected_seed': CANDIDATE_ID if selected else None, 'expected_seed': CANDIDATE_ID if params['label'] in TARGET_SHAPE_SET else None, 'route_kind': 'specialized' if selected else 'delegated', 'route_source': 'shape-specific-seed' if selected else 'd256-k32-parent', 'guard_id': '22e9_v12_highd_exact_guard' if selected else 'guard_miss', 'guard_condition': 'exact BF16 non-build high-D v12 RAG row' if selected else 'delegate to D256 K32 parent', 'feature_chunks': _feature_chunks_for_label(label) if selected and label is not None else None, 'split_count': _split_count_for_label(label) if selected and label is not None else None, 'group_count': _group_count_for_label(label) if selected and label is not None else None, 'producer_topology': 'M64_N64_tcgen05_tma_highd_chunked' if selected else None, 'merge_topology': 'fused_group_split_merge' if selected else None, 'classification': 'v12-highd-rag-seed' if selected else 'guard-miss'}) + return rows + +def _benchmark_payload(report: dict[str, Any], *, use_cupti: bool, shape_labels) -> dict[str, Any]: + rows = report.get('per_shape', {}) + timing_backends = sorted({row.get('timing_backend') for row in rows.values() if row.get('timing_backend')}) + return {'contract': report['contract'], 'contract_version': report['contract_version'], 'measured_entrypoint': BENCHMARK_ENTRYPOINT, 'candidate_entrypoint': ROUTE_ENTRYPOINT, 'candidate_id': CANDIDATE_ID, 'accelerated_shape_labels': list(TARGET_SHAPES), 'measured_shape_labels': list(shape_labels), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'timing_backends': timing_backends, 'topology': {label: {'producer': 'M64/N64 high-D tcgen05/TMA chunked', 'split_count': SHAPE_SPECS[label]['split_count'], 'group_count': SHAPE_SPECS[label]['group_count'], 'feature_chunks': SHAPE_SPECS[label]['feature_chunks'], 'merge': 'fused group split-list merge'} for label in TARGET_SHAPES}, 'route_trace': route_trace_for_contract_shapes(shape_labels), 'summary': report['summary'], 'performance': report['performance'], 'correctness': report['correctness'], 'rank_objective': report.get('rank_objective'), 'per_shape': report.get('per_shape', {}), 'report': report} + +def benchmark_knn_build_v12_highd_rag_22e9_v1(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES) -> dict[str, Any]: + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels) + return _benchmark_payload(report, use_cupti=use_cupti, shape_labels=shape_labels) diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_v12_highd_search_be66_v1.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_v12_highd_search_be66_v1.py new file mode 100644 index 00000000..c73c8cb3 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch/knn_build_v12_highd_search_be66_v1.py @@ -0,0 +1,206 @@ +"""v12 high-D rectangular search seed for kNN build/search. + +Minimum target architecture: sm_100a. This additive bucket-kernel candidate +routes the two BF16 non-build v12 high-D rectangular search rows through the +four-compute-warp M64/N64 tcgen05/TMA producer from the D768 build lineage, +retargeted by feature chunks, followed by the fused split-list merge. Guard +misses delegate to the existing high-D RAG sidecar lineage; this module does +not edit production dispatch. +""" +from __future__ import annotations +from json import loads as _json_loads +from .._dispatch_runtime import _capture_cuTensorMapEncodeTiled, _decode_capture, _import_dispatch_module, _ir_proxy +import argparse +import json +import os +from collections.abc import Callable +from functools import lru_cache +from typing import Any +from .._dispatch_runtime import dc as dc +from .. import _dispatch_runtime as eval_mod +from . import knn_build_common_d768_build_eeff_m64split_v1 as m64_4warp +from . import knn_build_v12_highd_rag_22e9_v1 as highd_rag_parent +from .._dispatch_runtime import pack_kernel_args +MODULE = 'loom.examples.weave.knn_build_v12_highd_search_be66_v1' +ROUTE_PREFIX = 'knn_build_v12_highd_search_be66_v1' +ROUTE_ENTRYPOINT = ''.join([format(MODULE, ''), ':launch_from_contract_inputs']) +BENCHMARK_ENTRYPOINT = ''.join([format(MODULE, ''), ':benchmark_knn_build_v12_highd_search_be66_v1']) +CANDIDATE_ID = 'knn_build_v12_highd_search_be66_v1' +SEARCH_D1024 = 'search_rect_common_d1024_b1_q256_m8192_k10' +SEARCH_D4096 = 'search_rect_common_d4096_b1_q128_m4096_k10' +TARGET_SHAPES = (SEARCH_D1024, SEARCH_D4096) +TARGET_SHAPE_SET = set(TARGET_SHAPES) +M64_BLOCK_Q = m64_4warp.M64_BLOCK_Q +M64_BLOCK_M = m64_4warp.M64_BLOCK_M +STAGE1_THREADS = m64_4warp.STAGE1_THREADS +K_TILE = m64_4warp.K_TILE +TOP_K_MAX = m64_4warp.TOP_K_MAX +GRID_DIM_DEFAULT = m64_4warp.GRID_DIM_DEFAULT +DEFAULT_SPLIT = _decode_capture(_json_loads('64')) +DEFAULT_GROUPS = _decode_capture(_json_loads('8')) +SHAPE_SPECS = _decode_capture(_json_loads('{"__dict_items__": [["search_rect_common_d1024_b1_q256_m8192_k10", {"__dict_items__": [["B", 1], ["Q", 256], ["M", 8192], ["D", 1024], ["K", 10], ["build", false], ["feature_chunks", 8], ["split_count", 64], ["group_count", 8]]}], ["search_rect_common_d4096_b1_q128_m4096_k10", {"__dict_items__": [["B", 1], ["Q", 128], ["M", 4096], ["D", 4096], ["K", 10], ["build", false], ["feature_chunks", 32], ["split_count", 64], ["group_count", 8]]}]]}')) + +def _ir_with_constants(ir_obj: Any, *, suffix: str, **updates: int) -> Any: + constants = tuple(((name, updates.get(name, value)) for name, value in ir_obj.constants)) + return dc.replace(ir_obj, symbol=''.join([format(ir_obj.symbol, ''), '_', format(suffix, '')]), constants=constants) + +@lru_cache(maxsize=4) +def _stage1_ir(feature_chunks: int) -> Any: + return _ir_with_constants(m64_4warp.stage1_m64_ir, suffix=''.join(['d', format(int(feature_chunks) * K_TILE, ''), '_be66_search_v1']), FEATURE_CHUNKS=int(feature_chunks)) + +def _verify_export_ir() -> Any: + verify_kernel = os.environ.get('LOOM_KNN_V12_HIGHD_SEARCH_BE66_VERIFY_KERNEL') + split_count = int(os.environ.get('LOOM_KNN_V12_HIGHD_SEARCH_BE66_VERIFY_SPLIT', DEFAULT_SPLIT)) + group_count = int(os.environ.get('LOOM_KNN_V12_HIGHD_SEARCH_BE66_VERIFY_GROUPS', DEFAULT_GROUPS)) + if verify_kernel == 'stage1_d1024': + return _stage1_ir(8) + if verify_kernel == 'stage1_d4096': + return _stage1_ir(32) + return m64_4warp.fused_parent._fused_merge_ir(split_count, group_count) +ir = _decode_capture(_json_loads('{"__ir__": "knn_build_non128_frontier_4be7_d768fused_merge_s64g8_4be7_d768fused_v1", "arg_keys": ["partial_dists", "partial_indices", "out_dists", "out_indices", "total_queries"], "cluster_dims": [1, 1, 1], "computed_smem_bytes": 1024, "constants": [["TOP_K_MAX", 10], ["GROUP_COUNT", 8], ["GROUP_SPLITS", 8]], "cta_group": 1, "threads": 32}')) + +@lru_cache(maxsize=4) +def _compiled_stage1(feature_chunks: int): + return m64_4warp.m64rag._compile_ir(_stage1_ir(int(feature_chunks))) + +@lru_cache(maxsize=8) +def _compiled_fused_merge(split_count: int, group_count: int): + return m64_4warp.fused_parent._compiled_fused_merge(int(split_count), int(group_count)) + +def _dtype_name(inputs: dict[str, Any], tensor_name: str='query') -> str: + tensor = inputs.get(tensor_name) + if tensor is not None and hasattr(tensor, 'dtype'): + return str(tensor.dtype).replace('torch.', '') + return str(inputs.get('dtype', '')).replace('torch.', '') + +def _matches_spec(inputs: dict[str, Any], spec: dict[str, Any]) -> bool: + return _dtype_name(inputs, 'query') == 'bfloat16' and _dtype_name(inputs, 'database') in ('', 'bfloat16') and (bool(inputs.get('build', False)) == bool(spec['build'])) and (int(inputs.get('B', -1)) == int(spec['B'])) and (int(inputs.get('Q', -1)) == int(spec['Q'])) and (int(inputs.get('M', -1)) == int(spec['M'])) and (int(inputs.get('D', -1)) == int(spec['D'])) and (int(inputs.get('K', -1)) == int(spec['K'])) + +def _target_label_for_inputs(inputs: dict[str, Any]) -> str | None: + label = inputs.get('label') + if label is not None: + label_s = str(label) + if label_s in TARGET_SHAPE_SET and _matches_spec(inputs, SHAPE_SPECS[label_s]): + return label_s + return None + for candidate_label, spec in SHAPE_SPECS.items(): + if _matches_spec(inputs, spec): + return candidate_label + return None + +def _split_count_for_label(label: str) -> int: + return int(SHAPE_SPECS[label]['split_count']) + +def _group_count_for_label(label: str) -> int: + group_count = int(SHAPE_SPECS[label]['group_count']) + m64_4warp.fused_parent._validate_group_shape(_split_count_for_label(label), group_count) + return group_count + +def _feature_chunks_for_label(label: str) -> int: + return int(SHAPE_SPECS[label]['feature_chunks']) + +def route_for_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> str: + label = _target_label_for_inputs(inputs) + if force_fallback or label is None: + return highd_rag_parent.route_for_contract_inputs(inputs, force_fallback=force_fallback) + spec = SHAPE_SPECS[label] + return ''.join([format(ROUTE_PREFIX, ''), ':', format(label, ''), ':d', format(int(spec['D']), ''), ':q', format(int(spec['Q']), ''), ':m', format(int(spec['M']), ''), ':m128n64:s', format(_split_count_for_label(label), ''), ':g', format(_group_count_for_label(label), ''), ':chunks', format(_feature_chunks_for_label(label), '')]) + +def _launch_search(inputs: dict[str, Any], label: str) -> None: + query = inputs['query'] + database = inputs['database'] + if str(query.dtype) != 'torch.bfloat16' or str(database.dtype) != 'torch.bfloat16': + raise ValueError('knn_build_v12_highd_search_be66_v1 supports bfloat16 inputs only') + bsz = int(inputs['B']) + n_query = int(inputs['Q']) + n_database = int(inputs['M']) + dim = int(inputs['D']) + top_k = int(inputs['K']) + feature_chunks = _feature_chunks_for_label(label) + split_count = _split_count_for_label(label) + group_count = _group_count_for_label(label) + tma_dim = feature_chunks * K_TILE + if dim != tma_dim: + raise ValueError(''.join([format(label, ''), ' expected D=', format(tma_dim, ''), ', got ', format(dim, '')])) + if top_k != TOP_K_MAX: + raise ValueError(''.join([format(label, ''), ' expected K=', format(TOP_K_MAX, ''), ', got ', format(top_k, '')])) + num_q_tiles = (n_query + M64_BLOCK_Q - 1) // M64_BLOCK_Q + num_db_tiles = (n_database + M64_BLOCK_M - 1) // M64_BLOCK_M + db_tiles_per_split = (num_db_tiles + split_count - 1) // split_count + total_work = bsz * num_q_tiles * split_count + total_queries = bsz * n_query + stage1_grid = min(total_work, GRID_DIM_DEFAULT) + merge_grid = min(total_queries, GRID_DIM_DEFAULT) + partial_dists, partial_indices = m64_4warp.split_parent._partial_buffers(split_count=split_count, bsz=bsz, n_query=n_query, top_k=top_k, device=query.device) + tmap_query = m64_4warp.m64rag.non128_base.base_v1._create_tensor_map_3d_oob_zero(query.data_ptr(), total_queries, M64_BLOCK_Q, tma_dim, K_TILE) + tmap_database = m64_4warp.m64rag.non128_base.base_v1._create_tensor_map_3d_oob_zero(database.data_ptr(), bsz * n_database, M64_BLOCK_M, tma_dim, K_TILE) + stage_ir = _stage1_ir(feature_chunks) + _compiled_stage1(feature_chunks).launch(grid=(stage1_grid, 1, 1), block=(STAGE1_THREADS, 1, 1), args=pack_kernel_args(stage_ir, tmap_query=tmap_query, tmap_database=tmap_database, query_sq=inputs['query_sq'], database_sq=inputs['database_sq'], partial_dists=partial_dists, partial_indices=partial_indices, B=bsz, Q=n_query, M=n_database, K=top_k, num_q_tiles=num_q_tiles, db_tiles_per_split=db_tiles_per_split, split_count=split_count, total_work=total_work), shared_mem=stage_ir.computed_smem_bytes) + merge_ir = m64_4warp.fused_parent._fused_merge_ir(split_count, group_count) + _compiled_fused_merge(split_count, group_count).launch(grid=(merge_grid, 1, 1), block=(m64_4warp.fused_parent.D768_FUSED_MERGE_THREADS, 1, 1), args=[partial_dists, partial_indices, inputs['out_dists'], inputs['out_indices'], total_queries], shared_mem=merge_ir.computed_smem_bytes) + +def launch_from_contract_inputs(inputs: dict[str, Any], *, force_fallback: bool=False) -> None: + label = _target_label_for_inputs(inputs) + if not force_fallback and label is not None: + _launch_search(inputs, label) + return + highd_rag_parent.launch_from_contract_inputs(inputs, force_fallback=force_fallback) + +def candidate(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs) + +def candidate_force_fallback(inputs: dict[str, Any]) -> None: + launch_from_contract_inputs(inputs, force_fallback=True) + +def evaluate_contract(*, shapes=None, correctness: bool=True, benchmark: bool=True, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + return eval_mod.evaluate(kernel_fn, shapes=shapes, correctness=correctness, benchmark=benchmark) + +def _select_contract_shapes(shape_labels): + if shape_labels is None: + wanted = set(TARGET_SHAPES) + else: + wanted = {str(label) for label in shape_labels} + selected = [shape for shape in eval_mod.CANONICAL_SHAPES if str(shape['label']) in wanted] + missing = wanted - {str(shape['label']) for shape in selected} + if missing: + raise ValueError(''.join(['unknown knn_build contract shape(s): ', format(sorted(missing), '')])) + return selected + +def compile_and_launch_knn_build(*, shape_labels=TARGET_SHAPES, benchmark: bool=False) -> dict[str, Any]: + report = evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=benchmark) + passed = bool(report.get('summary', {}).get('all_correct', False)) + report['passed'] = passed + report['all_pass'] = passed + return report + +def _set_bench_backend(use_cupti: bool): + previous = eval_mod.CONTRACT.bench.get('use_cupti', True) + eval_mod.CONTRACT.bench['use_cupti'] = bool(use_cupti) + return previous + +def _run_with_timing_backend(*, use_cupti: bool, shape_labels=TARGET_SHAPES, kernel_fn: Callable[[dict[str, Any]], Any]=candidate) -> dict[str, Any]: + prior_use_cupti = _set_bench_backend(use_cupti) + try: + return evaluate_contract(shapes=_select_contract_shapes(shape_labels), correctness=True, benchmark=True, kernel_fn=kernel_fn) + finally: + eval_mod.CONTRACT.bench['use_cupti'] = prior_use_cupti + +def route_trace_for_contract_shapes(shape_labels=TARGET_SHAPES, *, force_fallback: bool=False) -> list[dict[str, Any]]: + rows = [] + for shape in _select_contract_shapes(shape_labels): + params = dict(shape.get('params', {})) + params['label'] = shape['label'] + inputs = {'label': params['label'], 'B': params['B'], 'Q': params['Q'], 'M': params['M'], 'D': params['D'], 'K': params['K'], 'build': params['build'], 'dtype': params.get('dtype', 'bfloat16')} + label = _target_label_for_inputs(inputs) + selected = not force_fallback and label is not None + rows.append({'shape_key': params['label'], 'selected_route': route_for_contract_inputs(inputs, force_fallback=force_fallback), 'selected_entrypoint': ROUTE_ENTRYPOINT if selected else highd_rag_parent.ROUTE_ENTRYPOINT, 'selected_seed': CANDIDATE_ID if selected else None, 'expected_seed': CANDIDATE_ID if params['label'] in TARGET_SHAPE_SET else None, 'route_kind': 'specialized' if selected else 'delegated', 'route_source': 'shape-specific-seed' if selected else 'highd-rag-parent', 'guard_id': 'be66_v12_highd_search_exact_guard' if selected else 'guard_miss', 'guard_condition': 'exact BF16 non-build high-D rectangular search row' if selected else 'delegate to high-D RAG parent', 'feature_chunks': _feature_chunks_for_label(label) if selected and label is not None else None, 'split_count': _split_count_for_label(label) if selected and label is not None else None, 'group_count': _group_count_for_label(label) if selected and label is not None else None, 'producer_topology': 'M128_N64_tcgen05_tma_highd_chunked' if selected else None, 'merge_topology': 'fused_group_split_merge' if selected else None, 'classification': 'v12-highd-search-seed' if selected else 'guard-miss'}) + return rows + +def _benchmark_payload(report: dict[str, Any], *, use_cupti: bool, shape_labels) -> dict[str, Any]: + rows = report.get('per_shape', {}) + timing_backends = sorted({row.get('timing_backend') for row in rows.values() if row.get('timing_backend')}) + return {'contract': report['contract'], 'contract_version': report['contract_version'], 'measured_entrypoint': BENCHMARK_ENTRYPOINT, 'candidate_entrypoint': ROUTE_ENTRYPOINT, 'candidate_id': CANDIDATE_ID, 'accelerated_shape_labels': list(TARGET_SHAPES), 'measured_shape_labels': list(shape_labels), 'timing_backend_requested': 'cupti' if use_cupti else 'cuda_event', 'timing_backends': timing_backends, 'topology': {label: {'producer': 'M128/N64 high-D tcgen05/TMA chunked', 'split_count': SHAPE_SPECS[label]['split_count'], 'group_count': SHAPE_SPECS[label]['group_count'], 'feature_chunks': SHAPE_SPECS[label]['feature_chunks'], 'merge': 'fused group split-list merge'} for label in TARGET_SHAPES}, 'route_trace': route_trace_for_contract_shapes(shape_labels), 'summary': report['summary'], 'performance': report['performance'], 'correctness': report['correctness'], 'rank_objective': report.get('rank_objective'), 'per_shape': report.get('per_shape', {}), 'report': report} + +def benchmark_knn_build_v12_highd_search_be66_v1(*, use_cupti: bool=True, shape_labels=TARGET_SHAPES) -> dict[str, Any]: + report = _run_with_timing_backend(use_cupti=use_cupti, shape_labels=shape_labels) + return _benchmark_payload(report, use_cupti=use_cupti, shape_labels=shape_labels) diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch_runtime.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch_runtime.py new file mode 100644 index 00000000..f9eafcbf --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch_runtime.py @@ -0,0 +1,1151 @@ +from __future__ import annotations +_KERNEL_ALIAS_BY_IR_NAME = {'knn_build_evolve_7bfc_split_cg2_stage1_batch8_cond4_vmin_threshold_k5': 'dispatch_kernel_0000', 'knn_build_evolve_7bfc_k5_merge_s4_tree': 'dispatch_kernel_0001', 'knn_build_evolve_7bfc_split_merge': 'dispatch_kernel_0003', 'knn_build_evolve_7bfc_split_cg2_u2_stage1_k8split': 'dispatch_kernel_0004', 'knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_k8s8': 'dispatch_kernel_0005', 'knn_build_evolve_7bfc_k10_merge_s4_rowbase_cache': 'dispatch_kernel_0006', 'knn_build_evolve_7bfc_split_cg2_u2_stage1_k16split': 'dispatch_kernel_0008', 'knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_f8c3lowk_k16s16': 'dispatch_kernel_0009', 'knn_build_evolve_7bfc_split_cg2_u2_stage1_k12split': 'dispatch_kernel_0010', 'knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_k12s16': 'dispatch_kernel_0011', 'knn_build_evolve_7bfc_split_cg2_u2_stage1_k20split': 'dispatch_kernel_0012', 'knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_k20s16': 'dispatch_kernel_0013', 'knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_195e_q1024k8s16': 'dispatch_kernel_0014', 'knn_build_q4096_k8_fd9b_stage1_unordered_exact_prefill': 'dispatch_kernel_0015', 'knn_build_q4096_k8_fd9b_merge_s4_unordered_warp_select': 'dispatch_kernel_0016', 'knn_build_dim_midk_73a9_d64_split_stage1': 'dispatch_kernel_0017', 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'dispatch_kernel_0054', 'knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k30unordered_bad5k28unordered': 'dispatch_kernel_0055', 'knn_build_evolve_7bfc_k32_merge_s4_unordered_k30unordered_bad5k28unordered': 'dispatch_kernel_0056', 'knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered': 'dispatch_kernel_0057', 'knn_build_evolve_7bfc_k32_merge_s4_unordered_warp_select': 'dispatch_kernel_0058', 'knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k30unordered': 'dispatch_kernel_0059', 'knn_build_k30_q4096_6998_merge_s4_unordered_warp_select': 'dispatch_kernel_0060', 'knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k48over32': 'dispatch_kernel_0061', 'knn_build_k48_merge_s4_unordered_warp_select': 'dispatch_kernel_0062', 'knn_build_k64_stage1_tailinf_k64over32tailinfsplitgrid': 'dispatch_kernel_0063', 'knn_build_k64_merge_s8_unordered_warp_select_k64over32s8warpselect': 'dispatch_kernel_0064', 'knn_build_k20_mergeown_08ec_warp8_select_s2warp8': 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'{"computed_smem_bytes":17664,"constants":[["BLOCK_Q",64],["BLOCK_M",64],["K_TILE",64],["TOP_K_MAX",10]],"ir_name":"knn_build_common_d_5e7f_rag_d64_repair_stage1","kwargs":{"smem_bytes":17664,"validate":false},"threads":96}': 'dispatch_kernel_0229', '{"computed_smem_bytes":66816,"constants":[["BLOCK_Q",64],["BLOCK_M",64],["FEAT_D",128],["TOP_K_MAX",32],["ROWS_COVERED",32],["SPLIT_COUNT_CONST",141],["NUM_DB_TILES_CONST",1563],["TILES_FLOOR_CONST",11],["EXTRA_SPLITS_CONST",12],["DB_TILES_PER_SPLIT_CONST",12],["M_LIMIT",100000]],"ir_name":"knn_build_rag_microbucket_k32_q32rowld2exact_f653_v1_stage1_q32rowld2exact_f653_v1","kwargs":{"smem_bytes":66816,"validate":false},"threads":128}': 'dispatch_kernel_0230', '{"computed_smem_bytes":0,"constants":[["TOP_K_MAX",96],["SPLIT_COUNT",2]],"ir_name":"knn_build_k96_merge_s2_unordered_warp_select","kwargs":{"smem_bytes":0,"validate":false},"threads":128}': 'dispatch_kernel_0231', '{"computed_smem_bytes":0,"constants":[["TOP_K_MAX",10],["GROUP_COUNT",21],["GROUP_SPLITS",7]],"ir_name":"knn_build_q1m524_workfeed_s147_g21_register_merge","kwargs":{"smem_bytes":0,"validate":false},"threads":32}': 'dispatch_kernel_0232'} + +import ctypes +import importlib +import json +import threading +import sys +from contextlib import contextmanager +from contextvars import ContextVar +from dataclasses import dataclass, field, replace as _dataclass_replace +from importlib import resources +from types import SimpleNamespace + +from .kernels import get_kernel +from ._runtime import launch_stream_context, resolve_launch_defaults + + +_DISPATCH_LAUNCH_OPTIONS = ContextVar("dispatch_launch_options", default=(None, None)) + + +@contextmanager +def dispatch_launch_options(*, stream=None, timeout_ms=None): + token = _DISPATCH_LAUNCH_OPTIONS.set((stream, timeout_ms)) + try: + yield + finally: + _DISPATCH_LAUNCH_OPTIONS.reset(token) + + +def _resolved_launch_options(stream, timeout_ms): + default_stream, default_timeout_ms = _DISPATCH_LAUNCH_OPTIONS.get() + return ( + default_stream if stream is None else stream, + default_timeout_ms if timeout_ms is None else timeout_ms, + ) + + +_active_launch_capture = ContextVar("flashlib_active_launch_capture", default=None) +_pending_tensor_map_recipe = ContextVar("flashlib_pending_tensor_map_recipe", default=None) +_launch_capture_prepare_lock = threading.RLock() + + +def _replace(value, /, **changes): + replacer = getattr(value, "__replace__", None) + if callable(replacer): + return replacer(**changes) + return _dataclass_replace(value, **changes) + + +dc = SimpleNamespace(replace=_replace) + + +def _import_dispatch_module(short_name): + return importlib.import_module(f"{__package__}._dispatch.{short_name}") + + +_DISPATCH_OWNED_DICT_SUFFIXES = ("CACHE", "SCRATCH", "INPUTS", "OUTPUTS", "FLAGS") + + +def _cache_value_references_owned_object(value, owned_ids, seen): + identity = id(value) + if identity in owned_ids: + return True + if identity in seen: + return False + seen.add(identity) + if isinstance(value, dict): + return any( + _cache_value_references_owned_object(item, owned_ids, seen) + for pair in value.items() + for item in pair + ) + if isinstance(value, (tuple, list, set, frozenset)): + return any( + _cache_value_references_owned_object(item, owned_ids, seen) + for item in value + ) + return False + + +def release_dispatch_caches(owned_objects): + '''Clear route-owned tensor dictionaries after a prepared sequence binds. + + Generated dispatch modules may temporarily cache tensor-map descriptors and + workspaces while a route is prepared. A bound ``PreparedKernelSequence`` + retains every CUDA argument, so those module globals are no longer owners. + To avoid clearing dispatch registries or scalar statistics, this contract + is limited to dict-valued, private, uppercase names with an explicit + workspace/cache suffix below this generated package's ``_dispatch`` + namespace. + ''' + + prefix = f"{__package__}._dispatch." + owned_ids = { + id(value) + for value in owned_objects + if callable(getattr(value, "data_ptr", None)) + } + if not owned_ids: + return 0 + cleared = 0 + for module_name, module in tuple(sys.modules.items()): + if module is None or not module_name.startswith(prefix): + continue + for name, value in tuple(vars(module).items()): + if ( + name.startswith("_") + and name.endswith(_DISPATCH_OWNED_DICT_SUFFIXES) + and name.isupper() + and isinstance(value, dict) + ): + removed = False + for key, item in tuple(value.items()): + if _cache_value_references_owned_object(item, owned_ids, set()): + value.pop(key, None) + removed = True + cleared += int(removed) + return cleared + + +def _decode_capture(value): + if isinstance(value, dict) and "__ir__" in value: + return _ir_proxy( + value["__ir__"], + value.get("threads", 256), + value.get("computed_smem_bytes", 0), + value.get("cluster_dims", (1, 1, 1)), + value.get("cta_group", 1), + value.get("constants", ()), + value.get("arg_keys", ()), + ) + if isinstance(value, dict) and set(value) == {"__kernel__"}: + return DispatchKernel(value["__kernel__"]) + if isinstance(value, dict) and set(value) == {"__kernel_source__"}: + return value["__kernel_source__"] + if isinstance(value, dict) and set(value) == {"__tuple__"}: + return tuple(_decode_capture(item) for item in value["__tuple__"]) + if isinstance(value, dict) and set(value) == {"__dict_items__"}: + return { + _decode_capture(key): _decode_capture(item) + for key, item in value["__dict_items__"] + } + if isinstance(value, dict): + return {key: _decode_capture(item) for key, item in value.items()} + if isinstance(value, list): + return [_decode_capture(item) for item in value] + return value + + +@dataclass(frozen=True) +class _IRProxy: + symbol: str + threads: int = 256 + computed_smem_bytes: int = 0 + constants: tuple = () + grid: object = None + arg_keys: tuple = () + + def __replace__(self, /, **changes): + values = { + "symbol": self.symbol, + "threads": self.threads, + "computed_smem_bytes": self.computed_smem_bytes, + "constants": self.constants, + "grid": self.grid, + "arg_keys": self.arg_keys, + } + unknown = sorted(set(changes) - set(values)) + if unknown: + raise TypeError(f"unknown frozen WeaveIR field(s): {unknown}") + values.update(changes) + return _IRProxy(**values) + + +def _ir_proxy( + name, threads=256, computed_smem_bytes=0, cluster_dims=(1, 1, 1), + cta_group=1, constants=(), arg_keys=(), +): + return _IRProxy( + name.rpartition(":")[2], int(threads), int(computed_smem_bytes), + tuple(tuple(item) for item in constants), + SimpleNamespace(cluster_dims=tuple(cluster_dims), cta_group=int(cta_group)), + tuple(arg_keys), + ) + + +def pack_kernel_args(schedule, /, **bindings): + expected = tuple(schedule.arg_keys) + missing = sorted(set(expected) - set(bindings)) + unexpected = sorted(set(bindings) - set(expected)) + if missing or unexpected: + raise ValueError( + f"kernel argument bindings do not match frozen WeaveIR.args: " + f"missing={missing!r}, unexpected={unexpected!r}" + ) + return [bindings[key] for key in expected] + + +class PreparedKernelSequence: + def __init__( + self, + launches, + result, + input_bindings=(), + result_template=None, + tensor_map_bindings=(), + input_alias_topology=(), + stream=None, + ): + if not launches: + raise RuntimeError("prepared semantic route did not capture a CUDA launch") + self._launches = tuple(launches) + self._result = result + self._input_bindings = tuple(tuple(bindings) for bindings in input_bindings) + if self._input_bindings and len(self._input_bindings) != len(self._launches): + raise RuntimeError("prepared semantic route has corrupt input bindings") + self._result_template = result_template + self._input_alias_topology = tuple( + tuple(group) for group in input_alias_topology + ) + direct_input_keys = {key for bindings in self._input_bindings for _, key in bindings} + self._direct_input_keys = tuple(sorted(direct_input_keys)) + self._tensor_map_bindings = _own_tensor_map_bindings( + self._launches, + tuple(tensor_map_bindings), + stream=stream, + ) + self._bound_input_keys = tuple( + sorted(direct_input_keys | {binding.input_key for binding in self._tensor_map_bindings}) + ) + self._input_references_retained = True + + @property + def launch_count(self): + return len(self._launches) + + @property + def bound_input_keys(self): + return self._bound_input_keys + + def rebind_inputs( + self, + inputs, + *, + stream=None, + materialize_result=True, + preserve_prepared_stream=False, + retain_input_references=True, + ): + if not isinstance(materialize_result, bool): + raise TypeError("materialize_result must be a bool") + if not isinstance(preserve_prepared_stream, bool): + raise TypeError("preserve_prepared_stream must be a bool") + if not isinstance(retain_input_references, bool): + raise TypeError("retain_input_references must be a bool") + if preserve_prepared_stream and stream is not None: + raise ValueError("preserve_prepared_stream requires stream=None") + if materialize_result and not retain_input_references: + raise ValueError( + "materialize_result requires retain_input_references so the result remains valid" + ) + if not any(self._input_bindings) and not self._tensor_map_bindings: + raise RuntimeError( + "prepared semantic route has no input bindings; " + "capture it with capture_kernel_launches(inputs=...)" + ) + missing = sorted(set(self.bound_input_keys) - set(inputs)) + if missing: + raise KeyError(f"missing prepared semantic input binding(s): {missing!r}") + _validate_public_tensor_alias_topology(inputs, self._input_alias_topology) + pointer_values = None + inputs_already_scrubbed = False + if not retain_input_references: + pointer_values = {} + for key in self._direct_input_keys: + value = inputs[key] + data_ptr = getattr(value, "data_ptr", None) + if not callable(data_ptr): + raise TypeError(f"prepared CUDA tensor binding {key!r} is not tensor-like") + pointer_values[key] = int(data_ptr()) + inputs_already_scrubbed = not self._input_references_retained + with launch_stream_context(stream): + for binding in self._tensor_map_bindings: + binding.refresh(inputs[binding.input_key]) + for launch, bindings in zip(self._launches, self._input_bindings, strict=True): + launch.rebind_tensor_arguments( + bindings, + inputs, + stream=stream, + preserve_stream=preserve_prepared_stream, + retain_inputs=retain_input_references, + pointer_values=pointer_values, + inputs_already_scrubbed=inputs_already_scrubbed, + ) + self._input_references_retained = retain_input_references + # Stateful public runtimes may own the output independently of the + # semantic return tree. Let those callers skip recursively rebuilding + # a result they will not observe while retaining the default behavior + # for normal prepared-dispatch callers. + if materialize_result and self._result_template is not None: + self._result = _materialize_result_template(self._result_template, inputs) + return self + + def _rebind_stream_bound_scrubbed_inputs(self, inputs, *, stream): + '''Rebind one validated fixed-stream runtime slot without generic checks. + + This private path is valid only after a stateful wrapper selected the + sequence through a cache key containing the complete public pointer + alias topology, recorded every caller-owned tensor, and scrubbed the + sequence's caller references. Public prepared callers continue to use + :meth:`rebind_inputs` and its full validation. + ''' + if self._input_references_retained: + raise RuntimeError( + "fixed-stream semantic rebind requires scrubbed input references" + ) + if stream is None: + raise ValueError("fixed-stream semantic rebind requires an explicit stream") + pointer_values = {} + for key in self._direct_input_keys: + value = inputs[key] + data_ptr = getattr(value, "data_ptr", None) + if not callable(data_ptr): + raise TypeError(f"prepared CUDA tensor binding {key!r} is not tensor-like") + pointer_values[key] = int(data_ptr()) + for binding in self._tensor_map_bindings: + binding.rebind_stream_bound(inputs[binding.input_key], stream=stream) + for launch, bindings in zip(self._launches, self._input_bindings, strict=True): + launch.rebind_tensor_arguments( + bindings, + inputs, + preserve_stream=True, + retain_inputs=False, + pointer_values=pointer_values, + inputs_already_scrubbed=True, + ) + return self + + def release_bound_inputs(self): + '''Drop caller tensor references after their launch stream was recorded.''' + if self._input_references_retained: + for launch, bindings in zip(self._launches, self._input_bindings, strict=True): + keepalive = list(launch._keepalive) + for index, _key in bindings: + value = keepalive[index] + data_ptr = getattr(value, "data_ptr", None) + if callable(data_ptr): + keepalive[index] = int(data_ptr()) + launch._keepalive = tuple(keepalive) + self._input_references_retained = False + self._result = None + + def record_stream(self, stream): + '''Tie every tensor launch argument, including private scratch, to a stream.''' + if stream is None: + raise ValueError("prepared semantic record_stream requires an explicit stream") + seen = set() + for launch in self._launches: + for value in launch._keepalive: + identity = id(value) + record_stream = getattr(value, "record_stream", None) + if identity not in seen and callable(record_stream): + seen.add(identity) + record_stream(stream) + # Variant-bank descriptor tensors are slot-owned but only the active + # variant appears in a launch keepalive; record every variant so a + # non-synchronizing release stays allocator-safe. + for binding in self._tensor_map_bindings: + for value in binding.variants.values(): + identity = id(value) + record_stream = getattr(value, "record_stream", None) + if identity not in seen and callable(record_stream): + seen.add(identity) + record_stream(stream) + + def _finish_rebind(self, result): + self._result = result + return self + + def __call__(self, _inputs=None, *, stream=None, timeout_ms=None): + last = len(self._launches) - 1 + for index, launch in enumerate(self._launches): + launch.launch(stream=stream, timeout_ms=timeout_ms if index == last else None) + return self._result + + +class KernelLaunchCapture: + def __init__(self, *, stream=None, arch=None, inputs=None, rebind=None): + if rebind is not None and not isinstance(rebind, PreparedKernelSequence): + raise TypeError("rebind must be a PreparedKernelSequence") + if inputs is not None and rebind is not None: + raise ValueError("inputs and rebind are mutually exclusive capture modes") + if rebind is not None: + raise RuntimeError( + "capture(rebind=...) is unsupported because an in-place topology " + "update cannot be transactional; capture a new sequence instead" + ) + self.stream = stream + self.arch = arch + self._launches = [] + self._input_bindings = [] + self._input_key_by_identity = _public_tensor_input_identities(inputs) + self._input_key_by_pointer = _public_tensor_input_pointers(inputs) + self._input_alias_topology = _public_tensor_alias_topology(inputs) + self._tensor_map_bindings = {} + self._route_caches_released = False + self._rebind = rebind + self._rebind_index = 0 + self.host_data_reads = 0 + + @property + def host_data_dependent(self): + '''True when the route read device memory while its kernels were only + being recorded — its host branch decisions cannot be frozen.''' + return self.host_data_reads > 0 + + @property + def rebinding(self): + return self._rebind is not None + + def add(self, launch): + if self.rebinding: + raise RuntimeError("rebind capture requires launch topology, not a newly prepared launch") + self._launches.append(launch) + self._input_bindings.append( + () + if not self._input_key_by_identity + else tuple( + (index, self._input_key_by_identity[id(arg)]) + for index, arg in enumerate(launch._keepalive) + if id(arg) in self._input_key_by_identity + ) + ) + for arg in launch._keepalive: + recipe = getattr(arg, "_loom_tensor_map_recipe", None) + if recipe is None: + continue + source_pointer = int(recipe[2]) + input_key = self._input_key_by_pointer.get(source_pointer) + if input_key is None: + continue + self._tensor_map_bindings.setdefault( + id(arg), + _TensorMapBinding( + input_key=input_key, + tensor=arg, + recipe=tuple(recipe), + pointer=source_pointer, + ), + ) + + def add_kernel_launch( + self, + exported, + *, + mode, + grid, + block, + args, + arg_types, + shared_mem, + stream, + cluster_dims=None, + ): + resolved_arch, resolved_stream, _ = resolve_launch_defaults( + arch=self.arch, + stream=self.stream if self.stream is not None else stream, + timeout_ms=None, + ) + with launch_stream_context(resolved_stream): + kernel = exported.compile(arch=resolved_arch, options=["--use_fast_math"]) + kwargs = { + "grid": grid, + "block": block, + "args": tuple(args), + "arg_types": arg_types, + "shared_mem": shared_mem, + "stream": resolved_stream, + } + if self.rebinding: + if self._rebind_index >= self._rebind.launch_count: + raise RuntimeError( + "prepared semantic route launch-count mismatch: " + f"expected {self._rebind.launch_count}, captured more launches" + ) + prepared = self._rebind._launches[self._rebind_index] + if mode == "cluster": + kernel.rebind_launch_cluster( + prepared, cluster_dims=cluster_dims, **kwargs + ) + elif mode == "cooperative": + kernel.rebind_launch_cooperative(prepared, **kwargs) + elif mode == "regular": + kernel.rebind_launch(prepared, **kwargs) + else: + raise RuntimeError(f"unsupported captured launch mode: {mode!r}") + self._rebind_index += 1 + return + if mode == "cluster": + prepared = kernel.prepare_launch_cluster( + cluster_dims=cluster_dims, **kwargs + ) + elif mode == "cooperative": + prepared = kernel.prepare_launch_cooperative(**kwargs) + elif mode == "regular": + prepared = kernel.prepare_launch(**kwargs) + else: + raise RuntimeError(f"unsupported captured launch mode: {mode!r}") + self.add(prepared) + + def bind(self, result): + if self.rebinding: + if self._rebind_index != self._rebind.launch_count: + raise RuntimeError( + "prepared semantic route launch-count mismatch: " + f"expected {self._rebind.launch_count}, captured {self._rebind_index}" + ) + return self._rebind._finish_rebind(result) + result_template = _capture_result_template(result, self._input_key_by_identity) + route_cache_owned_objects = self._route_cache_owned_objects() + sequence = PreparedKernelSequence( + self._launches, + result, + self._input_bindings, + result_template, + tuple(self._tensor_map_bindings.values()), + self._input_alias_topology, + self.stream, + ) + self.release_route_caches(route_cache_owned_objects) + return sequence + + def _route_cache_owned_objects(self): + return tuple(arg for launch in self._launches for arg in launch._keepalive) + + def release_route_caches(self, owned_objects=None): + if self._route_caches_released: + return 0 + if owned_objects is None: + owned_objects = self._route_cache_owned_objects() + self._route_caches_released = True + return release_dispatch_caches(tuple(owned_objects)) + + +@dataclass(frozen=True) +class _BoundInputResult: + key: str + + +@dataclass +class _TensorMapBinding: + input_key: str + tensor: object + recipe: tuple + pointer: int + pointer_carriers: tuple = () + staging_slots: list = field(default_factory=list) + variants: dict = field(default_factory=dict) + variant_capacity: int = 4 + + def __post_init__(self): + if not self.variants: + self.variants[self.pointer] = self.tensor + + def _acquire_staging_slot(self, torch): + for slot in self.staging_slots: + if slot.event.query(): + return slot + slot = _TensorMapStagingSlot( + host_buffer=torch.empty(128, dtype=torch.uint8, pin_memory=True), + event=torch.cuda.Event(blocking=False, interprocess=False), + ) + self.staging_slots.append(slot) + return slot + + def _encode_into(self, pointer, tensor, *, stream=None): + from cuda.bindings import driver + import torch + + arguments = list(self.recipe) + arguments[2] = pointer + err, tmap = driver.cuTensorMapEncodeTiled(*arguments) + if err != 0: + raise RuntimeError(f"cuTensorMapEncodeTiled rebind failed: CUresult={err}") + slot = self._acquire_staging_slot(torch) + ctypes.memmove( + int(slot.host_buffer.data_ptr()), + int(tmap.getPtr()), + 128, + ) + tensor.copy_(slot.host_buffer, non_blocking=True) + slot.event.record(torch.cuda.current_stream() if stream is None else stream) + self.tensor = tensor + self.pointer = pointer + self.recipe = tuple(arguments) + tensor._loom_tensor_map_recipe = self.recipe + + def _activate(self, pointer, tensor): + descriptor_pointer = int(tensor.data_ptr()) + for carrier in self.pointer_carriers: + carrier.value = descriptor_pointer + arguments = list(self.recipe) + arguments[2] = pointer + self.tensor = tensor + self.pointer = pointer + self.recipe = tuple(arguments) + tensor._loom_tensor_map_recipe = self.recipe + + def refresh(self, source, *, stream=None): + pointer = int(source.data_ptr()) + if pointer == self.pointer: + return + self._encode_into(pointer, self.tensor, stream=stream) + # Generic public rebinding mutates the active descriptor in place. + # Reset the private variant bank so no stale pointer key can name it. + self.variants.clear() + self.variants[pointer] = self.tensor + + def rebind_stream_bound(self, source, *, stream): + if stream is None: + raise ValueError("stream-bound tensor-map rebind requires an explicit stream") + pointer = int(source.data_ptr()) + if pointer == self.pointer: + return + cached = self.variants.pop(pointer, None) + if cached is not None: + # LRU recency: re-insert the hit so eviction removes the + # least-recently-activated variant, not the newest one. + self.variants[pointer] = cached + self._activate(pointer, cached) + return + + import torch + + if len(self.variants) < self.variant_capacity: + tensor = torch.empty_like(self.tensor) + record_stream = getattr(tensor, "record_stream", None) + if callable(record_stream): + record_stream(stream) + else: + tensor = self.variants.pop(next(iter(self.variants))) + self._encode_into(pointer, tensor, stream=stream) + self.variants[pointer] = tensor + self._activate(pointer, tensor) + + +@dataclass +class _TensorMapStagingSlot: + host_buffer: object + event: object + + +def _own_tensor_map_bindings(launches, bindings, *, stream): + '''Clone cached descriptors and patch every launch to slot-owned storage.''' + + if not bindings: + return () + owned_by_identity = {} + owned_bindings = [] + with launch_stream_context(stream): + for binding in bindings: + original = binding.tensor + owned = original.clone() + owned._loom_tensor_map_recipe = binding.recipe + metadata = getattr(original, "_loom_tma_metadata", None) + if metadata is not None: + owned._loom_tma_metadata = metadata + owned_by_identity[id(original)] = owned + pointer_carriers = tuple( + launch._packed._prevent_gc[index] + for launch in launches + for index, arg in enumerate(launch._keepalive) + if id(arg) == id(original) + ) + if not pointer_carriers or any( + type(carrier) is not ctypes.c_void_p for carrier in pointer_carriers + ): + raise RuntimeError("captured tensor-map binding has invalid pointer carriers") + owned_bindings.append( + _TensorMapBinding( + input_key=binding.input_key, + tensor=owned, + recipe=binding.recipe, + pointer=binding.pointer, + pointer_carriers=pointer_carriers, + ) + ) + for launch in launches: + replacements = { + index: owned_by_identity[id(arg)] + for index, arg in enumerate(launch._keepalive) + if id(arg) in owned_by_identity + } + if replacements: + launch.rebind_arguments(replacements, stream=stream) + return tuple(owned_bindings) + + +def _public_tensor_input_identities(inputs): + if inputs is None: + return {} + if not hasattr(inputs, "items"): + raise TypeError("capture inputs must be a mapping") + identities = {} + for key, value in inputs.items(): + if ( + isinstance(key, str) + and not key.startswith("_") + and callable(getattr(value, "data_ptr", None)) + ): + identities.setdefault(id(value), key) + return identities + + +def _public_tensor_input_pointers(inputs): + if inputs is None: + return {} + pointers = {} + for key, value in inputs.items(): + if ( + isinstance(key, str) + and not key.startswith("_") + and callable(getattr(value, "data_ptr", None)) + ): + pointers.setdefault(int(value.data_ptr()), key) + return pointers + + +def _public_tensor_alias_topology(inputs, keys=None): + '''Return the complete pointer-equality partition of public tensor inputs.''' + + if inputs is None: + return () + if not hasattr(inputs, "items"): + raise TypeError("capture inputs must be a mapping") + if keys is None: + selected = [ + key + for key, value in inputs.items() + if ( + isinstance(key, str) + and not key.startswith("_") + and callable(getattr(value, "data_ptr", None)) + ) + ] + else: + selected = list(keys) + missing = sorted(set(selected) - set(inputs)) + if missing: + raise KeyError(f"missing prepared semantic alias binding(s): {missing!r}") + invalid = sorted( + key + for key in selected + if not callable(getattr(inputs[key], "data_ptr", None)) + ) + if invalid: + raise TypeError( + f"prepared semantic alias binding(s) must be tensor-like: {invalid!r}" + ) + groups = {} + for key in selected: + groups.setdefault(int(inputs[key].data_ptr()), []).append(key) + return tuple(sorted(tuple(sorted(group)) for group in groups.values())) + + +def _validate_public_tensor_alias_topology(inputs, expected): + if not expected: + return + keys = tuple(key for group in expected for key in group) + actual = _public_tensor_alias_topology(inputs, keys) + if actual != expected: + raise RuntimeError( + "prepared semantic public tensor alias topology changed: " + f"expected {expected!r}, got {actual!r}; capture a new sequence" + ) + + +def _capture_result_template(value, input_key_by_identity): + key = input_key_by_identity.get(id(value)) + if key is not None: + return _BoundInputResult(key) + if isinstance(value, tuple): + return tuple(_capture_result_template(item, input_key_by_identity) for item in value) + if isinstance(value, list): + return [_capture_result_template(item, input_key_by_identity) for item in value] + if isinstance(value, dict): + return { + key: _capture_result_template(item, input_key_by_identity) + for key, item in value.items() + } + return value + + +def _materialize_result_template(value, inputs): + if isinstance(value, _BoundInputResult): + return inputs[value.key] + if isinstance(value, tuple): + return tuple(_materialize_result_template(item, inputs) for item in value) + if isinstance(value, list): + return [_materialize_result_template(item, inputs) for item in value] + if isinstance(value, dict): + return {key: _materialize_result_template(item, inputs) for key, item in value.items()} + return value + + +@contextmanager +def capture_kernel_launches(*, stream=None, arch=None, inputs=None, rebind=None): + import torch + + with _launch_capture_prepare_lock: + if _active_launch_capture.get() is not None: + raise RuntimeError("nested kernel launch capture is not supported") + capture = KernelLaunchCapture(stream=stream, arch=arch, inputs=inputs, rebind=rebind) + token = _active_launch_capture.set(capture) + # Captured launches are marshalled, not run, so any device read the + # route performs mid-traversal (for example an ``overflow_flag.item()`` + # certification) observes memory its recorded kernels never wrote. + # Interpose ``torch.Tensor.item`` for the capture's duration and count + # CUDA-tensor reads; a nonzero count marks the capture + # ``host_data_dependent`` so the plan builder keeps that signature on + # the per-call launcher instead of freezing an unreproducible branch. + # Captures are serialized by ``_launch_capture_prepare_lock``, so the + # process-global interpose cannot nest. ``.item()`` is the only device + # read the vendored dispatch modules perform on their launch paths. + # Torch test doubles without a ``Tensor.item`` skip the interpose. + original_tensor_item = getattr(getattr(torch, "Tensor", None), "item", None) + + def _observed_item(tensor): + if getattr(tensor, "is_cuda", False): + capture.host_data_reads += 1 + return original_tensor_item(tensor) + + if original_tensor_item is not None: + torch.Tensor.item = _observed_item + try: + if stream is None: + yield capture + else: + with torch.cuda.stream(stream): + yield capture + finally: + if original_tensor_item is not None: + torch.Tensor.item = original_tensor_item + capture.release_route_caches() + _active_launch_capture.reset(token) + + +class DispatchKernel: + def __init__(self, alias, symbol=None): + self.exported = get_kernel(alias) + self.symbol = symbol or self.exported.spec.symbol + + def __enter__(self): + return self + + def __exit__(self, *args): + return None + + def launch(self, *, grid, block, args, shared_mem=0, stream=None, timeout_ms=None, **kwargs): + stream, timeout_ms = _resolved_launch_options(stream, timeout_ms) + capture = _active_launch_capture.get() + if capture is not None: + capture.add_kernel_launch( + self.exported, + mode="regular", + grid=grid, + block=block, + args=args, + arg_types=self.exported.arg_types, + shared_mem=shared_mem, + stream=stream, + ) + return + self.exported.launch( + *args, grid=grid, block=block, shared_mem=shared_mem, stream=stream, + timeout_ms=timeout_ms, options=["--use_fast_math"], + ) + + def launch_cluster( + self, *, grid, block, args, cluster_dims, shared_mem=0, stream=None, + timeout_ms=None, **kwargs + ): + stream, timeout_ms = _resolved_launch_options(stream, timeout_ms) + capture = _active_launch_capture.get() + if capture is not None: + capture.add_kernel_launch( + self.exported, + mode="cluster", + grid=grid, + block=block, + args=args, + arg_types=self.exported.arg_types, + cluster_dims=cluster_dims, + shared_mem=shared_mem, + stream=stream, + ) + return + arch, stream, timeout_ms = resolve_launch_defaults( + arch=None, + stream=stream, + timeout_ms=timeout_ms, + ) + with launch_stream_context(stream): + kernel = self.exported.compile(arch=arch, options=["--use_fast_math"]) + kernel.launch_cluster( + grid=grid, block=block, args=tuple(args), + arg_types=self.exported.arg_types, + cluster_dims=cluster_dims, shared_mem=shared_mem, stream=stream, + timeout_ms=timeout_ms, + ) + + def launch_cooperative( + self, *, grid, block, args, shared_mem=0, stream=None, timeout_ms=None, **kwargs + ): + stream, timeout_ms = _resolved_launch_options(stream, timeout_ms) + capture = _active_launch_capture.get() + if capture is not None: + capture.add_kernel_launch( + self.exported, + mode="cooperative", + grid=grid, + block=block, + args=args, + arg_types=self.exported.arg_types, + shared_mem=shared_mem, + stream=stream, + ) + return + arch, stream, timeout_ms = resolve_launch_defaults( + arch=None, + stream=stream, + timeout_ms=timeout_ms, + ) + with launch_stream_context(stream): + kernel = self.exported.compile(arch=arch, options=["--use_fast_math"]) + kernel.launch_cooperative( + grid=grid, block=block, args=tuple(args), + arg_types=self.exported.arg_types, + shared_mem=shared_mem, stream=stream, timeout_ms=timeout_ms, + ) + + def prepare_launch(self, **kwargs): + return _PreparedDispatchLaunch(self.launch, kwargs) + + def prepare_launch_cluster(self, **kwargs): + return _PreparedDispatchLaunch(self.launch_cluster, kwargs) + + def prepare_launch_cooperative(self, **kwargs): + return _PreparedDispatchLaunch(self.launch_cooperative, kwargs) + + +class _PreparedDispatchLaunch: + def __init__(self, launch, kwargs): + self._launch = launch + self._kwargs = dict(kwargs) + + def launch(self, timeout_ms=None): + kwargs = dict(self._kwargs) + if timeout_ms is not None: + kwargs["timeout_ms"] = timeout_ms + return self._launch(**kwargs) + + +CUDAKernel = DispatchKernel + + +def compile_cuda(source, **kwargs): + return source + + +def detect_gpu_arch(): + import torch + major, minor = torch.cuda.get_device_capability() + return f"sm_{major}{minor}a" + + +def _cuda_include_dirs(): + return [] + + +def arch_flag_for_cc(major, minor): + sm = int(major) * 10 + int(minor) + return f"sm_{sm}a" if sm >= 90 else f"sm_{sm}" + + +def _capture_cuTensorMapEncodeTiled(*arguments): + '''Encode a tensor map while retaining a pointer-rebind recipe.''' + from cuda.bindings import driver + + result = driver.cuTensorMapEncodeTiled(*arguments) + if result[0] == 0: + _pending_tensor_map_recipe.set(tuple(arguments)) + else: + _pending_tensor_map_recipe.set(None) + return result + + +def _tmap_to_device(tmap, metadata=None): + import torch + del metadata + recipe = _pending_tensor_map_recipe.get() + _pending_tensor_map_recipe.set(None) + host_ptr = tmap.getPtr() + raw = bytes((ctypes.c_ubyte * 128).from_address(host_ptr)) + host = torch.frombuffer(bytearray(raw), dtype=torch.uint8) + device = torch.empty(128, dtype=torch.uint8, device="cuda") + device.copy_(host) + if recipe is not None: + device._loom_tensor_map_recipe = recipe + return device + + +class Swizzle: + # Standalone spellings used only by stripped TMA metadata helpers. + SZ_128B = "128B" + SZ_64B = "64B" + SZ_32B = "32B" + NONE = "none" + + +class TensorMapMetadata: + # Compatibility carrier; frozen launch packing needs no metadata. + def __init__(self, **values): + self.__dict__.update(values) + + +def attach_tma_metadata(tensor, metadata): + tensor._loom_tensor_map_metadata = metadata + return tensor + + +def create_tensor_map(data_ptr, dim0, dim1, box0, box1, stride1_bytes): + '''Create a rank-2 BF16 128B-swizzled map with a rebind recipe.''' + from cuda.bindings import driver + + err, tmap = _capture_cuTensorMapEncodeTiled( + driver.CUtensorMapDataType.CU_TENSOR_MAP_DATA_TYPE_BFLOAT16, + 2, + data_ptr, + [driver.cuuint64_t(dim0), driver.cuuint64_t(dim1)], + [driver.cuuint64_t(stride1_bytes)], + [driver.cuuint32_t(box0), driver.cuuint32_t(box1)], + [driver.cuuint32_t(1), driver.cuuint32_t(1)], + driver.CUtensorMapInterleave.CU_TENSOR_MAP_INTERLEAVE_NONE, + driver.CUtensorMapSwizzle.CU_TENSOR_MAP_SWIZZLE_128B, + driver.CUtensorMapL2promotion.CU_TENSOR_MAP_L2_PROMOTION_NONE, + driver.CUtensorMapFloatOOBfill.CU_TENSOR_MAP_FLOAT_OOB_FILL_NONE, + ) + if err != 0: + raise RuntimeError(f"cuTensorMapEncodeTiled (2D BF16) failed: CUresult={err}") + return _tmap_to_device(tmap) + + +def _create_tensor_map_3d(data_ptr, global_height, shared_height, width, block_width, swizzle): + from cuda.bindings import driver + atoms = {"128B": 64, "64B": 32, "32B": 16} + swizzles = { + "128B": driver.CUtensorMapSwizzle.CU_TENSOR_MAP_SWIZZLE_128B, + "64B": driver.CUtensorMapSwizzle.CU_TENSOR_MAP_SWIZZLE_64B, + "32B": driver.CUtensorMapSwizzle.CU_TENSOR_MAP_SWIZZLE_32B, + } + try: + atom = atoms[swizzle] + swizzle_value = swizzles[swizzle] + except KeyError as exc: + raise ValueError(f"unsupported 3D tensor-map swizzle: {swizzle}") from exc + err, tmap = _capture_cuTensorMapEncodeTiled( + driver.CUtensorMapDataType.CU_TENSOR_MAP_DATA_TYPE_BFLOAT16, + 3, + data_ptr, + [driver.cuuint64_t(atom), driver.cuuint64_t(global_height), driver.cuuint64_t(width // atom)], + [driver.cuuint64_t(width * 2), driver.cuuint64_t(atom * 2)], + [driver.cuuint32_t(atom), driver.cuuint32_t(shared_height), driver.cuuint32_t(block_width // atom)], + [driver.cuuint32_t(1), driver.cuuint32_t(1), driver.cuuint32_t(1)], + driver.CUtensorMapInterleave.CU_TENSOR_MAP_INTERLEAVE_NONE, + swizzle_value, + driver.CUtensorMapL2promotion.CU_TENSOR_MAP_L2_PROMOTION_NONE, + driver.CUtensorMapFloatOOBfill.CU_TENSOR_MAP_FLOAT_OOB_FILL_NONE, + ) + if err != 0: + raise RuntimeError(f"cuTensorMapEncodeTiled failed: CUresult={err}") + return _tmap_to_device(tmap) + + +def create_tensor_map_3d(data_ptr, global_height, shared_height, width, block_width): + return _create_tensor_map_3d(data_ptr, global_height, shared_height, width, block_width, "128B") + + +def create_tensor_map_3d_64b(data_ptr, global_height, shared_height, width, block_width): + return _create_tensor_map_3d(data_ptr, global_height, shared_height, width, block_width, "64B") + + +def create_tensor_map_3d_32b(data_ptr, global_height, shared_height, width, block_width): + return _create_tensor_map_3d(data_ptr, global_height, shared_height, width, block_width, "32B") + + +def generate_kernel(ir, **kwargs): + request_key = json.dumps( + { + "ir_name": ir.symbol, + "constants": [[str(name), value] for name, value in ir.constants], + "threads": int(ir.threads), + "computed_smem_bytes": int(ir.computed_smem_bytes), + "kwargs": kwargs, + }, + sort_keys=True, separators=(",", ":"), default=repr, + ) + alias = _KERNEL_ALIAS_BY_REQUEST.get(request_key) + if alias is None: + alias = _KERNEL_ALIAS_BY_IR_NAME.get(ir.symbol) + if alias is None: + raise RuntimeError(f"uncaptured dispatcher specialization for {ir.symbol}") + return alias + + +def generate_kernel_bundle(*args, **kwargs): + raise RuntimeError("uncaptured dispatcher bundle specialization") + + +def _all_shapes(): + package = __package__ or __name__.rpartition(".")[0] + text = resources.files(package).joinpath("_dispatch_shapes.json").read_text(encoding="utf-8") + return json.loads(text) + + +class _CanonicalShapes: + '''Lazy contract-shape view backed by the exported plan ledger.''' + + def __iter__(self): + return iter(_all_shapes()) + + def __len__(self): + return len(_all_shapes()) + + def __getitem__(self, index): + return _all_shapes()[index] + + +CANONICAL_SHAPES = _CanonicalShapes() + + +def select_named_shapes(labels): + labels = [labels] if isinstance(labels, str) else list(labels) + by_label = {row["label"]: row for row in _all_shapes()} + return [by_label[label] for label in labels] + + +def evaluate(*args, **kwargs): + raise RuntimeError("Cake eval harness is not part of the standalone runtime") diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch_shapes.json b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch_shapes.json new file mode 100644 index 00000000..914bdae6 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_dispatch_shapes.json @@ -0,0 +1,2900 @@ +[ + { + "label": "flashml_correctness_b1_q256_m256_d128_k5", + "params": { + "B": 1, + "D": 128, + "K": 5, + "M": 256, + "Q": 256, + "benchmark": true, + "build": true, + "check_correctness": true, + "correctness_query_sample": 256, + "dtype": "bfloat16", + "recall_min": 0.99, + "seed": 606001, + "time_flashlib": true + }, + "recorded": { + "baseline_ms": 0.123361, + "baseline_name": "flashlib", + "kernel_ms": 0.049888, + "speedup_vs_baseline": 2.4727589801154584, + "timing_backend": "cupti" + } + }, + { + "label": "build_k_sweep_qm512_k1", + "params": { + "B": 1, + "D": 128, + "K": 1, + "M": 512, + "Q": 512, + "benchmark": true, + "build": true, + "check_correctness": true, + "correctness_query_sample": 512, + "dtype": "bfloat16", + "recall_min": 0.999, + "seed": 606049, + "time_flashlib": true + }, + "recorded": { + "baseline_ms": 0.104385, + "baseline_name": "flashlib", + "kernel_ms": 0.05152, + "speedup_vs_baseline": 2.0261063664596275, + "timing_backend": "cupti" + } + }, + { + "label": "build_k_sweep_qm512_k2", + "params": { + "B": 1, + "D": 128, + "K": 2, + "M": 512, + "Q": 512, + "benchmark": true, + "build": true, + "check_correctness": true, + "correctness_query_sample": 512, + "dtype": "bfloat16", + "recall_min": 0.999, + "seed": 606050, + "time_flashlib": true + }, + "recorded": { + "baseline_ms": 0.123905, + "baseline_name": "flashlib", + "kernel_ms": 0.056865, + "speedup_vs_baseline": 2.1789325595709137, + "timing_backend": "cupti" + } + }, + { + "label": "build_k_sweep_qm512_k4", + "params": { + "B": 1, + "D": 128, + "K": 4, + "M": 512, + "Q": 512, + "benchmark": true, + "build": true, + "check_correctness": true, + "correctness_query_sample": 512, + "diagnostic_class": "low_k_q512_k5_neighborhood", + "dtype": "bfloat16", + "recall_min": 0.999, + "seed": 606052, + "time_flashlib": true + }, + "recorded": { + "baseline_ms": 0.124322, + "baseline_name": "flashlib", + "kernel_ms": 0.057952, + "speedup_vs_baseline": 2.1452581446714523, + "timing_backend": "cupti" + } + }, + { + "label": "build_k_sweep_qm512_k5", + "params": { + "B": 1, + "D": 128, + "K": 5, + "M": 512, + "Q": 512, + "benchmark": true, + "build": true, + "check_correctness": true, + "correctness_query_sample": 512, + "dtype": "bfloat16", + "recall_min": 0.999, + "seed": 606053, + "time_flashlib": true + }, + "recorded": { + "baseline_ms": 0.124417, + "baseline_name": "flashlib", + "kernel_ms": 0.058912, + "speedup_vs_baseline": 2.1119126833242805, + "timing_backend": "cupti" + } + }, + { + "label": "build_k_sweep_qm512_k6", + "params": { + "B": 1, + "D": 128, + "K": 6, + "M": 512, + "Q": 512, + "benchmark": true, + "build": true, + "check_correctness": true, + "correctness_query_sample": 512, + "diagnostic_class": "low_k_q512_k5_neighborhood", + "dtype": "bfloat16", + "recall_min": 0.999, + "seed": 606054, + "time_flashlib": true + }, + "recorded": { + "baseline_ms": 0.125153, + "baseline_name": "flashlib", + "kernel_ms": 0.058976, + "speedup_vs_baseline": 2.1221005154639174, + "timing_backend": "cupti" + } + }, + { + "label": "build_k_sweep_qm512_k8", + "params": { + "B": 1, + "D": 128, + "K": 8, + "M": 512, + "Q": 512, + "benchmark": true, + "build": true, + "check_correctness": true, + "correctness_query_sample": 512, + "dtype": "bfloat16", + "recall_min": 0.999, + "seed": 606056, + "time_flashlib": true + }, + "recorded": { + "baseline_ms": 0.124193, + "baseline_name": "flashlib", + "kernel_ms": 0.046336, + "speedup_vs_baseline": 2.680270200276243, + "timing_backend": "cupti" + } + }, + { + "label": "build_k_sweep_qm512_k10", + "params": { + "B": 1, + "D": 128, + "K": 10, + "M": 512, + "Q": 512, + "benchmark": true, + "build": true, + "check_correctness": true, + "correctness_query_sample": 512, + "dtype": "bfloat16", + "recall_min": 0.999, + "seed": 606058, + "time_flashlib": true + }, + "recorded": { + "baseline_ms": 0.126306, + "baseline_name": "flashlib", + "kernel_ms": 0.046401, + "speedup_vs_baseline": 2.722053404021465, + "timing_backend": "cupti" + } + }, + { + "label": "build_qm1024_d128_k10", + "params": { + "B": 1, + "D": 128, + "K": 10, + "M": 1024, + "Q": 1024, + "benchmark": true, + "build": true, + "check_correctness": true, + "correctness_query_sample": 512, + "dtype": "bfloat16", + "recall_min": 0.999, + "seed": 606104, + "time_flashlib": true + }, + "recorded": { + "baseline_ms": 0.126849, + "baseline_name": "flashlib", + "kernel_ms": 0.047104, + "speedup_vs_baseline": 2.6929560122282608, + "timing_backend": "cupti" + } + }, + { + "label": "build_k_sweep_qm1024_k16", + "params": { + "B": 1, + "D": 128, + "K": 16, + "M": 1024, + "Q": 1024, + "benchmark": true, + "build": true, + "check_correctness": true, + "correctness_query_sample": 512, + "diagnostic_class": "mid_k_topk_bucket", + "dtype": "bfloat16", + "recall_min": 0.999, + "seed": 606116, + "time_flashlib": true + }, + "recorded": { + "baseline_ms": 0.125889, + "baseline_name": "flashlib", + "kernel_ms": 0.049281, + "speedup_vs_baseline": 2.5545139100261767, + "timing_backend": "cupti" + } + }, + { + "label": "build_k_sweep_qm1024_k12", + "params": { + "B": 1, + "D": 128, + "K": 12, + "M": 1024, + "Q": 1024, + "benchmark": true, + "build": 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"check_correctness": true, + "correctness_query_sample": 512, + "dtype": "bfloat16", + "recall_min": 0.999, + "seed": 606208, + "time_flashlib": true + }, + "recorded": { + "baseline_ms": 0.126433, + "baseline_name": "flashlib", + "kernel_ms": 0.046049, + "speedup_vs_baseline": 2.745618797368021, + "timing_backend": "cupti" + } + }, + { + "label": "build_qm1024_d128_k8", + "params": { + "B": 1, + "D": 128, + "K": 8, + "M": 1024, + "Q": 1024, + "benchmark": true, + "build": true, + "check_correctness": true, + "correctness_query_sample": 512, + "diagnostic_class": "low_k_build_frontier", + "dtype": "bfloat16", + "recall_min": 0.999, + "seed": 611108, + "time_flashlib": true + }, + "recorded": { + "baseline_ms": 0.125377, + "baseline_name": "flashlib", + "kernel_ms": 0.04688, + "speedup_vs_baseline": 2.674424061433447, + "timing_backend": "cupti" + } + }, + { + "label": "build_qm4096_d128_k8", + "params": { + "B": 1, + "D": 128, + "K": 8, + "M": 4096, + "Q": 4096, + "benchmark": true, + "build": true, + "check_correctness": true, + "correctness_query_sample": 512, + "diagnostic_class": "low_k_build_frontier", + "dtype": "bfloat16", + "recall_min": 0.999, + "seed": 611408, + "time_flashlib": true + }, + "recorded": { + "baseline_ms": 0.125441, + "baseline_name": "flashlib", + "kernel_ms": 0.089377, + "speedup_vs_baseline": 1.4035042572473904, + "timing_backend": "cupti" + } + }, + { + "label": "build_qm2048_d128_k10", + "params": { + "B": 1, + "D": 128, + "K": 10, + "M": 2048, + "Q": 2048, + "benchmark": true, + "build": true, + "check_correctness": true, + "correctness_query_sample": 512, + "dtype": "bfloat16", + "recall_min": 0.999, + "seed": 606210, + "time_flashlib": true + }, + "recorded": { + "baseline_ms": 0.125761, + "baseline_name": "flashlib", + "kernel_ms": 0.047265, + "speedup_vs_baseline": 2.6607637786945944, + "timing_backend": "cupti" + } + }, + { + "label": "build_dim_sweep_b1_q1024_m1024_d64_k10", + "params": { + "B": 1, + "D": 64, + "K": 10, + "M": 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field +from functools import lru_cache +from typing import Any + +from ._dispatch import knn_build_dispatch_q1m524_v10_d320recurrence_consumption_v1 as _root +from ._dispatch_runtime import _import_dispatch_module, dispatch_launch_options + +_WEAVE_PREFIX = 'loom.examples.weave.' +_ROOT_MODULE = 'knn_build_dispatch_q1m524_v10_d320recurrence_consumption_v1' +_ROOT_CALLABLE = 'launch_from_contract_inputs' +_EXACT_LAUNCH_SPECS = {} + + +@dataclass(frozen=True) +class RouteDecision: + """Resolved semantic route with a launcher that can be reused directly.""" + + route_id: str + launch_entrypoint: str + launcher: Callable[[dict[str, Any]], Any] = field(repr=False, compare=False) + exact_contract: bool = False + + def launch( + self, + inputs: dict[str, Any], + *, + stream: Any = None, + timeout_ms: float | None = None, + ) -> Any: + with dispatch_launch_options(stream=stream, timeout_ms=timeout_ms): + return self.launcher(inputs) + + +def _route_key(inputs: dict[str, Any]) -> tuple[int, int, int, int, int, str, bool, bool]: + dtype = str(inputs.get("dtype", "bfloat16")) + if dtype.startswith("torch."): + dtype = dtype[6:] + return ( + *(int(inputs[name]) for name in ("B", "Q", "M", "D", "K")), + dtype, + bool(inputs.get("self_search", False)), + bool(inputs.get("force_fallback", False)), + ) + + +@lru_cache(maxsize=None) +def _load_launcher(module_name: str, callable_name: str) -> Callable[[dict[str, Any]], Any]: + module = _import_dispatch_module(module_name) + launcher = getattr(module, callable_name, None) + if not callable(launcher): + raise RuntimeError(f"resolved dispatcher launcher is not callable: {module_name}:{callable_name}") + return launcher + + +@lru_cache(maxsize=None) +def _make_decision( + route_id: str, + module_name: str, + callable_name: str, + exact_contract: bool, +) -> RouteDecision: + return RouteDecision( + route_id=route_id, + launch_entrypoint=f"{_WEAVE_PREFIX}{module_name}:{callable_name}", + launcher=_load_launcher(module_name, callable_name), + exact_contract=exact_contract, + ) + + +def _entrypoint_spec(entrypoint: object) -> tuple[str, str] | None: + if not isinstance(entrypoint, str): + return None + module_name, separator, callable_name = entrypoint.partition(":") + if not separator or not module_name.startswith(_WEAVE_PREFIX) or not callable_name.isidentifier(): + return None + return module_name.removeprefix(_WEAVE_PREFIX), callable_name + + +def _generic_decision(inputs: dict[str, Any]) -> RouteDecision: + info_fn = getattr(_root, "route_info", None) + info = dict(info_fn(inputs)) if callable(info_fn) else {} + route_id = info.get("selected_route", info.get("route")) + if route_id is None: + select_route = getattr(_root, "selected_route", None) + route_id = select_route(inputs) if callable(select_route) else _ROOT_CALLABLE + # ``resolved_launch_entrypoint`` is an explicit launch contract and may + # bypass the root. ``selected_entrypoint`` is seed provenance: it can name + # a narrower module whose own guards would silently miss for this + # signature (the K11 prefix route reports its K64 seed there, and + # launching the seed with K=11 inputs falls through to a slower exact + # parent). Signatures without a launch contract go through the root + # dispatcher, which reproduces the frozen guard cascade exactly. + spec = _entrypoint_spec(info.get("resolved_launch_entrypoint")) + if spec is None: + spec = (_ROOT_MODULE, _ROOT_CALLABLE) + return _make_decision(str(route_id), *spec, False) + + +def resolve_route(inputs: dict[str, Any]) -> RouteDecision: + """Resolve once; exact exported shapes never re-enter the root dispatcher.""" + + spec = _EXACT_LAUNCH_SPECS.get(_route_key(inputs)) + if spec is None: + return _generic_decision(inputs) + return _make_decision(*spec, True) + + +class LaunchPlan: + """Per-signature resolved execution state for the exported hot path. + + Migration step 2 of ``PLAN_BASED_EXPORT_RUNTIME.md``: the guard cascade + (``resolve_route`` plus one captured dispatcher traversal) runs exactly + once, at construction. A hot call overwrites the recorded 8-byte pointer + carriers in place, refreshes device tensor-map descriptors only when a + bound pointer actually changed, and submits the already-marshalled + launches on their construction-time stream — no re-marshalling, no + per-launch stream query, no dispatcher re-entry. + """ + + __slots__ = ( + "route", + "sequence", + "torch_stream", + "stream_handle", + "_launches", + "_pointer_writers", + "_tma_bindings", + ) + + def __init__(self, route: RouteDecision, sequence: Any, *, torch_stream: Any, stream_handle: int): + launches = tuple(sequence._launches) + input_bindings = tuple(sequence._input_bindings) + if input_bindings and len(input_bindings) != len(launches): + raise RuntimeError("launch plan capture has corrupt input bindings") + writers: dict[str, list[Any]] = {} + for launch, bindings in zip(launches, input_bindings): + carriers = launch._packed._prevent_gc + for index, key in bindings: + writers.setdefault(key, []).append(carriers[index]) + self.route = route + self.sequence = sequence + self.torch_stream = torch_stream + self.stream_handle = int(stream_handle) + self._launches = launches + self._pointer_writers = tuple((key, tuple(items)) for key, items in writers.items()) + self._tma_bindings = tuple(sequence._tensor_map_bindings) + + @property + def launch_count(self) -> int: + return len(self._launches) + + @property + def bound_input_keys(self) -> tuple[str, ...]: + direct = {key for key, _carriers in self._pointer_writers} + derived = {binding.input_key for binding in self._tma_bindings} + return tuple(sorted(direct | derived)) + + def bind_hot(self, bindings: dict[str, Any]) -> None: + """Refresh tensor maps and overwrite bound pointer carriers in place. + + This is the host-side half of a hot call. Callers that enqueue their + own support launches between the plan's pointer binding and its + kernel submission (the KNN-build fused row norms) must call this + BEFORE those launches: a fresh-pointer tensor-map re-encode costs + host time, and paying it after any kernel is already enqueued turns + that host time into a GPU inter-kernel gap. + + Tensor maps go through the per-pointer variant bank: a pointer the + bank has seen keeps its device-resident descriptor, so re-activating + it is a handful of carrier writes with no ``cuTensorMapEncodeTiled``, + no pinned-staging refresh, and no H2D copy. The plan's signature slot + is stream-keyed and alias-keyed by the caller, which is the safety + contract ``rebind_stream_bound`` requires. + """ + + for binding in self._tma_bindings: + binding.rebind_stream_bound(bindings[binding.input_key], stream=self.torch_stream) + for key, carriers in self._pointer_writers: + pointer = bindings[key].data_ptr() + for carrier in carriers: + carrier.value = pointer + + def submit_hot(self, *, timeout_ms: float | None = None) -> None: + """Submit every prepared launch on the plan's construction stream.""" + + launches = self._launches + last_index = len(launches) - 1 + for index, launch in enumerate(launches): + launch.launch(stream=None, timeout_ms=timeout_ms if index == last_index else None) + + def launch_hot(self, bindings: dict[str, Any], *, timeout_ms: float | None = None) -> None: + """Patch bound pointer carriers from ``bindings`` and submit every launch.""" + + self.bind_hot(bindings) + self.submit_hot(timeout_ms=timeout_ms) + + def record_stream(self, stream: Any) -> None: + """Tie every plan-held launch argument to ``stream`` before release.""" + + self.sequence.record_stream(stream) + + +class PerCallRoutePlan: + """Per-signature plan for routes whose host logic reads device results. + + Capture observed the route reading GPU memory (for example an + ``overflow_flag.item()`` certification) while its kernels were only being + recorded, so a frozen launch list cannot reproduce the route's per-call + branch decisions — freezing would bake whichever branch the capture-time + garbage selected. These signatures keep the generic per-call launcher: + route resolution stays cached and the guard cascade still ran exactly + once, but every hot call re-executes the resolved route's host program. + Device-side repair (Migration step 3) makes such routes replayable. + """ + + __slots__ = ( + "route", + "torch_stream", + "stream_handle", + "launch_count", + "host_data_reads", + "_static_inputs", + "_pending_bindings", + ) + + def __init__( + self, + route: RouteDecision, + *, + torch_stream: Any, + stream_handle: int, + static_inputs: dict[str, Any], + launch_count: int, + host_data_reads: int, + ): + self.route = route + self.torch_stream = torch_stream + self.stream_handle = int(stream_handle) + self.launch_count = int(launch_count) + self.host_data_reads = int(host_data_reads) + self._static_inputs = dict(static_inputs) + self._pending_bindings = None + + def bind_hot(self, bindings: dict[str, Any]) -> None: + """Stage this call's tensor bindings for ``submit_hot``. + + Mirrors ``LaunchPlan``'s two-phase hot call so callers with support + launches use one code path. The caller's per-signature slot lock + serializes bind/submit pairs on a plan instance. + """ + + self._pending_bindings = dict(bindings) + + def submit_hot(self, *, timeout_ms: float | None = None) -> None: + """Re-execute the resolved route with the staged tensor bindings.""" + + bindings = self._pending_bindings + if bindings is None: + raise RuntimeError("PerCallRoutePlan.submit_hot requires a preceding bind_hot") + self._pending_bindings = None + self.launch_hot(bindings, timeout_ms=timeout_ms) + + def launch_hot(self, bindings: dict[str, Any], *, timeout_ms: float | None = None) -> None: + """Re-execute the resolved route with this call's tensor bindings. + + The torch stream context keeps the route's tensor operations (scratch + fills, the certification read-back) ordered with its kernel launches + on the plan's stream, exactly as the live evaluation path runs it. + """ + + import torch + + inputs = dict(self._static_inputs) + inputs.update(bindings) + with torch.cuda.stream(self.torch_stream): + self.route.launch(inputs, stream=self.torch_stream, timeout_ms=timeout_ms) + + def record_stream(self, stream: Any) -> None: + """Per-call plans hold no launch arguments; nothing to record.""" + + +def build_launch_plan( + inputs: dict[str, Any], + *, + stream: Any, + arch: str, + validate_result: Callable[[Any, dict[str, Any]], None] | None = None, + route: Any = None, +) -> LaunchPlan | PerCallRoutePlan: + """Run the guard cascade once and freeze its launches into a LaunchPlan. + + This is the per-signature slow path; ``resolve_route`` remains the single + source of routing truth. ``validate_result(result, inputs)`` must raise + when the resolved route's outputs cannot be retargeted by pointer + rebinding (for example outputs that are not caller-owned tensors). + Routes whose host logic read device memory during capture resolve to a + ``PerCallRoutePlan`` instead of a frozen launch list. + + ``route`` accepts an already-resolved decision from a sibling routing + layer with the same contract (``route_id``/``launch_entrypoint``/ + ``exact_contract`` plus ``launch(inputs, stream=..., timeout_ms=...)``), + for workloads whose exact-contract table lives outside this module (the + KNN-build direct-manifest resolver). It must come from that workload's + frozen routing surface, never from re-guessing the cascade. + """ + + from ._dispatch_runtime import capture_kernel_launches + from ._runtime import launch_context + + if stream is None: + raise ValueError("build_launch_plan requires a resolved torch CUDA stream, not None") + if route is None: + route = resolve_route(inputs) + with capture_kernel_launches(stream=stream, arch=arch, inputs=inputs) as captured: + with launch_context(arch=arch, stream=stream, timeout_ms=None): + result = route.launch(inputs, stream=stream, timeout_ms=None) + if validate_result is not None: + validate_result(result, inputs) + if captured.host_data_dependent: + static_inputs = { + key: value for key, value in inputs.items() if not callable(getattr(value, "data_ptr", None)) + } + return PerCallRoutePlan( + route, + torch_stream=stream, + stream_handle=int(stream.cuda_stream), + static_inputs=static_inputs, + launch_count=len(captured._launches), + host_data_reads=captured.host_data_reads, + ) + sequence = captured.bind(result) + return LaunchPlan( + route, + sequence, + torch_stream=stream, + stream_handle=int(stream.cuda_stream), + ) + + +class GraphCaptureUnsupported(RuntimeError): + """A plan's launches have no validated CUDA-graph capture path.""" + + +def _check_cu(err: Any, message: str) -> None: + code = err[0] if isinstance(err, tuple) else err + if int(code) != 0: + raise RuntimeError(f"{message}: CUresult={int(code)}") + + +class GraphExecPlan: + """One per-signature CUDA graph over a LaunchPlan plus support launches. + + Migration step 3 of ``PLAN_BASED_EXPORT_RUNTIME.md``: the signature's + stable kernel chain (support launches such as fused row norms, then the + frozen route launches) is stream-captured once at plan construction. A + hot call is host-only binding (the caller's ``plan.bind_hot`` plus + support pointer writes into the same persistent packed argument buffers), + then ``submit_hot``: every kernel node's packed buffer is pushed through + ``cuGraphExecKernelNodeSetParams`` and the chain replays with one + ``cuGraphLaunch`` on the plan's construction-time stream. Kernel-node + launch attributes recorded at capture (cluster dimensions, scheduling + preference) persist across exec-node parameter updates. + """ + + __slots__ = ( + "plan", + "_launches", + "_graph", + "_graph_exec", + "_node_params", + "_cu_stream", + "_set_params", + "_graph_launch", + "_cu_success", + "_destroyed", + ) + + def __init__(self, plan: LaunchPlan, launches, graph, graph_exec, node_params, cu_stream): + from cuda.bindings import driver + + self.plan = plan + self._launches = tuple(launches) + self._graph = graph + self._graph_exec = graph_exec + self._node_params = tuple(node_params) + self._cu_stream = cu_stream + self._set_params = driver.cuGraphExecKernelNodeSetParams + self._graph_launch = driver.cuGraphLaunch + self._cu_success = driver.CUresult.CUDA_SUCCESS + self._destroyed = False + + @property + def launch_count(self) -> int: + return len(self._launches) + + def submit_hot(self, *, timeout_ms: float | None = None) -> None: + """Push the persistent packed argument buffers and replay the graph. + + The caller must have completed every pointer/tensor-map bind for this + call (``plan.bind_hot`` plus support-launch binds) first; parameter + values are copied out of the packed buffers here. + """ + + if self._destroyed: + raise RuntimeError("graph plan was destroyed by a runtime clear()") + set_params = self._set_params + graph_exec = self._graph_exec + success = self._cu_success + for node, params in self._node_params: + (err,) = set_params(graph_exec, node, params) + if err != success: + _check_cu(err, "cuGraphExecKernelNodeSetParams failed") + (err,) = self._graph_launch(graph_exec, self._cu_stream) + if err != success: + _check_cu(err, "cuGraphLaunch failed") + if timeout_ms is not None: + self._launches[-1]._kernel._wait_with_timeout(self._cu_stream, timeout_ms) + + def destroy(self) -> None: + """Release the driver graph handles. Device work must be complete.""" + + if self._destroyed: + return + from cuda.bindings import driver + + self._destroyed = True + driver.cuGraphExecDestroy(self._graph_exec) + driver.cuGraphDestroy(self._graph) + + +def build_graph_exec_plan(plan: Any, *, support_launches: tuple = ()) -> GraphExecPlan: + """Capture ``support_launches`` then ``plan``'s launches into one graph. + + The per-signature slow path, run once at plan construction. Launches are + replayed onto a dedicated capture stream (graph construction only — no + kernel executes), each launch is mapped to its kernel node through the + capture stream's leaf-dependency query, and the captured topology is + hard-checked to contain exactly the expected kernel nodes so foreign + work (for example watchdog event records) can never silently ride along. + + Raises ``GraphCaptureUnsupported`` for plans that cannot replay from a + frozen kernel chain (per-call routes) and for launch modes without a + validated capture path (cooperative). Any other failure propagates — + a capture that should work but does not is an error, not a fallback. + """ + + import ctypes + import sys + from contextlib import nullcontext + + import torch + from cuda.bindings import driver + + if not isinstance(plan, LaunchPlan): + raise GraphCaptureUnsupported( + "only frozen LaunchPlans are graph-capturable; per-call routes re-execute host logic" + ) + launches = tuple(support_launches) + tuple(plan._launches) + for launch in launches: + if launch._mode not in ("regular", "cluster"): + raise GraphCaptureUnsupported( + f"launch mode {launch._mode!r} has no validated graph-capture path" + ) + + # Captured launches build graph nodes and do not execute, so loom's CUDA + # watchdog (present only when the in-repo runtime shares this process) + # must not record completion events for them: the event record would be + # captured as a foreign node and the poller would query a captured event. + watchdog = sys.modules.get("loom.runtime.cuda_watchdog") + suspend = getattr(watchdog, "suspend_tracking", None) + suspension = suspend() if callable(suspend) else nullcontext() + + capture_stream = torch.cuda.Stream(device=plan.torch_stream.device) + cu_capture = driver.CUstream(capture_stream.cuda_stream) + nodes = [] + with suspension: + (err,) = driver.cuStreamBeginCapture( + cu_capture, driver.CUstreamCaptureMode.CU_STREAM_CAPTURE_MODE_THREAD_LOCAL + ) + _check_cu(err, "cuStreamBeginCapture failed") + try: + for launch in launches: + launch.launch(stream=capture_stream, timeout_ms=None) + info = driver.cuStreamGetCaptureInfo(cu_capture) + _check_cu(info[0], "cuStreamGetCaptureInfo failed") + status, leaves = info[1], info[4] + if ( + status != driver.CUstreamCaptureStatus.CU_STREAM_CAPTURE_STATUS_ACTIVE + or len(leaves) != 1 + ): + raise RuntimeError( + "graph capture did not add exactly one leaf node for a prepared launch" + ) + nodes.append(leaves[0]) + except BaseException: + driver.cuStreamEndCapture(cu_capture) # abandon the partial capture + raise + err, graph = driver.cuStreamEndCapture(cu_capture) + _check_cu(err, "cuStreamEndCapture failed") + + try: + err, _probe, total_nodes = driver.cuGraphGetNodes(graph, 0) + _check_cu(err, "cuGraphGetNodes failed") + if int(total_nodes) != len(launches): + raise RuntimeError( + f"captured graph has {int(total_nodes)} nodes, expected {len(launches)}; " + "foreign work was injected into the capture" + ) + for node in nodes: + err, node_type = driver.cuGraphNodeGetType(node) + _check_cu(err, "cuGraphNodeGetType failed") + if node_type != driver.CUgraphNodeType.CU_GRAPH_NODE_TYPE_KERNEL: + raise RuntimeError("captured graph node is not a kernel node") + err, graph_exec = driver.cuGraphInstantiate(graph, 0) + _check_cu(err, "cuGraphInstantiate failed") + except BaseException: + driver.cuGraphDestroy(graph) + raise + + node_params = [] + for launch, node in zip(launches, nodes): + params = driver.CUDA_KERNEL_NODE_PARAMS() + params.func = launch._kernel._func + params.gridDimX, params.gridDimY, params.gridDimZ = launch._grid + params.blockDimX, params.blockDimY, params.blockDimZ = launch._block + params.sharedMemBytes = launch._shared_mem + params.kernelParams = ctypes.addressof(launch._packed) + params.extra = 0 + node_params.append((node, params)) + return GraphExecPlan( + plan, + launches, + graph, + graph_exec, + node_params, + driver.CUstream(plan.stream_handle), + ) diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_routes.json b/cake_exports/knn_build/src/flashlib_cake_knn_build/_routes.json new file mode 100644 index 00000000..f64d1798 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_routes.json @@ -0,0 +1,450 @@ +[ + { + "selected_route": "loom.examples.weave.knn_build_flashml_k5_bd4a_v1:launch_from_contract_inputs", + "shape": "flashml_correctness_b1_q256_m256_d128_k5" + }, + { + "selected_route": "loom.examples.weave.knn_build_k1_q512_group2_root_v1:q512_k1_s2", + "shape": "build_k_sweep_qm512_k1" + }, + { + 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"rag_microbatch_over32_d128_b1_q16_m100000_k48" + } +] diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_row_norm.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/_row_norm.cu new file mode 100644 index 00000000..ec9fc047 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_row_norm.cu @@ -0,0 +1,200 @@ +#include +#include + +namespace { + +struct Bf16Tag {}; +struct Fp16Tag {}; + +// Squared sum of the two BF16 values packed in one 32-bit word. BF16 is the +// top half of an FP32, so widening is a shift/mask on the float bit pattern — +// no conversion instruction and, crucially, no address-taken local staging. +__device__ __forceinline__ float squared_sum_word(unsigned int word, Bf16Tag) { + const float low = __uint_as_float(word << 16); + const float high = __uint_as_float(word & 0xffff0000u); + return fmaf(low, low, high * high); +} + +__device__ __forceinline__ float squared_sum_word(unsigned int word, Fp16Tag) { + const float2 values = __half22float2( + __halves2half2(__ushort_as_half(static_cast(word & 0xffffu)), + __ushort_as_half(static_cast(word >> 16)))); + return fmaf(values.x, values.x, values.y * values.y); +} + +template +__device__ __forceinline__ float squared_sum_vec8(uint4 chunk) { + const float sum_xy = squared_sum_word(chunk.x, DtypeTag{}) + squared_sum_word(chunk.y, DtypeTag{}); + const float sum_zw = squared_sum_word(chunk.z, DtypeTag{}) + squared_sum_word(chunk.w, DtypeTag{}); + return sum_xy + sum_zw; +} + +// Smallest power of two >= chunks, capped at the warp width. +__device__ __forceinline__ int lanes_for_chunks(int chunks) { + int lanes = 1; + while (lanes < chunks && lanes < 32) { + lanes <<= 1; + } + return lanes; +} + +// Sum across a power-of-two lane group; only the group leader's value is +// meaningful afterwards. All 32 lanes participate in every shuffle. +__device__ __forceinline__ float group_reduce(float value, int lanes) { + for (int offset = lanes >> 1; offset > 0; offset >>= 1) { + value += __shfl_down_sync(0xffffffffu, value, offset, lanes); + } + return value; +} + +// Vectorized main path: a lane group of `lanes` threads owns one row and +// streams it as 16-byte chunks, so a full warp always touches one contiguous +// 512-byte span per load wave (lane groups within a warp own consecutive +// rows). Grid-strides over rows. +template +__device__ void vec_grouped_rows( + const uint4* __restrict__ vectors, + float* __restrict__ output, + long long rows, + int chunks, + int lanes) { + const int rows_per_warp = 32 / lanes; + const int warp = threadIdx.x >> 5; + const int lane = threadIdx.x & 31; + const int group = lane / lanes; + const int lane_in_group = lane - group * lanes; + const long long rows_per_block = static_cast(blockDim.x >> 5) * rows_per_warp; + + for (long long row_base = static_cast(blockIdx.x) * rows_per_block; row_base < rows; + row_base += static_cast(gridDim.x) * rows_per_block) { + const long long row = row_base + static_cast(warp) * rows_per_warp + group; + float sum = 0.0f; + if (row < rows) { + const long long base = row * static_cast(chunks); + for (int chunk = lane_in_group; chunk < chunks; chunk += lanes) { + sum += squared_sum_vec8(vectors[base + chunk]); + } + } + sum = group_reduce(sum, lanes); + if (lane_in_group == 0 && row < rows) { + output[row] = sum; + } + } +} + +// Wide rows, few of them (query norms of high-D shapes): one warp per row +// would leave the device nearly idle, so the whole block strides one row and +// combines through shared memory. Grid-strides over rows. +template +__device__ void vec_block_per_row( + const uint4* __restrict__ vectors, + float* __restrict__ output, + long long rows, + int chunks) { + __shared__ float warp_sums[32]; + const int lane = threadIdx.x & 31; + const int warp = threadIdx.x >> 5; + + for (long long row = blockIdx.x; row < rows; row += gridDim.x) { + const long long base = row * static_cast(chunks); + float sum = 0.0f; + for (int chunk = threadIdx.x; chunk < chunks; chunk += blockDim.x) { + sum += squared_sum_vec8(vectors[base + chunk]); + } + sum = group_reduce(sum, 32); + if (lane == 0) { + warp_sums[warp] = sum; + } + __syncthreads(); + if (warp == 0) { + sum = lane < (blockDim.x >> 5) ? warp_sums[lane] : 0.0f; + sum = group_reduce(sum, 32); + if (lane == 0) { + output[row] = sum; + } + } + __syncthreads(); + } +} + +// Fallback for rows that cannot take 16-byte loads (dim not a multiple of 8, +// or a rebound pointer that is not 16-byte aligned): one warp per row with +// scalar loads. Correct for every dim >= 1; throughput is secondary here. +__device__ void scalar_rows( + const void* __restrict__ input, + float* __restrict__ output, + long long rows, + int dim, + int dtype_code) { + const int warp = threadIdx.x >> 5; + const int lane = threadIdx.x & 31; + const long long rows_per_block = blockDim.x >> 5; + + for (long long row_base = static_cast(blockIdx.x) * rows_per_block; row_base < rows; + row_base += static_cast(gridDim.x) * rows_per_block) { + const long long row = row_base + warp; + float sum = 0.0f; + if (row < rows) { + const long long base = row * static_cast(dim); + if (dtype_code == 0) { + const __nv_bfloat16* values = static_cast(input); + for (int column = lane; column < dim; column += 32) { + const float value = __bfloat162float(values[base + column]); + sum = fmaf(value, value, sum); + } + } else { + const __half* values = static_cast(input); + for (int column = lane; column < dim; column += 32) { + const float value = __half2float(values[base + column]); + sum = fmaf(value, value, sum); + } + } + } + sum = group_reduce(sum, 32); + if (lane == 0 && row < rows) { + output[row] = sum; + } + } +} + +constexpr int kBlockPerRowMaxRows = 512; +constexpr int kBlockPerRowMinChunks = 33; + +} // namespace + +// Fused row squared norms over a contiguous [rows, dim] half-precision +// matrix, FP32 out. The path taken depends only on (pointer alignment, dim, +// rows) — never on the launch geometry — and every path grid-strides its row +// space, so any grid/block yields correct results; the host-chosen geometry +// is purely a throughput hint. That keeps prepared-launch pointer rebinding +// safe: a rebound tensor that changes the alignment class falls back to the +// scalar path under the frozen geometry. +extern "C" __global__ void cake_row_squared_norm( + const void* input, + float* output, + long long rows, + int dim, + int dtype_code) { + const bool vectorizable = + (dim % 8 == 0) && ((reinterpret_cast(input) & 15ull) == 0); + if (!vectorizable) { + scalar_rows(input, output, rows, dim, dtype_code); + return; + } + const int chunks = dim >> 3; + const uint4* vectors = static_cast(input); + if (chunks >= kBlockPerRowMinChunks && rows <= kBlockPerRowMaxRows) { + if (dtype_code == 0) { + vec_block_per_row(vectors, output, rows, chunks); + } else { + vec_block_per_row(vectors, output, rows, chunks); + } + return; + } + const int lanes = lanes_for_chunks(chunks); + if (dtype_code == 0) { + vec_grouped_rows(vectors, output, rows, chunks, lanes); + } else { + vec_grouped_rows(vectors, output, rows, chunks, lanes); + } +} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_row_norm.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_row_norm.py new file mode 100644 index 00000000..fa05ca52 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_row_norm.py @@ -0,0 +1,187 @@ +"""Standalone fused row-squared norms; minimum architecture: sm_80.""" + +from __future__ import annotations + +from dataclasses import dataclass, field +from typing import Any + +from .kernels import ExportedKernel, KernelSpec + +_ROW_NORM_KERNEL = ExportedKernel( + KernelSpec( + name="cake_row_squared_norm", + symbol="cake_row_squared_norm", + source="_row_norm.cu", + threads=256, + shared_mem_bytes=0, + cluster_dims=(1, 1, 1), + launch_mode="standard", + parameters=( + {"name": "input", "ctype": "const void *"}, + {"name": "output", "ctype": "float *"}, + {"name": "rows", "ctype": "int64_t"}, + {"name": "dim", "ctype": "int32_t"}, + {"name": "dtype_code", "ctype": "int32_t"}, + ), + specializations={}, + compile_options=("--std=c++17",), + ) +) + + +def _validate(input_tensor: Any, output: Any) -> tuple[int, int, int]: + import torch + + if not isinstance(input_tensor, torch.Tensor) or not input_tensor.is_cuda: + raise TypeError("row-norm input must be a CUDA torch.Tensor") + if input_tensor.dtype not in (torch.bfloat16, torch.float16): + raise TypeError("row-norm input must have bfloat16 or float16 dtype") + if input_tensor.ndim != 3 or not input_tensor.is_contiguous(): + raise ValueError("row-norm input must be contiguous with shape [B, rows, D]") + if not isinstance(output, torch.Tensor) or not output.is_cuda: + raise TypeError("row-norm output must be a CUDA torch.Tensor") + if output.dtype is not torch.float32: + raise TypeError("row-norm output must have float32 dtype") + if tuple(output.shape) != tuple(input_tensor.shape[:-1]): + raise ValueError("row-norm output must have shape input.shape[:-1]") + if output.device != input_tensor.device or not output.is_contiguous(): + raise ValueError("row-norm output must be contiguous and on the input device") + bsz, rows, dim = map(int, input_tensor.shape) + return bsz * rows, dim, 0 if input_tensor.dtype is torch.bfloat16 else 1 + + +_GRID_CAP = 4096 + + +def _launch_geometry(rows: int, dim: int) -> tuple[int, int]: + """Throughput-hint grid/block for ``cake_row_squared_norm``. + + Mirrors the kernel's (rows, dim)-keyed path selection: block-per-row for + few wide rows, otherwise one power-of-two lane group per row with eight + warps per block. The kernel grid-strides its row space, so this geometry + only affects throughput — any grid is correct, which keeps prepared + launches safe under pointer rebinds. + """ + + if dim % 8 == 0: + chunks = dim // 8 + if chunks >= 33 and rows <= 512: + return max(1, min(rows, _GRID_CAP)), 256 + lanes = 1 + while lanes < chunks and lanes < 32: + lanes <<= 1 + else: + lanes = 32 + rows_per_block = 8 * (32 // lanes) + blocks = (rows + rows_per_block - 1) // rows_per_block + return max(1, min(blocks, _GRID_CAP)), 256 + + +@dataclass +class PreparedRowSquaredNorm: + """One pointer-rebindable fused row-norm launch.""" + + launch_plan: Any + rows: int + dim: int + dtype_code: int + _input_carrier: Any = field(default=None, repr=False) + + def rebind(self, input_tensor: Any, output: Any, *, stream: Any = None) -> None: + rows, dim, dtype_code = _validate(input_tensor, output) + if (rows, dim, dtype_code) != (self.rows, self.dim, self.dtype_code): + raise RuntimeError("row-norm prepared launch topology changed") + self.launch_plan.rebind_arguments({0: input_tensor, 1: output}, stream=stream) + + def launch(self, *, stream: Any = None, timeout_ms: float | None = None) -> None: + self.launch_plan.launch(stream=stream, timeout_ms=timeout_ms) + + def bind_hot(self, input_tensor: Any) -> None: + """Overwrite the input pointer carrier in place without submitting. + + Graph-captured runtimes bind here and replay the captured kernel + chain themselves; the carrier targets the same persistent packed + argument buffer the captured node's parameter update reads. + """ + + carrier = self._input_carrier + if carrier is None: + carrier = self.launch_plan._packed._prevent_gc[0] + self._input_carrier = carrier + carrier.value = input_tensor.data_ptr() + + def launch_hot(self, input_tensor: Any) -> None: + """Overwrite the input pointer carrier in place and submit the launch. + + The prepared output buffer stays bound (plan-owned scratch with a + stable pointer) and the launch goes to its preparation-time stream — + no re-marshal, no per-launch stream query. The caller's signature + cache must guarantee the tensor topology matches the preparation. + """ + + self.bind_hot(input_tensor) + self.launch_plan.launch(stream=None, timeout_ms=None) + + def record_stream(self, stream: Any) -> None: + """Tie every prepared launch argument to ``stream`` before release.""" + + for value in self.launch_plan._keepalive: + record_stream = getattr(value, "record_stream", None) + if callable(record_stream): + record_stream(stream) + + def release_bound_input(self, output: Any, *, stream: Any = None) -> None: + """Replace the caller-owned input keepalive with slot-owned scratch.""" + + self.launch_plan.rebind_arguments({0: output}, stream=stream) + + +def prepare_row_squared_norm( + input_tensor: Any, + output: Any, + *, + arch: str | None = None, + stream: Any = None, +) -> PreparedRowSquaredNorm: + rows, dim, dtype_code = _validate(input_tensor, output) + grid_x, threads = _launch_geometry(rows, dim) + launch_plan = _ROW_NORM_KERNEL.prepare_launch( + input_tensor, + output, + rows, + dim, + dtype_code, + grid=(grid_x, 1, 1), + block=(threads, 1, 1), + stream=stream, + arch=arch, + ) + return PreparedRowSquaredNorm( + launch_plan=launch_plan, + rows=rows, + dim=dim, + dtype_code=dtype_code, + ) + + +def row_squared_norm( + input_tensor: Any, + *, + output: Any = None, + arch: str | None = None, + stream: Any = None, + timeout_ms: float | None = None, +): + """Compute FP32 squared L2 norms with one CUDA activity.""" + + import torch + + if output is None: + output = torch.empty( + tuple(input_tensor.shape[:-1]), + dtype=torch.float32, + device=input_tensor.device, + ) + prepared = prepare_row_squared_norm(input_tensor, output, arch=arch, stream=stream) + prepared.launch(stream=stream, timeout_ms=timeout_ms) + return output diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_runtime.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/_runtime.py new file mode 100644 index 00000000..64de997a --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_runtime.py @@ -0,0 +1,912 @@ +from __future__ import annotations + +import contextlib +import contextvars +import ctypes +import os +import shutil +import threading +import time +from collections.abc import Iterator, Sequence +from typing import Any + +import torch +from cuda.bindings import driver, nvrtc + + +_LAUNCH_DEFAULTS: contextvars.ContextVar[tuple[str | None, Any, float | None] | None] = ( + contextvars.ContextVar("loom_export_launch_defaults", default=None) +) +_COMPILATION_CACHE_LOCK = threading.RLock() +_CUBIN_CACHE: dict[tuple[str, str, tuple[str, ...]], bytes] = {} +_MODULE_CACHE: dict[tuple[str, str, tuple[str, ...], int], "_LoadedModule"] = {} +_KERNEL_CACHE: dict[tuple[str, str, tuple[str, ...], int, str], "CUDAKernel"] = {} +_COMPILATION_CACHE_GENERATION = 0 +_RUNTIME_ACTIVITY_COUNTS = { + "source_reads": 0, + "nvrtc_compiles": 0, + "module_loads": 0, +} + + +def _record_runtime_activity(name: str) -> None: + with _COMPILATION_CACHE_LOCK: + _RUNTIME_ACTIVITY_COUNTS[name] += 1 + + +def record_source_read() -> None: + _record_runtime_activity("source_reads") + + +def runtime_activity_snapshot() -> dict[str, int]: + with _COMPILATION_CACHE_LOCK: + return dict(_RUNTIME_ACTIVITY_COUNTS) + + +class NVRTCError(RuntimeError): + def __init__(self, message: str, log: str): + self.log = log + super().__init__(f"{message}\n--- NVRTC log ---\n{log}") + + +def _check(err: int, msg: str) -> None: + if err != 0: + raise RuntimeError(f"{msg}: CUresult={err}") + + +def _nvrtc_check(err: int, msg: str) -> None: + if err != 0: + raise RuntimeError(f"{msg}: nvrtcResult={err}") + + +def _arch_flag_for_cc(major: int, minor: int) -> str: + sm = int(major) * 10 + int(minor) + return f"sm_{sm}a" if sm >= 90 else f"sm_{sm}" + + +def detect_gpu_arch() -> str: + forced = os.environ.get("LOOM_EXPORTED_FORCE_ARCH") or os.environ.get("LOOM_FORCE_ARCH") + if forced: + return forced + try: + (err,) = driver.cuInit(0) + if err != 0: + return "sm_100a" + err, dev = driver.cuDeviceGet(0) + if err != 0: + return "sm_100a" + err, major = driver.cuDeviceGetAttribute( + driver.CUdevice_attribute.CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR, dev + ) + err2, minor = driver.cuDeviceGetAttribute( + driver.CUdevice_attribute.CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR, dev + ) + if err != 0 or err2 != 0: + return "sm_100a" + return _arch_flag_for_cc(int(major), int(minor)) + except Exception: + return "sm_100a" + + +def resolve_gpu_arch(arch: str | None) -> str: + return detect_gpu_arch() if arch is None else str(arch) + + +def current_cuda_device_index() -> int: + return int(torch.cuda.current_device()) + + +def _is_torch_stream(stream: Any) -> bool: + return isinstance(stream, torch.cuda.Stream) + + +def launch_stream_context(stream: Any): + return torch.cuda.stream(stream) if _is_torch_stream(stream) else contextlib.nullcontext() + + +@contextlib.contextmanager +def launch_context( + *, + arch: str | None = None, + stream: Any = None, + timeout_ms: float | None = None, +) -> Iterator[None]: + '''Apply semantic-call launch defaults to every frozen kernel stage. + + The current PyTorch stream is captured lazily once before the first frozen + launch when no explicit stream is supplied. Passing a PyTorch stream also + makes dispatcher-side PyTorch tensor operations use that stream, not only + the generated driver launches. Context variables keep concurrent threads + and nested semantic calls isolated. + ''' + resolved_stream = stream + token = _LAUNCH_DEFAULTS.set((arch, resolved_stream, timeout_ms)) + stream_context = launch_stream_context(resolved_stream) + try: + with stream_context: + yield + finally: + _LAUNCH_DEFAULTS.reset(token) + + +def resolve_launch_defaults( + *, + arch: str | None, + stream: Any, + timeout_ms: float | None, +) -> tuple[str | None, Any, float | None]: + defaults = _LAUNCH_DEFAULTS.get() + if defaults is None: + return arch, stream, timeout_ms + default_arch, default_stream, default_timeout_ms = defaults + if stream is None and default_stream is None: + default_stream = torch.cuda.current_stream() + _LAUNCH_DEFAULTS.set((default_arch, default_stream, default_timeout_ms)) + return ( + default_arch if arch is None else arch, + default_stream if stream is None else stream, + default_timeout_ms if timeout_ms is None else timeout_ms, + ) + + +def compilation_cache_generation() -> int: + return _COMPILATION_CACHE_GENERATION + + +def _cuda_include_dirs() -> list[str]: + candidates = ["/usr/local/cuda/include"] + nvcc = shutil.which("nvcc") + if nvcc: + candidates.insert(0, os.path.join(os.path.dirname(os.path.dirname(nvcc)), "include")) + return [d for d in candidates if os.path.isdir(d)] + + +def _get_compile_log(prog: nvrtc.nvrtcProgram) -> str: + err, size = nvrtc.nvrtcGetProgramLogSize(prog) + if err != 0 or size <= 1: + return "" + log = b"\x00" * size + nvrtc.nvrtcGetProgramLog(prog, log) + return log.decode(errors="replace").rstrip("\x00") + + +def compile_cuda( + source: str, + *, + arch: str | None = None, + name: str = "kernel.cu", + options: list[str] | None = None, +) -> bytes: + _record_runtime_activity("nvrtc_compiles") + if arch is None: + arch = detect_gpu_arch() + opts = [ + f"--gpu-architecture={arch}", + "-std=c++17", + "-default-device", + ] + for include_dir in _cuda_include_dirs(): + opts.append(f"-I{include_dir}") + cccl = os.path.join(include_dir, "cccl") + if os.path.exists(os.path.join(cccl, "cuda", "std")): + opts.append(f"-I{cccl}") + if options: + opts.extend(options) + + err, prog = nvrtc.nvrtcCreateProgram(source.encode(), name.encode(), 0, [], []) + _nvrtc_check(err, "nvrtcCreateProgram failed") + try: + encoded_opts = [opt.encode() for opt in opts] + (err,) = nvrtc.nvrtcCompileProgram(prog, len(encoded_opts), encoded_opts) + if err != 0: + raise NVRTCError(f"Compilation failed for {name!r}", _get_compile_log(prog)) + err, size = nvrtc.nvrtcGetCUBINSize(prog) + _nvrtc_check(err, "nvrtcGetCUBINSize failed") + cubin = b"\x00" * size + (err,) = nvrtc.nvrtcGetCUBIN(prog, cubin) + _nvrtc_check(err, "nvrtcGetCUBIN failed") + return cubin + finally: + nvrtc.nvrtcDestroyProgram(prog) + + +def _is_tensor(arg: Any) -> bool: + return isinstance(arg, torch.Tensor) + + +def _marshal_arg(arg: Any, ctype: str) -> ctypes._SimpleCData: + normalized = " ".join(ctype.replace("__restrict__", "").split()) + if "*" in normalized: + if _is_tensor(arg): + return ctypes.c_void_p(arg.data_ptr()) + if isinstance(arg, ctypes.c_void_p): + return arg + if isinstance(arg, int) and not isinstance(arg, bool): + return ctypes.c_void_p(arg) + raise TypeError(f"Pointer argument {ctype!r} requires a CUDA tensor or integer device pointer, got {type(arg)}") + if normalized in {"bool", "_Bool"}: + if not isinstance(arg, bool): + raise TypeError(f"Scalar argument {ctype!r} requires bool, got {type(arg)}") + return ctypes.c_bool(arg) + integer_abis = { + "int8_t": (ctypes.c_int8, -(1 << 7), (1 << 7) - 1), + "signed char": (ctypes.c_int8, -(1 << 7), (1 << 7) - 1), + "uint8_t": (ctypes.c_uint8, 0, (1 << 8) - 1), + "unsigned char": (ctypes.c_uint8, 0, (1 << 8) - 1), + "int16_t": (ctypes.c_int16, -(1 << 15), (1 << 15) - 1), + "short": (ctypes.c_int16, -(1 << 15), (1 << 15) - 1), + "short int": (ctypes.c_int16, -(1 << 15), (1 << 15) - 1), + "uint16_t": (ctypes.c_uint16, 0, (1 << 16) - 1), + "unsigned short": (ctypes.c_uint16, 0, (1 << 16) - 1), + "unsigned short int": (ctypes.c_uint16, 0, (1 << 16) - 1), + "int32_t": (ctypes.c_int32, -(1 << 31), (1 << 31) - 1), + "int": (ctypes.c_int32, -(1 << 31), (1 << 31) - 1), + "signed int": (ctypes.c_int32, -(1 << 31), (1 << 31) - 1), + "uint32_t": (ctypes.c_uint32, 0, (1 << 32) - 1), + "unsigned int": (ctypes.c_uint32, 0, (1 << 32) - 1), + "int64_t": (ctypes.c_int64, -(1 << 63), (1 << 63) - 1), + "long long": (ctypes.c_int64, -(1 << 63), (1 << 63) - 1), + "uint64_t": (ctypes.c_uint64, 0, (1 << 64) - 1), + "unsigned long long": (ctypes.c_uint64, 0, (1 << 64) - 1), + "size_t": (ctypes.c_uint64, 0, (1 << 64) - 1), + } + integer_abi = integer_abis.get(normalized) + if integer_abi is not None: + if not isinstance(arg, int) or isinstance(arg, bool): + raise TypeError(f"Scalar argument {ctype!r} requires int, got {type(arg)}") + scalar_ctype, minimum, maximum = integer_abi + if arg < minimum or arg > maximum: + raise OverflowError(f"Scalar argument {ctype!r} is out of range: {arg}") + return scalar_ctype(arg) + if normalized in {"float", "double"}: + if not isinstance(arg, (int, float)) or isinstance(arg, bool): + raise TypeError(f"Scalar argument {ctype!r} requires int or float, got {type(arg)}") + return ctypes.c_float(arg) if normalized == "float" else ctypes.c_double(arg) + raise TypeError(f"Unsupported scalar ABI type {ctype!r}") + + +def _pack_args(args: Sequence[Any], arg_types: Sequence[str]) -> ctypes.Array: + if len(args) != len(arg_types): + raise ValueError(f"Argument count mismatch: got {len(args)}, expected {len(arg_types)}") + c_args = [_marshal_arg(arg, ctype) for arg, ctype in zip(args, arg_types, strict=True)] + ptrs = (ctypes.c_void_p * len(c_args))(*(ctypes.cast(ctypes.pointer(arg), ctypes.c_void_p) for arg in c_args)) + ptrs._prevent_gc = c_args # type: ignore[attr-defined] + return ptrs + + +class PreparedCUDAKernelLaunch: + # A fully marshalled generated-runtime launch reusable on the hot path. + + def __init__( + self, + kernel, + *, + mode, + grid, + block, + arg_types, + packed, + keepalive, + shared_mem, + cu_stream, + cluster_dims=None, + config=None, + ): + self._kernel = kernel + self._mode = mode + self._grid = tuple(grid) + self._block = tuple(block) + self._arg_types = tuple(arg_types) + self._packed = packed + self._keepalive = keepalive + self._shared_mem = shared_mem + self._cu_stream = cu_stream + self._cluster_dims = None if cluster_dims is None else tuple(cluster_dims) + self._config = config + + def rebind( + self, + kernel, + *, + mode, + grid, + block, + args, + arg_types, + shared_mem, + stream=None, + cluster_dims=None, + ): + # Reuse the existing void** and scalar carriers while replacing values. + + candidate_grid = tuple(grid) + candidate_block = tuple(block) + candidate_arg_types = tuple(arg_types) + candidate_cluster_dims = None if cluster_dims is None else tuple(cluster_dims) + mismatches = [] + if kernel is not self._kernel: + mismatches.append("kernel") + if mode != self._mode: + mismatches.append(f"mode ({self._mode!r} != {mode!r})") + if candidate_grid != self._grid: + mismatches.append(f"grid ({self._grid!r} != {candidate_grid!r})") + if candidate_block != self._block: + mismatches.append(f"block ({self._block!r} != {candidate_block!r})") + if int(shared_mem) != self._shared_mem: + mismatches.append(f"shared_mem ({self._shared_mem!r} != {int(shared_mem)!r})") + if candidate_cluster_dims != self._cluster_dims: + mismatches.append( + f"cluster_dims ({self._cluster_dims!r} != {candidate_cluster_dims!r})" + ) + if candidate_arg_types != self._arg_types: + mismatches.append("arg_types") + if mismatches: + raise RuntimeError( + "prepared CUDA launch topology mismatch: " + ", ".join(mismatches) + ) + + if len(args) != len(self._arg_types): + raise RuntimeError( + f"prepared CUDA launch argument count mismatch: " + f"expected {len(self._arg_types)}, got {len(args)}" + ) + return self.rebind_arguments(dict(enumerate(args)), stream=stream) + + def rebind_arguments(self, replacements, *, stream=None): + # Update selected ABI carriers without rebuilding or traversing the launch. + + old_c_args = self._packed._prevent_gc + if len(old_c_args) != len(self._arg_types): + raise RuntimeError("prepared CUDA launch has a corrupt packed argument array") + rebound = [] + for index, arg in replacements.items(): + if type(index) is not int or index < 0 or index >= len(self._arg_types): + raise IndexError(f"prepared CUDA launch argument index is out of range: {index!r}") + new_arg = _marshal_arg(arg, self._arg_types[index]) + old_arg = old_c_args[index] + if type(old_arg) is not type(new_arg): + raise RuntimeError( + f"prepared CUDA launch ABI mismatch at argument {index}: " + f"{type(old_arg).__name__} != {type(new_arg).__name__}" + ) + rebound.append((index, arg, old_arg, new_arg)) + + cu_stream = self._kernel._cu_stream(stream) + keepalive = list(self._keepalive) + for index, arg, old_arg, new_arg in rebound: + old_arg.value = new_arg.value + keepalive[index] = arg + self._keepalive = tuple(keepalive) + self._cu_stream = cu_stream + return self + + def rebind_tensor_arguments( + self, + bindings, + inputs, + *, + stream=None, + preserve_stream=False, + retain_inputs=True, + pointer_values=None, + inputs_already_scrubbed=False, + ): + # Captured public bindings are pointer-only. Update their existing + # c_void_p carriers directly instead of rematerializing ctypes objects. + + if not isinstance(preserve_stream, bool): + raise TypeError("preserve_stream must be a bool") + if not isinstance(retain_inputs, bool): + raise TypeError("retain_inputs must be a bool") + if not isinstance(inputs_already_scrubbed, bool): + raise TypeError("inputs_already_scrubbed must be a bool") + if inputs_already_scrubbed and retain_inputs: + raise ValueError("inputs_already_scrubbed requires retain_inputs=False") + if pointer_values is not None and retain_inputs: + raise ValueError("pointer_values requires retain_inputs=False") + old_c_args = self._packed._prevent_gc + if len(old_c_args) != len(self._arg_types): + raise RuntimeError("prepared CUDA launch has a corrupt packed argument array") + replacements = [] + for index, key in bindings: + if type(index) is not int or index < 0 or index >= len(self._arg_types): + raise IndexError(f"prepared CUDA launch argument index is out of range: {index!r}") + arg = None + if pointer_values is None: + try: + arg = inputs[key] + except KeyError: + raise KeyError(f"missing prepared CUDA tensor binding: {key!r}") from None + data_ptr = getattr(arg, "data_ptr", None) + if not callable(data_ptr): + raise TypeError(f"prepared CUDA tensor binding {key!r} is not tensor-like") + pointer = int(data_ptr()) + else: + try: + pointer = pointer_values[key] + except KeyError: + raise KeyError(f"missing prepared CUDA pointer binding: {key!r}") from None + if type(pointer) is not int: + raise TypeError(f"prepared CUDA pointer binding {key!r} is not an int") + old_arg = old_c_args[index] + if type(old_arg) is not ctypes.c_void_p: + raise RuntimeError( + f"prepared CUDA tensor binding {key!r} does not target a pointer carrier" + ) + if inputs_already_scrubbed and type(self._keepalive[index]) is not int: + raise RuntimeError( + f"prepared CUDA tensor binding {key!r} retained an unexpected caller reference" + ) + replacements.append((index, key, arg, pointer, old_arg)) + + keepalive = None if inputs_already_scrubbed else list(self._keepalive) + for index, key, arg, pointer, old_arg in replacements: + old_arg.value = pointer + if keepalive is not None: + if retain_inputs and arg is None: + arg = inputs[key] + keepalive[index] = arg if retain_inputs else pointer + if keepalive is not None: + self._keepalive = tuple(keepalive) + if not preserve_stream: + self._cu_stream = self._kernel._cu_stream(stream) + return self + + def _scrub_stream_bound_pointer_keepalives(self, bindings): + '''Drop selected tensor references without changing carriers or stream.''' + + old_c_args = self._packed._prevent_gc + if len(old_c_args) != len(self._arg_types): + raise RuntimeError("prepared CUDA launch has a corrupt packed argument array") + scrubbed = list(self._keepalive) + program = [] + for index, key in bindings: + if type(index) is not int or index < 0 or index >= len(self._arg_types): + raise IndexError(f"prepared CUDA launch argument index is out of range: {index!r}") + carrier = old_c_args[index] + if type(carrier) is not ctypes.c_void_p: + raise RuntimeError( + f"prepared CUDA tensor binding {key!r} does not target a pointer carrier" + ) + value = scrubbed[index] + data_ptr = getattr(value, "data_ptr", None) + if not callable(data_ptr): + raise RuntimeError( + f"prepared CUDA tensor binding {key!r} retained an unexpected value" + ) + pointer = int(data_ptr()) + if carrier.value != pointer: + raise RuntimeError( + f"prepared CUDA tensor binding {key!r} carrier disagrees with its keepalive" + ) + scrubbed[index] = pointer + program.append((key, carrier)) + self._keepalive = tuple(scrubbed) + return tuple(program) + + def launch(self, *, stream=None, timeout_ms=None): + kernel = self._kernel + if kernel._closed: + raise RuntimeError("Kernel has been unloaded") + cu_stream = self._cu_stream if stream is None else kernel._cu_stream(stream) + if self._mode == "cluster": + self._config.hStream = cu_stream + (err,) = driver.cuLaunchKernelEx(self._config, kernel._func, self._packed, 0) + api = "cuLaunchKernelEx" + elif self._mode == "cooperative": + (err,) = driver.cuLaunchCooperativeKernel( + kernel._func, + *self._grid, + *self._block, + self._shared_mem, + cu_stream, + self._packed, + ) + api = "cuLaunchCooperativeKernel" + else: + (err,) = driver.cuLaunchKernel( + kernel._func, + *self._grid, + *self._block, + self._shared_mem, + cu_stream, + self._packed, + 0, + ) + api = "cuLaunchKernel" + _check(err, f"{api} failed for {kernel._func_name!r}") + if timeout_ms is not None: + kernel._wait_with_timeout(cu_stream, timeout_ms) + + +class _LoadedModule: + def __init__(self, cubin: bytes): + _record_runtime_activity("module_loads") + torch.empty(0, device="cuda") + err, self._module = driver.cuModuleLoadData(cubin) + _check(err, "cuModuleLoadData failed") + self._functions: dict[str, Any] = {} + self._closed = False + + @property + def handle(self): + return self._module + + @property + def closed(self) -> bool: + return self._closed + + def function(self, func_name: str): + if self._closed: + raise RuntimeError("CUDA module has been unloaded") + function = self._functions.get(func_name) + if function is None: + err, function = driver.cuModuleGetFunction(self._module, func_name.encode()) + _check(err, f"cuModuleGetFunction failed for {func_name!r}") + self._functions[func_name] = function + return function + + def close(self) -> None: + if not self._closed: + driver.cuModuleUnload(self._module) + self._closed = True + + +class CUDAKernel: + def __init__(self, cubin: bytes, func_name: str): + self._module_owner = _LoadedModule(cubin) + self._owns_module = True + self._initialize_from_module(func_name) + + @classmethod + def from_loaded_module(cls, module: _LoadedModule, func_name: str) -> "CUDAKernel": + kernel = cls.__new__(cls) + kernel._module_owner = module + kernel._owns_module = False + kernel._initialize_from_module(func_name) + return kernel + + def _initialize_from_module(self, func_name: str) -> None: + self._closed = True + self._func_name = func_name + self._module = self._module_owner.handle + self._func = self._module_owner.function(func_name) + self._dynamic_smem_opt_in_bytes = 0 + self._closed = False + + @property + def closed(self) -> bool: + return self._closed + + def _ensure_dynamic_smem_opt_in(self, shared_mem: int) -> None: + if shared_mem <= 48 * 1024 or shared_mem <= self._dynamic_smem_opt_in_bytes: + return + (err,) = driver.cuFuncSetAttribute( + self._func, + driver.CUfunction_attribute.CU_FUNC_ATTRIBUTE_MAX_DYNAMIC_SHARED_SIZE_BYTES, + shared_mem, + ) + _check(err, "cuFuncSetAttribute MAX_DYNAMIC_SHARED_SIZE_BYTES failed") + self._dynamic_smem_opt_in_bytes = max(self._dynamic_smem_opt_in_bytes, shared_mem) + + def _cu_stream(self, stream=None): + if stream is None: + stream = torch.cuda.current_stream() + handle = getattr(stream, "cuda_stream", stream) + return driver.CUstream(int(handle)) + + def _wait_with_timeout(self, cu_stream: driver.CUstream, timeout_ms: float) -> None: + deadline = time.monotonic() + timeout_ms / 1000.0 + while True: + (err,) = driver.cuStreamQuery(cu_stream) + if err == 0: + return + if err != 600: + _check(err, f"cuStreamQuery failed for {self._func_name!r}") + if time.monotonic() >= deadline: + raise TimeoutError(f"Kernel {self._func_name!r} did not complete within {timeout_ms:.0f} ms") + time.sleep(0.001) + + def launch( + self, + *, + grid: tuple[int, int, int], + block: tuple[int, int, int], + args: Sequence[Any], + arg_types: Sequence[str], + shared_mem: int = 0, + stream=None, + timeout_ms: float | None = None, + ) -> None: + self.prepare_launch( + grid=grid, + block=block, + args=args, + arg_types=arg_types, + shared_mem=shared_mem, + stream=stream, + ).launch(timeout_ms=timeout_ms) + + def prepare_launch( + self, + *, + grid, + block, + args, + arg_types, + shared_mem=0, + stream=None, + ): + if self._closed: + raise RuntimeError("Kernel has been unloaded") + self._ensure_dynamic_smem_opt_in(shared_mem) + return PreparedCUDAKernelLaunch( + self, + mode="regular", + grid=grid, + block=block, + arg_types=arg_types, + packed=_pack_args(args, arg_types), + keepalive=tuple(args), + shared_mem=shared_mem, + cu_stream=self._cu_stream(stream), + ) + + def rebind_launch( + self, + prepared, + *, + grid, + block, + args, + arg_types, + shared_mem=0, + stream=None, + ): + if not isinstance(prepared, PreparedCUDAKernelLaunch): + raise TypeError("prepared must be a PreparedCUDAKernelLaunch") + return prepared.rebind( + self, + mode="regular", + grid=grid, + block=block, + args=args, + arg_types=arg_types, + shared_mem=shared_mem, + stream=stream, + ) + + def launch_cluster( + self, + *, + grid: tuple[int, int, int], + block: tuple[int, int, int], + args: Sequence[Any], + arg_types: Sequence[str], + cluster_dims: tuple[int, int, int], + shared_mem: int = 0, + stream=None, + timeout_ms: float | None = None, + ) -> None: + self.prepare_launch_cluster( + grid=grid, + block=block, + args=args, + arg_types=arg_types, + cluster_dims=cluster_dims, + shared_mem=shared_mem, + stream=stream, + ).launch(timeout_ms=timeout_ms) + + def prepare_launch_cluster( + self, + *, + grid, + block, + args, + arg_types, + cluster_dims, + shared_mem=0, + stream=None, + ): + if self._closed: + raise RuntimeError("Kernel has been unloaded") + self._ensure_dynamic_smem_opt_in(shared_mem) + packed = _pack_args(args, arg_types) + cu_stream = self._cu_stream(stream) + + attr_cluster = driver.CUlaunchAttribute() + attr_cluster.id = driver.CUlaunchAttributeID.CU_LAUNCH_ATTRIBUTE_CLUSTER_DIMENSION + attr_cluster.value.clusterDim.x = cluster_dims[0] + attr_cluster.value.clusterDim.y = cluster_dims[1] + attr_cluster.value.clusterDim.z = cluster_dims[2] + + attr_sched = driver.CUlaunchAttribute() + attr_sched.id = driver.CUlaunchAttributeID.CU_LAUNCH_ATTRIBUTE_CLUSTER_SCHEDULING_POLICY_PREFERENCE + attr_sched.value.clusterSchedulingPolicyPreference = ( + driver.CUclusterSchedulingPolicy.CU_CLUSTER_SCHEDULING_POLICY_SPREAD + ) + + config = driver.CUlaunchConfig() + config.gridDimX = grid[0] + config.gridDimY = grid[1] + config.gridDimZ = grid[2] + config.blockDimX = block[0] + config.blockDimY = block[1] + config.blockDimZ = block[2] + config.sharedMemBytes = shared_mem + config.hStream = cu_stream + config.attrs = [attr_cluster, attr_sched] + config.numAttrs = 2 + return PreparedCUDAKernelLaunch( + self, + mode="cluster", + grid=grid, + block=block, + arg_types=arg_types, + packed=packed, + keepalive=tuple(args), + shared_mem=shared_mem, + cu_stream=cu_stream, + cluster_dims=cluster_dims, + config=config, + ) + + def rebind_launch_cluster( + self, + prepared, + *, + grid, + block, + args, + arg_types, + cluster_dims, + shared_mem=0, + stream=None, + ): + if not isinstance(prepared, PreparedCUDAKernelLaunch): + raise TypeError("prepared must be a PreparedCUDAKernelLaunch") + return prepared.rebind( + self, + mode="cluster", + grid=grid, + block=block, + args=args, + arg_types=arg_types, + cluster_dims=cluster_dims, + shared_mem=shared_mem, + stream=stream, + ) + + def launch_cooperative( + self, + *, + grid: tuple[int, int, int], + block: tuple[int, int, int], + args: Sequence[Any], + arg_types: Sequence[str], + shared_mem: int = 0, + stream=None, + timeout_ms: float | None = None, + ) -> None: + self.prepare_launch_cooperative( + grid=grid, + block=block, + args=args, + arg_types=arg_types, + shared_mem=shared_mem, + stream=stream, + ).launch(timeout_ms=timeout_ms) + + def prepare_launch_cooperative( + self, + *, + grid, + block, + args, + arg_types, + shared_mem=0, + stream=None, + ): + if self._closed: + raise RuntimeError("Kernel has been unloaded") + self._ensure_dynamic_smem_opt_in(shared_mem) + return PreparedCUDAKernelLaunch( + self, + mode="cooperative", + grid=grid, + block=block, + arg_types=arg_types, + packed=_pack_args(args, arg_types), + keepalive=tuple(args), + shared_mem=shared_mem, + cu_stream=self._cu_stream(stream), + ) + + def rebind_launch_cooperative( + self, + prepared, + *, + grid, + block, + args, + arg_types, + shared_mem=0, + stream=None, + ): + if not isinstance(prepared, PreparedCUDAKernelLaunch): + raise TypeError("prepared must be a PreparedCUDAKernelLaunch") + return prepared.rebind( + self, + mode="cooperative", + grid=grid, + block=block, + args=args, + arg_types=arg_types, + shared_mem=shared_mem, + stream=stream, + ) + + def close(self) -> None: + if not self._closed: + if self._owns_module: + self._module_owner.close() + self._closed = True + + def __del__(self): + try: + self.close() + except Exception: + pass + + +def load_cached_kernel( + source: str, + *, + source_digest: str, + func_name: str, + arch: str, + device_index: int, + name: str, + options: Sequence[str] = (), +) -> CUDAKernel: + '''Compile/load one content-addressed kernel shared by equivalent aliases. + + Identical source, architecture, and options share NVRTC output. The active + device is added for module loading, and the function symbol only for the + final wrapper, so a multi-entrypoint translation unit loads one module per + device without conflating its functions. + ''' + option_tuple = tuple(options) + cubin_key = (source_digest, arch, option_tuple) + module_key = (*cubin_key, int(device_index)) + kernel_key = (*module_key, func_name) + with _COMPILATION_CACHE_LOCK: + kernel = _KERNEL_CACHE.get(kernel_key) + if kernel is not None and not kernel.closed: + return kernel + cubin = _CUBIN_CACHE.get(cubin_key) + if cubin is None: + cubin = compile_cuda(source, arch=arch, name=name, options=list(option_tuple)) + _CUBIN_CACHE[cubin_key] = cubin + module = _MODULE_CACHE.get(module_key) + if module is None or module.closed: + module = _LoadedModule(cubin) + _MODULE_CACHE[module_key] = module + kernel = CUDAKernel.from_loaded_module(module, func_name) + _KERNEL_CACHE[kernel_key] = kernel + return kernel + + +def clear_compilation_cache() -> None: + '''Clear process-local cubin/module caches (primarily for diagnostics).''' + global _COMPILATION_CACHE_GENERATION + with _COMPILATION_CACHE_LOCK: + for kernel in _KERNEL_CACHE.values(): + kernel._closed = True + for module in _MODULE_CACHE.values(): + module.close() + _KERNEL_CACHE.clear() + _MODULE_CACHE.clear() + _CUBIN_CACHE.clear() + _COMPILATION_CACHE_GENERATION += 1 diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/_shape_records.json b/cake_exports/knn_build/src/flashlib_cake_knn_build/_shape_records.json new file mode 100644 index 00000000..914bdae6 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/_shape_records.json @@ -0,0 +1,2900 @@ +[ + { + "label": "flashml_correctness_b1_q256_m256_d128_k5", + "params": { + "B": 1, + "D": 128, + "K": 5, + "M": 256, + "Q": 256, + "benchmark": true, + "build": true, + "check_correctness": true, + "correctness_query_sample": 256, + "dtype": "bfloat16", + "recall_min": 0.99, + "seed": 606001, + 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"speedup_vs_baseline": 2.0931910897936135, + "timing_backend": "cupti" + } + }, + { + "label": "build_over64_stress_qm4096_k96", + "params": { + "B": 1, + "D": 128, + "K": 96, + "M": 4096, + "Q": 4096, + "benchmark": true, + "build": true, + "check_correctness": true, + "correctness_query_sample": 256, + "diagnostic_class": "over64_topk_bottleneck", + "dtype": "bfloat16", + "recall_min": 0.999, + "seed": 609496, + "time_flashlib": true + }, + "recorded": { + "baseline_ms": 1.302762, + "baseline_name": "flashlib", + "kernel_ms": 0.45536350000000003, + "speedup_vs_baseline": 2.8609275886187624, + "timing_backend": "cupti" + } + }, + { + "label": "rag_online_common_d64_b1_q1_m262143_k10", + "params": { + "B": 1, + "D": 64, + "K": 10, + "M": 262143, + "Q": 1, + "benchmark": true, + "build": false, + "check_correctness": true, + "correctness_query_sample": 1, + "diagnostic_class": "v12_common_d_rag_online_irregular", + "dtype": "bfloat16", + "recall_min": 0.999, + "seed": 615064, + "time_flashlib": true + }, + "recorded": { + "baseline_ms": 0.126273, + "baseline_name": "flashlib", + "kernel_ms": 0.064641, + "speedup_vs_baseline": 1.9534505963707243, + "timing_backend": "cupti" + } + }, + { + "label": "rag_microbatch_common_d64_b1_q4_m100000_k10", + "params": { + "B": 1, + "D": 64, + "K": 10, + "M": 100000, + "Q": 4, + "benchmark": true, + "build": false, + "check_correctness": true, + "correctness_query_sample": 4, + "diagnostic_class": "v12_common_d_rag_microbatch_tail", + "dtype": "bfloat16", + "recall_min": 0.999, + "seed": 615164, + "time_flashlib": true + }, + "recorded": { + "baseline_ms": 0.126145, + "baseline_name": "flashlib", + "kernel_ms": 0.064577, + "speedup_vs_baseline": 1.9534044628892642, + "timing_backend": "cupti" + } + }, + { + "label": "rag_microbatch_common_d256_b1_q4_m100000_k10", + "params": { + "B": 1, + "D": 256, + "K": 10, + "M": 100000, + "Q": 4, + "benchmark": true, + "build": false, + "check_correctness": true, + "correctness_query_sample": 4, + "diagnostic_class": "v12_common_d_rag_microbatch_tail", + "dtype": "bfloat16", + "recall_min": 0.999, + "seed": 615256, + "time_flashlib": true + }, + "recorded": { + "baseline_ms": 0.131521, + "baseline_name": "flashlib", + "kernel_ms": 0.064769, + "speedup_vs_baseline": 2.0306164986336057, + "timing_backend": "cupti" + } + }, + { + "label": "rag_stream_common_d256_b1_q128_m100000_k10", + "params": { + "B": 1, + "D": 256, + "K": 10, + "M": 100000, + "Q": 128, + "benchmark": true, + "build": false, + "check_correctness": true, + "correctness_query_sample": 128, + "diagnostic_class": "v12_common_d_rag_streaming", + "dtype": "bfloat16", + "recall_min": 0.999, + "seed": 615356, + "time_flashlib": true + }, + "recorded": { + "baseline_ms": 0.148385, + "baseline_name": "flashlib", + "kernel_ms": 0.068256, + "speedup_vs_baseline": 2.17394807782466, + "timing_backend": "cupti" + } + }, + { + "label": "rag_microbatch_common_d768_b1_q8_m100000_k10", + "params": { + "B": 1, + "D": 768, + "K": 10, + "M": 100000, + "Q": 8, + "benchmark": true, + "build": false, + "check_correctness": true, + "correctness_query_sample": 8, + "diagnostic_class": "v12_common_d_rag_microbatch_tail", + "dtype": "bfloat16", + "recall_min": 0.999, + "seed": 615768, + "time_flashlib": true + }, + "recorded": { + "baseline_ms": 0.159201, + "baseline_name": "flashlib", + "kernel_ms": 0.092096, + "speedup_vs_baseline": 1.7286418519805422, + "timing_backend": "cupti" + } + }, + { + "label": "rag_microbatch_common_d1024_b1_q4_m100000_k10", + "params": { + "B": 1, + "D": 1024, + "K": 10, + "M": 100000, + "Q": 4, + "benchmark": true, + "build": false, + "check_correctness": true, + "correctness_query_sample": 4, + "diagnostic_class": "v12_common_d_rag_microbatch_tail", + "dtype": "bfloat16", + "recall_min": 0.999, + "seed": 616024, + "time_flashlib": true + }, + "recorded": { + "baseline_ms": 0.181954, + "baseline_name": "flashlib", + "kernel_ms": 0.116257, + "speedup_vs_baseline": 1.5651014562563974, + "timing_backend": "cupti" + } + }, + { + "label": "rag_online_common_d4096_b1_q1_m65536_k10", + "params": { + "B": 1, + "D": 4096, + "K": 10, + "M": 65536, + "Q": 1, + "benchmark": true, + "build": false, + "check_correctness": true, + "correctness_query_sample": 1, + "diagnostic_class": "v12_common_d_rag_online_highd", + "dtype": "bfloat16", + "recall_min": 0.999, + "seed": 616096, + "time_flashlib": true + }, + "recorded": { + "baseline_ms": 0.393123, + "baseline_name": "flashlib", + "kernel_ms": 0.298306, + "speedup_vs_baseline": 1.3178514679557232, + "timing_backend": "cupti" + } + }, + { + "label": "search_rect_common_d1024_b1_q256_m8192_k10", + "params": { + "B": 1, + "D": 1024, + "K": 10, + "M": 8192, + "Q": 256, + "benchmark": true, + "build": false, + "check_correctness": true, + "correctness_query_sample": 256, + "diagnostic_class": "v12_common_d_rectangular_search", + "dtype": "bfloat16", + "recall_min": 0.999, + "seed": 616124, + "time_flashlib": true + }, + "recorded": { + "baseline_ms": 0.149985, + "baseline_name": "flashlib", + "kernel_ms": 0.064193, + "speedup_vs_baseline": 2.3364697085351986, + "timing_backend": "cupti" + } + }, + { + "label": "search_rect_common_d4096_b1_q128_m4096_k10", + "params": { + "B": 1, + "D": 4096, + "K": 10, + "M": 4096, + "Q": 128, + "benchmark": true, + "build": false, + "check_correctness": true, + "correctness_query_sample": 128, + "diagnostic_class": "v12_common_d_rectangular_search", + "dtype": "bfloat16", + "recall_min": 0.999, + "seed": 616496, + "time_flashlib": true + }, + "recorded": { + "baseline_ms": 0.216001, + "baseline_name": "flashlib", + "kernel_ms": 0.063744, + "speedup_vs_baseline": 3.3885699046184743, + "timing_backend": "cupti" + } + }, + { + "label": "rag_microbatch_largek_common_d256_b1_q8_m100000_k32", + "params": { + "B": 1, + "D": 256, + "K": 32, + "M": 100000, + "Q": 8, + "benchmark": true, + "build": false, + "check_correctness": true, + "correctness_query_sample": 8, + "diagnostic_class": "v12_common_d_large_k_rag", + "dtype": "bfloat16", + "recall_min": 0.999, + "seed": 616332, + "time_flashlib": true + }, + "recorded": { + "baseline_ms": 0.133729, + "baseline_name": "flashlib", + "kernel_ms": 0.097057, + "speedup_vs_baseline": 1.3778398260815807, + "timing_backend": "cupti" + } + }, + { + "label": "rag_stream_largek_common_d256_b1_q128_m100000_k32", + "params": { + "B": 1, + "D": 256, + "K": 32, + "M": 100000, + "Q": 128, + "benchmark": true, + "build": false, + "check_correctness": true, + "correctness_query_sample": 128, + "diagnostic_class": "v12_common_d_large_k_rag", + "dtype": "bfloat16", + "recall_min": 0.999, + "seed": 616432, + "time_flashlib": true + }, + "recorded": { + "baseline_ms": 0.286754, + "baseline_name": "flashlib", + "kernel_ms": 0.224962, + "speedup_vs_baseline": 1.274677501089073, + "timing_backend": "cupti" + } + }, + { + "label": "rag_microbatch_over32_d128_b1_q16_m100000_k48", + "params": { + "B": 1, + "D": 128, + "K": 48, + "M": 100000, + "Q": 16, + "benchmark": true, + "build": false, + "check_correctness": true, + "correctness_query_sample": 16, + "diagnostic_class": "v12_rag_over32_topk", + "dtype": "bfloat16", + "recall_min": 0.999, + "seed": 616548, + "time_flashlib": true + }, + "recorded": { + "baseline_ms": 0.199393, + "baseline_name": "flashlib", + "kernel_ms": 0.103936, + "speedup_vs_baseline": 1.9184209513546797, + "timing_backend": "cupti" + } + } +] diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0000.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0000.cu new file mode 100644 index 00000000..5477e2c0 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0000.cu @@ -0,0 +1,719 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 128 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_STRIDE 32768 +#define SMEM_SMEM_DATABASE_OFF 33792 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_DATABASE_SQ_OFF 50176 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 50432 +#define THREADS 192 +#define BLOCK_Q 128 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_SMALL 5 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16_cta2( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + ".reg .b32 m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "tcgen05.mma.cta_group::2.kind::f16 [%0], %1, %2, %3, {m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step_cg2( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, %3, " + "{m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], %1;\n\t" + "}\n" + :: "r"(mbar_addr), "h"(cta_mask) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x16(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x16.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15}, [%16];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ uint32_t smem_addr(const void* ptr) { + uint32_t addr; + asm("{\n\t" + ".reg .u64 u64addr;\n\t" + "cvta.to.shared.u64 u64addr, %1;\n\t" + "cvt.u32.u64 %0, u64addr;\n\t" + "}\n" : "=r"(addr) : "l"(ptr)); + return addr; +} + + +__device__ __forceinline__ uint32_t mapa_to_rank(uint32_t local_addr, uint32_t rank) { + uint32_t remote; + asm volatile("mapa.shared::cluster.u32 %0, %1, %2;" + : "=r"(remote) : "r"(local_addr), "r"(rank)); + return remote; +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem_cta2( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global" + ".mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .b16 lo, hi;\n\t" + "mov.b32 {lo, hi}, %1;\n\t" + "tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], lo;\n\t" + "}\n" + :: "r"(mbar_addr), "r"((uint32_t)cta_mask) : "memory"); +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_evolve_7bfc_split_cg2_stage1_batch8_cond4_vmin_threshold_k5(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tile_pairs, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + const unsigned int clusters_x = gridDim.x / 2; + const unsigned int cluster_id = ((blockIdx.z * gridDim.y + blockIdx.y) * clusters_x) + blockIdx.x / 2; + const unsigned int num_clusters = clusters_x * gridDim.y * gridDim.z; + + int cta_rank; + asm volatile("mov.b32 %0, %%cluster_ctarank;" : "=r"(cta_rank)); + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 33792); + const int smem_database_addr = smem + 33792; + float* smem_database_sq = reinterpret_cast(smem_raw + 50176); + const int smem_database_sq_addr = smem + 50176; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 0, 2, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 16, 2, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=8 + mbarrier_init_pred(smem + 40, 8, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (128 columns, 128 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::2.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(128) : "memory"); + } + + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: load ---- + if (warp == 0) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 0) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx = cluster_id; work_idx < total_work; work_idx += num_clusters) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tile_pairs; + int q_tile_pair = query_work % num_q_tile_pairs; + int q_tile = q_tile_pair * 2 + cta_rank; + int off_q = q_tile * BLOCK_Q; + int global_q = batch_idx * Q + off_q; + int db_tile_start = split_idx * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((query_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(32768)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_query_addr), "l"(tmap_query), "r"(0), "r"(global_q), "r"(0), + "r"(((query_full_addr) & 0xFEFFFFFF)) : "memory"); + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int off_m = db_tile * BLOCK_M; + int global_m = batch_idx * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((database_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(16384)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_database_addr), "l"(tmap_database), "r"(0), "r"(global_m), "r"(0), + "r"(((database_full_addr) & 0xFEFFFFFF)) : "memory"); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 1) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + if (cta_rank == 0) { + #pragma unroll 1 + for (unsigned int work_idx_1 = cluster_id; work_idx_1 < total_work; work_idx_1 += num_clusters) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = (smem_query_addr >> 4) & 0x3FFF; + int _mma_b_lo_0 = (smem_database_addr >> 4) & 0x3FFF; + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\tmov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 270533776;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit_cg2_multicast(score_full_addr, (uint16_t)(3)); + elect_commit_cg2_multicast(database_empty_addr, (uint16_t)(3)); + } + elect_commit_cg2_multicast(query_empty_addr, (uint16_t)(3)); + } + } + } + // ---- Role: compute ---- + } else if (warp >= 2 && warp <= 5) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = cluster_id; work_idx_2 < total_work; work_idx_2 += num_clusters) { + int split_idx_1 = work_idx_2 % (unsigned int)split_count; + int query_work_1 = work_idx_2 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tile_pairs; + int q_tile_pair_1 = query_work_1 % num_q_tile_pairs; + int q_tile_1 = q_tile_pair_1 * 2 + cta_rank; + int off_q_1 = q_tile_1 * BLOCK_Q; + int q_idx = off_q_1 + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx_1 * Q + q_idx]; + } + float best_d[TOP_K_SMALL]; + int best_i[TOP_K_SMALL]; + float worst_d = 3.4e+38f; + int worst_pos = 0; + #pragma unroll + for (int kk = 0; kk < TOP_K_SMALL; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int db_start = db_tile_1 * BLOCK_M; + int db_sq_idx = db_start + (warp % 4 * 32 + lane); + if (warp % 4 * 32 + lane < BLOCK_M) { + if (db_sq_idx < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx_1 * M + db_sq_idx]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 0.0f; + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(cta_rank * BLOCK_Q + (warp % 4 * 32 << 16) << 16); + float _tmem_load_0[64]; + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x64.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31, %32, %33, %34, %35, %36, %37, %38, %39, %40, %41, %42, %43, %44, %45, %46, %47, %48, %49, %50, %51, %52, %53, %54, %55, %56, %57, %58, %59, %60, %61, %62, %63}, [%64];" + : "=f"(_tmem_load_0[0]), "=f"(_tmem_load_0[1]), "=f"(_tmem_load_0[2]), "=f"(_tmem_load_0[3]), "=f"(_tmem_load_0[4]), "=f"(_tmem_load_0[5]), "=f"(_tmem_load_0[6]), "=f"(_tmem_load_0[7]), "=f"(_tmem_load_0[8]), "=f"(_tmem_load_0[9]), "=f"(_tmem_load_0[10]), "=f"(_tmem_load_0[11]), "=f"(_tmem_load_0[12]), "=f"(_tmem_load_0[13]), "=f"(_tmem_load_0[14]), "=f"(_tmem_load_0[15]), "=f"(_tmem_load_0[16]), "=f"(_tmem_load_0[17]), "=f"(_tmem_load_0[18]), "=f"(_tmem_load_0[19]), "=f"(_tmem_load_0[20]), "=f"(_tmem_load_0[21]), "=f"(_tmem_load_0[22]), "=f"(_tmem_load_0[23]), "=f"(_tmem_load_0[24]), "=f"(_tmem_load_0[25]), "=f"(_tmem_load_0[26]), "=f"(_tmem_load_0[27]), "=f"(_tmem_load_0[28]), "=f"(_tmem_load_0[29]), "=f"(_tmem_load_0[30]), "=f"(_tmem_load_0[31]), "=f"(_tmem_load_0[32]), "=f"(_tmem_load_0[33]), "=f"(_tmem_load_0[34]), "=f"(_tmem_load_0[35]), "=f"(_tmem_load_0[36]), "=f"(_tmem_load_0[37]), "=f"(_tmem_load_0[38]), "=f"(_tmem_load_0[39]), "=f"(_tmem_load_0[40]), "=f"(_tmem_load_0[41]), "=f"(_tmem_load_0[42]), "=f"(_tmem_load_0[43]), "=f"(_tmem_load_0[44]), "=f"(_tmem_load_0[45]), "=f"(_tmem_load_0[46]), "=f"(_tmem_load_0[47]), "=f"(_tmem_load_0[48]), "=f"(_tmem_load_0[49]), "=f"(_tmem_load_0[50]), "=f"(_tmem_load_0[51]), "=f"(_tmem_load_0[52]), "=f"(_tmem_load_0[53]), "=f"(_tmem_load_0[54]), "=f"(_tmem_load_0[55]), "=f"(_tmem_load_0[56]), "=f"(_tmem_load_0[57]), "=f"(_tmem_load_0[58]), "=f"(_tmem_load_0[59]), "=f"(_tmem_load_0[60]), "=f"(_tmem_load_0[61]), "=f"(_tmem_load_0[62]), "=f"(_tmem_load_0[63]) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + if (elect_sync()) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cluster.b64 _, [%0];" + :: "r"((score_empty_addr) & 0xFEFFFFFF) : "memory"); + } + if (valid_q != 0) { + #pragma unroll 1 + for (int col_base = 0; col_base < 64; col_base += 8) { + float dist_vec0[4]; + dist_vec0[0] = _tmem_load_0[col_base]; + dist_vec0[1] = _tmem_load_0[col_base + 1]; + dist_vec0[2] = _tmem_load_0[col_base + 2]; + dist_vec0[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec0)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec0[4]; + db_sq_vec0[0] = smem_database_sq[col_base]; + db_sq_vec0[1] = smem_database_sq[col_base + 1]; + db_sq_vec0[2] = smem_database_sq[col_base + 2]; + db_sq_vec0[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec0)[_la], reinterpret_cast(db_sq_vec0)[_la]); + float dist_vec1[4]; + dist_vec1[0] = _tmem_load_0[col_base + 4]; + dist_vec1[1] = _tmem_load_0[col_base + 5]; + dist_vec1[2] = _tmem_load_0[col_base + 6]; + dist_vec1[3] = _tmem_load_0[col_base + 7]; + const float2 _fma_b2_2 = {-2.0f, -2.0f}; + const float2 _fma_c2_3 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec1)[_lf], _fma_b2_2, _fma_c2_3); + float db_sq_vec1[4]; + db_sq_vec1[0] = smem_database_sq[col_base + 4]; + db_sq_vec1[1] = smem_database_sq[col_base + 5]; + db_sq_vec1[2] = smem_database_sq[col_base + 6]; + db_sq_vec1[3] = smem_database_sq[col_base + 7]; + float _t1[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t1)[_la] = add_f32x2(reinterpret_cast(dist_vec1)[_la], reinterpret_cast(db_sq_vec1)[_la]); + float _t0_min = _t0[0]; + #pragma unroll + for (int _lr = 1; _lr < 4; _lr++) { + _t0_min = fminf(_t0_min, _t0[_lr]); + } + float group_min0 = _t0_min; + float _t1_min = _t1[0]; + #pragma unroll + for (int _lr = 1; _lr < 4; _lr++) { + _t1_min = fminf(_t1_min, _t1[_lr]); + } + float group_min1 = _t1_min; + if (group_min0 < worst_d) { + #pragma unroll + for (int vec_col = 0; vec_col < 4; vec_col++) { + int db_idx = db_start + col_base + vec_col; + if (db_idx < M) { + float dist = _t0[vec_col]; + if (dist < worst_d) { + best_d[worst_pos] = dist; + best_i[worst_pos] = db_idx; + int cmp01 = ((best_d[1] > best_d[0]) ? 1 : 0); + float max01_d = ((cmp01 != 0) ? best_d[1] : best_d[0]); + int max01_p = ((cmp01 != 0) ? 1 : 0); + int cmp23 = ((best_d[3] > best_d[2]) ? 1 : 0); + float max23_d = ((cmp23 != 0) ? best_d[3] : best_d[2]); + int max23_p = ((cmp23 != 0) ? 3 : 2); + int cmp0123 = ((max23_d > max01_d) ? 1 : 0); + float max0123_d = ((cmp0123 != 0) ? max23_d : max01_d); + int max0123_p = ((cmp0123 != 0) ? max23_p : max01_p); + int cmp_all = ((best_d[4] > max0123_d) ? 1 : 0); + worst_d = ((cmp_all != 0) ? best_d[4] : max0123_d); + worst_pos = ((cmp_all != 0) ? 4 : max0123_p); + } + } + } + } + if (group_min1 < worst_d) { + #pragma unroll + for (int vec_col_1 = 0; vec_col_1 < 4; vec_col_1++) { + int db_idx_1 = db_start + col_base + 4 + vec_col_1; + if (db_idx_1 < M) { + float dist_1 = _t1[vec_col_1]; + if (dist_1 < worst_d) { + best_d[worst_pos] = dist_1; + best_i[worst_pos] = db_idx_1; + int cmp01_1 = ((best_d[1] > best_d[0]) ? 1 : 0); + float max01_d_1 = ((cmp01_1 != 0) ? best_d[1] : best_d[0]); + int max01_p_1 = ((cmp01_1 != 0) ? 1 : 0); + int cmp23_1 = ((best_d[3] > best_d[2]) ? 1 : 0); + float max23_d_1 = ((cmp23_1 != 0) ? best_d[3] : best_d[2]); + int max23_p_1 = ((cmp23_1 != 0) ? 3 : 2); + int cmp0123_1 = ((max23_d_1 > max01_d_1) ? 1 : 0); + float max0123_d_1 = ((cmp0123_1 != 0) ? max23_d_1 : max01_d_1); + int max0123_p_1 = ((cmp0123_1 != 0) ? max23_p_1 : max01_p_1); + int cmp_all_1 = ((best_d[4] > max0123_d_1) ? 1 : 0); + worst_d = ((cmp_all_1 != 0) ? best_d[4] : max0123_d_1); + worst_pos = ((cmp_all_1 != 0) ? 4 : max0123_p_1); + } + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + if (valid_q != 0) { + int out_base = ((split_idx_1 * B + batch_idx_1) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_SMALL; out_k++) { + float selected_d = best_d[0]; + int selected_i = best_i[0]; + int selected_pos = 0; + #pragma unroll + for (int scan = 1; scan < TOP_K_SMALL; scan++) { + if (selected_d > best_d[scan]) { + selected_d = best_d[scan]; + selected_i = best_i[scan]; + selected_pos = scan; + } + } + if (out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = selected_d; + *((int*)(partial_indices + (out_base + out_k))) = selected_i; + } + best_d[selected_pos] = 3.4e+38f; + best_i[selected_pos] = -1; + } + } + } + } + } + + // Cleanup + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::2.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(128)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::2.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0001.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0001.cu new file mode 100644 index 00000000..831b402d --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0001.cu @@ -0,0 +1,95 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 256 +#define TOP_K_SMALL 5 + +#include + +extern "C" { + +__global__ __launch_bounds__(256, 1) void +kernel_knn_build_evolve_7bfc_k5_merge_s4_tree(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int B, int Q, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 256 + tid; + int stride = num_bids * 256; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int batch_idx = row / Q; + int q_idx = row - batch_idx * Q; + int base_row = (batch_idx * Q + q_idx) * TOP_K_SMALL; + int split_stride = B * Q * TOP_K_SMALL; + int partial_base0 = base_row; + int partial_base1 = base_row + split_stride; + int partial_base2 = partial_base1 + split_stride; + int partial_base3 = partial_base2 + split_stride; + int pos0 = 0; + int pos1 = 0; + int pos2 = 0; + int pos3 = 0; + int out_base = row * TOP_K_SMALL; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_SMALL; out_k++) { + float cand_d0 = partial_dists[partial_base0 + pos0]; + int cand_i0 = partial_indices[partial_base0 + pos0]; + float cand_d1 = partial_dists[partial_base1 + pos1]; + int cand_i1 = partial_indices[partial_base1 + pos1]; + float cand_d2 = partial_dists[partial_base2 + pos2]; + int cand_i2 = partial_indices[partial_base2 + pos2]; + float cand_d3 = partial_dists[partial_base3 + pos3]; + int cand_i3 = partial_indices[partial_base3 + pos3]; + int cmp01 = ((cand_d1 < cand_d0) ? 1 : 0); + float best01_d = ((cmp01 != 0) ? cand_d1 : cand_d0); + int best01_i = ((cmp01 != 0) ? cand_i1 : cand_i0); + int best01_split = ((cmp01 != 0) ? 1 : 0); + int cmp23 = ((cand_d3 < cand_d2) ? 1 : 0); + float best23_d = ((cmp23 != 0) ? cand_d3 : cand_d2); + int best23_i = ((cmp23 != 0) ? cand_i3 : cand_i2); + int best23_split = ((cmp23 != 0) ? 3 : 2); + int cmp_all = ((best23_d < best01_d) ? 1 : 0); + float best_d = ((cmp_all != 0) ? best23_d : best01_d); + int best_i = ((cmp_all != 0) ? best23_i : best01_i); + int best_split = ((cmp_all != 0) ? best23_split : best01_split); + *((float*)(out_dists + (out_base + out_k))) = best_d; + *((int*)(out_indices + (out_base + out_k))) = best_i; + if (best_split == 0) { + pos0 = pos0 + 1; + } else if (best_split == 1) { + pos1 = pos1 + 1; + } else { + if (best_split == 2) { + pos2 = pos2 + 1; + } else { + pos3 = pos3 + 1; + } + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0002.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0002.cu new file mode 100644 index 00000000..84c3ec88 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0002.cu @@ -0,0 +1,774 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 128 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_STRIDE 32768 +#define SMEM_SMEM_DATABASE_OFF 33792 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_DATABASE_SQ_OFF 50176 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 50432 +#define THREADS 192 +#define BLOCK_Q 128 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_MAX 10 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16_cta2( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + ".reg .b32 m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "tcgen05.mma.cta_group::2.kind::f16 [%0], %1, %2, %3, {m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step_cg2( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, %3, " + "{m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], %1;\n\t" + "}\n" + :: "r"(mbar_addr), "h"(cta_mask) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x16(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x16.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15}, [%16];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ uint32_t smem_addr(const void* ptr) { + uint32_t addr; + asm("{\n\t" + ".reg .u64 u64addr;\n\t" + "cvta.to.shared.u64 u64addr, %1;\n\t" + "cvt.u32.u64 %0, u64addr;\n\t" + "}\n" : "=r"(addr) : "l"(ptr)); + return addr; +} + + +__device__ __forceinline__ uint32_t mapa_to_rank(uint32_t local_addr, uint32_t rank) { + uint32_t remote; + asm volatile("mapa.shared::cluster.u32 %0, %1, %2;" + : "=r"(remote) : "r"(local_addr), "r"(rank)); + return remote; +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem_cta2( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global" + ".mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .b16 lo, hi;\n\t" + "mov.b32 {lo, hi}, %1;\n\t" + "tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], lo;\n\t" + "}\n" + :: "r"(mbar_addr), "r"((uint32_t)cta_mask) : "memory"); +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_evolve_7bfc_split_cg2_stage1_batch8_cond4_vmin_maxtree(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tile_pairs, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + const unsigned int clusters_x = gridDim.x / 2; + const unsigned int cluster_id = ((blockIdx.z * gridDim.y + blockIdx.y) * clusters_x) + blockIdx.x / 2; + const unsigned int num_clusters = clusters_x * gridDim.y * gridDim.z; + + int cta_rank; + asm volatile("mov.b32 %0, %%cluster_ctarank;" : "=r"(cta_rank)); + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 33792); + const int smem_database_addr = smem + 33792; + float* smem_database_sq = reinterpret_cast(smem_raw + 50176); + const int smem_database_sq_addr = smem + 50176; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 0, 2, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 16, 2, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=8 + mbarrier_init_pred(smem + 40, 8, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (128 columns, 128 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::2.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(128) : "memory"); + } + + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: load ---- + if (warp == 0) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 0) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx = cluster_id; work_idx < total_work; work_idx += num_clusters) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tile_pairs; + int q_tile_pair = query_work % num_q_tile_pairs; + int q_tile = q_tile_pair * 2 + cta_rank; + int off_q = q_tile * BLOCK_Q; + int global_q = batch_idx * Q + off_q; + int db_tile_start = split_idx * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((query_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(32768)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_query_addr), "l"(tmap_query), "r"(0), "r"(global_q), "r"(0), + "r"(((query_full_addr) & 0xFEFFFFFF)) : "memory"); + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int off_m = db_tile * BLOCK_M; + int global_m = batch_idx * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((database_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(16384)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_database_addr), "l"(tmap_database), "r"(0), "r"(global_m), "r"(0), + "r"(((database_full_addr) & 0xFEFFFFFF)) : "memory"); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 1) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + if (cta_rank == 0) { + #pragma unroll 1 + for (unsigned int work_idx_1 = cluster_id; work_idx_1 < total_work; work_idx_1 += num_clusters) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = (smem_query_addr >> 4) & 0x3FFF; + int _mma_b_lo_0 = (smem_database_addr >> 4) & 0x3FFF; + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\tmov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 270533776;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit_cg2_multicast(score_full_addr, (uint16_t)(3)); + elect_commit_cg2_multicast(database_empty_addr, (uint16_t)(3)); + } + elect_commit_cg2_multicast(query_empty_addr, (uint16_t)(3)); + } + } + } + // ---- Role: compute ---- + } else if (warp >= 2 && warp <= 5) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = cluster_id; work_idx_2 < total_work; work_idx_2 += num_clusters) { + int split_idx_1 = work_idx_2 % (unsigned int)split_count; + int query_work_1 = work_idx_2 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tile_pairs; + int q_tile_pair_1 = query_work_1 % num_q_tile_pairs; + int q_tile_1 = q_tile_pair_1 * 2 + cta_rank; + int off_q_1 = q_tile_1 * BLOCK_Q; + int q_idx = off_q_1 + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx_1 * Q + q_idx]; + } + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + float worst_d = 3.4e+38f; + int worst_pos = 0; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int db_start = db_tile_1 * BLOCK_M; + int db_sq_idx = db_start + (warp % 4 * 32 + lane); + if (warp % 4 * 32 + lane < BLOCK_M) { + if (db_sq_idx < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx_1 * M + db_sq_idx]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 0.0f; + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(cta_rank * BLOCK_Q + (warp % 4 * 32 << 16) << 16); + float _tmem_load_0[64]; + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x64.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31, %32, %33, %34, %35, %36, %37, %38, %39, %40, %41, %42, %43, %44, %45, %46, %47, %48, %49, %50, %51, %52, %53, %54, %55, %56, %57, %58, %59, %60, %61, %62, %63}, [%64];" + : "=f"(_tmem_load_0[0]), "=f"(_tmem_load_0[1]), "=f"(_tmem_load_0[2]), "=f"(_tmem_load_0[3]), "=f"(_tmem_load_0[4]), "=f"(_tmem_load_0[5]), "=f"(_tmem_load_0[6]), "=f"(_tmem_load_0[7]), "=f"(_tmem_load_0[8]), "=f"(_tmem_load_0[9]), "=f"(_tmem_load_0[10]), "=f"(_tmem_load_0[11]), "=f"(_tmem_load_0[12]), "=f"(_tmem_load_0[13]), "=f"(_tmem_load_0[14]), "=f"(_tmem_load_0[15]), "=f"(_tmem_load_0[16]), "=f"(_tmem_load_0[17]), "=f"(_tmem_load_0[18]), "=f"(_tmem_load_0[19]), "=f"(_tmem_load_0[20]), "=f"(_tmem_load_0[21]), "=f"(_tmem_load_0[22]), "=f"(_tmem_load_0[23]), "=f"(_tmem_load_0[24]), "=f"(_tmem_load_0[25]), "=f"(_tmem_load_0[26]), "=f"(_tmem_load_0[27]), "=f"(_tmem_load_0[28]), "=f"(_tmem_load_0[29]), "=f"(_tmem_load_0[30]), "=f"(_tmem_load_0[31]), "=f"(_tmem_load_0[32]), "=f"(_tmem_load_0[33]), "=f"(_tmem_load_0[34]), "=f"(_tmem_load_0[35]), "=f"(_tmem_load_0[36]), "=f"(_tmem_load_0[37]), "=f"(_tmem_load_0[38]), "=f"(_tmem_load_0[39]), "=f"(_tmem_load_0[40]), "=f"(_tmem_load_0[41]), "=f"(_tmem_load_0[42]), "=f"(_tmem_load_0[43]), "=f"(_tmem_load_0[44]), "=f"(_tmem_load_0[45]), "=f"(_tmem_load_0[46]), "=f"(_tmem_load_0[47]), "=f"(_tmem_load_0[48]), "=f"(_tmem_load_0[49]), "=f"(_tmem_load_0[50]), "=f"(_tmem_load_0[51]), "=f"(_tmem_load_0[52]), "=f"(_tmem_load_0[53]), "=f"(_tmem_load_0[54]), "=f"(_tmem_load_0[55]), "=f"(_tmem_load_0[56]), "=f"(_tmem_load_0[57]), "=f"(_tmem_load_0[58]), "=f"(_tmem_load_0[59]), "=f"(_tmem_load_0[60]), "=f"(_tmem_load_0[61]), "=f"(_tmem_load_0[62]), "=f"(_tmem_load_0[63]) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + if (elect_sync()) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cluster.b64 _, [%0];" + :: "r"((score_empty_addr) & 0xFEFFFFFF) : "memory"); + } + if (valid_q != 0) { + #pragma unroll 1 + for (int col_base = 0; col_base < 64; col_base += 8) { + float dist_vec0[4]; + dist_vec0[0] = _tmem_load_0[col_base]; + dist_vec0[1] = _tmem_load_0[col_base + 1]; + dist_vec0[2] = _tmem_load_0[col_base + 2]; + dist_vec0[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec0)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec0[4]; + db_sq_vec0[0] = smem_database_sq[col_base]; + db_sq_vec0[1] = smem_database_sq[col_base + 1]; + db_sq_vec0[2] = smem_database_sq[col_base + 2]; + db_sq_vec0[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec0)[_la], reinterpret_cast(db_sq_vec0)[_la]); + float dist_vec1[4]; + dist_vec1[0] = _tmem_load_0[col_base + 4]; + dist_vec1[1] = _tmem_load_0[col_base + 5]; + dist_vec1[2] = _tmem_load_0[col_base + 6]; + dist_vec1[3] = _tmem_load_0[col_base + 7]; + const float2 _fma_b2_2 = {-2.0f, -2.0f}; + const float2 _fma_c2_3 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec1)[_lf], _fma_b2_2, _fma_c2_3); + float db_sq_vec1[4]; + db_sq_vec1[0] = smem_database_sq[col_base + 4]; + db_sq_vec1[1] = smem_database_sq[col_base + 5]; + db_sq_vec1[2] = smem_database_sq[col_base + 6]; + db_sq_vec1[3] = smem_database_sq[col_base + 7]; + float _t1[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t1)[_la] = add_f32x2(reinterpret_cast(dist_vec1)[_la], reinterpret_cast(db_sq_vec1)[_la]); + float _t0_min = _t0[0]; + #pragma unroll + for (int _lr = 1; _lr < 4; _lr++) { + _t0_min = fminf(_t0_min, _t0[_lr]); + } + float group_min0 = _t0_min; + float _t1_min = _t1[0]; + #pragma unroll + for (int _lr = 1; _lr < 4; _lr++) { + _t1_min = fminf(_t1_min, _t1[_lr]); + } + float group_min1 = _t1_min; + if (group_min0 < worst_d) { + #pragma unroll + for (int vec_col = 0; vec_col < 4; vec_col++) { + int db_idx = db_start + col_base + vec_col; + if (db_idx < M) { + float dist = _t0[vec_col]; + if (dist < worst_d) { + best_d[worst_pos] = dist; + best_i[worst_pos] = db_idx; + int cmp01 = ((best_d[1] > best_d[0]) ? 1 : 0); + float max01_d = ((cmp01 != 0) ? best_d[1] : best_d[0]); + int max01_p = ((cmp01 != 0) ? 1 : 0); + int cmp23 = ((best_d[3] > best_d[2]) ? 1 : 0); + float max23_d = ((cmp23 != 0) ? best_d[3] : best_d[2]); + int max23_p = ((cmp23 != 0) ? 3 : 2); + int cmp45 = ((best_d[5] > best_d[4]) ? 1 : 0); + float max45_d = ((cmp45 != 0) ? best_d[5] : best_d[4]); + int max45_p = ((cmp45 != 0) ? 5 : 4); + int cmp67 = ((best_d[7] > best_d[6]) ? 1 : 0); + float max67_d = ((cmp67 != 0) ? best_d[7] : best_d[6]); + int max67_p = ((cmp67 != 0) ? 7 : 6); + int cmp89 = ((best_d[9] > best_d[8]) ? 1 : 0); + float max89_d = ((cmp89 != 0) ? best_d[9] : best_d[8]); + int max89_p = ((cmp89 != 0) ? 9 : 8); + int cmp0123 = ((max23_d > max01_d) ? 1 : 0); + float max0123_d = ((cmp0123 != 0) ? max23_d : max01_d); + int max0123_p = ((cmp0123 != 0) ? max23_p : max01_p); + int cmp4567 = ((max67_d > max45_d) ? 1 : 0); + float max4567_d = ((cmp4567 != 0) ? max67_d : max45_d); + int max4567_p = ((cmp4567 != 0) ? max67_p : max45_p); + int cmp0_7 = ((max4567_d > max0123_d) ? 1 : 0); + float max0_7_d = ((cmp0_7 != 0) ? max4567_d : max0123_d); + int max0_7_p = ((cmp0_7 != 0) ? max4567_p : max0123_p); + int cmp_all = ((max89_d > max0_7_d) ? 1 : 0); + worst_d = ((cmp_all != 0) ? max89_d : max0_7_d); + worst_pos = ((cmp_all != 0) ? max89_p : max0_7_p); + } + } + } + } + if (group_min1 < worst_d) { + #pragma unroll + for (int vec_col_1 = 0; vec_col_1 < 4; vec_col_1++) { + int db_idx_1 = db_start + col_base + 4 + vec_col_1; + if (db_idx_1 < M) { + float dist_1 = _t1[vec_col_1]; + if (dist_1 < worst_d) { + best_d[worst_pos] = dist_1; + best_i[worst_pos] = db_idx_1; + int cmp01_1 = ((best_d[1] > best_d[0]) ? 1 : 0); + float max01_d_1 = ((cmp01_1 != 0) ? best_d[1] : best_d[0]); + int max01_p_1 = ((cmp01_1 != 0) ? 1 : 0); + int cmp23_1 = ((best_d[3] > best_d[2]) ? 1 : 0); + float max23_d_1 = ((cmp23_1 != 0) ? best_d[3] : best_d[2]); + int max23_p_1 = ((cmp23_1 != 0) ? 3 : 2); + int cmp45_1 = ((best_d[5] > best_d[4]) ? 1 : 0); + float max45_d_1 = ((cmp45_1 != 0) ? best_d[5] : best_d[4]); + int max45_p_1 = ((cmp45_1 != 0) ? 5 : 4); + int cmp67_1 = ((best_d[7] > best_d[6]) ? 1 : 0); + float max67_d_1 = ((cmp67_1 != 0) ? best_d[7] : best_d[6]); + int max67_p_1 = ((cmp67_1 != 0) ? 7 : 6); + int cmp89_1 = ((best_d[9] > best_d[8]) ? 1 : 0); + float max89_d_1 = ((cmp89_1 != 0) ? best_d[9] : best_d[8]); + int max89_p_1 = ((cmp89_1 != 0) ? 9 : 8); + int cmp0123_1 = ((max23_d_1 > max01_d_1) ? 1 : 0); + float max0123_d_1 = ((cmp0123_1 != 0) ? max23_d_1 : max01_d_1); + int max0123_p_1 = ((cmp0123_1 != 0) ? max23_p_1 : max01_p_1); + int cmp4567_1 = ((max67_d_1 > max45_d_1) ? 1 : 0); + float max4567_d_1 = ((cmp4567_1 != 0) ? max67_d_1 : max45_d_1); + int max4567_p_1 = ((cmp4567_1 != 0) ? max67_p_1 : max45_p_1); + int cmp0_7_1 = ((max4567_d_1 > max0123_d_1) ? 1 : 0); + float max0_7_d_1 = ((cmp0_7_1 != 0) ? max4567_d_1 : max0123_d_1); + int max0_7_p_1 = ((cmp0_7_1 != 0) ? max4567_p_1 : max0123_p_1); + int cmp_all_1 = ((max89_d_1 > max0_7_d_1) ? 1 : 0); + worst_d = ((cmp_all_1 != 0) ? max89_d_1 : max0_7_d_1); + worst_pos = ((cmp_all_1 != 0) ? max89_p_1 : max0_7_p_1); + } + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + if (valid_q != 0) { + int out_base = ((split_idx_1 * B + batch_idx_1) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + int cmp01_min = ((best_d[1] < best_d[0]) ? 1 : 0); + float min01_d = ((cmp01_min != 0) ? best_d[1] : best_d[0]); + int min01_i = ((cmp01_min != 0) ? best_i[1] : best_i[0]); + int min01_p = ((cmp01_min != 0) ? 1 : 0); + int cmp23_min = ((best_d[3] < best_d[2]) ? 1 : 0); + float min23_d = ((cmp23_min != 0) ? best_d[3] : best_d[2]); + int min23_i = ((cmp23_min != 0) ? best_i[3] : best_i[2]); + int min23_p = ((cmp23_min != 0) ? 3 : 2); + int cmp45_min = ((best_d[5] < best_d[4]) ? 1 : 0); + float min45_d = ((cmp45_min != 0) ? best_d[5] : best_d[4]); + int min45_i = ((cmp45_min != 0) ? best_i[5] : best_i[4]); + int min45_p = ((cmp45_min != 0) ? 5 : 4); + int cmp67_min = ((best_d[7] < best_d[6]) ? 1 : 0); + float min67_d = ((cmp67_min != 0) ? best_d[7] : best_d[6]); + int min67_i = ((cmp67_min != 0) ? best_i[7] : best_i[6]); + int min67_p = ((cmp67_min != 0) ? 7 : 6); + int cmp89_min = ((best_d[9] < best_d[8]) ? 1 : 0); + float min89_d = ((cmp89_min != 0) ? best_d[9] : best_d[8]); + int min89_i = ((cmp89_min != 0) ? best_i[9] : best_i[8]); + int min89_p = ((cmp89_min != 0) ? 9 : 8); + int cmp0123_min = ((min23_d < min01_d) ? 1 : 0); + float min0123_d = ((cmp0123_min != 0) ? min23_d : min01_d); + int min0123_i = ((cmp0123_min != 0) ? min23_i : min01_i); + int min0123_p = ((cmp0123_min != 0) ? min23_p : min01_p); + int cmp4567_min = ((min67_d < min45_d) ? 1 : 0); + float min4567_d = ((cmp4567_min != 0) ? min67_d : min45_d); + int min4567_i = ((cmp4567_min != 0) ? min67_i : min45_i); + int min4567_p = ((cmp4567_min != 0) ? min67_p : min45_p); + int cmp0_7_min = ((min4567_d < min0123_d) ? 1 : 0); + float min0_7_d = ((cmp0_7_min != 0) ? min4567_d : min0123_d); + int min0_7_i = ((cmp0_7_min != 0) ? min4567_i : min0123_i); + int min0_7_p = ((cmp0_7_min != 0) ? min4567_p : min0123_p); + int cmp_all_min = ((min89_d < min0_7_d) ? 1 : 0); + float selected_d = ((cmp_all_min != 0) ? min89_d : min0_7_d); + int selected_i = ((cmp_all_min != 0) ? min89_i : min0_7_i); + int selected_pos = ((cmp_all_min != 0) ? min89_p : min0_7_p); + if (out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = selected_d; + *((int*)(partial_indices + (out_base + out_k))) = selected_i; + } + best_d[selected_pos] = 3.4e+38f; + best_i[selected_pos] = -1; + } + } + } + } + } + + // Cleanup + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::2.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(128)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::2.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0003.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0003.cu new file mode 100644 index 00000000..8a605980 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0003.cu @@ -0,0 +1,89 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 256 +#define TOP_K_MAX 10 + +#include + +extern "C" { + +__global__ __launch_bounds__(256, 1) void +kernel_knn_build_evolve_7bfc_split_merge(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int B, int Q, int K, int split_count, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 256 + tid; + int stride = num_bids * 256; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int batch_idx = row / Q; + int q_idx = row - batch_idx * Q; + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + #pragma unroll 1 + for (int split_idx = 0; split_idx < split_count; split_idx++) { + int partial_base = ((split_idx * B + batch_idx) * Q + q_idx) * K; + #pragma unroll + for (int cand_k = 0; cand_k < TOP_K_MAX; cand_k++) { + if (cand_k < K) { + float cand_d = partial_dists[partial_base + cand_k]; + int cand_i = partial_indices[partial_base + cand_k]; + if (cand_d < best_d[TOP_K_MAX - 1]) { + best_d[TOP_K_MAX - 1] = cand_d; + best_i[TOP_K_MAX - 1] = cand_i; + #pragma unroll + for (int pos = TOP_K_MAX - 1; pos >= 1; pos--) { + if (best_d[pos] < best_d[pos - 1]) { + float tmp_d = best_d[pos - 1]; + int tmp_i = best_i[pos - 1]; + best_d[pos - 1] = best_d[pos]; + best_i[pos - 1] = best_i[pos]; + best_d[pos] = tmp_d; + best_i[pos] = tmp_i; + } + } + } + } + } + } + int out_base = row * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + if (out_k < K) { + *((float*)(out_dists + (out_base + out_k))) = best_d[out_k]; + *((int*)(out_indices + (out_base + out_k))) = best_i[out_k]; + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0004.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0004.cu new file mode 100644 index 00000000..7725e60a --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0004.cu @@ -0,0 +1,657 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 128 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_STRIDE 32768 +#define SMEM_SMEM_DATABASE_OFF 33792 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_DATABASE_SQ_OFF 50176 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 50432 +#define THREADS 192 +#define BLOCK_Q 128 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_MAX 8 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16_cta2( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + ".reg .b32 m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "tcgen05.mma.cta_group::2.kind::f16 [%0], %1, %2, %3, {m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step_cg2( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, %3, " + "{m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], %1;\n\t" + "}\n" + :: "r"(mbar_addr), "h"(cta_mask) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x16(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x16.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15}, [%16];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ uint32_t smem_addr(const void* ptr) { + uint32_t addr; + asm("{\n\t" + ".reg .u64 u64addr;\n\t" + "cvta.to.shared.u64 u64addr, %1;\n\t" + "cvt.u32.u64 %0, u64addr;\n\t" + "}\n" : "=r"(addr) : "l"(ptr)); + return addr; +} + + +__device__ __forceinline__ uint32_t mapa_to_rank(uint32_t local_addr, uint32_t rank) { + uint32_t remote; + asm volatile("mapa.shared::cluster.u32 %0, %1, %2;" + : "=r"(remote) : "r"(local_addr), "r"(rank)); + return remote; +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem_cta2( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global" + ".mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .b16 lo, hi;\n\t" + "mov.b32 {lo, hi}, %1;\n\t" + "tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], lo;\n\t" + "}\n" + :: "r"(mbar_addr), "r"((uint32_t)cta_mask) : "memory"); +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_k8split(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tile_pairs, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + const unsigned int clusters_x = gridDim.x / 2; + const unsigned int cluster_id = ((blockIdx.z * gridDim.y + blockIdx.y) * clusters_x) + blockIdx.x / 2; + const unsigned int num_clusters = clusters_x * gridDim.y * gridDim.z; + + int cta_rank; + asm volatile("mov.b32 %0, %%cluster_ctarank;" : "=r"(cta_rank)); + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 33792); + const int smem_database_addr = smem + 33792; + float* smem_database_sq = reinterpret_cast(smem_raw + 50176); + const int smem_database_sq_addr = smem + 50176; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 0, 2, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 16, 2, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=8 + mbarrier_init_pred(smem + 40, 8, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (128 columns, 128 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::2.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(128) : "memory"); + } + + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: load ---- + if (warp == 0) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 0) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx = cluster_id; work_idx < total_work; work_idx += num_clusters) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tile_pairs; + int q_tile_pair = query_work % num_q_tile_pairs; + int q_tile = q_tile_pair * 2 + cta_rank; + int off_q = q_tile * BLOCK_Q; + int global_q = batch_idx * Q + off_q; + int db_tile_start = split_idx * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((query_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(32768)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_query_addr), "l"(tmap_query), "r"(0), "r"(global_q), "r"(0), + "r"(((query_full_addr) & 0xFEFFFFFF)) : "memory"); + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int off_m = db_tile * BLOCK_M; + int global_m = batch_idx * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((database_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(16384)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_database_addr), "l"(tmap_database), "r"(0), "r"(global_m), "r"(0), + "r"(((database_full_addr) & 0xFEFFFFFF)) : "memory"); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 1) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + if (cta_rank == 0) { + #pragma unroll 1 + for (unsigned int work_idx_1 = cluster_id; work_idx_1 < total_work; work_idx_1 += num_clusters) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = (smem_query_addr >> 4) & 0x3FFF; + int _mma_b_lo_0 = (smem_database_addr >> 4) & 0x3FFF; + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\tmov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 270533776;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit_cg2_multicast(score_full_addr, (uint16_t)(3)); + elect_commit_cg2_multicast(database_empty_addr, (uint16_t)(3)); + } + elect_commit_cg2_multicast(query_empty_addr, (uint16_t)(3)); + } + } + } + // ---- Role: compute ---- + } else if (warp >= 2 && warp <= 5) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = cluster_id; work_idx_2 < total_work; work_idx_2 += num_clusters) { + int split_idx_1 = work_idx_2 % (unsigned int)split_count; + int query_work_1 = work_idx_2 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tile_pairs; + int q_tile_pair_1 = query_work_1 % num_q_tile_pairs; + int q_tile_1 = q_tile_pair_1 * 2 + cta_rank; + int off_q_1 = q_tile_1 * BLOCK_Q; + int q_idx = off_q_1 + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx_1 * Q + q_idx]; + } + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int db_start = db_tile_1 * BLOCK_M; + int db_sq_idx = db_start + (warp % 4 * 32 + lane); + if (warp % 4 * 32 + lane < BLOCK_M) { + if (db_sq_idx < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx_1 * M + db_sq_idx]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 0.0f; + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(cta_rank * BLOCK_Q + (warp % 4 * 32 << 16) << 16); + float _tmem_load_0[64]; + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x64.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31, %32, %33, %34, %35, %36, %37, %38, %39, %40, %41, %42, %43, %44, %45, %46, %47, %48, %49, %50, %51, %52, %53, %54, %55, %56, %57, %58, %59, %60, %61, %62, %63}, [%64];" + : "=f"(_tmem_load_0[0]), "=f"(_tmem_load_0[1]), "=f"(_tmem_load_0[2]), "=f"(_tmem_load_0[3]), "=f"(_tmem_load_0[4]), "=f"(_tmem_load_0[5]), "=f"(_tmem_load_0[6]), "=f"(_tmem_load_0[7]), "=f"(_tmem_load_0[8]), "=f"(_tmem_load_0[9]), "=f"(_tmem_load_0[10]), "=f"(_tmem_load_0[11]), "=f"(_tmem_load_0[12]), "=f"(_tmem_load_0[13]), "=f"(_tmem_load_0[14]), "=f"(_tmem_load_0[15]), "=f"(_tmem_load_0[16]), "=f"(_tmem_load_0[17]), "=f"(_tmem_load_0[18]), "=f"(_tmem_load_0[19]), "=f"(_tmem_load_0[20]), "=f"(_tmem_load_0[21]), "=f"(_tmem_load_0[22]), "=f"(_tmem_load_0[23]), "=f"(_tmem_load_0[24]), "=f"(_tmem_load_0[25]), "=f"(_tmem_load_0[26]), "=f"(_tmem_load_0[27]), "=f"(_tmem_load_0[28]), "=f"(_tmem_load_0[29]), "=f"(_tmem_load_0[30]), "=f"(_tmem_load_0[31]), "=f"(_tmem_load_0[32]), "=f"(_tmem_load_0[33]), "=f"(_tmem_load_0[34]), "=f"(_tmem_load_0[35]), "=f"(_tmem_load_0[36]), "=f"(_tmem_load_0[37]), "=f"(_tmem_load_0[38]), "=f"(_tmem_load_0[39]), "=f"(_tmem_load_0[40]), "=f"(_tmem_load_0[41]), "=f"(_tmem_load_0[42]), "=f"(_tmem_load_0[43]), "=f"(_tmem_load_0[44]), "=f"(_tmem_load_0[45]), "=f"(_tmem_load_0[46]), "=f"(_tmem_load_0[47]), "=f"(_tmem_load_0[48]), "=f"(_tmem_load_0[49]), "=f"(_tmem_load_0[50]), "=f"(_tmem_load_0[51]), "=f"(_tmem_load_0[52]), "=f"(_tmem_load_0[53]), "=f"(_tmem_load_0[54]), "=f"(_tmem_load_0[55]), "=f"(_tmem_load_0[56]), "=f"(_tmem_load_0[57]), "=f"(_tmem_load_0[58]), "=f"(_tmem_load_0[59]), "=f"(_tmem_load_0[60]), "=f"(_tmem_load_0[61]), "=f"(_tmem_load_0[62]), "=f"(_tmem_load_0[63]) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + if (elect_sync()) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cluster.b64 _, [%0];" + :: "r"((score_empty_addr) & 0xFEFFFFFF) : "memory"); + } + if (valid_q != 0) { + #pragma unroll 2 + for (int col_base = 0; col_base < 64; col_base += 4) { + float dist_vec[4]; + dist_vec[0] = _tmem_load_0[col_base]; + dist_vec[1] = _tmem_load_0[col_base + 1]; + dist_vec[2] = _tmem_load_0[col_base + 2]; + dist_vec[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec[4]; + db_sq_vec[0] = smem_database_sq[col_base]; + db_sq_vec[1] = smem_database_sq[col_base + 1]; + db_sq_vec[2] = smem_database_sq[col_base + 2]; + db_sq_vec[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec)[_la], reinterpret_cast(db_sq_vec)[_la]); + float group_min = _t0[0]; + if (_t0[1] < group_min) { + group_min = _t0[1]; + } + if (_t0[2] < group_min) { + group_min = _t0[2]; + } + if (_t0[3] < group_min) { + group_min = _t0[3]; + } + if (group_min < best_d[TOP_K_MAX - 1]) { + #pragma unroll + for (int vec_col = 0; vec_col < 4; vec_col++) { + int db_idx = db_start + col_base + vec_col; + if (db_idx < M) { + float dist = _t0[vec_col]; + if (dist < best_d[TOP_K_MAX - 1]) { + best_d[TOP_K_MAX - 1] = dist; + best_i[TOP_K_MAX - 1] = db_idx; + #pragma unroll + for (int pos = TOP_K_MAX - 1; pos >= 1; pos--) { + if (best_d[pos] < best_d[pos - 1]) { + float tmp_d = best_d[pos - 1]; + int tmp_i = best_i[pos - 1]; + best_d[pos - 1] = best_d[pos]; + best_i[pos - 1] = best_i[pos]; + best_d[pos] = tmp_d; + best_i[pos] = tmp_i; + } + } + } + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + if (valid_q != 0) { + int out_base = ((split_idx_1 * B + batch_idx_1) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + if (out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = best_d[out_k]; + *((int*)(partial_indices + (out_base + out_k))) = best_i[out_k]; + } + } + } + } + } + } + + // Cleanup + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::2.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(128)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::2.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0005.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0005.cu new file mode 100644 index 00000000..98d8bc10 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0005.cu @@ -0,0 +1,84 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 32 +#define TOP_K_MAX 8 +#define SPLIT_COUNT 8 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_k8s8(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 32 + tid; + int stride = num_bids * 32; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int out_base = base_row; + int split_pos[SPLIT_COUNT]; + int split_base[SPLIT_COUNT]; + float cand_d[SPLIT_COUNT]; + int cand_i[SPLIT_COUNT]; + #pragma unroll + for (int split_idx = 0; split_idx < SPLIT_COUNT; split_idx++) { + split_pos[split_idx] = 0; + split_base[split_idx] = base_row + split_idx * split_stride; + cand_d[split_idx] = partial_dists[split_base[split_idx]]; + cand_i[split_idx] = partial_indices[split_base[split_idx]]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_d = cand_d[0]; + int best_i = cand_i[0]; + int best_split = 0; + #pragma unroll + for (int split_idx_1 = 1; split_idx_1 < SPLIT_COUNT; split_idx_1++) { + if (best_d > cand_d[split_idx_1]) { + best_d = cand_d[split_idx_1]; + best_i = cand_i[split_idx_1]; + best_split = split_idx_1; + } + } + *((float*)(out_dists + (out_base + out_k))) = best_d; + *((int*)(out_indices + (out_base + out_k))) = best_i; + split_pos[best_split] = split_pos[best_split] + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_pos = split_pos[best_split]; + int next_addr = split_base[best_split] + next_pos; + cand_d[best_split] = partial_dists[next_addr]; + cand_i[best_split] = partial_indices[next_addr]; + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0006.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0006.cu new file mode 100644 index 00000000..4d565cb0 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0006.cu @@ -0,0 +1,108 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 32 +#define TOP_K_MAX 10 +#define SPLIT_COUNT 4 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_evolve_7bfc_k10_merge_s4_rowbase_cache(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 32 + tid; + int stride = num_bids * 32; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int out_base = base_row; + int split_base0 = base_row; + int split_base1 = base_row + split_stride; + int split_base2 = split_base1 + split_stride; + int split_base3 = split_base2 + split_stride; + int pos0 = 0; + int pos1 = 0; + int pos2 = 0; + int pos3 = 0; + float cand_d0 = partial_dists[split_base0]; + int cand_i0 = partial_indices[split_base0]; + float cand_d1 = partial_dists[split_base1]; + int cand_i1 = partial_indices[split_base1]; + float cand_d2 = partial_dists[split_base2]; + int cand_i2 = partial_indices[split_base2]; + float cand_d3 = partial_dists[split_base3]; + int cand_i3 = partial_indices[split_base3]; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + int cand01_cmp = ((cand_d1 < cand_d0) ? 1 : 0); + float best01_d = ((cand01_cmp != 0) ? cand_d1 : cand_d0); + int best01_i = ((cand01_cmp != 0) ? cand_i1 : cand_i0); + int best01_split = ((cand01_cmp != 0) ? 1 : 0); + int cand23_cmp = ((cand_d3 < cand_d2) ? 1 : 0); + float best23_d = ((cand23_cmp != 0) ? cand_d3 : cand_d2); + int best23_i = ((cand23_cmp != 0) ? cand_i3 : cand_i2); + int best23_split = ((cand23_cmp != 0) ? 3 : 2); + int best_cmp = ((best23_d < best01_d) ? 1 : 0); + float best_d = ((best_cmp != 0) ? best23_d : best01_d); + int best_i = ((best_cmp != 0) ? best23_i : best01_i); + int best_split = ((best_cmp != 0) ? best23_split : best01_split); + *((float*)(out_dists + (out_base + out_k))) = best_d; + *((int*)(out_indices + (out_base + out_k))) = best_i; + if (out_k + 1 < TOP_K_MAX) { + if (best_split == 0) { + pos0 = pos0 + 1; + int next_addr0 = split_base0 + pos0; + cand_d0 = partial_dists[next_addr0]; + cand_i0 = partial_indices[next_addr0]; + } else if (best_split == 1) { + pos1 = pos1 + 1; + int next_addr1 = split_base1 + pos1; + cand_d1 = partial_dists[next_addr1]; + cand_i1 = partial_indices[next_addr1]; + } else { + if (best_split == 2) { + pos2 = pos2 + 1; + int next_addr2 = split_base2 + pos2; + cand_d2 = partial_dists[next_addr2]; + cand_i2 = partial_indices[next_addr2]; + } else { + pos3 = pos3 + 1; + int next_addr3 = split_base3 + pos3; + cand_d3 = partial_dists[next_addr3]; + cand_i3 = partial_indices[next_addr3]; + } + } + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0007.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0007.cu new file mode 100644 index 00000000..c5396a0e --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0007.cu @@ -0,0 +1,84 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 32 +#define TOP_K_MAX 10 +#define SPLIT_COUNT 7 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_evolve_7bfc_k10_merge_s7_rowbase_cache(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 32 + tid; + int stride = num_bids * 32; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int out_base = base_row; + int split_pos[SPLIT_COUNT]; + int split_base[SPLIT_COUNT]; + float cand_d[SPLIT_COUNT]; + int cand_i[SPLIT_COUNT]; + #pragma unroll + for (int split_idx = 0; split_idx < SPLIT_COUNT; split_idx++) { + split_pos[split_idx] = 0; + split_base[split_idx] = base_row + split_idx * split_stride; + cand_d[split_idx] = partial_dists[split_base[split_idx]]; + cand_i[split_idx] = partial_indices[split_base[split_idx]]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_d = cand_d[0]; + int best_i = cand_i[0]; + int best_split = 0; + #pragma unroll + for (int split_idx_1 = 1; split_idx_1 < SPLIT_COUNT; split_idx_1++) { + if (best_d > cand_d[split_idx_1]) { + best_d = cand_d[split_idx_1]; + best_i = cand_i[split_idx_1]; + best_split = split_idx_1; + } + } + *((float*)(out_dists + (out_base + out_k))) = best_d; + *((int*)(out_indices + (out_base + out_k))) = best_i; + split_pos[best_split] = split_pos[best_split] + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_pos = split_pos[best_split]; + int next_addr = split_base[best_split] + next_pos; + cand_d[best_split] = partial_dists[next_addr]; + cand_i[best_split] = partial_indices[next_addr]; + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0008.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0008.cu new file mode 100644 index 00000000..fe23938b --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0008.cu @@ -0,0 +1,657 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 128 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_STRIDE 32768 +#define SMEM_SMEM_DATABASE_OFF 33792 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_DATABASE_SQ_OFF 50176 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 50432 +#define THREADS 192 +#define BLOCK_Q 128 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_MAX 16 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16_cta2( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + ".reg .b32 m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "tcgen05.mma.cta_group::2.kind::f16 [%0], %1, %2, %3, {m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step_cg2( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, %3, " + "{m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], %1;\n\t" + "}\n" + :: "r"(mbar_addr), "h"(cta_mask) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x16(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x16.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15}, [%16];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ uint32_t smem_addr(const void* ptr) { + uint32_t addr; + asm("{\n\t" + ".reg .u64 u64addr;\n\t" + "cvta.to.shared.u64 u64addr, %1;\n\t" + "cvt.u32.u64 %0, u64addr;\n\t" + "}\n" : "=r"(addr) : "l"(ptr)); + return addr; +} + + +__device__ __forceinline__ uint32_t mapa_to_rank(uint32_t local_addr, uint32_t rank) { + uint32_t remote; + asm volatile("mapa.shared::cluster.u32 %0, %1, %2;" + : "=r"(remote) : "r"(local_addr), "r"(rank)); + return remote; +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem_cta2( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global" + ".mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .b16 lo, hi;\n\t" + "mov.b32 {lo, hi}, %1;\n\t" + "tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], lo;\n\t" + "}\n" + :: "r"(mbar_addr), "r"((uint32_t)cta_mask) : "memory"); +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_k16split(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tile_pairs, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + const unsigned int clusters_x = gridDim.x / 2; + const unsigned int cluster_id = ((blockIdx.z * gridDim.y + blockIdx.y) * clusters_x) + blockIdx.x / 2; + const unsigned int num_clusters = clusters_x * gridDim.y * gridDim.z; + + int cta_rank; + asm volatile("mov.b32 %0, %%cluster_ctarank;" : "=r"(cta_rank)); + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 33792); + const int smem_database_addr = smem + 33792; + float* smem_database_sq = reinterpret_cast(smem_raw + 50176); + const int smem_database_sq_addr = smem + 50176; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 0, 2, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 16, 2, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=8 + mbarrier_init_pred(smem + 40, 8, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (128 columns, 128 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::2.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(128) : "memory"); + } + + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: load ---- + if (warp == 0) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 0) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx = cluster_id; work_idx < total_work; work_idx += num_clusters) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tile_pairs; + int q_tile_pair = query_work % num_q_tile_pairs; + int q_tile = q_tile_pair * 2 + cta_rank; + int off_q = q_tile * BLOCK_Q; + int global_q = batch_idx * Q + off_q; + int db_tile_start = split_idx * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((query_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(32768)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_query_addr), "l"(tmap_query), "r"(0), "r"(global_q), "r"(0), + "r"(((query_full_addr) & 0xFEFFFFFF)) : "memory"); + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int off_m = db_tile * BLOCK_M; + int global_m = batch_idx * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((database_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(16384)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_database_addr), "l"(tmap_database), "r"(0), "r"(global_m), "r"(0), + "r"(((database_full_addr) & 0xFEFFFFFF)) : "memory"); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 1) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + if (cta_rank == 0) { + #pragma unroll 1 + for (unsigned int work_idx_1 = cluster_id; work_idx_1 < total_work; work_idx_1 += num_clusters) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = (smem_query_addr >> 4) & 0x3FFF; + int _mma_b_lo_0 = (smem_database_addr >> 4) & 0x3FFF; + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\tmov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 270533776;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit_cg2_multicast(score_full_addr, (uint16_t)(3)); + elect_commit_cg2_multicast(database_empty_addr, (uint16_t)(3)); + } + elect_commit_cg2_multicast(query_empty_addr, (uint16_t)(3)); + } + } + } + // ---- Role: compute ---- + } else if (warp >= 2 && warp <= 5) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = cluster_id; work_idx_2 < total_work; work_idx_2 += num_clusters) { + int split_idx_1 = work_idx_2 % (unsigned int)split_count; + int query_work_1 = work_idx_2 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tile_pairs; + int q_tile_pair_1 = query_work_1 % num_q_tile_pairs; + int q_tile_1 = q_tile_pair_1 * 2 + cta_rank; + int off_q_1 = q_tile_1 * BLOCK_Q; + int q_idx = off_q_1 + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx_1 * Q + q_idx]; + } + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int db_start = db_tile_1 * BLOCK_M; + int db_sq_idx = db_start + (warp % 4 * 32 + lane); + if (warp % 4 * 32 + lane < BLOCK_M) { + if (db_sq_idx < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx_1 * M + db_sq_idx]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 0.0f; + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(cta_rank * BLOCK_Q + (warp % 4 * 32 << 16) << 16); + float _tmem_load_0[64]; + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x64.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31, %32, %33, %34, %35, %36, %37, %38, %39, %40, %41, %42, %43, %44, %45, %46, %47, %48, %49, %50, %51, %52, %53, %54, %55, %56, %57, %58, %59, %60, %61, %62, %63}, [%64];" + : "=f"(_tmem_load_0[0]), "=f"(_tmem_load_0[1]), "=f"(_tmem_load_0[2]), "=f"(_tmem_load_0[3]), "=f"(_tmem_load_0[4]), "=f"(_tmem_load_0[5]), "=f"(_tmem_load_0[6]), "=f"(_tmem_load_0[7]), "=f"(_tmem_load_0[8]), "=f"(_tmem_load_0[9]), "=f"(_tmem_load_0[10]), "=f"(_tmem_load_0[11]), "=f"(_tmem_load_0[12]), "=f"(_tmem_load_0[13]), "=f"(_tmem_load_0[14]), "=f"(_tmem_load_0[15]), "=f"(_tmem_load_0[16]), "=f"(_tmem_load_0[17]), "=f"(_tmem_load_0[18]), "=f"(_tmem_load_0[19]), "=f"(_tmem_load_0[20]), "=f"(_tmem_load_0[21]), "=f"(_tmem_load_0[22]), "=f"(_tmem_load_0[23]), "=f"(_tmem_load_0[24]), "=f"(_tmem_load_0[25]), "=f"(_tmem_load_0[26]), "=f"(_tmem_load_0[27]), "=f"(_tmem_load_0[28]), "=f"(_tmem_load_0[29]), "=f"(_tmem_load_0[30]), "=f"(_tmem_load_0[31]), "=f"(_tmem_load_0[32]), "=f"(_tmem_load_0[33]), "=f"(_tmem_load_0[34]), "=f"(_tmem_load_0[35]), "=f"(_tmem_load_0[36]), "=f"(_tmem_load_0[37]), "=f"(_tmem_load_0[38]), "=f"(_tmem_load_0[39]), "=f"(_tmem_load_0[40]), "=f"(_tmem_load_0[41]), "=f"(_tmem_load_0[42]), "=f"(_tmem_load_0[43]), "=f"(_tmem_load_0[44]), "=f"(_tmem_load_0[45]), "=f"(_tmem_load_0[46]), "=f"(_tmem_load_0[47]), "=f"(_tmem_load_0[48]), "=f"(_tmem_load_0[49]), "=f"(_tmem_load_0[50]), "=f"(_tmem_load_0[51]), "=f"(_tmem_load_0[52]), "=f"(_tmem_load_0[53]), "=f"(_tmem_load_0[54]), "=f"(_tmem_load_0[55]), "=f"(_tmem_load_0[56]), "=f"(_tmem_load_0[57]), "=f"(_tmem_load_0[58]), "=f"(_tmem_load_0[59]), "=f"(_tmem_load_0[60]), "=f"(_tmem_load_0[61]), "=f"(_tmem_load_0[62]), "=f"(_tmem_load_0[63]) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + if (elect_sync()) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cluster.b64 _, [%0];" + :: "r"((score_empty_addr) & 0xFEFFFFFF) : "memory"); + } + if (valid_q != 0) { + #pragma unroll 2 + for (int col_base = 0; col_base < 64; col_base += 4) { + float dist_vec[4]; + dist_vec[0] = _tmem_load_0[col_base]; + dist_vec[1] = _tmem_load_0[col_base + 1]; + dist_vec[2] = _tmem_load_0[col_base + 2]; + dist_vec[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec[4]; + db_sq_vec[0] = smem_database_sq[col_base]; + db_sq_vec[1] = smem_database_sq[col_base + 1]; + db_sq_vec[2] = smem_database_sq[col_base + 2]; + db_sq_vec[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec)[_la], reinterpret_cast(db_sq_vec)[_la]); + float group_min = _t0[0]; + if (_t0[1] < group_min) { + group_min = _t0[1]; + } + if (_t0[2] < group_min) { + group_min = _t0[2]; + } + if (_t0[3] < group_min) { + group_min = _t0[3]; + } + if (group_min < best_d[TOP_K_MAX - 1]) { + #pragma unroll + for (int vec_col = 0; vec_col < 4; vec_col++) { + int db_idx = db_start + col_base + vec_col; + if (db_idx < M) { + float dist = _t0[vec_col]; + if (dist < best_d[TOP_K_MAX - 1]) { + best_d[TOP_K_MAX - 1] = dist; + best_i[TOP_K_MAX - 1] = db_idx; + #pragma unroll + for (int pos = TOP_K_MAX - 1; pos >= 1; pos--) { + if (best_d[pos] < best_d[pos - 1]) { + float tmp_d = best_d[pos - 1]; + int tmp_i = best_i[pos - 1]; + best_d[pos - 1] = best_d[pos]; + best_i[pos - 1] = best_i[pos]; + best_d[pos] = tmp_d; + best_i[pos] = tmp_i; + } + } + } + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + if (valid_q != 0) { + int out_base = ((split_idx_1 * B + batch_idx_1) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + if (out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = best_d[out_k]; + *((int*)(partial_indices + (out_base + out_k))) = best_i[out_k]; + } + } + } + } + } + } + + // Cleanup + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::2.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(128)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::2.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0009.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0009.cu new file mode 100644 index 00000000..392d262c --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0009.cu @@ -0,0 +1,84 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 32 +#define TOP_K_MAX 16 +#define SPLIT_COUNT 16 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_f8c3lowk_k16s16(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 32 + tid; + int stride = num_bids * 32; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int out_base = base_row; + int split_pos[SPLIT_COUNT]; + int split_base[SPLIT_COUNT]; + float cand_d[SPLIT_COUNT]; + int cand_i[SPLIT_COUNT]; + #pragma unroll + for (int split_idx = 0; split_idx < SPLIT_COUNT; split_idx++) { + split_pos[split_idx] = 0; + split_base[split_idx] = base_row + split_idx * split_stride; + cand_d[split_idx] = partial_dists[split_base[split_idx]]; + cand_i[split_idx] = partial_indices[split_base[split_idx]]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_d = cand_d[0]; + int best_i = cand_i[0]; + int best_split = 0; + #pragma unroll + for (int split_idx_1 = 1; split_idx_1 < SPLIT_COUNT; split_idx_1++) { + if (best_d > cand_d[split_idx_1]) { + best_d = cand_d[split_idx_1]; + best_i = cand_i[split_idx_1]; + best_split = split_idx_1; + } + } + *((float*)(out_dists + (out_base + out_k))) = best_d; + *((int*)(out_indices + (out_base + out_k))) = best_i; + split_pos[best_split] = split_pos[best_split] + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_pos = split_pos[best_split]; + int next_addr = split_base[best_split] + next_pos; + cand_d[best_split] = partial_dists[next_addr]; + cand_i[best_split] = partial_indices[next_addr]; + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0010.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0010.cu new file mode 100644 index 00000000..5b5a28fd --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0010.cu @@ -0,0 +1,657 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 128 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_STRIDE 32768 +#define SMEM_SMEM_DATABASE_OFF 33792 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_DATABASE_SQ_OFF 50176 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 50432 +#define THREADS 192 +#define BLOCK_Q 128 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_MAX 12 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16_cta2( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + ".reg .b32 m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "tcgen05.mma.cta_group::2.kind::f16 [%0], %1, %2, %3, {m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step_cg2( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, %3, " + "{m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], %1;\n\t" + "}\n" + :: "r"(mbar_addr), "h"(cta_mask) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x16(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x16.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15}, [%16];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ uint32_t smem_addr(const void* ptr) { + uint32_t addr; + asm("{\n\t" + ".reg .u64 u64addr;\n\t" + "cvta.to.shared.u64 u64addr, %1;\n\t" + "cvt.u32.u64 %0, u64addr;\n\t" + "}\n" : "=r"(addr) : "l"(ptr)); + return addr; +} + + +__device__ __forceinline__ uint32_t mapa_to_rank(uint32_t local_addr, uint32_t rank) { + uint32_t remote; + asm volatile("mapa.shared::cluster.u32 %0, %1, %2;" + : "=r"(remote) : "r"(local_addr), "r"(rank)); + return remote; +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem_cta2( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global" + ".mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .b16 lo, hi;\n\t" + "mov.b32 {lo, hi}, %1;\n\t" + "tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], lo;\n\t" + "}\n" + :: "r"(mbar_addr), "r"((uint32_t)cta_mask) : "memory"); +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_k12split(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tile_pairs, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + const unsigned int clusters_x = gridDim.x / 2; + const unsigned int cluster_id = ((blockIdx.z * gridDim.y + blockIdx.y) * clusters_x) + blockIdx.x / 2; + const unsigned int num_clusters = clusters_x * gridDim.y * gridDim.z; + + int cta_rank; + asm volatile("mov.b32 %0, %%cluster_ctarank;" : "=r"(cta_rank)); + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 33792); + const int smem_database_addr = smem + 33792; + float* smem_database_sq = reinterpret_cast(smem_raw + 50176); + const int smem_database_sq_addr = smem + 50176; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 0, 2, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 16, 2, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=8 + mbarrier_init_pred(smem + 40, 8, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (128 columns, 128 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::2.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(128) : "memory"); + } + + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: load ---- + if (warp == 0) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 0) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx = cluster_id; work_idx < total_work; work_idx += num_clusters) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tile_pairs; + int q_tile_pair = query_work % num_q_tile_pairs; + int q_tile = q_tile_pair * 2 + cta_rank; + int off_q = q_tile * BLOCK_Q; + int global_q = batch_idx * Q + off_q; + int db_tile_start = split_idx * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((query_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(32768)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_query_addr), "l"(tmap_query), "r"(0), "r"(global_q), "r"(0), + "r"(((query_full_addr) & 0xFEFFFFFF)) : "memory"); + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int off_m = db_tile * BLOCK_M; + int global_m = batch_idx * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((database_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(16384)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_database_addr), "l"(tmap_database), "r"(0), "r"(global_m), "r"(0), + "r"(((database_full_addr) & 0xFEFFFFFF)) : "memory"); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 1) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + if (cta_rank == 0) { + #pragma unroll 1 + for (unsigned int work_idx_1 = cluster_id; work_idx_1 < total_work; work_idx_1 += num_clusters) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = (smem_query_addr >> 4) & 0x3FFF; + int _mma_b_lo_0 = (smem_database_addr >> 4) & 0x3FFF; + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\tmov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 270533776;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit_cg2_multicast(score_full_addr, (uint16_t)(3)); + elect_commit_cg2_multicast(database_empty_addr, (uint16_t)(3)); + } + elect_commit_cg2_multicast(query_empty_addr, (uint16_t)(3)); + } + } + } + // ---- Role: compute ---- + } else if (warp >= 2 && warp <= 5) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = cluster_id; work_idx_2 < total_work; work_idx_2 += num_clusters) { + int split_idx_1 = work_idx_2 % (unsigned int)split_count; + int query_work_1 = work_idx_2 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tile_pairs; + int q_tile_pair_1 = query_work_1 % num_q_tile_pairs; + int q_tile_1 = q_tile_pair_1 * 2 + cta_rank; + int off_q_1 = q_tile_1 * BLOCK_Q; + int q_idx = off_q_1 + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx_1 * Q + q_idx]; + } + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int db_start = db_tile_1 * BLOCK_M; + int db_sq_idx = db_start + (warp % 4 * 32 + lane); + if (warp % 4 * 32 + lane < BLOCK_M) { + if (db_sq_idx < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx_1 * M + db_sq_idx]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 0.0f; + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(cta_rank * BLOCK_Q + (warp % 4 * 32 << 16) << 16); + float _tmem_load_0[64]; + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x64.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31, %32, %33, %34, %35, %36, %37, %38, %39, %40, %41, %42, %43, %44, %45, %46, %47, %48, %49, %50, %51, %52, %53, %54, %55, %56, %57, %58, %59, %60, %61, %62, %63}, [%64];" + : "=f"(_tmem_load_0[0]), "=f"(_tmem_load_0[1]), "=f"(_tmem_load_0[2]), "=f"(_tmem_load_0[3]), "=f"(_tmem_load_0[4]), "=f"(_tmem_load_0[5]), "=f"(_tmem_load_0[6]), "=f"(_tmem_load_0[7]), "=f"(_tmem_load_0[8]), "=f"(_tmem_load_0[9]), "=f"(_tmem_load_0[10]), "=f"(_tmem_load_0[11]), "=f"(_tmem_load_0[12]), "=f"(_tmem_load_0[13]), "=f"(_tmem_load_0[14]), "=f"(_tmem_load_0[15]), "=f"(_tmem_load_0[16]), "=f"(_tmem_load_0[17]), "=f"(_tmem_load_0[18]), "=f"(_tmem_load_0[19]), "=f"(_tmem_load_0[20]), "=f"(_tmem_load_0[21]), "=f"(_tmem_load_0[22]), "=f"(_tmem_load_0[23]), "=f"(_tmem_load_0[24]), "=f"(_tmem_load_0[25]), "=f"(_tmem_load_0[26]), "=f"(_tmem_load_0[27]), "=f"(_tmem_load_0[28]), "=f"(_tmem_load_0[29]), "=f"(_tmem_load_0[30]), "=f"(_tmem_load_0[31]), "=f"(_tmem_load_0[32]), "=f"(_tmem_load_0[33]), "=f"(_tmem_load_0[34]), "=f"(_tmem_load_0[35]), "=f"(_tmem_load_0[36]), "=f"(_tmem_load_0[37]), "=f"(_tmem_load_0[38]), "=f"(_tmem_load_0[39]), "=f"(_tmem_load_0[40]), "=f"(_tmem_load_0[41]), "=f"(_tmem_load_0[42]), "=f"(_tmem_load_0[43]), "=f"(_tmem_load_0[44]), "=f"(_tmem_load_0[45]), "=f"(_tmem_load_0[46]), "=f"(_tmem_load_0[47]), "=f"(_tmem_load_0[48]), "=f"(_tmem_load_0[49]), "=f"(_tmem_load_0[50]), "=f"(_tmem_load_0[51]), "=f"(_tmem_load_0[52]), "=f"(_tmem_load_0[53]), "=f"(_tmem_load_0[54]), "=f"(_tmem_load_0[55]), "=f"(_tmem_load_0[56]), "=f"(_tmem_load_0[57]), "=f"(_tmem_load_0[58]), "=f"(_tmem_load_0[59]), "=f"(_tmem_load_0[60]), "=f"(_tmem_load_0[61]), "=f"(_tmem_load_0[62]), "=f"(_tmem_load_0[63]) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + if (elect_sync()) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cluster.b64 _, [%0];" + :: "r"((score_empty_addr) & 0xFEFFFFFF) : "memory"); + } + if (valid_q != 0) { + #pragma unroll 2 + for (int col_base = 0; col_base < 64; col_base += 4) { + float dist_vec[4]; + dist_vec[0] = _tmem_load_0[col_base]; + dist_vec[1] = _tmem_load_0[col_base + 1]; + dist_vec[2] = _tmem_load_0[col_base + 2]; + dist_vec[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec[4]; + db_sq_vec[0] = smem_database_sq[col_base]; + db_sq_vec[1] = smem_database_sq[col_base + 1]; + db_sq_vec[2] = smem_database_sq[col_base + 2]; + db_sq_vec[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec)[_la], reinterpret_cast(db_sq_vec)[_la]); + float group_min = _t0[0]; + if (_t0[1] < group_min) { + group_min = _t0[1]; + } + if (_t0[2] < group_min) { + group_min = _t0[2]; + } + if (_t0[3] < group_min) { + group_min = _t0[3]; + } + if (group_min < best_d[TOP_K_MAX - 1]) { + #pragma unroll + for (int vec_col = 0; vec_col < 4; vec_col++) { + int db_idx = db_start + col_base + vec_col; + if (db_idx < M) { + float dist = _t0[vec_col]; + if (dist < best_d[TOP_K_MAX - 1]) { + best_d[TOP_K_MAX - 1] = dist; + best_i[TOP_K_MAX - 1] = db_idx; + #pragma unroll + for (int pos = TOP_K_MAX - 1; pos >= 1; pos--) { + if (best_d[pos] < best_d[pos - 1]) { + float tmp_d = best_d[pos - 1]; + int tmp_i = best_i[pos - 1]; + best_d[pos - 1] = best_d[pos]; + best_i[pos - 1] = best_i[pos]; + best_d[pos] = tmp_d; + best_i[pos] = tmp_i; + } + } + } + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + if (valid_q != 0) { + int out_base = ((split_idx_1 * B + batch_idx_1) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + if (out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = best_d[out_k]; + *((int*)(partial_indices + (out_base + out_k))) = best_i[out_k]; + } + } + } + } + } + } + + // Cleanup + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::2.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(128)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::2.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0011.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0011.cu new file mode 100644 index 00000000..247bd13b --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0011.cu @@ -0,0 +1,84 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 32 +#define TOP_K_MAX 12 +#define SPLIT_COUNT 16 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_k12s16(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 32 + tid; + int stride = num_bids * 32; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int out_base = base_row; + int split_pos[SPLIT_COUNT]; + int split_base[SPLIT_COUNT]; + float cand_d[SPLIT_COUNT]; + int cand_i[SPLIT_COUNT]; + #pragma unroll + for (int split_idx = 0; split_idx < SPLIT_COUNT; split_idx++) { + split_pos[split_idx] = 0; + split_base[split_idx] = base_row + split_idx * split_stride; + cand_d[split_idx] = partial_dists[split_base[split_idx]]; + cand_i[split_idx] = partial_indices[split_base[split_idx]]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_d = cand_d[0]; + int best_i = cand_i[0]; + int best_split = 0; + #pragma unroll + for (int split_idx_1 = 1; split_idx_1 < SPLIT_COUNT; split_idx_1++) { + if (best_d > cand_d[split_idx_1]) { + best_d = cand_d[split_idx_1]; + best_i = cand_i[split_idx_1]; + best_split = split_idx_1; + } + } + *((float*)(out_dists + (out_base + out_k))) = best_d; + *((int*)(out_indices + (out_base + out_k))) = best_i; + split_pos[best_split] = split_pos[best_split] + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_pos = split_pos[best_split]; + int next_addr = split_base[best_split] + next_pos; + cand_d[best_split] = partial_dists[next_addr]; + cand_i[best_split] = partial_indices[next_addr]; + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0012.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0012.cu new file mode 100644 index 00000000..1a57e046 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0012.cu @@ -0,0 +1,657 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 128 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_STRIDE 32768 +#define SMEM_SMEM_DATABASE_OFF 33792 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_DATABASE_SQ_OFF 50176 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 50432 +#define THREADS 192 +#define BLOCK_Q 128 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_MAX 20 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16_cta2( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + ".reg .b32 m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "tcgen05.mma.cta_group::2.kind::f16 [%0], %1, %2, %3, {m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step_cg2( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, %3, " + "{m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], %1;\n\t" + "}\n" + :: "r"(mbar_addr), "h"(cta_mask) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x16(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x16.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15}, [%16];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ uint32_t smem_addr(const void* ptr) { + uint32_t addr; + asm("{\n\t" + ".reg .u64 u64addr;\n\t" + "cvta.to.shared.u64 u64addr, %1;\n\t" + "cvt.u32.u64 %0, u64addr;\n\t" + "}\n" : "=r"(addr) : "l"(ptr)); + return addr; +} + + +__device__ __forceinline__ uint32_t mapa_to_rank(uint32_t local_addr, uint32_t rank) { + uint32_t remote; + asm volatile("mapa.shared::cluster.u32 %0, %1, %2;" + : "=r"(remote) : "r"(local_addr), "r"(rank)); + return remote; +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem_cta2( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global" + ".mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .b16 lo, hi;\n\t" + "mov.b32 {lo, hi}, %1;\n\t" + "tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], lo;\n\t" + "}\n" + :: "r"(mbar_addr), "r"((uint32_t)cta_mask) : "memory"); +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_k20split(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tile_pairs, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + const unsigned int clusters_x = gridDim.x / 2; + const unsigned int cluster_id = ((blockIdx.z * gridDim.y + blockIdx.y) * clusters_x) + blockIdx.x / 2; + const unsigned int num_clusters = clusters_x * gridDim.y * gridDim.z; + + int cta_rank; + asm volatile("mov.b32 %0, %%cluster_ctarank;" : "=r"(cta_rank)); + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 33792); + const int smem_database_addr = smem + 33792; + float* smem_database_sq = reinterpret_cast(smem_raw + 50176); + const int smem_database_sq_addr = smem + 50176; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 0, 2, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 16, 2, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=8 + mbarrier_init_pred(smem + 40, 8, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (128 columns, 128 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::2.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(128) : "memory"); + } + + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: load ---- + if (warp == 0) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 0) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx = cluster_id; work_idx < total_work; work_idx += num_clusters) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tile_pairs; + int q_tile_pair = query_work % num_q_tile_pairs; + int q_tile = q_tile_pair * 2 + cta_rank; + int off_q = q_tile * BLOCK_Q; + int global_q = batch_idx * Q + off_q; + int db_tile_start = split_idx * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((query_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(32768)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_query_addr), "l"(tmap_query), "r"(0), "r"(global_q), "r"(0), + "r"(((query_full_addr) & 0xFEFFFFFF)) : "memory"); + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int off_m = db_tile * BLOCK_M; + int global_m = batch_idx * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((database_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(16384)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_database_addr), "l"(tmap_database), "r"(0), "r"(global_m), "r"(0), + "r"(((database_full_addr) & 0xFEFFFFFF)) : "memory"); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 1) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + if (cta_rank == 0) { + #pragma unroll 1 + for (unsigned int work_idx_1 = cluster_id; work_idx_1 < total_work; work_idx_1 += num_clusters) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = (smem_query_addr >> 4) & 0x3FFF; + int _mma_b_lo_0 = (smem_database_addr >> 4) & 0x3FFF; + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\tmov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 270533776;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit_cg2_multicast(score_full_addr, (uint16_t)(3)); + elect_commit_cg2_multicast(database_empty_addr, (uint16_t)(3)); + } + elect_commit_cg2_multicast(query_empty_addr, (uint16_t)(3)); + } + } + } + // ---- Role: compute ---- + } else if (warp >= 2 && warp <= 5) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = cluster_id; work_idx_2 < total_work; work_idx_2 += num_clusters) { + int split_idx_1 = work_idx_2 % (unsigned int)split_count; + int query_work_1 = work_idx_2 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tile_pairs; + int q_tile_pair_1 = query_work_1 % num_q_tile_pairs; + int q_tile_1 = q_tile_pair_1 * 2 + cta_rank; + int off_q_1 = q_tile_1 * BLOCK_Q; + int q_idx = off_q_1 + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx_1 * Q + q_idx]; + } + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int db_start = db_tile_1 * BLOCK_M; + int db_sq_idx = db_start + (warp % 4 * 32 + lane); + if (warp % 4 * 32 + lane < BLOCK_M) { + if (db_sq_idx < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx_1 * M + db_sq_idx]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 0.0f; + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(cta_rank * BLOCK_Q + (warp % 4 * 32 << 16) << 16); + float _tmem_load_0[64]; + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x64.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31, %32, %33, %34, %35, %36, %37, %38, %39, %40, %41, %42, %43, %44, %45, %46, %47, %48, %49, %50, %51, %52, %53, %54, %55, %56, %57, %58, %59, %60, %61, %62, %63}, [%64];" + : "=f"(_tmem_load_0[0]), "=f"(_tmem_load_0[1]), "=f"(_tmem_load_0[2]), "=f"(_tmem_load_0[3]), "=f"(_tmem_load_0[4]), "=f"(_tmem_load_0[5]), "=f"(_tmem_load_0[6]), "=f"(_tmem_load_0[7]), "=f"(_tmem_load_0[8]), "=f"(_tmem_load_0[9]), "=f"(_tmem_load_0[10]), "=f"(_tmem_load_0[11]), "=f"(_tmem_load_0[12]), "=f"(_tmem_load_0[13]), "=f"(_tmem_load_0[14]), "=f"(_tmem_load_0[15]), "=f"(_tmem_load_0[16]), "=f"(_tmem_load_0[17]), "=f"(_tmem_load_0[18]), "=f"(_tmem_load_0[19]), "=f"(_tmem_load_0[20]), "=f"(_tmem_load_0[21]), "=f"(_tmem_load_0[22]), "=f"(_tmem_load_0[23]), "=f"(_tmem_load_0[24]), "=f"(_tmem_load_0[25]), "=f"(_tmem_load_0[26]), "=f"(_tmem_load_0[27]), "=f"(_tmem_load_0[28]), "=f"(_tmem_load_0[29]), "=f"(_tmem_load_0[30]), "=f"(_tmem_load_0[31]), "=f"(_tmem_load_0[32]), "=f"(_tmem_load_0[33]), "=f"(_tmem_load_0[34]), "=f"(_tmem_load_0[35]), "=f"(_tmem_load_0[36]), "=f"(_tmem_load_0[37]), "=f"(_tmem_load_0[38]), "=f"(_tmem_load_0[39]), "=f"(_tmem_load_0[40]), "=f"(_tmem_load_0[41]), "=f"(_tmem_load_0[42]), "=f"(_tmem_load_0[43]), "=f"(_tmem_load_0[44]), "=f"(_tmem_load_0[45]), "=f"(_tmem_load_0[46]), "=f"(_tmem_load_0[47]), "=f"(_tmem_load_0[48]), "=f"(_tmem_load_0[49]), "=f"(_tmem_load_0[50]), "=f"(_tmem_load_0[51]), "=f"(_tmem_load_0[52]), "=f"(_tmem_load_0[53]), "=f"(_tmem_load_0[54]), "=f"(_tmem_load_0[55]), "=f"(_tmem_load_0[56]), "=f"(_tmem_load_0[57]), "=f"(_tmem_load_0[58]), "=f"(_tmem_load_0[59]), "=f"(_tmem_load_0[60]), "=f"(_tmem_load_0[61]), "=f"(_tmem_load_0[62]), "=f"(_tmem_load_0[63]) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + if (elect_sync()) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cluster.b64 _, [%0];" + :: "r"((score_empty_addr) & 0xFEFFFFFF) : "memory"); + } + if (valid_q != 0) { + #pragma unroll 2 + for (int col_base = 0; col_base < 64; col_base += 4) { + float dist_vec[4]; + dist_vec[0] = _tmem_load_0[col_base]; + dist_vec[1] = _tmem_load_0[col_base + 1]; + dist_vec[2] = _tmem_load_0[col_base + 2]; + dist_vec[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec[4]; + db_sq_vec[0] = smem_database_sq[col_base]; + db_sq_vec[1] = smem_database_sq[col_base + 1]; + db_sq_vec[2] = smem_database_sq[col_base + 2]; + db_sq_vec[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec)[_la], reinterpret_cast(db_sq_vec)[_la]); + float group_min = _t0[0]; + if (_t0[1] < group_min) { + group_min = _t0[1]; + } + if (_t0[2] < group_min) { + group_min = _t0[2]; + } + if (_t0[3] < group_min) { + group_min = _t0[3]; + } + if (group_min < best_d[TOP_K_MAX - 1]) { + #pragma unroll + for (int vec_col = 0; vec_col < 4; vec_col++) { + int db_idx = db_start + col_base + vec_col; + if (db_idx < M) { + float dist = _t0[vec_col]; + if (dist < best_d[TOP_K_MAX - 1]) { + best_d[TOP_K_MAX - 1] = dist; + best_i[TOP_K_MAX - 1] = db_idx; + #pragma unroll + for (int pos = TOP_K_MAX - 1; pos >= 1; pos--) { + if (best_d[pos] < best_d[pos - 1]) { + float tmp_d = best_d[pos - 1]; + int tmp_i = best_i[pos - 1]; + best_d[pos - 1] = best_d[pos]; + best_i[pos - 1] = best_i[pos]; + best_d[pos] = tmp_d; + best_i[pos] = tmp_i; + } + } + } + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + if (valid_q != 0) { + int out_base = ((split_idx_1 * B + batch_idx_1) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + if (out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = best_d[out_k]; + *((int*)(partial_indices + (out_base + out_k))) = best_i[out_k]; + } + } + } + } + } + } + + // Cleanup + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::2.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(128)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::2.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0013.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0013.cu new file mode 100644 index 00000000..08aa77e9 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0013.cu @@ -0,0 +1,84 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 32 +#define TOP_K_MAX 20 +#define SPLIT_COUNT 16 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_k20s16(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 32 + tid; + int stride = num_bids * 32; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int out_base = base_row; + int split_pos[SPLIT_COUNT]; + int split_base[SPLIT_COUNT]; + float cand_d[SPLIT_COUNT]; + int cand_i[SPLIT_COUNT]; + #pragma unroll + for (int split_idx = 0; split_idx < SPLIT_COUNT; split_idx++) { + split_pos[split_idx] = 0; + split_base[split_idx] = base_row + split_idx * split_stride; + cand_d[split_idx] = partial_dists[split_base[split_idx]]; + cand_i[split_idx] = partial_indices[split_base[split_idx]]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_d = cand_d[0]; + int best_i = cand_i[0]; + int best_split = 0; + #pragma unroll + for (int split_idx_1 = 1; split_idx_1 < SPLIT_COUNT; split_idx_1++) { + if (best_d > cand_d[split_idx_1]) { + best_d = cand_d[split_idx_1]; + best_i = cand_i[split_idx_1]; + best_split = split_idx_1; + } + } + *((float*)(out_dists + (out_base + out_k))) = best_d; + *((int*)(out_indices + (out_base + out_k))) = best_i; + split_pos[best_split] = split_pos[best_split] + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_pos = split_pos[best_split]; + int next_addr = split_base[best_split] + next_pos; + cand_d[best_split] = partial_dists[next_addr]; + cand_i[best_split] = partial_indices[next_addr]; + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0014.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0014.cu new file mode 100644 index 00000000..69c65325 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0014.cu @@ -0,0 +1,84 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 32 +#define TOP_K_MAX 8 +#define SPLIT_COUNT 16 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_195e_q1024k8s16(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 32 + tid; + int stride = num_bids * 32; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int out_base = base_row; + int split_pos[SPLIT_COUNT]; + int split_base[SPLIT_COUNT]; + float cand_d[SPLIT_COUNT]; + int cand_i[SPLIT_COUNT]; + #pragma unroll + for (int split_idx = 0; split_idx < SPLIT_COUNT; split_idx++) { + split_pos[split_idx] = 0; + split_base[split_idx] = base_row + split_idx * split_stride; + cand_d[split_idx] = partial_dists[split_base[split_idx]]; + cand_i[split_idx] = partial_indices[split_base[split_idx]]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_d = cand_d[0]; + int best_i = cand_i[0]; + int best_split = 0; + #pragma unroll + for (int split_idx_1 = 1; split_idx_1 < SPLIT_COUNT; split_idx_1++) { + if (best_d > cand_d[split_idx_1]) { + best_d = cand_d[split_idx_1]; + best_i = cand_i[split_idx_1]; + best_split = split_idx_1; + } + } + *((float*)(out_dists + (out_base + out_k))) = best_d; + *((int*)(out_indices + (out_base + out_k))) = best_i; + split_pos[best_split] = split_pos[best_split] + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_pos = split_pos[best_split]; + int next_addr = split_base[best_split] + next_pos; + cand_d[best_split] = partial_dists[next_addr]; + cand_i[best_split] = partial_indices[next_addr]; + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0015.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0015.cu new file mode 100644 index 00000000..67a83850 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0015.cu @@ -0,0 +1,733 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 128 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_STRIDE 32768 +#define SMEM_SMEM_DATABASE_OFF 33792 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_DATABASE_SQ_OFF 50176 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 50432 +#define THREADS 192 +#define BLOCK_Q 128 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_MAX 8 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16_cta2( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + ".reg .b32 m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "tcgen05.mma.cta_group::2.kind::f16 [%0], %1, %2, %3, {m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step_cg2( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, %3, " + "{m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], %1;\n\t" + "}\n" + :: "r"(mbar_addr), "h"(cta_mask) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x16(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x16.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15}, [%16];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ uint32_t smem_addr(const void* ptr) { + uint32_t addr; + asm("{\n\t" + ".reg .u64 u64addr;\n\t" + "cvta.to.shared.u64 u64addr, %1;\n\t" + "cvt.u32.u64 %0, u64addr;\n\t" + "}\n" : "=r"(addr) : "l"(ptr)); + return addr; +} + + +__device__ __forceinline__ uint32_t mapa_to_rank(uint32_t local_addr, uint32_t rank) { + uint32_t remote; + asm volatile("mapa.shared::cluster.u32 %0, %1, %2;" + : "=r"(remote) : "r"(local_addr), "r"(rank)); + return remote; +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem_cta2( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global" + ".mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .b16 lo, hi;\n\t" + "mov.b32 {lo, hi}, %1;\n\t" + "tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], lo;\n\t" + "}\n" + :: "r"(mbar_addr), "r"((uint32_t)cta_mask) : "memory"); +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_q4096_k8_fd9b_stage1_unordered_exact_prefill(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tile_pairs, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + const unsigned int clusters_x = gridDim.x / 2; + const unsigned int cluster_id = ((blockIdx.z * gridDim.y + blockIdx.y) * clusters_x) + blockIdx.x / 2; + const unsigned int num_clusters = clusters_x * gridDim.y * gridDim.z; + + int cta_rank; + asm volatile("mov.b32 %0, %%cluster_ctarank;" : "=r"(cta_rank)); + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 33792); + const int smem_database_addr = smem + 33792; + float* smem_database_sq = reinterpret_cast(smem_raw + 50176); + const int smem_database_sq_addr = smem + 50176; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 0, 2, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 16, 2, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=8 + mbarrier_init_pred(smem + 40, 8, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (128 columns, 128 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::2.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(128) : "memory"); + } + + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: load ---- + if (warp == 0) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 0) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx = cluster_id; work_idx < total_work; work_idx += num_clusters) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tile_pairs; + int q_tile_pair = query_work % num_q_tile_pairs; + int q_tile = q_tile_pair * 2 + cta_rank; + int off_q = q_tile * BLOCK_Q; + int global_q = batch_idx * Q + off_q; + int db_tile_start = split_idx * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((query_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(32768)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_query_addr), "l"(tmap_query), "r"(0), "r"(global_q), "r"(0), + "r"(((query_full_addr) & 0xFEFFFFFF)) : "memory"); + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int off_m = db_tile * BLOCK_M; + int global_m = batch_idx * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((database_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(16384)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_database_addr), "l"(tmap_database), "r"(0), "r"(global_m), "r"(0), + "r"(((database_full_addr) & 0xFEFFFFFF)) : "memory"); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 1) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + if (cta_rank == 0) { + #pragma unroll 1 + for (unsigned int _work_idx = cluster_id; _work_idx < total_work; _work_idx += num_clusters) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = (smem_query_addr >> 4) & 0x3FFF; + int _mma_b_lo_0 = (smem_database_addr >> 4) & 0x3FFF; + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\tmov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 270533776;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit_cg2_multicast(score_full_addr, (uint16_t)(3)); + elect_commit_cg2_multicast(database_empty_addr, (uint16_t)(3)); + } + elect_commit_cg2_multicast(query_empty_addr, (uint16_t)(3)); + } + } + } + // ---- Role: compute ---- + } else if (warp >= 2 && warp <= 5) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_1 = cluster_id; work_idx_1 < total_work; work_idx_1 += num_clusters) { + int split_idx_1 = work_idx_1 % (unsigned int)split_count; + int query_work_1 = work_idx_1 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tile_pairs; + int q_tile_pair_1 = query_work_1 % num_q_tile_pairs; + int q_tile_1 = q_tile_pair_1 * 2 + cta_rank; + int off_q_1 = q_tile_1 * BLOCK_Q; + int q_idx = off_q_1 + (warp % 4 * 32 + lane); + float q_sq_val = query_sq[batch_idx_1 * Q + q_idx]; + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + float worst_d = 3.4e+38f; + int worst_pos = 0; + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int db_start = db_tile_1 * BLOCK_M; + int db_sq_idx = db_start + (warp % 4 * 32 + lane); + if (warp % 4 * 32 + lane < BLOCK_M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx_1 * M + db_sq_idx]; + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(cta_rank * BLOCK_Q + (warp % 4 * 32 << 16) << 16); + float _tmem_load_0[64]; + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x64.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31, %32, %33, %34, %35, %36, %37, %38, %39, %40, %41, %42, %43, %44, %45, %46, %47, %48, %49, %50, %51, %52, %53, %54, %55, %56, %57, %58, %59, %60, %61, %62, %63}, [%64];" + : "=f"(_tmem_load_0[0]), "=f"(_tmem_load_0[1]), "=f"(_tmem_load_0[2]), "=f"(_tmem_load_0[3]), "=f"(_tmem_load_0[4]), "=f"(_tmem_load_0[5]), "=f"(_tmem_load_0[6]), "=f"(_tmem_load_0[7]), "=f"(_tmem_load_0[8]), "=f"(_tmem_load_0[9]), "=f"(_tmem_load_0[10]), "=f"(_tmem_load_0[11]), "=f"(_tmem_load_0[12]), "=f"(_tmem_load_0[13]), "=f"(_tmem_load_0[14]), "=f"(_tmem_load_0[15]), "=f"(_tmem_load_0[16]), "=f"(_tmem_load_0[17]), "=f"(_tmem_load_0[18]), "=f"(_tmem_load_0[19]), "=f"(_tmem_load_0[20]), "=f"(_tmem_load_0[21]), "=f"(_tmem_load_0[22]), "=f"(_tmem_load_0[23]), "=f"(_tmem_load_0[24]), "=f"(_tmem_load_0[25]), "=f"(_tmem_load_0[26]), "=f"(_tmem_load_0[27]), "=f"(_tmem_load_0[28]), "=f"(_tmem_load_0[29]), "=f"(_tmem_load_0[30]), "=f"(_tmem_load_0[31]), "=f"(_tmem_load_0[32]), "=f"(_tmem_load_0[33]), "=f"(_tmem_load_0[34]), "=f"(_tmem_load_0[35]), "=f"(_tmem_load_0[36]), "=f"(_tmem_load_0[37]), "=f"(_tmem_load_0[38]), "=f"(_tmem_load_0[39]), "=f"(_tmem_load_0[40]), "=f"(_tmem_load_0[41]), "=f"(_tmem_load_0[42]), "=f"(_tmem_load_0[43]), "=f"(_tmem_load_0[44]), "=f"(_tmem_load_0[45]), "=f"(_tmem_load_0[46]), "=f"(_tmem_load_0[47]), "=f"(_tmem_load_0[48]), "=f"(_tmem_load_0[49]), "=f"(_tmem_load_0[50]), "=f"(_tmem_load_0[51]), "=f"(_tmem_load_0[52]), "=f"(_tmem_load_0[53]), "=f"(_tmem_load_0[54]), "=f"(_tmem_load_0[55]), "=f"(_tmem_load_0[56]), "=f"(_tmem_load_0[57]), "=f"(_tmem_load_0[58]), "=f"(_tmem_load_0[59]), "=f"(_tmem_load_0[60]), "=f"(_tmem_load_0[61]), "=f"(_tmem_load_0[62]), "=f"(_tmem_load_0[63]) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + if (elect_sync()) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cluster.b64 _, [%0];" + :: "r"((score_empty_addr) & 0xFEFFFFFF) : "memory"); + } + if (local_db_tile_1 == 0) { + #pragma unroll + for (int col_base = 0; col_base < TOP_K_MAX; col_base += 4) { + float dist_vec[4]; + dist_vec[0] = _tmem_load_0[col_base]; + dist_vec[1] = _tmem_load_0[col_base + 1]; + dist_vec[2] = _tmem_load_0[col_base + 2]; + dist_vec[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec[4]; + db_sq_vec[0] = smem_database_sq[col_base]; + db_sq_vec[1] = smem_database_sq[col_base + 1]; + db_sq_vec[2] = smem_database_sq[col_base + 2]; + db_sq_vec[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec)[_la], reinterpret_cast(db_sq_vec)[_la]); + #pragma unroll + for (int vec_col = 0; vec_col < 4; vec_col++) { + int slot = col_base + vec_col; + best_d[slot] = _t0[vec_col]; + best_i[slot] = db_start + slot; + } + } + worst_d = best_d[0]; + worst_pos = 0; + #pragma unroll + for (int scan_pos = 1; scan_pos < TOP_K_MAX; scan_pos++) { + if (worst_d < best_d[scan_pos]) { + worst_d = best_d[scan_pos]; + worst_pos = scan_pos; + } + } + #pragma unroll 2 + for (int col_base_1 = TOP_K_MAX; col_base_1 < 64; col_base_1 += 4) { + float dist_vec_1[4]; + dist_vec_1[0] = _tmem_load_0[col_base_1]; + dist_vec_1[1] = _tmem_load_0[col_base_1 + 1]; + dist_vec_1[2] = _tmem_load_0[col_base_1 + 2]; + dist_vec_1[3] = _tmem_load_0[col_base_1 + 3]; + const float2 _fma_b2_2 = {-2.0f, -2.0f}; + const float2 _fma_c2_3 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec_1)[_lf], _fma_b2_2, _fma_c2_3); + float db_sq_vec_1[4]; + db_sq_vec_1[0] = smem_database_sq[col_base_1]; + db_sq_vec_1[1] = smem_database_sq[col_base_1 + 1]; + db_sq_vec_1[2] = smem_database_sq[col_base_1 + 2]; + db_sq_vec_1[3] = smem_database_sq[col_base_1 + 3]; + float _t1[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t1)[_la] = add_f32x2(reinterpret_cast(dist_vec_1)[_la], reinterpret_cast(db_sq_vec_1)[_la]); + float group_min = _t1[0]; + if (_t1[1] < group_min) { + group_min = _t1[1]; + } + if (_t1[2] < group_min) { + group_min = _t1[2]; + } + if (_t1[3] < group_min) { + group_min = _t1[3]; + } + if (group_min < worst_d) { + #pragma unroll + for (int vec_col_1 = 0; vec_col_1 < 4; vec_col_1++) { + int db_idx = db_start + col_base_1 + vec_col_1; + float dist = _t1[vec_col_1]; + if (dist < worst_d) { + best_d[worst_pos] = dist; + best_i[worst_pos] = db_idx; + worst_d = best_d[0]; + worst_pos = 0; + #pragma unroll + for (int scan_pos_1 = 1; scan_pos_1 < TOP_K_MAX; scan_pos_1++) { + if (worst_d < best_d[scan_pos_1]) { + worst_d = best_d[scan_pos_1]; + worst_pos = scan_pos_1; + } + } + } + } + } + } + } else { + #pragma unroll 2 + for (int col_base_2 = 0; col_base_2 < 64; col_base_2 += 4) { + float dist_vec_2[4]; + dist_vec_2[0] = _tmem_load_0[col_base_2]; + dist_vec_2[1] = _tmem_load_0[col_base_2 + 1]; + dist_vec_2[2] = _tmem_load_0[col_base_2 + 2]; + dist_vec_2[3] = _tmem_load_0[col_base_2 + 3]; + const float2 _fma_b2_4 = {-2.0f, -2.0f}; + const float2 _fma_c2_5 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec_2)[_lf], _fma_b2_4, _fma_c2_5); + float db_sq_vec_2[4]; + db_sq_vec_2[0] = smem_database_sq[col_base_2]; + db_sq_vec_2[1] = smem_database_sq[col_base_2 + 1]; + db_sq_vec_2[2] = smem_database_sq[col_base_2 + 2]; + db_sq_vec_2[3] = smem_database_sq[col_base_2 + 3]; + float _t2[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t2)[_la] = add_f32x2(reinterpret_cast(dist_vec_2)[_la], reinterpret_cast(db_sq_vec_2)[_la]); + float group_min_1 = _t2[0]; + if (_t2[1] < group_min_1) { + group_min_1 = _t2[1]; + } + if (_t2[2] < group_min_1) { + group_min_1 = _t2[2]; + } + if (_t2[3] < group_min_1) { + group_min_1 = _t2[3]; + } + if (group_min_1 < worst_d) { + #pragma unroll + for (int vec_col_2 = 0; vec_col_2 < 4; vec_col_2++) { + int db_idx_1 = db_start + col_base_2 + vec_col_2; + float dist_1 = _t2[vec_col_2]; + if (dist_1 < worst_d) { + best_d[worst_pos] = dist_1; + best_i[worst_pos] = db_idx_1; + worst_d = best_d[0]; + worst_pos = 0; + #pragma unroll + for (int scan_pos_2 = 1; scan_pos_2 < TOP_K_MAX; scan_pos_2++) { + if (worst_d < best_d[scan_pos_2]) { + worst_d = best_d[scan_pos_2]; + worst_pos = scan_pos_2; + } + } + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + int out_base = ((split_idx_1 * B + batch_idx_1) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + *((float*)(partial_dists + (out_base + out_k))) = best_d[out_k]; + *((int*)(partial_indices + (out_base + out_k))) = best_i[out_k]; + } + } + } + } + + // Cleanup + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::2.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(128)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::2.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0016.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0016.cu new file mode 100644 index 00000000..23a55499 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0016.cu @@ -0,0 +1,121 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 128 +#define TOP_K_MAX 8 +#define SPLIT_COUNT 4 + +#include + +extern "C" { + +__global__ __launch_bounds__(128, 1) void +kernel_knn_build_q4096_k8_fd9b_merge_s4_unordered_warp_select(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int row = bid * 4 + warp; + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int cand_k = lane; + if (row < total_queries) { + float d0 = 3.4e+38f; + float d1 = 3.4e+38f; + float d2 = 3.4e+38f; + float d3 = 3.4e+38f; + int i0 = -1; + int i1 = -1; + int i2 = -1; + int i3 = -1; + if (cand_k < TOP_K_MAX) { + d0 = partial_dists[base_row + cand_k]; + i0 = partial_indices[base_row + cand_k]; + int base1 = base_row + split_stride; + d1 = partial_dists[base1 + cand_k]; + i1 = partial_indices[base1 + cand_k]; + int base2 = base1 + split_stride; + d2 = partial_dists[base2 + cand_k]; + i2 = partial_indices[base2 + cand_k]; + int base3 = base2 + split_stride; + d3 = partial_dists[base3 + cand_k]; + i3 = partial_indices[base3 + cand_k]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float winner_d = d0; + int winner_i = i0; + int winner_src = 0; + if (d1 < winner_d) { + winner_d = d1; + winner_i = i1; + winner_src = 1; + } + if (d2 < winner_d) { + winner_d = d2; + winner_i = i2; + winner_src = 2; + } + if (d3 < winner_d) { + winner_d = d3; + winner_i = i3; + winner_src = 3; + } + float warp_min = winner_d; + float _warp_reduce_0 = warp_min; + #pragma unroll + for (int offset = 16; offset > 0; offset >>= 1) + _warp_reduce_0 = fminf(_warp_reduce_0, __shfl_xor_sync(0xFFFFFFFF, _warp_reduce_0, offset)); + warp_min = _warp_reduce_0; + unsigned int _vote_0 = __ballot_sync(0xFFFFFFFF, winner_d == warp_min); + int owner_ballot = _vote_0; + int _ffs_0 = __ffs(owner_ballot); + int winner_lane = _ffs_0 - 1; + int _shfl_0 = __shfl_sync(0xFFFFFFFF, winner_i, winner_lane); + winner_i = _shfl_0; + int _shfl_1 = __shfl_sync(0xFFFFFFFF, winner_src, winner_lane); + winner_src = _shfl_1; + if (lane == 0) { + *((float*)(out_dists + (base_row + out_k))) = warp_min; + *((int*)(out_indices + (base_row + out_k))) = winner_i; + } + if (lane == winner_lane) { + if (winner_src == 0) { + d0 = 3.4e+38f; + } else if (winner_src == 1) { + d1 = 3.4e+38f; + } else { + if (winner_src == 2) { + d2 = 3.4e+38f; + } else { + d3 = 3.4e+38f; + } + } + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0017.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0017.cu new file mode 100644 index 00000000..dbd95174 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0017.cu @@ -0,0 +1,591 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 64 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 16384 +#define SMEM_SMEM_QUERY_STRIDE 16384 +#define SMEM_SMEM_DATABASE_OFF 17408 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 8192 +#define SMEM_SMEM_DATABASE_STRIDE 8192 +#define SMEM_SMEM_DATABASE_SQ_OFF 25600 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 25856 +#define THREADS 192 +#define BLOCK_Q 128 +#define BLOCK_M 64 +#define TOP_K_MAX 10 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "tcgen05.mma.cta_group::1.kind::f16 [%0], %1, %2, %3, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, %3, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit(int mbar_addr) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];\n\t" + "}\n" + :: "r"(mbar_addr)); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x32(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x32.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15," + " %16, %17, %18, %19, %20, %21, %22, %23," + " %24, %25, %26, %27, %28, %29, %30, %31}, [%32];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]), + "=f"(dst[16]), "=f"(dst[17]), "=f"(dst[18]), "=f"(dst[19]), + "=f"(dst[20]), "=f"(dst[21]), "=f"(dst[22]), "=f"(dst[23]), + "=f"(dst[24]), "=f"(dst[25]), "=f"(dst[26]), "=f"(dst[27]), + "=f"(dst[28]), "=f"(dst[29]), "=f"(dst[30]), "=f"(dst[31]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ float max_noftz(float a, float b) { + float c; + asm("max.f32 %0, %1, %2;" : "=f"(c) : "f"(a), "f"(b)); + return c; +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cta.global" + ".mbarrier::complete_tx::bytes" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit(int mbar_addr) { + asm volatile( + "tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ uint32_t make_warp_uniform(uint32_t val) { + uint32_t result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1f, 0xffffffff;" + : "=r"(result) : "r"(val)); + return result; +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_dim_midk_73a9_d64_split_stage1(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tiles, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 17408); + const int smem_database_addr = smem + 17408; + float* smem_database_sq = reinterpret_cast(smem_raw + 25600); + const int smem_database_sq_addr = smem + 25600; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 0, 1, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 16, 1, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=128 + mbarrier_init_pred(smem + 40, 128, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (64 columns, 64 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::1.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(64) : "memory"); + } + + __syncthreads(); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: load ---- + if (warp == 0) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 0) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx = bid; work_idx < total_work; work_idx += num_bids) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tiles; + int q_tile = query_work % num_q_tiles; + int off_q = q_tile * BLOCK_Q; + int global_q = batch_idx * Q + off_q; + int db_tile_start = split_idx * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + mbarrier_arrive_expect_tx(query_full_addr, 16384); + tma_3d_gmem2smem(smem_query_addr, tmap_query, 0, global_q, 0, query_full_addr); + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int off_m = db_tile * BLOCK_M; + int global_m = batch_idx * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + mbarrier_arrive_expect_tx(database_full_addr, 8192); + tma_3d_gmem2smem(smem_database_addr, tmap_database, 0, global_m, 0, database_full_addr); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 1) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_1 = bid; work_idx_1 < total_work; work_idx_1 += num_bids) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = make_warp_uniform((smem_query_addr >> 4) & 0x3FFF); + int _mma_b_lo_0 = make_warp_uniform((smem_database_addr >> 4) & 0x3FFF); + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 135267472;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit(score_full_addr); + elect_commit(database_empty_addr); + } + elect_commit(query_empty_addr); + } + } + // ---- Role: compute ---- + } else if (warp >= 2 && warp <= 5) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = bid; work_idx_2 < total_work; work_idx_2 += num_bids) { + int split_idx_1 = work_idx_2 % (unsigned int)split_count; + int query_work_1 = work_idx_2 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tiles; + int q_tile_1 = query_work_1 % num_q_tiles; + int off_q_1 = q_tile_1 * BLOCK_Q; + int q_idx = off_q_1 + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx_1 * Q + q_idx]; + } + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int db_start = db_tile_1 * BLOCK_M; + int db_sq_idx = db_start + (warp % 4 * 32 + lane); + if (warp % 4 * 32 + lane < BLOCK_M) { + if (db_sq_idx < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx_1 * M + db_sq_idx]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 0.0f; + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(warp % 4 * 32 << 16); + float _tmem_load_0[64]; + tmem_ld_x32(&_tmem_load_0[0], cross_addr); + tmem_ld_x32(&_tmem_load_0[32], cross_addr + 32); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_arrive(score_empty_addr); + if (valid_q != 0) { + #pragma unroll 2 + for (int col_base = 0; col_base < 64; col_base += 4) { + float dist_vec[4]; + dist_vec[0] = _tmem_load_0[col_base]; + dist_vec[1] = _tmem_load_0[col_base + 1]; + dist_vec[2] = _tmem_load_0[col_base + 2]; + dist_vec[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec[4]; + db_sq_vec[0] = smem_database_sq[col_base]; + db_sq_vec[1] = smem_database_sq[col_base + 1]; + db_sq_vec[2] = smem_database_sq[col_base + 2]; + db_sq_vec[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec)[_la], reinterpret_cast(db_sq_vec)[_la]); + float group_min = _t0[0]; + if (_t0[1] < group_min) { + group_min = _t0[1]; + } + if (_t0[2] < group_min) { + group_min = _t0[2]; + } + if (_t0[3] < group_min) { + group_min = _t0[3]; + } + if (group_min < best_d[TOP_K_MAX - 1]) { + #pragma unroll + for (int vec_col = 0; vec_col < 4; vec_col++) { + int db_idx = db_start + col_base + vec_col; + if (db_idx < M) { + float dist = _t0[vec_col]; + float _max_0 = max_noftz(dist, 0.0f); + dist = _max_0; + if (dist < best_d[TOP_K_MAX - 1]) { + best_d[TOP_K_MAX - 1] = dist; + best_i[TOP_K_MAX - 1] = db_idx; + #pragma unroll + for (int pos = TOP_K_MAX - 1; pos >= 1; pos--) { + if (best_d[pos] < best_d[pos - 1]) { + float tmp_d = best_d[pos - 1]; + int tmp_i = best_i[pos - 1]; + best_d[pos - 1] = best_d[pos]; + best_i[pos - 1] = best_i[pos]; + best_d[pos] = tmp_d; + best_i[pos] = tmp_i; + } + } + } + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + if (valid_q != 0) { + int out_base = ((split_idx_1 * B + batch_idx_1) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + if (out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = best_d[out_k]; + *((int*)(partial_indices + (out_base + out_k))) = best_i[out_k]; + } + } + } + } + } + } + + // Cleanup + __syncthreads(); // barrier before TMEM dealloc + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::1.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(64)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::1.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0018.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0018.cu new file mode 100644 index 00000000..badf1a9c --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0018.cu @@ -0,0 +1,83 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 32 +#define TOP_K_MAX 10 +#define SPLIT_COUNT 8 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_d64_build_aa88_k10_merge_s8_rowbase_cache(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 32 + tid; + int stride = num_bids * 32; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int split_pos[SPLIT_COUNT]; + int split_base[SPLIT_COUNT]; + float cand_d[SPLIT_COUNT]; + int cand_i[SPLIT_COUNT]; + #pragma unroll + for (int split_idx = 0; split_idx < SPLIT_COUNT; split_idx++) { + split_pos[split_idx] = 0; + split_base[split_idx] = base_row + split_idx * split_stride; + cand_d[split_idx] = partial_dists[split_base[split_idx]]; + cand_i[split_idx] = partial_indices[split_base[split_idx]]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_d = cand_d[0]; + int best_i = cand_i[0]; + int best_split = 0; + #pragma unroll + for (int split_idx_1 = 1; split_idx_1 < SPLIT_COUNT; split_idx_1++) { + if (best_d > cand_d[split_idx_1]) { + best_d = cand_d[split_idx_1]; + best_i = cand_i[split_idx_1]; + best_split = split_idx_1; + } + } + *((float*)(out_dists + (base_row + out_k))) = best_d; + *((int*)(out_indices + (base_row + out_k))) = best_i; + split_pos[best_split] = split_pos[best_split] + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_pos = split_pos[best_split]; + int next_addr = split_base[best_split] + next_pos; + cand_d[best_split] = partial_dists[next_addr]; + cand_i[best_split] = partial_indices[next_addr]; + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0019.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0019.cu new file mode 100644 index 00000000..2a2a77da --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0019.cu @@ -0,0 +1,601 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 64 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 16384 +#define SMEM_SMEM_QUERY_STRIDE 16384 +#define SMEM_SMEM_DATABASE_OFF 17408 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 8192 +#define SMEM_SMEM_DATABASE_STRIDE 8192 +#define SMEM_SMEM_DATABASE_SQ_OFF 25600 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 25856 +#define THREADS 192 +#define BLOCK_Q 128 +#define BLOCK_M 64 +#define TOP_K_MAX 10 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "tcgen05.mma.cta_group::1.kind::f16 [%0], %1, %2, %3, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, %3, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit(int mbar_addr) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];\n\t" + "}\n" + :: "r"(mbar_addr)); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x16(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x16.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15}, [%16];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ float max_noftz(float a, float b) { + float c; + asm("max.f32 %0, %1, %2;" : "=f"(c) : "f"(a), "f"(b)); + return c; +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cta.global" + ".mbarrier::complete_tx::bytes" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit(int mbar_addr) { + asm volatile( + "tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ uint32_t make_warp_uniform(uint32_t val) { + uint32_t result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1f, 0xffffffff;" + : "=r"(result) : "r"(val)); + return result; +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_d64_q4096_c271_stage1_unordered_syncdrop(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tiles, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 17408); + const int smem_database_addr = smem + 17408; + float* smem_database_sq = reinterpret_cast(smem_raw + 25600); + const int smem_database_sq_addr = smem + 25600; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 0, 1, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 16, 1, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=128 + mbarrier_init_pred(smem + 40, 128, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (64 columns, 64 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::1.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(64) : "memory"); + } + + __syncthreads(); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: load ---- + if (warp == 0) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 0) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx = bid; work_idx < total_work; work_idx += num_bids) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tiles; + int q_tile = query_work % num_q_tiles; + int off_q = q_tile * BLOCK_Q; + int global_q = batch_idx * Q + off_q; + int db_tile_start = split_idx * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + mbarrier_arrive_expect_tx(query_full_addr, 16384); + tma_3d_gmem2smem(smem_query_addr, tmap_query, 0, global_q, 0, query_full_addr); + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int off_m = db_tile * BLOCK_M; + int global_m = batch_idx * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + mbarrier_arrive_expect_tx(database_full_addr, 8192); + tma_3d_gmem2smem(smem_database_addr, tmap_database, 0, global_m, 0, database_full_addr); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 1) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_1 = bid; work_idx_1 < total_work; work_idx_1 += num_bids) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = make_warp_uniform((smem_query_addr >> 4) & 0x3FFF); + int _mma_b_lo_0 = make_warp_uniform((smem_database_addr >> 4) & 0x3FFF); + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 135267472;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit(score_full_addr); + elect_commit(database_empty_addr); + } + elect_commit(query_empty_addr); + } + } + // ---- Role: compute ---- + } else if (warp >= 2 && warp <= 5) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = bid; work_idx_2 < total_work; work_idx_2 += num_bids) { + int split_idx_1 = work_idx_2 % (unsigned int)split_count; + int query_work_1 = work_idx_2 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tiles; + int q_tile_1 = query_work_1 % num_q_tiles; + int off_q_1 = q_tile_1 * BLOCK_Q; + int q_idx = off_q_1 + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx_1 * Q + q_idx]; + } + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + float worst_d = 3.4e+38f; + int worst_pos = 0; + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int db_start = db_tile_1 * BLOCK_M; + int db_sq_idx = db_start + (warp % 4 * 32 + lane); + if (warp % 4 * 32 + lane < BLOCK_M) { + if (db_sq_idx < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx_1 * M + db_sq_idx]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 0.0f; + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(warp % 4 * 32 << 16); + float _tmem_load_0[64]; + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x64.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31, %32, %33, %34, %35, %36, %37, %38, %39, %40, %41, %42, %43, %44, %45, %46, %47, %48, %49, %50, %51, %52, %53, %54, %55, %56, %57, %58, %59, %60, %61, %62, %63}, [%64];" + : "=f"(_tmem_load_0[0]), "=f"(_tmem_load_0[1]), "=f"(_tmem_load_0[2]), "=f"(_tmem_load_0[3]), "=f"(_tmem_load_0[4]), "=f"(_tmem_load_0[5]), "=f"(_tmem_load_0[6]), "=f"(_tmem_load_0[7]), "=f"(_tmem_load_0[8]), "=f"(_tmem_load_0[9]), "=f"(_tmem_load_0[10]), "=f"(_tmem_load_0[11]), "=f"(_tmem_load_0[12]), "=f"(_tmem_load_0[13]), "=f"(_tmem_load_0[14]), "=f"(_tmem_load_0[15]), "=f"(_tmem_load_0[16]), "=f"(_tmem_load_0[17]), "=f"(_tmem_load_0[18]), "=f"(_tmem_load_0[19]), "=f"(_tmem_load_0[20]), "=f"(_tmem_load_0[21]), "=f"(_tmem_load_0[22]), "=f"(_tmem_load_0[23]), "=f"(_tmem_load_0[24]), "=f"(_tmem_load_0[25]), "=f"(_tmem_load_0[26]), "=f"(_tmem_load_0[27]), "=f"(_tmem_load_0[28]), "=f"(_tmem_load_0[29]), "=f"(_tmem_load_0[30]), "=f"(_tmem_load_0[31]), "=f"(_tmem_load_0[32]), "=f"(_tmem_load_0[33]), "=f"(_tmem_load_0[34]), "=f"(_tmem_load_0[35]), "=f"(_tmem_load_0[36]), "=f"(_tmem_load_0[37]), "=f"(_tmem_load_0[38]), "=f"(_tmem_load_0[39]), "=f"(_tmem_load_0[40]), "=f"(_tmem_load_0[41]), "=f"(_tmem_load_0[42]), "=f"(_tmem_load_0[43]), "=f"(_tmem_load_0[44]), "=f"(_tmem_load_0[45]), "=f"(_tmem_load_0[46]), "=f"(_tmem_load_0[47]), "=f"(_tmem_load_0[48]), "=f"(_tmem_load_0[49]), "=f"(_tmem_load_0[50]), "=f"(_tmem_load_0[51]), "=f"(_tmem_load_0[52]), "=f"(_tmem_load_0[53]), "=f"(_tmem_load_0[54]), "=f"(_tmem_load_0[55]), "=f"(_tmem_load_0[56]), "=f"(_tmem_load_0[57]), "=f"(_tmem_load_0[58]), "=f"(_tmem_load_0[59]), "=f"(_tmem_load_0[60]), "=f"(_tmem_load_0[61]), "=f"(_tmem_load_0[62]), "=f"(_tmem_load_0[63]) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + mbarrier_arrive(score_empty_addr); + if (valid_q != 0) { + #pragma unroll 2 + for (int col_base = 0; col_base < 64; col_base += 4) { + float dist_vec[4]; + dist_vec[0] = _tmem_load_0[col_base]; + dist_vec[1] = _tmem_load_0[col_base + 1]; + dist_vec[2] = _tmem_load_0[col_base + 2]; + dist_vec[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec[4]; + db_sq_vec[0] = smem_database_sq[col_base]; + db_sq_vec[1] = smem_database_sq[col_base + 1]; + db_sq_vec[2] = smem_database_sq[col_base + 2]; + db_sq_vec[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec)[_la], reinterpret_cast(db_sq_vec)[_la]); + float group_min = _t0[0]; + if (_t0[1] < group_min) { + group_min = _t0[1]; + } + if (_t0[2] < group_min) { + group_min = _t0[2]; + } + if (_t0[3] < group_min) { + group_min = _t0[3]; + } + if (group_min < worst_d) { + #pragma unroll + for (int vec_col = 0; vec_col < 4; vec_col++) { + int db_idx = db_start + col_base + vec_col; + if (db_idx < M) { + float dist = _t0[vec_col]; + float _max_0 = max_noftz(dist, 0.0f); + dist = _max_0; + if (dist < worst_d) { + best_d[worst_pos] = dist; + best_i[worst_pos] = db_idx; + worst_d = best_d[0]; + worst_pos = 0; + #pragma unroll + for (int scan_pos = 1; scan_pos < TOP_K_MAX; scan_pos++) { + if (worst_d < best_d[scan_pos]) { + worst_d = best_d[scan_pos]; + worst_pos = scan_pos; + } + } + } + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + if (valid_q != 0) { + int out_base = ((split_idx_1 * B + batch_idx_1) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + int selected_pos = 0; + float selected_d = best_d[0]; + int selected_i = best_i[0]; + #pragma unroll + for (int scan_pos_1 = 1; scan_pos_1 < TOP_K_MAX; scan_pos_1++) { + if (selected_d > best_d[scan_pos_1]) { + selected_d = best_d[scan_pos_1]; + selected_i = best_i[scan_pos_1]; + selected_pos = scan_pos_1; + } + } + if (out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = selected_d; + *((int*)(partial_indices + (out_base + out_k))) = selected_i; + } + best_d[selected_pos] = 3.4e+38f; + best_i[selected_pos] = -1; + } + } + } + } + } + + // Cleanup + __syncthreads(); // barrier before TMEM dealloc + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::1.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(64)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::1.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0020.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0020.cu new file mode 100644 index 00000000..42ed1084 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0020.cu @@ -0,0 +1,83 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 32 +#define TOP_K_MAX 10 +#define SPLIT_COUNT 4 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_d64_build_aa88_k10_merge_s8_rowbase_cache_s4(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 32 + tid; + int stride = num_bids * 32; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int split_pos[SPLIT_COUNT]; + int split_base[SPLIT_COUNT]; + float cand_d[SPLIT_COUNT]; + int cand_i[SPLIT_COUNT]; + #pragma unroll + for (int split_idx = 0; split_idx < SPLIT_COUNT; split_idx++) { + split_pos[split_idx] = 0; + split_base[split_idx] = base_row + split_idx * split_stride; + cand_d[split_idx] = partial_dists[split_base[split_idx]]; + cand_i[split_idx] = partial_indices[split_base[split_idx]]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_d = cand_d[0]; + int best_i = cand_i[0]; + int best_split = 0; + #pragma unroll + for (int split_idx_1 = 1; split_idx_1 < SPLIT_COUNT; split_idx_1++) { + if (best_d > cand_d[split_idx_1]) { + best_d = cand_d[split_idx_1]; + best_i = cand_i[split_idx_1]; + best_split = split_idx_1; + } + } + *((float*)(out_dists + (base_row + out_k))) = best_d; + *((int*)(out_indices + (base_row + out_k))) = best_i; + split_pos[best_split] = split_pos[best_split] + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_pos = split_pos[best_split]; + int next_addr = split_base[best_split] + next_pos; + cand_d[best_split] = partial_dists[next_addr]; + cand_i[best_split] = partial_indices[next_addr]; + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0021.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0021.cu new file mode 100644 index 00000000..791a066a --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0021.cu @@ -0,0 +1,601 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 64 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 24576 +#define SMEM_SMEM_QUERY_STRIDE 24576 +#define SMEM_SMEM_DATABASE_OFF 25600 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 12288 +#define SMEM_SMEM_DATABASE_STRIDE 12288 +#define SMEM_SMEM_DATABASE_SQ_OFF 37888 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 38144 +#define THREADS 192 +#define BLOCK_Q 128 +#define BLOCK_M 64 +#define TOP_K_MAX 10 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "tcgen05.mma.cta_group::1.kind::f16 [%0], %1, %2, %3, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, %3, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit(int mbar_addr) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];\n\t" + "}\n" + :: "r"(mbar_addr)); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x32(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x32.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15," + " %16, %17, %18, %19, %20, %21, %22, %23," + " %24, %25, %26, %27, %28, %29, %30, %31}, [%32];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]), + "=f"(dst[16]), "=f"(dst[17]), "=f"(dst[18]), "=f"(dst[19]), + "=f"(dst[20]), "=f"(dst[21]), "=f"(dst[22]), "=f"(dst[23]), + "=f"(dst[24]), "=f"(dst[25]), "=f"(dst[26]), "=f"(dst[27]), + "=f"(dst[28]), "=f"(dst[29]), "=f"(dst[30]), "=f"(dst[31]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ float max_noftz(float a, float b) { + float c; + asm("max.f32 %0, %1, %2;" : "=f"(c) : "f"(a), "f"(b)); + return c; +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cta.global" + ".mbarrier::complete_tx::bytes" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit(int mbar_addr) { + asm volatile( + "tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ uint32_t make_warp_uniform(uint32_t val) { + uint32_t result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1f, 0xffffffff;" + : "=r"(result) : "r"(val)); + return result; +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_non128_frontier_4be7_d96exact_stage1(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tiles, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 25600); + const int smem_database_addr = smem + 25600; + float* smem_database_sq = reinterpret_cast(smem_raw + 37888); + const int smem_database_sq_addr = smem + 37888; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 0, 1, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 16, 1, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=128 + mbarrier_init_pred(smem + 40, 128, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (64 columns, 64 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::1.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(64) : "memory"); + } + + __syncthreads(); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: load ---- + if (warp == 0) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 0) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx = bid; work_idx < total_work; work_idx += num_bids) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tiles; + int q_tile = query_work % num_q_tiles; + int off_q = q_tile * BLOCK_Q; + int global_q = batch_idx * Q + off_q; + int db_tile_start = split_idx * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + mbarrier_arrive_expect_tx(query_full_addr, 24576); + tma_3d_gmem2smem(smem_query_addr, tmap_query, 0, global_q, 0, query_full_addr); + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int off_m = db_tile * BLOCK_M; + int global_m = batch_idx * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + mbarrier_arrive_expect_tx(database_full_addr, 12288); + tma_3d_gmem2smem(smem_database_addr, tmap_database, 0, global_m, 0, database_full_addr); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 1) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_1 = bid; work_idx_1 < total_work; work_idx_1 += num_bids) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = make_warp_uniform((smem_query_addr >> 4) & 0x3FFF); + int _mma_b_lo_0 = make_warp_uniform((smem_database_addr >> 4) & 0x3FFF); + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "" + "mov.b32 adhi, 0x80004020;\n\t" + "mov.b32 bdhi, 0x80004020;\n\t" + "mov.b32 id, 135267472;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 510;\n\t" + "add.u32 blo, blo, 254;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 510;\n\t" + "add.u32 blo, blo, 254;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit(score_full_addr); + elect_commit(database_empty_addr); + } + elect_commit(query_empty_addr); + } + } + // ---- Role: compute ---- + } else if (warp >= 2 && warp <= 5) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = bid; work_idx_2 < total_work; work_idx_2 += num_bids) { + int split_idx_1 = work_idx_2 % (unsigned int)split_count; + int query_work_1 = work_idx_2 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tiles; + int q_tile_1 = query_work_1 % num_q_tiles; + int off_q_1 = q_tile_1 * BLOCK_Q; + int q_idx = off_q_1 + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx_1 * Q + q_idx]; + } + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int db_start = db_tile_1 * BLOCK_M; + int db_sq_idx = db_start + (warp % 4 * 32 + lane); + if (warp % 4 * 32 + lane < BLOCK_M) { + if (db_sq_idx < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx_1 * M + db_sq_idx]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 0.0f; + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(warp % 4 * 32 << 16); + float _tmem_load_0[64]; + tmem_ld_x32(&_tmem_load_0[0], cross_addr); + tmem_ld_x32(&_tmem_load_0[32], cross_addr + 32); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_arrive(score_empty_addr); + if (valid_q != 0) { + #pragma unroll 2 + for (int col_base = 0; col_base < 64; col_base += 4) { + float dist_vec[4]; + dist_vec[0] = _tmem_load_0[col_base]; + dist_vec[1] = _tmem_load_0[col_base + 1]; + dist_vec[2] = _tmem_load_0[col_base + 2]; + dist_vec[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec[4]; + db_sq_vec[0] = smem_database_sq[col_base]; + db_sq_vec[1] = smem_database_sq[col_base + 1]; + db_sq_vec[2] = smem_database_sq[col_base + 2]; + db_sq_vec[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec)[_la], reinterpret_cast(db_sq_vec)[_la]); + float group_min = _t0[0]; + if (_t0[1] < group_min) { + group_min = _t0[1]; + } + if (_t0[2] < group_min) { + group_min = _t0[2]; + } + if (_t0[3] < group_min) { + group_min = _t0[3]; + } + if (group_min < best_d[TOP_K_MAX - 1]) { + #pragma unroll + for (int vec_col = 0; vec_col < 4; vec_col++) { + int db_idx = db_start + col_base + vec_col; + if (db_idx < M) { + float dist = _t0[vec_col]; + float _max_0 = max_noftz(dist, 0.0f); + dist = _max_0; + if (dist < best_d[TOP_K_MAX - 1]) { + best_d[TOP_K_MAX - 1] = dist; + best_i[TOP_K_MAX - 1] = db_idx; + #pragma unroll + for (int pos = TOP_K_MAX - 1; pos >= 1; pos--) { + if (best_d[pos] < best_d[pos - 1]) { + float tmp_d = best_d[pos - 1]; + int tmp_i = best_i[pos - 1]; + best_d[pos - 1] = best_d[pos]; + best_i[pos - 1] = best_i[pos]; + best_d[pos] = tmp_d; + best_i[pos] = tmp_i; + } + } + } + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + if (valid_q != 0) { + int out_base = ((split_idx_1 * B + batch_idx_1) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + if (out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = best_d[out_k]; + *((int*)(partial_indices + (out_base + out_k))) = best_i[out_k]; + } + } + } + } + } + } + + // Cleanup + __syncthreads(); // barrier before TMEM dealloc + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::1.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(64)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::1.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0022.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0022.cu new file mode 100644 index 00000000..35d57f11 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0022.cu @@ -0,0 +1,83 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 32 +#define TOP_K_MAX 10 +#define SPLIT_COUNT 8 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_non128_frontier_3d5a_k10_merge_s8_rowbase_cache(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 32 + tid; + int stride = num_bids * 32; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int split_pos[SPLIT_COUNT]; + int split_base[SPLIT_COUNT]; + float cand_d[SPLIT_COUNT]; + int cand_i[SPLIT_COUNT]; + #pragma unroll + for (int split_idx = 0; split_idx < SPLIT_COUNT; split_idx++) { + split_pos[split_idx] = 0; + split_base[split_idx] = base_row + split_idx * split_stride; + cand_d[split_idx] = partial_dists[split_base[split_idx]]; + cand_i[split_idx] = partial_indices[split_base[split_idx]]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_d = cand_d[0]; + int best_i = cand_i[0]; + int best_split = 0; + #pragma unroll + for (int split_idx_1 = 1; split_idx_1 < SPLIT_COUNT; split_idx_1++) { + if (best_d > cand_d[split_idx_1]) { + best_d = cand_d[split_idx_1]; + best_i = cand_i[split_idx_1]; + best_split = split_idx_1; + } + } + *((float*)(out_dists + (base_row + out_k))) = best_d; + *((int*)(out_indices + (base_row + out_k))) = best_i; + split_pos[best_split] = split_pos[best_split] + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_pos = split_pos[best_split]; + int next_addr = split_base[best_split] + next_pos; + cand_d[best_split] = partial_dists[next_addr]; + cand_i[best_split] = partial_indices[next_addr]; + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0023.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0023.cu new file mode 100644 index 00000000..27f09ef1 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0023.cu @@ -0,0 +1,688 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 64 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 65536 +#define SMEM_SMEM_QUERY_STRIDE 65536 +#define SMEM_SMEM_DATABASE_OFF 66560 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 32768 +#define SMEM_SMEM_DATABASE_STRIDE 32768 +#define SMEM_SMEM_QUERY_LO_OFF 1024 +#define SMEM_SMEM_QUERY_LO_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_LO_STRIDE 32768 +#define SMEM_SMEM_QUERY_HI_OFF 33792 +#define SMEM_SMEM_QUERY_HI_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_HI_STRIDE 32768 +#define SMEM_SMEM_DATABASE_LO_OFF 66560 +#define SMEM_SMEM_DATABASE_LO_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_LO_STRIDE 16384 +#define SMEM_SMEM_DATABASE_HI_OFF 82944 +#define SMEM_SMEM_DATABASE_HI_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_HI_STRIDE 16384 +#define SMEM_SMEM_DATABASE_SQ_OFF 99328 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 99584 +#define THREADS 192 +#define BLOCK_Q 128 +#define BLOCK_M 64 +#define TOP_K_MAX 10 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "tcgen05.mma.cta_group::1.kind::f16 [%0], %1, %2, %3, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, %3, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit(int mbar_addr) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];\n\t" + "}\n" + :: "r"(mbar_addr)); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x32(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x32.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15," + " %16, %17, %18, %19, %20, %21, %22, %23," + " %24, %25, %26, %27, %28, %29, %30, %31}, [%32];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]), + "=f"(dst[16]), "=f"(dst[17]), "=f"(dst[18]), "=f"(dst[19]), + "=f"(dst[20]), "=f"(dst[21]), "=f"(dst[22]), "=f"(dst[23]), + "=f"(dst[24]), "=f"(dst[25]), "=f"(dst[26]), "=f"(dst[27]), + "=f"(dst[28]), "=f"(dst[29]), "=f"(dst[30]), "=f"(dst[31]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ float max_noftz(float a, float b) { + float c; + asm("max.f32 %0, %1, %2;" : "=f"(c) : "f"(a), "f"(b)); + return c; +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cta.global" + ".mbarrier::complete_tx::bytes" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit(int mbar_addr) { + asm volatile( + "tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ uint32_t make_warp_uniform(uint32_t val) { + uint32_t result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1f, 0xffffffff;" + : "=r"(result) : "r"(val)); + return result; +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_dim_midk_df2f_d256_split_stage1(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tiles, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 66560); + const int smem_database_addr = smem + 66560; + __nv_bfloat16* smem_query_lo = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_lo_addr = smem + 1024; + __nv_bfloat16* smem_query_hi = reinterpret_cast<__nv_bfloat16*>(smem_raw + 33792); + const int smem_query_hi_addr = smem + 33792; + __nv_bfloat16* smem_database_lo = reinterpret_cast<__nv_bfloat16*>(smem_raw + 66560); + const int smem_database_lo_addr = smem + 66560; + __nv_bfloat16* smem_database_hi = reinterpret_cast<__nv_bfloat16*>(smem_raw + 82944); + const int smem_database_hi_addr = smem + 82944; + float* smem_database_sq = reinterpret_cast(smem_raw + 99328); + const int smem_database_sq_addr = smem + 99328; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 0, 1, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 16, 1, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=128 + mbarrier_init_pred(smem + 40, 128, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (64 columns, 64 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::1.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(64) : "memory"); + } + + __syncthreads(); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: load ---- + if (warp == 0) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 0) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx = bid; work_idx < total_work; work_idx += num_bids) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tiles; + int q_tile = query_work % num_q_tiles; + int off_q = q_tile * BLOCK_Q; + int global_q = batch_idx * Q + off_q; + int db_tile_start = split_idx * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + mbarrier_arrive_expect_tx(query_full_addr, 65536); + tma_3d_gmem2smem(smem_query_addr, tmap_query, 0, global_q, 0, query_full_addr); + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int off_m = db_tile * BLOCK_M; + int global_m = batch_idx * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + mbarrier_arrive_expect_tx(database_full_addr, 32768); + tma_3d_gmem2smem(smem_database_addr, tmap_database, 0, global_m, 0, database_full_addr); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 1) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_1 = bid; work_idx_1 < total_work; work_idx_1 += num_bids) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = make_warp_uniform((smem_query_lo_addr >> 4) & 0x3FFF); + int _mma_b_lo_0 = make_warp_uniform((smem_database_lo_addr >> 4) & 0x3FFF); + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 135267472;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_1 = make_warp_uniform((smem_query_hi_addr >> 4) & 0x3FFF); + int _mma_b_lo_1 = make_warp_uniform((smem_database_hi_addr >> 4) & 0x3FFF); + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 135267472;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_1), "r"(_mma_b_lo_1), "r"(tmem_cross), "r"(1)); + elect_commit(score_full_addr); + elect_commit(database_empty_addr); + } + elect_commit(query_empty_addr); + } + } + // ---- Role: compute ---- + } else if (warp >= 2 && warp <= 5) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = bid; work_idx_2 < total_work; work_idx_2 += num_bids) { + int split_idx_1 = work_idx_2 % (unsigned int)split_count; + int query_work_1 = work_idx_2 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tiles; + int q_tile_1 = query_work_1 % num_q_tiles; + int off_q_1 = q_tile_1 * BLOCK_Q; + int q_idx = off_q_1 + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx_1 * Q + q_idx]; + } + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int db_start = db_tile_1 * BLOCK_M; + int db_sq_idx = db_start + (warp % 4 * 32 + lane); + if (warp % 4 * 32 + lane < BLOCK_M) { + if (db_sq_idx < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx_1 * M + db_sq_idx]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 0.0f; + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(warp % 4 * 32 << 16); + float _tmem_load_0[64]; + tmem_ld_x32(&_tmem_load_0[0], cross_addr); + tmem_ld_x32(&_tmem_load_0[32], cross_addr + 32); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_arrive(score_empty_addr); + if (valid_q != 0) { + #pragma unroll 2 + for (int col_base = 0; col_base < 64; col_base += 4) { + float dist_vec[4]; + dist_vec[0] = _tmem_load_0[col_base]; + dist_vec[1] = _tmem_load_0[col_base + 1]; + dist_vec[2] = _tmem_load_0[col_base + 2]; + dist_vec[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec[4]; + db_sq_vec[0] = smem_database_sq[col_base]; + db_sq_vec[1] = smem_database_sq[col_base + 1]; + db_sq_vec[2] = smem_database_sq[col_base + 2]; + db_sq_vec[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec)[_la], reinterpret_cast(db_sq_vec)[_la]); + float group_min = _t0[0]; + if (_t0[1] < group_min) { + group_min = _t0[1]; + } + if (_t0[2] < group_min) { + group_min = _t0[2]; + } + if (_t0[3] < group_min) { + group_min = _t0[3]; + } + if (group_min < best_d[TOP_K_MAX - 1]) { + #pragma unroll + for (int vec_col = 0; vec_col < 4; vec_col++) { + int db_idx = db_start + col_base + vec_col; + if (db_idx < M) { + float dist = _t0[vec_col]; + float _max_0 = max_noftz(dist, 0.0f); + dist = _max_0; + if (dist < best_d[TOP_K_MAX - 1]) { + best_d[TOP_K_MAX - 1] = dist; + best_i[TOP_K_MAX - 1] = db_idx; + #pragma unroll + for (int pos = TOP_K_MAX - 1; pos >= 1; pos--) { + if (best_d[pos] < best_d[pos - 1]) { + float tmp_d = best_d[pos - 1]; + int tmp_i = best_i[pos - 1]; + best_d[pos - 1] = best_d[pos]; + best_i[pos - 1] = best_i[pos]; + best_d[pos] = tmp_d; + best_i[pos] = tmp_i; + } + } + } + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + if (valid_q != 0) { + int out_base = ((split_idx_1 * B + batch_idx_1) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + if (out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = best_d[out_k]; + *((int*)(partial_indices + (out_base + out_k))) = best_i[out_k]; + } + } + } + } + } + } + + // Cleanup + __syncthreads(); // barrier before TMEM dealloc + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::1.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(64)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::1.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0024.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0024.cu new file mode 100644 index 00000000..ecbad810 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0024.cu @@ -0,0 +1,55 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 256 +#define D_PAD 256 + +#include + +extern "C" { + +__global__ __launch_bounds__(256, 1) void +kernel_knn_build_non128_frontier_7231_pad_bf16_rows_d256(__nv_bfloat16* __restrict__ src, __nv_bfloat16* __restrict__ dst, int rows, int src_cols, int total_elems) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start = bid * 256 + tid; + int stride = num_bids * 256; + #pragma unroll 1 + for (int elem = start; elem < total_elems; elem += stride) { + int row = elem / D_PAD; + int col = elem - row * D_PAD; + float val = 0.0f; + if (row < rows) { + if (col < src_cols) { + val = (float)src[row * src_cols + col]; + } + } + *((__nv_bfloat16*)(dst + elem)) = __float2bfloat16_rn(val); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0025.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0025.cu new file mode 100644 index 00000000..c5ebaeec --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0025.cu @@ -0,0 +1,84 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 32 +#define TOP_K_MAX 10 +#define SPLIT_COUNT 16 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_common_d_56f3_d256_q1024_k10_merge_rowbase_cache_s16(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 32 + tid; + int stride = num_bids * 32; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int split_pos[SPLIT_COUNT]; + int split_base[SPLIT_COUNT]; + float cand_d[SPLIT_COUNT]; + int cand_i[SPLIT_COUNT]; + #pragma unroll + for (int split_idx = 0; split_idx < SPLIT_COUNT; split_idx++) { + split_pos[split_idx] = 0; + split_base[split_idx] = base_row + split_idx * split_stride; + cand_d[split_idx] = partial_dists[split_base[split_idx]]; + cand_i[split_idx] = partial_indices[split_base[split_idx]]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_d = cand_d[0]; + int best_i = cand_i[0]; + int best_split = 0; + #pragma unroll + for (int split_idx_1 = 1; split_idx_1 < SPLIT_COUNT; split_idx_1++) { + if (best_d > cand_d[split_idx_1]) { + best_d = cand_d[split_idx_1]; + best_i = cand_i[split_idx_1]; + best_split = split_idx_1; + } + } + float out_d = ((best_i == row) ? 0.0f : best_d); + *((float*)(out_dists + (base_row + out_k))) = out_d; + *((int*)(out_indices + (base_row + out_k))) = best_i; + split_pos[best_split] = split_pos[best_split] + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_pos = split_pos[best_split]; + int next_addr = split_base[best_split] + next_pos; + cand_d[best_split] = partial_dists[next_addr]; + cand_i[best_split] = partial_indices[next_addr]; + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0026.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0026.cu new file mode 100644 index 00000000..07e1c8fb --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0026.cu @@ -0,0 +1,667 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 64 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_STRIDE 32768 +#define SMEM_SMEM_DATABASE_OFF 33792 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_DATABASE_SQ_OFF 50176 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 50432 +#define THREADS 192 +#define FEATURE_CHUNKS 6 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "tcgen05.mma.cta_group::1.kind::f16 [%0], %1, %2, %3, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, %3, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit(int mbar_addr) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];\n\t" + "}\n" + :: "r"(mbar_addr)); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x16(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x16.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15}, [%16];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ float max_noftz(float a, float b) { + float c; + asm("max.f32 %0, %1, %2;" : "=f"(c) : "f"(a), "f"(b)); + return c; +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cta.global" + ".mbarrier::complete_tx::bytes" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit(int mbar_addr) { + asm volatile( + "tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ uint32_t make_warp_uniform(uint32_t val) { + uint32_t result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1f, 0xffffffff;" + : "=r"(result) : "r"(val)); + return result; +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_common_d768_build_eeff_m64split_stage1(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tiles, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 33792); + const int smem_database_addr = smem + 33792; + float* smem_database_sq = reinterpret_cast(smem_raw + 50176); + const int smem_database_sq_addr = smem + 50176; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 0, 1, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 16, 1, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=128 + mbarrier_init_pred(smem + 40, 128, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (64 columns, 64 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::1.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(64) : "memory"); + } + + __syncthreads(); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: compute ---- + if (warp <= 3) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx = bid; work_idx < total_work; work_idx += num_bids) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tiles; + int q_tile = query_work % num_q_tiles; + int off_q = q_tile * 128; + int q_idx = off_q + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx * Q + q_idx]; + } + float best_d[10]; + int best_i[10]; + #pragma unroll + for (int kk = 0; kk < 10; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + int db_tile_start = split_idx * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int db_start = db_tile * 64; + int db_sq_idx = db_start + (warp % 4 * 32 + lane); + if (warp % 4 * 32 + lane < 64) { + if (db_sq_idx < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx * M + db_sq_idx]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 3.4e+38f; + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(warp % 4 * 32 << 16); + float _tmem_load_0[64]; + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x64.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31, %32, %33, %34, %35, %36, %37, %38, %39, %40, %41, %42, %43, %44, %45, %46, %47, %48, %49, %50, %51, %52, %53, %54, %55, %56, %57, %58, %59, %60, %61, %62, %63}, [%64];" + : "=f"(_tmem_load_0[0]), "=f"(_tmem_load_0[1]), "=f"(_tmem_load_0[2]), "=f"(_tmem_load_0[3]), "=f"(_tmem_load_0[4]), "=f"(_tmem_load_0[5]), "=f"(_tmem_load_0[6]), "=f"(_tmem_load_0[7]), "=f"(_tmem_load_0[8]), "=f"(_tmem_load_0[9]), "=f"(_tmem_load_0[10]), "=f"(_tmem_load_0[11]), "=f"(_tmem_load_0[12]), "=f"(_tmem_load_0[13]), "=f"(_tmem_load_0[14]), "=f"(_tmem_load_0[15]), "=f"(_tmem_load_0[16]), "=f"(_tmem_load_0[17]), "=f"(_tmem_load_0[18]), "=f"(_tmem_load_0[19]), "=f"(_tmem_load_0[20]), "=f"(_tmem_load_0[21]), "=f"(_tmem_load_0[22]), "=f"(_tmem_load_0[23]), "=f"(_tmem_load_0[24]), "=f"(_tmem_load_0[25]), "=f"(_tmem_load_0[26]), "=f"(_tmem_load_0[27]), "=f"(_tmem_load_0[28]), "=f"(_tmem_load_0[29]), "=f"(_tmem_load_0[30]), "=f"(_tmem_load_0[31]), "=f"(_tmem_load_0[32]), "=f"(_tmem_load_0[33]), "=f"(_tmem_load_0[34]), "=f"(_tmem_load_0[35]), "=f"(_tmem_load_0[36]), "=f"(_tmem_load_0[37]), "=f"(_tmem_load_0[38]), "=f"(_tmem_load_0[39]), "=f"(_tmem_load_0[40]), "=f"(_tmem_load_0[41]), "=f"(_tmem_load_0[42]), "=f"(_tmem_load_0[43]), "=f"(_tmem_load_0[44]), "=f"(_tmem_load_0[45]), "=f"(_tmem_load_0[46]), "=f"(_tmem_load_0[47]), "=f"(_tmem_load_0[48]), "=f"(_tmem_load_0[49]), "=f"(_tmem_load_0[50]), "=f"(_tmem_load_0[51]), "=f"(_tmem_load_0[52]), "=f"(_tmem_load_0[53]), "=f"(_tmem_load_0[54]), "=f"(_tmem_load_0[55]), "=f"(_tmem_load_0[56]), "=f"(_tmem_load_0[57]), "=f"(_tmem_load_0[58]), "=f"(_tmem_load_0[59]), "=f"(_tmem_load_0[60]), "=f"(_tmem_load_0[61]), "=f"(_tmem_load_0[62]), "=f"(_tmem_load_0[63]) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_arrive(score_empty_addr); + if (valid_q != 0) { + #pragma unroll 1 + for (int col_base = 0; col_base < 64; col_base += 4) { + float dist_vec[4]; + dist_vec[0] = _tmem_load_0[col_base]; + dist_vec[1] = _tmem_load_0[col_base + 1]; + dist_vec[2] = _tmem_load_0[col_base + 2]; + dist_vec[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec[4]; + db_sq_vec[0] = smem_database_sq[col_base]; + db_sq_vec[1] = smem_database_sq[col_base + 1]; + db_sq_vec[2] = smem_database_sq[col_base + 2]; + db_sq_vec[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec)[_la], reinterpret_cast(db_sq_vec)[_la]); + float _max_0 = max_noftz(_t0[0], 0.0f); + float cand0_d = _max_0; + float _max_1 = max_noftz(_t0[1], 0.0f); + float cand1_d = _max_1; + int cand0_i = db_start + col_base; + int cand1_i = cand0_i + 1; + if (cand0_i >= M) { + cand0_d = 3.4e+38f; + } + if (cand1_i >= M) { + cand1_d = 3.4e+38f; + } + if (cand0_d < best_d[9]) { + best_d[9] = cand0_d; + best_i[9] = cand0_i; + #pragma unroll + for (int kk_1 = 8; kk_1 >= 0; kk_1--) { + float lower0_d = best_d[kk_1 + 1]; + int lower0_i = best_i[kk_1 + 1]; + float upper0_d = best_d[kk_1]; + int upper0_i = best_i[kk_1]; + int swap0_up = ((lower0_d < upper0_d) ? 1 : 0); + best_d[kk_1] = ((swap0_up != 0) ? lower0_d : upper0_d); + best_i[kk_1] = ((swap0_up != 0) ? lower0_i : upper0_i); + best_d[kk_1 + 1] = ((swap0_up != 0) ? upper0_d : lower0_d); + best_i[kk_1 + 1] = ((swap0_up != 0) ? upper0_i : lower0_i); + } + } + if (cand1_d < best_d[9]) { + best_d[9] = cand1_d; + best_i[9] = cand1_i; + #pragma unroll + for (int kk_2 = 8; kk_2 >= 0; kk_2--) { + float lower1_d = best_d[kk_2 + 1]; + int lower1_i = best_i[kk_2 + 1]; + float upper1_d = best_d[kk_2]; + int upper1_i = best_i[kk_2]; + int swap1_up = ((lower1_d < upper1_d) ? 1 : 0); + best_d[kk_2] = ((swap1_up != 0) ? lower1_d : upper1_d); + best_i[kk_2] = ((swap1_up != 0) ? lower1_i : upper1_i); + best_d[kk_2 + 1] = ((swap1_up != 0) ? upper1_d : lower1_d); + best_i[kk_2 + 1] = ((swap1_up != 0) ? upper1_i : lower1_i); + } + } + float _max_2 = max_noftz(_t0[2], 0.0f); + float cand2_d = _max_2; + float _max_3 = max_noftz(_t0[3], 0.0f); + float cand3_d = _max_3; + int cand2_i = cand0_i + 2; + int cand3_i = cand0_i + 3; + if (cand2_i >= M) { + cand2_d = 3.4e+38f; + } + if (cand3_i >= M) { + cand3_d = 3.4e+38f; + } + if (cand2_d < best_d[9]) { + best_d[9] = cand2_d; + best_i[9] = cand2_i; + #pragma unroll + for (int kk_3 = 8; kk_3 >= 0; kk_3--) { + float lower0_d_1 = best_d[kk_3 + 1]; + int lower0_i_1 = best_i[kk_3 + 1]; + float upper0_d_1 = best_d[kk_3]; + int upper0_i_1 = best_i[kk_3]; + int swap0_up_1 = ((lower0_d_1 < upper0_d_1) ? 1 : 0); + best_d[kk_3] = ((swap0_up_1 != 0) ? lower0_d_1 : upper0_d_1); + best_i[kk_3] = ((swap0_up_1 != 0) ? lower0_i_1 : upper0_i_1); + best_d[kk_3 + 1] = ((swap0_up_1 != 0) ? upper0_d_1 : lower0_d_1); + best_i[kk_3 + 1] = ((swap0_up_1 != 0) ? upper0_i_1 : lower0_i_1); + } + } + if (cand3_d < best_d[9]) { + best_d[9] = cand3_d; + best_i[9] = cand3_i; + #pragma unroll + for (int kk_4 = 8; kk_4 >= 0; kk_4--) { + float lower1_d_1 = best_d[kk_4 + 1]; + int lower1_i_1 = best_i[kk_4 + 1]; + float upper1_d_1 = best_d[kk_4]; + int upper1_i_1 = best_i[kk_4]; + int swap1_up_1 = ((lower1_d_1 < upper1_d_1) ? 1 : 0); + best_d[kk_4] = ((swap1_up_1 != 0) ? lower1_d_1 : upper1_d_1); + best_i[kk_4] = ((swap1_up_1 != 0) ? lower1_i_1 : upper1_i_1); + best_d[kk_4 + 1] = ((swap1_up_1 != 0) ? upper1_d_1 : lower1_d_1); + best_i[kk_4 + 1] = ((swap1_up_1 != 0) ? upper1_i_1 : lower1_i_1); + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + if (valid_q != 0) { + int out_base = ((split_idx * B + batch_idx) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < 10; out_k++) { + if (out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = best_d[out_k]; + *((int*)(partial_indices + (out_base + out_k))) = best_i[out_k]; + } + } + } + } + } + // ---- Role: load ---- + } else if (warp == 4) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 4) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx_1 = bid; work_idx_1 < total_work; work_idx_1 += num_bids) { + int split_idx_1 = work_idx_1 % (unsigned int)split_count; + int query_work_1 = work_idx_1 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tiles; + int q_tile_1 = query_work_1 % num_q_tiles; + int off_q_1 = q_tile_1 * 128; + int global_q = batch_idx_1 * Q + off_q_1; + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int off_m = db_tile_1 * 64; + int global_m = batch_idx_1 * M + off_m; + #pragma unroll + for (int feat_chunk = 0; feat_chunk < FEATURE_CHUNKS; feat_chunk++) { + int feature_coord = feat_chunk * 2; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + mbarrier_arrive_expect_tx(query_full_addr, 32768); + tma_3d_gmem2smem(smem_query_addr, tmap_query, 0, global_q, feature_coord, query_full_addr); + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + mbarrier_arrive_expect_tx(database_full_addr, 16384); + tma_3d_gmem2smem(smem_database_addr, tmap_database, 0, global_m, feature_coord, database_full_addr); + } + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 5) { + { // mma_main + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_database_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = bid; work_idx_2 < total_work; work_idx_2 += num_bids) { + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + #pragma unroll + for (int feat_chunk_1 = 0; feat_chunk_1 < FEATURE_CHUNKS; feat_chunk_1++) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = make_warp_uniform((smem_query_addr >> 4) & 0x3FFF); + int _mma_b_lo_0 = make_warp_uniform((smem_database_addr >> 4) & 0x3FFF); + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 135267472;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(((((feat_chunk_1 == 0) ? 1 : 0)) ? 0 : 1))); + asm volatile("tcgen05.fence::after_thread_sync;"); + elect_commit(query_empty_addr); + elect_commit(database_empty_addr); + } + elect_commit(score_full_addr); + } + } + } + } + + // Cleanup + __syncthreads(); // barrier before TMEM dealloc + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::1.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(64)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::1.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0027.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0027.cu new file mode 100644 index 00000000..35be9a12 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0027.cu @@ -0,0 +1,141 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define SMEM_GROUP_DISTS_OFF 0 +#define SMEM_GROUP_DISTS_STAGE_BYTES 512 +#define SMEM_GROUP_DISTS_STRIDE 512 +#define SMEM_GROUP_INDICES_OFF 512 +#define SMEM_GROUP_INDICES_STAGE_BYTES 512 +#define SMEM_GROUP_INDICES_STRIDE 512 +#define SMEM_TOTAL 1024 +#define THREADS 32 +#define TOP_K_MAX 10 +#define GROUP_COUNT 8 +#define GROUP_SPLITS 2 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_non128_frontier_4be7_d768fused_merge_s16g8_4be7_d768fused_v1(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + float* group_dists = reinterpret_cast(smem_raw + 0); + const int group_dists_addr = smem + 0; + int* group_indices = reinterpret_cast(smem_raw + 512); + const int group_indices_addr = smem + 512; + + // === Task calls (dependency order) === + int split_pos[GROUP_SPLITS]; + int split_base[GROUP_SPLITS]; + float group_cand_d[GROUP_SPLITS]; + int group_cand_i[GROUP_SPLITS]; + int final_pos[GROUP_COUNT]; + float final_cand_d[GROUP_COUNT]; + int final_cand_i[GROUP_COUNT]; + #pragma unroll 1 + for (int row = bid; row < total_queries; row += num_bids) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + if (tid < GROUP_COUNT) { + int group_idx = tid; + int source_split0 = group_idx * GROUP_SPLITS; + int shared_base = group_idx * TOP_K_MAX; + #pragma unroll + for (int local_split = 0; local_split < GROUP_SPLITS; local_split++) { + split_pos[local_split] = 0; + int split_id = source_split0 + local_split; + split_base[local_split] = base_row + split_id * split_stride; + group_cand_d[local_split] = partial_dists[split_base[local_split]]; + group_cand_i[local_split] = partial_indices[split_base[local_split]]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_d = group_cand_d[0]; + int best_i = group_cand_i[0]; + int best_split = 0; + #pragma unroll + for (int local_split_1 = 1; local_split_1 < GROUP_SPLITS; local_split_1++) { + if (best_d > group_cand_d[local_split_1]) { + best_d = group_cand_d[local_split_1]; + best_i = group_cand_i[local_split_1]; + best_split = local_split_1; + } + } + group_dists[shared_base + out_k] = best_d; + group_indices[shared_base + out_k] = best_i; + split_pos[best_split] = split_pos[best_split] + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_pos = split_pos[best_split]; + int next_addr = split_base[best_split] + next_pos; + group_cand_d[best_split] = partial_dists[next_addr]; + group_cand_i[best_split] = partial_indices[next_addr]; + } + } + } + __syncthreads(); + if (tid == 0) { + #pragma unroll + for (int group_idx_1 = 0; group_idx_1 < GROUP_COUNT; group_idx_1++) { + final_pos[group_idx_1] = 0; + int group_base = group_idx_1 * TOP_K_MAX; + final_cand_d[group_idx_1] = group_dists[group_base]; + final_cand_i[group_idx_1] = group_indices[group_base]; + } + #pragma unroll + for (int out_k_1 = 0; out_k_1 < TOP_K_MAX; out_k_1++) { + float best_d_1 = final_cand_d[0]; + int best_i_1 = final_cand_i[0]; + int best_group = 0; + #pragma unroll + for (int group_idx_2 = 1; group_idx_2 < GROUP_COUNT; group_idx_2++) { + if (best_d_1 > final_cand_d[group_idx_2]) { + best_d_1 = final_cand_d[group_idx_2]; + best_i_1 = final_cand_i[group_idx_2]; + best_group = group_idx_2; + } + } + *((float*)(out_dists + (base_row + out_k_1))) = best_d_1; + *((int*)(out_indices + (base_row + out_k_1))) = best_i_1; + final_pos[best_group] = final_pos[best_group] + 1; + if (out_k_1 + 1 < TOP_K_MAX) { + int next_pos_1 = final_pos[best_group]; + int next_addr_1 = best_group * TOP_K_MAX + next_pos_1; + final_cand_d[best_group] = group_dists[next_addr_1]; + final_cand_i[best_group] = group_indices[next_addr_1]; + } + } + } + __syncthreads(); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0028.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0028.cu new file mode 100644 index 00000000..8246e344 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0028.cu @@ -0,0 +1,621 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 64 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_STRIDE 32768 +#define SMEM_SMEM_DATABASE_OFF 33792 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_DATABASE_SQ_OFF 50176 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 50432 +#define THREADS 192 +#define BLOCK_Q 128 +#define BLOCK_M 64 +#define K_TILE 128 +#define TOP_K_MAX 10 +#define FEATURE_CHUNKS 8 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "tcgen05.mma.cta_group::1.kind::f16 [%0], %1, %2, %3, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, %3, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit(int mbar_addr) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];\n\t" + "}\n" + :: "r"(mbar_addr)); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x32(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x32.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15," + " %16, %17, %18, %19, %20, %21, %22, %23," + " %24, %25, %26, %27, %28, %29, %30, %31}, [%32];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]), + "=f"(dst[16]), "=f"(dst[17]), "=f"(dst[18]), "=f"(dst[19]), + "=f"(dst[20]), "=f"(dst[21]), "=f"(dst[22]), "=f"(dst[23]), + "=f"(dst[24]), "=f"(dst[25]), "=f"(dst[26]), "=f"(dst[27]), + "=f"(dst[28]), "=f"(dst[29]), "=f"(dst[30]), "=f"(dst[31]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ float max_noftz(float a, float b) { + float c; + asm("max.f32 %0, %1, %2;" : "=f"(c) : "f"(a), "f"(b)); + return c; +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cta.global" + ".mbarrier::complete_tx::bytes" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit(int mbar_addr) { + asm volatile( + "tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ uint32_t make_warp_uniform(uint32_t val) { + uint32_t result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1f, 0xffffffff;" + : "=r"(result) : "r"(val)); + return result; +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_non128_frontier_7231_stage1_d1024(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tiles, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 33792); + const int smem_database_addr = smem + 33792; + float* smem_database_sq = reinterpret_cast(smem_raw + 50176); + const int smem_database_sq_addr = smem + 50176; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 0, 1, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 16, 1, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=128 + mbarrier_init_pred(smem + 40, 128, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (64 columns, 64 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::1.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(64) : "memory"); + } + + __syncthreads(); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: load ---- + if (warp == 0) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 0) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx = bid; work_idx < total_work; work_idx += num_bids) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tiles; + int q_tile = query_work % num_q_tiles; + int off_q = q_tile * BLOCK_Q; + int global_q = batch_idx * Q + off_q; + int db_tile_start = split_idx * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int off_m = db_tile * BLOCK_M; + int global_m = batch_idx * M + off_m; + #pragma unroll + for (int feat_chunk = 0; feat_chunk < FEATURE_CHUNKS; feat_chunk++) { + int feature_coord = feat_chunk * 2; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + mbarrier_arrive_expect_tx(query_full_addr, 32768); + tma_3d_gmem2smem(smem_query_addr, tmap_query, 0, global_q, feature_coord, query_full_addr); + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + mbarrier_arrive_expect_tx(database_full_addr, 16384); + tma_3d_gmem2smem(smem_database_addr, tmap_database, 0, global_m, feature_coord, database_full_addr); + } + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 1) { + { // mma_main + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_database_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_1 = bid; work_idx_1 < total_work; work_idx_1 += num_bids) { + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + #pragma unroll + for (int feat_chunk_1 = 0; feat_chunk_1 < FEATURE_CHUNKS; feat_chunk_1++) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = make_warp_uniform((smem_query_addr >> 4) & 0x3FFF); + int _mma_b_lo_0 = make_warp_uniform((smem_database_addr >> 4) & 0x3FFF); + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 135267472;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(((((feat_chunk_1 == 0) ? 1 : 0)) ? 0 : 1))); + asm volatile("tcgen05.fence::after_thread_sync;"); + elect_commit(query_empty_addr); + elect_commit(database_empty_addr); + } + elect_commit(score_full_addr); + } + } + } + // ---- Role: compute ---- + } else if (warp >= 2 && warp <= 5) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = bid; work_idx_2 < total_work; work_idx_2 += num_bids) { + int split_idx_1 = work_idx_2 % (unsigned int)split_count; + int query_work_1 = work_idx_2 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tiles; + int q_tile_1 = query_work_1 % num_q_tiles; + int off_q_1 = q_tile_1 * BLOCK_Q; + int q_idx = off_q_1 + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx_1 * Q + q_idx]; + } + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int db_start = db_tile_1 * BLOCK_M; + int db_sq_idx = db_start + (warp % 4 * 32 + lane); + if (warp % 4 * 32 + lane < BLOCK_M) { + if (db_sq_idx < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx_1 * M + db_sq_idx]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 0.0f; + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(warp % 4 * 32 << 16); + float _tmem_load_0[64]; + tmem_ld_x32(&_tmem_load_0[0], cross_addr); + tmem_ld_x32(&_tmem_load_0[32], cross_addr + 32); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_arrive(score_empty_addr); + if (valid_q != 0) { + #pragma unroll 2 + for (int col_base = 0; col_base < 64; col_base += 4) { + float dist_vec[4]; + dist_vec[0] = _tmem_load_0[col_base]; + dist_vec[1] = _tmem_load_0[col_base + 1]; + dist_vec[2] = _tmem_load_0[col_base + 2]; + dist_vec[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec[4]; + db_sq_vec[0] = smem_database_sq[col_base]; + db_sq_vec[1] = smem_database_sq[col_base + 1]; + db_sq_vec[2] = smem_database_sq[col_base + 2]; + db_sq_vec[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec)[_la], reinterpret_cast(db_sq_vec)[_la]); + float group_min = _t0[0]; + if (_t0[1] < group_min) { + group_min = _t0[1]; + } + if (_t0[2] < group_min) { + group_min = _t0[2]; + } + if (_t0[3] < group_min) { + group_min = _t0[3]; + } + if (group_min < best_d[TOP_K_MAX - 1]) { + #pragma unroll + for (int vec_col = 0; vec_col < 4; vec_col++) { + int db_idx = db_start + col_base + vec_col; + if (db_idx < M) { + float dist = _t0[vec_col]; + float _max_0 = max_noftz(dist, 0.0f); + dist = _max_0; + if (dist < best_d[TOP_K_MAX - 1]) { + best_d[TOP_K_MAX - 1] = dist; + best_i[TOP_K_MAX - 1] = db_idx; + #pragma unroll + for (int pos = TOP_K_MAX - 1; pos >= 1; pos--) { + if (best_d[pos] < best_d[pos - 1]) { + float tmp_d = best_d[pos - 1]; + int tmp_i = best_i[pos - 1]; + best_d[pos - 1] = best_d[pos]; + best_i[pos - 1] = best_i[pos]; + best_d[pos] = tmp_d; + best_i[pos] = tmp_i; + } + } + } + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + if (valid_q != 0) { + int out_base = ((split_idx_1 * B + batch_idx_1) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + if (out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = best_d[out_k]; + *((int*)(partial_indices + (out_base + out_k))) = best_i[out_k]; + } + } + } + } + } + } + + // Cleanup + __syncthreads(); // barrier before TMEM dealloc + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::1.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(64)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::1.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0029.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0029.cu new file mode 100644 index 00000000..0e044f07 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0029.cu @@ -0,0 +1,84 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 32 +#define TOP_K_MAX 10 +#define SPLIT_COUNT 8 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_common_d_56f3_k10_merge_rowbase_cache_s8(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 32 + tid; + int stride = num_bids * 32; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int split_pos[SPLIT_COUNT]; + int split_base[SPLIT_COUNT]; + float cand_d[SPLIT_COUNT]; + int cand_i[SPLIT_COUNT]; + #pragma unroll + for (int split_idx = 0; split_idx < SPLIT_COUNT; split_idx++) { + split_pos[split_idx] = 0; + split_base[split_idx] = base_row + split_idx * split_stride; + cand_d[split_idx] = partial_dists[split_base[split_idx]]; + cand_i[split_idx] = partial_indices[split_base[split_idx]]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_d = cand_d[0]; + int best_i = cand_i[0]; + int best_split = 0; + #pragma unroll + for (int split_idx_1 = 1; split_idx_1 < SPLIT_COUNT; split_idx_1++) { + if (best_d > cand_d[split_idx_1]) { + best_d = cand_d[split_idx_1]; + best_i = cand_i[split_idx_1]; + best_split = split_idx_1; + } + } + float out_d = ((best_i == row) ? 0.0f : best_d); + *((float*)(out_dists + (base_row + out_k))) = out_d; + *((int*)(out_indices + (base_row + out_k))) = best_i; + split_pos[best_split] = split_pos[best_split] + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_pos = split_pos[best_split]; + int next_addr = split_base[best_split] + next_pos; + cand_d[best_split] = partial_dists[next_addr]; + cand_i[best_split] = partial_indices[next_addr]; + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0030.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0030.cu new file mode 100644 index 00000000..610ce526 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0030.cu @@ -0,0 +1,621 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 64 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_STRIDE 32768 +#define SMEM_SMEM_DATABASE_OFF 33792 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_DATABASE_SQ_OFF 50176 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 50432 +#define THREADS 192 +#define BLOCK_Q 128 +#define BLOCK_M 64 +#define K_TILE 128 +#define TOP_K_MAX 10 +#define FEATURE_CHUNKS 32 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "tcgen05.mma.cta_group::1.kind::f16 [%0], %1, %2, %3, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, %3, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit(int mbar_addr) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];\n\t" + "}\n" + :: "r"(mbar_addr)); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x32(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x32.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15," + " %16, %17, %18, %19, %20, %21, %22, %23," + " %24, %25, %26, %27, %28, %29, %30, %31}, [%32];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]), + "=f"(dst[16]), "=f"(dst[17]), "=f"(dst[18]), "=f"(dst[19]), + "=f"(dst[20]), "=f"(dst[21]), "=f"(dst[22]), "=f"(dst[23]), + "=f"(dst[24]), "=f"(dst[25]), "=f"(dst[26]), "=f"(dst[27]), + "=f"(dst[28]), "=f"(dst[29]), "=f"(dst[30]), "=f"(dst[31]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ float max_noftz(float a, float b) { + float c; + asm("max.f32 %0, %1, %2;" : "=f"(c) : "f"(a), "f"(b)); + return c; +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cta.global" + ".mbarrier::complete_tx::bytes" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit(int mbar_addr) { + asm volatile( + "tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ uint32_t make_warp_uniform(uint32_t val) { + uint32_t result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1f, 0xffffffff;" + : "=r"(result) : "r"(val)); + return result; +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_non128_frontier_7231_stage1_d4096(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tiles, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 33792); + const int smem_database_addr = smem + 33792; + float* smem_database_sq = reinterpret_cast(smem_raw + 50176); + const int smem_database_sq_addr = smem + 50176; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 0, 1, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 16, 1, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=128 + mbarrier_init_pred(smem + 40, 128, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (64 columns, 64 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::1.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(64) : "memory"); + } + + __syncthreads(); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: load ---- + if (warp == 0) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 0) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx = bid; work_idx < total_work; work_idx += num_bids) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tiles; + int q_tile = query_work % num_q_tiles; + int off_q = q_tile * BLOCK_Q; + int global_q = batch_idx * Q + off_q; + int db_tile_start = split_idx * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int off_m = db_tile * BLOCK_M; + int global_m = batch_idx * M + off_m; + #pragma unroll + for (int feat_chunk = 0; feat_chunk < FEATURE_CHUNKS; feat_chunk++) { + int feature_coord = feat_chunk * 2; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + mbarrier_arrive_expect_tx(query_full_addr, 32768); + tma_3d_gmem2smem(smem_query_addr, tmap_query, 0, global_q, feature_coord, query_full_addr); + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + mbarrier_arrive_expect_tx(database_full_addr, 16384); + tma_3d_gmem2smem(smem_database_addr, tmap_database, 0, global_m, feature_coord, database_full_addr); + } + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 1) { + { // mma_main + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_database_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_1 = bid; work_idx_1 < total_work; work_idx_1 += num_bids) { + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + #pragma unroll + for (int feat_chunk_1 = 0; feat_chunk_1 < FEATURE_CHUNKS; feat_chunk_1++) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = make_warp_uniform((smem_query_addr >> 4) & 0x3FFF); + int _mma_b_lo_0 = make_warp_uniform((smem_database_addr >> 4) & 0x3FFF); + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 135267472;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(((((feat_chunk_1 == 0) ? 1 : 0)) ? 0 : 1))); + asm volatile("tcgen05.fence::after_thread_sync;"); + elect_commit(query_empty_addr); + elect_commit(database_empty_addr); + } + elect_commit(score_full_addr); + } + } + } + // ---- Role: compute ---- + } else if (warp >= 2 && warp <= 5) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = bid; work_idx_2 < total_work; work_idx_2 += num_bids) { + int split_idx_1 = work_idx_2 % (unsigned int)split_count; + int query_work_1 = work_idx_2 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tiles; + int q_tile_1 = query_work_1 % num_q_tiles; + int off_q_1 = q_tile_1 * BLOCK_Q; + int q_idx = off_q_1 + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx_1 * Q + q_idx]; + } + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int db_start = db_tile_1 * BLOCK_M; + int db_sq_idx = db_start + (warp % 4 * 32 + lane); + if (warp % 4 * 32 + lane < BLOCK_M) { + if (db_sq_idx < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx_1 * M + db_sq_idx]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 0.0f; + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(warp % 4 * 32 << 16); + float _tmem_load_0[64]; + tmem_ld_x32(&_tmem_load_0[0], cross_addr); + tmem_ld_x32(&_tmem_load_0[32], cross_addr + 32); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_arrive(score_empty_addr); + if (valid_q != 0) { + #pragma unroll 2 + for (int col_base = 0; col_base < 64; col_base += 4) { + float dist_vec[4]; + dist_vec[0] = _tmem_load_0[col_base]; + dist_vec[1] = _tmem_load_0[col_base + 1]; + dist_vec[2] = _tmem_load_0[col_base + 2]; + dist_vec[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec[4]; + db_sq_vec[0] = smem_database_sq[col_base]; + db_sq_vec[1] = smem_database_sq[col_base + 1]; + db_sq_vec[2] = smem_database_sq[col_base + 2]; + db_sq_vec[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec)[_la], reinterpret_cast(db_sq_vec)[_la]); + float group_min = _t0[0]; + if (_t0[1] < group_min) { + group_min = _t0[1]; + } + if (_t0[2] < group_min) { + group_min = _t0[2]; + } + if (_t0[3] < group_min) { + group_min = _t0[3]; + } + if (group_min < best_d[TOP_K_MAX - 1]) { + #pragma unroll + for (int vec_col = 0; vec_col < 4; vec_col++) { + int db_idx = db_start + col_base + vec_col; + if (db_idx < M) { + float dist = _t0[vec_col]; + float _max_0 = max_noftz(dist, 0.0f); + dist = _max_0; + if (dist < best_d[TOP_K_MAX - 1]) { + best_d[TOP_K_MAX - 1] = dist; + best_i[TOP_K_MAX - 1] = db_idx; + #pragma unroll + for (int pos = TOP_K_MAX - 1; pos >= 1; pos--) { + if (best_d[pos] < best_d[pos - 1]) { + float tmp_d = best_d[pos - 1]; + int tmp_i = best_i[pos - 1]; + best_d[pos - 1] = best_d[pos]; + best_i[pos - 1] = best_i[pos]; + best_d[pos] = tmp_d; + best_i[pos] = tmp_i; + } + } + } + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + if (valid_q != 0) { + int out_base = ((split_idx_1 * B + batch_idx_1) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + if (out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = best_d[out_k]; + *((int*)(partial_indices + (out_base + out_k))) = best_i[out_k]; + } + } + } + } + } + } + + // Cleanup + __syncthreads(); // barrier before TMEM dealloc + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::1.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(64)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::1.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0031.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0031.cu new file mode 100644 index 00000000..1779ec8e --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0031.cu @@ -0,0 +1,735 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 64 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 81920 +#define SMEM_SMEM_QUERY_STRIDE 81920 +#define SMEM_SMEM_DATABASE_OFF 82944 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 40960 +#define SMEM_SMEM_DATABASE_STRIDE 40960 +#define SMEM_SMEM_QUERY_LO_OFF 1024 +#define SMEM_SMEM_QUERY_LO_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_LO_STRIDE 32768 +#define SMEM_SMEM_QUERY_MID_OFF 33792 +#define SMEM_SMEM_QUERY_MID_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_MID_STRIDE 32768 +#define SMEM_SMEM_QUERY_TAIL_OFF 66560 +#define SMEM_SMEM_QUERY_TAIL_STAGE_BYTES 16384 +#define SMEM_SMEM_QUERY_TAIL_STRIDE 16384 +#define SMEM_SMEM_DATABASE_LO_OFF 82944 +#define SMEM_SMEM_DATABASE_LO_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_LO_STRIDE 16384 +#define SMEM_SMEM_DATABASE_MID_OFF 99328 +#define SMEM_SMEM_DATABASE_MID_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_MID_STRIDE 16384 +#define SMEM_SMEM_DATABASE_TAIL_OFF 115712 +#define SMEM_SMEM_DATABASE_TAIL_STAGE_BYTES 8192 +#define SMEM_SMEM_DATABASE_TAIL_STRIDE 8192 +#define SMEM_SMEM_DATABASE_SQ_OFF 123904 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 124160 +#define THREADS 192 +#define BLOCK_Q 128 +#define BLOCK_M 64 +#define TOP_K_MAX 10 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "tcgen05.mma.cta_group::1.kind::f16 [%0], %1, %2, %3, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, %3, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit(int mbar_addr) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];\n\t" + "}\n" + :: "r"(mbar_addr)); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x32(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x32.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15," + " %16, %17, %18, %19, %20, %21, %22, %23," + " %24, %25, %26, %27, %28, %29, %30, %31}, [%32];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]), + "=f"(dst[16]), "=f"(dst[17]), "=f"(dst[18]), "=f"(dst[19]), + "=f"(dst[20]), "=f"(dst[21]), "=f"(dst[22]), "=f"(dst[23]), + "=f"(dst[24]), "=f"(dst[25]), "=f"(dst[26]), "=f"(dst[27]), + "=f"(dst[28]), "=f"(dst[29]), "=f"(dst[30]), "=f"(dst[31]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ float max_noftz(float a, float b) { + float c; + asm("max.f32 %0, %1, %2;" : "=f"(c) : "f"(a), "f"(b)); + return c; +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cta.global" + ".mbarrier::complete_tx::bytes" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit(int mbar_addr) { + asm volatile( + "tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ uint32_t make_warp_uniform(uint32_t val) { + uint32_t result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1f, 0xffffffff;" + : "=r"(result) : "r"(val)); + return result; +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_non128_frontier_8227_d320tail_stage1(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tiles, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 82944); + const int smem_database_addr = smem + 82944; + __nv_bfloat16* smem_query_lo = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_lo_addr = smem + 1024; + __nv_bfloat16* smem_query_mid = reinterpret_cast<__nv_bfloat16*>(smem_raw + 33792); + const int smem_query_mid_addr = smem + 33792; + __nv_bfloat16* smem_query_tail = reinterpret_cast<__nv_bfloat16*>(smem_raw + 66560); + const int smem_query_tail_addr = smem + 66560; + __nv_bfloat16* smem_database_lo = reinterpret_cast<__nv_bfloat16*>(smem_raw + 82944); + const int smem_database_lo_addr = smem + 82944; + __nv_bfloat16* smem_database_mid = reinterpret_cast<__nv_bfloat16*>(smem_raw + 99328); + const int smem_database_mid_addr = smem + 99328; + __nv_bfloat16* smem_database_tail = reinterpret_cast<__nv_bfloat16*>(smem_raw + 115712); + const int smem_database_tail_addr = smem + 115712; + float* smem_database_sq = reinterpret_cast(smem_raw + 123904); + const int smem_database_sq_addr = smem + 123904; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 0, 1, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 16, 1, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=128 + mbarrier_init_pred(smem + 40, 128, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (64 columns, 64 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::1.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(64) : "memory"); + } + + __syncthreads(); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: load ---- + if (warp == 0) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 0) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx = bid; work_idx < total_work; work_idx += num_bids) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tiles; + int q_tile = query_work % num_q_tiles; + int off_q = q_tile * BLOCK_Q; + int global_q = batch_idx * Q + off_q; + int db_tile_start = split_idx * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + mbarrier_arrive_expect_tx(query_full_addr, 81920); + tma_3d_gmem2smem(smem_query_addr, tmap_query, 0, global_q, 0, query_full_addr); + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int off_m = db_tile * BLOCK_M; + int global_m = batch_idx * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + mbarrier_arrive_expect_tx(database_full_addr, 40960); + tma_3d_gmem2smem(smem_database_addr, tmap_database, 0, global_m, 0, database_full_addr); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 1) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_1 = bid; work_idx_1 < total_work; work_idx_1 += num_bids) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = make_warp_uniform((smem_query_lo_addr >> 4) & 0x3FFF); + int _mma_b_lo_0 = make_warp_uniform((smem_database_lo_addr >> 4) & 0x3FFF); + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 135267472;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_1 = make_warp_uniform((smem_query_mid_addr >> 4) & 0x3FFF); + int _mma_b_lo_1 = make_warp_uniform((smem_database_mid_addr >> 4) & 0x3FFF); + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 135267472;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_1), "r"(_mma_b_lo_1), "r"(tmem_cross), "r"(1)); + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_2 = make_warp_uniform((smem_query_tail_addr >> 4) & 0x3FFF); + int _mma_b_lo_2 = make_warp_uniform((smem_database_tail_addr >> 4) & 0x3FFF); + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 135267472;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_2), "r"(_mma_b_lo_2), "r"(tmem_cross), "r"(1)); + elect_commit(score_full_addr); + elect_commit(database_empty_addr); + } + elect_commit(query_empty_addr); + } + } + // ---- Role: compute ---- + } else if (warp >= 2 && warp <= 5) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = bid; work_idx_2 < total_work; work_idx_2 += num_bids) { + int split_idx_1 = work_idx_2 % (unsigned int)split_count; + int query_work_1 = work_idx_2 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tiles; + int q_tile_1 = query_work_1 % num_q_tiles; + int off_q_1 = q_tile_1 * BLOCK_Q; + int q_idx = off_q_1 + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx_1 * Q + q_idx]; + } + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int db_start = db_tile_1 * BLOCK_M; + int db_sq_idx = db_start + (warp % 4 * 32 + lane); + if (warp % 4 * 32 + lane < BLOCK_M) { + if (db_sq_idx < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx_1 * M + db_sq_idx]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 0.0f; + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(warp % 4 * 32 << 16); + float _tmem_load_0[64]; + tmem_ld_x32(&_tmem_load_0[0], cross_addr); + tmem_ld_x32(&_tmem_load_0[32], cross_addr + 32); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_arrive(score_empty_addr); + if (valid_q != 0) { + #pragma unroll 2 + for (int col_base = 0; col_base < 64; col_base += 4) { + float dist_vec[4]; + dist_vec[0] = _tmem_load_0[col_base]; + dist_vec[1] = _tmem_load_0[col_base + 1]; + dist_vec[2] = _tmem_load_0[col_base + 2]; + dist_vec[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec[4]; + db_sq_vec[0] = smem_database_sq[col_base]; + db_sq_vec[1] = smem_database_sq[col_base + 1]; + db_sq_vec[2] = smem_database_sq[col_base + 2]; + db_sq_vec[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec)[_la], reinterpret_cast(db_sq_vec)[_la]); + float group_min = _t0[0]; + if (_t0[1] < group_min) { + group_min = _t0[1]; + } + if (_t0[2] < group_min) { + group_min = _t0[2]; + } + if (_t0[3] < group_min) { + group_min = _t0[3]; + } + if (group_min < best_d[TOP_K_MAX - 1]) { + #pragma unroll + for (int vec_col = 0; vec_col < 4; vec_col++) { + int db_idx = db_start + col_base + vec_col; + if (db_idx < M) { + float dist = _t0[vec_col]; + float _max_0 = max_noftz(dist, 0.0f); + dist = _max_0; + if (dist < best_d[TOP_K_MAX - 1]) { + best_d[TOP_K_MAX - 1] = dist; + best_i[TOP_K_MAX - 1] = db_idx; + #pragma unroll + for (int pos = TOP_K_MAX - 1; pos >= 1; pos--) { + if (best_d[pos] < best_d[pos - 1]) { + float tmp_d = best_d[pos - 1]; + int tmp_i = best_i[pos - 1]; + best_d[pos - 1] = best_d[pos]; + best_i[pos - 1] = best_i[pos]; + best_d[pos] = tmp_d; + best_i[pos] = tmp_i; + } + } + } + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + if (valid_q != 0) { + int out_base = ((split_idx_1 * B + batch_idx_1) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + if (out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = best_d[out_k]; + *((int*)(partial_indices + (out_base + out_k))) = best_i[out_k]; + } + } + } + } + } + } + + // Cleanup + __syncthreads(); // barrier before TMEM dealloc + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::1.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(64)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::1.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0032.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0032.cu new file mode 100644 index 00000000..627c714c --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0032.cu @@ -0,0 +1,611 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 64 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_STRIDE 32768 +#define SMEM_SMEM_DATABASE_OFF 33792 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_DATABASE_SQ_OFF 50176 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 50432 +#define THREADS 192 +#define BLOCK_Q 128 +#define BLOCK_M 64 +#define TOP_K_MAX 10 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "tcgen05.mma.cta_group::1.kind::f16 [%0], %1, %2, %3, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, %3, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit(int mbar_addr) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];\n\t" + "}\n" + :: "r"(mbar_addr)); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x32(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x32.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15," + " %16, %17, %18, %19, %20, %21, %22, %23," + " %24, %25, %26, %27, %28, %29, %30, %31}, [%32];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]), + "=f"(dst[16]), "=f"(dst[17]), "=f"(dst[18]), "=f"(dst[19]), + "=f"(dst[20]), "=f"(dst[21]), "=f"(dst[22]), "=f"(dst[23]), + "=f"(dst[24]), "=f"(dst[25]), "=f"(dst[26]), "=f"(dst[27]), + "=f"(dst[28]), "=f"(dst[29]), "=f"(dst[30]), "=f"(dst[31]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ float max_noftz(float a, float b) { + float c; + asm("max.f32 %0, %1, %2;" : "=f"(c) : "f"(a), "f"(b)); + return c; +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cta.global" + ".mbarrier::complete_tx::bytes" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit(int mbar_addr) { + asm volatile( + "tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ uint32_t make_warp_uniform(uint32_t val) { + uint32_t result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1f, 0xffffffff;" + : "=r"(result) : "r"(val)); + return result; +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_dim_midk_df2f_fp16_split_stage1(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tiles, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + __half* smem_query = reinterpret_cast<__half*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __half* smem_database = reinterpret_cast<__half*>(smem_raw + 33792); + const int smem_database_addr = smem + 33792; + float* smem_database_sq = reinterpret_cast(smem_raw + 50176); + const int smem_database_sq_addr = smem + 50176; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 0, 1, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 16, 1, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=128 + mbarrier_init_pred(smem + 40, 128, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (64 columns, 64 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::1.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(64) : "memory"); + } + + __syncthreads(); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: load ---- + if (warp == 0) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 0) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx = bid; work_idx < total_work; work_idx += num_bids) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tiles; + int q_tile = query_work % num_q_tiles; + int off_q = q_tile * BLOCK_Q; + int global_q = batch_idx * Q + off_q; + int db_tile_start = split_idx * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + mbarrier_arrive_expect_tx(query_full_addr, 32768); + tma_3d_gmem2smem(smem_query_addr, tmap_query, 0, global_q, 0, query_full_addr); + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int off_m = db_tile * BLOCK_M; + int global_m = batch_idx * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + mbarrier_arrive_expect_tx(database_full_addr, 16384); + tma_3d_gmem2smem(smem_database_addr, tmap_database, 0, global_m, 0, database_full_addr); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 1) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_1 = bid; work_idx_1 < total_work; work_idx_1 += num_bids) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = make_warp_uniform((smem_query_addr >> 4) & 0x3FFF); + int _mma_b_lo_0 = make_warp_uniform((smem_database_addr >> 4) & 0x3FFF); + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 135266320;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit(score_full_addr); + elect_commit(database_empty_addr); + } + elect_commit(query_empty_addr); + } + } + // ---- Role: compute ---- + } else if (warp >= 2 && warp <= 5) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = bid; work_idx_2 < total_work; work_idx_2 += num_bids) { + int split_idx_1 = work_idx_2 % (unsigned int)split_count; + int query_work_1 = work_idx_2 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tiles; + int q_tile_1 = query_work_1 % num_q_tiles; + int off_q_1 = q_tile_1 * BLOCK_Q; + int q_idx = off_q_1 + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx_1 * Q + q_idx]; + } + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int db_start = db_tile_1 * BLOCK_M; + int db_sq_idx = db_start + (warp % 4 * 32 + lane); + if (warp % 4 * 32 + lane < BLOCK_M) { + if (db_sq_idx < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx_1 * M + db_sq_idx]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 0.0f; + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(warp % 4 * 32 << 16); + float _tmem_load_0[64]; + tmem_ld_x32(&_tmem_load_0[0], cross_addr); + tmem_ld_x32(&_tmem_load_0[32], cross_addr + 32); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_arrive(score_empty_addr); + if (valid_q != 0) { + #pragma unroll 2 + for (int col_base = 0; col_base < 64; col_base += 4) { + float dist_vec[4]; + dist_vec[0] = _tmem_load_0[col_base]; + dist_vec[1] = _tmem_load_0[col_base + 1]; + dist_vec[2] = _tmem_load_0[col_base + 2]; + dist_vec[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec[4]; + db_sq_vec[0] = smem_database_sq[col_base]; + db_sq_vec[1] = smem_database_sq[col_base + 1]; + db_sq_vec[2] = smem_database_sq[col_base + 2]; + db_sq_vec[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec)[_la], reinterpret_cast(db_sq_vec)[_la]); + float group_min = _t0[0]; + if (_t0[1] < group_min) { + group_min = _t0[1]; + } + if (_t0[2] < group_min) { + group_min = _t0[2]; + } + if (_t0[3] < group_min) { + group_min = _t0[3]; + } + if (group_min < best_d[TOP_K_MAX - 1]) { + #pragma unroll + for (int vec_col = 0; vec_col < 4; vec_col++) { + int db_idx = db_start + col_base + vec_col; + if (db_idx < M) { + float dist = _t0[vec_col]; + float _max_0 = max_noftz(dist, 0.0f); + dist = _max_0; + if (dist < best_d[TOP_K_MAX - 1]) { + best_d[TOP_K_MAX - 1] = dist; + best_i[TOP_K_MAX - 1] = db_idx; + #pragma unroll + for (int pos = TOP_K_MAX - 1; pos >= 1; pos--) { + if (best_d[pos] < best_d[pos - 1]) { + float tmp_d = best_d[pos - 1]; + int tmp_i = best_i[pos - 1]; + best_d[pos - 1] = best_d[pos]; + best_i[pos - 1] = best_i[pos]; + best_d[pos] = tmp_d; + best_i[pos] = tmp_i; + } + } + } + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + if (valid_q != 0) { + int out_base = ((split_idx_1 * B + batch_idx_1) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + if (out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = best_d[out_k]; + *((int*)(partial_indices + (out_base + out_k))) = best_i[out_k]; + } + } + } + } + } + } + + // Cleanup + __syncthreads(); // barrier before TMEM dealloc + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::1.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(64)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::1.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0033.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0033.cu new file mode 100644 index 00000000..6314e321 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0033.cu @@ -0,0 +1,83 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 32 +#define TOP_K_MAX 10 +#define SPLIT_COUNT 8 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_fp16_d128_lowfloor_fd37_k10_s8_merge(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 32 + tid; + int stride = num_bids * 32; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int split_pos[SPLIT_COUNT]; + int split_base[SPLIT_COUNT]; + float cand_d[SPLIT_COUNT]; + int cand_i[SPLIT_COUNT]; + #pragma unroll + for (int split_idx = 0; split_idx < SPLIT_COUNT; split_idx++) { + split_pos[split_idx] = 0; + split_base[split_idx] = base_row + split_idx * split_stride; + cand_d[split_idx] = partial_dists[split_base[split_idx]]; + cand_i[split_idx] = partial_indices[split_base[split_idx]]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_d = cand_d[0]; + int best_i = cand_i[0]; + int best_split = 0; + #pragma unroll + for (int split_idx_1 = 1; split_idx_1 < SPLIT_COUNT; split_idx_1++) { + if (best_d > cand_d[split_idx_1]) { + best_d = cand_d[split_idx_1]; + best_i = cand_i[split_idx_1]; + best_split = split_idx_1; + } + } + *((float*)(out_dists + (base_row + out_k))) = best_d; + *((int*)(out_indices + (base_row + out_k))) = best_i; + split_pos[best_split] = split_pos[best_split] + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_pos = split_pos[best_split]; + int next_addr = split_base[best_split] + next_pos; + cand_d[best_split] = partial_dists[next_addr]; + cand_i[best_split] = partial_indices[next_addr]; + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0034.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0034.cu new file mode 100644 index 00000000..2605dc5f --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0034.cu @@ -0,0 +1,657 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 128 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_STRIDE 32768 +#define SMEM_SMEM_DATABASE_OFF 33792 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_DATABASE_SQ_OFF 50176 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 50432 +#define THREADS 192 +#define BLOCK_Q 128 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_MAX 11 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16_cta2( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + ".reg .b32 m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "tcgen05.mma.cta_group::2.kind::f16 [%0], %1, %2, %3, {m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step_cg2( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, %3, " + "{m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], %1;\n\t" + "}\n" + :: "r"(mbar_addr), "h"(cta_mask) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x16(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x16.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15}, [%16];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ uint32_t smem_addr(const void* ptr) { + uint32_t addr; + asm("{\n\t" + ".reg .u64 u64addr;\n\t" + "cvta.to.shared.u64 u64addr, %1;\n\t" + "cvt.u32.u64 %0, u64addr;\n\t" + "}\n" : "=r"(addr) : "l"(ptr)); + return addr; +} + + +__device__ __forceinline__ uint32_t mapa_to_rank(uint32_t local_addr, uint32_t rank) { + uint32_t remote; + asm volatile("mapa.shared::cluster.u32 %0, %1, %2;" + : "=r"(remote) : "r"(local_addr), "r"(rank)); + return remote; +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem_cta2( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global" + ".mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .b16 lo, hi;\n\t" + "mov.b32 {lo, hi}, %1;\n\t" + "tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], lo;\n\t" + "}\n" + :: "r"(mbar_addr), "r"((uint32_t)cta_mask) : "memory"); +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_e080k11exact(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tile_pairs, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + const unsigned int clusters_x = gridDim.x / 2; + const unsigned int cluster_id = ((blockIdx.z * gridDim.y + blockIdx.y) * clusters_x) + blockIdx.x / 2; + const unsigned int num_clusters = clusters_x * gridDim.y * gridDim.z; + + int cta_rank; + asm volatile("mov.b32 %0, %%cluster_ctarank;" : "=r"(cta_rank)); + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 33792); + const int smem_database_addr = smem + 33792; + float* smem_database_sq = reinterpret_cast(smem_raw + 50176); + const int smem_database_sq_addr = smem + 50176; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 0, 2, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 16, 2, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=8 + mbarrier_init_pred(smem + 40, 8, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (128 columns, 128 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::2.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(128) : "memory"); + } + + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: load ---- + if (warp == 0) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 0) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx = cluster_id; work_idx < total_work; work_idx += num_clusters) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tile_pairs; + int q_tile_pair = query_work % num_q_tile_pairs; + int q_tile = q_tile_pair * 2 + cta_rank; + int off_q = q_tile * BLOCK_Q; + int global_q = batch_idx * Q + off_q; + int db_tile_start = split_idx * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((query_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(32768)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_query_addr), "l"(tmap_query), "r"(0), "r"(global_q), "r"(0), + "r"(((query_full_addr) & 0xFEFFFFFF)) : "memory"); + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int off_m = db_tile * BLOCK_M; + int global_m = batch_idx * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((database_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(16384)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_database_addr), "l"(tmap_database), "r"(0), "r"(global_m), "r"(0), + "r"(((database_full_addr) & 0xFEFFFFFF)) : "memory"); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 1) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + if (cta_rank == 0) { + #pragma unroll 1 + for (unsigned int work_idx_1 = cluster_id; work_idx_1 < total_work; work_idx_1 += num_clusters) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = (smem_query_addr >> 4) & 0x3FFF; + int _mma_b_lo_0 = (smem_database_addr >> 4) & 0x3FFF; + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\tmov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 270533776;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit_cg2_multicast(score_full_addr, (uint16_t)(3)); + elect_commit_cg2_multicast(database_empty_addr, (uint16_t)(3)); + } + elect_commit_cg2_multicast(query_empty_addr, (uint16_t)(3)); + } + } + } + // ---- Role: compute ---- + } else if (warp >= 2 && warp <= 5) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = cluster_id; work_idx_2 < total_work; work_idx_2 += num_clusters) { + int split_idx_1 = work_idx_2 % (unsigned int)split_count; + int query_work_1 = work_idx_2 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tile_pairs; + int q_tile_pair_1 = query_work_1 % num_q_tile_pairs; + int q_tile_1 = q_tile_pair_1 * 2 + cta_rank; + int off_q_1 = q_tile_1 * BLOCK_Q; + int q_idx = off_q_1 + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx_1 * Q + q_idx]; + } + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int db_start = db_tile_1 * BLOCK_M; + int db_sq_idx = db_start + (warp % 4 * 32 + lane); + if (warp % 4 * 32 + lane < BLOCK_M) { + if (db_sq_idx < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx_1 * M + db_sq_idx]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 0.0f; + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(cta_rank * BLOCK_Q + (warp % 4 * 32 << 16) << 16); + float _tmem_load_0[64]; + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x64.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31, %32, %33, %34, %35, %36, %37, %38, %39, %40, %41, %42, %43, %44, %45, %46, %47, %48, %49, %50, %51, %52, %53, %54, %55, %56, %57, %58, %59, %60, %61, %62, %63}, [%64];" + : "=f"(_tmem_load_0[0]), "=f"(_tmem_load_0[1]), "=f"(_tmem_load_0[2]), "=f"(_tmem_load_0[3]), "=f"(_tmem_load_0[4]), "=f"(_tmem_load_0[5]), "=f"(_tmem_load_0[6]), "=f"(_tmem_load_0[7]), "=f"(_tmem_load_0[8]), "=f"(_tmem_load_0[9]), "=f"(_tmem_load_0[10]), "=f"(_tmem_load_0[11]), "=f"(_tmem_load_0[12]), "=f"(_tmem_load_0[13]), "=f"(_tmem_load_0[14]), "=f"(_tmem_load_0[15]), "=f"(_tmem_load_0[16]), "=f"(_tmem_load_0[17]), "=f"(_tmem_load_0[18]), "=f"(_tmem_load_0[19]), "=f"(_tmem_load_0[20]), "=f"(_tmem_load_0[21]), "=f"(_tmem_load_0[22]), "=f"(_tmem_load_0[23]), "=f"(_tmem_load_0[24]), "=f"(_tmem_load_0[25]), "=f"(_tmem_load_0[26]), "=f"(_tmem_load_0[27]), "=f"(_tmem_load_0[28]), "=f"(_tmem_load_0[29]), "=f"(_tmem_load_0[30]), "=f"(_tmem_load_0[31]), "=f"(_tmem_load_0[32]), "=f"(_tmem_load_0[33]), "=f"(_tmem_load_0[34]), "=f"(_tmem_load_0[35]), "=f"(_tmem_load_0[36]), "=f"(_tmem_load_0[37]), "=f"(_tmem_load_0[38]), "=f"(_tmem_load_0[39]), "=f"(_tmem_load_0[40]), "=f"(_tmem_load_0[41]), "=f"(_tmem_load_0[42]), "=f"(_tmem_load_0[43]), "=f"(_tmem_load_0[44]), "=f"(_tmem_load_0[45]), "=f"(_tmem_load_0[46]), "=f"(_tmem_load_0[47]), "=f"(_tmem_load_0[48]), "=f"(_tmem_load_0[49]), "=f"(_tmem_load_0[50]), "=f"(_tmem_load_0[51]), "=f"(_tmem_load_0[52]), "=f"(_tmem_load_0[53]), "=f"(_tmem_load_0[54]), "=f"(_tmem_load_0[55]), "=f"(_tmem_load_0[56]), "=f"(_tmem_load_0[57]), "=f"(_tmem_load_0[58]), "=f"(_tmem_load_0[59]), "=f"(_tmem_load_0[60]), "=f"(_tmem_load_0[61]), "=f"(_tmem_load_0[62]), "=f"(_tmem_load_0[63]) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + if (elect_sync()) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cluster.b64 _, [%0];" + :: "r"((score_empty_addr) & 0xFEFFFFFF) : "memory"); + } + if (valid_q != 0) { + #pragma unroll 2 + for (int col_base = 0; col_base < 64; col_base += 4) { + float dist_vec[4]; + dist_vec[0] = _tmem_load_0[col_base]; + dist_vec[1] = _tmem_load_0[col_base + 1]; + dist_vec[2] = _tmem_load_0[col_base + 2]; + dist_vec[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec[4]; + db_sq_vec[0] = smem_database_sq[col_base]; + db_sq_vec[1] = smem_database_sq[col_base + 1]; + db_sq_vec[2] = smem_database_sq[col_base + 2]; + db_sq_vec[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec)[_la], reinterpret_cast(db_sq_vec)[_la]); + float group_min = _t0[0]; + if (_t0[1] < group_min) { + group_min = _t0[1]; + } + if (_t0[2] < group_min) { + group_min = _t0[2]; + } + if (_t0[3] < group_min) { + group_min = _t0[3]; + } + if (group_min < best_d[TOP_K_MAX - 1]) { + #pragma unroll + for (int vec_col = 0; vec_col < 4; vec_col++) { + int db_idx = db_start + col_base + vec_col; + if (db_idx < M) { + float dist = _t0[vec_col]; + if (dist < best_d[TOP_K_MAX - 1]) { + best_d[TOP_K_MAX - 1] = dist; + best_i[TOP_K_MAX - 1] = db_idx; + #pragma unroll + for (int pos = TOP_K_MAX - 1; pos >= 1; pos--) { + if (best_d[pos] < best_d[pos - 1]) { + float tmp_d = best_d[pos - 1]; + int tmp_i = best_i[pos - 1]; + best_d[pos - 1] = best_d[pos]; + best_i[pos - 1] = best_i[pos]; + best_d[pos] = tmp_d; + best_i[pos] = tmp_i; + } + } + } + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + if (valid_q != 0) { + int out_base = ((split_idx_1 * B + batch_idx_1) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + if (out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = best_d[out_k]; + *((int*)(partial_indices + (out_base + out_k))) = best_i[out_k]; + } + } + } + } + } + } + + // Cleanup + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::2.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(128)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::2.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0035.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0035.cu new file mode 100644 index 00000000..c454f780 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0035.cu @@ -0,0 +1,84 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 32 +#define TOP_K_MAX 11 +#define SPLIT_COUNT 8 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_e080k11s8exact(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 32 + tid; + int stride = num_bids * 32; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int out_base = base_row; + int split_pos[SPLIT_COUNT]; + int split_base[SPLIT_COUNT]; + float cand_d[SPLIT_COUNT]; + int cand_i[SPLIT_COUNT]; + #pragma unroll + for (int split_idx = 0; split_idx < SPLIT_COUNT; split_idx++) { + split_pos[split_idx] = 0; + split_base[split_idx] = base_row + split_idx * split_stride; + cand_d[split_idx] = partial_dists[split_base[split_idx]]; + cand_i[split_idx] = partial_indices[split_base[split_idx]]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_d = cand_d[0]; + int best_i = cand_i[0]; + int best_split = 0; + #pragma unroll + for (int split_idx_1 = 1; split_idx_1 < SPLIT_COUNT; split_idx_1++) { + if (best_d > cand_d[split_idx_1]) { + best_d = cand_d[split_idx_1]; + best_i = cand_i[split_idx_1]; + best_split = split_idx_1; + } + } + *((float*)(out_dists + (out_base + out_k))) = best_d; + *((int*)(out_indices + (out_base + out_k))) = best_i; + split_pos[best_split] = split_pos[best_split] + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_pos = split_pos[best_split]; + int next_addr = split_base[best_split] + next_pos; + cand_d[best_split] = partial_dists[next_addr]; + cand_i[best_split] = partial_indices[next_addr]; + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0036.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0036.cu new file mode 100644 index 00000000..5b5a28fd --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0036.cu @@ -0,0 +1,657 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 128 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_STRIDE 32768 +#define SMEM_SMEM_DATABASE_OFF 33792 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_DATABASE_SQ_OFF 50176 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 50432 +#define THREADS 192 +#define BLOCK_Q 128 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_MAX 12 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16_cta2( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + ".reg .b32 m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "tcgen05.mma.cta_group::2.kind::f16 [%0], %1, %2, %3, {m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step_cg2( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, %3, " + "{m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], %1;\n\t" + "}\n" + :: "r"(mbar_addr), "h"(cta_mask) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x16(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x16.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15}, [%16];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ uint32_t smem_addr(const void* ptr) { + uint32_t addr; + asm("{\n\t" + ".reg .u64 u64addr;\n\t" + "cvta.to.shared.u64 u64addr, %1;\n\t" + "cvt.u32.u64 %0, u64addr;\n\t" + "}\n" : "=r"(addr) : "l"(ptr)); + return addr; +} + + +__device__ __forceinline__ uint32_t mapa_to_rank(uint32_t local_addr, uint32_t rank) { + uint32_t remote; + asm volatile("mapa.shared::cluster.u32 %0, %1, %2;" + : "=r"(remote) : "r"(local_addr), "r"(rank)); + return remote; +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem_cta2( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global" + ".mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .b16 lo, hi;\n\t" + "mov.b32 {lo, hi}, %1;\n\t" + "tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], lo;\n\t" + "}\n" + :: "r"(mbar_addr), "r"((uint32_t)cta_mask) : "memory"); +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_k12split(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tile_pairs, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + const unsigned int clusters_x = gridDim.x / 2; + const unsigned int cluster_id = ((blockIdx.z * gridDim.y + blockIdx.y) * clusters_x) + blockIdx.x / 2; + const unsigned int num_clusters = clusters_x * gridDim.y * gridDim.z; + + int cta_rank; + asm volatile("mov.b32 %0, %%cluster_ctarank;" : "=r"(cta_rank)); + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 33792); + const int smem_database_addr = smem + 33792; + float* smem_database_sq = reinterpret_cast(smem_raw + 50176); + const int smem_database_sq_addr = smem + 50176; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 0, 2, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 16, 2, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=8 + mbarrier_init_pred(smem + 40, 8, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (128 columns, 128 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::2.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(128) : "memory"); + } + + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: load ---- + if (warp == 0) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 0) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx = cluster_id; work_idx < total_work; work_idx += num_clusters) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tile_pairs; + int q_tile_pair = query_work % num_q_tile_pairs; + int q_tile = q_tile_pair * 2 + cta_rank; + int off_q = q_tile * BLOCK_Q; + int global_q = batch_idx * Q + off_q; + int db_tile_start = split_idx * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((query_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(32768)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_query_addr), "l"(tmap_query), "r"(0), "r"(global_q), "r"(0), + "r"(((query_full_addr) & 0xFEFFFFFF)) : "memory"); + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int off_m = db_tile * BLOCK_M; + int global_m = batch_idx * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((database_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(16384)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_database_addr), "l"(tmap_database), "r"(0), "r"(global_m), "r"(0), + "r"(((database_full_addr) & 0xFEFFFFFF)) : "memory"); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 1) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + if (cta_rank == 0) { + #pragma unroll 1 + for (unsigned int work_idx_1 = cluster_id; work_idx_1 < total_work; work_idx_1 += num_clusters) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = (smem_query_addr >> 4) & 0x3FFF; + int _mma_b_lo_0 = (smem_database_addr >> 4) & 0x3FFF; + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\tmov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 270533776;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit_cg2_multicast(score_full_addr, (uint16_t)(3)); + elect_commit_cg2_multicast(database_empty_addr, (uint16_t)(3)); + } + elect_commit_cg2_multicast(query_empty_addr, (uint16_t)(3)); + } + } + } + // ---- Role: compute ---- + } else if (warp >= 2 && warp <= 5) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = cluster_id; work_idx_2 < total_work; work_idx_2 += num_clusters) { + int split_idx_1 = work_idx_2 % (unsigned int)split_count; + int query_work_1 = work_idx_2 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tile_pairs; + int q_tile_pair_1 = query_work_1 % num_q_tile_pairs; + int q_tile_1 = q_tile_pair_1 * 2 + cta_rank; + int off_q_1 = q_tile_1 * BLOCK_Q; + int q_idx = off_q_1 + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx_1 * Q + q_idx]; + } + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int db_start = db_tile_1 * BLOCK_M; + int db_sq_idx = db_start + (warp % 4 * 32 + lane); + if (warp % 4 * 32 + lane < BLOCK_M) { + if (db_sq_idx < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx_1 * M + db_sq_idx]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 0.0f; + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(cta_rank * BLOCK_Q + (warp % 4 * 32 << 16) << 16); + float _tmem_load_0[64]; + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x64.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31, %32, %33, %34, %35, %36, %37, %38, %39, %40, %41, %42, %43, %44, %45, %46, %47, %48, %49, %50, %51, %52, %53, %54, %55, %56, %57, %58, %59, %60, %61, %62, %63}, [%64];" + : "=f"(_tmem_load_0[0]), "=f"(_tmem_load_0[1]), "=f"(_tmem_load_0[2]), "=f"(_tmem_load_0[3]), "=f"(_tmem_load_0[4]), "=f"(_tmem_load_0[5]), "=f"(_tmem_load_0[6]), "=f"(_tmem_load_0[7]), "=f"(_tmem_load_0[8]), "=f"(_tmem_load_0[9]), "=f"(_tmem_load_0[10]), "=f"(_tmem_load_0[11]), "=f"(_tmem_load_0[12]), "=f"(_tmem_load_0[13]), "=f"(_tmem_load_0[14]), "=f"(_tmem_load_0[15]), "=f"(_tmem_load_0[16]), "=f"(_tmem_load_0[17]), "=f"(_tmem_load_0[18]), "=f"(_tmem_load_0[19]), "=f"(_tmem_load_0[20]), "=f"(_tmem_load_0[21]), "=f"(_tmem_load_0[22]), "=f"(_tmem_load_0[23]), "=f"(_tmem_load_0[24]), "=f"(_tmem_load_0[25]), "=f"(_tmem_load_0[26]), "=f"(_tmem_load_0[27]), "=f"(_tmem_load_0[28]), "=f"(_tmem_load_0[29]), "=f"(_tmem_load_0[30]), "=f"(_tmem_load_0[31]), "=f"(_tmem_load_0[32]), "=f"(_tmem_load_0[33]), "=f"(_tmem_load_0[34]), "=f"(_tmem_load_0[35]), "=f"(_tmem_load_0[36]), "=f"(_tmem_load_0[37]), "=f"(_tmem_load_0[38]), "=f"(_tmem_load_0[39]), "=f"(_tmem_load_0[40]), "=f"(_tmem_load_0[41]), "=f"(_tmem_load_0[42]), "=f"(_tmem_load_0[43]), "=f"(_tmem_load_0[44]), "=f"(_tmem_load_0[45]), "=f"(_tmem_load_0[46]), "=f"(_tmem_load_0[47]), "=f"(_tmem_load_0[48]), "=f"(_tmem_load_0[49]), "=f"(_tmem_load_0[50]), "=f"(_tmem_load_0[51]), "=f"(_tmem_load_0[52]), "=f"(_tmem_load_0[53]), "=f"(_tmem_load_0[54]), "=f"(_tmem_load_0[55]), "=f"(_tmem_load_0[56]), "=f"(_tmem_load_0[57]), "=f"(_tmem_load_0[58]), "=f"(_tmem_load_0[59]), "=f"(_tmem_load_0[60]), "=f"(_tmem_load_0[61]), "=f"(_tmem_load_0[62]), "=f"(_tmem_load_0[63]) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + if (elect_sync()) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cluster.b64 _, [%0];" + :: "r"((score_empty_addr) & 0xFEFFFFFF) : "memory"); + } + if (valid_q != 0) { + #pragma unroll 2 + for (int col_base = 0; col_base < 64; col_base += 4) { + float dist_vec[4]; + dist_vec[0] = _tmem_load_0[col_base]; + dist_vec[1] = _tmem_load_0[col_base + 1]; + dist_vec[2] = _tmem_load_0[col_base + 2]; + dist_vec[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec[4]; + db_sq_vec[0] = smem_database_sq[col_base]; + db_sq_vec[1] = smem_database_sq[col_base + 1]; + db_sq_vec[2] = smem_database_sq[col_base + 2]; + db_sq_vec[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec)[_la], reinterpret_cast(db_sq_vec)[_la]); + float group_min = _t0[0]; + if (_t0[1] < group_min) { + group_min = _t0[1]; + } + if (_t0[2] < group_min) { + group_min = _t0[2]; + } + if (_t0[3] < group_min) { + group_min = _t0[3]; + } + if (group_min < best_d[TOP_K_MAX - 1]) { + #pragma unroll + for (int vec_col = 0; vec_col < 4; vec_col++) { + int db_idx = db_start + col_base + vec_col; + if (db_idx < M) { + float dist = _t0[vec_col]; + if (dist < best_d[TOP_K_MAX - 1]) { + best_d[TOP_K_MAX - 1] = dist; + best_i[TOP_K_MAX - 1] = db_idx; + #pragma unroll + for (int pos = TOP_K_MAX - 1; pos >= 1; pos--) { + if (best_d[pos] < best_d[pos - 1]) { + float tmp_d = best_d[pos - 1]; + int tmp_i = best_i[pos - 1]; + best_d[pos - 1] = best_d[pos]; + best_i[pos - 1] = best_i[pos]; + best_d[pos] = tmp_d; + best_i[pos] = tmp_i; + } + } + } + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + if (valid_q != 0) { + int out_base = ((split_idx_1 * B + batch_idx_1) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + if (out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = best_d[out_k]; + *((int*)(partial_indices + (out_base + out_k))) = best_i[out_k]; + } + } + } + } + } + } + + // Cleanup + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::2.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(128)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::2.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0037.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0037.cu new file mode 100644 index 00000000..723233c2 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0037.cu @@ -0,0 +1,84 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 32 +#define TOP_K_MAX 12 +#define SPLIT_COUNT 8 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_k12s8(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 32 + tid; + int stride = num_bids * 32; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int out_base = base_row; + int split_pos[SPLIT_COUNT]; + int split_base[SPLIT_COUNT]; + float cand_d[SPLIT_COUNT]; + int cand_i[SPLIT_COUNT]; + #pragma unroll + for (int split_idx = 0; split_idx < SPLIT_COUNT; split_idx++) { + split_pos[split_idx] = 0; + split_base[split_idx] = base_row + split_idx * split_stride; + cand_d[split_idx] = partial_dists[split_base[split_idx]]; + cand_i[split_idx] = partial_indices[split_base[split_idx]]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_d = cand_d[0]; + int best_i = cand_i[0]; + int best_split = 0; + #pragma unroll + for (int split_idx_1 = 1; split_idx_1 < SPLIT_COUNT; split_idx_1++) { + if (best_d > cand_d[split_idx_1]) { + best_d = cand_d[split_idx_1]; + best_i = cand_i[split_idx_1]; + best_split = split_idx_1; + } + } + *((float*)(out_dists + (out_base + out_k))) = best_d; + *((int*)(out_indices + (out_base + out_k))) = best_i; + split_pos[best_split] = split_pos[best_split] + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_pos = split_pos[best_split]; + int next_addr = split_base[best_split] + next_pos; + cand_d[best_split] = partial_dists[next_addr]; + cand_i[best_split] = partial_indices[next_addr]; + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0038.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0038.cu new file mode 100644 index 00000000..12a9ef22 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0038.cu @@ -0,0 +1,657 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 128 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_STRIDE 32768 +#define SMEM_SMEM_DATABASE_OFF 33792 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_DATABASE_SQ_OFF 50176 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 50432 +#define THREADS 192 +#define BLOCK_Q 128 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_MAX 13 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16_cta2( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + ".reg .b32 m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "tcgen05.mma.cta_group::2.kind::f16 [%0], %1, %2, %3, {m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step_cg2( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, %3, " + "{m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], %1;\n\t" + "}\n" + :: "r"(mbar_addr), "h"(cta_mask) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x16(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x16.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15}, [%16];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ uint32_t smem_addr(const void* ptr) { + uint32_t addr; + asm("{\n\t" + ".reg .u64 u64addr;\n\t" + "cvta.to.shared.u64 u64addr, %1;\n\t" + "cvt.u32.u64 %0, u64addr;\n\t" + "}\n" : "=r"(addr) : "l"(ptr)); + return addr; +} + + +__device__ __forceinline__ uint32_t mapa_to_rank(uint32_t local_addr, uint32_t rank) { + uint32_t remote; + asm volatile("mapa.shared::cluster.u32 %0, %1, %2;" + : "=r"(remote) : "r"(local_addr), "r"(rank)); + return remote; +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem_cta2( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global" + ".mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .b16 lo, hi;\n\t" + "mov.b32 {lo, hi}, %1;\n\t" + "tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], lo;\n\t" + "}\n" + :: "r"(mbar_addr), "r"((uint32_t)cta_mask) : "memory"); +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_e080k13exact(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tile_pairs, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + const unsigned int clusters_x = gridDim.x / 2; + const unsigned int cluster_id = ((blockIdx.z * gridDim.y + blockIdx.y) * clusters_x) + blockIdx.x / 2; + const unsigned int num_clusters = clusters_x * gridDim.y * gridDim.z; + + int cta_rank; + asm volatile("mov.b32 %0, %%cluster_ctarank;" : "=r"(cta_rank)); + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 33792); + const int smem_database_addr = smem + 33792; + float* smem_database_sq = reinterpret_cast(smem_raw + 50176); + const int smem_database_sq_addr = smem + 50176; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 0, 2, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 16, 2, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=8 + mbarrier_init_pred(smem + 40, 8, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (128 columns, 128 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::2.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(128) : "memory"); + } + + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: load ---- + if (warp == 0) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 0) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx = cluster_id; work_idx < total_work; work_idx += num_clusters) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tile_pairs; + int q_tile_pair = query_work % num_q_tile_pairs; + int q_tile = q_tile_pair * 2 + cta_rank; + int off_q = q_tile * BLOCK_Q; + int global_q = batch_idx * Q + off_q; + int db_tile_start = split_idx * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((query_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(32768)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_query_addr), "l"(tmap_query), "r"(0), "r"(global_q), "r"(0), + "r"(((query_full_addr) & 0xFEFFFFFF)) : "memory"); + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int off_m = db_tile * BLOCK_M; + int global_m = batch_idx * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((database_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(16384)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_database_addr), "l"(tmap_database), "r"(0), "r"(global_m), "r"(0), + "r"(((database_full_addr) & 0xFEFFFFFF)) : "memory"); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 1) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + if (cta_rank == 0) { + #pragma unroll 1 + for (unsigned int work_idx_1 = cluster_id; work_idx_1 < total_work; work_idx_1 += num_clusters) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = (smem_query_addr >> 4) & 0x3FFF; + int _mma_b_lo_0 = (smem_database_addr >> 4) & 0x3FFF; + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\tmov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 270533776;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit_cg2_multicast(score_full_addr, (uint16_t)(3)); + elect_commit_cg2_multicast(database_empty_addr, (uint16_t)(3)); + } + elect_commit_cg2_multicast(query_empty_addr, (uint16_t)(3)); + } + } + } + // ---- Role: compute ---- + } else if (warp >= 2 && warp <= 5) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = cluster_id; work_idx_2 < total_work; work_idx_2 += num_clusters) { + int split_idx_1 = work_idx_2 % (unsigned int)split_count; + int query_work_1 = work_idx_2 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tile_pairs; + int q_tile_pair_1 = query_work_1 % num_q_tile_pairs; + int q_tile_1 = q_tile_pair_1 * 2 + cta_rank; + int off_q_1 = q_tile_1 * BLOCK_Q; + int q_idx = off_q_1 + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx_1 * Q + q_idx]; + } + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int db_start = db_tile_1 * BLOCK_M; + int db_sq_idx = db_start + (warp % 4 * 32 + lane); + if (warp % 4 * 32 + lane < BLOCK_M) { + if (db_sq_idx < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx_1 * M + db_sq_idx]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 0.0f; + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(cta_rank * BLOCK_Q + (warp % 4 * 32 << 16) << 16); + float _tmem_load_0[64]; + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x64.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31, %32, %33, %34, %35, %36, %37, %38, %39, %40, %41, %42, %43, %44, %45, %46, %47, %48, %49, %50, %51, %52, %53, %54, %55, %56, %57, %58, %59, %60, %61, %62, %63}, [%64];" + : "=f"(_tmem_load_0[0]), "=f"(_tmem_load_0[1]), "=f"(_tmem_load_0[2]), "=f"(_tmem_load_0[3]), "=f"(_tmem_load_0[4]), "=f"(_tmem_load_0[5]), "=f"(_tmem_load_0[6]), "=f"(_tmem_load_0[7]), "=f"(_tmem_load_0[8]), "=f"(_tmem_load_0[9]), "=f"(_tmem_load_0[10]), "=f"(_tmem_load_0[11]), "=f"(_tmem_load_0[12]), "=f"(_tmem_load_0[13]), "=f"(_tmem_load_0[14]), "=f"(_tmem_load_0[15]), "=f"(_tmem_load_0[16]), "=f"(_tmem_load_0[17]), "=f"(_tmem_load_0[18]), "=f"(_tmem_load_0[19]), "=f"(_tmem_load_0[20]), "=f"(_tmem_load_0[21]), "=f"(_tmem_load_0[22]), "=f"(_tmem_load_0[23]), "=f"(_tmem_load_0[24]), "=f"(_tmem_load_0[25]), "=f"(_tmem_load_0[26]), "=f"(_tmem_load_0[27]), "=f"(_tmem_load_0[28]), "=f"(_tmem_load_0[29]), "=f"(_tmem_load_0[30]), "=f"(_tmem_load_0[31]), "=f"(_tmem_load_0[32]), "=f"(_tmem_load_0[33]), "=f"(_tmem_load_0[34]), "=f"(_tmem_load_0[35]), "=f"(_tmem_load_0[36]), "=f"(_tmem_load_0[37]), "=f"(_tmem_load_0[38]), "=f"(_tmem_load_0[39]), "=f"(_tmem_load_0[40]), "=f"(_tmem_load_0[41]), "=f"(_tmem_load_0[42]), "=f"(_tmem_load_0[43]), "=f"(_tmem_load_0[44]), "=f"(_tmem_load_0[45]), "=f"(_tmem_load_0[46]), "=f"(_tmem_load_0[47]), "=f"(_tmem_load_0[48]), "=f"(_tmem_load_0[49]), "=f"(_tmem_load_0[50]), "=f"(_tmem_load_0[51]), "=f"(_tmem_load_0[52]), "=f"(_tmem_load_0[53]), "=f"(_tmem_load_0[54]), "=f"(_tmem_load_0[55]), "=f"(_tmem_load_0[56]), "=f"(_tmem_load_0[57]), "=f"(_tmem_load_0[58]), "=f"(_tmem_load_0[59]), "=f"(_tmem_load_0[60]), "=f"(_tmem_load_0[61]), "=f"(_tmem_load_0[62]), "=f"(_tmem_load_0[63]) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + if (elect_sync()) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cluster.b64 _, [%0];" + :: "r"((score_empty_addr) & 0xFEFFFFFF) : "memory"); + } + if (valid_q != 0) { + #pragma unroll 2 + for (int col_base = 0; col_base < 64; col_base += 4) { + float dist_vec[4]; + dist_vec[0] = _tmem_load_0[col_base]; + dist_vec[1] = _tmem_load_0[col_base + 1]; + dist_vec[2] = _tmem_load_0[col_base + 2]; + dist_vec[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec[4]; + db_sq_vec[0] = smem_database_sq[col_base]; + db_sq_vec[1] = smem_database_sq[col_base + 1]; + db_sq_vec[2] = smem_database_sq[col_base + 2]; + db_sq_vec[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec)[_la], reinterpret_cast(db_sq_vec)[_la]); + float group_min = _t0[0]; + if (_t0[1] < group_min) { + group_min = _t0[1]; + } + if (_t0[2] < group_min) { + group_min = _t0[2]; + } + if (_t0[3] < group_min) { + group_min = _t0[3]; + } + if (group_min < best_d[TOP_K_MAX - 1]) { + #pragma unroll + for (int vec_col = 0; vec_col < 4; vec_col++) { + int db_idx = db_start + col_base + vec_col; + if (db_idx < M) { + float dist = _t0[vec_col]; + if (dist < best_d[TOP_K_MAX - 1]) { + best_d[TOP_K_MAX - 1] = dist; + best_i[TOP_K_MAX - 1] = db_idx; + #pragma unroll + for (int pos = TOP_K_MAX - 1; pos >= 1; pos--) { + if (best_d[pos] < best_d[pos - 1]) { + float tmp_d = best_d[pos - 1]; + int tmp_i = best_i[pos - 1]; + best_d[pos - 1] = best_d[pos]; + best_i[pos - 1] = best_i[pos]; + best_d[pos] = tmp_d; + best_i[pos] = tmp_i; + } + } + } + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + if (valid_q != 0) { + int out_base = ((split_idx_1 * B + batch_idx_1) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + if (out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = best_d[out_k]; + *((int*)(partial_indices + (out_base + out_k))) = best_i[out_k]; + } + } + } + } + } + } + + // Cleanup + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::2.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(128)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::2.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0039.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0039.cu new file mode 100644 index 00000000..87baac74 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0039.cu @@ -0,0 +1,84 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 32 +#define TOP_K_MAX 13 +#define SPLIT_COUNT 8 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_e080k13s8exact(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 32 + tid; + int stride = num_bids * 32; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int out_base = base_row; + int split_pos[SPLIT_COUNT]; + int split_base[SPLIT_COUNT]; + float cand_d[SPLIT_COUNT]; + int cand_i[SPLIT_COUNT]; + #pragma unroll + for (int split_idx = 0; split_idx < SPLIT_COUNT; split_idx++) { + split_pos[split_idx] = 0; + split_base[split_idx] = base_row + split_idx * split_stride; + cand_d[split_idx] = partial_dists[split_base[split_idx]]; + cand_i[split_idx] = partial_indices[split_base[split_idx]]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_d = cand_d[0]; + int best_i = cand_i[0]; + int best_split = 0; + #pragma unroll + for (int split_idx_1 = 1; split_idx_1 < SPLIT_COUNT; split_idx_1++) { + if (best_d > cand_d[split_idx_1]) { + best_d = cand_d[split_idx_1]; + best_i = cand_i[split_idx_1]; + best_split = split_idx_1; + } + } + *((float*)(out_dists + (out_base + out_k))) = best_d; + *((int*)(out_indices + (out_base + out_k))) = best_i; + split_pos[best_split] = split_pos[best_split] + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_pos = split_pos[best_split]; + int next_addr = split_base[best_split] + next_pos; + cand_d[best_split] = partial_dists[next_addr]; + cand_i[best_split] = partial_indices[next_addr]; + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0040.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0040.cu new file mode 100644 index 00000000..fd9b65aa --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0040.cu @@ -0,0 +1,84 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 32 +#define TOP_K_MAX 20 +#define SPLIT_COUNT 8 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_k20s8(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 32 + tid; + int stride = num_bids * 32; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int out_base = base_row; + int split_pos[SPLIT_COUNT]; + int split_base[SPLIT_COUNT]; + float cand_d[SPLIT_COUNT]; + int cand_i[SPLIT_COUNT]; + #pragma unroll + for (int split_idx = 0; split_idx < SPLIT_COUNT; split_idx++) { + split_pos[split_idx] = 0; + split_base[split_idx] = base_row + split_idx * split_stride; + cand_d[split_idx] = partial_dists[split_base[split_idx]]; + cand_i[split_idx] = partial_indices[split_base[split_idx]]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_d = cand_d[0]; + int best_i = cand_i[0]; + int best_split = 0; + #pragma unroll + for (int split_idx_1 = 1; split_idx_1 < SPLIT_COUNT; split_idx_1++) { + if (best_d > cand_d[split_idx_1]) { + best_d = cand_d[split_idx_1]; + best_i = cand_i[split_idx_1]; + best_split = split_idx_1; + } + } + *((float*)(out_dists + (out_base + out_k))) = best_d; + *((int*)(out_indices + (out_base + out_k))) = best_i; + split_pos[best_split] = split_pos[best_split] + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_pos = split_pos[best_split]; + int next_addr = split_base[best_split] + next_pos; + cand_d[best_split] = partial_dists[next_addr]; + cand_i[best_split] = partial_indices[next_addr]; + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0041.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0041.cu new file mode 100644 index 00000000..416c3a8b --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0041.cu @@ -0,0 +1,657 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 128 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_STRIDE 32768 +#define SMEM_SMEM_DATABASE_OFF 33792 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_DATABASE_SQ_OFF 50176 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 50432 +#define THREADS 192 +#define BLOCK_Q 128 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_MAX 24 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16_cta2( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + ".reg .b32 m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "tcgen05.mma.cta_group::2.kind::f16 [%0], %1, %2, %3, {m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step_cg2( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, %3, " + "{m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], %1;\n\t" + "}\n" + :: "r"(mbar_addr), "h"(cta_mask) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x16(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x16.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15}, [%16];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ uint32_t smem_addr(const void* ptr) { + uint32_t addr; + asm("{\n\t" + ".reg .u64 u64addr;\n\t" + "cvta.to.shared.u64 u64addr, %1;\n\t" + "cvt.u32.u64 %0, u64addr;\n\t" + "}\n" : "=r"(addr) : "l"(ptr)); + return addr; +} + + +__device__ __forceinline__ uint32_t mapa_to_rank(uint32_t local_addr, uint32_t rank) { + uint32_t remote; + asm volatile("mapa.shared::cluster.u32 %0, %1, %2;" + : "=r"(remote) : "r"(local_addr), "r"(rank)); + return remote; +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem_cta2( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global" + ".mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .b16 lo, hi;\n\t" + "mov.b32 {lo, hi}, %1;\n\t" + "tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], lo;\n\t" + "}\n" + :: "r"(mbar_addr), "r"((uint32_t)cta_mask) : "memory"); +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_bad5midks8k24(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tile_pairs, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + const unsigned int clusters_x = gridDim.x / 2; + const unsigned int cluster_id = ((blockIdx.z * gridDim.y + blockIdx.y) * clusters_x) + blockIdx.x / 2; + const unsigned int num_clusters = clusters_x * gridDim.y * gridDim.z; + + int cta_rank; + asm volatile("mov.b32 %0, %%cluster_ctarank;" : "=r"(cta_rank)); + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 33792); + const int smem_database_addr = smem + 33792; + float* smem_database_sq = reinterpret_cast(smem_raw + 50176); + const int smem_database_sq_addr = smem + 50176; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 0, 2, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 16, 2, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=8 + mbarrier_init_pred(smem + 40, 8, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (128 columns, 128 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::2.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(128) : "memory"); + } + + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: load ---- + if (warp == 0) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 0) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx = cluster_id; work_idx < total_work; work_idx += num_clusters) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tile_pairs; + int q_tile_pair = query_work % num_q_tile_pairs; + int q_tile = q_tile_pair * 2 + cta_rank; + int off_q = q_tile * BLOCK_Q; + int global_q = batch_idx * Q + off_q; + int db_tile_start = split_idx * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((query_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(32768)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_query_addr), "l"(tmap_query), "r"(0), "r"(global_q), "r"(0), + "r"(((query_full_addr) & 0xFEFFFFFF)) : "memory"); + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int off_m = db_tile * BLOCK_M; + int global_m = batch_idx * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((database_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(16384)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_database_addr), "l"(tmap_database), "r"(0), "r"(global_m), "r"(0), + "r"(((database_full_addr) & 0xFEFFFFFF)) : "memory"); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 1) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + if (cta_rank == 0) { + #pragma unroll 1 + for (unsigned int work_idx_1 = cluster_id; work_idx_1 < total_work; work_idx_1 += num_clusters) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = (smem_query_addr >> 4) & 0x3FFF; + int _mma_b_lo_0 = (smem_database_addr >> 4) & 0x3FFF; + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\tmov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 270533776;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit_cg2_multicast(score_full_addr, (uint16_t)(3)); + elect_commit_cg2_multicast(database_empty_addr, (uint16_t)(3)); + } + elect_commit_cg2_multicast(query_empty_addr, (uint16_t)(3)); + } + } + } + // ---- Role: compute ---- + } else if (warp >= 2 && warp <= 5) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = cluster_id; work_idx_2 < total_work; work_idx_2 += num_clusters) { + int split_idx_1 = work_idx_2 % (unsigned int)split_count; + int query_work_1 = work_idx_2 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tile_pairs; + int q_tile_pair_1 = query_work_1 % num_q_tile_pairs; + int q_tile_1 = q_tile_pair_1 * 2 + cta_rank; + int off_q_1 = q_tile_1 * BLOCK_Q; + int q_idx = off_q_1 + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx_1 * Q + q_idx]; + } + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int db_start = db_tile_1 * BLOCK_M; + int db_sq_idx = db_start + (warp % 4 * 32 + lane); + if (warp % 4 * 32 + lane < BLOCK_M) { + if (db_sq_idx < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx_1 * M + db_sq_idx]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 0.0f; + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(cta_rank * BLOCK_Q + (warp % 4 * 32 << 16) << 16); + float _tmem_load_0[64]; + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x64.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31, %32, %33, %34, %35, %36, %37, %38, %39, %40, %41, %42, %43, %44, %45, %46, %47, %48, %49, %50, %51, %52, %53, %54, %55, %56, %57, %58, %59, %60, %61, %62, %63}, [%64];" + : "=f"(_tmem_load_0[0]), "=f"(_tmem_load_0[1]), "=f"(_tmem_load_0[2]), "=f"(_tmem_load_0[3]), "=f"(_tmem_load_0[4]), "=f"(_tmem_load_0[5]), "=f"(_tmem_load_0[6]), "=f"(_tmem_load_0[7]), "=f"(_tmem_load_0[8]), "=f"(_tmem_load_0[9]), "=f"(_tmem_load_0[10]), "=f"(_tmem_load_0[11]), "=f"(_tmem_load_0[12]), "=f"(_tmem_load_0[13]), "=f"(_tmem_load_0[14]), "=f"(_tmem_load_0[15]), "=f"(_tmem_load_0[16]), "=f"(_tmem_load_0[17]), "=f"(_tmem_load_0[18]), "=f"(_tmem_load_0[19]), "=f"(_tmem_load_0[20]), "=f"(_tmem_load_0[21]), "=f"(_tmem_load_0[22]), "=f"(_tmem_load_0[23]), "=f"(_tmem_load_0[24]), "=f"(_tmem_load_0[25]), "=f"(_tmem_load_0[26]), "=f"(_tmem_load_0[27]), "=f"(_tmem_load_0[28]), "=f"(_tmem_load_0[29]), "=f"(_tmem_load_0[30]), "=f"(_tmem_load_0[31]), "=f"(_tmem_load_0[32]), "=f"(_tmem_load_0[33]), "=f"(_tmem_load_0[34]), "=f"(_tmem_load_0[35]), "=f"(_tmem_load_0[36]), "=f"(_tmem_load_0[37]), "=f"(_tmem_load_0[38]), "=f"(_tmem_load_0[39]), "=f"(_tmem_load_0[40]), "=f"(_tmem_load_0[41]), "=f"(_tmem_load_0[42]), "=f"(_tmem_load_0[43]), "=f"(_tmem_load_0[44]), "=f"(_tmem_load_0[45]), "=f"(_tmem_load_0[46]), "=f"(_tmem_load_0[47]), "=f"(_tmem_load_0[48]), "=f"(_tmem_load_0[49]), "=f"(_tmem_load_0[50]), "=f"(_tmem_load_0[51]), "=f"(_tmem_load_0[52]), "=f"(_tmem_load_0[53]), "=f"(_tmem_load_0[54]), "=f"(_tmem_load_0[55]), "=f"(_tmem_load_0[56]), "=f"(_tmem_load_0[57]), "=f"(_tmem_load_0[58]), "=f"(_tmem_load_0[59]), "=f"(_tmem_load_0[60]), "=f"(_tmem_load_0[61]), "=f"(_tmem_load_0[62]), "=f"(_tmem_load_0[63]) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + if (elect_sync()) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cluster.b64 _, [%0];" + :: "r"((score_empty_addr) & 0xFEFFFFFF) : "memory"); + } + if (valid_q != 0) { + #pragma unroll 2 + for (int col_base = 0; col_base < 64; col_base += 4) { + float dist_vec[4]; + dist_vec[0] = _tmem_load_0[col_base]; + dist_vec[1] = _tmem_load_0[col_base + 1]; + dist_vec[2] = _tmem_load_0[col_base + 2]; + dist_vec[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec[4]; + db_sq_vec[0] = smem_database_sq[col_base]; + db_sq_vec[1] = smem_database_sq[col_base + 1]; + db_sq_vec[2] = smem_database_sq[col_base + 2]; + db_sq_vec[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec)[_la], reinterpret_cast(db_sq_vec)[_la]); + float group_min = _t0[0]; + if (_t0[1] < group_min) { + group_min = _t0[1]; + } + if (_t0[2] < group_min) { + group_min = _t0[2]; + } + if (_t0[3] < group_min) { + group_min = _t0[3]; + } + if (group_min < best_d[TOP_K_MAX - 1]) { + #pragma unroll + for (int vec_col = 0; vec_col < 4; vec_col++) { + int db_idx = db_start + col_base + vec_col; + if (db_idx < M) { + float dist = _t0[vec_col]; + if (dist < best_d[TOP_K_MAX - 1]) { + best_d[TOP_K_MAX - 1] = dist; + best_i[TOP_K_MAX - 1] = db_idx; + #pragma unroll + for (int pos = TOP_K_MAX - 1; pos >= 1; pos--) { + if (best_d[pos] < best_d[pos - 1]) { + float tmp_d = best_d[pos - 1]; + int tmp_i = best_i[pos - 1]; + best_d[pos - 1] = best_d[pos]; + best_i[pos - 1] = best_i[pos]; + best_d[pos] = tmp_d; + best_i[pos] = tmp_i; + } + } + } + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + if (valid_q != 0) { + int out_base = ((split_idx_1 * B + batch_idx_1) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + if (out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = best_d[out_k]; + *((int*)(partial_indices + (out_base + out_k))) = best_i[out_k]; + } + } + } + } + } + } + + // Cleanup + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::2.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(128)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::2.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0042.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0042.cu new file mode 100644 index 00000000..a255ce80 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0042.cu @@ -0,0 +1,84 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 32 +#define TOP_K_MAX 24 +#define SPLIT_COUNT 8 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_bad5midks8k24(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 32 + tid; + int stride = num_bids * 32; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int out_base = base_row; + int split_pos[SPLIT_COUNT]; + int split_base[SPLIT_COUNT]; + float cand_d[SPLIT_COUNT]; + int cand_i[SPLIT_COUNT]; + #pragma unroll + for (int split_idx = 0; split_idx < SPLIT_COUNT; split_idx++) { + split_pos[split_idx] = 0; + split_base[split_idx] = base_row + split_idx * split_stride; + cand_d[split_idx] = partial_dists[split_base[split_idx]]; + cand_i[split_idx] = partial_indices[split_base[split_idx]]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_d = cand_d[0]; + int best_i = cand_i[0]; + int best_split = 0; + #pragma unroll + for (int split_idx_1 = 1; split_idx_1 < SPLIT_COUNT; split_idx_1++) { + if (best_d > cand_d[split_idx_1]) { + best_d = cand_d[split_idx_1]; + best_i = cand_i[split_idx_1]; + best_split = split_idx_1; + } + } + *((float*)(out_dists + (out_base + out_k))) = best_d; + *((int*)(out_indices + (out_base + out_k))) = best_i; + split_pos[best_split] = split_pos[best_split] + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_pos = split_pos[best_split]; + int next_addr = split_base[best_split] + next_pos; + cand_d[best_split] = partial_dists[next_addr]; + cand_i[best_split] = partial_indices[next_addr]; + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0043.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0043.cu new file mode 100644 index 00000000..b7ca2332 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0043.cu @@ -0,0 +1,657 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 128 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_STRIDE 32768 +#define SMEM_SMEM_DATABASE_OFF 33792 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_DATABASE_SQ_OFF 50176 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 50432 +#define THREADS 192 +#define BLOCK_Q 128 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_MAX 28 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16_cta2( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + ".reg .b32 m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "tcgen05.mma.cta_group::2.kind::f16 [%0], %1, %2, %3, {m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step_cg2( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, %3, " + "{m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], %1;\n\t" + "}\n" + :: "r"(mbar_addr), "h"(cta_mask) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x16(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x16.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15}, [%16];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ uint32_t smem_addr(const void* ptr) { + uint32_t addr; + asm("{\n\t" + ".reg .u64 u64addr;\n\t" + "cvta.to.shared.u64 u64addr, %1;\n\t" + "cvt.u32.u64 %0, u64addr;\n\t" + "}\n" : "=r"(addr) : "l"(ptr)); + return addr; +} + + +__device__ __forceinline__ uint32_t mapa_to_rank(uint32_t local_addr, uint32_t rank) { + uint32_t remote; + asm volatile("mapa.shared::cluster.u32 %0, %1, %2;" + : "=r"(remote) : "r"(local_addr), "r"(rank)); + return remote; +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem_cta2( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global" + ".mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .b16 lo, hi;\n\t" + "mov.b32 {lo, hi}, %1;\n\t" + "tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], lo;\n\t" + "}\n" + :: "r"(mbar_addr), "r"((uint32_t)cta_mask) : "memory"); +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_bad5midks8k28(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tile_pairs, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + const unsigned int clusters_x = gridDim.x / 2; + const unsigned int cluster_id = ((blockIdx.z * gridDim.y + blockIdx.y) * clusters_x) + blockIdx.x / 2; + const unsigned int num_clusters = clusters_x * gridDim.y * gridDim.z; + + int cta_rank; + asm volatile("mov.b32 %0, %%cluster_ctarank;" : "=r"(cta_rank)); + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 33792); + const int smem_database_addr = smem + 33792; + float* smem_database_sq = reinterpret_cast(smem_raw + 50176); + const int smem_database_sq_addr = smem + 50176; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 0, 2, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 16, 2, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=8 + mbarrier_init_pred(smem + 40, 8, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (128 columns, 128 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::2.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(128) : "memory"); + } + + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: load ---- + if (warp == 0) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 0) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx = cluster_id; work_idx < total_work; work_idx += num_clusters) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tile_pairs; + int q_tile_pair = query_work % num_q_tile_pairs; + int q_tile = q_tile_pair * 2 + cta_rank; + int off_q = q_tile * BLOCK_Q; + int global_q = batch_idx * Q + off_q; + int db_tile_start = split_idx * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((query_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(32768)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_query_addr), "l"(tmap_query), "r"(0), "r"(global_q), "r"(0), + "r"(((query_full_addr) & 0xFEFFFFFF)) : "memory"); + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int off_m = db_tile * BLOCK_M; + int global_m = batch_idx * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((database_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(16384)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_database_addr), "l"(tmap_database), "r"(0), "r"(global_m), "r"(0), + "r"(((database_full_addr) & 0xFEFFFFFF)) : "memory"); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 1) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + if (cta_rank == 0) { + #pragma unroll 1 + for (unsigned int work_idx_1 = cluster_id; work_idx_1 < total_work; work_idx_1 += num_clusters) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = (smem_query_addr >> 4) & 0x3FFF; + int _mma_b_lo_0 = (smem_database_addr >> 4) & 0x3FFF; + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\tmov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 270533776;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit_cg2_multicast(score_full_addr, (uint16_t)(3)); + elect_commit_cg2_multicast(database_empty_addr, (uint16_t)(3)); + } + elect_commit_cg2_multicast(query_empty_addr, (uint16_t)(3)); + } + } + } + // ---- Role: compute ---- + } else if (warp >= 2 && warp <= 5) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = cluster_id; work_idx_2 < total_work; work_idx_2 += num_clusters) { + int split_idx_1 = work_idx_2 % (unsigned int)split_count; + int query_work_1 = work_idx_2 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tile_pairs; + int q_tile_pair_1 = query_work_1 % num_q_tile_pairs; + int q_tile_1 = q_tile_pair_1 * 2 + cta_rank; + int off_q_1 = q_tile_1 * BLOCK_Q; + int q_idx = off_q_1 + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx_1 * Q + q_idx]; + } + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int db_start = db_tile_1 * BLOCK_M; + int db_sq_idx = db_start + (warp % 4 * 32 + lane); + if (warp % 4 * 32 + lane < BLOCK_M) { + if (db_sq_idx < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx_1 * M + db_sq_idx]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 0.0f; + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(cta_rank * BLOCK_Q + (warp % 4 * 32 << 16) << 16); + float _tmem_load_0[64]; + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x64.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31, %32, %33, %34, %35, %36, %37, %38, %39, %40, %41, %42, %43, %44, %45, %46, %47, %48, %49, %50, %51, %52, %53, %54, %55, %56, %57, %58, %59, %60, %61, %62, %63}, [%64];" + : "=f"(_tmem_load_0[0]), "=f"(_tmem_load_0[1]), "=f"(_tmem_load_0[2]), "=f"(_tmem_load_0[3]), "=f"(_tmem_load_0[4]), "=f"(_tmem_load_0[5]), "=f"(_tmem_load_0[6]), "=f"(_tmem_load_0[7]), "=f"(_tmem_load_0[8]), "=f"(_tmem_load_0[9]), "=f"(_tmem_load_0[10]), "=f"(_tmem_load_0[11]), "=f"(_tmem_load_0[12]), "=f"(_tmem_load_0[13]), "=f"(_tmem_load_0[14]), "=f"(_tmem_load_0[15]), "=f"(_tmem_load_0[16]), "=f"(_tmem_load_0[17]), "=f"(_tmem_load_0[18]), "=f"(_tmem_load_0[19]), "=f"(_tmem_load_0[20]), "=f"(_tmem_load_0[21]), "=f"(_tmem_load_0[22]), "=f"(_tmem_load_0[23]), "=f"(_tmem_load_0[24]), "=f"(_tmem_load_0[25]), "=f"(_tmem_load_0[26]), "=f"(_tmem_load_0[27]), "=f"(_tmem_load_0[28]), "=f"(_tmem_load_0[29]), "=f"(_tmem_load_0[30]), "=f"(_tmem_load_0[31]), "=f"(_tmem_load_0[32]), "=f"(_tmem_load_0[33]), "=f"(_tmem_load_0[34]), "=f"(_tmem_load_0[35]), "=f"(_tmem_load_0[36]), "=f"(_tmem_load_0[37]), "=f"(_tmem_load_0[38]), "=f"(_tmem_load_0[39]), "=f"(_tmem_load_0[40]), "=f"(_tmem_load_0[41]), "=f"(_tmem_load_0[42]), "=f"(_tmem_load_0[43]), "=f"(_tmem_load_0[44]), "=f"(_tmem_load_0[45]), "=f"(_tmem_load_0[46]), "=f"(_tmem_load_0[47]), "=f"(_tmem_load_0[48]), "=f"(_tmem_load_0[49]), "=f"(_tmem_load_0[50]), "=f"(_tmem_load_0[51]), "=f"(_tmem_load_0[52]), "=f"(_tmem_load_0[53]), "=f"(_tmem_load_0[54]), "=f"(_tmem_load_0[55]), "=f"(_tmem_load_0[56]), "=f"(_tmem_load_0[57]), "=f"(_tmem_load_0[58]), "=f"(_tmem_load_0[59]), "=f"(_tmem_load_0[60]), "=f"(_tmem_load_0[61]), "=f"(_tmem_load_0[62]), "=f"(_tmem_load_0[63]) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + if (elect_sync()) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cluster.b64 _, [%0];" + :: "r"((score_empty_addr) & 0xFEFFFFFF) : "memory"); + } + if (valid_q != 0) { + #pragma unroll 2 + for (int col_base = 0; col_base < 64; col_base += 4) { + float dist_vec[4]; + dist_vec[0] = _tmem_load_0[col_base]; + dist_vec[1] = _tmem_load_0[col_base + 1]; + dist_vec[2] = _tmem_load_0[col_base + 2]; + dist_vec[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec[4]; + db_sq_vec[0] = smem_database_sq[col_base]; + db_sq_vec[1] = smem_database_sq[col_base + 1]; + db_sq_vec[2] = smem_database_sq[col_base + 2]; + db_sq_vec[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec)[_la], reinterpret_cast(db_sq_vec)[_la]); + float group_min = _t0[0]; + if (_t0[1] < group_min) { + group_min = _t0[1]; + } + if (_t0[2] < group_min) { + group_min = _t0[2]; + } + if (_t0[3] < group_min) { + group_min = _t0[3]; + } + if (group_min < best_d[TOP_K_MAX - 1]) { + #pragma unroll + for (int vec_col = 0; vec_col < 4; vec_col++) { + int db_idx = db_start + col_base + vec_col; + if (db_idx < M) { + float dist = _t0[vec_col]; + if (dist < best_d[TOP_K_MAX - 1]) { + best_d[TOP_K_MAX - 1] = dist; + best_i[TOP_K_MAX - 1] = db_idx; + #pragma unroll + for (int pos = TOP_K_MAX - 1; pos >= 1; pos--) { + if (best_d[pos] < best_d[pos - 1]) { + float tmp_d = best_d[pos - 1]; + int tmp_i = best_i[pos - 1]; + best_d[pos - 1] = best_d[pos]; + best_i[pos - 1] = best_i[pos]; + best_d[pos] = tmp_d; + best_i[pos] = tmp_i; + } + } + } + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + if (valid_q != 0) { + int out_base = ((split_idx_1 * B + batch_idx_1) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + if (out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = best_d[out_k]; + *((int*)(partial_indices + (out_base + out_k))) = best_i[out_k]; + } + } + } + } + } + } + + // Cleanup + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::2.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(128)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::2.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0044.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0044.cu new file mode 100644 index 00000000..8f475862 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0044.cu @@ -0,0 +1,84 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 32 +#define TOP_K_MAX 28 +#define SPLIT_COUNT 8 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_bad5midks8k28(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 32 + tid; + int stride = num_bids * 32; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int out_base = base_row; + int split_pos[SPLIT_COUNT]; + int split_base[SPLIT_COUNT]; + float cand_d[SPLIT_COUNT]; + int cand_i[SPLIT_COUNT]; + #pragma unroll + for (int split_idx = 0; split_idx < SPLIT_COUNT; split_idx++) { + split_pos[split_idx] = 0; + split_base[split_idx] = base_row + split_idx * split_stride; + cand_d[split_idx] = partial_dists[split_base[split_idx]]; + cand_i[split_idx] = partial_indices[split_base[split_idx]]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_d = cand_d[0]; + int best_i = cand_i[0]; + int best_split = 0; + #pragma unroll + for (int split_idx_1 = 1; split_idx_1 < SPLIT_COUNT; split_idx_1++) { + if (best_d > cand_d[split_idx_1]) { + best_d = cand_d[split_idx_1]; + best_i = cand_i[split_idx_1]; + best_split = split_idx_1; + } + } + *((float*)(out_dists + (out_base + out_k))) = best_d; + *((int*)(out_indices + (out_base + out_k))) = best_i; + split_pos[best_split] = split_pos[best_split] + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_pos = split_pos[best_split]; + int next_addr = split_base[best_split] + next_pos; + cand_d[best_split] = partial_dists[next_addr]; + cand_i[best_split] = partial_indices[next_addr]; + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0045.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0045.cu new file mode 100644 index 00000000..1a57e046 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0045.cu @@ -0,0 +1,657 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 128 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_STRIDE 32768 +#define SMEM_SMEM_DATABASE_OFF 33792 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_DATABASE_SQ_OFF 50176 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 50432 +#define THREADS 192 +#define BLOCK_Q 128 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_MAX 20 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16_cta2( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + ".reg .b32 m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "tcgen05.mma.cta_group::2.kind::f16 [%0], %1, %2, %3, {m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step_cg2( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, %3, " + "{m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], %1;\n\t" + "}\n" + :: "r"(mbar_addr), "h"(cta_mask) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x16(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x16.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15}, [%16];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ uint32_t smem_addr(const void* ptr) { + uint32_t addr; + asm("{\n\t" + ".reg .u64 u64addr;\n\t" + "cvta.to.shared.u64 u64addr, %1;\n\t" + "cvt.u32.u64 %0, u64addr;\n\t" + "}\n" : "=r"(addr) : "l"(ptr)); + return addr; +} + + +__device__ __forceinline__ uint32_t mapa_to_rank(uint32_t local_addr, uint32_t rank) { + uint32_t remote; + asm volatile("mapa.shared::cluster.u32 %0, %1, %2;" + : "=r"(remote) : "r"(local_addr), "r"(rank)); + return remote; +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem_cta2( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global" + ".mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .b16 lo, hi;\n\t" + "mov.b32 {lo, hi}, %1;\n\t" + "tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], lo;\n\t" + "}\n" + :: "r"(mbar_addr), "r"((uint32_t)cta_mask) : "memory"); +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_k20split(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tile_pairs, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + const unsigned int clusters_x = gridDim.x / 2; + const unsigned int cluster_id = ((blockIdx.z * gridDim.y + blockIdx.y) * clusters_x) + blockIdx.x / 2; + const unsigned int num_clusters = clusters_x * gridDim.y * gridDim.z; + + int cta_rank; + asm volatile("mov.b32 %0, %%cluster_ctarank;" : "=r"(cta_rank)); + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 33792); + const int smem_database_addr = smem + 33792; + float* smem_database_sq = reinterpret_cast(smem_raw + 50176); + const int smem_database_sq_addr = smem + 50176; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 0, 2, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 16, 2, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=8 + mbarrier_init_pred(smem + 40, 8, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (128 columns, 128 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::2.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(128) : "memory"); + } + + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: load ---- + if (warp == 0) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 0) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx = cluster_id; work_idx < total_work; work_idx += num_clusters) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tile_pairs; + int q_tile_pair = query_work % num_q_tile_pairs; + int q_tile = q_tile_pair * 2 + cta_rank; + int off_q = q_tile * BLOCK_Q; + int global_q = batch_idx * Q + off_q; + int db_tile_start = split_idx * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((query_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(32768)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_query_addr), "l"(tmap_query), "r"(0), "r"(global_q), "r"(0), + "r"(((query_full_addr) & 0xFEFFFFFF)) : "memory"); + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int off_m = db_tile * BLOCK_M; + int global_m = batch_idx * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((database_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(16384)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_database_addr), "l"(tmap_database), "r"(0), "r"(global_m), "r"(0), + "r"(((database_full_addr) & 0xFEFFFFFF)) : "memory"); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 1) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + if (cta_rank == 0) { + #pragma unroll 1 + for (unsigned int work_idx_1 = cluster_id; work_idx_1 < total_work; work_idx_1 += num_clusters) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = (smem_query_addr >> 4) & 0x3FFF; + int _mma_b_lo_0 = (smem_database_addr >> 4) & 0x3FFF; + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\tmov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 270533776;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit_cg2_multicast(score_full_addr, (uint16_t)(3)); + elect_commit_cg2_multicast(database_empty_addr, (uint16_t)(3)); + } + elect_commit_cg2_multicast(query_empty_addr, (uint16_t)(3)); + } + } + } + // ---- Role: compute ---- + } else if (warp >= 2 && warp <= 5) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = cluster_id; work_idx_2 < total_work; work_idx_2 += num_clusters) { + int split_idx_1 = work_idx_2 % (unsigned int)split_count; + int query_work_1 = work_idx_2 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tile_pairs; + int q_tile_pair_1 = query_work_1 % num_q_tile_pairs; + int q_tile_1 = q_tile_pair_1 * 2 + cta_rank; + int off_q_1 = q_tile_1 * BLOCK_Q; + int q_idx = off_q_1 + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx_1 * Q + q_idx]; + } + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int db_start = db_tile_1 * BLOCK_M; + int db_sq_idx = db_start + (warp % 4 * 32 + lane); + if (warp % 4 * 32 + lane < BLOCK_M) { + if (db_sq_idx < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx_1 * M + db_sq_idx]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 0.0f; + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(cta_rank * BLOCK_Q + (warp % 4 * 32 << 16) << 16); + float _tmem_load_0[64]; + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x64.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31, %32, %33, %34, %35, %36, %37, %38, %39, %40, %41, %42, %43, %44, %45, %46, %47, %48, %49, %50, %51, %52, %53, %54, %55, %56, %57, %58, %59, %60, %61, %62, %63}, [%64];" + : "=f"(_tmem_load_0[0]), "=f"(_tmem_load_0[1]), "=f"(_tmem_load_0[2]), "=f"(_tmem_load_0[3]), "=f"(_tmem_load_0[4]), "=f"(_tmem_load_0[5]), "=f"(_tmem_load_0[6]), "=f"(_tmem_load_0[7]), "=f"(_tmem_load_0[8]), "=f"(_tmem_load_0[9]), "=f"(_tmem_load_0[10]), "=f"(_tmem_load_0[11]), "=f"(_tmem_load_0[12]), "=f"(_tmem_load_0[13]), "=f"(_tmem_load_0[14]), "=f"(_tmem_load_0[15]), "=f"(_tmem_load_0[16]), "=f"(_tmem_load_0[17]), "=f"(_tmem_load_0[18]), "=f"(_tmem_load_0[19]), "=f"(_tmem_load_0[20]), "=f"(_tmem_load_0[21]), "=f"(_tmem_load_0[22]), "=f"(_tmem_load_0[23]), "=f"(_tmem_load_0[24]), "=f"(_tmem_load_0[25]), "=f"(_tmem_load_0[26]), "=f"(_tmem_load_0[27]), "=f"(_tmem_load_0[28]), "=f"(_tmem_load_0[29]), "=f"(_tmem_load_0[30]), "=f"(_tmem_load_0[31]), "=f"(_tmem_load_0[32]), "=f"(_tmem_load_0[33]), "=f"(_tmem_load_0[34]), "=f"(_tmem_load_0[35]), "=f"(_tmem_load_0[36]), "=f"(_tmem_load_0[37]), "=f"(_tmem_load_0[38]), "=f"(_tmem_load_0[39]), "=f"(_tmem_load_0[40]), "=f"(_tmem_load_0[41]), "=f"(_tmem_load_0[42]), "=f"(_tmem_load_0[43]), "=f"(_tmem_load_0[44]), "=f"(_tmem_load_0[45]), "=f"(_tmem_load_0[46]), "=f"(_tmem_load_0[47]), "=f"(_tmem_load_0[48]), "=f"(_tmem_load_0[49]), "=f"(_tmem_load_0[50]), "=f"(_tmem_load_0[51]), "=f"(_tmem_load_0[52]), "=f"(_tmem_load_0[53]), "=f"(_tmem_load_0[54]), "=f"(_tmem_load_0[55]), "=f"(_tmem_load_0[56]), "=f"(_tmem_load_0[57]), "=f"(_tmem_load_0[58]), "=f"(_tmem_load_0[59]), "=f"(_tmem_load_0[60]), "=f"(_tmem_load_0[61]), "=f"(_tmem_load_0[62]), "=f"(_tmem_load_0[63]) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + if (elect_sync()) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cluster.b64 _, [%0];" + :: "r"((score_empty_addr) & 0xFEFFFFFF) : "memory"); + } + if (valid_q != 0) { + #pragma unroll 2 + for (int col_base = 0; col_base < 64; col_base += 4) { + float dist_vec[4]; + dist_vec[0] = _tmem_load_0[col_base]; + dist_vec[1] = _tmem_load_0[col_base + 1]; + dist_vec[2] = _tmem_load_0[col_base + 2]; + dist_vec[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec[4]; + db_sq_vec[0] = smem_database_sq[col_base]; + db_sq_vec[1] = smem_database_sq[col_base + 1]; + db_sq_vec[2] = smem_database_sq[col_base + 2]; + db_sq_vec[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec)[_la], reinterpret_cast(db_sq_vec)[_la]); + float group_min = _t0[0]; + if (_t0[1] < group_min) { + group_min = _t0[1]; + } + if (_t0[2] < group_min) { + group_min = _t0[2]; + } + if (_t0[3] < group_min) { + group_min = _t0[3]; + } + if (group_min < best_d[TOP_K_MAX - 1]) { + #pragma unroll + for (int vec_col = 0; vec_col < 4; vec_col++) { + int db_idx = db_start + col_base + vec_col; + if (db_idx < M) { + float dist = _t0[vec_col]; + if (dist < best_d[TOP_K_MAX - 1]) { + best_d[TOP_K_MAX - 1] = dist; + best_i[TOP_K_MAX - 1] = db_idx; + #pragma unroll + for (int pos = TOP_K_MAX - 1; pos >= 1; pos--) { + if (best_d[pos] < best_d[pos - 1]) { + float tmp_d = best_d[pos - 1]; + int tmp_i = best_i[pos - 1]; + best_d[pos - 1] = best_d[pos]; + best_i[pos - 1] = best_i[pos]; + best_d[pos] = tmp_d; + best_i[pos] = tmp_i; + } + } + } + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + if (valid_q != 0) { + int out_base = ((split_idx_1 * B + batch_idx_1) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + if (out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = best_d[out_k]; + *((int*)(partial_indices + (out_base + out_k))) = best_i[out_k]; + } + } + } + } + } + } + + // Cleanup + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::2.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(128)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::2.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0046.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0046.cu new file mode 100644 index 00000000..b3846e29 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0046.cu @@ -0,0 +1,109 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 32 +#define TOP_K_MAX 20 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_evolve_7bfc_k32_merge_s4_rowbase_cache_k20split(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int K, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 32 + tid; + int stride = num_bids * 32; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int base_row = row * K; + int split_stride = total_queries * K; + int out_base = base_row; + int split_base0 = base_row; + int split_base1 = base_row + split_stride; + int split_base2 = split_base1 + split_stride; + int split_base3 = split_base2 + split_stride; + int pos0 = 0; + int pos1 = 0; + int pos2 = 0; + int pos3 = 0; + float cand_d0 = partial_dists[split_base0]; + int cand_i0 = partial_indices[split_base0]; + float cand_d1 = partial_dists[split_base1]; + int cand_i1 = partial_indices[split_base1]; + float cand_d2 = partial_dists[split_base2]; + int cand_i2 = partial_indices[split_base2]; + float cand_d3 = partial_dists[split_base3]; + int cand_i3 = partial_indices[split_base3]; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + if (out_k < K) { + int cand01_cmp = ((cand_d1 < cand_d0) ? 1 : 0); + float best01_d = ((cand01_cmp != 0) ? cand_d1 : cand_d0); + int best01_i = ((cand01_cmp != 0) ? cand_i1 : cand_i0); + int best01_split = ((cand01_cmp != 0) ? 1 : 0); + int cand23_cmp = ((cand_d3 < cand_d2) ? 1 : 0); + float best23_d = ((cand23_cmp != 0) ? cand_d3 : cand_d2); + int best23_i = ((cand23_cmp != 0) ? cand_i3 : cand_i2); + int best23_split = ((cand23_cmp != 0) ? 3 : 2); + int best_cmp = ((best23_d < best01_d) ? 1 : 0); + float best_d = ((best_cmp != 0) ? best23_d : best01_d); + int best_i = ((best_cmp != 0) ? best23_i : best01_i); + int best_split = ((best_cmp != 0) ? best23_split : best01_split); + *((float*)(out_dists + (out_base + out_k))) = best_d; + *((int*)(out_indices + (out_base + out_k))) = best_i; + if (out_k + 1 < K) { + if (best_split == 0) { + pos0 = pos0 + 1; + int next_addr0 = split_base0 + pos0; + cand_d0 = partial_dists[next_addr0]; + cand_i0 = partial_indices[next_addr0]; + } else if (best_split == 1) { + pos1 = pos1 + 1; + int next_addr1 = split_base1 + pos1; + cand_d1 = partial_dists[next_addr1]; + cand_i1 = partial_indices[next_addr1]; + } else { + if (best_split == 2) { + pos2 = pos2 + 1; + int next_addr2 = split_base2 + pos2; + cand_d2 = partial_dists[next_addr2]; + cand_i2 = partial_indices[next_addr2]; + } else { + pos3 = pos3 + 1; + int next_addr3 = split_base3 + pos3; + cand_d3 = partial_dists[next_addr3]; + cand_i3 = partial_indices[next_addr3]; + } + } + } + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0047.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0047.cu new file mode 100644 index 00000000..04911d3f --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0047.cu @@ -0,0 +1,655 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 128 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_STRIDE 32768 +#define SMEM_SMEM_DATABASE_OFF 33792 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_DATABASE_SQ_OFF 50176 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 50432 +#define THREADS 192 +#define BLOCK_Q 128 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_MAX 12 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16_cta2( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + ".reg .b32 m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "tcgen05.mma.cta_group::2.kind::f16 [%0], %1, %2, %3, {m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step_cg2( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, %3, " + "{m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], %1;\n\t" + "}\n" + :: "r"(mbar_addr), "h"(cta_mask) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x16(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x16.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15}, [%16];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ uint32_t smem_addr(const void* ptr) { + uint32_t addr; + asm("{\n\t" + ".reg .u64 u64addr;\n\t" + "cvta.to.shared.u64 u64addr, %1;\n\t" + "cvt.u32.u64 %0, u64addr;\n\t" + "}\n" : "=r"(addr) : "l"(ptr)); + return addr; +} + + +__device__ __forceinline__ uint32_t mapa_to_rank(uint32_t local_addr, uint32_t rank) { + uint32_t remote; + asm volatile("mapa.shared::cluster.u32 %0, %1, %2;" + : "=r"(remote) : "r"(local_addr), "r"(rank)); + return remote; +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem_cta2( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global" + ".mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .b16 lo, hi;\n\t" + "mov.b32 {lo, hi}, %1;\n\t" + "tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], lo;\n\t" + "}\n" + :: "r"(mbar_addr), "r"((uint32_t)cta_mask) : "memory"); +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k12unordered(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tile_pairs, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + const unsigned int clusters_x = gridDim.x / 2; + const unsigned int cluster_id = ((blockIdx.z * gridDim.y + blockIdx.y) * clusters_x) + blockIdx.x / 2; + const unsigned int num_clusters = clusters_x * gridDim.y * gridDim.z; + + int cta_rank; + asm volatile("mov.b32 %0, %%cluster_ctarank;" : "=r"(cta_rank)); + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 33792); + const int smem_database_addr = smem + 33792; + float* smem_database_sq = reinterpret_cast(smem_raw + 50176); + const int smem_database_sq_addr = smem + 50176; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 0, 2, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 16, 2, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=8 + mbarrier_init_pred(smem + 40, 8, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (128 columns, 128 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::2.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(128) : "memory"); + } + + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: load ---- + if (warp == 0) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 0) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx = cluster_id; work_idx < total_work; work_idx += num_clusters) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tile_pairs; + int q_tile_pair = query_work % num_q_tile_pairs; + int q_tile = q_tile_pair * 2 + cta_rank; + int off_q = q_tile * BLOCK_Q; + int global_q = batch_idx * Q + off_q; + int db_tile_start = split_idx * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((query_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(32768)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_query_addr), "l"(tmap_query), "r"(0), "r"(global_q), "r"(0), + "r"(((query_full_addr) & 0xFEFFFFFF)) : "memory"); + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int off_m = db_tile * BLOCK_M; + int global_m = batch_idx * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((database_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(16384)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_database_addr), "l"(tmap_database), "r"(0), "r"(global_m), "r"(0), + "r"(((database_full_addr) & 0xFEFFFFFF)) : "memory"); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 1) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + if (cta_rank == 0) { + #pragma unroll 1 + for (unsigned int work_idx_1 = cluster_id; work_idx_1 < total_work; work_idx_1 += num_clusters) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = (smem_query_addr >> 4) & 0x3FFF; + int _mma_b_lo_0 = (smem_database_addr >> 4) & 0x3FFF; + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\tmov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 270533776;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit_cg2_multicast(score_full_addr, (uint16_t)(3)); + elect_commit_cg2_multicast(database_empty_addr, (uint16_t)(3)); + } + elect_commit_cg2_multicast(query_empty_addr, (uint16_t)(3)); + } + } + } + // ---- Role: compute ---- + } else if (warp >= 2 && warp <= 5) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = cluster_id; work_idx_2 < total_work; work_idx_2 += num_clusters) { + int split_idx_1 = work_idx_2 % (unsigned int)split_count; + int query_work_1 = work_idx_2 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tile_pairs; + int q_tile_pair_1 = query_work_1 % num_q_tile_pairs; + int q_tile_1 = q_tile_pair_1 * 2 + cta_rank; + int off_q_1 = q_tile_1 * BLOCK_Q; + int q_idx = off_q_1 + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx_1 * Q + q_idx]; + } + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + float worst_d = 3.4e+38f; + int worst_pos = 0; + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int db_start = db_tile_1 * BLOCK_M; + int db_sq_idx = db_start + (warp % 4 * 32 + lane); + if (warp % 4 * 32 + lane < BLOCK_M) { + if (db_sq_idx < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx_1 * M + db_sq_idx]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 0.0f; + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(cta_rank * BLOCK_Q + (warp % 4 * 32 << 16) << 16); + float _tmem_load_0[64]; + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x64.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31, %32, %33, %34, %35, %36, %37, %38, %39, %40, %41, %42, %43, %44, %45, %46, %47, %48, %49, %50, %51, %52, %53, %54, %55, %56, %57, %58, %59, %60, %61, %62, %63}, [%64];" + : "=f"(_tmem_load_0[0]), "=f"(_tmem_load_0[1]), "=f"(_tmem_load_0[2]), "=f"(_tmem_load_0[3]), "=f"(_tmem_load_0[4]), "=f"(_tmem_load_0[5]), "=f"(_tmem_load_0[6]), "=f"(_tmem_load_0[7]), "=f"(_tmem_load_0[8]), "=f"(_tmem_load_0[9]), "=f"(_tmem_load_0[10]), "=f"(_tmem_load_0[11]), "=f"(_tmem_load_0[12]), "=f"(_tmem_load_0[13]), "=f"(_tmem_load_0[14]), "=f"(_tmem_load_0[15]), "=f"(_tmem_load_0[16]), "=f"(_tmem_load_0[17]), "=f"(_tmem_load_0[18]), "=f"(_tmem_load_0[19]), "=f"(_tmem_load_0[20]), "=f"(_tmem_load_0[21]), "=f"(_tmem_load_0[22]), "=f"(_tmem_load_0[23]), "=f"(_tmem_load_0[24]), "=f"(_tmem_load_0[25]), "=f"(_tmem_load_0[26]), "=f"(_tmem_load_0[27]), "=f"(_tmem_load_0[28]), "=f"(_tmem_load_0[29]), "=f"(_tmem_load_0[30]), "=f"(_tmem_load_0[31]), "=f"(_tmem_load_0[32]), "=f"(_tmem_load_0[33]), "=f"(_tmem_load_0[34]), "=f"(_tmem_load_0[35]), "=f"(_tmem_load_0[36]), "=f"(_tmem_load_0[37]), "=f"(_tmem_load_0[38]), "=f"(_tmem_load_0[39]), "=f"(_tmem_load_0[40]), "=f"(_tmem_load_0[41]), "=f"(_tmem_load_0[42]), "=f"(_tmem_load_0[43]), "=f"(_tmem_load_0[44]), "=f"(_tmem_load_0[45]), "=f"(_tmem_load_0[46]), "=f"(_tmem_load_0[47]), "=f"(_tmem_load_0[48]), "=f"(_tmem_load_0[49]), "=f"(_tmem_load_0[50]), "=f"(_tmem_load_0[51]), "=f"(_tmem_load_0[52]), "=f"(_tmem_load_0[53]), "=f"(_tmem_load_0[54]), "=f"(_tmem_load_0[55]), "=f"(_tmem_load_0[56]), "=f"(_tmem_load_0[57]), "=f"(_tmem_load_0[58]), "=f"(_tmem_load_0[59]), "=f"(_tmem_load_0[60]), "=f"(_tmem_load_0[61]), "=f"(_tmem_load_0[62]), "=f"(_tmem_load_0[63]) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + if (elect_sync()) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cluster.b64 _, [%0];" + :: "r"((score_empty_addr) & 0xFEFFFFFF) : "memory"); + } + if (valid_q != 0) { + #pragma unroll 2 + for (int col_base = 0; col_base < 64; col_base += 4) { + float dist_vec[4]; + dist_vec[0] = _tmem_load_0[col_base]; + dist_vec[1] = _tmem_load_0[col_base + 1]; + dist_vec[2] = _tmem_load_0[col_base + 2]; + dist_vec[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec[4]; + db_sq_vec[0] = smem_database_sq[col_base]; + db_sq_vec[1] = smem_database_sq[col_base + 1]; + db_sq_vec[2] = smem_database_sq[col_base + 2]; + db_sq_vec[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec)[_la], reinterpret_cast(db_sq_vec)[_la]); + float group_min = _t0[0]; + if (_t0[1] < group_min) { + group_min = _t0[1]; + } + if (_t0[2] < group_min) { + group_min = _t0[2]; + } + if (_t0[3] < group_min) { + group_min = _t0[3]; + } + if (group_min < worst_d) { + #pragma unroll + for (int vec_col = 0; vec_col < 4; vec_col++) { + int db_idx = db_start + col_base + vec_col; + if (db_idx < M) { + float dist = _t0[vec_col]; + if (dist < worst_d) { + best_d[worst_pos] = dist; + best_i[worst_pos] = db_idx; + worst_d = best_d[0]; + worst_pos = 0; + #pragma unroll + for (int scan_pos = 1; scan_pos < TOP_K_MAX; scan_pos++) { + if (worst_d < best_d[scan_pos]) { + worst_d = best_d[scan_pos]; + worst_pos = scan_pos; + } + } + } + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + if (valid_q != 0) { + int out_base = ((split_idx_1 * B + batch_idx_1) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + *((float*)(partial_dists + (out_base + out_k))) = best_d[out_k]; + *((int*)(partial_indices + (out_base + out_k))) = best_i[out_k]; + } + } + } + } + } + + // Cleanup + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::2.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(128)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::2.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0048.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0048.cu new file mode 100644 index 00000000..a7cca7b4 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0048.cu @@ -0,0 +1,85 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 32 +#define TOP_K_MAX 12 +#define SPLIT_COUNT 4 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_evolve_7bfc_k32_merge_s4_unordered_k12unordered(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 32 + tid; + int stride = num_bids * 32; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + float worst_d = 3.4e+38f; + int worst_pos = 0; + #pragma unroll + for (int split_idx = 0; split_idx < SPLIT_COUNT; split_idx++) { + int partial_base = base_row + split_idx * split_stride; + #pragma unroll + for (int cand_k = 0; cand_k < TOP_K_MAX; cand_k++) { + float cand_d = partial_dists[partial_base + cand_k]; + int cand_i = partial_indices[partial_base + cand_k]; + if (cand_d < worst_d) { + best_d[worst_pos] = cand_d; + best_i[worst_pos] = cand_i; + worst_d = best_d[0]; + worst_pos = 0; + #pragma unroll + for (int scan_pos = 1; scan_pos < TOP_K_MAX; scan_pos++) { + if (worst_d < best_d[scan_pos]) { + worst_d = best_d[scan_pos]; + worst_pos = scan_pos; + } + } + } + } + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + *((float*)(out_dists + (base_row + out_k))) = best_d[out_k]; + *((int*)(out_indices + (base_row + out_k))) = best_i[out_k]; + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0049.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0049.cu new file mode 100644 index 00000000..99341212 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0049.cu @@ -0,0 +1,655 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 128 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_STRIDE 32768 +#define SMEM_SMEM_DATABASE_OFF 33792 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_DATABASE_SQ_OFF 50176 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 50432 +#define THREADS 192 +#define BLOCK_Q 128 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_MAX 13 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16_cta2( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + ".reg .b32 m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "tcgen05.mma.cta_group::2.kind::f16 [%0], %1, %2, %3, {m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step_cg2( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, %3, " + "{m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], %1;\n\t" + "}\n" + :: "r"(mbar_addr), "h"(cta_mask) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x16(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x16.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15}, [%16];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ uint32_t smem_addr(const void* ptr) { + uint32_t addr; + asm("{\n\t" + ".reg .u64 u64addr;\n\t" + "cvta.to.shared.u64 u64addr, %1;\n\t" + "cvt.u32.u64 %0, u64addr;\n\t" + "}\n" : "=r"(addr) : "l"(ptr)); + return addr; +} + + +__device__ __forceinline__ uint32_t mapa_to_rank(uint32_t local_addr, uint32_t rank) { + uint32_t remote; + asm volatile("mapa.shared::cluster.u32 %0, %1, %2;" + : "=r"(remote) : "r"(local_addr), "r"(rank)); + return remote; +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem_cta2( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global" + ".mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .b16 lo, hi;\n\t" + "mov.b32 {lo, hi}, %1;\n\t" + "tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], lo;\n\t" + "}\n" + :: "r"(mbar_addr), "r"((uint32_t)cta_mask) : "memory"); +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_2c1ck13unordered(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tile_pairs, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + const unsigned int clusters_x = gridDim.x / 2; + const unsigned int cluster_id = ((blockIdx.z * gridDim.y + blockIdx.y) * clusters_x) + blockIdx.x / 2; + const unsigned int num_clusters = clusters_x * gridDim.y * gridDim.z; + + int cta_rank; + asm volatile("mov.b32 %0, %%cluster_ctarank;" : "=r"(cta_rank)); + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 33792); + const int smem_database_addr = smem + 33792; + float* smem_database_sq = reinterpret_cast(smem_raw + 50176); + const int smem_database_sq_addr = smem + 50176; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 0, 2, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 16, 2, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=8 + mbarrier_init_pred(smem + 40, 8, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (128 columns, 128 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::2.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(128) : "memory"); + } + + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: load ---- + if (warp == 0) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 0) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx = cluster_id; work_idx < total_work; work_idx += num_clusters) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tile_pairs; + int q_tile_pair = query_work % num_q_tile_pairs; + int q_tile = q_tile_pair * 2 + cta_rank; + int off_q = q_tile * BLOCK_Q; + int global_q = batch_idx * Q + off_q; + int db_tile_start = split_idx * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((query_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(32768)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_query_addr), "l"(tmap_query), "r"(0), "r"(global_q), "r"(0), + "r"(((query_full_addr) & 0xFEFFFFFF)) : "memory"); + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int off_m = db_tile * BLOCK_M; + int global_m = batch_idx * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((database_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(16384)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_database_addr), "l"(tmap_database), "r"(0), "r"(global_m), "r"(0), + "r"(((database_full_addr) & 0xFEFFFFFF)) : "memory"); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 1) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + if (cta_rank == 0) { + #pragma unroll 1 + for (unsigned int work_idx_1 = cluster_id; work_idx_1 < total_work; work_idx_1 += num_clusters) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = (smem_query_addr >> 4) & 0x3FFF; + int _mma_b_lo_0 = (smem_database_addr >> 4) & 0x3FFF; + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\tmov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 270533776;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit_cg2_multicast(score_full_addr, (uint16_t)(3)); + elect_commit_cg2_multicast(database_empty_addr, (uint16_t)(3)); + } + elect_commit_cg2_multicast(query_empty_addr, (uint16_t)(3)); + } + } + } + // ---- Role: compute ---- + } else if (warp >= 2 && warp <= 5) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = cluster_id; work_idx_2 < total_work; work_idx_2 += num_clusters) { + int split_idx_1 = work_idx_2 % (unsigned int)split_count; + int query_work_1 = work_idx_2 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tile_pairs; + int q_tile_pair_1 = query_work_1 % num_q_tile_pairs; + int q_tile_1 = q_tile_pair_1 * 2 + cta_rank; + int off_q_1 = q_tile_1 * BLOCK_Q; + int q_idx = off_q_1 + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx_1 * Q + q_idx]; + } + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + float worst_d = 3.4e+38f; + int worst_pos = 0; + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int db_start = db_tile_1 * BLOCK_M; + int db_sq_idx = db_start + (warp % 4 * 32 + lane); + if (warp % 4 * 32 + lane < BLOCK_M) { + if (db_sq_idx < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx_1 * M + db_sq_idx]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 0.0f; + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(cta_rank * BLOCK_Q + (warp % 4 * 32 << 16) << 16); + float _tmem_load_0[64]; + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x64.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31, %32, %33, %34, %35, %36, %37, %38, %39, %40, %41, %42, %43, %44, %45, %46, %47, %48, %49, %50, %51, %52, %53, %54, %55, %56, %57, %58, %59, %60, %61, %62, %63}, [%64];" + : "=f"(_tmem_load_0[0]), "=f"(_tmem_load_0[1]), "=f"(_tmem_load_0[2]), "=f"(_tmem_load_0[3]), "=f"(_tmem_load_0[4]), "=f"(_tmem_load_0[5]), "=f"(_tmem_load_0[6]), "=f"(_tmem_load_0[7]), "=f"(_tmem_load_0[8]), "=f"(_tmem_load_0[9]), "=f"(_tmem_load_0[10]), "=f"(_tmem_load_0[11]), "=f"(_tmem_load_0[12]), "=f"(_tmem_load_0[13]), "=f"(_tmem_load_0[14]), "=f"(_tmem_load_0[15]), "=f"(_tmem_load_0[16]), "=f"(_tmem_load_0[17]), "=f"(_tmem_load_0[18]), "=f"(_tmem_load_0[19]), "=f"(_tmem_load_0[20]), "=f"(_tmem_load_0[21]), "=f"(_tmem_load_0[22]), "=f"(_tmem_load_0[23]), "=f"(_tmem_load_0[24]), "=f"(_tmem_load_0[25]), "=f"(_tmem_load_0[26]), "=f"(_tmem_load_0[27]), "=f"(_tmem_load_0[28]), "=f"(_tmem_load_0[29]), "=f"(_tmem_load_0[30]), "=f"(_tmem_load_0[31]), "=f"(_tmem_load_0[32]), "=f"(_tmem_load_0[33]), "=f"(_tmem_load_0[34]), "=f"(_tmem_load_0[35]), "=f"(_tmem_load_0[36]), "=f"(_tmem_load_0[37]), "=f"(_tmem_load_0[38]), "=f"(_tmem_load_0[39]), "=f"(_tmem_load_0[40]), "=f"(_tmem_load_0[41]), "=f"(_tmem_load_0[42]), "=f"(_tmem_load_0[43]), "=f"(_tmem_load_0[44]), "=f"(_tmem_load_0[45]), "=f"(_tmem_load_0[46]), "=f"(_tmem_load_0[47]), "=f"(_tmem_load_0[48]), "=f"(_tmem_load_0[49]), "=f"(_tmem_load_0[50]), "=f"(_tmem_load_0[51]), "=f"(_tmem_load_0[52]), "=f"(_tmem_load_0[53]), "=f"(_tmem_load_0[54]), "=f"(_tmem_load_0[55]), "=f"(_tmem_load_0[56]), "=f"(_tmem_load_0[57]), "=f"(_tmem_load_0[58]), "=f"(_tmem_load_0[59]), "=f"(_tmem_load_0[60]), "=f"(_tmem_load_0[61]), "=f"(_tmem_load_0[62]), "=f"(_tmem_load_0[63]) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + if (elect_sync()) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cluster.b64 _, [%0];" + :: "r"((score_empty_addr) & 0xFEFFFFFF) : "memory"); + } + if (valid_q != 0) { + #pragma unroll 2 + for (int col_base = 0; col_base < 64; col_base += 4) { + float dist_vec[4]; + dist_vec[0] = _tmem_load_0[col_base]; + dist_vec[1] = _tmem_load_0[col_base + 1]; + dist_vec[2] = _tmem_load_0[col_base + 2]; + dist_vec[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec[4]; + db_sq_vec[0] = smem_database_sq[col_base]; + db_sq_vec[1] = smem_database_sq[col_base + 1]; + db_sq_vec[2] = smem_database_sq[col_base + 2]; + db_sq_vec[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec)[_la], reinterpret_cast(db_sq_vec)[_la]); + float group_min = _t0[0]; + if (_t0[1] < group_min) { + group_min = _t0[1]; + } + if (_t0[2] < group_min) { + group_min = _t0[2]; + } + if (_t0[3] < group_min) { + group_min = _t0[3]; + } + if (group_min < worst_d) { + #pragma unroll + for (int vec_col = 0; vec_col < 4; vec_col++) { + int db_idx = db_start + col_base + vec_col; + if (db_idx < M) { + float dist = _t0[vec_col]; + if (dist < worst_d) { + best_d[worst_pos] = dist; + best_i[worst_pos] = db_idx; + worst_d = best_d[0]; + worst_pos = 0; + #pragma unroll + for (int scan_pos = 1; scan_pos < TOP_K_MAX; scan_pos++) { + if (worst_d < best_d[scan_pos]) { + worst_d = best_d[scan_pos]; + worst_pos = scan_pos; + } + } + } + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + if (valid_q != 0) { + int out_base = ((split_idx_1 * B + batch_idx_1) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + *((float*)(partial_dists + (out_base + out_k))) = best_d[out_k]; + *((int*)(partial_indices + (out_base + out_k))) = best_i[out_k]; + } + } + } + } + } + + // Cleanup + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::2.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(128)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::2.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0050.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0050.cu new file mode 100644 index 00000000..d14f034b --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0050.cu @@ -0,0 +1,85 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 32 +#define TOP_K_MAX 13 +#define SPLIT_COUNT 4 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_evolve_7bfc_k32_merge_s4_unordered_2c1ck13unordered(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 32 + tid; + int stride = num_bids * 32; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + float worst_d = 3.4e+38f; + int worst_pos = 0; + #pragma unroll + for (int split_idx = 0; split_idx < SPLIT_COUNT; split_idx++) { + int partial_base = base_row + split_idx * split_stride; + #pragma unroll + for (int cand_k = 0; cand_k < TOP_K_MAX; cand_k++) { + float cand_d = partial_dists[partial_base + cand_k]; + int cand_i = partial_indices[partial_base + cand_k]; + if (cand_d < worst_d) { + best_d[worst_pos] = cand_d; + best_i[worst_pos] = cand_i; + worst_d = best_d[0]; + worst_pos = 0; + #pragma unroll + for (int scan_pos = 1; scan_pos < TOP_K_MAX; scan_pos++) { + if (worst_d < best_d[scan_pos]) { + worst_d = best_d[scan_pos]; + worst_pos = scan_pos; + } + } + } + } + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + *((float*)(out_dists + (base_row + out_k))) = best_d[out_k]; + *((int*)(out_indices + (base_row + out_k))) = best_i[out_k]; + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0051.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0051.cu new file mode 100644 index 00000000..bd7ea932 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0051.cu @@ -0,0 +1,655 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 128 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_STRIDE 32768 +#define SMEM_SMEM_DATABASE_OFF 33792 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_DATABASE_SQ_OFF 50176 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 50432 +#define THREADS 192 +#define BLOCK_Q 128 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_MAX 20 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16_cta2( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + ".reg .b32 m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "tcgen05.mma.cta_group::2.kind::f16 [%0], %1, %2, %3, {m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step_cg2( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, %3, " + "{m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], %1;\n\t" + "}\n" + :: "r"(mbar_addr), "h"(cta_mask) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x16(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x16.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15}, [%16];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ uint32_t smem_addr(const void* ptr) { + uint32_t addr; + asm("{\n\t" + ".reg .u64 u64addr;\n\t" + "cvta.to.shared.u64 u64addr, %1;\n\t" + "cvt.u32.u64 %0, u64addr;\n\t" + "}\n" : "=r"(addr) : "l"(ptr)); + return addr; +} + + +__device__ __forceinline__ uint32_t mapa_to_rank(uint32_t local_addr, uint32_t rank) { + uint32_t remote; + asm volatile("mapa.shared::cluster.u32 %0, %1, %2;" + : "=r"(remote) : "r"(local_addr), "r"(rank)); + return remote; +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem_cta2( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global" + ".mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .b16 lo, hi;\n\t" + "mov.b32 {lo, hi}, %1;\n\t" + "tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], lo;\n\t" + "}\n" + :: "r"(mbar_addr), "r"((uint32_t)cta_mask) : "memory"); +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k20unordered(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tile_pairs, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + const unsigned int clusters_x = gridDim.x / 2; + const unsigned int cluster_id = ((blockIdx.z * gridDim.y + blockIdx.y) * clusters_x) + blockIdx.x / 2; + const unsigned int num_clusters = clusters_x * gridDim.y * gridDim.z; + + int cta_rank; + asm volatile("mov.b32 %0, %%cluster_ctarank;" : "=r"(cta_rank)); + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 33792); + const int smem_database_addr = smem + 33792; + float* smem_database_sq = reinterpret_cast(smem_raw + 50176); + const int smem_database_sq_addr = smem + 50176; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 0, 2, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 16, 2, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=8 + mbarrier_init_pred(smem + 40, 8, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (128 columns, 128 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::2.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(128) : "memory"); + } + + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: load ---- + if (warp == 0) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 0) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx = cluster_id; work_idx < total_work; work_idx += num_clusters) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tile_pairs; + int q_tile_pair = query_work % num_q_tile_pairs; + int q_tile = q_tile_pair * 2 + cta_rank; + int off_q = q_tile * BLOCK_Q; + int global_q = batch_idx * Q + off_q; + int db_tile_start = split_idx * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((query_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(32768)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_query_addr), "l"(tmap_query), "r"(0), "r"(global_q), "r"(0), + "r"(((query_full_addr) & 0xFEFFFFFF)) : "memory"); + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int off_m = db_tile * BLOCK_M; + int global_m = batch_idx * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((database_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(16384)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_database_addr), "l"(tmap_database), "r"(0), "r"(global_m), "r"(0), + "r"(((database_full_addr) & 0xFEFFFFFF)) : "memory"); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 1) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + if (cta_rank == 0) { + #pragma unroll 1 + for (unsigned int work_idx_1 = cluster_id; work_idx_1 < total_work; work_idx_1 += num_clusters) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = (smem_query_addr >> 4) & 0x3FFF; + int _mma_b_lo_0 = (smem_database_addr >> 4) & 0x3FFF; + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\tmov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 270533776;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit_cg2_multicast(score_full_addr, (uint16_t)(3)); + elect_commit_cg2_multicast(database_empty_addr, (uint16_t)(3)); + } + elect_commit_cg2_multicast(query_empty_addr, (uint16_t)(3)); + } + } + } + // ---- Role: compute ---- + } else if (warp >= 2 && warp <= 5) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = cluster_id; work_idx_2 < total_work; work_idx_2 += num_clusters) { + int split_idx_1 = work_idx_2 % (unsigned int)split_count; + int query_work_1 = work_idx_2 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tile_pairs; + int q_tile_pair_1 = query_work_1 % num_q_tile_pairs; + int q_tile_1 = q_tile_pair_1 * 2 + cta_rank; + int off_q_1 = q_tile_1 * BLOCK_Q; + int q_idx = off_q_1 + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx_1 * Q + q_idx]; + } + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + float worst_d = 3.4e+38f; + int worst_pos = 0; + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int db_start = db_tile_1 * BLOCK_M; + int db_sq_idx = db_start + (warp % 4 * 32 + lane); + if (warp % 4 * 32 + lane < BLOCK_M) { + if (db_sq_idx < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx_1 * M + db_sq_idx]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 0.0f; + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(cta_rank * BLOCK_Q + (warp % 4 * 32 << 16) << 16); + float _tmem_load_0[64]; + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x64.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31, %32, %33, %34, %35, %36, %37, %38, %39, %40, %41, %42, %43, %44, %45, %46, %47, %48, %49, %50, %51, %52, %53, %54, %55, %56, %57, %58, %59, %60, %61, %62, %63}, [%64];" + : "=f"(_tmem_load_0[0]), "=f"(_tmem_load_0[1]), "=f"(_tmem_load_0[2]), "=f"(_tmem_load_0[3]), "=f"(_tmem_load_0[4]), "=f"(_tmem_load_0[5]), "=f"(_tmem_load_0[6]), "=f"(_tmem_load_0[7]), "=f"(_tmem_load_0[8]), "=f"(_tmem_load_0[9]), "=f"(_tmem_load_0[10]), "=f"(_tmem_load_0[11]), "=f"(_tmem_load_0[12]), "=f"(_tmem_load_0[13]), "=f"(_tmem_load_0[14]), "=f"(_tmem_load_0[15]), "=f"(_tmem_load_0[16]), "=f"(_tmem_load_0[17]), "=f"(_tmem_load_0[18]), "=f"(_tmem_load_0[19]), "=f"(_tmem_load_0[20]), "=f"(_tmem_load_0[21]), "=f"(_tmem_load_0[22]), "=f"(_tmem_load_0[23]), "=f"(_tmem_load_0[24]), "=f"(_tmem_load_0[25]), "=f"(_tmem_load_0[26]), "=f"(_tmem_load_0[27]), "=f"(_tmem_load_0[28]), "=f"(_tmem_load_0[29]), "=f"(_tmem_load_0[30]), "=f"(_tmem_load_0[31]), "=f"(_tmem_load_0[32]), "=f"(_tmem_load_0[33]), "=f"(_tmem_load_0[34]), "=f"(_tmem_load_0[35]), "=f"(_tmem_load_0[36]), "=f"(_tmem_load_0[37]), "=f"(_tmem_load_0[38]), "=f"(_tmem_load_0[39]), "=f"(_tmem_load_0[40]), "=f"(_tmem_load_0[41]), "=f"(_tmem_load_0[42]), "=f"(_tmem_load_0[43]), "=f"(_tmem_load_0[44]), "=f"(_tmem_load_0[45]), "=f"(_tmem_load_0[46]), "=f"(_tmem_load_0[47]), "=f"(_tmem_load_0[48]), "=f"(_tmem_load_0[49]), "=f"(_tmem_load_0[50]), "=f"(_tmem_load_0[51]), "=f"(_tmem_load_0[52]), "=f"(_tmem_load_0[53]), "=f"(_tmem_load_0[54]), "=f"(_tmem_load_0[55]), "=f"(_tmem_load_0[56]), "=f"(_tmem_load_0[57]), "=f"(_tmem_load_0[58]), "=f"(_tmem_load_0[59]), "=f"(_tmem_load_0[60]), "=f"(_tmem_load_0[61]), "=f"(_tmem_load_0[62]), "=f"(_tmem_load_0[63]) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + if (elect_sync()) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cluster.b64 _, [%0];" + :: "r"((score_empty_addr) & 0xFEFFFFFF) : "memory"); + } + if (valid_q != 0) { + #pragma unroll 2 + for (int col_base = 0; col_base < 64; col_base += 4) { + float dist_vec[4]; + dist_vec[0] = _tmem_load_0[col_base]; + dist_vec[1] = _tmem_load_0[col_base + 1]; + dist_vec[2] = _tmem_load_0[col_base + 2]; + dist_vec[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec[4]; + db_sq_vec[0] = smem_database_sq[col_base]; + db_sq_vec[1] = smem_database_sq[col_base + 1]; + db_sq_vec[2] = smem_database_sq[col_base + 2]; + db_sq_vec[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec)[_la], reinterpret_cast(db_sq_vec)[_la]); + float group_min = _t0[0]; + if (_t0[1] < group_min) { + group_min = _t0[1]; + } + if (_t0[2] < group_min) { + group_min = _t0[2]; + } + if (_t0[3] < group_min) { + group_min = _t0[3]; + } + if (group_min < worst_d) { + #pragma unroll + for (int vec_col = 0; vec_col < 4; vec_col++) { + int db_idx = db_start + col_base + vec_col; + if (db_idx < M) { + float dist = _t0[vec_col]; + if (dist < worst_d) { + best_d[worst_pos] = dist; + best_i[worst_pos] = db_idx; + worst_d = best_d[0]; + worst_pos = 0; + #pragma unroll + for (int scan_pos = 1; scan_pos < TOP_K_MAX; scan_pos++) { + if (worst_d < best_d[scan_pos]) { + worst_d = best_d[scan_pos]; + worst_pos = scan_pos; + } + } + } + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + if (valid_q != 0) { + int out_base = ((split_idx_1 * B + batch_idx_1) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + *((float*)(partial_dists + (out_base + out_k))) = best_d[out_k]; + *((int*)(partial_indices + (out_base + out_k))) = best_i[out_k]; + } + } + } + } + } + + // Cleanup + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::2.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(128)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::2.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0052.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0052.cu new file mode 100644 index 00000000..b6425c80 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0052.cu @@ -0,0 +1,85 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 32 +#define TOP_K_MAX 20 +#define SPLIT_COUNT 4 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_evolve_7bfc_k32_merge_s4_unordered_k20unordered(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 32 + tid; + int stride = num_bids * 32; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + float worst_d = 3.4e+38f; + int worst_pos = 0; + #pragma unroll + for (int split_idx = 0; split_idx < SPLIT_COUNT; split_idx++) { + int partial_base = base_row + split_idx * split_stride; + #pragma unroll + for (int cand_k = 0; cand_k < TOP_K_MAX; cand_k++) { + float cand_d = partial_dists[partial_base + cand_k]; + int cand_i = partial_indices[partial_base + cand_k]; + if (cand_d < worst_d) { + best_d[worst_pos] = cand_d; + best_i[worst_pos] = cand_i; + worst_d = best_d[0]; + worst_pos = 0; + #pragma unroll + for (int scan_pos = 1; scan_pos < TOP_K_MAX; scan_pos++) { + if (worst_d < best_d[scan_pos]) { + worst_d = best_d[scan_pos]; + worst_pos = scan_pos; + } + } + } + } + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + *((float*)(out_dists + (base_row + out_k))) = best_d[out_k]; + *((int*)(out_indices + (base_row + out_k))) = best_i[out_k]; + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0053.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0053.cu new file mode 100644 index 00000000..e66e55ea --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0053.cu @@ -0,0 +1,655 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 128 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_STRIDE 32768 +#define SMEM_SMEM_DATABASE_OFF 33792 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_DATABASE_SQ_OFF 50176 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 50432 +#define THREADS 192 +#define BLOCK_Q 128 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_MAX 24 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16_cta2( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + ".reg .b32 m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "tcgen05.mma.cta_group::2.kind::f16 [%0], %1, %2, %3, {m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step_cg2( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, %3, " + "{m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], %1;\n\t" + "}\n" + :: "r"(mbar_addr), "h"(cta_mask) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x16(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x16.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15}, [%16];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ uint32_t smem_addr(const void* ptr) { + uint32_t addr; + asm("{\n\t" + ".reg .u64 u64addr;\n\t" + "cvta.to.shared.u64 u64addr, %1;\n\t" + "cvt.u32.u64 %0, u64addr;\n\t" + "}\n" : "=r"(addr) : "l"(ptr)); + return addr; +} + + +__device__ __forceinline__ uint32_t mapa_to_rank(uint32_t local_addr, uint32_t rank) { + uint32_t remote; + asm volatile("mapa.shared::cluster.u32 %0, %1, %2;" + : "=r"(remote) : "r"(local_addr), "r"(rank)); + return remote; +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem_cta2( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global" + ".mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .b16 lo, hi;\n\t" + "mov.b32 {lo, hi}, %1;\n\t" + "tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], lo;\n\t" + "}\n" + :: "r"(mbar_addr), "r"((uint32_t)cta_mask) : "memory"); +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k30unordered_1074k24unordered(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tile_pairs, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + const unsigned int clusters_x = gridDim.x / 2; + const unsigned int cluster_id = ((blockIdx.z * gridDim.y + blockIdx.y) * clusters_x) + blockIdx.x / 2; + const unsigned int num_clusters = clusters_x * gridDim.y * gridDim.z; + + int cta_rank; + asm volatile("mov.b32 %0, %%cluster_ctarank;" : "=r"(cta_rank)); + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 33792); + const int smem_database_addr = smem + 33792; + float* smem_database_sq = reinterpret_cast(smem_raw + 50176); + const int smem_database_sq_addr = smem + 50176; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 0, 2, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 16, 2, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=8 + mbarrier_init_pred(smem + 40, 8, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (128 columns, 128 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::2.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(128) : "memory"); + } + + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: load ---- + if (warp == 0) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 0) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx = cluster_id; work_idx < total_work; work_idx += num_clusters) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tile_pairs; + int q_tile_pair = query_work % num_q_tile_pairs; + int q_tile = q_tile_pair * 2 + cta_rank; + int off_q = q_tile * BLOCK_Q; + int global_q = batch_idx * Q + off_q; + int db_tile_start = split_idx * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((query_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(32768)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_query_addr), "l"(tmap_query), "r"(0), "r"(global_q), "r"(0), + "r"(((query_full_addr) & 0xFEFFFFFF)) : "memory"); + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int off_m = db_tile * BLOCK_M; + int global_m = batch_idx * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((database_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(16384)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_database_addr), "l"(tmap_database), "r"(0), "r"(global_m), "r"(0), + "r"(((database_full_addr) & 0xFEFFFFFF)) : "memory"); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 1) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + if (cta_rank == 0) { + #pragma unroll 1 + for (unsigned int work_idx_1 = cluster_id; work_idx_1 < total_work; work_idx_1 += num_clusters) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = (smem_query_addr >> 4) & 0x3FFF; + int _mma_b_lo_0 = (smem_database_addr >> 4) & 0x3FFF; + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\tmov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 270533776;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit_cg2_multicast(score_full_addr, (uint16_t)(3)); + elect_commit_cg2_multicast(database_empty_addr, (uint16_t)(3)); + } + elect_commit_cg2_multicast(query_empty_addr, (uint16_t)(3)); + } + } + } + // ---- Role: compute ---- + } else if (warp >= 2 && warp <= 5) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = cluster_id; work_idx_2 < total_work; work_idx_2 += num_clusters) { + int split_idx_1 = work_idx_2 % (unsigned int)split_count; + int query_work_1 = work_idx_2 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tile_pairs; + int q_tile_pair_1 = query_work_1 % num_q_tile_pairs; + int q_tile_1 = q_tile_pair_1 * 2 + cta_rank; + int off_q_1 = q_tile_1 * BLOCK_Q; + int q_idx = off_q_1 + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx_1 * Q + q_idx]; + } + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + float worst_d = 3.4e+38f; + int worst_pos = 0; + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int db_start = db_tile_1 * BLOCK_M; + int db_sq_idx = db_start + (warp % 4 * 32 + lane); + if (warp % 4 * 32 + lane < BLOCK_M) { + if (db_sq_idx < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx_1 * M + db_sq_idx]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 0.0f; + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(cta_rank * BLOCK_Q + (warp % 4 * 32 << 16) << 16); + float _tmem_load_0[64]; + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x64.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31, %32, %33, %34, %35, %36, %37, %38, %39, %40, %41, %42, %43, %44, %45, %46, %47, %48, %49, %50, %51, %52, %53, %54, %55, %56, %57, %58, %59, %60, %61, %62, %63}, [%64];" + : "=f"(_tmem_load_0[0]), "=f"(_tmem_load_0[1]), "=f"(_tmem_load_0[2]), "=f"(_tmem_load_0[3]), "=f"(_tmem_load_0[4]), "=f"(_tmem_load_0[5]), "=f"(_tmem_load_0[6]), "=f"(_tmem_load_0[7]), "=f"(_tmem_load_0[8]), "=f"(_tmem_load_0[9]), "=f"(_tmem_load_0[10]), "=f"(_tmem_load_0[11]), "=f"(_tmem_load_0[12]), "=f"(_tmem_load_0[13]), "=f"(_tmem_load_0[14]), "=f"(_tmem_load_0[15]), "=f"(_tmem_load_0[16]), "=f"(_tmem_load_0[17]), "=f"(_tmem_load_0[18]), "=f"(_tmem_load_0[19]), "=f"(_tmem_load_0[20]), "=f"(_tmem_load_0[21]), "=f"(_tmem_load_0[22]), "=f"(_tmem_load_0[23]), "=f"(_tmem_load_0[24]), "=f"(_tmem_load_0[25]), "=f"(_tmem_load_0[26]), "=f"(_tmem_load_0[27]), "=f"(_tmem_load_0[28]), "=f"(_tmem_load_0[29]), "=f"(_tmem_load_0[30]), "=f"(_tmem_load_0[31]), "=f"(_tmem_load_0[32]), "=f"(_tmem_load_0[33]), "=f"(_tmem_load_0[34]), "=f"(_tmem_load_0[35]), "=f"(_tmem_load_0[36]), "=f"(_tmem_load_0[37]), "=f"(_tmem_load_0[38]), "=f"(_tmem_load_0[39]), "=f"(_tmem_load_0[40]), "=f"(_tmem_load_0[41]), "=f"(_tmem_load_0[42]), "=f"(_tmem_load_0[43]), "=f"(_tmem_load_0[44]), "=f"(_tmem_load_0[45]), "=f"(_tmem_load_0[46]), "=f"(_tmem_load_0[47]), "=f"(_tmem_load_0[48]), "=f"(_tmem_load_0[49]), "=f"(_tmem_load_0[50]), "=f"(_tmem_load_0[51]), "=f"(_tmem_load_0[52]), "=f"(_tmem_load_0[53]), "=f"(_tmem_load_0[54]), "=f"(_tmem_load_0[55]), "=f"(_tmem_load_0[56]), "=f"(_tmem_load_0[57]), "=f"(_tmem_load_0[58]), "=f"(_tmem_load_0[59]), "=f"(_tmem_load_0[60]), "=f"(_tmem_load_0[61]), "=f"(_tmem_load_0[62]), "=f"(_tmem_load_0[63]) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + if (elect_sync()) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cluster.b64 _, [%0];" + :: "r"((score_empty_addr) & 0xFEFFFFFF) : "memory"); + } + if (valid_q != 0) { + #pragma unroll 2 + for (int col_base = 0; col_base < 64; col_base += 4) { + float dist_vec[4]; + dist_vec[0] = _tmem_load_0[col_base]; + dist_vec[1] = _tmem_load_0[col_base + 1]; + dist_vec[2] = _tmem_load_0[col_base + 2]; + dist_vec[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec[4]; + db_sq_vec[0] = smem_database_sq[col_base]; + db_sq_vec[1] = smem_database_sq[col_base + 1]; + db_sq_vec[2] = smem_database_sq[col_base + 2]; + db_sq_vec[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec)[_la], reinterpret_cast(db_sq_vec)[_la]); + float group_min = _t0[0]; + if (_t0[1] < group_min) { + group_min = _t0[1]; + } + if (_t0[2] < group_min) { + group_min = _t0[2]; + } + if (_t0[3] < group_min) { + group_min = _t0[3]; + } + if (group_min < worst_d) { + #pragma unroll + for (int vec_col = 0; vec_col < 4; vec_col++) { + int db_idx = db_start + col_base + vec_col; + if (db_idx < M) { + float dist = _t0[vec_col]; + if (dist < worst_d) { + best_d[worst_pos] = dist; + best_i[worst_pos] = db_idx; + worst_d = best_d[0]; + worst_pos = 0; + #pragma unroll + for (int scan_pos = 1; scan_pos < TOP_K_MAX; scan_pos++) { + if (worst_d < best_d[scan_pos]) { + worst_d = best_d[scan_pos]; + worst_pos = scan_pos; + } + } + } + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + if (valid_q != 0) { + int out_base = ((split_idx_1 * B + batch_idx_1) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + *((float*)(partial_dists + (out_base + out_k))) = best_d[out_k]; + *((int*)(partial_indices + (out_base + out_k))) = best_i[out_k]; + } + } + } + } + } + + // Cleanup + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::2.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(128)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::2.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0054.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0054.cu new file mode 100644 index 00000000..f93d8af6 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0054.cu @@ -0,0 +1,121 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 128 +#define TOP_K_MAX 24 +#define SPLIT_COUNT 4 + +#include + +extern "C" { + +__global__ __launch_bounds__(128, 1) void +kernel_knn_build_1074_k24_q4096_merge_s4_unordered_warp_select(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int row = bid * 4 + warp; + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int cand_k = lane; + if (row < total_queries) { + float d0 = 3.4e+38f; + float d1 = 3.4e+38f; + float d2 = 3.4e+38f; + float d3 = 3.4e+38f; + int i0 = -1; + int i1 = -1; + int i2 = -1; + int i3 = -1; + if (cand_k < TOP_K_MAX) { + d0 = partial_dists[base_row + cand_k]; + i0 = partial_indices[base_row + cand_k]; + int base1 = base_row + split_stride; + d1 = partial_dists[base1 + cand_k]; + i1 = partial_indices[base1 + cand_k]; + int base2 = base1 + split_stride; + d2 = partial_dists[base2 + cand_k]; + i2 = partial_indices[base2 + cand_k]; + int base3 = base2 + split_stride; + d3 = partial_dists[base3 + cand_k]; + i3 = partial_indices[base3 + cand_k]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float winner_d = d0; + int winner_i = i0; + int winner_src = 0; + if (d1 < winner_d) { + winner_d = d1; + winner_i = i1; + winner_src = 1; + } + if (d2 < winner_d) { + winner_d = d2; + winner_i = i2; + winner_src = 2; + } + if (d3 < winner_d) { + winner_d = d3; + winner_i = i3; + winner_src = 3; + } + float warp_min = winner_d; + float _warp_reduce_0 = warp_min; + #pragma unroll + for (int offset = 16; offset > 0; offset >>= 1) + _warp_reduce_0 = fminf(_warp_reduce_0, __shfl_xor_sync(0xFFFFFFFF, _warp_reduce_0, offset)); + warp_min = _warp_reduce_0; + unsigned int _vote_0 = __ballot_sync(0xFFFFFFFF, winner_d == warp_min); + int owner_ballot = _vote_0; + int _ffs_0 = __ffs(owner_ballot); + int winner_lane = _ffs_0 - 1; + int _shfl_0 = __shfl_sync(0xFFFFFFFF, winner_i, winner_lane); + winner_i = _shfl_0; + int _shfl_1 = __shfl_sync(0xFFFFFFFF, winner_src, winner_lane); + winner_src = _shfl_1; + if (lane == 0) { + *((float*)(out_dists + (base_row + out_k))) = warp_min; + *((int*)(out_indices + (base_row + out_k))) = winner_i; + } + if (lane == winner_lane) { + if (winner_src == 0) { + d0 = 3.4e+38f; + } else if (winner_src == 1) { + d1 = 3.4e+38f; + } else { + if (winner_src == 2) { + d2 = 3.4e+38f; + } else { + d3 = 3.4e+38f; + } + } + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0055.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0055.cu new file mode 100644 index 00000000..0d49cccd --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0055.cu @@ -0,0 +1,655 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 128 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_STRIDE 32768 +#define SMEM_SMEM_DATABASE_OFF 33792 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_DATABASE_SQ_OFF 50176 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 50432 +#define THREADS 192 +#define BLOCK_Q 128 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_MAX 28 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16_cta2( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + ".reg .b32 m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "tcgen05.mma.cta_group::2.kind::f16 [%0], %1, %2, %3, {m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step_cg2( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, %3, " + "{m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], %1;\n\t" + "}\n" + :: "r"(mbar_addr), "h"(cta_mask) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x16(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x16.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15}, [%16];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ uint32_t smem_addr(const void* ptr) { + uint32_t addr; + asm("{\n\t" + ".reg .u64 u64addr;\n\t" + "cvta.to.shared.u64 u64addr, %1;\n\t" + "cvt.u32.u64 %0, u64addr;\n\t" + "}\n" : "=r"(addr) : "l"(ptr)); + return addr; +} + + +__device__ __forceinline__ uint32_t mapa_to_rank(uint32_t local_addr, uint32_t rank) { + uint32_t remote; + asm volatile("mapa.shared::cluster.u32 %0, %1, %2;" + : "=r"(remote) : "r"(local_addr), "r"(rank)); + return remote; +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem_cta2( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global" + ".mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .b16 lo, hi;\n\t" + "mov.b32 {lo, hi}, %1;\n\t" + "tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], lo;\n\t" + "}\n" + :: "r"(mbar_addr), "r"((uint32_t)cta_mask) : "memory"); +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k30unordered_bad5k28unordered(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tile_pairs, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + const unsigned int clusters_x = gridDim.x / 2; + const unsigned int cluster_id = ((blockIdx.z * gridDim.y + blockIdx.y) * clusters_x) + blockIdx.x / 2; + const unsigned int num_clusters = clusters_x * gridDim.y * gridDim.z; + + int cta_rank; + asm volatile("mov.b32 %0, %%cluster_ctarank;" : "=r"(cta_rank)); + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 33792); + const int smem_database_addr = smem + 33792; + float* smem_database_sq = reinterpret_cast(smem_raw + 50176); + const int smem_database_sq_addr = smem + 50176; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 0, 2, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 16, 2, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=8 + mbarrier_init_pred(smem + 40, 8, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (128 columns, 128 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::2.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(128) : "memory"); + } + + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: load ---- + if (warp == 0) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 0) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx = cluster_id; work_idx < total_work; work_idx += num_clusters) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tile_pairs; + int q_tile_pair = query_work % num_q_tile_pairs; + int q_tile = q_tile_pair * 2 + cta_rank; + int off_q = q_tile * BLOCK_Q; + int global_q = batch_idx * Q + off_q; + int db_tile_start = split_idx * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((query_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(32768)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_query_addr), "l"(tmap_query), "r"(0), "r"(global_q), "r"(0), + "r"(((query_full_addr) & 0xFEFFFFFF)) : "memory"); + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int off_m = db_tile * BLOCK_M; + int global_m = batch_idx * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((database_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(16384)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_database_addr), "l"(tmap_database), "r"(0), "r"(global_m), "r"(0), + "r"(((database_full_addr) & 0xFEFFFFFF)) : "memory"); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 1) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + if (cta_rank == 0) { + #pragma unroll 1 + for (unsigned int work_idx_1 = cluster_id; work_idx_1 < total_work; work_idx_1 += num_clusters) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = (smem_query_addr >> 4) & 0x3FFF; + int _mma_b_lo_0 = (smem_database_addr >> 4) & 0x3FFF; + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\tmov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 270533776;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit_cg2_multicast(score_full_addr, (uint16_t)(3)); + elect_commit_cg2_multicast(database_empty_addr, (uint16_t)(3)); + } + elect_commit_cg2_multicast(query_empty_addr, (uint16_t)(3)); + } + } + } + // ---- Role: compute ---- + } else if (warp >= 2 && warp <= 5) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = cluster_id; work_idx_2 < total_work; work_idx_2 += num_clusters) { + int split_idx_1 = work_idx_2 % (unsigned int)split_count; + int query_work_1 = work_idx_2 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tile_pairs; + int q_tile_pair_1 = query_work_1 % num_q_tile_pairs; + int q_tile_1 = q_tile_pair_1 * 2 + cta_rank; + int off_q_1 = q_tile_1 * BLOCK_Q; + int q_idx = off_q_1 + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx_1 * Q + q_idx]; + } + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + float worst_d = 3.4e+38f; + int worst_pos = 0; + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int db_start = db_tile_1 * BLOCK_M; + int db_sq_idx = db_start + (warp % 4 * 32 + lane); + if (warp % 4 * 32 + lane < BLOCK_M) { + if (db_sq_idx < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx_1 * M + db_sq_idx]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 0.0f; + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(cta_rank * BLOCK_Q + (warp % 4 * 32 << 16) << 16); + float _tmem_load_0[64]; + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x64.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31, %32, %33, %34, %35, %36, %37, %38, %39, %40, %41, %42, %43, %44, %45, %46, %47, %48, %49, %50, %51, %52, %53, %54, %55, %56, %57, %58, %59, %60, %61, %62, %63}, [%64];" + : "=f"(_tmem_load_0[0]), "=f"(_tmem_load_0[1]), "=f"(_tmem_load_0[2]), "=f"(_tmem_load_0[3]), "=f"(_tmem_load_0[4]), "=f"(_tmem_load_0[5]), "=f"(_tmem_load_0[6]), "=f"(_tmem_load_0[7]), "=f"(_tmem_load_0[8]), "=f"(_tmem_load_0[9]), "=f"(_tmem_load_0[10]), "=f"(_tmem_load_0[11]), "=f"(_tmem_load_0[12]), "=f"(_tmem_load_0[13]), "=f"(_tmem_load_0[14]), "=f"(_tmem_load_0[15]), "=f"(_tmem_load_0[16]), "=f"(_tmem_load_0[17]), "=f"(_tmem_load_0[18]), "=f"(_tmem_load_0[19]), "=f"(_tmem_load_0[20]), "=f"(_tmem_load_0[21]), "=f"(_tmem_load_0[22]), "=f"(_tmem_load_0[23]), "=f"(_tmem_load_0[24]), "=f"(_tmem_load_0[25]), "=f"(_tmem_load_0[26]), "=f"(_tmem_load_0[27]), "=f"(_tmem_load_0[28]), "=f"(_tmem_load_0[29]), "=f"(_tmem_load_0[30]), "=f"(_tmem_load_0[31]), "=f"(_tmem_load_0[32]), "=f"(_tmem_load_0[33]), "=f"(_tmem_load_0[34]), "=f"(_tmem_load_0[35]), "=f"(_tmem_load_0[36]), "=f"(_tmem_load_0[37]), "=f"(_tmem_load_0[38]), "=f"(_tmem_load_0[39]), "=f"(_tmem_load_0[40]), "=f"(_tmem_load_0[41]), "=f"(_tmem_load_0[42]), "=f"(_tmem_load_0[43]), "=f"(_tmem_load_0[44]), "=f"(_tmem_load_0[45]), "=f"(_tmem_load_0[46]), "=f"(_tmem_load_0[47]), "=f"(_tmem_load_0[48]), "=f"(_tmem_load_0[49]), "=f"(_tmem_load_0[50]), "=f"(_tmem_load_0[51]), "=f"(_tmem_load_0[52]), "=f"(_tmem_load_0[53]), "=f"(_tmem_load_0[54]), "=f"(_tmem_load_0[55]), "=f"(_tmem_load_0[56]), "=f"(_tmem_load_0[57]), "=f"(_tmem_load_0[58]), "=f"(_tmem_load_0[59]), "=f"(_tmem_load_0[60]), "=f"(_tmem_load_0[61]), "=f"(_tmem_load_0[62]), "=f"(_tmem_load_0[63]) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + if (elect_sync()) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cluster.b64 _, [%0];" + :: "r"((score_empty_addr) & 0xFEFFFFFF) : "memory"); + } + if (valid_q != 0) { + #pragma unroll 2 + for (int col_base = 0; col_base < 64; col_base += 4) { + float dist_vec[4]; + dist_vec[0] = _tmem_load_0[col_base]; + dist_vec[1] = _tmem_load_0[col_base + 1]; + dist_vec[2] = _tmem_load_0[col_base + 2]; + dist_vec[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec[4]; + db_sq_vec[0] = smem_database_sq[col_base]; + db_sq_vec[1] = smem_database_sq[col_base + 1]; + db_sq_vec[2] = smem_database_sq[col_base + 2]; + db_sq_vec[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec)[_la], reinterpret_cast(db_sq_vec)[_la]); + float group_min = _t0[0]; + if (_t0[1] < group_min) { + group_min = _t0[1]; + } + if (_t0[2] < group_min) { + group_min = _t0[2]; + } + if (_t0[3] < group_min) { + group_min = _t0[3]; + } + if (group_min < worst_d) { + #pragma unroll + for (int vec_col = 0; vec_col < 4; vec_col++) { + int db_idx = db_start + col_base + vec_col; + if (db_idx < M) { + float dist = _t0[vec_col]; + if (dist < worst_d) { + best_d[worst_pos] = dist; + best_i[worst_pos] = db_idx; + worst_d = best_d[0]; + worst_pos = 0; + #pragma unroll + for (int scan_pos = 1; scan_pos < TOP_K_MAX; scan_pos++) { + if (worst_d < best_d[scan_pos]) { + worst_d = best_d[scan_pos]; + worst_pos = scan_pos; + } + } + } + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + if (valid_q != 0) { + int out_base = ((split_idx_1 * B + batch_idx_1) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + *((float*)(partial_dists + (out_base + out_k))) = best_d[out_k]; + *((int*)(partial_indices + (out_base + out_k))) = best_i[out_k]; + } + } + } + } + } + + // Cleanup + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::2.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(128)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::2.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0056.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0056.cu new file mode 100644 index 00000000..0f472d6b --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0056.cu @@ -0,0 +1,85 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 32 +#define TOP_K_MAX 28 +#define SPLIT_COUNT 4 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_evolve_7bfc_k32_merge_s4_unordered_k30unordered_bad5k28unordered(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 32 + tid; + int stride = num_bids * 32; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + float worst_d = 3.4e+38f; + int worst_pos = 0; + #pragma unroll + for (int split_idx = 0; split_idx < SPLIT_COUNT; split_idx++) { + int partial_base = base_row + split_idx * split_stride; + #pragma unroll + for (int cand_k = 0; cand_k < TOP_K_MAX; cand_k++) { + float cand_d = partial_dists[partial_base + cand_k]; + int cand_i = partial_indices[partial_base + cand_k]; + if (cand_d < worst_d) { + best_d[worst_pos] = cand_d; + best_i[worst_pos] = cand_i; + worst_d = best_d[0]; + worst_pos = 0; + #pragma unroll + for (int scan_pos = 1; scan_pos < TOP_K_MAX; scan_pos++) { + if (worst_d < best_d[scan_pos]) { + worst_d = best_d[scan_pos]; + worst_pos = scan_pos; + } + } + } + } + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + *((float*)(out_dists + (base_row + out_k))) = best_d[out_k]; + *((int*)(out_indices + (base_row + out_k))) = best_i[out_k]; + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0057.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0057.cu new file mode 100644 index 00000000..60275371 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0057.cu @@ -0,0 +1,655 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 128 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_STRIDE 32768 +#define SMEM_SMEM_DATABASE_OFF 33792 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_DATABASE_SQ_OFF 50176 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 50432 +#define THREADS 192 +#define BLOCK_Q 128 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_MAX 32 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16_cta2( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + ".reg .b32 m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "tcgen05.mma.cta_group::2.kind::f16 [%0], %1, %2, %3, {m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step_cg2( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, %3, " + "{m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], %1;\n\t" + "}\n" + :: "r"(mbar_addr), "h"(cta_mask) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x16(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x16.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15}, [%16];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ uint32_t smem_addr(const void* ptr) { + uint32_t addr; + asm("{\n\t" + ".reg .u64 u64addr;\n\t" + "cvta.to.shared.u64 u64addr, %1;\n\t" + "cvt.u32.u64 %0, u64addr;\n\t" + "}\n" : "=r"(addr) : "l"(ptr)); + return addr; +} + + +__device__ __forceinline__ uint32_t mapa_to_rank(uint32_t local_addr, uint32_t rank) { + uint32_t remote; + asm volatile("mapa.shared::cluster.u32 %0, %1, %2;" + : "=r"(remote) : "r"(local_addr), "r"(rank)); + return remote; +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem_cta2( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global" + ".mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .b16 lo, hi;\n\t" + "mov.b32 {lo, hi}, %1;\n\t" + "tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], lo;\n\t" + "}\n" + :: "r"(mbar_addr), "r"((uint32_t)cta_mask) : "memory"); +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tile_pairs, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + const unsigned int clusters_x = gridDim.x / 2; + const unsigned int cluster_id = ((blockIdx.z * gridDim.y + blockIdx.y) * clusters_x) + blockIdx.x / 2; + const unsigned int num_clusters = clusters_x * gridDim.y * gridDim.z; + + int cta_rank; + asm volatile("mov.b32 %0, %%cluster_ctarank;" : "=r"(cta_rank)); + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 33792); + const int smem_database_addr = smem + 33792; + float* smem_database_sq = reinterpret_cast(smem_raw + 50176); + const int smem_database_sq_addr = smem + 50176; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 0, 2, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 16, 2, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=8 + mbarrier_init_pred(smem + 40, 8, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (128 columns, 128 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::2.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(128) : "memory"); + } + + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: load ---- + if (warp == 0) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 0) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx = cluster_id; work_idx < total_work; work_idx += num_clusters) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tile_pairs; + int q_tile_pair = query_work % num_q_tile_pairs; + int q_tile = q_tile_pair * 2 + cta_rank; + int off_q = q_tile * BLOCK_Q; + int global_q = batch_idx * Q + off_q; + int db_tile_start = split_idx * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((query_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(32768)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_query_addr), "l"(tmap_query), "r"(0), "r"(global_q), "r"(0), + "r"(((query_full_addr) & 0xFEFFFFFF)) : "memory"); + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int off_m = db_tile * BLOCK_M; + int global_m = batch_idx * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((database_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(16384)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_database_addr), "l"(tmap_database), "r"(0), "r"(global_m), "r"(0), + "r"(((database_full_addr) & 0xFEFFFFFF)) : "memory"); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 1) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + if (cta_rank == 0) { + #pragma unroll 1 + for (unsigned int work_idx_1 = cluster_id; work_idx_1 < total_work; work_idx_1 += num_clusters) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = (smem_query_addr >> 4) & 0x3FFF; + int _mma_b_lo_0 = (smem_database_addr >> 4) & 0x3FFF; + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\tmov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 270533776;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit_cg2_multicast(score_full_addr, (uint16_t)(3)); + elect_commit_cg2_multicast(database_empty_addr, (uint16_t)(3)); + } + elect_commit_cg2_multicast(query_empty_addr, (uint16_t)(3)); + } + } + } + // ---- Role: compute ---- + } else if (warp >= 2 && warp <= 5) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = cluster_id; work_idx_2 < total_work; work_idx_2 += num_clusters) { + int split_idx_1 = work_idx_2 % (unsigned int)split_count; + int query_work_1 = work_idx_2 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tile_pairs; + int q_tile_pair_1 = query_work_1 % num_q_tile_pairs; + int q_tile_1 = q_tile_pair_1 * 2 + cta_rank; + int off_q_1 = q_tile_1 * BLOCK_Q; + int q_idx = off_q_1 + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx_1 * Q + q_idx]; + } + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + float worst_d = 3.4e+38f; + int worst_pos = 0; + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int db_start = db_tile_1 * BLOCK_M; + int db_sq_idx = db_start + (warp % 4 * 32 + lane); + if (warp % 4 * 32 + lane < BLOCK_M) { + if (db_sq_idx < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx_1 * M + db_sq_idx]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 0.0f; + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(cta_rank * BLOCK_Q + (warp % 4 * 32 << 16) << 16); + float _tmem_load_0[64]; + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x64.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31, %32, %33, %34, %35, %36, %37, %38, %39, %40, %41, %42, %43, %44, %45, %46, %47, %48, %49, %50, %51, %52, %53, %54, %55, %56, %57, %58, %59, %60, %61, %62, %63}, [%64];" + : "=f"(_tmem_load_0[0]), "=f"(_tmem_load_0[1]), "=f"(_tmem_load_0[2]), "=f"(_tmem_load_0[3]), "=f"(_tmem_load_0[4]), "=f"(_tmem_load_0[5]), "=f"(_tmem_load_0[6]), "=f"(_tmem_load_0[7]), "=f"(_tmem_load_0[8]), "=f"(_tmem_load_0[9]), "=f"(_tmem_load_0[10]), "=f"(_tmem_load_0[11]), "=f"(_tmem_load_0[12]), "=f"(_tmem_load_0[13]), "=f"(_tmem_load_0[14]), "=f"(_tmem_load_0[15]), "=f"(_tmem_load_0[16]), "=f"(_tmem_load_0[17]), "=f"(_tmem_load_0[18]), "=f"(_tmem_load_0[19]), "=f"(_tmem_load_0[20]), "=f"(_tmem_load_0[21]), "=f"(_tmem_load_0[22]), "=f"(_tmem_load_0[23]), "=f"(_tmem_load_0[24]), "=f"(_tmem_load_0[25]), "=f"(_tmem_load_0[26]), "=f"(_tmem_load_0[27]), "=f"(_tmem_load_0[28]), "=f"(_tmem_load_0[29]), "=f"(_tmem_load_0[30]), "=f"(_tmem_load_0[31]), "=f"(_tmem_load_0[32]), "=f"(_tmem_load_0[33]), "=f"(_tmem_load_0[34]), "=f"(_tmem_load_0[35]), "=f"(_tmem_load_0[36]), "=f"(_tmem_load_0[37]), "=f"(_tmem_load_0[38]), "=f"(_tmem_load_0[39]), "=f"(_tmem_load_0[40]), "=f"(_tmem_load_0[41]), "=f"(_tmem_load_0[42]), "=f"(_tmem_load_0[43]), "=f"(_tmem_load_0[44]), "=f"(_tmem_load_0[45]), "=f"(_tmem_load_0[46]), "=f"(_tmem_load_0[47]), "=f"(_tmem_load_0[48]), "=f"(_tmem_load_0[49]), "=f"(_tmem_load_0[50]), "=f"(_tmem_load_0[51]), "=f"(_tmem_load_0[52]), "=f"(_tmem_load_0[53]), "=f"(_tmem_load_0[54]), "=f"(_tmem_load_0[55]), "=f"(_tmem_load_0[56]), "=f"(_tmem_load_0[57]), "=f"(_tmem_load_0[58]), "=f"(_tmem_load_0[59]), "=f"(_tmem_load_0[60]), "=f"(_tmem_load_0[61]), "=f"(_tmem_load_0[62]), "=f"(_tmem_load_0[63]) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + if (elect_sync()) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cluster.b64 _, [%0];" + :: "r"((score_empty_addr) & 0xFEFFFFFF) : "memory"); + } + if (valid_q != 0) { + #pragma unroll 2 + for (int col_base = 0; col_base < 64; col_base += 4) { + float dist_vec[4]; + dist_vec[0] = _tmem_load_0[col_base]; + dist_vec[1] = _tmem_load_0[col_base + 1]; + dist_vec[2] = _tmem_load_0[col_base + 2]; + dist_vec[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec[4]; + db_sq_vec[0] = smem_database_sq[col_base]; + db_sq_vec[1] = smem_database_sq[col_base + 1]; + db_sq_vec[2] = smem_database_sq[col_base + 2]; + db_sq_vec[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec)[_la], reinterpret_cast(db_sq_vec)[_la]); + float group_min = _t0[0]; + if (_t0[1] < group_min) { + group_min = _t0[1]; + } + if (_t0[2] < group_min) { + group_min = _t0[2]; + } + if (_t0[3] < group_min) { + group_min = _t0[3]; + } + if (group_min < worst_d) { + #pragma unroll + for (int vec_col = 0; vec_col < 4; vec_col++) { + int db_idx = db_start + col_base + vec_col; + if (db_idx < M) { + float dist = _t0[vec_col]; + if (dist < worst_d) { + best_d[worst_pos] = dist; + best_i[worst_pos] = db_idx; + worst_d = best_d[0]; + worst_pos = 0; + #pragma unroll + for (int scan_pos = 1; scan_pos < TOP_K_MAX; scan_pos++) { + if (worst_d < best_d[scan_pos]) { + worst_d = best_d[scan_pos]; + worst_pos = scan_pos; + } + } + } + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + if (valid_q != 0) { + int out_base = ((split_idx_1 * B + batch_idx_1) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + *((float*)(partial_dists + (out_base + out_k))) = best_d[out_k]; + *((int*)(partial_indices + (out_base + out_k))) = best_i[out_k]; + } + } + } + } + } + + // Cleanup + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::2.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(128)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::2.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0058.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0058.cu new file mode 100644 index 00000000..f8246ee1 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0058.cu @@ -0,0 +1,111 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 128 +#define TOP_K_MAX 32 +#define SPLIT_COUNT 4 + +#include + +extern "C" { + +__global__ __launch_bounds__(128, 1) void +kernel_knn_build_evolve_7bfc_k32_merge_s4_unordered_warp_select(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int row = bid * 4 + warp; + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int cand_k = lane; + if (row < total_queries) { + float d0 = partial_dists[base_row + cand_k]; + int i0 = partial_indices[base_row + cand_k]; + int base1 = base_row + split_stride; + float d1 = partial_dists[base1 + cand_k]; + int i1 = partial_indices[base1 + cand_k]; + int base2 = base1 + split_stride; + float d2 = partial_dists[base2 + cand_k]; + int i2 = partial_indices[base2 + cand_k]; + int base3 = base2 + split_stride; + float d3 = partial_dists[base3 + cand_k]; + int i3 = partial_indices[base3 + cand_k]; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float winner_d = d0; + int winner_i = i0; + int winner_src = 0; + if (d1 < winner_d) { + winner_d = d1; + winner_i = i1; + winner_src = 1; + } + if (d2 < winner_d) { + winner_d = d2; + winner_i = i2; + winner_src = 2; + } + if (d3 < winner_d) { + winner_d = d3; + winner_i = i3; + winner_src = 3; + } + float warp_min = winner_d; + float _warp_reduce_0 = warp_min; + #pragma unroll + for (int offset = 16; offset > 0; offset >>= 1) + _warp_reduce_0 = fminf(_warp_reduce_0, __shfl_xor_sync(0xFFFFFFFF, _warp_reduce_0, offset)); + warp_min = _warp_reduce_0; + unsigned int _vote_0 = __ballot_sync(0xFFFFFFFF, winner_d == warp_min); + int owner_ballot = _vote_0; + int _ffs_0 = __ffs(owner_ballot); + int winner_lane = _ffs_0 - 1; + int _shfl_0 = __shfl_sync(0xFFFFFFFF, winner_i, winner_lane); + winner_i = _shfl_0; + int _shfl_1 = __shfl_sync(0xFFFFFFFF, winner_src, winner_lane); + winner_src = _shfl_1; + if (lane == 0) { + *((float*)(out_dists + (base_row + out_k))) = warp_min; + *((int*)(out_indices + (base_row + out_k))) = winner_i; + } + if (lane == winner_lane) { + if (winner_src == 0) { + d0 = 3.4e+38f; + } else if (winner_src == 1) { + d1 = 3.4e+38f; + } else { + if (winner_src == 2) { + d2 = 3.4e+38f; + } else { + d3 = 3.4e+38f; + } + } + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0059.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0059.cu new file mode 100644 index 00000000..70b516e2 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0059.cu @@ -0,0 +1,655 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 128 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_STRIDE 32768 +#define SMEM_SMEM_DATABASE_OFF 33792 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_DATABASE_SQ_OFF 50176 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 50432 +#define THREADS 192 +#define BLOCK_Q 128 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_MAX 30 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16_cta2( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + ".reg .b32 m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "tcgen05.mma.cta_group::2.kind::f16 [%0], %1, %2, %3, {m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step_cg2( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, %3, " + "{m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], %1;\n\t" + "}\n" + :: "r"(mbar_addr), "h"(cta_mask) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x16(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x16.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15}, [%16];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ uint32_t smem_addr(const void* ptr) { + uint32_t addr; + asm("{\n\t" + ".reg .u64 u64addr;\n\t" + "cvta.to.shared.u64 u64addr, %1;\n\t" + "cvt.u32.u64 %0, u64addr;\n\t" + "}\n" : "=r"(addr) : "l"(ptr)); + return addr; +} + + +__device__ __forceinline__ uint32_t mapa_to_rank(uint32_t local_addr, uint32_t rank) { + uint32_t remote; + asm volatile("mapa.shared::cluster.u32 %0, %1, %2;" + : "=r"(remote) : "r"(local_addr), "r"(rank)); + return remote; +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem_cta2( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global" + ".mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .b16 lo, hi;\n\t" + "mov.b32 {lo, hi}, %1;\n\t" + "tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], lo;\n\t" + "}\n" + :: "r"(mbar_addr), "r"((uint32_t)cta_mask) : "memory"); +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k30unordered(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tile_pairs, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + const unsigned int clusters_x = gridDim.x / 2; + const unsigned int cluster_id = ((blockIdx.z * gridDim.y + blockIdx.y) * clusters_x) + blockIdx.x / 2; + const unsigned int num_clusters = clusters_x * gridDim.y * gridDim.z; + + int cta_rank; + asm volatile("mov.b32 %0, %%cluster_ctarank;" : "=r"(cta_rank)); + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 33792); + const int smem_database_addr = smem + 33792; + float* smem_database_sq = reinterpret_cast(smem_raw + 50176); + const int smem_database_sq_addr = smem + 50176; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 0, 2, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 16, 2, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=8 + mbarrier_init_pred(smem + 40, 8, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (128 columns, 128 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::2.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(128) : "memory"); + } + + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: load ---- + if (warp == 0) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 0) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx = cluster_id; work_idx < total_work; work_idx += num_clusters) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tile_pairs; + int q_tile_pair = query_work % num_q_tile_pairs; + int q_tile = q_tile_pair * 2 + cta_rank; + int off_q = q_tile * BLOCK_Q; + int global_q = batch_idx * Q + off_q; + int db_tile_start = split_idx * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((query_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(32768)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_query_addr), "l"(tmap_query), "r"(0), "r"(global_q), "r"(0), + "r"(((query_full_addr) & 0xFEFFFFFF)) : "memory"); + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int off_m = db_tile * BLOCK_M; + int global_m = batch_idx * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((database_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(16384)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_database_addr), "l"(tmap_database), "r"(0), "r"(global_m), "r"(0), + "r"(((database_full_addr) & 0xFEFFFFFF)) : "memory"); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 1) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + if (cta_rank == 0) { + #pragma unroll 1 + for (unsigned int work_idx_1 = cluster_id; work_idx_1 < total_work; work_idx_1 += num_clusters) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = (smem_query_addr >> 4) & 0x3FFF; + int _mma_b_lo_0 = (smem_database_addr >> 4) & 0x3FFF; + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\tmov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 270533776;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit_cg2_multicast(score_full_addr, (uint16_t)(3)); + elect_commit_cg2_multicast(database_empty_addr, (uint16_t)(3)); + } + elect_commit_cg2_multicast(query_empty_addr, (uint16_t)(3)); + } + } + } + // ---- Role: compute ---- + } else if (warp >= 2 && warp <= 5) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = cluster_id; work_idx_2 < total_work; work_idx_2 += num_clusters) { + int split_idx_1 = work_idx_2 % (unsigned int)split_count; + int query_work_1 = work_idx_2 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tile_pairs; + int q_tile_pair_1 = query_work_1 % num_q_tile_pairs; + int q_tile_1 = q_tile_pair_1 * 2 + cta_rank; + int off_q_1 = q_tile_1 * BLOCK_Q; + int q_idx = off_q_1 + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx_1 * Q + q_idx]; + } + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + float worst_d = 3.4e+38f; + int worst_pos = 0; + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int db_start = db_tile_1 * BLOCK_M; + int db_sq_idx = db_start + (warp % 4 * 32 + lane); + if (warp % 4 * 32 + lane < BLOCK_M) { + if (db_sq_idx < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx_1 * M + db_sq_idx]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 0.0f; + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(cta_rank * BLOCK_Q + (warp % 4 * 32 << 16) << 16); + float _tmem_load_0[64]; + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x64.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31, %32, %33, %34, %35, %36, %37, %38, %39, %40, %41, %42, %43, %44, %45, %46, %47, %48, %49, %50, %51, %52, %53, %54, %55, %56, %57, %58, %59, %60, %61, %62, %63}, [%64];" + : "=f"(_tmem_load_0[0]), "=f"(_tmem_load_0[1]), "=f"(_tmem_load_0[2]), "=f"(_tmem_load_0[3]), "=f"(_tmem_load_0[4]), "=f"(_tmem_load_0[5]), "=f"(_tmem_load_0[6]), "=f"(_tmem_load_0[7]), "=f"(_tmem_load_0[8]), "=f"(_tmem_load_0[9]), "=f"(_tmem_load_0[10]), "=f"(_tmem_load_0[11]), "=f"(_tmem_load_0[12]), "=f"(_tmem_load_0[13]), "=f"(_tmem_load_0[14]), "=f"(_tmem_load_0[15]), "=f"(_tmem_load_0[16]), "=f"(_tmem_load_0[17]), "=f"(_tmem_load_0[18]), "=f"(_tmem_load_0[19]), "=f"(_tmem_load_0[20]), "=f"(_tmem_load_0[21]), "=f"(_tmem_load_0[22]), "=f"(_tmem_load_0[23]), "=f"(_tmem_load_0[24]), "=f"(_tmem_load_0[25]), "=f"(_tmem_load_0[26]), "=f"(_tmem_load_0[27]), "=f"(_tmem_load_0[28]), "=f"(_tmem_load_0[29]), "=f"(_tmem_load_0[30]), "=f"(_tmem_load_0[31]), "=f"(_tmem_load_0[32]), "=f"(_tmem_load_0[33]), "=f"(_tmem_load_0[34]), "=f"(_tmem_load_0[35]), "=f"(_tmem_load_0[36]), "=f"(_tmem_load_0[37]), "=f"(_tmem_load_0[38]), "=f"(_tmem_load_0[39]), "=f"(_tmem_load_0[40]), "=f"(_tmem_load_0[41]), "=f"(_tmem_load_0[42]), "=f"(_tmem_load_0[43]), "=f"(_tmem_load_0[44]), "=f"(_tmem_load_0[45]), "=f"(_tmem_load_0[46]), "=f"(_tmem_load_0[47]), "=f"(_tmem_load_0[48]), "=f"(_tmem_load_0[49]), "=f"(_tmem_load_0[50]), "=f"(_tmem_load_0[51]), "=f"(_tmem_load_0[52]), "=f"(_tmem_load_0[53]), "=f"(_tmem_load_0[54]), "=f"(_tmem_load_0[55]), "=f"(_tmem_load_0[56]), "=f"(_tmem_load_0[57]), "=f"(_tmem_load_0[58]), "=f"(_tmem_load_0[59]), "=f"(_tmem_load_0[60]), "=f"(_tmem_load_0[61]), "=f"(_tmem_load_0[62]), "=f"(_tmem_load_0[63]) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + if (elect_sync()) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cluster.b64 _, [%0];" + :: "r"((score_empty_addr) & 0xFEFFFFFF) : "memory"); + } + if (valid_q != 0) { + #pragma unroll 2 + for (int col_base = 0; col_base < 64; col_base += 4) { + float dist_vec[4]; + dist_vec[0] = _tmem_load_0[col_base]; + dist_vec[1] = _tmem_load_0[col_base + 1]; + dist_vec[2] = _tmem_load_0[col_base + 2]; + dist_vec[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec[4]; + db_sq_vec[0] = smem_database_sq[col_base]; + db_sq_vec[1] = smem_database_sq[col_base + 1]; + db_sq_vec[2] = smem_database_sq[col_base + 2]; + db_sq_vec[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec)[_la], reinterpret_cast(db_sq_vec)[_la]); + float group_min = _t0[0]; + if (_t0[1] < group_min) { + group_min = _t0[1]; + } + if (_t0[2] < group_min) { + group_min = _t0[2]; + } + if (_t0[3] < group_min) { + group_min = _t0[3]; + } + if (group_min < worst_d) { + #pragma unroll + for (int vec_col = 0; vec_col < 4; vec_col++) { + int db_idx = db_start + col_base + vec_col; + if (db_idx < M) { + float dist = _t0[vec_col]; + if (dist < worst_d) { + best_d[worst_pos] = dist; + best_i[worst_pos] = db_idx; + worst_d = best_d[0]; + worst_pos = 0; + #pragma unroll + for (int scan_pos = 1; scan_pos < TOP_K_MAX; scan_pos++) { + if (worst_d < best_d[scan_pos]) { + worst_d = best_d[scan_pos]; + worst_pos = scan_pos; + } + } + } + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + if (valid_q != 0) { + int out_base = ((split_idx_1 * B + batch_idx_1) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + *((float*)(partial_dists + (out_base + out_k))) = best_d[out_k]; + *((int*)(partial_indices + (out_base + out_k))) = best_i[out_k]; + } + } + } + } + } + + // Cleanup + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::2.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(128)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::2.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0060.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0060.cu new file mode 100644 index 00000000..69ec3c7e --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0060.cu @@ -0,0 +1,121 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 128 +#define TOP_K_MAX 30 +#define SPLIT_COUNT 4 + +#include + +extern "C" { + +__global__ __launch_bounds__(128, 1) void +kernel_knn_build_k30_q4096_6998_merge_s4_unordered_warp_select(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int row = bid * 4 + warp; + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int cand_k = lane; + if (row < total_queries) { + float d0 = 3.4e+38f; + float d1 = 3.4e+38f; + float d2 = 3.4e+38f; + float d3 = 3.4e+38f; + int i0 = -1; + int i1 = -1; + int i2 = -1; + int i3 = -1; + if (cand_k < TOP_K_MAX) { + d0 = partial_dists[base_row + cand_k]; + i0 = partial_indices[base_row + cand_k]; + int base1 = base_row + split_stride; + d1 = partial_dists[base1 + cand_k]; + i1 = partial_indices[base1 + cand_k]; + int base2 = base1 + split_stride; + d2 = partial_dists[base2 + cand_k]; + i2 = partial_indices[base2 + cand_k]; + int base3 = base2 + split_stride; + d3 = partial_dists[base3 + cand_k]; + i3 = partial_indices[base3 + cand_k]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float winner_d = d0; + int winner_i = i0; + int winner_src = 0; + if (d1 < winner_d) { + winner_d = d1; + winner_i = i1; + winner_src = 1; + } + if (d2 < winner_d) { + winner_d = d2; + winner_i = i2; + winner_src = 2; + } + if (d3 < winner_d) { + winner_d = d3; + winner_i = i3; + winner_src = 3; + } + float warp_min = winner_d; + float _warp_reduce_0 = warp_min; + #pragma unroll + for (int offset = 16; offset > 0; offset >>= 1) + _warp_reduce_0 = fminf(_warp_reduce_0, __shfl_xor_sync(0xFFFFFFFF, _warp_reduce_0, offset)); + warp_min = _warp_reduce_0; + unsigned int _vote_0 = __ballot_sync(0xFFFFFFFF, winner_d == warp_min); + int owner_ballot = _vote_0; + int _ffs_0 = __ffs(owner_ballot); + int winner_lane = _ffs_0 - 1; + int _shfl_0 = __shfl_sync(0xFFFFFFFF, winner_i, winner_lane); + winner_i = _shfl_0; + int _shfl_1 = __shfl_sync(0xFFFFFFFF, winner_src, winner_lane); + winner_src = _shfl_1; + if (lane == 0) { + *((float*)(out_dists + (base_row + out_k))) = warp_min; + *((int*)(out_indices + (base_row + out_k))) = winner_i; + } + if (lane == winner_lane) { + if (winner_src == 0) { + d0 = 3.4e+38f; + } else if (winner_src == 1) { + d1 = 3.4e+38f; + } else { + if (winner_src == 2) { + d2 = 3.4e+38f; + } else { + d3 = 3.4e+38f; + } + } + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0061.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0061.cu new file mode 100644 index 00000000..a11c7ddb --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0061.cu @@ -0,0 +1,655 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 128 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_STRIDE 32768 +#define SMEM_SMEM_DATABASE_OFF 33792 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_DATABASE_SQ_OFF 50176 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 50432 +#define THREADS 192 +#define BLOCK_Q 128 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_MAX 48 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16_cta2( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + ".reg .b32 m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "tcgen05.mma.cta_group::2.kind::f16 [%0], %1, %2, %3, {m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step_cg2( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, %3, " + "{m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], %1;\n\t" + "}\n" + :: "r"(mbar_addr), "h"(cta_mask) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x16(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x16.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15}, [%16];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ uint32_t smem_addr(const void* ptr) { + uint32_t addr; + asm("{\n\t" + ".reg .u64 u64addr;\n\t" + "cvta.to.shared.u64 u64addr, %1;\n\t" + "cvt.u32.u64 %0, u64addr;\n\t" + "}\n" : "=r"(addr) : "l"(ptr)); + return addr; +} + + +__device__ __forceinline__ uint32_t mapa_to_rank(uint32_t local_addr, uint32_t rank) { + uint32_t remote; + asm volatile("mapa.shared::cluster.u32 %0, %1, %2;" + : "=r"(remote) : "r"(local_addr), "r"(rank)); + return remote; +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem_cta2( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global" + ".mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .b16 lo, hi;\n\t" + "mov.b32 {lo, hi}, %1;\n\t" + "tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], lo;\n\t" + "}\n" + :: "r"(mbar_addr), "r"((uint32_t)cta_mask) : "memory"); +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k48over32(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tile_pairs, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + const unsigned int clusters_x = gridDim.x / 2; + const unsigned int cluster_id = ((blockIdx.z * gridDim.y + blockIdx.y) * clusters_x) + blockIdx.x / 2; + const unsigned int num_clusters = clusters_x * gridDim.y * gridDim.z; + + int cta_rank; + asm volatile("mov.b32 %0, %%cluster_ctarank;" : "=r"(cta_rank)); + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 33792); + const int smem_database_addr = smem + 33792; + float* smem_database_sq = reinterpret_cast(smem_raw + 50176); + const int smem_database_sq_addr = smem + 50176; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 0, 2, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 16, 2, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=8 + mbarrier_init_pred(smem + 40, 8, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (128 columns, 128 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::2.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(128) : "memory"); + } + + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: load ---- + if (warp == 0) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 0) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx = cluster_id; work_idx < total_work; work_idx += num_clusters) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tile_pairs; + int q_tile_pair = query_work % num_q_tile_pairs; + int q_tile = q_tile_pair * 2 + cta_rank; + int off_q = q_tile * BLOCK_Q; + int global_q = batch_idx * Q + off_q; + int db_tile_start = split_idx * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((query_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(32768)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_query_addr), "l"(tmap_query), "r"(0), "r"(global_q), "r"(0), + "r"(((query_full_addr) & 0xFEFFFFFF)) : "memory"); + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int off_m = db_tile * BLOCK_M; + int global_m = batch_idx * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((database_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(16384)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_database_addr), "l"(tmap_database), "r"(0), "r"(global_m), "r"(0), + "r"(((database_full_addr) & 0xFEFFFFFF)) : "memory"); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 1) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + if (cta_rank == 0) { + #pragma unroll 1 + for (unsigned int work_idx_1 = cluster_id; work_idx_1 < total_work; work_idx_1 += num_clusters) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = (smem_query_addr >> 4) & 0x3FFF; + int _mma_b_lo_0 = (smem_database_addr >> 4) & 0x3FFF; + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\tmov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 270533776;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit_cg2_multicast(score_full_addr, (uint16_t)(3)); + elect_commit_cg2_multicast(database_empty_addr, (uint16_t)(3)); + } + elect_commit_cg2_multicast(query_empty_addr, (uint16_t)(3)); + } + } + } + // ---- Role: compute ---- + } else if (warp >= 2 && warp <= 5) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = cluster_id; work_idx_2 < total_work; work_idx_2 += num_clusters) { + int split_idx_1 = work_idx_2 % (unsigned int)split_count; + int query_work_1 = work_idx_2 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tile_pairs; + int q_tile_pair_1 = query_work_1 % num_q_tile_pairs; + int q_tile_1 = q_tile_pair_1 * 2 + cta_rank; + int off_q_1 = q_tile_1 * BLOCK_Q; + int q_idx = off_q_1 + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx_1 * Q + q_idx]; + } + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + float worst_d = 3.4e+38f; + int worst_pos = 0; + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int db_start = db_tile_1 * BLOCK_M; + int db_sq_idx = db_start + (warp % 4 * 32 + lane); + if (warp % 4 * 32 + lane < BLOCK_M) { + if (db_sq_idx < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx_1 * M + db_sq_idx]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 0.0f; + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(cta_rank * BLOCK_Q + (warp % 4 * 32 << 16) << 16); + float _tmem_load_0[64]; + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x64.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31, %32, %33, %34, %35, %36, %37, %38, %39, %40, %41, %42, %43, %44, %45, %46, %47, %48, %49, %50, %51, %52, %53, %54, %55, %56, %57, %58, %59, %60, %61, %62, %63}, [%64];" + : "=f"(_tmem_load_0[0]), "=f"(_tmem_load_0[1]), "=f"(_tmem_load_0[2]), "=f"(_tmem_load_0[3]), "=f"(_tmem_load_0[4]), "=f"(_tmem_load_0[5]), "=f"(_tmem_load_0[6]), "=f"(_tmem_load_0[7]), "=f"(_tmem_load_0[8]), "=f"(_tmem_load_0[9]), "=f"(_tmem_load_0[10]), "=f"(_tmem_load_0[11]), "=f"(_tmem_load_0[12]), "=f"(_tmem_load_0[13]), "=f"(_tmem_load_0[14]), "=f"(_tmem_load_0[15]), "=f"(_tmem_load_0[16]), "=f"(_tmem_load_0[17]), "=f"(_tmem_load_0[18]), "=f"(_tmem_load_0[19]), "=f"(_tmem_load_0[20]), "=f"(_tmem_load_0[21]), "=f"(_tmem_load_0[22]), "=f"(_tmem_load_0[23]), "=f"(_tmem_load_0[24]), "=f"(_tmem_load_0[25]), "=f"(_tmem_load_0[26]), "=f"(_tmem_load_0[27]), "=f"(_tmem_load_0[28]), "=f"(_tmem_load_0[29]), "=f"(_tmem_load_0[30]), "=f"(_tmem_load_0[31]), "=f"(_tmem_load_0[32]), "=f"(_tmem_load_0[33]), "=f"(_tmem_load_0[34]), "=f"(_tmem_load_0[35]), "=f"(_tmem_load_0[36]), "=f"(_tmem_load_0[37]), "=f"(_tmem_load_0[38]), "=f"(_tmem_load_0[39]), "=f"(_tmem_load_0[40]), "=f"(_tmem_load_0[41]), "=f"(_tmem_load_0[42]), "=f"(_tmem_load_0[43]), "=f"(_tmem_load_0[44]), "=f"(_tmem_load_0[45]), "=f"(_tmem_load_0[46]), "=f"(_tmem_load_0[47]), "=f"(_tmem_load_0[48]), "=f"(_tmem_load_0[49]), "=f"(_tmem_load_0[50]), "=f"(_tmem_load_0[51]), "=f"(_tmem_load_0[52]), "=f"(_tmem_load_0[53]), "=f"(_tmem_load_0[54]), "=f"(_tmem_load_0[55]), "=f"(_tmem_load_0[56]), "=f"(_tmem_load_0[57]), "=f"(_tmem_load_0[58]), "=f"(_tmem_load_0[59]), "=f"(_tmem_load_0[60]), "=f"(_tmem_load_0[61]), "=f"(_tmem_load_0[62]), "=f"(_tmem_load_0[63]) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + if (elect_sync()) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cluster.b64 _, [%0];" + :: "r"((score_empty_addr) & 0xFEFFFFFF) : "memory"); + } + if (valid_q != 0) { + #pragma unroll 2 + for (int col_base = 0; col_base < 64; col_base += 4) { + float dist_vec[4]; + dist_vec[0] = _tmem_load_0[col_base]; + dist_vec[1] = _tmem_load_0[col_base + 1]; + dist_vec[2] = _tmem_load_0[col_base + 2]; + dist_vec[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec[4]; + db_sq_vec[0] = smem_database_sq[col_base]; + db_sq_vec[1] = smem_database_sq[col_base + 1]; + db_sq_vec[2] = smem_database_sq[col_base + 2]; + db_sq_vec[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec)[_la], reinterpret_cast(db_sq_vec)[_la]); + float group_min = _t0[0]; + if (_t0[1] < group_min) { + group_min = _t0[1]; + } + if (_t0[2] < group_min) { + group_min = _t0[2]; + } + if (_t0[3] < group_min) { + group_min = _t0[3]; + } + if (group_min < worst_d) { + #pragma unroll + for (int vec_col = 0; vec_col < 4; vec_col++) { + int db_idx = db_start + col_base + vec_col; + if (db_idx < M) { + float dist = _t0[vec_col]; + if (dist < worst_d) { + best_d[worst_pos] = dist; + best_i[worst_pos] = db_idx; + worst_d = best_d[0]; + worst_pos = 0; + #pragma unroll + for (int scan_pos = 1; scan_pos < TOP_K_MAX; scan_pos++) { + if (worst_d < best_d[scan_pos]) { + worst_d = best_d[scan_pos]; + worst_pos = scan_pos; + } + } + } + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + if (valid_q != 0) { + int out_base = ((split_idx_1 * B + batch_idx_1) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + *((float*)(partial_dists + (out_base + out_k))) = best_d[out_k]; + *((int*)(partial_indices + (out_base + out_k))) = best_i[out_k]; + } + } + } + } + } + + // Cleanup + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::2.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(128)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::2.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0062.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0062.cu new file mode 100644 index 00000000..4a6dd682 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0062.cu @@ -0,0 +1,108 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 128 +#define TOP_K_MAX 48 +#define SPLIT_COUNT 4 + +#include + +extern "C" { + +__global__ __launch_bounds__(128, 1) void +kernel_knn_build_k48_merge_s4_unordered_warp_select(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int row = bid * 4 + warp; + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int cand_lo = lane; + int cand_hi = lane + 32; + if (row < total_queries) { + float cand_d[8]; + int cand_i[8]; + #pragma unroll + for (int slot = 0; slot < 8; slot++) { + cand_d[slot] = 3.4e+38f; + cand_i[slot] = -1; + } + #pragma unroll + for (int split_idx = 0; split_idx < SPLIT_COUNT; split_idx++) { + int split_base = base_row + split_idx * split_stride; + int lo_slot = split_idx * 2; + int hi_slot = lo_slot + 1; + cand_d[lo_slot] = partial_dists[split_base + cand_lo]; + cand_i[lo_slot] = partial_indices[split_base + cand_lo]; + if (cand_hi < TOP_K_MAX) { + cand_d[hi_slot] = partial_dists[split_base + cand_hi]; + cand_i[hi_slot] = partial_indices[split_base + cand_hi]; + } + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float winner_d = cand_d[0]; + int winner_i = cand_i[0]; + int winner_slot = 0; + #pragma unroll + for (int slot_1 = 1; slot_1 < 8; slot_1++) { + if (winner_d > cand_d[slot_1]) { + winner_d = cand_d[slot_1]; + winner_i = cand_i[slot_1]; + winner_slot = slot_1; + } + } + float warp_min = winner_d; + float _warp_reduce_0 = warp_min; + #pragma unroll + for (int offset = 16; offset > 0; offset >>= 1) + _warp_reduce_0 = fminf(_warp_reduce_0, __shfl_xor_sync(0xFFFFFFFF, _warp_reduce_0, offset)); + warp_min = _warp_reduce_0; + unsigned int _vote_0 = __ballot_sync(0xFFFFFFFF, winner_d == warp_min); + int owner_ballot = _vote_0; + int _ffs_0 = __ffs(owner_ballot); + int winner_lane = _ffs_0 - 1; + int _shfl_0 = __shfl_sync(0xFFFFFFFF, winner_i, winner_lane); + winner_i = _shfl_0; + int _shfl_1 = __shfl_sync(0xFFFFFFFF, winner_slot, winner_lane); + winner_slot = _shfl_1; + if (lane == 0) { + *((float*)(out_dists + (base_row + out_k))) = warp_min; + *((int*)(out_indices + (base_row + out_k))) = winner_i; + } + if (lane == winner_lane) { + #pragma unroll + for (int slot_2 = 0; slot_2 < 8; slot_2++) { + if (winner_slot == slot_2) { + cand_d[slot_2] = 3.4e+38f; + } + } + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0063.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0063.cu new file mode 100644 index 00000000..7770654c --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0063.cu @@ -0,0 +1,1143 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 128 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_STRIDE 32768 +#define SMEM_SMEM_DATABASE_OFF 33792 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_DATABASE_SQ_OFF 50176 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 50432 +#define THREADS 192 +#define BLOCK_Q 128 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_MAX 64 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16_cta2( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + ".reg .b32 m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "tcgen05.mma.cta_group::2.kind::f16 [%0], %1, %2, %3, {m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step_cg2( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, %3, " + "{m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], %1;\n\t" + "}\n" + :: "r"(mbar_addr), "h"(cta_mask) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x16(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x16.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15}, [%16];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ uint32_t smem_addr(const void* ptr) { + uint32_t addr; + asm("{\n\t" + ".reg .u64 u64addr;\n\t" + "cvta.to.shared.u64 u64addr, %1;\n\t" + "cvt.u32.u64 %0, u64addr;\n\t" + "}\n" : "=r"(addr) : "l"(ptr)); + return addr; +} + + +__device__ __forceinline__ uint32_t mapa_to_rank(uint32_t local_addr, uint32_t rank) { + uint32_t remote; + asm volatile("mapa.shared::cluster.u32 %0, %1, %2;" + : "=r"(remote) : "r"(local_addr), "r"(rank)); + return remote; +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem_cta2( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global" + ".mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .b16 lo, hi;\n\t" + "mov.b32 {lo, hi}, %1;\n\t" + "tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], lo;\n\t" + "}\n" + :: "r"(mbar_addr), "r"((uint32_t)cta_mask) : "memory"); +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_k64_stage1_tailinf_k64over32tailinfsplitgrid(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tile_pairs, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + const unsigned int clusters_x = gridDim.x / 2; + const unsigned int cluster_id = ((blockIdx.z * gridDim.y + blockIdx.y) * clusters_x) + blockIdx.x / 2; + const unsigned int num_clusters = clusters_x * gridDim.y * gridDim.z; + + int cta_rank; + asm volatile("mov.b32 %0, %%cluster_ctarank;" : "=r"(cta_rank)); + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 33792); + const int smem_database_addr = smem + 33792; + float* smem_database_sq = reinterpret_cast(smem_raw + 50176); + const int smem_database_sq_addr = smem + 50176; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 0, 2, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 16, 2, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=8 + mbarrier_init_pred(smem + 40, 8, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (128 columns, 128 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::2.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(128) : "memory"); + } + + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: compute ---- + if (warp <= 3) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx = cluster_id; work_idx < total_work; work_idx += num_clusters) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tile_pairs; + int q_tile_pair = query_work % num_q_tile_pairs; + int q_tile = q_tile_pair * 2 + cta_rank; + int off_q = q_tile * BLOCK_Q; + int q_idx = off_q + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx * Q + q_idx]; + } + float best_d[64]; + int best_i[64]; + #pragma unroll + for (int kk = 0; kk < 64; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + float worst_d = 3.4e+38f; + int worst_pos = 0; + int worst_chunk = 0; + float c0_worst_d = 3.4e+38f; + int c0_worst_pos = 0; + float c1_worst_d = 3.4e+38f; + int c1_worst_pos = 4; + float c2_worst_d = 3.4e+38f; + int c2_worst_pos = 8; + float c3_worst_d = 3.4e+38f; + int c3_worst_pos = 12; + float c4_worst_d = 3.4e+38f; + int c4_worst_pos = 16; + float c5_worst_d = 3.4e+38f; + int c5_worst_pos = 20; + float c6_worst_d = 3.4e+38f; + int c6_worst_pos = 24; + float c7_worst_d = 3.4e+38f; + int c7_worst_pos = 28; + float c8_worst_d = 3.4e+38f; + int c8_worst_pos = 32; + float c9_worst_d = 3.4e+38f; + int c9_worst_pos = 36; + float c10_worst_d = 3.4e+38f; + int c10_worst_pos = 40; + float c11_worst_d = 3.4e+38f; + int c11_worst_pos = 44; + float c12_worst_d = 3.4e+38f; + int c12_worst_pos = 48; + float c13_worst_d = 3.4e+38f; + int c13_worst_pos = 52; + float c14_worst_d = 3.4e+38f; + int c14_worst_pos = 56; + float c15_worst_d = 3.4e+38f; + int c15_worst_pos = 60; + int db_tile_start = split_idx * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int db_start = db_tile * BLOCK_M; + int db_sq_idx = db_start + (warp % 4 * 32 + lane); + if (warp % 4 * 32 + lane < BLOCK_M) { + if (db_sq_idx < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx * M + db_sq_idx]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 3.4e+38f; + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(cta_rank * BLOCK_Q + (warp % 4 * 32 << 16) << 16); + float _tmem_load_0[64]; + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x64.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31, %32, %33, %34, %35, %36, %37, %38, %39, %40, %41, %42, %43, %44, %45, %46, %47, %48, %49, %50, %51, %52, %53, %54, %55, %56, %57, %58, %59, %60, %61, %62, %63}, [%64];" + : "=f"(_tmem_load_0[0]), "=f"(_tmem_load_0[1]), "=f"(_tmem_load_0[2]), "=f"(_tmem_load_0[3]), "=f"(_tmem_load_0[4]), "=f"(_tmem_load_0[5]), "=f"(_tmem_load_0[6]), "=f"(_tmem_load_0[7]), "=f"(_tmem_load_0[8]), "=f"(_tmem_load_0[9]), "=f"(_tmem_load_0[10]), "=f"(_tmem_load_0[11]), "=f"(_tmem_load_0[12]), "=f"(_tmem_load_0[13]), "=f"(_tmem_load_0[14]), "=f"(_tmem_load_0[15]), "=f"(_tmem_load_0[16]), "=f"(_tmem_load_0[17]), "=f"(_tmem_load_0[18]), "=f"(_tmem_load_0[19]), "=f"(_tmem_load_0[20]), "=f"(_tmem_load_0[21]), "=f"(_tmem_load_0[22]), "=f"(_tmem_load_0[23]), "=f"(_tmem_load_0[24]), "=f"(_tmem_load_0[25]), "=f"(_tmem_load_0[26]), "=f"(_tmem_load_0[27]), "=f"(_tmem_load_0[28]), "=f"(_tmem_load_0[29]), "=f"(_tmem_load_0[30]), "=f"(_tmem_load_0[31]), "=f"(_tmem_load_0[32]), "=f"(_tmem_load_0[33]), "=f"(_tmem_load_0[34]), "=f"(_tmem_load_0[35]), "=f"(_tmem_load_0[36]), "=f"(_tmem_load_0[37]), "=f"(_tmem_load_0[38]), "=f"(_tmem_load_0[39]), "=f"(_tmem_load_0[40]), "=f"(_tmem_load_0[41]), "=f"(_tmem_load_0[42]), "=f"(_tmem_load_0[43]), "=f"(_tmem_load_0[44]), "=f"(_tmem_load_0[45]), "=f"(_tmem_load_0[46]), "=f"(_tmem_load_0[47]), "=f"(_tmem_load_0[48]), "=f"(_tmem_load_0[49]), "=f"(_tmem_load_0[50]), "=f"(_tmem_load_0[51]), "=f"(_tmem_load_0[52]), "=f"(_tmem_load_0[53]), "=f"(_tmem_load_0[54]), "=f"(_tmem_load_0[55]), "=f"(_tmem_load_0[56]), "=f"(_tmem_load_0[57]), "=f"(_tmem_load_0[58]), "=f"(_tmem_load_0[59]), "=f"(_tmem_load_0[60]), "=f"(_tmem_load_0[61]), "=f"(_tmem_load_0[62]), "=f"(_tmem_load_0[63]) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + if (elect_sync()) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cluster.b64 _, [%0];" + :: "r"((score_empty_addr) & 0xFEFFFFFF) : "memory"); + } + if (valid_q != 0) { + #pragma unroll 1 + for (int col_base = 0; col_base < 64; col_base += 4) { + float dist_vec[4]; + dist_vec[0] = _tmem_load_0[col_base]; + dist_vec[1] = _tmem_load_0[col_base + 1]; + dist_vec[2] = _tmem_load_0[col_base + 2]; + dist_vec[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec[4]; + db_sq_vec[0] = smem_database_sq[col_base]; + db_sq_vec[1] = smem_database_sq[col_base + 1]; + db_sq_vec[2] = smem_database_sq[col_base + 2]; + db_sq_vec[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec)[_la], reinterpret_cast(db_sq_vec)[_la]); + if (local_db_tile == 0) { + #pragma unroll + for (int vec_col = 0; vec_col < 4; vec_col++) { + int db_idx = db_start + col_base + vec_col; + int slot = col_base + vec_col; + best_d[slot] = _t0[vec_col]; + best_i[slot] = db_idx; + } + } else { + float group_min = _t0[0]; + if (_t0[1] < group_min) { + group_min = _t0[1]; + } + if (_t0[2] < group_min) { + group_min = _t0[2]; + } + if (_t0[3] < group_min) { + group_min = _t0[3]; + } + if (group_min < worst_d) { + float sort_d0 = _t0[0]; + float sort_d1 = _t0[1]; + float sort_d2 = _t0[2]; + float sort_d3 = _t0[3]; + int sort_col0 = 0; + int sort_col1 = 1; + int sort_col2 = 2; + int sort_col3 = 3; + float tmp_d = 0.0f; + int tmp_col = 0; + if (sort_d1 < sort_d0) { + tmp_d = sort_d0; + sort_d0 = sort_d1; + sort_d1 = tmp_d; + tmp_col = sort_col0; + sort_col0 = sort_col1; + sort_col1 = tmp_col; + } + if (sort_d3 < sort_d2) { + tmp_d = sort_d2; + sort_d2 = sort_d3; + sort_d3 = tmp_d; + tmp_col = sort_col2; + sort_col2 = sort_col3; + sort_col3 = tmp_col; + } + if (sort_d2 < sort_d0) { + tmp_d = sort_d0; + sort_d0 = sort_d2; + sort_d2 = tmp_d; + tmp_col = sort_col0; + sort_col0 = sort_col2; + sort_col2 = tmp_col; + } + if (sort_d3 < sort_d1) { + tmp_d = sort_d1; + sort_d1 = sort_d3; + sort_d3 = tmp_d; + tmp_col = sort_col1; + sort_col1 = sort_col3; + sort_col3 = tmp_col; + } + if (sort_d2 < sort_d1) { + tmp_d = sort_d1; + sort_d1 = sort_d2; + sort_d2 = tmp_d; + tmp_col = sort_col1; + sort_col1 = sort_col2; + sort_col2 = tmp_col; + } + #pragma unroll + for (int visit = 0; visit < 4; visit++) { + int vec_col_1 = sort_col0; + float dist = sort_d0; + if (visit == 1) { + vec_col_1 = sort_col1; + dist = sort_d1; + } + if (visit == 2) { + vec_col_1 = sort_col2; + dist = sort_d2; + } + if (visit == 3) { + vec_col_1 = sort_col3; + dist = sort_d3; + } + if (dist >= worst_d) { + break; + } + int db_idx_1 = db_start + col_base + vec_col_1; + best_d[worst_pos] = dist; + best_i[worst_pos] = db_idx_1; + int refresh_base = worst_chunk * 4; + float refresh_worst_d = best_d[refresh_base]; + int refresh_worst_pos = refresh_base; + #pragma unroll + for (int offset = 1; offset < 4; offset++) { + int scan_pos = refresh_base + offset; + if (best_d[scan_pos] > refresh_worst_d) { + refresh_worst_d = best_d[scan_pos]; + refresh_worst_pos = scan_pos; + } + } + if (worst_chunk == 0) { + c0_worst_d = refresh_worst_d; + c0_worst_pos = refresh_worst_pos; + } + if (worst_chunk == 1) { + c1_worst_d = refresh_worst_d; + c1_worst_pos = refresh_worst_pos; + } + if (worst_chunk == 2) { + c2_worst_d = refresh_worst_d; + c2_worst_pos = refresh_worst_pos; + } + if (worst_chunk == 3) { + c3_worst_d = refresh_worst_d; + c3_worst_pos = refresh_worst_pos; + } + if (worst_chunk == 4) { + c4_worst_d = refresh_worst_d; + c4_worst_pos = refresh_worst_pos; + } + if (worst_chunk == 5) { + c5_worst_d = refresh_worst_d; + c5_worst_pos = refresh_worst_pos; + } + if (worst_chunk == 6) { + c6_worst_d = refresh_worst_d; + c6_worst_pos = refresh_worst_pos; + } + if (worst_chunk == 7) { + c7_worst_d = refresh_worst_d; + c7_worst_pos = refresh_worst_pos; + } + if (worst_chunk == 8) { + c8_worst_d = refresh_worst_d; + c8_worst_pos = refresh_worst_pos; + } + if (worst_chunk == 9) { + c9_worst_d = refresh_worst_d; + c9_worst_pos = refresh_worst_pos; + } + if (worst_chunk == 10) { + c10_worst_d = refresh_worst_d; + c10_worst_pos = refresh_worst_pos; + } + if (worst_chunk == 11) { + c11_worst_d = refresh_worst_d; + c11_worst_pos = refresh_worst_pos; + } + if (worst_chunk == 12) { + c12_worst_d = refresh_worst_d; + c12_worst_pos = refresh_worst_pos; + } + if (worst_chunk == 13) { + c13_worst_d = refresh_worst_d; + c13_worst_pos = refresh_worst_pos; + } + if (worst_chunk == 14) { + c14_worst_d = refresh_worst_d; + c14_worst_pos = refresh_worst_pos; + } + if (worst_chunk == 15) { + c15_worst_d = refresh_worst_d; + c15_worst_pos = refresh_worst_pos; + } + worst_d = c0_worst_d; + worst_pos = c0_worst_pos; + worst_chunk = 0; + if (c1_worst_d > worst_d) { + worst_d = c1_worst_d; + worst_pos = c1_worst_pos; + worst_chunk = 1; + } + if (c2_worst_d > worst_d) { + worst_d = c2_worst_d; + worst_pos = c2_worst_pos; + worst_chunk = 2; + } + if (c3_worst_d > worst_d) { + worst_d = c3_worst_d; + worst_pos = c3_worst_pos; + worst_chunk = 3; + } + if (c4_worst_d > worst_d) { + worst_d = c4_worst_d; + worst_pos = c4_worst_pos; + worst_chunk = 4; + } + if (c5_worst_d > worst_d) { + worst_d = c5_worst_d; + worst_pos = c5_worst_pos; + worst_chunk = 5; + } + if (c6_worst_d > worst_d) { + worst_d = c6_worst_d; + worst_pos = c6_worst_pos; + worst_chunk = 6; + } + if (c7_worst_d > worst_d) { + worst_d = c7_worst_d; + worst_pos = c7_worst_pos; + worst_chunk = 7; + } + if (c8_worst_d > worst_d) { + worst_d = c8_worst_d; + worst_pos = c8_worst_pos; + worst_chunk = 8; + } + if (c9_worst_d > worst_d) { + worst_d = c9_worst_d; + worst_pos = c9_worst_pos; + worst_chunk = 9; + } + if (c10_worst_d > worst_d) { + worst_d = c10_worst_d; + worst_pos = c10_worst_pos; + worst_chunk = 10; + } + if (c11_worst_d > worst_d) { + worst_d = c11_worst_d; + worst_pos = c11_worst_pos; + worst_chunk = 11; + } + if (c12_worst_d > worst_d) { + worst_d = c12_worst_d; + worst_pos = c12_worst_pos; + worst_chunk = 12; + } + if (c13_worst_d > worst_d) { + worst_d = c13_worst_d; + worst_pos = c13_worst_pos; + worst_chunk = 13; + } + if (c14_worst_d > worst_d) { + worst_d = c14_worst_d; + worst_pos = c14_worst_pos; + worst_chunk = 14; + } + if (c15_worst_d > worst_d) { + worst_d = c15_worst_d; + worst_pos = c15_worst_pos; + worst_chunk = 15; + } + } + } + } + } + if (local_db_tile == 0) { + c0_worst_d = best_d[0]; + c0_worst_pos = 0; + #pragma unroll + for (int offset_1 = 1; offset_1 < 4; offset_1++) { + if (c0_worst_d < best_d[offset_1]) { + c0_worst_d = best_d[offset_1]; + c0_worst_pos = offset_1; + } + } + c1_worst_d = best_d[4]; + c1_worst_pos = 4; + #pragma unroll + for (int offset_2 = 1; offset_2 < 4; offset_2++) { + int scan_pos_1 = 4 + offset_2; + if (best_d[scan_pos_1] > c1_worst_d) { + c1_worst_d = best_d[scan_pos_1]; + c1_worst_pos = scan_pos_1; + } + } + c2_worst_d = best_d[8]; + c2_worst_pos = 8; + #pragma unroll + for (int offset_3 = 1; offset_3 < 4; offset_3++) { + int scan_pos_2 = 8 + offset_3; + if (best_d[scan_pos_2] > c2_worst_d) { + c2_worst_d = best_d[scan_pos_2]; + c2_worst_pos = scan_pos_2; + } + } + c3_worst_d = best_d[12]; + c3_worst_pos = 12; + #pragma unroll + for (int offset_4 = 1; offset_4 < 4; offset_4++) { + int scan_pos_3 = 12 + offset_4; + if (best_d[scan_pos_3] > c3_worst_d) { + c3_worst_d = best_d[scan_pos_3]; + c3_worst_pos = scan_pos_3; + } + } + c4_worst_d = best_d[16]; + c4_worst_pos = 16; + #pragma unroll + for (int offset_5 = 1; offset_5 < 4; offset_5++) { + int scan_pos_4 = 16 + offset_5; + if (best_d[scan_pos_4] > c4_worst_d) { + c4_worst_d = best_d[scan_pos_4]; + c4_worst_pos = scan_pos_4; + } + } + c5_worst_d = best_d[20]; + c5_worst_pos = 20; + #pragma unroll + for (int offset_6 = 1; offset_6 < 4; offset_6++) { + int scan_pos_5 = 20 + offset_6; + if (best_d[scan_pos_5] > c5_worst_d) { + c5_worst_d = best_d[scan_pos_5]; + c5_worst_pos = scan_pos_5; + } + } + c6_worst_d = best_d[24]; + c6_worst_pos = 24; + #pragma unroll + for (int offset_7 = 1; offset_7 < 4; offset_7++) { + int scan_pos_6 = 24 + offset_7; + if (best_d[scan_pos_6] > c6_worst_d) { + c6_worst_d = best_d[scan_pos_6]; + c6_worst_pos = scan_pos_6; + } + } + c7_worst_d = best_d[28]; + c7_worst_pos = 28; + #pragma unroll + for (int offset_8 = 1; offset_8 < 4; offset_8++) { + int scan_pos_7 = 28 + offset_8; + if (best_d[scan_pos_7] > c7_worst_d) { + c7_worst_d = best_d[scan_pos_7]; + c7_worst_pos = scan_pos_7; + } + } + c8_worst_d = best_d[32]; + c8_worst_pos = 32; + #pragma unroll + for (int offset_9 = 1; offset_9 < 4; offset_9++) { + int scan_pos_8 = 32 + offset_9; + if (best_d[scan_pos_8] > c8_worst_d) { + c8_worst_d = best_d[scan_pos_8]; + c8_worst_pos = scan_pos_8; + } + } + c9_worst_d = best_d[36]; + c9_worst_pos = 36; + #pragma unroll + for (int offset_10 = 1; offset_10 < 4; offset_10++) { + int scan_pos_9 = 36 + offset_10; + if (best_d[scan_pos_9] > c9_worst_d) { + c9_worst_d = best_d[scan_pos_9]; + c9_worst_pos = scan_pos_9; + } + } + c10_worst_d = best_d[40]; + c10_worst_pos = 40; + #pragma unroll + for (int offset_11 = 1; offset_11 < 4; offset_11++) { + int scan_pos_10 = 40 + offset_11; + if (best_d[scan_pos_10] > c10_worst_d) { + c10_worst_d = best_d[scan_pos_10]; + c10_worst_pos = scan_pos_10; + } + } + c11_worst_d = best_d[44]; + c11_worst_pos = 44; + #pragma unroll + for (int offset_12 = 1; offset_12 < 4; offset_12++) { + int scan_pos_11 = 44 + offset_12; + if (best_d[scan_pos_11] > c11_worst_d) { + c11_worst_d = best_d[scan_pos_11]; + c11_worst_pos = scan_pos_11; + } + } + c12_worst_d = best_d[48]; + c12_worst_pos = 48; + #pragma unroll + for (int offset_13 = 1; offset_13 < 4; offset_13++) { + int scan_pos_12 = 48 + offset_13; + if (best_d[scan_pos_12] > c12_worst_d) { + c12_worst_d = best_d[scan_pos_12]; + c12_worst_pos = scan_pos_12; + } + } + c13_worst_d = best_d[52]; + c13_worst_pos = 52; + #pragma unroll + for (int offset_14 = 1; offset_14 < 4; offset_14++) { + int scan_pos_13 = 52 + offset_14; + if (best_d[scan_pos_13] > c13_worst_d) { + c13_worst_d = best_d[scan_pos_13]; + c13_worst_pos = scan_pos_13; + } + } + c14_worst_d = best_d[56]; + c14_worst_pos = 56; + #pragma unroll + for (int offset_15 = 1; offset_15 < 4; offset_15++) { + int scan_pos_14 = 56 + offset_15; + if (best_d[scan_pos_14] > c14_worst_d) { + c14_worst_d = best_d[scan_pos_14]; + c14_worst_pos = scan_pos_14; + } + } + c15_worst_d = best_d[60]; + c15_worst_pos = 60; + #pragma unroll + for (int offset_16 = 1; offset_16 < 4; offset_16++) { + int scan_pos_15 = 60 + offset_16; + if (best_d[scan_pos_15] > c15_worst_d) { + c15_worst_d = best_d[scan_pos_15]; + c15_worst_pos = scan_pos_15; + } + } + worst_d = c0_worst_d; + worst_pos = c0_worst_pos; + worst_chunk = 0; + if (c1_worst_d > worst_d) { + worst_d = c1_worst_d; + worst_pos = c1_worst_pos; + worst_chunk = 1; + } + if (c2_worst_d > worst_d) { + worst_d = c2_worst_d; + worst_pos = c2_worst_pos; + worst_chunk = 2; + } + if (c3_worst_d > worst_d) { + worst_d = c3_worst_d; + worst_pos = c3_worst_pos; + worst_chunk = 3; + } + if (c4_worst_d > worst_d) { + worst_d = c4_worst_d; + worst_pos = c4_worst_pos; + worst_chunk = 4; + } + if (c5_worst_d > worst_d) { + worst_d = c5_worst_d; + worst_pos = c5_worst_pos; + worst_chunk = 5; + } + if (c6_worst_d > worst_d) { + worst_d = c6_worst_d; + worst_pos = c6_worst_pos; + worst_chunk = 6; + } + if (c7_worst_d > worst_d) { + worst_d = c7_worst_d; + worst_pos = c7_worst_pos; + worst_chunk = 7; + } + if (c8_worst_d > worst_d) { + worst_d = c8_worst_d; + worst_pos = c8_worst_pos; + worst_chunk = 8; + } + if (c9_worst_d > worst_d) { + worst_d = c9_worst_d; + worst_pos = c9_worst_pos; + worst_chunk = 9; + } + if (c10_worst_d > worst_d) { + worst_d = c10_worst_d; + worst_pos = c10_worst_pos; + worst_chunk = 10; + } + if (c11_worst_d > worst_d) { + worst_d = c11_worst_d; + worst_pos = c11_worst_pos; + worst_chunk = 11; + } + if (c12_worst_d > worst_d) { + worst_d = c12_worst_d; + worst_pos = c12_worst_pos; + worst_chunk = 12; + } + if (c13_worst_d > worst_d) { + worst_d = c13_worst_d; + worst_pos = c13_worst_pos; + worst_chunk = 13; + } + if (c14_worst_d > worst_d) { + worst_d = c14_worst_d; + worst_pos = c14_worst_pos; + worst_chunk = 14; + } + if (c15_worst_d > worst_d) { + worst_d = c15_worst_d; + worst_pos = c15_worst_pos; + worst_chunk = 15; + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + if (valid_q != 0) { + int out_base = ((split_idx * B + batch_idx) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < 64; out_k++) { + *((float*)(partial_dists + (out_base + out_k))) = best_d[out_k]; + *((int*)(partial_indices + (out_base + out_k))) = best_i[out_k]; + } + } + } + } + // ---- Role: load ---- + } else if (warp == 4) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 4) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx_1 = cluster_id; work_idx_1 < total_work; work_idx_1 += num_clusters) { + int split_idx_1 = work_idx_1 % (unsigned int)split_count; + int query_work_1 = work_idx_1 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tile_pairs; + int q_tile_pair_1 = query_work_1 % num_q_tile_pairs; + int q_tile_1 = q_tile_pair_1 * 2 + cta_rank; + int off_q_1 = q_tile_1 * BLOCK_Q; + int global_q = batch_idx_1 * Q + off_q_1; + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((query_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(32768)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_query_addr), "l"(tmap_query), "r"(0), "r"(global_q), "r"(0), + "r"(((query_full_addr) & 0xFEFFFFFF)) : "memory"); + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int off_m = db_tile_1 * BLOCK_M; + int global_m = batch_idx_1 * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((database_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(16384)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_database_addr), "l"(tmap_database), "r"(0), "r"(global_m), "r"(0), + "r"(((database_full_addr) & 0xFEFFFFFF)) : "memory"); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 5) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + if (cta_rank == 0) { + #pragma unroll 1 + for (unsigned int work_idx_2 = cluster_id; work_idx_2 < total_work; work_idx_2 += num_clusters) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = (smem_query_addr >> 4) & 0x3FFF; + int _mma_b_lo_0 = (smem_database_addr >> 4) & 0x3FFF; + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\tmov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 270533776;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit_cg2_multicast(score_full_addr, (uint16_t)(3)); + elect_commit_cg2_multicast(database_empty_addr, (uint16_t)(3)); + } + elect_commit_cg2_multicast(query_empty_addr, (uint16_t)(3)); + } + } + } + } + + // Cleanup + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::2.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(128)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::2.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0064.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0064.cu new file mode 100644 index 00000000..b389f353 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0064.cu @@ -0,0 +1,99 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 128 +#define TOP_K_MAX 64 +#define SPLIT_COUNT 8 + +#include + +extern "C" { + +__global__ __launch_bounds__(128, 1) void +kernel_knn_build_k64_merge_s8_unordered_warp_select_k64over32s8warpselect(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int row = bid * 4 + warp; + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int cand_lo = lane; + int cand_hi = lane + 32; + if (row < total_queries) { + float cand_d[16]; + int cand_i[16]; + #pragma unroll + for (int split_idx = 0; split_idx < 8; split_idx++) { + int split_base = base_row + split_idx * split_stride; + cand_d[split_idx * 2] = partial_dists[split_base + cand_lo]; + cand_i[split_idx * 2] = partial_indices[split_base + cand_lo]; + cand_d[split_idx * 2 + 1] = partial_dists[split_base + cand_hi]; + cand_i[split_idx * 2 + 1] = partial_indices[split_base + cand_hi]; + } + #pragma unroll + for (int out_k = 0; out_k < 64; out_k++) { + float winner_d = cand_d[0]; + int winner_i = cand_i[0]; + int winner_slot = 0; + #pragma unroll + for (int slot = 1; slot < 16; slot++) { + if (winner_d > cand_d[slot]) { + winner_d = cand_d[slot]; + winner_i = cand_i[slot]; + winner_slot = slot; + } + } + float warp_min = winner_d; + float _warp_reduce_0 = warp_min; + #pragma unroll + for (int offset = 16; offset > 0; offset >>= 1) + _warp_reduce_0 = fminf(_warp_reduce_0, __shfl_xor_sync(0xFFFFFFFF, _warp_reduce_0, offset)); + warp_min = _warp_reduce_0; + unsigned int _vote_0 = __ballot_sync(0xFFFFFFFF, winner_d == warp_min); + int owner_ballot = _vote_0; + int _ffs_0 = __ffs(owner_ballot); + int winner_lane = _ffs_0 - 1; + int _shfl_0 = __shfl_sync(0xFFFFFFFF, winner_i, winner_lane); + winner_i = _shfl_0; + int _shfl_1 = __shfl_sync(0xFFFFFFFF, winner_slot, winner_lane); + winner_slot = _shfl_1; + if (lane == 0) { + *((float*)(out_dists + (base_row + out_k))) = warp_min; + *((int*)(out_indices + (base_row + out_k))) = winner_i; + } + if (lane == winner_lane) { + #pragma unroll + for (int slot_1 = 0; slot_1 < 16; slot_1++) { + if (winner_slot == slot_1) { + cand_d[slot_1] = 3.4e+38f; + } + } + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0065.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0065.cu new file mode 100644 index 00000000..bd7ea932 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0065.cu @@ -0,0 +1,655 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 128 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_STRIDE 32768 +#define SMEM_SMEM_DATABASE_OFF 33792 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_DATABASE_SQ_OFF 50176 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 50432 +#define THREADS 192 +#define BLOCK_Q 128 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_MAX 20 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16_cta2( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + ".reg .b32 m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "tcgen05.mma.cta_group::2.kind::f16 [%0], %1, %2, %3, {m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step_cg2( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, %3, " + "{m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], %1;\n\t" + "}\n" + :: "r"(mbar_addr), "h"(cta_mask) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x16(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x16.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15}, [%16];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ uint32_t smem_addr(const void* ptr) { + uint32_t addr; + asm("{\n\t" + ".reg .u64 u64addr;\n\t" + "cvta.to.shared.u64 u64addr, %1;\n\t" + "cvt.u32.u64 %0, u64addr;\n\t" + "}\n" : "=r"(addr) : "l"(ptr)); + return addr; +} + + +__device__ __forceinline__ uint32_t mapa_to_rank(uint32_t local_addr, uint32_t rank) { + uint32_t remote; + asm volatile("mapa.shared::cluster.u32 %0, %1, %2;" + : "=r"(remote) : "r"(local_addr), "r"(rank)); + return remote; +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem_cta2( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global" + ".mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .b16 lo, hi;\n\t" + "mov.b32 {lo, hi}, %1;\n\t" + "tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], lo;\n\t" + "}\n" + :: "r"(mbar_addr), "r"((uint32_t)cta_mask) : "memory"); +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k20unordered(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tile_pairs, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + const unsigned int clusters_x = gridDim.x / 2; + const unsigned int cluster_id = ((blockIdx.z * gridDim.y + blockIdx.y) * clusters_x) + blockIdx.x / 2; + const unsigned int num_clusters = clusters_x * gridDim.y * gridDim.z; + + int cta_rank; + asm volatile("mov.b32 %0, %%cluster_ctarank;" : "=r"(cta_rank)); + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 33792); + const int smem_database_addr = smem + 33792; + float* smem_database_sq = reinterpret_cast(smem_raw + 50176); + const int smem_database_sq_addr = smem + 50176; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 0, 2, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 16, 2, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=8 + mbarrier_init_pred(smem + 40, 8, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (128 columns, 128 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::2.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(128) : "memory"); + } + + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: load ---- + if (warp == 0) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 0) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx = cluster_id; work_idx < total_work; work_idx += num_clusters) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tile_pairs; + int q_tile_pair = query_work % num_q_tile_pairs; + int q_tile = q_tile_pair * 2 + cta_rank; + int off_q = q_tile * BLOCK_Q; + int global_q = batch_idx * Q + off_q; + int db_tile_start = split_idx * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((query_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(32768)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_query_addr), "l"(tmap_query), "r"(0), "r"(global_q), "r"(0), + "r"(((query_full_addr) & 0xFEFFFFFF)) : "memory"); + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int off_m = db_tile * BLOCK_M; + int global_m = batch_idx * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((database_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(16384)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_database_addr), "l"(tmap_database), "r"(0), "r"(global_m), "r"(0), + "r"(((database_full_addr) & 0xFEFFFFFF)) : "memory"); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 1) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + if (cta_rank == 0) { + #pragma unroll 1 + for (unsigned int work_idx_1 = cluster_id; work_idx_1 < total_work; work_idx_1 += num_clusters) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = (smem_query_addr >> 4) & 0x3FFF; + int _mma_b_lo_0 = (smem_database_addr >> 4) & 0x3FFF; + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\tmov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 270533776;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit_cg2_multicast(score_full_addr, (uint16_t)(3)); + elect_commit_cg2_multicast(database_empty_addr, (uint16_t)(3)); + } + elect_commit_cg2_multicast(query_empty_addr, (uint16_t)(3)); + } + } + } + // ---- Role: compute ---- + } else if (warp >= 2 && warp <= 5) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = cluster_id; work_idx_2 < total_work; work_idx_2 += num_clusters) { + int split_idx_1 = work_idx_2 % (unsigned int)split_count; + int query_work_1 = work_idx_2 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tile_pairs; + int q_tile_pair_1 = query_work_1 % num_q_tile_pairs; + int q_tile_1 = q_tile_pair_1 * 2 + cta_rank; + int off_q_1 = q_tile_1 * BLOCK_Q; + int q_idx = off_q_1 + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx_1 * Q + q_idx]; + } + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + float worst_d = 3.4e+38f; + int worst_pos = 0; + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int db_start = db_tile_1 * BLOCK_M; + int db_sq_idx = db_start + (warp % 4 * 32 + lane); + if (warp % 4 * 32 + lane < BLOCK_M) { + if (db_sq_idx < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx_1 * M + db_sq_idx]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 0.0f; + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(cta_rank * BLOCK_Q + (warp % 4 * 32 << 16) << 16); + float _tmem_load_0[64]; + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x64.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31, %32, %33, %34, %35, %36, %37, %38, %39, %40, %41, %42, %43, %44, %45, %46, %47, %48, %49, %50, %51, %52, %53, %54, %55, %56, %57, %58, %59, %60, %61, %62, %63}, [%64];" + : "=f"(_tmem_load_0[0]), "=f"(_tmem_load_0[1]), "=f"(_tmem_load_0[2]), "=f"(_tmem_load_0[3]), "=f"(_tmem_load_0[4]), "=f"(_tmem_load_0[5]), "=f"(_tmem_load_0[6]), "=f"(_tmem_load_0[7]), "=f"(_tmem_load_0[8]), "=f"(_tmem_load_0[9]), "=f"(_tmem_load_0[10]), "=f"(_tmem_load_0[11]), "=f"(_tmem_load_0[12]), "=f"(_tmem_load_0[13]), "=f"(_tmem_load_0[14]), "=f"(_tmem_load_0[15]), "=f"(_tmem_load_0[16]), "=f"(_tmem_load_0[17]), "=f"(_tmem_load_0[18]), "=f"(_tmem_load_0[19]), "=f"(_tmem_load_0[20]), "=f"(_tmem_load_0[21]), "=f"(_tmem_load_0[22]), "=f"(_tmem_load_0[23]), "=f"(_tmem_load_0[24]), "=f"(_tmem_load_0[25]), "=f"(_tmem_load_0[26]), "=f"(_tmem_load_0[27]), "=f"(_tmem_load_0[28]), "=f"(_tmem_load_0[29]), "=f"(_tmem_load_0[30]), "=f"(_tmem_load_0[31]), "=f"(_tmem_load_0[32]), "=f"(_tmem_load_0[33]), "=f"(_tmem_load_0[34]), "=f"(_tmem_load_0[35]), "=f"(_tmem_load_0[36]), "=f"(_tmem_load_0[37]), "=f"(_tmem_load_0[38]), "=f"(_tmem_load_0[39]), "=f"(_tmem_load_0[40]), "=f"(_tmem_load_0[41]), "=f"(_tmem_load_0[42]), "=f"(_tmem_load_0[43]), "=f"(_tmem_load_0[44]), "=f"(_tmem_load_0[45]), "=f"(_tmem_load_0[46]), "=f"(_tmem_load_0[47]), "=f"(_tmem_load_0[48]), "=f"(_tmem_load_0[49]), "=f"(_tmem_load_0[50]), "=f"(_tmem_load_0[51]), "=f"(_tmem_load_0[52]), "=f"(_tmem_load_0[53]), "=f"(_tmem_load_0[54]), "=f"(_tmem_load_0[55]), "=f"(_tmem_load_0[56]), "=f"(_tmem_load_0[57]), "=f"(_tmem_load_0[58]), "=f"(_tmem_load_0[59]), "=f"(_tmem_load_0[60]), "=f"(_tmem_load_0[61]), "=f"(_tmem_load_0[62]), "=f"(_tmem_load_0[63]) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + if (elect_sync()) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cluster.b64 _, [%0];" + :: "r"((score_empty_addr) & 0xFEFFFFFF) : "memory"); + } + if (valid_q != 0) { + #pragma unroll 2 + for (int col_base = 0; col_base < 64; col_base += 4) { + float dist_vec[4]; + dist_vec[0] = _tmem_load_0[col_base]; + dist_vec[1] = _tmem_load_0[col_base + 1]; + dist_vec[2] = _tmem_load_0[col_base + 2]; + dist_vec[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec[4]; + db_sq_vec[0] = smem_database_sq[col_base]; + db_sq_vec[1] = smem_database_sq[col_base + 1]; + db_sq_vec[2] = smem_database_sq[col_base + 2]; + db_sq_vec[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec)[_la], reinterpret_cast(db_sq_vec)[_la]); + float group_min = _t0[0]; + if (_t0[1] < group_min) { + group_min = _t0[1]; + } + if (_t0[2] < group_min) { + group_min = _t0[2]; + } + if (_t0[3] < group_min) { + group_min = _t0[3]; + } + if (group_min < worst_d) { + #pragma unroll + for (int vec_col = 0; vec_col < 4; vec_col++) { + int db_idx = db_start + col_base + vec_col; + if (db_idx < M) { + float dist = _t0[vec_col]; + if (dist < worst_d) { + best_d[worst_pos] = dist; + best_i[worst_pos] = db_idx; + worst_d = best_d[0]; + worst_pos = 0; + #pragma unroll + for (int scan_pos = 1; scan_pos < TOP_K_MAX; scan_pos++) { + if (worst_d < best_d[scan_pos]) { + worst_d = best_d[scan_pos]; + worst_pos = scan_pos; + } + } + } + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + if (valid_q != 0) { + int out_base = ((split_idx_1 * B + batch_idx_1) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + *((float*)(partial_dists + (out_base + out_k))) = best_d[out_k]; + *((int*)(partial_indices + (out_base + out_k))) = best_i[out_k]; + } + } + } + } + } + + // Cleanup + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::2.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(128)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::2.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0066.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0066.cu new file mode 100644 index 00000000..91cfb39d --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0066.cu @@ -0,0 +1,100 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 256 +#define TOP_K_MAX 20 +#define SPLIT_COUNT 2 + +#include + +extern "C" { + +__global__ __launch_bounds__(256, 1) void +kernel_knn_build_k20_mergeown_08ec_warp8_select_s2warp8(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int row = bid * 8 + warp; + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int cand_k = lane; + if (row < total_queries) { + float cand_d[SPLIT_COUNT]; + int cand_i[SPLIT_COUNT]; + #pragma unroll + for (int split_idx = 0; split_idx < SPLIT_COUNT; split_idx++) { + cand_d[split_idx] = 3.4e+38f; + cand_i[split_idx] = -1; + if (cand_k < TOP_K_MAX) { + int split_base = base_row + split_idx * split_stride; + cand_d[split_idx] = partial_dists[split_base + cand_k]; + cand_i[split_idx] = partial_indices[split_base + cand_k]; + } + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float winner_d = cand_d[0]; + int winner_i = cand_i[0]; + int winner_src = 0; + #pragma unroll + for (int split_idx_1 = 1; split_idx_1 < SPLIT_COUNT; split_idx_1++) { + if (winner_d > cand_d[split_idx_1]) { + winner_d = cand_d[split_idx_1]; + winner_i = cand_i[split_idx_1]; + winner_src = split_idx_1; + } + } + float warp_min = winner_d; + float _warp_reduce_0 = warp_min; + #pragma unroll + for (int offset = 16; offset > 0; offset >>= 1) + _warp_reduce_0 = fminf(_warp_reduce_0, __shfl_xor_sync(0xFFFFFFFF, _warp_reduce_0, offset)); + warp_min = _warp_reduce_0; + unsigned int _vote_0 = __ballot_sync(0xFFFFFFFF, winner_d == warp_min); + int owner_ballot = _vote_0; + int _ffs_0 = __ffs(owner_ballot); + int winner_lane = _ffs_0 - 1; + int _shfl_0 = __shfl_sync(0xFFFFFFFF, winner_i, winner_lane); + winner_i = _shfl_0; + int _shfl_1 = __shfl_sync(0xFFFFFFFF, winner_src, winner_lane); + winner_src = _shfl_1; + if (lane == 0) { + *((float*)(out_dists + (base_row + out_k))) = warp_min; + *((int*)(out_indices + (base_row + out_k))) = winner_i; + } + if (lane == winner_lane) { + #pragma unroll + for (int split_idx_2 = 0; split_idx_2 < SPLIT_COUNT; split_idx_2++) { + if (winner_src == split_idx_2) { + cand_d[split_idx_2] = 3.4e+38f; + } + } + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0067.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0067.cu new file mode 100644 index 00000000..0e9014e9 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0067.cu @@ -0,0 +1,700 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 128 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_STRIDE 32768 +#define SMEM_SMEM_DATABASE_OFF 33792 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_DATABASE_SQ_OFF 50176 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 50432 +#define THREADS 192 +#define BLOCK_Q 128 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_MAX 32 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16_cta2( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + ".reg .b32 m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "tcgen05.mma.cta_group::2.kind::f16 [%0], %1, %2, %3, {m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step_cg2( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, %3, " + "{m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], %1;\n\t" + "}\n" + :: "r"(mbar_addr), "h"(cta_mask) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x16(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x16.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15}, [%16];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ uint32_t smem_addr(const void* ptr) { + uint32_t addr; + asm("{\n\t" + ".reg .u64 u64addr;\n\t" + "cvta.to.shared.u64 u64addr, %1;\n\t" + "cvt.u32.u64 %0, u64addr;\n\t" + "}\n" : "=r"(addr) : "l"(ptr)); + return addr; +} + + +__device__ __forceinline__ uint32_t mapa_to_rank(uint32_t local_addr, uint32_t rank) { + uint32_t remote; + asm volatile("mapa.shared::cluster.u32 %0, %1, %2;" + : "=r"(remote) : "r"(local_addr), "r"(rank)); + return remote; +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem_cta2( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global" + ".mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .b16 lo, hi;\n\t" + "mov.b32 {lo, hi}, %1;\n\t" + "tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], lo;\n\t" + "}\n" + :: "r"(mbar_addr), "r"((uint32_t)cta_mask) : "memory"); +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_large_square_k32_stage1_chunkworst(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tile_pairs, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + const unsigned int clusters_x = gridDim.x / 2; + const unsigned int cluster_id = ((blockIdx.z * gridDim.y + blockIdx.y) * clusters_x) + blockIdx.x / 2; + const unsigned int num_clusters = clusters_x * gridDim.y * gridDim.z; + + int cta_rank; + asm volatile("mov.b32 %0, %%cluster_ctarank;" : "=r"(cta_rank)); + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 33792); + const int smem_database_addr = smem + 33792; + float* smem_database_sq = reinterpret_cast(smem_raw + 50176); + const int smem_database_sq_addr = smem + 50176; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 0, 2, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 16, 2, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=8 + mbarrier_init_pred(smem + 40, 8, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (128 columns, 128 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::2.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(128) : "memory"); + } + + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: compute ---- + if (warp <= 3) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx = cluster_id; work_idx < total_work; work_idx += num_clusters) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tile_pairs; + int q_tile_pair = query_work % num_q_tile_pairs; + int q_tile = q_tile_pair * 2 + cta_rank; + int off_q = q_tile * BLOCK_Q; + int q_idx = off_q + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx * Q + q_idx]; + } + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + float worst_d = 3.4e+38f; + int worst_pos = 0; + int worst_chunk = 0; + float c0_worst_d = 3.4e+38f; + int c0_worst_pos = 0; + float c1_worst_d = 3.4e+38f; + int c1_worst_pos = 8; + float c2_worst_d = 3.4e+38f; + int c2_worst_pos = 16; + float c3_worst_d = 3.4e+38f; + int c3_worst_pos = 24; + int db_tile_start = split_idx * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int db_start = db_tile * BLOCK_M; + int db_sq_idx = db_start + (warp % 4 * 32 + lane); + if (warp % 4 * 32 + lane < BLOCK_M) { + if (db_sq_idx < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx * M + db_sq_idx]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 0.0f; + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(cta_rank * BLOCK_Q + (warp % 4 * 32 << 16) << 16); + float _tmem_load_0[64]; + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x64.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31, %32, %33, %34, %35, %36, %37, %38, %39, %40, %41, %42, %43, %44, %45, %46, %47, %48, %49, %50, %51, %52, %53, %54, %55, %56, %57, %58, %59, %60, %61, %62, %63}, [%64];" + : "=f"(_tmem_load_0[0]), "=f"(_tmem_load_0[1]), "=f"(_tmem_load_0[2]), "=f"(_tmem_load_0[3]), "=f"(_tmem_load_0[4]), "=f"(_tmem_load_0[5]), "=f"(_tmem_load_0[6]), "=f"(_tmem_load_0[7]), "=f"(_tmem_load_0[8]), "=f"(_tmem_load_0[9]), "=f"(_tmem_load_0[10]), "=f"(_tmem_load_0[11]), "=f"(_tmem_load_0[12]), "=f"(_tmem_load_0[13]), "=f"(_tmem_load_0[14]), "=f"(_tmem_load_0[15]), "=f"(_tmem_load_0[16]), "=f"(_tmem_load_0[17]), "=f"(_tmem_load_0[18]), "=f"(_tmem_load_0[19]), "=f"(_tmem_load_0[20]), "=f"(_tmem_load_0[21]), "=f"(_tmem_load_0[22]), "=f"(_tmem_load_0[23]), "=f"(_tmem_load_0[24]), "=f"(_tmem_load_0[25]), "=f"(_tmem_load_0[26]), "=f"(_tmem_load_0[27]), "=f"(_tmem_load_0[28]), "=f"(_tmem_load_0[29]), "=f"(_tmem_load_0[30]), "=f"(_tmem_load_0[31]), "=f"(_tmem_load_0[32]), "=f"(_tmem_load_0[33]), "=f"(_tmem_load_0[34]), "=f"(_tmem_load_0[35]), "=f"(_tmem_load_0[36]), "=f"(_tmem_load_0[37]), "=f"(_tmem_load_0[38]), "=f"(_tmem_load_0[39]), "=f"(_tmem_load_0[40]), "=f"(_tmem_load_0[41]), "=f"(_tmem_load_0[42]), "=f"(_tmem_load_0[43]), "=f"(_tmem_load_0[44]), "=f"(_tmem_load_0[45]), "=f"(_tmem_load_0[46]), "=f"(_tmem_load_0[47]), "=f"(_tmem_load_0[48]), "=f"(_tmem_load_0[49]), "=f"(_tmem_load_0[50]), "=f"(_tmem_load_0[51]), "=f"(_tmem_load_0[52]), "=f"(_tmem_load_0[53]), "=f"(_tmem_load_0[54]), "=f"(_tmem_load_0[55]), "=f"(_tmem_load_0[56]), "=f"(_tmem_load_0[57]), "=f"(_tmem_load_0[58]), "=f"(_tmem_load_0[59]), "=f"(_tmem_load_0[60]), "=f"(_tmem_load_0[61]), "=f"(_tmem_load_0[62]), "=f"(_tmem_load_0[63]) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + if (elect_sync()) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cluster.b64 _, [%0];" + :: "r"((score_empty_addr) & 0xFEFFFFFF) : "memory"); + } + if (valid_q != 0) { + #pragma unroll 1 + for (int col_base = 0; col_base < 64; col_base += 4) { + float dist_vec[4]; + dist_vec[0] = _tmem_load_0[col_base]; + dist_vec[1] = _tmem_load_0[col_base + 1]; + dist_vec[2] = _tmem_load_0[col_base + 2]; + dist_vec[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec[4]; + db_sq_vec[0] = smem_database_sq[col_base]; + db_sq_vec[1] = smem_database_sq[col_base + 1]; + db_sq_vec[2] = smem_database_sq[col_base + 2]; + db_sq_vec[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec)[_la], reinterpret_cast(db_sq_vec)[_la]); + float group_min = _t0[0]; + if (_t0[1] < group_min) { + group_min = _t0[1]; + } + if (_t0[2] < group_min) { + group_min = _t0[2]; + } + if (_t0[3] < group_min) { + group_min = _t0[3]; + } + if (group_min < worst_d) { + #pragma unroll + for (int vec_col = 0; vec_col < 4; vec_col++) { + int db_idx = db_start + col_base + vec_col; + if (db_idx < M) { + float dist = _t0[vec_col]; + if (dist < worst_d) { + best_d[worst_pos] = dist; + best_i[worst_pos] = db_idx; + int refresh_base = worst_chunk * 8; + float refresh_worst_d = best_d[refresh_base]; + int refresh_worst_pos = refresh_base; + #pragma unroll + for (int offset = 1; offset < 8; offset++) { + int scan_pos = refresh_base + offset; + if (best_d[scan_pos] > refresh_worst_d) { + refresh_worst_d = best_d[scan_pos]; + refresh_worst_pos = scan_pos; + } + } + if (worst_chunk == 0) { + c0_worst_d = refresh_worst_d; + c0_worst_pos = refresh_worst_pos; + } + if (worst_chunk == 1) { + c1_worst_d = refresh_worst_d; + c1_worst_pos = refresh_worst_pos; + } + if (worst_chunk == 2) { + c2_worst_d = refresh_worst_d; + c2_worst_pos = refresh_worst_pos; + } + if (worst_chunk == 3) { + c3_worst_d = refresh_worst_d; + c3_worst_pos = refresh_worst_pos; + } + worst_d = c0_worst_d; + worst_pos = c0_worst_pos; + worst_chunk = 0; + if (c1_worst_d > worst_d) { + worst_d = c1_worst_d; + worst_pos = c1_worst_pos; + worst_chunk = 1; + } + if (c2_worst_d > worst_d) { + worst_d = c2_worst_d; + worst_pos = c2_worst_pos; + worst_chunk = 2; + } + if (c3_worst_d > worst_d) { + worst_d = c3_worst_d; + worst_pos = c3_worst_pos; + worst_chunk = 3; + } + } + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + if (valid_q != 0) { + int out_base = ((split_idx * B + batch_idx) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + *((float*)(partial_dists + (out_base + out_k))) = best_d[out_k]; + *((int*)(partial_indices + (out_base + out_k))) = best_i[out_k]; + } + } + } + } + // ---- Role: load ---- + } else if (warp == 4) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 4) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx_1 = cluster_id; work_idx_1 < total_work; work_idx_1 += num_clusters) { + int split_idx_1 = work_idx_1 % (unsigned int)split_count; + int query_work_1 = work_idx_1 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tile_pairs; + int q_tile_pair_1 = query_work_1 % num_q_tile_pairs; + int q_tile_1 = q_tile_pair_1 * 2 + cta_rank; + int off_q_1 = q_tile_1 * BLOCK_Q; + int global_q = batch_idx_1 * Q + off_q_1; + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((query_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(32768)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_query_addr), "l"(tmap_query), "r"(0), "r"(global_q), "r"(0), + "r"(((query_full_addr) & 0xFEFFFFFF)) : "memory"); + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int off_m = db_tile_1 * BLOCK_M; + int global_m = batch_idx_1 * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((database_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(16384)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_database_addr), "l"(tmap_database), "r"(0), "r"(global_m), "r"(0), + "r"(((database_full_addr) & 0xFEFFFFFF)) : "memory"); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 5) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + if (cta_rank == 0) { + #pragma unroll 1 + for (unsigned int work_idx_2 = cluster_id; work_idx_2 < total_work; work_idx_2 += num_clusters) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = (smem_query_addr >> 4) & 0x3FFF; + int _mma_b_lo_0 = (smem_database_addr >> 4) & 0x3FFF; + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\tmov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 270533776;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit_cg2_multicast(score_full_addr, (uint16_t)(3)); + elect_commit_cg2_multicast(database_empty_addr, (uint16_t)(3)); + } + elect_commit_cg2_multicast(query_empty_addr, (uint16_t)(3)); + } + } + } + } + + // Cleanup + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::2.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(128)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::2.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0068.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0068.cu new file mode 100644 index 00000000..bc43410a --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0068.cu @@ -0,0 +1,88 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 256 +#define TOP_K_MAX 32 + +#include + +extern "C" { + +__global__ __launch_bounds__(256, 1) void +kernel_knn_build_large_square_k32_s2_warp8_merge(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int row = bid * 8 + warp; + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int cand_k = lane; + if (row < total_queries) { + float d0 = partial_dists[base_row + cand_k]; + int i0 = partial_indices[base_row + cand_k]; + int base1 = base_row + split_stride; + float d1 = partial_dists[base1 + cand_k]; + int i1 = partial_indices[base1 + cand_k]; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float winner_d = d0; + int winner_i = i0; + int winner_src = 0; + if (d1 < winner_d) { + winner_d = d1; + winner_i = i1; + winner_src = 1; + } + float warp_min = winner_d; + float _warp_reduce_0 = warp_min; + #pragma unroll + for (int offset = 16; offset > 0; offset >>= 1) + _warp_reduce_0 = fminf(_warp_reduce_0, __shfl_xor_sync(0xFFFFFFFF, _warp_reduce_0, offset)); + warp_min = _warp_reduce_0; + unsigned int _vote_0 = __ballot_sync(0xFFFFFFFF, winner_d == warp_min); + int owner_ballot = _vote_0; + int _ffs_0 = __ffs(owner_ballot); + int winner_lane = _ffs_0 - 1; + int _shfl_0 = __shfl_sync(0xFFFFFFFF, winner_i, winner_lane); + winner_i = _shfl_0; + int _shfl_1 = __shfl_sync(0xFFFFFFFF, winner_src, winner_lane); + winner_src = _shfl_1; + if (lane == 0) { + *((float*)(out_dists + (base_row + out_k))) = warp_min; + *((int*)(out_indices + (base_row + out_k))) = winner_i; + } + if (lane == winner_lane) { + if (winner_src == 0) { + d0 = 3.4e+38f; + } else { + d1 = 3.4e+38f; + } + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0069.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0069.cu new file mode 100644 index 00000000..7790e1ac --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0069.cu @@ -0,0 +1,84 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 32 +#define TOP_K_MAX 10 +#define SPLIT_COUNT 16 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_evolve_7bfc_k10_merge_s7_rowbase_cache_rectd15e_s16(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 32 + tid; + int stride = num_bids * 32; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int out_base = base_row; + int split_pos[SPLIT_COUNT]; + int split_base[SPLIT_COUNT]; + float cand_d[SPLIT_COUNT]; + int cand_i[SPLIT_COUNT]; + #pragma unroll + for (int split_idx = 0; split_idx < SPLIT_COUNT; split_idx++) { + split_pos[split_idx] = 0; + split_base[split_idx] = base_row + split_idx * split_stride; + cand_d[split_idx] = partial_dists[split_base[split_idx]]; + cand_i[split_idx] = partial_indices[split_base[split_idx]]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_d = cand_d[0]; + int best_i = cand_i[0]; + int best_split = 0; + #pragma unroll + for (int split_idx_1 = 1; split_idx_1 < SPLIT_COUNT; split_idx_1++) { + if (best_d > cand_d[split_idx_1]) { + best_d = cand_d[split_idx_1]; + best_i = cand_i[split_idx_1]; + best_split = split_idx_1; + } + } + *((float*)(out_dists + (out_base + out_k))) = best_d; + *((int*)(out_indices + (out_base + out_k))) = best_i; + split_pos[best_split] = split_pos[best_split] + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_pos = split_pos[best_split]; + int next_addr = split_base[best_split] + next_pos; + cand_d[best_split] = partial_dists[next_addr]; + cand_i[best_split] = partial_indices[next_addr]; + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0070.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0070.cu new file mode 100644 index 00000000..79f47327 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0070.cu @@ -0,0 +1,84 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 8 +#define TOP_K_MAX 10 +#define SPLIT_COUNT 16 + +#include + +extern "C" { + +__global__ __launch_bounds__(8, 1) void +kernel_knn_build_rect_d64_23be_s16_cached_merge(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 8 + tid; + int stride = num_bids * 8; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int out_base = base_row; + int split_pos[SPLIT_COUNT]; + int split_base[SPLIT_COUNT]; + float cand_d[SPLIT_COUNT]; + int cand_i[SPLIT_COUNT]; + #pragma unroll + for (int split_idx = 0; split_idx < SPLIT_COUNT; split_idx++) { + split_pos[split_idx] = 0; + split_base[split_idx] = base_row + split_idx * split_stride; + cand_d[split_idx] = partial_dists[split_base[split_idx]]; + cand_i[split_idx] = partial_indices[split_base[split_idx]]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_d = cand_d[0]; + int best_i = cand_i[0]; + int best_split = 0; + #pragma unroll + for (int split_idx_1 = 1; split_idx_1 < SPLIT_COUNT; split_idx_1++) { + if (best_d > cand_d[split_idx_1]) { + best_d = cand_d[split_idx_1]; + best_i = cand_i[split_idx_1]; + best_split = split_idx_1; + } + } + *((float*)(out_dists + (out_base + out_k))) = best_d; + *((int*)(out_indices + (out_base + out_k))) = best_i; + split_pos[best_split] = split_pos[best_split] + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_pos = split_pos[best_split]; + int next_addr = split_base[best_split] + next_pos; + cand_d[best_split] = partial_dists[next_addr]; + cand_i[best_split] = partial_indices[next_addr]; + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0071.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0071.cu new file mode 100644 index 00000000..42d8e58e --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0071.cu @@ -0,0 +1,604 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 64 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 16384 +#define SMEM_SMEM_QUERY_STRIDE 16384 +#define SMEM_SMEM_DATABASE_OFF 17408 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 8192 +#define SMEM_SMEM_DATABASE_STRIDE 8192 +#define SMEM_SMEM_DATABASE_SQ_OFF 25600 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 25856 +#define THREADS 192 +#define BLOCK_Q 128 +#define BLOCK_M 64 +#define TOP_K_MAX 10 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "tcgen05.mma.cta_group::1.kind::f16 [%0], %1, %2, %3, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, %3, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit(int mbar_addr) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];\n\t" + "}\n" + :: "r"(mbar_addr)); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x32(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x32.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15," + " %16, %17, %18, %19, %20, %21, %22, %23," + " %24, %25, %26, %27, %28, %29, %30, %31}, [%32];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]), + "=f"(dst[16]), "=f"(dst[17]), "=f"(dst[18]), "=f"(dst[19]), + "=f"(dst[20]), "=f"(dst[21]), "=f"(dst[22]), "=f"(dst[23]), + "=f"(dst[24]), "=f"(dst[25]), "=f"(dst[26]), "=f"(dst[27]), + "=f"(dst[28]), "=f"(dst[29]), "=f"(dst[30]), "=f"(dst[31]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ float max_noftz(float a, float b) { + float c; + asm("max.f32 %0, %1, %2;" : "=f"(c) : "f"(a), "f"(b)); + return c; +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cta.global" + ".mbarrier::complete_tx::bytes" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit(int mbar_addr) { + asm volatile( + "tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ uint32_t make_warp_uniform(uint32_t val) { + uint32_t result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1f, 0xffffffff;" + : "=r"(result) : "r"(val)); + return result; +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_rect_d64_23be_unordered_stage1(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tiles, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 17408); + const int smem_database_addr = smem + 17408; + float* smem_database_sq = reinterpret_cast(smem_raw + 25600); + const int smem_database_sq_addr = smem + 25600; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 0, 1, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 16, 1, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=128 + mbarrier_init_pred(smem + 40, 128, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (64 columns, 64 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::1.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(64) : "memory"); + } + + __syncthreads(); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: load ---- + if (warp == 0) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 0) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx = bid; work_idx < total_work; work_idx += num_bids) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tiles; + int q_tile = query_work % num_q_tiles; + int off_q = q_tile * BLOCK_Q; + int global_q = batch_idx * Q + off_q; + int db_tile_start = split_idx * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + mbarrier_arrive_expect_tx(query_full_addr, 16384); + tma_3d_gmem2smem(smem_query_addr, tmap_query, 0, global_q, 0, query_full_addr); + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int off_m = db_tile * BLOCK_M; + int global_m = batch_idx * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + mbarrier_arrive_expect_tx(database_full_addr, 8192); + tma_3d_gmem2smem(smem_database_addr, tmap_database, 0, global_m, 0, database_full_addr); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 1) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_1 = bid; work_idx_1 < total_work; work_idx_1 += num_bids) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = make_warp_uniform((smem_query_addr >> 4) & 0x3FFF); + int _mma_b_lo_0 = make_warp_uniform((smem_database_addr >> 4) & 0x3FFF); + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 135267472;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit(score_full_addr); + elect_commit(database_empty_addr); + } + elect_commit(query_empty_addr); + } + } + // ---- Role: compute ---- + } else if (warp >= 2 && warp <= 5) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = bid; work_idx_2 < total_work; work_idx_2 += num_bids) { + int split_idx_1 = work_idx_2 % (unsigned int)split_count; + int query_work_1 = work_idx_2 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tiles; + int q_tile_1 = query_work_1 % num_q_tiles; + int off_q_1 = q_tile_1 * BLOCK_Q; + int q_idx = off_q_1 + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx_1 * Q + q_idx]; + } + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + float worst_d = 3.4e+38f; + int worst_pos = 0; + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int db_start = db_tile_1 * BLOCK_M; + int db_sq_idx = db_start + (warp % 4 * 32 + lane); + if (warp % 4 * 32 + lane < BLOCK_M) { + if (db_sq_idx < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx_1 * M + db_sq_idx]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 0.0f; + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(warp % 4 * 32 << 16); + float _tmem_load_0[64]; + tmem_ld_x32(&_tmem_load_0[0], cross_addr); + tmem_ld_x32(&_tmem_load_0[32], cross_addr + 32); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_arrive(score_empty_addr); + if (valid_q != 0) { + #pragma unroll 2 + for (int col_base = 0; col_base < 64; col_base += 4) { + float dist_vec[4]; + dist_vec[0] = _tmem_load_0[col_base]; + dist_vec[1] = _tmem_load_0[col_base + 1]; + dist_vec[2] = _tmem_load_0[col_base + 2]; + dist_vec[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec[4]; + db_sq_vec[0] = smem_database_sq[col_base]; + db_sq_vec[1] = smem_database_sq[col_base + 1]; + db_sq_vec[2] = smem_database_sq[col_base + 2]; + db_sq_vec[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec)[_la], reinterpret_cast(db_sq_vec)[_la]); + float group_min = _t0[0]; + if (_t0[1] < group_min) { + group_min = _t0[1]; + } + if (_t0[2] < group_min) { + group_min = _t0[2]; + } + if (_t0[3] < group_min) { + group_min = _t0[3]; + } + if (group_min < worst_d) { + #pragma unroll + for (int vec_col = 0; vec_col < 4; vec_col++) { + int db_idx = db_start + col_base + vec_col; + if (db_idx < M) { + float dist = _t0[vec_col]; + float _max_0 = max_noftz(dist, 0.0f); + dist = _max_0; + if (dist < worst_d) { + best_d[worst_pos] = dist; + best_i[worst_pos] = db_idx; + worst_d = best_d[0]; + worst_pos = 0; + #pragma unroll + for (int scan_pos = 1; scan_pos < TOP_K_MAX; scan_pos++) { + if (worst_d < best_d[scan_pos]) { + worst_d = best_d[scan_pos]; + worst_pos = scan_pos; + } + } + } + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + if (valid_q != 0) { + int out_base = ((split_idx_1 * B + batch_idx_1) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + int selected_pos = 0; + float selected_d = best_d[0]; + int selected_i = best_i[0]; + #pragma unroll + for (int scan_pos_1 = 1; scan_pos_1 < TOP_K_MAX; scan_pos_1++) { + if (selected_d > best_d[scan_pos_1]) { + selected_d = best_d[scan_pos_1]; + selected_i = best_i[scan_pos_1]; + selected_pos = scan_pos_1; + } + } + if (out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = selected_d; + *((int*)(partial_indices + (out_base + out_k))) = selected_i; + } + best_d[selected_pos] = 3.4e+38f; + best_i[selected_pos] = -1; + } + } + } + } + } + + // Cleanup + __syncthreads(); // barrier before TMEM dealloc + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::1.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(64)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::1.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0072.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0072.cu new file mode 100644 index 00000000..f9a98ebf --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0072.cu @@ -0,0 +1,129 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 128 +#define TOP_K_MAX 10 +#define SPLIT_COUNT 48 + +#include + +extern "C" { + +__global__ __launch_bounds__(128, 1) void +kernel_knn_build_d128_rag_q128_k10_s74_warp_merge_d320_s48_f556_v2(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int row = bid * 4 + warp; + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int lane_idx = lane; + if (row < total_queries) { + int split0 = lane_idx; + int base0 = base_row + split0 * split_stride; + int pos0 = 0; + float d0 = partial_dists[base0]; + int i0 = partial_indices[base0]; + int split1 = lane_idx + 32; + int base1 = base_row + split1 * split_stride; + int pos1 = 0; + float d1 = 3.4e+38f; + int i1 = -1; + if (split1 < SPLIT_COUNT) { + d1 = partial_dists[base1]; + i1 = partial_indices[base1]; + } + int split2 = lane_idx + 64; + int base2 = base_row + split2 * split_stride; + int pos2 = 0; + float d2 = 3.4e+38f; + int i2 = -1; + if (split2 < SPLIT_COUNT) { + d2 = partial_dists[base2]; + i2 = partial_indices[base2]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float winner_d = d0; + int winner_i = i0; + int winner_src = 0; + if (d1 < winner_d) { + winner_d = d1; + winner_i = i1; + winner_src = 1; + } + if (d2 < winner_d) { + winner_d = d2; + winner_i = i2; + winner_src = 2; + } + float warp_min = winner_d; + float _warp_reduce_0 = warp_min; + #pragma unroll + for (int offset = 16; offset > 0; offset >>= 1) + _warp_reduce_0 = fminf(_warp_reduce_0, __shfl_xor_sync(0xFFFFFFFF, _warp_reduce_0, offset)); + warp_min = _warp_reduce_0; + unsigned int _vote_0 = __ballot_sync(0xFFFFFFFF, winner_d == warp_min); + int owner_ballot = _vote_0; + int _ffs_0 = __ffs(owner_ballot); + int winner_lane = _ffs_0 - 1; + int _shfl_0 = __shfl_sync(0xFFFFFFFF, winner_i, winner_lane); + winner_i = _shfl_0; + int _shfl_1 = __shfl_sync(0xFFFFFFFF, winner_src, winner_lane); + winner_src = _shfl_1; + if (lane == 0) { + *((float*)(out_dists + (base_row + out_k))) = warp_min; + *((int*)(out_indices + (base_row + out_k))) = winner_i; + } + if (lane == winner_lane) { + if (winner_src == 0) { + pos0 = pos0 + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_addr0 = base0 + pos0; + d0 = partial_dists[next_addr0]; + i0 = partial_indices[next_addr0]; + } + } else if (winner_src == 1) { + pos1 = pos1 + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_addr1 = base1 + pos1; + d1 = partial_dists[next_addr1]; + i1 = partial_indices[next_addr1]; + } + } else { + pos2 = pos2 + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_addr2 = base2 + pos2; + d2 = partial_dists[next_addr2]; + i2 = partial_indices[next_addr2]; + } + } + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0073.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0073.cu new file mode 100644 index 00000000..18fc6361 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0073.cu @@ -0,0 +1,621 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 64 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_STRIDE 32768 +#define SMEM_SMEM_DATABASE_OFF 33792 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_DATABASE_SQ_OFF 50176 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 50432 +#define THREADS 192 +#define BLOCK_Q 128 +#define BLOCK_M 64 +#define K_TILE 128 +#define TOP_K_MAX 10 +#define FEATURE_CHUNKS 2 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "tcgen05.mma.cta_group::1.kind::f16 [%0], %1, %2, %3, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, %3, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit(int mbar_addr) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];\n\t" + "}\n" + :: "r"(mbar_addr)); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x32(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x32.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15," + " %16, %17, %18, %19, %20, %21, %22, %23," + " %24, %25, %26, %27, %28, %29, %30, %31}, [%32];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]), + "=f"(dst[16]), "=f"(dst[17]), "=f"(dst[18]), "=f"(dst[19]), + "=f"(dst[20]), "=f"(dst[21]), "=f"(dst[22]), "=f"(dst[23]), + "=f"(dst[24]), "=f"(dst[25]), "=f"(dst[26]), "=f"(dst[27]), + "=f"(dst[28]), "=f"(dst[29]), "=f"(dst[30]), "=f"(dst[31]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ float max_noftz(float a, float b) { + float c; + asm("max.f32 %0, %1, %2;" : "=f"(c) : "f"(a), "f"(b)); + return c; +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cta.global" + ".mbarrier::complete_tx::bytes" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit(int mbar_addr) { + asm volatile( + "tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ uint32_t make_warp_uniform(uint32_t val) { + uint32_t result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1f, 0xffffffff;" + : "=r"(result) : "r"(val)); + return result; +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_non128_frontier_7231_stage1_d256(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tiles, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 33792); + const int smem_database_addr = smem + 33792; + float* smem_database_sq = reinterpret_cast(smem_raw + 50176); + const int smem_database_sq_addr = smem + 50176; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 0, 1, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 16, 1, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=128 + mbarrier_init_pred(smem + 40, 128, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (64 columns, 64 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::1.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(64) : "memory"); + } + + __syncthreads(); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: load ---- + if (warp == 0) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 0) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx = bid; work_idx < total_work; work_idx += num_bids) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tiles; + int q_tile = query_work % num_q_tiles; + int off_q = q_tile * BLOCK_Q; + int global_q = batch_idx * Q + off_q; + int db_tile_start = split_idx * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int off_m = db_tile * BLOCK_M; + int global_m = batch_idx * M + off_m; + #pragma unroll + for (int feat_chunk = 0; feat_chunk < FEATURE_CHUNKS; feat_chunk++) { + int feature_coord = feat_chunk * 2; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + mbarrier_arrive_expect_tx(query_full_addr, 32768); + tma_3d_gmem2smem(smem_query_addr, tmap_query, 0, global_q, feature_coord, query_full_addr); + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + mbarrier_arrive_expect_tx(database_full_addr, 16384); + tma_3d_gmem2smem(smem_database_addr, tmap_database, 0, global_m, feature_coord, database_full_addr); + } + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 1) { + { // mma_main + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_database_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_1 = bid; work_idx_1 < total_work; work_idx_1 += num_bids) { + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + #pragma unroll + for (int feat_chunk_1 = 0; feat_chunk_1 < FEATURE_CHUNKS; feat_chunk_1++) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = make_warp_uniform((smem_query_addr >> 4) & 0x3FFF); + int _mma_b_lo_0 = make_warp_uniform((smem_database_addr >> 4) & 0x3FFF); + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 135267472;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(((((feat_chunk_1 == 0) ? 1 : 0)) ? 0 : 1))); + asm volatile("tcgen05.fence::after_thread_sync;"); + elect_commit(query_empty_addr); + elect_commit(database_empty_addr); + } + elect_commit(score_full_addr); + } + } + } + // ---- Role: compute ---- + } else if (warp >= 2 && warp <= 5) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = bid; work_idx_2 < total_work; work_idx_2 += num_bids) { + int split_idx_1 = work_idx_2 % (unsigned int)split_count; + int query_work_1 = work_idx_2 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tiles; + int q_tile_1 = query_work_1 % num_q_tiles; + int off_q_1 = q_tile_1 * BLOCK_Q; + int q_idx = off_q_1 + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx_1 * Q + q_idx]; + } + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int db_start = db_tile_1 * BLOCK_M; + int db_sq_idx = db_start + (warp % 4 * 32 + lane); + if (warp % 4 * 32 + lane < BLOCK_M) { + if (db_sq_idx < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx_1 * M + db_sq_idx]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 0.0f; + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(warp % 4 * 32 << 16); + float _tmem_load_0[64]; + tmem_ld_x32(&_tmem_load_0[0], cross_addr); + tmem_ld_x32(&_tmem_load_0[32], cross_addr + 32); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_arrive(score_empty_addr); + if (valid_q != 0) { + #pragma unroll 2 + for (int col_base = 0; col_base < 64; col_base += 4) { + float dist_vec[4]; + dist_vec[0] = _tmem_load_0[col_base]; + dist_vec[1] = _tmem_load_0[col_base + 1]; + dist_vec[2] = _tmem_load_0[col_base + 2]; + dist_vec[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec[4]; + db_sq_vec[0] = smem_database_sq[col_base]; + db_sq_vec[1] = smem_database_sq[col_base + 1]; + db_sq_vec[2] = smem_database_sq[col_base + 2]; + db_sq_vec[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec)[_la], reinterpret_cast(db_sq_vec)[_la]); + float group_min = _t0[0]; + if (_t0[1] < group_min) { + group_min = _t0[1]; + } + if (_t0[2] < group_min) { + group_min = _t0[2]; + } + if (_t0[3] < group_min) { + group_min = _t0[3]; + } + if (group_min < best_d[TOP_K_MAX - 1]) { + #pragma unroll + for (int vec_col = 0; vec_col < 4; vec_col++) { + int db_idx = db_start + col_base + vec_col; + if (db_idx < M) { + float dist = _t0[vec_col]; + float _max_0 = max_noftz(dist, 0.0f); + dist = _max_0; + if (dist < best_d[TOP_K_MAX - 1]) { + best_d[TOP_K_MAX - 1] = dist; + best_i[TOP_K_MAX - 1] = db_idx; + #pragma unroll + for (int pos = TOP_K_MAX - 1; pos >= 1; pos--) { + if (best_d[pos] < best_d[pos - 1]) { + float tmp_d = best_d[pos - 1]; + int tmp_i = best_i[pos - 1]; + best_d[pos - 1] = best_d[pos]; + best_i[pos - 1] = best_i[pos]; + best_d[pos] = tmp_d; + best_i[pos] = tmp_i; + } + } + } + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + if (valid_q != 0) { + int out_base = ((split_idx_1 * B + batch_idx_1) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + if (out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = best_d[out_k]; + *((int*)(partial_indices + (out_base + out_k))) = best_i[out_k]; + } + } + } + } + } + } + + // Cleanup + __syncthreads(); // barrier before TMEM dealloc + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::1.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(64)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::1.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0074.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0074.cu new file mode 100644 index 00000000..8de30ec8 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0074.cu @@ -0,0 +1,621 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 64 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_STRIDE 32768 +#define SMEM_SMEM_DATABASE_OFF 33792 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_DATABASE_SQ_OFF 50176 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 50432 +#define THREADS 192 +#define BLOCK_Q 128 +#define BLOCK_M 64 +#define K_TILE 128 +#define TOP_K_MAX 10 +#define FEATURE_CHUNKS 6 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "tcgen05.mma.cta_group::1.kind::f16 [%0], %1, %2, %3, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, %3, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit(int mbar_addr) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];\n\t" + "}\n" + :: "r"(mbar_addr)); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x32(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x32.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15," + " %16, %17, %18, %19, %20, %21, %22, %23," + " %24, %25, %26, %27, %28, %29, %30, %31}, [%32];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]), + "=f"(dst[16]), "=f"(dst[17]), "=f"(dst[18]), "=f"(dst[19]), + "=f"(dst[20]), "=f"(dst[21]), "=f"(dst[22]), "=f"(dst[23]), + "=f"(dst[24]), "=f"(dst[25]), "=f"(dst[26]), "=f"(dst[27]), + "=f"(dst[28]), "=f"(dst[29]), "=f"(dst[30]), "=f"(dst[31]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ float max_noftz(float a, float b) { + float c; + asm("max.f32 %0, %1, %2;" : "=f"(c) : "f"(a), "f"(b)); + return c; +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cta.global" + ".mbarrier::complete_tx::bytes" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit(int mbar_addr) { + asm volatile( + "tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ uint32_t make_warp_uniform(uint32_t val) { + uint32_t result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1f, 0xffffffff;" + : "=r"(result) : "r"(val)); + return result; +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_non128_frontier_7231_stage1_d768(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tiles, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 33792); + const int smem_database_addr = smem + 33792; + float* smem_database_sq = reinterpret_cast(smem_raw + 50176); + const int smem_database_sq_addr = smem + 50176; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 0, 1, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 16, 1, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=128 + mbarrier_init_pred(smem + 40, 128, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (64 columns, 64 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::1.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(64) : "memory"); + } + + __syncthreads(); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: load ---- + if (warp == 0) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 0) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx = bid; work_idx < total_work; work_idx += num_bids) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tiles; + int q_tile = query_work % num_q_tiles; + int off_q = q_tile * BLOCK_Q; + int global_q = batch_idx * Q + off_q; + int db_tile_start = split_idx * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int off_m = db_tile * BLOCK_M; + int global_m = batch_idx * M + off_m; + #pragma unroll + for (int feat_chunk = 0; feat_chunk < FEATURE_CHUNKS; feat_chunk++) { + int feature_coord = feat_chunk * 2; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + mbarrier_arrive_expect_tx(query_full_addr, 32768); + tma_3d_gmem2smem(smem_query_addr, tmap_query, 0, global_q, feature_coord, query_full_addr); + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + mbarrier_arrive_expect_tx(database_full_addr, 16384); + tma_3d_gmem2smem(smem_database_addr, tmap_database, 0, global_m, feature_coord, database_full_addr); + } + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 1) { + { // mma_main + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_database_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_1 = bid; work_idx_1 < total_work; work_idx_1 += num_bids) { + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + #pragma unroll + for (int feat_chunk_1 = 0; feat_chunk_1 < FEATURE_CHUNKS; feat_chunk_1++) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = make_warp_uniform((smem_query_addr >> 4) & 0x3FFF); + int _mma_b_lo_0 = make_warp_uniform((smem_database_addr >> 4) & 0x3FFF); + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 135267472;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(((((feat_chunk_1 == 0) ? 1 : 0)) ? 0 : 1))); + asm volatile("tcgen05.fence::after_thread_sync;"); + elect_commit(query_empty_addr); + elect_commit(database_empty_addr); + } + elect_commit(score_full_addr); + } + } + } + // ---- Role: compute ---- + } else if (warp >= 2 && warp <= 5) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = bid; work_idx_2 < total_work; work_idx_2 += num_bids) { + int split_idx_1 = work_idx_2 % (unsigned int)split_count; + int query_work_1 = work_idx_2 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tiles; + int q_tile_1 = query_work_1 % num_q_tiles; + int off_q_1 = q_tile_1 * BLOCK_Q; + int q_idx = off_q_1 + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx_1 * Q + q_idx]; + } + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int db_start = db_tile_1 * BLOCK_M; + int db_sq_idx = db_start + (warp % 4 * 32 + lane); + if (warp % 4 * 32 + lane < BLOCK_M) { + if (db_sq_idx < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx_1 * M + db_sq_idx]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 0.0f; + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(warp % 4 * 32 << 16); + float _tmem_load_0[64]; + tmem_ld_x32(&_tmem_load_0[0], cross_addr); + tmem_ld_x32(&_tmem_load_0[32], cross_addr + 32); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_arrive(score_empty_addr); + if (valid_q != 0) { + #pragma unroll 2 + for (int col_base = 0; col_base < 64; col_base += 4) { + float dist_vec[4]; + dist_vec[0] = _tmem_load_0[col_base]; + dist_vec[1] = _tmem_load_0[col_base + 1]; + dist_vec[2] = _tmem_load_0[col_base + 2]; + dist_vec[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec[4]; + db_sq_vec[0] = smem_database_sq[col_base]; + db_sq_vec[1] = smem_database_sq[col_base + 1]; + db_sq_vec[2] = smem_database_sq[col_base + 2]; + db_sq_vec[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec)[_la], reinterpret_cast(db_sq_vec)[_la]); + float group_min = _t0[0]; + if (_t0[1] < group_min) { + group_min = _t0[1]; + } + if (_t0[2] < group_min) { + group_min = _t0[2]; + } + if (_t0[3] < group_min) { + group_min = _t0[3]; + } + if (group_min < best_d[TOP_K_MAX - 1]) { + #pragma unroll + for (int vec_col = 0; vec_col < 4; vec_col++) { + int db_idx = db_start + col_base + vec_col; + if (db_idx < M) { + float dist = _t0[vec_col]; + float _max_0 = max_noftz(dist, 0.0f); + dist = _max_0; + if (dist < best_d[TOP_K_MAX - 1]) { + best_d[TOP_K_MAX - 1] = dist; + best_i[TOP_K_MAX - 1] = db_idx; + #pragma unroll + for (int pos = TOP_K_MAX - 1; pos >= 1; pos--) { + if (best_d[pos] < best_d[pos - 1]) { + float tmp_d = best_d[pos - 1]; + int tmp_i = best_i[pos - 1]; + best_d[pos - 1] = best_d[pos]; + best_i[pos - 1] = best_i[pos]; + best_d[pos] = tmp_d; + best_i[pos] = tmp_i; + } + } + } + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + if (valid_q != 0) { + int out_base = ((split_idx_1 * B + batch_idx_1) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + if (out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = best_d[out_k]; + *((int*)(partial_indices + (out_base + out_k))) = best_i[out_k]; + } + } + } + } + } + } + + // Cleanup + __syncthreads(); // barrier before TMEM dealloc + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::1.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(64)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::1.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0075.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0075.cu new file mode 100644 index 00000000..5e85727b --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0075.cu @@ -0,0 +1,141 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define SMEM_GROUP_DISTS_OFF 0 +#define SMEM_GROUP_DISTS_STAGE_BYTES 512 +#define SMEM_GROUP_DISTS_STRIDE 512 +#define SMEM_GROUP_INDICES_OFF 512 +#define SMEM_GROUP_INDICES_STAGE_BYTES 512 +#define SMEM_GROUP_INDICES_STRIDE 512 +#define SMEM_TOTAL 1024 +#define THREADS 32 +#define TOP_K_MAX 10 +#define GROUP_COUNT 8 +#define GROUP_SPLITS 4 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_non128_frontier_4be7_d768fused_merge_s32g8_4be7_d768fused_v1(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + float* group_dists = reinterpret_cast(smem_raw + 0); + const int group_dists_addr = smem + 0; + int* group_indices = reinterpret_cast(smem_raw + 512); + const int group_indices_addr = smem + 512; + + // === Task calls (dependency order) === + int split_pos[GROUP_SPLITS]; + int split_base[GROUP_SPLITS]; + float group_cand_d[GROUP_SPLITS]; + int group_cand_i[GROUP_SPLITS]; + int final_pos[GROUP_COUNT]; + float final_cand_d[GROUP_COUNT]; + int final_cand_i[GROUP_COUNT]; + #pragma unroll 1 + for (int row = bid; row < total_queries; row += num_bids) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + if (tid < GROUP_COUNT) { + int group_idx = tid; + int source_split0 = group_idx * GROUP_SPLITS; + int shared_base = group_idx * TOP_K_MAX; + #pragma unroll + for (int local_split = 0; local_split < GROUP_SPLITS; local_split++) { + split_pos[local_split] = 0; + int split_id = source_split0 + local_split; + split_base[local_split] = base_row + split_id * split_stride; + group_cand_d[local_split] = partial_dists[split_base[local_split]]; + group_cand_i[local_split] = partial_indices[split_base[local_split]]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_d = group_cand_d[0]; + int best_i = group_cand_i[0]; + int best_split = 0; + #pragma unroll + for (int local_split_1 = 1; local_split_1 < GROUP_SPLITS; local_split_1++) { + if (best_d > group_cand_d[local_split_1]) { + best_d = group_cand_d[local_split_1]; + best_i = group_cand_i[local_split_1]; + best_split = local_split_1; + } + } + group_dists[shared_base + out_k] = best_d; + group_indices[shared_base + out_k] = best_i; + split_pos[best_split] = split_pos[best_split] + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_pos = split_pos[best_split]; + int next_addr = split_base[best_split] + next_pos; + group_cand_d[best_split] = partial_dists[next_addr]; + group_cand_i[best_split] = partial_indices[next_addr]; + } + } + } + __syncthreads(); + if (tid == 0) { + #pragma unroll + for (int group_idx_1 = 0; group_idx_1 < GROUP_COUNT; group_idx_1++) { + final_pos[group_idx_1] = 0; + int group_base = group_idx_1 * TOP_K_MAX; + final_cand_d[group_idx_1] = group_dists[group_base]; + final_cand_i[group_idx_1] = group_indices[group_base]; + } + #pragma unroll + for (int out_k_1 = 0; out_k_1 < TOP_K_MAX; out_k_1++) { + float best_d_1 = final_cand_d[0]; + int best_i_1 = final_cand_i[0]; + int best_group = 0; + #pragma unroll + for (int group_idx_2 = 1; group_idx_2 < GROUP_COUNT; group_idx_2++) { + if (best_d_1 > final_cand_d[group_idx_2]) { + best_d_1 = final_cand_d[group_idx_2]; + best_i_1 = final_cand_i[group_idx_2]; + best_group = group_idx_2; + } + } + *((float*)(out_dists + (base_row + out_k_1))) = best_d_1; + *((int*)(out_indices + (base_row + out_k_1))) = best_i_1; + final_pos[best_group] = final_pos[best_group] + 1; + if (out_k_1 + 1 < TOP_K_MAX) { + int next_pos_1 = final_pos[best_group]; + int next_addr_1 = best_group * TOP_K_MAX + next_pos_1; + final_cand_d[best_group] = group_dists[next_addr_1]; + final_cand_i[best_group] = group_indices[next_addr_1]; + } + } + } + __syncthreads(); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0076.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0076.cu new file mode 100644 index 00000000..d6ea50ab --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0076.cu @@ -0,0 +1,121 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 128 +#define TOP_K_MAX 20 +#define SPLIT_COUNT 4 + +#include + +extern "C" { + +__global__ __launch_bounds__(128, 1) void +kernel_knn_build_evolve_7bfc_k20_merge_s4_unordered_warp_select(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int row = bid * 4 + warp; + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int cand_k = lane; + if (row < total_queries) { + float d0 = 3.4e+38f; + float d1 = 3.4e+38f; + float d2 = 3.4e+38f; + float d3 = 3.4e+38f; + int i0 = -1; + int i1 = -1; + int i2 = -1; + int i3 = -1; + if (cand_k < TOP_K_MAX) { + d0 = partial_dists[base_row + cand_k]; + i0 = partial_indices[base_row + cand_k]; + int base1 = base_row + split_stride; + d1 = partial_dists[base1 + cand_k]; + i1 = partial_indices[base1 + cand_k]; + int base2 = base1 + split_stride; + d2 = partial_dists[base2 + cand_k]; + i2 = partial_indices[base2 + cand_k]; + int base3 = base2 + split_stride; + d3 = partial_dists[base3 + cand_k]; + i3 = partial_indices[base3 + cand_k]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float winner_d = d0; + int winner_i = i0; + int winner_src = 0; + if (d1 < winner_d) { + winner_d = d1; + winner_i = i1; + winner_src = 1; + } + if (d2 < winner_d) { + winner_d = d2; + winner_i = i2; + winner_src = 2; + } + if (d3 < winner_d) { + winner_d = d3; + winner_i = i3; + winner_src = 3; + } + float warp_min = winner_d; + float _warp_reduce_0 = warp_min; + #pragma unroll + for (int offset = 16; offset > 0; offset >>= 1) + _warp_reduce_0 = fminf(_warp_reduce_0, __shfl_xor_sync(0xFFFFFFFF, _warp_reduce_0, offset)); + warp_min = _warp_reduce_0; + unsigned int _vote_0 = __ballot_sync(0xFFFFFFFF, winner_d == warp_min); + int owner_ballot = _vote_0; + int _ffs_0 = __ffs(owner_ballot); + int winner_lane = _ffs_0 - 1; + int _shfl_0 = __shfl_sync(0xFFFFFFFF, winner_i, winner_lane); + winner_i = _shfl_0; + int _shfl_1 = __shfl_sync(0xFFFFFFFF, winner_src, winner_lane); + winner_src = _shfl_1; + if (lane == 0) { + *((float*)(out_dists + (base_row + out_k))) = warp_min; + *((int*)(out_indices + (base_row + out_k))) = winner_i; + } + if (lane == winner_lane) { + if (winner_src == 0) { + d0 = 3.4e+38f; + } else if (winner_src == 1) { + d1 = 3.4e+38f; + } else { + if (winner_src == 2) { + d2 = 3.4e+38f; + } else { + d3 = 3.4e+38f; + } + } + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0077.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0077.cu new file mode 100644 index 00000000..db6cf6d3 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0077.cu @@ -0,0 +1,100 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 128 +#define TOP_K_MAX 20 +#define SPLIT_COUNT_CONST 12 + +#include + +extern "C" { + +__global__ __launch_bounds__(128, 1) void +kernel_knn_build_rect_d128_k20_q1536_warp4_merge(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int row = bid * 4 + warp; + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int cand_k = lane; + if (row < total_queries) { + float cand_d[SPLIT_COUNT_CONST]; + int cand_i[SPLIT_COUNT_CONST]; + #pragma unroll + for (int split_idx = 0; split_idx < SPLIT_COUNT_CONST; split_idx++) { + cand_d[split_idx] = 3.4e+38f; + cand_i[split_idx] = -1; + if (cand_k < TOP_K_MAX) { + int split_base = base_row + split_idx * split_stride; + cand_d[split_idx] = partial_dists[split_base + cand_k]; + cand_i[split_idx] = partial_indices[split_base + cand_k]; + } + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float winner_d = cand_d[0]; + int winner_i = cand_i[0]; + int winner_src = 0; + #pragma unroll + for (int split_idx_1 = 1; split_idx_1 < SPLIT_COUNT_CONST; split_idx_1++) { + if (winner_d > cand_d[split_idx_1]) { + winner_d = cand_d[split_idx_1]; + winner_i = cand_i[split_idx_1]; + winner_src = split_idx_1; + } + } + float warp_min = winner_d; + float _warp_reduce_0 = warp_min; + #pragma unroll + for (int offset = 16; offset > 0; offset >>= 1) + _warp_reduce_0 = fminf(_warp_reduce_0, __shfl_xor_sync(0xFFFFFFFF, _warp_reduce_0, offset)); + warp_min = _warp_reduce_0; + unsigned int _vote_0 = __ballot_sync(0xFFFFFFFF, winner_d == warp_min); + int owner_ballot = _vote_0; + int _ffs_0 = __ffs(owner_ballot); + int winner_lane = _ffs_0 - 1; + int _shfl_0 = __shfl_sync(0xFFFFFFFF, winner_i, winner_lane); + winner_i = _shfl_0; + int _shfl_1 = __shfl_sync(0xFFFFFFFF, winner_src, winner_lane); + winner_src = _shfl_1; + if (lane == 0) { + *((float*)(out_dists + (base_row + out_k))) = warp_min; + *((int*)(out_indices + (base_row + out_k))) = winner_i; + } + if (lane == winner_lane) { + #pragma unroll + for (int split_idx_2 = 0; split_idx_2 < SPLIT_COUNT_CONST; split_idx_2++) { + if (winner_src == split_idx_2) { + cand_d[split_idx_2] = 3.4e+38f; + } + } + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0078.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0078.cu new file mode 100644 index 00000000..7770654c --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0078.cu @@ -0,0 +1,1143 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 128 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_STRIDE 32768 +#define SMEM_SMEM_DATABASE_OFF 33792 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_DATABASE_SQ_OFF 50176 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 50432 +#define THREADS 192 +#define BLOCK_Q 128 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_MAX 64 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16_cta2( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + ".reg .b32 m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "tcgen05.mma.cta_group::2.kind::f16 [%0], %1, %2, %3, {m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step_cg2( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, %3, " + "{m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], %1;\n\t" + "}\n" + :: "r"(mbar_addr), "h"(cta_mask) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x16(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x16.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15}, [%16];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ uint32_t smem_addr(const void* ptr) { + uint32_t addr; + asm("{\n\t" + ".reg .u64 u64addr;\n\t" + "cvta.to.shared.u64 u64addr, %1;\n\t" + "cvt.u32.u64 %0, u64addr;\n\t" + "}\n" : "=r"(addr) : "l"(ptr)); + return addr; +} + + +__device__ __forceinline__ uint32_t mapa_to_rank(uint32_t local_addr, uint32_t rank) { + uint32_t remote; + asm volatile("mapa.shared::cluster.u32 %0, %1, %2;" + : "=r"(remote) : "r"(local_addr), "r"(rank)); + return remote; +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem_cta2( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global" + ".mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .b16 lo, hi;\n\t" + "mov.b32 {lo, hi}, %1;\n\t" + "tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], lo;\n\t" + "}\n" + :: "r"(mbar_addr), "r"((uint32_t)cta_mask) : "memory"); +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_k64_stage1_tailinf_k64over32tailinfsplitgrid(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tile_pairs, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + const unsigned int clusters_x = gridDim.x / 2; + const unsigned int cluster_id = ((blockIdx.z * gridDim.y + blockIdx.y) * clusters_x) + blockIdx.x / 2; + const unsigned int num_clusters = clusters_x * gridDim.y * gridDim.z; + + int cta_rank; + asm volatile("mov.b32 %0, %%cluster_ctarank;" : "=r"(cta_rank)); + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 33792); + const int smem_database_addr = smem + 33792; + float* smem_database_sq = reinterpret_cast(smem_raw + 50176); + const int smem_database_sq_addr = smem + 50176; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 0, 2, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 16, 2, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=8 + mbarrier_init_pred(smem + 40, 8, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (128 columns, 128 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::2.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(128) : "memory"); + } + + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: compute ---- + if (warp <= 3) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx = cluster_id; work_idx < total_work; work_idx += num_clusters) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tile_pairs; + int q_tile_pair = query_work % num_q_tile_pairs; + int q_tile = q_tile_pair * 2 + cta_rank; + int off_q = q_tile * BLOCK_Q; + int q_idx = off_q + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx * Q + q_idx]; + } + float best_d[64]; + int best_i[64]; + #pragma unroll + for (int kk = 0; kk < 64; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + float worst_d = 3.4e+38f; + int worst_pos = 0; + int worst_chunk = 0; + float c0_worst_d = 3.4e+38f; + int c0_worst_pos = 0; + float c1_worst_d = 3.4e+38f; + int c1_worst_pos = 4; + float c2_worst_d = 3.4e+38f; + int c2_worst_pos = 8; + float c3_worst_d = 3.4e+38f; + int c3_worst_pos = 12; + float c4_worst_d = 3.4e+38f; + int c4_worst_pos = 16; + float c5_worst_d = 3.4e+38f; + int c5_worst_pos = 20; + float c6_worst_d = 3.4e+38f; + int c6_worst_pos = 24; + float c7_worst_d = 3.4e+38f; + int c7_worst_pos = 28; + float c8_worst_d = 3.4e+38f; + int c8_worst_pos = 32; + float c9_worst_d = 3.4e+38f; + int c9_worst_pos = 36; + float c10_worst_d = 3.4e+38f; + int c10_worst_pos = 40; + float c11_worst_d = 3.4e+38f; + int c11_worst_pos = 44; + float c12_worst_d = 3.4e+38f; + int c12_worst_pos = 48; + float c13_worst_d = 3.4e+38f; + int c13_worst_pos = 52; + float c14_worst_d = 3.4e+38f; + int c14_worst_pos = 56; + float c15_worst_d = 3.4e+38f; + int c15_worst_pos = 60; + int db_tile_start = split_idx * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int db_start = db_tile * BLOCK_M; + int db_sq_idx = db_start + (warp % 4 * 32 + lane); + if (warp % 4 * 32 + lane < BLOCK_M) { + if (db_sq_idx < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx * M + db_sq_idx]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 3.4e+38f; + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(cta_rank * BLOCK_Q + (warp % 4 * 32 << 16) << 16); + float _tmem_load_0[64]; + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x64.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31, %32, %33, %34, %35, %36, %37, %38, %39, %40, %41, %42, %43, %44, %45, %46, %47, %48, %49, %50, %51, %52, %53, %54, %55, %56, %57, %58, %59, %60, %61, %62, %63}, [%64];" + : "=f"(_tmem_load_0[0]), "=f"(_tmem_load_0[1]), "=f"(_tmem_load_0[2]), "=f"(_tmem_load_0[3]), "=f"(_tmem_load_0[4]), "=f"(_tmem_load_0[5]), "=f"(_tmem_load_0[6]), "=f"(_tmem_load_0[7]), "=f"(_tmem_load_0[8]), "=f"(_tmem_load_0[9]), "=f"(_tmem_load_0[10]), "=f"(_tmem_load_0[11]), "=f"(_tmem_load_0[12]), "=f"(_tmem_load_0[13]), "=f"(_tmem_load_0[14]), "=f"(_tmem_load_0[15]), "=f"(_tmem_load_0[16]), "=f"(_tmem_load_0[17]), "=f"(_tmem_load_0[18]), "=f"(_tmem_load_0[19]), "=f"(_tmem_load_0[20]), "=f"(_tmem_load_0[21]), "=f"(_tmem_load_0[22]), "=f"(_tmem_load_0[23]), "=f"(_tmem_load_0[24]), "=f"(_tmem_load_0[25]), "=f"(_tmem_load_0[26]), "=f"(_tmem_load_0[27]), "=f"(_tmem_load_0[28]), "=f"(_tmem_load_0[29]), "=f"(_tmem_load_0[30]), "=f"(_tmem_load_0[31]), "=f"(_tmem_load_0[32]), "=f"(_tmem_load_0[33]), "=f"(_tmem_load_0[34]), "=f"(_tmem_load_0[35]), "=f"(_tmem_load_0[36]), "=f"(_tmem_load_0[37]), "=f"(_tmem_load_0[38]), "=f"(_tmem_load_0[39]), "=f"(_tmem_load_0[40]), "=f"(_tmem_load_0[41]), "=f"(_tmem_load_0[42]), "=f"(_tmem_load_0[43]), "=f"(_tmem_load_0[44]), "=f"(_tmem_load_0[45]), "=f"(_tmem_load_0[46]), "=f"(_tmem_load_0[47]), "=f"(_tmem_load_0[48]), "=f"(_tmem_load_0[49]), "=f"(_tmem_load_0[50]), "=f"(_tmem_load_0[51]), "=f"(_tmem_load_0[52]), "=f"(_tmem_load_0[53]), "=f"(_tmem_load_0[54]), "=f"(_tmem_load_0[55]), "=f"(_tmem_load_0[56]), "=f"(_tmem_load_0[57]), "=f"(_tmem_load_0[58]), "=f"(_tmem_load_0[59]), "=f"(_tmem_load_0[60]), "=f"(_tmem_load_0[61]), "=f"(_tmem_load_0[62]), "=f"(_tmem_load_0[63]) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + if (elect_sync()) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cluster.b64 _, [%0];" + :: "r"((score_empty_addr) & 0xFEFFFFFF) : "memory"); + } + if (valid_q != 0) { + #pragma unroll 1 + for (int col_base = 0; col_base < 64; col_base += 4) { + float dist_vec[4]; + dist_vec[0] = _tmem_load_0[col_base]; + dist_vec[1] = _tmem_load_0[col_base + 1]; + dist_vec[2] = _tmem_load_0[col_base + 2]; + dist_vec[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec[4]; + db_sq_vec[0] = smem_database_sq[col_base]; + db_sq_vec[1] = smem_database_sq[col_base + 1]; + db_sq_vec[2] = smem_database_sq[col_base + 2]; + db_sq_vec[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec)[_la], reinterpret_cast(db_sq_vec)[_la]); + if (local_db_tile == 0) { + #pragma unroll + for (int vec_col = 0; vec_col < 4; vec_col++) { + int db_idx = db_start + col_base + vec_col; + int slot = col_base + vec_col; + best_d[slot] = _t0[vec_col]; + best_i[slot] = db_idx; + } + } else { + float group_min = _t0[0]; + if (_t0[1] < group_min) { + group_min = _t0[1]; + } + if (_t0[2] < group_min) { + group_min = _t0[2]; + } + if (_t0[3] < group_min) { + group_min = _t0[3]; + } + if (group_min < worst_d) { + float sort_d0 = _t0[0]; + float sort_d1 = _t0[1]; + float sort_d2 = _t0[2]; + float sort_d3 = _t0[3]; + int sort_col0 = 0; + int sort_col1 = 1; + int sort_col2 = 2; + int sort_col3 = 3; + float tmp_d = 0.0f; + int tmp_col = 0; + if (sort_d1 < sort_d0) { + tmp_d = sort_d0; + sort_d0 = sort_d1; + sort_d1 = tmp_d; + tmp_col = sort_col0; + sort_col0 = sort_col1; + sort_col1 = tmp_col; + } + if (sort_d3 < sort_d2) { + tmp_d = sort_d2; + sort_d2 = sort_d3; + sort_d3 = tmp_d; + tmp_col = sort_col2; + sort_col2 = sort_col3; + sort_col3 = tmp_col; + } + if (sort_d2 < sort_d0) { + tmp_d = sort_d0; + sort_d0 = sort_d2; + sort_d2 = tmp_d; + tmp_col = sort_col0; + sort_col0 = sort_col2; + sort_col2 = tmp_col; + } + if (sort_d3 < sort_d1) { + tmp_d = sort_d1; + sort_d1 = sort_d3; + sort_d3 = tmp_d; + tmp_col = sort_col1; + sort_col1 = sort_col3; + sort_col3 = tmp_col; + } + if (sort_d2 < sort_d1) { + tmp_d = sort_d1; + sort_d1 = sort_d2; + sort_d2 = tmp_d; + tmp_col = sort_col1; + sort_col1 = sort_col2; + sort_col2 = tmp_col; + } + #pragma unroll + for (int visit = 0; visit < 4; visit++) { + int vec_col_1 = sort_col0; + float dist = sort_d0; + if (visit == 1) { + vec_col_1 = sort_col1; + dist = sort_d1; + } + if (visit == 2) { + vec_col_1 = sort_col2; + dist = sort_d2; + } + if (visit == 3) { + vec_col_1 = sort_col3; + dist = sort_d3; + } + if (dist >= worst_d) { + break; + } + int db_idx_1 = db_start + col_base + vec_col_1; + best_d[worst_pos] = dist; + best_i[worst_pos] = db_idx_1; + int refresh_base = worst_chunk * 4; + float refresh_worst_d = best_d[refresh_base]; + int refresh_worst_pos = refresh_base; + #pragma unroll + for (int offset = 1; offset < 4; offset++) { + int scan_pos = refresh_base + offset; + if (best_d[scan_pos] > refresh_worst_d) { + refresh_worst_d = best_d[scan_pos]; + refresh_worst_pos = scan_pos; + } + } + if (worst_chunk == 0) { + c0_worst_d = refresh_worst_d; + c0_worst_pos = refresh_worst_pos; + } + if (worst_chunk == 1) { + c1_worst_d = refresh_worst_d; + c1_worst_pos = refresh_worst_pos; + } + if (worst_chunk == 2) { + c2_worst_d = refresh_worst_d; + c2_worst_pos = refresh_worst_pos; + } + if (worst_chunk == 3) { + c3_worst_d = refresh_worst_d; + c3_worst_pos = refresh_worst_pos; + } + if (worst_chunk == 4) { + c4_worst_d = refresh_worst_d; + c4_worst_pos = refresh_worst_pos; + } + if (worst_chunk == 5) { + c5_worst_d = refresh_worst_d; + c5_worst_pos = refresh_worst_pos; + } + if (worst_chunk == 6) { + c6_worst_d = refresh_worst_d; + c6_worst_pos = refresh_worst_pos; + } + if (worst_chunk == 7) { + c7_worst_d = refresh_worst_d; + c7_worst_pos = refresh_worst_pos; + } + if (worst_chunk == 8) { + c8_worst_d = refresh_worst_d; + c8_worst_pos = refresh_worst_pos; + } + if (worst_chunk == 9) { + c9_worst_d = refresh_worst_d; + c9_worst_pos = refresh_worst_pos; + } + if (worst_chunk == 10) { + c10_worst_d = refresh_worst_d; + c10_worst_pos = refresh_worst_pos; + } + if (worst_chunk == 11) { + c11_worst_d = refresh_worst_d; + c11_worst_pos = refresh_worst_pos; + } + if (worst_chunk == 12) { + c12_worst_d = refresh_worst_d; + c12_worst_pos = refresh_worst_pos; + } + if (worst_chunk == 13) { + c13_worst_d = refresh_worst_d; + c13_worst_pos = refresh_worst_pos; + } + if (worst_chunk == 14) { + c14_worst_d = refresh_worst_d; + c14_worst_pos = refresh_worst_pos; + } + if (worst_chunk == 15) { + c15_worst_d = refresh_worst_d; + c15_worst_pos = refresh_worst_pos; + } + worst_d = c0_worst_d; + worst_pos = c0_worst_pos; + worst_chunk = 0; + if (c1_worst_d > worst_d) { + worst_d = c1_worst_d; + worst_pos = c1_worst_pos; + worst_chunk = 1; + } + if (c2_worst_d > worst_d) { + worst_d = c2_worst_d; + worst_pos = c2_worst_pos; + worst_chunk = 2; + } + if (c3_worst_d > worst_d) { + worst_d = c3_worst_d; + worst_pos = c3_worst_pos; + worst_chunk = 3; + } + if (c4_worst_d > worst_d) { + worst_d = c4_worst_d; + worst_pos = c4_worst_pos; + worst_chunk = 4; + } + if (c5_worst_d > worst_d) { + worst_d = c5_worst_d; + worst_pos = c5_worst_pos; + worst_chunk = 5; + } + if (c6_worst_d > worst_d) { + worst_d = c6_worst_d; + worst_pos = c6_worst_pos; + worst_chunk = 6; + } + if (c7_worst_d > worst_d) { + worst_d = c7_worst_d; + worst_pos = c7_worst_pos; + worst_chunk = 7; + } + if (c8_worst_d > worst_d) { + worst_d = c8_worst_d; + worst_pos = c8_worst_pos; + worst_chunk = 8; + } + if (c9_worst_d > worst_d) { + worst_d = c9_worst_d; + worst_pos = c9_worst_pos; + worst_chunk = 9; + } + if (c10_worst_d > worst_d) { + worst_d = c10_worst_d; + worst_pos = c10_worst_pos; + worst_chunk = 10; + } + if (c11_worst_d > worst_d) { + worst_d = c11_worst_d; + worst_pos = c11_worst_pos; + worst_chunk = 11; + } + if (c12_worst_d > worst_d) { + worst_d = c12_worst_d; + worst_pos = c12_worst_pos; + worst_chunk = 12; + } + if (c13_worst_d > worst_d) { + worst_d = c13_worst_d; + worst_pos = c13_worst_pos; + worst_chunk = 13; + } + if (c14_worst_d > worst_d) { + worst_d = c14_worst_d; + worst_pos = c14_worst_pos; + worst_chunk = 14; + } + if (c15_worst_d > worst_d) { + worst_d = c15_worst_d; + worst_pos = c15_worst_pos; + worst_chunk = 15; + } + } + } + } + } + if (local_db_tile == 0) { + c0_worst_d = best_d[0]; + c0_worst_pos = 0; + #pragma unroll + for (int offset_1 = 1; offset_1 < 4; offset_1++) { + if (c0_worst_d < best_d[offset_1]) { + c0_worst_d = best_d[offset_1]; + c0_worst_pos = offset_1; + } + } + c1_worst_d = best_d[4]; + c1_worst_pos = 4; + #pragma unroll + for (int offset_2 = 1; offset_2 < 4; offset_2++) { + int scan_pos_1 = 4 + offset_2; + if (best_d[scan_pos_1] > c1_worst_d) { + c1_worst_d = best_d[scan_pos_1]; + c1_worst_pos = scan_pos_1; + } + } + c2_worst_d = best_d[8]; + c2_worst_pos = 8; + #pragma unroll + for (int offset_3 = 1; offset_3 < 4; offset_3++) { + int scan_pos_2 = 8 + offset_3; + if (best_d[scan_pos_2] > c2_worst_d) { + c2_worst_d = best_d[scan_pos_2]; + c2_worst_pos = scan_pos_2; + } + } + c3_worst_d = best_d[12]; + c3_worst_pos = 12; + #pragma unroll + for (int offset_4 = 1; offset_4 < 4; offset_4++) { + int scan_pos_3 = 12 + offset_4; + if (best_d[scan_pos_3] > c3_worst_d) { + c3_worst_d = best_d[scan_pos_3]; + c3_worst_pos = scan_pos_3; + } + } + c4_worst_d = best_d[16]; + c4_worst_pos = 16; + #pragma unroll + for (int offset_5 = 1; offset_5 < 4; offset_5++) { + int scan_pos_4 = 16 + offset_5; + if (best_d[scan_pos_4] > c4_worst_d) { + c4_worst_d = best_d[scan_pos_4]; + c4_worst_pos = scan_pos_4; + } + } + c5_worst_d = best_d[20]; + c5_worst_pos = 20; + #pragma unroll + for (int offset_6 = 1; offset_6 < 4; offset_6++) { + int scan_pos_5 = 20 + offset_6; + if (best_d[scan_pos_5] > c5_worst_d) { + c5_worst_d = best_d[scan_pos_5]; + c5_worst_pos = scan_pos_5; + } + } + c6_worst_d = best_d[24]; + c6_worst_pos = 24; + #pragma unroll + for (int offset_7 = 1; offset_7 < 4; offset_7++) { + int scan_pos_6 = 24 + offset_7; + if (best_d[scan_pos_6] > c6_worst_d) { + c6_worst_d = best_d[scan_pos_6]; + c6_worst_pos = scan_pos_6; + } + } + c7_worst_d = best_d[28]; + c7_worst_pos = 28; + #pragma unroll + for (int offset_8 = 1; offset_8 < 4; offset_8++) { + int scan_pos_7 = 28 + offset_8; + if (best_d[scan_pos_7] > c7_worst_d) { + c7_worst_d = best_d[scan_pos_7]; + c7_worst_pos = scan_pos_7; + } + } + c8_worst_d = best_d[32]; + c8_worst_pos = 32; + #pragma unroll + for (int offset_9 = 1; offset_9 < 4; offset_9++) { + int scan_pos_8 = 32 + offset_9; + if (best_d[scan_pos_8] > c8_worst_d) { + c8_worst_d = best_d[scan_pos_8]; + c8_worst_pos = scan_pos_8; + } + } + c9_worst_d = best_d[36]; + c9_worst_pos = 36; + #pragma unroll + for (int offset_10 = 1; offset_10 < 4; offset_10++) { + int scan_pos_9 = 36 + offset_10; + if (best_d[scan_pos_9] > c9_worst_d) { + c9_worst_d = best_d[scan_pos_9]; + c9_worst_pos = scan_pos_9; + } + } + c10_worst_d = best_d[40]; + c10_worst_pos = 40; + #pragma unroll + for (int offset_11 = 1; offset_11 < 4; offset_11++) { + int scan_pos_10 = 40 + offset_11; + if (best_d[scan_pos_10] > c10_worst_d) { + c10_worst_d = best_d[scan_pos_10]; + c10_worst_pos = scan_pos_10; + } + } + c11_worst_d = best_d[44]; + c11_worst_pos = 44; + #pragma unroll + for (int offset_12 = 1; offset_12 < 4; offset_12++) { + int scan_pos_11 = 44 + offset_12; + if (best_d[scan_pos_11] > c11_worst_d) { + c11_worst_d = best_d[scan_pos_11]; + c11_worst_pos = scan_pos_11; + } + } + c12_worst_d = best_d[48]; + c12_worst_pos = 48; + #pragma unroll + for (int offset_13 = 1; offset_13 < 4; offset_13++) { + int scan_pos_12 = 48 + offset_13; + if (best_d[scan_pos_12] > c12_worst_d) { + c12_worst_d = best_d[scan_pos_12]; + c12_worst_pos = scan_pos_12; + } + } + c13_worst_d = best_d[52]; + c13_worst_pos = 52; + #pragma unroll + for (int offset_14 = 1; offset_14 < 4; offset_14++) { + int scan_pos_13 = 52 + offset_14; + if (best_d[scan_pos_13] > c13_worst_d) { + c13_worst_d = best_d[scan_pos_13]; + c13_worst_pos = scan_pos_13; + } + } + c14_worst_d = best_d[56]; + c14_worst_pos = 56; + #pragma unroll + for (int offset_15 = 1; offset_15 < 4; offset_15++) { + int scan_pos_14 = 56 + offset_15; + if (best_d[scan_pos_14] > c14_worst_d) { + c14_worst_d = best_d[scan_pos_14]; + c14_worst_pos = scan_pos_14; + } + } + c15_worst_d = best_d[60]; + c15_worst_pos = 60; + #pragma unroll + for (int offset_16 = 1; offset_16 < 4; offset_16++) { + int scan_pos_15 = 60 + offset_16; + if (best_d[scan_pos_15] > c15_worst_d) { + c15_worst_d = best_d[scan_pos_15]; + c15_worst_pos = scan_pos_15; + } + } + worst_d = c0_worst_d; + worst_pos = c0_worst_pos; + worst_chunk = 0; + if (c1_worst_d > worst_d) { + worst_d = c1_worst_d; + worst_pos = c1_worst_pos; + worst_chunk = 1; + } + if (c2_worst_d > worst_d) { + worst_d = c2_worst_d; + worst_pos = c2_worst_pos; + worst_chunk = 2; + } + if (c3_worst_d > worst_d) { + worst_d = c3_worst_d; + worst_pos = c3_worst_pos; + worst_chunk = 3; + } + if (c4_worst_d > worst_d) { + worst_d = c4_worst_d; + worst_pos = c4_worst_pos; + worst_chunk = 4; + } + if (c5_worst_d > worst_d) { + worst_d = c5_worst_d; + worst_pos = c5_worst_pos; + worst_chunk = 5; + } + if (c6_worst_d > worst_d) { + worst_d = c6_worst_d; + worst_pos = c6_worst_pos; + worst_chunk = 6; + } + if (c7_worst_d > worst_d) { + worst_d = c7_worst_d; + worst_pos = c7_worst_pos; + worst_chunk = 7; + } + if (c8_worst_d > worst_d) { + worst_d = c8_worst_d; + worst_pos = c8_worst_pos; + worst_chunk = 8; + } + if (c9_worst_d > worst_d) { + worst_d = c9_worst_d; + worst_pos = c9_worst_pos; + worst_chunk = 9; + } + if (c10_worst_d > worst_d) { + worst_d = c10_worst_d; + worst_pos = c10_worst_pos; + worst_chunk = 10; + } + if (c11_worst_d > worst_d) { + worst_d = c11_worst_d; + worst_pos = c11_worst_pos; + worst_chunk = 11; + } + if (c12_worst_d > worst_d) { + worst_d = c12_worst_d; + worst_pos = c12_worst_pos; + worst_chunk = 12; + } + if (c13_worst_d > worst_d) { + worst_d = c13_worst_d; + worst_pos = c13_worst_pos; + worst_chunk = 13; + } + if (c14_worst_d > worst_d) { + worst_d = c14_worst_d; + worst_pos = c14_worst_pos; + worst_chunk = 14; + } + if (c15_worst_d > worst_d) { + worst_d = c15_worst_d; + worst_pos = c15_worst_pos; + worst_chunk = 15; + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + if (valid_q != 0) { + int out_base = ((split_idx * B + batch_idx) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < 64; out_k++) { + *((float*)(partial_dists + (out_base + out_k))) = best_d[out_k]; + *((int*)(partial_indices + (out_base + out_k))) = best_i[out_k]; + } + } + } + } + // ---- Role: load ---- + } else if (warp == 4) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 4) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx_1 = cluster_id; work_idx_1 < total_work; work_idx_1 += num_clusters) { + int split_idx_1 = work_idx_1 % (unsigned int)split_count; + int query_work_1 = work_idx_1 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tile_pairs; + int q_tile_pair_1 = query_work_1 % num_q_tile_pairs; + int q_tile_1 = q_tile_pair_1 * 2 + cta_rank; + int off_q_1 = q_tile_1 * BLOCK_Q; + int global_q = batch_idx_1 * Q + off_q_1; + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((query_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(32768)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_query_addr), "l"(tmap_query), "r"(0), "r"(global_q), "r"(0), + "r"(((query_full_addr) & 0xFEFFFFFF)) : "memory"); + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int off_m = db_tile_1 * BLOCK_M; + int global_m = batch_idx_1 * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((database_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(16384)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_database_addr), "l"(tmap_database), "r"(0), "r"(global_m), "r"(0), + "r"(((database_full_addr) & 0xFEFFFFFF)) : "memory"); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 5) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + if (cta_rank == 0) { + #pragma unroll 1 + for (unsigned int work_idx_2 = cluster_id; work_idx_2 < total_work; work_idx_2 += num_clusters) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = (smem_query_addr >> 4) & 0x3FFF; + int _mma_b_lo_0 = (smem_database_addr >> 4) & 0x3FFF; + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\tmov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 270533776;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit_cg2_multicast(score_full_addr, (uint16_t)(3)); + elect_commit_cg2_multicast(database_empty_addr, (uint16_t)(3)); + } + elect_commit_cg2_multicast(query_empty_addr, (uint16_t)(3)); + } + } + } + } + + // Cleanup + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::2.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(128)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::2.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0079.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0079.cu new file mode 100644 index 00000000..b389f353 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0079.cu @@ -0,0 +1,99 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 128 +#define TOP_K_MAX 64 +#define SPLIT_COUNT 8 + +#include + +extern "C" { + +__global__ __launch_bounds__(128, 1) void +kernel_knn_build_k64_merge_s8_unordered_warp_select_k64over32s8warpselect(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int row = bid * 4 + warp; + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int cand_lo = lane; + int cand_hi = lane + 32; + if (row < total_queries) { + float cand_d[16]; + int cand_i[16]; + #pragma unroll + for (int split_idx = 0; split_idx < 8; split_idx++) { + int split_base = base_row + split_idx * split_stride; + cand_d[split_idx * 2] = partial_dists[split_base + cand_lo]; + cand_i[split_idx * 2] = partial_indices[split_base + cand_lo]; + cand_d[split_idx * 2 + 1] = partial_dists[split_base + cand_hi]; + cand_i[split_idx * 2 + 1] = partial_indices[split_base + cand_hi]; + } + #pragma unroll + for (int out_k = 0; out_k < 64; out_k++) { + float winner_d = cand_d[0]; + int winner_i = cand_i[0]; + int winner_slot = 0; + #pragma unroll + for (int slot = 1; slot < 16; slot++) { + if (winner_d > cand_d[slot]) { + winner_d = cand_d[slot]; + winner_i = cand_i[slot]; + winner_slot = slot; + } + } + float warp_min = winner_d; + float _warp_reduce_0 = warp_min; + #pragma unroll + for (int offset = 16; offset > 0; offset >>= 1) + _warp_reduce_0 = fminf(_warp_reduce_0, __shfl_xor_sync(0xFFFFFFFF, _warp_reduce_0, offset)); + warp_min = _warp_reduce_0; + unsigned int _vote_0 = __ballot_sync(0xFFFFFFFF, winner_d == warp_min); + int owner_ballot = _vote_0; + int _ffs_0 = __ffs(owner_ballot); + int winner_lane = _ffs_0 - 1; + int _shfl_0 = __shfl_sync(0xFFFFFFFF, winner_i, winner_lane); + winner_i = _shfl_0; + int _shfl_1 = __shfl_sync(0xFFFFFFFF, winner_slot, winner_lane); + winner_slot = _shfl_1; + if (lane == 0) { + *((float*)(out_dists + (base_row + out_k))) = warp_min; + *((int*)(out_indices + (base_row + out_k))) = winner_i; + } + if (lane == winner_lane) { + #pragma unroll + for (int slot_1 = 0; slot_1 < 16; slot_1++) { + if (winner_slot == slot_1) { + cand_d[slot_1] = 3.4e+38f; + } + } + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0080.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0080.cu new file mode 100644 index 00000000..09f833b2 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0080.cu @@ -0,0 +1,581 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 64 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 16384 +#define SMEM_SMEM_QUERY_STRIDE 16384 +#define SMEM_SMEM_DATABASE_OFF 17408 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_LOCAL_D_OFF 34048 +#define SMEM_SMEM_LOCAL_D_STAGE_BYTES 1280 +#define SMEM_SMEM_LOCAL_D_STRIDE 1280 +#define SMEM_SMEM_LOCAL_I_OFF 35328 +#define SMEM_SMEM_LOCAL_I_STAGE_BYTES 1280 +#define SMEM_SMEM_LOCAL_I_STRIDE 1280 +#define SMEM_TOTAL 36608 +#define THREADS 96 +#define BLOCK_Q 64 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_MAX 10 +#define ROWS_COVERED 1 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "tcgen05.mma.cta_group::1.kind::f16 [%0], %1, %2, %3, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, %3, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit(int mbar_addr) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];\n\t" + "}\n" + :: "r"(mbar_addr)); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x32(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x32.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15," + " %16, %17, %18, %19, %20, %21, %22, %23," + " %24, %25, %26, %27, %28, %29, %30, %31}, [%32];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]), + "=f"(dst[16]), "=f"(dst[17]), "=f"(dst[18]), "=f"(dst[19]), + "=f"(dst[20]), "=f"(dst[21]), "=f"(dst[22]), "=f"(dst[23]), + "=f"(dst[24]), "=f"(dst[25]), "=f"(dst[26]), "=f"(dst[27]), + "=f"(dst[28]), "=f"(dst[29]), "=f"(dst[30]), "=f"(dst[31]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ float max_noftz(float a, float b) { + float c; + asm("max.f32 %0, %1, %2;" : "=f"(c) : "f"(a), "f"(b)); + return c; +} + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cta.global" + ".mbarrier::complete_tx::bytes" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit(int mbar_addr) { + asm volatile( + "tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ uint32_t make_warp_uniform(uint32_t val) { + uint32_t result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1f, 0xffffffff;" + : "=r"(result) : "r"(val)); + return result; +} + +extern "C" { + +__global__ __launch_bounds__(96, 1) void +kernel_knn_build_ragonline_mbucket_4fc7_q1m262_v2_stage1_q1_k10_m64_halfrow(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tiles, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 17408); + const int smem_database_addr = smem + 17408; + float* smem_local_d = reinterpret_cast(smem_raw + 34048); + const int smem_local_d_addr = smem + 34048; + int* smem_local_i = reinterpret_cast(smem_raw + 35328); + const int smem_local_i_addr = smem + 35328; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 0, 1, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 16, 1, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 40, 1, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (64 columns, 64 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::1.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(64) : "memory"); + } + + __syncthreads(); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: compute ---- + if (warp == 0) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx = bid; work_idx < total_work; work_idx += num_bids) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tiles; + int q_tile = query_work % num_q_tiles; + int off_q = q_tile * BLOCK_Q; + int row = lane / 4; + int lane_col = lane % 4; + int slot = lane_col; + int q_idx = off_q + row; + int valid_row = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_row != 0) { + q_sq_val = query_sq[batch_idx * Q + q_idx]; + } + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + int db_tile_start = split_idx * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int db_start = db_tile * BLOCK_M; + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + float _tmem_load_0[32]; + asm volatile( + "tcgen05.ld.sync.aligned.16x256b.x8.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31}, [%32];" + : "=r"(*reinterpret_cast(&_tmem_load_0[0])), "=r"(*reinterpret_cast(&_tmem_load_0[1])), "=r"(*reinterpret_cast(&_tmem_load_0[2])), "=r"(*reinterpret_cast(&_tmem_load_0[3])), "=r"(*reinterpret_cast(&_tmem_load_0[4])), "=r"(*reinterpret_cast(&_tmem_load_0[5])), "=r"(*reinterpret_cast(&_tmem_load_0[6])), "=r"(*reinterpret_cast(&_tmem_load_0[7])), "=r"(*reinterpret_cast(&_tmem_load_0[8])), "=r"(*reinterpret_cast(&_tmem_load_0[9])), "=r"(*reinterpret_cast(&_tmem_load_0[10])), "=r"(*reinterpret_cast(&_tmem_load_0[11])), "=r"(*reinterpret_cast(&_tmem_load_0[12])), "=r"(*reinterpret_cast(&_tmem_load_0[13])), "=r"(*reinterpret_cast(&_tmem_load_0[14])), "=r"(*reinterpret_cast(&_tmem_load_0[15])), "=r"(*reinterpret_cast(&_tmem_load_0[16])), "=r"(*reinterpret_cast(&_tmem_load_0[17])), "=r"(*reinterpret_cast(&_tmem_load_0[18])), "=r"(*reinterpret_cast(&_tmem_load_0[19])), "=r"(*reinterpret_cast(&_tmem_load_0[20])), "=r"(*reinterpret_cast(&_tmem_load_0[21])), "=r"(*reinterpret_cast(&_tmem_load_0[22])), "=r"(*reinterpret_cast(&_tmem_load_0[23])), "=r"(*reinterpret_cast(&_tmem_load_0[24])), "=r"(*reinterpret_cast(&_tmem_load_0[25])), "=r"(*reinterpret_cast(&_tmem_load_0[26])), "=r"(*reinterpret_cast(&_tmem_load_0[27])), "=r"(*reinterpret_cast(&_tmem_load_0[28])), "=r"(*reinterpret_cast(&_tmem_load_0[29])), "=r"(*reinterpret_cast(&_tmem_load_0[30])), "=r"(*reinterpret_cast(&_tmem_load_0[31])) + : "r"(taddr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + if (elect_sync()) { + mbarrier_arrive(score_empty_addr); + } + #pragma unroll + for (int repeat = 0; repeat < 8; repeat++) { + const int reg_base = repeat * 4; + int col_base = repeat * 8 + lane_col * 2; + int db_idx0 = db_start + col_base; + int db_idx1 = db_idx0 + 1; + float cand0_d = 3.4e+38f; + float cand1_d = 3.4e+38f; + if (valid_row != 0 && db_idx0 < M) { + float _max_0 = max_noftz(q_sq_val + database_sq[batch_idx * M + db_idx0] - 2.0f * _tmem_load_0[reg_base], 0.0f); + cand0_d = _max_0; + } + if (valid_row != 0 && db_idx1 < M) { + float _max_1 = max_noftz(q_sq_val + database_sq[batch_idx * M + db_idx1] - 2.0f * _tmem_load_0[reg_base + 1], 0.0f); + cand1_d = _max_1; + } + int take1 = ((cand1_d < cand0_d) ? 1 : 0); + if (best_d[9] > ((take1 != 0) ? cand1_d : cand0_d)) { + best_d[9] = ((take1 != 0) ? cand1_d : cand0_d); + best_i[9] = ((take1 != 0) ? db_idx1 : db_idx0); + #pragma unroll + for (int kk_1 = 8; kk_1 >= 0; kk_1--) { + float lower0_d = best_d[kk_1 + 1]; + int lower0_i = best_i[kk_1 + 1]; + float upper0_d = best_d[kk_1]; + int upper0_i = best_i[kk_1]; + int swap0_up = ((lower0_d < upper0_d) ? 1 : 0); + best_d[kk_1] = ((swap0_up != 0) ? lower0_d : upper0_d); + best_i[kk_1] = ((swap0_up != 0) ? lower0_i : upper0_i); + best_d[kk_1 + 1] = ((swap0_up != 0) ? upper0_d : lower0_d); + best_i[kk_1 + 1] = ((swap0_up != 0) ? upper0_i : lower0_i); + } + if (best_d[9] > ((take1 != 0) ? cand0_d : cand1_d)) { + best_d[9] = ((take1 != 0) ? cand0_d : cand1_d); + best_i[9] = ((take1 != 0) ? db_idx0 : db_idx1); + #pragma unroll + for (int kk_2 = 8; kk_2 >= 0; kk_2--) { + float lower1_d = best_d[kk_2 + 1]; + int lower1_i = best_i[kk_2 + 1]; + float upper1_d = best_d[kk_2]; + int upper1_i = best_i[kk_2]; + int swap1_up = ((lower1_d < upper1_d) ? 1 : 0); + best_d[kk_2] = ((swap1_up != 0) ? lower1_d : upper1_d); + best_i[kk_2] = ((swap1_up != 0) ? lower1_i : upper1_i); + best_d[kk_2 + 1] = ((swap1_up != 0) ? upper1_d : lower1_d); + best_i[kk_2 + 1] = ((swap1_up != 0) ? upper1_i : lower1_i); + } + } + } + } + } + int slot_base = (row * 4 + slot) * TOP_K_MAX; + #pragma unroll + for (int kk_3 = 0; kk_3 < TOP_K_MAX; kk_3++) { + smem_local_d[slot_base + kk_3] = best_d[kk_3]; + smem_local_i[slot_base + kk_3] = best_i[kk_3]; + } + asm volatile("barrier.sync 8, %0;" :: "r"(32)); + if (tid < ROWS_COVERED) { + int out_row = tid; + int out_q_idx = off_q + out_row; + float head_d[4]; + int head_i[4]; + int head_k[4]; + #pragma unroll + for (int slot_idx = 0; slot_idx < 4; slot_idx++) { + int local_base = (out_row * 4 + slot_idx) * TOP_K_MAX; + head_k[slot_idx] = 0; + head_d[slot_idx] = smem_local_d[local_base]; + head_i[slot_idx] = smem_local_i[local_base]; + } + int out_base = ((split_idx * B + batch_idx) * Q + out_q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float winner_d = head_d[0]; + int winner_i = head_i[0]; + int winner_slot = 0; + #pragma unroll + for (int slot_idx_1 = 1; slot_idx_1 < 4; slot_idx_1++) { + float cand_d = head_d[slot_idx_1]; + int take = ((cand_d < winner_d) ? 1 : 0); + winner_d = ((take != 0) ? cand_d : winner_d); + winner_i = ((take != 0) ? head_i[slot_idx_1] : winner_i); + winner_slot = ((take != 0) ? slot_idx_1 : winner_slot); + } + if (out_q_idx < Q && out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = winner_d; + *((int*)(partial_indices + (out_base + out_k))) = winner_i; + } + #pragma unroll + for (int slot_idx_2 = 0; slot_idx_2 < 4; slot_idx_2++) { + if (winner_slot == slot_idx_2) { + int next_head = head_k[slot_idx_2] + 1; + head_k[slot_idx_2] = next_head; + head_d[slot_idx_2] = 3.4e+38f; + head_i[slot_idx_2] = -1; + if (next_head < TOP_K_MAX) { + int local_base_1 = (out_row * 4 + slot_idx_2) * TOP_K_MAX; + head_d[slot_idx_2] = smem_local_d[local_base_1 + next_head]; + head_i[slot_idx_2] = smem_local_i[local_base_1 + next_head]; + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(32)); + } + } + // ---- Role: load ---- + } else if (warp == 1) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 1) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx_1 = bid; work_idx_1 < total_work; work_idx_1 += num_bids) { + int split_idx_1 = work_idx_1 % (unsigned int)split_count; + int query_work_1 = work_idx_1 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tiles; + int q_tile_1 = query_work_1 % num_q_tiles; + int off_q_1 = q_tile_1 * BLOCK_Q; + int global_q = batch_idx_1 * Q + off_q_1; + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + mbarrier_arrive_expect_tx(query_full_addr, 16384); + tma_3d_gmem2smem(smem_query_addr, tmap_query, 0, global_q, 0, query_full_addr); + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int off_m = db_tile_1 * BLOCK_M; + int global_m = batch_idx_1 * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + mbarrier_arrive_expect_tx(database_full_addr, 16384); + tma_3d_gmem2smem(smem_database_addr, tmap_database, 0, global_m, 0, database_full_addr); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 2) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = bid; work_idx_2 < total_work; work_idx_2 += num_bids) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = make_warp_uniform((smem_query_addr >> 4) & 0x3FFF); + int _mma_b_lo_0 = make_warp_uniform((smem_database_addr >> 4) & 0x3FFF); + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 68158608;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 506;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit(score_full_addr); + elect_commit(database_empty_addr); + } + elect_commit(query_empty_addr); + } + } + } + + // Cleanup + __syncthreads(); // barrier before TMEM dealloc + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::1.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(64)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::1.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0081.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0081.cu new file mode 100644 index 00000000..3b490242 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0081.cu @@ -0,0 +1,141 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define SMEM_GROUP_DISTS_OFF 0 +#define SMEM_GROUP_DISTS_STAGE_BYTES 512 +#define SMEM_GROUP_DISTS_STRIDE 512 +#define SMEM_GROUP_INDICES_OFF 512 +#define SMEM_GROUP_INDICES_STAGE_BYTES 512 +#define SMEM_GROUP_INDICES_STRIDE 512 +#define SMEM_TOTAL 1024 +#define THREADS 32 +#define TOP_K_MAX 10 +#define GROUP_COUNT 12 +#define GROUP_SPLITS 12 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_rag_microbatch_4a72_k10_fused_group_final_merge_s144g12_4a72_v1(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + float* group_dists = reinterpret_cast(smem_raw + 0); + const int group_dists_addr = smem + 0; + int* group_indices = reinterpret_cast(smem_raw + 512); + const int group_indices_addr = smem + 512; + + // === Task calls (dependency order) === + int split_pos[GROUP_SPLITS]; + int split_base[GROUP_SPLITS]; + float group_cand_d[GROUP_SPLITS]; + int group_cand_i[GROUP_SPLITS]; + int final_pos[GROUP_COUNT]; + float final_cand_d[GROUP_COUNT]; + int final_cand_i[GROUP_COUNT]; + #pragma unroll 1 + for (int row = bid; row < total_queries; row += num_bids) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + if (tid < GROUP_COUNT) { + int group_idx = tid; + int source_split0 = group_idx * GROUP_SPLITS; + int shared_base = group_idx * TOP_K_MAX; + #pragma unroll + for (int local_split = 0; local_split < GROUP_SPLITS; local_split++) { + split_pos[local_split] = 0; + int split_id = source_split0 + local_split; + split_base[local_split] = base_row + split_id * split_stride; + group_cand_d[local_split] = partial_dists[split_base[local_split]]; + group_cand_i[local_split] = partial_indices[split_base[local_split]]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_d = group_cand_d[0]; + int best_i = group_cand_i[0]; + int best_split = 0; + #pragma unroll + for (int local_split_1 = 1; local_split_1 < GROUP_SPLITS; local_split_1++) { + if (best_d > group_cand_d[local_split_1]) { + best_d = group_cand_d[local_split_1]; + best_i = group_cand_i[local_split_1]; + best_split = local_split_1; + } + } + group_dists[shared_base + out_k] = best_d; + group_indices[shared_base + out_k] = best_i; + split_pos[best_split] = split_pos[best_split] + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_pos = split_pos[best_split]; + int next_addr = split_base[best_split] + next_pos; + group_cand_d[best_split] = partial_dists[next_addr]; + group_cand_i[best_split] = partial_indices[next_addr]; + } + } + } + __syncthreads(); + if (tid == 0) { + #pragma unroll + for (int group_idx_1 = 0; group_idx_1 < GROUP_COUNT; group_idx_1++) { + final_pos[group_idx_1] = 0; + int group_base = group_idx_1 * TOP_K_MAX; + final_cand_d[group_idx_1] = group_dists[group_base]; + final_cand_i[group_idx_1] = group_indices[group_base]; + } + #pragma unroll + for (int out_k_1 = 0; out_k_1 < TOP_K_MAX; out_k_1++) { + float best_d_1 = final_cand_d[0]; + int best_i_1 = final_cand_i[0]; + int best_group = 0; + #pragma unroll + for (int group_idx_2 = 1; group_idx_2 < GROUP_COUNT; group_idx_2++) { + if (best_d_1 > final_cand_d[group_idx_2]) { + best_d_1 = final_cand_d[group_idx_2]; + best_i_1 = final_cand_i[group_idx_2]; + best_group = group_idx_2; + } + } + *((float*)(out_dists + (base_row + out_k_1))) = best_d_1; + *((int*)(out_indices + (base_row + out_k_1))) = best_i_1; + final_pos[best_group] = final_pos[best_group] + 1; + if (out_k_1 + 1 < TOP_K_MAX) { + int next_pos_1 = final_pos[best_group]; + int next_addr_1 = best_group * TOP_K_MAX + next_pos_1; + final_cand_d[best_group] = group_dists[next_addr_1]; + final_cand_i[best_group] = group_indices[next_addr_1]; + } + } + } + __syncthreads(); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0082.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0082.cu new file mode 100644 index 00000000..5a44d6be --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0082.cu @@ -0,0 +1,642 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 128 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 16384 +#define SMEM_SMEM_QUERY_STRIDE 16384 +#define SMEM_SMEM_DATABASE_OFF 17408 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 32768 +#define SMEM_SMEM_DATABASE_STRIDE 32768 +#define SMEM_SMEM_LOCAL_D_OFF 50432 +#define SMEM_SMEM_LOCAL_D_STAGE_BYTES 1280 +#define SMEM_SMEM_LOCAL_D_STRIDE 1280 +#define SMEM_SMEM_LOCAL_I_OFF 51712 +#define SMEM_SMEM_LOCAL_I_STAGE_BYTES 1280 +#define SMEM_SMEM_LOCAL_I_STRIDE 1280 +#define SMEM_TOTAL 52992 +#define THREADS 96 +#define BLOCK_Q 64 +#define BLOCK_M 128 +#define FEAT_D 128 +#define TOP_K_MAX 10 +#define ROWS_COVERED 1 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "tcgen05.mma.cta_group::1.kind::f16 [%0], %1, %2, %3, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, %3, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit(int mbar_addr) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];\n\t" + "}\n" + :: "r"(mbar_addr)); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x32(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x32.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15," + " %16, %17, %18, %19, %20, %21, %22, %23," + " %24, %25, %26, %27, %28, %29, %30, %31}, [%32];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]), + "=f"(dst[16]), "=f"(dst[17]), "=f"(dst[18]), "=f"(dst[19]), + "=f"(dst[20]), "=f"(dst[21]), "=f"(dst[22]), "=f"(dst[23]), + "=f"(dst[24]), "=f"(dst[25]), "=f"(dst[26]), "=f"(dst[27]), + "=f"(dst[28]), "=f"(dst[29]), "=f"(dst[30]), "=f"(dst[31]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ float max_noftz(float a, float b) { + float c; + asm("max.f32 %0, %1, %2;" : "=f"(c) : "f"(a), "f"(b)); + return c; +} + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cta.global" + ".mbarrier::complete_tx::bytes" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit(int mbar_addr) { + asm volatile( + "tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ uint32_t make_warp_uniform(uint32_t val) { + uint32_t result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1f, 0xffffffff;" + : "=r"(result) : "r"(val)); + return result; +} + +extern "C" { + +__global__ __launch_bounds__(96, 1) void +kernel_knn_build_ragonline_mbucket_ea43_q1m524_n128_stage1(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tiles, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 17408); + const int smem_database_addr = smem + 17408; + float* smem_local_d = reinterpret_cast(smem_raw + 50432); + const int smem_local_d_addr = smem + 50432; + int* smem_local_i = reinterpret_cast(smem_raw + 51712); + const int smem_local_i_addr = smem + 51712; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 0, 1, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 16, 1, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 40, 1, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (128 columns, 128 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::1.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(128) : "memory"); + } + + __syncthreads(); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: compute ---- + if (warp == 0) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx = bid; work_idx < total_work; work_idx += num_bids) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tiles; + int q_tile = query_work % num_q_tiles; + int off_q = q_tile * BLOCK_Q; + int row = lane / 4; + int lane_col = lane % 4; + int slot = lane_col; + int q_idx = off_q + row; + int valid_row = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_row != 0) { + q_sq_val = query_sq[batch_idx * Q + q_idx]; + } + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + int db_tile_start = split_idx * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int db_start = db_tile * BLOCK_M; + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + float _tmem_load_0[32]; + asm volatile( + "tcgen05.ld.sync.aligned.16x256b.x8.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31}, [%32];" + : "=r"(*reinterpret_cast(&_tmem_load_0[0])), "=r"(*reinterpret_cast(&_tmem_load_0[1])), "=r"(*reinterpret_cast(&_tmem_load_0[2])), "=r"(*reinterpret_cast(&_tmem_load_0[3])), "=r"(*reinterpret_cast(&_tmem_load_0[4])), "=r"(*reinterpret_cast(&_tmem_load_0[5])), "=r"(*reinterpret_cast(&_tmem_load_0[6])), "=r"(*reinterpret_cast(&_tmem_load_0[7])), "=r"(*reinterpret_cast(&_tmem_load_0[8])), "=r"(*reinterpret_cast(&_tmem_load_0[9])), "=r"(*reinterpret_cast(&_tmem_load_0[10])), "=r"(*reinterpret_cast(&_tmem_load_0[11])), "=r"(*reinterpret_cast(&_tmem_load_0[12])), "=r"(*reinterpret_cast(&_tmem_load_0[13])), "=r"(*reinterpret_cast(&_tmem_load_0[14])), "=r"(*reinterpret_cast(&_tmem_load_0[15])), "=r"(*reinterpret_cast(&_tmem_load_0[16])), "=r"(*reinterpret_cast(&_tmem_load_0[17])), "=r"(*reinterpret_cast(&_tmem_load_0[18])), "=r"(*reinterpret_cast(&_tmem_load_0[19])), "=r"(*reinterpret_cast(&_tmem_load_0[20])), "=r"(*reinterpret_cast(&_tmem_load_0[21])), "=r"(*reinterpret_cast(&_tmem_load_0[22])), "=r"(*reinterpret_cast(&_tmem_load_0[23])), "=r"(*reinterpret_cast(&_tmem_load_0[24])), "=r"(*reinterpret_cast(&_tmem_load_0[25])), "=r"(*reinterpret_cast(&_tmem_load_0[26])), "=r"(*reinterpret_cast(&_tmem_load_0[27])), "=r"(*reinterpret_cast(&_tmem_load_0[28])), "=r"(*reinterpret_cast(&_tmem_load_0[29])), "=r"(*reinterpret_cast(&_tmem_load_0[30])), "=r"(*reinterpret_cast(&_tmem_load_0[31])) + : "r"(taddr) + : "memory"); + float _tmem_load_1[32]; + asm volatile( + "tcgen05.ld.sync.aligned.16x256b.x8.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31}, [%32];" + : "=r"(*reinterpret_cast(&_tmem_load_1[0])), "=r"(*reinterpret_cast(&_tmem_load_1[1])), "=r"(*reinterpret_cast(&_tmem_load_1[2])), "=r"(*reinterpret_cast(&_tmem_load_1[3])), "=r"(*reinterpret_cast(&_tmem_load_1[4])), "=r"(*reinterpret_cast(&_tmem_load_1[5])), "=r"(*reinterpret_cast(&_tmem_load_1[6])), "=r"(*reinterpret_cast(&_tmem_load_1[7])), "=r"(*reinterpret_cast(&_tmem_load_1[8])), "=r"(*reinterpret_cast(&_tmem_load_1[9])), "=r"(*reinterpret_cast(&_tmem_load_1[10])), "=r"(*reinterpret_cast(&_tmem_load_1[11])), "=r"(*reinterpret_cast(&_tmem_load_1[12])), "=r"(*reinterpret_cast(&_tmem_load_1[13])), "=r"(*reinterpret_cast(&_tmem_load_1[14])), "=r"(*reinterpret_cast(&_tmem_load_1[15])), "=r"(*reinterpret_cast(&_tmem_load_1[16])), "=r"(*reinterpret_cast(&_tmem_load_1[17])), "=r"(*reinterpret_cast(&_tmem_load_1[18])), "=r"(*reinterpret_cast(&_tmem_load_1[19])), "=r"(*reinterpret_cast(&_tmem_load_1[20])), "=r"(*reinterpret_cast(&_tmem_load_1[21])), "=r"(*reinterpret_cast(&_tmem_load_1[22])), "=r"(*reinterpret_cast(&_tmem_load_1[23])), "=r"(*reinterpret_cast(&_tmem_load_1[24])), "=r"(*reinterpret_cast(&_tmem_load_1[25])), "=r"(*reinterpret_cast(&_tmem_load_1[26])), "=r"(*reinterpret_cast(&_tmem_load_1[27])), "=r"(*reinterpret_cast(&_tmem_load_1[28])), "=r"(*reinterpret_cast(&_tmem_load_1[29])), "=r"(*reinterpret_cast(&_tmem_load_1[30])), "=r"(*reinterpret_cast(&_tmem_load_1[31])) + : "r"(taddr + 64) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + if (elect_sync()) { + mbarrier_arrive(score_empty_addr); + } + #pragma unroll + for (int repeat = 0; repeat < 8; repeat++) { + const int reg_base = repeat * 4; + int col_base = repeat * 8 + lane_col * 2; + int db_idx0 = db_start + col_base; + int db_idx1 = db_idx0 + 1; + float cand0_d = 3.4e+38f; + float cand1_d = 3.4e+38f; + float dot0 = _tmem_load_0[reg_base]; + float dot1 = _tmem_load_0[reg_base + 1]; + if (valid_row != 0 && db_idx0 < M) { + float _max_0 = max_noftz(q_sq_val + database_sq[batch_idx * M + db_idx0] - 2.0f * dot0, 0.0f); + cand0_d = _max_0; + } + if (valid_row != 0 && db_idx1 < M) { + float _max_1 = max_noftz(q_sq_val + database_sq[batch_idx * M + db_idx1] - 2.0f * dot1, 0.0f); + cand1_d = _max_1; + } + int take1 = ((cand1_d < cand0_d) ? 1 : 0); + if (best_d[9] > ((take1 != 0) ? cand1_d : cand0_d)) { + best_d[9] = ((take1 != 0) ? cand1_d : cand0_d); + best_i[9] = ((take1 != 0) ? db_idx1 : db_idx0); + #pragma unroll + for (int kk_1 = 8; kk_1 >= 0; kk_1--) { + float lower0_d = best_d[kk_1 + 1]; + int lower0_i = best_i[kk_1 + 1]; + float upper0_d = best_d[kk_1]; + int upper0_i = best_i[kk_1]; + int swap0_up = ((lower0_d < upper0_d) ? 1 : 0); + best_d[kk_1] = ((swap0_up != 0) ? lower0_d : upper0_d); + best_i[kk_1] = ((swap0_up != 0) ? lower0_i : upper0_i); + best_d[kk_1 + 1] = ((swap0_up != 0) ? upper0_d : lower0_d); + best_i[kk_1 + 1] = ((swap0_up != 0) ? upper0_i : lower0_i); + } + if (best_d[9] > ((take1 != 0) ? cand0_d : cand1_d)) { + best_d[9] = ((take1 != 0) ? cand0_d : cand1_d); + best_i[9] = ((take1 != 0) ? db_idx0 : db_idx1); + #pragma unroll + for (int kk_2 = 8; kk_2 >= 0; kk_2--) { + float lower1_d = best_d[kk_2 + 1]; + int lower1_i = best_i[kk_2 + 1]; + float upper1_d = best_d[kk_2]; + int upper1_i = best_i[kk_2]; + int swap1_up = ((lower1_d < upper1_d) ? 1 : 0); + best_d[kk_2] = ((swap1_up != 0) ? lower1_d : upper1_d); + best_i[kk_2] = ((swap1_up != 0) ? lower1_i : upper1_i); + best_d[kk_2 + 1] = ((swap1_up != 0) ? upper1_d : lower1_d); + best_i[kk_2 + 1] = ((swap1_up != 0) ? upper1_i : lower1_i); + } + } + } + } + #pragma unroll + for (int repeat_hi = 0; repeat_hi < 8; repeat_hi++) { + const int reg_base_hi = repeat_hi * 4; + int col_base_hi = 64 + repeat_hi * 8 + lane_col * 2; + int db_idx_hi0 = db_start + col_base_hi; + int db_idx_hi1 = db_idx_hi0 + 1; + float cand_hi0_d = 3.4e+38f; + float cand_hi1_d = 3.4e+38f; + float dot_hi0 = _tmem_load_1[reg_base_hi]; + float dot_hi1 = _tmem_load_1[reg_base_hi + 1]; + if (valid_row != 0 && db_idx_hi0 < M) { + float _max_2 = max_noftz(q_sq_val + database_sq[batch_idx * M + db_idx_hi0] - 2.0f * dot_hi0, 0.0f); + cand_hi0_d = _max_2; + } + if (valid_row != 0 && db_idx_hi1 < M) { + float _max_3 = max_noftz(q_sq_val + database_sq[batch_idx * M + db_idx_hi1] - 2.0f * dot_hi1, 0.0f); + cand_hi1_d = _max_3; + } + int take_hi1 = ((cand_hi1_d < cand_hi0_d) ? 1 : 0); + if (best_d[9] > ((take_hi1 != 0) ? cand_hi1_d : cand_hi0_d)) { + best_d[9] = ((take_hi1 != 0) ? cand_hi1_d : cand_hi0_d); + best_i[9] = ((take_hi1 != 0) ? db_idx_hi1 : db_idx_hi0); + #pragma unroll + for (int kk_3 = 8; kk_3 >= 0; kk_3--) { + float lower0_d_1 = best_d[kk_3 + 1]; + int lower0_i_1 = best_i[kk_3 + 1]; + float upper0_d_1 = best_d[kk_3]; + int upper0_i_1 = best_i[kk_3]; + int swap0_up_1 = ((lower0_d_1 < upper0_d_1) ? 1 : 0); + best_d[kk_3] = ((swap0_up_1 != 0) ? lower0_d_1 : upper0_d_1); + best_i[kk_3] = ((swap0_up_1 != 0) ? lower0_i_1 : upper0_i_1); + best_d[kk_3 + 1] = ((swap0_up_1 != 0) ? upper0_d_1 : lower0_d_1); + best_i[kk_3 + 1] = ((swap0_up_1 != 0) ? upper0_i_1 : lower0_i_1); + } + if (best_d[9] > ((take_hi1 != 0) ? cand_hi0_d : cand_hi1_d)) { + best_d[9] = ((take_hi1 != 0) ? cand_hi0_d : cand_hi1_d); + best_i[9] = ((take_hi1 != 0) ? db_idx_hi0 : db_idx_hi1); + #pragma unroll + for (int kk_4 = 8; kk_4 >= 0; kk_4--) { + float lower1_d_1 = best_d[kk_4 + 1]; + int lower1_i_1 = best_i[kk_4 + 1]; + float upper1_d_1 = best_d[kk_4]; + int upper1_i_1 = best_i[kk_4]; + int swap1_up_1 = ((lower1_d_1 < upper1_d_1) ? 1 : 0); + best_d[kk_4] = ((swap1_up_1 != 0) ? lower1_d_1 : upper1_d_1); + best_i[kk_4] = ((swap1_up_1 != 0) ? lower1_i_1 : upper1_i_1); + best_d[kk_4 + 1] = ((swap1_up_1 != 0) ? upper1_d_1 : lower1_d_1); + best_i[kk_4 + 1] = ((swap1_up_1 != 0) ? upper1_i_1 : lower1_i_1); + } + } + } + } + } + int slot_base = (row * 4 + slot) * TOP_K_MAX; + #pragma unroll + for (int kk_5 = 0; kk_5 < TOP_K_MAX; kk_5++) { + smem_local_d[slot_base + kk_5] = best_d[kk_5]; + smem_local_i[slot_base + kk_5] = best_i[kk_5]; + } + asm volatile("barrier.sync 8, %0;" :: "r"(32)); + if (tid < ROWS_COVERED) { + int out_row = tid; + int out_q_idx = off_q + out_row; + float head_d[4]; + int head_i[4]; + int head_k[4]; + #pragma unroll + for (int slot_idx = 0; slot_idx < 4; slot_idx++) { + int local_base = (out_row * 4 + slot_idx) * TOP_K_MAX; + head_k[slot_idx] = 0; + head_d[slot_idx] = smem_local_d[local_base]; + head_i[slot_idx] = smem_local_i[local_base]; + } + int out_base = ((split_idx * B + batch_idx) * Q + out_q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float winner_d = head_d[0]; + int winner_i = head_i[0]; + int winner_slot = 0; + #pragma unroll + for (int slot_idx_1 = 1; slot_idx_1 < 4; slot_idx_1++) { + float cand_d = head_d[slot_idx_1]; + int take = ((cand_d < winner_d) ? 1 : 0); + winner_d = ((take != 0) ? cand_d : winner_d); + winner_i = ((take != 0) ? head_i[slot_idx_1] : winner_i); + winner_slot = ((take != 0) ? slot_idx_1 : winner_slot); + } + if (out_q_idx < Q && out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = winner_d; + *((int*)(partial_indices + (out_base + out_k))) = winner_i; + } + #pragma unroll + for (int slot_idx_2 = 0; slot_idx_2 < 4; slot_idx_2++) { + if (winner_slot == slot_idx_2) { + int next_head = head_k[slot_idx_2] + 1; + head_k[slot_idx_2] = next_head; + head_d[slot_idx_2] = 3.4e+38f; + head_i[slot_idx_2] = -1; + if (next_head < TOP_K_MAX) { + int local_base_1 = (out_row * 4 + slot_idx_2) * TOP_K_MAX; + head_d[slot_idx_2] = smem_local_d[local_base_1 + next_head]; + head_i[slot_idx_2] = smem_local_i[local_base_1 + next_head]; + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(32)); + } + } + // ---- Role: load ---- + } else if (warp == 1) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 1) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx_1 = bid; work_idx_1 < total_work; work_idx_1 += num_bids) { + int split_idx_1 = work_idx_1 % (unsigned int)split_count; + int query_work_1 = work_idx_1 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tiles; + int q_tile_1 = query_work_1 % num_q_tiles; + int off_q_1 = q_tile_1 * BLOCK_Q; + int global_q = batch_idx_1 * Q + off_q_1; + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + mbarrier_arrive_expect_tx(query_full_addr, 16384); + tma_3d_gmem2smem(smem_query_addr, tmap_query, 0, global_q, 0, query_full_addr); + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int off_m = db_tile_1 * BLOCK_M; + int global_m = batch_idx_1 * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + mbarrier_arrive_expect_tx(database_full_addr, 32768); + tma_3d_gmem2smem(smem_database_addr, tmap_database, 0, global_m, 0, database_full_addr); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 2) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = bid; work_idx_2 < total_work; work_idx_2 += num_bids) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = make_warp_uniform((smem_query_addr >> 4) & 0x3FFF); + int _mma_b_lo_0 = make_warp_uniform((smem_database_addr >> 4) & 0x3FFF); + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 69207184;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 506;\n\t" + "add.u32 blo, blo, 1018;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit(score_full_addr); + elect_commit(database_empty_addr); + } + elect_commit(query_empty_addr); + } + } + } + + // Cleanup + __syncthreads(); // barrier before TMEM dealloc + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::1.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(128)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::1.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0083.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0083.cu new file mode 100644 index 00000000..21bc0036 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0083.cu @@ -0,0 +1,141 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define SMEM_GROUP_DISTS_OFF 0 +#define SMEM_GROUP_DISTS_STAGE_BYTES 512 +#define SMEM_GROUP_DISTS_STRIDE 512 +#define SMEM_GROUP_INDICES_OFF 512 +#define SMEM_GROUP_INDICES_STAGE_BYTES 512 +#define SMEM_GROUP_INDICES_STRIDE 512 +#define SMEM_TOTAL 1024 +#define THREADS 32 +#define TOP_K_MAX 10 +#define GROUP_COUNT 7 +#define GROUP_SPLITS 21 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_rag_microbatch_4a72_k10_fused_group_final_merge_s147g7_4a72_v1(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + float* group_dists = reinterpret_cast(smem_raw + 0); + const int group_dists_addr = smem + 0; + int* group_indices = reinterpret_cast(smem_raw + 512); + const int group_indices_addr = smem + 512; + + // === Task calls (dependency order) === + int split_pos[GROUP_SPLITS]; + int split_base[GROUP_SPLITS]; + float group_cand_d[GROUP_SPLITS]; + int group_cand_i[GROUP_SPLITS]; + int final_pos[GROUP_COUNT]; + float final_cand_d[GROUP_COUNT]; + int final_cand_i[GROUP_COUNT]; + #pragma unroll 1 + for (int row = bid; row < total_queries; row += num_bids) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + if (tid < GROUP_COUNT) { + int group_idx = tid; + int source_split0 = group_idx * GROUP_SPLITS; + int shared_base = group_idx * TOP_K_MAX; + #pragma unroll + for (int local_split = 0; local_split < GROUP_SPLITS; local_split++) { + split_pos[local_split] = 0; + int split_id = source_split0 + local_split; + split_base[local_split] = base_row + split_id * split_stride; + group_cand_d[local_split] = partial_dists[split_base[local_split]]; + group_cand_i[local_split] = partial_indices[split_base[local_split]]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_d = group_cand_d[0]; + int best_i = group_cand_i[0]; + int best_split = 0; + #pragma unroll + for (int local_split_1 = 1; local_split_1 < GROUP_SPLITS; local_split_1++) { + if (best_d > group_cand_d[local_split_1]) { + best_d = group_cand_d[local_split_1]; + best_i = group_cand_i[local_split_1]; + best_split = local_split_1; + } + } + group_dists[shared_base + out_k] = best_d; + group_indices[shared_base + out_k] = best_i; + split_pos[best_split] = split_pos[best_split] + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_pos = split_pos[best_split]; + int next_addr = split_base[best_split] + next_pos; + group_cand_d[best_split] = partial_dists[next_addr]; + group_cand_i[best_split] = partial_indices[next_addr]; + } + } + } + __syncthreads(); + if (tid == 0) { + #pragma unroll + for (int group_idx_1 = 0; group_idx_1 < GROUP_COUNT; group_idx_1++) { + final_pos[group_idx_1] = 0; + int group_base = group_idx_1 * TOP_K_MAX; + final_cand_d[group_idx_1] = group_dists[group_base]; + final_cand_i[group_idx_1] = group_indices[group_base]; + } + #pragma unroll + for (int out_k_1 = 0; out_k_1 < TOP_K_MAX; out_k_1++) { + float best_d_1 = final_cand_d[0]; + int best_i_1 = final_cand_i[0]; + int best_group = 0; + #pragma unroll + for (int group_idx_2 = 1; group_idx_2 < GROUP_COUNT; group_idx_2++) { + if (best_d_1 > final_cand_d[group_idx_2]) { + best_d_1 = final_cand_d[group_idx_2]; + best_i_1 = final_cand_i[group_idx_2]; + best_group = group_idx_2; + } + } + *((float*)(out_dists + (base_row + out_k_1))) = best_d_1; + *((int*)(out_indices + (base_row + out_k_1))) = best_i_1; + final_pos[best_group] = final_pos[best_group] + 1; + if (out_k_1 + 1 < TOP_K_MAX) { + int next_pos_1 = final_pos[best_group]; + int next_addr_1 = best_group * TOP_K_MAX + next_pos_1; + final_cand_d[best_group] = group_dists[next_addr_1]; + final_cand_i[best_group] = group_indices[next_addr_1]; + } + } + } + __syncthreads(); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0084.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0084.cu new file mode 100644 index 00000000..cc258e9f --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0084.cu @@ -0,0 +1,641 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 64 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 16384 +#define SMEM_SMEM_QUERY_STRIDE 16384 +#define SMEM_SMEM_DATABASE_OFF 17408 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_LOCAL_D_OFF 34048 +#define SMEM_SMEM_LOCAL_D_STAGE_BYTES 10240 +#define SMEM_SMEM_LOCAL_D_STRIDE 10240 +#define SMEM_SMEM_LOCAL_I_OFF 44288 +#define SMEM_SMEM_LOCAL_I_STAGE_BYTES 10240 +#define SMEM_SMEM_LOCAL_I_STRIDE 10240 +#define SMEM_TOTAL 54528 +#define THREADS 192 +#define BLOCK_Q 64 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_MAX 10 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "tcgen05.mma.cta_group::1.kind::f16 [%0], %1, %2, %3, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, %3, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit(int mbar_addr) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];\n\t" + "}\n" + :: "r"(mbar_addr)); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x32(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x32.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15," + " %16, %17, %18, %19, %20, %21, %22, %23," + " %24, %25, %26, %27, %28, %29, %30, %31}, [%32];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]), + "=f"(dst[16]), "=f"(dst[17]), "=f"(dst[18]), "=f"(dst[19]), + "=f"(dst[20]), "=f"(dst[21]), "=f"(dst[22]), "=f"(dst[23]), + "=f"(dst[24]), "=f"(dst[25]), "=f"(dst[26]), "=f"(dst[27]), + "=f"(dst[28]), "=f"(dst[29]), "=f"(dst[30]), "=f"(dst[31]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ float max_noftz(float a, float b) { + float c; + asm("max.f32 %0, %1, %2;" : "=f"(c) : "f"(a), "f"(b)); + return c; +} + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cta.global" + ".mbarrier::complete_tx::bytes" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit(int mbar_addr) { + asm volatile( + "tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ uint32_t make_warp_uniform(uint32_t val) { + uint32_t result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1f, 0xffffffff;" + : "=r"(result) : "r"(val)); + return result; +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_rag_stream_k10_q128_1bed_rowld_stage1(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tiles, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 17408); + const int smem_database_addr = smem + 17408; + float* smem_local_d = reinterpret_cast(smem_raw + 34048); + const int smem_local_d_addr = smem + 34048; + int* smem_local_i = reinterpret_cast(smem_raw + 44288); + const int smem_local_i_addr = smem + 44288; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 0, 1, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 16, 1, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=4 + mbarrier_init_pred(smem + 40, 4, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (64 columns, 64 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::1.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(64) : "memory"); + } + + __syncthreads(); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: compute ---- + if (warp <= 3) { + { // compute_main + int warp_id_in_role = (warp - 0); + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx = bid; work_idx < total_work; work_idx += num_bids) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tiles; + int q_tile = query_work % num_q_tiles; + int off_q = q_tile * BLOCK_Q; + int tmem_row_origin = warp_id_in_role * 32; + int logical_row_origin = warp_id_in_role * 16; + int row_top = logical_row_origin + lane / 4; + int row_bot = row_top + 8; + int lane_col = lane % 4; + int slot = lane_col; + int q_top = off_q + row_top; + int q_bot = off_q + row_bot; + int valid_top = ((q_top < Q) ? 1 : 0); + int valid_bot = ((q_bot < Q) ? 1 : 0); + float q_sq_top = 0.0f; + float q_sq_bot = 0.0f; + if (valid_top != 0) { + q_sq_top = query_sq[batch_idx * Q + q_top]; + } + if (valid_bot != 0) { + q_sq_bot = query_sq[batch_idx * Q + q_bot]; + } + float best_top_d[TOP_K_MAX]; + float best_bot_d[TOP_K_MAX]; + int best_top_i[TOP_K_MAX]; + int best_bot_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_top_d[kk] = 3.4e+38f; + best_bot_d[kk] = 3.4e+38f; + best_top_i[kk] = -1; + best_bot_i[kk] = -1; + } + int db_tile_start = split_idx * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int db_start = db_tile * BLOCK_M; + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(tmem_row_origin << 16); + float _tmem_load_0[32]; + asm volatile( + "tcgen05.ld.sync.aligned.16x256b.x8.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31}, [%32];" + : "=r"(*reinterpret_cast(&_tmem_load_0[0])), "=r"(*reinterpret_cast(&_tmem_load_0[1])), "=r"(*reinterpret_cast(&_tmem_load_0[2])), "=r"(*reinterpret_cast(&_tmem_load_0[3])), "=r"(*reinterpret_cast(&_tmem_load_0[4])), "=r"(*reinterpret_cast(&_tmem_load_0[5])), "=r"(*reinterpret_cast(&_tmem_load_0[6])), "=r"(*reinterpret_cast(&_tmem_load_0[7])), "=r"(*reinterpret_cast(&_tmem_load_0[8])), "=r"(*reinterpret_cast(&_tmem_load_0[9])), "=r"(*reinterpret_cast(&_tmem_load_0[10])), "=r"(*reinterpret_cast(&_tmem_load_0[11])), "=r"(*reinterpret_cast(&_tmem_load_0[12])), "=r"(*reinterpret_cast(&_tmem_load_0[13])), "=r"(*reinterpret_cast(&_tmem_load_0[14])), "=r"(*reinterpret_cast(&_tmem_load_0[15])), "=r"(*reinterpret_cast(&_tmem_load_0[16])), "=r"(*reinterpret_cast(&_tmem_load_0[17])), "=r"(*reinterpret_cast(&_tmem_load_0[18])), "=r"(*reinterpret_cast(&_tmem_load_0[19])), "=r"(*reinterpret_cast(&_tmem_load_0[20])), "=r"(*reinterpret_cast(&_tmem_load_0[21])), "=r"(*reinterpret_cast(&_tmem_load_0[22])), "=r"(*reinterpret_cast(&_tmem_load_0[23])), "=r"(*reinterpret_cast(&_tmem_load_0[24])), "=r"(*reinterpret_cast(&_tmem_load_0[25])), "=r"(*reinterpret_cast(&_tmem_load_0[26])), "=r"(*reinterpret_cast(&_tmem_load_0[27])), "=r"(*reinterpret_cast(&_tmem_load_0[28])), "=r"(*reinterpret_cast(&_tmem_load_0[29])), "=r"(*reinterpret_cast(&_tmem_load_0[30])), "=r"(*reinterpret_cast(&_tmem_load_0[31])) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + if (elect_sync()) { + mbarrier_arrive(score_empty_addr); + } + #pragma unroll + for (int repeat = 0; repeat < 8; repeat++) { + const int reg_base = repeat * 4; + int col_base = repeat * 8 + lane_col * 2; + int db_idx0 = db_start + col_base; + int db_idx1 = db_idx0 + 1; + float top_d0 = 3.4e+38f; + float top_d1 = 3.4e+38f; + if (valid_top != 0 && db_idx0 < M) { + float _max_0 = max_noftz(q_sq_top + database_sq[batch_idx * M + db_idx0] - 2.0f * _tmem_load_0[reg_base], 0.0f); + top_d0 = _max_0; + } + if (valid_top != 0 && db_idx1 < M) { + float _max_1 = max_noftz(q_sq_top + database_sq[batch_idx * M + db_idx1] - 2.0f * _tmem_load_0[reg_base + 1], 0.0f); + top_d1 = _max_1; + } + int top_take1 = ((top_d1 < top_d0) ? 1 : 0); + if (best_top_d[9] > ((top_take1 != 0) ? top_d1 : top_d0)) { + best_top_d[9] = ((top_take1 != 0) ? top_d1 : top_d0); + best_top_i[9] = ((top_take1 != 0) ? db_idx1 : db_idx0); + #pragma unroll + for (int kk_1 = 8; kk_1 >= 0; kk_1--) { + float lower0_d = best_top_d[kk_1 + 1]; + int lower0_i = best_top_i[kk_1 + 1]; + float upper0_d = best_top_d[kk_1]; + int upper0_i = best_top_i[kk_1]; + int swap0_up = ((lower0_d < upper0_d) ? 1 : 0); + best_top_d[kk_1] = ((swap0_up != 0) ? lower0_d : upper0_d); + best_top_i[kk_1] = ((swap0_up != 0) ? lower0_i : upper0_i); + best_top_d[kk_1 + 1] = ((swap0_up != 0) ? upper0_d : lower0_d); + best_top_i[kk_1 + 1] = ((swap0_up != 0) ? upper0_i : lower0_i); + } + if (best_top_d[9] > ((top_take1 != 0) ? top_d0 : top_d1)) { + best_top_d[9] = ((top_take1 != 0) ? top_d0 : top_d1); + best_top_i[9] = ((top_take1 != 0) ? db_idx0 : db_idx1); + #pragma unroll + for (int kk_2 = 8; kk_2 >= 0; kk_2--) { + float lower1_d = best_top_d[kk_2 + 1]; + int lower1_i = best_top_i[kk_2 + 1]; + float upper1_d = best_top_d[kk_2]; + int upper1_i = best_top_i[kk_2]; + int swap1_up = ((lower1_d < upper1_d) ? 1 : 0); + best_top_d[kk_2] = ((swap1_up != 0) ? lower1_d : upper1_d); + best_top_i[kk_2] = ((swap1_up != 0) ? lower1_i : upper1_i); + best_top_d[kk_2 + 1] = ((swap1_up != 0) ? upper1_d : lower1_d); + best_top_i[kk_2 + 1] = ((swap1_up != 0) ? upper1_i : lower1_i); + } + } + } + float bot_d0 = 3.4e+38f; + float bot_d1 = 3.4e+38f; + if (valid_bot != 0 && db_idx0 < M) { + float _max_2 = max_noftz(q_sq_bot + database_sq[batch_idx * M + db_idx0] - 2.0f * _tmem_load_0[reg_base + 2], 0.0f); + bot_d0 = _max_2; + } + if (valid_bot != 0 && db_idx1 < M) { + float _max_3 = max_noftz(q_sq_bot + database_sq[batch_idx * M + db_idx1] - 2.0f * _tmem_load_0[reg_base + 3], 0.0f); + bot_d1 = _max_3; + } + int bot_take1 = ((bot_d1 < bot_d0) ? 1 : 0); + if (best_bot_d[9] > ((bot_take1 != 0) ? bot_d1 : bot_d0)) { + best_bot_d[9] = ((bot_take1 != 0) ? bot_d1 : bot_d0); + best_bot_i[9] = ((bot_take1 != 0) ? db_idx1 : db_idx0); + #pragma unroll + for (int kk_3 = 8; kk_3 >= 0; kk_3--) { + float lower0_d_1 = best_bot_d[kk_3 + 1]; + int lower0_i_1 = best_bot_i[kk_3 + 1]; + float upper0_d_1 = best_bot_d[kk_3]; + int upper0_i_1 = best_bot_i[kk_3]; + int swap0_up_1 = ((lower0_d_1 < upper0_d_1) ? 1 : 0); + best_bot_d[kk_3] = ((swap0_up_1 != 0) ? lower0_d_1 : upper0_d_1); + best_bot_i[kk_3] = ((swap0_up_1 != 0) ? lower0_i_1 : upper0_i_1); + best_bot_d[kk_3 + 1] = ((swap0_up_1 != 0) ? upper0_d_1 : lower0_d_1); + best_bot_i[kk_3 + 1] = ((swap0_up_1 != 0) ? upper0_i_1 : lower0_i_1); + } + if (best_bot_d[9] > ((bot_take1 != 0) ? bot_d0 : bot_d1)) { + best_bot_d[9] = ((bot_take1 != 0) ? bot_d0 : bot_d1); + best_bot_i[9] = ((bot_take1 != 0) ? db_idx0 : db_idx1); + #pragma unroll + for (int kk_4 = 8; kk_4 >= 0; kk_4--) { + float lower1_d_1 = best_bot_d[kk_4 + 1]; + int lower1_i_1 = best_bot_i[kk_4 + 1]; + float upper1_d_1 = best_bot_d[kk_4]; + int upper1_i_1 = best_bot_i[kk_4]; + int swap1_up_1 = ((lower1_d_1 < upper1_d_1) ? 1 : 0); + best_bot_d[kk_4] = ((swap1_up_1 != 0) ? lower1_d_1 : upper1_d_1); + best_bot_i[kk_4] = ((swap1_up_1 != 0) ? lower1_i_1 : upper1_i_1); + best_bot_d[kk_4 + 1] = ((swap1_up_1 != 0) ? upper1_d_1 : lower1_d_1); + best_bot_i[kk_4 + 1] = ((swap1_up_1 != 0) ? upper1_i_1 : lower1_i_1); + } + } + } + } + } + int top_slot_base = (row_top * 4 + slot) * TOP_K_MAX; + int bot_slot_base = (row_bot * 4 + slot) * TOP_K_MAX; + #pragma unroll + for (int kk_5 = 0; kk_5 < TOP_K_MAX; kk_5++) { + smem_local_d[top_slot_base + kk_5] = best_top_d[kk_5]; + smem_local_i[top_slot_base + kk_5] = best_top_i[kk_5]; + smem_local_d[bot_slot_base + kk_5] = best_bot_d[kk_5]; + smem_local_i[bot_slot_base + kk_5] = best_bot_i[kk_5]; + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + if (tid < BLOCK_Q) { + int row = tid; + int q_idx = off_q + row; + float head_d[4]; + int head_i[4]; + int head_k[4]; + #pragma unroll + for (int slot_idx = 0; slot_idx < 4; slot_idx++) { + int local_base = (row * 4 + slot_idx) * TOP_K_MAX; + head_k[slot_idx] = 0; + head_d[slot_idx] = smem_local_d[local_base]; + head_i[slot_idx] = smem_local_i[local_base]; + } + int out_base = ((split_idx * B + batch_idx) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float winner_d = head_d[0]; + int winner_i = head_i[0]; + int winner_slot = 0; + #pragma unroll + for (int slot_idx_1 = 1; slot_idx_1 < 4; slot_idx_1++) { + float cand_d = head_d[slot_idx_1]; + int take = ((cand_d < winner_d) ? 1 : 0); + winner_d = ((take != 0) ? cand_d : winner_d); + winner_i = ((take != 0) ? head_i[slot_idx_1] : winner_i); + winner_slot = ((take != 0) ? slot_idx_1 : winner_slot); + } + if (q_idx < Q && out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = winner_d; + *((int*)(partial_indices + (out_base + out_k))) = winner_i; + } + #pragma unroll + for (int slot_idx_2 = 0; slot_idx_2 < 4; slot_idx_2++) { + if (winner_slot == slot_idx_2) { + int next_head = head_k[slot_idx_2] + 1; + head_k[slot_idx_2] = next_head; + head_d[slot_idx_2] = 3.4e+38f; + head_i[slot_idx_2] = -1; + if (next_head < TOP_K_MAX) { + int local_base_1 = (row * 4 + slot_idx_2) * TOP_K_MAX; + head_d[slot_idx_2] = smem_local_d[local_base_1 + next_head]; + head_i[slot_idx_2] = smem_local_i[local_base_1 + next_head]; + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + } + // ---- Role: load ---- + } else if (warp == 4) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 4) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx_1 = bid; work_idx_1 < total_work; work_idx_1 += num_bids) { + int split_idx_1 = work_idx_1 % (unsigned int)split_count; + int query_work_1 = work_idx_1 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tiles; + int q_tile_1 = query_work_1 % num_q_tiles; + int off_q_1 = q_tile_1 * BLOCK_Q; + int global_q = batch_idx_1 * Q + off_q_1; + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + mbarrier_arrive_expect_tx(query_full_addr, 16384); + tma_3d_gmem2smem(smem_query_addr, tmap_query, 0, global_q, 0, query_full_addr); + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int off_m = db_tile_1 * BLOCK_M; + int global_m = batch_idx_1 * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + mbarrier_arrive_expect_tx(database_full_addr, 16384); + tma_3d_gmem2smem(smem_database_addr, tmap_database, 0, global_m, 0, database_full_addr); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 5) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = bid; work_idx_2 < total_work; work_idx_2 += num_bids) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = make_warp_uniform((smem_query_addr >> 4) & 0x3FFF); + int _mma_b_lo_0 = make_warp_uniform((smem_database_addr >> 4) & 0x3FFF); + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 68158608;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 506;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit(score_full_addr); + elect_commit(database_empty_addr); + } + elect_commit(query_empty_addr); + } + } + } + + // Cleanup + __syncthreads(); // barrier before TMEM dealloc + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::1.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(64)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::1.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0085.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0085.cu new file mode 100644 index 00000000..82cb0088 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0085.cu @@ -0,0 +1,129 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 128 +#define TOP_K_MAX 10 +#define SPLIT_COUNT 74 + +#include + +extern "C" { + +__global__ __launch_bounds__(128, 1) void +kernel_knn_build_d128_rag_q128_k10_s74_warp_merge_rowld_s74_1bed_v1(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int row = bid * 4 + warp; + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int lane_idx = lane; + if (row < total_queries) { + int split0 = lane_idx; + int base0 = base_row + split0 * split_stride; + int pos0 = 0; + float d0 = partial_dists[base0]; + int i0 = partial_indices[base0]; + int split1 = lane_idx + 32; + int base1 = base_row + split1 * split_stride; + int pos1 = 0; + float d1 = 3.4e+38f; + int i1 = -1; + if (split1 < SPLIT_COUNT) { + d1 = partial_dists[base1]; + i1 = partial_indices[base1]; + } + int split2 = lane_idx + 64; + int base2 = base_row + split2 * split_stride; + int pos2 = 0; + float d2 = 3.4e+38f; + int i2 = -1; + if (split2 < SPLIT_COUNT) { + d2 = partial_dists[base2]; + i2 = partial_indices[base2]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float winner_d = d0; + int winner_i = i0; + int winner_src = 0; + if (d1 < winner_d) { + winner_d = d1; + winner_i = i1; + winner_src = 1; + } + if (d2 < winner_d) { + winner_d = d2; + winner_i = i2; + winner_src = 2; + } + float warp_min = winner_d; + float _warp_reduce_0 = warp_min; + #pragma unroll + for (int offset = 16; offset > 0; offset >>= 1) + _warp_reduce_0 = fminf(_warp_reduce_0, __shfl_xor_sync(0xFFFFFFFF, _warp_reduce_0, offset)); + warp_min = _warp_reduce_0; + unsigned int _vote_0 = __ballot_sync(0xFFFFFFFF, winner_d == warp_min); + int owner_ballot = _vote_0; + int _ffs_0 = __ffs(owner_ballot); + int winner_lane = _ffs_0 - 1; + int _shfl_0 = __shfl_sync(0xFFFFFFFF, winner_i, winner_lane); + winner_i = _shfl_0; + int _shfl_1 = __shfl_sync(0xFFFFFFFF, winner_src, winner_lane); + winner_src = _shfl_1; + if (lane == 0) { + *((float*)(out_dists + (base_row + out_k))) = warp_min; + *((int*)(out_indices + (base_row + out_k))) = winner_i; + } + if (lane == winner_lane) { + if (winner_src == 0) { + pos0 = pos0 + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_addr0 = base0 + pos0; + d0 = partial_dists[next_addr0]; + i0 = partial_indices[next_addr0]; + } + } else if (winner_src == 1) { + pos1 = pos1 + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_addr1 = base1 + pos1; + d1 = partial_dists[next_addr1]; + i1 = partial_indices[next_addr1]; + } + } else { + pos2 = pos2 + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_addr2 = base2 + pos2; + d2 = partial_dists[next_addr2]; + i2 = partial_indices[next_addr2]; + } + } + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0086.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0086.cu new file mode 100644 index 00000000..2b1c2718 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0086.cu @@ -0,0 +1,94 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 128 +#define TOP_K_MAX 20 + +#include + +extern "C" { + +__global__ __launch_bounds__(128, 1) void +kernel_knn_build_k20_large_lowfanout_s2_warp_select(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int row = bid * 4 + warp; + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int cand_k = lane; + if (row < total_queries) { + float d0 = 3.4e+38f; + float d1 = 3.4e+38f; + int i0 = -1; + int i1 = -1; + if (cand_k < TOP_K_MAX) { + d0 = partial_dists[base_row + cand_k]; + i0 = partial_indices[base_row + cand_k]; + int base1 = base_row + split_stride; + d1 = partial_dists[base1 + cand_k]; + i1 = partial_indices[base1 + cand_k]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float winner_d = d0; + int winner_i = i0; + int winner_src = 0; + if (d1 < winner_d) { + winner_d = d1; + winner_i = i1; + winner_src = 1; + } + float warp_min = winner_d; + float _warp_reduce_0 = warp_min; + #pragma unroll + for (int offset = 16; offset > 0; offset >>= 1) + _warp_reduce_0 = fminf(_warp_reduce_0, __shfl_xor_sync(0xFFFFFFFF, _warp_reduce_0, offset)); + warp_min = _warp_reduce_0; + unsigned int _vote_0 = __ballot_sync(0xFFFFFFFF, winner_d == warp_min); + int owner_ballot = _vote_0; + int _ffs_0 = __ffs(owner_ballot); + int winner_lane = _ffs_0 - 1; + int _shfl_0 = __shfl_sync(0xFFFFFFFF, winner_i, winner_lane); + winner_i = _shfl_0; + int _shfl_1 = __shfl_sync(0xFFFFFFFF, winner_src, winner_lane); + winner_src = _shfl_1; + if (lane == 0) { + *((float*)(out_dists + (base_row + out_k))) = warp_min; + *((int*)(out_indices + (base_row + out_k))) = winner_i; + } + if (lane == winner_lane) { + if (winner_src == 0) { + d0 = 3.4e+38f; + } else { + d1 = 3.4e+38f; + } + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0087.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0087.cu new file mode 100644 index 00000000..36a3ac7d --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0087.cu @@ -0,0 +1,717 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 64 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_STRIDE 32768 +#define SMEM_SMEM_DATABASE_OFF 33792 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_DATABASE_SQ_OFF 50176 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 50432 +#define THREADS 192 +#define BLOCK_Q 128 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_MAX 10 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "tcgen05.mma.cta_group::1.kind::f16 [%0], %1, %2, %3, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, %3, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit(int mbar_addr) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];\n\t" + "}\n" + :: "r"(mbar_addr)); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x16(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x16.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15}, [%16];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cta.global" + ".mbarrier::complete_tx::bytes" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit(int mbar_addr) { + asm volatile( + "tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ uint32_t make_warp_uniform(uint32_t val) { + uint32_t result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1f, 0xffffffff;" + : "=r"(result) : "r"(val)); + return result; +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_rag_microbatch_4a72_v2_stage1_k10_cta1_maxtree(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tiles, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 33792); + const int smem_database_addr = smem + 33792; + float* smem_database_sq = reinterpret_cast(smem_raw + 50176); + const int smem_database_sq_addr = smem + 50176; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 0, 1, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 16, 1, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=128 + mbarrier_init_pred(smem + 40, 128, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (64 columns, 64 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::1.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(64) : "memory"); + } + + __syncthreads(); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: load ---- + if (warp == 0) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 0) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx = bid; work_idx < total_work; work_idx += num_bids) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tiles; + int q_tile = query_work % num_q_tiles; + int off_q = q_tile * BLOCK_Q; + int global_q = batch_idx * Q + off_q; + int db_tile_start = split_idx * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + mbarrier_arrive_expect_tx(query_full_addr, 32768); + tma_3d_gmem2smem(smem_query_addr, tmap_query, 0, global_q, 0, query_full_addr); + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int off_m = db_tile * BLOCK_M; + int global_m = batch_idx * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + mbarrier_arrive_expect_tx(database_full_addr, 16384); + tma_3d_gmem2smem(smem_database_addr, tmap_database, 0, global_m, 0, database_full_addr); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 1) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_1 = bid; work_idx_1 < total_work; work_idx_1 += num_bids) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = make_warp_uniform((smem_query_addr >> 4) & 0x3FFF); + int _mma_b_lo_0 = make_warp_uniform((smem_database_addr >> 4) & 0x3FFF); + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 135267472;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit(score_full_addr); + elect_commit(database_empty_addr); + } + elect_commit(query_empty_addr); + } + } + // ---- Role: compute ---- + } else if (warp >= 2 && warp <= 5) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = bid; work_idx_2 < total_work; work_idx_2 += num_bids) { + int split_idx_1 = work_idx_2 % (unsigned int)split_count; + int query_work_1 = work_idx_2 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tiles; + int q_tile_1 = query_work_1 % num_q_tiles; + int off_q_1 = q_tile_1 * BLOCK_Q; + int q_idx = off_q_1 + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx_1 * Q + q_idx]; + } + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + float worst_d = 3.4e+38f; + int worst_pos = 0; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int db_start = db_tile_1 * BLOCK_M; + int db_sq_idx = db_start + (warp % 4 * 32 + lane); + if (warp % 4 * 32 + lane < BLOCK_M) { + if (db_sq_idx < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx_1 * M + db_sq_idx]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 0.0f; + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(warp % 4 * 32 << 16); + float _tmem_load_0[64]; + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x64.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31, %32, %33, %34, %35, %36, %37, %38, %39, %40, %41, %42, %43, %44, %45, %46, %47, %48, %49, %50, %51, %52, %53, %54, %55, %56, %57, %58, %59, %60, %61, %62, %63}, [%64];" + : "=f"(_tmem_load_0[0]), "=f"(_tmem_load_0[1]), "=f"(_tmem_load_0[2]), "=f"(_tmem_load_0[3]), "=f"(_tmem_load_0[4]), "=f"(_tmem_load_0[5]), "=f"(_tmem_load_0[6]), "=f"(_tmem_load_0[7]), "=f"(_tmem_load_0[8]), "=f"(_tmem_load_0[9]), "=f"(_tmem_load_0[10]), "=f"(_tmem_load_0[11]), "=f"(_tmem_load_0[12]), "=f"(_tmem_load_0[13]), "=f"(_tmem_load_0[14]), "=f"(_tmem_load_0[15]), "=f"(_tmem_load_0[16]), "=f"(_tmem_load_0[17]), "=f"(_tmem_load_0[18]), "=f"(_tmem_load_0[19]), "=f"(_tmem_load_0[20]), "=f"(_tmem_load_0[21]), "=f"(_tmem_load_0[22]), "=f"(_tmem_load_0[23]), "=f"(_tmem_load_0[24]), "=f"(_tmem_load_0[25]), "=f"(_tmem_load_0[26]), "=f"(_tmem_load_0[27]), "=f"(_tmem_load_0[28]), "=f"(_tmem_load_0[29]), "=f"(_tmem_load_0[30]), "=f"(_tmem_load_0[31]), "=f"(_tmem_load_0[32]), "=f"(_tmem_load_0[33]), "=f"(_tmem_load_0[34]), "=f"(_tmem_load_0[35]), "=f"(_tmem_load_0[36]), "=f"(_tmem_load_0[37]), "=f"(_tmem_load_0[38]), "=f"(_tmem_load_0[39]), "=f"(_tmem_load_0[40]), "=f"(_tmem_load_0[41]), "=f"(_tmem_load_0[42]), "=f"(_tmem_load_0[43]), "=f"(_tmem_load_0[44]), "=f"(_tmem_load_0[45]), "=f"(_tmem_load_0[46]), "=f"(_tmem_load_0[47]), "=f"(_tmem_load_0[48]), "=f"(_tmem_load_0[49]), "=f"(_tmem_load_0[50]), "=f"(_tmem_load_0[51]), "=f"(_tmem_load_0[52]), "=f"(_tmem_load_0[53]), "=f"(_tmem_load_0[54]), "=f"(_tmem_load_0[55]), "=f"(_tmem_load_0[56]), "=f"(_tmem_load_0[57]), "=f"(_tmem_load_0[58]), "=f"(_tmem_load_0[59]), "=f"(_tmem_load_0[60]), "=f"(_tmem_load_0[61]), "=f"(_tmem_load_0[62]), "=f"(_tmem_load_0[63]) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + mbarrier_arrive(score_empty_addr); + if (valid_q != 0) { + #pragma unroll 1 + for (int col_base = 0; col_base < 64; col_base += 8) { + float dist_vec0[4]; + dist_vec0[0] = _tmem_load_0[col_base]; + dist_vec0[1] = _tmem_load_0[col_base + 1]; + dist_vec0[2] = _tmem_load_0[col_base + 2]; + dist_vec0[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec0)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec0[4]; + db_sq_vec0[0] = smem_database_sq[col_base]; + db_sq_vec0[1] = smem_database_sq[col_base + 1]; + db_sq_vec0[2] = smem_database_sq[col_base + 2]; + db_sq_vec0[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec0)[_la], reinterpret_cast(db_sq_vec0)[_la]); + float dist_vec1[4]; + dist_vec1[0] = _tmem_load_0[col_base + 4]; + dist_vec1[1] = _tmem_load_0[col_base + 5]; + dist_vec1[2] = _tmem_load_0[col_base + 6]; + dist_vec1[3] = _tmem_load_0[col_base + 7]; + const float2 _fma_b2_2 = {-2.0f, -2.0f}; + const float2 _fma_c2_3 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec1)[_lf], _fma_b2_2, _fma_c2_3); + float db_sq_vec1[4]; + db_sq_vec1[0] = smem_database_sq[col_base + 4]; + db_sq_vec1[1] = smem_database_sq[col_base + 5]; + db_sq_vec1[2] = smem_database_sq[col_base + 6]; + db_sq_vec1[3] = smem_database_sq[col_base + 7]; + float _t1[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t1)[_la] = add_f32x2(reinterpret_cast(dist_vec1)[_la], reinterpret_cast(db_sq_vec1)[_la]); + float _t0_min = _t0[0]; + #pragma unroll + for (int _lr = 1; _lr < 4; _lr++) { + _t0_min = fminf(_t0_min, _t0[_lr]); + } + float group_min0 = _t0_min; + float _t1_min = _t1[0]; + #pragma unroll + for (int _lr = 1; _lr < 4; _lr++) { + _t1_min = fminf(_t1_min, _t1[_lr]); + } + float group_min1 = _t1_min; + if (group_min0 < worst_d) { + #pragma unroll + for (int vec_col = 0; vec_col < 4; vec_col++) { + int db_idx = db_start + col_base + vec_col; + if (db_idx < M) { + float dist = _t0[vec_col]; + if (dist < worst_d) { + best_d[worst_pos] = dist; + best_i[worst_pos] = db_idx; + int cmp01 = ((best_d[1] > best_d[0]) ? 1 : 0); + float max01_d = ((cmp01 != 0) ? best_d[1] : best_d[0]); + int max01_p = ((cmp01 != 0) ? 1 : 0); + int cmp23 = ((best_d[3] > best_d[2]) ? 1 : 0); + float max23_d = ((cmp23 != 0) ? best_d[3] : best_d[2]); + int max23_p = ((cmp23 != 0) ? 3 : 2); + int cmp45 = ((best_d[5] > best_d[4]) ? 1 : 0); + float max45_d = ((cmp45 != 0) ? best_d[5] : best_d[4]); + int max45_p = ((cmp45 != 0) ? 5 : 4); + int cmp67 = ((best_d[7] > best_d[6]) ? 1 : 0); + float max67_d = ((cmp67 != 0) ? best_d[7] : best_d[6]); + int max67_p = ((cmp67 != 0) ? 7 : 6); + int cmp89 = ((best_d[9] > best_d[8]) ? 1 : 0); + float max89_d = ((cmp89 != 0) ? best_d[9] : best_d[8]); + int max89_p = ((cmp89 != 0) ? 9 : 8); + int cmp0123 = ((max23_d > max01_d) ? 1 : 0); + float max0123_d = ((cmp0123 != 0) ? max23_d : max01_d); + int max0123_p = ((cmp0123 != 0) ? max23_p : max01_p); + int cmp4567 = ((max67_d > max45_d) ? 1 : 0); + float max4567_d = ((cmp4567 != 0) ? max67_d : max45_d); + int max4567_p = ((cmp4567 != 0) ? max67_p : max45_p); + int cmp0_7 = ((max4567_d > max0123_d) ? 1 : 0); + float max0_7_d = ((cmp0_7 != 0) ? max4567_d : max0123_d); + int max0_7_p = ((cmp0_7 != 0) ? max4567_p : max0123_p); + int cmp_all = ((max89_d > max0_7_d) ? 1 : 0); + worst_d = ((cmp_all != 0) ? max89_d : max0_7_d); + worst_pos = ((cmp_all != 0) ? max89_p : max0_7_p); + } + } + } + } + if (group_min1 < worst_d) { + #pragma unroll + for (int vec_col_1 = 0; vec_col_1 < 4; vec_col_1++) { + int db_idx_1 = db_start + col_base + 4 + vec_col_1; + if (db_idx_1 < M) { + float dist_1 = _t1[vec_col_1]; + if (dist_1 < worst_d) { + best_d[worst_pos] = dist_1; + best_i[worst_pos] = db_idx_1; + int cmp01_1 = ((best_d[1] > best_d[0]) ? 1 : 0); + float max01_d_1 = ((cmp01_1 != 0) ? best_d[1] : best_d[0]); + int max01_p_1 = ((cmp01_1 != 0) ? 1 : 0); + int cmp23_1 = ((best_d[3] > best_d[2]) ? 1 : 0); + float max23_d_1 = ((cmp23_1 != 0) ? best_d[3] : best_d[2]); + int max23_p_1 = ((cmp23_1 != 0) ? 3 : 2); + int cmp45_1 = ((best_d[5] > best_d[4]) ? 1 : 0); + float max45_d_1 = ((cmp45_1 != 0) ? best_d[5] : best_d[4]); + int max45_p_1 = ((cmp45_1 != 0) ? 5 : 4); + int cmp67_1 = ((best_d[7] > best_d[6]) ? 1 : 0); + float max67_d_1 = ((cmp67_1 != 0) ? best_d[7] : best_d[6]); + int max67_p_1 = ((cmp67_1 != 0) ? 7 : 6); + int cmp89_1 = ((best_d[9] > best_d[8]) ? 1 : 0); + float max89_d_1 = ((cmp89_1 != 0) ? best_d[9] : best_d[8]); + int max89_p_1 = ((cmp89_1 != 0) ? 9 : 8); + int cmp0123_1 = ((max23_d_1 > max01_d_1) ? 1 : 0); + float max0123_d_1 = ((cmp0123_1 != 0) ? max23_d_1 : max01_d_1); + int max0123_p_1 = ((cmp0123_1 != 0) ? max23_p_1 : max01_p_1); + int cmp4567_1 = ((max67_d_1 > max45_d_1) ? 1 : 0); + float max4567_d_1 = ((cmp4567_1 != 0) ? max67_d_1 : max45_d_1); + int max4567_p_1 = ((cmp4567_1 != 0) ? max67_p_1 : max45_p_1); + int cmp0_7_1 = ((max4567_d_1 > max0123_d_1) ? 1 : 0); + float max0_7_d_1 = ((cmp0_7_1 != 0) ? max4567_d_1 : max0123_d_1); + int max0_7_p_1 = ((cmp0_7_1 != 0) ? max4567_p_1 : max0123_p_1); + int cmp_all_1 = ((max89_d_1 > max0_7_d_1) ? 1 : 0); + worst_d = ((cmp_all_1 != 0) ? max89_d_1 : max0_7_d_1); + worst_pos = ((cmp_all_1 != 0) ? max89_p_1 : max0_7_p_1); + } + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + if (valid_q != 0) { + int out_base = ((split_idx_1 * B + batch_idx_1) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + int cmp01_min = ((best_d[1] < best_d[0]) ? 1 : 0); + float min01_d = ((cmp01_min != 0) ? best_d[1] : best_d[0]); + int min01_i = ((cmp01_min != 0) ? best_i[1] : best_i[0]); + int min01_p = ((cmp01_min != 0) ? 1 : 0); + int cmp23_min = ((best_d[3] < best_d[2]) ? 1 : 0); + float min23_d = ((cmp23_min != 0) ? best_d[3] : best_d[2]); + int min23_i = ((cmp23_min != 0) ? best_i[3] : best_i[2]); + int min23_p = ((cmp23_min != 0) ? 3 : 2); + int cmp45_min = ((best_d[5] < best_d[4]) ? 1 : 0); + float min45_d = ((cmp45_min != 0) ? best_d[5] : best_d[4]); + int min45_i = ((cmp45_min != 0) ? best_i[5] : best_i[4]); + int min45_p = ((cmp45_min != 0) ? 5 : 4); + int cmp67_min = ((best_d[7] < best_d[6]) ? 1 : 0); + float min67_d = ((cmp67_min != 0) ? best_d[7] : best_d[6]); + int min67_i = ((cmp67_min != 0) ? best_i[7] : best_i[6]); + int min67_p = ((cmp67_min != 0) ? 7 : 6); + int cmp89_min = ((best_d[9] < best_d[8]) ? 1 : 0); + float min89_d = ((cmp89_min != 0) ? best_d[9] : best_d[8]); + int min89_i = ((cmp89_min != 0) ? best_i[9] : best_i[8]); + int min89_p = ((cmp89_min != 0) ? 9 : 8); + int cmp0123_min = ((min23_d < min01_d) ? 1 : 0); + float min0123_d = ((cmp0123_min != 0) ? min23_d : min01_d); + int min0123_i = ((cmp0123_min != 0) ? min23_i : min01_i); + int min0123_p = ((cmp0123_min != 0) ? min23_p : min01_p); + int cmp4567_min = ((min67_d < min45_d) ? 1 : 0); + float min4567_d = ((cmp4567_min != 0) ? min67_d : min45_d); + int min4567_i = ((cmp4567_min != 0) ? min67_i : min45_i); + int min4567_p = ((cmp4567_min != 0) ? min67_p : min45_p); + int cmp0_7_min = ((min4567_d < min0123_d) ? 1 : 0); + float min0_7_d = ((cmp0_7_min != 0) ? min4567_d : min0123_d); + int min0_7_i = ((cmp0_7_min != 0) ? min4567_i : min0123_i); + int min0_7_p = ((cmp0_7_min != 0) ? min4567_p : min0123_p); + int cmp_all_min = ((min89_d < min0_7_d) ? 1 : 0); + float selected_d = ((cmp_all_min != 0) ? min89_d : min0_7_d); + int selected_i = ((cmp_all_min != 0) ? min89_i : min0_7_i); + int selected_pos = ((cmp_all_min != 0) ? min89_p : min0_7_p); + if (out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = selected_d; + *((int*)(partial_indices + (out_base + out_k))) = selected_i; + } + best_d[selected_pos] = 3.4e+38f; + best_i[selected_pos] = -1; + } + } + } + } + } + + // Cleanup + __syncthreads(); // barrier before TMEM dealloc + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::1.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(64)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::1.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0088.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0088.cu new file mode 100644 index 00000000..c0f449c4 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0088.cu @@ -0,0 +1,141 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define SMEM_GROUP_DISTS_OFF 0 +#define SMEM_GROUP_DISTS_STAGE_BYTES 512 +#define SMEM_GROUP_DISTS_STRIDE 512 +#define SMEM_GROUP_INDICES_OFF 512 +#define SMEM_GROUP_INDICES_STAGE_BYTES 512 +#define SMEM_GROUP_INDICES_STRIDE 512 +#define SMEM_TOTAL 1024 +#define THREADS 32 +#define TOP_K_MAX 10 +#define GROUP_COUNT 12 +#define GROUP_SPLITS 12 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_rag_microbatch_4a72_v2_k10_fused_group_final_merge_s144g12_4a72_v2(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + float* group_dists = reinterpret_cast(smem_raw + 0); + const int group_dists_addr = smem + 0; + int* group_indices = reinterpret_cast(smem_raw + 512); + const int group_indices_addr = smem + 512; + + // === Task calls (dependency order) === + int split_pos[GROUP_SPLITS]; + int split_base[GROUP_SPLITS]; + float group_cand_d[GROUP_SPLITS]; + int group_cand_i[GROUP_SPLITS]; + int final_pos[GROUP_COUNT]; + float final_cand_d[GROUP_COUNT]; + int final_cand_i[GROUP_COUNT]; + #pragma unroll 1 + for (int row = bid; row < total_queries; row += num_bids) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + if (tid < GROUP_COUNT) { + int group_idx = tid; + int source_split0 = group_idx * GROUP_SPLITS; + int shared_base = group_idx * TOP_K_MAX; + #pragma unroll + for (int local_split = 0; local_split < GROUP_SPLITS; local_split++) { + split_pos[local_split] = 0; + int split_id = source_split0 + local_split; + split_base[local_split] = base_row + split_id * split_stride; + group_cand_d[local_split] = partial_dists[split_base[local_split]]; + group_cand_i[local_split] = partial_indices[split_base[local_split]]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_d = group_cand_d[0]; + int best_i = group_cand_i[0]; + int best_split = 0; + #pragma unroll + for (int local_split_1 = 1; local_split_1 < GROUP_SPLITS; local_split_1++) { + if (best_d > group_cand_d[local_split_1]) { + best_d = group_cand_d[local_split_1]; + best_i = group_cand_i[local_split_1]; + best_split = local_split_1; + } + } + group_dists[shared_base + out_k] = best_d; + group_indices[shared_base + out_k] = best_i; + split_pos[best_split] = split_pos[best_split] + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_pos = split_pos[best_split]; + int next_addr = split_base[best_split] + next_pos; + group_cand_d[best_split] = partial_dists[next_addr]; + group_cand_i[best_split] = partial_indices[next_addr]; + } + } + } + __syncthreads(); + if (tid == 0) { + #pragma unroll + for (int group_idx_1 = 0; group_idx_1 < GROUP_COUNT; group_idx_1++) { + final_pos[group_idx_1] = 0; + int group_base = group_idx_1 * TOP_K_MAX; + final_cand_d[group_idx_1] = group_dists[group_base]; + final_cand_i[group_idx_1] = group_indices[group_base]; + } + #pragma unroll + for (int out_k_1 = 0; out_k_1 < TOP_K_MAX; out_k_1++) { + float best_d_1 = final_cand_d[0]; + int best_i_1 = final_cand_i[0]; + int best_group = 0; + #pragma unroll + for (int group_idx_2 = 1; group_idx_2 < GROUP_COUNT; group_idx_2++) { + if (best_d_1 > final_cand_d[group_idx_2]) { + best_d_1 = final_cand_d[group_idx_2]; + best_i_1 = final_cand_i[group_idx_2]; + best_group = group_idx_2; + } + } + *((float*)(out_dists + (base_row + out_k_1))) = best_d_1; + *((int*)(out_indices + (base_row + out_k_1))) = best_i_1; + final_pos[best_group] = final_pos[best_group] + 1; + if (out_k_1 + 1 < TOP_K_MAX) { + int next_pos_1 = final_pos[best_group]; + int next_addr_1 = best_group * TOP_K_MAX + next_pos_1; + final_cand_d[best_group] = group_dists[next_addr_1]; + final_cand_i[best_group] = group_indices[next_addr_1]; + } + } + } + __syncthreads(); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0089.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0089.cu new file mode 100644 index 00000000..202701c0 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0089.cu @@ -0,0 +1,622 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 128 +#define TMEM_ACC_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 16384 +#define SMEM_SMEM_QUERY_STRIDE 16384 +#define SMEM_SMEM_DATABASE_OFF 17408 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 32768 +#define SMEM_SMEM_DATABASE_STRIDE 32768 +#define SMEM_SMEM_LOCAL_D_OFF 50176 +#define SMEM_SMEM_LOCAL_D_STAGE_BYTES 20480 +#define SMEM_SMEM_LOCAL_D_STRIDE 20480 +#define SMEM_SMEM_LOCAL_I_OFF 70656 +#define SMEM_SMEM_LOCAL_I_STAGE_BYTES 20480 +#define SMEM_SMEM_LOCAL_I_STRIDE 20480 +#define SMEM_TOTAL 91392 +#define THREADS 512 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "tcgen05.mma.cta_group::1.kind::f16 [%0], %1, %2, %3, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, %3, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit(int mbar_addr) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];\n\t" + "}\n" + :: "r"(mbar_addr)); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x32(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x32.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15," + " %16, %17, %18, %19, %20, %21, %22, %23," + " %24, %25, %26, %27, %28, %29, %30, %31}, [%32];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]), + "=f"(dst[16]), "=f"(dst[17]), "=f"(dst[18]), "=f"(dst[19]), + "=f"(dst[20]), "=f"(dst[21]), "=f"(dst[22]), "=f"(dst[23]), + "=f"(dst[24]), "=f"(dst[25]), "=f"(dst[26]), "=f"(dst[27]), + "=f"(dst[28]), "=f"(dst[29]), "=f"(dst[30]), "=f"(dst[31]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ float max_noftz(float a, float b) { + float c; + asm("max.f32 %0, %1, %2;" : "=f"(c) : "f"(a), "f"(b)); + return c; +} + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit(int mbar_addr) { + asm volatile( + "tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ uint32_t make_warp_uniform(uint32_t val) { + uint32_t result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1f, 0xffffffff;" + : "=r"(result) : "r"(val)); + return result; +} + +extern "C" { + +__global__ __launch_bounds__(512, 1) void +kernel_knn_build_rag_microbatch_m64_d4f7_stage1(__nv_bfloat16* __restrict__ query, __nv_bfloat16* __restrict__ database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tiles, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 17408); + const int smem_database_addr = smem + 17408; + float* smem_local_d = reinterpret_cast(smem_raw + 50176); + const int smem_local_d_addr = smem + 50176; + int* smem_local_i = reinterpret_cast(smem_raw + 70656); + const int smem_local_i_addr = smem + 70656; + + // Mbarrier init (1 groups, 1 barriers) + // Mbarriers at smem_raw[0..8) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // mma_done: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 0, 1, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (128 columns, 128 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 8); + if (warp == 0) { + int _tmem_hold = smem + 8; + asm volatile("tcgen05.alloc.cta_group::1.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(128) : "memory"); + } + + __syncthreads(); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define mma_done_addr (mbar_base + 0) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_acc = taddr; + + // === Task calls (dependency order) === + unsigned int _phase_mma_done_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx = bid; work_idx < total_work; work_idx += num_bids) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tiles; + int q_tile = query_work % num_q_tiles; + int off_q = q_tile * 64; + #pragma unroll 1 + for (int e_vec = tid; e_vec < 1024; e_vec += 512) { + int q_elem = e_vec * 8; + int q_row = q_elem / 128; + int d_col = q_elem - q_row * 128; + int q_idx = off_q + q_row; + float q_vals[8]; + unsigned int q_pack[4]; + #pragma unroll + for (int vi = 0; vi < 8; vi++) { + q_vals[vi] = 0.0f; + } + if (q_idx < Q) { + int q_addr = (batch_idx * Q + q_idx) * 128 + d_col; + { + const uint4* _vptr_0 = reinterpret_cast(query + (unsigned long long)q_addr + 0); + uint4 _vld_0[1]; + #pragma unroll + for (int _blk = 0; _blk < 1; _blk++) { + _vld_0[_blk] = _vptr_0[_blk]; + __nv_bfloat16* _velems_0 = reinterpret_cast<__nv_bfloat16*>(&_vld_0[_blk]); + #pragma unroll + for (int _j = 0; _j < 8; _j++) + q_vals[0 + _blk * 8 + _j] = __bfloat162float(_velems_0[_j]); + } + } + } + #pragma unroll + for (int _lp = 0; _lp < 4; _lp++) { + __nv_bfloat162 _bf2 = __float22bfloat162_rn(make_float2(q_vals[_lp*2 + 0], q_vals[_lp*2+1 + 0])); + q_pack[_lp] = *(uint32_t*)&_bf2; + } + int q_store_addr = (smem_query_addr + (unsigned int)(d_col / 64 * 8192 + q_row * 128 + d_col % 64 * 2 ^ (d_col / 64 * 8192 + q_row * 128 + d_col % 64 * 2 >> 7 & 7) << 4)); + asm volatile("st.shared.v4.b32 [%0], {%1,%2,%3,%4};" :: "r"(q_store_addr), "r"(q_pack[0]), "r"(q_pack[1]), "r"(q_pack[2]), "r"(q_pack[3]) : "memory"); + } + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); + __syncthreads(); + const int row_group = warp % 4; + const int col_block = warp / 4; + const int tmem_row_origin = row_group * 32; + const int logical_row_origin = row_group * 16; + int row_top = logical_row_origin + lane / 4; + int row_bot = row_top + 8; + const int lane_col = lane % 4; + const int slot = col_block * 4 + lane_col; + int q_top = off_q + row_top; + int q_bot = off_q + row_bot; + int valid_top = ((q_top < Q) ? 1 : 0); + int valid_bot = ((q_bot < Q) ? 1 : 0); + float q_sq_top = 0.0f; + float q_sq_bot = 0.0f; + if (valid_top != 0) { + q_sq_top = query_sq[batch_idx * Q + q_top]; + } + if (valid_bot != 0) { + q_sq_bot = query_sq[batch_idx * Q + q_bot]; + } + float best_top_d[10]; + float best_bot_d[10]; + int best_top_i[10]; + int best_bot_i[10]; + #pragma unroll + for (int kk = 0; kk < 10; kk++) { + best_top_d[kk] = 3.4e+38f; + best_bot_d[kk] = 3.4e+38f; + best_top_i[kk] = -1; + best_bot_i[kk] = -1; + } + int db_tile_start = split_idx * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int db_start = db_tile * 128; + #pragma unroll 1 + for (int e_vec_1 = tid; e_vec_1 < 2048; e_vec_1 += 512) { + int db_elem = e_vec_1 * 8; + int db_row = db_elem / 128; + int d_col_1 = db_elem - db_row * 128; + int db_idx = db_start + db_row; + float db_vals[8]; + unsigned int db_pack[4]; + #pragma unroll + for (int vi_1 = 0; vi_1 < 8; vi_1++) { + db_vals[vi_1] = 0.0f; + } + if (db_idx < M) { + int db_addr = (batch_idx * M + db_idx) * 128 + d_col_1; + { + const uint4* _vptr_1 = reinterpret_cast(database + (unsigned long long)db_addr + 0); + uint4 _vld_1[1]; + #pragma unroll + for (int _blk = 0; _blk < 1; _blk++) { + _vld_1[_blk] = _vptr_1[_blk]; + __nv_bfloat16* _velems_1 = reinterpret_cast<__nv_bfloat16*>(&_vld_1[_blk]); + #pragma unroll + for (int _j = 0; _j < 8; _j++) + db_vals[0 + _blk * 8 + _j] = __bfloat162float(_velems_1[_j]); + } + } + } + #pragma unroll + for (int _lp = 0; _lp < 4; _lp++) { + __nv_bfloat162 _bf2 = __float22bfloat162_rn(make_float2(db_vals[_lp*2 + 0], db_vals[_lp*2+1 + 0])); + db_pack[_lp] = *(uint32_t*)&_bf2; + } + int b_store_addr = (smem_database_addr + (unsigned int)(d_col_1 / 64 * 16384 + db_row * 128 + d_col_1 % 64 * 2 ^ (d_col_1 / 64 * 16384 + db_row * 128 + d_col_1 % 64 * 2 >> 7 & 7) << 4)); + asm volatile("st.shared.v4.b32 [%0], {%1,%2,%3,%4};" :: "r"(b_store_addr), "r"(db_pack[0]), "r"(db_pack[1]), "r"(db_pack[2]), "r"(db_pack[3]) : "memory"); + } + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); + __syncthreads(); + if (warp == 0) { + int _mma_a_lo_0 = make_warp_uniform((smem_query_addr >> 4) & 0x3FFF); + int _mma_b_lo_0 = make_warp_uniform((smem_database_addr >> 4) & 0x3FFF); + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 69207184;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 506;\n\t" + "add.u32 blo, blo, 1018;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_acc), "r"(0)); + elect_commit(mma_done_addr); + } + mbarrier_wait(mma_done_addr, _phase_mma_done_0); + _phase_mma_done_0 ^= 1; + if (warp < 8) { + float _tmem_load_0[32]; + asm volatile( + "tcgen05.ld.sync.aligned.16x256b.x8.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31}, [%32];" + : "=r"(*reinterpret_cast(&_tmem_load_0[0])), "=r"(*reinterpret_cast(&_tmem_load_0[1])), "=r"(*reinterpret_cast(&_tmem_load_0[2])), "=r"(*reinterpret_cast(&_tmem_load_0[3])), "=r"(*reinterpret_cast(&_tmem_load_0[4])), "=r"(*reinterpret_cast(&_tmem_load_0[5])), "=r"(*reinterpret_cast(&_tmem_load_0[6])), "=r"(*reinterpret_cast(&_tmem_load_0[7])), "=r"(*reinterpret_cast(&_tmem_load_0[8])), "=r"(*reinterpret_cast(&_tmem_load_0[9])), "=r"(*reinterpret_cast(&_tmem_load_0[10])), "=r"(*reinterpret_cast(&_tmem_load_0[11])), "=r"(*reinterpret_cast(&_tmem_load_0[12])), "=r"(*reinterpret_cast(&_tmem_load_0[13])), "=r"(*reinterpret_cast(&_tmem_load_0[14])), "=r"(*reinterpret_cast(&_tmem_load_0[15])), "=r"(*reinterpret_cast(&_tmem_load_0[16])), "=r"(*reinterpret_cast(&_tmem_load_0[17])), "=r"(*reinterpret_cast(&_tmem_load_0[18])), "=r"(*reinterpret_cast(&_tmem_load_0[19])), "=r"(*reinterpret_cast(&_tmem_load_0[20])), "=r"(*reinterpret_cast(&_tmem_load_0[21])), "=r"(*reinterpret_cast(&_tmem_load_0[22])), "=r"(*reinterpret_cast(&_tmem_load_0[23])), "=r"(*reinterpret_cast(&_tmem_load_0[24])), "=r"(*reinterpret_cast(&_tmem_load_0[25])), "=r"(*reinterpret_cast(&_tmem_load_0[26])), "=r"(*reinterpret_cast(&_tmem_load_0[27])), "=r"(*reinterpret_cast(&_tmem_load_0[28])), "=r"(*reinterpret_cast(&_tmem_load_0[29])), "=r"(*reinterpret_cast(&_tmem_load_0[30])), "=r"(*reinterpret_cast(&_tmem_load_0[31])) + : "r"(taddr + (unsigned int)(tmem_row_origin << 16) + (unsigned int)(col_block * 64)) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;"); + #pragma unroll + for (int repeat = 0; repeat < 8; repeat++) { + const int reg_base = repeat * 4; + const int col_base = col_block * 64 + repeat * 8 + lane_col * 2; + int db_idx0 = db_start + col_base; + int db_idx1 = db_idx0 + 1; + float top_d0 = 3.4e+38f; + float top_d1 = 3.4e+38f; + if (valid_top != 0 && db_idx0 < M) { + float _max_0 = max_noftz(q_sq_top + database_sq[batch_idx * M + db_idx0] - 2.0f * _tmem_load_0[reg_base], 0.0f); + top_d0 = _max_0; + } + if (valid_top != 0 && db_idx1 < M) { + float _max_1 = max_noftz(q_sq_top + database_sq[batch_idx * M + db_idx1] - 2.0f * _tmem_load_0[reg_base + 1], 0.0f); + top_d1 = _max_1; + } + int top_take1 = ((top_d1 < top_d0) ? 1 : 0); + if (best_top_d[9] > ((top_take1 != 0) ? top_d1 : top_d0)) { + best_top_d[9] = ((top_take1 != 0) ? top_d1 : top_d0); + best_top_i[9] = ((top_take1 != 0) ? db_idx1 : db_idx0); + #pragma unroll + for (int kk_1 = 8; kk_1 >= 0; kk_1--) { + float lower0_d = best_top_d[kk_1 + 1]; + int lower0_i = best_top_i[kk_1 + 1]; + float upper0_d = best_top_d[kk_1]; + int upper0_i = best_top_i[kk_1]; + int swap0_up = ((lower0_d < upper0_d) ? 1 : 0); + best_top_d[kk_1] = ((swap0_up != 0) ? lower0_d : upper0_d); + best_top_i[kk_1] = ((swap0_up != 0) ? lower0_i : upper0_i); + best_top_d[kk_1 + 1] = ((swap0_up != 0) ? upper0_d : lower0_d); + best_top_i[kk_1 + 1] = ((swap0_up != 0) ? upper0_i : lower0_i); + } + if (best_top_d[9] > ((top_take1 != 0) ? top_d0 : top_d1)) { + best_top_d[9] = ((top_take1 != 0) ? top_d0 : top_d1); + best_top_i[9] = ((top_take1 != 0) ? db_idx0 : db_idx1); + #pragma unroll + for (int kk_2 = 8; kk_2 >= 0; kk_2--) { + float lower1_d = best_top_d[kk_2 + 1]; + int lower1_i = best_top_i[kk_2 + 1]; + float upper1_d = best_top_d[kk_2]; + int upper1_i = best_top_i[kk_2]; + int swap1_up = ((lower1_d < upper1_d) ? 1 : 0); + best_top_d[kk_2] = ((swap1_up != 0) ? lower1_d : upper1_d); + best_top_i[kk_2] = ((swap1_up != 0) ? lower1_i : upper1_i); + best_top_d[kk_2 + 1] = ((swap1_up != 0) ? upper1_d : lower1_d); + best_top_i[kk_2 + 1] = ((swap1_up != 0) ? upper1_i : lower1_i); + } + } + } + float bot_d0 = 3.4e+38f; + float bot_d1 = 3.4e+38f; + if (valid_bot != 0 && db_idx0 < M) { + float _max_2 = max_noftz(q_sq_bot + database_sq[batch_idx * M + db_idx0] - 2.0f * _tmem_load_0[reg_base + 2], 0.0f); + bot_d0 = _max_2; + } + if (valid_bot != 0 && db_idx1 < M) { + float _max_3 = max_noftz(q_sq_bot + database_sq[batch_idx * M + db_idx1] - 2.0f * _tmem_load_0[reg_base + 3], 0.0f); + bot_d1 = _max_3; + } + int bot_take1 = ((bot_d1 < bot_d0) ? 1 : 0); + if (best_bot_d[9] > ((bot_take1 != 0) ? bot_d1 : bot_d0)) { + best_bot_d[9] = ((bot_take1 != 0) ? bot_d1 : bot_d0); + best_bot_i[9] = ((bot_take1 != 0) ? db_idx1 : db_idx0); + #pragma unroll + for (int kk_3 = 8; kk_3 >= 0; kk_3--) { + float lower0_d_1 = best_bot_d[kk_3 + 1]; + int lower0_i_1 = best_bot_i[kk_3 + 1]; + float upper0_d_1 = best_bot_d[kk_3]; + int upper0_i_1 = best_bot_i[kk_3]; + int swap0_up_1 = ((lower0_d_1 < upper0_d_1) ? 1 : 0); + best_bot_d[kk_3] = ((swap0_up_1 != 0) ? lower0_d_1 : upper0_d_1); + best_bot_i[kk_3] = ((swap0_up_1 != 0) ? lower0_i_1 : upper0_i_1); + best_bot_d[kk_3 + 1] = ((swap0_up_1 != 0) ? upper0_d_1 : lower0_d_1); + best_bot_i[kk_3 + 1] = ((swap0_up_1 != 0) ? upper0_i_1 : lower0_i_1); + } + if (best_bot_d[9] > ((bot_take1 != 0) ? bot_d0 : bot_d1)) { + best_bot_d[9] = ((bot_take1 != 0) ? bot_d0 : bot_d1); + best_bot_i[9] = ((bot_take1 != 0) ? db_idx0 : db_idx1); + #pragma unroll + for (int kk_4 = 8; kk_4 >= 0; kk_4--) { + float lower1_d_1 = best_bot_d[kk_4 + 1]; + int lower1_i_1 = best_bot_i[kk_4 + 1]; + float upper1_d_1 = best_bot_d[kk_4]; + int upper1_i_1 = best_bot_i[kk_4]; + int swap1_up_1 = ((lower1_d_1 < upper1_d_1) ? 1 : 0); + best_bot_d[kk_4] = ((swap1_up_1 != 0) ? lower1_d_1 : upper1_d_1); + best_bot_i[kk_4] = ((swap1_up_1 != 0) ? lower1_i_1 : upper1_i_1); + best_bot_d[kk_4 + 1] = ((swap1_up_1 != 0) ? upper1_d_1 : lower1_d_1); + best_bot_i[kk_4 + 1] = ((swap1_up_1 != 0) ? upper1_i_1 : lower1_i_1); + } + } + } + } + } + __syncthreads(); + } + if (warp < 8) { + int top_slot_base = (row_top * 8 + slot) * 10; + int bot_slot_base = (row_bot * 8 + slot) * 10; + #pragma unroll + for (int kk_5 = 0; kk_5 < 10; kk_5++) { + smem_local_d[top_slot_base + kk_5] = best_top_d[kk_5]; + smem_local_i[top_slot_base + kk_5] = best_top_i[kk_5]; + smem_local_d[bot_slot_base + kk_5] = best_bot_d[kk_5]; + smem_local_i[bot_slot_base + kk_5] = best_bot_i[kk_5]; + } + } + __syncthreads(); + if (tid < 64) { + int row = tid; + int q_idx_1 = off_q + row; + if (q_idx_1 < Q) { + float head_d[8]; + int head_i[8]; + int head_k[8]; + #pragma unroll + for (int slot_idx = 0; slot_idx < 8; slot_idx++) { + int local_base = (row * 8 + slot_idx) * 10; + head_k[slot_idx] = 0; + head_d[slot_idx] = smem_local_d[local_base]; + head_i[slot_idx] = smem_local_i[local_base]; + } + int out_base = ((split_idx * B + batch_idx) * Q + q_idx_1) * K; + #pragma unroll + for (int out_k = 0; out_k < 10; out_k++) { + float winner_d = head_d[0]; + int winner_i = head_i[0]; + int winner_slot = 0; + #pragma unroll + for (int slot_idx_1 = 1; slot_idx_1 < 8; slot_idx_1++) { + float cand_d = head_d[slot_idx_1]; + int take = ((cand_d < winner_d) ? 1 : 0); + winner_d = ((take != 0) ? cand_d : winner_d); + winner_i = ((take != 0) ? head_i[slot_idx_1] : winner_i); + winner_slot = ((take != 0) ? slot_idx_1 : winner_slot); + } + if (out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = winner_d; + *((int*)(partial_indices + (out_base + out_k))) = winner_i; + } + #pragma unroll + for (int slot_idx_2 = 0; slot_idx_2 < 8; slot_idx_2++) { + if (winner_slot == slot_idx_2) { + int next_head = head_k[slot_idx_2] + 1; + head_k[slot_idx_2] = next_head; + head_d[slot_idx_2] = 3.4e+38f; + head_i[slot_idx_2] = -1; + if (next_head < 10) { + int local_base_1 = (row * 8 + slot_idx_2) * 10; + head_d[slot_idx_2] = smem_local_d[local_base_1 + next_head]; + head_i[slot_idx_2] = smem_local_i[local_base_1 + next_head]; + } + } + } + } + } + } + __syncthreads(); + } + + // Cleanup + __syncthreads(); + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::1.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(128)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::1.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0090.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0090.cu new file mode 100644 index 00000000..11601c19 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0090.cu @@ -0,0 +1,141 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define SMEM_GROUP_DISTS_OFF 0 +#define SMEM_GROUP_DISTS_STAGE_BYTES 512 +#define SMEM_GROUP_DISTS_STRIDE 512 +#define SMEM_GROUP_INDICES_OFF 512 +#define SMEM_GROUP_INDICES_STAGE_BYTES 512 +#define SMEM_GROUP_INDICES_STRIDE 512 +#define SMEM_TOTAL 1024 +#define THREADS 32 +#define TOP_K_MAX 10 +#define GROUP_COUNT 8 +#define GROUP_SPLITS 17 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_rag_microbatch_4a72_k10_fused_group_final_merge_s136g8_4a72_v1(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + float* group_dists = reinterpret_cast(smem_raw + 0); + const int group_dists_addr = smem + 0; + int* group_indices = reinterpret_cast(smem_raw + 512); + const int group_indices_addr = smem + 512; + + // === Task calls (dependency order) === + int split_pos[GROUP_SPLITS]; + int split_base[GROUP_SPLITS]; + float group_cand_d[GROUP_SPLITS]; + int group_cand_i[GROUP_SPLITS]; + int final_pos[GROUP_COUNT]; + float final_cand_d[GROUP_COUNT]; + int final_cand_i[GROUP_COUNT]; + #pragma unroll 1 + for (int row = bid; row < total_queries; row += num_bids) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + if (tid < GROUP_COUNT) { + int group_idx = tid; + int source_split0 = group_idx * GROUP_SPLITS; + int shared_base = group_idx * TOP_K_MAX; + #pragma unroll + for (int local_split = 0; local_split < GROUP_SPLITS; local_split++) { + split_pos[local_split] = 0; + int split_id = source_split0 + local_split; + split_base[local_split] = base_row + split_id * split_stride; + group_cand_d[local_split] = partial_dists[split_base[local_split]]; + group_cand_i[local_split] = partial_indices[split_base[local_split]]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_d = group_cand_d[0]; + int best_i = group_cand_i[0]; + int best_split = 0; + #pragma unroll + for (int local_split_1 = 1; local_split_1 < GROUP_SPLITS; local_split_1++) { + if (best_d > group_cand_d[local_split_1]) { + best_d = group_cand_d[local_split_1]; + best_i = group_cand_i[local_split_1]; + best_split = local_split_1; + } + } + group_dists[shared_base + out_k] = best_d; + group_indices[shared_base + out_k] = best_i; + split_pos[best_split] = split_pos[best_split] + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_pos = split_pos[best_split]; + int next_addr = split_base[best_split] + next_pos; + group_cand_d[best_split] = partial_dists[next_addr]; + group_cand_i[best_split] = partial_indices[next_addr]; + } + } + } + __syncthreads(); + if (tid == 0) { + #pragma unroll + for (int group_idx_1 = 0; group_idx_1 < GROUP_COUNT; group_idx_1++) { + final_pos[group_idx_1] = 0; + int group_base = group_idx_1 * TOP_K_MAX; + final_cand_d[group_idx_1] = group_dists[group_base]; + final_cand_i[group_idx_1] = group_indices[group_base]; + } + #pragma unroll + for (int out_k_1 = 0; out_k_1 < TOP_K_MAX; out_k_1++) { + float best_d_1 = final_cand_d[0]; + int best_i_1 = final_cand_i[0]; + int best_group = 0; + #pragma unroll + for (int group_idx_2 = 1; group_idx_2 < GROUP_COUNT; group_idx_2++) { + if (best_d_1 > final_cand_d[group_idx_2]) { + best_d_1 = final_cand_d[group_idx_2]; + best_i_1 = final_cand_i[group_idx_2]; + best_group = group_idx_2; + } + } + *((float*)(out_dists + (base_row + out_k_1))) = best_d_1; + *((int*)(out_indices + (base_row + out_k_1))) = best_i_1; + final_pos[best_group] = final_pos[best_group] + 1; + if (out_k_1 + 1 < TOP_K_MAX) { + int next_pos_1 = final_pos[best_group]; + int next_addr_1 = best_group * TOP_K_MAX + next_pos_1; + final_cand_d[best_group] = group_dists[next_addr_1]; + final_cand_i[best_group] = group_indices[next_addr_1]; + } + } + } + __syncthreads(); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0091.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0091.cu new file mode 100644 index 00000000..ed3cf69a --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0091.cu @@ -0,0 +1,619 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 64 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 16384 +#define SMEM_SMEM_QUERY_STRIDE 16384 +#define SMEM_SMEM_DATABASE_OFF 17408 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_DATABASE_SQ_OFF 33792 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 34048 +#define THREADS 96 +#define FEATURE_CHUNKS 6 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "tcgen05.mma.cta_group::1.kind::f16 [%0], %1, %2, %3, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, %3, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit(int mbar_addr) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];\n\t" + "}\n" + :: "r"(mbar_addr)); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x16(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x16.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15}, [%16];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ float max_noftz(float a, float b) { + float c; + asm("max.f32 %0, %1, %2;" : "=f"(c) : "f"(a), "f"(b)); + return c; +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cta.global" + ".mbarrier::complete_tx::bytes" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit(int mbar_addr) { + asm volatile( + "tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ uint32_t make_warp_uniform(uint32_t val) { + uint32_t result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1f, 0xffffffff;" + : "=r"(result) : "r"(val)); + return result; +} + +extern "C" { + +__global__ __launch_bounds__(96, 1) void +kernel_knn_build_non128_frontier_7ee5_m64rag_stage1(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tiles, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 17408); + const int smem_database_addr = smem + 17408; + float* smem_database_sq = reinterpret_cast(smem_raw + 33792); + const int smem_database_sq_addr = smem + 33792; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 0, 1, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 16, 1, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=32 + mbarrier_init_pred(smem + 40, 32, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (64 columns, 64 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::1.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(64) : "memory"); + } + + __syncthreads(); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: compute ---- + if (warp == 0) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx = bid; work_idx < total_work; work_idx += num_bids) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tiles; + int q_tile = query_work % num_q_tiles; + int off_q = q_tile * 64; + int q_idx = off_q + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx * Q + q_idx]; + } + float best_d[10]; + int best_i[10]; + #pragma unroll + for (int kk = 0; kk < 10; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + int db_tile_start = split_idx * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int db_start = db_tile * 64; + int db_sq_idx0 = db_start + (warp % 4 * 32 + lane); + if (db_sq_idx0 < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx * M + db_sq_idx0]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 3.4e+38f; + } + int db_col1 = warp % 4 * 32 + lane + 32; + int db_sq_idx1 = db_start + db_col1; + if (db_sq_idx1 < M) { + smem_database_sq[db_col1] = database_sq[batch_idx * M + db_sq_idx1]; + } else { + smem_database_sq[db_col1] = 3.4e+38f; + } + asm volatile("barrier.sync 8, %0;" :: "r"(32)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(warp % 4 * 32 << 16); + float _tmem_load_0[64]; + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x64.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31, %32, %33, %34, %35, %36, %37, %38, %39, %40, %41, %42, %43, %44, %45, %46, %47, %48, %49, %50, %51, %52, %53, %54, %55, %56, %57, %58, %59, %60, %61, %62, %63}, [%64];" + : "=f"(_tmem_load_0[0]), "=f"(_tmem_load_0[1]), "=f"(_tmem_load_0[2]), "=f"(_tmem_load_0[3]), "=f"(_tmem_load_0[4]), "=f"(_tmem_load_0[5]), "=f"(_tmem_load_0[6]), "=f"(_tmem_load_0[7]), "=f"(_tmem_load_0[8]), "=f"(_tmem_load_0[9]), "=f"(_tmem_load_0[10]), "=f"(_tmem_load_0[11]), "=f"(_tmem_load_0[12]), "=f"(_tmem_load_0[13]), "=f"(_tmem_load_0[14]), "=f"(_tmem_load_0[15]), "=f"(_tmem_load_0[16]), "=f"(_tmem_load_0[17]), "=f"(_tmem_load_0[18]), "=f"(_tmem_load_0[19]), "=f"(_tmem_load_0[20]), "=f"(_tmem_load_0[21]), "=f"(_tmem_load_0[22]), "=f"(_tmem_load_0[23]), "=f"(_tmem_load_0[24]), "=f"(_tmem_load_0[25]), "=f"(_tmem_load_0[26]), "=f"(_tmem_load_0[27]), "=f"(_tmem_load_0[28]), "=f"(_tmem_load_0[29]), "=f"(_tmem_load_0[30]), "=f"(_tmem_load_0[31]), "=f"(_tmem_load_0[32]), "=f"(_tmem_load_0[33]), "=f"(_tmem_load_0[34]), "=f"(_tmem_load_0[35]), "=f"(_tmem_load_0[36]), "=f"(_tmem_load_0[37]), "=f"(_tmem_load_0[38]), "=f"(_tmem_load_0[39]), "=f"(_tmem_load_0[40]), "=f"(_tmem_load_0[41]), "=f"(_tmem_load_0[42]), "=f"(_tmem_load_0[43]), "=f"(_tmem_load_0[44]), "=f"(_tmem_load_0[45]), "=f"(_tmem_load_0[46]), "=f"(_tmem_load_0[47]), "=f"(_tmem_load_0[48]), "=f"(_tmem_load_0[49]), "=f"(_tmem_load_0[50]), "=f"(_tmem_load_0[51]), "=f"(_tmem_load_0[52]), "=f"(_tmem_load_0[53]), "=f"(_tmem_load_0[54]), "=f"(_tmem_load_0[55]), "=f"(_tmem_load_0[56]), "=f"(_tmem_load_0[57]), "=f"(_tmem_load_0[58]), "=f"(_tmem_load_0[59]), "=f"(_tmem_load_0[60]), "=f"(_tmem_load_0[61]), "=f"(_tmem_load_0[62]), "=f"(_tmem_load_0[63]) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(32)); + mbarrier_arrive(score_empty_addr); + if (valid_q != 0) { + #pragma unroll 1 + for (int col_base = 0; col_base < 64; col_base += 4) { + float dist_vec[4]; + dist_vec[0] = _tmem_load_0[col_base]; + dist_vec[1] = _tmem_load_0[col_base + 1]; + dist_vec[2] = _tmem_load_0[col_base + 2]; + dist_vec[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec[4]; + db_sq_vec[0] = smem_database_sq[col_base]; + db_sq_vec[1] = smem_database_sq[col_base + 1]; + db_sq_vec[2] = smem_database_sq[col_base + 2]; + db_sq_vec[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec)[_la], reinterpret_cast(db_sq_vec)[_la]); + float group_min = _t0[0]; + if (_t0[1] < group_min) { + group_min = _t0[1]; + } + if (_t0[2] < group_min) { + group_min = _t0[2]; + } + if (_t0[3] < group_min) { + group_min = _t0[3]; + } + if (group_min < best_d[9]) { + #pragma unroll + for (int vec_col = 0; vec_col < 4; vec_col++) { + int db_idx = db_start + col_base + vec_col; + if (db_idx < M) { + float _max_0 = max_noftz(_t0[vec_col], 0.0f); + float dist = _max_0; + if (dist < best_d[9]) { + best_d[9] = dist; + best_i[9] = db_idx; + #pragma unroll + for (int pos = 9; pos >= 1; pos--) { + if (best_d[pos] < best_d[pos - 1]) { + float tmp_d = best_d[pos - 1]; + int tmp_i = best_i[pos - 1]; + best_d[pos - 1] = best_d[pos]; + best_i[pos - 1] = best_i[pos]; + best_d[pos] = tmp_d; + best_i[pos] = tmp_i; + } + } + } + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(32)); + } + if (valid_q != 0) { + int out_base = ((split_idx * B + batch_idx) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < 10; out_k++) { + if (out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = best_d[out_k]; + *((int*)(partial_indices + (out_base + out_k))) = best_i[out_k]; + } + } + } + } + } + // ---- Role: load ---- + } else if (warp == 1) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 1) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx_1 = bid; work_idx_1 < total_work; work_idx_1 += num_bids) { + int split_idx_1 = work_idx_1 % (unsigned int)split_count; + int query_work_1 = work_idx_1 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tiles; + int q_tile_1 = query_work_1 % num_q_tiles; + int off_q_1 = q_tile_1 * 64; + int global_q = batch_idx_1 * Q + off_q_1; + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int off_m = db_tile_1 * 64; + int global_m = batch_idx_1 * M + off_m; + #pragma unroll + for (int feat_chunk = 0; feat_chunk < FEATURE_CHUNKS; feat_chunk++) { + int feature_coord = feat_chunk * 2; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + mbarrier_arrive_expect_tx(query_full_addr, 16384); + tma_3d_gmem2smem(smem_query_addr, tmap_query, 0, global_q, feature_coord, query_full_addr); + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + mbarrier_arrive_expect_tx(database_full_addr, 16384); + tma_3d_gmem2smem(smem_database_addr, tmap_database, 0, global_m, feature_coord, database_full_addr); + } + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 2) { + { // mma_main + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_database_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = bid; work_idx_2 < total_work; work_idx_2 += num_bids) { + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + #pragma unroll + for (int feat_chunk_1 = 0; feat_chunk_1 < FEATURE_CHUNKS; feat_chunk_1++) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = make_warp_uniform((smem_query_addr >> 4) & 0x3FFF); + int _mma_b_lo_0 = make_warp_uniform((smem_database_addr >> 4) & 0x3FFF); + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 68158608;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 506;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(((((feat_chunk_1 == 0) ? 1 : 0)) ? 0 : 1))); + asm volatile("tcgen05.fence::after_thread_sync;"); + elect_commit(query_empty_addr); + elect_commit(database_empty_addr); + } + elect_commit(score_full_addr); + } + } + } + } + + // Cleanup + __syncthreads(); // barrier before TMEM dealloc + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::1.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(64)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::1.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0092.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0092.cu new file mode 100644 index 00000000..185a31a4 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0092.cu @@ -0,0 +1,141 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define SMEM_GROUP_DISTS_OFF 0 +#define SMEM_GROUP_DISTS_STAGE_BYTES 512 +#define SMEM_GROUP_DISTS_STRIDE 512 +#define SMEM_GROUP_INDICES_OFF 512 +#define SMEM_GROUP_INDICES_STAGE_BYTES 512 +#define SMEM_GROUP_INDICES_STRIDE 512 +#define SMEM_TOTAL 1024 +#define THREADS 32 +#define TOP_K_MAX 10 +#define GROUP_COUNT 8 +#define GROUP_SPLITS 9 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_non128_frontier_4be7_d768fused_merge_s72g8_4be7_d768fused_v1(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + float* group_dists = reinterpret_cast(smem_raw + 0); + const int group_dists_addr = smem + 0; + int* group_indices = reinterpret_cast(smem_raw + 512); + const int group_indices_addr = smem + 512; + + // === Task calls (dependency order) === + int split_pos[GROUP_SPLITS]; + int split_base[GROUP_SPLITS]; + float group_cand_d[GROUP_SPLITS]; + int group_cand_i[GROUP_SPLITS]; + int final_pos[GROUP_COUNT]; + float final_cand_d[GROUP_COUNT]; + int final_cand_i[GROUP_COUNT]; + #pragma unroll 1 + for (int row = bid; row < total_queries; row += num_bids) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + if (tid < GROUP_COUNT) { + int group_idx = tid; + int source_split0 = group_idx * GROUP_SPLITS; + int shared_base = group_idx * TOP_K_MAX; + #pragma unroll + for (int local_split = 0; local_split < GROUP_SPLITS; local_split++) { + split_pos[local_split] = 0; + int split_id = source_split0 + local_split; + split_base[local_split] = base_row + split_id * split_stride; + group_cand_d[local_split] = partial_dists[split_base[local_split]]; + group_cand_i[local_split] = partial_indices[split_base[local_split]]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_d = group_cand_d[0]; + int best_i = group_cand_i[0]; + int best_split = 0; + #pragma unroll + for (int local_split_1 = 1; local_split_1 < GROUP_SPLITS; local_split_1++) { + if (best_d > group_cand_d[local_split_1]) { + best_d = group_cand_d[local_split_1]; + best_i = group_cand_i[local_split_1]; + best_split = local_split_1; + } + } + group_dists[shared_base + out_k] = best_d; + group_indices[shared_base + out_k] = best_i; + split_pos[best_split] = split_pos[best_split] + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_pos = split_pos[best_split]; + int next_addr = split_base[best_split] + next_pos; + group_cand_d[best_split] = partial_dists[next_addr]; + group_cand_i[best_split] = partial_indices[next_addr]; + } + } + } + __syncthreads(); + if (tid == 0) { + #pragma unroll + for (int group_idx_1 = 0; group_idx_1 < GROUP_COUNT; group_idx_1++) { + final_pos[group_idx_1] = 0; + int group_base = group_idx_1 * TOP_K_MAX; + final_cand_d[group_idx_1] = group_dists[group_base]; + final_cand_i[group_idx_1] = group_indices[group_base]; + } + #pragma unroll + for (int out_k_1 = 0; out_k_1 < TOP_K_MAX; out_k_1++) { + float best_d_1 = final_cand_d[0]; + int best_i_1 = final_cand_i[0]; + int best_group = 0; + #pragma unroll + for (int group_idx_2 = 1; group_idx_2 < GROUP_COUNT; group_idx_2++) { + if (best_d_1 > final_cand_d[group_idx_2]) { + best_d_1 = final_cand_d[group_idx_2]; + best_i_1 = final_cand_i[group_idx_2]; + best_group = group_idx_2; + } + } + *((float*)(out_dists + (base_row + out_k_1))) = best_d_1; + *((int*)(out_indices + (base_row + out_k_1))) = best_i_1; + final_pos[best_group] = final_pos[best_group] + 1; + if (out_k_1 + 1 < TOP_K_MAX) { + int next_pos_1 = final_pos[best_group]; + int next_addr_1 = best_group * TOP_K_MAX + next_pos_1; + final_cand_d[best_group] = group_dists[next_addr_1]; + final_cand_i[best_group] = group_indices[next_addr_1]; + } + } + } + __syncthreads(); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0093.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0093.cu new file mode 100644 index 00000000..2753a146 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0093.cu @@ -0,0 +1,602 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 128 +#define TMEM_ACC_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 8192 +#define SMEM_SMEM_QUERY_STRIDE 8192 +#define SMEM_SMEM_DATABASE_OFF 9216 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_LOCAL_D_OFF 25600 +#define SMEM_SMEM_LOCAL_D_STAGE_BYTES 20480 +#define SMEM_SMEM_LOCAL_D_STRIDE 20480 +#define SMEM_SMEM_LOCAL_I_OFF 46080 +#define SMEM_SMEM_LOCAL_I_STAGE_BYTES 20480 +#define SMEM_SMEM_LOCAL_I_STRIDE 20480 +#define SMEM_TOTAL 66816 +#define THREADS 512 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "tcgen05.mma.cta_group::1.kind::f16 [%0], %1, %2, %3, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, %3, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit(int mbar_addr) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];\n\t" + "}\n" + :: "r"(mbar_addr)); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x32(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x32.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15," + " %16, %17, %18, %19, %20, %21, %22, %23," + " %24, %25, %26, %27, %28, %29, %30, %31}, [%32];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]), + "=f"(dst[16]), "=f"(dst[17]), "=f"(dst[18]), "=f"(dst[19]), + "=f"(dst[20]), "=f"(dst[21]), "=f"(dst[22]), "=f"(dst[23]), + "=f"(dst[24]), "=f"(dst[25]), "=f"(dst[26]), "=f"(dst[27]), + "=f"(dst[28]), "=f"(dst[29]), "=f"(dst[30]), "=f"(dst[31]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ float max_noftz(float a, float b) { + float c; + asm("max.f32 %0, %1, %2;" : "=f"(c) : "f"(a), "f"(b)); + return c; +} + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit(int mbar_addr) { + asm volatile( + "tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ uint32_t make_warp_uniform(uint32_t val) { + uint32_t result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1f, 0xffffffff;" + : "=r"(result) : "r"(val)); + return result; +} + +extern "C" { + +__global__ __launch_bounds__(512, 1) void +kernel_knn_build_common_d_1438_rag_d64_m128_stage1(__nv_bfloat16* __restrict__ query, __nv_bfloat16* __restrict__ database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tiles, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 9216); + const int smem_database_addr = smem + 9216; + float* smem_local_d = reinterpret_cast(smem_raw + 25600); + const int smem_local_d_addr = smem + 25600; + int* smem_local_i = reinterpret_cast(smem_raw + 46080); + const int smem_local_i_addr = smem + 46080; + + // Mbarrier init (1 groups, 1 barriers) + // Mbarriers at smem_raw[0..8) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // mma_done: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 0, 1, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (128 columns, 128 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 8); + if (warp == 0) { + int _tmem_hold = smem + 8; + asm volatile("tcgen05.alloc.cta_group::1.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(128) : "memory"); + } + + __syncthreads(); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define mma_done_addr (mbar_base + 0) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_acc = taddr; + + // === Task calls (dependency order) === + unsigned int _phase_mma_done_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx = bid; work_idx < total_work; work_idx += num_bids) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tiles; + int q_tile = query_work % num_q_tiles; + int off_q = q_tile * 64; + #pragma unroll 1 + for (int e_vec = tid; e_vec < 512; e_vec += 512) { + int q_elem = e_vec * 8; + int q_row = q_elem / 64; + int d_col = q_elem - q_row * 64; + int q_idx = off_q + q_row; + float q_vals[8]; + unsigned int q_pack[4]; + #pragma unroll + for (int vi = 0; vi < 8; vi++) { + q_vals[vi] = 0.0f; + } + if (q_idx < Q) { + int q_addr = (batch_idx * Q + q_idx) * 64 + d_col; + { + const uint4* _vptr_0 = reinterpret_cast(query + (unsigned long long)q_addr + 0); + uint4 _vld_0[1]; + #pragma unroll + for (int _blk = 0; _blk < 1; _blk++) { + _vld_0[_blk] = _vptr_0[_blk]; + __nv_bfloat16* _velems_0 = reinterpret_cast<__nv_bfloat16*>(&_vld_0[_blk]); + #pragma unroll + for (int _j = 0; _j < 8; _j++) + q_vals[0 + _blk * 8 + _j] = __bfloat162float(_velems_0[_j]); + } + } + } + #pragma unroll + for (int _lp = 0; _lp < 4; _lp++) { + __nv_bfloat162 _bf2 = __float22bfloat162_rn(make_float2(q_vals[_lp*2 + 0], q_vals[_lp*2+1 + 0])); + q_pack[_lp] = *(uint32_t*)&_bf2; + } + int q_store_addr = (smem_query_addr + (unsigned int)(d_col / 64 * 8192 + q_row * 128 + d_col % 64 * 2 ^ (d_col / 64 * 8192 + q_row * 128 + d_col % 64 * 2 >> 7 & 7) << 4)); + asm volatile("st.shared.v4.b32 [%0], {%1,%2,%3,%4};" :: "r"(q_store_addr), "r"(q_pack[0]), "r"(q_pack[1]), "r"(q_pack[2]), "r"(q_pack[3]) : "memory"); + } + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); + __syncthreads(); + const int row_group = warp % 4; + const int col_block = warp / 4; + const int tmem_row_origin = row_group * 32; + const int logical_row_origin = row_group * 16; + int row_top = logical_row_origin + lane / 4; + int row_bot = row_top + 8; + const int lane_col = lane % 4; + const int slot = col_block * 4 + lane_col; + int q_top = off_q + row_top; + int q_bot = off_q + row_bot; + int valid_top = ((q_top < Q) ? 1 : 0); + int valid_bot = ((q_bot < Q) ? 1 : 0); + float q_sq_top = 0.0f; + float q_sq_bot = 0.0f; + if (valid_top != 0) { + q_sq_top = query_sq[batch_idx * Q + q_top]; + } + if (valid_bot != 0) { + q_sq_bot = query_sq[batch_idx * Q + q_bot]; + } + float best_top_d[10]; + float best_bot_d[10]; + int best_top_i[10]; + int best_bot_i[10]; + #pragma unroll + for (int kk = 0; kk < 10; kk++) { + best_top_d[kk] = 3.4e+38f; + best_bot_d[kk] = 3.4e+38f; + best_top_i[kk] = -1; + best_bot_i[kk] = -1; + } + int db_tile_start = split_idx * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int db_start = db_tile * 128; + #pragma unroll 1 + for (int e_vec_1 = tid; e_vec_1 < 1024; e_vec_1 += 512) { + int db_elem = e_vec_1 * 8; + int db_row = db_elem / 64; + int d_col_1 = db_elem - db_row * 64; + int db_idx = db_start + db_row; + float db_vals[8]; + unsigned int db_pack[4]; + #pragma unroll + for (int vi_1 = 0; vi_1 < 8; vi_1++) { + db_vals[vi_1] = 0.0f; + } + if (db_idx < M) { + int db_addr = (batch_idx * M + db_idx) * 64 + d_col_1; + { + const uint4* _vptr_1 = reinterpret_cast(database + (unsigned long long)db_addr + 0); + uint4 _vld_1[1]; + #pragma unroll + for (int _blk = 0; _blk < 1; _blk++) { + _vld_1[_blk] = _vptr_1[_blk]; + __nv_bfloat16* _velems_1 = reinterpret_cast<__nv_bfloat16*>(&_vld_1[_blk]); + #pragma unroll + for (int _j = 0; _j < 8; _j++) + db_vals[0 + _blk * 8 + _j] = __bfloat162float(_velems_1[_j]); + } + } + } + #pragma unroll + for (int _lp = 0; _lp < 4; _lp++) { + __nv_bfloat162 _bf2 = __float22bfloat162_rn(make_float2(db_vals[_lp*2 + 0], db_vals[_lp*2+1 + 0])); + db_pack[_lp] = *(uint32_t*)&_bf2; + } + int b_store_addr = (smem_database_addr + (unsigned int)(d_col_1 / 64 * 16384 + db_row * 128 + d_col_1 % 64 * 2 ^ (d_col_1 / 64 * 16384 + db_row * 128 + d_col_1 % 64 * 2 >> 7 & 7) << 4)); + asm volatile("st.shared.v4.b32 [%0], {%1,%2,%3,%4};" :: "r"(b_store_addr), "r"(db_pack[0]), "r"(db_pack[1]), "r"(db_pack[2]), "r"(db_pack[3]) : "memory"); + } + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); + __syncthreads(); + if (warp == 0) { + int _mma_a_lo_0 = make_warp_uniform((smem_query_addr >> 4) & 0x3FFF); + int _mma_b_lo_0 = make_warp_uniform((smem_database_addr >> 4) & 0x3FFF); + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 69207184;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_acc), "r"(0)); + elect_commit(mma_done_addr); + } + mbarrier_wait(mma_done_addr, _phase_mma_done_0); + _phase_mma_done_0 ^= 1; + if (warp < 8) { + float _tmem_load_0[32]; + asm volatile( + "tcgen05.ld.sync.aligned.16x256b.x8.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31}, [%32];" + : "=r"(*reinterpret_cast(&_tmem_load_0[0])), "=r"(*reinterpret_cast(&_tmem_load_0[1])), "=r"(*reinterpret_cast(&_tmem_load_0[2])), "=r"(*reinterpret_cast(&_tmem_load_0[3])), "=r"(*reinterpret_cast(&_tmem_load_0[4])), "=r"(*reinterpret_cast(&_tmem_load_0[5])), "=r"(*reinterpret_cast(&_tmem_load_0[6])), "=r"(*reinterpret_cast(&_tmem_load_0[7])), "=r"(*reinterpret_cast(&_tmem_load_0[8])), "=r"(*reinterpret_cast(&_tmem_load_0[9])), "=r"(*reinterpret_cast(&_tmem_load_0[10])), "=r"(*reinterpret_cast(&_tmem_load_0[11])), "=r"(*reinterpret_cast(&_tmem_load_0[12])), "=r"(*reinterpret_cast(&_tmem_load_0[13])), "=r"(*reinterpret_cast(&_tmem_load_0[14])), "=r"(*reinterpret_cast(&_tmem_load_0[15])), "=r"(*reinterpret_cast(&_tmem_load_0[16])), "=r"(*reinterpret_cast(&_tmem_load_0[17])), "=r"(*reinterpret_cast(&_tmem_load_0[18])), "=r"(*reinterpret_cast(&_tmem_load_0[19])), "=r"(*reinterpret_cast(&_tmem_load_0[20])), "=r"(*reinterpret_cast(&_tmem_load_0[21])), "=r"(*reinterpret_cast(&_tmem_load_0[22])), "=r"(*reinterpret_cast(&_tmem_load_0[23])), "=r"(*reinterpret_cast(&_tmem_load_0[24])), "=r"(*reinterpret_cast(&_tmem_load_0[25])), "=r"(*reinterpret_cast(&_tmem_load_0[26])), "=r"(*reinterpret_cast(&_tmem_load_0[27])), "=r"(*reinterpret_cast(&_tmem_load_0[28])), "=r"(*reinterpret_cast(&_tmem_load_0[29])), "=r"(*reinterpret_cast(&_tmem_load_0[30])), "=r"(*reinterpret_cast(&_tmem_load_0[31])) + : "r"(taddr + (unsigned int)(tmem_row_origin << 16) + (unsigned int)(col_block * 64)) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;"); + #pragma unroll + for (int repeat = 0; repeat < 8; repeat++) { + const int reg_base = repeat * 4; + const int col_base = col_block * 64 + repeat * 8 + lane_col * 2; + int db_idx0 = db_start + col_base; + int db_idx1 = db_idx0 + 1; + float top_d0 = 3.4e+38f; + float top_d1 = 3.4e+38f; + if (valid_top != 0 && db_idx0 < M) { + float _max_0 = max_noftz(q_sq_top + database_sq[batch_idx * M + db_idx0] - 2.0f * _tmem_load_0[reg_base], 0.0f); + top_d0 = _max_0; + } + if (valid_top != 0 && db_idx1 < M) { + float _max_1 = max_noftz(q_sq_top + database_sq[batch_idx * M + db_idx1] - 2.0f * _tmem_load_0[reg_base + 1], 0.0f); + top_d1 = _max_1; + } + int top_take1 = ((top_d1 < top_d0) ? 1 : 0); + if (best_top_d[9] > ((top_take1 != 0) ? top_d1 : top_d0)) { + best_top_d[9] = ((top_take1 != 0) ? top_d1 : top_d0); + best_top_i[9] = ((top_take1 != 0) ? db_idx1 : db_idx0); + #pragma unroll + for (int kk_1 = 8; kk_1 >= 0; kk_1--) { + float lower0_d = best_top_d[kk_1 + 1]; + int lower0_i = best_top_i[kk_1 + 1]; + float upper0_d = best_top_d[kk_1]; + int upper0_i = best_top_i[kk_1]; + int swap0_up = ((lower0_d < upper0_d) ? 1 : 0); + best_top_d[kk_1] = ((swap0_up != 0) ? lower0_d : upper0_d); + best_top_i[kk_1] = ((swap0_up != 0) ? lower0_i : upper0_i); + best_top_d[kk_1 + 1] = ((swap0_up != 0) ? upper0_d : lower0_d); + best_top_i[kk_1 + 1] = ((swap0_up != 0) ? upper0_i : lower0_i); + } + if (best_top_d[9] > ((top_take1 != 0) ? top_d0 : top_d1)) { + best_top_d[9] = ((top_take1 != 0) ? top_d0 : top_d1); + best_top_i[9] = ((top_take1 != 0) ? db_idx0 : db_idx1); + #pragma unroll + for (int kk_2 = 8; kk_2 >= 0; kk_2--) { + float lower1_d = best_top_d[kk_2 + 1]; + int lower1_i = best_top_i[kk_2 + 1]; + float upper1_d = best_top_d[kk_2]; + int upper1_i = best_top_i[kk_2]; + int swap1_up = ((lower1_d < upper1_d) ? 1 : 0); + best_top_d[kk_2] = ((swap1_up != 0) ? lower1_d : upper1_d); + best_top_i[kk_2] = ((swap1_up != 0) ? lower1_i : upper1_i); + best_top_d[kk_2 + 1] = ((swap1_up != 0) ? upper1_d : lower1_d); + best_top_i[kk_2 + 1] = ((swap1_up != 0) ? upper1_i : lower1_i); + } + } + } + float bot_d0 = 3.4e+38f; + float bot_d1 = 3.4e+38f; + if (valid_bot != 0 && db_idx0 < M) { + float _max_2 = max_noftz(q_sq_bot + database_sq[batch_idx * M + db_idx0] - 2.0f * _tmem_load_0[reg_base + 2], 0.0f); + bot_d0 = _max_2; + } + if (valid_bot != 0 && db_idx1 < M) { + float _max_3 = max_noftz(q_sq_bot + database_sq[batch_idx * M + db_idx1] - 2.0f * _tmem_load_0[reg_base + 3], 0.0f); + bot_d1 = _max_3; + } + int bot_take1 = ((bot_d1 < bot_d0) ? 1 : 0); + if (best_bot_d[9] > ((bot_take1 != 0) ? bot_d1 : bot_d0)) { + best_bot_d[9] = ((bot_take1 != 0) ? bot_d1 : bot_d0); + best_bot_i[9] = ((bot_take1 != 0) ? db_idx1 : db_idx0); + #pragma unroll + for (int kk_3 = 8; kk_3 >= 0; kk_3--) { + float lower0_d_1 = best_bot_d[kk_3 + 1]; + int lower0_i_1 = best_bot_i[kk_3 + 1]; + float upper0_d_1 = best_bot_d[kk_3]; + int upper0_i_1 = best_bot_i[kk_3]; + int swap0_up_1 = ((lower0_d_1 < upper0_d_1) ? 1 : 0); + best_bot_d[kk_3] = ((swap0_up_1 != 0) ? lower0_d_1 : upper0_d_1); + best_bot_i[kk_3] = ((swap0_up_1 != 0) ? lower0_i_1 : upper0_i_1); + best_bot_d[kk_3 + 1] = ((swap0_up_1 != 0) ? upper0_d_1 : lower0_d_1); + best_bot_i[kk_3 + 1] = ((swap0_up_1 != 0) ? upper0_i_1 : lower0_i_1); + } + if (best_bot_d[9] > ((bot_take1 != 0) ? bot_d0 : bot_d1)) { + best_bot_d[9] = ((bot_take1 != 0) ? bot_d0 : bot_d1); + best_bot_i[9] = ((bot_take1 != 0) ? db_idx0 : db_idx1); + #pragma unroll + for (int kk_4 = 8; kk_4 >= 0; kk_4--) { + float lower1_d_1 = best_bot_d[kk_4 + 1]; + int lower1_i_1 = best_bot_i[kk_4 + 1]; + float upper1_d_1 = best_bot_d[kk_4]; + int upper1_i_1 = best_bot_i[kk_4]; + int swap1_up_1 = ((lower1_d_1 < upper1_d_1) ? 1 : 0); + best_bot_d[kk_4] = ((swap1_up_1 != 0) ? lower1_d_1 : upper1_d_1); + best_bot_i[kk_4] = ((swap1_up_1 != 0) ? lower1_i_1 : upper1_i_1); + best_bot_d[kk_4 + 1] = ((swap1_up_1 != 0) ? upper1_d_1 : lower1_d_1); + best_bot_i[kk_4 + 1] = ((swap1_up_1 != 0) ? upper1_i_1 : lower1_i_1); + } + } + } + } + } + __syncthreads(); + } + if (warp < 8) { + int top_slot_base = (row_top * 8 + slot) * 10; + int bot_slot_base = (row_bot * 8 + slot) * 10; + #pragma unroll + for (int kk_5 = 0; kk_5 < 10; kk_5++) { + smem_local_d[top_slot_base + kk_5] = best_top_d[kk_5]; + smem_local_i[top_slot_base + kk_5] = best_top_i[kk_5]; + smem_local_d[bot_slot_base + kk_5] = best_bot_d[kk_5]; + smem_local_i[bot_slot_base + kk_5] = best_bot_i[kk_5]; + } + } + __syncthreads(); + if (tid < 64) { + int row = tid; + int q_idx_1 = off_q + row; + if (q_idx_1 < Q) { + float head_d[8]; + int head_i[8]; + int head_k[8]; + #pragma unroll + for (int slot_idx = 0; slot_idx < 8; slot_idx++) { + int local_base = (row * 8 + slot_idx) * 10; + head_k[slot_idx] = 0; + head_d[slot_idx] = smem_local_d[local_base]; + head_i[slot_idx] = smem_local_i[local_base]; + } + int out_base = ((split_idx * B + batch_idx) * Q + q_idx_1) * K; + #pragma unroll + for (int out_k = 0; out_k < 10; out_k++) { + float winner_d = head_d[0]; + int winner_i = head_i[0]; + int winner_slot = 0; + #pragma unroll + for (int slot_idx_1 = 1; slot_idx_1 < 8; slot_idx_1++) { + float cand_d = head_d[slot_idx_1]; + int take = ((cand_d < winner_d) ? 1 : 0); + winner_d = ((take != 0) ? cand_d : winner_d); + winner_i = ((take != 0) ? head_i[slot_idx_1] : winner_i); + winner_slot = ((take != 0) ? slot_idx_1 : winner_slot); + } + if (out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = winner_d; + *((int*)(partial_indices + (out_base + out_k))) = winner_i; + } + #pragma unroll + for (int slot_idx_2 = 0; slot_idx_2 < 8; slot_idx_2++) { + if (winner_slot == slot_idx_2) { + int next_head = head_k[slot_idx_2] + 1; + head_k[slot_idx_2] = next_head; + head_d[slot_idx_2] = 3.4e+38f; + head_i[slot_idx_2] = -1; + if (next_head < 10) { + int local_base_1 = (row * 8 + slot_idx_2) * 10; + head_d[slot_idx_2] = smem_local_d[local_base_1 + next_head]; + head_i[slot_idx_2] = smem_local_i[local_base_1 + next_head]; + } + } + } + } + } + } + __syncthreads(); + } + + // Cleanup + __syncthreads(); + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::1.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(128)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::1.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0094.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0094.cu new file mode 100644 index 00000000..00bff3ad --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0094.cu @@ -0,0 +1,141 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define SMEM_GROUP_DISTS_OFF 0 +#define SMEM_GROUP_DISTS_STAGE_BYTES 512 +#define SMEM_GROUP_DISTS_STRIDE 512 +#define SMEM_GROUP_INDICES_OFF 512 +#define SMEM_GROUP_INDICES_STAGE_BYTES 512 +#define SMEM_GROUP_INDICES_STRIDE 512 +#define SMEM_TOTAL 1024 +#define THREADS 32 +#define TOP_K_MAX 10 +#define GROUP_COUNT 8 +#define GROUP_SPLITS 17 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_non128_frontier_4be7_d768fused_merge_s136g8_4be7_d768fused_v1(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + float* group_dists = reinterpret_cast(smem_raw + 0); + const int group_dists_addr = smem + 0; + int* group_indices = reinterpret_cast(smem_raw + 512); + const int group_indices_addr = smem + 512; + + // === Task calls (dependency order) === + int split_pos[GROUP_SPLITS]; + int split_base[GROUP_SPLITS]; + float group_cand_d[GROUP_SPLITS]; + int group_cand_i[GROUP_SPLITS]; + int final_pos[GROUP_COUNT]; + float final_cand_d[GROUP_COUNT]; + int final_cand_i[GROUP_COUNT]; + #pragma unroll 1 + for (int row = bid; row < total_queries; row += num_bids) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + if (tid < GROUP_COUNT) { + int group_idx = tid; + int source_split0 = group_idx * GROUP_SPLITS; + int shared_base = group_idx * TOP_K_MAX; + #pragma unroll + for (int local_split = 0; local_split < GROUP_SPLITS; local_split++) { + split_pos[local_split] = 0; + int split_id = source_split0 + local_split; + split_base[local_split] = base_row + split_id * split_stride; + group_cand_d[local_split] = partial_dists[split_base[local_split]]; + group_cand_i[local_split] = partial_indices[split_base[local_split]]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_d = group_cand_d[0]; + int best_i = group_cand_i[0]; + int best_split = 0; + #pragma unroll + for (int local_split_1 = 1; local_split_1 < GROUP_SPLITS; local_split_1++) { + if (best_d > group_cand_d[local_split_1]) { + best_d = group_cand_d[local_split_1]; + best_i = group_cand_i[local_split_1]; + best_split = local_split_1; + } + } + group_dists[shared_base + out_k] = best_d; + group_indices[shared_base + out_k] = best_i; + split_pos[best_split] = split_pos[best_split] + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_pos = split_pos[best_split]; + int next_addr = split_base[best_split] + next_pos; + group_cand_d[best_split] = partial_dists[next_addr]; + group_cand_i[best_split] = partial_indices[next_addr]; + } + } + } + __syncthreads(); + if (tid == 0) { + #pragma unroll + for (int group_idx_1 = 0; group_idx_1 < GROUP_COUNT; group_idx_1++) { + final_pos[group_idx_1] = 0; + int group_base = group_idx_1 * TOP_K_MAX; + final_cand_d[group_idx_1] = group_dists[group_base]; + final_cand_i[group_idx_1] = group_indices[group_base]; + } + #pragma unroll + for (int out_k_1 = 0; out_k_1 < TOP_K_MAX; out_k_1++) { + float best_d_1 = final_cand_d[0]; + int best_i_1 = final_cand_i[0]; + int best_group = 0; + #pragma unroll + for (int group_idx_2 = 1; group_idx_2 < GROUP_COUNT; group_idx_2++) { + if (best_d_1 > final_cand_d[group_idx_2]) { + best_d_1 = final_cand_d[group_idx_2]; + best_i_1 = final_cand_i[group_idx_2]; + best_group = group_idx_2; + } + } + *((float*)(out_dists + (base_row + out_k_1))) = best_d_1; + *((int*)(out_indices + (base_row + out_k_1))) = best_i_1; + final_pos[best_group] = final_pos[best_group] + 1; + if (out_k_1 + 1 < TOP_K_MAX) { + int next_pos_1 = final_pos[best_group]; + int next_addr_1 = best_group * TOP_K_MAX + next_pos_1; + final_cand_d[best_group] = group_dists[next_addr_1]; + final_cand_i[best_group] = group_indices[next_addr_1]; + } + } + } + __syncthreads(); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0095.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0095.cu new file mode 100644 index 00000000..d891be3a --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0095.cu @@ -0,0 +1,619 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 64 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 16384 +#define SMEM_SMEM_QUERY_STRIDE 16384 +#define SMEM_SMEM_DATABASE_OFF 17408 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_DATABASE_SQ_OFF 33792 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 34048 +#define THREADS 96 +#define FEATURE_CHUNKS 2 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "tcgen05.mma.cta_group::1.kind::f16 [%0], %1, %2, %3, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, %3, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit(int mbar_addr) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];\n\t" + "}\n" + :: "r"(mbar_addr)); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x16(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x16.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15}, [%16];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ float max_noftz(float a, float b) { + float c; + asm("max.f32 %0, %1, %2;" : "=f"(c) : "f"(a), "f"(b)); + return c; +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cta.global" + ".mbarrier::complete_tx::bytes" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit(int mbar_addr) { + asm volatile( + "tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ uint32_t make_warp_uniform(uint32_t val) { + uint32_t result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1f, 0xffffffff;" + : "=r"(result) : "r"(val)); + return result; +} + +extern "C" { + +__global__ __launch_bounds__(96, 1) void +kernel_knn_build_non128_frontier_7ee5_m64rag_stage1_d256_5e7f_rag_d64d256_v1(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tiles, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 17408); + const int smem_database_addr = smem + 17408; + float* smem_database_sq = reinterpret_cast(smem_raw + 33792); + const int smem_database_sq_addr = smem + 33792; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 0, 1, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 16, 1, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=32 + mbarrier_init_pred(smem + 40, 32, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (64 columns, 64 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::1.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(64) : "memory"); + } + + __syncthreads(); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: compute ---- + if (warp == 0) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx = bid; work_idx < total_work; work_idx += num_bids) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tiles; + int q_tile = query_work % num_q_tiles; + int off_q = q_tile * 64; + int q_idx = off_q + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx * Q + q_idx]; + } + float best_d[10]; + int best_i[10]; + #pragma unroll + for (int kk = 0; kk < 10; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + int db_tile_start = split_idx * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int db_start = db_tile * 64; + int db_sq_idx0 = db_start + (warp % 4 * 32 + lane); + if (db_sq_idx0 < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx * M + db_sq_idx0]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 3.4e+38f; + } + int db_col1 = warp % 4 * 32 + lane + 32; + int db_sq_idx1 = db_start + db_col1; + if (db_sq_idx1 < M) { + smem_database_sq[db_col1] = database_sq[batch_idx * M + db_sq_idx1]; + } else { + smem_database_sq[db_col1] = 3.4e+38f; + } + asm volatile("barrier.sync 8, %0;" :: "r"(32)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(warp % 4 * 32 << 16); + float _tmem_load_0[64]; + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x64.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31, %32, %33, %34, %35, %36, %37, %38, %39, %40, %41, %42, %43, %44, %45, %46, %47, %48, %49, %50, %51, %52, %53, %54, %55, %56, %57, %58, %59, %60, %61, %62, %63}, [%64];" + : "=f"(_tmem_load_0[0]), "=f"(_tmem_load_0[1]), "=f"(_tmem_load_0[2]), "=f"(_tmem_load_0[3]), "=f"(_tmem_load_0[4]), "=f"(_tmem_load_0[5]), "=f"(_tmem_load_0[6]), "=f"(_tmem_load_0[7]), "=f"(_tmem_load_0[8]), "=f"(_tmem_load_0[9]), "=f"(_tmem_load_0[10]), "=f"(_tmem_load_0[11]), "=f"(_tmem_load_0[12]), "=f"(_tmem_load_0[13]), "=f"(_tmem_load_0[14]), "=f"(_tmem_load_0[15]), "=f"(_tmem_load_0[16]), "=f"(_tmem_load_0[17]), "=f"(_tmem_load_0[18]), "=f"(_tmem_load_0[19]), "=f"(_tmem_load_0[20]), "=f"(_tmem_load_0[21]), "=f"(_tmem_load_0[22]), "=f"(_tmem_load_0[23]), "=f"(_tmem_load_0[24]), "=f"(_tmem_load_0[25]), "=f"(_tmem_load_0[26]), "=f"(_tmem_load_0[27]), "=f"(_tmem_load_0[28]), "=f"(_tmem_load_0[29]), "=f"(_tmem_load_0[30]), "=f"(_tmem_load_0[31]), "=f"(_tmem_load_0[32]), "=f"(_tmem_load_0[33]), "=f"(_tmem_load_0[34]), "=f"(_tmem_load_0[35]), "=f"(_tmem_load_0[36]), "=f"(_tmem_load_0[37]), "=f"(_tmem_load_0[38]), "=f"(_tmem_load_0[39]), "=f"(_tmem_load_0[40]), "=f"(_tmem_load_0[41]), "=f"(_tmem_load_0[42]), "=f"(_tmem_load_0[43]), "=f"(_tmem_load_0[44]), "=f"(_tmem_load_0[45]), "=f"(_tmem_load_0[46]), "=f"(_tmem_load_0[47]), "=f"(_tmem_load_0[48]), "=f"(_tmem_load_0[49]), "=f"(_tmem_load_0[50]), "=f"(_tmem_load_0[51]), "=f"(_tmem_load_0[52]), "=f"(_tmem_load_0[53]), "=f"(_tmem_load_0[54]), "=f"(_tmem_load_0[55]), "=f"(_tmem_load_0[56]), "=f"(_tmem_load_0[57]), "=f"(_tmem_load_0[58]), "=f"(_tmem_load_0[59]), "=f"(_tmem_load_0[60]), "=f"(_tmem_load_0[61]), "=f"(_tmem_load_0[62]), "=f"(_tmem_load_0[63]) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(32)); + mbarrier_arrive(score_empty_addr); + if (valid_q != 0) { + #pragma unroll 1 + for (int col_base = 0; col_base < 64; col_base += 4) { + float dist_vec[4]; + dist_vec[0] = _tmem_load_0[col_base]; + dist_vec[1] = _tmem_load_0[col_base + 1]; + dist_vec[2] = _tmem_load_0[col_base + 2]; + dist_vec[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec[4]; + db_sq_vec[0] = smem_database_sq[col_base]; + db_sq_vec[1] = smem_database_sq[col_base + 1]; + db_sq_vec[2] = smem_database_sq[col_base + 2]; + db_sq_vec[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec)[_la], reinterpret_cast(db_sq_vec)[_la]); + float group_min = _t0[0]; + if (_t0[1] < group_min) { + group_min = _t0[1]; + } + if (_t0[2] < group_min) { + group_min = _t0[2]; + } + if (_t0[3] < group_min) { + group_min = _t0[3]; + } + if (group_min < best_d[9]) { + #pragma unroll + for (int vec_col = 0; vec_col < 4; vec_col++) { + int db_idx = db_start + col_base + vec_col; + if (db_idx < M) { + float _max_0 = max_noftz(_t0[vec_col], 0.0f); + float dist = _max_0; + if (dist < best_d[9]) { + best_d[9] = dist; + best_i[9] = db_idx; + #pragma unroll + for (int pos = 9; pos >= 1; pos--) { + if (best_d[pos] < best_d[pos - 1]) { + float tmp_d = best_d[pos - 1]; + int tmp_i = best_i[pos - 1]; + best_d[pos - 1] = best_d[pos]; + best_i[pos - 1] = best_i[pos]; + best_d[pos] = tmp_d; + best_i[pos] = tmp_i; + } + } + } + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(32)); + } + if (valid_q != 0) { + int out_base = ((split_idx * B + batch_idx) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < 10; out_k++) { + if (out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = best_d[out_k]; + *((int*)(partial_indices + (out_base + out_k))) = best_i[out_k]; + } + } + } + } + } + // ---- Role: load ---- + } else if (warp == 1) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 1) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx_1 = bid; work_idx_1 < total_work; work_idx_1 += num_bids) { + int split_idx_1 = work_idx_1 % (unsigned int)split_count; + int query_work_1 = work_idx_1 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tiles; + int q_tile_1 = query_work_1 % num_q_tiles; + int off_q_1 = q_tile_1 * 64; + int global_q = batch_idx_1 * Q + off_q_1; + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int off_m = db_tile_1 * 64; + int global_m = batch_idx_1 * M + off_m; + #pragma unroll + for (int feat_chunk = 0; feat_chunk < FEATURE_CHUNKS; feat_chunk++) { + int feature_coord = feat_chunk * 2; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + mbarrier_arrive_expect_tx(query_full_addr, 16384); + tma_3d_gmem2smem(smem_query_addr, tmap_query, 0, global_q, feature_coord, query_full_addr); + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + mbarrier_arrive_expect_tx(database_full_addr, 16384); + tma_3d_gmem2smem(smem_database_addr, tmap_database, 0, global_m, feature_coord, database_full_addr); + } + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 2) { + { // mma_main + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_database_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = bid; work_idx_2 < total_work; work_idx_2 += num_bids) { + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + #pragma unroll + for (int feat_chunk_1 = 0; feat_chunk_1 < FEATURE_CHUNKS; feat_chunk_1++) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = make_warp_uniform((smem_query_addr >> 4) & 0x3FFF); + int _mma_b_lo_0 = make_warp_uniform((smem_database_addr >> 4) & 0x3FFF); + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 68158608;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 506;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(((((feat_chunk_1 == 0) ? 1 : 0)) ? 0 : 1))); + asm volatile("tcgen05.fence::after_thread_sync;"); + elect_commit(query_empty_addr); + elect_commit(database_empty_addr); + } + elect_commit(score_full_addr); + } + } + } + } + + // Cleanup + __syncthreads(); // barrier before TMEM dealloc + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::1.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(64)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::1.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0096.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0096.cu new file mode 100644 index 00000000..e6c5b1f5 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0096.cu @@ -0,0 +1,141 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define SMEM_GROUP_DISTS_OFF 0 +#define SMEM_GROUP_DISTS_STAGE_BYTES 512 +#define SMEM_GROUP_DISTS_STRIDE 512 +#define SMEM_GROUP_INDICES_OFF 512 +#define SMEM_GROUP_INDICES_STAGE_BYTES 512 +#define SMEM_GROUP_INDICES_STRIDE 512 +#define SMEM_TOTAL 1024 +#define THREADS 32 +#define TOP_K_MAX 10 +#define GROUP_COUNT 8 +#define GROUP_SPLITS 18 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_non128_frontier_4be7_d768fused_merge_s144g8_4be7_d768fused_v1(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + float* group_dists = reinterpret_cast(smem_raw + 0); + const int group_dists_addr = smem + 0; + int* group_indices = reinterpret_cast(smem_raw + 512); + const int group_indices_addr = smem + 512; + + // === Task calls (dependency order) === + int split_pos[GROUP_SPLITS]; + int split_base[GROUP_SPLITS]; + float group_cand_d[GROUP_SPLITS]; + int group_cand_i[GROUP_SPLITS]; + int final_pos[GROUP_COUNT]; + float final_cand_d[GROUP_COUNT]; + int final_cand_i[GROUP_COUNT]; + #pragma unroll 1 + for (int row = bid; row < total_queries; row += num_bids) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + if (tid < GROUP_COUNT) { + int group_idx = tid; + int source_split0 = group_idx * GROUP_SPLITS; + int shared_base = group_idx * TOP_K_MAX; + #pragma unroll + for (int local_split = 0; local_split < GROUP_SPLITS; local_split++) { + split_pos[local_split] = 0; + int split_id = source_split0 + local_split; + split_base[local_split] = base_row + split_id * split_stride; + group_cand_d[local_split] = partial_dists[split_base[local_split]]; + group_cand_i[local_split] = partial_indices[split_base[local_split]]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_d = group_cand_d[0]; + int best_i = group_cand_i[0]; + int best_split = 0; + #pragma unroll + for (int local_split_1 = 1; local_split_1 < GROUP_SPLITS; local_split_1++) { + if (best_d > group_cand_d[local_split_1]) { + best_d = group_cand_d[local_split_1]; + best_i = group_cand_i[local_split_1]; + best_split = local_split_1; + } + } + group_dists[shared_base + out_k] = best_d; + group_indices[shared_base + out_k] = best_i; + split_pos[best_split] = split_pos[best_split] + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_pos = split_pos[best_split]; + int next_addr = split_base[best_split] + next_pos; + group_cand_d[best_split] = partial_dists[next_addr]; + group_cand_i[best_split] = partial_indices[next_addr]; + } + } + } + __syncthreads(); + if (tid == 0) { + #pragma unroll + for (int group_idx_1 = 0; group_idx_1 < GROUP_COUNT; group_idx_1++) { + final_pos[group_idx_1] = 0; + int group_base = group_idx_1 * TOP_K_MAX; + final_cand_d[group_idx_1] = group_dists[group_base]; + final_cand_i[group_idx_1] = group_indices[group_base]; + } + #pragma unroll + for (int out_k_1 = 0; out_k_1 < TOP_K_MAX; out_k_1++) { + float best_d_1 = final_cand_d[0]; + int best_i_1 = final_cand_i[0]; + int best_group = 0; + #pragma unroll + for (int group_idx_2 = 1; group_idx_2 < GROUP_COUNT; group_idx_2++) { + if (best_d_1 > final_cand_d[group_idx_2]) { + best_d_1 = final_cand_d[group_idx_2]; + best_i_1 = final_cand_i[group_idx_2]; + best_group = group_idx_2; + } + } + *((float*)(out_dists + (base_row + out_k_1))) = best_d_1; + *((int*)(out_indices + (base_row + out_k_1))) = best_i_1; + final_pos[best_group] = final_pos[best_group] + 1; + if (out_k_1 + 1 < TOP_K_MAX) { + int next_pos_1 = final_pos[best_group]; + int next_addr_1 = best_group * TOP_K_MAX + next_pos_1; + final_cand_d[best_group] = group_dists[next_addr_1]; + final_cand_i[best_group] = group_indices[next_addr_1]; + } + } + } + __syncthreads(); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0097.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0097.cu new file mode 100644 index 00000000..6b84f13b --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0097.cu @@ -0,0 +1,619 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 64 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 16384 +#define SMEM_SMEM_QUERY_STRIDE 16384 +#define SMEM_SMEM_DATABASE_OFF 17408 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_DATABASE_SQ_OFF 33792 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 34048 +#define THREADS 96 +#define FEATURE_CHUNKS 8 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "tcgen05.mma.cta_group::1.kind::f16 [%0], %1, %2, %3, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, %3, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit(int mbar_addr) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];\n\t" + "}\n" + :: "r"(mbar_addr)); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x16(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x16.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15}, [%16];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ float max_noftz(float a, float b) { + float c; + asm("max.f32 %0, %1, %2;" : "=f"(c) : "f"(a), "f"(b)); + return c; +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cta.global" + ".mbarrier::complete_tx::bytes" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit(int mbar_addr) { + asm volatile( + "tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ uint32_t make_warp_uniform(uint32_t val) { + uint32_t result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1f, 0xffffffff;" + : "=r"(result) : "r"(val)); + return result; +} + +extern "C" { + +__global__ __launch_bounds__(96, 1) void +kernel_knn_build_non128_frontier_7ee5_m64rag_stage1_d1024_5e7f_highd_v1(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tiles, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 17408); + const int smem_database_addr = smem + 17408; + float* smem_database_sq = reinterpret_cast(smem_raw + 33792); + const int smem_database_sq_addr = smem + 33792; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 0, 1, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 16, 1, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=32 + mbarrier_init_pred(smem + 40, 32, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (64 columns, 64 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::1.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(64) : "memory"); + } + + __syncthreads(); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: compute ---- + if (warp == 0) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx = bid; work_idx < total_work; work_idx += num_bids) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tiles; + int q_tile = query_work % num_q_tiles; + int off_q = q_tile * 64; + int q_idx = off_q + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx * Q + q_idx]; + } + float best_d[10]; + int best_i[10]; + #pragma unroll + for (int kk = 0; kk < 10; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + int db_tile_start = split_idx * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int db_start = db_tile * 64; + int db_sq_idx0 = db_start + (warp % 4 * 32 + lane); + if (db_sq_idx0 < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx * M + db_sq_idx0]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 3.4e+38f; + } + int db_col1 = warp % 4 * 32 + lane + 32; + int db_sq_idx1 = db_start + db_col1; + if (db_sq_idx1 < M) { + smem_database_sq[db_col1] = database_sq[batch_idx * M + db_sq_idx1]; + } else { + smem_database_sq[db_col1] = 3.4e+38f; + } + asm volatile("barrier.sync 8, %0;" :: "r"(32)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(warp % 4 * 32 << 16); + float _tmem_load_0[64]; + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x64.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31, %32, %33, %34, %35, %36, %37, %38, %39, %40, %41, %42, %43, %44, %45, %46, %47, %48, %49, %50, %51, %52, %53, %54, %55, %56, %57, %58, %59, %60, %61, %62, %63}, [%64];" + : "=f"(_tmem_load_0[0]), "=f"(_tmem_load_0[1]), "=f"(_tmem_load_0[2]), "=f"(_tmem_load_0[3]), "=f"(_tmem_load_0[4]), "=f"(_tmem_load_0[5]), "=f"(_tmem_load_0[6]), "=f"(_tmem_load_0[7]), "=f"(_tmem_load_0[8]), "=f"(_tmem_load_0[9]), "=f"(_tmem_load_0[10]), "=f"(_tmem_load_0[11]), "=f"(_tmem_load_0[12]), "=f"(_tmem_load_0[13]), "=f"(_tmem_load_0[14]), "=f"(_tmem_load_0[15]), "=f"(_tmem_load_0[16]), "=f"(_tmem_load_0[17]), "=f"(_tmem_load_0[18]), "=f"(_tmem_load_0[19]), "=f"(_tmem_load_0[20]), "=f"(_tmem_load_0[21]), "=f"(_tmem_load_0[22]), "=f"(_tmem_load_0[23]), "=f"(_tmem_load_0[24]), "=f"(_tmem_load_0[25]), "=f"(_tmem_load_0[26]), "=f"(_tmem_load_0[27]), "=f"(_tmem_load_0[28]), "=f"(_tmem_load_0[29]), "=f"(_tmem_load_0[30]), "=f"(_tmem_load_0[31]), "=f"(_tmem_load_0[32]), "=f"(_tmem_load_0[33]), "=f"(_tmem_load_0[34]), "=f"(_tmem_load_0[35]), "=f"(_tmem_load_0[36]), "=f"(_tmem_load_0[37]), "=f"(_tmem_load_0[38]), "=f"(_tmem_load_0[39]), "=f"(_tmem_load_0[40]), "=f"(_tmem_load_0[41]), "=f"(_tmem_load_0[42]), "=f"(_tmem_load_0[43]), "=f"(_tmem_load_0[44]), "=f"(_tmem_load_0[45]), "=f"(_tmem_load_0[46]), "=f"(_tmem_load_0[47]), "=f"(_tmem_load_0[48]), "=f"(_tmem_load_0[49]), "=f"(_tmem_load_0[50]), "=f"(_tmem_load_0[51]), "=f"(_tmem_load_0[52]), "=f"(_tmem_load_0[53]), "=f"(_tmem_load_0[54]), "=f"(_tmem_load_0[55]), "=f"(_tmem_load_0[56]), "=f"(_tmem_load_0[57]), "=f"(_tmem_load_0[58]), "=f"(_tmem_load_0[59]), "=f"(_tmem_load_0[60]), "=f"(_tmem_load_0[61]), "=f"(_tmem_load_0[62]), "=f"(_tmem_load_0[63]) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(32)); + mbarrier_arrive(score_empty_addr); + if (valid_q != 0) { + #pragma unroll 1 + for (int col_base = 0; col_base < 64; col_base += 4) { + float dist_vec[4]; + dist_vec[0] = _tmem_load_0[col_base]; + dist_vec[1] = _tmem_load_0[col_base + 1]; + dist_vec[2] = _tmem_load_0[col_base + 2]; + dist_vec[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec[4]; + db_sq_vec[0] = smem_database_sq[col_base]; + db_sq_vec[1] = smem_database_sq[col_base + 1]; + db_sq_vec[2] = smem_database_sq[col_base + 2]; + db_sq_vec[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec)[_la], reinterpret_cast(db_sq_vec)[_la]); + float group_min = _t0[0]; + if (_t0[1] < group_min) { + group_min = _t0[1]; + } + if (_t0[2] < group_min) { + group_min = _t0[2]; + } + if (_t0[3] < group_min) { + group_min = _t0[3]; + } + if (group_min < best_d[9]) { + #pragma unroll + for (int vec_col = 0; vec_col < 4; vec_col++) { + int db_idx = db_start + col_base + vec_col; + if (db_idx < M) { + float _max_0 = max_noftz(_t0[vec_col], 0.0f); + float dist = _max_0; + if (dist < best_d[9]) { + best_d[9] = dist; + best_i[9] = db_idx; + #pragma unroll + for (int pos = 9; pos >= 1; pos--) { + if (best_d[pos] < best_d[pos - 1]) { + float tmp_d = best_d[pos - 1]; + int tmp_i = best_i[pos - 1]; + best_d[pos - 1] = best_d[pos]; + best_i[pos - 1] = best_i[pos]; + best_d[pos] = tmp_d; + best_i[pos] = tmp_i; + } + } + } + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(32)); + } + if (valid_q != 0) { + int out_base = ((split_idx * B + batch_idx) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < 10; out_k++) { + if (out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = best_d[out_k]; + *((int*)(partial_indices + (out_base + out_k))) = best_i[out_k]; + } + } + } + } + } + // ---- Role: load ---- + } else if (warp == 1) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 1) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx_1 = bid; work_idx_1 < total_work; work_idx_1 += num_bids) { + int split_idx_1 = work_idx_1 % (unsigned int)split_count; + int query_work_1 = work_idx_1 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tiles; + int q_tile_1 = query_work_1 % num_q_tiles; + int off_q_1 = q_tile_1 * 64; + int global_q = batch_idx_1 * Q + off_q_1; + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int off_m = db_tile_1 * 64; + int global_m = batch_idx_1 * M + off_m; + #pragma unroll + for (int feat_chunk = 0; feat_chunk < FEATURE_CHUNKS; feat_chunk++) { + int feature_coord = feat_chunk * 2; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + mbarrier_arrive_expect_tx(query_full_addr, 16384); + tma_3d_gmem2smem(smem_query_addr, tmap_query, 0, global_q, feature_coord, query_full_addr); + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + mbarrier_arrive_expect_tx(database_full_addr, 16384); + tma_3d_gmem2smem(smem_database_addr, tmap_database, 0, global_m, feature_coord, database_full_addr); + } + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 2) { + { // mma_main + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_database_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = bid; work_idx_2 < total_work; work_idx_2 += num_bids) { + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + #pragma unroll + for (int feat_chunk_1 = 0; feat_chunk_1 < FEATURE_CHUNKS; feat_chunk_1++) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = make_warp_uniform((smem_query_addr >> 4) & 0x3FFF); + int _mma_b_lo_0 = make_warp_uniform((smem_database_addr >> 4) & 0x3FFF); + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 68158608;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 506;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(((((feat_chunk_1 == 0) ? 1 : 0)) ? 0 : 1))); + asm volatile("tcgen05.fence::after_thread_sync;"); + elect_commit(query_empty_addr); + elect_commit(database_empty_addr); + } + elect_commit(score_full_addr); + } + } + } + } + + // Cleanup + __syncthreads(); // barrier before TMEM dealloc + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::1.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(64)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::1.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0098.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0098.cu new file mode 100644 index 00000000..a118af57 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0098.cu @@ -0,0 +1,141 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define SMEM_GROUP_DISTS_OFF 0 +#define SMEM_GROUP_DISTS_STAGE_BYTES 512 +#define SMEM_GROUP_DISTS_STRIDE 512 +#define SMEM_GROUP_INDICES_OFF 512 +#define SMEM_GROUP_INDICES_STAGE_BYTES 512 +#define SMEM_GROUP_INDICES_STRIDE 512 +#define SMEM_TOTAL 1024 +#define THREADS 32 +#define TOP_K_MAX 10 +#define GROUP_COUNT 12 +#define GROUP_SPLITS 12 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_non128_frontier_4be7_d768fused_merge_s144g12_4be7_d768fused_v1(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + float* group_dists = reinterpret_cast(smem_raw + 0); + const int group_dists_addr = smem + 0; + int* group_indices = reinterpret_cast(smem_raw + 512); + const int group_indices_addr = smem + 512; + + // === Task calls (dependency order) === + int split_pos[GROUP_SPLITS]; + int split_base[GROUP_SPLITS]; + float group_cand_d[GROUP_SPLITS]; + int group_cand_i[GROUP_SPLITS]; + int final_pos[GROUP_COUNT]; + float final_cand_d[GROUP_COUNT]; + int final_cand_i[GROUP_COUNT]; + #pragma unroll 1 + for (int row = bid; row < total_queries; row += num_bids) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + if (tid < GROUP_COUNT) { + int group_idx = tid; + int source_split0 = group_idx * GROUP_SPLITS; + int shared_base = group_idx * TOP_K_MAX; + #pragma unroll + for (int local_split = 0; local_split < GROUP_SPLITS; local_split++) { + split_pos[local_split] = 0; + int split_id = source_split0 + local_split; + split_base[local_split] = base_row + split_id * split_stride; + group_cand_d[local_split] = partial_dists[split_base[local_split]]; + group_cand_i[local_split] = partial_indices[split_base[local_split]]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_d = group_cand_d[0]; + int best_i = group_cand_i[0]; + int best_split = 0; + #pragma unroll + for (int local_split_1 = 1; local_split_1 < GROUP_SPLITS; local_split_1++) { + if (best_d > group_cand_d[local_split_1]) { + best_d = group_cand_d[local_split_1]; + best_i = group_cand_i[local_split_1]; + best_split = local_split_1; + } + } + group_dists[shared_base + out_k] = best_d; + group_indices[shared_base + out_k] = best_i; + split_pos[best_split] = split_pos[best_split] + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_pos = split_pos[best_split]; + int next_addr = split_base[best_split] + next_pos; + group_cand_d[best_split] = partial_dists[next_addr]; + group_cand_i[best_split] = partial_indices[next_addr]; + } + } + } + __syncthreads(); + if (tid == 0) { + #pragma unroll + for (int group_idx_1 = 0; group_idx_1 < GROUP_COUNT; group_idx_1++) { + final_pos[group_idx_1] = 0; + int group_base = group_idx_1 * TOP_K_MAX; + final_cand_d[group_idx_1] = group_dists[group_base]; + final_cand_i[group_idx_1] = group_indices[group_base]; + } + #pragma unroll + for (int out_k_1 = 0; out_k_1 < TOP_K_MAX; out_k_1++) { + float best_d_1 = final_cand_d[0]; + int best_i_1 = final_cand_i[0]; + int best_group = 0; + #pragma unroll + for (int group_idx_2 = 1; group_idx_2 < GROUP_COUNT; group_idx_2++) { + if (best_d_1 > final_cand_d[group_idx_2]) { + best_d_1 = final_cand_d[group_idx_2]; + best_i_1 = final_cand_i[group_idx_2]; + best_group = group_idx_2; + } + } + *((float*)(out_dists + (base_row + out_k_1))) = best_d_1; + *((int*)(out_indices + (base_row + out_k_1))) = best_i_1; + final_pos[best_group] = final_pos[best_group] + 1; + if (out_k_1 + 1 < TOP_K_MAX) { + int next_pos_1 = final_pos[best_group]; + int next_addr_1 = best_group * TOP_K_MAX + next_pos_1; + final_cand_d[best_group] = group_dists[next_addr_1]; + final_cand_i[best_group] = group_indices[next_addr_1]; + } + } + } + __syncthreads(); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0099.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0099.cu new file mode 100644 index 00000000..f449c8b3 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0099.cu @@ -0,0 +1,619 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 64 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 16384 +#define SMEM_SMEM_QUERY_STRIDE 16384 +#define SMEM_SMEM_DATABASE_OFF 17408 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_DATABASE_SQ_OFF 33792 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 34048 +#define THREADS 96 +#define FEATURE_CHUNKS 32 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "tcgen05.mma.cta_group::1.kind::f16 [%0], %1, %2, %3, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, %3, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit(int mbar_addr) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];\n\t" + "}\n" + :: "r"(mbar_addr)); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x16(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x16.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15}, [%16];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ float max_noftz(float a, float b) { + float c; + asm("max.f32 %0, %1, %2;" : "=f"(c) : "f"(a), "f"(b)); + return c; +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cta.global" + ".mbarrier::complete_tx::bytes" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit(int mbar_addr) { + asm volatile( + "tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ uint32_t make_warp_uniform(uint32_t val) { + uint32_t result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1f, 0xffffffff;" + : "=r"(result) : "r"(val)); + return result; +} + +extern "C" { + +__global__ __launch_bounds__(96, 1) void +kernel_knn_build_non128_frontier_7ee5_m64rag_stage1_d4096_5e7f_highd_v1(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tiles, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 17408); + const int smem_database_addr = smem + 17408; + float* smem_database_sq = reinterpret_cast(smem_raw + 33792); + const int smem_database_sq_addr = smem + 33792; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 0, 1, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 16, 1, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=32 + mbarrier_init_pred(smem + 40, 32, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (64 columns, 64 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::1.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(64) : "memory"); + } + + __syncthreads(); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: compute ---- + if (warp == 0) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx = bid; work_idx < total_work; work_idx += num_bids) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tiles; + int q_tile = query_work % num_q_tiles; + int off_q = q_tile * 64; + int q_idx = off_q + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx * Q + q_idx]; + } + float best_d[10]; + int best_i[10]; + #pragma unroll + for (int kk = 0; kk < 10; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + int db_tile_start = split_idx * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int db_start = db_tile * 64; + int db_sq_idx0 = db_start + (warp % 4 * 32 + lane); + if (db_sq_idx0 < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx * M + db_sq_idx0]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 3.4e+38f; + } + int db_col1 = warp % 4 * 32 + lane + 32; + int db_sq_idx1 = db_start + db_col1; + if (db_sq_idx1 < M) { + smem_database_sq[db_col1] = database_sq[batch_idx * M + db_sq_idx1]; + } else { + smem_database_sq[db_col1] = 3.4e+38f; + } + asm volatile("barrier.sync 8, %0;" :: "r"(32)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(warp % 4 * 32 << 16); + float _tmem_load_0[64]; + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x64.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31, %32, %33, %34, %35, %36, %37, %38, %39, %40, %41, %42, %43, %44, %45, %46, %47, %48, %49, %50, %51, %52, %53, %54, %55, %56, %57, %58, %59, %60, %61, %62, %63}, [%64];" + : "=f"(_tmem_load_0[0]), "=f"(_tmem_load_0[1]), "=f"(_tmem_load_0[2]), "=f"(_tmem_load_0[3]), "=f"(_tmem_load_0[4]), "=f"(_tmem_load_0[5]), "=f"(_tmem_load_0[6]), "=f"(_tmem_load_0[7]), "=f"(_tmem_load_0[8]), "=f"(_tmem_load_0[9]), "=f"(_tmem_load_0[10]), "=f"(_tmem_load_0[11]), "=f"(_tmem_load_0[12]), "=f"(_tmem_load_0[13]), "=f"(_tmem_load_0[14]), "=f"(_tmem_load_0[15]), "=f"(_tmem_load_0[16]), "=f"(_tmem_load_0[17]), "=f"(_tmem_load_0[18]), "=f"(_tmem_load_0[19]), "=f"(_tmem_load_0[20]), "=f"(_tmem_load_0[21]), "=f"(_tmem_load_0[22]), "=f"(_tmem_load_0[23]), "=f"(_tmem_load_0[24]), "=f"(_tmem_load_0[25]), "=f"(_tmem_load_0[26]), "=f"(_tmem_load_0[27]), "=f"(_tmem_load_0[28]), "=f"(_tmem_load_0[29]), "=f"(_tmem_load_0[30]), "=f"(_tmem_load_0[31]), "=f"(_tmem_load_0[32]), "=f"(_tmem_load_0[33]), "=f"(_tmem_load_0[34]), "=f"(_tmem_load_0[35]), "=f"(_tmem_load_0[36]), "=f"(_tmem_load_0[37]), "=f"(_tmem_load_0[38]), "=f"(_tmem_load_0[39]), "=f"(_tmem_load_0[40]), "=f"(_tmem_load_0[41]), "=f"(_tmem_load_0[42]), "=f"(_tmem_load_0[43]), "=f"(_tmem_load_0[44]), "=f"(_tmem_load_0[45]), "=f"(_tmem_load_0[46]), "=f"(_tmem_load_0[47]), "=f"(_tmem_load_0[48]), "=f"(_tmem_load_0[49]), "=f"(_tmem_load_0[50]), "=f"(_tmem_load_0[51]), "=f"(_tmem_load_0[52]), "=f"(_tmem_load_0[53]), "=f"(_tmem_load_0[54]), "=f"(_tmem_load_0[55]), "=f"(_tmem_load_0[56]), "=f"(_tmem_load_0[57]), "=f"(_tmem_load_0[58]), "=f"(_tmem_load_0[59]), "=f"(_tmem_load_0[60]), "=f"(_tmem_load_0[61]), "=f"(_tmem_load_0[62]), "=f"(_tmem_load_0[63]) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(32)); + mbarrier_arrive(score_empty_addr); + if (valid_q != 0) { + #pragma unroll 1 + for (int col_base = 0; col_base < 64; col_base += 4) { + float dist_vec[4]; + dist_vec[0] = _tmem_load_0[col_base]; + dist_vec[1] = _tmem_load_0[col_base + 1]; + dist_vec[2] = _tmem_load_0[col_base + 2]; + dist_vec[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec[4]; + db_sq_vec[0] = smem_database_sq[col_base]; + db_sq_vec[1] = smem_database_sq[col_base + 1]; + db_sq_vec[2] = smem_database_sq[col_base + 2]; + db_sq_vec[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec)[_la], reinterpret_cast(db_sq_vec)[_la]); + float group_min = _t0[0]; + if (_t0[1] < group_min) { + group_min = _t0[1]; + } + if (_t0[2] < group_min) { + group_min = _t0[2]; + } + if (_t0[3] < group_min) { + group_min = _t0[3]; + } + if (group_min < best_d[9]) { + #pragma unroll + for (int vec_col = 0; vec_col < 4; vec_col++) { + int db_idx = db_start + col_base + vec_col; + if (db_idx < M) { + float _max_0 = max_noftz(_t0[vec_col], 0.0f); + float dist = _max_0; + if (dist < best_d[9]) { + best_d[9] = dist; + best_i[9] = db_idx; + #pragma unroll + for (int pos = 9; pos >= 1; pos--) { + if (best_d[pos] < best_d[pos - 1]) { + float tmp_d = best_d[pos - 1]; + int tmp_i = best_i[pos - 1]; + best_d[pos - 1] = best_d[pos]; + best_i[pos - 1] = best_i[pos]; + best_d[pos] = tmp_d; + best_i[pos] = tmp_i; + } + } + } + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(32)); + } + if (valid_q != 0) { + int out_base = ((split_idx * B + batch_idx) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < 10; out_k++) { + if (out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = best_d[out_k]; + *((int*)(partial_indices + (out_base + out_k))) = best_i[out_k]; + } + } + } + } + } + // ---- Role: load ---- + } else if (warp == 1) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 1) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx_1 = bid; work_idx_1 < total_work; work_idx_1 += num_bids) { + int split_idx_1 = work_idx_1 % (unsigned int)split_count; + int query_work_1 = work_idx_1 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tiles; + int q_tile_1 = query_work_1 % num_q_tiles; + int off_q_1 = q_tile_1 * 64; + int global_q = batch_idx_1 * Q + off_q_1; + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int off_m = db_tile_1 * 64; + int global_m = batch_idx_1 * M + off_m; + #pragma unroll + for (int feat_chunk = 0; feat_chunk < FEATURE_CHUNKS; feat_chunk++) { + int feature_coord = feat_chunk * 2; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + mbarrier_arrive_expect_tx(query_full_addr, 16384); + tma_3d_gmem2smem(smem_query_addr, tmap_query, 0, global_q, feature_coord, query_full_addr); + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + mbarrier_arrive_expect_tx(database_full_addr, 16384); + tma_3d_gmem2smem(smem_database_addr, tmap_database, 0, global_m, feature_coord, database_full_addr); + } + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 2) { + { // mma_main + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_database_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = bid; work_idx_2 < total_work; work_idx_2 += num_bids) { + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + #pragma unroll + for (int feat_chunk_1 = 0; feat_chunk_1 < FEATURE_CHUNKS; feat_chunk_1++) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = make_warp_uniform((smem_query_addr >> 4) & 0x3FFF); + int _mma_b_lo_0 = make_warp_uniform((smem_database_addr >> 4) & 0x3FFF); + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 68158608;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 506;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(((((feat_chunk_1 == 0) ? 1 : 0)) ? 0 : 1))); + asm volatile("tcgen05.fence::after_thread_sync;"); + elect_commit(query_empty_addr); + elect_commit(database_empty_addr); + } + elect_commit(score_full_addr); + } + } + } + } + + // Cleanup + __syncthreads(); // barrier before TMEM dealloc + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::1.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(64)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::1.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0100.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0100.cu new file mode 100644 index 00000000..f7dcc7c0 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0100.cu @@ -0,0 +1,141 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define SMEM_GROUP_DISTS_OFF 0 +#define SMEM_GROUP_DISTS_STAGE_BYTES 512 +#define SMEM_GROUP_DISTS_STRIDE 512 +#define SMEM_GROUP_INDICES_OFF 512 +#define SMEM_GROUP_INDICES_STAGE_BYTES 512 +#define SMEM_GROUP_INDICES_STRIDE 512 +#define SMEM_TOTAL 1024 +#define THREADS 32 +#define TOP_K_MAX 10 +#define GROUP_COUNT 8 +#define GROUP_SPLITS 16 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_non128_frontier_4be7_d768fused_merge_s128g8_4be7_d768fused_v1(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + float* group_dists = reinterpret_cast(smem_raw + 0); + const int group_dists_addr = smem + 0; + int* group_indices = reinterpret_cast(smem_raw + 512); + const int group_indices_addr = smem + 512; + + // === Task calls (dependency order) === + int split_pos[GROUP_SPLITS]; + int split_base[GROUP_SPLITS]; + float group_cand_d[GROUP_SPLITS]; + int group_cand_i[GROUP_SPLITS]; + int final_pos[GROUP_COUNT]; + float final_cand_d[GROUP_COUNT]; + int final_cand_i[GROUP_COUNT]; + #pragma unroll 1 + for (int row = bid; row < total_queries; row += num_bids) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + if (tid < GROUP_COUNT) { + int group_idx = tid; + int source_split0 = group_idx * GROUP_SPLITS; + int shared_base = group_idx * TOP_K_MAX; + #pragma unroll + for (int local_split = 0; local_split < GROUP_SPLITS; local_split++) { + split_pos[local_split] = 0; + int split_id = source_split0 + local_split; + split_base[local_split] = base_row + split_id * split_stride; + group_cand_d[local_split] = partial_dists[split_base[local_split]]; + group_cand_i[local_split] = partial_indices[split_base[local_split]]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_d = group_cand_d[0]; + int best_i = group_cand_i[0]; + int best_split = 0; + #pragma unroll + for (int local_split_1 = 1; local_split_1 < GROUP_SPLITS; local_split_1++) { + if (best_d > group_cand_d[local_split_1]) { + best_d = group_cand_d[local_split_1]; + best_i = group_cand_i[local_split_1]; + best_split = local_split_1; + } + } + group_dists[shared_base + out_k] = best_d; + group_indices[shared_base + out_k] = best_i; + split_pos[best_split] = split_pos[best_split] + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_pos = split_pos[best_split]; + int next_addr = split_base[best_split] + next_pos; + group_cand_d[best_split] = partial_dists[next_addr]; + group_cand_i[best_split] = partial_indices[next_addr]; + } + } + } + __syncthreads(); + if (tid == 0) { + #pragma unroll + for (int group_idx_1 = 0; group_idx_1 < GROUP_COUNT; group_idx_1++) { + final_pos[group_idx_1] = 0; + int group_base = group_idx_1 * TOP_K_MAX; + final_cand_d[group_idx_1] = group_dists[group_base]; + final_cand_i[group_idx_1] = group_indices[group_base]; + } + #pragma unroll + for (int out_k_1 = 0; out_k_1 < TOP_K_MAX; out_k_1++) { + float best_d_1 = final_cand_d[0]; + int best_i_1 = final_cand_i[0]; + int best_group = 0; + #pragma unroll + for (int group_idx_2 = 1; group_idx_2 < GROUP_COUNT; group_idx_2++) { + if (best_d_1 > final_cand_d[group_idx_2]) { + best_d_1 = final_cand_d[group_idx_2]; + best_i_1 = final_cand_i[group_idx_2]; + best_group = group_idx_2; + } + } + *((float*)(out_dists + (base_row + out_k_1))) = best_d_1; + *((int*)(out_indices + (base_row + out_k_1))) = best_i_1; + final_pos[best_group] = final_pos[best_group] + 1; + if (out_k_1 + 1 < TOP_K_MAX) { + int next_pos_1 = final_pos[best_group]; + int next_addr_1 = best_group * TOP_K_MAX + next_pos_1; + final_cand_d[best_group] = group_dists[next_addr_1]; + final_cand_i[best_group] = group_indices[next_addr_1]; + } + } + } + __syncthreads(); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0101.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0101.cu new file mode 100644 index 00000000..ff37ea57 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0101.cu @@ -0,0 +1,141 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define SMEM_GROUP_DISTS_OFF 0 +#define SMEM_GROUP_DISTS_STAGE_BYTES 512 +#define SMEM_GROUP_DISTS_STRIDE 512 +#define SMEM_GROUP_INDICES_OFF 512 +#define SMEM_GROUP_INDICES_STAGE_BYTES 512 +#define SMEM_GROUP_INDICES_STRIDE 512 +#define SMEM_TOTAL 1024 +#define THREADS 32 +#define TOP_K_MAX 10 +#define GROUP_COUNT 8 +#define GROUP_SPLITS 16 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_rag_microbatch_4a72_k10_fused_group_final_merge_s128g8_4a72_v1(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + float* group_dists = reinterpret_cast(smem_raw + 0); + const int group_dists_addr = smem + 0; + int* group_indices = reinterpret_cast(smem_raw + 512); + const int group_indices_addr = smem + 512; + + // === Task calls (dependency order) === + int split_pos[GROUP_SPLITS]; + int split_base[GROUP_SPLITS]; + float group_cand_d[GROUP_SPLITS]; + int group_cand_i[GROUP_SPLITS]; + int final_pos[GROUP_COUNT]; + float final_cand_d[GROUP_COUNT]; + int final_cand_i[GROUP_COUNT]; + #pragma unroll 1 + for (int row = bid; row < total_queries; row += num_bids) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + if (tid < GROUP_COUNT) { + int group_idx = tid; + int source_split0 = group_idx * GROUP_SPLITS; + int shared_base = group_idx * TOP_K_MAX; + #pragma unroll + for (int local_split = 0; local_split < GROUP_SPLITS; local_split++) { + split_pos[local_split] = 0; + int split_id = source_split0 + local_split; + split_base[local_split] = base_row + split_id * split_stride; + group_cand_d[local_split] = partial_dists[split_base[local_split]]; + group_cand_i[local_split] = partial_indices[split_base[local_split]]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_d = group_cand_d[0]; + int best_i = group_cand_i[0]; + int best_split = 0; + #pragma unroll + for (int local_split_1 = 1; local_split_1 < GROUP_SPLITS; local_split_1++) { + if (best_d > group_cand_d[local_split_1]) { + best_d = group_cand_d[local_split_1]; + best_i = group_cand_i[local_split_1]; + best_split = local_split_1; + } + } + group_dists[shared_base + out_k] = best_d; + group_indices[shared_base + out_k] = best_i; + split_pos[best_split] = split_pos[best_split] + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_pos = split_pos[best_split]; + int next_addr = split_base[best_split] + next_pos; + group_cand_d[best_split] = partial_dists[next_addr]; + group_cand_i[best_split] = partial_indices[next_addr]; + } + } + } + __syncthreads(); + if (tid == 0) { + #pragma unroll + for (int group_idx_1 = 0; group_idx_1 < GROUP_COUNT; group_idx_1++) { + final_pos[group_idx_1] = 0; + int group_base = group_idx_1 * TOP_K_MAX; + final_cand_d[group_idx_1] = group_dists[group_base]; + final_cand_i[group_idx_1] = group_indices[group_base]; + } + #pragma unroll + for (int out_k_1 = 0; out_k_1 < TOP_K_MAX; out_k_1++) { + float best_d_1 = final_cand_d[0]; + int best_i_1 = final_cand_i[0]; + int best_group = 0; + #pragma unroll + for (int group_idx_2 = 1; group_idx_2 < GROUP_COUNT; group_idx_2++) { + if (best_d_1 > final_cand_d[group_idx_2]) { + best_d_1 = final_cand_d[group_idx_2]; + best_i_1 = final_cand_i[group_idx_2]; + best_group = group_idx_2; + } + } + *((float*)(out_dists + (base_row + out_k_1))) = best_d_1; + *((int*)(out_indices + (base_row + out_k_1))) = best_i_1; + final_pos[best_group] = final_pos[best_group] + 1; + if (out_k_1 + 1 < TOP_K_MAX) { + int next_pos_1 = final_pos[best_group]; + int next_addr_1 = best_group * TOP_K_MAX + next_pos_1; + final_cand_d[best_group] = group_dists[next_addr_1]; + final_cand_i[best_group] = group_indices[next_addr_1]; + } + } + } + __syncthreads(); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0102.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0102.cu new file mode 100644 index 00000000..24592b1e --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0102.cu @@ -0,0 +1,581 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 64 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 16384 +#define SMEM_SMEM_QUERY_STRIDE 16384 +#define SMEM_SMEM_DATABASE_OFF 17408 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_LOCAL_D_OFF 34048 +#define SMEM_SMEM_LOCAL_D_STAGE_BYTES 4096 +#define SMEM_SMEM_LOCAL_D_STRIDE 4096 +#define SMEM_SMEM_LOCAL_I_OFF 38144 +#define SMEM_SMEM_LOCAL_I_STAGE_BYTES 4096 +#define SMEM_SMEM_LOCAL_I_STRIDE 4096 +#define SMEM_TOTAL 42240 +#define THREADS 96 +#define BLOCK_Q 64 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_MAX 32 +#define ROWS_COVERED 8 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "tcgen05.mma.cta_group::1.kind::f16 [%0], %1, %2, %3, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, %3, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit(int mbar_addr) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];\n\t" + "}\n" + :: "r"(mbar_addr)); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x32(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x32.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15," + " %16, %17, %18, %19, %20, %21, %22, %23," + " %24, %25, %26, %27, %28, %29, %30, %31}, [%32];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]), + "=f"(dst[16]), "=f"(dst[17]), "=f"(dst[18]), "=f"(dst[19]), + "=f"(dst[20]), "=f"(dst[21]), "=f"(dst[22]), "=f"(dst[23]), + "=f"(dst[24]), "=f"(dst[25]), "=f"(dst[26]), "=f"(dst[27]), + "=f"(dst[28]), "=f"(dst[29]), "=f"(dst[30]), "=f"(dst[31]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ float max_noftz(float a, float b) { + float c; + asm("max.f32 %0, %1, %2;" : "=f"(c) : "f"(a), "f"(b)); + return c; +} + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cta.global" + ".mbarrier::complete_tx::bytes" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit(int mbar_addr) { + asm volatile( + "tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ uint32_t make_warp_uniform(uint32_t val) { + uint32_t result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1f, 0xffffffff;" + : "=r"(result) : "r"(val)); + return result; +} + +extern "C" { + +__global__ __launch_bounds__(96, 1) void +kernel_knn_build_rag_microbucket_k32q8half_0077_v1_stage1_q8_k32_m64_halfrow_q8half_0077_v1(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tiles, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 17408); + const int smem_database_addr = smem + 17408; + float* smem_local_d = reinterpret_cast(smem_raw + 34048); + const int smem_local_d_addr = smem + 34048; + int* smem_local_i = reinterpret_cast(smem_raw + 38144); + const int smem_local_i_addr = smem + 38144; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 0, 1, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 16, 1, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 40, 1, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (64 columns, 64 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::1.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(64) : "memory"); + } + + __syncthreads(); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: compute ---- + if (warp == 0) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx = bid; work_idx < total_work; work_idx += num_bids) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tiles; + int q_tile = query_work % num_q_tiles; + int off_q = q_tile * BLOCK_Q; + int row = lane / 4; + int lane_col = lane % 4; + int slot = lane_col; + int q_idx = off_q + row; + int valid_row = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_row != 0) { + q_sq_val = query_sq[batch_idx * Q + q_idx]; + } + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + int db_tile_start = split_idx * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int db_start = db_tile * BLOCK_M; + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + float _tmem_load_0[32]; + asm volatile( + "tcgen05.ld.sync.aligned.16x256b.x8.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31}, [%32];" + : "=r"(*reinterpret_cast(&_tmem_load_0[0])), "=r"(*reinterpret_cast(&_tmem_load_0[1])), "=r"(*reinterpret_cast(&_tmem_load_0[2])), "=r"(*reinterpret_cast(&_tmem_load_0[3])), "=r"(*reinterpret_cast(&_tmem_load_0[4])), "=r"(*reinterpret_cast(&_tmem_load_0[5])), "=r"(*reinterpret_cast(&_tmem_load_0[6])), "=r"(*reinterpret_cast(&_tmem_load_0[7])), "=r"(*reinterpret_cast(&_tmem_load_0[8])), "=r"(*reinterpret_cast(&_tmem_load_0[9])), "=r"(*reinterpret_cast(&_tmem_load_0[10])), "=r"(*reinterpret_cast(&_tmem_load_0[11])), "=r"(*reinterpret_cast(&_tmem_load_0[12])), "=r"(*reinterpret_cast(&_tmem_load_0[13])), "=r"(*reinterpret_cast(&_tmem_load_0[14])), "=r"(*reinterpret_cast(&_tmem_load_0[15])), "=r"(*reinterpret_cast(&_tmem_load_0[16])), "=r"(*reinterpret_cast(&_tmem_load_0[17])), "=r"(*reinterpret_cast(&_tmem_load_0[18])), "=r"(*reinterpret_cast(&_tmem_load_0[19])), "=r"(*reinterpret_cast(&_tmem_load_0[20])), "=r"(*reinterpret_cast(&_tmem_load_0[21])), "=r"(*reinterpret_cast(&_tmem_load_0[22])), "=r"(*reinterpret_cast(&_tmem_load_0[23])), "=r"(*reinterpret_cast(&_tmem_load_0[24])), "=r"(*reinterpret_cast(&_tmem_load_0[25])), "=r"(*reinterpret_cast(&_tmem_load_0[26])), "=r"(*reinterpret_cast(&_tmem_load_0[27])), "=r"(*reinterpret_cast(&_tmem_load_0[28])), "=r"(*reinterpret_cast(&_tmem_load_0[29])), "=r"(*reinterpret_cast(&_tmem_load_0[30])), "=r"(*reinterpret_cast(&_tmem_load_0[31])) + : "r"(taddr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + if (elect_sync()) { + mbarrier_arrive(score_empty_addr); + } + #pragma unroll + for (int repeat = 0; repeat < 8; repeat++) { + const int reg_base = repeat * 4; + int col_base = repeat * 8 + lane_col * 2; + int db_idx0 = db_start + col_base; + int db_idx1 = db_idx0 + 1; + float cand0_d = 3.4e+38f; + float cand1_d = 3.4e+38f; + if (valid_row != 0 && db_idx0 < M) { + float _max_0 = max_noftz(q_sq_val + database_sq[batch_idx * M + db_idx0] - 2.0f * _tmem_load_0[reg_base], 0.0f); + cand0_d = _max_0; + } + if (valid_row != 0 && db_idx1 < M) { + float _max_1 = max_noftz(q_sq_val + database_sq[batch_idx * M + db_idx1] - 2.0f * _tmem_load_0[reg_base + 1], 0.0f); + cand1_d = _max_1; + } + int take1 = ((cand1_d < cand0_d) ? 1 : 0); + if (best_d[31] > ((take1 != 0) ? cand1_d : cand0_d)) { + best_d[31] = ((take1 != 0) ? cand1_d : cand0_d); + best_i[31] = ((take1 != 0) ? db_idx1 : db_idx0); + #pragma unroll + for (int kk_1 = 30; kk_1 >= 0; kk_1--) { + float lower0_d = best_d[kk_1 + 1]; + int lower0_i = best_i[kk_1 + 1]; + float upper0_d = best_d[kk_1]; + int upper0_i = best_i[kk_1]; + int swap0_up = ((lower0_d < upper0_d) ? 1 : 0); + best_d[kk_1] = ((swap0_up != 0) ? lower0_d : upper0_d); + best_i[kk_1] = ((swap0_up != 0) ? lower0_i : upper0_i); + best_d[kk_1 + 1] = ((swap0_up != 0) ? upper0_d : lower0_d); + best_i[kk_1 + 1] = ((swap0_up != 0) ? upper0_i : lower0_i); + } + if (best_d[31] > ((take1 != 0) ? cand0_d : cand1_d)) { + best_d[31] = ((take1 != 0) ? cand0_d : cand1_d); + best_i[31] = ((take1 != 0) ? db_idx0 : db_idx1); + #pragma unroll + for (int kk_2 = 30; kk_2 >= 0; kk_2--) { + float lower1_d = best_d[kk_2 + 1]; + int lower1_i = best_i[kk_2 + 1]; + float upper1_d = best_d[kk_2]; + int upper1_i = best_i[kk_2]; + int swap1_up = ((lower1_d < upper1_d) ? 1 : 0); + best_d[kk_2] = ((swap1_up != 0) ? lower1_d : upper1_d); + best_i[kk_2] = ((swap1_up != 0) ? lower1_i : upper1_i); + best_d[kk_2 + 1] = ((swap1_up != 0) ? upper1_d : lower1_d); + best_i[kk_2 + 1] = ((swap1_up != 0) ? upper1_i : lower1_i); + } + } + } + } + } + int slot_base = (row * 4 + slot) * TOP_K_MAX; + #pragma unroll + for (int kk_3 = 0; kk_3 < TOP_K_MAX; kk_3++) { + smem_local_d[slot_base + kk_3] = best_d[kk_3]; + smem_local_i[slot_base + kk_3] = best_i[kk_3]; + } + asm volatile("barrier.sync 8, %0;" :: "r"(32)); + if (tid < ROWS_COVERED) { + int out_row = tid; + int out_q_idx = off_q + out_row; + float head_d[4]; + int head_i[4]; + int head_k[4]; + #pragma unroll + for (int slot_idx = 0; slot_idx < 4; slot_idx++) { + int local_base = (out_row * 4 + slot_idx) * TOP_K_MAX; + head_k[slot_idx] = 0; + head_d[slot_idx] = smem_local_d[local_base]; + head_i[slot_idx] = smem_local_i[local_base]; + } + int out_base = ((split_idx * B + batch_idx) * Q + out_q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float winner_d = head_d[0]; + int winner_i = head_i[0]; + int winner_slot = 0; + #pragma unroll + for (int slot_idx_1 = 1; slot_idx_1 < 4; slot_idx_1++) { + float cand_d = head_d[slot_idx_1]; + int take = ((cand_d < winner_d) ? 1 : 0); + winner_d = ((take != 0) ? cand_d : winner_d); + winner_i = ((take != 0) ? head_i[slot_idx_1] : winner_i); + winner_slot = ((take != 0) ? slot_idx_1 : winner_slot); + } + if (out_q_idx < Q && out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = winner_d; + *((int*)(partial_indices + (out_base + out_k))) = winner_i; + } + #pragma unroll + for (int slot_idx_2 = 0; slot_idx_2 < 4; slot_idx_2++) { + if (winner_slot == slot_idx_2) { + int next_head = head_k[slot_idx_2] + 1; + head_k[slot_idx_2] = next_head; + head_d[slot_idx_2] = 3.4e+38f; + head_i[slot_idx_2] = -1; + if (next_head < TOP_K_MAX) { + int local_base_1 = (out_row * 4 + slot_idx_2) * TOP_K_MAX; + head_d[slot_idx_2] = smem_local_d[local_base_1 + next_head]; + head_i[slot_idx_2] = smem_local_i[local_base_1 + next_head]; + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(32)); + } + } + // ---- Role: load ---- + } else if (warp == 1) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 1) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx_1 = bid; work_idx_1 < total_work; work_idx_1 += num_bids) { + int split_idx_1 = work_idx_1 % (unsigned int)split_count; + int query_work_1 = work_idx_1 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tiles; + int q_tile_1 = query_work_1 % num_q_tiles; + int off_q_1 = q_tile_1 * BLOCK_Q; + int global_q = batch_idx_1 * Q + off_q_1; + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + mbarrier_arrive_expect_tx(query_full_addr, 16384); + tma_3d_gmem2smem(smem_query_addr, tmap_query, 0, global_q, 0, query_full_addr); + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int off_m = db_tile_1 * BLOCK_M; + int global_m = batch_idx_1 * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + mbarrier_arrive_expect_tx(database_full_addr, 16384); + tma_3d_gmem2smem(smem_database_addr, tmap_database, 0, global_m, 0, database_full_addr); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 2) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = bid; work_idx_2 < total_work; work_idx_2 += num_bids) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = make_warp_uniform((smem_query_addr >> 4) & 0x3FFF); + int _mma_b_lo_0 = make_warp_uniform((smem_database_addr >> 4) & 0x3FFF); + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 68158608;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 506;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit(score_full_addr); + elect_commit(database_empty_addr); + } + elect_commit(query_empty_addr); + } + } + } + + // Cleanup + __syncthreads(); // barrier before TMEM dealloc + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::1.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(64)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::1.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0103.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0103.cu new file mode 100644 index 00000000..d2881412 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0103.cu @@ -0,0 +1,114 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 128 +#define TOP_K_MAX 32 +#define SPLIT_COUNT 144 +#define SPLITS_PER_LANE 5 +#define ROWS_PER_CTA 1 + +#include + +extern "C" { + +__global__ __launch_bounds__(128, 1) void +kernel_knn_build_rag_microbucket_k32warpmerge_0077_v1_warp_row_merge_k32s144_0077_v1(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int row = bid * ROWS_PER_CTA + warp; + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + if (warp < ROWS_PER_CTA && row < total_queries) { + int split_pos[SPLITS_PER_LANE]; + int split_id_for_slot[SPLITS_PER_LANE]; + float cand_d[SPLITS_PER_LANE]; + int cand_i[SPLITS_PER_LANE]; + #pragma unroll + for (int slot = 0; slot < SPLITS_PER_LANE; slot++) { + int split_id = slot * 32 + lane; + split_id_for_slot[slot] = split_id; + split_pos[slot] = 0; + cand_d[slot] = 3.4e+38f; + cand_i[slot] = -1; + if (split_id < SPLIT_COUNT) { + int source_addr = base_row + split_id * split_stride; + cand_d[slot] = partial_dists[source_addr]; + cand_i[slot] = partial_indices[source_addr]; + } + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float lane_best_d = cand_d[0]; + int lane_best_i = cand_i[0]; + int lane_best_slot = 0; + #pragma unroll + for (int slot_1 = 1; slot_1 < SPLITS_PER_LANE; slot_1++) { + if (lane_best_d > cand_d[slot_1]) { + lane_best_d = cand_d[slot_1]; + lane_best_i = cand_i[slot_1]; + lane_best_slot = slot_1; + } + } + float warp_min = lane_best_d; + float _warp_reduce_0 = warp_min; + #pragma unroll + for (int offset = 16; offset > 0; offset >>= 1) + _warp_reduce_0 = fminf(_warp_reduce_0, __shfl_xor_sync(0xFFFFFFFF, _warp_reduce_0, offset)); + warp_min = _warp_reduce_0; + unsigned int _vote_0 = __ballot_sync(0xFFFFFFFF, lane_best_d == warp_min); + int owner_ballot = _vote_0; + int _ffs_0 = __ffs(owner_ballot); + int winner_lane = _ffs_0 - 1; + int _shfl_0 = __shfl_sync(0xFFFFFFFF, lane_best_i, winner_lane); + int winner_i = _shfl_0; + int _shfl_1 = __shfl_sync(0xFFFFFFFF, lane_best_slot, winner_lane); + int winner_slot = _shfl_1; + if (lane == 0) { + *((float*)(out_dists + (base_row + out_k))) = warp_min; + *((int*)(out_indices + (base_row + out_k))) = winner_i; + } + if (lane == winner_lane) { + #pragma unroll + for (int slot_2 = 0; slot_2 < SPLITS_PER_LANE; slot_2++) { + if (winner_slot == slot_2) { + int next_pos = split_pos[slot_2] + 1; + split_pos[slot_2] = next_pos; + cand_d[slot_2] = 3.4e+38f; + cand_i[slot_2] = -1; + if (next_pos < TOP_K_MAX) { + int next_addr = base_row + split_id_for_slot[slot_2] * split_stride + next_pos; + cand_d[slot_2] = partial_dists[next_addr]; + cand_i[slot_2] = partial_indices[next_addr]; + } + } + } + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0104.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0104.cu new file mode 100644 index 00000000..a94ea7c9 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0104.cu @@ -0,0 +1,607 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 64 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 16384 +#define SMEM_SMEM_QUERY_STRIDE 16384 +#define SMEM_SMEM_DATABASE_OFF 17408 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_UPPER_DOTS_OFF 34048 +#define SMEM_SMEM_UPPER_DOTS_STAGE_BYTES 2048 +#define SMEM_SMEM_UPPER_DOTS_STRIDE 2048 +#define SMEM_SMEM_LOCAL_D_OFF 36096 +#define SMEM_SMEM_LOCAL_D_STAGE_BYTES 8192 +#define SMEM_SMEM_LOCAL_D_STRIDE 8192 +#define SMEM_SMEM_LOCAL_I_OFF 44288 +#define SMEM_SMEM_LOCAL_I_STAGE_BYTES 8192 +#define SMEM_SMEM_LOCAL_I_STRIDE 8192 +#define SMEM_TOTAL 52480 +#define THREADS 128 +#define BLOCK_Q 64 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_MAX 32 +#define ROWS_COVERED 16 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "tcgen05.mma.cta_group::1.kind::f16 [%0], %1, %2, %3, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, %3, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit(int mbar_addr) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];\n\t" + "}\n" + :: "r"(mbar_addr)); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x32(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x32.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15," + " %16, %17, %18, %19, %20, %21, %22, %23," + " %24, %25, %26, %27, %28, %29, %30, %31}, [%32];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]), + "=f"(dst[16]), "=f"(dst[17]), "=f"(dst[18]), "=f"(dst[19]), + "=f"(dst[20]), "=f"(dst[21]), "=f"(dst[22]), "=f"(dst[23]), + "=f"(dst[24]), "=f"(dst[25]), "=f"(dst[26]), "=f"(dst[27]), + "=f"(dst[28]), "=f"(dst[29]), "=f"(dst[30]), "=f"(dst[31]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ float max_noftz(float a, float b) { + float c; + asm("max.f32 %0, %1, %2;" : "=f"(c) : "f"(a), "f"(b)); + return c; +} + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cta.global" + ".mbarrier::complete_tx::bytes" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit(int mbar_addr) { + asm volatile( + "tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ uint32_t make_warp_uniform(uint32_t val) { + uint32_t result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1f, 0xffffffff;" + : "=r"(result) : "r"(val)); + return result; +} + +extern "C" { + +__global__ __launch_bounds__(128, 1) void +kernel_knn_build_rag_microbucket_k32_q16irreg2warp_a444_v2_stage1_q16_rowld1_2warp_q16dual2warp_56ed_v1(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tiles, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 17408); + const int smem_database_addr = smem + 17408; + float* smem_upper_dots = reinterpret_cast(smem_raw + 34048); + const int smem_upper_dots_addr = smem + 34048; + float* smem_local_d = reinterpret_cast(smem_raw + 36096); + const int smem_local_d_addr = smem + 36096; + int* smem_local_i = reinterpret_cast(smem_raw + 44288); + const int smem_local_i_addr = smem + 44288; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 0, 1, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 16, 1, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 40, 2, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (64 columns, 64 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::1.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(64) : "memory"); + } + + __syncthreads(); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: compute ---- + if (warp <= 1) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx = bid; work_idx < total_work; work_idx += num_bids) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tiles; + int q_tile = query_work % num_q_tiles; + int off_q = q_tile * BLOCK_Q; + int row = warp * 8 + lane / 4; + int lane_col = lane % 4; + int slot = lane_col; + int q_idx = off_q + row; + int valid_row = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_row != 0) { + q_sq_val = query_sq[batch_idx * Q + q_idx]; + } + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + int db_tile_start = split_idx * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int db_start = db_tile * BLOCK_M; + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + float _tmem_load_0[32]; + asm volatile( + "tcgen05.ld.sync.aligned.16x256b.x8.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31}, [%32];" + : "=r"(*reinterpret_cast(&_tmem_load_0[0])), "=r"(*reinterpret_cast(&_tmem_load_0[1])), "=r"(*reinterpret_cast(&_tmem_load_0[2])), "=r"(*reinterpret_cast(&_tmem_load_0[3])), "=r"(*reinterpret_cast(&_tmem_load_0[4])), "=r"(*reinterpret_cast(&_tmem_load_0[5])), "=r"(*reinterpret_cast(&_tmem_load_0[6])), "=r"(*reinterpret_cast(&_tmem_load_0[7])), "=r"(*reinterpret_cast(&_tmem_load_0[8])), "=r"(*reinterpret_cast(&_tmem_load_0[9])), "=r"(*reinterpret_cast(&_tmem_load_0[10])), "=r"(*reinterpret_cast(&_tmem_load_0[11])), "=r"(*reinterpret_cast(&_tmem_load_0[12])), "=r"(*reinterpret_cast(&_tmem_load_0[13])), "=r"(*reinterpret_cast(&_tmem_load_0[14])), "=r"(*reinterpret_cast(&_tmem_load_0[15])), "=r"(*reinterpret_cast(&_tmem_load_0[16])), "=r"(*reinterpret_cast(&_tmem_load_0[17])), "=r"(*reinterpret_cast(&_tmem_load_0[18])), "=r"(*reinterpret_cast(&_tmem_load_0[19])), "=r"(*reinterpret_cast(&_tmem_load_0[20])), "=r"(*reinterpret_cast(&_tmem_load_0[21])), "=r"(*reinterpret_cast(&_tmem_load_0[22])), "=r"(*reinterpret_cast(&_tmem_load_0[23])), "=r"(*reinterpret_cast(&_tmem_load_0[24])), "=r"(*reinterpret_cast(&_tmem_load_0[25])), "=r"(*reinterpret_cast(&_tmem_load_0[26])), "=r"(*reinterpret_cast(&_tmem_load_0[27])), "=r"(*reinterpret_cast(&_tmem_load_0[28])), "=r"(*reinterpret_cast(&_tmem_load_0[29])), "=r"(*reinterpret_cast(&_tmem_load_0[30])), "=r"(*reinterpret_cast(&_tmem_load_0[31])) + : "r"(taddr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + if (warp == 0) { + int scratch_row = lane / 4; + #pragma unroll + for (int repeat = 0; repeat < 8; repeat++) { + const int reg_base = repeat * 4; + int scratch_col = repeat * 8 + lane_col * 2; + int scratch_base = scratch_row * 64 + scratch_col; + smem_upper_dots[scratch_base] = _tmem_load_0[reg_base + 2]; + smem_upper_dots[scratch_base + 1] = _tmem_load_0[reg_base + 3]; + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(64)); + if (elect_sync()) { + mbarrier_arrive(score_empty_addr); + } + #pragma unroll + for (int repeat_1 = 0; repeat_1 < 8; repeat_1++) { + const int reg_base_1 = repeat_1 * 4; + int col_base = repeat_1 * 8 + lane_col * 2; + int db_idx0 = db_start + col_base; + int db_idx1 = db_idx0 + 1; + float dot0 = _tmem_load_0[reg_base_1]; + float dot1 = _tmem_load_0[reg_base_1 + 1]; + if (warp != 0) { + int scratch_row_1 = lane / 4; + int scratch_base_1 = scratch_row_1 * 64 + col_base; + dot0 = smem_upper_dots[scratch_base_1]; + dot1 = smem_upper_dots[scratch_base_1 + 1]; + } + float cand0_d = 3.4e+38f; + float cand1_d = 3.4e+38f; + if (valid_row != 0 && db_idx0 < M) { + float _max_0 = max_noftz(q_sq_val + database_sq[batch_idx * M + db_idx0] - 2.0f * dot0, 0.0f); + cand0_d = _max_0; + } + if (valid_row != 0 && db_idx1 < M) { + float _max_1 = max_noftz(q_sq_val + database_sq[batch_idx * M + db_idx1] - 2.0f * dot1, 0.0f); + cand1_d = _max_1; + } + int take1 = ((cand1_d < cand0_d) ? 1 : 0); + if (best_d[31] > ((take1 != 0) ? cand1_d : cand0_d)) { + best_d[31] = ((take1 != 0) ? cand1_d : cand0_d); + best_i[31] = ((take1 != 0) ? db_idx1 : db_idx0); + #pragma unroll + for (int kk_1 = 30; kk_1 >= 0; kk_1--) { + float lower0_d = best_d[kk_1 + 1]; + int lower0_i = best_i[kk_1 + 1]; + float upper0_d = best_d[kk_1]; + int upper0_i = best_i[kk_1]; + int swap0_up = ((lower0_d < upper0_d) ? 1 : 0); + best_d[kk_1] = ((swap0_up != 0) ? lower0_d : upper0_d); + best_i[kk_1] = ((swap0_up != 0) ? lower0_i : upper0_i); + best_d[kk_1 + 1] = ((swap0_up != 0) ? upper0_d : lower0_d); + best_i[kk_1 + 1] = ((swap0_up != 0) ? upper0_i : lower0_i); + } + if (best_d[31] > ((take1 != 0) ? cand0_d : cand1_d)) { + best_d[31] = ((take1 != 0) ? cand0_d : cand1_d); + best_i[31] = ((take1 != 0) ? db_idx0 : db_idx1); + #pragma unroll + for (int kk_2 = 30; kk_2 >= 0; kk_2--) { + float lower1_d = best_d[kk_2 + 1]; + int lower1_i = best_i[kk_2 + 1]; + float upper1_d = best_d[kk_2]; + int upper1_i = best_i[kk_2]; + int swap1_up = ((lower1_d < upper1_d) ? 1 : 0); + best_d[kk_2] = ((swap1_up != 0) ? lower1_d : upper1_d); + best_i[kk_2] = ((swap1_up != 0) ? lower1_i : upper1_i); + best_d[kk_2 + 1] = ((swap1_up != 0) ? upper1_d : lower1_d); + best_i[kk_2 + 1] = ((swap1_up != 0) ? upper1_i : lower1_i); + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(64)); + } + int slot_base = (row * 4 + slot) * TOP_K_MAX; + #pragma unroll + for (int kk_3 = 0; kk_3 < TOP_K_MAX; kk_3++) { + smem_local_d[slot_base + kk_3] = best_d[kk_3]; + smem_local_i[slot_base + kk_3] = best_i[kk_3]; + } + asm volatile("barrier.sync 8, %0;" :: "r"(64)); + if (tid < ROWS_COVERED) { + int out_row = tid; + int out_q_idx = off_q + out_row; + float head_d[4]; + int head_i[4]; + int head_k[4]; + #pragma unroll + for (int slot_idx = 0; slot_idx < 4; slot_idx++) { + int local_base = (out_row * 4 + slot_idx) * TOP_K_MAX; + head_k[slot_idx] = 0; + head_d[slot_idx] = smem_local_d[local_base]; + head_i[slot_idx] = smem_local_i[local_base]; + } + int out_base = ((split_idx * B + batch_idx) * Q + out_q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float winner_d = head_d[0]; + int winner_i = head_i[0]; + int winner_slot = 0; + #pragma unroll + for (int slot_idx_1 = 1; slot_idx_1 < 4; slot_idx_1++) { + float cand_d = head_d[slot_idx_1]; + int take = ((cand_d < winner_d) ? 1 : 0); + winner_d = ((take != 0) ? cand_d : winner_d); + winner_i = ((take != 0) ? head_i[slot_idx_1] : winner_i); + winner_slot = ((take != 0) ? slot_idx_1 : winner_slot); + } + if (out_q_idx < Q && out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = winner_d; + *((int*)(partial_indices + (out_base + out_k))) = winner_i; + } + #pragma unroll + for (int slot_idx_2 = 0; slot_idx_2 < 4; slot_idx_2++) { + if (winner_slot == slot_idx_2) { + int next_head = head_k[slot_idx_2] + 1; + head_k[slot_idx_2] = next_head; + head_d[slot_idx_2] = 3.4e+38f; + head_i[slot_idx_2] = -1; + if (next_head < TOP_K_MAX) { + int local_base_1 = (out_row * 4 + slot_idx_2) * TOP_K_MAX; + head_d[slot_idx_2] = smem_local_d[local_base_1 + next_head]; + head_i[slot_idx_2] = smem_local_i[local_base_1 + next_head]; + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(64)); + } + } + // ---- Role: load ---- + } else if (warp == 2) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 2) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx_1 = bid; work_idx_1 < total_work; work_idx_1 += num_bids) { + int split_idx_1 = work_idx_1 % (unsigned int)split_count; + int query_work_1 = work_idx_1 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tiles; + int q_tile_1 = query_work_1 % num_q_tiles; + int off_q_1 = q_tile_1 * BLOCK_Q; + int global_q = batch_idx_1 * Q + off_q_1; + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + mbarrier_arrive_expect_tx(query_full_addr, 16384); + tma_3d_gmem2smem(smem_query_addr, tmap_query, 0, global_q, 0, query_full_addr); + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int off_m = db_tile_1 * BLOCK_M; + int global_m = batch_idx_1 * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + mbarrier_arrive_expect_tx(database_full_addr, 16384); + tma_3d_gmem2smem(smem_database_addr, tmap_database, 0, global_m, 0, database_full_addr); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 3) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = bid; work_idx_2 < total_work; work_idx_2 += num_bids) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = make_warp_uniform((smem_query_addr >> 4) & 0x3FFF); + int _mma_b_lo_0 = make_warp_uniform((smem_database_addr >> 4) & 0x3FFF); + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 68158608;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 506;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit(score_full_addr); + elect_commit(database_empty_addr); + } + elect_commit(query_empty_addr); + } + } + } + + // Cleanup + __syncthreads(); // barrier before TMEM dealloc + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::1.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(64)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::1.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0105.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0105.cu new file mode 100644 index 00000000..c995ae30 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0105.cu @@ -0,0 +1,114 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 128 +#define TOP_K_MAX 32 +#define SPLIT_COUNT 144 +#define SPLITS_PER_LANE 5 +#define ROWS_PER_CTA 4 + +#include + +extern "C" { + +__global__ __launch_bounds__(128, 1) void +kernel_knn_build_rag_microbucket_k32warpmerge_0077_v1_warp_row_merge_k32s144r4_56ed_v1(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int row = bid * ROWS_PER_CTA + warp; + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + if (warp < ROWS_PER_CTA && row < total_queries) { + int split_pos[SPLITS_PER_LANE]; + int split_id_for_slot[SPLITS_PER_LANE]; + float cand_d[SPLITS_PER_LANE]; + int cand_i[SPLITS_PER_LANE]; + #pragma unroll + for (int slot = 0; slot < SPLITS_PER_LANE; slot++) { + int split_id = slot * 32 + lane; + split_id_for_slot[slot] = split_id; + split_pos[slot] = 0; + cand_d[slot] = 3.4e+38f; + cand_i[slot] = -1; + if (split_id < SPLIT_COUNT) { + int source_addr = base_row + split_id * split_stride; + cand_d[slot] = partial_dists[source_addr]; + cand_i[slot] = partial_indices[source_addr]; + } + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float lane_best_d = cand_d[0]; + int lane_best_i = cand_i[0]; + int lane_best_slot = 0; + #pragma unroll + for (int slot_1 = 1; slot_1 < SPLITS_PER_LANE; slot_1++) { + if (lane_best_d > cand_d[slot_1]) { + lane_best_d = cand_d[slot_1]; + lane_best_i = cand_i[slot_1]; + lane_best_slot = slot_1; + } + } + float warp_min = lane_best_d; + float _warp_reduce_0 = warp_min; + #pragma unroll + for (int offset = 16; offset > 0; offset >>= 1) + _warp_reduce_0 = fminf(_warp_reduce_0, __shfl_xor_sync(0xFFFFFFFF, _warp_reduce_0, offset)); + warp_min = _warp_reduce_0; + unsigned int _vote_0 = __ballot_sync(0xFFFFFFFF, lane_best_d == warp_min); + int owner_ballot = _vote_0; + int _ffs_0 = __ffs(owner_ballot); + int winner_lane = _ffs_0 - 1; + int _shfl_0 = __shfl_sync(0xFFFFFFFF, lane_best_i, winner_lane); + int winner_i = _shfl_0; + int _shfl_1 = __shfl_sync(0xFFFFFFFF, lane_best_slot, winner_lane); + int winner_slot = _shfl_1; + if (lane == 0) { + *((float*)(out_dists + (base_row + out_k))) = warp_min; + *((int*)(out_indices + (base_row + out_k))) = winner_i; + } + if (lane == winner_lane) { + #pragma unroll + for (int slot_2 = 0; slot_2 < SPLITS_PER_LANE; slot_2++) { + if (winner_slot == slot_2) { + int next_pos = split_pos[slot_2] + 1; + split_pos[slot_2] = next_pos; + cand_d[slot_2] = 3.4e+38f; + cand_i[slot_2] = -1; + if (next_pos < TOP_K_MAX) { + int next_addr = base_row + split_id_for_slot[slot_2] * split_stride + next_pos; + cand_d[slot_2] = partial_dists[next_addr]; + cand_i[slot_2] = partial_indices[next_addr]; + } + } + } + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0106.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0106.cu new file mode 100644 index 00000000..c7ed64ba --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0106.cu @@ -0,0 +1,642 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 64 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 16384 +#define SMEM_SMEM_QUERY_STRIDE 16384 +#define SMEM_SMEM_DATABASE_OFF 17408 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_LOCAL_D_OFF 34048 +#define SMEM_SMEM_LOCAL_D_STAGE_BYTES 16384 +#define SMEM_SMEM_LOCAL_D_STRIDE 16384 +#define SMEM_SMEM_LOCAL_I_OFF 50432 +#define SMEM_SMEM_LOCAL_I_STAGE_BYTES 16384 +#define SMEM_SMEM_LOCAL_I_STRIDE 16384 +#define SMEM_TOTAL 66816 +#define THREADS 128 +#define BLOCK_Q 64 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_MAX 32 +#define ROWS_COVERED 24 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "tcgen05.mma.cta_group::1.kind::f16 [%0], %1, %2, %3, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, %3, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit(int mbar_addr) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];\n\t" + "}\n" + :: "r"(mbar_addr)); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x32(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x32.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15," + " %16, %17, %18, %19, %20, %21, %22, %23," + " %24, %25, %26, %27, %28, %29, %30, %31}, [%32];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]), + "=f"(dst[16]), "=f"(dst[17]), "=f"(dst[18]), "=f"(dst[19]), + "=f"(dst[20]), "=f"(dst[21]), "=f"(dst[22]), "=f"(dst[23]), + "=f"(dst[24]), "=f"(dst[25]), "=f"(dst[26]), "=f"(dst[27]), + "=f"(dst[28]), "=f"(dst[29]), "=f"(dst[30]), "=f"(dst[31]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ float max_noftz(float a, float b) { + float c; + asm("max.f32 %0, %1, %2;" : "=f"(c) : "f"(a), "f"(b)); + return c; +} + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cta.global" + ".mbarrier::complete_tx::bytes" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit(int mbar_addr) { + asm volatile( + "tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ uint32_t make_warp_uniform(uint32_t val) { + uint32_t result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1f, 0xffffffff;" + : "=r"(result) : "r"(val)); + return result; +} + +extern "C" { + +__global__ __launch_bounds__(128, 1) void +kernel_knn_build_rag_microbucket_k32rowld1warp_0077_v1_stage1_q32_k32_m64_rowld2_q24rowld2_24dc_v1(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tiles, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 17408); + const int smem_database_addr = smem + 17408; + float* smem_local_d = reinterpret_cast(smem_raw + 34048); + const int smem_local_d_addr = smem + 34048; + int* smem_local_i = reinterpret_cast(smem_raw + 50432); + const int smem_local_i_addr = smem + 50432; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 0, 1, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 16, 1, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 40, 2, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (64 columns, 64 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::1.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(64) : "memory"); + } + + __syncthreads(); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: compute ---- + if (warp <= 1) { + { // compute_main + int warp_id_in_role = (warp - 0); + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx = bid; work_idx < total_work; work_idx += num_bids) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tiles; + int q_tile = query_work % num_q_tiles; + int off_q = q_tile * BLOCK_Q; + int tmem_row_origin = warp_id_in_role * 32; + int logical_row_origin = warp_id_in_role * 16; + int row_top = logical_row_origin + lane / 4; + int row_bot = row_top + 8; + int lane_col = lane % 4; + int slot = lane_col; + int q_top = off_q + row_top; + int q_bot = off_q + row_bot; + int valid_top = ((q_top < Q) ? 1 : 0); + int valid_bot = ((q_bot < Q) ? 1 : 0); + float q_sq_top = 0.0f; + float q_sq_bot = 0.0f; + if (valid_top != 0) { + q_sq_top = query_sq[batch_idx * Q + q_top]; + } + if (valid_bot != 0) { + q_sq_bot = query_sq[batch_idx * Q + q_bot]; + } + float best_top_d[TOP_K_MAX]; + float best_bot_d[TOP_K_MAX]; + int best_top_i[TOP_K_MAX]; + int best_bot_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_top_d[kk] = 3.4e+38f; + best_bot_d[kk] = 3.4e+38f; + best_top_i[kk] = -1; + best_bot_i[kk] = -1; + } + int db_tile_start = split_idx * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int db_start = db_tile * BLOCK_M; + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(tmem_row_origin << 16); + float _tmem_load_0[32]; + asm volatile( + "tcgen05.ld.sync.aligned.16x256b.x8.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31}, [%32];" + : "=r"(*reinterpret_cast(&_tmem_load_0[0])), "=r"(*reinterpret_cast(&_tmem_load_0[1])), "=r"(*reinterpret_cast(&_tmem_load_0[2])), "=r"(*reinterpret_cast(&_tmem_load_0[3])), "=r"(*reinterpret_cast(&_tmem_load_0[4])), "=r"(*reinterpret_cast(&_tmem_load_0[5])), "=r"(*reinterpret_cast(&_tmem_load_0[6])), "=r"(*reinterpret_cast(&_tmem_load_0[7])), "=r"(*reinterpret_cast(&_tmem_load_0[8])), "=r"(*reinterpret_cast(&_tmem_load_0[9])), "=r"(*reinterpret_cast(&_tmem_load_0[10])), "=r"(*reinterpret_cast(&_tmem_load_0[11])), "=r"(*reinterpret_cast(&_tmem_load_0[12])), "=r"(*reinterpret_cast(&_tmem_load_0[13])), "=r"(*reinterpret_cast(&_tmem_load_0[14])), "=r"(*reinterpret_cast(&_tmem_load_0[15])), "=r"(*reinterpret_cast(&_tmem_load_0[16])), "=r"(*reinterpret_cast(&_tmem_load_0[17])), "=r"(*reinterpret_cast(&_tmem_load_0[18])), "=r"(*reinterpret_cast(&_tmem_load_0[19])), "=r"(*reinterpret_cast(&_tmem_load_0[20])), "=r"(*reinterpret_cast(&_tmem_load_0[21])), "=r"(*reinterpret_cast(&_tmem_load_0[22])), "=r"(*reinterpret_cast(&_tmem_load_0[23])), "=r"(*reinterpret_cast(&_tmem_load_0[24])), "=r"(*reinterpret_cast(&_tmem_load_0[25])), "=r"(*reinterpret_cast(&_tmem_load_0[26])), "=r"(*reinterpret_cast(&_tmem_load_0[27])), "=r"(*reinterpret_cast(&_tmem_load_0[28])), "=r"(*reinterpret_cast(&_tmem_load_0[29])), "=r"(*reinterpret_cast(&_tmem_load_0[30])), "=r"(*reinterpret_cast(&_tmem_load_0[31])) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + if (elect_sync()) { + mbarrier_arrive(score_empty_addr); + } + #pragma unroll + for (int repeat = 0; repeat < 8; repeat++) { + const int reg_base = repeat * 4; + int col_base = repeat * 8 + lane_col * 2; + int db_idx0 = db_start + col_base; + int db_idx1 = db_idx0 + 1; + float top_d0 = 3.4e+38f; + float top_d1 = 3.4e+38f; + if (valid_top != 0 && db_idx0 < M) { + float _max_0 = max_noftz(q_sq_top + database_sq[batch_idx * M + db_idx0] - 2.0f * _tmem_load_0[reg_base], 0.0f); + top_d0 = _max_0; + } + if (valid_top != 0 && db_idx1 < M) { + float _max_1 = max_noftz(q_sq_top + database_sq[batch_idx * M + db_idx1] - 2.0f * _tmem_load_0[reg_base + 1], 0.0f); + top_d1 = _max_1; + } + int top_take1 = ((top_d1 < top_d0) ? 1 : 0); + if (best_top_d[31] > ((top_take1 != 0) ? top_d1 : top_d0)) { + best_top_d[31] = ((top_take1 != 0) ? top_d1 : top_d0); + best_top_i[31] = ((top_take1 != 0) ? db_idx1 : db_idx0); + #pragma unroll + for (int kk_1 = 30; kk_1 >= 0; kk_1--) { + float lower0_d = best_top_d[kk_1 + 1]; + int lower0_i = best_top_i[kk_1 + 1]; + float upper0_d = best_top_d[kk_1]; + int upper0_i = best_top_i[kk_1]; + int swap0_up = ((lower0_d < upper0_d) ? 1 : 0); + best_top_d[kk_1] = ((swap0_up != 0) ? lower0_d : upper0_d); + best_top_i[kk_1] = ((swap0_up != 0) ? lower0_i : upper0_i); + best_top_d[kk_1 + 1] = ((swap0_up != 0) ? upper0_d : lower0_d); + best_top_i[kk_1 + 1] = ((swap0_up != 0) ? upper0_i : lower0_i); + } + if (best_top_d[31] > ((top_take1 != 0) ? top_d0 : top_d1)) { + best_top_d[31] = ((top_take1 != 0) ? top_d0 : top_d1); + best_top_i[31] = ((top_take1 != 0) ? db_idx0 : db_idx1); + #pragma unroll + for (int kk_2 = 30; kk_2 >= 0; kk_2--) { + float lower1_d = best_top_d[kk_2 + 1]; + int lower1_i = best_top_i[kk_2 + 1]; + float upper1_d = best_top_d[kk_2]; + int upper1_i = best_top_i[kk_2]; + int swap1_up = ((lower1_d < upper1_d) ? 1 : 0); + best_top_d[kk_2] = ((swap1_up != 0) ? lower1_d : upper1_d); + best_top_i[kk_2] = ((swap1_up != 0) ? lower1_i : upper1_i); + best_top_d[kk_2 + 1] = ((swap1_up != 0) ? upper1_d : lower1_d); + best_top_i[kk_2 + 1] = ((swap1_up != 0) ? upper1_i : lower1_i); + } + } + } + float bot_d0 = 3.4e+38f; + float bot_d1 = 3.4e+38f; + if (valid_bot != 0 && db_idx0 < M) { + float _max_2 = max_noftz(q_sq_bot + database_sq[batch_idx * M + db_idx0] - 2.0f * _tmem_load_0[reg_base + 2], 0.0f); + bot_d0 = _max_2; + } + if (valid_bot != 0 && db_idx1 < M) { + float _max_3 = max_noftz(q_sq_bot + database_sq[batch_idx * M + db_idx1] - 2.0f * _tmem_load_0[reg_base + 3], 0.0f); + bot_d1 = _max_3; + } + int bot_take1 = ((bot_d1 < bot_d0) ? 1 : 0); + if (best_bot_d[31] > ((bot_take1 != 0) ? bot_d1 : bot_d0)) { + best_bot_d[31] = ((bot_take1 != 0) ? bot_d1 : bot_d0); + best_bot_i[31] = ((bot_take1 != 0) ? db_idx1 : db_idx0); + #pragma unroll + for (int kk_3 = 30; kk_3 >= 0; kk_3--) { + float lower0_d_1 = best_bot_d[kk_3 + 1]; + int lower0_i_1 = best_bot_i[kk_3 + 1]; + float upper0_d_1 = best_bot_d[kk_3]; + int upper0_i_1 = best_bot_i[kk_3]; + int swap0_up_1 = ((lower0_d_1 < upper0_d_1) ? 1 : 0); + best_bot_d[kk_3] = ((swap0_up_1 != 0) ? lower0_d_1 : upper0_d_1); + best_bot_i[kk_3] = ((swap0_up_1 != 0) ? lower0_i_1 : upper0_i_1); + best_bot_d[kk_3 + 1] = ((swap0_up_1 != 0) ? upper0_d_1 : lower0_d_1); + best_bot_i[kk_3 + 1] = ((swap0_up_1 != 0) ? upper0_i_1 : lower0_i_1); + } + if (best_bot_d[31] > ((bot_take1 != 0) ? bot_d0 : bot_d1)) { + best_bot_d[31] = ((bot_take1 != 0) ? bot_d0 : bot_d1); + best_bot_i[31] = ((bot_take1 != 0) ? db_idx0 : db_idx1); + #pragma unroll + for (int kk_4 = 30; kk_4 >= 0; kk_4--) { + float lower1_d_1 = best_bot_d[kk_4 + 1]; + int lower1_i_1 = best_bot_i[kk_4 + 1]; + float upper1_d_1 = best_bot_d[kk_4]; + int upper1_i_1 = best_bot_i[kk_4]; + int swap1_up_1 = ((lower1_d_1 < upper1_d_1) ? 1 : 0); + best_bot_d[kk_4] = ((swap1_up_1 != 0) ? lower1_d_1 : upper1_d_1); + best_bot_i[kk_4] = ((swap1_up_1 != 0) ? lower1_i_1 : upper1_i_1); + best_bot_d[kk_4 + 1] = ((swap1_up_1 != 0) ? upper1_d_1 : lower1_d_1); + best_bot_i[kk_4 + 1] = ((swap1_up_1 != 0) ? upper1_i_1 : lower1_i_1); + } + } + } + } + } + int top_slot_base = (row_top * 4 + slot) * TOP_K_MAX; + int bot_slot_base = (row_bot * 4 + slot) * TOP_K_MAX; + #pragma unroll + for (int kk_5 = 0; kk_5 < TOP_K_MAX; kk_5++) { + smem_local_d[top_slot_base + kk_5] = best_top_d[kk_5]; + smem_local_i[top_slot_base + kk_5] = best_top_i[kk_5]; + smem_local_d[bot_slot_base + kk_5] = best_bot_d[kk_5]; + smem_local_i[bot_slot_base + kk_5] = best_bot_i[kk_5]; + } + asm volatile("barrier.sync 8, %0;" :: "r"(64)); + if (tid < ROWS_COVERED) { + int row = tid; + int q_idx = off_q + row; + float head_d[4]; + int head_i[4]; + int head_k[4]; + #pragma unroll + for (int slot_idx = 0; slot_idx < 4; slot_idx++) { + int local_base = (row * 4 + slot_idx) * TOP_K_MAX; + head_k[slot_idx] = 0; + head_d[slot_idx] = smem_local_d[local_base]; + head_i[slot_idx] = smem_local_i[local_base]; + } + int out_base = ((split_idx * B + batch_idx) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float winner_d = head_d[0]; + int winner_i = head_i[0]; + int winner_slot = 0; + #pragma unroll + for (int slot_idx_1 = 1; slot_idx_1 < 4; slot_idx_1++) { + float cand_d = head_d[slot_idx_1]; + int take = ((cand_d < winner_d) ? 1 : 0); + winner_d = ((take != 0) ? cand_d : winner_d); + winner_i = ((take != 0) ? head_i[slot_idx_1] : winner_i); + winner_slot = ((take != 0) ? slot_idx_1 : winner_slot); + } + if (q_idx < Q && out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = winner_d; + *((int*)(partial_indices + (out_base + out_k))) = winner_i; + } + #pragma unroll + for (int slot_idx_2 = 0; slot_idx_2 < 4; slot_idx_2++) { + if (winner_slot == slot_idx_2) { + int next_head = head_k[slot_idx_2] + 1; + head_k[slot_idx_2] = next_head; + head_d[slot_idx_2] = 3.4e+38f; + head_i[slot_idx_2] = -1; + if (next_head < TOP_K_MAX) { + int local_base_1 = (row * 4 + slot_idx_2) * TOP_K_MAX; + head_d[slot_idx_2] = smem_local_d[local_base_1 + next_head]; + head_i[slot_idx_2] = smem_local_i[local_base_1 + next_head]; + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(64)); + } + } + // ---- Role: load ---- + } else if (warp == 2) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 2) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx_1 = bid; work_idx_1 < total_work; work_idx_1 += num_bids) { + int split_idx_1 = work_idx_1 % (unsigned int)split_count; + int query_work_1 = work_idx_1 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tiles; + int q_tile_1 = query_work_1 % num_q_tiles; + int off_q_1 = q_tile_1 * BLOCK_Q; + int global_q = batch_idx_1 * Q + off_q_1; + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + mbarrier_arrive_expect_tx(query_full_addr, 16384); + tma_3d_gmem2smem(smem_query_addr, tmap_query, 0, global_q, 0, query_full_addr); + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int off_m = db_tile_1 * BLOCK_M; + int global_m = batch_idx_1 * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + mbarrier_arrive_expect_tx(database_full_addr, 16384); + tma_3d_gmem2smem(smem_database_addr, tmap_database, 0, global_m, 0, database_full_addr); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 3) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = bid; work_idx_2 < total_work; work_idx_2 += num_bids) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = make_warp_uniform((smem_query_addr >> 4) & 0x3FFF); + int _mma_b_lo_0 = make_warp_uniform((smem_database_addr >> 4) & 0x3FFF); + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 68158608;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 506;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit(score_full_addr); + elect_commit(database_empty_addr); + } + elect_commit(query_empty_addr); + } + } + } + + // Cleanup + __syncthreads(); // barrier before TMEM dealloc + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::1.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(64)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::1.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0107.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0107.cu new file mode 100644 index 00000000..2bc57358 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0107.cu @@ -0,0 +1,114 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 128 +#define TOP_K_MAX 32 +#define SPLIT_COUNT 144 +#define SPLITS_PER_LANE 5 +#define ROWS_PER_CTA 4 + +#include + +extern "C" { + +__global__ __launch_bounds__(128, 1) void +kernel_knn_build_rag_microbucket_k32warpmerge_0077_v1_warp_row_merge_k32q24s144r4_24dc_v1(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int row = bid * ROWS_PER_CTA + warp; + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + if (warp < ROWS_PER_CTA && row < total_queries) { + int split_pos[SPLITS_PER_LANE]; + int split_id_for_slot[SPLITS_PER_LANE]; + float cand_d[SPLITS_PER_LANE]; + int cand_i[SPLITS_PER_LANE]; + #pragma unroll + for (int slot = 0; slot < SPLITS_PER_LANE; slot++) { + int split_id = slot * 32 + lane; + split_id_for_slot[slot] = split_id; + split_pos[slot] = 0; + cand_d[slot] = 3.4e+38f; + cand_i[slot] = -1; + if (split_id < SPLIT_COUNT) { + int source_addr = base_row + split_id * split_stride; + cand_d[slot] = partial_dists[source_addr]; + cand_i[slot] = partial_indices[source_addr]; + } + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float lane_best_d = cand_d[0]; + int lane_best_i = cand_i[0]; + int lane_best_slot = 0; + #pragma unroll + for (int slot_1 = 1; slot_1 < SPLITS_PER_LANE; slot_1++) { + if (lane_best_d > cand_d[slot_1]) { + lane_best_d = cand_d[slot_1]; + lane_best_i = cand_i[slot_1]; + lane_best_slot = slot_1; + } + } + float warp_min = lane_best_d; + float _warp_reduce_0 = warp_min; + #pragma unroll + for (int offset = 16; offset > 0; offset >>= 1) + _warp_reduce_0 = fminf(_warp_reduce_0, __shfl_xor_sync(0xFFFFFFFF, _warp_reduce_0, offset)); + warp_min = _warp_reduce_0; + unsigned int _vote_0 = __ballot_sync(0xFFFFFFFF, lane_best_d == warp_min); + int owner_ballot = _vote_0; + int _ffs_0 = __ffs(owner_ballot); + int winner_lane = _ffs_0 - 1; + int _shfl_0 = __shfl_sync(0xFFFFFFFF, lane_best_i, winner_lane); + int winner_i = _shfl_0; + int _shfl_1 = __shfl_sync(0xFFFFFFFF, lane_best_slot, winner_lane); + int winner_slot = _shfl_1; + if (lane == 0) { + *((float*)(out_dists + (base_row + out_k))) = warp_min; + *((int*)(out_indices + (base_row + out_k))) = winner_i; + } + if (lane == winner_lane) { + #pragma unroll + for (int slot_2 = 0; slot_2 < SPLITS_PER_LANE; slot_2++) { + if (winner_slot == slot_2) { + int next_pos = split_pos[slot_2] + 1; + split_pos[slot_2] = next_pos; + cand_d[slot_2] = 3.4e+38f; + cand_i[slot_2] = -1; + if (next_pos < TOP_K_MAX) { + int next_addr = base_row + split_id_for_slot[slot_2] * split_stride + next_pos; + cand_d[slot_2] = partial_dists[next_addr]; + cand_i[slot_2] = partial_indices[next_addr]; + } + } + } + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0108.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0108.cu new file mode 100644 index 00000000..47373522 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0108.cu @@ -0,0 +1,114 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 128 +#define TOP_K_MAX 32 +#define SPLIT_COUNT 288 +#define SPLITS_PER_LANE 9 +#define ROWS_PER_CTA 4 + +#include + +extern "C" { + +__global__ __launch_bounds__(128, 1) void +kernel_knn_build_rag_microbucket_k32warpmerge_0077_v1_warp_row_merge_k32s288r4_56ed_v1(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int row = bid * ROWS_PER_CTA + warp; + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + if (warp < ROWS_PER_CTA && row < total_queries) { + int split_pos[SPLITS_PER_LANE]; + int split_id_for_slot[SPLITS_PER_LANE]; + float cand_d[SPLITS_PER_LANE]; + int cand_i[SPLITS_PER_LANE]; + #pragma unroll + for (int slot = 0; slot < SPLITS_PER_LANE; slot++) { + int split_id = slot * 32 + lane; + split_id_for_slot[slot] = split_id; + split_pos[slot] = 0; + cand_d[slot] = 3.4e+38f; + cand_i[slot] = -1; + if (split_id < SPLIT_COUNT) { + int source_addr = base_row + split_id * split_stride; + cand_d[slot] = partial_dists[source_addr]; + cand_i[slot] = partial_indices[source_addr]; + } + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float lane_best_d = cand_d[0]; + int lane_best_i = cand_i[0]; + int lane_best_slot = 0; + #pragma unroll + for (int slot_1 = 1; slot_1 < SPLITS_PER_LANE; slot_1++) { + if (lane_best_d > cand_d[slot_1]) { + lane_best_d = cand_d[slot_1]; + lane_best_i = cand_i[slot_1]; + lane_best_slot = slot_1; + } + } + float warp_min = lane_best_d; + float _warp_reduce_0 = warp_min; + #pragma unroll + for (int offset = 16; offset > 0; offset >>= 1) + _warp_reduce_0 = fminf(_warp_reduce_0, __shfl_xor_sync(0xFFFFFFFF, _warp_reduce_0, offset)); + warp_min = _warp_reduce_0; + unsigned int _vote_0 = __ballot_sync(0xFFFFFFFF, lane_best_d == warp_min); + int owner_ballot = _vote_0; + int _ffs_0 = __ffs(owner_ballot); + int winner_lane = _ffs_0 - 1; + int _shfl_0 = __shfl_sync(0xFFFFFFFF, lane_best_i, winner_lane); + int winner_i = _shfl_0; + int _shfl_1 = __shfl_sync(0xFFFFFFFF, lane_best_slot, winner_lane); + int winner_slot = _shfl_1; + if (lane == 0) { + *((float*)(out_dists + (base_row + out_k))) = warp_min; + *((int*)(out_indices + (base_row + out_k))) = winner_i; + } + if (lane == winner_lane) { + #pragma unroll + for (int slot_2 = 0; slot_2 < SPLITS_PER_LANE; slot_2++) { + if (winner_slot == slot_2) { + int next_pos = split_pos[slot_2] + 1; + split_pos[slot_2] = next_pos; + cand_d[slot_2] = 3.4e+38f; + cand_i[slot_2] = -1; + if (next_pos < TOP_K_MAX) { + int next_addr = base_row + split_id_for_slot[slot_2] * split_stride + next_pos; + cand_d[slot_2] = partial_dists[next_addr]; + cand_i[slot_2] = partial_indices[next_addr]; + } + } + } + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0109.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0109.cu new file mode 100644 index 00000000..67d328cb --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0109.cu @@ -0,0 +1,652 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 64 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 16384 +#define SMEM_SMEM_QUERY_STRIDE 16384 +#define SMEM_SMEM_DATABASE_OFF 17408 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_LOCAL_D_OFF 34048 +#define SMEM_SMEM_LOCAL_D_STAGE_BYTES 16384 +#define SMEM_SMEM_LOCAL_D_STRIDE 16384 +#define SMEM_SMEM_LOCAL_I_OFF 50432 +#define SMEM_SMEM_LOCAL_I_STAGE_BYTES 16384 +#define SMEM_SMEM_LOCAL_I_STRIDE 16384 +#define SMEM_TOTAL 66816 +#define THREADS 128 +#define BLOCK_Q 64 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_MAX 32 +#define ROWS_COVERED 32 +#define SPLIT_COUNT_CONST 141 +#define NUM_DB_TILES_CONST 1563 +#define TILES_FLOOR_CONST 11 +#define EXTRA_SPLITS_CONST 12 +#define DB_TILES_PER_SPLIT_CONST 12 +#define M_LIMIT 100000 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "tcgen05.mma.cta_group::1.kind::f16 [%0], %1, %2, %3, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, %3, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit(int mbar_addr) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];\n\t" + "}\n" + :: "r"(mbar_addr)); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x32(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x32.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15," + " %16, %17, %18, %19, %20, %21, %22, %23," + " %24, %25, %26, %27, %28, %29, %30, %31}, [%32];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]), + "=f"(dst[16]), "=f"(dst[17]), "=f"(dst[18]), "=f"(dst[19]), + "=f"(dst[20]), "=f"(dst[21]), "=f"(dst[22]), "=f"(dst[23]), + "=f"(dst[24]), "=f"(dst[25]), "=f"(dst[26]), "=f"(dst[27]), + "=f"(dst[28]), "=f"(dst[29]), "=f"(dst[30]), "=f"(dst[31]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ float max_noftz(float a, float b) { + float c; + asm("max.f32 %0, %1, %2;" : "=f"(c) : "f"(a), "f"(b)); + return c; +} + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cta.global" + ".mbarrier::complete_tx::bytes" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit(int mbar_addr) { + asm volatile( + "tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ uint32_t make_warp_uniform(uint32_t val) { + uint32_t result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1f, 0xffffffff;" + : "=r"(result) : "r"(val)); + return result; +} + +extern "C" { + +__global__ __launch_bounds__(128, 1) void +kernel_knn_build_rag_microbucket_k32_q32rowld2exact_s141_72d1_v1_stage1_q32rowld2exact_f653_v1(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tiles, int num_db_tiles, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 17408); + const int smem_database_addr = smem + 17408; + float* smem_local_d = reinterpret_cast(smem_raw + 34048); + const int smem_local_d_addr = smem + 34048; + int* smem_local_i = reinterpret_cast(smem_raw + 50432); + const int smem_local_i_addr = smem + 50432; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 0, 1, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 16, 1, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 40, 2, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (64 columns, 64 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::1.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(64) : "memory"); + } + + __syncthreads(); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: compute ---- + if (warp <= 1) { + { // compute_main + int warp_id_in_role = (warp - 0); + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx = bid; work_idx < SPLIT_COUNT_CONST; work_idx += num_bids) { + int split_idx = work_idx % (unsigned int)SPLIT_COUNT_CONST; + int tmem_row_origin = warp_id_in_role * 32; + int logical_row_origin = warp_id_in_role * 16; + int row_top = logical_row_origin + lane / 4; + int row_bot = row_top + 8; + int lane_col = lane % 4; + int slot = lane_col; + float q_sq_top = query_sq[row_top]; + float q_sq_bot = query_sq[row_bot]; + float best_top_d[TOP_K_MAX]; + float best_bot_d[TOP_K_MAX]; + int best_top_i[TOP_K_MAX]; + int best_bot_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_top_d[kk] = 3.4e+38f; + best_bot_d[kk] = 3.4e+38f; + best_top_i[kk] = -1; + best_bot_i[kk] = -1; + } + int extra_before = split_idx; + if (extra_before > EXTRA_SPLITS_CONST) { + extra_before = EXTRA_SPLITS_CONST; + } + int split_tile_count = TILES_FLOOR_CONST; + if (split_idx < EXTRA_SPLITS_CONST) { + split_tile_count = TILES_FLOOR_CONST + 1; + } + int db_tile_start = split_idx * TILES_FLOOR_CONST + extra_before; + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < DB_TILES_PER_SPLIT_CONST; local_db_tile++) { + if (split_tile_count > local_db_tile) { + int db_tile = db_tile_start + local_db_tile; + int db_start = db_tile * BLOCK_M; + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(tmem_row_origin << 16); + float _tmem_load_0[32]; + asm volatile( + "tcgen05.ld.sync.aligned.16x256b.x8.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31}, [%32];" + : "=r"(*reinterpret_cast(&_tmem_load_0[0])), "=r"(*reinterpret_cast(&_tmem_load_0[1])), "=r"(*reinterpret_cast(&_tmem_load_0[2])), "=r"(*reinterpret_cast(&_tmem_load_0[3])), "=r"(*reinterpret_cast(&_tmem_load_0[4])), "=r"(*reinterpret_cast(&_tmem_load_0[5])), "=r"(*reinterpret_cast(&_tmem_load_0[6])), "=r"(*reinterpret_cast(&_tmem_load_0[7])), "=r"(*reinterpret_cast(&_tmem_load_0[8])), "=r"(*reinterpret_cast(&_tmem_load_0[9])), "=r"(*reinterpret_cast(&_tmem_load_0[10])), "=r"(*reinterpret_cast(&_tmem_load_0[11])), "=r"(*reinterpret_cast(&_tmem_load_0[12])), "=r"(*reinterpret_cast(&_tmem_load_0[13])), "=r"(*reinterpret_cast(&_tmem_load_0[14])), "=r"(*reinterpret_cast(&_tmem_load_0[15])), "=r"(*reinterpret_cast(&_tmem_load_0[16])), "=r"(*reinterpret_cast(&_tmem_load_0[17])), "=r"(*reinterpret_cast(&_tmem_load_0[18])), "=r"(*reinterpret_cast(&_tmem_load_0[19])), "=r"(*reinterpret_cast(&_tmem_load_0[20])), "=r"(*reinterpret_cast(&_tmem_load_0[21])), "=r"(*reinterpret_cast(&_tmem_load_0[22])), "=r"(*reinterpret_cast(&_tmem_load_0[23])), "=r"(*reinterpret_cast(&_tmem_load_0[24])), "=r"(*reinterpret_cast(&_tmem_load_0[25])), "=r"(*reinterpret_cast(&_tmem_load_0[26])), "=r"(*reinterpret_cast(&_tmem_load_0[27])), "=r"(*reinterpret_cast(&_tmem_load_0[28])), "=r"(*reinterpret_cast(&_tmem_load_0[29])), "=r"(*reinterpret_cast(&_tmem_load_0[30])), "=r"(*reinterpret_cast(&_tmem_load_0[31])) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + if (elect_sync()) { + mbarrier_arrive(score_empty_addr); + } + #pragma unroll + for (int repeat = 0; repeat < 8; repeat++) { + const int reg_base = repeat * 4; + int col_base = repeat * 8 + lane_col * 2; + int db_idx0 = db_start + col_base; + int db_idx1 = db_idx0 + 1; + float top_d0 = 3.4e+38f; + float top_d1 = 3.4e+38f; + if (db_idx0 < M_LIMIT) { + float _max_0 = max_noftz(q_sq_top + database_sq[db_idx0] - 2.0f * _tmem_load_0[reg_base], 0.0f); + top_d0 = _max_0; + } + if (db_idx1 < M_LIMIT) { + float _max_1 = max_noftz(q_sq_top + database_sq[db_idx1] - 2.0f * _tmem_load_0[reg_base + 1], 0.0f); + top_d1 = _max_1; + } + int top_take1 = ((top_d1 < top_d0) ? 1 : 0); + if (best_top_d[31] > ((top_take1 != 0) ? top_d1 : top_d0)) { + best_top_d[31] = ((top_take1 != 0) ? top_d1 : top_d0); + best_top_i[31] = ((top_take1 != 0) ? db_idx1 : db_idx0); + #pragma unroll + for (int kk_1 = 30; kk_1 >= 0; kk_1--) { + float lower0_d = best_top_d[kk_1 + 1]; + int lower0_i = best_top_i[kk_1 + 1]; + float upper0_d = best_top_d[kk_1]; + int upper0_i = best_top_i[kk_1]; + int swap0_up = ((lower0_d < upper0_d) ? 1 : 0); + best_top_d[kk_1] = ((swap0_up != 0) ? lower0_d : upper0_d); + best_top_i[kk_1] = ((swap0_up != 0) ? lower0_i : upper0_i); + best_top_d[kk_1 + 1] = ((swap0_up != 0) ? upper0_d : lower0_d); + best_top_i[kk_1 + 1] = ((swap0_up != 0) ? upper0_i : lower0_i); + } + if (best_top_d[31] > ((top_take1 != 0) ? top_d0 : top_d1)) { + best_top_d[31] = ((top_take1 != 0) ? top_d0 : top_d1); + best_top_i[31] = ((top_take1 != 0) ? db_idx0 : db_idx1); + #pragma unroll + for (int kk_2 = 30; kk_2 >= 0; kk_2--) { + float lower1_d = best_top_d[kk_2 + 1]; + int lower1_i = best_top_i[kk_2 + 1]; + float upper1_d = best_top_d[kk_2]; + int upper1_i = best_top_i[kk_2]; + int swap1_up = ((lower1_d < upper1_d) ? 1 : 0); + best_top_d[kk_2] = ((swap1_up != 0) ? lower1_d : upper1_d); + best_top_i[kk_2] = ((swap1_up != 0) ? lower1_i : upper1_i); + best_top_d[kk_2 + 1] = ((swap1_up != 0) ? upper1_d : lower1_d); + best_top_i[kk_2 + 1] = ((swap1_up != 0) ? upper1_i : lower1_i); + } + } + } + float bot_d0 = 3.4e+38f; + float bot_d1 = 3.4e+38f; + if (db_idx0 < M_LIMIT) { + float _max_2 = max_noftz(q_sq_bot + database_sq[db_idx0] - 2.0f * _tmem_load_0[reg_base + 2], 0.0f); + bot_d0 = _max_2; + } + if (db_idx1 < M_LIMIT) { + float _max_3 = max_noftz(q_sq_bot + database_sq[db_idx1] - 2.0f * _tmem_load_0[reg_base + 3], 0.0f); + bot_d1 = _max_3; + } + int bot_take1 = ((bot_d1 < bot_d0) ? 1 : 0); + if (best_bot_d[31] > ((bot_take1 != 0) ? bot_d1 : bot_d0)) { + best_bot_d[31] = ((bot_take1 != 0) ? bot_d1 : bot_d0); + best_bot_i[31] = ((bot_take1 != 0) ? db_idx1 : db_idx0); + #pragma unroll + for (int kk_3 = 30; kk_3 >= 0; kk_3--) { + float lower0_d_1 = best_bot_d[kk_3 + 1]; + int lower0_i_1 = best_bot_i[kk_3 + 1]; + float upper0_d_1 = best_bot_d[kk_3]; + int upper0_i_1 = best_bot_i[kk_3]; + int swap0_up_1 = ((lower0_d_1 < upper0_d_1) ? 1 : 0); + best_bot_d[kk_3] = ((swap0_up_1 != 0) ? lower0_d_1 : upper0_d_1); + best_bot_i[kk_3] = ((swap0_up_1 != 0) ? lower0_i_1 : upper0_i_1); + best_bot_d[kk_3 + 1] = ((swap0_up_1 != 0) ? upper0_d_1 : lower0_d_1); + best_bot_i[kk_3 + 1] = ((swap0_up_1 != 0) ? upper0_i_1 : lower0_i_1); + } + if (best_bot_d[31] > ((bot_take1 != 0) ? bot_d0 : bot_d1)) { + best_bot_d[31] = ((bot_take1 != 0) ? bot_d0 : bot_d1); + best_bot_i[31] = ((bot_take1 != 0) ? db_idx0 : db_idx1); + #pragma unroll + for (int kk_4 = 30; kk_4 >= 0; kk_4--) { + float lower1_d_1 = best_bot_d[kk_4 + 1]; + int lower1_i_1 = best_bot_i[kk_4 + 1]; + float upper1_d_1 = best_bot_d[kk_4]; + int upper1_i_1 = best_bot_i[kk_4]; + int swap1_up_1 = ((lower1_d_1 < upper1_d_1) ? 1 : 0); + best_bot_d[kk_4] = ((swap1_up_1 != 0) ? lower1_d_1 : upper1_d_1); + best_bot_i[kk_4] = ((swap1_up_1 != 0) ? lower1_i_1 : upper1_i_1); + best_bot_d[kk_4 + 1] = ((swap1_up_1 != 0) ? upper1_d_1 : lower1_d_1); + best_bot_i[kk_4 + 1] = ((swap1_up_1 != 0) ? upper1_i_1 : lower1_i_1); + } + } + } + } + } + } + int top_slot_base = (row_top * 4 + slot) * TOP_K_MAX; + int bot_slot_base = (row_bot * 4 + slot) * TOP_K_MAX; + #pragma unroll + for (int kk_5 = 0; kk_5 < TOP_K_MAX; kk_5++) { + smem_local_d[top_slot_base + kk_5] = best_top_d[kk_5]; + smem_local_i[top_slot_base + kk_5] = best_top_i[kk_5]; + smem_local_d[bot_slot_base + kk_5] = best_bot_d[kk_5]; + smem_local_i[bot_slot_base + kk_5] = best_bot_i[kk_5]; + } + asm volatile("barrier.sync 8, %0;" :: "r"(64)); + if (tid < ROWS_COVERED) { + int row = tid; + float head_d[4]; + int head_i[4]; + int head_k[4]; + #pragma unroll + for (int slot_idx = 0; slot_idx < 4; slot_idx++) { + int local_base = (row * 4 + slot_idx) * TOP_K_MAX; + head_k[slot_idx] = 0; + head_d[slot_idx] = smem_local_d[local_base]; + head_i[slot_idx] = smem_local_i[local_base]; + } + int out_base = (split_idx * ROWS_COVERED + row) * TOP_K_MAX; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float winner_d = head_d[0]; + int winner_i = head_i[0]; + int winner_slot = 0; + #pragma unroll + for (int slot_idx_1 = 1; slot_idx_1 < 4; slot_idx_1++) { + float cand_d = head_d[slot_idx_1]; + int take = ((cand_d < winner_d) ? 1 : 0); + winner_d = ((take != 0) ? cand_d : winner_d); + winner_i = ((take != 0) ? head_i[slot_idx_1] : winner_i); + winner_slot = ((take != 0) ? slot_idx_1 : winner_slot); + } + *((float*)(partial_dists + (out_base + out_k))) = winner_d; + *((int*)(partial_indices + (out_base + out_k))) = winner_i; + #pragma unroll + for (int slot_idx_2 = 0; slot_idx_2 < 4; slot_idx_2++) { + if (winner_slot == slot_idx_2) { + int next_head = head_k[slot_idx_2] + 1; + head_k[slot_idx_2] = next_head; + head_d[slot_idx_2] = 3.4e+38f; + head_i[slot_idx_2] = -1; + if (next_head < TOP_K_MAX) { + int local_base_1 = (row * 4 + slot_idx_2) * TOP_K_MAX; + head_d[slot_idx_2] = smem_local_d[local_base_1 + next_head]; + head_i[slot_idx_2] = smem_local_i[local_base_1 + next_head]; + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(64)); + } + } + // ---- Role: load ---- + } else if (warp == 2) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 2) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx_1 = bid; work_idx_1 < SPLIT_COUNT_CONST; work_idx_1 += num_bids) { + int split_idx_1 = work_idx_1 % (unsigned int)SPLIT_COUNT_CONST; + int extra_before_1 = split_idx_1; + if (extra_before_1 > EXTRA_SPLITS_CONST) { + extra_before_1 = EXTRA_SPLITS_CONST; + } + int split_tile_count_1 = TILES_FLOOR_CONST; + if (split_idx_1 < EXTRA_SPLITS_CONST) { + split_tile_count_1 = TILES_FLOOR_CONST + 1; + } + int db_tile_start_1 = split_idx_1 * TILES_FLOOR_CONST + extra_before_1; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + mbarrier_arrive_expect_tx(query_full_addr, 16384); + tma_3d_gmem2smem(smem_query_addr, tmap_query, 0, 0, 0, query_full_addr); + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < DB_TILES_PER_SPLIT_CONST; local_db_tile_1++) { + if (split_tile_count_1 > local_db_tile_1) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int off_m = db_tile_1 * BLOCK_M; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + mbarrier_arrive_expect_tx(database_full_addr, 16384); + tma_3d_gmem2smem(smem_database_addr, tmap_database, 0, off_m, 0, database_full_addr); + } + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 3) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = bid; work_idx_2 < SPLIT_COUNT_CONST; work_idx_2 += num_bids) { + int split_idx_2 = work_idx_2 % (unsigned int)SPLIT_COUNT_CONST; + int split_tile_count_2 = TILES_FLOOR_CONST; + if (split_idx_2 < EXTRA_SPLITS_CONST) { + split_tile_count_2 = TILES_FLOOR_CONST + 1; + } + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int local_db_tile_2 = 0; local_db_tile_2 < DB_TILES_PER_SPLIT_CONST; local_db_tile_2++) { + if (split_tile_count_2 > local_db_tile_2) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = make_warp_uniform((smem_query_addr >> 4) & 0x3FFF); + int _mma_b_lo_0 = make_warp_uniform((smem_database_addr >> 4) & 0x3FFF); + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 68158608;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 506;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit(score_full_addr); + elect_commit(database_empty_addr); + } + } + elect_commit(query_empty_addr); + } + } + } + + // Cleanup + __syncthreads(); // barrier before TMEM dealloc + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::1.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(64)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::1.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0110.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0110.cu new file mode 100644 index 00000000..96e946b1 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0110.cu @@ -0,0 +1,114 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 128 +#define TOP_K_MAX 32 +#define SPLIT_COUNT 141 +#define SPLITS_PER_LANE 5 +#define ROWS_PER_CTA 4 + +#include + +extern "C" { + +__global__ __launch_bounds__(128, 1) void +kernel_knn_build_rag_microbucket_k32warpmerge_0077_v1_warp_row_merge_k32q32exact_s141r4_f653_v1(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int row = bid * ROWS_PER_CTA + warp; + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + if (warp < ROWS_PER_CTA && row < total_queries) { + int split_pos[SPLITS_PER_LANE]; + int split_id_for_slot[SPLITS_PER_LANE]; + float cand_d[SPLITS_PER_LANE]; + int cand_i[SPLITS_PER_LANE]; + #pragma unroll + for (int slot = 0; slot < SPLITS_PER_LANE; slot++) { + int split_id = slot * 32 + lane; + split_id_for_slot[slot] = split_id; + split_pos[slot] = 0; + cand_d[slot] = 3.4e+38f; + cand_i[slot] = -1; + if (split_id < SPLIT_COUNT) { + int source_addr = base_row + split_id * split_stride; + cand_d[slot] = partial_dists[source_addr]; + cand_i[slot] = partial_indices[source_addr]; + } + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float lane_best_d = cand_d[0]; + int lane_best_i = cand_i[0]; + int lane_best_slot = 0; + #pragma unroll + for (int slot_1 = 1; slot_1 < SPLITS_PER_LANE; slot_1++) { + if (lane_best_d > cand_d[slot_1]) { + lane_best_d = cand_d[slot_1]; + lane_best_i = cand_i[slot_1]; + lane_best_slot = slot_1; + } + } + float warp_min = lane_best_d; + float _warp_reduce_0 = warp_min; + #pragma unroll + for (int offset = 16; offset > 0; offset >>= 1) + _warp_reduce_0 = fminf(_warp_reduce_0, __shfl_xor_sync(0xFFFFFFFF, _warp_reduce_0, offset)); + warp_min = _warp_reduce_0; + unsigned int _vote_0 = __ballot_sync(0xFFFFFFFF, lane_best_d == warp_min); + int owner_ballot = _vote_0; + int _ffs_0 = __ffs(owner_ballot); + int winner_lane = _ffs_0 - 1; + int _shfl_0 = __shfl_sync(0xFFFFFFFF, lane_best_i, winner_lane); + int winner_i = _shfl_0; + int _shfl_1 = __shfl_sync(0xFFFFFFFF, lane_best_slot, winner_lane); + int winner_slot = _shfl_1; + if (lane == 0) { + *((float*)(out_dists + (base_row + out_k))) = warp_min; + *((int*)(out_indices + (base_row + out_k))) = winner_i; + } + if (lane == winner_lane) { + #pragma unroll + for (int slot_2 = 0; slot_2 < SPLITS_PER_LANE; slot_2++) { + if (winner_slot == slot_2) { + int next_pos = split_pos[slot_2] + 1; + split_pos[slot_2] = next_pos; + cand_d[slot_2] = 3.4e+38f; + cand_i[slot_2] = -1; + if (next_pos < TOP_K_MAX) { + int next_addr = base_row + split_id_for_slot[slot_2] * split_stride + next_pos; + cand_d[slot_2] = partial_dists[next_addr]; + cand_i[slot_2] = partial_indices[next_addr]; + } + } + } + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0111.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0111.cu new file mode 100644 index 00000000..3b9f9514 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0111.cu @@ -0,0 +1,114 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 128 +#define TOP_K_MAX 32 +#define SPLIT_COUNT 148 +#define SPLITS_PER_LANE 5 +#define ROWS_PER_CTA 4 + +#include + +extern "C" { + +__global__ __launch_bounds__(128, 1) void +kernel_knn_build_rag_microbucket_k32warpmerge_0077_v1_warp_row_merge_k32s148r4_56ed_v1(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int row = bid * ROWS_PER_CTA + warp; + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + if (warp < ROWS_PER_CTA && row < total_queries) { + int split_pos[SPLITS_PER_LANE]; + int split_id_for_slot[SPLITS_PER_LANE]; + float cand_d[SPLITS_PER_LANE]; + int cand_i[SPLITS_PER_LANE]; + #pragma unroll + for (int slot = 0; slot < SPLITS_PER_LANE; slot++) { + int split_id = slot * 32 + lane; + split_id_for_slot[slot] = split_id; + split_pos[slot] = 0; + cand_d[slot] = 3.4e+38f; + cand_i[slot] = -1; + if (split_id < SPLIT_COUNT) { + int source_addr = base_row + split_id * split_stride; + cand_d[slot] = partial_dists[source_addr]; + cand_i[slot] = partial_indices[source_addr]; + } + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float lane_best_d = cand_d[0]; + int lane_best_i = cand_i[0]; + int lane_best_slot = 0; + #pragma unroll + for (int slot_1 = 1; slot_1 < SPLITS_PER_LANE; slot_1++) { + if (lane_best_d > cand_d[slot_1]) { + lane_best_d = cand_d[slot_1]; + lane_best_i = cand_i[slot_1]; + lane_best_slot = slot_1; + } + } + float warp_min = lane_best_d; + float _warp_reduce_0 = warp_min; + #pragma unroll + for (int offset = 16; offset > 0; offset >>= 1) + _warp_reduce_0 = fminf(_warp_reduce_0, __shfl_xor_sync(0xFFFFFFFF, _warp_reduce_0, offset)); + warp_min = _warp_reduce_0; + unsigned int _vote_0 = __ballot_sync(0xFFFFFFFF, lane_best_d == warp_min); + int owner_ballot = _vote_0; + int _ffs_0 = __ffs(owner_ballot); + int winner_lane = _ffs_0 - 1; + int _shfl_0 = __shfl_sync(0xFFFFFFFF, lane_best_i, winner_lane); + int winner_i = _shfl_0; + int _shfl_1 = __shfl_sync(0xFFFFFFFF, lane_best_slot, winner_lane); + int winner_slot = _shfl_1; + if (lane == 0) { + *((float*)(out_dists + (base_row + out_k))) = warp_min; + *((int*)(out_indices + (base_row + out_k))) = winner_i; + } + if (lane == winner_lane) { + #pragma unroll + for (int slot_2 = 0; slot_2 < SPLITS_PER_LANE; slot_2++) { + if (winner_slot == slot_2) { + int next_pos = split_pos[slot_2] + 1; + split_pos[slot_2] = next_pos; + cand_d[slot_2] = 3.4e+38f; + cand_i[slot_2] = -1; + if (next_pos < TOP_K_MAX) { + int next_addr = base_row + split_id_for_slot[slot_2] * split_stride + next_pos; + cand_d[slot_2] = partial_dists[next_addr]; + cand_i[slot_2] = partial_indices[next_addr]; + } + } + } + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0112.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0112.cu new file mode 100644 index 00000000..fa8ff634 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0112.cu @@ -0,0 +1,622 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 128 +#define TMEM_ACC_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 16384 +#define SMEM_SMEM_QUERY_STRIDE 16384 +#define SMEM_SMEM_DATABASE_OFF 17408 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 32768 +#define SMEM_SMEM_DATABASE_STRIDE 32768 +#define SMEM_SMEM_LOCAL_D_OFF 50176 +#define SMEM_SMEM_LOCAL_D_STAGE_BYTES 65536 +#define SMEM_SMEM_LOCAL_D_STRIDE 65536 +#define SMEM_SMEM_LOCAL_I_OFF 115712 +#define SMEM_SMEM_LOCAL_I_STAGE_BYTES 65536 +#define SMEM_SMEM_LOCAL_I_STRIDE 65536 +#define SMEM_TOTAL 181504 +#define THREADS 256 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "tcgen05.mma.cta_group::1.kind::f16 [%0], %1, %2, %3, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, %3, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit(int mbar_addr) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];\n\t" + "}\n" + :: "r"(mbar_addr)); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x32(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x32.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15," + " %16, %17, %18, %19, %20, %21, %22, %23," + " %24, %25, %26, %27, %28, %29, %30, %31}, [%32];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]), + "=f"(dst[16]), "=f"(dst[17]), "=f"(dst[18]), "=f"(dst[19]), + "=f"(dst[20]), "=f"(dst[21]), "=f"(dst[22]), "=f"(dst[23]), + "=f"(dst[24]), "=f"(dst[25]), "=f"(dst[26]), "=f"(dst[27]), + "=f"(dst[28]), "=f"(dst[29]), "=f"(dst[30]), "=f"(dst[31]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ float max_noftz(float a, float b) { + float c; + asm("max.f32 %0, %1, %2;" : "=f"(c) : "f"(a), "f"(b)); + return c; +} + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit(int mbar_addr) { + asm volatile( + "tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ uint32_t make_warp_uniform(uint32_t val) { + uint32_t result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1f, 0xffffffff;" + : "=r"(result) : "r"(val)); + return result; +} + +extern "C" { + +__global__ __launch_bounds__(256, 1) void +kernel_knn_build_rag_stream_k32_q128m100000_staticn128_664a_stage1(__nv_bfloat16* __restrict__ query, __nv_bfloat16* __restrict__ database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tiles, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 17408); + const int smem_database_addr = smem + 17408; + float* smem_local_d = reinterpret_cast(smem_raw + 50176); + const int smem_local_d_addr = smem + 50176; + int* smem_local_i = reinterpret_cast(smem_raw + 115712); + const int smem_local_i_addr = smem + 115712; + + // Mbarrier init (1 groups, 1 barriers) + // Mbarriers at smem_raw[0..8) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // mma_done: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 0, 1, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (128 columns, 128 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 8); + if (warp == 0) { + int _tmem_hold = smem + 8; + asm volatile("tcgen05.alloc.cta_group::1.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(128) : "memory"); + } + + __syncthreads(); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define mma_done_addr (mbar_base + 0) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_acc = taddr; + + // === Task calls (dependency order) === + unsigned int _phase_mma_done_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx = bid; work_idx < total_work; work_idx += num_bids) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tiles; + int q_tile = query_work % num_q_tiles; + int off_q = q_tile * 64; + #pragma unroll 1 + for (int e_vec = tid; e_vec < 1024; e_vec += 256) { + int q_elem = e_vec * 8; + int q_row = q_elem / 128; + int d_col = q_elem - q_row * 128; + int q_idx = off_q + q_row; + float q_vals[8]; + unsigned int q_pack[4]; + #pragma unroll + for (int vi = 0; vi < 8; vi++) { + q_vals[vi] = 0.0f; + } + if (q_idx < Q) { + int q_addr = (batch_idx * Q + q_idx) * 128 + d_col; + { + const uint4* _vptr_0 = reinterpret_cast(query + (unsigned long long)q_addr + 0); + uint4 _vld_0[1]; + #pragma unroll + for (int _blk = 0; _blk < 1; _blk++) { + _vld_0[_blk] = _vptr_0[_blk]; + __nv_bfloat16* _velems_0 = reinterpret_cast<__nv_bfloat16*>(&_vld_0[_blk]); + #pragma unroll + for (int _j = 0; _j < 8; _j++) + q_vals[0 + _blk * 8 + _j] = __bfloat162float(_velems_0[_j]); + } + } + } + #pragma unroll + for (int _lp = 0; _lp < 4; _lp++) { + __nv_bfloat162 _bf2 = __float22bfloat162_rn(make_float2(q_vals[_lp*2 + 0], q_vals[_lp*2+1 + 0])); + q_pack[_lp] = *(uint32_t*)&_bf2; + } + int q_store_addr = (smem_query_addr + (unsigned int)(d_col / 64 * 8192 + q_row * 128 + d_col % 64 * 2 ^ (d_col / 64 * 8192 + q_row * 128 + d_col % 64 * 2 >> 7 & 7) << 4)); + asm volatile("st.shared.v4.b32 [%0], {%1,%2,%3,%4};" :: "r"(q_store_addr), "r"(q_pack[0]), "r"(q_pack[1]), "r"(q_pack[2]), "r"(q_pack[3]) : "memory"); + } + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); + __syncthreads(); + const int row_group = warp % 4; + const int col_block = warp / 4; + const int tmem_row_origin = row_group * 32; + const int logical_row_origin = row_group * 16; + int row_top = logical_row_origin + lane / 4; + int row_bot = row_top + 8; + const int lane_col = lane % 4; + const int slot = col_block * 4 + lane_col; + int q_top = off_q + row_top; + int q_bot = off_q + row_bot; + int valid_top = ((q_top < Q) ? 1 : 0); + int valid_bot = ((q_bot < Q) ? 1 : 0); + float q_sq_top = 0.0f; + float q_sq_bot = 0.0f; + if (valid_top != 0) { + q_sq_top = query_sq[batch_idx * Q + q_top]; + } + if (valid_bot != 0) { + q_sq_bot = query_sq[batch_idx * Q + q_bot]; + } + float best_top_d[32]; + float best_bot_d[32]; + int best_top_i[32]; + int best_bot_i[32]; + #pragma unroll + for (int kk = 0; kk < 32; kk++) { + best_top_d[kk] = 3.4e+38f; + best_bot_d[kk] = 3.4e+38f; + best_top_i[kk] = -1; + best_bot_i[kk] = -1; + } + int db_tile_start = split_idx * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int db_start = db_tile * 128; + #pragma unroll 1 + for (int e_vec_1 = tid; e_vec_1 < 2048; e_vec_1 += 256) { + int db_elem = e_vec_1 * 8; + int db_row = db_elem / 128; + int d_col_1 = db_elem - db_row * 128; + int db_idx = db_start + db_row; + float db_vals[8]; + unsigned int db_pack[4]; + #pragma unroll + for (int vi_1 = 0; vi_1 < 8; vi_1++) { + db_vals[vi_1] = 0.0f; + } + if (db_idx < M) { + int db_addr = (batch_idx * M + db_idx) * 128 + d_col_1; + { + const uint4* _vptr_1 = reinterpret_cast(database + (unsigned long long)db_addr + 0); + uint4 _vld_1[1]; + #pragma unroll + for (int _blk = 0; _blk < 1; _blk++) { + _vld_1[_blk] = _vptr_1[_blk]; + __nv_bfloat16* _velems_1 = reinterpret_cast<__nv_bfloat16*>(&_vld_1[_blk]); + #pragma unroll + for (int _j = 0; _j < 8; _j++) + db_vals[0 + _blk * 8 + _j] = __bfloat162float(_velems_1[_j]); + } + } + } + #pragma unroll + for (int _lp = 0; _lp < 4; _lp++) { + __nv_bfloat162 _bf2 = __float22bfloat162_rn(make_float2(db_vals[_lp*2 + 0], db_vals[_lp*2+1 + 0])); + db_pack[_lp] = *(uint32_t*)&_bf2; + } + int b_store_addr = (smem_database_addr + (unsigned int)(d_col_1 / 64 * 16384 + db_row * 128 + d_col_1 % 64 * 2 ^ (d_col_1 / 64 * 16384 + db_row * 128 + d_col_1 % 64 * 2 >> 7 & 7) << 4)); + asm volatile("st.shared.v4.b32 [%0], {%1,%2,%3,%4};" :: "r"(b_store_addr), "r"(db_pack[0]), "r"(db_pack[1]), "r"(db_pack[2]), "r"(db_pack[3]) : "memory"); + } + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); + __syncthreads(); + if (warp == 0) { + int _mma_a_lo_0 = make_warp_uniform((smem_query_addr >> 4) & 0x3FFF); + int _mma_b_lo_0 = make_warp_uniform((smem_database_addr >> 4) & 0x3FFF); + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 69207184;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 506;\n\t" + "add.u32 blo, blo, 1018;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_acc), "r"(0)); + elect_commit(mma_done_addr); + } + mbarrier_wait(mma_done_addr, _phase_mma_done_0); + _phase_mma_done_0 ^= 1; + if (warp < 8) { + float _tmem_load_0[32]; + asm volatile( + "tcgen05.ld.sync.aligned.16x256b.x8.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31}, [%32];" + : "=r"(*reinterpret_cast(&_tmem_load_0[0])), "=r"(*reinterpret_cast(&_tmem_load_0[1])), "=r"(*reinterpret_cast(&_tmem_load_0[2])), "=r"(*reinterpret_cast(&_tmem_load_0[3])), "=r"(*reinterpret_cast(&_tmem_load_0[4])), "=r"(*reinterpret_cast(&_tmem_load_0[5])), "=r"(*reinterpret_cast(&_tmem_load_0[6])), "=r"(*reinterpret_cast(&_tmem_load_0[7])), "=r"(*reinterpret_cast(&_tmem_load_0[8])), "=r"(*reinterpret_cast(&_tmem_load_0[9])), "=r"(*reinterpret_cast(&_tmem_load_0[10])), "=r"(*reinterpret_cast(&_tmem_load_0[11])), "=r"(*reinterpret_cast(&_tmem_load_0[12])), "=r"(*reinterpret_cast(&_tmem_load_0[13])), "=r"(*reinterpret_cast(&_tmem_load_0[14])), "=r"(*reinterpret_cast(&_tmem_load_0[15])), "=r"(*reinterpret_cast(&_tmem_load_0[16])), "=r"(*reinterpret_cast(&_tmem_load_0[17])), "=r"(*reinterpret_cast(&_tmem_load_0[18])), "=r"(*reinterpret_cast(&_tmem_load_0[19])), "=r"(*reinterpret_cast(&_tmem_load_0[20])), "=r"(*reinterpret_cast(&_tmem_load_0[21])), "=r"(*reinterpret_cast(&_tmem_load_0[22])), "=r"(*reinterpret_cast(&_tmem_load_0[23])), "=r"(*reinterpret_cast(&_tmem_load_0[24])), "=r"(*reinterpret_cast(&_tmem_load_0[25])), "=r"(*reinterpret_cast(&_tmem_load_0[26])), "=r"(*reinterpret_cast(&_tmem_load_0[27])), "=r"(*reinterpret_cast(&_tmem_load_0[28])), "=r"(*reinterpret_cast(&_tmem_load_0[29])), "=r"(*reinterpret_cast(&_tmem_load_0[30])), "=r"(*reinterpret_cast(&_tmem_load_0[31])) + : "r"(taddr + (unsigned int)(tmem_row_origin << 16) + (unsigned int)(col_block * 64)) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;"); + #pragma unroll + for (int repeat = 0; repeat < 8; repeat++) { + const int reg_base = repeat * 4; + const int col_base = col_block * 64 + repeat * 8 + lane_col * 2; + int db_idx0 = db_start + col_base; + int db_idx1 = db_idx0 + 1; + float top_d0 = 3.4e+38f; + float top_d1 = 3.4e+38f; + if (valid_top != 0 && db_idx0 < M) { + float _max_0 = max_noftz(q_sq_top + database_sq[batch_idx * M + db_idx0] - 2.0f * _tmem_load_0[reg_base], 0.0f); + top_d0 = _max_0; + } + if (valid_top != 0 && db_idx1 < M) { + float _max_1 = max_noftz(q_sq_top + database_sq[batch_idx * M + db_idx1] - 2.0f * _tmem_load_0[reg_base + 1], 0.0f); + top_d1 = _max_1; + } + int top_take1 = ((top_d1 < top_d0) ? 1 : 0); + if (best_top_d[9] > ((top_take1 != 0) ? top_d1 : top_d0)) { + best_top_d[9] = ((top_take1 != 0) ? top_d1 : top_d0); + best_top_i[9] = ((top_take1 != 0) ? db_idx1 : db_idx0); + #pragma unroll + for (int kk_1 = 8; kk_1 >= 0; kk_1--) { + float lower0_d = best_top_d[kk_1 + 1]; + int lower0_i = best_top_i[kk_1 + 1]; + float upper0_d = best_top_d[kk_1]; + int upper0_i = best_top_i[kk_1]; + int swap0_up = ((lower0_d < upper0_d) ? 1 : 0); + best_top_d[kk_1] = ((swap0_up != 0) ? lower0_d : upper0_d); + best_top_i[kk_1] = ((swap0_up != 0) ? lower0_i : upper0_i); + best_top_d[kk_1 + 1] = ((swap0_up != 0) ? upper0_d : lower0_d); + best_top_i[kk_1 + 1] = ((swap0_up != 0) ? upper0_i : lower0_i); + } + if (best_top_d[9] > ((top_take1 != 0) ? top_d0 : top_d1)) { + best_top_d[9] = ((top_take1 != 0) ? top_d0 : top_d1); + best_top_i[9] = ((top_take1 != 0) ? db_idx0 : db_idx1); + #pragma unroll + for (int kk_2 = 8; kk_2 >= 0; kk_2--) { + float lower1_d = best_top_d[kk_2 + 1]; + int lower1_i = best_top_i[kk_2 + 1]; + float upper1_d = best_top_d[kk_2]; + int upper1_i = best_top_i[kk_2]; + int swap1_up = ((lower1_d < upper1_d) ? 1 : 0); + best_top_d[kk_2] = ((swap1_up != 0) ? lower1_d : upper1_d); + best_top_i[kk_2] = ((swap1_up != 0) ? lower1_i : upper1_i); + best_top_d[kk_2 + 1] = ((swap1_up != 0) ? upper1_d : lower1_d); + best_top_i[kk_2 + 1] = ((swap1_up != 0) ? upper1_i : lower1_i); + } + } + } + float bot_d0 = 3.4e+38f; + float bot_d1 = 3.4e+38f; + if (valid_bot != 0 && db_idx0 < M) { + float _max_2 = max_noftz(q_sq_bot + database_sq[batch_idx * M + db_idx0] - 2.0f * _tmem_load_0[reg_base + 2], 0.0f); + bot_d0 = _max_2; + } + if (valid_bot != 0 && db_idx1 < M) { + float _max_3 = max_noftz(q_sq_bot + database_sq[batch_idx * M + db_idx1] - 2.0f * _tmem_load_0[reg_base + 3], 0.0f); + bot_d1 = _max_3; + } + int bot_take1 = ((bot_d1 < bot_d0) ? 1 : 0); + if (best_bot_d[9] > ((bot_take1 != 0) ? bot_d1 : bot_d0)) { + best_bot_d[9] = ((bot_take1 != 0) ? bot_d1 : bot_d0); + best_bot_i[9] = ((bot_take1 != 0) ? db_idx1 : db_idx0); + #pragma unroll + for (int kk_3 = 8; kk_3 >= 0; kk_3--) { + float lower0_d_1 = best_bot_d[kk_3 + 1]; + int lower0_i_1 = best_bot_i[kk_3 + 1]; + float upper0_d_1 = best_bot_d[kk_3]; + int upper0_i_1 = best_bot_i[kk_3]; + int swap0_up_1 = ((lower0_d_1 < upper0_d_1) ? 1 : 0); + best_bot_d[kk_3] = ((swap0_up_1 != 0) ? lower0_d_1 : upper0_d_1); + best_bot_i[kk_3] = ((swap0_up_1 != 0) ? lower0_i_1 : upper0_i_1); + best_bot_d[kk_3 + 1] = ((swap0_up_1 != 0) ? upper0_d_1 : lower0_d_1); + best_bot_i[kk_3 + 1] = ((swap0_up_1 != 0) ? upper0_i_1 : lower0_i_1); + } + if (best_bot_d[9] > ((bot_take1 != 0) ? bot_d0 : bot_d1)) { + best_bot_d[9] = ((bot_take1 != 0) ? bot_d0 : bot_d1); + best_bot_i[9] = ((bot_take1 != 0) ? db_idx0 : db_idx1); + #pragma unroll + for (int kk_4 = 8; kk_4 >= 0; kk_4--) { + float lower1_d_1 = best_bot_d[kk_4 + 1]; + int lower1_i_1 = best_bot_i[kk_4 + 1]; + float upper1_d_1 = best_bot_d[kk_4]; + int upper1_i_1 = best_bot_i[kk_4]; + int swap1_up_1 = ((lower1_d_1 < upper1_d_1) ? 1 : 0); + best_bot_d[kk_4] = ((swap1_up_1 != 0) ? lower1_d_1 : upper1_d_1); + best_bot_i[kk_4] = ((swap1_up_1 != 0) ? lower1_i_1 : upper1_i_1); + best_bot_d[kk_4 + 1] = ((swap1_up_1 != 0) ? upper1_d_1 : lower1_d_1); + best_bot_i[kk_4 + 1] = ((swap1_up_1 != 0) ? upper1_i_1 : lower1_i_1); + } + } + } + } + } + __syncthreads(); + } + if (warp < 8) { + int top_slot_base = (row_top * 8 + slot) * 32; + int bot_slot_base = (row_bot * 8 + slot) * 32; + #pragma unroll + for (int kk_5 = 0; kk_5 < 32; kk_5++) { + smem_local_d[top_slot_base + kk_5] = best_top_d[kk_5]; + smem_local_i[top_slot_base + kk_5] = best_top_i[kk_5]; + smem_local_d[bot_slot_base + kk_5] = best_bot_d[kk_5]; + smem_local_i[bot_slot_base + kk_5] = best_bot_i[kk_5]; + } + } + __syncthreads(); + if (tid < 64) { + int row = tid; + int q_idx_1 = off_q + row; + if (q_idx_1 < Q) { + float head_d[8]; + int head_i[8]; + int head_k[8]; + #pragma unroll + for (int slot_idx = 0; slot_idx < 8; slot_idx++) { + int local_base = (row * 8 + slot_idx) * 32; + head_k[slot_idx] = 0; + head_d[slot_idx] = smem_local_d[local_base]; + head_i[slot_idx] = smem_local_i[local_base]; + } + int out_base = ((split_idx * B + batch_idx) * Q + q_idx_1) * K; + #pragma unroll + for (int out_k = 0; out_k < 32; out_k++) { + float winner_d = head_d[0]; + int winner_i = head_i[0]; + int winner_slot = 0; + #pragma unroll + for (int slot_idx_1 = 1; slot_idx_1 < 8; slot_idx_1++) { + float cand_d = head_d[slot_idx_1]; + int take = ((cand_d < winner_d) ? 1 : 0); + winner_d = ((take != 0) ? cand_d : winner_d); + winner_i = ((take != 0) ? head_i[slot_idx_1] : winner_i); + winner_slot = ((take != 0) ? slot_idx_1 : winner_slot); + } + if (out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = winner_d; + *((int*)(partial_indices + (out_base + out_k))) = winner_i; + } + #pragma unroll + for (int slot_idx_2 = 0; slot_idx_2 < 8; slot_idx_2++) { + if (winner_slot == slot_idx_2) { + int next_head = head_k[slot_idx_2] + 1; + head_k[slot_idx_2] = next_head; + head_d[slot_idx_2] = 3.4e+38f; + head_i[slot_idx_2] = -1; + if (next_head < 32) { + int local_base_1 = (row * 8 + slot_idx_2) * 32; + head_d[slot_idx_2] = smem_local_d[local_base_1 + next_head]; + head_i[slot_idx_2] = smem_local_i[local_base_1 + next_head]; + } + } + } + } + } + } + __syncthreads(); + } + + // Cleanup + __syncthreads(); + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::1.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(128)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::1.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0113.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0113.cu new file mode 100644 index 00000000..e1ce01af --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0113.cu @@ -0,0 +1,141 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define SMEM_GROUP_DISTS_OFF 0 +#define SMEM_GROUP_DISTS_STAGE_BYTES 1024 +#define SMEM_GROUP_DISTS_STRIDE 1024 +#define SMEM_GROUP_INDICES_OFF 1024 +#define SMEM_GROUP_INDICES_STAGE_BYTES 1024 +#define SMEM_GROUP_INDICES_STRIDE 1024 +#define SMEM_TOTAL 2048 +#define THREADS 32 +#define TOP_K_MAX 32 +#define GROUP_COUNT 8 +#define GROUP_SPLITS 9 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_rag_frontier_7399_k32_fused_group_final_merge_k32s72g8_4fbf_v6(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + float* group_dists = reinterpret_cast(smem_raw + 0); + const int group_dists_addr = smem + 0; + int* group_indices = reinterpret_cast(smem_raw + 1024); + const int group_indices_addr = smem + 1024; + + // === Task calls (dependency order) === + int split_pos[GROUP_SPLITS]; + int split_base[GROUP_SPLITS]; + float group_cand_d[GROUP_SPLITS]; + int group_cand_i[GROUP_SPLITS]; + int final_pos[GROUP_COUNT]; + float final_cand_d[GROUP_COUNT]; + int final_cand_i[GROUP_COUNT]; + #pragma unroll 1 + for (int row = bid; row < total_queries; row += num_bids) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + if (tid < GROUP_COUNT) { + int group_idx = tid; + int source_split0 = group_idx * GROUP_SPLITS; + int shared_base = group_idx * TOP_K_MAX; + #pragma unroll + for (int local_split = 0; local_split < GROUP_SPLITS; local_split++) { + split_pos[local_split] = 0; + int split_id = source_split0 + local_split; + split_base[local_split] = base_row + split_id * split_stride; + group_cand_d[local_split] = partial_dists[split_base[local_split]]; + group_cand_i[local_split] = partial_indices[split_base[local_split]]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_d = group_cand_d[0]; + int best_i = group_cand_i[0]; + int best_split = 0; + #pragma unroll + for (int local_split_1 = 1; local_split_1 < GROUP_SPLITS; local_split_1++) { + if (best_d > group_cand_d[local_split_1]) { + best_d = group_cand_d[local_split_1]; + best_i = group_cand_i[local_split_1]; + best_split = local_split_1; + } + } + group_dists[shared_base + out_k] = best_d; + group_indices[shared_base + out_k] = best_i; + split_pos[best_split] = split_pos[best_split] + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_pos = split_pos[best_split]; + int next_addr = split_base[best_split] + next_pos; + group_cand_d[best_split] = partial_dists[next_addr]; + group_cand_i[best_split] = partial_indices[next_addr]; + } + } + } + __syncthreads(); + if (tid == 0) { + #pragma unroll + for (int group_idx_1 = 0; group_idx_1 < GROUP_COUNT; group_idx_1++) { + final_pos[group_idx_1] = 0; + int group_base = group_idx_1 * TOP_K_MAX; + final_cand_d[group_idx_1] = group_dists[group_base]; + final_cand_i[group_idx_1] = group_indices[group_base]; + } + #pragma unroll + for (int out_k_1 = 0; out_k_1 < TOP_K_MAX; out_k_1++) { + float best_d_1 = final_cand_d[0]; + int best_i_1 = final_cand_i[0]; + int best_group = 0; + #pragma unroll + for (int group_idx_2 = 1; group_idx_2 < GROUP_COUNT; group_idx_2++) { + if (best_d_1 > final_cand_d[group_idx_2]) { + best_d_1 = final_cand_d[group_idx_2]; + best_i_1 = final_cand_i[group_idx_2]; + best_group = group_idx_2; + } + } + *((float*)(out_dists + (base_row + out_k_1))) = best_d_1; + *((int*)(out_indices + (base_row + out_k_1))) = best_i_1; + final_pos[best_group] = final_pos[best_group] + 1; + if (out_k_1 + 1 < TOP_K_MAX) { + int next_pos_1 = final_pos[best_group]; + int next_addr_1 = best_group * TOP_K_MAX + next_pos_1; + final_cand_d[best_group] = group_dists[next_addr_1]; + final_cand_i[best_group] = group_indices[next_addr_1]; + } + } + } + __syncthreads(); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0114.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0114.cu new file mode 100644 index 00000000..b181596e --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0114.cu @@ -0,0 +1,641 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 64 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 16384 +#define SMEM_SMEM_QUERY_STRIDE 16384 +#define SMEM_SMEM_DATABASE_OFF 17408 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_LOCAL_D_OFF 34048 +#define SMEM_SMEM_LOCAL_D_STAGE_BYTES 32768 +#define SMEM_SMEM_LOCAL_D_STRIDE 32768 +#define SMEM_SMEM_LOCAL_I_OFF 66816 +#define SMEM_SMEM_LOCAL_I_STAGE_BYTES 32768 +#define SMEM_SMEM_LOCAL_I_STRIDE 32768 +#define SMEM_TOTAL 99584 +#define THREADS 192 +#define BLOCK_Q 64 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_MAX 32 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "tcgen05.mma.cta_group::1.kind::f16 [%0], %1, %2, %3, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, %3, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit(int mbar_addr) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];\n\t" + "}\n" + :: "r"(mbar_addr)); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x32(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x32.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15," + " %16, %17, %18, %19, %20, %21, %22, %23," + " %24, %25, %26, %27, %28, %29, %30, %31}, [%32];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]), + "=f"(dst[16]), "=f"(dst[17]), "=f"(dst[18]), "=f"(dst[19]), + "=f"(dst[20]), "=f"(dst[21]), "=f"(dst[22]), "=f"(dst[23]), + "=f"(dst[24]), "=f"(dst[25]), "=f"(dst[26]), "=f"(dst[27]), + "=f"(dst[28]), "=f"(dst[29]), "=f"(dst[30]), "=f"(dst[31]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ float max_noftz(float a, float b) { + float c; + asm("max.f32 %0, %1, %2;" : "=f"(c) : "f"(a), "f"(b)); + return c; +} + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cta.global" + ".mbarrier::complete_tx::bytes" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit(int mbar_addr) { + asm volatile( + "tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ uint32_t make_warp_uniform(uint32_t val) { + uint32_t result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1f, 0xffffffff;" + : "=r"(result) : "r"(val)); + return result; +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_rag_microbucket_q32rowld_e5db_v1_stage1_q32_k32_m64(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tiles, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 17408); + const int smem_database_addr = smem + 17408; + float* smem_local_d = reinterpret_cast(smem_raw + 34048); + const int smem_local_d_addr = smem + 34048; + int* smem_local_i = reinterpret_cast(smem_raw + 66816); + const int smem_local_i_addr = smem + 66816; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 0, 1, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 16, 1, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=4 + mbarrier_init_pred(smem + 40, 4, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (64 columns, 64 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::1.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(64) : "memory"); + } + + __syncthreads(); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: compute ---- + if (warp <= 3) { + { // compute_main + int warp_id_in_role = (warp - 0); + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx = bid; work_idx < total_work; work_idx += num_bids) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tiles; + int q_tile = query_work % num_q_tiles; + int off_q = q_tile * BLOCK_Q; + int tmem_row_origin = warp_id_in_role * 32; + int logical_row_origin = warp_id_in_role * 16; + int row_top = logical_row_origin + lane / 4; + int row_bot = row_top + 8; + int lane_col = lane % 4; + int slot = lane_col; + int q_top = off_q + row_top; + int q_bot = off_q + row_bot; + int valid_top = ((q_top < Q) ? 1 : 0); + int valid_bot = ((q_bot < Q) ? 1 : 0); + float q_sq_top = 0.0f; + float q_sq_bot = 0.0f; + if (valid_top != 0) { + q_sq_top = query_sq[batch_idx * Q + q_top]; + } + if (valid_bot != 0) { + q_sq_bot = query_sq[batch_idx * Q + q_bot]; + } + float best_top_d[TOP_K_MAX]; + float best_bot_d[TOP_K_MAX]; + int best_top_i[TOP_K_MAX]; + int best_bot_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_top_d[kk] = 3.4e+38f; + best_bot_d[kk] = 3.4e+38f; + best_top_i[kk] = -1; + best_bot_i[kk] = -1; + } + int db_tile_start = split_idx * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int db_start = db_tile * BLOCK_M; + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(tmem_row_origin << 16); + float _tmem_load_0[32]; + asm volatile( + "tcgen05.ld.sync.aligned.16x256b.x8.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31}, [%32];" + : "=r"(*reinterpret_cast(&_tmem_load_0[0])), "=r"(*reinterpret_cast(&_tmem_load_0[1])), "=r"(*reinterpret_cast(&_tmem_load_0[2])), "=r"(*reinterpret_cast(&_tmem_load_0[3])), "=r"(*reinterpret_cast(&_tmem_load_0[4])), "=r"(*reinterpret_cast(&_tmem_load_0[5])), "=r"(*reinterpret_cast(&_tmem_load_0[6])), "=r"(*reinterpret_cast(&_tmem_load_0[7])), "=r"(*reinterpret_cast(&_tmem_load_0[8])), "=r"(*reinterpret_cast(&_tmem_load_0[9])), "=r"(*reinterpret_cast(&_tmem_load_0[10])), "=r"(*reinterpret_cast(&_tmem_load_0[11])), "=r"(*reinterpret_cast(&_tmem_load_0[12])), "=r"(*reinterpret_cast(&_tmem_load_0[13])), "=r"(*reinterpret_cast(&_tmem_load_0[14])), "=r"(*reinterpret_cast(&_tmem_load_0[15])), "=r"(*reinterpret_cast(&_tmem_load_0[16])), "=r"(*reinterpret_cast(&_tmem_load_0[17])), "=r"(*reinterpret_cast(&_tmem_load_0[18])), "=r"(*reinterpret_cast(&_tmem_load_0[19])), "=r"(*reinterpret_cast(&_tmem_load_0[20])), "=r"(*reinterpret_cast(&_tmem_load_0[21])), "=r"(*reinterpret_cast(&_tmem_load_0[22])), "=r"(*reinterpret_cast(&_tmem_load_0[23])), "=r"(*reinterpret_cast(&_tmem_load_0[24])), "=r"(*reinterpret_cast(&_tmem_load_0[25])), "=r"(*reinterpret_cast(&_tmem_load_0[26])), "=r"(*reinterpret_cast(&_tmem_load_0[27])), "=r"(*reinterpret_cast(&_tmem_load_0[28])), "=r"(*reinterpret_cast(&_tmem_load_0[29])), "=r"(*reinterpret_cast(&_tmem_load_0[30])), "=r"(*reinterpret_cast(&_tmem_load_0[31])) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + if (elect_sync()) { + mbarrier_arrive(score_empty_addr); + } + #pragma unroll + for (int repeat = 0; repeat < 8; repeat++) { + const int reg_base = repeat * 4; + int col_base = repeat * 8 + lane_col * 2; + int db_idx0 = db_start + col_base; + int db_idx1 = db_idx0 + 1; + float top_d0 = 3.4e+38f; + float top_d1 = 3.4e+38f; + if (valid_top != 0 && db_idx0 < M) { + float _max_0 = max_noftz(q_sq_top + database_sq[batch_idx * M + db_idx0] - 2.0f * _tmem_load_0[reg_base], 0.0f); + top_d0 = _max_0; + } + if (valid_top != 0 && db_idx1 < M) { + float _max_1 = max_noftz(q_sq_top + database_sq[batch_idx * M + db_idx1] - 2.0f * _tmem_load_0[reg_base + 1], 0.0f); + top_d1 = _max_1; + } + int top_take1 = ((top_d1 < top_d0) ? 1 : 0); + if (best_top_d[31] > ((top_take1 != 0) ? top_d1 : top_d0)) { + best_top_d[31] = ((top_take1 != 0) ? top_d1 : top_d0); + best_top_i[31] = ((top_take1 != 0) ? db_idx1 : db_idx0); + #pragma unroll + for (int kk_1 = 30; kk_1 >= 0; kk_1--) { + float lower0_d = best_top_d[kk_1 + 1]; + int lower0_i = best_top_i[kk_1 + 1]; + float upper0_d = best_top_d[kk_1]; + int upper0_i = best_top_i[kk_1]; + int swap0_up = ((lower0_d < upper0_d) ? 1 : 0); + best_top_d[kk_1] = ((swap0_up != 0) ? lower0_d : upper0_d); + best_top_i[kk_1] = ((swap0_up != 0) ? lower0_i : upper0_i); + best_top_d[kk_1 + 1] = ((swap0_up != 0) ? upper0_d : lower0_d); + best_top_i[kk_1 + 1] = ((swap0_up != 0) ? upper0_i : lower0_i); + } + if (best_top_d[31] > ((top_take1 != 0) ? top_d0 : top_d1)) { + best_top_d[31] = ((top_take1 != 0) ? top_d0 : top_d1); + best_top_i[31] = ((top_take1 != 0) ? db_idx0 : db_idx1); + #pragma unroll + for (int kk_2 = 30; kk_2 >= 0; kk_2--) { + float lower1_d = best_top_d[kk_2 + 1]; + int lower1_i = best_top_i[kk_2 + 1]; + float upper1_d = best_top_d[kk_2]; + int upper1_i = best_top_i[kk_2]; + int swap1_up = ((lower1_d < upper1_d) ? 1 : 0); + best_top_d[kk_2] = ((swap1_up != 0) ? lower1_d : upper1_d); + best_top_i[kk_2] = ((swap1_up != 0) ? lower1_i : upper1_i); + best_top_d[kk_2 + 1] = ((swap1_up != 0) ? upper1_d : lower1_d); + best_top_i[kk_2 + 1] = ((swap1_up != 0) ? upper1_i : lower1_i); + } + } + } + float bot_d0 = 3.4e+38f; + float bot_d1 = 3.4e+38f; + if (valid_bot != 0 && db_idx0 < M) { + float _max_2 = max_noftz(q_sq_bot + database_sq[batch_idx * M + db_idx0] - 2.0f * _tmem_load_0[reg_base + 2], 0.0f); + bot_d0 = _max_2; + } + if (valid_bot != 0 && db_idx1 < M) { + float _max_3 = max_noftz(q_sq_bot + database_sq[batch_idx * M + db_idx1] - 2.0f * _tmem_load_0[reg_base + 3], 0.0f); + bot_d1 = _max_3; + } + int bot_take1 = ((bot_d1 < bot_d0) ? 1 : 0); + if (best_bot_d[31] > ((bot_take1 != 0) ? bot_d1 : bot_d0)) { + best_bot_d[31] = ((bot_take1 != 0) ? bot_d1 : bot_d0); + best_bot_i[31] = ((bot_take1 != 0) ? db_idx1 : db_idx0); + #pragma unroll + for (int kk_3 = 30; kk_3 >= 0; kk_3--) { + float lower0_d_1 = best_bot_d[kk_3 + 1]; + int lower0_i_1 = best_bot_i[kk_3 + 1]; + float upper0_d_1 = best_bot_d[kk_3]; + int upper0_i_1 = best_bot_i[kk_3]; + int swap0_up_1 = ((lower0_d_1 < upper0_d_1) ? 1 : 0); + best_bot_d[kk_3] = ((swap0_up_1 != 0) ? lower0_d_1 : upper0_d_1); + best_bot_i[kk_3] = ((swap0_up_1 != 0) ? lower0_i_1 : upper0_i_1); + best_bot_d[kk_3 + 1] = ((swap0_up_1 != 0) ? upper0_d_1 : lower0_d_1); + best_bot_i[kk_3 + 1] = ((swap0_up_1 != 0) ? upper0_i_1 : lower0_i_1); + } + if (best_bot_d[31] > ((bot_take1 != 0) ? bot_d0 : bot_d1)) { + best_bot_d[31] = ((bot_take1 != 0) ? bot_d0 : bot_d1); + best_bot_i[31] = ((bot_take1 != 0) ? db_idx0 : db_idx1); + #pragma unroll + for (int kk_4 = 30; kk_4 >= 0; kk_4--) { + float lower1_d_1 = best_bot_d[kk_4 + 1]; + int lower1_i_1 = best_bot_i[kk_4 + 1]; + float upper1_d_1 = best_bot_d[kk_4]; + int upper1_i_1 = best_bot_i[kk_4]; + int swap1_up_1 = ((lower1_d_1 < upper1_d_1) ? 1 : 0); + best_bot_d[kk_4] = ((swap1_up_1 != 0) ? lower1_d_1 : upper1_d_1); + best_bot_i[kk_4] = ((swap1_up_1 != 0) ? lower1_i_1 : upper1_i_1); + best_bot_d[kk_4 + 1] = ((swap1_up_1 != 0) ? upper1_d_1 : lower1_d_1); + best_bot_i[kk_4 + 1] = ((swap1_up_1 != 0) ? upper1_i_1 : lower1_i_1); + } + } + } + } + } + int top_slot_base = (row_top * 4 + slot) * TOP_K_MAX; + int bot_slot_base = (row_bot * 4 + slot) * TOP_K_MAX; + #pragma unroll + for (int kk_5 = 0; kk_5 < TOP_K_MAX; kk_5++) { + smem_local_d[top_slot_base + kk_5] = best_top_d[kk_5]; + smem_local_i[top_slot_base + kk_5] = best_top_i[kk_5]; + smem_local_d[bot_slot_base + kk_5] = best_bot_d[kk_5]; + smem_local_i[bot_slot_base + kk_5] = best_bot_i[kk_5]; + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + if (tid < BLOCK_Q) { + int row = tid; + int q_idx = off_q + row; + float head_d[4]; + int head_i[4]; + int head_k[4]; + #pragma unroll + for (int slot_idx = 0; slot_idx < 4; slot_idx++) { + int local_base = (row * 4 + slot_idx) * TOP_K_MAX; + head_k[slot_idx] = 0; + head_d[slot_idx] = smem_local_d[local_base]; + head_i[slot_idx] = smem_local_i[local_base]; + } + int out_base = ((split_idx * B + batch_idx) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float winner_d = head_d[0]; + int winner_i = head_i[0]; + int winner_slot = 0; + #pragma unroll + for (int slot_idx_1 = 1; slot_idx_1 < 4; slot_idx_1++) { + float cand_d = head_d[slot_idx_1]; + int take = ((cand_d < winner_d) ? 1 : 0); + winner_d = ((take != 0) ? cand_d : winner_d); + winner_i = ((take != 0) ? head_i[slot_idx_1] : winner_i); + winner_slot = ((take != 0) ? slot_idx_1 : winner_slot); + } + if (q_idx < Q && out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = winner_d; + *((int*)(partial_indices + (out_base + out_k))) = winner_i; + } + #pragma unroll + for (int slot_idx_2 = 0; slot_idx_2 < 4; slot_idx_2++) { + if (winner_slot == slot_idx_2) { + int next_head = head_k[slot_idx_2] + 1; + head_k[slot_idx_2] = next_head; + head_d[slot_idx_2] = 3.4e+38f; + head_i[slot_idx_2] = -1; + if (next_head < TOP_K_MAX) { + int local_base_1 = (row * 4 + slot_idx_2) * TOP_K_MAX; + head_d[slot_idx_2] = smem_local_d[local_base_1 + next_head]; + head_i[slot_idx_2] = smem_local_i[local_base_1 + next_head]; + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + } + // ---- Role: load ---- + } else if (warp == 4) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 4) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx_1 = bid; work_idx_1 < total_work; work_idx_1 += num_bids) { + int split_idx_1 = work_idx_1 % (unsigned int)split_count; + int query_work_1 = work_idx_1 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tiles; + int q_tile_1 = query_work_1 % num_q_tiles; + int off_q_1 = q_tile_1 * BLOCK_Q; + int global_q = batch_idx_1 * Q + off_q_1; + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + mbarrier_arrive_expect_tx(query_full_addr, 16384); + tma_3d_gmem2smem(smem_query_addr, tmap_query, 0, global_q, 0, query_full_addr); + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int off_m = db_tile_1 * BLOCK_M; + int global_m = batch_idx_1 * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + mbarrier_arrive_expect_tx(database_full_addr, 16384); + tma_3d_gmem2smem(smem_database_addr, tmap_database, 0, global_m, 0, database_full_addr); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 5) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = bid; work_idx_2 < total_work; work_idx_2 += num_bids) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = make_warp_uniform((smem_query_addr >> 4) & 0x3FFF); + int _mma_b_lo_0 = make_warp_uniform((smem_database_addr >> 4) & 0x3FFF); + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 68158608;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 506;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit(score_full_addr); + elect_commit(database_empty_addr); + } + elect_commit(query_empty_addr); + } + } + } + + // Cleanup + __syncthreads(); // barrier before TMEM dealloc + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::1.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(64)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::1.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0115.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0115.cu new file mode 100644 index 00000000..18df0e53 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0115.cu @@ -0,0 +1,114 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 128 +#define TOP_K_MAX 32 +#define SPLIT_COUNT 72 +#define SPLITS_PER_LANE 3 +#define ROWS_PER_CTA 1 + +#include + +extern "C" { + +__global__ __launch_bounds__(128, 1) void +kernel_knn_build_rag_microbucket_k32warpmerge_0077_v1_warp_row_merge_k32s72_0077_v1(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int row = bid * ROWS_PER_CTA + warp; + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + if (warp < ROWS_PER_CTA && row < total_queries) { + int split_pos[SPLITS_PER_LANE]; + int split_id_for_slot[SPLITS_PER_LANE]; + float cand_d[SPLITS_PER_LANE]; + int cand_i[SPLITS_PER_LANE]; + #pragma unroll + for (int slot = 0; slot < SPLITS_PER_LANE; slot++) { + int split_id = slot * 32 + lane; + split_id_for_slot[slot] = split_id; + split_pos[slot] = 0; + cand_d[slot] = 3.4e+38f; + cand_i[slot] = -1; + if (split_id < SPLIT_COUNT) { + int source_addr = base_row + split_id * split_stride; + cand_d[slot] = partial_dists[source_addr]; + cand_i[slot] = partial_indices[source_addr]; + } + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float lane_best_d = cand_d[0]; + int lane_best_i = cand_i[0]; + int lane_best_slot = 0; + #pragma unroll + for (int slot_1 = 1; slot_1 < SPLITS_PER_LANE; slot_1++) { + if (lane_best_d > cand_d[slot_1]) { + lane_best_d = cand_d[slot_1]; + lane_best_i = cand_i[slot_1]; + lane_best_slot = slot_1; + } + } + float warp_min = lane_best_d; + float _warp_reduce_0 = warp_min; + #pragma unroll + for (int offset = 16; offset > 0; offset >>= 1) + _warp_reduce_0 = fminf(_warp_reduce_0, __shfl_xor_sync(0xFFFFFFFF, _warp_reduce_0, offset)); + warp_min = _warp_reduce_0; + unsigned int _vote_0 = __ballot_sync(0xFFFFFFFF, lane_best_d == warp_min); + int owner_ballot = _vote_0; + int _ffs_0 = __ffs(owner_ballot); + int winner_lane = _ffs_0 - 1; + int _shfl_0 = __shfl_sync(0xFFFFFFFF, lane_best_i, winner_lane); + int winner_i = _shfl_0; + int _shfl_1 = __shfl_sync(0xFFFFFFFF, lane_best_slot, winner_lane); + int winner_slot = _shfl_1; + if (lane == 0) { + *((float*)(out_dists + (base_row + out_k))) = warp_min; + *((int*)(out_indices + (base_row + out_k))) = winner_i; + } + if (lane == winner_lane) { + #pragma unroll + for (int slot_2 = 0; slot_2 < SPLITS_PER_LANE; slot_2++) { + if (winner_slot == slot_2) { + int next_pos = split_pos[slot_2] + 1; + split_pos[slot_2] = next_pos; + cand_d[slot_2] = 3.4e+38f; + cand_i[slot_2] = -1; + if (next_pos < TOP_K_MAX) { + int next_addr = base_row + split_id_for_slot[slot_2] * split_stride + next_pos; + cand_d[slot_2] = partial_dists[next_addr]; + cand_i[slot_2] = partial_indices[next_addr]; + } + } + } + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0116.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0116.cu new file mode 100644 index 00000000..5742624a --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0116.cu @@ -0,0 +1,84 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 32 +#define TOP_K_MAX 10 +#define SPLIT_COUNT 72 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_k10s72_4e09(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 32 + tid; + int stride = num_bids * 32; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int out_base = base_row; + int split_pos[SPLIT_COUNT]; + int split_base[SPLIT_COUNT]; + float cand_d[SPLIT_COUNT]; + int cand_i[SPLIT_COUNT]; + #pragma unroll + for (int split_idx = 0; split_idx < SPLIT_COUNT; split_idx++) { + split_pos[split_idx] = 0; + split_base[split_idx] = base_row + split_idx * split_stride; + cand_d[split_idx] = partial_dists[split_base[split_idx]]; + cand_i[split_idx] = partial_indices[split_base[split_idx]]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_d = cand_d[0]; + int best_i = cand_i[0]; + int best_split = 0; + #pragma unroll + for (int split_idx_1 = 1; split_idx_1 < SPLIT_COUNT; split_idx_1++) { + if (best_d > cand_d[split_idx_1]) { + best_d = cand_d[split_idx_1]; + best_i = cand_i[split_idx_1]; + best_split = split_idx_1; + } + } + *((float*)(out_dists + (out_base + out_k))) = best_d; + *((int*)(out_indices + (out_base + out_k))) = best_i; + split_pos[best_split] = split_pos[best_split] + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_pos = split_pos[best_split]; + int next_addr = split_base[best_split] + next_pos; + cand_d[best_split] = partial_dists[next_addr]; + cand_i[best_split] = partial_indices[next_addr]; + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0117.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0117.cu new file mode 100644 index 00000000..47781c4b --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0117.cu @@ -0,0 +1,84 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 32 +#define TOP_K_MAX 10 +#define SPLIT_COUNT 8 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_evolve_7bfc_k10_merge_s7_rowbase_cache_rect4452_s8(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 32 + tid; + int stride = num_bids * 32; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int out_base = base_row; + int split_pos[SPLIT_COUNT]; + int split_base[SPLIT_COUNT]; + float cand_d[SPLIT_COUNT]; + int cand_i[SPLIT_COUNT]; + #pragma unroll + for (int split_idx = 0; split_idx < SPLIT_COUNT; split_idx++) { + split_pos[split_idx] = 0; + split_base[split_idx] = base_row + split_idx * split_stride; + cand_d[split_idx] = partial_dists[split_base[split_idx]]; + cand_i[split_idx] = partial_indices[split_base[split_idx]]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_d = cand_d[0]; + int best_i = cand_i[0]; + int best_split = 0; + #pragma unroll + for (int split_idx_1 = 1; split_idx_1 < SPLIT_COUNT; split_idx_1++) { + if (best_d > cand_d[split_idx_1]) { + best_d = cand_d[split_idx_1]; + best_i = cand_i[split_idx_1]; + best_split = split_idx_1; + } + } + *((float*)(out_dists + (out_base + out_k))) = best_d; + *((int*)(out_indices + (out_base + out_k))) = best_i; + split_pos[best_split] = split_pos[best_split] + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_pos = split_pos[best_split]; + int next_addr = split_base[best_split] + next_pos; + cand_d[best_split] = partial_dists[next_addr]; + cand_i[best_split] = partial_indices[next_addr]; + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0118.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0118.cu new file mode 100644 index 00000000..7770654c --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0118.cu @@ -0,0 +1,1143 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 128 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_STRIDE 32768 +#define SMEM_SMEM_DATABASE_OFF 33792 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_DATABASE_SQ_OFF 50176 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 50432 +#define THREADS 192 +#define BLOCK_Q 128 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_MAX 64 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16_cta2( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + ".reg .b32 m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "tcgen05.mma.cta_group::2.kind::f16 [%0], %1, %2, %3, {m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step_cg2( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, %3, " + "{m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], %1;\n\t" + "}\n" + :: "r"(mbar_addr), "h"(cta_mask) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x16(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x16.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15}, [%16];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ uint32_t smem_addr(const void* ptr) { + uint32_t addr; + asm("{\n\t" + ".reg .u64 u64addr;\n\t" + "cvta.to.shared.u64 u64addr, %1;\n\t" + "cvt.u32.u64 %0, u64addr;\n\t" + "}\n" : "=r"(addr) : "l"(ptr)); + return addr; +} + + +__device__ __forceinline__ uint32_t mapa_to_rank(uint32_t local_addr, uint32_t rank) { + uint32_t remote; + asm volatile("mapa.shared::cluster.u32 %0, %1, %2;" + : "=r"(remote) : "r"(local_addr), "r"(rank)); + return remote; +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem_cta2( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global" + ".mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .b16 lo, hi;\n\t" + "mov.b32 {lo, hi}, %1;\n\t" + "tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], lo;\n\t" + "}\n" + :: "r"(mbar_addr), "r"((uint32_t)cta_mask) : "memory"); +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_k64_stage1_tailinf_k64over32tailinfsplitgrid(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tile_pairs, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + const unsigned int clusters_x = gridDim.x / 2; + const unsigned int cluster_id = ((blockIdx.z * gridDim.y + blockIdx.y) * clusters_x) + blockIdx.x / 2; + const unsigned int num_clusters = clusters_x * gridDim.y * gridDim.z; + + int cta_rank; + asm volatile("mov.b32 %0, %%cluster_ctarank;" : "=r"(cta_rank)); + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 33792); + const int smem_database_addr = smem + 33792; + float* smem_database_sq = reinterpret_cast(smem_raw + 50176); + const int smem_database_sq_addr = smem + 50176; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 0, 2, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 16, 2, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=8 + mbarrier_init_pred(smem + 40, 8, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (128 columns, 128 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::2.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(128) : "memory"); + } + + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: compute ---- + if (warp <= 3) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx = cluster_id; work_idx < total_work; work_idx += num_clusters) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tile_pairs; + int q_tile_pair = query_work % num_q_tile_pairs; + int q_tile = q_tile_pair * 2 + cta_rank; + int off_q = q_tile * BLOCK_Q; + int q_idx = off_q + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx * Q + q_idx]; + } + float best_d[64]; + int best_i[64]; + #pragma unroll + for (int kk = 0; kk < 64; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + float worst_d = 3.4e+38f; + int worst_pos = 0; + int worst_chunk = 0; + float c0_worst_d = 3.4e+38f; + int c0_worst_pos = 0; + float c1_worst_d = 3.4e+38f; + int c1_worst_pos = 4; + float c2_worst_d = 3.4e+38f; + int c2_worst_pos = 8; + float c3_worst_d = 3.4e+38f; + int c3_worst_pos = 12; + float c4_worst_d = 3.4e+38f; + int c4_worst_pos = 16; + float c5_worst_d = 3.4e+38f; + int c5_worst_pos = 20; + float c6_worst_d = 3.4e+38f; + int c6_worst_pos = 24; + float c7_worst_d = 3.4e+38f; + int c7_worst_pos = 28; + float c8_worst_d = 3.4e+38f; + int c8_worst_pos = 32; + float c9_worst_d = 3.4e+38f; + int c9_worst_pos = 36; + float c10_worst_d = 3.4e+38f; + int c10_worst_pos = 40; + float c11_worst_d = 3.4e+38f; + int c11_worst_pos = 44; + float c12_worst_d = 3.4e+38f; + int c12_worst_pos = 48; + float c13_worst_d = 3.4e+38f; + int c13_worst_pos = 52; + float c14_worst_d = 3.4e+38f; + int c14_worst_pos = 56; + float c15_worst_d = 3.4e+38f; + int c15_worst_pos = 60; + int db_tile_start = split_idx * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int db_start = db_tile * BLOCK_M; + int db_sq_idx = db_start + (warp % 4 * 32 + lane); + if (warp % 4 * 32 + lane < BLOCK_M) { + if (db_sq_idx < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx * M + db_sq_idx]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 3.4e+38f; + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(cta_rank * BLOCK_Q + (warp % 4 * 32 << 16) << 16); + float _tmem_load_0[64]; + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x64.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31, %32, %33, %34, %35, %36, %37, %38, %39, %40, %41, %42, %43, %44, %45, %46, %47, %48, %49, %50, %51, %52, %53, %54, %55, %56, %57, %58, %59, %60, %61, %62, %63}, [%64];" + : "=f"(_tmem_load_0[0]), "=f"(_tmem_load_0[1]), "=f"(_tmem_load_0[2]), "=f"(_tmem_load_0[3]), "=f"(_tmem_load_0[4]), "=f"(_tmem_load_0[5]), "=f"(_tmem_load_0[6]), "=f"(_tmem_load_0[7]), "=f"(_tmem_load_0[8]), "=f"(_tmem_load_0[9]), "=f"(_tmem_load_0[10]), "=f"(_tmem_load_0[11]), "=f"(_tmem_load_0[12]), "=f"(_tmem_load_0[13]), "=f"(_tmem_load_0[14]), "=f"(_tmem_load_0[15]), "=f"(_tmem_load_0[16]), "=f"(_tmem_load_0[17]), "=f"(_tmem_load_0[18]), "=f"(_tmem_load_0[19]), "=f"(_tmem_load_0[20]), "=f"(_tmem_load_0[21]), "=f"(_tmem_load_0[22]), "=f"(_tmem_load_0[23]), "=f"(_tmem_load_0[24]), "=f"(_tmem_load_0[25]), "=f"(_tmem_load_0[26]), "=f"(_tmem_load_0[27]), "=f"(_tmem_load_0[28]), "=f"(_tmem_load_0[29]), "=f"(_tmem_load_0[30]), "=f"(_tmem_load_0[31]), "=f"(_tmem_load_0[32]), "=f"(_tmem_load_0[33]), "=f"(_tmem_load_0[34]), "=f"(_tmem_load_0[35]), "=f"(_tmem_load_0[36]), "=f"(_tmem_load_0[37]), "=f"(_tmem_load_0[38]), "=f"(_tmem_load_0[39]), "=f"(_tmem_load_0[40]), "=f"(_tmem_load_0[41]), "=f"(_tmem_load_0[42]), "=f"(_tmem_load_0[43]), "=f"(_tmem_load_0[44]), "=f"(_tmem_load_0[45]), "=f"(_tmem_load_0[46]), "=f"(_tmem_load_0[47]), "=f"(_tmem_load_0[48]), "=f"(_tmem_load_0[49]), "=f"(_tmem_load_0[50]), "=f"(_tmem_load_0[51]), "=f"(_tmem_load_0[52]), "=f"(_tmem_load_0[53]), "=f"(_tmem_load_0[54]), "=f"(_tmem_load_0[55]), "=f"(_tmem_load_0[56]), "=f"(_tmem_load_0[57]), "=f"(_tmem_load_0[58]), "=f"(_tmem_load_0[59]), "=f"(_tmem_load_0[60]), "=f"(_tmem_load_0[61]), "=f"(_tmem_load_0[62]), "=f"(_tmem_load_0[63]) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + if (elect_sync()) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cluster.b64 _, [%0];" + :: "r"((score_empty_addr) & 0xFEFFFFFF) : "memory"); + } + if (valid_q != 0) { + #pragma unroll 1 + for (int col_base = 0; col_base < 64; col_base += 4) { + float dist_vec[4]; + dist_vec[0] = _tmem_load_0[col_base]; + dist_vec[1] = _tmem_load_0[col_base + 1]; + dist_vec[2] = _tmem_load_0[col_base + 2]; + dist_vec[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec[4]; + db_sq_vec[0] = smem_database_sq[col_base]; + db_sq_vec[1] = smem_database_sq[col_base + 1]; + db_sq_vec[2] = smem_database_sq[col_base + 2]; + db_sq_vec[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec)[_la], reinterpret_cast(db_sq_vec)[_la]); + if (local_db_tile == 0) { + #pragma unroll + for (int vec_col = 0; vec_col < 4; vec_col++) { + int db_idx = db_start + col_base + vec_col; + int slot = col_base + vec_col; + best_d[slot] = _t0[vec_col]; + best_i[slot] = db_idx; + } + } else { + float group_min = _t0[0]; + if (_t0[1] < group_min) { + group_min = _t0[1]; + } + if (_t0[2] < group_min) { + group_min = _t0[2]; + } + if (_t0[3] < group_min) { + group_min = _t0[3]; + } + if (group_min < worst_d) { + float sort_d0 = _t0[0]; + float sort_d1 = _t0[1]; + float sort_d2 = _t0[2]; + float sort_d3 = _t0[3]; + int sort_col0 = 0; + int sort_col1 = 1; + int sort_col2 = 2; + int sort_col3 = 3; + float tmp_d = 0.0f; + int tmp_col = 0; + if (sort_d1 < sort_d0) { + tmp_d = sort_d0; + sort_d0 = sort_d1; + sort_d1 = tmp_d; + tmp_col = sort_col0; + sort_col0 = sort_col1; + sort_col1 = tmp_col; + } + if (sort_d3 < sort_d2) { + tmp_d = sort_d2; + sort_d2 = sort_d3; + sort_d3 = tmp_d; + tmp_col = sort_col2; + sort_col2 = sort_col3; + sort_col3 = tmp_col; + } + if (sort_d2 < sort_d0) { + tmp_d = sort_d0; + sort_d0 = sort_d2; + sort_d2 = tmp_d; + tmp_col = sort_col0; + sort_col0 = sort_col2; + sort_col2 = tmp_col; + } + if (sort_d3 < sort_d1) { + tmp_d = sort_d1; + sort_d1 = sort_d3; + sort_d3 = tmp_d; + tmp_col = sort_col1; + sort_col1 = sort_col3; + sort_col3 = tmp_col; + } + if (sort_d2 < sort_d1) { + tmp_d = sort_d1; + sort_d1 = sort_d2; + sort_d2 = tmp_d; + tmp_col = sort_col1; + sort_col1 = sort_col2; + sort_col2 = tmp_col; + } + #pragma unroll + for (int visit = 0; visit < 4; visit++) { + int vec_col_1 = sort_col0; + float dist = sort_d0; + if (visit == 1) { + vec_col_1 = sort_col1; + dist = sort_d1; + } + if (visit == 2) { + vec_col_1 = sort_col2; + dist = sort_d2; + } + if (visit == 3) { + vec_col_1 = sort_col3; + dist = sort_d3; + } + if (dist >= worst_d) { + break; + } + int db_idx_1 = db_start + col_base + vec_col_1; + best_d[worst_pos] = dist; + best_i[worst_pos] = db_idx_1; + int refresh_base = worst_chunk * 4; + float refresh_worst_d = best_d[refresh_base]; + int refresh_worst_pos = refresh_base; + #pragma unroll + for (int offset = 1; offset < 4; offset++) { + int scan_pos = refresh_base + offset; + if (best_d[scan_pos] > refresh_worst_d) { + refresh_worst_d = best_d[scan_pos]; + refresh_worst_pos = scan_pos; + } + } + if (worst_chunk == 0) { + c0_worst_d = refresh_worst_d; + c0_worst_pos = refresh_worst_pos; + } + if (worst_chunk == 1) { + c1_worst_d = refresh_worst_d; + c1_worst_pos = refresh_worst_pos; + } + if (worst_chunk == 2) { + c2_worst_d = refresh_worst_d; + c2_worst_pos = refresh_worst_pos; + } + if (worst_chunk == 3) { + c3_worst_d = refresh_worst_d; + c3_worst_pos = refresh_worst_pos; + } + if (worst_chunk == 4) { + c4_worst_d = refresh_worst_d; + c4_worst_pos = refresh_worst_pos; + } + if (worst_chunk == 5) { + c5_worst_d = refresh_worst_d; + c5_worst_pos = refresh_worst_pos; + } + if (worst_chunk == 6) { + c6_worst_d = refresh_worst_d; + c6_worst_pos = refresh_worst_pos; + } + if (worst_chunk == 7) { + c7_worst_d = refresh_worst_d; + c7_worst_pos = refresh_worst_pos; + } + if (worst_chunk == 8) { + c8_worst_d = refresh_worst_d; + c8_worst_pos = refresh_worst_pos; + } + if (worst_chunk == 9) { + c9_worst_d = refresh_worst_d; + c9_worst_pos = refresh_worst_pos; + } + if (worst_chunk == 10) { + c10_worst_d = refresh_worst_d; + c10_worst_pos = refresh_worst_pos; + } + if (worst_chunk == 11) { + c11_worst_d = refresh_worst_d; + c11_worst_pos = refresh_worst_pos; + } + if (worst_chunk == 12) { + c12_worst_d = refresh_worst_d; + c12_worst_pos = refresh_worst_pos; + } + if (worst_chunk == 13) { + c13_worst_d = refresh_worst_d; + c13_worst_pos = refresh_worst_pos; + } + if (worst_chunk == 14) { + c14_worst_d = refresh_worst_d; + c14_worst_pos = refresh_worst_pos; + } + if (worst_chunk == 15) { + c15_worst_d = refresh_worst_d; + c15_worst_pos = refresh_worst_pos; + } + worst_d = c0_worst_d; + worst_pos = c0_worst_pos; + worst_chunk = 0; + if (c1_worst_d > worst_d) { + worst_d = c1_worst_d; + worst_pos = c1_worst_pos; + worst_chunk = 1; + } + if (c2_worst_d > worst_d) { + worst_d = c2_worst_d; + worst_pos = c2_worst_pos; + worst_chunk = 2; + } + if (c3_worst_d > worst_d) { + worst_d = c3_worst_d; + worst_pos = c3_worst_pos; + worst_chunk = 3; + } + if (c4_worst_d > worst_d) { + worst_d = c4_worst_d; + worst_pos = c4_worst_pos; + worst_chunk = 4; + } + if (c5_worst_d > worst_d) { + worst_d = c5_worst_d; + worst_pos = c5_worst_pos; + worst_chunk = 5; + } + if (c6_worst_d > worst_d) { + worst_d = c6_worst_d; + worst_pos = c6_worst_pos; + worst_chunk = 6; + } + if (c7_worst_d > worst_d) { + worst_d = c7_worst_d; + worst_pos = c7_worst_pos; + worst_chunk = 7; + } + if (c8_worst_d > worst_d) { + worst_d = c8_worst_d; + worst_pos = c8_worst_pos; + worst_chunk = 8; + } + if (c9_worst_d > worst_d) { + worst_d = c9_worst_d; + worst_pos = c9_worst_pos; + worst_chunk = 9; + } + if (c10_worst_d > worst_d) { + worst_d = c10_worst_d; + worst_pos = c10_worst_pos; + worst_chunk = 10; + } + if (c11_worst_d > worst_d) { + worst_d = c11_worst_d; + worst_pos = c11_worst_pos; + worst_chunk = 11; + } + if (c12_worst_d > worst_d) { + worst_d = c12_worst_d; + worst_pos = c12_worst_pos; + worst_chunk = 12; + } + if (c13_worst_d > worst_d) { + worst_d = c13_worst_d; + worst_pos = c13_worst_pos; + worst_chunk = 13; + } + if (c14_worst_d > worst_d) { + worst_d = c14_worst_d; + worst_pos = c14_worst_pos; + worst_chunk = 14; + } + if (c15_worst_d > worst_d) { + worst_d = c15_worst_d; + worst_pos = c15_worst_pos; + worst_chunk = 15; + } + } + } + } + } + if (local_db_tile == 0) { + c0_worst_d = best_d[0]; + c0_worst_pos = 0; + #pragma unroll + for (int offset_1 = 1; offset_1 < 4; offset_1++) { + if (c0_worst_d < best_d[offset_1]) { + c0_worst_d = best_d[offset_1]; + c0_worst_pos = offset_1; + } + } + c1_worst_d = best_d[4]; + c1_worst_pos = 4; + #pragma unroll + for (int offset_2 = 1; offset_2 < 4; offset_2++) { + int scan_pos_1 = 4 + offset_2; + if (best_d[scan_pos_1] > c1_worst_d) { + c1_worst_d = best_d[scan_pos_1]; + c1_worst_pos = scan_pos_1; + } + } + c2_worst_d = best_d[8]; + c2_worst_pos = 8; + #pragma unroll + for (int offset_3 = 1; offset_3 < 4; offset_3++) { + int scan_pos_2 = 8 + offset_3; + if (best_d[scan_pos_2] > c2_worst_d) { + c2_worst_d = best_d[scan_pos_2]; + c2_worst_pos = scan_pos_2; + } + } + c3_worst_d = best_d[12]; + c3_worst_pos = 12; + #pragma unroll + for (int offset_4 = 1; offset_4 < 4; offset_4++) { + int scan_pos_3 = 12 + offset_4; + if (best_d[scan_pos_3] > c3_worst_d) { + c3_worst_d = best_d[scan_pos_3]; + c3_worst_pos = scan_pos_3; + } + } + c4_worst_d = best_d[16]; + c4_worst_pos = 16; + #pragma unroll + for (int offset_5 = 1; offset_5 < 4; offset_5++) { + int scan_pos_4 = 16 + offset_5; + if (best_d[scan_pos_4] > c4_worst_d) { + c4_worst_d = best_d[scan_pos_4]; + c4_worst_pos = scan_pos_4; + } + } + c5_worst_d = best_d[20]; + c5_worst_pos = 20; + #pragma unroll + for (int offset_6 = 1; offset_6 < 4; offset_6++) { + int scan_pos_5 = 20 + offset_6; + if (best_d[scan_pos_5] > c5_worst_d) { + c5_worst_d = best_d[scan_pos_5]; + c5_worst_pos = scan_pos_5; + } + } + c6_worst_d = best_d[24]; + c6_worst_pos = 24; + #pragma unroll + for (int offset_7 = 1; offset_7 < 4; offset_7++) { + int scan_pos_6 = 24 + offset_7; + if (best_d[scan_pos_6] > c6_worst_d) { + c6_worst_d = best_d[scan_pos_6]; + c6_worst_pos = scan_pos_6; + } + } + c7_worst_d = best_d[28]; + c7_worst_pos = 28; + #pragma unroll + for (int offset_8 = 1; offset_8 < 4; offset_8++) { + int scan_pos_7 = 28 + offset_8; + if (best_d[scan_pos_7] > c7_worst_d) { + c7_worst_d = best_d[scan_pos_7]; + c7_worst_pos = scan_pos_7; + } + } + c8_worst_d = best_d[32]; + c8_worst_pos = 32; + #pragma unroll + for (int offset_9 = 1; offset_9 < 4; offset_9++) { + int scan_pos_8 = 32 + offset_9; + if (best_d[scan_pos_8] > c8_worst_d) { + c8_worst_d = best_d[scan_pos_8]; + c8_worst_pos = scan_pos_8; + } + } + c9_worst_d = best_d[36]; + c9_worst_pos = 36; + #pragma unroll + for (int offset_10 = 1; offset_10 < 4; offset_10++) { + int scan_pos_9 = 36 + offset_10; + if (best_d[scan_pos_9] > c9_worst_d) { + c9_worst_d = best_d[scan_pos_9]; + c9_worst_pos = scan_pos_9; + } + } + c10_worst_d = best_d[40]; + c10_worst_pos = 40; + #pragma unroll + for (int offset_11 = 1; offset_11 < 4; offset_11++) { + int scan_pos_10 = 40 + offset_11; + if (best_d[scan_pos_10] > c10_worst_d) { + c10_worst_d = best_d[scan_pos_10]; + c10_worst_pos = scan_pos_10; + } + } + c11_worst_d = best_d[44]; + c11_worst_pos = 44; + #pragma unroll + for (int offset_12 = 1; offset_12 < 4; offset_12++) { + int scan_pos_11 = 44 + offset_12; + if (best_d[scan_pos_11] > c11_worst_d) { + c11_worst_d = best_d[scan_pos_11]; + c11_worst_pos = scan_pos_11; + } + } + c12_worst_d = best_d[48]; + c12_worst_pos = 48; + #pragma unroll + for (int offset_13 = 1; offset_13 < 4; offset_13++) { + int scan_pos_12 = 48 + offset_13; + if (best_d[scan_pos_12] > c12_worst_d) { + c12_worst_d = best_d[scan_pos_12]; + c12_worst_pos = scan_pos_12; + } + } + c13_worst_d = best_d[52]; + c13_worst_pos = 52; + #pragma unroll + for (int offset_14 = 1; offset_14 < 4; offset_14++) { + int scan_pos_13 = 52 + offset_14; + if (best_d[scan_pos_13] > c13_worst_d) { + c13_worst_d = best_d[scan_pos_13]; + c13_worst_pos = scan_pos_13; + } + } + c14_worst_d = best_d[56]; + c14_worst_pos = 56; + #pragma unroll + for (int offset_15 = 1; offset_15 < 4; offset_15++) { + int scan_pos_14 = 56 + offset_15; + if (best_d[scan_pos_14] > c14_worst_d) { + c14_worst_d = best_d[scan_pos_14]; + c14_worst_pos = scan_pos_14; + } + } + c15_worst_d = best_d[60]; + c15_worst_pos = 60; + #pragma unroll + for (int offset_16 = 1; offset_16 < 4; offset_16++) { + int scan_pos_15 = 60 + offset_16; + if (best_d[scan_pos_15] > c15_worst_d) { + c15_worst_d = best_d[scan_pos_15]; + c15_worst_pos = scan_pos_15; + } + } + worst_d = c0_worst_d; + worst_pos = c0_worst_pos; + worst_chunk = 0; + if (c1_worst_d > worst_d) { + worst_d = c1_worst_d; + worst_pos = c1_worst_pos; + worst_chunk = 1; + } + if (c2_worst_d > worst_d) { + worst_d = c2_worst_d; + worst_pos = c2_worst_pos; + worst_chunk = 2; + } + if (c3_worst_d > worst_d) { + worst_d = c3_worst_d; + worst_pos = c3_worst_pos; + worst_chunk = 3; + } + if (c4_worst_d > worst_d) { + worst_d = c4_worst_d; + worst_pos = c4_worst_pos; + worst_chunk = 4; + } + if (c5_worst_d > worst_d) { + worst_d = c5_worst_d; + worst_pos = c5_worst_pos; + worst_chunk = 5; + } + if (c6_worst_d > worst_d) { + worst_d = c6_worst_d; + worst_pos = c6_worst_pos; + worst_chunk = 6; + } + if (c7_worst_d > worst_d) { + worst_d = c7_worst_d; + worst_pos = c7_worst_pos; + worst_chunk = 7; + } + if (c8_worst_d > worst_d) { + worst_d = c8_worst_d; + worst_pos = c8_worst_pos; + worst_chunk = 8; + } + if (c9_worst_d > worst_d) { + worst_d = c9_worst_d; + worst_pos = c9_worst_pos; + worst_chunk = 9; + } + if (c10_worst_d > worst_d) { + worst_d = c10_worst_d; + worst_pos = c10_worst_pos; + worst_chunk = 10; + } + if (c11_worst_d > worst_d) { + worst_d = c11_worst_d; + worst_pos = c11_worst_pos; + worst_chunk = 11; + } + if (c12_worst_d > worst_d) { + worst_d = c12_worst_d; + worst_pos = c12_worst_pos; + worst_chunk = 12; + } + if (c13_worst_d > worst_d) { + worst_d = c13_worst_d; + worst_pos = c13_worst_pos; + worst_chunk = 13; + } + if (c14_worst_d > worst_d) { + worst_d = c14_worst_d; + worst_pos = c14_worst_pos; + worst_chunk = 14; + } + if (c15_worst_d > worst_d) { + worst_d = c15_worst_d; + worst_pos = c15_worst_pos; + worst_chunk = 15; + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + if (valid_q != 0) { + int out_base = ((split_idx * B + batch_idx) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < 64; out_k++) { + *((float*)(partial_dists + (out_base + out_k))) = best_d[out_k]; + *((int*)(partial_indices + (out_base + out_k))) = best_i[out_k]; + } + } + } + } + // ---- Role: load ---- + } else if (warp == 4) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 4) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx_1 = cluster_id; work_idx_1 < total_work; work_idx_1 += num_clusters) { + int split_idx_1 = work_idx_1 % (unsigned int)split_count; + int query_work_1 = work_idx_1 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tile_pairs; + int q_tile_pair_1 = query_work_1 % num_q_tile_pairs; + int q_tile_1 = q_tile_pair_1 * 2 + cta_rank; + int off_q_1 = q_tile_1 * BLOCK_Q; + int global_q = batch_idx_1 * Q + off_q_1; + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((query_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(32768)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_query_addr), "l"(tmap_query), "r"(0), "r"(global_q), "r"(0), + "r"(((query_full_addr) & 0xFEFFFFFF)) : "memory"); + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int off_m = db_tile_1 * BLOCK_M; + int global_m = batch_idx_1 * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((database_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(16384)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_database_addr), "l"(tmap_database), "r"(0), "r"(global_m), "r"(0), + "r"(((database_full_addr) & 0xFEFFFFFF)) : "memory"); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 5) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + if (cta_rank == 0) { + #pragma unroll 1 + for (unsigned int work_idx_2 = cluster_id; work_idx_2 < total_work; work_idx_2 += num_clusters) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = (smem_query_addr >> 4) & 0x3FFF; + int _mma_b_lo_0 = (smem_database_addr >> 4) & 0x3FFF; + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\tmov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 270533776;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit_cg2_multicast(score_full_addr, (uint16_t)(3)); + elect_commit_cg2_multicast(database_empty_addr, (uint16_t)(3)); + } + elect_commit_cg2_multicast(query_empty_addr, (uint16_t)(3)); + } + } + } + } + + // Cleanup + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::2.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(128)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::2.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0119.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0119.cu new file mode 100644 index 00000000..b389f353 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0119.cu @@ -0,0 +1,99 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 128 +#define TOP_K_MAX 64 +#define SPLIT_COUNT 8 + +#include + +extern "C" { + +__global__ __launch_bounds__(128, 1) void +kernel_knn_build_k64_merge_s8_unordered_warp_select_k64over32s8warpselect(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int row = bid * 4 + warp; + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int cand_lo = lane; + int cand_hi = lane + 32; + if (row < total_queries) { + float cand_d[16]; + int cand_i[16]; + #pragma unroll + for (int split_idx = 0; split_idx < 8; split_idx++) { + int split_base = base_row + split_idx * split_stride; + cand_d[split_idx * 2] = partial_dists[split_base + cand_lo]; + cand_i[split_idx * 2] = partial_indices[split_base + cand_lo]; + cand_d[split_idx * 2 + 1] = partial_dists[split_base + cand_hi]; + cand_i[split_idx * 2 + 1] = partial_indices[split_base + cand_hi]; + } + #pragma unroll + for (int out_k = 0; out_k < 64; out_k++) { + float winner_d = cand_d[0]; + int winner_i = cand_i[0]; + int winner_slot = 0; + #pragma unroll + for (int slot = 1; slot < 16; slot++) { + if (winner_d > cand_d[slot]) { + winner_d = cand_d[slot]; + winner_i = cand_i[slot]; + winner_slot = slot; + } + } + float warp_min = winner_d; + float _warp_reduce_0 = warp_min; + #pragma unroll + for (int offset = 16; offset > 0; offset >>= 1) + _warp_reduce_0 = fminf(_warp_reduce_0, __shfl_xor_sync(0xFFFFFFFF, _warp_reduce_0, offset)); + warp_min = _warp_reduce_0; + unsigned int _vote_0 = __ballot_sync(0xFFFFFFFF, winner_d == warp_min); + int owner_ballot = _vote_0; + int _ffs_0 = __ffs(owner_ballot); + int winner_lane = _ffs_0 - 1; + int _shfl_0 = __shfl_sync(0xFFFFFFFF, winner_i, winner_lane); + winner_i = _shfl_0; + int _shfl_1 = __shfl_sync(0xFFFFFFFF, winner_slot, winner_lane); + winner_slot = _shfl_1; + if (lane == 0) { + *((float*)(out_dists + (base_row + out_k))) = warp_min; + *((int*)(out_indices + (base_row + out_k))) = winner_i; + } + if (lane == winner_lane) { + #pragma unroll + for (int slot_1 = 0; slot_1 < 16; slot_1++) { + if (winner_slot == slot_1) { + cand_d[slot_1] = 3.4e+38f; + } + } + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0120.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0120.cu new file mode 100644 index 00000000..d7412360 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0120.cu @@ -0,0 +1,766 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 128 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_STRIDE 32768 +#define SMEM_SMEM_DATABASE_OFF 33792 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_DATABASE_SQ_OFF 50176 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 50432 +#define THREADS 192 +#define BLOCK_Q 128 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_MAX 96 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16_cta2( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + ".reg .b32 m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "tcgen05.mma.cta_group::2.kind::f16 [%0], %1, %2, %3, {m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step_cg2( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, %3, " + "{m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], %1;\n\t" + "}\n" + :: "r"(mbar_addr), "h"(cta_mask) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x16(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x16.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15}, [%16];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ uint32_t smem_addr(const void* ptr) { + uint32_t addr; + asm("{\n\t" + ".reg .u64 u64addr;\n\t" + "cvta.to.shared.u64 u64addr, %1;\n\t" + "cvt.u32.u64 %0, u64addr;\n\t" + "}\n" : "=r"(addr) : "l"(ptr)); + return addr; +} + + +__device__ __forceinline__ uint32_t mapa_to_rank(uint32_t local_addr, uint32_t rank) { + uint32_t remote; + asm volatile("mapa.shared::cluster.u32 %0, %1, %2;" + : "=r"(remote) : "r"(local_addr), "r"(rank)); + return remote; +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem_cta2( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global" + ".mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .b16 lo, hi;\n\t" + "mov.b32 {lo, hi}, %1;\n\t" + "tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], lo;\n\t" + "}\n" + :: "r"(mbar_addr), "r"((uint32_t)cta_mask) : "memory"); +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_k96_stage1_exact_prefill_q1024_k96over64exactprefillq1024_e5db(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tile_pairs, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + const unsigned int clusters_x = gridDim.x / 2; + const unsigned int cluster_id = ((blockIdx.z * gridDim.y + blockIdx.y) * clusters_x) + blockIdx.x / 2; + const unsigned int num_clusters = clusters_x * gridDim.y * gridDim.z; + + int cta_rank; + asm volatile("mov.b32 %0, %%cluster_ctarank;" : "=r"(cta_rank)); + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 33792); + const int smem_database_addr = smem + 33792; + float* smem_database_sq = reinterpret_cast(smem_raw + 50176); + const int smem_database_sq_addr = smem + 50176; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 0, 2, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 16, 2, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=8 + mbarrier_init_pred(smem + 40, 8, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (128 columns, 128 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::2.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(128) : "memory"); + } + + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: compute ---- + if (warp <= 3) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx = cluster_id; work_idx < total_work; work_idx += num_clusters) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tile_pairs; + int q_tile_pair = query_work % num_q_tile_pairs; + int q_tile = q_tile_pair * 2 + cta_rank; + int off_q = q_tile * BLOCK_Q; + int q_idx = off_q + (warp % 4 * 32 + lane); + float q_sq_val = query_sq[batch_idx * Q + q_idx]; + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + float chunk_worst_d[24]; + int chunk_worst_pos[24]; + float worst_d = 3.4e+38f; + int worst_pos = 0; + int worst_chunk = 0; + int db_tile_start = split_idx * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int db_start = db_tile * BLOCK_M; + int db_sq_idx = db_start + (warp % 4 * 32 + lane); + if (warp % 4 * 32 + lane < BLOCK_M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx * M + db_sq_idx]; + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(cta_rank * BLOCK_Q + (warp % 4 * 32 << 16) << 16); + float _tmem_load_0[64]; + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x64.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31, %32, %33, %34, %35, %36, %37, %38, %39, %40, %41, %42, %43, %44, %45, %46, %47, %48, %49, %50, %51, %52, %53, %54, %55, %56, %57, %58, %59, %60, %61, %62, %63}, [%64];" + : "=f"(_tmem_load_0[0]), "=f"(_tmem_load_0[1]), "=f"(_tmem_load_0[2]), "=f"(_tmem_load_0[3]), "=f"(_tmem_load_0[4]), "=f"(_tmem_load_0[5]), "=f"(_tmem_load_0[6]), "=f"(_tmem_load_0[7]), "=f"(_tmem_load_0[8]), "=f"(_tmem_load_0[9]), "=f"(_tmem_load_0[10]), "=f"(_tmem_load_0[11]), "=f"(_tmem_load_0[12]), "=f"(_tmem_load_0[13]), "=f"(_tmem_load_0[14]), "=f"(_tmem_load_0[15]), "=f"(_tmem_load_0[16]), "=f"(_tmem_load_0[17]), "=f"(_tmem_load_0[18]), "=f"(_tmem_load_0[19]), "=f"(_tmem_load_0[20]), "=f"(_tmem_load_0[21]), "=f"(_tmem_load_0[22]), "=f"(_tmem_load_0[23]), "=f"(_tmem_load_0[24]), "=f"(_tmem_load_0[25]), "=f"(_tmem_load_0[26]), "=f"(_tmem_load_0[27]), "=f"(_tmem_load_0[28]), "=f"(_tmem_load_0[29]), "=f"(_tmem_load_0[30]), "=f"(_tmem_load_0[31]), "=f"(_tmem_load_0[32]), "=f"(_tmem_load_0[33]), "=f"(_tmem_load_0[34]), "=f"(_tmem_load_0[35]), "=f"(_tmem_load_0[36]), "=f"(_tmem_load_0[37]), "=f"(_tmem_load_0[38]), "=f"(_tmem_load_0[39]), "=f"(_tmem_load_0[40]), "=f"(_tmem_load_0[41]), "=f"(_tmem_load_0[42]), "=f"(_tmem_load_0[43]), "=f"(_tmem_load_0[44]), "=f"(_tmem_load_0[45]), "=f"(_tmem_load_0[46]), "=f"(_tmem_load_0[47]), "=f"(_tmem_load_0[48]), "=f"(_tmem_load_0[49]), "=f"(_tmem_load_0[50]), "=f"(_tmem_load_0[51]), "=f"(_tmem_load_0[52]), "=f"(_tmem_load_0[53]), "=f"(_tmem_load_0[54]), "=f"(_tmem_load_0[55]), "=f"(_tmem_load_0[56]), "=f"(_tmem_load_0[57]), "=f"(_tmem_load_0[58]), "=f"(_tmem_load_0[59]), "=f"(_tmem_load_0[60]), "=f"(_tmem_load_0[61]), "=f"(_tmem_load_0[62]), "=f"(_tmem_load_0[63]) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + if (elect_sync()) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cluster.b64 _, [%0];" + :: "r"((score_empty_addr) & 0xFEFFFFFF) : "memory"); + } + #pragma unroll 1 + for (int col_base = 0; col_base < 64; col_base += 4) { + float dist_vec[4]; + dist_vec[0] = _tmem_load_0[col_base]; + dist_vec[1] = _tmem_load_0[col_base + 1]; + dist_vec[2] = _tmem_load_0[col_base + 2]; + dist_vec[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec[4]; + db_sq_vec[0] = smem_database_sq[col_base]; + db_sq_vec[1] = smem_database_sq[col_base + 1]; + db_sq_vec[2] = smem_database_sq[col_base + 2]; + db_sq_vec[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec)[_la], reinterpret_cast(db_sq_vec)[_la]); + int prefill_group = 0; + if (local_db_tile == 0) { + prefill_group = 1; + } + if (local_db_tile == 1) { + if (col_base < 32) { + prefill_group = 1; + } + } + if (prefill_group != 0) { + int slot_base = local_db_tile * 64 + col_base; + best_d[slot_base] = _t0[0]; + best_i[slot_base] = db_start + col_base; + best_d[slot_base + 1] = _t0[1]; + best_i[slot_base + 1] = db_start + col_base + 1; + best_d[slot_base + 2] = _t0[2]; + best_i[slot_base + 2] = db_start + col_base + 2; + best_d[slot_base + 3] = _t0[3]; + best_i[slot_base + 3] = db_start + col_base + 3; + if (local_db_tile == 1) { + if (col_base == 28) { + #pragma unroll + for (int chunk = 0; chunk < 24; chunk++) { + int chunk_base = chunk * 4; + chunk_worst_d[chunk] = best_d[chunk_base]; + chunk_worst_pos[chunk] = chunk_base; + #pragma unroll + for (int offset = 1; offset < 4; offset++) { + int scan_pos = chunk_base + offset; + if (best_d[scan_pos] > chunk_worst_d[chunk]) { + chunk_worst_d[chunk] = best_d[scan_pos]; + chunk_worst_pos[chunk] = scan_pos; + } + } + } + worst_d = chunk_worst_d[0]; + worst_pos = chunk_worst_pos[0]; + worst_chunk = 0; + #pragma unroll + for (int chunk_1 = 1; chunk_1 < 24; chunk_1++) { + if (worst_d < chunk_worst_d[chunk_1]) { + worst_d = chunk_worst_d[chunk_1]; + worst_pos = chunk_worst_pos[chunk_1]; + worst_chunk = chunk_1; + } + } + } + } + } else { + float group_min = _t0[0]; + if (_t0[1] < group_min) { + group_min = _t0[1]; + } + if (_t0[2] < group_min) { + group_min = _t0[2]; + } + if (_t0[3] < group_min) { + group_min = _t0[3]; + } + if (group_min < worst_d) { + float sort_d0 = _t0[0]; + float sort_d1 = _t0[1]; + float sort_d2 = _t0[2]; + float sort_d3 = _t0[3]; + int sort_col0 = 0; + int sort_col1 = 1; + int sort_col2 = 2; + int sort_col3 = 3; + float tmp_d = 0.0f; + int tmp_col = 0; + if (sort_d1 < sort_d0) { + tmp_d = sort_d0; + sort_d0 = sort_d1; + sort_d1 = tmp_d; + tmp_col = sort_col0; + sort_col0 = sort_col1; + sort_col1 = tmp_col; + } + if (sort_d3 < sort_d2) { + tmp_d = sort_d2; + sort_d2 = sort_d3; + sort_d3 = tmp_d; + tmp_col = sort_col2; + sort_col2 = sort_col3; + sort_col3 = tmp_col; + } + if (sort_d2 < sort_d0) { + tmp_d = sort_d0; + sort_d0 = sort_d2; + sort_d2 = tmp_d; + tmp_col = sort_col0; + sort_col0 = sort_col2; + sort_col2 = tmp_col; + } + if (sort_d3 < sort_d1) { + tmp_d = sort_d1; + sort_d1 = sort_d3; + sort_d3 = tmp_d; + tmp_col = sort_col1; + sort_col1 = sort_col3; + sort_col3 = tmp_col; + } + if (sort_d2 < sort_d1) { + tmp_d = sort_d1; + sort_d1 = sort_d2; + sort_d2 = tmp_d; + tmp_col = sort_col1; + sort_col1 = sort_col2; + sort_col2 = tmp_col; + } + #pragma unroll + for (int visit = 0; visit < 4; visit++) { + int vec_col = sort_col0; + float dist = sort_d0; + if (visit == 1) { + vec_col = sort_col1; + dist = sort_d1; + } + if (visit == 2) { + vec_col = sort_col2; + dist = sort_d2; + } + if (visit == 3) { + vec_col = sort_col3; + dist = sort_d3; + } + if (dist >= worst_d) { + break; + } + int db_idx = db_start + col_base + vec_col; + best_d[worst_pos] = dist; + best_i[worst_pos] = db_idx; + int refresh_base = worst_chunk * 4; + chunk_worst_d[worst_chunk] = best_d[refresh_base]; + chunk_worst_pos[worst_chunk] = refresh_base; + #pragma unroll + for (int offset_1 = 1; offset_1 < 4; offset_1++) { + int scan_pos_1 = refresh_base + offset_1; + if (best_d[scan_pos_1] > chunk_worst_d[worst_chunk]) { + chunk_worst_d[worst_chunk] = best_d[scan_pos_1]; + chunk_worst_pos[worst_chunk] = scan_pos_1; + } + } + worst_d = chunk_worst_d[0]; + worst_pos = chunk_worst_pos[0]; + worst_chunk = 0; + #pragma unroll + for (int chunk_2 = 1; chunk_2 < 24; chunk_2++) { + if (worst_d < chunk_worst_d[chunk_2]) { + worst_d = chunk_worst_d[chunk_2]; + worst_pos = chunk_worst_pos[chunk_2]; + worst_chunk = chunk_2; + } + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + int out_base = ((split_idx * B + batch_idx) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + *((float*)(partial_dists + (out_base + out_k))) = best_d[out_k]; + *((int*)(partial_indices + (out_base + out_k))) = best_i[out_k]; + } + } + } + // ---- Role: load ---- + } else if (warp == 4) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 4) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx_1 = cluster_id; work_idx_1 < total_work; work_idx_1 += num_clusters) { + int split_idx_1 = work_idx_1 % (unsigned int)split_count; + int query_work_1 = work_idx_1 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tile_pairs; + int q_tile_pair_1 = query_work_1 % num_q_tile_pairs; + int q_tile_1 = q_tile_pair_1 * 2 + cta_rank; + int off_q_1 = q_tile_1 * BLOCK_Q; + int global_q = batch_idx_1 * Q + off_q_1; + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((query_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(32768)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_query_addr), "l"(tmap_query), "r"(0), "r"(global_q), "r"(0), + "r"(((query_full_addr) & 0xFEFFFFFF)) : "memory"); + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int off_m = db_tile_1 * BLOCK_M; + int global_m = batch_idx_1 * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((database_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(16384)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_database_addr), "l"(tmap_database), "r"(0), "r"(global_m), "r"(0), + "r"(((database_full_addr) & 0xFEFFFFFF)) : "memory"); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 5) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + if (cta_rank == 0) { + #pragma unroll 1 + for (unsigned int work_idx_2 = cluster_id; work_idx_2 < total_work; work_idx_2 += num_clusters) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = (smem_query_addr >> 4) & 0x3FFF; + int _mma_b_lo_0 = (smem_database_addr >> 4) & 0x3FFF; + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\tmov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 270533776;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit_cg2_multicast(score_full_addr, (uint16_t)(3)); + elect_commit_cg2_multicast(database_empty_addr, (uint16_t)(3)); + } + elect_commit_cg2_multicast(query_empty_addr, (uint16_t)(3)); + } + } + } + } + + // Cleanup + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::2.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(128)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::2.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0121.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0121.cu new file mode 100644 index 00000000..aa3d05e4 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0121.cu @@ -0,0 +1,123 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 32 +#define TOP_K_MAX 96 +#define SPLIT_COUNT 2 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_k96_merge_s8_unordered_chunkprefill_k96over64s2chunkprefill_f9d1(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 32 + tid; + int stride = num_bids * 32; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + float best_d[96]; + int best_i[96]; + #pragma unroll + for (int cand_k = 0; cand_k < 96; cand_k++) { + best_d[cand_k] = partial_dists[base_row + cand_k]; + best_i[cand_k] = partial_indices[base_row + cand_k]; + } + float chunk_worst_d[12]; + int chunk_worst_pos[12]; + #pragma unroll + for (int chunk = 0; chunk < 12; chunk++) { + int chunk_base = chunk * 8; + chunk_worst_d[chunk] = best_d[chunk_base]; + chunk_worst_pos[chunk] = chunk_base; + #pragma unroll + for (int offset = 1; offset < 8; offset++) { + int scan_pos = chunk_base + offset; + if (best_d[scan_pos] > chunk_worst_d[chunk]) { + chunk_worst_d[chunk] = best_d[scan_pos]; + chunk_worst_pos[chunk] = scan_pos; + } + } + } + float worst_d = chunk_worst_d[0]; + int worst_pos = chunk_worst_pos[0]; + int worst_chunk = 0; + #pragma unroll + for (int chunk_1 = 1; chunk_1 < 12; chunk_1++) { + if (worst_d < chunk_worst_d[chunk_1]) { + worst_d = chunk_worst_d[chunk_1]; + worst_pos = chunk_worst_pos[chunk_1]; + worst_chunk = chunk_1; + } + } + #pragma unroll + for (int split_idx = 1; split_idx < SPLIT_COUNT; split_idx++) { + int partial_base = base_row + split_idx * split_stride; + #pragma unroll + for (int cand_k_1 = 0; cand_k_1 < 96; cand_k_1++) { + float cand_d = partial_dists[partial_base + cand_k_1]; + int cand_i = partial_indices[partial_base + cand_k_1]; + if (cand_d < worst_d) { + best_d[worst_pos] = cand_d; + best_i[worst_pos] = cand_i; + int refresh_base = worst_chunk * 8; + chunk_worst_d[worst_chunk] = best_d[refresh_base]; + chunk_worst_pos[worst_chunk] = refresh_base; + #pragma unroll + for (int offset_1 = 1; offset_1 < 8; offset_1++) { + int scan_pos_1 = refresh_base + offset_1; + if (best_d[scan_pos_1] > chunk_worst_d[worst_chunk]) { + chunk_worst_d[worst_chunk] = best_d[scan_pos_1]; + chunk_worst_pos[worst_chunk] = scan_pos_1; + } + } + worst_d = chunk_worst_d[0]; + worst_pos = chunk_worst_pos[0]; + worst_chunk = 0; + #pragma unroll + for (int chunk_2 = 1; chunk_2 < 12; chunk_2++) { + if (worst_d < chunk_worst_d[chunk_2]) { + worst_d = chunk_worst_d[chunk_2]; + worst_pos = chunk_worst_pos[chunk_2]; + worst_chunk = chunk_2; + } + } + } + } + } + #pragma unroll + for (int out_k = 0; out_k < 96; out_k++) { + *((float*)(out_dists + (base_row + out_k))) = best_d[out_k]; + *((int*)(out_indices + (base_row + out_k))) = best_i[out_k]; + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0122.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0122.cu new file mode 100644 index 00000000..47e86968 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0122.cu @@ -0,0 +1,123 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 32 +#define TOP_K_MAX 96 +#define SPLIT_COUNT 4 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_k96_merge_s8_unordered_chunkprefill_k96over64s4chunkprefill_f9d1(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 32 + tid; + int stride = num_bids * 32; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + float best_d[96]; + int best_i[96]; + #pragma unroll + for (int cand_k = 0; cand_k < 96; cand_k++) { + best_d[cand_k] = partial_dists[base_row + cand_k]; + best_i[cand_k] = partial_indices[base_row + cand_k]; + } + float chunk_worst_d[12]; + int chunk_worst_pos[12]; + #pragma unroll + for (int chunk = 0; chunk < 12; chunk++) { + int chunk_base = chunk * 8; + chunk_worst_d[chunk] = best_d[chunk_base]; + chunk_worst_pos[chunk] = chunk_base; + #pragma unroll + for (int offset = 1; offset < 8; offset++) { + int scan_pos = chunk_base + offset; + if (best_d[scan_pos] > chunk_worst_d[chunk]) { + chunk_worst_d[chunk] = best_d[scan_pos]; + chunk_worst_pos[chunk] = scan_pos; + } + } + } + float worst_d = chunk_worst_d[0]; + int worst_pos = chunk_worst_pos[0]; + int worst_chunk = 0; + #pragma unroll + for (int chunk_1 = 1; chunk_1 < 12; chunk_1++) { + if (worst_d < chunk_worst_d[chunk_1]) { + worst_d = chunk_worst_d[chunk_1]; + worst_pos = chunk_worst_pos[chunk_1]; + worst_chunk = chunk_1; + } + } + #pragma unroll + for (int split_idx = 1; split_idx < SPLIT_COUNT; split_idx++) { + int partial_base = base_row + split_idx * split_stride; + #pragma unroll + for (int cand_k_1 = 0; cand_k_1 < 96; cand_k_1++) { + float cand_d = partial_dists[partial_base + cand_k_1]; + int cand_i = partial_indices[partial_base + cand_k_1]; + if (cand_d < worst_d) { + best_d[worst_pos] = cand_d; + best_i[worst_pos] = cand_i; + int refresh_base = worst_chunk * 8; + chunk_worst_d[worst_chunk] = best_d[refresh_base]; + chunk_worst_pos[worst_chunk] = refresh_base; + #pragma unroll + for (int offset_1 = 1; offset_1 < 8; offset_1++) { + int scan_pos_1 = refresh_base + offset_1; + if (best_d[scan_pos_1] > chunk_worst_d[worst_chunk]) { + chunk_worst_d[worst_chunk] = best_d[scan_pos_1]; + chunk_worst_pos[worst_chunk] = scan_pos_1; + } + } + worst_d = chunk_worst_d[0]; + worst_pos = chunk_worst_pos[0]; + worst_chunk = 0; + #pragma unroll + for (int chunk_2 = 1; chunk_2 < 12; chunk_2++) { + if (worst_d < chunk_worst_d[chunk_2]) { + worst_d = chunk_worst_d[chunk_2]; + worst_pos = chunk_worst_pos[chunk_2]; + worst_chunk = chunk_2; + } + } + } + } + } + #pragma unroll + for (int out_k = 0; out_k < 96; out_k++) { + *((float*)(out_dists + (base_row + out_k))) = best_d[out_k]; + *((int*)(out_indices + (base_row + out_k))) = best_i[out_k]; + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0123.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0123.cu new file mode 100644 index 00000000..a41c2773 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0123.cu @@ -0,0 +1,561 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 64 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 8192 +#define SMEM_SMEM_QUERY_STRIDE 8192 +#define SMEM_SMEM_DATABASE_OFF 9216 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 8192 +#define SMEM_SMEM_DATABASE_STRIDE 8192 +#define SMEM_SMEM_LOCAL_D_OFF 17664 +#define SMEM_SMEM_LOCAL_D_STAGE_BYTES 1280 +#define SMEM_SMEM_LOCAL_D_STRIDE 1280 +#define SMEM_SMEM_LOCAL_I_OFF 18944 +#define SMEM_SMEM_LOCAL_I_STAGE_BYTES 1280 +#define SMEM_SMEM_LOCAL_I_STRIDE 1280 +#define SMEM_TOTAL 20224 +#define THREADS 96 +#define BLOCK_Q 64 +#define BLOCK_M 64 +#define FEAT_D 64 +#define TOP_K_MAX 10 +#define ROWS_COVERED 4 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "tcgen05.mma.cta_group::1.kind::f16 [%0], %1, %2, %3, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, %3, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit(int mbar_addr) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];\n\t" + "}\n" + :: "r"(mbar_addr)); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x32(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x32.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15," + " %16, %17, %18, %19, %20, %21, %22, %23," + " %24, %25, %26, %27, %28, %29, %30, %31}, [%32];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]), + "=f"(dst[16]), "=f"(dst[17]), "=f"(dst[18]), "=f"(dst[19]), + "=f"(dst[20]), "=f"(dst[21]), "=f"(dst[22]), "=f"(dst[23]), + "=f"(dst[24]), "=f"(dst[25]), "=f"(dst[26]), "=f"(dst[27]), + "=f"(dst[28]), "=f"(dst[29]), "=f"(dst[30]), "=f"(dst[31]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ float max_noftz(float a, float b) { + float c; + asm("max.f32 %0, %1, %2;" : "=f"(c) : "f"(a), "f"(b)); + return c; +} + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cta.global" + ".mbarrier::complete_tx::bytes" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit(int mbar_addr) { + asm volatile( + "tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ uint32_t make_warp_uniform(uint32_t val) { + uint32_t result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1f, 0xffffffff;" + : "=r"(result) : "r"(val)); + return result; +} + +extern "C" { + +__global__ __launch_bounds__(96, 1) void +kernel_knn_build_v12_d64_tail_017a_stage1(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tiles, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 9216); + const int smem_database_addr = smem + 9216; + float* smem_local_d = reinterpret_cast(smem_raw + 17664); + const int smem_local_d_addr = smem + 17664; + int* smem_local_i = reinterpret_cast(smem_raw + 18944); + const int smem_local_i_addr = smem + 18944; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 0, 1, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 16, 1, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 40, 1, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (64 columns, 64 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::1.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(64) : "memory"); + } + + __syncthreads(); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: compute ---- + if (warp == 0) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx = bid; work_idx < total_work; work_idx += num_bids) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tiles; + int q_tile = query_work % num_q_tiles; + int off_q = q_tile * BLOCK_Q; + int row = lane / 4; + int lane_col = lane % 4; + int slot = lane_col; + int q_idx = off_q + row; + int valid_row = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_row != 0) { + q_sq_val = query_sq[batch_idx * Q + q_idx]; + } + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + int db_tile_start = split_idx * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int db_start = db_tile * BLOCK_M; + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + float _tmem_load_0[32]; + asm volatile( + "tcgen05.ld.sync.aligned.16x256b.x8.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31}, [%32];" + : "=r"(*reinterpret_cast(&_tmem_load_0[0])), "=r"(*reinterpret_cast(&_tmem_load_0[1])), "=r"(*reinterpret_cast(&_tmem_load_0[2])), "=r"(*reinterpret_cast(&_tmem_load_0[3])), "=r"(*reinterpret_cast(&_tmem_load_0[4])), "=r"(*reinterpret_cast(&_tmem_load_0[5])), "=r"(*reinterpret_cast(&_tmem_load_0[6])), "=r"(*reinterpret_cast(&_tmem_load_0[7])), "=r"(*reinterpret_cast(&_tmem_load_0[8])), "=r"(*reinterpret_cast(&_tmem_load_0[9])), "=r"(*reinterpret_cast(&_tmem_load_0[10])), "=r"(*reinterpret_cast(&_tmem_load_0[11])), "=r"(*reinterpret_cast(&_tmem_load_0[12])), "=r"(*reinterpret_cast(&_tmem_load_0[13])), "=r"(*reinterpret_cast(&_tmem_load_0[14])), "=r"(*reinterpret_cast(&_tmem_load_0[15])), "=r"(*reinterpret_cast(&_tmem_load_0[16])), "=r"(*reinterpret_cast(&_tmem_load_0[17])), "=r"(*reinterpret_cast(&_tmem_load_0[18])), "=r"(*reinterpret_cast(&_tmem_load_0[19])), "=r"(*reinterpret_cast(&_tmem_load_0[20])), "=r"(*reinterpret_cast(&_tmem_load_0[21])), "=r"(*reinterpret_cast(&_tmem_load_0[22])), "=r"(*reinterpret_cast(&_tmem_load_0[23])), "=r"(*reinterpret_cast(&_tmem_load_0[24])), "=r"(*reinterpret_cast(&_tmem_load_0[25])), "=r"(*reinterpret_cast(&_tmem_load_0[26])), "=r"(*reinterpret_cast(&_tmem_load_0[27])), "=r"(*reinterpret_cast(&_tmem_load_0[28])), "=r"(*reinterpret_cast(&_tmem_load_0[29])), "=r"(*reinterpret_cast(&_tmem_load_0[30])), "=r"(*reinterpret_cast(&_tmem_load_0[31])) + : "r"(taddr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + if (elect_sync()) { + mbarrier_arrive(score_empty_addr); + } + #pragma unroll + for (int repeat = 0; repeat < 8; repeat++) { + const int reg_base = repeat * 4; + int col_base = repeat * 8 + lane_col * 2; + int db_idx0 = db_start + col_base; + int db_idx1 = db_idx0 + 1; + float cand0_d = 3.4e+38f; + float cand1_d = 3.4e+38f; + if (valid_row != 0 && db_idx0 < M) { + float _max_0 = max_noftz(q_sq_val + database_sq[batch_idx * M + db_idx0] - 2.0f * _tmem_load_0[reg_base], 0.0f); + cand0_d = _max_0; + } + if (valid_row != 0 && db_idx1 < M) { + float _max_1 = max_noftz(q_sq_val + database_sq[batch_idx * M + db_idx1] - 2.0f * _tmem_load_0[reg_base + 1], 0.0f); + cand1_d = _max_1; + } + int take1 = ((cand1_d < cand0_d) ? 1 : 0); + if (best_d[9] > ((take1 != 0) ? cand1_d : cand0_d)) { + best_d[9] = ((take1 != 0) ? cand1_d : cand0_d); + best_i[9] = ((take1 != 0) ? db_idx1 : db_idx0); + #pragma unroll + for (int kk_1 = 8; kk_1 >= 0; kk_1--) { + float lower0_d = best_d[kk_1 + 1]; + int lower0_i = best_i[kk_1 + 1]; + float upper0_d = best_d[kk_1]; + int upper0_i = best_i[kk_1]; + int swap0_up = ((lower0_d < upper0_d) ? 1 : 0); + best_d[kk_1] = ((swap0_up != 0) ? lower0_d : upper0_d); + best_i[kk_1] = ((swap0_up != 0) ? lower0_i : upper0_i); + best_d[kk_1 + 1] = ((swap0_up != 0) ? upper0_d : lower0_d); + best_i[kk_1 + 1] = ((swap0_up != 0) ? upper0_i : lower0_i); + } + if (best_d[9] > ((take1 != 0) ? cand0_d : cand1_d)) { + best_d[9] = ((take1 != 0) ? cand0_d : cand1_d); + best_i[9] = ((take1 != 0) ? db_idx0 : db_idx1); + #pragma unroll + for (int kk_2 = 8; kk_2 >= 0; kk_2--) { + float lower1_d = best_d[kk_2 + 1]; + int lower1_i = best_i[kk_2 + 1]; + float upper1_d = best_d[kk_2]; + int upper1_i = best_i[kk_2]; + int swap1_up = ((lower1_d < upper1_d) ? 1 : 0); + best_d[kk_2] = ((swap1_up != 0) ? lower1_d : upper1_d); + best_i[kk_2] = ((swap1_up != 0) ? lower1_i : upper1_i); + best_d[kk_2 + 1] = ((swap1_up != 0) ? upper1_d : lower1_d); + best_i[kk_2 + 1] = ((swap1_up != 0) ? upper1_i : lower1_i); + } + } + } + } + } + int slot_base = (row * 4 + slot) * TOP_K_MAX; + #pragma unroll + for (int kk_3 = 0; kk_3 < TOP_K_MAX; kk_3++) { + smem_local_d[slot_base + kk_3] = best_d[kk_3]; + smem_local_i[slot_base + kk_3] = best_i[kk_3]; + } + asm volatile("barrier.sync 8, %0;" :: "r"(32)); + if (tid < ROWS_COVERED) { + int out_row = tid; + int out_q_idx = off_q + out_row; + float head_d[4]; + int head_i[4]; + int head_k[4]; + #pragma unroll + for (int slot_idx = 0; slot_idx < 4; slot_idx++) { + int local_base = (out_row * 4 + slot_idx) * TOP_K_MAX; + head_k[slot_idx] = 0; + head_d[slot_idx] = smem_local_d[local_base]; + head_i[slot_idx] = smem_local_i[local_base]; + } + int out_base = ((split_idx * B + batch_idx) * Q + out_q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float winner_d = head_d[0]; + int winner_i = head_i[0]; + int winner_slot = 0; + #pragma unroll + for (int slot_idx_1 = 1; slot_idx_1 < 4; slot_idx_1++) { + float cand_d = head_d[slot_idx_1]; + int take = ((cand_d < winner_d) ? 1 : 0); + winner_d = ((take != 0) ? cand_d : winner_d); + winner_i = ((take != 0) ? head_i[slot_idx_1] : winner_i); + winner_slot = ((take != 0) ? slot_idx_1 : winner_slot); + } + if (out_q_idx < Q && out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = winner_d; + *((int*)(partial_indices + (out_base + out_k))) = winner_i; + } + #pragma unroll + for (int slot_idx_2 = 0; slot_idx_2 < 4; slot_idx_2++) { + if (winner_slot == slot_idx_2) { + int next_head = head_k[slot_idx_2] + 1; + head_k[slot_idx_2] = next_head; + head_d[slot_idx_2] = 3.4e+38f; + head_i[slot_idx_2] = -1; + if (next_head < TOP_K_MAX) { + int local_base_1 = (out_row * 4 + slot_idx_2) * TOP_K_MAX; + head_d[slot_idx_2] = smem_local_d[local_base_1 + next_head]; + head_i[slot_idx_2] = smem_local_i[local_base_1 + next_head]; + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(32)); + } + } + // ---- Role: load ---- + } else if (warp == 1) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 1) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx_1 = bid; work_idx_1 < total_work; work_idx_1 += num_bids) { + int split_idx_1 = work_idx_1 % (unsigned int)split_count; + int query_work_1 = work_idx_1 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tiles; + int q_tile_1 = query_work_1 % num_q_tiles; + int off_q_1 = q_tile_1 * BLOCK_Q; + int global_q = batch_idx_1 * Q + off_q_1; + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + mbarrier_arrive_expect_tx(query_full_addr, 8192); + tma_3d_gmem2smem(smem_query_addr, tmap_query, 0, global_q, 0, query_full_addr); + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int off_m = db_tile_1 * BLOCK_M; + int global_m = batch_idx_1 * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + mbarrier_arrive_expect_tx(database_full_addr, 8192); + tma_3d_gmem2smem(smem_database_addr, tmap_database, 0, global_m, 0, database_full_addr); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 2) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + #pragma unroll 1 + for (unsigned int _work_idx = bid; _work_idx < total_work; _work_idx += num_bids) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = make_warp_uniform((smem_query_addr >> 4) & 0x3FFF); + int _mma_b_lo_0 = make_warp_uniform((smem_database_addr >> 4) & 0x3FFF); + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 68158608;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit(score_full_addr); + elect_commit(database_empty_addr); + } + elect_commit(query_empty_addr); + } + } + } + + // Cleanup + __syncthreads(); // barrier before TMEM dealloc + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::1.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(64)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::1.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0124.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0124.cu new file mode 100644 index 00000000..85b11a8d --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0124.cu @@ -0,0 +1,667 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 64 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_STRIDE 32768 +#define SMEM_SMEM_DATABASE_OFF 33792 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_DATABASE_SQ_OFF 50176 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 50432 +#define THREADS 192 +#define FEATURE_CHUNKS 2 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "tcgen05.mma.cta_group::1.kind::f16 [%0], %1, %2, %3, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, %3, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit(int mbar_addr) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];\n\t" + "}\n" + :: "r"(mbar_addr)); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x16(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x16.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15}, [%16];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ float max_noftz(float a, float b) { + float c; + asm("max.f32 %0, %1, %2;" : "=f"(c) : "f"(a), "f"(b)); + return c; +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cta.global" + ".mbarrier::complete_tx::bytes" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit(int mbar_addr) { + asm volatile( + "tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ uint32_t make_warp_uniform(uint32_t val) { + uint32_t result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1f, 0xffffffff;" + : "=r"(result) : "r"(val)); + return result; +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_common_d768_build_eeff_m64split_stage1_d256_q128_k10_59fe_v1(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tiles, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 33792); + const int smem_database_addr = smem + 33792; + float* smem_database_sq = reinterpret_cast(smem_raw + 50176); + const int smem_database_sq_addr = smem + 50176; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 0, 1, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 16, 1, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=128 + mbarrier_init_pred(smem + 40, 128, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (64 columns, 64 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::1.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(64) : "memory"); + } + + __syncthreads(); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: compute ---- + if (warp <= 3) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx = bid; work_idx < total_work; work_idx += num_bids) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tiles; + int q_tile = query_work % num_q_tiles; + int off_q = q_tile * 128; + int q_idx = off_q + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx * Q + q_idx]; + } + float best_d[10]; + int best_i[10]; + #pragma unroll + for (int kk = 0; kk < 10; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + int db_tile_start = split_idx * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int db_start = db_tile * 64; + int db_sq_idx = db_start + (warp % 4 * 32 + lane); + if (warp % 4 * 32 + lane < 64) { + if (db_sq_idx < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx * M + db_sq_idx]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 3.4e+38f; + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(warp % 4 * 32 << 16); + float _tmem_load_0[64]; + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x64.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31, %32, %33, %34, %35, %36, %37, %38, %39, %40, %41, %42, %43, %44, %45, %46, %47, %48, %49, %50, %51, %52, %53, %54, %55, %56, %57, %58, %59, %60, %61, %62, %63}, [%64];" + : "=f"(_tmem_load_0[0]), "=f"(_tmem_load_0[1]), "=f"(_tmem_load_0[2]), "=f"(_tmem_load_0[3]), "=f"(_tmem_load_0[4]), "=f"(_tmem_load_0[5]), "=f"(_tmem_load_0[6]), "=f"(_tmem_load_0[7]), "=f"(_tmem_load_0[8]), "=f"(_tmem_load_0[9]), "=f"(_tmem_load_0[10]), "=f"(_tmem_load_0[11]), "=f"(_tmem_load_0[12]), "=f"(_tmem_load_0[13]), "=f"(_tmem_load_0[14]), "=f"(_tmem_load_0[15]), "=f"(_tmem_load_0[16]), "=f"(_tmem_load_0[17]), "=f"(_tmem_load_0[18]), "=f"(_tmem_load_0[19]), "=f"(_tmem_load_0[20]), "=f"(_tmem_load_0[21]), "=f"(_tmem_load_0[22]), "=f"(_tmem_load_0[23]), "=f"(_tmem_load_0[24]), "=f"(_tmem_load_0[25]), "=f"(_tmem_load_0[26]), "=f"(_tmem_load_0[27]), "=f"(_tmem_load_0[28]), "=f"(_tmem_load_0[29]), "=f"(_tmem_load_0[30]), "=f"(_tmem_load_0[31]), "=f"(_tmem_load_0[32]), "=f"(_tmem_load_0[33]), "=f"(_tmem_load_0[34]), "=f"(_tmem_load_0[35]), "=f"(_tmem_load_0[36]), "=f"(_tmem_load_0[37]), "=f"(_tmem_load_0[38]), "=f"(_tmem_load_0[39]), "=f"(_tmem_load_0[40]), "=f"(_tmem_load_0[41]), "=f"(_tmem_load_0[42]), "=f"(_tmem_load_0[43]), "=f"(_tmem_load_0[44]), "=f"(_tmem_load_0[45]), "=f"(_tmem_load_0[46]), "=f"(_tmem_load_0[47]), "=f"(_tmem_load_0[48]), "=f"(_tmem_load_0[49]), "=f"(_tmem_load_0[50]), "=f"(_tmem_load_0[51]), "=f"(_tmem_load_0[52]), "=f"(_tmem_load_0[53]), "=f"(_tmem_load_0[54]), "=f"(_tmem_load_0[55]), "=f"(_tmem_load_0[56]), "=f"(_tmem_load_0[57]), "=f"(_tmem_load_0[58]), "=f"(_tmem_load_0[59]), "=f"(_tmem_load_0[60]), "=f"(_tmem_load_0[61]), "=f"(_tmem_load_0[62]), "=f"(_tmem_load_0[63]) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_arrive(score_empty_addr); + if (valid_q != 0) { + #pragma unroll 1 + for (int col_base = 0; col_base < 64; col_base += 4) { + float dist_vec[4]; + dist_vec[0] = _tmem_load_0[col_base]; + dist_vec[1] = _tmem_load_0[col_base + 1]; + dist_vec[2] = _tmem_load_0[col_base + 2]; + dist_vec[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec[4]; + db_sq_vec[0] = smem_database_sq[col_base]; + db_sq_vec[1] = smem_database_sq[col_base + 1]; + db_sq_vec[2] = smem_database_sq[col_base + 2]; + db_sq_vec[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec)[_la], reinterpret_cast(db_sq_vec)[_la]); + float _max_0 = max_noftz(_t0[0], 0.0f); + float cand0_d = _max_0; + float _max_1 = max_noftz(_t0[1], 0.0f); + float cand1_d = _max_1; + int cand0_i = db_start + col_base; + int cand1_i = cand0_i + 1; + if (cand0_i >= M) { + cand0_d = 3.4e+38f; + } + if (cand1_i >= M) { + cand1_d = 3.4e+38f; + } + if (cand0_d < best_d[9]) { + best_d[9] = cand0_d; + best_i[9] = cand0_i; + #pragma unroll + for (int kk_1 = 8; kk_1 >= 0; kk_1--) { + float lower0_d = best_d[kk_1 + 1]; + int lower0_i = best_i[kk_1 + 1]; + float upper0_d = best_d[kk_1]; + int upper0_i = best_i[kk_1]; + int swap0_up = ((lower0_d < upper0_d) ? 1 : 0); + best_d[kk_1] = ((swap0_up != 0) ? lower0_d : upper0_d); + best_i[kk_1] = ((swap0_up != 0) ? lower0_i : upper0_i); + best_d[kk_1 + 1] = ((swap0_up != 0) ? upper0_d : lower0_d); + best_i[kk_1 + 1] = ((swap0_up != 0) ? upper0_i : lower0_i); + } + } + if (cand1_d < best_d[9]) { + best_d[9] = cand1_d; + best_i[9] = cand1_i; + #pragma unroll + for (int kk_2 = 8; kk_2 >= 0; kk_2--) { + float lower1_d = best_d[kk_2 + 1]; + int lower1_i = best_i[kk_2 + 1]; + float upper1_d = best_d[kk_2]; + int upper1_i = best_i[kk_2]; + int swap1_up = ((lower1_d < upper1_d) ? 1 : 0); + best_d[kk_2] = ((swap1_up != 0) ? lower1_d : upper1_d); + best_i[kk_2] = ((swap1_up != 0) ? lower1_i : upper1_i); + best_d[kk_2 + 1] = ((swap1_up != 0) ? upper1_d : lower1_d); + best_i[kk_2 + 1] = ((swap1_up != 0) ? upper1_i : lower1_i); + } + } + float _max_2 = max_noftz(_t0[2], 0.0f); + float cand2_d = _max_2; + float _max_3 = max_noftz(_t0[3], 0.0f); + float cand3_d = _max_3; + int cand2_i = cand0_i + 2; + int cand3_i = cand0_i + 3; + if (cand2_i >= M) { + cand2_d = 3.4e+38f; + } + if (cand3_i >= M) { + cand3_d = 3.4e+38f; + } + if (cand2_d < best_d[9]) { + best_d[9] = cand2_d; + best_i[9] = cand2_i; + #pragma unroll + for (int kk_3 = 8; kk_3 >= 0; kk_3--) { + float lower0_d_1 = best_d[kk_3 + 1]; + int lower0_i_1 = best_i[kk_3 + 1]; + float upper0_d_1 = best_d[kk_3]; + int upper0_i_1 = best_i[kk_3]; + int swap0_up_1 = ((lower0_d_1 < upper0_d_1) ? 1 : 0); + best_d[kk_3] = ((swap0_up_1 != 0) ? lower0_d_1 : upper0_d_1); + best_i[kk_3] = ((swap0_up_1 != 0) ? lower0_i_1 : upper0_i_1); + best_d[kk_3 + 1] = ((swap0_up_1 != 0) ? upper0_d_1 : lower0_d_1); + best_i[kk_3 + 1] = ((swap0_up_1 != 0) ? upper0_i_1 : lower0_i_1); + } + } + if (cand3_d < best_d[9]) { + best_d[9] = cand3_d; + best_i[9] = cand3_i; + #pragma unroll + for (int kk_4 = 8; kk_4 >= 0; kk_4--) { + float lower1_d_1 = best_d[kk_4 + 1]; + int lower1_i_1 = best_i[kk_4 + 1]; + float upper1_d_1 = best_d[kk_4]; + int upper1_i_1 = best_i[kk_4]; + int swap1_up_1 = ((lower1_d_1 < upper1_d_1) ? 1 : 0); + best_d[kk_4] = ((swap1_up_1 != 0) ? lower1_d_1 : upper1_d_1); + best_i[kk_4] = ((swap1_up_1 != 0) ? lower1_i_1 : upper1_i_1); + best_d[kk_4 + 1] = ((swap1_up_1 != 0) ? upper1_d_1 : lower1_d_1); + best_i[kk_4 + 1] = ((swap1_up_1 != 0) ? upper1_i_1 : lower1_i_1); + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + if (valid_q != 0) { + int out_base = ((split_idx * B + batch_idx) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < 10; out_k++) { + if (out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = best_d[out_k]; + *((int*)(partial_indices + (out_base + out_k))) = best_i[out_k]; + } + } + } + } + } + // ---- Role: load ---- + } else if (warp == 4) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 4) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx_1 = bid; work_idx_1 < total_work; work_idx_1 += num_bids) { + int split_idx_1 = work_idx_1 % (unsigned int)split_count; + int query_work_1 = work_idx_1 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tiles; + int q_tile_1 = query_work_1 % num_q_tiles; + int off_q_1 = q_tile_1 * 128; + int global_q = batch_idx_1 * Q + off_q_1; + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int off_m = db_tile_1 * 64; + int global_m = batch_idx_1 * M + off_m; + #pragma unroll + for (int feat_chunk = 0; feat_chunk < FEATURE_CHUNKS; feat_chunk++) { + int feature_coord = feat_chunk * 2; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + mbarrier_arrive_expect_tx(query_full_addr, 32768); + tma_3d_gmem2smem(smem_query_addr, tmap_query, 0, global_q, feature_coord, query_full_addr); + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + mbarrier_arrive_expect_tx(database_full_addr, 16384); + tma_3d_gmem2smem(smem_database_addr, tmap_database, 0, global_m, feature_coord, database_full_addr); + } + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 5) { + { // mma_main + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_database_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = bid; work_idx_2 < total_work; work_idx_2 += num_bids) { + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + #pragma unroll + for (int feat_chunk_1 = 0; feat_chunk_1 < FEATURE_CHUNKS; feat_chunk_1++) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = make_warp_uniform((smem_query_addr >> 4) & 0x3FFF); + int _mma_b_lo_0 = make_warp_uniform((smem_database_addr >> 4) & 0x3FFF); + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 135267472;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(((((feat_chunk_1 == 0) ? 1 : 0)) ? 0 : 1))); + asm volatile("tcgen05.fence::after_thread_sync;"); + elect_commit(query_empty_addr); + elect_commit(database_empty_addr); + } + elect_commit(score_full_addr); + } + } + } + } + + // Cleanup + __syncthreads(); // barrier before TMEM dealloc + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::1.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(64)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::1.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0125.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0125.cu new file mode 100644 index 00000000..61352a95 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0125.cu @@ -0,0 +1,619 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 64 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 16384 +#define SMEM_SMEM_QUERY_STRIDE 16384 +#define SMEM_SMEM_DATABASE_OFF 17408 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_DATABASE_SQ_OFF 33792 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 34048 +#define THREADS 96 +#define FEATURE_CHUNKS 6 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "tcgen05.mma.cta_group::1.kind::f16 [%0], %1, %2, %3, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, %3, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit(int mbar_addr) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];\n\t" + "}\n" + :: "r"(mbar_addr)); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x16(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x16.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15}, [%16];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ float max_noftz(float a, float b) { + float c; + asm("max.f32 %0, %1, %2;" : "=f"(c) : "f"(a), "f"(b)); + return c; +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cta.global" + ".mbarrier::complete_tx::bytes" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit(int mbar_addr) { + asm volatile( + "tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ uint32_t make_warp_uniform(uint32_t val) { + uint32_t result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1f, 0xffffffff;" + : "=r"(result) : "r"(val)); + return result; +} + +extern "C" { + +__global__ __launch_bounds__(96, 1) void +kernel_knn_build_non128_frontier_7ee5_m64rag_stage1_d768_5e7f_highd_v1(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tiles, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 17408); + const int smem_database_addr = smem + 17408; + float* smem_database_sq = reinterpret_cast(smem_raw + 33792); + const int smem_database_sq_addr = smem + 33792; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 0, 1, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 16, 1, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=32 + mbarrier_init_pred(smem + 40, 32, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (64 columns, 64 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::1.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(64) : "memory"); + } + + __syncthreads(); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: compute ---- + if (warp == 0) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx = bid; work_idx < total_work; work_idx += num_bids) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tiles; + int q_tile = query_work % num_q_tiles; + int off_q = q_tile * 64; + int q_idx = off_q + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx * Q + q_idx]; + } + float best_d[10]; + int best_i[10]; + #pragma unroll + for (int kk = 0; kk < 10; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + int db_tile_start = split_idx * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int db_start = db_tile * 64; + int db_sq_idx0 = db_start + (warp % 4 * 32 + lane); + if (db_sq_idx0 < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx * M + db_sq_idx0]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 3.4e+38f; + } + int db_col1 = warp % 4 * 32 + lane + 32; + int db_sq_idx1 = db_start + db_col1; + if (db_sq_idx1 < M) { + smem_database_sq[db_col1] = database_sq[batch_idx * M + db_sq_idx1]; + } else { + smem_database_sq[db_col1] = 3.4e+38f; + } + asm volatile("barrier.sync 8, %0;" :: "r"(32)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(warp % 4 * 32 << 16); + float _tmem_load_0[64]; + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x64.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31, %32, %33, %34, %35, %36, %37, %38, %39, %40, %41, %42, %43, %44, %45, %46, %47, %48, %49, %50, %51, %52, %53, %54, %55, %56, %57, %58, %59, %60, %61, %62, %63}, [%64];" + : "=f"(_tmem_load_0[0]), "=f"(_tmem_load_0[1]), "=f"(_tmem_load_0[2]), "=f"(_tmem_load_0[3]), "=f"(_tmem_load_0[4]), "=f"(_tmem_load_0[5]), "=f"(_tmem_load_0[6]), "=f"(_tmem_load_0[7]), "=f"(_tmem_load_0[8]), "=f"(_tmem_load_0[9]), "=f"(_tmem_load_0[10]), "=f"(_tmem_load_0[11]), "=f"(_tmem_load_0[12]), "=f"(_tmem_load_0[13]), "=f"(_tmem_load_0[14]), "=f"(_tmem_load_0[15]), "=f"(_tmem_load_0[16]), "=f"(_tmem_load_0[17]), "=f"(_tmem_load_0[18]), "=f"(_tmem_load_0[19]), "=f"(_tmem_load_0[20]), "=f"(_tmem_load_0[21]), "=f"(_tmem_load_0[22]), "=f"(_tmem_load_0[23]), "=f"(_tmem_load_0[24]), "=f"(_tmem_load_0[25]), "=f"(_tmem_load_0[26]), "=f"(_tmem_load_0[27]), "=f"(_tmem_load_0[28]), "=f"(_tmem_load_0[29]), "=f"(_tmem_load_0[30]), "=f"(_tmem_load_0[31]), "=f"(_tmem_load_0[32]), "=f"(_tmem_load_0[33]), "=f"(_tmem_load_0[34]), "=f"(_tmem_load_0[35]), "=f"(_tmem_load_0[36]), "=f"(_tmem_load_0[37]), "=f"(_tmem_load_0[38]), "=f"(_tmem_load_0[39]), "=f"(_tmem_load_0[40]), "=f"(_tmem_load_0[41]), "=f"(_tmem_load_0[42]), "=f"(_tmem_load_0[43]), "=f"(_tmem_load_0[44]), "=f"(_tmem_load_0[45]), "=f"(_tmem_load_0[46]), "=f"(_tmem_load_0[47]), "=f"(_tmem_load_0[48]), "=f"(_tmem_load_0[49]), "=f"(_tmem_load_0[50]), "=f"(_tmem_load_0[51]), "=f"(_tmem_load_0[52]), "=f"(_tmem_load_0[53]), "=f"(_tmem_load_0[54]), "=f"(_tmem_load_0[55]), "=f"(_tmem_load_0[56]), "=f"(_tmem_load_0[57]), "=f"(_tmem_load_0[58]), "=f"(_tmem_load_0[59]), "=f"(_tmem_load_0[60]), "=f"(_tmem_load_0[61]), "=f"(_tmem_load_0[62]), "=f"(_tmem_load_0[63]) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(32)); + mbarrier_arrive(score_empty_addr); + if (valid_q != 0) { + #pragma unroll 1 + for (int col_base = 0; col_base < 64; col_base += 4) { + float dist_vec[4]; + dist_vec[0] = _tmem_load_0[col_base]; + dist_vec[1] = _tmem_load_0[col_base + 1]; + dist_vec[2] = _tmem_load_0[col_base + 2]; + dist_vec[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec[4]; + db_sq_vec[0] = smem_database_sq[col_base]; + db_sq_vec[1] = smem_database_sq[col_base + 1]; + db_sq_vec[2] = smem_database_sq[col_base + 2]; + db_sq_vec[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec)[_la], reinterpret_cast(db_sq_vec)[_la]); + float group_min = _t0[0]; + if (_t0[1] < group_min) { + group_min = _t0[1]; + } + if (_t0[2] < group_min) { + group_min = _t0[2]; + } + if (_t0[3] < group_min) { + group_min = _t0[3]; + } + if (group_min < best_d[9]) { + #pragma unroll + for (int vec_col = 0; vec_col < 4; vec_col++) { + int db_idx = db_start + col_base + vec_col; + if (db_idx < M) { + float _max_0 = max_noftz(_t0[vec_col], 0.0f); + float dist = _max_0; + if (dist < best_d[9]) { + best_d[9] = dist; + best_i[9] = db_idx; + #pragma unroll + for (int pos = 9; pos >= 1; pos--) { + if (best_d[pos] < best_d[pos - 1]) { + float tmp_d = best_d[pos - 1]; + int tmp_i = best_i[pos - 1]; + best_d[pos - 1] = best_d[pos]; + best_i[pos - 1] = best_i[pos]; + best_d[pos] = tmp_d; + best_i[pos] = tmp_i; + } + } + } + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(32)); + } + if (valid_q != 0) { + int out_base = ((split_idx * B + batch_idx) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < 10; out_k++) { + if (out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = best_d[out_k]; + *((int*)(partial_indices + (out_base + out_k))) = best_i[out_k]; + } + } + } + } + } + // ---- Role: load ---- + } else if (warp == 1) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 1) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx_1 = bid; work_idx_1 < total_work; work_idx_1 += num_bids) { + int split_idx_1 = work_idx_1 % (unsigned int)split_count; + int query_work_1 = work_idx_1 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tiles; + int q_tile_1 = query_work_1 % num_q_tiles; + int off_q_1 = q_tile_1 * 64; + int global_q = batch_idx_1 * Q + off_q_1; + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int off_m = db_tile_1 * 64; + int global_m = batch_idx_1 * M + off_m; + #pragma unroll + for (int feat_chunk = 0; feat_chunk < FEATURE_CHUNKS; feat_chunk++) { + int feature_coord = feat_chunk * 2; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + mbarrier_arrive_expect_tx(query_full_addr, 16384); + tma_3d_gmem2smem(smem_query_addr, tmap_query, 0, global_q, feature_coord, query_full_addr); + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + mbarrier_arrive_expect_tx(database_full_addr, 16384); + tma_3d_gmem2smem(smem_database_addr, tmap_database, 0, global_m, feature_coord, database_full_addr); + } + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 2) { + { // mma_main + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_database_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = bid; work_idx_2 < total_work; work_idx_2 += num_bids) { + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + #pragma unroll + for (int feat_chunk_1 = 0; feat_chunk_1 < FEATURE_CHUNKS; feat_chunk_1++) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = make_warp_uniform((smem_query_addr >> 4) & 0x3FFF); + int _mma_b_lo_0 = make_warp_uniform((smem_database_addr >> 4) & 0x3FFF); + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 68158608;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 506;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(((((feat_chunk_1 == 0) ? 1 : 0)) ? 0 : 1))); + asm volatile("tcgen05.fence::after_thread_sync;"); + elect_commit(query_empty_addr); + elect_commit(database_empty_addr); + } + elect_commit(score_full_addr); + } + } + } + } + + // Cleanup + __syncthreads(); // barrier before TMEM dealloc + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::1.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(64)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::1.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0126.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0126.cu new file mode 100644 index 00000000..4f77a366 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0126.cu @@ -0,0 +1,667 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 64 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_STRIDE 32768 +#define SMEM_SMEM_DATABASE_OFF 33792 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_DATABASE_SQ_OFF 50176 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 50432 +#define THREADS 192 +#define FEATURE_CHUNKS 8 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "tcgen05.mma.cta_group::1.kind::f16 [%0], %1, %2, %3, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, %3, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit(int mbar_addr) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];\n\t" + "}\n" + :: "r"(mbar_addr)); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x16(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x16.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15}, [%16];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ float max_noftz(float a, float b) { + float c; + asm("max.f32 %0, %1, %2;" : "=f"(c) : "f"(a), "f"(b)); + return c; +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cta.global" + ".mbarrier::complete_tx::bytes" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit(int mbar_addr) { + asm volatile( + "tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ uint32_t make_warp_uniform(uint32_t val) { + uint32_t result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1f, 0xffffffff;" + : "=r"(result) : "r"(val)); + return result; +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_common_d768_build_eeff_m64split_stage1_d1024_be66_search_v1(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tiles, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 33792); + const int smem_database_addr = smem + 33792; + float* smem_database_sq = reinterpret_cast(smem_raw + 50176); + const int smem_database_sq_addr = smem + 50176; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 0, 1, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 16, 1, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=128 + mbarrier_init_pred(smem + 40, 128, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (64 columns, 64 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::1.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(64) : "memory"); + } + + __syncthreads(); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: compute ---- + if (warp <= 3) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx = bid; work_idx < total_work; work_idx += num_bids) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tiles; + int q_tile = query_work % num_q_tiles; + int off_q = q_tile * 128; + int q_idx = off_q + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx * Q + q_idx]; + } + float best_d[10]; + int best_i[10]; + #pragma unroll + for (int kk = 0; kk < 10; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + int db_tile_start = split_idx * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int db_start = db_tile * 64; + int db_sq_idx = db_start + (warp % 4 * 32 + lane); + if (warp % 4 * 32 + lane < 64) { + if (db_sq_idx < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx * M + db_sq_idx]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 3.4e+38f; + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(warp % 4 * 32 << 16); + float _tmem_load_0[64]; + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x64.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31, %32, %33, %34, %35, %36, %37, %38, %39, %40, %41, %42, %43, %44, %45, %46, %47, %48, %49, %50, %51, %52, %53, %54, %55, %56, %57, %58, %59, %60, %61, %62, %63}, [%64];" + : "=f"(_tmem_load_0[0]), "=f"(_tmem_load_0[1]), "=f"(_tmem_load_0[2]), "=f"(_tmem_load_0[3]), "=f"(_tmem_load_0[4]), "=f"(_tmem_load_0[5]), "=f"(_tmem_load_0[6]), "=f"(_tmem_load_0[7]), "=f"(_tmem_load_0[8]), "=f"(_tmem_load_0[9]), "=f"(_tmem_load_0[10]), "=f"(_tmem_load_0[11]), "=f"(_tmem_load_0[12]), "=f"(_tmem_load_0[13]), "=f"(_tmem_load_0[14]), "=f"(_tmem_load_0[15]), "=f"(_tmem_load_0[16]), "=f"(_tmem_load_0[17]), "=f"(_tmem_load_0[18]), "=f"(_tmem_load_0[19]), "=f"(_tmem_load_0[20]), "=f"(_tmem_load_0[21]), "=f"(_tmem_load_0[22]), "=f"(_tmem_load_0[23]), "=f"(_tmem_load_0[24]), "=f"(_tmem_load_0[25]), "=f"(_tmem_load_0[26]), "=f"(_tmem_load_0[27]), "=f"(_tmem_load_0[28]), "=f"(_tmem_load_0[29]), "=f"(_tmem_load_0[30]), "=f"(_tmem_load_0[31]), "=f"(_tmem_load_0[32]), "=f"(_tmem_load_0[33]), "=f"(_tmem_load_0[34]), "=f"(_tmem_load_0[35]), "=f"(_tmem_load_0[36]), "=f"(_tmem_load_0[37]), "=f"(_tmem_load_0[38]), "=f"(_tmem_load_0[39]), "=f"(_tmem_load_0[40]), "=f"(_tmem_load_0[41]), "=f"(_tmem_load_0[42]), "=f"(_tmem_load_0[43]), "=f"(_tmem_load_0[44]), "=f"(_tmem_load_0[45]), "=f"(_tmem_load_0[46]), "=f"(_tmem_load_0[47]), "=f"(_tmem_load_0[48]), "=f"(_tmem_load_0[49]), "=f"(_tmem_load_0[50]), "=f"(_tmem_load_0[51]), "=f"(_tmem_load_0[52]), "=f"(_tmem_load_0[53]), "=f"(_tmem_load_0[54]), "=f"(_tmem_load_0[55]), "=f"(_tmem_load_0[56]), "=f"(_tmem_load_0[57]), "=f"(_tmem_load_0[58]), "=f"(_tmem_load_0[59]), "=f"(_tmem_load_0[60]), "=f"(_tmem_load_0[61]), "=f"(_tmem_load_0[62]), "=f"(_tmem_load_0[63]) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_arrive(score_empty_addr); + if (valid_q != 0) { + #pragma unroll 1 + for (int col_base = 0; col_base < 64; col_base += 4) { + float dist_vec[4]; + dist_vec[0] = _tmem_load_0[col_base]; + dist_vec[1] = _tmem_load_0[col_base + 1]; + dist_vec[2] = _tmem_load_0[col_base + 2]; + dist_vec[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec[4]; + db_sq_vec[0] = smem_database_sq[col_base]; + db_sq_vec[1] = smem_database_sq[col_base + 1]; + db_sq_vec[2] = smem_database_sq[col_base + 2]; + db_sq_vec[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec)[_la], reinterpret_cast(db_sq_vec)[_la]); + float _max_0 = max_noftz(_t0[0], 0.0f); + float cand0_d = _max_0; + float _max_1 = max_noftz(_t0[1], 0.0f); + float cand1_d = _max_1; + int cand0_i = db_start + col_base; + int cand1_i = cand0_i + 1; + if (cand0_i >= M) { + cand0_d = 3.4e+38f; + } + if (cand1_i >= M) { + cand1_d = 3.4e+38f; + } + if (cand0_d < best_d[9]) { + best_d[9] = cand0_d; + best_i[9] = cand0_i; + #pragma unroll + for (int kk_1 = 8; kk_1 >= 0; kk_1--) { + float lower0_d = best_d[kk_1 + 1]; + int lower0_i = best_i[kk_1 + 1]; + float upper0_d = best_d[kk_1]; + int upper0_i = best_i[kk_1]; + int swap0_up = ((lower0_d < upper0_d) ? 1 : 0); + best_d[kk_1] = ((swap0_up != 0) ? lower0_d : upper0_d); + best_i[kk_1] = ((swap0_up != 0) ? lower0_i : upper0_i); + best_d[kk_1 + 1] = ((swap0_up != 0) ? upper0_d : lower0_d); + best_i[kk_1 + 1] = ((swap0_up != 0) ? upper0_i : lower0_i); + } + } + if (cand1_d < best_d[9]) { + best_d[9] = cand1_d; + best_i[9] = cand1_i; + #pragma unroll + for (int kk_2 = 8; kk_2 >= 0; kk_2--) { + float lower1_d = best_d[kk_2 + 1]; + int lower1_i = best_i[kk_2 + 1]; + float upper1_d = best_d[kk_2]; + int upper1_i = best_i[kk_2]; + int swap1_up = ((lower1_d < upper1_d) ? 1 : 0); + best_d[kk_2] = ((swap1_up != 0) ? lower1_d : upper1_d); + best_i[kk_2] = ((swap1_up != 0) ? lower1_i : upper1_i); + best_d[kk_2 + 1] = ((swap1_up != 0) ? upper1_d : lower1_d); + best_i[kk_2 + 1] = ((swap1_up != 0) ? upper1_i : lower1_i); + } + } + float _max_2 = max_noftz(_t0[2], 0.0f); + float cand2_d = _max_2; + float _max_3 = max_noftz(_t0[3], 0.0f); + float cand3_d = _max_3; + int cand2_i = cand0_i + 2; + int cand3_i = cand0_i + 3; + if (cand2_i >= M) { + cand2_d = 3.4e+38f; + } + if (cand3_i >= M) { + cand3_d = 3.4e+38f; + } + if (cand2_d < best_d[9]) { + best_d[9] = cand2_d; + best_i[9] = cand2_i; + #pragma unroll + for (int kk_3 = 8; kk_3 >= 0; kk_3--) { + float lower0_d_1 = best_d[kk_3 + 1]; + int lower0_i_1 = best_i[kk_3 + 1]; + float upper0_d_1 = best_d[kk_3]; + int upper0_i_1 = best_i[kk_3]; + int swap0_up_1 = ((lower0_d_1 < upper0_d_1) ? 1 : 0); + best_d[kk_3] = ((swap0_up_1 != 0) ? lower0_d_1 : upper0_d_1); + best_i[kk_3] = ((swap0_up_1 != 0) ? lower0_i_1 : upper0_i_1); + best_d[kk_3 + 1] = ((swap0_up_1 != 0) ? upper0_d_1 : lower0_d_1); + best_i[kk_3 + 1] = ((swap0_up_1 != 0) ? upper0_i_1 : lower0_i_1); + } + } + if (cand3_d < best_d[9]) { + best_d[9] = cand3_d; + best_i[9] = cand3_i; + #pragma unroll + for (int kk_4 = 8; kk_4 >= 0; kk_4--) { + float lower1_d_1 = best_d[kk_4 + 1]; + int lower1_i_1 = best_i[kk_4 + 1]; + float upper1_d_1 = best_d[kk_4]; + int upper1_i_1 = best_i[kk_4]; + int swap1_up_1 = ((lower1_d_1 < upper1_d_1) ? 1 : 0); + best_d[kk_4] = ((swap1_up_1 != 0) ? lower1_d_1 : upper1_d_1); + best_i[kk_4] = ((swap1_up_1 != 0) ? lower1_i_1 : upper1_i_1); + best_d[kk_4 + 1] = ((swap1_up_1 != 0) ? upper1_d_1 : lower1_d_1); + best_i[kk_4 + 1] = ((swap1_up_1 != 0) ? upper1_i_1 : lower1_i_1); + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + if (valid_q != 0) { + int out_base = ((split_idx * B + batch_idx) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < 10; out_k++) { + if (out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = best_d[out_k]; + *((int*)(partial_indices + (out_base + out_k))) = best_i[out_k]; + } + } + } + } + } + // ---- Role: load ---- + } else if (warp == 4) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 4) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx_1 = bid; work_idx_1 < total_work; work_idx_1 += num_bids) { + int split_idx_1 = work_idx_1 % (unsigned int)split_count; + int query_work_1 = work_idx_1 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tiles; + int q_tile_1 = query_work_1 % num_q_tiles; + int off_q_1 = q_tile_1 * 128; + int global_q = batch_idx_1 * Q + off_q_1; + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int off_m = db_tile_1 * 64; + int global_m = batch_idx_1 * M + off_m; + #pragma unroll + for (int feat_chunk = 0; feat_chunk < FEATURE_CHUNKS; feat_chunk++) { + int feature_coord = feat_chunk * 2; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + mbarrier_arrive_expect_tx(query_full_addr, 32768); + tma_3d_gmem2smem(smem_query_addr, tmap_query, 0, global_q, feature_coord, query_full_addr); + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + mbarrier_arrive_expect_tx(database_full_addr, 16384); + tma_3d_gmem2smem(smem_database_addr, tmap_database, 0, global_m, feature_coord, database_full_addr); + } + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 5) { + { // mma_main + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_database_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = bid; work_idx_2 < total_work; work_idx_2 += num_bids) { + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + #pragma unroll + for (int feat_chunk_1 = 0; feat_chunk_1 < FEATURE_CHUNKS; feat_chunk_1++) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = make_warp_uniform((smem_query_addr >> 4) & 0x3FFF); + int _mma_b_lo_0 = make_warp_uniform((smem_database_addr >> 4) & 0x3FFF); + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 135267472;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(((((feat_chunk_1 == 0) ? 1 : 0)) ? 0 : 1))); + asm volatile("tcgen05.fence::after_thread_sync;"); + elect_commit(query_empty_addr); + elect_commit(database_empty_addr); + } + elect_commit(score_full_addr); + } + } + } + } + + // Cleanup + __syncthreads(); // barrier before TMEM dealloc + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::1.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(64)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::1.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0127.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0127.cu new file mode 100644 index 00000000..188b479f --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0127.cu @@ -0,0 +1,141 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define SMEM_GROUP_DISTS_OFF 0 +#define SMEM_GROUP_DISTS_STAGE_BYTES 512 +#define SMEM_GROUP_DISTS_STRIDE 512 +#define SMEM_GROUP_INDICES_OFF 512 +#define SMEM_GROUP_INDICES_STAGE_BYTES 512 +#define SMEM_GROUP_INDICES_STRIDE 512 +#define SMEM_TOTAL 1024 +#define THREADS 32 +#define TOP_K_MAX 10 +#define GROUP_COUNT 8 +#define GROUP_SPLITS 8 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_non128_frontier_4be7_d768fused_merge_s64g8_4be7_d768fused_v1(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + float* group_dists = reinterpret_cast(smem_raw + 0); + const int group_dists_addr = smem + 0; + int* group_indices = reinterpret_cast(smem_raw + 512); + const int group_indices_addr = smem + 512; + + // === Task calls (dependency order) === + int split_pos[GROUP_SPLITS]; + int split_base[GROUP_SPLITS]; + float group_cand_d[GROUP_SPLITS]; + int group_cand_i[GROUP_SPLITS]; + int final_pos[GROUP_COUNT]; + float final_cand_d[GROUP_COUNT]; + int final_cand_i[GROUP_COUNT]; + #pragma unroll 1 + for (int row = bid; row < total_queries; row += num_bids) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + if (tid < GROUP_COUNT) { + int group_idx = tid; + int source_split0 = group_idx * GROUP_SPLITS; + int shared_base = group_idx * TOP_K_MAX; + #pragma unroll + for (int local_split = 0; local_split < GROUP_SPLITS; local_split++) { + split_pos[local_split] = 0; + int split_id = source_split0 + local_split; + split_base[local_split] = base_row + split_id * split_stride; + group_cand_d[local_split] = partial_dists[split_base[local_split]]; + group_cand_i[local_split] = partial_indices[split_base[local_split]]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_d = group_cand_d[0]; + int best_i = group_cand_i[0]; + int best_split = 0; + #pragma unroll + for (int local_split_1 = 1; local_split_1 < GROUP_SPLITS; local_split_1++) { + if (best_d > group_cand_d[local_split_1]) { + best_d = group_cand_d[local_split_1]; + best_i = group_cand_i[local_split_1]; + best_split = local_split_1; + } + } + group_dists[shared_base + out_k] = best_d; + group_indices[shared_base + out_k] = best_i; + split_pos[best_split] = split_pos[best_split] + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_pos = split_pos[best_split]; + int next_addr = split_base[best_split] + next_pos; + group_cand_d[best_split] = partial_dists[next_addr]; + group_cand_i[best_split] = partial_indices[next_addr]; + } + } + } + __syncthreads(); + if (tid == 0) { + #pragma unroll + for (int group_idx_1 = 0; group_idx_1 < GROUP_COUNT; group_idx_1++) { + final_pos[group_idx_1] = 0; + int group_base = group_idx_1 * TOP_K_MAX; + final_cand_d[group_idx_1] = group_dists[group_base]; + final_cand_i[group_idx_1] = group_indices[group_base]; + } + #pragma unroll + for (int out_k_1 = 0; out_k_1 < TOP_K_MAX; out_k_1++) { + float best_d_1 = final_cand_d[0]; + int best_i_1 = final_cand_i[0]; + int best_group = 0; + #pragma unroll + for (int group_idx_2 = 1; group_idx_2 < GROUP_COUNT; group_idx_2++) { + if (best_d_1 > final_cand_d[group_idx_2]) { + best_d_1 = final_cand_d[group_idx_2]; + best_i_1 = final_cand_i[group_idx_2]; + best_group = group_idx_2; + } + } + *((float*)(out_dists + (base_row + out_k_1))) = best_d_1; + *((int*)(out_indices + (base_row + out_k_1))) = best_i_1; + final_pos[best_group] = final_pos[best_group] + 1; + if (out_k_1 + 1 < TOP_K_MAX) { + int next_pos_1 = final_pos[best_group]; + int next_addr_1 = best_group * TOP_K_MAX + next_pos_1; + final_cand_d[best_group] = group_dists[next_addr_1]; + final_cand_i[best_group] = group_indices[next_addr_1]; + } + } + } + __syncthreads(); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0128.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0128.cu new file mode 100644 index 00000000..ad7f6c0a --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0128.cu @@ -0,0 +1,667 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 64 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_STRIDE 32768 +#define SMEM_SMEM_DATABASE_OFF 33792 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_DATABASE_SQ_OFF 50176 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 50432 +#define THREADS 192 +#define FEATURE_CHUNKS 32 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "tcgen05.mma.cta_group::1.kind::f16 [%0], %1, %2, %3, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, %3, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit(int mbar_addr) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];\n\t" + "}\n" + :: "r"(mbar_addr)); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x16(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x16.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15}, [%16];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ float max_noftz(float a, float b) { + float c; + asm("max.f32 %0, %1, %2;" : "=f"(c) : "f"(a), "f"(b)); + return c; +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cta.global" + ".mbarrier::complete_tx::bytes" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit(int mbar_addr) { + asm volatile( + "tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ uint32_t make_warp_uniform(uint32_t val) { + uint32_t result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1f, 0xffffffff;" + : "=r"(result) : "r"(val)); + return result; +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_common_d768_build_eeff_m64split_stage1_d4096_be66_search_v1(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tiles, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 33792); + const int smem_database_addr = smem + 33792; + float* smem_database_sq = reinterpret_cast(smem_raw + 50176); + const int smem_database_sq_addr = smem + 50176; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 0, 1, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 16, 1, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=128 + mbarrier_init_pred(smem + 40, 128, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (64 columns, 64 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::1.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(64) : "memory"); + } + + __syncthreads(); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: compute ---- + if (warp <= 3) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx = bid; work_idx < total_work; work_idx += num_bids) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tiles; + int q_tile = query_work % num_q_tiles; + int off_q = q_tile * 128; + int q_idx = off_q + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx * Q + q_idx]; + } + float best_d[10]; + int best_i[10]; + #pragma unroll + for (int kk = 0; kk < 10; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + int db_tile_start = split_idx * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int db_start = db_tile * 64; + int db_sq_idx = db_start + (warp % 4 * 32 + lane); + if (warp % 4 * 32 + lane < 64) { + if (db_sq_idx < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx * M + db_sq_idx]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 3.4e+38f; + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(warp % 4 * 32 << 16); + float _tmem_load_0[64]; + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x64.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31, %32, %33, %34, %35, %36, %37, %38, %39, %40, %41, %42, %43, %44, %45, %46, %47, %48, %49, %50, %51, %52, %53, %54, %55, %56, %57, %58, %59, %60, %61, %62, %63}, [%64];" + : "=f"(_tmem_load_0[0]), "=f"(_tmem_load_0[1]), "=f"(_tmem_load_0[2]), "=f"(_tmem_load_0[3]), "=f"(_tmem_load_0[4]), "=f"(_tmem_load_0[5]), "=f"(_tmem_load_0[6]), "=f"(_tmem_load_0[7]), "=f"(_tmem_load_0[8]), "=f"(_tmem_load_0[9]), "=f"(_tmem_load_0[10]), "=f"(_tmem_load_0[11]), "=f"(_tmem_load_0[12]), "=f"(_tmem_load_0[13]), "=f"(_tmem_load_0[14]), "=f"(_tmem_load_0[15]), "=f"(_tmem_load_0[16]), "=f"(_tmem_load_0[17]), "=f"(_tmem_load_0[18]), "=f"(_tmem_load_0[19]), "=f"(_tmem_load_0[20]), "=f"(_tmem_load_0[21]), "=f"(_tmem_load_0[22]), "=f"(_tmem_load_0[23]), "=f"(_tmem_load_0[24]), "=f"(_tmem_load_0[25]), "=f"(_tmem_load_0[26]), "=f"(_tmem_load_0[27]), "=f"(_tmem_load_0[28]), "=f"(_tmem_load_0[29]), "=f"(_tmem_load_0[30]), "=f"(_tmem_load_0[31]), "=f"(_tmem_load_0[32]), "=f"(_tmem_load_0[33]), "=f"(_tmem_load_0[34]), "=f"(_tmem_load_0[35]), "=f"(_tmem_load_0[36]), "=f"(_tmem_load_0[37]), "=f"(_tmem_load_0[38]), "=f"(_tmem_load_0[39]), "=f"(_tmem_load_0[40]), "=f"(_tmem_load_0[41]), "=f"(_tmem_load_0[42]), "=f"(_tmem_load_0[43]), "=f"(_tmem_load_0[44]), "=f"(_tmem_load_0[45]), "=f"(_tmem_load_0[46]), "=f"(_tmem_load_0[47]), "=f"(_tmem_load_0[48]), "=f"(_tmem_load_0[49]), "=f"(_tmem_load_0[50]), "=f"(_tmem_load_0[51]), "=f"(_tmem_load_0[52]), "=f"(_tmem_load_0[53]), "=f"(_tmem_load_0[54]), "=f"(_tmem_load_0[55]), "=f"(_tmem_load_0[56]), "=f"(_tmem_load_0[57]), "=f"(_tmem_load_0[58]), "=f"(_tmem_load_0[59]), "=f"(_tmem_load_0[60]), "=f"(_tmem_load_0[61]), "=f"(_tmem_load_0[62]), "=f"(_tmem_load_0[63]) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_arrive(score_empty_addr); + if (valid_q != 0) { + #pragma unroll 1 + for (int col_base = 0; col_base < 64; col_base += 4) { + float dist_vec[4]; + dist_vec[0] = _tmem_load_0[col_base]; + dist_vec[1] = _tmem_load_0[col_base + 1]; + dist_vec[2] = _tmem_load_0[col_base + 2]; + dist_vec[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec[4]; + db_sq_vec[0] = smem_database_sq[col_base]; + db_sq_vec[1] = smem_database_sq[col_base + 1]; + db_sq_vec[2] = smem_database_sq[col_base + 2]; + db_sq_vec[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec)[_la], reinterpret_cast(db_sq_vec)[_la]); + float _max_0 = max_noftz(_t0[0], 0.0f); + float cand0_d = _max_0; + float _max_1 = max_noftz(_t0[1], 0.0f); + float cand1_d = _max_1; + int cand0_i = db_start + col_base; + int cand1_i = cand0_i + 1; + if (cand0_i >= M) { + cand0_d = 3.4e+38f; + } + if (cand1_i >= M) { + cand1_d = 3.4e+38f; + } + if (cand0_d < best_d[9]) { + best_d[9] = cand0_d; + best_i[9] = cand0_i; + #pragma unroll + for (int kk_1 = 8; kk_1 >= 0; kk_1--) { + float lower0_d = best_d[kk_1 + 1]; + int lower0_i = best_i[kk_1 + 1]; + float upper0_d = best_d[kk_1]; + int upper0_i = best_i[kk_1]; + int swap0_up = ((lower0_d < upper0_d) ? 1 : 0); + best_d[kk_1] = ((swap0_up != 0) ? lower0_d : upper0_d); + best_i[kk_1] = ((swap0_up != 0) ? lower0_i : upper0_i); + best_d[kk_1 + 1] = ((swap0_up != 0) ? upper0_d : lower0_d); + best_i[kk_1 + 1] = ((swap0_up != 0) ? upper0_i : lower0_i); + } + } + if (cand1_d < best_d[9]) { + best_d[9] = cand1_d; + best_i[9] = cand1_i; + #pragma unroll + for (int kk_2 = 8; kk_2 >= 0; kk_2--) { + float lower1_d = best_d[kk_2 + 1]; + int lower1_i = best_i[kk_2 + 1]; + float upper1_d = best_d[kk_2]; + int upper1_i = best_i[kk_2]; + int swap1_up = ((lower1_d < upper1_d) ? 1 : 0); + best_d[kk_2] = ((swap1_up != 0) ? lower1_d : upper1_d); + best_i[kk_2] = ((swap1_up != 0) ? lower1_i : upper1_i); + best_d[kk_2 + 1] = ((swap1_up != 0) ? upper1_d : lower1_d); + best_i[kk_2 + 1] = ((swap1_up != 0) ? upper1_i : lower1_i); + } + } + float _max_2 = max_noftz(_t0[2], 0.0f); + float cand2_d = _max_2; + float _max_3 = max_noftz(_t0[3], 0.0f); + float cand3_d = _max_3; + int cand2_i = cand0_i + 2; + int cand3_i = cand0_i + 3; + if (cand2_i >= M) { + cand2_d = 3.4e+38f; + } + if (cand3_i >= M) { + cand3_d = 3.4e+38f; + } + if (cand2_d < best_d[9]) { + best_d[9] = cand2_d; + best_i[9] = cand2_i; + #pragma unroll + for (int kk_3 = 8; kk_3 >= 0; kk_3--) { + float lower0_d_1 = best_d[kk_3 + 1]; + int lower0_i_1 = best_i[kk_3 + 1]; + float upper0_d_1 = best_d[kk_3]; + int upper0_i_1 = best_i[kk_3]; + int swap0_up_1 = ((lower0_d_1 < upper0_d_1) ? 1 : 0); + best_d[kk_3] = ((swap0_up_1 != 0) ? lower0_d_1 : upper0_d_1); + best_i[kk_3] = ((swap0_up_1 != 0) ? lower0_i_1 : upper0_i_1); + best_d[kk_3 + 1] = ((swap0_up_1 != 0) ? upper0_d_1 : lower0_d_1); + best_i[kk_3 + 1] = ((swap0_up_1 != 0) ? upper0_i_1 : lower0_i_1); + } + } + if (cand3_d < best_d[9]) { + best_d[9] = cand3_d; + best_i[9] = cand3_i; + #pragma unroll + for (int kk_4 = 8; kk_4 >= 0; kk_4--) { + float lower1_d_1 = best_d[kk_4 + 1]; + int lower1_i_1 = best_i[kk_4 + 1]; + float upper1_d_1 = best_d[kk_4]; + int upper1_i_1 = best_i[kk_4]; + int swap1_up_1 = ((lower1_d_1 < upper1_d_1) ? 1 : 0); + best_d[kk_4] = ((swap1_up_1 != 0) ? lower1_d_1 : upper1_d_1); + best_i[kk_4] = ((swap1_up_1 != 0) ? lower1_i_1 : upper1_i_1); + best_d[kk_4 + 1] = ((swap1_up_1 != 0) ? upper1_d_1 : lower1_d_1); + best_i[kk_4 + 1] = ((swap1_up_1 != 0) ? upper1_i_1 : lower1_i_1); + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + if (valid_q != 0) { + int out_base = ((split_idx * B + batch_idx) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < 10; out_k++) { + if (out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = best_d[out_k]; + *((int*)(partial_indices + (out_base + out_k))) = best_i[out_k]; + } + } + } + } + } + // ---- Role: load ---- + } else if (warp == 4) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 4) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx_1 = bid; work_idx_1 < total_work; work_idx_1 += num_bids) { + int split_idx_1 = work_idx_1 % (unsigned int)split_count; + int query_work_1 = work_idx_1 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tiles; + int q_tile_1 = query_work_1 % num_q_tiles; + int off_q_1 = q_tile_1 * 128; + int global_q = batch_idx_1 * Q + off_q_1; + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int off_m = db_tile_1 * 64; + int global_m = batch_idx_1 * M + off_m; + #pragma unroll + for (int feat_chunk = 0; feat_chunk < FEATURE_CHUNKS; feat_chunk++) { + int feature_coord = feat_chunk * 2; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + mbarrier_arrive_expect_tx(query_full_addr, 32768); + tma_3d_gmem2smem(smem_query_addr, tmap_query, 0, global_q, feature_coord, query_full_addr); + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + mbarrier_arrive_expect_tx(database_full_addr, 16384); + tma_3d_gmem2smem(smem_database_addr, tmap_database, 0, global_m, feature_coord, database_full_addr); + } + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 5) { + { // mma_main + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_database_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = bid; work_idx_2 < total_work; work_idx_2 += num_bids) { + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + #pragma unroll + for (int feat_chunk_1 = 0; feat_chunk_1 < FEATURE_CHUNKS; feat_chunk_1++) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = make_warp_uniform((smem_query_addr >> 4) & 0x3FFF); + int _mma_b_lo_0 = make_warp_uniform((smem_database_addr >> 4) & 0x3FFF); + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 135267472;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(((((feat_chunk_1 == 0) ? 1 : 0)) ? 0 : 1))); + asm volatile("tcgen05.fence::after_thread_sync;"); + elect_commit(query_empty_addr); + elect_commit(database_empty_addr); + } + elect_commit(score_full_addr); + } + } + } + } + + // Cleanup + __syncthreads(); // barrier before TMEM dealloc + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::1.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(64)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::1.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0129.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0129.cu new file mode 100644 index 00000000..f9636327 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0129.cu @@ -0,0 +1,650 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 64 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 16384 +#define SMEM_SMEM_QUERY_STRIDE 16384 +#define SMEM_SMEM_DATABASE_OFF 17408 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_LOCAL_D_OFF 34048 +#define SMEM_SMEM_LOCAL_D_STAGE_BYTES 32768 +#define SMEM_SMEM_LOCAL_D_STRIDE 32768 +#define SMEM_SMEM_LOCAL_I_OFF 66816 +#define SMEM_SMEM_LOCAL_I_STAGE_BYTES 32768 +#define SMEM_SMEM_LOCAL_I_STRIDE 32768 +#define SMEM_TOTAL 99584 +#define THREADS 192 +#define BLOCK_Q 64 +#define BLOCK_M 64 +#define K_TILE 128 +#define FEATURE_CHUNKS 2 +#define TOP_K_MAX 32 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "tcgen05.mma.cta_group::1.kind::f16 [%0], %1, %2, %3, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, %3, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit(int mbar_addr) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];\n\t" + "}\n" + :: "r"(mbar_addr)); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x32(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x32.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15," + " %16, %17, %18, %19, %20, %21, %22, %23," + " %24, %25, %26, %27, %28, %29, %30, %31}, [%32];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]), + "=f"(dst[16]), "=f"(dst[17]), "=f"(dst[18]), "=f"(dst[19]), + "=f"(dst[20]), "=f"(dst[21]), "=f"(dst[22]), "=f"(dst[23]), + "=f"(dst[24]), "=f"(dst[25]), "=f"(dst[26]), "=f"(dst[27]), + "=f"(dst[28]), "=f"(dst[29]), "=f"(dst[30]), "=f"(dst[31]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ float max_noftz(float a, float b) { + float c; + asm("max.f32 %0, %1, %2;" : "=f"(c) : "f"(a), "f"(b)); + return c; +} + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cta.global" + ".mbarrier::complete_tx::bytes" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit(int mbar_addr) { + asm volatile( + "tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ uint32_t make_warp_uniform(uint32_t val) { + uint32_t result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1f, 0xffffffff;" + : "=r"(result) : "r"(val)); + return result; +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_v12_d256_k32_tail_59fe_v1_stage1_rowld(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tiles, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 17408); + const int smem_database_addr = smem + 17408; + float* smem_local_d = reinterpret_cast(smem_raw + 34048); + const int smem_local_d_addr = smem + 34048; + int* smem_local_i = reinterpret_cast(smem_raw + 66816); + const int smem_local_i_addr = smem + 66816; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 0, 1, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 16, 1, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=4 + mbarrier_init_pred(smem + 40, 4, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (64 columns, 64 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::1.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(64) : "memory"); + } + + __syncthreads(); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: compute ---- + if (warp <= 3) { + { // compute_main + int warp_id_in_role = (warp - 0); + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx = bid; work_idx < total_work; work_idx += num_bids) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tiles; + int q_tile = query_work % num_q_tiles; + int off_q = q_tile * BLOCK_Q; + int tmem_row_origin = warp_id_in_role * 32; + int logical_row_origin = warp_id_in_role * 16; + int row_top = logical_row_origin + lane / 4; + int row_bot = row_top + 8; + int lane_col = lane % 4; + int slot = lane_col; + int q_top = off_q + row_top; + int q_bot = off_q + row_bot; + int valid_top = ((q_top < Q) ? 1 : 0); + int valid_bot = ((q_bot < Q) ? 1 : 0); + float q_sq_top = 0.0f; + float q_sq_bot = 0.0f; + if (valid_top != 0) { + q_sq_top = query_sq[batch_idx * Q + q_top]; + } + if (valid_bot != 0) { + q_sq_bot = query_sq[batch_idx * Q + q_bot]; + } + float best_top_d[TOP_K_MAX]; + float best_bot_d[TOP_K_MAX]; + int best_top_i[TOP_K_MAX]; + int best_bot_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_top_d[kk] = 3.4e+38f; + best_bot_d[kk] = 3.4e+38f; + best_top_i[kk] = -1; + best_bot_i[kk] = -1; + } + int db_tile_start = split_idx * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int db_start = db_tile * BLOCK_M; + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(tmem_row_origin << 16); + float _tmem_load_0[32]; + asm volatile( + "tcgen05.ld.sync.aligned.16x256b.x8.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31}, [%32];" + : "=r"(*reinterpret_cast(&_tmem_load_0[0])), "=r"(*reinterpret_cast(&_tmem_load_0[1])), "=r"(*reinterpret_cast(&_tmem_load_0[2])), "=r"(*reinterpret_cast(&_tmem_load_0[3])), "=r"(*reinterpret_cast(&_tmem_load_0[4])), "=r"(*reinterpret_cast(&_tmem_load_0[5])), "=r"(*reinterpret_cast(&_tmem_load_0[6])), "=r"(*reinterpret_cast(&_tmem_load_0[7])), "=r"(*reinterpret_cast(&_tmem_load_0[8])), "=r"(*reinterpret_cast(&_tmem_load_0[9])), "=r"(*reinterpret_cast(&_tmem_load_0[10])), "=r"(*reinterpret_cast(&_tmem_load_0[11])), "=r"(*reinterpret_cast(&_tmem_load_0[12])), "=r"(*reinterpret_cast(&_tmem_load_0[13])), "=r"(*reinterpret_cast(&_tmem_load_0[14])), "=r"(*reinterpret_cast(&_tmem_load_0[15])), "=r"(*reinterpret_cast(&_tmem_load_0[16])), "=r"(*reinterpret_cast(&_tmem_load_0[17])), "=r"(*reinterpret_cast(&_tmem_load_0[18])), "=r"(*reinterpret_cast(&_tmem_load_0[19])), "=r"(*reinterpret_cast(&_tmem_load_0[20])), "=r"(*reinterpret_cast(&_tmem_load_0[21])), "=r"(*reinterpret_cast(&_tmem_load_0[22])), "=r"(*reinterpret_cast(&_tmem_load_0[23])), "=r"(*reinterpret_cast(&_tmem_load_0[24])), "=r"(*reinterpret_cast(&_tmem_load_0[25])), "=r"(*reinterpret_cast(&_tmem_load_0[26])), "=r"(*reinterpret_cast(&_tmem_load_0[27])), "=r"(*reinterpret_cast(&_tmem_load_0[28])), "=r"(*reinterpret_cast(&_tmem_load_0[29])), "=r"(*reinterpret_cast(&_tmem_load_0[30])), "=r"(*reinterpret_cast(&_tmem_load_0[31])) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + if (elect_sync()) { + mbarrier_arrive(score_empty_addr); + } + #pragma unroll + for (int repeat = 0; repeat < 8; repeat++) { + const int reg_base = repeat * 4; + int col_base = repeat * 8 + lane_col * 2; + int db_idx0 = db_start + col_base; + int db_idx1 = db_idx0 + 1; + float top_d0 = 3.4e+38f; + float top_d1 = 3.4e+38f; + if (valid_top != 0 && db_idx0 < M) { + float _max_0 = max_noftz(q_sq_top + database_sq[batch_idx * M + db_idx0] - 2.0f * _tmem_load_0[reg_base], 0.0f); + top_d0 = _max_0; + } + if (valid_top != 0 && db_idx1 < M) { + float _max_1 = max_noftz(q_sq_top + database_sq[batch_idx * M + db_idx1] - 2.0f * _tmem_load_0[reg_base + 1], 0.0f); + top_d1 = _max_1; + } + int top_take1 = ((top_d1 < top_d0) ? 1 : 0); + if (best_top_d[31] > ((top_take1 != 0) ? top_d1 : top_d0)) { + best_top_d[31] = ((top_take1 != 0) ? top_d1 : top_d0); + best_top_i[31] = ((top_take1 != 0) ? db_idx1 : db_idx0); + #pragma unroll + for (int kk_1 = 30; kk_1 >= 0; kk_1--) { + float lower0_d = best_top_d[kk_1 + 1]; + int lower0_i = best_top_i[kk_1 + 1]; + float upper0_d = best_top_d[kk_1]; + int upper0_i = best_top_i[kk_1]; + int swap0_up = ((lower0_d < upper0_d) ? 1 : 0); + best_top_d[kk_1] = ((swap0_up != 0) ? lower0_d : upper0_d); + best_top_i[kk_1] = ((swap0_up != 0) ? lower0_i : upper0_i); + best_top_d[kk_1 + 1] = ((swap0_up != 0) ? upper0_d : lower0_d); + best_top_i[kk_1 + 1] = ((swap0_up != 0) ? upper0_i : lower0_i); + } + if (best_top_d[31] > ((top_take1 != 0) ? top_d0 : top_d1)) { + best_top_d[31] = ((top_take1 != 0) ? top_d0 : top_d1); + best_top_i[31] = ((top_take1 != 0) ? db_idx0 : db_idx1); + #pragma unroll + for (int kk_2 = 30; kk_2 >= 0; kk_2--) { + float lower1_d = best_top_d[kk_2 + 1]; + int lower1_i = best_top_i[kk_2 + 1]; + float upper1_d = best_top_d[kk_2]; + int upper1_i = best_top_i[kk_2]; + int swap1_up = ((lower1_d < upper1_d) ? 1 : 0); + best_top_d[kk_2] = ((swap1_up != 0) ? lower1_d : upper1_d); + best_top_i[kk_2] = ((swap1_up != 0) ? lower1_i : upper1_i); + best_top_d[kk_2 + 1] = ((swap1_up != 0) ? upper1_d : lower1_d); + best_top_i[kk_2 + 1] = ((swap1_up != 0) ? upper1_i : lower1_i); + } + } + } + float bot_d0 = 3.4e+38f; + float bot_d1 = 3.4e+38f; + if (valid_bot != 0 && db_idx0 < M) { + float _max_2 = max_noftz(q_sq_bot + database_sq[batch_idx * M + db_idx0] - 2.0f * _tmem_load_0[reg_base + 2], 0.0f); + bot_d0 = _max_2; + } + if (valid_bot != 0 && db_idx1 < M) { + float _max_3 = max_noftz(q_sq_bot + database_sq[batch_idx * M + db_idx1] - 2.0f * _tmem_load_0[reg_base + 3], 0.0f); + bot_d1 = _max_3; + } + int bot_take1 = ((bot_d1 < bot_d0) ? 1 : 0); + if (best_bot_d[31] > ((bot_take1 != 0) ? bot_d1 : bot_d0)) { + best_bot_d[31] = ((bot_take1 != 0) ? bot_d1 : bot_d0); + best_bot_i[31] = ((bot_take1 != 0) ? db_idx1 : db_idx0); + #pragma unroll + for (int kk_3 = 30; kk_3 >= 0; kk_3--) { + float lower0_d_1 = best_bot_d[kk_3 + 1]; + int lower0_i_1 = best_bot_i[kk_3 + 1]; + float upper0_d_1 = best_bot_d[kk_3]; + int upper0_i_1 = best_bot_i[kk_3]; + int swap0_up_1 = ((lower0_d_1 < upper0_d_1) ? 1 : 0); + best_bot_d[kk_3] = ((swap0_up_1 != 0) ? lower0_d_1 : upper0_d_1); + best_bot_i[kk_3] = ((swap0_up_1 != 0) ? lower0_i_1 : upper0_i_1); + best_bot_d[kk_3 + 1] = ((swap0_up_1 != 0) ? upper0_d_1 : lower0_d_1); + best_bot_i[kk_3 + 1] = ((swap0_up_1 != 0) ? upper0_i_1 : lower0_i_1); + } + if (best_bot_d[31] > ((bot_take1 != 0) ? bot_d0 : bot_d1)) { + best_bot_d[31] = ((bot_take1 != 0) ? bot_d0 : bot_d1); + best_bot_i[31] = ((bot_take1 != 0) ? db_idx0 : db_idx1); + #pragma unroll + for (int kk_4 = 30; kk_4 >= 0; kk_4--) { + float lower1_d_1 = best_bot_d[kk_4 + 1]; + int lower1_i_1 = best_bot_i[kk_4 + 1]; + float upper1_d_1 = best_bot_d[kk_4]; + int upper1_i_1 = best_bot_i[kk_4]; + int swap1_up_1 = ((lower1_d_1 < upper1_d_1) ? 1 : 0); + best_bot_d[kk_4] = ((swap1_up_1 != 0) ? lower1_d_1 : upper1_d_1); + best_bot_i[kk_4] = ((swap1_up_1 != 0) ? lower1_i_1 : upper1_i_1); + best_bot_d[kk_4 + 1] = ((swap1_up_1 != 0) ? upper1_d_1 : lower1_d_1); + best_bot_i[kk_4 + 1] = ((swap1_up_1 != 0) ? upper1_i_1 : lower1_i_1); + } + } + } + } + } + int top_slot_base = (row_top * 4 + slot) * TOP_K_MAX; + int bot_slot_base = (row_bot * 4 + slot) * TOP_K_MAX; + #pragma unroll + for (int kk_5 = 0; kk_5 < TOP_K_MAX; kk_5++) { + smem_local_d[top_slot_base + kk_5] = best_top_d[kk_5]; + smem_local_i[top_slot_base + kk_5] = best_top_i[kk_5]; + smem_local_d[bot_slot_base + kk_5] = best_bot_d[kk_5]; + smem_local_i[bot_slot_base + kk_5] = best_bot_i[kk_5]; + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + if (tid < BLOCK_Q) { + int row = tid; + int q_idx = off_q + row; + float head_d[4]; + int head_i[4]; + int head_k[4]; + #pragma unroll + for (int slot_idx = 0; slot_idx < 4; slot_idx++) { + int local_base = (row * 4 + slot_idx) * TOP_K_MAX; + head_k[slot_idx] = 0; + head_d[slot_idx] = smem_local_d[local_base]; + head_i[slot_idx] = smem_local_i[local_base]; + } + int out_base = ((split_idx * B + batch_idx) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float winner_d = head_d[0]; + int winner_i = head_i[0]; + int winner_slot = 0; + #pragma unroll + for (int slot_idx_1 = 1; slot_idx_1 < 4; slot_idx_1++) { + float cand_d = head_d[slot_idx_1]; + int take = ((cand_d < winner_d) ? 1 : 0); + winner_d = ((take != 0) ? cand_d : winner_d); + winner_i = ((take != 0) ? head_i[slot_idx_1] : winner_i); + winner_slot = ((take != 0) ? slot_idx_1 : winner_slot); + } + if (q_idx < Q && out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = winner_d; + *((int*)(partial_indices + (out_base + out_k))) = winner_i; + } + #pragma unroll + for (int slot_idx_2 = 0; slot_idx_2 < 4; slot_idx_2++) { + if (winner_slot == slot_idx_2) { + int next_head = head_k[slot_idx_2] + 1; + head_k[slot_idx_2] = next_head; + head_d[slot_idx_2] = 3.4e+38f; + head_i[slot_idx_2] = -1; + if (next_head < TOP_K_MAX) { + int local_base_1 = (row * 4 + slot_idx_2) * TOP_K_MAX; + head_d[slot_idx_2] = smem_local_d[local_base_1 + next_head]; + head_i[slot_idx_2] = smem_local_i[local_base_1 + next_head]; + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + } + // ---- Role: load ---- + } else if (warp == 4) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 4) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx_1 = bid; work_idx_1 < total_work; work_idx_1 += num_bids) { + int split_idx_1 = work_idx_1 % (unsigned int)split_count; + int query_work_1 = work_idx_1 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tiles; + int q_tile_1 = query_work_1 % num_q_tiles; + int off_q_1 = q_tile_1 * BLOCK_Q; + int global_q = batch_idx_1 * Q + off_q_1; + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int off_m = db_tile_1 * BLOCK_M; + int global_m = batch_idx_1 * M + off_m; + #pragma unroll + for (int feat_chunk = 0; feat_chunk < FEATURE_CHUNKS; feat_chunk++) { + int feature_coord = feat_chunk * 2; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + mbarrier_arrive_expect_tx(query_full_addr, 16384); + tma_3d_gmem2smem(smem_query_addr, tmap_query, 0, global_q, feature_coord, query_full_addr); + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + mbarrier_arrive_expect_tx(database_full_addr, 16384); + tma_3d_gmem2smem(smem_database_addr, tmap_database, 0, global_m, feature_coord, database_full_addr); + } + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 5) { + { // mma_main + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_database_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = bid; work_idx_2 < total_work; work_idx_2 += num_bids) { + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + #pragma unroll + for (int feat_chunk_1 = 0; feat_chunk_1 < FEATURE_CHUNKS; feat_chunk_1++) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = make_warp_uniform((smem_query_addr >> 4) & 0x3FFF); + int _mma_b_lo_0 = make_warp_uniform((smem_database_addr >> 4) & 0x3FFF); + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 68158608;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 506;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(((((feat_chunk_1 == 0) ? 1 : 0)) ? 0 : 1))); + asm volatile("tcgen05.fence::after_thread_sync;"); + elect_commit(query_empty_addr); + elect_commit(database_empty_addr); + } + elect_commit(score_full_addr); + } + } + } + } + + // Cleanup + __syncthreads(); // barrier before TMEM dealloc + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::1.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(64)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::1.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0130.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0130.cu new file mode 100644 index 00000000..10931946 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0130.cu @@ -0,0 +1,114 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 128 +#define TOP_K_MAX 32 +#define SPLIT_COUNT 64 +#define SPLITS_PER_LANE 2 +#define ROWS_PER_CTA 1 + +#include + +extern "C" { + +__global__ __launch_bounds__(128, 1) void +kernel_knn_build_rag_microbucket_k32warpmerge_0077_v1_warp_row_merge_k32s64_0077_v1(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int row = bid * ROWS_PER_CTA + warp; + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + if (warp < ROWS_PER_CTA && row < total_queries) { + int split_pos[SPLITS_PER_LANE]; + int split_id_for_slot[SPLITS_PER_LANE]; + float cand_d[SPLITS_PER_LANE]; + int cand_i[SPLITS_PER_LANE]; + #pragma unroll + for (int slot = 0; slot < SPLITS_PER_LANE; slot++) { + int split_id = slot * 32 + lane; + split_id_for_slot[slot] = split_id; + split_pos[slot] = 0; + cand_d[slot] = 3.4e+38f; + cand_i[slot] = -1; + if (split_id < SPLIT_COUNT) { + int source_addr = base_row + split_id * split_stride; + cand_d[slot] = partial_dists[source_addr]; + cand_i[slot] = partial_indices[source_addr]; + } + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float lane_best_d = cand_d[0]; + int lane_best_i = cand_i[0]; + int lane_best_slot = 0; + #pragma unroll + for (int slot_1 = 1; slot_1 < SPLITS_PER_LANE; slot_1++) { + if (lane_best_d > cand_d[slot_1]) { + lane_best_d = cand_d[slot_1]; + lane_best_i = cand_i[slot_1]; + lane_best_slot = slot_1; + } + } + float warp_min = lane_best_d; + float _warp_reduce_0 = warp_min; + #pragma unroll + for (int offset = 16; offset > 0; offset >>= 1) + _warp_reduce_0 = fminf(_warp_reduce_0, __shfl_xor_sync(0xFFFFFFFF, _warp_reduce_0, offset)); + warp_min = _warp_reduce_0; + unsigned int _vote_0 = __ballot_sync(0xFFFFFFFF, lane_best_d == warp_min); + int owner_ballot = _vote_0; + int _ffs_0 = __ffs(owner_ballot); + int winner_lane = _ffs_0 - 1; + int _shfl_0 = __shfl_sync(0xFFFFFFFF, lane_best_i, winner_lane); + int winner_i = _shfl_0; + int _shfl_1 = __shfl_sync(0xFFFFFFFF, lane_best_slot, winner_lane); + int winner_slot = _shfl_1; + if (lane == 0) { + *((float*)(out_dists + (base_row + out_k))) = warp_min; + *((int*)(out_indices + (base_row + out_k))) = winner_i; + } + if (lane == winner_lane) { + #pragma unroll + for (int slot_2 = 0; slot_2 < SPLITS_PER_LANE; slot_2++) { + if (winner_slot == slot_2) { + int next_pos = split_pos[slot_2] + 1; + split_pos[slot_2] = next_pos; + cand_d[slot_2] = 3.4e+38f; + cand_i[slot_2] = -1; + if (next_pos < TOP_K_MAX) { + int next_addr = base_row + split_id_for_slot[slot_2] * split_stride + next_pos; + cand_d[slot_2] = partial_dists[next_addr]; + cand_i[slot_2] = partial_indices[next_addr]; + } + } + } + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0131.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0131.cu new file mode 100644 index 00000000..34dd4c84 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0131.cu @@ -0,0 +1,607 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 64 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 16384 +#define SMEM_SMEM_QUERY_STRIDE 16384 +#define SMEM_SMEM_DATABASE_OFF 17408 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_UPPER_DOTS_OFF 34048 +#define SMEM_SMEM_UPPER_DOTS_STAGE_BYTES 2048 +#define SMEM_SMEM_UPPER_DOTS_STRIDE 2048 +#define SMEM_SMEM_LOCAL_D_OFF 36096 +#define SMEM_SMEM_LOCAL_D_STAGE_BYTES 12288 +#define SMEM_SMEM_LOCAL_D_STRIDE 12288 +#define SMEM_SMEM_LOCAL_I_OFF 48384 +#define SMEM_SMEM_LOCAL_I_STAGE_BYTES 12288 +#define SMEM_SMEM_LOCAL_I_STRIDE 12288 +#define SMEM_TOTAL 60672 +#define THREADS 128 +#define BLOCK_Q_CONST 64 +#define BLOCK_M_CONST 64 +#define FEAT_D_CONST 128 +#define TOP_K_MAX 48 +#define ROWS_COVERED_CONST 16 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "tcgen05.mma.cta_group::1.kind::f16 [%0], %1, %2, %3, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, %3, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit(int mbar_addr) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];\n\t" + "}\n" + :: "r"(mbar_addr)); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x32(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x32.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15," + " %16, %17, %18, %19, %20, %21, %22, %23," + " %24, %25, %26, %27, %28, %29, %30, %31}, [%32];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]), + "=f"(dst[16]), "=f"(dst[17]), "=f"(dst[18]), "=f"(dst[19]), + "=f"(dst[20]), "=f"(dst[21]), "=f"(dst[22]), "=f"(dst[23]), + "=f"(dst[24]), "=f"(dst[25]), "=f"(dst[26]), "=f"(dst[27]), + "=f"(dst[28]), "=f"(dst[29]), "=f"(dst[30]), "=f"(dst[31]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ float max_noftz(float a, float b) { + float c; + asm("max.f32 %0, %1, %2;" : "=f"(c) : "f"(a), "f"(b)); + return c; +} + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cta.global" + ".mbarrier::complete_tx::bytes" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit(int mbar_addr) { + asm volatile( + "tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ uint32_t make_warp_uniform(uint32_t val) { + uint32_t result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1f, 0xffffffff;" + : "=r"(result) : "r"(val)); + return result; +} + +extern "C" { + +__global__ __launch_bounds__(128, 1) void +kernel_knn_build_v12_d128_q16_k48_dd2b_v1_stage1(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tiles, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 17408); + const int smem_database_addr = smem + 17408; + float* smem_upper_dots = reinterpret_cast(smem_raw + 34048); + const int smem_upper_dots_addr = smem + 34048; + float* smem_local_d = reinterpret_cast(smem_raw + 36096); + const int smem_local_d_addr = smem + 36096; + int* smem_local_i = reinterpret_cast(smem_raw + 48384); + const int smem_local_i_addr = smem + 48384; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 0, 1, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 16, 1, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 40, 2, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (64 columns, 64 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::1.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(64) : "memory"); + } + + __syncthreads(); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: compute ---- + if (warp <= 1) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx = bid; work_idx < total_work; work_idx += num_bids) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tiles; + int q_tile = query_work % num_q_tiles; + int off_q = q_tile * BLOCK_Q_CONST; + int row = warp * 8 + lane / 4; + int lane_col = lane % 4; + int slot = lane_col; + int q_idx = off_q + row; + int valid_row = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_row != 0) { + q_sq_val = query_sq[batch_idx * Q + q_idx]; + } + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + int db_tile_start = split_idx * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int db_start = db_tile * BLOCK_M_CONST; + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + float _tmem_load_0[32]; + asm volatile( + "tcgen05.ld.sync.aligned.16x256b.x8.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31}, [%32];" + : "=r"(*reinterpret_cast(&_tmem_load_0[0])), "=r"(*reinterpret_cast(&_tmem_load_0[1])), "=r"(*reinterpret_cast(&_tmem_load_0[2])), "=r"(*reinterpret_cast(&_tmem_load_0[3])), "=r"(*reinterpret_cast(&_tmem_load_0[4])), "=r"(*reinterpret_cast(&_tmem_load_0[5])), "=r"(*reinterpret_cast(&_tmem_load_0[6])), "=r"(*reinterpret_cast(&_tmem_load_0[7])), "=r"(*reinterpret_cast(&_tmem_load_0[8])), "=r"(*reinterpret_cast(&_tmem_load_0[9])), "=r"(*reinterpret_cast(&_tmem_load_0[10])), "=r"(*reinterpret_cast(&_tmem_load_0[11])), "=r"(*reinterpret_cast(&_tmem_load_0[12])), "=r"(*reinterpret_cast(&_tmem_load_0[13])), "=r"(*reinterpret_cast(&_tmem_load_0[14])), "=r"(*reinterpret_cast(&_tmem_load_0[15])), "=r"(*reinterpret_cast(&_tmem_load_0[16])), "=r"(*reinterpret_cast(&_tmem_load_0[17])), "=r"(*reinterpret_cast(&_tmem_load_0[18])), "=r"(*reinterpret_cast(&_tmem_load_0[19])), "=r"(*reinterpret_cast(&_tmem_load_0[20])), "=r"(*reinterpret_cast(&_tmem_load_0[21])), "=r"(*reinterpret_cast(&_tmem_load_0[22])), "=r"(*reinterpret_cast(&_tmem_load_0[23])), "=r"(*reinterpret_cast(&_tmem_load_0[24])), "=r"(*reinterpret_cast(&_tmem_load_0[25])), "=r"(*reinterpret_cast(&_tmem_load_0[26])), "=r"(*reinterpret_cast(&_tmem_load_0[27])), "=r"(*reinterpret_cast(&_tmem_load_0[28])), "=r"(*reinterpret_cast(&_tmem_load_0[29])), "=r"(*reinterpret_cast(&_tmem_load_0[30])), "=r"(*reinterpret_cast(&_tmem_load_0[31])) + : "r"(taddr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + if (warp == 0) { + int scratch_row = lane / 4; + #pragma unroll + for (int repeat = 0; repeat < 8; repeat++) { + const int reg_base = repeat * 4; + int scratch_col = repeat * 8 + lane_col * 2; + int scratch_base = scratch_row * 64 + scratch_col; + smem_upper_dots[scratch_base] = _tmem_load_0[reg_base + 2]; + smem_upper_dots[scratch_base + 1] = _tmem_load_0[reg_base + 3]; + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(64)); + if (elect_sync()) { + mbarrier_arrive(score_empty_addr); + } + #pragma unroll + for (int repeat_1 = 0; repeat_1 < 8; repeat_1++) { + const int reg_base_1 = repeat_1 * 4; + int col_base = repeat_1 * 8 + lane_col * 2; + int db_idx0 = db_start + col_base; + int db_idx1 = db_idx0 + 1; + float dot0 = _tmem_load_0[reg_base_1]; + float dot1 = _tmem_load_0[reg_base_1 + 1]; + if (warp != 0) { + int scratch_row_1 = lane / 4; + int scratch_base_1 = scratch_row_1 * 64 + col_base; + dot0 = smem_upper_dots[scratch_base_1]; + dot1 = smem_upper_dots[scratch_base_1 + 1]; + } + float cand0_d = 3.4e+38f; + float cand1_d = 3.4e+38f; + if (valid_row != 0 && db_idx0 < M) { + float _max_0 = max_noftz(q_sq_val + database_sq[batch_idx * M + db_idx0] - 2.0f * dot0, 0.0f); + cand0_d = _max_0; + } + if (valid_row != 0 && db_idx1 < M) { + float _max_1 = max_noftz(q_sq_val + database_sq[batch_idx * M + db_idx1] - 2.0f * dot1, 0.0f); + cand1_d = _max_1; + } + int take1 = ((cand1_d < cand0_d) ? 1 : 0); + if (best_d[47] > ((take1 != 0) ? cand1_d : cand0_d)) { + best_d[47] = ((take1 != 0) ? cand1_d : cand0_d); + best_i[47] = ((take1 != 0) ? db_idx1 : db_idx0); + #pragma unroll + for (int kk_1 = 46; kk_1 >= 0; kk_1--) { + float lower0_d = best_d[kk_1 + 1]; + int lower0_i = best_i[kk_1 + 1]; + float upper0_d = best_d[kk_1]; + int upper0_i = best_i[kk_1]; + int swap0_up = ((lower0_d < upper0_d) ? 1 : 0); + best_d[kk_1] = ((swap0_up != 0) ? lower0_d : upper0_d); + best_i[kk_1] = ((swap0_up != 0) ? lower0_i : upper0_i); + best_d[kk_1 + 1] = ((swap0_up != 0) ? upper0_d : lower0_d); + best_i[kk_1 + 1] = ((swap0_up != 0) ? upper0_i : lower0_i); + } + if (best_d[47] > ((take1 != 0) ? cand0_d : cand1_d)) { + best_d[47] = ((take1 != 0) ? cand0_d : cand1_d); + best_i[47] = ((take1 != 0) ? db_idx0 : db_idx1); + #pragma unroll + for (int kk_2 = 46; kk_2 >= 0; kk_2--) { + float lower1_d = best_d[kk_2 + 1]; + int lower1_i = best_i[kk_2 + 1]; + float upper1_d = best_d[kk_2]; + int upper1_i = best_i[kk_2]; + int swap1_up = ((lower1_d < upper1_d) ? 1 : 0); + best_d[kk_2] = ((swap1_up != 0) ? lower1_d : upper1_d); + best_i[kk_2] = ((swap1_up != 0) ? lower1_i : upper1_i); + best_d[kk_2 + 1] = ((swap1_up != 0) ? upper1_d : lower1_d); + best_i[kk_2 + 1] = ((swap1_up != 0) ? upper1_i : lower1_i); + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(64)); + } + int slot_base = (row * 4 + slot) * TOP_K_MAX; + #pragma unroll + for (int kk_3 = 0; kk_3 < TOP_K_MAX; kk_3++) { + smem_local_d[slot_base + kk_3] = best_d[kk_3]; + smem_local_i[slot_base + kk_3] = best_i[kk_3]; + } + asm volatile("barrier.sync 8, %0;" :: "r"(64)); + if (tid < ROWS_COVERED_CONST) { + int out_row = tid; + int out_q_idx = off_q + out_row; + float head_d[4]; + int head_i[4]; + int head_k[4]; + #pragma unroll + for (int slot_idx = 0; slot_idx < 4; slot_idx++) { + int local_base = (out_row * 4 + slot_idx) * TOP_K_MAX; + head_k[slot_idx] = 0; + head_d[slot_idx] = smem_local_d[local_base]; + head_i[slot_idx] = smem_local_i[local_base]; + } + int out_base = ((split_idx * B + batch_idx) * Q + out_q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float winner_d = head_d[0]; + int winner_i = head_i[0]; + int winner_slot = 0; + #pragma unroll + for (int slot_idx_1 = 1; slot_idx_1 < 4; slot_idx_1++) { + float cand_d = head_d[slot_idx_1]; + int take = ((cand_d < winner_d) ? 1 : 0); + winner_d = ((take != 0) ? cand_d : winner_d); + winner_i = ((take != 0) ? head_i[slot_idx_1] : winner_i); + winner_slot = ((take != 0) ? slot_idx_1 : winner_slot); + } + if (out_q_idx < Q && out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = winner_d; + *((int*)(partial_indices + (out_base + out_k))) = winner_i; + } + #pragma unroll + for (int slot_idx_2 = 0; slot_idx_2 < 4; slot_idx_2++) { + if (winner_slot == slot_idx_2) { + int next_head = head_k[slot_idx_2] + 1; + head_k[slot_idx_2] = next_head; + head_d[slot_idx_2] = 3.4e+38f; + head_i[slot_idx_2] = -1; + if (next_head < TOP_K_MAX) { + int local_base_1 = (out_row * 4 + slot_idx_2) * TOP_K_MAX; + head_d[slot_idx_2] = smem_local_d[local_base_1 + next_head]; + head_i[slot_idx_2] = smem_local_i[local_base_1 + next_head]; + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(64)); + } + } + // ---- Role: load ---- + } else if (warp == 2) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 2) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx_1 = bid; work_idx_1 < total_work; work_idx_1 += num_bids) { + int split_idx_1 = work_idx_1 % (unsigned int)split_count; + int query_work_1 = work_idx_1 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tiles; + int q_tile_1 = query_work_1 % num_q_tiles; + int off_q_1 = q_tile_1 * BLOCK_Q_CONST; + int global_q = batch_idx_1 * Q + off_q_1; + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + mbarrier_arrive_expect_tx(query_full_addr, 16384); + tma_3d_gmem2smem(smem_query_addr, tmap_query, 0, global_q, 0, query_full_addr); + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int off_m = db_tile_1 * BLOCK_M_CONST; + int global_m = batch_idx_1 * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + mbarrier_arrive_expect_tx(database_full_addr, 16384); + tma_3d_gmem2smem(smem_database_addr, tmap_database, 0, global_m, 0, database_full_addr); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 3) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + #pragma unroll 1 + for (unsigned int _work_idx = bid; _work_idx < total_work; _work_idx += num_bids) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = make_warp_uniform((smem_query_addr >> 4) & 0x3FFF); + int _mma_b_lo_0 = make_warp_uniform((smem_database_addr >> 4) & 0x3FFF); + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 68158608;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 506;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit(score_full_addr); + elect_commit(database_empty_addr); + } + elect_commit(query_empty_addr); + } + } + } + + // Cleanup + __syncthreads(); // barrier before TMEM dealloc + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::1.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(64)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::1.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0132.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0132.cu new file mode 100644 index 00000000..8b40ea14 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0132.cu @@ -0,0 +1,114 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 128 +#define TOP_K_MAX 48 +#define SPLIT_COUNT 144 +#define SPLITS_PER_LANE 5 +#define ROWS_PER_CTA 4 + +#include + +extern "C" { + +__global__ __launch_bounds__(128, 1) void +kernel_knn_build_rag_microbucket_k32warpmerge_0077_v1_warp_row_merge_k48s144r4_dd2b_v1(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int row = bid * ROWS_PER_CTA + warp; + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + if (warp < ROWS_PER_CTA && row < total_queries) { + int split_pos[SPLITS_PER_LANE]; + int split_id_for_slot[SPLITS_PER_LANE]; + float cand_d[SPLITS_PER_LANE]; + int cand_i[SPLITS_PER_LANE]; + #pragma unroll + for (int slot = 0; slot < SPLITS_PER_LANE; slot++) { + int split_id = slot * 32 + lane; + split_id_for_slot[slot] = split_id; + split_pos[slot] = 0; + cand_d[slot] = 3.4e+38f; + cand_i[slot] = -1; + if (split_id < SPLIT_COUNT) { + int source_addr = base_row + split_id * split_stride; + cand_d[slot] = partial_dists[source_addr]; + cand_i[slot] = partial_indices[source_addr]; + } + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float lane_best_d = cand_d[0]; + int lane_best_i = cand_i[0]; + int lane_best_slot = 0; + #pragma unroll + for (int slot_1 = 1; slot_1 < SPLITS_PER_LANE; slot_1++) { + if (lane_best_d > cand_d[slot_1]) { + lane_best_d = cand_d[slot_1]; + lane_best_i = cand_i[slot_1]; + lane_best_slot = slot_1; + } + } + float warp_min = lane_best_d; + float _warp_reduce_0 = warp_min; + #pragma unroll + for (int offset = 16; offset > 0; offset >>= 1) + _warp_reduce_0 = fminf(_warp_reduce_0, __shfl_xor_sync(0xFFFFFFFF, _warp_reduce_0, offset)); + warp_min = _warp_reduce_0; + unsigned int _vote_0 = __ballot_sync(0xFFFFFFFF, lane_best_d == warp_min); + int owner_ballot = _vote_0; + int _ffs_0 = __ffs(owner_ballot); + int winner_lane = _ffs_0 - 1; + int _shfl_0 = __shfl_sync(0xFFFFFFFF, lane_best_i, winner_lane); + int winner_i = _shfl_0; + int _shfl_1 = __shfl_sync(0xFFFFFFFF, lane_best_slot, winner_lane); + int winner_slot = _shfl_1; + if (lane == 0) { + *((float*)(out_dists + (base_row + out_k))) = warp_min; + *((int*)(out_indices + (base_row + out_k))) = winner_i; + } + if (lane == winner_lane) { + #pragma unroll + for (int slot_2 = 0; slot_2 < SPLITS_PER_LANE; slot_2++) { + if (winner_slot == slot_2) { + int next_pos = split_pos[slot_2] + 1; + split_pos[slot_2] = next_pos; + cand_d[slot_2] = 3.4e+38f; + cand_i[slot_2] = -1; + if (next_pos < TOP_K_MAX) { + int next_addr = base_row + split_id_for_slot[slot_2] * split_stride + next_pos; + cand_d[slot_2] = partial_dists[next_addr]; + cand_i[slot_2] = partial_indices[next_addr]; + } + } + } + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0133.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0133.cu new file mode 100644 index 00000000..4f2e4816 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0133.cu @@ -0,0 +1,114 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 128 +#define TOP_K 10 +#define SPLITS 72 +#define LANESLOTS 3 +#define ROWS 4 + +#include + +extern "C" { + +__global__ __launch_bounds__(128, 1) void +kernel_knn_build_rag_stream_k10_s72_warp_row_merge_34da(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int row = bid * ROWS + warp; + int base_row = row * TOP_K; + int split_stride = total_queries * TOP_K; + if (warp < ROWS && row < total_queries) { + int split_pos[LANESLOTS]; + int split_id_for_slot[LANESLOTS]; + float cand_d[LANESLOTS]; + int cand_i[LANESLOTS]; + #pragma unroll + for (int slot = 0; slot < LANESLOTS; slot++) { + int split_id = slot * 32 + lane; + split_id_for_slot[slot] = split_id; + split_pos[slot] = 0; + cand_d[slot] = 3.4e+38f; + cand_i[slot] = -1; + if (split_id < SPLITS) { + int source_addr = base_row + split_id * split_stride; + cand_d[slot] = partial_dists[source_addr]; + cand_i[slot] = partial_indices[source_addr]; + } + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K; out_k++) { + float lane_best_d = cand_d[0]; + int lane_best_i = cand_i[0]; + int lane_best_slot = 0; + #pragma unroll + for (int slot_1 = 1; slot_1 < LANESLOTS; slot_1++) { + if (lane_best_d > cand_d[slot_1]) { + lane_best_d = cand_d[slot_1]; + lane_best_i = cand_i[slot_1]; + lane_best_slot = slot_1; + } + } + float warp_min = lane_best_d; + float _warp_reduce_0 = warp_min; + #pragma unroll + for (int offset = 16; offset > 0; offset >>= 1) + _warp_reduce_0 = fminf(_warp_reduce_0, __shfl_xor_sync(0xFFFFFFFF, _warp_reduce_0, offset)); + warp_min = _warp_reduce_0; + unsigned int _vote_0 = __ballot_sync(0xFFFFFFFF, lane_best_d == warp_min); + int owner_ballot = _vote_0; + int _ffs_0 = __ffs(owner_ballot); + int winner_lane = _ffs_0 - 1; + int _shfl_0 = __shfl_sync(0xFFFFFFFF, lane_best_i, winner_lane); + int winner_i = _shfl_0; + int _shfl_1 = __shfl_sync(0xFFFFFFFF, lane_best_slot, winner_lane); + int winner_slot = _shfl_1; + if (lane == 0) { + *((float*)(out_dists + (base_row + out_k))) = warp_min; + *((int*)(out_indices + (base_row + out_k))) = winner_i; + } + if (lane == winner_lane) { + #pragma unroll + for (int slot_2 = 0; slot_2 < LANESLOTS; slot_2++) { + if (winner_slot == slot_2) { + int next_pos = split_pos[slot_2] + 1; + split_pos[slot_2] = next_pos; + cand_d[slot_2] = 3.4e+38f; + cand_i[slot_2] = -1; + if (next_pos < TOP_K) { + int next_addr = base_row + split_id_for_slot[slot_2] * split_stride + next_pos; + cand_d[slot_2] = partial_dists[next_addr]; + cand_i[slot_2] = partial_indices[next_addr]; + } + } + } + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0134.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0134.cu new file mode 100644 index 00000000..749e2ca2 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0134.cu @@ -0,0 +1,84 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 32 +#define TOP_K_MAX 10 +#define SPLIT_COUNT 64 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_k10s64_3d97(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 32 + tid; + int stride = num_bids * 32; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int out_base = base_row; + int split_pos[SPLIT_COUNT]; + int split_base[SPLIT_COUNT]; + float cand_d[SPLIT_COUNT]; + int cand_i[SPLIT_COUNT]; + #pragma unroll + for (int split_idx = 0; split_idx < SPLIT_COUNT; split_idx++) { + split_pos[split_idx] = 0; + split_base[split_idx] = base_row + split_idx * split_stride; + cand_d[split_idx] = partial_dists[split_base[split_idx]]; + cand_i[split_idx] = partial_indices[split_base[split_idx]]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_d = cand_d[0]; + int best_i = cand_i[0]; + int best_split = 0; + #pragma unroll + for (int split_idx_1 = 1; split_idx_1 < SPLIT_COUNT; split_idx_1++) { + if (best_d > cand_d[split_idx_1]) { + best_d = cand_d[split_idx_1]; + best_i = cand_i[split_idx_1]; + best_split = split_idx_1; + } + } + *((float*)(out_dists + (out_base + out_k))) = best_d; + *((int*)(out_indices + (out_base + out_k))) = best_i; + split_pos[best_split] = split_pos[best_split] + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_pos = split_pos[best_split]; + int next_addr = split_base[best_split] + next_pos; + cand_d[best_split] = partial_dists[next_addr]; + cand_i[best_split] = partial_indices[next_addr]; + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0135.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0135.cu new file mode 100644 index 00000000..e7baa2af --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0135.cu @@ -0,0 +1,484 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 64 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_STRIDE 32768 +#define SMEM_SMEM_DATABASE_OFF 33792 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_TOTAL 50176 +#define THREADS 192 +#define BLOCK_Q 128 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_MAX 10 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "tcgen05.mma.cta_group::1.kind::f16 [%0], %1, %2, %3, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, %3, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit(int mbar_addr) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];\n\t" + "}\n" + :: "r"(mbar_addr)); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x32(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x32.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15," + " %16, %17, %18, %19, %20, %21, %22, %23," + " %24, %25, %26, %27, %28, %29, %30, %31}, [%32];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]), + "=f"(dst[16]), "=f"(dst[17]), "=f"(dst[18]), "=f"(dst[19]), + "=f"(dst[20]), "=f"(dst[21]), "=f"(dst[22]), "=f"(dst[23]), + "=f"(dst[24]), "=f"(dst[25]), "=f"(dst[26]), "=f"(dst[27]), + "=f"(dst[28]), "=f"(dst[29]), "=f"(dst[30]), "=f"(dst[31]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ float max_noftz(float a, float b) { + float c; + asm("max.f32 %0, %1, %2;" : "=f"(c) : "f"(a), "f"(b)); + return c; +} + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cta.global" + ".mbarrier::complete_tx::bytes" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit(int mbar_addr) { + asm volatile( + "tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ uint32_t make_warp_uniform(uint32_t val) { + uint32_t result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1f, 0xffffffff;" + : "=r"(result) : "r"(val)); + return result; +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_evolve_7bfc_v1(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ out_dists, int* __restrict__ out_indices, int B, int Q, int M, int K, int num_q_tiles, int num_db_tiles, int total_tiles) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 33792); + const int smem_database_addr = smem + 33792; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 0, 1, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 16, 1, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=128 + mbarrier_init_pred(smem + 40, 128, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (64 columns, 64 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::1.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(64) : "memory"); + } + + __syncthreads(); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: load ---- + if (warp == 0) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 0) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int tile_idx = bid; tile_idx < total_tiles; tile_idx += num_bids) { + int batch_idx = tile_idx / (unsigned int)num_q_tiles; + int q_tile = tile_idx % (unsigned int)num_q_tiles; + int off_q = q_tile * BLOCK_Q; + int global_q = batch_idx * Q + off_q; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + mbarrier_arrive_expect_tx(query_full_addr, 32768); + tma_3d_gmem2smem(smem_query_addr, tmap_query, 0, global_q, 0, query_full_addr); + #pragma unroll 1 + for (int db_tile = 0; db_tile < num_db_tiles; db_tile++) { + int off_m = db_tile * BLOCK_M; + int global_m = batch_idx * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + mbarrier_arrive_expect_tx(database_full_addr, 16384); + tma_3d_gmem2smem(smem_database_addr, tmap_database, 0, global_m, 0, database_full_addr); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 1) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + #pragma unroll 1 + for (unsigned int tile_idx_1 = bid; tile_idx_1 < total_tiles; tile_idx_1 += num_bids) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _db_tile = 0; _db_tile < num_db_tiles; _db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = make_warp_uniform((smem_query_addr >> 4) & 0x3FFF); + int _mma_b_lo_0 = make_warp_uniform((smem_database_addr >> 4) & 0x3FFF); + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 135267472;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit(score_full_addr); + elect_commit(database_empty_addr); + } + elect_commit(query_empty_addr); + } + } + // ---- Role: compute ---- + } else if (warp >= 2 && warp <= 5) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int tile_idx_2 = bid; tile_idx_2 < total_tiles; tile_idx_2 += num_bids) { + int batch_idx_1 = tile_idx_2 / (unsigned int)num_q_tiles; + int q_tile_1 = tile_idx_2 % (unsigned int)num_q_tiles; + int off_q_1 = q_tile_1 * BLOCK_Q; + int q_idx = off_q_1 + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx_1 * Q + q_idx]; + } + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + #pragma unroll 1 + for (int db_tile_1 = 0; db_tile_1 < num_db_tiles; db_tile_1++) { + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(warp % 4 * 32 << 16); + float _tmem_load_0[64]; + tmem_ld_x32(&_tmem_load_0[0], cross_addr); + tmem_ld_x32(&_tmem_load_0[32], cross_addr + 32); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + mbarrier_arrive(score_empty_addr); + int db_start = db_tile_1 * BLOCK_M; + #pragma unroll + for (int col = 0; col < 64; col++) { + int db_idx = db_start + col; + if (valid_q != 0 && db_idx < M) { + float db_sq_val = database_sq[batch_idx_1 * M + db_idx]; + float dist = q_sq_val + db_sq_val - 2.0f * _tmem_load_0[col]; + float _max_0 = max_noftz(dist, 0.0f); + dist = _max_0; + if (dist < best_d[TOP_K_MAX - 1]) { + best_d[TOP_K_MAX - 1] = dist; + best_i[TOP_K_MAX - 1] = db_idx; + #pragma unroll + for (int pos = TOP_K_MAX - 1; pos >= 1; pos--) { + if (best_d[pos] < best_d[pos - 1]) { + float tmp_d = best_d[pos - 1]; + int tmp_i = best_i[pos - 1]; + best_d[pos - 1] = best_d[pos]; + best_i[pos - 1] = best_i[pos]; + best_d[pos] = tmp_d; + best_i[pos] = tmp_i; + } + } + } + } + } + } + if (valid_q != 0) { + int out_base = (batch_idx_1 * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + if (out_k < K) { + *((float*)(out_dists + (out_base + out_k))) = best_d[out_k]; + *((int*)(out_indices + (out_base + out_k))) = best_i[out_k]; + } + } + } + } + } + } + + // Cleanup + __syncthreads(); // barrier before TMEM dealloc + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::1.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(64)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::1.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0136.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0136.cu new file mode 100644 index 00000000..e1e19789 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0136.cu @@ -0,0 +1,587 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 64 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_STRIDE 32768 +#define SMEM_SMEM_DATABASE_OFF 33792 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_DATABASE_SQ_OFF 50176 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 50432 +#define THREADS 192 +#define BLOCK_Q 128 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_MAX 10 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "tcgen05.mma.cta_group::1.kind::f16 [%0], %1, %2, %3, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, %3, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit(int mbar_addr) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];\n\t" + "}\n" + :: "r"(mbar_addr)); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x16(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x16.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15}, [%16];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cta.global" + ".mbarrier::complete_tx::bytes" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit(int mbar_addr) { + asm volatile( + "tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ uint32_t make_warp_uniform(uint32_t val) { + uint32_t result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1f, 0xffffffff;" + : "=r"(result) : "r"(val)); + return result; +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_evolve_7bfc_split_stage1(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tiles, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 33792); + const int smem_database_addr = smem + 33792; + float* smem_database_sq = reinterpret_cast(smem_raw + 50176); + const int smem_database_sq_addr = smem + 50176; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 0, 1, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 16, 1, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=128 + mbarrier_init_pred(smem + 40, 128, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (64 columns, 64 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::1.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(64) : "memory"); + } + + __syncthreads(); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: load ---- + if (warp == 0) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 0) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx = bid; work_idx < total_work; work_idx += num_bids) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tiles; + int q_tile = query_work % num_q_tiles; + int off_q = q_tile * BLOCK_Q; + int global_q = batch_idx * Q + off_q; + int db_tile_start = split_idx * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + mbarrier_arrive_expect_tx(query_full_addr, 32768); + tma_3d_gmem2smem(smem_query_addr, tmap_query, 0, global_q, 0, query_full_addr); + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int off_m = db_tile * BLOCK_M; + int global_m = batch_idx * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + mbarrier_arrive_expect_tx(database_full_addr, 16384); + tma_3d_gmem2smem(smem_database_addr, tmap_database, 0, global_m, 0, database_full_addr); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 1) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_1 = bid; work_idx_1 < total_work; work_idx_1 += num_bids) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + int _mma_a_lo_0 = make_warp_uniform((smem_query_addr >> 4) & 0x3FFF); + int _mma_b_lo_0 = make_warp_uniform((smem_database_addr >> 4) & 0x3FFF); + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 135267472;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit(score_full_addr); + elect_commit(database_empty_addr); + } + elect_commit(query_empty_addr); + } + } + // ---- Role: compute ---- + } else if (warp >= 2 && warp <= 5) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = bid; work_idx_2 < total_work; work_idx_2 += num_bids) { + int split_idx_1 = work_idx_2 % (unsigned int)split_count; + int query_work_1 = work_idx_2 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tiles; + int q_tile_1 = query_work_1 % num_q_tiles; + int off_q_1 = q_tile_1 * BLOCK_Q; + int q_idx = off_q_1 + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx_1 * Q + q_idx]; + } + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int db_start = db_tile_1 * BLOCK_M; + int db_sq_idx = db_start + (warp % 4 * 32 + lane); + if (warp % 4 * 32 + lane < BLOCK_M) { + if (db_sq_idx < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx_1 * M + db_sq_idx]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 0.0f; + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(warp % 4 * 32 << 16); + float _tmem_load_0[64]; + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x64.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31, %32, %33, %34, %35, %36, %37, %38, %39, %40, %41, %42, %43, %44, %45, %46, %47, %48, %49, %50, %51, %52, %53, %54, %55, %56, %57, %58, %59, %60, %61, %62, %63}, [%64];" + : "=f"(_tmem_load_0[0]), "=f"(_tmem_load_0[1]), "=f"(_tmem_load_0[2]), "=f"(_tmem_load_0[3]), "=f"(_tmem_load_0[4]), "=f"(_tmem_load_0[5]), "=f"(_tmem_load_0[6]), "=f"(_tmem_load_0[7]), "=f"(_tmem_load_0[8]), "=f"(_tmem_load_0[9]), "=f"(_tmem_load_0[10]), "=f"(_tmem_load_0[11]), "=f"(_tmem_load_0[12]), "=f"(_tmem_load_0[13]), "=f"(_tmem_load_0[14]), "=f"(_tmem_load_0[15]), "=f"(_tmem_load_0[16]), "=f"(_tmem_load_0[17]), "=f"(_tmem_load_0[18]), "=f"(_tmem_load_0[19]), "=f"(_tmem_load_0[20]), "=f"(_tmem_load_0[21]), "=f"(_tmem_load_0[22]), "=f"(_tmem_load_0[23]), "=f"(_tmem_load_0[24]), "=f"(_tmem_load_0[25]), "=f"(_tmem_load_0[26]), "=f"(_tmem_load_0[27]), "=f"(_tmem_load_0[28]), "=f"(_tmem_load_0[29]), "=f"(_tmem_load_0[30]), "=f"(_tmem_load_0[31]), "=f"(_tmem_load_0[32]), "=f"(_tmem_load_0[33]), "=f"(_tmem_load_0[34]), "=f"(_tmem_load_0[35]), "=f"(_tmem_load_0[36]), "=f"(_tmem_load_0[37]), "=f"(_tmem_load_0[38]), "=f"(_tmem_load_0[39]), "=f"(_tmem_load_0[40]), "=f"(_tmem_load_0[41]), "=f"(_tmem_load_0[42]), "=f"(_tmem_load_0[43]), "=f"(_tmem_load_0[44]), "=f"(_tmem_load_0[45]), "=f"(_tmem_load_0[46]), "=f"(_tmem_load_0[47]), "=f"(_tmem_load_0[48]), "=f"(_tmem_load_0[49]), "=f"(_tmem_load_0[50]), "=f"(_tmem_load_0[51]), "=f"(_tmem_load_0[52]), "=f"(_tmem_load_0[53]), "=f"(_tmem_load_0[54]), "=f"(_tmem_load_0[55]), "=f"(_tmem_load_0[56]), "=f"(_tmem_load_0[57]), "=f"(_tmem_load_0[58]), "=f"(_tmem_load_0[59]), "=f"(_tmem_load_0[60]), "=f"(_tmem_load_0[61]), "=f"(_tmem_load_0[62]), "=f"(_tmem_load_0[63]) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + mbarrier_arrive(score_empty_addr); + if (valid_q != 0) { + #pragma unroll + for (int col_base = 0; col_base < 64; col_base += 4) { + float dist_vec[4]; + dist_vec[0] = _tmem_load_0[col_base]; + dist_vec[1] = _tmem_load_0[col_base + 1]; + dist_vec[2] = _tmem_load_0[col_base + 2]; + dist_vec[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec[4]; + db_sq_vec[0] = smem_database_sq[col_base]; + db_sq_vec[1] = smem_database_sq[col_base + 1]; + db_sq_vec[2] = smem_database_sq[col_base + 2]; + db_sq_vec[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec)[_la], reinterpret_cast(db_sq_vec)[_la]); + #pragma unroll + for (int vec_col = 0; vec_col < 4; vec_col++) { + int db_idx = db_start + col_base + vec_col; + if (db_idx < M) { + float dist = _t0[vec_col]; + if (dist < best_d[TOP_K_MAX - 1]) { + best_d[TOP_K_MAX - 1] = dist; + best_i[TOP_K_MAX - 1] = db_idx; + #pragma unroll + for (int pos = TOP_K_MAX - 1; pos >= 1; pos--) { + if (best_d[pos] < best_d[pos - 1]) { + float tmp_d = best_d[pos - 1]; + int tmp_i = best_i[pos - 1]; + best_d[pos - 1] = best_d[pos]; + best_i[pos - 1] = best_i[pos]; + best_d[pos] = tmp_d; + best_i[pos] = tmp_i; + } + } + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + if (valid_q != 0) { + int out_base = ((split_idx_1 * B + batch_idx_1) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + if (out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = best_d[out_k]; + *((int*)(partial_indices + (out_base + out_k))) = best_i[out_k]; + } + } + } + } + } + } + + // Cleanup + __syncthreads(); // barrier before TMEM dealloc + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::1.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(64)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::1.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0137.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0137.cu new file mode 100644 index 00000000..624d908d --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0137.cu @@ -0,0 +1,657 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 128 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_STRIDE 32768 +#define SMEM_SMEM_DATABASE_OFF 33792 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_DATABASE_SQ_OFF 50176 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 50432 +#define THREADS 192 +#define BLOCK_Q 128 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_MAX 10 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16_cta2( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + ".reg .b32 m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "tcgen05.mma.cta_group::2.kind::f16 [%0], %1, %2, %3, {m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step_cg2( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, %3, " + "{m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], %1;\n\t" + "}\n" + :: "r"(mbar_addr), "h"(cta_mask) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x16(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x16.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15}, [%16];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ uint32_t smem_addr(const void* ptr) { + uint32_t addr; + asm("{\n\t" + ".reg .u64 u64addr;\n\t" + "cvta.to.shared.u64 u64addr, %1;\n\t" + "cvt.u32.u64 %0, u64addr;\n\t" + "}\n" : "=r"(addr) : "l"(ptr)); + return addr; +} + + +__device__ __forceinline__ uint32_t mapa_to_rank(uint32_t local_addr, uint32_t rank) { + uint32_t remote; + asm volatile("mapa.shared::cluster.u32 %0, %1, %2;" + : "=r"(remote) : "r"(local_addr), "r"(rank)); + return remote; +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem_cta2( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global" + ".mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .b16 lo, hi;\n\t" + "mov.b32 {lo, hi}, %1;\n\t" + "tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], lo;\n\t" + "}\n" + :: "r"(mbar_addr), "r"((uint32_t)cta_mask) : "memory"); +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tile_pairs, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + const unsigned int clusters_x = gridDim.x / 2; + const unsigned int cluster_id = ((blockIdx.z * gridDim.y + blockIdx.y) * clusters_x) + blockIdx.x / 2; + const unsigned int num_clusters = clusters_x * gridDim.y * gridDim.z; + + int cta_rank; + asm volatile("mov.b32 %0, %%cluster_ctarank;" : "=r"(cta_rank)); + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 33792); + const int smem_database_addr = smem + 33792; + float* smem_database_sq = reinterpret_cast(smem_raw + 50176); + const int smem_database_sq_addr = smem + 50176; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 0, 2, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 16, 2, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=8 + mbarrier_init_pred(smem + 40, 8, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (128 columns, 128 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::2.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(128) : "memory"); + } + + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: load ---- + if (warp == 0) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 0) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx = cluster_id; work_idx < total_work; work_idx += num_clusters) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tile_pairs; + int q_tile_pair = query_work % num_q_tile_pairs; + int q_tile = q_tile_pair * 2 + cta_rank; + int off_q = q_tile * BLOCK_Q; + int global_q = batch_idx * Q + off_q; + int db_tile_start = split_idx * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((query_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(32768)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_query_addr), "l"(tmap_query), "r"(0), "r"(global_q), "r"(0), + "r"(((query_full_addr) & 0xFEFFFFFF)) : "memory"); + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int off_m = db_tile * BLOCK_M; + int global_m = batch_idx * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((database_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(16384)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_database_addr), "l"(tmap_database), "r"(0), "r"(global_m), "r"(0), + "r"(((database_full_addr) & 0xFEFFFFFF)) : "memory"); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 1) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + if (cta_rank == 0) { + #pragma unroll 1 + for (unsigned int work_idx_1 = cluster_id; work_idx_1 < total_work; work_idx_1 += num_clusters) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = (smem_query_addr >> 4) & 0x3FFF; + int _mma_b_lo_0 = (smem_database_addr >> 4) & 0x3FFF; + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\tmov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 270533776;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit_cg2_multicast(score_full_addr, (uint16_t)(3)); + elect_commit_cg2_multicast(database_empty_addr, (uint16_t)(3)); + } + elect_commit_cg2_multicast(query_empty_addr, (uint16_t)(3)); + } + } + } + // ---- Role: compute ---- + } else if (warp >= 2 && warp <= 5) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = cluster_id; work_idx_2 < total_work; work_idx_2 += num_clusters) { + int split_idx_1 = work_idx_2 % (unsigned int)split_count; + int query_work_1 = work_idx_2 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tile_pairs; + int q_tile_pair_1 = query_work_1 % num_q_tile_pairs; + int q_tile_1 = q_tile_pair_1 * 2 + cta_rank; + int off_q_1 = q_tile_1 * BLOCK_Q; + int q_idx = off_q_1 + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx_1 * Q + q_idx]; + } + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int db_start = db_tile_1 * BLOCK_M; + int db_sq_idx = db_start + (warp % 4 * 32 + lane); + if (warp % 4 * 32 + lane < BLOCK_M) { + if (db_sq_idx < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx_1 * M + db_sq_idx]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 0.0f; + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(cta_rank * BLOCK_Q + (warp % 4 * 32 << 16) << 16); + float _tmem_load_0[64]; + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x64.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31, %32, %33, %34, %35, %36, %37, %38, %39, %40, %41, %42, %43, %44, %45, %46, %47, %48, %49, %50, %51, %52, %53, %54, %55, %56, %57, %58, %59, %60, %61, %62, %63}, [%64];" + : "=f"(_tmem_load_0[0]), "=f"(_tmem_load_0[1]), "=f"(_tmem_load_0[2]), "=f"(_tmem_load_0[3]), "=f"(_tmem_load_0[4]), "=f"(_tmem_load_0[5]), "=f"(_tmem_load_0[6]), "=f"(_tmem_load_0[7]), "=f"(_tmem_load_0[8]), "=f"(_tmem_load_0[9]), "=f"(_tmem_load_0[10]), "=f"(_tmem_load_0[11]), "=f"(_tmem_load_0[12]), "=f"(_tmem_load_0[13]), "=f"(_tmem_load_0[14]), "=f"(_tmem_load_0[15]), "=f"(_tmem_load_0[16]), "=f"(_tmem_load_0[17]), "=f"(_tmem_load_0[18]), "=f"(_tmem_load_0[19]), "=f"(_tmem_load_0[20]), "=f"(_tmem_load_0[21]), "=f"(_tmem_load_0[22]), "=f"(_tmem_load_0[23]), "=f"(_tmem_load_0[24]), "=f"(_tmem_load_0[25]), "=f"(_tmem_load_0[26]), "=f"(_tmem_load_0[27]), "=f"(_tmem_load_0[28]), "=f"(_tmem_load_0[29]), "=f"(_tmem_load_0[30]), "=f"(_tmem_load_0[31]), "=f"(_tmem_load_0[32]), "=f"(_tmem_load_0[33]), "=f"(_tmem_load_0[34]), "=f"(_tmem_load_0[35]), "=f"(_tmem_load_0[36]), "=f"(_tmem_load_0[37]), "=f"(_tmem_load_0[38]), "=f"(_tmem_load_0[39]), "=f"(_tmem_load_0[40]), "=f"(_tmem_load_0[41]), "=f"(_tmem_load_0[42]), "=f"(_tmem_load_0[43]), "=f"(_tmem_load_0[44]), "=f"(_tmem_load_0[45]), "=f"(_tmem_load_0[46]), "=f"(_tmem_load_0[47]), "=f"(_tmem_load_0[48]), "=f"(_tmem_load_0[49]), "=f"(_tmem_load_0[50]), "=f"(_tmem_load_0[51]), "=f"(_tmem_load_0[52]), "=f"(_tmem_load_0[53]), "=f"(_tmem_load_0[54]), "=f"(_tmem_load_0[55]), "=f"(_tmem_load_0[56]), "=f"(_tmem_load_0[57]), "=f"(_tmem_load_0[58]), "=f"(_tmem_load_0[59]), "=f"(_tmem_load_0[60]), "=f"(_tmem_load_0[61]), "=f"(_tmem_load_0[62]), "=f"(_tmem_load_0[63]) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + if (elect_sync()) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cluster.b64 _, [%0];" + :: "r"((score_empty_addr) & 0xFEFFFFFF) : "memory"); + } + if (valid_q != 0) { + #pragma unroll 2 + for (int col_base = 0; col_base < 64; col_base += 4) { + float dist_vec[4]; + dist_vec[0] = _tmem_load_0[col_base]; + dist_vec[1] = _tmem_load_0[col_base + 1]; + dist_vec[2] = _tmem_load_0[col_base + 2]; + dist_vec[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec[4]; + db_sq_vec[0] = smem_database_sq[col_base]; + db_sq_vec[1] = smem_database_sq[col_base + 1]; + db_sq_vec[2] = smem_database_sq[col_base + 2]; + db_sq_vec[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec)[_la], reinterpret_cast(db_sq_vec)[_la]); + float group_min = _t0[0]; + if (_t0[1] < group_min) { + group_min = _t0[1]; + } + if (_t0[2] < group_min) { + group_min = _t0[2]; + } + if (_t0[3] < group_min) { + group_min = _t0[3]; + } + if (group_min < best_d[TOP_K_MAX - 1]) { + #pragma unroll + for (int vec_col = 0; vec_col < 4; vec_col++) { + int db_idx = db_start + col_base + vec_col; + if (db_idx < M) { + float dist = _t0[vec_col]; + if (dist < best_d[TOP_K_MAX - 1]) { + best_d[TOP_K_MAX - 1] = dist; + best_i[TOP_K_MAX - 1] = db_idx; + #pragma unroll + for (int pos = TOP_K_MAX - 1; pos >= 1; pos--) { + if (best_d[pos] < best_d[pos - 1]) { + float tmp_d = best_d[pos - 1]; + int tmp_i = best_i[pos - 1]; + best_d[pos - 1] = best_d[pos]; + best_i[pos - 1] = best_i[pos]; + best_d[pos] = tmp_d; + best_i[pos] = tmp_i; + } + } + } + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + if (valid_q != 0) { + int out_base = ((split_idx_1 * B + batch_idx_1) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + if (out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = best_d[out_k]; + *((int*)(partial_indices + (out_base + out_k))) = best_i[out_k]; + } + } + } + } + } + } + + // Cleanup + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::2.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(128)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::2.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0138.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0138.cu new file mode 100644 index 00000000..ed57bb5c --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0138.cu @@ -0,0 +1,76 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 256 +#define TOP_K_MAX 10 +#define SPLIT_COUNT 4 + +#include + +extern "C" { + +__global__ __launch_bounds__(256, 1) void +kernel_knn_build_evolve_7bfc_k10_merge_s4(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int B, int Q, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 256 + tid; + int stride = num_bids * 256; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int batch_idx = row / Q; + int q_idx = row - batch_idx * Q; + int split_pos[SPLIT_COUNT]; + #pragma unroll + for (int split_idx = 0; split_idx < SPLIT_COUNT; split_idx++) { + split_pos[split_idx] = 0; + } + int out_base = row * TOP_K_MAX; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_d = 3.4e+38f; + int best_i = -1; + int best_split = 0; + #pragma unroll + for (int split_idx_1 = 0; split_idx_1 < SPLIT_COUNT; split_idx_1++) { + int partial_base = ((split_idx_1 * B + batch_idx) * Q + q_idx) * TOP_K_MAX; + int cand_pos = split_pos[split_idx_1]; + float cand_d = partial_dists[partial_base + cand_pos]; + int cand_i = partial_indices[partial_base + cand_pos]; + if (cand_d < best_d) { + best_d = cand_d; + best_i = cand_i; + best_split = split_idx_1; + } + } + *((float*)(out_dists + (out_base + out_k))) = best_d; + *((int*)(out_indices + (out_base + out_k))) = best_i; + split_pos[best_split] = split_pos[best_split] + 1; + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0139.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0139.cu new file mode 100644 index 00000000..d9fdc922 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0139.cu @@ -0,0 +1,76 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 256 +#define TOP_K_MAX 10 +#define SPLIT_COUNT 7 + +#include + +extern "C" { + +__global__ __launch_bounds__(256, 1) void +kernel_knn_build_evolve_7bfc_k10_merge_s7(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int B, int Q, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 256 + tid; + int stride = num_bids * 256; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int batch_idx = row / Q; + int q_idx = row - batch_idx * Q; + int split_pos[SPLIT_COUNT]; + #pragma unroll + for (int split_idx = 0; split_idx < SPLIT_COUNT; split_idx++) { + split_pos[split_idx] = 0; + } + int out_base = row * TOP_K_MAX; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_d = 3.4e+38f; + int best_i = -1; + int best_split = 0; + #pragma unroll + for (int split_idx_1 = 0; split_idx_1 < SPLIT_COUNT; split_idx_1++) { + int partial_base = ((split_idx_1 * B + batch_idx) * Q + q_idx) * TOP_K_MAX; + int cand_pos = split_pos[split_idx_1]; + float cand_d = partial_dists[partial_base + cand_pos]; + int cand_i = partial_indices[partial_base + cand_pos]; + if (cand_d < best_d) { + best_d = cand_d; + best_i = cand_i; + best_split = split_idx_1; + } + } + *((float*)(out_dists + (out_base + out_k))) = best_d; + *((int*)(out_indices + (out_base + out_k))) = best_i; + split_pos[best_split] = split_pos[best_split] + 1; + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0140.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0140.cu new file mode 100644 index 00000000..9f5d8394 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0140.cu @@ -0,0 +1,724 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 128 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_STRIDE 32768 +#define SMEM_SMEM_DATABASE_OFF 33792 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_DATABASE_SQ_OFF 50176 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 50432 +#define THREADS 192 +#define BLOCK_Q 128 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_SMALL 5 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16_cta2( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + ".reg .b32 m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "tcgen05.mma.cta_group::2.kind::f16 [%0], %1, %2, %3, {m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step_cg2( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, %3, " + "{m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], %1;\n\t" + "}\n" + :: "r"(mbar_addr), "h"(cta_mask) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x16(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x16.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15}, [%16];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ uint32_t smem_addr(const void* ptr) { + uint32_t addr; + asm("{\n\t" + ".reg .u64 u64addr;\n\t" + "cvta.to.shared.u64 u64addr, %1;\n\t" + "cvt.u32.u64 %0, u64addr;\n\t" + "}\n" : "=r"(addr) : "l"(ptr)); + return addr; +} + + +__device__ __forceinline__ uint32_t mapa_to_rank(uint32_t local_addr, uint32_t rank) { + uint32_t remote; + asm volatile("mapa.shared::cluster.u32 %0, %1, %2;" + : "=r"(remote) : "r"(local_addr), "r"(rank)); + return remote; +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem_cta2( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global" + ".mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .b16 lo, hi;\n\t" + "mov.b32 {lo, hi}, %1;\n\t" + "tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], lo;\n\t" + "}\n" + :: "r"(mbar_addr), "r"((uint32_t)cta_mask) : "memory"); +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_evolve_7bfc_split_cg2_stage1_batch8_cond4_vmin_threshold_k5_mintree(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tile_pairs, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + const unsigned int clusters_x = gridDim.x / 2; + const unsigned int cluster_id = ((blockIdx.z * gridDim.y + blockIdx.y) * clusters_x) + blockIdx.x / 2; + const unsigned int num_clusters = clusters_x * gridDim.y * gridDim.z; + + int cta_rank; + asm volatile("mov.b32 %0, %%cluster_ctarank;" : "=r"(cta_rank)); + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 33792); + const int smem_database_addr = smem + 33792; + float* smem_database_sq = reinterpret_cast(smem_raw + 50176); + const int smem_database_sq_addr = smem + 50176; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 0, 2, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 16, 2, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=8 + mbarrier_init_pred(smem + 40, 8, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (128 columns, 128 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::2.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(128) : "memory"); + } + + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: load ---- + if (warp == 0) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 0) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx = cluster_id; work_idx < total_work; work_idx += num_clusters) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tile_pairs; + int q_tile_pair = query_work % num_q_tile_pairs; + int q_tile = q_tile_pair * 2 + cta_rank; + int off_q = q_tile * BLOCK_Q; + int global_q = batch_idx * Q + off_q; + int db_tile_start = split_idx * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((query_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(32768)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_query_addr), "l"(tmap_query), "r"(0), "r"(global_q), "r"(0), + "r"(((query_full_addr) & 0xFEFFFFFF)) : "memory"); + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int off_m = db_tile * BLOCK_M; + int global_m = batch_idx * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((database_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(16384)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_database_addr), "l"(tmap_database), "r"(0), "r"(global_m), "r"(0), + "r"(((database_full_addr) & 0xFEFFFFFF)) : "memory"); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 1) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + if (cta_rank == 0) { + #pragma unroll 1 + for (unsigned int work_idx_1 = cluster_id; work_idx_1 < total_work; work_idx_1 += num_clusters) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = (smem_query_addr >> 4) & 0x3FFF; + int _mma_b_lo_0 = (smem_database_addr >> 4) & 0x3FFF; + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\tmov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 270533776;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit_cg2_multicast(score_full_addr, (uint16_t)(3)); + elect_commit_cg2_multicast(database_empty_addr, (uint16_t)(3)); + } + elect_commit_cg2_multicast(query_empty_addr, (uint16_t)(3)); + } + } + } + // ---- Role: compute ---- + } else if (warp >= 2 && warp <= 5) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = cluster_id; work_idx_2 < total_work; work_idx_2 += num_clusters) { + int split_idx_1 = work_idx_2 % (unsigned int)split_count; + int query_work_1 = work_idx_2 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tile_pairs; + int q_tile_pair_1 = query_work_1 % num_q_tile_pairs; + int q_tile_1 = q_tile_pair_1 * 2 + cta_rank; + int off_q_1 = q_tile_1 * BLOCK_Q; + int q_idx = off_q_1 + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx_1 * Q + q_idx]; + } + float best_d[TOP_K_SMALL]; + int best_i[TOP_K_SMALL]; + float worst_d = 3.4e+38f; + int worst_pos = 0; + #pragma unroll + for (int kk = 0; kk < TOP_K_SMALL; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int db_start = db_tile_1 * BLOCK_M; + int db_sq_idx = db_start + (warp % 4 * 32 + lane); + if (warp % 4 * 32 + lane < BLOCK_M) { + if (db_sq_idx < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx_1 * M + db_sq_idx]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 0.0f; + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(cta_rank * BLOCK_Q + (warp % 4 * 32 << 16) << 16); + float _tmem_load_0[64]; + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x64.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31, %32, %33, %34, %35, %36, %37, %38, %39, %40, %41, %42, %43, %44, %45, %46, %47, %48, %49, %50, %51, %52, %53, %54, %55, %56, %57, %58, %59, %60, %61, %62, %63}, [%64];" + : "=f"(_tmem_load_0[0]), "=f"(_tmem_load_0[1]), "=f"(_tmem_load_0[2]), "=f"(_tmem_load_0[3]), "=f"(_tmem_load_0[4]), "=f"(_tmem_load_0[5]), "=f"(_tmem_load_0[6]), "=f"(_tmem_load_0[7]), "=f"(_tmem_load_0[8]), "=f"(_tmem_load_0[9]), "=f"(_tmem_load_0[10]), "=f"(_tmem_load_0[11]), "=f"(_tmem_load_0[12]), "=f"(_tmem_load_0[13]), "=f"(_tmem_load_0[14]), "=f"(_tmem_load_0[15]), "=f"(_tmem_load_0[16]), "=f"(_tmem_load_0[17]), "=f"(_tmem_load_0[18]), "=f"(_tmem_load_0[19]), "=f"(_tmem_load_0[20]), "=f"(_tmem_load_0[21]), "=f"(_tmem_load_0[22]), "=f"(_tmem_load_0[23]), "=f"(_tmem_load_0[24]), "=f"(_tmem_load_0[25]), "=f"(_tmem_load_0[26]), "=f"(_tmem_load_0[27]), "=f"(_tmem_load_0[28]), "=f"(_tmem_load_0[29]), "=f"(_tmem_load_0[30]), "=f"(_tmem_load_0[31]), "=f"(_tmem_load_0[32]), "=f"(_tmem_load_0[33]), "=f"(_tmem_load_0[34]), "=f"(_tmem_load_0[35]), "=f"(_tmem_load_0[36]), "=f"(_tmem_load_0[37]), "=f"(_tmem_load_0[38]), "=f"(_tmem_load_0[39]), "=f"(_tmem_load_0[40]), "=f"(_tmem_load_0[41]), "=f"(_tmem_load_0[42]), "=f"(_tmem_load_0[43]), "=f"(_tmem_load_0[44]), "=f"(_tmem_load_0[45]), "=f"(_tmem_load_0[46]), "=f"(_tmem_load_0[47]), "=f"(_tmem_load_0[48]), "=f"(_tmem_load_0[49]), "=f"(_tmem_load_0[50]), "=f"(_tmem_load_0[51]), "=f"(_tmem_load_0[52]), "=f"(_tmem_load_0[53]), "=f"(_tmem_load_0[54]), "=f"(_tmem_load_0[55]), "=f"(_tmem_load_0[56]), "=f"(_tmem_load_0[57]), "=f"(_tmem_load_0[58]), "=f"(_tmem_load_0[59]), "=f"(_tmem_load_0[60]), "=f"(_tmem_load_0[61]), "=f"(_tmem_load_0[62]), "=f"(_tmem_load_0[63]) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + if (elect_sync()) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cluster.b64 _, [%0];" + :: "r"((score_empty_addr) & 0xFEFFFFFF) : "memory"); + } + if (valid_q != 0) { + #pragma unroll 1 + for (int col_base = 0; col_base < 64; col_base += 8) { + float dist_vec0[4]; + dist_vec0[0] = _tmem_load_0[col_base]; + dist_vec0[1] = _tmem_load_0[col_base + 1]; + dist_vec0[2] = _tmem_load_0[col_base + 2]; + dist_vec0[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec0)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec0[4]; + db_sq_vec0[0] = smem_database_sq[col_base]; + db_sq_vec0[1] = smem_database_sq[col_base + 1]; + db_sq_vec0[2] = smem_database_sq[col_base + 2]; + db_sq_vec0[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec0)[_la], reinterpret_cast(db_sq_vec0)[_la]); + float dist_vec1[4]; + dist_vec1[0] = _tmem_load_0[col_base + 4]; + dist_vec1[1] = _tmem_load_0[col_base + 5]; + dist_vec1[2] = _tmem_load_0[col_base + 6]; + dist_vec1[3] = _tmem_load_0[col_base + 7]; + const float2 _fma_b2_2 = {-2.0f, -2.0f}; + const float2 _fma_c2_3 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec1)[_lf], _fma_b2_2, _fma_c2_3); + float db_sq_vec1[4]; + db_sq_vec1[0] = smem_database_sq[col_base + 4]; + db_sq_vec1[1] = smem_database_sq[col_base + 5]; + db_sq_vec1[2] = smem_database_sq[col_base + 6]; + db_sq_vec1[3] = smem_database_sq[col_base + 7]; + float _t1[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t1)[_la] = add_f32x2(reinterpret_cast(dist_vec1)[_la], reinterpret_cast(db_sq_vec1)[_la]); + float _t0_min = _t0[0]; + #pragma unroll + for (int _lr = 1; _lr < 4; _lr++) { + _t0_min = fminf(_t0_min, _t0[_lr]); + } + float group_min0 = _t0_min; + float _t1_min = _t1[0]; + #pragma unroll + for (int _lr = 1; _lr < 4; _lr++) { + _t1_min = fminf(_t1_min, _t1[_lr]); + } + float group_min1 = _t1_min; + if (group_min0 < worst_d) { + #pragma unroll + for (int vec_col = 0; vec_col < 4; vec_col++) { + int db_idx = db_start + col_base + vec_col; + if (db_idx < M) { + float dist = _t0[vec_col]; + if (dist < worst_d) { + best_d[worst_pos] = dist; + best_i[worst_pos] = db_idx; + int cmp01 = ((best_d[1] > best_d[0]) ? 1 : 0); + float max01_d = ((cmp01 != 0) ? best_d[1] : best_d[0]); + int max01_p = ((cmp01 != 0) ? 1 : 0); + int cmp23 = ((best_d[3] > best_d[2]) ? 1 : 0); + float max23_d = ((cmp23 != 0) ? best_d[3] : best_d[2]); + int max23_p = ((cmp23 != 0) ? 3 : 2); + int cmp0123 = ((max23_d > max01_d) ? 1 : 0); + float max0123_d = ((cmp0123 != 0) ? max23_d : max01_d); + int max0123_p = ((cmp0123 != 0) ? max23_p : max01_p); + int cmp_all = ((best_d[4] > max0123_d) ? 1 : 0); + worst_d = ((cmp_all != 0) ? best_d[4] : max0123_d); + worst_pos = ((cmp_all != 0) ? 4 : max0123_p); + } + } + } + } + if (group_min1 < worst_d) { + #pragma unroll + for (int vec_col_1 = 0; vec_col_1 < 4; vec_col_1++) { + int db_idx_1 = db_start + col_base + 4 + vec_col_1; + if (db_idx_1 < M) { + float dist_1 = _t1[vec_col_1]; + if (dist_1 < worst_d) { + best_d[worst_pos] = dist_1; + best_i[worst_pos] = db_idx_1; + int cmp01_1 = ((best_d[1] > best_d[0]) ? 1 : 0); + float max01_d_1 = ((cmp01_1 != 0) ? best_d[1] : best_d[0]); + int max01_p_1 = ((cmp01_1 != 0) ? 1 : 0); + int cmp23_1 = ((best_d[3] > best_d[2]) ? 1 : 0); + float max23_d_1 = ((cmp23_1 != 0) ? best_d[3] : best_d[2]); + int max23_p_1 = ((cmp23_1 != 0) ? 3 : 2); + int cmp0123_1 = ((max23_d_1 > max01_d_1) ? 1 : 0); + float max0123_d_1 = ((cmp0123_1 != 0) ? max23_d_1 : max01_d_1); + int max0123_p_1 = ((cmp0123_1 != 0) ? max23_p_1 : max01_p_1); + int cmp_all_1 = ((best_d[4] > max0123_d_1) ? 1 : 0); + worst_d = ((cmp_all_1 != 0) ? best_d[4] : max0123_d_1); + worst_pos = ((cmp_all_1 != 0) ? 4 : max0123_p_1); + } + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + if (valid_q != 0) { + int out_base = ((split_idx_1 * B + batch_idx_1) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_SMALL; out_k++) { + int cmp01_2 = ((best_d[1] < best_d[0]) ? 1 : 0); + float min01_d = ((cmp01_2 != 0) ? best_d[1] : best_d[0]); + int min01_i = ((cmp01_2 != 0) ? best_i[1] : best_i[0]); + int min01_p = ((cmp01_2 != 0) ? 1 : 0); + int cmp23_2 = ((best_d[3] < best_d[2]) ? 1 : 0); + float min23_d = ((cmp23_2 != 0) ? best_d[3] : best_d[2]); + int min23_i = ((cmp23_2 != 0) ? best_i[3] : best_i[2]); + int min23_p = ((cmp23_2 != 0) ? 3 : 2); + int cmp0123_2 = ((min23_d < min01_d) ? 1 : 0); + float min0123_d = ((cmp0123_2 != 0) ? min23_d : min01_d); + int min0123_i = ((cmp0123_2 != 0) ? min23_i : min01_i); + int min0123_p = ((cmp0123_2 != 0) ? min23_p : min01_p); + int cmp_all_2 = ((best_d[4] < min0123_d) ? 1 : 0); + float selected_d = ((cmp_all_2 != 0) ? best_d[4] : min0123_d); + int selected_i = ((cmp_all_2 != 0) ? best_i[4] : min0123_i); + int selected_pos = ((cmp_all_2 != 0) ? 4 : min0123_p); + if (out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = selected_d; + *((int*)(partial_indices + (out_base + out_k))) = selected_i; + } + best_d[selected_pos] = 3.4e+38f; + best_i[selected_pos] = -1; + } + } + } + } + } + + // Cleanup + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::2.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(128)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::2.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0141.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0141.cu new file mode 100644 index 00000000..66308b7e --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0141.cu @@ -0,0 +1,93 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 256 +#define TOP_K_SMALL 5 + +#include + +extern "C" { + +__global__ __launch_bounds__(256, 1) void +kernel_knn_build_evolve_7bfc_k5_merge_s4_tree_rowbase(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 256 + tid; + int stride = num_bids * 256; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int base_row = row * TOP_K_SMALL; + int split_stride = total_queries * TOP_K_SMALL; + int partial_base0 = base_row; + int partial_base1 = base_row + split_stride; + int partial_base2 = partial_base1 + split_stride; + int partial_base3 = partial_base2 + split_stride; + int pos0 = 0; + int pos1 = 0; + int pos2 = 0; + int pos3 = 0; + int out_base = row * TOP_K_SMALL; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_SMALL; out_k++) { + float cand_d0 = partial_dists[partial_base0 + pos0]; + int cand_i0 = partial_indices[partial_base0 + pos0]; + float cand_d1 = partial_dists[partial_base1 + pos1]; + int cand_i1 = partial_indices[partial_base1 + pos1]; + float cand_d2 = partial_dists[partial_base2 + pos2]; + int cand_i2 = partial_indices[partial_base2 + pos2]; + float cand_d3 = partial_dists[partial_base3 + pos3]; + int cand_i3 = partial_indices[partial_base3 + pos3]; + int cmp01 = ((cand_d1 < cand_d0) ? 1 : 0); + float best01_d = ((cmp01 != 0) ? cand_d1 : cand_d0); + int best01_i = ((cmp01 != 0) ? cand_i1 : cand_i0); + int best01_split = ((cmp01 != 0) ? 1 : 0); + int cmp23 = ((cand_d3 < cand_d2) ? 1 : 0); + float best23_d = ((cmp23 != 0) ? cand_d3 : cand_d2); + int best23_i = ((cmp23 != 0) ? cand_i3 : cand_i2); + int best23_split = ((cmp23 != 0) ? 3 : 2); + int cmp_all = ((best23_d < best01_d) ? 1 : 0); + float best_d = ((cmp_all != 0) ? best23_d : best01_d); + int best_i = ((cmp_all != 0) ? best23_i : best01_i); + int best_split = ((cmp_all != 0) ? best23_split : best01_split); + *((float*)(out_dists + (out_base + out_k))) = best_d; + *((int*)(out_indices + (out_base + out_k))) = best_i; + if (best_split == 0) { + pos0 = pos0 + 1; + } else if (best_split == 1) { + pos1 = pos1 + 1; + } else { + if (best_split == 2) { + pos2 = pos2 + 1; + } else { + pos3 = pos3 + 1; + } + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0142.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0142.cu new file mode 100644 index 00000000..c36bb734 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0142.cu @@ -0,0 +1,84 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 64 +#define TOP_K_MAX 10 +#define SPLIT_COUNT 7 + +#include + +extern "C" { + +__global__ __launch_bounds__(64, 1) void +kernel_knn_build_evolve_7bfc_k10_merge_s7_rowbase_cache(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 64 + tid; + int stride = num_bids * 64; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int out_base = base_row; + int split_pos[SPLIT_COUNT]; + int split_base[SPLIT_COUNT]; + float cand_d[SPLIT_COUNT]; + int cand_i[SPLIT_COUNT]; + #pragma unroll + for (int split_idx = 0; split_idx < SPLIT_COUNT; split_idx++) { + split_pos[split_idx] = 0; + split_base[split_idx] = base_row + split_idx * split_stride; + cand_d[split_idx] = partial_dists[split_base[split_idx]]; + cand_i[split_idx] = partial_indices[split_base[split_idx]]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_d = cand_d[0]; + int best_i = cand_i[0]; + int best_split = 0; + #pragma unroll + for (int split_idx_1 = 1; split_idx_1 < SPLIT_COUNT; split_idx_1++) { + if (best_d > cand_d[split_idx_1]) { + best_d = cand_d[split_idx_1]; + best_i = cand_i[split_idx_1]; + best_split = split_idx_1; + } + } + *((float*)(out_dists + (out_base + out_k))) = best_d; + *((int*)(out_indices + (out_base + out_k))) = best_i; + split_pos[best_split] = split_pos[best_split] + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_pos = split_pos[best_split]; + int next_addr = split_base[best_split] + next_pos; + cand_d[best_split] = partial_dists[next_addr]; + cand_i[best_split] = partial_indices[next_addr]; + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0143.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0143.cu new file mode 100644 index 00000000..f535d18f --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0143.cu @@ -0,0 +1,749 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 128 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_STRIDE 32768 +#define SMEM_SMEM_DATABASE_OFF 33792 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_DATABASE_SQ_OFF 50176 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 50432 +#define THREADS 192 +#define BLOCK_Q 128 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_MAX 10 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16_cta2( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + ".reg .b32 m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "tcgen05.mma.cta_group::2.kind::f16 [%0], %1, %2, %3, {m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step_cg2( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, %3, " + "{m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], %1;\n\t" + "}\n" + :: "r"(mbar_addr), "h"(cta_mask) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x16(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x16.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15}, [%16];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ uint32_t smem_addr(const void* ptr) { + uint32_t addr; + asm("{\n\t" + ".reg .u64 u64addr;\n\t" + "cvta.to.shared.u64 u64addr, %1;\n\t" + "cvt.u32.u64 %0, u64addr;\n\t" + "}\n" : "=r"(addr) : "l"(ptr)); + return addr; +} + + +__device__ __forceinline__ uint32_t mapa_to_rank(uint32_t local_addr, uint32_t rank) { + uint32_t remote; + asm volatile("mapa.shared::cluster.u32 %0, %1, %2;" + : "=r"(remote) : "r"(local_addr), "r"(rank)); + return remote; +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem_cta2( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global" + ".mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .b16 lo, hi;\n\t" + "mov.b32 {lo, hi}, %1;\n\t" + "tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], lo;\n\t" + "}\n" + :: "r"(mbar_addr), "r"((uint32_t)cta_mask) : "memory"); +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_evolve_7bfc_split_cg2_stage1_batch8_cond4_vmin_maxtree(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tile_pairs, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + const unsigned int clusters_x = gridDim.x / 2; + const unsigned int cluster_id = ((blockIdx.z * gridDim.y + blockIdx.y) * clusters_x) + blockIdx.x / 2; + const unsigned int num_clusters = clusters_x * gridDim.y * gridDim.z; + + int cta_rank; + asm volatile("mov.b32 %0, %%cluster_ctarank;" : "=r"(cta_rank)); + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 33792); + const int smem_database_addr = smem + 33792; + float* smem_database_sq = reinterpret_cast(smem_raw + 50176); + const int smem_database_sq_addr = smem + 50176; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 0, 2, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 16, 2, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=8 + mbarrier_init_pred(smem + 40, 8, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (128 columns, 128 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::2.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(128) : "memory"); + } + + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: load ---- + if (warp == 0) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 0) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx = cluster_id; work_idx < total_work; work_idx += num_clusters) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tile_pairs; + int q_tile_pair = query_work % num_q_tile_pairs; + int q_tile = q_tile_pair * 2 + cta_rank; + int off_q = q_tile * BLOCK_Q; + int global_q = batch_idx * Q + off_q; + int db_tile_start = split_idx * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((query_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(32768)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_query_addr), "l"(tmap_query), "r"(0), "r"(global_q), "r"(0), + "r"(((query_full_addr) & 0xFEFFFFFF)) : "memory"); + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int off_m = db_tile * BLOCK_M; + int global_m = batch_idx * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((database_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(16384)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_database_addr), "l"(tmap_database), "r"(0), "r"(global_m), "r"(0), + "r"(((database_full_addr) & 0xFEFFFFFF)) : "memory"); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 1) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + if (cta_rank == 0) { + #pragma unroll 1 + for (unsigned int work_idx_1 = cluster_id; work_idx_1 < total_work; work_idx_1 += num_clusters) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = (smem_query_addr >> 4) & 0x3FFF; + int _mma_b_lo_0 = (smem_database_addr >> 4) & 0x3FFF; + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\tmov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 270533776;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit_cg2_multicast(score_full_addr, (uint16_t)(3)); + elect_commit_cg2_multicast(database_empty_addr, (uint16_t)(3)); + } + elect_commit_cg2_multicast(query_empty_addr, (uint16_t)(3)); + } + } + } + // ---- Role: compute ---- + } else if (warp >= 2 && warp <= 5) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = cluster_id; work_idx_2 < total_work; work_idx_2 += num_clusters) { + int split_idx_1 = work_idx_2 % (unsigned int)split_count; + int query_work_1 = work_idx_2 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tile_pairs; + int q_tile_pair_1 = query_work_1 % num_q_tile_pairs; + int q_tile_1 = q_tile_pair_1 * 2 + cta_rank; + int off_q_1 = q_tile_1 * BLOCK_Q; + int q_idx = off_q_1 + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx_1 * Q + q_idx]; + } + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + float worst_d = 3.4e+38f; + int worst_pos = 0; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int db_start = db_tile_1 * BLOCK_M; + int db_sq_idx = db_start + (warp % 4 * 32 + lane); + if (warp % 4 * 32 + lane < BLOCK_M) { + if (db_sq_idx < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx_1 * M + db_sq_idx]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 0.0f; + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(cta_rank * BLOCK_Q + (warp % 4 * 32 << 16) << 16); + float _tmem_load_0[64]; + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x64.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31, %32, %33, %34, %35, %36, %37, %38, %39, %40, %41, %42, %43, %44, %45, %46, %47, %48, %49, %50, %51, %52, %53, %54, %55, %56, %57, %58, %59, %60, %61, %62, %63}, [%64];" + : "=f"(_tmem_load_0[0]), "=f"(_tmem_load_0[1]), "=f"(_tmem_load_0[2]), "=f"(_tmem_load_0[3]), "=f"(_tmem_load_0[4]), "=f"(_tmem_load_0[5]), "=f"(_tmem_load_0[6]), "=f"(_tmem_load_0[7]), "=f"(_tmem_load_0[8]), "=f"(_tmem_load_0[9]), "=f"(_tmem_load_0[10]), "=f"(_tmem_load_0[11]), "=f"(_tmem_load_0[12]), "=f"(_tmem_load_0[13]), "=f"(_tmem_load_0[14]), "=f"(_tmem_load_0[15]), "=f"(_tmem_load_0[16]), "=f"(_tmem_load_0[17]), "=f"(_tmem_load_0[18]), "=f"(_tmem_load_0[19]), "=f"(_tmem_load_0[20]), "=f"(_tmem_load_0[21]), "=f"(_tmem_load_0[22]), "=f"(_tmem_load_0[23]), "=f"(_tmem_load_0[24]), "=f"(_tmem_load_0[25]), "=f"(_tmem_load_0[26]), "=f"(_tmem_load_0[27]), "=f"(_tmem_load_0[28]), "=f"(_tmem_load_0[29]), "=f"(_tmem_load_0[30]), "=f"(_tmem_load_0[31]), "=f"(_tmem_load_0[32]), "=f"(_tmem_load_0[33]), "=f"(_tmem_load_0[34]), "=f"(_tmem_load_0[35]), "=f"(_tmem_load_0[36]), "=f"(_tmem_load_0[37]), "=f"(_tmem_load_0[38]), "=f"(_tmem_load_0[39]), "=f"(_tmem_load_0[40]), "=f"(_tmem_load_0[41]), "=f"(_tmem_load_0[42]), "=f"(_tmem_load_0[43]), "=f"(_tmem_load_0[44]), "=f"(_tmem_load_0[45]), "=f"(_tmem_load_0[46]), "=f"(_tmem_load_0[47]), "=f"(_tmem_load_0[48]), "=f"(_tmem_load_0[49]), "=f"(_tmem_load_0[50]), "=f"(_tmem_load_0[51]), "=f"(_tmem_load_0[52]), "=f"(_tmem_load_0[53]), "=f"(_tmem_load_0[54]), "=f"(_tmem_load_0[55]), "=f"(_tmem_load_0[56]), "=f"(_tmem_load_0[57]), "=f"(_tmem_load_0[58]), "=f"(_tmem_load_0[59]), "=f"(_tmem_load_0[60]), "=f"(_tmem_load_0[61]), "=f"(_tmem_load_0[62]), "=f"(_tmem_load_0[63]) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + if (elect_sync()) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cluster.b64 _, [%0];" + :: "r"((score_empty_addr) & 0xFEFFFFFF) : "memory"); + } + if (valid_q != 0) { + #pragma unroll 1 + for (int col_base = 0; col_base < 64; col_base += 8) { + float dist_vec0[4]; + dist_vec0[0] = _tmem_load_0[col_base]; + dist_vec0[1] = _tmem_load_0[col_base + 1]; + dist_vec0[2] = _tmem_load_0[col_base + 2]; + dist_vec0[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec0)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec0[4]; + db_sq_vec0[0] = smem_database_sq[col_base]; + db_sq_vec0[1] = smem_database_sq[col_base + 1]; + db_sq_vec0[2] = smem_database_sq[col_base + 2]; + db_sq_vec0[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec0)[_la], reinterpret_cast(db_sq_vec0)[_la]); + float dist_vec1[4]; + dist_vec1[0] = _tmem_load_0[col_base + 4]; + dist_vec1[1] = _tmem_load_0[col_base + 5]; + dist_vec1[2] = _tmem_load_0[col_base + 6]; + dist_vec1[3] = _tmem_load_0[col_base + 7]; + const float2 _fma_b2_2 = {-2.0f, -2.0f}; + const float2 _fma_c2_3 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec1)[_lf], _fma_b2_2, _fma_c2_3); + float db_sq_vec1[4]; + db_sq_vec1[0] = smem_database_sq[col_base + 4]; + db_sq_vec1[1] = smem_database_sq[col_base + 5]; + db_sq_vec1[2] = smem_database_sq[col_base + 6]; + db_sq_vec1[3] = smem_database_sq[col_base + 7]; + float _t1[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t1)[_la] = add_f32x2(reinterpret_cast(dist_vec1)[_la], reinterpret_cast(db_sq_vec1)[_la]); + float _t0_min = _t0[0]; + #pragma unroll + for (int _lr = 1; _lr < 4; _lr++) { + _t0_min = fminf(_t0_min, _t0[_lr]); + } + float group_min0 = _t0_min; + float _t1_min = _t1[0]; + #pragma unroll + for (int _lr = 1; _lr < 4; _lr++) { + _t1_min = fminf(_t1_min, _t1[_lr]); + } + float group_min1 = _t1_min; + if (group_min0 < worst_d) { + #pragma unroll + for (int vec_col = 0; vec_col < 4; vec_col++) { + int db_idx = db_start + col_base + vec_col; + if (db_idx < M) { + float dist = _t0[vec_col]; + if (dist < worst_d) { + best_d[worst_pos] = dist; + best_i[worst_pos] = db_idx; + int cmp01 = ((best_d[1] > best_d[0]) ? 1 : 0); + float max01_d = ((cmp01 != 0) ? best_d[1] : best_d[0]); + int max01_p = ((cmp01 != 0) ? 1 : 0); + int cmp23 = ((best_d[3] > best_d[2]) ? 1 : 0); + float max23_d = ((cmp23 != 0) ? best_d[3] : best_d[2]); + int max23_p = ((cmp23 != 0) ? 3 : 2); + int cmp45 = ((best_d[5] > best_d[4]) ? 1 : 0); + float max45_d = ((cmp45 != 0) ? best_d[5] : best_d[4]); + int max45_p = ((cmp45 != 0) ? 5 : 4); + int cmp67 = ((best_d[7] > best_d[6]) ? 1 : 0); + float max67_d = ((cmp67 != 0) ? best_d[7] : best_d[6]); + int max67_p = ((cmp67 != 0) ? 7 : 6); + int cmp89 = ((best_d[9] > best_d[8]) ? 1 : 0); + float max89_d = ((cmp89 != 0) ? best_d[9] : best_d[8]); + int max89_p = ((cmp89 != 0) ? 9 : 8); + int cmp0123 = ((max23_d > max01_d) ? 1 : 0); + float max0123_d = ((cmp0123 != 0) ? max23_d : max01_d); + int max0123_p = ((cmp0123 != 0) ? max23_p : max01_p); + int cmp4567 = ((max67_d > max45_d) ? 1 : 0); + float max4567_d = ((cmp4567 != 0) ? max67_d : max45_d); + int max4567_p = ((cmp4567 != 0) ? max67_p : max45_p); + int cmp0_7 = ((max4567_d > max0123_d) ? 1 : 0); + float max0_7_d = ((cmp0_7 != 0) ? max4567_d : max0123_d); + int max0_7_p = ((cmp0_7 != 0) ? max4567_p : max0123_p); + int cmp_all = ((max89_d > max0_7_d) ? 1 : 0); + worst_d = ((cmp_all != 0) ? max89_d : max0_7_d); + worst_pos = ((cmp_all != 0) ? max89_p : max0_7_p); + } + } + } + } + if (group_min1 < worst_d) { + #pragma unroll + for (int vec_col_1 = 0; vec_col_1 < 4; vec_col_1++) { + int db_idx_1 = db_start + col_base + 4 + vec_col_1; + if (db_idx_1 < M) { + float dist_1 = _t1[vec_col_1]; + if (dist_1 < worst_d) { + best_d[worst_pos] = dist_1; + best_i[worst_pos] = db_idx_1; + int cmp01_1 = ((best_d[1] > best_d[0]) ? 1 : 0); + float max01_d_1 = ((cmp01_1 != 0) ? best_d[1] : best_d[0]); + int max01_p_1 = ((cmp01_1 != 0) ? 1 : 0); + int cmp23_1 = ((best_d[3] > best_d[2]) ? 1 : 0); + float max23_d_1 = ((cmp23_1 != 0) ? best_d[3] : best_d[2]); + int max23_p_1 = ((cmp23_1 != 0) ? 3 : 2); + int cmp45_1 = ((best_d[5] > best_d[4]) ? 1 : 0); + float max45_d_1 = ((cmp45_1 != 0) ? best_d[5] : best_d[4]); + int max45_p_1 = ((cmp45_1 != 0) ? 5 : 4); + int cmp67_1 = ((best_d[7] > best_d[6]) ? 1 : 0); + float max67_d_1 = ((cmp67_1 != 0) ? best_d[7] : best_d[6]); + int max67_p_1 = ((cmp67_1 != 0) ? 7 : 6); + int cmp89_1 = ((best_d[9] > best_d[8]) ? 1 : 0); + float max89_d_1 = ((cmp89_1 != 0) ? best_d[9] : best_d[8]); + int max89_p_1 = ((cmp89_1 != 0) ? 9 : 8); + int cmp0123_1 = ((max23_d_1 > max01_d_1) ? 1 : 0); + float max0123_d_1 = ((cmp0123_1 != 0) ? max23_d_1 : max01_d_1); + int max0123_p_1 = ((cmp0123_1 != 0) ? max23_p_1 : max01_p_1); + int cmp4567_1 = ((max67_d_1 > max45_d_1) ? 1 : 0); + float max4567_d_1 = ((cmp4567_1 != 0) ? max67_d_1 : max45_d_1); + int max4567_p_1 = ((cmp4567_1 != 0) ? max67_p_1 : max45_p_1); + int cmp0_7_1 = ((max4567_d_1 > max0123_d_1) ? 1 : 0); + float max0_7_d_1 = ((cmp0_7_1 != 0) ? max4567_d_1 : max0123_d_1); + int max0_7_p_1 = ((cmp0_7_1 != 0) ? max4567_p_1 : max0123_p_1); + int cmp_all_1 = ((max89_d_1 > max0_7_d_1) ? 1 : 0); + worst_d = ((cmp_all_1 != 0) ? max89_d_1 : max0_7_d_1); + worst_pos = ((cmp_all_1 != 0) ? max89_p_1 : max0_7_p_1); + } + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + if (valid_q != 0) { + int out_base = ((split_idx_1 * B + batch_idx_1) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float selected_d = best_d[0]; + int selected_i = best_i[0]; + int selected_pos = 0; + #pragma unroll + for (int scan = 1; scan < TOP_K_MAX; scan++) { + if (selected_d > best_d[scan]) { + selected_d = best_d[scan]; + selected_i = best_i[scan]; + selected_pos = scan; + } + } + if (out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = selected_d; + *((int*)(partial_indices + (out_base + out_k))) = selected_i; + } + best_d[selected_pos] = 3.4e+38f; + best_i[selected_pos] = -1; + } + } + } + } + } + + // Cleanup + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::2.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(128)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::2.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0144.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0144.cu new file mode 100644 index 00000000..9f5d8394 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0144.cu @@ -0,0 +1,724 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 128 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_STRIDE 32768 +#define SMEM_SMEM_DATABASE_OFF 33792 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_DATABASE_SQ_OFF 50176 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 50432 +#define THREADS 192 +#define BLOCK_Q 128 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_SMALL 5 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16_cta2( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + ".reg .b32 m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "tcgen05.mma.cta_group::2.kind::f16 [%0], %1, %2, %3, {m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step_cg2( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, %3, " + "{m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], %1;\n\t" + "}\n" + :: "r"(mbar_addr), "h"(cta_mask) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x16(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x16.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15}, [%16];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ uint32_t smem_addr(const void* ptr) { + uint32_t addr; + asm("{\n\t" + ".reg .u64 u64addr;\n\t" + "cvta.to.shared.u64 u64addr, %1;\n\t" + "cvt.u32.u64 %0, u64addr;\n\t" + "}\n" : "=r"(addr) : "l"(ptr)); + return addr; +} + + +__device__ __forceinline__ uint32_t mapa_to_rank(uint32_t local_addr, uint32_t rank) { + uint32_t remote; + asm volatile("mapa.shared::cluster.u32 %0, %1, %2;" + : "=r"(remote) : "r"(local_addr), "r"(rank)); + return remote; +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem_cta2( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global" + ".mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .b16 lo, hi;\n\t" + "mov.b32 {lo, hi}, %1;\n\t" + "tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], lo;\n\t" + "}\n" + :: "r"(mbar_addr), "r"((uint32_t)cta_mask) : "memory"); +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_evolve_7bfc_split_cg2_stage1_batch8_cond4_vmin_threshold_k5_mintree(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tile_pairs, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + const unsigned int clusters_x = gridDim.x / 2; + const unsigned int cluster_id = ((blockIdx.z * gridDim.y + blockIdx.y) * clusters_x) + blockIdx.x / 2; + const unsigned int num_clusters = clusters_x * gridDim.y * gridDim.z; + + int cta_rank; + asm volatile("mov.b32 %0, %%cluster_ctarank;" : "=r"(cta_rank)); + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 33792); + const int smem_database_addr = smem + 33792; + float* smem_database_sq = reinterpret_cast(smem_raw + 50176); + const int smem_database_sq_addr = smem + 50176; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 0, 2, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 16, 2, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=8 + mbarrier_init_pred(smem + 40, 8, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (128 columns, 128 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::2.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(128) : "memory"); + } + + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: load ---- + if (warp == 0) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 0) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx = cluster_id; work_idx < total_work; work_idx += num_clusters) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tile_pairs; + int q_tile_pair = query_work % num_q_tile_pairs; + int q_tile = q_tile_pair * 2 + cta_rank; + int off_q = q_tile * BLOCK_Q; + int global_q = batch_idx * Q + off_q; + int db_tile_start = split_idx * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((query_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(32768)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_query_addr), "l"(tmap_query), "r"(0), "r"(global_q), "r"(0), + "r"(((query_full_addr) & 0xFEFFFFFF)) : "memory"); + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int off_m = db_tile * BLOCK_M; + int global_m = batch_idx * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((database_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(16384)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_database_addr), "l"(tmap_database), "r"(0), "r"(global_m), "r"(0), + "r"(((database_full_addr) & 0xFEFFFFFF)) : "memory"); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 1) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + if (cta_rank == 0) { + #pragma unroll 1 + for (unsigned int work_idx_1 = cluster_id; work_idx_1 < total_work; work_idx_1 += num_clusters) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = (smem_query_addr >> 4) & 0x3FFF; + int _mma_b_lo_0 = (smem_database_addr >> 4) & 0x3FFF; + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\tmov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 270533776;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit_cg2_multicast(score_full_addr, (uint16_t)(3)); + elect_commit_cg2_multicast(database_empty_addr, (uint16_t)(3)); + } + elect_commit_cg2_multicast(query_empty_addr, (uint16_t)(3)); + } + } + } + // ---- Role: compute ---- + } else if (warp >= 2 && warp <= 5) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = cluster_id; work_idx_2 < total_work; work_idx_2 += num_clusters) { + int split_idx_1 = work_idx_2 % (unsigned int)split_count; + int query_work_1 = work_idx_2 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tile_pairs; + int q_tile_pair_1 = query_work_1 % num_q_tile_pairs; + int q_tile_1 = q_tile_pair_1 * 2 + cta_rank; + int off_q_1 = q_tile_1 * BLOCK_Q; + int q_idx = off_q_1 + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx_1 * Q + q_idx]; + } + float best_d[TOP_K_SMALL]; + int best_i[TOP_K_SMALL]; + float worst_d = 3.4e+38f; + int worst_pos = 0; + #pragma unroll + for (int kk = 0; kk < TOP_K_SMALL; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int db_start = db_tile_1 * BLOCK_M; + int db_sq_idx = db_start + (warp % 4 * 32 + lane); + if (warp % 4 * 32 + lane < BLOCK_M) { + if (db_sq_idx < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx_1 * M + db_sq_idx]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 0.0f; + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(cta_rank * BLOCK_Q + (warp % 4 * 32 << 16) << 16); + float _tmem_load_0[64]; + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x64.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31, %32, %33, %34, %35, %36, %37, %38, %39, %40, %41, %42, %43, %44, %45, %46, %47, %48, %49, %50, %51, %52, %53, %54, %55, %56, %57, %58, %59, %60, %61, %62, %63}, [%64];" + : "=f"(_tmem_load_0[0]), "=f"(_tmem_load_0[1]), "=f"(_tmem_load_0[2]), "=f"(_tmem_load_0[3]), "=f"(_tmem_load_0[4]), "=f"(_tmem_load_0[5]), "=f"(_tmem_load_0[6]), "=f"(_tmem_load_0[7]), "=f"(_tmem_load_0[8]), "=f"(_tmem_load_0[9]), "=f"(_tmem_load_0[10]), "=f"(_tmem_load_0[11]), "=f"(_tmem_load_0[12]), "=f"(_tmem_load_0[13]), "=f"(_tmem_load_0[14]), "=f"(_tmem_load_0[15]), "=f"(_tmem_load_0[16]), "=f"(_tmem_load_0[17]), "=f"(_tmem_load_0[18]), "=f"(_tmem_load_0[19]), "=f"(_tmem_load_0[20]), "=f"(_tmem_load_0[21]), "=f"(_tmem_load_0[22]), "=f"(_tmem_load_0[23]), "=f"(_tmem_load_0[24]), "=f"(_tmem_load_0[25]), "=f"(_tmem_load_0[26]), "=f"(_tmem_load_0[27]), "=f"(_tmem_load_0[28]), "=f"(_tmem_load_0[29]), "=f"(_tmem_load_0[30]), "=f"(_tmem_load_0[31]), "=f"(_tmem_load_0[32]), "=f"(_tmem_load_0[33]), "=f"(_tmem_load_0[34]), "=f"(_tmem_load_0[35]), "=f"(_tmem_load_0[36]), "=f"(_tmem_load_0[37]), "=f"(_tmem_load_0[38]), "=f"(_tmem_load_0[39]), "=f"(_tmem_load_0[40]), "=f"(_tmem_load_0[41]), "=f"(_tmem_load_0[42]), "=f"(_tmem_load_0[43]), "=f"(_tmem_load_0[44]), "=f"(_tmem_load_0[45]), "=f"(_tmem_load_0[46]), "=f"(_tmem_load_0[47]), "=f"(_tmem_load_0[48]), "=f"(_tmem_load_0[49]), "=f"(_tmem_load_0[50]), "=f"(_tmem_load_0[51]), "=f"(_tmem_load_0[52]), "=f"(_tmem_load_0[53]), "=f"(_tmem_load_0[54]), "=f"(_tmem_load_0[55]), "=f"(_tmem_load_0[56]), "=f"(_tmem_load_0[57]), "=f"(_tmem_load_0[58]), "=f"(_tmem_load_0[59]), "=f"(_tmem_load_0[60]), "=f"(_tmem_load_0[61]), "=f"(_tmem_load_0[62]), "=f"(_tmem_load_0[63]) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + if (elect_sync()) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cluster.b64 _, [%0];" + :: "r"((score_empty_addr) & 0xFEFFFFFF) : "memory"); + } + if (valid_q != 0) { + #pragma unroll 1 + for (int col_base = 0; col_base < 64; col_base += 8) { + float dist_vec0[4]; + dist_vec0[0] = _tmem_load_0[col_base]; + dist_vec0[1] = _tmem_load_0[col_base + 1]; + dist_vec0[2] = _tmem_load_0[col_base + 2]; + dist_vec0[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec0)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec0[4]; + db_sq_vec0[0] = smem_database_sq[col_base]; + db_sq_vec0[1] = smem_database_sq[col_base + 1]; + db_sq_vec0[2] = smem_database_sq[col_base + 2]; + db_sq_vec0[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec0)[_la], reinterpret_cast(db_sq_vec0)[_la]); + float dist_vec1[4]; + dist_vec1[0] = _tmem_load_0[col_base + 4]; + dist_vec1[1] = _tmem_load_0[col_base + 5]; + dist_vec1[2] = _tmem_load_0[col_base + 6]; + dist_vec1[3] = _tmem_load_0[col_base + 7]; + const float2 _fma_b2_2 = {-2.0f, -2.0f}; + const float2 _fma_c2_3 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec1)[_lf], _fma_b2_2, _fma_c2_3); + float db_sq_vec1[4]; + db_sq_vec1[0] = smem_database_sq[col_base + 4]; + db_sq_vec1[1] = smem_database_sq[col_base + 5]; + db_sq_vec1[2] = smem_database_sq[col_base + 6]; + db_sq_vec1[3] = smem_database_sq[col_base + 7]; + float _t1[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t1)[_la] = add_f32x2(reinterpret_cast(dist_vec1)[_la], reinterpret_cast(db_sq_vec1)[_la]); + float _t0_min = _t0[0]; + #pragma unroll + for (int _lr = 1; _lr < 4; _lr++) { + _t0_min = fminf(_t0_min, _t0[_lr]); + } + float group_min0 = _t0_min; + float _t1_min = _t1[0]; + #pragma unroll + for (int _lr = 1; _lr < 4; _lr++) { + _t1_min = fminf(_t1_min, _t1[_lr]); + } + float group_min1 = _t1_min; + if (group_min0 < worst_d) { + #pragma unroll + for (int vec_col = 0; vec_col < 4; vec_col++) { + int db_idx = db_start + col_base + vec_col; + if (db_idx < M) { + float dist = _t0[vec_col]; + if (dist < worst_d) { + best_d[worst_pos] = dist; + best_i[worst_pos] = db_idx; + int cmp01 = ((best_d[1] > best_d[0]) ? 1 : 0); + float max01_d = ((cmp01 != 0) ? best_d[1] : best_d[0]); + int max01_p = ((cmp01 != 0) ? 1 : 0); + int cmp23 = ((best_d[3] > best_d[2]) ? 1 : 0); + float max23_d = ((cmp23 != 0) ? best_d[3] : best_d[2]); + int max23_p = ((cmp23 != 0) ? 3 : 2); + int cmp0123 = ((max23_d > max01_d) ? 1 : 0); + float max0123_d = ((cmp0123 != 0) ? max23_d : max01_d); + int max0123_p = ((cmp0123 != 0) ? max23_p : max01_p); + int cmp_all = ((best_d[4] > max0123_d) ? 1 : 0); + worst_d = ((cmp_all != 0) ? best_d[4] : max0123_d); + worst_pos = ((cmp_all != 0) ? 4 : max0123_p); + } + } + } + } + if (group_min1 < worst_d) { + #pragma unroll + for (int vec_col_1 = 0; vec_col_1 < 4; vec_col_1++) { + int db_idx_1 = db_start + col_base + 4 + vec_col_1; + if (db_idx_1 < M) { + float dist_1 = _t1[vec_col_1]; + if (dist_1 < worst_d) { + best_d[worst_pos] = dist_1; + best_i[worst_pos] = db_idx_1; + int cmp01_1 = ((best_d[1] > best_d[0]) ? 1 : 0); + float max01_d_1 = ((cmp01_1 != 0) ? best_d[1] : best_d[0]); + int max01_p_1 = ((cmp01_1 != 0) ? 1 : 0); + int cmp23_1 = ((best_d[3] > best_d[2]) ? 1 : 0); + float max23_d_1 = ((cmp23_1 != 0) ? best_d[3] : best_d[2]); + int max23_p_1 = ((cmp23_1 != 0) ? 3 : 2); + int cmp0123_1 = ((max23_d_1 > max01_d_1) ? 1 : 0); + float max0123_d_1 = ((cmp0123_1 != 0) ? max23_d_1 : max01_d_1); + int max0123_p_1 = ((cmp0123_1 != 0) ? max23_p_1 : max01_p_1); + int cmp_all_1 = ((best_d[4] > max0123_d_1) ? 1 : 0); + worst_d = ((cmp_all_1 != 0) ? best_d[4] : max0123_d_1); + worst_pos = ((cmp_all_1 != 0) ? 4 : max0123_p_1); + } + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + if (valid_q != 0) { + int out_base = ((split_idx_1 * B + batch_idx_1) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_SMALL; out_k++) { + int cmp01_2 = ((best_d[1] < best_d[0]) ? 1 : 0); + float min01_d = ((cmp01_2 != 0) ? best_d[1] : best_d[0]); + int min01_i = ((cmp01_2 != 0) ? best_i[1] : best_i[0]); + int min01_p = ((cmp01_2 != 0) ? 1 : 0); + int cmp23_2 = ((best_d[3] < best_d[2]) ? 1 : 0); + float min23_d = ((cmp23_2 != 0) ? best_d[3] : best_d[2]); + int min23_i = ((cmp23_2 != 0) ? best_i[3] : best_i[2]); + int min23_p = ((cmp23_2 != 0) ? 3 : 2); + int cmp0123_2 = ((min23_d < min01_d) ? 1 : 0); + float min0123_d = ((cmp0123_2 != 0) ? min23_d : min01_d); + int min0123_i = ((cmp0123_2 != 0) ? min23_i : min01_i); + int min0123_p = ((cmp0123_2 != 0) ? min23_p : min01_p); + int cmp_all_2 = ((best_d[4] < min0123_d) ? 1 : 0); + float selected_d = ((cmp_all_2 != 0) ? best_d[4] : min0123_d); + int selected_i = ((cmp_all_2 != 0) ? best_i[4] : min0123_i); + int selected_pos = ((cmp_all_2 != 0) ? 4 : min0123_p); + if (out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = selected_d; + *((int*)(partial_indices + (out_base + out_k))) = selected_i; + } + best_d[selected_pos] = 3.4e+38f; + best_i[selected_pos] = -1; + } + } + } + } + } + + // Cleanup + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::2.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(128)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::2.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0145.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0145.cu new file mode 100644 index 00000000..66308b7e --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0145.cu @@ -0,0 +1,93 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 256 +#define TOP_K_SMALL 5 + +#include + +extern "C" { + +__global__ __launch_bounds__(256, 1) void +kernel_knn_build_evolve_7bfc_k5_merge_s4_tree_rowbase(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 256 + tid; + int stride = num_bids * 256; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int base_row = row * TOP_K_SMALL; + int split_stride = total_queries * TOP_K_SMALL; + int partial_base0 = base_row; + int partial_base1 = base_row + split_stride; + int partial_base2 = partial_base1 + split_stride; + int partial_base3 = partial_base2 + split_stride; + int pos0 = 0; + int pos1 = 0; + int pos2 = 0; + int pos3 = 0; + int out_base = row * TOP_K_SMALL; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_SMALL; out_k++) { + float cand_d0 = partial_dists[partial_base0 + pos0]; + int cand_i0 = partial_indices[partial_base0 + pos0]; + float cand_d1 = partial_dists[partial_base1 + pos1]; + int cand_i1 = partial_indices[partial_base1 + pos1]; + float cand_d2 = partial_dists[partial_base2 + pos2]; + int cand_i2 = partial_indices[partial_base2 + pos2]; + float cand_d3 = partial_dists[partial_base3 + pos3]; + int cand_i3 = partial_indices[partial_base3 + pos3]; + int cmp01 = ((cand_d1 < cand_d0) ? 1 : 0); + float best01_d = ((cmp01 != 0) ? cand_d1 : cand_d0); + int best01_i = ((cmp01 != 0) ? cand_i1 : cand_i0); + int best01_split = ((cmp01 != 0) ? 1 : 0); + int cmp23 = ((cand_d3 < cand_d2) ? 1 : 0); + float best23_d = ((cmp23 != 0) ? cand_d3 : cand_d2); + int best23_i = ((cmp23 != 0) ? cand_i3 : cand_i2); + int best23_split = ((cmp23 != 0) ? 3 : 2); + int cmp_all = ((best23_d < best01_d) ? 1 : 0); + float best_d = ((cmp_all != 0) ? best23_d : best01_d); + int best_i = ((cmp_all != 0) ? best23_i : best01_i); + int best_split = ((cmp_all != 0) ? best23_split : best01_split); + *((float*)(out_dists + (out_base + out_k))) = best_d; + *((int*)(out_indices + (out_base + out_k))) = best_i; + if (best_split == 0) { + pos0 = pos0 + 1; + } else if (best_split == 1) { + pos1 = pos1 + 1; + } else { + if (best_split == 2) { + pos2 = pos2 + 1; + } else { + pos3 = pos3 + 1; + } + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0146.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0146.cu new file mode 100644 index 00000000..4d565cb0 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0146.cu @@ -0,0 +1,108 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 32 +#define TOP_K_MAX 10 +#define SPLIT_COUNT 4 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_evolve_7bfc_k10_merge_s4_rowbase_cache(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 32 + tid; + int stride = num_bids * 32; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int out_base = base_row; + int split_base0 = base_row; + int split_base1 = base_row + split_stride; + int split_base2 = split_base1 + split_stride; + int split_base3 = split_base2 + split_stride; + int pos0 = 0; + int pos1 = 0; + int pos2 = 0; + int pos3 = 0; + float cand_d0 = partial_dists[split_base0]; + int cand_i0 = partial_indices[split_base0]; + float cand_d1 = partial_dists[split_base1]; + int cand_i1 = partial_indices[split_base1]; + float cand_d2 = partial_dists[split_base2]; + int cand_i2 = partial_indices[split_base2]; + float cand_d3 = partial_dists[split_base3]; + int cand_i3 = partial_indices[split_base3]; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + int cand01_cmp = ((cand_d1 < cand_d0) ? 1 : 0); + float best01_d = ((cand01_cmp != 0) ? cand_d1 : cand_d0); + int best01_i = ((cand01_cmp != 0) ? cand_i1 : cand_i0); + int best01_split = ((cand01_cmp != 0) ? 1 : 0); + int cand23_cmp = ((cand_d3 < cand_d2) ? 1 : 0); + float best23_d = ((cand23_cmp != 0) ? cand_d3 : cand_d2); + int best23_i = ((cand23_cmp != 0) ? cand_i3 : cand_i2); + int best23_split = ((cand23_cmp != 0) ? 3 : 2); + int best_cmp = ((best23_d < best01_d) ? 1 : 0); + float best_d = ((best_cmp != 0) ? best23_d : best01_d); + int best_i = ((best_cmp != 0) ? best23_i : best01_i); + int best_split = ((best_cmp != 0) ? best23_split : best01_split); + *((float*)(out_dists + (out_base + out_k))) = best_d; + *((int*)(out_indices + (out_base + out_k))) = best_i; + if (out_k + 1 < TOP_K_MAX) { + if (best_split == 0) { + pos0 = pos0 + 1; + int next_addr0 = split_base0 + pos0; + cand_d0 = partial_dists[next_addr0]; + cand_i0 = partial_indices[next_addr0]; + } else if (best_split == 1) { + pos1 = pos1 + 1; + int next_addr1 = split_base1 + pos1; + cand_d1 = partial_dists[next_addr1]; + cand_i1 = partial_indices[next_addr1]; + } else { + if (best_split == 2) { + pos2 = pos2 + 1; + int next_addr2 = split_base2 + pos2; + cand_d2 = partial_dists[next_addr2]; + cand_i2 = partial_indices[next_addr2]; + } else { + pos3 = pos3 + 1; + int next_addr3 = split_base3 + pos3; + cand_d3 = partial_dists[next_addr3]; + cand_i3 = partial_indices[next_addr3]; + } + } + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0147.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0147.cu new file mode 100644 index 00000000..60275371 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0147.cu @@ -0,0 +1,655 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 128 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_STRIDE 32768 +#define SMEM_SMEM_DATABASE_OFF 33792 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_DATABASE_SQ_OFF 50176 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 50432 +#define THREADS 192 +#define BLOCK_Q 128 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_MAX 32 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16_cta2( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + ".reg .b32 m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "tcgen05.mma.cta_group::2.kind::f16 [%0], %1, %2, %3, {m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step_cg2( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, %3, " + "{m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], %1;\n\t" + "}\n" + :: "r"(mbar_addr), "h"(cta_mask) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x16(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x16.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15}, [%16];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ uint32_t smem_addr(const void* ptr) { + uint32_t addr; + asm("{\n\t" + ".reg .u64 u64addr;\n\t" + "cvta.to.shared.u64 u64addr, %1;\n\t" + "cvt.u32.u64 %0, u64addr;\n\t" + "}\n" : "=r"(addr) : "l"(ptr)); + return addr; +} + + +__device__ __forceinline__ uint32_t mapa_to_rank(uint32_t local_addr, uint32_t rank) { + uint32_t remote; + asm volatile("mapa.shared::cluster.u32 %0, %1, %2;" + : "=r"(remote) : "r"(local_addr), "r"(rank)); + return remote; +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem_cta2( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global" + ".mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .b16 lo, hi;\n\t" + "mov.b32 {lo, hi}, %1;\n\t" + "tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], lo;\n\t" + "}\n" + :: "r"(mbar_addr), "r"((uint32_t)cta_mask) : "memory"); +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tile_pairs, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + const unsigned int clusters_x = gridDim.x / 2; + const unsigned int cluster_id = ((blockIdx.z * gridDim.y + blockIdx.y) * clusters_x) + blockIdx.x / 2; + const unsigned int num_clusters = clusters_x * gridDim.y * gridDim.z; + + int cta_rank; + asm volatile("mov.b32 %0, %%cluster_ctarank;" : "=r"(cta_rank)); + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 33792); + const int smem_database_addr = smem + 33792; + float* smem_database_sq = reinterpret_cast(smem_raw + 50176); + const int smem_database_sq_addr = smem + 50176; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 0, 2, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 16, 2, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=8 + mbarrier_init_pred(smem + 40, 8, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (128 columns, 128 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::2.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(128) : "memory"); + } + + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: load ---- + if (warp == 0) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 0) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx = cluster_id; work_idx < total_work; work_idx += num_clusters) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tile_pairs; + int q_tile_pair = query_work % num_q_tile_pairs; + int q_tile = q_tile_pair * 2 + cta_rank; + int off_q = q_tile * BLOCK_Q; + int global_q = batch_idx * Q + off_q; + int db_tile_start = split_idx * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((query_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(32768)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_query_addr), "l"(tmap_query), "r"(0), "r"(global_q), "r"(0), + "r"(((query_full_addr) & 0xFEFFFFFF)) : "memory"); + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int off_m = db_tile * BLOCK_M; + int global_m = batch_idx * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((database_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(16384)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_database_addr), "l"(tmap_database), "r"(0), "r"(global_m), "r"(0), + "r"(((database_full_addr) & 0xFEFFFFFF)) : "memory"); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 1) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + if (cta_rank == 0) { + #pragma unroll 1 + for (unsigned int work_idx_1 = cluster_id; work_idx_1 < total_work; work_idx_1 += num_clusters) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = (smem_query_addr >> 4) & 0x3FFF; + int _mma_b_lo_0 = (smem_database_addr >> 4) & 0x3FFF; + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\tmov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 270533776;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit_cg2_multicast(score_full_addr, (uint16_t)(3)); + elect_commit_cg2_multicast(database_empty_addr, (uint16_t)(3)); + } + elect_commit_cg2_multicast(query_empty_addr, (uint16_t)(3)); + } + } + } + // ---- Role: compute ---- + } else if (warp >= 2 && warp <= 5) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = cluster_id; work_idx_2 < total_work; work_idx_2 += num_clusters) { + int split_idx_1 = work_idx_2 % (unsigned int)split_count; + int query_work_1 = work_idx_2 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tile_pairs; + int q_tile_pair_1 = query_work_1 % num_q_tile_pairs; + int q_tile_1 = q_tile_pair_1 * 2 + cta_rank; + int off_q_1 = q_tile_1 * BLOCK_Q; + int q_idx = off_q_1 + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx_1 * Q + q_idx]; + } + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + float worst_d = 3.4e+38f; + int worst_pos = 0; + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int db_start = db_tile_1 * BLOCK_M; + int db_sq_idx = db_start + (warp % 4 * 32 + lane); + if (warp % 4 * 32 + lane < BLOCK_M) { + if (db_sq_idx < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx_1 * M + db_sq_idx]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 0.0f; + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(cta_rank * BLOCK_Q + (warp % 4 * 32 << 16) << 16); + float _tmem_load_0[64]; + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x64.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31, %32, %33, %34, %35, %36, %37, %38, %39, %40, %41, %42, %43, %44, %45, %46, %47, %48, %49, %50, %51, %52, %53, %54, %55, %56, %57, %58, %59, %60, %61, %62, %63}, [%64];" + : "=f"(_tmem_load_0[0]), "=f"(_tmem_load_0[1]), "=f"(_tmem_load_0[2]), "=f"(_tmem_load_0[3]), "=f"(_tmem_load_0[4]), "=f"(_tmem_load_0[5]), "=f"(_tmem_load_0[6]), "=f"(_tmem_load_0[7]), "=f"(_tmem_load_0[8]), "=f"(_tmem_load_0[9]), "=f"(_tmem_load_0[10]), "=f"(_tmem_load_0[11]), "=f"(_tmem_load_0[12]), "=f"(_tmem_load_0[13]), "=f"(_tmem_load_0[14]), "=f"(_tmem_load_0[15]), "=f"(_tmem_load_0[16]), "=f"(_tmem_load_0[17]), "=f"(_tmem_load_0[18]), "=f"(_tmem_load_0[19]), "=f"(_tmem_load_0[20]), "=f"(_tmem_load_0[21]), "=f"(_tmem_load_0[22]), "=f"(_tmem_load_0[23]), "=f"(_tmem_load_0[24]), "=f"(_tmem_load_0[25]), "=f"(_tmem_load_0[26]), "=f"(_tmem_load_0[27]), "=f"(_tmem_load_0[28]), "=f"(_tmem_load_0[29]), "=f"(_tmem_load_0[30]), "=f"(_tmem_load_0[31]), "=f"(_tmem_load_0[32]), "=f"(_tmem_load_0[33]), "=f"(_tmem_load_0[34]), "=f"(_tmem_load_0[35]), "=f"(_tmem_load_0[36]), "=f"(_tmem_load_0[37]), "=f"(_tmem_load_0[38]), "=f"(_tmem_load_0[39]), "=f"(_tmem_load_0[40]), "=f"(_tmem_load_0[41]), "=f"(_tmem_load_0[42]), "=f"(_tmem_load_0[43]), "=f"(_tmem_load_0[44]), "=f"(_tmem_load_0[45]), "=f"(_tmem_load_0[46]), "=f"(_tmem_load_0[47]), "=f"(_tmem_load_0[48]), "=f"(_tmem_load_0[49]), "=f"(_tmem_load_0[50]), "=f"(_tmem_load_0[51]), "=f"(_tmem_load_0[52]), "=f"(_tmem_load_0[53]), "=f"(_tmem_load_0[54]), "=f"(_tmem_load_0[55]), "=f"(_tmem_load_0[56]), "=f"(_tmem_load_0[57]), "=f"(_tmem_load_0[58]), "=f"(_tmem_load_0[59]), "=f"(_tmem_load_0[60]), "=f"(_tmem_load_0[61]), "=f"(_tmem_load_0[62]), "=f"(_tmem_load_0[63]) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + if (elect_sync()) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cluster.b64 _, [%0];" + :: "r"((score_empty_addr) & 0xFEFFFFFF) : "memory"); + } + if (valid_q != 0) { + #pragma unroll 2 + for (int col_base = 0; col_base < 64; col_base += 4) { + float dist_vec[4]; + dist_vec[0] = _tmem_load_0[col_base]; + dist_vec[1] = _tmem_load_0[col_base + 1]; + dist_vec[2] = _tmem_load_0[col_base + 2]; + dist_vec[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec[4]; + db_sq_vec[0] = smem_database_sq[col_base]; + db_sq_vec[1] = smem_database_sq[col_base + 1]; + db_sq_vec[2] = smem_database_sq[col_base + 2]; + db_sq_vec[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec)[_la], reinterpret_cast(db_sq_vec)[_la]); + float group_min = _t0[0]; + if (_t0[1] < group_min) { + group_min = _t0[1]; + } + if (_t0[2] < group_min) { + group_min = _t0[2]; + } + if (_t0[3] < group_min) { + group_min = _t0[3]; + } + if (group_min < worst_d) { + #pragma unroll + for (int vec_col = 0; vec_col < 4; vec_col++) { + int db_idx = db_start + col_base + vec_col; + if (db_idx < M) { + float dist = _t0[vec_col]; + if (dist < worst_d) { + best_d[worst_pos] = dist; + best_i[worst_pos] = db_idx; + worst_d = best_d[0]; + worst_pos = 0; + #pragma unroll + for (int scan_pos = 1; scan_pos < TOP_K_MAX; scan_pos++) { + if (worst_d < best_d[scan_pos]) { + worst_d = best_d[scan_pos]; + worst_pos = scan_pos; + } + } + } + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + if (valid_q != 0) { + int out_base = ((split_idx_1 * B + batch_idx_1) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + *((float*)(partial_dists + (out_base + out_k))) = best_d[out_k]; + *((int*)(partial_indices + (out_base + out_k))) = best_i[out_k]; + } + } + } + } + } + + // Cleanup + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::2.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(128)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::2.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0148.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0148.cu new file mode 100644 index 00000000..37eef13e --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0148.cu @@ -0,0 +1,85 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 32 +#define TOP_K_MAX 32 +#define SPLIT_COUNT 4 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_evolve_7bfc_k32_merge_s4_unordered(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 32 + tid; + int stride = num_bids * 32; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + float worst_d = 3.4e+38f; + int worst_pos = 0; + #pragma unroll + for (int split_idx = 0; split_idx < SPLIT_COUNT; split_idx++) { + int partial_base = base_row + split_idx * split_stride; + #pragma unroll + for (int cand_k = 0; cand_k < TOP_K_MAX; cand_k++) { + float cand_d = partial_dists[partial_base + cand_k]; + int cand_i = partial_indices[partial_base + cand_k]; + if (cand_d < worst_d) { + best_d[worst_pos] = cand_d; + best_i[worst_pos] = cand_i; + worst_d = best_d[0]; + worst_pos = 0; + #pragma unroll + for (int scan_pos = 1; scan_pos < TOP_K_MAX; scan_pos++) { + if (worst_d < best_d[scan_pos]) { + worst_d = best_d[scan_pos]; + worst_pos = scan_pos; + } + } + } + } + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + *((float*)(out_dists + (base_row + out_k))) = best_d[out_k]; + *((int*)(out_indices + (base_row + out_k))) = best_i[out_k]; + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0149.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0149.cu new file mode 100644 index 00000000..3f5c5e7a --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0149.cu @@ -0,0 +1,84 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 32 +#define TOP_K_MAX 30 +#define SPLIT_COUNT 8 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 32 + tid; + int stride = num_bids * 32; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int out_base = base_row; + int split_pos[SPLIT_COUNT]; + int split_base[SPLIT_COUNT]; + float cand_d[SPLIT_COUNT]; + int cand_i[SPLIT_COUNT]; + #pragma unroll + for (int split_idx = 0; split_idx < SPLIT_COUNT; split_idx++) { + split_pos[split_idx] = 0; + split_base[split_idx] = base_row + split_idx * split_stride; + cand_d[split_idx] = partial_dists[split_base[split_idx]]; + cand_i[split_idx] = partial_indices[split_base[split_idx]]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_d = cand_d[0]; + int best_i = cand_i[0]; + int best_split = 0; + #pragma unroll + for (int split_idx_1 = 1; split_idx_1 < SPLIT_COUNT; split_idx_1++) { + if (best_d > cand_d[split_idx_1]) { + best_d = cand_d[split_idx_1]; + best_i = cand_i[split_idx_1]; + best_split = split_idx_1; + } + } + *((float*)(out_dists + (out_base + out_k))) = best_d; + *((int*)(out_indices + (out_base + out_k))) = best_i; + split_pos[best_split] = split_pos[best_split] + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_pos = split_pos[best_split]; + int next_addr = split_base[best_split] + next_pos; + cand_d[best_split] = partial_dists[next_addr]; + cand_i[best_split] = partial_indices[next_addr]; + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0150.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0150.cu new file mode 100644 index 00000000..723233c2 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0150.cu @@ -0,0 +1,84 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 32 +#define TOP_K_MAX 12 +#define SPLIT_COUNT 8 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_k12s8(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 32 + tid; + int stride = num_bids * 32; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int out_base = base_row; + int split_pos[SPLIT_COUNT]; + int split_base[SPLIT_COUNT]; + float cand_d[SPLIT_COUNT]; + int cand_i[SPLIT_COUNT]; + #pragma unroll + for (int split_idx = 0; split_idx < SPLIT_COUNT; split_idx++) { + split_pos[split_idx] = 0; + split_base[split_idx] = base_row + split_idx * split_stride; + cand_d[split_idx] = partial_dists[split_base[split_idx]]; + cand_i[split_idx] = partial_indices[split_base[split_idx]]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_d = cand_d[0]; + int best_i = cand_i[0]; + int best_split = 0; + #pragma unroll + for (int split_idx_1 = 1; split_idx_1 < SPLIT_COUNT; split_idx_1++) { + if (best_d > cand_d[split_idx_1]) { + best_d = cand_d[split_idx_1]; + best_i = cand_i[split_idx_1]; + best_split = split_idx_1; + } + } + *((float*)(out_dists + (out_base + out_k))) = best_d; + *((int*)(out_indices + (out_base + out_k))) = best_i; + split_pos[best_split] = split_pos[best_split] + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_pos = split_pos[best_split]; + int next_addr = split_base[best_split] + next_pos; + cand_d[best_split] = partial_dists[next_addr]; + cand_i[best_split] = partial_indices[next_addr]; + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0151.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0151.cu new file mode 100644 index 00000000..fd9b65aa --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0151.cu @@ -0,0 +1,84 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 32 +#define TOP_K_MAX 20 +#define SPLIT_COUNT 8 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_k20s8(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 32 + tid; + int stride = num_bids * 32; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int out_base = base_row; + int split_pos[SPLIT_COUNT]; + int split_base[SPLIT_COUNT]; + float cand_d[SPLIT_COUNT]; + int cand_i[SPLIT_COUNT]; + #pragma unroll + for (int split_idx = 0; split_idx < SPLIT_COUNT; split_idx++) { + split_pos[split_idx] = 0; + split_base[split_idx] = base_row + split_idx * split_stride; + cand_d[split_idx] = partial_dists[split_base[split_idx]]; + cand_i[split_idx] = partial_indices[split_base[split_idx]]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_d = cand_d[0]; + int best_i = cand_i[0]; + int best_split = 0; + #pragma unroll + for (int split_idx_1 = 1; split_idx_1 < SPLIT_COUNT; split_idx_1++) { + if (best_d > cand_d[split_idx_1]) { + best_d = cand_d[split_idx_1]; + best_i = cand_i[split_idx_1]; + best_split = split_idx_1; + } + } + *((float*)(out_dists + (out_base + out_k))) = best_d; + *((int*)(out_indices + (out_base + out_k))) = best_i; + split_pos[best_split] = split_pos[best_split] + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_pos = split_pos[best_split]; + int next_addr = split_base[best_split] + next_pos; + cand_d[best_split] = partial_dists[next_addr]; + cand_i[best_split] = partial_indices[next_addr]; + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0152.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0152.cu new file mode 100644 index 00000000..e6a8c654 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0152.cu @@ -0,0 +1,84 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 32 +#define TOP_K_MAX 8 +#define SPLIT_COUNT 7 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_evolve_7bfc_k10_merge_s7_rowbase_cache_k8s7(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 32 + tid; + int stride = num_bids * 32; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int out_base = base_row; + int split_pos[SPLIT_COUNT]; + int split_base[SPLIT_COUNT]; + float cand_d[SPLIT_COUNT]; + int cand_i[SPLIT_COUNT]; + #pragma unroll + for (int split_idx = 0; split_idx < SPLIT_COUNT; split_idx++) { + split_pos[split_idx] = 0; + split_base[split_idx] = base_row + split_idx * split_stride; + cand_d[split_idx] = partial_dists[split_base[split_idx]]; + cand_i[split_idx] = partial_indices[split_base[split_idx]]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_d = cand_d[0]; + int best_i = cand_i[0]; + int best_split = 0; + #pragma unroll + for (int split_idx_1 = 1; split_idx_1 < SPLIT_COUNT; split_idx_1++) { + if (best_d > cand_d[split_idx_1]) { + best_d = cand_d[split_idx_1]; + best_i = cand_i[split_idx_1]; + best_split = split_idx_1; + } + } + *((float*)(out_dists + (out_base + out_k))) = best_d; + *((int*)(out_indices + (out_base + out_k))) = best_i; + split_pos[best_split] = split_pos[best_split] + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_pos = split_pos[best_split]; + int next_addr = split_base[best_split] + next_pos; + cand_d[best_split] = partial_dists[next_addr]; + cand_i[best_split] = partial_indices[next_addr]; + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0153.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0153.cu new file mode 100644 index 00000000..08aa77e9 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0153.cu @@ -0,0 +1,84 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 32 +#define TOP_K_MAX 20 +#define SPLIT_COUNT 16 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_k20s16(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 32 + tid; + int stride = num_bids * 32; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int out_base = base_row; + int split_pos[SPLIT_COUNT]; + int split_base[SPLIT_COUNT]; + float cand_d[SPLIT_COUNT]; + int cand_i[SPLIT_COUNT]; + #pragma unroll + for (int split_idx = 0; split_idx < SPLIT_COUNT; split_idx++) { + split_pos[split_idx] = 0; + split_base[split_idx] = base_row + split_idx * split_stride; + cand_d[split_idx] = partial_dists[split_base[split_idx]]; + cand_i[split_idx] = partial_indices[split_base[split_idx]]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_d = cand_d[0]; + int best_i = cand_i[0]; + int best_split = 0; + #pragma unroll + for (int split_idx_1 = 1; split_idx_1 < SPLIT_COUNT; split_idx_1++) { + if (best_d > cand_d[split_idx_1]) { + best_d = cand_d[split_idx_1]; + best_i = cand_i[split_idx_1]; + best_split = split_idx_1; + } + } + *((float*)(out_dists + (out_base + out_k))) = best_d; + *((int*)(out_indices + (out_base + out_k))) = best_i; + split_pos[best_split] = split_pos[best_split] + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_pos = split_pos[best_split]; + int next_addr = split_base[best_split] + next_pos; + cand_d[best_split] = partial_dists[next_addr]; + cand_i[best_split] = partial_indices[next_addr]; + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0154.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0154.cu new file mode 100644 index 00000000..b1925bd5 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0154.cu @@ -0,0 +1,484 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 64 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_STRIDE 32768 +#define SMEM_SMEM_DATABASE_OFF 33792 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_TOTAL 50176 +#define THREADS 192 +#define BLOCK_Q 128 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_MAX 10 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "tcgen05.mma.cta_group::1.kind::f16 [%0], %1, %2, %3, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, %3, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit(int mbar_addr) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];\n\t" + "}\n" + :: "r"(mbar_addr)); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x32(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x32.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15," + " %16, %17, %18, %19, %20, %21, %22, %23," + " %24, %25, %26, %27, %28, %29, %30, %31}, [%32];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]), + "=f"(dst[16]), "=f"(dst[17]), "=f"(dst[18]), "=f"(dst[19]), + "=f"(dst[20]), "=f"(dst[21]), "=f"(dst[22]), "=f"(dst[23]), + "=f"(dst[24]), "=f"(dst[25]), "=f"(dst[26]), "=f"(dst[27]), + "=f"(dst[28]), "=f"(dst[29]), "=f"(dst[30]), "=f"(dst[31]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ float max_noftz(float a, float b) { + float c; + asm("max.f32 %0, %1, %2;" : "=f"(c) : "f"(a), "f"(b)); + return c; +} + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cta.global" + ".mbarrier::complete_tx::bytes" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit(int mbar_addr) { + asm volatile( + "tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ uint32_t make_warp_uniform(uint32_t val) { + uint32_t result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1f, 0xffffffff;" + : "=r"(result) : "r"(val)); + return result; +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_evolve_7bfc_fp16_d128_base(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ out_dists, int* __restrict__ out_indices, int B, int Q, int M, int K, int num_q_tiles, int num_db_tiles, int total_tiles) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + __half* smem_query = reinterpret_cast<__half*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __half* smem_database = reinterpret_cast<__half*>(smem_raw + 33792); + const int smem_database_addr = smem + 33792; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 0, 1, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 16, 1, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=128 + mbarrier_init_pred(smem + 40, 128, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (64 columns, 64 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::1.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(64) : "memory"); + } + + __syncthreads(); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: load ---- + if (warp == 0) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 0) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int tile_idx = bid; tile_idx < total_tiles; tile_idx += num_bids) { + int batch_idx = tile_idx / (unsigned int)num_q_tiles; + int q_tile = tile_idx % (unsigned int)num_q_tiles; + int off_q = q_tile * BLOCK_Q; + int global_q = batch_idx * Q + off_q; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + mbarrier_arrive_expect_tx(query_full_addr, 32768); + tma_3d_gmem2smem(smem_query_addr, tmap_query, 0, global_q, 0, query_full_addr); + #pragma unroll 1 + for (int db_tile = 0; db_tile < num_db_tiles; db_tile++) { + int off_m = db_tile * BLOCK_M; + int global_m = batch_idx * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + mbarrier_arrive_expect_tx(database_full_addr, 16384); + tma_3d_gmem2smem(smem_database_addr, tmap_database, 0, global_m, 0, database_full_addr); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 1) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + #pragma unroll 1 + for (unsigned int tile_idx_1 = bid; tile_idx_1 < total_tiles; tile_idx_1 += num_bids) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _db_tile = 0; _db_tile < num_db_tiles; _db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = make_warp_uniform((smem_query_addr >> 4) & 0x3FFF); + int _mma_b_lo_0 = make_warp_uniform((smem_database_addr >> 4) & 0x3FFF); + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 135266320;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit(score_full_addr); + elect_commit(database_empty_addr); + } + elect_commit(query_empty_addr); + } + } + // ---- Role: compute ---- + } else if (warp >= 2 && warp <= 5) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int tile_idx_2 = bid; tile_idx_2 < total_tiles; tile_idx_2 += num_bids) { + int batch_idx_1 = tile_idx_2 / (unsigned int)num_q_tiles; + int q_tile_1 = tile_idx_2 % (unsigned int)num_q_tiles; + int off_q_1 = q_tile_1 * BLOCK_Q; + int q_idx = off_q_1 + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx_1 * Q + q_idx]; + } + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + #pragma unroll 1 + for (int db_tile_1 = 0; db_tile_1 < num_db_tiles; db_tile_1++) { + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(warp % 4 * 32 << 16); + float _tmem_load_0[64]; + tmem_ld_x32(&_tmem_load_0[0], cross_addr); + tmem_ld_x32(&_tmem_load_0[32], cross_addr + 32); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + mbarrier_arrive(score_empty_addr); + int db_start = db_tile_1 * BLOCK_M; + #pragma unroll + for (int col = 0; col < 64; col++) { + int db_idx = db_start + col; + if (valid_q != 0 && db_idx < M) { + float db_sq_val = database_sq[batch_idx_1 * M + db_idx]; + float dist = q_sq_val + db_sq_val - 2.0f * _tmem_load_0[col]; + float _max_0 = max_noftz(dist, 0.0f); + dist = _max_0; + if (dist < best_d[TOP_K_MAX - 1]) { + best_d[TOP_K_MAX - 1] = dist; + best_i[TOP_K_MAX - 1] = db_idx; + #pragma unroll + for (int pos = TOP_K_MAX - 1; pos >= 1; pos--) { + if (best_d[pos] < best_d[pos - 1]) { + float tmp_d = best_d[pos - 1]; + int tmp_i = best_i[pos - 1]; + best_d[pos - 1] = best_d[pos]; + best_i[pos - 1] = best_i[pos]; + best_d[pos] = tmp_d; + best_i[pos] = tmp_i; + } + } + } + } + } + } + if (valid_q != 0) { + int out_base = (batch_idx_1 * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + if (out_k < K) { + *((float*)(out_dists + (out_base + out_k))) = best_d[out_k]; + *((int*)(out_indices + (out_base + out_k))) = best_i[out_k]; + } + } + } + } + } + } + + // Cleanup + __syncthreads(); // barrier before TMEM dealloc + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::1.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(64)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::1.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0155.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0155.cu new file mode 100644 index 00000000..0feb4d2d --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0155.cu @@ -0,0 +1,560 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 64 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 65536 +#define SMEM_SMEM_QUERY_STRIDE 65536 +#define SMEM_SMEM_DATABASE_OFF 66560 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 32768 +#define SMEM_SMEM_DATABASE_STRIDE 32768 +#define SMEM_SMEM_QUERY_LO_OFF 1024 +#define SMEM_SMEM_QUERY_LO_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_LO_STRIDE 32768 +#define SMEM_SMEM_QUERY_HI_OFF 33792 +#define SMEM_SMEM_QUERY_HI_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_HI_STRIDE 32768 +#define SMEM_SMEM_DATABASE_LO_OFF 66560 +#define SMEM_SMEM_DATABASE_LO_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_LO_STRIDE 16384 +#define SMEM_SMEM_DATABASE_HI_OFF 82944 +#define SMEM_SMEM_DATABASE_HI_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_HI_STRIDE 16384 +#define SMEM_TOTAL 99328 +#define THREADS 192 +#define BLOCK_Q 128 +#define BLOCK_M 64 +#define TOP_K_MAX 10 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "tcgen05.mma.cta_group::1.kind::f16 [%0], %1, %2, %3, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, %3, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit(int mbar_addr) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];\n\t" + "}\n" + :: "r"(mbar_addr)); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x32(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x32.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15," + " %16, %17, %18, %19, %20, %21, %22, %23," + " %24, %25, %26, %27, %28, %29, %30, %31}, [%32];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]), + "=f"(dst[16]), "=f"(dst[17]), "=f"(dst[18]), "=f"(dst[19]), + "=f"(dst[20]), "=f"(dst[21]), "=f"(dst[22]), "=f"(dst[23]), + "=f"(dst[24]), "=f"(dst[25]), "=f"(dst[26]), "=f"(dst[27]), + "=f"(dst[28]), "=f"(dst[29]), "=f"(dst[30]), "=f"(dst[31]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ float max_noftz(float a, float b) { + float c; + asm("max.f32 %0, %1, %2;" : "=f"(c) : "f"(a), "f"(b)); + return c; +} + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cta.global" + ".mbarrier::complete_tx::bytes" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit(int mbar_addr) { + asm volatile( + "tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ uint32_t make_warp_uniform(uint32_t val) { + uint32_t result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1f, 0xffffffff;" + : "=r"(result) : "r"(val)); + return result; +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_evolve_7bfc_d256_twomma_base(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ out_dists, int* __restrict__ out_indices, int B, int Q, int M, int K, int num_q_tiles, int num_db_tiles, int total_tiles) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 66560); + const int smem_database_addr = smem + 66560; + __nv_bfloat16* smem_query_lo = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_lo_addr = smem + 1024; + __nv_bfloat16* smem_query_hi = reinterpret_cast<__nv_bfloat16*>(smem_raw + 33792); + const int smem_query_hi_addr = smem + 33792; + __nv_bfloat16* smem_database_lo = reinterpret_cast<__nv_bfloat16*>(smem_raw + 66560); + const int smem_database_lo_addr = smem + 66560; + __nv_bfloat16* smem_database_hi = reinterpret_cast<__nv_bfloat16*>(smem_raw + 82944); + const int smem_database_hi_addr = smem + 82944; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 0, 1, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 16, 1, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=128 + mbarrier_init_pred(smem + 40, 128, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (64 columns, 64 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::1.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(64) : "memory"); + } + + __syncthreads(); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: load ---- + if (warp == 0) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 0) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int tile_idx = bid; tile_idx < total_tiles; tile_idx += num_bids) { + int batch_idx = tile_idx / (unsigned int)num_q_tiles; + int q_tile = tile_idx % (unsigned int)num_q_tiles; + int off_q = q_tile * BLOCK_Q; + int global_q = batch_idx * Q + off_q; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + mbarrier_arrive_expect_tx(query_full_addr, 65536); + tma_3d_gmem2smem(smem_query_addr, tmap_query, 0, global_q, 0, query_full_addr); + #pragma unroll 1 + for (int db_tile = 0; db_tile < num_db_tiles; db_tile++) { + int off_m = db_tile * BLOCK_M; + int global_m = batch_idx * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + mbarrier_arrive_expect_tx(database_full_addr, 32768); + tma_3d_gmem2smem(smem_database_addr, tmap_database, 0, global_m, 0, database_full_addr); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 1) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + #pragma unroll 1 + for (unsigned int tile_idx_1 = bid; tile_idx_1 < total_tiles; tile_idx_1 += num_bids) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _db_tile = 0; _db_tile < num_db_tiles; _db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = make_warp_uniform((smem_query_lo_addr >> 4) & 0x3FFF); + int _mma_b_lo_0 = make_warp_uniform((smem_database_lo_addr >> 4) & 0x3FFF); + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 135267472;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_1 = make_warp_uniform((smem_query_hi_addr >> 4) & 0x3FFF); + int _mma_b_lo_1 = make_warp_uniform((smem_database_hi_addr >> 4) & 0x3FFF); + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 135267472;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_1), "r"(_mma_b_lo_1), "r"(tmem_cross), "r"(1)); + elect_commit(score_full_addr); + elect_commit(database_empty_addr); + } + elect_commit(query_empty_addr); + } + } + // ---- Role: compute ---- + } else if (warp >= 2 && warp <= 5) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int tile_idx_2 = bid; tile_idx_2 < total_tiles; tile_idx_2 += num_bids) { + int batch_idx_1 = tile_idx_2 / (unsigned int)num_q_tiles; + int q_tile_1 = tile_idx_2 % (unsigned int)num_q_tiles; + int off_q_1 = q_tile_1 * BLOCK_Q; + int q_idx = off_q_1 + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx_1 * Q + q_idx]; + } + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + #pragma unroll 1 + for (int db_tile_1 = 0; db_tile_1 < num_db_tiles; db_tile_1++) { + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(warp % 4 * 32 << 16); + float _tmem_load_0[64]; + tmem_ld_x32(&_tmem_load_0[0], cross_addr); + tmem_ld_x32(&_tmem_load_0[32], cross_addr + 32); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + mbarrier_arrive(score_empty_addr); + int db_start = db_tile_1 * BLOCK_M; + #pragma unroll + for (int col = 0; col < 64; col++) { + int db_idx = db_start + col; + if (valid_q != 0 && db_idx < M) { + float db_sq_val = database_sq[batch_idx_1 * M + db_idx]; + float dist = q_sq_val + db_sq_val - 2.0f * _tmem_load_0[col]; + float _max_0 = max_noftz(dist, 0.0f); + dist = _max_0; + if (dist < best_d[TOP_K_MAX - 1]) { + best_d[TOP_K_MAX - 1] = dist; + best_i[TOP_K_MAX - 1] = db_idx; + #pragma unroll + for (int pos = TOP_K_MAX - 1; pos >= 1; pos--) { + if (best_d[pos] < best_d[pos - 1]) { + float tmp_d = best_d[pos - 1]; + int tmp_i = best_i[pos - 1]; + best_d[pos - 1] = best_d[pos]; + best_i[pos - 1] = best_i[pos]; + best_d[pos] = tmp_d; + best_i[pos] = tmp_i; + } + } + } + } + } + } + if (valid_q != 0) { + int out_base = (batch_idx_1 * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + if (out_k < K) { + *((float*)(out_dists + (out_base + out_k))) = best_d[out_k]; + *((int*)(out_indices + (out_base + out_k))) = best_i[out_k]; + } + } + } + } + } + } + + // Cleanup + __syncthreads(); // barrier before TMEM dealloc + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::1.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(64)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::1.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0156.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0156.cu new file mode 100644 index 00000000..5ade68db --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0156.cu @@ -0,0 +1,463 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 64 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 16384 +#define SMEM_SMEM_QUERY_STRIDE 16384 +#define SMEM_SMEM_DATABASE_OFF 17408 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 8192 +#define SMEM_SMEM_DATABASE_STRIDE 8192 +#define SMEM_TOTAL 25600 +#define THREADS 192 +#define BLOCK_Q 128 +#define BLOCK_M 64 +#define TOP_K_MAX 10 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "tcgen05.mma.cta_group::1.kind::f16 [%0], %1, %2, %3, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, %3, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit(int mbar_addr) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];\n\t" + "}\n" + :: "r"(mbar_addr)); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x32(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x32.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15," + " %16, %17, %18, %19, %20, %21, %22, %23," + " %24, %25, %26, %27, %28, %29, %30, %31}, [%32];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]), + "=f"(dst[16]), "=f"(dst[17]), "=f"(dst[18]), "=f"(dst[19]), + "=f"(dst[20]), "=f"(dst[21]), "=f"(dst[22]), "=f"(dst[23]), + "=f"(dst[24]), "=f"(dst[25]), "=f"(dst[26]), "=f"(dst[27]), + "=f"(dst[28]), "=f"(dst[29]), "=f"(dst[30]), "=f"(dst[31]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ float max_noftz(float a, float b) { + float c; + asm("max.f32 %0, %1, %2;" : "=f"(c) : "f"(a), "f"(b)); + return c; +} + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cta.global" + ".mbarrier::complete_tx::bytes" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit(int mbar_addr) { + asm volatile( + "tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ uint32_t make_warp_uniform(uint32_t val) { + uint32_t result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1f, 0xffffffff;" + : "=r"(result) : "r"(val)); + return result; +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_evolve_7bfc_d64_tcgen05_base(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ out_dists, int* __restrict__ out_indices, int B, int Q, int M, int K, int num_q_tiles, int num_db_tiles, int total_tiles) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 17408); + const int smem_database_addr = smem + 17408; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 0, 1, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 16, 1, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=128 + mbarrier_init_pred(smem + 40, 128, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (64 columns, 64 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::1.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(64) : "memory"); + } + + __syncthreads(); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: load ---- + if (warp == 0) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 0) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int tile_idx = bid; tile_idx < total_tiles; tile_idx += num_bids) { + int batch_idx = tile_idx / (unsigned int)num_q_tiles; + int q_tile = tile_idx % (unsigned int)num_q_tiles; + int off_q = q_tile * BLOCK_Q; + int global_q = batch_idx * Q + off_q; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + mbarrier_arrive_expect_tx(query_full_addr, 16384); + tma_3d_gmem2smem(smem_query_addr, tmap_query, 0, global_q, 0, query_full_addr); + #pragma unroll 1 + for (int db_tile = 0; db_tile < num_db_tiles; db_tile++) { + int off_m = db_tile * BLOCK_M; + int global_m = batch_idx * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + mbarrier_arrive_expect_tx(database_full_addr, 8192); + tma_3d_gmem2smem(smem_database_addr, tmap_database, 0, global_m, 0, database_full_addr); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 1) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + #pragma unroll 1 + for (unsigned int tile_idx_1 = bid; tile_idx_1 < total_tiles; tile_idx_1 += num_bids) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _db_tile = 0; _db_tile < num_db_tiles; _db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = make_warp_uniform((smem_query_addr >> 4) & 0x3FFF); + int _mma_b_lo_0 = make_warp_uniform((smem_database_addr >> 4) & 0x3FFF); + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 135267472;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit(score_full_addr); + elect_commit(database_empty_addr); + } + elect_commit(query_empty_addr); + } + } + // ---- Role: compute ---- + } else if (warp >= 2 && warp <= 5) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int tile_idx_2 = bid; tile_idx_2 < total_tiles; tile_idx_2 += num_bids) { + int batch_idx_1 = tile_idx_2 / (unsigned int)num_q_tiles; + int q_tile_1 = tile_idx_2 % (unsigned int)num_q_tiles; + int off_q_1 = q_tile_1 * BLOCK_Q; + int q_idx = off_q_1 + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx_1 * Q + q_idx]; + } + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + #pragma unroll 1 + for (int db_tile_1 = 0; db_tile_1 < num_db_tiles; db_tile_1++) { + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(warp % 4 * 32 << 16); + float _tmem_load_0[64]; + tmem_ld_x32(&_tmem_load_0[0], cross_addr); + tmem_ld_x32(&_tmem_load_0[32], cross_addr + 32); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + mbarrier_arrive(score_empty_addr); + int db_start = db_tile_1 * BLOCK_M; + #pragma unroll + for (int col = 0; col < 64; col++) { + int db_idx = db_start + col; + if (valid_q != 0 && db_idx < M) { + float db_sq_val = database_sq[batch_idx_1 * M + db_idx]; + float dist = q_sq_val + db_sq_val - 2.0f * _tmem_load_0[col]; + float _max_0 = max_noftz(dist, 0.0f); + dist = _max_0; + if (dist < best_d[TOP_K_MAX - 1]) { + best_d[TOP_K_MAX - 1] = dist; + best_i[TOP_K_MAX - 1] = db_idx; + #pragma unroll + for (int pos = TOP_K_MAX - 1; pos >= 1; pos--) { + if (best_d[pos] < best_d[pos - 1]) { + float tmp_d = best_d[pos - 1]; + int tmp_i = best_i[pos - 1]; + best_d[pos - 1] = best_d[pos]; + best_i[pos - 1] = best_i[pos]; + best_d[pos] = tmp_d; + best_i[pos] = tmp_i; + } + } + } + } + } + } + if (valid_q != 0) { + int out_base = (batch_idx_1 * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + if (out_k < K) { + *((float*)(out_dists + (out_base + out_k))) = best_d[out_k]; + *((int*)(out_indices + (out_base + out_k))) = best_i[out_k]; + } + } + } + } + } + } + + // Cleanup + __syncthreads(); // barrier before TMEM dealloc + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::1.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(64)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::1.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0157.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0157.cu new file mode 100644 index 00000000..a5b636ed --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0157.cu @@ -0,0 +1,150 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 128 +#define TOP_K_MAX 20 +#define SPLIT_COUNT 4 + +#include + +extern "C" { + +__global__ __launch_bounds__(128, 1) void +kernel_knn_build_evolve_7bfc_k20_merge_s4_unordered_warp_select_splitmajor(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int row = bid * 4 + warp; + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int cand_k = lane; + if (row < total_queries) { + float d0 = 3.4e+38f; + float d1 = 3.4e+38f; + float d2 = 3.4e+38f; + float d3 = 3.4e+38f; + int i0 = -1; + int i1 = -1; + int i2 = -1; + int i3 = -1; + if (cand_k < TOP_K_MAX) { + d0 = partial_dists[base_row + cand_k]; + i0 = partial_indices[base_row + cand_k]; + int base1 = base_row + split_stride; + d1 = partial_dists[base1 + cand_k]; + i1 = partial_indices[base1 + cand_k]; + int base2 = base1 + split_stride; + d2 = partial_dists[base2 + cand_k]; + i2 = partial_indices[base2 + cand_k]; + int base3 = base2 + split_stride; + d3 = partial_dists[base3 + cand_k]; + i3 = partial_indices[base3 + cand_k]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float winner_d = d0; + int winner_i = i0; + int winner_src = 0; + if (d1 < winner_d) { + winner_d = d1; + winner_i = i1; + winner_src = 1; + } + if (d2 < winner_d) { + winner_d = d2; + winner_i = i2; + winner_src = 2; + } + if (d3 < winner_d) { + winner_d = d3; + winner_i = i3; + winner_src = 3; + } + float warp_min = winner_d; + float _warp_reduce_0 = warp_min; + #pragma unroll + for (int offset = 16; offset > 0; offset >>= 1) + _warp_reduce_0 = fminf(_warp_reduce_0, __shfl_xor_sync(0xFFFFFFFF, _warp_reduce_0, offset)); + warp_min = _warp_reduce_0; + int src0_owner = 0; + int src1_owner = 0; + int src2_owner = 0; + int src3_owner = 0; + if (winner_d == warp_min) { + if (winner_src == 0) { + src0_owner = 1; + } else if (winner_src == 1) { + src1_owner = 1; + } else { + if (winner_src == 2) { + src2_owner = 1; + } else { + src3_owner = 1; + } + } + } + unsigned int _vote_0 = __ballot_sync(0xFFFFFFFF, src0_owner != 0); + int owner_ballot = _vote_0; + if (owner_ballot == 0) { + unsigned int _vote_1 = __ballot_sync(0xFFFFFFFF, src1_owner != 0); + owner_ballot = _vote_1; + if (owner_ballot == 0) { + unsigned int _vote_2 = __ballot_sync(0xFFFFFFFF, src2_owner != 0); + owner_ballot = _vote_2; + if (owner_ballot == 0) { + unsigned int _vote_3 = __ballot_sync(0xFFFFFFFF, src3_owner != 0); + owner_ballot = _vote_3; + } + } + } + int _clz_0 = __clz(owner_ballot); + int winner_lane = 31 - _clz_0; + int _shfl_0 = __shfl_sync(0xFFFFFFFF, winner_i, winner_lane); + winner_i = _shfl_0; + int _shfl_1 = __shfl_sync(0xFFFFFFFF, winner_src, winner_lane); + winner_src = _shfl_1; + if (lane == 0) { + *((float*)(out_dists + (base_row + out_k))) = warp_min; + *((int*)(out_indices + (base_row + out_k))) = winner_i; + } + if (lane == winner_lane) { + if (winner_src == 0) { + d0 = 3.4e+38f; + } else if (winner_src == 1) { + d1 = 3.4e+38f; + } else { + if (winner_src == 2) { + d2 = 3.4e+38f; + } else { + d3 = 3.4e+38f; + } + } + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0158.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0158.cu new file mode 100644 index 00000000..bd7ea932 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0158.cu @@ -0,0 +1,655 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 128 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_STRIDE 32768 +#define SMEM_SMEM_DATABASE_OFF 33792 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_DATABASE_SQ_OFF 50176 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 50432 +#define THREADS 192 +#define BLOCK_Q 128 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_MAX 20 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16_cta2( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + ".reg .b32 m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "tcgen05.mma.cta_group::2.kind::f16 [%0], %1, %2, %3, {m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step_cg2( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, %3, " + "{m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], %1;\n\t" + "}\n" + :: "r"(mbar_addr), "h"(cta_mask) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x16(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x16.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15}, [%16];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ uint32_t smem_addr(const void* ptr) { + uint32_t addr; + asm("{\n\t" + ".reg .u64 u64addr;\n\t" + "cvta.to.shared.u64 u64addr, %1;\n\t" + "cvt.u32.u64 %0, u64addr;\n\t" + "}\n" : "=r"(addr) : "l"(ptr)); + return addr; +} + + +__device__ __forceinline__ uint32_t mapa_to_rank(uint32_t local_addr, uint32_t rank) { + uint32_t remote; + asm volatile("mapa.shared::cluster.u32 %0, %1, %2;" + : "=r"(remote) : "r"(local_addr), "r"(rank)); + return remote; +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem_cta2( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global" + ".mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .b16 lo, hi;\n\t" + "mov.b32 {lo, hi}, %1;\n\t" + "tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], lo;\n\t" + "}\n" + :: "r"(mbar_addr), "r"((uint32_t)cta_mask) : "memory"); +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k20unordered(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tile_pairs, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + const unsigned int clusters_x = gridDim.x / 2; + const unsigned int cluster_id = ((blockIdx.z * gridDim.y + blockIdx.y) * clusters_x) + blockIdx.x / 2; + const unsigned int num_clusters = clusters_x * gridDim.y * gridDim.z; + + int cta_rank; + asm volatile("mov.b32 %0, %%cluster_ctarank;" : "=r"(cta_rank)); + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 33792); + const int smem_database_addr = smem + 33792; + float* smem_database_sq = reinterpret_cast(smem_raw + 50176); + const int smem_database_sq_addr = smem + 50176; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 0, 2, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 16, 2, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=8 + mbarrier_init_pred(smem + 40, 8, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (128 columns, 128 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::2.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(128) : "memory"); + } + + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: load ---- + if (warp == 0) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 0) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx = cluster_id; work_idx < total_work; work_idx += num_clusters) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tile_pairs; + int q_tile_pair = query_work % num_q_tile_pairs; + int q_tile = q_tile_pair * 2 + cta_rank; + int off_q = q_tile * BLOCK_Q; + int global_q = batch_idx * Q + off_q; + int db_tile_start = split_idx * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((query_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(32768)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_query_addr), "l"(tmap_query), "r"(0), "r"(global_q), "r"(0), + "r"(((query_full_addr) & 0xFEFFFFFF)) : "memory"); + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int off_m = db_tile * BLOCK_M; + int global_m = batch_idx * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((database_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(16384)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_database_addr), "l"(tmap_database), "r"(0), "r"(global_m), "r"(0), + "r"(((database_full_addr) & 0xFEFFFFFF)) : "memory"); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 1) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + if (cta_rank == 0) { + #pragma unroll 1 + for (unsigned int work_idx_1 = cluster_id; work_idx_1 < total_work; work_idx_1 += num_clusters) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = (smem_query_addr >> 4) & 0x3FFF; + int _mma_b_lo_0 = (smem_database_addr >> 4) & 0x3FFF; + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\tmov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 270533776;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit_cg2_multicast(score_full_addr, (uint16_t)(3)); + elect_commit_cg2_multicast(database_empty_addr, (uint16_t)(3)); + } + elect_commit_cg2_multicast(query_empty_addr, (uint16_t)(3)); + } + } + } + // ---- Role: compute ---- + } else if (warp >= 2 && warp <= 5) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = cluster_id; work_idx_2 < total_work; work_idx_2 += num_clusters) { + int split_idx_1 = work_idx_2 % (unsigned int)split_count; + int query_work_1 = work_idx_2 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tile_pairs; + int q_tile_pair_1 = query_work_1 % num_q_tile_pairs; + int q_tile_1 = q_tile_pair_1 * 2 + cta_rank; + int off_q_1 = q_tile_1 * BLOCK_Q; + int q_idx = off_q_1 + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx_1 * Q + q_idx]; + } + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + float worst_d = 3.4e+38f; + int worst_pos = 0; + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int db_start = db_tile_1 * BLOCK_M; + int db_sq_idx = db_start + (warp % 4 * 32 + lane); + if (warp % 4 * 32 + lane < BLOCK_M) { + if (db_sq_idx < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx_1 * M + db_sq_idx]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 0.0f; + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(cta_rank * BLOCK_Q + (warp % 4 * 32 << 16) << 16); + float _tmem_load_0[64]; + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x64.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31, %32, %33, %34, %35, %36, %37, %38, %39, %40, %41, %42, %43, %44, %45, %46, %47, %48, %49, %50, %51, %52, %53, %54, %55, %56, %57, %58, %59, %60, %61, %62, %63}, [%64];" + : "=f"(_tmem_load_0[0]), "=f"(_tmem_load_0[1]), "=f"(_tmem_load_0[2]), "=f"(_tmem_load_0[3]), "=f"(_tmem_load_0[4]), "=f"(_tmem_load_0[5]), "=f"(_tmem_load_0[6]), "=f"(_tmem_load_0[7]), "=f"(_tmem_load_0[8]), "=f"(_tmem_load_0[9]), "=f"(_tmem_load_0[10]), "=f"(_tmem_load_0[11]), "=f"(_tmem_load_0[12]), "=f"(_tmem_load_0[13]), "=f"(_tmem_load_0[14]), "=f"(_tmem_load_0[15]), "=f"(_tmem_load_0[16]), "=f"(_tmem_load_0[17]), "=f"(_tmem_load_0[18]), "=f"(_tmem_load_0[19]), "=f"(_tmem_load_0[20]), "=f"(_tmem_load_0[21]), "=f"(_tmem_load_0[22]), "=f"(_tmem_load_0[23]), "=f"(_tmem_load_0[24]), "=f"(_tmem_load_0[25]), "=f"(_tmem_load_0[26]), "=f"(_tmem_load_0[27]), "=f"(_tmem_load_0[28]), "=f"(_tmem_load_0[29]), "=f"(_tmem_load_0[30]), "=f"(_tmem_load_0[31]), "=f"(_tmem_load_0[32]), "=f"(_tmem_load_0[33]), "=f"(_tmem_load_0[34]), "=f"(_tmem_load_0[35]), "=f"(_tmem_load_0[36]), "=f"(_tmem_load_0[37]), "=f"(_tmem_load_0[38]), "=f"(_tmem_load_0[39]), "=f"(_tmem_load_0[40]), "=f"(_tmem_load_0[41]), "=f"(_tmem_load_0[42]), "=f"(_tmem_load_0[43]), "=f"(_tmem_load_0[44]), "=f"(_tmem_load_0[45]), "=f"(_tmem_load_0[46]), "=f"(_tmem_load_0[47]), "=f"(_tmem_load_0[48]), "=f"(_tmem_load_0[49]), "=f"(_tmem_load_0[50]), "=f"(_tmem_load_0[51]), "=f"(_tmem_load_0[52]), "=f"(_tmem_load_0[53]), "=f"(_tmem_load_0[54]), "=f"(_tmem_load_0[55]), "=f"(_tmem_load_0[56]), "=f"(_tmem_load_0[57]), "=f"(_tmem_load_0[58]), "=f"(_tmem_load_0[59]), "=f"(_tmem_load_0[60]), "=f"(_tmem_load_0[61]), "=f"(_tmem_load_0[62]), "=f"(_tmem_load_0[63]) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + if (elect_sync()) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cluster.b64 _, [%0];" + :: "r"((score_empty_addr) & 0xFEFFFFFF) : "memory"); + } + if (valid_q != 0) { + #pragma unroll 2 + for (int col_base = 0; col_base < 64; col_base += 4) { + float dist_vec[4]; + dist_vec[0] = _tmem_load_0[col_base]; + dist_vec[1] = _tmem_load_0[col_base + 1]; + dist_vec[2] = _tmem_load_0[col_base + 2]; + dist_vec[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec[4]; + db_sq_vec[0] = smem_database_sq[col_base]; + db_sq_vec[1] = smem_database_sq[col_base + 1]; + db_sq_vec[2] = smem_database_sq[col_base + 2]; + db_sq_vec[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec)[_la], reinterpret_cast(db_sq_vec)[_la]); + float group_min = _t0[0]; + if (_t0[1] < group_min) { + group_min = _t0[1]; + } + if (_t0[2] < group_min) { + group_min = _t0[2]; + } + if (_t0[3] < group_min) { + group_min = _t0[3]; + } + if (group_min < worst_d) { + #pragma unroll + for (int vec_col = 0; vec_col < 4; vec_col++) { + int db_idx = db_start + col_base + vec_col; + if (db_idx < M) { + float dist = _t0[vec_col]; + if (dist < worst_d) { + best_d[worst_pos] = dist; + best_i[worst_pos] = db_idx; + worst_d = best_d[0]; + worst_pos = 0; + #pragma unroll + for (int scan_pos = 1; scan_pos < TOP_K_MAX; scan_pos++) { + if (worst_d < best_d[scan_pos]) { + worst_d = best_d[scan_pos]; + worst_pos = scan_pos; + } + } + } + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + if (valid_q != 0) { + int out_base = ((split_idx_1 * B + batch_idx_1) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + *((float*)(partial_dists + (out_base + out_k))) = best_d[out_k]; + *((int*)(partial_indices + (out_base + out_k))) = best_i[out_k]; + } + } + } + } + } + + // Cleanup + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::2.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(128)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::2.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0159.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0159.cu new file mode 100644 index 00000000..3ca77503 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0159.cu @@ -0,0 +1,85 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 32 +#define TOP_K_MAX 20 +#define SPLIT_COUNT 4 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_k20_mergeown_08ec_s4_rowbase_lane(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 32 + lane; + int stride = num_bids * 32; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + float worst_d = 3.4e+38f; + int worst_pos = 0; + #pragma unroll + for (int split_idx = 0; split_idx < SPLIT_COUNT; split_idx++) { + int partial_base = base_row + split_idx * split_stride; + #pragma unroll + for (int cand_k = 0; cand_k < TOP_K_MAX; cand_k++) { + float cand_d = partial_dists[partial_base + cand_k]; + int cand_i = partial_indices[partial_base + cand_k]; + if (cand_d < worst_d) { + best_d[worst_pos] = cand_d; + best_i[worst_pos] = cand_i; + worst_d = best_d[0]; + worst_pos = 0; + #pragma unroll + for (int scan_pos = 1; scan_pos < TOP_K_MAX; scan_pos++) { + if (worst_d < best_d[scan_pos]) { + worst_d = best_d[scan_pos]; + worst_pos = scan_pos; + } + } + } + } + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + *((float*)(out_dists + (base_row + out_k))) = best_d[out_k]; + *((int*)(out_indices + (base_row + out_k))) = best_i[out_k]; + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0160.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0160.cu new file mode 100644 index 00000000..84f6b52d --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0160.cu @@ -0,0 +1,106 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 128 +#define TOP_K_MAX 20 + +#include + +extern "C" { + +__global__ __launch_bounds__(128, 1) void +kernel_knn_build_k20_large_rect_s3_warp_select(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int row = bid * 4 + warp; + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int cand_k = lane; + if (row < total_queries) { + float d0 = 3.4e+38f; + float d1 = 3.4e+38f; + float d2 = 3.4e+38f; + int i0 = -1; + int i1 = -1; + int i2 = -1; + if (cand_k < TOP_K_MAX) { + d0 = partial_dists[base_row + cand_k]; + i0 = partial_indices[base_row + cand_k]; + int base1 = base_row + split_stride; + d1 = partial_dists[base1 + cand_k]; + i1 = partial_indices[base1 + cand_k]; + int base2 = base1 + split_stride; + d2 = partial_dists[base2 + cand_k]; + i2 = partial_indices[base2 + cand_k]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float winner_d = d0; + int winner_i = i0; + int winner_src = 0; + if (d1 < winner_d) { + winner_d = d1; + winner_i = i1; + winner_src = 1; + } + if (d2 < winner_d) { + winner_d = d2; + winner_i = i2; + winner_src = 2; + } + float warp_min = winner_d; + float _warp_reduce_0 = warp_min; + #pragma unroll + for (int offset = 16; offset > 0; offset >>= 1) + _warp_reduce_0 = fminf(_warp_reduce_0, __shfl_xor_sync(0xFFFFFFFF, _warp_reduce_0, offset)); + warp_min = _warp_reduce_0; + unsigned int _vote_0 = __ballot_sync(0xFFFFFFFF, winner_d == warp_min); + int owner_ballot = _vote_0; + int _ffs_0 = __ffs(owner_ballot); + int winner_lane = _ffs_0 - 1; + int _shfl_0 = __shfl_sync(0xFFFFFFFF, winner_i, winner_lane); + winner_i = _shfl_0; + int _shfl_1 = __shfl_sync(0xFFFFFFFF, winner_src, winner_lane); + winner_src = _shfl_1; + if (lane == 0) { + *((float*)(out_dists + (base_row + out_k))) = warp_min; + *((int*)(out_indices + (base_row + out_k))) = winner_i; + } + if (lane == winner_lane) { + if (winner_src == 0) { + d0 = 3.4e+38f; + } else if (winner_src == 1) { + d1 = 3.4e+38f; + } else { + d2 = 3.4e+38f; + } + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0161.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0161.cu new file mode 100644 index 00000000..84c3ec88 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0161.cu @@ -0,0 +1,774 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 128 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_STRIDE 32768 +#define SMEM_SMEM_DATABASE_OFF 33792 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_DATABASE_SQ_OFF 50176 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 50432 +#define THREADS 192 +#define BLOCK_Q 128 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_MAX 10 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16_cta2( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + ".reg .b32 m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "tcgen05.mma.cta_group::2.kind::f16 [%0], %1, %2, %3, {m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step_cg2( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, %3, " + "{m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], %1;\n\t" + "}\n" + :: "r"(mbar_addr), "h"(cta_mask) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x16(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x16.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15}, [%16];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ uint32_t smem_addr(const void* ptr) { + uint32_t addr; + asm("{\n\t" + ".reg .u64 u64addr;\n\t" + "cvta.to.shared.u64 u64addr, %1;\n\t" + "cvt.u32.u64 %0, u64addr;\n\t" + "}\n" : "=r"(addr) : "l"(ptr)); + return addr; +} + + +__device__ __forceinline__ uint32_t mapa_to_rank(uint32_t local_addr, uint32_t rank) { + uint32_t remote; + asm volatile("mapa.shared::cluster.u32 %0, %1, %2;" + : "=r"(remote) : "r"(local_addr), "r"(rank)); + return remote; +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem_cta2( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global" + ".mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .b16 lo, hi;\n\t" + "mov.b32 {lo, hi}, %1;\n\t" + "tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], lo;\n\t" + "}\n" + :: "r"(mbar_addr), "r"((uint32_t)cta_mask) : "memory"); +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_evolve_7bfc_split_cg2_stage1_batch8_cond4_vmin_maxtree(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tile_pairs, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + const unsigned int clusters_x = gridDim.x / 2; + const unsigned int cluster_id = ((blockIdx.z * gridDim.y + blockIdx.y) * clusters_x) + blockIdx.x / 2; + const unsigned int num_clusters = clusters_x * gridDim.y * gridDim.z; + + int cta_rank; + asm volatile("mov.b32 %0, %%cluster_ctarank;" : "=r"(cta_rank)); + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 33792); + const int smem_database_addr = smem + 33792; + float* smem_database_sq = reinterpret_cast(smem_raw + 50176); + const int smem_database_sq_addr = smem + 50176; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 0, 2, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 16, 2, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=8 + mbarrier_init_pred(smem + 40, 8, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (128 columns, 128 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::2.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(128) : "memory"); + } + + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: load ---- + if (warp == 0) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 0) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx = cluster_id; work_idx < total_work; work_idx += num_clusters) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tile_pairs; + int q_tile_pair = query_work % num_q_tile_pairs; + int q_tile = q_tile_pair * 2 + cta_rank; + int off_q = q_tile * BLOCK_Q; + int global_q = batch_idx * Q + off_q; + int db_tile_start = split_idx * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((query_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(32768)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_query_addr), "l"(tmap_query), "r"(0), "r"(global_q), "r"(0), + "r"(((query_full_addr) & 0xFEFFFFFF)) : "memory"); + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int off_m = db_tile * BLOCK_M; + int global_m = batch_idx * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((database_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(16384)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_database_addr), "l"(tmap_database), "r"(0), "r"(global_m), "r"(0), + "r"(((database_full_addr) & 0xFEFFFFFF)) : "memory"); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 1) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + if (cta_rank == 0) { + #pragma unroll 1 + for (unsigned int work_idx_1 = cluster_id; work_idx_1 < total_work; work_idx_1 += num_clusters) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = (smem_query_addr >> 4) & 0x3FFF; + int _mma_b_lo_0 = (smem_database_addr >> 4) & 0x3FFF; + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\tmov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 270533776;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit_cg2_multicast(score_full_addr, (uint16_t)(3)); + elect_commit_cg2_multicast(database_empty_addr, (uint16_t)(3)); + } + elect_commit_cg2_multicast(query_empty_addr, (uint16_t)(3)); + } + } + } + // ---- Role: compute ---- + } else if (warp >= 2 && warp <= 5) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = cluster_id; work_idx_2 < total_work; work_idx_2 += num_clusters) { + int split_idx_1 = work_idx_2 % (unsigned int)split_count; + int query_work_1 = work_idx_2 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tile_pairs; + int q_tile_pair_1 = query_work_1 % num_q_tile_pairs; + int q_tile_1 = q_tile_pair_1 * 2 + cta_rank; + int off_q_1 = q_tile_1 * BLOCK_Q; + int q_idx = off_q_1 + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx_1 * Q + q_idx]; + } + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + float worst_d = 3.4e+38f; + int worst_pos = 0; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int db_start = db_tile_1 * BLOCK_M; + int db_sq_idx = db_start + (warp % 4 * 32 + lane); + if (warp % 4 * 32 + lane < BLOCK_M) { + if (db_sq_idx < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx_1 * M + db_sq_idx]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 0.0f; + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(cta_rank * BLOCK_Q + (warp % 4 * 32 << 16) << 16); + float _tmem_load_0[64]; + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x64.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31, %32, %33, %34, %35, %36, %37, %38, %39, %40, %41, %42, %43, %44, %45, %46, %47, %48, %49, %50, %51, %52, %53, %54, %55, %56, %57, %58, %59, %60, %61, %62, %63}, [%64];" + : "=f"(_tmem_load_0[0]), "=f"(_tmem_load_0[1]), "=f"(_tmem_load_0[2]), "=f"(_tmem_load_0[3]), "=f"(_tmem_load_0[4]), "=f"(_tmem_load_0[5]), "=f"(_tmem_load_0[6]), "=f"(_tmem_load_0[7]), "=f"(_tmem_load_0[8]), "=f"(_tmem_load_0[9]), "=f"(_tmem_load_0[10]), "=f"(_tmem_load_0[11]), "=f"(_tmem_load_0[12]), "=f"(_tmem_load_0[13]), "=f"(_tmem_load_0[14]), "=f"(_tmem_load_0[15]), "=f"(_tmem_load_0[16]), "=f"(_tmem_load_0[17]), "=f"(_tmem_load_0[18]), "=f"(_tmem_load_0[19]), "=f"(_tmem_load_0[20]), "=f"(_tmem_load_0[21]), "=f"(_tmem_load_0[22]), "=f"(_tmem_load_0[23]), "=f"(_tmem_load_0[24]), "=f"(_tmem_load_0[25]), "=f"(_tmem_load_0[26]), "=f"(_tmem_load_0[27]), "=f"(_tmem_load_0[28]), "=f"(_tmem_load_0[29]), "=f"(_tmem_load_0[30]), "=f"(_tmem_load_0[31]), "=f"(_tmem_load_0[32]), "=f"(_tmem_load_0[33]), "=f"(_tmem_load_0[34]), "=f"(_tmem_load_0[35]), "=f"(_tmem_load_0[36]), "=f"(_tmem_load_0[37]), "=f"(_tmem_load_0[38]), "=f"(_tmem_load_0[39]), "=f"(_tmem_load_0[40]), "=f"(_tmem_load_0[41]), "=f"(_tmem_load_0[42]), "=f"(_tmem_load_0[43]), "=f"(_tmem_load_0[44]), "=f"(_tmem_load_0[45]), "=f"(_tmem_load_0[46]), "=f"(_tmem_load_0[47]), "=f"(_tmem_load_0[48]), "=f"(_tmem_load_0[49]), "=f"(_tmem_load_0[50]), "=f"(_tmem_load_0[51]), "=f"(_tmem_load_0[52]), "=f"(_tmem_load_0[53]), "=f"(_tmem_load_0[54]), "=f"(_tmem_load_0[55]), "=f"(_tmem_load_0[56]), "=f"(_tmem_load_0[57]), "=f"(_tmem_load_0[58]), "=f"(_tmem_load_0[59]), "=f"(_tmem_load_0[60]), "=f"(_tmem_load_0[61]), "=f"(_tmem_load_0[62]), "=f"(_tmem_load_0[63]) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + if (elect_sync()) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cluster.b64 _, [%0];" + :: "r"((score_empty_addr) & 0xFEFFFFFF) : "memory"); + } + if (valid_q != 0) { + #pragma unroll 1 + for (int col_base = 0; col_base < 64; col_base += 8) { + float dist_vec0[4]; + dist_vec0[0] = _tmem_load_0[col_base]; + dist_vec0[1] = _tmem_load_0[col_base + 1]; + dist_vec0[2] = _tmem_load_0[col_base + 2]; + dist_vec0[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec0)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec0[4]; + db_sq_vec0[0] = smem_database_sq[col_base]; + db_sq_vec0[1] = smem_database_sq[col_base + 1]; + db_sq_vec0[2] = smem_database_sq[col_base + 2]; + db_sq_vec0[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec0)[_la], reinterpret_cast(db_sq_vec0)[_la]); + float dist_vec1[4]; + dist_vec1[0] = _tmem_load_0[col_base + 4]; + dist_vec1[1] = _tmem_load_0[col_base + 5]; + dist_vec1[2] = _tmem_load_0[col_base + 6]; + dist_vec1[3] = _tmem_load_0[col_base + 7]; + const float2 _fma_b2_2 = {-2.0f, -2.0f}; + const float2 _fma_c2_3 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec1)[_lf], _fma_b2_2, _fma_c2_3); + float db_sq_vec1[4]; + db_sq_vec1[0] = smem_database_sq[col_base + 4]; + db_sq_vec1[1] = smem_database_sq[col_base + 5]; + db_sq_vec1[2] = smem_database_sq[col_base + 6]; + db_sq_vec1[3] = smem_database_sq[col_base + 7]; + float _t1[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t1)[_la] = add_f32x2(reinterpret_cast(dist_vec1)[_la], reinterpret_cast(db_sq_vec1)[_la]); + float _t0_min = _t0[0]; + #pragma unroll + for (int _lr = 1; _lr < 4; _lr++) { + _t0_min = fminf(_t0_min, _t0[_lr]); + } + float group_min0 = _t0_min; + float _t1_min = _t1[0]; + #pragma unroll + for (int _lr = 1; _lr < 4; _lr++) { + _t1_min = fminf(_t1_min, _t1[_lr]); + } + float group_min1 = _t1_min; + if (group_min0 < worst_d) { + #pragma unroll + for (int vec_col = 0; vec_col < 4; vec_col++) { + int db_idx = db_start + col_base + vec_col; + if (db_idx < M) { + float dist = _t0[vec_col]; + if (dist < worst_d) { + best_d[worst_pos] = dist; + best_i[worst_pos] = db_idx; + int cmp01 = ((best_d[1] > best_d[0]) ? 1 : 0); + float max01_d = ((cmp01 != 0) ? best_d[1] : best_d[0]); + int max01_p = ((cmp01 != 0) ? 1 : 0); + int cmp23 = ((best_d[3] > best_d[2]) ? 1 : 0); + float max23_d = ((cmp23 != 0) ? best_d[3] : best_d[2]); + int max23_p = ((cmp23 != 0) ? 3 : 2); + int cmp45 = ((best_d[5] > best_d[4]) ? 1 : 0); + float max45_d = ((cmp45 != 0) ? best_d[5] : best_d[4]); + int max45_p = ((cmp45 != 0) ? 5 : 4); + int cmp67 = ((best_d[7] > best_d[6]) ? 1 : 0); + float max67_d = ((cmp67 != 0) ? best_d[7] : best_d[6]); + int max67_p = ((cmp67 != 0) ? 7 : 6); + int cmp89 = ((best_d[9] > best_d[8]) ? 1 : 0); + float max89_d = ((cmp89 != 0) ? best_d[9] : best_d[8]); + int max89_p = ((cmp89 != 0) ? 9 : 8); + int cmp0123 = ((max23_d > max01_d) ? 1 : 0); + float max0123_d = ((cmp0123 != 0) ? max23_d : max01_d); + int max0123_p = ((cmp0123 != 0) ? max23_p : max01_p); + int cmp4567 = ((max67_d > max45_d) ? 1 : 0); + float max4567_d = ((cmp4567 != 0) ? max67_d : max45_d); + int max4567_p = ((cmp4567 != 0) ? max67_p : max45_p); + int cmp0_7 = ((max4567_d > max0123_d) ? 1 : 0); + float max0_7_d = ((cmp0_7 != 0) ? max4567_d : max0123_d); + int max0_7_p = ((cmp0_7 != 0) ? max4567_p : max0123_p); + int cmp_all = ((max89_d > max0_7_d) ? 1 : 0); + worst_d = ((cmp_all != 0) ? max89_d : max0_7_d); + worst_pos = ((cmp_all != 0) ? max89_p : max0_7_p); + } + } + } + } + if (group_min1 < worst_d) { + #pragma unroll + for (int vec_col_1 = 0; vec_col_1 < 4; vec_col_1++) { + int db_idx_1 = db_start + col_base + 4 + vec_col_1; + if (db_idx_1 < M) { + float dist_1 = _t1[vec_col_1]; + if (dist_1 < worst_d) { + best_d[worst_pos] = dist_1; + best_i[worst_pos] = db_idx_1; + int cmp01_1 = ((best_d[1] > best_d[0]) ? 1 : 0); + float max01_d_1 = ((cmp01_1 != 0) ? best_d[1] : best_d[0]); + int max01_p_1 = ((cmp01_1 != 0) ? 1 : 0); + int cmp23_1 = ((best_d[3] > best_d[2]) ? 1 : 0); + float max23_d_1 = ((cmp23_1 != 0) ? best_d[3] : best_d[2]); + int max23_p_1 = ((cmp23_1 != 0) ? 3 : 2); + int cmp45_1 = ((best_d[5] > best_d[4]) ? 1 : 0); + float max45_d_1 = ((cmp45_1 != 0) ? best_d[5] : best_d[4]); + int max45_p_1 = ((cmp45_1 != 0) ? 5 : 4); + int cmp67_1 = ((best_d[7] > best_d[6]) ? 1 : 0); + float max67_d_1 = ((cmp67_1 != 0) ? best_d[7] : best_d[6]); + int max67_p_1 = ((cmp67_1 != 0) ? 7 : 6); + int cmp89_1 = ((best_d[9] > best_d[8]) ? 1 : 0); + float max89_d_1 = ((cmp89_1 != 0) ? best_d[9] : best_d[8]); + int max89_p_1 = ((cmp89_1 != 0) ? 9 : 8); + int cmp0123_1 = ((max23_d_1 > max01_d_1) ? 1 : 0); + float max0123_d_1 = ((cmp0123_1 != 0) ? max23_d_1 : max01_d_1); + int max0123_p_1 = ((cmp0123_1 != 0) ? max23_p_1 : max01_p_1); + int cmp4567_1 = ((max67_d_1 > max45_d_1) ? 1 : 0); + float max4567_d_1 = ((cmp4567_1 != 0) ? max67_d_1 : max45_d_1); + int max4567_p_1 = ((cmp4567_1 != 0) ? max67_p_1 : max45_p_1); + int cmp0_7_1 = ((max4567_d_1 > max0123_d_1) ? 1 : 0); + float max0_7_d_1 = ((cmp0_7_1 != 0) ? max4567_d_1 : max0123_d_1); + int max0_7_p_1 = ((cmp0_7_1 != 0) ? max4567_p_1 : max0123_p_1); + int cmp_all_1 = ((max89_d_1 > max0_7_d_1) ? 1 : 0); + worst_d = ((cmp_all_1 != 0) ? max89_d_1 : max0_7_d_1); + worst_pos = ((cmp_all_1 != 0) ? max89_p_1 : max0_7_p_1); + } + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + if (valid_q != 0) { + int out_base = ((split_idx_1 * B + batch_idx_1) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + int cmp01_min = ((best_d[1] < best_d[0]) ? 1 : 0); + float min01_d = ((cmp01_min != 0) ? best_d[1] : best_d[0]); + int min01_i = ((cmp01_min != 0) ? best_i[1] : best_i[0]); + int min01_p = ((cmp01_min != 0) ? 1 : 0); + int cmp23_min = ((best_d[3] < best_d[2]) ? 1 : 0); + float min23_d = ((cmp23_min != 0) ? best_d[3] : best_d[2]); + int min23_i = ((cmp23_min != 0) ? best_i[3] : best_i[2]); + int min23_p = ((cmp23_min != 0) ? 3 : 2); + int cmp45_min = ((best_d[5] < best_d[4]) ? 1 : 0); + float min45_d = ((cmp45_min != 0) ? best_d[5] : best_d[4]); + int min45_i = ((cmp45_min != 0) ? best_i[5] : best_i[4]); + int min45_p = ((cmp45_min != 0) ? 5 : 4); + int cmp67_min = ((best_d[7] < best_d[6]) ? 1 : 0); + float min67_d = ((cmp67_min != 0) ? best_d[7] : best_d[6]); + int min67_i = ((cmp67_min != 0) ? best_i[7] : best_i[6]); + int min67_p = ((cmp67_min != 0) ? 7 : 6); + int cmp89_min = ((best_d[9] < best_d[8]) ? 1 : 0); + float min89_d = ((cmp89_min != 0) ? best_d[9] : best_d[8]); + int min89_i = ((cmp89_min != 0) ? best_i[9] : best_i[8]); + int min89_p = ((cmp89_min != 0) ? 9 : 8); + int cmp0123_min = ((min23_d < min01_d) ? 1 : 0); + float min0123_d = ((cmp0123_min != 0) ? min23_d : min01_d); + int min0123_i = ((cmp0123_min != 0) ? min23_i : min01_i); + int min0123_p = ((cmp0123_min != 0) ? min23_p : min01_p); + int cmp4567_min = ((min67_d < min45_d) ? 1 : 0); + float min4567_d = ((cmp4567_min != 0) ? min67_d : min45_d); + int min4567_i = ((cmp4567_min != 0) ? min67_i : min45_i); + int min4567_p = ((cmp4567_min != 0) ? min67_p : min45_p); + int cmp0_7_min = ((min4567_d < min0123_d) ? 1 : 0); + float min0_7_d = ((cmp0_7_min != 0) ? min4567_d : min0123_d); + int min0_7_i = ((cmp0_7_min != 0) ? min4567_i : min0123_i); + int min0_7_p = ((cmp0_7_min != 0) ? min4567_p : min0123_p); + int cmp_all_min = ((min89_d < min0_7_d) ? 1 : 0); + float selected_d = ((cmp_all_min != 0) ? min89_d : min0_7_d); + int selected_i = ((cmp_all_min != 0) ? min89_i : min0_7_i); + int selected_pos = ((cmp_all_min != 0) ? min89_p : min0_7_p); + if (out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = selected_d; + *((int*)(partial_indices + (out_base + out_k))) = selected_i; + } + best_d[selected_pos] = 3.4e+38f; + best_i[selected_pos] = -1; + } + } + } + } + } + + // Cleanup + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::2.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(128)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::2.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0162.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0162.cu new file mode 100644 index 00000000..28f3a0e8 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0162.cu @@ -0,0 +1,757 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 128 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_STRIDE 32768 +#define SMEM_SMEM_DATABASE_OFF 33792 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_DATABASE_SQ_OFF 50176 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 50432 +#define THREADS 192 +#define BLOCK_Q 128 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_MAX 32 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16_cta2( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + ".reg .b32 m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "tcgen05.mma.cta_group::2.kind::f16 [%0], %1, %2, %3, {m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step_cg2( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, %3, " + "{m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], %1;\n\t" + "}\n" + :: "r"(mbar_addr), "h"(cta_mask) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x16(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x16.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15}, [%16];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ uint32_t smem_addr(const void* ptr) { + uint32_t addr; + asm("{\n\t" + ".reg .u64 u64addr;\n\t" + "cvta.to.shared.u64 u64addr, %1;\n\t" + "cvt.u32.u64 %0, u64addr;\n\t" + "}\n" : "=r"(addr) : "l"(ptr)); + return addr; +} + + +__device__ __forceinline__ uint32_t mapa_to_rank(uint32_t local_addr, uint32_t rank) { + uint32_t remote; + asm volatile("mapa.shared::cluster.u32 %0, %1, %2;" + : "=r"(remote) : "r"(local_addr), "r"(rank)); + return remote; +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem_cta2( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global" + ".mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .b16 lo, hi;\n\t" + "mov.b32 {lo, hi}, %1;\n\t" + "tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], lo;\n\t" + "}\n" + :: "r"(mbar_addr), "r"((uint32_t)cta_mask) : "memory"); +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_rag_frontier_b6d4_stage1_k32_sort4earlystop(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tile_pairs, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + const unsigned int clusters_x = gridDim.x / 2; + const unsigned int cluster_id = ((blockIdx.z * gridDim.y + blockIdx.y) * clusters_x) + blockIdx.x / 2; + const unsigned int num_clusters = clusters_x * gridDim.y * gridDim.z; + + int cta_rank; + asm volatile("mov.b32 %0, %%cluster_ctarank;" : "=r"(cta_rank)); + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 33792); + const int smem_database_addr = smem + 33792; + float* smem_database_sq = reinterpret_cast(smem_raw + 50176); + const int smem_database_sq_addr = smem + 50176; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 0, 2, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 16, 2, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=8 + mbarrier_init_pred(smem + 40, 8, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (128 columns, 128 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::2.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(128) : "memory"); + } + + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: load ---- + if (warp == 0) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 0) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx = cluster_id; work_idx < total_work; work_idx += num_clusters) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tile_pairs; + int q_tile_pair = query_work % num_q_tile_pairs; + int q_tile = q_tile_pair * 2 + cta_rank; + int off_q = q_tile * BLOCK_Q; + int global_q = batch_idx * Q + off_q; + int db_tile_start = split_idx * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((query_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(32768)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_query_addr), "l"(tmap_query), "r"(0), "r"(global_q), "r"(0), + "r"(((query_full_addr) & 0xFEFFFFFF)) : "memory"); + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int off_m = db_tile * BLOCK_M; + int global_m = batch_idx * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((database_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(16384)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_database_addr), "l"(tmap_database), "r"(0), "r"(global_m), "r"(0), + "r"(((database_full_addr) & 0xFEFFFFFF)) : "memory"); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 1) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + if (cta_rank == 0) { + #pragma unroll 1 + for (unsigned int work_idx_1 = cluster_id; work_idx_1 < total_work; work_idx_1 += num_clusters) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = (smem_query_addr >> 4) & 0x3FFF; + int _mma_b_lo_0 = (smem_database_addr >> 4) & 0x3FFF; + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\tmov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 270533776;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit_cg2_multicast(score_full_addr, (uint16_t)(3)); + elect_commit_cg2_multicast(database_empty_addr, (uint16_t)(3)); + } + elect_commit_cg2_multicast(query_empty_addr, (uint16_t)(3)); + } + } + } + // ---- Role: compute ---- + } else if (warp >= 2 && warp <= 5) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = cluster_id; work_idx_2 < total_work; work_idx_2 += num_clusters) { + int split_idx_1 = work_idx_2 % (unsigned int)split_count; + int query_work_1 = work_idx_2 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tile_pairs; + int q_tile_pair_1 = query_work_1 % num_q_tile_pairs; + int q_tile_1 = q_tile_pair_1 * 2 + cta_rank; + int off_q_1 = q_tile_1 * BLOCK_Q; + int q_idx = off_q_1 + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx_1 * Q + q_idx]; + } + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + float chunk_worst_d[4]; + int chunk_worst_pos[4]; + #pragma unroll + for (int chunk = 0; chunk < 4; chunk++) { + int chunk_base = chunk * 8; + chunk_worst_d[chunk] = 3.4e+38f; + chunk_worst_pos[chunk] = chunk_base; + } + float worst_d = 3.4e+38f; + int worst_pos = 0; + int worst_chunk = 0; + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int db_start = db_tile_1 * BLOCK_M; + int db_sq_idx = db_start + (warp % 4 * 32 + lane); + if (warp % 4 * 32 + lane < BLOCK_M) { + if (db_sq_idx < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx_1 * M + db_sq_idx]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 0.0f; + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(cta_rank * BLOCK_Q + (warp % 4 * 32 << 16) << 16); + float _tmem_load_0[64]; + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x64.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31, %32, %33, %34, %35, %36, %37, %38, %39, %40, %41, %42, %43, %44, %45, %46, %47, %48, %49, %50, %51, %52, %53, %54, %55, %56, %57, %58, %59, %60, %61, %62, %63}, [%64];" + : "=f"(_tmem_load_0[0]), "=f"(_tmem_load_0[1]), "=f"(_tmem_load_0[2]), "=f"(_tmem_load_0[3]), "=f"(_tmem_load_0[4]), "=f"(_tmem_load_0[5]), "=f"(_tmem_load_0[6]), "=f"(_tmem_load_0[7]), "=f"(_tmem_load_0[8]), "=f"(_tmem_load_0[9]), "=f"(_tmem_load_0[10]), "=f"(_tmem_load_0[11]), "=f"(_tmem_load_0[12]), "=f"(_tmem_load_0[13]), "=f"(_tmem_load_0[14]), "=f"(_tmem_load_0[15]), "=f"(_tmem_load_0[16]), "=f"(_tmem_load_0[17]), "=f"(_tmem_load_0[18]), "=f"(_tmem_load_0[19]), "=f"(_tmem_load_0[20]), "=f"(_tmem_load_0[21]), "=f"(_tmem_load_0[22]), "=f"(_tmem_load_0[23]), "=f"(_tmem_load_0[24]), "=f"(_tmem_load_0[25]), "=f"(_tmem_load_0[26]), "=f"(_tmem_load_0[27]), "=f"(_tmem_load_0[28]), "=f"(_tmem_load_0[29]), "=f"(_tmem_load_0[30]), "=f"(_tmem_load_0[31]), "=f"(_tmem_load_0[32]), "=f"(_tmem_load_0[33]), "=f"(_tmem_load_0[34]), "=f"(_tmem_load_0[35]), "=f"(_tmem_load_0[36]), "=f"(_tmem_load_0[37]), "=f"(_tmem_load_0[38]), "=f"(_tmem_load_0[39]), "=f"(_tmem_load_0[40]), "=f"(_tmem_load_0[41]), "=f"(_tmem_load_0[42]), "=f"(_tmem_load_0[43]), "=f"(_tmem_load_0[44]), "=f"(_tmem_load_0[45]), "=f"(_tmem_load_0[46]), "=f"(_tmem_load_0[47]), "=f"(_tmem_load_0[48]), "=f"(_tmem_load_0[49]), "=f"(_tmem_load_0[50]), "=f"(_tmem_load_0[51]), "=f"(_tmem_load_0[52]), "=f"(_tmem_load_0[53]), "=f"(_tmem_load_0[54]), "=f"(_tmem_load_0[55]), "=f"(_tmem_load_0[56]), "=f"(_tmem_load_0[57]), "=f"(_tmem_load_0[58]), "=f"(_tmem_load_0[59]), "=f"(_tmem_load_0[60]), "=f"(_tmem_load_0[61]), "=f"(_tmem_load_0[62]), "=f"(_tmem_load_0[63]) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + if (elect_sync()) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cluster.b64 _, [%0];" + :: "r"((score_empty_addr) & 0xFEFFFFFF) : "memory"); + } + if (valid_q != 0) { + #pragma unroll 2 + for (int col_base = 0; col_base < 64; col_base += 4) { + float dist_vec[4]; + dist_vec[0] = _tmem_load_0[col_base]; + dist_vec[1] = _tmem_load_0[col_base + 1]; + dist_vec[2] = _tmem_load_0[col_base + 2]; + dist_vec[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec[4]; + db_sq_vec[0] = smem_database_sq[col_base]; + db_sq_vec[1] = smem_database_sq[col_base + 1]; + db_sq_vec[2] = smem_database_sq[col_base + 2]; + db_sq_vec[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec)[_la], reinterpret_cast(db_sq_vec)[_la]); + float group_min = _t0[0]; + if (_t0[1] < group_min) { + group_min = _t0[1]; + } + if (_t0[2] < group_min) { + group_min = _t0[2]; + } + if (_t0[3] < group_min) { + group_min = _t0[3]; + } + if (group_min < worst_d) { + float sort_d0 = _t0[0]; + float sort_d1 = _t0[1]; + float sort_d2 = _t0[2]; + float sort_d3 = _t0[3]; + int sort_col0 = 0; + int sort_col1 = 1; + int sort_col2 = 2; + int sort_col3 = 3; + float tmp_d = 0.0f; + int tmp_col = 0; + if (sort_d1 < sort_d0) { + tmp_d = sort_d0; + sort_d0 = sort_d1; + sort_d1 = tmp_d; + tmp_col = sort_col0; + sort_col0 = sort_col1; + sort_col1 = tmp_col; + } + if (sort_d3 < sort_d2) { + tmp_d = sort_d2; + sort_d2 = sort_d3; + sort_d3 = tmp_d; + tmp_col = sort_col2; + sort_col2 = sort_col3; + sort_col3 = tmp_col; + } + if (sort_d2 < sort_d0) { + tmp_d = sort_d0; + sort_d0 = sort_d2; + sort_d2 = tmp_d; + tmp_col = sort_col0; + sort_col0 = sort_col2; + sort_col2 = tmp_col; + } + if (sort_d3 < sort_d1) { + tmp_d = sort_d1; + sort_d1 = sort_d3; + sort_d3 = tmp_d; + tmp_col = sort_col1; + sort_col1 = sort_col3; + sort_col3 = tmp_col; + } + if (sort_d2 < sort_d1) { + tmp_d = sort_d1; + sort_d1 = sort_d2; + sort_d2 = tmp_d; + tmp_col = sort_col1; + sort_col1 = sort_col2; + sort_col2 = tmp_col; + } + #pragma unroll + for (int visit = 0; visit < 4; visit++) { + int vec_col = sort_col0; + float dist = sort_d0; + if (visit == 1) { + vec_col = sort_col1; + dist = sort_d1; + } + if (visit == 2) { + vec_col = sort_col2; + dist = sort_d2; + } + if (visit == 3) { + vec_col = sort_col3; + dist = sort_d3; + } + if (dist >= worst_d) { + break; + } + int db_idx = db_start + col_base + vec_col; + if (db_idx < M) { + if (dist < worst_d) { + best_d[worst_pos] = dist; + best_i[worst_pos] = db_idx; + int refresh_base = worst_chunk * 8; + chunk_worst_d[worst_chunk] = best_d[refresh_base]; + chunk_worst_pos[worst_chunk] = refresh_base; + #pragma unroll + for (int offset = 1; offset < 8; offset++) { + int scan_pos = refresh_base + offset; + if (best_d[scan_pos] > chunk_worst_d[worst_chunk]) { + chunk_worst_d[worst_chunk] = best_d[scan_pos]; + chunk_worst_pos[worst_chunk] = scan_pos; + } + } + worst_d = chunk_worst_d[0]; + worst_pos = chunk_worst_pos[0]; + worst_chunk = 0; + #pragma unroll + for (int chunk_1 = 1; chunk_1 < 4; chunk_1++) { + if (worst_d < chunk_worst_d[chunk_1]) { + worst_d = chunk_worst_d[chunk_1]; + worst_pos = chunk_worst_pos[chunk_1]; + worst_chunk = chunk_1; + } + } + } + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + if (valid_q != 0) { + int out_base = ((split_idx_1 * B + batch_idx_1) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_out_d = best_d[0]; + int best_out_i = best_i[0]; + int best_out_pos = 0; + #pragma unroll + for (int scan_pos_1 = 1; scan_pos_1 < TOP_K_MAX; scan_pos_1++) { + if (best_out_d > best_d[scan_pos_1]) { + best_out_d = best_d[scan_pos_1]; + best_out_i = best_i[scan_pos_1]; + best_out_pos = scan_pos_1; + } + } + if (out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = best_out_d; + *((int*)(partial_indices + (out_base + out_k))) = best_out_i; + } + best_d[best_out_pos] = 3.4e+38f; + } + } + } + } + } + + // Cleanup + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::2.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(128)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::2.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0163.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0163.cu new file mode 100644 index 00000000..0a42c5c5 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0163.cu @@ -0,0 +1,141 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define SMEM_GROUP_DISTS_OFF 0 +#define SMEM_GROUP_DISTS_STAGE_BYTES 1024 +#define SMEM_GROUP_DISTS_STRIDE 1024 +#define SMEM_GROUP_INDICES_OFF 1024 +#define SMEM_GROUP_INDICES_STAGE_BYTES 1024 +#define SMEM_GROUP_INDICES_STRIDE 1024 +#define SMEM_TOTAL 2048 +#define THREADS 32 +#define TOP_K_MAX 32 +#define GROUP_COUNT 8 +#define GROUP_SPLITS 9 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_rag_frontier_7399_k32_fused_group_final_merge(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + float* group_dists = reinterpret_cast(smem_raw + 0); + const int group_dists_addr = smem + 0; + int* group_indices = reinterpret_cast(smem_raw + 1024); + const int group_indices_addr = smem + 1024; + + // === Task calls (dependency order) === + int split_pos[GROUP_SPLITS]; + int split_base[GROUP_SPLITS]; + float group_cand_d[GROUP_SPLITS]; + int group_cand_i[GROUP_SPLITS]; + int final_pos[GROUP_COUNT]; + float final_cand_d[GROUP_COUNT]; + int final_cand_i[GROUP_COUNT]; + #pragma unroll 1 + for (int row = bid; row < total_queries; row += num_bids) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + if (tid < GROUP_COUNT) { + int group_idx = tid; + int source_split0 = group_idx * GROUP_SPLITS; + int shared_base = group_idx * TOP_K_MAX; + #pragma unroll + for (int local_split = 0; local_split < GROUP_SPLITS; local_split++) { + split_pos[local_split] = 0; + int split_id = source_split0 + local_split; + split_base[local_split] = base_row + split_id * split_stride; + group_cand_d[local_split] = partial_dists[split_base[local_split]]; + group_cand_i[local_split] = partial_indices[split_base[local_split]]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_d = group_cand_d[0]; + int best_i = group_cand_i[0]; + int best_split = 0; + #pragma unroll + for (int local_split_1 = 1; local_split_1 < GROUP_SPLITS; local_split_1++) { + if (best_d > group_cand_d[local_split_1]) { + best_d = group_cand_d[local_split_1]; + best_i = group_cand_i[local_split_1]; + best_split = local_split_1; + } + } + group_dists[shared_base + out_k] = best_d; + group_indices[shared_base + out_k] = best_i; + split_pos[best_split] = split_pos[best_split] + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_pos = split_pos[best_split]; + int next_addr = split_base[best_split] + next_pos; + group_cand_d[best_split] = partial_dists[next_addr]; + group_cand_i[best_split] = partial_indices[next_addr]; + } + } + } + __syncthreads(); + if (tid == 0) { + #pragma unroll + for (int group_idx_1 = 0; group_idx_1 < GROUP_COUNT; group_idx_1++) { + final_pos[group_idx_1] = 0; + int group_base = group_idx_1 * TOP_K_MAX; + final_cand_d[group_idx_1] = group_dists[group_base]; + final_cand_i[group_idx_1] = group_indices[group_base]; + } + #pragma unroll + for (int out_k_1 = 0; out_k_1 < TOP_K_MAX; out_k_1++) { + float best_d_1 = final_cand_d[0]; + int best_i_1 = final_cand_i[0]; + int best_group = 0; + #pragma unroll + for (int group_idx_2 = 1; group_idx_2 < GROUP_COUNT; group_idx_2++) { + if (best_d_1 > final_cand_d[group_idx_2]) { + best_d_1 = final_cand_d[group_idx_2]; + best_i_1 = final_cand_i[group_idx_2]; + best_group = group_idx_2; + } + } + *((float*)(out_dists + (base_row + out_k_1))) = best_d_1; + *((int*)(out_indices + (base_row + out_k_1))) = best_i_1; + final_pos[best_group] = final_pos[best_group] + 1; + if (out_k_1 + 1 < TOP_K_MAX) { + int next_pos_1 = final_pos[best_group]; + int next_addr_1 = best_group * TOP_K_MAX + next_pos_1; + final_cand_d[best_group] = group_dists[next_addr_1]; + final_cand_i[best_group] = group_indices[next_addr_1]; + } + } + } + __syncthreads(); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0164.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0164.cu new file mode 100644 index 00000000..f878663e --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0164.cu @@ -0,0 +1,84 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 32 +#define TOP_K_MAX 32 +#define SPLIT_COUNT 32 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_k32s32_4b5c(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 32 + tid; + int stride = num_bids * 32; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int out_base = base_row; + int split_pos[SPLIT_COUNT]; + int split_base[SPLIT_COUNT]; + float cand_d[SPLIT_COUNT]; + int cand_i[SPLIT_COUNT]; + #pragma unroll + for (int split_idx = 0; split_idx < SPLIT_COUNT; split_idx++) { + split_pos[split_idx] = 0; + split_base[split_idx] = base_row + split_idx * split_stride; + cand_d[split_idx] = partial_dists[split_base[split_idx]]; + cand_i[split_idx] = partial_indices[split_base[split_idx]]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_d = cand_d[0]; + int best_i = cand_i[0]; + int best_split = 0; + #pragma unroll + for (int split_idx_1 = 1; split_idx_1 < SPLIT_COUNT; split_idx_1++) { + if (best_d > cand_d[split_idx_1]) { + best_d = cand_d[split_idx_1]; + best_i = cand_i[split_idx_1]; + best_split = split_idx_1; + } + } + *((float*)(out_dists + (out_base + out_k))) = best_d; + *((int*)(out_indices + (out_base + out_k))) = best_i; + split_pos[best_split] = split_pos[best_split] + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_pos = split_pos[best_split]; + int next_addr = split_base[best_split] + next_pos; + cand_d[best_split] = partial_dists[next_addr]; + cand_i[best_split] = partial_indices[next_addr]; + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0165.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0165.cu new file mode 100644 index 00000000..3f377496 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0165.cu @@ -0,0 +1,655 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 128 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_STRIDE 32768 +#define SMEM_SMEM_DATABASE_OFF 33792 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_DATABASE_SQ_OFF 50176 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 50432 +#define THREADS 192 +#define BLOCK_Q 128 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_MAX 96 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16_cta2( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + ".reg .b32 m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "tcgen05.mma.cta_group::2.kind::f16 [%0], %1, %2, %3, {m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step_cg2( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, %3, " + "{m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], %1;\n\t" + "}\n" + :: "r"(mbar_addr), "h"(cta_mask) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x16(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x16.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15}, [%16];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ uint32_t smem_addr(const void* ptr) { + uint32_t addr; + asm("{\n\t" + ".reg .u64 u64addr;\n\t" + "cvta.to.shared.u64 u64addr, %1;\n\t" + "cvt.u32.u64 %0, u64addr;\n\t" + "}\n" : "=r"(addr) : "l"(ptr)); + return addr; +} + + +__device__ __forceinline__ uint32_t mapa_to_rank(uint32_t local_addr, uint32_t rank) { + uint32_t remote; + asm volatile("mapa.shared::cluster.u32 %0, %1, %2;" + : "=r"(remote) : "r"(local_addr), "r"(rank)); + return remote; +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem_cta2( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global" + ".mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .b16 lo, hi;\n\t" + "mov.b32 {lo, hi}, %1;\n\t" + "tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], lo;\n\t" + "}\n" + :: "r"(mbar_addr), "r"((uint32_t)cta_mask) : "memory"); +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_k96over64(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tile_pairs, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + const unsigned int clusters_x = gridDim.x / 2; + const unsigned int cluster_id = ((blockIdx.z * gridDim.y + blockIdx.y) * clusters_x) + blockIdx.x / 2; + const unsigned int num_clusters = clusters_x * gridDim.y * gridDim.z; + + int cta_rank; + asm volatile("mov.b32 %0, %%cluster_ctarank;" : "=r"(cta_rank)); + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 33792); + const int smem_database_addr = smem + 33792; + float* smem_database_sq = reinterpret_cast(smem_raw + 50176); + const int smem_database_sq_addr = smem + 50176; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 0, 2, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 16, 2, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=8 + mbarrier_init_pred(smem + 40, 8, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (128 columns, 128 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::2.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(128) : "memory"); + } + + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: load ---- + if (warp == 0) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 0) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx = cluster_id; work_idx < total_work; work_idx += num_clusters) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tile_pairs; + int q_tile_pair = query_work % num_q_tile_pairs; + int q_tile = q_tile_pair * 2 + cta_rank; + int off_q = q_tile * BLOCK_Q; + int global_q = batch_idx * Q + off_q; + int db_tile_start = split_idx * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((query_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(32768)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_query_addr), "l"(tmap_query), "r"(0), "r"(global_q), "r"(0), + "r"(((query_full_addr) & 0xFEFFFFFF)) : "memory"); + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int off_m = db_tile * BLOCK_M; + int global_m = batch_idx * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((database_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(16384)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_database_addr), "l"(tmap_database), "r"(0), "r"(global_m), "r"(0), + "r"(((database_full_addr) & 0xFEFFFFFF)) : "memory"); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 1) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + if (cta_rank == 0) { + #pragma unroll 1 + for (unsigned int work_idx_1 = cluster_id; work_idx_1 < total_work; work_idx_1 += num_clusters) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = (smem_query_addr >> 4) & 0x3FFF; + int _mma_b_lo_0 = (smem_database_addr >> 4) & 0x3FFF; + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\tmov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 270533776;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit_cg2_multicast(score_full_addr, (uint16_t)(3)); + elect_commit_cg2_multicast(database_empty_addr, (uint16_t)(3)); + } + elect_commit_cg2_multicast(query_empty_addr, (uint16_t)(3)); + } + } + } + // ---- Role: compute ---- + } else if (warp >= 2 && warp <= 5) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = cluster_id; work_idx_2 < total_work; work_idx_2 += num_clusters) { + int split_idx_1 = work_idx_2 % (unsigned int)split_count; + int query_work_1 = work_idx_2 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tile_pairs; + int q_tile_pair_1 = query_work_1 % num_q_tile_pairs; + int q_tile_1 = q_tile_pair_1 * 2 + cta_rank; + int off_q_1 = q_tile_1 * BLOCK_Q; + int q_idx = off_q_1 + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx_1 * Q + q_idx]; + } + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + float worst_d = 3.4e+38f; + int worst_pos = 0; + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int db_start = db_tile_1 * BLOCK_M; + int db_sq_idx = db_start + (warp % 4 * 32 + lane); + if (warp % 4 * 32 + lane < BLOCK_M) { + if (db_sq_idx < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx_1 * M + db_sq_idx]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 0.0f; + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(cta_rank * BLOCK_Q + (warp % 4 * 32 << 16) << 16); + float _tmem_load_0[64]; + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x64.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31, %32, %33, %34, %35, %36, %37, %38, %39, %40, %41, %42, %43, %44, %45, %46, %47, %48, %49, %50, %51, %52, %53, %54, %55, %56, %57, %58, %59, %60, %61, %62, %63}, [%64];" + : "=f"(_tmem_load_0[0]), "=f"(_tmem_load_0[1]), "=f"(_tmem_load_0[2]), "=f"(_tmem_load_0[3]), "=f"(_tmem_load_0[4]), "=f"(_tmem_load_0[5]), "=f"(_tmem_load_0[6]), "=f"(_tmem_load_0[7]), "=f"(_tmem_load_0[8]), "=f"(_tmem_load_0[9]), "=f"(_tmem_load_0[10]), "=f"(_tmem_load_0[11]), "=f"(_tmem_load_0[12]), "=f"(_tmem_load_0[13]), "=f"(_tmem_load_0[14]), "=f"(_tmem_load_0[15]), "=f"(_tmem_load_0[16]), "=f"(_tmem_load_0[17]), "=f"(_tmem_load_0[18]), "=f"(_tmem_load_0[19]), "=f"(_tmem_load_0[20]), "=f"(_tmem_load_0[21]), "=f"(_tmem_load_0[22]), "=f"(_tmem_load_0[23]), "=f"(_tmem_load_0[24]), "=f"(_tmem_load_0[25]), "=f"(_tmem_load_0[26]), "=f"(_tmem_load_0[27]), "=f"(_tmem_load_0[28]), "=f"(_tmem_load_0[29]), "=f"(_tmem_load_0[30]), "=f"(_tmem_load_0[31]), "=f"(_tmem_load_0[32]), "=f"(_tmem_load_0[33]), "=f"(_tmem_load_0[34]), "=f"(_tmem_load_0[35]), "=f"(_tmem_load_0[36]), "=f"(_tmem_load_0[37]), "=f"(_tmem_load_0[38]), "=f"(_tmem_load_0[39]), "=f"(_tmem_load_0[40]), "=f"(_tmem_load_0[41]), "=f"(_tmem_load_0[42]), "=f"(_tmem_load_0[43]), "=f"(_tmem_load_0[44]), "=f"(_tmem_load_0[45]), "=f"(_tmem_load_0[46]), "=f"(_tmem_load_0[47]), "=f"(_tmem_load_0[48]), "=f"(_tmem_load_0[49]), "=f"(_tmem_load_0[50]), "=f"(_tmem_load_0[51]), "=f"(_tmem_load_0[52]), "=f"(_tmem_load_0[53]), "=f"(_tmem_load_0[54]), "=f"(_tmem_load_0[55]), "=f"(_tmem_load_0[56]), "=f"(_tmem_load_0[57]), "=f"(_tmem_load_0[58]), "=f"(_tmem_load_0[59]), "=f"(_tmem_load_0[60]), "=f"(_tmem_load_0[61]), "=f"(_tmem_load_0[62]), "=f"(_tmem_load_0[63]) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + if (elect_sync()) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cluster.b64 _, [%0];" + :: "r"((score_empty_addr) & 0xFEFFFFFF) : "memory"); + } + if (valid_q != 0) { + #pragma unroll 2 + for (int col_base = 0; col_base < 64; col_base += 4) { + float dist_vec[4]; + dist_vec[0] = _tmem_load_0[col_base]; + dist_vec[1] = _tmem_load_0[col_base + 1]; + dist_vec[2] = _tmem_load_0[col_base + 2]; + dist_vec[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec[4]; + db_sq_vec[0] = smem_database_sq[col_base]; + db_sq_vec[1] = smem_database_sq[col_base + 1]; + db_sq_vec[2] = smem_database_sq[col_base + 2]; + db_sq_vec[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec)[_la], reinterpret_cast(db_sq_vec)[_la]); + float group_min = _t0[0]; + if (_t0[1] < group_min) { + group_min = _t0[1]; + } + if (_t0[2] < group_min) { + group_min = _t0[2]; + } + if (_t0[3] < group_min) { + group_min = _t0[3]; + } + if (group_min < worst_d) { + #pragma unroll + for (int vec_col = 0; vec_col < 4; vec_col++) { + int db_idx = db_start + col_base + vec_col; + if (db_idx < M) { + float dist = _t0[vec_col]; + if (dist < worst_d) { + best_d[worst_pos] = dist; + best_i[worst_pos] = db_idx; + worst_d = best_d[0]; + worst_pos = 0; + #pragma unroll + for (int scan_pos = 1; scan_pos < TOP_K_MAX; scan_pos++) { + if (worst_d < best_d[scan_pos]) { + worst_d = best_d[scan_pos]; + worst_pos = scan_pos; + } + } + } + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + if (valid_q != 0) { + int out_base = ((split_idx_1 * B + batch_idx_1) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + *((float*)(partial_dists + (out_base + out_k))) = best_d[out_k]; + *((int*)(partial_indices + (out_base + out_k))) = best_i[out_k]; + } + } + } + } + } + + // Cleanup + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::2.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(128)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::2.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0166.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0166.cu new file mode 100644 index 00000000..710b25ea --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0166.cu @@ -0,0 +1,123 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 32 +#define TOP_K_MAX 96 +#define SPLIT_COUNT 8 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_k96_merge_s8_unordered_chunkprefill_k96over64s8chunkprefill(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 32 + tid; + int stride = num_bids * 32; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + float best_d[96]; + int best_i[96]; + #pragma unroll + for (int cand_k = 0; cand_k < 96; cand_k++) { + best_d[cand_k] = partial_dists[base_row + cand_k]; + best_i[cand_k] = partial_indices[base_row + cand_k]; + } + float chunk_worst_d[12]; + int chunk_worst_pos[12]; + #pragma unroll + for (int chunk = 0; chunk < 12; chunk++) { + int chunk_base = chunk * 8; + chunk_worst_d[chunk] = best_d[chunk_base]; + chunk_worst_pos[chunk] = chunk_base; + #pragma unroll + for (int offset = 1; offset < 8; offset++) { + int scan_pos = chunk_base + offset; + if (best_d[scan_pos] > chunk_worst_d[chunk]) { + chunk_worst_d[chunk] = best_d[scan_pos]; + chunk_worst_pos[chunk] = scan_pos; + } + } + } + float worst_d = chunk_worst_d[0]; + int worst_pos = chunk_worst_pos[0]; + int worst_chunk = 0; + #pragma unroll + for (int chunk_1 = 1; chunk_1 < 12; chunk_1++) { + if (worst_d < chunk_worst_d[chunk_1]) { + worst_d = chunk_worst_d[chunk_1]; + worst_pos = chunk_worst_pos[chunk_1]; + worst_chunk = chunk_1; + } + } + #pragma unroll + for (int split_idx = 1; split_idx < SPLIT_COUNT; split_idx++) { + int partial_base = base_row + split_idx * split_stride; + #pragma unroll + for (int cand_k_1 = 0; cand_k_1 < 96; cand_k_1++) { + float cand_d = partial_dists[partial_base + cand_k_1]; + int cand_i = partial_indices[partial_base + cand_k_1]; + if (cand_d < worst_d) { + best_d[worst_pos] = cand_d; + best_i[worst_pos] = cand_i; + int refresh_base = worst_chunk * 8; + chunk_worst_d[worst_chunk] = best_d[refresh_base]; + chunk_worst_pos[worst_chunk] = refresh_base; + #pragma unroll + for (int offset_1 = 1; offset_1 < 8; offset_1++) { + int scan_pos_1 = refresh_base + offset_1; + if (best_d[scan_pos_1] > chunk_worst_d[worst_chunk]) { + chunk_worst_d[worst_chunk] = best_d[scan_pos_1]; + chunk_worst_pos[worst_chunk] = scan_pos_1; + } + } + worst_d = chunk_worst_d[0]; + worst_pos = chunk_worst_pos[0]; + worst_chunk = 0; + #pragma unroll + for (int chunk_2 = 1; chunk_2 < 12; chunk_2++) { + if (worst_d < chunk_worst_d[chunk_2]) { + worst_d = chunk_worst_d[chunk_2]; + worst_pos = chunk_worst_pos[chunk_2]; + worst_chunk = chunk_2; + } + } + } + } + } + #pragma unroll + for (int out_k = 0; out_k < 96; out_k++) { + *((float*)(out_dists + (base_row + out_k))) = best_d[out_k]; + *((int*)(out_indices + (base_row + out_k))) = best_i[out_k]; + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0167.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0167.cu new file mode 100644 index 00000000..3d8f367f --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0167.cu @@ -0,0 +1,691 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 128 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_STRIDE 32768 +#define SMEM_SMEM_DATABASE_OFF 33792 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_DATABASE_SQ_OFF 50176 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 50432 +#define THREADS 192 +#define BLOCK_Q 128 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_MAX 32 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16_cta2( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + ".reg .b32 m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "tcgen05.mma.cta_group::2.kind::f16 [%0], %1, %2, %3, {m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step_cg2( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, %3, " + "{m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], %1;\n\t" + "}\n" + :: "r"(mbar_addr), "h"(cta_mask) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x16(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x16.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15}, [%16];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ uint32_t smem_addr(const void* ptr) { + uint32_t addr; + asm("{\n\t" + ".reg .u64 u64addr;\n\t" + "cvta.to.shared.u64 u64addr, %1;\n\t" + "cvt.u32.u64 %0, u64addr;\n\t" + "}\n" : "=r"(addr) : "l"(ptr)); + return addr; +} + + +__device__ __forceinline__ uint32_t mapa_to_rank(uint32_t local_addr, uint32_t rank) { + uint32_t remote; + asm volatile("mapa.shared::cluster.u32 %0, %1, %2;" + : "=r"(remote) : "r"(local_addr), "r"(rank)); + return remote; +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem_cta2( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global" + ".mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .b16 lo, hi;\n\t" + "mov.b32 {lo, hi}, %1;\n\t" + "tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], lo;\n\t" + "}\n" + :: "r"(mbar_addr), "r"((uint32_t)cta_mask) : "memory"); +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_rag_frontier_b6d4_stage1_k32_chunked(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tile_pairs, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + const unsigned int clusters_x = gridDim.x / 2; + const unsigned int cluster_id = ((blockIdx.z * gridDim.y + blockIdx.y) * clusters_x) + blockIdx.x / 2; + const unsigned int num_clusters = clusters_x * gridDim.y * gridDim.z; + + int cta_rank; + asm volatile("mov.b32 %0, %%cluster_ctarank;" : "=r"(cta_rank)); + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 33792); + const int smem_database_addr = smem + 33792; + float* smem_database_sq = reinterpret_cast(smem_raw + 50176); + const int smem_database_sq_addr = smem + 50176; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 0, 2, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 16, 2, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=8 + mbarrier_init_pred(smem + 40, 8, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (128 columns, 128 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::2.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(128) : "memory"); + } + + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: load ---- + if (warp == 0) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 0) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx = cluster_id; work_idx < total_work; work_idx += num_clusters) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tile_pairs; + int q_tile_pair = query_work % num_q_tile_pairs; + int q_tile = q_tile_pair * 2 + cta_rank; + int off_q = q_tile * BLOCK_Q; + int global_q = batch_idx * Q + off_q; + int db_tile_start = split_idx * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((query_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(32768)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_query_addr), "l"(tmap_query), "r"(0), "r"(global_q), "r"(0), + "r"(((query_full_addr) & 0xFEFFFFFF)) : "memory"); + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int off_m = db_tile * BLOCK_M; + int global_m = batch_idx * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((database_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(16384)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_database_addr), "l"(tmap_database), "r"(0), "r"(global_m), "r"(0), + "r"(((database_full_addr) & 0xFEFFFFFF)) : "memory"); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 1) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + if (cta_rank == 0) { + #pragma unroll 1 + for (unsigned int work_idx_1 = cluster_id; work_idx_1 < total_work; work_idx_1 += num_clusters) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = (smem_query_addr >> 4) & 0x3FFF; + int _mma_b_lo_0 = (smem_database_addr >> 4) & 0x3FFF; + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\tmov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 270533776;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit_cg2_multicast(score_full_addr, (uint16_t)(3)); + elect_commit_cg2_multicast(database_empty_addr, (uint16_t)(3)); + } + elect_commit_cg2_multicast(query_empty_addr, (uint16_t)(3)); + } + } + } + // ---- Role: compute ---- + } else if (warp >= 2 && warp <= 5) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = cluster_id; work_idx_2 < total_work; work_idx_2 += num_clusters) { + int split_idx_1 = work_idx_2 % (unsigned int)split_count; + int query_work_1 = work_idx_2 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tile_pairs; + int q_tile_pair_1 = query_work_1 % num_q_tile_pairs; + int q_tile_1 = q_tile_pair_1 * 2 + cta_rank; + int off_q_1 = q_tile_1 * BLOCK_Q; + int q_idx = off_q_1 + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx_1 * Q + q_idx]; + } + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + float chunk_worst_d[4]; + int chunk_worst_pos[4]; + #pragma unroll + for (int chunk = 0; chunk < 4; chunk++) { + int chunk_base = chunk * 8; + chunk_worst_d[chunk] = 3.4e+38f; + chunk_worst_pos[chunk] = chunk_base; + } + float worst_d = 3.4e+38f; + int worst_pos = 0; + int worst_chunk = 0; + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int db_start = db_tile_1 * BLOCK_M; + int db_sq_idx = db_start + (warp % 4 * 32 + lane); + if (warp % 4 * 32 + lane < BLOCK_M) { + if (db_sq_idx < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx_1 * M + db_sq_idx]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 0.0f; + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(cta_rank * BLOCK_Q + (warp % 4 * 32 << 16) << 16); + float _tmem_load_0[64]; + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x64.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31, %32, %33, %34, %35, %36, %37, %38, %39, %40, %41, %42, %43, %44, %45, %46, %47, %48, %49, %50, %51, %52, %53, %54, %55, %56, %57, %58, %59, %60, %61, %62, %63}, [%64];" + : "=f"(_tmem_load_0[0]), "=f"(_tmem_load_0[1]), "=f"(_tmem_load_0[2]), "=f"(_tmem_load_0[3]), "=f"(_tmem_load_0[4]), "=f"(_tmem_load_0[5]), "=f"(_tmem_load_0[6]), "=f"(_tmem_load_0[7]), "=f"(_tmem_load_0[8]), "=f"(_tmem_load_0[9]), "=f"(_tmem_load_0[10]), "=f"(_tmem_load_0[11]), "=f"(_tmem_load_0[12]), "=f"(_tmem_load_0[13]), "=f"(_tmem_load_0[14]), "=f"(_tmem_load_0[15]), "=f"(_tmem_load_0[16]), "=f"(_tmem_load_0[17]), "=f"(_tmem_load_0[18]), "=f"(_tmem_load_0[19]), "=f"(_tmem_load_0[20]), "=f"(_tmem_load_0[21]), "=f"(_tmem_load_0[22]), "=f"(_tmem_load_0[23]), "=f"(_tmem_load_0[24]), "=f"(_tmem_load_0[25]), "=f"(_tmem_load_0[26]), "=f"(_tmem_load_0[27]), "=f"(_tmem_load_0[28]), "=f"(_tmem_load_0[29]), "=f"(_tmem_load_0[30]), "=f"(_tmem_load_0[31]), "=f"(_tmem_load_0[32]), "=f"(_tmem_load_0[33]), "=f"(_tmem_load_0[34]), "=f"(_tmem_load_0[35]), "=f"(_tmem_load_0[36]), "=f"(_tmem_load_0[37]), "=f"(_tmem_load_0[38]), "=f"(_tmem_load_0[39]), "=f"(_tmem_load_0[40]), "=f"(_tmem_load_0[41]), "=f"(_tmem_load_0[42]), "=f"(_tmem_load_0[43]), "=f"(_tmem_load_0[44]), "=f"(_tmem_load_0[45]), "=f"(_tmem_load_0[46]), "=f"(_tmem_load_0[47]), "=f"(_tmem_load_0[48]), "=f"(_tmem_load_0[49]), "=f"(_tmem_load_0[50]), "=f"(_tmem_load_0[51]), "=f"(_tmem_load_0[52]), "=f"(_tmem_load_0[53]), "=f"(_tmem_load_0[54]), "=f"(_tmem_load_0[55]), "=f"(_tmem_load_0[56]), "=f"(_tmem_load_0[57]), "=f"(_tmem_load_0[58]), "=f"(_tmem_load_0[59]), "=f"(_tmem_load_0[60]), "=f"(_tmem_load_0[61]), "=f"(_tmem_load_0[62]), "=f"(_tmem_load_0[63]) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + if (elect_sync()) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cluster.b64 _, [%0];" + :: "r"((score_empty_addr) & 0xFEFFFFFF) : "memory"); + } + if (valid_q != 0) { + #pragma unroll 2 + for (int col_base = 0; col_base < 64; col_base += 4) { + float dist_vec[4]; + dist_vec[0] = _tmem_load_0[col_base]; + dist_vec[1] = _tmem_load_0[col_base + 1]; + dist_vec[2] = _tmem_load_0[col_base + 2]; + dist_vec[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec[4]; + db_sq_vec[0] = smem_database_sq[col_base]; + db_sq_vec[1] = smem_database_sq[col_base + 1]; + db_sq_vec[2] = smem_database_sq[col_base + 2]; + db_sq_vec[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec)[_la], reinterpret_cast(db_sq_vec)[_la]); + float group_min = _t0[0]; + if (_t0[1] < group_min) { + group_min = _t0[1]; + } + if (_t0[2] < group_min) { + group_min = _t0[2]; + } + if (_t0[3] < group_min) { + group_min = _t0[3]; + } + if (group_min < worst_d) { + #pragma unroll + for (int vec_col = 0; vec_col < 4; vec_col++) { + int db_idx = db_start + col_base + vec_col; + if (db_idx < M) { + float dist = _t0[vec_col]; + if (dist < worst_d) { + best_d[worst_pos] = dist; + best_i[worst_pos] = db_idx; + int refresh_base = worst_chunk * 8; + chunk_worst_d[worst_chunk] = best_d[refresh_base]; + chunk_worst_pos[worst_chunk] = refresh_base; + #pragma unroll + for (int offset = 1; offset < 8; offset++) { + int scan_pos = refresh_base + offset; + if (best_d[scan_pos] > chunk_worst_d[worst_chunk]) { + chunk_worst_d[worst_chunk] = best_d[scan_pos]; + chunk_worst_pos[worst_chunk] = scan_pos; + } + } + worst_d = chunk_worst_d[0]; + worst_pos = chunk_worst_pos[0]; + worst_chunk = 0; + #pragma unroll + for (int chunk_1 = 1; chunk_1 < 4; chunk_1++) { + if (worst_d < chunk_worst_d[chunk_1]) { + worst_d = chunk_worst_d[chunk_1]; + worst_pos = chunk_worst_pos[chunk_1]; + worst_chunk = chunk_1; + } + } + } + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + if (valid_q != 0) { + int out_base = ((split_idx_1 * B + batch_idx_1) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_out_d = best_d[0]; + int best_out_i = best_i[0]; + int best_out_pos = 0; + #pragma unroll + for (int scan_pos_1 = 1; scan_pos_1 < TOP_K_MAX; scan_pos_1++) { + if (best_out_d > best_d[scan_pos_1]) { + best_out_d = best_d[scan_pos_1]; + best_out_i = best_i[scan_pos_1]; + best_out_pos = scan_pos_1; + } + } + if (out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = best_out_d; + *((int*)(partial_indices + (out_base + out_k))) = best_out_i; + } + best_d[best_out_pos] = 3.4e+38f; + } + } + } + } + } + + // Cleanup + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::2.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(128)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::2.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0168.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0168.cu new file mode 100644 index 00000000..60275371 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0168.cu @@ -0,0 +1,655 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 128 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_STRIDE 32768 +#define SMEM_SMEM_DATABASE_OFF 33792 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_DATABASE_SQ_OFF 50176 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 50432 +#define THREADS 192 +#define BLOCK_Q 128 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_MAX 32 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16_cta2( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + ".reg .b32 m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "tcgen05.mma.cta_group::2.kind::f16 [%0], %1, %2, %3, {m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step_cg2( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, %3, " + "{m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], %1;\n\t" + "}\n" + :: "r"(mbar_addr), "h"(cta_mask) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x16(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x16.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15}, [%16];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ uint32_t smem_addr(const void* ptr) { + uint32_t addr; + asm("{\n\t" + ".reg .u64 u64addr;\n\t" + "cvta.to.shared.u64 u64addr, %1;\n\t" + "cvt.u32.u64 %0, u64addr;\n\t" + "}\n" : "=r"(addr) : "l"(ptr)); + return addr; +} + + +__device__ __forceinline__ uint32_t mapa_to_rank(uint32_t local_addr, uint32_t rank) { + uint32_t remote; + asm volatile("mapa.shared::cluster.u32 %0, %1, %2;" + : "=r"(remote) : "r"(local_addr), "r"(rank)); + return remote; +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem_cta2( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global" + ".mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .b16 lo, hi;\n\t" + "mov.b32 {lo, hi}, %1;\n\t" + "tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], lo;\n\t" + "}\n" + :: "r"(mbar_addr), "r"((uint32_t)cta_mask) : "memory"); +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tile_pairs, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + const unsigned int clusters_x = gridDim.x / 2; + const unsigned int cluster_id = ((blockIdx.z * gridDim.y + blockIdx.y) * clusters_x) + blockIdx.x / 2; + const unsigned int num_clusters = clusters_x * gridDim.y * gridDim.z; + + int cta_rank; + asm volatile("mov.b32 %0, %%cluster_ctarank;" : "=r"(cta_rank)); + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 33792); + const int smem_database_addr = smem + 33792; + float* smem_database_sq = reinterpret_cast(smem_raw + 50176); + const int smem_database_sq_addr = smem + 50176; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 0, 2, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 16, 2, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=8 + mbarrier_init_pred(smem + 40, 8, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (128 columns, 128 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::2.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(128) : "memory"); + } + + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: load ---- + if (warp == 0) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 0) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx = cluster_id; work_idx < total_work; work_idx += num_clusters) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tile_pairs; + int q_tile_pair = query_work % num_q_tile_pairs; + int q_tile = q_tile_pair * 2 + cta_rank; + int off_q = q_tile * BLOCK_Q; + int global_q = batch_idx * Q + off_q; + int db_tile_start = split_idx * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((query_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(32768)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_query_addr), "l"(tmap_query), "r"(0), "r"(global_q), "r"(0), + "r"(((query_full_addr) & 0xFEFFFFFF)) : "memory"); + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int off_m = db_tile * BLOCK_M; + int global_m = batch_idx * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((database_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(16384)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_database_addr), "l"(tmap_database), "r"(0), "r"(global_m), "r"(0), + "r"(((database_full_addr) & 0xFEFFFFFF)) : "memory"); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 1) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + if (cta_rank == 0) { + #pragma unroll 1 + for (unsigned int work_idx_1 = cluster_id; work_idx_1 < total_work; work_idx_1 += num_clusters) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = (smem_query_addr >> 4) & 0x3FFF; + int _mma_b_lo_0 = (smem_database_addr >> 4) & 0x3FFF; + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\tmov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 270533776;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit_cg2_multicast(score_full_addr, (uint16_t)(3)); + elect_commit_cg2_multicast(database_empty_addr, (uint16_t)(3)); + } + elect_commit_cg2_multicast(query_empty_addr, (uint16_t)(3)); + } + } + } + // ---- Role: compute ---- + } else if (warp >= 2 && warp <= 5) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = cluster_id; work_idx_2 < total_work; work_idx_2 += num_clusters) { + int split_idx_1 = work_idx_2 % (unsigned int)split_count; + int query_work_1 = work_idx_2 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tile_pairs; + int q_tile_pair_1 = query_work_1 % num_q_tile_pairs; + int q_tile_1 = q_tile_pair_1 * 2 + cta_rank; + int off_q_1 = q_tile_1 * BLOCK_Q; + int q_idx = off_q_1 + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx_1 * Q + q_idx]; + } + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + float worst_d = 3.4e+38f; + int worst_pos = 0; + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int db_start = db_tile_1 * BLOCK_M; + int db_sq_idx = db_start + (warp % 4 * 32 + lane); + if (warp % 4 * 32 + lane < BLOCK_M) { + if (db_sq_idx < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx_1 * M + db_sq_idx]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 0.0f; + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(cta_rank * BLOCK_Q + (warp % 4 * 32 << 16) << 16); + float _tmem_load_0[64]; + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x64.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31, %32, %33, %34, %35, %36, %37, %38, %39, %40, %41, %42, %43, %44, %45, %46, %47, %48, %49, %50, %51, %52, %53, %54, %55, %56, %57, %58, %59, %60, %61, %62, %63}, [%64];" + : "=f"(_tmem_load_0[0]), "=f"(_tmem_load_0[1]), "=f"(_tmem_load_0[2]), "=f"(_tmem_load_0[3]), "=f"(_tmem_load_0[4]), "=f"(_tmem_load_0[5]), "=f"(_tmem_load_0[6]), "=f"(_tmem_load_0[7]), "=f"(_tmem_load_0[8]), "=f"(_tmem_load_0[9]), "=f"(_tmem_load_0[10]), "=f"(_tmem_load_0[11]), "=f"(_tmem_load_0[12]), "=f"(_tmem_load_0[13]), "=f"(_tmem_load_0[14]), "=f"(_tmem_load_0[15]), "=f"(_tmem_load_0[16]), "=f"(_tmem_load_0[17]), "=f"(_tmem_load_0[18]), "=f"(_tmem_load_0[19]), "=f"(_tmem_load_0[20]), "=f"(_tmem_load_0[21]), "=f"(_tmem_load_0[22]), "=f"(_tmem_load_0[23]), "=f"(_tmem_load_0[24]), "=f"(_tmem_load_0[25]), "=f"(_tmem_load_0[26]), "=f"(_tmem_load_0[27]), "=f"(_tmem_load_0[28]), "=f"(_tmem_load_0[29]), "=f"(_tmem_load_0[30]), "=f"(_tmem_load_0[31]), "=f"(_tmem_load_0[32]), "=f"(_tmem_load_0[33]), "=f"(_tmem_load_0[34]), "=f"(_tmem_load_0[35]), "=f"(_tmem_load_0[36]), "=f"(_tmem_load_0[37]), "=f"(_tmem_load_0[38]), "=f"(_tmem_load_0[39]), "=f"(_tmem_load_0[40]), "=f"(_tmem_load_0[41]), "=f"(_tmem_load_0[42]), "=f"(_tmem_load_0[43]), "=f"(_tmem_load_0[44]), "=f"(_tmem_load_0[45]), "=f"(_tmem_load_0[46]), "=f"(_tmem_load_0[47]), "=f"(_tmem_load_0[48]), "=f"(_tmem_load_0[49]), "=f"(_tmem_load_0[50]), "=f"(_tmem_load_0[51]), "=f"(_tmem_load_0[52]), "=f"(_tmem_load_0[53]), "=f"(_tmem_load_0[54]), "=f"(_tmem_load_0[55]), "=f"(_tmem_load_0[56]), "=f"(_tmem_load_0[57]), "=f"(_tmem_load_0[58]), "=f"(_tmem_load_0[59]), "=f"(_tmem_load_0[60]), "=f"(_tmem_load_0[61]), "=f"(_tmem_load_0[62]), "=f"(_tmem_load_0[63]) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + if (elect_sync()) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cluster.b64 _, [%0];" + :: "r"((score_empty_addr) & 0xFEFFFFFF) : "memory"); + } + if (valid_q != 0) { + #pragma unroll 2 + for (int col_base = 0; col_base < 64; col_base += 4) { + float dist_vec[4]; + dist_vec[0] = _tmem_load_0[col_base]; + dist_vec[1] = _tmem_load_0[col_base + 1]; + dist_vec[2] = _tmem_load_0[col_base + 2]; + dist_vec[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec[4]; + db_sq_vec[0] = smem_database_sq[col_base]; + db_sq_vec[1] = smem_database_sq[col_base + 1]; + db_sq_vec[2] = smem_database_sq[col_base + 2]; + db_sq_vec[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec)[_la], reinterpret_cast(db_sq_vec)[_la]); + float group_min = _t0[0]; + if (_t0[1] < group_min) { + group_min = _t0[1]; + } + if (_t0[2] < group_min) { + group_min = _t0[2]; + } + if (_t0[3] < group_min) { + group_min = _t0[3]; + } + if (group_min < worst_d) { + #pragma unroll + for (int vec_col = 0; vec_col < 4; vec_col++) { + int db_idx = db_start + col_base + vec_col; + if (db_idx < M) { + float dist = _t0[vec_col]; + if (dist < worst_d) { + best_d[worst_pos] = dist; + best_i[worst_pos] = db_idx; + worst_d = best_d[0]; + worst_pos = 0; + #pragma unroll + for (int scan_pos = 1; scan_pos < TOP_K_MAX; scan_pos++) { + if (worst_d < best_d[scan_pos]) { + worst_d = best_d[scan_pos]; + worst_pos = scan_pos; + } + } + } + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + if (valid_q != 0) { + int out_base = ((split_idx_1 * B + batch_idx_1) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + *((float*)(partial_dists + (out_base + out_k))) = best_d[out_k]; + *((int*)(partial_indices + (out_base + out_k))) = best_i[out_k]; + } + } + } + } + } + + // Cleanup + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::2.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(128)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::2.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0169.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0169.cu new file mode 100644 index 00000000..37eef13e --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0169.cu @@ -0,0 +1,85 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 32 +#define TOP_K_MAX 32 +#define SPLIT_COUNT 4 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_evolve_7bfc_k32_merge_s4_unordered(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 32 + tid; + int stride = num_bids * 32; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + float worst_d = 3.4e+38f; + int worst_pos = 0; + #pragma unroll + for (int split_idx = 0; split_idx < SPLIT_COUNT; split_idx++) { + int partial_base = base_row + split_idx * split_stride; + #pragma unroll + for (int cand_k = 0; cand_k < TOP_K_MAX; cand_k++) { + float cand_d = partial_dists[partial_base + cand_k]; + int cand_i = partial_indices[partial_base + cand_k]; + if (cand_d < worst_d) { + best_d[worst_pos] = cand_d; + best_i[worst_pos] = cand_i; + worst_d = best_d[0]; + worst_pos = 0; + #pragma unroll + for (int scan_pos = 1; scan_pos < TOP_K_MAX; scan_pos++) { + if (worst_d < best_d[scan_pos]) { + worst_d = best_d[scan_pos]; + worst_pos = scan_pos; + } + } + } + } + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + *((float*)(out_dists + (base_row + out_k))) = best_d[out_k]; + *((int*)(out_indices + (base_row + out_k))) = best_i[out_k]; + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0170.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0170.cu new file mode 100644 index 00000000..3f5c5e7a --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0170.cu @@ -0,0 +1,84 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 32 +#define TOP_K_MAX 30 +#define SPLIT_COUNT 8 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 32 + tid; + int stride = num_bids * 32; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int out_base = base_row; + int split_pos[SPLIT_COUNT]; + int split_base[SPLIT_COUNT]; + float cand_d[SPLIT_COUNT]; + int cand_i[SPLIT_COUNT]; + #pragma unroll + for (int split_idx = 0; split_idx < SPLIT_COUNT; split_idx++) { + split_pos[split_idx] = 0; + split_base[split_idx] = base_row + split_idx * split_stride; + cand_d[split_idx] = partial_dists[split_base[split_idx]]; + cand_i[split_idx] = partial_indices[split_base[split_idx]]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_d = cand_d[0]; + int best_i = cand_i[0]; + int best_split = 0; + #pragma unroll + for (int split_idx_1 = 1; split_idx_1 < SPLIT_COUNT; split_idx_1++) { + if (best_d > cand_d[split_idx_1]) { + best_d = cand_d[split_idx_1]; + best_i = cand_i[split_idx_1]; + best_split = split_idx_1; + } + } + *((float*)(out_dists + (out_base + out_k))) = best_d; + *((int*)(out_indices + (out_base + out_k))) = best_i; + split_pos[best_split] = split_pos[best_split] + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_pos = split_pos[best_split]; + int next_addr = split_base[best_split] + next_pos; + cand_d[best_split] = partial_dists[next_addr]; + cand_i[best_split] = partial_indices[next_addr]; + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0171.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0171.cu new file mode 100644 index 00000000..723233c2 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0171.cu @@ -0,0 +1,84 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 32 +#define TOP_K_MAX 12 +#define SPLIT_COUNT 8 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_k12s8(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 32 + tid; + int stride = num_bids * 32; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int out_base = base_row; + int split_pos[SPLIT_COUNT]; + int split_base[SPLIT_COUNT]; + float cand_d[SPLIT_COUNT]; + int cand_i[SPLIT_COUNT]; + #pragma unroll + for (int split_idx = 0; split_idx < SPLIT_COUNT; split_idx++) { + split_pos[split_idx] = 0; + split_base[split_idx] = base_row + split_idx * split_stride; + cand_d[split_idx] = partial_dists[split_base[split_idx]]; + cand_i[split_idx] = partial_indices[split_base[split_idx]]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_d = cand_d[0]; + int best_i = cand_i[0]; + int best_split = 0; + #pragma unroll + for (int split_idx_1 = 1; split_idx_1 < SPLIT_COUNT; split_idx_1++) { + if (best_d > cand_d[split_idx_1]) { + best_d = cand_d[split_idx_1]; + best_i = cand_i[split_idx_1]; + best_split = split_idx_1; + } + } + *((float*)(out_dists + (out_base + out_k))) = best_d; + *((int*)(out_indices + (out_base + out_k))) = best_i; + split_pos[best_split] = split_pos[best_split] + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_pos = split_pos[best_split]; + int next_addr = split_base[best_split] + next_pos; + cand_d[best_split] = partial_dists[next_addr]; + cand_i[best_split] = partial_indices[next_addr]; + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0172.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0172.cu new file mode 100644 index 00000000..fd9b65aa --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0172.cu @@ -0,0 +1,84 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 32 +#define TOP_K_MAX 20 +#define SPLIT_COUNT 8 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_k20s8(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 32 + tid; + int stride = num_bids * 32; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int out_base = base_row; + int split_pos[SPLIT_COUNT]; + int split_base[SPLIT_COUNT]; + float cand_d[SPLIT_COUNT]; + int cand_i[SPLIT_COUNT]; + #pragma unroll + for (int split_idx = 0; split_idx < SPLIT_COUNT; split_idx++) { + split_pos[split_idx] = 0; + split_base[split_idx] = base_row + split_idx * split_stride; + cand_d[split_idx] = partial_dists[split_base[split_idx]]; + cand_i[split_idx] = partial_indices[split_base[split_idx]]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_d = cand_d[0]; + int best_i = cand_i[0]; + int best_split = 0; + #pragma unroll + for (int split_idx_1 = 1; split_idx_1 < SPLIT_COUNT; split_idx_1++) { + if (best_d > cand_d[split_idx_1]) { + best_d = cand_d[split_idx_1]; + best_i = cand_i[split_idx_1]; + best_split = split_idx_1; + } + } + *((float*)(out_dists + (out_base + out_k))) = best_d; + *((int*)(out_indices + (out_base + out_k))) = best_i; + split_pos[best_split] = split_pos[best_split] + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_pos = split_pos[best_split]; + int next_addr = split_base[best_split] + next_pos; + cand_d[best_split] = partial_dists[next_addr]; + cand_i[best_split] = partial_indices[next_addr]; + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0173.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0173.cu new file mode 100644 index 00000000..e6a8c654 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0173.cu @@ -0,0 +1,84 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 32 +#define TOP_K_MAX 8 +#define SPLIT_COUNT 7 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_evolve_7bfc_k10_merge_s7_rowbase_cache_k8s7(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 32 + tid; + int stride = num_bids * 32; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int out_base = base_row; + int split_pos[SPLIT_COUNT]; + int split_base[SPLIT_COUNT]; + float cand_d[SPLIT_COUNT]; + int cand_i[SPLIT_COUNT]; + #pragma unroll + for (int split_idx = 0; split_idx < SPLIT_COUNT; split_idx++) { + split_pos[split_idx] = 0; + split_base[split_idx] = base_row + split_idx * split_stride; + cand_d[split_idx] = partial_dists[split_base[split_idx]]; + cand_i[split_idx] = partial_indices[split_base[split_idx]]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_d = cand_d[0]; + int best_i = cand_i[0]; + int best_split = 0; + #pragma unroll + for (int split_idx_1 = 1; split_idx_1 < SPLIT_COUNT; split_idx_1++) { + if (best_d > cand_d[split_idx_1]) { + best_d = cand_d[split_idx_1]; + best_i = cand_i[split_idx_1]; + best_split = split_idx_1; + } + } + *((float*)(out_dists + (out_base + out_k))) = best_d; + *((int*)(out_indices + (out_base + out_k))) = best_i; + split_pos[best_split] = split_pos[best_split] + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_pos = split_pos[best_split]; + int next_addr = split_base[best_split] + next_pos; + cand_d[best_split] = partial_dists[next_addr]; + cand_i[best_split] = partial_indices[next_addr]; + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0174.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0174.cu new file mode 100644 index 00000000..98d8bc10 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0174.cu @@ -0,0 +1,84 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 32 +#define TOP_K_MAX 8 +#define SPLIT_COUNT 8 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_k8s8(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 32 + tid; + int stride = num_bids * 32; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int out_base = base_row; + int split_pos[SPLIT_COUNT]; + int split_base[SPLIT_COUNT]; + float cand_d[SPLIT_COUNT]; + int cand_i[SPLIT_COUNT]; + #pragma unroll + for (int split_idx = 0; split_idx < SPLIT_COUNT; split_idx++) { + split_pos[split_idx] = 0; + split_base[split_idx] = base_row + split_idx * split_stride; + cand_d[split_idx] = partial_dists[split_base[split_idx]]; + cand_i[split_idx] = partial_indices[split_base[split_idx]]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_d = cand_d[0]; + int best_i = cand_i[0]; + int best_split = 0; + #pragma unroll + for (int split_idx_1 = 1; split_idx_1 < SPLIT_COUNT; split_idx_1++) { + if (best_d > cand_d[split_idx_1]) { + best_d = cand_d[split_idx_1]; + best_i = cand_i[split_idx_1]; + best_split = split_idx_1; + } + } + *((float*)(out_dists + (out_base + out_k))) = best_d; + *((int*)(out_indices + (out_base + out_k))) = best_i; + split_pos[best_split] = split_pos[best_split] + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_pos = split_pos[best_split]; + int next_addr = split_base[best_split] + next_pos; + cand_d[best_split] = partial_dists[next_addr]; + cand_i[best_split] = partial_indices[next_addr]; + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0175.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0175.cu new file mode 100644 index 00000000..08aa77e9 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0175.cu @@ -0,0 +1,84 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 32 +#define TOP_K_MAX 20 +#define SPLIT_COUNT 16 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_k20s16(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 32 + tid; + int stride = num_bids * 32; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int out_base = base_row; + int split_pos[SPLIT_COUNT]; + int split_base[SPLIT_COUNT]; + float cand_d[SPLIT_COUNT]; + int cand_i[SPLIT_COUNT]; + #pragma unroll + for (int split_idx = 0; split_idx < SPLIT_COUNT; split_idx++) { + split_pos[split_idx] = 0; + split_base[split_idx] = base_row + split_idx * split_stride; + cand_d[split_idx] = partial_dists[split_base[split_idx]]; + cand_i[split_idx] = partial_indices[split_base[split_idx]]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_d = cand_d[0]; + int best_i = cand_i[0]; + int best_split = 0; + #pragma unroll + for (int split_idx_1 = 1; split_idx_1 < SPLIT_COUNT; split_idx_1++) { + if (best_d > cand_d[split_idx_1]) { + best_d = cand_d[split_idx_1]; + best_i = cand_i[split_idx_1]; + best_split = split_idx_1; + } + } + *((float*)(out_dists + (out_base + out_k))) = best_d; + *((int*)(out_indices + (out_base + out_k))) = best_i; + split_pos[best_split] = split_pos[best_split] + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_pos = split_pos[best_split]; + int next_addr = split_base[best_split] + next_pos; + cand_d[best_split] = partial_dists[next_addr]; + cand_i[best_split] = partial_indices[next_addr]; + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0176.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0176.cu new file mode 100644 index 00000000..b0dfa0af --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0176.cu @@ -0,0 +1,753 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 128 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_STRIDE 32768 +#define SMEM_SMEM_DATABASE_OFF 33792 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_DATABASE_SQ_OFF 50176 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 50432 +#define THREADS 192 +#define BLOCK_Q 128 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_MAX 32 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16_cta2( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + ".reg .b32 m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "tcgen05.mma.cta_group::2.kind::f16 [%0], %1, %2, %3, {m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step_cg2( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, %3, " + "{m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], %1;\n\t" + "}\n" + :: "r"(mbar_addr), "h"(cta_mask) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x16(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x16.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15}, [%16];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ uint32_t smem_addr(const void* ptr) { + uint32_t addr; + asm("{\n\t" + ".reg .u64 u64addr;\n\t" + "cvta.to.shared.u64 u64addr, %1;\n\t" + "cvt.u32.u64 %0, u64addr;\n\t" + "}\n" : "=r"(addr) : "l"(ptr)); + return addr; +} + + +__device__ __forceinline__ uint32_t mapa_to_rank(uint32_t local_addr, uint32_t rank) { + uint32_t remote; + asm volatile("mapa.shared::cluster.u32 %0, %1, %2;" + : "=r"(remote) : "r"(local_addr), "r"(rank)); + return remote; +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem_cta2( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global" + ".mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .b16 lo, hi;\n\t" + "mov.b32 {lo, hi}, %1;\n\t" + "tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], lo;\n\t" + "}\n" + :: "r"(mbar_addr), "r"((uint32_t)cta_mask) : "memory"); +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_rag_frontier_4fbf_v7_stage1_k32_sort4earlystop_tailinf(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tile_pairs, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + const unsigned int clusters_x = gridDim.x / 2; + const unsigned int cluster_id = ((blockIdx.z * gridDim.y + blockIdx.y) * clusters_x) + blockIdx.x / 2; + const unsigned int num_clusters = clusters_x * gridDim.y * gridDim.z; + + int cta_rank; + asm volatile("mov.b32 %0, %%cluster_ctarank;" : "=r"(cta_rank)); + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 33792); + const int smem_database_addr = smem + 33792; + float* smem_database_sq = reinterpret_cast(smem_raw + 50176); + const int smem_database_sq_addr = smem + 50176; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 0, 2, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 16, 2, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=8 + mbarrier_init_pred(smem + 40, 8, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (128 columns, 128 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::2.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(128) : "memory"); + } + + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: load ---- + if (warp == 0) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 0) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx = cluster_id; work_idx < total_work; work_idx += num_clusters) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tile_pairs; + int q_tile_pair = query_work % num_q_tile_pairs; + int q_tile = q_tile_pair * 2 + cta_rank; + int off_q = q_tile * BLOCK_Q; + int global_q = batch_idx * Q + off_q; + int db_tile_start = split_idx * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((query_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(32768)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_query_addr), "l"(tmap_query), "r"(0), "r"(global_q), "r"(0), + "r"(((query_full_addr) & 0xFEFFFFFF)) : "memory"); + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int off_m = db_tile * BLOCK_M; + int global_m = batch_idx * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((database_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(16384)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_database_addr), "l"(tmap_database), "r"(0), "r"(global_m), "r"(0), + "r"(((database_full_addr) & 0xFEFFFFFF)) : "memory"); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 1) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + if (cta_rank == 0) { + #pragma unroll 1 + for (unsigned int work_idx_1 = cluster_id; work_idx_1 < total_work; work_idx_1 += num_clusters) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = (smem_query_addr >> 4) & 0x3FFF; + int _mma_b_lo_0 = (smem_database_addr >> 4) & 0x3FFF; + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\tmov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 270533776;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit_cg2_multicast(score_full_addr, (uint16_t)(3)); + elect_commit_cg2_multicast(database_empty_addr, (uint16_t)(3)); + } + elect_commit_cg2_multicast(query_empty_addr, (uint16_t)(3)); + } + } + } + // ---- Role: compute ---- + } else if (warp >= 2 && warp <= 5) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = cluster_id; work_idx_2 < total_work; work_idx_2 += num_clusters) { + int split_idx_1 = work_idx_2 % (unsigned int)split_count; + int query_work_1 = work_idx_2 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tile_pairs; + int q_tile_pair_1 = query_work_1 % num_q_tile_pairs; + int q_tile_1 = q_tile_pair_1 * 2 + cta_rank; + int off_q_1 = q_tile_1 * BLOCK_Q; + int q_idx = off_q_1 + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx_1 * Q + q_idx]; + } + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + float chunk_worst_d[4]; + int chunk_worst_pos[4]; + #pragma unroll + for (int chunk = 0; chunk < 4; chunk++) { + int chunk_base = chunk * 8; + chunk_worst_d[chunk] = 3.4e+38f; + chunk_worst_pos[chunk] = chunk_base; + } + float worst_d = 3.4e+38f; + int worst_pos = 0; + int worst_chunk = 0; + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int db_start = db_tile_1 * BLOCK_M; + int db_sq_idx = db_start + (warp % 4 * 32 + lane); + if (warp % 4 * 32 + lane < BLOCK_M) { + if (db_sq_idx < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx_1 * M + db_sq_idx]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 3.4e+38f; + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(cta_rank * BLOCK_Q + (warp % 4 * 32 << 16) << 16); + float _tmem_load_0[64]; + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x64.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31, %32, %33, %34, %35, %36, %37, %38, %39, %40, %41, %42, %43, %44, %45, %46, %47, %48, %49, %50, %51, %52, %53, %54, %55, %56, %57, %58, %59, %60, %61, %62, %63}, [%64];" + : "=f"(_tmem_load_0[0]), "=f"(_tmem_load_0[1]), "=f"(_tmem_load_0[2]), "=f"(_tmem_load_0[3]), "=f"(_tmem_load_0[4]), "=f"(_tmem_load_0[5]), "=f"(_tmem_load_0[6]), "=f"(_tmem_load_0[7]), "=f"(_tmem_load_0[8]), "=f"(_tmem_load_0[9]), "=f"(_tmem_load_0[10]), "=f"(_tmem_load_0[11]), "=f"(_tmem_load_0[12]), "=f"(_tmem_load_0[13]), "=f"(_tmem_load_0[14]), "=f"(_tmem_load_0[15]), "=f"(_tmem_load_0[16]), "=f"(_tmem_load_0[17]), "=f"(_tmem_load_0[18]), "=f"(_tmem_load_0[19]), "=f"(_tmem_load_0[20]), "=f"(_tmem_load_0[21]), "=f"(_tmem_load_0[22]), "=f"(_tmem_load_0[23]), "=f"(_tmem_load_0[24]), "=f"(_tmem_load_0[25]), "=f"(_tmem_load_0[26]), "=f"(_tmem_load_0[27]), "=f"(_tmem_load_0[28]), "=f"(_tmem_load_0[29]), "=f"(_tmem_load_0[30]), "=f"(_tmem_load_0[31]), "=f"(_tmem_load_0[32]), "=f"(_tmem_load_0[33]), "=f"(_tmem_load_0[34]), "=f"(_tmem_load_0[35]), "=f"(_tmem_load_0[36]), "=f"(_tmem_load_0[37]), "=f"(_tmem_load_0[38]), "=f"(_tmem_load_0[39]), "=f"(_tmem_load_0[40]), "=f"(_tmem_load_0[41]), "=f"(_tmem_load_0[42]), "=f"(_tmem_load_0[43]), "=f"(_tmem_load_0[44]), "=f"(_tmem_load_0[45]), "=f"(_tmem_load_0[46]), "=f"(_tmem_load_0[47]), "=f"(_tmem_load_0[48]), "=f"(_tmem_load_0[49]), "=f"(_tmem_load_0[50]), "=f"(_tmem_load_0[51]), "=f"(_tmem_load_0[52]), "=f"(_tmem_load_0[53]), "=f"(_tmem_load_0[54]), "=f"(_tmem_load_0[55]), "=f"(_tmem_load_0[56]), "=f"(_tmem_load_0[57]), "=f"(_tmem_load_0[58]), "=f"(_tmem_load_0[59]), "=f"(_tmem_load_0[60]), "=f"(_tmem_load_0[61]), "=f"(_tmem_load_0[62]), "=f"(_tmem_load_0[63]) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + if (elect_sync()) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cluster.b64 _, [%0];" + :: "r"((score_empty_addr) & 0xFEFFFFFF) : "memory"); + } + if (valid_q != 0) { + #pragma unroll 2 + for (int col_base = 0; col_base < 64; col_base += 4) { + float dist_vec[4]; + dist_vec[0] = _tmem_load_0[col_base]; + dist_vec[1] = _tmem_load_0[col_base + 1]; + dist_vec[2] = _tmem_load_0[col_base + 2]; + dist_vec[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec[4]; + db_sq_vec[0] = smem_database_sq[col_base]; + db_sq_vec[1] = smem_database_sq[col_base + 1]; + db_sq_vec[2] = smem_database_sq[col_base + 2]; + db_sq_vec[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec)[_la], reinterpret_cast(db_sq_vec)[_la]); + float group_min = _t0[0]; + if (_t0[1] < group_min) { + group_min = _t0[1]; + } + if (_t0[2] < group_min) { + group_min = _t0[2]; + } + if (_t0[3] < group_min) { + group_min = _t0[3]; + } + if (group_min < worst_d) { + float sort_d0 = _t0[0]; + float sort_d1 = _t0[1]; + float sort_d2 = _t0[2]; + float sort_d3 = _t0[3]; + int sort_col0 = 0; + int sort_col1 = 1; + int sort_col2 = 2; + int sort_col3 = 3; + float tmp_d = 0.0f; + int tmp_col = 0; + if (sort_d1 < sort_d0) { + tmp_d = sort_d0; + sort_d0 = sort_d1; + sort_d1 = tmp_d; + tmp_col = sort_col0; + sort_col0 = sort_col1; + sort_col1 = tmp_col; + } + if (sort_d3 < sort_d2) { + tmp_d = sort_d2; + sort_d2 = sort_d3; + sort_d3 = tmp_d; + tmp_col = sort_col2; + sort_col2 = sort_col3; + sort_col3 = tmp_col; + } + if (sort_d2 < sort_d0) { + tmp_d = sort_d0; + sort_d0 = sort_d2; + sort_d2 = tmp_d; + tmp_col = sort_col0; + sort_col0 = sort_col2; + sort_col2 = tmp_col; + } + if (sort_d3 < sort_d1) { + tmp_d = sort_d1; + sort_d1 = sort_d3; + sort_d3 = tmp_d; + tmp_col = sort_col1; + sort_col1 = sort_col3; + sort_col3 = tmp_col; + } + if (sort_d2 < sort_d1) { + tmp_d = sort_d1; + sort_d1 = sort_d2; + sort_d2 = tmp_d; + tmp_col = sort_col1; + sort_col1 = sort_col2; + sort_col2 = tmp_col; + } + #pragma unroll + for (int visit = 0; visit < 4; visit++) { + int vec_col = sort_col0; + float dist = sort_d0; + if (visit == 1) { + vec_col = sort_col1; + dist = sort_d1; + } + if (visit == 2) { + vec_col = sort_col2; + dist = sort_d2; + } + if (visit == 3) { + vec_col = sort_col3; + dist = sort_d3; + } + if (dist >= worst_d) { + break; + } + int db_idx = db_start + col_base + vec_col; + best_d[worst_pos] = dist; + best_i[worst_pos] = db_idx; + int refresh_base = worst_chunk * 8; + chunk_worst_d[worst_chunk] = best_d[refresh_base]; + chunk_worst_pos[worst_chunk] = refresh_base; + #pragma unroll + for (int offset = 1; offset < 8; offset++) { + int scan_pos = refresh_base + offset; + if (best_d[scan_pos] > chunk_worst_d[worst_chunk]) { + chunk_worst_d[worst_chunk] = best_d[scan_pos]; + chunk_worst_pos[worst_chunk] = scan_pos; + } + } + worst_d = chunk_worst_d[0]; + worst_pos = chunk_worst_pos[0]; + worst_chunk = 0; + #pragma unroll + for (int chunk_1 = 1; chunk_1 < 4; chunk_1++) { + if (worst_d < chunk_worst_d[chunk_1]) { + worst_d = chunk_worst_d[chunk_1]; + worst_pos = chunk_worst_pos[chunk_1]; + worst_chunk = chunk_1; + } + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + if (valid_q != 0) { + int out_base = ((split_idx_1 * B + batch_idx_1) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_out_d = best_d[0]; + int best_out_i = best_i[0]; + int best_out_pos = 0; + #pragma unroll + for (int scan_pos_1 = 1; scan_pos_1 < TOP_K_MAX; scan_pos_1++) { + if (best_out_d > best_d[scan_pos_1]) { + best_out_d = best_d[scan_pos_1]; + best_out_i = best_i[scan_pos_1]; + best_out_pos = scan_pos_1; + } + } + if (out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = best_out_d; + *((int*)(partial_indices + (out_base + out_k))) = best_out_i; + } + best_d[best_out_pos] = 3.4e+38f; + } + } + } + } + } + + // Cleanup + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::2.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(128)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::2.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0177.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0177.cu new file mode 100644 index 00000000..5f2b5929 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0177.cu @@ -0,0 +1,753 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 128 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_STRIDE 32768 +#define SMEM_SMEM_DATABASE_OFF 33792 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_DATABASE_SQ_OFF 50176 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 50432 +#define THREADS 192 +#define BLOCK_Q 128 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_MAX 32 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16_cta2( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + ".reg .b32 m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "tcgen05.mma.cta_group::2.kind::f16 [%0], %1, %2, %3, {m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step_cg2( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, %3, " + "{m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], %1;\n\t" + "}\n" + :: "r"(mbar_addr), "h"(cta_mask) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x16(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x16.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15}, [%16];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ uint32_t smem_addr(const void* ptr) { + uint32_t addr; + asm("{\n\t" + ".reg .u64 u64addr;\n\t" + "cvta.to.shared.u64 u64addr, %1;\n\t" + "cvt.u32.u64 %0, u64addr;\n\t" + "}\n" : "=r"(addr) : "l"(ptr)); + return addr; +} + + +__device__ __forceinline__ uint32_t mapa_to_rank(uint32_t local_addr, uint32_t rank) { + uint32_t remote; + asm volatile("mapa.shared::cluster.u32 %0, %1, %2;" + : "=r"(remote) : "r"(local_addr), "r"(rank)); + return remote; +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem_cta2( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global" + ".mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .b16 lo, hi;\n\t" + "mov.b32 {lo, hi}, %1;\n\t" + "tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], lo;\n\t" + "}\n" + :: "r"(mbar_addr), "r"((uint32_t)cta_mask) : "memory"); +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_rag_frontier_4fbf_stage1_k32_sort4earlystop_tailinf(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tile_pairs, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + const unsigned int clusters_x = gridDim.x / 2; + const unsigned int cluster_id = ((blockIdx.z * gridDim.y + blockIdx.y) * clusters_x) + blockIdx.x / 2; + const unsigned int num_clusters = clusters_x * gridDim.y * gridDim.z; + + int cta_rank; + asm volatile("mov.b32 %0, %%cluster_ctarank;" : "=r"(cta_rank)); + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 33792); + const int smem_database_addr = smem + 33792; + float* smem_database_sq = reinterpret_cast(smem_raw + 50176); + const int smem_database_sq_addr = smem + 50176; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 0, 2, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 16, 2, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=8 + mbarrier_init_pred(smem + 40, 8, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (128 columns, 128 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::2.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(128) : "memory"); + } + + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: load ---- + if (warp == 0) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 0) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx = cluster_id; work_idx < total_work; work_idx += num_clusters) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tile_pairs; + int q_tile_pair = query_work % num_q_tile_pairs; + int q_tile = q_tile_pair * 2 + cta_rank; + int off_q = q_tile * BLOCK_Q; + int global_q = batch_idx * Q + off_q; + int db_tile_start = split_idx * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((query_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(32768)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_query_addr), "l"(tmap_query), "r"(0), "r"(global_q), "r"(0), + "r"(((query_full_addr) & 0xFEFFFFFF)) : "memory"); + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int off_m = db_tile * BLOCK_M; + int global_m = batch_idx * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((database_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(16384)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_database_addr), "l"(tmap_database), "r"(0), "r"(global_m), "r"(0), + "r"(((database_full_addr) & 0xFEFFFFFF)) : "memory"); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 1) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + if (cta_rank == 0) { + #pragma unroll 1 + for (unsigned int work_idx_1 = cluster_id; work_idx_1 < total_work; work_idx_1 += num_clusters) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = (smem_query_addr >> 4) & 0x3FFF; + int _mma_b_lo_0 = (smem_database_addr >> 4) & 0x3FFF; + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\tmov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 270533776;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit_cg2_multicast(score_full_addr, (uint16_t)(3)); + elect_commit_cg2_multicast(database_empty_addr, (uint16_t)(3)); + } + elect_commit_cg2_multicast(query_empty_addr, (uint16_t)(3)); + } + } + } + // ---- Role: compute ---- + } else if (warp >= 2 && warp <= 5) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = cluster_id; work_idx_2 < total_work; work_idx_2 += num_clusters) { + int split_idx_1 = work_idx_2 % (unsigned int)split_count; + int query_work_1 = work_idx_2 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tile_pairs; + int q_tile_pair_1 = query_work_1 % num_q_tile_pairs; + int q_tile_1 = q_tile_pair_1 * 2 + cta_rank; + int off_q_1 = q_tile_1 * BLOCK_Q; + int q_idx = off_q_1 + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx_1 * Q + q_idx]; + } + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + float chunk_worst_d[4]; + int chunk_worst_pos[4]; + #pragma unroll + for (int chunk = 0; chunk < 4; chunk++) { + int chunk_base = chunk * 8; + chunk_worst_d[chunk] = 3.4e+38f; + chunk_worst_pos[chunk] = chunk_base; + } + float worst_d = 3.4e+38f; + int worst_pos = 0; + int worst_chunk = 0; + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int db_start = db_tile_1 * BLOCK_M; + int db_sq_idx = db_start + (warp % 4 * 32 + lane); + if (warp % 4 * 32 + lane < BLOCK_M) { + if (db_sq_idx < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx_1 * M + db_sq_idx]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 3.4e+38f; + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(cta_rank * BLOCK_Q + (warp % 4 * 32 << 16) << 16); + float _tmem_load_0[64]; + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x64.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31, %32, %33, %34, %35, %36, %37, %38, %39, %40, %41, %42, %43, %44, %45, %46, %47, %48, %49, %50, %51, %52, %53, %54, %55, %56, %57, %58, %59, %60, %61, %62, %63}, [%64];" + : "=f"(_tmem_load_0[0]), "=f"(_tmem_load_0[1]), "=f"(_tmem_load_0[2]), "=f"(_tmem_load_0[3]), "=f"(_tmem_load_0[4]), "=f"(_tmem_load_0[5]), "=f"(_tmem_load_0[6]), "=f"(_tmem_load_0[7]), "=f"(_tmem_load_0[8]), "=f"(_tmem_load_0[9]), "=f"(_tmem_load_0[10]), "=f"(_tmem_load_0[11]), "=f"(_tmem_load_0[12]), "=f"(_tmem_load_0[13]), "=f"(_tmem_load_0[14]), "=f"(_tmem_load_0[15]), "=f"(_tmem_load_0[16]), "=f"(_tmem_load_0[17]), "=f"(_tmem_load_0[18]), "=f"(_tmem_load_0[19]), "=f"(_tmem_load_0[20]), "=f"(_tmem_load_0[21]), "=f"(_tmem_load_0[22]), "=f"(_tmem_load_0[23]), "=f"(_tmem_load_0[24]), "=f"(_tmem_load_0[25]), "=f"(_tmem_load_0[26]), "=f"(_tmem_load_0[27]), "=f"(_tmem_load_0[28]), "=f"(_tmem_load_0[29]), "=f"(_tmem_load_0[30]), "=f"(_tmem_load_0[31]), "=f"(_tmem_load_0[32]), "=f"(_tmem_load_0[33]), "=f"(_tmem_load_0[34]), "=f"(_tmem_load_0[35]), "=f"(_tmem_load_0[36]), "=f"(_tmem_load_0[37]), "=f"(_tmem_load_0[38]), "=f"(_tmem_load_0[39]), "=f"(_tmem_load_0[40]), "=f"(_tmem_load_0[41]), "=f"(_tmem_load_0[42]), "=f"(_tmem_load_0[43]), "=f"(_tmem_load_0[44]), "=f"(_tmem_load_0[45]), "=f"(_tmem_load_0[46]), "=f"(_tmem_load_0[47]), "=f"(_tmem_load_0[48]), "=f"(_tmem_load_0[49]), "=f"(_tmem_load_0[50]), "=f"(_tmem_load_0[51]), "=f"(_tmem_load_0[52]), "=f"(_tmem_load_0[53]), "=f"(_tmem_load_0[54]), "=f"(_tmem_load_0[55]), "=f"(_tmem_load_0[56]), "=f"(_tmem_load_0[57]), "=f"(_tmem_load_0[58]), "=f"(_tmem_load_0[59]), "=f"(_tmem_load_0[60]), "=f"(_tmem_load_0[61]), "=f"(_tmem_load_0[62]), "=f"(_tmem_load_0[63]) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + if (elect_sync()) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cluster.b64 _, [%0];" + :: "r"((score_empty_addr) & 0xFEFFFFFF) : "memory"); + } + if (valid_q != 0) { + #pragma unroll 2 + for (int col_base = 0; col_base < 64; col_base += 4) { + float dist_vec[4]; + dist_vec[0] = _tmem_load_0[col_base]; + dist_vec[1] = _tmem_load_0[col_base + 1]; + dist_vec[2] = _tmem_load_0[col_base + 2]; + dist_vec[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec[4]; + db_sq_vec[0] = smem_database_sq[col_base]; + db_sq_vec[1] = smem_database_sq[col_base + 1]; + db_sq_vec[2] = smem_database_sq[col_base + 2]; + db_sq_vec[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec)[_la], reinterpret_cast(db_sq_vec)[_la]); + float group_min = _t0[0]; + if (_t0[1] < group_min) { + group_min = _t0[1]; + } + if (_t0[2] < group_min) { + group_min = _t0[2]; + } + if (_t0[3] < group_min) { + group_min = _t0[3]; + } + if (group_min < worst_d) { + float sort_d0 = _t0[0]; + float sort_d1 = _t0[1]; + float sort_d2 = _t0[2]; + float sort_d3 = _t0[3]; + int sort_col0 = 0; + int sort_col1 = 1; + int sort_col2 = 2; + int sort_col3 = 3; + float tmp_d = 0.0f; + int tmp_col = 0; + if (sort_d1 < sort_d0) { + tmp_d = sort_d0; + sort_d0 = sort_d1; + sort_d1 = tmp_d; + tmp_col = sort_col0; + sort_col0 = sort_col1; + sort_col1 = tmp_col; + } + if (sort_d3 < sort_d2) { + tmp_d = sort_d2; + sort_d2 = sort_d3; + sort_d3 = tmp_d; + tmp_col = sort_col2; + sort_col2 = sort_col3; + sort_col3 = tmp_col; + } + if (sort_d2 < sort_d0) { + tmp_d = sort_d0; + sort_d0 = sort_d2; + sort_d2 = tmp_d; + tmp_col = sort_col0; + sort_col0 = sort_col2; + sort_col2 = tmp_col; + } + if (sort_d3 < sort_d1) { + tmp_d = sort_d1; + sort_d1 = sort_d3; + sort_d3 = tmp_d; + tmp_col = sort_col1; + sort_col1 = sort_col3; + sort_col3 = tmp_col; + } + if (sort_d2 < sort_d1) { + tmp_d = sort_d1; + sort_d1 = sort_d2; + sort_d2 = tmp_d; + tmp_col = sort_col1; + sort_col1 = sort_col2; + sort_col2 = tmp_col; + } + #pragma unroll + for (int visit = 0; visit < 4; visit++) { + int vec_col = sort_col0; + float dist = sort_d0; + if (visit == 1) { + vec_col = sort_col1; + dist = sort_d1; + } + if (visit == 2) { + vec_col = sort_col2; + dist = sort_d2; + } + if (visit == 3) { + vec_col = sort_col3; + dist = sort_d3; + } + if (dist >= worst_d) { + break; + } + int db_idx = db_start + col_base + vec_col; + best_d[worst_pos] = dist; + best_i[worst_pos] = db_idx; + int refresh_base = worst_chunk * 8; + chunk_worst_d[worst_chunk] = best_d[refresh_base]; + chunk_worst_pos[worst_chunk] = refresh_base; + #pragma unroll + for (int offset = 1; offset < 8; offset++) { + int scan_pos = refresh_base + offset; + if (best_d[scan_pos] > chunk_worst_d[worst_chunk]) { + chunk_worst_d[worst_chunk] = best_d[scan_pos]; + chunk_worst_pos[worst_chunk] = scan_pos; + } + } + worst_d = chunk_worst_d[0]; + worst_pos = chunk_worst_pos[0]; + worst_chunk = 0; + #pragma unroll + for (int chunk_1 = 1; chunk_1 < 4; chunk_1++) { + if (worst_d < chunk_worst_d[chunk_1]) { + worst_d = chunk_worst_d[chunk_1]; + worst_pos = chunk_worst_pos[chunk_1]; + worst_chunk = chunk_1; + } + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + if (valid_q != 0) { + int out_base = ((split_idx_1 * B + batch_idx_1) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_out_d = best_d[0]; + int best_out_i = best_i[0]; + int best_out_pos = 0; + #pragma unroll + for (int scan_pos_1 = 1; scan_pos_1 < TOP_K_MAX; scan_pos_1++) { + if (best_out_d > best_d[scan_pos_1]) { + best_out_d = best_d[scan_pos_1]; + best_out_i = best_i[scan_pos_1]; + best_out_pos = scan_pos_1; + } + } + if (out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = best_out_d; + *((int*)(partial_indices + (out_base + out_k))) = best_out_i; + } + best_d[best_out_pos] = 3.4e+38f; + } + } + } + } + } + + // Cleanup + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::2.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(128)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::2.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0178.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0178.cu new file mode 100644 index 00000000..2f6328b9 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0178.cu @@ -0,0 +1,657 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 128 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_STRIDE 32768 +#define SMEM_SMEM_DATABASE_OFF 33792 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_DATABASE_SQ_OFF 50176 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 50432 +#define THREADS 192 +#define BLOCK_Q 128 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_MAX 24 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16_cta2( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + ".reg .b32 m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "tcgen05.mma.cta_group::2.kind::f16 [%0], %1, %2, %3, {m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step_cg2( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, %3, " + "{m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], %1;\n\t" + "}\n" + :: "r"(mbar_addr), "h"(cta_mask) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x16(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x16.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15}, [%16];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ uint32_t smem_addr(const void* ptr) { + uint32_t addr; + asm("{\n\t" + ".reg .u64 u64addr;\n\t" + "cvta.to.shared.u64 u64addr, %1;\n\t" + "cvt.u32.u64 %0, u64addr;\n\t" + "}\n" : "=r"(addr) : "l"(ptr)); + return addr; +} + + +__device__ __forceinline__ uint32_t mapa_to_rank(uint32_t local_addr, uint32_t rank) { + uint32_t remote; + asm volatile("mapa.shared::cluster.u32 %0, %1, %2;" + : "=r"(remote) : "r"(local_addr), "r"(rank)); + return remote; +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem_cta2( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global" + ".mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .b16 lo, hi;\n\t" + "mov.b32 {lo, hi}, %1;\n\t" + "tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], lo;\n\t" + "}\n" + :: "r"(mbar_addr), "r"((uint32_t)cta_mask) : "memory"); +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_bad5k24s8(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tile_pairs, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + const unsigned int clusters_x = gridDim.x / 2; + const unsigned int cluster_id = ((blockIdx.z * gridDim.y + blockIdx.y) * clusters_x) + blockIdx.x / 2; + const unsigned int num_clusters = clusters_x * gridDim.y * gridDim.z; + + int cta_rank; + asm volatile("mov.b32 %0, %%cluster_ctarank;" : "=r"(cta_rank)); + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 33792); + const int smem_database_addr = smem + 33792; + float* smem_database_sq = reinterpret_cast(smem_raw + 50176); + const int smem_database_sq_addr = smem + 50176; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 0, 2, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 16, 2, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=8 + mbarrier_init_pred(smem + 40, 8, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (128 columns, 128 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::2.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(128) : "memory"); + } + + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: load ---- + if (warp == 0) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 0) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx = cluster_id; work_idx < total_work; work_idx += num_clusters) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tile_pairs; + int q_tile_pair = query_work % num_q_tile_pairs; + int q_tile = q_tile_pair * 2 + cta_rank; + int off_q = q_tile * BLOCK_Q; + int global_q = batch_idx * Q + off_q; + int db_tile_start = split_idx * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((query_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(32768)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_query_addr), "l"(tmap_query), "r"(0), "r"(global_q), "r"(0), + "r"(((query_full_addr) & 0xFEFFFFFF)) : "memory"); + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int off_m = db_tile * BLOCK_M; + int global_m = batch_idx * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((database_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(16384)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_database_addr), "l"(tmap_database), "r"(0), "r"(global_m), "r"(0), + "r"(((database_full_addr) & 0xFEFFFFFF)) : "memory"); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 1) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + if (cta_rank == 0) { + #pragma unroll 1 + for (unsigned int work_idx_1 = cluster_id; work_idx_1 < total_work; work_idx_1 += num_clusters) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = (smem_query_addr >> 4) & 0x3FFF; + int _mma_b_lo_0 = (smem_database_addr >> 4) & 0x3FFF; + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\tmov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 270533776;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit_cg2_multicast(score_full_addr, (uint16_t)(3)); + elect_commit_cg2_multicast(database_empty_addr, (uint16_t)(3)); + } + elect_commit_cg2_multicast(query_empty_addr, (uint16_t)(3)); + } + } + } + // ---- Role: compute ---- + } else if (warp >= 2 && warp <= 5) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = cluster_id; work_idx_2 < total_work; work_idx_2 += num_clusters) { + int split_idx_1 = work_idx_2 % (unsigned int)split_count; + int query_work_1 = work_idx_2 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tile_pairs; + int q_tile_pair_1 = query_work_1 % num_q_tile_pairs; + int q_tile_1 = q_tile_pair_1 * 2 + cta_rank; + int off_q_1 = q_tile_1 * BLOCK_Q; + int q_idx = off_q_1 + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx_1 * Q + q_idx]; + } + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int db_start = db_tile_1 * BLOCK_M; + int db_sq_idx = db_start + (warp % 4 * 32 + lane); + if (warp % 4 * 32 + lane < BLOCK_M) { + if (db_sq_idx < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx_1 * M + db_sq_idx]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 0.0f; + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(cta_rank * BLOCK_Q + (warp % 4 * 32 << 16) << 16); + float _tmem_load_0[64]; + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x64.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31, %32, %33, %34, %35, %36, %37, %38, %39, %40, %41, %42, %43, %44, %45, %46, %47, %48, %49, %50, %51, %52, %53, %54, %55, %56, %57, %58, %59, %60, %61, %62, %63}, [%64];" + : "=f"(_tmem_load_0[0]), "=f"(_tmem_load_0[1]), "=f"(_tmem_load_0[2]), "=f"(_tmem_load_0[3]), "=f"(_tmem_load_0[4]), "=f"(_tmem_load_0[5]), "=f"(_tmem_load_0[6]), "=f"(_tmem_load_0[7]), "=f"(_tmem_load_0[8]), "=f"(_tmem_load_0[9]), "=f"(_tmem_load_0[10]), "=f"(_tmem_load_0[11]), "=f"(_tmem_load_0[12]), "=f"(_tmem_load_0[13]), "=f"(_tmem_load_0[14]), "=f"(_tmem_load_0[15]), "=f"(_tmem_load_0[16]), "=f"(_tmem_load_0[17]), "=f"(_tmem_load_0[18]), "=f"(_tmem_load_0[19]), "=f"(_tmem_load_0[20]), "=f"(_tmem_load_0[21]), "=f"(_tmem_load_0[22]), "=f"(_tmem_load_0[23]), "=f"(_tmem_load_0[24]), "=f"(_tmem_load_0[25]), "=f"(_tmem_load_0[26]), "=f"(_tmem_load_0[27]), "=f"(_tmem_load_0[28]), "=f"(_tmem_load_0[29]), "=f"(_tmem_load_0[30]), "=f"(_tmem_load_0[31]), "=f"(_tmem_load_0[32]), "=f"(_tmem_load_0[33]), "=f"(_tmem_load_0[34]), "=f"(_tmem_load_0[35]), "=f"(_tmem_load_0[36]), "=f"(_tmem_load_0[37]), "=f"(_tmem_load_0[38]), "=f"(_tmem_load_0[39]), "=f"(_tmem_load_0[40]), "=f"(_tmem_load_0[41]), "=f"(_tmem_load_0[42]), "=f"(_tmem_load_0[43]), "=f"(_tmem_load_0[44]), "=f"(_tmem_load_0[45]), "=f"(_tmem_load_0[46]), "=f"(_tmem_load_0[47]), "=f"(_tmem_load_0[48]), "=f"(_tmem_load_0[49]), "=f"(_tmem_load_0[50]), "=f"(_tmem_load_0[51]), "=f"(_tmem_load_0[52]), "=f"(_tmem_load_0[53]), "=f"(_tmem_load_0[54]), "=f"(_tmem_load_0[55]), "=f"(_tmem_load_0[56]), "=f"(_tmem_load_0[57]), "=f"(_tmem_load_0[58]), "=f"(_tmem_load_0[59]), "=f"(_tmem_load_0[60]), "=f"(_tmem_load_0[61]), "=f"(_tmem_load_0[62]), "=f"(_tmem_load_0[63]) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + if (elect_sync()) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cluster.b64 _, [%0];" + :: "r"((score_empty_addr) & 0xFEFFFFFF) : "memory"); + } + if (valid_q != 0) { + #pragma unroll 2 + for (int col_base = 0; col_base < 64; col_base += 4) { + float dist_vec[4]; + dist_vec[0] = _tmem_load_0[col_base]; + dist_vec[1] = _tmem_load_0[col_base + 1]; + dist_vec[2] = _tmem_load_0[col_base + 2]; + dist_vec[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec[4]; + db_sq_vec[0] = smem_database_sq[col_base]; + db_sq_vec[1] = smem_database_sq[col_base + 1]; + db_sq_vec[2] = smem_database_sq[col_base + 2]; + db_sq_vec[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec)[_la], reinterpret_cast(db_sq_vec)[_la]); + float group_min = _t0[0]; + if (_t0[1] < group_min) { + group_min = _t0[1]; + } + if (_t0[2] < group_min) { + group_min = _t0[2]; + } + if (_t0[3] < group_min) { + group_min = _t0[3]; + } + if (group_min < best_d[TOP_K_MAX - 1]) { + #pragma unroll + for (int vec_col = 0; vec_col < 4; vec_col++) { + int db_idx = db_start + col_base + vec_col; + if (db_idx < M) { + float dist = _t0[vec_col]; + if (dist < best_d[TOP_K_MAX - 1]) { + best_d[TOP_K_MAX - 1] = dist; + best_i[TOP_K_MAX - 1] = db_idx; + #pragma unroll + for (int pos = TOP_K_MAX - 1; pos >= 1; pos--) { + if (best_d[pos] < best_d[pos - 1]) { + float tmp_d = best_d[pos - 1]; + int tmp_i = best_i[pos - 1]; + best_d[pos - 1] = best_d[pos]; + best_i[pos - 1] = best_i[pos]; + best_d[pos] = tmp_d; + best_i[pos] = tmp_i; + } + } + } + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + if (valid_q != 0) { + int out_base = ((split_idx_1 * B + batch_idx_1) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + if (out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = best_d[out_k]; + *((int*)(partial_indices + (out_base + out_k))) = best_i[out_k]; + } + } + } + } + } + } + + // Cleanup + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::2.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(128)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::2.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0179.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0179.cu new file mode 100644 index 00000000..9c4337d5 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0179.cu @@ -0,0 +1,657 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 128 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_STRIDE 32768 +#define SMEM_SMEM_DATABASE_OFF 33792 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_DATABASE_SQ_OFF 50176 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 50432 +#define THREADS 192 +#define BLOCK_Q 128 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_MAX 28 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16_cta2( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + ".reg .b32 m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "tcgen05.mma.cta_group::2.kind::f16 [%0], %1, %2, %3, {m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step_cg2( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, %3, " + "{m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], %1;\n\t" + "}\n" + :: "r"(mbar_addr), "h"(cta_mask) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x16(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x16.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15}, [%16];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ uint32_t smem_addr(const void* ptr) { + uint32_t addr; + asm("{\n\t" + ".reg .u64 u64addr;\n\t" + "cvta.to.shared.u64 u64addr, %1;\n\t" + "cvt.u32.u64 %0, u64addr;\n\t" + "}\n" : "=r"(addr) : "l"(ptr)); + return addr; +} + + +__device__ __forceinline__ uint32_t mapa_to_rank(uint32_t local_addr, uint32_t rank) { + uint32_t remote; + asm volatile("mapa.shared::cluster.u32 %0, %1, %2;" + : "=r"(remote) : "r"(local_addr), "r"(rank)); + return remote; +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem_cta2( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global" + ".mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .b16 lo, hi;\n\t" + "mov.b32 {lo, hi}, %1;\n\t" + "tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], lo;\n\t" + "}\n" + :: "r"(mbar_addr), "r"((uint32_t)cta_mask) : "memory"); +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_bad5k28s8(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tile_pairs, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + const unsigned int clusters_x = gridDim.x / 2; + const unsigned int cluster_id = ((blockIdx.z * gridDim.y + blockIdx.y) * clusters_x) + blockIdx.x / 2; + const unsigned int num_clusters = clusters_x * gridDim.y * gridDim.z; + + int cta_rank; + asm volatile("mov.b32 %0, %%cluster_ctarank;" : "=r"(cta_rank)); + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 33792); + const int smem_database_addr = smem + 33792; + float* smem_database_sq = reinterpret_cast(smem_raw + 50176); + const int smem_database_sq_addr = smem + 50176; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 0, 2, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 16, 2, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=8 + mbarrier_init_pred(smem + 40, 8, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (128 columns, 128 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::2.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(128) : "memory"); + } + + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: load ---- + if (warp == 0) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 0) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx = cluster_id; work_idx < total_work; work_idx += num_clusters) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tile_pairs; + int q_tile_pair = query_work % num_q_tile_pairs; + int q_tile = q_tile_pair * 2 + cta_rank; + int off_q = q_tile * BLOCK_Q; + int global_q = batch_idx * Q + off_q; + int db_tile_start = split_idx * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((query_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(32768)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_query_addr), "l"(tmap_query), "r"(0), "r"(global_q), "r"(0), + "r"(((query_full_addr) & 0xFEFFFFFF)) : "memory"); + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int off_m = db_tile * BLOCK_M; + int global_m = batch_idx * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((database_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(16384)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_database_addr), "l"(tmap_database), "r"(0), "r"(global_m), "r"(0), + "r"(((database_full_addr) & 0xFEFFFFFF)) : "memory"); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 1) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + if (cta_rank == 0) { + #pragma unroll 1 + for (unsigned int work_idx_1 = cluster_id; work_idx_1 < total_work; work_idx_1 += num_clusters) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = (smem_query_addr >> 4) & 0x3FFF; + int _mma_b_lo_0 = (smem_database_addr >> 4) & 0x3FFF; + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\tmov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 270533776;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit_cg2_multicast(score_full_addr, (uint16_t)(3)); + elect_commit_cg2_multicast(database_empty_addr, (uint16_t)(3)); + } + elect_commit_cg2_multicast(query_empty_addr, (uint16_t)(3)); + } + } + } + // ---- Role: compute ---- + } else if (warp >= 2 && warp <= 5) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = cluster_id; work_idx_2 < total_work; work_idx_2 += num_clusters) { + int split_idx_1 = work_idx_2 % (unsigned int)split_count; + int query_work_1 = work_idx_2 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tile_pairs; + int q_tile_pair_1 = query_work_1 % num_q_tile_pairs; + int q_tile_1 = q_tile_pair_1 * 2 + cta_rank; + int off_q_1 = q_tile_1 * BLOCK_Q; + int q_idx = off_q_1 + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx_1 * Q + q_idx]; + } + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int db_start = db_tile_1 * BLOCK_M; + int db_sq_idx = db_start + (warp % 4 * 32 + lane); + if (warp % 4 * 32 + lane < BLOCK_M) { + if (db_sq_idx < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx_1 * M + db_sq_idx]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 0.0f; + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(cta_rank * BLOCK_Q + (warp % 4 * 32 << 16) << 16); + float _tmem_load_0[64]; + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x64.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31, %32, %33, %34, %35, %36, %37, %38, %39, %40, %41, %42, %43, %44, %45, %46, %47, %48, %49, %50, %51, %52, %53, %54, %55, %56, %57, %58, %59, %60, %61, %62, %63}, [%64];" + : "=f"(_tmem_load_0[0]), "=f"(_tmem_load_0[1]), "=f"(_tmem_load_0[2]), "=f"(_tmem_load_0[3]), "=f"(_tmem_load_0[4]), "=f"(_tmem_load_0[5]), "=f"(_tmem_load_0[6]), "=f"(_tmem_load_0[7]), "=f"(_tmem_load_0[8]), "=f"(_tmem_load_0[9]), "=f"(_tmem_load_0[10]), "=f"(_tmem_load_0[11]), "=f"(_tmem_load_0[12]), "=f"(_tmem_load_0[13]), "=f"(_tmem_load_0[14]), "=f"(_tmem_load_0[15]), "=f"(_tmem_load_0[16]), "=f"(_tmem_load_0[17]), "=f"(_tmem_load_0[18]), "=f"(_tmem_load_0[19]), "=f"(_tmem_load_0[20]), "=f"(_tmem_load_0[21]), "=f"(_tmem_load_0[22]), "=f"(_tmem_load_0[23]), "=f"(_tmem_load_0[24]), "=f"(_tmem_load_0[25]), "=f"(_tmem_load_0[26]), "=f"(_tmem_load_0[27]), "=f"(_tmem_load_0[28]), "=f"(_tmem_load_0[29]), "=f"(_tmem_load_0[30]), "=f"(_tmem_load_0[31]), "=f"(_tmem_load_0[32]), "=f"(_tmem_load_0[33]), "=f"(_tmem_load_0[34]), "=f"(_tmem_load_0[35]), "=f"(_tmem_load_0[36]), "=f"(_tmem_load_0[37]), "=f"(_tmem_load_0[38]), "=f"(_tmem_load_0[39]), "=f"(_tmem_load_0[40]), "=f"(_tmem_load_0[41]), "=f"(_tmem_load_0[42]), "=f"(_tmem_load_0[43]), "=f"(_tmem_load_0[44]), "=f"(_tmem_load_0[45]), "=f"(_tmem_load_0[46]), "=f"(_tmem_load_0[47]), "=f"(_tmem_load_0[48]), "=f"(_tmem_load_0[49]), "=f"(_tmem_load_0[50]), "=f"(_tmem_load_0[51]), "=f"(_tmem_load_0[52]), "=f"(_tmem_load_0[53]), "=f"(_tmem_load_0[54]), "=f"(_tmem_load_0[55]), "=f"(_tmem_load_0[56]), "=f"(_tmem_load_0[57]), "=f"(_tmem_load_0[58]), "=f"(_tmem_load_0[59]), "=f"(_tmem_load_0[60]), "=f"(_tmem_load_0[61]), "=f"(_tmem_load_0[62]), "=f"(_tmem_load_0[63]) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + if (elect_sync()) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cluster.b64 _, [%0];" + :: "r"((score_empty_addr) & 0xFEFFFFFF) : "memory"); + } + if (valid_q != 0) { + #pragma unroll 2 + for (int col_base = 0; col_base < 64; col_base += 4) { + float dist_vec[4]; + dist_vec[0] = _tmem_load_0[col_base]; + dist_vec[1] = _tmem_load_0[col_base + 1]; + dist_vec[2] = _tmem_load_0[col_base + 2]; + dist_vec[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec[4]; + db_sq_vec[0] = smem_database_sq[col_base]; + db_sq_vec[1] = smem_database_sq[col_base + 1]; + db_sq_vec[2] = smem_database_sq[col_base + 2]; + db_sq_vec[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec)[_la], reinterpret_cast(db_sq_vec)[_la]); + float group_min = _t0[0]; + if (_t0[1] < group_min) { + group_min = _t0[1]; + } + if (_t0[2] < group_min) { + group_min = _t0[2]; + } + if (_t0[3] < group_min) { + group_min = _t0[3]; + } + if (group_min < best_d[TOP_K_MAX - 1]) { + #pragma unroll + for (int vec_col = 0; vec_col < 4; vec_col++) { + int db_idx = db_start + col_base + vec_col; + if (db_idx < M) { + float dist = _t0[vec_col]; + if (dist < best_d[TOP_K_MAX - 1]) { + best_d[TOP_K_MAX - 1] = dist; + best_i[TOP_K_MAX - 1] = db_idx; + #pragma unroll + for (int pos = TOP_K_MAX - 1; pos >= 1; pos--) { + if (best_d[pos] < best_d[pos - 1]) { + float tmp_d = best_d[pos - 1]; + int tmp_i = best_i[pos - 1]; + best_d[pos - 1] = best_d[pos]; + best_i[pos - 1] = best_i[pos]; + best_d[pos] = tmp_d; + best_i[pos] = tmp_i; + } + } + } + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + if (valid_q != 0) { + int out_base = ((split_idx_1 * B + batch_idx_1) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + if (out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = best_d[out_k]; + *((int*)(partial_indices + (out_base + out_k))) = best_i[out_k]; + } + } + } + } + } + } + + // Cleanup + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::2.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(128)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::2.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0180.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0180.cu new file mode 100644 index 00000000..e24373f6 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0180.cu @@ -0,0 +1,84 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 32 +#define TOP_K_MAX 24 +#define SPLIT_COUNT 8 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_bad5k24s8(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 32 + tid; + int stride = num_bids * 32; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int out_base = base_row; + int split_pos[SPLIT_COUNT]; + int split_base[SPLIT_COUNT]; + float cand_d[SPLIT_COUNT]; + int cand_i[SPLIT_COUNT]; + #pragma unroll + for (int split_idx = 0; split_idx < SPLIT_COUNT; split_idx++) { + split_pos[split_idx] = 0; + split_base[split_idx] = base_row + split_idx * split_stride; + cand_d[split_idx] = partial_dists[split_base[split_idx]]; + cand_i[split_idx] = partial_indices[split_base[split_idx]]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_d = cand_d[0]; + int best_i = cand_i[0]; + int best_split = 0; + #pragma unroll + for (int split_idx_1 = 1; split_idx_1 < SPLIT_COUNT; split_idx_1++) { + if (best_d > cand_d[split_idx_1]) { + best_d = cand_d[split_idx_1]; + best_i = cand_i[split_idx_1]; + best_split = split_idx_1; + } + } + *((float*)(out_dists + (out_base + out_k))) = best_d; + *((int*)(out_indices + (out_base + out_k))) = best_i; + split_pos[best_split] = split_pos[best_split] + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_pos = split_pos[best_split]; + int next_addr = split_base[best_split] + next_pos; + cand_d[best_split] = partial_dists[next_addr]; + cand_i[best_split] = partial_indices[next_addr]; + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0181.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0181.cu new file mode 100644 index 00000000..12630ddc --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0181.cu @@ -0,0 +1,84 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 32 +#define TOP_K_MAX 28 +#define SPLIT_COUNT 8 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_bad5k28s8(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 32 + tid; + int stride = num_bids * 32; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int out_base = base_row; + int split_pos[SPLIT_COUNT]; + int split_base[SPLIT_COUNT]; + float cand_d[SPLIT_COUNT]; + int cand_i[SPLIT_COUNT]; + #pragma unroll + for (int split_idx = 0; split_idx < SPLIT_COUNT; split_idx++) { + split_pos[split_idx] = 0; + split_base[split_idx] = base_row + split_idx * split_stride; + cand_d[split_idx] = partial_dists[split_base[split_idx]]; + cand_i[split_idx] = partial_indices[split_base[split_idx]]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_d = cand_d[0]; + int best_i = cand_i[0]; + int best_split = 0; + #pragma unroll + for (int split_idx_1 = 1; split_idx_1 < SPLIT_COUNT; split_idx_1++) { + if (best_d > cand_d[split_idx_1]) { + best_d = cand_d[split_idx_1]; + best_i = cand_i[split_idx_1]; + best_split = split_idx_1; + } + } + *((float*)(out_dists + (out_base + out_k))) = best_d; + *((int*)(out_indices + (out_base + out_k))) = best_i; + split_pos[best_split] = split_pos[best_split] + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_pos = split_pos[best_split]; + int next_addr = split_base[best_split] + next_pos; + cand_d[best_split] = partial_dists[next_addr]; + cand_i[best_split] = partial_indices[next_addr]; + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0182.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0182.cu new file mode 100644 index 00000000..56ea0c2e --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0182.cu @@ -0,0 +1,88 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 128 +#define TOP_K_MAX 32 + +#include + +extern "C" { + +__global__ __launch_bounds__(128, 1) void +kernel_knn_build_large_square_k32_s2_warp_select(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int row = bid * 4 + warp; + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int cand_k = lane; + if (row < total_queries) { + float d0 = partial_dists[base_row + cand_k]; + int i0 = partial_indices[base_row + cand_k]; + int base1 = base_row + split_stride; + float d1 = partial_dists[base1 + cand_k]; + int i1 = partial_indices[base1 + cand_k]; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float winner_d = d0; + int winner_i = i0; + int winner_src = 0; + if (d1 < winner_d) { + winner_d = d1; + winner_i = i1; + winner_src = 1; + } + float warp_min = winner_d; + float _warp_reduce_0 = warp_min; + #pragma unroll + for (int offset = 16; offset > 0; offset >>= 1) + _warp_reduce_0 = fminf(_warp_reduce_0, __shfl_xor_sync(0xFFFFFFFF, _warp_reduce_0, offset)); + warp_min = _warp_reduce_0; + unsigned int _vote_0 = __ballot_sync(0xFFFFFFFF, winner_d == warp_min); + int owner_ballot = _vote_0; + int _ffs_0 = __ffs(owner_ballot); + int winner_lane = _ffs_0 - 1; + int _shfl_0 = __shfl_sync(0xFFFFFFFF, winner_i, winner_lane); + winner_i = _shfl_0; + int _shfl_1 = __shfl_sync(0xFFFFFFFF, winner_src, winner_lane); + winner_src = _shfl_1; + if (lane == 0) { + *((float*)(out_dists + (base_row + out_k))) = warp_min; + *((int*)(out_indices + (base_row + out_k))) = winner_i; + } + if (lane == winner_lane) { + if (winner_src == 0) { + d0 = 3.4e+38f; + } else { + d1 = 3.4e+38f; + } + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0183.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0183.cu new file mode 100644 index 00000000..e41ca210 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0183.cu @@ -0,0 +1,84 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 64 +#define TOP_K_MAX 10 +#define SPLIT_COUNT 16 + +#include + +extern "C" { + +__global__ __launch_bounds__(64, 1) void +kernel_knn_build_rect_d64_cf49_s16_cached_merge(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 64 + tid; + int stride = num_bids * 64; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int out_base = base_row; + int split_pos[SPLIT_COUNT]; + int split_base[SPLIT_COUNT]; + float cand_d[SPLIT_COUNT]; + int cand_i[SPLIT_COUNT]; + #pragma unroll + for (int split_idx = 0; split_idx < SPLIT_COUNT; split_idx++) { + split_pos[split_idx] = 0; + split_base[split_idx] = base_row + split_idx * split_stride; + cand_d[split_idx] = partial_dists[split_base[split_idx]]; + cand_i[split_idx] = partial_indices[split_base[split_idx]]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_d = cand_d[0]; + int best_i = cand_i[0]; + int best_split = 0; + #pragma unroll + for (int split_idx_1 = 1; split_idx_1 < SPLIT_COUNT; split_idx_1++) { + if (best_d > cand_d[split_idx_1]) { + best_d = cand_d[split_idx_1]; + best_i = cand_i[split_idx_1]; + best_split = split_idx_1; + } + } + *((float*)(out_dists + (out_base + out_k))) = best_d; + *((int*)(out_indices + (out_base + out_k))) = best_i; + split_pos[best_split] = split_pos[best_split] + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_pos = split_pos[best_split]; + int next_addr = split_base[best_split] + next_pos; + cand_d[best_split] = partial_dists[next_addr]; + cand_i[best_split] = partial_indices[next_addr]; + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0184.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0184.cu new file mode 100644 index 00000000..8fe5d66a --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0184.cu @@ -0,0 +1,150 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define SMEM_GROUP_D_OFF 0 +#define SMEM_GROUP_D_STAGE_BYTES 160 +#define SMEM_GROUP_D_STRIDE 160 +#define SMEM_GROUP_I_OFF 160 +#define SMEM_GROUP_I_STAGE_BYTES 160 +#define SMEM_GROUP_I_STRIDE 160 +#define SMEM_TOTAL 512 +#define THREADS 128 +#define TOP_K_MAX 10 +#define SPLIT_COUNT 72 +#define MERGE_GROUPS 4 +#define SPLITS_PER_GROUP 18 + +#include + +extern "C" { + +__global__ __launch_bounds__(128, 1) void +kernel_knn_build_ragonline_mbucket_aa88_q1m_s72_k10_coop_merge(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + float* group_d = reinterpret_cast(smem_raw + 0); + const int group_d_addr = smem + 0; + int* group_i = reinterpret_cast(smem_raw + 160); + const int group_i_addr = smem + 160; + + // === Task calls (dependency order) === + int row = bid; + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int group = warp; + int split_idx = group * SPLITS_PER_GROUP + lane; + int split_pos = 0; + float cand_d = 3.4e+38f; + int cand_i = -1; + if (row < total_queries) { + if (lane < SPLITS_PER_GROUP) { + if (split_idx < SPLIT_COUNT) { + int split_base = base_row + split_idx * split_stride; + cand_d = partial_dists[split_base]; + cand_i = partial_indices[split_base]; + } + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float warp_min = cand_d; + float _warp_reduce_0 = warp_min; + #pragma unroll + for (int offset = 16; offset > 0; offset >>= 1) + _warp_reduce_0 = fminf(_warp_reduce_0, __shfl_xor_sync(0xFFFFFFFF, _warp_reduce_0, offset)); + warp_min = _warp_reduce_0; + unsigned int _vote_0 = __ballot_sync(0xFFFFFFFF, cand_d == warp_min); + int owner_ballot = _vote_0; + int _ffs_0 = __ffs(owner_ballot); + int winner_lane = _ffs_0 - 1; + int _shfl_0 = __shfl_sync(0xFFFFFFFF, cand_i, winner_lane); + int winner_i = _shfl_0; + if (lane == 0) { + int group_slot = group * TOP_K_MAX + out_k; + group_d[group_slot] = warp_min; + group_i[group_slot] = winner_i; + } + if (lane == winner_lane) { + split_pos = split_pos + 1; + if (split_pos < TOP_K_MAX) { + int next_addr = base_row + split_idx * split_stride + split_pos; + cand_d = partial_dists[next_addr]; + cand_i = partial_indices[next_addr]; + } else { + cand_d = 3.4e+38f; + cand_i = -1; + } + } + } + } + asm volatile("barrier.sync 15, %0;" :: "r"(128)); + if (row < total_queries) { + if (warp == 0) { + int group_pos = 0; + float final_d = 3.4e+38f; + int final_i = -1; + if (lane < MERGE_GROUPS) { + final_d = group_d[lane * TOP_K_MAX]; + final_i = group_i[lane * TOP_K_MAX]; + } + #pragma unroll + for (int out_k_1 = 0; out_k_1 < TOP_K_MAX; out_k_1++) { + float warp_min_1 = final_d; + float _warp_reduce_1 = warp_min_1; + #pragma unroll + for (int offset = 16; offset > 0; offset >>= 1) + _warp_reduce_1 = fminf(_warp_reduce_1, __shfl_xor_sync(0xFFFFFFFF, _warp_reduce_1, offset)); + warp_min_1 = _warp_reduce_1; + unsigned int _vote_1 = __ballot_sync(0xFFFFFFFF, final_d == warp_min_1); + int owner_ballot_1 = _vote_1; + int _ffs_1 = __ffs(owner_ballot_1); + int winner_lane_1 = _ffs_1 - 1; + int _shfl_1 = __shfl_sync(0xFFFFFFFF, final_i, winner_lane_1); + int winner_i_1 = _shfl_1; + if (lane == 0) { + *((float*)(out_dists + (base_row + out_k_1))) = warp_min_1; + *((int*)(out_indices + (base_row + out_k_1))) = winner_i_1; + } + if (lane == winner_lane_1) { + group_pos = group_pos + 1; + if (group_pos < TOP_K_MAX) { + int next_slot = lane * TOP_K_MAX + group_pos; + final_d = group_d[next_slot]; + final_i = group_i[next_slot]; + } else { + final_d = 3.4e+38f; + final_i = -1; + } + } + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0185.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0185.cu new file mode 100644 index 00000000..44bddf03 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0185.cu @@ -0,0 +1,150 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define SMEM_GROUP_D_OFF 0 +#define SMEM_GROUP_D_STAGE_BYTES 160 +#define SMEM_GROUP_D_STRIDE 160 +#define SMEM_GROUP_I_OFF 160 +#define SMEM_GROUP_I_STAGE_BYTES 160 +#define SMEM_GROUP_I_STRIDE 160 +#define SMEM_TOTAL 512 +#define THREADS 128 +#define TOP_K_MAX 10 +#define SPLIT_COUNT 74 +#define MERGE_GROUPS 4 +#define SPLITS_PER_GROUP 19 + +#include + +extern "C" { + +__global__ __launch_bounds__(128, 1) void +kernel_knn_build_ragonline_mbucket_aa88_q1m_s72_k10_coop_merge_s74_m250(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + float* group_d = reinterpret_cast(smem_raw + 0); + const int group_d_addr = smem + 0; + int* group_i = reinterpret_cast(smem_raw + 160); + const int group_i_addr = smem + 160; + + // === Task calls (dependency order) === + int row = bid; + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int group = warp; + int split_idx = group * SPLITS_PER_GROUP + lane; + int split_pos = 0; + float cand_d = 3.4e+38f; + int cand_i = -1; + if (row < total_queries) { + if (lane < SPLITS_PER_GROUP) { + if (split_idx < SPLIT_COUNT) { + int split_base = base_row + split_idx * split_stride; + cand_d = partial_dists[split_base]; + cand_i = partial_indices[split_base]; + } + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float warp_min = cand_d; + float _warp_reduce_0 = warp_min; + #pragma unroll + for (int offset = 16; offset > 0; offset >>= 1) + _warp_reduce_0 = fminf(_warp_reduce_0, __shfl_xor_sync(0xFFFFFFFF, _warp_reduce_0, offset)); + warp_min = _warp_reduce_0; + unsigned int _vote_0 = __ballot_sync(0xFFFFFFFF, cand_d == warp_min); + int owner_ballot = _vote_0; + int _ffs_0 = __ffs(owner_ballot); + int winner_lane = _ffs_0 - 1; + int _shfl_0 = __shfl_sync(0xFFFFFFFF, cand_i, winner_lane); + int winner_i = _shfl_0; + if (lane == 0) { + int group_slot = group * TOP_K_MAX + out_k; + group_d[group_slot] = warp_min; + group_i[group_slot] = winner_i; + } + if (lane == winner_lane) { + split_pos = split_pos + 1; + if (split_pos < TOP_K_MAX) { + int next_addr = base_row + split_idx * split_stride + split_pos; + cand_d = partial_dists[next_addr]; + cand_i = partial_indices[next_addr]; + } else { + cand_d = 3.4e+38f; + cand_i = -1; + } + } + } + } + asm volatile("barrier.sync 15, %0;" :: "r"(128)); + if (row < total_queries) { + if (warp == 0) { + int group_pos = 0; + float final_d = 3.4e+38f; + int final_i = -1; + if (lane < MERGE_GROUPS) { + final_d = group_d[lane * TOP_K_MAX]; + final_i = group_i[lane * TOP_K_MAX]; + } + #pragma unroll + for (int out_k_1 = 0; out_k_1 < TOP_K_MAX; out_k_1++) { + float warp_min_1 = final_d; + float _warp_reduce_1 = warp_min_1; + #pragma unroll + for (int offset = 16; offset > 0; offset >>= 1) + _warp_reduce_1 = fminf(_warp_reduce_1, __shfl_xor_sync(0xFFFFFFFF, _warp_reduce_1, offset)); + warp_min_1 = _warp_reduce_1; + unsigned int _vote_1 = __ballot_sync(0xFFFFFFFF, final_d == warp_min_1); + int owner_ballot_1 = _vote_1; + int _ffs_1 = __ffs(owner_ballot_1); + int winner_lane_1 = _ffs_1 - 1; + int _shfl_1 = __shfl_sync(0xFFFFFFFF, final_i, winner_lane_1); + int winner_i_1 = _shfl_1; + if (lane == 0) { + *((float*)(out_dists + (base_row + out_k_1))) = warp_min_1; + *((int*)(out_indices + (base_row + out_k_1))) = winner_i_1; + } + if (lane == winner_lane_1) { + group_pos = group_pos + 1; + if (group_pos < TOP_K_MAX) { + int next_slot = lane * TOP_K_MAX + group_pos; + final_d = group_d[next_slot]; + final_i = group_i[next_slot]; + } else { + final_d = 3.4e+38f; + final_i = -1; + } + } + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0186.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0186.cu new file mode 100644 index 00000000..8c615fb6 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0186.cu @@ -0,0 +1,702 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 64 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_STRIDE 32768 +#define SMEM_SMEM_DATABASE_OFF 33792 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_DATABASE_SQ_OFF 50176 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 50432 +#define THREADS 96 +#define BLOCK_Q 128 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_MAX 32 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "tcgen05.mma.cta_group::1.kind::f16 [%0], %1, %2, %3, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, %3, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit(int mbar_addr) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];\n\t" + "}\n" + :: "r"(mbar_addr)); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x16(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x16.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15}, [%16];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cta.global" + ".mbarrier::complete_tx::bytes" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit(int mbar_addr) { + asm volatile( + "tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ uint32_t make_warp_uniform(uint32_t val) { + uint32_t result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1f, 0xffffffff;" + : "=r"(result) : "r"(val)); + return result; +} + +extern "C" { + +__global__ __launch_bounds__(96, 1) void +kernel_knn_build_rag_microbucket_5093_v1_stage1_k32_tailinf_cta1_compactwarp(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tiles, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 33792); + const int smem_database_addr = smem + 33792; + float* smem_database_sq = reinterpret_cast(smem_raw + 50176); + const int smem_database_sq_addr = smem + 50176; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 0, 1, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 16, 1, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=32 + mbarrier_init_pred(smem + 40, 32, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (64 columns, 64 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::1.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(64) : "memory"); + } + + __syncthreads(); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: compute ---- + if (warp == 0) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx = bid; work_idx < total_work; work_idx += num_bids) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tiles; + int q_tile = query_work % num_q_tiles; + int off_q = q_tile * BLOCK_Q; + int q_idx = off_q + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx * Q + q_idx]; + } + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + float chunk_worst_d[4]; + int chunk_worst_pos[4]; + #pragma unroll + for (int chunk = 0; chunk < 4; chunk++) { + int chunk_base = chunk * 8; + chunk_worst_d[chunk] = 3.4e+38f; + chunk_worst_pos[chunk] = chunk_base; + } + float worst_d = 3.4e+38f; + int worst_pos = 0; + int worst_chunk = 0; + int db_tile_start = split_idx * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int db_start = db_tile * BLOCK_M; + int db_sq_idx0 = db_start + (warp % 4 * 32 + lane); + if (db_sq_idx0 < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx * M + db_sq_idx0]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 3.4e+38f; + } + int db_col1 = warp % 4 * 32 + lane + 32; + int db_sq_idx1 = db_start + db_col1; + if (db_sq_idx1 < M) { + smem_database_sq[db_col1] = database_sq[batch_idx * M + db_sq_idx1]; + } else { + smem_database_sq[db_col1] = 3.4e+38f; + } + asm volatile("barrier.sync 8, %0;" :: "r"(32)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(warp % 4 * 32 << 16); + float _tmem_load_0[64]; + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x64.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31, %32, %33, %34, %35, %36, %37, %38, %39, %40, %41, %42, %43, %44, %45, %46, %47, %48, %49, %50, %51, %52, %53, %54, %55, %56, %57, %58, %59, %60, %61, %62, %63}, [%64];" + : "=f"(_tmem_load_0[0]), "=f"(_tmem_load_0[1]), "=f"(_tmem_load_0[2]), "=f"(_tmem_load_0[3]), "=f"(_tmem_load_0[4]), "=f"(_tmem_load_0[5]), "=f"(_tmem_load_0[6]), "=f"(_tmem_load_0[7]), "=f"(_tmem_load_0[8]), "=f"(_tmem_load_0[9]), "=f"(_tmem_load_0[10]), "=f"(_tmem_load_0[11]), "=f"(_tmem_load_0[12]), "=f"(_tmem_load_0[13]), "=f"(_tmem_load_0[14]), "=f"(_tmem_load_0[15]), "=f"(_tmem_load_0[16]), "=f"(_tmem_load_0[17]), "=f"(_tmem_load_0[18]), "=f"(_tmem_load_0[19]), "=f"(_tmem_load_0[20]), "=f"(_tmem_load_0[21]), "=f"(_tmem_load_0[22]), "=f"(_tmem_load_0[23]), "=f"(_tmem_load_0[24]), "=f"(_tmem_load_0[25]), "=f"(_tmem_load_0[26]), "=f"(_tmem_load_0[27]), "=f"(_tmem_load_0[28]), "=f"(_tmem_load_0[29]), "=f"(_tmem_load_0[30]), "=f"(_tmem_load_0[31]), "=f"(_tmem_load_0[32]), "=f"(_tmem_load_0[33]), "=f"(_tmem_load_0[34]), "=f"(_tmem_load_0[35]), "=f"(_tmem_load_0[36]), "=f"(_tmem_load_0[37]), "=f"(_tmem_load_0[38]), "=f"(_tmem_load_0[39]), "=f"(_tmem_load_0[40]), "=f"(_tmem_load_0[41]), "=f"(_tmem_load_0[42]), "=f"(_tmem_load_0[43]), "=f"(_tmem_load_0[44]), "=f"(_tmem_load_0[45]), "=f"(_tmem_load_0[46]), "=f"(_tmem_load_0[47]), "=f"(_tmem_load_0[48]), "=f"(_tmem_load_0[49]), "=f"(_tmem_load_0[50]), "=f"(_tmem_load_0[51]), "=f"(_tmem_load_0[52]), "=f"(_tmem_load_0[53]), "=f"(_tmem_load_0[54]), "=f"(_tmem_load_0[55]), "=f"(_tmem_load_0[56]), "=f"(_tmem_load_0[57]), "=f"(_tmem_load_0[58]), "=f"(_tmem_load_0[59]), "=f"(_tmem_load_0[60]), "=f"(_tmem_load_0[61]), "=f"(_tmem_load_0[62]), "=f"(_tmem_load_0[63]) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(32)); + mbarrier_arrive(score_empty_addr); + if (valid_q != 0) { + #pragma unroll 1 + for (int col_base = 0; col_base < 64; col_base += 4) { + float dist_vec[4]; + dist_vec[0] = _tmem_load_0[col_base]; + dist_vec[1] = _tmem_load_0[col_base + 1]; + dist_vec[2] = _tmem_load_0[col_base + 2]; + dist_vec[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec[4]; + db_sq_vec[0] = smem_database_sq[col_base]; + db_sq_vec[1] = smem_database_sq[col_base + 1]; + db_sq_vec[2] = smem_database_sq[col_base + 2]; + db_sq_vec[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec)[_la], reinterpret_cast(db_sq_vec)[_la]); + float group_min = _t0[0]; + if (_t0[1] < group_min) { + group_min = _t0[1]; + } + if (_t0[2] < group_min) { + group_min = _t0[2]; + } + if (_t0[3] < group_min) { + group_min = _t0[3]; + } + if (group_min < worst_d) { + float sort_d0 = _t0[0]; + float sort_d1 = _t0[1]; + float sort_d2 = _t0[2]; + float sort_d3 = _t0[3]; + int sort_col0 = 0; + int sort_col1 = 1; + int sort_col2 = 2; + int sort_col3 = 3; + float tmp_d = 0.0f; + int tmp_col = 0; + if (sort_d1 < sort_d0) { + tmp_d = sort_d0; + sort_d0 = sort_d1; + sort_d1 = tmp_d; + tmp_col = sort_col0; + sort_col0 = sort_col1; + sort_col1 = tmp_col; + } + if (sort_d3 < sort_d2) { + tmp_d = sort_d2; + sort_d2 = sort_d3; + sort_d3 = tmp_d; + tmp_col = sort_col2; + sort_col2 = sort_col3; + sort_col3 = tmp_col; + } + if (sort_d2 < sort_d0) { + tmp_d = sort_d0; + sort_d0 = sort_d2; + sort_d2 = tmp_d; + tmp_col = sort_col0; + sort_col0 = sort_col2; + sort_col2 = tmp_col; + } + if (sort_d3 < sort_d1) { + tmp_d = sort_d1; + sort_d1 = sort_d3; + sort_d3 = tmp_d; + tmp_col = sort_col1; + sort_col1 = sort_col3; + sort_col3 = tmp_col; + } + if (sort_d2 < sort_d1) { + tmp_d = sort_d1; + sort_d1 = sort_d2; + sort_d2 = tmp_d; + tmp_col = sort_col1; + sort_col1 = sort_col2; + sort_col2 = tmp_col; + } + #pragma unroll + for (int visit = 0; visit < 4; visit++) { + int vec_col = sort_col0; + float dist = sort_d0; + if (visit == 1) { + vec_col = sort_col1; + dist = sort_d1; + } + if (visit == 2) { + vec_col = sort_col2; + dist = sort_d2; + } + if (visit == 3) { + vec_col = sort_col3; + dist = sort_d3; + } + if (dist >= worst_d) { + break; + } + int db_idx = db_start + col_base + vec_col; + best_d[worst_pos] = dist; + best_i[worst_pos] = db_idx; + int refresh_base = worst_chunk * 8; + chunk_worst_d[worst_chunk] = best_d[refresh_base]; + chunk_worst_pos[worst_chunk] = refresh_base; + #pragma unroll + for (int offset = 1; offset < 8; offset++) { + int scan_pos = refresh_base + offset; + if (best_d[scan_pos] > chunk_worst_d[worst_chunk]) { + chunk_worst_d[worst_chunk] = best_d[scan_pos]; + chunk_worst_pos[worst_chunk] = scan_pos; + } + } + worst_d = chunk_worst_d[0]; + worst_pos = chunk_worst_pos[0]; + worst_chunk = 0; + #pragma unroll + for (int chunk_1 = 1; chunk_1 < 4; chunk_1++) { + if (worst_d < chunk_worst_d[chunk_1]) { + worst_d = chunk_worst_d[chunk_1]; + worst_pos = chunk_worst_pos[chunk_1]; + worst_chunk = chunk_1; + } + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(32)); + } + if (valid_q != 0) { + int out_base = ((split_idx * B + batch_idx) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_out_d = best_d[0]; + int best_out_i = best_i[0]; + int best_out_pos = 0; + #pragma unroll + for (int scan_pos_1 = 1; scan_pos_1 < TOP_K_MAX; scan_pos_1++) { + if (best_out_d > best_d[scan_pos_1]) { + best_out_d = best_d[scan_pos_1]; + best_out_i = best_i[scan_pos_1]; + best_out_pos = scan_pos_1; + } + } + if (out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = best_out_d; + *((int*)(partial_indices + (out_base + out_k))) = best_out_i; + } + best_d[best_out_pos] = 3.4e+38f; + } + } + } + } + // ---- Role: load ---- + } else if (warp == 1) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 1) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx_1 = bid; work_idx_1 < total_work; work_idx_1 += num_bids) { + int split_idx_1 = work_idx_1 % (unsigned int)split_count; + int query_work_1 = work_idx_1 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tiles; + int q_tile_1 = query_work_1 % num_q_tiles; + int off_q_1 = q_tile_1 * BLOCK_Q; + int global_q = batch_idx_1 * Q + off_q_1; + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + mbarrier_arrive_expect_tx(query_full_addr, 32768); + tma_3d_gmem2smem(smem_query_addr, tmap_query, 0, global_q, 0, query_full_addr); + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int off_m = db_tile_1 * BLOCK_M; + int global_m = batch_idx_1 * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + mbarrier_arrive_expect_tx(database_full_addr, 16384); + tma_3d_gmem2smem(smem_database_addr, tmap_database, 0, global_m, 0, database_full_addr); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 2) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = bid; work_idx_2 < total_work; work_idx_2 += num_bids) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = make_warp_uniform((smem_query_addr >> 4) & 0x3FFF); + int _mma_b_lo_0 = make_warp_uniform((smem_database_addr >> 4) & 0x3FFF); + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 135267472;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit(score_full_addr); + elect_commit(database_empty_addr); + } + elect_commit(query_empty_addr); + } + } + } + + // Cleanup + __syncthreads(); // barrier before TMEM dealloc + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::1.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(64)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::1.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0187.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0187.cu new file mode 100644 index 00000000..ebd248f0 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0187.cu @@ -0,0 +1,697 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 64 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_STRIDE 32768 +#define SMEM_SMEM_DATABASE_OFF 33792 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_DATABASE_SQ_OFF 50176 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 50432 +#define THREADS 192 +#define BLOCK_Q 128 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_MAX 32 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "tcgen05.mma.cta_group::1.kind::f16 [%0], %1, %2, %3, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, %3, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit(int mbar_addr) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];\n\t" + "}\n" + :: "r"(mbar_addr)); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x16(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x16.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15}, [%16];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cta.global" + ".mbarrier::complete_tx::bytes" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit(int mbar_addr) { + asm volatile( + "tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ uint32_t make_warp_uniform(uint32_t val) { + uint32_t result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1f, 0xffffffff;" + : "=r"(result) : "r"(val)); + return result; +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_rag_microbucket_3505_v3_stage1_k32_tailinf_cta1(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tiles, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 33792); + const int smem_database_addr = smem + 33792; + float* smem_database_sq = reinterpret_cast(smem_raw + 50176); + const int smem_database_sq_addr = smem + 50176; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 0, 1, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 16, 1, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=128 + mbarrier_init_pred(smem + 40, 128, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (64 columns, 64 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::1.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(64) : "memory"); + } + + __syncthreads(); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: load ---- + if (warp == 0) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 0) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx = bid; work_idx < total_work; work_idx += num_bids) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tiles; + int q_tile = query_work % num_q_tiles; + int off_q = q_tile * BLOCK_Q; + int global_q = batch_idx * Q + off_q; + int db_tile_start = split_idx * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + mbarrier_arrive_expect_tx(query_full_addr, 32768); + tma_3d_gmem2smem(smem_query_addr, tmap_query, 0, global_q, 0, query_full_addr); + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int off_m = db_tile * BLOCK_M; + int global_m = batch_idx * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + mbarrier_arrive_expect_tx(database_full_addr, 16384); + tma_3d_gmem2smem(smem_database_addr, tmap_database, 0, global_m, 0, database_full_addr); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 1) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_1 = bid; work_idx_1 < total_work; work_idx_1 += num_bids) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = make_warp_uniform((smem_query_addr >> 4) & 0x3FFF); + int _mma_b_lo_0 = make_warp_uniform((smem_database_addr >> 4) & 0x3FFF); + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 135267472;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit(score_full_addr); + elect_commit(database_empty_addr); + } + elect_commit(query_empty_addr); + } + } + // ---- Role: compute ---- + } else if (warp >= 2 && warp <= 5) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = bid; work_idx_2 < total_work; work_idx_2 += num_bids) { + int split_idx_1 = work_idx_2 % (unsigned int)split_count; + int query_work_1 = work_idx_2 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tiles; + int q_tile_1 = query_work_1 % num_q_tiles; + int off_q_1 = q_tile_1 * BLOCK_Q; + int q_idx = off_q_1 + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx_1 * Q + q_idx]; + } + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + float chunk_worst_d[4]; + int chunk_worst_pos[4]; + #pragma unroll + for (int chunk = 0; chunk < 4; chunk++) { + int chunk_base = chunk * 8; + chunk_worst_d[chunk] = 3.4e+38f; + chunk_worst_pos[chunk] = chunk_base; + } + float worst_d = 3.4e+38f; + int worst_pos = 0; + int worst_chunk = 0; + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int db_start = db_tile_1 * BLOCK_M; + int db_sq_idx = db_start + (warp % 4 * 32 + lane); + if (warp % 4 * 32 + lane < BLOCK_M) { + if (db_sq_idx < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx_1 * M + db_sq_idx]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 3.4e+38f; + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(warp % 4 * 32 << 16); + float _tmem_load_0[64]; + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x64.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31, %32, %33, %34, %35, %36, %37, %38, %39, %40, %41, %42, %43, %44, %45, %46, %47, %48, %49, %50, %51, %52, %53, %54, %55, %56, %57, %58, %59, %60, %61, %62, %63}, [%64];" + : "=f"(_tmem_load_0[0]), "=f"(_tmem_load_0[1]), "=f"(_tmem_load_0[2]), "=f"(_tmem_load_0[3]), "=f"(_tmem_load_0[4]), "=f"(_tmem_load_0[5]), "=f"(_tmem_load_0[6]), "=f"(_tmem_load_0[7]), "=f"(_tmem_load_0[8]), "=f"(_tmem_load_0[9]), "=f"(_tmem_load_0[10]), "=f"(_tmem_load_0[11]), "=f"(_tmem_load_0[12]), "=f"(_tmem_load_0[13]), "=f"(_tmem_load_0[14]), "=f"(_tmem_load_0[15]), "=f"(_tmem_load_0[16]), "=f"(_tmem_load_0[17]), "=f"(_tmem_load_0[18]), "=f"(_tmem_load_0[19]), "=f"(_tmem_load_0[20]), "=f"(_tmem_load_0[21]), "=f"(_tmem_load_0[22]), "=f"(_tmem_load_0[23]), "=f"(_tmem_load_0[24]), "=f"(_tmem_load_0[25]), "=f"(_tmem_load_0[26]), "=f"(_tmem_load_0[27]), "=f"(_tmem_load_0[28]), "=f"(_tmem_load_0[29]), "=f"(_tmem_load_0[30]), "=f"(_tmem_load_0[31]), "=f"(_tmem_load_0[32]), "=f"(_tmem_load_0[33]), "=f"(_tmem_load_0[34]), "=f"(_tmem_load_0[35]), "=f"(_tmem_load_0[36]), "=f"(_tmem_load_0[37]), "=f"(_tmem_load_0[38]), "=f"(_tmem_load_0[39]), "=f"(_tmem_load_0[40]), "=f"(_tmem_load_0[41]), "=f"(_tmem_load_0[42]), "=f"(_tmem_load_0[43]), "=f"(_tmem_load_0[44]), "=f"(_tmem_load_0[45]), "=f"(_tmem_load_0[46]), "=f"(_tmem_load_0[47]), "=f"(_tmem_load_0[48]), "=f"(_tmem_load_0[49]), "=f"(_tmem_load_0[50]), "=f"(_tmem_load_0[51]), "=f"(_tmem_load_0[52]), "=f"(_tmem_load_0[53]), "=f"(_tmem_load_0[54]), "=f"(_tmem_load_0[55]), "=f"(_tmem_load_0[56]), "=f"(_tmem_load_0[57]), "=f"(_tmem_load_0[58]), "=f"(_tmem_load_0[59]), "=f"(_tmem_load_0[60]), "=f"(_tmem_load_0[61]), "=f"(_tmem_load_0[62]), "=f"(_tmem_load_0[63]) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_arrive(score_empty_addr); + if (valid_q != 0) { + #pragma unroll 1 + for (int col_base = 0; col_base < 64; col_base += 4) { + float dist_vec[4]; + dist_vec[0] = _tmem_load_0[col_base]; + dist_vec[1] = _tmem_load_0[col_base + 1]; + dist_vec[2] = _tmem_load_0[col_base + 2]; + dist_vec[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec[4]; + db_sq_vec[0] = smem_database_sq[col_base]; + db_sq_vec[1] = smem_database_sq[col_base + 1]; + db_sq_vec[2] = smem_database_sq[col_base + 2]; + db_sq_vec[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec)[_la], reinterpret_cast(db_sq_vec)[_la]); + float group_min = _t0[0]; + if (_t0[1] < group_min) { + group_min = _t0[1]; + } + if (_t0[2] < group_min) { + group_min = _t0[2]; + } + if (_t0[3] < group_min) { + group_min = _t0[3]; + } + if (group_min < worst_d) { + float sort_d0 = _t0[0]; + float sort_d1 = _t0[1]; + float sort_d2 = _t0[2]; + float sort_d3 = _t0[3]; + int sort_col0 = 0; + int sort_col1 = 1; + int sort_col2 = 2; + int sort_col3 = 3; + float tmp_d = 0.0f; + int tmp_col = 0; + if (sort_d1 < sort_d0) { + tmp_d = sort_d0; + sort_d0 = sort_d1; + sort_d1 = tmp_d; + tmp_col = sort_col0; + sort_col0 = sort_col1; + sort_col1 = tmp_col; + } + if (sort_d3 < sort_d2) { + tmp_d = sort_d2; + sort_d2 = sort_d3; + sort_d3 = tmp_d; + tmp_col = sort_col2; + sort_col2 = sort_col3; + sort_col3 = tmp_col; + } + if (sort_d2 < sort_d0) { + tmp_d = sort_d0; + sort_d0 = sort_d2; + sort_d2 = tmp_d; + tmp_col = sort_col0; + sort_col0 = sort_col2; + sort_col2 = tmp_col; + } + if (sort_d3 < sort_d1) { + tmp_d = sort_d1; + sort_d1 = sort_d3; + sort_d3 = tmp_d; + tmp_col = sort_col1; + sort_col1 = sort_col3; + sort_col3 = tmp_col; + } + if (sort_d2 < sort_d1) { + tmp_d = sort_d1; + sort_d1 = sort_d2; + sort_d2 = tmp_d; + tmp_col = sort_col1; + sort_col1 = sort_col2; + sort_col2 = tmp_col; + } + #pragma unroll + for (int visit = 0; visit < 4; visit++) { + int vec_col = sort_col0; + float dist = sort_d0; + if (visit == 1) { + vec_col = sort_col1; + dist = sort_d1; + } + if (visit == 2) { + vec_col = sort_col2; + dist = sort_d2; + } + if (visit == 3) { + vec_col = sort_col3; + dist = sort_d3; + } + if (dist >= worst_d) { + break; + } + int db_idx = db_start + col_base + vec_col; + best_d[worst_pos] = dist; + best_i[worst_pos] = db_idx; + int refresh_base = worst_chunk * 8; + chunk_worst_d[worst_chunk] = best_d[refresh_base]; + chunk_worst_pos[worst_chunk] = refresh_base; + #pragma unroll + for (int offset = 1; offset < 8; offset++) { + int scan_pos = refresh_base + offset; + if (best_d[scan_pos] > chunk_worst_d[worst_chunk]) { + chunk_worst_d[worst_chunk] = best_d[scan_pos]; + chunk_worst_pos[worst_chunk] = scan_pos; + } + } + worst_d = chunk_worst_d[0]; + worst_pos = chunk_worst_pos[0]; + worst_chunk = 0; + #pragma unroll + for (int chunk_1 = 1; chunk_1 < 4; chunk_1++) { + if (worst_d < chunk_worst_d[chunk_1]) { + worst_d = chunk_worst_d[chunk_1]; + worst_pos = chunk_worst_pos[chunk_1]; + worst_chunk = chunk_1; + } + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + if (valid_q != 0) { + int out_base = ((split_idx_1 * B + batch_idx_1) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_out_d = best_d[0]; + int best_out_i = best_i[0]; + int best_out_pos = 0; + #pragma unroll + for (int scan_pos_1 = 1; scan_pos_1 < TOP_K_MAX; scan_pos_1++) { + if (best_out_d > best_d[scan_pos_1]) { + best_out_d = best_d[scan_pos_1]; + best_out_i = best_i[scan_pos_1]; + best_out_pos = scan_pos_1; + } + } + if (out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = best_out_d; + *((int*)(partial_indices + (out_base + out_k))) = best_out_i; + } + best_d[best_out_pos] = 3.4e+38f; + } + } + } + } + } + + // Cleanup + __syncthreads(); // barrier before TMEM dealloc + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::1.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(64)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::1.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0188.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0188.cu new file mode 100644 index 00000000..c4926980 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0188.cu @@ -0,0 +1,84 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 8 +#define TOP_K_MAX 10 +#define SPLIT_COUNT 16 + +#include + +extern "C" { + +__global__ __launch_bounds__(8, 1) void +kernel_knn_build_rect_d64_cf49_s16_cached_merge(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 8 + tid; + int stride = num_bids * 8; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int out_base = base_row; + int split_pos[SPLIT_COUNT]; + int split_base[SPLIT_COUNT]; + float cand_d[SPLIT_COUNT]; + int cand_i[SPLIT_COUNT]; + #pragma unroll + for (int split_idx = 0; split_idx < SPLIT_COUNT; split_idx++) { + split_pos[split_idx] = 0; + split_base[split_idx] = base_row + split_idx * split_stride; + cand_d[split_idx] = partial_dists[split_base[split_idx]]; + cand_i[split_idx] = partial_indices[split_base[split_idx]]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_d = cand_d[0]; + int best_i = cand_i[0]; + int best_split = 0; + #pragma unroll + for (int split_idx_1 = 1; split_idx_1 < SPLIT_COUNT; split_idx_1++) { + if (best_d > cand_d[split_idx_1]) { + best_d = cand_d[split_idx_1]; + best_i = cand_i[split_idx_1]; + best_split = split_idx_1; + } + } + *((float*)(out_dists + (out_base + out_k))) = best_d; + *((int*)(out_indices + (out_base + out_k))) = best_i; + split_pos[best_split] = split_pos[best_split] + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_pos = split_pos[best_split]; + int next_addr = split_base[best_split] + next_pos; + cand_d[best_split] = partial_dists[next_addr]; + cand_i[best_split] = partial_indices[next_addr]; + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0189.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0189.cu new file mode 100644 index 00000000..49f9f391 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0189.cu @@ -0,0 +1,741 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 128 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_STRIDE 32768 +#define SMEM_SMEM_DATABASE_OFF 33792 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_DATABASE_SQ_OFF 50176 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 50432 +#define THREADS 192 +#define BLOCK_Q 128 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_MAX 96 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16_cta2( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + ".reg .b32 m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "tcgen05.mma.cta_group::2.kind::f16 [%0], %1, %2, %3, {m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step_cg2( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, %3, " + "{m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], %1;\n\t" + "}\n" + :: "r"(mbar_addr), "h"(cta_mask) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x16(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x16.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15}, [%16];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ uint32_t smem_addr(const void* ptr) { + uint32_t addr; + asm("{\n\t" + ".reg .u64 u64addr;\n\t" + "cvta.to.shared.u64 u64addr, %1;\n\t" + "cvt.u32.u64 %0, u64addr;\n\t" + "}\n" : "=r"(addr) : "l"(ptr)); + return addr; +} + + +__device__ __forceinline__ uint32_t mapa_to_rank(uint32_t local_addr, uint32_t rank) { + uint32_t remote; + asm volatile("mapa.shared::cluster.u32 %0, %1, %2;" + : "=r"(remote) : "r"(local_addr), "r"(rank)); + return remote; +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem_cta2( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global" + ".mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .b16 lo, hi;\n\t" + "mov.b32 {lo, hi}, %1;\n\t" + "tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], lo;\n\t" + "}\n" + :: "r"(mbar_addr), "r"((uint32_t)cta_mask) : "memory"); +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_k96_stage1_sort4_chunked_k96over64sort4chunked(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tile_pairs, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + const unsigned int clusters_x = gridDim.x / 2; + const unsigned int cluster_id = ((blockIdx.z * gridDim.y + blockIdx.y) * clusters_x) + blockIdx.x / 2; + const unsigned int num_clusters = clusters_x * gridDim.y * gridDim.z; + + int cta_rank; + asm volatile("mov.b32 %0, %%cluster_ctarank;" : "=r"(cta_rank)); + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 33792); + const int smem_database_addr = smem + 33792; + float* smem_database_sq = reinterpret_cast(smem_raw + 50176); + const int smem_database_sq_addr = smem + 50176; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 0, 2, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 16, 2, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=8 + mbarrier_init_pred(smem + 40, 8, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (128 columns, 128 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::2.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(128) : "memory"); + } + + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: compute ---- + if (warp <= 3) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx = cluster_id; work_idx < total_work; work_idx += num_clusters) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tile_pairs; + int q_tile_pair = query_work % num_q_tile_pairs; + int q_tile = q_tile_pair * 2 + cta_rank; + int off_q = q_tile * BLOCK_Q; + int q_idx = off_q + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx * Q + q_idx]; + } + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + float chunk_worst_d[24]; + int chunk_worst_pos[24]; + #pragma unroll + for (int chunk = 0; chunk < 24; chunk++) { + int chunk_base = chunk * 4; + chunk_worst_d[chunk] = 3.4e+38f; + chunk_worst_pos[chunk] = chunk_base; + } + float worst_d = 3.4e+38f; + int worst_pos = 0; + int worst_chunk = 0; + int db_tile_start = split_idx * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int db_start = db_tile * BLOCK_M; + int db_sq_idx = db_start + (warp % 4 * 32 + lane); + if (warp % 4 * 32 + lane < BLOCK_M) { + if (db_sq_idx < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx * M + db_sq_idx]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 0.0f; + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(cta_rank * BLOCK_Q + (warp % 4 * 32 << 16) << 16); + float _tmem_load_0[64]; + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x64.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31, %32, %33, %34, %35, %36, %37, %38, %39, %40, %41, %42, %43, %44, %45, %46, %47, %48, %49, %50, %51, %52, %53, %54, %55, %56, %57, %58, %59, %60, %61, %62, %63}, [%64];" + : "=f"(_tmem_load_0[0]), "=f"(_tmem_load_0[1]), "=f"(_tmem_load_0[2]), "=f"(_tmem_load_0[3]), "=f"(_tmem_load_0[4]), "=f"(_tmem_load_0[5]), "=f"(_tmem_load_0[6]), "=f"(_tmem_load_0[7]), "=f"(_tmem_load_0[8]), "=f"(_tmem_load_0[9]), "=f"(_tmem_load_0[10]), "=f"(_tmem_load_0[11]), "=f"(_tmem_load_0[12]), "=f"(_tmem_load_0[13]), "=f"(_tmem_load_0[14]), "=f"(_tmem_load_0[15]), "=f"(_tmem_load_0[16]), "=f"(_tmem_load_0[17]), "=f"(_tmem_load_0[18]), "=f"(_tmem_load_0[19]), "=f"(_tmem_load_0[20]), "=f"(_tmem_load_0[21]), "=f"(_tmem_load_0[22]), "=f"(_tmem_load_0[23]), "=f"(_tmem_load_0[24]), "=f"(_tmem_load_0[25]), "=f"(_tmem_load_0[26]), "=f"(_tmem_load_0[27]), "=f"(_tmem_load_0[28]), "=f"(_tmem_load_0[29]), "=f"(_tmem_load_0[30]), "=f"(_tmem_load_0[31]), "=f"(_tmem_load_0[32]), "=f"(_tmem_load_0[33]), "=f"(_tmem_load_0[34]), "=f"(_tmem_load_0[35]), "=f"(_tmem_load_0[36]), "=f"(_tmem_load_0[37]), "=f"(_tmem_load_0[38]), "=f"(_tmem_load_0[39]), "=f"(_tmem_load_0[40]), "=f"(_tmem_load_0[41]), "=f"(_tmem_load_0[42]), "=f"(_tmem_load_0[43]), "=f"(_tmem_load_0[44]), "=f"(_tmem_load_0[45]), "=f"(_tmem_load_0[46]), "=f"(_tmem_load_0[47]), "=f"(_tmem_load_0[48]), "=f"(_tmem_load_0[49]), "=f"(_tmem_load_0[50]), "=f"(_tmem_load_0[51]), "=f"(_tmem_load_0[52]), "=f"(_tmem_load_0[53]), "=f"(_tmem_load_0[54]), "=f"(_tmem_load_0[55]), "=f"(_tmem_load_0[56]), "=f"(_tmem_load_0[57]), "=f"(_tmem_load_0[58]), "=f"(_tmem_load_0[59]), "=f"(_tmem_load_0[60]), "=f"(_tmem_load_0[61]), "=f"(_tmem_load_0[62]), "=f"(_tmem_load_0[63]) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + if (elect_sync()) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cluster.b64 _, [%0];" + :: "r"((score_empty_addr) & 0xFEFFFFFF) : "memory"); + } + if (valid_q != 0) { + #pragma unroll 1 + for (int col_base = 0; col_base < 64; col_base += 4) { + float dist_vec[4]; + dist_vec[0] = _tmem_load_0[col_base]; + dist_vec[1] = _tmem_load_0[col_base + 1]; + dist_vec[2] = _tmem_load_0[col_base + 2]; + dist_vec[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec[4]; + db_sq_vec[0] = smem_database_sq[col_base]; + db_sq_vec[1] = smem_database_sq[col_base + 1]; + db_sq_vec[2] = smem_database_sq[col_base + 2]; + db_sq_vec[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec)[_la], reinterpret_cast(db_sq_vec)[_la]); + float group_min = _t0[0]; + if (_t0[1] < group_min) { + group_min = _t0[1]; + } + if (_t0[2] < group_min) { + group_min = _t0[2]; + } + if (_t0[3] < group_min) { + group_min = _t0[3]; + } + if (group_min < worst_d) { + float sort_d0 = _t0[0]; + float sort_d1 = _t0[1]; + float sort_d2 = _t0[2]; + float sort_d3 = _t0[3]; + int sort_col0 = 0; + int sort_col1 = 1; + int sort_col2 = 2; + int sort_col3 = 3; + float tmp_d = 0.0f; + int tmp_col = 0; + if (sort_d1 < sort_d0) { + tmp_d = sort_d0; + sort_d0 = sort_d1; + sort_d1 = tmp_d; + tmp_col = sort_col0; + sort_col0 = sort_col1; + sort_col1 = tmp_col; + } + if (sort_d3 < sort_d2) { + tmp_d = sort_d2; + sort_d2 = sort_d3; + sort_d3 = tmp_d; + tmp_col = sort_col2; + sort_col2 = sort_col3; + sort_col3 = tmp_col; + } + if (sort_d2 < sort_d0) { + tmp_d = sort_d0; + sort_d0 = sort_d2; + sort_d2 = tmp_d; + tmp_col = sort_col0; + sort_col0 = sort_col2; + sort_col2 = tmp_col; + } + if (sort_d3 < sort_d1) { + tmp_d = sort_d1; + sort_d1 = sort_d3; + sort_d3 = tmp_d; + tmp_col = sort_col1; + sort_col1 = sort_col3; + sort_col3 = tmp_col; + } + if (sort_d2 < sort_d1) { + tmp_d = sort_d1; + sort_d1 = sort_d2; + sort_d2 = tmp_d; + tmp_col = sort_col1; + sort_col1 = sort_col2; + sort_col2 = tmp_col; + } + #pragma unroll + for (int visit = 0; visit < 4; visit++) { + int vec_col = sort_col0; + float dist = sort_d0; + if (visit == 1) { + vec_col = sort_col1; + dist = sort_d1; + } + if (visit == 2) { + vec_col = sort_col2; + dist = sort_d2; + } + if (visit == 3) { + vec_col = sort_col3; + dist = sort_d3; + } + if (dist >= worst_d) { + break; + } + int db_idx = db_start + col_base + vec_col; + if (db_idx < M) { + best_d[worst_pos] = dist; + best_i[worst_pos] = db_idx; + int refresh_base = worst_chunk * 4; + chunk_worst_d[worst_chunk] = best_d[refresh_base]; + chunk_worst_pos[worst_chunk] = refresh_base; + #pragma unroll + for (int offset = 1; offset < 4; offset++) { + int scan_pos = refresh_base + offset; + if (best_d[scan_pos] > chunk_worst_d[worst_chunk]) { + chunk_worst_d[worst_chunk] = best_d[scan_pos]; + chunk_worst_pos[worst_chunk] = scan_pos; + } + } + worst_d = chunk_worst_d[0]; + worst_pos = chunk_worst_pos[0]; + worst_chunk = 0; + #pragma unroll + for (int chunk_1 = 1; chunk_1 < 24; chunk_1++) { + if (worst_d < chunk_worst_d[chunk_1]) { + worst_d = chunk_worst_d[chunk_1]; + worst_pos = chunk_worst_pos[chunk_1]; + worst_chunk = chunk_1; + } + } + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + if (valid_q != 0) { + int out_base = ((split_idx * B + batch_idx) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + *((float*)(partial_dists + (out_base + out_k))) = best_d[out_k]; + *((int*)(partial_indices + (out_base + out_k))) = best_i[out_k]; + } + } + } + } + // ---- Role: load ---- + } else if (warp == 4) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 4) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx_1 = cluster_id; work_idx_1 < total_work; work_idx_1 += num_clusters) { + int split_idx_1 = work_idx_1 % (unsigned int)split_count; + int query_work_1 = work_idx_1 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tile_pairs; + int q_tile_pair_1 = query_work_1 % num_q_tile_pairs; + int q_tile_1 = q_tile_pair_1 * 2 + cta_rank; + int off_q_1 = q_tile_1 * BLOCK_Q; + int global_q = batch_idx_1 * Q + off_q_1; + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((query_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(32768)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_query_addr), "l"(tmap_query), "r"(0), "r"(global_q), "r"(0), + "r"(((query_full_addr) & 0xFEFFFFFF)) : "memory"); + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int off_m = db_tile_1 * BLOCK_M; + int global_m = batch_idx_1 * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((database_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(16384)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_database_addr), "l"(tmap_database), "r"(0), "r"(global_m), "r"(0), + "r"(((database_full_addr) & 0xFEFFFFFF)) : "memory"); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 5) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + if (cta_rank == 0) { + #pragma unroll 1 + for (unsigned int work_idx_2 = cluster_id; work_idx_2 < total_work; work_idx_2 += num_clusters) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = (smem_query_addr >> 4) & 0x3FFF; + int _mma_b_lo_0 = (smem_database_addr >> 4) & 0x3FFF; + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\tmov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 270533776;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit_cg2_multicast(score_full_addr, (uint16_t)(3)); + elect_commit_cg2_multicast(database_empty_addr, (uint16_t)(3)); + } + elect_commit_cg2_multicast(query_empty_addr, (uint16_t)(3)); + } + } + } + } + + // Cleanup + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::2.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(128)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::2.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0190.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0190.cu new file mode 100644 index 00000000..710b25ea --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0190.cu @@ -0,0 +1,123 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 32 +#define TOP_K_MAX 96 +#define SPLIT_COUNT 8 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_k96_merge_s8_unordered_chunkprefill_k96over64s8chunkprefill(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 32 + tid; + int stride = num_bids * 32; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + float best_d[96]; + int best_i[96]; + #pragma unroll + for (int cand_k = 0; cand_k < 96; cand_k++) { + best_d[cand_k] = partial_dists[base_row + cand_k]; + best_i[cand_k] = partial_indices[base_row + cand_k]; + } + float chunk_worst_d[12]; + int chunk_worst_pos[12]; + #pragma unroll + for (int chunk = 0; chunk < 12; chunk++) { + int chunk_base = chunk * 8; + chunk_worst_d[chunk] = best_d[chunk_base]; + chunk_worst_pos[chunk] = chunk_base; + #pragma unroll + for (int offset = 1; offset < 8; offset++) { + int scan_pos = chunk_base + offset; + if (best_d[scan_pos] > chunk_worst_d[chunk]) { + chunk_worst_d[chunk] = best_d[scan_pos]; + chunk_worst_pos[chunk] = scan_pos; + } + } + } + float worst_d = chunk_worst_d[0]; + int worst_pos = chunk_worst_pos[0]; + int worst_chunk = 0; + #pragma unroll + for (int chunk_1 = 1; chunk_1 < 12; chunk_1++) { + if (worst_d < chunk_worst_d[chunk_1]) { + worst_d = chunk_worst_d[chunk_1]; + worst_pos = chunk_worst_pos[chunk_1]; + worst_chunk = chunk_1; + } + } + #pragma unroll + for (int split_idx = 1; split_idx < SPLIT_COUNT; split_idx++) { + int partial_base = base_row + split_idx * split_stride; + #pragma unroll + for (int cand_k_1 = 0; cand_k_1 < 96; cand_k_1++) { + float cand_d = partial_dists[partial_base + cand_k_1]; + int cand_i = partial_indices[partial_base + cand_k_1]; + if (cand_d < worst_d) { + best_d[worst_pos] = cand_d; + best_i[worst_pos] = cand_i; + int refresh_base = worst_chunk * 8; + chunk_worst_d[worst_chunk] = best_d[refresh_base]; + chunk_worst_pos[worst_chunk] = refresh_base; + #pragma unroll + for (int offset_1 = 1; offset_1 < 8; offset_1++) { + int scan_pos_1 = refresh_base + offset_1; + if (best_d[scan_pos_1] > chunk_worst_d[worst_chunk]) { + chunk_worst_d[worst_chunk] = best_d[scan_pos_1]; + chunk_worst_pos[worst_chunk] = scan_pos_1; + } + } + worst_d = chunk_worst_d[0]; + worst_pos = chunk_worst_pos[0]; + worst_chunk = 0; + #pragma unroll + for (int chunk_2 = 1; chunk_2 < 12; chunk_2++) { + if (worst_d < chunk_worst_d[chunk_2]) { + worst_d = chunk_worst_d[chunk_2]; + worst_pos = chunk_worst_pos[chunk_2]; + worst_chunk = chunk_2; + } + } + } + } + } + #pragma unroll + for (int out_k = 0; out_k < 96; out_k++) { + *((float*)(out_dists + (base_row + out_k))) = best_d[out_k]; + *((int*)(out_indices + (base_row + out_k))) = best_i[out_k]; + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0191.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0191.cu new file mode 100644 index 00000000..9432d6e6 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0191.cu @@ -0,0 +1,702 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 64 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 16384 +#define SMEM_SMEM_QUERY_STRIDE 16384 +#define SMEM_SMEM_DATABASE_OFF 17408 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_DATABASE_SQ_OFF 33792 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 34048 +#define THREADS 96 +#define BLOCK_Q 64 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_MAX 32 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "tcgen05.mma.cta_group::1.kind::f16 [%0], %1, %2, %3, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, %3, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit(int mbar_addr) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];\n\t" + "}\n" + :: "r"(mbar_addr)); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x16(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x16.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15}, [%16];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cta.global" + ".mbarrier::complete_tx::bytes" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit(int mbar_addr) { + asm volatile( + "tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ uint32_t make_warp_uniform(uint32_t val) { + uint32_t result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1f, 0xffffffff;" + : "=r"(result) : "r"(val)); + return result; +} + +extern "C" { + +__global__ __launch_bounds__(96, 1) void +kernel_knn_build_rag_microbucket_3505_v9_stage1_q8_k32_m64(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tiles, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 17408); + const int smem_database_addr = smem + 17408; + float* smem_database_sq = reinterpret_cast(smem_raw + 33792); + const int smem_database_sq_addr = smem + 33792; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 0, 1, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 16, 1, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=32 + mbarrier_init_pred(smem + 40, 32, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (64 columns, 64 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::1.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(64) : "memory"); + } + + __syncthreads(); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: compute ---- + if (warp == 0) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx = bid; work_idx < total_work; work_idx += num_bids) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tiles; + int q_tile = query_work % num_q_tiles; + int off_q = q_tile * BLOCK_Q; + int q_idx = off_q + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx * Q + q_idx]; + } + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + float chunk_worst_d[4]; + int chunk_worst_pos[4]; + #pragma unroll + for (int chunk = 0; chunk < 4; chunk++) { + int chunk_base = chunk * 8; + chunk_worst_d[chunk] = 3.4e+38f; + chunk_worst_pos[chunk] = chunk_base; + } + float worst_d = 3.4e+38f; + int worst_pos = 0; + int worst_chunk = 0; + int db_tile_start = split_idx * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int db_start = db_tile * BLOCK_M; + int db_sq_idx0 = db_start + (warp % 4 * 32 + lane); + if (db_sq_idx0 < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx * M + db_sq_idx0]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 3.4e+38f; + } + int db_col1 = warp % 4 * 32 + lane + 32; + int db_sq_idx1 = db_start + db_col1; + if (db_sq_idx1 < M) { + smem_database_sq[db_col1] = database_sq[batch_idx * M + db_sq_idx1]; + } else { + smem_database_sq[db_col1] = 3.4e+38f; + } + asm volatile("barrier.sync 8, %0;" :: "r"(32)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(warp % 4 * 32 << 16); + float _tmem_load_0[64]; + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x64.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31, %32, %33, %34, %35, %36, %37, %38, %39, %40, %41, %42, %43, %44, %45, %46, %47, %48, %49, %50, %51, %52, %53, %54, %55, %56, %57, %58, %59, %60, %61, %62, %63}, [%64];" + : "=f"(_tmem_load_0[0]), "=f"(_tmem_load_0[1]), "=f"(_tmem_load_0[2]), "=f"(_tmem_load_0[3]), "=f"(_tmem_load_0[4]), "=f"(_tmem_load_0[5]), "=f"(_tmem_load_0[6]), "=f"(_tmem_load_0[7]), "=f"(_tmem_load_0[8]), "=f"(_tmem_load_0[9]), "=f"(_tmem_load_0[10]), "=f"(_tmem_load_0[11]), "=f"(_tmem_load_0[12]), "=f"(_tmem_load_0[13]), "=f"(_tmem_load_0[14]), "=f"(_tmem_load_0[15]), "=f"(_tmem_load_0[16]), "=f"(_tmem_load_0[17]), "=f"(_tmem_load_0[18]), "=f"(_tmem_load_0[19]), "=f"(_tmem_load_0[20]), "=f"(_tmem_load_0[21]), "=f"(_tmem_load_0[22]), "=f"(_tmem_load_0[23]), "=f"(_tmem_load_0[24]), "=f"(_tmem_load_0[25]), "=f"(_tmem_load_0[26]), "=f"(_tmem_load_0[27]), "=f"(_tmem_load_0[28]), "=f"(_tmem_load_0[29]), "=f"(_tmem_load_0[30]), "=f"(_tmem_load_0[31]), "=f"(_tmem_load_0[32]), "=f"(_tmem_load_0[33]), "=f"(_tmem_load_0[34]), "=f"(_tmem_load_0[35]), "=f"(_tmem_load_0[36]), "=f"(_tmem_load_0[37]), "=f"(_tmem_load_0[38]), "=f"(_tmem_load_0[39]), "=f"(_tmem_load_0[40]), "=f"(_tmem_load_0[41]), "=f"(_tmem_load_0[42]), "=f"(_tmem_load_0[43]), "=f"(_tmem_load_0[44]), "=f"(_tmem_load_0[45]), "=f"(_tmem_load_0[46]), "=f"(_tmem_load_0[47]), "=f"(_tmem_load_0[48]), "=f"(_tmem_load_0[49]), "=f"(_tmem_load_0[50]), "=f"(_tmem_load_0[51]), "=f"(_tmem_load_0[52]), "=f"(_tmem_load_0[53]), "=f"(_tmem_load_0[54]), "=f"(_tmem_load_0[55]), "=f"(_tmem_load_0[56]), "=f"(_tmem_load_0[57]), "=f"(_tmem_load_0[58]), "=f"(_tmem_load_0[59]), "=f"(_tmem_load_0[60]), "=f"(_tmem_load_0[61]), "=f"(_tmem_load_0[62]), "=f"(_tmem_load_0[63]) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(32)); + mbarrier_arrive(score_empty_addr); + if (valid_q != 0) { + #pragma unroll 1 + for (int col_base = 0; col_base < 64; col_base += 4) { + float dist_vec[4]; + dist_vec[0] = _tmem_load_0[col_base]; + dist_vec[1] = _tmem_load_0[col_base + 1]; + dist_vec[2] = _tmem_load_0[col_base + 2]; + dist_vec[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec[4]; + db_sq_vec[0] = smem_database_sq[col_base]; + db_sq_vec[1] = smem_database_sq[col_base + 1]; + db_sq_vec[2] = smem_database_sq[col_base + 2]; + db_sq_vec[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec)[_la], reinterpret_cast(db_sq_vec)[_la]); + float group_min = _t0[0]; + if (_t0[1] < group_min) { + group_min = _t0[1]; + } + if (_t0[2] < group_min) { + group_min = _t0[2]; + } + if (_t0[3] < group_min) { + group_min = _t0[3]; + } + if (group_min < worst_d) { + float sort_d0 = _t0[0]; + float sort_d1 = _t0[1]; + float sort_d2 = _t0[2]; + float sort_d3 = _t0[3]; + int sort_col0 = 0; + int sort_col1 = 1; + int sort_col2 = 2; + int sort_col3 = 3; + float tmp_d = 0.0f; + int tmp_col = 0; + if (sort_d1 < sort_d0) { + tmp_d = sort_d0; + sort_d0 = sort_d1; + sort_d1 = tmp_d; + tmp_col = sort_col0; + sort_col0 = sort_col1; + sort_col1 = tmp_col; + } + if (sort_d3 < sort_d2) { + tmp_d = sort_d2; + sort_d2 = sort_d3; + sort_d3 = tmp_d; + tmp_col = sort_col2; + sort_col2 = sort_col3; + sort_col3 = tmp_col; + } + if (sort_d2 < sort_d0) { + tmp_d = sort_d0; + sort_d0 = sort_d2; + sort_d2 = tmp_d; + tmp_col = sort_col0; + sort_col0 = sort_col2; + sort_col2 = tmp_col; + } + if (sort_d3 < sort_d1) { + tmp_d = sort_d1; + sort_d1 = sort_d3; + sort_d3 = tmp_d; + tmp_col = sort_col1; + sort_col1 = sort_col3; + sort_col3 = tmp_col; + } + if (sort_d2 < sort_d1) { + tmp_d = sort_d1; + sort_d1 = sort_d2; + sort_d2 = tmp_d; + tmp_col = sort_col1; + sort_col1 = sort_col2; + sort_col2 = tmp_col; + } + #pragma unroll + for (int visit = 0; visit < 4; visit++) { + int vec_col = sort_col0; + float dist = sort_d0; + if (visit == 1) { + vec_col = sort_col1; + dist = sort_d1; + } + if (visit == 2) { + vec_col = sort_col2; + dist = sort_d2; + } + if (visit == 3) { + vec_col = sort_col3; + dist = sort_d3; + } + if (dist >= worst_d) { + break; + } + int db_idx = db_start + col_base + vec_col; + best_d[worst_pos] = dist; + best_i[worst_pos] = db_idx; + int refresh_base = worst_chunk * 8; + chunk_worst_d[worst_chunk] = best_d[refresh_base]; + chunk_worst_pos[worst_chunk] = refresh_base; + #pragma unroll + for (int offset = 1; offset < 8; offset++) { + int scan_pos = refresh_base + offset; + if (best_d[scan_pos] > chunk_worst_d[worst_chunk]) { + chunk_worst_d[worst_chunk] = best_d[scan_pos]; + chunk_worst_pos[worst_chunk] = scan_pos; + } + } + worst_d = chunk_worst_d[0]; + worst_pos = chunk_worst_pos[0]; + worst_chunk = 0; + #pragma unroll + for (int chunk_1 = 1; chunk_1 < 4; chunk_1++) { + if (worst_d < chunk_worst_d[chunk_1]) { + worst_d = chunk_worst_d[chunk_1]; + worst_pos = chunk_worst_pos[chunk_1]; + worst_chunk = chunk_1; + } + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(32)); + } + if (valid_q != 0) { + int out_base = ((split_idx * B + batch_idx) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_out_d = best_d[0]; + int best_out_i = best_i[0]; + int best_out_pos = 0; + #pragma unroll + for (int scan_pos_1 = 1; scan_pos_1 < TOP_K_MAX; scan_pos_1++) { + if (best_out_d > best_d[scan_pos_1]) { + best_out_d = best_d[scan_pos_1]; + best_out_i = best_i[scan_pos_1]; + best_out_pos = scan_pos_1; + } + } + if (out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = best_out_d; + *((int*)(partial_indices + (out_base + out_k))) = best_out_i; + } + best_d[best_out_pos] = 3.4e+38f; + } + } + } + } + // ---- Role: load ---- + } else if (warp == 1) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 1) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx_1 = bid; work_idx_1 < total_work; work_idx_1 += num_bids) { + int split_idx_1 = work_idx_1 % (unsigned int)split_count; + int query_work_1 = work_idx_1 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tiles; + int q_tile_1 = query_work_1 % num_q_tiles; + int off_q_1 = q_tile_1 * BLOCK_Q; + int global_q = batch_idx_1 * Q + off_q_1; + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + mbarrier_arrive_expect_tx(query_full_addr, 16384); + tma_3d_gmem2smem(smem_query_addr, tmap_query, 0, global_q, 0, query_full_addr); + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int off_m = db_tile_1 * BLOCK_M; + int global_m = batch_idx_1 * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + mbarrier_arrive_expect_tx(database_full_addr, 16384); + tma_3d_gmem2smem(smem_database_addr, tmap_database, 0, global_m, 0, database_full_addr); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 2) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = bid; work_idx_2 < total_work; work_idx_2 += num_bids) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = make_warp_uniform((smem_query_addr >> 4) & 0x3FFF); + int _mma_b_lo_0 = make_warp_uniform((smem_database_addr >> 4) & 0x3FFF); + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 68158608;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 506;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit(score_full_addr); + elect_commit(database_empty_addr); + } + elect_commit(query_empty_addr); + } + } + } + + // Cleanup + __syncthreads(); // barrier before TMEM dealloc + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::1.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(64)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::1.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0192.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0192.cu new file mode 100644 index 00000000..bdff2ba3 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0192.cu @@ -0,0 +1,129 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 128 +#define TOP_K_MAX 10 +#define SPLIT_COUNT 74 + +#include + +extern "C" { + +__global__ __launch_bounds__(128, 1) void +kernel_knn_build_d128_rag_q128_k10_s74_warp_merge(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int row = bid * 4 + warp; + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int lane_idx = lane; + if (row < total_queries) { + int split0 = lane_idx; + int base0 = base_row + split0 * split_stride; + int pos0 = 0; + float d0 = partial_dists[base0]; + int i0 = partial_indices[base0]; + int split1 = lane_idx + 32; + int base1 = base_row + split1 * split_stride; + int pos1 = 0; + float d1 = 3.4e+38f; + int i1 = -1; + if (split1 < SPLIT_COUNT) { + d1 = partial_dists[base1]; + i1 = partial_indices[base1]; + } + int split2 = lane_idx + 64; + int base2 = base_row + split2 * split_stride; + int pos2 = 0; + float d2 = 3.4e+38f; + int i2 = -1; + if (split2 < SPLIT_COUNT) { + d2 = partial_dists[base2]; + i2 = partial_indices[base2]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float winner_d = d0; + int winner_i = i0; + int winner_src = 0; + if (d1 < winner_d) { + winner_d = d1; + winner_i = i1; + winner_src = 1; + } + if (d2 < winner_d) { + winner_d = d2; + winner_i = i2; + winner_src = 2; + } + float warp_min = winner_d; + float _warp_reduce_0 = warp_min; + #pragma unroll + for (int offset = 16; offset > 0; offset >>= 1) + _warp_reduce_0 = fminf(_warp_reduce_0, __shfl_xor_sync(0xFFFFFFFF, _warp_reduce_0, offset)); + warp_min = _warp_reduce_0; + unsigned int _vote_0 = __ballot_sync(0xFFFFFFFF, winner_d == warp_min); + int owner_ballot = _vote_0; + int _ffs_0 = __ffs(owner_ballot); + int winner_lane = _ffs_0 - 1; + int _shfl_0 = __shfl_sync(0xFFFFFFFF, winner_i, winner_lane); + winner_i = _shfl_0; + int _shfl_1 = __shfl_sync(0xFFFFFFFF, winner_src, winner_lane); + winner_src = _shfl_1; + if (lane == 0) { + *((float*)(out_dists + (base_row + out_k))) = warp_min; + *((int*)(out_indices + (base_row + out_k))) = winner_i; + } + if (lane == winner_lane) { + if (winner_src == 0) { + pos0 = pos0 + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_addr0 = base0 + pos0; + d0 = partial_dists[next_addr0]; + i0 = partial_indices[next_addr0]; + } + } else if (winner_src == 1) { + pos1 = pos1 + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_addr1 = base1 + pos1; + d1 = partial_dists[next_addr1]; + i1 = partial_indices[next_addr1]; + } + } else { + pos2 = pos2 + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_addr2 = base2 + pos2; + d2 = partial_dists[next_addr2]; + i2 = partial_indices[next_addr2]; + } + } + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0193.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0193.cu new file mode 100644 index 00000000..5b5a28fd --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0193.cu @@ -0,0 +1,657 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 128 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_STRIDE 32768 +#define SMEM_SMEM_DATABASE_OFF 33792 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_DATABASE_SQ_OFF 50176 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 50432 +#define THREADS 192 +#define BLOCK_Q 128 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_MAX 12 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16_cta2( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + ".reg .b32 m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "tcgen05.mma.cta_group::2.kind::f16 [%0], %1, %2, %3, {m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step_cg2( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, %3, " + "{m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], %1;\n\t" + "}\n" + :: "r"(mbar_addr), "h"(cta_mask) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x16(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x16.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15}, [%16];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ uint32_t smem_addr(const void* ptr) { + uint32_t addr; + asm("{\n\t" + ".reg .u64 u64addr;\n\t" + "cvta.to.shared.u64 u64addr, %1;\n\t" + "cvt.u32.u64 %0, u64addr;\n\t" + "}\n" : "=r"(addr) : "l"(ptr)); + return addr; +} + + +__device__ __forceinline__ uint32_t mapa_to_rank(uint32_t local_addr, uint32_t rank) { + uint32_t remote; + asm volatile("mapa.shared::cluster.u32 %0, %1, %2;" + : "=r"(remote) : "r"(local_addr), "r"(rank)); + return remote; +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem_cta2( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global" + ".mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .b16 lo, hi;\n\t" + "mov.b32 {lo, hi}, %1;\n\t" + "tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], lo;\n\t" + "}\n" + :: "r"(mbar_addr), "r"((uint32_t)cta_mask) : "memory"); +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_k12split(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tile_pairs, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + const unsigned int clusters_x = gridDim.x / 2; + const unsigned int cluster_id = ((blockIdx.z * gridDim.y + blockIdx.y) * clusters_x) + blockIdx.x / 2; + const unsigned int num_clusters = clusters_x * gridDim.y * gridDim.z; + + int cta_rank; + asm volatile("mov.b32 %0, %%cluster_ctarank;" : "=r"(cta_rank)); + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 33792); + const int smem_database_addr = smem + 33792; + float* smem_database_sq = reinterpret_cast(smem_raw + 50176); + const int smem_database_sq_addr = smem + 50176; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 0, 2, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 16, 2, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=8 + mbarrier_init_pred(smem + 40, 8, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (128 columns, 128 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::2.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(128) : "memory"); + } + + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: load ---- + if (warp == 0) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 0) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx = cluster_id; work_idx < total_work; work_idx += num_clusters) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tile_pairs; + int q_tile_pair = query_work % num_q_tile_pairs; + int q_tile = q_tile_pair * 2 + cta_rank; + int off_q = q_tile * BLOCK_Q; + int global_q = batch_idx * Q + off_q; + int db_tile_start = split_idx * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((query_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(32768)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_query_addr), "l"(tmap_query), "r"(0), "r"(global_q), "r"(0), + "r"(((query_full_addr) & 0xFEFFFFFF)) : "memory"); + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int off_m = db_tile * BLOCK_M; + int global_m = batch_idx * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((database_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(16384)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_database_addr), "l"(tmap_database), "r"(0), "r"(global_m), "r"(0), + "r"(((database_full_addr) & 0xFEFFFFFF)) : "memory"); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 1) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + if (cta_rank == 0) { + #pragma unroll 1 + for (unsigned int work_idx_1 = cluster_id; work_idx_1 < total_work; work_idx_1 += num_clusters) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = (smem_query_addr >> 4) & 0x3FFF; + int _mma_b_lo_0 = (smem_database_addr >> 4) & 0x3FFF; + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\tmov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 270533776;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit_cg2_multicast(score_full_addr, (uint16_t)(3)); + elect_commit_cg2_multicast(database_empty_addr, (uint16_t)(3)); + } + elect_commit_cg2_multicast(query_empty_addr, (uint16_t)(3)); + } + } + } + // ---- Role: compute ---- + } else if (warp >= 2 && warp <= 5) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = cluster_id; work_idx_2 < total_work; work_idx_2 += num_clusters) { + int split_idx_1 = work_idx_2 % (unsigned int)split_count; + int query_work_1 = work_idx_2 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tile_pairs; + int q_tile_pair_1 = query_work_1 % num_q_tile_pairs; + int q_tile_1 = q_tile_pair_1 * 2 + cta_rank; + int off_q_1 = q_tile_1 * BLOCK_Q; + int q_idx = off_q_1 + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx_1 * Q + q_idx]; + } + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int db_start = db_tile_1 * BLOCK_M; + int db_sq_idx = db_start + (warp % 4 * 32 + lane); + if (warp % 4 * 32 + lane < BLOCK_M) { + if (db_sq_idx < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx_1 * M + db_sq_idx]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 0.0f; + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(cta_rank * BLOCK_Q + (warp % 4 * 32 << 16) << 16); + float _tmem_load_0[64]; + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x64.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31, %32, %33, %34, %35, %36, %37, %38, %39, %40, %41, %42, %43, %44, %45, %46, %47, %48, %49, %50, %51, %52, %53, %54, %55, %56, %57, %58, %59, %60, %61, %62, %63}, [%64];" + : "=f"(_tmem_load_0[0]), "=f"(_tmem_load_0[1]), "=f"(_tmem_load_0[2]), "=f"(_tmem_load_0[3]), "=f"(_tmem_load_0[4]), "=f"(_tmem_load_0[5]), "=f"(_tmem_load_0[6]), "=f"(_tmem_load_0[7]), "=f"(_tmem_load_0[8]), "=f"(_tmem_load_0[9]), "=f"(_tmem_load_0[10]), "=f"(_tmem_load_0[11]), "=f"(_tmem_load_0[12]), "=f"(_tmem_load_0[13]), "=f"(_tmem_load_0[14]), "=f"(_tmem_load_0[15]), "=f"(_tmem_load_0[16]), "=f"(_tmem_load_0[17]), "=f"(_tmem_load_0[18]), "=f"(_tmem_load_0[19]), "=f"(_tmem_load_0[20]), "=f"(_tmem_load_0[21]), "=f"(_tmem_load_0[22]), "=f"(_tmem_load_0[23]), "=f"(_tmem_load_0[24]), "=f"(_tmem_load_0[25]), "=f"(_tmem_load_0[26]), "=f"(_tmem_load_0[27]), "=f"(_tmem_load_0[28]), "=f"(_tmem_load_0[29]), "=f"(_tmem_load_0[30]), "=f"(_tmem_load_0[31]), "=f"(_tmem_load_0[32]), "=f"(_tmem_load_0[33]), "=f"(_tmem_load_0[34]), "=f"(_tmem_load_0[35]), "=f"(_tmem_load_0[36]), "=f"(_tmem_load_0[37]), "=f"(_tmem_load_0[38]), "=f"(_tmem_load_0[39]), "=f"(_tmem_load_0[40]), "=f"(_tmem_load_0[41]), "=f"(_tmem_load_0[42]), "=f"(_tmem_load_0[43]), "=f"(_tmem_load_0[44]), "=f"(_tmem_load_0[45]), "=f"(_tmem_load_0[46]), "=f"(_tmem_load_0[47]), "=f"(_tmem_load_0[48]), "=f"(_tmem_load_0[49]), "=f"(_tmem_load_0[50]), "=f"(_tmem_load_0[51]), "=f"(_tmem_load_0[52]), "=f"(_tmem_load_0[53]), "=f"(_tmem_load_0[54]), "=f"(_tmem_load_0[55]), "=f"(_tmem_load_0[56]), "=f"(_tmem_load_0[57]), "=f"(_tmem_load_0[58]), "=f"(_tmem_load_0[59]), "=f"(_tmem_load_0[60]), "=f"(_tmem_load_0[61]), "=f"(_tmem_load_0[62]), "=f"(_tmem_load_0[63]) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + if (elect_sync()) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cluster.b64 _, [%0];" + :: "r"((score_empty_addr) & 0xFEFFFFFF) : "memory"); + } + if (valid_q != 0) { + #pragma unroll 2 + for (int col_base = 0; col_base < 64; col_base += 4) { + float dist_vec[4]; + dist_vec[0] = _tmem_load_0[col_base]; + dist_vec[1] = _tmem_load_0[col_base + 1]; + dist_vec[2] = _tmem_load_0[col_base + 2]; + dist_vec[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec[4]; + db_sq_vec[0] = smem_database_sq[col_base]; + db_sq_vec[1] = smem_database_sq[col_base + 1]; + db_sq_vec[2] = smem_database_sq[col_base + 2]; + db_sq_vec[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec)[_la], reinterpret_cast(db_sq_vec)[_la]); + float group_min = _t0[0]; + if (_t0[1] < group_min) { + group_min = _t0[1]; + } + if (_t0[2] < group_min) { + group_min = _t0[2]; + } + if (_t0[3] < group_min) { + group_min = _t0[3]; + } + if (group_min < best_d[TOP_K_MAX - 1]) { + #pragma unroll + for (int vec_col = 0; vec_col < 4; vec_col++) { + int db_idx = db_start + col_base + vec_col; + if (db_idx < M) { + float dist = _t0[vec_col]; + if (dist < best_d[TOP_K_MAX - 1]) { + best_d[TOP_K_MAX - 1] = dist; + best_i[TOP_K_MAX - 1] = db_idx; + #pragma unroll + for (int pos = TOP_K_MAX - 1; pos >= 1; pos--) { + if (best_d[pos] < best_d[pos - 1]) { + float tmp_d = best_d[pos - 1]; + int tmp_i = best_i[pos - 1]; + best_d[pos - 1] = best_d[pos]; + best_i[pos - 1] = best_i[pos]; + best_d[pos] = tmp_d; + best_i[pos] = tmp_i; + } + } + } + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + if (valid_q != 0) { + int out_base = ((split_idx_1 * B + batch_idx_1) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + if (out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = best_d[out_k]; + *((int*)(partial_indices + (out_base + out_k))) = best_i[out_k]; + } + } + } + } + } + } + + // Cleanup + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::2.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(128)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::2.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0194.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0194.cu new file mode 100644 index 00000000..19f9bf0b --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0194.cu @@ -0,0 +1,109 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 32 +#define TOP_K_MAX 11 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_evolve_7bfc_k32_merge_s4_rowbase_cache_e080k11s4exact(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int K, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 32 + tid; + int stride = num_bids * 32; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int base_row = row * K; + int split_stride = total_queries * K; + int out_base = base_row; + int split_base0 = base_row; + int split_base1 = base_row + split_stride; + int split_base2 = split_base1 + split_stride; + int split_base3 = split_base2 + split_stride; + int pos0 = 0; + int pos1 = 0; + int pos2 = 0; + int pos3 = 0; + float cand_d0 = partial_dists[split_base0]; + int cand_i0 = partial_indices[split_base0]; + float cand_d1 = partial_dists[split_base1]; + int cand_i1 = partial_indices[split_base1]; + float cand_d2 = partial_dists[split_base2]; + int cand_i2 = partial_indices[split_base2]; + float cand_d3 = partial_dists[split_base3]; + int cand_i3 = partial_indices[split_base3]; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + if (out_k < K) { + int cand01_cmp = ((cand_d1 < cand_d0) ? 1 : 0); + float best01_d = ((cand01_cmp != 0) ? cand_d1 : cand_d0); + int best01_i = ((cand01_cmp != 0) ? cand_i1 : cand_i0); + int best01_split = ((cand01_cmp != 0) ? 1 : 0); + int cand23_cmp = ((cand_d3 < cand_d2) ? 1 : 0); + float best23_d = ((cand23_cmp != 0) ? cand_d3 : cand_d2); + int best23_i = ((cand23_cmp != 0) ? cand_i3 : cand_i2); + int best23_split = ((cand23_cmp != 0) ? 3 : 2); + int best_cmp = ((best23_d < best01_d) ? 1 : 0); + float best_d = ((best_cmp != 0) ? best23_d : best01_d); + int best_i = ((best_cmp != 0) ? best23_i : best01_i); + int best_split = ((best_cmp != 0) ? best23_split : best01_split); + *((float*)(out_dists + (out_base + out_k))) = best_d; + *((int*)(out_indices + (out_base + out_k))) = best_i; + if (out_k + 1 < K) { + if (best_split == 0) { + pos0 = pos0 + 1; + int next_addr0 = split_base0 + pos0; + cand_d0 = partial_dists[next_addr0]; + cand_i0 = partial_indices[next_addr0]; + } else if (best_split == 1) { + pos1 = pos1 + 1; + int next_addr1 = split_base1 + pos1; + cand_d1 = partial_dists[next_addr1]; + cand_i1 = partial_indices[next_addr1]; + } else { + if (best_split == 2) { + pos2 = pos2 + 1; + int next_addr2 = split_base2 + pos2; + cand_d2 = partial_dists[next_addr2]; + cand_i2 = partial_indices[next_addr2]; + } else { + pos3 = pos3 + 1; + int next_addr3 = split_base3 + pos3; + cand_d3 = partial_dists[next_addr3]; + cand_i3 = partial_indices[next_addr3]; + } + } + } + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0195.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0195.cu new file mode 100644 index 00000000..b7e1b795 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0195.cu @@ -0,0 +1,109 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 32 +#define TOP_K_MAX 13 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_evolve_7bfc_k32_merge_s4_rowbase_cache_e080k13s4exact(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int K, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 32 + tid; + int stride = num_bids * 32; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int base_row = row * K; + int split_stride = total_queries * K; + int out_base = base_row; + int split_base0 = base_row; + int split_base1 = base_row + split_stride; + int split_base2 = split_base1 + split_stride; + int split_base3 = split_base2 + split_stride; + int pos0 = 0; + int pos1 = 0; + int pos2 = 0; + int pos3 = 0; + float cand_d0 = partial_dists[split_base0]; + int cand_i0 = partial_indices[split_base0]; + float cand_d1 = partial_dists[split_base1]; + int cand_i1 = partial_indices[split_base1]; + float cand_d2 = partial_dists[split_base2]; + int cand_i2 = partial_indices[split_base2]; + float cand_d3 = partial_dists[split_base3]; + int cand_i3 = partial_indices[split_base3]; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + if (out_k < K) { + int cand01_cmp = ((cand_d1 < cand_d0) ? 1 : 0); + float best01_d = ((cand01_cmp != 0) ? cand_d1 : cand_d0); + int best01_i = ((cand01_cmp != 0) ? cand_i1 : cand_i0); + int best01_split = ((cand01_cmp != 0) ? 1 : 0); + int cand23_cmp = ((cand_d3 < cand_d2) ? 1 : 0); + float best23_d = ((cand23_cmp != 0) ? cand_d3 : cand_d2); + int best23_i = ((cand23_cmp != 0) ? cand_i3 : cand_i2); + int best23_split = ((cand23_cmp != 0) ? 3 : 2); + int best_cmp = ((best23_d < best01_d) ? 1 : 0); + float best_d = ((best_cmp != 0) ? best23_d : best01_d); + int best_i = ((best_cmp != 0) ? best23_i : best01_i); + int best_split = ((best_cmp != 0) ? best23_split : best01_split); + *((float*)(out_dists + (out_base + out_k))) = best_d; + *((int*)(out_indices + (out_base + out_k))) = best_i; + if (out_k + 1 < K) { + if (best_split == 0) { + pos0 = pos0 + 1; + int next_addr0 = split_base0 + pos0; + cand_d0 = partial_dists[next_addr0]; + cand_i0 = partial_indices[next_addr0]; + } else if (best_split == 1) { + pos1 = pos1 + 1; + int next_addr1 = split_base1 + pos1; + cand_d1 = partial_dists[next_addr1]; + cand_i1 = partial_indices[next_addr1]; + } else { + if (best_split == 2) { + pos2 = pos2 + 1; + int next_addr2 = split_base2 + pos2; + cand_d2 = partial_dists[next_addr2]; + cand_i2 = partial_indices[next_addr2]; + } else { + pos3 = pos3 + 1; + int next_addr3 = split_base3 + pos3; + cand_d3 = partial_dists[next_addr3]; + cand_i3 = partial_indices[next_addr3]; + } + } + } + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0196.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0196.cu new file mode 100644 index 00000000..60275371 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0196.cu @@ -0,0 +1,655 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 128 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_STRIDE 32768 +#define SMEM_SMEM_DATABASE_OFF 33792 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_DATABASE_SQ_OFF 50176 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 50432 +#define THREADS 192 +#define BLOCK_Q 128 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_MAX 32 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16_cta2( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + ".reg .b32 m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "tcgen05.mma.cta_group::2.kind::f16 [%0], %1, %2, %3, {m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step_cg2( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, %3, " + "{m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], %1;\n\t" + "}\n" + :: "r"(mbar_addr), "h"(cta_mask) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x16(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x16.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15}, [%16];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ uint32_t smem_addr(const void* ptr) { + uint32_t addr; + asm("{\n\t" + ".reg .u64 u64addr;\n\t" + "cvta.to.shared.u64 u64addr, %1;\n\t" + "cvt.u32.u64 %0, u64addr;\n\t" + "}\n" : "=r"(addr) : "l"(ptr)); + return addr; +} + + +__device__ __forceinline__ uint32_t mapa_to_rank(uint32_t local_addr, uint32_t rank) { + uint32_t remote; + asm volatile("mapa.shared::cluster.u32 %0, %1, %2;" + : "=r"(remote) : "r"(local_addr), "r"(rank)); + return remote; +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem_cta2( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global" + ".mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .b16 lo, hi;\n\t" + "mov.b32 {lo, hi}, %1;\n\t" + "tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], lo;\n\t" + "}\n" + :: "r"(mbar_addr), "r"((uint32_t)cta_mask) : "memory"); +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tile_pairs, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + const unsigned int clusters_x = gridDim.x / 2; + const unsigned int cluster_id = ((blockIdx.z * gridDim.y + blockIdx.y) * clusters_x) + blockIdx.x / 2; + const unsigned int num_clusters = clusters_x * gridDim.y * gridDim.z; + + int cta_rank; + asm volatile("mov.b32 %0, %%cluster_ctarank;" : "=r"(cta_rank)); + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 33792); + const int smem_database_addr = smem + 33792; + float* smem_database_sq = reinterpret_cast(smem_raw + 50176); + const int smem_database_sq_addr = smem + 50176; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 0, 2, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 16, 2, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=8 + mbarrier_init_pred(smem + 40, 8, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (128 columns, 128 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::2.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(128) : "memory"); + } + + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: load ---- + if (warp == 0) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 0) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx = cluster_id; work_idx < total_work; work_idx += num_clusters) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tile_pairs; + int q_tile_pair = query_work % num_q_tile_pairs; + int q_tile = q_tile_pair * 2 + cta_rank; + int off_q = q_tile * BLOCK_Q; + int global_q = batch_idx * Q + off_q; + int db_tile_start = split_idx * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((query_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(32768)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_query_addr), "l"(tmap_query), "r"(0), "r"(global_q), "r"(0), + "r"(((query_full_addr) & 0xFEFFFFFF)) : "memory"); + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int off_m = db_tile * BLOCK_M; + int global_m = batch_idx * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((database_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(16384)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_database_addr), "l"(tmap_database), "r"(0), "r"(global_m), "r"(0), + "r"(((database_full_addr) & 0xFEFFFFFF)) : "memory"); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 1) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + if (cta_rank == 0) { + #pragma unroll 1 + for (unsigned int work_idx_1 = cluster_id; work_idx_1 < total_work; work_idx_1 += num_clusters) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = (smem_query_addr >> 4) & 0x3FFF; + int _mma_b_lo_0 = (smem_database_addr >> 4) & 0x3FFF; + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\tmov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 270533776;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit_cg2_multicast(score_full_addr, (uint16_t)(3)); + elect_commit_cg2_multicast(database_empty_addr, (uint16_t)(3)); + } + elect_commit_cg2_multicast(query_empty_addr, (uint16_t)(3)); + } + } + } + // ---- Role: compute ---- + } else if (warp >= 2 && warp <= 5) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = cluster_id; work_idx_2 < total_work; work_idx_2 += num_clusters) { + int split_idx_1 = work_idx_2 % (unsigned int)split_count; + int query_work_1 = work_idx_2 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tile_pairs; + int q_tile_pair_1 = query_work_1 % num_q_tile_pairs; + int q_tile_1 = q_tile_pair_1 * 2 + cta_rank; + int off_q_1 = q_tile_1 * BLOCK_Q; + int q_idx = off_q_1 + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx_1 * Q + q_idx]; + } + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + float worst_d = 3.4e+38f; + int worst_pos = 0; + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int db_start = db_tile_1 * BLOCK_M; + int db_sq_idx = db_start + (warp % 4 * 32 + lane); + if (warp % 4 * 32 + lane < BLOCK_M) { + if (db_sq_idx < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx_1 * M + db_sq_idx]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 0.0f; + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(cta_rank * BLOCK_Q + (warp % 4 * 32 << 16) << 16); + float _tmem_load_0[64]; + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x64.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31, %32, %33, %34, %35, %36, %37, %38, %39, %40, %41, %42, %43, %44, %45, %46, %47, %48, %49, %50, %51, %52, %53, %54, %55, %56, %57, %58, %59, %60, %61, %62, %63}, [%64];" + : "=f"(_tmem_load_0[0]), "=f"(_tmem_load_0[1]), "=f"(_tmem_load_0[2]), "=f"(_tmem_load_0[3]), "=f"(_tmem_load_0[4]), "=f"(_tmem_load_0[5]), "=f"(_tmem_load_0[6]), "=f"(_tmem_load_0[7]), "=f"(_tmem_load_0[8]), "=f"(_tmem_load_0[9]), "=f"(_tmem_load_0[10]), "=f"(_tmem_load_0[11]), "=f"(_tmem_load_0[12]), "=f"(_tmem_load_0[13]), "=f"(_tmem_load_0[14]), "=f"(_tmem_load_0[15]), "=f"(_tmem_load_0[16]), "=f"(_tmem_load_0[17]), "=f"(_tmem_load_0[18]), "=f"(_tmem_load_0[19]), "=f"(_tmem_load_0[20]), "=f"(_tmem_load_0[21]), "=f"(_tmem_load_0[22]), "=f"(_tmem_load_0[23]), "=f"(_tmem_load_0[24]), "=f"(_tmem_load_0[25]), "=f"(_tmem_load_0[26]), "=f"(_tmem_load_0[27]), "=f"(_tmem_load_0[28]), "=f"(_tmem_load_0[29]), "=f"(_tmem_load_0[30]), "=f"(_tmem_load_0[31]), "=f"(_tmem_load_0[32]), "=f"(_tmem_load_0[33]), "=f"(_tmem_load_0[34]), "=f"(_tmem_load_0[35]), "=f"(_tmem_load_0[36]), "=f"(_tmem_load_0[37]), "=f"(_tmem_load_0[38]), "=f"(_tmem_load_0[39]), "=f"(_tmem_load_0[40]), "=f"(_tmem_load_0[41]), "=f"(_tmem_load_0[42]), "=f"(_tmem_load_0[43]), "=f"(_tmem_load_0[44]), "=f"(_tmem_load_0[45]), "=f"(_tmem_load_0[46]), "=f"(_tmem_load_0[47]), "=f"(_tmem_load_0[48]), "=f"(_tmem_load_0[49]), "=f"(_tmem_load_0[50]), "=f"(_tmem_load_0[51]), "=f"(_tmem_load_0[52]), "=f"(_tmem_load_0[53]), "=f"(_tmem_load_0[54]), "=f"(_tmem_load_0[55]), "=f"(_tmem_load_0[56]), "=f"(_tmem_load_0[57]), "=f"(_tmem_load_0[58]), "=f"(_tmem_load_0[59]), "=f"(_tmem_load_0[60]), "=f"(_tmem_load_0[61]), "=f"(_tmem_load_0[62]), "=f"(_tmem_load_0[63]) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + if (elect_sync()) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cluster.b64 _, [%0];" + :: "r"((score_empty_addr) & 0xFEFFFFFF) : "memory"); + } + if (valid_q != 0) { + #pragma unroll 2 + for (int col_base = 0; col_base < 64; col_base += 4) { + float dist_vec[4]; + dist_vec[0] = _tmem_load_0[col_base]; + dist_vec[1] = _tmem_load_0[col_base + 1]; + dist_vec[2] = _tmem_load_0[col_base + 2]; + dist_vec[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec[4]; + db_sq_vec[0] = smem_database_sq[col_base]; + db_sq_vec[1] = smem_database_sq[col_base + 1]; + db_sq_vec[2] = smem_database_sq[col_base + 2]; + db_sq_vec[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec)[_la], reinterpret_cast(db_sq_vec)[_la]); + float group_min = _t0[0]; + if (_t0[1] < group_min) { + group_min = _t0[1]; + } + if (_t0[2] < group_min) { + group_min = _t0[2]; + } + if (_t0[3] < group_min) { + group_min = _t0[3]; + } + if (group_min < worst_d) { + #pragma unroll + for (int vec_col = 0; vec_col < 4; vec_col++) { + int db_idx = db_start + col_base + vec_col; + if (db_idx < M) { + float dist = _t0[vec_col]; + if (dist < worst_d) { + best_d[worst_pos] = dist; + best_i[worst_pos] = db_idx; + worst_d = best_d[0]; + worst_pos = 0; + #pragma unroll + for (int scan_pos = 1; scan_pos < TOP_K_MAX; scan_pos++) { + if (worst_d < best_d[scan_pos]) { + worst_d = best_d[scan_pos]; + worst_pos = scan_pos; + } + } + } + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + if (valid_q != 0) { + int out_base = ((split_idx_1 * B + batch_idx_1) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + *((float*)(partial_dists + (out_base + out_k))) = best_d[out_k]; + *((int*)(partial_indices + (out_base + out_k))) = best_i[out_k]; + } + } + } + } + } + + // Cleanup + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::2.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(128)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::2.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0197.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0197.cu new file mode 100644 index 00000000..37eef13e --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0197.cu @@ -0,0 +1,85 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 32 +#define TOP_K_MAX 32 +#define SPLIT_COUNT 4 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_evolve_7bfc_k32_merge_s4_unordered(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 32 + tid; + int stride = num_bids * 32; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + float worst_d = 3.4e+38f; + int worst_pos = 0; + #pragma unroll + for (int split_idx = 0; split_idx < SPLIT_COUNT; split_idx++) { + int partial_base = base_row + split_idx * split_stride; + #pragma unroll + for (int cand_k = 0; cand_k < TOP_K_MAX; cand_k++) { + float cand_d = partial_dists[partial_base + cand_k]; + int cand_i = partial_indices[partial_base + cand_k]; + if (cand_d < worst_d) { + best_d[worst_pos] = cand_d; + best_i[worst_pos] = cand_i; + worst_d = best_d[0]; + worst_pos = 0; + #pragma unroll + for (int scan_pos = 1; scan_pos < TOP_K_MAX; scan_pos++) { + if (worst_d < best_d[scan_pos]) { + worst_d = best_d[scan_pos]; + worst_pos = scan_pos; + } + } + } + } + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + *((float*)(out_dists + (base_row + out_k))) = best_d[out_k]; + *((int*)(out_indices + (base_row + out_k))) = best_i[out_k]; + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0198.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0198.cu new file mode 100644 index 00000000..3f5c5e7a --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0198.cu @@ -0,0 +1,84 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 32 +#define TOP_K_MAX 30 +#define SPLIT_COUNT 8 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 32 + tid; + int stride = num_bids * 32; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int out_base = base_row; + int split_pos[SPLIT_COUNT]; + int split_base[SPLIT_COUNT]; + float cand_d[SPLIT_COUNT]; + int cand_i[SPLIT_COUNT]; + #pragma unroll + for (int split_idx = 0; split_idx < SPLIT_COUNT; split_idx++) { + split_pos[split_idx] = 0; + split_base[split_idx] = base_row + split_idx * split_stride; + cand_d[split_idx] = partial_dists[split_base[split_idx]]; + cand_i[split_idx] = partial_indices[split_base[split_idx]]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_d = cand_d[0]; + int best_i = cand_i[0]; + int best_split = 0; + #pragma unroll + for (int split_idx_1 = 1; split_idx_1 < SPLIT_COUNT; split_idx_1++) { + if (best_d > cand_d[split_idx_1]) { + best_d = cand_d[split_idx_1]; + best_i = cand_i[split_idx_1]; + best_split = split_idx_1; + } + } + *((float*)(out_dists + (out_base + out_k))) = best_d; + *((int*)(out_indices + (out_base + out_k))) = best_i; + split_pos[best_split] = split_pos[best_split] + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_pos = split_pos[best_split]; + int next_addr = split_base[best_split] + next_pos; + cand_d[best_split] = partial_dists[next_addr]; + cand_i[best_split] = partial_indices[next_addr]; + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0199.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0199.cu new file mode 100644 index 00000000..90a9fb6a --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0199.cu @@ -0,0 +1,638 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 64 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 16384 +#define SMEM_SMEM_QUERY_STRIDE 16384 +#define SMEM_SMEM_DATABASE_OFF 17408 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_LOCAL_D_OFF 34048 +#define SMEM_SMEM_LOCAL_D_STAGE_BYTES 8192 +#define SMEM_SMEM_LOCAL_D_STRIDE 8192 +#define SMEM_SMEM_LOCAL_I_OFF 42240 +#define SMEM_SMEM_LOCAL_I_STAGE_BYTES 8192 +#define SMEM_SMEM_LOCAL_I_STRIDE 8192 +#define SMEM_TOTAL 50432 +#define THREADS 96 +#define BLOCK_Q 64 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_MAX 32 +#define ROWS_COVERED 16 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "tcgen05.mma.cta_group::1.kind::f16 [%0], %1, %2, %3, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, %3, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit(int mbar_addr) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];\n\t" + "}\n" + :: "r"(mbar_addr)); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x32(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x32.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15," + " %16, %17, %18, %19, %20, %21, %22, %23," + " %24, %25, %26, %27, %28, %29, %30, %31}, [%32];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]), + "=f"(dst[16]), "=f"(dst[17]), "=f"(dst[18]), "=f"(dst[19]), + "=f"(dst[20]), "=f"(dst[21]), "=f"(dst[22]), "=f"(dst[23]), + "=f"(dst[24]), "=f"(dst[25]), "=f"(dst[26]), "=f"(dst[27]), + "=f"(dst[28]), "=f"(dst[29]), "=f"(dst[30]), "=f"(dst[31]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ float max_noftz(float a, float b) { + float c; + asm("max.f32 %0, %1, %2;" : "=f"(c) : "f"(a), "f"(b)); + return c; +} + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cta.global" + ".mbarrier::complete_tx::bytes" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit(int mbar_addr) { + asm volatile( + "tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ uint32_t make_warp_uniform(uint32_t val) { + uint32_t result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1f, 0xffffffff;" + : "=r"(result) : "r"(val)); + return result; +} + +extern "C" { + +__global__ __launch_bounds__(96, 1) void +kernel_knn_build_rag_microbucket_k32rowld1warp_0077_v1_stage1_q16_k32_m64_rowld1_q16rowld1_0077_v1(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tiles, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 17408); + const int smem_database_addr = smem + 17408; + float* smem_local_d = reinterpret_cast(smem_raw + 34048); + const int smem_local_d_addr = smem + 34048; + int* smem_local_i = reinterpret_cast(smem_raw + 42240); + const int smem_local_i_addr = smem + 42240; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 0, 1, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 16, 1, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 40, 1, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (64 columns, 64 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::1.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(64) : "memory"); + } + + __syncthreads(); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: compute ---- + if (warp == 0) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx = bid; work_idx < total_work; work_idx += num_bids) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tiles; + int q_tile = query_work % num_q_tiles; + int off_q = q_tile * BLOCK_Q; + int row_top = lane / 4; + int row_bot = row_top + 8; + int lane_col = lane % 4; + int slot = lane_col; + int q_top = off_q + row_top; + int q_bot = off_q + row_bot; + int valid_top = ((q_top < Q) ? 1 : 0); + int valid_bot = ((q_bot < Q) ? 1 : 0); + float q_sq_top = 0.0f; + float q_sq_bot = 0.0f; + if (valid_top != 0) { + q_sq_top = query_sq[batch_idx * Q + q_top]; + } + if (valid_bot != 0) { + q_sq_bot = query_sq[batch_idx * Q + q_bot]; + } + float best_top_d[TOP_K_MAX]; + float best_bot_d[TOP_K_MAX]; + int best_top_i[TOP_K_MAX]; + int best_bot_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_top_d[kk] = 3.4e+38f; + best_bot_d[kk] = 3.4e+38f; + best_top_i[kk] = -1; + best_bot_i[kk] = -1; + } + int db_tile_start = split_idx * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int db_start = db_tile * BLOCK_M; + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + float _tmem_load_0[32]; + asm volatile( + "tcgen05.ld.sync.aligned.16x256b.x8.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31}, [%32];" + : "=r"(*reinterpret_cast(&_tmem_load_0[0])), "=r"(*reinterpret_cast(&_tmem_load_0[1])), "=r"(*reinterpret_cast(&_tmem_load_0[2])), "=r"(*reinterpret_cast(&_tmem_load_0[3])), "=r"(*reinterpret_cast(&_tmem_load_0[4])), "=r"(*reinterpret_cast(&_tmem_load_0[5])), "=r"(*reinterpret_cast(&_tmem_load_0[6])), "=r"(*reinterpret_cast(&_tmem_load_0[7])), "=r"(*reinterpret_cast(&_tmem_load_0[8])), "=r"(*reinterpret_cast(&_tmem_load_0[9])), "=r"(*reinterpret_cast(&_tmem_load_0[10])), "=r"(*reinterpret_cast(&_tmem_load_0[11])), "=r"(*reinterpret_cast(&_tmem_load_0[12])), "=r"(*reinterpret_cast(&_tmem_load_0[13])), "=r"(*reinterpret_cast(&_tmem_load_0[14])), "=r"(*reinterpret_cast(&_tmem_load_0[15])), "=r"(*reinterpret_cast(&_tmem_load_0[16])), "=r"(*reinterpret_cast(&_tmem_load_0[17])), "=r"(*reinterpret_cast(&_tmem_load_0[18])), "=r"(*reinterpret_cast(&_tmem_load_0[19])), "=r"(*reinterpret_cast(&_tmem_load_0[20])), "=r"(*reinterpret_cast(&_tmem_load_0[21])), "=r"(*reinterpret_cast(&_tmem_load_0[22])), "=r"(*reinterpret_cast(&_tmem_load_0[23])), "=r"(*reinterpret_cast(&_tmem_load_0[24])), "=r"(*reinterpret_cast(&_tmem_load_0[25])), "=r"(*reinterpret_cast(&_tmem_load_0[26])), "=r"(*reinterpret_cast(&_tmem_load_0[27])), "=r"(*reinterpret_cast(&_tmem_load_0[28])), "=r"(*reinterpret_cast(&_tmem_load_0[29])), "=r"(*reinterpret_cast(&_tmem_load_0[30])), "=r"(*reinterpret_cast(&_tmem_load_0[31])) + : "r"(taddr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + if (elect_sync()) { + mbarrier_arrive(score_empty_addr); + } + #pragma unroll + for (int repeat = 0; repeat < 8; repeat++) { + const int reg_base = repeat * 4; + int col_base = repeat * 8 + lane_col * 2; + int db_idx0 = db_start + col_base; + int db_idx1 = db_idx0 + 1; + float top_d0 = 3.4e+38f; + float top_d1 = 3.4e+38f; + if (valid_top != 0 && db_idx0 < M) { + float _max_0 = max_noftz(q_sq_top + database_sq[batch_idx * M + db_idx0] - 2.0f * _tmem_load_0[reg_base], 0.0f); + top_d0 = _max_0; + } + if (valid_top != 0 && db_idx1 < M) { + float _max_1 = max_noftz(q_sq_top + database_sq[batch_idx * M + db_idx1] - 2.0f * _tmem_load_0[reg_base + 1], 0.0f); + top_d1 = _max_1; + } + int top_take1 = ((top_d1 < top_d0) ? 1 : 0); + if (best_top_d[31] > ((top_take1 != 0) ? top_d1 : top_d0)) { + best_top_d[31] = ((top_take1 != 0) ? top_d1 : top_d0); + best_top_i[31] = ((top_take1 != 0) ? db_idx1 : db_idx0); + #pragma unroll + for (int kk_1 = 30; kk_1 >= 0; kk_1--) { + float lower0_d = best_top_d[kk_1 + 1]; + int lower0_i = best_top_i[kk_1 + 1]; + float upper0_d = best_top_d[kk_1]; + int upper0_i = best_top_i[kk_1]; + int swap0_up = ((lower0_d < upper0_d) ? 1 : 0); + best_top_d[kk_1] = ((swap0_up != 0) ? lower0_d : upper0_d); + best_top_i[kk_1] = ((swap0_up != 0) ? lower0_i : upper0_i); + best_top_d[kk_1 + 1] = ((swap0_up != 0) ? upper0_d : lower0_d); + best_top_i[kk_1 + 1] = ((swap0_up != 0) ? upper0_i : lower0_i); + } + if (best_top_d[31] > ((top_take1 != 0) ? top_d0 : top_d1)) { + best_top_d[31] = ((top_take1 != 0) ? top_d0 : top_d1); + best_top_i[31] = ((top_take1 != 0) ? db_idx0 : db_idx1); + #pragma unroll + for (int kk_2 = 30; kk_2 >= 0; kk_2--) { + float lower1_d = best_top_d[kk_2 + 1]; + int lower1_i = best_top_i[kk_2 + 1]; + float upper1_d = best_top_d[kk_2]; + int upper1_i = best_top_i[kk_2]; + int swap1_up = ((lower1_d < upper1_d) ? 1 : 0); + best_top_d[kk_2] = ((swap1_up != 0) ? lower1_d : upper1_d); + best_top_i[kk_2] = ((swap1_up != 0) ? lower1_i : upper1_i); + best_top_d[kk_2 + 1] = ((swap1_up != 0) ? upper1_d : lower1_d); + best_top_i[kk_2 + 1] = ((swap1_up != 0) ? upper1_i : lower1_i); + } + } + } + float bot_d0 = 3.4e+38f; + float bot_d1 = 3.4e+38f; + if (valid_bot != 0 && db_idx0 < M) { + float _max_2 = max_noftz(q_sq_bot + database_sq[batch_idx * M + db_idx0] - 2.0f * _tmem_load_0[reg_base + 2], 0.0f); + bot_d0 = _max_2; + } + if (valid_bot != 0 && db_idx1 < M) { + float _max_3 = max_noftz(q_sq_bot + database_sq[batch_idx * M + db_idx1] - 2.0f * _tmem_load_0[reg_base + 3], 0.0f); + bot_d1 = _max_3; + } + int bot_take1 = ((bot_d1 < bot_d0) ? 1 : 0); + if (best_bot_d[31] > ((bot_take1 != 0) ? bot_d1 : bot_d0)) { + best_bot_d[31] = ((bot_take1 != 0) ? bot_d1 : bot_d0); + best_bot_i[31] = ((bot_take1 != 0) ? db_idx1 : db_idx0); + #pragma unroll + for (int kk_3 = 30; kk_3 >= 0; kk_3--) { + float lower0_d_1 = best_bot_d[kk_3 + 1]; + int lower0_i_1 = best_bot_i[kk_3 + 1]; + float upper0_d_1 = best_bot_d[kk_3]; + int upper0_i_1 = best_bot_i[kk_3]; + int swap0_up_1 = ((lower0_d_1 < upper0_d_1) ? 1 : 0); + best_bot_d[kk_3] = ((swap0_up_1 != 0) ? lower0_d_1 : upper0_d_1); + best_bot_i[kk_3] = ((swap0_up_1 != 0) ? lower0_i_1 : upper0_i_1); + best_bot_d[kk_3 + 1] = ((swap0_up_1 != 0) ? upper0_d_1 : lower0_d_1); + best_bot_i[kk_3 + 1] = ((swap0_up_1 != 0) ? upper0_i_1 : lower0_i_1); + } + if (best_bot_d[31] > ((bot_take1 != 0) ? bot_d0 : bot_d1)) { + best_bot_d[31] = ((bot_take1 != 0) ? bot_d0 : bot_d1); + best_bot_i[31] = ((bot_take1 != 0) ? db_idx0 : db_idx1); + #pragma unroll + for (int kk_4 = 30; kk_4 >= 0; kk_4--) { + float lower1_d_1 = best_bot_d[kk_4 + 1]; + int lower1_i_1 = best_bot_i[kk_4 + 1]; + float upper1_d_1 = best_bot_d[kk_4]; + int upper1_i_1 = best_bot_i[kk_4]; + int swap1_up_1 = ((lower1_d_1 < upper1_d_1) ? 1 : 0); + best_bot_d[kk_4] = ((swap1_up_1 != 0) ? lower1_d_1 : upper1_d_1); + best_bot_i[kk_4] = ((swap1_up_1 != 0) ? lower1_i_1 : upper1_i_1); + best_bot_d[kk_4 + 1] = ((swap1_up_1 != 0) ? upper1_d_1 : lower1_d_1); + best_bot_i[kk_4 + 1] = ((swap1_up_1 != 0) ? upper1_i_1 : lower1_i_1); + } + } + } + } + } + int top_slot_base = (row_top * 4 + slot) * TOP_K_MAX; + int bot_slot_base = (row_bot * 4 + slot) * TOP_K_MAX; + #pragma unroll + for (int kk_5 = 0; kk_5 < TOP_K_MAX; kk_5++) { + smem_local_d[top_slot_base + kk_5] = best_top_d[kk_5]; + smem_local_i[top_slot_base + kk_5] = best_top_i[kk_5]; + smem_local_d[bot_slot_base + kk_5] = best_bot_d[kk_5]; + smem_local_i[bot_slot_base + kk_5] = best_bot_i[kk_5]; + } + asm volatile("barrier.sync 8, %0;" :: "r"(32)); + if (tid < ROWS_COVERED) { + int row = tid; + int q_idx = off_q + row; + float head_d[4]; + int head_i[4]; + int head_k[4]; + #pragma unroll + for (int slot_idx = 0; slot_idx < 4; slot_idx++) { + int local_base = (row * 4 + slot_idx) * TOP_K_MAX; + head_k[slot_idx] = 0; + head_d[slot_idx] = smem_local_d[local_base]; + head_i[slot_idx] = smem_local_i[local_base]; + } + int out_base = ((split_idx * B + batch_idx) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float winner_d = head_d[0]; + int winner_i = head_i[0]; + int winner_slot = 0; + #pragma unroll + for (int slot_idx_1 = 1; slot_idx_1 < 4; slot_idx_1++) { + float cand_d = head_d[slot_idx_1]; + int take = ((cand_d < winner_d) ? 1 : 0); + winner_d = ((take != 0) ? cand_d : winner_d); + winner_i = ((take != 0) ? head_i[slot_idx_1] : winner_i); + winner_slot = ((take != 0) ? slot_idx_1 : winner_slot); + } + if (q_idx < Q && out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = winner_d; + *((int*)(partial_indices + (out_base + out_k))) = winner_i; + } + #pragma unroll + for (int slot_idx_2 = 0; slot_idx_2 < 4; slot_idx_2++) { + if (winner_slot == slot_idx_2) { + int next_head = head_k[slot_idx_2] + 1; + head_k[slot_idx_2] = next_head; + head_d[slot_idx_2] = 3.4e+38f; + head_i[slot_idx_2] = -1; + if (next_head < TOP_K_MAX) { + int local_base_1 = (row * 4 + slot_idx_2) * TOP_K_MAX; + head_d[slot_idx_2] = smem_local_d[local_base_1 + next_head]; + head_i[slot_idx_2] = smem_local_i[local_base_1 + next_head]; + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(32)); + } + } + // ---- Role: load ---- + } else if (warp == 1) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 1) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx_1 = bid; work_idx_1 < total_work; work_idx_1 += num_bids) { + int split_idx_1 = work_idx_1 % (unsigned int)split_count; + int query_work_1 = work_idx_1 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tiles; + int q_tile_1 = query_work_1 % num_q_tiles; + int off_q_1 = q_tile_1 * BLOCK_Q; + int global_q = batch_idx_1 * Q + off_q_1; + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + mbarrier_arrive_expect_tx(query_full_addr, 16384); + tma_3d_gmem2smem(smem_query_addr, tmap_query, 0, global_q, 0, query_full_addr); + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int off_m = db_tile_1 * BLOCK_M; + int global_m = batch_idx_1 * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + mbarrier_arrive_expect_tx(database_full_addr, 16384); + tma_3d_gmem2smem(smem_database_addr, tmap_database, 0, global_m, 0, database_full_addr); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 2) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = bid; work_idx_2 < total_work; work_idx_2 += num_bids) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = make_warp_uniform((smem_query_addr >> 4) & 0x3FFF); + int _mma_b_lo_0 = make_warp_uniform((smem_database_addr >> 4) & 0x3FFF); + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 68158608;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 506;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit(score_full_addr); + elect_commit(database_empty_addr); + } + elect_commit(query_empty_addr); + } + } + } + + // Cleanup + __syncthreads(); // barrier before TMEM dealloc + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::1.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(64)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::1.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0200.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0200.cu new file mode 100644 index 00000000..5f1d29fb --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0200.cu @@ -0,0 +1,642 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 64 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 16384 +#define SMEM_SMEM_QUERY_STRIDE 16384 +#define SMEM_SMEM_DATABASE_OFF 17408 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_LOCAL_D_OFF 34048 +#define SMEM_SMEM_LOCAL_D_STAGE_BYTES 16384 +#define SMEM_SMEM_LOCAL_D_STRIDE 16384 +#define SMEM_SMEM_LOCAL_I_OFF 50432 +#define SMEM_SMEM_LOCAL_I_STAGE_BYTES 16384 +#define SMEM_SMEM_LOCAL_I_STRIDE 16384 +#define SMEM_TOTAL 66816 +#define THREADS 128 +#define BLOCK_Q 64 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_MAX 32 +#define ROWS_COVERED 32 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "tcgen05.mma.cta_group::1.kind::f16 [%0], %1, %2, %3, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, %3, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit(int mbar_addr) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];\n\t" + "}\n" + :: "r"(mbar_addr)); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x32(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x32.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15," + " %16, %17, %18, %19, %20, %21, %22, %23," + " %24, %25, %26, %27, %28, %29, %30, %31}, [%32];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]), + "=f"(dst[16]), "=f"(dst[17]), "=f"(dst[18]), "=f"(dst[19]), + "=f"(dst[20]), "=f"(dst[21]), "=f"(dst[22]), "=f"(dst[23]), + "=f"(dst[24]), "=f"(dst[25]), "=f"(dst[26]), "=f"(dst[27]), + "=f"(dst[28]), "=f"(dst[29]), "=f"(dst[30]), "=f"(dst[31]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ float max_noftz(float a, float b) { + float c; + asm("max.f32 %0, %1, %2;" : "=f"(c) : "f"(a), "f"(b)); + return c; +} + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cta.global" + ".mbarrier::complete_tx::bytes" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit(int mbar_addr) { + asm volatile( + "tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ uint32_t make_warp_uniform(uint32_t val) { + uint32_t result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1f, 0xffffffff;" + : "=r"(result) : "r"(val)); + return result; +} + +extern "C" { + +__global__ __launch_bounds__(128, 1) void +kernel_knn_build_rag_microbucket_k32rowld1warp_0077_v1_stage1_q32_k32_m64_rowld2_q32rowld2_0077_v1(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tiles, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 17408); + const int smem_database_addr = smem + 17408; + float* smem_local_d = reinterpret_cast(smem_raw + 34048); + const int smem_local_d_addr = smem + 34048; + int* smem_local_i = reinterpret_cast(smem_raw + 50432); + const int smem_local_i_addr = smem + 50432; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 0, 1, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 16, 1, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 40, 2, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (64 columns, 64 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::1.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(64) : "memory"); + } + + __syncthreads(); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: compute ---- + if (warp <= 1) { + { // compute_main + int warp_id_in_role = (warp - 0); + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx = bid; work_idx < total_work; work_idx += num_bids) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tiles; + int q_tile = query_work % num_q_tiles; + int off_q = q_tile * BLOCK_Q; + int tmem_row_origin = warp_id_in_role * 32; + int logical_row_origin = warp_id_in_role * 16; + int row_top = logical_row_origin + lane / 4; + int row_bot = row_top + 8; + int lane_col = lane % 4; + int slot = lane_col; + int q_top = off_q + row_top; + int q_bot = off_q + row_bot; + int valid_top = ((q_top < Q) ? 1 : 0); + int valid_bot = ((q_bot < Q) ? 1 : 0); + float q_sq_top = 0.0f; + float q_sq_bot = 0.0f; + if (valid_top != 0) { + q_sq_top = query_sq[batch_idx * Q + q_top]; + } + if (valid_bot != 0) { + q_sq_bot = query_sq[batch_idx * Q + q_bot]; + } + float best_top_d[TOP_K_MAX]; + float best_bot_d[TOP_K_MAX]; + int best_top_i[TOP_K_MAX]; + int best_bot_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_top_d[kk] = 3.4e+38f; + best_bot_d[kk] = 3.4e+38f; + best_top_i[kk] = -1; + best_bot_i[kk] = -1; + } + int db_tile_start = split_idx * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int db_start = db_tile * BLOCK_M; + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(tmem_row_origin << 16); + float _tmem_load_0[32]; + asm volatile( + "tcgen05.ld.sync.aligned.16x256b.x8.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31}, [%32];" + : "=r"(*reinterpret_cast(&_tmem_load_0[0])), "=r"(*reinterpret_cast(&_tmem_load_0[1])), "=r"(*reinterpret_cast(&_tmem_load_0[2])), "=r"(*reinterpret_cast(&_tmem_load_0[3])), "=r"(*reinterpret_cast(&_tmem_load_0[4])), "=r"(*reinterpret_cast(&_tmem_load_0[5])), "=r"(*reinterpret_cast(&_tmem_load_0[6])), "=r"(*reinterpret_cast(&_tmem_load_0[7])), "=r"(*reinterpret_cast(&_tmem_load_0[8])), "=r"(*reinterpret_cast(&_tmem_load_0[9])), "=r"(*reinterpret_cast(&_tmem_load_0[10])), "=r"(*reinterpret_cast(&_tmem_load_0[11])), "=r"(*reinterpret_cast(&_tmem_load_0[12])), "=r"(*reinterpret_cast(&_tmem_load_0[13])), "=r"(*reinterpret_cast(&_tmem_load_0[14])), "=r"(*reinterpret_cast(&_tmem_load_0[15])), "=r"(*reinterpret_cast(&_tmem_load_0[16])), "=r"(*reinterpret_cast(&_tmem_load_0[17])), "=r"(*reinterpret_cast(&_tmem_load_0[18])), "=r"(*reinterpret_cast(&_tmem_load_0[19])), "=r"(*reinterpret_cast(&_tmem_load_0[20])), "=r"(*reinterpret_cast(&_tmem_load_0[21])), "=r"(*reinterpret_cast(&_tmem_load_0[22])), "=r"(*reinterpret_cast(&_tmem_load_0[23])), "=r"(*reinterpret_cast(&_tmem_load_0[24])), "=r"(*reinterpret_cast(&_tmem_load_0[25])), "=r"(*reinterpret_cast(&_tmem_load_0[26])), "=r"(*reinterpret_cast(&_tmem_load_0[27])), "=r"(*reinterpret_cast(&_tmem_load_0[28])), "=r"(*reinterpret_cast(&_tmem_load_0[29])), "=r"(*reinterpret_cast(&_tmem_load_0[30])), "=r"(*reinterpret_cast(&_tmem_load_0[31])) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + if (elect_sync()) { + mbarrier_arrive(score_empty_addr); + } + #pragma unroll + for (int repeat = 0; repeat < 8; repeat++) { + const int reg_base = repeat * 4; + int col_base = repeat * 8 + lane_col * 2; + int db_idx0 = db_start + col_base; + int db_idx1 = db_idx0 + 1; + float top_d0 = 3.4e+38f; + float top_d1 = 3.4e+38f; + if (valid_top != 0 && db_idx0 < M) { + float _max_0 = max_noftz(q_sq_top + database_sq[batch_idx * M + db_idx0] - 2.0f * _tmem_load_0[reg_base], 0.0f); + top_d0 = _max_0; + } + if (valid_top != 0 && db_idx1 < M) { + float _max_1 = max_noftz(q_sq_top + database_sq[batch_idx * M + db_idx1] - 2.0f * _tmem_load_0[reg_base + 1], 0.0f); + top_d1 = _max_1; + } + int top_take1 = ((top_d1 < top_d0) ? 1 : 0); + if (best_top_d[31] > ((top_take1 != 0) ? top_d1 : top_d0)) { + best_top_d[31] = ((top_take1 != 0) ? top_d1 : top_d0); + best_top_i[31] = ((top_take1 != 0) ? db_idx1 : db_idx0); + #pragma unroll + for (int kk_1 = 30; kk_1 >= 0; kk_1--) { + float lower0_d = best_top_d[kk_1 + 1]; + int lower0_i = best_top_i[kk_1 + 1]; + float upper0_d = best_top_d[kk_1]; + int upper0_i = best_top_i[kk_1]; + int swap0_up = ((lower0_d < upper0_d) ? 1 : 0); + best_top_d[kk_1] = ((swap0_up != 0) ? lower0_d : upper0_d); + best_top_i[kk_1] = ((swap0_up != 0) ? lower0_i : upper0_i); + best_top_d[kk_1 + 1] = ((swap0_up != 0) ? upper0_d : lower0_d); + best_top_i[kk_1 + 1] = ((swap0_up != 0) ? upper0_i : lower0_i); + } + if (best_top_d[31] > ((top_take1 != 0) ? top_d0 : top_d1)) { + best_top_d[31] = ((top_take1 != 0) ? top_d0 : top_d1); + best_top_i[31] = ((top_take1 != 0) ? db_idx0 : db_idx1); + #pragma unroll + for (int kk_2 = 30; kk_2 >= 0; kk_2--) { + float lower1_d = best_top_d[kk_2 + 1]; + int lower1_i = best_top_i[kk_2 + 1]; + float upper1_d = best_top_d[kk_2]; + int upper1_i = best_top_i[kk_2]; + int swap1_up = ((lower1_d < upper1_d) ? 1 : 0); + best_top_d[kk_2] = ((swap1_up != 0) ? lower1_d : upper1_d); + best_top_i[kk_2] = ((swap1_up != 0) ? lower1_i : upper1_i); + best_top_d[kk_2 + 1] = ((swap1_up != 0) ? upper1_d : lower1_d); + best_top_i[kk_2 + 1] = ((swap1_up != 0) ? upper1_i : lower1_i); + } + } + } + float bot_d0 = 3.4e+38f; + float bot_d1 = 3.4e+38f; + if (valid_bot != 0 && db_idx0 < M) { + float _max_2 = max_noftz(q_sq_bot + database_sq[batch_idx * M + db_idx0] - 2.0f * _tmem_load_0[reg_base + 2], 0.0f); + bot_d0 = _max_2; + } + if (valid_bot != 0 && db_idx1 < M) { + float _max_3 = max_noftz(q_sq_bot + database_sq[batch_idx * M + db_idx1] - 2.0f * _tmem_load_0[reg_base + 3], 0.0f); + bot_d1 = _max_3; + } + int bot_take1 = ((bot_d1 < bot_d0) ? 1 : 0); + if (best_bot_d[31] > ((bot_take1 != 0) ? bot_d1 : bot_d0)) { + best_bot_d[31] = ((bot_take1 != 0) ? bot_d1 : bot_d0); + best_bot_i[31] = ((bot_take1 != 0) ? db_idx1 : db_idx0); + #pragma unroll + for (int kk_3 = 30; kk_3 >= 0; kk_3--) { + float lower0_d_1 = best_bot_d[kk_3 + 1]; + int lower0_i_1 = best_bot_i[kk_3 + 1]; + float upper0_d_1 = best_bot_d[kk_3]; + int upper0_i_1 = best_bot_i[kk_3]; + int swap0_up_1 = ((lower0_d_1 < upper0_d_1) ? 1 : 0); + best_bot_d[kk_3] = ((swap0_up_1 != 0) ? lower0_d_1 : upper0_d_1); + best_bot_i[kk_3] = ((swap0_up_1 != 0) ? lower0_i_1 : upper0_i_1); + best_bot_d[kk_3 + 1] = ((swap0_up_1 != 0) ? upper0_d_1 : lower0_d_1); + best_bot_i[kk_3 + 1] = ((swap0_up_1 != 0) ? upper0_i_1 : lower0_i_1); + } + if (best_bot_d[31] > ((bot_take1 != 0) ? bot_d0 : bot_d1)) { + best_bot_d[31] = ((bot_take1 != 0) ? bot_d0 : bot_d1); + best_bot_i[31] = ((bot_take1 != 0) ? db_idx0 : db_idx1); + #pragma unroll + for (int kk_4 = 30; kk_4 >= 0; kk_4--) { + float lower1_d_1 = best_bot_d[kk_4 + 1]; + int lower1_i_1 = best_bot_i[kk_4 + 1]; + float upper1_d_1 = best_bot_d[kk_4]; + int upper1_i_1 = best_bot_i[kk_4]; + int swap1_up_1 = ((lower1_d_1 < upper1_d_1) ? 1 : 0); + best_bot_d[kk_4] = ((swap1_up_1 != 0) ? lower1_d_1 : upper1_d_1); + best_bot_i[kk_4] = ((swap1_up_1 != 0) ? lower1_i_1 : upper1_i_1); + best_bot_d[kk_4 + 1] = ((swap1_up_1 != 0) ? upper1_d_1 : lower1_d_1); + best_bot_i[kk_4 + 1] = ((swap1_up_1 != 0) ? upper1_i_1 : lower1_i_1); + } + } + } + } + } + int top_slot_base = (row_top * 4 + slot) * TOP_K_MAX; + int bot_slot_base = (row_bot * 4 + slot) * TOP_K_MAX; + #pragma unroll + for (int kk_5 = 0; kk_5 < TOP_K_MAX; kk_5++) { + smem_local_d[top_slot_base + kk_5] = best_top_d[kk_5]; + smem_local_i[top_slot_base + kk_5] = best_top_i[kk_5]; + smem_local_d[bot_slot_base + kk_5] = best_bot_d[kk_5]; + smem_local_i[bot_slot_base + kk_5] = best_bot_i[kk_5]; + } + asm volatile("barrier.sync 8, %0;" :: "r"(64)); + if (tid < ROWS_COVERED) { + int row = tid; + int q_idx = off_q + row; + float head_d[4]; + int head_i[4]; + int head_k[4]; + #pragma unroll + for (int slot_idx = 0; slot_idx < 4; slot_idx++) { + int local_base = (row * 4 + slot_idx) * TOP_K_MAX; + head_k[slot_idx] = 0; + head_d[slot_idx] = smem_local_d[local_base]; + head_i[slot_idx] = smem_local_i[local_base]; + } + int out_base = ((split_idx * B + batch_idx) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float winner_d = head_d[0]; + int winner_i = head_i[0]; + int winner_slot = 0; + #pragma unroll + for (int slot_idx_1 = 1; slot_idx_1 < 4; slot_idx_1++) { + float cand_d = head_d[slot_idx_1]; + int take = ((cand_d < winner_d) ? 1 : 0); + winner_d = ((take != 0) ? cand_d : winner_d); + winner_i = ((take != 0) ? head_i[slot_idx_1] : winner_i); + winner_slot = ((take != 0) ? slot_idx_1 : winner_slot); + } + if (q_idx < Q && out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = winner_d; + *((int*)(partial_indices + (out_base + out_k))) = winner_i; + } + #pragma unroll + for (int slot_idx_2 = 0; slot_idx_2 < 4; slot_idx_2++) { + if (winner_slot == slot_idx_2) { + int next_head = head_k[slot_idx_2] + 1; + head_k[slot_idx_2] = next_head; + head_d[slot_idx_2] = 3.4e+38f; + head_i[slot_idx_2] = -1; + if (next_head < TOP_K_MAX) { + int local_base_1 = (row * 4 + slot_idx_2) * TOP_K_MAX; + head_d[slot_idx_2] = smem_local_d[local_base_1 + next_head]; + head_i[slot_idx_2] = smem_local_i[local_base_1 + next_head]; + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(64)); + } + } + // ---- Role: load ---- + } else if (warp == 2) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 2) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx_1 = bid; work_idx_1 < total_work; work_idx_1 += num_bids) { + int split_idx_1 = work_idx_1 % (unsigned int)split_count; + int query_work_1 = work_idx_1 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tiles; + int q_tile_1 = query_work_1 % num_q_tiles; + int off_q_1 = q_tile_1 * BLOCK_Q; + int global_q = batch_idx_1 * Q + off_q_1; + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + mbarrier_arrive_expect_tx(query_full_addr, 16384); + tma_3d_gmem2smem(smem_query_addr, tmap_query, 0, global_q, 0, query_full_addr); + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int off_m = db_tile_1 * BLOCK_M; + int global_m = batch_idx_1 * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + mbarrier_arrive_expect_tx(database_full_addr, 16384); + tma_3d_gmem2smem(smem_database_addr, tmap_database, 0, global_m, 0, database_full_addr); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 3) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = bid; work_idx_2 < total_work; work_idx_2 += num_bids) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = make_warp_uniform((smem_query_addr >> 4) & 0x3FFF); + int _mma_b_lo_0 = make_warp_uniform((smem_database_addr >> 4) & 0x3FFF); + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 68158608;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 506;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit(score_full_addr); + elect_commit(database_empty_addr); + } + elect_commit(query_empty_addr); + } + } + } + + // Cleanup + __syncthreads(); // barrier before TMEM dealloc + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::1.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(64)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::1.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0201.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0201.cu new file mode 100644 index 00000000..aa3d05e4 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0201.cu @@ -0,0 +1,123 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 32 +#define TOP_K_MAX 96 +#define SPLIT_COUNT 2 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_k96_merge_s8_unordered_chunkprefill_k96over64s2chunkprefill_f9d1(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 32 + tid; + int stride = num_bids * 32; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + float best_d[96]; + int best_i[96]; + #pragma unroll + for (int cand_k = 0; cand_k < 96; cand_k++) { + best_d[cand_k] = partial_dists[base_row + cand_k]; + best_i[cand_k] = partial_indices[base_row + cand_k]; + } + float chunk_worst_d[12]; + int chunk_worst_pos[12]; + #pragma unroll + for (int chunk = 0; chunk < 12; chunk++) { + int chunk_base = chunk * 8; + chunk_worst_d[chunk] = best_d[chunk_base]; + chunk_worst_pos[chunk] = chunk_base; + #pragma unroll + for (int offset = 1; offset < 8; offset++) { + int scan_pos = chunk_base + offset; + if (best_d[scan_pos] > chunk_worst_d[chunk]) { + chunk_worst_d[chunk] = best_d[scan_pos]; + chunk_worst_pos[chunk] = scan_pos; + } + } + } + float worst_d = chunk_worst_d[0]; + int worst_pos = chunk_worst_pos[0]; + int worst_chunk = 0; + #pragma unroll + for (int chunk_1 = 1; chunk_1 < 12; chunk_1++) { + if (worst_d < chunk_worst_d[chunk_1]) { + worst_d = chunk_worst_d[chunk_1]; + worst_pos = chunk_worst_pos[chunk_1]; + worst_chunk = chunk_1; + } + } + #pragma unroll + for (int split_idx = 1; split_idx < SPLIT_COUNT; split_idx++) { + int partial_base = base_row + split_idx * split_stride; + #pragma unroll + for (int cand_k_1 = 0; cand_k_1 < 96; cand_k_1++) { + float cand_d = partial_dists[partial_base + cand_k_1]; + int cand_i = partial_indices[partial_base + cand_k_1]; + if (cand_d < worst_d) { + best_d[worst_pos] = cand_d; + best_i[worst_pos] = cand_i; + int refresh_base = worst_chunk * 8; + chunk_worst_d[worst_chunk] = best_d[refresh_base]; + chunk_worst_pos[worst_chunk] = refresh_base; + #pragma unroll + for (int offset_1 = 1; offset_1 < 8; offset_1++) { + int scan_pos_1 = refresh_base + offset_1; + if (best_d[scan_pos_1] > chunk_worst_d[worst_chunk]) { + chunk_worst_d[worst_chunk] = best_d[scan_pos_1]; + chunk_worst_pos[worst_chunk] = scan_pos_1; + } + } + worst_d = chunk_worst_d[0]; + worst_pos = chunk_worst_pos[0]; + worst_chunk = 0; + #pragma unroll + for (int chunk_2 = 1; chunk_2 < 12; chunk_2++) { + if (worst_d < chunk_worst_d[chunk_2]) { + worst_d = chunk_worst_d[chunk_2]; + worst_pos = chunk_worst_pos[chunk_2]; + worst_chunk = chunk_2; + } + } + } + } + } + #pragma unroll + for (int out_k = 0; out_k < 96; out_k++) { + *((float*)(out_dists + (base_row + out_k))) = best_d[out_k]; + *((int*)(out_indices + (base_row + out_k))) = best_i[out_k]; + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0202.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0202.cu new file mode 100644 index 00000000..326efe01 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0202.cu @@ -0,0 +1,777 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 128 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_STRIDE 32768 +#define SMEM_SMEM_DATABASE_OFF 33792 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_DATABASE_SQ_OFF 50176 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 50432 +#define THREADS 192 +#define BLOCK_Q 128 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_MAX 96 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16_cta2( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + ".reg .b32 m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "tcgen05.mma.cta_group::2.kind::f16 [%0], %1, %2, %3, {m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step_cg2( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, %3, " + "{m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], %1;\n\t" + "}\n" + :: "r"(mbar_addr), "h"(cta_mask) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x16(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x16.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15}, [%16];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ uint32_t smem_addr(const void* ptr) { + uint32_t addr; + asm("{\n\t" + ".reg .u64 u64addr;\n\t" + "cvta.to.shared.u64 u64addr, %1;\n\t" + "cvt.u32.u64 %0, u64addr;\n\t" + "}\n" : "=r"(addr) : "l"(ptr)); + return addr; +} + + +__device__ __forceinline__ uint32_t mapa_to_rank(uint32_t local_addr, uint32_t rank) { + uint32_t remote; + asm volatile("mapa.shared::cluster.u32 %0, %1, %2;" + : "=r"(remote) : "r"(local_addr), "r"(rank)); + return remote; +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem_cta2( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global" + ".mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .b16 lo, hi;\n\t" + "mov.b32 {lo, hi}, %1;\n\t" + "tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], lo;\n\t" + "}\n" + :: "r"(mbar_addr), "r"((uint32_t)cta_mask) : "memory"); +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_k96_stage1_sort4_prefill_q1024_k96over64sort4prefillq1024_8c56(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tile_pairs, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + const unsigned int clusters_x = gridDim.x / 2; + const unsigned int cluster_id = ((blockIdx.z * gridDim.y + blockIdx.y) * clusters_x) + blockIdx.x / 2; + const unsigned int num_clusters = clusters_x * gridDim.y * gridDim.z; + + int cta_rank; + asm volatile("mov.b32 %0, %%cluster_ctarank;" : "=r"(cta_rank)); + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 33792); + const int smem_database_addr = smem + 33792; + float* smem_database_sq = reinterpret_cast(smem_raw + 50176); + const int smem_database_sq_addr = smem + 50176; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 0, 2, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 16, 2, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=8 + mbarrier_init_pred(smem + 40, 8, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (128 columns, 128 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::2.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(128) : "memory"); + } + + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: compute ---- + if (warp <= 3) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx = cluster_id; work_idx < total_work; work_idx += num_clusters) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tile_pairs; + int q_tile_pair = query_work % num_q_tile_pairs; + int q_tile = q_tile_pair * 2 + cta_rank; + int off_q = q_tile * BLOCK_Q; + int q_idx = off_q + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx * Q + q_idx]; + } + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + float chunk_worst_d[24]; + int chunk_worst_pos[24]; + #pragma unroll + for (int chunk = 0; chunk < 24; chunk++) { + int chunk_base = chunk * 4; + chunk_worst_d[chunk] = 3.4e+38f; + chunk_worst_pos[chunk] = chunk_base; + } + float worst_d = 3.4e+38f; + int worst_pos = 0; + int worst_chunk = 0; + int fill_count = 0; + int db_tile_start = split_idx * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int db_start = db_tile * BLOCK_M; + int db_sq_idx = db_start + (warp % 4 * 32 + lane); + if (warp % 4 * 32 + lane < BLOCK_M) { + if (db_sq_idx < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx * M + db_sq_idx]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 0.0f; + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(cta_rank * BLOCK_Q + (warp % 4 * 32 << 16) << 16); + float _tmem_load_0[64]; + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x64.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31, %32, %33, %34, %35, %36, %37, %38, %39, %40, %41, %42, %43, %44, %45, %46, %47, %48, %49, %50, %51, %52, %53, %54, %55, %56, %57, %58, %59, %60, %61, %62, %63}, [%64];" + : "=f"(_tmem_load_0[0]), "=f"(_tmem_load_0[1]), "=f"(_tmem_load_0[2]), "=f"(_tmem_load_0[3]), "=f"(_tmem_load_0[4]), "=f"(_tmem_load_0[5]), "=f"(_tmem_load_0[6]), "=f"(_tmem_load_0[7]), "=f"(_tmem_load_0[8]), "=f"(_tmem_load_0[9]), "=f"(_tmem_load_0[10]), "=f"(_tmem_load_0[11]), "=f"(_tmem_load_0[12]), "=f"(_tmem_load_0[13]), "=f"(_tmem_load_0[14]), "=f"(_tmem_load_0[15]), "=f"(_tmem_load_0[16]), "=f"(_tmem_load_0[17]), "=f"(_tmem_load_0[18]), "=f"(_tmem_load_0[19]), "=f"(_tmem_load_0[20]), "=f"(_tmem_load_0[21]), "=f"(_tmem_load_0[22]), "=f"(_tmem_load_0[23]), "=f"(_tmem_load_0[24]), "=f"(_tmem_load_0[25]), "=f"(_tmem_load_0[26]), "=f"(_tmem_load_0[27]), "=f"(_tmem_load_0[28]), "=f"(_tmem_load_0[29]), "=f"(_tmem_load_0[30]), "=f"(_tmem_load_0[31]), "=f"(_tmem_load_0[32]), "=f"(_tmem_load_0[33]), "=f"(_tmem_load_0[34]), "=f"(_tmem_load_0[35]), "=f"(_tmem_load_0[36]), "=f"(_tmem_load_0[37]), "=f"(_tmem_load_0[38]), "=f"(_tmem_load_0[39]), "=f"(_tmem_load_0[40]), "=f"(_tmem_load_0[41]), "=f"(_tmem_load_0[42]), "=f"(_tmem_load_0[43]), "=f"(_tmem_load_0[44]), "=f"(_tmem_load_0[45]), "=f"(_tmem_load_0[46]), "=f"(_tmem_load_0[47]), "=f"(_tmem_load_0[48]), "=f"(_tmem_load_0[49]), "=f"(_tmem_load_0[50]), "=f"(_tmem_load_0[51]), "=f"(_tmem_load_0[52]), "=f"(_tmem_load_0[53]), "=f"(_tmem_load_0[54]), "=f"(_tmem_load_0[55]), "=f"(_tmem_load_0[56]), "=f"(_tmem_load_0[57]), "=f"(_tmem_load_0[58]), "=f"(_tmem_load_0[59]), "=f"(_tmem_load_0[60]), "=f"(_tmem_load_0[61]), "=f"(_tmem_load_0[62]), "=f"(_tmem_load_0[63]) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + if (elect_sync()) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cluster.b64 _, [%0];" + :: "r"((score_empty_addr) & 0xFEFFFFFF) : "memory"); + } + if (valid_q != 0) { + #pragma unroll 1 + for (int col_base = 0; col_base < 64; col_base += 4) { + float dist_vec[4]; + dist_vec[0] = _tmem_load_0[col_base]; + dist_vec[1] = _tmem_load_0[col_base + 1]; + dist_vec[2] = _tmem_load_0[col_base + 2]; + dist_vec[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec[4]; + db_sq_vec[0] = smem_database_sq[col_base]; + db_sq_vec[1] = smem_database_sq[col_base + 1]; + db_sq_vec[2] = smem_database_sq[col_base + 2]; + db_sq_vec[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec)[_la], reinterpret_cast(db_sq_vec)[_la]); + float group_min = _t0[0]; + if (_t0[1] < group_min) { + group_min = _t0[1]; + } + if (_t0[2] < group_min) { + group_min = _t0[2]; + } + if (_t0[3] < group_min) { + group_min = _t0[3]; + } + if (group_min < worst_d) { + float sort_d0 = _t0[0]; + float sort_d1 = _t0[1]; + float sort_d2 = _t0[2]; + float sort_d3 = _t0[3]; + int sort_col0 = 0; + int sort_col1 = 1; + int sort_col2 = 2; + int sort_col3 = 3; + float tmp_d = 0.0f; + int tmp_col = 0; + if (sort_d1 < sort_d0) { + tmp_d = sort_d0; + sort_d0 = sort_d1; + sort_d1 = tmp_d; + tmp_col = sort_col0; + sort_col0 = sort_col1; + sort_col1 = tmp_col; + } + if (sort_d3 < sort_d2) { + tmp_d = sort_d2; + sort_d2 = sort_d3; + sort_d3 = tmp_d; + tmp_col = sort_col2; + sort_col2 = sort_col3; + sort_col3 = tmp_col; + } + if (sort_d2 < sort_d0) { + tmp_d = sort_d0; + sort_d0 = sort_d2; + sort_d2 = tmp_d; + tmp_col = sort_col0; + sort_col0 = sort_col2; + sort_col2 = tmp_col; + } + if (sort_d3 < sort_d1) { + tmp_d = sort_d1; + sort_d1 = sort_d3; + sort_d3 = tmp_d; + tmp_col = sort_col1; + sort_col1 = sort_col3; + sort_col3 = tmp_col; + } + if (sort_d2 < sort_d1) { + tmp_d = sort_d1; + sort_d1 = sort_d2; + sort_d2 = tmp_d; + tmp_col = sort_col1; + sort_col1 = sort_col2; + sort_col2 = tmp_col; + } + #pragma unroll + for (int visit = 0; visit < 4; visit++) { + int vec_col = sort_col0; + float dist = sort_d0; + if (visit == 1) { + vec_col = sort_col1; + dist = sort_d1; + } + if (visit == 2) { + vec_col = sort_col2; + dist = sort_d2; + } + if (visit == 3) { + vec_col = sort_col3; + dist = sort_d3; + } + int db_idx = db_start + col_base + vec_col; + if (fill_count < TOP_K_MAX) { + if (db_idx < M) { + best_d[fill_count] = dist; + best_i[fill_count] = db_idx; + fill_count += 1; + if (fill_count == TOP_K_MAX) { + #pragma unroll + for (int chunk_1 = 0; chunk_1 < 24; chunk_1++) { + int chunk_base_1 = chunk_1 * 4; + chunk_worst_d[chunk_1] = best_d[chunk_base_1]; + chunk_worst_pos[chunk_1] = chunk_base_1; + #pragma unroll + for (int offset = 1; offset < 4; offset++) { + int scan_pos = chunk_base_1 + offset; + if (best_d[scan_pos] > chunk_worst_d[chunk_1]) { + chunk_worst_d[chunk_1] = best_d[scan_pos]; + chunk_worst_pos[chunk_1] = scan_pos; + } + } + } + worst_d = chunk_worst_d[0]; + worst_pos = chunk_worst_pos[0]; + worst_chunk = 0; + #pragma unroll + for (int chunk_2 = 1; chunk_2 < 24; chunk_2++) { + if (worst_d < chunk_worst_d[chunk_2]) { + worst_d = chunk_worst_d[chunk_2]; + worst_pos = chunk_worst_pos[chunk_2]; + worst_chunk = chunk_2; + } + } + } + } + } else { + if (dist >= worst_d) { + break; + } + if (db_idx < M) { + best_d[worst_pos] = dist; + best_i[worst_pos] = db_idx; + int refresh_base = worst_chunk * 4; + chunk_worst_d[worst_chunk] = best_d[refresh_base]; + chunk_worst_pos[worst_chunk] = refresh_base; + #pragma unroll + for (int offset_1 = 1; offset_1 < 4; offset_1++) { + int scan_pos_1 = refresh_base + offset_1; + if (best_d[scan_pos_1] > chunk_worst_d[worst_chunk]) { + chunk_worst_d[worst_chunk] = best_d[scan_pos_1]; + chunk_worst_pos[worst_chunk] = scan_pos_1; + } + } + worst_d = chunk_worst_d[0]; + worst_pos = chunk_worst_pos[0]; + worst_chunk = 0; + #pragma unroll + for (int chunk_3 = 1; chunk_3 < 24; chunk_3++) { + if (worst_d < chunk_worst_d[chunk_3]) { + worst_d = chunk_worst_d[chunk_3]; + worst_pos = chunk_worst_pos[chunk_3]; + worst_chunk = chunk_3; + } + } + } + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + if (valid_q != 0) { + int out_base = ((split_idx * B + batch_idx) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + *((float*)(partial_dists + (out_base + out_k))) = best_d[out_k]; + *((int*)(partial_indices + (out_base + out_k))) = best_i[out_k]; + } + } + } + } + // ---- Role: load ---- + } else if (warp == 4) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 4) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx_1 = cluster_id; work_idx_1 < total_work; work_idx_1 += num_clusters) { + int split_idx_1 = work_idx_1 % (unsigned int)split_count; + int query_work_1 = work_idx_1 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tile_pairs; + int q_tile_pair_1 = query_work_1 % num_q_tile_pairs; + int q_tile_1 = q_tile_pair_1 * 2 + cta_rank; + int off_q_1 = q_tile_1 * BLOCK_Q; + int global_q = batch_idx_1 * Q + off_q_1; + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((query_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(32768)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_query_addr), "l"(tmap_query), "r"(0), "r"(global_q), "r"(0), + "r"(((query_full_addr) & 0xFEFFFFFF)) : "memory"); + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int off_m = db_tile_1 * BLOCK_M; + int global_m = batch_idx_1 * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((database_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(16384)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_database_addr), "l"(tmap_database), "r"(0), "r"(global_m), "r"(0), + "r"(((database_full_addr) & 0xFEFFFFFF)) : "memory"); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 5) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + if (cta_rank == 0) { + #pragma unroll 1 + for (unsigned int work_idx_2 = cluster_id; work_idx_2 < total_work; work_idx_2 += num_clusters) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = (smem_query_addr >> 4) & 0x3FFF; + int _mma_b_lo_0 = (smem_database_addr >> 4) & 0x3FFF; + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\tmov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 270533776;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit_cg2_multicast(score_full_addr, (uint16_t)(3)); + elect_commit_cg2_multicast(database_empty_addr, (uint16_t)(3)); + } + elect_commit_cg2_multicast(query_empty_addr, (uint16_t)(3)); + } + } + } + } + + // Cleanup + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::2.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(128)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::2.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0203.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0203.cu new file mode 100644 index 00000000..a47846da --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0203.cu @@ -0,0 +1,755 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 64 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 98304 +#define SMEM_SMEM_QUERY_STRIDE 98304 +#define SMEM_SMEM_DATABASE_OFF 99328 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 49152 +#define SMEM_SMEM_DATABASE_STRIDE 49152 +#define SMEM_SMEM_QUERY_LO_OFF 1024 +#define SMEM_SMEM_QUERY_LO_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_LO_STRIDE 32768 +#define SMEM_SMEM_QUERY_MID_OFF 33792 +#define SMEM_SMEM_QUERY_MID_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_MID_STRIDE 32768 +#define SMEM_SMEM_QUERY_HI_OFF 66560 +#define SMEM_SMEM_QUERY_HI_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_HI_STRIDE 32768 +#define SMEM_SMEM_DATABASE_LO_OFF 99328 +#define SMEM_SMEM_DATABASE_LO_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_LO_STRIDE 16384 +#define SMEM_SMEM_DATABASE_MID_OFF 115712 +#define SMEM_SMEM_DATABASE_MID_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_MID_STRIDE 16384 +#define SMEM_SMEM_DATABASE_HI_OFF 132096 +#define SMEM_SMEM_DATABASE_HI_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_HI_STRIDE 16384 +#define SMEM_SMEM_DATABASE_SQ_OFF 148480 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 148736 +#define THREADS 192 +#define BLOCK_Q 128 +#define BLOCK_M 64 +#define TOP_K_MAX 10 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "tcgen05.mma.cta_group::1.kind::f16 [%0], %1, %2, %3, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, %3, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit(int mbar_addr) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];\n\t" + "}\n" + :: "r"(mbar_addr)); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x32(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x32.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15," + " %16, %17, %18, %19, %20, %21, %22, %23," + " %24, %25, %26, %27, %28, %29, %30, %31}, [%32];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]), + "=f"(dst[16]), "=f"(dst[17]), "=f"(dst[18]), "=f"(dst[19]), + "=f"(dst[20]), "=f"(dst[21]), "=f"(dst[22]), "=f"(dst[23]), + "=f"(dst[24]), "=f"(dst[25]), "=f"(dst[26]), "=f"(dst[27]), + "=f"(dst[28]), "=f"(dst[29]), "=f"(dst[30]), "=f"(dst[31]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ float max_noftz(float a, float b) { + float c; + asm("max.f32 %0, %1, %2;" : "=f"(c) : "f"(a), "f"(b)); + return c; +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cta.global" + ".mbarrier::complete_tx::bytes" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit(int mbar_addr) { + asm volatile( + "tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ uint32_t make_warp_uniform(uint32_t val) { + uint32_t result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1f, 0xffffffff;" + : "=r"(result) : "r"(val)); + return result; +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_non128_frontier_8199_d384_stage1(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tiles, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 99328); + const int smem_database_addr = smem + 99328; + __nv_bfloat16* smem_query_lo = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_lo_addr = smem + 1024; + __nv_bfloat16* smem_query_mid = reinterpret_cast<__nv_bfloat16*>(smem_raw + 33792); + const int smem_query_mid_addr = smem + 33792; + __nv_bfloat16* smem_query_hi = reinterpret_cast<__nv_bfloat16*>(smem_raw + 66560); + const int smem_query_hi_addr = smem + 66560; + __nv_bfloat16* smem_database_lo = reinterpret_cast<__nv_bfloat16*>(smem_raw + 99328); + const int smem_database_lo_addr = smem + 99328; + __nv_bfloat16* smem_database_mid = reinterpret_cast<__nv_bfloat16*>(smem_raw + 115712); + const int smem_database_mid_addr = smem + 115712; + __nv_bfloat16* smem_database_hi = reinterpret_cast<__nv_bfloat16*>(smem_raw + 132096); + const int smem_database_hi_addr = smem + 132096; + float* smem_database_sq = reinterpret_cast(smem_raw + 148480); + const int smem_database_sq_addr = smem + 148480; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 0, 1, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 16, 1, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=128 + mbarrier_init_pred(smem + 40, 128, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (64 columns, 64 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::1.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(64) : "memory"); + } + + __syncthreads(); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: load ---- + if (warp == 0) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 0) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx = bid; work_idx < total_work; work_idx += num_bids) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tiles; + int q_tile = query_work % num_q_tiles; + int off_q = q_tile * BLOCK_Q; + int global_q = batch_idx * Q + off_q; + int db_tile_start = split_idx * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + mbarrier_arrive_expect_tx(query_full_addr, 98304); + tma_3d_gmem2smem(smem_query_addr, tmap_query, 0, global_q, 0, query_full_addr); + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int off_m = db_tile * BLOCK_M; + int global_m = batch_idx * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + mbarrier_arrive_expect_tx(database_full_addr, 49152); + tma_3d_gmem2smem(smem_database_addr, tmap_database, 0, global_m, 0, database_full_addr); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 1) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_1 = bid; work_idx_1 < total_work; work_idx_1 += num_bids) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = make_warp_uniform((smem_query_lo_addr >> 4) & 0x3FFF); + int _mma_b_lo_0 = make_warp_uniform((smem_database_lo_addr >> 4) & 0x3FFF); + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 135267472;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_1 = make_warp_uniform((smem_query_mid_addr >> 4) & 0x3FFF); + int _mma_b_lo_1 = make_warp_uniform((smem_database_mid_addr >> 4) & 0x3FFF); + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 135267472;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_1), "r"(_mma_b_lo_1), "r"(tmem_cross), "r"(1)); + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_2 = make_warp_uniform((smem_query_hi_addr >> 4) & 0x3FFF); + int _mma_b_lo_2 = make_warp_uniform((smem_database_hi_addr >> 4) & 0x3FFF); + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 135267472;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_2), "r"(_mma_b_lo_2), "r"(tmem_cross), "r"(1)); + elect_commit(score_full_addr); + elect_commit(database_empty_addr); + } + elect_commit(query_empty_addr); + } + } + // ---- Role: compute ---- + } else if (warp >= 2 && warp <= 5) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = bid; work_idx_2 < total_work; work_idx_2 += num_bids) { + int split_idx_1 = work_idx_2 % (unsigned int)split_count; + int query_work_1 = work_idx_2 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tiles; + int q_tile_1 = query_work_1 % num_q_tiles; + int off_q_1 = q_tile_1 * BLOCK_Q; + int q_idx = off_q_1 + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx_1 * Q + q_idx]; + } + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int db_start = db_tile_1 * BLOCK_M; + int db_sq_idx = db_start + (warp % 4 * 32 + lane); + if (warp % 4 * 32 + lane < BLOCK_M) { + if (db_sq_idx < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx_1 * M + db_sq_idx]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 0.0f; + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(warp % 4 * 32 << 16); + float _tmem_load_0[64]; + tmem_ld_x32(&_tmem_load_0[0], cross_addr); + tmem_ld_x32(&_tmem_load_0[32], cross_addr + 32); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_arrive(score_empty_addr); + if (valid_q != 0) { + #pragma unroll 2 + for (int col_base = 0; col_base < 64; col_base += 4) { + float dist_vec[4]; + dist_vec[0] = _tmem_load_0[col_base]; + dist_vec[1] = _tmem_load_0[col_base + 1]; + dist_vec[2] = _tmem_load_0[col_base + 2]; + dist_vec[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec[4]; + db_sq_vec[0] = smem_database_sq[col_base]; + db_sq_vec[1] = smem_database_sq[col_base + 1]; + db_sq_vec[2] = smem_database_sq[col_base + 2]; + db_sq_vec[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec)[_la], reinterpret_cast(db_sq_vec)[_la]); + float group_min = _t0[0]; + if (_t0[1] < group_min) { + group_min = _t0[1]; + } + if (_t0[2] < group_min) { + group_min = _t0[2]; + } + if (_t0[3] < group_min) { + group_min = _t0[3]; + } + if (group_min < best_d[TOP_K_MAX - 1]) { + #pragma unroll + for (int vec_col = 0; vec_col < 4; vec_col++) { + int db_idx = db_start + col_base + vec_col; + if (db_idx < M) { + float dist = _t0[vec_col]; + float _max_0 = max_noftz(dist, 0.0f); + dist = _max_0; + if (dist < best_d[TOP_K_MAX - 1]) { + best_d[TOP_K_MAX - 1] = dist; + best_i[TOP_K_MAX - 1] = db_idx; + #pragma unroll + for (int pos = TOP_K_MAX - 1; pos >= 1; pos--) { + if (best_d[pos] < best_d[pos - 1]) { + float tmp_d = best_d[pos - 1]; + int tmp_i = best_i[pos - 1]; + best_d[pos - 1] = best_d[pos]; + best_i[pos - 1] = best_i[pos]; + best_d[pos] = tmp_d; + best_i[pos] = tmp_i; + } + } + } + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + if (valid_q != 0) { + int out_base = ((split_idx_1 * B + batch_idx_1) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + if (out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = best_d[out_k]; + *((int*)(partial_indices + (out_base + out_k))) = best_i[out_k]; + } + } + } + } + } + } + + // Cleanup + __syncthreads(); // barrier before TMEM dealloc + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::1.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(64)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::1.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0204.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0204.cu new file mode 100644 index 00000000..9cb65c8b --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0204.cu @@ -0,0 +1,621 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 64 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 16384 +#define SMEM_SMEM_QUERY_STRIDE 16384 +#define SMEM_SMEM_DATABASE_OFF 17408 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_LOCAL_D_OFF 34048 +#define SMEM_SMEM_LOCAL_D_STAGE_BYTES 31744 +#define SMEM_SMEM_LOCAL_D_STRIDE 31744 +#define SMEM_SMEM_LOCAL_I_OFF 65792 +#define SMEM_SMEM_LOCAL_I_STAGE_BYTES 31744 +#define SMEM_SMEM_LOCAL_I_STRIDE 31744 +#define SMEM_TOTAL 97536 +#define THREADS 128 +#define BLOCK_Q 64 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_MAX 31 +#define ROWS_COVERED 32 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "tcgen05.mma.cta_group::1.kind::f16 [%0], %1, %2, %3, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, %3, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit(int mbar_addr) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];\n\t" + "}\n" + :: "r"(mbar_addr)); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x32(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x32.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15," + " %16, %17, %18, %19, %20, %21, %22, %23," + " %24, %25, %26, %27, %28, %29, %30, %31}, [%32];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]), + "=f"(dst[16]), "=f"(dst[17]), "=f"(dst[18]), "=f"(dst[19]), + "=f"(dst[20]), "=f"(dst[21]), "=f"(dst[22]), "=f"(dst[23]), + "=f"(dst[24]), "=f"(dst[25]), "=f"(dst[26]), "=f"(dst[27]), + "=f"(dst[28]), "=f"(dst[29]), "=f"(dst[30]), "=f"(dst[31]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ float max_noftz(float a, float b) { + float c; + asm("max.f32 %0, %1, %2;" : "=f"(c) : "f"(a), "f"(b)); + return c; +} + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cta.global" + ".mbarrier::complete_tx::bytes" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit(int mbar_addr) { + asm volatile( + "tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ uint32_t make_warp_uniform(uint32_t val) { + uint32_t result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1f, 0xffffffff;" + : "=r"(result) : "r"(val)); + return result; +} + +extern "C" { + +__global__ __launch_bounds__(128, 1) void +kernel_knn_build_rag_microbucket_q32_k31_c3d2_v1_stage1_q32k31_c3d2_v1(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tiles, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 17408); + const int smem_database_addr = smem + 17408; + float* smem_local_d = reinterpret_cast(smem_raw + 34048); + const int smem_local_d_addr = smem + 34048; + int* smem_local_i = reinterpret_cast(smem_raw + 65792); + const int smem_local_i_addr = smem + 65792; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 0, 1, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 16, 1, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 40, 2, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (64 columns, 64 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::1.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(64) : "memory"); + } + + __syncthreads(); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: compute ---- + if (warp <= 1) { + { // compute_main + int warp_id_in_role = (warp - 0); + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx = bid; work_idx < total_work; work_idx += num_bids) { + int split_idx = work_idx % (unsigned int)split_count; + int tmem_row_origin = warp_id_in_role * 32; + int logical_row_origin = warp_id_in_role * 16; + int row_top = logical_row_origin + lane / 4; + int row_bot = row_top + 8; + int lane_col = lane % 4; + int slot = lane_col; + int q_top = row_top; + int q_bot = row_bot; + float q_sq_top = query_sq[q_top]; + float q_sq_bot = query_sq[q_bot]; + float best_top_d[TOP_K_MAX]; + float best_bot_d[TOP_K_MAX]; + int best_top_i[TOP_K_MAX]; + int best_bot_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_top_d[kk] = 3.4e+38f; + best_bot_d[kk] = 3.4e+38f; + best_top_i[kk] = -1; + best_bot_i[kk] = -1; + } + int db_tile_start = split_idx * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int db_start = db_tile * BLOCK_M; + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(tmem_row_origin << 16); + float _tmem_load_0[32]; + asm volatile( + "tcgen05.ld.sync.aligned.16x256b.x8.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31}, [%32];" + : "=r"(*reinterpret_cast(&_tmem_load_0[0])), "=r"(*reinterpret_cast(&_tmem_load_0[1])), "=r"(*reinterpret_cast(&_tmem_load_0[2])), "=r"(*reinterpret_cast(&_tmem_load_0[3])), "=r"(*reinterpret_cast(&_tmem_load_0[4])), "=r"(*reinterpret_cast(&_tmem_load_0[5])), "=r"(*reinterpret_cast(&_tmem_load_0[6])), "=r"(*reinterpret_cast(&_tmem_load_0[7])), "=r"(*reinterpret_cast(&_tmem_load_0[8])), "=r"(*reinterpret_cast(&_tmem_load_0[9])), "=r"(*reinterpret_cast(&_tmem_load_0[10])), "=r"(*reinterpret_cast(&_tmem_load_0[11])), "=r"(*reinterpret_cast(&_tmem_load_0[12])), "=r"(*reinterpret_cast(&_tmem_load_0[13])), "=r"(*reinterpret_cast(&_tmem_load_0[14])), "=r"(*reinterpret_cast(&_tmem_load_0[15])), "=r"(*reinterpret_cast(&_tmem_load_0[16])), "=r"(*reinterpret_cast(&_tmem_load_0[17])), "=r"(*reinterpret_cast(&_tmem_load_0[18])), "=r"(*reinterpret_cast(&_tmem_load_0[19])), "=r"(*reinterpret_cast(&_tmem_load_0[20])), "=r"(*reinterpret_cast(&_tmem_load_0[21])), "=r"(*reinterpret_cast(&_tmem_load_0[22])), "=r"(*reinterpret_cast(&_tmem_load_0[23])), "=r"(*reinterpret_cast(&_tmem_load_0[24])), "=r"(*reinterpret_cast(&_tmem_load_0[25])), "=r"(*reinterpret_cast(&_tmem_load_0[26])), "=r"(*reinterpret_cast(&_tmem_load_0[27])), "=r"(*reinterpret_cast(&_tmem_load_0[28])), "=r"(*reinterpret_cast(&_tmem_load_0[29])), "=r"(*reinterpret_cast(&_tmem_load_0[30])), "=r"(*reinterpret_cast(&_tmem_load_0[31])) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + if (elect_sync()) { + mbarrier_arrive(score_empty_addr); + } + #pragma unroll + for (int repeat = 0; repeat < 8; repeat++) { + const int reg_base = repeat * 4; + int col_base = repeat * 8 + lane_col * 2; + int db_idx0 = db_start + col_base; + int db_idx1 = db_idx0 + 1; + float top_d0 = 3.4e+38f; + float top_d1 = 3.4e+38f; + if (db_idx0 < M) { + float _max_0 = max_noftz(q_sq_top + database_sq[db_idx0] - 2.0f * _tmem_load_0[reg_base], 0.0f); + top_d0 = _max_0; + } + if (db_idx1 < M) { + float _max_1 = max_noftz(q_sq_top + database_sq[db_idx1] - 2.0f * _tmem_load_0[reg_base + 1], 0.0f); + top_d1 = _max_1; + } + int top_take1 = ((top_d1 < top_d0) ? 1 : 0); + if (best_top_d[30] > ((top_take1 != 0) ? top_d1 : top_d0)) { + best_top_d[30] = ((top_take1 != 0) ? top_d1 : top_d0); + best_top_i[30] = ((top_take1 != 0) ? db_idx1 : db_idx0); + #pragma unroll + for (int kk_1 = 29; kk_1 >= 0; kk_1--) { + float lower0_d = best_top_d[kk_1 + 1]; + int lower0_i = best_top_i[kk_1 + 1]; + float upper0_d = best_top_d[kk_1]; + int upper0_i = best_top_i[kk_1]; + int swap0_up = ((lower0_d < upper0_d) ? 1 : 0); + best_top_d[kk_1] = ((swap0_up != 0) ? lower0_d : upper0_d); + best_top_i[kk_1] = ((swap0_up != 0) ? lower0_i : upper0_i); + best_top_d[kk_1 + 1] = ((swap0_up != 0) ? upper0_d : lower0_d); + best_top_i[kk_1 + 1] = ((swap0_up != 0) ? upper0_i : lower0_i); + } + if (best_top_d[30] > ((top_take1 != 0) ? top_d0 : top_d1)) { + best_top_d[30] = ((top_take1 != 0) ? top_d0 : top_d1); + best_top_i[30] = ((top_take1 != 0) ? db_idx0 : db_idx1); + #pragma unroll + for (int kk_2 = 29; kk_2 >= 0; kk_2--) { + float lower1_d = best_top_d[kk_2 + 1]; + int lower1_i = best_top_i[kk_2 + 1]; + float upper1_d = best_top_d[kk_2]; + int upper1_i = best_top_i[kk_2]; + int swap1_up = ((lower1_d < upper1_d) ? 1 : 0); + best_top_d[kk_2] = ((swap1_up != 0) ? lower1_d : upper1_d); + best_top_i[kk_2] = ((swap1_up != 0) ? lower1_i : upper1_i); + best_top_d[kk_2 + 1] = ((swap1_up != 0) ? upper1_d : lower1_d); + best_top_i[kk_2 + 1] = ((swap1_up != 0) ? upper1_i : lower1_i); + } + } + } + float bot_d0 = 3.4e+38f; + float bot_d1 = 3.4e+38f; + if (db_idx0 < M) { + float _max_2 = max_noftz(q_sq_bot + database_sq[db_idx0] - 2.0f * _tmem_load_0[reg_base + 2], 0.0f); + bot_d0 = _max_2; + } + if (db_idx1 < M) { + float _max_3 = max_noftz(q_sq_bot + database_sq[db_idx1] - 2.0f * _tmem_load_0[reg_base + 3], 0.0f); + bot_d1 = _max_3; + } + int bot_take1 = ((bot_d1 < bot_d0) ? 1 : 0); + if (best_bot_d[30] > ((bot_take1 != 0) ? bot_d1 : bot_d0)) { + best_bot_d[30] = ((bot_take1 != 0) ? bot_d1 : bot_d0); + best_bot_i[30] = ((bot_take1 != 0) ? db_idx1 : db_idx0); + #pragma unroll + for (int kk_3 = 29; kk_3 >= 0; kk_3--) { + float lower0_d_1 = best_bot_d[kk_3 + 1]; + int lower0_i_1 = best_bot_i[kk_3 + 1]; + float upper0_d_1 = best_bot_d[kk_3]; + int upper0_i_1 = best_bot_i[kk_3]; + int swap0_up_1 = ((lower0_d_1 < upper0_d_1) ? 1 : 0); + best_bot_d[kk_3] = ((swap0_up_1 != 0) ? lower0_d_1 : upper0_d_1); + best_bot_i[kk_3] = ((swap0_up_1 != 0) ? lower0_i_1 : upper0_i_1); + best_bot_d[kk_3 + 1] = ((swap0_up_1 != 0) ? upper0_d_1 : lower0_d_1); + best_bot_i[kk_3 + 1] = ((swap0_up_1 != 0) ? upper0_i_1 : lower0_i_1); + } + if (best_bot_d[30] > ((bot_take1 != 0) ? bot_d0 : bot_d1)) { + best_bot_d[30] = ((bot_take1 != 0) ? bot_d0 : bot_d1); + best_bot_i[30] = ((bot_take1 != 0) ? db_idx0 : db_idx1); + #pragma unroll + for (int kk_4 = 29; kk_4 >= 0; kk_4--) { + float lower1_d_1 = best_bot_d[kk_4 + 1]; + int lower1_i_1 = best_bot_i[kk_4 + 1]; + float upper1_d_1 = best_bot_d[kk_4]; + int upper1_i_1 = best_bot_i[kk_4]; + int swap1_up_1 = ((lower1_d_1 < upper1_d_1) ? 1 : 0); + best_bot_d[kk_4] = ((swap1_up_1 != 0) ? lower1_d_1 : upper1_d_1); + best_bot_i[kk_4] = ((swap1_up_1 != 0) ? lower1_i_1 : upper1_i_1); + best_bot_d[kk_4 + 1] = ((swap1_up_1 != 0) ? upper1_d_1 : lower1_d_1); + best_bot_i[kk_4 + 1] = ((swap1_up_1 != 0) ? upper1_i_1 : lower1_i_1); + } + } + } + } + } + int top_slot_base = (row_top * 4 + slot) * TOP_K_MAX; + int bot_slot_base = (row_bot * 4 + slot) * TOP_K_MAX; + #pragma unroll + for (int kk_5 = 0; kk_5 < TOP_K_MAX; kk_5++) { + smem_local_d[top_slot_base + kk_5] = best_top_d[kk_5]; + smem_local_i[top_slot_base + kk_5] = best_top_i[kk_5]; + smem_local_d[bot_slot_base + kk_5] = best_bot_d[kk_5]; + smem_local_i[bot_slot_base + kk_5] = best_bot_i[kk_5]; + } + asm volatile("barrier.sync 8, %0;" :: "r"(64)); + if (tid < ROWS_COVERED) { + int row = tid; + float head_d[4]; + int head_i[4]; + int head_k[4]; + #pragma unroll + for (int slot_idx = 0; slot_idx < 4; slot_idx++) { + int local_base = (row * 4 + slot_idx) * TOP_K_MAX; + head_k[slot_idx] = 0; + head_d[slot_idx] = smem_local_d[local_base]; + head_i[slot_idx] = smem_local_i[local_base]; + } + int out_base = (split_idx * ROWS_COVERED + row) * TOP_K_MAX; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float winner_d = head_d[0]; + int winner_i = head_i[0]; + int winner_slot = 0; + #pragma unroll + for (int slot_idx_1 = 1; slot_idx_1 < 4; slot_idx_1++) { + float cand_d = head_d[slot_idx_1]; + int take = ((cand_d < winner_d) ? 1 : 0); + winner_d = ((take != 0) ? cand_d : winner_d); + winner_i = ((take != 0) ? head_i[slot_idx_1] : winner_i); + winner_slot = ((take != 0) ? slot_idx_1 : winner_slot); + } + *((float*)(partial_dists + (out_base + out_k))) = winner_d; + *((int*)(partial_indices + (out_base + out_k))) = winner_i; + #pragma unroll + for (int slot_idx_2 = 0; slot_idx_2 < 4; slot_idx_2++) { + if (winner_slot == slot_idx_2) { + int next_head = head_k[slot_idx_2] + 1; + head_k[slot_idx_2] = next_head; + head_d[slot_idx_2] = 3.4e+38f; + head_i[slot_idx_2] = -1; + if (next_head < TOP_K_MAX) { + int local_base_1 = (row * 4 + slot_idx_2) * TOP_K_MAX; + head_d[slot_idx_2] = smem_local_d[local_base_1 + next_head]; + head_i[slot_idx_2] = smem_local_i[local_base_1 + next_head]; + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(64)); + } + } + // ---- Role: load ---- + } else if (warp == 2) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 2) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx_1 = bid; work_idx_1 < total_work; work_idx_1 += num_bids) { + int split_idx_1 = work_idx_1 % (unsigned int)split_count; + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + mbarrier_arrive_expect_tx(query_full_addr, 16384); + tma_3d_gmem2smem(smem_query_addr, tmap_query, 0, 0, 0, query_full_addr); + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int off_m = db_tile_1 * BLOCK_M; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + mbarrier_arrive_expect_tx(database_full_addr, 16384); + tma_3d_gmem2smem(smem_database_addr, tmap_database, 0, off_m, 0, database_full_addr); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 3) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + #pragma unroll 1 + for (unsigned int _work_idx = bid; _work_idx < total_work; _work_idx += num_bids) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = make_warp_uniform((smem_query_addr >> 4) & 0x3FFF); + int _mma_b_lo_0 = make_warp_uniform((smem_database_addr >> 4) & 0x3FFF); + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 68158608;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 506;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit(score_full_addr); + elect_commit(database_empty_addr); + } + elect_commit(query_empty_addr); + } + } + } + + // Cleanup + __syncthreads(); // barrier before TMEM dealloc + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::1.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(64)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::1.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0205.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0205.cu new file mode 100644 index 00000000..29ffc43b --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0205.cu @@ -0,0 +1,621 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 64 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 16384 +#define SMEM_SMEM_QUERY_STRIDE 16384 +#define SMEM_SMEM_DATABASE_OFF 17408 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_LOCAL_D_OFF 34048 +#define SMEM_SMEM_LOCAL_D_STAGE_BYTES 16384 +#define SMEM_SMEM_LOCAL_D_STRIDE 16384 +#define SMEM_SMEM_LOCAL_I_OFF 50432 +#define SMEM_SMEM_LOCAL_I_STAGE_BYTES 16384 +#define SMEM_SMEM_LOCAL_I_STRIDE 16384 +#define SMEM_TOTAL 66816 +#define THREADS 128 +#define BLOCK_Q 64 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_MAX 32 +#define ROWS_COVERED 32 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "tcgen05.mma.cta_group::1.kind::f16 [%0], %1, %2, %3, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, %3, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit(int mbar_addr) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];\n\t" + "}\n" + :: "r"(mbar_addr)); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x32(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x32.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15," + " %16, %17, %18, %19, %20, %21, %22, %23," + " %24, %25, %26, %27, %28, %29, %30, %31}, [%32];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]), + "=f"(dst[16]), "=f"(dst[17]), "=f"(dst[18]), "=f"(dst[19]), + "=f"(dst[20]), "=f"(dst[21]), "=f"(dst[22]), "=f"(dst[23]), + "=f"(dst[24]), "=f"(dst[25]), "=f"(dst[26]), "=f"(dst[27]), + "=f"(dst[28]), "=f"(dst[29]), "=f"(dst[30]), "=f"(dst[31]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ float max_noftz(float a, float b) { + float c; + asm("max.f32 %0, %1, %2;" : "=f"(c) : "f"(a), "f"(b)); + return c; +} + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cta.global" + ".mbarrier::complete_tx::bytes" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit(int mbar_addr) { + asm volatile( + "tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ uint32_t make_warp_uniform(uint32_t val) { + uint32_t result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1f, 0xffffffff;" + : "=r"(result) : "r"(val)); + return result; +} + +extern "C" { + +__global__ __launch_bounds__(128, 1) void +kernel_knn_build_rag_microbucket_k32_f590_q32exact_v1_stage1_q32exact_f590_v1(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tiles, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 17408); + const int smem_database_addr = smem + 17408; + float* smem_local_d = reinterpret_cast(smem_raw + 34048); + const int smem_local_d_addr = smem + 34048; + int* smem_local_i = reinterpret_cast(smem_raw + 50432); + const int smem_local_i_addr = smem + 50432; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 0, 1, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 16, 1, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 40, 2, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (64 columns, 64 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::1.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(64) : "memory"); + } + + __syncthreads(); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: compute ---- + if (warp <= 1) { + { // compute_main + int warp_id_in_role = (warp - 0); + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx = bid; work_idx < total_work; work_idx += num_bids) { + int split_idx = work_idx % (unsigned int)split_count; + int tmem_row_origin = warp_id_in_role * 32; + int logical_row_origin = warp_id_in_role * 16; + int row_top = logical_row_origin + lane / 4; + int row_bot = row_top + 8; + int lane_col = lane % 4; + int slot = lane_col; + int q_top = row_top; + int q_bot = row_bot; + float q_sq_top = query_sq[q_top]; + float q_sq_bot = query_sq[q_bot]; + float best_top_d[TOP_K_MAX]; + float best_bot_d[TOP_K_MAX]; + int best_top_i[TOP_K_MAX]; + int best_bot_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_top_d[kk] = 3.4e+38f; + best_bot_d[kk] = 3.4e+38f; + best_top_i[kk] = -1; + best_bot_i[kk] = -1; + } + int db_tile_start = split_idx * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int db_start = db_tile * BLOCK_M; + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(tmem_row_origin << 16); + float _tmem_load_0[32]; + asm volatile( + "tcgen05.ld.sync.aligned.16x256b.x8.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31}, [%32];" + : "=r"(*reinterpret_cast(&_tmem_load_0[0])), "=r"(*reinterpret_cast(&_tmem_load_0[1])), "=r"(*reinterpret_cast(&_tmem_load_0[2])), "=r"(*reinterpret_cast(&_tmem_load_0[3])), "=r"(*reinterpret_cast(&_tmem_load_0[4])), "=r"(*reinterpret_cast(&_tmem_load_0[5])), "=r"(*reinterpret_cast(&_tmem_load_0[6])), "=r"(*reinterpret_cast(&_tmem_load_0[7])), "=r"(*reinterpret_cast(&_tmem_load_0[8])), "=r"(*reinterpret_cast(&_tmem_load_0[9])), "=r"(*reinterpret_cast(&_tmem_load_0[10])), "=r"(*reinterpret_cast(&_tmem_load_0[11])), "=r"(*reinterpret_cast(&_tmem_load_0[12])), "=r"(*reinterpret_cast(&_tmem_load_0[13])), "=r"(*reinterpret_cast(&_tmem_load_0[14])), "=r"(*reinterpret_cast(&_tmem_load_0[15])), "=r"(*reinterpret_cast(&_tmem_load_0[16])), "=r"(*reinterpret_cast(&_tmem_load_0[17])), "=r"(*reinterpret_cast(&_tmem_load_0[18])), "=r"(*reinterpret_cast(&_tmem_load_0[19])), "=r"(*reinterpret_cast(&_tmem_load_0[20])), "=r"(*reinterpret_cast(&_tmem_load_0[21])), "=r"(*reinterpret_cast(&_tmem_load_0[22])), "=r"(*reinterpret_cast(&_tmem_load_0[23])), "=r"(*reinterpret_cast(&_tmem_load_0[24])), "=r"(*reinterpret_cast(&_tmem_load_0[25])), "=r"(*reinterpret_cast(&_tmem_load_0[26])), "=r"(*reinterpret_cast(&_tmem_load_0[27])), "=r"(*reinterpret_cast(&_tmem_load_0[28])), "=r"(*reinterpret_cast(&_tmem_load_0[29])), "=r"(*reinterpret_cast(&_tmem_load_0[30])), "=r"(*reinterpret_cast(&_tmem_load_0[31])) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + if (elect_sync()) { + mbarrier_arrive(score_empty_addr); + } + #pragma unroll + for (int repeat = 0; repeat < 8; repeat++) { + const int reg_base = repeat * 4; + int col_base = repeat * 8 + lane_col * 2; + int db_idx0 = db_start + col_base; + int db_idx1 = db_idx0 + 1; + float top_d0 = 3.4e+38f; + float top_d1 = 3.4e+38f; + if (db_idx0 < M) { + float _max_0 = max_noftz(q_sq_top + database_sq[db_idx0] - 2.0f * _tmem_load_0[reg_base], 0.0f); + top_d0 = _max_0; + } + if (db_idx1 < M) { + float _max_1 = max_noftz(q_sq_top + database_sq[db_idx1] - 2.0f * _tmem_load_0[reg_base + 1], 0.0f); + top_d1 = _max_1; + } + int top_take1 = ((top_d1 < top_d0) ? 1 : 0); + if (best_top_d[31] > ((top_take1 != 0) ? top_d1 : top_d0)) { + best_top_d[31] = ((top_take1 != 0) ? top_d1 : top_d0); + best_top_i[31] = ((top_take1 != 0) ? db_idx1 : db_idx0); + #pragma unroll + for (int kk_1 = 30; kk_1 >= 0; kk_1--) { + float lower0_d = best_top_d[kk_1 + 1]; + int lower0_i = best_top_i[kk_1 + 1]; + float upper0_d = best_top_d[kk_1]; + int upper0_i = best_top_i[kk_1]; + int swap0_up = ((lower0_d < upper0_d) ? 1 : 0); + best_top_d[kk_1] = ((swap0_up != 0) ? lower0_d : upper0_d); + best_top_i[kk_1] = ((swap0_up != 0) ? lower0_i : upper0_i); + best_top_d[kk_1 + 1] = ((swap0_up != 0) ? upper0_d : lower0_d); + best_top_i[kk_1 + 1] = ((swap0_up != 0) ? upper0_i : lower0_i); + } + if (best_top_d[31] > ((top_take1 != 0) ? top_d0 : top_d1)) { + best_top_d[31] = ((top_take1 != 0) ? top_d0 : top_d1); + best_top_i[31] = ((top_take1 != 0) ? db_idx0 : db_idx1); + #pragma unroll + for (int kk_2 = 30; kk_2 >= 0; kk_2--) { + float lower1_d = best_top_d[kk_2 + 1]; + int lower1_i = best_top_i[kk_2 + 1]; + float upper1_d = best_top_d[kk_2]; + int upper1_i = best_top_i[kk_2]; + int swap1_up = ((lower1_d < upper1_d) ? 1 : 0); + best_top_d[kk_2] = ((swap1_up != 0) ? lower1_d : upper1_d); + best_top_i[kk_2] = ((swap1_up != 0) ? lower1_i : upper1_i); + best_top_d[kk_2 + 1] = ((swap1_up != 0) ? upper1_d : lower1_d); + best_top_i[kk_2 + 1] = ((swap1_up != 0) ? upper1_i : lower1_i); + } + } + } + float bot_d0 = 3.4e+38f; + float bot_d1 = 3.4e+38f; + if (db_idx0 < M) { + float _max_2 = max_noftz(q_sq_bot + database_sq[db_idx0] - 2.0f * _tmem_load_0[reg_base + 2], 0.0f); + bot_d0 = _max_2; + } + if (db_idx1 < M) { + float _max_3 = max_noftz(q_sq_bot + database_sq[db_idx1] - 2.0f * _tmem_load_0[reg_base + 3], 0.0f); + bot_d1 = _max_3; + } + int bot_take1 = ((bot_d1 < bot_d0) ? 1 : 0); + if (best_bot_d[31] > ((bot_take1 != 0) ? bot_d1 : bot_d0)) { + best_bot_d[31] = ((bot_take1 != 0) ? bot_d1 : bot_d0); + best_bot_i[31] = ((bot_take1 != 0) ? db_idx1 : db_idx0); + #pragma unroll + for (int kk_3 = 30; kk_3 >= 0; kk_3--) { + float lower0_d_1 = best_bot_d[kk_3 + 1]; + int lower0_i_1 = best_bot_i[kk_3 + 1]; + float upper0_d_1 = best_bot_d[kk_3]; + int upper0_i_1 = best_bot_i[kk_3]; + int swap0_up_1 = ((lower0_d_1 < upper0_d_1) ? 1 : 0); + best_bot_d[kk_3] = ((swap0_up_1 != 0) ? lower0_d_1 : upper0_d_1); + best_bot_i[kk_3] = ((swap0_up_1 != 0) ? lower0_i_1 : upper0_i_1); + best_bot_d[kk_3 + 1] = ((swap0_up_1 != 0) ? upper0_d_1 : lower0_d_1); + best_bot_i[kk_3 + 1] = ((swap0_up_1 != 0) ? upper0_i_1 : lower0_i_1); + } + if (best_bot_d[31] > ((bot_take1 != 0) ? bot_d0 : bot_d1)) { + best_bot_d[31] = ((bot_take1 != 0) ? bot_d0 : bot_d1); + best_bot_i[31] = ((bot_take1 != 0) ? db_idx0 : db_idx1); + #pragma unroll + for (int kk_4 = 30; kk_4 >= 0; kk_4--) { + float lower1_d_1 = best_bot_d[kk_4 + 1]; + int lower1_i_1 = best_bot_i[kk_4 + 1]; + float upper1_d_1 = best_bot_d[kk_4]; + int upper1_i_1 = best_bot_i[kk_4]; + int swap1_up_1 = ((lower1_d_1 < upper1_d_1) ? 1 : 0); + best_bot_d[kk_4] = ((swap1_up_1 != 0) ? lower1_d_1 : upper1_d_1); + best_bot_i[kk_4] = ((swap1_up_1 != 0) ? lower1_i_1 : upper1_i_1); + best_bot_d[kk_4 + 1] = ((swap1_up_1 != 0) ? upper1_d_1 : lower1_d_1); + best_bot_i[kk_4 + 1] = ((swap1_up_1 != 0) ? upper1_i_1 : lower1_i_1); + } + } + } + } + } + int top_slot_base = (row_top * 4 + slot) * TOP_K_MAX; + int bot_slot_base = (row_bot * 4 + slot) * TOP_K_MAX; + #pragma unroll + for (int kk_5 = 0; kk_5 < TOP_K_MAX; kk_5++) { + smem_local_d[top_slot_base + kk_5] = best_top_d[kk_5]; + smem_local_i[top_slot_base + kk_5] = best_top_i[kk_5]; + smem_local_d[bot_slot_base + kk_5] = best_bot_d[kk_5]; + smem_local_i[bot_slot_base + kk_5] = best_bot_i[kk_5]; + } + asm volatile("barrier.sync 8, %0;" :: "r"(64)); + if (tid < ROWS_COVERED) { + int row = tid; + float head_d[4]; + int head_i[4]; + int head_k[4]; + #pragma unroll + for (int slot_idx = 0; slot_idx < 4; slot_idx++) { + int local_base = (row * 4 + slot_idx) * TOP_K_MAX; + head_k[slot_idx] = 0; + head_d[slot_idx] = smem_local_d[local_base]; + head_i[slot_idx] = smem_local_i[local_base]; + } + int out_base = (split_idx * ROWS_COVERED + row) * TOP_K_MAX; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float winner_d = head_d[0]; + int winner_i = head_i[0]; + int winner_slot = 0; + #pragma unroll + for (int slot_idx_1 = 1; slot_idx_1 < 4; slot_idx_1++) { + float cand_d = head_d[slot_idx_1]; + int take = ((cand_d < winner_d) ? 1 : 0); + winner_d = ((take != 0) ? cand_d : winner_d); + winner_i = ((take != 0) ? head_i[slot_idx_1] : winner_i); + winner_slot = ((take != 0) ? slot_idx_1 : winner_slot); + } + *((float*)(partial_dists + (out_base + out_k))) = winner_d; + *((int*)(partial_indices + (out_base + out_k))) = winner_i; + #pragma unroll + for (int slot_idx_2 = 0; slot_idx_2 < 4; slot_idx_2++) { + if (winner_slot == slot_idx_2) { + int next_head = head_k[slot_idx_2] + 1; + head_k[slot_idx_2] = next_head; + head_d[slot_idx_2] = 3.4e+38f; + head_i[slot_idx_2] = -1; + if (next_head < TOP_K_MAX) { + int local_base_1 = (row * 4 + slot_idx_2) * TOP_K_MAX; + head_d[slot_idx_2] = smem_local_d[local_base_1 + next_head]; + head_i[slot_idx_2] = smem_local_i[local_base_1 + next_head]; + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(64)); + } + } + // ---- Role: load ---- + } else if (warp == 2) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 2) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx_1 = bid; work_idx_1 < total_work; work_idx_1 += num_bids) { + int split_idx_1 = work_idx_1 % (unsigned int)split_count; + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + mbarrier_arrive_expect_tx(query_full_addr, 16384); + tma_3d_gmem2smem(smem_query_addr, tmap_query, 0, 0, 0, query_full_addr); + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int off_m = db_tile_1 * BLOCK_M; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + mbarrier_arrive_expect_tx(database_full_addr, 16384); + tma_3d_gmem2smem(smem_database_addr, tmap_database, 0, off_m, 0, database_full_addr); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 3) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + #pragma unroll 1 + for (unsigned int _work_idx = bid; _work_idx < total_work; _work_idx += num_bids) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = make_warp_uniform((smem_query_addr >> 4) & 0x3FFF); + int _mma_b_lo_0 = make_warp_uniform((smem_database_addr >> 4) & 0x3FFF); + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 68158608;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 506;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit(score_full_addr); + elect_commit(database_empty_addr); + } + elect_commit(query_empty_addr); + } + } + } + + // Cleanup + __syncthreads(); // barrier before TMEM dealloc + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::1.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(64)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::1.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0206.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0206.cu new file mode 100644 index 00000000..e2aec76b --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0206.cu @@ -0,0 +1,641 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 64 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 16384 +#define SMEM_SMEM_QUERY_STRIDE 16384 +#define SMEM_SMEM_DATABASE_OFF 17408 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_LOCAL_D_OFF 34048 +#define SMEM_SMEM_LOCAL_D_STAGE_BYTES 12288 +#define SMEM_SMEM_LOCAL_D_STRIDE 12288 +#define SMEM_SMEM_LOCAL_I_OFF 46336 +#define SMEM_SMEM_LOCAL_I_STAGE_BYTES 12288 +#define SMEM_SMEM_LOCAL_I_STRIDE 12288 +#define SMEM_TOTAL 58624 +#define THREADS 192 +#define BLOCK_Q 64 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_MAX 12 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "tcgen05.mma.cta_group::1.kind::f16 [%0], %1, %2, %3, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, %3, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit(int mbar_addr) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];\n\t" + "}\n" + :: "r"(mbar_addr)); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x32(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x32.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15," + " %16, %17, %18, %19, %20, %21, %22, %23," + " %24, %25, %26, %27, %28, %29, %30, %31}, [%32];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]), + "=f"(dst[16]), "=f"(dst[17]), "=f"(dst[18]), "=f"(dst[19]), + "=f"(dst[20]), "=f"(dst[21]), "=f"(dst[22]), "=f"(dst[23]), + "=f"(dst[24]), "=f"(dst[25]), "=f"(dst[26]), "=f"(dst[27]), + "=f"(dst[28]), "=f"(dst[29]), "=f"(dst[30]), "=f"(dst[31]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ float max_noftz(float a, float b) { + float c; + asm("max.f32 %0, %1, %2;" : "=f"(c) : "f"(a), "f"(b)); + return c; +} + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cta.global" + ".mbarrier::complete_tx::bytes" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit(int mbar_addr) { + asm volatile( + "tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ uint32_t make_warp_uniform(uint32_t val) { + uint32_t result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1f, 0xffffffff;" + : "=r"(result) : "r"(val)); + return result; +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_rag_microbucket_k12_2f22_q48exact_v1_stage1(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tiles, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 17408); + const int smem_database_addr = smem + 17408; + float* smem_local_d = reinterpret_cast(smem_raw + 34048); + const int smem_local_d_addr = smem + 34048; + int* smem_local_i = reinterpret_cast(smem_raw + 46336); + const int smem_local_i_addr = smem + 46336; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 0, 1, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 16, 1, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=4 + mbarrier_init_pred(smem + 40, 4, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (64 columns, 64 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::1.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(64) : "memory"); + } + + __syncthreads(); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: compute ---- + if (warp <= 3) { + { // compute_main + int warp_id_in_role = (warp - 0); + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx = bid; work_idx < total_work; work_idx += num_bids) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tiles; + int q_tile = query_work % num_q_tiles; + int off_q = q_tile * BLOCK_Q; + int tmem_row_origin = warp_id_in_role * 32; + int logical_row_origin = warp_id_in_role * 16; + int row_top = logical_row_origin + lane / 4; + int row_bot = row_top + 8; + int lane_col = lane % 4; + int slot = lane_col; + int q_top = off_q + row_top; + int q_bot = off_q + row_bot; + int valid_top = ((q_top < Q) ? 1 : 0); + int valid_bot = ((q_bot < Q) ? 1 : 0); + float q_sq_top = 0.0f; + float q_sq_bot = 0.0f; + if (valid_top != 0) { + q_sq_top = query_sq[batch_idx * Q + q_top]; + } + if (valid_bot != 0) { + q_sq_bot = query_sq[batch_idx * Q + q_bot]; + } + float best_top_d[TOP_K_MAX]; + float best_bot_d[TOP_K_MAX]; + int best_top_i[TOP_K_MAX]; + int best_bot_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_top_d[kk] = 3.4e+38f; + best_bot_d[kk] = 3.4e+38f; + best_top_i[kk] = -1; + best_bot_i[kk] = -1; + } + int db_tile_start = split_idx * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int db_start = db_tile * BLOCK_M; + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(tmem_row_origin << 16); + float _tmem_load_0[32]; + asm volatile( + "tcgen05.ld.sync.aligned.16x256b.x8.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31}, [%32];" + : "=r"(*reinterpret_cast(&_tmem_load_0[0])), "=r"(*reinterpret_cast(&_tmem_load_0[1])), "=r"(*reinterpret_cast(&_tmem_load_0[2])), "=r"(*reinterpret_cast(&_tmem_load_0[3])), "=r"(*reinterpret_cast(&_tmem_load_0[4])), "=r"(*reinterpret_cast(&_tmem_load_0[5])), "=r"(*reinterpret_cast(&_tmem_load_0[6])), "=r"(*reinterpret_cast(&_tmem_load_0[7])), "=r"(*reinterpret_cast(&_tmem_load_0[8])), "=r"(*reinterpret_cast(&_tmem_load_0[9])), "=r"(*reinterpret_cast(&_tmem_load_0[10])), "=r"(*reinterpret_cast(&_tmem_load_0[11])), "=r"(*reinterpret_cast(&_tmem_load_0[12])), "=r"(*reinterpret_cast(&_tmem_load_0[13])), "=r"(*reinterpret_cast(&_tmem_load_0[14])), "=r"(*reinterpret_cast(&_tmem_load_0[15])), "=r"(*reinterpret_cast(&_tmem_load_0[16])), "=r"(*reinterpret_cast(&_tmem_load_0[17])), "=r"(*reinterpret_cast(&_tmem_load_0[18])), "=r"(*reinterpret_cast(&_tmem_load_0[19])), "=r"(*reinterpret_cast(&_tmem_load_0[20])), "=r"(*reinterpret_cast(&_tmem_load_0[21])), "=r"(*reinterpret_cast(&_tmem_load_0[22])), "=r"(*reinterpret_cast(&_tmem_load_0[23])), "=r"(*reinterpret_cast(&_tmem_load_0[24])), "=r"(*reinterpret_cast(&_tmem_load_0[25])), "=r"(*reinterpret_cast(&_tmem_load_0[26])), "=r"(*reinterpret_cast(&_tmem_load_0[27])), "=r"(*reinterpret_cast(&_tmem_load_0[28])), "=r"(*reinterpret_cast(&_tmem_load_0[29])), "=r"(*reinterpret_cast(&_tmem_load_0[30])), "=r"(*reinterpret_cast(&_tmem_load_0[31])) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + if (elect_sync()) { + mbarrier_arrive(score_empty_addr); + } + #pragma unroll + for (int repeat = 0; repeat < 8; repeat++) { + const int reg_base = repeat * 4; + int col_base = repeat * 8 + lane_col * 2; + int db_idx0 = db_start + col_base; + int db_idx1 = db_idx0 + 1; + float top_d0 = 3.4e+38f; + float top_d1 = 3.4e+38f; + if (valid_top != 0 && db_idx0 < M) { + float _max_0 = max_noftz(q_sq_top + database_sq[batch_idx * M + db_idx0] - 2.0f * _tmem_load_0[reg_base], 0.0f); + top_d0 = _max_0; + } + if (valid_top != 0 && db_idx1 < M) { + float _max_1 = max_noftz(q_sq_top + database_sq[batch_idx * M + db_idx1] - 2.0f * _tmem_load_0[reg_base + 1], 0.0f); + top_d1 = _max_1; + } + int top_take1 = ((top_d1 < top_d0) ? 1 : 0); + if (best_top_d[11] > ((top_take1 != 0) ? top_d1 : top_d0)) { + best_top_d[11] = ((top_take1 != 0) ? top_d1 : top_d0); + best_top_i[11] = ((top_take1 != 0) ? db_idx1 : db_idx0); + #pragma unroll + for (int kk_1 = 10; kk_1 >= 0; kk_1--) { + float lower0_d = best_top_d[kk_1 + 1]; + int lower0_i = best_top_i[kk_1 + 1]; + float upper0_d = best_top_d[kk_1]; + int upper0_i = best_top_i[kk_1]; + int swap0_up = ((lower0_d < upper0_d) ? 1 : 0); + best_top_d[kk_1] = ((swap0_up != 0) ? lower0_d : upper0_d); + best_top_i[kk_1] = ((swap0_up != 0) ? lower0_i : upper0_i); + best_top_d[kk_1 + 1] = ((swap0_up != 0) ? upper0_d : lower0_d); + best_top_i[kk_1 + 1] = ((swap0_up != 0) ? upper0_i : lower0_i); + } + if (best_top_d[11] > ((top_take1 != 0) ? top_d0 : top_d1)) { + best_top_d[11] = ((top_take1 != 0) ? top_d0 : top_d1); + best_top_i[11] = ((top_take1 != 0) ? db_idx0 : db_idx1); + #pragma unroll + for (int kk_2 = 10; kk_2 >= 0; kk_2--) { + float lower1_d = best_top_d[kk_2 + 1]; + int lower1_i = best_top_i[kk_2 + 1]; + float upper1_d = best_top_d[kk_2]; + int upper1_i = best_top_i[kk_2]; + int swap1_up = ((lower1_d < upper1_d) ? 1 : 0); + best_top_d[kk_2] = ((swap1_up != 0) ? lower1_d : upper1_d); + best_top_i[kk_2] = ((swap1_up != 0) ? lower1_i : upper1_i); + best_top_d[kk_2 + 1] = ((swap1_up != 0) ? upper1_d : lower1_d); + best_top_i[kk_2 + 1] = ((swap1_up != 0) ? upper1_i : lower1_i); + } + } + } + float bot_d0 = 3.4e+38f; + float bot_d1 = 3.4e+38f; + if (valid_bot != 0 && db_idx0 < M) { + float _max_2 = max_noftz(q_sq_bot + database_sq[batch_idx * M + db_idx0] - 2.0f * _tmem_load_0[reg_base + 2], 0.0f); + bot_d0 = _max_2; + } + if (valid_bot != 0 && db_idx1 < M) { + float _max_3 = max_noftz(q_sq_bot + database_sq[batch_idx * M + db_idx1] - 2.0f * _tmem_load_0[reg_base + 3], 0.0f); + bot_d1 = _max_3; + } + int bot_take1 = ((bot_d1 < bot_d0) ? 1 : 0); + if (best_bot_d[11] > ((bot_take1 != 0) ? bot_d1 : bot_d0)) { + best_bot_d[11] = ((bot_take1 != 0) ? bot_d1 : bot_d0); + best_bot_i[11] = ((bot_take1 != 0) ? db_idx1 : db_idx0); + #pragma unroll + for (int kk_3 = 10; kk_3 >= 0; kk_3--) { + float lower0_d_1 = best_bot_d[kk_3 + 1]; + int lower0_i_1 = best_bot_i[kk_3 + 1]; + float upper0_d_1 = best_bot_d[kk_3]; + int upper0_i_1 = best_bot_i[kk_3]; + int swap0_up_1 = ((lower0_d_1 < upper0_d_1) ? 1 : 0); + best_bot_d[kk_3] = ((swap0_up_1 != 0) ? lower0_d_1 : upper0_d_1); + best_bot_i[kk_3] = ((swap0_up_1 != 0) ? lower0_i_1 : upper0_i_1); + best_bot_d[kk_3 + 1] = ((swap0_up_1 != 0) ? upper0_d_1 : lower0_d_1); + best_bot_i[kk_3 + 1] = ((swap0_up_1 != 0) ? upper0_i_1 : lower0_i_1); + } + if (best_bot_d[11] > ((bot_take1 != 0) ? bot_d0 : bot_d1)) { + best_bot_d[11] = ((bot_take1 != 0) ? bot_d0 : bot_d1); + best_bot_i[11] = ((bot_take1 != 0) ? db_idx0 : db_idx1); + #pragma unroll + for (int kk_4 = 10; kk_4 >= 0; kk_4--) { + float lower1_d_1 = best_bot_d[kk_4 + 1]; + int lower1_i_1 = best_bot_i[kk_4 + 1]; + float upper1_d_1 = best_bot_d[kk_4]; + int upper1_i_1 = best_bot_i[kk_4]; + int swap1_up_1 = ((lower1_d_1 < upper1_d_1) ? 1 : 0); + best_bot_d[kk_4] = ((swap1_up_1 != 0) ? lower1_d_1 : upper1_d_1); + best_bot_i[kk_4] = ((swap1_up_1 != 0) ? lower1_i_1 : upper1_i_1); + best_bot_d[kk_4 + 1] = ((swap1_up_1 != 0) ? upper1_d_1 : lower1_d_1); + best_bot_i[kk_4 + 1] = ((swap1_up_1 != 0) ? upper1_i_1 : lower1_i_1); + } + } + } + } + } + int top_slot_base = (row_top * 4 + slot) * TOP_K_MAX; + int bot_slot_base = (row_bot * 4 + slot) * TOP_K_MAX; + #pragma unroll + for (int kk_5 = 0; kk_5 < TOP_K_MAX; kk_5++) { + smem_local_d[top_slot_base + kk_5] = best_top_d[kk_5]; + smem_local_i[top_slot_base + kk_5] = best_top_i[kk_5]; + smem_local_d[bot_slot_base + kk_5] = best_bot_d[kk_5]; + smem_local_i[bot_slot_base + kk_5] = best_bot_i[kk_5]; + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + if (tid < BLOCK_Q) { + int row = tid; + int q_idx = off_q + row; + float head_d[4]; + int head_i[4]; + int head_k[4]; + #pragma unroll + for (int slot_idx = 0; slot_idx < 4; slot_idx++) { + int local_base = (row * 4 + slot_idx) * TOP_K_MAX; + head_k[slot_idx] = 0; + head_d[slot_idx] = smem_local_d[local_base]; + head_i[slot_idx] = smem_local_i[local_base]; + } + int out_base = ((split_idx * B + batch_idx) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float winner_d = head_d[0]; + int winner_i = head_i[0]; + int winner_slot = 0; + #pragma unroll + for (int slot_idx_1 = 1; slot_idx_1 < 4; slot_idx_1++) { + float cand_d = head_d[slot_idx_1]; + int take = ((cand_d < winner_d) ? 1 : 0); + winner_d = ((take != 0) ? cand_d : winner_d); + winner_i = ((take != 0) ? head_i[slot_idx_1] : winner_i); + winner_slot = ((take != 0) ? slot_idx_1 : winner_slot); + } + if (q_idx < Q && out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = winner_d; + *((int*)(partial_indices + (out_base + out_k))) = winner_i; + } + #pragma unroll + for (int slot_idx_2 = 0; slot_idx_2 < 4; slot_idx_2++) { + if (winner_slot == slot_idx_2) { + int next_head = head_k[slot_idx_2] + 1; + head_k[slot_idx_2] = next_head; + head_d[slot_idx_2] = 3.4e+38f; + head_i[slot_idx_2] = -1; + if (next_head < TOP_K_MAX) { + int local_base_1 = (row * 4 + slot_idx_2) * TOP_K_MAX; + head_d[slot_idx_2] = smem_local_d[local_base_1 + next_head]; + head_i[slot_idx_2] = smem_local_i[local_base_1 + next_head]; + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + } + // ---- Role: load ---- + } else if (warp == 4) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 4) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx_1 = bid; work_idx_1 < total_work; work_idx_1 += num_bids) { + int split_idx_1 = work_idx_1 % (unsigned int)split_count; + int query_work_1 = work_idx_1 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tiles; + int q_tile_1 = query_work_1 % num_q_tiles; + int off_q_1 = q_tile_1 * BLOCK_Q; + int global_q = batch_idx_1 * Q + off_q_1; + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + mbarrier_arrive_expect_tx(query_full_addr, 16384); + tma_3d_gmem2smem(smem_query_addr, tmap_query, 0, global_q, 0, query_full_addr); + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int off_m = db_tile_1 * BLOCK_M; + int global_m = batch_idx_1 * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + mbarrier_arrive_expect_tx(database_full_addr, 16384); + tma_3d_gmem2smem(smem_database_addr, tmap_database, 0, global_m, 0, database_full_addr); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 5) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + #pragma unroll 1 + for (unsigned int _work_idx = bid; _work_idx < total_work; _work_idx += num_bids) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = make_warp_uniform((smem_query_addr >> 4) & 0x3FFF); + int _mma_b_lo_0 = make_warp_uniform((smem_database_addr >> 4) & 0x3FFF); + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 68158608;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 506;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit(score_full_addr); + elect_commit(database_empty_addr); + } + elect_commit(query_empty_addr); + } + } + } + + // Cleanup + __syncthreads(); // barrier before TMEM dealloc + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::1.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(64)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::1.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0207.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0207.cu new file mode 100644 index 00000000..dc82d508 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0207.cu @@ -0,0 +1,675 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 64 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 16384 +#define SMEM_SMEM_QUERY_STRIDE 16384 +#define SMEM_SMEM_DATABASE_OFF 17408 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_LOCAL_D_OFF 34048 +#define SMEM_SMEM_LOCAL_D_STAGE_BYTES 16384 +#define SMEM_SMEM_LOCAL_D_STRIDE 16384 +#define SMEM_SMEM_LOCAL_I_OFF 50432 +#define SMEM_SMEM_LOCAL_I_STAGE_BYTES 16384 +#define SMEM_SMEM_LOCAL_I_STRIDE 16384 +#define SMEM_TOTAL 66816 +#define THREADS 128 +#define BLOCK_Q 64 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_MAX 32 +#define ROWS_COVERED 32 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "tcgen05.mma.cta_group::1.kind::f16 [%0], %1, %2, %3, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, %3, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit(int mbar_addr) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];\n\t" + "}\n" + :: "r"(mbar_addr)); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x32(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x32.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15," + " %16, %17, %18, %19, %20, %21, %22, %23," + " %24, %25, %26, %27, %28, %29, %30, %31}, [%32];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]), + "=f"(dst[16]), "=f"(dst[17]), "=f"(dst[18]), "=f"(dst[19]), + "=f"(dst[20]), "=f"(dst[21]), "=f"(dst[22]), "=f"(dst[23]), + "=f"(dst[24]), "=f"(dst[25]), "=f"(dst[26]), "=f"(dst[27]), + "=f"(dst[28]), "=f"(dst[29]), "=f"(dst[30]), "=f"(dst[31]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ float max_noftz(float a, float b) { + float c; + asm("max.f32 %0, %1, %2;" : "=f"(c) : "f"(a), "f"(b)); + return c; +} + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cta.global" + ".mbarrier::complete_tx::bytes" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit(int mbar_addr) { + asm volatile( + "tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ uint32_t make_warp_uniform(uint32_t val) { + uint32_t result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1f, 0xffffffff;" + : "=r"(result) : "r"(val)); + return result; +} + +extern "C" { + +__global__ __launch_bounds__(128, 1) void +kernel_knn_build_rag_microbucket_k32_q32rowld2uneven_f653_v1_stage1_q32rowld2uneven_f653_v1(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tiles, int num_db_tiles, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 17408); + const int smem_database_addr = smem + 17408; + float* smem_local_d = reinterpret_cast(smem_raw + 34048); + const int smem_local_d_addr = smem + 34048; + int* smem_local_i = reinterpret_cast(smem_raw + 50432); + const int smem_local_i_addr = smem + 50432; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 0, 1, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 16, 1, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 40, 2, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (64 columns, 64 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::1.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(64) : "memory"); + } + + __syncthreads(); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: compute ---- + if (warp <= 1) { + { // compute_main + int warp_id_in_role = (warp - 0); + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx = bid; work_idx < total_work; work_idx += num_bids) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tiles; + int q_tile = query_work % num_q_tiles; + int off_q = q_tile * BLOCK_Q; + int tmem_row_origin = warp_id_in_role * 32; + int logical_row_origin = warp_id_in_role * 16; + int row_top = logical_row_origin + lane / 4; + int row_bot = row_top + 8; + int lane_col = lane % 4; + int slot = lane_col; + int q_top = off_q + row_top; + int q_bot = off_q + row_bot; + int valid_top = ((q_top < Q) ? 1 : 0); + int valid_bot = ((q_bot < Q) ? 1 : 0); + float q_sq_top = 0.0f; + float q_sq_bot = 0.0f; + if (valid_top != 0) { + q_sq_top = query_sq[batch_idx * Q + q_top]; + } + if (valid_bot != 0) { + q_sq_bot = query_sq[batch_idx * Q + q_bot]; + } + float best_top_d[TOP_K_MAX]; + float best_bot_d[TOP_K_MAX]; + int best_top_i[TOP_K_MAX]; + int best_bot_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_top_d[kk] = 3.4e+38f; + best_bot_d[kk] = 3.4e+38f; + best_top_i[kk] = -1; + best_bot_i[kk] = -1; + } + int tiles_floor = num_db_tiles / split_count; + int extra_splits = num_db_tiles - tiles_floor * split_count; + int extra_before = split_idx; + if (extra_before > extra_splits) { + extra_before = extra_splits; + } + int split_tile_count = tiles_floor; + if (split_idx < extra_splits) { + split_tile_count = tiles_floor + 1; + } + int db_tile_start = split_idx * tiles_floor + extra_before; + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + if (split_tile_count > local_db_tile) { + int db_tile = db_tile_start + local_db_tile; + int db_start = db_tile * BLOCK_M; + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(tmem_row_origin << 16); + float _tmem_load_0[32]; + asm volatile( + "tcgen05.ld.sync.aligned.16x256b.x8.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31}, [%32];" + : "=r"(*reinterpret_cast(&_tmem_load_0[0])), "=r"(*reinterpret_cast(&_tmem_load_0[1])), "=r"(*reinterpret_cast(&_tmem_load_0[2])), "=r"(*reinterpret_cast(&_tmem_load_0[3])), "=r"(*reinterpret_cast(&_tmem_load_0[4])), "=r"(*reinterpret_cast(&_tmem_load_0[5])), "=r"(*reinterpret_cast(&_tmem_load_0[6])), "=r"(*reinterpret_cast(&_tmem_load_0[7])), "=r"(*reinterpret_cast(&_tmem_load_0[8])), "=r"(*reinterpret_cast(&_tmem_load_0[9])), "=r"(*reinterpret_cast(&_tmem_load_0[10])), "=r"(*reinterpret_cast(&_tmem_load_0[11])), "=r"(*reinterpret_cast(&_tmem_load_0[12])), "=r"(*reinterpret_cast(&_tmem_load_0[13])), "=r"(*reinterpret_cast(&_tmem_load_0[14])), "=r"(*reinterpret_cast(&_tmem_load_0[15])), "=r"(*reinterpret_cast(&_tmem_load_0[16])), "=r"(*reinterpret_cast(&_tmem_load_0[17])), "=r"(*reinterpret_cast(&_tmem_load_0[18])), "=r"(*reinterpret_cast(&_tmem_load_0[19])), "=r"(*reinterpret_cast(&_tmem_load_0[20])), "=r"(*reinterpret_cast(&_tmem_load_0[21])), "=r"(*reinterpret_cast(&_tmem_load_0[22])), "=r"(*reinterpret_cast(&_tmem_load_0[23])), "=r"(*reinterpret_cast(&_tmem_load_0[24])), "=r"(*reinterpret_cast(&_tmem_load_0[25])), "=r"(*reinterpret_cast(&_tmem_load_0[26])), "=r"(*reinterpret_cast(&_tmem_load_0[27])), "=r"(*reinterpret_cast(&_tmem_load_0[28])), "=r"(*reinterpret_cast(&_tmem_load_0[29])), "=r"(*reinterpret_cast(&_tmem_load_0[30])), "=r"(*reinterpret_cast(&_tmem_load_0[31])) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + if (elect_sync()) { + mbarrier_arrive(score_empty_addr); + } + #pragma unroll + for (int repeat = 0; repeat < 8; repeat++) { + const int reg_base = repeat * 4; + int col_base = repeat * 8 + lane_col * 2; + int db_idx0 = db_start + col_base; + int db_idx1 = db_idx0 + 1; + float top_d0 = 3.4e+38f; + float top_d1 = 3.4e+38f; + if (valid_top != 0 && db_idx0 < M) { + float _max_0 = max_noftz(q_sq_top + database_sq[batch_idx * M + db_idx0] - 2.0f * _tmem_load_0[reg_base], 0.0f); + top_d0 = _max_0; + } + if (valid_top != 0 && db_idx1 < M) { + float _max_1 = max_noftz(q_sq_top + database_sq[batch_idx * M + db_idx1] - 2.0f * _tmem_load_0[reg_base + 1], 0.0f); + top_d1 = _max_1; + } + int top_take1 = ((top_d1 < top_d0) ? 1 : 0); + if (best_top_d[31] > ((top_take1 != 0) ? top_d1 : top_d0)) { + best_top_d[31] = ((top_take1 != 0) ? top_d1 : top_d0); + best_top_i[31] = ((top_take1 != 0) ? db_idx1 : db_idx0); + #pragma unroll + for (int kk_1 = 30; kk_1 >= 0; kk_1--) { + float lower0_d = best_top_d[kk_1 + 1]; + int lower0_i = best_top_i[kk_1 + 1]; + float upper0_d = best_top_d[kk_1]; + int upper0_i = best_top_i[kk_1]; + int swap0_up = ((lower0_d < upper0_d) ? 1 : 0); + best_top_d[kk_1] = ((swap0_up != 0) ? lower0_d : upper0_d); + best_top_i[kk_1] = ((swap0_up != 0) ? lower0_i : upper0_i); + best_top_d[kk_1 + 1] = ((swap0_up != 0) ? upper0_d : lower0_d); + best_top_i[kk_1 + 1] = ((swap0_up != 0) ? upper0_i : lower0_i); + } + if (best_top_d[31] > ((top_take1 != 0) ? top_d0 : top_d1)) { + best_top_d[31] = ((top_take1 != 0) ? top_d0 : top_d1); + best_top_i[31] = ((top_take1 != 0) ? db_idx0 : db_idx1); + #pragma unroll + for (int kk_2 = 30; kk_2 >= 0; kk_2--) { + float lower1_d = best_top_d[kk_2 + 1]; + int lower1_i = best_top_i[kk_2 + 1]; + float upper1_d = best_top_d[kk_2]; + int upper1_i = best_top_i[kk_2]; + int swap1_up = ((lower1_d < upper1_d) ? 1 : 0); + best_top_d[kk_2] = ((swap1_up != 0) ? lower1_d : upper1_d); + best_top_i[kk_2] = ((swap1_up != 0) ? lower1_i : upper1_i); + best_top_d[kk_2 + 1] = ((swap1_up != 0) ? upper1_d : lower1_d); + best_top_i[kk_2 + 1] = ((swap1_up != 0) ? upper1_i : lower1_i); + } + } + } + float bot_d0 = 3.4e+38f; + float bot_d1 = 3.4e+38f; + if (valid_bot != 0 && db_idx0 < M) { + float _max_2 = max_noftz(q_sq_bot + database_sq[batch_idx * M + db_idx0] - 2.0f * _tmem_load_0[reg_base + 2], 0.0f); + bot_d0 = _max_2; + } + if (valid_bot != 0 && db_idx1 < M) { + float _max_3 = max_noftz(q_sq_bot + database_sq[batch_idx * M + db_idx1] - 2.0f * _tmem_load_0[reg_base + 3], 0.0f); + bot_d1 = _max_3; + } + int bot_take1 = ((bot_d1 < bot_d0) ? 1 : 0); + if (best_bot_d[31] > ((bot_take1 != 0) ? bot_d1 : bot_d0)) { + best_bot_d[31] = ((bot_take1 != 0) ? bot_d1 : bot_d0); + best_bot_i[31] = ((bot_take1 != 0) ? db_idx1 : db_idx0); + #pragma unroll + for (int kk_3 = 30; kk_3 >= 0; kk_3--) { + float lower0_d_1 = best_bot_d[kk_3 + 1]; + int lower0_i_1 = best_bot_i[kk_3 + 1]; + float upper0_d_1 = best_bot_d[kk_3]; + int upper0_i_1 = best_bot_i[kk_3]; + int swap0_up_1 = ((lower0_d_1 < upper0_d_1) ? 1 : 0); + best_bot_d[kk_3] = ((swap0_up_1 != 0) ? lower0_d_1 : upper0_d_1); + best_bot_i[kk_3] = ((swap0_up_1 != 0) ? lower0_i_1 : upper0_i_1); + best_bot_d[kk_3 + 1] = ((swap0_up_1 != 0) ? upper0_d_1 : lower0_d_1); + best_bot_i[kk_3 + 1] = ((swap0_up_1 != 0) ? upper0_i_1 : lower0_i_1); + } + if (best_bot_d[31] > ((bot_take1 != 0) ? bot_d0 : bot_d1)) { + best_bot_d[31] = ((bot_take1 != 0) ? bot_d0 : bot_d1); + best_bot_i[31] = ((bot_take1 != 0) ? db_idx0 : db_idx1); + #pragma unroll + for (int kk_4 = 30; kk_4 >= 0; kk_4--) { + float lower1_d_1 = best_bot_d[kk_4 + 1]; + int lower1_i_1 = best_bot_i[kk_4 + 1]; + float upper1_d_1 = best_bot_d[kk_4]; + int upper1_i_1 = best_bot_i[kk_4]; + int swap1_up_1 = ((lower1_d_1 < upper1_d_1) ? 1 : 0); + best_bot_d[kk_4] = ((swap1_up_1 != 0) ? lower1_d_1 : upper1_d_1); + best_bot_i[kk_4] = ((swap1_up_1 != 0) ? lower1_i_1 : upper1_i_1); + best_bot_d[kk_4 + 1] = ((swap1_up_1 != 0) ? upper1_d_1 : lower1_d_1); + best_bot_i[kk_4 + 1] = ((swap1_up_1 != 0) ? upper1_i_1 : lower1_i_1); + } + } + } + } + } + } + int top_slot_base = (row_top * 4 + slot) * TOP_K_MAX; + int bot_slot_base = (row_bot * 4 + slot) * TOP_K_MAX; + #pragma unroll + for (int kk_5 = 0; kk_5 < TOP_K_MAX; kk_5++) { + smem_local_d[top_slot_base + kk_5] = best_top_d[kk_5]; + smem_local_i[top_slot_base + kk_5] = best_top_i[kk_5]; + smem_local_d[bot_slot_base + kk_5] = best_bot_d[kk_5]; + smem_local_i[bot_slot_base + kk_5] = best_bot_i[kk_5]; + } + asm volatile("barrier.sync 8, %0;" :: "r"(64)); + if (tid < ROWS_COVERED) { + int row = tid; + int q_idx = off_q + row; + float head_d[4]; + int head_i[4]; + int head_k[4]; + #pragma unroll + for (int slot_idx = 0; slot_idx < 4; slot_idx++) { + int local_base = (row * 4 + slot_idx) * TOP_K_MAX; + head_k[slot_idx] = 0; + head_d[slot_idx] = smem_local_d[local_base]; + head_i[slot_idx] = smem_local_i[local_base]; + } + int out_base = ((split_idx * B + batch_idx) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float winner_d = head_d[0]; + int winner_i = head_i[0]; + int winner_slot = 0; + #pragma unroll + for (int slot_idx_1 = 1; slot_idx_1 < 4; slot_idx_1++) { + float cand_d = head_d[slot_idx_1]; + int take = ((cand_d < winner_d) ? 1 : 0); + winner_d = ((take != 0) ? cand_d : winner_d); + winner_i = ((take != 0) ? head_i[slot_idx_1] : winner_i); + winner_slot = ((take != 0) ? slot_idx_1 : winner_slot); + } + if (q_idx < Q && out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = winner_d; + *((int*)(partial_indices + (out_base + out_k))) = winner_i; + } + #pragma unroll + for (int slot_idx_2 = 0; slot_idx_2 < 4; slot_idx_2++) { + if (winner_slot == slot_idx_2) { + int next_head = head_k[slot_idx_2] + 1; + head_k[slot_idx_2] = next_head; + head_d[slot_idx_2] = 3.4e+38f; + head_i[slot_idx_2] = -1; + if (next_head < TOP_K_MAX) { + int local_base_1 = (row * 4 + slot_idx_2) * TOP_K_MAX; + head_d[slot_idx_2] = smem_local_d[local_base_1 + next_head]; + head_i[slot_idx_2] = smem_local_i[local_base_1 + next_head]; + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(64)); + } + } + // ---- Role: load ---- + } else if (warp == 2) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 2) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx_1 = bid; work_idx_1 < total_work; work_idx_1 += num_bids) { + int split_idx_1 = work_idx_1 % (unsigned int)split_count; + int query_work_1 = work_idx_1 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tiles; + int q_tile_1 = query_work_1 % num_q_tiles; + int off_q_1 = q_tile_1 * BLOCK_Q; + int global_q = batch_idx_1 * Q + off_q_1; + int tiles_floor_1 = num_db_tiles / split_count; + int extra_splits_1 = num_db_tiles - tiles_floor_1 * split_count; + int extra_before_1 = split_idx_1; + if (extra_before_1 > extra_splits_1) { + extra_before_1 = extra_splits_1; + } + int split_tile_count_1 = tiles_floor_1; + if (split_idx_1 < extra_splits_1) { + split_tile_count_1 = tiles_floor_1 + 1; + } + int db_tile_start_1 = split_idx_1 * tiles_floor_1 + extra_before_1; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + mbarrier_arrive_expect_tx(query_full_addr, 16384); + tma_3d_gmem2smem(smem_query_addr, tmap_query, 0, global_q, 0, query_full_addr); + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + if (split_tile_count_1 > local_db_tile_1) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int off_m = db_tile_1 * BLOCK_M; + int global_m = batch_idx_1 * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + mbarrier_arrive_expect_tx(database_full_addr, 16384); + tma_3d_gmem2smem(smem_database_addr, tmap_database, 0, global_m, 0, database_full_addr); + } + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 3) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = bid; work_idx_2 < total_work; work_idx_2 += num_bids) { + int split_idx_2 = work_idx_2 % (unsigned int)split_count; + int tiles_floor_2 = num_db_tiles / split_count; + int extra_splits_2 = num_db_tiles - tiles_floor_2 * split_count; + int split_tile_count_2 = tiles_floor_2; + if (split_idx_2 < extra_splits_2) { + split_tile_count_2 = tiles_floor_2 + 1; + } + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int local_db_tile_2 = 0; local_db_tile_2 < db_tiles_per_split; local_db_tile_2++) { + if (split_tile_count_2 > local_db_tile_2) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = make_warp_uniform((smem_query_addr >> 4) & 0x3FFF); + int _mma_b_lo_0 = make_warp_uniform((smem_database_addr >> 4) & 0x3FFF); + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 68158608;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 506;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit(score_full_addr); + elect_commit(database_empty_addr); + } + } + elect_commit(query_empty_addr); + } + } + } + + // Cleanup + __syncthreads(); // barrier before TMEM dealloc + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::1.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(64)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::1.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0208.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0208.cu new file mode 100644 index 00000000..577a13f9 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0208.cu @@ -0,0 +1,642 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 64 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 16384 +#define SMEM_SMEM_QUERY_STRIDE 16384 +#define SMEM_SMEM_DATABASE_OFF 17408 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_LOCAL_D_OFF 34048 +#define SMEM_SMEM_LOCAL_D_STAGE_BYTES 16384 +#define SMEM_SMEM_LOCAL_D_STRIDE 16384 +#define SMEM_SMEM_LOCAL_I_OFF 50432 +#define SMEM_SMEM_LOCAL_I_STAGE_BYTES 16384 +#define SMEM_SMEM_LOCAL_I_STRIDE 16384 +#define SMEM_TOTAL 66816 +#define THREADS 128 +#define BLOCK_Q 64 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_MAX 32 +#define ROWS_COVERED 32 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "tcgen05.mma.cta_group::1.kind::f16 [%0], %1, %2, %3, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, %3, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit(int mbar_addr) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];\n\t" + "}\n" + :: "r"(mbar_addr)); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x32(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x32.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15," + " %16, %17, %18, %19, %20, %21, %22, %23," + " %24, %25, %26, %27, %28, %29, %30, %31}, [%32];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]), + "=f"(dst[16]), "=f"(dst[17]), "=f"(dst[18]), "=f"(dst[19]), + "=f"(dst[20]), "=f"(dst[21]), "=f"(dst[22]), "=f"(dst[23]), + "=f"(dst[24]), "=f"(dst[25]), "=f"(dst[26]), "=f"(dst[27]), + "=f"(dst[28]), "=f"(dst[29]), "=f"(dst[30]), "=f"(dst[31]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ float max_noftz(float a, float b) { + float c; + asm("max.f32 %0, %1, %2;" : "=f"(c) : "f"(a), "f"(b)); + return c; +} + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cta.global" + ".mbarrier::complete_tx::bytes" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit(int mbar_addr) { + asm volatile( + "tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ uint32_t make_warp_uniform(uint32_t val) { + uint32_t result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1f, 0xffffffff;" + : "=r"(result) : "r"(val)); + return result; +} + +extern "C" { + +__global__ __launch_bounds__(128, 1) void +kernel_knn_build_rag_microbucket_k32rowld1warp_0077_v1_stage1_q32_k32_m64_rowld2_q32rowld2_f653_v1(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tiles, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 17408); + const int smem_database_addr = smem + 17408; + float* smem_local_d = reinterpret_cast(smem_raw + 34048); + const int smem_local_d_addr = smem + 34048; + int* smem_local_i = reinterpret_cast(smem_raw + 50432); + const int smem_local_i_addr = smem + 50432; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 0, 1, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 16, 1, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 40, 2, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (64 columns, 64 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::1.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(64) : "memory"); + } + + __syncthreads(); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: compute ---- + if (warp <= 1) { + { // compute_main + int warp_id_in_role = (warp - 0); + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx = bid; work_idx < total_work; work_idx += num_bids) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tiles; + int q_tile = query_work % num_q_tiles; + int off_q = q_tile * BLOCK_Q; + int tmem_row_origin = warp_id_in_role * 32; + int logical_row_origin = warp_id_in_role * 16; + int row_top = logical_row_origin + lane / 4; + int row_bot = row_top + 8; + int lane_col = lane % 4; + int slot = lane_col; + int q_top = off_q + row_top; + int q_bot = off_q + row_bot; + int valid_top = ((q_top < Q) ? 1 : 0); + int valid_bot = ((q_bot < Q) ? 1 : 0); + float q_sq_top = 0.0f; + float q_sq_bot = 0.0f; + if (valid_top != 0) { + q_sq_top = query_sq[batch_idx * Q + q_top]; + } + if (valid_bot != 0) { + q_sq_bot = query_sq[batch_idx * Q + q_bot]; + } + float best_top_d[TOP_K_MAX]; + float best_bot_d[TOP_K_MAX]; + int best_top_i[TOP_K_MAX]; + int best_bot_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_top_d[kk] = 3.4e+38f; + best_bot_d[kk] = 3.4e+38f; + best_top_i[kk] = -1; + best_bot_i[kk] = -1; + } + int db_tile_start = split_idx * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int db_start = db_tile * BLOCK_M; + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(tmem_row_origin << 16); + float _tmem_load_0[32]; + asm volatile( + "tcgen05.ld.sync.aligned.16x256b.x8.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31}, [%32];" + : "=r"(*reinterpret_cast(&_tmem_load_0[0])), "=r"(*reinterpret_cast(&_tmem_load_0[1])), "=r"(*reinterpret_cast(&_tmem_load_0[2])), "=r"(*reinterpret_cast(&_tmem_load_0[3])), "=r"(*reinterpret_cast(&_tmem_load_0[4])), "=r"(*reinterpret_cast(&_tmem_load_0[5])), "=r"(*reinterpret_cast(&_tmem_load_0[6])), "=r"(*reinterpret_cast(&_tmem_load_0[7])), "=r"(*reinterpret_cast(&_tmem_load_0[8])), "=r"(*reinterpret_cast(&_tmem_load_0[9])), "=r"(*reinterpret_cast(&_tmem_load_0[10])), "=r"(*reinterpret_cast(&_tmem_load_0[11])), "=r"(*reinterpret_cast(&_tmem_load_0[12])), "=r"(*reinterpret_cast(&_tmem_load_0[13])), "=r"(*reinterpret_cast(&_tmem_load_0[14])), "=r"(*reinterpret_cast(&_tmem_load_0[15])), "=r"(*reinterpret_cast(&_tmem_load_0[16])), "=r"(*reinterpret_cast(&_tmem_load_0[17])), "=r"(*reinterpret_cast(&_tmem_load_0[18])), "=r"(*reinterpret_cast(&_tmem_load_0[19])), "=r"(*reinterpret_cast(&_tmem_load_0[20])), "=r"(*reinterpret_cast(&_tmem_load_0[21])), "=r"(*reinterpret_cast(&_tmem_load_0[22])), "=r"(*reinterpret_cast(&_tmem_load_0[23])), "=r"(*reinterpret_cast(&_tmem_load_0[24])), "=r"(*reinterpret_cast(&_tmem_load_0[25])), "=r"(*reinterpret_cast(&_tmem_load_0[26])), "=r"(*reinterpret_cast(&_tmem_load_0[27])), "=r"(*reinterpret_cast(&_tmem_load_0[28])), "=r"(*reinterpret_cast(&_tmem_load_0[29])), "=r"(*reinterpret_cast(&_tmem_load_0[30])), "=r"(*reinterpret_cast(&_tmem_load_0[31])) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + if (elect_sync()) { + mbarrier_arrive(score_empty_addr); + } + #pragma unroll + for (int repeat = 0; repeat < 8; repeat++) { + const int reg_base = repeat * 4; + int col_base = repeat * 8 + lane_col * 2; + int db_idx0 = db_start + col_base; + int db_idx1 = db_idx0 + 1; + float top_d0 = 3.4e+38f; + float top_d1 = 3.4e+38f; + if (valid_top != 0 && db_idx0 < M) { + float _max_0 = max_noftz(q_sq_top + database_sq[batch_idx * M + db_idx0] - 2.0f * _tmem_load_0[reg_base], 0.0f); + top_d0 = _max_0; + } + if (valid_top != 0 && db_idx1 < M) { + float _max_1 = max_noftz(q_sq_top + database_sq[batch_idx * M + db_idx1] - 2.0f * _tmem_load_0[reg_base + 1], 0.0f); + top_d1 = _max_1; + } + int top_take1 = ((top_d1 < top_d0) ? 1 : 0); + if (best_top_d[31] > ((top_take1 != 0) ? top_d1 : top_d0)) { + best_top_d[31] = ((top_take1 != 0) ? top_d1 : top_d0); + best_top_i[31] = ((top_take1 != 0) ? db_idx1 : db_idx0); + #pragma unroll + for (int kk_1 = 30; kk_1 >= 0; kk_1--) { + float lower0_d = best_top_d[kk_1 + 1]; + int lower0_i = best_top_i[kk_1 + 1]; + float upper0_d = best_top_d[kk_1]; + int upper0_i = best_top_i[kk_1]; + int swap0_up = ((lower0_d < upper0_d) ? 1 : 0); + best_top_d[kk_1] = ((swap0_up != 0) ? lower0_d : upper0_d); + best_top_i[kk_1] = ((swap0_up != 0) ? lower0_i : upper0_i); + best_top_d[kk_1 + 1] = ((swap0_up != 0) ? upper0_d : lower0_d); + best_top_i[kk_1 + 1] = ((swap0_up != 0) ? upper0_i : lower0_i); + } + if (best_top_d[31] > ((top_take1 != 0) ? top_d0 : top_d1)) { + best_top_d[31] = ((top_take1 != 0) ? top_d0 : top_d1); + best_top_i[31] = ((top_take1 != 0) ? db_idx0 : db_idx1); + #pragma unroll + for (int kk_2 = 30; kk_2 >= 0; kk_2--) { + float lower1_d = best_top_d[kk_2 + 1]; + int lower1_i = best_top_i[kk_2 + 1]; + float upper1_d = best_top_d[kk_2]; + int upper1_i = best_top_i[kk_2]; + int swap1_up = ((lower1_d < upper1_d) ? 1 : 0); + best_top_d[kk_2] = ((swap1_up != 0) ? lower1_d : upper1_d); + best_top_i[kk_2] = ((swap1_up != 0) ? lower1_i : upper1_i); + best_top_d[kk_2 + 1] = ((swap1_up != 0) ? upper1_d : lower1_d); + best_top_i[kk_2 + 1] = ((swap1_up != 0) ? upper1_i : lower1_i); + } + } + } + float bot_d0 = 3.4e+38f; + float bot_d1 = 3.4e+38f; + if (valid_bot != 0 && db_idx0 < M) { + float _max_2 = max_noftz(q_sq_bot + database_sq[batch_idx * M + db_idx0] - 2.0f * _tmem_load_0[reg_base + 2], 0.0f); + bot_d0 = _max_2; + } + if (valid_bot != 0 && db_idx1 < M) { + float _max_3 = max_noftz(q_sq_bot + database_sq[batch_idx * M + db_idx1] - 2.0f * _tmem_load_0[reg_base + 3], 0.0f); + bot_d1 = _max_3; + } + int bot_take1 = ((bot_d1 < bot_d0) ? 1 : 0); + if (best_bot_d[31] > ((bot_take1 != 0) ? bot_d1 : bot_d0)) { + best_bot_d[31] = ((bot_take1 != 0) ? bot_d1 : bot_d0); + best_bot_i[31] = ((bot_take1 != 0) ? db_idx1 : db_idx0); + #pragma unroll + for (int kk_3 = 30; kk_3 >= 0; kk_3--) { + float lower0_d_1 = best_bot_d[kk_3 + 1]; + int lower0_i_1 = best_bot_i[kk_3 + 1]; + float upper0_d_1 = best_bot_d[kk_3]; + int upper0_i_1 = best_bot_i[kk_3]; + int swap0_up_1 = ((lower0_d_1 < upper0_d_1) ? 1 : 0); + best_bot_d[kk_3] = ((swap0_up_1 != 0) ? lower0_d_1 : upper0_d_1); + best_bot_i[kk_3] = ((swap0_up_1 != 0) ? lower0_i_1 : upper0_i_1); + best_bot_d[kk_3 + 1] = ((swap0_up_1 != 0) ? upper0_d_1 : lower0_d_1); + best_bot_i[kk_3 + 1] = ((swap0_up_1 != 0) ? upper0_i_1 : lower0_i_1); + } + if (best_bot_d[31] > ((bot_take1 != 0) ? bot_d0 : bot_d1)) { + best_bot_d[31] = ((bot_take1 != 0) ? bot_d0 : bot_d1); + best_bot_i[31] = ((bot_take1 != 0) ? db_idx0 : db_idx1); + #pragma unroll + for (int kk_4 = 30; kk_4 >= 0; kk_4--) { + float lower1_d_1 = best_bot_d[kk_4 + 1]; + int lower1_i_1 = best_bot_i[kk_4 + 1]; + float upper1_d_1 = best_bot_d[kk_4]; + int upper1_i_1 = best_bot_i[kk_4]; + int swap1_up_1 = ((lower1_d_1 < upper1_d_1) ? 1 : 0); + best_bot_d[kk_4] = ((swap1_up_1 != 0) ? lower1_d_1 : upper1_d_1); + best_bot_i[kk_4] = ((swap1_up_1 != 0) ? lower1_i_1 : upper1_i_1); + best_bot_d[kk_4 + 1] = ((swap1_up_1 != 0) ? upper1_d_1 : lower1_d_1); + best_bot_i[kk_4 + 1] = ((swap1_up_1 != 0) ? upper1_i_1 : lower1_i_1); + } + } + } + } + } + int top_slot_base = (row_top * 4 + slot) * TOP_K_MAX; + int bot_slot_base = (row_bot * 4 + slot) * TOP_K_MAX; + #pragma unroll + for (int kk_5 = 0; kk_5 < TOP_K_MAX; kk_5++) { + smem_local_d[top_slot_base + kk_5] = best_top_d[kk_5]; + smem_local_i[top_slot_base + kk_5] = best_top_i[kk_5]; + smem_local_d[bot_slot_base + kk_5] = best_bot_d[kk_5]; + smem_local_i[bot_slot_base + kk_5] = best_bot_i[kk_5]; + } + asm volatile("barrier.sync 8, %0;" :: "r"(64)); + if (tid < ROWS_COVERED) { + int row = tid; + int q_idx = off_q + row; + float head_d[4]; + int head_i[4]; + int head_k[4]; + #pragma unroll + for (int slot_idx = 0; slot_idx < 4; slot_idx++) { + int local_base = (row * 4 + slot_idx) * TOP_K_MAX; + head_k[slot_idx] = 0; + head_d[slot_idx] = smem_local_d[local_base]; + head_i[slot_idx] = smem_local_i[local_base]; + } + int out_base = ((split_idx * B + batch_idx) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float winner_d = head_d[0]; + int winner_i = head_i[0]; + int winner_slot = 0; + #pragma unroll + for (int slot_idx_1 = 1; slot_idx_1 < 4; slot_idx_1++) { + float cand_d = head_d[slot_idx_1]; + int take = ((cand_d < winner_d) ? 1 : 0); + winner_d = ((take != 0) ? cand_d : winner_d); + winner_i = ((take != 0) ? head_i[slot_idx_1] : winner_i); + winner_slot = ((take != 0) ? slot_idx_1 : winner_slot); + } + if (q_idx < Q && out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = winner_d; + *((int*)(partial_indices + (out_base + out_k))) = winner_i; + } + #pragma unroll + for (int slot_idx_2 = 0; slot_idx_2 < 4; slot_idx_2++) { + if (winner_slot == slot_idx_2) { + int next_head = head_k[slot_idx_2] + 1; + head_k[slot_idx_2] = next_head; + head_d[slot_idx_2] = 3.4e+38f; + head_i[slot_idx_2] = -1; + if (next_head < TOP_K_MAX) { + int local_base_1 = (row * 4 + slot_idx_2) * TOP_K_MAX; + head_d[slot_idx_2] = smem_local_d[local_base_1 + next_head]; + head_i[slot_idx_2] = smem_local_i[local_base_1 + next_head]; + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(64)); + } + } + // ---- Role: load ---- + } else if (warp == 2) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 2) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx_1 = bid; work_idx_1 < total_work; work_idx_1 += num_bids) { + int split_idx_1 = work_idx_1 % (unsigned int)split_count; + int query_work_1 = work_idx_1 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tiles; + int q_tile_1 = query_work_1 % num_q_tiles; + int off_q_1 = q_tile_1 * BLOCK_Q; + int global_q = batch_idx_1 * Q + off_q_1; + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + mbarrier_arrive_expect_tx(query_full_addr, 16384); + tma_3d_gmem2smem(smem_query_addr, tmap_query, 0, global_q, 0, query_full_addr); + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int off_m = db_tile_1 * BLOCK_M; + int global_m = batch_idx_1 * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + mbarrier_arrive_expect_tx(database_full_addr, 16384); + tma_3d_gmem2smem(smem_database_addr, tmap_database, 0, global_m, 0, database_full_addr); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 3) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = bid; work_idx_2 < total_work; work_idx_2 += num_bids) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = make_warp_uniform((smem_query_addr >> 4) & 0x3FFF); + int _mma_b_lo_0 = make_warp_uniform((smem_database_addr >> 4) & 0x3FFF); + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 68158608;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 506;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit(score_full_addr); + elect_commit(database_empty_addr); + } + elect_commit(query_empty_addr); + } + } + } + + // Cleanup + __syncthreads(); // barrier before TMEM dealloc + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::1.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(64)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::1.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0209.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0209.cu new file mode 100644 index 00000000..0c71404d --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0209.cu @@ -0,0 +1,625 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 64 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 16384 +#define SMEM_SMEM_QUERY_STRIDE 16384 +#define SMEM_SMEM_DATABASE_OFF 17408 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_LOCAL_D_OFF 34048 +#define SMEM_SMEM_LOCAL_D_STAGE_BYTES 16384 +#define SMEM_SMEM_LOCAL_D_STRIDE 16384 +#define SMEM_SMEM_LOCAL_I_OFF 50432 +#define SMEM_SMEM_LOCAL_I_STAGE_BYTES 16384 +#define SMEM_SMEM_LOCAL_I_STRIDE 16384 +#define SMEM_TOTAL 66816 +#define THREADS 128 +#define BLOCK_Q 64 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_MAX 32 +#define ROWS_COVERED 31 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "tcgen05.mma.cta_group::1.kind::f16 [%0], %1, %2, %3, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, %3, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit(int mbar_addr) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];\n\t" + "}\n" + :: "r"(mbar_addr)); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x32(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x32.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15," + " %16, %17, %18, %19, %20, %21, %22, %23," + " %24, %25, %26, %27, %28, %29, %30, %31}, [%32];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]), + "=f"(dst[16]), "=f"(dst[17]), "=f"(dst[18]), "=f"(dst[19]), + "=f"(dst[20]), "=f"(dst[21]), "=f"(dst[22]), "=f"(dst[23]), + "=f"(dst[24]), "=f"(dst[25]), "=f"(dst[26]), "=f"(dst[27]), + "=f"(dst[28]), "=f"(dst[29]), "=f"(dst[30]), "=f"(dst[31]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ float max_noftz(float a, float b) { + float c; + asm("max.f32 %0, %1, %2;" : "=f"(c) : "f"(a), "f"(b)); + return c; +} + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cta.global" + ".mbarrier::complete_tx::bytes" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit(int mbar_addr) { + asm volatile( + "tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ uint32_t make_warp_uniform(uint32_t val) { + uint32_t result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1f, 0xffffffff;" + : "=r"(result) : "r"(val)); + return result; +} + +extern "C" { + +__global__ __launch_bounds__(128, 1) void +kernel_knn_build_rag_microbucket_k32_0cb5_q31tail_v2_stage1_q31exact_0cb5_v2(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tiles, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 17408); + const int smem_database_addr = smem + 17408; + float* smem_local_d = reinterpret_cast(smem_raw + 34048); + const int smem_local_d_addr = smem + 34048; + int* smem_local_i = reinterpret_cast(smem_raw + 50432); + const int smem_local_i_addr = smem + 50432; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 0, 1, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 16, 1, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 40, 2, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (64 columns, 64 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::1.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(64) : "memory"); + } + + __syncthreads(); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: compute ---- + if (warp <= 1) { + { // compute_main + int warp_id_in_role = (warp - 0); + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx = bid; work_idx < total_work; work_idx += num_bids) { + int split_idx = work_idx % (unsigned int)split_count; + int tmem_row_origin = warp_id_in_role * 32; + int logical_row_origin = warp_id_in_role * 16; + int row_top = logical_row_origin + lane / 4; + int row_bot = row_top + 8; + int lane_col = lane % 4; + int slot = lane_col; + int q_top = row_top; + int q_bot = row_bot; + int valid_bot = ((q_bot < ROWS_COVERED) ? 1 : 0); + float q_sq_top = query_sq[q_top]; + float q_sq_bot = 0.0f; + if (valid_bot != 0) { + q_sq_bot = query_sq[q_bot]; + } + float best_top_d[TOP_K_MAX]; + float best_bot_d[TOP_K_MAX]; + int best_top_i[TOP_K_MAX]; + int best_bot_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_top_d[kk] = 3.4e+38f; + best_bot_d[kk] = 3.4e+38f; + best_top_i[kk] = -1; + best_bot_i[kk] = -1; + } + int db_tile_start = split_idx * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int db_start = db_tile * BLOCK_M; + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(tmem_row_origin << 16); + float _tmem_load_0[32]; + asm volatile( + "tcgen05.ld.sync.aligned.16x256b.x8.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31}, [%32];" + : "=r"(*reinterpret_cast(&_tmem_load_0[0])), "=r"(*reinterpret_cast(&_tmem_load_0[1])), "=r"(*reinterpret_cast(&_tmem_load_0[2])), "=r"(*reinterpret_cast(&_tmem_load_0[3])), "=r"(*reinterpret_cast(&_tmem_load_0[4])), "=r"(*reinterpret_cast(&_tmem_load_0[5])), "=r"(*reinterpret_cast(&_tmem_load_0[6])), "=r"(*reinterpret_cast(&_tmem_load_0[7])), "=r"(*reinterpret_cast(&_tmem_load_0[8])), "=r"(*reinterpret_cast(&_tmem_load_0[9])), "=r"(*reinterpret_cast(&_tmem_load_0[10])), "=r"(*reinterpret_cast(&_tmem_load_0[11])), "=r"(*reinterpret_cast(&_tmem_load_0[12])), "=r"(*reinterpret_cast(&_tmem_load_0[13])), "=r"(*reinterpret_cast(&_tmem_load_0[14])), "=r"(*reinterpret_cast(&_tmem_load_0[15])), "=r"(*reinterpret_cast(&_tmem_load_0[16])), "=r"(*reinterpret_cast(&_tmem_load_0[17])), "=r"(*reinterpret_cast(&_tmem_load_0[18])), "=r"(*reinterpret_cast(&_tmem_load_0[19])), "=r"(*reinterpret_cast(&_tmem_load_0[20])), "=r"(*reinterpret_cast(&_tmem_load_0[21])), "=r"(*reinterpret_cast(&_tmem_load_0[22])), "=r"(*reinterpret_cast(&_tmem_load_0[23])), "=r"(*reinterpret_cast(&_tmem_load_0[24])), "=r"(*reinterpret_cast(&_tmem_load_0[25])), "=r"(*reinterpret_cast(&_tmem_load_0[26])), "=r"(*reinterpret_cast(&_tmem_load_0[27])), "=r"(*reinterpret_cast(&_tmem_load_0[28])), "=r"(*reinterpret_cast(&_tmem_load_0[29])), "=r"(*reinterpret_cast(&_tmem_load_0[30])), "=r"(*reinterpret_cast(&_tmem_load_0[31])) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + if (elect_sync()) { + mbarrier_arrive(score_empty_addr); + } + #pragma unroll + for (int repeat = 0; repeat < 8; repeat++) { + const int reg_base = repeat * 4; + int col_base = repeat * 8 + lane_col * 2; + int db_idx0 = db_start + col_base; + int db_idx1 = db_idx0 + 1; + float top_d0 = 3.4e+38f; + float top_d1 = 3.4e+38f; + if (db_idx0 < M) { + float _max_0 = max_noftz(q_sq_top + database_sq[db_idx0] - 2.0f * _tmem_load_0[reg_base], 0.0f); + top_d0 = _max_0; + } + if (db_idx1 < M) { + float _max_1 = max_noftz(q_sq_top + database_sq[db_idx1] - 2.0f * _tmem_load_0[reg_base + 1], 0.0f); + top_d1 = _max_1; + } + int top_take1 = ((top_d1 < top_d0) ? 1 : 0); + if (best_top_d[31] > ((top_take1 != 0) ? top_d1 : top_d0)) { + best_top_d[31] = ((top_take1 != 0) ? top_d1 : top_d0); + best_top_i[31] = ((top_take1 != 0) ? db_idx1 : db_idx0); + #pragma unroll + for (int kk_1 = 30; kk_1 >= 0; kk_1--) { + float lower0_d = best_top_d[kk_1 + 1]; + int lower0_i = best_top_i[kk_1 + 1]; + float upper0_d = best_top_d[kk_1]; + int upper0_i = best_top_i[kk_1]; + int swap0_up = ((lower0_d < upper0_d) ? 1 : 0); + best_top_d[kk_1] = ((swap0_up != 0) ? lower0_d : upper0_d); + best_top_i[kk_1] = ((swap0_up != 0) ? lower0_i : upper0_i); + best_top_d[kk_1 + 1] = ((swap0_up != 0) ? upper0_d : lower0_d); + best_top_i[kk_1 + 1] = ((swap0_up != 0) ? upper0_i : lower0_i); + } + if (best_top_d[31] > ((top_take1 != 0) ? top_d0 : top_d1)) { + best_top_d[31] = ((top_take1 != 0) ? top_d0 : top_d1); + best_top_i[31] = ((top_take1 != 0) ? db_idx0 : db_idx1); + #pragma unroll + for (int kk_2 = 30; kk_2 >= 0; kk_2--) { + float lower1_d = best_top_d[kk_2 + 1]; + int lower1_i = best_top_i[kk_2 + 1]; + float upper1_d = best_top_d[kk_2]; + int upper1_i = best_top_i[kk_2]; + int swap1_up = ((lower1_d < upper1_d) ? 1 : 0); + best_top_d[kk_2] = ((swap1_up != 0) ? lower1_d : upper1_d); + best_top_i[kk_2] = ((swap1_up != 0) ? lower1_i : upper1_i); + best_top_d[kk_2 + 1] = ((swap1_up != 0) ? upper1_d : lower1_d); + best_top_i[kk_2 + 1] = ((swap1_up != 0) ? upper1_i : lower1_i); + } + } + } + float bot_d0 = 3.4e+38f; + float bot_d1 = 3.4e+38f; + if (valid_bot != 0 && db_idx0 < M) { + float _max_2 = max_noftz(q_sq_bot + database_sq[db_idx0] - 2.0f * _tmem_load_0[reg_base + 2], 0.0f); + bot_d0 = _max_2; + } + if (valid_bot != 0 && db_idx1 < M) { + float _max_3 = max_noftz(q_sq_bot + database_sq[db_idx1] - 2.0f * _tmem_load_0[reg_base + 3], 0.0f); + bot_d1 = _max_3; + } + int bot_take1 = ((bot_d1 < bot_d0) ? 1 : 0); + if (best_bot_d[31] > ((bot_take1 != 0) ? bot_d1 : bot_d0)) { + best_bot_d[31] = ((bot_take1 != 0) ? bot_d1 : bot_d0); + best_bot_i[31] = ((bot_take1 != 0) ? db_idx1 : db_idx0); + #pragma unroll + for (int kk_3 = 30; kk_3 >= 0; kk_3--) { + float lower0_d_1 = best_bot_d[kk_3 + 1]; + int lower0_i_1 = best_bot_i[kk_3 + 1]; + float upper0_d_1 = best_bot_d[kk_3]; + int upper0_i_1 = best_bot_i[kk_3]; + int swap0_up_1 = ((lower0_d_1 < upper0_d_1) ? 1 : 0); + best_bot_d[kk_3] = ((swap0_up_1 != 0) ? lower0_d_1 : upper0_d_1); + best_bot_i[kk_3] = ((swap0_up_1 != 0) ? lower0_i_1 : upper0_i_1); + best_bot_d[kk_3 + 1] = ((swap0_up_1 != 0) ? upper0_d_1 : lower0_d_1); + best_bot_i[kk_3 + 1] = ((swap0_up_1 != 0) ? upper0_i_1 : lower0_i_1); + } + if (best_bot_d[31] > ((bot_take1 != 0) ? bot_d0 : bot_d1)) { + best_bot_d[31] = ((bot_take1 != 0) ? bot_d0 : bot_d1); + best_bot_i[31] = ((bot_take1 != 0) ? db_idx0 : db_idx1); + #pragma unroll + for (int kk_4 = 30; kk_4 >= 0; kk_4--) { + float lower1_d_1 = best_bot_d[kk_4 + 1]; + int lower1_i_1 = best_bot_i[kk_4 + 1]; + float upper1_d_1 = best_bot_d[kk_4]; + int upper1_i_1 = best_bot_i[kk_4]; + int swap1_up_1 = ((lower1_d_1 < upper1_d_1) ? 1 : 0); + best_bot_d[kk_4] = ((swap1_up_1 != 0) ? lower1_d_1 : upper1_d_1); + best_bot_i[kk_4] = ((swap1_up_1 != 0) ? lower1_i_1 : upper1_i_1); + best_bot_d[kk_4 + 1] = ((swap1_up_1 != 0) ? upper1_d_1 : lower1_d_1); + best_bot_i[kk_4 + 1] = ((swap1_up_1 != 0) ? upper1_i_1 : lower1_i_1); + } + } + } + } + } + int top_slot_base = (row_top * 4 + slot) * TOP_K_MAX; + int bot_slot_base = (row_bot * 4 + slot) * TOP_K_MAX; + #pragma unroll + for (int kk_5 = 0; kk_5 < TOP_K_MAX; kk_5++) { + smem_local_d[top_slot_base + kk_5] = best_top_d[kk_5]; + smem_local_i[top_slot_base + kk_5] = best_top_i[kk_5]; + smem_local_d[bot_slot_base + kk_5] = best_bot_d[kk_5]; + smem_local_i[bot_slot_base + kk_5] = best_bot_i[kk_5]; + } + asm volatile("barrier.sync 8, %0;" :: "r"(64)); + if (tid < ROWS_COVERED) { + int row = tid; + float head_d[4]; + int head_i[4]; + int head_k[4]; + #pragma unroll + for (int slot_idx = 0; slot_idx < 4; slot_idx++) { + int local_base = (row * 4 + slot_idx) * TOP_K_MAX; + head_k[slot_idx] = 0; + head_d[slot_idx] = smem_local_d[local_base]; + head_i[slot_idx] = smem_local_i[local_base]; + } + int out_base = (split_idx * ROWS_COVERED + row) * TOP_K_MAX; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float winner_d = head_d[0]; + int winner_i = head_i[0]; + int winner_slot = 0; + #pragma unroll + for (int slot_idx_1 = 1; slot_idx_1 < 4; slot_idx_1++) { + float cand_d = head_d[slot_idx_1]; + int take = ((cand_d < winner_d) ? 1 : 0); + winner_d = ((take != 0) ? cand_d : winner_d); + winner_i = ((take != 0) ? head_i[slot_idx_1] : winner_i); + winner_slot = ((take != 0) ? slot_idx_1 : winner_slot); + } + *((float*)(partial_dists + (out_base + out_k))) = winner_d; + *((int*)(partial_indices + (out_base + out_k))) = winner_i; + #pragma unroll + for (int slot_idx_2 = 0; slot_idx_2 < 4; slot_idx_2++) { + if (winner_slot == slot_idx_2) { + int next_head = head_k[slot_idx_2] + 1; + head_k[slot_idx_2] = next_head; + head_d[slot_idx_2] = 3.4e+38f; + head_i[slot_idx_2] = -1; + if (next_head < TOP_K_MAX) { + int local_base_1 = (row * 4 + slot_idx_2) * TOP_K_MAX; + head_d[slot_idx_2] = smem_local_d[local_base_1 + next_head]; + head_i[slot_idx_2] = smem_local_i[local_base_1 + next_head]; + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(64)); + } + } + // ---- Role: load ---- + } else if (warp == 2) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 2) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx_1 = bid; work_idx_1 < total_work; work_idx_1 += num_bids) { + int split_idx_1 = work_idx_1 % (unsigned int)split_count; + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + mbarrier_arrive_expect_tx(query_full_addr, 16384); + tma_3d_gmem2smem(smem_query_addr, tmap_query, 0, 0, 0, query_full_addr); + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int off_m = db_tile_1 * BLOCK_M; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + mbarrier_arrive_expect_tx(database_full_addr, 16384); + tma_3d_gmem2smem(smem_database_addr, tmap_database, 0, off_m, 0, database_full_addr); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 3) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + #pragma unroll 1 + for (unsigned int _work_idx = bid; _work_idx < total_work; _work_idx += num_bids) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = make_warp_uniform((smem_query_addr >> 4) & 0x3FFF); + int _mma_b_lo_0 = make_warp_uniform((smem_database_addr >> 4) & 0x3FFF); + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 68158608;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 506;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit(score_full_addr); + elect_commit(database_empty_addr); + } + elect_commit(query_empty_addr); + } + } + } + + // Cleanup + __syncthreads(); // barrier before TMEM dealloc + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::1.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(64)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::1.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0210.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0210.cu new file mode 100644 index 00000000..f45b3728 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0210.cu @@ -0,0 +1,607 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 64 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 16384 +#define SMEM_SMEM_QUERY_STRIDE 16384 +#define SMEM_SMEM_DATABASE_OFF 17408 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_UPPER_DOTS_OFF 34048 +#define SMEM_SMEM_UPPER_DOTS_STAGE_BYTES 2048 +#define SMEM_SMEM_UPPER_DOTS_STRIDE 2048 +#define SMEM_SMEM_LOCAL_D_OFF 36096 +#define SMEM_SMEM_LOCAL_D_STAGE_BYTES 8192 +#define SMEM_SMEM_LOCAL_D_STRIDE 8192 +#define SMEM_SMEM_LOCAL_I_OFF 44288 +#define SMEM_SMEM_LOCAL_I_STAGE_BYTES 8192 +#define SMEM_SMEM_LOCAL_I_STRIDE 8192 +#define SMEM_TOTAL 52480 +#define THREADS 128 +#define BLOCK_Q 64 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_MAX 32 +#define ROWS_COVERED 16 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "tcgen05.mma.cta_group::1.kind::f16 [%0], %1, %2, %3, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, %3, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit(int mbar_addr) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];\n\t" + "}\n" + :: "r"(mbar_addr)); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x32(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x32.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15," + " %16, %17, %18, %19, %20, %21, %22, %23," + " %24, %25, %26, %27, %28, %29, %30, %31}, [%32];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]), + "=f"(dst[16]), "=f"(dst[17]), "=f"(dst[18]), "=f"(dst[19]), + "=f"(dst[20]), "=f"(dst[21]), "=f"(dst[22]), "=f"(dst[23]), + "=f"(dst[24]), "=f"(dst[25]), "=f"(dst[26]), "=f"(dst[27]), + "=f"(dst[28]), "=f"(dst[29]), "=f"(dst[30]), "=f"(dst[31]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ float max_noftz(float a, float b) { + float c; + asm("max.f32 %0, %1, %2;" : "=f"(c) : "f"(a), "f"(b)); + return c; +} + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cta.global" + ".mbarrier::complete_tx::bytes" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit(int mbar_addr) { + asm volatile( + "tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ uint32_t make_warp_uniform(uint32_t val) { + uint32_t result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1f, 0xffffffff;" + : "=r"(result) : "r"(val)); + return result; +} + +extern "C" { + +__global__ __launch_bounds__(128, 1) void +kernel_knn_build_rag_microbucket_k32_q16irreg2warp_a444_v2_stage1_q16_rowld1_2warp_q16irreg2warp_a444_v2(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tiles, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 17408); + const int smem_database_addr = smem + 17408; + float* smem_upper_dots = reinterpret_cast(smem_raw + 34048); + const int smem_upper_dots_addr = smem + 34048; + float* smem_local_d = reinterpret_cast(smem_raw + 36096); + const int smem_local_d_addr = smem + 36096; + int* smem_local_i = reinterpret_cast(smem_raw + 44288); + const int smem_local_i_addr = smem + 44288; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 0, 1, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 16, 1, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 40, 2, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (64 columns, 64 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::1.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(64) : "memory"); + } + + __syncthreads(); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: compute ---- + if (warp <= 1) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx = bid; work_idx < total_work; work_idx += num_bids) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tiles; + int q_tile = query_work % num_q_tiles; + int off_q = q_tile * BLOCK_Q; + int row = warp * 8 + lane / 4; + int lane_col = lane % 4; + int slot = lane_col; + int q_idx = off_q + row; + int valid_row = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_row != 0) { + q_sq_val = query_sq[batch_idx * Q + q_idx]; + } + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + int db_tile_start = split_idx * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int db_start = db_tile * BLOCK_M; + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + float _tmem_load_0[32]; + asm volatile( + "tcgen05.ld.sync.aligned.16x256b.x8.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31}, [%32];" + : "=r"(*reinterpret_cast(&_tmem_load_0[0])), "=r"(*reinterpret_cast(&_tmem_load_0[1])), "=r"(*reinterpret_cast(&_tmem_load_0[2])), "=r"(*reinterpret_cast(&_tmem_load_0[3])), "=r"(*reinterpret_cast(&_tmem_load_0[4])), "=r"(*reinterpret_cast(&_tmem_load_0[5])), "=r"(*reinterpret_cast(&_tmem_load_0[6])), "=r"(*reinterpret_cast(&_tmem_load_0[7])), "=r"(*reinterpret_cast(&_tmem_load_0[8])), "=r"(*reinterpret_cast(&_tmem_load_0[9])), "=r"(*reinterpret_cast(&_tmem_load_0[10])), "=r"(*reinterpret_cast(&_tmem_load_0[11])), "=r"(*reinterpret_cast(&_tmem_load_0[12])), "=r"(*reinterpret_cast(&_tmem_load_0[13])), "=r"(*reinterpret_cast(&_tmem_load_0[14])), "=r"(*reinterpret_cast(&_tmem_load_0[15])), "=r"(*reinterpret_cast(&_tmem_load_0[16])), "=r"(*reinterpret_cast(&_tmem_load_0[17])), "=r"(*reinterpret_cast(&_tmem_load_0[18])), "=r"(*reinterpret_cast(&_tmem_load_0[19])), "=r"(*reinterpret_cast(&_tmem_load_0[20])), "=r"(*reinterpret_cast(&_tmem_load_0[21])), "=r"(*reinterpret_cast(&_tmem_load_0[22])), "=r"(*reinterpret_cast(&_tmem_load_0[23])), "=r"(*reinterpret_cast(&_tmem_load_0[24])), "=r"(*reinterpret_cast(&_tmem_load_0[25])), "=r"(*reinterpret_cast(&_tmem_load_0[26])), "=r"(*reinterpret_cast(&_tmem_load_0[27])), "=r"(*reinterpret_cast(&_tmem_load_0[28])), "=r"(*reinterpret_cast(&_tmem_load_0[29])), "=r"(*reinterpret_cast(&_tmem_load_0[30])), "=r"(*reinterpret_cast(&_tmem_load_0[31])) + : "r"(taddr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + if (warp == 0) { + int scratch_row = lane / 4; + #pragma unroll + for (int repeat = 0; repeat < 8; repeat++) { + const int reg_base = repeat * 4; + int scratch_col = repeat * 8 + lane_col * 2; + int scratch_base = scratch_row * 64 + scratch_col; + smem_upper_dots[scratch_base] = _tmem_load_0[reg_base + 2]; + smem_upper_dots[scratch_base + 1] = _tmem_load_0[reg_base + 3]; + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(64)); + if (elect_sync()) { + mbarrier_arrive(score_empty_addr); + } + #pragma unroll + for (int repeat_1 = 0; repeat_1 < 8; repeat_1++) { + const int reg_base_1 = repeat_1 * 4; + int col_base = repeat_1 * 8 + lane_col * 2; + int db_idx0 = db_start + col_base; + int db_idx1 = db_idx0 + 1; + float dot0 = _tmem_load_0[reg_base_1]; + float dot1 = _tmem_load_0[reg_base_1 + 1]; + if (warp != 0) { + int scratch_row_1 = lane / 4; + int scratch_base_1 = scratch_row_1 * 64 + col_base; + dot0 = smem_upper_dots[scratch_base_1]; + dot1 = smem_upper_dots[scratch_base_1 + 1]; + } + float cand0_d = 3.4e+38f; + float cand1_d = 3.4e+38f; + if (valid_row != 0 && db_idx0 < M) { + float _max_0 = max_noftz(q_sq_val + database_sq[batch_idx * M + db_idx0] - 2.0f * dot0, 0.0f); + cand0_d = _max_0; + } + if (valid_row != 0 && db_idx1 < M) { + float _max_1 = max_noftz(q_sq_val + database_sq[batch_idx * M + db_idx1] - 2.0f * dot1, 0.0f); + cand1_d = _max_1; + } + int take1 = ((cand1_d < cand0_d) ? 1 : 0); + if (best_d[31] > ((take1 != 0) ? cand1_d : cand0_d)) { + best_d[31] = ((take1 != 0) ? cand1_d : cand0_d); + best_i[31] = ((take1 != 0) ? db_idx1 : db_idx0); + #pragma unroll + for (int kk_1 = 30; kk_1 >= 0; kk_1--) { + float lower0_d = best_d[kk_1 + 1]; + int lower0_i = best_i[kk_1 + 1]; + float upper0_d = best_d[kk_1]; + int upper0_i = best_i[kk_1]; + int swap0_up = ((lower0_d < upper0_d) ? 1 : 0); + best_d[kk_1] = ((swap0_up != 0) ? lower0_d : upper0_d); + best_i[kk_1] = ((swap0_up != 0) ? lower0_i : upper0_i); + best_d[kk_1 + 1] = ((swap0_up != 0) ? upper0_d : lower0_d); + best_i[kk_1 + 1] = ((swap0_up != 0) ? upper0_i : lower0_i); + } + if (best_d[31] > ((take1 != 0) ? cand0_d : cand1_d)) { + best_d[31] = ((take1 != 0) ? cand0_d : cand1_d); + best_i[31] = ((take1 != 0) ? db_idx0 : db_idx1); + #pragma unroll + for (int kk_2 = 30; kk_2 >= 0; kk_2--) { + float lower1_d = best_d[kk_2 + 1]; + int lower1_i = best_i[kk_2 + 1]; + float upper1_d = best_d[kk_2]; + int upper1_i = best_i[kk_2]; + int swap1_up = ((lower1_d < upper1_d) ? 1 : 0); + best_d[kk_2] = ((swap1_up != 0) ? lower1_d : upper1_d); + best_i[kk_2] = ((swap1_up != 0) ? lower1_i : upper1_i); + best_d[kk_2 + 1] = ((swap1_up != 0) ? upper1_d : lower1_d); + best_i[kk_2 + 1] = ((swap1_up != 0) ? upper1_i : lower1_i); + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(64)); + } + int slot_base = (row * 4 + slot) * TOP_K_MAX; + #pragma unroll + for (int kk_3 = 0; kk_3 < TOP_K_MAX; kk_3++) { + smem_local_d[slot_base + kk_3] = best_d[kk_3]; + smem_local_i[slot_base + kk_3] = best_i[kk_3]; + } + asm volatile("barrier.sync 8, %0;" :: "r"(64)); + if (tid < ROWS_COVERED) { + int out_row = tid; + int out_q_idx = off_q + out_row; + float head_d[4]; + int head_i[4]; + int head_k[4]; + #pragma unroll + for (int slot_idx = 0; slot_idx < 4; slot_idx++) { + int local_base = (out_row * 4 + slot_idx) * TOP_K_MAX; + head_k[slot_idx] = 0; + head_d[slot_idx] = smem_local_d[local_base]; + head_i[slot_idx] = smem_local_i[local_base]; + } + int out_base = ((split_idx * B + batch_idx) * Q + out_q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float winner_d = head_d[0]; + int winner_i = head_i[0]; + int winner_slot = 0; + #pragma unroll + for (int slot_idx_1 = 1; slot_idx_1 < 4; slot_idx_1++) { + float cand_d = head_d[slot_idx_1]; + int take = ((cand_d < winner_d) ? 1 : 0); + winner_d = ((take != 0) ? cand_d : winner_d); + winner_i = ((take != 0) ? head_i[slot_idx_1] : winner_i); + winner_slot = ((take != 0) ? slot_idx_1 : winner_slot); + } + if (out_q_idx < Q && out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = winner_d; + *((int*)(partial_indices + (out_base + out_k))) = winner_i; + } + #pragma unroll + for (int slot_idx_2 = 0; slot_idx_2 < 4; slot_idx_2++) { + if (winner_slot == slot_idx_2) { + int next_head = head_k[slot_idx_2] + 1; + head_k[slot_idx_2] = next_head; + head_d[slot_idx_2] = 3.4e+38f; + head_i[slot_idx_2] = -1; + if (next_head < TOP_K_MAX) { + int local_base_1 = (out_row * 4 + slot_idx_2) * TOP_K_MAX; + head_d[slot_idx_2] = smem_local_d[local_base_1 + next_head]; + head_i[slot_idx_2] = smem_local_i[local_base_1 + next_head]; + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(64)); + } + } + // ---- Role: load ---- + } else if (warp == 2) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 2) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx_1 = bid; work_idx_1 < total_work; work_idx_1 += num_bids) { + int split_idx_1 = work_idx_1 % (unsigned int)split_count; + int query_work_1 = work_idx_1 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tiles; + int q_tile_1 = query_work_1 % num_q_tiles; + int off_q_1 = q_tile_1 * BLOCK_Q; + int global_q = batch_idx_1 * Q + off_q_1; + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + mbarrier_arrive_expect_tx(query_full_addr, 16384); + tma_3d_gmem2smem(smem_query_addr, tmap_query, 0, global_q, 0, query_full_addr); + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int off_m = db_tile_1 * BLOCK_M; + int global_m = batch_idx_1 * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + mbarrier_arrive_expect_tx(database_full_addr, 16384); + tma_3d_gmem2smem(smem_database_addr, tmap_database, 0, global_m, 0, database_full_addr); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 3) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = bid; work_idx_2 < total_work; work_idx_2 += num_bids) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = make_warp_uniform((smem_query_addr >> 4) & 0x3FFF); + int _mma_b_lo_0 = make_warp_uniform((smem_database_addr >> 4) & 0x3FFF); + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 68158608;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 506;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit(score_full_addr); + elect_commit(database_empty_addr); + } + elect_commit(query_empty_addr); + } + } + } + + // Cleanup + __syncthreads(); // barrier before TMEM dealloc + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::1.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(64)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::1.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0211.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0211.cu new file mode 100644 index 00000000..72e36055 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0211.cu @@ -0,0 +1,641 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 64 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 16384 +#define SMEM_SMEM_QUERY_STRIDE 16384 +#define SMEM_SMEM_DATABASE_OFF 17408 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_LOCAL_D_OFF 34048 +#define SMEM_SMEM_LOCAL_D_STAGE_BYTES 32768 +#define SMEM_SMEM_LOCAL_D_STRIDE 32768 +#define SMEM_SMEM_LOCAL_I_OFF 66816 +#define SMEM_SMEM_LOCAL_I_STAGE_BYTES 32768 +#define SMEM_SMEM_LOCAL_I_STRIDE 32768 +#define SMEM_TOTAL 99584 +#define THREADS 192 +#define BLOCK_Q 64 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_MAX 32 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "tcgen05.mma.cta_group::1.kind::f16 [%0], %1, %2, %3, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, %3, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit(int mbar_addr) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];\n\t" + "}\n" + :: "r"(mbar_addr)); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x32(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x32.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15," + " %16, %17, %18, %19, %20, %21, %22, %23," + " %24, %25, %26, %27, %28, %29, %30, %31}, [%32];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]), + "=f"(dst[16]), "=f"(dst[17]), "=f"(dst[18]), "=f"(dst[19]), + "=f"(dst[20]), "=f"(dst[21]), "=f"(dst[22]), "=f"(dst[23]), + "=f"(dst[24]), "=f"(dst[25]), "=f"(dst[26]), "=f"(dst[27]), + "=f"(dst[28]), "=f"(dst[29]), "=f"(dst[30]), "=f"(dst[31]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ float max_noftz(float a, float b) { + float c; + asm("max.f32 %0, %1, %2;" : "=f"(c) : "f"(a), "f"(b)); + return c; +} + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cta.global" + ".mbarrier::complete_tx::bytes" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit(int mbar_addr) { + asm volatile( + "tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ uint32_t make_warp_uniform(uint32_t val) { + uint32_t result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1f, 0xffffffff;" + : "=r"(result) : "r"(val)); + return result; +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_rag_microbucket_q32rowld_e5db_v1_stage1_q32_k32_m64_q128rowld_60fb_v1(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tiles, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 17408); + const int smem_database_addr = smem + 17408; + float* smem_local_d = reinterpret_cast(smem_raw + 34048); + const int smem_local_d_addr = smem + 34048; + int* smem_local_i = reinterpret_cast(smem_raw + 66816); + const int smem_local_i_addr = smem + 66816; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 0, 1, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 16, 1, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=4 + mbarrier_init_pred(smem + 40, 4, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (64 columns, 64 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::1.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(64) : "memory"); + } + + __syncthreads(); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: compute ---- + if (warp <= 3) { + { // compute_main + int warp_id_in_role = (warp - 0); + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx = bid; work_idx < total_work; work_idx += num_bids) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tiles; + int q_tile = query_work % num_q_tiles; + int off_q = q_tile * BLOCK_Q; + int tmem_row_origin = warp_id_in_role * 32; + int logical_row_origin = warp_id_in_role * 16; + int row_top = logical_row_origin + lane / 4; + int row_bot = row_top + 8; + int lane_col = lane % 4; + int slot = lane_col; + int q_top = off_q + row_top; + int q_bot = off_q + row_bot; + int valid_top = ((q_top < Q) ? 1 : 0); + int valid_bot = ((q_bot < Q) ? 1 : 0); + float q_sq_top = 0.0f; + float q_sq_bot = 0.0f; + if (valid_top != 0) { + q_sq_top = query_sq[batch_idx * Q + q_top]; + } + if (valid_bot != 0) { + q_sq_bot = query_sq[batch_idx * Q + q_bot]; + } + float best_top_d[TOP_K_MAX]; + float best_bot_d[TOP_K_MAX]; + int best_top_i[TOP_K_MAX]; + int best_bot_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_top_d[kk] = 3.4e+38f; + best_bot_d[kk] = 3.4e+38f; + best_top_i[kk] = -1; + best_bot_i[kk] = -1; + } + int db_tile_start = split_idx * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int db_start = db_tile * BLOCK_M; + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(tmem_row_origin << 16); + float _tmem_load_0[32]; + asm volatile( + "tcgen05.ld.sync.aligned.16x256b.x8.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31}, [%32];" + : "=r"(*reinterpret_cast(&_tmem_load_0[0])), "=r"(*reinterpret_cast(&_tmem_load_0[1])), "=r"(*reinterpret_cast(&_tmem_load_0[2])), "=r"(*reinterpret_cast(&_tmem_load_0[3])), "=r"(*reinterpret_cast(&_tmem_load_0[4])), "=r"(*reinterpret_cast(&_tmem_load_0[5])), "=r"(*reinterpret_cast(&_tmem_load_0[6])), "=r"(*reinterpret_cast(&_tmem_load_0[7])), "=r"(*reinterpret_cast(&_tmem_load_0[8])), "=r"(*reinterpret_cast(&_tmem_load_0[9])), "=r"(*reinterpret_cast(&_tmem_load_0[10])), "=r"(*reinterpret_cast(&_tmem_load_0[11])), "=r"(*reinterpret_cast(&_tmem_load_0[12])), "=r"(*reinterpret_cast(&_tmem_load_0[13])), "=r"(*reinterpret_cast(&_tmem_load_0[14])), "=r"(*reinterpret_cast(&_tmem_load_0[15])), "=r"(*reinterpret_cast(&_tmem_load_0[16])), "=r"(*reinterpret_cast(&_tmem_load_0[17])), "=r"(*reinterpret_cast(&_tmem_load_0[18])), "=r"(*reinterpret_cast(&_tmem_load_0[19])), "=r"(*reinterpret_cast(&_tmem_load_0[20])), "=r"(*reinterpret_cast(&_tmem_load_0[21])), "=r"(*reinterpret_cast(&_tmem_load_0[22])), "=r"(*reinterpret_cast(&_tmem_load_0[23])), "=r"(*reinterpret_cast(&_tmem_load_0[24])), "=r"(*reinterpret_cast(&_tmem_load_0[25])), "=r"(*reinterpret_cast(&_tmem_load_0[26])), "=r"(*reinterpret_cast(&_tmem_load_0[27])), "=r"(*reinterpret_cast(&_tmem_load_0[28])), "=r"(*reinterpret_cast(&_tmem_load_0[29])), "=r"(*reinterpret_cast(&_tmem_load_0[30])), "=r"(*reinterpret_cast(&_tmem_load_0[31])) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + if (elect_sync()) { + mbarrier_arrive(score_empty_addr); + } + #pragma unroll + for (int repeat = 0; repeat < 8; repeat++) { + const int reg_base = repeat * 4; + int col_base = repeat * 8 + lane_col * 2; + int db_idx0 = db_start + col_base; + int db_idx1 = db_idx0 + 1; + float top_d0 = 3.4e+38f; + float top_d1 = 3.4e+38f; + if (valid_top != 0 && db_idx0 < M) { + float _max_0 = max_noftz(q_sq_top + database_sq[batch_idx * M + db_idx0] - 2.0f * _tmem_load_0[reg_base], 0.0f); + top_d0 = _max_0; + } + if (valid_top != 0 && db_idx1 < M) { + float _max_1 = max_noftz(q_sq_top + database_sq[batch_idx * M + db_idx1] - 2.0f * _tmem_load_0[reg_base + 1], 0.0f); + top_d1 = _max_1; + } + int top_take1 = ((top_d1 < top_d0) ? 1 : 0); + if (best_top_d[31] > ((top_take1 != 0) ? top_d1 : top_d0)) { + best_top_d[31] = ((top_take1 != 0) ? top_d1 : top_d0); + best_top_i[31] = ((top_take1 != 0) ? db_idx1 : db_idx0); + #pragma unroll + for (int kk_1 = 30; kk_1 >= 0; kk_1--) { + float lower0_d = best_top_d[kk_1 + 1]; + int lower0_i = best_top_i[kk_1 + 1]; + float upper0_d = best_top_d[kk_1]; + int upper0_i = best_top_i[kk_1]; + int swap0_up = ((lower0_d < upper0_d) ? 1 : 0); + best_top_d[kk_1] = ((swap0_up != 0) ? lower0_d : upper0_d); + best_top_i[kk_1] = ((swap0_up != 0) ? lower0_i : upper0_i); + best_top_d[kk_1 + 1] = ((swap0_up != 0) ? upper0_d : lower0_d); + best_top_i[kk_1 + 1] = ((swap0_up != 0) ? upper0_i : lower0_i); + } + if (best_top_d[31] > ((top_take1 != 0) ? top_d0 : top_d1)) { + best_top_d[31] = ((top_take1 != 0) ? top_d0 : top_d1); + best_top_i[31] = ((top_take1 != 0) ? db_idx0 : db_idx1); + #pragma unroll + for (int kk_2 = 30; kk_2 >= 0; kk_2--) { + float lower1_d = best_top_d[kk_2 + 1]; + int lower1_i = best_top_i[kk_2 + 1]; + float upper1_d = best_top_d[kk_2]; + int upper1_i = best_top_i[kk_2]; + int swap1_up = ((lower1_d < upper1_d) ? 1 : 0); + best_top_d[kk_2] = ((swap1_up != 0) ? lower1_d : upper1_d); + best_top_i[kk_2] = ((swap1_up != 0) ? lower1_i : upper1_i); + best_top_d[kk_2 + 1] = ((swap1_up != 0) ? upper1_d : lower1_d); + best_top_i[kk_2 + 1] = ((swap1_up != 0) ? upper1_i : lower1_i); + } + } + } + float bot_d0 = 3.4e+38f; + float bot_d1 = 3.4e+38f; + if (valid_bot != 0 && db_idx0 < M) { + float _max_2 = max_noftz(q_sq_bot + database_sq[batch_idx * M + db_idx0] - 2.0f * _tmem_load_0[reg_base + 2], 0.0f); + bot_d0 = _max_2; + } + if (valid_bot != 0 && db_idx1 < M) { + float _max_3 = max_noftz(q_sq_bot + database_sq[batch_idx * M + db_idx1] - 2.0f * _tmem_load_0[reg_base + 3], 0.0f); + bot_d1 = _max_3; + } + int bot_take1 = ((bot_d1 < bot_d0) ? 1 : 0); + if (best_bot_d[31] > ((bot_take1 != 0) ? bot_d1 : bot_d0)) { + best_bot_d[31] = ((bot_take1 != 0) ? bot_d1 : bot_d0); + best_bot_i[31] = ((bot_take1 != 0) ? db_idx1 : db_idx0); + #pragma unroll + for (int kk_3 = 30; kk_3 >= 0; kk_3--) { + float lower0_d_1 = best_bot_d[kk_3 + 1]; + int lower0_i_1 = best_bot_i[kk_3 + 1]; + float upper0_d_1 = best_bot_d[kk_3]; + int upper0_i_1 = best_bot_i[kk_3]; + int swap0_up_1 = ((lower0_d_1 < upper0_d_1) ? 1 : 0); + best_bot_d[kk_3] = ((swap0_up_1 != 0) ? lower0_d_1 : upper0_d_1); + best_bot_i[kk_3] = ((swap0_up_1 != 0) ? lower0_i_1 : upper0_i_1); + best_bot_d[kk_3 + 1] = ((swap0_up_1 != 0) ? upper0_d_1 : lower0_d_1); + best_bot_i[kk_3 + 1] = ((swap0_up_1 != 0) ? upper0_i_1 : lower0_i_1); + } + if (best_bot_d[31] > ((bot_take1 != 0) ? bot_d0 : bot_d1)) { + best_bot_d[31] = ((bot_take1 != 0) ? bot_d0 : bot_d1); + best_bot_i[31] = ((bot_take1 != 0) ? db_idx0 : db_idx1); + #pragma unroll + for (int kk_4 = 30; kk_4 >= 0; kk_4--) { + float lower1_d_1 = best_bot_d[kk_4 + 1]; + int lower1_i_1 = best_bot_i[kk_4 + 1]; + float upper1_d_1 = best_bot_d[kk_4]; + int upper1_i_1 = best_bot_i[kk_4]; + int swap1_up_1 = ((lower1_d_1 < upper1_d_1) ? 1 : 0); + best_bot_d[kk_4] = ((swap1_up_1 != 0) ? lower1_d_1 : upper1_d_1); + best_bot_i[kk_4] = ((swap1_up_1 != 0) ? lower1_i_1 : upper1_i_1); + best_bot_d[kk_4 + 1] = ((swap1_up_1 != 0) ? upper1_d_1 : lower1_d_1); + best_bot_i[kk_4 + 1] = ((swap1_up_1 != 0) ? upper1_i_1 : lower1_i_1); + } + } + } + } + } + int top_slot_base = (row_top * 4 + slot) * TOP_K_MAX; + int bot_slot_base = (row_bot * 4 + slot) * TOP_K_MAX; + #pragma unroll + for (int kk_5 = 0; kk_5 < TOP_K_MAX; kk_5++) { + smem_local_d[top_slot_base + kk_5] = best_top_d[kk_5]; + smem_local_i[top_slot_base + kk_5] = best_top_i[kk_5]; + smem_local_d[bot_slot_base + kk_5] = best_bot_d[kk_5]; + smem_local_i[bot_slot_base + kk_5] = best_bot_i[kk_5]; + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + if (tid < BLOCK_Q) { + int row = tid; + int q_idx = off_q + row; + float head_d[4]; + int head_i[4]; + int head_k[4]; + #pragma unroll + for (int slot_idx = 0; slot_idx < 4; slot_idx++) { + int local_base = (row * 4 + slot_idx) * TOP_K_MAX; + head_k[slot_idx] = 0; + head_d[slot_idx] = smem_local_d[local_base]; + head_i[slot_idx] = smem_local_i[local_base]; + } + int out_base = ((split_idx * B + batch_idx) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float winner_d = head_d[0]; + int winner_i = head_i[0]; + int winner_slot = 0; + #pragma unroll + for (int slot_idx_1 = 1; slot_idx_1 < 4; slot_idx_1++) { + float cand_d = head_d[slot_idx_1]; + int take = ((cand_d < winner_d) ? 1 : 0); + winner_d = ((take != 0) ? cand_d : winner_d); + winner_i = ((take != 0) ? head_i[slot_idx_1] : winner_i); + winner_slot = ((take != 0) ? slot_idx_1 : winner_slot); + } + if (q_idx < Q && out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = winner_d; + *((int*)(partial_indices + (out_base + out_k))) = winner_i; + } + #pragma unroll + for (int slot_idx_2 = 0; slot_idx_2 < 4; slot_idx_2++) { + if (winner_slot == slot_idx_2) { + int next_head = head_k[slot_idx_2] + 1; + head_k[slot_idx_2] = next_head; + head_d[slot_idx_2] = 3.4e+38f; + head_i[slot_idx_2] = -1; + if (next_head < TOP_K_MAX) { + int local_base_1 = (row * 4 + slot_idx_2) * TOP_K_MAX; + head_d[slot_idx_2] = smem_local_d[local_base_1 + next_head]; + head_i[slot_idx_2] = smem_local_i[local_base_1 + next_head]; + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + } + // ---- Role: load ---- + } else if (warp == 4) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 4) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx_1 = bid; work_idx_1 < total_work; work_idx_1 += num_bids) { + int split_idx_1 = work_idx_1 % (unsigned int)split_count; + int query_work_1 = work_idx_1 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tiles; + int q_tile_1 = query_work_1 % num_q_tiles; + int off_q_1 = q_tile_1 * BLOCK_Q; + int global_q = batch_idx_1 * Q + off_q_1; + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + mbarrier_arrive_expect_tx(query_full_addr, 16384); + tma_3d_gmem2smem(smem_query_addr, tmap_query, 0, global_q, 0, query_full_addr); + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int off_m = db_tile_1 * BLOCK_M; + int global_m = batch_idx_1 * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + mbarrier_arrive_expect_tx(database_full_addr, 16384); + tma_3d_gmem2smem(smem_database_addr, tmap_database, 0, global_m, 0, database_full_addr); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 5) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = bid; work_idx_2 < total_work; work_idx_2 += num_bids) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = make_warp_uniform((smem_query_addr >> 4) & 0x3FFF); + int _mma_b_lo_0 = make_warp_uniform((smem_database_addr >> 4) & 0x3FFF); + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 68158608;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 506;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit(score_full_addr); + elect_commit(database_empty_addr); + } + elect_commit(query_empty_addr); + } + } + } + + // Cleanup + __syncthreads(); // barrier before TMEM dealloc + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::1.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(64)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::1.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0212.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0212.cu new file mode 100644 index 00000000..4cfda108 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0212.cu @@ -0,0 +1,591 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 64 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 8192 +#define SMEM_SMEM_QUERY_STRIDE 8192 +#define SMEM_SMEM_DATABASE_OFF 9216 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 8192 +#define SMEM_SMEM_DATABASE_STRIDE 8192 +#define SMEM_SMEM_DATABASE_SQ_OFF 17408 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 17664 +#define THREADS 96 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "tcgen05.mma.cta_group::1.kind::f16 [%0], %1, %2, %3, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, %3, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit(int mbar_addr) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];\n\t" + "}\n" + :: "r"(mbar_addr)); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x16(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x16.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15}, [%16];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ float max_noftz(float a, float b) { + float c; + asm("max.f32 %0, %1, %2;" : "=f"(c) : "f"(a), "f"(b)); + return c; +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cta.global" + ".mbarrier::complete_tx::bytes" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit(int mbar_addr) { + asm volatile( + "tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ uint32_t make_warp_uniform(uint32_t val) { + uint32_t result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1f, 0xffffffff;" + : "=r"(result) : "r"(val)); + return result; +} + +extern "C" { + +__global__ __launch_bounds__(96, 1) void +kernel_knn_build_common_d_5e7f_rag_d64_m64_stage1(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tiles, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 9216); + const int smem_database_addr = smem + 9216; + float* smem_database_sq = reinterpret_cast(smem_raw + 17408); + const int smem_database_sq_addr = smem + 17408; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 0, 1, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 16, 1, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=32 + mbarrier_init_pred(smem + 40, 32, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (64 columns, 64 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::1.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(64) : "memory"); + } + + __syncthreads(); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: compute ---- + if (warp == 0) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx = bid; work_idx < total_work; work_idx += num_bids) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tiles; + int q_tile = query_work % num_q_tiles; + int off_q = q_tile * 64; + int q_idx = off_q + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx * Q + q_idx]; + } + float best_d[10]; + int best_i[10]; + #pragma unroll + for (int kk = 0; kk < 10; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + int db_tile_start = split_idx * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int db_start = db_tile * 64; + int db_sq_idx0 = db_start + (warp % 4 * 32 + lane); + if (db_sq_idx0 < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx * M + db_sq_idx0]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 3.4e+38f; + } + int db_col1 = warp % 4 * 32 + lane + 32; + int db_sq_idx1 = db_start + db_col1; + if (db_sq_idx1 < M) { + smem_database_sq[db_col1] = database_sq[batch_idx * M + db_sq_idx1]; + } else { + smem_database_sq[db_col1] = 3.4e+38f; + } + asm volatile("barrier.sync 8, %0;" :: "r"(32)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(warp % 4 * 32 << 16); + float _tmem_load_0[64]; + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x64.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31, %32, %33, %34, %35, %36, %37, %38, %39, %40, %41, %42, %43, %44, %45, %46, %47, %48, %49, %50, %51, %52, %53, %54, %55, %56, %57, %58, %59, %60, %61, %62, %63}, [%64];" + : "=f"(_tmem_load_0[0]), "=f"(_tmem_load_0[1]), "=f"(_tmem_load_0[2]), "=f"(_tmem_load_0[3]), "=f"(_tmem_load_0[4]), "=f"(_tmem_load_0[5]), "=f"(_tmem_load_0[6]), "=f"(_tmem_load_0[7]), "=f"(_tmem_load_0[8]), "=f"(_tmem_load_0[9]), "=f"(_tmem_load_0[10]), "=f"(_tmem_load_0[11]), "=f"(_tmem_load_0[12]), "=f"(_tmem_load_0[13]), "=f"(_tmem_load_0[14]), "=f"(_tmem_load_0[15]), "=f"(_tmem_load_0[16]), "=f"(_tmem_load_0[17]), "=f"(_tmem_load_0[18]), "=f"(_tmem_load_0[19]), "=f"(_tmem_load_0[20]), "=f"(_tmem_load_0[21]), "=f"(_tmem_load_0[22]), "=f"(_tmem_load_0[23]), "=f"(_tmem_load_0[24]), "=f"(_tmem_load_0[25]), "=f"(_tmem_load_0[26]), "=f"(_tmem_load_0[27]), "=f"(_tmem_load_0[28]), "=f"(_tmem_load_0[29]), "=f"(_tmem_load_0[30]), "=f"(_tmem_load_0[31]), "=f"(_tmem_load_0[32]), "=f"(_tmem_load_0[33]), "=f"(_tmem_load_0[34]), "=f"(_tmem_load_0[35]), "=f"(_tmem_load_0[36]), "=f"(_tmem_load_0[37]), "=f"(_tmem_load_0[38]), "=f"(_tmem_load_0[39]), "=f"(_tmem_load_0[40]), "=f"(_tmem_load_0[41]), "=f"(_tmem_load_0[42]), "=f"(_tmem_load_0[43]), "=f"(_tmem_load_0[44]), "=f"(_tmem_load_0[45]), "=f"(_tmem_load_0[46]), "=f"(_tmem_load_0[47]), "=f"(_tmem_load_0[48]), "=f"(_tmem_load_0[49]), "=f"(_tmem_load_0[50]), "=f"(_tmem_load_0[51]), "=f"(_tmem_load_0[52]), "=f"(_tmem_load_0[53]), "=f"(_tmem_load_0[54]), "=f"(_tmem_load_0[55]), "=f"(_tmem_load_0[56]), "=f"(_tmem_load_0[57]), "=f"(_tmem_load_0[58]), "=f"(_tmem_load_0[59]), "=f"(_tmem_load_0[60]), "=f"(_tmem_load_0[61]), "=f"(_tmem_load_0[62]), "=f"(_tmem_load_0[63]) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(32)); + mbarrier_arrive(score_empty_addr); + if (valid_q != 0) { + #pragma unroll 1 + for (int col_base = 0; col_base < 64; col_base += 4) { + float dist_vec[4]; + dist_vec[0] = _tmem_load_0[col_base]; + dist_vec[1] = _tmem_load_0[col_base + 1]; + dist_vec[2] = _tmem_load_0[col_base + 2]; + dist_vec[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec[4]; + db_sq_vec[0] = smem_database_sq[col_base]; + db_sq_vec[1] = smem_database_sq[col_base + 1]; + db_sq_vec[2] = smem_database_sq[col_base + 2]; + db_sq_vec[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec)[_la], reinterpret_cast(db_sq_vec)[_la]); + float group_min = _t0[0]; + if (_t0[1] < group_min) { + group_min = _t0[1]; + } + if (_t0[2] < group_min) { + group_min = _t0[2]; + } + if (_t0[3] < group_min) { + group_min = _t0[3]; + } + if (group_min < best_d[9]) { + #pragma unroll + for (int vec_col = 0; vec_col < 4; vec_col++) { + int db_idx = db_start + col_base + vec_col; + if (db_idx < M) { + float _max_0 = max_noftz(_t0[vec_col], 0.0f); + float dist = _max_0; + if (dist < best_d[9]) { + best_d[9] = dist; + best_i[9] = db_idx; + #pragma unroll + for (int pos = 9; pos >= 1; pos--) { + if (best_d[pos] < best_d[pos - 1]) { + float tmp_d = best_d[pos - 1]; + int tmp_i = best_i[pos - 1]; + best_d[pos - 1] = best_d[pos]; + best_i[pos - 1] = best_i[pos]; + best_d[pos] = tmp_d; + best_i[pos] = tmp_i; + } + } + } + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(32)); + } + if (valid_q != 0) { + int out_base = ((split_idx * B + batch_idx) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < 10; out_k++) { + if (out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = best_d[out_k]; + *((int*)(partial_indices + (out_base + out_k))) = best_i[out_k]; + } + } + } + } + } + // ---- Role: load ---- + } else if (warp == 1) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 1) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx_1 = bid; work_idx_1 < total_work; work_idx_1 += num_bids) { + int split_idx_1 = work_idx_1 % (unsigned int)split_count; + int query_work_1 = work_idx_1 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tiles; + int q_tile_1 = query_work_1 % num_q_tiles; + int off_q_1 = q_tile_1 * 64; + int global_q = batch_idx_1 * Q + off_q_1; + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + mbarrier_arrive_expect_tx(query_full_addr, 8192); + tma_3d_gmem2smem(smem_query_addr, tmap_query, 0, global_q, 0, query_full_addr); + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int off_m = db_tile_1 * 64; + int global_m = batch_idx_1 * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + mbarrier_arrive_expect_tx(database_full_addr, 8192); + tma_3d_gmem2smem(smem_database_addr, tmap_database, 0, global_m, 0, database_full_addr); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 2) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = bid; work_idx_2 < total_work; work_idx_2 += num_bids) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = make_warp_uniform((smem_query_addr >> 4) & 0x3FFF); + int _mma_b_lo_0 = make_warp_uniform((smem_database_addr >> 4) & 0x3FFF); + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 68158608;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + asm volatile("tcgen05.fence::after_thread_sync;"); + elect_commit(score_full_addr); + elect_commit(database_empty_addr); + } + elect_commit(query_empty_addr); + } + } + } + + // Cleanup + __syncthreads(); // barrier before TMEM dealloc + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::1.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(64)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::1.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0213.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0213.cu new file mode 100644 index 00000000..0f6b6147 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0213.cu @@ -0,0 +1,697 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 64 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_STRIDE 32768 +#define SMEM_SMEM_DATABASE_OFF 33792 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_DATABASE_SQ_OFF 50176 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 50432 +#define THREADS 192 +#define BLOCK_Q 128 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_MAX 32 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "tcgen05.mma.cta_group::1.kind::f16 [%0], %1, %2, %3, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, %3, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit(int mbar_addr) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];\n\t" + "}\n" + :: "r"(mbar_addr)); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x16(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x16.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15}, [%16];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cta.global" + ".mbarrier::complete_tx::bytes" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit(int mbar_addr) { + asm volatile( + "tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ uint32_t make_warp_uniform(uint32_t val) { + uint32_t result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1f, 0xffffffff;" + : "=r"(result) : "r"(val)); + return result; +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_rag_microbucket_3505_v2_stage1_k32_tailinf_cta1(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tiles, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 33792); + const int smem_database_addr = smem + 33792; + float* smem_database_sq = reinterpret_cast(smem_raw + 50176); + const int smem_database_sq_addr = smem + 50176; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 0, 1, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 16, 1, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=128 + mbarrier_init_pred(smem + 40, 128, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (64 columns, 64 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::1.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(64) : "memory"); + } + + __syncthreads(); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: load ---- + if (warp == 0) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 0) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx = bid; work_idx < total_work; work_idx += num_bids) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tiles; + int q_tile = query_work % num_q_tiles; + int off_q = q_tile * BLOCK_Q; + int global_q = batch_idx * Q + off_q; + int db_tile_start = split_idx * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + mbarrier_arrive_expect_tx(query_full_addr, 32768); + tma_3d_gmem2smem(smem_query_addr, tmap_query, 0, global_q, 0, query_full_addr); + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int off_m = db_tile * BLOCK_M; + int global_m = batch_idx * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + mbarrier_arrive_expect_tx(database_full_addr, 16384); + tma_3d_gmem2smem(smem_database_addr, tmap_database, 0, global_m, 0, database_full_addr); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 1) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_1 = bid; work_idx_1 < total_work; work_idx_1 += num_bids) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = make_warp_uniform((smem_query_addr >> 4) & 0x3FFF); + int _mma_b_lo_0 = make_warp_uniform((smem_database_addr >> 4) & 0x3FFF); + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 135267472;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit(score_full_addr); + elect_commit(database_empty_addr); + } + elect_commit(query_empty_addr); + } + } + // ---- Role: compute ---- + } else if (warp >= 2 && warp <= 5) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = bid; work_idx_2 < total_work; work_idx_2 += num_bids) { + int split_idx_1 = work_idx_2 % (unsigned int)split_count; + int query_work_1 = work_idx_2 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tiles; + int q_tile_1 = query_work_1 % num_q_tiles; + int off_q_1 = q_tile_1 * BLOCK_Q; + int q_idx = off_q_1 + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx_1 * Q + q_idx]; + } + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + float chunk_worst_d[4]; + int chunk_worst_pos[4]; + #pragma unroll + for (int chunk = 0; chunk < 4; chunk++) { + int chunk_base = chunk * 8; + chunk_worst_d[chunk] = 3.4e+38f; + chunk_worst_pos[chunk] = chunk_base; + } + float worst_d = 3.4e+38f; + int worst_pos = 0; + int worst_chunk = 0; + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int db_start = db_tile_1 * BLOCK_M; + int db_sq_idx = db_start + (warp % 4 * 32 + lane); + if (warp % 4 * 32 + lane < BLOCK_M) { + if (db_sq_idx < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx_1 * M + db_sq_idx]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 3.4e+38f; + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(warp % 4 * 32 << 16); + float _tmem_load_0[64]; + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x64.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31, %32, %33, %34, %35, %36, %37, %38, %39, %40, %41, %42, %43, %44, %45, %46, %47, %48, %49, %50, %51, %52, %53, %54, %55, %56, %57, %58, %59, %60, %61, %62, %63}, [%64];" + : "=f"(_tmem_load_0[0]), "=f"(_tmem_load_0[1]), "=f"(_tmem_load_0[2]), "=f"(_tmem_load_0[3]), "=f"(_tmem_load_0[4]), "=f"(_tmem_load_0[5]), "=f"(_tmem_load_0[6]), "=f"(_tmem_load_0[7]), "=f"(_tmem_load_0[8]), "=f"(_tmem_load_0[9]), "=f"(_tmem_load_0[10]), "=f"(_tmem_load_0[11]), "=f"(_tmem_load_0[12]), "=f"(_tmem_load_0[13]), "=f"(_tmem_load_0[14]), "=f"(_tmem_load_0[15]), "=f"(_tmem_load_0[16]), "=f"(_tmem_load_0[17]), "=f"(_tmem_load_0[18]), "=f"(_tmem_load_0[19]), "=f"(_tmem_load_0[20]), "=f"(_tmem_load_0[21]), "=f"(_tmem_load_0[22]), "=f"(_tmem_load_0[23]), "=f"(_tmem_load_0[24]), "=f"(_tmem_load_0[25]), "=f"(_tmem_load_0[26]), "=f"(_tmem_load_0[27]), "=f"(_tmem_load_0[28]), "=f"(_tmem_load_0[29]), "=f"(_tmem_load_0[30]), "=f"(_tmem_load_0[31]), "=f"(_tmem_load_0[32]), "=f"(_tmem_load_0[33]), "=f"(_tmem_load_0[34]), "=f"(_tmem_load_0[35]), "=f"(_tmem_load_0[36]), "=f"(_tmem_load_0[37]), "=f"(_tmem_load_0[38]), "=f"(_tmem_load_0[39]), "=f"(_tmem_load_0[40]), "=f"(_tmem_load_0[41]), "=f"(_tmem_load_0[42]), "=f"(_tmem_load_0[43]), "=f"(_tmem_load_0[44]), "=f"(_tmem_load_0[45]), "=f"(_tmem_load_0[46]), "=f"(_tmem_load_0[47]), "=f"(_tmem_load_0[48]), "=f"(_tmem_load_0[49]), "=f"(_tmem_load_0[50]), "=f"(_tmem_load_0[51]), "=f"(_tmem_load_0[52]), "=f"(_tmem_load_0[53]), "=f"(_tmem_load_0[54]), "=f"(_tmem_load_0[55]), "=f"(_tmem_load_0[56]), "=f"(_tmem_load_0[57]), "=f"(_tmem_load_0[58]), "=f"(_tmem_load_0[59]), "=f"(_tmem_load_0[60]), "=f"(_tmem_load_0[61]), "=f"(_tmem_load_0[62]), "=f"(_tmem_load_0[63]) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_arrive(score_empty_addr); + if (valid_q != 0) { + #pragma unroll 2 + for (int col_base = 0; col_base < 64; col_base += 4) { + float dist_vec[4]; + dist_vec[0] = _tmem_load_0[col_base]; + dist_vec[1] = _tmem_load_0[col_base + 1]; + dist_vec[2] = _tmem_load_0[col_base + 2]; + dist_vec[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec[4]; + db_sq_vec[0] = smem_database_sq[col_base]; + db_sq_vec[1] = smem_database_sq[col_base + 1]; + db_sq_vec[2] = smem_database_sq[col_base + 2]; + db_sq_vec[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec)[_la], reinterpret_cast(db_sq_vec)[_la]); + float group_min = _t0[0]; + if (_t0[1] < group_min) { + group_min = _t0[1]; + } + if (_t0[2] < group_min) { + group_min = _t0[2]; + } + if (_t0[3] < group_min) { + group_min = _t0[3]; + } + if (group_min < worst_d) { + float sort_d0 = _t0[0]; + float sort_d1 = _t0[1]; + float sort_d2 = _t0[2]; + float sort_d3 = _t0[3]; + int sort_col0 = 0; + int sort_col1 = 1; + int sort_col2 = 2; + int sort_col3 = 3; + float tmp_d = 0.0f; + int tmp_col = 0; + if (sort_d1 < sort_d0) { + tmp_d = sort_d0; + sort_d0 = sort_d1; + sort_d1 = tmp_d; + tmp_col = sort_col0; + sort_col0 = sort_col1; + sort_col1 = tmp_col; + } + if (sort_d3 < sort_d2) { + tmp_d = sort_d2; + sort_d2 = sort_d3; + sort_d3 = tmp_d; + tmp_col = sort_col2; + sort_col2 = sort_col3; + sort_col3 = tmp_col; + } + if (sort_d2 < sort_d0) { + tmp_d = sort_d0; + sort_d0 = sort_d2; + sort_d2 = tmp_d; + tmp_col = sort_col0; + sort_col0 = sort_col2; + sort_col2 = tmp_col; + } + if (sort_d3 < sort_d1) { + tmp_d = sort_d1; + sort_d1 = sort_d3; + sort_d3 = tmp_d; + tmp_col = sort_col1; + sort_col1 = sort_col3; + sort_col3 = tmp_col; + } + if (sort_d2 < sort_d1) { + tmp_d = sort_d1; + sort_d1 = sort_d2; + sort_d2 = tmp_d; + tmp_col = sort_col1; + sort_col1 = sort_col2; + sort_col2 = tmp_col; + } + #pragma unroll + for (int visit = 0; visit < 4; visit++) { + int vec_col = sort_col0; + float dist = sort_d0; + if (visit == 1) { + vec_col = sort_col1; + dist = sort_d1; + } + if (visit == 2) { + vec_col = sort_col2; + dist = sort_d2; + } + if (visit == 3) { + vec_col = sort_col3; + dist = sort_d3; + } + if (dist >= worst_d) { + break; + } + int db_idx = db_start + col_base + vec_col; + best_d[worst_pos] = dist; + best_i[worst_pos] = db_idx; + int refresh_base = worst_chunk * 8; + chunk_worst_d[worst_chunk] = best_d[refresh_base]; + chunk_worst_pos[worst_chunk] = refresh_base; + #pragma unroll + for (int offset = 1; offset < 8; offset++) { + int scan_pos = refresh_base + offset; + if (best_d[scan_pos] > chunk_worst_d[worst_chunk]) { + chunk_worst_d[worst_chunk] = best_d[scan_pos]; + chunk_worst_pos[worst_chunk] = scan_pos; + } + } + worst_d = chunk_worst_d[0]; + worst_pos = chunk_worst_pos[0]; + worst_chunk = 0; + #pragma unroll + for (int chunk_1 = 1; chunk_1 < 4; chunk_1++) { + if (worst_d < chunk_worst_d[chunk_1]) { + worst_d = chunk_worst_d[chunk_1]; + worst_pos = chunk_worst_pos[chunk_1]; + worst_chunk = chunk_1; + } + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + if (valid_q != 0) { + int out_base = ((split_idx_1 * B + batch_idx_1) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_out_d = best_d[0]; + int best_out_i = best_i[0]; + int best_out_pos = 0; + #pragma unroll + for (int scan_pos_1 = 1; scan_pos_1 < TOP_K_MAX; scan_pos_1++) { + if (best_out_d > best_d[scan_pos_1]) { + best_out_d = best_d[scan_pos_1]; + best_out_i = best_i[scan_pos_1]; + best_out_pos = scan_pos_1; + } + } + if (out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = best_out_d; + *((int*)(partial_indices + (out_base + out_k))) = best_out_i; + } + best_d[best_out_pos] = 3.4e+38f; + } + } + } + } + } + + // Cleanup + __syncthreads(); // barrier before TMEM dealloc + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::1.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(64)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::1.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0214.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0214.cu new file mode 100644 index 00000000..60275371 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0214.cu @@ -0,0 +1,655 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 128 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_STRIDE 32768 +#define SMEM_SMEM_DATABASE_OFF 33792 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_DATABASE_SQ_OFF 50176 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 50432 +#define THREADS 192 +#define BLOCK_Q 128 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_MAX 32 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16_cta2( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + ".reg .b32 m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "tcgen05.mma.cta_group::2.kind::f16 [%0], %1, %2, %3, {m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step_cg2( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, %3, " + "{m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], %1;\n\t" + "}\n" + :: "r"(mbar_addr), "h"(cta_mask) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x16(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x16.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15}, [%16];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ uint32_t smem_addr(const void* ptr) { + uint32_t addr; + asm("{\n\t" + ".reg .u64 u64addr;\n\t" + "cvta.to.shared.u64 u64addr, %1;\n\t" + "cvt.u32.u64 %0, u64addr;\n\t" + "}\n" : "=r"(addr) : "l"(ptr)); + return addr; +} + + +__device__ __forceinline__ uint32_t mapa_to_rank(uint32_t local_addr, uint32_t rank) { + uint32_t remote; + asm volatile("mapa.shared::cluster.u32 %0, %1, %2;" + : "=r"(remote) : "r"(local_addr), "r"(rank)); + return remote; +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem_cta2( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global" + ".mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .b16 lo, hi;\n\t" + "mov.b32 {lo, hi}, %1;\n\t" + "tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], lo;\n\t" + "}\n" + :: "r"(mbar_addr), "r"((uint32_t)cta_mask) : "memory"); +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tile_pairs, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + const unsigned int clusters_x = gridDim.x / 2; + const unsigned int cluster_id = ((blockIdx.z * gridDim.y + blockIdx.y) * clusters_x) + blockIdx.x / 2; + const unsigned int num_clusters = clusters_x * gridDim.y * gridDim.z; + + int cta_rank; + asm volatile("mov.b32 %0, %%cluster_ctarank;" : "=r"(cta_rank)); + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 33792); + const int smem_database_addr = smem + 33792; + float* smem_database_sq = reinterpret_cast(smem_raw + 50176); + const int smem_database_sq_addr = smem + 50176; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 0, 2, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 16, 2, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=8 + mbarrier_init_pred(smem + 40, 8, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (128 columns, 128 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::2.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(128) : "memory"); + } + + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: load ---- + if (warp == 0) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 0) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx = cluster_id; work_idx < total_work; work_idx += num_clusters) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tile_pairs; + int q_tile_pair = query_work % num_q_tile_pairs; + int q_tile = q_tile_pair * 2 + cta_rank; + int off_q = q_tile * BLOCK_Q; + int global_q = batch_idx * Q + off_q; + int db_tile_start = split_idx * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((query_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(32768)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_query_addr), "l"(tmap_query), "r"(0), "r"(global_q), "r"(0), + "r"(((query_full_addr) & 0xFEFFFFFF)) : "memory"); + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int off_m = db_tile * BLOCK_M; + int global_m = batch_idx * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((database_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(16384)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_database_addr), "l"(tmap_database), "r"(0), "r"(global_m), "r"(0), + "r"(((database_full_addr) & 0xFEFFFFFF)) : "memory"); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 1) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + if (cta_rank == 0) { + #pragma unroll 1 + for (unsigned int work_idx_1 = cluster_id; work_idx_1 < total_work; work_idx_1 += num_clusters) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = (smem_query_addr >> 4) & 0x3FFF; + int _mma_b_lo_0 = (smem_database_addr >> 4) & 0x3FFF; + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\tmov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 270533776;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit_cg2_multicast(score_full_addr, (uint16_t)(3)); + elect_commit_cg2_multicast(database_empty_addr, (uint16_t)(3)); + } + elect_commit_cg2_multicast(query_empty_addr, (uint16_t)(3)); + } + } + } + // ---- Role: compute ---- + } else if (warp >= 2 && warp <= 5) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = cluster_id; work_idx_2 < total_work; work_idx_2 += num_clusters) { + int split_idx_1 = work_idx_2 % (unsigned int)split_count; + int query_work_1 = work_idx_2 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tile_pairs; + int q_tile_pair_1 = query_work_1 % num_q_tile_pairs; + int q_tile_1 = q_tile_pair_1 * 2 + cta_rank; + int off_q_1 = q_tile_1 * BLOCK_Q; + int q_idx = off_q_1 + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx_1 * Q + q_idx]; + } + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + float worst_d = 3.4e+38f; + int worst_pos = 0; + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int db_start = db_tile_1 * BLOCK_M; + int db_sq_idx = db_start + (warp % 4 * 32 + lane); + if (warp % 4 * 32 + lane < BLOCK_M) { + if (db_sq_idx < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx_1 * M + db_sq_idx]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 0.0f; + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(cta_rank * BLOCK_Q + (warp % 4 * 32 << 16) << 16); + float _tmem_load_0[64]; + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x64.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31, %32, %33, %34, %35, %36, %37, %38, %39, %40, %41, %42, %43, %44, %45, %46, %47, %48, %49, %50, %51, %52, %53, %54, %55, %56, %57, %58, %59, %60, %61, %62, %63}, [%64];" + : "=f"(_tmem_load_0[0]), "=f"(_tmem_load_0[1]), "=f"(_tmem_load_0[2]), "=f"(_tmem_load_0[3]), "=f"(_tmem_load_0[4]), "=f"(_tmem_load_0[5]), "=f"(_tmem_load_0[6]), "=f"(_tmem_load_0[7]), "=f"(_tmem_load_0[8]), "=f"(_tmem_load_0[9]), "=f"(_tmem_load_0[10]), "=f"(_tmem_load_0[11]), "=f"(_tmem_load_0[12]), "=f"(_tmem_load_0[13]), "=f"(_tmem_load_0[14]), "=f"(_tmem_load_0[15]), "=f"(_tmem_load_0[16]), "=f"(_tmem_load_0[17]), "=f"(_tmem_load_0[18]), "=f"(_tmem_load_0[19]), "=f"(_tmem_load_0[20]), "=f"(_tmem_load_0[21]), "=f"(_tmem_load_0[22]), "=f"(_tmem_load_0[23]), "=f"(_tmem_load_0[24]), "=f"(_tmem_load_0[25]), "=f"(_tmem_load_0[26]), "=f"(_tmem_load_0[27]), "=f"(_tmem_load_0[28]), "=f"(_tmem_load_0[29]), "=f"(_tmem_load_0[30]), "=f"(_tmem_load_0[31]), "=f"(_tmem_load_0[32]), "=f"(_tmem_load_0[33]), "=f"(_tmem_load_0[34]), "=f"(_tmem_load_0[35]), "=f"(_tmem_load_0[36]), "=f"(_tmem_load_0[37]), "=f"(_tmem_load_0[38]), "=f"(_tmem_load_0[39]), "=f"(_tmem_load_0[40]), "=f"(_tmem_load_0[41]), "=f"(_tmem_load_0[42]), "=f"(_tmem_load_0[43]), "=f"(_tmem_load_0[44]), "=f"(_tmem_load_0[45]), "=f"(_tmem_load_0[46]), "=f"(_tmem_load_0[47]), "=f"(_tmem_load_0[48]), "=f"(_tmem_load_0[49]), "=f"(_tmem_load_0[50]), "=f"(_tmem_load_0[51]), "=f"(_tmem_load_0[52]), "=f"(_tmem_load_0[53]), "=f"(_tmem_load_0[54]), "=f"(_tmem_load_0[55]), "=f"(_tmem_load_0[56]), "=f"(_tmem_load_0[57]), "=f"(_tmem_load_0[58]), "=f"(_tmem_load_0[59]), "=f"(_tmem_load_0[60]), "=f"(_tmem_load_0[61]), "=f"(_tmem_load_0[62]), "=f"(_tmem_load_0[63]) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + if (elect_sync()) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cluster.b64 _, [%0];" + :: "r"((score_empty_addr) & 0xFEFFFFFF) : "memory"); + } + if (valid_q != 0) { + #pragma unroll 2 + for (int col_base = 0; col_base < 64; col_base += 4) { + float dist_vec[4]; + dist_vec[0] = _tmem_load_0[col_base]; + dist_vec[1] = _tmem_load_0[col_base + 1]; + dist_vec[2] = _tmem_load_0[col_base + 2]; + dist_vec[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec[4]; + db_sq_vec[0] = smem_database_sq[col_base]; + db_sq_vec[1] = smem_database_sq[col_base + 1]; + db_sq_vec[2] = smem_database_sq[col_base + 2]; + db_sq_vec[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec)[_la], reinterpret_cast(db_sq_vec)[_la]); + float group_min = _t0[0]; + if (_t0[1] < group_min) { + group_min = _t0[1]; + } + if (_t0[2] < group_min) { + group_min = _t0[2]; + } + if (_t0[3] < group_min) { + group_min = _t0[3]; + } + if (group_min < worst_d) { + #pragma unroll + for (int vec_col = 0; vec_col < 4; vec_col++) { + int db_idx = db_start + col_base + vec_col; + if (db_idx < M) { + float dist = _t0[vec_col]; + if (dist < worst_d) { + best_d[worst_pos] = dist; + best_i[worst_pos] = db_idx; + worst_d = best_d[0]; + worst_pos = 0; + #pragma unroll + for (int scan_pos = 1; scan_pos < TOP_K_MAX; scan_pos++) { + if (worst_d < best_d[scan_pos]) { + worst_d = best_d[scan_pos]; + worst_pos = scan_pos; + } + } + } + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + if (valid_q != 0) { + int out_base = ((split_idx_1 * B + batch_idx_1) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + *((float*)(partial_dists + (out_base + out_k))) = best_d[out_k]; + *((int*)(partial_indices + (out_base + out_k))) = best_i[out_k]; + } + } + } + } + } + + // Cleanup + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::2.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(128)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::2.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0215.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0215.cu new file mode 100644 index 00000000..37eef13e --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0215.cu @@ -0,0 +1,85 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 32 +#define TOP_K_MAX 32 +#define SPLIT_COUNT 4 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_evolve_7bfc_k32_merge_s4_unordered(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 32 + tid; + int stride = num_bids * 32; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + float worst_d = 3.4e+38f; + int worst_pos = 0; + #pragma unroll + for (int split_idx = 0; split_idx < SPLIT_COUNT; split_idx++) { + int partial_base = base_row + split_idx * split_stride; + #pragma unroll + for (int cand_k = 0; cand_k < TOP_K_MAX; cand_k++) { + float cand_d = partial_dists[partial_base + cand_k]; + int cand_i = partial_indices[partial_base + cand_k]; + if (cand_d < worst_d) { + best_d[worst_pos] = cand_d; + best_i[worst_pos] = cand_i; + worst_d = best_d[0]; + worst_pos = 0; + #pragma unroll + for (int scan_pos = 1; scan_pos < TOP_K_MAX; scan_pos++) { + if (worst_d < best_d[scan_pos]) { + worst_d = best_d[scan_pos]; + worst_pos = scan_pos; + } + } + } + } + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + *((float*)(out_dists + (base_row + out_k))) = best_d[out_k]; + *((int*)(out_indices + (base_row + out_k))) = best_i[out_k]; + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0216.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0216.cu new file mode 100644 index 00000000..3f5c5e7a --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0216.cu @@ -0,0 +1,84 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 32 +#define TOP_K_MAX 30 +#define SPLIT_COUNT 8 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 32 + tid; + int stride = num_bids * 32; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int out_base = base_row; + int split_pos[SPLIT_COUNT]; + int split_base[SPLIT_COUNT]; + float cand_d[SPLIT_COUNT]; + int cand_i[SPLIT_COUNT]; + #pragma unroll + for (int split_idx = 0; split_idx < SPLIT_COUNT; split_idx++) { + split_pos[split_idx] = 0; + split_base[split_idx] = base_row + split_idx * split_stride; + cand_d[split_idx] = partial_dists[split_base[split_idx]]; + cand_i[split_idx] = partial_indices[split_base[split_idx]]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_d = cand_d[0]; + int best_i = cand_i[0]; + int best_split = 0; + #pragma unroll + for (int split_idx_1 = 1; split_idx_1 < SPLIT_COUNT; split_idx_1++) { + if (best_d > cand_d[split_idx_1]) { + best_d = cand_d[split_idx_1]; + best_i = cand_i[split_idx_1]; + best_split = split_idx_1; + } + } + *((float*)(out_dists + (out_base + out_k))) = best_d; + *((int*)(out_indices + (out_base + out_k))) = best_i; + split_pos[best_split] = split_pos[best_split] + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_pos = split_pos[best_split]; + int next_addr = split_base[best_split] + next_pos; + cand_d[best_split] = partial_dists[next_addr]; + cand_i[best_split] = partial_indices[next_addr]; + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0217.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0217.cu new file mode 100644 index 00000000..723233c2 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0217.cu @@ -0,0 +1,84 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 32 +#define TOP_K_MAX 12 +#define SPLIT_COUNT 8 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_k12s8(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 32 + tid; + int stride = num_bids * 32; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int out_base = base_row; + int split_pos[SPLIT_COUNT]; + int split_base[SPLIT_COUNT]; + float cand_d[SPLIT_COUNT]; + int cand_i[SPLIT_COUNT]; + #pragma unroll + for (int split_idx = 0; split_idx < SPLIT_COUNT; split_idx++) { + split_pos[split_idx] = 0; + split_base[split_idx] = base_row + split_idx * split_stride; + cand_d[split_idx] = partial_dists[split_base[split_idx]]; + cand_i[split_idx] = partial_indices[split_base[split_idx]]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_d = cand_d[0]; + int best_i = cand_i[0]; + int best_split = 0; + #pragma unroll + for (int split_idx_1 = 1; split_idx_1 < SPLIT_COUNT; split_idx_1++) { + if (best_d > cand_d[split_idx_1]) { + best_d = cand_d[split_idx_1]; + best_i = cand_i[split_idx_1]; + best_split = split_idx_1; + } + } + *((float*)(out_dists + (out_base + out_k))) = best_d; + *((int*)(out_indices + (out_base + out_k))) = best_i; + split_pos[best_split] = split_pos[best_split] + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_pos = split_pos[best_split]; + int next_addr = split_base[best_split] + next_pos; + cand_d[best_split] = partial_dists[next_addr]; + cand_i[best_split] = partial_indices[next_addr]; + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0218.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0218.cu new file mode 100644 index 00000000..e6a8c654 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0218.cu @@ -0,0 +1,84 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 32 +#define TOP_K_MAX 8 +#define SPLIT_COUNT 7 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_evolve_7bfc_k10_merge_s7_rowbase_cache_k8s7(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 32 + tid; + int stride = num_bids * 32; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int out_base = base_row; + int split_pos[SPLIT_COUNT]; + int split_base[SPLIT_COUNT]; + float cand_d[SPLIT_COUNT]; + int cand_i[SPLIT_COUNT]; + #pragma unroll + for (int split_idx = 0; split_idx < SPLIT_COUNT; split_idx++) { + split_pos[split_idx] = 0; + split_base[split_idx] = base_row + split_idx * split_stride; + cand_d[split_idx] = partial_dists[split_base[split_idx]]; + cand_i[split_idx] = partial_indices[split_base[split_idx]]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_d = cand_d[0]; + int best_i = cand_i[0]; + int best_split = 0; + #pragma unroll + for (int split_idx_1 = 1; split_idx_1 < SPLIT_COUNT; split_idx_1++) { + if (best_d > cand_d[split_idx_1]) { + best_d = cand_d[split_idx_1]; + best_i = cand_i[split_idx_1]; + best_split = split_idx_1; + } + } + *((float*)(out_dists + (out_base + out_k))) = best_d; + *((int*)(out_indices + (out_base + out_k))) = best_i; + split_pos[best_split] = split_pos[best_split] + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_pos = split_pos[best_split]; + int next_addr = split_base[best_split] + next_pos; + cand_d[best_split] = partial_dists[next_addr]; + cand_i[best_split] = partial_indices[next_addr]; + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0219.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0219.cu new file mode 100644 index 00000000..98d8bc10 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0219.cu @@ -0,0 +1,84 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 32 +#define TOP_K_MAX 8 +#define SPLIT_COUNT 8 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_evolve_7bfc_k30_merge_s8_rowbase_cache_k8s8(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 32 + tid; + int stride = num_bids * 32; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int out_base = base_row; + int split_pos[SPLIT_COUNT]; + int split_base[SPLIT_COUNT]; + float cand_d[SPLIT_COUNT]; + int cand_i[SPLIT_COUNT]; + #pragma unroll + for (int split_idx = 0; split_idx < SPLIT_COUNT; split_idx++) { + split_pos[split_idx] = 0; + split_base[split_idx] = base_row + split_idx * split_stride; + cand_d[split_idx] = partial_dists[split_base[split_idx]]; + cand_i[split_idx] = partial_indices[split_base[split_idx]]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_d = cand_d[0]; + int best_i = cand_i[0]; + int best_split = 0; + #pragma unroll + for (int split_idx_1 = 1; split_idx_1 < SPLIT_COUNT; split_idx_1++) { + if (best_d > cand_d[split_idx_1]) { + best_d = cand_d[split_idx_1]; + best_i = cand_i[split_idx_1]; + best_split = split_idx_1; + } + } + *((float*)(out_dists + (out_base + out_k))) = best_d; + *((int*)(out_indices + (out_base + out_k))) = best_i; + split_pos[best_split] = split_pos[best_split] + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_pos = split_pos[best_split]; + int next_addr = split_base[best_split] + next_pos; + cand_d[best_split] = partial_dists[next_addr]; + cand_i[best_split] = partial_indices[next_addr]; + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0220.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0220.cu new file mode 100644 index 00000000..869d51dd --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0220.cu @@ -0,0 +1,655 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 128 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 32768 +#define SMEM_SMEM_QUERY_STRIDE 32768 +#define SMEM_SMEM_DATABASE_OFF 33792 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_DATABASE_SQ_OFF 50176 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 50432 +#define THREADS 192 +#define BLOCK_Q 128 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_MAX 8 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16_cta2( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + ".reg .b32 m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "tcgen05.mma.cta_group::2.kind::f16 [%0], %1, %2, %3, {m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step_cg2( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\t" + "mov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, %3, " + "{m0, m1, m2, m3, m4, m5, m6, m7}, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], %1;\n\t" + "}\n" + :: "r"(mbar_addr), "h"(cta_mask) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x16(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x16.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15}, [%16];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ uint32_t smem_addr(const void* ptr) { + uint32_t addr; + asm("{\n\t" + ".reg .u64 u64addr;\n\t" + "cvta.to.shared.u64 u64addr, %1;\n\t" + "cvt.u32.u64 %0, u64addr;\n\t" + "}\n" : "=r"(addr) : "l"(ptr)); + return addr; +} + + +__device__ __forceinline__ uint32_t mapa_to_rank(uint32_t local_addr, uint32_t rank) { + uint32_t remote; + asm volatile("mapa.shared::cluster.u32 %0, %1, %2;" + : "=r"(remote) : "r"(local_addr), "r"(rank)); + return remote; +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem_cta2( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global" + ".mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit_cg2_multicast(int mbar_addr, uint16_t cta_mask) { + asm volatile( + "{\n\t" + ".reg .b16 lo, hi;\n\t" + "mov.b32 {lo, hi}, %1;\n\t" + "tcgen05.commit.cta_group::2.mbarrier::arrive::one" + ".shared::cluster.multicast::cluster.b64 [%0], lo;\n\t" + "}\n" + :: "r"(mbar_addr), "r"((uint32_t)cta_mask) : "memory"); +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_evolve_7bfc_split_cg2_u2_stage1_k32_unordered_fd9b_k8unordered(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tile_pairs, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + const unsigned int clusters_x = gridDim.x / 2; + const unsigned int cluster_id = ((blockIdx.z * gridDim.y + blockIdx.y) * clusters_x) + blockIdx.x / 2; + const unsigned int num_clusters = clusters_x * gridDim.y * gridDim.z; + + int cta_rank; + asm volatile("mov.b32 %0, %%cluster_ctarank;" : "=r"(cta_rank)); + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 33792); + const int smem_database_addr = smem + 33792; + float* smem_database_sq = reinterpret_cast(smem_raw + 50176); + const int smem_database_sq_addr = smem + 50176; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 0, 2, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 16, 2, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=8 + mbarrier_init_pred(smem + 40, 8, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (128 columns, 128 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::2.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(128) : "memory"); + } + + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: load ---- + if (warp == 0) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 0) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx = cluster_id; work_idx < total_work; work_idx += num_clusters) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tile_pairs; + int q_tile_pair = query_work % num_q_tile_pairs; + int q_tile = q_tile_pair * 2 + cta_rank; + int off_q = q_tile * BLOCK_Q; + int global_q = batch_idx * Q + off_q; + int db_tile_start = split_idx * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((query_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(32768)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_query_addr), "l"(tmap_query), "r"(0), "r"(global_q), "r"(0), + "r"(((query_full_addr) & 0xFEFFFFFF)) : "memory"); + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int off_m = db_tile * BLOCK_M; + int global_m = batch_idx * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cluster.b64 _, [%0], %1;" + :: "r"((database_full_addr) & 0xFEFFFFFF), "r"((uint32_t)(16384)) : "memory"); + asm volatile( + "cp.async.bulk.tensor.3d.shared::cluster.global.mbarrier::complete_tx::bytes.cta_group::2" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(smem_database_addr), "l"(tmap_database), "r"(0), "r"(global_m), "r"(0), + "r"(((database_full_addr) & 0xFEFFFFFF)) : "memory"); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 1) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + if (cta_rank == 0) { + #pragma unroll 1 + for (unsigned int work_idx_1 = cluster_id; work_idx_1 < total_work; work_idx_1 += num_clusters) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = (smem_query_addr >> 4) & 0x3FFF; + int _mma_b_lo_0 = (smem_database_addr >> 4) & 0x3FFF; + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id, m0, m1, m2, m3, m4, m5, m6, m7;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "mov.b32 m0, 0; mov.b32 m1, 0; mov.b32 m2, 0; mov.b32 m3, 0;\n\tmov.b32 m4, 0; mov.b32 m5, 0; mov.b32 m6, 0; mov.b32 m7, 0;\n\t" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 270533776;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 1018;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::2.kind::f16 [%2], da, db, id, {m0, m1, m2, m3, m4, m5, m6, m7}, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit_cg2_multicast(score_full_addr, (uint16_t)(3)); + elect_commit_cg2_multicast(database_empty_addr, (uint16_t)(3)); + } + elect_commit_cg2_multicast(query_empty_addr, (uint16_t)(3)); + } + } + } + // ---- Role: compute ---- + } else if (warp >= 2 && warp <= 5) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = cluster_id; work_idx_2 < total_work; work_idx_2 += num_clusters) { + int split_idx_1 = work_idx_2 % (unsigned int)split_count; + int query_work_1 = work_idx_2 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tile_pairs; + int q_tile_pair_1 = query_work_1 % num_q_tile_pairs; + int q_tile_1 = q_tile_pair_1 * 2 + cta_rank; + int off_q_1 = q_tile_1 * BLOCK_Q; + int q_idx = off_q_1 + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx_1 * Q + q_idx]; + } + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + float worst_d = 3.4e+38f; + int worst_pos = 0; + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int db_start = db_tile_1 * BLOCK_M; + int db_sq_idx = db_start + (warp % 4 * 32 + lane); + if (warp % 4 * 32 + lane < BLOCK_M) { + if (db_sq_idx < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx_1 * M + db_sq_idx]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 0.0f; + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(cta_rank * BLOCK_Q + (warp % 4 * 32 << 16) << 16); + float _tmem_load_0[64]; + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x64.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31, %32, %33, %34, %35, %36, %37, %38, %39, %40, %41, %42, %43, %44, %45, %46, %47, %48, %49, %50, %51, %52, %53, %54, %55, %56, %57, %58, %59, %60, %61, %62, %63}, [%64];" + : "=f"(_tmem_load_0[0]), "=f"(_tmem_load_0[1]), "=f"(_tmem_load_0[2]), "=f"(_tmem_load_0[3]), "=f"(_tmem_load_0[4]), "=f"(_tmem_load_0[5]), "=f"(_tmem_load_0[6]), "=f"(_tmem_load_0[7]), "=f"(_tmem_load_0[8]), "=f"(_tmem_load_0[9]), "=f"(_tmem_load_0[10]), "=f"(_tmem_load_0[11]), "=f"(_tmem_load_0[12]), "=f"(_tmem_load_0[13]), "=f"(_tmem_load_0[14]), "=f"(_tmem_load_0[15]), "=f"(_tmem_load_0[16]), "=f"(_tmem_load_0[17]), "=f"(_tmem_load_0[18]), "=f"(_tmem_load_0[19]), "=f"(_tmem_load_0[20]), "=f"(_tmem_load_0[21]), "=f"(_tmem_load_0[22]), "=f"(_tmem_load_0[23]), "=f"(_tmem_load_0[24]), "=f"(_tmem_load_0[25]), "=f"(_tmem_load_0[26]), "=f"(_tmem_load_0[27]), "=f"(_tmem_load_0[28]), "=f"(_tmem_load_0[29]), "=f"(_tmem_load_0[30]), "=f"(_tmem_load_0[31]), "=f"(_tmem_load_0[32]), "=f"(_tmem_load_0[33]), "=f"(_tmem_load_0[34]), "=f"(_tmem_load_0[35]), "=f"(_tmem_load_0[36]), "=f"(_tmem_load_0[37]), "=f"(_tmem_load_0[38]), "=f"(_tmem_load_0[39]), "=f"(_tmem_load_0[40]), "=f"(_tmem_load_0[41]), "=f"(_tmem_load_0[42]), "=f"(_tmem_load_0[43]), "=f"(_tmem_load_0[44]), "=f"(_tmem_load_0[45]), "=f"(_tmem_load_0[46]), "=f"(_tmem_load_0[47]), "=f"(_tmem_load_0[48]), "=f"(_tmem_load_0[49]), "=f"(_tmem_load_0[50]), "=f"(_tmem_load_0[51]), "=f"(_tmem_load_0[52]), "=f"(_tmem_load_0[53]), "=f"(_tmem_load_0[54]), "=f"(_tmem_load_0[55]), "=f"(_tmem_load_0[56]), "=f"(_tmem_load_0[57]), "=f"(_tmem_load_0[58]), "=f"(_tmem_load_0[59]), "=f"(_tmem_load_0[60]), "=f"(_tmem_load_0[61]), "=f"(_tmem_load_0[62]), "=f"(_tmem_load_0[63]) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + if (elect_sync()) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cluster.b64 _, [%0];" + :: "r"((score_empty_addr) & 0xFEFFFFFF) : "memory"); + } + if (valid_q != 0) { + #pragma unroll 2 + for (int col_base = 0; col_base < 64; col_base += 4) { + float dist_vec[4]; + dist_vec[0] = _tmem_load_0[col_base]; + dist_vec[1] = _tmem_load_0[col_base + 1]; + dist_vec[2] = _tmem_load_0[col_base + 2]; + dist_vec[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec[4]; + db_sq_vec[0] = smem_database_sq[col_base]; + db_sq_vec[1] = smem_database_sq[col_base + 1]; + db_sq_vec[2] = smem_database_sq[col_base + 2]; + db_sq_vec[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec)[_la], reinterpret_cast(db_sq_vec)[_la]); + float group_min = _t0[0]; + if (_t0[1] < group_min) { + group_min = _t0[1]; + } + if (_t0[2] < group_min) { + group_min = _t0[2]; + } + if (_t0[3] < group_min) { + group_min = _t0[3]; + } + if (group_min < worst_d) { + #pragma unroll + for (int vec_col = 0; vec_col < 4; vec_col++) { + int db_idx = db_start + col_base + vec_col; + if (db_idx < M) { + float dist = _t0[vec_col]; + if (dist < worst_d) { + best_d[worst_pos] = dist; + best_i[worst_pos] = db_idx; + worst_d = best_d[0]; + worst_pos = 0; + #pragma unroll + for (int scan_pos = 1; scan_pos < TOP_K_MAX; scan_pos++) { + if (worst_d < best_d[scan_pos]) { + worst_d = best_d[scan_pos]; + worst_pos = scan_pos; + } + } + } + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + if (valid_q != 0) { + int out_base = ((split_idx_1 * B + batch_idx_1) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + *((float*)(partial_dists + (out_base + out_k))) = best_d[out_k]; + *((int*)(partial_indices + (out_base + out_k))) = best_i[out_k]; + } + } + } + } + } + + // Cleanup + asm volatile("barrier.cluster.arrive.release.aligned;"); + asm volatile("barrier.cluster.wait.acquire.aligned;"); + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::2.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(128)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::2.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0221.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0221.cu new file mode 100644 index 00000000..d7d29360 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0221.cu @@ -0,0 +1,93 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 128 +#define TOP_K_MAX 10 +#define SPLIT_COUNT 8 +#define GROUP_COUNT 4 +#define GROUP_SPLITS 2 +#define GROUPS_PER_CTA 4 + +#include + +extern "C" { + +__global__ __launch_bounds__(128, 1) void +kernel_knn_build_d64_q4096_c271_twostage_group_reduce(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ reduced_dists, int* __restrict__ reduced_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int group_linear = bid * GROUPS_PER_CTA + warp; + int total_groups = total_queries * GROUP_COUNT; + if (warp < GROUPS_PER_CTA && group_linear < total_groups) { + int row = group_linear / GROUP_COUNT; + int group_idx = group_linear - row * GROUP_COUNT; + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int source_split0 = group_idx * GROUP_SPLITS; + int out_base = base_row + group_idx * split_stride; + int split_pos = 0; + int split_id = source_split0 + lane; + float cand_d = 3.4e+38f; + int cand_i = -1; + if (lane < GROUP_SPLITS) { + int source_addr = base_row + split_id * split_stride; + cand_d = partial_dists[source_addr]; + cand_i = partial_indices[source_addr]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float warp_min = cand_d; + float _warp_reduce_0 = warp_min; + #pragma unroll + for (int offset = 16; offset > 0; offset >>= 1) + _warp_reduce_0 = fminf(_warp_reduce_0, __shfl_xor_sync(0xFFFFFFFF, _warp_reduce_0, offset)); + warp_min = _warp_reduce_0; + unsigned int _vote_0 = __ballot_sync(0xFFFFFFFF, cand_d == warp_min); + int owner_ballot = _vote_0; + int _ffs_0 = __ffs(owner_ballot); + int winner_lane = _ffs_0 - 1; + int _shfl_0 = __shfl_sync(0xFFFFFFFF, cand_i, winner_lane); + int winner_i = _shfl_0; + if (lane == 0) { + *((float*)(reduced_dists + (out_base + out_k))) = warp_min; + *((int*)(reduced_indices + (out_base + out_k))) = winner_i; + } + if (lane == winner_lane) { + split_pos = split_pos + 1; + cand_d = 3.4e+38f; + cand_i = -1; + if (split_pos < TOP_K_MAX) { + int next_addr = base_row + split_id * split_stride + split_pos; + cand_d = partial_dists[next_addr]; + cand_i = partial_indices[next_addr]; + } + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0222.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0222.cu new file mode 100644 index 00000000..b5919b87 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0222.cu @@ -0,0 +1,83 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 32 +#define TOP_K_MAX 10 +#define SPLIT_COUNT 5 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_d64_build_aa88_k10_merge_s8_rowbase_cache_c271_s5(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 32 + tid; + int stride = num_bids * 32; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int split_pos[SPLIT_COUNT]; + int split_base[SPLIT_COUNT]; + float cand_d[SPLIT_COUNT]; + int cand_i[SPLIT_COUNT]; + #pragma unroll + for (int split_idx = 0; split_idx < SPLIT_COUNT; split_idx++) { + split_pos[split_idx] = 0; + split_base[split_idx] = base_row + split_idx * split_stride; + cand_d[split_idx] = partial_dists[split_base[split_idx]]; + cand_i[split_idx] = partial_indices[split_base[split_idx]]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_d = cand_d[0]; + int best_i = cand_i[0]; + int best_split = 0; + #pragma unroll + for (int split_idx_1 = 1; split_idx_1 < SPLIT_COUNT; split_idx_1++) { + if (best_d > cand_d[split_idx_1]) { + best_d = cand_d[split_idx_1]; + best_i = cand_i[split_idx_1]; + best_split = split_idx_1; + } + } + *((float*)(out_dists + (base_row + out_k))) = best_d; + *((int*)(out_indices + (base_row + out_k))) = best_i; + split_pos[best_split] = split_pos[best_split] + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_pos = split_pos[best_split]; + int next_addr = split_base[best_split] + next_pos; + cand_d[best_split] = partial_dists[next_addr]; + cand_i[best_split] = partial_indices[next_addr]; + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0223.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0223.cu new file mode 100644 index 00000000..398576c7 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0223.cu @@ -0,0 +1,83 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 32 +#define TOP_K_MAX 10 +#define SPLIT_COUNT 6 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_d64_build_aa88_k10_merge_s8_rowbase_cache_c271_s6(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 32 + tid; + int stride = num_bids * 32; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int split_pos[SPLIT_COUNT]; + int split_base[SPLIT_COUNT]; + float cand_d[SPLIT_COUNT]; + int cand_i[SPLIT_COUNT]; + #pragma unroll + for (int split_idx = 0; split_idx < SPLIT_COUNT; split_idx++) { + split_pos[split_idx] = 0; + split_base[split_idx] = base_row + split_idx * split_stride; + cand_d[split_idx] = partial_dists[split_base[split_idx]]; + cand_i[split_idx] = partial_indices[split_base[split_idx]]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_d = cand_d[0]; + int best_i = cand_i[0]; + int best_split = 0; + #pragma unroll + for (int split_idx_1 = 1; split_idx_1 < SPLIT_COUNT; split_idx_1++) { + if (best_d > cand_d[split_idx_1]) { + best_d = cand_d[split_idx_1]; + best_i = cand_i[split_idx_1]; + best_split = split_idx_1; + } + } + *((float*)(out_dists + (base_row + out_k))) = best_d; + *((int*)(out_indices + (base_row + out_k))) = best_i; + split_pos[best_split] = split_pos[best_split] + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_pos = split_pos[best_split]; + int next_addr = split_base[best_split] + next_pos; + cand_d[best_split] = partial_dists[next_addr]; + cand_i[best_split] = partial_indices[next_addr]; + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0224.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0224.cu new file mode 100644 index 00000000..1386e690 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0224.cu @@ -0,0 +1,588 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 64 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 16384 +#define SMEM_SMEM_QUERY_STRIDE 16384 +#define SMEM_SMEM_DATABASE_OFF 17408 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 8192 +#define SMEM_SMEM_DATABASE_STRIDE 8192 +#define SMEM_SMEM_DATABASE_SQ_OFF 25600 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 25856 +#define THREADS 192 +#define BLOCK_Q 128 +#define BLOCK_M 64 +#define TOP_K_MAX 10 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "tcgen05.mma.cta_group::1.kind::f16 [%0], %1, %2, %3, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, %3, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit(int mbar_addr) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];\n\t" + "}\n" + :: "r"(mbar_addr)); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x16(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x16.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15}, [%16];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ float max_noftz(float a, float b) { + float c; + asm("max.f32 %0, %1, %2;" : "=f"(c) : "f"(a), "f"(b)); + return c; +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cta.global" + ".mbarrier::complete_tx::bytes" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit(int mbar_addr) { + asm volatile( + "tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ uint32_t make_warp_uniform(uint32_t val) { + uint32_t result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1f, 0xffffffff;" + : "=r"(result) : "r"(val)); + return result; +} + +extern "C" { + +__global__ __launch_bounds__(192, 1) void +kernel_knn_build_d64_q4096_c271_stage1_syncdrop(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tiles, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 17408); + const int smem_database_addr = smem + 17408; + float* smem_database_sq = reinterpret_cast(smem_raw + 25600); + const int smem_database_sq_addr = smem + 25600; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 0, 1, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 16, 1, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=128 + mbarrier_init_pred(smem + 40, 128, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (64 columns, 64 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::1.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(64) : "memory"); + } + + __syncthreads(); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: load ---- + if (warp == 0) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 0) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx = bid; work_idx < total_work; work_idx += num_bids) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tiles; + int q_tile = query_work % num_q_tiles; + int off_q = q_tile * BLOCK_Q; + int global_q = batch_idx * Q + off_q; + int db_tile_start = split_idx * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + mbarrier_arrive_expect_tx(query_full_addr, 16384); + tma_3d_gmem2smem(smem_query_addr, tmap_query, 0, global_q, 0, query_full_addr); + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int off_m = db_tile * BLOCK_M; + int global_m = batch_idx * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + mbarrier_arrive_expect_tx(database_full_addr, 8192); + tma_3d_gmem2smem(smem_database_addr, tmap_database, 0, global_m, 0, database_full_addr); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 1) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_1 = bid; work_idx_1 < total_work; work_idx_1 += num_bids) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = make_warp_uniform((smem_query_addr >> 4) & 0x3FFF); + int _mma_b_lo_0 = make_warp_uniform((smem_database_addr >> 4) & 0x3FFF); + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 135267472;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit(score_full_addr); + elect_commit(database_empty_addr); + } + elect_commit(query_empty_addr); + } + } + // ---- Role: compute ---- + } else if (warp >= 2 && warp <= 5) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = bid; work_idx_2 < total_work; work_idx_2 += num_bids) { + int split_idx_1 = work_idx_2 % (unsigned int)split_count; + int query_work_1 = work_idx_2 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tiles; + int q_tile_1 = query_work_1 % num_q_tiles; + int off_q_1 = q_tile_1 * BLOCK_Q; + int q_idx = off_q_1 + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx_1 * Q + q_idx]; + } + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int db_start = db_tile_1 * BLOCK_M; + int db_sq_idx = db_start + (warp % 4 * 32 + lane); + if (warp % 4 * 32 + lane < BLOCK_M) { + if (db_sq_idx < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx_1 * M + db_sq_idx]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 0.0f; + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(warp % 4 * 32 << 16); + float _tmem_load_0[64]; + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x64.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31, %32, %33, %34, %35, %36, %37, %38, %39, %40, %41, %42, %43, %44, %45, %46, %47, %48, %49, %50, %51, %52, %53, %54, %55, %56, %57, %58, %59, %60, %61, %62, %63}, [%64];" + : "=f"(_tmem_load_0[0]), "=f"(_tmem_load_0[1]), "=f"(_tmem_load_0[2]), "=f"(_tmem_load_0[3]), "=f"(_tmem_load_0[4]), "=f"(_tmem_load_0[5]), "=f"(_tmem_load_0[6]), "=f"(_tmem_load_0[7]), "=f"(_tmem_load_0[8]), "=f"(_tmem_load_0[9]), "=f"(_tmem_load_0[10]), "=f"(_tmem_load_0[11]), "=f"(_tmem_load_0[12]), "=f"(_tmem_load_0[13]), "=f"(_tmem_load_0[14]), "=f"(_tmem_load_0[15]), "=f"(_tmem_load_0[16]), "=f"(_tmem_load_0[17]), "=f"(_tmem_load_0[18]), "=f"(_tmem_load_0[19]), "=f"(_tmem_load_0[20]), "=f"(_tmem_load_0[21]), "=f"(_tmem_load_0[22]), "=f"(_tmem_load_0[23]), "=f"(_tmem_load_0[24]), "=f"(_tmem_load_0[25]), "=f"(_tmem_load_0[26]), "=f"(_tmem_load_0[27]), "=f"(_tmem_load_0[28]), "=f"(_tmem_load_0[29]), "=f"(_tmem_load_0[30]), "=f"(_tmem_load_0[31]), "=f"(_tmem_load_0[32]), "=f"(_tmem_load_0[33]), "=f"(_tmem_load_0[34]), "=f"(_tmem_load_0[35]), "=f"(_tmem_load_0[36]), "=f"(_tmem_load_0[37]), "=f"(_tmem_load_0[38]), "=f"(_tmem_load_0[39]), "=f"(_tmem_load_0[40]), "=f"(_tmem_load_0[41]), "=f"(_tmem_load_0[42]), "=f"(_tmem_load_0[43]), "=f"(_tmem_load_0[44]), "=f"(_tmem_load_0[45]), "=f"(_tmem_load_0[46]), "=f"(_tmem_load_0[47]), "=f"(_tmem_load_0[48]), "=f"(_tmem_load_0[49]), "=f"(_tmem_load_0[50]), "=f"(_tmem_load_0[51]), "=f"(_tmem_load_0[52]), "=f"(_tmem_load_0[53]), "=f"(_tmem_load_0[54]), "=f"(_tmem_load_0[55]), "=f"(_tmem_load_0[56]), "=f"(_tmem_load_0[57]), "=f"(_tmem_load_0[58]), "=f"(_tmem_load_0[59]), "=f"(_tmem_load_0[60]), "=f"(_tmem_load_0[61]), "=f"(_tmem_load_0[62]), "=f"(_tmem_load_0[63]) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + mbarrier_arrive(score_empty_addr); + if (valid_q != 0) { + #pragma unroll 2 + for (int col_base = 0; col_base < 64; col_base += 4) { + float dist_vec[4]; + dist_vec[0] = _tmem_load_0[col_base]; + dist_vec[1] = _tmem_load_0[col_base + 1]; + dist_vec[2] = _tmem_load_0[col_base + 2]; + dist_vec[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec[4]; + db_sq_vec[0] = smem_database_sq[col_base]; + db_sq_vec[1] = smem_database_sq[col_base + 1]; + db_sq_vec[2] = smem_database_sq[col_base + 2]; + db_sq_vec[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec)[_la], reinterpret_cast(db_sq_vec)[_la]); + float group_min = _t0[0]; + if (_t0[1] < group_min) { + group_min = _t0[1]; + } + if (_t0[2] < group_min) { + group_min = _t0[2]; + } + if (_t0[3] < group_min) { + group_min = _t0[3]; + } + if (group_min < best_d[TOP_K_MAX - 1]) { + #pragma unroll + for (int vec_col = 0; vec_col < 4; vec_col++) { + int db_idx = db_start + col_base + vec_col; + if (db_idx < M) { + float dist = _t0[vec_col]; + float _max_0 = max_noftz(dist, 0.0f); + dist = _max_0; + if (dist < best_d[TOP_K_MAX - 1]) { + best_d[TOP_K_MAX - 1] = dist; + best_i[TOP_K_MAX - 1] = db_idx; + #pragma unroll + for (int pos = TOP_K_MAX - 1; pos >= 1; pos--) { + if (best_d[pos] < best_d[pos - 1]) { + float tmp_d = best_d[pos - 1]; + int tmp_i = best_i[pos - 1]; + best_d[pos - 1] = best_d[pos]; + best_i[pos - 1] = best_i[pos]; + best_d[pos] = tmp_d; + best_i[pos] = tmp_i; + } + } + } + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(128)); + } + if (valid_q != 0) { + int out_base = ((split_idx_1 * B + batch_idx_1) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + if (out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = best_d[out_k]; + *((int*)(partial_indices + (out_base + out_k))) = best_i[out_k]; + } + } + } + } + } + } + + // Cleanup + __syncthreads(); // barrier before TMEM dealloc + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::1.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(64)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::1.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0225.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0225.cu new file mode 100644 index 00000000..b5919b87 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0225.cu @@ -0,0 +1,83 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 32 +#define TOP_K_MAX 10 +#define SPLIT_COUNT 5 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_d64_build_aa88_k10_merge_s8_rowbase_cache_c271_s5(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 32 + tid; + int stride = num_bids * 32; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int split_pos[SPLIT_COUNT]; + int split_base[SPLIT_COUNT]; + float cand_d[SPLIT_COUNT]; + int cand_i[SPLIT_COUNT]; + #pragma unroll + for (int split_idx = 0; split_idx < SPLIT_COUNT; split_idx++) { + split_pos[split_idx] = 0; + split_base[split_idx] = base_row + split_idx * split_stride; + cand_d[split_idx] = partial_dists[split_base[split_idx]]; + cand_i[split_idx] = partial_indices[split_base[split_idx]]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_d = cand_d[0]; + int best_i = cand_i[0]; + int best_split = 0; + #pragma unroll + for (int split_idx_1 = 1; split_idx_1 < SPLIT_COUNT; split_idx_1++) { + if (best_d > cand_d[split_idx_1]) { + best_d = cand_d[split_idx_1]; + best_i = cand_i[split_idx_1]; + best_split = split_idx_1; + } + } + *((float*)(out_dists + (base_row + out_k))) = best_d; + *((int*)(out_indices + (base_row + out_k))) = best_i; + split_pos[best_split] = split_pos[best_split] + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_pos = split_pos[best_split]; + int next_addr = split_base[best_split] + next_pos; + cand_d[best_split] = partial_dists[next_addr]; + cand_i[best_split] = partial_indices[next_addr]; + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0226.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0226.cu new file mode 100644 index 00000000..398576c7 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0226.cu @@ -0,0 +1,83 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 32 +#define TOP_K_MAX 10 +#define SPLIT_COUNT 6 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_d64_build_aa88_k10_merge_s8_rowbase_cache_c271_s6(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int start_row = bid * 32 + tid; + int stride = num_bids * 32; + #pragma unroll 1 + for (int row = start_row; row < total_queries; row += stride) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int split_pos[SPLIT_COUNT]; + int split_base[SPLIT_COUNT]; + float cand_d[SPLIT_COUNT]; + int cand_i[SPLIT_COUNT]; + #pragma unroll + for (int split_idx = 0; split_idx < SPLIT_COUNT; split_idx++) { + split_pos[split_idx] = 0; + split_base[split_idx] = base_row + split_idx * split_stride; + cand_d[split_idx] = partial_dists[split_base[split_idx]]; + cand_i[split_idx] = partial_indices[split_base[split_idx]]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float best_d = cand_d[0]; + int best_i = cand_i[0]; + int best_split = 0; + #pragma unroll + for (int split_idx_1 = 1; split_idx_1 < SPLIT_COUNT; split_idx_1++) { + if (best_d > cand_d[split_idx_1]) { + best_d = cand_d[split_idx_1]; + best_i = cand_i[split_idx_1]; + best_split = split_idx_1; + } + } + *((float*)(out_dists + (base_row + out_k))) = best_d; + *((int*)(out_indices + (base_row + out_k))) = best_i; + split_pos[best_split] = split_pos[best_split] + 1; + if (out_k + 1 < TOP_K_MAX) { + int next_pos = split_pos[best_split]; + int next_addr = split_base[best_split] + next_pos; + cand_d[best_split] = partial_dists[next_addr]; + cand_i[best_split] = partial_indices[next_addr]; + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0227.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0227.cu new file mode 100644 index 00000000..0bd74eff --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0227.cu @@ -0,0 +1,146 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_DIST_OFF 0 +#define SMEM_SMEM_DIST_STAGE_BYTES 10240 +#define SMEM_SMEM_DIST_STRIDE 10240 +#define SMEM_SMEM_IDX_OFF 10240 +#define SMEM_SMEM_IDX_STAGE_BYTES 10240 +#define SMEM_SMEM_IDX_STRIDE 10240 +#define SMEM_TOTAL 20480 +#define THREADS 256 +#define K_MAX_ 10 +#define THREADS_ 256 + +#include + +extern "C" { + +__global__ __launch_bounds__(256, 1) void +kernel_knn_build_common_d_generic_direct_v1(__nv_bfloat16* __restrict__ query, __nv_bfloat16* __restrict__ database, float* __restrict__ out_dists, int* __restrict__ out_indices, int B, int Q, int M, int K, int D) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + float* smem_dist = reinterpret_cast(smem_raw + 0); + const int smem_dist_addr = smem + 0; + int* smem_idx = reinterpret_cast(smem_raw + 10240); + const int smem_idx_addr = smem + 10240; + + // === Task calls (dependency order) === + int work_id = bid; + int batch_id = work_id / Q; + int q_row = work_id - batch_id * Q; + if (batch_id < B) { + unsigned long long q_base = (unsigned long long)((batch_id * Q + q_row) * D); + float best_d[10]; + int best_i[10]; + #pragma unroll + for (int kk = 0; kk < K_MAX_; kk++) { + best_d[kk] = LOOM_INF; + best_i[kk] = -1; + } + #pragma unroll 1 + for (int m_row = tid; m_row < M; m_row += THREADS_) { + unsigned long long db_base = (unsigned long long)((batch_id * M + m_row) * D); + float dist = 0.0f; + #pragma unroll 1 + for (int d_col = 0; d_col < D; d_col++) { + float q_val = (float)query[q_base + (unsigned long long)d_col]; + float db_val = (float)database[db_base + (unsigned long long)d_col]; + float diff = q_val - db_val; + dist += diff * diff; + } + if (dist < best_d[K_MAX_ - 1]) { + float carry_d = dist; + int carry_i = m_row; + #pragma unroll + for (int kk_1 = 0; kk_1 < K_MAX_; kk_1++) { + float old_d = best_d[kk_1]; + int old_i = best_i[kk_1]; + int take = ((carry_d < old_d) ? 1 : 0); + best_d[kk_1] = ((take != 0) ? carry_d : old_d); + best_i[kk_1] = ((take != 0) ? carry_i : old_i); + carry_d = ((take != 0) ? old_d : carry_d); + carry_i = ((take != 0) ? old_i : carry_i); + } + } + } + #pragma unroll + for (int kk_2 = 0; kk_2 < K_MAX_; kk_2++) { + int smem_off = tid * K_MAX_ + kk_2; + smem_dist[smem_off] = best_d[kk_2]; + smem_idx[smem_off] = best_i[kk_2]; + } + __syncthreads(); + if (tid == 0) { + float final_d[10]; + int final_i[10]; + #pragma unroll + for (int kk_3 = 0; kk_3 < K_MAX_; kk_3++) { + final_d[kk_3] = LOOM_INF; + final_i[kk_3] = -1; + } + #pragma unroll 1 + for (int src_thread = 0; src_thread < THREADS_; src_thread++) { + #pragma unroll + for (int src_k = 0; src_k < K_MAX_; src_k++) { + int smem_off_1 = src_thread * K_MAX_ + src_k; + int cand_i = smem_idx[smem_off_1]; + if (cand_i >= 0) { + float cand_d = smem_dist[smem_off_1]; + if (cand_d < final_d[K_MAX_ - 1]) { + float carry_d_1 = cand_d; + int carry_i_1 = cand_i; + #pragma unroll + for (int kk_4 = 0; kk_4 < K_MAX_; kk_4++) { + float old_d_1 = final_d[kk_4]; + int old_i_1 = final_i[kk_4]; + int take_1 = ((carry_d_1 < old_d_1) ? 1 : 0); + final_d[kk_4] = ((take_1 != 0) ? carry_d_1 : old_d_1); + final_i[kk_4] = ((take_1 != 0) ? carry_i_1 : old_i_1); + carry_d_1 = ((take_1 != 0) ? old_d_1 : carry_d_1); + carry_i_1 = ((take_1 != 0) ? old_i_1 : carry_i_1); + } + } + } + } + } + unsigned long long out_base = (unsigned long long)((batch_id * Q + q_row) * K); + #pragma unroll + for (int kk_5 = 0; kk_5 < K_MAX_; kk_5++) { + if (kk_5 < K) { + out_dists[out_base + (unsigned long long)kk_5] = final_d[kk_5]; + out_indices[out_base + (unsigned long long)kk_5] = final_i[kk_5]; + } + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0228.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0228.cu new file mode 100644 index 00000000..0bd74eff --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0228.cu @@ -0,0 +1,146 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_DIST_OFF 0 +#define SMEM_SMEM_DIST_STAGE_BYTES 10240 +#define SMEM_SMEM_DIST_STRIDE 10240 +#define SMEM_SMEM_IDX_OFF 10240 +#define SMEM_SMEM_IDX_STAGE_BYTES 10240 +#define SMEM_SMEM_IDX_STRIDE 10240 +#define SMEM_TOTAL 20480 +#define THREADS 256 +#define K_MAX_ 10 +#define THREADS_ 256 + +#include + +extern "C" { + +__global__ __launch_bounds__(256, 1) void +kernel_knn_build_common_d_generic_direct_v1(__nv_bfloat16* __restrict__ query, __nv_bfloat16* __restrict__ database, float* __restrict__ out_dists, int* __restrict__ out_indices, int B, int Q, int M, int K, int D) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + float* smem_dist = reinterpret_cast(smem_raw + 0); + const int smem_dist_addr = smem + 0; + int* smem_idx = reinterpret_cast(smem_raw + 10240); + const int smem_idx_addr = smem + 10240; + + // === Task calls (dependency order) === + int work_id = bid; + int batch_id = work_id / Q; + int q_row = work_id - batch_id * Q; + if (batch_id < B) { + unsigned long long q_base = (unsigned long long)((batch_id * Q + q_row) * D); + float best_d[10]; + int best_i[10]; + #pragma unroll + for (int kk = 0; kk < K_MAX_; kk++) { + best_d[kk] = LOOM_INF; + best_i[kk] = -1; + } + #pragma unroll 1 + for (int m_row = tid; m_row < M; m_row += THREADS_) { + unsigned long long db_base = (unsigned long long)((batch_id * M + m_row) * D); + float dist = 0.0f; + #pragma unroll 1 + for (int d_col = 0; d_col < D; d_col++) { + float q_val = (float)query[q_base + (unsigned long long)d_col]; + float db_val = (float)database[db_base + (unsigned long long)d_col]; + float diff = q_val - db_val; + dist += diff * diff; + } + if (dist < best_d[K_MAX_ - 1]) { + float carry_d = dist; + int carry_i = m_row; + #pragma unroll + for (int kk_1 = 0; kk_1 < K_MAX_; kk_1++) { + float old_d = best_d[kk_1]; + int old_i = best_i[kk_1]; + int take = ((carry_d < old_d) ? 1 : 0); + best_d[kk_1] = ((take != 0) ? carry_d : old_d); + best_i[kk_1] = ((take != 0) ? carry_i : old_i); + carry_d = ((take != 0) ? old_d : carry_d); + carry_i = ((take != 0) ? old_i : carry_i); + } + } + } + #pragma unroll + for (int kk_2 = 0; kk_2 < K_MAX_; kk_2++) { + int smem_off = tid * K_MAX_ + kk_2; + smem_dist[smem_off] = best_d[kk_2]; + smem_idx[smem_off] = best_i[kk_2]; + } + __syncthreads(); + if (tid == 0) { + float final_d[10]; + int final_i[10]; + #pragma unroll + for (int kk_3 = 0; kk_3 < K_MAX_; kk_3++) { + final_d[kk_3] = LOOM_INF; + final_i[kk_3] = -1; + } + #pragma unroll 1 + for (int src_thread = 0; src_thread < THREADS_; src_thread++) { + #pragma unroll + for (int src_k = 0; src_k < K_MAX_; src_k++) { + int smem_off_1 = src_thread * K_MAX_ + src_k; + int cand_i = smem_idx[smem_off_1]; + if (cand_i >= 0) { + float cand_d = smem_dist[smem_off_1]; + if (cand_d < final_d[K_MAX_ - 1]) { + float carry_d_1 = cand_d; + int carry_i_1 = cand_i; + #pragma unroll + for (int kk_4 = 0; kk_4 < K_MAX_; kk_4++) { + float old_d_1 = final_d[kk_4]; + int old_i_1 = final_i[kk_4]; + int take_1 = ((carry_d_1 < old_d_1) ? 1 : 0); + final_d[kk_4] = ((take_1 != 0) ? carry_d_1 : old_d_1); + final_i[kk_4] = ((take_1 != 0) ? carry_i_1 : old_i_1); + carry_d_1 = ((take_1 != 0) ? old_d_1 : carry_d_1); + carry_i_1 = ((take_1 != 0) ? old_i_1 : carry_i_1); + } + } + } + } + } + unsigned long long out_base = (unsigned long long)((batch_id * Q + q_row) * K); + #pragma unroll + for (int kk_5 = 0; kk_5 < K_MAX_; kk_5++) { + if (kk_5 < K) { + out_dists[out_base + (unsigned long long)kk_5] = final_d[kk_5]; + out_indices[out_base + (unsigned long long)kk_5] = final_i[kk_5]; + } + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0229.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0229.cu new file mode 100644 index 00000000..8f681a55 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0229.cu @@ -0,0 +1,607 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 64 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 8192 +#define SMEM_SMEM_QUERY_STRIDE 8192 +#define SMEM_SMEM_DATABASE_OFF 9216 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 8192 +#define SMEM_SMEM_DATABASE_STRIDE 8192 +#define SMEM_SMEM_DATABASE_SQ_OFF 17408 +#define SMEM_SMEM_DATABASE_SQ_STAGE_BYTES 256 +#define SMEM_SMEM_DATABASE_SQ_STRIDE 256 +#define SMEM_TOTAL 17664 +#define THREADS 96 +#define BLOCK_Q 64 +#define BLOCK_M 64 +#define K_TILE 64 +#define TOP_K_MAX 10 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "tcgen05.mma.cta_group::1.kind::f16 [%0], %1, %2, %3, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, %3, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit(int mbar_addr) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];\n\t" + "}\n" + :: "r"(mbar_addr)); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x16(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x16.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15}, [%16];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ float max_noftz(float a, float b) { + float c; + asm("max.f32 %0, %1, %2;" : "=f"(c) : "f"(a), "f"(b)); + return c; +} + + +__device__ __forceinline__ void fma_f32x2_inplace(float2* a, float2 b, float2 c) { + unsigned long long r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(r) + : "l"(*(unsigned long long*)a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + *(unsigned long long*)a = r; +} + +__device__ __forceinline__ void mul_f32x2_inplace(float2* a, float2 b) { + asm("mul.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void add_f32x2_inplace(float2* a, float2 b) { + asm("add.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ void sub_f32x2_inplace(float2* a, float2 b) { + asm("sub.rn.ftz.f32x2 %0, %0, %1;" + : "+l"(*(unsigned long long*)a) : "l"(*(unsigned long long*)&b)); +} + +__device__ __forceinline__ float2 add_f32x2(float2 a, float2 b) { + float2 r; + asm("add.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ float2 sub_f32x2(float2 a, float2 b) { + float2 r; + asm("sub.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +__device__ __forceinline__ void fma_scale_x32( + float* sv, const float2* scale2, const float2* neg_max2) +{ + float2* sv_2 = reinterpret_cast(sv); + #pragma unroll + for (int j = 0; j < 16; j++) + fma_f32x2_inplace(&sv_2[j], *scale2, *neg_max2); +} + +__device__ __forceinline__ float2 fma_f32x2(float2 a, float2 b, float2 c) { + float2 r; + asm("fma.rn.ftz.f32x2 %0, %1, %2, %3;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b), + "l"(*(unsigned long long*)&c)); + return r; +} + +__device__ __forceinline__ float2 mul_f32x2(float2 a, float2 b) { + float2 r; + asm("mul.rn.ftz.f32x2 %0, %1, %2;" + : "=l"(*(unsigned long long*)&r) + : "l"(*(unsigned long long*)&a), "l"(*(unsigned long long*)&b)); + return r; +} + +// ex2_emulation_f32x2 defined in softmax_frag_exp2_cast helper (or standalone) + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cta.global" + ".mbarrier::complete_tx::bytes" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit(int mbar_addr) { + asm volatile( + "tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ uint32_t make_warp_uniform(uint32_t val) { + uint32_t result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1f, 0xffffffff;" + : "=r"(result) : "r"(val)); + return result; +} + +extern "C" { + +__global__ __launch_bounds__(96, 1) void +kernel_knn_build_common_d_5e7f_rag_d64_repair_stage1(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tiles, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 9216); + const int smem_database_addr = smem + 9216; + float* smem_database_sq = reinterpret_cast(smem_raw + 17408); + const int smem_database_sq_addr = smem + 17408; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 0, 1, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 16, 1, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=32 + mbarrier_init_pred(smem + 40, 32, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (64 columns, 64 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::1.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(64) : "memory"); + } + + __syncthreads(); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: compute ---- + if (warp == 0) { + { // compute_main + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx = bid; work_idx < total_work; work_idx += num_bids) { + int split_idx = work_idx % (unsigned int)split_count; + int query_work = work_idx / (unsigned int)split_count; + int batch_idx = query_work / num_q_tiles; + int q_tile = query_work % num_q_tiles; + int off_q = q_tile * BLOCK_Q; + int q_idx = off_q + (warp % 4 * 32 + lane); + int valid_q = ((q_idx < Q) ? 1 : 0); + float q_sq_val = 0.0f; + if (valid_q != 0) { + q_sq_val = query_sq[batch_idx * Q + q_idx]; + } + float best_d[TOP_K_MAX]; + int best_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_d[kk] = 3.4e+38f; + best_i[kk] = -1; + } + float worst_d = 3.4e+38f; + int worst_pos = 0; + int db_tile_start = split_idx * db_tiles_per_split; + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < db_tiles_per_split; local_db_tile++) { + int db_tile = db_tile_start + local_db_tile; + int db_start = db_tile * BLOCK_M; + int db_sq_idx0 = db_start + (warp % 4 * 32 + lane); + if (db_sq_idx0 < M) { + smem_database_sq[warp % 4 * 32 + lane] = database_sq[batch_idx * M + db_sq_idx0]; + } else { + smem_database_sq[warp % 4 * 32 + lane] = 3.4e+38f; + } + int db_col1 = warp % 4 * 32 + lane + 32; + int db_sq_idx1 = db_start + db_col1; + if (db_sq_idx1 < M) { + smem_database_sq[db_col1] = database_sq[batch_idx * M + db_sq_idx1]; + } else { + smem_database_sq[db_col1] = 3.4e+38f; + } + asm volatile("barrier.sync 8, %0;" :: "r"(32)); + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(warp % 4 * 32 << 16); + float _tmem_load_0[64]; + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x64.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31, %32, %33, %34, %35, %36, %37, %38, %39, %40, %41, %42, %43, %44, %45, %46, %47, %48, %49, %50, %51, %52, %53, %54, %55, %56, %57, %58, %59, %60, %61, %62, %63}, [%64];" + : "=f"(_tmem_load_0[0]), "=f"(_tmem_load_0[1]), "=f"(_tmem_load_0[2]), "=f"(_tmem_load_0[3]), "=f"(_tmem_load_0[4]), "=f"(_tmem_load_0[5]), "=f"(_tmem_load_0[6]), "=f"(_tmem_load_0[7]), "=f"(_tmem_load_0[8]), "=f"(_tmem_load_0[9]), "=f"(_tmem_load_0[10]), "=f"(_tmem_load_0[11]), "=f"(_tmem_load_0[12]), "=f"(_tmem_load_0[13]), "=f"(_tmem_load_0[14]), "=f"(_tmem_load_0[15]), "=f"(_tmem_load_0[16]), "=f"(_tmem_load_0[17]), "=f"(_tmem_load_0[18]), "=f"(_tmem_load_0[19]), "=f"(_tmem_load_0[20]), "=f"(_tmem_load_0[21]), "=f"(_tmem_load_0[22]), "=f"(_tmem_load_0[23]), "=f"(_tmem_load_0[24]), "=f"(_tmem_load_0[25]), "=f"(_tmem_load_0[26]), "=f"(_tmem_load_0[27]), "=f"(_tmem_load_0[28]), "=f"(_tmem_load_0[29]), "=f"(_tmem_load_0[30]), "=f"(_tmem_load_0[31]), "=f"(_tmem_load_0[32]), "=f"(_tmem_load_0[33]), "=f"(_tmem_load_0[34]), "=f"(_tmem_load_0[35]), "=f"(_tmem_load_0[36]), "=f"(_tmem_load_0[37]), "=f"(_tmem_load_0[38]), "=f"(_tmem_load_0[39]), "=f"(_tmem_load_0[40]), "=f"(_tmem_load_0[41]), "=f"(_tmem_load_0[42]), "=f"(_tmem_load_0[43]), "=f"(_tmem_load_0[44]), "=f"(_tmem_load_0[45]), "=f"(_tmem_load_0[46]), "=f"(_tmem_load_0[47]), "=f"(_tmem_load_0[48]), "=f"(_tmem_load_0[49]), "=f"(_tmem_load_0[50]), "=f"(_tmem_load_0[51]), "=f"(_tmem_load_0[52]), "=f"(_tmem_load_0[53]), "=f"(_tmem_load_0[54]), "=f"(_tmem_load_0[55]), "=f"(_tmem_load_0[56]), "=f"(_tmem_load_0[57]), "=f"(_tmem_load_0[58]), "=f"(_tmem_load_0[59]), "=f"(_tmem_load_0[60]), "=f"(_tmem_load_0[61]), "=f"(_tmem_load_0[62]), "=f"(_tmem_load_0[63]) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + asm volatile("barrier.sync 8, %0;" :: "r"(32)); + mbarrier_arrive(score_empty_addr); + if (valid_q != 0) { + #pragma unroll 1 + for (int col_base = 0; col_base < 64; col_base += 4) { + float dist_vec[4]; + dist_vec[0] = _tmem_load_0[col_base]; + dist_vec[1] = _tmem_load_0[col_base + 1]; + dist_vec[2] = _tmem_load_0[col_base + 2]; + dist_vec[3] = _tmem_load_0[col_base + 3]; + const float2 _fma_b2_0 = {-2.0f, -2.0f}; + const float2 _fma_c2_1 = {q_sq_val, q_sq_val}; + #pragma unroll + for (int _lf = 0; _lf < 2; _lf++) + fma_f32x2_inplace(&reinterpret_cast(dist_vec)[_lf], _fma_b2_0, _fma_c2_1); + float db_sq_vec[4]; + db_sq_vec[0] = smem_database_sq[col_base]; + db_sq_vec[1] = smem_database_sq[col_base + 1]; + db_sq_vec[2] = smem_database_sq[col_base + 2]; + db_sq_vec[3] = smem_database_sq[col_base + 3]; + float _t0[4]; + #pragma unroll + for (int _la = 0; _la < 2; _la++) + reinterpret_cast(_t0)[_la] = add_f32x2(reinterpret_cast(dist_vec)[_la], reinterpret_cast(db_sq_vec)[_la]); + float group_min = _t0[0]; + if (_t0[1] < group_min) { + group_min = _t0[1]; + } + if (_t0[2] < group_min) { + group_min = _t0[2]; + } + if (_t0[3] < group_min) { + group_min = _t0[3]; + } + if (group_min < worst_d) { + #pragma unroll + for (int vec_col = 0; vec_col < 4; vec_col++) { + int db_idx = db_start + col_base + vec_col; + if (db_idx < M) { + float _max_0 = max_noftz(_t0[vec_col], 0.0f); + float dist = _max_0; + if (dist < worst_d) { + best_d[worst_pos] = dist; + best_i[worst_pos] = db_idx; + worst_d = best_d[0]; + worst_pos = 0; + #pragma unroll + for (int scan_pos = 1; scan_pos < TOP_K_MAX; scan_pos++) { + if (worst_d < best_d[scan_pos]) { + worst_d = best_d[scan_pos]; + worst_pos = scan_pos; + } + } + } + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(32)); + } + if (valid_q != 0) { + int out_base = ((split_idx * B + batch_idx) * Q + q_idx) * K; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + int selected_pos = 0; + float selected_d = best_d[0]; + int selected_i = best_i[0]; + #pragma unroll + for (int scan_pos_1 = 1; scan_pos_1 < TOP_K_MAX; scan_pos_1++) { + if (selected_d > best_d[scan_pos_1]) { + selected_d = best_d[scan_pos_1]; + selected_i = best_i[scan_pos_1]; + selected_pos = scan_pos_1; + } + } + if (out_k < K) { + *((float*)(partial_dists + (out_base + out_k))) = selected_d; + *((int*)(partial_indices + (out_base + out_k))) = selected_i; + } + best_d[selected_pos] = 3.4e+38f; + best_i[selected_pos] = -1; + } + } + } + } + // ---- Role: load ---- + } else if (warp == 1) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 1) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx_1 = bid; work_idx_1 < total_work; work_idx_1 += num_bids) { + int split_idx_1 = work_idx_1 % (unsigned int)split_count; + int query_work_1 = work_idx_1 / (unsigned int)split_count; + int batch_idx_1 = query_work_1 / num_q_tiles; + int q_tile_1 = query_work_1 % num_q_tiles; + int off_q_1 = q_tile_1 * BLOCK_Q; + int global_q = batch_idx_1 * Q + off_q_1; + int db_tile_start_1 = split_idx_1 * db_tiles_per_split; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + mbarrier_arrive_expect_tx(query_full_addr, 8192); + tma_3d_gmem2smem(smem_query_addr, tmap_query, 0, global_q, 0, query_full_addr); + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < db_tiles_per_split; local_db_tile_1++) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int off_m = db_tile_1 * BLOCK_M; + int global_m = batch_idx_1 * M + off_m; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + mbarrier_arrive_expect_tx(database_full_addr, 8192); + tma_3d_gmem2smem(smem_database_addr, tmap_database, 0, global_m, 0, database_full_addr); + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 2) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = bid; work_idx_2 < total_work; work_idx_2 += num_bids) { + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int _local_db_tile = 0; _local_db_tile < db_tiles_per_split; _local_db_tile++) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = make_warp_uniform((smem_query_addr >> 4) & 0x3FFF); + int _mma_b_lo_0 = make_warp_uniform((smem_database_addr >> 4) & 0x3FFF); + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 68158608;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit(score_full_addr); + elect_commit(database_empty_addr); + } + elect_commit(query_empty_addr); + } + } + } + + // Cleanup + __syncthreads(); // barrier before TMEM dealloc + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::1.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(64)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::1.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0230.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0230.cu new file mode 100644 index 00000000..eb524d3f --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0230.cu @@ -0,0 +1,652 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +#define LOOM_INF CUDART_INF_F +#define TMEM_NCOLS 64 +#define TMEM_CROSS_OFFSET 0 +#define NUM_MAIN_STAGES 1 +#define SMEM_SMEM_QUERY_OFF 1024 +#define SMEM_SMEM_QUERY_STAGE_BYTES 16384 +#define SMEM_SMEM_QUERY_STRIDE 16384 +#define SMEM_SMEM_DATABASE_OFF 17408 +#define SMEM_SMEM_DATABASE_STAGE_BYTES 16384 +#define SMEM_SMEM_DATABASE_STRIDE 16384 +#define SMEM_SMEM_LOCAL_D_OFF 34048 +#define SMEM_SMEM_LOCAL_D_STAGE_BYTES 16384 +#define SMEM_SMEM_LOCAL_D_STRIDE 16384 +#define SMEM_SMEM_LOCAL_I_OFF 50432 +#define SMEM_SMEM_LOCAL_I_STAGE_BYTES 16384 +#define SMEM_SMEM_LOCAL_I_STRIDE 16384 +#define SMEM_TOTAL 66816 +#define THREADS 128 +#define BLOCK_Q 64 +#define BLOCK_M 64 +#define FEAT_D 128 +#define TOP_K_MAX 32 +#define ROWS_COVERED 32 +#define SPLIT_COUNT_CONST 141 +#define NUM_DB_TILES_CONST 1563 +#define TILES_FLOOR_CONST 11 +#define EXTRA_SPLITS_CONST 12 +#define DB_TILES_PER_SPLIT_CONST 12 +#define M_LIMIT 100000 + +#include + +__device__ __forceinline__ uint32_t elect_sync() { + uint32_t pred = 0; + asm volatile( + "{\n\t" + ".reg .pred %%px;\n\t" + "elect.sync _|%%px, %1;\n\t" + "@%%px mov.s32 %0, 1;\n\t" + "}\n" + : "+r"(pred) + : "r"(0xFFFFFFFF)); + return pred; +} + + +__device__ __forceinline__ void mbarrier_init(int mbar_addr, int count) { + asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" + :: "r"(mbar_addr), "r"(count)); +} + + +__device__ __forceinline__ uint32_t mbarrier_try_wait(int mbar_addr, int phase) { + uint32_t token; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "mbarrier.try_wait.parity.shared::cta.b64" + " P1, [%1], %2;\n\t" + "selp.u32 %0, 1, 0, P1;\n\t" + "}\n" + : "=r"(token) + : "r"(mbar_addr), "r"(phase) : "memory"); + return token; +} + +__device__ __forceinline__ void mbarrier_wait(int mbar_addr, int phase) { + uint32_t ticks = 0x989680; + asm volatile( + "{\n\t" + ".reg .pred P1;\n\t" + "LAB_WAIT:\n\t" + "mbarrier.try_wait.parity.acquire.cta.shared::cta.b64" + " P1, [%0], %1, %2;\n\t" + "@P1 bra.uni DONE;\n\t" + "bra.uni LAB_WAIT;\n\t" + "DONE:\n\t" + "}\n" + :: "r"(mbar_addr), "r"(phase), "r"(ticks) : "memory"); +} + +__device__ __forceinline__ void mbarrier_wait_token(int mbar_addr, int phase, uint32_t token) { + if (token == 0) { + mbarrier_wait(mbar_addr, phase); + } +} + + +__device__ __forceinline__ void tcgen05_mma_f16( + int taddr, uint64_t a_desc, uint64_t b_desc, + uint32_t i_desc, int enable_input_d) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "tcgen05.mma.cta_group::1.kind::f16 [%0], %1, %2, %3, p;\n\t" + "}\n" + :: "r"(taddr), "l"(a_desc), "l"(b_desc), + "r"(i_desc), "r"(enable_input_d)); +} + + +__device__ __forceinline__ uint64_t desc_encode(uint64_t x) { + return (x & 0x3FFFFULL) >> 4ULL; +} + + +__device__ __forceinline__ void mma_ss_step( + int a_lo, int b_lo, int taddr, uint32_t i_desc, int enable_d, + uint32_t a_dhi, uint32_t b_dhi) { + asm volatile( + "{\n\t" + ".reg .pred leader, p;\n\t" + ".reg .b32 adhi, bdhi;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p, %4, 0;\n\t" + "mov.b32 adhi, %5;\n\t" + "mov.b32 bdhi, %6;\n\t" + "mov.b64 da, {%0, adhi};\n\t" + "mov.b64 db, {%1, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, %3, p;\n\t" + "}\n" + :: "r"(a_lo), "r"(b_lo), "r"(taddr), "r"(i_desc), "r"(enable_d), "r"(a_dhi), "r"(b_dhi)); +} + + +__device__ __forceinline__ void elect_commit(int mbar_addr) { + asm volatile( + "{\n\t" + ".reg .pred leader;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "@leader tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];\n\t" + "}\n" + :: "r"(mbar_addr)); +} + + +__device__ __forceinline__ void mbarrier_arrive(int mbar_addr) { + asm volatile( + "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void mbarrier_arrive_expect_tx(int mbar_addr, uint32_t bytes) { + asm volatile( + "mbarrier.arrive.expect_tx.release.cta.shared::cta.b64 _, [%0], %1;" + :: "r"(mbar_addr), "r"(bytes) : "memory"); +} + + +__device__ __forceinline__ void tmem_ld_x32(float* dst, int tmem_addr) { + asm volatile( + "tcgen05.ld.sync.aligned.32x32b.x32.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7," + " %8, %9, %10, %11, %12, %13, %14, %15," + " %16, %17, %18, %19, %20, %21, %22, %23," + " %24, %25, %26, %27, %28, %29, %30, %31}, [%32];" + : "=f"(dst[0]), "=f"(dst[1]), "=f"(dst[2]), "=f"(dst[3]), + "=f"(dst[4]), "=f"(dst[5]), "=f"(dst[6]), "=f"(dst[7]), + "=f"(dst[8]), "=f"(dst[9]), "=f"(dst[10]), "=f"(dst[11]), + "=f"(dst[12]), "=f"(dst[13]), "=f"(dst[14]), "=f"(dst[15]), + "=f"(dst[16]), "=f"(dst[17]), "=f"(dst[18]), "=f"(dst[19]), + "=f"(dst[20]), "=f"(dst[21]), "=f"(dst[22]), "=f"(dst[23]), + "=f"(dst[24]), "=f"(dst[25]), "=f"(dst[26]), "=f"(dst[27]), + "=f"(dst[28]), "=f"(dst[29]), "=f"(dst[30]), "=f"(dst[31]) + : "r"(tmem_addr)); +} + + +__device__ __forceinline__ void mbarrier_init_pred(int mbar_addr, uint32_t count, uint32_t pred) { + asm volatile( + "{\n\t" + ".reg .pred p;\n\t" + "setp.ne.b32 p, %2, 0;\n\t" + "@p mbarrier.init.shared::cta.b64 [%0], %1;\n\t" + "}\n" :: "r"(mbar_addr), "r"(count), "r"(pred)); +} + + +__device__ __forceinline__ float max_noftz(float a, float b) { + float c; + asm("max.f32 %0, %1, %2;" : "=f"(c) : "f"(a), "f"(b)); + return c; +} + + +__device__ __forceinline__ void fence_async_shared() { + asm volatile("fence.proxy.async.shared::cta;" ::: "memory"); +} + + +__device__ __forceinline__ uint64_t make_smem_desc(int addr) { + const int SBO = 1024; + return desc_encode(addr) + | (desc_encode(SBO) << 32ULL) + | (1ULL << 46ULL) + | (2ULL << 61ULL); +} + + +__device__ __forceinline__ void tma_3d_gmem2smem( + int dst, const void *tmap_ptr, int x, int y, int z, int mbar_addr) { + asm volatile( + "cp.async.bulk.tensor.3d.shared::cta.global" + ".mbarrier::complete_tx::bytes" + " [%0], [%1, {%2, %3, %4}], [%5];" + :: "r"(dst), "l"(tmap_ptr), "r"(x), "r"(y), "r"(z), + "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ void tcgen05_commit(int mbar_addr) { + asm volatile( + "tcgen05.commit.cta_group::1.mbarrier::arrive::one" + ".shared::cluster.b64 [%0];" + :: "r"(mbar_addr) : "memory"); +} + + +__device__ __forceinline__ uint32_t make_warp_uniform(uint32_t val) { + uint32_t result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1f, 0xffffffff;" + : "=r"(result) : "r"(val)); + return result; +} + +extern "C" { + +__global__ __launch_bounds__(128, 1) void +kernel_knn_build_rag_microbucket_k32_q32rowld2exact_f653_v1_stage1_q32rowld2exact_f653_v1(const void* __restrict__ tmap_query, const void* __restrict__ tmap_database, float* __restrict__ query_sq, float* __restrict__ database_sq, float* __restrict__ partial_dists, int* __restrict__ partial_indices, int B, int Q, int M, int K, int num_q_tiles, int num_db_tiles, int db_tiles_per_split, int split_count, int total_work) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + extern __shared__ __align__(1024) char smem_raw[]; + int smem; + smem = (int)(unsigned long long)__cvta_generic_to_shared(smem_raw); + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // Kernel setup ops + __nv_bfloat16* smem_query = reinterpret_cast<__nv_bfloat16*>(smem_raw + 1024); + const int smem_query_addr = smem + 1024; + __nv_bfloat16* smem_database = reinterpret_cast<__nv_bfloat16*>(smem_raw + 17408); + const int smem_database_addr = smem + 17408; + float* smem_local_d = reinterpret_cast(smem_raw + 34048); + const int smem_local_d_addr = smem + 34048; + int* smem_local_i = reinterpret_cast(smem_raw + 50432); + const int smem_local_i_addr = smem + 50432; + + // Mbarrier init (6 groups, 6 barriers) + // Mbarriers at smem_raw[0..48) + + if (warp == 0) { + uint32_t leader = elect_sync(); + // query_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 0, 1, leader); + // query_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 8, 1, leader); + // database_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 16, 1, leader); + // database_empty: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 24, 1, leader); + // score_full: 1 barriers, init_count=1 + mbarrier_init_pred(smem + 32, 1, leader); + // score_empty: 1 barriers, init_count=2 + mbarrier_init_pred(smem + 40, 2, leader); + asm volatile("fence.mbarrier_init.release.cluster;"); + } + + __syncthreads(); + + // TMEM alloc (64 columns, 64 used) + volatile int* tmem_addr_storage = (volatile int*)(smem_raw + 48); + if (warp == 0) { + int _tmem_hold = smem + 48; + asm volatile("tcgen05.alloc.cta_group::1.sync.aligned.shared::cta.b32 [%0], %1;" :: "r"(_tmem_hold), "r"(64) : "memory"); + } + + __syncthreads(); + asm volatile("tcgen05.fence::after_thread_sync;"); + + const int mbar_base = smem; + #define query_full_addr (mbar_base + 0) + #define query_empty_addr (mbar_base + 8) + #define database_full_addr (mbar_base + 16) + #define database_empty_addr (mbar_base + 24) + #define score_full_addr (mbar_base + 32) + #define score_empty_addr (mbar_base + 40) + const int taddr = tmem_addr_storage[0]; + + // Kernel post-init ops + const int tmem_cross = taddr; + + // ---- Role: compute ---- + if (warp <= 1) { + { // compute_main + int warp_id_in_role = (warp - 0); + unsigned int _phase_score_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx = bid; work_idx < SPLIT_COUNT_CONST; work_idx += num_bids) { + int split_idx = work_idx % (unsigned int)SPLIT_COUNT_CONST; + int tmem_row_origin = warp_id_in_role * 32; + int logical_row_origin = warp_id_in_role * 16; + int row_top = logical_row_origin + lane / 4; + int row_bot = row_top + 8; + int lane_col = lane % 4; + int slot = lane_col; + float q_sq_top = query_sq[row_top]; + float q_sq_bot = query_sq[row_bot]; + float best_top_d[TOP_K_MAX]; + float best_bot_d[TOP_K_MAX]; + int best_top_i[TOP_K_MAX]; + int best_bot_i[TOP_K_MAX]; + #pragma unroll + for (int kk = 0; kk < TOP_K_MAX; kk++) { + best_top_d[kk] = 3.4e+38f; + best_bot_d[kk] = 3.4e+38f; + best_top_i[kk] = -1; + best_bot_i[kk] = -1; + } + int extra_before = split_idx; + if (extra_before > EXTRA_SPLITS_CONST) { + extra_before = EXTRA_SPLITS_CONST; + } + int split_tile_count = TILES_FLOOR_CONST; + if (split_idx < EXTRA_SPLITS_CONST) { + split_tile_count = TILES_FLOOR_CONST + 1; + } + int db_tile_start = split_idx * TILES_FLOOR_CONST + extra_before; + #pragma unroll 1 + for (int local_db_tile = 0; local_db_tile < DB_TILES_PER_SPLIT_CONST; local_db_tile++) { + if (split_tile_count > local_db_tile) { + int db_tile = db_tile_start + local_db_tile; + int db_start = db_tile * BLOCK_M; + mbarrier_wait(score_full_addr, _phase_score_full_0); + _phase_score_full_0 ^= 1; + int cross_addr = taddr + (unsigned int)(tmem_row_origin << 16); + float _tmem_load_0[32]; + asm volatile( + "tcgen05.ld.sync.aligned.16x256b.x8.b32" + " {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31}, [%32];" + : "=r"(*reinterpret_cast(&_tmem_load_0[0])), "=r"(*reinterpret_cast(&_tmem_load_0[1])), "=r"(*reinterpret_cast(&_tmem_load_0[2])), "=r"(*reinterpret_cast(&_tmem_load_0[3])), "=r"(*reinterpret_cast(&_tmem_load_0[4])), "=r"(*reinterpret_cast(&_tmem_load_0[5])), "=r"(*reinterpret_cast(&_tmem_load_0[6])), "=r"(*reinterpret_cast(&_tmem_load_0[7])), "=r"(*reinterpret_cast(&_tmem_load_0[8])), "=r"(*reinterpret_cast(&_tmem_load_0[9])), "=r"(*reinterpret_cast(&_tmem_load_0[10])), "=r"(*reinterpret_cast(&_tmem_load_0[11])), "=r"(*reinterpret_cast(&_tmem_load_0[12])), "=r"(*reinterpret_cast(&_tmem_load_0[13])), "=r"(*reinterpret_cast(&_tmem_load_0[14])), "=r"(*reinterpret_cast(&_tmem_load_0[15])), "=r"(*reinterpret_cast(&_tmem_load_0[16])), "=r"(*reinterpret_cast(&_tmem_load_0[17])), "=r"(*reinterpret_cast(&_tmem_load_0[18])), "=r"(*reinterpret_cast(&_tmem_load_0[19])), "=r"(*reinterpret_cast(&_tmem_load_0[20])), "=r"(*reinterpret_cast(&_tmem_load_0[21])), "=r"(*reinterpret_cast(&_tmem_load_0[22])), "=r"(*reinterpret_cast(&_tmem_load_0[23])), "=r"(*reinterpret_cast(&_tmem_load_0[24])), "=r"(*reinterpret_cast(&_tmem_load_0[25])), "=r"(*reinterpret_cast(&_tmem_load_0[26])), "=r"(*reinterpret_cast(&_tmem_load_0[27])), "=r"(*reinterpret_cast(&_tmem_load_0[28])), "=r"(*reinterpret_cast(&_tmem_load_0[29])), "=r"(*reinterpret_cast(&_tmem_load_0[30])), "=r"(*reinterpret_cast(&_tmem_load_0[31])) + : "r"(cross_addr) + : "memory"); + asm volatile("tcgen05.wait::ld.sync.aligned;" ::: "memory"); + if (elect_sync()) { + mbarrier_arrive(score_empty_addr); + } + #pragma unroll + for (int repeat = 0; repeat < 8; repeat++) { + const int reg_base = repeat * 4; + int col_base = repeat * 8 + lane_col * 2; + int db_idx0 = db_start + col_base; + int db_idx1 = db_idx0 + 1; + float top_d0 = 3.4e+38f; + float top_d1 = 3.4e+38f; + if (db_idx0 < M_LIMIT) { + float _max_0 = max_noftz(q_sq_top + database_sq[db_idx0] - 2.0f * _tmem_load_0[reg_base], 0.0f); + top_d0 = _max_0; + } + if (db_idx1 < M_LIMIT) { + float _max_1 = max_noftz(q_sq_top + database_sq[db_idx1] - 2.0f * _tmem_load_0[reg_base + 1], 0.0f); + top_d1 = _max_1; + } + int top_take1 = ((top_d1 < top_d0) ? 1 : 0); + if (best_top_d[31] > ((top_take1 != 0) ? top_d1 : top_d0)) { + best_top_d[31] = ((top_take1 != 0) ? top_d1 : top_d0); + best_top_i[31] = ((top_take1 != 0) ? db_idx1 : db_idx0); + #pragma unroll + for (int kk_1 = 30; kk_1 >= 0; kk_1--) { + float lower0_d = best_top_d[kk_1 + 1]; + int lower0_i = best_top_i[kk_1 + 1]; + float upper0_d = best_top_d[kk_1]; + int upper0_i = best_top_i[kk_1]; + int swap0_up = ((lower0_d < upper0_d) ? 1 : 0); + best_top_d[kk_1] = ((swap0_up != 0) ? lower0_d : upper0_d); + best_top_i[kk_1] = ((swap0_up != 0) ? lower0_i : upper0_i); + best_top_d[kk_1 + 1] = ((swap0_up != 0) ? upper0_d : lower0_d); + best_top_i[kk_1 + 1] = ((swap0_up != 0) ? upper0_i : lower0_i); + } + if (best_top_d[31] > ((top_take1 != 0) ? top_d0 : top_d1)) { + best_top_d[31] = ((top_take1 != 0) ? top_d0 : top_d1); + best_top_i[31] = ((top_take1 != 0) ? db_idx0 : db_idx1); + #pragma unroll + for (int kk_2 = 30; kk_2 >= 0; kk_2--) { + float lower1_d = best_top_d[kk_2 + 1]; + int lower1_i = best_top_i[kk_2 + 1]; + float upper1_d = best_top_d[kk_2]; + int upper1_i = best_top_i[kk_2]; + int swap1_up = ((lower1_d < upper1_d) ? 1 : 0); + best_top_d[kk_2] = ((swap1_up != 0) ? lower1_d : upper1_d); + best_top_i[kk_2] = ((swap1_up != 0) ? lower1_i : upper1_i); + best_top_d[kk_2 + 1] = ((swap1_up != 0) ? upper1_d : lower1_d); + best_top_i[kk_2 + 1] = ((swap1_up != 0) ? upper1_i : lower1_i); + } + } + } + float bot_d0 = 3.4e+38f; + float bot_d1 = 3.4e+38f; + if (db_idx0 < M_LIMIT) { + float _max_2 = max_noftz(q_sq_bot + database_sq[db_idx0] - 2.0f * _tmem_load_0[reg_base + 2], 0.0f); + bot_d0 = _max_2; + } + if (db_idx1 < M_LIMIT) { + float _max_3 = max_noftz(q_sq_bot + database_sq[db_idx1] - 2.0f * _tmem_load_0[reg_base + 3], 0.0f); + bot_d1 = _max_3; + } + int bot_take1 = ((bot_d1 < bot_d0) ? 1 : 0); + if (best_bot_d[31] > ((bot_take1 != 0) ? bot_d1 : bot_d0)) { + best_bot_d[31] = ((bot_take1 != 0) ? bot_d1 : bot_d0); + best_bot_i[31] = ((bot_take1 != 0) ? db_idx1 : db_idx0); + #pragma unroll + for (int kk_3 = 30; kk_3 >= 0; kk_3--) { + float lower0_d_1 = best_bot_d[kk_3 + 1]; + int lower0_i_1 = best_bot_i[kk_3 + 1]; + float upper0_d_1 = best_bot_d[kk_3]; + int upper0_i_1 = best_bot_i[kk_3]; + int swap0_up_1 = ((lower0_d_1 < upper0_d_1) ? 1 : 0); + best_bot_d[kk_3] = ((swap0_up_1 != 0) ? lower0_d_1 : upper0_d_1); + best_bot_i[kk_3] = ((swap0_up_1 != 0) ? lower0_i_1 : upper0_i_1); + best_bot_d[kk_3 + 1] = ((swap0_up_1 != 0) ? upper0_d_1 : lower0_d_1); + best_bot_i[kk_3 + 1] = ((swap0_up_1 != 0) ? upper0_i_1 : lower0_i_1); + } + if (best_bot_d[31] > ((bot_take1 != 0) ? bot_d0 : bot_d1)) { + best_bot_d[31] = ((bot_take1 != 0) ? bot_d0 : bot_d1); + best_bot_i[31] = ((bot_take1 != 0) ? db_idx0 : db_idx1); + #pragma unroll + for (int kk_4 = 30; kk_4 >= 0; kk_4--) { + float lower1_d_1 = best_bot_d[kk_4 + 1]; + int lower1_i_1 = best_bot_i[kk_4 + 1]; + float upper1_d_1 = best_bot_d[kk_4]; + int upper1_i_1 = best_bot_i[kk_4]; + int swap1_up_1 = ((lower1_d_1 < upper1_d_1) ? 1 : 0); + best_bot_d[kk_4] = ((swap1_up_1 != 0) ? lower1_d_1 : upper1_d_1); + best_bot_i[kk_4] = ((swap1_up_1 != 0) ? lower1_i_1 : upper1_i_1); + best_bot_d[kk_4 + 1] = ((swap1_up_1 != 0) ? upper1_d_1 : lower1_d_1); + best_bot_i[kk_4 + 1] = ((swap1_up_1 != 0) ? upper1_i_1 : lower1_i_1); + } + } + } + } + } + } + int top_slot_base = (row_top * 4 + slot) * TOP_K_MAX; + int bot_slot_base = (row_bot * 4 + slot) * TOP_K_MAX; + #pragma unroll + for (int kk_5 = 0; kk_5 < TOP_K_MAX; kk_5++) { + smem_local_d[top_slot_base + kk_5] = best_top_d[kk_5]; + smem_local_i[top_slot_base + kk_5] = best_top_i[kk_5]; + smem_local_d[bot_slot_base + kk_5] = best_bot_d[kk_5]; + smem_local_i[bot_slot_base + kk_5] = best_bot_i[kk_5]; + } + asm volatile("barrier.sync 8, %0;" :: "r"(64)); + if (tid < ROWS_COVERED) { + int row = tid; + float head_d[4]; + int head_i[4]; + int head_k[4]; + #pragma unroll + for (int slot_idx = 0; slot_idx < 4; slot_idx++) { + int local_base = (row * 4 + slot_idx) * TOP_K_MAX; + head_k[slot_idx] = 0; + head_d[slot_idx] = smem_local_d[local_base]; + head_i[slot_idx] = smem_local_i[local_base]; + } + int out_base = (split_idx * ROWS_COVERED + row) * TOP_K_MAX; + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float winner_d = head_d[0]; + int winner_i = head_i[0]; + int winner_slot = 0; + #pragma unroll + for (int slot_idx_1 = 1; slot_idx_1 < 4; slot_idx_1++) { + float cand_d = head_d[slot_idx_1]; + int take = ((cand_d < winner_d) ? 1 : 0); + winner_d = ((take != 0) ? cand_d : winner_d); + winner_i = ((take != 0) ? head_i[slot_idx_1] : winner_i); + winner_slot = ((take != 0) ? slot_idx_1 : winner_slot); + } + *((float*)(partial_dists + (out_base + out_k))) = winner_d; + *((int*)(partial_indices + (out_base + out_k))) = winner_i; + #pragma unroll + for (int slot_idx_2 = 0; slot_idx_2 < 4; slot_idx_2++) { + if (winner_slot == slot_idx_2) { + int next_head = head_k[slot_idx_2] + 1; + head_k[slot_idx_2] = next_head; + head_d[slot_idx_2] = 3.4e+38f; + head_i[slot_idx_2] = -1; + if (next_head < TOP_K_MAX) { + int local_base_1 = (row * 4 + slot_idx_2) * TOP_K_MAX; + head_d[slot_idx_2] = smem_local_d[local_base_1 + next_head]; + head_i[slot_idx_2] = smem_local_i[local_base_1 + next_head]; + } + } + } + } + } + asm volatile("barrier.sync 8, %0;" :: "r"(64)); + } + } + // ---- Role: load ---- + } else if (warp == 2) { + { // load_main + unsigned int _phase_query_empty_0 = 1; + unsigned int _phase_database_empty_0 = 1; + if (warp == 2) { + if (elect_sync()) { + #pragma unroll 1 + for (unsigned int work_idx_1 = bid; work_idx_1 < SPLIT_COUNT_CONST; work_idx_1 += num_bids) { + int split_idx_1 = work_idx_1 % (unsigned int)SPLIT_COUNT_CONST; + int extra_before_1 = split_idx_1; + if (extra_before_1 > EXTRA_SPLITS_CONST) { + extra_before_1 = EXTRA_SPLITS_CONST; + } + int split_tile_count_1 = TILES_FLOOR_CONST; + if (split_idx_1 < EXTRA_SPLITS_CONST) { + split_tile_count_1 = TILES_FLOOR_CONST + 1; + } + int db_tile_start_1 = split_idx_1 * TILES_FLOOR_CONST + extra_before_1; + mbarrier_wait(query_empty_addr, _phase_query_empty_0); + _phase_query_empty_0 ^= 1; + mbarrier_arrive_expect_tx(query_full_addr, 16384); + tma_3d_gmem2smem(smem_query_addr, tmap_query, 0, 0, 0, query_full_addr); + #pragma unroll 1 + for (int local_db_tile_1 = 0; local_db_tile_1 < DB_TILES_PER_SPLIT_CONST; local_db_tile_1++) { + if (split_tile_count_1 > local_db_tile_1) { + int db_tile_1 = db_tile_start_1 + local_db_tile_1; + int off_m = db_tile_1 * BLOCK_M; + mbarrier_wait(database_empty_addr, _phase_database_empty_0); + _phase_database_empty_0 ^= 1; + mbarrier_arrive_expect_tx(database_full_addr, 16384); + tma_3d_gmem2smem(smem_database_addr, tmap_database, 0, off_m, 0, database_full_addr); + } + } + } + } + } + } + // ---- Role: mma ---- + } else if (warp == 3) { + { // mma_main + unsigned int _phase_query_full_0 = 0; + unsigned int _phase_score_empty_0 = 1; + unsigned int _phase_database_full_0 = 0; + #pragma unroll 1 + for (unsigned int work_idx_2 = bid; work_idx_2 < SPLIT_COUNT_CONST; work_idx_2 += num_bids) { + int split_idx_2 = work_idx_2 % (unsigned int)SPLIT_COUNT_CONST; + int split_tile_count_2 = TILES_FLOOR_CONST; + if (split_idx_2 < EXTRA_SPLITS_CONST) { + split_tile_count_2 = TILES_FLOOR_CONST + 1; + } + mbarrier_wait(query_full_addr, _phase_query_full_0); + _phase_query_full_0 ^= 1; + #pragma unroll 1 + for (int local_db_tile_2 = 0; local_db_tile_2 < DB_TILES_PER_SPLIT_CONST; local_db_tile_2++) { + if (split_tile_count_2 > local_db_tile_2) { + mbarrier_wait(score_empty_addr, _phase_score_empty_0); + _phase_score_empty_0 ^= 1; + mbarrier_wait(database_full_addr, _phase_database_full_0); + _phase_database_full_0 ^= 1; + asm volatile("tcgen05.fence::after_thread_sync;"); + int _mma_a_lo_0 = make_warp_uniform((smem_query_addr >> 4) & 0x3FFF); + int _mma_b_lo_0 = make_warp_uniform((smem_database_addr >> 4) & 0x3FFF); + asm volatile( + "{\n\t" + ".reg .pred leader, p0, p1;\n\t" + ".reg .b32 adhi, bdhi, alo, blo, id;\n\t" + ".reg .b64 da, db;\n\t" + "elect.sync _|leader, 0xFFFFFFFF;\n\t" + "setp.ne.b32 p0, %3, 0;\n\t" + "setp.ne.b32 p1, 1, 0;\n\t" + "" + "mov.b32 adhi, 0x40004040;\n\t" + "mov.b32 bdhi, 0x40004040;\n\t" + "mov.b32 id, 68158608;\n\t" + "mov.b32 alo, %0;\n\t" + "mov.b32 blo, %1;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p0;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 506;\n\t" + "add.u32 blo, blo, 506;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "add.u32 alo, alo, 2;\n\t" + "add.u32 blo, blo, 2;\n\t" + "mov.b64 da, {alo, adhi};\n\t" + "mov.b64 db, {blo, bdhi};\n\t" + "@leader tcgen05.mma.cta_group::1.kind::f16 [%2], da, db, id, p1;\n\t" + "}\n" + :: "r"(_mma_a_lo_0), "r"(_mma_b_lo_0), "r"(tmem_cross), "r"(0)); + elect_commit(score_full_addr); + elect_commit(database_empty_addr); + } + } + elect_commit(query_empty_addr); + } + } + } + + // Cleanup + __syncthreads(); // barrier before TMEM dealloc + + if (warp == 0) { + asm volatile("tcgen05.dealloc.cta_group::1.sync.aligned.b32 %0, %1;" :: "r"(tmem_addr_storage[0]), "r"(64)); + asm volatile("tcgen05.relinquish_alloc_permit.cta_group::1.sync.aligned;"); + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0231.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0231.cu new file mode 100644 index 00000000..1820fa5a --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0231.cu @@ -0,0 +1,103 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 128 +#define TOP_K_MAX 96 +#define SPLIT_COUNT 2 + +#include + +extern "C" { + +__global__ __launch_bounds__(128, 1) void +kernel_knn_build_k96_merge_s2_unordered_warp_select(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int row = bid * 4 + warp; + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + int cand0 = lane; + int cand1 = lane + 32; + int cand2 = lane + 64; + if (row < total_queries) { + float cand_d[6]; + int cand_i[6]; + #pragma unroll + for (int split_idx = 0; split_idx < SPLIT_COUNT; split_idx++) { + int split_base = base_row + split_idx * split_stride; + int slot_base = split_idx * 3; + cand_d[slot_base] = partial_dists[split_base + cand0]; + cand_i[slot_base] = partial_indices[split_base + cand0]; + cand_d[slot_base + 1] = partial_dists[split_base + cand1]; + cand_i[slot_base + 1] = partial_indices[split_base + cand1]; + cand_d[slot_base + 2] = partial_dists[split_base + cand2]; + cand_i[slot_base + 2] = partial_indices[split_base + cand2]; + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float winner_d = cand_d[0]; + int winner_i = cand_i[0]; + int winner_slot = 0; + #pragma unroll + for (int slot = 1; slot < 6; slot++) { + if (winner_d > cand_d[slot]) { + winner_d = cand_d[slot]; + winner_i = cand_i[slot]; + winner_slot = slot; + } + } + float warp_min = winner_d; + float _warp_reduce_0 = warp_min; + #pragma unroll + for (int offset = 16; offset > 0; offset >>= 1) + _warp_reduce_0 = fminf(_warp_reduce_0, __shfl_xor_sync(0xFFFFFFFF, _warp_reduce_0, offset)); + warp_min = _warp_reduce_0; + unsigned int _vote_0 = __ballot_sync(0xFFFFFFFF, winner_d == warp_min); + int owner_ballot = _vote_0; + int _ffs_0 = __ffs(owner_ballot); + int winner_lane = _ffs_0 - 1; + int _shfl_0 = __shfl_sync(0xFFFFFFFF, winner_i, winner_lane); + winner_i = _shfl_0; + int _shfl_1 = __shfl_sync(0xFFFFFFFF, winner_slot, winner_lane); + winner_slot = _shfl_1; + if (lane == 0) { + *((float*)(out_dists + (base_row + out_k))) = warp_min; + *((int*)(out_indices + (base_row + out_k))) = winner_i; + } + if (lane == winner_lane) { + #pragma unroll + for (int slot_1 = 0; slot_1 < 6; slot_1++) { + if (winner_slot == slot_1) { + cand_d[slot_1] = 3.4e+38f; + } + } + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0232.cu b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0232.cu new file mode 100644 index 00000000..35edb308 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/cuda/dispatch_kernel_0232.cu @@ -0,0 +1,116 @@ +typedef unsigned char uint8_t; +typedef unsigned short uint16_t; +typedef unsigned int uint32_t; +typedef unsigned long long uint64_t; +typedef signed int int32_t; +typedef short int int16_t; + +#include + +__device__ __forceinline__ int make_warp_uniform(int x) { + int result; + asm volatile("shfl.sync.idx.b32 %0, %1, 0, 0x1F, 0xFFFFFFFF;" + : "=r"(result) : "r"(x)); + return result; +} + +#define LOOM_INF CUDART_INF_F +#define NUM_MAIN_STAGES 1 +#define THREADS 32 +#define TOP_K_MAX 10 +#define GROUP_COUNT 21 +#define GROUP_SPLITS 7 + +#include + +extern "C" { + +__global__ __launch_bounds__(32, 1) void +kernel_knn_build_q1m524_workfeed_s147_g21_register_merge(float* __restrict__ partial_dists, int* __restrict__ partial_indices, float* __restrict__ out_dists, int* __restrict__ out_indices, int total_queries) +{ + const int tid = threadIdx.x; + const int warp = make_warp_uniform(tid / 32); + const int lane = tid % 32; + + + const int bid = blockIdx.x; + const int num_bids = gridDim.x; + + // === Task calls (dependency order) === + int split_pos[GROUP_SPLITS]; + int split_base[GROUP_SPLITS]; + float group_cand_d[GROUP_SPLITS]; + int group_cand_i[GROUP_SPLITS]; + #pragma unroll 1 + for (int row = bid; row < total_queries; row += num_bids) { + int base_row = row * TOP_K_MAX; + int split_stride = total_queries * TOP_K_MAX; + float group_best_d = 3.4e+38f; + int group_best_i = -1; + int group_best_split = 0; + if (tid < GROUP_COUNT) { + int group_idx = tid; + int source_split0 = group_idx * GROUP_SPLITS; + #pragma unroll + for (int local_split = 0; local_split < GROUP_SPLITS; local_split++) { + split_pos[local_split] = 0; + int split_id = source_split0 + local_split; + split_base[local_split] = base_row + split_id * split_stride; + group_cand_d[local_split] = partial_dists[split_base[local_split]]; + group_cand_i[local_split] = partial_indices[split_base[local_split]]; + } + group_best_d = group_cand_d[0]; + group_best_i = group_cand_i[0]; + #pragma unroll + for (int local_split_1 = 1; local_split_1 < GROUP_SPLITS; local_split_1++) { + if (group_best_d > group_cand_d[local_split_1]) { + group_best_d = group_cand_d[local_split_1]; + group_best_i = group_cand_i[local_split_1]; + group_best_split = local_split_1; + } + } + } + #pragma unroll + for (int out_k = 0; out_k < TOP_K_MAX; out_k++) { + float warp_min = group_best_d; + float _warp_reduce_0 = warp_min; + #pragma unroll + for (int offset = 16; offset > 0; offset >>= 1) + _warp_reduce_0 = fminf(_warp_reduce_0, __shfl_xor_sync(0xFFFFFFFF, _warp_reduce_0, offset)); + warp_min = _warp_reduce_0; + unsigned int _vote_0 = __ballot_sync(0xFFFFFFFF, group_best_d == warp_min); + int owner_ballot = _vote_0; + int _ffs_0 = __ffs(owner_ballot); + int winner_lane = _ffs_0 - 1; + int _shfl_0 = __shfl_sync(0xFFFFFFFF, group_best_i, winner_lane); + int winner_i = _shfl_0; + if (tid == 0) { + *((float*)(out_dists + (base_row + out_k))) = warp_min; + *((int*)(out_indices + (base_row + out_k))) = winner_i; + } + if (tid == winner_lane) { + if (out_k + 1 < TOP_K_MAX) { + split_pos[group_best_split] = split_pos[group_best_split] + 1; + int next_pos = split_pos[group_best_split]; + int next_addr = split_base[group_best_split] + next_pos; + group_cand_d[group_best_split] = partial_dists[next_addr]; + group_cand_i[group_best_split] = partial_indices[next_addr]; + group_best_d = group_cand_d[0]; + group_best_i = group_cand_i[0]; + group_best_split = 0; + #pragma unroll + for (int local_split_2 = 1; local_split_2 < GROUP_SPLITS; local_split_2++) { + if (group_best_d > group_cand_d[local_split_2]) { + group_best_d = group_cand_d[local_split_2]; + group_best_i = group_cand_i[local_split_2]; + group_best_split = local_split_2; + } + } + } + } + } + } +} + +} // extern "C" + diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/interface.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/interface.py new file mode 100644 index 00000000..65077c93 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/interface.py @@ -0,0 +1,1018 @@ +from __future__ import annotations + +from collections import OrderedDict +from dataclasses import dataclass, field +from functools import wraps +from threading import Condition, Event, RLock +from typing import Any + +from . import _direct_plan as _direct_plan_runtime +from ._direct_plan import PreparedDirectRoute, prepare_route +from ._launch_plan import GraphCaptureUnsupported, build_graph_exec_plan, build_launch_plan + +SEMANTIC_ENTRYPOINT = ( + "loom.examples.weave.knn_build_dispatch_q1m524_v10_d320recurrence_consumption_v1:launch_from_contract_inputs" +) +_PREPARE_LOCK = RLock() + + +@dataclass(frozen=True) +class PreparedKNNBuild: + """Fixed inputs and a direct route resolved once outside the hot path.""" + + inputs: dict[str, Any] + launch_plan: PreparedDirectRoute + shape_label: str | None + stream: Any = None + timeout_ms: float | None = None + + @property + def selected_route(self) -> str: + return self.launch_plan.route_id + + +@dataclass +class _KNNBuildRuntimeSlot: + """One stream-bound, signature-specialized LaunchPlan plus support state. + + ``plan`` freezes the resolved route's leaf launches (or keeps the cached + per-call launcher for host-data-dependent routes). ``norm_launches`` are + the route-required fused row-norm support launches, submitted by pointer + overwrite before the plan; their outputs are the slot-owned ``query_sq``/ + ``database_sq`` scratch with stable pointers. ``graph`` is the signature's + captured CUDA graph over the full norm + route kernel chain; when set, + a hot call binds pointers host-side and replays the graph instead of + submitting the prepared launches one by one (``graph_capture_error`` + records why a frozen plan stayed on the prepared path). ``default_outputs`` + alternates between two plan-owned pairs so consecutive default-output + calls never hand the caller the allocation the previous call returned. + ``default_flip`` is only read or written while ``lock`` is held. + """ + + plan: Any + route: Any + norm_launches: tuple[tuple[str, Any], ...] + query_sq: Any + database_sq: Any + internal_query_norm: bool + internal_database_norm: bool + norm_compute_fields: tuple[str, ...] + default_outputs: tuple[tuple[Any, Any], tuple[Any, Any]] + graph: Any = None + graph_capture_error: str | None = None + lock: Any = field(default_factory=RLock, repr=False) + default_flip: int = 0 + + +@dataclass +class _PendingPreparation: + event: Event = field(default_factory=Event, repr=False) + error: BaseException | None = field(default=None, repr=False) + + +def _guard_runtime_compute(method: Any) -> Any: + """Keep clear() from crossing an in-progress lookup/rebind/enqueue call.""" + + @wraps(method) + def guarded(self: Any, *args: Any, **kwargs: Any) -> Any: + with self._lifecycle: + while self._clearing: + self._lifecycle.wait() + self._active_calls += 1 + try: + return method(self, *args, **kwargs) + finally: + with self._lifecycle: + self._active_calls -= 1 + if self._active_calls == 0: + self._lifecycle.notify_all() + + return guarded + + +def _prepare_inputs( + query: Any, + database: Any, + k: int, + *, + build: bool, + shape_label: str | None, + out: tuple[Any, Any] | None, + query_sq: Any = None, + database_sq: Any = None, + defer_missing_norms: bool = False, +) -> dict[str, Any]: + """Validate the public ABI and allocate one fixed set of intermediates.""" + import torch + + if not isinstance(query, torch.Tensor) or not query.is_cuda: + raise TypeError("query must be a CUDA torch.Tensor") + if not isinstance(database, torch.Tensor) or not database.is_cuda: + raise TypeError("database must be a CUDA torch.Tensor") + if query.dtype not in (torch.bfloat16, torch.float16) or database.dtype != query.dtype: + raise TypeError("query and database must have the same bfloat16 or float16 dtype") + if query.ndim != 3 or not query.is_contiguous(): + raise ValueError("query must be contiguous with shape [B, Q, D]") + if database.ndim != 3 or not database.is_contiguous(): + raise ValueError("database must be contiguous with shape [B, N, D]") + bsz, n_query, dim = map(int, query.shape) + db_bsz, n_database, db_dim = map(int, database.shape) + if (db_bsz, db_dim) != (bsz, dim) or query.device != database.device: + raise ValueError("query and database batch/feature dimensions and device must match") + if build and (n_query != n_database or query.data_ptr() != database.data_ptr()): + raise ValueError("build=True requires query to alias database and Q == M") + k = int(k) + if not 0 < k <= n_database: + raise ValueError(f"k must be in [1, {n_database}], got {k}") + expected = (bsz, n_query, k) + if out is None: + out = ( + torch.empty(expected, dtype=torch.float32, device=database.device), + torch.empty(expected, dtype=torch.int32, device=database.device), + ) + out_dists, out_indices = out + if tuple(out_dists.shape) != expected or tuple(out_indices.shape) != expected: + raise ValueError(f"out tensors must have shape {expected}") + if out_dists.dtype is not torch.float32 or out_indices.dtype is not torch.int32: + raise TypeError("out must be (float32 distances, int32 indices)") + if out_dists.device != database.device or out_indices.device != database.device: + raise ValueError("out tensors must be on the query/database device") + if not out_dists.is_contiguous() or not out_indices.is_contiguous(): + raise ValueError("out tensors must be contiguous") + if build: + if query_sq is None and database_sq is not None: + _validate_norm("database_sq", database_sq, (bsz, n_database), database.device) + query_sq = database_sq + elif query_sq is None and not defer_missing_norms: + query_sq = (query.float() ** 2).sum(-1).contiguous() + elif query_sq is not None: + _validate_norm("query_sq", query_sq, (bsz, n_query), database.device) + if database_sq is not None and database_sq is not query_sq: + raise ValueError("build=True requires query_sq and database_sq to alias when both are provided") + database_sq = query_sq + else: + if query_sq is None and not defer_missing_norms: + query_sq = (query.float() ** 2).sum(-1).contiguous() + elif query_sq is not None: + _validate_norm("query_sq", query_sq, (bsz, n_query), database.device) + if database_sq is None and not defer_missing_norms: + database_sq = (database.float() ** 2).sum(-1).contiguous() + elif database_sq is not None: + _validate_norm("database_sq", database_sq, (bsz, n_database), database.device) + return { + "label": shape_label, + "B": bsz, + "Q": n_query, + "M": n_database, + "D": dim, + "K": k, + "dtype": str(database.dtype).removeprefix("torch."), + "build": bool(build), + "query": query, + "database": database, + "query_sq": query_sq, + "database_sq": database_sq, + "out_dists": out_dists, + "out_indices": out_indices, + } + + +def _validate_norm(name: str, value: Any, expected: tuple[int, int], device: Any) -> None: + import torch + + if not isinstance(value, torch.Tensor) or not value.is_cuda: + raise TypeError(f"{name} must be a CUDA torch.Tensor") + if tuple(value.shape) != expected: + raise ValueError(f"{name} must have shape {expected}") + if value.dtype is not torch.float32: + raise TypeError(f"{name} must have float32 dtype") + if value.device != device: + raise ValueError(f"{name} must be on the query/database device") + if not value.is_contiguous(): + raise ValueError(f"{name} must be contiguous") + + +def _tensor_device_index(tensor: Any) -> int: + import torch + + index = tensor.device.index + return int(torch.cuda.current_device() if index is None else index) + + +def _runtime_device_index(device: Any) -> int: + import torch + + if device is None: + return int(torch.cuda.current_device()) + if isinstance(device, int) and not isinstance(device, bool): + return int(device) + resolved = torch.device(device) + if resolved.type != "cuda": + raise ValueError(f"KNN-build runtime requires a CUDA device, got {resolved}") + return int(torch.cuda.current_device() if resolved.index is None else resolved.index) + + +def _validate_timeout(timeout_ms: float | None) -> float | None: + if timeout_ms is None: + return None + value = float(timeout_ms) + if value <= 0: + raise ValueError("timeout_ms must be positive") + return value + + +def _record_stream_tensors(tensors: tuple[Any, ...], stream: Any) -> None: + seen: set[int] = set() + for tensor in tensors: + if tensor is None: + continue + identity = id(tensor) + if identity in seen: + continue + seen.add(identity) + record_stream = getattr(tensor, "record_stream", None) + if callable(record_stream): + record_stream(stream) + + +def _validate_compute_tensors( + torch: Any, + query: Any, + database: Any, + k: int, + *, + build: bool, + device_index: int, +) -> tuple[int, int, int, int]: + """Validate the public compute ABI without allocating anything.""" + + if not isinstance(query, torch.Tensor) or not query.is_cuda: + raise TypeError("query must be a CUDA torch.Tensor") + if not isinstance(database, torch.Tensor) or not database.is_cuda: + raise TypeError("database must be a CUDA torch.Tensor") + if query.dtype not in (torch.bfloat16, torch.float16) or database.dtype != query.dtype: + raise TypeError("query and database must have the same bfloat16 or float16 dtype") + if query.ndim != 3 or not query.is_contiguous(): + raise ValueError("query must be contiguous with shape [B, Q, D]") + if database.ndim != 3 or not database.is_contiguous(): + raise ValueError("database must be contiguous with shape [B, N, D]") + bsz, n_query, dim = map(int, query.shape) + db_bsz, n_database, db_dim = map(int, database.shape) + if (db_bsz, db_dim) != (bsz, dim) or query.device != database.device: + raise ValueError("query and database batch/feature dimensions and device must match") + if build and (n_query != n_database or query.data_ptr() != database.data_ptr()): + raise ValueError("build=True requires query to alias database and Q == M") + input_device_index = _tensor_device_index(query) + if input_device_index != device_index: + raise ValueError( + f"KNN-build runtime is bound to cuda:{device_index}, but query is on cuda:{input_device_index}" + ) + k = int(k) + if not 0 < k <= n_database: + raise ValueError(f"k must be in [1, {n_database}], got {k}") + return bsz, n_query, n_database, dim + + +def _validate_compute_outputs( + torch: Any, + out: tuple[Any, Any], + expected: tuple[int, int, int], + device: Any, +) -> tuple[Any, Any]: + if not isinstance(out, (tuple, list)) or len(out) != 2: + raise TypeError("out must be a (distances, indices) pair") + out_dists, out_indices = out + if not all(isinstance(item, torch.Tensor) and item.is_cuda for item in (out_dists, out_indices)): + raise TypeError("out tensors must be CUDA torch.Tensor objects") + if tuple(out_dists.shape) != expected or tuple(out_indices.shape) != expected: + raise ValueError(f"out tensors must have shape {expected}") + if out_dists.dtype is not torch.float32 or out_indices.dtype is not torch.int32: + raise TypeError("out must be (float32 distances, int32 indices)") + if out_dists.device != device or out_indices.device != device: + raise ValueError("out tensors must be on the query/database device") + if not out_dists.is_contiguous() or not out_indices.is_contiguous(): + raise ValueError("out tensors must be contiguous") + return out_dists, out_indices + + +def _validate_compute_norms( + query_sq: Any, + database_sq: Any, + *, + build: bool, + bsz: int, + n_query: int, + n_database: int, + device: Any, +) -> tuple[Any, Any]: + """Apply the public norm-alias rules without computing missing norms.""" + + if build: + if query_sq is None and database_sq is not None: + _validate_norm("database_sq", database_sq, (bsz, n_database), device) + query_sq = database_sq + elif query_sq is not None: + _validate_norm("query_sq", query_sq, (bsz, n_query), device) + if database_sq is not None and database_sq is not query_sq: + raise ValueError("build=True requires query_sq and database_sq to alias when both are provided") + database_sq = query_sq + else: + if query_sq is not None: + _validate_norm("query_sq", query_sq, (bsz, n_query), device) + if database_sq is not None: + _validate_norm("database_sq", database_sq, (bsz, n_database), device) + return query_sq, database_sq + + +def _require_owned_outputs(outputs: Any, inputs: dict[str, Any]) -> None: + """Reject routes whose hot path would require an uncaptured output copy.""" + + owned = (inputs["out_dists"], inputs["out_indices"]) + if outputs is None: + normalized = owned + elif isinstance(outputs, (tuple, list)) and len(outputs) == 2: + normalized = (outputs[0], outputs[1]) + elif isinstance(outputs, dict): + distances = outputs.get("distances", outputs.get("dists", outputs.get("out_dists"))) + indices = outputs.get("indices", outputs.get("idxs", outputs.get("out_indices"))) + if distances is None or indices is None: + raise TypeError("knn_build dispatcher output dict must contain distances and indices") + normalized = (distances, indices) + else: + raise TypeError("knn_build dispatcher must return (distances, indices), a matching dict, or write outputs") + if any(source is not destination for destination, source in zip(owned, normalized)): + raise RuntimeError("prepared KNN-build route must write caller-owned outputs through captured launches") + + +class KNNBuildRuntime: + """One device runtime with reusable launch plans keyed by shape and stream.""" + + def __init__( + self, + *, + device: Any = None, + arch: str | None = None, + timeout_ms: float | None = None, + max_cached_shapes: int | None = None, + compile: str = "lazy", + ) -> None: + import torch + + if compile != "lazy": + raise ValueError("compile must be 'lazy'; use warmup() to eagerly prepare known shapes") + self.device_index = _runtime_device_index(device) + if max_cached_shapes is not None: + if isinstance(max_cached_shapes, bool) or not isinstance(max_cached_shapes, int): + raise TypeError("max_cached_shapes must be a positive integer or None") + if int(max_cached_shapes) <= 0: + raise ValueError("max_cached_shapes must be positive") + max_cached_shapes = int(max_cached_shapes) + self.timeout_ms = _validate_timeout(timeout_ms) + self.max_cached_shapes = max_cached_shapes + with torch.cuda.device(self.device_index): + detected_arch = str(_direct_plan_runtime.detect_gpu_arch()) + self.arch = detected_arch if arch is None else str(arch) + if self.arch != detected_arch: + raise ValueError( + f"KNN-build runtime arch must match its device: requested {self.arch}, detected {detected_arch}" + ) + self._cache: OrderedDict[tuple[Any, ...], _KNNBuildRuntimeSlot] = OrderedDict() + self._preparing: dict[tuple[Any, ...], _PendingPreparation] = {} + self._cache_lock = RLock() + self._lifecycle = Condition(RLock()) + self._active_calls = 0 + self._clearing = False + self._hits = 0 + self._misses = 0 + + @_guard_runtime_compute + def compute( + self, + query: Any, + database: Any, + k: int, + *, + build: bool = False, + shape_label: str | None = None, + out: tuple[Any, Any] | None = None, + query_sq: Any = None, + database_sq: Any = None, + stream: Any = None, + timeout_ms: float | None = None, + return_info: bool = False, + ): + """Run one KNN build/search through this signature's launch plan. + + A cache miss runs the frozen guard cascade once and freezes its exact + leaf launches (plus the route-required fused row-norm support + launches) into a per-signature plan. Cache hits overwrite the + recorded pointer carriers in place and submit the prepared launches + on the plan's stream — no dispatch re-evaluation, no argument + re-marshalling, no per-launch stream query, and no per-call + default-output allocation (defaults ping-pong between two plan-owned + pairs, so consecutive default-output calls never alias). + """ + + import torch + + bsz, n_query, n_database, dim = _validate_compute_tensors( + torch, + query, + database, + k, + build=build, + device_index=self.device_index, + ) + k = int(k) + build = bool(build) + effective_timeout_ms = self.timeout_ms if timeout_ms is None else _validate_timeout(timeout_ms) + if stream is None: + resolved_stream = torch.cuda.current_stream(self.device_index) + else: + resolved_stream = stream + stream_device = getattr(resolved_stream, "device", None) + stream_device_index = getattr(stream_device, "index", stream_device) + if stream_device_index is not None and int(stream_device_index) != self.device_index: + raise ValueError( + f"KNN-build stream device {stream_device_index} does not match runtime device {self.device_index}" + ) + stream_handle = int(resolved_stream.cuda_stream) + query_sq, database_sq = _validate_compute_norms( + query_sq, + database_sq, + build=build, + bsz=bsz, + n_query=n_query, + n_database=n_database, + device=database.device, + ) + out_pair = ( + None if out is None else _validate_compute_outputs(torch, out, (bsz, n_query, k), database.device) + ) + if query_sq is None or database_sq is None: + norm_alias = bool(build and query_sq is None and database_sq is None) + else: + norm_alias = int(query_sq.data_ptr()) == int(database_sq.data_ptr()) + dtype_name = "bfloat16" if query.dtype is torch.bfloat16 else "float16" + key = ( + self.device_index, + self.arch, + bsz, + n_query, + n_database, + dim, + k, + dtype_name, + build, + int(query.data_ptr()) == int(database.data_ptr()), + query_sq is None, + database_sq is None, + norm_alias, + stream_handle, + ) + with self._cache_lock: + slot = self._cache.get(key) + if slot is not None: + self._cache.move_to_end(key) + self._hits += 1 + cache_hit = slot is not None + owns_slot_lock = False + if slot is None: + slot, owns_slot_lock = self._create_slot( + key, + query=query, + database=database, + k=k, + build=build, + shape_label=shape_label, + dtype_name=dtype_name, + shape=(bsz, n_query, n_database, dim), + out_pair=out_pair, + query_sq=query_sq, + database_sq=database_sq, + stream=resolved_stream, + ) + cache_hit = not owns_slot_lock + if not owns_slot_lock: + slot.lock.acquire() + try: + if out_pair is None: + out_dists, out_indices = slot.default_outputs[slot.default_flip] + slot.default_flip ^= 1 + else: + out_dists, out_indices = out_pair + bindings = { + "query": query, + "database": database, + "query_sq": slot.query_sq if slot.internal_query_norm else query_sq, + "database_sq": slot.database_sq if slot.internal_database_norm else database_sq, + "out_dists": out_dists, + "out_indices": out_indices, + } + try: + # Bind first (tensor-map refresh + pointer overwrite are host + # work), then enqueue the norm support launches, then submit + # the plan's kernels. Binding after the norms would turn a + # fresh-pointer tensor-map re-encode into a GPU inter-kernel + # gap between the norm kernel and the route's first kernel. + # A captured signature does every bind host-side, then one + # graph replay covers the whole norm + route kernel chain. + if slot.graph is not None: + slot.plan.bind_hot(bindings) + for norm_input_key, norm_launch in slot.norm_launches: + norm_launch.bind_hot(bindings[norm_input_key]) + slot.graph.submit_hot(timeout_ms=effective_timeout_ms) + else: + slot.plan.bind_hot(bindings) + for norm_input_key, norm_launch in slot.norm_launches: + norm_launch.launch_hot(bindings[norm_input_key]) + slot.plan.submit_hot(timeout_ms=effective_timeout_ms) + finally: + # Slot-owned scratch and pooled default outputs are allocated + # on the plan's stream and only released through clear(); + # caller-provided tensors may live on another stream, so + # record those for allocator safety even on partial + # submission. + _record_stream_tensors( + (query, database, query_sq, database_sq) + + (() if out_pair is None else out_pair), + resolved_stream, + ) + finally: + slot.lock.release() + result = (out_dists, out_indices) + if not return_info: + return result + route = slot.route + info = { + "semantic_entrypoint": SEMANTIC_ENTRYPOINT, + "selected_route": route.route_id, + "launch_entrypoint": route.launch_entrypoint, + "exact_launch_plan": route.exact_contract, + "shape_label": getattr(route, "shape_label", None), + "prepared_launch_count": int(slot.plan.launch_count), + "runtime_launch_count": int(slot.plan.launch_count) + len(slot.norm_launches), + "norm_launch_count": len(slot.norm_launches), + "norm_compute_fields": list(slot.norm_compute_fields), + "norm_mode": ( + "internal_fused_row_norm:" + ",".join(slot.norm_compute_fields) + if slot.norm_compute_fields + else "route_elided_internal_norms" + if slot.internal_query_norm or slot.internal_database_norm + else "explicit_precomputed" + ), + "hot_launch_path": "cuda_graph" if slot.graph is not None else "prepared_launches", + "graph_kernel_count": None if slot.graph is None else int(slot.graph.launch_count), + "graph_capture_error": slot.graph_capture_error, + "arch": self.arch, + "device_index": self.device_index, + "stream_handle": stream_handle, + "runtime_cache_hit": cache_hit, + } + return result, info + + def _create_slot( + self, + key: tuple[Any, ...], + *, + query: Any, + database: Any, + k: int, + build: bool, + shape_label: str | None, + dtype_name: str, + shape: tuple[int, int, int, int], + out_pair: tuple[Any, Any] | None, + query_sq: Any, + database_sq: Any, + stream: Any, + ) -> tuple[_KNNBuildRuntimeSlot, bool]: + """Construct and publish this signature's LaunchPlan (the slow path). + + Returns ``(slot, owns_slot_lock)``. A newly published slot is returned + with its lock held so the constructing call launches first; when + another thread published the slot while this one waited, the existing + slot is returned unlocked and counted as a cache hit. Preparation + never holds the runtime cache lock, so resident hot shapes stay + unblocked while a cold signature captures. + """ + + while True: + with self._cache_lock: + slot = self._cache.get(key) + if slot is not None: + self._cache.move_to_end(key) + self._hits += 1 + return slot, False + pending = self._preparing.get(key) + if pending is None: + if ( + self.max_cached_shapes is not None + and len(self._cache) + len(self._preparing) >= self.max_cached_shapes + ): + raise RuntimeError( + "KNNBuildRuntime cache is full; call clear() only after in-flight work completes" + ) + pending = _PendingPreparation() + self._preparing[key] = pending + break + pending.event.wait() + if pending.error is not None: + raise RuntimeError("KNN-build slot preparation failed in another thread") from pending.error + + slot: _KNNBuildRuntimeSlot | None = None + try: + import torch + + from ._row_norm import prepare_row_squared_norm + + bsz, n_query, n_database, dim = shape + expected = (bsz, n_query, k) + internal_query_norm = query_sq is None + internal_database_norm = database_sq is None + with torch.cuda.device(self.device_index), torch.cuda.stream(stream): + default_outputs = tuple( + ( + torch.empty(expected, dtype=torch.float32, device=database.device), + torch.empty(expected, dtype=torch.int32, device=database.device), + ) + for _pair in range(2) + ) + if internal_query_norm: + slot_query_sq = torch.empty( + (bsz, n_query), + dtype=torch.float32, + device=query.device, + ) + else: + slot_query_sq = query_sq + if build and internal_database_norm: + slot_database_sq = slot_query_sq + elif internal_database_norm: + slot_database_sq = torch.empty( + (bsz, n_database), + dtype=torch.float32, + device=database.device, + ) + else: + slot_database_sq = database_sq + capture_dists, capture_indices = out_pair if out_pair is not None else default_outputs[0] + inputs = { + "label": shape_label, + "B": bsz, + "Q": n_query, + "M": n_database, + "D": dim, + "K": k, + "dtype": dtype_name, + "build": build, + "query": query, + "database": database, + "query_sq": slot_query_sq, + "database_sq": slot_database_sq, + "out_dists": capture_dists, + "out_indices": capture_indices, + } + with _PREPARE_LOCK: + route = _direct_plan_runtime.resolve_route(inputs) + plan = build_launch_plan( + inputs, + stream=stream, + arch=self.arch, + validate_result=_require_owned_outputs, + route=route, + ) + bound_keys = getattr(plan, "bound_input_keys", None) + bound = None if bound_keys is None else set(bound_keys) + # A frozen plan reports exactly which public inputs its + # captured launches bind; routes that compute norms + # in-kernel skip the support launches entirely. A per-call + # plan re-executes the route's host program, so provide + # every internal norm it could consume. + compute_query_norm = internal_query_norm and ( + bound is None or "query_sq" in bound or build and "database_sq" in bound + ) + compute_database_norm = internal_database_norm and not build and ( + bound is None or "database_sq" in bound + ) + norm_launches: list[tuple[str, Any]] = [] + if compute_query_norm: + norm_launches.append( + ( + "query", + prepare_row_squared_norm(query, slot_query_sq, arch=self.arch, stream=stream), + ) + ) + if compute_database_norm: + norm_launches.append( + ( + "database", + prepare_row_squared_norm(database, slot_database_sq, arch=self.arch, stream=stream), + ) + ) + # Capture the signature's stable kernel chain (norms first, + # then the frozen route launches — the exact hot submission + # order) into one CUDA graph. Per-call routes and launch + # modes without a validated capture path stay on the + # prepared-launch hot path, with the reason recorded; + # any other capture failure is an error, not a fallback. + graph_plan = None + graph_capture_error: str | None = None + try: + graph_plan = build_graph_exec_plan( + plan, + support_launches=tuple( + norm_launch.launch_plan for _key, norm_launch in norm_launches + ), + ) + except GraphCaptureUnsupported as unsupported: + graph_capture_error = str(unsupported) + norm_compute_fields = tuple( + name + for name, enabled in ( + ("query_sq", compute_query_norm), + ("database_sq", compute_database_norm), + ) + if enabled + ) + slot = _KNNBuildRuntimeSlot( + plan=plan, + route=route, + norm_launches=tuple(norm_launches), + query_sq=slot_query_sq, + database_sq=slot_database_sq, + internal_query_norm=internal_query_norm, + internal_database_norm=internal_database_norm, + norm_compute_fields=norm_compute_fields, + default_outputs=default_outputs, + graph=graph_plan, + graph_capture_error=graph_capture_error, + ) + slot.lock.acquire() + except BaseException as error: + if slot is not None: + try: + slot.lock.release() + except RuntimeError: + pass + with self._cache_lock: + self._preparing.pop(key, None) + pending.error = error + pending.event.set() + raise + publication_committed = False + try: + with self._cache_lock: + old_misses = self._misses + try: + self._cache[key] = slot + self._misses = old_misses + 1 + self._preparing.pop(key, None) + pending.event.set() + publication_committed = True + except BaseException as error: + if self._cache.get(key) is slot: + self._cache.pop(key, None) + self._misses = old_misses + if self._preparing.get(key) is pending: + self._preparing.pop(key, None) + pending.error = error + pending.event.set() + raise + except BaseException as error: + if not publication_committed: + with self._cache_lock: + if self._cache.get(key) is slot: + self._cache.pop(key, None) + self._misses -= 1 + if self._preparing.get(key) is pending: + self._preparing.pop(key, None) + if pending.error is None: + pending.error = error + pending.event.set() + slot.lock.release() + raise + return slot, True + + def cache_info(self) -> dict[str, int | None]: + with self._cache_lock: + return { + "hits": self._hits, + "misses": self._misses, + "size": len(self._cache), + "max_cached_shapes": self.max_cached_shapes, + } + + def clear(self, *, synchronize: bool = True) -> None: + """Drop cached plans after host calls finish. + + The default waits for device completion. With ``synchronize=False``, + every plan-held launch argument, slot-owned norm buffer, and pooled + default output is tied to its plan's stream via ``record_stream`` + before release, keeping tensor storage allocator-safe; the caller + remains responsible for observing asynchronous completion. + """ + import torch + + with self._lifecycle: + while self._clearing: + self._lifecycle.wait() + try: + self._clearing = True + while self._active_calls: + self._lifecycle.wait() + if synchronize: + with torch.cuda.device(self.device_index): + torch.cuda.synchronize() + with self._cache_lock: + if synchronize: + # Device work is complete; release the driver graph + # handles eagerly. A non-synchronizing clear only + # drops the Python references — an executing graph + # must never be destroyed underneath the device. + for slot in self._cache.values(): + if slot.graph is not None: + slot.graph.destroy() + else: + for slot in self._cache.values(): + plan_stream = slot.plan.torch_stream + slot.plan.record_stream(plan_stream) + for _input_key, norm_launch in slot.norm_launches: + norm_launch.record_stream(plan_stream) + _record_stream_tensors( + (slot.query_sq, slot.database_sq) + + tuple(tensor for pair in slot.default_outputs for tensor in pair), + plan_stream, + ) + self._cache.clear() + self._hits = 0 + self._misses = 0 + finally: + self._clearing = False + self._lifecycle.notify_all() + + def warmup(self, *args: Any, synchronize: bool = True, **kwargs: Any): + result = self.compute(*args, **kwargs) + if synchronize: + import torch + + with torch.cuda.device(self.device_index): + torch.cuda.synchronize() + return result + + +def init( + device: Any = None, + arch: str | None = None, + timeout_ms: float | None = None, + max_cached_shapes: int | None = None, + compile: str = "lazy", +) -> KNNBuildRuntime: + """Initialize one reusable KNN-build runtime without binding input tensors.""" + + return KNNBuildRuntime( + device=device, + arch=arch, + timeout_ms=timeout_ms, + max_cached_shapes=max_cached_shapes, + compile=compile, + ) + + +def prepare_knn_build( + query: Any, + database: Any, + k: int, + *, + build: bool = False, + shape_label: str | None = None, + out: tuple[Any, Any] | None = None, + arch: str | None = None, + stream: Any = None, + timeout_ms: float | None = None, +) -> PreparedKNNBuild: + """Prepare norms, outputs, scratch, and a fully marshalled direct leaf.""" + + import torch + + if not isinstance(query, torch.Tensor) or not query.is_cuda: + raise TypeError("query must be a CUDA torch.Tensor") + device_index = query.device.index + if device_index is None: + device_index = torch.cuda.current_device() + with torch.cuda.device(device_index): + resolved_stream = torch.cuda.current_stream(device_index) if stream is None else stream + stream_device = getattr(resolved_stream, "device", None) + stream_device_index = getattr(stream_device, "index", stream_device) + if stream_device_index is not None and int(stream_device_index) != int(device_index): + raise ValueError( + f"KNN-build stream device {stream_device_index} does not match input device {device_index}" + ) + with torch.cuda.stream(resolved_stream): + inputs = _prepare_inputs(query, database, k, build=build, shape_label=shape_label, out=out) + launch_plan = prepare_route(inputs, arch=arch, stream=resolved_stream) + return PreparedKNNBuild( + inputs=inputs, + launch_plan=launch_plan, + shape_label=launch_plan.shape_label, + stream=resolved_stream, + timeout_ms=timeout_ms, + ) + + +def knn_build_prepared( + prepared: PreparedKNNBuild, + *, + arch: str | None = None, + stream: Any = None, + timeout_ms: float | None = None, + return_info: bool = False, +): + """Launch a fixed route without re-entering the parent dispatcher or importer.""" + if not isinstance(prepared, PreparedKNNBuild): + raise TypeError("prepared must be returned by prepare_knn_build") + plan = prepared.launch_plan + if arch is not None and str(arch) != plan.arch: + raise ValueError(f"prepared KNN-build route targets {plan.arch}, requested incompatible arch {arch}") + plan.launch( + prepared.inputs, + stream=stream, + timeout_ms=prepared.timeout_ms if timeout_ms is None else timeout_ms, + ) + out = (prepared.inputs["out_dists"], prepared.inputs["out_indices"]) + if not return_info: + return out + info = { + "semantic_entrypoint": SEMANTIC_ENTRYPOINT, + "selected_route": prepared.selected_route, + "launch_entrypoint": plan.launch_entrypoint, + "exact_launch_plan": plan.exact_contract, + "shape_label": prepared.shape_label, + "prepared_launch_count": plan.launch_count, + "arch": plan.arch, + "device_index": plan.device_index, + "stream_handle": plan.stream_handle, + } + return out, info + + +_DEFAULT_RUNTIME_LOCK = RLock() +_DEFAULT_RUNTIMES: dict[int, KNNBuildRuntime] = {} + + +def _default_runtime(device_index: int) -> KNNBuildRuntime: + """Return the process-wide per-device runtime backing ``knn_build``.""" + + runtime = _DEFAULT_RUNTIMES.get(device_index) + if runtime is None: + with _DEFAULT_RUNTIME_LOCK: + runtime = _DEFAULT_RUNTIMES.get(device_index) + if runtime is None: + runtime = KNNBuildRuntime(device=device_index) + _DEFAULT_RUNTIMES[device_index] = runtime + return runtime + + +def knn_build( + query: Any, + database: Any, + k: int, + *, + build: bool = False, + shape_label: str | None = None, + out: tuple[Any, Any] | None = None, + arch: str | None = None, + stream: Any = None, + timeout_ms: float | None = None, + return_info: bool = False, +): + """Run one KNN build/search through the per-device signature plan cache. + + The first call for a signature runs the frozen guard cascade once to + construct its launch plan; subsequent calls are pointer-overwrite + launches on the plan's stream. A caller-provided ``out=`` pair is + written through; default outputs come from the signature's plan-owned + pool, so a default-output result must be consumed (or cloned) before + two further default-output calls of the same signature overwrite its + storage. + """ + import torch + + if not isinstance(query, torch.Tensor) or not getattr(query, "is_cuda", False): + raise TypeError("query must be a CUDA torch.Tensor") + runtime = _default_runtime(_tensor_device_index(query)) + if arch is not None and str(arch) != runtime.arch: + raise ValueError( + f"KNN-build launch arch must match the active device: requested {arch}, detected {runtime.arch}" + ) + return runtime.compute( + query, + database, + k, + build=build, + shape_label=shape_label, + out=out, + stream=stream, + timeout_ms=timeout_ms, + return_info=return_info, + ) diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/kernels.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/kernels.py new file mode 100644 index 00000000..b4c3ffac --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/kernels.py @@ -0,0 +1,6025 @@ +from __future__ import annotations + +import json +from dataclasses import dataclass +from importlib import resources +from typing import TYPE_CHECKING, Any + +if TYPE_CHECKING: + from ._runtime import CUDAKernel + + +@dataclass(frozen=True) +class KernelSpec: + name: str + symbol: str + source: str + threads: int + shared_mem_bytes: int + cluster_dims: tuple[int, int, int] + launch_mode: str + parameters: tuple[dict[str, str], ...] + specializations: dict[str, int | str] + compile_options: tuple[str, ...] + + @staticmethod + def from_manifest(entry: dict[str, Any]) -> "KernelSpec": + return KernelSpec( + name=entry["name"], + symbol=entry["symbol"], + source=entry["source"], + threads=int(entry["threads"]), + shared_mem_bytes=int(entry["shared_mem_bytes"]), + cluster_dims=tuple(int(v) for v in entry["cluster_dims"]), + launch_mode=entry["launch_mode"], + parameters=tuple(entry["parameters"]), + specializations=dict(entry.get("specializations", {})), + compile_options=tuple(str(option) for option in entry.get("compile_options", ())), + ) + + +class ExportedKernel: + def __init__(self, spec: KernelSpec): + self.spec = spec + self._compiled: dict[tuple[str, tuple[str, ...], int, int], "CUDAKernel"] = {} + self._arg_types = tuple(parameter["ctype"] for parameter in spec.parameters) + self._default_block = (spec.threads, 1, 1) + self._default_shared_mem = spec.shared_mem_bytes + self._source_cache: str | None = None + self._source_digest: str | None = None + + @property + def parameters(self) -> tuple[dict[str, str], ...]: + return self.spec.parameters + + @property + def arg_types(self) -> tuple[str, ...]: + return self._arg_types + + def source_text(self) -> str: + if self._source_cache is None: + from ._runtime import record_source_read + + package = __package__ or __name__.rpartition(".")[0] + self._source_cache = resources.files(package).joinpath(self.spec.source).read_text(encoding="utf-8") + record_source_read() + return self._source_cache + + def compile(self, *, arch: str | None = None, options: list[str] | None = None) -> "CUDAKernel": + effective_options = tuple(dict.fromkeys((*self.spec.compile_options, *(options or ())))) + from ._runtime import ( + compilation_cache_generation, + current_cuda_device_index, + load_cached_kernel, + resolve_gpu_arch, + ) + + resolved_arch = resolve_gpu_arch(arch) + device_index = current_cuda_device_index() + generation = compilation_cache_generation() + key = (resolved_arch, effective_options, device_index, generation) + kernel = self._compiled.get(key) + if kernel is None or kernel.closed: + source = self.source_text() + if self._source_digest is None: + import hashlib + + self._source_digest = hashlib.sha256(source.encode("utf-8")).hexdigest() + kernel = load_cached_kernel( + source, + source_digest=self._source_digest, + func_name=self.spec.symbol, + arch=resolved_arch, + device_index=device_index, + name=f"{self.spec.name}.cu", + options=effective_options, + ) + self._compiled[key] = kernel + return kernel + + def launch( + self, + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, + ) -> None: + from ._runtime import resolve_launch_defaults + + arch, stream, timeout_ms = resolve_launch_defaults( + arch=arch, + stream=stream, + timeout_ms=timeout_ms, + ) + prepared = self.prepare_launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + arch=arch, + options=options, + ) + prepared.launch(timeout_ms=timeout_ms) + + def prepare_launch( + self, + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + arch: str | None = None, + options: list[str] | None = None, + ): + # Compile and fully marshal one launch without submitting GPU work. + + if len(args) != len(self.spec.parameters): + expected = ", ".join(p["name"] for p in self.spec.parameters) + raise TypeError(f"{self.spec.name} expects {len(self.spec.parameters)} args ({expected}), got {len(args)}") + from ._runtime import launch_stream_context, resolve_launch_defaults + + arch, stream, _ = resolve_launch_defaults( + arch=arch, + stream=stream, + timeout_ms=None, + ) + if block is None: + block = self._default_block + if shared_mem is None: + shared_mem = self._default_shared_mem + with launch_stream_context(stream): + kernel = self.compile(arch=arch, options=options) + if self.spec.launch_mode == "cluster": + return kernel.prepare_launch_cluster( + grid=grid, + block=block, + args=args, + arg_types=self._arg_types, + cluster_dims=self.spec.cluster_dims, + shared_mem=shared_mem, + stream=stream, + ) + if self.spec.launch_mode == "cooperative": + return kernel.prepare_launch_cooperative( + grid=grid, + block=block, + args=args, + arg_types=self._arg_types, + shared_mem=shared_mem, + stream=stream, + ) + return kernel.prepare_launch( + grid=grid, + block=block, + args=args, + arg_types=self._arg_types, + shared_mem=shared_mem, + stream=stream, + ) + + +def _load_manifest() -> dict[str, Any]: + package = __package__ or __name__.rpartition(".")[0] + return json.loads(resources.files(package).joinpath("manifest.json").read_text(encoding="utf-8")) + + +_MANIFEST = _load_manifest() +KERNELS = {entry["name"]: ExportedKernel(KernelSpec.from_manifest(entry)) for entry in _MANIFEST["kernels"]} + + +def get_kernel(name: str) -> ExportedKernel: + try: + return KERNELS[name] + except KeyError as exc: + available = ", ".join(sorted(KERNELS)) + raise KeyError(f"Unknown exported kernel {name!r}. Available: {available}") from exc + + +dispatch_kernel_0000 = get_kernel('dispatch_kernel_0000') + + +def launch_dispatch_kernel_0000( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0000.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0001 = get_kernel('dispatch_kernel_0001') + + +def launch_dispatch_kernel_0001( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0001.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0002 = get_kernel('dispatch_kernel_0002') + + +def launch_dispatch_kernel_0002( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0002.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0003 = get_kernel('dispatch_kernel_0003') + + +def launch_dispatch_kernel_0003( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0003.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0004 = get_kernel('dispatch_kernel_0004') + + +def launch_dispatch_kernel_0004( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0004.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0005 = get_kernel('dispatch_kernel_0005') + + +def launch_dispatch_kernel_0005( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0005.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0006 = get_kernel('dispatch_kernel_0006') + + +def launch_dispatch_kernel_0006( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0006.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0007 = get_kernel('dispatch_kernel_0007') + + +def launch_dispatch_kernel_0007( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0007.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0008 = get_kernel('dispatch_kernel_0008') + + +def launch_dispatch_kernel_0008( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0008.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0009 = get_kernel('dispatch_kernel_0009') + + +def launch_dispatch_kernel_0009( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0009.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0010 = get_kernel('dispatch_kernel_0010') + + +def launch_dispatch_kernel_0010( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0010.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0011 = get_kernel('dispatch_kernel_0011') + + +def launch_dispatch_kernel_0011( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0011.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0012 = get_kernel('dispatch_kernel_0012') + + +def launch_dispatch_kernel_0012( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0012.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0013 = get_kernel('dispatch_kernel_0013') + + +def launch_dispatch_kernel_0013( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0013.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0014 = get_kernel('dispatch_kernel_0014') + + +def launch_dispatch_kernel_0014( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0014.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0015 = get_kernel('dispatch_kernel_0015') + + +def launch_dispatch_kernel_0015( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0015.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0016 = get_kernel('dispatch_kernel_0016') + + +def launch_dispatch_kernel_0016( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0016.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0017 = get_kernel('dispatch_kernel_0017') + + +def launch_dispatch_kernel_0017( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0017.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0018 = get_kernel('dispatch_kernel_0018') + + +def launch_dispatch_kernel_0018( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0018.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0019 = get_kernel('dispatch_kernel_0019') + + +def launch_dispatch_kernel_0019( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0019.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0020 = get_kernel('dispatch_kernel_0020') + + +def launch_dispatch_kernel_0020( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0020.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0021 = get_kernel('dispatch_kernel_0021') + + +def launch_dispatch_kernel_0021( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0021.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0022 = get_kernel('dispatch_kernel_0022') + + +def launch_dispatch_kernel_0022( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0022.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0023 = get_kernel('dispatch_kernel_0023') + + +def launch_dispatch_kernel_0023( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0023.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0024 = get_kernel('dispatch_kernel_0024') + + +def launch_dispatch_kernel_0024( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0024.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0025 = get_kernel('dispatch_kernel_0025') + + +def launch_dispatch_kernel_0025( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0025.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0026 = get_kernel('dispatch_kernel_0026') + + +def launch_dispatch_kernel_0026( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0026.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0027 = get_kernel('dispatch_kernel_0027') + + +def launch_dispatch_kernel_0027( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0027.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0028 = get_kernel('dispatch_kernel_0028') + + +def launch_dispatch_kernel_0028( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0028.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0029 = get_kernel('dispatch_kernel_0029') + + +def launch_dispatch_kernel_0029( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0029.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0030 = get_kernel('dispatch_kernel_0030') + + +def launch_dispatch_kernel_0030( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0030.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0031 = get_kernel('dispatch_kernel_0031') + + +def launch_dispatch_kernel_0031( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0031.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0032 = get_kernel('dispatch_kernel_0032') + + +def launch_dispatch_kernel_0032( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0032.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0033 = get_kernel('dispatch_kernel_0033') + + +def launch_dispatch_kernel_0033( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0033.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0034 = get_kernel('dispatch_kernel_0034') + + +def launch_dispatch_kernel_0034( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0034.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0035 = get_kernel('dispatch_kernel_0035') + + +def launch_dispatch_kernel_0035( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0035.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0036 = get_kernel('dispatch_kernel_0036') + + +def launch_dispatch_kernel_0036( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0036.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0037 = get_kernel('dispatch_kernel_0037') + + +def launch_dispatch_kernel_0037( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0037.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0038 = get_kernel('dispatch_kernel_0038') + + +def launch_dispatch_kernel_0038( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0038.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0039 = get_kernel('dispatch_kernel_0039') + + +def launch_dispatch_kernel_0039( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0039.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0040 = get_kernel('dispatch_kernel_0040') + + +def launch_dispatch_kernel_0040( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0040.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0041 = get_kernel('dispatch_kernel_0041') + + +def launch_dispatch_kernel_0041( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0041.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0042 = get_kernel('dispatch_kernel_0042') + + +def launch_dispatch_kernel_0042( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0042.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0043 = get_kernel('dispatch_kernel_0043') + + +def launch_dispatch_kernel_0043( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0043.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0044 = get_kernel('dispatch_kernel_0044') + + +def launch_dispatch_kernel_0044( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0044.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0045 = get_kernel('dispatch_kernel_0045') + + +def launch_dispatch_kernel_0045( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0045.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0046 = get_kernel('dispatch_kernel_0046') + + +def launch_dispatch_kernel_0046( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0046.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0047 = get_kernel('dispatch_kernel_0047') + + +def launch_dispatch_kernel_0047( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0047.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0048 = get_kernel('dispatch_kernel_0048') + + +def launch_dispatch_kernel_0048( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0048.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0049 = get_kernel('dispatch_kernel_0049') + + +def launch_dispatch_kernel_0049( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0049.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0050 = get_kernel('dispatch_kernel_0050') + + +def launch_dispatch_kernel_0050( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0050.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0051 = get_kernel('dispatch_kernel_0051') + + +def launch_dispatch_kernel_0051( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0051.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0052 = get_kernel('dispatch_kernel_0052') + + +def launch_dispatch_kernel_0052( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0052.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0053 = get_kernel('dispatch_kernel_0053') + + +def launch_dispatch_kernel_0053( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0053.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0054 = get_kernel('dispatch_kernel_0054') + + +def launch_dispatch_kernel_0054( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0054.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0055 = get_kernel('dispatch_kernel_0055') + + +def launch_dispatch_kernel_0055( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0055.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0056 = get_kernel('dispatch_kernel_0056') + + +def launch_dispatch_kernel_0056( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0056.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0057 = get_kernel('dispatch_kernel_0057') + + +def launch_dispatch_kernel_0057( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0057.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0058 = get_kernel('dispatch_kernel_0058') + + +def launch_dispatch_kernel_0058( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0058.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0059 = get_kernel('dispatch_kernel_0059') + + +def launch_dispatch_kernel_0059( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0059.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0060 = get_kernel('dispatch_kernel_0060') + + +def launch_dispatch_kernel_0060( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0060.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0061 = get_kernel('dispatch_kernel_0061') + + +def launch_dispatch_kernel_0061( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0061.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0062 = get_kernel('dispatch_kernel_0062') + + +def launch_dispatch_kernel_0062( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0062.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0063 = get_kernel('dispatch_kernel_0063') + + +def launch_dispatch_kernel_0063( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0063.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0064 = get_kernel('dispatch_kernel_0064') + + +def launch_dispatch_kernel_0064( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0064.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0065 = get_kernel('dispatch_kernel_0065') + + +def launch_dispatch_kernel_0065( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0065.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0066 = get_kernel('dispatch_kernel_0066') + + +def launch_dispatch_kernel_0066( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0066.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0067 = get_kernel('dispatch_kernel_0067') + + +def launch_dispatch_kernel_0067( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0067.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0068 = get_kernel('dispatch_kernel_0068') + + +def launch_dispatch_kernel_0068( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0068.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0069 = get_kernel('dispatch_kernel_0069') + + +def launch_dispatch_kernel_0069( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0069.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0070 = get_kernel('dispatch_kernel_0070') + + +def launch_dispatch_kernel_0070( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0070.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0071 = get_kernel('dispatch_kernel_0071') + + +def launch_dispatch_kernel_0071( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0071.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0072 = get_kernel('dispatch_kernel_0072') + + +def launch_dispatch_kernel_0072( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0072.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0073 = get_kernel('dispatch_kernel_0073') + + +def launch_dispatch_kernel_0073( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0073.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0074 = get_kernel('dispatch_kernel_0074') + + +def launch_dispatch_kernel_0074( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0074.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0075 = get_kernel('dispatch_kernel_0075') + + +def launch_dispatch_kernel_0075( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0075.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0076 = get_kernel('dispatch_kernel_0076') + + +def launch_dispatch_kernel_0076( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0076.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0077 = get_kernel('dispatch_kernel_0077') + + +def launch_dispatch_kernel_0077( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0077.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0078 = get_kernel('dispatch_kernel_0078') + + +def launch_dispatch_kernel_0078( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0078.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0079 = get_kernel('dispatch_kernel_0079') + + +def launch_dispatch_kernel_0079( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0079.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0080 = get_kernel('dispatch_kernel_0080') + + +def launch_dispatch_kernel_0080( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0080.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0081 = get_kernel('dispatch_kernel_0081') + + +def launch_dispatch_kernel_0081( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0081.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0082 = get_kernel('dispatch_kernel_0082') + + +def launch_dispatch_kernel_0082( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0082.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0083 = get_kernel('dispatch_kernel_0083') + + +def launch_dispatch_kernel_0083( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0083.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0084 = get_kernel('dispatch_kernel_0084') + + +def launch_dispatch_kernel_0084( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0084.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0085 = get_kernel('dispatch_kernel_0085') + + +def launch_dispatch_kernel_0085( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0085.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0086 = get_kernel('dispatch_kernel_0086') + + +def launch_dispatch_kernel_0086( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0086.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0087 = get_kernel('dispatch_kernel_0087') + + +def launch_dispatch_kernel_0087( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0087.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0088 = get_kernel('dispatch_kernel_0088') + + +def launch_dispatch_kernel_0088( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0088.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0089 = get_kernel('dispatch_kernel_0089') + + +def launch_dispatch_kernel_0089( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0089.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0090 = get_kernel('dispatch_kernel_0090') + + +def launch_dispatch_kernel_0090( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0090.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0091 = get_kernel('dispatch_kernel_0091') + + +def launch_dispatch_kernel_0091( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0091.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0092 = get_kernel('dispatch_kernel_0092') + + +def launch_dispatch_kernel_0092( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0092.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0093 = get_kernel('dispatch_kernel_0093') + + +def launch_dispatch_kernel_0093( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0093.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0094 = get_kernel('dispatch_kernel_0094') + + +def launch_dispatch_kernel_0094( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0094.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0095 = get_kernel('dispatch_kernel_0095') + + +def launch_dispatch_kernel_0095( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0095.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0096 = get_kernel('dispatch_kernel_0096') + + +def launch_dispatch_kernel_0096( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0096.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0097 = get_kernel('dispatch_kernel_0097') + + +def launch_dispatch_kernel_0097( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0097.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0098 = get_kernel('dispatch_kernel_0098') + + +def launch_dispatch_kernel_0098( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0098.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0099 = get_kernel('dispatch_kernel_0099') + + +def launch_dispatch_kernel_0099( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0099.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0100 = get_kernel('dispatch_kernel_0100') + + +def launch_dispatch_kernel_0100( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0100.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0101 = get_kernel('dispatch_kernel_0101') + + +def launch_dispatch_kernel_0101( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0101.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0102 = get_kernel('dispatch_kernel_0102') + + +def launch_dispatch_kernel_0102( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0102.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0103 = get_kernel('dispatch_kernel_0103') + + +def launch_dispatch_kernel_0103( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0103.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0104 = get_kernel('dispatch_kernel_0104') + + +def launch_dispatch_kernel_0104( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0104.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0105 = get_kernel('dispatch_kernel_0105') + + +def launch_dispatch_kernel_0105( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0105.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0106 = get_kernel('dispatch_kernel_0106') + + +def launch_dispatch_kernel_0106( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0106.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0107 = get_kernel('dispatch_kernel_0107') + + +def launch_dispatch_kernel_0107( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0107.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0108 = get_kernel('dispatch_kernel_0108') + + +def launch_dispatch_kernel_0108( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0108.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0109 = get_kernel('dispatch_kernel_0109') + + +def launch_dispatch_kernel_0109( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0109.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0110 = get_kernel('dispatch_kernel_0110') + + +def launch_dispatch_kernel_0110( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0110.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0111 = get_kernel('dispatch_kernel_0111') + + +def launch_dispatch_kernel_0111( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0111.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0112 = get_kernel('dispatch_kernel_0112') + + +def launch_dispatch_kernel_0112( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0112.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0113 = get_kernel('dispatch_kernel_0113') + + +def launch_dispatch_kernel_0113( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0113.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0114 = get_kernel('dispatch_kernel_0114') + + +def launch_dispatch_kernel_0114( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0114.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0115 = get_kernel('dispatch_kernel_0115') + + +def launch_dispatch_kernel_0115( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0115.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0116 = get_kernel('dispatch_kernel_0116') + + +def launch_dispatch_kernel_0116( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0116.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0117 = get_kernel('dispatch_kernel_0117') + + +def launch_dispatch_kernel_0117( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0117.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0118 = get_kernel('dispatch_kernel_0118') + + +def launch_dispatch_kernel_0118( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0118.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0119 = get_kernel('dispatch_kernel_0119') + + +def launch_dispatch_kernel_0119( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0119.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0120 = get_kernel('dispatch_kernel_0120') + + +def launch_dispatch_kernel_0120( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0120.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0121 = get_kernel('dispatch_kernel_0121') + + +def launch_dispatch_kernel_0121( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0121.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0122 = get_kernel('dispatch_kernel_0122') + + +def launch_dispatch_kernel_0122( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0122.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0123 = get_kernel('dispatch_kernel_0123') + + +def launch_dispatch_kernel_0123( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0123.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0124 = get_kernel('dispatch_kernel_0124') + + +def launch_dispatch_kernel_0124( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0124.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0125 = get_kernel('dispatch_kernel_0125') + + +def launch_dispatch_kernel_0125( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0125.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0126 = get_kernel('dispatch_kernel_0126') + + +def launch_dispatch_kernel_0126( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0126.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0127 = get_kernel('dispatch_kernel_0127') + + +def launch_dispatch_kernel_0127( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0127.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0128 = get_kernel('dispatch_kernel_0128') + + +def launch_dispatch_kernel_0128( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0128.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0129 = get_kernel('dispatch_kernel_0129') + + +def launch_dispatch_kernel_0129( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0129.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0130 = get_kernel('dispatch_kernel_0130') + + +def launch_dispatch_kernel_0130( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0130.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0131 = get_kernel('dispatch_kernel_0131') + + +def launch_dispatch_kernel_0131( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0131.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0132 = get_kernel('dispatch_kernel_0132') + + +def launch_dispatch_kernel_0132( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0132.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0133 = get_kernel('dispatch_kernel_0133') + + +def launch_dispatch_kernel_0133( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0133.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0134 = get_kernel('dispatch_kernel_0134') + + +def launch_dispatch_kernel_0134( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0134.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0135 = get_kernel('dispatch_kernel_0135') + + +def launch_dispatch_kernel_0135( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0135.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0136 = get_kernel('dispatch_kernel_0136') + + +def launch_dispatch_kernel_0136( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0136.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0137 = get_kernel('dispatch_kernel_0137') + + +def launch_dispatch_kernel_0137( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0137.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0138 = get_kernel('dispatch_kernel_0138') + + +def launch_dispatch_kernel_0138( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0138.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0139 = get_kernel('dispatch_kernel_0139') + + +def launch_dispatch_kernel_0139( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0139.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0140 = get_kernel('dispatch_kernel_0140') + + +def launch_dispatch_kernel_0140( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0140.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0141 = get_kernel('dispatch_kernel_0141') + + +def launch_dispatch_kernel_0141( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0141.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0142 = get_kernel('dispatch_kernel_0142') + + +def launch_dispatch_kernel_0142( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0142.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0143 = get_kernel('dispatch_kernel_0143') + + +def launch_dispatch_kernel_0143( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0143.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0144 = get_kernel('dispatch_kernel_0144') + + +def launch_dispatch_kernel_0144( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0144.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0145 = get_kernel('dispatch_kernel_0145') + + +def launch_dispatch_kernel_0145( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0145.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0146 = get_kernel('dispatch_kernel_0146') + + +def launch_dispatch_kernel_0146( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0146.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0147 = get_kernel('dispatch_kernel_0147') + + +def launch_dispatch_kernel_0147( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0147.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0148 = get_kernel('dispatch_kernel_0148') + + +def launch_dispatch_kernel_0148( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0148.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0149 = get_kernel('dispatch_kernel_0149') + + +def launch_dispatch_kernel_0149( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0149.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0150 = get_kernel('dispatch_kernel_0150') + + +def launch_dispatch_kernel_0150( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0150.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0151 = get_kernel('dispatch_kernel_0151') + + +def launch_dispatch_kernel_0151( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0151.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0152 = get_kernel('dispatch_kernel_0152') + + +def launch_dispatch_kernel_0152( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0152.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0153 = get_kernel('dispatch_kernel_0153') + + +def launch_dispatch_kernel_0153( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0153.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0154 = get_kernel('dispatch_kernel_0154') + + +def launch_dispatch_kernel_0154( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0154.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0155 = get_kernel('dispatch_kernel_0155') + + +def launch_dispatch_kernel_0155( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0155.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0156 = get_kernel('dispatch_kernel_0156') + + +def launch_dispatch_kernel_0156( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0156.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0157 = get_kernel('dispatch_kernel_0157') + + +def launch_dispatch_kernel_0157( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0157.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0158 = get_kernel('dispatch_kernel_0158') + + +def launch_dispatch_kernel_0158( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0158.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0159 = get_kernel('dispatch_kernel_0159') + + +def launch_dispatch_kernel_0159( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0159.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0160 = get_kernel('dispatch_kernel_0160') + + +def launch_dispatch_kernel_0160( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0160.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0161 = get_kernel('dispatch_kernel_0161') + + +def launch_dispatch_kernel_0161( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0161.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0162 = get_kernel('dispatch_kernel_0162') + + +def launch_dispatch_kernel_0162( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0162.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0163 = get_kernel('dispatch_kernel_0163') + + +def launch_dispatch_kernel_0163( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0163.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0164 = get_kernel('dispatch_kernel_0164') + + +def launch_dispatch_kernel_0164( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0164.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0165 = get_kernel('dispatch_kernel_0165') + + +def launch_dispatch_kernel_0165( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0165.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0166 = get_kernel('dispatch_kernel_0166') + + +def launch_dispatch_kernel_0166( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0166.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0167 = get_kernel('dispatch_kernel_0167') + + +def launch_dispatch_kernel_0167( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0167.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0168 = get_kernel('dispatch_kernel_0168') + + +def launch_dispatch_kernel_0168( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0168.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0169 = get_kernel('dispatch_kernel_0169') + + +def launch_dispatch_kernel_0169( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0169.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0170 = get_kernel('dispatch_kernel_0170') + + +def launch_dispatch_kernel_0170( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0170.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0171 = get_kernel('dispatch_kernel_0171') + + +def launch_dispatch_kernel_0171( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0171.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0172 = get_kernel('dispatch_kernel_0172') + + +def launch_dispatch_kernel_0172( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0172.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0173 = get_kernel('dispatch_kernel_0173') + + +def launch_dispatch_kernel_0173( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0173.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0174 = get_kernel('dispatch_kernel_0174') + + +def launch_dispatch_kernel_0174( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0174.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0175 = get_kernel('dispatch_kernel_0175') + + +def launch_dispatch_kernel_0175( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0175.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0176 = get_kernel('dispatch_kernel_0176') + + +def launch_dispatch_kernel_0176( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0176.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0177 = get_kernel('dispatch_kernel_0177') + + +def launch_dispatch_kernel_0177( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0177.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0178 = get_kernel('dispatch_kernel_0178') + + +def launch_dispatch_kernel_0178( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0178.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0179 = get_kernel('dispatch_kernel_0179') + + +def launch_dispatch_kernel_0179( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0179.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0180 = get_kernel('dispatch_kernel_0180') + + +def launch_dispatch_kernel_0180( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0180.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0181 = get_kernel('dispatch_kernel_0181') + + +def launch_dispatch_kernel_0181( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0181.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0182 = get_kernel('dispatch_kernel_0182') + + +def launch_dispatch_kernel_0182( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0182.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0183 = get_kernel('dispatch_kernel_0183') + + +def launch_dispatch_kernel_0183( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0183.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0184 = get_kernel('dispatch_kernel_0184') + + +def launch_dispatch_kernel_0184( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0184.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0185 = get_kernel('dispatch_kernel_0185') + + +def launch_dispatch_kernel_0185( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0185.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0186 = get_kernel('dispatch_kernel_0186') + + +def launch_dispatch_kernel_0186( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0186.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0187 = get_kernel('dispatch_kernel_0187') + + +def launch_dispatch_kernel_0187( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0187.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0188 = get_kernel('dispatch_kernel_0188') + + +def launch_dispatch_kernel_0188( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0188.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0189 = get_kernel('dispatch_kernel_0189') + + +def launch_dispatch_kernel_0189( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0189.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0190 = get_kernel('dispatch_kernel_0190') + + +def launch_dispatch_kernel_0190( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0190.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0191 = get_kernel('dispatch_kernel_0191') + + +def launch_dispatch_kernel_0191( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0191.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0192 = get_kernel('dispatch_kernel_0192') + + +def launch_dispatch_kernel_0192( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0192.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0193 = get_kernel('dispatch_kernel_0193') + + +def launch_dispatch_kernel_0193( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0193.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0194 = get_kernel('dispatch_kernel_0194') + + +def launch_dispatch_kernel_0194( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0194.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0195 = get_kernel('dispatch_kernel_0195') + + +def launch_dispatch_kernel_0195( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0195.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0196 = get_kernel('dispatch_kernel_0196') + + +def launch_dispatch_kernel_0196( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0196.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0197 = get_kernel('dispatch_kernel_0197') + + +def launch_dispatch_kernel_0197( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0197.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0198 = get_kernel('dispatch_kernel_0198') + + +def launch_dispatch_kernel_0198( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0198.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0199 = get_kernel('dispatch_kernel_0199') + + +def launch_dispatch_kernel_0199( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0199.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0200 = get_kernel('dispatch_kernel_0200') + + +def launch_dispatch_kernel_0200( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0200.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0201 = get_kernel('dispatch_kernel_0201') + + +def launch_dispatch_kernel_0201( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0201.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0202 = get_kernel('dispatch_kernel_0202') + + +def launch_dispatch_kernel_0202( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0202.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0203 = get_kernel('dispatch_kernel_0203') + + +def launch_dispatch_kernel_0203( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0203.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0204 = get_kernel('dispatch_kernel_0204') + + +def launch_dispatch_kernel_0204( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0204.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0205 = get_kernel('dispatch_kernel_0205') + + +def launch_dispatch_kernel_0205( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0205.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0206 = get_kernel('dispatch_kernel_0206') + + +def launch_dispatch_kernel_0206( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0206.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0207 = get_kernel('dispatch_kernel_0207') + + +def launch_dispatch_kernel_0207( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0207.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0208 = get_kernel('dispatch_kernel_0208') + + +def launch_dispatch_kernel_0208( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0208.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0209 = get_kernel('dispatch_kernel_0209') + + +def launch_dispatch_kernel_0209( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0209.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0210 = get_kernel('dispatch_kernel_0210') + + +def launch_dispatch_kernel_0210( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0210.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0211 = get_kernel('dispatch_kernel_0211') + + +def launch_dispatch_kernel_0211( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0211.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0212 = get_kernel('dispatch_kernel_0212') + + +def launch_dispatch_kernel_0212( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0212.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0213 = get_kernel('dispatch_kernel_0213') + + +def launch_dispatch_kernel_0213( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0213.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0214 = get_kernel('dispatch_kernel_0214') + + +def launch_dispatch_kernel_0214( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0214.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0215 = get_kernel('dispatch_kernel_0215') + + +def launch_dispatch_kernel_0215( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0215.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0216 = get_kernel('dispatch_kernel_0216') + + +def launch_dispatch_kernel_0216( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0216.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0217 = get_kernel('dispatch_kernel_0217') + + +def launch_dispatch_kernel_0217( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0217.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0218 = get_kernel('dispatch_kernel_0218') + + +def launch_dispatch_kernel_0218( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0218.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0219 = get_kernel('dispatch_kernel_0219') + + +def launch_dispatch_kernel_0219( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0219.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0220 = get_kernel('dispatch_kernel_0220') + + +def launch_dispatch_kernel_0220( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0220.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0221 = get_kernel('dispatch_kernel_0221') + + +def launch_dispatch_kernel_0221( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0221.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0222 = get_kernel('dispatch_kernel_0222') + + +def launch_dispatch_kernel_0222( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0222.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0223 = get_kernel('dispatch_kernel_0223') + + +def launch_dispatch_kernel_0223( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0223.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0224 = get_kernel('dispatch_kernel_0224') + + +def launch_dispatch_kernel_0224( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0224.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0225 = get_kernel('dispatch_kernel_0225') + + +def launch_dispatch_kernel_0225( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0225.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0226 = get_kernel('dispatch_kernel_0226') + + +def launch_dispatch_kernel_0226( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0226.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0227 = get_kernel('dispatch_kernel_0227') + + +def launch_dispatch_kernel_0227( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0227.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0228 = get_kernel('dispatch_kernel_0228') + + +def launch_dispatch_kernel_0228( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0228.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0229 = get_kernel('dispatch_kernel_0229') + + +def launch_dispatch_kernel_0229( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0229.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0230 = get_kernel('dispatch_kernel_0230') + + +def launch_dispatch_kernel_0230( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0230.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0231 = get_kernel('dispatch_kernel_0231') + + +def launch_dispatch_kernel_0231( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0231.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) + + +dispatch_kernel_0232 = get_kernel('dispatch_kernel_0232') + + +def launch_dispatch_kernel_0232( + *args, + grid: tuple[int, int, int], + block: tuple[int, int, int] | None = None, + shared_mem: int | None = None, + stream=None, + timeout_ms: float | None = None, + arch: str | None = None, + options: list[str] | None = None, +): + return dispatch_kernel_0232.launch( + *args, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=stream, + timeout_ms=timeout_ms, + arch=arch, + options=options, + ) diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/manifest.json b/cake_exports/knn_build/src/flashlib_cake_knn_build/manifest.json new file mode 100644 index 00000000..879e58c8 --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/manifest.json @@ -0,0 +1,16765 @@ +{ + "export_plan": { + "entrypoints": { + "correctness_test": "tests/test_correctness.py", + "performance_benchmark": "benchmarks/benchmark.py", + "python_interface": "src/flashlib_cake_knn_build/interface.py" + }, + "file": "plan.json", + "kernel_count": 233, + "metadata": { + "baseline": "FlashLib", + "inventory": "canonical112 v10 D320-recurrence production dispatcher", + "launch_resolution": "init-once device runtime with lazy per-shape/per-stream pointer-rebindable direct sequences", + "registry_correctness_shapes": 112, + "registry_performance_shapes": 112, + "workload": "knn_build" + }, + "name": "flashlib-knn-build-production", 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"flashlib_cake_knn_build.launch_dispatch_kernel_0225", + "flashlib_cake_knn_build.launch_dispatch_kernel_0226", + "flashlib_cake_knn_build.launch_dispatch_kernel_0227", + "flashlib_cake_knn_build.launch_dispatch_kernel_0228", + "flashlib_cake_knn_build.launch_dispatch_kernel_0229", + "flashlib_cake_knn_build.launch_dispatch_kernel_0230", + "flashlib_cake_knn_build.launch_dispatch_kernel_0231", + "flashlib_cake_knn_build.launch_dispatch_kernel_0232" + ], + "tensor_interop": "dlpack-zero-copy" + } +} diff --git a/cake_exports/knn_build/src/flashlib_cake_knn_build/tvm_ffi.py b/cake_exports/knn_build/src/flashlib_cake_knn_build/tvm_ffi.py new file mode 100644 index 00000000..0d7087fc --- /dev/null +++ b/cake_exports/knn_build/src/flashlib_cake_knn_build/tvm_ffi.py @@ -0,0 +1,179 @@ +"""Optional Apache TVM FFI adapters for the exported kernel package.""" + +from __future__ import annotations + +import contextlib +import importlib +import json +from pathlib import Path +from typing import Any + +from .kernels import get_kernel + + +_PACKAGE = 'flashlib_cake_knn_build' +_KERNEL_ALIASES = ['dispatch_kernel_0000', 'dispatch_kernel_0001', 'dispatch_kernel_0002', 'dispatch_kernel_0003', 'dispatch_kernel_0004', 'dispatch_kernel_0005', 'dispatch_kernel_0006', 'dispatch_kernel_0007', 'dispatch_kernel_0008', 'dispatch_kernel_0009', 'dispatch_kernel_0010', 'dispatch_kernel_0011', 'dispatch_kernel_0012', 'dispatch_kernel_0013', 'dispatch_kernel_0014', 'dispatch_kernel_0015', 'dispatch_kernel_0016', 'dispatch_kernel_0017', 'dispatch_kernel_0018', 'dispatch_kernel_0019', 'dispatch_kernel_0020', 'dispatch_kernel_0021', 'dispatch_kernel_0022', 'dispatch_kernel_0023', 'dispatch_kernel_0024', 'dispatch_kernel_0025', 'dispatch_kernel_0026', 'dispatch_kernel_0027', 'dispatch_kernel_0028', 'dispatch_kernel_0029', 'dispatch_kernel_0030', 'dispatch_kernel_0031', 'dispatch_kernel_0032', 'dispatch_kernel_0033', 'dispatch_kernel_0034', 'dispatch_kernel_0035', 'dispatch_kernel_0036', 'dispatch_kernel_0037', 'dispatch_kernel_0038', 'dispatch_kernel_0039', 'dispatch_kernel_0040', 'dispatch_kernel_0041', 'dispatch_kernel_0042', 'dispatch_kernel_0043', 'dispatch_kernel_0044', 'dispatch_kernel_0045', 'dispatch_kernel_0046', 'dispatch_kernel_0047', 'dispatch_kernel_0048', 'dispatch_kernel_0049', 'dispatch_kernel_0050', 'dispatch_kernel_0051', 'dispatch_kernel_0052', 'dispatch_kernel_0053', 'dispatch_kernel_0054', 'dispatch_kernel_0055', 'dispatch_kernel_0056', 'dispatch_kernel_0057', 'dispatch_kernel_0058', 'dispatch_kernel_0059', 'dispatch_kernel_0060', 'dispatch_kernel_0061', 'dispatch_kernel_0062', 'dispatch_kernel_0063', 'dispatch_kernel_0064', 'dispatch_kernel_0065', 'dispatch_kernel_0066', 'dispatch_kernel_0067', 'dispatch_kernel_0068', 'dispatch_kernel_0069', 'dispatch_kernel_0070', 'dispatch_kernel_0071', 'dispatch_kernel_0072', 'dispatch_kernel_0073', 'dispatch_kernel_0074', 'dispatch_kernel_0075', 'dispatch_kernel_0076', 'dispatch_kernel_0077', 'dispatch_kernel_0078', 'dispatch_kernel_0079', 'dispatch_kernel_0080', 'dispatch_kernel_0081', 'dispatch_kernel_0082', 'dispatch_kernel_0083', 'dispatch_kernel_0084', 'dispatch_kernel_0085', 'dispatch_kernel_0086', 'dispatch_kernel_0087', 'dispatch_kernel_0088', 'dispatch_kernel_0089', 'dispatch_kernel_0090', 'dispatch_kernel_0091', 'dispatch_kernel_0092', 'dispatch_kernel_0093', 'dispatch_kernel_0094', 'dispatch_kernel_0095', 'dispatch_kernel_0096', 'dispatch_kernel_0097', 'dispatch_kernel_0098', 'dispatch_kernel_0099', 'dispatch_kernel_0100', 'dispatch_kernel_0101', 'dispatch_kernel_0102', 'dispatch_kernel_0103', 'dispatch_kernel_0104', 'dispatch_kernel_0105', 'dispatch_kernel_0106', 'dispatch_kernel_0107', 'dispatch_kernel_0108', 'dispatch_kernel_0109', 'dispatch_kernel_0110', 'dispatch_kernel_0111', 'dispatch_kernel_0112', 'dispatch_kernel_0113', 'dispatch_kernel_0114', 'dispatch_kernel_0115', 'dispatch_kernel_0116', 'dispatch_kernel_0117', 'dispatch_kernel_0118', 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'dispatch_kernel_0160', 'dispatch_kernel_0161', 'dispatch_kernel_0162', 'dispatch_kernel_0163', 'dispatch_kernel_0164', 'dispatch_kernel_0165', 'dispatch_kernel_0166', 'dispatch_kernel_0167', 'dispatch_kernel_0168', 'dispatch_kernel_0169', 'dispatch_kernel_0170', 'dispatch_kernel_0171', 'dispatch_kernel_0172', 'dispatch_kernel_0173', 'dispatch_kernel_0174', 'dispatch_kernel_0175', 'dispatch_kernel_0176', 'dispatch_kernel_0177', 'dispatch_kernel_0178', 'dispatch_kernel_0179', 'dispatch_kernel_0180', 'dispatch_kernel_0181', 'dispatch_kernel_0182', 'dispatch_kernel_0183', 'dispatch_kernel_0184', 'dispatch_kernel_0185', 'dispatch_kernel_0186', 'dispatch_kernel_0187', 'dispatch_kernel_0188', 'dispatch_kernel_0189', 'dispatch_kernel_0190', 'dispatch_kernel_0191', 'dispatch_kernel_0192', 'dispatch_kernel_0193', 'dispatch_kernel_0194', 'dispatch_kernel_0195', 'dispatch_kernel_0196', 'dispatch_kernel_0197', 'dispatch_kernel_0198', 'dispatch_kernel_0199', 'dispatch_kernel_0200', 'dispatch_kernel_0201', 'dispatch_kernel_0202', 'dispatch_kernel_0203', 'dispatch_kernel_0204', 'dispatch_kernel_0205', 'dispatch_kernel_0206', 'dispatch_kernel_0207', 'dispatch_kernel_0208', 'dispatch_kernel_0209', 'dispatch_kernel_0210', 'dispatch_kernel_0211', 'dispatch_kernel_0212', 'dispatch_kernel_0213', 'dispatch_kernel_0214', 'dispatch_kernel_0215', 'dispatch_kernel_0216', 'dispatch_kernel_0217', 'dispatch_kernel_0218', 'dispatch_kernel_0219', 'dispatch_kernel_0220', 'dispatch_kernel_0221', 'dispatch_kernel_0222', 'dispatch_kernel_0223', 'dispatch_kernel_0224', 'dispatch_kernel_0225', 'dispatch_kernel_0226', 'dispatch_kernel_0227', 'dispatch_kernel_0228', 'dispatch_kernel_0229', 'dispatch_kernel_0230', 'dispatch_kernel_0231', 'dispatch_kernel_0232'] +_REGISTERED: dict[str, tuple[str, ...]] = {} + + +class _RawCUDAStream: + def __init__(self, handle: int): + self.cuda_stream = int(handle) + + +def _manifest() -> dict[str, Any]: + return json.loads(Path(__file__).with_name("manifest.json").read_text(encoding="utf-8")) + + +def _planned_public_exports() -> dict[str, str] | None: + export_plan = _manifest().get("export_plan", {}) + if "tvm_ffi_exports" in export_plan: + return dict(export_plan["tvm_ffi_exports"]) + if "package_exports" in export_plan: + return dict(export_plan["package_exports"]) + return None + + +def _public_export_names() -> tuple[str, ...]: + planned = _planned_public_exports() + if planned is not None: + return tuple(planned) + package = importlib.import_module(__package__) + excluded = { + "KERNELS", + "ExportedKernel", + "get_kernel", + "register_tvm_ffi", + "tvm_ffi_function_names", + *(_KERNEL_ALIASES), + *(f"launch_{name}" for name in _KERNEL_ALIASES), + } + return tuple( + name + for name in getattr(package, "__all__", ()) + if name not in excluded and callable(getattr(package, name, None)) + ) + + +def tvm_ffi_function_names(namespace: str | None = None) -> tuple[str, ...]: + """Return the deterministic TVM FFI global names this package registers.""" + + prefix = namespace or _PACKAGE + public_names = [f"{prefix}.{name}" for name in _public_export_names()] + kernel_names = [f"{prefix}.launch_{name}" for name in _KERNEL_ALIASES] + return tuple(public_names + kernel_names) + + +def _tensor_stream(arg: Any, tvm_ffi: Any) -> tuple[int, int] | None: + if not isinstance(arg, tvm_ffi.Tensor): + return None + device = arg.device + device_type = getattr(device, "type", None) or str(device).split(":", 1)[0] + if str(device_type) != "cuda": + return None + device_id = int(getattr(device, "index", 0)) + return device_id, int(tvm_ffi.get_raw_stream(device)) + + +def _convert_arg(arg: Any, tvm_ffi: Any) -> Any: + if not isinstance(arg, tvm_ffi.Tensor): + return arg + import torch + + return torch.from_dlpack(arg) + + +def _torch_stream_context(stream: tuple[int, int] | None): + if stream is None: + return contextlib.nullcontext() + import torch + + device_id, handle = stream + return torch.cuda.stream(torch.cuda.ExternalStream(handle, device=device_id)) + + +def _semantic_target(public_name: str): + planned = _planned_public_exports() + target = None if planned is None else planned.get(public_name) + if target is None: + return getattr(importlib.import_module(__package__), public_name) + module_name, separator, attr = target.partition(":") + if not separator: + raise ValueError(f"invalid package export target: {target!r}") + module = importlib.import_module(module_name, package=__package__) + return getattr(module, attr) + + +def _semantic_wrapper(public_name: str, tvm_ffi: Any): + function = _semantic_target(public_name) + + def call(*args): + stream = next((item for arg in args if (item := _tensor_stream(arg, tvm_ffi)) is not None), None) + converted = tuple(_convert_arg(arg, tvm_ffi) for arg in args) + with _torch_stream_context(stream): + return function(*converted) + + return call + + +def _kernel_wrapper(alias: str, tvm_ffi: Any): + kernel = get_kernel(alias) + parameter_count = len(kernel.parameters) + + def call(*args): + expected = parameter_count + 7 + if len(args) != expected: + raise TypeError( + f"{alias} TVM FFI launch expects {expected} positional arguments " + f"({parameter_count} kernel + grid xyz + block xyz + shared_mem), got {len(args)}" + ) + kernel_args = args[:parameter_count] + config = tuple(int(value) for value in args[parameter_count:]) + grid = config[:3] + block = config[3:6] + shared_mem = config[6] + stream = next( + (item for arg in kernel_args if (item := _tensor_stream(arg, tvm_ffi)) is not None), + None, + ) + converted = tuple(_convert_arg(arg, tvm_ffi) for arg in kernel_args) + return kernel.launch( + *converted, + grid=grid, + block=block, + shared_mem=shared_mem, + stream=_RawCUDAStream(stream[1]) if stream is not None else None, + ) + + return call + + +def register_tvm_ffi(namespace: str | None = None, *, override: bool = False) -> tuple[str, ...]: + """Register semantic and low-level launch functions in Apache TVM FFI.""" + + try: + import tvm_ffi + except ImportError as exc: + raise ImportError( + 'TVM FFI support requires `python -m pip install -e ".[tvm-ffi]"`' + ) from exc + + prefix = namespace or _PACKAGE + if prefix in _REGISTERED and not override: + return _REGISTERED[prefix] + + registered: list[str] = [] + for public_name in _public_export_names(): + name = f"{prefix}.{public_name}" + tvm_ffi.register_global_func( + name, _semantic_wrapper(public_name, tvm_ffi), override=override + ) + registered.append(name) + for alias in _KERNEL_ALIASES: + name = f"{prefix}.launch_{alias}" + tvm_ffi.register_global_func(name, _kernel_wrapper(alias, tvm_ffi), override=override) + registered.append(name) + result = tuple(registered) + _REGISTERED[prefix] = result + return result + diff --git a/cake_exports/knn_build/tests/test_benchmark_harness.py b/cake_exports/knn_build/tests/test_benchmark_harness.py new file mode 100644 index 00000000..76ca7724 --- /dev/null +++ b/cake_exports/knn_build/tests/test_benchmark_harness.py @@ -0,0 +1,64 @@ +from __future__ import annotations + +import json +import os +import subprocess +import sys +from pathlib import Path + + +ROOT = Path(__file__).resolve().parents[1] + + +def test_benchmark_harness_metadata_mode(tmp_path): + output = tmp_path / "benchmark_metadata.json" + env = os.environ.copy() + env["PYTHONPATH"] = str(ROOT / "src") + result = subprocess.run( + [ + sys.executable, + "benchmarks/benchmark_exported_kernels.py", + "--metadata-only", + "--json", + str(output), + ], + cwd=ROOT, + env=env, + check=False, + text=True, + capture_output=True, + ) + + assert result.returncode == 0, result.stdout + result.stderr + payload = json.loads(output.read_text(encoding="utf-8")) + assert payload["benchmark"] == "exported_kernel_compile" + assert payload["metadata_only"] is True + assert payload["kernels"] + assert payload["summary"]["kernel_count"] == len(payload["kernels"]) + + +def test_shape_benchmark_metadata_mode(tmp_path): + output = tmp_path / "shape_benchmark_metadata.json" + env = os.environ.copy() + env["PYTHONPATH"] = str(ROOT / "src") + result = subprocess.run( + [ + sys.executable, + "benchmarks/benchmark_shapes.py", + "--metadata-only", + "--json", + str(output), + ], + cwd=ROOT, + env=env, + check=False, + text=True, + capture_output=True, + ) + + assert result.returncode == 0, result.stdout + result.stderr + payload = json.loads(output.read_text(encoding="utf-8")) + assert payload["benchmark"] == "exported_kernel_shapes" + assert payload["metadata_only"] is True + assert payload["timing_backend_requested"] == "cupti" + diff --git a/cake_exports/knn_build/tests/test_correctness.py b/cake_exports/knn_build/tests/test_correctness.py new file mode 100644 index 00000000..ec3a30c6 --- /dev/null +++ b/cake_exports/knn_build/tests/test_correctness.py @@ -0,0 +1,94 @@ +from __future__ import annotations + +import importlib.util +import sys +from pathlib import Path + +import pytest + +ROOT = Path(__file__).resolve().parents[1] +BENCHMARKS = ROOT / "benchmarks" +if str(BENCHMARKS) not in sys.path: + sys.path.insert(0, str(BENCHMARKS)) + + +def _benchmark_module(): + spec = importlib.util.spec_from_file_location("knn_build_benchmark", BENCHMARKS / "benchmark.py") + assert spec is not None and spec.loader is not None + module = importlib.util.module_from_spec(spec) + spec.loader.exec_module(module) + return module + + +BENCHMARK = _benchmark_module() + + +def test_knn_build_runtime_api_is_exported(): + from flashlib_cake_knn_build import ( + KNNBuildRuntime, + init, + knn_build, + knn_build_prepared, + prepare_knn_build, + ) + + assert KNNBuildRuntime is not None + assert callable(init) + assert callable(knn_build) + assert callable(prepare_knn_build) + assert callable(knn_build_prepared) + + +@pytest.mark.export_validation_shape +@pytest.mark.parametrize("name", list(BENCHMARK.SHAPES)) +def test_knn_build_matches_reference(name: str): + torch = pytest.importorskip("torch") + if not torch.cuda.is_available(): + pytest.skip("CUDA GPU required for exported-kernel correctness") + from flashlib_cake_knn_build import init + + runtime = init() + try: + result = BENCHMARK._run_shape( + name, + BENCHMARK.SHAPES[name], + runtime=runtime, + arch=None, + correctness=True, + benchmark=False, + ) + finally: + runtime.clear() + assert result["route_matches_expected"], result + assert result["correct"], result + + +def test_knn_build_runtime_reuses_shape_a_b_a(): + torch = pytest.importorskip("torch") + if not torch.cuda.is_available(): + pytest.skip("CUDA GPU required for exported-kernel correctness") + from flashlib_cake_knn_build import init + + labels = ( + "flashml_correctness_b1_q256_m256_d128_k5", + "rag_online_b1_q1_m65536_d128_k10", + "flashml_correctness_b1_q256_m256_d128_k5", + ) + runtime = init() + try: + results = [ + BENCHMARK._run_shape( + label, + BENCHMARK.SHAPES[label], + runtime=runtime, + arch=None, + correctness=True, + benchmark=False, + ) + for label in labels + ] + assert all(result["correct"] and result["route_matches_expected"] for result in results) + assert results[-1]["first_shape_lookup_cache_hit"] is True + assert runtime.cache_info()["size"] == 2 + finally: + runtime.clear() diff --git a/cake_exports/knn_build/tests/test_exported_kernels.py b/cake_exports/knn_build/tests/test_exported_kernels.py new file mode 100644 index 00000000..df32db85 --- /dev/null +++ b/cake_exports/knn_build/tests/test_exported_kernels.py @@ -0,0 +1,165 @@ +from __future__ import annotations + +import importlib +import json +import sys +import types +from pathlib import Path + +import pytest + + +ROOT = Path(__file__).resolve().parents[1] +SRC = ROOT / "src" +if str(SRC) not in sys.path: + sys.path.insert(0, str(SRC)) + +PACKAGE_NAME = 'flashlib_cake_knn_build' + + +def _manifest() -> dict: + return json.loads((SRC / PACKAGE_NAME / "manifest.json").read_text(encoding="utf-8")) + + +def test_manifest_matches_package_exports(): + pkg = importlib.import_module(PACKAGE_NAME) + manifest = _manifest() + names = [entry["name"] for entry in manifest["kernels"]] + + assert names + assert set(pkg.KERNELS) == set(names) + assert manifest["package"] == PACKAGE_NAME + package_exports = manifest.get("export_plan", {}).get("package_exports", {}) + for public_name in package_exports: + assert hasattr(pkg, public_name), public_name + assert public_name in pkg.__all__, public_name + + if "export_plan" in manifest: + entrypoints = manifest["export_plan"].get("entrypoints", {}) + assert set(entrypoints) == { + "python_interface", + "correctness_test", + "performance_benchmark", + } + for path in entrypoints.values(): + assert (ROOT / path).is_file(), path + + +def test_manifest_sources_exist_and_contain_symbols(): + manifest = _manifest() + package_dir = SRC / PACKAGE_NAME + + for entry in manifest["kernels"]: + source_path = package_dir / entry["source"] + assert source_path.is_file(), entry["source"] + source = source_path.read_text(encoding="utf-8") + assert entry["symbol"] in source + assert entry["parameters"] + + +def test_package_import_and_source_text_do_not_require_cuda_runtime(): + pkg = importlib.import_module(PACKAGE_NAME) + + for name, kernel in pkg.KERNELS.items(): + assert kernel.source_text().startswith("typedef "), name + assert kernel.parameters + + +def test_exported_repo_docs_and_benchmarks_exist(): + assert (ROOT / "README.md").is_file() + assert (ROOT / "RESULTS.md").is_file() + assert (ROOT / "benchmarks" / "benchmark_exported_kernels.py").is_file() + assert (ROOT / "benchmarks" / "benchmark_shapes.py").is_file() + assert (ROOT / "benchmarks" / "workload.py").is_file() + assert (SRC / PACKAGE_NAME / "tvm_ffi.py").is_file() + pyproject = (ROOT / "pyproject.toml").read_text(encoding="utf-8") + assert 'cake-std==0.1.13.dev20260704+g7b8dbc8' in pyproject + + +def test_tvm_ffi_adapter_registers_low_level_functions_without_import_time_dependency(monkeypatch): + pkg = importlib.import_module(PACKAGE_NAME) + registrations = {} + + class FakeTensor: + pass + + def register_global_func(name, function, *, override=False): + assert override is False + registrations[name] = function + + fake_tvm_ffi = types.SimpleNamespace( + Tensor=FakeTensor, + register_global_func=register_global_func, + get_raw_stream=lambda device: 0, + ) + monkeypatch.setitem(sys.modules, "tvm_ffi", fake_tvm_ffi) + + expected = pkg.tvm_ffi_function_names("export_test") + registered = pkg.register_tvm_ffi("export_test") + assert registered == expected + assert set(registrations) == set(expected) + assert registered == pkg.register_tvm_ffi("export_test") + assert len(registrations) == len(expected) + + +def test_benchmark_runtime_requires_cupti_without_event_or_wall_clock_fallback(): + source = (SRC / PACKAGE_NAME / "_benchmark.py").read_text(encoding="utf-8") + assert "activity_register_callbacks" in source + assert "torch.cuda.Event" not in source + assert "perf_counter" not in source + assert "active_union_times_ms" in source + assert "activity_counts" in source + assert "launch_activity_counts" in source + assert "kernel_activity_counts" in source + assert "submission_times_ms" in source + assert "synchronized_e2e_times_ms" in source + assert "cold_first_call_host_enqueue_ms" in source + assert "cold_first_call_synchronized_e2e_ms" in source + helper = source.split("def _complete_l2_flush_before_bracket", 1)[1].split( + "def _correlate", 1 + )[0] + assert helper.index("flusher.flush()") < helper.index("synchronize()") + measured_loop = source.split("for _ in range(bench_iters):", 1)[1] + assert measured_loop.index("_complete_l2_flush_before_bracket") < measured_loop.index( + "start = cupti.get_timestamp()" + ) + + +def test_benchmark_runtime_preserves_exact_activity_diagnostics(): + benchmark = importlib.import_module(f"{PACKAGE_NAME}._benchmark") + timing = benchmark._correlate( + [(0, 100, 10_000)], + [(10, 11, 1), (20, 21, 2)], + [(1_000, 4_000, 1), (2_000, 5_000, 2)], + ) + assert timing.gpu_span_ms == [0.004] + assert timing.kernel_sum_ms == [0.006] + assert timing.active_union_ms == [0.004] + assert timing.inter_kernel_gap_ms == [0.0] + assert timing.activity_count == [2] + assert timing.launch_activity_count == [2] + assert timing.kernel_activity_count == [2] + with pytest.raises(ValueError, match="must be 'cupti'"): + benchmark.BenchResult(times_ms=[1.0], backend="cuda_event") + + +def test_runtime_has_content_cache_and_launch_context(): + source = (SRC / PACKAGE_NAME / "_runtime.py").read_text(encoding="utf-8") + assert "def launch_context" in source + assert "def load_cached_kernel" in source + assert "_CUBIN_CACHE" in source + assert "_MODULE_CACHE" in source + assert "_KERNEL_CACHE" in source + kernels_source = (SRC / PACKAGE_NAME / "kernels.py").read_text(encoding="utf-8") + assert "self._arg_types = tuple" in kernels_source + assert "self._default_block" in kernels_source + + +def test_launch_argument_count_is_checked_before_compilation(): + pkg = importlib.import_module(PACKAGE_NAME) + kernel = next(iter(pkg.KERNELS.values())) + bad_args = [object()] * (len(kernel.parameters) + 1) + + with pytest.raises(TypeError, match="expects"): + kernel.launch(*bad_args, grid=(1, 1, 1)) +