[MIGraphX EP] Fix stream_ nullptr bug and enable end-to-end HIP graph capture. Identical diff and commit message as PR 2a. The AMD ROCm fork

.agents/skills/cuda-attention-kernel-patterns/SKILL.md

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+---
+name: cuda-attention-kernel-patterns
+description: Patterns and pitfalls for the ONNX domain Attention operator (opset 23/24) CUDA implementation. Use when modifying the dispatch cascade in core/providers/cuda/llm/attention.cc, writing mask/bias CUDA kernels, debugging attention test routing, or adding features to the ONNX Attention op. NOT for contrib domain MultiHeadAttention/GroupQueryAttention.
+---
+
+# ONNX Domain Attention (Opset 23/24) CUDA Patterns
+
+Reusable knowledge from ONNX Attention CUDA development in ORT.
+
+> **Scope**: This skill covers the **ONNX domain** `Attention` operator (opset 23/24)
+> implemented at `core/providers/cuda/llm/attention.cc`. This is **separate from** the
+> contrib domain `MultiHeadAttention` / `GroupQueryAttention` at `contrib_ops/cuda/bert/`.
+> They share some underlying kernels (CUTLASS FMHA, Flash Attention) and infrastructure
+> (`attention_softmax.h`) but have **different dispatch logic, parameter structs, and eligibility checks**.
+>
+> - **Shared infrastructure**: CUTLASS FMHA kernel, Flash kernel, unified unfused kernel
+>   (`unfused_attention.cu`), `attention_softmax.h`, `attention_impl.cu` (contrib only)
+> - **ONNX-specific**: Dispatch cascade in `attention.cc`, `ConvertAttnMaskToBias`,
+>   `mask_filter_value` cap, parameter bridge to contrib structs, `attention_mask_impl.cu`
+> - **Contrib-specific**: Own dispatch in contrib MHA/GQA ops, uses `contrib::AttentionParameters`
+>   directly, has XQA kernel, past-present buffer sharing
+
+## 1. Runner Dispatch Cascade
+
+CUDA attention dispatches in priority order: **Flash → MEA (Memory Efficient) → Unified Unfused Attention**.
+
+```
+// onnxruntime/core/providers/cuda/llm/attention.cc — ComputeInternal()
+Flash eligible?      → RunFlashAttention()
+  ↓ no
+MEA eligible?        → RunMemoryEfficientAttention()
+  ↓ no
+Unified Unfused      → RunUnfusedAttention()
+  (handles both MHA and GQA via reshape-Q trick)
+```
+
+**Flash eligibility**: fp16/bf16 only, SM≥8.0 (Ampere+), `head_size == v_head_size`, `head_size <= 256`, no `output_qk`, `attn_mask == nullptr`. Uses `mha_fwd` / `mha_fwd_kvcache`.
+
+**MEA eligibility**: SM50+/53+/80+ by dtype, `head_size <= 1024` and divisible by 8 (enforced by `has_memory_efficient_attention`), no `output_qk`. GQA additionally requires `head_size == v_head_size` (for `LaunchUngroup`); decode also requires it (for `LaunchConcatNewToPastKV`). Bias stride must satisfy `total_sequence_length % 4 == 0`. GQA with FP32 is excluded (LaunchUngroup only has fp16/bf16 instantiations). Supports `softcap + attn_mask` — CUTLASS applies softcap before bias in kernel tiles, matching ONNX spec ordering (onnx/onnx#7867, supersedes the now-closed onnx/onnx#7865 issue).
+
+**Unified Unfused Attention**: Always available as the final fallback. Handles both MHA (`num_heads == kv_num_heads`, group=1) and GQA (`num_heads != kv_num_heads`, group>1) via a reshape-Q trick with stride-based cuBLAS batched GEMM (no K/V head replication). Uses FP32 QK scratch for precision. Supports all features:
+- softcap + attn_mask (spec-correct ordering)
+- output_qk (kQK mode: copies raw QK before softcap/mask mutations)
+- past_key + past_value with `head_size != v_head_size` (separate K/V concat)
+- causal masking, nonpad_kv_seqlen, all dtypes (fp16/bf16/fp32)
+
+## 2. CUTLASS kLog2e Overflow
+
+CUTLASS `iterative_softmax` multiplies all attention scores by `kLog2e ≈ 1.4427` internally (for `exp2f` instead of `expf`). For float/bf16:
+
+```
+mask_filter_value = std::numeric_limits<float>::lowest() ≈ -3.40e+38
+-3.40e+38 × 1.4427 ≈ -4.91e+38  →  overflows fp32  →  -inf
+```
+
+When all values become `-inf`, CUTLASS's special-case path produces `s_prime=0` → `1/s_prime=inf` → `0 × inf = NaN`.
+
+**Fix**: Cap `mask_filter_value` to `-1.0e+30f` in `ConvertAttnMaskToBias`. This value is safe: `1e30 × 1.4427 ≈ 1.4e30 << FLT_MAX`, and `exp(-1e30) ≈ 0` (effectively masked).
+
+**fp16 is NOT affected**: `lowest() = -65504`, and `-65504 × 1.4427 ≈ -94500` stays within fp32 range.
+
+This cap is ONLY applied in MEA paths. The unfused path uses `lowest()` directly (its softmax subtracts max first, avoiding overflow).
+
+**Subtlety**: When bias is present (`kSupportsBias=true`), CUTLASS pre-applies `p.scale` to QK (line 858) and uses `scaling=1.0f` in the softmax loop (line 981). So the full `kLog2e` multiplier hits the bias-dominated values — the overflow is head_size-independent. Without bias, `scaling = p.scale * kLog2e = kLog2e/sqrt(head_size)`, which is much smaller.
+
+## 3. Bias Alignment
+
+CUTLASS FMHA requires the attention bias row stride to satisfy minimum alignment. The bias has shape `[B, H, S, T]` where `T = total_sequence_length` is the row stride.
+
+```cpp
+constexpr int min_bias_align = 4;  // elements, not bytes
+if (parameters.total_sequence_length % min_bias_align != 0) {
+    mea_eligible = false;  // fall through to unfused
+}
+```
+
+**Impact on tests**: If a test uses `total_sequence_length` not divisible by 4 (e.g., past=5 + new=6 = 11), MEA is rejected and unfused handles it. To test MEA with bias, ensure `total_sequence_length % 4 == 0`.
+
+## 4. Softcap Ordering
+
+ONNX Attention opset 23/24 spec ordering (per onnx/onnx#7867, which superseded
+the now-closed onnx/onnx#7865 issue, and onnx/onnx#7913 which swapped
+`qk_matmul_output_mode` values 1 and 2 to align with the corrected pipeline):
+
+```
+scale * (Q @ K^T)        # stage 0: raw scaled QK
+    |
+softcap (if > 0)         # stage 1: tanh(qk / softcap) * softcap
+    |
++ attn_bias / + attn_mask # stage 2: additive (mask -inf survives to stage 3)
+    |
+softmax                  # stage 3
+    |
+@ V
+```
+
+`qk_matmul_output_mode` integer values follow pipeline stage order:
+0 = raw scale*QK, 1 = post-softcap (pre-mask), 2 = post-mask/bias (pre-softmax),
+3 = post-softmax.
+
+CUDA implementation status (all spec-correct):
+- **MEA (CUTLASS)**: `kernel_forward.h` applies softcap inside the score-compute
+  tile loop BEFORE `attn_bias` is added.
+- **Flash**: `mha_fwd` / `mha_fwd_kvcache` handle softcap natively; reject
+  explicit `attn_mask`, so ordering with float mask is moot for this path.
+- **Unfused**: `UnfusedSoftmaxKernel` does `QK -> scale -> softcap -> add bias -> softmax`
+  (all fused).
+
+CPU implementation status: `core/providers/cpu/llm/attention.cc::ComputeAttentionProbs<T>`
+applies softcap BEFORE the mask add (post-fix; pre-fix it inverted the order
+and leaked probability through masked positions).
+
+Why this ordering matters: a -inf in `attn_mask` must survive to softmax. If
+softcap were applied AFTER the mask-add, then `tanh(-inf/softcap) * softcap = -softcap`
+(a finite value), and softmax would assign non-zero weight to the masked
+position — leaking poison V values into the output. The CUDA-side guard tests
+at `test_onnx_attention/test_gqa.py:1501` and `:1761`, and the CPU-side guards
+at `TestONNXAttentionCPUSoftcapMaskOrdering` in the same file, exercise this
+property by combining small softcap, a -inf mask entry, and a poison V value.
+
+## 5. Grid-Stride Loops for CUDA Kernels
+
+Always cap grid size to prevent exceeding `gridDim.x` limits, and use grid-stride loops for large workloads:
+
+```cpp
+constexpr int64_t kMaxGridDimX = 65535;
+int threads = static_cast<int>(std::min(static_cast<int64_t>(max_threads_per_block), total));
+int64_t blocks = (total + threads - 1) / threads;
+unsigned int grid_size = static_cast<unsigned int>(std::min(blocks, kMaxGridDimX));
+
+MyKernel<<<grid_size, threads, 0, stream>>>(...);
+
+// Inside the kernel:
+for (int64_t idx = blockIdx.x * blockDim.x + threadIdx.x;
+     idx < total;
+     idx += static_cast<int64_t>(gridDim.x) * blockDim.x) {
+    // work
+}
+```
+
+**Never** cast `int64_t` block count directly to `unsigned int` without capping — it silently truncates.
+
+Always call `CUDA_CALL(cudaGetLastError())` after kernel launches in standalone helper functions. This is the established pattern in the file (see `ConcatPastToPresent`, `PastPresentBufferShare`).
+
+## 6. Fully-Masked Batches
+
+All-false bool masks or `seqlens_k=0` produce NaN in CUTLASS MEA.
+
+**Additive-bias path** (bool mask converted to bias): Fixed by capping `mask_filter_value` to `-1e+30f` (see section 2). CUTLASS then naturally computes uniform softmax → mean(V).
+
+**Nonpad path** (`seqlens_k=0`): CUTLASS skips all K/V positions → `s_prime=0` → NaN. Fixed by `ZeroOutputForFullyMaskedBatches` kernel which zeros output for batches where `seqlens_k[b] == 0`. Note: this produces zeros, not mean(V) — a cross-EP consistency TODO exists.
+
+**CPU/Unfused behavior**: `mask_filter_value = lowest()` (not `-inf`). All masked values are equal → `softmax(equal) = 1/N` → output = mean(V). This is the spec reference.
+
+## 7. Test Runner Targeting
+
+Use `ScopedEnvironmentVariables` to force specific CUDA runners:
+
+```cpp
+// Force MEA (disable Flash)
+ScopedEnvironmentVariables scoped_env({
+    {"ORT_DISABLE_FLASH_ATTENTION", "1"},
+});
+
+// Force Unfused (disable both Flash and MEA)
+ScopedEnvironmentVariables scoped_env({
+    {"ORT_DISABLE_FLASH_ATTENTION", "1"},
+    {"ORT_DISABLE_MEMORY_EFFICIENT_ATTENTION", "1"},
+});
+```
+
+**Always verify which runner a test actually hits.** A test designed for MEA may silently fall to unfused if:
+- `total_sequence_length % 4 != 0` (bias alignment)
+- `head_size != v_head_size` (decode path)
+- fp32 dtype with GQA (LaunchUngroup fp16/bf16 only)
+- fp32 dtype on SM < 80
+
+Enable verbose logging to confirm: `LOGS_DEFAULT(VERBOSE) << "ONNX Attention: using ..."`.
+
+## 8. Cross-EP Consistency
+
+CPU is the spec reference implementation. CUDA outputs should match CPU for all valid inputs.
+
+- CPU uses `mask_filter_value = std::numeric_limits<T>::lowest()` (finite, not `-inf`)
+- CPU softmax: subtract-max-first → works correctly with extreme finite values
+- CPU handles fully-masked batches naturally (uniform softmax → mean(V))
+
+Run tests with `disable_cpu=false` to always validate against CPU. The C++ test framework (`RunTest4D`) supports `disable_cpu`, `disable_cuda`, `disable_dml` flags.
+
+## 9. File Locations
+
+### ONNX Domain (this op's code)
+
+| File | Purpose |
+|------|---------|
+| `core/providers/cuda/llm/attention.cc` | ONNX Attention CUDA dispatch: Flash/MEA/Unfused cascade, `ConvertAttnMaskToBias`, parameter setup |
+| `core/providers/cuda/llm/attention_mask_impl.cu` | ONNX-specific mask/bias CUDA kernels: bool→bias, nonpad→seqlens_k, ZeroOutput, bias composition |
+| `core/providers/cuda/llm/attention_mask_impl.h` | Declarations for ONNX mask/bias kernels |
+| `core/providers/cpu/llm/attention.cc` | CPU reference implementation (ONNX domain) |
+| `core/providers/cpu/llm/attention_helper.h` | ONNX parameter validation and shape computation |
+| `test/providers/cpu/llm/attention_op_test.cc` | C++ attention tests (all EPs) |
+| `test/python/transformers/test_onnx_attention/test_mha.py` | Python parity tests |
+| `test/python/transformers/test_onnx_attention/common.py` | Python test utilities and reference `attention_ref()` |
+
+### Shared Infrastructure (used by both ONNX and contrib ops)
+
+| File | Purpose |
+|------|---------|
+| `contrib_ops/cuda/bert/unfused_attention.cu` | Unified unfused attention: QK GEMM (FP32), fused softmax kernel (scale+softcap+bias+causal), V GEMM. Handles MHA and GQA. |
+| `contrib_ops/cuda/bert/unfused_attention.h` | `UnfusedAttentionParams`, `LaunchUnfusedAttention`, workspace size |
+| `contrib_ops/cuda/bert/attention_impl.cu` | Legacy unfused `QkvToContext` (contrib MHA only). Also `ApplySoftcap`, `ConcatPastToPresent` |
+| `contrib_ops/cuda/bert/attention_softmax.h` | CUDA softmax kernels (`ComputeSoftmax`, `ComputeSoftmaxWithRawMask`) — used by legacy contrib path |
+| `contrib_ops/cuda/bert/cutlass_fmha/` | CUTLASS FMHA (Memory Efficient Attention) kernels |
+| `contrib_ops/cuda/bert/flash_attention/` | Flash Attention kernels |
+
+### Contrib Domain (separate ops, NOT covered by this skill)
+
+| File | Purpose |
+|------|---------|
+| `contrib_ops/cuda/bert/multihead_attention.cu` | Contrib `MultiHeadAttention` — own dispatch, uses `contrib::AttentionParameters` directly |
+| `contrib_ops/cuda/bert/group_query_attention.cu` | Contrib `GroupQueryAttention` — has XQA kernel, past-present buffer sharing |
+
+## 10. Parameter Bridge (ONNX → Contrib)
+
+The ONNX Attention op uses `attention_helper::AttentionParameters` (in `core/providers/cpu/llm/attention_parameters.h`). The unified unfused kernel (`LaunchUnfusedAttention`) uses its own `UnfusedAttentionParams` struct populated directly from ONNX parameters in `RunUnfusedAttention`.
+
+The contrib `QkvToContext` function (used by contrib MHA, NOT by ONNX Attention) uses `contrib::AttentionParameters`. ONNX Attention does **not** bridge to `contrib::AttentionParameters` — it routes through the unified unfused kernel instead.
+
+## 11. Causal Alignment
+
+The ONNX spec defines two causal alignment modes based on where query positions sit in the full attention matrix:
+
+- **Upper-left**: `q_i` attends to `kv[0..i]`. Query positions start at 0 in the full matrix.
+- **Lower-right**: `q_i` attends to `kv[kv_len - q_len + i..kv_len - 1]`. Query positions are at the end.
+
+**ONNX spec rule**: `is_causal=1` always means upper-left in the full matrix. When `past_key` provides context, `past_sequence_length` shifts the query start position forward — the resulting `[S_q × total_kv]` sub-matrix effectively has lower-right alignment.
+
+### Per-kernel behavior
+
+| Kernel | Alignment | Mechanism |
+|--------|-----------|-----------|
+| **Flash** | Lower-right only | `is_causal` flag → `seqlen_k - seqlen_q` offset in kernel. No top-left option. |
+| **MEA (CUTLASS)** | Both | `causal_from_top_left` flag in `MemoryEfficientAttentionParams`. `true` → `CausalFromTopLeft` (offset=0). `false` → `CausalFromBottomRight` (offset = num_keys - num_queries). |
+| **Unfused** | Both | `past_kv_length` param. `0` → upper-left. `total_kv - S_q` → lower-right. |
+
+### Dispatch logic in attention.cc
+
+```cpp
+// Flash cannot do upper-left → guarded by causal_cross_no_past
+bool causal_cross_no_past = parameters.is_causal &&
+    parameters.q_sequence_length != parameters.total_sequence_length &&
+    parameters.past_sequence_length == 0;
+
+// Flash: skip when causal_cross_no_past (no top-left support)
+// MEA: NOT skipped — handles it via causal_from_top_left = (past_sequence_length == 0)
+// Unfused: always correct via past_kv_length = parameters.past_sequence_length
+```
+
+### When S_q == S_kv
+
+Upper-left and lower-right produce **identical** results when `S_q == S_kv` (the offset is 0 either way). The alignment distinction only matters for cross-attention shapes (`S_q != S_kv`).
+
+### TensorScatter decode (opset 24 external KV cache)
+
+TensorScatter manages KV cache externally — `past_key` is nullptr but K/V already contain the full sequence. Per the ONNX spec, `is_causal` with `S_q != S_kv` and no `past_key` means upper-left (q[0] sees only kv[0]), which is **not meaningful for decode**.
+
+**Correct pattern**: TensorScatter decode must use `is_causal=0` and rely on `nonpad_kv_seqlen` to bound the active KV range. Models using `is_causal=1` with TensorScatter decode have a spec-invalid combination.

.agents/skills/ort-build/SKILL.md

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+---
+name: ort-build
+description: Build ONNX Runtime from source. Use this skill when asked to build, compile, or generate CMake files for ONNX Runtime.
+---
+
+# Building ONNX Runtime
+
+The build scripts `build.sh` (Linux/macOS) and `build.bat` (Windows) delegate to `tools/ci_build/build.py`.
+
+## Build phases
+
+Three phases, controlled by flags:
+
+- `--update` — generate CMake build files
+- `--build` — compile (add `--parallel` to speed this up)
+- `--test` — run tests
+
+For native builds, if none are specified (and `--skip_tests` is not passed), **all three run by default**. For cross-compiled builds, the default is `--update` + `--build` only.
+
+### When to use `--update`
+
+You need `--update` when:
+- First build in a new build directory
+- New source files are added (some CMake targets use glob patterns, others use explicit file lists — re-run to pick up new files either way)
+- CMake configuration changes (new flags, updated CMakeLists.txt)
+
+You do **not** need `--update` when only modifying existing `.cc`/`.h` files — just use `--build`. Skipping it saves time.
+
+## Examples
+
+```bash
+# Full build (update + build + test)
+./build.sh --config Release --parallel
+.\build.bat --config Release --parallel     # Windows
+
+# Just regenerate CMake files
+./build.sh --config Release --update
+
+# Just compile (skip CMake regeneration and tests)
+./build.sh --config Release --build --parallel
+
+# Just run tests (after a prior build)
+./build.sh --config Release --test
+
+# Build with CUDA execution provider
+./build.sh --config Release --parallel --use_cuda --cuda_home /usr/local/cuda --cudnn_home /usr/local/cuda
+
+# Build Python wheel
+./build.sh --config Release --parallel --build_wheel
+
+# Build a specific CMake target (much faster than a full build)
+./build.sh --config Release --build --parallel --target onnxruntime_common
+
+# Load flags from an option file (one flag per line)
+./build.sh "@./custom_options.opt" --build --parallel
+```
+
+## Key flags
+
+| Flag | Description |
+|------|-------------|
+| `--config` | `Debug`, `MinSizeRel`, `Release`, or `RelWithDebInfo` |
+| `--parallel` | Enable parallel compilation (recommended) |
+| `--skip_tests` | Skip running tests after build |
+| `--build_wheel` | Build the Python wheel package |
+| `--use_cuda` | Enable CUDA EP. Requires `--cuda_home`/`--cudnn_home` or `CUDA_HOME`/`CUDNN_HOME` env vars. On Windows, only `cuda_home`/`CUDA_HOME` is validated. |
+| `--target T` | Build a specific CMake target (requires `--build`; e.g., `onnxruntime_common`, `onnxruntime_test_all`) |
+| `--build_dir` | Build output directory |
+
+## Build output path
+
+Default: `build/<Platform>/<Config>/` where Platform is `Linux`, `MacOS`, or `Windows`.
+
+With Visual Studio multi-config generators, the config name appears twice (e.g., `build/Windows/Release/Release/`).
+
+It may be customized with `--build_dir`.
+
+## Agent tips
+
+- **Activate a Python virtual environment** before building. See "Python > Virtual environment" in `AGENTS.md`.
+- **Prefer `python tools/ci_build/build.py` directly** over `build.bat`/`build.sh` when redirecting output. The `.bat` wrapper runs in `cmd.exe`, which breaks PowerShell redirection.
+- **Redirect output to a file** (e.g., `> build_log.txt 2>&1`). Build output is large and will overflow terminal buffers.
+- **Run builds in the background** — a full build can take tens of minutes to over an hour. Poll the log for `"Build complete"` or errors.
+- **Use `--parallel`** by default unless the user says otherwise.
+- Ask the user what they want to build (config, execution providers, wheel, etc.) if not clear from their prompt.