cuda-attention-kernel-patterns

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)

Eligibility anchors (symbols are stable; line numbers as of cc34d0b914)

head_size=512 IS routed to MEA, but its MEA kernel is NOT portably launchable — so it is not a robust test probe. By the predicate, 512 > 256 fails Flash and 512 ≤ 1024 with 512 & 7 == 0 passes the MEA predicate, so dispatch selects MEA — but the MEA eligibility check (memory_efficient_attention.h:68-73) gates only on SM + head&7==0 + head<=1024, with no shared-memory check. For head_size=512 FP16 the CUTLASS MEA SharedStorage exceeds the dynamic-smem opt-in cap on capacity-limited arches (sm86 ~99KB, sm80 ~163KB, sm90 ~227KB — non-monotonic, no clean SM-version guard). fmha_launch_template.h calls cudaFuncSetAttribute(..., cudaFuncAttributeMaxDynamicSharedMemorySize, ...) but ignores its return value and launches anyway, so on sm86 the kernel dies at launch with CUDA failure 1: invalid argument — there is no fallback to unfused (live bug #28388; its fix PR #28383 was never merged). So head_size=512's MEA kernel launches only on large-smem arches like sm90/H100.

To force the MEA path portably in a test, use ORT_DISABLE_FLASH_ATTENTION=1 with a small head_size (e.g. 64) whose SharedStorage fits every target arch — NOT head_size=512. Also guard with SKIP_IF_MEA_NOT_COMPILED (see §7) so a MEA-OFF build SKIPs rather than false-greens via the (correct) unfused fallback.

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.

QUICK_BUILD caveat (false hypothesis trap). (General principle — build flags can silently reroute kernel dispatch — lives in the ort-build skill, "Agent tips". The attention-specific instance:) With onnxruntime_QUICK_BUILD=ON (-DORT_QUICK_BUILD), Flash is compiled for head_dim 128 only: flash_api.h:147 is_supported<T> returns false for head_size != 128, and static_switch.h:80 HEADDIM_SWITCH only instantiates kHeadDim=128. So under QUICK_BUILD nearly every shape routes to MEA, not FlashAttention-2. If a head_size!=128 test "fails only on some SM", suspect MEA (CUTLASS, arch-independent), NOT a Flash/FA2 hardware bug. head_size=512 is routed to MEA in all MEA-enabled builds (Flash caps at 256), but its MEA kernel fails to launch on small-smem GPUs — see the head_size=512 caveat above (#28388).

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.

constexpr int min_bias_align = 4;  // elements, not bytes
if (parameters.total_sequence_length 

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):

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:

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 Rows and Batches

All-false bool masks, an all--inf attn_mask row, or a causal/nonpad frontier with no allowed key produce NaN in CUTLASS MEA (the uniform/empty softmax degenerates: s_prime=0 → 1/s_prime=inf → 0 × inf = NaN). Per onnx/onnx#8068 (Bug-2), a fully-masked query row — one with no key allowed by the composed causal + nonpad + mask constraints — must output a zero row (Y = 0), not mean-of-V.

This Y = 0 behavior is now consistent on BOTH EPs (the earlier mean(V)-vs-zero cross-EP divergence is RESOLVED — there is no longer an open TODO here):

• CUDA: ZeroFullyMaskedRowsKernel (in attention_mask_impl.cu) runs after the MEA/CUTLASS output and zeros each fully-masked row with a select (not multiply, so 0 @ V = 0 even when V is poisoned). It detects a fully-masked row with an exact per-key predicate (within the causal/nonpad frontier AND the additive-bias slot is above the mask sentinel), matching the onnx#8068 isneginf-of-row-max reference. A finite (even very negative) user bias is not the sentinel, so its key stays unmasked and the row is left untouched.
• CPU: core/providers/cpu/llm/attention.cc applies the same Bug-2 guard — after softmax it zeros any row whose composed frontier admitted no unmasked key.

Additive-bias path (bool mask converted to bias): mask_filter_value is capped to -1e+30f (see section 2) so CUTLASS does not overflow to NaN; a row that is nonetheless fully masked is then zeroed by the per-row guard above.

Whole-batch empty (seqlens_k[b] == 0): the structural case where an entire batch has zero valid keys is additionally handled by ZeroOutputForFullyMaskedBatches, which zeros that batch's output. (The per-row guard covers the finer-grained case where only some query rows are fully masked.)

qk_matmul_output_mode (mode 3 / post-softmax debug output): for a fully-masked row the mode-3 snapshot is mandated to be 0 (zero row), consistent with Y = 0, per the onnx#8068 SIG decision (this superseded the earlier "unspecified" proposal). The CPU post-softmax snapshot is taken after the row-zeroing guard — matching the onnx reference and the v23/v24 function bodies, where the guard runs before the mode-3 capture — so the debug tensor reflects the same zero row as the output. Note this mode-3=0 behavior is served by the CPU path: CUDA qk_matmul_output_mode beyond kNone/kQK (i.e. kPostSoftCap/kPostMaskBias/kPostSoftMax) returns NOT_IMPLEMENTED (attention.cc), so an agent must not assume CUDA produces mode-3=0.

7. Test Runner Targeting

Use ScopedEnvironmentVariables to force specific CUDA runners:

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 ...".

SKIP_IF_MEA_NOT_COMPILED is a local gtest macro (defined in test/providers/cpu/llm/attention_op_test.cc) that GTEST_SKIPs — rather than silently passes — when USE_MEMORY_EFFICIENT_ATTENTION is OFF, so an MEA-targeted test cannot false-green via the (correct) unfused fallback. Use it in any test that must prove the MEA path ran (see the ort-test skill → "Verify which path/kernel actually executed").

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 zeros fully-masked query rows (onnx#8068 Bug-2 guard) — output Y = 0, matching CUDA's ZeroFullyMaskedRowsKernel. (Earlier docs claimed CPU produced mean(V) here; that divergence is resolved — both EPs now emit a zero row.)

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)

Shared Infrastructure (used by both ONNX and contrib ops)

Contrib Domain (separate ops, NOT covered by this skill)

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 (a.k.a. top-left): q_i attends to kv[0..i]. Query positions start at 0 in the full matrix.
• Bottom-right (a.k.a. lower-right): q_i attends to kv[0 .. kv_len - q_len + i] — i.e. keys j with j <= i + offset, where offset = kv_len - q_len (clamped >= 0). The causal diagonal is anchored at the end of the key axis. This is the term onnx/onnx#8068 uses; kernel flags spell it CausalFromBottomRight.

ONNX spec rule: causal alignment depends on how the KV context is supplied.

• Internal cache / no cache (past_key, or plain self-attention): is_causal=1 is 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 is effectively bottom-right.
• External / static cache (nonpad_kv_seqlen, no past_key, opset 24): per onnx/onnx#8068, is_causal=1 uses bottom-right (offset-aware) alignment — query in-block index i attends key j iff j <= i + offset[b], where offset[b] = nonpad_kv_seqlen[b] - q_sequence_length (clamped to >= 0).

Per-kernel behavior

Dispatch logic in attention.cc

When S_q == S_kv

Upper-left and bottom-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, with nonpad_kv_seqlen[b] giving each batch's valid (non-padded) key count. Per onnx/onnx#8068, is_causal=1 with an external/static KV cache (no past_key) uses bottom-right (offset-aware) alignment: query in-block index i attends key j iff j <= i + offset[b], where offset[b] = nonpad_kv_seqlen[b] - q_sequence_length (clamped to >= 0). For decode (q_sequence_length == 1) the single query row therefore attends all nonpad_kv_seqlen[b] valid keys — the meaningful, spec-correct result (not the degenerate "q[0] sees only kv[0]" of upper-left).

Correct pattern: is_causal=1 with TensorScatter + nonpad_kv_seqlen (no past_key) is valid and supported for both decode and continued-prefill — it yields bottom-right causal attention bounded by the per-batch valid-key count. (is_causal=0 is also valid where a model wants no causal masking.) The earlier is_causal=1 NOT_IMPLEMENTED reject was removed in the onnx#8068 alignment work; the only still-invalid combination is nonpad_kv_seqlen together with past_key (mutually exclusive internal-vs-external cache, enforced at validation in attention_helper.h).

12. Signed Offsets in CUTLASS FMHA (uint wrap hazard)

This is a specific instance of the general signed-vs-unsigned wrap bug class — see AGENTS.md → "Signed vs unsigned on negative-capable differences" for the principle. Below are the attention-specific fix sites in cutlass_fmha/kernel_forward.h. See §11 for what the offset means (bottom-right alignment); this section is purely the signed-arithmetic hazard.

Any FMHA offset computed as a difference of counts — canonically causal_diagonal_offset = num_keys - num_queries (CausalFromBottomRight) — is negative whenever num_keys < num_queries (cross-attention / KV-trimmed / nonpad_kv_seqlen[b] < q_len, onnx#8068 / ORT #28904). It must be stored and compared as int32_t; a uint32_t wraps the negative value to ~4.29e9 (0xFFFFFFFE), the causal-mask guard min(iter_key_start + kKeysPerBlock, num_keys) >= query_start + offset becomes permanently false, the per-element causal mask is silently skipped, and boundary query rows over-attend one extra key.

Fix sites in cutlass_fmha/kernel_forward.h (symbols are stable; lines as of cc34d0b914)

Rules when editing kernel_forward.h (or any FMHA kernel)

• Keep causal_diagonal_offset int32_t.
• query_start in the iteration kernels is uint32_t — static_cast<int32_t>(query_start) before adding the offset in ANY relational guard.
• The same hazard is dormant but real at the window_size > 0 guard: harden it the same way even though opset-24 Attention currently pins window=-1 (a future sliding-window / KV-trim caller could combine window_size>0 with a negative offset).
• Tests that exercise this need a negative offset: num_keys < num_queries. Force the MEA path portably with ORT_DISABLE_FLASH_ATTENTION=1 + a small head_size (e.g. 64) — not head_size=512, whose MEA launch is arch-fragile on small-smem GPUs (#28388, see §1). The regression tests live in test/providers/cpu/llm/attention_op_test.cc (Attention_Causal_NonPadKVSeqLen_MEA_*), guarded by SKIP_IF_MEA_NOT_COMPILED.*