Enable cuDNN SDPA for contrib Attention

[mastryukov1990]

Description

Enable the existing cuDNN SDPA runner for the CUDA implementation of the contrib Attention operator when:

• the input type is FP16 or BF16;
• the mask is absent or is a compact 1D key sequence-length mask;
• past/present KV cache inputs are not used; and
• cuDNN reports support for the concrete attention shape.

On Hopper and newer GPUs, cuDNN SDPA is auto-preferred consistently with MultiHeadAttention. Explicit kernel selection continues to be respected. Raw attention masks and cache paths remain on their existing kernels.

The new test forces cuDNN SDPA and uses a partial sequence-length mask, verifying that the masked key/value token is excluded from both output positions.

Motivation and Context

Transformer optimizer fusions can produce com.microsoft::Attention nodes with compact sequence-length masks. Although cuDNN SDPA was enabled for MultiHeadAttention and GroupQueryAttention in #28849, the legacy fused Attention path did not dispatch to the shared cuDNN runner. On an H100, all 24 attention nodes in a BGE encoder consequently selected the math kernel.

This change reuses the existing cuDNN implementation and eligibility checks rather than adding another attention kernel.

Performance

Exact A/B comparison using the same patched build of current main, with cuDNN auto-dispatch disabled for the baseline via ORT_ENABLE_CUDNN_FLASH_ATTENTION=0:

Batch × sequence	Baseline rows/s	cuDNN SDPA rows/s	Speedup
65 × 696	156.80	546.11	3.48×
12 × 1600	50.50	209.29	4.14×
12 × 3072	20.03	93.43	4.67×

Environment: NVIDIA H100 (SM90), CUDA 12.8, cuDNN frontend 1.24, FP16 BGE encoder with right-padded batches. Debug dispatch confirmed CUDNN_FLASH_ATTENTION for 24/24 fused Attention nodes.

Numerical comparison against the same build with cuDNN disabled, over sampled embeddings:

• maximum absolute difference: 0.001465;
• minimum cosine similarity: 0.999955.

Validation

• Full CUDA build completed successfully.
• ContribOpAttentionTest.CudnnFlashAttentionWithKeySequenceLengthMask: passed and reported SdpaKernel=CUDNN_FLASH_ATTENTION.
• ContribOpAttentionTest.*: 46/46 passed (one pre-existing disabled test).
• clang-format --dry-run --Werror on all changed files.
• git diff --check.

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[mastryukov1990]

@tianleiwu Could you please take a look and approve the pending GitHub Actions workflows when you have a chance? This PR extends the cuDNN SDPA integration from #28849 to the fused contrib Attention operator with compact 1D sequence-length masks. I validated the dispatch on H100 (24/24 BGE attention nodes selected cuDNN), the full ContribOpAttentionTest.* suite passed (46/46), and the exact same-build A/B showed a 3.48–4.67× throughput improvement. Thanks!

[Copilot]

Pull request overview

This PR extends the CUDA implementation of the contrib com.microsoft::Attention operator to reuse the existing cuDNN SDPA (Flash Attention) runner when eligible (FP16/BF16, no raw mask, no KV cache, and cuDNN reports support), aligning its dispatch behavior more closely with existing MultiHeadAttention/GroupQueryAttention integration.

Changes:

• Add cuDNN SDPA enable/auto-enable flags to the contrib CUDA Attention kernel and compute cuDNN eligibility early in dispatch.
• Route eligible contrib Attention executions to AttentionKernel_CudnnFlashAttention, including temp-space allocator wiring for the cuDNN runner.
• Add a new contrib Attention test using a 1D key sequence-length mask intended to exercise the cuDNN SDPA path.

Reviewed changes

Copilot reviewed 3 out of 3 changed files in this pull request and generated 1 comment.

File	Description
onnxruntime/test/contrib_ops/attention_op_test.cc	Adds a new CUDA-focused test case using a 1D key sequence-length mask and env-var kernel selection controls.
onnxruntime/contrib_ops/cuda/bert/attention.h	Adds member flags to track explicit and auto cuDNN SDPA enablement for contrib Attention.
onnxruntime/contrib_ops/cuda/bert/attention.cc	Implements cuDNN SDPA eligibility checks and dispatch integration for contrib Attention, and propagates the selection into workspace sizing and execution data.

[mastryukov1990]

@tianleiwu The follow-up commit 55624f9 addresses the review feedback by asserting the actual cuDNN kernel selection. I validated it on H100: the targeted test passed (1/1) and the full ContribOpAttentionTest.* suite passed (46/46). All 31 workflows are awaiting approval again after the push. Could you please approve/start them when you have a chance? Thanks!

[mastryukov1990]

The Linux TensorRT CI failure appears unrelated to this change. The Build Linux TensorRT x64 Release job failed while the Gradle wrapper was downloading gradle-8.7-bin.zip from services.gradle.org:

java.net.SocketTimeoutException: Connect timed out

The sibling TensorRT CUDA Minimal build completed successfully, so this looks like a transient network/infrastructure failure. A rerun of the failed job should be sufficient.

Failed job: https://github.com/microsoft/onnxruntime/actions/runs/29374987406/job/87230486564

[mastryukov1990]

Sorry for tagging you again, @tianleiwu. The failed Linux TensorRT CI job appears to be a transient Gradle download timeout. Could you please rerun the failed job when you have a chance? Thank you!

[tianleiwu]

Sorry for tagging you again, @tianleiwu. The failed Linux TensorRT CI job appears to be a transient Gradle download timeout. Could you please rerun the failed job when you have a chance? Thank you!

@mastryukov1990, CI pipeline has some issues right now (some jobs are queued forever). I could help trigger CIs later.

[mastryukov1990]

Hi @tianleiwu, sorry to follow up again. The PR currently has 20 successful workflows and 11 cancelled ones (mostly Windows workflows, plus ONNX Runtime CUDA Builds); none are queued or running now. Is the CI infrastructure healthy enough to rerun the cancelled workflows at this point, or should we wait a bit longer? If it is safe now, could you please trigger them? Thank you!

[tianleiwu]

@mastryukov1990, please merge latest main branch, which has a commit for CI pool change.

[mastryukov1990]

@tianleiwu Done — I merged the latest main (f05b21861c) into the PR branch in 9ec8479931. The merge was conflict-free, and the PR diff remains limited to the original three files. The new workflows are now awaiting approval. Thank you!

[tianleiwu]

Reviewed the cuDNN SDPA enablement for the fused contrib Attention kernel. The change reuses the shared cudnn_sdpa::is_stable/is_supported predicate and AttentionKernel_CudnnFlashAttention dispatch already used by MultiHeadAttention, so no new kernel is introduced.

Verified against the surrounding paths:

• Mutual exclusion preserved — cudnn_sdpa_supported correctly gates off flash, the fused TRT runner, and memory-efficient attention; the QkvToContext <= 1 fused-kernel assert still holds.
• QKV format is valid — with cuDNN selected, PrepareQkv_Attention falls into the unfused branch producing Q_K_V_BNSH, which CudnnFlashAttention/build_graph explicitly accept and stride correctly.
• Workspace sizing is correct — GetAttentionWorkspaceSize(..., cudnn_sdpa_supported, false) returns the full qkv_bytes transpose buffer (previously this arg was hard-coded false).
• Safe default — AttentionData::kernel_type defaults to AttentionKernel_Default, so non-eligible calls keep their existing dispatch.
• Test validates both kernel selection (SdpaKernel=CUDNN_FLASH_ATTENTION) and numerics (masked key/value token excluded from both outputs), and skips cleanly when cuDNN is unavailable.

No blocking issues. One maintainability suggestion left inline regarding the implicit attention_bias safety invariant.

[tianleiwu]

Unlike the flash path (nullptr == attention_bias) and unlike MultiHeadAttention (which adds an explicit cudnn_sdpa_bias_ok term), this predicate does not mention attention_bias. An Attention node with an attention_bias input plus no/1D mask will now newly dispatch to cuDNN and forward the bias.

This is correct here only because the fused Attention op is always self-attention with no past/present, so sequence_length == total_sequence_length and cuDNN never selects the bottom-right causal alignment that is incompatible with bias (exactly the case MHA's cudnn_sdpa_bias_ok protects against). Broadcast dims are also set by CheckInputs, so data flow is fine.

The safety relies on an implicit, untested invariant. Consider a short comment documenting why bias is safe here (s_q == s_kv ⇒ TOP_LEFT causal), and/or a test covering the attention_bias + cuDNN combination, so a future change that relaxes the no-past/present restriction doesn't silently route an unsupported bias + bottom-right case to cuDNN.