[sfc-gh-goliaro]

[Perf][FA4] Warm up dense (paged) FA4 attention kernels

Repo: vllm-project/vllm · Branch: fa4-dense-cutedsl-warmup

Summary

The CuTeDSL warmup path (register_cutedsl_warmup_provider) currently only warms the
FA4 MLA prefill kernel. Standard GQA models on the FLASH_ATTN backend
(FlashAttentionForwardSm100) therefore JIT-compile the dense prefill specialization
(q_stage=2, paged, seqused_k) on the first serving request — a large one-time latency
spike (~8 s) that jit_monitor explicitly flags as
CuTeDSL JIT compilation during inference: FlashAttentionForwardSm100.

This registers the dense FA4 backend (FlashAttentionMetadataBuilder) as a CuTeDSL warmup
provider (new vllm/model_executor/warmup/fa4_dense_config.py) and precompiles the dense
decode and prefill specializations at startup, mirroring the existing MLA-prefill provider
in mla/prefill/flash_attn.py.

• Restricted to the vLLM-compile path (CompilationMode.VLLM_COMPILE), where query and
  paged-KV tensors have the contiguous/paged strides the compiled kernel bakes. Under
  enforce_eager, tensors can be strided views, so warmup is skipped and eager falls back
  to stock in-serving JIT (unchanged).
• Emits fp8-descale variants when the KV cache is fp8.

Dependency (why this is a draft)

Requires the paged compile-spec support in vllm-flash-attention:
vllm-project/flash-attention#158 (compile_flash_attn_varlen_func_from_specs:
seqused_k/page_table/k_stride/descale). The cmake GIT_TAG here points at that
PR's commit; it must be updated to the merged flash-attn commit before this can build
in CI. Kept as a draft until #158 lands.

Not a duplicate

• [Feat][1/N] CuTeDSL warmup infrastructure, FA4 MLA #46182 (merged) and [1/N][Feat][Perf] Add new warmup infrastructure for JITs #47451 (open) add the CuTeDSL warmup infrastructure and apply it
  to FA4 MLA prefill only. Neither touches the dense backend
  (vllm/v1/attention/backends/flash_attn.py) nor warms FlashAttentionForwardSm100 for
  standard GQA models. This builds on the merged [Feat][1/N] CuTeDSL warmup infrastructure, FA4 MLA #46182 provider registry and fills that gap.
• [Feat] Add runtime monitor for post-warmup CuTeDSL compilation #46167 adds the runtime monitor for post-warmup CuTeDSL compiles (the detector), not the
  dense fix. Searched open PRs/issues for dense-FA4 / FlashAttentionForwardSm100 warmup —
  none found.

Tests run + results

Hardware: 1× B200 (SM100), vLLM built from this branch. Throughput via
vllm bench throughput --attention-backend FLASH_ATTN --dataset-name random --input-len 1024 --output-len 1024 --num-prompts 256, cold CuTeDSL cache, VLLM_PLUGINS="".

Model	Metric	Before (stock main)	After (this PR)	FlashInfer ref
Llama-3.1-8B (bf16)	output tok/s	19,935	54,957 (2.75×)	61,320
Llama-3.1-8B (bf16)	in-serving CuTeDSL JIT	1 (~8 s spike)	0	0
Qwen3-VL-8B-Instruct-FP8	output tok/s	21,875	64,567 (2.95×)	73,984
Qwen3-VL-8B-Instruct-FP8	in-serving CuTeDSL JIT	1	0	0

• Single-pass FA4-vs-FlashInfer throughput gap: 3.0× → ~1.12×.
• jit_monitor: 0 CuTeDSL JIT compilation during inference during serving (was 1, the
  dense prefill kernel).
• Eager fallback: --enforce-eager runs to completion (rc=0), warmup skipped — no regression.

Model evaluation (output correctness)

Greedy generation (temperature=0) with FLASH_ATTN vs FLASHINFER on Llama-3.1-8B is
byte-identical across test prompts (e.g. "17 × 23" → "391", primes → "2, 3, 5, 7, 11").
Warmup only pre-populates the same compile_key the engine would JIT, so numerics are
unchanged.

Residual warmed gap — out of scope for this PR (kernel, not warmup)

After cold-start is eliminated, a ~12% gap remains on decode-heavy workloads. Isolated
attention-kernel microbenchmarks (FA4 vs FlashInfer trtllm, identical paged inputs, outputs
validated to match, CUDA-event timed) attribute it entirely to the FA4 decode kernel;
prefill is at parity or faster:

Phase	Scenario (heads 32/8, hd128, bf16, paged)	FA4 vs FlashInfer
decode	batch 64, ctx 1024	1.55× slower
decode	batch 256, ctx 1024	1.59× slower
decode	batch 256, ctx 4096	1.38× slower
prefill	batch 1, seq 4096	0.80× (FA4 faster)
prefill	batch 4, seq 2048	0.97× (parity)
prefill	batch 8, seq 1024	1.02× (parity)

CUDA graphs (which vLLM already uses for FA4 decode as FULL graphs) eliminate FA4's per-call
host overhead, so at serving batch sizes these pure-kernel ratios drive the end-to-end gap.
Closing it would require FA4 decode-kernel work (a dedicated q_stage=1 kernel / better
split-KV vs FlashInfer's trtllm-gen decode cubin) — a separate effort, not this PR.

Reviewer notes

• ruff check / ruff format clean on the changed files; run pre-commit before merging.
• Scope: this PR closes the cold-start gap only; the residual decode-kernel gap above is
  intentionally not addressed here.

🤖 AI-assisted change; a human must review every line and run the tests/evals before submitting.

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Claude Code Review

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[LopezCastroRoberto]

Thanks for extending the FA4 warmup coverage. Great work, @sfc-gh-goliaro!

Left some comments.

[LopezCastroRoberto]

Since you’ve identified the lowest-level compile keys here already, could we use q_stage instead of higher-level variables such as prefill size? e.g., for FA4 MLA

vllm/vllm/model_executor/warmup/fa4_cutedsl_config.py

Line 147 in 68ea76e

q_stage1_q = 1

In general, I think we should identify the atomic factors that determine different compile keys, and it looks like you've already identified most of them. I would use the atomic ones whenever possible.

[sfc-gh-goliaro]

Done. Now driven by the atomic factor: q_stage=2 via max_seqlen_q = tile_m + 1 (SM100 tile 128), q_stage=1 via max_seqlen_q=1. This is the same pattern as the MLA config.

[LopezCastroRoberto]

Same thing for this one, i.e., ideally we should try to find the atomic factors that trigger a different compile-key. Probably batch size is now the fundamental factor?

[sfc-gh-goliaro]

Probably batch size is now the fundamental factor?

Hmm not batch, batch isn't in compile_key. The atomic decode factor is is_split_kv (num_splits>1); batch only flips it through FA4's split heuristic, and serving passes num_splits=0. Now enumerated directly via num_splits $\in$ {1,2} , which also adds the split-KV decode kernel the old num_splits=1-only version missed.

[LopezCastroRoberto]

Ideally, this should use current_platform, not torch.cuda directly. See https://github.com/vllm-project/vllm/pull/46182/changes#r3501268810

[sfc-gh-goliaro]

Done! now using current_platform.get_device_capability()

[LopezCastroRoberto]

What about SM90 and SM120? What are the compile-keys in that case?

[sfc-gh-goliaro]

Added an arch-family helper. Per the FA4 selector: q_stage=2 exists only on SM100/SM110 (SM90 & SM120 are always q_stage=1); split-KV is rejected on SM120; fp8 is SM100-only. So we emit: SM90 {q1}x{split,no-split}, SM100/110 {q1,q2}x{split,no-split}, SM120 {q1}. Unknown arches skip warmup (fall back to JIT).

[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @sfc-gh-goliaro.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

[sfc-gh-goliaro]

Merged the changes from main and addressed @LopezCastroRoberto's comments: the warmup now enumerates atomic compile_key factors (q_stage, is_split_kv) directly, uses current_platform, and covers all FA4 arch families (SM90/SM100-110/SM120).

Dropped the fp8/descale branch: the FA4 fp8 kernel writes a bf16 output, but compile_flash_attn_varlen_func_from_specs hardcodes the compile-only output dtype to q_dtype (no out_dtype), so an fp8-in/bf16-out spec can't be built. fp8-KV dense serving falls back to in-serving JIT until that helper gains an out_dtype.

Validated on B200 (SM100): all 4 bf16 dense specs JIT-compile and select the intended q_stage/is_split_kv/paged/seqused_k kernels.

Still draft as cmake pin stays at the vllm-project/flash-attention#158 commit until it lands in main.

[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @sfc-gh-goliaro.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

[LopezCastroRoberto]

Thanks a lot for doing this, @sfc-gh-goliaro!

Would you mind moving this to the warmup contract we're trying to establish? See: #47451. Specifically vllm/v1/attention/backends/mla/prefill/flash_attn.py. RFC here: #47456

I think it should be very straightforward in your case, since you identified the core compile-keys already.

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