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Large QSA sparse prefill launches over BF16 KV caches on SM90 run a CUDA/CuTe kernel instead of the one-split Triton kernel. Both GEMMs are transposed so the selected keys take the wgmma M dimension; the output gate is fused into the epilogue with the Triton kernel's BF16 rounding. FP8 caches, decode-config launches and other devices keep the Triton path; VLLM_QSA_SM90_NATIVE=0 disables the native kernel. Co-authored-by: Claude Signed-off-by: Milo <7bit2333@gmail.com>
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Is the speedup mainly from swapAB MMA? Have you tried doing swapAB for the triton kernel? |
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Purpose
On Hopper, QSA sparse prefill attention for Qwen3.8-Flash-Next runs
_qsa_sparse_paged_gqa_splitk_kernelwith the(32, 1, 1)prefill config. Every query attends to its own 2048 selected tokens, and one KV group has only 12 (TP1/TP2), 6 (TP4) or 3 (TP8) query heads, so the Triton kernel is limited to one warp andmma.syncwith M padded to 16. On H20, ncu shows it is not memory bound: DRAM reads are under 1% of peak, and L2 hit rate is 99% because one request's K/V for one layer fits in L2. The tensor pipe is 58% busy withmath_pipe_throttleas the top stall, which is 28% of BF16 peak. RetuningBLOCK_N/ warps / stages does not help, and a transposed Triton variant that could usewgmmaneeds 142 KB of shared memory per program and runs 2x slower.This PR adds a CUDA/CuTe kernel for that path with the same semantics as the one-split Triton kernel. It is used only on SM90 with a BF16 KV cache and prefill-config launches above 2048 (row, KV head) programs. Everything else keeps the Triton kernel:
wgmmaM dimension:S^T[64x16] = K·Q^TandO^T[256x16] += V^T·P^T, bothm64n16k16. The online softmax runs per head column, with one cross-warp max exchange per 64-key tile.cp.asyncdirectly into the swizzledwgmmalayouts.wgmmaon it. Token metadata is resolved two tiles ahead, overlapping QK. Shared memory is 75 KB and registers are 156 with no spills, so 3 CTAs fit per SM.sigmoid(gate)in FP32.The kernel lives in
csrc/libtorch_stable/attention/qsa/qsa_sm90.cuand is built only for9.0a. The op_C::qsa_sparse_prefill_sm90is registered in that file, so it exists only when it was built.qsa_sparse_paged_attentiondispatches to it after its argument checks. Unsupported layouts (K/V rows not 16-byte aligned, a cache span over 64 GiB, group > 16, head_dim != 256) log once and fall back to Triton.VLLM_QSA_SM90_NATIVE=0disables the kernel.Not a duplicate. I searched open and closed PRs for
qsa sm90,qsa hopper,qsa h20,qsa prefill kernel,qsa native,qsa wgmma,qsa cuda,sparse gqaandQwen4Exp sparse attention kernel, and found no SM90 kernel for this path:sm90value of itsVLLM_QSA_ATTENTION_BACKEND.(32, 1, 1).Test Plan
All runs were on 8x H20 (96 GB), CUDA 13.3, torch 2.13.0, on this branch built with
TORCH_CUDA_ARCH_LIST=9.0a. Native vs Triton is toggled withVLLM_QSA_SM90_NATIVE=1/0.Test Result
Unit tests:
test_qsa_reference.pypasses, 83 tests (73 existing + 10 new). The native kernel's max abs difference from the Triton kernel is at most 2e-3 on production-shaped inputs, and at most one BF16 ulp of the gated output elsewhere.compute-sanitizer: memcheck 0 errors, racecheck 0 hazards, synccheck 0 errors, initcheck 0 errors.
Kernel (H20),
benchmark_qsa_sm90_prefill.py, median ms throughqsa_sparse_paged_attention:All shapes
Every benchmarked shape above the dispatch threshold is faster: 1.47-1.57x at TP1/TP2 (group 12) and 1.32-1.41x at TP4/TP8 (group 6/3, where N=16 padding costs more). The production-sized TP1 chunk (15,094 rows, full 2048-token selections) goes from 18.44 ms to 11.94 ms (1.54x). Replays of real indexer selections dumped from a server go from 18.02/18.08 ms to 11.87/11.85 ms (1.52x/1.53x). The fused gate epilogue costs 2.2-2.9% of kernel time.
Serving (TP4, H20), 16k-token prompts, 8 output tokens, concurrency 16, 128 prompts, two seeds:
In our TP1 DP4/EP4 multimodal deployment on an older base, QSA per call went from 17.5 ms to 11.4 ms in nsys, and end-to-end throughput rose 3.9%.
Accuracy,
tests/evals/qwen4_exp, Qwen3.8-Flash-Next-FP8, TP4, same seeds and generation config:Both arms pass every run, and AIME25 misses come from the same hard problems in both arms: #13 in all six runs, and #14 and #29 in both arms. The one extra native miss at seed 1236 (#29) hit the 32k
max_tokenslimit before a final answer.GSM8K and AIME25 prompts are short, so most of their prefill launches stay under the dispatch threshold. To exercise the native path on long contexts, I also compared both arms on 4 long prompts (7.8k-31k tokens, greedy, 128 tokens). Both servers scored the same token sequence (prompt + Triton's greedy continuation) with
prompt_logprobs:Every disagreement is at a near tie. For scale, repeating the same greedy request on the same server, where the repeat hits the prefix cache, changes the output on 3 of 4 prompts for Triton and 2 of 4 for native.
Notes for reviewers
Co-authored-bytrailer. I have reviewed every changed line.Essential Elements of an Effective PR Description Checklist
supported_models.mdandexamplesfor a new model.