[Bugfix] Pad unaligned N in SM12x CUTLASS blockwise FP8 GEMM#48588
[Bugfix] Pad unaligned N in SM12x CUTLASS blockwise FP8 GEMM#48588jszzr wants to merge 1 commit into
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…oject#47990) The SM120 CUTLASS blockwise FP8 kernels reject every problem whose weight output dim N is not a multiple of the 128x128 scale block (CUTLASS can_implement fails for all three tile configs), so any DeepSeek-shaped block-FP8 checkpoint (kv_a_proj_with_mqa N=576) crashes on engine startup once the layer routes to the CUTLASS kernel. Zero-pad N to the scale block at weight load time and slice the padding off after the GEMM, following the pattern the per-tensor CUTLASS FP8 kernel already uses for its 16-element alignment. The padded rows land in the last, already-present scale block, so the scale tensor needs no change. The shared padding helpers move to module scope, and the block kernel base derives the logical output width from the layer config instead of the (possibly padded) weight. Padded CUTLASS is 1.9-2.9x faster than the Triton fallback at decode batch sizes on the DeepSeek kv_a_proj shape, measured on an RTX PRO 5000 Blackwell. Fixes vllm-project#47990 Signed-off-by: Zhirui <69025003+jszzr@users.noreply.github.com>
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Verified on a second SM120 device (RTX PRO 6000 Blackwell Max-Q, CUDA 13.0, torch 2.11.0+cu130): the -k cutlass selection of test_block_fp8.py fails on current main with the exact Invalid status from #47990 and passes all 10 on this branch, and re-running my shape sweep from the issue through the patched wrapper (fp32-dequant reference, N in {64, 192, 576, 2112} x M up to 1024) gives 2-3e-3 rel err everywhere, same as aligned shapes. Perf direction matches here too: padded CUTLASS including the slice copy beat the default-config Triton fallback at every M I tried on the kv_a_proj shape, ~1.4-2.3x at decode-ish M (co-tenanted box, so take the exact ratios loosely). Agreed on the #47988 interaction: I'll keep the N%128 guard there until this lands, then drop it and keep only the E8M0 upcast -- the two PRs collide textually in cutlass.py, so whoever lands second rebases, happy for that to be me.
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Thanks for the independent verification on a second SM120 device @waynehacking8! Full CI is currently gated on the first-contribution pre-run-check — would appreciate a maintainer adding the |
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Purpose
Fixes #47990.
The SM120 CUTLASS c3x blockwise FP8 GEMM rejects every problem whose weight output dim N is not a multiple of 128: all three tile configs in
scaled_mm_blockwise_sm120_fp8_dispatch.cuhfail CUTLASScan_implement. N=576 iskv_a_proj_with_mqa(512+64) in DeepSeek-V3-family models, so any block-FP8 DeepSeek-shaped checkpoint crashes at engine startup withcutlass_gemm_caller ... Invalid statuswhenever the layer routes to the CUTLASS kernel (e.g. wherever DeepGEMM is unavailable or auto-disabled on SM12x, see #47436 / #47169).This implements the padding remediation suggested in the issue: zero-pad the weight N to the next multiple of 128 once at load time, and slice the padded columns off after the GEMM. The padded rows land inside the last, already-present scale block (checkpoint scales have
ceil(N/128)rows), so the scale tensor needs no change. This is the same patternCutlassFP8ScaledMMLinearKernelalready uses for its 16-element alignment padding, and NVFP4 uses viapad_nvfp4_weight_for_cutlass.Changes:
scaled_mm/cutlass.py: move the_pad_to_alignment/padded_weight_loaderhelpers fromCutlassFP8ScaledMMLinearKernelto module scope so both CUTLASS kernels share them;CutlassFp8BlockScaledMMKernel.process_weights_after_loadingpads N on SM12x (installingpadded_weight_loaderso weight reloads keep working) andapply_block_scaled_mmslices the output back to the logical width; the module-levelcutlass_scaled_mmwrapper (test surface) pads per call.scaled_mm/BlockScaledMMLinearKernel.py: the baseapply_weightsderives the logical output width fromconfig.weight_shape[0]instead ofweight.shape[0], since the weight may now carry padding.tests/kernels/quantization/test_block_fp8.py: parametrizetest_w8a8_block_fp8_cutlass_matmulover M x (N, K) including the previously-failing N=576 / N=2112 plus an aligned control, and add a layer-level test covering load-time padding, output slicing, bias, and a 3D input, compared head-to-head againstTritonFp8BlockScaledMMKernel(both share the baseapply_weightsandQuantFP8, isolating the GEMM and pad/slice).Why padding instead of falling back to Triton
#47988 adds a selection-level guard that declines unaligned N on SM12x so those layers fall through to Triton. That is correct for safety, but keeps DeepSeek-shaped layers off the faster kernel. Measured on an RTX PRO 5000 Blackwell (SM120, CUDA 13.0, torch 2.11.0+cu130), padded CUTLASS including the output-slice copy vs
w8a8_triton_block_scaled_mm(default config; no tuned JSON exists for this device):Correctness sweep vs the fp32-dequant native reference: rel err <= 2e-8 across M in {1, 32, 83, 256} x N in {576, 2112}, identical to Triton's error.
If this lands, the SM12x
N % 128guard in #47988 becomes unnecessary (its E8M0 handling is orthogonal and still needed) — happy to coordinate, cc @waynehacking8.The remaining kernel-level gap (native ragged-N support in the CUTLASS SM120 blockwise collectives) is upstream CUTLASS work; padding at the integration layer costs at most 127 zero columns on a single projection (64 x 7168 FP8 = 448 KiB for DeepSeek's kv_a_proj).
Test Plan
All executed on RTX PRO 5000 Blackwell (SM120), CUDA 13.0, torch 2.11.0+cu130, precompiled kernels at merge-base:
pytest tests/kernels/quantization/test_block_fp8.py -k cutlass: 10 passed (the N=576 case fails on current main withInvalid status).test_block_fp8.py: no new failures (the pre-existing SM120 DeepGEMM failures are identical on clean main).pytest tests/kernels/quantization/test_cutlass_scaled_mm.py -k fp8: 261 passed (validates the helper refactor; the file's int8 tests fail on SM120 with "Int8 not supported on SM120" both before and after this change).pre-commit(ruff check/format, typos, SPDX, etc.) andmypyclean on the changed files.kv_a_proj_with_mqaN=576,--load-format dummy,--linear-backend cutlass, dense-only to avoid unrelated SM120 MoE issues):Selected CutlassFp8BlockScaledMMKernel for Fp8LinearMethod->EngineCore failed to start ... RuntimeError: cutlass_gemm_caller ... Invalid statusTest Result
See above; all listed tests pass on SM120 hardware.
AI tooling assisted with this change; I reviewed all changes and ran every test and benchmark above on my own SM120 hardware.