[Test] Use UE8M0 weight scales in block-FP8 DeepGEMM matmul test when the E8M0 oracle is active - #51372
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… the E8M0 oracle is active test_w8a8_block_fp8_deep_gemm_matmul quantizes weights with arbitrary float32 block scales, but SM100/SM120 mxfp8 DeepGEMM kernels require power-of-two (UE8M0) weight scales. Production satisfies this contract via requant_weight_ue8m0_inplace before dispatching to DeepGEMM; the test bypassed it and all 100 parametrizations failed with rel_diff=nan on RTX PRO 6000 Blackwell (SM120). Quantize test weights with use_ue8m0=is_deep_gemm_e8m0_used(), matching the production weight-processing path and the existing idiom in tests/kernels/moe/test_deepgemm.py. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> Signed-off-by: BabyDrangoner <148877251+BabyDrangoner@users.noreply.github.com>
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Purpose
tests/kernels/quantization/test_block_fp8.py::test_w8a8_block_fp8_deep_gemm_matmulfails in all 100 collected parametrizations on Blackwell SM120 (RTX PRO 6000) withrel_diff = nan: the DeepGEMM output tensor is entirely NaN while the reference is finite.The test quantizes weights with
per_block_cast_to_fp8(B_fp32, block_size=block_size), which defaults touse_ue8m0=Falseand produces arbitrary float32 block scales. The SM100/SM120 mxfp8 DeepGEMM kernels require power-of-two (UE8M0-representable) weight scales. Production code satisfies this contract before dispatching to DeepGEMM —deepgemm_post_process_fp8_weight_blockcallsrequant_weight_ue8m0_inplacewhen the E8M0 oracle is active — but the test callsfp8_gemm_ntdirectly with non-power-of-two weight scales, so the kernel silently emits NaN.Isolation on SM120 (M=128, N=512, K=512) shows NaN tracks only the weight-scale format; the activation-scale format does not matter:
This change quantizes the test weights with
use_ue8m0=is_deep_gemm_e8m0_used(), matching the production weight-processing path. The MoE DeepGEMM test (tests/kernels/moe/test_deepgemm.py) already does this (per_block_cast_to_fp8(..., use_ue8m0=True)), which is why the grouped MoE tests pass on SM120 while the dense test fails. The activation side already follows the oracle viaper_token_group_quant_fp8's default.On Hopper the oracle also returns True by default, so weight scales become power-of-two there as well; the reference matmul uses the same scales, so the comparison stays self-consistent. With
VLLM_USE_DEEP_GEMM_E8M0=0or without DeepGEMM the call degrades to the current behavior (use_ue8m0=False).Not covered here:
test_w8a8_block_fp8_cutlass_matmulandtest_w8a8_block_fp8_torch_scaled_mm_matmulalso fail on SM120 but with a different root cause (cutlass_gemm_caller Invalid status, related to #48588), and forcingVLLM_USE_DEEP_GEMM_E8M0=0on SM120 still fails at the library level because the mxfp8 hardware path cannot consume float32 scales — both are separate issues from this test-contract fix.Related: #50796 and #48962 touch the DeepGEMM build/packaging only; #47258 / #50332 cover the Blackwell MoE accuracy auto-disable. None of them touch this test contract.
Test Plan
On RTX PRO 6000 Blackwell (SM120), CUDA 13.0, torch 2.11.0, commit 58fcaa0:
Test Result
Before:
100 failed, 25 skipped(-k deep_gemm), every failurerel_diff = nan.After:
100 passed, 25 skipped; worst observedrel_diff5.9e-08. Full file:2 failed, 321 passed, 150 skipped— the 2 remaining failures are the pre-existing unrelated CUTLASS/torch scaled_mm SM120 issues noted above, unchanged by this PR.Not run locally: Hopper/SM100 (no hardware); needs upstream CI confirmation there.
Essential Elements of an Effective PR Description Checklist
supported_models.mdandexamplesfor a new model.AI assistance disclosure: This PR was developed with AI assistance (GitHub Copilot CLI). The failure was reproduced, root-caused, and verified on real SM120 hardware as described above; all reported numbers are from actual runs.