[TRTLLM-6960][fix] enable scaled_mm tests #6936
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📝 WalkthroughWalkthroughUpdated a unit test to run on SM90 by removing a skip, and tightened the primary assertion tolerance from 1 to 0.01. The SM90-specific CUTLASS comparison remains unchanged with its own tolerance. Changes
Estimated code review effort🎯 2 (Simple) | ⏱️ ~6 minutes Suggested reviewers
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tests/unittest/_torch/thop/test_scaled_mm.py (1)
72-75: Tightening atol to 0.01 may introduce flakiness for fp16/bf16; consider dtype-aware tolerances.FP8 paths accumulating into fp16/bf16 can show larger absolute error near zero. Keeping rtol=1% is fine, but an absolute 0.01 can be too strict for half/bfloat16 across drivers/kernels. Suggest dtype-conditional tolerances to keep the test stable while remaining strict.
Apply this diff to make tolerances dtype-aware:
- np.testing.assert_allclose(ref.float().cpu(), - output.float().cpu(), - atol=0.01, - rtol=0.01) + # Tolerances tuned per output dtype to balance strictness and stability across kernels/drivers. + if output_dtype == torch.float32: + atol, rtol = 0.01, 0.01 + else: # torch.float16 or torch.bfloat16 + atol, rtol = 0.1, 0.02 + np.testing.assert_allclose(ref.float().cpu(), + output.float().cpu(), + atol=atol, + rtol=rtol)If you prefer PyTorch’s native checker, switching to torch.testing.assert_close with dtype-specific tolerances is also an option. Do you want me to draft that variant as well?
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tests/unittest/_torch/thop/test_scaled_mm.py(1 hunks)
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PR_Github #15414 [ run ] triggered by Bot |
Signed-off-by: Zhenhuan Chen <chenzhh3671@gmail.com>
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PR_Github #15423 [ run ] triggered by Bot |
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PR_Github #15414 [ run ] completed with state |
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x86 tests succeeded, but SBSA tests time out. Seems no accuracy issue. Will try again. cc @hlu1 |
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PR_Github #15556 [ run ] triggered by Bot |
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PR_Github #15583 [ run ] triggered by Bot |
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PR_Github #15583 [ run ] completed with state |
This reverts commit 2bb90ba.
This reverts commit 2bb90ba. Signed-off-by: Iman Tabrizian <itabrizian@nvidia.com>
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