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This gives a fair comparison between eager and other modes. The constraints mentioned in the comment seem to have been fixed at least for Blackwell.
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Pull Request Overview
This PR enables the use of torch._grouped_mm in eager mode for benchmarking purposes, providing a fair comparison between eager and other modes. Previously, the function was only used during compilation (via torch.compiler.is_compiling() check). The constraints that prevented eager mode usage have been resolved.
Key changes:
- Replaced
torch.compiler.is_compiling()check with availability check based on_grouped_mmvariable - Added
elseclause to set_grouped_mm = Nonefor torch versions < 2.8.0 - Removed outdated comment about constraints requiring offsets to be multiples of 16
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crcrpar
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Nov 11, 2025
Co-authored-by: Masaki <mkozuki@nvidia.com>
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This gives a fair comparison between eager and other modes.
The constraints mentioned in the comment seem to have been fixed by pytorch/pytorch#161407
python thunder/benchmarks/benchmark_inference.pyat head runs fine on both Blackwell and Ampere.