enable torch.compile for mxfp8_cublas recipe #1841
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/ao/1841
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Summary: This PR enables `MXLinear` with `mxfp8_cublas` recipe to use torch.compile. The current approach is a short term workaround until pytorch/pytorch#148461 is done. Since we can't use e8m0 in torchinductor or triton yet, we create a custom op wrapper around `torch._scaled_mm` which takes `uint8` scales and does the cast to e8m0 inside the wrapper, where torchinductor can't see it. Test Plan: ``` // this now works (although performance is not ideal due to #1788) python benchmarks/float8/profile_lowp_training.py ~/local/tmp/20250305_test --mx_recipe_name mxfp8_cublas // we can also uncomment the hardware check and run the unit test pytest test/prototype/mx_formats -s -k test_linear_compile ``` Reviewers: Subscribers: Tasks: Tags: ghstack-source-id: 033d817549f80d7d0d8cf549f748411cc1f3ac6a ghstack-comment-id: 2701679811 Pull Request resolved: #1841
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Summary: This PR enables `MXLinear` with `mxfp8_cublas` recipe to use torch.compile. The current approach is a short term workaround until pytorch/pytorch#147873 is done. Since we can't use e8m0 in torchinductor or triton yet, we create a custom op wrapper around `torch._scaled_mm` which takes `uint8` scales and does the cast to e8m0 inside the wrapper, where torchinductor can't see it. Test Plan: ``` // this now works (although performance is not ideal due to #1788) python benchmarks/float8/profile_lowp_training.py ~/local/tmp/20250305_test --mx_recipe_name mxfp8_cublas // we can also uncomment the hardware check and run the unit test pytest test/prototype/mx_formats -s -k test_linear_compile ``` Reviewers: Subscribers: Tasks: Tags: ghstack-source-id: f3ebd12edcb746b8abf992d00711ce2bdbb7fcf2 ghstack-comment-id: 2701679811 Pull Request resolved: #1841
vkuzo
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Summary: This PR enables `MXLinear` with `mxfp8_cublas` recipe to use torch.compile. The current approach is a short term workaround until pytorch/pytorch#147873 is done. Since we can't use e8m0 in torchinductor or triton yet, we create a custom op wrapper around `torch._scaled_mm` which takes `uint8` scales and does the cast to e8m0 inside the wrapper, where torchinductor can't see it. Test Plan: ``` // this now works (although performance is not ideal due to #1788) python benchmarks/float8/profile_lowp_training.py ~/local/tmp/20250305_test --mx_recipe_name mxfp8_cublas // we can also uncomment the hardware check and run the unit test pytest test/prototype/mx_formats -s -k test_linear_compile ``` Reviewers: Subscribers: Tasks: Tags: ghstack-source-id: e5687e308db0a54c6083c58cfec5cc49626622f1 ghstack-comment-id: 2701679811 Pull Request resolved: #1841
danielvegamyhre
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Mar 6, 2025
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| is_sm_at_least_100(), | ||
| reason="triton does not work yet on CUDA capability 10.0", | ||
| ) | ||
| @pytest.mark.skipif( | ||
| not is_sm_at_least_100(), | ||
| reason="MX gemms require CUDA capability 10.0", | ||
| ) |
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Combining skip if is_sm_at_least_100() with skip if not is_sm_at_least_100() will prevent the test from ever running, so I just want to confirm, is this test intentionally being skipped until the new release of pytorch (with triton that supports compute capability 10.0) is part of CI?
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yes, that's corrrect. It's skipped in CI because we don't have B200s in CI, and it's skipped locally because it requires building triton from source. I uncomment these tests if I need to run them, for now.
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Summary:
This PR enables
MXLinearwithmxfp8_cublasrecipe to usetorch.compile.
The current approach is a short term workaround until
pytorch/pytorch#148461 is done. Since we can't
use e8m0 in torchinductor or triton yet, we create a custom op wrapper
around
torch._scaled_mmwhich takesuint8scales and does the cast toe8m0 inside the wrapper, where torchinductor can't see it.
Test Plan:
Reviewers:
Subscribers:
Tasks:
Tags: