Make Inductor benchmarker more compatible with Triton do_bench #160921
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/160921
Note: Links to docs will display an error until the docs builds have been completed. ✅ No FailuresAs of commit 75bc584 with merge base 82c7a1e ( This comment was automatically generated by Dr. CI and updates every 15 minutes. |
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| @@ -183,7 +183,7 @@ def L2_cache_size(self: Self) -> int: | |||
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| def get_event_pairs( | |||
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| ) -> list[tuple[torch.cuda.Event, torch.cuda.Event]]: | |||
| ) -> List[tuple[torch.cuda.Event, torch.cuda.Event]]: | |||
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…ch#160921) Common benchmark suites like TritonBench uses `triton.testing.do_bench` for kernel timing measurement which is not always fair for all backends. E.g. it includes torch.compile Dynamo invocation overhead and hence doesn't reflect real-world model use case where Dynamo overhead is usually hidden. I also opened a PR to use this timing measurement function on TritonBench side: meta-pytorch/tritonbench#333. But regardless of whether that PR can land, I think we should enhance Inductor benchmark_gpu to match do_bench features, to make it easier to people to migrate. Pull Request resolved: pytorch#160921 Approved by: https://github.com/BoyuanFeng
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Common benchmark suites like TritonBench uses
triton.testing.do_benchfor kernel timing measurement which is not always fair for all backends. E.g. it includes torch.compile Dynamo invocation overhead and hence doesn't reflect real-world model use case where Dynamo overhead is usually hidden.I also opened a PR to use this timing measurement function on TritonBench side: meta-pytorch/tritonbench#333. But regardless of whether that PR can land, I think we should enhance Inductor benchmark_gpu to match do_bench features, to make it easier to people to migrate.
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov @coconutruben