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Remove uses of deleted operations by exclamaforte · Pull Request #139447 · pytorch/pytorch · GitHub
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Remove uses of deleted operations
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/139447
Note: Links to docs will display an error until the docs builds have been completed. âś… No FailuresAs of commit cf7058f with merge base d72a308 ( This comment was automatically generated by Dr. CI and updates every 15 minutes. |
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hey mind checking workplace ? |
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nice!
Summary: Delete the uses of deleted nodes. The double for-loop is icky here, but N should be pretty small and removing it requires refactoring the datastructures involved, which is a bigger endeavor. Test Plan: Normal test coverage should be sufficient. There were a couple of spots in the scheduler code that didn't check users being deleted, so I'll run a perf test to see what impact that has, and to make sure N^2 doesn't affect compile times.
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@pytorchbot merge |
Merge startedYour change will be merged once all checks pass (ETA 0-4 Hours). Learn more about merging in the wiki. Questions? Feedback? Please reach out to the PyTorch DevX Team |
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resolves: pytorch#138721 Summary: Delete the uses of deleted nodes. The double for-loop is icky here, but N should be pretty small and removing it requires refactoring the datastructures involved, which is a bigger endeavor. Test Plan: Normal test coverage should be sufficient. There were a couple of spots in the scheduler code that didn't check users being deleted, so I'll run a perf test to see what impact that has, and to make sure N^2 doesn't affect compile times. Perf: https://hud.pytorch.org/benchmark/compilers?dashboard=torchinductor&startTime=Tue%2C%2029%20Oct%202024%2017%3A41%3A36%20GMT&stopTime=Tue%2C%2005%20Nov%202024%2018%3A41%3A36%20GMT&granularity=hour&suite=torchbench&mode=inference&dtype=bfloat16&deviceName=cuda%20(a100)&lBranch=exclamaforte/prune-deleted-users&lCommit=5cb1aa6f7d8a52acdae0c7cf36b8c2d536d7f0d1&rBranch=main&rCommit=f4ee5a243dbb31e6310e5632b1c87898b299df2c off of nov4 nightly Pull Request resolved: pytorch#139447 Approved by: https://github.com/eellison
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resolves: pytorch#138721 Summary: Delete the uses of deleted nodes. The double for-loop is icky here, but N should be pretty small and removing it requires refactoring the datastructures involved, which is a bigger endeavor. Test Plan: Normal test coverage should be sufficient. There were a couple of spots in the scheduler code that didn't check users being deleted, so I'll run a perf test to see what impact that has, and to make sure N^2 doesn't affect compile times. Perf: https://hud.pytorch.org/benchmark/compilers?dashboard=torchinductor&startTime=Tue%2C%2029%20Oct%202024%2017%3A41%3A36%20GMT&stopTime=Tue%2C%2005%20Nov%202024%2018%3A41%3A36%20GMT&granularity=hour&suite=torchbench&mode=inference&dtype=bfloat16&deviceName=cuda%20(a100)&lBranch=exclamaforte/prune-deleted-users&lCommit=5cb1aa6f7d8a52acdae0c7cf36b8c2d536d7f0d1&rBranch=main&rCommit=f4ee5a243dbb31e6310e5632b1c87898b299df2c off of nov4 nightly Pull Request resolved: pytorch#139447 Approved by: https://github.com/eellison
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resolves: pytorch#138721 Summary: Delete the uses of deleted nodes. The double for-loop is icky here, but N should be pretty small and removing it requires refactoring the datastructures involved, which is a bigger endeavor. Test Plan: Normal test coverage should be sufficient. There were a couple of spots in the scheduler code that didn't check users being deleted, so I'll run a perf test to see what impact that has, and to make sure N^2 doesn't affect compile times. Perf: https://hud.pytorch.org/benchmark/compilers?dashboard=torchinductor&startTime=Tue%2C%2029%20Oct%202024%2017%3A41%3A36%20GMT&stopTime=Tue%2C%2005%20Nov%202024%2018%3A41%3A36%20GMT&granularity=hour&suite=torchbench&mode=inference&dtype=bfloat16&deviceName=cuda%20(a100)&lBranch=exclamaforte/prune-deleted-users&lCommit=5cb1aa6f7d8a52acdae0c7cf36b8c2d536d7f0d1&rBranch=main&rCommit=f4ee5a243dbb31e6310e5632b1c87898b299df2c off of nov4 nightly Pull Request resolved: pytorch#139447 Approved by: https://github.com/eellison
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resolves: pytorch#138721 Summary: Delete the uses of deleted nodes. The double for-loop is icky here, but N should be pretty small and removing it requires refactoring the datastructures involved, which is a bigger endeavor. Test Plan: Normal test coverage should be sufficient. There were a couple of spots in the scheduler code that didn't check users being deleted, so I'll run a perf test to see what impact that has, and to make sure N^2 doesn't affect compile times. Perf: https://hud.pytorch.org/benchmark/compilers?dashboard=torchinductor&startTime=Tue%2C%2029%20Oct%202024%2017%3A41%3A36%20GMT&stopTime=Tue%2C%2005%20Nov%202024%2018%3A41%3A36%20GMT&granularity=hour&suite=torchbench&mode=inference&dtype=bfloat16&deviceName=cuda%20(a100)&lBranch=exclamaforte/prune-deleted-users&lCommit=5cb1aa6f7d8a52acdae0c7cf36b8c2d536d7f0d1&rBranch=main&rCommit=f4ee5a243dbb31e6310e5632b1c87898b299df2c off of nov4 nightly Pull Request resolved: pytorch#139447 Approved by: https://github.com/eellison
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resolves: #138721
Summary:
Delete the uses of deleted nodes. The double for-loop is icky here, but N should
be pretty small and removing it requires refactoring the datastructures
involved, which is a bigger endeavor.
Test Plan:
Normal test coverage should be sufficient. There were a couple of spots in the
scheduler code that didn't check users being deleted, so I'll run a perf test to see
what impact that has, and to make sure N^2 doesn't affect compile times.
Perf:
https://hud.pytorch.org/benchmark/compilers?dashboard=torchinductor&startTime=Tue%2C%2029%20Oct%202024%2017%3A41%3A36%20GMT&stopTime=Tue%2C%2005%20Nov%202024%2018%3A41%3A36%20GMT&granularity=hour&suite=torchbench&mode=inference&dtype=bfloat16&deviceName=cuda%20(a100)&lBranch=exclamaforte/prune-deleted-users&lCommit=5cb1aa6f7d8a52acdae0c7cf36b8c2d536d7f0d1&rBranch=main&rCommit=f4ee5a243dbb31e6310e5632b1c87898b299df2c
off of nov4 nightly
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @ColinPeppler @amjames @desertfire @chauhang @aakhundov