[MPS] Fix gradient issues with NLL and Smooth_L1 loss ops #94226
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razarmehr
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Feb 6, 2023
razarmehr
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- Fix correctness issues with nll_loss_backward(), smooth_l1_loss_backward() and cross_entropy_backward() by taking grad_output into account when computing those loss ops
- Add numel()==0 check to prevent crashes
- Clean up and formatting
- This should fix cross_entropy_backward() too - Take grad_output into account when computing nll_loss_backward() - Clean up
- Take grad_output into account when computing smooth_l1_backward() - Add numel()==0 check to prevent crashes - Clean up and formatting
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/94226
Note: Links to docs will display an error until the docs builds have been completed. ✅ No FailuresAs of commit d860e0a: This comment was automatically generated by Dr. CI and updates every 15 minutes. |
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Feb 6, 2023
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@pytorchbot merge -f "MPS tests are green" |
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Merge startedYour change will be merged immediately since you used the force (-f) flag, bypassing any CI checks (ETA: 1-5 minutes). Learn more about merging in the wiki. Questions? Feedback? Please reach out to the PyTorch DevX Team |
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