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zero the gradients after updating weights #3447

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zero the gradients after updating weights #3447

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@bintang-aswam bintang-aswam commented Jul 10, 2025 •
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Manually zero the gradients after updating weights by using machine epsilon for standard float (64-bit).

Fixes #ISSUE_NUMBER

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  • The issue that is being fixed is referred in the description (see above "Fixes #ISSUE_NUMBER")
  • Only one issue is addressed in this pull request
  • Labels from the issue that this PR is fixing are added to this pull request
  • No unnecessary issues are included into this pull request.

cc @albanD @jbschlosser

@bintang-aswam
zero the gradients after updating weights
d20395f 
Manually zero the gradients after updating weights by using machine epsilon for standard float (64-bit).
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pytorch-bot bot commented Jul 10, 2025 •
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🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/tutorials/3447

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@svekars svekars added the core Tutorials of any level of difficulty related to the core pytorch functionality label Jul 18, 2025
albanD
albanD reviewed Jul 21, 2025
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Would you be able to share details on why do you want to do this change.
Setting them to None is equivalent to setting them to exactly 0. which is best no?

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Would you be able to share details on why do you want to do this change. Setting them to None is equivalent to setting them to exactly 0. which is best no?

answer:
@albanD, In other words, setting them to weight.grad = None is equivalent to setting them to weight.grad.zero_(). However, there are few conceptual difference in the context of numerically zeroing between zeroing the gradients vs tensor-multiplication of loss by machine-epsilon as follows:
image

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