fix(pt): fix zero inputs for LayerNorm

[njzjz]

Fix #4064.

Summary by CodeRabbit

• Bug Fixes
  • Improved robustness of layer normalization by handling empty input tensors, ensuring consistent output without errors.

[coderabbitai]

Walkthrough

Walkthrough

The modification enhances the layer normalization implementation by adding a conditional check in the forward method. It verifies if the input tensor is non-empty before computing the mean and variance. If the tensor is empty, it bypasses normalization, ensuring that the output matches the input. This change prevents potential errors related to empty tensors during normalization.

Changes

Files	Change Summary
deepmd/pt/model/network/layernorm.py	Added a check for non-empty input tensor in the forward method to prevent errors during normalization.

Assessment against linked issues

Objective	Addressed	Explanation
UserWarning: var_mean(): degrees of freedom is <= 0 (Issue #4064)	✅

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Recent review details

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Review profile: CHILL

Commits

Files that changed from the base of the PR and between 0c59ef3 and e15b97b.

Files selected for processing (1)

• deepmd/pt/model/network/layernorm.py (1 hunks)

Additional comments not posted (1)

deepmd/pt/model/network/layernorm.py (1)

99-103: Robust handling of empty input tensors in LayerNorm.

The introduction of the conditional check if xx.numel() > 0 enhances the robustness of the layer normalization by gracefully handling the case of empty input tensors. When xx is non-empty, the code computes the variance and mean using torch.var_mean and normalizes xx accordingly. This approach is more reliable compared to using xx.mean and xx.var separately, as it avoids potential errors when using jit models for inference.

If xx is empty, the code directly assigns yy to xx, effectively bypassing the normalization step. This ensures that the output remains consistent with the input in such cases, preventing potential errors that could occur during normalization.

Overall, these changes align with the issue description and provide a robust solution to handle zero inputs in the LayerNorm function.

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[codecov]

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 83.02%. Comparing base (96ed5df) to head (e15b97b).
Report is 6 commits behind head on devel.

Additional details and impacted files

@@           Coverage Diff           @@
##            devel    #4134   +/-   ##
=======================================
  Coverage   83.02%   83.02%           
=======================================
  Files         532      532           
  Lines       52187    52189    +2     
  Branches     3030     3030           
=======================================
+ Hits        43330    43332    +2     
- Misses       7911     7912    +1     
+ Partials      946      945    -1     

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