fix(pt): fix zero inputs for LayerNorm (#4134)

Fix #4064.

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## Summary by CodeRabbit

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

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Signed-off-by: Jinzhe Zeng <[email protected]>

deepmd/pt/model/network/layernorm.py

@@ -96,8 +96,11 @@ def forward(
         # variance = xx.var(dim=-1, unbiased=False, keepdim=True)
         # The following operation is the same as above, but will not raise error when using jit model to inference.
         # See https://github.com/pytorch/pytorch/issues/85792
-        variance, mean = torch.var_mean(xx, dim=-1, unbiased=False, keepdim=True)
-        yy = (xx - mean) / torch.sqrt(variance + self.eps)
+        if xx.numel() > 0:
+            variance, mean = torch.var_mean(xx, dim=-1, unbiased=False, keepdim=True)
+            yy = (xx - mean) / torch.sqrt(variance + self.eps)
+        else:
+            yy = xx
         if self.matrix is not None and self.bias is not None:
             yy = yy * self.matrix + self.bias
         return yy