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Clarify clip_grad_norm_ patch on TROUBLESHOOTING.md #1898

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Apr 9, 2020
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Clarify clip_grad_norm_ patch on TROUBLESHOOTING.md #1898

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2 changes: 1 addition & 1 deletion TROUBLESHOOTING.md
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Expand Up @@ -95,7 +95,7 @@ If your model shows bad performance, keep in mind the following caveats:
- For most ops we can lower them to XLA to fix it. Checkout [metrics report section](#metrics-report) to find out the missing ops and open a feature request on [GitHub](https://github.com/pytorch/xla/issues).
- Even when a PyTorch tensor is known as a scalar, avoid using `tensor.item()`. Keep it as a tensor and use tensor operations on it.
- Use `torch.where` to substitute control flow when applicable.
E.g. The control flow with `item()` used in [clip_grad_norm_](https://github.com/pytorch/pytorch/blob/de19eeee99a2a282fc441f637b23d8e50c75ecd1/torch/nn/utils/clip_grad.py#L33) can be simply replaced by `torch.where` with dramatical performance improvement.
E.g. The control flow with `item()` used in [clip_grad_norm_](https://github.com/pytorch/pytorch/blob/de19eeee99a2a282fc441f637b23d8e50c75ecd1/torch/nn/utils/clip_grad.py#L33) is problematic and impacts performance, so we have [patched](https://github.com/pytorch/xla/blob/master/torch_patches/X10-clip_grad.diff) `clip_grad_norm_` by calling `torch.where` instead, which gives us a dramatic performance improvement.
```python
...
else:
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