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https://github.com/wassname/pytorch-lightning.git
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e85a646a41
* fix grad norm formula * grad-norm tracker test * fixed seed and explicit rtol in grad norm tracking test * a docstring for grad-norms and forced cast to float of norm_type * support for inf-norm * renamed the grad norm test * docs * fixed language in docstring * Apply suggestions from code review Co-authored-by: Jirka <jirka@pytorchlightning.ai> Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>
42 lines
1.3 KiB
Python
42 lines
1.3 KiB
Python
"""
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Module to describe gradients
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"""
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from typing import Dict, Union
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import torch
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class GradInformation(torch.nn.Module):
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def grad_norm(self, norm_type: Union[float, int, str]) -> Dict[str, float]:
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"""Compute each parameter's gradient's norm and their overall norm.
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The overall norm is computed over all gradients together, as if they
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were concatenated into a single vector.
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Args:
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norm_type: The type of the used p-norm, cast to float if necessary.
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Can be ``'inf'`` for infinity norm.
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Return:
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norms: The dictionary of p-norms of each parameter's gradient and
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a special entry for the total p-norm of the gradients viewed
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as a single vector.
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"""
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norm_type = float(norm_type)
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norms, all_norms = {}, []
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for name, p in self.named_parameters():
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if p.grad is None:
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continue
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param_norm = float(p.grad.data.norm(norm_type))
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norms[f'grad_{norm_type}_norm_{name}'] = round(param_norm, 3)
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all_norms.append(param_norm)
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total_norm = float(torch.tensor(all_norms).norm(norm_type))
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norms[f'grad_{norm_type}_norm_total'] = round(total_norm, 3)
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return norms
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