57 lines
1.8 KiB
Python
57 lines
1.8 KiB
Python
# Copyright The PyTorch Lightning team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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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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from torch.nn import Module
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class GradInformation(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, 4)
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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, 4)
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return norms
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