53 lines
2.1 KiB
Python
53 lines
2.1 KiB
Python
# Copyright The PyTorch Lightning team.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
"""Utilities to describe gradients."""
|
|
from typing import Dict, Union
|
|
|
|
import torch
|
|
from torch.nn import Module
|
|
|
|
|
|
def grad_norm(module: Module, norm_type: Union[float, int, str], group_separator: str = "/") -> Dict[str, float]:
|
|
"""Compute each parameter's gradient's norm and their overall norm.
|
|
|
|
The overall norm is computed over all gradients together, as if they
|
|
were concatenated into a single vector.
|
|
|
|
Args:
|
|
module: :class:`torch.nn.Module` to inspect.
|
|
norm_type: The type of the used p-norm, cast to float if necessary.
|
|
Can be ``'inf'`` for infinity norm.
|
|
group_separator: The separator string used by the logger to group
|
|
the gradients norms in their own subfolder instead of the logs one.
|
|
|
|
Return:
|
|
norms: The dictionary of p-norms of each parameter's gradient and
|
|
a special entry for the total p-norm of the gradients viewed
|
|
as a single vector.
|
|
"""
|
|
norm_type = float(norm_type)
|
|
if norm_type <= 0:
|
|
raise ValueError(f"`norm_type` must be a positive number or 'inf' (infinity norm). Got {norm_type}")
|
|
|
|
norms = {
|
|
f"grad_{norm_type}_norm{group_separator}{name}": p.grad.data.norm(norm_type).item()
|
|
for name, p in module.named_parameters()
|
|
if p.grad is not None
|
|
}
|
|
if norms:
|
|
total_norm = torch.tensor(list(norms.values())).norm(norm_type).item()
|
|
norms[f"grad_{norm_type}_norm_total"] = total_norm
|
|
norms = {k: round(v, 4) for k, v in norms.items()}
|
|
return norms
|