1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
|
import warnings
import torch
from torch._six import inf
from typing import Union, Iterable
_tensor_or_tensors = Union[torch.Tensor, Iterable[torch.Tensor]]
def clip_grad_norm_(parameters: _tensor_or_tensors, max_norm: float, norm_type: float = 2.0) -> torch.Tensor:
r"""Clips gradient norm of an iterable of parameters.
The norm is computed over all gradients together, as if they were
concatenated into a single vector. Gradients are modified in-place.
Arguments:
parameters (Iterable[Tensor] or Tensor): an iterable of Tensors or a
single Tensor that will have gradients normalized
max_norm (float or int): max norm of the gradients
norm_type (float or int): type of the used p-norm. Can be ``'inf'`` for
infinity norm.
Returns:
Total norm of the parameters (viewed as a single vector).
"""
if isinstance(parameters, torch.Tensor):
parameters = [parameters]
parameters = [p for p in parameters if p.grad is not None]
max_norm = float(max_norm)
norm_type = float(norm_type)
if len(parameters) == 0:
return torch.tensor(0.)
device = parameters[0].grad.device
if norm_type == inf:
total_norm = max(p.grad.detach().abs().max().to(device) for p in parameters)
else:
total_norm = torch.norm(torch.stack([torch.norm(p.grad.detach(), norm_type).to(device) for p in parameters]), norm_type)
clip_coef = max_norm / (total_norm + 1e-6)
if clip_coef < 1:
for p in parameters:
p.grad.detach().mul_(clip_coef.to(p.grad.device))
return total_norm
def clip_grad_norm(parameters: _tensor_or_tensors, max_norm: float, norm_type: float = 2.) -> torch.Tensor:
r"""Clips gradient norm of an iterable of parameters.
.. warning::
This method is now deprecated in favor of
:func:`torch.nn.utils.clip_grad_norm_`.
"""
warnings.warn("torch.nn.utils.clip_grad_norm is now deprecated in favor "
"of torch.nn.utils.clip_grad_norm_.", stacklevel=2)
return clip_grad_norm_(parameters, max_norm, norm_type)
def clip_grad_value_(parameters: _tensor_or_tensors, clip_value: float) -> None:
r"""Clips gradient of an iterable of parameters at specified value.
Gradients are modified in-place.
Arguments:
parameters (Iterable[Tensor] or Tensor): an iterable of Tensors or a
single Tensor that will have gradients normalized
clip_value (float or int): maximum allowed value of the gradients.
The gradients are clipped in the range
:math:`\left[\text{-clip\_value}, \text{clip\_value}\right]`
"""
if isinstance(parameters, torch.Tensor):
parameters = [parameters]
clip_value = float(clip_value)
for p in filter(lambda p: p.grad is not None, parameters):
p.grad.data.clamp_(min=-clip_value, max=clip_value)
|
