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Source code for torch.nn.utils.clip_grad

import warnings
import torch
from torch._six import inf
from typing import Union, Iterable

_tensor_or_tensors = Union[torch.Tensor, Iterable[torch.Tensor]]


[docs]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)
[docs]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)

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