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Source code for torch.nn.utils.clip_grad

import warnings
import functools
from typing import Union, Iterable, List, Dict, Tuple, Optional, cast

import torch
from torch import Tensor
from torch.utils._foreach_utils import _group_tensors_by_device_and_dtype, _has_foreach_support, _device_has_foreach_support

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

__all__ = ['clip_grad_norm_', 'clip_grad_norm', 'clip_grad_value_']

def _no_grad(func):
    """
    This wrapper is needed to avoid a circular import when using @torch.no_grad on the exposed functions
    clip_grad_norm_ and clip_grad_value_ themselves.
    """
    def _no_grad_wrapper(*args, **kwargs):
        with torch.no_grad():
            return func(*args, **kwargs)
    functools.update_wrapper(_no_grad_wrapper, func)
    return _no_grad_wrapper

[docs]@_no_grad def clip_grad_norm_( parameters: _tensor_or_tensors, max_norm: float, norm_type: float = 2.0, error_if_nonfinite: bool = False, foreach: Optional[bool] = None) -> torch.Tensor: r"""Clip the 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. Args: parameters (Iterable[Tensor] or Tensor): an iterable of Tensors or a single Tensor that will have gradients normalized max_norm (float): max norm of the gradients norm_type (float): type of the used p-norm. Can be ``'inf'`` for infinity norm. error_if_nonfinite (bool): if True, an error is thrown if the total norm of the gradients from :attr:`parameters` is ``nan``, ``inf``, or ``-inf``. Default: False (will switch to True in the future) foreach (bool): use the faster foreach-based implementation. If ``None``, use the foreach implementation for CUDA and CPU native tensors and silently fall back to the slow implementation for other device types. Default: ``None`` Returns: Total norm of the parameter gradients (viewed as a single vector). """ if isinstance(parameters, torch.Tensor): parameters = [parameters] grads = [p.grad for p in parameters if p.grad is not None] max_norm = float(max_norm) norm_type = float(norm_type) if len(grads) == 0: return torch.tensor(0.) first_device = grads[0].device grouped_grads: Dict[Tuple[torch.device, torch.dtype], Tuple[List[List[Tensor]], List[int]]] \ = _group_tensors_by_device_and_dtype([grads]) # type: ignore[assignment] norms: List[Tensor] = [] for ((device, _), ([device_grads], _)) in grouped_grads.items(): # type: ignore[assignment] if ( (foreach is None and _has_foreach_support(device_grads, device)) or (foreach and _device_has_foreach_support(device)) ): norms.extend(torch._foreach_norm(device_grads, norm_type)) elif foreach: raise RuntimeError(f'foreach=True was passed, but can\'t use the foreach API on {device.type} tensors') else: norms.extend([torch.linalg.vector_norm(g, norm_type) for g in device_grads]) total_norm = torch.linalg.vector_norm(torch.stack([norm.to(first_device) for norm in norms]), norm_type) if error_if_nonfinite and torch.logical_or(total_norm.isnan(), total_norm.isinf()): raise RuntimeError( f'The total norm of order {norm_type} for gradients from ' '`parameters` is non-finite, so it cannot be clipped. To disable ' 'this error and scale the gradients by the non-finite norm anyway, ' 'set `error_if_nonfinite=False`') clip_coef = max_norm / (total_norm + 1e-6) # Note: multiplying by the clamped coef is redundant when the coef is clamped to 1, but doing so # avoids a `if clip_coef < 1:` conditional which can require a CPU <=> device synchronization # when the gradients do not reside in CPU memory. clip_coef_clamped = torch.clamp(clip_coef, max=1.0) for ((device, _), ([device_grads], _)) in grouped_grads.items(): # type: ignore[assignment] if ( (foreach is None and _has_foreach_support(device_grads, device)) or (foreach and _device_has_foreach_support(device)) ): torch._foreach_mul_(device_grads, clip_coef_clamped.to(device)) elif foreach: raise RuntimeError(f'foreach=True was passed, but can\'t use the foreach API on {device.type} tensors') else: clip_coef_clamped_device = clip_coef_clamped.to(device) for g in device_grads: g.mul_(clip_coef_clamped_device) return total_norm
[docs]def clip_grad_norm( parameters: _tensor_or_tensors, max_norm: float, norm_type: float = 2., error_if_nonfinite: bool = False, foreach: Optional[bool] = None) -> torch.Tensor: r"""Clip the 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, error_if_nonfinite, foreach)
[docs]@_no_grad def clip_grad_value_(parameters: _tensor_or_tensors, clip_value: float, foreach: Optional[bool] = None) -> None: r"""Clip the gradients of an iterable of parameters at specified value. Gradients are modified in-place. Args: parameters (Iterable[Tensor] or Tensor): an iterable of Tensors or a single Tensor that will have gradients normalized clip_value (float): maximum allowed value of the gradients. The gradients are clipped in the range :math:`\left[\text{-clip\_value}, \text{clip\_value}\right]` foreach (bool): use the faster foreach-based implementation If ``None``, use the foreach implementation for CUDA and CPU native tensors and silently fall back to the slow implementation for other device types. Default: ``None`` """ if isinstance(parameters, torch.Tensor): parameters = [parameters] clip_value = float(clip_value) grads = [p.grad for p in parameters if p.grad is not None] grouped_grads = _group_tensors_by_device_and_dtype([grads]) for ((device, _), ([grads], _)) in grouped_grads.items(): # type: ignore[assignment] if ( (foreach is None and _has_foreach_support(cast(List[Tensor], grads), device=device)) or (foreach and _device_has_foreach_support(device)) ): torch._foreach_clamp_min_(cast(List[Tensor], grads), -clip_value) torch._foreach_clamp_max_(cast(List[Tensor], grads), clip_value) elif foreach: raise RuntimeError(f'foreach=True was passed, but can\'t use the foreach API on {device.type} tensors') else: for grad in grads: cast(Tensor, grad).clamp_(min=-clip_value, max=clip_value)

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