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| 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 | |
| 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 | |
| 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) | |
| 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) | |