152 lines
7.0 KiB
Python
152 lines
7.0 KiB
Python
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
|
|
|
|
@_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
|
|
|
|
|
|
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)
|
|
|
|
|
|
@_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)
|