nn/utils/clip_grad.py

# mypy: allow-untyped-decorators
# mypy: allow-untyped-defs
import functools
from typing import cast, Dict, Iterable, List, Optional, Tuple, Union
from typing_extensions import deprecated

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


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


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


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 the norms of the individual gradients of all parameters,
    as if the norms of the individual gradients 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(0.0)
    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


@deprecated(
    "`torch.nn.utils.clip_grad_norm` is now deprecated "
    "in favor of `torch.nn.utils.clip_grad_norm_`.",
    category=FutureWarning,
)
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.

    .. warning::
        This method is now deprecated in favor of
        :func:`torch.nn.utils.clip_grad_norm_`.
    """
    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)