# Copyright 2022-2024 MetaOPT Team. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
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# ==============================================================================
"""Hook utilities."""
from __future__ import annotations
from typing import Callable
import torch
from torchopt import pytree
from torchopt.base import EmptyState, GradientTransformation
from torchopt.typing import OptState, Params, Updates
__all__ = ['zero_nan_hook', 'nan_to_num_hook', 'register_hook']
[docs]
def zero_nan_hook(g: torch.Tensor) -> torch.Tensor:
"""Replace ``nan`` with zero."""
return g.nan_to_num(nan=0.0)
[docs]
def nan_to_num_hook(
nan: float = 0.0,
posinf: float | None = None,
neginf: float | None = None,
) -> Callable[[torch.Tensor], torch.Tensor]:
"""Return a ``nan`` to num hook to replace ``nan`` / ``+inf`` / ``-inf`` with the given numbers."""
def hook(g: torch.Tensor) -> torch.Tensor:
"""Replace ``nan`` / ``+inf`` / ``-inf`` with the given numbers."""
return g.nan_to_num(nan=nan, posinf=posinf, neginf=neginf)
return hook
[docs]
def register_hook(hook: Callable[[torch.Tensor], torch.Tensor | None]) -> GradientTransformation:
"""Stateless identity transformation that leaves input gradients untouched.
This function passes through the *gradient updates* unchanged.
Returns:
An ``(init_fn, update_fn)`` tuple.
"""
def init_fn(params: Params) -> OptState: # pylint: disable=unused-argument
return EmptyState()
def update_fn(
updates: Updates,
state: OptState,
*,
params: Params | None = None, # pylint: disable=unused-argument
inplace: bool = True, # pylint: disable=unused-argument
) -> tuple[Updates, OptState]:
def f(g: torch.Tensor) -> torch.utils.hooks.RemovableHandle:
return g.register_hook(hook)
pytree.tree_map_(f, updates)
return updates, state
return GradientTransformation(init_fn, update_fn)