# 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,
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# ==============================================================================
"""Preset transformations that replaces updates with non-finite values to the given numbers."""
from __future__ import annotations
import torch
from torchopt import pytree
from torchopt.base import EmptyState, GradientTransformation
from torchopt.typing import OptState, Params, Updates
[docs]
def nan_to_num(
nan: float = 0.0,
posinf: float | None = None,
neginf: float | None = None,
) -> GradientTransformation:
"""Replace updates with values ``nan`` / ``+inf`` / ``-inf`` to the given numbers.
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,
) -> tuple[Updates, OptState]:
if inplace:
def f(g: torch.Tensor) -> torch.Tensor:
return g.nan_to_num_(nan=nan, posinf=posinf, neginf=neginf)
else:
def f(g: torch.Tensor) -> torch.Tensor:
return g.nan_to_num(nan=nan, posinf=posinf, neginf=neginf)
new_updates = pytree.tree_map(f, updates)
return new_updates, state
return GradientTransformation(init_fn, update_fn)