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# ==============================================================================
# This file is modified from:
# https://github.com/deepmind/optax/blob/master/optax/_src/update.py
# ==============================================================================
# Copyright 2019 DeepMind Technologies Limited. All Rights Reserved.
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# you may not use this file except in compliance with the License.
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# ==============================================================================
"""Helper functions for applying updates."""
from __future__ import annotations
import torch
from torchopt import pytree
from torchopt.typing import Params, Updates
__all__ = ['apply_updates']
[docs]
def apply_updates(params: Params, updates: Updates, *, inplace: bool = True) -> Params:
"""Apply an update to the corresponding parameters.
This is a utility functions that applies an update to a set of parameters, and then returns the
updated parameters to the caller. As an example, the update may be a gradient transformed by a
sequence of :class:`GradientTransformations`. This function is exposed for convenience, but it
just adds updates and parameters; you may also apply updates to parameters manually, using
:func:`tree_map` (e.g. if you want to manipulate updates in custom ways before applying them).
Args:
params (tree of Tensor): A tree of parameters.
updates (tree of Tensor): A tree of updates, the tree structure and the shape of the leaf
nodes must match that of ``params``.
inplace (bool, optional): If :data:`True`, will update params in a inplace manner.
(default: :data:`True`)
Returns:
Updated parameters, with same structure, shape and type as ``params``.
"""
if inplace:
def f(p: torch.Tensor, u: torch.Tensor | None) -> torch.Tensor:
if u is not None:
p.data.add_(u)
return p
else:
def f(p: torch.Tensor, u: torch.Tensor | None) -> torch.Tensor:
return p.add(u) if u is not None else p
return pytree.tree_map(f, params, updates)