# Copyright 2022-2023 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
# limitations under the License.
# ==============================================================================
"""Adamax optimizer."""
from __future__ import annotations
from typing import Iterable
import torch
from torchopt import alias
from torchopt.optim.base import Optimizer
from torchopt.typing import ScalarOrSchedule
__all__ = ['AdaMax', 'Adamax']
[docs]class AdaMax(Optimizer):
"""The classic AdaMax optimizer.
See Also:
- The functional AdaMax optimizer: :func:`torchopt.adamax`.
- The differentiable meta-AdaMax optimizer: :class:`torchopt.MetaAdaMax`.
"""
# pylint: disable-next=too-many-arguments
def __init__(
self,
params: Iterable[torch.Tensor],
lr: ScalarOrSchedule = 2e-3,
betas: tuple[float, float] = (0.9, 0.999),
eps: float = 1e-8,
weight_decay: float = 0.0,
) -> None:
r"""Initialize the AdaMax optimizer.
Args:
params (iterable of Tensor): An iterable of :class:`torch.Tensor`\s. Specifies what
tensors should be optimized.
lr (float or callable, optional): This is a fixed global scaling factor or a learning rate
scheduler. (default: :const:`1e-3`)
betas (tuple of float, optional): Coefficients used for computing running averages of
gradient and its square. (default: :const:`(0.9, 0.999)`)
eps (float, optional): A small constant applied to the square root (as in the AdaMax paper)
to avoid dividing by zero when rescaling.
(default: :const:`1e-6`)
weight_decay (float, optional): Weight decay, add L2 penalty to parameters.
(default: :const:`0.0`)
"""
super().__init__(
params,
alias.adamax(
lr=lr,
betas=betas,
eps=eps,
weight_decay=weight_decay,
moment_requires_grad=False,
),
)
Adamax = AdaMax # alias for PyTorch compatibility