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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# http://www.apache.org/licenses/LICENSE-2.0
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# ==============================================================================
"""Adam 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__ = ['Adam']
[docs]
class Adam(Optimizer):
"""The classic Adam optimizer.
See Also:
- The functional Adam optimizer: :func:`torchopt.adam`.
- The differentiable meta-Adam optimizer: :class:`torchopt.MetaAdam`.
"""
# pylint: disable-next=too-many-arguments
def __init__(
self,
params: Iterable[torch.Tensor],
lr: ScalarOrSchedule = 1e-3,
betas: tuple[float, float] = (0.9, 0.999),
eps: float = 1e-8,
weight_decay: float = 0.0,
*,
eps_root: float = 0.0,
maximize: bool = False,
use_accelerated_op: bool = False,
) -> None:
r"""Initialize the Adam 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 denominator outside of the square
root (as in the Adam paper) to avoid dividing by zero when rescaling.
(default: :const:`1e-8`)
weight_decay (float, optional): Weight decay, add L2 penalty to parameters.
(default: :const:`0.0`)
eps_root (float, optional): A small constant applied to denominator inside the square
root (as in RMSProp), to avoid dividing by zero when rescaling. This is needed for
example when computing (meta-)gradients through Adam. (default: :const:`0.0`)
moment_requires_grad (bool, optional): If :data:`True` the momentums will be created
with flag ``requires_grad=True``, this flag is often used in Meta-Learning
algorithms. (default: :data:`False`)
maximize (bool, optional): Maximize the params based on the objective, instead of
minimizing. (default: :data:`False`)
use_accelerated_op (bool, optional): If :data:`True` use our implemented fused operator.
(default: :data:`False`)
"""
super().__init__(
params,
alias.adam(
lr=lr,
betas=betas,
eps=eps,
weight_decay=weight_decay,
eps_root=eps_root,
moment_requires_grad=False,
maximize=maximize,
use_accelerated_op=use_accelerated_op,
),
)