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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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# ==============================================================================
"""Preset :class:`GradientTransformation` for the RAdam optimizer."""
from __future__ import annotations
from torchopt.alias.utils import (
_get_use_chain_flat,
flip_sign_and_add_weight_decay,
scale_by_neg_lr,
)
from torchopt.combine import chain
from torchopt.transform import scale_by_radam
from torchopt.typing import GradientTransformation, ScalarOrSchedule
__all__ = ['radam']
# pylint: disable-next=too-many-arguments
[docs]def radam(
lr: ScalarOrSchedule = 1e-3,
betas: tuple[float, float] = (0.9, 0.999),
eps: float = 1e-6,
weight_decay: float = 0.0,
*,
moment_requires_grad: bool = False,
) -> GradientTransformation:
"""Create a functional version of the RAdam optimizer.
RAdam is a variance of the adaptive learning rate rectified optimizer.
References:
- Liu, 2019: https://arxiv.org/abs/1908.03265
Args:
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 RAdam 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`)
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`)
Returns:
The corresponding :class:`GradientTransformation` instance.
See Also:
The functional optimizer wrapper :class:`torchopt.FuncOptimizer`.
"""
b1, b2 = betas # pylint: disable=invalid-name
# pylint: disable=unneeded-not
if not (callable(lr) or lr >= 0.0): # pragma: no cover
raise ValueError(f'Invalid learning rate: {lr}')
if not 0 <= b1 <= 1: # pragma: no cover
raise ValueError(f'Invalid rho value: {b1}')
if not 0 <= b2 <= 1: # pragma: no cover
raise ValueError(f'Invalid rho value: {b2}')
if not eps >= 0.0: # pragma: no cover
raise ValueError(f'Invalid epsilon value: {eps}')
if not weight_decay >= 0.0: # pragma: no cover
raise ValueError(f'Invalid weight_decay value: {weight_decay}')
# pylint: enable=unneeded-not
chain_fn = chain
flip_sign_and_add_weight_decay_fn = flip_sign_and_add_weight_decay
radam_scaler_fn = scale_by_radam
scale_by_neg_lr_fn = scale_by_neg_lr
if _get_use_chain_flat(): # default behavior
chain_fn = chain_fn.flat # type: ignore[attr-defined]
flip_sign_and_add_weight_decay_fn = flip_sign_and_add_weight_decay_fn.flat # type: ignore[attr-defined]
radam_scaler_fn = radam_scaler_fn.flat # type: ignore[attr-defined]
scale_by_neg_lr_fn = scale_by_neg_lr_fn.flat # type: ignore[attr-defined]
return chain_fn(
flip_sign_and_add_weight_decay_fn(weight_decay=weight_decay, maximize=False),
radam_scaler_fn(
b1=b1,
b2=b2,
eps=eps,
moment_requires_grad=moment_requires_grad,
),
scale_by_neg_lr_fn(lr),
)