Source code for torchopt.optim.meta.adamax

# Copyright 2022 MetaOPT Team. All Rights Reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
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#     http://www.apache.org/licenses/LICENSE-2.0
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# ==============================================================================
"""Differentiable Adamax optimizer."""

from __future__ import annotations

import torch.nn as nn

from torchopt import alias
from torchopt.optim.meta.base import MetaOptimizer
from torchopt.typing import ScalarOrSchedule


__all__ = ['MetaAdaMax', 'MetaAdamax']


class MetaAdaMax(MetaOptimizer):
    """The differentiable AdaMax optimizer.

    See Also:
        - The functional AdaMax optimizer: :func:`torchopt.adamax`.
        - The classic AdaMax optimizer: :class:`torchopt.Adamax`.
    """

    # pylint: disable-next=too-many-arguments
    def __init__(
        self,
        module: nn.Module,
        lr: ScalarOrSchedule = 2e-3,
        betas: tuple[float, float] = (0.9, 0.999),
        eps: float = 1e-8,
        weight_decay: float = 0.0,
        *,
        moment_requires_grad: bool = True,
    ) -> None:
        """Initialize the meta AdaMax optimizer.

        Args:
            module (nn.Module): A network whose parameters 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`)
            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`)
        """
        super().__init__(
            module,
            alias.adamax(
                lr=lr,
                betas=betas,
                eps=eps,
                weight_decay=weight_decay,
                moment_requires_grad=moment_requires_grad,
            ),
        )


MetaAdamax = MetaAdaMax  # alias for PyTorch compatibility