Source code for torchopt.optim.meta.adadelta

# Copyright 2022-2024 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 Adadelta optimizer."""

from __future__ import annotations

from typing import TYPE_CHECKING

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


if TYPE_CHECKING:
    import torch.nn as nn

    from torchopt.typing import ScalarOrSchedule


__all__ = ['MetaAdaDelta', 'MetaAdadelta']




class MetaAdaDelta(MetaOptimizer):
    """The differentiable AdaDelta optimizer.

    See Also:
        - The functional AdaDelta optimizer: :func:`torchopt.adadetla`.
        - The classic AdaDelta optimizer: :class:`torchopt.Adadelta`.
    """

    # pylint: disable-next=too-many-arguments
    def __init__(
        self,
        module: nn.Module,
        lr: ScalarOrSchedule = 1.0,
        rho: float = 0.9,
        eps: float = 1e-6,
        weight_decay: float = 0.0,
        *,
        moment_requires_grad: bool = True,
    ) -> None:
        """Initialize the meta AdaDelta 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`)
            rho (float, optional): Coefficients used for computing running averages of  gradient and its square.
                (default: :const:`0.9`)
            eps (float, optional): A small constant applied to the square root (as in the AdaDelta 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.adadelta(
                lr=lr,
                rho=rho,
                eps=eps,
                weight_decay=weight_decay,
                moment_requires_grad=moment_requires_grad,
            ),
        )



MetaAdadelta = MetaAdaDelta  # alias for PyTorch compatibility