Source code for torchopt.optim.meta.adagrad

# Copyright 2022-2023 MetaOPT Team. All Rights Reserved.
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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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# ==============================================================================
"""Differentiable AdaGrad 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__ = ['MetaAdaGrad', 'MetaAdagrad']


class MetaAdaGrad(MetaOptimizer):
    """The differentiable AdaGrad optimizer.

    See Also:
        - The functional AdaGrad optimizer: :func:`torchopt.adagrad`.
        - The classic AdaGrad optimizer: :class:`torchopt.Adagrad`.
    """

    # pylint: disable-next=too-many-arguments
    def __init__(
        self,
        module: nn.Module,
        lr: ScalarOrSchedule = 1e-2,
        lr_decay: float = 0.0,
        weight_decay: float = 0.0,
        initial_accumulator_value: float = 0.0,
        eps: float = 1e-10,
        *,
        maximize: bool = False,
    ) -> None:
        """Initialize the meta AdaGrad 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-2`)
            lr_decay (float, optional): Learning rate decay. (default: :const:`0.0`)
            weight_decay (float, optional): Weight decay, add L2 penalty to parameters.
                (default: :const:`0.0`)
            initial_accumulator_value (float, optional): Initial value for the accumulator.
                (default: :const:`0.0`)
            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-10`)
            maximize (bool, optional): Maximize the params based on the objective, instead of
                minimizing. (default: :data:`False`)
        """
        super().__init__(
            module,
            alias.adagrad(
                lr=lr,
                lr_decay=lr_decay,
                weight_decay=weight_decay,
                initial_accumulator_value=initial_accumulator_value,
                eps=eps,
                maximize=maximize,
            ),
        )


MetaAdagrad = MetaAdaGrad  # alias for PyTorch compatibility