MegEngine/imperative/python/megengine/optimizer/adadelta.py

# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
from typing import Iterable, Union

import numpy as np

from ..functional import sqrt
from ..tensor_nn import Buffer, Parameter
from .distributed_optimizer import DistributedOptimizer


class Adadelta(DistributedOptimizer):
    r"""Implements Adadelta algorithm.

    It has been proposed in `"ADADELTA: An Adaptive Learning Rate Method" <https://arxiv.org/abs/1212.5701>`_.

    :param params: iterable of parameters to optimize or dicts defining
        parameter groups.
    :param lr: coefficient that scale delta before it is applied
        to the parameters (default: 1.0).
    :param rho: coefficient used for computing a running average
        of squared gradients (default: 0.9).
    :param eps: term added to the denominator to improve
        numerical stability (default: 1e-6).
    :param weight_decay: weight decay (L2 penalty) (default: 0).
    """

    def __init__(
        self,
        params: Union[Iterable[Parameter], dict],
        lr: float = 1.0,
        rho: float = 0.9,
        eps: float = 1e-6,
        weight_decay: float = 0.0,
        **kwargs
    ):
        assert lr >= 0.0, "Invalid learning rate: {}".format(lr)
        assert rho >= 0.0 and rho <= 1.0, "Invalid rho value: {}".format(rho)
        assert eps >= 0.0, "Invalid epsilon value: {}".format(eps)
        assert weight_decay >= 0.0, "Invalid weight_decay value: {}".format(
            weight_decay
        )

        defaults = dict(lr=lr, rho=rho, eps=eps, weight_decay=weight_decay)
        super().__init__(params, defaults, **kwargs)

    def _create_state(self, param_group):
        for param in param_group["params"]:
            self._add_state(param, "square_avg")
            self._add_state(param, "acc_delta")
            self._add_state(param, "step", initializer=0.0)

    def _updates(self, param_group):
        lr = param_group["lr"]
        weight_decay = param_group["weight_decay"]
        rho = param_group["rho"]
        eps = param_group["eps"]

        for param in param_group["params"]:

            if param.__wrapped__ in self._grad_skip:
                self._grad_skip.remove(param.__wrapped__)
                continue

            if not isinstance(param.grad, Buffer):
                raise TypeError(
                    "grad must be a Buffer, maybe you forget to call backward()?"
                )

            if not param.requires_grad:
                continue

            states = self._state[param]
            step = states["step"]
            step += 1.0
            grad = param.grad
            if weight_decay != 0.0:
                grad += param * weight_decay

            square_avg = states["square_avg"]
            acc_delta = states["acc_delta"]
            square_avg = rho * square_avg + (1 - rho) * grad ** 2
            std = sqrt(square_avg + eps)
            delta = sqrt(acc_delta + eps) / std * grad
            param -= lr * delta
            acc_delta = rho * acc_delta + (1 - rho) * delta ** 2
            states["square_avg"]._reset(square_avg)
            states["acc_delta"]._reset(acc_delta)

        assert len(self._grad_skip) == 0