MegEngine/imperative/python/megengine/optimizer/sgd.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

from ..core.tensor.tensor import Tensor
from ..tensor import Parameter, tensor
from .optimizer import Optimizer


class SGD(Optimizer):
    r"""
    Implements stochastic gradient descent.

    Nesterov momentum is based on the formula from
    `"On the importance of initialization and momentum in deep learning" <http://www.cs.toronto.edu/%7Ehinton/absps/momentum.pdf>`_ .

    :param params: iterable of parameters to optimize or dicts defining
            parameter groups.
    :param lr: learning rate.
    :param momentum: momentum factor. Default: 0.0
    :param weight_decay: weight decay (L2 penalty). Default: 0.0
    """

    def __init__(
        self,
        params: Union[Iterable[Parameter], dict],
        lr: float,
        momentum: float = 0.0,
        weight_decay: float = 0.0,
    ):
        assert lr >= 0.0, "Invalid learning rate: {}".format(lr)
        assert momentum >= 0.0, "Invalid momentum value: {}".format(momentum)
        assert weight_decay >= 0.0, "Invalid weight_decay value: {}".format(
            weight_decay
        )

        defaults = dict(lr=lr, momentum=momentum, weight_decay=weight_decay)
        super().__init__(params, defaults)

    def _create_state(self, param_group):
        if param_group["momentum"] != 0.0:
            for param in param_group["params"]:
                self._add_state(param, "momentum_buffer")

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

        # since `conver_inputs` is disabled for param updates,
        # scalar should be explicitly tansforred to tensor
        _lr = tensor([lr])
        _weight_decay = tensor([weight_decay])
        _momentum = tensor([momentum])

        for param in param_group["params"]:
            if param.grad is None:
                continue

            grad = param.grad
            if weight_decay != 0.0:
                grad += param * _weight_decay

            if momentum:
                v = self._state[param]["momentum_buffer"]
                v = _momentum * v + grad
                param -= _lr * v
                self._state[param]["momentum_buffer"]._reset(v)
            else:
                param -= _lr * grad