megengine.optimizer.sgd 源代码

# -*- 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.
import os
from typing import Iterable, Union

from ..functional.inplace import _inplace_add_
from ..tensor import Parameter, tensor
from .optimizer import Optimizer


[文档]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]) inplace_mode = int(os.getenv("MEGENGINE_INPLACE_UPDATE", "0")) if inplace_mode: _neg_lr = tensor([-lr]) c1 = tensor([1.0]) 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 inplace_mode: if momentum: v = self._state[param]["momentum_buffer"] _inplace_add_(v, grad, alpha=_momentum, beta=c1) _inplace_add_(param, v, alpha=c1, beta=_neg_lr) else: _inplace_add_(param, grad, alpha=c1, beta=_neg_lr) continue if momentum: v = self._state[param]["momentum_buffer"] # v = v * _momentum + grad v *= _momentum v += grad param -= _lr * v else: param -= _lr * grad