# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2021 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>`_ .
Args:
params: iterable of parameters to optimize or dicts defining
parameter groups.
lr: learning rate.
momentum: momentum factor. Default: 0.0
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)
self._disable_type_convert = True
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 = 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