basecls.solver.optimizer.sgd 源代码
#!/usr/bin/env python3
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
import os
from typing import Iterable, Union
from megengine import Parameter, tensor
from megengine.functional.inplace import _inplace_add_
from megengine.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``
nesterov: enables Nesterov momentum. Default: ``False``
weight_decay: weight decay (L2 penalty). Default: ``0.0``
"""
def __init__(
self,
params: Union[Iterable[Parameter], dict],
lr: float,
momentum: float = 0.0,
nesterov: bool = False,
weight_decay: float = 0.0,
):
if lr < 0.0:
raise ValueError("Invalid learning rate: {}".format(lr))
if momentum < 0.0:
raise ValueError("Invalid momentum value: {}".format(momentum))
if weight_decay < 0.0:
raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
if nesterov and momentum <= 0:
raise ValueError("Nesterov momentum requires a momentum")
defaults = dict(lr=lr, momentum=momentum, weight_decay=weight_decay)
super().__init__(params, defaults)
self.nesterov = nesterov
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 != 0.0:
v = self._state[param]["momentum_buffer"]
_inplace_add_(v, grad, alpha=_momentum, beta=c1)
if self.nesterov:
grad = grad + v * _momentum
else:
grad = v
_inplace_add_(param, grad, alpha=c1, beta=_neg_lr)
continue
if momentum != 0.0:
v = self._state[param]["momentum_buffer"]
v *= _momentum
v += grad
if self.nesterov:
grad = grad + v * _momentum
else:
grad = v
param -= _lr * grad