# Copyright 2023 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
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# ============================================================================
"""sgd"""
from __future__ import absolute_import
from mindspore.ops import functional as F, composite as C, operations as P
from mindspore.common.tensor import Tensor
import mindspore.common.dtype as mstype
from mindspore import _checkparam as Validator
from mindspore.experimental.optim.optimizer import Optimizer
from mindspore import jit
_sgd_opt = C.MultitypeFuncGraph("sgd_opt")
@_sgd_opt.register("Function", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor")
def _tensor_run_opt_ext(opt, momentum, learning_rate, gradient, weight, accum, stat):
"""Apply sgd optimizer to the weight parameter using Tensor."""
opt(weight, gradient, learning_rate, accum, momentum, stat)
return True
[文档]class SGD(Optimizer):
r"""
Stochastic Gradient Descent optimizer.
.. math::
v_{t+1} = u \ast v_{t} + gradient \ast (1-dampening)
If nesterov is True:
.. math::
p_{t+1} = p_{t} - lr \ast (gradient + u \ast v_{t+1})
If nesterov is False:
.. math::
p_{t+1} = p_{t} - lr \ast v_{t+1}
To be noticed, for the first step, :math:`v_{t+1} = gradient`.
Here : where p, v and u denote the parameters, accum, and momentum respectively.
.. warning::
This is an experimental optimizer API that is subject to change.
This module must be used with lr scheduler module in `LRScheduler Class
<https://www.mindspore.cn/docs/en/r2.3.1/api_python/mindspore.experimental.html#lrscheduler-class>`_ .
Args:
params (Union[list(Parameter), list(dict)]): list of parameters to optimize or dicts defining
parameter groups.
lr (Union[int, float, Tensor]): learning rate.
momentum (Union[int, float], optional): momentum factor. Default: ``0``.
weight_decay (float, optional): weight decay (L2 penalty). Default: ``0.``.
dampening (Union[int, float], optional): dampening for momentum. Default: ``0``.
nesterov (bool, optional): enables Nesterov momentum. Default: ``False``.
Keyword Args:
maximize (bool, optional): maximize the params based on the objective, instead of minimizing.
Default: ``False``.
Inputs:
- **gradients** (tuple[Tensor]) - The gradients of `params`.
Raises:
ValueError: If the learning rate is not int, float or Tensor.
ValueError: If the learning rate is less than 0.
ValueError: If the `momentum` or `weight_decay` value is less than 0.0.
ValueError: If the `momentum`, `dampening` or `weight_decay` value is not int or float.
ValueError: If the `nesterov` and `maximize` is not bool.
ValueError: If the `nesterov` is true, `momentum` is not positive or `dampening` is not 0.0.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> from mindspore import nn
>>> from mindspore.experimental import optim
>>> # Define the network structure of LeNet5. Refer to
>>> # https://gitee.com/mindspore/docs/blob/r2.3.1/docs/mindspore/code/lenet.py
>>> net = LeNet5()
>>> loss_fn = nn.SoftmaxCrossEntropyWithLogits(sparse=True)
>>> optimizer = optim.SGD(net.trainable_params(), lr=0.1)
>>> def forward_fn(data, label):
... logits = net(data)
... loss = loss_fn(logits, label)
... return loss, logits
>>> grad_fn = mindspore.value_and_grad(forward_fn, None, optimizer.parameters, has_aux=True)
>>> def train_step(data, label):
... (loss, _), grads = grad_fn(data, label)
... optimizer(grads)
... return loss
"""
def __init__(self, params, lr, momentum=0, dampening=0, weight_decay=0.0, nesterov=False, *,
maximize=False):
Validator.check_value_type("lr", lr, [float, int, Tensor], self.cls_name)
if lr < 0.0:
raise ValueError("Invalid learning rate: {}".format(lr))
Validator.check_value_type("momentum", momentum, [int, float], self.cls_name)
if momentum < 0.0:
raise ValueError("Invalid momentum value: {}".format(momentum))
momentum = float(momentum)
Validator.check_value_type("nesterov", nesterov, [bool], self.cls_name)
Validator.check_value_type("maximize", maximize, [bool], self.cls_name)
defaults = dict(lr=lr, momentum=momentum, dampening=dampening,
weight_decay=weight_decay, nesterov=nesterov,
maximize=maximize, grad_centralization=False)
super(SGD, self).__init__(params, defaults)
for group in self.param_groups:
Validator.check_value_type("dampening", group.get("dampening"), [int, float], self.cls_name)
group["dampening"] = float(group.get("dampening"))
if nesterov and (momentum <= 0.0 or dampening != 0.0):
raise ValueError("For 'SGD', if 'nesterov' is true, 'momentum' must be > 0.0 and 'dampening' must "
"equal to 0.0, but got 'momentum' {}, 'dampening' {}".format(momentum, dampening))
self.accum = self.parameters.clone(prefix="accum", init='zeros')
self.stat = self.parameters.clone(prefix="stat", init='ones')
self.op_cast = P.Cast()
@jit
def implementation(self, momentum, lr, group_id, gradients, maximize, dampening, weight_decay, nesterov):
"""Extract the common computing part for acceleration"""
start_id = self.group_start_id[group_id]
end_id = self.group_start_id[group_id + 1]
momentum = self.op_cast(momentum, mstype.float32)
opt = P.SGD(dampening, weight_decay, nesterov)
grads = tuple([grad if not maximize else F.neg(grad) for grad in gradients[start_id: end_id]])
self.hyper_map(F.partial(_sgd_opt, opt, momentum, lr), grads,
self.parameters[start_id: end_id], self.accum[start_id: end_id],
self.stat[start_id: end_id])
return True
def construct(self, gradients):
for group_id, group in enumerate(self.param_groups):
lr = self.lrs[group_id]
if isinstance(group.get("lr"), float):
lr = self.op_cast(group.get("lr"), mstype.float32)
self.implementation(group.get("momentum"), lr, group_id, gradients, group.get("maximize"),
group.get("dampening"),
group.get("weight_decay"), group.get("nesterov"))
return True