# Copyright 2020 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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# See the License for the specific language governing permissions and
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# ============================================================================
"""sgd"""
from mindspore.ops import functional as F, composite as C, operations as P
from mindspore.common.parameter import Parameter
from mindspore.common.tensor import Tensor
import mindspore.common.dtype as mstype
from mindspore._checkparam import Validator as validator
from .optimizer import Optimizer
_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."""
success = True
success = F.depend(success, opt(weight, gradient, learning_rate, accum, momentum, stat))
return success
[docs]class SGD(Optimizer):
r"""
Implements stochastic gradient descent (optionally with momentum).
Introduction to SGD can be found at https://en.wikipedia.org/wiki/Stochastic_gradient_descent.
Nesterov momentum is based on the formula from paper `On the importance of initialization and
momentum in deep learning <http://proceedings.mlr.press/v28/sutskever13.html>`_.
Note:
When separating parameter groups, the weight decay in each group will be applied on the parameters if the
weight decay is positive. When not separating parameter groups, the `weight_decay` in the API will be applied
on the parameters without 'beta' or 'gamma' in their names if `weight_decay` is positive.
To improve parameter groups performance, the customized order of parameters can be supported.
.. 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 Flase:
.. math::
p_{t+1} = p_{t} - lr \ast v_{t+1}
To be noticed, for the first step, v_{t+1} = gradient
Here : where p, v and u denote the parameters, accum, and momentum respectively.
Args:
params (Union[list[Parameter], list[dict]]): When the `params` is a list of `Parameter` which will be updated,
the element in `params` must be class `Parameter`. When the `params` is a list of `dict`, the "params",
"lr", "weight_decay" and "order_params" are the keys can be parsed.
- params: Required. The value must be a list of `Parameter`.
- lr: Optional. If "lr" in the keys, the value of corresponding learning rate will be used.
If not, the `learning_rate` in the API will be used.
- weight_decay: Optional. If "weight_decay" in the keys, the value of corresponding weight decay
will be used. If not, the `weight_decay` in the API will be used.
- order_params: Optional. If "order_params" in the keys, the value must be the order of parameters and
the order will be followed in optimizer. There are no other keys in the `dict` and the parameters which
in the value of 'order_params' must be in one of group parameters.
learning_rate (Union[float, Tensor, Iterable, LearningRateSchedule]): A value or a graph for the learning rate.
When the learning_rate is an Iterable or a Tensor in a 1D dimension, use dynamic learning rate, then
the i-th step will take the i-th value as the learning rate. When the learning_rate is LearningRateSchedule,
use dynamic learning rate, the i-th learning rate will be calculated during the process of training
according to the formula of LearningRateSchedule. When the learning_rate is a float or a Tensor in a zero
dimension, use fixed learning rate. Other cases are not supported. The float learning rate must be
equal to or greater than 0. If the type of `learning_rate` is int, it will be converted to float.
Default: 0.1.
momentum (float): A floating point value the momentum. must be at least 0.0. Default: 0.0.
dampening (float): A floating point value of dampening for momentum. must be at least 0.0. Default: 0.0.
weight_decay (float): Weight decay (L2 penalty). It must be equal to or greater than 0. Default: 0.0.
nesterov (bool): Enables the Nesterov momentum. If use nesterov, momentum must be positive,
and dampening must equal to 0.0. Default: False.
loss_scale (float): A floating point value for the loss scale, which must be larger
than 0.0. Default: 1.0.
Inputs:
- **gradients** (tuple[Tensor]) - The gradients of `params`, the shape is the same as `params`.
Outputs:
Tensor[bool], the value is True.
Raises:
ValueError: If the momentum, dampening or weight_decay value is less than 0.0.
Examples:
>>> net = Net()
>>> #1) All parameters use the same learning rate and weight decay
>>> optim = nn.SGD(params=net.trainable_params())
>>>
>>> #2) Use parameter groups and set different values
>>> conv_params = list(filter(lambda x: 'conv' in x.name, net.trainable_params()))
>>> no_conv_params = list(filter(lambda x: 'conv' not in x.name, net.trainable_params()))
>>> group_params = [{'params': conv_params, 'weight_decay': 0.01},
>>> {'params': no_conv_params, 'lr': 0.01},
>>> {'order_params': net.trainable_params()}]
>>> optim = nn.SGD(group_params, learning_rate=0.1, weight_decay=0.0)
>>> # The conv_params's parameters will use a learning rate of default value 0.1 and a weight decay of 0.01.
>>> # The no_conv_params's parameters will use a learning rate of 0.01 and a weight decay of default value 0.0.
>>> # The final parameters order in which the optimizer will be followed is the value of 'order_params'.
>>>
>>> loss = nn.SoftmaxCrossEntropyWithLogits()
>>> model = Model(net, loss_fn=loss, optimizer=optim)
"""
def __init__(self, params, learning_rate=0.1, momentum=0.0, dampening=0.0, weight_decay=0.0, nesterov=False,
loss_scale=1.0):
super(SGD, self).__init__(learning_rate, params, weight_decay, loss_scale)
if isinstance(momentum, int):
momentum = float(momentum)
if not isinstance(momentum, float):
raise TypeError("momentum should be float number!")
if isinstance(momentum, float) and momentum < 0.0:
raise ValueError("momentum should be at least 0.0, but got momentum {}".format(momentum))
if isinstance(dampening, int):
dampening = float(dampening)
if not isinstance(dampening, float):
raise TypeError("dampening should be float number")
if dampening < 0.0:
raise ValueError("dampening should be at least 0.0, but got dampening {}".format(dampening))
self.dampening = dampening
if isinstance(weight_decay, int):
weight_decay = float(weight_decay)
validator.check_value_type("nesterov", nesterov, [bool], self.cls_name)
if nesterov and (momentum <= 0.0 or dampening != 0.0):
raise ValueError("If use nesterov, momentum must be positive and dampening must equal to 0.0,"
"but got momentum {}, dampening {}".format(momentum, dampening))
self.nesterov = nesterov
self.opt = P.SGD(dampening, weight_decay, nesterov)
self.momentum = Parameter(Tensor(momentum, mstype.float32), name="momentum")
self.accum = self.parameters.clone(prefix="accum", init='zeros')
self.stat = self.parameters.clone(prefix="stat", init='ones')
self.hyper_map = C.HyperMap()
def construct(self, gradients):
params = self.parameters
accum = self.accum
stat = self.stat
gradients = self.scale_grad(gradients)
lr = self.get_lr()
if self.is_group_lr:
success = self.hyper_map(F.partial(_sgd_opt, self.opt, self.momentum), lr, gradients, params, accum, stat)
else:
success = self.hyper_map(F.partial(_sgd_opt, self.opt, self.momentum, lr), gradients, params, accum, stat)
return success