# 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
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# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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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. Momentum is optional.
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>`_.
.. 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, v_{t+1} = gradient
Here : where p, v and u denote the parameters, accum, and momentum respectively.
Note:
When separating parameter groups, if you want to centralize the gradient, set grad_centralization to True,
but the gradient centralization can only be applied to the parameters of the convolution layer.
If the parameters of the non convolution layer are set to True, an error will be reported.
To improve parameter groups performance, the customized order of parameters can be supported.
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.
- 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.
- grad_centralization: Optional. The data type of "grad_centralization" is Bool. If "grad_centralization"
is in the keys, the set value will be used. If not, the `grad_centralization` is False by default.
This parameter only works on the convolution layer.
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. In general, use
the default value. Only when `FixedLossScaleManager` is used for training and the `drop_overflow_update` in
`FixedLossScaleManager` is set to False, then this value needs to be the same as the `loss_scale` in
`FixedLossScaleManager`. Refer to class :class:`mindspore.FixedLossScaleManager` for more details.
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.
Supported Platforms:
``Ascend`` ``GPU``
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,'grad_centralization':True},
... {'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 default learning rate of 0.1 default weight decay of 0.0 and grad
>>> # centralization of True.
>>> # The no_conv_params's parameters will use learning rate of 0.01 and default weight decay of 0.0 and grad
>>> # centralization of False.
>>> # 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)
gradients = self.gradients_centralization(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