Source code for mindspore.nn.optim.sgd

# 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
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""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): """ 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` should 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 should 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 should 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' should be in one of group parameters. learning_rate (Union[float, Tensor, Iterable, LearningRateSchedule]): A value or graph for the learning rate. When the learning_rate is a Iterable or a Tensor with dimension of 1, 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 with dimension of 0, use fixed learning rate. Other cases are not supported. The float learning rate should 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. should be at least 0.0. Default: 0.0. dampening (float): A floating point value of dampening for momentum. should be at least 0.0. Default: 0.0. weight_decay (float): Weight decay (L2 penalty). It should be in range [0.0, 1.0]. 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. Should be not less than 1.0. 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 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)) 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) 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