Source code for mindspore.nn.optim.sgd

# Copyright 2020 Huawei Technologies Co., Ltd
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# Licensed under the Apache License, Version 2.0 (the "License");
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# http://www.apache.org/licenses/LICENSE-2.0
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"""sgd"""
from mindspore.ops import functional as F, composite as C, operations as P
from mindspore.common.initializer import initializer
from mindspore.common.parameter import Parameter
from mindspore._checkparam import ParamValidator as validator
import mindspore.common.dtype as mstype
from .optimizer import Optimizer, grad_scale

sgd_opt = C.MultitypeFuncGraph("sgd_opt")


@sgd_opt.register("Function", "Number", "Number", "Tensor", "Tensor", "Tensor", "Tensor")
def _tensor_run_opt(opt, learning_rate, momentum, gradient, weight, accum, stat):
    """Apply sgd optimizer to the weight parameter."""
    success = True
    success = F.depend(success, opt(weight, gradient, learning_rate, accum, momentum, stat))
    return success


@sgd_opt.register("Function", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor")
def _tensor_run_opt_ext(opt, learning_rate, momentum, 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


@sgd_opt.register("Function", "Tensor", "Number", "Tensor", "Tensor", "Tensor", "Tensor")
def _tensor_run_opt_dyn(opt, learning_rate, momentum, gradient, weight, accum, stat):
    """Apply sgd optimizer to the weight parameter using dynamic learning rate."""
    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>`_. Args: params (list[Parameter]): A list of parameter, which will be updated. The element in `params` should be class mindspore.Parameter. learning_rate (float): A floating point value for the learning rate. Default: 0.1. momentum (float): A floating point value the momentum. Default: 0. dampening (float): A floating point value of dampening for momentum. Default: 0. weight_decay (float): Weight decay (L2 penalty). Default: 0. nesterov (bool): Enables the Nesterov momentum. Default: False. loss_scale (float): A floating point value for the loss scale. 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() >>> loss = nn.SoftmaxCrossEntropyWithLogits() >>> optim = nn.SGD(params=net.trainable_params()) >>> model = Model(net, loss_fn=loss, optimizer=optim, metrics=None) """ 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) if isinstance(momentum, float) and momentum < 0.0: raise ValueError("momentum should be at least 0.0, but got momentum {}".format(momentum)) if dampening < 0.0: raise ValueError("dampening should be at least 0.0, but got dampening {}".format(dampening)) self.dampening = dampening if weight_decay < 0.0: raise ValueError("weight_decay should be at least 0.0, but got weight_decay {}".format(weight_decay)) self.weight_decay = weight_decay validator.check_type("nesterov", nesterov, [bool]) self.nesterov = nesterov self.opt = P.SGD(dampening, weight_decay, nesterov) self.dynamic_lr = False self.gather = None self.global_step = None self.axis = None if not isinstance(learning_rate, float): self.dynamic_lr = True self.gather = P.GatherV2() self.assignadd = P.AssignAdd() self.global_step = Parameter(initializer(0, [1], mstype.int32), name="global_step") self.axis = 0 self.momentum = Parameter(momentum, name="momentum") self.params = self.parameters self.accum = self.params.clone(prefix="accum", init='zeros') self.stat = self.params.clone(prefix="stat", init='ones') self.hyper_map = C.HyperMap() self.weight_decay = weight_decay * loss_scale self.reciprocal_scale = 1.0 / loss_scale def construct(self, gradients): params = self.params accum = self.accum stat = self.stat if self.reciprocal_scale != 1.0: gradients = self.hyper_map(F.partial(grad_scale, self.reciprocal_scale), gradients) if self.dynamic_lr: lr = self.gather(self.learning_rate, self.global_step, self.axis) F.control_depend(lr, self.assignadd(self.global_step, 1)) else: lr = self.learning_rate success = self.hyper_map(F.partial(sgd_opt, self.opt, lr, self.momentum), gradients, params, accum, stat) return success