Source code for mindspore.nn.optim.momentum

# 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.
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# ============================================================================
from mindspore.ops import functional as F, composite as C, operations as P
from mindspore.ops import _selected_ops
from mindspore.common.parameter import Parameter
from mindspore.common.tensor import Tensor
import mindspore.common.dtype as mstype
from mindspore._checkparam import check_bool
from mindspore._checkparam import Validator as validator
from .optimizer import Optimizer

_momentum_opt = C.MultitypeFuncGraph("momentum_opt")

@_momentum_opt.register("Function", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Bool")
def _tensor_run_opt_ext(opt, momentum, learning_rate, gradient, weight, moment, ps_parameter):
    """Apply momentum optimizer to the weight parameter using Tensor."""
    success = True
    if ps_parameter:
        op_shape = P.Shape()
        _ps_pull = P.Pull()
        _ps_push = P.Push("ApplyMomentum", [])
        shapes = (op_shape(learning_rate), op_shape(gradient), op_shape(momentum))
        success = F.depend(success, _ps_pull(_ps_push((learning_rate, gradient, momentum), shapes), weight))
        success = F.depend(success, opt(weight, moment, learning_rate, gradient, momentum))
    return success

[docs]class Momentum(Optimizer): r""" Implements the Momentum algorithm. Refer to the paper on the importance of initialization and momentum in deep learning for more details. 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} = v_{t-1} \ast u + gradients If use_nesterov is True: .. math:: p_{t} = p_{t-1} - (grad \ast lr + v_{t} \ast u \ast lr) If use_nesterov is Flase: .. math:: p_{t} = p_{t-1} - lr \ast v_{t} Here: where grad, lr, p, v and u denote the gradients, learning_rate, params, moments, 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 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 should be equal to or greater than 0. If the type of `learning_rate` is int, it will be converted to float. momentum (float): Hyperparameter of type float, means momentum for the moving average. It should be at least 0.0. weight_decay (int, float): Weight decay (L2 penalty). It should be equal to or greater than 0.0. Default: 0.0. loss_scale (int, float): A floating point value for the loss scale. It should be greater than 0.0. Default: 1.0. use_nesterov (bool): Enable Nesterov momentum. Default: False. Inputs: - **gradients** (tuple[Tensor]) - The gradients of `params`, the shape is the same as `params`. Outputs: tuple[bool], all elements are True. Raises: ValueError: If the momentum is less than 0.0. Examples: >>> net = Net() >>> #1) All parameters use the same learning rate and weight decay >>> optim = nn.Momentum(params=net.trainable_params(), learning_rate=0.1, momentum=0.9) >>> >>> #2) Use parameter groups and set different values >>> conv_params = list(filter(lambda x: 'conv' in, net.trainable_params())) >>> no_conv_params = list(filter(lambda x: 'conv' not in, 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.Momentum(group_params, learning_rate=0.1, momentum=0.9, 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, metrics=None) """ def __init__(self, params, learning_rate, momentum, weight_decay=0.0, loss_scale=1.0, use_nesterov=False): super(Momentum, self).__init__(learning_rate, params, weight_decay, loss_scale) validator.check_value_type("momentum", momentum, [float], self.cls_name) if isinstance(momentum, float) and momentum < 0.0: raise ValueError("momentum should be at least 0.0, but got momentum {}".format(momentum)) self.momentum = Parameter(Tensor(momentum, mstype.float32), name="momentum") self.params = self.parameters self.use_nesterov = check_bool(use_nesterov) self.moments = self.params.clone(prefix="moments", init='zeros') self.hyper_map = C.HyperMap() self.opt = _selected_ops.ApplyMomentum(use_nesterov=self.use_nesterov) def construct(self, gradients): params = self.params moments = self.moments gradients = self.decay_weight(gradients) gradients = self.scale_grad(gradients) lr = self.get_lr() if self.is_group_lr: success = self.hyper_map(F.partial(_momentum_opt, self.opt, self.momentum), lr, gradients, params, moments, self.ps_parameters) else: success = self.hyper_map(F.partial(_momentum_opt, self.opt, self.momentum, lr), gradients, params, moments, self.ps_parameters) return success