# Source code for mindspore.nn.optim.momentum

# Copyright 2020 Huawei Technologies Co., Ltd
#
# 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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# Unless required by applicable law or agreed to in writing, software
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# ============================================================================
"""momentum"""
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", [])
success = F.depend(success, _ps_pull(_ps_push((learning_rate, gradient, momentum), shapes), weight))
else:
success = F.depend(success, opt(weight, moment, learning_rate, gradient, momentum))
return success

[docs]class Momentum(Optimizer):
"""
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 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.
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 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.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)