# 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.
# ============================================================================
"""PROXIMAL_ADA_GRAD"""
from mindspore.ops import functional as F, composite as C, operations as P
from mindspore.common import Tensor
import mindspore.common.dtype as mstype
from mindspore._checkparam import Validator as validator
from mindspore._checkparam import Rel
from .optimizer import Optimizer
_proximal_ada_grad_opt = C.MultitypeFuncGraph("proximal_ada_grad_opt")
@_proximal_ada_grad_opt.register("Function", "Function", "Tensor", "Tensor", "Tensor", "RowTensor", "Tensor",
"Tensor")
def _tensor_run_opt_with_sparse(opt, sparse_opt, l1, l2, learning_rate, gradient, weight, accum):
"""Apply sparse proximal_ada_grad optimizer to the weight parameter."""
success = True
success = F.depend(success, sparse_opt(weight, accum, learning_rate, l1, l2, gradient.values, gradient.indices))
return success
@_proximal_ada_grad_opt.register("Function", "Function", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor")
def _tensor_run_opt(opt, sparse_opt, l1, l2, learning_rate, gradient, weight, accum):
"""Apply proximal_ada_grad optimizer to the weight parameter."""
success = True
success = F.depend(success, opt(weight, accum, learning_rate, l1, l2, gradient))
return success
def _check_param_value(accum, l1, l2, use_locking, prim_name=None):
"""Check inputs param."""
validator.check_value_type("accum", accum, [float], prim_name)
validator.check_value_type("l1", l1, [float], prim_name)
validator.check_value_type("l2", l2, [float], prim_name)
validator.check_value_type("use_locking", use_locking, [bool], prim_name)
validator.check_number_range("accum", accum, 0.0, float("inf"), Rel.INC_LEFT, prim_name)
validator.check_number_range("l1", l1, 0.0, float("inf"), Rel.INC_LEFT, prim_name)
validator.check_number_range("l2", l2, 0.0, float("inf"), Rel.INC_LEFT, prim_name)
[docs]class ProximalAdagrad(Optimizer):
"""
Implement the ProximalAdagrad algorithm with ApplyProximalAdagrad Operator.
ProximalAdagrad is an online Learning and Stochastic Optimization.
Refer to paper `Efficient Learning using Forward-Backward Splitting
<http://papers.nips.cc//paper/3793-efficient-learning-using-forward-backward-splitting.pdf>`_.
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.
The sparse strategy is applied while the SparseGatherV2 operator being used for forward network.
The sparse feature is under continuous development. The sparse
behavior is currently performed on the CPU.
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.
- 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 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.
accum (float): The starting value for accumulators, must be zero or positive values. Default: 0.1.
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: 0.001.
l1 (float): l1 regularization strength, must be greater than or equal to zero. Default: 0.0.
l2 (float): l2 regularization strength, must be greater than or equal to zero. Default: 0.0.
use_locking (bool): If true, use locks for updating operation. Default: False.
loss_scale (float): Value for the loss scale. It must be greater than 0.0. Default: 1.0.
weight_decay (float): Weight decay value to multiply weight, must be zero or positive value. Default: 0.0.
Inputs:
- **grads** (tuple[Tensor]) - The gradients of `params` in the optimizer, the shape is the same as the `params`
in optimizer.
Outputs:
Tensor[bool], the value is True.
Examples:
>>> net = Net()
>>> #1) All parameters use the same learning rate and weight decay
>>> optim = nn.ProximalAdagrad(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.ProximalAdagrad(group_params, learning_rate=0.1, weight_decay=0.0)
>>> # The conv_params's parameters will use default learning rate of 0.1 and weight decay of 0.01.
>>> # The no_conv_params's parameters will use learning rate of 0.01 and default weight decay of 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, accum=0.1, learning_rate=0.001, l1=0.0, l2=0.0,
use_locking=False, loss_scale=1.0, weight_decay=0.0):
super(ProximalAdagrad, self).__init__(learning_rate, params, weight_decay, loss_scale)
_check_param_value(accum, l1, l2, use_locking, self.cls_name)
self.accum = self.parameters.clone(prefix="accum", init=accum)
self.l1 = Tensor(l1, mstype.float32)
self.l2 = Tensor(l2, mstype.float32)
self.hyper_map = C.HyperMap()
self.opt = P.ApplyProximalAdagrad(use_locking=use_locking)
self.sparse_opt = P.FusedSparseProximalAdagrad(use_locking=use_locking)
def construct(self, grads):
params = self.parameters
accum = self.accum
grads = self.decay_weight(grads)
grads = self.scale_grad(grads)
lr = self.get_lr()
if self.is_group_lr:
success = self.map_(F.partial(_proximal_ada_grad_opt, self.opt, self.sparse_opt, self.l1, self.l2), lr,
grads, params, accum)
else:
success = self.map_(F.partial(_proximal_ada_grad_opt, self.opt, self.sparse_opt, self.l1, self.l2, lr),
grads, params, accum)
return success