# Copyright 2023 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.
# ============================================================================
"""adadelta"""
from __future__ import absolute_import
from mindspore.ops import functional as F, composite as C, operations as P
import mindspore.common.dtype as mstype
from mindspore.experimental.optim.optimizer import Optimizer, check_not_less_than, check_not_less_than_without_equal
from mindspore import _checkparam as validator
from mindspore import jit
_adadelta_opt = C.MultitypeFuncGraph("adadelta_opt")
@_adadelta_opt.register("Function", "Number", "Number", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor")
def _tensor_run_opt(opt, rho, epsilon, learning_rate, weight, accum, accum_update, gradient):
"""Apply adadelta optimizer to the weight parameter."""
success = True
success = F.depend(success, opt(weight, accum, accum_update, learning_rate, rho, epsilon, gradient))
return success
[文档]class Adadelta(Optimizer):
r"""
Implements Adadelta algorithm.
.. math::
\begin{aligned}
&\rule{150mm}{0.4pt} \\
&\textbf{input} : \gamma \text{ (lr)}, \: \theta_0 \text{ (params)},
\: f(\theta) \text{ (objective)}, \: \rho \text{ (decay)},
\: \lambda \text{ (weight decay)} \\
&\textbf{initialize} : v_0 \leftarrow 0 \: \text{ (square avg)},
\: u_0 \leftarrow 0 \: \text{ (accumulate variables)} \\[-1.ex]
&\rule{110mm}{0.4pt} \\
&\textbf{for} \: t=1 \: \textbf{to} \: \ldots \: \textbf{do} \\
&\hspace{5mm}g_t \leftarrow \nabla_{\theta} f_t (\theta_{t-1}) \\
&\hspace{5mm}if \: \lambda \neq 0 \\
&\hspace{10mm} g_t \leftarrow g_t + \lambda \theta_{t-1} \\
&\hspace{5mm} v_t \leftarrow v_{t-1} \rho + g^2_t (1 - \rho) \\
&\hspace{5mm}\Delta x_t \leftarrow \frac{\sqrt{u_{t-1} +
\epsilon }}{ \sqrt{v_t + \epsilon} }g_t \hspace{21mm} \\
&\hspace{5mm} u_t \leftarrow u_{t-1} \rho +
\Delta x^2_t (1 - \rho) \\
&\hspace{5mm}\theta_t \leftarrow \theta_{t-1} - \gamma \Delta x_t \\
&\rule{110mm}{0.4pt} \\[-1.ex]
&\bf{return} \: \theta_t \\[-1.ex]
&\rule{110mm}{0.4pt} \\[-1.ex]
\end{aligned}
.. warning::
This is an experimental optimizer API that is subject to change.
This module must be used with lr scheduler module in `LRScheduler Class
<https://www.mindspore.cn/docs/en/r2.3.1/api_python/mindspore.experimental.html#lrscheduler-class>`_ .
Args:
params (Union[list(Parameter), list(dict)]): list of parameters to optimize or dicts defining
parameter groups.
lr (Union[int, float, Tensor], optional): learning rate. Default: ``1.0``.
rho (float, optional): coefficient used for computing a running average
of squared gradients. :math:`\rho` in the formula above. Default: ``0.9``.
eps (float, optional): term added to the denominator to improve
numerical stability. :math:`\epsilon` in the formula above. Default: ``1e-6``.
weight_decay (float, optional): weight decay (L2 penalty). Default: ``0.``.
Keyword Args:
maximize (bool, optional): maximize the params based on the objective, instead of minimizing.
Default: ``False``.
Inputs:
- **gradients** (tuple[Tensor]) - The gradients of `params`.
Raises:
ValueError: If the learning rate is not int, float or Tensor.
ValueError: If the learning rate is less than 0.
ValueError: If the `eps` is less than or equal to 0.0.
ValueError: If the `rho` is not in the range of [0, 1].
ValueError: If the `weight_decay` is less than 0.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> from mindspore import nn
>>> from mindspore.experimental import optim
>>> # Define the network structure of LeNet5. Refer to
>>> # https://gitee.com/mindspore/docs/blob/r2.3.1/docs/mindspore/code/lenet.py
>>> net = LeNet5()
>>> loss_fn = nn.SoftmaxCrossEntropyWithLogits(sparse=True)
>>> optimizer = optim.Adadelta(net.trainable_params(), lr=0.1)
>>> def forward_fn(data, label):
... logits = net(data)
... loss = loss_fn(logits, label)
... return loss, logits
>>> grad_fn = mindspore.value_and_grad(forward_fn, None, optimizer.parameters, has_aux=True)
>>> def train_step(data, label):
... (loss, _), grads = grad_fn(data, label)
... optimizer(grads)
... return loss
"""
def __init__(self, params, lr=1.0, rho=0.9, eps=1e-6, weight_decay=0.0, *, maximize=False):
check_not_less_than_without_equal(lr, "lr", self.cls_name)
check_not_less_than_without_equal(eps, "eps", self.cls_name)
check_not_less_than(weight_decay, "weight_decay", self.cls_name)
validator.check_float_range(rho, 0., 1., validator.INC_BOTH, "rho", self.cls_name)
defaults = dict(
lr=lr,
rho=rho,
eps=eps,
weight_decay=weight_decay,
maximize=maximize,
)
super(Adadelta, self).__init__(params, defaults)
self.accum = self.parameters.clone(prefix="accum", init=0)
self.accum_update = self.parameters.clone(prefix="accum_update", init=0)
self.opt = P.ApplyAdadelta()
self.op_cast = P.Cast()
@jit
def implementation(self, lr, rho, eps, maximize, weight_decay, start_id, end_id, gradients):
"""Extract the common computing part for acceleration"""
params = self.parameters[start_id: end_id]
grads = tuple([grad if not maximize else F.neg(grad) for grad in gradients[start_id: end_id]])
grads = self._decay_weight(weight_decay, params, grads)
accum = self.accum[start_id: end_id]
accum_update = self.accum_update[start_id: end_id]
self.hyper_map(F.partial(_adadelta_opt, self.opt, rho, eps, lr),
params, accum, accum_update, grads)
return True
def construct(self, gradients):
for group_id, group in enumerate(self.param_groups):
lr = self.lrs[group_id]
if isinstance(group.get("lr"), float):
lr = self.op_cast(group.get("lr"), mstype.float32)
maximize = group.get("maximize")
rho = group["rho"]
eps = group["eps"]
start_id = self.group_start_id[group_id]
end_id = self.group_start_id[group_id + 1]
weight_decay = group["weight_decay"]
self.implementation(lr, rho, eps, maximize, weight_decay, start_id, end_id, gradients)
return True