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PaddleSpeech/paddlespeech/s2t/training/optimizer/adadelta.py

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# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
import paddle
from paddle.fluid import framework
from paddle.optimizer import Optimizer
__all__ = []
class SimpleAdadelta(Optimizer):
r"""
**Notes: This API does not support sparse parameter optimization.**
Adadelta Optimizer. Please refer to this for details:
`ADADELTA: AN ADAPTIVE LEARNING RATE METHOD <https://arxiv.org/abs/1212.5701>`_.
The update is done as follows:
.. math::
E(g_t^2) &= \rho * E(g_{t-1}^2) + (1-\rho) * g^2
learning\_rate &= \sqrt{ ( E(dx_{t-1}^2) + \epsilon ) / ( E(g_t^2) + \epsilon ) }
E(dx_t^2) &= \rho * E(dx_{t-1}^2) + (1-\rho) * (-g*learning\_rate)^2
Args:
learning_rate (float|Tensor|LearningRateDecay, optional): The learning rate used to update ``Parameter``.
It can be a float value, a ``Tensor`` with a float type or a LearningRateDecay. The default value is 0.001.
epsilon (float): a small float number for numeric stability. Default 1.0e-6.
rho (float): a floating point value indicating the decay rate. Default 0.95.
parameters (list|tuple, optional): List/Tuple of ``Tensor`` to update to minimize ``loss``. \
This parameter is required in dygraph mode. And you can specify different options for \
different parameter groups such as the learning rate, weight decay, etc, \
then the parameters are list of dict. Note that the learning_rate in paramter groups \
represents the scale of base learning_rate. \
The default value is None in static mode, at this time all parameters will be updated.
weight_decay (float|WeightDecayRegularizer, optional): The strategy of regularization. \
It canbe a float value as coeff of L2 regularization or \
:ref:`api_fluid_regularizer_L1Decay`, :ref:`api_fluid_regularizer_L2Decay`.
If a parameter has set regularizer using :ref:`api_fluid_ParamAttr` already, \
the regularization setting here in optimizer will be ignored for this parameter. \
Otherwise, the regularization setting here in optimizer will take effect. \
Default None, meaning there is no regularization.
foreach (bool, optional): whether foreach implementation of optimizer is used. The default value is None.
maximize (bool, optional): maximize the params based on the objective, instead of minimizing.
The default value is False.
name (str, optional): The default value is None. Normally there is no need for user
to set this property. For more information, please refer to
:ref:`api_guide_Name` .
Examples:
.. code-block:: python
import paddle
from paddlespeech.s2t.training.optimizer.adadelta import SimpleAdadelta
inp = paddle.uniform([10, 10], dtype="float32", min=-0.1, max=0.1)
linear = paddle.nn.Linear(10, 10)
out = linear(inp)
loss = paddle.mean(out)
adadelta = SimpleAdadelta(learning_rate=0.1, parameters=linear.parameters(), weight_decay=0.01)
out.backward()
adadelta.step()
adadelta.clear_grad()
"""
def __init__(
self,
learning_rate=0.001,
epsilon=1.0e-6,
rho=0.95,
parameters=None,
weight_decay=0.0,
foreach=None,
maximize=False,
name=None, ):
if learning_rate is None:
raise ValueError("learning_rate is not set.")
if epsilon is None:
raise ValueError("epsilon is not set.")
if rho is None:
raise ValueError("rho is not set.")
super(SimpleAdadelta, self).__init__(
learning_rate=learning_rate,
parameters=parameters,
weight_decay=weight_decay,
name=name, )
self._epsilon = epsilon
self._rho = rho
self.state = 0 # self.state is 0 or 1, use to control init square_avgs and acc_deltas
self._weight_decay = weight_decay
self._learning_rate = learning_rate
self._foreach = foreach
self._maximize = maximize
self.square_avgs = []
self.acc_deltas = []
@paddle.no_grad()
@framework.dygraph_only
def step(self):
"""Performs a single optimization step.
Args:
closure (Callable, optional): A closure that reevaluates the model
and returns the loss.
"""
if not isinstance(self._parameter_list[0], dict):
params_grads = []
for param in self._parameter_list:
if param.stop_gradient:
continue
if param._grad_ivar() is not None:
grad_var = param._grad_ivar()
params_grads.append((param, grad_var))
if self.state == 0:
self.square_avg = paddle.zeros_like(param)
self.acc_delta = paddle.zeros_like(param)
self.square_avgs.append(self.square_avg)
self.acc_deltas.append(self.acc_delta)
else:
# optimize parameters in groups
params_grads = []
for idx, param_group in enumerate(self._param_groups):
for param in param_group['params']:
if param.stop_gradient:
continue
if param._grad_ivar() is not None:
grad_var = param._grad_ivar()
params_grads.append((param, grad_var))
if self.state == 0:
self.square_avg = paddle.zeros_like(param)
self.acc_delta = paddle.zeros_like(param)
self.square_avgs.append(self.square_avg)
self.acc_deltas.append(self.acc_delta)
self.state = 1
adadelta(
params_grads,
square_avgs=self.square_avgs,
acc_deltas=self.acc_deltas,
learning_rate=self._learning_rate,
rho=self._rho,
epsilon=self._epsilon,
weight_decay=self._weight_decay,
foreach=self._foreach,
maximize=self._maximize)
def adadelta(params_grads,
square_avgs,
acc_deltas,
foreach=None,
*,
learning_rate: float,
rho: float,
epsilon: float,
weight_decay: float,
maximize: bool):
if foreach is None:
# if foreach is None, set False
foreach = False
if not foreach:
# optimizer is used
func = _single_tensor_adadelta
func(
params_grads,
square_avgs,
acc_deltas,
learning_rate=learning_rate,
rho=rho,
epsilon=epsilon,
weight_decay=weight_decay,
maximize=maximize)
def _single_tensor_adadelta(params_grads,
square_avgs,
acc_deltas,
*,
learning_rate: float,
rho: float,
epsilon: float,
weight_decay: float,
maximize: bool):
"""
Calculate variables(square_avgs, acc_deltas) and update parameters.
"""
for (params_grad, square_avg, acc_delta) in zip(params_grads, square_avgs,
acc_deltas):
param, grad = params_grad
grad = grad if not maximize else -grad
if weight_decay != 0:
grad.set_value(grad.add(paddle.multiply(param, weight_decay)))
if paddle.is_complex(param):
square_avg = paddle.as_real(square_avg)
acc_delta = paddle.as_real(acc_delta)
grad = paddle.as_real(grad)
# square_avg = square_avg * rho + (1-rho) * grad * grad
square_avg.set_value(
paddle.multiply(square_avg, paddle.to_tensor(rho)).add(
paddle.multiply(paddle.to_tensor(1 - rho), grad.square())))
# std = (square_avg + eps).sqrt()
std = square_avg.add(paddle.to_tensor(epsilon)).sqrt_()
# delta = std / (acc_delta + eps).sqrt() * grad
delta = (paddle.multiply(
paddle.divide(
acc_delta.add(paddle.to_tensor(epsilon)).sqrt_(), std), grad))
# acc_delta = acc_delta * rho + (1-rho) * delta * delta
acc_delta.set_value(
paddle.multiply(acc_delta, paddle.to_tensor(rho)).add(
paddle.multiply(paddle.to_tensor(1 - rho), delta.square())))
if paddle.is_complex(param):
delta = paddle.as_real(delta)
# param = param - delta*learning_rate
param.set_value(
param.add(
paddle.multiply(
delta.astype('float32'), paddle.to_tensor(-learning_rate))))