[ASR] add python simple adadelta optimizer, test=asr (#2925)
* add simple adeadelta optimizer. * remove useless log * remove useless and fluid import. * add framework.dygraph_only backpull/2944/head
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# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import paddle
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from paddle.fluid import framework
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from paddle.optimizer import Optimizer
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__all__ = []
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class SimpleAdadelta(Optimizer):
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r"""
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**Notes: This API does not support sparse parameter optimization.**
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Adadelta Optimizer. Please refer to this for details:
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`ADADELTA: AN ADAPTIVE LEARNING RATE METHOD <https://arxiv.org/abs/1212.5701>`_.
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The update is done as follows:
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.. math::
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E(g_t^2) &= \rho * E(g_{t-1}^2) + (1-\rho) * g^2
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learning\_rate &= \sqrt{ ( E(dx_{t-1}^2) + \epsilon ) / ( E(g_t^2) + \epsilon ) }
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E(dx_t^2) &= \rho * E(dx_{t-1}^2) + (1-\rho) * (-g*learning\_rate)^2
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Args:
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learning_rate (float|Tensor|LearningRateDecay, optional): The learning rate used to update ``Parameter``.
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It can be a float value, a ``Tensor`` with a float type or a LearningRateDecay. The default value is 0.001.
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epsilon (float): a small float number for numeric stability. Default 1.0e-6.
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rho (float): a floating point value indicating the decay rate. Default 0.95.
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parameters (list|tuple, optional): List/Tuple of ``Tensor`` to update to minimize ``loss``. \
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This parameter is required in dygraph mode. And you can specify different options for \
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different parameter groups such as the learning rate, weight decay, etc, \
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then the parameters are list of dict. Note that the learning_rate in paramter groups \
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represents the scale of base learning_rate. \
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The default value is None in static mode, at this time all parameters will be updated.
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weight_decay (float|WeightDecayRegularizer, optional): The strategy of regularization. \
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It canbe a float value as coeff of L2 regularization or \
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:ref:`api_fluid_regularizer_L1Decay`, :ref:`api_fluid_regularizer_L2Decay`.
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If a parameter has set regularizer using :ref:`api_fluid_ParamAttr` already, \
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the regularization setting here in optimizer will be ignored for this parameter. \
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Otherwise, the regularization setting here in optimizer will take effect. \
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Default None, meaning there is no regularization.
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foreach (bool, optional): whether foreach implementation of optimizer is used. The default value is None.
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maximize (bool, optional): maximize the params based on the objective, instead of minimizing.
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The default value is False.
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name (str, optional): The default value is None. Normally there is no need for user
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to set this property. For more information, please refer to
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:ref:`api_guide_Name` .
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Examples:
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.. code-block:: python
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import paddle
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from paddlespeech.s2t.training.optimizer.adadelta import SimpleAdadelta
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inp = paddle.uniform([10, 10], dtype="float32", min=-0.1, max=0.1)
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linear = paddle.nn.Linear(10, 10)
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out = linear(inp)
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loss = paddle.mean(out)
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adadelta = SimpleAdadelta(learning_rate=0.1, parameters=linear.parameters(), weight_decay=0.01)
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out.backward()
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adadelta.step()
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adadelta.clear_grad()
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"""
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def __init__(
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self,
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learning_rate=0.001,
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epsilon=1.0e-6,
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rho=0.95,
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parameters=None,
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weight_decay=0.0,
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foreach=None,
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maximize=False,
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name=None, ):
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if learning_rate is None:
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raise ValueError("learning_rate is not set.")
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if epsilon is None:
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raise ValueError("epsilon is not set.")
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if rho is None:
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raise ValueError("rho is not set.")
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super(SimpleAdadelta, self).__init__(
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learning_rate=learning_rate,
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parameters=parameters,
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weight_decay=weight_decay,
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name=name, )
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self._epsilon = epsilon
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self._rho = rho
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self.state = 0 # self.state is 0 or 1, use to control init square_avgs and acc_deltas
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self._weight_decay = weight_decay
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self._learning_rate = learning_rate
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self._foreach = foreach
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self._maximize = maximize
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self.square_avgs = []
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self.acc_deltas = []
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@paddle.no_grad()
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@framework.dygraph_only
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def step(self):
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"""Performs a single optimization step.
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Args:
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closure (Callable, optional): A closure that reevaluates the model
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and returns the loss.
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"""
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if not isinstance(self._parameter_list[0], dict):
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params_grads = []
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for param in self._parameter_list:
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if param.stop_gradient:
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continue
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if param._grad_ivar() is not None:
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grad_var = param._grad_ivar()
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params_grads.append((param, grad_var))
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if self.state == 0:
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self.square_avg = paddle.zeros_like(param)
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self.acc_delta = paddle.zeros_like(param)
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self.square_avgs.append(self.square_avg)
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self.acc_deltas.append(self.acc_delta)
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else:
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# optimize parameters in groups
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params_grads = []
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for idx, param_group in enumerate(self._param_groups):
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for param in param_group['params']:
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if param.stop_gradient:
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continue
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if param._grad_ivar() is not None:
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grad_var = param._grad_ivar()
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params_grads.append((param, grad_var))
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if self.state == 0:
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self.square_avg = paddle.zeros_like(param)
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self.acc_delta = paddle.zeros_like(param)
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self.square_avgs.append(self.square_avg)
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self.acc_deltas.append(self.acc_delta)
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self.state = 1
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adadelta(
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params_grads,
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square_avgs=self.square_avgs,
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acc_deltas=self.acc_deltas,
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learning_rate=self._learning_rate,
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rho=self._rho,
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epsilon=self._epsilon,
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weight_decay=self._weight_decay,
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foreach=self._foreach,
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maximize=self._maximize)
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def adadelta(params_grads,
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square_avgs,
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acc_deltas,
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foreach=None,
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*,
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learning_rate: float,
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rho: float,
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epsilon: float,
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weight_decay: float,
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maximize: bool):
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if foreach is None:
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# if foreach is None, set False
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foreach = False
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if not foreach:
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# optimizer is used
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func = _single_tensor_adadelta
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func(
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params_grads,
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square_avgs,
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acc_deltas,
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learning_rate=learning_rate,
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rho=rho,
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epsilon=epsilon,
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weight_decay=weight_decay,
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maximize=maximize)
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def _single_tensor_adadelta(params_grads,
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square_avgs,
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acc_deltas,
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*,
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learning_rate: float,
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rho: float,
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epsilon: float,
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weight_decay: float,
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maximize: bool):
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"""
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Calculate variables(square_avgs, acc_deltas) and update parameters.
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"""
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for (params_grad, square_avg, acc_delta) in zip(params_grads, square_avgs,
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acc_deltas):
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param, grad = params_grad
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grad = grad if not maximize else -grad
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if weight_decay != 0:
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grad.set_value(grad.add(paddle.multiply(param, weight_decay)))
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if paddle.is_complex(param):
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square_avg = paddle.as_real(square_avg)
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acc_delta = paddle.as_real(acc_delta)
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grad = paddle.as_real(grad)
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# square_avg = square_avg * rho + (1-rho) * grad * grad
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square_avg.set_value(
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paddle.multiply(square_avg, paddle.to_tensor(rho)).add(
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paddle.multiply(paddle.to_tensor(1 - rho), grad.square())))
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# std = (square_avg + eps).sqrt()
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std = square_avg.add(paddle.to_tensor(epsilon)).sqrt_()
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# delta = std / (acc_delta + eps).sqrt() * grad
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delta = (paddle.multiply(
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paddle.divide(
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acc_delta.add(paddle.to_tensor(epsilon)).sqrt_(), std), grad))
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# acc_delta = acc_delta * rho + (1-rho) * delta * delta
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acc_delta.set_value(
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paddle.multiply(acc_delta, paddle.to_tensor(rho)).add(
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paddle.multiply(paddle.to_tensor(1 - rho), delta.square())))
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if paddle.is_complex(param):
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delta = paddle.as_real(delta)
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# param = param - delta*learning_rate
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param.set_value(
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param.add(
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paddle.multiply(
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delta.astype('float32'), paddle.to_tensor(-learning_rate))))
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