diff --git a/paddlespeech/s2t/training/optimizer.py b/paddlespeech/s2t/training/optimizer/__init__.py similarity index 99% rename from paddlespeech/s2t/training/optimizer.py rename to paddlespeech/s2t/training/optimizer/__init__.py index f7f70c570..aafdc5b6a 100644 --- a/paddlespeech/s2t/training/optimizer.py +++ b/paddlespeech/s2t/training/optimizer/__init__.py @@ -19,7 +19,6 @@ from typing import Text import paddle from paddle.optimizer import Optimizer from paddle.regularizer import L2Decay - from paddlespeech.s2t.training.gradclip import ClipGradByGlobalNormWithLog from paddlespeech.s2t.utils.dynamic_import import dynamic_import from paddlespeech.s2t.utils.dynamic_import import instance_class diff --git a/paddlespeech/s2t/training/optimizer/adadelta.py b/paddlespeech/s2t/training/optimizer/adadelta.py new file mode 100644 index 000000000..900b697c5 --- /dev/null +++ b/paddlespeech/s2t/training/optimizer/adadelta.py @@ -0,0 +1,239 @@ +# 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 `_. + + 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))))