text: #   Copyright (c) 2020 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
  
  from paddle.fluid.optimizer import Momentum, DGCMomentumOptimizer
  from .meta_optimizer_base import MetaOptimizerBase
  import logging
  
  
  class DGCOptimizer(MetaOptimizerBase):
      def __init__(self, optimizer):
          super(DGCOptimizer, self).__init__(optimizer)
          self.inner_opt = optimizer
          self.dgc_opt = None
          # we do not allow meta optimizer to be inner optimizer currently
          self.meta_optimizers_white_list = []
          self.meta_optimizers_black_list = []
  
      def _set_basic_info(self, loss, role_maker, user_defined_optimizer,
                          user_defined_strategy):
          super(DGCOptimizer, self)._set_basic_info(
              loss, role_maker, user_defined_optimizer, user_defined_strategy)
  
          opt = self.inner_opt
          if not isinstance(opt, Momentum):
              return
  
          configs = self.user_defined_strategy.dgc_configs
          if len(configs['sparsity']) == 0:
              # default is [0.999]
              configs['sparsity'] = [0.999]
  
          self.dgc_opt = DGCMomentumOptimizer(
              learning_rate=opt._learning_rate,
              momentum=opt._momentum,
              rampup_begin_step=configs['rampup_begin_step'],
              rampup_step=configs['rampup_step'],
              sparsity=configs['sparsity'],
              parameter_list=opt._parameter_list,
              use_nesterov=opt._use_nesterov,
              num_trainers=self.role_maker._worker_num(),
              regularization=opt.regularization,
              grad_clip=opt._grad_clip,
              name=opt._name)
  
      def _can_apply(self):
          if not self.role_maker._is_collective:
              return False
  
          if self.user_defined_strategy.dgc:
              if not isinstance(self.inner_opt, Momentum):
                  logging.warn("dgc only works on Momentum optimizer")
                  return False
              if self.role_maker._worker_num() <= 1:
                  logging.warn("dgc only works on multi cards")
                  return False
  
              return True
  
          return False
  
      def _disable_strategy(self, dist_strategy):
          dist_strategy.dgc = False
          dist_strategy.dgc_configs = {}
  
      def _enable_strategy(self, dist_strategy, context):
          dist_strategy.dgc = True
          dist_strategy.dgc_configs = {"rampup_begin_step": 0, "rampup_step": 1}
  
      def backward(self,
                   loss,
                   startup_program=None,
                   parameter_list=None,
                   no_grad_set=None,
                   callbacks=None):
          return self.dgc_opt.backward(loss, startup_program, parameter_list,
                                       no_grad_set, callbacks)
  
      def apply_gradients(self, params_grads):
          return self.dgc_opt.apply_gradients(params_grads=params_grads)
  
      def apply_optimize(self, loss, startup_program, params_grads):
          return self.dgc_opt.apply_optimize(
              loss, startup_program=startup_program, params_grads=params_grads)
  
      def minimize_impl(self,
                        loss,
                        startup_program=None,
                        parameter_list=None,
                        no_grad_set=None):
          optimize_ops, params_grads = \
              self.dgc_opt.minimize(loss, startup_program,
                                    parameter_list, no_grad_set)
          return optimize_ops, params_grads
  