add dist_multi_trainer for distributed training, add trainer_factory and device_worker_factory so that we can easily extend new training mode, add pull dense worker which is a singleton for parameter fetching

paddle/fluid/framework/async_executor.cc

@@ -104,6 +104,7 @@ void AsyncExecutor::SaveModel(const std::string& path) {
 }
 
 void AsyncExecutor::RunFromFile(const ProgramDesc& main_program,
+<<<<<<< HEAD
                                 const std::string& data_feed_desc_str,
                                 const std::vector<std::string>& filelist,
                                 const int thread_num,
@@ -192,6 +193,25 @@ void AsyncExecutor::RunFromFile(const ProgramDesc& main_program,
     _pull_dense_thread->stop();
   }
 #endif
+=======
+                                const std::string& trainer_desc_str,
+                                const bool debug) {
+  TrainerDesc trainer_desc;
+  google::protobuf::TextFormat::ParseFromString(trainer_desc_str,
+                                                &trainer_desc);
+  std::shared_ptr<TrainerBase> trainer;
+  trainer = TrainerFactory::CreateTrainer(trainer_desc.class_name());
+  // initialize trainer
+  trainer->Initialize(trainer_desc);
+  // trainer->SetRootScope(root_scope_);
+  trainer->SetDebug(debug);
+  // prepare training environment and helper environment
+  trainer->InitTrainerEnv(main_program, place_);
+  trainer->InitOtherEnv(main_program);
+  // training and finalize training
+  trainer->Run();
+  trainer->Finalize();
+>>>>>>> add dist_multi_trainer for distributed training, add trainer_factory and device_worker_factory so that we can easily extend new training mode, add pull dense worker which is a singleton for parameter fetching
   root_scope_->DropKids();
 
   return;