trainer.h

/* Copyright (c) 2018 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. */

#pragma once

#include <fstream>
#include <memory>
#include <mutex>  // NOLINT
#include <string>
#include <thread>  // NOLINT
#include <vector>

#include "paddle/fluid/framework/data_feed.h"
#include "paddle/fluid/framework/data_set.h"
#include "paddle/fluid/framework/device_worker.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/program_desc.h"
#include "paddle/fluid/framework/reader.h"
#include "paddle/fluid/framework/trainer_desc.pb.h"
#include "paddle/fluid/framework/variable_helper.h"
#include "paddle/fluid/operators/reader/blocking_queue.h"
#include "paddle/fluid/platform/port.h"

namespace paddle {
namespace framework {

class TrainerBase {
 public:
  TrainerBase() {}
  virtual ~TrainerBase() {}
  // model memory are hosted in root_scope
  void SetScope(Scope* root_scope);
  void SetDebug(const bool debug) { debug_ = debug; }
  void SetDataset(Dataset* dataset_ptr) { dataset_ptr_ = dataset_ptr; }
  virtual void Initialize(const TrainerDesc& trainer_desc,
                          Dataset* data_set) = 0;
  virtual void InitTrainerEnv(const ProgramDesc& main_program,
                              const platform::Place& place) = 0;
  virtual void InitOtherEnv(const ProgramDesc& main_program) = 0;
  virtual void Run() = 0;
  virtual void Finalize() = 0;

 protected:
  Scope* root_scope_;
  bool debug_;
  Dataset* dataset_ptr_;
};

// general trainer for async execution
// local trainer and distributed trainer are supported
// depends on the assigned device_worker
class MultiTrainer : public TrainerBase {
 public:
  MultiTrainer() {}
  virtual ~MultiTrainer() {}
  virtual void Initialize(const TrainerDesc& trainer_desc, Dataset* data_set);
  virtual void InitTrainerEnv(const ProgramDesc& main_program,
                              const platform::Place& place);
  virtual void InitOtherEnv(const ProgramDesc& main_program) {}
  virtual void Run();
  virtual void Finalize();

 protected:
  int thread_num_;
  std::vector<std::thread> threads_;
  std::vector<DataFeed*> readers_;
  std::vector<std::shared_ptr<DeviceWorker>> workers_;
};

class DistMultiTrainer : public MultiTrainer {
 public:
  DistMultiTrainer() {}
  virtual ~DistMultiTrainer() {}
  virtual void Initialize(const TrainerDesc& trainer_desc, Dataset* data_set);
  virtual void InitOtherEnv(const ProgramDesc& main_program);
  virtual void Run();
  virtual void Finalize();
  virtual void FinalizeDumpEnv();
  virtual void InitDumpEnv();
  virtual void DumpWork();

 protected:
  std::shared_ptr<paddle::framework::PullDenseWorker> pull_dense_worker_;
  std::thread dump_thread_;
  std::shared_ptr<FILE> fp_;
  std::shared_ptr<paddle::framework::ChannelObject<std::string>> queue_;

  bool need_dump_field_;
  std::string dump_fields_path_;
  std::string dump_converter_;
  std::vector<std::string> dump_fields_;
  int mpi_rank_;
};

#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
class PipelineTrainer : public TrainerBase {
 public:
  PipelineTrainer() {}
  ~PipelineTrainer() override {}
  void Initialize(const TrainerDesc& trainer_desc, Dataset* data_set) override;
  void InitTrainerEnv(const ProgramDesc& main_program,
                      const platform::Place& place) override;
  void InitOtherEnv(const ProgramDesc& main_program) override {}
  void Run() override;
  void Finalize() override;

 protected:
  int section_num_;
  int pipeline_num_;
  int scope_queue_size_;
  int sync_steps_;

  SectionWorkerParameter pipeline_config_;

  // The in/output var names for each section
  std::vector<std::unique_ptr<std::vector<std::string>>> in_var_names_;
  std::vector<std::unique_ptr<std::vector<std::string>>> out_var_names_;

  // Counter for the running thread
  std::vector<std::vector<int*>> worker_count_;
  std::vector<std::vector<std::unique_ptr<std::mutex>>> worker_count_mutex_;

  // worker: [section_id][pipeline_id][thread_id]
  std::vector<std::vector<
      std::vector<std::shared_ptr<paddle::framework::DeviceWorker>>>>
      workers_;
  std::vector<std::thread> section_threads_;

  // We use scope to maintain context info, and scopes
  // will be deliverd between different sections.
  std::vector<std::vector<std::unique_ptr<ScopeQueue>>> scope_queues_;
  std::vector<Scope*> pipeline_scopes_;

  // The parameters that should be syncronized between different cards using
  // nccl all-reduce
  std::shared_ptr<std::vector<std::string>> param_need_sync_;
  std::vector<std::unique_ptr<SyncFunctor>> sync_functors_;
  std::shared_ptr<platform::NCCLContextMap> nccl_ctx_map_;

  std::vector<DataFeed*> readers_;

  void InitFirstScopeQueue(ScopeQueue* scope_queue, int pipeline_id,
                           const ProgramDesc& main_program);
  void CopyParameters(const Scope& root_scope, int pipeline_id);
  void construct_sync_functor();
};
#endif
}  // namespace framework
}  // namespace paddle