device_worker.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 <map>
#include <memory>
#include <mutex>  // NOLINT
#include <string>
#include <thread>  // NOLINT
#include <vector>

#include "paddle/fluid/framework/data_feed.h"
#include "paddle/fluid/framework/fleet/fleet_wrapper.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/op_registry.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/place.h"
#include "paddle/fluid/platform/timer.h"

namespace paddle {
namespace framework {

class PullDenseWorker {
 public:
  virtual ~PullDenseWorker() {}
  virtual void Initialize(const TrainerDesc& param);
  int Start();
  void Stop();
  void SetRootScope(Scope* scope) { root_scope_ = scope; }
  void IncreaseThreadVersion(int thread_id, uint64_t table_id);
  void ResetThreadVersion(uint64_t table_id);
  void Wait(std::vector<::std::future<int32_t>>* status_vec);
  static std::shared_ptr<PullDenseWorker> s_instance_;
  static std::shared_ptr<PullDenseWorker> GetInstance() {
    if (NULL == s_instance_) {
      s_instance_.reset(new paddle::framework::PullDenseWorker());
    }
    return s_instance_;
  }

 private:
  PullDenseWorker() : root_scope_(NULL) {}
  void Run();
  bool CheckUpdateParam(uint64_t table_id);

 private:
  std::shared_ptr<paddle::framework::FleetWrapper> fleet_ptr_;
  PullDenseWorkerParameter param_;
  Scope* root_scope_;
  bool running_;

  std::map<uint64_t, uint64_t> last_versions_;
  std::map<uint64_t, uint64_t> current_version_;
  std::mutex mutex_for_version_;
  std::map<uint64_t, std::vector<uint64_t>> training_versions_;
  std::map<uint64_t, std::vector<std::string>> dense_value_names_;

  std::thread t_;
  int thread_num_;
  int sleep_time_ms_;
  int threshold_;

  std::vector<::std::future<int32_t>> pull_dense_status_;
  uint32_t pull_dense_fail_times_ = 0;
  std::vector<float> base_norm_param_;
  std::vector<float> mean_;
  std::vector<float> scale_;
  float squared_sum_epsilon_ = 1e-4;
  std::mutex mutex_for_mean_scale_;
  float total_batch_num_ = 0;
};

// should incorporate different type of device
class DeviceWorker {
 public:
  DeviceWorker() {}
  virtual ~DeviceWorker() {}
  virtual void Initialize(const TrainerDesc& desc) = 0;
  virtual void SetDeviceIndex(int tid) = 0;
  virtual void TrainFiles() = 0;
  virtual void TrainFilesWithProfiler() = 0;
  virtual void CreateDeviceResource(const ProgramDesc& main_prog) = 0;
  // will make this zero copy in the future
  virtual void BindingDataFeedMemory() = 0;
  virtual void SetRootScope(Scope* root_scope);
  virtual void SetDataFeed(const std::shared_ptr<DataFeed>& data_feed);
  virtual void SetPlace(const paddle::platform::Place& place) {
    place_ = place;
  }

 protected:
  Scope* root_scope_;
  paddle::platform::Place place_;
  std::shared_ptr<DataFeed> device_reader_;
};

class CPUWorkerBase : public DeviceWorker {
 public:
  CPUWorkerBase() {}
  virtual ~CPUWorkerBase() {}
  virtual void SetDeviceIndex(int tid) { thread_id_ = tid; }
  virtual void TrainFiles() = 0;
  virtual void TrainFilesWithProfiler() {}
  virtual void CreateDeviceResource(const ProgramDesc& main_prog) {}

 protected:
  int thread_id_;
};

class HogwildWorker : public CPUWorkerBase {
 public:
  HogwildWorker() {}
  virtual ~HogwildWorker() {}
  virtual void Initialize(const TrainerDesc& desc) {}
  virtual void TrainFiles();
  virtual void TrainFilesWithProfiler();
  virtual void CreateDeviceResource(const ProgramDesc& main_prog);
  virtual void BindingDataFeedMemory();

 protected:
  void CreateThreadOperators(const ProgramDesc& program);
  void CreateThreadScope(const ProgramDesc& program);
  std::vector<std::string> op_names_;
  std::vector<OperatorBase*> ops_;
  Scope* thread_scope_;
  std::vector<std::string> fetch_var_names_;
  std::vector<std::vector<float>> fetch_values_;
};

class DownpourWorker : public HogwildWorker {
 public:
  DownpourWorker() {}
  virtual ~DownpourWorker() {}
  virtual void Initialize(const TrainerDesc& desc);
  virtual void TrainFiles();

 protected:
  std::shared_ptr<paddle::framework::FleetWrapper> fleet_ptr_;
  std::shared_ptr<paddle::framework::PullDenseWorker> pull_dense_worker_;
  void FillSparseValue(size_t table_id);
  void PushGradients();
  void CollectLabelInfo(size_t table_id);

 private:
  DownpourWorkerParameter param_;
  // just save the value in param_ for easy access
  std::map<uint64_t, std::string> label_var_name_;
  std::map<uint64_t, std::vector<std::string>> sparse_key_names_;
  std::map<uint64_t, std::vector<std::string>> sparse_value_names_;
  std::map<uint64_t, std::vector<std::string>> sparse_grad_names_;
  std::map<uint64_t, std::vector<std::string>> dense_value_names_;
  std::map<uint64_t, std::vector<std::string>> dense_grad_names_;

  // feasign
  std::map<uint64_t, std::vector<uint64_t>> features_;
  // feasign stats
  std::map<uint64_t, std::vector<float>> feature_labels_;
  // feasign embedding
  std::map<uint64_t, std::vector<std::vector<float>>> feature_values_;
  // feasign embedding gradient
  std::map<uint64_t, std::vector<std::vector<float>>> feature_grads_;
  // skipped ops
  std::vector<std::string> skip_ops_;

  std::shared_ptr<PullDenseWorker> _pull_dense_worker;
  std::vector<::std::future<int32_t>> push_sparse_status_;
  std::vector<::std::future<int32_t>> push_dense_status_;
};

}  // namespace framework
}  // namespace paddle