heterbox_trainer.cc

/* Copyright (c) 2016 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. */

#include <cstdlib>
#include <string>
#include <vector>
#include "io/fs.h"
#include "paddle/fluid/framework/data_feed_factory.h"
#include "paddle/fluid/framework/data_set.h"
#include "paddle/fluid/framework/device_worker_factory.h"
#include "paddle/fluid/framework/fleet/fleet_wrapper.h"
#include "paddle/fluid/framework/trainer.h"
#if (defined PADDLE_WITH_CUDA || defined PADDLE_WITH_HIP || \
     defined PADDLE_WITH_XPU) &&                            \
    (defined PADDLE_WITH_PSLIB)
#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
#include "paddle/fluid/platform/cuda_device_guard.h"
#endif
namespace paddle {
namespace framework {

void HeterBoxTrainer::Initialize(const TrainerDesc& trainer_desc,
                                 Dataset* dataset) {
  thread_num_ = trainer_desc.thread_num();
  param_ = trainer_desc.downpour_param();
  for (int i = 0; i < param_.dense_table_size(); ++i) {
    uint64_t table_id = static_cast<uint64_t>(param_.dense_table(i).table_id());
    auto table = param_.dense_table(i);
    dense_grad_names_[table_id].resize(table.dense_grad_name_size());
    for (int j = 0; j < table.dense_grad_name_size(); ++j) {
      dense_grad_names_[table_id][j] = table.dense_grad_name(j);
    }
  }
  RegisterHeterCallback();
  scale_datanorm_ = trainer_desc.scale_datanorm();
  int place_num = trainer_desc.worker_places_size();
  const std::vector<paddle::framework::DataFeed*> readers =
      dataset->GetReaders();
  for (int i = 0; i < place_num; ++i) {
    int num = trainer_desc.worker_places(i);
#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
    platform::CUDAPlace place = platform::CUDAPlace(num);
    platform::CUDADeviceGuard guard(place.device);
    gpuStream_t stream;
#ifdef PADDLE_WITH_HIP
    PADDLE_ENFORCE_CUDA_SUCCESS(hipStreamCreate(&stream));
#else
    PADDLE_ENFORCE_CUDA_SUCCESS(cudaStreamCreate(&stream));
#endif
    copy_streams_.push_back(stream);
    places_.push_back(place);
    gpuEvent_t event;
#ifdef PADDLE_WITH_HIP
    PADDLE_ENFORCE_CUDA_SUCCESS(
        hipEventCreateWithFlags(&event, hipEventDisableTiming));
#else
    PADDLE_ENFORCE_CUDA_SUCCESS(
        cudaEventCreateWithFlags(&event, cudaEventDisableTiming));
#endif
    events_.push_back(event);
#endif
#ifdef PADDLE_WITH_XPU
    platform::XPUPlace place = platform::XPUPlace(num);
    places_.push_back(place);
#endif
  }
  for (int i = 0; i < trainer_desc.downpour_param().stat_var_names_size();
       i++) {
    need_merge_var_names_.push_back(
        trainer_desc.downpour_param().stat_var_names(i));
  }
  VLOG(3) << "going to initialize pull dense worker";
  pull_dense_worker_ = PullDenseWorker::GetInstance();
  pull_dense_worker_->Initialize(trainer_desc);
  VLOG(3) << "initialize pull dense worker";
  SetDebug(trainer_desc.debug());
  fleet_ptr_ = FleetWrapper::GetInstance();
  trainer_desc_ = trainer_desc;
  workers_.resize(place_num);
  for (int i = 0; i < place_num; ++i) {
    workers_[i] = DeviceWorkerFactory::CreateDeviceWorker(
        trainer_desc.device_worker_name());
    workers_[i]->SetDeviceIndex(i);
    workers_[i]->SetDataFeed(readers[i]);
    workers_[i]->Initialize(trainer_desc);
    workers_[i]->SetWorkerNum(place_num);
  }
}

void HeterBoxTrainer::DumpWork(int tid) {}

void HeterBoxTrainer::RegisterHeterCallback() {
  auto fleet_ptr = FleetWrapper::GetInstance();
  fleet_ptr->RegisterHeterCallback([this](int worker, int taskid) {
    // workers_[worker]->Schedule(taskid);
  });
}

void HeterBoxTrainer::InitTrainerEnv(const ProgramDesc& main_program,
                                     const platform::Place& place) {
  for (size_t i = 0; i < places_.size(); ++i) {
    workers_[i]->SetPlace(places_[i]);
    workers_[i]->SetStream(copy_streams_[i]);
    workers_[i]->SetEvent(events_[i]);
    workers_[i]->SetReaderPlace(platform::CPUPlace());
    workers_[i]->SetRootScope(root_scope_);
    workers_[i]->CreateDeviceResource(main_program);  // Program
    workers_[i]->BindingDataFeedMemory();
#ifdef PADDLE_WITH_PSLIB
    workers_[i]->CacheProgram(main_program);
#endif
  }
  for (size_t num = 0; num < places_.size(); ++num) {
    auto place = places_[num];
    Scope* scope = workers_[num]->GetThreadScope();
    auto stream = copy_streams_[num];
    auto event = events_[num];
    auto dev_id = BOOST_GET_CONST(platform::CUDAPlace, place).device;
    platform::CUDADeviceGuard guard(dev_id);
    auto& block = main_program.Block(0);
    for (auto& var : block.AllVars()) {
      if (var->Persistable()) {
        auto name = var->Name();
        Variable* root_var = root_scope_->FindVar(name);
        if (!root_var) {
          continue;
        }
        LoDTensor* root_tensor = root_var->GetMutable<LoDTensor>();
        auto* ptr = scope->Var(name);
        InitializeVariable(ptr, proto::VarType::LOD_TENSOR);
        LoDTensor* thread_tensor = ptr->GetMutable<LoDTensor>();

#define HeterMemcpyFunc(cpp_type, proto_type)                           \
  do {                                                                  \
    if (root_tensor->type() == proto_type) {                            \
      HeterMemCpy<cpp_type>(thread_tensor, root_tensor, place, stream); \
    }                                                                   \
  } while (0)
        _ForEachDataType_(HeterMemcpyFunc);
      }
    }
#ifdef PADDLE_WITH_HIP
    PADDLE_ENFORCE_CUDA_SUCCESS(hipEventRecord(event, stream));
    hipEventSynchronize(event);
#else
    PADDLE_ENFORCE_CUDA_SUCCESS(cudaEventRecord(event, stream));
    cudaEventSynchronize(event);
#endif
  }
  place_ = place;
}

template <typename T>
void HeterBoxTrainer::HeterMemCpy(LoDTensor* thread_tensor,
                                  LoDTensor* root_tensor,
                                  const paddle::platform::Place& thread_place,
                                  gpuStream_t stream) {
  T* thread_ptr =
      thread_tensor->mutable_data<T>(root_tensor->dims(), thread_place);
  T* root_ptr = root_tensor->data<T>();
  if (platform::is_cpu_place(root_tensor->place())) {
    memory::Copy(BOOST_GET_CONST(platform::CUDAPlace, thread_place), thread_ptr,
                 platform::CPUPlace(), root_ptr,
                 sizeof(T) * root_tensor->numel(), stream);
  } else {
    memory::Copy(BOOST_GET_CONST(platform::CUDAPlace, thread_place), thread_ptr,
                 BOOST_GET_CONST(platform::CUDAPlace, root_tensor->place()),
                 root_ptr, sizeof(T) * root_tensor->numel(), stream);
  }
}

void HeterBoxTrainer::InitOtherEnv(const ProgramDesc& main_program) {
  pull_dense_worker_->SetRootScope(root_scope_);
  pull_dense_worker_->CreatePinVar();
  for (size_t i = 0; i < places_.size(); ++i) {
    pull_dense_worker_->AddThreadScope(workers_[i]->GetThreadScope());
    pull_dense_worker_->AddPlace(places_[i]);
#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
    pull_dense_worker_->AddStream(copy_streams_[i]);
#endif
  }
  VLOG(3) << "init other env done.";
}

void HeterBoxTrainer::Run() {
  int pull_thread_num = 3 * places_.size();
  for (size_t thidx = 0; thidx < places_.size(); ++thidx) {
    workers_[thidx]->device_reader_->Start();
    std::dynamic_pointer_cast<paddle::framework::HeterBoxWorker>(
        workers_[thidx])
        ->ResetStat();
  }
  for (int i = 0; i < pull_thread_num; ++i) {
    int worker_id = i % places_.size();
    pull_threads_.push_back(
        std::thread(&DeviceWorker::ProduceTasks, workers_[worker_id].get()));
  }
  for (size_t thidx = 0; thidx < places_.size(); ++thidx) {
    threads_.push_back(
        std::thread(&DeviceWorker::TrainFiles, workers_[thidx].get()));
  }
}

template <typename T>
void HeterBoxTrainer::MergeToRootScope(LoDTensor* root_tensor,
                                       LoDTensor* tensor) {
  LoDTensor tmp_root;
  TensorCopy(*root_tensor, platform::CPUPlace(), &tmp_root);
  T* tmp_root_data = tmp_root.data<T>();
  LoDTensor tmp_tensor;
  TensorCopy(*tensor, platform::CPUPlace(), &tmp_tensor);
  T* data = tmp_tensor.data<T>();
  for (int i = 0; i < tmp_tensor.numel(); i++) {
    tmp_root_data[i] += data[i];
  }
  TensorCopy(tmp_root, platform::CPUPlace(), root_tensor);
}

Scope* HeterBoxTrainer::GetWorkerScope(int thread_id) { return nullptr; }

void HeterBoxTrainer::Finalize() {
  for (auto& th : pull_threads_) {
    th.join();
  }
  for (auto& th : threads_) {
    th.join();
  }
  for (size_t i = 0; i < need_merge_var_names_.size(); i++) {
    Variable* root_var = root_scope_->FindVar(need_merge_var_names_[i]);
    if (root_var == nullptr) {
      continue;
    }
    LoDTensor* root_tensor = root_var->GetMutable<LoDTensor>();

    for (size_t j = 0; j < places_.size(); j++) {
      Scope* cur_thread_scope = workers_[j]->GetThreadScope();
      Variable* thread_var =
          cur_thread_scope->FindVar(need_merge_var_names_[i]);
      if (thread_var == nullptr) {
        continue;
      }
      LoDTensor* thread_tensor = thread_var->GetMutable<LoDTensor>();
#define MergeCallback(cpp_type, proto_type)                                    \
  do {                                                                         \
    if (root_tensor->type() == proto_type) {                                   \
      if (thread_tensor->type() != proto_type) {                               \
        VLOG(0) << "Error: thread id=" << j << ", need_merge_var_names_[" << i \
                << "] " << need_merge_var_names_[i]                            \
                << ", root tensor type=" << root_tensor->type()                \
                << ", thread tensor type=" << thread_tensor->type();           \
        exit(-1);                                                              \
      }                                                                        \
      MergeToRootScope<cpp_type>(root_tensor, thread_tensor);                  \
    }                                                                          \
  } while (0)
      _ForEachDataType_(MergeCallback);
    }
  }
  pull_dense_worker_->MergeDenseParam();
  root_scope_->DropKids();
}
}  // namespace framework
}  // namespace paddle
#endif