pipeline_trainer.cc

// Copyright (c) 2019 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.

#if defined(PADDLE_WITH_NCCL)
#include <map>
#include "paddle/fluid/framework/data_feed_factory.h"
#include "paddle/fluid/framework/device_worker_factory.h"
#include "paddle/fluid/framework/trainer.h"
#include "paddle/fluid/framework/trainer_desc.pb.h"

namespace paddle {
namespace framework {

void PipelineTrainer::Initialize(const TrainerDesc& trainer_desc,
                                 Dataset* dataset) {
  const auto& section_params = trainer_desc.section_param();
  num_microbatches_ = section_params.num_microbatches();
  VLOG(3) << "Number of microbatches per minibatch: " << num_microbatches_;
  trainer_desc_ = trainer_desc;
  start_cpu_core_id_ = section_params.start_cpu_core_id();

  // SetDataset(dataset);
  ParseDumpConfig(trainer_desc);
  // get filelist from trainer_desc here
  // const std::vector<paddle::framework::DataFeed*> readers =
  // VLOG(3) << "Number of program sections: " << section_num_;
  //    dataset->GetReaders();
  // VLOG(3) << "readers num: " << readers.size();
  // int num_readers = readers.size();
  // PADDLE_ENFORCE_EQ(num_readers, 1,
  //                  platform::errors::InvalidArgument(
  //                      "Number of dataset readers for pipeline "
  //                      "must be 1 now, but the value you give is %d.",
  //                      num_readers));
  // auto* reader = readers[0];

  // workers_.resize(section_num_);
  // for (int i = 0; i < section_num_; ++i) {
  //  const auto& section_config = section_params.section_config(i);
  //  platform::Place place;
  //  int place_id = section_config.place_id();
  //  switch (section_config.place()) {
  //    case SectionConfig::CPUPlace:
  //      place = platform::CPUPlace();
  //      break;
  //    case SectionConfig::CUDAPlace:
  //      // Note that one section has at most one GPU place in one pipeline
  //      PADDLE_ENFORCE_GE(
  //          place_id, 0,
  //          platform::errors::InvalidArgument(
  //              "The place_id value for CUDAPlace shoud be greater "
  //              "than or equal to 0, but the value you give is %d.",
  //              place_id));
  //      place = platform::CUDAPlace(place_id);
  //      break;
  //    case SectionConfig::CUDAPinnedPlace:
  //      place = platform::CUDAPinnedPlace();
  //      break;
  //    default:
  //      PADDLE_ENFORCE_NOT_NULL(nullptr,
  //                              platform::errors::InvalidArgument(
  //                                  "Unkown place type in SectionConfig: %d",
  //                                  section_config.place()));
  //  }
  //  places_.emplace_back(place);
  //  VLOG(3) << "Device worker place: " << place << ", device id: " << place_id
  //          << ", section: " << i;

  //  workers_[i] = DeviceWorkerFactory::CreateDeviceWorker(
  //      trainer_desc.device_worker_name());
  //  auto this_worker =
  //      std::dynamic_pointer_cast<paddle::framework::SectionWorker>(
  //          workers_[i]);
  //  if (i == 0) {
  //    // we only set reader for the first section
  //    this_worker->SetDataFeed(reader);
  //    this_worker->SetReaderPlace(place);
  //  }
  //  this_worker->SetThreadIndex(i);
  //  this_worker->SetSectionIndex(i);
  //  this_worker->SetPlace(place);
  //  this_worker->Initialize(trainer_desc);
  //  this_worker->SetMicrobatchNum(num_microbatches_);
  //}
  const auto& section_config = section_params.section_config();
  int place_id = section_config.place_id();
  PADDLE_ENFORCE_GE(place_id, 0,
                    platform::errors::InvalidArgument(
                        "The place_id value for CUDAPlace shoud be "
                        "non-negative, but the value given is %d.",
                        place_id));
  place_ = platform::CUDAPlace(place_id);
  worker_ = DeviceWorkerFactory::CreateDeviceWorker(
      trainer_desc.device_worker_name());
  auto this_worker =
      std::dynamic_pointer_cast<paddle::framework::SectionWorker>(worker_);
  this_worker->SetPlace(place_);
  this_worker->Initialize(trainer_desc);
  this_worker->SetMicrobatchNum(num_microbatches_);

  // set debug here
  SetDebug(trainer_desc.debug());
}

void PipelineTrainer::InitOtherEnv(const ProgramDesc& main_program) {
  if (need_dump_field_) {
    InitDumpEnv();
  }
  VLOG(3) << "init other env done.";
}

std::string PipelineTrainer::GetDumpPath(int tid) {
  return string::format_string("%s/part-%05d", dump_fields_path_.c_str(), tid);
}

void PipelineTrainer::InitDumpEnv() {
  queue_ = paddle::framework::MakeChannel<std::string>();
  // TODO(sandyhouse): should make it as a config
  dump_thread_num_ = 1;
  for (int i = 0; i < dump_thread_num_; i++) {
    dump_thread_.push_back(
        std::thread(std::bind(&TrainerBase::DumpWork, this, i)));
  }
}

// void PipelineTrainer::CopyParameters(int section_id, int microbatch_id,
//                                      const ProgramDesc& program,
//                                      const platform::Place& place) {
//   auto& global_block = program.Block(0);
//   std::map<std::string, int> param_map;
//   for (auto& var : global_block.AllVars()) {
//     if (var->Persistable()) {
//       param_map[var->Name()] = 1;
//     }
//   }
//   for (auto& var : global_block.AllVars()) {
//     bool is_param_grad = false;
//     size_t pos = 0;
//     if ((pos = var->Name().find(kGradVarSuffix)) != std::string::npos) {
//       auto prefix_name = var->Name().substr(0, pos);
//       if (param_map.find(prefix_name) != param_map.end()) {
//         is_param_grad = true;
//       }
//     }
//     VLOG(3) << "Var name: " << var->Name();
//     if ((var->Persistable() || is_param_grad) && microbatch_id == 0) {
//       auto* ptr = root_scope_->FindVar(var->Name());
//       auto* new_ptr = minibatch_scopes_[section_id]->Var(var->Name());
//       VLOG(3) << "Create persistable var " << var->Name() << " for minibatch
//       "
//               << section_id << ", which pointer is " << new_ptr;
//       InitializeVariable(new_ptr, var->GetType());
//       if (is_param_grad) {
//         continue;
//       }
//       const LoDTensor& root_tensor = ptr->Get<LoDTensor>();
//       LoDTensor* minibatch_tensor = new_ptr->GetMutable<LoDTensor>();
//       TensorCopy(*static_cast<const Tensor*>(&root_tensor), place,
//                  static_cast<Tensor*>(minibatch_tensor));
//     } else if (!var->Persistable() && !is_param_grad) {
//       auto* ptr =
//           microbatch_scopes_[section_id][microbatch_id]->Var(var->Name());
//       VLOG(3) << "Create variable " << var->Name() << " for section "
//               << section_id << " microbatch " << microbatch_id
//               << ", which pointer is " << ptr;
//       InitializeVariable(ptr, var->GetType());
//     }
//   }
// }

void PipelineTrainer::CopyParameters(int microbatch_id,
                                     const ProgramDesc& program,
                                     const platform::Place& place) {
  auto& global_block = program.Block(0);
  std::map<std::string, int> param_map;
  for (auto& var : global_block.AllVars()) {
    if (var->Persistable()) {
      param_map[var->Name()] = 1;
    }
  }
  for (auto& var : global_block.AllVars()) {
    bool is_param_grad = false;
    size_t pos = 0;
    if ((pos = var->Name().find(kGradVarSuffix)) != std::string::npos) {
      auto prefix_name = var->Name().substr(0, pos);
      if (param_map.find(prefix_name) != param_map.end()) {
        is_param_grad = true;
      }
    }
    VLOG(3) << "Var name: " << var->Name();
    if (is_param_grad && microbatch_id == 0) {
      auto* ptr = minibatch_scope_->Var(var->Name());
      InitializeVariable(ptr, var->GetType());
      VLOG(3) << "Create grad for persistable var: " << var->Name()
              << ", which pointer is " << ptr;
    } else if (!var->Persistable() && !is_param_grad) {
      auto* ptr = microbatch_scopes_[microbatch_id]->Var(var->Name());
      VLOG(3) << "Create variable " << var->Name() << " microbatch "
              << ", which pointer is " << ptr;
      InitializeVariable(ptr, var->GetType());
    }
  }
}

// void PipelineTrainer::GetSkipVars(int section_id, const ProgramDesc& program)
// {
//   auto& global_block = program.Block(0);
//   for (auto& op : global_block.AllOps()) {
//     if (op->Type() != "enqueue") {
//       continue;
//     }
//     auto input_arg_names = op->InputArgumentNames();
//     PADDLE_ENFORCE_EQ(input_arg_names.size(), 1,
//                       platform::errors::InvalidArgument(
//                           "Number of input arguments for enqueue op must be
//                           1, "
//                           "but the value is %d.",
//                           input_arg_names.size()));
//     std::string input_arg_name = input_arg_names[0];
//     if (input_arg_name.rfind("@GRAD") != input_arg_name.size() - 5) {
//       skip_vars_[section_id].emplace_back(input_arg_name);
//       VLOG(3) << "add skip var name: " << input_arg_name;
//     }
//   }
// }

void PipelineTrainer::GetSkipVars(const ProgramDesc& program) {
  auto& global_block = program.Block(0);
  for (auto& op : global_block.AllOps()) {
    if (op->Type() != "c_send") {
      continue;
    }
    auto input_arg_names = op->InputArgumentNames();
    PADDLE_ENFORCE_EQ(input_arg_names.size(), 1,
                      platform::errors::InvalidArgument(
                          "Number of input arguments for c_send op must be 1, "
                          "but the value given is %d.",
                          input_arg_names.size()));
    std::string input_arg_name = input_arg_names[0];
    if (input_arg_name.rfind("@GRAD") != input_arg_name.size() - 5) {
      skip_vars_.emplace_back(input_arg_name);
      VLOG(3) << "add skip var name: " << input_arg_name;
    }
  }
}

void PipelineTrainer::InitTrainerEnv(const ProgramDesc& main_program,
                                     const platform::Place& place) {
  PADDLE_ENFORCE_NOT_NULL(root_scope_, platform::errors::InvalidArgument(
                                           "root_scope_ can not be nullptr"));
  auto start_cpu_id = trainer_desc_.section_param().start_cpu_core_id();
  SectionWorker::cpu_id_.store(start_cpu_id);
  // minibatch_scopes_.resize(section_num_);
  // microbatch_scopes_.resize(section_num_);
  // minibatch_scopes_.resize(1);
  microbatch_scopes_.resize(num_microbatches_);
  // skip_vars_.resize(section_num_);

  VLOG(3) << "Init ScopeQueues and create all scopes";
  // for (int i = 0; i < section_num_; ++i) {
  minibatch_scope_ = &root_scope_->NewScope();
  std::shared_ptr<framework::ProgramDesc> program;
  program.reset(new ProgramDesc(
      trainer_desc_.section_param().section_config().program_desc()));
  // trainer_desc_.section_param().section_config(i).program_desc()));
  // microbatch_scopes_[i].resize(num_microbatches_);
  for (int j = 0; j < num_microbatches_; ++j) {
    // microbatch_scopes_[j] = &minibatch_scopes_[i]->NewScope();
    microbatch_scopes_[j] = &minibatch_scope_->NewScope();
    // CopyParameters(i, j, *program, places_[i]);
    CopyParameters(j, *program, place_);
  }
  // GetSkipVars(i, *program);
  GetSkipVars(*program);
  // }

  // for (int i = 0; i < section_num_; ++i) {
  auto this_worker =
      std::dynamic_pointer_cast<paddle::framework::SectionWorker>(worker_);
  // workers_[i]);
  this_worker->SetRootScope(root_scope_);
  this_worker->SetMinibatchScope(minibatch_scope_);
  // this_worker->SetMicrobatchScopes(microbatch_scopes_[i]);
  this_worker->SetMicrobatchScopes(microbatch_scopes_);
  // this_worker->SetSkipVars(skip_vars_[i]);
  //}
}

void PipelineTrainer::Run() {
  VLOG(3) << "Going to run";
  // for (int i = 0; i < section_num_; ++i) {
  if (!debug_) {
    section_thread_ = std::thread(&DeviceWorker::TrainFiles, worker_.get());
    // section_threads_.push_back(
    // std::thread(&DeviceWorker::TrainFiles, workers_.get()));
    // std::thread(&DeviceWorker::TrainFiles, workers_[i].get()));
  } else {
    section_thread_ =
        std::thread(&DeviceWorker::TrainFilesWithProfiler, worker_.get());
    // section_threads_.push_back(std::thread(
    // &DeviceWorker::TrainFilesWithProfiler, workers_.get()));
    // &DeviceWorker::TrainFilesWithProfiler, workers_[i].get()));
  }
  //}
}

void PipelineTrainer::Finalize() {
  // for (auto& th : section_threads_) {
  //  th.join();
  //}
  section_thread_.join();
  if (need_dump_field_) {
    FinalizeDumpEnv();
  }
  // VLOG(3) << "copying back parameters. ";
  // for (int i = 0; i < section_num_; ++i) {
  //   std::shared_ptr<framework::ProgramDesc> program;
  //   program.reset(new ProgramDesc(
  //       trainer_desc_.section_param().section_config(i).program_desc()));
  //   for (int j = 0; j < num_microbatches_; ++j) {
  //     auto& global_block = program->Block(0);
  //     for (auto& var : global_block.AllVars()) {
  //       if (var->Persistable()) {
  //         auto* ptr = root_scope_->FindVar(var->Name());
  //         LoDTensor* root_tensor = ptr->GetMutable<LoDTensor>();
  //         auto* minibatch_ptr = minibatch_scopes_[i]->Var(var->Name());
  //         const LoDTensor& minibatch_tensor =
  //         minibatch_ptr->Get<LoDTensor>();
  //         TensorCopy(*static_cast<const Tensor*>(&minibatch_tensor),
  //         places_[0],
  //                    static_cast<Tensor*>(root_tensor));
  //         VLOG(3) << "Copy persitable var " << var->Name() << " to root
  //         scope";
  //       }
  //     }
  //   }
  // }
  root_scope_->DropKids();
  // SectionWorker::ResetBatchId();
}

Scope* PipelineTrainer::GetWorkerScope(int thread_id) {
  return microbatch_scopes_[0];
}

}  // end namespace framework
}  // end namespace paddle
#endif