hogwild_worker.cc

/* 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. */

#include "paddle/fluid/framework/data_type.h"
#include "paddle/fluid/framework/device_worker.h"
#include "paddle/fluid/framework/device_worker_factory.h"
#include "paddle/fluid/operators/controlflow/conditional_block_op_helper.h"
#include "paddle/fluid/platform/cpu_helper.h"
#include "paddle/fluid/platform/lodtensor_printer.h"

#if defined PADDLE_WITH_PSCORE
#include "paddle/fluid/distributed/service/communicator.h"
#endif

namespace paddle {
namespace framework {

void HogwildWorker::Initialize(const TrainerDesc &desc) {
  fetch_config_ = desc.fetch_config();
  param_ = desc.hogwild_param();
  skip_ops_.resize(param_.skip_ops_size());
  for (int i = 0; i < param_.skip_ops_size(); ++i) {
    skip_ops_[i] = param_.skip_ops(i);
  }
  use_cvm_ = desc.use_cvm();
  thread_barrier_ = desc.thread_barrier();

  for (int i = 0; i < param_.stat_var_names_size(); ++i) {
    stat_var_name_map_[param_.stat_var_names(i)] = 1;
  }
}

void HogwildWorker::CreateThreadOperators(const ProgramDesc &program) {
  auto &block = program.Block(0);
  op_names_.clear();
  for (auto &op_desc : block.AllOps()) {
    std::unique_ptr<OperatorBase> local_op = OpRegistry::CreateOp(*op_desc);
    op_names_.push_back(op_desc->Type());
    OperatorBase *local_op_ptr = local_op.release();
    ops_.push_back(local_op_ptr);
    continue;
  }
  operators::PrepareSafeEagerDeletionOnConditionalOpAndConditionalGradOp(
      program, 0, ops_);
}

void HogwildWorker::CreateThreadScope(const ProgramDesc &program) {
  auto &block = program.Block(0);

  PADDLE_ENFORCE_NOT_NULL(
      root_scope_,
      platform::errors::NotFound(
          "Root scope should be set before creating thread scope."));

  thread_scope_ = &root_scope_->NewScope();

  for (auto &var : block.AllVars()) {
    all_param_.push_back(var->Name());
    if (var->Persistable()) {
      auto *ptr = root_scope_->Var(var->Name());
      InitializeVariable(ptr, var->GetType());
      if (stat_var_name_map_.find(var->Name()) != stat_var_name_map_.end() &&
          thread_id_ != 0) {
        int tensor_dim =
            root_scope_->FindVar(var->Name())->GetMutable<LoDTensor>()->numel();
        auto *ptr1 = thread_scope_->Var(var->Name());
        InitializeVariable(ptr1, var->GetType());
        LoDTensor *thread_tensor = ptr1->GetMutable<LoDTensor>();
        LoDTensor *root_tensor =
            root_scope_->FindVar(var->Name())->GetMutable<LoDTensor>();
#define MemsetCallback(cpp_type, proto_type)                     \
  do {                                                           \
    if (root_tensor->type() == proto_type) {                     \
      SetZero<cpp_type>(thread_tensor, root_tensor, tensor_dim); \
    }                                                            \
  } while (0)
        _ForEachDataType_(MemsetCallback);
      }
    } else {
      auto *ptr = thread_scope_->Var(var->Name());
      InitializeVariable(ptr, var->GetType());
    }
  }
}

template <typename T>
void HogwildWorker::SetZero(LoDTensor *tensor, LoDTensor *root_tensor,
                            int tensor_dim) {
  T *ptr = tensor->mutable_data<T>(root_tensor->dims(), platform::CPUPlace());
  memset(ptr, 0, sizeof(T) * tensor_dim);
}

void HogwildWorker::BindingDataFeedMemory() {
  const std::vector<std::string> &input_feed =
      device_reader_->GetUseSlotAlias();
  for (auto name : input_feed) {
    device_reader_->AddFeedVar(thread_scope_->FindVar(name), name);
  }
}

void HogwildWorker::CreateDeviceResource(const ProgramDesc &main_prog) {
  CreateThreadScope(main_prog);
  CreateThreadOperators(main_prog);
}

void HogwildWorker::TrainFilesWithProfiler() {
  platform::SetNumThreads(1);
  device_reader_->Start();
  std::vector<double> op_total_time;
  std::vector<std::string> op_name;
  for (auto &op : ops_) {
    op_name.push_back(op->Type());
  }
  op_total_time.resize(ops_.size());
  for (size_t i = 0; i < op_total_time.size(); ++i) {
    op_total_time[i] = 0.0;
  }
  platform::Timer timeline;
  double total_time = 0.0;
  double read_time = 0.0;
  int cur_batch;
  int batch_cnt = 0;
  timeline.Start();
  uint64_t total_inst = 0;
  while ((cur_batch = device_reader_->Next()) > 0) {
    VLOG(3) << "read a batch in thread " << thread_id_;
    timeline.Pause();
    read_time += timeline.ElapsedSec();
    total_time += timeline.ElapsedSec();
    for (size_t i = 0; i < ops_.size(); ++i) {
      bool need_skip = false;
      for (auto t = 0u; t < skip_ops_.size(); ++t) {
        if (ops_[i]->Type().find(skip_ops_[t]) != std::string::npos) {
          need_skip = true;
          break;
        }
      }
      timeline.Start();
      VLOG(3) << "Going to run op " << op_name[i];
      if (!need_skip) {
        ops_[i]->Run(*thread_scope_, place_);
      }
      VLOG(3) << "Op " << op_name[i] << " Finished";
      timeline.Pause();
      op_total_time[i] += timeline.ElapsedSec();
      total_time += timeline.ElapsedSec();
    }

    if (need_dump_field_) {
      DumpField(*thread_scope_, dump_mode_, dump_interval_);
    }
    if (need_dump_param_ && thread_id_ == 0) {
      DumpParam(*thread_scope_, batch_cnt);
    }

    total_inst += cur_batch;
    ++batch_cnt;
    PrintFetchVars();
    if (thread_id_ == 0) {
      if (batch_cnt > 0 && batch_cnt % 100 == 0) {
        for (size_t i = 0; i < ops_.size(); ++i) {
          fprintf(stderr, "op_name:[%zu][%s], op_mean_time:[%fs]\n", i,
                  op_name[i].c_str(), op_total_time[i] / batch_cnt);
        }
        fprintf(stderr, "mean read time: %fs\n", read_time / batch_cnt);
        fprintf(stderr, "IO percent: %f\n", read_time / total_time * 100);
        fprintf(stderr, "%6.2f instances/s\n", total_inst / total_time);
      }
    }
    thread_scope_->DropKids();
    timeline.Start();
  }

  if (need_dump_field_ || need_dump_param_) {
    writer_.Flush();
  }

#if defined PADDLE_WITH_PSCORE
  if (thread_barrier_) {
    paddle::distributed::Communicator::GetInstance()->BarrierTriggerDecrement();
  }
#endif
}

void HogwildWorker::TrainFiles() {
  platform::SetNumThreads(1);

  // how to accumulate fetched values here
  device_reader_->Start();
  int cur_batch;
  while ((cur_batch = device_reader_->Next()) > 0) {
    for (auto &op : ops_) {
      bool need_skip = false;
      for (auto t = 0u; t < skip_ops_.size(); ++t) {
        if (op->Type().find(skip_ops_[t]) != std::string::npos) {
          need_skip = true;
          break;
        }
      }
      if (!need_skip) {
        op->Run(*thread_scope_, place_);
      }
    }

    PrintFetchVars();
    thread_scope_->DropKids();
  }
#if defined PADDLE_WITH_PSCORE
  if (thread_barrier_) {
    paddle::distributed::Communicator::GetInstance()->BarrierTriggerDecrement();
  }
#endif
}

void HogwildWorker::PrintFetchVars() {
  // call count
  batch_num_++;
  int batch_per_print = fetch_config_.print_period();
  if (thread_id_ == 0) {
    if (batch_num_ % batch_per_print == 0) {
      int fetch_var_num = fetch_config_.fetch_var_names_size();
      for (int i = 0; i < fetch_var_num; ++i) {
        platform::PrintVar(thread_scope_, fetch_config_.fetch_var_names(i),
                           fetch_config_.fetch_var_str_format(i));
      }
    }
  }
}

}  // end namespace framework
}  // end namespace paddle