async_executor.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 "paddle/fluid/framework/async_executor.h"
#include <stdio.h>
#include <string.h>
#include <fcntl.h>
#include <sys/types.h>
#include <sys/stat.h>
#include <unistd.h>
#include <fstream>
#include <iostream>
#include <map>
#include <algorithm>
#include "google/protobuf/message.h"
#include "google/protobuf/text_format.h"
#include "google/protobuf/io/zero_copy_stream_impl.h"

#include "gflags/gflags.h"
#include "paddle/fluid/framework/feed_fetch_method.h"
#include "paddle/fluid/framework/feed_fetch_type.h"
#include "paddle/fluid/framework/lod_rank_table.h"
#include "paddle/fluid/framework/lod_tensor_array.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/reader.h"
#include "paddle/fluid/platform/place.h"
#include "paddle/fluid/inference/io.h"
#include "paddle/fluid/framework/executor_thread_worker.h"
#include "paddle/fluid/framework/data_feed_factory.h"
#include "paddle/fluid/pybind/pybind.h"

namespace paddle {
namespace framework {

static void ReadBinaryFile(const std::string& filename,
                             std::string* content) {
  std::string &contents = *content;
  std::ifstream fin(filename, std::ios::in | std::ios::binary);
  if (!fin.good()) {
    LOG(ERROR) << "Cannot open file " << filename.c_str();
  }
  fin.seekg(0, std::ios::end);
  contents.clear();
  contents.resize(fin.tellg());
  fin.seekg(0, std::ios::beg);
  fin.read(&contents[0], contents.size());
  fin.close();
}

static void SaveModel(
    const std::unique_ptr<ProgramDesc> & main_program,
    Scope* scope,
    const std::vector<std::string> & param_names,
    const std::string & model_name,
    bool save_combine) {
  auto place = platform::CPUPlace();
  const BlockDesc& global_block = main_program->Block(0);
  std::vector<std::string> paralist;

  for (auto* var : global_block.AllVars()) {
    bool is_model_param = false;
    for (auto param_name : param_names) {
      if (var->Name() == param_name) {
        is_model_param = true;
        break;
      }
    }

    if (!is_model_param)  continue;

    if (!save_combine) {
      LOG(ERROR) << "model var name: " << var->Name().c_str();

      paddle::framework::AttributeMap attrs;
      attrs.insert({"file_path", model_name + "/" + var->Name()});
      auto save_op = paddle::framework::OpRegistry::CreateOp(
                                                      "save",
                                                      {{"X", {var->Name()}}},
                                                      {},
                                                      attrs);
      save_op->Run(*scope, place);
    } else {
      paralist.push_back(var->Name());
    }
  }

  if (save_combine) {
    std::sort(paralist.begin(), paralist.end());
    paddle::framework::AttributeMap attrs;
    attrs.insert({"file_path", model_name});
    auto save_op = paddle::framework::OpRegistry::CreateOp(
                                                      "save_combine",
                                                      {{"X", paralist}},
                                                      {},
                                                      attrs);

    save_op->Run(*scope, place);
  }
}   // end SaveModel

AsyncExecutor::AsyncExecutor(Scope& scope, const platform::Place& place)
    : root_scope_(scope), place_(place) {}

void AsyncExecutor::CreateThreads(
    ExecutorThreadWorker* worker,
    const ProgramDesc& main_program,
    const std::shared_ptr<DataFeed>& reader,
    const std::vector<std::string>& fetch_var_names,
    Scope& root_scope,
    const int thread_index) {
  worker->SetThreadId(thread_index);
  worker->SetRootScope(&root_scope);
  worker->CreateThreadResource(main_program, place_);
  worker->SetDataFeed(reader);
  worker->SetFetchVarNames(fetch_var_names);
  worker->BindingDataFeedMemory();
}

void AsyncExecutor::CheckFiles(
    const std::vector<std::string>& files) {
  // function for user to check file formats
  // should be exposed to users
}

void AsyncExecutor::SetModelPrefix(const std::string& model_prefix) {
  model_prefix_ = model_prefix;
}

std::vector<float> AsyncExecutor::RunFromFile(
    const ProgramDesc& main_program,
    const std::string& data_feed_desc_str,
    const std::vector<std::string>& filelist,
    const int thread_num,
    const std::vector<std::string>& fetch_var_names) {
  std::vector<std::thread> threads;

  DataFeedDesc data_feed_desc;
  google::protobuf::TextFormat::ParseFromString(data_feed_desc_str, &data_feed_desc);
  /*
    readerDesc: protobuf description for reader initlization
    argument: class_name, batch_size, use_slot, queue_size, buffer_size, padding_index
    
    reader: 
    1) each thread has a reader, reader will read input data and 
    put it into input queue
    2) each reader has a Next() iterface, that can fetch an instance
    from the input queue
   */
  // todo: should be factory method for creating datafeed
  std::vector<std::shared_ptr<DataFeed> > readers;
  readers.resize(thread_num);
  for (unsigned int i = 0; i < readers.size(); ++i) {
    readers[i] = DataFeedFactory::CreateDataFeed(data_feed_desc.name());
  }

  std::vector<std::shared_ptr<ExecutorThreadWorker> > workers;
  workers.resize(thread_num);
  for (auto& worker : workers) {
    worker.reset(new ExecutorThreadWorker);
  }

  // prepare thread resource here
  for (int thidx = 0; thidx < thread_num; ++thidx) {
    CreateThreads(workers[thidx].get(), main_program,
                  readers[thidx], fetch_var_names, root_scope_, thidx);
  }

  // start executing ops in multiple threads
  for (int thidx = 0; thidx < thread_num; ++thidx) {
    threads.push_back(std::thread(&ExecutorThreadWorker::TrainFiles,
                                  workers[thidx].get()));
  }

  for (auto& th : threads) {
    th.join();
  }

  std::vector<float> fetch_values;
  fetch_values.resize(fetch_var_names.size(), 0);

  std::vector<std::vector<float>*> fetch_value_vectors;
  fetch_value_vectors.resize(thread_num);
  for (int i = 0; i < thread_num; ++i) {
    fetch_value_vectors[i] = &workers[i]->GetFetchValues();
  }

  for (unsigned int i = 0; i < fetch_var_names.size(); ++i) {
    float value = 0.0;
    for (int j = 0; j < thread_num; ++j) {
      value += fetch_value_vectors[j]->at(i);
    }
    value /= thread_num;
    fetch_values[i] = value;
  }

  return fetch_values;
}

void AsyncExecutor::LoadInitModel() {
  auto place = paddle::platform::CPUPlace();
  auto* executor = new paddle::framework::Executor(place);

  std::string init_prog_file = model_path_ + "/" + init_prog_file_;
  std::string init_model_file = model_path_ + "/" + init_model_file_;

  struct stat stat_buf;

  if (stat(init_prog_file.c_str(), &stat_buf) == 0 &&
      S_ISREG(stat_buf.st_mode) &&
      stat(init_model_file.c_str(), &stat_buf) == 0 &&
      S_ISREG(stat_buf.st_mode)) {
    paddle::inference::Load(executor,
                          GetRootScope(),
                          model_path_ + "/" + init_prog_file_,
                          model_path_ + "/" + init_model_file_);
  }
}
}   // einit_modelnd namespace framework
}   // end namespace paddle

/* vim: set expandtab ts=2 sw=2 sts=2 tw=100: */