data_feed.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 "google/protobuf/io/zero_copy_stream_impl.h"
#include "google/protobuf/message.h"
#include "google/protobuf/text_format.h"

#include "gflags/gflags.h"
#include "paddle/fluid/framework/data_feed.h"
#include "paddle/fluid/framework/feed_fetch_method.h"
#include "paddle/fluid/framework/feed_fetch_type.h"

namespace paddle {
namespace framework {

std::vector<std::string> DataFeed::filelist_;
size_t DataFeed::file_idx_;
std::mutex DataFeed::mutex_for_pick_file_;
bool DataFeed::finish_set_filelist_;

void DataFeed::AddFeedVar(Variable* var, const std::string& name) {
  CheckInit();
  for (size_t i = 0; i < use_slots_.size(); ++i) {
    if (name == use_slots_[i]) {
      if (use_slots_is_dense_[i]) {
        feed_vec_[i] = MixTensor(var->GetMutable<Tensor>());
      } else {
        feed_vec_[i] = MixTensor(var->GetMutable<LoDTensor>());
      }
    }
  }
}

bool DataFeed::SetFileList(const std::vector<std::string>& files) {
  std::unique_lock<std::mutex> lock(mutex_for_pick_file_);
  CheckInit();
  if (finish_set_filelist_) {
    VLOG(3) << "info: you have set the filelist.";
    return false;
  }
  PADDLE_ENFORCE(files.size(), "You have set an empty filelist.");
  filelist_.assign(files.begin(), files.end());
  file_idx_ = 0;

  finish_set_filelist_ = true;
  return true;
}

void DataFeed::SetBatchSize(int batch_size) {
  PADDLE_ENFORCE(batch_size > 0, "Illegal batch size: %d.", batch_size);
  default_batch_size_ = batch_size;
}

bool DataFeed::PickOneFile(std::string* filename) {
  std::unique_lock<std::mutex> lock(mutex_for_pick_file_);
  if (file_idx_ == filelist_.size()) {
    return false;
  }
  *filename = filelist_[file_idx_++];
  LOG(ERROR) << "pick file:" << *filename;
  return true;
}

void DataFeed::CheckInit() {
  PADDLE_ENFORCE(finish_init_, "Initialization did not succeed.");
}

void DataFeed::CheckSetFileList() {
  PADDLE_ENFORCE(finish_set_filelist_, "Set filelist did not succeed.");
}

void DataFeed::CheckStart() {
  PADDLE_ENFORCE(finish_start_, "Datafeed has not started running yet.");
}

template <typename T>
void PrivateQueueDataFeed<T>::SetQueueSize(int queue_size) {
  PADDLE_ENFORCE(queue_size > 0, "Illegal queue size: %d.", queue_size);
  queue_size_ = queue_size;
  queue_ = std::unique_ptr<paddle::operators::reader::BlockingQueue<T>>(
      new paddle::operators::reader::BlockingQueue<T>(queue_size_));
}

template <typename T>
bool PrivateQueueDataFeed<T>::Start() {
  CheckSetFileList();
  read_thread_ = std::thread(&PrivateQueueDataFeed::ReadThread, this);
  read_thread_.detach();

  finish_start_ = true;
  return true;
}

template <typename T>
void PrivateQueueDataFeed<T>::ReadThread() {
  std::string filename;
  while (PickOneFile(&filename)) {
    file_.open(filename.c_str());  // is_text_feed
    PADDLE_ENFORCE(file_.good(), "Open file<%s> fail.", filename.c_str());
    T instance;
    while (ParseOneInstance(&instance)) {
      queue_->Send(instance);
    }
    file_.close();
  }
  queue_->Close();
}

template <typename T>
int PrivateQueueDataFeed<T>::Next() {
  CheckStart();
  int index = 0;
  T instance;
  T ins_vec;
  while (index < default_batch_size_) {
    if (!queue_->Receive(&instance)) {
      break;
    }
    AddInstanceToInsVec(&ins_vec, instance, index++);
  }
  batch_size_ = index;
  if (batch_size_ != 0) {
    PutToFeedVec(ins_vec);
  }
  return batch_size_;
}

#ifdef _WIN32
template class PrivateQueueDataFeed<std::vector<MultiSlotType>>;
#endif

void MultiSlotDataFeed::Init(
    const paddle::framework::DataFeedDesc& data_feed_desc) {
  finish_init_ = false;
  finish_set_filelist_ = false;
  finish_start_ = false;

  PADDLE_ENFORCE(data_feed_desc.has_multi_slot_desc(),
                 "Multi_slot_desc has not been set.");
  paddle::framework::MultiSlotDesc multi_slot_desc =
      data_feed_desc.multi_slot_desc();
  SetBatchSize(data_feed_desc.batch_size());
  SetQueueSize(data_feed_desc.batch_size());
  size_t all_slot_num = multi_slot_desc.slots_size();
  all_slots_.resize(all_slot_num);
  all_slots_type_.resize(all_slot_num);
  use_slots_index_.resize(all_slot_num);
  use_slots_.clear();
  use_slots_is_dense_.clear();
  for (size_t i = 0; i < all_slot_num; ++i) {
    const auto& slot = multi_slot_desc.slots(i);
    all_slots_[i] = slot.name();
    all_slots_type_[i] = slot.type();
    use_slots_index_[i] = slot.is_used() ? use_slots_.size() : -1;
    if (slot.is_used()) {
      use_slots_.push_back(all_slots_[i]);
      use_slots_is_dense_.push_back(slot.is_dense());
    }
  }
  feed_vec_.resize(use_slots_.size());
  finish_init_ = true;
}

bool MultiSlotDataFeed::CheckFile(const char* filename) {
  CheckInit();  // get info of slots
  std::ifstream fin(filename);
  if (!fin.good()) {
    VLOG(1) << "error: open file<" << filename << "> fail";
    return false;
  }
  std::string line;
  int instance_cout = 0;
  std::string all_slots_alias = "";
  for (const auto& alias : all_slots_) {
    all_slots_alias += alias + " ";
  }
  std::string use_slots_alias = "";
  for (const auto& alias : use_slots_) {
    use_slots_alias += alias + " ";
  }
  VLOG(3) << "total slots num: " << all_slots_.size();
  VLOG(3) << "total slots alias: " << all_slots_alias;
  VLOG(3) << "used slots num: " << use_slots_.size();
  VLOG(3) << "used slots alias: " << use_slots_alias;
  while (getline(fin, line)) {
    ++instance_cout;
    const char* str = line.c_str();
    char* endptr = const_cast<char*>(str);
    int len = line.length();
    for (size_t i = 0; i < all_slots_.size(); ++i) {
      int num = strtol(endptr, &endptr, 10);
      if (num < 0) {
        VLOG(1) << "error: the number of ids is a negative number: " << num;
        VLOG(1) << "please check line<" << instance_cout << "> in file<"
                << filename << ">";
        return false;
      } else if (num == 0) {
        VLOG(1)
            << "error: the number of ids can not be zero, you need "
               "padding it in data generator; or if there is something wrong"
               " with the data, please check if the data contains unresolvable "
               "characters.";
        VLOG(1) << "please check line<" << instance_cout << "> in file<"
                << filename << ">";
        return false;
      } else if (errno == ERANGE || num > INT_MAX) {
        VLOG(1) << "error: the number of ids greater than INT_MAX";
        VLOG(1) << "please check line<" << instance_cout << "> in file<"
                << filename << ">";
        return false;
      }
      if (all_slots_type_[i] == "float") {
        for (int i = 0; i < num; ++i) {
          strtof(endptr, &endptr);
          if (errno == ERANGE) {
            VLOG(1) << "error: the value is out of the range of "
                       "representable values for float";
            VLOG(1) << "please check line<" << instance_cout << "> in file<"
                    << filename << ">";
            return false;
          }
          if (i + 1 != num && endptr - str == len) {
            VLOG(1) << "error: there is a wrong with the number of ids.";
            VLOG(1) << "please check line<" << instance_cout << "> in file<"
                    << filename << ">";
            return false;
          }
        }
      } else if (all_slots_type_[i] == "uint64") {
        for (int i = 0; i < num; ++i) {
          strtoull(endptr, &endptr, 10);
          if (errno == ERANGE) {
            VLOG(1) << "error: the value is out of the range of "
                       "representable values for uint64_t";
            VLOG(1) << "please check line<" << instance_cout << "> in file<"
                    << filename << ">";
            return false;
          }
          if (i + 1 != num && endptr - str == len) {
            VLOG(1) << "error: there is a wrong with the number of ids.";
            VLOG(1) << "please check line<" << instance_cout << "> in file<"
                    << filename << ">";
            return false;
          }
        }
      } else {
        VLOG(1) << "error: this type<" << all_slots_type_[i]
                << "> is not supported";
        return false;
      }
    }
    if (endptr - str != len) {
      VLOG(1) << "error: there is some data at the end of the line.";
      VLOG(1) << "please check line<" << instance_cout << "> in file<"
              << filename << ">";
      return false;
    }
  }
  VLOG(3) << "instances cout: " << instance_cout;
  VLOG(3) << "The file format is correct";
  return true;
}

bool MultiSlotDataFeed::ParseOneInstance(std::vector<MultiSlotType>* instance) {
  std::string line;
  if (getline(file_, line)) {
    int use_slots_num = use_slots_.size();
    instance->resize(use_slots_num);
    // parse line
    const char* str = line.c_str();
    char* endptr = const_cast<char*>(str);
    int pos = 0;
    for (size_t i = 0; i < use_slots_index_.size(); ++i) {
      int idx = use_slots_index_[i];
      int num = strtol(&str[pos], &endptr, 10);
      PADDLE_ENFORCE(
          num,
          "The number of ids can not be zero, you need padding "
          "it in data generator; or if there is something wrong with "
          "the data, please check if the data contains unresolvable "
          "characters.\nplease check this error line: %s",
          str);
      if (idx != -1) {
        (*instance)[idx].Init(all_slots_type_[i]);
        if ((*instance)[idx].GetType()[0] == 'f') {  // float
          for (int j = 0; j < num; ++j) {
            float feasign = strtof(endptr, &endptr);
            (*instance)[idx].AddValue(feasign);
          }
        } else if ((*instance)[idx].GetType()[0] == 'u') {  // uint64
          for (int j = 0; j < num; ++j) {
            uint64_t feasign = (uint64_t)strtoull(endptr, &endptr, 10);
            (*instance)[idx].AddValue(feasign);
          }
        }
        pos = endptr - str;
      } else {
        for (int j = 0; j <= num; ++j) {
          pos = line.find_first_of(' ', pos + 1);
        }
      }
    }
  } else {
    return false;
  }
  return true;
}

void MultiSlotDataFeed::AddInstanceToInsVec(
    std::vector<MultiSlotType>* ins_vec,
    const std::vector<MultiSlotType>& instance, int index) {
  if (index == 0) {
    ins_vec->resize(instance.size());
    for (size_t i = 0; i < instance.size(); ++i) {
      (*ins_vec)[i].Init(instance[i].GetType());
      (*ins_vec)[i].InitOffset();
    }
  }
  for (size_t i = 0; i < instance.size(); ++i) {
    (*ins_vec)[i].AddIns(instance[i]);
  }
}

void MultiSlotDataFeed::PutToFeedVec(
    const std::vector<MultiSlotType>& ins_vec) {
  for (size_t i = 0; i < use_slots_.size(); ++i) {
    const auto& type = ins_vec[i].GetType();
    const auto& offset = ins_vec[i].GetOffset();
    int total_instance = static_cast<int>(offset.back());
    if (type[0] == 'f') {  // float
      const auto& feasign = ins_vec[i].GetFloatData();
      if (feed_vec_[i].IsDense()) {
        int size_in_each_batch = total_instance / batch_size_;
        float* tensor_ptr = feed_vec_[i].GetTensor()->mutable_data<float>(
            {batch_size_, size_in_each_batch}, platform::CPUPlace());
        memcpy(tensor_ptr, &feasign[0], total_instance * sizeof(float));
      } else {
        float* tensor_ptr = feed_vec_[i].GetLoDTensor()->mutable_data<float>(
            {total_instance, 1}, platform::CPUPlace());
        memcpy(tensor_ptr, &feasign[0], total_instance * sizeof(float));
        LoD data_lod{offset};
        feed_vec_[i].GetLoDTensor()->set_lod(data_lod);
      }
    } else if (type[0] == 'u') {  // uint64
      // no uint64_t type in paddlepaddle
      const auto& feasign = ins_vec[i].GetUint64Data();
      if (feed_vec_[i].IsDense()) {
        int size_in_each_batch = total_instance / batch_size_;
        int64_t* tensor_ptr = feed_vec_[i].GetTensor()->mutable_data<int64_t>(
            {batch_size_, size_in_each_batch}, platform::CPUPlace());
        memcpy(tensor_ptr, &feasign[0], total_instance * sizeof(int64_t));
      } else {
        int64_t* tensor_ptr =
            feed_vec_[i].GetLoDTensor()->mutable_data<int64_t>(
                {total_instance, 1}, platform::CPUPlace());
        memcpy(tensor_ptr, &feasign[0], total_instance * sizeof(int64_t));
        LoD data_lod{offset};
        feed_vec_[i].GetLoDTensor()->set_lod(data_lod);
      }
    }
  }
}

}  // namespace framework
}  // namespace paddle