paddlepaddle--paddle/paddle/fluid/framework/data_feed.h

/* 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. */

#pragma once

#if defined _WIN32 || defined __APPLE__
#else
#define _LINUX
#endif

#include <fstream>
#include <future>  // NOLINT
#include <memory>
#include <mutex>  // NOLINT
#include <random>
#include <sstream>
#include <string>
#include <thread>  // NOLINT
#include <unordered_map>
#include <unordered_set>
#include <utility>
#include <vector>
#include "paddle/common/macros.h"
#include "paddle/fluid/framework/archive.h"
#include "paddle/fluid/framework/blocking_queue.h"
#include "paddle/fluid/framework/channel.h"
#include "paddle/fluid/framework/fleet/fleet_wrapper.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/variable.h"
#include "paddle/phi/core/framework/data_feed.pb.h"
#include "paddle/phi/core/framework/reader.h"
#include "paddle/phi/core/platform/timer.h"
#include "paddle/utils/string/string_helper.h"
#if defined(PADDLE_WITH_CUDA)
#include "paddle/fluid/framework/fleet/heter_ps/gpu_graph_utils.h"
#include "paddle/phi/core/cuda_stream.h"
#include "paddle/phi/core/platform/cuda_device_guard.h"
#include "paddle/phi/core/platform/device/gpu/gpu_info.h"
#endif
#include "paddle/common/flags.h"

COMMON_DECLARE_int32(record_pool_max_size);
COMMON_DECLARE_int32(slotpool_thread_num);
COMMON_DECLARE_bool(enable_slotpool_wait_release);
COMMON_DECLARE_bool(enable_slotrecord_reset_shrink);

namespace paddle {
namespace framework {
class DataFeedDesc;
class Scope;
class Variable;
class NeighborSampleResult;
class NodeQueryResult;
template <typename KeyType, typename ValType>
class HashTable;
}  // namespace framework
}  // namespace paddle

namespace phi {
class DenseTensor;
}  // namespace phi

namespace paddle {
namespace framework {

// DataFeed is the base virtual class for all other DataFeeds.
// It is used to read files and parse the data for subsequent trainer.
// Example:
//   DataFeed* reader =
//   paddle::framework::DataFeedFactory::CreateDataFeed(data_feed_name);
//   reader->Init(data_feed_desc); // data_feed_desc is a protobuf object
//   reader->SetFileList(filelist);
//   const std::vector<std::string> & use_slot_alias =
//   reader->GetUseSlotAlias();
//   for (auto name: use_slot_alias){ // for binding memory
//     reader->AddFeedVar(scope->Var(name), name);
//   }
//   reader->Start();
//   while (reader->Next()) {
//      // trainer do something
//   }

template <typename T>
struct SlotValues {
  std::vector<T> slot_values;
  std::vector<uint32_t> slot_offsets;

  void add_values(const T* values, uint32_t num) {
    if (slot_offsets.empty()) {
      slot_offsets.push_back(0);
    }
    if (num > 0) {
      slot_values.insert(slot_values.end(), values, values + num);
    }
    slot_offsets.push_back(static_cast<uint32_t>(slot_values.size()));
  }
  T* get_values(int idx, size_t* size) {
    uint32_t& offset = slot_offsets[idx];
    (*size) = slot_offsets[idx + 1] - offset;
    return &slot_values[offset];
  }
  void add_slot_feasigns(const std::vector<std::vector<T>>& slot_feasigns,
                         uint32_t fea_num) {
    slot_values.reserve(fea_num);
    int slot_num = static_cast<int>(slot_feasigns.size());
    slot_offsets.resize(slot_num + 1);
    for (int i = 0; i < slot_num; ++i) {
      auto& slot_val = slot_feasigns[i];
      slot_offsets[i] = static_cast<uint32_t>(slot_values.size());
      uint32_t num = static_cast<uint32_t>(slot_val.size());
      if (num > 0) {
        slot_values.insert(slot_values.end(), slot_val.begin(), slot_val.end());
      }
    }
    slot_offsets[slot_num] = slot_values.size();
  }
  void clear(bool shrink) {
    slot_offsets.clear();
    slot_values.clear();
    if (shrink) {
      slot_values.shrink_to_fit();
      slot_offsets.shrink_to_fit();
    }
  }
};
union FeatureFeasign {
  uint64_t uint64_feasign_;
  float float_feasign_;
};

struct FeatureItem {
  FeatureItem() {}
  FeatureItem(FeatureFeasign sign, uint16_t slot) {
    this->sign() = sign;
    this->slot() = slot;
  }
  FeatureFeasign& sign() {
    return *(reinterpret_cast<FeatureFeasign*>(sign_buffer()));
  }
  const FeatureFeasign& sign() const {
    const FeatureFeasign* ret =
        reinterpret_cast<FeatureFeasign*>(sign_buffer());
    return *ret;
  }
  uint16_t& slot() { return slot_; }
  const uint16_t& slot() const { return slot_; }

 private:
  char* sign_buffer() const { return const_cast<char*>(sign_); }
  char sign_[sizeof(FeatureFeasign)];
  uint16_t slot_;
};

struct AllSlotInfo {
  std::string slot;
  std::string type;
  int used_idx;
  int slot_value_idx;
};
struct UsedSlotInfo {
  int idx;
  int slot_value_idx;
  std::string slot;
  std::string type;
  bool dense;
  std::vector<int> local_shape;
  int total_dims_without_inductive;
  int inductive_shape_index;
};
struct SlotRecordObject {
  uint64_t search_id;
  uint32_t rank;
  uint32_t cmatch;
  std::string ins_id_;
  SlotValues<uint64_t> slot_uint64_feasigns_;
  SlotValues<float> slot_float_feasigns_;

  ~SlotRecordObject() { clear(true); }
  void reset(void) { clear(FLAGS_enable_slotrecord_reset_shrink); }
  void clear(bool shrink) {
    slot_uint64_feasigns_.clear(shrink);
    slot_float_feasigns_.clear(shrink);
  }
};
using SlotRecord = SlotRecordObject*;
// sizeof Record is much less than std::vector<MultiSlotType>
struct Record {
  std::vector<FeatureItem> uint64_feasigns_;
  std::vector<FeatureItem> float_feasigns_;
  std::string ins_id_;
  std::string content_;
  uint64_t search_id;
  uint32_t rank;
  uint32_t cmatch;
  std::string uid_;
};

inline SlotRecord make_slotrecord() {
  static const size_t slot_record_byte_size = sizeof(SlotRecordObject);
  void* p = malloc(slot_record_byte_size);
  new (p) SlotRecordObject;
  return reinterpret_cast<SlotRecordObject*>(p);
}

inline void free_slotrecord(SlotRecordObject* p) {
  p->~SlotRecordObject();
  free(p);
}

template <class T>
class SlotObjAllocator {
 public:
  explicit SlotObjAllocator(std::function<void(T*)> deleter)
      : free_nodes_(NULL), capacity_(0), deleter_(deleter) {}
  ~SlotObjAllocator() { clear(); }

  void clear() {
    T* tmp = NULL;
    while (free_nodes_ != NULL) {
      tmp = reinterpret_cast<T*>(reinterpret_cast<void*>(free_nodes_));
      free_nodes_ = free_nodes_->next;
      deleter_(tmp);
      --capacity_;
    }
    PADDLE_ENFORCE_EQ(capacity_,
                      static_cast<size_t>(0),
                      common::errors::InvalidArgument(
                          "There still are some nodes are not deleted"));
  }
  T* acquire(void) {
    T* x = NULL;
    x = reinterpret_cast<T*>(reinterpret_cast<void*>(free_nodes_));
    free_nodes_ = free_nodes_->next;
    --capacity_;
    return x;
  }
  void release(T* x) {
    Node* node = reinterpret_cast<Node*>(reinterpret_cast<void*>(x));
    node->next = free_nodes_;
    free_nodes_ = node;
    ++capacity_;
  }
  size_t capacity(void) { return capacity_; }

 private:
  struct alignas(T) Node {
    union {
      Node* next;
      char data[sizeof(T)];
    };
  };
  Node* free_nodes_;  // a list
  size_t capacity_;
  std::function<void(T*)> deleter_ = nullptr;
};
static const int OBJPOOL_BLOCK_SIZE = 10000;
class SlotObjPool {
 public:
  SlotObjPool()
      : max_capacity_(FLAGS_record_pool_max_size), alloc_(free_slotrecord) {
    ins_chan_ = MakeChannel<SlotRecord>();
    ins_chan_->SetBlockSize(OBJPOOL_BLOCK_SIZE);
    for (int i = 0; i < FLAGS_slotpool_thread_num; ++i) {
      threads_.push_back(std::thread([this]() { run(); }));
    }
    disable_pool_ = false;
    count_ = 0;
  }
  ~SlotObjPool() {
    ins_chan_->Close();
    for (auto& t : threads_) {
      t.join();
    }
  }
  void disable_pool(bool disable) { disable_pool_ = disable; }
  void set_max_capacity(size_t max_capacity) { max_capacity_ = max_capacity; }
  void get(std::vector<SlotRecord>* output, int n) {
    output->resize(n);
    return get(&(*output)[0], n);
  }
  void get(SlotRecord* output, int n) {
    int size = 0;
    mutex_.lock();
    int left = static_cast<int>(alloc_.capacity());
    if (left > 0) {
      size = (left >= n) ? n : left;
      for (int i = 0; i < size; ++i) {
        output[i] = alloc_.acquire();
      }
    }
    mutex_.unlock();
    count_ += n;
    if (size == n) {
      return;
    }
    for (int i = size; i < n; ++i) {
      output[i] = make_slotrecord();
    }
  }
  void put(std::vector<SlotRecord>* input) {
    size_t size = input->size();
    if (size == 0) {
      return;
    }
    put(&(*input)[0], size);
    input->clear();
  }
  void put(SlotRecord* input, size_t size) {
    PADDLE_ENFORCE_EQ(ins_chan_->WriteMove(size, input),
                      size,
                      common::errors::InvalidArgument(
                          "Incompatible size of input with given size"));
  }
  void run(void) {
    std::vector<SlotRecord> input;
    while (ins_chan_->ReadOnce(input, OBJPOOL_BLOCK_SIZE)) {
      if (input.empty()) {
        continue;
      }
      // over max capacity
      size_t n = input.size();
      count_ -= n;
      if (disable_pool_ || n + capacity() > max_capacity_) {
        for (auto& t : input) {
          free_slotrecord(t);
        }
      } else {
        for (auto& t : input) {
          t->reset();
        }
        mutex_.lock();
        for (auto& t : input) {
          alloc_.release(t);
        }
        mutex_.unlock();
      }
      input.clear();
    }
  }
  void clear(void) {
    platform::Timer timeline;
    timeline.Start();
    mutex_.lock();
    alloc_.clear();
    mutex_.unlock();
    // wait release channel data
    if (FLAGS_enable_slotpool_wait_release) {
      while (!ins_chan_->Empty()) {
        sleep(1);
      }
    }
    timeline.Pause();
    VLOG(3) << "clear slot pool data size=" << count_.load()
            << ", span=" << timeline.ElapsedSec();
  }
  size_t capacity(void) {
    mutex_.lock();
    size_t total = alloc_.capacity();
    mutex_.unlock();
    return total;
  }

 private:
  size_t max_capacity_;
  Channel<SlotRecord> ins_chan_;
  std::vector<std::thread> threads_;
  std::mutex mutex_;
  SlotObjAllocator<SlotRecordObject> alloc_;
  bool disable_pool_;
  std::atomic<long> count_;  // NOLINT
};

inline SlotObjPool& SlotRecordPool() {
  static SlotObjPool pool;
  return pool;
}
struct PvInstanceObject {
  std::vector<Record*> ads;
  void merge_instance(Record* ins) { ads.push_back(ins); }
};

using PvInstance = PvInstanceObject*;

inline PvInstance make_pv_instance() { return new PvInstanceObject(); }

struct SlotConf {
  std::string name;
  std::string type;
  int use_slots_index;
  int use_slots_is_dense;
};

class CustomParser {
 public:
  CustomParser() {}
  virtual ~CustomParser() {}
  virtual void Init(const std::vector<SlotConf>& slots) = 0;
  virtual bool Init(const std::vector<AllSlotInfo>& slots) = 0;
  virtual bool PreLoad(const std::vector<AllSlotInfo>& slots) { return true; }
  virtual void Reset() {}
  virtual void ParseOneInstance(const char* str, Record* instance) = 0;
  virtual int ParseInstance(int len UNUSED,
                            const char* str UNUSED,
                            std::vector<Record>* instances UNUSED) {
    return 0;
  }
  virtual bool ParseOneInstance(
      const std::string& line UNUSED,
      std::function<void(std::vector<SlotRecord>&, int)> GetInsFunc
          UNUSED) {  // NOLINT
    return true;
  }
  virtual bool ParseFileInstance(
      std::function<int(char* buf, int len)> ReadBuffFunc UNUSED,
      std::function<void(std::vector<SlotRecord>&, int, int)> PullRecordsFunc
          UNUSED,           // NOLINT
      int& lines UNUSED) {  // NOLINT
    return false;
  }
};

struct UsedSlotGpuType {
  int is_uint64_value;
  int slot_value_idx;
};

#if defined(PADDLE_WITH_CUDA) && defined(PADDLE_WITH_HETERPS)
template <typename T>
struct CudaBuffer {
  T* cu_buffer;
  uint64_t buf_size;

  CudaBuffer<T>() {
    cu_buffer = NULL;
    buf_size = 0;
  }
  ~CudaBuffer<T>() { free(); }
  T* data() { return cu_buffer; }
  uint64_t size() { return buf_size; }
  void malloc(uint64_t size) {
    buf_size = size;
    CUDA_CHECK(
        cudaMalloc(reinterpret_cast<void**>(&cu_buffer), size * sizeof(T)));
  }
  void free() {
    if (cu_buffer != NULL) {
      CUDA_CHECK(cudaFree(cu_buffer));
      cu_buffer = NULL;
    }
    buf_size = 0;
  }
  void resize(uint64_t size) {
    if (size <= buf_size) {
      return;
    }
    free();
    malloc(size);
  }
};
template <typename T>
struct HostBuffer {
  T* host_buffer;
  size_t buf_size;
  size_t data_len;

  HostBuffer<T>() {
    host_buffer = NULL;
    buf_size = 0;
    data_len = 0;
  }
  ~HostBuffer<T>() { free(); }

  T* data() { return host_buffer; }
  const T* data() const { return host_buffer; }
  size_t size() const { return data_len; }
  void clear() { free(); }
  T& back() { return host_buffer[data_len - 1]; }

  T& operator[](size_t i) { return host_buffer[i]; }
  const T& operator[](size_t i) const { return host_buffer[i]; }
  void malloc(size_t len) {
    buf_size = len;
    CUDA_CHECK(cudaHostAlloc(reinterpret_cast<void**>(&host_buffer),
                             buf_size * sizeof(T),
                             cudaHostAllocDefault));
    PADDLE_ENFORCE_NOT_NULL(host_buffer,
                            common::errors::ResourceExhausted(
                                "Alloc memory failed on CUDA, please Check"));
  }
  void free() {
    if (host_buffer != NULL) {
      CUDA_CHECK(cudaFreeHost(host_buffer));
      host_buffer = NULL;
    }
    buf_size = 0;
  }
  void resize(size_t size) {
    if (size <= buf_size) {
      data_len = size;
      return;
    }
    data_len = size;
    free();
    malloc(size);
  }
};

struct BatchCPUValue {
  HostBuffer<int> h_uint64_lens;
  HostBuffer<uint64_t> h_uint64_keys;
  HostBuffer<int> h_uint64_offset;

  HostBuffer<int> h_float_lens;
  HostBuffer<float> h_float_keys;
  HostBuffer<int> h_float_offset;

  HostBuffer<int> h_rank;
  HostBuffer<int> h_cmatch;
  HostBuffer<int> h_ad_offset;
};

struct BatchGPUValue {
  CudaBuffer<int> d_uint64_lens;
  CudaBuffer<uint64_t> d_uint64_keys;
  CudaBuffer<int> d_uint64_offset;

  CudaBuffer<int> d_float_lens;
  CudaBuffer<float> d_float_keys;
  CudaBuffer<int> d_float_offset;

  CudaBuffer<int> d_rank;
  CudaBuffer<int> d_cmatch;
  CudaBuffer<int> d_ad_offset;
};

class MiniBatchGpuPack {
 public:
  MiniBatchGpuPack(const phi::Place& place,
                   const std::vector<UsedSlotInfo>& infos,
                   phi::StreamId stream_id);
  ~MiniBatchGpuPack();
  bool is_use() { return is_using_; }
  void set_use_flag(bool is_use) { is_using_ = is_use; }
  void reset(const phi::Place& place);
  void pack_instance(const SlotRecord* ins_vec, int num);
  int ins_num() { return ins_num_; }
  int pv_num() { return pv_num_; }
  BatchGPUValue& value() { return value_; }
  BatchCPUValue& cpu_value() { return buf_; }
  UsedSlotGpuType* get_gpu_slots(void) {
    return reinterpret_cast<UsedSlotGpuType*>(gpu_slots_.data());
  }
  SlotRecord* get_records(void) { return &ins_vec_[0]; }

  // tensor gpu memory reused
  void resize_tensor(void) {
    if (used_float_num_ > 0) {
      int float_total_len = buf_.h_float_lens.back();
      if (float_total_len > 0) {
        float_tensor_.mutable_data<float>({float_total_len, 1}, this->place_);
      }
    }
    if (used_uint64_num_ > 0) {
      int uint64_total_len = buf_.h_uint64_lens.back();
      if (uint64_total_len > 0) {
        uint64_tensor_.mutable_data<int64_t>({uint64_total_len, 1},
                                             this->place_);
      }
    }
  }
  phi::DenseTensor& float_tensor(void) { return float_tensor_; }
  phi::DenseTensor& uint64_tensor(void) { return uint64_tensor_; }
  std::vector<DenseTensor>& float_tensor_vec(void) { return float_tensor_vec_; }
  std::vector<DenseTensor>& uint64_tensor_vec(void) {
    return uint64_tensor_vec_;
  }

  HostBuffer<size_t>& offsets(void) { return offsets_; }
  HostBuffer<void*>& h_tensor_ptrs(void) { return h_tensor_ptrs_; }

  void* gpu_slot_offsets(void) { return gpu_slot_offsets_->ptr(); }

  void* slot_buf_ptr(void) { return slot_buf_ptr_->ptr(); }

  void resize_gpu_slot_offsets(const size_t slot_total_bytes) {
    if (gpu_slot_offsets_ == nullptr) {
      gpu_slot_offsets_ = memory::AllocShared(place_, slot_total_bytes);
    } else if (gpu_slot_offsets_->size() < slot_total_bytes) {
      auto buf = memory::AllocShared(place_, slot_total_bytes);
      gpu_slot_offsets_.swap(buf);
      buf = nullptr;
    }
  }
  const std::string& get_lineid(int idx) {
    if (enable_pv_) {
      return ins_vec_[idx]->ins_id_;
    }
    return batch_ins_[idx]->ins_id_;
  }

  cudaStream_t get_stream() { return stream_; }

 private:
  void transfer_to_gpu(void);
  void pack_all_data(const SlotRecord* ins_vec, int num);
  void pack_uint64_data(const SlotRecord* ins_vec, int num);
  void pack_float_data(const SlotRecord* ins_vec, int num);

 public:
  template <typename T>
  void copy_host2device(CudaBuffer<T>* buf, const T* val, size_t size) {
    if (size == 0) {
      return;
    }
    buf->resize(size);
    CUDA_CHECK(cudaMemcpyAsync(
        buf->data(), val, size * sizeof(T), cudaMemcpyHostToDevice, stream_));
  }
  template <typename T>
  void copy_host2device(CudaBuffer<T>* buf, const HostBuffer<T>& val) {
    copy_host2device(buf, val.data(), val.size());
  }

 private:
  bool is_using_ = false;
  phi::Place place_;
  std::unique_ptr<phi::CUDAStream> stream_holder_;
  cudaStream_t stream_;
  BatchGPUValue value_;
  BatchCPUValue buf_;
  int ins_num_ = 0;
  int pv_num_ = 0;

  bool enable_pv_ = false;
  int used_float_num_ = 0;
  int used_uint64_num_ = 0;
  int used_slot_size_ = 0;

  CudaBuffer<UsedSlotGpuType> gpu_slots_;
  std::vector<UsedSlotGpuType> gpu_used_slots_;
  std::vector<SlotRecord> ins_vec_;
  const SlotRecord* batch_ins_ = nullptr;

  // uint64 tensor
  DenseTensor uint64_tensor_;
  std::vector<DenseTensor> uint64_tensor_vec_;
  // float tensor
  DenseTensor float_tensor_;
  std::vector<DenseTensor> float_tensor_vec_;
  // batch
  HostBuffer<size_t> offsets_;
  HostBuffer<void*> h_tensor_ptrs_;

  std::shared_ptr<phi::Allocation> gpu_slot_offsets_ = nullptr;
  std::shared_ptr<phi::Allocation> slot_buf_ptr_ = nullptr;
};
class MiniBatchGpuPackMgr {
  static const int MAX_DEVICE_NUM = 16;

 public:
  MiniBatchGpuPackMgr() {
    pack_list_.resize(MAX_DEVICE_NUM);
    for (int i = 0; i < MAX_DEVICE_NUM; ++i) {
      pack_list_[i].clear();
    }
  }
  ~MiniBatchGpuPackMgr() {
    for (int i = 0; i < MAX_DEVICE_NUM; ++i) {
      for (size_t j = 0; j < pack_list_[i].size(); j++) {
        if (pack_list_[i][j] == nullptr) {
          continue;
        }
        delete pack_list_[i][j];
        pack_list_[i][j] = nullptr;
      }
    }
  }

  // thread unsafe
  MiniBatchGpuPack* get(const phi::Place& place,
                        const std::vector<UsedSlotInfo>& infos) {
    int device_id = place.GetDeviceId();
    for (size_t i = 0; i < pack_list_[device_id].size(); i++) {
      if (!pack_list_[device_id][i]->is_use()) {
        pack_list_[device_id][i]->set_use_flag(true);
        pack_list_[device_id][i]->reset(place);
        return pack_list_[device_id][i];
      }
    }
    {
      std::lock_guard<std::mutex> lock(mutex_);
      if (!alloc_stream_map_.count(device_id)) {
        alloc_stream_map_.emplace(device_id, new phi::CUDAStream(place));
      }
    }
    phi::StreamId alloc_stream_id = reinterpret_cast<phi::StreamId>(
        alloc_stream_map_[device_id]->raw_stream());
    auto* pack = new MiniBatchGpuPack(place, infos, alloc_stream_id);
    pack->set_use_flag(true);
    pack_list_[device_id].push_back(pack);
    return pack;
  }

 private:
  std::vector<std::vector<MiniBatchGpuPack*>> pack_list_;
  std::unordered_map<int, std::unique_ptr<phi::CUDAStream>> alloc_stream_map_;
  std::mutex mutex_;
};
// global mgr
inline MiniBatchGpuPackMgr& BatchGpuPackMgr() {
  static MiniBatchGpuPackMgr mgr;
  return mgr;
}
#endif

typedef paddle::framework::CustomParser* (*CreateParserObjectFunc)();

class DLManager {
  struct DLHandle {
    void* module;
    paddle::framework::CustomParser* parser;
  };

 public:
  DLManager() {}

  ~DLManager() {
#ifdef _LINUX
    std::lock_guard<std::mutex> lock(mutex_);
    for (auto it = handle_map_.begin(); it != handle_map_.end(); ++it) {
      delete it->second.parser;
      dlclose(it->second.module);
    }
#endif
  }

  bool Close(const std::string& name) {
#ifdef _LINUX
    auto it = handle_map_.find(name);
    if (it == handle_map_.end()) {
      return true;
    }
    delete it->second.parser;
    dlclose(it->second.module);
#endif
    VLOG(0) << "Not implement in windows";
    return false;
  }

  paddle::framework::CustomParser* Load(const std::string& name,
                                        const std::vector<SlotConf>& conf) {
#ifdef _LINUX
    std::lock_guard<std::mutex> lock(mutex_);
    DLHandle handle;
    std::map<std::string, DLHandle>::iterator it = handle_map_.find(name);
    if (it != handle_map_.end()) {
      return it->second.parser;
    }

    handle.module = dlopen(name.c_str(), RTLD_NOW);
    if (handle.module == nullptr) {
      VLOG(0) << "Create so of " << name << " fail, " << dlerror();
      return nullptr;
    }

    CreateParserObjectFunc create_parser_func =
        (CreateParserObjectFunc)dlsym(handle.module, "CreateParserObject");
    handle.parser = create_parser_func();
    handle.parser->Init(conf);
    handle_map_.insert({name, handle});

    return handle.parser;
#endif
    VLOG(0) << "Not implement in windows";
    return nullptr;
  }

  paddle::framework::CustomParser* Load(const std::string& name,
                                        const std::vector<AllSlotInfo>& conf) {
#ifdef _LINUX
    std::lock_guard<std::mutex> lock(mutex_);
    DLHandle handle;
    std::map<std::string, DLHandle>::iterator it = handle_map_.find(name);
    if (it != handle_map_.end()) {
      return it->second.parser;
    }
    handle.module = dlopen(name.c_str(), RTLD_NOW);
    if (handle.module == nullptr) {
      VLOG(0) << "Create so of " << name << " fail";
      exit(-1);
      return nullptr;
    }

    CreateParserObjectFunc create_parser_func =
        (CreateParserObjectFunc)dlsym(handle.module, "CreateParserObject");
    handle.parser = create_parser_func();
    handle.parser->Init(conf);
    handle_map_.insert({name, handle});

    return handle.parser;
#endif
    VLOG(0) << "Not implement in windows";
    return nullptr;
  }

  paddle::framework::CustomParser* ReLoad(const std::string& name,
                                          const std::vector<SlotConf>& conf) {
    Close(name);
    return Load(name, conf);
  }

 private:
  std::mutex mutex_;
  std::map<std::string, DLHandle> handle_map_;
};

struct engine_wrapper_t {
  std::default_random_engine engine;
#if !defined(_WIN32)
  engine_wrapper_t() {
    struct timespec tp;
    clock_gettime(CLOCK_REALTIME, &tp);
    double cur_time = tp.tv_sec + tp.tv_nsec * 1e-9;
    static std::atomic<uint64_t> x(0);
    std::seed_seq sseq = {x++, x++, x++, (uint64_t)(cur_time * 1000)};
    engine.seed(sseq);
  }
#endif
};

struct BufState {
  int left;
  int right;
  int central_word;
  int step;
  engine_wrapper_t random_engine_;

  int len;
  int cursor;
  int row_num;

  int batch_size;
  int walk_len;
  std::vector<int>* window;

  BufState() {}
  ~BufState() {}

  void Init(int graph_batch_size,
            int graph_walk_len,
            std::vector<int>* graph_window) {
    batch_size = graph_batch_size;
    walk_len = graph_walk_len;
    window = graph_window;

    left = 0;
    right = window->size() - 1;
    central_word = -1;
    step = -1;

    len = 0;
    cursor = 0;
    row_num = 0;
    for (size_t i = 0; i < graph_window->size(); i++) {
      VLOG(2) << "graph_window[" << i << "] = " << (*graph_window)[i];
    }
  }

  void Reset(int total_rows) {
    cursor = 0;
    row_num = total_rows;
    int tmp_len = cursor + batch_size > row_num ? row_num - cursor : batch_size;
    len = tmp_len;
    central_word = -1;
    step = -1;
    GetNextCentralWord();
  }

  int GetNextStep() {
    step++;
    // Checking out-of-bound by MakeInsPair
    if (step <= right) {
      return 1;
    }
    return 0;
  }

  void Debug() {
    VLOG(2) << "left: " << left << " right: " << right
            << " central_word: " << central_word << " step: " << step
            << " cursor: " << cursor << " len: " << len
            << " row_num: " << row_num;
  }

  int GetNextCentralWord() {
    if (++central_word >= walk_len) {
      return 0;
    }
    int window_size = window->size() / 2;
    int random_window = random_engine_.engine() % window_size + 1;
    left = window_size - random_window;
    right = window_size + random_window - 1;
    VLOG(2) << "random window: " << random_window << " window[" << left
            << "] = " << (*window)[left] << " window[" << right
            << "] = " << (*window)[right];
    // Checking out-of-bound by MakeInsPair
    step = left;
    return 1;
  }

  int GetNextBatch() {
    cursor += len;
    if (row_num - cursor < 0) {
      return 0;
    }
    int tmp_len = cursor + batch_size > row_num ? row_num - cursor : batch_size;
    if (tmp_len == 0) {
      return 0;
    }
    len = tmp_len;
    central_word = -1;
    step = -1;
    GetNextCentralWord();
    return tmp_len != 0;
  }
};

/// Related behaviors and events during sampling
const int EVENT_FINISH_EPOCH = 0;     // End of sampling single epoch
const int EVENT_CONTINUE_SAMPLE = 1;  // Continue sampling
const int EVENT_WALKBUF_FULL = 2;  // d_walk is full, end current pass sampling
const int EVENT_NOT_SWITCH = 0;    // Continue sampling on the current metapath.
const int EVENT_SWITCH_METAPATH =
    1;  // Switch to the next metapath to perform sampling

struct GraphDataGeneratorConfig {
  bool need_walk_ntype;
  bool enable_pair_label;
  bool gpu_graph_training;
  bool sage_mode;
  bool get_degree;
  bool weighted_sample;
  bool return_weight;
  bool is_multi_node;
  bool is_thread_sharding;
  int batch_size;
  int slot_num;
  int walk_degree;
  int walk_len;
  int window;
  int gpuid;
  int thread_id;
  int once_sample_startid_len;
  int node_type_num;
  int debug_mode;
  int excluded_train_pair_len;
  int edge_to_id_len;
  int tensor_pair_num;
  uint32_t tensor_num_of_one_pair;
  uint32_t tensor_num_of_one_subgraph;
  size_t buf_size;
  size_t once_max_sample_keynum;
  int64_t reindex_table_size;
  uint64_t train_table_cap;
  uint64_t infer_table_cap;
  int accumulate_num;
  std::vector<int> window_step;
  std::vector<int> samples;
  std::shared_ptr<phi::Allocation> d_excluded_train_pair;
  std::shared_ptr<phi::Allocation> d_pair_label_conf;
  std::set<int> infer_node_type_index_set;
};

class GraphDataGenerator {
 public:
  GraphDataGenerator() {}
  virtual ~GraphDataGenerator() {}
  void SetConfig(const paddle::framework::DataFeedDesc& data_feed_desc);
  void AllocResource(int thread_id, std::vector<phi::DenseTensor*> feed_vec);
  void AllocTrainResource(int thread_id);
  void SetFeedVec(std::vector<phi::DenseTensor*> feed_vec);
  void SetFeedInfo(std::vector<UsedSlotInfo>* feed_info);
  int GenerateBatch();
  void DoWalkandSage();
  int FillSlotFeature(uint64_t* d_walk);
  int FillIdShowClkTensor(int total_instance, bool gpu_graph_training);
  int FillGraphIdShowClkTensor(int uniq_instance,
                               int total_instance,
                               int index);
  int FillGraphIdShowClkTensorAccum(int index);
  int FillGraphSlotFeature(
      int total_instance,
      bool gpu_graph_training,
      std::shared_ptr<phi::Allocation> final_sage_nodes = nullptr);
  int FillGraphSlotFeatureAccum(bool gpu_graph_training, int index);
  int FillSlotFeature(uint64_t* d_walk,
                      size_t key_num,
                      int tensor_pair_idx,
                      int accum = 0);
  int FillFloatFeature(uint64_t* d_walk, size_t key_num, int tensor_pair_idx);
  int GetPathNum() { return total_row_[0]; }
  void ResetPathNum() { total_row_[0] = 0; }
  int GetGraphBatchsize() { return conf_.batch_size; }
  void SetNewBatchsize(int batch_num) {
    if (!conf_.gpu_graph_training) {
      conf_.batch_size = (total_row_[0] + batch_num - 1) / batch_num;
    } else {
      return;
    }
  }
  bool GetSageMode() { return conf_.sage_mode; }
  bool GetMultiNodeMode() { return conf_.is_multi_node; }
  bool GetTrainState() { return conf_.gpu_graph_training; }
  void ResetEpochFinish() { epoch_finish_ = false; }
  void reset_pass_end() { pass_end_ = 0; }
  void ClearSampleState();
  void DumpWalkPath(std::string dump_path, size_t dump_rate);
  void DumpSampleNeighbors(std::string dump_path);
  void SetDeviceKeys(std::vector<uint64_t>* device_keys UNUSED,
                     int type UNUSED) {
    // type_to_index_[type] = h_device_keys_.size();
    // h_device_keys_.push_back(device_keys);
  }
  int GetTrainMemoryDataSize() {
    // use only for is_multi_node = True, sage_mode = True, gpu_graph_training =
    // True
    if (!global_train_flag_) {
      return 0;
    } else {
      return total_row_[0];
    }
  }
  std::vector<uint64_t>& GetHostVec() { return host_vec_; }
  std::vector<uint32_t>& GetHostRanks() { return host_ranks_; }
  std::shared_ptr<HashTable<uint64_t, uint32_t>> GetKeys2RankTable() {
    return keys2rank_table_;
  }

  bool get_epoch_finish() { return epoch_finish_; }
  int get_pass_end() { return pass_end_; }
  void clear_gpu_mem();
  int dynamic_adjust_batch_num_for_sage();

 protected:
  bool DoWalkForInfer();
  void DoSageForInfer();
  bool DoWalkForTrain();
  void DoSageForTrain();

  // key: key id,
  // value: dest machine rank id
  // Two usage: dup keys and cache dest machine rank of keys
  std::shared_ptr<HashTable<uint64_t, uint32_t>> keys2rank_table_;
  GraphDataGeneratorConfig conf_;
  std::vector<size_t> infer_cursor_;
  std::vector<size_t> jump_rows_;
  int64_t* id_tensor_ptr_;
  int* index_tensor_ptr_;
  int64_t* show_tensor_ptr_;
  int64_t* clk_tensor_ptr_;
  int* degree_tensor_ptr_;
  int32_t* pair_label_ptr_;

  cudaStream_t train_stream_;
  cudaStream_t sample_stream_;
  phi::Place place_;
  std::vector<phi::DenseTensor*> feed_vec_;
  std::vector<UsedSlotInfo>* feed_info_;  // adapt for float feature
  std::vector<size_t> offset_;
  std::vector<std::vector<std::shared_ptr<phi::Allocation>>> d_device_keys_;
  std::vector<std::shared_ptr<phi::Allocation>> d_train_metapath_keys_;

  std::vector<std::shared_ptr<phi::Allocation>> d_walk_;
  std::vector<std::shared_ptr<phi::Allocation>> d_walk_ntype_;
  std::shared_ptr<phi::Allocation> d_feature_list_;
  std::shared_ptr<phi::Allocation> d_feature_;
  std::vector<std::shared_ptr<phi::Allocation>> d_random_row_;
  std::vector<std::shared_ptr<phi::Allocation>> d_random_row_col_shift_;
  std::shared_ptr<phi::Allocation> d_uniq_node_num_;
  std::shared_ptr<phi::Allocation> d_slot_feature_num_map_;
  std::shared_ptr<phi::Allocation> d_actual_slot_id_map_;
  std::shared_ptr<phi::Allocation> d_fea_offset_map_;

  std::vector<std::shared_ptr<phi::Allocation>> d_pair_label_buf_;
  std::vector<std::shared_ptr<phi::Allocation>> d_ins_buf_;
  std::shared_ptr<phi::Allocation> d_feature_size_list_buf_;
  std::shared_ptr<phi::Allocation> d_feature_size_prefixsum_buf_;
  std::vector<std::shared_ptr<phi::Allocation>> d_pair_num_;
  std::shared_ptr<phi::Allocation> d_slot_tensor_ptr_;
  std::shared_ptr<phi::Allocation> d_slot_lod_tensor_ptr_;
  std::vector<std::shared_ptr<phi::Allocation>> edge_type_graph_;

  // sage mode batch data
  std::vector<std::shared_ptr<phi::Allocation>> pair_label_vec_;
  std::vector<std::shared_ptr<phi::Allocation>> inverse_vec_;
  std::vector<std::shared_ptr<phi::Allocation>> final_sage_nodes_vec_;
  std::vector<std::shared_ptr<phi::Allocation>> node_degree_vec_;
  std::vector<int> uniq_instance_vec_;
  std::vector<int> total_instance_vec_;
  std::vector<std::vector<std::shared_ptr<phi::Allocation>>> graph_edges_vec_;
  std::vector<std::vector<std::vector<int>>> edges_split_num_vec_;

  int sage_batch_count_;
  int sage_batch_num_;
  bool global_train_flag_ = 0;
  std::vector<int> ins_buf_pair_len_;
  int id_offset_of_feed_vec_;

  // size of a d_walk buf
  int repeat_time_;
  std::vector<BufState> buf_state_;
  int float_slot_num_ = 0;  // float slot num
  int uint_slot_num_ = 0;   // uint slot num
  std::vector<int> h_slot_feature_num_map_;
  int fea_num_per_node_;
  std::vector<int> shuffle_seed_;
  bool epoch_finish_;
  int pass_end_ = 0;
  std::vector<uint64_t> host_vec_;
  std::vector<uint32_t> host_ranks_;
  std::vector<std::vector<uint64_t>> h_device_keys_len_;
  std::vector<uint64_t> h_train_metapath_keys_len_;
  uint64_t copy_unique_len_;
  std::vector<int> total_row_;
  std::vector<size_t> infer_node_start_;
  std::vector<size_t> infer_node_end_;
  std::string infer_node_type_;
  DenseTensor multi_node_sync_stat_;
};

class DataFeed {
 public:
  DataFeed() {
    mutex_for_pick_file_ = nullptr;
    file_idx_ = nullptr;
    mutex_for_fea_num_ = nullptr;
    total_fea_num_ = nullptr;
  }
  virtual ~DataFeed() {}
  virtual void Init(const DataFeedDesc& data_feed_desc) = 0;
  virtual bool CheckFile(const char* filename UNUSED) {
    PADDLE_THROW(common::errors::Unimplemented(
        "This function(CheckFile) is not implemented."));
  }
  // Set filelist for DataFeed.
  // Pay attention that it must init all readers before call this function.
  // Otherwise, Init() function will init finish_set_filelist_ flag.
  virtual bool SetFileList(const std::vector<std::string>& files);
  virtual bool Start() = 0;

  // The trainer calls the Next() function, and the DataFeed will load a new
  // batch to the feed_vec. The return value of this function is the batch
  // size of the current batch.
  virtual int Next() = 0;
  // Get all slots' alias which defined in protofile
  virtual const std::vector<std::string>& GetAllSlotAlias() {
    return all_slots_;
  }
  // Get used slots' alias which defined in protofile
  virtual const std::vector<std::string>& GetUseSlotAlias() {
    return use_slots_;
  }
  // This function is used for binding feed_vec memory
  virtual void AddFeedVar(Variable* var, const std::string& name);

  // This function is used for binding feed_vec memory in a given scope
  virtual void AssignFeedVar(const Scope& scope);

  virtual std::vector<std::string> GetInputVarNames() {
    return std::vector<std::string>();
  }

  // This function will do nothing at default
  virtual void SetInputPvChannel(void* channel UNUSED) {}
  // This function will do nothing at default
  virtual void SetOutputPvChannel(void* channel UNUSED) {}
  // This function will do nothing at default
  virtual void SetConsumePvChannel(void* channel UNUSED) {}

  // This function will do nothing at default
  virtual void SetInputChannel(void* channel UNUSED) {}
  // This function will do nothing at default
  virtual void SetOutputChannel(void* channel UNUSED) {}
  // This function will do nothing at default
  virtual void SetConsumeChannel(void* channel UNUSED) {}
  // This function will do nothing at default
  virtual void SetThreadId(int thread_id UNUSED) {}
  // This function will do nothing at default
  virtual void SetThreadNum(int thread_num UNUSED) {}
  // This function will do nothing at default
  virtual void SetParseInsId(bool parse_ins_id UNUSED) {}
  virtual void SetParseUid(bool parse_uid UNUSED) {}
  virtual void SetParseContent(bool parse_content UNUSED) {}
  virtual void SetParseLogKey(bool parse_logkey UNUSED) {}
  virtual void SetEnablePvMerge(bool enable_pv_merge UNUSED) {}
  virtual void SetCurrentPhase(int current_phase UNUSED) {}
#if defined(PADDLE_WITH_PSCORE) && defined(PADDLE_WITH_HETERPS)
  virtual void InitGraphResource() {}
  virtual void InitGraphTrainResource() {}
  virtual void SetDeviceKeys(std::vector<uint64_t>* device_keys, int type) {
    gpu_graph_data_generator_.SetDeviceKeys(device_keys, type);
  }
#endif

  virtual void SetGpuGraphMode(int gpu_graph_mode) {
    gpu_graph_mode_ = gpu_graph_mode;
  }
  virtual void SetFileListMutex(std::mutex* mutex) {
    mutex_for_pick_file_ = mutex;
  }
  virtual void SetFeaNumMutex(std::mutex* mutex) { mutex_for_fea_num_ = mutex; }
  virtual void SetFileListIndex(size_t* file_index) { file_idx_ = file_index; }
  virtual void SetFeaNum(uint64_t* fea_num) { total_fea_num_ = fea_num; }
  virtual const std::vector<std::string>& GetInsIdVec() const {
    return ins_id_vec_;
  }
  virtual const std::vector<std::string>& GetInsContentVec() const {
    return ins_content_vec_;
  }
  virtual void SetCurBatchSize(const int batch_size) {
    batch_size_ = batch_size;
  }
  virtual int GetCurBatchSize() {
    if (gpu_graph_mode_) {
#if defined(PADDLE_WITH_PSCORE) && defined(PADDLE_WITH_HETERPS)
      return gpu_graph_data_generator_.GetGraphBatchsize();
#else
      // gpu_graph_mode_ set true only when
      // PADDLE_WITH_HETERPS=ON and PADDLE_WITH_PSCORE=ON
      VLOG(1) << "Error: GetCurBatchSize() gpu_graph_mode_ "
              << "set true only when "
              << "PADDLE_WITH_HETERPS=ON and PADDLE_WITH_PSCORE=ON";
      return 0;
#endif
    } else {
      return batch_size_;
    }
  }
  virtual void SetNewBatchsize(int batch_num) {
#if defined(PADDLE_WITH_PSCORE) && defined(PADDLE_WITH_HETERPS)
    if (gpu_graph_mode_) {
      gpu_graph_data_generator_.SetNewBatchsize(batch_num);
    }
#endif
  }
  virtual bool GetSageMode() {
    if (gpu_graph_mode_) {
#if defined(PADDLE_WITH_PSCORE) && defined(PADDLE_WITH_HETERPS)
      return gpu_graph_data_generator_.GetSageMode();
#else
      return 0;
#endif
    } else {
      return 0;
    }
  }
  virtual bool GetMultiNodeMode() {
    if (gpu_graph_mode_) {
#if defined(PADDLE_WITH_HETERPS)
      return gpu_graph_data_generator_.GetMultiNodeMode();
#else
      return 0;
#endif
    } else {
      return 0;
    }
  }
  virtual int GetGraphPathNum() {
    if (gpu_graph_mode_) {
#if defined(PADDLE_WITH_PSCORE) && defined(PADDLE_WITH_HETERPS)
      return gpu_graph_data_generator_.GetPathNum();
#else
      return 0;
#endif
    } else {
      return 0;
    }
  }
  virtual bool GetTrainState() {
    if (gpu_graph_mode_) {
#if defined(PADDLE_WITH_PSCORE) && defined(PADDLE_WITH_HETERPS)
      return gpu_graph_data_generator_.GetTrainState();
#else
      return 0;
#endif
    } else {
      return 0;
    }
  }
  virtual int GetTrainMemoryDataSize() {
    if (gpu_graph_mode_) {
#if defined(PADDLE_WITH_PSCORE) && defined(PADDLE_WITH_HETERPS)
      return gpu_graph_data_generator_.GetTrainMemoryDataSize();
#else
      return 0;
#endif
    } else {
      return 0;
    }
  }

#if defined(PADDLE_WITH_PSCORE) && defined(PADDLE_WITH_HETERPS)
  virtual std::vector<uint64_t>* GetHostVec() {
    if (gpu_graph_mode_) {
      return &(gpu_graph_data_generator_.GetHostVec());
    } else {
      return nullptr;
    }
  }

  virtual std::vector<uint32_t>* GetHostRanks() {
    if (gpu_graph_mode_) {
      return &(gpu_graph_data_generator_.GetHostRanks());
    } else {
      return nullptr;
    }
  }

  virtual void clear_gpu_mem() {
    if (gpu_graph_mode_) {
      gpu_graph_data_generator_.clear_gpu_mem();
    }
  }

  virtual bool get_epoch_finish() {
    if (gpu_graph_mode_) {
      return gpu_graph_data_generator_.get_epoch_finish();
    } else {
      return 0;
    }
  }

  virtual int get_pass_end() {
    if (gpu_graph_mode_) {
      return gpu_graph_data_generator_.get_pass_end();
    } else {
      return 0;
    }
  }

  virtual void reset_pass_end() {
    if (gpu_graph_mode_) {
      gpu_graph_data_generator_.reset_pass_end();
    }
  }

  virtual void ResetPathNum() {
    if (gpu_graph_mode_) {
      gpu_graph_data_generator_.ResetPathNum();
    }
  }

  virtual void ClearSampleState() {
    if (gpu_graph_mode_) {
      gpu_graph_data_generator_.ClearSampleState();
    }
  }

  virtual void ResetEpochFinish() {
    if (gpu_graph_mode_) {
      gpu_graph_data_generator_.ResetEpochFinish();
    }
  }

  virtual void DoWalkandSage() {
    PADDLE_THROW(common::errors::Unimplemented(
        "This function(DoWalkandSage) is not implemented."));
  }

  std::shared_ptr<HashTable<uint64_t, uint32_t>> GetKeys2RankTable() {
    if (gpu_graph_mode_) {
      return gpu_graph_data_generator_.GetKeys2RankTable();
    } else {
      return nullptr;
    }
  }

#endif

  virtual bool IsTrainMode() { return train_mode_; }
  virtual void LoadIntoMemory() {
    PADDLE_THROW(common::errors::Unimplemented(
        "This function(LoadIntoMemory) is not implemented."));
  }
  virtual void SetPlace(const phi::Place& place) { place_ = place; }
  virtual const phi::Place& GetPlace() const { return place_; }

#if defined(PADDLE_WITH_CUDA) && defined(PADDLE_WITH_HETERPS)
  virtual MiniBatchGpuPack* get_pack(MiniBatchGpuPack* last_pack) {
    return nullptr;
  }

  virtual void PackToScope(MiniBatchGpuPack* pack, const Scope* scope) {
    PADDLE_THROW(common::errors::Unimplemented(
        "This function(PackToScope) is not implemented."));
  }
  virtual void SetInsIdVec(MiniBatchGpuPack* pack) {}
#endif

  virtual void DumpWalkPath(std::string dump_path UNUSED,
                            size_t dump_rate UNUSED) {
    PADDLE_THROW(common::errors::Unimplemented(
        "This function(DumpWalkPath) is not implemented."));
  }
  virtual void DumpSampleNeighbors(std::string dump_path) {
    PADDLE_THROW(common::errors::Unimplemented(
        "This function(DumpSampleNeighbors) is not implemented"));
  }

 protected:
  // The following three functions are used to check if it is executed in this
  // order:
  //   Init() -> SetFileList() -> Start() -> Next()
  virtual void CheckInit();
  virtual void CheckSetFileList();
  virtual void CheckStart();
  virtual void SetBatchSize(
      int batch);  // batch size will be set in Init() function
  // This function is used to pick one file from the global filelist(thread
  // safe).
  virtual bool PickOneFile(std::string* filename);
  virtual void CopyToFeedTensor(void* dst, const void* src, size_t size);

  std::vector<std::string> filelist_;
  size_t* file_idx_;
  std::mutex* mutex_for_pick_file_;
  std::mutex* mutex_for_fea_num_ = nullptr;
  uint64_t* total_fea_num_ = nullptr;
  uint64_t fea_num_ = 0;

  // the alias of used slots, and its order is determined by
  // data_feed_desc(proto object)
  std::vector<std::string> use_slots_;
  std::vector<bool> use_slots_is_dense_;

  // the alias of all slots, and its order is determined by data_feed_desc(proto
  // object)
  std::vector<std::string> all_slots_;
  std::vector<std::string> all_slots_type_;
  std::vector<std::vector<int>> use_slots_shape_;
  std::vector<int> inductive_shape_index_;
  std::vector<int> total_dims_without_inductive_;
  // For the inductive shape passed within data
  std::vector<std::vector<int>> multi_inductive_shape_index_;
  std::vector<int>
      use_slots_index_;  // -1: not used; >=0: the index of use_slots_

  // The data read by DataFeed will be stored here
  std::vector<phi::DenseTensor*> feed_vec_;
  phi::DenseTensor* rank_offset_;

  // the batch size defined by user
  int default_batch_size_;
  // current batch size
  int batch_size_;

  bool finish_init_;
  bool finish_set_filelist_;
  bool finish_start_;
  std::string pipe_command_;
  std::string so_parser_name_;
  std::vector<SlotConf> slot_conf_;
  std::vector<std::string> ins_id_vec_;
  std::vector<std::string> ins_content_vec_;
  phi::Place place_;
  std::string uid_slot_;

  // The input type of pipe reader, 0 for one sample, 1 for one batch
  int input_type_;
  int gpu_graph_mode_ = 0;
#if defined(PADDLE_WITH_PSCORE) && defined(PADDLE_WITH_HETERPS)
  GraphDataGenerator gpu_graph_data_generator_;
#endif
  bool train_mode_;
};

// PrivateQueueDataFeed is the base virtual class for other DataFeeds.
// It use a read-thread to read file and parse data to a private-queue
// (thread level), and get data from this queue when trainer call Next().
template <typename T>
class PrivateQueueDataFeed : public DataFeed {
 public:
  PrivateQueueDataFeed() {}
  virtual ~PrivateQueueDataFeed() {}
  virtual bool Start();
  virtual int Next();

 protected:
  // The thread implementation function for reading file and parse.
  virtual void ReadThread();
  // This function is used to set private-queue size, and the most
  // efficient when the queue size is close to the batch size.
  virtual void SetQueueSize(int queue_size);
  // The reading and parsing method called in the ReadThread.
  virtual bool ParseOneInstance(T* instance) = 0;
  virtual bool ParseOneInstanceFromPipe(T* instance) = 0;
  // This function is used to put instance to vec_ins
  virtual void AddInstanceToInsVec(T* vec_ins,
                                   const T& instance,
                                   int index) = 0;
  // This function is used to put ins_vec to feed_vec
  virtual void PutToFeedVec(const T& ins_vec) = 0;

  // The thread for read files
  std::thread read_thread_;
  // using ifstream one line and one line parse is faster
  // than using fread one buffer and one buffer parse.
  //   for a 601M real data:
  //     ifstream one line and one line parse: 6034 ms
  //     fread one buffer and one buffer parse: 7097 ms
  std::ifstream file_;
  std::shared_ptr<FILE> fp_;
  size_t queue_size_;
  string::LineFileReader reader_;
  // The queue for store parsed data
  std::shared_ptr<paddle::framework::ChannelObject<T>> queue_;
};

template <typename T>
class InMemoryDataFeed : public DataFeed {
 public:
  InMemoryDataFeed();
  virtual ~InMemoryDataFeed() {}
  virtual void Init(const DataFeedDesc& data_feed_desc) = 0;
  virtual bool Start();
  virtual int Next();
  virtual void SetInputPvChannel(void* channel);
  virtual void SetOutputPvChannel(void* channel);
  virtual void SetConsumePvChannel(void* channel);

  virtual void SetInputChannel(void* channel);
  virtual void SetOutputChannel(void* channel);
  virtual void SetConsumeChannel(void* channel);
  virtual void SetThreadId(int thread_id);
  virtual void SetThreadNum(int thread_num);
  virtual void SetParseInsId(bool parse_ins_id);
  virtual void SetParseUid(bool parse_uid);
  virtual void SetParseContent(bool parse_content);
  virtual void SetParseLogKey(bool parse_logkey);
  virtual void SetEnablePvMerge(bool enable_pv_merge);
  virtual void SetCurrentPhase(int current_phase);
  virtual void LoadIntoMemory();
  virtual void LoadIntoMemoryFromSo();
  virtual void SetRecord(T* records) { records_ = records; }
  int GetDefaultBatchSize() { return default_batch_size_; }
  void AddBatchOffset(const std::pair<int, int>& offset) {
    batch_offsets_.push_back(offset);
  }

 protected:
  virtual bool ParseOneInstance(T* instance) = 0;
  virtual bool ParseOneInstanceFromPipe(T* instance) = 0;
  virtual void ParseOneInstanceFromSo(const char* str UNUSED,
                                      T* instance UNUSED,
                                      CustomParser* parser UNUSED) {}
  virtual int ParseInstanceFromSo(int len UNUSED,
                                  const char* str UNUSED,
                                  std::vector<T>* instances UNUSED,
                                  CustomParser* parser UNUSED) {
    return 0;
  }
  virtual void PutToFeedVec(const std::vector<T>& ins_vec) = 0;
  virtual void PutToFeedVec(const T* ins_vec, int num) = 0;

  std::vector<std::vector<float>> batch_float_feasigns_;
  std::vector<std::vector<uint64_t>> batch_uint64_feasigns_;
  std::vector<std::vector<size_t>> offset_;
  std::vector<bool> visit_;

  int thread_id_;
  int thread_num_;
  bool parse_ins_id_;
  bool parse_uid_;
  bool parse_content_;
  bool parse_logkey_;
  bool enable_pv_merge_;
  int current_phase_{-1};  // only for untest
  std::ifstream file_;
  std::shared_ptr<FILE> fp_;
  paddle::framework::ChannelObject<T>* input_channel_;
  paddle::framework::ChannelObject<T>* output_channel_;
  paddle::framework::ChannelObject<T>* consume_channel_;

  paddle::framework::ChannelObject<PvInstance>* input_pv_channel_;
  paddle::framework::ChannelObject<PvInstance>* output_pv_channel_;
  paddle::framework::ChannelObject<PvInstance>* consume_pv_channel_;

  std::vector<std::pair<int, int>> batch_offsets_;
  uint64_t offset_index_ = 0;
  bool enable_heterps_ = false;
  T* records_ = nullptr;
};

// This class define the data type of instance(ins_vec) in MultiSlotDataFeed
class MultiSlotType {
 public:
  MultiSlotType() {}
  ~MultiSlotType() {}
  void Init(const std::string& type, size_t reserved_size = 0) {
    CheckType(type);
    if (type_[0] == 'f') {
      float_feasign_.clear();
      if (reserved_size) {
        float_feasign_.reserve(reserved_size);
      }
    } else if (type_[0] == 'u') {
      uint64_feasign_.clear();
      if (reserved_size) {
        uint64_feasign_.reserve(reserved_size);
      }
    }
    type_ = type;
  }
  void InitOffset(size_t max_batch_size = 0) {
    if (max_batch_size > 0) {
      offset_.reserve(max_batch_size + 1);
    }
    offset_.resize(1);
    // DenseTensor' lod is counted from 0, the size of lod
    // is one size larger than the size of data.
    offset_[0] = 0;
  }
  const std::vector<size_t>& GetOffset() const { return offset_; }
  std::vector<size_t>& MutableOffset() { return offset_; }
  void AddValue(const float v) {
    CheckFloat();
    float_feasign_.push_back(v);
  }
  void AddValue(const uint64_t v) {
    CheckUint64();
    uint64_feasign_.push_back(v);
  }
  void CopyValues(const float* input, size_t size) {
    CheckFloat();
    float_feasign_.resize(size);
    memcpy(float_feasign_.data(), input, size * sizeof(float));
  }
  void CopyValues(const uint64_t* input, size_t size) {
    CheckUint64();
    uint64_feasign_.resize(size);
    memcpy(uint64_feasign_.data(), input, size * sizeof(uint64_t));
  }
  void AddIns(const MultiSlotType& ins) {
    if (ins.GetType()[0] == 'f') {  // float
      CheckFloat();
      auto& vec = ins.GetFloatData();
      offset_.push_back(offset_.back() + vec.size());
      float_feasign_.insert(float_feasign_.end(), vec.begin(), vec.end());
    } else if (ins.GetType()[0] == 'u') {  // uint64
      CheckUint64();
      auto& vec = ins.GetUint64Data();
      offset_.push_back(offset_.back() + vec.size());
      uint64_feasign_.insert(uint64_feasign_.end(), vec.begin(), vec.end());
    }
  }
  void AppendValues(const uint64_t* input, size_t size) {
    CheckUint64();
    offset_.push_back(offset_.back() + size);
    uint64_feasign_.insert(uint64_feasign_.end(), input, input + size);
  }
  void AppendValues(const float* input, size_t size) {
    CheckFloat();
    offset_.push_back(offset_.back() + size);

    float_feasign_.insert(float_feasign_.end(), input, input + size);
  }
  const std::vector<float>& GetFloatData() const { return float_feasign_; }
  std::vector<float>& MutableFloatData() { return float_feasign_; }
  const std::vector<uint64_t>& GetUint64Data() const { return uint64_feasign_; }
  std::vector<uint64_t>& MutableUint64Data() { return uint64_feasign_; }
  const std::string& GetType() const { return type_; }
  size_t GetBatchSize() { return offset_.size() - 1; }
  std::string& MutableType() { return type_; }

  std::string DebugString() {
    std::stringstream ss;

    ss << "\ntype: " << type_ << "\n";
    ss << "offset: ";
    ss << "[";
    for (const size_t& i : offset_) {
      ss << offset_[i] << ",";
    }
    ss << "]\ndata: [";
    if (type_[0] == 'f') {
      for (const float& i : float_feasign_) {
        ss << i << ",";
      }
    } else {
      for (const uint64_t& i : uint64_feasign_) {
        ss << i << ",";
      }
    }
    ss << "]\n";
    return ss.str();
  }

 private:
  void CheckType(const std::string& type) const {
    PADDLE_ENFORCE_EQ((type == "uint64" || type == "float"),
                      true,
                      common::errors::InvalidArgument(
                          "MultiSlotType error, expect type is uint64 or "
                          "float, but received type is %s.",
                          type));
  }
  void CheckFloat() const {
    PADDLE_ENFORCE_EQ(
        type_[0],
        'f',
        common::errors::InvalidArgument(
            "MultiSlotType error, add %s value to float slot.", type_));
  }
  void CheckUint64() const {
    PADDLE_ENFORCE_EQ(
        type_[0],
        'u',
        common::errors::InvalidArgument(
            "MultiSlotType error, add %s value to uint64 slot.", type_));
  }
  std::vector<float> float_feasign_;
  std::vector<uint64_t> uint64_feasign_;
  std::string type_;
  std::vector<size_t> offset_;
};

template <class AR>
paddle::framework::Archive<AR>& operator<<(paddle::framework::Archive<AR>& ar,
                                           const MultiSlotType& ins) {
  ar << ins.GetType();
#ifdef _LINUX
  ar << ins.GetOffset();
#else
  const auto& offset = ins.GetOffset();
  ar << (uint64_t)offset.size();
  for (const size_t& x : offset) {
    ar << (const uint64_t)x;
  }
#endif
  ar << ins.GetFloatData();
  ar << ins.GetUint64Data();
  return ar;
}

template <class AR>
paddle::framework::Archive<AR>& operator>>(paddle::framework::Archive<AR>& ar,
                                           MultiSlotType& ins) {
  ar >> ins.MutableType();
#ifdef _LINUX
  ar >> ins.MutableOffset();
#else
  auto& offset = ins.MutableOffset();
  offset.resize(ar.template Get<uint64_t>());
  for (size_t& x : offset) {
    uint64_t t;
    ar >> t;
    x = static_cast<size_t>(t);
  }
#endif
  ar >> ins.MutableFloatData();
  ar >> ins.MutableUint64Data();
  return ar;
}

struct RecordCandidate {
  std::string ins_id_;
  std::unordered_multimap<uint16_t, FeatureFeasign> feas_;
  size_t shadow_index_ = -1;  // Optimization for Reservoir Sample

  RecordCandidate() {}
  RecordCandidate(const Record& rec,
                  const std::unordered_set<uint16_t>& slot_index_to_replace) {
    for (const auto& fea : rec.uint64_feasigns_) {
      if (slot_index_to_replace.find(fea.slot()) !=
          slot_index_to_replace.end()) {
        feas_.insert({fea.slot(), fea.sign()});
      }
    }
  }

  RecordCandidate& operator=(const Record& rec) {
    feas_.clear();
    ins_id_ = rec.ins_id_;
    for (auto& fea : rec.uint64_feasigns_) {
      feas_.insert({fea.slot(), fea.sign()});
    }
    return *this;
  }
};

class RecordCandidateList {
 public:
  RecordCandidateList() = default;
  RecordCandidateList(const RecordCandidateList& UNUSED) {}

  size_t Size() { return cur_size_; }
  void ReSize(size_t length);

  void ReInit();
  void ReInitPass() {
    for (size_t i = 0; i < cur_size_; ++i) {
      if (candidate_list_[i].shadow_index_ != i) {
        candidate_list_[i].ins_id_ =
            candidate_list_[candidate_list_[i].shadow_index_].ins_id_;
        candidate_list_[i].feas_.swap(
            candidate_list_[candidate_list_[i].shadow_index_].feas_);
        candidate_list_[i].shadow_index_ = i;
      }
    }
    candidate_list_.resize(cur_size_);
  }

  void AddAndGet(const Record& record, RecordCandidate* result);
  void AddAndGet(const Record& record, size_t& index_result) {  // NOLINT
    // std::unique_lock<std::mutex> lock(mutex_);
    size_t index = 0;
    ++total_size_;
    auto fleet_ptr = FleetWrapper::GetInstance();
    if (!full_) {
      candidate_list_.emplace_back(record, slot_index_to_replace_);
      candidate_list_.back().shadow_index_ = cur_size_;
      ++cur_size_;
      full_ = (cur_size_ == capacity_);
    } else {
      index = fleet_ptr->LocalRandomEngine()() % total_size_;
      if (index < capacity_) {
        candidate_list_.emplace_back(record, slot_index_to_replace_);
        candidate_list_[index].shadow_index_ = candidate_list_.size() - 1;
      }
    }
    index = fleet_ptr->LocalRandomEngine()() % cur_size_;
    index_result = candidate_list_[index].shadow_index_;
  }
  const RecordCandidate& Get(size_t index) const {
    PADDLE_ENFORCE_LT(
        index,
        candidate_list_.size(),
        common::errors::OutOfRange("Your index [%lu] exceeds the number of "
                                   "elements in candidate_list[%lu].",
                                   index,
                                   candidate_list_.size()));
    return candidate_list_[index];
  }
  void SetSlotIndexToReplace(
      const std::unordered_set<uint16_t>& slot_index_to_replace) {
    slot_index_to_replace_ = slot_index_to_replace;
  }

 private:
  size_t capacity_ = 0;
  std::mutex mutex_;
  bool full_ = false;
  size_t cur_size_ = 0;
  size_t total_size_ = 0;
  std::vector<RecordCandidate> candidate_list_;
  std::unordered_set<uint16_t> slot_index_to_replace_;
};

template <class AR>
paddle::framework::Archive<AR>& operator<<(paddle::framework::Archive<AR>& ar,
                                           const FeatureFeasign& fk) {
  ar << fk.uint64_feasign_;
  ar << fk.float_feasign_;
  return ar;
}

template <class AR>
paddle::framework::Archive<AR>& operator>>(paddle::framework::Archive<AR>& ar,
                                           FeatureFeasign& fk) {
  ar >> fk.uint64_feasign_;
  ar >> fk.float_feasign_;
  return ar;
}

template <class AR>
paddle::framework::Archive<AR>& operator<<(paddle::framework::Archive<AR>& ar,
                                           const FeatureItem& fi) {
  ar << fi.sign();
  ar << fi.slot();
  return ar;
}

template <class AR>
paddle::framework::Archive<AR>& operator>>(paddle::framework::Archive<AR>& ar,
                                           FeatureItem& fi) {
  ar >> fi.sign();
  ar >> fi.slot();
  return ar;
}

template <class AR>
paddle::framework::Archive<AR>& operator<<(paddle::framework::Archive<AR>& ar,
                                           const Record& r) {
  ar << r.uint64_feasigns_;
  ar << r.float_feasigns_;
  ar << r.ins_id_;
  return ar;
}

template <class AR>
paddle::framework::Archive<AR>& operator>>(paddle::framework::Archive<AR>& ar,
                                           Record& r) {
  ar >> r.uint64_feasigns_;
  ar >> r.float_feasigns_;
  ar >> r.ins_id_;
  return ar;
}

// This DataFeed is used to feed multi-slot type data.
// The format of multi-slot type data:
//   [n feasign_0 feasign_1 ... feasign_n]*
class MultiSlotDataFeed
    : public PrivateQueueDataFeed<std::vector<MultiSlotType>> {
 public:
  MultiSlotDataFeed() {}
  virtual ~MultiSlotDataFeed() {}
  virtual void Init(const DataFeedDesc& data_feed_desc);
  virtual bool CheckFile(const char* filename);

 protected:
  virtual void ReadThread();
  virtual void AddInstanceToInsVec(std::vector<MultiSlotType>* vec_ins,
                                   const std::vector<MultiSlotType>& instance,
                                   int index);
  virtual bool ParseOneInstance(std::vector<MultiSlotType>* instance);
  virtual bool ParseOneInstanceFromPipe(std::vector<MultiSlotType>* instance);
  virtual void PutToFeedVec(const std::vector<MultiSlotType>& ins_vec);
};

class MultiSlotInMemoryDataFeed : public InMemoryDataFeed<Record> {
 public:
  MultiSlotInMemoryDataFeed() {}
  virtual ~MultiSlotInMemoryDataFeed() {}
  virtual void Init(const DataFeedDesc& data_feed_desc);
  // void SetRecord(Record* records) { records_ = records; }

 protected:
  virtual bool ParseOneInstance(Record* instance);
  virtual bool ParseOneInstanceFromPipe(Record* instance);
  virtual void ParseOneInstanceFromSo(const char* str UNUSED,
                                      Record* instance UNUSED,
                                      CustomParser* parser UNUSED) {}
  virtual int ParseInstanceFromSo(int len,
                                  const char* str,
                                  std::vector<Record>* instances,
                                  CustomParser* parser);
  virtual void PutToFeedVec(const std::vector<Record>& ins_vec);
  virtual void GetMsgFromLogKey(const std::string& log_key,
                                uint64_t* search_id,
                                uint32_t* cmatch,
                                uint32_t* rank);
  virtual void PutToFeedVec(const Record* ins_vec, int num);
};

class SlotRecordInMemoryDataFeed : public InMemoryDataFeed<SlotRecord> {
 public:
  SlotRecordInMemoryDataFeed() = default;
  virtual ~SlotRecordInMemoryDataFeed();
  void Init(const DataFeedDesc& data_feed_desc) override;
  void LoadIntoMemory() override;
  void ExpandSlotRecord(SlotRecord* ins);

 protected:
  bool Start() override;
  int Next() override;
  bool ParseOneInstance(SlotRecord* instance UNUSED) override { return false; }
  bool ParseOneInstanceFromPipe(SlotRecord* instance UNUSED) override {
    return false;
  }
  // virtual void ParseOneInstanceFromSo(const char* str, T* instance,
  //                                    CustomParser* parser) {}
  void PutToFeedVec(const std::vector<SlotRecord>& ins_vec UNUSED) override {}

  virtual void LoadIntoMemoryByCommand(void);
  virtual void LoadIntoMemoryByLib(void);
  virtual void LoadIntoMemoryByLine(void);
  virtual void LoadIntoMemoryByFile(void);
  void SetInputChannel(void* channel) override {
    input_channel_ = static_cast<ChannelObject<SlotRecord>*>(channel);
  }
  bool ParseOneInstance(const std::string& line, SlotRecord* rec);
  void PutToFeedVec(const SlotRecord* ins_vec, int num) override;
  void AssignFeedVar(const Scope& scope) override;
  std::vector<std::string> GetInputVarNames() override {
    std::vector<std::string> var_names;
    for (int i = 0; i < use_slot_size_; ++i) {
      var_names.push_back(used_slots_info_[i].slot);
    }
    return var_names;
  }
#if defined(PADDLE_WITH_CUDA) && defined(PADDLE_WITH_HETERPS)
  void BuildSlotBatchGPU(const int ins_num, MiniBatchGpuPack* pack);

  virtual MiniBatchGpuPack* get_pack(MiniBatchGpuPack* last_pack);

  virtual void PackToScope(MiniBatchGpuPack* pack,
                           const Scope* scope = nullptr);

  void FillSlotValueOffset(const int ins_num,
                           const int used_slot_num,
                           size_t* slot_value_offsets,
                           const int* uint64_offsets,
                           const int uint64_slot_size,
                           const int* float_offsets,
                           const int float_slot_size,
                           const UsedSlotGpuType* used_slots,
                           cudaStream_t stream);
  void CopyForTensor(const int ins_num,
                     const int used_slot_num,
                     void** dest,
                     const size_t* slot_value_offsets,
                     const uint64_t* uint64_feas,
                     const int* uint64_offsets,
                     const int* uint64_ins_lens,
                     const int uint64_slot_size,
                     const float* float_feas,
                     const int* float_offsets,
                     const int* float_ins_lens,
                     const int float_slot_size,
                     const UsedSlotGpuType* used_slots,
                     cudaStream_t stream);
#endif

#if defined(PADDLE_WITH_PSCORE) && defined(PADDLE_WITH_HETERPS)
  virtual void InitGraphResource(void);
  virtual void InitGraphTrainResource(void);
  virtual void DoWalkandSage();
  void SetInsIdVec(MiniBatchGpuPack* pack) override {
    if (parse_ins_id_) {
      size_t ins_num = pack->ins_num();
      ins_id_vec_.clear();
      ins_id_vec_.resize(ins_num);
      for (size_t i = 0; i < ins_num; i++) {
        ins_id_vec_[i] = pack->get_lineid(i);
      }
    }
  }
#endif
  void DumpWalkPath(std::string dump_path, size_t dump_rate) override;
  void DumpSampleNeighbors(std::string dump_path) override;

  float sample_rate_ = 1.0f;
  int use_slot_size_ = 0;
  int float_use_slot_size_ = 0;
  int uint64_use_slot_size_ = 0;
  std::vector<AllSlotInfo> all_slots_info_;
  std::vector<UsedSlotInfo> used_slots_info_;
  size_t float_total_dims_size_ = 0;
  std::vector<int> float_total_dims_without_inductives_;

#if defined(PADDLE_WITH_CUDA) && defined(PADDLE_WITH_HETERPS)
  int pack_thread_num_{5};
  std::vector<std::thread> pack_threads_;
  std::vector<MiniBatchGpuPack*> pack_vec_;
  BlockingQueue<MiniBatchGpuPack*> free_pack_queue_;
  BlockingQueue<MiniBatchGpuPack*> using_pack_queue_;
  std::atomic<bool> pack_is_end_{false};
  std::atomic<uint64_t> pack_offset_index_{0};
  MiniBatchGpuPack* last_pack_{nullptr};
  std::atomic<bool> stop_token_{false};
  std::atomic<int> thread_count_{0};
  std::mutex pack_mutex_;

  // async infershape
  std::map<const Scope*, std::vector<phi::DenseTensor*>> scope_feed_vec_;
#endif
};

class PaddleBoxDataFeed : public MultiSlotInMemoryDataFeed {
 public:
  PaddleBoxDataFeed() {}
  virtual ~PaddleBoxDataFeed() {}

 protected:
  virtual void Init(const DataFeedDesc& data_feed_desc);
  virtual bool Start();
  virtual int Next();
  virtual void AssignFeedVar(const Scope& scope);
  virtual void PutToFeedVec(const std::vector<PvInstance>& pv_vec);
  virtual void PutToFeedVec(const std::vector<Record*>& ins_vec);
  virtual int GetCurrentPhase();
  virtual void GetRankOffset(const std::vector<PvInstance>& pv_vec,
                             int ins_number);
  std::string rank_offset_name_;
  int pv_batch_size_;
};

#if (defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)) && !defined(_WIN32)
template <typename T>
class PrivateInstantDataFeed : public DataFeed {
 public:
  PrivateInstantDataFeed() {}
  virtual ~PrivateInstantDataFeed() {}
  void Init(const DataFeedDesc& data_feed_desc) override;
  bool Start() override { return true; }
  int Next() override;

 protected:
  // The batched data buffer
  std::vector<MultiSlotType> ins_vec_;

  // This function is used to preprocess with a given filename, e.g. open it or
  // mmap
  virtual bool Preprocess(const std::string& filename) = 0;

  // This function is used to postprocess system resource such as closing file
  // NOTICE: Ensure that it is safe to call before Preprocess
  virtual bool Postprocess() = 0;

  // The reading and parsing method.
  virtual bool ParseOneMiniBatch() = 0;

  // This function is used to put ins_vec to feed_vec
  virtual void PutToFeedVec();
};

class MultiSlotFileInstantDataFeed
    : public PrivateInstantDataFeed<std::vector<MultiSlotType>> {
 public:
  MultiSlotFileInstantDataFeed() {}
  virtual ~MultiSlotFileInstantDataFeed() {}

 protected:
  int fd_{-1};
  char* buffer_{nullptr};
  size_t end_{0};
  size_t offset_{0};

  bool Preprocess(const std::string& filename) override;

  bool Postprocess() override;

  bool ParseOneMiniBatch() override;
};
#endif

}  // namespace framework
}  // namespace paddle