mindspore2022/mindspore/ccsrc/debug/debug_services.cc

/**
 * Copyright 2019-2020 Huawei Technologies Co., Ltd
 *
 * 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 "debug/debug_services.h"
#include <dirent.h>
#include <fstream>
#include <algorithm>
#include <map>
#include <unordered_set>
#ifdef ONLINE_DBG_MODE
#include "backend/session/anf_runtime_algorithm.h"
#endif
#include "debug/debugger/tensor_summary.h"
#ifdef ONLINE_DBG_MODE
namespace mindspore {
#endif
DebugServices::DebugServices() {
  tensor_loader_ = new TensorLoader();
  uint32_t iter_num = -1;
  tensor_loader_->set_iter_num(iter_num);
}

DebugServices::DebugServices(const DebugServices &other) {
  tensor_loader_ = other.tensor_loader_;
  watchpoint_table = other.watchpoint_table;
}

DebugServices &DebugServices::operator=(const DebugServices &other) {
  if (this != &other) {
    tensor_loader_ = other.tensor_loader_;
    watchpoint_table = other.watchpoint_table;
  }
  return *this;
}

DebugServices::~DebugServices() { delete tensor_loader_; }

void DebugServices::AddWatchpoint(
  unsigned int id, unsigned int watch_condition, float parameter,
  const std::vector<std::tuple<std::string, bool>> &check_node_list, const std::vector<parameter_t> &parameter_list,
  const std::vector<std::tuple<std::string, std::vector<uint32_t>>> *check_node_device_list,
  const std::vector<std::tuple<std::string, std::vector<uint32_t>>> *check_node_graph_list) {
  std::lock_guard<std::mutex> lg(lock_);

  watchpoint_t watchpoint_item;
  watchpoint_item.id = id;
  watchpoint_item.condition.type = static_cast<CONDITION_TYPE>(watch_condition);
  watchpoint_item.condition.parameter = parameter;
  watchpoint_item.check_node_list = check_node_list;
  if (check_node_device_list != nullptr) {
    watchpoint_item.check_node_device_list = *check_node_device_list;
  }
  if (check_node_graph_list != nullptr) {
    watchpoint_item.check_node_graph_list = *check_node_graph_list;
  }
  watchpoint_item.parameter_list = parameter_list;
  watchpoint_table[id] = watchpoint_item;
}

void DebugServices::RemoveWatchpoint(unsigned int id) {
  std::lock_guard<std::mutex> lg(lock_);
  watchpoint_table.erase(id);
}

std::unique_ptr<ITensorSummary> GetSummaryPtr(const std::shared_ptr<TensorData> &tensor, void *previous_tensor_ptr,
                                              uint32_t num_elements, int tensor_dtype) {
  switch (tensor_dtype) {
    case DbgDataType::DT_UINT8: {
      return std::make_unique<TensorSummary<uint8_t>>(tensor->GetDataPtr(), previous_tensor_ptr, num_elements);
    }
    case DbgDataType::DT_INT8: {
      return std::make_unique<TensorSummary<int8_t>>(tensor->GetDataPtr(), previous_tensor_ptr, num_elements);
    }
    case DbgDataType::DT_UINT16: {
      return std::make_unique<TensorSummary<uint16_t>>(tensor->GetDataPtr(), previous_tensor_ptr, num_elements);
    }
    case DbgDataType::DT_INT16: {
      return std::make_unique<TensorSummary<int16_t>>(tensor->GetDataPtr(), previous_tensor_ptr, num_elements);
    }
    case DbgDataType::DT_UINT32: {
      return std::make_unique<TensorSummary<uint32_t>>(tensor->GetDataPtr(), previous_tensor_ptr, num_elements);
    }
    case DbgDataType::DT_INT32:
    case DbgDataType::DT_BASE_INT: {
      return std::make_unique<TensorSummary<int32_t>>(tensor->GetDataPtr(), previous_tensor_ptr, num_elements);
    }
    case DbgDataType::DT_UINT64: {
      return std::make_unique<TensorSummary<uint64_t>>(tensor->GetDataPtr(), previous_tensor_ptr, num_elements);
    }
    case DbgDataType::DT_INT64: {
      return std::make_unique<TensorSummary<int64_t>>(tensor->GetDataPtr(), previous_tensor_ptr, num_elements);
    }
    case DbgDataType::DT_FLOAT16: {
      return std::make_unique<TensorSummary<float16>>(tensor->GetDataPtr(), previous_tensor_ptr, num_elements);
    }
    case DbgDataType::DT_FLOAT32:
    case DbgDataType::DT_BASE_FLOAT: {
      return std::make_unique<TensorSummary<float>>(tensor->GetDataPtr(), previous_tensor_ptr, num_elements);
    }
    case DbgDataType::DT_FLOAT64: {
      return std::make_unique<TensorSummary<double>>(tensor->GetDataPtr(), previous_tensor_ptr, num_elements);
    }
    case DbgDataType::DT_BOOL: {
      return std::make_unique<TensorSummary<bool>>(tensor->GetDataPtr(), previous_tensor_ptr, num_elements);
    }
    default:
      MS_LOG(INFO) << "Unsupported tensor type";
      // return a null pointer
      return std::unique_ptr<TensorSummary<int32_t>>{};
  }
}

#ifdef OFFLINE_DBG_MODE
void *DebugServices::GetPrevTensor(const std::shared_ptr<TensorData> &tensor, bool previous_iter_tensor_needed) {
  void *previous_tensor_ptr = nullptr;
  std::shared_ptr<TensorData> tensor_prev;
  if (previous_iter_tensor_needed && tensor->GetIteration() > 1) {
    // read data in offline mode
    std::vector<std::shared_ptr<TensorData>> result_list_prev;
    ReadDumpedTensor(std::vector<std::string>{tensor->GetName()}, std::vector<size_t>{tensor->GetSlot()},
                     std::vector<unsigned int>{tensor->GetDeviceId()},
                     std::vector<unsigned int>{tensor->GetIteration() - 1},
                     std::vector<unsigned int>{tensor->GetRootGraphId()}, &result_list_prev);
    tensor_prev = result_list_prev[0];
    if (!tensor_prev->GetByteSize()) {
      tensor_prev.reset();
    } else {
      previous_tensor_ptr = tensor_prev->GetDataPtr();
    }
  }
  return previous_tensor_ptr;
}
#endif

void DebugServices::AddWatchPointsToCheck(bool init_dbg_suspend, bool step_end, bool recheck,
                                          const std::string &tensor_name, const std::string &tensor_name_no_slot,
                                          bool *previous_iter_tensor_needed, std::string *qualified_tensor_name,
                                          std::vector<watchpoint_t> *watchpoints_to_check) {
  for (auto w_table_item : watchpoint_table) {
    auto wp = std::get<1>(w_table_item);
    // check ONLY init conditions on initial suspended state.
    // skip other conditions on initial suspended state
    if (init_dbg_suspend && (wp.condition.type != INIT)) continue;
    // skip init condition if not init suspend
    if ((wp.condition.type == INIT) && !init_dbg_suspend) continue;
    // check change conditions only on step end.
    if (wp.change_condition() && !step_end) continue;
    // if recheck, ignore the cache results and reanalyze everything.
    // if not a recheck, check only unanalyzed tensors
    if (!recheck && wp_id_cache[tensor_name].count(wp.id)) continue;
    std::string found = wp.FindQualifiedTensorName(tensor_name_no_slot);
    if (!found.empty()) {
      *qualified_tensor_name = found;
      watchpoints_to_check->push_back(w_table_item.second);
#ifdef OFFLINE_DBG_MODE
      if (wp.change_condition()) {
        *previous_iter_tensor_needed = true;
      }
#endif
    }
  }
}

void DebugServices::AddAnalyzedTensorToCache(const bool recheck, const unsigned int id,
                                             const std::string &tensor_name) {
  // add analyzed tensor to cache
  if (!recheck) {
    wp_id_cache[tensor_name].insert(id);
  }
}

void DebugServices::CheckWatchpoints(std::vector<std::string> *name, std::vector<std::string> *slot,
                                     std::vector<int> *condition, std::vector<unsigned int> *watchpoint_id,
                                     std::vector<std::vector<parameter_t>> *parameters,
                                     std::vector<int32_t> *error_codes, const std::vector<std::string> &op_overflows,
                                     std::vector<std::shared_ptr<TensorData>> *tensor_list, const bool init_dbg_suspend,
                                     const bool step_end, const bool recheck, std::vector<unsigned int> *device_id,
                                     std::vector<unsigned int> *root_graph_id) {
  std::lock_guard<std::mutex> lg(lock_);
  if (watchpoint_table.empty()) return;

  for (auto &tensor : *tensor_list) {
#ifdef OFFLINE_DBG_MODE
    // read data in offline mode
    std::vector<std::shared_ptr<TensorData>> result_list;
    ReadDumpedTensor(std::vector<std::string>{tensor->GetName()}, std::vector<size_t>{tensor->GetSlot()},
                     std::vector<unsigned int>{tensor->GetDeviceId()},
                     std::vector<unsigned int>{tensor->GetIteration()},
                     std::vector<unsigned int>{tensor->GetRootGraphId()}, &result_list);
    tensor = result_list[0];
    if (!tensor->GetByteSize()) {
      tensor.reset();
      continue;
    }
#endif

    const auto tensor_name = tensor->GetName();
    const auto tensor_name_no_slot = tensor_name.substr(0, tensor_name.find_first_of(':'));
    const auto tensor_slot = std::to_string(tensor->GetSlot());
    // no elements to analyze
    if (tensor->GetByteSize() == 0) continue;
    int tensor_dtype = tensor->GetType();
    std::vector<watchpoint_t> watchpoints_to_check;
    std::string qualified_tensor_name;
    bool previous_iter_tensor_needed = false;
    // Add do nothing line in case offline debug is off, prevent unused var warning
    (void)previous_iter_tensor_needed;
    AddWatchPointsToCheck(init_dbg_suspend, step_end, recheck, tensor_name, tensor_name_no_slot,
                          &previous_iter_tensor_needed, &qualified_tensor_name, &watchpoints_to_check);
    // no wp set on current tensor
    if (watchpoints_to_check.empty()) continue;

    uint32_t num_elements = tensor->GetNumElements();

#ifdef OFFLINE_DBG_MODE
    void *previous_tensor_ptr = GetPrevTensor(tensor, previous_iter_tensor_needed);
#else
    void *previous_tensor_ptr =
      tensor_loader_->GetPrevTensor(tensor_name) ? tensor_loader_->GetPrevTensor(tensor_name)->GetDataPtr() : nullptr;
#endif

    std::unique_ptr<ITensorSummary> base_summary_ptr;
    if (!(watchpoints_to_check.size() == 1 && watchpoints_to_check[0].condition.type == IS_OVERFLOW)) {
      base_summary_ptr = GetSummaryPtr(tensor, previous_tensor_ptr, num_elements, tensor_dtype);
      if (base_summary_ptr != nullptr) {
        base_summary_ptr->SummarizeTensor(watchpoints_to_check);
      }
    }
    for (auto &wp : watchpoints_to_check) {
      bool is_hit = false;
      int error_code = 0;
      std::vector<parameter_t> parameter_list = {};
      if (wp.condition.type == IS_OVERFLOW) {
        is_hit = (std::find(op_overflows.begin(), op_overflows.end(), tensor_name_no_slot) != op_overflows.end());
      } else if (base_summary_ptr != nullptr) {
        auto item = base_summary_ptr->IsWatchpointHit(wp);
        is_hit = std::get<0>(item);
        error_code = std::get<1>(item);
        parameter_list = std::get<2>(item);
      }
      AddAnalyzedTensorToCache(recheck, wp.id, tensor_name);

      if (is_hit || error_code) {
        name->push_back(qualified_tensor_name);
        slot->push_back(tensor_slot);
        condition->push_back(wp.condition.type);
        watchpoint_id->push_back(wp.id);
        if (device_id != nullptr) {
          device_id->push_back(tensor->GetDeviceId());
        }
        if (root_graph_id != nullptr) {
          root_graph_id->push_back(tensor->GetRootGraphId());
        }
        parameters->push_back(parameter_list);
        error_codes->push_back(error_code);
      }
    }

#ifdef OFFLINE_DBG_MODE
    // in offline mode remove the need for the data
    tensor.reset();
#endif
  }
}

#ifdef OFFLINE_DBG_MODE
void DebugServices::GetSlotInfo(const std::string &file_name, const std::string &dump_name,
                                const std::string &specific_dump_dir, std::vector<size_t> *slot_list) {
  if (is_sync_mode) {
    // get the slot from the name
    std::string delimiter = "_";
    unsigned int start_pos = dump_name.length();
    unsigned int end_pos = file_name.find(delimiter, start_pos);
    std::string item = file_name.substr(start_pos, end_pos - start_pos);
    slot_list->push_back(std::stoul(item));
  } else {
    std::string out_dir = "/tmp/" + file_name;
    std::string input_file = specific_dump_dir + "/" + file_name;
    std::string log_enabled = DbgLogger::verbose ? "" : "> /dev/null";
    std::string convert_command =
      "python /usr/local/Ascend/toolkit/tools/operator_cmp/compare/msaccucmp.pyc convert -d " + input_file + " -out " +
      out_dir + " -t bin " + log_enabled;
    (void)(system(convert_command.c_str()) + 1);
    convert_command = "python /usr/local/Ascend/toolkit/tools/operator_cmp/compare/msaccucmp.pyc convert -d " +
                      input_file + " -out " + out_dir + " -f NCHW -t bin " + log_enabled;
    (void)(system(convert_command.c_str()) + 1);

    std::string prefix_converted_dump_file_name = file_name + ".output.";
    DIR *convert_dir_ptr = opendir(out_dir.c_str());
    if (convert_dir_ptr != nullptr) {
      struct dirent *convert_dir_contents = nullptr;
      while ((convert_dir_contents = readdir(convert_dir_ptr)) != NULL) {
        if (convert_dir_contents->d_type == DT_REG) {
          std::string converted_file_name = convert_dir_contents->d_name;
          std::size_t nd_file = converted_file_name.rfind(".ND.bin");
          std::size_t fractal_z_file = converted_file_name.rfind(".FRACTAL_Z.bin");
          std::size_t nchw_file = converted_file_name.rfind(".NCHW.bin");
          if (nd_file == std::string::npos && nchw_file == std::string::npos && fractal_z_file == std::string::npos) {
            continue;
          }
          std::size_t found_c = converted_file_name.find(prefix_converted_dump_file_name);
          if (found_c != 0) {
            continue;
          }
          std::size_t slot_start_pos = prefix_converted_dump_file_name.length();
          std::size_t slot_end_pos = converted_file_name.find(".", slot_start_pos) - 1;
          std::string slot_item = converted_file_name.substr(slot_start_pos, slot_end_pos - slot_start_pos + 1);
          slot_list->push_back(std::stoul(slot_item));
        }
      }
    } else {
      MS_LOG(INFO) << out_dir << " directory does not exist!";
    }
    closedir(convert_dir_ptr);

    // std::string delete_cmd = "rm -rf " + out_dir;
    // system(delete_cmd.c_str());
  }
}

std::size_t DebugServices::GetShapeTypeInfo(const std::string &specific_dump_dir, std::size_t slot,
                                            const std::string &prefix_dump_file_name, std::string *file_name,
                                            std::string *type_name, std::string *out_dir, std::vector<int64_t> *shape) {
  std::size_t found = 0;
  if (is_sync_mode) {
    found = file_name->rfind(prefix_dump_file_name, 0);
  } else {
    std::string file_name_w_o_prefix = file_name->substr(file_name->find('.') + 1);
    found = file_name_w_o_prefix.rfind(prefix_dump_file_name, 0);
  }
  if (found != 0) {
    return found;
  }
  if (is_sync_mode) {
    // found a file, now get the shape and type
    // find "_shape_" in the filename
    std::string shape_delimiter = "_shape_";
    unsigned int str_pos = file_name->find(shape_delimiter) + shape_delimiter.length();

    // read numbers with '_' delimter until you read a non-number, that will be the type name
    bool number_found = true;
    std::string delimiter = "_";
    while (number_found) {
      unsigned int end_pos = file_name->find(delimiter, str_pos);
      std::string item = file_name->substr(str_pos, end_pos - str_pos);
      bool is_number = !item.empty() && std::find_if(item.begin(), item.end(),
                                                     [](unsigned char c) { return !std::isdigit(c); }) == item.end();

      if (is_number) {
        shape->push_back(std::stoul(item));
        str_pos = end_pos + 1;
      } else {
        *type_name = item;
        number_found = false;
      }
    }
  } else {
    *out_dir = "/tmp/" + *file_name;
    std::string input_file = specific_dump_dir + "/" + *file_name;
    std::string log_enabled = DbgLogger::verbose ? "" : "> /dev/null";
    std::string convert_command =
      "python /usr/local/Ascend/toolkit/tools/operator_cmp/compare/msaccucmp.pyc convert -d " + input_file + " -out " +
      *out_dir + " -t bin " + log_enabled;
    (void)(system(convert_command.c_str()) + 1);
    convert_command = "python /usr/local/Ascend/toolkit/tools/operator_cmp/compare/msaccucmp.pyc convert -d " +
                      input_file + " -out " + *out_dir + " -f NCHW -t bin " + log_enabled;
    (void)(system(convert_command.c_str()) + 1);

    std::string prefix_converted_dump_file_name = *file_name + ".output." + std::to_string(slot);
    *file_name = "";
    DIR *convert_dir_ptr = opendir(out_dir->c_str());
    if (convert_dir_ptr != nullptr) {
      struct dirent *convert_dir_contents = nullptr;
      while ((convert_dir_contents = readdir(convert_dir_ptr)) != NULL) {
        if (convert_dir_contents->d_type == DT_REG) {
          std::string converted_file_name = convert_dir_contents->d_name;
          std::size_t nd_file = converted_file_name.rfind(".ND.bin");
          std::size_t fractal_z_file = converted_file_name.rfind(".FRACTAL_Z.bin");
          std::size_t nchw_file = converted_file_name.rfind(".NCHW.bin");
          if (nd_file == std::string::npos && nchw_file == std::string::npos && fractal_z_file == std::string::npos) {
            continue;
          }
          std::size_t found_c = converted_file_name.rfind(prefix_converted_dump_file_name, 0);
          if (found_c != 0) {
            continue;
          }
          *file_name = converted_file_name;
        }
      }
    } else {
      MS_LOG(INFO) << *out_dir << " directory does not exist!";
    }
    closedir(convert_dir_ptr);

    if (*file_name == "") {
      MS_LOG(WARNING) << out_dir << ": no valid files found post msaccucmp exec";
      return 1;
    }

    // std::string delete_cmd = "rm -rf " + out_dir;
    // system(delete_cmd.c_str());

    // found a file, now get the shape and type
    std::stringstream check_filename(*file_name);
    std::vector<std::string> tokens;
    std::string intermediate;

    while (getline(check_filename, intermediate, '.')) {
      tokens.push_back(intermediate);
    }
    *type_name = tokens[8];

    std::string shape_str = tokens[7];
    std::stringstream check_shape(shape_str);
    while (getline(check_shape, intermediate, '_')) {
      shape->push_back(std::stoul(intermediate));
    }
  }
  return 0;
}

void DebugServices::ReadDumpedTensor(std::vector<std::string> backend_name, std::vector<size_t> slot,
                                     std::vector<unsigned int> device_id, std::vector<unsigned int> iteration,
                                     std::vector<unsigned int> root_graph_id,
                                     std::vector<std::shared_ptr<TensorData>> *result_list) {
  for (unsigned int i = 0; i < backend_name.size(); i++) {
    // form prefix of the tensor file to read from graph pb node name
    std::string dump_style_kernel_name = backend_name[i];
    const std::string strsrc = "/";

    std::string strdst;
    if (is_sync_mode) {
      strdst = "--";
    } else {
      strdst = "_";
    }

    std::string::size_type pos = 0;
    std::string::size_type srclen = strsrc.size();
    std::string::size_type dstlen = strdst.size();

    // remove slot from name
    std::size_t found_colon = dump_style_kernel_name.find_last_of(":");
    dump_style_kernel_name = dump_style_kernel_name.substr(0, found_colon);

    while ((pos = dump_style_kernel_name.find(strsrc, pos)) != std::string::npos) {
      dump_style_kernel_name.replace(pos, srclen, strdst);
      pos += dstlen;
    }

    std::string prefix_dump_file_name = dump_style_kernel_name;
    if (is_sync_mode) {
      prefix_dump_file_name += "_output_" + std::to_string(slot[i]) + "_";
    }

    std::string specific_dump_dir;
    if (is_sync_mode) {
      specific_dump_dir =
        dump_dir + "/device_" + std::to_string(device_id[i]) + "/iteration_" + std::to_string(iteration[i]);
    } else {
      specific_dump_dir = dump_dir + "/device_" + std::to_string(device_id[i]) + "/" + net_name + "_graph_" +
                          std::to_string(root_graph_id[i]) + "/" + std::to_string(root_graph_id[i]) + "/" +
                          std::to_string(iteration[i]);
    }

    // search files in dir for the one that meets the filename prefix and read the file into memory
    DIR *d;
    d = opendir(specific_dump_dir.c_str());
    std::vector<char> *buffer = NULL;
    std::string type_name = "";
    std::vector<int64_t> shape;
    uint64_t data_size = 0;
    if (d != nullptr) {
      struct dirent *dir = nullptr;
      while ((dir = readdir(d)) != NULL) {
        if (dir->d_type == DT_REG) {
          std::string file_name = dir->d_name;
          std::string out_dir;
          std::size_t found = GetShapeTypeInfo(specific_dump_dir, slot[i], prefix_dump_file_name, &file_name,
                                               &type_name, &out_dir, &shape);
          if (found != 0) {
            continue;
          }

          // read the tensor data from the file
          std::string file_path;
          if (is_sync_mode) {
            file_path = specific_dump_dir + "/" + file_name;
          } else {
            file_path = out_dir + "/" + file_name;
          }

          std::ifstream infile;
          infile.open(file_path.c_str(), std::ios::binary | std::ios::ate);
          if (!infile.is_open()) {
            MS_LOG(ERROR) << "Failed to open bin file " << file_name;
            break;
          }
          uint64_t file_size = infile.tellg();
          infile.seekg(0, std::ios::beg);
          buffer = new std::vector<char>(file_size);
          if (!infile.read(buffer->data(), file_size)) {
            MS_LOG(ERROR) << "Failed to read in bin file " << file_name;
            break;
          }
          data_size = file_size;
          infile.close();
        }
      }
    } else {
      MS_LOG(INFO) << "directory does not exist!";
    }
    closedir(d);

    // call LoadNewTensor to store tensor in internal cache
    auto tensor_data = std::make_shared<TensorData>();
    tensor_data->SetName(backend_name[i]);
    tensor_data->SetExecutionOrder(0);
    tensor_data->SetSlot(slot[i]);
    tensor_data->SetIteration(iteration[i]);
    tensor_data->SetDeviceId(device_id[i]);
    tensor_data->SetRootGraphId(root_graph_id[i]);
    if (data_size) {
      tensor_data->SetDataPtr(buffer->data());
    } else {
      tensor_data->SetDataPtr(NULL);
    }
    tensor_data->SetByteSize(data_size);
    tensor_data->SetType(type_name);
    tensor_data->SetShape(shape);
    if (data_size) {
      tensor_loader_->LoadNewTensor(tensor_data, false);
    }

    // add to result_list
    result_list->push_back(tensor_data);
  }
}

void ReplaceSrcFileName(const bool is_sync_mode, std::string *dump_style_name) {
  const std::string strsrc = "/";
  std::string strdst;
  if (is_sync_mode) {
    strdst = "--";
  } else {
    strdst = "_";
  }
  std::string::size_type pos = 0;
  std::string::size_type srclen = strsrc.size();
  std::string::size_type dstlen = strdst.size();

  while ((pos = dump_style_name->find(strsrc, pos)) != std::string::npos) {
    dump_style_name->replace(pos, srclen, strdst);
    pos += dstlen;
  }
}

std::vector<std::shared_ptr<TensorData>> DebugServices::ReadNeededDumpedTensors(unsigned int iteration) {
  // get a list of nodes and the devices they are on to monitor
  std::vector<std::shared_ptr<TensorData>> tensor_list;
  std::map<std::tuple<uint32_t, uint32_t>, std::unordered_set<std::string>> device_and_graph_to_nodes;
  for (auto w_table_item : watchpoint_table) {
    auto wp = std::get<1>(w_table_item);
    for (auto check_node : wp.check_node_list) {
      unsigned int index = 0;
      std::string w_name = std::get<0>(check_node);
      bool w_is_param = std::get<1>(check_node);

      std::string node_name = w_name;
      if (w_is_param) {
        std::size_t found = node_name.find_last_of("/");
        node_name = node_name.substr(found + 1);
      }

      std::vector<uint32_t> devices = std::get<1>(wp.check_node_device_list[index]);
      std::vector<uint32_t> graphs = std::get<1>(wp.check_node_graph_list[index]);
      for (auto device : devices) {
        for (auto graph : graphs) {
          std::tuple<uint32_t, uint32_t> key(device, graph);
          device_and_graph_to_nodes[key].insert(node_name);
        }
      }

      index++;
    }
  }

  // scan each device/iteration dir for the watched nodes for each device, and add to tensor_list
  // as they are found
  for (auto const &device_and_graph_item : device_and_graph_to_nodes) {
    std::tuple<uint32_t, uint32_t> device_and_graph = device_and_graph_item.first;
    uint32_t device_id = std::get<0>(device_and_graph);
    uint32_t root_graph_id = std::get<1>(device_and_graph);
    std::unordered_set<std::string> wp_nodes = device_and_graph_item.second;
    std::vector<std::tuple<std::string, std::string>> proto_to_dump;

    std::string specific_dump_dir;
    if (is_sync_mode) {
      specific_dump_dir = dump_dir + "/device_" + std::to_string(device_id) + "/iteration_" + std::to_string(iteration);
    } else {
      specific_dump_dir = dump_dir + "/device_" + std::to_string(device_id) + "/" + net_name + "_graph_" +
                          std::to_string(root_graph_id) + "/" + std::to_string(root_graph_id) + "/" +
                          std::to_string(iteration);
    }

    // convert node names to dump style
    for (auto node : wp_nodes) {
      std::string orig_name = node;
      std::string dump_style_name = node;
      ReplaceSrcFileName(is_sync_mode, &dump_style_name);

      if (is_sync_mode) {
        dump_style_name.append("_output_");
      }

      proto_to_dump.push_back(std::tuple<std::string, std::string>(orig_name, dump_style_name));
    }

    // search files in dir for the one that meets the filename prefix and read the file into memory
    DIR *d;
    d = opendir(specific_dump_dir.c_str());
    if (d != nullptr) {
      struct dirent *dir = nullptr;
      while ((dir = readdir(d)) != NULL) {
        if (dir->d_type == DT_REG) {
          std::string file_name = dir->d_name;
          for (auto &node : proto_to_dump) {
            std::string dump_name = std::get<1>(node);
            std::size_t found = 0;

            if (is_sync_mode) {
              found = file_name.rfind(dump_name, 0);
            } else {
              std::string file_name_w_o_prefix = file_name.substr(file_name.find('.') + 1);
              found = file_name_w_o_prefix.rfind(dump_name, 0);
            }

            if (found == 0) {
              std::vector<size_t> slot_list;
              GetSlotInfo(file_name, dump_name, specific_dump_dir, &slot_list);
              for (auto slot : slot_list) {
                // add a TensorData entry (data will be read when needed)
                std::vector<int64_t> shape;
                std::string orig_name = std::get<0>(node);
                auto tensor_data = std::make_shared<TensorData>();
                tensor_data->SetName(orig_name);
                tensor_data->SetExecutionOrder(0);
                tensor_data->SetSlot(slot);
                tensor_data->SetIteration(iteration);
                tensor_data->SetDeviceId(device_id);
                tensor_data->SetRootGraphId(root_graph_id);
                tensor_data->SetDataPtr(NULL);
                tensor_data->SetByteSize(0);
                tensor_data->SetType("");
                tensor_data->SetShape(shape);

                tensor_list.push_back(tensor_data);
              }
              break;
            }
          }
        }
      }
    }
  }

  return tensor_list;
}
#endif

void DebugServices::ReadNodesTensors(std::vector<std::string> name, std::vector<std::string> *ret_name,
                                     std::vector<char *> *data_ptr, std::vector<ssize_t> *data_size,
                                     std::vector<unsigned int> *dtype, std::vector<std::vector<int64_t>> *shape) {
  std::vector<std::tuple<std::string, std::shared_ptr<TensorData>>> result_list;
  tensor_loader_->SearchTensors(name, &result_list);

  for (auto result : result_list) {
    if (!std::get<1>(result)) {
      continue;
    }
    ret_name->push_back(std::get<0>(result));
    data_ptr->push_back(reinterpret_cast<char *>(std::get<1>(result)->GetDataPtr()));
    data_size->push_back(std::get<1>(result)->GetByteSize());
    dtype->push_back(std::get<1>(result)->GetType());
    shape->push_back(std::get<1>(result)->GetShape());
  }
}

#ifdef ONLINE_DBG_MODE
bool DebugServices::IsWatchPoint(const std::string &kernel_name, const CNodePtr &kernel) const {
  bool ret = false;
  for (auto w_table_item : watchpoint_table) {
    auto check_node_list = std::get<1>(w_table_item).check_node_list;
    for (auto check_node : check_node_list) {
      std::string w_name = std::get<0>(check_node);
      bool w_type = std::get<1>(check_node);
      if ((w_type == true &&
           ((kernel_name.find(w_name) != string::npos && kernel_name.rfind(w_name, 0) == 0) || w_name == "*")) ||
          (w_type == false && (kernel_name == w_name || IsWatchPointNodeInput(w_name, kernel)))) {
        ret = true;
        return ret;
      }
    }
  }
  return ret;
}

bool DebugServices::IsWatchPointNodeInput(const std::string &w_name, const CNodePtr &kernel) const {
  if (kernel) {
    auto input_size = AnfAlgo::GetInputTensorNum(kernel);
    for (size_t j = 0; j < input_size; ++j) {
      auto input_kernel = kernel->input(j + 1);
      std::string input_kernel_name = input_kernel->fullname_with_scope();
      auto found = w_name.find_last_of('/');
      if (found != std::string::npos && w_name.substr(found + 1) == input_kernel_name) return true;
    }
    return false;
  } else {
    return false;
  }
}
#endif

void DebugServices::EmptyTensor() { tensor_loader_->EmptyTensor(); }

std::vector<std::shared_ptr<TensorData>> DebugServices::GetTensor() const { return tensor_loader_->GetTensor(); }

std::vector<std::shared_ptr<TensorData>> DebugServices::GetNodeTensorMap(const std::string &node_name) const {
  return tensor_loader_->GetNodeTensorMap(node_name);
}

uint32_t DebugServices::GetTensorLoaderIterNum() const { return tensor_loader_->GetIterNum(); }

void DebugServices::SetTensorLoaderIterNum(uint32_t iter_num) { tensor_loader_->set_iter_num(iter_num); }

void DebugServices::EmptyPrevTensor() { tensor_loader_->EmptyPrevTensor(); }

void DebugServices::EmptyCurrentTensor() { tensor_loader_->EmptyCurrentTensor(); }

#ifdef ONLINE_DBG_MODE
bool DebugServices::DumpTensorToFile(const std::string &tensor_name, bool trans_flag, const std::string &filepath,
                                     const std::string &host_fmt, const std::vector<int64_t> &host_shape,
                                     TypeId host_type, TypeId addr_type_id, const std::string &addr_format,
                                     size_t slot) const {
  return tensor_loader_->DumpTensorToFile(tensor_name, trans_flag, filepath, host_fmt, host_shape, host_type,
                                          addr_type_id, addr_format, slot);
}
#endif

bool DebugServices::LoadNewTensor(const std::shared_ptr<TensorData> &tensor, bool keep_prev) {
  return tensor_loader_->LoadNewTensor(tensor, keep_prev);
}

std::unordered_map<unsigned int, DebugServices::watchpoint_t> DebugServices::GetWatchpointTable() {
  return watchpoint_table;
}

void DebugServices::ResetLoadedTensors() {
  wp_id_cache.clear();
  MS_LOG(INFO) << "Resetting loaded tensors";
  tensor_loader_->MoveParametersCurrentToPrev();
  tensor_loader_->EmptyCurrentTensor();
  // will move parameters from previous to current map
  tensor_loader_->SwapCurrentPrev();
}

#ifdef ONLINE_DBG_MODE
std::vector<std::shared_ptr<TensorData>> DebugServices::GetNodeTensor(const CNodePtr &kernel) {
  MS_EXCEPTION_IF_NULL(kernel);
  std::vector<std::shared_ptr<TensorData>> result;
  auto output_size = AnfAlgo::GetOutputTensorNum(kernel);
  auto kernel_name = kernel->fullname_with_scope();
  for (size_t j = 0; j < output_size; ++j) {
    auto tensor_name_with_slot = kernel_name + ":" + std::to_string(j);
    auto tensor = tensor_loader_->GetTensor(tensor_name_with_slot);
    if (tensor) result.push_back(tensor);
  }
  return result;
}
#endif

bool DebugServices::TensorExistsInCurrent(std::string tensor_name) {
  return tensor_loader_->TensorExistsInCurrent(tensor_name);
}
void DebugServices::MoveTensorCurrentToPrev(std::string tensor_name) {
  tensor_loader_->MoveTensorCurrentToPrev(tensor_name);
}

void DebugServices::SetNetName(std::string net_name) { this->net_name = net_name; }

std::string DebugServices::GetNetName() { return net_name; }

void DebugServices::SetDumpDir(std::string dump_dir) { this->dump_dir = dump_dir; }

std::string DebugServices::GetDumpDir() { return dump_dir; }

void DebugServices::SetSyncMode(bool is_sync_mode) { this->is_sync_mode = is_sync_mode; }

bool DebugServices::GetSyncMode() { return is_sync_mode; }

#ifdef ONLINE_DBG_MODE
}  // namespace mindspore
#endif