mindspore/mindspore/ccsrc/pipeline/pipeline.cc

/**
 * This is the C++ adaptation and derivative work of Myia (https://github.com/mila-iqia/myia/).
 *
 * Copyright 2019 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 "pipeline/pipeline.h"

#include <sstream>
#include <map>
#include <unordered_map>
#include <cstdlib>
#include <algorithm>

#include "pipeline/pass.h"
#include "pipeline/parse/data_converter.h"
#include "optimizer/ad/dfunctor.h"
#include "debug/anf_ir_dump.h"
#include "debug/anf_ir_utils.h"
#include "utils/config_manager.h"
#include "utils/convert_utils.h"
#include "utils/utils.h"
#include "utils/base_ref.h"
#include "vm/segment_runner.h"
#include "parallel/context.h"
#include "parallel/graph_util/get_parallel_info.h"
#include "device/kernel_runtime_manager.h"
#include "debug/trace.h"

#if (ENABLE_GE || ENABLE_D)
#include "pipeline/pipeline_ge.h"
#include "transform/convert.h"
#include "transform/df_graph_manager.h"
#endif

namespace mindspore {
// namespace to support intermediate representation definition
namespace pipeline {
using Tensor = mindspore::tensor::Tensor;
using MetaTensor = mindspore::tensor::MetaTensor;
using TensorOrderMap = std::map<std::string, std::shared_ptr<Tensor>>;
using mindspore::abstract::AbstractTensor;
using mindspore::abstract::AbstractTensorPtr;
using mindspore::abstract::AbstractTuple;
using mindspore::abstract::AbstractTuplePtr;

const char IR_TYPE_ANF[] = "anf_ir";
const char IR_TYPE_ONNX[] = "onnx_ir";

ExecutorPyPtr ExecutorPy::executor_ = nullptr;
std::mutex ExecutorPy::instance_lock_;

std::unordered_map<abstract::AbstractBasePtrList, int, abstract::AbstractBasePtrListHasher,
                   abstract::AbstractBasePtrListEqual>
  g_args_cache;

namespace {
std::string GetBaseNameForIR(int stage_idx, const std::string& action_name) {
  std::ostringstream oss;
  auto ms_context = MsContext::GetInstance();
  if (ms_context == nullptr) {
    MS_LOG(EXCEPTION) << "ms_context is nullptr";
  }
  auto save_graphs_path = ms_context->save_graphs_path();
  if (save_graphs_path.empty()) {
    save_graphs_path = ".";
  }
  oss << save_graphs_path << "/" << stage_idx << "_" << action_name;
  return oss.str();
}
}  // namespace

py::tuple GenerateKey(const std::string& name, const std::unordered_map<std::string, py::object>& defaults) {
  MS_LOG(DEBUG) << "GenerateKey args size:" << defaults.size();
  abstract::AbstractBasePtrList args_spec;

  for (auto arg : defaults) {
    if (py::isinstance<py::module>(arg.second)) {
      MS_LOG(EXCEPTION) << "GenerateKey failed, argument input should not be py::module";
    }
    ValuePtr converted = nullptr;
    if (!parse::ConvertData(arg.second, &converted)) {
      MS_LOG(EXCEPTION) << "GenerateKey convert arg failed";
    }
    args_spec.push_back(abstract::FromValue(converted, true));
  }
  if (g_args_cache.count(args_spec) == 0) {
    static int key = 0;
    MS_LOG(INFO) << "Start new args and compile key:" << key;
    g_args_cache[args_spec] = key++;
  }
  py::tuple argSpec = py::tuple(2);
  argSpec[0] = name;
  argSpec[1] = g_args_cache[args_spec];
  return argSpec;
}

py::bool_ VerifyInputSignature(const py::list input_signature, const py::tuple inputs) {
  MS_LOG(DEBUG) << "Verify args size:" << inputs.size();
  if (inputs.size() != input_signature.size()) {
    MS_LOG(ERROR) << "Signature size not equal to args size";
    return false;
  }

  size_t count = 0;
  for (auto arg_obj : inputs) {
    if (py::hasattr(arg_obj, PYTHON_TENSOR_FLAG)) {
      MS_LOG(DEBUG) << "Verify Tensor";
      std::shared_ptr<Tensor> m_tensor = arg_obj.cast<std::shared_ptr<Tensor>>();
      if (m_tensor == nullptr) {
        MS_LOG(ERROR) << "Verify Tensor error, get ptr is null";
        return false;
      }
      std::shared_ptr<MetaTensor> sig = input_signature[count].cast<std::shared_ptr<MetaTensor>>();
      std::vector<int> sig_shape = sig->shape();
      TypePtr sig_type = sig->Dtype();

      std::vector<int> tensor_shape = m_tensor->shape_c();
      if (tensor_shape != sig_shape) {
        MS_LOG(ERROR) << "Python input shape is incompatible with input_signature";
        return false;
      }

      if (*m_tensor->Dtype() != *sig_type) {
        MS_LOG(ERROR) << "Python input type(" << m_tensor->Dtype()->ToString() << ") incompatible with input_signature("
                      << sig_type->ToString() << ")";
        return false;
      }
    }
    count++;
  }

  return true;
}

ExecutorPy::ExecutorPy() {}

ResourcePtr ExecutorPy::GetResource(const std::string& phase) {
  MS_LOG(DEBUG) << "Phase size:" << info_.size();
  if (info_.count(phase) == 0) {
    return nullptr;
  }
  return info_[phase]->resource;
}

FuncGraphPtr ExecutorPy::GetFuncGraph(const std::string& phase) {
  if (info_.count(phase) == 0) {
    MS_LOG(EXCEPTION) << "No phase in executor:" << GetPhasePrefix(phase);
  }
  return info_[phase]->func_graph;
}

std::size_t ExecutorPy::ArgListSize(const std::string& phase) {
  if (info_.count(phase) == 0) {
    MS_LOG(EXCEPTION) << "No phase in executor:" << GetPhasePrefix(phase);
  }
  return info_[phase]->arg_list_size;
}

compile::VmEvalFuncPtr ExecutorPy::GetVmEvalFunc(const std::string& phase) {
  ResourcePtr res = GetResource(phase);
  MS_EXCEPTION_IF_NULL(res);
  if (res->results().find(kOutput) != res->results().end() && res->results()[kOutput].is<compile::VmEvalFuncPtr>()) {
    return res->results()[kOutput].cast<compile::VmEvalFuncPtr>();
  }
  MS_LOG(ERROR) << "GetVmEvalFunc vm model can't find kOutput:" << kOutput;
  return nullptr;
}

bool ExecutorPy::HasCompiled(const std::string& phase) const {
  if (info_.count(phase) == 0) {
    return false;
  }
  return true;
}

py::bytes ExecutorPy::GetFuncGraphProto(const std::string& phase, const std::string& ir_type) {
  FuncGraphPtr fg_ptr = GetFuncGraph(phase);
  if (fg_ptr == nullptr) {
    for (auto& item : info_) {
      MS_LOG(DEBUG) << "Phase key is: " << item.first;
    }
    MS_LOG(EXCEPTION) << "Can not find func graph " << phase;
  }

  if (ir_type == IR_TYPE_ANF) {
    std::string proto_str = GetFuncGraphProtoString(fg_ptr);
    if (proto_str.empty()) {
      MS_LOG(EXCEPTION) << "Graph proto is empty.";
    }
    return proto_str;
  }

  if (ir_type == IR_TYPE_ONNX) {
    std::string proto_str = GetOnnxProtoString(fg_ptr);
    if (proto_str.empty()) {
      MS_LOG(EXCEPTION) << "Graph proto is empty.";
    }
    return proto_str;
  }

  MS_LOG(EXCEPTION) << "Unknown ir type: " << ir_type;
}

py::dict ExecutorPy::GetParameterLayout(const std::string& phase) {
  MS_LOG(DEBUG) << "GetParameterLayout!";
  std::string layout_graph = phase + kStepParallelGraph;
  auto graph = GetFuncGraph(layout_graph);
  return mindspore::parallel::GetParameterLayout(graph);
}

py::dict ExecutorPy::GetCNodeStrategy(const std::string& phase) {
  MS_LOG(DEBUG) << "GetCNodeStrategy!";
  std::string layout_graph = phase + kStepParallelGraph;
  auto graph = GetFuncGraph(layout_graph);
  return mindspore::parallel::GetCNodeStrategy(graph);
}

py::dict ExecutorPy::GetAllreduceFusion(const std::string& phase) {
  MS_LOG(INFO) << "GetAllreduceFusion!";
  auto graph = GetFuncGraph(phase);
  return mindspore::parallel::GetAllreduceFusion(graph);
}

void ExecutorPy::DelNetRes(const std::string& id) {
#ifdef ENABLE_GE
  FinalizeGe();
#endif
  if (executor_ != nullptr) {
    bool flag = false;
    auto tmp_info = info_;
    for (auto& item : tmp_info) {
      if (item.first.find(id) != string::npos) {
        MS_LOG(INFO) << "Delete network res:" << item.first;
        (void)info_.erase(item.first);
        flag = true;
      }
    }

    MS_LOG(INFO) << "Delete flag:" << flag;
#ifdef ENABLE_GE
    if (flag && info_.size() == 0) {
      // because Ge only support one Session exist at the same time ,so we delete the old one
      transform::DfGraphManager::GetInstance().DeleteGraphRunner();
      transform::DfGraphManager::GetInstance().EraseAnfGraph();
      transform::DfGraphManager::GetInstance().DeleteGeSession();
    }
#endif
  }
}

void ExecutorPy::ClearRes() {
  MS_LOG(INFO) << "Clean executor resource!";
  executor_ = nullptr;
}

ExecutorPy::~ExecutorPy() {
  MS_LOG(INFO) << "Release Executor!";
  ConfigManager::GetInstance().ResetConfig();
}

void ExecutorPy::SaveCompiledGraph(const std::string& phase_s) {
  // save the graph to ExecutorPy
  FuncGraphPtr func_graph = info_[phase_s]->resource->func_graph();
  MS_EXCEPTION_IF_NULL(func_graph);
  MS_EXCEPTION_IF_NULL(parallel::ParallelContext::GetInstance());
  std::string parallel_mode = parallel::ParallelContext::GetInstance()->parallel_mode();

  MS_LOG(INFO) << "Save compiled func graph(" << func_graph->ToString() << ") phase(" << phase_s << ")!";
  info_[phase_s]->func_graph = func_graph;
  if ((func_graph != nullptr) &&
      ((parallel_mode == parallel::AUTO_PARALLEL) || (parallel_mode == parallel::SEMI_AUTO_PARALLEL))) {
    MS_LOG(DEBUG) << "Save model parallel parameter layout graph!";
    func_graph = info_[phase_s]->resource->results()[kStepParallelGraph].cast<FuncGraphPtr>();
    ExecutorInfoPtr executor_info = std::make_shared<ExecutorInfo>();
    std::string layout_graph = phase_s + kStepParallelGraph;
    executor_info->func_graph = func_graph;
    info_[layout_graph] = executor_info;
  } else {
    MS_LOG(DEBUG) << "Save model parallel parameter layout graph null!";
  }
  MS_LOG(INFO) << "End save compiled func graph!";
}

bool ExecutorPy::ChangeExportGeirUseVmFlag(bool use_vm, const std::string& phase_s) const {
  std::string phase_prefix = GetPhasePrefix(phase_s);

  if (use_vm && phase_prefix == "export") {
    MS_LOG(INFO) << "Use ge backend to export geir";
    use_vm = false;
  }
  return use_vm;
}

void ExecutorPy::GetGeBackendPolicy() const {
  auto ms_context = MsContext::GetInstance();
  MS_EXCEPTION_IF_NULL(ms_context);
  std::string backend = ms_context->backend_policy();
  if (backend != "ge") {
    MS_LOG(EXCEPTION) << "" << backend << " backend policy is not supported under ge backend!";
  }
}

bool ExecutorPy::CompileInner(const py::object& obj, const py::tuple& args, const py::object& phase, bool use_vm) {
  MS_LOG(DEBUG) << "Start ExecutorPy compile!";
  if ((!py::isinstance<py::str>(phase))) {
    MS_LOG(ERROR) << "Arg phase must be string.";
    return false;
  }
  // check the arg valid?
  if (py::isinstance<py::none>(obj)) {
    MS_LOG(ERROR) << "Find error: parse obj is None.";
    return false;
  }
#ifdef ENABLE_GE
  GetGeBackendPolicy();
#endif
  ExecutorInfoPtr executor_info = std::make_shared<ExecutorInfo>();
  std::string phase_s = py::cast<std::string>(phase);
  MS_LOG(INFO) << "ExecutorPy compile phase:" << phase_s << "!";
  ResourcePtr resource = std::make_shared<Resource>(obj);
  std::vector<ActionItem> p_actions;

  use_vm = ChangeExportGeirUseVmFlag(use_vm, phase_s);

  std::string backend = MsContext::GetInstance()->backend_policy();
  if (use_vm && backend != "ge") {
    // Create backend and session
    resource->results()[kBackend] = compile::CreateBackend();
    p_actions = VmPipeline();
  } else {
    p_actions = GePipeline();
  }

  std::shared_ptr<Pipeline> pip = std::make_shared<Pipeline>(resource, p_actions);

  // get the parameters items and add the value to args_spec
  abstract::AbstractBasePtrList args_spec;
  std::size_t size = args.size();
  for (std::size_t i = 0; i < size; i++) {
    ValuePtr converted = nullptr;
    bool succ = parse::ConvertData(args[i], &converted);
    if (!succ) {
      MS_LOG(EXCEPTION) << "Args convert error";
    }
    bool broaden = true;
    args_spec.push_back(abstract::FromValue(converted, broaden));
  }

  resource->set_args_spec(args_spec);
  executor_info->arg_list_size = size;
  executor_info->resource = resource;
  info_[phase_s] = executor_info;
  pip->Run();

  // save the run graph func to MsPipeLine
  SaveCompiledGraph(phase_s);

  resource->Clean();
  // Reclaim all resource used by optimizer;
  ReclaimOptimizer();

  MS_LOG(INFO) << "End ExecutorPy compile!";
  return true;
}

void ExecutorPy::ReleaseResource(const py::object& phase) {
  ResourcePtr res = GetResource(py::cast<std::string>(phase));
  if (res != nullptr) {
    res->Clean();
  }
  // Reclaim all resource used by optimizer;
  ReclaimOptimizer();
}

static std::string PrintArgs(const py::tuple& args) {
  py::print(args);
  return "";
}

bool ExecutorPy::Compile(const py::object& obj, const py::tuple& args, const py::object& phase, bool use_vm) {
  bool ret_value = false;

  try {
    MS_LOG(DEBUG) << PrintArgs(args);
    ret_value = CompileInner(obj, args, phase, use_vm);
  } catch (const py::error_already_set& ex) {
    // print function call stack info before release
    std::ostringstream oss;
    trace::TraceGraphInfer();
    trace::GetInferStackInfo(oss);
    // call py::print to output function call stack to STDOUT, in case of output the log to file, the user can see
    // these info from screen, no need to open log file to find these info
    py::print(oss.str());
    MS_LOG(ERROR) << oss.str();
    ReleaseResource(phase);

    // re-throw this exception to Python interpreter to handle it
    throw(py::error_already_set(ex));
  } catch (const py::type_error& ex) {
    ReleaseResource(phase);
    throw py::type_error(ex);
  } catch (const py::value_error& ex) {
    ReleaseResource(phase);
    throw py::value_error(ex);
  } catch (const std::exception& ex) {
    ReleaseResource(phase);
    // re-throw this exception to Python interpreter to handle it
    throw(std::runtime_error(ex.what()));
  } catch (...) {
    ReleaseResource(phase);
    std::string exName(abi::__cxa_current_exception_type()->name());
    MS_LOG(EXCEPTION) << "Error occurred when compile graph. Exception name: " << exName;
  }

  return ret_value;
}

#ifdef ENABLE_LOAD_ANF_IR
// get MindSpore Intermediate Representation File
std::string GetMsIrFile(void) {
  std::string file;
  const char* path = getenv("MS_IR_FILE");
  if (path == nullptr) {
    return file;
  }

  char real_path[PATH_MAX] = {0};
  if (realpath(path, real_path) == nullptr) {
    MS_LOG(ERROR) << "MS IR path error, " << path;
    return file;
  }
  file = real_path;
  return file;
}

void RunPipelineAction(const ActionItem& action, pipeline::ResourcePtr resource, bool* result) {
  MS_EXCEPTION_IF_NULL(resource);
  MS_EXCEPTION_IF_NULL(result);

  std::string ir_file = GetMsIrFile();
  (void)parse::python_adapter::set_python_scoped();
  if (ir_file.empty()) {
    *result = action.second(resource);
    return;
  }

  // when in loading anf ir mode, action `parse` do nothing
  if (action.first == "parse") {
    parse::PythonAdapter::SetPythonEnvFlag(true);
    return;
  }

  // load MindSpore IR from file
  if (action.first == "symbol_resolve") {
    MS_LOG(DEBUG) << "" << action.first << " read ir file: " << ir_file;
    std::vector<FuncGraphPtr> graphs = ImportIR(ir_file);
    if (graphs.size() == 0) {
      MS_LOG(EXCEPTION) << "" << action.first << " read ir file " << ir_file << " failed as no graph found";
    }
    auto manager = resource->manager();
    MS_EXCEPTION_IF_NULL(manager);
    for (auto& graph : graphs) {
      manager->AddFuncGraph(graph);
    }
    resource->set_func_graph(graphs[0]);
    return;
  }

  // do normal action when not in `parse` and `symbol_resolve` stage
  *result = action.second(resource);
}
#endif

void Pipeline::Run() {
  MS_LOG(INFO) << "Pipeline run";
  MS_EXCEPTION_IF_NULL(resource_);
  FuncGraphPtr user_graph = nullptr;

  WITH(MsProfile::GetProfile())[&user_graph, this]() {
    int i = 0;
    for (auto& action : actions_) {
#ifdef ENABLE_TIMELINE
      DumpTime& dump_time = DumpTime::GetInstance();
      dump_time.Record(action.first, GetTime(), true);
#endif
      bool result = true;
      WITH(MsProfile::GetProfile()->Step(action.first))[&result, &action, this]() {
        MS_LOG(DEBUG) << "Action " << action.first << " start ...";
#ifdef ENABLE_LOAD_ANF_IR
        RunPipelineAction(action, resource_, &result);
#else
        result = action.second(resource_);
#endif
        MS_LOG(DEBUG) << "Action " << action.first << " end.";
      };
      if (!result) {
        MS_LOG(EXCEPTION) << "Pipeline running to end, failed in step:" << action.first;
      }
      if (MsContext::GetInstance()->save_graphs_flag() && resource_->func_graph() != nullptr) {
        auto graph = resource_->func_graph();
        if (graph != nullptr) {
          user_graph = graph;
          std::string base_name = GetBaseNameForIR(i, action.first);

          // generate IR file in dot format, which can be converted to svg file using graphviz dot command
          draw::Draw(base_name + ".dot", graph);
          // generate IR file in human readable format
          DumpIR(base_name + ".ir", graph);
          // generate IR file in a heavily commented format, which can also be reloaded
          if (action.first != "parse") {
            ExportIR(base_name + ".dat", std::to_string(i), graph);
          }
        }
#ifdef MS_DEBUG
        // Dump graph cnode list
        MS_LOG(INFO) << "Show CNode list after " << action.first;
        graph->DumpCNodeList();
#endif
      }
      if (resource_->func_graph() != nullptr) {
        auto func_graph = resource_->func_graph();
        if (func_graph->has_flag(GRAPH_FLAG_HAS_EFFECT)) {
          func_graph->EraseUnusedNodeInOrder();
          func_graph->CheckOrder();
          for (auto fg : func_graph->func_graphs_used_total()) {
            MS_LOG(DEBUG) << "Check order graph " << fg->ToString() << ".";
            fg->EraseUnusedNodeInOrder();
            fg->CheckOrder();
          }
        }
      }
      i++;
#ifdef ENABLE_TIMELINE
      dump_time.Record(action.first, GetTime(), false);
#endif
    }
  };
#ifdef ENABLE_PROFILE
  MsProfile::Print();
  MsProfile::Reset();
#endif

  if (MsContext::GetInstance()->save_graphs_flag() && (user_graph != nullptr)) {
    std::string user_graph_file = GetFilePathName("ModelDigraph.dot");
    MS_LOG(DEBUG) << "Save user graph to: " << user_graph_file;
    draw::DrawUserFuncGraph(user_graph_file, user_graph);

#ifdef ENABLE_DUMP_IR
    std::string filename = GetFilePathName("ms_output.pb");
    ChangeFileMode(filename, S_IRWXU);
    std::ofstream ofs(filename);
    if (!ofs.is_open()) {
      MS_LOG(ERROR) << "Open file '" << filename << "' failed!";
      return;
    }
    ofs << GetFuncGraphProtoString(user_graph);
    ofs.close();
    // set file mode to read only by user
    ChangeFileMode(filename, S_IRUSR);
#endif
  }
  MS_LOG(INFO) << "End";
}

void ExecutorPy::ProcessVmArg(const py::tuple& args, const std::string& phase, VectorRef* arg_list) {
  std::size_t size = args.size();

  for (std::size_t i = 0; i < size; i++) {
    py::object arg = args[i];
    auto ms_context = MsContext::GetInstance();
    if (ms_context->backend_policy() == kMsConvert && py::isinstance<py::array>(arg)) {
      MS_LOG(EXCEPTION) << "Args[" << i << "] is numpy array, not tensor";
    }
    (*arg_list).push_back(arg);
  }

  ResourcePtr res = GetResource(phase);
  MS_EXCEPTION_IF_NULL(res);
  auto graph = res->func_graph();
  MS_EXCEPTION_IF_NULL(graph);
  std::vector<AnfNodePtr> graph_params = graph->parameters();
  std::size_t graph_params_size = graph_params.size();
  if ((*arg_list).size() != graph_params_size) {
    // maybe some default parameter
    for (std::size_t i = (*arg_list).size(); i < graph_params_size; i++) {
      MS_EXCEPTION_IF_NULL(graph_params[i]);
      py::object obj = dyn_cast<Parameter>(graph_params[i])->default_param();
      py::object p_value = py::cast<py::object>(parse::python_adapter::GetPyObjAttr(obj, "default_input"));
      (*arg_list).push_back(p_value);
    }
  }
}

py::object ExecutorPy::Run(const py::tuple& args, const py::object& phase) {
  std::size_t size = args.size();
  if (!py::isinstance<py::str>(phase)) {
    MS_LOG(EXCEPTION) << "Run failed, phase input is not a str";
  }
  auto phase_s = py::cast<std::string>(phase);
  std::string backend = MsContext::GetInstance()->backend_policy();
#ifdef ENABLE_GE
  if (backend == "ge") {
    return ExecDFGraph(info_, args, phase_s);
  }
#else
  if (backend == "ge") {
    std::shared_ptr<py::object> ret_val = std::make_shared<py::object>();
    if (info_.count(phase_s) != 0 && info_[phase_s]->func_graph != nullptr) {
      if (IsGraphOutputValueNodeOrParameter(info_[phase_s]->func_graph->output(), args, ret_val)) {
        return *ret_val;
      }
    }
    if (args.size() > 0) {
      return args[0];
    }
    return args;
  }
#endif
  std::size_t full_arg_size = ArgListSize(phase_s);
  if (size > full_arg_size) {
    MS_LOG(WARNING) << "The arg num : size = " << size << ". full_arg_size = " << full_arg_size;
  }
  VectorRef arg_list;
  ProcessVmArg(args, phase_s, &arg_list);

  compile::VmEvalFuncPtr run = GetVmEvalFunc(phase_s);
  if (run == nullptr) {
    MS_LOG(EXCEPTION) << "Can't find run graph func for " << phase_s;
  }

  MS_LOG(DEBUG) << "Eval run" << backend;
  BaseRef value = (*run)(arg_list);
  MS_LOG(DEBUG) << "Run end";
  return BaseRefToPyData(value);
}

FuncGraphPtr ExecutorPy::BuildGraph(const py::dict& init_params, const std::string& phase,
                                    const py::object& broadcast_params) {
#if (ENABLE_GE || ENABLE_D)
  return BuildDFGraph(info_, init_params, phase, broadcast_params);
#else
  return nullptr;
#endif
}

void ExecutorPy::RunInitGraph(const py::dict& init_params, const std::string& phase) {
#if ENABLE_GE
  RunGEInitGraph(init_params, phase);
#endif
}

bool InitExecDataset(const std::string& queue_name, int64_t iter_num, int64_t batch_size,
                     const std::vector<TypePtr>& types, const std::vector<std::vector<int64_t>>& shapes,
                     const std::vector<int64_t>& input_indexes, const std::string& phase) {
  std::string name = MsContext::GetInstance()->backend_policy();
  if (name == kMsConvert || name == kMsVm) {
    return InitExecDatasetVm(queue_name, iter_num, batch_size, types, shapes, input_indexes);
  }
#if ENABLE_GE
  return InitExecDatasetGe(queue_name, iter_num, batch_size, types, shapes, input_indexes, phase);
#else
  std::string backend = MsContext::GetInstance()->backend_policy();
  if (backend == "ge") {
    return true;
  }
#endif
  return false;
}

bool InitExecDatasetVm(const std::string& queue_name, int64_t size, int64_t batch_size,
                       const std::vector<TypePtr>& types, const std::vector<std::vector<int64_t>>& shapes,
                       const std::vector<int64_t>& input_indexes) {
  MS_LOG(INFO) << "Start InitDataSet Entry";
  std::vector<int> int_input_indexes;
  (void)std::transform(input_indexes.begin(), input_indexes.end(), std::back_inserter(int_input_indexes),
                       [](int64_t item) { return static_cast<int>(item); });
  std::vector<std::vector<int>> int_shapes;
  (void)std::transform(shapes.begin(), shapes.end(), std::back_inserter(int_shapes),
                       [](const std::vector<int64_t>& item) {
                         std::vector<int> vector_item;
                         (void)std::transform(item.begin(), item.end(), std::back_inserter(vector_item),
                                              [](int64_t inner_item) { return static_cast<int>(inner_item); });
                         return vector_item;
                       });
  auto p_init = std::make_shared<Primitive>("InitDataSetQueue");
  p_init->set_attr("queue_name", MakeValue(queue_name));
  p_init->set_attr("size", MakeValue(static_cast<int>(size)));
  p_init->set_attr("batch_size", MakeValue(static_cast<int>(batch_size)));
  p_init->set_attr("types", MakeValue(types));
  p_init->set_attr("shapes", MakeValue(int_shapes));
  p_init->set_attr("input_indexes", MakeValue(int_input_indexes));

  const std::vector<std::string> empty_str_list;
  p_init->set_attr("input_names", MakeValue(empty_str_list));
  p_init->set_attr("output_names", MakeValue(empty_str_list));

  FuncGraphPtr func_graph = std::make_shared<FuncGraph>();
  auto app_init = std::make_shared<CNode>(AnfNodePtrList{NewValueNode(p_init)}, func_graph);
  func_graph->set_output(app_init);
  auto manager = MakeManager();
  manager->AddFuncGraph(func_graph);

  // AbstractNone indicates there is no output for this apply node.
  auto abstract_none = std::make_shared<abstract::AbstractNone>();
  app_init->set_abstract(abstract_none);

  auto backend = compile::CreateBackend();
  MS_EXCEPTION_IF_NULL(backend);
  auto convert_fn = backend->convert_fn();
  MS_EXCEPTION_IF_NULL(convert_fn);
  // Convert CNodeList to LinConvertResult.
  ConfigManager::GetInstance().set_iter_num(1);
  auto runner = convert_fn({app_init});
  backend->Link(runner.graph_id);
  ConfigManager::GetInstance().set_iter_num(size);

  if (!(*runner.run)) {
    // empty function
    MS_LOG(EXCEPTION) << "Backend " << backend->name() << " unsupported tdt dataset.";
  }

  // launch init dataset runner without inputs and outputs
  VectorRef args;
  auto fn = runner.run;
  (void)(*fn)(args);
  MS_LOG(DEBUG) << "InitDataSetVm End.";
  return true;
}

void ResetOpId() { mindspore::id_generator::reset_id(); }

void InitHccl() {
#ifdef ENABLE_GE
  (void)InitGe();
#else
  mindspore::parse::python_adapter::set_python_env_flag(true);
  auto ms_context = MsContext::GetInstance();
  MS_EXCEPTION_IF_NULL(ms_context);
  (void)ms_context->OpenTsd();
  uint32_t device_id = ms_context->device_id();
  std::string device_name = ms_context->device_target();

  if (ms_context->backend_policy() == "ms" && ms_context->device_target() == kAscendDevice) {
    auto runtime_instance = device::KernelRuntimeManager::Instance().GetKernelRuntime(device_name, device_id);
    MS_EXCEPTION_IF_NULL(runtime_instance);
    if (!runtime_instance->Init()) {
      MS_LOG(ERROR) << "Kernel runtime init error.";
      return;
    }
  }
#endif
}

void FinalizeHccl() {
#ifdef ENABLE_GE
  (void)FinalizeGe();
#else
  device::KernelRuntimeManager::Instance().ClearRuntimeResource();
#endif
}

void ExportGraph(const std::string& file_name, const std::string&, const std::string& phase) {
#if (ENABLE_GE || ENABLE_D)
  ExportDFGraph(file_name, phase);
#endif
  MS_LOG(WARNING) << "In ut test no export_graph";
}

void ReleaseGeTsd() {
  auto context_ptr = MsContext::GetInstance();
  if (context_ptr != nullptr) {
    (void)context_ptr->FinalizeGe(true);
    (void)context_ptr->CloseTsd(true);
  }
}

void InitGe() {
  // set python env flag
  mindspore::parse::python_adapter::set_python_env_flag(true);
  // open tsd before ge initialize
  auto ms_context = MsContext::GetInstance();
  MS_EXCEPTION_IF_NULL(ms_context);
  if (!ms_context->OpenTsd()) {
    MS_LOG(EXCEPTION) << "Open tsd failed";
  }
  (void)ms_context->InitGe();
}

void FinalizeGe() {
  auto context_ptr = MsContext::GetInstance();
  MS_EXCEPTION_IF_NULL(context_ptr);
  (void)context_ptr->FinalizeGe();
  (void)context_ptr->CloseTsd();
}

void ClearResAtexit() {
  MS_LOG(DEBUG) << "Pipeline clear all resource";
  device::KernelRuntimeManager::Instance().ClearRuntimeResource();

  ad::g_k_prims.clear();

  abstract::ClearPrimEvaluatorMap();
  compile::ClearConvertCache();
  pipeline::GetMethodMap().clear();
  pipeline::ExecutorPy::ClearRes();
#ifdef ENABLE_GE
  transform::DfGraphManager::GetInstance().ClearGraph();
  transform::DfGraphConvertor::get_adpt_map().clear();
#endif
  ReleaseGeTsd();
  parse::python_adapter::ResetPythonScope();
}
}  // namespace pipeline
}  // namespace mindspore