/**
 * 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 "hybrid/executor/subgraph_executor.h"
#include "graph/ge_context.h"
#include "hybrid/executor/worker/task_compile_engine.h"
#include "hybrid/executor/worker/execution_engine.h"
#include "hybrid/node_executor/node_executor.h"

namespace ge {
namespace hybrid {
namespace {
constexpr int kDefaultThreadNum = 4;
constexpr int kDefaultQueueSize = 16;
constexpr int kDataInputIndex = 0;
}

SubgraphExecutor::SubgraphExecutor(const GraphItem *graph_item, GraphExecutionContext *context, bool force_infer_shape)
    : graph_item_(graph_item),
      context_(context),
      force_infer_shape_(force_infer_shape),
      pre_run_pool_(kDefaultThreadNum),
      ready_queue_(kDefaultQueueSize) {
}

SubgraphExecutor::~SubgraphExecutor() {
  GELOGD("[%s] SubgraphExecutor destroyed.", graph_item_->GetName().c_str());
}

Status SubgraphExecutor::Init(const std::vector<TensorValue> &inputs,
                              const std::vector<ConstGeTensorDescPtr> &input_desc) {
  subgraph_context_.reset(new(std::nothrow)SubgraphContext(graph_item_, context_));
  GE_CHECK_NOTNULL(subgraph_context_);
  GE_CHK_STATUS_RET(subgraph_context_->Init(),
      "[Init][SubgraphContext][%s] Failed to init subgraph context.", graph_item_->GetName().c_str());

  shape_inference_engine_.reset(new(std::nothrow) ShapeInferenceEngine(context_, subgraph_context_.get()));
  GE_CHECK_NOTNULL(shape_inference_engine_);

  if (graph_item_->IsDynamic()) {
    GE_CHK_STATUS_RET(InitInputsForUnknownShape(inputs, input_desc),
                      "[%s] Failed to set inputs.",
                      graph_item_->GetName().c_str());
  } else {
    GE_CHK_STATUS_RET(InitInputsForKnownShape(inputs),
        "[Invoke][InitInputsForKnownShape][%s] Failed to init subgraph executor for known shape subgraph.",
        graph_item_->GetName().c_str());
  }

  return SUCCESS;
}

Status SubgraphExecutor::InitInputsForUnknownShape(const std::vector<TensorValue> &inputs,
                                                   const std::vector<ConstGeTensorDescPtr> &input_desc) {
  // Number of inputs of parent node should be greater or equal than that of subgraph
  auto input_nodes = graph_item_->GetInputNodes();
  if (inputs.size() < input_nodes.size()) {
    GELOGE(INTERNAL_ERROR,
        "[Check][Size][%s] Number of inputs [%zu] is not sufficient for subgraph which needs [%zu] inputs.",
        graph_item_->GetName().c_str(), inputs.size(), input_nodes.size());
    REPORT_INNER_ERROR("E19999",
        "[%s] Number of inputs [%zu] is not sufficient for subgraph which needs [%zu] inputs,"
        "check invalid when SubgraphExecutor %s.",
        graph_item_->GetName().c_str(), inputs.size(), input_nodes.size(), __FUNCTION__);
    return INTERNAL_ERROR;
  }

  for (size_t i = 0; i < input_nodes.size(); ++i) {
    auto &input_node = input_nodes[i];
    if (input_node == nullptr) {
      GELOGD("[%s] Input[%zu] is not needed by subgraph, skip it.", graph_item_->GetName().c_str(), i);
      continue;
    }

    auto &input_tensor = inputs[i];
    GELOGD("[%s] Set input tensor[%zu] to inputs with index = %d, tensor = %s",
           graph_item_->GetName().c_str(),
           i,
           input_node->input_start,
           input_tensor.DebugString().c_str());

    GE_CHK_STATUS_RET(subgraph_context_->SetInput(*input_node, kDataInputIndex, input_tensor),
        "[Invoke][SetInput] failed for grap_item[%s] input tensor[%zu]", graph_item_->GetName().c_str(), i);

    if (force_infer_shape_ || input_node->is_dynamic) {
      GELOGD("[%s] Start to update input[%zu] for subgraph data node.", graph_item_->GetName().c_str(), i);
      GE_CHECK_LE(i + 1, input_desc.size());
      const auto &tensor_desc = input_desc[i];
      GE_CHECK_NOTNULL(tensor_desc);
      auto node_state = subgraph_context_->GetOrCreateNodeState(input_node);
      GE_CHECK_NOTNULL(node_state);
      node_state->GetShapeInferenceState().UpdateInputShape(0, *tensor_desc);
    }
  }

  GELOGD("[%s] Done setting inputs.", graph_item_->GetName().c_str());
  return SUCCESS;
}

Status SubgraphExecutor::InitInputsForKnownShape(const std::vector<TensorValue> &inputs) {
  auto &input_index_mapping = graph_item_->GetInputIndexMapping();
  for (size_t i = 0; i < input_index_mapping.size(); ++i) {
    auto &parent_input_index = input_index_mapping[i];
    if (static_cast<size_t>(parent_input_index) >= inputs.size()) {
      GELOGE(INTERNAL_ERROR,
          "[Check][Size][%s] Number of inputs [%zu] is not sufficient for subgraph which needs at lease [%d] inputs",
          graph_item_->GetName().c_str(), inputs.size(), parent_input_index + 1);
      REPORT_INNER_ERROR("E19999",
          "[%s] Number of inputs [%zu] is not sufficient for subgraph which needs at lease [%d] inputs,"
          "check invalid when %s.",
          graph_item_->GetName().c_str(), inputs.size(), parent_input_index + 1, __FUNCTION__);
      return INTERNAL_ERROR;
    }

    auto &input_tensor = inputs[parent_input_index];
    subgraph_context_->SetInput(static_cast<int>(i), input_tensor);
    GELOGD("[%s] Set input tensor[%zu] with inputs with index = %d, tensor = %s",
           graph_item_->GetName().c_str(),
           i,
           parent_input_index,
           input_tensor.DebugString().c_str());
  }

  return SUCCESS;
}

Status SubgraphExecutor::ExecuteAsync(const std::vector<TensorValue> &inputs,
                                      const std::vector<ConstGeTensorDescPtr> &input_desc,
                                      const std::vector<TensorValue> &outputs) {
  GELOGD("[%s] is dynamic = %s", graph_item_->GetName().c_str(), graph_item_->IsDynamic() ? "true" : "false");
  GE_CHK_STATUS_RET(Init(inputs, input_desc), "[Invoke][Init]failed for [%s].", graph_item_->GetName().c_str());
  if (!outputs.empty()) {
    GE_CHK_STATUS_RET(EnableOutputZeroCopy(outputs),
        "[Invoke][EnableOutputZeroCopy] Failed by user provided outputs.");
  }
  if (!graph_item_->IsDynamic()) {
    return ExecuteAsyncForKnownShape(inputs);
  }

  HYBRID_CHK_STATUS_RET(ScheduleTasks(), "[%s] Failed to execute tasks.", graph_item_->GetName().c_str());
  GELOGD("[%s] Done executing subgraph successfully.", graph_item_->GetName().c_str());
  return SUCCESS;
}

Status SubgraphExecutor::ExecuteAsync(const std::vector<TensorValue> &inputs,
                                      const std::vector<ConstGeTensorDescPtr> &input_desc) {
  return ExecuteAsync(inputs, input_desc, {});
}

Status SubgraphExecutor::ExecuteAsyncForKnownShape(const std::vector<TensorValue> &inputs) {
  GELOGD("[%s] subgraph is not dynamic.", graph_item_->GetName().c_str());
  if (graph_item_->GetAllNodes().size() != 1) {
    GELOGE(INTERNAL_ERROR,
           "[%s] Invalid known shape subgraph. node size = %zu",
           graph_item_->GetName().c_str(),
           graph_item_->GetAllNodes().size());
    return INTERNAL_ERROR;
  }

  auto node_item = graph_item_->GetAllNodes()[0];
  GE_CHECK_NOTNULL(node_item);
  auto node_state = subgraph_context_->GetOrCreateNodeState(node_item);
  GE_CHECK_NOTNULL(node_state);
  node_state->SetKernelTask(node_item->kernel_task);

  known_shape_task_context_ = TaskContext::Create(node_state.get(), context_, subgraph_context_.get());
  GE_CHECK_NOTNULL(known_shape_task_context_);

  HYBRID_CHK_STATUS_RET(ExecutionEngine::ExecuteAsync(*node_state, known_shape_task_context_, *context_),
                        "[%s] Failed to execute node [%s] for known subgraph.",
                        graph_item_->GetName().c_str(),
                        known_shape_task_context_->GetNodeName());

  GELOGD("[%s] Done execute non-dynamic subgraph successfully.", graph_item_->GetName().c_str());
  return SUCCESS;
}

Status SubgraphExecutor::ExecuteAsync(TaskContext &task_context) {
  std::vector<TensorValue> inputs;
  std::vector<ConstGeTensorDescPtr> input_desc;
  for (int i = 0; i < task_context.NumInputs(); ++i) {
    auto tensor = task_context.GetInput(i);
    GE_CHECK_NOTNULL(tensor);
    inputs.emplace_back(*tensor);
    input_desc.emplace_back(task_context.GetInputDesc(i));
  }

  GE_CHK_STATUS_RET(ExecuteAsync(inputs, input_desc),
      "[Invoke][ExecuteAsync] failed for [%s].", graph_item_->GetName().c_str());

  GE_CHK_STATUS_RET(SetOutputsToParentNode(task_context),
      "[Invoke][SetOutputsToParentNode][%s] Failed to set output shapes to parent node.",
      graph_item_->GetName().c_str());
  return SUCCESS;
}

Status SubgraphExecutor::PrepareNodes(int group) {
  GELOGD("[%s] Start to prepare nodes. group = %d",
         graph_item_->GetName().c_str(),
         group);
  auto &all_nodes = graph_item_->GetAllNodes(group);
  for (auto all_node : all_nodes) {
    auto &node_item = *all_node;
    // for while op
    if (force_infer_shape_ && !node_item.is_dynamic) {
      GELOGD("[%s] Force infer shape is set, updating node to dynamic.", node_item.NodeName().c_str());
      auto &mutable_node_item = const_cast<NodeItem &>(node_item);
      mutable_node_item.SetToDynamic();
    }

    GELOGD("[%s] Start to prepare node [%s].", graph_item_->GetName().c_str(), node_item.NodeName().c_str());
    auto node_state = subgraph_context_->GetOrCreateNodeState(&node_item);
    GE_CHECK_NOTNULL(node_state);
    auto p_node_state = node_state.get();

    if (node_item.node_type != NETOUTPUT) {
      // only do shape inference and compilation for nodes with dynamic shapes.
      if (node_item.is_dynamic) {
        auto prepare_future = pre_run_pool_.commit([this, p_node_state]() -> Status {
          GetContext().SetSessionId(context_->session_id);
          GetContext().SetContextId(context_->context_id);
          GE_CHK_STATUS_RET_NOLOG(InferShape(shape_inference_engine_.get(), *p_node_state));
          return PrepareForExecution(context_, *p_node_state);
        });

        p_node_state->SetPrepareFuture(std::move(prepare_future));
      } else {
        GELOGD("[%s] Skipping shape inference and compilation for node with static shape.",
               node_item.NodeName().c_str());
        if (node_item.kernel_task == nullptr) {
          GELOGW("[%s] Node of static shape got no task.", node_item.NodeName().c_str());
          GE_CHK_STATUS_RET(TaskCompileEngine::Compile(*p_node_state, context_),
              "[Invoke][Compile] failed for [%s].", p_node_state->GetName().c_str());
        } else {
          node_state->SetKernelTask(node_item.kernel_task);
        }
        auto unique_task_context =
            TaskContext::Create(node_state.get(), context_, subgraph_context_.get());
        GE_CHECK_NOTNULL(unique_task_context);
        const auto &task = node_state->GetKernelTask();
        if (task == nullptr) {
          GELOGE(INTERNAL_ERROR, "[Get][KernelTask] failed for[%s], NodeTask is null.", node_state->GetName().c_str());
          REPORT_CALL_ERROR("E19999", "invoke GetKernelTask failed for %s when %s, nodetask is null.",
              node_state->GetName().c_str(), __FUNCTION__);
          return INTERNAL_ERROR;
        }
        auto shared_task_context = std::shared_ptr<TaskContext>(unique_task_context.release());
        node_state->SetTaskContext(shared_task_context);
      }
    }

    if (!ready_queue_.Push(p_node_state)) {
      if (context_->is_eos_) {
        GELOGD("Got end of sequence");
        return SUCCESS;
      }
      GELOGE(INTERNAL_ERROR, "[Check][State][%s] Error occurs while launching tasks. quit from preparing nodes.",
          graph_item_->GetName().c_str());
      REPORT_INNER_ERROR("E19999", "[%s] Error occurs while launching tasks. quit from preparing nodes when %s.",
          graph_item_->GetName().c_str(), __FUNCTION__);
      return INTERNAL_ERROR;
    }

    GELOGD("[%s] Push node [%s] to queue.", graph_item_->GetName().c_str(), node_item.NodeName().c_str());
  }

  GELOGD("[%s] Done preparing nodes successfully.", graph_item_->GetName().c_str());
  return SUCCESS;
}

Status SubgraphExecutor::InferShape(ShapeInferenceEngine *shape_inference_engine, NodeState &node_state) const {
  HYBRID_CHK_STATUS_RET(shape_inference_engine->InferShape(node_state),
      "[Invoke][InferShape] failed for [%s].", node_state.GetName().c_str());
  HYBRID_CHK_STATUS_RET(shape_inference_engine->PropagateOutputShapes(node_state),
      "[Invoke][PropagateOutputShapes] failed for [%s].", node_state.GetName().c_str());
  return SUCCESS;
}

Status SubgraphExecutor::PrepareForExecution(GraphExecutionContext *ctx, NodeState &node_state) {
  auto &node_item = *node_state.GetNodeItem();
  if (node_item.kernel_task == nullptr) {
    GE_CHK_STATUS_RET(TaskCompileEngine::Compile(node_state, ctx),
        "[Invoke][Compile] Failed for node[%s]", node_state.GetName().c_str());
  } else {
    node_state.SetKernelTask(node_item.kernel_task);
  }
  auto unique_task_context = TaskContext::Create(&node_state, context_, subgraph_context_.get());
  GE_CHECK_NOTNULL(unique_task_context);
  const auto &task = node_state.GetKernelTask();
  if (task == nullptr) {
    GELOGE(INTERNAL_ERROR, "[Invoke][GetKernelTask] failed for[%s], NodeTask is null.", node_state.GetName().c_str());
    REPORT_CALL_ERROR("E19999", "invoke GetKernelTask failed for %s, NodeTask is null when %s.",
        node_state.GetName().c_str(), __FUNCTION__);
    return INTERNAL_ERROR;
  }
  auto shared_task_context = std::shared_ptr<TaskContext>(unique_task_context.release());
  node_state.SetTaskContext(shared_task_context);
  GE_CHK_RT_RET(rtCtxSetCurrent(ctx->rt_context));
  RECORD_COMPILE_EVENT(ctx, node_item.NodeName().c_str(), "[UpdateTilingData] start");
  GE_CHK_STATUS_RET_NOLOG(task->UpdateTilingData(*shared_task_context)); // update op_desc before alloc ws
  RECORD_COMPILE_EVENT(ctx, node_item.NodeName().c_str(), "[UpdateTilingData] end");
  return SUCCESS;
}

Status SubgraphExecutor::LaunchTasks() {
  while (true) {
    NodeState *node_state = nullptr;
    if (!ready_queue_.Pop(node_state)) {
      GELOGE(INTERNAL_ERROR, "[Invoke][Pop] failed for [%s].", graph_item_->GetName().c_str());
      REPORT_CALL_ERROR("E19999", "invoke pop failed for %s when %s", graph_item_->GetName().c_str(), __FUNCTION__);
      return INTERNAL_ERROR;
    }

    if (node_state == nullptr) {
      GELOGD("[%s] Got EOF from queue.", graph_item_->GetName().c_str());
      return SUCCESS;
    }

    if (node_state->GetType() == NETOUTPUT) {
      // Wait for all inputs become valid
      // after PrepareNodes returned. all output tensors and shapes are valid
      GE_CHK_STATUS_RET_NOLOG(node_state->GetShapeInferenceState().AwaitShapesReady(*context_));
      GE_CHK_STATUS_RET_NOLOG(node_state->AwaitInputTensors(*context_));
      GELOGD("[%s] Done executing node successfully.", node_state->GetName().c_str());
      continue;
    }

    GE_CHK_STATUS_RET_NOLOG(node_state->WaitForPrepareDone());

    GELOGD("[%s] Start to execute.", node_state->GetName().c_str());
    auto shared_task_context = node_state->GetTaskContext();
    GE_CHECK_NOTNULL(shared_task_context);
    shared_task_context->SetForceInferShape(force_infer_shape_);
    HYBRID_CHK_STATUS_RET(ExecutionEngine::ExecuteAsync(*node_state, shared_task_context, *context_),
        "[Invoke][ExecuteAsync] failed for [%s].", node_state->GetName().c_str());
    GELOGD("[%s] Done executing node successfully.", node_state->GetName().c_str());
  }
}

Status SubgraphExecutor::ScheduleTasks(int group) {
  GELOGD("[%s] Start to schedule prepare workers.", graph_item_->GetName().c_str());
  auto prepare_future = std::async(std::launch::async, [&]() -> Status {
    GetContext().SetSessionId(context_->session_id);
    GetContext().SetContextId(context_->context_id);
    auto ret = PrepareNodes(group);
    ready_queue_.Push(nullptr);
    return ret;
  });

  GELOGD("[%s] Start to execute subgraph.", graph_item_->GetName().c_str());
  auto ret = LaunchTasks();
  if (ret != SUCCESS) {
    subgraph_context_->OnError(ret);
    context_->SetErrorCode(ret);
    ready_queue_.Stop();
    prepare_future.wait();
    return ret;
  }

  GE_CHK_STATUS_RET(prepare_future.get(),
      "[Invoke][get] [%s] Error occurred in task preparation.", graph_item_->GetName().c_str());

  GELOGD("[%s] Done launching all tasks successfully.", graph_item_->GetName().c_str());
  return SUCCESS;
}

Status SubgraphExecutor::GetOutputs(vector<TensorValue> &outputs) {
  return subgraph_context_->GetOutputs(outputs);
}

Status SubgraphExecutor::GetOutputs(vector<TensorValue> &outputs, std::vector<ConstGeTensorDescPtr> &output_desc) {
  GE_CHK_STATUS_RET(GetOutputs(outputs), "[Invoke][GetOutputs] failed for [%s].", graph_item_->GetName().c_str());

  // copy output data from op to designated position
  GE_CHK_STATUS_RET(graph_item_->GetOutputDescList(output_desc),
      "[Invoke][GetOutputDescList][%s] Failed to get output tensor desc.", graph_item_->GetName().c_str());
  if (outputs.size() != output_desc.size()) {
    GELOGE(INTERNAL_ERROR,
        "[Check][Size]Number of outputs(%zu) mismatch number of output_desc(%zu).",
        outputs.size(), output_desc.size());
    REPORT_INNER_ERROR("E19999", "Number of outputs(%zu) mismatch number of output_desc(%zu),"
        "check invlid when SubgraphExecutor %s.",
        outputs.size(), output_desc.size(), __FUNCTION__);
    return INTERNAL_ERROR;
  }
  return SUCCESS;
}

Status SubgraphExecutor::Synchronize() {
  GELOGD("[%s] Synchronize start.", graph_item_->GetName().c_str());
  GE_CHK_STATUS_RET_NOLOG(context_->Synchronize(context_->stream));
  GELOGD("[%s] Done synchronizing successfully.", graph_item_->GetName().c_str());
  return SUCCESS;
}

Status SubgraphExecutor::SetOutputsToParentNode(TaskContext &task_context) {
  // get output tensors and tensor desc list
  std::vector<TensorValue> outputs;
  std::vector<ConstGeTensorDescPtr> output_desc_list;
  GE_CHK_STATUS_RET(subgraph_context_->GetOutputs(outputs),
      "[Invoke][GetOutputs][%s] Failed to get output tensors.", graph_item_->GetName().c_str());
  GE_CHK_STATUS_RET(graph_item_->GetOutputDescList(output_desc_list),
      "[Invoke][GetOutputDescList][%s] Failed to get output tensor desc.", graph_item_->GetName().c_str());

  if (outputs.size() != output_desc_list.size()) {
    GELOGE(INTERNAL_ERROR, "[Check][Size][%s] num of output tensors = %zu, num of output tensor desc = %zu not equal",
        graph_item_->GetName().c_str(), outputs.size(), output_desc_list.size());
    REPORT_INNER_ERROR("E19999",
        "%s num of output tensors = %zu, num of output tensor desc = %zu not equal,"
        "check invalid when SubgraphExecutor %s",
        graph_item_->GetName().c_str(), outputs.size(), output_desc_list.size(), __FUNCTION__);
    return INTERNAL_ERROR;
  }

  // mapping to parent task context
  for (size_t i = 0; i < outputs.size(); ++i) {
    int parent_output_index = graph_item_->GetParentOutputIndex(i);
    GE_CHECK_GE(parent_output_index, 0);
    // update tensor
    GELOGD("[%s] Updating output[%zu] to parent output[%d]",
           graph_item_->GetName().c_str(),
           i,
           parent_output_index);

    GELOGD("[%s] Updating output tensor, index = %d, tensor = %s",
           graph_item_->GetName().c_str(),
           parent_output_index,
           outputs[i].DebugString().c_str());
    GE_CHK_STATUS_RET(task_context.SetOutput(parent_output_index, outputs[i]));

    // updating shapes. dynamic format/dtype is not supported.
    // It should be noted that even the subgraph is of known shape, it is also necessary to update parent output desc,
    // for instance, IfOp may have two known-shaped subgraphs of different output shapes
    const auto &output_desc = output_desc_list[i];
    auto parent_output_desc = task_context.MutableOutputDesc(parent_output_index);
    GE_CHECK_NOTNULL(parent_output_desc);
    GELOGD("[%s] Updating output shape[%d] from [%s] to [%s]",
           graph_item_->GetName().c_str(),
           parent_output_index,
           parent_output_desc->MutableShape().ToString().c_str(),
           output_desc->GetShape().ToString().c_str());
    parent_output_desc->SetShape(output_desc->GetShape());

    GELOGD("[%s] Updating output original shape[%d] from [%s] to [%s]",
           graph_item_->GetName().c_str(),
           parent_output_index,
           parent_output_desc->GetOriginShape().ToString().c_str(),
           output_desc->GetOriginShape().ToString().c_str());
    parent_output_desc->SetOriginShape(output_desc->GetOriginShape());
  }

  return SUCCESS;
}

Status SubgraphExecutor::EnableOutputZeroCopy(const vector<TensorValue> &outputs) {
  GELOGD("To enable zero copy, output number = %zu", outputs.size());
  const auto &output_edges = graph_item_->GetOutputEdges();
  // Op -> MetOutput, set the output tensor of Op that output to the NetOutput node
  if (outputs.size() != output_edges.size()) {
    GELOGE(PARAM_INVALID, "[Check][Size]Output number mismatches, expect = %zu, but given = %zu",
        output_edges.size(), outputs.size());
    REPORT_INNER_ERROR("E19999", "Output number mismatches, expect = %zu, but given = %zu when %s",
        output_edges.size(), outputs.size(), __FUNCTION__);
    return PARAM_INVALID;
  }

  for (size_t i = 0; i < outputs.size(); ++i) {
    auto &output_tensor = outputs[i];
    auto &output_node = output_edges[i].first;
    int output_idx = output_edges[i].second;
    GELOGD("[%s] Set output tensor[%zu] to [%s]'s output[%d], tensor = %s",
           graph_item_->GetName().c_str(),
           i,
           output_node->NodeName().c_str(),
           output_idx,
           output_tensor.DebugString().c_str());

    GE_CHK_STATUS_RET(subgraph_context_->SetOutput(*output_node, output_idx, output_tensor),
        "[Invoke][SetOutput][%s] Failed to set input tensor[%zu]", graph_item_->GetName().c_str(), i);
  }

  GELOGD("Done enabling zero copy for outputs successfully.");
  return SUCCESS;
}

Status SubgraphExecutor::PartialExecuteAsync(int task_group) {
  return ScheduleTasks(task_group);
}

Status SubgraphExecutor::InitForPartialExecution(const vector<TensorValue> &inputs,
                                                 const vector<ConstGeTensorDescPtr> &input_desc) {
  return Init(inputs, input_desc);
}
}  // namespace hybrid
}  // namespace ge