Fix ut.

ge/hybrid/executor/hybrid_model_executor.cc

@@ -70,7 +70,7 @@ Status HybridModelExecutor::Execute(HybridModelExecutor::ExecuteArgs &args) {
     context_.profiler->Dump(std::cout);
     context_.profiler->Reset();
   }
-  root_graph_executor_->ResetContext();
+  root_graph_executor_->ReleaseContext();
 
   context_.iteration += 1;
   if (ret == END_OF_SEQUENCE) {

ge/hybrid/executor/node_state.h

@@ -177,6 +177,10 @@ struct NodeState {
   void SetTaskContext(std::shared_ptr<TaskContext> &task_context);
   std::shared_ptr<TaskContext> GetTaskContext();
 
+  void SetSkipInferShape(bool skip_infershape) { skip_infershape_ = skip_infershape; }
+
+  bool GetSkipInferShape() const { return skip_infershape_; }
+
  private:
   bool IsScheduleReady() const;
   void SetDataSchedule(const NodeState &node_state, const std::function<void(const NodeItem *)> &ready);
@@ -204,6 +208,7 @@ struct NodeState {
   int merge_index_ = -1; // Use for Execute (Reset after Executed).
   int switch_index_ = -1; // Use for Schedule (Reset after Prepared).
   int group_ = -1;
+  bool skip_infershape_ = false;
 };
 }  // namespace hybrid
 }  // namespace ge

ge/hybrid/executor/subgraph_executor.cc

@@ -110,6 +110,7 @@ Status SubgraphExecutor::InitInputsForUnknownShape(const std::vector<TensorValue
       output_desc->SetShape(tensor_desc->GetShape());
       output_desc->SetOriginShape(tensor_desc->GetOriginShape());
       output_desc->SetDataType(tensor_desc->GetDataType());
+      node_state->SetSkipInferShape(true);
     }
   }
 

ge/hybrid/executor/subgraph_executor.h

@@ -41,7 +41,7 @@ class SubgraphExecutor {
 
   Status PartialExecuteAsync(int task_group);
 
-  void ResetContext() { subgraph_context_.reset(nullptr); }
+  void ReleaseContext() { subgraph_context_.reset(nullptr); }
 
   /**
    * Execute subgraph async, output tensor address(not data) and output tensor descriptions are

ge/hybrid/executor/worker/shape_inference_engine.cc

@@ -70,7 +70,7 @@ Status ShapeInferenceEngine::InferShape(NodeState &node_state) {
   // Do shape inference
   // Skipping infer shape of input node.
   GELOGD("[%s] Start to invoke InferShapeAndType", node_item.NodeName().c_str());
-  if (node_state.GetType() != DATA_TYPE && node_state.GetType() != AIPP_DATA_TYPE) {
+  if (node_state.GetSkipInferShape()) {
     RECORD_SHAPE_INFERENCE_EVENT(execution_context_, node_item.NodeName().c_str(), "[InferShapeAndType] Start");
     GE_CHK_STATUS_RET(ShapeRefiner::InferShapeAndTypeForRunning(node_item.node, true),
         "[Invoke][InferShapeAndType] for %s failed.", node_item.NodeName().c_str());

ge/single_op/single_op_model.cc

@@ -49,8 +49,8 @@ const uint32_t kOutputIndexOfData = 0;
 constexpr char const *kAttrSupportDynamicShape = "support_dynamicshape";
 
 Status CheckHostMem(const std::vector<string> &dependencies, const NodePtr &node, bool &is_host_mem) {
+  auto op_desc = node->GetOpDesc();
   for (const auto &input_name : dependencies) {
-    auto op_desc = node->GetOpDesc();
     int input_index = op_desc->GetInputIndexByName(input_name);
     if (input_index < 0) {
       GELOGE(INTERNAL_ERROR, "[Get][InputIndex]failed, node:[%s] inputname: %s.",
@@ -60,11 +60,7 @@ Status CheckHostMem(const std::vector<string> &dependencies, const NodePtr &node
       return INTERNAL_ERROR;
     }
 
-    const auto &in_anchor = node->GetInDataAnchor(input_index);
-    GE_CHECK_NOTNULL(in_anchor);
-    const auto &peer_out_anchor = in_anchor->GetPeerOutAnchor();
-    GE_CHECK_NOTNULL(peer_out_anchor);
-    const auto &src_node = peer_out_anchor->GetOwnerNode();
+    const auto &src_node = NodeUtils::GetInDataNodeByIndex(*node, input_index);
     GE_CHECK_NOTNULL(src_node);
     auto src_op_desc = src_node->GetOpDesc();
     GE_CHECK_NOTNULL(src_op_desc);

tests/ut/ge/single_op/single_op_model_unittest.cc

@@ -213,7 +213,7 @@ TEST_F(UtestSingleOpModel, test_build_dynamic_op) {
 
   // make graph
   ut::GraphBuilder builder = ut::GraphBuilder("graph");
-  auto data = builder.AddNode("Data", "Data", 0, 1);
+  auto data = builder.AddNode("Data", "Data", 1, 1);
   auto transdata = builder.AddNode("Transdata", "Transdata", 1, 1);
   auto netoutput = builder.AddNode("Netoutput", "NetOutput", 1, 0);
   builder.AddDataEdge(data, 0, transdata, 0);
@@ -228,11 +228,6 @@ TEST_F(UtestSingleOpModel, test_build_dynamic_op) {
   op_desc->SetOpInferDepends(depend_names);
   (void)AttrUtils::SetBool(op_desc, kAttrSupportDynamicShape, true);
 
-  auto tensor = std::make_shared<GeTensor>();
-  auto data_desc = data->GetOpDesc();
-  auto tensor_desc = data_desc->MutableInputDesc(0);
-  AttrUtils::SetTensor(tensor_desc, "_value", tensor);
-
   // set task_def
   auto model_task_def = make_shared<domi::ModelTaskDef>();
   domi::TaskDef *task_def = model_task_def->add_task();
@@ -249,6 +244,12 @@ TEST_F(UtestSingleOpModel, test_build_dynamic_op) {
 
   op_desc->impl_->input_name_idx_["Data"] = 0;
   model.BuildDynamicOp(res, dynamic_single_op);
+
+  auto tensor = std::make_shared<GeTensor>();
+  auto data_desc = data->GetOpDesc();
+  auto tensor_desc = data_desc->MutableInputDesc(0);
+  AttrUtils::SetTensor(tensor_desc, "_value", tensor);
+  model.BuildDynamicOp(res, dynamic_single_op);
 }
 
 TEST_F(UtestSingleOpModel, test_host_mem) {