!15228 fix abstract of parameter

From: @hangangqiang
Reviewed-by: @zhang_xue_tong,@ddwsky
Signed-off-by: @zhang_xue_tong

mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/topk_infer.c

@@ -38,9 +38,12 @@ int TopKInferShape(const TensorC *const *inputs, size_t inputs_size, TensorC **o
   if (!parameter->infer_flag_) {
     return NNACL_INFER_INVALID;
   }
+  const TensorC *input_k_tensor = inputs[1];
+  if (input_k_tensor->data_ == NULL) {
+    return NNACL_INFER_INVALID;
+  }
 
   TopkParameter *param = (TopkParameter *)parameter;
-  const TensorC *input_k_tensor = inputs[1];
   param->k_ = ((int32_t *)input_k_tensor->data_)[0];
 
   int out_shape[MAX_SHAPE_SIZE];

mindspore/lite/src/lite_model.h

@@ -75,12 +75,14 @@ class LiteModel : public Model {
       } else {
         node->name_ = c_node->name()->c_str();
       }
-      auto count = c_node->inputIndex()->size();
+      if (c_node->inputIndex() != nullptr) {
-      for (uint32_t j = 0; j < count; ++j) {
+        auto count = c_node->inputIndex()->size();
-        node->input_indices_.push_back(size_t(c_node->inputIndex()->template GetAs<uint32_t>(j)));
+        for (uint32_t j = 0; j < count; ++j) {
+          node->input_indices_.push_back(size_t(c_node->inputIndex()->template GetAs<uint32_t>(j)));
+        }
       }
       if (c_node->outputIndex() != nullptr) {
-        count = c_node->outputIndex()->size();
+        auto count = c_node->outputIndex()->size();
         for (uint32_t j = 0; j < count; ++j) {
           node->output_indices_.push_back(size_t(c_node->outputIndex()->template GetAs<uint32_t>(j)));
         }

mindspore/lite/test/CMakeLists.txt

@@ -247,6 +247,7 @@ if(ENABLE_CONVERTER)
             ${LITE_DIR}/tools/optimizer/fusion/tf_bidirection_gru_fusion.cc
             ${LITE_DIR}/tools/optimizer/fusion/tf_bidirection_gru_cf_fusion.cc
             ${LITE_DIR}/tools/optimizer/fusion/matmul_add_fusion.cc
+            ${LITE_DIR}/tools/optimizer/fusion/mul_add_fusion.cc
             ${LITE_DIR}/tools/optimizer/fusion/gelu_fusion.cc
             ${LITE_DIR}/tools/optimizer/fusion/tf_gelu_fusion.cc
             ${LITE_DIR}/tools/optimizer/fusion/onnx_gelu_fusion.cc

mindspore/lite/tools/common/tensor_util.cc

@@ -73,6 +73,40 @@ tensor::TensorPtr CreateTensorInfo(const void *data, size_t data_size, const std
   return tensor_info;
 }
 
+AbstractBasePtr CreateTensorAbstract(const std::vector<int64_t> &shape, TypeId data_type) {
+  auto tensor_info = CreateTensorInfo(nullptr, 0, shape, data_type);
+  if (tensor_info == nullptr) {
+    MS_LOG(ERROR) << "Create tensor info failed";
+    return nullptr;
+  }
+  auto abstract = tensor_info->ToAbstract();
+  if (abstract == nullptr) {
+    MS_LOG(ERROR) << "Create tensor abstarct failed";
+    return nullptr;
+  }
+  return abstract;
+}
+
+int SetParameterAbstractAndParam(const ParameterPtr &parameter, const void *data, size_t data_size,
+                                 const std::vector<int64_t> &shape, TypeId data_type) {
+  if (parameter == nullptr) {
+    MS_LOG(ERROR) << "Input parameter is nullptr";
+    return RET_INPUT_PARAM_INVALID;
+  }
+  auto tensor_info = CreateTensorInfo(data, data_size, shape, data_type);
+  if (tensor_info == nullptr) {
+    MS_LOG(ERROR) << "Create tensor info failed";
+    return RET_ERROR;
+  }
+  auto abstract = tensor_info->ToAbstract();
+  if (abstract == nullptr) {
+    MS_LOG(ERROR) << "Create tensor abstarct failed";
+    return RET_ERROR;
+  }
+  parameter->set_abstract(abstract);
+  return RET_OK;
+}
+
 int SetTensorData(const tensor::TensorPtr &tensor_info, const void *data, size_t data_size) {
   if (tensor_info == nullptr) {
     MS_LOG(ERROR) << "tensor info is nullptr.";

mindspore/lite/tools/common/tensor_util.h

@@ -46,6 +46,11 @@ std::unique_ptr<QuantParamT> GetTensorQuantParam(const std::unique_ptr<TensorT>
 tensor::TensorPtr CreateTensorInfo(const void *data, size_t data_size, const std::vector<int64_t> &shape,
                                    TypeId data_type);
 
+AbstractBasePtr CreateTensorAbstract(const std::vector<int64_t> &shape, TypeId data_type);
+
+int SetParameterAbstractAndParam(const ParameterPtr &parameter, const void *data, size_t data_size,
+                                 const std::vector<int64_t> &shape, TypeId data_type);
+
 int SetTensorData(const tensor::TensorPtr &tensor_info, const void *data, size_t data_size);
 
 std::unique_ptr<schema::TensorT> CreateTensorTFromTensorInfo(const tensor::TensorPtr &tensor_info,

mindspore/lite/tools/converter/CMakeLists.txt

@@ -54,6 +54,7 @@ file(GLOB_RECURSE CONVERTER_SRC RELATIVE ${CMAKE_CURRENT_SOURCE_DIR}
         ../optimizer/fusion/tf_bidirection_gru_fusion.cc
         ../optimizer/fusion/tf_bidirection_gru_cf_fusion.cc
         ../optimizer/fusion/matmul_add_fusion.cc
+        ../optimizer/fusion/mul_add_fusion.cc
         ../optimizer/fusion/gelu_fusion.cc
         ../optimizer/fusion/tf_gelu_fusion.cc
         ../optimizer/fusion/onnx_gelu_fusion.cc

mindspore/lite/tools/converter/converter.cc

@@ -70,7 +70,7 @@ MetaGraphT *Converter::Convert(const std::unique_ptr<converter::Flags> &flag) {
   }
   MS_LOG(INFO) << "Run anfTransform success";
 
-  // protobuf -> flatbuf
+  // protobuf -> flatbuffer
   auto meta_graph = Export(graph, false, false, flag->trainModel);
   if (meta_graph == nullptr) {
     MS_LOG(ERROR) << "Export to meta graph return nullptr";

mindspore/lite/tools/converter/graphdef_transform.cc

@@ -39,7 +39,6 @@
 
 using std::string;
 namespace mindspore::lite {
-
 std::vector<schema::CNodeT *> GraphDefTransform::GetGraphNodes() {
   std::vector<schema::CNodeT *> old_nodes{};
   old_nodes.resize(graph_defT_->nodes.size());
@@ -71,54 +70,7 @@ int GraphDefTransform::Transform(const converter::Flags &ctx) {
     }
   }
 
-  // generate and infer quant parameters
+  // format transpose global optimize
-  {
-    Optimizer infer_quant_param_pass;
-    infer_quant_param_pass.AddPass(new (std::nothrow) TopologicalSortPass());
-    infer_quant_param_pass.AddPass(new (std::nothrow) InferQuantParamPass());
-    status = infer_quant_param_pass.Run(graph_defT_);
-    if (status != RET_OK && status != RET_NO_CHANGE) {
-      MS_LOG(ERROR) << "Run infer_quant_param_pass graphPasses Failed";
-      return status;
-    }
-  }
-
-  {
-    // format transform
-    // init old node indices
-    auto old_nodes = GetGraphNodes();
-
-    Optimizer format_trans_optimizer;
-    format_trans_optimizer.AddPass(new (std::nothrow) SubgraphNodePass(old_nodes));
-    format_trans_optimizer.AddPass(new (std::nothrow) TopologicalSortPass());
-    if (ctx.fmk != converter::FmkType_TF) {
-      auto infer_shape_pass = new (std::nothrow) InferShapePass();
-      if (infer_shape_pass == nullptr) {
-        MS_LOG(ERROR) << "new InferShapePass failed";
-        return RET_MEMORY_FAILED;
-      }
-      infer_shape_pass->set_fmk_type(ctx.fmk);
-      format_trans_optimizer.AddPass(infer_shape_pass);
-    }
-    status = format_trans_optimizer.Run(graph_defT_);
-    if (status != RET_OK && status != RET_NO_CHANGE && status != RET_INFER_INVALID) {
-      MS_LOG(ERROR) << "Run format_trans_optimizer graphPasses Failed";
-      return status;
-    }
-  }
-  {
-    // init old node indices
-    auto old_nodes = GetGraphNodes();
-    Optimizer format_trans_optimizer;
-    format_trans_optimizer.AddPass(new (std::nothrow) IsolatedNodeRemovePass());
-    format_trans_optimizer.AddPass(new (std::nothrow) SubgraphNodePass(old_nodes));
-    status = format_trans_optimizer.Run(graph_defT_);
-    if (status != RET_OK && status != RET_NO_CHANGE && status != RET_INFER_INVALID) {
-      MS_LOG(ERROR) << "Run format_trans_optimizer graphPasses Failed";
-      return status;
-    }
-  }
-
   {
     // init old node indices
     auto old_nodes = GetGraphNodes();
@@ -134,20 +86,13 @@ int GraphDefTransform::Transform(const converter::Flags &ctx) {
     }
   }
 
-  // postconvert pass
+  // node replace
-  {
+  if (!ctx.trainModel) {
     // init old node indices
     auto old_nodes = GetGraphNodes();
     Optimizer replace_optimizer;
-    if (!ctx.trainModel) {
+    replace_optimizer.AddPass(new (std::nothrow) InferShapePass(ctx.fmk));
-      auto batch_norm_scale_pass = new (std::nothrow) BatchNormConvertScalePass();
+    replace_optimizer.AddPass(new (std::nothrow) BatchNormConvertScalePass(ctx.fmk));
-      if (batch_norm_scale_pass == nullptr) {
-        MS_LOG(ERROR) << "new batch_norm_scale_pass failed.";
-        return RET_ERROR;
-      }
-      batch_norm_scale_pass->SetFmk(ctx.fmk);
-      replace_optimizer.AddPass(batch_norm_scale_pass);
-    }
     replace_optimizer.AddPass(new (std::nothrow) IsolatedNodeRemovePass());
     replace_optimizer.AddPass(new SubgraphNodePass(old_nodes));
     status = replace_optimizer.Run(graph_defT_);
@@ -157,6 +102,7 @@ int GraphDefTransform::Transform(const converter::Flags &ctx) {
     }
   }
 
+  // node fusion
   {
     // init old node indices
     auto old_nodes = GetGraphNodes();
@@ -171,19 +117,14 @@ int GraphDefTransform::Transform(const converter::Flags &ctx) {
     }
   }
 
-  // do quantization
+  // quantization
   if (ctx.fmk != converter::FmkType_TF) {
     // init old node indices
     auto old_nodes = GetGraphNodes();
     Optimizer tensor_quant_optimizer;
     tensor_quant_optimizer.AddPass(new (std::nothrow) TopologicalSortPass());
-    auto infer_shape_pass = new (std::nothrow) InferShapePass();
+    tensor_quant_optimizer.AddPass(new (std::nothrow) InferQuantParamPass());
-    if (infer_shape_pass == nullptr) {
+    tensor_quant_optimizer.AddPass(new (std::nothrow) InferShapePass(ctx.fmk));
-      MS_LOG(ERROR) << "new InferShapePass failed";
-      return RET_MEMORY_FAILED;
-    }
-    infer_shape_pass->set_fmk_type(ctx.fmk);
-    tensor_quant_optimizer.AddPass(infer_shape_pass);
     tensor_quant_optimizer.AddPass(new (std::nothrow) TensorQuantPass());
     tensor_quant_optimizer.AddPass(new (std::nothrow) SubgraphNodePass(old_nodes));
     status = tensor_quant_optimizer.Run(graph_defT_);
@@ -193,38 +134,17 @@ int GraphDefTransform::Transform(const converter::Flags &ctx) {
     }
   }
 
-  // insert quantNode and deQuantNode
+  // quantization
   if (ctx.fmk != converter::FmkType_TF) {
     // init old node indices
-    auto old_nodes = GetGraphNodes();
     Optimizer quant_node_optimizer;
-    quant_node_optimizer.AddPass(new (std::nothrow) SubgraphNodePass(old_nodes));
     quant_node_optimizer.AddPass(new (std::nothrow) TopologicalSortPass());
-    auto infer_shape_pass = new (std::nothrow) InferShapePass();
+    auto old_nodes = GetGraphNodes();
-    if (infer_shape_pass == nullptr) {
+    quant_node_optimizer.AddPass(new (std::nothrow) InferShapePass(ctx.fmk));
-      MS_LOG(ERROR) << "new InferShapePass failed";
+    quant_node_optimizer.AddPass(new (std::nothrow) DTypeTransPass(ctx.inputDataType, ctx.outputDataType));
-      return RET_MEMORY_FAILED;
-    }
-    infer_shape_pass->set_fmk_type(ctx.fmk);
-    quant_node_optimizer.AddPass(infer_shape_pass);
-    status = quant_node_optimizer.Run(graph_defT_);
-    if (status != RET_OK && status != RET_NO_CHANGE) {
-      MS_LOG(ERROR) << "Run quant_node_optimizer graphPasses Failed";
-      return status;
-    }
-    auto old_nodes2 = GetGraphNodes();
-    quant_node_optimizer.AddPass(new (std::nothrow) InferQuantParamPass());
-    auto dtype_trans_pass = new (std::nothrow) DTypeTransPass();
-    if (dtype_trans_pass == nullptr) {
-      MS_LOG(ERROR) << "new dtype_trans_pass failed";
-      return RET_MEMORY_FAILED;
-    }
-    dtype_trans_pass->set_input_data_dtype(ctx.inputDataType);
-    dtype_trans_pass->set_output_data_dtype(ctx.outputDataType);
-    quant_node_optimizer.AddPass(dtype_trans_pass);
     quant_node_optimizer.AddPass(new (std::nothrow) QuantCastFusionPass());
     quant_node_optimizer.AddPass(new (std::nothrow) IsolatedNodeRemovePass());
-    quant_node_optimizer.AddPass(new (std::nothrow) SubgraphNodePass(old_nodes2));
+    quant_node_optimizer.AddPass(new (std::nothrow) SubgraphNodePass(old_nodes));
     status = quant_node_optimizer.Run(graph_defT_);
     if (status != RET_OK && status != RET_NO_CHANGE) {
       MS_LOG(ERROR) << "Run quant_node_optimizer graphPasses Failed";
@@ -232,7 +152,7 @@ int GraphDefTransform::Transform(const converter::Flags &ctx) {
     }
   }
 
-  // switch pass
+  // controlflow pass
   {
     // init old node indices
     auto old_nodes = GetGraphNodes();
@@ -240,6 +160,7 @@ int GraphDefTransform::Transform(const converter::Flags &ctx) {
     switch_optimizer.AddPass(new (std::nothrow) SwitchPass());
     switch_optimizer.AddPass(new (std::nothrow) IsolatedNodeRemovePass());
     switch_optimizer.AddPass(new (std::nothrow) SubgraphNodePass(old_nodes));
+    switch_optimizer.AddPass(new (std::nothrow) SubgraphTensorPass());
     status = switch_optimizer.Run(graph_defT_);
     if (status != RET_OK && status != RET_NO_CHANGE) {
       MS_LOG(ERROR) << "Run switch_optimizer Failed";
@@ -247,34 +168,11 @@ int GraphDefTransform::Transform(const converter::Flags &ctx) {
     }
   }
 
-  // subgraph tensor pass
-  {
-    Optimizer subgraph_tensor_optimizer;
-    subgraph_tensor_optimizer.AddPass(new (std::nothrow) SubgraphTensorPass());
-    status = subgraph_tensor_optimizer.Run(graph_defT_);
-    if (status != RET_OK && status != RET_NO_CHANGE) {
-      MS_LOG(ERROR) << "Run subgraph tensor pass Failed";
-      return status;
-    }
-  }
-
-  // tensor name
-  {
-    // init old node indices
-    auto old_nodes = GetGraphNodes();
-    Optimizer name_optimizer;
-    name_optimizer.AddPass(new (std::nothrow) SubgraphNodePass(old_nodes));
-    name_optimizer.AddPass(new (std::nothrow) TopologicalSortPass());
-    name_optimizer.AddPass(new (std::nothrow) TensorNamePass());
-    status = name_optimizer.Run(graph_defT_);
-    if (status != RET_OK && status != RET_NO_CHANGE) {
-      MS_LOG(ERROR) << "Run name_optimizer graphPasses Failed";
-      return status;
-    }
-  }
-
   {
     Optimizer nested_loop_optimizer;
+    auto old_nodes = GetGraphNodes();
+    nested_loop_optimizer.AddPass(new (std::nothrow) SubgraphNodePass(old_nodes));
+    nested_loop_optimizer.AddPass(new (std::nothrow) TopologicalSortPass());
     nested_loop_optimizer.AddPass(new (std::nothrow) NestedLoopExpandPass());
     status = nested_loop_optimizer.Run(graph_defT_);
     if (status != RET_OK && status != RET_NO_CHANGE) {
@@ -284,30 +182,16 @@ int GraphDefTransform::Transform(const converter::Flags &ctx) {
   }
 
   {
-    Optimizer quant_param_optimizer;
+    Optimizer forming_model_optimizer;
-    quant_param_optimizer.AddPass(new (std::nothrow) SetUnusedQuantParamToDefaultPass());
+    forming_model_optimizer.AddPass(new (std::nothrow) InferShapePass(ctx.fmk));
-    status = quant_param_optimizer.Run(graph_defT_);
+    forming_model_optimizer.AddPass(new (std::nothrow) SetUnusedQuantParamToDefaultPass());
-    if (status != RET_OK && status != RET_NO_CHANGE) {
+    forming_model_optimizer.AddPass(new (std::nothrow) TensorNamePass());
-      MS_LOG(ERROR) << "Run quant_param_optimizer graphPasses Failed";
+    status = forming_model_optimizer.Run(graph_defT_);
-      return status;
-    }
-  }
-
-  {
-    Optimizer infer_shape_optimizer;
-    auto infer_shape_pass = new (std::nothrow) InferShapePass();
-    if (infer_shape_pass == nullptr) {
-      MS_LOG(ERROR) << "new InferShapePass failed";
-      return RET_MEMORY_FAILED;
-    }
-    infer_shape_pass->set_fmk_type(ctx.fmk);
-    infer_shape_optimizer.AddPass(infer_shape_pass);
-    status = infer_shape_optimizer.Run(graph_defT_);
     if (status != RET_OK) {
       MS_LOG(ERROR) << "Run InferShapeOptimizer graphPasses Failed.";
       return status;
     }
   }
   return RET_OK;
-}  // namespace mindspore::lite
+}
 }  // namespace mindspore::lite

mindspore/lite/tools/converter/legacy_optimizer/graph/batchnorm_convert_scale_pass.h

@@ -36,14 +36,12 @@ struct BNWeightTensors {
 };
 class BatchNormConvertScalePass : public GraphPass {
  public:
-  BatchNormConvertScalePass() = default;
+  explicit BatchNormConvertScalePass(converter::FmkType fmk) : fmkType(fmk) {}
 
   ~BatchNormConvertScalePass() = default;
 
   STATUS Run(MetaGraphT *graph) override;
 
-  void SetFmk(converter::FmkType fmk) { this->fmkType = fmk; }
-
  protected:
   STATUS GetTransParam(MetaGraphT *graph, const std::unique_ptr<CNodeT> &bnNode);
 

mindspore/lite/tools/converter/legacy_optimizer/graph/dtype_trans_pass.cc

@@ -276,10 +276,5 @@ NodeIter DTypeTransPass::InsertDTypeTransNode(schema::MetaGraphT *graph, NodeIte
   return InsertNode(graph, exist_node_iter, place, inout_idx, std::move(trans_node), error_code, &insert_num,
                     castOpCopyer);
 }
-
-void DTypeTransPass::set_input_data_dtype(TypeId data_type) { this->input_data_dtype = data_type; }
-
-void DTypeTransPass::set_output_data_dtype(TypeId data_type) { this->output_data_dtype = data_type; }
-
 }  // namespace lite
 }  // namespace mindspore

mindspore/lite/tools/converter/legacy_optimizer/graph/dtype_trans_pass.h

@@ -30,16 +30,13 @@ enum DTypeTransNodeType { kInt8ToFP32, kFP32ToInt8, kUInt8ToInt8, kInt8ToUInt8 }
 
 class DTypeTransPass : public GraphPass {
  public:
-  DTypeTransPass() : id_(0) {}
+  DTypeTransPass(TypeId model_input_data_type, TypeId model_output_data_type)
+      : id_(0), input_data_dtype(model_input_data_type), output_data_dtype(model_output_data_type) {}
 
   ~DTypeTransPass() override = default;
 
   STATUS Run(schema::MetaGraphT *graph) override;
 
-  void set_input_data_dtype(TypeId data_type);
-
-  void set_output_data_dtype(TypeId dataType);
-
  private:
   STATUS DoModelInputDTypeTrans(schema::MetaGraphT *graph);
 

mindspore/lite/tools/converter/legacy_optimizer/graph/infershape_pass.h

@@ -39,14 +39,10 @@ struct InferTensor {
 
 class InferShapePass : public GraphPass {
  public:
-  InferShapePass() = default;
+  explicit InferShapePass(converter::FmkType fmk_type) : fmk_type_(fmk_type) {}
-
+  ~InferShapePass() override = default;
-  ~InferShapePass() = default;
-
   STATUS Run(MetaGraphT *graph) override;
 
-  void set_fmk_type(converter::FmkType fmk_type) { this->fmk_type_ = fmk_type; }
-
  private:
   void InitSearchTensor(MetaGraphT *graph);
   void AddNextInferShapeNode(std::vector<uint32_t> output_tensor_node_indexes, size_t index);

mindspore/lite/tools/converter/model_parser.h

@@ -34,8 +34,28 @@ class ModelParser {
 
   virtual ~ModelParser() = default;
 
-  virtual FuncGraphPtr Parse(const std::string &model_file, const std::string &weight_file,
+  FuncGraphPtr Parse(const std::string &model_file, const std::string &weight_file, const QuantType &quant_type) {
-                             const QuantType &quant_type) = 0;
+    auto ret = ParseToFuncGraph(model_file, weight_file, quant_type);
+    if (ret != RET_OK) {
+      MS_LOG(ERROR) << "Parse to func graph failed : " << ret;
+      return nullptr;
+    }
+    ret = PostAdjust();
+    if (ret != RET_OK) {
+      MS_LOG(ERROR) << "Adjust func graph failed : " << ret;
+      return nullptr;
+    }
+    return this->res_graph_;
+  }
+
+ protected:
+  virtual int ParseToFuncGraph(const std::string &model_file, const std::string &weight_file,
+                               const QuantType &quant_type) = 0;
+
+  virtual int PostAdjust() = 0;
+
+ protected:
+  FuncGraphPtr res_graph_ = nullptr;
 };
 }  // namespace mindspore::lite
 

mindspore/lite/tools/converter/ops/while.cc

@@ -15,6 +15,7 @@
  */
 
 #include <vector>
+#include "tools/common/tensor_util.h"
 #include "tools/converter/ops/while.h"
 #include "utils/check_convert_utils.h"
 #include "abstract/primitive_infer_map.h"
@@ -55,7 +56,9 @@ AbstractBasePtr WhileInfer(const abstract::AnalysisEnginePtr &, const PrimitiveP
   AbstractBasePtrList output;
   for (int64_t i = 0; i < (int64_t)input_args.size(); i++) {
     auto shape = CheckAndConvertUtils::ConvertShapePtrToShapeMap(input_args[i]->BuildShape())[kShape];
-    output.push_back(std::make_shared<abstract::AbstractTensor>(input_args[i]->BuildType(), shape));
+    auto abstract_tensor = lite::CreateTensorAbstract(shape, input_args[i]->BuildType()->type_id());
+    MS_EXCEPTION_IF_NULL(abstract_tensor);
+    output.push_back(abstract_tensor);
   }
   return std::make_shared<abstract::AbstractTuple>(output);
 }

mindspore/lite/tools/converter/parser/caffe/caffe_model_parser.cc

@@ -41,34 +41,34 @@ CaffeModelParser::CaffeModelParser() = default;
 
 CaffeModelParser::~CaffeModelParser() = default;
 
-FuncGraphPtr CaffeModelParser::Parse(const std::string &model_file, const std::string &weight_file,
+int CaffeModelParser::ParseToFuncGraph(const std::string &model_file, const std::string &weight_file,
-                                     const QuantType &quant_type) {
+                                       const QuantType &quant_type) {
   STATUS status = InitOriginModel(model_file, weight_file);
   if (status != RET_OK) {
     ReturnCode::GetSingleReturnCode()->UpdateReturnCode(status);
-    return nullptr;
+    return status;
   }
-  func_graph_ptr_ = std::make_shared<FuncGraph>();
+  res_graph_ = std::make_shared<FuncGraph>();
   status = ConvertGraphInputs();
   if (status != RET_OK) {
     ReturnCode::GetSingleReturnCode()->UpdateReturnCode(status);
-    return nullptr;
+    return status;
   }
 
   status = ConvertLayers();
   if (status != RET_OK) {
     ReturnCode::GetSingleReturnCode()->UpdateReturnCode(status);
-    return nullptr;
+    return status;
   }
 
   status = ConvertGraphOutputs();
   if (status != RET_OK) {
     ReturnCode::GetSingleReturnCode()->UpdateReturnCode(status);
-    return nullptr;
+    return status;
   }
-  func_graph_ptr_->set_attr("graph_name", MakeValue("main_graph"));
+  res_graph_->set_attr("graph_name", MakeValue("main_graph"));
-  func_graph_ptr_->set_attr("fmk", MakeValue(static_cast<int>(converter::FmkType_CAFFE)));
+  res_graph_->set_attr("fmk", MakeValue(static_cast<int>(converter::FmkType_CAFFE)));
-  return func_graph_ptr_;
+  return RET_OK;
 }
 
 STATUS CaffeModelParser::ConvertLayers() {
@@ -134,7 +134,7 @@ STATUS CaffeModelParser::ConvertLayers() {
     std::vector<AnfNodePtr> op_inputs = {NewValueNode(std::shared_ptr<ops::PrimitiveC>(primitive_c))};
     op_inputs.insert(op_inputs.end(), input_nodes.begin(), input_nodes.end());
     op_inputs.insert(op_inputs.end(), const_parameters.begin(), const_parameters.end());
-    auto new_cnode = func_graph_ptr_->NewCNode(op_inputs);
+    auto new_cnode = res_graph_->NewCNode(op_inputs);
     new_cnode->set_fullname_with_scope(layer.name());
 
     // convert outputs
@@ -194,14 +194,17 @@ STATUS CaffeModelParser::ConvertGraphInputs() {
   for (int i = 0; i < caffe_model_.layer_size(); i++) {
     auto layer = caffe_model_.layer(i);
     if (layer.type() == "Input") {
-      auto parameter = func_graph_ptr_->add_parameter();
+      auto parameter = res_graph_->add_parameter();
       std::vector<int64_t> shape;
       for (int j = 0; j < layer.input_param().shape(0).dim_size(); j++) {
         shape.push_back(layer.input_param().shape(0).dim(j));
       }
-      auto type_ptr = TypeIdToType(TypeId::kNumberTypeFloat32);
+      auto abstract = CreateTensorAbstract(shape, kNumberTypeFloat32);
-      auto abstract_tensor = std::make_shared<abstract::AbstractTensor>(type_ptr, shape);
+      if (abstract == nullptr) {
-      parameter->set_abstract(abstract_tensor);
+        MS_LOG(ERROR) << "Create tensor abstarct failed";
+        return RET_ERROR;
+      }
+      parameter->set_abstract(abstract);
       parameter->set_name("graph_input-" + std::to_string(i));
       nodes_.insert(std::pair(layer.top(0), parameter));
     }
@@ -220,10 +223,13 @@ STATUS CaffeModelParser::ConvertGraphInputs() {
           shape.push_back(caffe_model_.input_dim(j));
         }
       }
-      auto parameter = func_graph_ptr_->add_parameter();
+      auto parameter = res_graph_->add_parameter();
-      auto type_ptr = TypeIdToType(TypeId::kNumberTypeFloat32);
+      auto abstract = CreateTensorAbstract(shape, kNumberTypeFloat32);
-      auto abstract_tensor = std::make_shared<abstract::AbstractTensor>(type_ptr, shape);
+      if (abstract == nullptr) {
-      parameter->set_abstract(abstract_tensor);
+        MS_LOG(ERROR) << "Create tensor abstarct failed";
+        return RET_ERROR;
+      }
+      parameter->set_abstract(abstract);
       parameter->set_name("graph_input-" + caffe_model_.input(i));
       nodes_.insert(std::pair(caffe_model_.input(i), parameter));
     }
@@ -234,10 +240,18 @@ STATUS CaffeModelParser::ConvertGraphInputs() {
       for (int j = 0; j < shape.dim_size(); j++) {
         shape_vector.push_back(shape.dim(j));
       }
-      auto parameter = func_graph_ptr_->add_parameter();
+      auto parameter = res_graph_->add_parameter();
-      auto type_ptr = TypeIdToType(TypeId::kNumberTypeFloat32);
+      auto tensor_info = CreateTensorInfo(nullptr, 0, shape_vector, kNumberTypeFloat32);
-      auto abstract_tensor = std::make_shared<abstract::AbstractTensor>(type_ptr, shape_vector);
+      if (tensor_info == nullptr) {
-      parameter->set_abstract(abstract_tensor);
+        MS_LOG(ERROR) << "Create tensor info failed";
+        return RET_ERROR;
+      }
+      auto abstract = tensor_info->ToAbstract();
+      if (abstract == nullptr) {
+        MS_LOG(ERROR) << "Create tensor abstarct failed";
+        return RET_ERROR;
+      }
+      parameter->set_abstract(abstract);
       parameter->set_name("graph_input-" + caffe_model_.input(i));
       nodes_.insert(std::pair(caffe_model_.input(i), parameter));
     }
@@ -265,7 +279,7 @@ STATUS CaffeModelParser::ConvertGraphOutputs() {
       auto cnode = nodes_.find(output_node)->second;
       make_tuple_inputs.emplace_back(cnode);
     }
-    auto make_tuple_cnode = func_graph_ptr_->NewCNode(make_tuple_inputs);
+    auto make_tuple_cnode = res_graph_->NewCNode(make_tuple_inputs);
     make_tuple_cnode->set_fullname_with_scope("return tuple");
 
     std::vector<AnfNodePtr> op_inputs;
@@ -277,9 +291,9 @@ STATUS CaffeModelParser::ConvertGraphOutputs() {
     auto value_node = NewValueNode(return_prim_ptr);
     op_inputs.emplace_back(value_node);
     op_inputs.emplace_back(make_tuple_cnode);
-    auto cnode = func_graph_ptr_->NewCNode(op_inputs);
+    auto cnode = res_graph_->NewCNode(op_inputs);
     cnode->set_fullname_with_scope("Return");
-    func_graph_ptr_->set_return(cnode);
+    res_graph_->set_return(cnode);
   } else {
     auto returnPrim = std::make_shared<ops::Return>();
     if (returnPrim == nullptr) {
@@ -298,9 +312,9 @@ STATUS CaffeModelParser::ConvertGraphOutputs() {
       return RET_NOT_FIND_OP;
     }
     opInputs.emplace_back(cnode);
-    auto returnCnode = func_graph_ptr_->NewCNode(opInputs);
+    auto returnCnode = res_graph_->NewCNode(opInputs);
     returnCnode->set_fullname_with_scope("Return");
-    func_graph_ptr_->set_return(returnCnode);
+    res_graph_->set_return(returnCnode);
   }
   return RET_OK;
 }
@@ -333,7 +347,7 @@ STATUS CaffeModelParser::ConvertBlobs(const caffe::LayerParameter &layer, std::v
     ConvertShape(layer.blobs(i), &shape);
 
     // cal Weight num
-    auto parameter = func_graph_ptr_->add_parameter();
+    auto parameter = res_graph_->add_parameter();
     auto type_ptr = TypeIdToType(TypeId::kNumberTypeFloat32);
     std::vector<int64_t> shape_vector;
     (void)std::transform(shape.begin(), shape.end(), std::back_inserter(shape_vector),
@@ -402,17 +416,25 @@ STATUS CaffeModelParser::ConvertBottom(const caffe::LayerParameter &layer, std::
 }
 
 STATUS CaffeModelParser::ConvertTop(const caffe::LayerParameter &layer, const CNodePtr &cnode) {
-  auto type_ptr = TypeIdToType(TypeId::kNumberTypeFloat32);
-  std::vector<int64_t> shape_vector;
   if (layer.top_size() == 1) {
-    cnode->set_abstract(std::make_shared<abstract::AbstractTensor>(type_ptr, shape_vector));
+    auto abstract = CreateTensorAbstract({}, kNumberTypeFloat32);
+    if (abstract == nullptr) {
+      MS_LOG(ERROR) << "Create tensor abstarct failed";
+      return RET_ERROR;
+    }
+    cnode->set_abstract(abstract);
     nodes_[layer.top(0)] = cnode;
     return RET_OK;
   }
 
   AbstractBasePtrList abstract_list;
   for (int i = 0; i < layer.top_size(); i++) {
-    abstract_list.emplace_back(std::make_shared<abstract::AbstractTensor>(type_ptr, shape_vector));
+    auto abstract = CreateTensorAbstract({}, kNumberTypeFloat32);
+    if (abstract == nullptr) {
+      MS_LOG(ERROR) << "Create tensor abstarct failed";
+      return RET_ERROR;
+    }
+    abstract_list.emplace_back(abstract);
     auto tuple_get_item_prim_ptr = std::make_shared<ops::TupleGetItem>();
     if (tuple_get_item_prim_ptr == nullptr) {
       MS_LOG(ERROR) << "new TupleGetItem failed";
@@ -421,7 +443,7 @@ STATUS CaffeModelParser::ConvertTop(const caffe::LayerParameter &layer, const CN
     auto tuple_get_item_prim = NewValueNode(tuple_get_item_prim_ptr);
     auto get_item_value = NewValueNode(MakeValue<int>(i));
     std::vector<AnfNodePtr> inputs{tuple_get_item_prim, cnode, get_item_value};
-    CNodePtr get_item_cnode = func_graph_ptr_->NewCNode(inputs);
+    CNodePtr get_item_cnode = res_graph_->NewCNode(inputs);
     get_item_cnode->set_fullname_with_scope(layer.top(i));
     nodes_[layer.top(i)] = get_item_cnode;
   }
@@ -446,4 +468,6 @@ std::string CaffeModelParser::GetOriginLayerName(const std::string &layer_name)
   }
   return layer.name();
 }
+
+int CaffeModelParser::PostAdjust() { return RET_OK; }
 }  // namespace mindspore::lite

mindspore/lite/tools/converter/parser/caffe/caffe_model_parser.h

@@ -32,8 +32,10 @@ class CaffeModelParser : public ModelParser {
 
   ~CaffeModelParser() override;
 
-  FuncGraphPtr Parse(const std::string &model_file, const std::string &weight_file,
+  int ParseToFuncGraph(const std::string &model_file, const std::string &weight_file,
-                     const QuantType &quant_type) override;
+                       const QuantType &quant_type) override;
+
+  int PostAdjust() override;
 
  private:
   STATUS InitOriginModel(const std::string &model_file, const std::string &weight_file);
@@ -59,7 +61,6 @@ class CaffeModelParser : public ModelParser {
   caffe::NetParameter caffe_weight_;
   std::unordered_map<std::string, caffe::LayerParameter> caffe_layers_;
   std::unordered_map<std::string, AnfNodePtr> nodes_;
-  FuncGraphPtr func_graph_ptr_;
 };
 }  // namespace mindspore::lite
 

mindspore/lite/tools/converter/parser/onnx/onnx_model_parser.cc

@@ -45,31 +45,31 @@ static const std::unordered_map<int, mindspore::TypeId> TYPE_MAP = {
   {onnx::TensorProto_DataType_FLOAT, mindspore::kNumberTypeFloat32},
   {onnx::TensorProto_DataType_BOOL, mindspore::kNumberTypeBool}};
 
-FuncGraphPtr OnnxModelParser::Parse(const std::string &model_file, const std::string &weight_file,
+int OnnxModelParser::ParseToFuncGraph(const std::string &model_file, const std::string &weight_file,
-                                    const QuantType &quant_type) {
+                                      const QuantType &quant_type) {
   NotSupportOp::GetInstance()->set_fmk_type("ONNX");
-  anf_root_graph_ = std::make_shared<FuncGraph>();
+  res_graph_ = std::make_shared<FuncGraph>();
   auto status = InitOriginModel(model_file);
   if (RET_OK != status) {
     ReturnCode::GetSingleReturnCode()->UpdateReturnCode(status);
     MS_LOG(ERROR) << "init origin model failed.";
-    return nullptr;
+    return status;
   }
 
-  status = ConvertOnnxGraph(onnx_root_graph_, anf_root_graph_, &anf_nodes_map_, {}, "root_node");
+  status = ConvertOnnxGraph(onnx_root_graph_, res_graph_, &anf_nodes_map_, {}, "root_node");
   if (RET_OK != status) {
     ReturnCode::GetSingleReturnCode()->UpdateReturnCode(status);
     MS_LOG(ERROR) << "convert onnx graph failed.";
-    return nullptr;
+    return status;
   }
-  static auto root_func_manager = Manage(anf_root_graph_);
+  static auto root_func_manager = Manage(res_graph_);
   for (auto &subgraph : all_subgraphs_) {
     subgraph->set_manager(root_func_manager);
     subgraph->set_attr("fmk", MakeValue(static_cast<int>(converter::FmkType_ONNX)));
   }
-  anf_root_graph_->set_attr("graph_name", MakeValue("main_graph"));
+  res_graph_->set_attr("graph_name", MakeValue("main_graph"));
-  anf_root_graph_->set_attr("fmk", MakeValue(static_cast<int>(converter::FmkType_ONNX)));
+  res_graph_->set_attr("fmk", MakeValue(static_cast<int>(converter::FmkType_ONNX)));
-  return anf_root_graph_;
+  return RET_OK;
 }
 
 STATUS OnnxModelParser::InitOriginModel(const std::string &model_file) {
@@ -88,9 +88,9 @@ STATUS OnnxModelParser::InitOriginModel(const std::string &model_file) {
   OnnxNodeParser::set_opset_version(onnx_model_.opset_import().Get(0).version());
   onnx_root_graph_ = onnx_model_.graph();
   if (OnnxNodeParser::opset_version() > 15) {
-    anf_root_graph_->set_attr("fmk", MakeValue(static_cast<int>(converter::FmkType_ONNX)));
+    res_graph_->set_attr("fmk", MakeValue(static_cast<int>(converter::FmkType_ONNX)));
   } else {
-    anf_root_graph_->set_attr("fmk", MakeValue(static_cast<int>(converter::FmkType_ONNX_LOW_VERSION)));
+    res_graph_->set_attr("fmk", MakeValue(static_cast<int>(converter::FmkType_ONNX_LOW_VERSION)));
   }
   return RET_OK;
 }
@@ -170,13 +170,16 @@ STATUS OnnxModelParser::ConvertGraphInputs(const onnx::GraphProto &onnx_graph, c
                     << static_cast<onnx::TensorProto_DataType>(input_value.type().tensor_type().elem_type());
       return RET_ERROR;
     }
-    auto type_ptr = TypeIdToType(data_type);
     std::vector<int64_t> shape_vector;
     auto onnx_shape = input_value.type().tensor_type().shape().dim();
     std::transform(onnx_shape.begin(), onnx_shape.end(), std::back_inserter(shape_vector),
                    [](const onnx::TensorShapeProto_Dimension &val) { return static_cast<int64_t>(val.dim_value()); });
     std::replace(shape_vector.begin(), shape_vector.end(), 0, -1);
-    auto abstract_tensor = std::make_shared<abstract::AbstractTensor>(type_ptr, shape_vector);
+    auto abstract_tensor = CreateTensorAbstract(shape_vector, data_type);
+    if (abstract_tensor == nullptr) {
+      MS_LOG(ERROR) << "Create tensor abstarct failed";
+      return RET_ERROR;
+    }
     parameter->set_abstract(abstract_tensor);
     parameter->set_name(input_value.name());
     anf_nodes_map->emplace(input_value.name(), parameter);
@@ -490,17 +493,23 @@ STATUS OnnxModelParser::BuildOpOutputs(const onnx::NodeProto &onnx_node, const F
     return RET_NULL_PTR;
   }
   if (onnx_node.output_size() == 1) {
-    auto type_ptr = TypeIdToType(kNumberTypeFloat32);
+    auto abstract_tensor = CreateTensorAbstract({}, kNumberTypeFloat32);
-    std::vector<int64_t> shape_vector;
+    if (abstract_tensor == nullptr) {
-    cnode->set_abstract(std::make_shared<abstract::AbstractTensor>(type_ptr, shape_vector));
+      MS_LOG(ERROR) << "Create tensor abstarct failed";
+      return RET_ERROR;
+    }
+    cnode->set_abstract(abstract_tensor);
     anf_nodes_map->emplace(onnx_node.output(0), cnode);
   } else {
     AbstractBasePtrList abstract_list;
     int op_idx = 0;
     for (const auto &output_name : onnx_node.output()) {
-      std::vector<int64_t> shape_vector;
+      auto abstract_tensor = CreateTensorAbstract({}, kNumberTypeFloat32);
-      auto type_ptr = TypeIdToType(kNumberTypeFloat32);
+      if (abstract_tensor == nullptr) {
-      abstract_list.emplace_back(std::make_shared<abstract::AbstractTensor>(type_ptr, shape_vector));
+        MS_LOG(ERROR) << "Create tensor abstarct failed";
+        return RET_ERROR;
+      }
+      abstract_list.emplace_back(abstract_tensor);
       auto tuple_get_item_prim_ptr = std::make_shared<ops::TupleGetItem>();
       if (tuple_get_item_prim_ptr == nullptr) {
         MS_LOG(ERROR) << "new TupleGetItem failed";
@@ -687,7 +696,11 @@ ParameterPtr CreateConstParamter(const FuncGraphPtr &anf_graph, int val) {
     return nullptr;
   }
   auto const_node = anf_graph->add_parameter();
-  auto const_abstract = std::make_shared<abstract::AbstractTensor>(kInt32, std::vector<int64_t>());
+  auto const_abstract = CreateTensorAbstract({}, kNumberTypeInt32);
+  if (const_abstract == nullptr) {
+    MS_LOG(ERROR) << "Create tensor abstarct failed";
+    return nullptr;
+  }
   const_node->set_abstract(const_abstract);
   int *tensor_data = new (std::nothrow) int[1];
   if (tensor_data == nullptr) {
@@ -834,9 +847,16 @@ STATUS OnnxModelParser::AddTensorArrayEdge(const FuncGraphPtr &anf_graph, std::v
   for (int i = 0; i < act_output_num; i++) {
     // tensor_array need as root while input
     auto while_tensor_array_input = anf_root_graph->add_parameter();
-    std::vector<int64_t> shape_vector;
+    auto tensor_info = CreateTensorInfo(nullptr, 0, {}, kObjectTypeTensorType);
-    auto abstract_tensor = std::make_shared<abstract::AbstractTensor>(kTensorType, shape_vector);
+    if (tensor_info == nullptr) {
-    auto tensor_info = std::make_shared<tensor::Tensor>(kObjectTypeTensorType, shape_vector);
+      MS_LOG(ERROR) << "Create tensor info failed";
+      return RET_ERROR;
+    }
+    auto abstract_tensor = tensor_info->ToAbstract();
+    if (abstract_tensor == nullptr) {
+      MS_LOG(ERROR) << "Create tensor abstarct failed";
+      return RET_ERROR;
+    }
     while_tensor_array_input->set_abstract(abstract_tensor);
     while_tensor_array_input->set_default_param(tensor_info);
     while_tensor_array_input->set_name(loop_node_name + "_scan_outputs_tensorarray");
@@ -975,7 +995,11 @@ STATUS OnnxModelParser::BuildCondGraph(const FuncGraphPtr &cond_graph, const Anf
     auto input_paramter = cond_graph->add_parameter();
     input_paramter->set_name(cond_graph_name + "_input_" + std::to_string(i) + "_parameter");
     auto root_while_inputs = root_while_node->cast<CNodePtr>()->inputs();
-    auto input_abstract = std::make_shared<abstract::AbstractTensor>(kInt32, std::vector<int64_t>());
+    auto input_abstract = CreateTensorAbstract({}, kNumberTypeInt32);
+    if (input_abstract == nullptr) {
+      MS_LOG(ERROR) << "Create tensor abstarct failed";
+      return RET_ERROR;
+    }
     input_paramter->set_abstract(input_abstract);
     if (i == 0) {
       auto zero_parameter = CreateConstParamter(cond_graph, 0);
@@ -987,7 +1011,11 @@ STATUS OnnxModelParser::BuildCondGraph(const FuncGraphPtr &cond_graph, const Anf
         MS_LOG(ERROR) << "new cnode error";
         return RET_ERROR;
       }
-      auto less_abstract = std::make_shared<abstract::AbstractTensor>(kBool, std::vector<int64_t>());
+      auto less_abstract = CreateTensorAbstract({}, kNumberTypeBool);
+      if (less_abstract == nullptr) {
+        MS_LOG(ERROR) << "Create tensor abstarct failed";
+        return RET_ERROR;
+      }
       less_cnode->set_abstract(less_abstract);
       less_cnode->set_fullname_with_scope(cond_graph_name + "_less_cnode");
     }
@@ -1020,12 +1048,11 @@ STATUS OnnxModelParser::BuildParameterNodeForQuantParam(const void *data, const
     MS_LOG(ERROR) << "quant param type don't support.";
     return RET_NOT_SUPPORT;
   }
-  std::vector<int64_t> shape_vector;
+  auto parameter_node = res_graph_->add_parameter();
-  auto parameter_node = anf_root_graph_->add_parameter();
+  auto abstract_tensor = CreateTensorAbstract({}, type);
-  auto abstract_tensor = std::make_shared<abstract::AbstractTensor>(TypeIdToType(type), shape_vector);
   if (abstract_tensor == nullptr) {
-    MS_LOG(ERROR) << "new abstract_tensor failed";
+    MS_LOG(ERROR) << "Create tensor abstarct failed";
-    return RET_MEMORY_FAILED;
+    return RET_ERROR;
   }
   parameter_node->set_abstract(abstract_tensor);
   parameter_node->set_name(name);
@@ -1051,9 +1078,12 @@ STATUS OnnxModelParser::BuildParameterNode(const ParameterPtr &parameter_node, c
     MS_LOG(ERROR) << "not support onnx data type " << static_cast<onnx::TensorProto_DataType>(tensor.data_type());
     return RET_ERROR;
   }
-  auto type_ptr = TypeIdToType(data_type);
   std::vector<int64_t> shape_vector(tensor.dims().begin(), tensor.dims().end());
-  auto abstract_tensor = std::make_shared<abstract::AbstractTensor>(type_ptr, shape_vector);
+  auto abstract_tensor = CreateTensorAbstract(shape_vector, data_type);
+  if (abstract_tensor == nullptr) {
+    MS_LOG(ERROR) << "Create tensor abstarct failed";
+    return RET_ERROR;
+  }
   parameter_node->set_abstract(abstract_tensor);
   parameter_node->set_name(tensor.name());
 
@@ -1142,5 +1172,7 @@ TypeId OnnxModelParser::GetDataTypeFromOnnx(onnx::TensorProto_DataType onnx_type
   }
   return iter->second;
 }
+
+int OnnxModelParser::PostAdjust() { return 0; }
 }  // namespace lite
 }  // namespace mindspore

mindspore/lite/tools/converter/parser/onnx/onnx_model_parser.h

@@ -40,14 +40,17 @@ class OnnxModelParser : public ModelParser {
 
   ~OnnxModelParser() override = default;
 
-  FuncGraphPtr Parse(const std::string &model_file, const std::string &weight_file,
+  int ParseToFuncGraph(const std::string &model_file, const std::string &weight_file,
-                     const QuantType &quant_type) override;
+                       const QuantType &quant_type) override;
+
+  int PostAdjust() override;
+
   static TypeId GetDataTypeFromOnnx(onnx::TensorProto_DataType onnx_type);
   static STATUS CopyOnnxTensorData(const onnx::TensorProto &onnx_const_tensor,
                                    const tensor::TensorPtr &param_value_lite);
+  STATUS InitOriginModel(const std::string &model_file);
 
  private:
-  STATUS InitOriginModel(const std::string &model_file);
   STATUS ConvertNodes(const onnx::GraphProto &onnx_graph, const FuncGraphPtr &func_graph_ptr,
                       std::unordered_map<std::string, AnfNodePtr> *anf_nodes_map, std::vector<AnfNodePtr> *graph_inputs,
                       const std::string &root_node_name);
@@ -94,7 +97,6 @@ class OnnxModelParser : public ModelParser {
   std::unordered_map<std::string, AnfNodePtr> anf_nodes_map_;
   std::unordered_map<std::string, std::unordered_map<std::string, AnfNodePtr> *> control_nodes_map_;
   std::unordered_map<std::string, std::string> child_root_map_;  // for nest control flow node
-  FuncGraphPtr anf_root_graph_ = nullptr;
 };
 }  // namespace lite
 }  // namespace mindspore

mindspore/lite/tools/converter/parser/tf/tf_model_parser.cc

@@ -418,18 +418,17 @@ STATUS TFModelParser::ConvertParameter(const tensorflow::NodeDef &node, const Pa
     type = TensorFlowUtils::GetTFDataType(attr_value.type());
   }
 
-  std::vector<int> shape;
+  std::vector<int64_t> shape;
   if (TensorFlowUtils::FindAttrValue(node, "shape", &attr_value)) {
     auto &shape_attr = attr_value.shape();
     for (int i = 0; i < shape_attr.dim_size(); ++i) {
       shape.push_back(shape_attr.dim(i).size());
     }
   }
-  std::vector<int64_t> shape_vector(shape.begin(), shape.end());
 
   if (TensorFlowUtils::FindAttrValue(node, "value", &attr_value)) {
     MS_LOG(INFO) << "Found value attr, means it has default value";
-    auto status = ConvertConstTensor(node, attr_value, type, parameter, &shape_vector);
+    auto status = ConvertConstTensor(node, attr_value, type, parameter, &shape);
     if (status != RET_OK) {
       MS_LOG(ERROR) << "convert const tensor failed.";
       return status;
@@ -438,10 +437,10 @@ STATUS TFModelParser::ConvertParameter(const tensorflow::NodeDef &node, const Pa
     graph_input_names_.emplace_back(node.name());  // only root graph need set graph input names
   }
 
-  auto type_ptr = TypeIdToType(type == kNumberTypeInt64 ? kNumberTypeInt32 : type);
+  type = (type == kNumberTypeInt64) ? kNumberTypeInt32 : type;
-  auto abstract_tensor = std::make_shared<abstract::AbstractTensor>(type_ptr, shape_vector);
+  auto abstract_tensor = CreateTensorAbstract(shape, type);
   if (abstract_tensor == nullptr) {
-    MS_LOG(ERROR) << "abstract_tensor is nullptr";
+    MS_LOG(ERROR) << "Create tensor abstarct failed";
     return RET_ERROR;
   }
   parameter->set_name(node.name());
@@ -474,51 +473,51 @@ STATUS TFModelParser::ConvertGraphInputsAndConsts(
   }
   return RET_OK;
 }
-FuncGraphPtr paserTfFuction() { return nullptr; }
+
-FuncGraphPtr TFModelParser::Parse(const std::string &modelFile, const std::string &weightFile,
+int TFModelParser::ParseToFuncGraph(const std::string &modelFile, const std::string &weightFile,
-                                  const QuantType &quantType) {
+                                    const QuantType &quantType) {
   NotSupportOp::GetInstance()->set_fmk_type("TF");
   auto status = ValidateFileStr(modelFile, ".pb");
   if (status != RET_OK) {
     MS_LOG(ERROR) << "INPUT ILLEGAL: modelFile must be *.pb";
     ReturnCode::GetSingleReturnCode()->UpdateReturnCode(status);
-    return nullptr;
+    return status;
   }
   tf_root_graph_ = std::make_unique<tensorflow::GraphDef>();
   if (tf_root_graph_ == nullptr) {
     MS_LOG(ERROR) << "tf_root_graph_ is nullptr";
     ReturnCode::GetSingleReturnCode()->UpdateReturnCode(RET_ERROR);
-    return nullptr;
+    return status;
   }
   status = ReadProtoFromBinaryFile((const char *)modelFile.c_str(), tf_root_graph_.get());
   if (status != RET_OK) {
     MS_LOG(ERROR) << "Open modelFile for TF converter failed!";
     ReturnCode::GetSingleReturnCode()->UpdateReturnCode(status);
-    return nullptr;
+    return status;
   }
-  anf_root_graph_ = std::make_shared<FuncGraph>();
+  res_graph_ = std::make_shared<FuncGraph>();
-  if (anf_root_graph_ == nullptr) {
+  if (res_graph_ == nullptr) {
     MS_LOG(ERROR) << "funGraphPtr is nullptr";
     ReturnCode::GetSingleReturnCode()->UpdateReturnCode(RET_ERROR);
-    return nullptr;
+    return status;
   }
-  anf_root_graph_->set_attr("graph_name", MakeValue("main_graph"));
+  res_graph_->set_attr("graph_name", MakeValue("main_graph"));
-  anf_root_graph_->set_attr("fmk", MakeValue(static_cast<int>(converter::FmkType_TF)));
+  res_graph_->set_attr("fmk", MakeValue(static_cast<int>(converter::FmkType_TF)));
 
   for (int i = 0; i < tf_root_graph_->node_size(); i++) {
     auto &node_def = tf_root_graph_->node(i);
     tf_root_graph_nodes_[node_def.name()] = &node_def;
   }
 
-  status = ConvertGraphInputsAndConsts(tf_root_graph_nodes_, anf_root_graph_, &anf_root_node_map_);
+  status = ConvertGraphInputsAndConsts(tf_root_graph_nodes_, res_graph_, &anf_root_node_map_);
   if (status != RET_OK) {
     ReturnCode::GetSingleReturnCode()->UpdateReturnCode(status);
-    return nullptr;
+    return status;
   }
   bool success_flag = true;
   for (int i = 0; i < tf_root_graph_->node_size(); i++) {
     auto &node_def = tf_root_graph_->node(i);
-    status = ConvertOps(node_def, tf_root_graph_nodes_, anf_root_graph_, &anf_root_node_map_);
+    status = ConvertOps(node_def, tf_root_graph_nodes_, res_graph_, &anf_root_node_map_);
     ReturnCode::GetSingleReturnCode()->UpdateReturnCode(status);
     if (status != RET_OK) {
       success_flag = false;
@@ -526,7 +525,7 @@ FuncGraphPtr TFModelParser::Parse(const std::string &modelFile, const std::strin
   }
   if (!success_flag) {
     MS_LOG(ERROR) << "Convert ops failed.";
-    return nullptr;
+    return RET_ERROR;
   }
 
   if (!nodes_with_null_input_.empty()) {
@@ -534,7 +533,7 @@ FuncGraphPtr TFModelParser::Parse(const std::string &modelFile, const std::strin
     if (status != RET_OK) {
       MS_LOG(ERROR) << "Connect null inputs failed.";
       ReturnCode::GetSingleReturnCode()->UpdateReturnCode(status);
-      return nullptr;
+      return status;
     }
   }
 
@@ -542,17 +541,17 @@ FuncGraphPtr TFModelParser::Parse(const std::string &modelFile, const std::strin
   if (status != RET_OK) {
     MS_LOG(ERROR) << "Convert graph outputs failed.";
     ReturnCode::GetSingleReturnCode()->UpdateReturnCode(status);
-    return nullptr;
+    return status;
   }
 
   status = ConvertSubgraph();
   if (status != RET_OK) {
     MS_LOG(ERROR) << "Convert subgraph failed.";
     ReturnCode::GetSingleReturnCode()->UpdateReturnCode(status);
-    return nullptr;
+    return status;
   }
 
-  return anf_root_graph_;
+  return RET_OK;
 }
 
 STATUS TFModelParser::ConvertSubgraphInputs(std::map<std::string, const tensorflow::NodeDef *> *tf_sub_node_map,
@@ -746,7 +745,7 @@ STATUS TFModelParser::ControlFlowNodePostProcess(const std::map<CNodePtr, FuncGr
     MS_LOG(ERROR) << "while cond body size error";
     return RET_ERROR;
   }
-  static auto root_func_manager = Manage(anf_root_graph_);
+  static auto root_func_manager = Manage(res_graph_);
 
   for (auto &kv : first_func_map) {
     auto control_flow_node = kv.first;
@@ -758,7 +757,7 @@ STATUS TFModelParser::ControlFlowNodePostProcess(const std::map<CNodePtr, FuncGr
     auto second_value_node = NewValueNode(second_sub_graph);
     auto inputs = control_flow_node->inputs();
     inputs.insert(inputs.begin() + 1, {first_value_node, second_value_node});
-    auto new_node = anf_root_graph_->NewCNode(inputs);  // must create new node, otherwise node_users won't update
+    auto new_node = res_graph_->NewCNode(inputs);  // must create new node, otherwise node_users won't update
     if (new_node == nullptr) {
       MS_LOG(ERROR) << "new node failed";
       return RET_ERROR;
@@ -812,43 +811,46 @@ STATUS TFModelParser::ConvertOutputTensor(const tensorflow::NodeDef &op, const C
   if (output_size == 0) {
     return RET_OK;
   } else if (output_size == 1) {
-    auto type = kFloat32;
+    auto type = kNumberTypeFloat32;
-    std::vector<int64_t> shape_vector;
     if (IsTensorListOp(anf_node)) {
-      type = TypeIdToType(kObjectTypeTensorType);
+      type = kObjectTypeTensorType;
     }
-    auto abstract = std::make_shared<abstract::AbstractTensor>(type, shape_vector);
+    auto abstract_tensor = CreateTensorAbstract({}, type);
-    if (abstract == nullptr) {
+    if (abstract_tensor == nullptr) {
-      MS_LOG(ERROR) << "create AbstractTensor failed";
+      MS_LOG(ERROR) << "Create tensor abstarct failed";
       return RET_ERROR;
     }
-    anf_node->set_abstract(abstract);
+    anf_node->set_abstract(abstract_tensor);
     anf_node_map->insert(std::pair(op.name(), anf_node));
   } else {
-    AbstractBasePtrList abstractList;
+    AbstractBasePtrList abstract_list;
     for (int output_idx = 0; output_idx < output_size; output_idx++) {
-      std::vector<int64_t> shape_vector;
+      auto abstract_tensor = CreateTensorAbstract({}, kNumberTypeFloat32);
-      abstractList.emplace_back(std::make_shared<abstract::AbstractTensor>(kFloat32, shape_vector));
+      if (abstract_tensor == nullptr) {
-      auto tupleGetItemPrimPtr = std::make_shared<ops::TupleGetItem>();
+        MS_LOG(ERROR) << "Create tensor abstarct failed";
-      if (tupleGetItemPrimPtr == nullptr) {
+        return RET_ERROR;
+      }
+      abstract_list.emplace_back(abstract_tensor);
+      auto tuple_get_item_prim_ptr = std::make_shared<ops::TupleGetItem>();
+      if (tuple_get_item_prim_ptr == nullptr) {
         MS_LOG(ERROR) << "new TupleGetItem failed";
         return RET_NULL_PTR;
       }
-      auto tupleGetItemPrim = NewValueNode(tupleGetItemPrimPtr);
+      auto tuple_get_item_prim = NewValueNode(tuple_get_item_prim_ptr);
-      auto getItemValue = NewValueNode(MakeValue<int>(output_idx));
+      auto get_item_value = NewValueNode(MakeValue<int>(output_idx));
-      std::vector<AnfNodePtr> inputs{tupleGetItemPrim, anf_node, getItemValue};
+      std::vector<AnfNodePtr> inputs{tuple_get_item_prim, anf_node, get_item_value};
-      CNodePtr getItemCNode = anf_graph->NewCNode(inputs);
+      CNodePtr get_item_cnode = anf_graph->NewCNode(inputs);
       std::string output_item_name = anf_node->fullname_with_scope() + "_getitem_" + std::to_string(output_idx);
-      auto abstract = std::make_shared<abstract::AbstractTensor>(kFloat32, shape_vector);
+      auto get_item_abstract = CreateTensorAbstract({}, kNumberTypeFloat32);
-      if (abstract == nullptr) {
+      if (get_item_abstract == nullptr) {
-        MS_LOG(ERROR) << "create AbstractTensor failed";
+        MS_LOG(ERROR) << "Create tensor abstarct failed";
         return RET_ERROR;
       }
-      getItemCNode->set_abstract(abstract);
+      get_item_cnode->set_abstract(get_item_abstract);
-      getItemCNode->set_fullname_with_scope(output_item_name);
+      get_item_cnode->set_fullname_with_scope(output_item_name);
-      anf_node_map->insert(std::pair(op.name() + ":" + std::to_string(output_idx), getItemCNode));
+      anf_node_map->insert(std::pair(op.name() + ":" + std::to_string(output_idx), get_item_cnode));
     }
-    anf_node->set_abstract(std::make_shared<abstract::AbstractTuple>(abstractList));
+    anf_node->set_abstract(std::make_shared<abstract::AbstractTuple>(abstract_list));
   }
   return RET_OK;
 }
@@ -1022,7 +1024,7 @@ STATUS TFModelParser::ConvertRootGraphOutputs() {
       }
     }
   }
-  auto status = MakeAnfGraphOutputs(&output_nodes, anf_root_graph_);
+  auto status = MakeAnfGraphOutputs(&output_nodes, res_graph_);
   if (status != RET_OK) {
     MS_LOG(ERROR) << "make anf graph outputs node error";
     return status;
@@ -1070,5 +1072,7 @@ STATUS TFModelParser::MakeAnfGraphOutputs(std::vector<AnfNodePtr> *output_nodes,
   }
   return RET_OK;
 }
+
+int TFModelParser::PostAdjust() { return 0; }
 }  // namespace lite
 }  // namespace mindspore

mindspore/lite/tools/converter/parser/tf/tf_model_parser.h

@@ -36,9 +36,11 @@ namespace lite {
 class TFModelParser : public ModelParser {
  public:
   TFModelParser() = default;
-  ~TFModelParser() = default;
+  ~TFModelParser() override = default;
 
-  FuncGraphPtr Parse(const std::string &modelFile, const std::string &weightFile, const QuantType &quantType);
+  int ParseToFuncGraph(const std::string &modelFile, const std::string &weightFile, const QuantType &quantType);
+
+  int PostAdjust() override;
 
  private:
   static STATUS ConvertConstVariant(const tensorflow::TensorProto &tensor_proto, tensor::TensorPtr *tensor_info);
@@ -87,7 +89,6 @@ class TFModelParser : public ModelParser {
 
   STATUS ConnectNullInput();
 
-  FuncGraphPtr anf_root_graph_;
   std::unique_ptr<tensorflow::GraphDef> tf_root_graph_;                     // tf root graph def
   std::map<std::string, const tensorflow::NodeDef *> tf_root_graph_nodes_;  // tf root graph node map
   std::unordered_map<std::string, AnfNodePtr> anf_root_node_map_;

mindspore/lite/tools/converter/parser/tflite/tflite_model_parser.cc

@@ -43,46 +43,46 @@ std::unique_ptr<tflite::ModelT> TfliteModelParser::ReadTfliteModel(const char *m
   return tflite::UnPackModel(tflite_model_buf_);
 }
 
-FuncGraphPtr TfliteModelParser::Parse(const std::string &model_file, const std::string &weight_file,
+int TfliteModelParser::ParseToFuncGraph(const std::string &model_file, const std::string &weight_file,
-                                      const QuantType &quant_type) {
+                                        const QuantType &quant_type) {
   // load graph
   tflite_model_ = ReadTfliteModel(model_file.c_str());
   if (tflite_model_ == nullptr) {
     MS_LOG(ERROR) << "read tflite model failed";
     ReturnCode::GetSingleReturnCode()->UpdateReturnCode(RET_GRAPH_FILE_ERR);
-    return nullptr;
+    return RET_GRAPH_FILE_ERR;
   }
 
   if (tflite_model_->subgraphs.size() != 1) {
     MS_LOG(ERROR) << "read tflite model subgraphs failed";
     ReturnCode::GetSingleReturnCode()->UpdateReturnCode(RET_GRAPH_FILE_ERR);
-    return nullptr;
+    return RET_GRAPH_FILE_ERR;
   }
-  func_graph_ = std::make_shared<FuncGraph>();
+  res_graph_ = std::make_shared<FuncGraph>();
-  func_graph_->set_attr("fmk", MakeValue(static_cast<int>(converter::FmkType_TFLITE)));
+  res_graph_->set_attr("fmk", MakeValue(static_cast<int>(converter::FmkType_TFLITE)));
 
   auto status = ConvertGraphInputs();
   if (status != RET_OK) {
     MS_LOG(ERROR) << "Convert graph inputs failed.";
     ReturnCode::GetSingleReturnCode()->UpdateReturnCode(status);
-    return nullptr;
+    return status;
   }
 
   status = ConvertOps();
   if (status != RET_OK) {
     MS_LOG(ERROR) << "Convert ops failed.";
     ReturnCode::GetSingleReturnCode()->UpdateReturnCode(status);
-    return nullptr;
+    return status;
   }
 
   status = ConvertGraphOutputs();
   if (status != RET_OK) {
     MS_LOG(ERROR) << "Convert graph outputs failed.";
     ReturnCode::GetSingleReturnCode()->UpdateReturnCode(status);
-    return nullptr;
+    return status;
   }
-  func_graph_->set_attr("graph_name", MakeValue("main_graph"));
+  res_graph_->set_attr("graph_name", MakeValue("main_graph"));
-  return func_graph_;
+  return RET_OK;
 }
 
 std::string GetTensorName(size_t index, const tflite::BuiltinOperator &op_type, const std::string &op_name) {
@@ -158,7 +158,7 @@ STATUS TfliteModelParser::ConvertOps() {
       } else {
         tensor_name = GetTensorName(i, tflite_op_type, op_name);
       }
-      auto parameter = func_graph_->add_parameter();
+      auto parameter = res_graph_->add_parameter();
       status = ConvertConstTensor(input_tensor.get(), parameter, tensor_name);
       if (status != RET_OK) {
         MS_LOG(ERROR) << "convert " << op_name << " node: " << input_idx << " const node failed.";
@@ -168,7 +168,7 @@ STATUS TfliteModelParser::ConvertOps() {
       op_inputs.emplace_back(parameter);
       nodes_.insert(std::pair(input_idx, parameter));
     }
-    auto new_cnode = func_graph_->NewCNode(op_inputs);
+    auto new_cnode = res_graph_->NewCNode(op_inputs);
     new_cnode->set_fullname_with_scope(op_name);
 
     // parse outputs
@@ -284,13 +284,16 @@ STATUS TfliteModelParser::ConvertGraphInputs() {
     if (tflite_graph_input < 0) {
       tflite_graph_input = tflite_graph_input + tflite_subgraph->tensors.size();
     }
-    auto parameter = func_graph_->add_parameter();
+    auto parameter = res_graph_->add_parameter();
     const auto &tensor = tflite_subgraph->tensors.at(tflite_graph_input);
     std::vector<int64_t> shape_vector;
     (void)std::transform(tensor->shape.begin(), tensor->shape.end(), std::back_inserter(shape_vector),
                          [](const int32_t &value) { return static_cast<int64_t>(value); });
-    auto type_ptr = TypeIdToType(GetTfliteDataType(tensor->type));
+    auto abstract_tensor = CreateTensorAbstract(shape_vector, GetTfliteDataType(tensor->type));
-    auto abstract_tensor = std::make_shared<abstract::AbstractTensor>(type_ptr, shape_vector);
+    if (abstract_tensor == nullptr) {
+      MS_LOG(ERROR) << "Create tensor abstarct failed";
+      return RET_ERROR;
+    }
     parameter->set_abstract(abstract_tensor);
     parameter->set_name("graph_input-" + std::to_string(tflite_graph_input));
     nodes_.insert(std::pair(tflite_graph_input, parameter));
@@ -318,7 +321,7 @@ STATUS TfliteModelParser::ConvertGraphOutputs() {
       }
       make_tuple_inputs.emplace_back(cnode);
     }
-    auto make_tuple_cnode = func_graph_->NewCNode(make_tuple_inputs);
+    auto make_tuple_cnode = res_graph_->NewCNode(make_tuple_inputs);
     make_tuple_cnode->set_fullname_with_scope("return tuple");
 
     std::vector<AnfNodePtr> op_inputs;
@@ -330,9 +333,9 @@ STATUS TfliteModelParser::ConvertGraphOutputs() {
     auto value_node = NewValueNode(return_prim_ptr);
     op_inputs.emplace_back(value_node);
     op_inputs.emplace_back(make_tuple_cnode);
-    auto cnode = func_graph_->NewCNode(op_inputs);
+    auto cnode = res_graph_->NewCNode(op_inputs);
     cnode->set_fullname_with_scope("Return");
-    func_graph_->set_return(cnode);
+    res_graph_->set_return(cnode);
   } else {
     auto returnPrim = std::make_shared<ops::Return>();
     if (returnPrim == nullptr) {
@@ -350,9 +353,9 @@ STATUS TfliteModelParser::ConvertGraphOutputs() {
       return RET_NOT_FIND_OP;
     }
     op_inputs.emplace_back(cnode);
-    auto returnCnode = func_graph_->NewCNode(op_inputs);
+    auto returnCnode = res_graph_->NewCNode(op_inputs);
     returnCnode->set_fullname_with_scope("Return");
-    func_graph_->set_return(returnCnode);
+    res_graph_->set_return(returnCnode);
   }
   return RET_OK;
 }
@@ -436,8 +439,12 @@ STATUS TfliteModelParser::ConvertOutputTensor(const tflite::OperatorT *op, const
     std::vector<int64_t> shape_vector;
     (void)std::transform(tensor->shape.begin(), tensor->shape.end(), std::back_inserter(shape_vector),
                          [](const int32_t &value) { return static_cast<int64_t>(value); });
-    auto type_ptr = TypeIdToType(GetTfliteDataType(tensor->type));
+    auto abstract_tensor = CreateTensorAbstract(shape_vector, GetTfliteDataType(tensor->type));
-    dst_cnode->set_abstract(std::make_shared<abstract::AbstractTensor>(type_ptr, shape_vector));
+    if (abstract_tensor == nullptr) {
+      MS_LOG(ERROR) << "Create tensor abstarct failed";
+      return RET_ERROR;
+    }
+    dst_cnode->set_abstract(abstract_tensor);
     nodes_.insert(std::pair(op->outputs.front(), dst_cnode));
   } else {
     AbstractBasePtrList abstract_list;
@@ -450,8 +457,12 @@ STATUS TfliteModelParser::ConvertOutputTensor(const tflite::OperatorT *op, const
       std::vector<int64_t> shape_vector;
       (void)std::transform(tensor->shape.begin(), tensor->shape.end(), std::back_inserter(shape_vector),
                            [](const int32_t &value) { return static_cast<int64_t>(value); });
-      auto type_ptr = TypeIdToType(GetTfliteDataType(tensor->type));
+      auto abstract_tensor = CreateTensorAbstract(shape_vector, GetTfliteDataType(tensor->type));
-      abstract_list.emplace_back(std::make_shared<abstract::AbstractTensor>(type_ptr, shape_vector));
+      if (abstract_tensor == nullptr) {
+        MS_LOG(ERROR) << "Create tensor abstarct failed";
+        return RET_ERROR;
+      }
+      abstract_list.emplace_back(abstract_tensor);
       auto tuple_get_item_prim_ptr = std::make_shared<ops::TupleGetItem>();
       if (tuple_get_item_prim_ptr == nullptr) {
         MS_LOG(ERROR) << "new TupleGetItem failed";
@@ -460,7 +471,7 @@ STATUS TfliteModelParser::ConvertOutputTensor(const tflite::OperatorT *op, const
       auto tuple_get_item_prim = NewValueNode(tuple_get_item_prim_ptr);
       auto get_item_value = NewValueNode(MakeValue<int>(op_idx));
       std::vector<AnfNodePtr> inputs{tuple_get_item_prim, dst_cnode, get_item_value};
-      CNodePtr get_item_cnode = func_graph_->NewCNode(inputs);
+      CNodePtr get_item_cnode = res_graph_->NewCNode(inputs);
       get_item_cnode->set_fullname_with_scope(dst_cnode->fullname_with_scope() + "_getitem_" + std::to_string(op_idx));
       nodes_.insert(std::pair(output_idx, get_item_cnode));
       op_idx++;
@@ -469,4 +480,6 @@ STATUS TfliteModelParser::ConvertOutputTensor(const tflite::OperatorT *op, const
   }
   return RET_OK;
 }
+
+int TfliteModelParser::PostAdjust() { return 0; }
 }  // namespace mindspore::lite

mindspore/lite/tools/converter/parser/tflite/tflite_model_parser.h

@@ -32,13 +32,14 @@ class TfliteModelParser : public ModelParser {
 
   ~TfliteModelParser() override = default;
 
-  FuncGraphPtr Parse(const std::string &model_file, const std::string &weight_file,
+  int ParseToFuncGraph(const std::string &model_file, const std::string &weight_file,
-                     const QuantType &quant_type) override;
+                       const QuantType &quant_type) override;
+
+  int PostAdjust() override;
 
  private:
   std::unordered_map<int, AnfNodePtr> nodes_;
   std::unique_ptr<tflite::ModelT> tflite_model_;
-  FuncGraphPtr func_graph_;
   char *tflite_model_buf_ = nullptr;
   std::unique_ptr<tflite::ModelT> ReadTfliteModel(const char *model_path);
   STATUS ConvertConstTensor(const tflite::TensorT *tensor, const ParameterPtr &parameter,

mindspore/lite/tools/optimizer/common/gllo_utils.cc

@@ -399,6 +399,24 @@ int CheckIfCNodeIsNull(const CNodePtr &node) {
   return lite::RET_OK;
 }
 
+int CheckIfParameterIsNull(const ParameterPtr &node) {
+  if (node == nullptr) {
+    MS_LOG(ERROR) << "The Parameter is null.";
+    lite::ReturnCode::GetSingleReturnCode()->UpdateReturnCode(lite::RET_NULL_PTR);
+    return lite::RET_NULL_PTR;
+  }
+  return lite::RET_OK;
+}
+
+int CheckIfValueNodeIsNull(const ValueNodePtr &node) {
+  if (node == nullptr) {
+    MS_LOG(ERROR) << "The ValueNode is null.";
+    lite::ReturnCode::GetSingleReturnCode()->UpdateReturnCode(lite::RET_NULL_PTR);
+    return lite::RET_NULL_PTR;
+  }
+  return lite::RET_OK;
+}
+
 int CheckIfVarIsNull(const VarPtr &var) {
   if (var == nullptr) {
     MS_LOG(ERROR) << "The Var is null.";

mindspore/lite/tools/optimizer/common/gllo_utils.h

@@ -57,6 +57,10 @@ int CheckIfAnfNodeIsNull(const AnfNodePtr &node);
 
 int CheckIfCNodeIsNull(const CNodePtr &node);
 
+int CheckIfParameterIsNull(const ParameterPtr &node);
+
+int CheckIfValueNodeIsNull(const ValueNodePtr &node);
+
 int CheckIfVarIsNull(const VarPtr &var);
 
 int CheckInputSize(const CNodePtr &node, int size);

mindspore/lite/tools/optimizer/fusion/mul_add_fusion.cc

@@ -0,0 +1,294 @@
+/**
+ * Copyright 2021 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 "tools/optimizer/fusion/mul_add_fusion.h"
+#include <memory>
+#include "ops/fusion/mul_fusion.h"
+#include "ops/fusion/add_fusion.h"
+#include "ops/fusion/scale_fusion.h"
+#include "ops/op_utils.h"
+#include "tools/optimizer/common/gllo_utils.h"
+
+namespace mindspore::opt {
+namespace {
+constexpr size_t kMulInputsLength = 3;
+constexpr size_t kAddInputsLength = 3;
+}  // namespace
+
+const BaseRef MulAddFusion::DefinePattern() const {
+  auto mul_var = std::make_shared<CondVar>(IsSpecifiedNode<&prim::kPrimMulFusion>);
+  auto add_var = std::make_shared<CondVar>(IsSpecifiedNode<&prim::kPrimAddFusion>);
+  return VectorRef({add_var, mul_var});
+}
+
+bool MulAddFusion::ScaleInputShapeValid() const {
+  MS_ASSERT(scale_tensor_ != nullptr);
+  MS_ASSERT(bias_tensor_ != nullptr);
+  auto scale_shape = scale_tensor_->shape_c();
+  auto offset_shape = bias_tensor_->shape_c();
+  if (mul_input_shape_.size() < scale_shape.size() || scale_shape.size() == 0) {
+    return false;
+  }
+  size_t rank_diff = mul_input_shape_.size() - scale_shape.size();
+  for (size_t i = 0; i < scale_shape.size(); ++i) {
+    if (mul_input_shape_[i + rank_diff] != scale_shape[i]) {
+      return false;
+    }
+  }
+  if (scale_shape != offset_shape) {
+    return false;
+  }
+  return true;
+}
+
+bool MulAddFusion::CheckMulNode(const FuncGraphPtr &func_graph) const {
+  MS_ASSERT(func_graph != nullptr);
+  if (mul_anode_ == nullptr) {
+    return false;
+  }
+  if (IsMultiOutputTensors(func_graph, mul_anode_)) {
+    MS_LOG(DEBUG) << "Mul op has multi-output";
+    return false;
+  }
+  auto mul_node = mul_anode_->cast<CNodePtr>();
+  if (!CheckPrimitiveType(mul_node, prim::kPrimMulFusion)) {
+    MS_LOG(DEBUG) << "Mul add fusion pass match only mul or add";
+    return false;
+  }
+  auto mul_primitive = GetValueNode<std::shared_ptr<ops::MulFusion>>(mul_node->input(0));
+  MS_ASSERT(mul_primitive != nullptr);
+  auto mul_act_type = mul_primitive->get_activation_type();
+  if (mul_act_type != ActivationType::NO_ACTIVATION) {
+    MS_LOG(DEBUG) << "Only support mul node with no activation";
+    return false;
+  }
+  if (CheckIfCNodeIsNull(mul_node) != lite::RET_OK || CheckInputSize(mul_node, kMulInputsLength) != lite::RET_OK) {
+    MS_LOG(DEBUG) << "Mul op is null or has error input size";
+    return false;
+  }
+  // find mul's const input and mul input
+  AnfNodePtr mul_pre_input_node = nullptr;
+  AnfNodePtr mul_pre_const_node = nullptr;
+  auto mul_pre_node_1 = mul_node->input(1);
+  if (CheckIfAnfNodeIsNull(mul_pre_node_1) != lite::RET_OK) {
+    MS_LOG(DEBUG) << "Pre-node of mul op is nullptr";
+    return false;
+  }
+  auto mul_pre_node_2 = mul_node->input(2);
+  if (CheckIfAnfNodeIsNull(mul_pre_node_2) != lite::RET_OK) {
+    MS_LOG(DEBUG) << "Pre-node of mul op is nullptr";
+    return false;
+  }
+  if (utils::isa<CNodePtr>(mul_pre_node_1) && !utils::isa<CNodePtr>(mul_pre_node_2)) {
+    mul_pre_input_node = mul_pre_node_1;
+    mul_pre_const_node = mul_pre_node_2;
+  } else if (!utils::isa<CNodePtr>(mul_pre_node_1) && utils::isa<CNodePtr>(mul_pre_node_2)) {
+    mul_pre_input_node = mul_pre_node_1;
+    mul_pre_const_node = mul_pre_node_2;
+  } else {
+    MS_LOG(DEBUG) << "Mul op should has a cnode input and a const input";
+    return false;
+  }
+  // check mul's const input
+  tensor::TensorPtr mul_tensor = nullptr;
+  if (utils::isa<ParameterPtr>(mul_pre_const_node)) {
+    auto mul_bias_node = mul_pre_const_node->cast<ParameterPtr>();
+    MS_ASSERT(mul_bias_node != nullptr);
+    if (!mul_bias_node->has_default()) {
+      MS_LOG(DEBUG) << "Const input of mul op should has data";
+      return false;
+    }
+    mul_tensor = mul_bias_node->default_param()->cast<tensor::TensorPtr>();
+  } else if (utils::isa<ValueNodePtr>(mul_pre_const_node)) {
+    auto mul_bias_node = mul_pre_const_node->cast<ValueNodePtr>();
+    MS_ASSERT(mul_bias_node != nullptr);
+    if (mul_bias_node->value() == nullptr) {
+      MS_LOG(DEBUG) << "Const input of mul op should has data";
+      return false;
+    }
+    mul_tensor = mul_bias_node->value()->cast<tensor::TensorPtr>();
+  } else {
+    MS_ASSERT(false);
+  }
+  if (mul_tensor == nullptr) {
+    MS_LOG(DEBUG) << "Const input of add op should has data";
+    return false;
+  }
+  mul_input_anode_ = mul_pre_input_node;
+  mul_const_anode_ = mul_pre_const_node;
+  scale_tensor_ = mul_tensor;
+  return true;
+}
+
+bool MulAddFusion::CheckAddNode() const {
+  if (add_anode_ == nullptr) {
+    return false;
+  }
+  auto add_cnode = add_anode_->cast<CNodePtr>();
+  if (CheckIfCNodeIsNull(add_cnode) != lite::RET_OK || CheckInputSize(add_cnode, kAddInputsLength) != lite::RET_OK) {
+    MS_LOG(DEBUG) << "Add op is null or has error input size";
+    return false;
+  }
+  if (!CheckPrimitiveType(add_cnode, prim::kPrimAddFusion)) {
+    MS_LOG(DEBUG) << "Mul add fusion pass match only mul or add";
+    return false;
+  }
+  auto add_primitive = GetValueNode<std::shared_ptr<ops::AddFusion>>(add_cnode->input(0));
+  MS_ASSERT(add_primitive != nullptr);
+  auto add_act_type = add_primitive->get_activation_type();
+  if (add_act_type != ActivationType::RELU && add_act_type != ActivationType::RELU6 &&
+      add_act_type != ActivationType::NO_ACTIVATION) {
+    MS_LOG(DEBUG) << "Only support add node with relu or relu6 or no activation";
+    return false;
+  }
+  scale_act_type_ = add_act_type;
+  // find add's const input and mul input
+  AnfNodePtr add_pre_input_node = nullptr;
+  AnfNodePtr add_pre_const_node = nullptr;
+  auto add_pre_node_1 = add_cnode->input(1);
+  if (CheckIfAnfNodeIsNull(add_pre_node_1) != lite::RET_OK) {
+    MS_LOG(DEBUG) << "Pre-node of add op is nullptr";
+    return false;
+  }
+  auto add_pre_node_2 = add_cnode->input(2);
+  if (CheckIfAnfNodeIsNull(add_pre_node_2) != lite::RET_OK) {
+    MS_LOG(DEBUG) << "Pre-node of add op is nullptr";
+    return false;
+  }
+  if (utils::isa<CNodePtr>(add_pre_node_1) && !utils::isa<CNodePtr>(add_pre_node_2)) {
+    add_pre_input_node = add_pre_node_1;
+    add_pre_const_node = add_pre_node_2;
+  } else if (!utils::isa<CNodePtr>(add_pre_node_1) && utils::isa<CNodePtr>(add_pre_node_2)) {
+    add_pre_input_node = add_pre_node_2;
+    add_pre_const_node = add_pre_node_1;
+  } else {
+    MS_LOG(DEBUG) << "Add op should has a cnode input and a const input";
+    return false;
+  }
+  // check add's const input
+  tensor::TensorPtr add_tensor = nullptr;
+  if (utils::isa<ParameterPtr>(add_pre_const_node)) {
+    auto add_bias_node = add_pre_const_node->cast<ParameterPtr>();
+    MS_ASSERT(add_bias_node != nullptr);
+    if (!add_bias_node->has_default()) {
+      MS_LOG(DEBUG) << "Const input of add op should has data";
+      return false;
+    }
+    add_tensor = add_bias_node->default_param()->cast<tensor::TensorPtr>();
+  } else if (utils::isa<ValueNodePtr>(add_pre_const_node)) {
+    auto add_bias_node = add_pre_const_node->cast<ValueNodePtr>();
+    MS_ASSERT(add_bias_node != nullptr);
+    if (add_bias_node->value() == nullptr) {
+      MS_LOG(DEBUG) << "Const input of add op should has data";
+      return false;
+    }
+    add_tensor = add_bias_node->value()->cast<tensor::TensorPtr>();
+  } else {
+    MS_ASSERT(false);
+  }
+  if (add_tensor == nullptr) {
+    MS_LOG(DEBUG) << "Const input of add op should has data";
+    return false;
+  }
+  mul_anode_ = add_pre_input_node;
+  add_const_anode_ = add_pre_const_node;
+  bias_tensor_ = add_tensor;
+  return true;
+}
+
+bool MulAddFusion::GetMulInputShape() const {
+  MS_ASSERT(mul_input_anode_ != nullptr);
+  ShapeVector mul_input_shape;
+  AbstractBasePtr mul_input_abstract = nullptr;
+  if (utils::isa<ParameterPtr>(mul_input_anode_)) {
+    auto mul_input_node = mul_input_anode_->cast<ParameterPtr>();
+    MS_ASSERT(mul_bias_node != nullptr);
+    mul_input_abstract = mul_input_node->abstract();
+  } else if (utils::isa<ValueNodePtr>(mul_input_anode_)) {
+    auto mul_input_node = mul_input_anode_->cast<ValueNodePtr>();
+    MS_ASSERT(mul_input_node != nullptr);
+    mul_input_abstract = mul_input_node->abstract();
+  } else if (utils::isa<CNodePtr>(mul_input_anode_)) {
+    auto mul_input_node = mul_input_anode_->cast<CNodePtr>();
+    MS_ASSERT(mul_input_node != nullptr);
+    mul_input_abstract = mul_input_node->abstract();
+  } else {
+    MS_ASSERT(false);
+  }
+  if (mul_input_abstract == nullptr) {
+    MS_LOG(DEBUG) << "Mul input node has no abstract";
+    return false;
+  }
+  if (!utils::isa<abstract::AbstractTensorPtr>(mul_input_abstract)) {
+    MS_LOG(DEBUG) << "Abstract of mul input node should be AbstractTensor";
+    return false;
+  }
+  auto abstract_tensor = utils::cast<abstract::AbstractTensorPtr>(mul_input_abstract);
+  MS_ASSERT(abstract_tensor != nullptr);
+  MS_ASSERT(abstract_tensor->BuildShape() != nullptr);
+  if (!utils::isa<abstract::ShapePtr>(abstract_tensor->BuildShape())) {
+    MS_LOG(DEBUG) << "BuildShape of abstract of mul input node should be ShapePtr";
+    return false;
+  }
+  mul_input_shape_ = utils::cast<abstract::ShapePtr>(abstract_tensor->BuildShape())->shape();
+  return true;
+}
+
+const AnfNodePtr MulAddFusion::Process(const FuncGraphPtr &func_graph, const AnfNodePtr &node, const EquivPtr &) const {
+  MS_ASSERT(func_graph != nullptr);
+  MS_ASSERT(node != nullptr);
+  if (CheckIfFuncGraphIsNull(func_graph) != lite::RET_OK || CheckIfAnfNodeIsNull(node) != lite::RET_OK) {
+    lite::ReturnCode::GetSingleReturnCode()->UpdateReturnCode(lite::RET_NULL_PTR);
+    return nullptr;
+  }
+  add_anode_ = node;
+  if (!CheckAddNode()) {
+    MS_LOG(DEBUG) << "Add op is not suit for mul-add-fusion: " << node->fullname_with_scope();
+    return nullptr;
+  }
+  MS_ASSERT(mul_anode_ != nullptr);
+  MS_ASSERT(bias_tensor_ != nullptr);
+  MS_ASSERT(add_const_anode_ != nullptr);
+  if (!CheckMulNode(func_graph)) {
+    MS_LOG(DEBUG) << "Mul op is not suit for mul-add-fusion: " << mul_anode_->fullname_with_scope();
+    return nullptr;
+  }
+  MS_ASSERT(mul_input_anode_ != nullptr);
+  MS_ASSERT(scale_tensor_ != nullptr);
+  MS_ASSERT(mul_const_anode_ != nullptr);
+  if (!GetMulInputShape()) {
+    MS_LOG(DEBUG) << "Get input shape of mul op failed";
+    return nullptr;
+  }
+  // scale requires scale shape tail sub of input shape, scale shape same as bias shape
+  if (!ScaleInputShapeValid()) {
+    MS_LOG(DEBUG) << "Check input shape, scale shape and bias shape failed";
+    return nullptr;
+  }
+  // create scale primitive
+  auto scale_primitive = new (std::nothrow) mindspore::ops::ScaleFusion();
+  if (scale_primitive == nullptr) {
+    MS_LOG(ERROR) << "new scale primitive failed";
+    return nullptr;
+  }
+  scale_primitive->set_activation_type(scale_act_type_);
+  scale_primitive->set_axis(0 - bias_tensor_->shape_c().size());
+  // create scale op
+  auto scale_node = func_graph->NewCNode(std::shared_ptr<ops::PrimitiveC>(scale_primitive),
+                                         {mul_input_anode_, mul_const_anode_, add_const_anode_});
+  return scale_node;
+}
+}  // namespace mindspore::opt

mindspore/lite/tools/optimizer/fusion/mul_add_fusion.h

@@ -0,0 +1,53 @@
+/**
+ * Copyright 2021 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.
+ */
+
+#ifndef MINDSPORE_LITE_SRC_PASS_FUSION_MUL_ADD_FUSION_H_
+#define MINDSPORE_LITE_SRC_PASS_FUSION_MUL_ADD_FUSION_H_
+
+#include <string>
+#include "backend/optimizer/common/optimizer.h"
+#include "utils/check_convert_utils.h"
+
+namespace mindspore {
+namespace opt {
+class MulAddFusion : public PatternProcessPass {
+ public:
+  explicit MulAddFusion(bool multigraph = true, const std::string &name = "conv_activation_fusion")
+      : PatternProcessPass(name, multigraph) {}
+  ~MulAddFusion() override = default;
+  const BaseRef DefinePattern() const override;
+  const AnfNodePtr Process(const FuncGraphPtr &, const AnfNodePtr &, const EquivPtr &) const override;
+
+ private:
+  bool CheckMulNode(const FuncGraphPtr &func_graph) const;
+  bool CheckAddNode() const;
+  bool GetMulInputShape() const;
+  bool ScaleInputShapeValid() const;
+
+ private:
+  mutable AnfNodePtr mul_anode_ = nullptr;
+  mutable AnfNodePtr mul_input_anode_ = nullptr;
+  mutable AnfNodePtr mul_const_anode_ = nullptr;
+  mutable ShapeVector mul_input_shape_;
+  mutable AnfNodePtr add_anode_ = nullptr;
+  mutable AnfNodePtr add_const_anode_ = nullptr;
+  mutable tensor::TensorPtr scale_tensor_ = nullptr;
+  mutable tensor::TensorPtr bias_tensor_ = nullptr;
+  mutable ActivationType scale_act_type_ = ActivationType::NO_ACTIVATION;
+};
+}  // namespace opt
+}  // namespace mindspore
+#endif  // MINDSPORE_LITE_SRC_PASS_FUSION_CONV_ACTIVATION_FUSION_H_

mindspore/lite/tools/optimizer/fusion/tf_bidirection_gru_fusion.cc

@@ -256,11 +256,12 @@ ParameterPtr TfBidirectionGruFusion::AddDefaultParameter(const FuncGraphPtr &fun
   auto parameter = func_graph->add_parameter();
   parameter->set_name(name);
   std::vector<int64_t> shape_vector(shape.begin(), shape.end());
-  auto abstract_tensor = std::make_shared<abstract::AbstractTensor>(TypeIdToType(type), shape_vector);
+  auto abstract = lite::CreateTensorAbstract(shape_vector, type);
-  if (abstract_tensor == nullptr) {
+  if (abstract == nullptr) {
+    MS_LOG(ERROR) << "Create tensor abstarct failed";
     return nullptr;
   }
-  parameter->set_abstract(abstract_tensor);
+  parameter->set_abstract(abstract);
 
   auto gate_weight_default = std::make_shared<tensor::Tensor>(type, shape_vector);
   if (gate_weight_default == nullptr) {

mindspore/lite/tools/optimizer/fusion/tflite_lstm_cell_fusion.cc

@@ -502,13 +502,12 @@ CNodePtr TfliteLstmCellFusion::CreateOutputGetItem(const FuncGraphPtr &func_grap
     return nullptr;
   }
   CNodePtr get_item_cnode = func_graph->NewCNode(tuple_get_item_prim, {node, get_item_value});
-  std::vector<int64_t> shape_vector;
+  auto abstract = lite::CreateTensorAbstract({}, kNumberTypeFloat32);
-  auto abstract_tensor = std::make_shared<abstract::AbstractTensor>(kFloat32, shape_vector);
+  if (abstract == nullptr) {
-  if (abstract_tensor == nullptr) {
+    MS_LOG(ERROR) << "Create tensor abstarct failed";
-    MS_LOG(ERROR) << "create abstract_tensor failed";
     return nullptr;
   }
-  get_item_cnode->set_abstract(abstract_tensor);
+  get_item_cnode->set_abstract(abstract);
   get_item_cnode->set_fullname_with_scope(node->fullname_with_scope() + "_output_getitem_" +
                                           std::to_string(item_index));
   return get_item_cnode;
@@ -581,13 +580,12 @@ STATUS TfliteLstmCellFusion::SetAbstractTuple(const CNodePtr &cnode, const int o
   MS_ASSERT(cnode != nullptr);
   AbstractBasePtrList abstract_list;
   for (int i = 0; i < output_num; ++i) {
-    std::vector<int64_t> shape_vector;
+    auto abstract = lite::CreateTensorAbstract({}, kNumberTypeFloat32);
-    auto abstract_tensor = std::make_shared<abstract::AbstractTensor>(kFloat32, shape_vector);
+    if (abstract == nullptr) {
-    if (abstract_tensor == nullptr) {
+      MS_LOG(ERROR) << "Create tensor abstarct failed";
-      MS_LOG(ERROR) << "create abstract_tensor failed";
       return RET_ERROR;
     }
-    abstract_list.emplace_back(abstract_tensor);
+    abstract_list.emplace_back(abstract);
   }
   auto abstract_tuple = std::make_shared<abstract::AbstractTuple>(abstract_list);
   if (abstract_tuple == nullptr) {

mindspore/lite/tools/optimizer/graph/functionalize_while.cc

@@ -23,6 +23,7 @@
 #include "ops/return.h"
 #include "ops/tuple_get_item.h"
 #include "tools/converter/ops/while.h"
+#include "tools/common/tensor_util.h"
 
 namespace {
 mindspore::ValueNodePtr GetWhileAnfPrim() {
@@ -207,9 +208,13 @@ STATUS FunctionalizeWhile::UpdateExitNodeUser() {
       auto node_users = manager->node_users()[node];
       for (auto &node_user : node_users) {
         // new getitem
-        AbstractBasePtrList abstractList;
+        AbstractBasePtrList abstract_list;
-        std::vector<int64_t> shape_vector;
+        auto abstract = lite::CreateTensorAbstract({}, kNumberTypeFloat32);
-        abstractList.emplace_back(std::make_shared<abstract::AbstractTensor>(kFloat32, shape_vector));
+        if (abstract == nullptr) {
+          MS_LOG(ERROR) << "Create tensor abstarct failed";
+          return RET_ERROR;
+        }
+        abstract_list.emplace_back(abstract);
         auto tuple_get_item_prim_ptr = std::make_shared<ops::TupleGetItem>();
         if (tuple_get_item_prim_ptr == nullptr) {
           MS_LOG(ERROR) << "GetTupleGetItemPrim return nullptr";
@@ -225,12 +230,12 @@ STATUS FunctionalizeWhile::UpdateExitNodeUser() {
         std::vector<AnfNodePtr> inputs{tuple_get_item_prim, while_node_, getItemValue};
         CNodePtr get_item_node = fg_->NewCNode(inputs);
         std::string output_item_name = while_node_->fullname_with_scope() + "_getitem_" + std::to_string(output_idx);
-        auto abstract = std::make_shared<abstract::AbstractTensor>(kFloat32, shape_vector);
+        auto get_item_node_abstract = lite::CreateTensorAbstract({}, kNumberTypeFloat32);
-        if (abstract == nullptr) {
+        if (get_item_node_abstract == nullptr) {
-          MS_LOG(ERROR) << "create AbstractTensor failed";
+          MS_LOG(ERROR) << "Create get_item_node_abstract failed";
-          return RET_NULL_PTR;
+          return RET_ERROR;
         }
-        get_item_node->set_abstract(abstract);
+        get_item_node->set_abstract(get_item_node_abstract);
         get_item_node->set_fullname_with_scope(output_item_name);
         // set
         if (fg_->nodes().contains(node_user.first)) {

mindspore/lite/tools/optimizer/graph/group_depthwise_op_convert_pass.cc

@@ -22,6 +22,7 @@
 #include "src/tensor.h"
 #include "tools/converter/quantizer/quant_cast.h"
 #include "src/common/log_adapter.h"
+#include "tools/common/tensor_util.h"
 #include "securec/include/securec.h"
 
 namespace mindspore::opt {
@@ -101,13 +102,16 @@ bool GroupDepthwiseOpConvertPass::Run(const FuncGraphPtr &graph) {
         return false;
       }
       auto type_id = static_cast<TypeId>(weight_value->data_type());
-      auto type_ptr = TypeIdToType(type_id);
       auto shape = weight_value->shape();
       std::vector<int64_t> shape_vector;
       (void)std::transform(shape.begin(), shape.end(), std::back_inserter(shape_vector),
                            [](const int32_t &value) { return static_cast<int64_t>(value); });
-      auto abstract_tensor = std::make_shared<abstract::AbstractTensor>(type_ptr, shape_vector);
+      auto abstract = lite::CreateTensorAbstract(shape_vector, type_id);
-      weight_node->set_abstract(abstract_tensor);
+      if (abstract == nullptr) {
+        MS_LOG(ERROR) << "Create tensor abstarct failed";
+        return RET_ERROR;
+      }
+      weight_node->set_abstract(abstract);
     }
   }
   return true;

mindspore/lite/tools/optimizer/graph/infershape_pass.cc

@@ -21,6 +21,7 @@
 #include "tools/common/node_util.h"
 #include "tools/common/tensor_util.h"
 #include "src/common/common.h"
+#include "src/common/tensor_util.h"
 #include "src/ops/populate/populate_register.h"
 #include "src/ops/ops_utils.h"
 #include "src/runtime/infer_manager.h"
@@ -28,19 +29,6 @@
 namespace mindspore::opt {
 namespace {
 constexpr size_t INITIAL_SIZE = 1024;
-tensor::TensorPtr NewTensorInfo(lite::Tensor *tensor) {
-  std::vector<int> shape(tensor->shape());
-  std::vector<int64_t> shape_vector;
-  std::transform(shape.begin(), shape.end(), std::back_inserter(shape_vector),
-                 [](const int32_t &value) { return static_cast<int64_t>(value); });
-  auto tensor_info = std::make_shared<tensor::Tensor>(tensor->data_type(), shape_vector);
-  if (tensor_info == nullptr) {
-    MS_LOG(ERROR) << "new tensor::Tensor failed";
-    return nullptr;
-  }
-  return tensor_info;
-}
-
 bool IsSpecialType(const CNodePtr &cnode) {
   if (CheckPrimitiveType(cnode, prim::kPrimTupleGetItem) || CheckPrimitiveType(cnode, prim::kPrimDepend) ||
       CheckPrimitiveType(cnode, prim::kPrimMakeTuple) || CheckPrimitiveType(cnode, prim::kPrimReturn) ||
@@ -75,21 +63,14 @@ STATUS GetTensorInfoFromAbstract(tensor::TensorPtr *tensor_info, const CNodePtr
 }
 }  // namespace
 
-abstract::AbstractTensorPtr InferShapePass::ConvertLiteTensorToAbstractTensor(lite::Tensor *tensor) {
+abstract::AbstractBasePtr InferShapePass::ConvertLiteTensorToAbstract(lite::Tensor *tensor) {
   MS_ASSERT(nullptr != tensor);
-  std::vector<int> shape(tensor->shape());
+  auto shape = tensor->shape();
   auto type_id = static_cast<TypeId>(tensor->data_type());
-  auto type_ptr = TypeIdToType(type_id);
   std::vector<int64_t> shape_vector(shape.begin(), shape.end());
-  auto new_abstract = std::make_shared<abstract::AbstractTensor>(type_ptr, shape_vector);
+  auto tensor_info = lite::CreateTensorInfo(nullptr, 0, shape_vector, type_id);
-  if (new_abstract == nullptr) {
-    MS_LOG(ERROR) << "new AbstractTensor failed";
-    return nullptr;
-  }
-
-  auto tensor_info = NewTensorInfo(tensor);
   if (tensor_info == nullptr) {
-    MS_LOG(ERROR) << "new tensor::Tensor failed";
+    MS_LOG(DEBUG) << "Create tensor info failed";
     return nullptr;
   }
 
@@ -112,8 +93,12 @@ abstract::AbstractTensorPtr InferShapePass::ConvertLiteTensorToAbstractTensor(li
       return nullptr;
     }
   }
-  new_abstract->set_value(tensor_info);
+  auto abstract = tensor_info->ToAbstract();
-  return new_abstract;
+  if (abstract == nullptr) {
+    MS_LOG(DEBUG) << "Create tensor abstarct failed";
+    return nullptr;
+  }
+  return abstract;
 }
 
 STATUS InferShapePass::SetParameterAbstract(const ParameterPtr &parameter) {
@@ -143,8 +128,6 @@ STATUS InferShapePass::SetParameterAbstract(const ParameterPtr &parameter) {
   std::vector<int32_t> shape;
   (void)std::transform(shape_vector.begin(), shape_vector.end(), std::back_inserter(shape),
                        [](const int64_t &value) { return static_cast<int32_t>(value); });
-
-  auto new_abstract = std::make_shared<abstract::AbstractTensor>(type_ptr, shape_vector);
   auto new_tensor_info = std::make_shared<tensor::Tensor>(type_ptr->type_id(), shape_vector);
   if (parameter->has_default()) {
     auto old_tensor_info = std::dynamic_pointer_cast<tensor::Tensor>(parameter->default_param());
@@ -155,7 +138,11 @@ STATUS InferShapePass::SetParameterAbstract(const ParameterPtr &parameter) {
       return RET_ERROR;
     }
   }
-  new_abstract->set_value(new_tensor_info);
+  auto new_abstract = new_tensor_info->ToAbstract();
+  if (new_abstract == nullptr) {
+    MS_LOG(ERROR) << "Create tensor abstarct failed";
+    return RET_ERROR;
+  }
   parameter->set_abstract(new_abstract);
   return RET_OK;
 }
@@ -304,7 +291,7 @@ STATUS InferShapePass::SetCNodeAbstract(const std::vector<lite::Tensor *> &outpu
   }
   if (output_tensors.size() == 1) {
     auto tensor = output_tensors.front();
-    auto new_abstract = ConvertLiteTensorToAbstractTensor(tensor);
+    auto new_abstract = ConvertLiteTensorToAbstract(tensor);
     if (new_abstract == nullptr) {
       return RET_ERROR;
     }
@@ -313,7 +300,7 @@ STATUS InferShapePass::SetCNodeAbstract(const std::vector<lite::Tensor *> &outpu
     AbstractBasePtrList abstract_list;
     for (size_t i = 0; i < output_tensors.size(); i++) {
       auto tensor = output_tensors.front();
-      auto new_abstract = ConvertLiteTensorToAbstractTensor(tensor);
+      auto new_abstract = ConvertLiteTensorToAbstract(tensor);
       if (new_abstract == nullptr) {
         return RET_ERROR;
       }

mindspore/lite/tools/optimizer/graph/infershape_pass.h

@@ -36,7 +36,7 @@ class InferShapePass : public Pass {
 
  private:
   void FreeTensors(std::vector<lite::Tensor *> *tensors);
-  abstract::AbstractTensorPtr ConvertLiteTensorToAbstractTensor(lite::Tensor *tensor);
+  abstract::AbstractBasePtr ConvertLiteTensorToAbstract(lite::Tensor *tensor);
   STATUS GetCNodeInputTensors(const CNodePtr &cnode, std::vector<lite::Tensor *> *input_tensors);
   STATUS GetCNodeOutputTensors(const CNodePtr &cnode, std::vector<lite::Tensor *> *output_tensors);
   STATUS SetParameterAbstract(const ParameterPtr &parameter);

mindspore/lite/tools/optimizer/graph/mindir_adjust_pass.cc

@@ -179,23 +179,23 @@ int MindirAdjustPass::ValueNodeInt64Convert(AnfNodePtr anf_node) {
   if (!utils::isa<ValueNodePtr>(anf_node)) {
     return lite::RET_NO_CHANGE;
   }
-  auto valueNode = anf_node->cast<ValueNodePtr>();
+  auto value_node = anf_node->cast<ValueNodePtr>();
-  if (valueNode->abstract() == nullptr) {
+  if (value_node->abstract() == nullptr) {
     return lite::RET_NO_CHANGE;
   }
-  auto abstractTensor = utils::cast<abstract::AbstractTensorPtr>(valueNode->abstract());
+  auto abstract_tensor = utils::cast<abstract::AbstractTensorPtr>(value_node->abstract());
-  if (abstractTensor == nullptr) {
+  if (abstract_tensor == nullptr) {
     return lite::RET_NO_CHANGE;
   }
-  auto value = abstractTensor->GetValueTrack();
+  auto value = abstract_tensor->GetValueTrack();
   if (value != nullptr && value->isa<tensor::Tensor>()) {
-    if (abstractTensor->element() == nullptr) {
+    if (abstract_tensor->element() == nullptr) {
       MS_LOG(ERROR) << "abstractTensor->element() is nullptr.";
       return RET_ERROR;
     }
-    auto typePtr = abstractTensor->element()->GetTypeTrack();
+    auto type_ptr = abstract_tensor->element()->GetTypeTrack();
-    if (typePtr->type_id() == kNumberTypeInt64) {
+    if (type_ptr->type_id() == kNumberTypeInt64) {
-      auto shape_vector = utils::cast<abstract::ShapePtr>(abstractTensor->BuildShape())->shape();
+      auto shape_vector = utils::cast<abstract::ShapePtr>(abstract_tensor->BuildShape())->shape();
       auto dest_tensor_info = std::make_shared<tensor::Tensor>(kNumberTypeInt32, shape_vector);
       auto *dest_data_buf = reinterpret_cast<int32_t *>(dest_tensor_info->data_c());
       auto src_tensor_info = value->cast<tensor::TensorPtr>();
@@ -204,10 +204,10 @@ int MindirAdjustPass::ValueNodeInt64Convert(AnfNodePtr anf_node) {
       for (int i = 0; i < dest_tensor_info->ElementsNum(); i++) {
         dest_data_buf[i] = src_data_buf[i];
       }
-      abstractTensor->set_value(dest_tensor_info);
+      abstract_tensor->set_value(dest_tensor_info);
-      abstractTensor->set_type(TypeIdToType(kNumberTypeInt32));
+      abstract_tensor->set_type(TypeIdToType(kNumberTypeInt32));
-      abstractTensor->element()->set_type(TypeIdToType(kNumberTypeInt32));
+      abstract_tensor->element()->set_type(TypeIdToType(kNumberTypeInt32));
-      valueNode->set_value(dest_tensor_info);
+      value_node->set_value(dest_tensor_info);
     }
   }
   return lite::RET_NO_CHANGE;

mindspore/lite/tools/optimizer/graph/weight_format_transform_pass.cc

@@ -19,6 +19,7 @@
 #include <vector>
 #include "ops/transpose.h"
 #include "tools/optimizer/common/gllo_utils.h"
+#include "tools/common/tensor_util.h"
 
 using mindspore::lite::converter::FmkType_CAFFE;
 using mindspore::lite::converter::FmkType_MS;
@@ -92,9 +93,20 @@ lite::STATUS WeightFormatTransformPass::TransposeInsertForWeightSharing(const Fu
   auto perm_node = BuildIntVecParameterNode(graph, perm, weight_node->fullname_with_scope() + "_perm");
   auto prim = std::make_shared<ops::Transpose>();
   auto transpose_node = graph->NewCNode(prim, {weight_node, perm_node});
-  auto type_ptr = TypeIdToType(kTypeUnknown);
+  if (!weight_node->has_default()) {
-  std::vector<int64_t> shape_vector;
+    MS_LOG(DEBUG) << "Weight parameter should has default parameter.";
-  auto abstract = std::make_shared<abstract::AbstractTensor>(type_ptr, shape_vector);
+    return lite::RET_ERROR;
+  }
+  auto weight_tensor = weight_node->default_param()->cast<tensor::TensorPtr>();
+  if (weight_tensor == nullptr) {
+    MS_LOG(DEBUG) << "Default parameter of weight parameter should be a tensor.";
+    return lite::RET_ERROR;
+  }
+  auto abstract = lite::CreateTensorAbstract(weight_tensor->shape_c(), weight_tensor->data_type());
+  if (abstract == nullptr) {
+    MS_LOG(ERROR) << "Create tensor abstarct failed";
+    return RET_ERROR;
+  }
   transpose_node->set_abstract(abstract);
   transpose_node->set_fullname_with_scope(weight_node->fullname_with_scope() + "_post");
   for (auto &adjust_node : adjust_nodes) {
@@ -177,11 +189,14 @@ lite::STATUS WeightFormatTransformPass::ConvWeightFormatTrans(const FuncGraphPtr
       return false;
     }
     auto type_id = static_cast<TypeId>(weight_value->data_type());
-    auto type_ptr = TypeIdToType(type_id);
     auto shape = weight_value->shape();
     std::vector<int64_t> shape_vector(shape.begin(), shape.end());
-    auto abstract_tensor = std::make_shared<abstract::AbstractTensor>(type_ptr, shape_vector);
+    auto abstract = lite::CreateTensorAbstract(shape_vector, type_id);
-    weight_node->set_abstract(abstract_tensor);
+    if (abstract == nullptr) {
+      MS_LOG(ERROR) << "Create tensor abstarct failed";
+      return RET_ERROR;
+    }
+    weight_node->set_abstract(abstract);
   }
   return RET_OK;
 }