graph compile performance optimization

Signed-off-by: Nzhoufeng <zhoufeng54@huawei.com>

mindspore/ccsrc/backend/kernel_compiler/kernel_build_info.cc

@@ -52,13 +52,13 @@ TypeId KernelBuildInfo::GetOutputDeviceType(size_t output_index) const {
   return outputs_device_type_[output_index];
 }
 
-std::vector<std::string> KernelBuildInfo::GetAllInputFormats() const { return inputs_format_; }
+const std::vector<std::string> &KernelBuildInfo::GetAllInputFormats() const { return inputs_format_; }
 
-std::vector<std::string> KernelBuildInfo::GetAllOutputFormats() const { return outputs_format_; }
+const std::vector<std::string> &KernelBuildInfo::GetAllOutputFormats() const { return outputs_format_; }
 
-std::vector<TypeId> KernelBuildInfo::GetAllInputDeviceTypes() const { return inputs_device_type_; }
+const std::vector<TypeId> &KernelBuildInfo::GetAllInputDeviceTypes() const { return inputs_device_type_; }
 
-std::vector<TypeId> KernelBuildInfo::GetAllOutputDeviceTypes() const { return outputs_device_type_; }
+const std::vector<TypeId> &KernelBuildInfo::GetAllOutputDeviceTypes() const { return outputs_device_type_; }
 
 size_t KernelBuildInfo::GetInputNum() const { return inputs_format_.size(); }
 

mindspore/ccsrc/backend/kernel_compiler/kernel_build_info.h

@@ -63,13 +63,13 @@ class KernelBuildInfo {
 
   std::vector<Axis> GetOutputReshapeType(size_t input_index) const;
 
-  std::vector<std::string> GetAllInputFormats() const;
+  const std::vector<std::string> &GetAllInputFormats() const;
 
-  std::vector<std::string> GetAllOutputFormats() const;
+  const std::vector<std::string> &GetAllOutputFormats() const;
 
-  std::vector<TypeId> GetAllInputDeviceTypes() const;
+  const std::vector<TypeId> &GetAllInputDeviceTypes() const;
 
-  std::vector<TypeId> GetAllOutputDeviceTypes() const;
+  const std::vector<TypeId> &GetAllOutputDeviceTypes() const;
 
   std::vector<std::vector<Axis>> GetAllOutputReshapeType() const;
 

mindspore/ccsrc/backend/kernel_compiler/kernel_fusion.h

@@ -25,6 +25,10 @@ namespace kernel {
  * @brief fuse op and return a callable mod
  */
 struct FusionScopeInfo {
+  FusionScopeInfo() {}
+  FusionScopeInfo(int32_t id, const std::vector<AnfNodePtr> &in, const std::vector<AnfNodePtr> &comp,
+                  const std::vector<AnfNodePtr> &out)
+      : scope_id(id), input_nodes(in), compute_nodes(comp), output_nodes(out) {}
   int32_t scope_id;
   std::vector<AnfNodePtr> input_nodes;
   std::vector<AnfNodePtr> compute_nodes;

mindspore/ccsrc/backend/kernel_compiler/oplib/opinfo.h

@@ -59,13 +59,13 @@ class OpIOInfo {
   ~OpIOInfo() = default;
 
   int index() const { return index_; }
-  std::string name() const { return name_; }
+  const std::string &name() const { return name_; }
   bool need_compile() const { return need_compile_; }
-  std::string param_type() const { return param_type_; }
-  std::string reshape_type() const { return reshape_type_; }
-  std::string shape() const { return shape_; }
-  std::vector<std::string> dtypes() const { return dtypes_; }
-  std::vector<std::string> formats() const { return formats_; }
+  const std::string &param_type() const { return param_type_; }
+  const std::string &reshape_type() const { return reshape_type_; }
+  const std::string &shape() const { return shape_; }
+  const std::vector<std::string> &dtypes() const { return dtypes_; }
+  const std::vector<std::string> &formats() const { return formats_; }
 
   void set_index(const int index) { index_ = index; }
   void set_name(const std::string &name) { name_ = name; }

mindspore/ccsrc/backend/kernel_compiler/oplib/oplib.cc

@@ -336,13 +336,11 @@ std::shared_ptr<OpInfo> OpLib::FindOp(const std::string &op_name, OpImplyType im
                   << ", current op num: " << op_info_.size();
     return nullptr;
   }
+  std::string target_processor = is_gpu ? kCUDA : kAiCore;
   for (const auto &op_info : op_info_) {
     MS_EXCEPTION_IF_NULL(op_info);
     if (op_info->op_name() == op_name && op_info->imply_type() == imply_type) {
-      auto akg_processor_match = [&]() {
-        return is_gpu ? op_info->processor() == kCUDA : op_info->processor() == kAiCore;
-      };
-      if (imply_type != kAKG || akg_processor_match()) {
+      if (imply_type != kAKG || op_info->processor() == target_processor) {
         return op_info;
       }
     }

mindspore/ccsrc/backend/kernel_compiler/tbe/tbe_kernel_select/tbe_kernel_select.cc

@@ -82,7 +82,6 @@ void TbeKernelSelect::TbeMetadataInfoEx() {
   }
   // check support
   FilterInVaildKernelInfo();
-  MS_LOG(INFO) << "End get kernel build info size: " << kernel_info_list_->size() << ", after tbe select.";
 }
 
 void TbeKernelSelect::GetCommonPatternKernelInfo(const OpInfo &op_info) {
@@ -221,38 +220,37 @@ void TbeKernelSelect::FilterInVaildKernelInfo() {
     MS_LOG(INFO) << "Warning: get kernel build info failed.";
     return;
   }
-  auto kernel_build_info_iter = kernel_info_list_->begin();
-  while (kernel_build_info_iter != kernel_info_list_->end()) {
-    if (!FilterInVaildShape(kernel_build_info_iter)) {
-      MS_LOG(INFO) << "Filter invaild shape, filter item info: " << (*kernel_build_info_iter)->ToString();
-      kernel_build_info_iter = kernel_info_list_->erase(kernel_build_info_iter);
+  std::vector<std::shared_ptr<KernelBuildInfo>> new_kernel_info_list;
+  for (auto iter = kernel_info_list_->begin(); iter != kernel_info_list_->end(); ++iter) {
+    if (!FilterInVaildShape(iter)) {
+      MS_LOG(INFO) << "Filter invaild shape, filter item info: " << (*iter)->ToString();
       continue;
     }
-    if (!TbeCheckSupported(kernel_build_info_iter)) {
-      MS_LOG(INFO) << "Check support shape, filter item info: " << (*kernel_build_info_iter)->ToString();
-      kernel_build_info_iter = kernel_info_list_->erase(kernel_build_info_iter);
+    if (!TbeCheckSupported(iter)) {
+      MS_LOG(INFO) << "Check support shape, filter item info: " << (*iter)->ToString();
       continue;
     }
-    kernel_build_info_iter++;
+    new_kernel_info_list.emplace_back(*iter);
   }
+  (*kernel_info_list_) = new_kernel_info_list;
 }
 
 bool TbeKernelSelect::FilterInVaildShape(
   const mindspore::kernel::TbeKernelSelect::KernelBuildInfoIter &kernel_build_info_iter) {
   MS_EXCEPTION_IF_NULL((*kernel_build_info_iter));
-  auto kernel_build_info_inputs_format = (*kernel_build_info_iter)->GetAllInputFormats();
+  const auto &kernel_build_info_inputs_format = (*kernel_build_info_iter)->GetAllInputFormats();
   for (size_t i = 0; i < kernel_build_info_inputs_format.size(); ++i) {
     auto shape = AnfAlgo::GetPrevNodeOutputInferShape(cnode_ptr_, i);
-    auto format = kernel_build_info_inputs_format.at(i);
+    const auto &format = kernel_build_info_inputs_format[i];
     if (!IsShapeMatchFormat(shape, format)) {
       MS_LOG(INFO) << "The " << i << "th input check failed.";
       return false;
     }
   }
-  auto kernel_build_info_outputs_format = (*kernel_build_info_iter)->GetAllOutputFormats();
+  const auto &kernel_build_info_outputs_format = (*kernel_build_info_iter)->GetAllOutputFormats();
   for (size_t j = 0; j < kernel_build_info_outputs_format.size(); ++j) {
     auto shape = AnfAlgo::GetOutputInferShape(cnode_ptr_, j);
-    auto format = kernel_build_info_outputs_format.at(j);
+    const auto &format = kernel_build_info_outputs_format[j];
     if (!IsShapeMatchFormat(shape, format)) {
       MS_LOG(INFO) << "The " << j << "th input check failed.";
       return false;
@@ -344,12 +342,12 @@ bool TbeKernelSelect::GenBuilderItem(bool is_input, size_t kernel_build_info_ind
   size_t io_info_num = ios_info.size();
   for (; io_info_index < io_info_num && real_io_tensor_index < real_io_tensor_num; io_info_index++) {
     std::shared_ptr<OpIOInfo> io_info_item = ios_info[io_info_index];
-    auto kernel_build_info_dtype = io_info_item->dtypes().at(kernel_build_info_index);
+    const auto &kernel_build_info_dtype = io_info_item->dtypes()[kernel_build_info_index];
     std::string kernel_build_info_format;
     if (!io_info_item->formats().empty()) {
-      kernel_build_info_format = io_info_item->formats().at(kernel_build_info_index);
+      kernel_build_info_format = io_info_item->formats()[kernel_build_info_index];
     }
-    std::string io_param_type = io_info_item->param_type();
+    const std::string &io_param_type = io_info_item->param_type();
     std::vector<Axis> reshape_type;
     StringToAxisVector(io_info_item->reshape_type(), &reshape_type);
     if (io_param_type == kParamTypeDynamic) {
@@ -367,6 +365,7 @@ bool TbeKernelSelect::GenBuilderItem(bool is_input, size_t kernel_build_info_ind
         }
         dynamic_input_index++;
         real_io_tensor_index += dynamic_input_size;
+
       } else {
         if (ios_info.size() != 1) {
           MS_LOG(EXCEPTION) << "if output is dynamic, so output must has one output.";
@@ -388,7 +387,6 @@ bool TbeKernelSelect::GenBuilderItem(bool is_input, size_t kernel_build_info_ind
       MS_LOG(EXCEPTION) << "op info's param type is not match: " << io_param_type;
     }
   }
-
   if (io_info_index != io_info_num) {
     MS_LOG(INFO) << "Warning: io_info_index(" << io_info_index << ") != io_info_num(" << io_info_num
                  << "), this node may has optional input/output.";

mindspore/ccsrc/backend/optimizer/ascend/ascend_helper.cc

@@ -51,11 +51,11 @@ AnfNodePtr CreateReshapeNode(const FuncGraphPtr &func_graph, const AnfNodePtr &i
 AnfNodePtr AddTransOpNodeToGraph(const FuncGraphPtr &func_graph, const AnfNodePtr &node,
                                  const KernelSelectPtr &kernel_select, size_t insert_index, bool is_insert_input) {
   AnfNodePtr trans_node = nullptr;
-  AnfNodePtr input_node = node;
+  AnfNodePtr input_node = nullptr;
   CNodePtr trans_data = nullptr;
   std::string input_format = is_insert_input ? kOpFormat_DEFAULT : AnfAlgo::GetOutputFormat(node, 0);
   std::string dst_format = is_insert_input ? AnfAlgo::GetInputFormat(node, 0) : kOpFormat_DEFAULT;
-  std::vector<kernel::Axis> padding_axis = AnfAlgo::GetOutputReshapeType(node, 0);
+  std::vector<kernel::Axis> padding_axis;
   MS_EXCEPTION_IF_NULL(node);
   // if insert transdata for input we need to change the input
   if (is_insert_input) {
@@ -66,12 +66,17 @@ AnfNodePtr AddTransOpNodeToGraph(const FuncGraphPtr &func_graph, const AnfNodePt
     dst_format = AnfAlgo::GetInputFormat(cnode, insert_index);
     input_node = AnfAlgo::GetInputNode(cnode, insert_index);
     padding_axis = AnfAlgo::GetInputReshapeType(node, insert_index);
+  } else {
+    input_node = node;
+    padding_axis = AnfAlgo::GetOutputReshapeType(node, 0);
   }
+
+  auto input_node_out_shape = AnfAlgo::GetOutputInferShape(input_node, 0);
   bool need_padding = false;
   if (is_insert_input) {
-    need_padding = (trans::IsNeedPadding(dst_format, AnfAlgo::GetOutputInferShape(input_node, 0).size()));
+    need_padding = (trans::IsNeedPadding(dst_format, input_node_out_shape.size()));
   } else {
-    need_padding = (trans::IsNeedPadding(input_format, AnfAlgo::GetOutputInferShape(input_node, 0).size()));
+    need_padding = (trans::IsNeedPadding(input_format, input_node_out_shape.size()));
   }
   if (!need_padding) {
     // don't need padding insert transdata only
@@ -80,8 +85,7 @@ AnfNodePtr AddTransOpNodeToGraph(const FuncGraphPtr &func_graph, const AnfNodePt
   } else if (is_insert_input) {
     // if need padding & is input need insert a transdata
     // reshape[padding shape] -> transdata[padding shape] -> node
-    auto padding_shape =
-      trans::PaddingShapeTo4d(AnfAlgo::GetOutputInferShape(input_node, 0), AnfAlgo::GetInputReshapeType(node, 0));
+    auto padding_shape = trans::PaddingShapeTo4d(input_node_out_shape, AnfAlgo::GetInputReshapeType(node, 0));
     auto reshape_node = CreateReshapeNode(func_graph, input_node, kernel_select, padding_shape);
     trans_data = NewTransOpNode(func_graph, reshape_node, kernel_select, need_padding, prim::KPrimTransData->name());
     trans_node = trans_data;
@@ -89,8 +93,7 @@ AnfNodePtr AddTransOpNodeToGraph(const FuncGraphPtr &func_graph, const AnfNodePt
     // if need padding & is output need insert a transdata
     // node -> transdata[padding shape] -> reshape[ori_shape]
     trans_data = NewTransOpNode(func_graph, input_node, kernel_select, need_padding, prim::KPrimTransData->name());
-    auto reshape_node =
-      CreateReshapeNode(func_graph, trans_data, kernel_select, AnfAlgo::GetOutputInferShape(input_node, 0));
+    auto reshape_node = CreateReshapeNode(func_graph, trans_data, kernel_select, input_node_out_shape);
     trans_node = reshape_node;
   }
   // refresh the transdata's format to ori format & dst format
@@ -140,10 +143,10 @@ AnfNodePtr InsertTransOpForMultipleOutput(const FuncGraphPtr &func_graph, const
                                           const KernelSelectPtr &kernel_select) {
   MS_EXCEPTION_IF_NULL(func_graph);
   MS_EXCEPTION_IF_NULL(node);
-  std::vector<AnfNodePtr> make_tuple_inputs;
-  make_tuple_inputs.push_back(NewValueNode(prim::kPrimMakeTuple));
+  std::vector<AnfNodePtr> make_tuple_inputs = {NewValueNode(prim::kPrimMakeTuple)};
   auto kernel_graph = func_graph->cast<KernelGraphPtr>();
-  for (size_t output_idx = 0; output_idx < AnfAlgo::GetOutputTensorNum(node); ++output_idx) {
+  size_t out_num = AnfAlgo::GetOutputTensorNum(node);
+  for (size_t output_idx = 0; output_idx < out_num; ++output_idx) {
     std::string output_format = AnfAlgo::GetOutputFormat(node, output_idx);
     if (output_format == kOpFormat_NC1KHKWHWC0) {
       MS_LOG(EXCEPTION) << "Got the special format" << output_format << " when insert the transdata node "
@@ -151,12 +154,12 @@ AnfNodePtr InsertTransOpForMultipleOutput(const FuncGraphPtr &func_graph, const
     }
     auto tuple_getitem = CreatTupleGetItemNode(func_graph, node, output_idx);
     std::vector<size_t> origin_shape = AnfAlgo::GetOutputInferShape(node, output_idx);
-    if (kCommonFormatSet.find(output_format) == kCommonFormatSet.end() && origin_shape.size() > 1) {
+    if (origin_shape.size() > 1 && kCommonFormatSet.find(output_format) == kCommonFormatSet.end()) {
       auto trans_op = AddTransOpNodeToGraph(func_graph, tuple_getitem, kernel_select, 0, false);
       if (kernel_graph != nullptr && kernel_graph->IsInternalOutput(node, output_idx)) {
         kernel_graph->ReplaceInternalOutput(node, trans_op, output_idx, 0);
       }
-      make_tuple_inputs.emplace_back(trans_op);
+      make_tuple_inputs.push_back(trans_op);
     } else {
       // No need insert trans op.
       make_tuple_inputs.push_back(tuple_getitem);
@@ -188,15 +191,11 @@ CNodePtr NewTransOpNode(const FuncGraphPtr &func_graph, const AnfNodePtr &input,
                         const bool need_padding, const std::string &op_name) {
   MS_EXCEPTION_IF_NULL(func_graph);
   MS_EXCEPTION_IF_NULL(input);
-  std::vector<AnfNodePtr> trans_inputs;
-  auto prim = std::make_shared<Primitive>(op_name);
-  trans_inputs.push_back(NewValueNode(prim));
-  trans_inputs.push_back(input);
-  CNodePtr trans_node = func_graph->NewCNode(trans_inputs);
+  CNodePtr trans_node = func_graph->NewCNode({NewValueNode(std::make_shared<Primitive>(op_name)), input});
   MS_EXCEPTION_IF_NULL(trans_node);
-  auto padding_axis = AnfAlgo::GetOutputReshapeType(input, 0);
   if (need_padding) {
     // if need padding we should set the transdata node's shape to the padding shape
+    auto padding_axis = AnfAlgo::GetOutputReshapeType(input, 0);
     AnfAlgo::SetOutputInferTypeAndShape({AnfAlgo::GetOutputInferDataType(input, 0)},
                                         {trans::PaddingShapeTo4d(AnfAlgo::GetOutputInferShape(input, 0), padding_axis)},
                                         trans_node.get());
@@ -224,11 +223,7 @@ AnfNodePtr AddCastOpNodeToGraph(const FuncGraphPtr &func_graph, const AnfNodePtr
   MS_EXCEPTION_IF_NULL(func_graph);
   std::string input_format = format;
   std::string output_format = format;
-  std::vector<AnfNodePtr> new_cast_inputs;
-  auto prim = std::make_shared<Primitive>(prim::kPrimCast->name());
-  new_cast_inputs.push_back(NewValueNode(prim));
-  new_cast_inputs.push_back(input);
-  CNodePtr cast = func_graph->NewCNode(new_cast_inputs);
+  CNodePtr cast = func_graph->NewCNode({NewValueNode(std::make_shared<Primitive>(prim::kPrimCast->name())), input});
   MS_EXCEPTION_IF_NULL(cast);
   // set kernel build info
   kernel::KernelBuildInfo::KernelBuildInfoBuilder builder;
@@ -280,7 +275,8 @@ AnfNodePtr InsertTransOpForInput(const FuncGraphPtr &func_graph, const AnfNodePt
   auto cnode = node->cast<CNodePtr>();
   MS_EXCEPTION_IF_NULL(cnode);
   std::vector<AnfNodePtr> new_inputs = {AnfAlgo::GetCNodePrimitiveNode(cnode)};
-  for (size_t input_index = 0; input_index < AnfAlgo::GetInputTensorNum(cnode); ++input_index) {
+  size_t in_num = AnfAlgo::GetInputTensorNum(cnode);
+  for (size_t input_index = 0; input_index < in_num; ++input_index) {
     AnfNodePtr input_node = GetTransInputNodePtr(func_graph, cnode, input_index, kernel_select);
     MS_EXCEPTION_IF_NULL(input_node);
     new_inputs.push_back(input_node);
@@ -301,8 +297,10 @@ AnfNodePtr InsertTransOpForInput(const FuncGraphPtr &func_graph, const AnfNodePt
 CNodePtr InsertCastForInput(const FuncGraphPtr &func_graph, const CNodePtr &cnode) {
   MS_EXCEPTION_IF_NULL(cnode);
   std::vector<AnfNodePtr> new_inputs = {AnfAlgo::GetCNodePrimitiveNode(cnode)};
-  for (size_t input_index = 0; input_index < AnfAlgo::GetInputTensorNum(cnode); ++input_index) {
-    const auto infer_type = AnfAlgo::GetPrevNodeOutputInferDataType(cnode, input_index);
+  size_t in_num = AnfAlgo::GetInputTensorNum(cnode);
+  for (size_t input_index = 0; input_index < in_num; ++input_index) {
+    auto prev_node = AnfAlgo::GetPrevNodeOutput(cnode, input_index);
+    const auto infer_type = AnfAlgo::GetOutputInferDataType(prev_node.first, prev_node.second);
     TypeId origin_type(kTypeUnknown);
     auto cur_input = AnfAlgo::GetInputNode(cnode, input_index);
     auto kernel_with_index = AnfAlgo::VisitKernel(cur_input, 0);
@@ -311,20 +309,19 @@ CNodePtr InsertCastForInput(const FuncGraphPtr &func_graph, const CNodePtr &cnod
       // weight
       origin_type = AnfAlgo::GetPrevNodeOutputPrecision(cnode, input_index);
       if (origin_type == kTypeUnknown) {
-        origin_type = AnfAlgo::GetPrevNodeOutputDeviceDataType(cnode, input_index);
+        origin_type = AnfAlgo::GetOutputDeviceDataType(prev_node.first, prev_node.second);
       }
     } else {
       // feature map
-      origin_type = AnfAlgo::GetPrevNodeOutputInferDataType(cnode, input_index);
+      origin_type = AnfAlgo::GetOutputInferDataType(prev_node.first, prev_node.second);
     }
     const std::string dev_fmt = AnfAlgo::GetInputFormat(cnode, input_index);
-    const std::vector<size_t> origin_shape = AnfAlgo::GetPrevNodeOutputInferShape(cnode, input_index);
-    const TypeId device_type = AnfAlgo::GetInputDeviceDataType(cnode, input_index);
+    const std::vector<size_t> origin_shape = AnfAlgo::GetOutputInferShape(prev_node.first, prev_node.second);
     // In graph kernel, we check parameter,
     // the eliminate pass will not eliminate this case, so we just do not insert the noused cast.
     if (func_graph->has_attr(FUNC_GRAPH_ATTR_GRAPH_KERNEL) && IsValueNode<tensor::Tensor>(cur_input)) {
       new_inputs.push_back(cur_input);
-    } else if (origin_type != device_type) {
+    } else if (TypeId device_type = AnfAlgo::GetInputDeviceDataType(cnode, input_index); origin_type != device_type) {
       auto cast =
         AddCastOpNodeToGraph(func_graph, cur_input, dev_fmt, origin_type, device_type, origin_shape, infer_type);
       MS_EXCEPTION_IF_NULL(cast);

mindspore/ccsrc/backend/optimizer/ascend/buffer_fusion/ub_pattern_fusion.cc

@@ -120,8 +120,8 @@ kernel::KernelBuildInfoPtr CreateFusionOpKernelInfo(const std::vector<AnfNodePtr
   std::vector<TypeId> inputs_data_type;
   for (const auto &input : inputs_list) {
     auto real_input = AnfAlgo::VisitKernel(input, 0);
-    inputs_format.push_back(AnfAlgo::GetOutputFormat(real_input.first, real_input.second));
-    inputs_data_type.push_back(AnfAlgo::GetOutputDeviceDataType(real_input.first, real_input.second));
+    inputs_format.emplace_back(AnfAlgo::GetOutputFormat(real_input.first, real_input.second));
+    inputs_data_type.emplace_back(AnfAlgo::GetOutputDeviceDataType(real_input.first, real_input.second));
   }
   // outputs format and data type
   std::vector<std::string> outputs_format;
@@ -130,13 +130,13 @@ kernel::KernelBuildInfoPtr CreateFusionOpKernelInfo(const std::vector<AnfNodePtr
     if (AnfAlgo::GetCNodeName(output) == prim::kPrimTupleGetItem->name()) {
       auto tuple_getitem = output->cast<CNodePtr>();
       MS_EXCEPTION_IF_NULL(tuple_getitem);
-      outputs_format.push_back(AnfAlgo::GetOutputFormat(
+      outputs_format.emplace_back(AnfAlgo::GetOutputFormat(
         tuple_getitem->input(1), IntToSize(GetValue<int>(GetValueNode(tuple_getitem->input(2))))));
-      outputs_data_type.push_back(AnfAlgo::GetOutputDeviceDataType(
+      outputs_data_type.emplace_back(AnfAlgo::GetOutputDeviceDataType(
         tuple_getitem->input(1), IntToSize(GetValue<int>(GetValueNode(tuple_getitem->input(2))))));
     } else {
-      outputs_format.push_back(AnfAlgo::GetOutputFormat(output, 0));
-      outputs_data_type.push_back(AnfAlgo::GetOutputDeviceDataType(output, 0));
+      outputs_format.emplace_back(AnfAlgo::GetOutputFormat(output, 0));
+      outputs_data_type.emplace_back(AnfAlgo::GetOutputDeviceDataType(output, 0));
     }
   }
   builder.SetInputsFormat(inputs_format);
@@ -229,7 +229,7 @@ void GetFusionScopeInputNodeList(const session::KernelGraph &kernel_graph,
 
   for (auto &buffer_fusion_info : *buffer_fusion_infos) {
     auto fusion_id = buffer_fusion_info.first;
-    auto fusion_info = buffer_fusion_info.second;
+    const auto &fusion_info = buffer_fusion_info.second;
     for (const auto &node : fusion_info.anf_nodes) {
       auto cnode = node->cast<CNodePtr>();
       MS_EXCEPTION_IF_NULL(cnode);
@@ -237,10 +237,10 @@ void GetFusionScopeInputNodeList(const session::KernelGraph &kernel_graph,
         auto real_input = AnfAlgo::VisitKernel(cnode->input(idx), 0);
         if (std::find(fusion_info.anf_nodes.begin(), fusion_info.anf_nodes.end(), real_input.first) ==
             fusion_info.anf_nodes.end()) {
-          if (std::find((*buffer_fusion_infos)[fusion_id].inputs_list.begin(),
-                        (*buffer_fusion_infos)[fusion_id].inputs_list.end(),
-                        cnode->input(idx)) == (*buffer_fusion_infos)[fusion_id].inputs_list.end()) {
-            (*buffer_fusion_infos)[fusion_id].inputs_list.push_back(cnode->input(idx));
+          if (auto in = cnode->input(idx); std::find((*buffer_fusion_infos)[fusion_id].inputs_list.begin(),
+                                                     (*buffer_fusion_infos)[fusion_id].inputs_list.end(),
+                                                     in) == (*buffer_fusion_infos)[fusion_id].inputs_list.end()) {
+            (*buffer_fusion_infos)[fusion_id].inputs_list.push_back(in);
           }
         }
       }
@@ -277,7 +277,7 @@ void GetFusionScopeOutputNodeList(session::KernelGraph *kernel_graph,
 
   for (auto &buffer_fusion_info : *buffer_fusion_infos) {
     auto fusion_id = buffer_fusion_info.first;
-    auto fusion_info = buffer_fusion_info.second;
+    const auto &fusion_info = buffer_fusion_info.second;
     for (const auto &node : fusion_info.anf_nodes) {
       if (AnfAlgo::GetOutputTensorNum(node) == 1) {
         for (auto use_node : manager->node_users()[node]) {
@@ -294,7 +294,7 @@ void GetFusionScopeOutputNodeList(session::KernelGraph *kernel_graph,
                        std::back_inserter(tuple_getitem_nodes),
                        [](const std::pair<AnfNodePtr, int> &use_node) { return use_node.first; });
         std::sort(tuple_getitem_nodes.begin(), tuple_getitem_nodes.end(), TupleGetitemNodeCompare);
-        for (auto getitem : tuple_getitem_nodes) {
+        for (auto &getitem : tuple_getitem_nodes) {
           MS_EXCEPTION_IF_NULL(getitem);
           auto getitem_ptr = getitem->cast<CNodePtr>();
           auto input2 = getitem_ptr->input(2);
@@ -304,7 +304,7 @@ void GetFusionScopeOutputNodeList(session::KernelGraph *kernel_graph,
             (*buffer_fusion_infos)[fusion_id].outputs_list.push_back(stub_node);
           }
           prev_idx = output_idx + 1;
-          for (auto item_use_node : manager->node_users()[getitem]) {
+          for (auto &item_use_node : manager->node_users()[getitem]) {
             if (std::find(fusion_info.anf_nodes.begin(), fusion_info.anf_nodes.end(), item_use_node.first) ==
                 fusion_info.anf_nodes.end()) {
               (*buffer_fusion_infos)[fusion_id].outputs_list.push_back(getitem);
@@ -365,31 +365,25 @@ bool UbPatternFusion::FuseBufferFusionPattern(session::KernelGraph *kernel_graph
   MS_EXCEPTION_IF_NULL(kernel_graph);
   bool change = false;
   std::unordered_map<int32_t, BufferFusionInfo_t> buffer_fusion_infos;
-  buffer_fusion_infos.clear();
   GetBufferFusionInfo(kernel_graph, &buffer_fusion_infos);
 
   std::vector<mindspore::kernel::FusionScopeInfo> fusion_scope_infos;
-  for (auto &buffer_fusion_info : buffer_fusion_infos) {
-    mindspore::kernel::FusionScopeInfo fusion_scope_info;
-    fusion_scope_info.scope_id = buffer_fusion_info.first;
-    fusion_scope_info.input_nodes = buffer_fusion_info.second.inputs_list;
-    fusion_scope_info.compute_nodes = buffer_fusion_info.second.anf_nodes;
-    fusion_scope_info.output_nodes = buffer_fusion_info.second.outputs_list;
-    fusion_scope_infos.push_back(fusion_scope_info);
-#ifdef DEBUG
-    DumpFusionScopeInfo(fusion_scope_info);
-#endif
-  }
+  std::transform(
+    buffer_fusion_infos.begin(), buffer_fusion_infos.end(), std::back_inserter(fusion_scope_infos),
+    [](const std::pair<int32_t, BufferFusionInfo_t> &buffer_fusion_info) -> mindspore::kernel::FusionScopeInfo {
+      return mindspore::kernel::FusionScopeInfo(buffer_fusion_info.first, buffer_fusion_info.second.inputs_list,
+                                                buffer_fusion_info.second.anf_nodes,
+                                                buffer_fusion_info.second.outputs_list);
+    });
   auto kernel_mods = mindspore::kernel::KernelFusion(fusion_scope_infos);
-  std::vector<int32_t> fusion_ids;
+  std::set<int32_t> fusion_ids;
   for (auto &buffer_fusion_info : buffer_fusion_infos) {
     MS_LOG(DEBUG) << "anf node size: " << buffer_fusion_info.second.anf_nodes.size()
                   << ", inputs_list size: " << buffer_fusion_info.second.inputs_list.size()
                   << ", outputs list size: " << buffer_fusion_info.second.outputs_list.size();
-    fusion_ids.push_back(buffer_fusion_info.first);
+    fusion_ids.insert(buffer_fusion_info.first);
   }
   // Replace fusion op from return to head
-  std::sort(fusion_ids.begin(), fusion_ids.end());
   for (auto &fusion_id : fusion_ids) {
     // Get kernel mod when supporting tbe
     if (kernel_mods.find(fusion_id) == kernel_mods.end() || kernel_mods[fusion_id] == nullptr) {
@@ -414,9 +408,10 @@ bool UbPatternFusion::ReplaceFusionOp(std::unordered_map<int32_t, BufferFusionIn
   std::vector<TypeId> types;
   std::vector<std::vector<size_t>> shapes;
   for (const auto &out_node : buffer_fusion_info.outputs_list) {
-    for (size_t idx = 0; idx < AnfAlgo::GetOutputTensorNum(out_node); ++idx) {
-      types.push_back(AnfAlgo::GetOutputInferDataType(out_node, idx));
-      shapes.push_back(AnfAlgo::GetOutputInferShape(out_node, idx));
+    size_t out_num = AnfAlgo::GetOutputTensorNum(out_node);
+    for (size_t idx = 0; idx < out_num; ++idx) {
+      types.emplace_back(AnfAlgo::GetOutputInferDataType(out_node, idx));
+      shapes.emplace_back(AnfAlgo::GetOutputInferShape(out_node, idx));
     }
   }
   if (types.empty() || shapes.empty()) {

mindspore/ccsrc/backend/optimizer/ascend/format_type/check_consistency.cc

@@ -30,12 +30,13 @@ namespace {
 bool CheckFormatForConsistency(const CNodePtr &node, const size_t input_index) {
   MS_EXCEPTION_IF_NULL(node);
   // get prior node's device output format
-  string pre_output_format = AnfAlgo::GetPrevNodeOutputFormat(node, input_index);
+  auto prev_node = AnfAlgo::GetPrevNodeOutput(node, input_index);
+  string pre_output_format = AnfAlgo::GetOutputFormat(prev_node.first, prev_node.second);
   string selected_input_format = AnfAlgo::GetInputFormat(node, input_index);
   if (pre_output_format == selected_input_format) {
     return true;
   }
-  auto input_origin_shape = AnfAlgo::GetPrevNodeOutputInferShape(node, input_index);
+  auto input_origin_shape = AnfAlgo::GetOutputInferShape(prev_node.first, prev_node.second);
   if (pre_output_format == kOpFormat_DEFAULT || selected_input_format == kOpFormat_DEFAULT) {
     string checking_format = (pre_output_format == kOpFormat_DEFAULT) ? selected_input_format : pre_output_format;
     // when input shape size is 1D, default format and NC1HWC0 are compatible
@@ -87,7 +88,8 @@ const AnfNodePtr CheckConsistency::Process(const FuncGraphPtr &, const AnfNodePt
 
   for (auto &t : todos) {
     CNodePtr cnode = t->cast<CNodePtr>();
-    for (size_t i = 0; i < AnfAlgo::GetInputTensorNum(cnode); i++) {
+    size_t in_num = AnfAlgo::GetInputTensorNum(cnode);
+    for (size_t i = 0; i < in_num; ++i) {
       if (!CheckFormatForConsistency(cnode, i) || !CheckDataTypeForConsistency(cnode, i)) {
         MS_LOG(EXCEPTION) << "Found inconsistent format or data type! Op: " << AnfAlgo::GetCNodeName(cnode) << "["
                           << cnode->DebugString() << "]";

mindspore/ccsrc/backend/optimizer/ascend/format_type/insert_cast.cc

@@ -39,9 +39,10 @@ AnfNodePtr InsertCastForMultipleOutput(const FuncGraphPtr &func_graph, const CNo
   MS_EXCEPTION_IF_NULL(cnode);
   std::vector<AnfNodePtr> make_tuple_inputs;
   AbstractBasePtrList abstract_list;
-  make_tuple_inputs.push_back(NewValueNode(prim::kPrimMakeTuple));
+  make_tuple_inputs.emplace_back(NewValueNode(prim::kPrimMakeTuple));
   auto kernel_graph = func_graph->cast<KernelGraphPtr>();
-  for (size_t output_idx = 0; output_idx < AnfAlgo::GetOutputTensorNum(cnode); ++output_idx) {
+  size_t out_num = AnfAlgo::GetOutputTensorNum(cnode);
+  for (size_t output_idx = 0; output_idx < out_num; ++output_idx) {
     AnfNodePtr replace_node = nullptr;
     const auto origin_shape = AnfAlgo::GetOutputInferShape(cnode, output_idx);
     const auto infer_type = AnfAlgo::GetOutputInferDataType(cnode, output_idx);
@@ -74,7 +75,7 @@ AnfNodePtr InsertCastForMultipleOutput(const FuncGraphPtr &func_graph, const CNo
     } else {
       replace_node = getitem;
     }
-    abstract_list.push_back(replace_node->abstract());
+    abstract_list.emplace_back(replace_node->abstract());
     make_tuple_inputs.push_back(replace_node);
   }
   AnfNodePtr make_tuple = func_graph->NewCNode(make_tuple_inputs);

mindspore/ccsrc/backend/optimizer/ascend/ir_fission/addn_fission.cc

@@ -27,7 +27,7 @@ AnfNodePtr CreateNewAddn(const FuncGraphPtr &func_graph, const CNodePtr &origin_
   MS_EXCEPTION_IF_NULL(origin_addn_cnode);
   std::vector<AnfNodePtr> new_addn_inputs{NewValueNode(std::make_shared<Primitive>(prim::kPrimAddN->name()))};
   for (size_t i = begin_index; i < begin_index + offset; ++i) {
-    new_addn_inputs.push_back(origin_addn_cnode->input(i));
+    new_addn_inputs.emplace_back(origin_addn_cnode->input(i));
   }
   CNodePtr new_addn = func_graph->NewCNode(new_addn_inputs);
   MS_EXCEPTION_IF_NULL(new_addn);
@@ -66,7 +66,7 @@ const AnfNodePtr AddnFission::Process(const FuncGraphPtr &func_graph, const AnfN
       cur_input_index += inputs_divisor_;
     }
     for (size_t i = cur_input_index; i <= origin_input_size; i++) {
-      base_addn_inputs.push_back(new_cnode->input(i));
+      base_addn_inputs.emplace_back(new_cnode->input(i));
     }
     CNodePtr base_addn = func_graph->NewCNode(base_addn_inputs);
     MS_EXCEPTION_IF_NULL(base_addn);

mindspore/ccsrc/backend/optimizer/ascend/ir_fission/batch_norm_bert_fission.cc

@@ -37,7 +37,7 @@ bool GetBatchNormOutputs(const FuncGraphPtr &func_graph, const AnfNodePtr &bn, s
   }
   size_t output_num = 0;
   for (const auto &node_index : manager->node_users()[bn]) {
-    AnfNodePtr output = node_index.first;
+    const AnfNodePtr &output = node_index.first;
     MS_EXCEPTION_IF_NULL(output);
     if (!IsPrimitiveCNode(output, prim::kPrimTupleGetItem)) {
       continue;

mindspore/ccsrc/backend/optimizer/ascend/ir_fission/batch_norm_grad_infer_fission.cc

@@ -32,7 +32,7 @@ bool CheckOutputsIndex(const FuncGraphPtr &func_graph, const AnfNodePtr &node) {
     return false;
   }
   for (const auto &node_index : manager->node_users()[node]) {
-    AnfNodePtr output = node_index.first;
+    const AnfNodePtr &output = node_index.first;
     MS_EXCEPTION_IF_NULL(output);
     if (!IsPrimitiveCNode(output, prim::kPrimTupleGetItem)) {
       continue;

mindspore/ccsrc/backend/optimizer/ascend/ir_fission/batch_norm_grad_split.cc

@@ -33,7 +33,7 @@ void CreateOutputsOfUpdateGrad(const FuncGraphPtr &graph, const CNodePtr &bn_gra
                                std::vector<AnfNodePtr> *bn_update_grad_outputs) {
   MS_EXCEPTION_IF_NULL(graph);
   MS_EXCEPTION_IF_NULL(bn_grad_node);
-  auto bn_grad_inputs = bn_grad_node->inputs();
+  const auto &bn_grad_inputs = bn_grad_node->inputs();
   if (bn_grad_inputs.size() < kBNGradInputNum) {
     MS_LOG(EXCEPTION) << "BNGrad has wrong inputs size";
   }
@@ -58,7 +58,8 @@ void CreateOutputsOfReduceGrad(const FuncGraphPtr &graph, const CNodePtr &bn_gra
                                std::vector<AnfNodePtr> *bn_reduce_grad_outputs) {
   MS_EXCEPTION_IF_NULL(graph);
   MS_EXCEPTION_IF_NULL(bn_grad_node);
-  auto bn_grad_inputs = bn_grad_node->inputs();
+  MS_EXCEPTION_IF_NULL(bn_reduce_grad_outputs);
+  const auto &bn_grad_inputs = bn_grad_node->inputs();
   if (bn_grad_inputs.size() < kBNGradInputNum) {
     MS_LOG(EXCEPTION) << "BNGrad has wrong inputs size";
   }

mindspore/ccsrc/backend/optimizer/ascend/ir_fission/concat_fission.cc

@@ -25,9 +25,9 @@ AnfNodePtr CreateNewConcat(const FuncGraphPtr &func_graph, const CNodePtr &origi
                            size_t offset) {
   MS_EXCEPTION_IF_NULL(func_graph);
   MS_EXCEPTION_IF_NULL(origin_concat_cnode);
-  std::vector<AnfNodePtr> new_concat_inputs{NewValueNode(std::make_shared<Primitive>(prim::kPrimConcat->name()))};
+  std::vector<AnfNodePtr> new_concat_inputs = {NewValueNode(std::make_shared<Primitive>(prim::kPrimConcat->name()))};
   for (size_t i = begin_index; i < begin_index + offset; ++i) {
-    new_concat_inputs.push_back(origin_concat_cnode->input(i));
+    new_concat_inputs.emplace_back(origin_concat_cnode->input(i));
   }
   CNodePtr new_concat = func_graph->NewCNode(new_concat_inputs);
   MS_EXCEPTION_IF_NULL(new_concat);
@@ -83,7 +83,7 @@ const AnfNodePtr ConcatFission::Process(const FuncGraphPtr &func_graph, const An
       cur_input_index += inputs_divisor_;
     }
     for (size_t i = cur_input_index; i <= origin_input_size; i++) {
-      base_concat_inputs.push_back(new_cnode->input(i));
+      base_concat_inputs.emplace_back(new_cnode->input(i));
     }
     CNodePtr base_concat = func_graph->NewCNode(base_concat_inputs);
     MS_EXCEPTION_IF_NULL(base_concat);

mindspore/ccsrc/backend/optimizer/ascend/ir_fission/lars_v2_fission.cc

@@ -31,9 +31,8 @@ void CreateOutputsOfSquareSumAll(const FuncGraphPtr &graph, const CNodePtr &lars
     MS_LOG(EXCEPTION) << "Op lars_v2's input not equal " << kLarsV2InputNum;
   }
 
-  std::vector<AnfNodePtr> inputs = {NewValueNode(std::make_shared<Primitive>(kSquareSumAllOpName))};
-  inputs.push_back(lars_v2->input(1));
-  inputs.push_back(lars_v2->input(2));
+  std::vector<AnfNodePtr> inputs = {NewValueNode(std::make_shared<Primitive>(kSquareSumAllOpName)), lars_v2->input(1),
+                                    lars_v2->input(2)};
   auto square_sum_all = graph->NewCNode(inputs);
   MS_EXCEPTION_IF_NULL(square_sum_all);
   square_sum_all->set_scope(lars_v2->scope());
@@ -56,13 +55,13 @@ CNodePtr CreateLarsV2Update(const FuncGraphPtr &graph, const CNodePtr &lars_v2,
   if (lars_v2->size() != kLarsV2InputNum) {
     MS_LOG(EXCEPTION) << "Op lars_v2's input not equal " << kLarsV2InputNum;
   }
-  std::vector<AnfNodePtr> inputs = {NewValueNode(std::make_shared<Primitive>(kLarsV2UpdateOpName))};
-  inputs.push_back(lars_v2->input(1));
-  inputs.push_back(lars_v2->input(2));
-  inputs.push_back(square_sum_all_outputs[0]);
-  inputs.push_back(square_sum_all_outputs[1]);
-  inputs.push_back(lars_v2->input(3));
-  inputs.push_back(lars_v2->input(4));
+  std::vector<AnfNodePtr> inputs = {NewValueNode(std::make_shared<Primitive>(kLarsV2UpdateOpName)),
+                                    lars_v2->input(1),
+                                    lars_v2->input(2),
+                                    square_sum_all_outputs[0],
+                                    square_sum_all_outputs[1],
+                                    lars_v2->input(3),
+                                    lars_v2->input(4)};
   auto lars_v2_update = graph->NewCNode(inputs);
   MS_EXCEPTION_IF_NULL(lars_v2_update);
   lars_v2_update->set_scope(lars_v2->scope());

mindspore/ccsrc/backend/optimizer/ascend/ir_fission/layer_norm_grad_split.cc

@@ -32,6 +32,7 @@ void LayerNormGradSplit::CreateOutputsOfLayerNormXBackprop(
   std::vector<AnfNodePtr> *layer_norm_x_backprop_outputs) const {
   MS_EXCEPTION_IF_NULL(graph);
   MS_EXCEPTION_IF_NULL(layer_norm_grad);
+  MS_EXCEPTION_IF_NULL(layer_norm_x_backprop_outputs);
   auto prim = std::make_shared<Primitive>(kLayerNormXBackpropOpName);
   std::vector<AnfNodePtr> layer_norm_x_backprop_inputs = {NewValueNode(prim)};
   for (size_t i = 1; i < layer_norm_grad->inputs().size(); ++i) {

mindspore/ccsrc/backend/optimizer/ascend/ir_fission/pack_fission.cc

@@ -83,11 +83,11 @@ const AnfNodePtr PackFission::Process(const FuncGraphPtr &func_graph, const AnfN
   size_t cur_input_index = 1;
   // Divide the inputs of pack by inputs_divisor_.
   while (origin_input_size - cur_input_index + 1 >= inputs_divisor_) {
-    base_concat_inputs.push_back(CreateNewPack(func_graph, cnode, cur_input_index, inputs_divisor_));
+    base_concat_inputs.emplace_back(CreateNewPack(func_graph, cnode, cur_input_index, inputs_divisor_));
     cur_input_index += inputs_divisor_;
   }
   if (cur_input_index <= origin_input_size) {
-    base_concat_inputs.push_back(
+    base_concat_inputs.emplace_back(
       CreateNewPack(func_graph, cnode, cur_input_index, origin_input_size - cur_input_index + 1));
   }
 

mindspore/ccsrc/backend/optimizer/ascend/ir_fission/split_fission.cc

@@ -96,17 +96,16 @@ void CreateOutputShapeAndTypeId(const CNodePtr &origin_cnode, int split_dim, int
 void SetAttrAndAbstractForBaseSplitv(const CNodePtr &origin_cnode, const CNodePtr &base_splitv,
                                      const std::vector<int> &size_splits_base, int split_dim, int num_split) {
   SetAttrForSplitVNode(base_splitv, size_splits_base, split_dim, num_split);
-  std::vector<TypeId> base_type_ids;
-  std::vector<std::vector<size_t>> base_output_shapes_base;
   auto output_shape = AnfAlgo::GetOutputInferShape(origin_cnode, 0);
   TypeId type_id = AnfAlgo::GetOutputInferDataType(origin_cnode, 0);
+  std::vector<TypeId> base_type_ids(num_split, type_id);
+  std::vector<std::vector<size_t>> base_output_shapes_base;
   if (split_dim < 0) {
     split_dim += output_shape.size();
   }
   for (int i = 0; i < num_split; ++i) {
     output_shape[split_dim] = size_splits_base[i];
     base_output_shapes_base.emplace_back(output_shape);
-    base_type_ids.emplace_back(type_id);
   }
   AnfAlgo::SetOutputInferTypeAndShape(base_type_ids, base_output_shapes_base, base_splitv.get());
 }
@@ -118,17 +117,14 @@ AnfNodePtr DoFission(const FuncGraphPtr &func_graph, const CNodePtr &cnode, int
 
   // Create new size_splits for "size_splits" attr of each new Splitv node which has full inputs.
   auto small_split_size = SizeToInt(GetSmallSplitSize(cnode, split_dim, num_split));
-  std::vector<int> size_splits_new;
-  for (int i = 0; i < divisor; ++i) {
-    size_splits_new.emplace_back(small_split_size);
-  }
+  std::vector<int> size_splits_new(divisor, small_split_size);
   // Create new output shape and new output type id for each new Splitv node which has full inputs.
   std::vector<TypeId> new_type_ids;
   std::vector<std::vector<size_t>> new_output_shapes;
   CreateOutputShapeAndTypeId(cnode, split_dim, small_split_size, divisor, &new_type_ids, &new_output_shapes);
 
   // Create make_tuple input to create a make_tuple for replacing the old Split node.
-  std::vector<AnfNodePtr> make_tuple_inputs{NewValueNode(prim::kPrimMakeTuple)};
+  std::vector<AnfNodePtr> make_tuple_inputs = {NewValueNode(prim::kPrimMakeTuple)};
   // Start to divide the outputs of Split.
   std::vector<int> size_splits_base;
   const auto base_split_size = divisor * small_split_size;
@@ -147,10 +143,7 @@ AnfNodePtr DoFission(const FuncGraphPtr &func_graph, const CNodePtr &cnode, int
     auto last_node_num_split = num_split - cur_output_index;
     if (last_node_num_split > 1) {
       CNodePtr new_splitv = CreateSplitVNode(func_graph, CreateTupleGetItem(func_graph, base_splitv, nodes_num));
-      std::vector<int> size_splits_new_last;
-      for (int i = 0; i < last_node_num_split; ++i) {
-        size_splits_new_last.emplace_back(small_split_size);
-      }
+      std::vector<int> size_splits_new_last(last_node_num_split, small_split_size);
       SetAttrForSplitVNode(new_splitv, size_splits_new_last, split_dim, last_node_num_split);
       // Create new output shape and new output type id for the last Splitv node
       std::vector<TypeId> last_new_type_ids;

mindspore/ccsrc/backend/optimizer/ascend/ir_fusion/fused_batch_norm_fusion.cc

@@ -44,7 +44,7 @@ void GetBNOutput(const FuncGraphPtr &func_graph, const AnfNodePtr &bn, std::vect
     MS_LOG(EXCEPTION) << "The bn node " << bn->DebugString() << " should has some outputs";
   }
   for (const auto &node_index : manager->node_users()[bn]) {
-    AnfNodePtr output = node_index.first;
+    const AnfNodePtr &output = node_index.first;
     MS_EXCEPTION_IF_NULL(output);
     bn_outputs->push_back(output);
   }

mindspore/ccsrc/backend/optimizer/common/helper.cc

@@ -313,9 +313,9 @@ void CreateMultipleOutputsOfAnfNode(const FuncGraphPtr &func_graph, const AnfNod
   MS_EXCEPTION_IF_NULL(node);
   MS_EXCEPTION_IF_NULL(outputs);
   for (size_t i = 0; i < output_num; i++) {
-    auto idx = NewValueNode(SizeToInt(i));
-    MS_EXCEPTION_IF_NULL(idx);
     int temp = SizeToInt(i);
+    auto idx = NewValueNode(temp);
+    MS_EXCEPTION_IF_NULL(idx);
     auto imm = std::make_shared<Int32Imm>(temp);
     auto abstract_scalar = std::make_shared<abstract::AbstractScalar>(imm);
     idx->set_abstract(abstract_scalar);

mindspore/ccsrc/backend/session/anf_runtime_algorithm.cc

@@ -745,7 +745,7 @@ void AnfRuntimeAlgorithm::SetOutputInferTypeAndShape(const std::vector<TypeId> &
     for (size_t i = 0; i < types.size(); ++i) {
       std::vector<int> shape_int;
       std::transform(shapes[i].begin(), shapes[i].end(), std::back_inserter(shape_int), SizeToInt);
-      abstract_list.push_back(std::make_shared<AbstractTensor>(TypeIdToType(types[i]), shape_int));
+      abstract_list.emplace_back(std::make_shared<AbstractTensor>(TypeIdToType(types[i]), shape_int));
     }
     auto abstract_tuple = std::make_shared<AbstractTuple>(abstract_list);
     node->set_abstract(abstract_tuple);

mindspore/ccsrc/runtime/device/ascend/kernel_select_ascend.cc

@@ -550,6 +550,7 @@ KernelSelectStatus SelectKernelInfo(const CNodePtr &kernel_node, KernelType kern
   kernel::KernelQuery(kernel_node, &kernel_info_list, kernel_type);
   auto select_status = SetMatchedKernelInfo(kernel_node, kernel_info_list);
   // If aicore not find valid kernel info reloading aicpu kernel info list to find it
+
   if (select_status == kNoMatched) {
     MS_LOG(WARNING) << "The node [" << kernel_node->DebugString()
                     << "] cannot find valid TBE kernel info, try to get aicpu kernel info";

mindspore/ccsrc/utils/utils.h

@@ -20,6 +20,7 @@
 #include <unistd.h>
 #include <fcntl.h>
 #include <sys/stat.h>
+#include <sys/time.h>
 #include <string>
 #include <vector>
 #include <set>
@@ -337,5 +338,33 @@ static inline void ChangeFileMode(const std::string &file_name, mode_t mode) {
     MS_LOG(DEBUG) << "File `" << file_name << "` change mode failed! May be not exist.";
   }
 }
+
+static inline uint64_t GetCurrentUSec() {
+  struct timeval tv;
+  int ret = gettimeofday(&tv, nullptr);
+  if (ret != 0) {
+    MS_LOG(EXCEPTION) << "Fail gettimeofday, ret = " << ret;
+  }
+  return static_cast<uint64_t>(tv.tv_usec + tv.tv_sec * 1000000);
+}
+
+#define PROF_START(stage) uint64_t start_usec_##stage = mindspore::GetCurrentUSec()
+#define PROF_END(stage)                                                                         \
+  do {                                                                                          \
+    uint64_t end_usec_##stage = mindspore::GetCurrentUSec();                                    \
+    MS_LOG(INFO) << #stage << " costs " << (end_usec_##stage - start_usec_##stage) << " usec."; \
+  } while (0)
+
+#define PROF_MULTI_DEFINE(stage)     \
+  static uint64_t total_##stage = 0; \
+  static uint64_t count_##stage = 0;
+
+#define PROF_MULTI_START(stage) uint64_t start_usec_##stage = mindspore::GetCurrentUSec()
+
+#define PROF_MULTI_END(stage)                              \
+  ++count_##stage;                                         \
+  uint64_t end_usec_##stage = mindspore::GetCurrentUSec(); \
+  total_##stage += (end_usec_##stage - start_usec_##stage)
+
 }  // namespace mindspore
 #endif  // MINDSPORE_CCSRC_UTILS_UTILS_H_