!47778 Clean code warning.

Merge pull request !47778 from Margaret_wangrui/clean_code

mindspore/ccsrc/backend/common/pass/convert_tuple_input_to_dynamic_input.cc

@@ -1,5 +1,5 @@
 /**
- * Copyright 2020-2022 Huawei Technologies Co., Ltd
+ * Copyright 2020-2023 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.
@@ -45,7 +45,7 @@ int64_t SplitTupleInputs(const FuncGraphPtr &graph, const AnfNodePtr &tuple_inpu
       MS_EXCEPTION_IF_NULL(dyn_input_node);
       // Handle tuple nested scenes.
       if (dyn_input_node->isa<CNode>() && common::AnfAlgo::CheckPrimitiveType(dyn_input_node, prim::kPrimMakeTuple)) {
-        input_size += SplitTupleInputs(graph, dyn_input_node, plant_inputs);
+        input_size += LongToSize(SplitTupleInputs(graph, dyn_input_node, plant_inputs));
         continue;
       }
       (void)plant_inputs->emplace_back(dyn_input_node);

mindspore/ccsrc/frontend/operator/composite/composite.cc

@@ -1,7 +1,7 @@
 /**
  * This is the C++ adaptation and derivative work of Myia (https://github.com/mila-iqia/myia/).
  *
- * Copyright 2019-2022 Huawei Technologies Co., Ltd
+ * Copyright 2019-2023 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.
@@ -145,7 +145,7 @@ AnfNodePtr HyperMap::FullMake(const std::shared_ptr<List> &type, const FuncGraph
     MS_LOG(EXCEPTION) << "The lists in HyperMap should have the same length. " << oss.str();
   }
 
-  // cannot use shared_from_base() also known as this, as it will make a reference cycle on
+  // Cannot use shared_from_base() also known as this, as it will make a reference cycle on
   // hypermap and graph generated, it will cause memory leak.
   auto fn_rec = NewValueNode(std::make_shared<HyperMap>(*this));
   constexpr size_t kPrimHoldLen = 1;
@@ -205,7 +205,7 @@ AnfNodePtr HyperMap::FullMake(const std::shared_ptr<Tuple> &type, const FuncGrap
     MS_LOG(EXCEPTION) << "The length of tuples in HyperMap must be the same. " << oss.str();
   }
 
-  // cannot use shared_from_base() also known as this, as it will make a reference cycle on
+  // Cannot use shared_from_base() also known as this, as it will make a reference cycle on
   // hypermap and graph generated, it will cause memory leak.
   auto fn_rec = NewValueNode(std::make_shared<HyperMap>(*this));
   constexpr size_t kPrimHoldLen = 1;
@@ -462,7 +462,7 @@ FuncGraphPtr PyExecuteGradient::GenerateFuncGraph(const AbstractBasePtrList &arg
   // Make fprop first result, PyExecute's forward result.
   AnfNodePtr out = fg->NewCNodeInOrder(params);
 
-  // make fprop second result, PyExecute's backward function.
+  // Make fprop second result, PyExecute's backward function.
   FuncGraphPtr bprop = std::make_shared<FuncGraph>();
 
   ss.str(std::string());
@@ -1252,7 +1252,7 @@ FuncGraphPtr VmapOperation::GenerateFuncGraph(const AbstractBasePtrList &args_sp
   vmap_fg->set_flag(FUNC_GRAPH_FLAG_CORE, true);
   vmap_fg->debug_info()->set_name(ss.str());
 
-  // add parameter for `fn`, `in_axes` and `out_axes` respectively.
+  // Add parameter for `fn`, `in_axes` and `out_axes` respectively.
   ParameterPtr param_graph = vmap_fg->add_parameter();
   (void)vmap_fg->add_parameter();
   (void)vmap_fg->add_parameter();

mindspore/ccsrc/frontend/optimizer/ad/dfunctor.cc

@@ -1,5 +1,5 @@
 /**
- * Copyright 2020-2022 Huawei Technologies Co., Ltd
+ * Copyright 2020-2023 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.
@@ -62,7 +62,7 @@ DFunctor::DFunctor(const FuncGraphPtr &primal_graph, const pipeline::ResourceBas
   const auto &info = primal_graph->GetEffectInfo();
   if (is_top_ && info.back_mem) {
     // Add Umonad arg for top graph.
-    tape_->add_parameter();
+    (void)tape_->add_parameter();
   }
 }
 

mindspore/ccsrc/frontend/optimizer/clean.cc

@@ -1,7 +1,7 @@
 /**
  * This is the C++ adaptation and derivative work of Myia (https://github.com/mila-iqia/myia/).
  *
- * Copyright 2019-2022 Huawei Technologies Co., Ltd
+ * Copyright 2019-2023 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.
@@ -226,7 +226,7 @@ class SimplifyDataStructuresRewriter : public BaseRewriter {
   }
 
   // DictGetItem --> PyExecute()
-  AnfNodePtr RebuidDictGetItem(const CNodePtr &node) const {
+  AnfNodePtr RebuildDictGetItem(const CNodePtr &node) const {
     MS_EXCEPTION_IF_NULL(node);
     // Inputs should be [dict_setitem, dict, item]
     const size_t expect_inputs_size = 3;
@@ -291,7 +291,7 @@ class SimplifyDataStructuresRewriter : public BaseRewriter {
   AnfNodePtr ConvertDictGetItem(const CNodePtr &node) {
     static const auto support_fallback_runtime = (common::GetEnv("MS_DEV_ENABLE_FALLBACK_RUNTIME") != "0");
     if (support_fallback_runtime && is_dict_output_) {
-      return RebuidDictGetItem(node);
+      return RebuildDictGetItem(node);
     }
     return ConvertDictGetItemToTupleGetItem(node);
   }

mindspore/ccsrc/frontend/optimizer/irpass/cast_eliminate.cc

@@ -86,7 +86,7 @@ void CastSameTypeEliminater::Visit(const AnfNodePtr &node) {
   }
 }
 
-bool TwoCastEliminater::CheckTwoTypes(const std::map<TypeId, int> &type_map, TypeId type1, TypeId type2) {
+bool TwoCastEliminater::CheckTwoTypes(const std::map<TypeId, int> &type_map, TypeId type1, TypeId type2) const {
   auto type1_iter = type_map.find(type1);
   auto type2_iter = type_map.find(type2);
   if (type1_iter != type_map.end() && type2_iter != type_map.end()) {
@@ -96,7 +96,7 @@ bool TwoCastEliminater::CheckTwoTypes(const std::map<TypeId, int> &type_map, Typ
 }
 
 bool TwoCastEliminater::CheckThreeTypes(const std::map<TypeId, int> &type_map, TypeId type1, TypeId type2,
-                                        TypeId type3) {
+                                        TypeId type3) const {
   auto type1_iter = type_map.find(type1);
   auto type2_iter = type_map.find(type2);
   auto type3_iter = type_map.find(type3);
@@ -107,8 +107,8 @@ bool TwoCastEliminater::CheckThreeTypes(const std::map<TypeId, int> &type_map, T
 }
 
 // {prim::kPrimCast, {prim::kPrimCast, X, Y}, T}  -> {prim::kPrimCast, X, T}
-// x_type <= y_type <= t_type or x_type >= y_type >= t_type
-bool TwoCastEliminater::CheckTypesIsIncrementalOrDecreasing() {
+// y_type == t_type or x_type <= y_type or x_type >= y_type >= t_type
+bool TwoCastEliminater::CheckTypesIsIncreasingOrDecreasing() {
   auto x_type = x_->Type();
   if (x_type->isa<TensorType>()) {
     x_type = x_type->cast<TensorTypePtr>()->element();
@@ -128,18 +128,21 @@ bool TwoCastEliminater::CheckTypesIsIncrementalOrDecreasing() {
   auto x_type_id = x_type->type_id();
   auto y_type_id = y_type->type_id();
   auto t_type_id = t_type->type_id();
+  // y_type == t_type
   if (y_type_id == t_type_id) {
     return true;
   }
-  // If the precision is incremental or decreasing, the cast can be eliminated.
+  // If the precision is increasing or decreasing, the cast can be eliminated.
   // x_type <= y_type
-  bool incremental = CheckTwoTypes(int_map_, x_type_id, y_type_id) || CheckTwoTypes(uint_map_, x_type_id, y_type_id) ||
-                     CheckTwoTypes(float_map_, x_type_id, y_type_id);
+  bool increasing = CheckTwoTypes(int_map_, x_type_id, y_type_id) || CheckTwoTypes(uint_map_, x_type_id, y_type_id) ||
+                    CheckTwoTypes(float_map_, x_type_id, y_type_id);
+  if (increasing) {
+    return true;
+  }
   //  x_type >= y_type >= t_type
-  bool decreasing = CheckThreeTypes(int_map_, t_type_id, y_type_id, x_type_id) ||
-                    CheckThreeTypes(uint_map_, t_type_id, y_type_id, x_type_id) ||
-                    CheckThreeTypes(float_map_, t_type_id, y_type_id, x_type_id);
-  return incremental || decreasing;
+  return CheckThreeTypes(int_map_, t_type_id, y_type_id, x_type_id) ||
+         CheckThreeTypes(uint_map_, t_type_id, y_type_id, x_type_id) ||
+         CheckThreeTypes(float_map_, t_type_id, y_type_id, x_type_id);
 }
 
 // {prim::kPrimCast, {prim::kPrimCast, X, Y}, T}
@@ -150,7 +153,7 @@ AnfNodePtr TwoCastEliminater::operator()(const OptimizerPtr &, const AnfNodePtr
   if (x_ == nullptr || t_ == nullptr || y_ == nullptr) {
     return nullptr;
   }
-  if (CheckTypesIsIncrementalOrDecreasing()) {
+  if (CheckTypesIsIncreasingOrDecreasing()) {
     auto cast_op = python_adapter::GetPyFn("mindspore.ops.operations", "Cast")();
     ValuePtr cast = parse::data_converter::PyDataToValue(cast_op);
     auto cnode = NewCNode({NewValueNode(cast), x_, t_}, node->func_graph());

mindspore/ccsrc/frontend/optimizer/irpass/cast_eliminate.h

@@ -51,9 +51,9 @@ class TwoCastEliminater : public AnfVisitor {
   }
 
  private:
-  bool CheckTypesIsIncrementalOrDecreasing();
-  bool CheckTwoTypes(const std::map<TypeId, int> &type_map, TypeId type1, TypeId type2);
-  bool CheckThreeTypes(const std::map<TypeId, int> &type_map, TypeId type1, TypeId type2, TypeId type3);
+  bool CheckTypesIsIncreasingOrDecreasing();
+  bool CheckTwoTypes(const std::map<TypeId, int> &type_map, TypeId type1, TypeId type2) const;
+  bool CheckThreeTypes(const std::map<TypeId, int> &type_map, TypeId type1, TypeId type2, TypeId type3) const;
   std::map<TypeId, int> int_map_ = {
     {kNumberTypeInt, 0}, {kNumberTypeInt8, 1}, {kNumberTypeInt16, 2}, {kNumberTypeInt32, 3}, {kNumberTypeInt64, 4}};
   std::map<TypeId, int> uint_map_ = {{kNumberTypeUInt, 0},

mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/gru_cpu_kernel.cc

@@ -1,5 +1,5 @@
 /**
- * Copyright 2022 Huawei Technologies Co., Ltd
+ * Copyright 2022-2023 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.
@@ -174,7 +174,8 @@ bool GRUCpuKernelMod::Launch(const std::vector<kernel::AddressPtr> &inputs, cons
                   reinterpret_cast<float *>(inputs[kInputWeightIndex]->addr) + weight_size_ + weight_h_size_);
   } else {
     auto size = GetSize(bias_desc_);
-    if (memset_s(GetDataHandle(bias_memory_), size, 0, size)) {
+    auto ret = memset_s(GetDataHandle(bias_memory_), size, 0, size);
+    if (ret != EOK) {
       MS_LOG(EXCEPTION) << "Bias memset error";
     }
   }

mindspore/ccsrc/plugin/device/cpu/optimizer/print_value_type.h

@@ -1,5 +1,5 @@
 /**
- * Copyright 2022 Huawei Technologies Co., Ltd
+ * Copyright 2022-2023 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.
@@ -23,7 +23,7 @@ namespace mindspore {
 namespace opt {
 class PrintValueType : public Pass {
  public:
-  explicit PrintValueType(const std::string &name) : Pass("print_value_type") {}
+  explicit PrintValueType(const std::string &) : Pass("print_value_type") {}
   ~PrintValueType() override = default;
   bool Run(const FuncGraphPtr &graph) override;
 };

mindspore/core/ir/map_tensor.h

@@ -1,5 +1,5 @@
 /**
- * Copyright 2022 Huawei Technologies Co., Ltd
+ * Copyright 2022-2023 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.
@@ -71,6 +71,7 @@ class MS_CORE_API MapTensor final : public Tensor {
     key_shape_ = {abstract::Shape::kShapeDimAny};
     shape_ = {abstract::Shape::kShapeDimAny};
     (void)shape_.insert(shape_.cend(), value_shape.cbegin(), value_shape.cend());
+    size_ = shape_[0];
     ShapeVector key_shape = {abstract::Shape::kShapeDimAny};
     key_tensor_ = std::make_shared<Tensor>(key_dtype, key_shape);
     value_tensor_ = std::make_shared<Tensor>(value_dtype, shape_);
@@ -90,13 +91,13 @@ class MS_CORE_API MapTensor final : public Tensor {
   MapTensor(const TensorPtr &key_tensor, const TensorPtr &value_tensor, const TensorPtr &status_tensor,
             const ValuePtr &default_value, const ValuePtr &permit_filter_value = nullptr,
             const ValuePtr &evict_filter_value = nullptr)
-      : default_value_(default_value) {
-    key_dtype_ = key_tensor->data_type();
+      : key_dtype_(key_tensor->data_type()), default_value_(default_value) {
     data_type_ = value_tensor->data_type();
     shape_ = value_tensor->shape();
     key_shape_ = key_tensor->shape();
     value_shape_.clear();
     (void)value_shape_.insert(value_shape_.cend(), shape_.cbegin() + 1, shape_.cend());
+    size_ = shape_.size() != 0 ? shape_[0] : (abstract::Shape::kShapeDimAny);
     key_tensor_ = key_tensor;
     value_tensor_ = value_tensor;
     status_tensor_ = status_tensor;