[ME] Code static check.

mindspore/core/abstract/prim_nn.cc

@@ -1,5 +1,5 @@
 /**
- * Copyright 2019-2021 Huawei Technologies Co., Ltd
+ * Copyright 2019-2022 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.
@@ -32,7 +32,9 @@ const size_t padding_start_idx = 0;
 int64_t GetAndCheckFormat(const ValuePtr &value) {
   int64_t data_format;
   bool result = CheckAndConvertUtils::GetDataFormatEnumValue(value, &data_format);
-  if (!result || (data_format != Format::NHWC && data_format != Format::NCHW && data_format != Format::NCDHW)) {
+  if (!result ||
+      (data_format != static_cast<int64_t>(Format::NHWC) && data_format != static_cast<int64_t>(Format::NCHW) &&
+       data_format != static_cast<int64_t>(Format::NCDHW))) {
     MS_LOG(EXCEPTION) << "data format is invalid, only support NCHW, NHWC and NCDHW";
   }
   return data_format;
@@ -80,11 +82,12 @@ AbstractBasePtr InferImplPooling(const AnalysisEnginePtr &, const PrimitivePtr &
   auto pad_mode_ptr = primitive->GetAttr("pad_mode");
   if (pad_mode_ptr != nullptr) {
     int64_t pad_mode;
+    const size_t middle = 2;
     CheckAndConvertUtils::GetPadModEnumValue(pad_mode_ptr, &pad_mode, true);
-    if (pad_mode == PadMode::VALID) {
+    if (pad_mode == static_cast<int64_t>(PadMode::VALID)) {
       padding = 0;
-    } else if (pad_mode == PadMode::SAME) {
-      padding = (window - 1) / 2;
+    } else if (pad_mode == static_cast<int64_t>(PadMode::SAME)) {
+      padding = (window - 1) / middle;
     }
   }
   std::set<std::string> available_mode{"max", "avg"};
@@ -95,9 +98,9 @@ AbstractBasePtr InferImplPooling(const AnalysisEnginePtr &, const PrimitivePtr &
       MS_LOG(EXCEPTION) << "Unsupported pooling mode: " << mode << ".";
     }
   }
-
-  int64_t h_out = ((h_input + 2 * padding - (window - 1) - 1) / stride) + 1;
-  int64_t w_out = ((w_input + 2 * padding - (window - 1) - 1) / stride) + 1;
+  const size_t twice = 2;
+  int64_t h_out = (((h_input + twice * padding - (window - 1)) - 1) / stride) + 1;
+  int64_t w_out = (((w_input + twice * padding - (window - 1)) - 1) / stride) + 1;
   ShapeVector shape_out = {input_shape->shape()[0], input_shape->shape()[1], h_out, w_out};
   AbstractBasePtr ret = input_tensor->Broaden();
   ret->set_shape(std::make_shared<Shape>(shape_out));
@@ -153,7 +156,7 @@ AbstractBasePtr InferImplBatchNorm(const AnalysisEnginePtr &, const PrimitivePtr
   int64_t data_format = GetAndCheckFormat(data_format_ptr);
 
   size_t c_axis = 1;
-  if (data_format == Format::NHWC) {
+  if (data_format == static_cast<int64_t>(Format::NHWC)) {
     c_axis = 3;
   }
   for (size_t i = 1; i < args_spec_list.size(); ++i) {
@@ -204,30 +207,34 @@ void Conv2DPadFunction(std::vector<int64_t> *output_hw, std::vector<int64_t> *pa
                        const int64_t x_w, const std::vector<int64_t> &kernel, const std::vector<int64_t> &stride,
                        const std::vector<int64_t> &dilation, const int64_t &pad_mode,
                        const std::vector<int64_t> &padding) {
-  if (pad_mode == PadMode::VALID) {
+  const size_t middle = 2;
+  const size_t second_index = 2;
+  const size_t third_index = 3;
+  if (pad_mode == static_cast<int64_t>(PadMode::VALID)) {
     output_hw->push_back(static_cast<int64_t>(std::ceil(((x_h * 1.0) - dilation[0] * (kernel[0] - 1)) / stride[0])));
     output_hw->push_back(static_cast<int64_t>(std::ceil(((x_w * 1.0) - dilation[1] * (kernel[1] - 1)) / stride[1])));
     const size_t nhwc = 4;
     (void)pad_list->insert(pad_list->begin(), nhwc, 0);
-  } else if (pad_mode == PadMode::SAME) {
+  } else if (pad_mode == static_cast<int64_t>(PadMode::SAME)) {
     output_hw->push_back(static_cast<int64_t>(std::ceil((x_h * 1.0) / stride[0])));
     output_hw->push_back(static_cast<int64_t>(std::ceil((x_w * 1.0) / stride[1])));
     int64_t pad_needed_h = (output_hw->at(0) - 1) * stride[0] + dilation[0] * (kernel[0] - 1) + 1 - x_h;
     pad_needed_h = std::max((int64_t)0, pad_needed_h);
-    pad_list->push_back(static_cast<int64_t>(std::floor(pad_needed_h / 2)));
+    pad_list->push_back(static_cast<int64_t>(std::floor(pad_needed_h / middle)));
     pad_list->push_back(pad_needed_h - pad_list->at(0));
     int64_t pad_needed_w = (output_hw->at(1) - 1) * stride[1] + dilation[1] * (kernel[1] - 1) + 1 - x_w;
     pad_needed_w = std::max((int64_t)0, pad_needed_w);
-    pad_list->push_back(static_cast<int64_t>(std::floor(pad_needed_w / 2)));
-    pad_list->push_back(pad_needed_w - pad_list->at(2));
-  } else if (pad_mode == PadMode::PAD) {
+    pad_list->push_back(static_cast<int64_t>(std::floor(pad_needed_w / middle)));
+    pad_list->push_back(pad_needed_w - pad_list->at(middle));
+  } else if (pad_mode == static_cast<int64_t>(PadMode::PAD)) {
     (void)pad_list->insert(pad_list->begin(), padding.begin(), padding.end());
     output_hw->push_back(static_cast<int64_t>(std::floor(
-      1 + ((x_h * 1.0) + pad_list->at(0) + pad_list->at(1) - kernel[0] - (kernel[0] - 1) * (dilation[0] - 1)) /
+      1 + (((x_h * 1.0) + pad_list->at(0) + pad_list->at(1) - kernel[0]) - (kernel[0] - 1) * (dilation[0] - 1)) /
             stride[0])));
-    output_hw->push_back(static_cast<int64_t>(std::floor(
-      1 + ((x_w * 1.0) + pad_list->at(2) + pad_list->at(3) - kernel[1] - (kernel[1] - 1) * (dilation[1] - 1)) /
-            stride[1])));
+    output_hw->push_back(static_cast<int64_t>(
+      std::floor(1 + (((x_w * 1.0) + pad_list->at(second_index) + pad_list->at(third_index) - kernel[1]) -
+                      (kernel[1] - 1) * (dilation[1] - 1)) /
+                       stride[1])));
   }
 }
 

mindspore/core/base/complex_storage.h

@@ -1,5 +1,5 @@
 /**
- * Copyright 2021 Huawei Technologies Co., Ltd
+ * Copyright 2021-2022 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 @@ struct alignas(sizeof(T) * 2) ComplexStorage {
 
 template <typename T>
 inline bool operator==(const ComplexStorage<T> &lhs, const ComplexStorage<T> &rhs) {
-  return lhs.real_ == rhs.real_ && lhs.imag_ == rhs.imag_;
+  return (lhs.real_ - rhs.real_ == 0) && (lhs.imag_ - rhs.imag_ == 0);
 }
 
 template <typename T>

mindspore/core/base/effect_info.h

@@ -1,5 +1,5 @@
 /**
- * Copyright 2019 Huawei Technologies Co., Ltd
+ * Copyright 2019-2022 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.
@@ -52,6 +52,8 @@ class EffectInfoHolder {
   // Unset effect info.
   void UnsetEffectInfo() { effect_info_ = {EffectInfo::kUnknown, false, false}; }
 
+  ~EffectInfoHolder() {}
+
  protected:
   EffectInfo effect_info_;
 };

mindspore/core/base/float16.h

@@ -1,5 +1,5 @@
 /**
- * Copyright 2020 Huawei Technologies Co., Ltd
+ * Copyright 2020-2022 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.
@@ -98,8 +98,8 @@ class Float16 {
     return *this;
   }
 
-  static float ToFloat32(Float16 f16) {
-    constexpr Union32 magic = {113 << 23};
+  static float ToFloat32(const Float16 &f16) {
+    constexpr Union32 magic = {.u = 113 << 23};
     constexpr uint32_t exponent_adjust = ((127 - 15) << 23);
     constexpr uint32_t inf_extra_exp_adjust = ((128 - 16) << 23);
     constexpr uint32_t zero_extra_exp_adjust = (1 << 23);
@@ -130,9 +130,9 @@ class Float16 {
  private:
   static uint16_t FromFloat32(float f32) {
     constexpr uint32_t magic = {113 << 23};
-    constexpr Union32 f32infty = {255 << 23};
-    constexpr Union32 f16max = {(127 + 16) << 23};
-    constexpr Union32 denorm_magic = {((127 - 15) + (23 - 10) + 1) << 23};
+    constexpr Union32 f32infty = {.u = 255 << 23};
+    constexpr Union32 f16max = {.u = (127 + 16) << 23};
+    constexpr Union32 denorm_magic = {.u = ((127 - 15) + (23 - 10) + 1) << 23};
     constexpr unsigned int exponent_bits = 13;
     constexpr unsigned int sign_bit_shift = 16;
     constexpr unsigned int sign_mask = 0x80000000u;
@@ -275,11 +275,11 @@ struct numeric_limits<float16> {
 // std::numeric_limits<const volatile T>
 // https://stackoverflow.com/a/16519653/
 template <>
-struct numeric_limits<const mindspore::Float16> : numeric_limits<mindspore::Float16> {};
+struct numeric_limits<const mindspore::Float16> : private numeric_limits<mindspore::Float16> {};
 template <>
-struct numeric_limits<volatile mindspore::Float16> : numeric_limits<mindspore::Float16> {};
+struct numeric_limits<volatile mindspore::Float16> : private numeric_limits<mindspore::Float16> {};
 template <>
-struct numeric_limits<const volatile mindspore::Float16> : numeric_limits<mindspore::Float16> {};
+struct numeric_limits<const volatile mindspore::Float16> : private numeric_limits<mindspore::Float16> {};
 }  // namespace std
 
 // Implements standard math functions for float16.
@@ -306,6 +306,6 @@ inline float16 pow(const float16 &a, const float16 &b) {
 
 #endif  // ENABLE_ARM32 || ENABLE_ARM64
 
-inline float half_to_float(float16 h) { return static_cast<float>(h); }
+inline float half_to_float(const float16 &h) { return static_cast<float>(h); }
 
 #endif  // MINDSPORE_CORE_BASE_FLOAT16_H_

mindspore/core/ir/anf.h

@@ -1,7 +1,7 @@
 /**
  * This is the C++ adaptation and derivative work of Myia (https://github.com/mila-iqia/myia/).
  *
- * Copyright 2019-2021 Huawei Technologies Co., Ltd
+ * Copyright 2019-2022 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.
@@ -168,7 +168,7 @@ class MS_CORE_API AnfNode : public Base {
   /// \brief Obtain the pointer of KernelInfoDevice.
   ///
   /// \return The pointer of KernelInfoDevice.
-  const KernelInfoDevicePtr &kernel_info_ptr() { return kernel_info_; }
+  const KernelInfoDevicePtr &kernel_info_ptr() const { return kernel_info_; }
 
   /// \brief Set device kernel program information.
   ///
@@ -340,7 +340,7 @@ class MS_CORE_API AnfNode : public Base {
   /// \brief Check if there is an interpret node.
   ///
   /// \return True if there is an interpret node, otherwise false.
-  bool interpret() { return interpret_; }
+  bool interpret() const { return interpret_; }
 
   /// \brief Whether to use interpretation
   ///
@@ -558,7 +558,7 @@ class MS_CORE_API CNode final : public AnfNode, public EffectInfoHolder {
   /// \brief Check if is_load_ is set.
   ///
   /// \return True if is_load_ is set, otherwise false.
-  bool get_load_flag() { return is_load_; }
+  bool get_load_flag() const { return is_load_; }
 
   /// \brief Get func_graph_as_var of this CNode.
   ///
@@ -805,7 +805,7 @@ class MS_CORE_API Parameter final : public ANode {
   /// \brief Set the default parameter.
   ///
   /// \param[in] param The default parameter.
-  void set_default_param(ValuePtr param) {
+  void set_default_param(const ValuePtr &param) {
     default_param_ = param;
     has_default_ = true;
   }
@@ -876,7 +876,7 @@ class MS_CORE_API Parameter final : public ANode {
   /// \brief Get groups attr in FracZ format.
   ///
   /// \return Groups attr in FracZ format.
-  int64_t fracz_group() { return format_attrs_.fracz_group; }
+  int64_t fracz_group() const { return format_attrs_.fracz_group; }
 
   /// \brief Set input_size attr in FracNZ_RNN or ND_RNN_Bias format.
   ///
@@ -886,7 +886,7 @@ class MS_CORE_API Parameter final : public ANode {
   /// \brief Get input_size attr in FracNZ_RNN or ND_RNN_Bias format.
   ///
   /// \return input_size attr in FracNZ_RNN or ND_RNN_Bias format.
-  int64_t input_size() { return format_attrs_.input_size; }
+  int64_t input_size() const { return format_attrs_.input_size; }
 
   /// \brief Set hidden_size attr in FracNZ_RNN or ND_RNN_Bias format.
   ///
@@ -896,7 +896,7 @@ class MS_CORE_API Parameter final : public ANode {
   /// \brief Get hidden_size attr in FracNZ_RNN or ND_RNN_Bias format.
   ///
   /// \return hidden_size attr in FracNZ_RNN or ND_RNN_Bias format.
-  int64_t hidden_size() { return format_attrs_.hidden_size; }
+  int64_t hidden_size() const { return format_attrs_.hidden_size; }
 
  private:
   struct FormatAttr {
@@ -946,11 +946,7 @@ class MS_CORE_API Value : public Base {
   /// \brief Get the abstract value of Value.
   ///
   /// \return Abstract value of Value.
-  virtual abstract::AbstractBasePtr ToAbstract() {
-    MS_LOG(EXCEPTION) << "ToAbstract error";
-    abstract::AbstractBasePtr result;
-    return result;
-  }
+  virtual abstract::AbstractBasePtr ToAbstract() { MS_LOG(EXCEPTION) << "ToAbstract error"; }
 
   /// \brief Check whether the input is the current Value object.
   ///
@@ -994,7 +990,7 @@ class MS_CORE_API ValueNode final : public ANode {
   ~ValueNode() override = default;
   MS_DECLARE_PARENT(ValueNode, ANode);
 
-  void set_func_graph(const FuncGraphPtr &func_graph) override {
+  void set_func_graph(const FuncGraphPtr &) override {
     MS_EXCEPTION(ValueError) << "ValueNode should not set its func_graph.";
   }
 

mindspore/core/ir/device_sync.h

@@ -1,5 +1,5 @@
 /**
- * Copyright 2020 Huawei Technologies Co., Ltd
+ * Copyright 2020-2022 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.
@@ -32,16 +32,15 @@ class DeviceSync {
  public:
   // Used to sync data between different device addresses, only need the data size and data ptr. The CPU device doesn't
   // need use the interfaces, so need the default implementation.
-  virtual bool SyncDeviceToHost(size_t size, void *host_ptr) const { return true; }
-  virtual bool SyncHostToDevice(size_t size, const void *host_ptr) const { return true; }
+  virtual bool SyncDeviceToHost(size_t, void *) const { return true; }
+  virtual bool SyncHostToDevice(size_t, const void *) const { return true; }
 
   // Used to sync data between host tensor and device address, additional need the data shape and data type.
   virtual bool SyncDeviceToHost(const ShapeVector &shape, size_t size, TypeId type, void *host_ptr) const = 0;
   virtual bool SyncHostToDevice(const ShapeVector &shape, size_t size, TypeId type, const void *host_ptr,
                                 const std::string &format = "DefaultFormat") const = 0;
-  virtual bool SyncDeviceToDevice(const DeviceSync *src_device_addr) const { return true; }
-  virtual bool AsyncDeviceToDevice(const ShapeVector &shape, size_t size, TypeId type, const void *src_ptr,
-                                   const std::string &format) const {
+  virtual bool SyncDeviceToDevice(const DeviceSync *) const { return true; }
+  virtual bool AsyncDeviceToDevice(const ShapeVector &, size_t, TypeId type, const void *, const std::string &) const {
     return true;
   }
 
@@ -66,6 +65,8 @@ class DeviceSync {
   void DecreaseRefCount() { ref_count_--; }
   void ResetRefCount() { ref_count_ = original_ref_count_; }
 
+  virtual ~DeviceSync() {}
+
  protected:
   mutable size_t original_ref_count_{1};
   // It will be decreased in the running, and reset by original_ref_count_ when it is zero.

mindspore/core/ir/dtype/container.cc

@@ -1,5 +1,5 @@
 /**
- * Copyright 2019-2021 Huawei Technologies Co., Ltd
+ * Copyright 2019-2022 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.
@@ -15,7 +15,6 @@
  */
 
 #include "ir/dtype/container.h"
-#include <string>
 #include <cstdlib>
 #include <algorithm>
 #include "utils/log_adapter.h"

mindspore/core/ir/dtype/number.cc

@@ -1,5 +1,5 @@
 /**
- * Copyright 2019 Huawei Technologies Co., Ltd
+ * Copyright 2019-2022 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.
@@ -15,7 +15,6 @@
  */
 
 #include "ir/dtype/number.h"
-#include <string>
 #include <cstdlib>
 #include <algorithm>
 #include "utils/log_adapter.h"

mindspore/core/ir/dtype/ref.cc

@@ -1,5 +1,5 @@
 /**
- * Copyright 2019 Huawei Technologies Co., Ltd
+ * Copyright 2019-2022 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.
@@ -15,7 +15,6 @@
  */
 
 #include "ir/dtype/ref.h"
-#include <string>
 #include <cstdlib>
 #include <algorithm>
 #include "utils/log_adapter.h"

mindspore/core/ir/dtype/tensor_type.cc

@@ -1,5 +1,5 @@
 /**
- * Copyright 2019 Huawei Technologies Co., Ltd
+ * Copyright 2019-2022 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.
@@ -15,7 +15,6 @@
  */
 
 #include "ir/dtype/tensor_type.h"
-#include <string>
 #include <cstdlib>
 #include <algorithm>
 #include "utils/log_adapter.h"

mindspore/core/ir/dtype/type.cc

@@ -1,7 +1,7 @@
 /**
  * This is the C++ adaptation and derivative work of Myia (https://github.com/mila-iqia/myia/).
  *
- * Copyright 2019-2021 Huawei Technologies Co., Ltd
+ * Copyright 2019-2022 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.
@@ -20,7 +20,6 @@
 
 #include <algorithm>
 #include <cstdlib>
-#include <string>
 #include <climits>
 
 #include "ir/dtype/number.h"

mindspore/core/ir/dtype_extends.cc

@@ -1,5 +1,5 @@
 /**
- * Copyright 2020-2021 Huawei Technologies Co., Ltd
+ * Copyright 2020-2022 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.
@@ -15,7 +15,6 @@
  */
 
 #include "ir/dtype.h"
-#include <string>
 #include <cstdlib>
 #include <algorithm>
 #include "utils/log_adapter.h"

mindspore/core/ir/func_graph.cc

@@ -1,7 +1,7 @@
 /**
  * This is the C++ adaptation and derivative work of Myia (https://github.com/mila-iqia/myia/).
  *
- * Copyright 2019-2021 Huawei Technologies Co., Ltd
+ * Copyright 2019-2022 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.
@@ -578,14 +578,14 @@ AnfNodePtr FuncGraph::GetVariableArgParameter() {
       MS_LOG(EXCEPTION) << "Length of parameters is " << parameters_.size() << ", hyper_param_count is "
                         << hyper_param_count_ << ", parameters is less than 2 + hyper_param_count";
     }
-    return parameters_[parameters_.size() - hyper_param_count_ - min_param_num];
+    return parameters_[(parameters_.size() - hyper_param_count_) - min_param_num];
   }
 
   if (parameters_.size() < hyper_param_count_ + 1) {
     MS_LOG(EXCEPTION) << "Length of parameters is " << parameters_.size() << ", hyper_param_count is "
                       << hyper_param_count_ << ", parameters is less than 1 + hyper_param_count";
   }
-  return parameters_[parameters_.size() - hyper_param_count_ - 1];
+  return parameters_[(parameters_.size() - hyper_param_count_) - 1];
 }
 
 std::string FuncGraph::GetVariableArgName() {
@@ -600,7 +600,8 @@ std::string FuncGraph::GetVariableArgName() {
       MS_LOG(EXCEPTION) << "Length of parameters is " << parameters_.size() << ", hyper_param_count is "
                         << hyper_param_count_ << ", parameters is less than 2 + hyper_param_count";
     }
-    const auto &parameter = parameters_[parameters_.size() - hyper_param_count_ - min_param_num]->cast<ParameterPtr>();
+    const auto &parameter =
+      parameters_[(parameters_.size() - hyper_param_count_) - min_param_num]->cast<ParameterPtr>();
     MS_EXCEPTION_IF_NULL(parameter);
     return parameter->name();
   }
@@ -609,7 +610,7 @@ std::string FuncGraph::GetVariableArgName() {
     MS_LOG(EXCEPTION) << "Length of parameters is " << parameters_.size() << ", hyper_param_count is "
                       << hyper_param_count_ << ", parameters is less than 1 + hyper_param_count";
   }
-  const auto &parameter = parameters_[parameters_.size() - hyper_param_count_ - 1]->cast<ParameterPtr>();
+  const auto &parameter = parameters_[(parameters_.size() - hyper_param_count_) - 1]->cast<ParameterPtr>();
   MS_EXCEPTION_IF_NULL(parameter);
   return parameter->name();
 }
@@ -620,7 +621,7 @@ AnfNodePtr FuncGraph::GetVariableKwargParameter() {
       MS_LOG(EXCEPTION) << "Length of parameters is " << parameters_.size() << ", hyper_param_count is "
                         << hyper_param_count_ << ", parameters is less than 1 + hyper_param_count";
     }
-    return parameters_[parameters_.size() - hyper_param_count_ - 1];
+    return parameters_[(parameters_.size() - hyper_param_count_) - 1];
   }
   return nullptr;
 }
@@ -631,7 +632,7 @@ std::string FuncGraph::GetVariableKwargName() {
       MS_LOG(EXCEPTION) << "Length of parameters is " << parameters_.size() << ", hyper_param_count is "
                         << hyper_param_count_ << ", parameters is less than 1 + hyper_param_count";
     }
-    const auto &parameter = parameters_[parameters_.size() - hyper_param_count_ - 1]->cast<ParameterPtr>();
+    const auto &parameter = parameters_[(parameters_.size() - hyper_param_count_) - 1]->cast<ParameterPtr>();
     MS_EXCEPTION_IF_NULL(parameter);
     return parameter->name();
   }
@@ -646,7 +647,7 @@ int FuncGraph::GetPositionalArgsCount() const {
   if (has_vararg_) {
     count--;
   }
-  return count - kwonlyargs_count_ - SizeToInt(hyper_param_count_);
+  return (count - kwonlyargs_count_) - SizeToInt(hyper_param_count_);
 }
 
 AnfNodePtr FuncGraph::GetParameterByName(const std::string &name) {

mindspore/core/ir/func_graph.h

@@ -1,7 +1,7 @@
 /**
  * This is the C++ adaptation and derivative work of Myia (https://github.com/mila-iqia/myia/).
  *
- * Copyright 2019-2021 Huawei Technologies Co., Ltd
+ * Copyright 2019-2022 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.
@@ -95,13 +95,6 @@ const char kFuncGraphFlagBackPropEntry[] = "BackPropEntry";
 const char kFuncGraphFlagReAutoMonad[] = "ReAutoMonad";
 const char kFuncGraphFlagRecursive[] = "Recursive";
 
-namespace abstract {
-class AbstractKeywordArg;
-using AbstractKeywordArgPtr = std::shared_ptr<AbstractKeywordArg>;
-class AbstractFunction;
-using AbstractFunctionPtr = std::shared_ptr<AbstractFunction>;
-}  // namespace abstract
-
 class FuncGraph : public deprecated::api::FuncGraph, public FuncGraphBase, public EffectInfoHolder {
  public:
   using Drawer = std::function<void(const std::string &, const FuncGraphPtr &)>;
@@ -348,14 +341,16 @@ class FuncGraph : public deprecated::api::FuncGraph, public FuncGraphBase, publi
 
   bool stub() const { return stub_; }
   void set_stub(bool stub) { stub_ = stub; }
-  static void set_drawer(Drawer drawer) { drawer_ = drawer; }
+  static void set_drawer(const Drawer &drawer) { drawer_ = drawer; }
   std::shared_ptr<bool> switch_input() const { return switch_input_; }
-  void set_switch_input(std::shared_ptr<bool> switch_input) { switch_input_ = switch_input; }
+  void set_switch_input(const std::shared_ptr<bool> &switch_input) { switch_input_ = switch_input; }
   std::shared_ptr<bool> switch_layer_input() const { return switch_layer_input_; }
-  void set_switch_layer_input(std::shared_ptr<bool> switch_layer_input) { switch_layer_input_ = switch_layer_input; }
+  void set_switch_layer_input(const std::shared_ptr<bool> &switch_layer_input) {
+    switch_layer_input_ = switch_layer_input;
+  }
   bool ContainMultiTarget();
   bool IsMultiTarget() const { return exist_multi_target_; }
-  int64_t stage() { return stage_; }
+  int64_t stage() const { return stage_; }
   void set_stage(int64_t stage) { stage_ = stage; }
 
   bool dropped() const { return dropped_; }

mindspore/core/ir/func_graph_extends.cc

@@ -1,7 +1,7 @@
 /**
  * This is the C++ adaptation and derivative work of Myia (https://github.com/mila-iqia/myia/).
  *
- * Copyright 2020-2021 Huawei Technologies Co., Ltd
+ * Copyright 2020-2022 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.
@@ -252,7 +252,7 @@ FuncGraphPtr FuncGraph::GenerateGraph(const AbstractBasePtrList &args_spec_list)
   }
   FuncGraphPtr specialized_graph = BasicClone(shared_from_base<FuncGraph>());
   size_t kwarg_count = kwarg_list.size();
-  int pos_args_input_count = SizeToInt(arguments_count - kwarg_count - hyper_param_count_);
+  int pos_args_input_count = SizeToInt((arguments_count - kwarg_count) - hyper_param_count_);
   int pos_args_count = std::min(pos_args_input_count, this->GetPositionalArgsCount());
   int variable_args_count = pos_args_input_count - pos_args_count;
   std::vector<AnfNodePtr> specialized_parameter_list;

mindspore/core/ir/kernel_info_dev.h

@@ -1,5 +1,5 @@
 /**
- * Copyright 2019 Huawei Technologies Co., Ltd
+ * Copyright 2019-2022 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.
@@ -85,6 +85,8 @@ class KernelInfoDevice {
 
   RuntimeCacheScope runtime_cache() { return RuntimeCacheScope(runtime_cache_, mu_); }
 
+  virtual ~KernelInfoDevice() {}
+
  private:
   RuntimeCache runtime_cache_;
   std::mutex mu_;

mindspore/core/ops/base_operator.h

@@ -1,5 +1,5 @@
 /**
- * Copyright 2021 Huawei Technologies Co., Ltd
+ * Copyright 2021-2022 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.
@@ -31,11 +31,6 @@ class AbstractBase;
 using AbstractBasePtr = std::shared_ptr<abstract::AbstractBase>;
 }  // namespace abstract
 
-namespace mindspore {
-class Primitive;
-using PrimitivePtr = std::shared_ptr<Primitive>;
-}  // namespace mindspore
-
 namespace mindspore {
 namespace ops {
 class BaseOperator : public api::Primitive {