modify static check

mindspore/lite/src/common/common.h

@@ -14,8 +14,8 @@
  * limitations under the License.
  */
 
-#ifndef MINDSPORE_LITE_COMMON_COMMON_H_
-#define MINDSPORE_LITE_COMMON_COMMON_H_
+#ifndef MINDSPORE_LITE_SRC_COMMON_COMMON_H_
+#define MINDSPORE_LITE_SRC_COMMON_COMMON_H_
 
 #include <string>
 #include "src/tensor.h"
@@ -55,4 +55,4 @@ static const schema::Format DEFAULT_FORMAT = schema::Format::Format_NCHW;
 }  // namespace lite
 }  // namespace mindspore
 
-#endif  // MINDSPORE_LITE_COMMON_COMMON_H_
+#endif  // MINDSPORE_LITE_SRC_COMMON_COMMON_H_

mindspore/lite/src/common/file_utils.h

@@ -14,8 +14,8 @@
  * limitations under the License.
  */
 
-#ifndef MINDSPORE_LITE_COMMON_FILE_UTILS_H_
-#define MINDSPORE_LITE_COMMON_FILE_UTILS_H_
+#ifndef MINDSPORE_LITE_SRC_COMMON_FILE_UTILS_H_
+#define MINDSPORE_LITE_SRC_COMMON_FILE_UTILS_H_
 
 #include <cstdio>
 #include <cstdlib>
@@ -48,13 +48,14 @@ void WriteToTxt(const std::string &file_path, void *data, size_t element_size) {
   out_file.close();
 }
 
-inline int WriteToBin(const std::string &file_path, void *data, size_t size) {
+inline int WriteToBin(const std::string &file_path, void *data, const size_t size) {
   std::ofstream out_file;
   out_file.open(file_path.c_str(), std::ios::binary);
   if (!out_file.good() || !out_file.is_open()) {
     return -1;
   }
   out_file.write(reinterpret_cast<char *>(data), size);
+  out_file.close();
   return 0;
 }
 
@@ -63,4 +64,4 @@ std::string GetAndroidPackagePath();
 }  // namespace lite
 }  // namespace mindspore
 
-#endif  // MINDSPORE_LITE_COMMON_FILE_UTILS_H_
+#endif  // MINDSPORE_LITE_SRC_COMMON_FILE_UTILS_H_

mindspore/lite/src/common/graph_util.h

@@ -14,8 +14,8 @@
  * limitations under the License.
  */
 
-#ifndef MINDSPORE_LITE_COMMON_GRAPH_UTIL_H_
-#define MINDSPORE_LITE_COMMON_GRAPH_UTIL_H_
+#ifndef MINDSPORE_LITE_SRC_COMMON_GRAPH_UTIL_H_
+#define MINDSPORE_LITE_SRC_COMMON_GRAPH_UTIL_H_
 
 #include <string>
 #include <unordered_map>
@@ -35,8 +35,8 @@ std::vector<size_t> GetGraphInputNodes(const lite::Model *model);
 
 std::vector<size_t> GetGraphOutputNodes(const lite::Model *model);
 
-std::vector<size_t> GetLinkedPostNodeIdx(const lite::Model *model, const size_t tensor_idx);
+std::vector<size_t> GetLinkedPostNodeIdx(const lite::Model *model, size_t tensor_idx);
 }  // namespace lite
 }  // namespace mindspore
 
-#endif  // MINDSPORE_LITE_COMMON_GRAPH_UTIL_H_
+#endif  // MINDSPORE_LITE_SRC_COMMON_GRAPH_UTIL_H_

mindspore/lite/src/common/log_adapter.h

@@ -14,8 +14,8 @@
  * limitations under the License.
  */
 
-#ifndef MINDSPORE_LITE_COMMON_LOG_ADAPTER_H_
-#define MINDSPORE_LITE_COMMON_LOG_ADAPTER_H_
+#ifndef MINDSPORE_LITE_SRC_COMMON_LOG_ADAPTER_H_
+#define MINDSPORE_LITE_SRC_COMMON_LOG_ADAPTER_H_
 #ifdef USE_GLOG
 #include "utils/log_adapter.h"
 #else
@@ -113,4 +113,4 @@ class LogWriter {
 #define MS_ASSERT(f) ((void)0)
 #endif
 #endif
-#endif  // MINDSPORE_LITE_COMMON_LOG_ADAPTER_H_
+#endif  // MINDSPORE_LITE_SRC_COMMON_LOG_ADAPTER_H_

mindspore/lite/src/common/string_util.h

@@ -14,8 +14,8 @@
  * limitations under the License.
  */
 
-#ifndef MINDSPORE_LITE_COMMON_STRING_UTIL_H_
-#define MINDSPORE_LITE_COMMON_STRING_UTIL_H_
+#ifndef MINDSPORE_LITE_SRC_COMMON_STRING_UTIL_H_
+#define MINDSPORE_LITE_SRC_COMMON_STRING_UTIL_H_
 
 #include <vector>
 #include <string>
@@ -52,4 +52,4 @@ uint64_t StringHash64(const char *s, size_t len);
 }  // namespace lite
 }  // namespace mindspore
 
-#endif  // MINDSPORE_LITE_COMMON_STRING_UTIL_H_
+#endif  // MINDSPORE_LITE_SRC_COMMON_STRING_UTIL_H_

mindspore/lite/src/common/utils.h

@@ -14,10 +14,9 @@
  * limitations under the License.
  */
 
-#ifndef MINDSPORE_LITE_COMMON_UTILS_H_
-#define MINDSPORE_LITE_COMMON_UTILS_H_
+#ifndef MINDSPORE_LITE_SRC_COMMON_UTILS_H_
+#define MINDSPORE_LITE_SRC_COMMON_UTILS_H_
 
-#include <stdint.h>
 #include <ctime>
 #include <cstdint>
 #include <vector>
@@ -186,4 +185,4 @@ inline Option<bool> GenericParseValue(const std::string &value) {
 }  // namespace lite
 }  // namespace mindspore
 
-#endif  // MINDSPORE_LITE_COMMON_UTILS_H_
+#endif  // MINDSPORE_LITE_SRC_COMMON_UTILS_H_

mindspore/lite/src/executor.h

@@ -35,7 +35,7 @@ class Executor {
                   const KernelCallBack &before = nullptr, const KernelCallBack &after = nullptr);
 
  protected:
-  int CheckInputs(std::vector<Tensor *> &in_tensors);
+  static int CheckInputs(std::vector<Tensor *> &in_tensors);
 };
 
 }  // namespace mindspore::lite

mindspore/lite/src/inner_context.cc

@@ -52,10 +52,10 @@ int InnerContext::Init() {
 }
 
 InnerContext::~InnerContext() {
-  if (this->thread_pool_ != NULL) {
+  if (this->thread_pool_ != nullptr) {
     DestroyThreadPool(this->thread_pool_);
     free(this->thread_pool_);
-    this->thread_pool_ = NULL;
+    this->thread_pool_ = nullptr;
   }
 }
 

mindspore/lite/src/kernel_registry.cc

@@ -73,7 +73,7 @@ int KernelRegistry::GetCreatorFuncIndex(const kernel::KernelKey desc) {
   return index;
 }
 
-void KernelRegistry::RegKernel(const KernelKey desc, kernel::KernelCreator creator) {
+void KernelRegistry::RegKernel(const KernelKey desc, const kernel::KernelCreator creator) {
   int index = GetCreatorFuncIndex(desc);
   if (index >= array_size_) {
     MS_LOG(ERROR) << "invalid kernel key, arch " << desc.arch << ", data_type" << desc.data_type << ",op type "
@@ -97,8 +97,6 @@ void KernelRegistry::RegKernel(const KERNEL_ARCH arch, const TypeId data_type, c
 
 bool KernelRegistry::Merge(const std::unordered_map<KernelKey, KernelCreator> &new_creators) { return false; }
 
-const kernel::KernelCreator *KernelRegistry::GetCreatorArrays() { return creator_arrays_; }
-
 kernel::LiteKernel *KernelRegistry::GetKernel(const std::vector<Tensor *> &in_tensors,
                                               const std::vector<Tensor *> &out_tensors, const PrimitiveC *primitive,
                                               const InnerContext *ctx, const kernel::KernelKey &key) {
@@ -124,5 +122,5 @@ kernel::LiteKernel *KernelRegistry::GetKernel(const std::vector<Tensor *> &in_te
   return nullptr;
 }
 
-KernelRegistry::~KernelRegistry() {}
+KernelRegistry::~KernelRegistry() = default;
 }  // namespace mindspore::lite

mindspore/lite/src/kernel_registry.h

@@ -35,9 +35,8 @@ class KernelRegistry {
   virtual ~KernelRegistry();
 
   static KernelRegistry *GetInstance();
-  int Init();
+  static int Init();
   virtual kernel::KernelCreator GetCreator(const kernel::KernelKey &desc);
-  const kernel::KernelCreator *GetCreatorArrays();
   int GetCreatorFuncIndex(kernel::KernelKey desc);
   void RegKernel(kernel::KernelKey desc, kernel::KernelCreator creator);
   void RegKernel(kernel::KERNEL_ARCH arch, TypeId data_type, schema::PrimitiveType type, kernel::KernelCreator creator);
@@ -51,12 +50,11 @@ class KernelRegistry {
   static const int op_type_length_{PrimitiveType_MAX - PrimitiveType_MIN + 1};
   static const int array_size_{device_type_length_ * data_type_length_ * op_type_length_};
   kernel::KernelCreator creator_arrays_[array_size_] = {nullptr};
-  std::vector<OpParameter *> op_parameters_;
 };
 
 class KernelRegistrar {
  public:
-  KernelRegistrar(const kernel::KernelKey &desc, kernel::KernelCreator creator) {
+  KernelRegistrar(const kernel::KernelKey &desc, const kernel::KernelCreator creator) {
     KernelRegistry::GetInstance()->RegKernel(desc, creator);
   }
   ~KernelRegistrar() = default;

mindspore/lite/src/lite_kernel.cc

@@ -93,7 +93,12 @@ int LiteKernel::PreProcess() {
   auto outputs = this->out_tensors();
   for (auto *output : outputs) {
     MS_ASSERT(output != nullptr);
-    output->MallocData();
+
+    auto ret = output->MallocData();
+    if (ret != RET_OK) {
+      MS_LOG(ERROR) << "MallocData failed";
+      return ret;
+    }
   }
   return RET_OK;
 }
@@ -242,42 +247,6 @@ std::vector<lite::Tensor *> LiteKernelUtil::SubgraphOutputTensors(const std::vec
   return output_tensors;
 }
 
-int LiteKernelUtil::TopologicalSortKernels(std::vector<kernel::LiteKernel *> *kernels) {
-  auto old_kernels = *kernels;
-  kernels->clear();
-  std::queue<kernel::LiteKernel *> kernel_queue;
-  for (auto kernel : old_kernels) {
-    if (kernel->in_kernels().empty()) {
-      kernel_queue.push(kernel);
-      kernels->emplace_back(kernel);
-    }
-  }
-  while (!kernel_queue.empty()) {
-    auto cur_kernel = kernel_queue.front();
-    kernel_queue.pop();
-    MS_ASSERT(cur_kernel != nullptr);
-    auto next_kernels = cur_kernel->out_kernels();
-    for (auto next_kernel : next_kernels) {
-      auto in_kernels = next_kernel->in_kernels();
-      if (lite::IsContain(*kernels, const_cast<kernel::LiteKernel *>(next_kernel))) {
-        MS_LOG(ERROR) << "TopologicalSortKernels failed, loop exist";
-        return RET_ERROR;
-      }
-      if (std::all_of(in_kernels.begin(), in_kernels.end(), [&](const kernel::LiteKernel *in_kernel) {
-            return lite::IsContain(*kernels, const_cast<kernel::LiteKernel *>(in_kernel));
-          })) {
-        kernel_queue.push(next_kernel);
-      }
-    }
-  }
-  if (kernels->size() != old_kernels.size()) {
-    MS_LOG(ERROR) << "TopologicalSortKernels failed, kernels size before sort: " << old_kernels.size()
-                  << ", kernels size after sort: " << kernels->size();
-    return RET_ERROR;
-  }
-  return RET_OK;
-}
-
 void LiteKernelUtil::InitIOKernels(std::vector<kernel::LiteKernel *> &kernels) {
   for (auto *kernel : kernels) {
     // clean io kernels
@@ -308,5 +277,5 @@ void LiteKernelUtil::InitTensorRefCount(std::vector<kernel::LiteKernel *> &kerne
   }
 }
 
-int LiteKernelUtil::SetInput(LiteKernel &kernelMod, std::vector<lite::Tensor *> inputs) { return -1; }
+int LiteKernelUtil::SetInput(LiteKernel &kernelMod, const std::vector<lite::Tensor *> &inputs) { return -1; }
 }  // namespace mindspore::kernel

mindspore/lite/src/lite_kernel.h

@@ -202,11 +202,9 @@ class LiteKernelUtil {
 
   static std::vector<lite::Tensor *> SubgraphOutputTensors(const std::vector<kernel::LiteKernel *> &kernels);
 
-  static int TopologicalSortKernels(std::vector<kernel::LiteKernel *> *kernels);
-
   static void InitTensorRefCount(std::vector<kernel::LiteKernel *> &kernels);
 
-  static int SetInput(LiteKernel &kernelMod, std::vector<lite::Tensor *> inputs);
+  static int SetInput(LiteKernel &kernelMod, const std::vector<lite::Tensor *> &inputs);
 };
 }  // namespace mindspore::kernel
 

mindspore/lite/src/lite_session.cc

@@ -573,7 +573,11 @@ int LiteSession::Resize(const std::vector<mindspore::tensor::MSTensor *> &inputs
 }  // namespace lite
 
 session::LiteSession *session::LiteSession::CreateSession(const lite::Context *context) {
-  auto session = new lite::LiteSession();
+  auto session = new (std::nothrow) lite::LiteSession();
+  if (session == nullptr) {
+    MS_LOG(ERROR) << "create sesssion failed";
+    return nullptr;
+  }
   auto ret = session->Init(context);
   if (ret != mindspore::lite::RET_OK) {
     MS_LOG(ERROR) << "init sesssion failed";

mindspore/lite/src/lite_session.h

@@ -66,7 +66,7 @@ class LiteSession : public session::LiteSession {
              const std::vector<std::vector<int>> &dims) override;
 
  protected:
-  void ConvertTensorsQuantParam(const schema::Tensor *src_tensor, lite::Tensor *dst_tensor);
+  static void ConvertTensorsQuantParam(const schema::Tensor *src_tensor, lite::Tensor *dst_tensor);
 
   int ConvertTensorsData(const lite::Model *model, size_t tensor_index, const schema::Tensor *src_tensor,
                          lite::Tensor *dst_tensor);

mindspore/lite/src/ops/pooling_grad.cc

@@ -198,11 +198,9 @@ int PoolingGrad::InferShape(std::vector<Tensor *> inputs_, std::vector<Tensor *>
     }
   }
   auto grad_output = outputs_.at(0);
-  // todo: fmk type
   auto output_shape = input->shape();
   grad_output->set_shape(output_shape);
   grad_output->set_data_type(input->data_type());
-  // todo: temp fix
   grad_output->set_format(input->format());
   return RET_OK;
 }

mindspore/lite/src/ops/primitive_c.cc

@@ -193,7 +193,6 @@ void PrimitiveC::FillDefaultInputQuantParamIfNeed(const size_t &inputSize) {
     quants.emplace_back(tmpQuantParam);
     input_quant_param_.insert(input_quant_param_.end(), inputSize - input_quant_param_.size(), quants);
   }
-
   if (input_quant_param_.size() == kDoubleNum) {
     quants.clear();
     quantParam.min = 0.0;
@@ -234,8 +233,8 @@ void PrimitiveC::PopulaterInputQuantParam(const Primitive &prim, const std::vect
   if (filterMin != nullptr && filterMax != nullptr) {
     auto filterMinPtr = filterMin->cast<TensorPtr>();
     auto filterMaxPtr = filterMax->cast<TensorPtr>();
-    float *minBuf = static_cast<float *>(filterMinPtr->data_c());
-    float *maxBuf = static_cast<float *>(filterMaxPtr->data_c());
+    auto *minBuf = static_cast<float *>(filterMinPtr->data_c());
+    auto *maxBuf = static_cast<float *>(filterMaxPtr->data_c());
     quantParam.min = FLT_MAX;
     quantParam.max = FLT_MIN;
     for (int i = 0; i < filterMinPtr->ElementsNum(); ++i) {
@@ -264,8 +263,8 @@ void PrimitiveC::PopulaterOutputQuantParam(const Primitive &prim, bool narrowRan
   if (outputMin != nullptr && outputMax != nullptr) {
     auto outputMinPtr = outputMin->cast<TensorPtr>();
     auto outputMaxPtr = outputMax->cast<TensorPtr>();
-    float *minBuf = static_cast<float *>(outputMinPtr->data_c());
-    float *maxBuf = static_cast<float *>(outputMaxPtr->data_c());
+    auto *minBuf = static_cast<float *>(outputMinPtr->data_c());
+    auto *maxBuf = static_cast<float *>(outputMaxPtr->data_c());
     quantParam.min = *minBuf;
     quantParam.max = *maxBuf;
     auto ret = quant::CalQuantizationParams(&quantParam, quantParam.min, quantParam.max, narrowRangeQuantParam,
@@ -285,14 +284,14 @@ void PrimitiveC::PopulaterOutputQuantParam(const Primitive &prim, bool narrowRan
 
 void PrimitiveC::PopulaterQuantParam(const Primitive &prim, const std::vector<AnfNodePtr> &inputs) {
   auto narrow_range = prim.GetAttr("narrow_range");
-  bool narrowRangeQuantParam = narrow_range != nullptr ? GetValue<bool>(narrow_range) : false;
+  bool narrowRangeQuantParam = narrow_range != nullptr && GetValue<bool>(narrow_range);
   auto num_bits = prim.GetAttr("num_bits");
   int32_t numbitsRangeQuantParam = num_bits != nullptr ? GetValue<int64_t>(num_bits) : 8;
   PopulaterInputQuantParam(prim, inputs, narrowRangeQuantParam, numbitsRangeQuantParam);
   PopulaterOutputQuantParam(prim, narrowRangeQuantParam, numbitsRangeQuantParam);
 }
 
-void PrimitiveC::GetAttrDataFromInput(const AnfNodePtr inputNode, std::vector<int> *data) {
+void PrimitiveC::GetAttrDataFromInput(const AnfNodePtr &inputNode, std::vector<int> *data) {
   if (inputNode->isa<ValueNode>()) {
     auto valNode = inputNode->cast<ValueNodePtr>();
     MS_ASSERT(valNode != nullptr);
@@ -362,12 +361,12 @@ void PrimitiveC::ClearInputOutputQuantParam() {
   output_quant_param_.clear();
 }
 
-void PrimitiveC::AddInputQuantParam(std::vector<schema::QuantParamT> quant_param) {
+void PrimitiveC::AddInputQuantParam(const std::vector<schema::QuantParamT> &quant_param) {
   this->input_quant_param_.emplace_back(quant_param);
 }
 std::vector<std::vector<schema::QuantParamT>> PrimitiveC::input_quant_params() const { return input_quant_param_; }
 
-void PrimitiveC::AddOutputQuantParam(std::vector<schema::QuantParamT> quant_param) {
+void PrimitiveC::AddOutputQuantParam(const std::vector<schema::QuantParamT> &quant_param) {
   this->output_quant_param_.emplace_back(quant_param);
 }
 std::vector<std::vector<schema::QuantParamT>> PrimitiveC::output_quant_params() const { return output_quant_param_; }
@@ -383,7 +382,7 @@ std::shared_ptr<PrimitiveC> GetReturnPrim() {
     return nullptr;
   }
   return_primitiveT->value.type = schema::PrimitiveType_Return;
-  return_primitiveT->value.value = new schema::ReturnT;
+  return_primitiveT->value.value = new (std::nothrow) schema::ReturnT;
   if (return_primitiveT->value.value == nullptr) {
     MS_LOG(ERROR) << "new ReturnT failed";
     delete (return_primitiveT);
@@ -393,13 +392,13 @@ std::shared_ptr<PrimitiveC> GetReturnPrim() {
 }
 
 std::shared_ptr<PrimitiveC> GetMakeTuplePrim() {
-  auto make_tuple_primitiveT = new schema::PrimitiveT;
+  auto make_tuple_primitiveT = new (std::nothrow) schema::PrimitiveT;
   if (make_tuple_primitiveT == nullptr) {
     MS_LOG(ERROR) << "new PrimitiveT failed";
     return nullptr;
   }
   make_tuple_primitiveT->value.type = schema::PrimitiveType_MakeTuple;
-  make_tuple_primitiveT->value.value = new schema::MakeTupleT;
+  make_tuple_primitiveT->value.value = new (std::nothrow) schema::MakeTupleT;
   if (make_tuple_primitiveT->value.value == nullptr) {
     MS_LOG(ERROR) << "new MakeTupleT failed";
     delete (make_tuple_primitiveT);
@@ -409,13 +408,13 @@ std::shared_ptr<PrimitiveC> GetMakeTuplePrim() {
 }
 
 std::shared_ptr<PrimitiveC> GetTupleGetItemPrim() {
-  auto tuple_get_item_primitiveT = new schema::PrimitiveT();
+  auto tuple_get_item_primitiveT = new (std::nothrow) schema::PrimitiveT();
   if (tuple_get_item_primitiveT == nullptr) {
     MS_LOG(ERROR) << "new PrimitiveT failed";
     return nullptr;
   }
   tuple_get_item_primitiveT->value.type = schema::PrimitiveType_TupleGetItem;
-  tuple_get_item_primitiveT->value.value = new schema::TupleGetItemT;
+  tuple_get_item_primitiveT->value.value = new (std::nothrow) schema::TupleGetItemT;
   if (tuple_get_item_primitiveT->value.value == nullptr) {
     MS_LOG(ERROR) << "new TupleGetItemT failed";
     delete (tuple_get_item_primitiveT);
@@ -594,310 +593,310 @@ PrimitiveC *PrimitiveC::Create(mindspore::schema::PrimitiveT *primitive) {
   auto op_type = primitive->value.type;
   switch (op_type) {
     case schema::PrimitiveType_SoftMax:
-      return new SoftMax(primitive);
+      return new (std::nothrow) SoftMax(primitive);
     case schema::PrimitiveType_Activation:
-      return new Activation(primitive);
+      return new (std::nothrow) Activation(primitive);
     case schema::PrimitiveType_Conv2D:
-      return new Conv2D(primitive);
+      return new (std::nothrow) Conv2D(primitive);
     case schema::PrimitiveType_DeConv2D:
-      return new DeConv2D(primitive);
+      return new (std::nothrow) DeConv2D(primitive);
     case schema::PrimitiveType_Reduce:
-      return new Reduce(primitive);
+      return new (std::nothrow) Reduce(primitive);
     case schema::PrimitiveType_Pooling:
-      return new Pooling(primitive);
+      return new (std::nothrow) Pooling(primitive);
     case schema::PrimitiveType_ROIPooling:
-      return new ROIPooling(primitive);
+      return new (std::nothrow) ROIPooling(primitive);
     case schema::PrimitiveType_DepthwiseConv2D:
-      return new DepthwiseConv2D(primitive);
+      return new (std::nothrow) DepthwiseConv2D(primitive);
     case schema::PrimitiveType_FusedBatchNorm:
-      return new FusedBatchNorm(primitive);
+      return new (std::nothrow) FusedBatchNorm(primitive);
     case schema::PrimitiveType_BatchNorm:
-      return new BatchNorm(primitive);
+      return new (std::nothrow) BatchNorm(primitive);
     case schema::PrimitiveType_FullConnection:
-      return new FullConnection(primitive);
+      return new (std::nothrow) FullConnection(primitive);
     case schema::PrimitiveType_Power:
-      return new Power(primitive);
+      return new (std::nothrow) Power(primitive);
     case schema::PrimitiveType_Pad:
-      return new Pad(primitive);
+      return new (std::nothrow) Pad(primitive);
     case schema::PrimitiveType_Range:
-      return new Range(primitive);
+      return new (std::nothrow) Range(primitive);
     case schema::PrimitiveType_Mul:
-      return new Mul(primitive);
+      return new (std::nothrow) Mul(primitive);
     case schema::PrimitiveType_Add:
-      return new Add(primitive);
+      return new (std::nothrow) Add(primitive);
     case schema::PrimitiveType_Sub:
-      return new Sub(primitive);
+      return new (std::nothrow) Sub(primitive);
     case schema::PrimitiveType_Div:
-      return new Div(primitive);
+      return new (std::nothrow) Div(primitive);
     case schema::PrimitiveType_BiasAdd:
-      return new BiasAdd(primitive);
+      return new (std::nothrow) BiasAdd(primitive);
     case schema::PrimitiveType_ExpandDims:
-      return new ExpandDims(primitive);
+      return new (std::nothrow) ExpandDims(primitive);
     case schema::PrimitiveType_ArgMax:
-      return new ArgMax(primitive);
+      return new (std::nothrow) ArgMax(primitive);
     case schema::PrimitiveType_ArgMin:
-      return new ArgMin(primitive);
+      return new (std::nothrow) ArgMin(primitive);
     case schema::PrimitiveType_Cast:
-      return new Cast(primitive);
+      return new (std::nothrow) Cast(primitive);
     case schema::PrimitiveType_Reshape:
-      return new Reshape(primitive);
+      return new (std::nothrow) Reshape(primitive);
     case schema::PrimitiveType_Scale:
-      return new Scale(primitive);
+      return new (std::nothrow) Scale(primitive);
     case schema::PrimitiveType_Eltwise:
-      return new Eltwise(primitive);
+      return new (std::nothrow) Eltwise(primitive);
     case schema::PrimitiveType_Ceil:
-      return new Ceil(primitive);
+      return new (std::nothrow) Ceil(primitive);
     case schema::PrimitiveType_Concat:
-      return new Concat(primitive);
+      return new (std::nothrow) Concat(primitive);
     case schema::PrimitiveType_Fill:
-      return new Fill(primitive);
+      return new (std::nothrow) Fill(primitive);
     case schema::PrimitiveType_Nhwc2Nchw:
-      return new Nhwc2Nchw(primitive);
+      return new (std::nothrow) Nhwc2Nchw(primitive);
     case schema::PrimitiveType_Nchw2Nhwc:
-      return new Nchw2Nhwc(primitive);
+      return new (std::nothrow) Nchw2Nhwc(primitive);
     case schema::PrimitiveType_Transpose:
-      return new Transpose(primitive);
+      return new (std::nothrow) Transpose(primitive);
     case schema::PrimitiveType_Slice:
-      return new Slice(primitive);
+      return new (std::nothrow) Slice(primitive);
     case schema::PrimitiveType_Squeeze:
-      return new Squeeze(primitive);
+      return new (std::nothrow) Squeeze(primitive);
     case schema::PrimitiveType_Flatten:
-      return new Flatten(primitive);
+      return new (std::nothrow) Flatten(primitive);
     case schema::PrimitiveType_Mean:
-      return new Mean(primitive);
+      return new (std::nothrow) Mean(primitive);
     case schema::PrimitiveType_Stack:
-      return new Stack(primitive);
+      return new (std::nothrow) Stack(primitive);
     case schema::PrimitiveType_Crop:
-      return new Crop(primitive);
+      return new (std::nothrow) Crop(primitive);
     case schema::PrimitiveType_SquaredDifference:
-      return new SquaredDifference(primitive);
+      return new (std::nothrow) SquaredDifference(primitive);
     case schema::PrimitiveType_AddN:
-      return new AddN(primitive);
+      return new (std::nothrow) AddN(primitive);
     case schema::PrimitiveType_Abs:
-      return new Abs(primitive);
+      return new (std::nothrow) Abs(primitive);
     case schema::PrimitiveType_Sin:
-      return new Sin(primitive);
+      return new (std::nothrow) Sin(primitive);
     case schema::PrimitiveType_Cos:
-      return new Cos(primitive);
+      return new (std::nothrow) Cos(primitive);
     case schema::PrimitiveType_Log:
-      return new Log(primitive);
+      return new (std::nothrow) Log(primitive);
     case schema::PrimitiveType_Sqrt:
-      return new Sqrt(primitive);
+      return new (std::nothrow) Sqrt(primitive);
     case schema::PrimitiveType_Rsqrt:
-      return new Rsqrt(primitive);
+      return new (std::nothrow) Rsqrt(primitive);
     case schema::PrimitiveType_Square:
-      return new Square(primitive);
+      return new (std::nothrow) Square(primitive);
     case schema::PrimitiveType_Exp:
-      return new Exp(primitive);
+      return new (std::nothrow) Exp(primitive);
     case schema::PrimitiveType_Gather:
-      return new Gather(primitive);
+      return new (std::nothrow) Gather(primitive);
     case schema::PrimitiveType_GatherNd:
-      return new GatherNd(primitive);
+      return new (std::nothrow) GatherNd(primitive);
     case schema::PrimitiveType_LocalResponseNormalization:
-      return new LocalResponseNormalization(primitive);
+      return new (std::nothrow) LocalResponseNormalization(primitive);
     case schema::PrimitiveType_Maximum:
-      return new Maximum(primitive);
+      return new (std::nothrow) Maximum(primitive);
     case schema::PrimitiveType_Minimum:
-      return new Minimum(primitive);
+      return new (std::nothrow) Minimum(primitive);
     case schema::PrimitiveType_StridedSlice:
-      return new StridedSlice(primitive);
+      return new (std::nothrow) StridedSlice(primitive);
     case schema::PrimitiveType_LeakyReLU:
       return new (std::nothrow) LeakyReLU(primitive);
     case schema::PrimitiveType_PReLU:
       return new (std::nothrow) PReLU(primitive);
     case schema::PrimitiveType_Round:
-      return new Round(primitive);
+      return new (std::nothrow) Round(primitive);
     case schema::PrimitiveType_Reverse:
-      return new Reverse(primitive);
+      return new (std::nothrow) Reverse(primitive);
     case schema::PrimitiveType_ReverseSequence:
-      return new ReverseSequence(primitive);
+      return new (std::nothrow) ReverseSequence(primitive);
     case schema::PrimitiveType_LogicalAnd:
-      return new LogicalAnd(primitive);
+      return new (std::nothrow) LogicalAnd(primitive);
     case schema::PrimitiveType_LogicalOr:
-      return new LogicalOr(primitive);
+      return new (std::nothrow) LogicalOr(primitive);
     case schema::PrimitiveType_LogicalNot:
-      return new LogicalNot(primitive);
+      return new (std::nothrow) LogicalNot(primitive);
     case schema::PrimitiveType_FloorDiv:
-      return new FloorDiv(primitive);
+      return new (std::nothrow) FloorDiv(primitive);
     case schema::PrimitiveType_FloorMod:
-      return new FloorMod(primitive);
+      return new (std::nothrow) FloorMod(primitive);
     case schema::PrimitiveType_Equal:
-      return new Equal(primitive);
+      return new (std::nothrow) Equal(primitive);
     case schema::PrimitiveType_NotEqual:
-      return new NotEqual(primitive);
+      return new (std::nothrow) NotEqual(primitive);
     case schema::PrimitiveType_Less:
-      return new Less(primitive);
+      return new (std::nothrow) Less(primitive);
     case schema::PrimitiveType_LessEqual:
-      return new LessEqual(primitive);
+      return new (std::nothrow) LessEqual(primitive);
     case schema::PrimitiveType_Greater:
-      return new Greater(primitive);
+      return new (std::nothrow) Greater(primitive);
     case schema::PrimitiveType_GreaterEqual:
-      return new GreaterEqual(primitive);
+      return new (std::nothrow) GreaterEqual(primitive);
     case schema::PrimitiveType_Floor:
-      return new Floor(primitive);
+      return new (std::nothrow) Floor(primitive);
     case schema::PrimitiveType_Split:
-      return new Split(primitive);
+      return new (std::nothrow) Split(primitive);
     case schema::PrimitiveType_OneHot:
-      return new OneHot(primitive);
+      return new (std::nothrow) OneHot(primitive);
     case schema::PrimitiveType_PriorBox:
-      return new PriorBox(primitive);
+      return new (std::nothrow) PriorBox(primitive);
     case schema::PrimitiveType_SpaceToDepth:
-      return new SpaceToDepth(primitive);
+      return new (std::nothrow) SpaceToDepth(primitive);
     case schema::PrimitiveType_Tile:
-      return new Tile(primitive);
+      return new (std::nothrow) Tile(primitive);
     case schema::PrimitiveType_Resize:
-      return new Resize(primitive);
+      return new (std::nothrow) Resize(primitive);
     case schema::PrimitiveType_Unstack:
-      return new Unstack(primitive);
+      return new (std::nothrow) Unstack(primitive);
     case schema::PrimitiveType_Unique:
-      return new Unique(primitive);
+      return new (std::nothrow) Unique(primitive);
     case schema::PrimitiveType_TopK:
-      return new TopK(primitive);
+      return new (std::nothrow) TopK(primitive);
     case schema::PrimitiveType_MatMul:
-      return new MatMul(primitive);
+      return new (std::nothrow) MatMul(primitive);
     case schema::PrimitiveType_QuantDTypeCast:
-      return new QuantDTypeCast(primitive);
+      return new (std::nothrow) QuantDTypeCast(primitive);
     case schema::PrimitiveType_EmbeddingLookup:
-      return new EmbeddingLookup(primitive);
+      return new (std::nothrow) EmbeddingLookup(primitive);
     case schema::PrimitiveType_Elu:
-      return new Elu(primitive);
+      return new (std::nothrow) Elu(primitive);
     case schema::PrimitiveType_DeDepthwiseConv2D:
-      return new DeDepthwiseConv2D(primitive);
+      return new (std::nothrow) DeDepthwiseConv2D(primitive);
     case schema::PrimitiveType_Shape:
-      return new Shape(primitive);
+      return new (std::nothrow) Shape(primitive);
     case schema::PrimitiveType_Unsqueeze:
-      return new Unsqueeze(primitive);
+      return new (std::nothrow) Unsqueeze(primitive);
     case schema::PrimitiveType_BatchToSpace:
     case schema::PrimitiveType_BatchToSpaceND:
-      return new BatchToSpace(primitive);
+      return new (std::nothrow) BatchToSpace(primitive);
     case schema::PrimitiveType_SpaceToBatch:
-      return new SpaceToBatch(primitive);
+      return new (std::nothrow) SpaceToBatch(primitive);
     case schema::PrimitiveType_SpaceToBatchND:
-      return new SpaceToBatchND(primitive);
+      return new (std::nothrow) SpaceToBatchND(primitive);
     case schema::PrimitiveType_BroadcastTo:
-      return new BroadcastTo(primitive);
+      return new (std::nothrow) BroadcastTo(primitive);
     case schema::PrimitiveType_DepthToSpace:
-      return new DepthToSpace(primitive);
+      return new (std::nothrow) DepthToSpace(primitive);
     case schema::PrimitiveType_Lstm:
-      return new Lstm(primitive);
+      return new (std::nothrow) Lstm(primitive);
     case schema::PrimitiveType_ZerosLike:
-      return new ZerosLike(primitive);
+      return new (std::nothrow) ZerosLike(primitive);
     case schema::PrimitiveType_MakeTuple:
-      return new MakeTuple(primitive);
+      return new (std::nothrow) MakeTuple(primitive);
     case schema::PrimitiveType_Where:
-      return new Where(primitive);
+      return new (std::nothrow) Where(primitive);
     case schema::PrimitiveType_ScatterND:
-      return new ScatterND(primitive);
+      return new (std::nothrow) ScatterND(primitive);
     case schema::PrimitiveType_ConstantOfShape:
-      return new ConstantOfShape(primitive);
+      return new (std::nothrow) ConstantOfShape(primitive);
     case schema::PrimitiveType_L2Norm:
-      return new L2Norm(primitive);
+      return new (std::nothrow) L2Norm(primitive);
     case schema::PrimitiveType_SparseToDense:
-      return new SparseToDense(primitive);
+      return new (std::nothrow) SparseToDense(primitive);
     case schema::PrimitiveType_DetectionPostProcess:
-      return new DetectionPostProcess(primitive);
+      return new (std::nothrow) DetectionPostProcess(primitive);
     case schema::PrimitiveType_Dropout:
-      return new Dropout(primitive);
+      return new (std::nothrow) Dropout(primitive);
     case schema::PrimitiveType_Neg:
-      return new Neg(primitive);
+      return new (std::nothrow) Neg(primitive);
     case schema::PrimitiveType_RealDiv:
-      return new RealDiv(primitive);
+      return new (std::nothrow) RealDiv(primitive);
     case schema::PrimitiveType_LshProjection:
-      return new LshProjection(primitive);
+      return new (std::nothrow) LshProjection(primitive);
     case schema::PrimitiveType_HashtableLookup:
-      return new HashtableLookup(primitive);
+      return new (std::nothrow) HashtableLookup(primitive);
     case schema::PrimitiveType_SkipGram:
-      return new SkipGram(primitive);
+      return new (std::nothrow) SkipGram(primitive);
     case schema::PrimitiveType_Clip:
-      return new Clip(primitive);
+      return new (std::nothrow) Clip(primitive);
     case schema::PrimitiveType_CustomPredict:
-      return new CustomPredict(primitive);
+      return new (std::nothrow) CustomPredict(primitive);
     case schema::PrimitiveType_CustomNormalize:
-      return new CustomNormalize(primitive);
+      return new (std::nothrow) CustomNormalize(primitive);
     case schema::PrimitiveType_CustomExtractFeatures:
-      return new CustomExtractFeatures(primitive);
+      return new (std::nothrow) CustomExtractFeatures(primitive);
     case schema::PrimitiveType_Upsample:
-      return new Upsample(primitive);
+      return new (std::nothrow) Upsample(primitive);
     case schema::PrimitiveType_LayerNorm:
-      return new LayerNorm(primitive);
+      return new (std::nothrow) LayerNorm(primitive);
     case schema::PrimitiveType_NonMaxSuppression:
-      return new NonMaxSuppression(primitive);
+      return new (std::nothrow) NonMaxSuppression(primitive);
     case schema::PrimitiveType_Identity:
-      return new Identity(primitive);
+      return new (std::nothrow) Identity(primitive);
     case schema::PrimitiveType_Rfft:
-      return new Rfft(primitive);
+      return new (std::nothrow) Rfft(primitive);
     case schema::PrimitiveType_FftReal:
-      return new FftReal(primitive);
+      return new (std::nothrow) FftReal(primitive);
     case schema::PrimitiveType_FftImag:
-      return new FftImag(primitive);
+      return new (std::nothrow) FftImag(primitive);
     case schema::PrimitiveType_AudioSpectrogram:
-      return new AudioSpectrogram(primitive);
+      return new (std::nothrow) AudioSpectrogram(primitive);
     case schema::PrimitiveType_Mfcc:
-      return new Mfcc(primitive);
+      return new (std::nothrow) Mfcc(primitive);
     case schema::PrimitiveType_InstanceNorm:
-      return new InstanceNorm(primitive);
+      return new (std::nothrow) InstanceNorm(primitive);
     case schema::PrimitiveType_While:
-      return new While(primitive);
+      return new (std::nothrow) While(primitive);
     case schema::PrimitiveType_OnnxInt8Quantize:
-      return new Quant(primitive);
+      return new (std::nothrow) Quant(primitive);
     case schema::PrimitiveType_OnnxInt8Dequantize:
-      return new Dequant(primitive);
+      return new (std::nothrow) Dequant(primitive);
 
 #ifdef SUPPORT_TRAIN
     case schema::PrimitiveType_ActivationGrad:
-      return new ActivationGrad(primitive);
+      return new (std::nothrow) ActivationGrad(primitive);
     case schema::PrimitiveType_PoolingGrad:
-      return new PoolingGrad(primitive);
+      return new (std::nothrow) PoolingGrad(primitive);
     case schema::PrimitiveType_Conv2DGradFilter:
-      return new Conv2DGradFilter(primitive);
+      return new (std::nothrow) Conv2DGradFilter(primitive);
     case schema::PrimitiveType_Conv2DGradInput:
-      return new Conv2DGradInput(primitive);
+      return new (std::nothrow) Conv2DGradInput(primitive);
     case schema::PrimitiveType_GroupConv2DGradInput:
-      return new GroupConv2DGradInput(primitive);
+      return new (std::nothrow) GroupConv2DGradInput(primitive);
     case schema::PrimitiveType_BiasGrad:
-      return new BiasGrad(primitive);
+      return new (std::nothrow) BiasGrad(primitive);
     case schema::PrimitiveType_ApplyMomentum:
-      return new ApplyMomentum(primitive);
+      return new (std::nothrow) ApplyMomentum(primitive);
     case schema::PrimitiveType_BNGrad:
-      return new BNGrad(primitive);
+      return new (std::nothrow) BNGrad(primitive);
     case schema::PrimitiveType_AddGrad:
-      return new ArithmeticGrad(primitive);
+      return new (std::nothrow) ArithmeticGrad(primitive);
     case schema::PrimitiveType_SubGrad:
-      return new ArithmeticGrad(primitive);
+      return new (std::nothrow) ArithmeticGrad(primitive);
     case schema::PrimitiveType_MulGrad:
-      return new ArithmeticGrad(primitive);
+      return new (std::nothrow) ArithmeticGrad(primitive);
     case schema::PrimitiveType_DivGrad:
-      return new ArithmeticGrad(primitive);
+      return new (std::nothrow) ArithmeticGrad(primitive);
     case schema::PrimitiveType_SoftmaxCrossEntropy:
-      return new SoftmaxCrossEntropy(primitive);
+      return new (std::nothrow) SoftmaxCrossEntropy(primitive);
     case schema::PrimitiveType_PowerGrad:
-      return new PowerGrad(primitive);
+      return new (std::nothrow) PowerGrad(primitive);
     case schema::PrimitiveType_Depend:
-      return new Depend(primitive);
+      return new (std::nothrow) Depend(primitive);
     case schema::PrimitiveType_ControlDepend:
-      return new ControlDepend(primitive);
+      return new (std::nothrow) ControlDepend(primitive);
     case schema::PrimitiveType_FlattenGrad:
-      return new FlattenGrad(primitive);
+      return new (std::nothrow) FlattenGrad(primitive);
     case schema::PrimitiveType_NegGrad:
-      return new NegGrad(primitive);
+      return new (std::nothrow) NegGrad(primitive);
     case schema::PrimitiveType_LogGrad:
-      return new LogGrad(primitive);
+      return new (std::nothrow) LogGrad(primitive);
     case schema::PrimitiveType_Sgd:
-      return new Sgd(primitive);
+      return new (std::nothrow) Sgd(primitive);
     case schema::PrimitiveType_Adam:
-      return new Adam(primitive);
+      return new (std::nothrow) Adam(primitive);
     case schema::PrimitiveType_Assign:
-      return new Assign(primitive);
+      return new (std::nothrow) Assign(primitive);
     case schema::PrimitiveType_AssignAdd:
-      return new AssignAdd(primitive);
+      return new (std::nothrow) AssignAdd(primitive);
     case schema::PrimitiveType_OnesLike:
-      return new OnesLike(primitive);
+      return new (std::nothrow) OnesLike(primitive);
     case schema::PrimitiveType_UnsortedSegmentSum:
-      return new UnsortedSegmentSum(primitive);
+      return new (std::nothrow) UnsortedSegmentSum(primitive);
     case schema::PrimitiveType_BinaryCrossEntropyGrad:
-      return new BinaryCrossEntropyGrad(primitive);
+      return new (std::nothrow) BinaryCrossEntropyGrad(primitive);
     case schema::PrimitiveType_BinaryCrossEntropy:
-      return new BinaryCrossEntropy(primitive);
+      return new (std::nothrow) BinaryCrossEntropy(primitive);
 
 #endif
     default:

mindspore/lite/src/ops/primitive_c.h

@@ -77,9 +77,9 @@ class PrimitiveC : public mindspore::Primitive {
 
   void ClearPrimitiveT();
 
-  bool operator==(const Value &rhs) const {
+  bool operator==(const Value &rhs) const override {
     if (rhs.isa<PrimitiveC>()) {
-      auto other_prim = static_cast<const PrimitiveC &>(rhs);
+      auto other_prim = dynamic_cast<const PrimitiveC &>(rhs);
       auto a = this->primitive_->value.type;
       auto b = other_prim.primitive_->value.type;
       return a == b;
@@ -102,11 +102,11 @@ class PrimitiveC : public mindspore::Primitive {
 
   void ClearInputOutputQuantParam();
 
-  void AddInputQuantParam(std::vector<schema::QuantParamT> quant_param);
+  void AddInputQuantParam(const std::vector<schema::QuantParamT> &quant_param);
 
   std::vector<std::vector<schema::QuantParamT>> input_quant_params() const;
 
-  void AddOutputQuantParam(std::vector<schema::QuantParamT> quant_param);
+  void AddOutputQuantParam(const std::vector<schema::QuantParamT> &quant_param);
 
   std::vector<std::vector<schema::QuantParamT>> output_quant_params() const;
 
@@ -124,7 +124,7 @@ class PrimitiveC : public mindspore::Primitive {
 
   static PrimitiveC *Create(mindspore::schema::PrimitiveT *primitive);
 
-  void GetAttrDataFromInput(const AnfNodePtr inputNode, std::vector<int> *data);
+  static void GetAttrDataFromInput(const AnfNodePtr &inputNode, std::vector<int> *data);
 
   static std::shared_ptr<PrimitiveC> Create(const Primitive &prim, const std::vector<AnfNodePtr> &inputs,
                                             const schema::QuantType &quantType);
@@ -133,7 +133,7 @@ class PrimitiveC : public mindspore::Primitive {
                                 bool narrowRangeQuantParam, int32_t numbitsRangeQuantParam);
   void PopulaterOutputQuantParam(const Primitive &prim, bool narrowRangeQuantParam, int32_t numbitsRangeQuantParam);
   void PopulaterQuantParam(const Primitive &prim, const std::vector<AnfNodePtr> &inputs);
-  void CalFloatScopeByMeanAndStddev(const double &mean, const double &stdDev, float *mMin, float *mMax);
+  static void CalFloatScopeByMeanAndStddev(const double &mean, const double &stdDev, float *mMin, float *mMax);
 
  protected:
   virtual int UnPackAttr(const Primitive &prim, const std::vector<AnfNodePtr> &inputs) { return RET_ERROR; }

mindspore/lite/src/ops/sub.cc

@@ -52,7 +52,6 @@ int Sub::UnPackAttr(const Primitive &prim, const std::vector<AnfNodePtr> &inputs
       this->primitive_ = nullptr;
       return RET_ERROR;
     }
-    // todo: confirm the activationType
     attr->activationType = schema::ActivationType_NO_ACTIVATION;
     this->primitive_->value.value = attr;
   }

mindspore/lite/src/param_value_lite.h

@@ -37,15 +37,15 @@ class ParamValueLite : public Value {
   }
 
   size_t tensor_size() const { return tensor_size_; }
-  void set_tensor_size(size_t size) { tensor_size_ = size; }
+  void set_tensor_size(const size_t size) { tensor_size_ = size; }
   void *tensor_addr() const { return tensor_addr_; }
   void set_tensor_addr(void *addr) { tensor_addr_ = addr; }
 
   std::vector<int> tensor_shape() const { return tensor_shape_; }
-  void set_tensor_shape(std::vector<int> tensor_shape) { tensor_shape_ = std::move(tensor_shape); }
+  void set_tensor_shape(const std::vector<int> &tensor_shape) { tensor_shape_ = tensor_shape; }
 
   TypeId tensor_type() const { return type_id_; }
-  void set_tensor_type(TypeId type_id) { type_id_ = type_id; }
+  void set_tensor_type(const TypeId type_id) { type_id_ = type_id; }
 
   int tensor_shape_size() const {
     int size = 1;

mindspore/lite/src/runtime/allocator.cc

@@ -23,7 +23,7 @@ std::shared_ptr<Allocator> Allocator::Create() {
   return std::shared_ptr<Allocator>(new (std::nothrow) DefaultAllocator());
 }
 
-DefaultAllocator::DefaultAllocator() {}
+DefaultAllocator::DefaultAllocator() = default;
 
 DefaultAllocator::~DefaultAllocator() { Clear(); }
 
@@ -94,13 +94,13 @@ size_t DefaultAllocator::GetTotalSize() {
   Lock();
   size_t totalSize = 0;
 
-  for (auto it = allocated_list_.begin(); it != allocated_list_.end(); it++) {
-    auto membuf = it->second;
+  for (auto &it : allocated_list_) {
+    auto membuf = it.second;
     totalSize += membuf->size;
   }
 
-  for (auto it = free_list_.begin(); it != free_list_.end(); it++) {
-    auto membuf = it->second;
+  for (auto &it : free_list_) {
+    auto membuf = it.second;
     totalSize += membuf->size;
   }
   UnLock();
@@ -110,13 +110,13 @@ size_t DefaultAllocator::GetTotalSize() {
 void DefaultAllocator::Clear() {
   Lock();
 
-  for (auto it = allocated_list_.begin(); it != allocated_list_.end(); it++) {
-    free(it->second);
+  for (auto &it : allocated_list_) {
+    free(it.second);
   }
   allocated_list_.clear();
 
-  for (auto it = free_list_.begin(); it != free_list_.end(); it++) {
-    free(it->second);
+  for (auto &it : free_list_) {
+    free(it.second);
   }
   free_list_.clear();
   UnLock();

mindspore/lite/src/runtime/allocator.h

@@ -34,7 +34,7 @@ struct AllocatorContext {
 class Allocator {
  public:
   Allocator() : name("default") {}
-  virtual ~Allocator() {}
+  virtual ~Allocator() = default;
   virtual void *Malloc(size_t size) = 0;
   virtual void Free(void *ptr) = 0;
   virtual void SetContext(const AllocatorContext &ctx) {}

mindspore/lite/src/runtime/parallel_executor.cc

@@ -31,7 +31,7 @@ int ParallelExecutor::Prepare(const std::vector<mindspore::kernel::LiteKernel *>
 }
 
 static int RunKernel(void *data, int index) {
-  ParallelExecutor *executor = reinterpret_cast<ParallelExecutor *>(data);
+  auto *executor = reinterpret_cast<ParallelExecutor *>(data);
   auto kernel = executor->GetReadyKernel(index);
   auto ret = kernel->Run();
   executor->SetResult(index, ret);
@@ -65,16 +65,19 @@ int ParallelExecutor::Run(std::vector<Tensor *> &in_tensors, std::vector<Tensor
   kernel::LiteKernelUtil::InitTensorRefCount(kernels);
 
   for (auto kernel : kernels) {
-    if (kernel->in_kernels().size() == 0) {
+    if (kernel->in_kernels().empty()) {
       readyKernels.emplace_back(kernel);
       continue;
     }
     refCount[kernel] = kernel->in_kernels().size();
   }
   std::vector<kernel::LiteKernel *> newReadyKernels;
-  while (readyKernels.size() > 0) {
+  while (!readyKernels.empty()) {
     results.resize(readyKernels.size(), RET_OK);
-    ParallelLaunch(thread_pool_, RunKernel, this, readyKernels.size());
+    if (0 != ParallelLaunch(thread_pool_, RunKernel, this, readyKernels.size())) {
+      MS_LOG(ERROR) << "ParallelLaunch failed ";
+      return RET_ERROR;
+    }
 
     if (std::find_if(results.begin(), results.end(), [](const int &ret) { return (ret != 0); }) != results.end()) {
       return RET_ERROR;

mindspore/lite/src/runtime/parallel_executor.h

@@ -14,8 +14,8 @@
  * limitations under the License.
  */
 
-#ifndef MINDSPORE_LITE_PARALLEL_EXECUTOR_H_
-#define MINDSPORE_LITE_PARALLEL_EXECUTOR_H_
+#ifndef MINDSPORE_LITE_SRC_RUNTIME_PARALLEL_EXECUTOR_H_
+#define MINDSPORE_LITE_SRC_RUNTIME_PARALLEL_EXECUTOR_H_
 
 #include <vector>
 #include <unordered_map>
@@ -28,7 +28,7 @@ namespace mindspore::lite {
 class ParallelExecutor : public Executor {
  public:
   ParallelExecutor() = default;
-  virtual ~ParallelExecutor();
+  ~ParallelExecutor() override;
 
   int Prepare(const std::vector<kernel::LiteKernel *> &kernels) override;
 
@@ -42,8 +42,8 @@ class ParallelExecutor : public Executor {
   std::unordered_map<kernel::LiteKernel *, size_t> refCount;
   std::vector<kernel::LiteKernel *> readyKernels;
   std::vector<int> results;
-  struct ThreadPool *thread_pool_ = NULL;
+  struct ThreadPool *thread_pool_ = nullptr;
 };
 
 }  // namespace mindspore::lite
-#endif
+#endif  // MINDSPORE_LITE_SRC_RUNTIME_PARALLEL_EXECUTOR_H_

mindspore/lite/src/runtime/runtime_api.cc

@@ -16,7 +16,6 @@
 
 #include "src/runtime/runtime_api.h"
 #include <mutex>
-#include <string>
 #include "src/common/log_adapter.h"
 
 static std::mutex gWorkspaceMutex;

mindspore/lite/src/runtime/runtime_api.h

@@ -13,8 +13,8 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-#ifndef PREDICT_SRC_RUNTIME_RUNTIME_API_H_
-#define PREDICT_SRC_RUNTIME_RUNTIME_API_H_
+#ifndef MINDSPORE_LITE_SRC_RUNTIME_RUNTIME_API_H_
+#define MINDSPORE_LITE_SRC_RUNTIME_RUNTIME_API_H_
 #include <memory>
 
 #ifndef INTERNAL_API_DLL
@@ -40,4 +40,4 @@ INTERNAL_API_DLL int LiteBackendRegisterSystemLibSymbol(const char *name, void *
 #ifdef __cplusplus
 }
 #endif
-#endif  // PREDICT_SRC_RUNTIME_RUNTIME_API_H_
+#endif  // MINDSPORE_LITE_SRC_RUNTIME_RUNTIME_API_H_

mindspore/lite/src/runtime/thread_pool.c

@@ -846,24 +846,6 @@ ThreadPool *CreateThreadPool(int thread_num, int mode) {
   return thread_pool;
 }
 
-void ActivateThreadPool(struct ThreadPool *thread_pool) {
-  if (thread_pool == NULL) {
-    LOG_ERROR("get thread pool instane failed");
-    return;
-  }
-  ThreadList *thread_list = thread_pool->thread_list;
-  if (thread_list == NULL) {
-    LOG_ERROR("thread pool's list is null");
-    return;
-  }
-  Thread *thread = thread_list->head;
-  while (thread != NULL) {
-    sem_post(&thread->sem);
-    thread->activate = true;
-    thread = thread->next;
-  }
-}
-
 void DeactivateThreadPool(struct ThreadPool *thread_pool) {
   if (thread_pool == NULL) {
     LOG_ERROR("get thread pool instane failed");
@@ -903,11 +885,3 @@ void DestroyThreadPool(struct ThreadPool *thread_pool) {
   thread_pool->thread_list = NULL;
   LOG_INFO("destroy thread pool success");
 }
-
-int GetCurrentThreadNum(struct ThreadPool *thread_pool) {
-  if (thread_pool == NULL) {
-    LOG_ERROR("get thread pool instane failed");
-    return 0;
-  }
-  return thread_pool->thread_num;
-}

mindspore/lite/src/runtime/thread_pool.h

@@ -48,24 +48,12 @@ int ParallelLaunch(struct ThreadPool *thread_pool, int (*job)(void *, int), void
  */
 int BindThreads(struct ThreadPool *thread_pool, bool is_bind, int mode);
 
-/**
- * activate the thread pool
- * @param thread_pool_id
- */
-void ActivateThreadPool(struct ThreadPool *thread_pool);
-
 /**
  * deactivate the thread pool
  * @param thread_pool_id
  */
 void DeactivateThreadPool(struct ThreadPool *thread_pool);
 
-/**
- *
- * @return current thread num
- */
-int GetCurrentThreadNum(struct ThreadPool *thread_pool);
-
 /**
  * destroy thread pool, and release resource
  */

mindspore/lite/src/scheduler.cc

@@ -72,10 +72,6 @@ int Scheduler::ReSizeKernels(const std::vector<kernel::LiteKernel *> &kernels) {
       return RET_ERROR;
     }
     auto sub_graph = reinterpret_cast<kernel::SubGraphKernel *>(kernel);
-    if (sub_graph == nullptr) {
-      MS_LOG(ERROR) << "node " << kernel->name() << " is neither a kernel or a sub_graph";
-      return RET_ERROR;
-    }
     auto ret = sub_graph->ReSize(infer_shape_interrupt);
     if (ret == RET_INFER_INVALID) {
       MS_LOG(INFO) << "InferShape is interrupted";
@@ -133,7 +129,7 @@ int Scheduler::InferShape(const lite::Model *model, std::vector<Tensor *> *tenso
   return RET_OK;
 }
 
-int Scheduler::BuildKernels(const lite::Model *model, std::vector<Tensor *> *tensors,
+int Scheduler::BuildKernels(const lite::Model *model, const std::vector<Tensor *> *tensors,
                             std::vector<kernel::LiteKernel *> *kernels) {
   MS_ASSERT(model != nullptr);
   MS_ASSERT(tensors != nullptr);
@@ -245,21 +241,21 @@ kernel::SubGraphKernel *Scheduler::CreateSubGraphKernel(const std::vector<kernel
   std::vector<kernel::LiteKernel *> output_kernels = kernel::LiteKernelUtil::SubgraphOutputKernels(kernels);
   if (type == kernel::kGpuSubGraph) {
 #if SUPPORT_GPU
-    auto sub_kernel =
-      new kernel::SubGraphOpenCLKernel(input_tensors, output_tensors, input_kernels, output_kernels, kernels, context_);
+    auto sub_kernel = new (std::nothrow)
+      kernel::SubGraphOpenCLKernel(input_tensors, output_tensors, input_kernels, output_kernels, kernels, context_);
     return sub_kernel;
 #else
     return nullptr;
 #endif
   }
   if (type == kernel::kCpuFP16SubGraph) {
-    auto sub_kernel =
-      new kernel::CpuFp16SubGraph(input_tensors, output_tensors, input_kernels, output_kernels, kernels, context_);
+    auto sub_kernel = new (std::nothrow)
+      kernel::CpuFp16SubGraph(input_tensors, output_tensors, input_kernels, output_kernels, kernels, context_);
     return sub_kernel;
   }
   if (type == kernel::kCpuFP32SubGraph) {
-    auto sub_kernel =
-      new kernel::CpuFp32SubGraph(input_tensors, output_tensors, input_kernels, output_kernels, kernels, context_);
+    auto sub_kernel = new (std::nothrow)
+      kernel::CpuFp32SubGraph(input_tensors, output_tensors, input_kernels, output_kernels, kernels, context_);
     return sub_kernel;
   }
   return nullptr;
@@ -345,7 +341,7 @@ void Scheduler::SetKernelTensorDataType(kernel::LiteKernel *kernel) {
   }
 }
 
-kernel::SubGraphType Scheduler::GetKernelSubGraphType(kernel::LiteKernel *kernel) {
+kernel::SubGraphType Scheduler::GetKernelSubGraphType(const kernel::LiteKernel *kernel) {
   if (kernel == nullptr) {
     return kernel::kNotSubGraph;
   }

mindspore/lite/src/scheduler.h

@@ -38,7 +38,7 @@ class Scheduler {
   kernel::LiteKernel *ScheduleNode(const std::vector<Tensor *> &in_tensors, const std::vector<Tensor *> &out_tensors,
                                    const mindspore::lite::PrimitiveC *primitive, const Model::Node *cnode);
 
-  int BuildKernels(const lite::Model *model, std::vector<Tensor *> *tensors,
+  int BuildKernels(const lite::Model *model, const std::vector<Tensor *> *tensors,
                    std::vector<kernel::LiteKernel *> *kernels);
 
   static int InferShape(const lite::Model *model, std::vector<Tensor *> *tensors);
@@ -55,7 +55,7 @@ class Scheduler {
 
   static void SetKernelTensorDataType(kernel::LiteKernel *kernel);
 
-  static kernel::SubGraphType GetKernelSubGraphType(kernel::LiteKernel *kernel);
+  static kernel::SubGraphType GetKernelSubGraphType(const kernel::LiteKernel *kernel);
 
  protected:
   InnerContext *context_ = nullptr;

mindspore/lite/src/sub_graph_kernel.cc

@@ -125,7 +125,11 @@ int SubGraphKernel::ReSize(bool is_interrupt) {
     std::vector<lite::Tensor *> inputs = kernel->in_tensors();
     std::vector<lite::Tensor *> outputs = kernel->out_tensors();
     for (auto &output : outputs) {
-      output->FreeData();
+      auto ret = output->FreeData();
+      if (ret != RET_OK) {
+        MS_LOG(ERROR) << "FreeData failed";
+        return RET_ERROR;
+      }
     }
     primitive->set_infer_flag(!is_interrupt);
     auto ret = primitive->InferShape(inputs, outputs);

mindspore/lite/src/sub_graph_kernel.h

@@ -77,7 +77,7 @@ class CpuSubGraph : public SubGraphKernel {
                        const std::vector<LiteKernel *> &nodes, const lite::InnerContext *ctx)
       : SubGraphKernel(inputs, outputs, in_kernels, out_kernels, nodes, ctx) {
     subgraph_type_ = kCpuFP32SubGraph;
-    this->executor_ = new mindspore::lite::Executor;
+    this->executor_ = new (std::nothrow) mindspore::lite::Executor;
   }
 
   ~CpuSubGraph() override { delete this->executor_; }

mindspore/lite/src/tensor.cc

@@ -18,15 +18,15 @@
 #include <string>
 #include <utility>
 #include <algorithm>
+#include <functional>
 #include "src/tensor.h"
 #include "securec/include/securec.h"
 #include "include/errorcode.h"
 
-namespace mindspore {
-namespace lite {
+namespace mindspore::lite {
 #define kMaxMallocSize 1024 * 1024 * 100
-Tensor::Tensor(const TypeId data_type, const std::vector<int> &shape, const schema::Format &format, Category category)
-    : data_type_(data_type), shape_(shape), format_(format), category_(category) {}
+Tensor::Tensor(const TypeId data_type, std::vector<int> shape, const schema::Format &format, Category category)
+    : data_type_(data_type), shape_(std::move(shape)), format_(format), category_(category) {}
 
 Tensor::Tensor(const Tensor &tensor) {
   auto ret = CopyTensor(tensor, true);
@@ -234,7 +234,7 @@ int32_t Tensor::ElementsC4Num() const {
   return result;
 }
 
-int Tensor::DimensionSize(size_t index) const {
+int Tensor::DimensionSize(const size_t index) const {
   int dim_size = -1;
   if (index < shape_.size()) {
     dim_size = shape_[index];
@@ -277,12 +277,12 @@ std::string Tensor::ToString() const {
   return oss.str();
 }
 
-int Tensor::MallocData(mindspore::lite::Allocator *allocator) {
+int Tensor::MallocData(const mindspore::lite::Allocator *allocator) {
   if (nullptr != this->data_) {
     return RET_OK;
   }
   if (allocator != nullptr) {
-    allocator_ = allocator;
+    allocator_ = const_cast<mindspore::lite::Allocator *>(allocator);
   }
   if (allocator_ == nullptr) {
     this->data_ = malloc(this->Size());
@@ -341,5 +341,4 @@ std::vector<tensor::MSTensor *> TensorVectorCast(const std::vector<Tensor *> &sr
   std::transform(src.begin(), src.end(), target.begin(), [](Tensor *t) { return dynamic_cast<tensor::MSTensor *>(t); });
   return target;
 }
-}  // namespace lite
-}  // namespace mindspore
+}  // namespace mindspore::lite

mindspore/lite/src/tensor.h

@@ -14,8 +14,8 @@
  * limitations under the License.
  */
 
-#ifndef MINDSPORE_LITE_SRC_IR_TENSOR_H_
-#define MINDSPORE_LITE_SRC_IR_TENSOR_H_
+#ifndef MINDSPORE_LITE_SRC_TENSOR_H_
+#define MINDSPORE_LITE_SRC_TENSOR_H_
 
 #include <memory>
 #include <vector>
@@ -49,18 +49,18 @@ class Tensor : public mindspore::tensor::MSTensor {
   };
   Tensor() = default;
 
-  Tensor(const TypeId data_type, const std::vector<int> &shape,
-         const schema::Format &format = schema::Format::Format_NHWC, Category category = VAR);
+  Tensor(TypeId data_type, std::vector<int> shape, const schema::Format &format = schema::Format::Format_NHWC,
+         Category category = VAR);
 
   Tensor(const Tensor &tensor);
 
-  virtual ~Tensor();
+  ~Tensor() override;
 
   int CopyTensorData(const Tensor &srcTensor);
 
   int CopyTensor(const Tensor &srcTensor, bool copyData = false);
 
-  virtual Tensor &operator=(const Tensor &tensor);
+  Tensor &operator=(const Tensor &tensor);
 
   virtual bool operator==(const Tensor &tensor);
 
@@ -92,7 +92,7 @@ class Tensor : public mindspore::tensor::MSTensor {
 
   mindspore::lite::Allocator *allocator() const { return this->allocator_; }
 
-  int MallocData(mindspore::lite::Allocator *allocator = nullptr);
+  int MallocData(const mindspore::lite::Allocator *allocator = nullptr);
 
   int FreeData();
 
@@ -108,7 +108,7 @@ class Tensor : public mindspore::tensor::MSTensor {
 
   schema::Format format() { return this->format_; }
 
-  size_t ref_count() { return this->ref_count_; }
+  size_t ref_count() const { return this->ref_count_; }
 
   void set_ref_count(size_t ref_count) { this->ref_count_ = ref_count; }
 
@@ -218,4 +218,4 @@ std::vector<tensor::MSTensor *> TensorVectorCast(const std::vector<Tensor *> &sr
 }  // namespace mindspore
 
 using TensorPtr = std::shared_ptr<mindspore::lite::Tensor>;
-#endif  // MINDSPORE_LITE_SRC_IR_TENSOR_H_
+#endif  // MINDSPORE_LITE_SRC_TENSOR_H_