!3004 fixed codedex issues

Merge pull request !3004 from mxm/fix_codedex

mindspore/ccsrc/ir/pattern_matcher.h

@@ -541,6 +541,9 @@ class PConstant : public PBase<PConstant<T> > {
         data_out[i] *= data_2[0];
       }
     } else {
+      if (in_data_2_size < out_data_size) {
+        MS_EXCEPTION(ValueError) << "in_data_2_size is smaller than out_data_size.";
+      }
       for (int i = 0; i < out_data_size; i++) {
         data_out[i] *= data_2[i];
       }
@@ -595,33 +598,41 @@ class PConstant : public PBase<PConstant<T> > {
       return nullptr;
     }
 
-    void *data_out;
+    auto new_tensor_ptr = std::make_shared<tensor::Tensor>(tensor_3_type_ptr->type_id(), tensor_out_shape);
+    size_t mem_size = GetTypeByte(tensor_3_type_ptr) * IntToSize(new_tensor_ptr->ElementsNum());
+    char *data = reinterpret_cast<char *>(new_tensor_ptr->data_c());
 
+    int ret = 0;
+    void *data_out = nullptr;
     if ((tensor_3_type_ptr->type_id() == TypeId::kNumberTypeFloat32) ||
         (tensor_3_type_ptr->type_id() == TypeId::kNumberTypeFloat)) {
       Multiply<float>(tensor_ptr_1->data_c(), tensor_ptr_1->DataSize(), tensor_ptr_2->data_c(),
                       tensor_ptr_2->DataSize(), &data_out, data_out_size);
+      ret = memcpy_s(data, mem_size, data_out, mem_size);
+      delete[] reinterpret_cast<float *>(data_out);
     } else {
       if (tensor_3_type_ptr->type_id() == TypeId::kNumberTypeFloat64) {
         Multiply<double>(tensor_ptr_1->data_c(), tensor_ptr_1->DataSize(), tensor_ptr_2->data_c(),
                          tensor_ptr_2->DataSize(), &data_out, data_out_size);
+        ret = memcpy_s(data, mem_size, data_out, mem_size);
+        delete[] reinterpret_cast<double *>(data_out);
       } else {
         if ((tensor_3_type_ptr->type_id() == TypeId::kNumberTypeInt32) ||
             (tensor_3_type_ptr->type_id() == TypeId::kNumberTypeInt)) {
           Multiply<int>(tensor_ptr_1->data_c(), tensor_ptr_1->DataSize(), tensor_ptr_2->data_c(),
                         tensor_ptr_2->DataSize(), &data_out, data_out_size);
+          ret = memcpy_s(data, mem_size, data_out, mem_size);
+          delete[] reinterpret_cast<int *>(data_out);
         } else {
           // Un-support data types
           return nullptr;
         }
       }
     }
-
-    auto new_tensor_ptr = std::make_shared<tensor::Tensor>(tensor_3_type_ptr->type_id(), tensor_out_shape);
-    size_t mem_size = GetTypeByte(tensor_3_type_ptr) * IntToSize(new_tensor_ptr->ElementsNum());
-    char *data = reinterpret_cast<char *>(new_tensor_ptr->data_c());
-    memcpy(data, data_out, mem_size);
-
+    if (ret != 0) {
+      MS_LOG(EXCEPTION) << "memcpy_s error, errorno " << ret << ", source size " << mem_size << "dest size"
+                        << new_tensor_ptr->DataSize();
+    }
     auto new_vnode = NewValueNode(new_tensor_ptr);
     new_vnode->set_abstract(new_tensor_ptr->ToAbstract());
     return new_vnode;

mindspore/ccsrc/ir/tensor.cc

@@ -125,6 +125,7 @@ template <typename T>
 class TensorDataImpl : public TensorData {
  public:
   explicit TensorDataImpl(const std::vector<int> &shape) : ndim_(shape.size()), data_size_(SizeOf(shape)) {}
+  ~TensorDataImpl() = default;
 
   TensorDataImpl(const std::vector<int> &shape, void *data, size_t data_len)
       : ndim_(shape.size()), data_size_(SizeOf(shape)), data_(CopyData<T>(shape, data, data_len)) {}
@@ -288,7 +289,7 @@ class TensorDataImpl : public TensorData {
 };
 
 template <typename... Args>
-TensorDataPtr MakeTensorData(TypeId data_type, const std::vector<int> &shape, Args... args) {
+TensorDataPtr MakeTensorData(TypeId data_type, const std::vector<int> &shape, const Args... args) {
   switch (data_type) {
     case kNumberTypeBool:
     case kNumberTypeUInt8:

mindspore/ccsrc/optimizer/ad/dfunctor.cc

@@ -99,14 +99,14 @@ void DFunctor::BackPropagateFv(const AnfNodePtr &fv, const AnfNodePtr &din) {
       fv_adjoint = anfnode_to_adjoin_indirect_fv_.find(fv);
     }
   }
-  auto key = tape_->NewCNode({NewValueNode(prim::kPrimEmbed), fv_adjoint->second->k()});
-  fv_adjoint->second->RegisterKUser(key, 1);
+  auto node = tape_->NewCNode({NewValueNode(prim::kPrimEmbed), fv_adjoint->second->k()});
+  fv_adjoint->second->RegisterKUser(node, 1);
   auto default_val = tape_->NewCNode({NewValueNode(prim::GetPythonOps("zeros_like")), fv_adjoint->second->k()});
   fv_adjoint->second->RegisterKUser(default_val, 1);
-  auto dfv = tape_->NewCNode({NewValueNode(prim::kPrimEnvGetItem), din, key, default_val});
+  auto dfv = tape_->NewCNode({NewValueNode(prim::kPrimEnvGetItem), din, node, default_val});
   MS_LOG(DEBUG) << "BackPropagateFv find adjoint in anfnode_to_adjoin_ or anfnode_to_adjoin_indirect_fv_ fv "
                 << fv->func_graph()->ToString() << " " << fv->ToString() << ".";
-  MS_LOG(DEBUG) << "BackPropagateFv get item from " << din->ToString() << " key " << key->ToString() << ".";
+  MS_LOG(DEBUG) << "BackPropagateFv get item from " << din->ToString() << " key " << node->ToString() << ".";
   fv_adjoint->second->AccumulateDout(dfv);
 }
 
@@ -279,13 +279,13 @@ AnfNodePtr DFunctor::AttachFvDoutToTape(const AnfNodePtr &grad_fv) {
     if (fv_adjoint == anfnode_to_adjoin_.end()) {
       MS_LOG(EXCEPTION) << "AttachFvDoutToTape fv adjoint does not exist " << fv->ToString() << ".";
     }
-    auto key = tape_->NewCNode({NewValueNode(prim::kPrimEmbed), fv_adjoint->second->k()});
-    fv_adjoint->second->RegisterKUser(key, 1);
+    auto node = tape_->NewCNode({NewValueNode(prim::kPrimEmbed), fv_adjoint->second->k()});
+    fv_adjoint->second->RegisterKUser(node, 1);
     auto sens = fv_adjoint->second->dout();
     new_grad_fv = tape_->NewCNode({
       NewValueNode(prim::kPrimEnvSetItem),
       new_grad_fv,
-      key,
+      node,
       sens,
     });
     fv_adjoint->second->RegisterDoutUser(new_grad_fv->cast<CNodePtr>(), 3);
@@ -301,13 +301,13 @@ AnfNodePtr DFunctor::AttachIndirectFvDoutToTape(const AnfNodePtr &grad_fv) {
   for (auto &fv_adjoint : anfnode_to_adjoin_indirect_fv_) {
     MS_LOG(DEBUG) << "AttachIndirectFvDoutToTape backprop indirect fv " << fv_adjoint.first->ToString() << " "
                   << primal_graph_->ToString() << ".";
-    auto key = tape_->NewCNode({NewValueNode(prim::kPrimEmbed), fv_adjoint.second->k()});
-    fv_adjoint.second->RegisterKUser(key, 1);
+    auto node = tape_->NewCNode({NewValueNode(prim::kPrimEmbed), fv_adjoint.second->k()});
+    fv_adjoint.second->RegisterKUser(node, 1);
     auto sens = fv_adjoint.second->dout();
     new_grad_fv = tape_->NewCNode({
       NewValueNode(prim::kPrimEnvSetItem),
       new_grad_fv,
-      key,
+      node,
       sens,
     });
     fv_adjoint.second->RegisterDoutUser(new_grad_fv->cast<CNodePtr>(), 3);

mindspore/ccsrc/utils/load_onnx/anf_converter.cc

@@ -60,6 +60,9 @@ int AnfConverter::ValidateFileStr(const std::string &modelFile, std::string file
 bool AnfConverter::ReadOnnxFromBinary(const std::string &modelFile, google::protobuf::Message *onnx_model) {
   std::unique_ptr<char> onnx_file(new (std::nothrow) char[PATH_MAX]{0});
   int fd = open(onnx_file.get(), O_RDONLY);
+  if (fd < 0) {
+    MS_LOG(EXCEPTION) << "failed to open file";
+  }
   google::protobuf::io::FileInputStream input(fd);
   google::protobuf::io::CodedInputStream code_input(&input);
   code_input.SetTotalBytesLimit(INT_MAX, 536870912);
@@ -85,7 +88,7 @@ std::shared_ptr<FuncGraph> AnfConverter::RunAnfConverter(const std::string &file
     MS_LOG(ERROR) << "Trans data not support input format!";
   } else {
     modelFile = flagItem.substr(pos + 1);
-    std::cout << "input protobuf file path is: " << flagItem.substr(pos + 1) << std::endl;
+    std::cout << "input protobuf file path is: " << modelFile << std::endl;
   }
 
   if (ValidateFileStr(modelFile, ".pb") != 0) {

mindspore/ccsrc/utils/load_onnx/anf_model_parser.cc

@@ -119,7 +119,10 @@ bool MSANFModelParser::BuildParameterForFuncGraph(const ParameterPtr &node, cons
     std::string initial_data = initialize_proto.raw_data();
     auto *tensor_data_buf = reinterpret_cast<uint8_t *>(tensor_info->data_c());
     MS_EXCEPTION_IF_NULL(tensor_data_buf);
-    memcpy_s(tensor_data_buf, tensor_info->data().nbytes(), initial_data.data(), initial_data.size());
+    auto ret = memcpy_s(tensor_data_buf, tensor_info->data().nbytes(), initial_data.data(), initial_data.size());
+    if (ret != 0) {
+      MS_LOG(EXCEPTION) << "memcpy_s error, errorno" << ret;
+    }
 
     auto param_value = std::make_shared<ParamValue>();
     MS_EXCEPTION_IF_NULL(param_value);
@@ -249,7 +252,11 @@ bool MSANFModelParser::ObtainValueNodeInTensorForm(const std::string &value_node
   tensor::TensorPtr tensor_info = std::make_shared<tensor::Tensor>(kDefaultValueSwitchMap[attr_tensor_type], shape);
   const std::string &tensor_buf = attr_tensor.raw_data();
   auto *tensor_data_buf = reinterpret_cast<uint8_t *>(tensor_info->data_c());
-  memcpy_s(tensor_data_buf, tensor_info->data().nbytes(), tensor_buf.data(), tensor_buf.size());
+  auto ret = memcpy_s(tensor_data_buf, tensor_info->data().nbytes(), tensor_buf.data(), tensor_buf.size());
+  if (ret != 0) {
+    MS_LOG(EXCEPTION) << "memcpy_s error, errorno" << ret;
+  }
+
   auto new_value_node = NewValueNode(MakeValue(tensor_info));
   MS_EXCEPTION_IF_NULL(new_value_node);
   auto tensor_abstract = tensor_info->ToAbstract();
@@ -336,7 +343,6 @@ bool MSANFModelParser::GetAttrValueForValueNode(const std::string &ref_attr_name
       MS_LOG(ERROR) << "parse ValueNode value don't support input of ref_attr_name";
       return false;
   }
-  return true;
 }
 
 bool MSANFModelParser::BuildValueNodeForFuncGraph(const onnx::NodeProto &node_proto) {

mindspore/ccsrc/utils/load_onnx/anf_model_parser.h

@@ -32,7 +32,7 @@ using uint64 = uint64_t;
 using float16 = Eigen::half;
 class MSANFModelParser {
  public:
-  MSANFModelParser() = default;
+  MSANFModelParser() : producer_name_(""), model_version_(0), ir_version_(0) {}
   ~MSANFModelParser() = default;
 
   FuncGraphPtr Parse(const onnx::ModelProto &model_proto);