[onert] Simplify code using SFINAE technique

This commit simplify code using the SFINAE technique. ONE-DCO-1.0-Signed-off-by: ragmani <[email protected]>
Samsung · Feb 19, 2025 · 95600ac · 95600ac
1 parent c425c09
commit 95600ac
Show file tree

Hide file tree

Showing 6 changed files with 11 additions and 14 deletions.
diff --git a/runtime/onert/backend/trix/Convert.h b/runtime/onert/backend/trix/Convert.h
@@ -42,7 +42,7 @@ data_type convertDataType(const ir::DataType type);
  * @param tensors Tensors that have data information
  * @param info    tensors_data_info to be set
  */
-template <typename T, std::enable_if_t<std::is_base_of<IPortableTensor, T>::value, bool> = true>
+template <typename T, std::enable_if_t<std::is_base_of_v<IPortableTensor, T>, bool> = true>
 void setDataInfo(const std::vector<T *> &tensors, tensors_data_info *info)
 {
   info->num_info = static_cast<uint32_t>(tensors.size());
@@ -61,7 +61,7 @@ void setDataInfo(const std::vector<T *> &tensors, tensors_data_info *info)
  * @param tensors Tensors that have buffer information
  * @param buf     generic_buffers to be set
  */
-template <typename T, std::enable_if_t<std::is_base_of<IPortableTensor, T>::value, bool> = true>
+template <typename T, std::enable_if_t<std::is_base_of_v<IPortableTensor, T>, bool> = true>
 void setBuffers(const std::vector<T *> &tensors, generic_buffers *buf)
 {
   buf->num_buffers = static_cast<uint32_t>(tensors.size());

diff --git a/runtime/onert/core/include/ir/Model.h b/runtime/onert/core/include/ir/Model.h
@@ -138,7 +138,7 @@ class Model
    *
    * @return true if the model has only typename Graph, otherwise false
    */
-  template <typename Graph, std::enable_if_t<std::is_base_of<IGraph, Graph>::value, bool> = true>
+  template <typename Graph, std::enable_if_t<std::is_base_of_v<IGraph, Graph>, bool> = true>
   bool hasOnly()
   {
     for (const auto &e : _subgraphs)
@@ -193,8 +193,6 @@ class Model
   }
 
 private:
-  // TODO: Apply Heterogeneous lookup for unordered containers (transparent hashing) since C++20
-  //       to use `std::string_view` with lookup functions in unordered containers
   std::unordered_map<std::string, std::unique_ptr<const ir::Data>> _metadatas;
 };
 } // namespace onert::ir

diff --git a/runtime/onert/core/include/ir/train/operation/UntrainableOperation.h b/runtime/onert/core/include/ir/train/operation/UntrainableOperation.h
@@ -30,7 +30,7 @@ namespace onert::ir::train::operation
 // `UntrainableOperation` wraps operations that are not yet supported for training.
 // This class can be removed if all operations are supported for training.
 template <typename OperationType,
-          typename = std::enable_if_t<std::is_base_of<Operation, OperationType>::value>>
+          std::enable_if_t<std::is_base_of_v<Operation, OperationType>, bool> = true>
 class UntrainableOperation : public OperationType, public TrainableOperation
 {
 public:

diff --git a/runtime/onert/core/src/compiler/CompilerHelpers.h b/runtime/onert/core/src/compiler/CompilerHelpers.h
@@ -33,7 +33,7 @@ namespace onert::compiler
  * @return    Shape inferer map
  */
 template <typename LoweredGraphType,
-          typename = std::enable_if_t<std::is_base_of<ILoweredGraph, LoweredGraphType>::value>>
+          std::enable_if_t<std::is_base_of_v<ILoweredGraph, LoweredGraphType>, bool> = true>
 static std::unordered_map<ir::SubgraphIndex, std::unique_ptr<StaticShapeInferer>>
 createStaticShapeInferers(
   const std::unordered_map<ir::SubgraphIndex, std::unique_ptr<LoweredGraphType>> &lowered_subgs)

diff --git a/runtime/onert/core/src/exec/IPermuteFunction.cc b/runtime/onert/core/src/exec/IPermuteFunction.cc
@@ -139,8 +139,8 @@ void dequantize(const backend::ITensor *src_tensor, backend::ITensor *dst_tensor
 }
 
 template <typename SRC_T, typename DST_T,
-          std::enable_if_t<std::is_base_of<backend::ITensor, SRC_T>::value &&
-                             std::is_base_of<backend::ITensor, DST_T>::value,
+          std::enable_if_t<std::is_base_of_v<backend::ITensor, SRC_T> &&
+                             std::is_base_of_v<backend::ITensor, DST_T>,
                            bool> = true>
 void typeAwareQuantize(const SRC_T *src_tensor, DST_T *dst_tensor, const ir::PermuteType &type)
 {

diff --git a/runtime/onert/core/src/util/ShapeInference.cc b/runtime/onert/core/src/util/ShapeInference.cc
@@ -35,12 +35,11 @@ namespace onert::shape_inference
 namespace
 {
 
-template <typename T, typename U>
-typename std::enable_if<std::is_integral<T>::value && std::is_integral<U>::value,
-                        typename std::common_type<T, U>::type>::type
-ceil_div(T dividend, U divisor)
+template <typename T, typename U,
+          std::enable_if_t<std::is_integral_v<T> && std::is_integral_v<U>, bool> = true>
+std::common_type_t<T, U> ceil_div(T dividend, U divisor)
 {
-  assert(dividend > 0 && divisor > 0 && "this implementations is for positive numbers only");
+  assert(dividend > 0 && divisor > 0 && "this implementation is for positive numbers only");
   return (dividend + divisor - 1) / divisor;
 }