From aeddb45be8c8dc8ffd6fd134dfc121c692fdb81a Mon Sep 17 00:00:00 2001
From: Eunju Yang <ej.yang@samsung.com>
Date: Fri, 29 Nov 2024 15:04:30 +0900
Subject: [PATCH] [ GPU/OpenCL ] change addition_layer_cl to inherit
 LayerImplCl

- This commit updates addition_layer_cl.cpp/h to inherit LayerImplCl.
- This commit implements registerClKernels() of addition_layer_cl layer.
- This commit update cl_context.cpp (applying addition_layer_cl's update)

Self evaluation:

Build test: [X]Passed [ ]Failed [ ]Skipped
Run test: [X]Passed [ ]Failed [ ]Skipped

Signed-off-by: Eunju Yang <ej.yang@samsung.com>
---
 nntrainer/cl_context.cpp                      |  8 +++++---
 .../layers/cl_layers/addition_layer_cl.h      | 19 +++++++++++++++----
 2 files changed, 20 insertions(+), 7 deletions(-)

diff --git a/nntrainer/cl_context.cpp b/nntrainer/cl_context.cpp
index b6c200792b..25c2d66c2b 100644
--- a/nntrainer/cl_context.cpp
+++ b/nntrainer/cl_context.cpp
@@ -39,9 +39,11 @@ static void add_default_object(ClContext &cc) {
                        ml::train::LayerType::LAYER_FC);
   }
 
-  cc.registerFactory(nntrainer::createLayer<AdditionLayerCL>,
-                     AdditionLayerCL::type,
-                     ml::train::LayerType::LAYER_ADDITION);
+  if (AdditionLayerCL::registerClKernels()) {
+    cc.registerFactory(nntrainer::createLayer<AdditionLayerCL>,
+                       AdditionLayerCL::type,
+                       ml::train::LayerType::LAYER_ADDITION);
+  }
 
   // @todo swiglulayercl also needs to be updated.
   cc.registerFactory(nntrainer::createLayer<SwiGLULayerCl>, SwiGLULayerCl::type,
diff --git a/nntrainer/layers/cl_layers/addition_layer_cl.h b/nntrainer/layers/cl_layers/addition_layer_cl.h
index a2b8f55f67..eb2acd2604 100644
--- a/nntrainer/layers/cl_layers/addition_layer_cl.h
+++ b/nntrainer/layers/cl_layers/addition_layer_cl.h
@@ -18,6 +18,7 @@
 #include <cl_context.h>
 #include <common_properties.h>
 #include <layer_devel.h>
+#include <layer_impl_cl.h>
 
 namespace nntrainer {
 
@@ -25,12 +26,12 @@ namespace nntrainer {
  * @class   AdditionLayerCL
  * @brief   Addition Layer
  */
-class AdditionLayerCL : public Layer {
+class AdditionLayerCL : public LayerImplCl {
 public:
   /**
    * @brief     Constructor of Addition Layer
    */
-  AdditionLayerCL() : Layer(), add_props(props::Print()) {}
+  AdditionLayerCL() : LayerImplCl(), add_props(props::Print()) {}
 
   /**
    * @brief     Destructor of Addition Layer
@@ -93,10 +94,20 @@ class AdditionLayerCL : public Layer {
    */
   const std::string getType() const override { return AdditionLayerCL::type; };
 
-  std::tuple<props::Print>
-    add_props; /**< fc layer properties : unit - number of output neurons */
+  /**
+   * @brief     registerClKernels for addition_layer_cl
+   * @details   registerClKernels for addition_layer_cl always returns true
+   * without any specific action for kernel registeration. It only uses
+   * cl_blas_kernels and there is no specific kernels for this. If there are
+   * specific kernels for this, it should be updated to register the kernels .
+   */
+  static bool registerClKernels() { return true; };
 
   inline static const std::string type = "addition";
+
+private:
+  std::tuple<props::Print>
+    add_props; /**< fc layer properties : unit - number of output neurons */
 };
 
 } // namespace nntrainer