Apply cpu::ops::PadLayer to PadLayer in train

Samsung · Jan 29, 2024 · dab9c59 · dab9c59
1 parent 2a2d86d
commit dab9c59
Show file tree

Hide file tree

Showing 3 changed files with 6 additions and 57 deletions.
diff --git a/runtime/onert/backend/cpu/ops/PadLayer.h b/runtime/onert/backend/cpu/ops/PadLayer.h
@@ -46,7 +46,7 @@ class PadLayer : public ::onert::exec::IFunction
 
   void run() override;
 
-private:
+protected:
   const IPortableTensor *_input;
   IPortableTensor *_output;
 

diff --git a/runtime/onert/backend/train/ops/PadLayer.cc b/runtime/onert/backend/train/ops/PadLayer.cc
@@ -27,19 +27,11 @@ namespace train
 namespace ops
 {
 
-PadLayer::PadLayer()
-  : _input(nullptr), _output(nullptr), _padData(), _padRank(), _constantValueData(),
-    _back_prop_input{nullptr}, _back_prop_output{nullptr}
+PadLayer::PadLayer() : cpu::ops::PadLayer(), _back_prop_input{nullptr}, _back_prop_output{nullptr}
 {
   // DO NOTHING
 }
 
-template <typename T> void PadLayer::padImpl(const T *constant_value_data)
-{
-  nnfw::cker::Pad<T>(_padData, _padRank, getShape(_input), getBuffer<T>(_input), getShape(_output),
-                     getBuffer<T>(_output), constant_value_data);
-}
-
 template <typename T> void PadLayer::depad()
 {
   nnfw::cker::train::Depad<T>(_padData, _padRank, getShape(_back_prop_output),
@@ -51,48 +43,12 @@ void PadLayer::configure(const IPortableTensor *input, IPortableTensor *output,
                          const int32_t *padData, int32_t padRank, const void *constantValueData,
                          IPortableTensor *back_prop_input, const IPortableTensor *back_prop_output)
 {
-  _input = input;
-  _output = output;
-  memcpy(_padData, padData, sizeof(_padData));
-  _padRank = padRank;
-  _constantValueData.v = constantValueData;
+  cpu::ops::PadLayer::configure(input, output, padData, padRank, constantValueData);
   _back_prop_input = back_prop_input;
   _back_prop_output = back_prop_output;
 }
 
-void PadLayer::forward(bool)
-{
-  switch (_input->data_type())
-  {
-    case OperandType::FLOAT32:
-      padImpl<float>(_constantValueData.f);
-      break;
-    case OperandType::QUANT_UINT8_ASYMM:
-      if (_constantValueData.u8 == nullptr)
-      {
-        uint8_t pad_value = static_cast<uint8_t>(_output->data_zero_point());
-        padImpl<uint8_t>(&pad_value);
-      }
-      else
-      {
-        padImpl<uint8_t>(_constantValueData.u8);
-      }
-      break;
-    case OperandType::QUANT_INT8_ASYMM:
-      if (_constantValueData.i8 == nullptr)
-      {
-        int8_t pad_value = static_cast<int8_t>(_output->data_zero_point());
-        padImpl<int8_t>(&pad_value);
-      }
-      else
-      {
-        padImpl<int8_t>(_constantValueData.i8);
-      }
-      break;
-    default:
-      throw std::runtime_error{"Pad: unsupported data type"};
-  }
-}
+void PadLayer::forward(bool) { cpu::ops::PadLayer::run(); }
 
 void PadLayer::backward()
 {

diff --git a/runtime/onert/backend/train/ops/PadLayer.h b/runtime/onert/backend/train/ops/PadLayer.h
@@ -17,6 +17,7 @@
 #ifndef __ONERT_BACKEND_TRAIN_OPS_PADLAYER_H__
 #define __ONERT_BACKEND_TRAIN_OPS_PADLAYER_H__
 
+#include <ops/PadLayer.h>
 #include <backend/IPortableTensor.h>
 #include "OperationUtils.h"
 
@@ -33,13 +34,12 @@ namespace ops
 
 // Note, this is pad with mode=`CONSTANT`: it doesn't support `REFLECT` and
 // `SYMMETRIC`
-class PadLayer : public ::onert::exec::train::ITrainableFunction
+class PadLayer : public ::onert::exec::train::ITrainableFunction, public cpu::ops::PadLayer
 {
 public:
   PadLayer();
 
 public:
-  template <typename T> void padImpl(const T *constant_value_data);
   template <typename T> void depad();
 
   void configure(const IPortableTensor *input, IPortableTensor *output, const int32_t *padData,
@@ -49,13 +49,6 @@ class PadLayer : public ::onert::exec::train::ITrainableFunction
   void backward() override;
 
 private:
-  const IPortableTensor *_input;
-  IPortableTensor *_output;
-
-  int32_t _padData[8];
-  int32_t _padRank;
-  ConstDataPtr _constantValueData;
-
   IPortableTensor *_back_prop_input;
   const IPortableTensor *_back_prop_output;
 };