1 files changed, 92 insertions, 0 deletions
diff --git a/runtime/onert/backend/cpu/kernel/DivLayer.cc b/runtime/onert/backend/cpu/kernel/DivLayer.cc
new file mode 100644
index 000000000..ec9daae9b
--- /dev/null
+++ b/runtime/onert/backend/cpu/kernel/DivLayer.cc
@@ -0,0 +1,92 @@
+/*
+ * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "DivLayer.h"
+
+#include <cker/operation/BinaryArithmeticOps.h>
+
+namespace onert
+{
+namespace backend
+{
+namespace cpu
+{
+namespace kernel
+{
+
+void DivLayer::divFloat32()
+{
+  float output_activation_min, output_activation_max;
+  CalculateActivationRangeFloat(_activation, &output_activation_min, &output_activation_max);
+  nnfw::cker::BinaryArithmeticOpParam op_params;
+  op_params.type = nnfw::cker::BinaryArithmeticOpType::DIV;
+  op_params.float_activation_max = output_activation_max;
+  op_params.float_activation_min = output_activation_min;
+
+  if (!HaveSameShapes(_lhs, _rhs))
+  {
+    nnfw::cker::BroadcastBinaryArithmeticOpSlow(
+        op_params, convertToExtendedCkerShape(_lhs),
+        reinterpret_cast<const float *>(_lhs->buffer()), convertToExtendedCkerShape(_rhs),
+        reinterpret_cast<const float *>(_rhs->buffer()), convertToExtendedCkerShape(_output),
+        reinterpret_cast<float *>(_output->buffer()));
+    return;
+  }
+
+  nnfw::cker::BinaryArithmeticOp(
+      op_params, convertTensorToCkerShape(_lhs), reinterpret_cast<const float *>(_lhs->buffer()),
+      convertTensorToCkerShape(_rhs), reinterpret_cast<const float *>(_rhs->buffer()),
+      convertTensorToCkerShape(_output), reinterpret_cast<float *>(_output->buffer()));
+}
+
+void DivLayer::divQuant8()
+{
+  int32_t output_activation_min, output_activation_max;
+  CalculateActivationRangeUint8(_activation, _output, &output_activation_min,
+                                &output_activation_max);
+  // nnfw::cker::BinaryArithmeticOpParam op_params;
+  // op_params.quantized_activation_max = output_activation_max;
+  // op_params.quantized_activation_min = output_activation_min;
+
+  // cker quant8 div is not implemented yet
+  throw std::runtime_error{"Div NYI for quantized"};
+}
+
+void DivLayer::configure(const operand::Tensor *lhs, const operand::Tensor *rhs,
+                         const ir::Activation activation, operand::Tensor *output)
+{
+  _lhs = lhs;
+  _rhs = rhs;
+  _activation = activation;
+  _output = output;
+}
+
+void DivLayer::run()
+{
+  if (_output->data_type() == OperandType::FLOAT32)
+  {
+    divFloat32();
+  }
+  else if (_output->data_type() == OperandType::QUANT8_ASYMM)
+  {
+    divQuant8();
+  }
+}
+
+} // namespace kernel
+} // namespace cpu
+} // namespace backend
+} // namespace onert