1 files changed, 0 insertions, 116 deletions
diff --git a/runtime/onert/backend/cpu/ops/MulLayer.cc b/runtime/onert/backend/cpu/ops/MulLayer.cc
deleted file mode 100644
index eef73edf3..000000000
--- a/runtime/onert/backend/cpu/ops/MulLayer.cc
+++ /dev/null
@@ -1,116 +0,0 @@
-/*
- * 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 "MulLayer.h"
-
-#include <cker/operation/BinaryArithmeticOps.h>
-
-namespace onert
-{
-namespace backend
-{
-namespace cpu
-{
-namespace ops
-{
-
-void MulLayer::mulFloat32()
-{
-  float output_activation_min = 0, output_activation_max = 0;
-  CalculateActivationRange(_activation, &output_activation_min, &output_activation_max);
-  nnfw::cker::BinaryArithmeticOpParam op_params;
-  op_params.float_activation_max = output_activation_max;
-  op_params.float_activation_min = output_activation_min;
-
-  const bool need_broadcast =
-      nnfw::cker::ProcessBroadcastShapes(getTensorShape(_lhs), getTensorShape(_rhs), &op_params);
-  if (need_broadcast)
-  {
-    nnfw::cker::BroadcastBinaryArithmeticOp<nnfw::cker::BinaryArithmeticOpType::MUL>(
-        op_params, getTensorShape(_lhs), reinterpret_cast<const float *>(_lhs->buffer()),
-        getTensorShape(_rhs), reinterpret_cast<const float *>(_rhs->buffer()),
-        getTensorShape(_output), reinterpret_cast<float *>(_output->buffer()));
-    return;
-  }
-
-  nnfw::cker::BinaryArithmeticOp<nnfw::cker::BinaryArithmeticOpType::MUL>(
-      op_params, getTensorShape(_lhs), reinterpret_cast<const float *>(_lhs->buffer()),
-      getTensorShape(_rhs), reinterpret_cast<const float *>(_rhs->buffer()),
-      getTensorShape(_output), reinterpret_cast<float *>(_output->buffer()));
-}
-
-void MulLayer::mulQuant8()
-{
-  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;
-  op_params.input1_offset = -_lhs->data_offset();
-  op_params.input2_offset = -_rhs->data_offset();
-  op_params.output_offset = _output->data_offset();
-
-  double real_multiplier = _lhs->data_scale() * _rhs->data_scale() / _output->data_scale();
-  QuantizeMultiplier(real_multiplier, &op_params.output_multiplier, &op_params.output_shift);
-
-  const bool need_broadcast =
-      nnfw::cker::ProcessBroadcastShapes(getTensorShape(_lhs), getTensorShape(_rhs), &op_params);
-  if (need_broadcast)
-  {
-    nnfw::cker::BroadcastBinaryArithmeticOp<nnfw::cker::BinaryArithmeticOpType::MUL>(
-        op_params, getTensorShape(_lhs), reinterpret_cast<const uint8_t *>(_lhs->buffer()),
-        getTensorShape(_rhs), reinterpret_cast<const uint8_t *>(_rhs->buffer()),
-        getTensorShape(_output), reinterpret_cast<uint8_t *>(_output->buffer()));
-    return;
-  }
-
-  nnfw::cker::BinaryArithmeticOp<nnfw::cker::BinaryArithmeticOpType::MUL>(
-      op_params, getTensorShape(_lhs), reinterpret_cast<const uint8_t *>(_lhs->buffer()),
-      getTensorShape(_rhs), reinterpret_cast<const uint8_t *>(_rhs->buffer()),
-      getTensorShape(_output), reinterpret_cast<uint8_t *>(_output->buffer()));
-}
-
-void MulLayer::configure(const IPortableTensor *lhs, const IPortableTensor *rhs,
-                         const ir::Activation activation, IPortableTensor *output)
-{
-  _lhs = lhs;
-  _rhs = rhs;
-  _activation = activation;
-  _output = output;
-}
-
-void MulLayer::run()
-{
-  if (_output->data_type() == OperandType::FLOAT32)
-  {
-    mulFloat32();
-  }
-  else if (_output->data_type() == OperandType::QUANT_UINT8_ASYMM)
-  {
-    mulQuant8();
-  }
-  else
-  {
-    throw std::runtime_error{"Mul: unsupported data type"};
-  }
-}
-
-} // namespace ops
-} // namespace cpu
-} // namespace backend
-} // namespace onert