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/*
* 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
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