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/*
* Copyright (c) 2020 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 "ElementwiseBinaryLayer.h"
#include "OperationUtils.h"
#include <cker/operation/LogicalOr.h>
#include <cker/operation/MaxMin.h>
namespace onert
{
namespace backend
{
namespace cpu
{
namespace ops
{
namespace
{
template <typename T>
void logicalOrGeneric(const IPortableTensor *lhs, const IPortableTensor *rhs,
IPortableTensor *output)
{
if (!HaveSameShapes(lhs, rhs))
{
nnfw::cker::LogicalOrBroadcast<T>(
getTensorShape(lhs), reinterpret_cast<const T *>(lhs->buffer()), getTensorShape(rhs),
reinterpret_cast<const T *>(rhs->buffer()), getTensorShape(output),
reinterpret_cast<T *>(output->buffer()));
}
else
{
nnfw::cker::LogicalOrElementwise<T>(
getTensorShape(lhs), reinterpret_cast<const T *>(lhs->buffer()),
reinterpret_cast<const T *>(rhs->buffer()), reinterpret_cast<T *>(output->buffer()));
}
}
template <typename T>
void maximumGeneric(const IPortableTensor *lhs, const IPortableTensor *rhs, IPortableTensor *output)
{
nnfw::cker::Max<T>(getTensorShape(lhs), reinterpret_cast<const T *>(lhs->buffer()),
getTensorShape(rhs), reinterpret_cast<const T *>(rhs->buffer()),
getTensorShape(output), reinterpret_cast<T *>(output->buffer()));
}
template <typename T>
void minimumGeneric(const IPortableTensor *lhs, const IPortableTensor *rhs, IPortableTensor *output)
{
nnfw::cker::Min<T>(getTensorShape(lhs), reinterpret_cast<const T *>(lhs->buffer()),
getTensorShape(rhs), reinterpret_cast<const T *>(rhs->buffer()),
getTensorShape(output), reinterpret_cast<T *>(output->buffer()));
}
bool haveSameQauntInfo(const IPortableTensor *lhs, const IPortableTensor *rhs,
const IPortableTensor *output)
{
return (lhs->data_scale() == rhs->data_scale() && lhs->data_scale() == output->data_scale()) &&
(lhs->data_offset() == rhs->data_offset() && lhs->data_offset() == output->data_offset());
}
} // namespace
void ElementwiseBinaryLayer::configure(const IPortableTensor *lhs, const IPortableTensor *rhs,
IPortableTensor *output, const ElementwiseBinaryType op_type)
{
assert(lhs != nullptr);
assert(rhs != nullptr);
assert(output != nullptr);
_lhs = lhs;
_rhs = rhs;
_output = output;
switch (op_type)
{
case ElementwiseBinaryType::kLogicalOr:
if ((_lhs->data_type() == OperandType::BOOL8) && (_rhs->data_type() == OperandType::BOOL8))
{
_kernel = logicalOrGeneric<bool>;
}
else
{
throw std::runtime_error{"LogicalOr: Unsupported data type"};
}
break;
case ElementwiseBinaryType::kMax:
if (_lhs->data_type() == OperandType::QUANT_UINT8_ASYMM)
{
if (!haveSameQauntInfo(_lhs, _rhs, _output))
{
throw std::runtime_error("Max NYI for quantized");
}
_kernel = maximumGeneric<uint8_t>;
}
else if (_lhs->data_type() == OperandType::FLOAT32)
{
_kernel = maximumGeneric<float>;
}
else
{
throw std::runtime_error{"Max: unsupported data type"};
}
break;
case ElementwiseBinaryType::kMin:
if (_lhs->data_type() == OperandType::QUANT_UINT8_ASYMM)
{
if (!haveSameQauntInfo(_lhs, _rhs, _output))
{
throw std::runtime_error("Min NYI for quantized");
}
_kernel = minimumGeneric<uint8_t>;
}
else if (_lhs->data_type() == OperandType::INT32)
{
_kernel = minimumGeneric<int32_t>;
}
else if (_lhs->data_type() == OperandType::FLOAT32)
{
_kernel = minimumGeneric<float>;
}
else
{
throw std::runtime_error{"Min: unsupported data type"};
}
break;
default:
throw std::runtime_error{"ElementwiseBinary: Unsupported ElementwiseBinary type"};
}
}
void ElementwiseBinaryLayer::run() { _kernel(_lhs, _rhs, _output); }
} // namespace ops
} // namespace cpu
} // namespace backend
} // namespace onert
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