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//
// Copyright © 2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include "RefElementwiseBinaryWorkload.hpp"
#include "Decoders.hpp"
#include "ElementwiseFunction.hpp"
#include "Encoders.hpp"
#include "RefWorkloadUtils.hpp"
#include "Maximum.hpp"
#include "Minimum.hpp"
#include "SquaredDifference.hpp"
#include "Power.hpp"
#include <Profiling.hpp>
#include <armnn/TypesUtils.hpp>
#include <functional>
namespace armnn
{
template<typename DataType>
void ExecuteFunction(std::vector<ITensorHandle*> inputs,
std::vector<ITensorHandle*> outputs,
BinaryOperation operation)
{
const TensorInfo& inputInfo0 = GetTensorInfo(inputs[0]);
const TensorInfo& inputInfo1 = GetTensorInfo(inputs[1]);
const TensorInfo& outputInfo = GetTensorInfo(outputs[0]);
const TensorShape& inShape0 = inputInfo0.GetShape();
const TensorShape& inShape1 = inputInfo1.GetShape();
const TensorShape& outShape = outputInfo.GetShape();
std::unique_ptr<Decoder<DataType>> input0 = MakeDecoder<DataType>(inputInfo0, inputs[0]->Map());
std::unique_ptr<Decoder<DataType>> input1 = MakeDecoder<DataType>(inputInfo1, inputs[1]->Map());
std::unique_ptr<Encoder<DataType>> output = MakeEncoder<DataType>(outputInfo, outputs[0]->Map());
using AddFunction = ElementwiseBinaryFunction<std::plus<DataType>>;
using DivFunction = ElementwiseBinaryFunction<std::divides<DataType>>;
using MaximumFunction = ElementwiseBinaryFunction<armnn::maximum<DataType>>;
using MinimumFunction = ElementwiseBinaryFunction<armnn::minimum<DataType>>;
using MulFunction = ElementwiseBinaryFunction<std::multiplies<DataType>>;
using SubFunction = ElementwiseBinaryFunction<std::minus<DataType>>;
using SqDiffFunction = ElementwiseBinaryFunction<armnn::squaredDifference<DataType>>;
using PowerFunction = ElementwiseBinaryFunction<armnn::power<DataType>>;
switch (operation)
{
case BinaryOperation::Add:
{
AddFunction(inShape0, inShape1, outShape, *input0, *input1, *output);
break;
}
case BinaryOperation::Div:
{
DivFunction(inShape0, inShape1, outShape, *input0, *input1, *output);
break;
}
case BinaryOperation::Maximum:
{
MaximumFunction(inShape0, inShape1, outShape, *input0, *input1, *output);
break;
}
case BinaryOperation::Minimum:
{
MinimumFunction(inShape0, inShape1, outShape, *input0, *input1, *output);
break;
}
case BinaryOperation::Mul:
{
MulFunction(inShape0, inShape1, outShape, *input0, *input1, *output);
break;
}
case BinaryOperation::Sub:
{
SubFunction(inShape0, inShape1, outShape, *input0, *input1, *output);
break;
}
case BinaryOperation::SqDiff:
{
SqDiffFunction(inShape0, inShape1, outShape, *input0, *input1, *output);
break;
}
case BinaryOperation::Power:
{
PowerFunction(inShape0, inShape1, outShape, *input0, *input1, *output);
break;
}
default:
{
throw InvalidArgumentException(std::string("Unsupported binary operation ") +
GetBinaryOperationAsCString(operation), CHECK_LOCATION());
}
}
}
RefElementwiseBinaryWorkload::RefElementwiseBinaryWorkload(const ElementwiseBinaryQueueDescriptor& desc,
const WorkloadInfo& info)
: RefBaseWorkload<ElementwiseBinaryQueueDescriptor>(desc, info)
{}
void RefElementwiseBinaryWorkload::Execute() const
{
Execute(m_Data.m_Inputs, m_Data.m_Outputs);
}
void RefElementwiseBinaryWorkload::ExecuteAsync(ExecutionData& executionData)
{
WorkingMemDescriptor* workingMemDescriptor = static_cast<WorkingMemDescriptor*>(executionData.m_Data);
Execute(workingMemDescriptor->m_Inputs, workingMemDescriptor->m_Outputs);
}
void RefElementwiseBinaryWorkload::Execute(std::vector<ITensorHandle*> inputs,
std::vector<ITensorHandle*> outputs) const
{
ARMNN_SCOPED_PROFILING_EVENT(Compute::CpuRef, "RefElementwiseBinaryWorkload_Execute");
if (GetTensorInfo(inputs[0]).GetDataType() == DataType::Signed32)
{
ExecuteFunction<int32_t>(inputs, outputs, m_Data.m_Parameters.m_Operation);
}
else
{
ExecuteFunction<float>(inputs, outputs, m_Data.m_Parameters.m_Operation);
}
}
} // namespace armnn
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