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//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include "RefConstantWorkload.hpp"
#include "RefWorkloadUtils.hpp"
#include <armnn/Types.hpp>
#include <boost/assert.hpp>
#include <cstring>
namespace armnn
{
template <armnn::DataType DataType>
void RefConstantWorkload<DataType>::Execute() const
{
// Considering the reference backend independently, it could be possible to initialise the intermediate tensor
// created by the layer output handler at workload construction time, rather than at workload execution time.
// However, this is not an option for other backends (e.g. CL). For consistency, we prefer to align all
// implementations.
// A similar argument can be made about performing the memory copy in the first place (the layer output handler
// could have a non-owning reference to the layer output tensor managed by the const input layer); again, this is
// not an option for other backends, and the extra complexity required to make this work for the reference backend
// may not be worth the effort (skipping a memory copy in the first inference).
ARMNN_SCOPED_PROFILING_EVENT(Compute::CpuRef, "RefConstantWorkload_Execute");
if (!m_RanOnce)
{
const ConstantQueueDescriptor& data = this->m_Data;
BOOST_ASSERT(data.m_LayerOutput != nullptr);
const TensorInfo& outputInfo = GetTensorInfo(data.m_Outputs[0]);
BOOST_ASSERT(data.m_LayerOutput->GetTensorInfo().GetNumBytes() == outputInfo.GetNumBytes());
memcpy(GetOutputTensorData<void>(0, data), data.m_LayerOutput->GetConstTensor<void>(),
outputInfo.GetNumBytes());
m_RanOnce = true;
}
}
template class RefConstantWorkload<DataType::Float32>;
template class RefConstantWorkload<DataType::QuantisedAsymm8>;
template class RefConstantWorkload<DataType::Signed32>;
} //namespace armnn
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