ClSoftmaxUint8Workload.cpp

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//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//

#include "ClSoftmaxUint8Workload.hpp"
#include "ClWorkloadUtils.hpp"

#include <aclCommon/ArmComputeUtils.hpp>
#include <cl/ClTensorHandle.hpp>
#include <backendsCommon/CpuTensorHandle.hpp>

namespace armnn
{

ClSoftmaxUint8Workload::ClSoftmaxUint8Workload(const SoftmaxQueueDescriptor& descriptor, const WorkloadInfo& info,
                                               std::shared_ptr<arm_compute::MemoryManagerOnDemand>& memoryManager)
    : Uint8Workload<SoftmaxQueueDescriptor>(descriptor, info)
    , m_SoftmaxLayer(memoryManager)
{
    m_Data.ValidateInputsOutputs("ClSoftmaxUint8Workload", 1, 1);

    arm_compute::ICLTensor& input  = static_cast<ClTensorHandle*>(m_Data.m_Inputs[0])->GetTensor();
    arm_compute::ICLTensor& output = static_cast<ClTensorHandle*>(m_Data.m_Outputs[0])->GetTensor();

    const auto outputQuantization = output.info()->quantization_info();

    if ((!outputQuantization.scale().empty() && outputQuantization.scale()[0] != (1.0f / 256.0f)) ||
        (!outputQuantization.offset().empty() && outputQuantization.offset()[0] != 0) ||
         outputQuantization.scale().empty() || outputQuantization.offset().empty())
    {
        throw InvalidArgumentException(
            "Invalid quantization for output. Only scale = 1.0f / 256.0f and offset = 0 supported");
    }

    unsigned int aclAxis = ComputeSoftmaxAclAxis(m_Data.m_Parameters, info.m_InputTensorInfos[0]);
    m_SoftmaxLayer.configure(&input, &output, descriptor.m_Parameters.m_Beta, aclAxis);
}

void ClSoftmaxUint8Workload::Execute() const
{
    ARMNN_SCOPED_PROFILING_EVENT_CL("ClSoftmaxUint8Workload_Execute");
    RunClFunction(m_SoftmaxLayer, CHECK_LOCATION());
}

} //namespace armnn