src/backends/neon/workloads/NeonDepthwiseConvolutionWorkload.cpp

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

#include "NeonDepthwiseConvolutionWorkload.hpp"

#include <aclCommon/ArmComputeTensorUtils.hpp>
#include <neon/NeonLayerSupport.hpp>
#include <backendsCommon/CpuTensorHandle.hpp>
#include <backendsCommon/DataLayoutIndexed.hpp>

namespace armnn
{

using namespace armcomputetensorutils;

arm_compute::Status NeonDepthwiseConvolutionWorkloadValidate(const TensorInfo& input,
    const TensorInfo& output,
    const DepthwiseConvolution2dDescriptor& descriptor,
    const TensorInfo& weights,
    const Optional<TensorInfo>& biases)
{
    const arm_compute::TensorInfo aclInputInfo =
        BuildArmComputeTensorInfo(input, descriptor.m_DataLayout);
    const arm_compute::TensorInfo aclOutputInfo =
        BuildArmComputeTensorInfo(output, descriptor.m_DataLayout);
    const arm_compute::TensorInfo aclWeightsInfo =
        BuildArmComputeTensorInfo(weights, descriptor.m_DataLayout);

    arm_compute::TensorInfo aclBiasesInfo;
    arm_compute::TensorInfo *optionalAclBiasesInfo = nullptr;

    if (descriptor.m_BiasEnabled)
    {
        BOOST_ASSERT(biases.has_value());

        aclBiasesInfo = BuildArmComputeTensorInfo(biases.value(), descriptor.m_DataLayout);
        optionalAclBiasesInfo = &aclBiasesInfo;
    }

    const arm_compute::PadStrideInfo aclPadStrideInfo =
        BuildArmComputePadStrideInfo(descriptor);
    const unsigned int aclDepthMultiplier = weights.GetShape()[0];

    return arm_compute::NEDepthwiseConvolutionLayer::validate(&aclInputInfo,
                                                              &aclWeightsInfo,
                                                              optionalAclBiasesInfo,
                                                              &aclOutputInfo,
                                                              aclPadStrideInfo,
                                                              aclDepthMultiplier);
}

NeonDepthwiseConvolutionWorkload::NeonDepthwiseConvolutionWorkload(
    const DepthwiseConvolution2dQueueDescriptor& descriptor,
    const WorkloadInfo& info)
    : BaseWorkload<DepthwiseConvolution2dQueueDescriptor>(descriptor, info)
{
    const TensorInfo& weightInfo = m_Data.m_Weight->GetTensorInfo();

    m_KernelTensor = std::make_unique<arm_compute::Tensor>();
    BuildArmComputeTensor(*m_KernelTensor, weightInfo, m_Data.m_Parameters.m_DataLayout);

    INeonTensorHandle* inputTensorHandle = static_cast<INeonTensorHandle*>(m_Data.m_Inputs[0]);
    INeonTensorHandle* outputTensorHandle =  static_cast<INeonTensorHandle*>(m_Data.m_Outputs[0]);
    DataLayoutIndexed dataLayoutIndex(m_Data.m_Parameters.m_DataLayout);

    if (m_Data.m_Parameters.m_BiasEnabled)
    {
        m_BiasTensor = std::make_unique<arm_compute::Tensor>();
        BuildArmComputeTensor(*m_BiasTensor, m_Data.m_Bias->GetTensorInfo(), m_Data.m_Parameters.m_DataLayout);
    }
    else
    {
        // Workaround for COMPMID-1813
        m_BiasTensor = std::make_unique<arm_compute::Tensor>();
        TensorInfo biasTensorInfo({weightInfo.GetShape()[dataLayoutIndex.GetChannelsIndex()]},
                                  weightInfo.GetDataType() == DataType::QuantisedAsymm8 ? DataType::Signed32 :
                                                                                          weightInfo.GetDataType(),
                                  weightInfo.GetQuantizationScale() *
                                  info.m_InputTensorInfos[0].GetQuantizationScale());
        BuildArmComputeTensor(*m_BiasTensor, biasTensorInfo, m_Data.m_Parameters.m_DataLayout);
    }

    arm_compute::PadStrideInfo padStrideInfo(m_Data.m_Parameters.m_StrideX,
                                             m_Data.m_Parameters.m_StrideY,
                                             m_Data.m_Parameters.m_PadLeft,
                                             m_Data.m_Parameters.m_PadRight,
                                             m_Data.m_Parameters.m_PadTop,
                                             m_Data.m_Parameters.m_PadBottom,
                                             arm_compute::DimensionRoundingType::FLOOR);

    m_Data.ValidateInputsOutputs("NeonDepthwiseConvolutionWorkload", 1, 1);

    arm_compute::ITensor& input  = inputTensorHandle->GetTensor();
    arm_compute::ITensor& output = outputTensorHandle->GetTensor();

    arm_compute::DataLayout aclDataLayout = ConvertDataLayout(m_Data.m_Parameters.m_DataLayout);
    input.info()->set_data_layout(aclDataLayout);
    output.info()->set_data_layout(aclDataLayout);

    bool use3x3Optimisation = weightInfo.GetShape()[dataLayoutIndex.GetWidthIndex()] == 3 &&
                              weightInfo.GetShape()[dataLayoutIndex.GetHeightIndex()] == 3;

    if (use3x3Optimisation)
    {
        m_pDepthwiseConvolutionLayer = std::make_unique<arm_compute::NEDepthwiseConvolutionLayer3x3>();
        static_cast<arm_compute::NEDepthwiseConvolutionLayer3x3*>(
            m_pDepthwiseConvolutionLayer.get())->configure(&input,
                                                           m_KernelTensor.get(),
                                                           m_BiasTensor.get(),
                                                           &output,
                                                           padStrideInfo);
    }
    else
    {
        m_pDepthwiseConvolutionLayer = std::make_unique<arm_compute::NEDepthwiseConvolutionLayer>();
        static_cast<arm_compute::NEDepthwiseConvolutionLayer*>(
            m_pDepthwiseConvolutionLayer.get())->configure(&input,
                                                           m_KernelTensor.get(),
                                                           m_BiasTensor.get(),
                                                           &output,
                                                           padStrideInfo);
    }

    BOOST_ASSERT(m_pDepthwiseConvolutionLayer);

    InitializeArmComputeTensorData(*m_KernelTensor, m_Data.m_Weight);

    if (m_Data.m_Parameters.m_BiasEnabled)
    {
        InitializeArmComputeTensorData(*m_BiasTensor, m_Data.m_Bias);
    }
    else
    {
        InitialiseArmComputeTensorEmpty(*m_BiasTensor);
    }

    m_pDepthwiseConvolutionLayer->prepare();
    FreeUnusedTensors();
}

void NeonDepthwiseConvolutionWorkload::Execute() const
{
    ARMNN_SCOPED_PROFILING_EVENT_NEON("NeonDepthwiseConvolutionWorkload_Execute");
    BOOST_ASSERT(m_pDepthwiseConvolutionLayer);

    m_pDepthwiseConvolutionLayer->run();
}

void NeonDepthwiseConvolutionWorkload::FreeUnusedTensors()
{
    FreeTensorIfUnused(m_KernelTensor);
    FreeTensorIfUnused(m_BiasTensor);
}

} //namespace armnn