NeonDepthwiseConvolutionWorkload.cpp

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

#include "NeonDepthwiseConvolutionWorkload.hpp"

#include "NeonWorkloadUtils.hpp"

#include <armnnUtils/DataLayoutIndexed.hpp>

#include <aclCommon/ArmComputeTensorUtils.hpp>
#include <aclCommon/ArmComputeUtils.hpp>

#include <neon/NeonLayerSupport.hpp>

#include <armnn/backends/TensorHandle.hpp>
#include <backendsCommon/WorkloadUtils.hpp>

#include <arm_compute/runtime/NEON/functions/NEDepthwiseConvolutionLayer.h>

using namespace armnnUtils;

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 ActivationDescriptor* activationDescriptor)
{
    const arm_compute::TensorInfo aclInputInfo = BuildArmComputeTensorInfo(input, descriptor.m_DataLayout);
    const arm_compute::TensorInfo aclOutputInfo = BuildArmComputeTensorInfo(output, descriptor.m_DataLayout);

    // ArmNN's weight format is usually [ M, I, H, W ] but for depthwise its [ 1, H, W, I*M]
    // Permute to [ 1, I * M, H, W ] (if NCHW), as required by the compute library
    unsigned int aclDepthMultiplier;
    TensorInfo weightsPermuted;
    std::tie(weightsPermuted, aclDepthMultiplier) = Convert1HWOTensorInfoToAcl(weights, input, descriptor.m_DataLayout);

    // Convert the weights into the compute library format
    const arm_compute::TensorInfo aclWeightsInfo = BuildArmComputeTensorInfo(weightsPermuted, descriptor.m_DataLayout);

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

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

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

    arm_compute::PadStrideInfo aclPadStrideInfo = BuildArmComputePadStrideInfo(descriptor);
    const arm_compute::Size2D aclDilationInfo = BuildArmComputeSize2D(
        descriptor.m_DilationX, descriptor.m_DilationY);

    const arm_compute::ActivationLayerInfo activationInfo = ConvertActivationDescriptorToAclActivationLayerInfo(
        activationDescriptor);

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

NeonDepthwiseConvolutionWorkload::NeonDepthwiseConvolutionWorkload(
    const DepthwiseConvolution2dQueueDescriptor& descriptor,
    const WorkloadInfo& info)
    : NeonBaseWorkload<DepthwiseConvolution2dQueueDescriptor>(descriptor, info)
{
    // ArmNN's weight format for depthwise is [ 1, H, W, I*M ]
    auto& weightInfo = m_Data.m_Weight->GetTensorInfo();

    ConstTensor weightsPermuted;
    unsigned int depthMultiplier;
    std::unique_ptr<unsigned char[]> permuteBuffer(new unsigned char[weightInfo.GetNumBytes()]);
    std::tie(weightsPermuted, depthMultiplier) = Convert1HWOTensorToAcl(m_Data.m_Weight,
                                                                        info.m_InputTensorInfos[0],
                                                                        m_Data.m_Parameters.m_DataLayout,
                                                                        permuteBuffer.get());

    // Convert the weights into the compute library format
    m_KernelTensor = std::make_unique<arm_compute::Tensor>();
    BuildArmComputeTensor(*m_KernelTensor, weightsPermuted.GetInfo(), 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);
    }

    const arm_compute::Size2D aclDilationInfo = BuildArmComputeSize2D(
        m_Data.m_Parameters.m_DilationX, m_Data.m_Parameters.m_DilationY);

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

    IAclTensorHandle* inputTensorHandle = static_cast<IAclTensorHandle*>(m_Data.m_Inputs[0]);
    IAclTensorHandle* outputTensorHandle = static_cast<IAclTensorHandle*>(m_Data.m_Outputs[0]);

    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);

    arm_compute::PadStrideInfo padStrideInfo = BuildArmComputePadStrideInfo(m_Data.m_Parameters);

    const arm_compute::ActivationLayerInfo activationInfo = ConvertAdditionalInfoToAclActivationLayerInfo(descriptor);

    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,
                                                       depthMultiplier,
                                                       activationInfo,
                                                       aclDilationInfo);

    // Add details for profiling output
    WorkloadInfo detailsInfo;

    detailsInfo.m_InputTensorInfos = info.m_InputTensorInfos;
    detailsInfo.m_OutputTensorInfos = info.m_OutputTensorInfos;
    detailsInfo.m_WeightsTensorInfo = armnn::Optional<armnn::TensorInfo>(descriptor.m_Weight->GetTensorInfo());
    if (descriptor.m_Parameters.m_BiasEnabled)
    {
        detailsInfo.m_BiasTensorInfo = armnn::Optional<armnn::TensorInfo>(descriptor.m_Bias->GetTensorInfo());
    }

    // Report Profiling Details
    ARMNN_REPORT_PROFILING_WORKLOAD_DESC("NeonDepthwiseConvolution2dWorkload_Construct",
                                         descriptor.m_Parameters,
                                         detailsInfo,
                                         this->GetGuid());

    ARMNN_ASSERT(m_pDepthwiseConvolutionLayer);

    ScopedTensorHandle weightsPermutedHandle(weightsPermuted);
    InitializeArmComputeTensorData(*m_KernelTensor, &weightsPermutedHandle);

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

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

void NeonDepthwiseConvolutionWorkload::Execute() const
{
    ARMNN_SCOPED_PROFILING_EVENT_NEON_GUID("NeonDepthwiseConvolutionWorkload_Execute", this->GetGuid());
    ARMNN_ASSERT(m_pDepthwiseConvolutionLayer);

    m_pDepthwiseConvolutionLayer->run();
}

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

} //namespace armnn