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//
// Copyright © 2017 Arm Ltd. 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)
: BaseWorkload<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
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