src/backends/neon/workloads/NeonConvolution2dWorkload.cpp

   1 //
   2 // Copyright © 2017 Arm Ltd. All rights reserved.
   3 // SPDX-License-Identifier: MIT
   4 //
   5
   6 #include "NeonConvolution2dWorkload.hpp"
   7
   8 #include <backends/CpuTensorHandle.hpp>
   9 #include <backends/aclCommon/ArmComputeTensorUtils.hpp>
  10 #include <backends/neon/NeonLayerSupport.hpp>
  11
  12 #include <armnn/Types.hpp>
  13 #include <armnnUtils/Half.hpp>
  14
  15 namespace armnn
  16 {
  17
  18 using namespace armcomputetensorutils;
  19
  20 arm_compute::Status NeonConvolution2dWorkloadValidate(const TensorInfo& input,
  21     const TensorInfo& output,
  22     const Convolution2dDescriptor& descriptor,
  23     const TensorInfo& weights,
  24     const Optional<TensorInfo>& biases)
  25 {
  26     const arm_compute::TensorInfo aclInputInfo = BuildArmComputeTensorInfo(input, descriptor.m_DataLayout);
  27     const arm_compute::TensorInfo aclOutputInfo = BuildArmComputeTensorInfo(output, descriptor.m_DataLayout);
  28     const arm_compute::TensorInfo aclWeightsInfo = BuildArmComputeTensorInfo(weights, descriptor.m_DataLayout);
  29
  30     arm_compute::TensorInfo aclBiasesInfo;
  31     arm_compute::TensorInfo *optionalAclBiasesInfo = nullptr;
  32
  33     if (descriptor.m_BiasEnabled)
  34     {
  35         BOOST_ASSERT(biases.has_value());
  36
  37         aclBiasesInfo = BuildArmComputeTensorInfo(biases.value(), descriptor.m_DataLayout);
  38         optionalAclBiasesInfo = &aclBiasesInfo;
  39     }
  40
  41     arm_compute::PadStrideInfo layerInfo = BuildArmComputePadStrideInfo(descriptor);
  42
  43     return arm_compute::NEConvolutionLayer::validate(&aclInputInfo,
  44                                                      &aclWeightsInfo,
  45                                                      optionalAclBiasesInfo,
  46                                                      &aclOutputInfo,
  47                                                      layerInfo);
  48 }
  49
  50 NeonConvolution2dWorkload::NeonConvolution2dWorkload(
  51     const Convolution2dQueueDescriptor& descriptor, const WorkloadInfo& info,
  52     std::shared_ptr<arm_compute::MemoryManagerOnDemand>& memoryManager)
  53     : BaseWorkload<Convolution2dQueueDescriptor>(descriptor, info)
  54 {
  55     using arm_compute::NEDirectConvolutionLayer;
  56
  57     m_Data.ValidateInputsOutputs("NeonConvolution2dWorkload", 1, 1);
  58
  59     // todo: check tensor shapes match.
  60
  61     arm_compute::ITensor& input = boost::polymorphic_downcast<INeonTensorHandle*>(m_Data.m_Inputs[0])->GetTensor();
  62     arm_compute::ITensor& output = boost::polymorphic_downcast<INeonTensorHandle*>(m_Data.m_Outputs[0])->GetTensor();
  63
  64     arm_compute::DataLayout aclDataLayout = ConvertDataLayout(m_Data.m_Parameters.m_DataLayout);
  65     input.info()->set_data_layout(aclDataLayout);
  66     output.info()->set_data_layout(aclDataLayout);
  67
  68     m_KernelTensor = std::make_unique<arm_compute::Tensor>();
  69     BuildArmComputeTensor(*m_KernelTensor, m_Data.m_Weight->GetTensorInfo(), m_Data.m_Parameters.m_DataLayout);
  70
  71     if (m_Data.m_Parameters.m_BiasEnabled)
  72     {
  73         m_BiasTensor = std::make_unique<arm_compute::Tensor>();
  74         BuildArmComputeTensor(*m_BiasTensor, m_Data.m_Bias->GetTensorInfo(), m_Data.m_Parameters.m_DataLayout);
  75     }
  76
  77     arm_compute::PadStrideInfo padStrideInfo(m_Data.m_Parameters.m_StrideX,
  78                                              m_Data.m_Parameters.m_StrideY,
  79                                              m_Data.m_Parameters.m_PadLeft,
  80                                              m_Data.m_Parameters.m_PadRight,
  81                                              m_Data.m_Parameters.m_PadTop,
  82                                              m_Data.m_Parameters.m_PadBottom,
  83                                              arm_compute::DimensionRoundingType::FLOOR);
  84
  85     const bool preferDirectConvolution =
  86         IsNeonDirectConvolutionPreferred(m_Data.m_Weight->GetTensorInfo(),
  87                                          m_Data.m_Parameters);
  88
  89     if (preferDirectConvolution)
  90     {
  91         auto directConvolutionLayer = std::make_unique<arm_compute::NEDirectConvolutionLayer>(memoryManager);
  92         directConvolutionLayer->configure(&input,
  93                                           m_KernelTensor.get(),
  94                                           m_BiasTensor.get(),
  95                                           &output,
  96                                           padStrideInfo);
  97         m_ConvolutionLayer.reset(directConvolutionLayer.release());
  98     }
  99     else
 100     {
 101         auto convolutionLayer = std::make_unique<arm_compute::NEConvolutionLayer>(memoryManager);
 102         convolutionLayer->configure(&input,
 103                                     m_KernelTensor.get(),
 104                                     m_BiasTensor.get(),
 105                                     &output,
 106                                     padStrideInfo);
 107         m_ConvolutionLayer.reset(convolutionLayer.release());
 108     }
 109     BOOST_ASSERT(m_ConvolutionLayer);
 110
 111     InitializeArmComputeTensorData(*m_KernelTensor, m_Data.m_Weight);
 112
 113     if (m_Data.m_Parameters.m_BiasEnabled)
 114     {
 115         InitializeArmComputeTensorData(*m_BiasTensor, m_Data.m_Bias);
 116     }
 117
 118     m_ConvolutionLayer->prepare();
 119     FreeUnusedTensors();
 120 }
 121
 122 void NeonConvolution2dWorkload::Execute() const
 123 {
 124     ARMNN_SCOPED_PROFILING_EVENT_NEON("NeonConvolution2dWorkload_Execute");
 125     m_ConvolutionLayer->run();
 126 }
 127
 128 void NeonConvolution2dWorkload::FreeUnusedTensors()
 129 {
 130     FreeTensorIfUnused(m_KernelTensor);
 131     FreeTensorIfUnused(m_BiasTensor);
 132 }
 133
 134 } //namespace armnn