From fd899966cb881f5bb1ccce7903253a32d360419d Mon Sep 17 00:00:00 2001 From: Matthew Bentham Date: Mon, 31 Dec 2018 15:49:42 +0000 Subject: MLCE-82 Add Neon Mean support and unit tests Factor out new BuildArmComputeReductionCoordinates function from CL backend into ArmComputeTensorUtils. Update NEON LayerSupport and WorkloadFactory objects Change-Id: Icc975ec699199bffafbdb207323df509d35e1e04 --- src/backends/aclCommon/ArmComputeTensorUtils.cpp | 42 +++++++++++++++++ src/backends/aclCommon/ArmComputeTensorUtils.hpp | 5 ++ src/backends/cl/workloads/ClMeanWorkload.cpp | 60 +++--------------------- src/backends/neon/NeonLayerSupport.cpp | 11 +++-- src/backends/neon/NeonWorkloadFactory.cpp | 2 +- src/backends/neon/backend.mk | 1 + src/backends/neon/test/NeonLayerTests.cpp | 15 ++++++ src/backends/neon/workloads/CMakeLists.txt | 2 + src/backends/neon/workloads/NeonMeanWorkload.cpp | 53 +++++++++++++++++++++ src/backends/neon/workloads/NeonMeanWorkload.hpp | 30 ++++++++++++ src/backends/neon/workloads/NeonWorkloads.hpp | 1 + 11 files changed, 162 insertions(+), 60 deletions(-) create mode 100644 src/backends/neon/workloads/NeonMeanWorkload.cpp create mode 100644 src/backends/neon/workloads/NeonMeanWorkload.hpp (limited to 'src/backends') diff --git a/src/backends/aclCommon/ArmComputeTensorUtils.cpp b/src/backends/aclCommon/ArmComputeTensorUtils.cpp index 6b55948693..a2d7d8c797 100644 --- a/src/backends/aclCommon/ArmComputeTensorUtils.cpp +++ b/src/backends/aclCommon/ArmComputeTensorUtils.cpp @@ -31,6 +31,48 @@ arm_compute::DataType GetArmComputeDataType(armnn::DataType dataType) } } +arm_compute::Coordinates BuildArmComputeReductionCoordinates(size_t inputDimensions, + unsigned int originalInputRank, + const std::vector& armnnAxes) +{ + arm_compute::Coordinates outAclCoords; + + if (armnnAxes.empty()) + { + // If no reduction axes were provided, then the input must be reduced along all dimensions. + // Since Compute Library does not accept an empty vector as the reduction dimensions, we then + // manually create a vector including all the input dimensions (in reversed order) as: + // + // { inputDimensions - 1, inputDimensions - 2, ..., 1, 0 } + // + outAclCoords.set_num_dimensions(inputDimensions); + std::generate(outAclCoords.begin(), outAclCoords.end(), [d = inputDimensions - 1] () mutable { return d--; }); + } + else + { + // Create a vector of reduction dimensions (in reversed order) with the given reduction axes. + // + // Adjust the given reduction axes according to the original rank of the input tensor (before ACL applied any + // dimension correction). + // For example, if the input tensor originally had 4 dimensions, and one of the reduction axes was 2, then the + // new value for that reduction axis should be 1. + // + // Example: + // ArmNN input shape = { 1, 1, 3, 2 } -> ACL input shape = { 2, 3 } + // ArmNN reduction axis = { 2 } -> ACL reduction axis = { 1 } + // ArmNN reduction axis = { 3 } -> ACL reduction axis = { 0 } + // + // The transformation: ACL reduction axis index = original rank - ArmNN reduction axis index - 1 + // + outAclCoords.set_num_dimensions(armnnAxes.size()); + std::transform(armnnAxes.begin(), armnnAxes.end(), + outAclCoords.begin(), + [originalInputRank](unsigned int i){ return originalInputRank - i - 1; }); + } + + return outAclCoords; +} + arm_compute::TensorShape BuildArmComputeTensorShape(const armnn::TensorShape& tensorShape) { arm_compute::TensorShape shape; diff --git a/src/backends/aclCommon/ArmComputeTensorUtils.hpp b/src/backends/aclCommon/ArmComputeTensorUtils.hpp index 2a14d6548c..fbd850c687 100644 --- a/src/backends/aclCommon/ArmComputeTensorUtils.hpp +++ b/src/backends/aclCommon/ArmComputeTensorUtils.hpp @@ -24,6 +24,11 @@ namespace armcomputetensorutils /// Utility function to map an armnn::DataType to corresponding arm_compute::DataType. arm_compute::DataType GetArmComputeDataType(armnn::DataType dataType); +/// Utility function used to set up an arm_compute::Coordinates from a vector of ArmNN Axes for reduction functions +arm_compute::Coordinates BuildArmComputeReductionCoordinates(size_t inputDimensions, + unsigned int originalInputRank, + const std::vector& armnnAxes); + /// Utility function used to setup an arm_compute::TensorShape object from an armnn::TensorShape. arm_compute::TensorShape BuildArmComputeTensorShape(const armnn::TensorShape& tensorShape); diff --git a/src/backends/cl/workloads/ClMeanWorkload.cpp b/src/backends/cl/workloads/ClMeanWorkload.cpp index 960fca2732..470b6a883d 100644 --- a/src/backends/cl/workloads/ClMeanWorkload.cpp +++ b/src/backends/cl/workloads/ClMeanWorkload.cpp @@ -10,50 +10,6 @@ #include "ClWorkloadUtils.hpp" -namespace -{ - -void ConvertArmnnAxesToAclCoordinates(size_t inputDimensions, - unsigned int originalInputRank, - const std::vector& armnnAxes, - arm_compute::Coordinates& outAclCoords) -{ - if (armnnAxes.empty()) - { - // If no reduction axes were provided, then the input must be reduced along all dimensions. - // Since arm_compute::CLReduceMean does not accept an empty vector as the reduction dimensions, we then - // manually create a vector including all the input dimensions (in reversed order) as: - // - // { inputDimensions - 1, inputDimensions - 2, ..., 1, 0 } - // - outAclCoords.set_num_dimensions(inputDimensions); - std::generate(outAclCoords.begin(), outAclCoords.end(), [d = inputDimensions - 1] () mutable { return d--; }); - } - else - { - // Create a vector of reduction dimensions (in reversed order) with the given reduction axes. - // - // Adjust the given reduction axes according to the original rank of the input tensor (before ACL applied any - // dimension correction). - // For example, if the input tensor originally had 4 dimensions, and one of the reduction axes was 2, then the - // new value for that reduction axis should be 1. - // - // Example: - // ArmNN input shape = { 1, 1, 3, 2 } -> ACL input shape = { 2, 3 } - // ArmNN reduction axis = { 2 } -> ACL reduction axis = { 1 } - // ArmNN reduction axis = { 3 } -> ACL reduction axis = { 0 } - // - // The transformation: ACL reduction axis index = original rank - ArmNN reduction axis index - 1 - // - outAclCoords.set_num_dimensions(armnnAxes.size()); - std::transform(armnnAxes.begin(), armnnAxes.end(), - outAclCoords.begin(), - [originalInputRank](unsigned int i){ return originalInputRank - i - 1; }); - } -} - -} // anonymous namespace - namespace armnn { using namespace armcomputetensorutils; @@ -65,11 +21,9 @@ arm_compute::Status ClMeanValidate(const TensorInfo& input, const arm_compute::TensorInfo aclInputInfo = armcomputetensorutils::BuildArmComputeTensorInfo(input); const arm_compute::TensorInfo aclOutputInfo = armcomputetensorutils::BuildArmComputeTensorInfo(output); - arm_compute::Coordinates coords; - ConvertArmnnAxesToAclCoordinates(aclInputInfo.num_dimensions(), - input.GetNumDimensions(), - desc.m_Axis, - coords); + arm_compute::Coordinates coords = BuildArmComputeReductionCoordinates(aclInputInfo.num_dimensions(), + input.GetNumDimensions(), + desc.m_Axis); return arm_compute::CLReduceMean::validate(&aclInputInfo, coords, desc.m_KeepDims, &aclOutputInfo); } @@ -82,11 +36,9 @@ ClMeanWorkload::ClMeanWorkload(const MeanQueueDescriptor& descriptor, const Work arm_compute::ICLTensor& input = static_cast(m_Data.m_Inputs[0])->GetTensor(); arm_compute::ICLTensor& output = static_cast(m_Data.m_Outputs[0])->GetTensor(); - arm_compute::Coordinates coords; - ConvertArmnnAxesToAclCoordinates(input.info()->num_dimensions(), - info.m_InputTensorInfos[0].GetNumDimensions(), - m_Data.m_Parameters.m_Axis, - coords); + arm_compute::Coordinates coords = BuildArmComputeReductionCoordinates(input.info()->num_dimensions(), + info.m_InputTensorInfos[0].GetNumDimensions(), + m_Data.m_Parameters.m_Axis); m_Layer.configure(&input, coords, m_Data.m_Parameters.m_KeepDims, &output); } diff --git a/src/backends/neon/NeonLayerSupport.cpp b/src/backends/neon/NeonLayerSupport.cpp index 7efdf159c9..93c1123675 100644 --- a/src/backends/neon/NeonLayerSupport.cpp +++ b/src/backends/neon/NeonLayerSupport.cpp @@ -24,6 +24,7 @@ #include "workloads/NeonDepthwiseConvolutionWorkload.hpp" #include "workloads/NeonL2NormalizationFloatWorkload.hpp" #include "workloads/NeonMaximumWorkload.hpp" +#include "workloads/NeonMeanWorkload.hpp" #include "workloads/NeonMergerWorkload.hpp" #include "workloads/NeonMultiplicationFloatWorkload.hpp" #include "workloads/NeonNormalizationFloatWorkload.hpp" @@ -364,11 +365,11 @@ bool NeonLayerSupport::IsMeanSupported(const TensorInfo& input, const MeanDescriptor& descriptor, Optional reasonIfUnsupported) const { - ignore_unused(input); - ignore_unused(output); - ignore_unused(descriptor); - ignore_unused(reasonIfUnsupported); - return false; + FORWARD_WORKLOAD_VALIDATE_FUNC(NeonMeanWorkloadValidate, + reasonIfUnsupported, + input, + output, + descriptor); } bool NeonLayerSupport::IsMergerSupported(const std::vector inputs, diff --git a/src/backends/neon/NeonWorkloadFactory.cpp b/src/backends/neon/NeonWorkloadFactory.cpp index 85e5768571..e635f0cc8d 100644 --- a/src/backends/neon/NeonWorkloadFactory.cpp +++ b/src/backends/neon/NeonWorkloadFactory.cpp @@ -273,7 +273,7 @@ std::unique_ptr NeonWorkloadFactory::CreateMaximum(const MaximumQueue std::unique_ptr NeonWorkloadFactory::CreateMean(const MeanQueueDescriptor& descriptor, const WorkloadInfo& info) const { - return MakeWorkloadHelper(descriptor, info); + return std::make_unique(descriptor, info); } std::unique_ptr NeonWorkloadFactory::CreatePad(const PadQueueDescriptor& descriptor, diff --git a/src/backends/neon/backend.mk b/src/backends/neon/backend.mk index fdfd696fbe..d4f414eba1 100644 --- a/src/backends/neon/backend.mk +++ b/src/backends/neon/backend.mk @@ -26,6 +26,7 @@ BACKEND_SOURCES := \ workloads/NeonL2NormalizationFloatWorkload.cpp \ workloads/NeonLstmFloatWorkload.cpp \ workloads/NeonMaximumWorkload.cpp \ + workloads/NeonMeanWorkload.cpp \ workloads/NeonMergerWorkload.cpp \ workloads/NeonMultiplicationFloatWorkload.cpp \ workloads/NeonNormalizationFloatWorkload.cpp \ diff --git a/src/backends/neon/test/NeonLayerTests.cpp b/src/backends/neon/test/NeonLayerTests.cpp index 37933e0254..5b83b2bfa8 100644 --- a/src/backends/neon/test/NeonLayerTests.cpp +++ b/src/backends/neon/test/NeonLayerTests.cpp @@ -399,6 +399,21 @@ ARMNN_AUTO_TEST_CASE(LstmLayerFloat32NoCifgNoPeepholeNoProjection, ARMNN_AUTO_TEST_CASE(LstmLayerFloat32NoCifgWithPeepholeWithProjection, LstmLayerFloat32NoCifgWithPeepholeWithProjectionTest) +// Mean +ARMNN_AUTO_TEST_CASE(MeanUint8Simple, MeanUint8SimpleTest) +ARMNN_AUTO_TEST_CASE(MeanUint8SimpleAxis, MeanUint8SimpleAxisTest) +ARMNN_AUTO_TEST_CASE(MeanUint8KeepDims, MeanUint8KeepDimsTest) +ARMNN_AUTO_TEST_CASE(MeanUint8MultipleDims, MeanUint8MultipleDimsTest) +ARMNN_AUTO_TEST_CASE(MeanVtsUint8, MeanVtsUint8Test) + +ARMNN_AUTO_TEST_CASE(MeanFloatSimple, MeanFloatSimpleTest) +ARMNN_AUTO_TEST_CASE(MeanFloatSimpleAxis, MeanFloatSimpleAxisTest) +ARMNN_AUTO_TEST_CASE(MeanFloatKeepDims, MeanFloatKeepDimsTest) +ARMNN_AUTO_TEST_CASE(MeanFloatMultipleDims, MeanFloatMultipleDimsTest) +ARMNN_AUTO_TEST_CASE(MeanVtsFloat1, MeanVtsFloat1Test) +ARMNN_AUTO_TEST_CASE(MeanVtsFloat2, MeanVtsFloat2Test) +ARMNN_AUTO_TEST_CASE(MeanVtsFloat3, MeanVtsFloat3Test) + // Max ARMNN_AUTO_TEST_CASE(SimpleMaximum, MaximumSimpleTest) ARMNN_AUTO_TEST_CASE(MaximumBroadcast1Element, MaximumBroadcast1ElementTest) diff --git a/src/backends/neon/workloads/CMakeLists.txt b/src/backends/neon/workloads/CMakeLists.txt index 7b0251ce04..b7dfc3fcfd 100644 --- a/src/backends/neon/workloads/CMakeLists.txt +++ b/src/backends/neon/workloads/CMakeLists.txt @@ -30,6 +30,8 @@ list(APPEND armnnNeonBackendWorkloads_sources NeonLstmFloatWorkload.hpp NeonMaximumWorkload.cpp NeonMaximumWorkload.hpp + NeonMeanWorkload.cpp + NeonMeanWorkload.hpp NeonMergerWorkload.cpp NeonMergerWorkload.hpp NeonMultiplicationFloatWorkload.cpp diff --git a/src/backends/neon/workloads/NeonMeanWorkload.cpp b/src/backends/neon/workloads/NeonMeanWorkload.cpp new file mode 100644 index 0000000000..d736e42e0e --- /dev/null +++ b/src/backends/neon/workloads/NeonMeanWorkload.cpp @@ -0,0 +1,53 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#include "NeonMeanWorkload.hpp" + +#include + +#include + +#include "NeonWorkloadUtils.hpp" + +namespace armnn +{ +using namespace armcomputetensorutils; + +arm_compute::Status NeonMeanWorkloadValidate(const TensorInfo& input, + const TensorInfo& output, + const MeanDescriptor& desc) +{ + const arm_compute::TensorInfo aclInputInfo = armcomputetensorutils::BuildArmComputeTensorInfo(input); + const arm_compute::TensorInfo aclOutputInfo = armcomputetensorutils::BuildArmComputeTensorInfo(output); + + arm_compute::Coordinates coords = BuildArmComputeReductionCoordinates(aclInputInfo.num_dimensions(), + input.GetNumDimensions(), + desc.m_Axis); + + return arm_compute::NEReduceMean::validate(&aclInputInfo, coords, desc.m_KeepDims, &aclOutputInfo); +} + +NeonMeanWorkload::NeonMeanWorkload(const MeanQueueDescriptor& descriptor, const WorkloadInfo& info) + : BaseWorkload(descriptor, info) +{ + m_Data.ValidateInputsOutputs("NeonMeanWorkload", 1, 1); + + arm_compute::ITensor& input = static_cast(m_Data.m_Inputs[0])->GetTensor(); + arm_compute::ITensor& output = static_cast(m_Data.m_Outputs[0])->GetTensor(); + + arm_compute::Coordinates coords = BuildArmComputeReductionCoordinates(input.info()->num_dimensions(), + info.m_InputTensorInfos[0].GetNumDimensions(), + m_Data.m_Parameters.m_Axis); + + m_Layer.configure(&input, coords, m_Data.m_Parameters.m_KeepDims, &output); +} + +void NeonMeanWorkload::Execute() const +{ + ARMNN_SCOPED_PROFILING_EVENT_NEON("NeonMeanWorkload_Execute"); + m_Layer.run(); +} + +} //namespace armnn diff --git a/src/backends/neon/workloads/NeonMeanWorkload.hpp b/src/backends/neon/workloads/NeonMeanWorkload.hpp new file mode 100644 index 0000000000..055b52a011 --- /dev/null +++ b/src/backends/neon/workloads/NeonMeanWorkload.hpp @@ -0,0 +1,30 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#pragma once + +#include + +#include + +namespace armnn +{ + +arm_compute::Status NeonMeanWorkloadValidate(const TensorInfo& input, + const TensorInfo& output, + const MeanDescriptor& desc); + +class NeonMeanWorkload : public BaseWorkload +{ +public: + NeonMeanWorkload(const MeanQueueDescriptor& descriptor, const WorkloadInfo& info); + + void Execute() const override; + +private: + mutable arm_compute::NEReduceMean m_Layer; +}; + +} //namespace armnn diff --git a/src/backends/neon/workloads/NeonWorkloads.hpp b/src/backends/neon/workloads/NeonWorkloads.hpp index 1f08d039ae..a5ef0dcb2d 100644 --- a/src/backends/neon/workloads/NeonWorkloads.hpp +++ b/src/backends/neon/workloads/NeonWorkloads.hpp @@ -17,6 +17,7 @@ #include "NeonL2NormalizationFloatWorkload.hpp" #include "NeonLstmFloatWorkload.hpp" #include "NeonMaximumWorkload.hpp" +#include "NeonMeanWorkload.hpp" #include "NeonMergerWorkload.hpp" #include "NeonMultiplicationFloatWorkload.hpp" #include "NeonNormalizationFloatWorkload.hpp" -- cgit v1.2.1