diff options
Diffstat (limited to 'src/backends/aclCommon')
| -rw-r--r-- | src/backends/aclCommon/ArmComputeTensorHandle.hpp | 25 | ||||
| -rw-r--r-- | src/backends/aclCommon/ArmComputeTensorUtils.cpp | 247 | ||||
| -rw-r--r-- | src/backends/aclCommon/ArmComputeTensorUtils.hpp | 247 | ||||
| -rw-r--r-- | src/backends/aclCommon/ArmComputeUtils.hpp | 173 | ||||
| -rw-r--r-- | src/backends/aclCommon/BaseMemoryManager.cpp | 108 | ||||
| -rw-r--r-- | src/backends/aclCommon/BaseMemoryManager.hpp | 96 | ||||
| -rw-r--r-- | src/backends/aclCommon/CMakeLists.txt | 23 | ||||
| -rw-r--r-- | src/backends/aclCommon/common.cmake | 10 | ||||
| -rw-r--r-- | src/backends/aclCommon/common.mk | 20 | ||||
| -rw-r--r-- | src/backends/aclCommon/test/ArmComputeTensorUtilsTests.cpp | 46 | ||||
| -rw-r--r-- | src/backends/aclCommon/test/CMakeLists.txt | 17 | ||||
| -rw-r--r-- | src/backends/aclCommon/test/CreateWorkloadClNeon.hpp | 111 | ||||
| -rw-r--r-- | src/backends/aclCommon/test/MemCopyTestImpl.hpp | 94 | ||||
| -rw-r--r-- | src/backends/aclCommon/test/MemCopyTests.cpp | 77 |
14 files changed, 0 insertions, 1294 deletions
diff --git a/src/backends/aclCommon/ArmComputeTensorHandle.hpp b/src/backends/aclCommon/ArmComputeTensorHandle.hpp deleted file mode 100644 index ae7df2ec95..0000000000 --- a/src/backends/aclCommon/ArmComputeTensorHandle.hpp +++ /dev/null @@ -1,25 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include <armnn/backends/ITensorHandle.hpp> - -#include <arm_compute/runtime/IMemoryGroup.h> -#include <arm_compute/runtime/Tensor.h> - -namespace armnn -{ - -class IAclTensorHandle : public ITensorHandle -{ -public: - virtual arm_compute::ITensor& GetTensor() = 0; - virtual arm_compute::ITensor const& GetTensor() const = 0; - virtual arm_compute::DataType GetDataType() const = 0; - virtual void SetMemoryGroup(const std::shared_ptr<arm_compute::IMemoryGroup>& memoryGroup) = 0; -}; - -} //namespace armnn
\ No newline at end of file diff --git a/src/backends/aclCommon/ArmComputeTensorUtils.cpp b/src/backends/aclCommon/ArmComputeTensorUtils.cpp deleted file mode 100644 index 49fef5bf17..0000000000 --- a/src/backends/aclCommon/ArmComputeTensorUtils.cpp +++ /dev/null @@ -1,247 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// -#include <aclCommon/ArmComputeTensorUtils.hpp> -#include <aclCommon/ArmComputeUtils.hpp> - -#include "armnn/Exceptions.hpp" -#include <armnn/Descriptors.hpp> - -namespace armnn -{ -namespace armcomputetensorutils -{ - -arm_compute::DataType GetArmComputeDataType(armnn::DataType dataType, bool multiScales) -{ - switch(dataType) - { - case armnn::DataType::Boolean: - return arm_compute::DataType::U8; - case armnn::DataType::Float16: - return arm_compute::DataType::F16; - case armnn::DataType::Float32: - return arm_compute::DataType::F32; - case armnn::DataType::QAsymmS8: - return arm_compute::DataType::QASYMM8_SIGNED; - case armnn::DataType::QAsymmU8: - return arm_compute::DataType::QASYMM8; - case armnn::DataType::QSymmS16: - return arm_compute::DataType::QSYMM16; - case armnn::DataType::QSymmS8: - { - return multiScales ? arm_compute::DataType::QSYMM8_PER_CHANNEL : arm_compute::DataType::QSYMM8; - } - ARMNN_NO_DEPRECATE_WARN_BEGIN - case armnn::DataType::QuantizedSymm8PerAxis: - return arm_compute::DataType::QSYMM8_PER_CHANNEL; - ARMNN_NO_DEPRECATE_WARN_END - case armnn::DataType::Signed32: - return arm_compute::DataType::S32; - default: - BOOST_ASSERT_MSG(false, "Unknown data type"); - return arm_compute::DataType::UNKNOWN; - } -} - -arm_compute::Coordinates BuildArmComputeReductionCoordinates(size_t inputDimensions, - unsigned int originalInputRank, - const std::vector<unsigned int>& 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; - - // armnn tensors are (batch, channels, height, width). - // arm_compute tensors are (width, height, channels, batch). - for (unsigned int i = 0; i < tensorShape.GetNumDimensions(); i++) - { - // Note that our dimensions are stored in the opposite order to ACL's. - shape.set(tensorShape.GetNumDimensions() - i - 1, tensorShape[i], false); - - // TensorShape::set() flattens leading ones, so that batch size 1 cannot happen. - // arm_compute tensors expect this. - } - - // prevent arm_compute issue where tensor is flattened to nothing - if (shape.num_dimensions() == 0) - { - shape.set_num_dimensions(1); - } - - return shape; -} - -// Utility function used to build a TensorInfo object, that can be used to initialise -// ARM Compute Tensor and CLTensor allocators. -arm_compute::TensorInfo BuildArmComputeTensorInfo(const armnn::TensorInfo& tensorInfo) -{ - bool multiScales = tensorInfo.HasMultipleQuantizationScales(); - const arm_compute::TensorShape aclTensorShape = BuildArmComputeTensorShape(tensorInfo.GetShape()); - const arm_compute::DataType aclDataType = GetArmComputeDataType(tensorInfo.GetDataType(), multiScales); - - const arm_compute::QuantizationInfo aclQuantizationInfo = multiScales ? - arm_compute::QuantizationInfo(tensorInfo.GetQuantizationScales()) : - arm_compute::QuantizationInfo(tensorInfo.GetQuantizationScale(), tensorInfo.GetQuantizationOffset()); - - return arm_compute::TensorInfo(aclTensorShape, 1, aclDataType, aclQuantizationInfo); -} - -arm_compute::TensorInfo BuildArmComputeTensorInfo(const armnn::TensorInfo& tensorInfo, - armnn::DataLayout dataLayout) -{ - arm_compute::TensorInfo aclTensorInfo = BuildArmComputeTensorInfo(tensorInfo); - aclTensorInfo.set_data_layout(ConvertDataLayout(dataLayout)); - - return aclTensorInfo; -} - -arm_compute::DataLayout ConvertDataLayout(armnn::DataLayout dataLayout) -{ - switch(dataLayout) - { - case armnn::DataLayout::NHWC : return arm_compute::DataLayout::NHWC; - - case armnn::DataLayout::NCHW : return arm_compute::DataLayout::NCHW; - - default: throw InvalidArgumentException("Unknown armnn::DataLayout: [" + - std::to_string(static_cast<int>(dataLayout)) + "]"); - } -} - -arm_compute::PoolingLayerInfo BuildArmComputePoolingLayerInfo(const Pooling2dDescriptor& descriptor, - bool fpMixedPrecision) -{ - using arm_compute::PoolingType; - using arm_compute::DimensionRoundingType; - using arm_compute::PadStrideInfo; - using arm_compute::PoolingLayerInfo; - using arm_compute::Size2D; - using arm_compute::DataLayout; - - // Resolve ARM Compute layer parameters. - const PoolingType poolingType = ConvertPoolingAlgorithmToAclPoolingType(descriptor.m_PoolType); - - const DataLayout dataLayout = ConvertDataLayout(descriptor.m_DataLayout); - - bool isGlobalPooling = (descriptor.m_StrideX==0 && descriptor.m_StrideY==0); - //use specific constructor if global pooling - if(isGlobalPooling) - { - return arm_compute::PoolingLayerInfo(poolingType, dataLayout); - } - - const DimensionRoundingType rounding = ConvertOutputShapeRoundingToAclDimensionRoundingType( - descriptor.m_OutputShapeRounding); - const PadStrideInfo padStrideInfo(descriptor.m_StrideX, - descriptor.m_StrideY, - descriptor.m_PadLeft, - descriptor.m_PadRight, - descriptor.m_PadTop, - descriptor.m_PadBottom, - rounding); - - const bool excludePadding = (descriptor.m_PaddingMethod == PaddingMethod::Exclude); - - const Size2D poolSize(descriptor.m_PoolWidth, descriptor.m_PoolHeight); - - return arm_compute::PoolingLayerInfo(poolingType, poolSize, dataLayout, padStrideInfo, excludePadding, - fpMixedPrecision); -} - -arm_compute::NormalizationLayerInfo BuildArmComputeNormalizationLayerInfo(const NormalizationDescriptor& descriptor) -{ - const arm_compute::NormType normType = - ConvertNormalizationAlgorithmChannelToAclNormType(descriptor.m_NormChannelType); - return arm_compute::NormalizationLayerInfo(normType, - descriptor.m_NormSize, - descriptor.m_Alpha, - descriptor.m_Beta, - descriptor.m_K, - false); -} - -arm_compute::PermutationVector BuildArmComputePermutationVector(const armnn::PermutationVector& perm) -{ - arm_compute::PermutationVector aclPerm; - - unsigned int start = 0; - while ((start < perm.GetSize()) && (start == perm[start])) - { - ++start; - } - - for (unsigned int i = start; i < perm.GetSize(); ++i) - { - aclPerm.set(i - start, perm[i] - start); - } - - return aclPerm; -} - -arm_compute::Size2D BuildArmComputeSize2D(const unsigned int width, const unsigned int height) -{ - return arm_compute::Size2D(width, height); -} - -arm_compute::PixelValue GetPixelValue(arm_compute::ITensor& input, float pixelValue) -{ - switch (input.info()->data_type()) - { - case arm_compute::DataType::F16: - return arm_compute::PixelValue(static_cast<Half>(pixelValue)); - case arm_compute::DataType::F32: - return arm_compute::PixelValue(pixelValue); - case arm_compute::DataType::QASYMM8: - return arm_compute::PixelValue(static_cast<uint8_t>(pixelValue)); - case arm_compute::DataType::QSYMM16: - return arm_compute::PixelValue(static_cast<int16_t>(pixelValue)); - case arm_compute::DataType::QSYMM8_PER_CHANNEL: - return arm_compute::PixelValue(static_cast<int8_t>(pixelValue)); - default: - throw InvalidArgumentException("Unsupported DataType: [" + - std::to_string(static_cast<int>(input.info()->data_type())) + "]"); - } -} - -} // namespace armcomputetensorutils -} // namespace armnn diff --git a/src/backends/aclCommon/ArmComputeTensorUtils.hpp b/src/backends/aclCommon/ArmComputeTensorUtils.hpp deleted file mode 100644 index b4ff0f72ff..0000000000 --- a/src/backends/aclCommon/ArmComputeTensorUtils.hpp +++ /dev/null @@ -1,247 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// -#pragma once - -#include <armnn/Tensor.hpp> -#include <armnn/DescriptorsFwd.hpp> - -#include <arm_compute/core/ITensor.h> -#include <arm_compute/core/TensorInfo.h> -#include <arm_compute/core/Types.h> -#include <arm_compute/core/Size2D.h> - -#include <Half.hpp> - -#include <boost/cast.hpp> - -namespace armnn -{ -class ITensorHandle; - -namespace armcomputetensorutils -{ - -/// Utility function to map an armnn::DataType to corresponding arm_compute::DataType. -arm_compute::DataType GetArmComputeDataType(armnn::DataType dataType, bool multiScales); - -/// 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<unsigned int>& armnnAxes); - -/// Utility function used to setup an arm_compute::TensorShape object from an armnn::TensorShape. -arm_compute::TensorShape BuildArmComputeTensorShape(const armnn::TensorShape& tensorShape); - -/// Utility function used to setup an arm_compute::ITensorInfo object whose dimensions are based on the given -/// armnn::ITensorInfo. -arm_compute::TensorInfo BuildArmComputeTensorInfo(const armnn::TensorInfo& tensorInfo); - -/// Utility function used to setup an arm_compute::ITensorInfo object whose dimensions are based on the given -/// armnn::ITensorInfo. -/// armnn::DataLayout. -arm_compute::TensorInfo BuildArmComputeTensorInfo(const armnn::TensorInfo& tensorInfo, - armnn::DataLayout dataLayout); - -/// Utility function used to convert armnn::DataLayout to arm_compute::DataLayout -/// armnn::DataLayout. -arm_compute::DataLayout ConvertDataLayout(armnn::DataLayout dataLayout); - -/// Utility function used to setup an arm_compute::PoolingLayerInfo object from given -/// armnn::Pooling2dDescriptor -/// bool fpMixedPrecision -arm_compute::PoolingLayerInfo BuildArmComputePoolingLayerInfo(const Pooling2dDescriptor& descriptor, - bool fpMixedPrecision = false); - -/// Utility function to setup an arm_compute::NormalizationLayerInfo object from an armnn::NormalizationDescriptor. -arm_compute::NormalizationLayerInfo BuildArmComputeNormalizationLayerInfo(const NormalizationDescriptor& desc); - -/// Utility function used to setup an arm_compute::PermutationVector object from an armnn::PermutationVector. -arm_compute::PermutationVector BuildArmComputePermutationVector(const armnn::PermutationVector& vector); - -/// Utility function used to setup an arm_compute::Size2D object from width and height values. -arm_compute::Size2D BuildArmComputeSize2D(const unsigned int width, const unsigned int height); - -/// Gets the appropriate PixelValue for the input DataType -arm_compute::PixelValue GetPixelValue(arm_compute::ITensor& input, float pixelValue); - -/// Utility function used to setup an arm_compute::PadStrideInfo object from an armnn layer descriptor. -template <typename Descriptor> -arm_compute::PadStrideInfo BuildArmComputePadStrideInfo(const Descriptor &descriptor) -{ - return arm_compute::PadStrideInfo(descriptor.m_StrideX, - descriptor.m_StrideY, - descriptor.m_PadLeft, - descriptor.m_PadRight, - descriptor.m_PadTop, - descriptor.m_PadBottom, - arm_compute::DimensionRoundingType::FLOOR); -} - -/// Sets up the given ArmCompute tensor's dimensions based on the given ArmNN tensor. -template <typename Tensor> -void BuildArmComputeTensor(Tensor& tensor, const armnn::TensorInfo& tensorInfo) -{ - tensor.allocator()->init(BuildArmComputeTensorInfo(tensorInfo)); -} - -/// Sets up the given ArmCompute tensor's dimensions based on the given ArmNN tensor. -template <typename Tensor> -void BuildArmComputeTensor(Tensor& tensor, const armnn::TensorInfo& tensorInfo, DataLayout dataLayout) -{ - tensor.allocator()->init(BuildArmComputeTensorInfo(tensorInfo, dataLayout)); -} - -template <typename Tensor> -void InitialiseArmComputeTensorEmpty(Tensor& tensor) -{ - tensor.allocator()->allocate(); -} - -/// Utility function to free unused tensors after a workload is configured and prepared -template <typename Tensor> -void FreeTensorIfUnused(std::unique_ptr<Tensor>& tensor) -{ - if (tensor && !tensor->is_used()) - { - tensor.reset(nullptr); - } -} - -// Helper function to obtain byte offset into tensor data -inline size_t GetTensorOffset(const arm_compute::ITensorInfo& info, - uint32_t depthIndex, - uint32_t batchIndex, - uint32_t channelIndex, - uint32_t y, - uint32_t x) -{ - arm_compute::Coordinates coords; - coords.set(4, static_cast<int>(depthIndex)); - coords.set(3, static_cast<int>(batchIndex)); - coords.set(2, static_cast<int>(channelIndex)); - coords.set(1, static_cast<int>(y)); - coords.set(0, static_cast<int>(x)); - return boost::numeric_cast<size_t>(info.offset_element_in_bytes(coords)); -} - -// Helper function to obtain element offset into data buffer representing tensor data (assuming no strides). -inline size_t GetLinearBufferOffset(const arm_compute::ITensorInfo& info, - uint32_t depthIndex, - uint32_t batchIndex, - uint32_t channelIndex, - uint32_t y, - uint32_t x) -{ - const arm_compute::TensorShape& shape = info.tensor_shape(); - uint32_t width = static_cast<uint32_t>(shape[0]); - uint32_t height = static_cast<uint32_t>(shape[1]); - uint32_t numChannels = static_cast<uint32_t>(shape[2]); - uint32_t numBatches = static_cast<uint32_t>(shape[3]); - return (((depthIndex * numBatches + batchIndex) * numChannels + channelIndex) * height + y) * width + x; -} - -template <typename T> -void CopyArmComputeITensorData(const arm_compute::ITensor& srcTensor, T* dstData) -{ - // If MaxNumOfTensorDimensions is increased, this loop will need fixing. - static_assert(MaxNumOfTensorDimensions == 5, "Please update CopyArmComputeITensorData"); - { - const arm_compute::ITensorInfo& info = *srcTensor.info(); - const arm_compute::TensorShape& shape = info.tensor_shape(); - const uint8_t* const bufferPtr = srcTensor.buffer(); - uint32_t width = static_cast<uint32_t>(shape[0]); - uint32_t height = static_cast<uint32_t>(shape[1]); - uint32_t numChannels = static_cast<uint32_t>(shape[2]); - uint32_t numBatches = static_cast<uint32_t>(shape[3]); - uint32_t depth = static_cast<uint32_t>(shape[4]); - - for (unsigned int depthIndex = 0; depthIndex < depth; ++depthIndex) - { - for (unsigned int batchIndex = 0; batchIndex < numBatches; ++batchIndex) - { - for (unsigned int channelIndex = 0; channelIndex < numChannels; ++channelIndex) - { - for (unsigned int y = 0; y < height; ++y) - { - // Copies one row from arm_compute tensor buffer to linear memory buffer. - // A row is the largest contiguous region we can copy, as the tensor data may be using strides. - memcpy( - dstData + GetLinearBufferOffset(info, depthIndex, batchIndex, channelIndex, y, 0), - bufferPtr + GetTensorOffset(info, depthIndex, batchIndex, channelIndex, y, 0), - width * sizeof(T)); - } - } - } - } - } -} - -template <typename T> -void CopyArmComputeITensorData(const T* srcData, arm_compute::ITensor& dstTensor) -{ - // If MaxNumOfTensorDimensions is increased, this loop will need fixing. - static_assert(MaxNumOfTensorDimensions == 5, "Please update CopyArmComputeITensorData"); - { - const arm_compute::ITensorInfo& info = *dstTensor.info(); - const arm_compute::TensorShape& shape = info.tensor_shape(); - uint8_t* const bufferPtr = dstTensor.buffer(); - uint32_t width = static_cast<uint32_t>(shape[0]); - uint32_t height = static_cast<uint32_t>(shape[1]); - uint32_t numChannels = static_cast<uint32_t>(shape[2]); - uint32_t numBatches = static_cast<uint32_t>(shape[3]); - uint32_t depth = static_cast<uint32_t>(shape[4]); - - for (unsigned int depthIndex = 0; depthIndex < depth; ++depthIndex) - { - for (unsigned int batchIndex = 0; batchIndex < numBatches; ++batchIndex) - { - for (unsigned int channelIndex = 0; channelIndex < numChannels; ++channelIndex) - { - for (unsigned int y = 0; y < height; ++y) - { - // Copies one row from linear memory buffer to arm_compute tensor buffer. - // A row is the largest contiguous region we can copy, as the tensor data may be using strides. - memcpy( - bufferPtr + GetTensorOffset(info, depthIndex, batchIndex, channelIndex, y, 0), - srcData + GetLinearBufferOffset(info, depthIndex, batchIndex, channelIndex, y, 0), - width * sizeof(T)); - } - } - } - } - } -} - -/// Construct a TensorShape object from an ArmCompute object based on arm_compute::Dimensions. -/// \tparam ArmComputeType Any type that implements the Dimensions interface -/// \tparam T Shape value type -/// \param shapelike An ArmCompute object that implements the Dimensions interface -/// \param initial A default value to initialise the shape with -/// \return A TensorShape object filled from the Acl shapelike object. -template<typename ArmComputeType, typename T> -TensorShape GetTensorShape(const ArmComputeType& shapelike, T initial) -{ - std::vector<unsigned int> s(MaxNumOfTensorDimensions, initial); - for (unsigned int i=0; i < shapelike.num_dimensions(); ++i) - { - s[(shapelike.num_dimensions()-1)-i] = boost::numeric_cast<unsigned int>(shapelike[i]); - } - return TensorShape(boost::numeric_cast<unsigned int>(shapelike.num_dimensions()), s.data()); -}; - -/// Get the strides from an ACL strides object -inline TensorShape GetStrides(const arm_compute::Strides& strides) -{ - return GetTensorShape(strides, 0U); -} - -/// Get the shape from an ACL shape object -inline TensorShape GetShape(const arm_compute::TensorShape& shape) -{ - return GetTensorShape(shape, 1U); -} - -} // namespace armcomputetensorutils -} // namespace armnn diff --git a/src/backends/aclCommon/ArmComputeUtils.hpp b/src/backends/aclCommon/ArmComputeUtils.hpp deleted file mode 100644 index 4d690901c6..0000000000 --- a/src/backends/aclCommon/ArmComputeUtils.hpp +++ /dev/null @@ -1,173 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// -#pragma once - -#include <armnn/Descriptors.hpp> -#include <armnn/Tensor.hpp> - -#include <arm_compute/core/Types.h> - -#include <boost/assert.hpp> - -namespace armnn -{ - -inline arm_compute::NormalizationLayerInfo -CreateAclNormalizationLayerInfoForL2Normalization(const armnn::TensorInfo& tensorInfo, - armnn::DataLayout dataLayout) -{ - unsigned int depthDimension = dataLayout == armnn::DataLayout::NCHW ? 1 : 3; - const unsigned int depth = tensorInfo.GetShape()[depthDimension]; - - // At the time of writing, {CL|Neon}L2Normalization performs the reduction only along dimension 0. This version of - // L2 Normalization always performs the reduction along the depth axis, though. Thus, we repurpose - // {CL|Neon}NormalizationLayers to act as depthwise L2 normalizations by carefully chosing the normalization - // parameters. - // - // Please refer to both the reference implementation of the normalization layer and the implementation of - // {CL|Neon}NormalizationLayer when checking the derivations for the parameter values below. - - // Make sure normalization covers the entire depth range. ACL requires the normalization size to be odd. - // CL: This does not result in extra kernel threads not doing any work: See usage of the RADIUS parameter in - // ACL's normalization_layer_cross_map() CL function. - const uint32_t normSize = depth * 2u + 1u; - - // See ACL's NormalizationLayerInfo::scale_coeff() definition. - // For the reference implementation, to make alpha_ become 1, we'd have to use alpha = normSize instead. - const float alpha = 1.0f; - - // Don't offset the reduction. - const float kappa = 0.0f; - - // pow(reduction, -0.5) = 1 / sqrt(reduction) - const float beta = 0.5f; - - return arm_compute::NormalizationLayerInfo(arm_compute::NormType::CROSS_MAP, normSize, alpha, beta, kappa, false); -} - -inline arm_compute::ActivationLayerInfo::ActivationFunction -ConvertActivationFunctionToAclActivationFunction(ActivationFunction armnnFunction) -{ - using AclActivationFunction = arm_compute::ActivationLayerInfo::ActivationFunction; - - switch (armnnFunction) - { - case ActivationFunction::Linear: return AclActivationFunction::LINEAR; - // Arm compute's 'logistic' function is non-parameterized, so it is exactly a sigmoid function. - case ActivationFunction::Sigmoid: return AclActivationFunction::LOGISTIC; - case ActivationFunction::ReLu: return AclActivationFunction::RELU; - case ActivationFunction::BoundedReLu: return AclActivationFunction::LU_BOUNDED_RELU; - case ActivationFunction::SoftReLu: return AclActivationFunction::SOFT_RELU; - case ActivationFunction::LeakyReLu: return AclActivationFunction::LEAKY_RELU; - case ActivationFunction::Abs: return AclActivationFunction::ABS; - case ActivationFunction::Sqrt: return AclActivationFunction::SQRT; - case ActivationFunction::Square: return AclActivationFunction::SQUARE; - case ActivationFunction::TanH: return AclActivationFunction::TANH; - default: throw InvalidArgumentException("Unsupported activation function"); - } -} - -inline arm_compute::ActivationLayerInfo -ConvertActivationDescriptorToAclActivationLayerInfo(const ActivationDescriptor& actDesc) -{ - return arm_compute::ActivationLayerInfo(ConvertActivationFunctionToAclActivationFunction(actDesc.m_Function), - actDesc.m_A, actDesc.m_B); -} - -inline arm_compute::PoolingType ConvertPoolingAlgorithmToAclPoolingType(PoolingAlgorithm poolingAlgorithm) -{ - using arm_compute::PoolingType; - - switch (poolingAlgorithm) - { - case PoolingAlgorithm::Max: return PoolingType::MAX; - case PoolingAlgorithm::Average: return PoolingType::AVG; - case PoolingAlgorithm::L2: return PoolingType::L2; - default: throw InvalidArgumentException("Unsupported pooling algorithm"); - } -} - -inline arm_compute::DimensionRoundingType ConvertOutputShapeRoundingToAclDimensionRoundingType(OutputShapeRounding - rounding) -{ - using arm_compute::DimensionRoundingType; - - switch (rounding) - { - case OutputShapeRounding::Ceiling: return DimensionRoundingType::CEIL; - case OutputShapeRounding::Floor: return DimensionRoundingType::FLOOR; - default: throw InvalidArgumentException("Unsupported Output Shape Rounding type"); - } -} - -inline arm_compute::NormType -ConvertNormalizationAlgorithmChannelToAclNormType(NormalizationAlgorithmChannel channelType) -{ - using arm_compute::NormType; - switch (channelType) - { - case NormalizationAlgorithmChannel::Across: return NormType::CROSS_MAP; - case NormalizationAlgorithmChannel::Within: return NormType::IN_MAP_2D; - default: throw InvalidArgumentException("Unsupported normalization algorithm channel type"); - } -} - -inline arm_compute::FullyConnectedLayerInfo -ConvertFullyConnectedDescriptorToAclFullyConnectedLayerInfo(const FullyConnectedDescriptor& fullyConnectedDesc) -{ - arm_compute::FullyConnectedLayerInfo fc_info; - fc_info.transpose_weights = fullyConnectedDesc.m_TransposeWeightMatrix; - return fc_info; -} - -inline arm_compute::InterpolationPolicy ConvertResizeMethodToAclInterpolationPolicy(ResizeMethod resizeMethod) -{ - switch (resizeMethod) - { - case ResizeMethod::Bilinear: - return arm_compute::InterpolationPolicy::BILINEAR; - case ResizeMethod::NearestNeighbor: - return arm_compute::InterpolationPolicy::NEAREST_NEIGHBOR; - default: - throw InvalidArgumentException("Unsupported resize method"); - } -} - -inline unsigned int ComputeSoftmaxAclAxis(const SoftmaxDescriptor& softmaxDesc, const armnn::TensorInfo& tensor) -{ - // Detect the Android default value of -1 and return the ACL default value of 1. - if (softmaxDesc.m_Axis == -1) - { - return 1; - } - - unsigned int dim = tensor.GetNumDimensions(); - - BOOST_ASSERT(dim != 0); - - // Currently ArmNN support axis 1. - return dim - 1; -} - -inline std::set<unsigned int> ComputeSplitAxis(const armnn::SplitterDescriptor& desc, const TensorShape& input) -{ - unsigned int numSplit = desc.GetNumViews(); - unsigned int numDimensions = desc.GetNumDimensions(); - std::set<unsigned int> splitAxis; - - for (unsigned int i = 0; i < numSplit; ++i) - { - for (unsigned int dimIdx = 0; dimIdx < numDimensions; ++dimIdx) - { - if (desc.GetViewSizes(i)[dimIdx] != input[dimIdx]) - { - splitAxis.insert(dimIdx); - } - } - } - return splitAxis; -} - -} // namespace armnn diff --git a/src/backends/aclCommon/BaseMemoryManager.cpp b/src/backends/aclCommon/BaseMemoryManager.cpp deleted file mode 100644 index 844fbcd4ca..0000000000 --- a/src/backends/aclCommon/BaseMemoryManager.cpp +++ /dev/null @@ -1,108 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// -#include "BaseMemoryManager.hpp" - -#if defined(ARMCOMPUTENEON_ENABLED) || defined(ARMCOMPUTECL_ENABLED) -#include "arm_compute/runtime/BlobLifetimeManager.h" -#include "arm_compute/runtime/PoolManager.h" -#include "arm_compute/runtime/OffsetLifetimeManager.h" -#endif - -#include <boost/polymorphic_cast.hpp> - -namespace armnn -{ - -#if defined(ARMCOMPUTENEON_ENABLED) || defined(ARMCOMPUTECL_ENABLED) -BaseMemoryManager::BaseMemoryManager(std::unique_ptr<arm_compute::IAllocator> alloc, - MemoryAffinity memoryAffinity) -{ - BOOST_ASSERT(alloc); - m_Allocator = std::move(alloc); - - m_IntraLayerMemoryMgr = CreateArmComputeMemoryManager(memoryAffinity); - m_InterLayerMemoryMgr = CreateArmComputeMemoryManager(memoryAffinity); -} - -std::shared_ptr<arm_compute::MemoryManagerOnDemand> -BaseMemoryManager::CreateArmComputeMemoryManager(MemoryAffinity memoryAffinity) -{ - std::shared_ptr<arm_compute::ILifetimeManager> lifetimeManager = nullptr; - - if (memoryAffinity == MemoryAffinity::Buffer) - { - lifetimeManager = std::make_shared<arm_compute::BlobLifetimeManager>(); - } - else - { - lifetimeManager = std::make_shared<arm_compute::OffsetLifetimeManager>(); - } - - auto poolManager = std::make_shared<arm_compute::PoolManager>(); - auto memoryManager = std::make_shared<arm_compute::MemoryManagerOnDemand>(lifetimeManager, poolManager); - - return memoryManager; -} - -void BaseMemoryManager::Acquire() -{ - static const size_t s_NumPools = 1; - - // Allocate memory pools for intra-layer memory manager - BOOST_ASSERT(m_IntraLayerMemoryMgr); - m_IntraLayerMemoryMgr->populate(*m_Allocator, s_NumPools); - - // Allocate memory pools for inter-layer memory manager - BOOST_ASSERT(m_InterLayerMemoryMgr); - m_InterLayerMemoryMgr->populate(*m_Allocator, s_NumPools); - - // Acquire inter-layer memory group. NOTE: This has to come after allocating the pools - BOOST_ASSERT(m_InterLayerMemoryGroup); - m_InterLayerMemoryGroup->acquire(); -} - -void BaseMemoryManager::Release() -{ - // Release inter-layer memory group. NOTE: This has to come before releasing the pools - BOOST_ASSERT(m_InterLayerMemoryGroup); - m_InterLayerMemoryGroup->release(); - - // Release memory pools managed by intra-layer memory manager - BOOST_ASSERT(m_IntraLayerMemoryMgr); - m_IntraLayerMemoryMgr->clear(); - - // Release memory pools managed by inter-layer memory manager - BOOST_ASSERT(m_InterLayerMemoryMgr); - m_InterLayerMemoryMgr->clear(); -} -#else -void BaseMemoryManager::Acquire() -{ - // No-op if neither NEON nor CL enabled -} - -void BaseMemoryManager::Release() -{ - // No-op if neither NEON nor CL enabled -} -#endif - -#if defined(ARMCOMPUTENEON_ENABLED) -std::shared_ptr<arm_compute::IMemoryGroup> -NeonMemoryManager::CreateMemoryGroup(const std::shared_ptr<arm_compute::MemoryManagerOnDemand>& memoryManager) -{ - return std::make_shared<arm_compute::MemoryGroup>(memoryManager); -} -#endif - -#if defined(ARMCOMPUTECL_ENABLED) -std::shared_ptr<arm_compute::IMemoryGroup> -ClMemoryManager::CreateMemoryGroup(const std::shared_ptr<arm_compute::MemoryManagerOnDemand>& memoryManager) -{ - return std::make_shared<arm_compute::MemoryGroup>(memoryManager); -} -#endif - -} diff --git a/src/backends/aclCommon/BaseMemoryManager.hpp b/src/backends/aclCommon/BaseMemoryManager.hpp deleted file mode 100644 index 9d2dbf7838..0000000000 --- a/src/backends/aclCommon/BaseMemoryManager.hpp +++ /dev/null @@ -1,96 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// -#pragma once - -#include <armnn/backends/IMemoryManager.hpp> -#include <backendsCommon/WorkloadFactory.hpp> - -#if defined(ARMCOMPUTENEON_ENABLED) || defined(ARMCOMPUTECL_ENABLED) -#include <arm_compute/runtime/MemoryGroup.h> -#endif - -#if defined(ARMCOMPUTENEON_ENABLED) || defined(ARMCOMPUTECL_ENABLED) -#include <arm_compute/runtime/IAllocator.h> -#include <arm_compute/runtime/IMemoryGroup.h> -#include <arm_compute/runtime/MemoryManagerOnDemand.h> -#endif - -namespace armnn -{ - -class BaseMemoryManager : public IMemoryManager -{ -public: - enum class MemoryAffinity - { - Buffer, - Offset - }; - - BaseMemoryManager() { } - virtual ~BaseMemoryManager() { } - - void Acquire() override; - void Release() override; - -#if defined(ARMCOMPUTENEON_ENABLED) || defined(ARMCOMPUTECL_ENABLED) - BaseMemoryManager(std::unique_ptr<arm_compute::IAllocator> alloc, MemoryAffinity memoryAffinity); - - std::shared_ptr<arm_compute::MemoryManagerOnDemand>& GetIntraLayerManager() { return m_IntraLayerMemoryMgr; } - std::shared_ptr<arm_compute::MemoryManagerOnDemand>& GetInterLayerManager() { return m_InterLayerMemoryMgr; } - std::shared_ptr<arm_compute::IMemoryGroup>& GetInterLayerMemoryGroup() { return m_InterLayerMemoryGroup; } - -protected: - std::unique_ptr<arm_compute::IAllocator> m_Allocator; - std::shared_ptr<arm_compute::MemoryManagerOnDemand> m_IntraLayerMemoryMgr; - std::shared_ptr<arm_compute::MemoryManagerOnDemand> m_InterLayerMemoryMgr; - std::shared_ptr<arm_compute::IMemoryGroup> m_InterLayerMemoryGroup; - - std::shared_ptr<arm_compute::MemoryManagerOnDemand> CreateArmComputeMemoryManager(MemoryAffinity memoryAffinity); - - virtual std::shared_ptr<arm_compute::IMemoryGroup> - CreateMemoryGroup(const std::shared_ptr<arm_compute::MemoryManagerOnDemand>& memoryManager) = 0; -#endif -}; - -#if defined(ARMCOMPUTENEON_ENABLED) -class NeonMemoryManager : public BaseMemoryManager -{ -public: - NeonMemoryManager() {} - virtual ~NeonMemoryManager() {} - - NeonMemoryManager(std::unique_ptr<arm_compute::IAllocator> alloc, MemoryAffinity memoryAffinity) - : BaseMemoryManager(std::move(alloc), memoryAffinity) - { - m_InterLayerMemoryGroup = CreateMemoryGroup(m_InterLayerMemoryMgr); - } - -protected: - std::shared_ptr<arm_compute::IMemoryGroup> - CreateMemoryGroup(const std::shared_ptr<arm_compute::MemoryManagerOnDemand>& memoryManager) override; -}; -#endif - -#if defined(ARMCOMPUTECL_ENABLED) -class ClMemoryManager : public BaseMemoryManager -{ -public: - ClMemoryManager() {} - virtual ~ClMemoryManager() {} - - ClMemoryManager(std::unique_ptr<arm_compute::IAllocator> alloc) - : BaseMemoryManager(std::move(alloc), MemoryAffinity::Buffer) - { - m_InterLayerMemoryGroup = CreateMemoryGroup(m_InterLayerMemoryMgr); - } - -protected: - std::shared_ptr<arm_compute::IMemoryGroup> - CreateMemoryGroup(const std::shared_ptr<arm_compute::MemoryManagerOnDemand>& memoryManager) override; -}; -#endif - -} //namespace armnn diff --git a/src/backends/aclCommon/CMakeLists.txt b/src/backends/aclCommon/CMakeLists.txt deleted file mode 100644 index d4ff541985..0000000000 --- a/src/backends/aclCommon/CMakeLists.txt +++ /dev/null @@ -1,23 +0,0 @@ -# -# Copyright © 2017 Arm Ltd. All rights reserved. -# SPDX-License-Identifier: MIT -# - -list(APPEND armnnAclCommon_sources - ArmComputeTensorHandle.hpp - ArmComputeTensorUtils.hpp - ArmComputeTensorUtils.cpp - ArmComputeUtils.hpp - BaseMemoryManager.cpp - BaseMemoryManager.hpp -) - -if(BUILD_UNIT_TESTS) - add_subdirectory(test) -endif() - -add_library(armnnAclCommon OBJECT ${armnnAclCommon_sources}) -target_include_directories(armnnAclCommon PRIVATE ${PROJECT_SOURCE_DIR}/src/armnn) -target_include_directories(armnnAclCommon PRIVATE ${PROJECT_SOURCE_DIR}/src/armnnUtils) -target_include_directories(armnnAclCommon PRIVATE ${PROJECT_SOURCE_DIR}/src/backends) -target_include_directories(armnnAclCommon PRIVATE ${PROJECT_SOURCE_DIR}/src/profiling) diff --git a/src/backends/aclCommon/common.cmake b/src/backends/aclCommon/common.cmake deleted file mode 100644 index 89be236a7f..0000000000 --- a/src/backends/aclCommon/common.cmake +++ /dev/null @@ -1,10 +0,0 @@ -# -# Copyright © 2017 Arm Ltd. All rights reserved. -# SPDX-License-Identifier: MIT -# - -if(ARMCOMPUTENEON OR ARMCOMPUTECL) - add_subdirectory(${PROJECT_SOURCE_DIR}/src/backends/aclCommon) - list(APPEND armnnLibraries armnnAclCommon) - list(APPEND armnnUnitTestLibraries armnnAclCommonUnitTests) -endif() diff --git a/src/backends/aclCommon/common.mk b/src/backends/aclCommon/common.mk deleted file mode 100644 index 0ba966af14..0000000000 --- a/src/backends/aclCommon/common.mk +++ /dev/null @@ -1,20 +0,0 @@ -# -# Copyright © 2017 ARM Ltd. All rights reserved. -# SPDX-License-Identifier: MIT -# - -# COMMON_SOURCES contains the list of files to be included -# in the Android build and it is picked up by the Android.mk -# file in the root of ArmNN - -COMMON_SOURCES := \ - ArmComputeTensorUtils.cpp \ - BaseMemoryManager.cpp - -# COMMON_TEST_SOURCES contains the list of files to be included -# in the Android unit test build (armnn-tests) and it is picked -# up by the Android.mk file in the root of ArmNN - -COMMON_TEST_SOURCES := \ - test/ArmComputeTensorUtilsTests.cpp \ - test/MemCopyTests.cpp diff --git a/src/backends/aclCommon/test/ArmComputeTensorUtilsTests.cpp b/src/backends/aclCommon/test/ArmComputeTensorUtilsTests.cpp deleted file mode 100644 index 4ab748806c..0000000000 --- a/src/backends/aclCommon/test/ArmComputeTensorUtilsTests.cpp +++ /dev/null @@ -1,46 +0,0 @@ -// -// Copyright © 2019 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include <aclCommon/ArmComputeTensorUtils.hpp> - -#include <boost/test/unit_test.hpp> - -using namespace armnn::armcomputetensorutils; - -BOOST_AUTO_TEST_SUITE(ArmComputeTensorUtils) - -BOOST_AUTO_TEST_CASE(BuildArmComputeTensorInfoTest) -{ - - const armnn::TensorShape tensorShape = { 1, 2, 3, 4 }; - const armnn::DataType dataType = armnn::DataType::QAsymmU8; - - const std::vector<float> quantScales = { 1.5f, 2.5f, 3.5f, 4.5f }; - const float quantScale = quantScales[0]; - const int32_t quantOffset = 128; - - // Tensor info with per-tensor quantization - const armnn::TensorInfo tensorInfo0(tensorShape, dataType, quantScale, quantOffset); - const arm_compute::TensorInfo aclTensorInfo0 = BuildArmComputeTensorInfo(tensorInfo0); - - const arm_compute::TensorShape& aclTensorShape = aclTensorInfo0.tensor_shape(); - BOOST_CHECK(aclTensorShape.num_dimensions() == tensorShape.GetNumDimensions()); - for(unsigned int i = 0u; i < tensorShape.GetNumDimensions(); ++i) - { - // NOTE: arm_compute tensor dimensions are stored in the opposite order - BOOST_CHECK(aclTensorShape[i] == tensorShape[tensorShape.GetNumDimensions() - i - 1]); - } - - BOOST_CHECK(aclTensorInfo0.data_type() == arm_compute::DataType::QASYMM8); - BOOST_CHECK(aclTensorInfo0.quantization_info().scale()[0] == quantScale); - - // Tensor info with per-axis quantization - const armnn::TensorInfo tensorInfo1(tensorShape, dataType, quantScales, 0); - const arm_compute::TensorInfo aclTensorInfo1 = BuildArmComputeTensorInfo(tensorInfo1); - - BOOST_CHECK(aclTensorInfo1.quantization_info().scale() == quantScales); -} - -BOOST_AUTO_TEST_SUITE_END() diff --git a/src/backends/aclCommon/test/CMakeLists.txt b/src/backends/aclCommon/test/CMakeLists.txt deleted file mode 100644 index 087e56669b..0000000000 --- a/src/backends/aclCommon/test/CMakeLists.txt +++ /dev/null @@ -1,17 +0,0 @@ -# -# Copyright © 2017 Arm Ltd. All rights reserved. -# SPDX-License-Identifier: MIT -# - -list(APPEND armnnAclCommonUnitTests_sources - ArmComputeTensorUtilsTests.cpp - CreateWorkloadClNeon.hpp - MemCopyTests.cpp - MemCopyTestImpl.hpp -) - -add_library(armnnAclCommonUnitTests OBJECT ${armnnAclCommonUnitTests_sources}) -target_include_directories(armnnAclCommonUnitTests PRIVATE ${PROJECT_SOURCE_DIR}/src/armnn) -target_include_directories(armnnAclCommonUnitTests PRIVATE ${PROJECT_SOURCE_DIR}/src/armnnUtils) -target_include_directories(armnnAclCommonUnitTests PRIVATE ${PROJECT_SOURCE_DIR}/src/backends) -target_include_directories(armnnAclCommonUnitTests PRIVATE ${PROJECT_SOURCE_DIR}/src/profiling) diff --git a/src/backends/aclCommon/test/CreateWorkloadClNeon.hpp b/src/backends/aclCommon/test/CreateWorkloadClNeon.hpp deleted file mode 100644 index 83cec2a746..0000000000 --- a/src/backends/aclCommon/test/CreateWorkloadClNeon.hpp +++ /dev/null @@ -1,111 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// -#pragma once - -#include <test/CreateWorkload.hpp> - -#include <backendsCommon/MemCopyWorkload.hpp> -#include <reference/RefWorkloadFactory.hpp> -#include <reference/RefTensorHandle.hpp> - -#if defined(ARMCOMPUTECL_ENABLED) -#include <cl/ClTensorHandle.hpp> -#endif - -#if defined(ARMCOMPUTENEON_ENABLED) -#include <neon/NeonTensorHandle.hpp> -#endif - -using namespace armnn; - -namespace -{ - -using namespace std; - -template<typename IComputeTensorHandle> -boost::test_tools::predicate_result CompareTensorHandleShape(IComputeTensorHandle* tensorHandle, - std::initializer_list<unsigned int> expectedDimensions) -{ - arm_compute::ITensorInfo* info = tensorHandle->GetTensor().info(); - - auto infoNumDims = info->num_dimensions(); - auto numExpectedDims = expectedDimensions.size(); - if (infoNumDims != numExpectedDims) - { - boost::test_tools::predicate_result res(false); - res.message() << "Different number of dimensions [" << info->num_dimensions() - << "!=" << expectedDimensions.size() << "]"; - return res; - } - - size_t i = info->num_dimensions() - 1; - - for (unsigned int expectedDimension : expectedDimensions) - { - if (info->dimension(i) != expectedDimension) - { - boost::test_tools::predicate_result res(false); - res.message() << "For dimension " << i << - " expected size " << expectedDimension << - " got " << info->dimension(i); - return res; - } - - i--; - } - - return true; -} - -template<typename IComputeTensorHandle> -void CreateMemCopyWorkloads(IWorkloadFactory& factory) -{ - TensorHandleFactoryRegistry registry; - Graph graph; - RefWorkloadFactory refFactory; - - // Creates the layers we're testing. - Layer* const layer1 = graph.AddLayer<MemCopyLayer>("layer1"); - Layer* const layer2 = graph.AddLayer<MemCopyLayer>("layer2"); - - // Creates extra layers. - Layer* const input = graph.AddLayer<InputLayer>(0, "input"); - Layer* const output = graph.AddLayer<OutputLayer>(0, "output"); - - // Connects up. - TensorInfo tensorInfo({2, 3}, DataType::Float32); - Connect(input, layer1, tensorInfo); - Connect(layer1, layer2, tensorInfo); - Connect(layer2, output, tensorInfo); - - input->CreateTensorHandles(registry, refFactory); - layer1->CreateTensorHandles(registry, factory); - layer2->CreateTensorHandles(registry, refFactory); - output->CreateTensorHandles(registry, refFactory); - - // make the workloads and check them - auto workload1 = MakeAndCheckWorkload<CopyMemGenericWorkload>(*layer1, factory); - auto workload2 = MakeAndCheckWorkload<CopyMemGenericWorkload>(*layer2, refFactory); - - MemCopyQueueDescriptor queueDescriptor1 = workload1->GetData(); - BOOST_TEST(queueDescriptor1.m_Inputs.size() == 1); - BOOST_TEST(queueDescriptor1.m_Outputs.size() == 1); - auto inputHandle1 = boost::polymorphic_downcast<RefTensorHandle*>(queueDescriptor1.m_Inputs[0]); - auto outputHandle1 = boost::polymorphic_downcast<IComputeTensorHandle*>(queueDescriptor1.m_Outputs[0]); - BOOST_TEST((inputHandle1->GetTensorInfo() == TensorInfo({2, 3}, DataType::Float32))); - BOOST_TEST(CompareTensorHandleShape<IComputeTensorHandle>(outputHandle1, {2, 3})); - - - MemCopyQueueDescriptor queueDescriptor2 = workload2->GetData(); - BOOST_TEST(queueDescriptor2.m_Inputs.size() == 1); - BOOST_TEST(queueDescriptor2.m_Outputs.size() == 1); - auto inputHandle2 = boost::polymorphic_downcast<IComputeTensorHandle*>(queueDescriptor2.m_Inputs[0]); - auto outputHandle2 = boost::polymorphic_downcast<RefTensorHandle*>(queueDescriptor2.m_Outputs[0]); - BOOST_TEST(CompareTensorHandleShape<IComputeTensorHandle>(inputHandle2, {2, 3})); - BOOST_TEST((outputHandle2->GetTensorInfo() == TensorInfo({2, 3}, DataType::Float32))); -} - -} //namespace diff --git a/src/backends/aclCommon/test/MemCopyTestImpl.hpp b/src/backends/aclCommon/test/MemCopyTestImpl.hpp deleted file mode 100644 index 570c6027f3..0000000000 --- a/src/backends/aclCommon/test/MemCopyTestImpl.hpp +++ /dev/null @@ -1,94 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// -#pragma once - -#include <ResolveType.hpp> - -#include <armnn/backends/IBackendInternal.hpp> - -#include <backendsCommon/test/LayerTests.hpp> -#include <backendsCommon/test/TensorCopyUtils.hpp> -#include <backendsCommon/test/WorkloadFactoryHelper.hpp> -#include <backendsCommon/test/WorkloadTestUtils.hpp> - -#include <test/TensorHelpers.hpp> - -#include <boost/multi_array.hpp> - -namespace -{ - -template<armnn::DataType dataType, typename T = armnn::ResolveType<dataType>> -LayerTestResult<T, 4> MemCopyTest(armnn::IWorkloadFactory& srcWorkloadFactory, - armnn::IWorkloadFactory& dstWorkloadFactory, - bool withSubtensors) -{ - const std::array<unsigned int, 4> shapeData = { { 1u, 1u, 6u, 5u } }; - const armnn::TensorShape tensorShape(4, shapeData.data()); - const armnn::TensorInfo tensorInfo(tensorShape, dataType); - boost::multi_array<T, 4> inputData = MakeTensor<T, 4>(tensorInfo, std::vector<T>( - { - 1, 2, 3, 4, 5, - 6, 7, 8, 9, 10, - 11, 12, 13, 14, 15, - 16, 17, 18, 19, 20, - 21, 22, 23, 24, 25, - 26, 27, 28, 29, 30, - }) - ); - - LayerTestResult<T, 4> ret(tensorInfo); - ret.outputExpected = inputData; - - boost::multi_array<T, 4> outputData(shapeData); - - auto inputTensorHandle = srcWorkloadFactory.CreateTensorHandle(tensorInfo); - auto outputTensorHandle = dstWorkloadFactory.CreateTensorHandle(tensorInfo); - - AllocateAndCopyDataToITensorHandle(inputTensorHandle.get(), inputData.data()); - outputTensorHandle->Allocate(); - - armnn::MemCopyQueueDescriptor memCopyQueueDesc; - armnn::WorkloadInfo workloadInfo; - - const unsigned int origin[4] = {}; - - auto workloadInput = (withSubtensors && srcWorkloadFactory.SupportsSubTensors()) - ? srcWorkloadFactory.CreateSubTensorHandle(*inputTensorHandle, tensorShape, origin) - : std::move(inputTensorHandle); - auto workloadOutput = (withSubtensors && dstWorkloadFactory.SupportsSubTensors()) - ? dstWorkloadFactory.CreateSubTensorHandle(*outputTensorHandle, tensorShape, origin) - : std::move(outputTensorHandle); - - AddInputToWorkload(memCopyQueueDesc, workloadInfo, tensorInfo, workloadInput.get()); - AddOutputToWorkload(memCopyQueueDesc, workloadInfo, tensorInfo, workloadOutput.get()); - - dstWorkloadFactory.CreateMemCopy(memCopyQueueDesc, workloadInfo)->Execute(); - - CopyDataFromITensorHandle(outputData.data(), workloadOutput.get()); - ret.output = outputData; - - return ret; -} - -template<typename SrcWorkloadFactory, - typename DstWorkloadFactory, - armnn::DataType dataType, - typename T = armnn::ResolveType<dataType>> -LayerTestResult<T, 4> MemCopyTest(bool withSubtensors) -{ - armnn::IBackendInternal::IMemoryManagerSharedPtr srcMemoryManager = - WorkloadFactoryHelper<SrcWorkloadFactory>::GetMemoryManager(); - - armnn::IBackendInternal::IMemoryManagerSharedPtr dstMemoryManager = - WorkloadFactoryHelper<DstWorkloadFactory>::GetMemoryManager(); - - SrcWorkloadFactory srcWorkloadFactory = WorkloadFactoryHelper<SrcWorkloadFactory>::GetFactory(srcMemoryManager); - DstWorkloadFactory dstWorkloadFactory = WorkloadFactoryHelper<DstWorkloadFactory>::GetFactory(dstMemoryManager); - - return MemCopyTest<dataType>(srcWorkloadFactory, dstWorkloadFactory, withSubtensors); -} - -} // anonymous namespace diff --git a/src/backends/aclCommon/test/MemCopyTests.cpp b/src/backends/aclCommon/test/MemCopyTests.cpp deleted file mode 100644 index 3e26364354..0000000000 --- a/src/backends/aclCommon/test/MemCopyTests.cpp +++ /dev/null @@ -1,77 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include <aclCommon/ArmComputeTensorUtils.hpp> -#include <aclCommon/test/MemCopyTestImpl.hpp> - -#if defined(ARMCOMPUTECL_ENABLED) && defined(ARMCOMPUTENEON_ENABLED) -#include <cl/ClWorkloadFactory.hpp> -#include <cl/test/ClContextControlFixture.hpp> -#include <cl/test/ClWorkloadFactoryHelper.hpp> - -#include <neon/NeonWorkloadFactory.hpp> -#include <neon/test/NeonWorkloadFactoryHelper.hpp> -#endif - -#include <boost/test/unit_test.hpp> - -BOOST_AUTO_TEST_SUITE(MemCopyCommon) - -BOOST_AUTO_TEST_CASE(AclTypeConversions) -{ - arm_compute::Strides strides(1, 2, 3, 4); - armnn::TensorShape convertedStrides = armnn::armcomputetensorutils::GetStrides(strides); - - BOOST_TEST(convertedStrides[0] == 4); - BOOST_TEST(convertedStrides[1] == 3); - BOOST_TEST(convertedStrides[2] == 2); - BOOST_TEST(convertedStrides[3] == 1); - - arm_compute::TensorShape shape(5, 6, 7, 8); - armnn::TensorShape convertedshape = armnn::armcomputetensorutils::GetShape(shape); - - BOOST_TEST(convertedshape[0] == 8); - BOOST_TEST(convertedshape[1] == 7); - BOOST_TEST(convertedshape[2] == 6); - BOOST_TEST(convertedshape[3] == 5); -} - -BOOST_AUTO_TEST_SUITE_END() - -#if defined(ARMCOMPUTECL_ENABLED) && defined(ARMCOMPUTENEON_ENABLED) - -BOOST_FIXTURE_TEST_SUITE(MemCopyClNeon, ClContextControlFixture) - -BOOST_AUTO_TEST_CASE(CopyBetweenNeonAndGpu) -{ - LayerTestResult<float, 4> result = - MemCopyTest<armnn::NeonWorkloadFactory, armnn::ClWorkloadFactory, armnn::DataType::Float32>(false); - BOOST_TEST(CompareTensors(result.output, result.outputExpected)); -} - -BOOST_AUTO_TEST_CASE(CopyBetweenGpuAndNeon) -{ - LayerTestResult<float, 4> result = - MemCopyTest<armnn::ClWorkloadFactory, armnn::NeonWorkloadFactory, armnn::DataType::Float32>(false); - BOOST_TEST(CompareTensors(result.output, result.outputExpected)); -} - -BOOST_AUTO_TEST_CASE(CopyBetweenNeonAndGpuWithSubtensors) -{ - LayerTestResult<float, 4> result = - MemCopyTest<armnn::NeonWorkloadFactory, armnn::ClWorkloadFactory, armnn::DataType::Float32>(true); - BOOST_TEST(CompareTensors(result.output, result.outputExpected)); -} - -BOOST_AUTO_TEST_CASE(CopyBetweenGpuAndNeonWithSubtensors) -{ - LayerTestResult<float, 4> result = - MemCopyTest<armnn::ClWorkloadFactory, armnn::NeonWorkloadFactory, armnn::DataType::Float32>(true); - BOOST_TEST(CompareTensors(result.output, result.outputExpected)); -} - -BOOST_AUTO_TEST_SUITE_END() - -#endif |