| // |
| // Copyright © 2017 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "NeonLayerSupport.hpp" |
| #include "NeonBackendId.hpp" |
| #include "NeonBackendModelContext.hpp" |
| |
| #include <armnn/Exceptions.hpp> |
| #include <armnn/Tensor.hpp> |
| #include <armnn/Types.hpp> |
| #include <armnn/BackendRegistry.hpp> |
| |
| #include <InternalTypes.hpp> |
| #include <LayerSupportCommon.hpp> |
| #include <armnn/utility/IgnoreUnused.hpp> |
| #include <armnn/utility/PolymorphicDowncast.hpp> |
| |
| #if defined(ARMCOMPUTENEON_ENABLED) |
| #include <aclCommon/ArmComputeUtils.hpp> |
| #include <aclCommon/ArmComputeTensorUtils.hpp> |
| #include "workloads/NeonAbsWorkload.hpp" |
| #include "workloads/NeonAdditionWorkload.hpp" |
| #include "workloads/NeonActivationWorkload.hpp" |
| #include "workloads/NeonArgMinMaxWorkload.hpp" |
| #include "workloads/NeonBatchNormalizationWorkload.hpp" |
| #include "workloads/NeonBatchToSpaceNdWorkload.hpp" |
| #include "workloads/NeonCastWorkload.hpp" |
| #include "workloads/NeonChannelShuffleWorkload.hpp" |
| #include "workloads/NeonComparisonWorkload.hpp" |
| #include "workloads/NeonConcatWorkload.hpp" |
| #include "workloads/NeonConstantWorkload.hpp" |
| #include "workloads/NeonConvolution2dWorkload.hpp" |
| #include "workloads/NeonConvolution3dWorkload.hpp" |
| #include "workloads/NeonDepthToSpaceWorkload.hpp" |
| #include "workloads/NeonDepthwiseConvolutionWorkload.hpp" |
| #include "workloads/NeonDequantizeWorkload.hpp" |
| #include "workloads/NeonExpWorkload.hpp" |
| #include "workloads/NeonInstanceNormalizationWorkload.hpp" |
| #include "workloads/NeonL2NormalizationFloatWorkload.hpp" |
| #include "workloads/NeonLogWorkload.hpp" |
| #include "workloads/NeonLogSoftmaxWorkload.hpp" |
| #include "workloads/NeonLogicalAndWorkload.hpp" |
| #include "workloads/NeonLogicalNotWorkload.hpp" |
| #include "workloads/NeonLogicalOrWorkload.hpp" |
| #include "workloads/NeonLstmFloatWorkload.hpp" |
| #include "workloads/NeonMaximumWorkload.hpp" |
| #include "workloads/NeonMeanWorkload.hpp" |
| #include "workloads/NeonMinimumWorkload.hpp" |
| #include "workloads/NeonMultiplicationWorkload.hpp" |
| #include "workloads/NeonDivisionWorkload.hpp" |
| #include "workloads/NeonNegWorkload.hpp" |
| #include "workloads/NeonNormalizationFloatWorkload.hpp" |
| #include "workloads/NeonFullyConnectedWorkload.hpp" |
| #include "workloads/NeonGatherWorkload.hpp" |
| #include "workloads/NeonGatherNdWorkload.hpp" |
| #include "workloads/NeonPadWorkload.hpp" |
| #include "workloads/NeonPermuteWorkload.hpp" |
| #include "workloads/NeonPooling2dWorkload.hpp" |
| #include "workloads/NeonPooling3dWorkload.hpp" |
| #include "workloads/NeonPreluWorkload.hpp" |
| #include "workloads/NeonQLstmWorkload.hpp" |
| #include "workloads/NeonQuantizeWorkload.hpp" |
| #include "workloads/NeonQuantizedLstmWorkload.hpp" |
| #include "workloads/NeonReduceWorkload.hpp" |
| #include "workloads/NeonReshapeWorkload.hpp" |
| #include "workloads/NeonResizeWorkload.hpp" |
| #include "workloads/NeonRsqrtWorkload.hpp" |
| #include "workloads/NeonSinWorkload.hpp" |
| #include "workloads/NeonSliceWorkload.hpp" |
| #include "workloads/NeonSoftmaxWorkload.hpp" |
| #include "workloads/NeonSpaceToBatchNdWorkload.hpp" |
| #include "workloads/NeonSpaceToDepthWorkload.hpp" |
| #include "workloads/NeonSplitterWorkload.hpp" |
| #include "workloads/NeonSqrtWorkload.hpp" |
| #include "workloads/NeonStackWorkload.hpp" |
| #include "workloads/NeonStridedSliceWorkload.hpp" |
| #include "workloads/NeonSubtractionWorkload.hpp" |
| #include "workloads/NeonTransposeConvolution2dWorkload.hpp" |
| #include "workloads/NeonTransposeWorkload.hpp" |
| #include "workloads/NeonUnidirectionalSequenceLstmFloatWorkload.hpp" |
| #include "workloads/NeonUnidirectionalSequenceLstmWorkload.hpp" |
| #endif |
| |
| namespace armnn |
| { |
| |
| namespace |
| { |
| |
| template< typename ... Args> |
| bool IsNeonBackendSupported(Optional<std::string&> reasonIfUnsupported, Args... args) |
| { |
| IgnoreUnused(reasonIfUnsupported, (args)...); |
| #if defined(ARMCOMPUTENEON_ENABLED) |
| return true; |
| #else |
| SetValueChecked(reasonIfUnsupported, "The armnn library has been built without NEON support"); |
| return false; |
| #endif |
| } |
| |
| template<typename FloatFunc, typename Uint8Func, typename ... Params> |
| bool IsSupportedForDataTypeNeon(Optional<std::string&> reasonIfUnsupported, |
| DataType dataType, |
| FloatFunc floatFuncPtr, |
| Uint8Func uint8FuncPtr, |
| Params&&... params) |
| { |
| return IsNeonBackendSupported(reasonIfUnsupported) && |
| IsSupportedForDataTypeGeneric(reasonIfUnsupported, |
| dataType, |
| floatFuncPtr, |
| floatFuncPtr, |
| uint8FuncPtr, |
| &FalseFunc<>, |
| &FalseFunc<>, |
| std::forward<Params>(params)...); |
| } |
| |
| #if defined(ARMCOMPUTENEON_ENABLED) |
| template<class FuncType, class... Args> |
| inline bool IsWorkloadSupported(FuncType& func, Optional<std::string&> reasonIfUnsupported, Args&&... args) |
| { |
| arm_compute::Status aclStatus = func(std::forward<Args>(args)...); |
| const bool supported = (aclStatus.error_code() == arm_compute::ErrorCode::OK); |
| if (!supported && reasonIfUnsupported) |
| { |
| reasonIfUnsupported.value() = aclStatus.error_description(); |
| } |
| return supported; |
| } |
| |
| #define FORWARD_WORKLOAD_VALIDATE_FUNC(func, reasonIfUnsupported, ...) \ |
| return IsWorkloadSupported(func, reasonIfUnsupported, __VA_ARGS__); |
| #else |
| #define FORWARD_WORKLOAD_VALIDATE_FUNC(func, reasonIfUnsupported, ...) \ |
| return IsNeonBackendSupported(reasonIfUnsupported, __VA_ARGS__); |
| #endif |
| } // anonymous namespace |
| |
| NeonLayerSupport::NeonLayerSupport(const IBackendInternal::IBackendSpecificModelContextPtr& modelContextPtr) |
| : m_ModelContextPtr(modelContextPtr) |
| { |
| } |
| |
| NeonLayerSupport::NeonLayerSupport() |
| : m_ModelContextPtr(nullptr) |
| { |
| } |
| |
| bool NeonLayerSupport::IsLayerSupported(const LayerType& type, |
| const std::vector<TensorInfo>& infos, |
| const BaseDescriptor& descriptor, |
| const Optional<LstmInputParamsInfo>& lstmParamsInfo, |
| const Optional<QuantizedLstmInputParamsInfo>& quantizedLstmParamsInfo, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| switch (type) |
| { |
| case LayerType::Activation: |
| return IsActivationSupported(infos[0], |
| infos[1], |
| *(PolymorphicDowncast<const ActivationDescriptor*>(&descriptor)), |
| reasonIfUnsupported); |
| case LayerType::Addition: |
| return IsAdditionSupported(infos[0], infos[1], infos[2], reasonIfUnsupported); |
| case LayerType::ArgMinMax: |
| return IsArgMinMaxSupported(infos[0], |
| infos[1], |
| *(PolymorphicDowncast<const ArgMinMaxDescriptor*>(&descriptor)), |
| reasonIfUnsupported); |
| case LayerType::BatchNormalization: |
| return IsBatchNormalizationSupported(infos[0], |
| infos[1], |
| infos[2], |
| infos[3], |
| infos[4], |
| infos[5], |
| *(PolymorphicDowncast<const BatchNormalizationDescriptor*> |
| (&descriptor)), |
| reasonIfUnsupported); |
| case LayerType::BatchToSpaceNd: |
| return IsBatchToSpaceNdSupported(infos[0], |
| infos[1], |
| *(PolymorphicDowncast<const BatchToSpaceNdDescriptor*>(&descriptor)), |
| reasonIfUnsupported); |
| case LayerType::Cast: |
| return IsCastSupported(infos[0], infos[1], reasonIfUnsupported); |
| case LayerType::ChannelShuffle: |
| return IsChannelShuffleSupported(infos[0], |
| infos[1], |
| *(PolymorphicDowncast<const ChannelShuffleDescriptor*>(&descriptor)), |
| reasonIfUnsupported); |
| case LayerType::Comparison: |
| return IsComparisonSupported(infos[0], |
| infos[1], |
| infos[2], |
| *(PolymorphicDowncast<const ComparisonDescriptor*>(&descriptor)), |
| reasonIfUnsupported); |
| case LayerType::Concat: |
| { |
| std::vector<const TensorInfo*> inputInfos; |
| for (uint32_t i = 0; i < (infos.size() - 1); i++) |
| { |
| inputInfos.push_back(&infos[i]); |
| } |
| return IsConcatSupported(inputInfos, |
| infos[infos.size() - 1], |
| *(PolymorphicDowncast<const OriginsDescriptor*>(&descriptor)), |
| reasonIfUnsupported); |
| } |
| case LayerType::Constant: |
| return IsConstantSupported(infos[0], reasonIfUnsupported); |
| case LayerType::ConvertBf16ToFp32: |
| return IsConvertBf16ToFp32Supported(infos[0], infos[1], reasonIfUnsupported); |
| case LayerType::ConvertFp16ToFp32: |
| return IsConvertFp16ToFp32Supported(infos[0], infos[1], reasonIfUnsupported); |
| case LayerType::ConvertFp32ToBf16: |
| return IsConvertFp32ToBf16Supported(infos[0], infos[1], reasonIfUnsupported); |
| case LayerType::ConvertFp32ToFp16: |
| return IsConvertFp32ToFp16Supported(infos[0], infos[1], reasonIfUnsupported); |
| case LayerType::Convolution2d: |
| { |
| if (infos.size() != 4) |
| { |
| throw InvalidArgumentException("Invalid number of TransposeConvolution2d TensorInfos. " |
| "TensorInfos should be of format: {input, output, weights, biases}."); |
| } |
| |
| auto desc = *(PolymorphicDowncast<const Convolution2dDescriptor*>(&descriptor)); |
| if (infos[3] == TensorInfo()) |
| { |
| return IsConvolution2dSupported(infos[0], |
| infos[1], |
| desc, |
| infos[2], |
| EmptyOptional(), |
| reasonIfUnsupported); |
| } |
| else |
| { |
| return IsConvolution2dSupported(infos[0], |
| infos[1], |
| desc, |
| infos[2], |
| infos[3], |
| reasonIfUnsupported); |
| } |
| } |
| case LayerType::Convolution3d: |
| { |
| if (infos.size() != 4) |
| { |
| throw InvalidArgumentException("Invalid number of Convolution3d TensorInfos. " |
| "TensorInfos should be of format: {input, output, weights, biases}."); |
| } |
| |
| auto desc = *(PolymorphicDowncast<const Convolution3dDescriptor*>(&descriptor)); |
| if (infos[3] == TensorInfo()) |
| { |
| return IsConvolution3dSupported(infos[0], |
| infos[1], |
| desc, |
| infos[2], |
| EmptyOptional(), |
| reasonIfUnsupported); |
| } |
| else |
| { |
| return IsConvolution3dSupported(infos[0], |
| infos[1], |
| desc, |
| infos[2], |
| infos[3], |
| reasonIfUnsupported); |
| } |
| } |
| case LayerType::DepthToSpace: |
| return IsDepthToSpaceSupported(infos[0], |
| infos[1], |
| *(PolymorphicDowncast<const DepthToSpaceDescriptor*>(&descriptor)), |
| reasonIfUnsupported); |
| case LayerType::DepthwiseConvolution2d: |
| { |
| if (infos.size() != 4) |
| { |
| throw InvalidArgumentException("Invalid number of DepthwiseConvolution2d TensorInfos. " |
| "TensorInfos should be of format: {input, output, weights, biases}."); |
| } |
| |
| auto desc = *(PolymorphicDowncast<const DepthwiseConvolution2dDescriptor*>(&descriptor)); |
| if (infos[3] == TensorInfo()) |
| { |
| return IsDepthwiseConvolutionSupported(infos[0], |
| infos[1], |
| desc, |
| infos[2], |
| EmptyOptional(), |
| reasonIfUnsupported); |
| } |
| else |
| { |
| return IsDepthwiseConvolutionSupported(infos[0], |
| infos[1], |
| desc, |
| infos[2], |
| infos[3], |
| reasonIfUnsupported); |
| } |
| } |
| case LayerType::Dequantize: |
| return IsDequantizeSupported(infos[0], infos[1], reasonIfUnsupported); |
| case LayerType::DetectionPostProcess: |
| { |
| auto desc = *(PolymorphicDowncast<const DetectionPostProcessDescriptor*>(&descriptor)); |
| return LayerSupportBase::IsDetectionPostProcessSupported(infos[0], |
| infos[1], |
| infos[2], |
| infos[3], |
| infos[4], |
| infos[5], |
| infos[6], |
| desc, |
| reasonIfUnsupported); |
| } |
| case LayerType::Division: |
| return IsDivisionSupported(infos[0], infos[1], infos[2], reasonIfUnsupported); |
| case LayerType::ElementwiseUnary: |
| return IsElementwiseUnarySupported(infos[0], |
| infos[1], |
| *(PolymorphicDowncast<const ElementwiseUnaryDescriptor*>(&descriptor)), |
| reasonIfUnsupported); |
| case LayerType::Fill: |
| return IsFillSupported(infos[0], |
| infos[1], |
| *(PolymorphicDowncast<const FillDescriptor*>(&descriptor)), |
| reasonIfUnsupported); |
| case LayerType::Floor: |
| return IsFloorSupported(infos[0], infos[1], reasonIfUnsupported); |
| case LayerType::FullyConnected: |
| return IsFullyConnectedSupported(infos[0], |
| infos[1], |
| infos[2], |
| infos[3], |
| *(PolymorphicDowncast<const FullyConnectedDescriptor*>(&descriptor)), |
| reasonIfUnsupported); |
| case LayerType::Gather: |
| return IsGatherSupported(infos[0], |
| infos[1], |
| infos[2], |
| *(PolymorphicDowncast<const GatherDescriptor*>(&descriptor)), |
| reasonIfUnsupported); |
| case LayerType::GatherNd: |
| return IsGatherNdSupported(infos[0], |
| infos[1], |
| infos[2], |
| reasonIfUnsupported); |
| case LayerType::Input: |
| return IsInputSupported(infos[0], reasonIfUnsupported); |
| case LayerType::InstanceNormalization: |
| return IsInstanceNormalizationSupported(infos[0], |
| infos[1], |
| *(PolymorphicDowncast<const InstanceNormalizationDescriptor*> |
| (&descriptor)), |
| reasonIfUnsupported); |
| case LayerType::L2Normalization: |
| return IsL2NormalizationSupported(infos[0], |
| infos[1], |
| *(PolymorphicDowncast<const L2NormalizationDescriptor*>(&descriptor)), |
| reasonIfUnsupported); |
| case LayerType::LogicalBinary: |
| return IsLogicalBinarySupported(infos[0], |
| infos[1], |
| infos[2], |
| *(PolymorphicDowncast<const LogicalBinaryDescriptor*>(&descriptor)), |
| reasonIfUnsupported); |
| case LayerType::LogSoftmax: |
| return IsLogSoftmaxSupported(infos[0], |
| infos[1], |
| *(PolymorphicDowncast<const LogSoftmaxDescriptor*>(&descriptor)), |
| reasonIfUnsupported); |
| case LayerType::Lstm: |
| return IsLstmSupported(infos[0], |
| infos[1], |
| infos[2], |
| infos[3], |
| infos[4], |
| infos[5], |
| infos[6], |
| *(PolymorphicDowncast<const LstmDescriptor*>(&descriptor)), |
| lstmParamsInfo.value(), |
| reasonIfUnsupported); |
| case LayerType::Map: |
| return true; |
| case LayerType::Maximum: |
| return IsMaximumSupported(infos[0], infos[1], infos[2], reasonIfUnsupported); |
| case LayerType::Mean: |
| return IsMeanSupported(infos[0], |
| infos[1], |
| *(PolymorphicDowncast<const MeanDescriptor*>(&descriptor)), |
| reasonIfUnsupported); |
| case LayerType::MemCopy: |
| return LayerSupportBase::IsMemCopySupported(infos[0], infos[1], reasonIfUnsupported); |
| case LayerType::MemImport: |
| return LayerSupportBase::IsMemImportSupported(infos[0], infos[1], reasonIfUnsupported); |
| case LayerType::Merge: |
| return LayerSupportBase::IsMergeSupported(infos[0], |
| infos[1], |
| infos[2], |
| reasonIfUnsupported); |
| case LayerType::Minimum: |
| return IsMinimumSupported(infos[0], infos[1], infos[2], reasonIfUnsupported); |
| case LayerType::Multiplication: |
| return IsMultiplicationSupported(infos[0], infos[1], infos[2], reasonIfUnsupported); |
| case LayerType::Normalization: |
| return IsNormalizationSupported(infos[0], |
| infos[1], |
| *(PolymorphicDowncast<const NormalizationDescriptor*>(&descriptor)), |
| reasonIfUnsupported); |
| case LayerType::Output: |
| return IsOutputSupported(infos[0], reasonIfUnsupported); |
| case LayerType::Pad: |
| return IsPadSupported(infos[0], |
| infos[1], |
| *(PolymorphicDowncast<const PadDescriptor*>(&descriptor)), |
| reasonIfUnsupported); |
| case LayerType::Permute: |
| return IsPermuteSupported(infos[0], |
| infos[1], |
| *(PolymorphicDowncast<const PermuteDescriptor*>(&descriptor)), |
| reasonIfUnsupported); |
| case LayerType::Pooling2d: |
| return IsPooling2dSupported(infos[0], |
| infos[1], |
| *(PolymorphicDowncast<const Pooling2dDescriptor*>(&descriptor)), |
| reasonIfUnsupported); |
| case LayerType::Pooling3d: |
| return IsPooling3dSupported(infos[0], |
| infos[1], |
| *(PolymorphicDowncast<const Pooling3dDescriptor*>(&descriptor)), |
| reasonIfUnsupported); |
| case LayerType::Prelu: |
| return IsPreluSupported(infos[0], infos[1], infos[2], reasonIfUnsupported); |
| case LayerType::QLstm: |
| return IsQLstmSupported(infos[0], |
| infos[1], |
| infos[2], |
| infos[3], |
| infos[4], |
| infos[5], |
| *(PolymorphicDowncast<const QLstmDescriptor*>(&descriptor)), |
| lstmParamsInfo.value(), |
| reasonIfUnsupported); |
| case LayerType::Quantize: |
| return IsQuantizeSupported(infos[0], infos[1], reasonIfUnsupported); |
| case LayerType::QuantizedLstm: |
| return IsQuantizedLstmSupported(infos[0], |
| infos[1], |
| infos[2], |
| infos[3], |
| infos[4], |
| quantizedLstmParamsInfo.value(), |
| reasonIfUnsupported); |
| case LayerType::Rank: |
| return true; |
| case LayerType::Reshape: |
| return IsReshapeSupported(infos[0], |
| infos[1], |
| *(PolymorphicDowncast<const ReshapeDescriptor*>(&descriptor)), |
| reasonIfUnsupported); |
| case LayerType::Resize: |
| return IsResizeSupported(infos[0], |
| infos[1], |
| *(PolymorphicDowncast<const ResizeDescriptor*>(&descriptor)), |
| reasonIfUnsupported); |
| case LayerType::Reduce: |
| return IsReduceSupported(infos[0], |
| infos[1], |
| *(PolymorphicDowncast<const ReduceDescriptor*>(&descriptor)), |
| reasonIfUnsupported); |
| case LayerType::Shape: |
| return LayerSupportBase::IsShapeSupported(infos[0], |
| infos[1], |
| reasonIfUnsupported); |
| case LayerType::Slice: |
| return IsSliceSupported(infos[0], |
| infos[1], |
| *(PolymorphicDowncast<const SliceDescriptor*>(&descriptor)), |
| reasonIfUnsupported); |
| case LayerType::Softmax: |
| return IsSoftmaxSupported(infos[0], |
| infos[1], |
| *(PolymorphicDowncast<const SoftmaxDescriptor*>(&descriptor)), |
| reasonIfUnsupported); |
| case LayerType::SpaceToBatchNd: |
| return IsSpaceToBatchNdSupported(infos[0], |
| infos[1], |
| *(PolymorphicDowncast<const SpaceToBatchNdDescriptor*>(&descriptor)), |
| reasonIfUnsupported); |
| case LayerType::SpaceToDepth: |
| return IsSpaceToDepthSupported(infos[0], |
| infos[1], |
| *(PolymorphicDowncast<const SpaceToDepthDescriptor*>(&descriptor)), |
| reasonIfUnsupported); |
| case LayerType::Splitter: |
| { |
| std::vector<TensorInfo> outputInfos; |
| for (uint32_t i = 1; i < infos.size(); i++) |
| { |
| outputInfos.push_back(infos[i]); |
| } |
| return IsSplitterSupported(infos[0], |
| {outputInfos.begin(), outputInfos.end()}, |
| *(PolymorphicDowncast<const ViewsDescriptor*>(&descriptor)), |
| reasonIfUnsupported); |
| } |
| case LayerType::Stack: |
| { |
| std::vector<const TensorInfo*> inputInfos; |
| for (uint32_t i = 0; i < infos.size() - 1; i++) |
| { |
| inputInfos.push_back(&infos[i]); |
| } |
| return IsStackSupported(inputInfos, |
| infos[infos.size() - 1], |
| *(PolymorphicDowncast<const StackDescriptor*>(&descriptor)), |
| reasonIfUnsupported); |
| } |
| case LayerType::StridedSlice: |
| return IsStridedSliceSupported(infos[0], |
| infos[1], |
| *(PolymorphicDowncast<const StridedSliceDescriptor*>(&descriptor)), |
| reasonIfUnsupported); |
| case LayerType::Subtraction: |
| return IsSubtractionSupported(infos[0], infos[1], infos[2], reasonIfUnsupported); |
| case LayerType::Transpose: |
| return IsTransposeSupported(infos[0], |
| infos[1], |
| *(PolymorphicDowncast<const TransposeDescriptor*>(&descriptor)), |
| reasonIfUnsupported); |
| case LayerType::TransposeConvolution2d: |
| { |
| if (infos.size() != 4) |
| { |
| throw InvalidArgumentException("Invalid number of TransposeConvolution2d TensorInfos. " |
| "TensorInfos should be of format: {input, output, weights, biases}."); |
| } |
| |
| auto desc = *(PolymorphicDowncast<const TransposeConvolution2dDescriptor*>(&descriptor)); |
| if (infos[3] == TensorInfo()) |
| { |
| return IsTransposeConvolution2dSupported(infos[0], |
| infos[1], |
| desc, |
| infos[2], |
| EmptyOptional(), |
| reasonIfUnsupported); |
| } |
| else |
| { |
| return IsTransposeConvolution2dSupported(infos[0], |
| infos[1], |
| desc, |
| infos[2], |
| infos[3], |
| reasonIfUnsupported); |
| } |
| } |
| case LayerType::UnidirectionalSequenceLstm: |
| return IsUnidirectionalSequenceLstmSupported(infos[0], |
| infos[1], |
| infos[2], |
| infos[3], |
| infos[4], |
| infos[5], |
| *(PolymorphicDowncast<const |
| UnidirectionalSequenceLstmDescriptor*>(&descriptor)), |
| lstmParamsInfo.value(), |
| reasonIfUnsupported); |
| case LayerType::Unmap: |
| return true; |
| default: |
| // layers not supported in neon by default: |
| // debug, fakequantization, precompiled, |
| // standin, switch |
| return false; |
| } |
| } |
| |
| bool NeonLayerSupport::IsActivationSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| const ActivationDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| IgnoreUnused(descriptor); |
| FORWARD_WORKLOAD_VALIDATE_FUNC(NeonActivationWorkloadValidate, |
| reasonIfUnsupported, |
| input, |
| output, |
| descriptor); |
| } |
| |
| bool NeonLayerSupport::IsAdditionSupported(const TensorInfo& input0, |
| const TensorInfo& input1, |
| const TensorInfo& output, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| FORWARD_WORKLOAD_VALIDATE_FUNC(NeonAdditionWorkloadValidate, |
| reasonIfUnsupported, |
| input0, |
| input1, |
| output, |
| nullptr); |
| } |
| |
| bool NeonLayerSupport::IsArgMinMaxSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| const ArgMinMaxDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| FORWARD_WORKLOAD_VALIDATE_FUNC(NeonArgMinMaxWorkloadValidate, |
| reasonIfUnsupported, |
| input, |
| output, |
| descriptor); |
| } |
| |
| bool NeonLayerSupport::IsBatchNormalizationSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| const TensorInfo& mean, |
| const TensorInfo& var, |
| const TensorInfo& beta, |
| const TensorInfo& gamma, |
| const BatchNormalizationDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| FORWARD_WORKLOAD_VALIDATE_FUNC(NeonBatchNormalizationValidate, |
| reasonIfUnsupported, |
| input, |
| output, |
| mean, |
| var, |
| beta, |
| gamma, |
| descriptor, |
| nullptr); |
| } |
| |
| bool NeonLayerSupport::IsBatchToSpaceNdSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| const BatchToSpaceNdDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| FORWARD_WORKLOAD_VALIDATE_FUNC(NeonBatchToSpaceNdWorkloadValidate, |
| reasonIfUnsupported, |
| input, |
| output, |
| descriptor); |
| } |
| |
| bool NeonLayerSupport::IsCastSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| FORWARD_WORKLOAD_VALIDATE_FUNC(NeonCastValidate, |
| reasonIfUnsupported, |
| input, |
| output); |
| } |
| |
| bool NeonLayerSupport::IsChannelShuffleSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| const ChannelShuffleDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| FORWARD_WORKLOAD_VALIDATE_FUNC(NeonChannelShuffleValidate, |
| reasonIfUnsupported, |
| input, |
| output, |
| descriptor); |
| } |
| |
| bool NeonLayerSupport::IsComparisonSupported(const TensorInfo& input0, |
| const TensorInfo& input1, |
| const TensorInfo& output, |
| const ComparisonDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| |
| FORWARD_WORKLOAD_VALIDATE_FUNC(NeonComparisonWorkloadValidate, |
| reasonIfUnsupported, |
| input0, |
| input1, |
| output, |
| descriptor); |
| } |
| |
| bool NeonLayerSupport::IsConcatSupported(const std::vector<const TensorInfo*> inputs, |
| const TensorInfo& output, |
| const OriginsDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| if (descriptor.GetNumDimensions() <= descriptor.GetConcatAxis()) |
| { |
| SetValueChecked(reasonIfUnsupported, "Neon Concat: Concat axis > Number of dimensions."); |
| return false; |
| } |
| |
| unsigned int concatInnerAxis = (descriptor.GetNumDimensions() - descriptor.GetConcatAxis()) - 1; |
| if(concatInnerAxis < 3) // Width, height, or channels |
| { |
| FORWARD_WORKLOAD_VALIDATE_FUNC(NeonConcatWorkloadValidate, |
| reasonIfUnsupported, |
| inputs, |
| output, |
| descriptor); |
| } |
| else if (concatInnerAxis == 3) |
| { |
| for (auto& input : inputs) |
| { |
| if (input && !output.IsTypeSpaceMatch(*input)) // Cannot use sub-tensors if the types are not same space |
| { |
| SetValueChecked(reasonIfUnsupported, "Neon Concat: Types and quantization parameters must match."); |
| return false; |
| } |
| } |
| return true; // Sub-tensors support concat along batch |
| } |
| else // > 4 dimensions not supported. |
| { |
| SetValueChecked(reasonIfUnsupported, "Neon Concat: Maximum of 4 dimensions supported."); |
| return false; |
| } |
| } |
| |
| bool NeonLayerSupport::IsConstantSupported(const TensorInfo& output, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| FORWARD_WORKLOAD_VALIDATE_FUNC(NeonConstantWorkloadValidate, |
| reasonIfUnsupported, |
| output); |
| } |
| |
| bool NeonLayerSupport::IsConvertBf16ToFp32Supported(const TensorInfo& input, |
| const TensorInfo& output, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| armnn::IgnoreUnused(input); |
| armnn::IgnoreUnused(output); |
| armnn::IgnoreUnused(reasonIfUnsupported); |
| return true; |
| } |
| |
| bool NeonLayerSupport::IsConvertFp16ToFp32Supported(const TensorInfo& input, |
| const TensorInfo& output, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| armnn::IgnoreUnused(input); |
| armnn::IgnoreUnused(output); |
| armnn::IgnoreUnused(reasonIfUnsupported); |
| return true; |
| } |
| |
| bool NeonLayerSupport::IsConvertFp32ToBf16Supported(const TensorInfo& input, |
| const TensorInfo& output, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| armnn::IgnoreUnused(input); |
| armnn::IgnoreUnused(output); |
| armnn::IgnoreUnused(reasonIfUnsupported); |
| return true; |
| } |
| |
| bool NeonLayerSupport::IsConvertFp32ToFp16Supported(const TensorInfo& input, |
| const TensorInfo& output, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| armnn::IgnoreUnused(input); |
| armnn::IgnoreUnused(output); |
| armnn::IgnoreUnused(reasonIfUnsupported); |
| return true; |
| } |
| |
| bool NeonLayerSupport::IsConvolution2dSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| const Convolution2dDescriptor& descriptor, |
| const TensorInfo& weights, |
| const Optional<TensorInfo>& biases, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| bool isFastMathEnabled = false; |
| #if defined(ARMCOMPUTENEON_ENABLED) |
| if (m_ModelContextPtr) |
| { |
| if (m_ModelContextPtr.get() != nullptr) |
| { |
| auto modelOptions = dynamic_cast<NeonBackendModelContext*>(m_ModelContextPtr.get()); |
| if (modelOptions) |
| { |
| isFastMathEnabled = modelOptions->IsFastMathEnabled(); |
| } |
| } |
| } |
| #endif |
| |
| FORWARD_WORKLOAD_VALIDATE_FUNC(NeonConvolution2dWorkloadValidate, |
| reasonIfUnsupported, |
| input, |
| output, |
| descriptor, |
| weights, |
| biases, |
| isFastMathEnabled, |
| nullptr); |
| } |
| |
| bool NeonLayerSupport::IsConvolution3dSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| const Convolution3dDescriptor& descriptor, |
| const TensorInfo& weights, |
| const Optional<TensorInfo>& biases, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| bool isFastMathEnabled = false; |
| #if defined(ARMCOMPUTENEON_ENABLED) |
| if (m_ModelContextPtr) |
| { |
| if (m_ModelContextPtr.get() != nullptr) |
| { |
| auto modelOptions = dynamic_cast<NeonBackendModelContext*>(m_ModelContextPtr.get()); |
| if (modelOptions) |
| { |
| isFastMathEnabled = modelOptions->IsFastMathEnabled(); |
| } |
| } |
| } |
| #endif |
| |
| FORWARD_WORKLOAD_VALIDATE_FUNC(NeonConvolution3dWorkloadValidate, |
| reasonIfUnsupported, |
| input, |
| output, |
| descriptor, |
| weights, |
| biases, |
| isFastMathEnabled, |
| nullptr); |
| } |
| |
| bool NeonLayerSupport::IsDepthToSpaceSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| const DepthToSpaceDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| FORWARD_WORKLOAD_VALIDATE_FUNC(NeonDepthToSpaceWorkloadValidate, |
| reasonIfUnsupported, |
| input, |
| output, |
| descriptor); |
| } |
| |
| bool NeonLayerSupport::IsDepthwiseConvolutionSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| const DepthwiseConvolution2dDescriptor& descriptor, |
| const TensorInfo& weights, |
| const Optional<TensorInfo>& biases, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| FORWARD_WORKLOAD_VALIDATE_FUNC(NeonDepthwiseConvolutionWorkloadValidate, |
| reasonIfUnsupported, |
| input, |
| output, |
| descriptor, |
| weights, |
| biases, |
| nullptr); |
| } |
| |
| bool NeonLayerSupport::IsDequantizeSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| FORWARD_WORKLOAD_VALIDATE_FUNC(NeonDequantizeWorkloadValidate, |
| reasonIfUnsupported, |
| input, |
| output); |
| } |
| |
| bool NeonLayerSupport::IsDilatedDepthwiseConvolutionSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| const DepthwiseConvolution2dDescriptor& descriptor, |
| const TensorInfo& weights, |
| const Optional<TensorInfo>& biases, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| FORWARD_WORKLOAD_VALIDATE_FUNC(NeonDepthwiseConvolutionWorkloadValidate, |
| reasonIfUnsupported, |
| input, |
| output, |
| descriptor, |
| weights, |
| biases, |
| nullptr); |
| } |
| |
| bool NeonLayerSupport::IsElementwiseUnarySupported(const TensorInfo& input, |
| const TensorInfo& output, |
| const ElementwiseUnaryDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| switch(descriptor.m_Operation) |
| { |
| case UnaryOperation::Abs: |
| FORWARD_WORKLOAD_VALIDATE_FUNC(NeonAbsWorkloadValidate, |
| reasonIfUnsupported, |
| input, |
| output); |
| case UnaryOperation::Exp: |
| FORWARD_WORKLOAD_VALIDATE_FUNC(NeonExpWorkloadValidate, |
| reasonIfUnsupported, |
| input, |
| output); |
| case UnaryOperation::LogicalNot: |
| FORWARD_WORKLOAD_VALIDATE_FUNC(NeonLogicalNotWorkloadValidate, |
| reasonIfUnsupported, |
| input, |
| output); |
| case UnaryOperation::Log: |
| FORWARD_WORKLOAD_VALIDATE_FUNC(NeonLogWorkloadValidate, |
| reasonIfUnsupported, |
| input, |
| output); |
| case UnaryOperation::Neg: |
| FORWARD_WORKLOAD_VALIDATE_FUNC(NeonNegWorkloadValidate, |
| reasonIfUnsupported, |
| input, |
| output); |
| case UnaryOperation::Rsqrt: |
| FORWARD_WORKLOAD_VALIDATE_FUNC(NeonRsqrtWorkloadValidate, |
| reasonIfUnsupported, |
| input, |
| output); |
| case UnaryOperation::Sin: |
| FORWARD_WORKLOAD_VALIDATE_FUNC(NeonSinWorkloadValidate, |
| reasonIfUnsupported, |
| input, |
| output); |
| case UnaryOperation::Sqrt: |
| FORWARD_WORKLOAD_VALIDATE_FUNC(NeonSqrtWorkloadValidate, |
| reasonIfUnsupported, |
| input, |
| output); |
| default: |
| return false; |
| } |
| } |
| |
| bool NeonLayerSupport::IsFillSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| const FillDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| armnn::IgnoreUnused(input); |
| armnn::IgnoreUnused(output); |
| armnn::IgnoreUnused(descriptor); |
| |
| return IsNeonBackendSupported(reasonIfUnsupported); |
| } |
| |
| bool NeonLayerSupport::IsFloorSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| armnn::IgnoreUnused(output); |
| return IsNeonBackendSupported(reasonIfUnsupported) && |
| IsSupportedForDataTypeGeneric(reasonIfUnsupported, |
| input.GetDataType(), |
| &FalseFuncF16<>, |
| &TrueFunc<>, |
| &FalseFuncU8<>, |
| &FalseFuncI32<>, |
| &FalseFuncU8<>); |
| } |
| |
| bool NeonLayerSupport::IsFullyConnectedSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| const TensorInfo& weights, |
| const TensorInfo& biases, |
| const FullyConnectedDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| FORWARD_WORKLOAD_VALIDATE_FUNC(NeonFullyConnectedWorkloadValidate, |
| reasonIfUnsupported, |
| input, |
| output, |
| weights, |
| biases, |
| descriptor, |
| nullptr); |
| } |
| |
| bool NeonLayerSupport::IsGatherSupported(const TensorInfo& input0, |
| const TensorInfo& input1, |
| const TensorInfo& output, |
| const GatherDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| FORWARD_WORKLOAD_VALIDATE_FUNC(NeonGatherWorkloadValidate, |
| reasonIfUnsupported, |
| input0, |
| input1, |
| output, |
| descriptor); |
| } |
| |
| bool NeonLayerSupport::IsGatherNdSupported(const TensorInfo& input0, |
| const TensorInfo& input1, |
| const TensorInfo& output, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| FORWARD_WORKLOAD_VALIDATE_FUNC(NeonGatherNdWorkloadValidate, |
| reasonIfUnsupported, |
| input0, |
| input1, |
| output); |
| } |
| |
| bool NeonLayerSupport::IsInputSupported(const TensorInfo& input, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| return IsNeonBackendSupported(reasonIfUnsupported, input); |
| } |
| |
| bool NeonLayerSupport::IsInstanceNormalizationSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| const InstanceNormalizationDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| FORWARD_WORKLOAD_VALIDATE_FUNC(NeonInstanceNormalizationWorkloadValidate, |
| reasonIfUnsupported, |
| input, |
| output, |
| descriptor); |
| } |
| |
| bool NeonLayerSupport::IsL2NormalizationSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| const L2NormalizationDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| FORWARD_WORKLOAD_VALIDATE_FUNC(NeonL2NormalizationWorkloadValidate, reasonIfUnsupported, input, output, descriptor); |
| } |
| |
| bool NeonLayerSupport::IsLogicalBinarySupported(const TensorInfo& input0, |
| const TensorInfo& input1, |
| const TensorInfo& output, |
| const LogicalBinaryDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| switch(descriptor.m_Operation) |
| { |
| case LogicalBinaryOperation::LogicalAnd: |
| FORWARD_WORKLOAD_VALIDATE_FUNC(NeonLogicalAndWorkloadValidate, |
| reasonIfUnsupported, |
| input0, |
| input1, |
| output); |
| case LogicalBinaryOperation::LogicalOr: |
| FORWARD_WORKLOAD_VALIDATE_FUNC(NeonLogicalOrWorkloadValidate, |
| reasonIfUnsupported, |
| input0, |
| input1, |
| output); |
| default: |
| return false; |
| } |
| } |
| |
| bool NeonLayerSupport::IsLogSoftmaxSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| const LogSoftmaxDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| FORWARD_WORKLOAD_VALIDATE_FUNC(NeonLogSoftmaxWorkloadValidate, reasonIfUnsupported, input, output, descriptor); |
| } |
| |
| bool NeonLayerSupport::IsLstmSupported(const TensorInfo& input, |
| const TensorInfo& outputStateIn, |
| const TensorInfo& cellStateIn, |
| const TensorInfo& scratchBuffer, |
| const TensorInfo& outputStateOut, |
| const TensorInfo& cellStateOut, |
| const TensorInfo& output, |
| const LstmDescriptor& descriptor, |
| const LstmInputParamsInfo& paramsInfo, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| FORWARD_WORKLOAD_VALIDATE_FUNC(NeonLstmFloatWorkloadValidate, |
| reasonIfUnsupported, |
| input, |
| outputStateIn, |
| cellStateIn, |
| scratchBuffer, |
| outputStateOut, |
| cellStateOut, |
| output, |
| descriptor, |
| paramsInfo); |
| } |
| |
| bool NeonLayerSupport::IsMaximumSupported(const TensorInfo& input0, |
| const TensorInfo& input1, |
| const TensorInfo& output, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| FORWARD_WORKLOAD_VALIDATE_FUNC(NeonMaximumWorkloadValidate, |
| reasonIfUnsupported, |
| input0, |
| input1, |
| output); |
| } |
| |
| bool NeonLayerSupport::IsMeanSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| const MeanDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| FORWARD_WORKLOAD_VALIDATE_FUNC(NeonMeanWorkloadValidate, |
| reasonIfUnsupported, |
| input, |
| output, |
| descriptor); |
| } |
| |
| bool NeonLayerSupport::IsMinimumSupported(const TensorInfo& input0, |
| const TensorInfo& input1, |
| const TensorInfo& output, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| FORWARD_WORKLOAD_VALIDATE_FUNC(NeonMinimumWorkloadValidate, |
| reasonIfUnsupported, |
| input0, |
| input1, |
| output); |
| } |
| |
| bool NeonLayerSupport::IsMultiplicationSupported(const TensorInfo& input0, |
| const TensorInfo& input1, |
| const TensorInfo& output, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| FORWARD_WORKLOAD_VALIDATE_FUNC(NeonMultiplicationWorkloadValidate, |
| reasonIfUnsupported, |
| input0, |
| input1, |
| output, |
| nullptr); |
| } |
| |
| bool NeonLayerSupport::IsDivisionSupported(const TensorInfo& input0, |
| const TensorInfo& input1, |
| const TensorInfo& output, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| FORWARD_WORKLOAD_VALIDATE_FUNC(NeonDivisionWorkloadValidate, |
| reasonIfUnsupported, |
| input0, |
| input1, |
| output, |
| nullptr); |
| } |
| |
| bool NeonLayerSupport::IsNormalizationSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| const NormalizationDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| FORWARD_WORKLOAD_VALIDATE_FUNC(NeonNormalizationWorkloadValidate, |
| reasonIfUnsupported, |
| input, |
| output, |
| descriptor); |
| } |
| |
| bool NeonLayerSupport::IsOutputSupported(const TensorInfo& output, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| return IsNeonBackendSupported(reasonIfUnsupported, output); |
| } |
| |
| bool NeonLayerSupport::IsPadSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| const PadDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| FORWARD_WORKLOAD_VALIDATE_FUNC(NeonPadWorkloadValidate, |
| reasonIfUnsupported, |
| input, |
| output, |
| descriptor); |
| } |
| |
| bool NeonLayerSupport::IsPermuteSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| const PermuteDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| FORWARD_WORKLOAD_VALIDATE_FUNC(NeonPermuteWorkloadValidate, reasonIfUnsupported, input, output, descriptor); |
| } |
| |
| bool NeonLayerSupport::IsPooling2dSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| const Pooling2dDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| FORWARD_WORKLOAD_VALIDATE_FUNC(NeonPooling2dWorkloadValidate, reasonIfUnsupported, input, output, descriptor); |
| } |
| |
| bool NeonLayerSupport::IsPooling3dSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| const Pooling3dDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| FORWARD_WORKLOAD_VALIDATE_FUNC(NeonPooling3dWorkloadValidate, reasonIfUnsupported, input, output, descriptor); |
| } |
| |
| bool NeonLayerSupport::IsPreluSupported(const armnn::TensorInfo &input, |
| const armnn::TensorInfo &alpha, |
| const armnn::TensorInfo &output, |
| armnn::Optional<std::string &> reasonIfUnsupported) const |
| { |
| FORWARD_WORKLOAD_VALIDATE_FUNC(NeonPreluWorkloadValidate, reasonIfUnsupported, input, alpha, output); |
| } |
| |
| bool NeonLayerSupport::IsQLstmSupported(const TensorInfo& input, |
| const TensorInfo& previousOutputIn, |
| const TensorInfo& previousCellStateIn, |
| const TensorInfo& outputStateOut, |
| const TensorInfo& cellStateOut, |
| const TensorInfo& output, |
| const QLstmDescriptor& descriptor, |
| const LstmInputParamsInfo& paramsInfo, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| // Check required here in order to pass IsLayerSupported for datatypes tests |
| if (input.GetDataType() == armnn::DataType::QAsymmS8 && |
| previousOutputIn.GetDataType() == armnn::DataType::QAsymmS8 && |
| previousCellStateIn.GetDataType() == armnn::DataType::QSymmS16 && |
| outputStateOut.GetDataType() == armnn::DataType::QAsymmS8 && |
| cellStateOut.GetDataType() == armnn::DataType::QSymmS16 && |
| output.GetDataType() == armnn::DataType::QAsymmS8) |
| { |
| FORWARD_WORKLOAD_VALIDATE_FUNC(NeonQLstmWorkloadValidate, |
| reasonIfUnsupported, |
| input, |
| previousCellStateIn, |
| previousOutputIn, |
| cellStateOut, |
| outputStateOut, |
| output, |
| descriptor, |
| paramsInfo); |
| } |
| else |
| { |
| return false; |
| } |
| } |
| |
| bool NeonLayerSupport::IsQuantizeSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| FORWARD_WORKLOAD_VALIDATE_FUNC(NeonQuantizeWorkloadValidate, |
| reasonIfUnsupported, |
| input, |
| output); |
| } |
| |
| bool NeonLayerSupport::IsQuantizedLstmSupported(const TensorInfo& input, |
| const TensorInfo& cellStateIn, |
| const TensorInfo& outputStateIn, |
| const TensorInfo& cellStateOut, |
| const TensorInfo& outputStateOut, |
| const QuantizedLstmInputParamsInfo& paramsInfo, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| FORWARD_WORKLOAD_VALIDATE_FUNC(NeonQuantizedLstmWorkloadValidate, |
| reasonIfUnsupported, |
| input, |
| cellStateIn, |
| outputStateIn, |
| cellStateOut, |
| outputStateOut, |
| paramsInfo); |
| } |
| |
| bool NeonLayerSupport::IsReduceSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| const ReduceDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| FORWARD_WORKLOAD_VALIDATE_FUNC(NeonReduceWorkloadValidate, |
| reasonIfUnsupported, |
| input, |
| output, |
| descriptor); |
| } |
| |
| bool NeonLayerSupport::IsReshapeSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| const ReshapeDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| armnn::IgnoreUnused(descriptor); |
| FORWARD_WORKLOAD_VALIDATE_FUNC(NeonReshapeWorkloadValidate, |
| reasonIfUnsupported, |
| input, |
| output); |
| } |
| |
| bool NeonLayerSupport::IsResizeSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| const ResizeDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| FORWARD_WORKLOAD_VALIDATE_FUNC(NeonResizeWorkloadValidate, |
| reasonIfUnsupported, |
| input, |
| output, |
| descriptor); |
| } |
| |
| bool NeonLayerSupport::IsSliceSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| const SliceDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| FORWARD_WORKLOAD_VALIDATE_FUNC(NeonSliceWorkloadValidate, |
| reasonIfUnsupported, |
| input, |
| output, |
| descriptor); |
| } |
| |
| bool NeonLayerSupport::IsSoftmaxSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| const SoftmaxDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| FORWARD_WORKLOAD_VALIDATE_FUNC(NeonSoftmaxWorkloadValidate, reasonIfUnsupported, input, output, descriptor); |
| } |
| |
| bool NeonLayerSupport::IsSpaceToBatchNdSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| const SpaceToBatchNdDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| FORWARD_WORKLOAD_VALIDATE_FUNC(NeonSpaceToBatchNdWorkloadValidate, |
| reasonIfUnsupported, |
| input, |
| output, |
| descriptor); |
| } |
| |
| bool NeonLayerSupport::IsSpaceToDepthSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| const SpaceToDepthDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| FORWARD_WORKLOAD_VALIDATE_FUNC(NeonSpaceToDepthWorkloadValidate, |
| reasonIfUnsupported, |
| input, |
| output, |
| descriptor); |
| } |
| |
| bool NeonLayerSupport::IsSplitterSupported(const TensorInfo& input, |
| const std::vector<std::reference_wrapper<TensorInfo>>& outputs, |
| const ViewsDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| #if defined(ARMCOMPUTENEON_ENABLED) |
| // Split along the last dimension, cannot use sub-tensors |
| // as width and height of the sub-tensors do not match |
| // the width and height of the parent tensor |
| // in case of input with more than 2D. |
| std::set<unsigned int> splitAxis = ComputeSplitAxis(descriptor, input.GetShape()); |
| if (descriptor.GetNumDimensions() > 2 && splitAxis.size() == 1 && |
| *splitAxis.begin() == descriptor.GetNumDimensions() - 1 ) |
| { |
| FORWARD_WORKLOAD_VALIDATE_FUNC(NeonSplitterWorkloadValidate, |
| reasonIfUnsupported, |
| input, |
| outputs, |
| *splitAxis.begin()); |
| } |
| #endif |
| IgnoreUnused(descriptor); |
| for (auto output : outputs) |
| { |
| if (!input.IsTypeSpaceMatch(output)) // Cannot use sub-tensors if the types are not same space |
| { |
| SetValueChecked(reasonIfUnsupported, "Neon Splitter: Types and quantization parameters must match."); |
| return false; |
| } |
| } |
| return true; |
| } |
| |
| bool NeonLayerSupport::IsStackSupported(const std::vector<const TensorInfo*>& inputs, |
| const TensorInfo& output, |
| const StackDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| FORWARD_WORKLOAD_VALIDATE_FUNC(NeonStackWorkloadValidate, |
| reasonIfUnsupported, |
| inputs, |
| output, |
| descriptor); |
| } |
| |
| bool NeonLayerSupport::IsStridedSliceSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| const StridedSliceDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| FORWARD_WORKLOAD_VALIDATE_FUNC(NeonStridedSliceWorkloadValidate, |
| reasonIfUnsupported, |
| input, |
| output, |
| descriptor); |
| } |
| |
| bool NeonLayerSupport::IsSubtractionSupported(const TensorInfo& input0, |
| const TensorInfo& input1, |
| const TensorInfo& output, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| FORWARD_WORKLOAD_VALIDATE_FUNC(NeonSubtractionWorkloadValidate, |
| reasonIfUnsupported, |
| input0, |
| input1, |
| output, |
| nullptr); |
| } |
| |
| bool NeonLayerSupport::IsTransposeConvolution2dSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| const TransposeConvolution2dDescriptor& descriptor, |
| const TensorInfo& weights, |
| const Optional<TensorInfo>& biases, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| FORWARD_WORKLOAD_VALIDATE_FUNC(NeonTransposeConvolution2dWorkloadValidate, |
| reasonIfUnsupported, |
| input, |
| output, |
| descriptor, |
| weights, |
| biases); |
| } |
| |
| bool NeonLayerSupport::IsTransposeSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| const TransposeDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| FORWARD_WORKLOAD_VALIDATE_FUNC(NeonTransposeWorkloadValidate, reasonIfUnsupported, input, output, descriptor); |
| } |
| |
| bool NeonLayerSupport::IsUnidirectionalSequenceLstmSupported(const TensorInfo& input, |
| const TensorInfo& outputStateIn, |
| const TensorInfo& cellStateIn, |
| const TensorInfo& outputStateOut, |
| const TensorInfo& cellStateOut, |
| const TensorInfo& output, |
| const UnidirectionalSequenceLstmDescriptor& descriptor, |
| const LstmInputParamsInfo& paramsInfo, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| if (input.GetDataType() == armnn::DataType::QAsymmS8 && |
| outputStateIn.GetDataType() == armnn::DataType::QAsymmS8 && |
| cellStateIn.GetDataType() == armnn::DataType::QSymmS16 && |
| outputStateOut.GetDataType() == armnn::DataType::QAsymmS8 && |
| cellStateOut.GetDataType() == armnn::DataType::QSymmS16 && |
| output.GetDataType() == armnn::DataType::QAsymmS8) |
| { |
| FORWARD_WORKLOAD_VALIDATE_FUNC(NeonUnidirectionalSequenceLstmWorkloadValidate, |
| reasonIfUnsupported, |
| input, |
| outputStateIn, |
| cellStateIn, |
| outputStateOut, |
| cellStateOut, |
| output, |
| descriptor, |
| paramsInfo); |
| } |
| else |
| { |
| FORWARD_WORKLOAD_VALIDATE_FUNC(NeonUnidirectionalSequenceLstmFloatWorkloadValidate, |
| reasonIfUnsupported, |
| input, |
| outputStateIn, |
| cellStateIn, |
| outputStateOut, |
| cellStateOut, |
| output, |
| descriptor, |
| paramsInfo); |
| } |
| } |
| |
| } // namespace armnn |
