| // |
| // Copyright © 2017 Arm Ltd. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "RefLayerSupport.hpp" |
| #include "RefBackendId.hpp" |
| |
| #include <InternalTypes.hpp> |
| #include <LayerSupportCommon.hpp> |
| #include <armnn/Types.hpp> |
| #include <armnn/Descriptors.hpp> |
| |
| #include <backendsCommon/BackendRegistry.hpp> |
| |
| #include <boost/core/ignore_unused.hpp> |
| |
| #include <vector> |
| #include <algorithm> |
| #include <array> |
| |
| using namespace boost; |
| |
| namespace armnn |
| { |
| |
| namespace |
| { |
| |
| template<typename Float32Func, typename Uint8Func, typename ... Params> |
| bool IsSupportedForDataTypeRef(Optional<std::string&> reasonIfUnsupported, |
| DataType dataType, |
| Float32Func floatFuncPtr, |
| Uint8Func uint8FuncPtr, |
| Params&&... params) |
| { |
| return IsSupportedForDataTypeGeneric(reasonIfUnsupported, |
| dataType, |
| &FalseFunc<Params...>, |
| floatFuncPtr, |
| uint8FuncPtr, |
| &FalseFunc<Params...>, |
| &FalseFunc<Params...>, |
| std::forward<Params>(params)...); |
| } |
| |
| } // anonymous namespace |
| |
| |
| namespace |
| { |
| template<typename F> |
| bool CheckSupportRule(F rule, Optional<std::string&> reasonIfUnsupported, const char* reason) |
| { |
| bool supported = rule(); |
| if (!supported && reason) |
| { |
| reasonIfUnsupported.value() += std::string(reason) + "\n"; // Append the reason on a new line |
| } |
| return supported; |
| } |
| |
| struct Rule |
| { |
| bool operator()() const |
| { |
| return m_Res; |
| } |
| |
| bool m_Res = true; |
| }; |
| |
| template<typename T> |
| bool AllTypesAreEqualImpl(T t) |
| { |
| return true; |
| } |
| |
| template<typename T, typename... Rest> |
| bool AllTypesAreEqualImpl(T t1, T t2, Rest... rest) |
| { |
| static_assert(std::is_same<T, TensorInfo>::value, "Type T must be a TensorInfo"); |
| |
| return (t1.GetDataType() == t2.GetDataType()) && AllTypesAreEqualImpl(t2, rest...); |
| } |
| |
| struct TypesAreEqual : public Rule |
| { |
| template<typename ... Ts> |
| TypesAreEqual(const Ts&... ts) |
| { |
| m_Res = AllTypesAreEqualImpl(ts...); |
| } |
| }; |
| |
| struct QuantizationParametersAreEqual : public Rule |
| { |
| QuantizationParametersAreEqual(const TensorInfo& info0, const TensorInfo& info1) |
| { |
| m_Res = info0.GetQuantizationScale() == info1.GetQuantizationScale() && |
| info0.GetQuantizationOffset() == info1.GetQuantizationOffset(); |
| } |
| }; |
| |
| struct TypeAnyOf : public Rule |
| { |
| template<typename Container> |
| TypeAnyOf(const TensorInfo& info, const Container& c) |
| { |
| m_Res = std::any_of(c.begin(), c.end(), [&info](DataType dt) |
| { |
| return dt == info.GetDataType(); |
| }); |
| } |
| }; |
| |
| struct ShapesAreSameRank : public Rule |
| { |
| ShapesAreSameRank(const TensorInfo& info0, const TensorInfo& info1) |
| { |
| m_Res = info0.GetShape().GetNumDimensions() == info1.GetShape().GetNumDimensions(); |
| } |
| }; |
| |
| struct ShapesAreSameTotalSize : public Rule |
| { |
| ShapesAreSameTotalSize(const TensorInfo& info0, const TensorInfo& info1) |
| { |
| m_Res = info0.GetNumElements() == info1.GetNumElements(); |
| } |
| }; |
| |
| struct ShapesAreBroadcastCompatible : public Rule |
| { |
| unsigned int CalcInputSize(const TensorShape& in, const TensorShape& out, unsigned int idx) |
| { |
| unsigned int offset = out.GetNumDimensions() - in.GetNumDimensions(); |
| unsigned int sizeIn = (idx < offset) ? 1 : in[idx-offset]; |
| return sizeIn; |
| } |
| |
| ShapesAreBroadcastCompatible(const TensorInfo& in0, const TensorInfo& in1, const TensorInfo& out) |
| { |
| const TensorShape& shape0 = in0.GetShape(); |
| const TensorShape& shape1 = in1.GetShape(); |
| const TensorShape& outShape = out.GetShape(); |
| |
| for (unsigned int i=0; i < outShape.GetNumDimensions() && m_Res; i++) |
| { |
| unsigned int sizeOut = outShape[i]; |
| unsigned int sizeIn0 = CalcInputSize(shape0, outShape, i); |
| unsigned int sizeIn1 = CalcInputSize(shape1, outShape, i); |
| |
| m_Res &= ((sizeIn0 == sizeOut) || (sizeIn0 == 1)) && |
| ((sizeIn1 == sizeOut) || (sizeIn1 == 1)); |
| } |
| } |
| }; |
| } // namespace |
| |
| |
| bool RefLayerSupport::IsActivationSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| const ActivationDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| bool supported = true; |
| |
| // Define supported types. |
| std::array<DataType,3> supportedTypes = { |
| DataType::Float32, |
| DataType::QuantisedAsymm8, |
| DataType::QuantisedSymm16 |
| }; |
| |
| supported &= CheckSupportRule(TypeAnyOf(input, supportedTypes), reasonIfUnsupported, |
| "Reference activation: input type not supported."); |
| |
| supported &= CheckSupportRule(TypeAnyOf(output, supportedTypes), reasonIfUnsupported, |
| "Reference activation: output type not supported."); |
| |
| supported &= CheckSupportRule(TypesAreEqual(input, output), reasonIfUnsupported, |
| "Reference activation: input and output types mismatched."); |
| |
| supported &= CheckSupportRule(ShapesAreSameRank(input, output), reasonIfUnsupported, |
| "Reference activation: input and output shapes are of different rank."); |
| |
| |
| struct ActivationFunctionSupported : public Rule |
| { |
| ActivationFunctionSupported(const ActivationDescriptor& desc) |
| { |
| switch(desc.m_Function) |
| { |
| case ActivationFunction::Abs: |
| case ActivationFunction::BoundedReLu: |
| case ActivationFunction::LeakyReLu: |
| case ActivationFunction::Linear: |
| case ActivationFunction::ReLu: |
| case ActivationFunction::Sigmoid: |
| case ActivationFunction::SoftReLu: |
| case ActivationFunction::Sqrt: |
| case ActivationFunction::Square: |
| case ActivationFunction::TanH: |
| { |
| m_Res = true; |
| break; |
| } |
| default: |
| { |
| m_Res = false; |
| break; |
| } |
| } |
| } |
| }; |
| |
| // Function is supported |
| supported &= CheckSupportRule(ActivationFunctionSupported(descriptor), reasonIfUnsupported, |
| "Reference activation: function not supported."); |
| |
| return supported; |
| } |
| |
| bool RefLayerSupport::IsAdditionSupported(const TensorInfo& input0, |
| const TensorInfo& input1, |
| const TensorInfo& output, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| bool supported = true; |
| |
| std::array<DataType,3> supportedTypes = { |
| DataType::Float32, |
| DataType::QuantisedAsymm8, |
| DataType::QuantisedSymm16 |
| }; |
| |
| supported &= CheckSupportRule(TypeAnyOf(input0, supportedTypes), reasonIfUnsupported, |
| "Reference addition: input 0 is not a supported type."); |
| |
| supported &= CheckSupportRule(TypeAnyOf(input1, supportedTypes), reasonIfUnsupported, |
| "Reference addition: input 1 is not a supported type."); |
| |
| supported &= CheckSupportRule(TypeAnyOf(output, supportedTypes), reasonIfUnsupported, |
| "Reference addition: output is not a supported type."); |
| |
| supported &= CheckSupportRule(TypesAreEqual(input0, input1), reasonIfUnsupported, |
| "Reference addition: input 0 and Input 1 types are mismatched"); |
| |
| supported &= CheckSupportRule(TypesAreEqual(input0, output), reasonIfUnsupported, |
| "Reference addition: input and output types are mismatched"); |
| |
| supported &= CheckSupportRule(ShapesAreBroadcastCompatible(input0, input1, output), reasonIfUnsupported, |
| "Reference addition: shapes are not suitable for implicit broadcast."); |
| |
| return supported; |
| } |
| |
| bool RefLayerSupport::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 |
| { |
| ignore_unused(output); |
| ignore_unused(mean); |
| ignore_unused(var); |
| ignore_unused(beta); |
| ignore_unused(gamma); |
| ignore_unused(descriptor); |
| return IsSupportedForDataTypeRef(reasonIfUnsupported, |
| input.GetDataType(), |
| &TrueFunc<>, |
| &TrueFunc<>); |
| } |
| |
| bool RefLayerSupport::IsBatchToSpaceNdSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| const BatchToSpaceNdDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| ignore_unused(descriptor); |
| return (IsSupportedForDataTypeRef(reasonIfUnsupported, |
| input.GetDataType(), |
| &TrueFunc<>, |
| &TrueFunc<>) && |
| IsSupportedForDataTypeRef(reasonIfUnsupported, |
| output.GetDataType(), |
| &TrueFunc<>, |
| &TrueFunc<>)); |
| } |
| |
| bool RefLayerSupport::IsConstantSupported(const TensorInfo& output, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| return IsSupportedForDataTypeGeneric(reasonIfUnsupported, |
| output.GetDataType(), |
| &FalseFunc<>, |
| &TrueFunc<>, |
| &TrueFunc<>, |
| &TrueFunc<>, |
| &FalseFunc<>); |
| } |
| |
| bool RefLayerSupport::IsConvertFp16ToFp32Supported(const TensorInfo& input, |
| const TensorInfo& output, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| return (IsSupportedForDataTypeGeneric(reasonIfUnsupported, |
| input.GetDataType(), |
| &TrueFunc<>, |
| &FalseInputFuncF32<>, |
| &FalseFuncU8<>, |
| &FalseFuncI32<>, |
| &FalseFuncU8<>) && |
| IsSupportedForDataTypeGeneric(reasonIfUnsupported, |
| output.GetDataType(), |
| &FalseOutputFuncF16<>, |
| &TrueFunc<>, |
| &FalseFuncU8<>, |
| &FalseFuncI32<>, |
| &FalseFuncU8<>)); |
| } |
| |
| bool RefLayerSupport::IsConvertFp32ToFp16Supported(const TensorInfo& input, |
| const TensorInfo& output, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| return (IsSupportedForDataTypeGeneric(reasonIfUnsupported, |
| input.GetDataType(), |
| &FalseInputFuncF16<>, |
| &TrueFunc<>, |
| &FalseFuncU8<>, |
| &FalseFuncI32<>, |
| &FalseFuncU8<>) && |
| IsSupportedForDataTypeGeneric(reasonIfUnsupported, |
| output.GetDataType(), |
| &TrueFunc<>, |
| &FalseOutputFuncF32<>, |
| &FalseFuncU8<>, |
| &FalseFuncI32<>, |
| &FalseFuncU8<>)); |
| } |
| |
| bool RefLayerSupport::IsConvolution2dSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| const Convolution2dDescriptor& descriptor, |
| const TensorInfo& weights, |
| const Optional<TensorInfo>& biases, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| ignore_unused(output); |
| ignore_unused(descriptor); |
| ignore_unused(weights); |
| ignore_unused(biases); |
| return IsSupportedForDataTypeRef(reasonIfUnsupported, |
| input.GetDataType(), |
| &TrueFunc<>, |
| &TrueFunc<>); |
| } |
| |
| bool RefLayerSupport::IsDebugSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| ignore_unused(output); |
| return IsSupportedForDataTypeRef(reasonIfUnsupported, |
| input.GetDataType(), |
| &TrueFunc<>, |
| &TrueFunc<>); |
| } |
| |
| bool RefLayerSupport::IsDepthwiseConvolutionSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| const DepthwiseConvolution2dDescriptor& descriptor, |
| const TensorInfo& weights, |
| const Optional<TensorInfo>& biases, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| ignore_unused(output); |
| ignore_unused(descriptor); |
| ignore_unused(weights); |
| ignore_unused(biases); |
| return IsSupportedForDataTypeRef(reasonIfUnsupported, |
| input.GetDataType(), |
| &TrueFunc<>, |
| &TrueFunc<>); |
| } |
| |
| bool RefLayerSupport::IsDequantizeSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| bool supported = true; |
| |
| std::array<DataType,2> supportedInputTypes = { |
| DataType::QuantisedAsymm8, |
| DataType::QuantisedSymm16 |
| }; |
| |
| supported &= CheckSupportRule(TypeAnyOf(input, supportedInputTypes), reasonIfUnsupported, |
| "Reference dequantize: input type not supported."); |
| |
| std::array<DataType,2> supportedOutputTypes = { |
| DataType::Float32, |
| }; |
| |
| supported &= CheckSupportRule(TypeAnyOf(output, supportedOutputTypes), reasonIfUnsupported, |
| "Reference dequantize: output type not supported."); |
| |
| supported &= CheckSupportRule(ShapesAreSameTotalSize(input, output), reasonIfUnsupported, |
| "Reference dequantize: input and output shapes have different num total elements."); |
| |
| return supported; |
| } |
| |
| bool RefLayerSupport::IsDetectionPostProcessSupported(const armnn::TensorInfo& input0, |
| const armnn::TensorInfo& input1, |
| const armnn::DetectionPostProcessDescriptor& descriptor, |
| armnn::Optional<std::string&> reasonIfUnsupported) const |
| { |
| ignore_unused(input1); |
| return IsSupportedForDataTypeRef(reasonIfUnsupported, |
| input0.GetDataType(), |
| &TrueFunc<>, |
| &TrueFunc<>); |
| } |
| |
| bool RefLayerSupport::IsDivisionSupported(const TensorInfo& input0, |
| const TensorInfo& input1, |
| const TensorInfo& output, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| bool supported = true; |
| |
| std::array<DataType,3> supportedTypes = { |
| DataType::Float32, |
| DataType::QuantisedAsymm8, |
| DataType::QuantisedSymm16 |
| }; |
| |
| supported &= CheckSupportRule(TypeAnyOf(input0, supportedTypes), reasonIfUnsupported, |
| "Reference division: input 0 is not a supported type."); |
| |
| supported &= CheckSupportRule(TypeAnyOf(input1, supportedTypes), reasonIfUnsupported, |
| "Reference division: input 1 is not a supported type."); |
| |
| supported &= CheckSupportRule(TypeAnyOf(output, supportedTypes), reasonIfUnsupported, |
| "Reference division: output is not a supported type."); |
| |
| supported &= CheckSupportRule(TypesAreEqual(input0, input1), reasonIfUnsupported, |
| "Reference division: input 0 and Input 1 types are mismatched"); |
| |
| supported &= CheckSupportRule(TypesAreEqual(input0, output), reasonIfUnsupported, |
| "Reference division: input and output types are mismatched"); |
| |
| supported &= CheckSupportRule(ShapesAreBroadcastCompatible(input0, input1, output), reasonIfUnsupported, |
| "Reference division: shapes are not suitable for implicit broadcast."); |
| |
| return supported; |
| } |
| |
| bool RefLayerSupport::IsEqualSupported(const TensorInfo& input0, |
| const TensorInfo& input1, |
| const TensorInfo& output, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| ignore_unused(input0); |
| ignore_unused(input1); |
| ignore_unused(output); |
| ignore_unused(reasonIfUnsupported); |
| return IsSupportedForDataTypeRef(reasonIfUnsupported, |
| input0.GetDataType(), |
| &TrueFunc<>, |
| &TrueFunc<>); |
| } |
| |
| bool RefLayerSupport::IsFakeQuantizationSupported(const TensorInfo& input, |
| const FakeQuantizationDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| ignore_unused(descriptor); |
| return IsSupportedForDataTypeRef(reasonIfUnsupported, |
| input.GetDataType(), |
| &TrueFunc<>, |
| &FalseFuncU8<>); |
| } |
| |
| bool RefLayerSupport::IsFloorSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| ignore_unused(output); |
| return IsSupportedForDataTypeRef(reasonIfUnsupported, |
| input.GetDataType(), |
| &TrueFunc<>, |
| &FalseFuncU8<>); |
| } |
| |
| bool RefLayerSupport::IsFullyConnectedSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| const TensorInfo& weights, |
| const TensorInfo& biases, |
| const FullyConnectedDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| ignore_unused(output); |
| ignore_unused(weights); |
| ignore_unused(biases); |
| ignore_unused(descriptor); |
| return IsSupportedForDataTypeRef(reasonIfUnsupported, |
| input.GetDataType(), |
| &TrueFunc<>, |
| &TrueFunc<>); |
| } |
| |
| bool RefLayerSupport::IsGatherSupported(const armnn::TensorInfo& input0, |
| const armnn::TensorInfo& input1, |
| const armnn::TensorInfo& output, |
| armnn::Optional<std::string&> reasonIfUnsupported) const |
| { |
| ignore_unused(input1); |
| ignore_unused(output); |
| return IsSupportedForDataTypeRef(reasonIfUnsupported, |
| input0.GetDataType(), |
| &TrueFunc<>, |
| &TrueFunc<>); |
| } |
| |
| bool RefLayerSupport::IsGreaterSupported(const TensorInfo& input0, |
| const TensorInfo& input1, |
| const TensorInfo& output, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| ignore_unused(input0); |
| ignore_unused(input1); |
| ignore_unused(output); |
| ignore_unused(reasonIfUnsupported); |
| return IsSupportedForDataTypeRef(reasonIfUnsupported, |
| input0.GetDataType(), |
| &TrueFunc<>, |
| &TrueFunc<>); |
| } |
| |
| bool RefLayerSupport::IsInputSupported(const TensorInfo& input, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| return IsSupportedForDataTypeRef(reasonIfUnsupported, |
| input.GetDataType(), |
| &TrueFunc<>, |
| &TrueFunc<>); |
| } |
| |
| bool RefLayerSupport::IsL2NormalizationSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| const L2NormalizationDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| ignore_unused(output); |
| ignore_unused(descriptor); |
| return IsSupportedForDataTypeRef(reasonIfUnsupported, |
| input.GetDataType(), |
| &TrueFunc<>, |
| &FalseFuncU8<>); |
| } |
| |
| bool RefLayerSupport::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 TensorInfo& inputToForgetWeights, |
| const TensorInfo& inputToCellWeights, |
| const TensorInfo& inputToOutputWeights, |
| const TensorInfo& recurrentToForgetWeights, |
| const TensorInfo& recurrentToCellWeights, |
| const TensorInfo& recurrentToOutputWeights, |
| const TensorInfo& forgetGateBias, |
| const TensorInfo& cellBias, |
| const TensorInfo& outputGateBias, |
| const TensorInfo* inputToInputWeights, |
| const TensorInfo* recurrentToInputWeights, |
| const TensorInfo* cellToInputWeights, |
| const TensorInfo* inputGateBias, |
| const TensorInfo* projectionWeights, |
| const TensorInfo* projectionBias, |
| const TensorInfo* cellToForgetWeights, |
| const TensorInfo* cellToOutputWeights, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| ignore_unused(descriptor); |
| ignore_unused(inputToForgetWeights); |
| ignore_unused(inputToCellWeights); |
| ignore_unused(inputToOutputWeights); |
| ignore_unused(recurrentToForgetWeights); |
| ignore_unused(recurrentToCellWeights); |
| ignore_unused(recurrentToOutputWeights); |
| ignore_unused(forgetGateBias); |
| ignore_unused(cellBias); |
| ignore_unused(outputGateBias); |
| ignore_unused(inputToInputWeights); |
| ignore_unused(recurrentToInputWeights); |
| ignore_unused(cellToInputWeights); |
| ignore_unused(inputGateBias); |
| ignore_unused(projectionWeights); |
| ignore_unused(projectionBias); |
| ignore_unused(cellToForgetWeights); |
| ignore_unused(cellToOutputWeights); |
| |
| bool supported = true; |
| |
| std::array<DataType,2> supportedTypes = { |
| DataType::Float32 |
| }; |
| |
| supported &= CheckSupportRule(TypeAnyOf(input, supportedTypes), reasonIfUnsupported, |
| "Reference Lstm: input is not a supported type."); |
| |
| supported &= CheckSupportRule(TypesAreEqual(input, outputStateIn), reasonIfUnsupported, |
| "Reference Lstm: input and outputStateIn types are mismatched"); |
| |
| supported &= CheckSupportRule(TypesAreEqual(input, cellStateIn), reasonIfUnsupported, |
| "Reference Lstm: input and cellStateIn types are mismatched"); |
| |
| supported &= CheckSupportRule(TypesAreEqual(input, scratchBuffer), reasonIfUnsupported, |
| "Reference Lstm: input and scratchBuffer types are mismatched"); |
| |
| supported &= CheckSupportRule(TypesAreEqual(input, outputStateOut), reasonIfUnsupported, |
| "Reference Lstm: input and outputStateOut types are mismatched"); |
| |
| supported &= CheckSupportRule(TypesAreEqual(input, cellStateOut), reasonIfUnsupported, |
| "Reference Lstm: input and cellStateOut types are mismatched"); |
| |
| supported &= CheckSupportRule(TypesAreEqual(input, output), reasonIfUnsupported, |
| "Reference Lstm: input and output types are mismatched"); |
| |
| return supported; |
| } |
| |
| bool RefLayerSupport::IsMaximumSupported(const TensorInfo& input0, |
| const TensorInfo& input1, |
| const TensorInfo& output, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| bool supported = true; |
| |
| std::array<DataType,3> supportedTypes = { |
| DataType::Float32, |
| DataType::QuantisedAsymm8, |
| DataType::QuantisedSymm16 |
| }; |
| |
| supported &= CheckSupportRule(TypeAnyOf(input0, supportedTypes), reasonIfUnsupported, |
| "Reference maximum: input 0 is not a supported type."); |
| |
| supported &= CheckSupportRule(TypeAnyOf(input1, supportedTypes), reasonIfUnsupported, |
| "Reference maximum: input 1 is not a supported type."); |
| |
| supported &= CheckSupportRule(TypeAnyOf(output, supportedTypes), reasonIfUnsupported, |
| "Reference maximum: output is not a supported type."); |
| |
| supported &= CheckSupportRule(TypesAreEqual(input0, input1), reasonIfUnsupported, |
| "Reference maximum: input 0 and Input 1 types are mismatched"); |
| |
| supported &= CheckSupportRule(TypesAreEqual(input0, output), reasonIfUnsupported, |
| "Reference maximum: input and output types are mismatched"); |
| |
| supported &= CheckSupportRule(ShapesAreBroadcastCompatible(input0, input1, output), reasonIfUnsupported, |
| "Reference maximum: shapes are not suitable for implicit broadcast."); |
| |
| return supported; |
| } |
| |
| bool RefLayerSupport::IsMeanSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| const MeanDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| ignore_unused(output); |
| ignore_unused(descriptor); |
| return IsSupportedForDataTypeRef(reasonIfUnsupported, |
| input.GetDataType(), |
| &TrueFunc<>, |
| &TrueFunc<>); |
| } |
| |
| bool RefLayerSupport::IsMergerSupported(const std::vector<const TensorInfo*> inputs, |
| const TensorInfo& output, |
| const OriginsDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| ignore_unused(descriptor); |
| ignore_unused(output); |
| return IsSupportedForDataTypeRef(reasonIfUnsupported, |
| inputs[0]->GetDataType(), |
| &TrueFunc<>, |
| &TrueFunc<>); |
| } |
| |
| bool RefLayerSupport::IsMemCopySupported(const TensorInfo &input, |
| const TensorInfo &output, |
| Optional<std::string &> reasonIfUnsupported) const |
| { |
| ignore_unused(output); |
| return IsSupportedForDataTypeGeneric(reasonIfUnsupported, |
| input.GetDataType(), |
| &TrueFunc<>, |
| &TrueFunc<>, |
| &TrueFunc<>, |
| &FalseFuncI32<>, |
| &TrueFunc<>); |
| } |
| |
| bool RefLayerSupport::IsMinimumSupported(const TensorInfo& input0, |
| const TensorInfo& input1, |
| const TensorInfo& output, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| bool supported = true; |
| |
| std::array<DataType,3> supportedTypes = { |
| DataType::Float32, |
| DataType::QuantisedAsymm8, |
| DataType::QuantisedSymm16 |
| }; |
| |
| supported &= CheckSupportRule(TypeAnyOf(input0, supportedTypes), reasonIfUnsupported, |
| "Reference minimum: input 0 is not a supported type."); |
| |
| supported &= CheckSupportRule(TypeAnyOf(input1, supportedTypes), reasonIfUnsupported, |
| "Reference minimum: input 1 is not a supported type."); |
| |
| supported &= CheckSupportRule(TypeAnyOf(output, supportedTypes), reasonIfUnsupported, |
| "Reference minimum: output is not a supported type."); |
| |
| supported &= CheckSupportRule(TypesAreEqual(input0, input1), reasonIfUnsupported, |
| "Reference minimum: input 0 and Input 1 types are mismatched"); |
| |
| supported &= CheckSupportRule(TypesAreEqual(input0, output), reasonIfUnsupported, |
| "Reference minimum: input and output types are mismatched"); |
| |
| supported &= CheckSupportRule(ShapesAreBroadcastCompatible(input0, input1, output), reasonIfUnsupported, |
| "Reference minimum: shapes are not suitable for implicit broadcast."); |
| |
| return supported; |
| } |
| |
| bool RefLayerSupport::IsMultiplicationSupported(const TensorInfo& input0, |
| const TensorInfo& input1, |
| const TensorInfo& output, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| bool supported = true; |
| |
| std::array<DataType,3> supportedTypes = { |
| DataType::Float32, |
| DataType::QuantisedAsymm8, |
| DataType::QuantisedSymm16 |
| }; |
| |
| supported &= CheckSupportRule(TypeAnyOf(input0, supportedTypes), reasonIfUnsupported, |
| "Reference multiplication: input 0 is not a supported type."); |
| |
| supported &= CheckSupportRule(TypeAnyOf(input1, supportedTypes), reasonIfUnsupported, |
| "Reference multiplication: input 1 is not a supported type."); |
| |
| supported &= CheckSupportRule(TypeAnyOf(output, supportedTypes), reasonIfUnsupported, |
| "Reference multiplication: output is not a supported type."); |
| |
| supported &= CheckSupportRule(TypesAreEqual(input0, input1), reasonIfUnsupported, |
| "Reference multiplication: input 0 and Input 1 types are mismatched"); |
| |
| supported &= CheckSupportRule(TypesAreEqual(input0, output), reasonIfUnsupported, |
| "Reference multiplication: input and output types are mismatched"); |
| |
| supported &= CheckSupportRule(ShapesAreBroadcastCompatible(input0, input1, output), reasonIfUnsupported, |
| "Reference multiplication: shapes are not suitable for implicit broadcast."); |
| |
| return supported; |
| } |
| |
| bool RefLayerSupport::IsNormalizationSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| const NormalizationDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| ignore_unused(output); |
| ignore_unused(descriptor); |
| return IsSupportedForDataTypeRef(reasonIfUnsupported, |
| input.GetDataType(), |
| &TrueFunc<>, |
| &FalseFuncU8<>); |
| } |
| |
| bool RefLayerSupport::IsOutputSupported(const TensorInfo& output, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| return IsSupportedForDataTypeGeneric(reasonIfUnsupported, |
| output.GetDataType(), |
| &TrueFunc<>, |
| &TrueFunc<>, |
| &TrueFunc<>, |
| &FalseFuncI32<>, |
| &TrueFunc<>); |
| } |
| |
| bool RefLayerSupport::IsPadSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| const PadDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| ignore_unused(output); |
| ignore_unused(descriptor); |
| return IsSupportedForDataTypeRef(reasonIfUnsupported, |
| input.GetDataType(), |
| &TrueFunc<>, |
| &TrueFunc<>); |
| } |
| |
| bool RefLayerSupport::IsPermuteSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| const PermuteDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| ignore_unused(output); |
| ignore_unused(descriptor); |
| return IsSupportedForDataTypeRef(reasonIfUnsupported, |
| input.GetDataType(), |
| &TrueFunc<>, |
| &TrueFunc<>); |
| } |
| |
| bool RefLayerSupport::IsPooling2dSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| const Pooling2dDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| ignore_unused(output); |
| ignore_unused(descriptor); |
| return IsSupportedForDataTypeRef(reasonIfUnsupported, |
| input.GetDataType(), |
| &TrueFunc<>, |
| &TrueFunc<>); |
| } |
| |
| bool RefLayerSupport::IsQuantizeSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| bool supported = true; |
| |
| // Define supported output types. |
| std::array<DataType,2> supportedInputTypes = { |
| DataType::Float32, |
| }; |
| |
| supported &= CheckSupportRule(TypeAnyOf(input, supportedInputTypes), reasonIfUnsupported, |
| "Reference quantize: input type not supported."); |
| |
| // Define supported output types. |
| std::array<DataType,2> supportedOutputTypes = { |
| DataType::QuantisedAsymm8, |
| DataType::QuantisedSymm16 |
| }; |
| supported &= CheckSupportRule(TypeAnyOf(output, supportedOutputTypes), reasonIfUnsupported, |
| "Reference quantize: output type not supported."); |
| |
| supported &= CheckSupportRule(ShapesAreSameTotalSize(input, output), reasonIfUnsupported, |
| "Reference quantize: input and output shapes have different num total elements."); |
| |
| return supported; |
| } |
| |
| bool RefLayerSupport::IsReshapeSupported(const TensorInfo& input, |
| const ReshapeDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| ignore_unused(descriptor); |
| return IsSupportedForDataTypeRef(reasonIfUnsupported, |
| input.GetDataType(), |
| &TrueFunc<>, |
| &TrueFunc<>); |
| } |
| |
| bool RefLayerSupport::IsResizeBilinearSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| ignore_unused(output); |
| return IsSupportedForDataTypeRef(reasonIfUnsupported, |
| input.GetDataType(), |
| &TrueFunc<>, |
| &TrueFunc<>); |
| } |
| |
| bool RefLayerSupport::IsRsqrtSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| ignore_unused(output); |
| return IsSupportedForDataTypeRef(reasonIfUnsupported, |
| input.GetDataType(), |
| &TrueFunc<>, |
| &FalseFuncU8<>); |
| } |
| |
| bool RefLayerSupport::IsSoftmaxSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| const SoftmaxDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| ignore_unused(output); |
| ignore_unused(descriptor); |
| return IsSupportedForDataTypeRef(reasonIfUnsupported, |
| input.GetDataType(), |
| &TrueFunc<>, |
| &TrueFunc<>); |
| } |
| |
| bool RefLayerSupport::IsSpaceToBatchNdSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| const SpaceToBatchNdDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| ignore_unused(output); |
| ignore_unused(descriptor); |
| return IsSupportedForDataTypeRef(reasonIfUnsupported, |
| input.GetDataType(), |
| &TrueFunc<>, |
| &TrueFunc<>); |
| } |
| |
| bool RefLayerSupport::IsSplitterSupported(const TensorInfo& input, |
| const ViewsDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| ignore_unused(descriptor); |
| return IsSupportedForDataTypeRef(reasonIfUnsupported, |
| input.GetDataType(), |
| &TrueFunc<>, |
| &TrueFunc<>); |
| } |
| |
| bool RefLayerSupport::IsStridedSliceSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| const StridedSliceDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| ignore_unused(output); |
| ignore_unused(descriptor); |
| return IsSupportedForDataTypeRef(reasonIfUnsupported, |
| input.GetDataType(), |
| &TrueFunc<>, |
| &TrueFunc<>); |
| } |
| |
| bool RefLayerSupport::IsSubtractionSupported(const TensorInfo& input0, |
| const TensorInfo& input1, |
| const TensorInfo& output, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| bool supported = true; |
| |
| std::array<DataType,3> supportedTypes = { |
| DataType::Float32, |
| DataType::QuantisedAsymm8, |
| DataType::QuantisedSymm16 |
| }; |
| |
| supported &= CheckSupportRule(TypeAnyOf(input0, supportedTypes), reasonIfUnsupported, |
| "Reference subtraction: input 0 is not a supported type."); |
| |
| supported &= CheckSupportRule(TypeAnyOf(input1, supportedTypes), reasonIfUnsupported, |
| "Reference subtraction: input 1 is not a supported type."); |
| |
| supported &= CheckSupportRule(TypeAnyOf(output, supportedTypes), reasonIfUnsupported, |
| "Reference subtraction: output is not a supported type."); |
| |
| supported &= CheckSupportRule(TypesAreEqual(input0, input1), reasonIfUnsupported, |
| "Reference subtraction: input 0 and Input 1 types are mismatched"); |
| |
| supported &= CheckSupportRule(TypesAreEqual(input0, output), reasonIfUnsupported, |
| "Reference subtraction: input and output types are mismatched"); |
| |
| supported &= CheckSupportRule(ShapesAreBroadcastCompatible(input0, input1, output), reasonIfUnsupported, |
| "Reference subtraction: shapes are not suitable for implicit broadcast."); |
| |
| return supported; |
| } |
| |
| } // namespace armnn |
