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Diffstat (limited to 'src/backends/backendsCommon/test/IsLayerSupportedTestImpl.hpp')
| -rw-r--r-- | src/backends/backendsCommon/test/IsLayerSupportedTestImpl.hpp | 565 |
1 files changed, 565 insertions, 0 deletions
diff --git a/src/backends/backendsCommon/test/IsLayerSupportedTestImpl.hpp b/src/backends/backendsCommon/test/IsLayerSupportedTestImpl.hpp new file mode 100644 index 0000000000..2c992bc10b --- /dev/null +++ b/src/backends/backendsCommon/test/IsLayerSupportedTestImpl.hpp @@ -0,0 +1,565 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// +#pragma once + +#include <Graph.hpp> + +#include <backendsCommon/WorkloadFactory.hpp> + +#include <boost/core/ignore_unused.hpp> + +namespace +{ +armnn::Graph dummyGraph; + +// Make a dummy TensorInfo object. +template<armnn::DataType DataType> +armnn::TensorInfo MakeDummyTensorInfo() +{ + return armnn::TensorInfo({2,2,2,2}, DataType); +} + + +// Make a dummy WorkloadInfo using a dummy TensorInfo. +template<armnn::DataType DataType> +armnn::WorkloadInfo MakeDummyWorkloadInfo(unsigned int numInputs, unsigned int numOutputs) +{ + armnn::WorkloadInfo info; + for (unsigned int i=0; i < numInputs; i++) + { + info.m_InputTensorInfos.push_back(MakeDummyTensorInfo<DataType>()); + } + for (unsigned int o=0; o < numOutputs; o++) + { + info.m_OutputTensorInfos.push_back(MakeDummyTensorInfo<DataType>()); + } + return info; +} + +// Template class to create a dummy layer (2 parameters). +template<typename LayerType, typename DescType = typename LayerType::DescriptorType> +struct DummyLayer +{ + DummyLayer() + { + m_Layer = dummyGraph.AddLayer<LayerType>(DescType(), ""); + } + ~DummyLayer() + { + dummyGraph.EraseLayer(m_Layer); + } + LayerType* m_Layer; +}; + +// Template class to create a dummy layer (1 parameter). +template<typename LayerType> +struct DummyLayer<LayerType, void> +{ + DummyLayer() + { + m_Layer = dummyGraph.AddLayer<LayerType>(""); + } + ~DummyLayer() + { + dummyGraph.EraseLayer(m_Layer); + } + LayerType* m_Layer; +}; + +template<> +struct DummyLayer<armnn::BatchNormalizationLayer> +{ + DummyLayer() + { + m_Layer = dummyGraph.AddLayer<armnn::BatchNormalizationLayer>(armnn::BatchNormalizationDescriptor(), ""); + m_Layer->m_Mean = std::make_unique<armnn::ScopedCpuTensorHandle>( + armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32)); + m_Layer->m_Variance = std::make_unique<armnn::ScopedCpuTensorHandle>( + armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32)); + m_Layer->m_Beta = std::make_unique<armnn::ScopedCpuTensorHandle>( + armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32)); + m_Layer->m_Gamma = std::make_unique<armnn::ScopedCpuTensorHandle>( + armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32)); + } + ~DummyLayer() + { + dummyGraph.EraseLayer(m_Layer); + } + armnn::BatchNormalizationLayer* m_Layer; + +}; + +template<> +struct DummyLayer<armnn::ConstantLayer, void> +{ + DummyLayer() + { + m_Layer = dummyGraph.AddLayer<armnn::ConstantLayer>(""); + } + ~DummyLayer() + { + dummyGraph.EraseLayer(m_Layer); + } + armnn::ConstantLayer* m_Layer; +}; + +template<> +struct DummyLayer<armnn::InputLayer, armnn::LayerBindingId> +{ + DummyLayer() + { + m_Layer = dummyGraph.AddLayer<armnn::InputLayer>(armnn::LayerBindingId(), ""); + + } + ~DummyLayer() + { + dummyGraph.EraseLayer(m_Layer); + } + armnn::InputLayer* m_Layer; +}; + +template<> +struct DummyLayer<armnn::MergerLayer> +{ + DummyLayer() + { + armnn::OriginsDescriptor desc(2); + m_Layer = dummyGraph.AddLayer<armnn::MergerLayer>(desc, ""); + + } + ~DummyLayer() + { + dummyGraph.EraseLayer(m_Layer); + } + armnn::MergerLayer* m_Layer; +}; + +template<> +struct DummyLayer<armnn::OutputLayer, armnn::LayerBindingId> +{ + DummyLayer() + { + m_Layer = dummyGraph.AddLayer<armnn::OutputLayer>(armnn::LayerBindingId(), ""); + + } + ~DummyLayer() + { + dummyGraph.EraseLayer(m_Layer); + } + armnn::OutputLayer* m_Layer; +}; + +template<> +struct DummyLayer<armnn::SplitterLayer> +{ + DummyLayer() + { + armnn::ViewsDescriptor desc(1); + m_Layer = dummyGraph.AddLayer<armnn::SplitterLayer>(desc, ""); + + } + ~DummyLayer() + { + dummyGraph.EraseLayer(m_Layer); + } + armnn::SplitterLayer* m_Layer; +}; + +template <typename ConvolutionLayerType> +struct DummyConvolutionLayer +{ + DummyConvolutionLayer() + { + typename ConvolutionLayerType::DescriptorType desc; + m_Layer = dummyGraph.AddLayer<ConvolutionLayerType>(desc, ""); + m_Layer->m_Weight = std::make_unique<armnn::ScopedCpuTensorHandle>( + armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32)); + m_Layer->m_Bias = std::make_unique<armnn::ScopedCpuTensorHandle>( + armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32)); + } + ~DummyConvolutionLayer() + { + dummyGraph.EraseLayer(m_Layer); + } + ConvolutionLayerType* m_Layer; +}; + +template<> +struct DummyLayer<armnn::Convolution2dLayer> + : public DummyConvolutionLayer<armnn::Convolution2dLayer> +{ +}; + +template<> +struct DummyLayer<armnn::DepthwiseConvolution2dLayer> + : public DummyConvolutionLayer<armnn::DepthwiseConvolution2dLayer> +{ +}; + +template <typename LstmLayerType> +struct DummyLstmLayer +{ + DummyLstmLayer() + { + typename LstmLayerType::DescriptorType desc; + desc.m_CifgEnabled = false; + + m_Layer = dummyGraph.AddLayer<LstmLayerType>(armnn::LstmDescriptor(), ""); + m_Layer->m_BasicParameters.m_InputToForgetWeights = std::make_unique<armnn::ScopedCpuTensorHandle>( + armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32)); + m_Layer->m_BasicParameters.m_InputToCellWeights = std::make_unique<armnn::ScopedCpuTensorHandle>( + armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32)); + m_Layer->m_BasicParameters.m_InputToOutputWeights = std::make_unique<armnn::ScopedCpuTensorHandle>( + armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32)); + m_Layer->m_BasicParameters.m_RecurrentToForgetWeights = std::make_unique<armnn::ScopedCpuTensorHandle>( + armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32)); + m_Layer->m_BasicParameters.m_RecurrentToCellWeights = std::make_unique<armnn::ScopedCpuTensorHandle>( + armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32)); + m_Layer->m_BasicParameters.m_RecurrentToOutputWeights = std::make_unique<armnn::ScopedCpuTensorHandle>( + armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32)); + m_Layer->m_BasicParameters.m_ForgetGateBias = std::make_unique<armnn::ScopedCpuTensorHandle>( + armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32)); + m_Layer->m_BasicParameters.m_CellBias = std::make_unique<armnn::ScopedCpuTensorHandle>( + armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32)); + m_Layer->m_BasicParameters.m_OutputGateBias = std::make_unique<armnn::ScopedCpuTensorHandle>( + armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32)); + + m_Layer->m_CifgParameters.m_InputToInputWeights = std::make_unique<armnn::ScopedCpuTensorHandle>( + armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32)); + m_Layer->m_CifgParameters.m_RecurrentToInputWeights = std::make_unique<armnn::ScopedCpuTensorHandle>( + armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32)); + m_Layer->m_CifgParameters.m_CellToInputWeights = std::make_unique<armnn::ScopedCpuTensorHandle>( + armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32)); + m_Layer->m_CifgParameters.m_InputGateBias = std::make_unique<armnn::ScopedCpuTensorHandle>( + armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32)); + } + ~DummyLstmLayer() + { + dummyGraph.EraseLayer(m_Layer); + } + armnn::LstmLayer* m_Layer; +}; + +template<> +struct DummyLayer<armnn::LstmLayer> + : public DummyLstmLayer<armnn::LstmLayer> +{ +}; + +template<> +struct DummyLayer<armnn::FullyConnectedLayer> +{ + DummyLayer() + { + armnn::FullyConnectedLayer::DescriptorType desc; + m_Layer = dummyGraph.AddLayer<armnn::FullyConnectedLayer>(desc, ""); + m_Layer->m_Weight = std::make_unique<armnn::ScopedCpuTensorHandle>( + armnn::TensorInfo(armnn::TensorShape({1,1,1,1}), armnn::DataType::Float32)); + } + ~DummyLayer() + { + dummyGraph.EraseLayer(m_Layer); + } + armnn::FullyConnectedLayer* m_Layer; +}; + +// Tag for giving LayerType entries a unique strong type each. +template<armnn::LayerType> +struct Tag{}; + +#define DECLARE_LAYER_POLICY_CUSTOM_PARAM(name, descType) \ +template<armnn::DataType DataType> \ +struct LayerTypePolicy<armnn::LayerType::name, DataType> \ +{ \ + using Type = armnn::name##Layer; \ + using Desc = descType; \ + using QueueDesc = armnn::name##QueueDescriptor; \ + constexpr static const char* NameStr = #name; \ + \ + static std::unique_ptr<armnn::IWorkload> MakeDummyWorkload(armnn::IWorkloadFactory *factory, \ + unsigned int nIn, unsigned int nOut) \ + { \ + QueueDesc desc; \ + armnn::WorkloadInfo info = MakeDummyWorkloadInfo<DataType>(nIn, nOut); \ + return factory->Create##name(desc, info); \ + } \ +}; + +// Define a layer policy specialization for use with the IsLayerSupported tests. +// Use this version for layers whose constructor takes 1 parameter(name). +#define DECLARE_LAYER_POLICY_1_PARAM(name) DECLARE_LAYER_POLICY_CUSTOM_PARAM(name, void) + +// Define a layer policy specialization for use with the IsLayerSupported tests. +// Use this version for layers whose constructor takes 2 parameters(descriptor and name). +#define DECLARE_LAYER_POLICY_2_PARAM(name) DECLARE_LAYER_POLICY_CUSTOM_PARAM(name, armnn::name##Descriptor) + +// Layer policy template. +template<armnn::LayerType Type, armnn::DataType DataType> +struct LayerTypePolicy; + +// Every entry in the armnn::LayerType enum must be accounted for below. +DECLARE_LAYER_POLICY_2_PARAM(Activation) + +DECLARE_LAYER_POLICY_1_PARAM(Addition) + +DECLARE_LAYER_POLICY_2_PARAM(BatchNormalization) + +DECLARE_LAYER_POLICY_1_PARAM(Constant) + +DECLARE_LAYER_POLICY_1_PARAM(ConvertFp16ToFp32) + +DECLARE_LAYER_POLICY_1_PARAM(ConvertFp32ToFp16) + +DECLARE_LAYER_POLICY_2_PARAM(Convolution2d) + +DECLARE_LAYER_POLICY_1_PARAM(MemCopy) + +DECLARE_LAYER_POLICY_2_PARAM(DepthwiseConvolution2d) + +DECLARE_LAYER_POLICY_2_PARAM(FakeQuantization) + +DECLARE_LAYER_POLICY_1_PARAM(Floor) + +DECLARE_LAYER_POLICY_2_PARAM(FullyConnected) + +DECLARE_LAYER_POLICY_CUSTOM_PARAM(Input, armnn::LayerBindingId) + +DECLARE_LAYER_POLICY_2_PARAM(L2Normalization) + +DECLARE_LAYER_POLICY_2_PARAM(Lstm) + +DECLARE_LAYER_POLICY_2_PARAM(Mean) + +DECLARE_LAYER_POLICY_2_PARAM(Merger) + +DECLARE_LAYER_POLICY_1_PARAM(Multiplication) + +DECLARE_LAYER_POLICY_2_PARAM(Normalization) + +DECLARE_LAYER_POLICY_CUSTOM_PARAM(Output, armnn::LayerBindingId) + +DECLARE_LAYER_POLICY_2_PARAM(Pad) + +DECLARE_LAYER_POLICY_2_PARAM(Permute) + +DECLARE_LAYER_POLICY_2_PARAM(Pooling2d) + +DECLARE_LAYER_POLICY_1_PARAM(Division) + +DECLARE_LAYER_POLICY_2_PARAM(ResizeBilinear) + +DECLARE_LAYER_POLICY_2_PARAM(Reshape) + +DECLARE_LAYER_POLICY_2_PARAM(Softmax) + +DECLARE_LAYER_POLICY_2_PARAM(SpaceToBatchNd) + +DECLARE_LAYER_POLICY_2_PARAM(Splitter) + +DECLARE_LAYER_POLICY_1_PARAM(Subtraction) + + +// Generic implementation to get the number of input slots for a given layer type; +template<armnn::LayerType Type> +unsigned int GetNumInputs(const armnn::Layer& layer) +{ + return layer.GetNumInputSlots(); +} + +// Generic implementation to get the number of output slots for a given layer type; +template<armnn::LayerType Type> +unsigned int GetNumOutputs(const armnn::Layer& layer) +{ + return layer.GetNumOutputSlots(); +} + +template<> +unsigned int GetNumInputs<armnn::LayerType::Merger>(const armnn::Layer& layer) +{ + boost::ignore_unused(layer); + return 2; +} + +// Tests that the IsLayerSupported() function returns the correct value. +// We determined the correct value by *trying* to create the relevant workload and seeing if it matches what we expect. +// Returns true if expectations are met, otherwise returns false. +template<typename FactoryType, armnn::DataType DataType, armnn::LayerType Type> +bool IsLayerSupportedTest(FactoryType *factory, Tag<Type>) +{ + using LayerPolicy = LayerTypePolicy<Type, DataType>; + using LayerType = typename LayerPolicy::Type; + using LayerDesc = typename LayerPolicy::Desc; + DummyLayer<LayerType, LayerDesc> layer; + + unsigned int numIn = GetNumInputs<Type>(*layer.m_Layer); + unsigned int numOut = GetNumOutputs<Type>(*layer.m_Layer); + + // Make another dummy layer just to make IsLayerSupported have valid inputs. + DummyLayer<armnn::ConstantLayer, void> previousLayer; + // Set output of the previous layer to a dummy tensor. + armnn::TensorInfo output = MakeDummyTensorInfo<DataType>(); + previousLayer.m_Layer->GetOutputSlot(0).SetTensorInfo(output); + // Connect all outputs of the previous layer to inputs of tested layer. + for (unsigned int i = 0; i < numIn; i++) + { + armnn::IOutputSlot& previousLayerOutputSlot = previousLayer.m_Layer->GetOutputSlot(0); + armnn::IInputSlot& layerInputSlot = layer.m_Layer->GetInputSlot(i); + previousLayerOutputSlot.Connect(layerInputSlot); + } + // Set outputs of tested layer to a dummy tensor. + for (unsigned int i = 0; i < numOut; i++) + { + layer.m_Layer->GetOutputSlot(0).SetTensorInfo(output); + } + + std::string layerName = LayerPolicy::NameStr; + std::string reasonIfUnsupported; + if (FactoryType::IsLayerSupported(*layer.m_Layer, DataType, reasonIfUnsupported)) + { + std::string errorMsg = " layer expected support but found none."; + try + { + bool retVal = LayerPolicy::MakeDummyWorkload(factory, numIn, numOut).get() != nullptr; + BOOST_CHECK_MESSAGE(retVal, layerName << errorMsg); + return retVal; + } + catch(const armnn::InvalidArgumentException& e) + { + boost::ignore_unused(e); + // This is ok since we throw InvalidArgumentException when creating the dummy workload. + return true; + } + catch(const std::exception& e) + { + errorMsg = e.what(); + BOOST_TEST_ERROR(layerName << ": " << errorMsg); + return false; + } + catch(...) + { + errorMsg = "Unexpected error while testing support for "; + BOOST_TEST_ERROR(errorMsg << layerName); + return false; + } + } + else + { + std::string errorMsg = "layer expected no support (giving reason: " + reasonIfUnsupported + ") but found some."; + try + { + bool retVal = LayerPolicy::MakeDummyWorkload(factory, numIn, numOut).get() == nullptr; + BOOST_CHECK_MESSAGE(retVal, layerName << errorMsg); + return retVal; + } + // These two exceptions are ok: For workloads that are partially supported, attempting to instantiate them + // using parameters that make IsLayerSupported() return false should throw an + // InvalidArgumentException or UnimplementedException. + catch(const armnn::InvalidArgumentException& e) + { + boost::ignore_unused(e); + return true; + } + catch(const armnn::UnimplementedException& e) + { + boost::ignore_unused(e); + return true; + } + catch(const std::exception& e) + { + errorMsg = e.what(); + BOOST_TEST_ERROR(layerName << ": " << errorMsg); + return false; + } + catch(...) + { + errorMsg = "Unexpected error while testing support for "; + BOOST_TEST_ERROR(errorMsg << layerName); + return false; + } + } +} + +// Helper function to compute the next type in the LayerType enum. +constexpr armnn::LayerType NextType(armnn::LayerType type) +{ + return static_cast<armnn::LayerType>(static_cast<int>(type)+1); +} + +// Termination function for determining the end of the LayerType enumeration. +template<typename FactoryType, armnn::DataType DataType, armnn::LayerType Type> +bool IsLayerSupportedTestsImpl(FactoryType *factory, Tag<armnn::LayerType::LastLayer>) +{ + return IsLayerSupportedTest<FactoryType, DataType, Type>(factory, Tag<Type>()); +}; + +// Recursive function to test and enter in the LayerType enum and then iterate on the next entry. +template<typename FactoryType, armnn::DataType DataType, armnn::LayerType Type> +bool IsLayerSupportedTestsImpl(FactoryType *factory, Tag<Type>) +{ + bool v = IsLayerSupportedTest<FactoryType, DataType, Type>(factory, Tag<Type>()); + + return v && + IsLayerSupportedTestsImpl<FactoryType, DataType, NextType(Type)> + (factory, Tag<NextType(Type)>()); +}; + +// Helper function to pass through to the test framework. +template<typename FactoryType, armnn::DataType DataType> +bool IsLayerSupportedTests(FactoryType *factory) +{ + return IsLayerSupportedTestsImpl<FactoryType, DataType>(factory, Tag<armnn::LayerType::FirstLayer>()); +}; + +template<armnn::LayerType Type> +bool TestLayerTypeMatches() +{ + using LayerPolicy = LayerTypePolicy<Type, armnn::DataType::Float32>; + using LayerType = typename LayerPolicy::Type; + using LayerDesc = typename LayerPolicy::Desc; + DummyLayer<LayerType, LayerDesc> layer; + + std::stringstream ss; + ss << LayerPolicy::NameStr << " layer type mismatches expected layer type value."; + bool v = Type == layer.m_Layer->GetType(); + BOOST_CHECK_MESSAGE(v, ss.str()); + return v; +}; + +template<armnn::LayerType Type> +bool LayerTypeMatchesTestImpl(Tag<armnn::LayerType::LastLayer>) +{ + return TestLayerTypeMatches<Type>(); +}; + +template<armnn::LayerType Type> +bool LayerTypeMatchesTestImpl(Tag<Type>) +{ + return TestLayerTypeMatches<Type>() && + LayerTypeMatchesTestImpl<NextType(Type)>(Tag<NextType(Type)>()); +}; + +template<typename FactoryType, typename LayerType, armnn::DataType InputDataType , armnn::DataType OutputDataType> +bool IsConvertLayerSupportedTests(std::string& reasonIfUnsupported) +{ + armnn::Graph graph; + LayerType* const layer = graph.AddLayer<LayerType>("LayerName"); + + armnn::Layer* const input = graph.AddLayer<armnn::InputLayer>(0, "input"); + armnn::Layer* const output = graph.AddLayer<armnn::OutputLayer>(0, "output"); + + armnn::TensorInfo inputTensorInfo({1, 3, 2, 3}, InputDataType); + armnn::TensorInfo outputTensorInfo({1, 3, 2, 3}, OutputDataType); + + input->GetOutputSlot(0).Connect(layer->GetInputSlot(0)); + input->GetOutputHandler(0).SetTensorInfo(inputTensorInfo); + layer->GetOutputSlot(0).Connect(output->GetInputSlot(0)); + layer->GetOutputHandler(0).SetTensorInfo(outputTensorInfo); + + bool result = FactoryType::IsLayerSupported(*layer, InputDataType, reasonIfUnsupported); + + return result; +}; + +} //namespace |