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Diffstat (limited to 'src/armnnTestUtils/CreateWorkload.hpp')
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diff --git a/src/armnnTestUtils/CreateWorkload.hpp b/src/armnnTestUtils/CreateWorkload.hpp new file mode 100644 index 0000000000..ea8a436177 --- /dev/null +++ b/src/armnnTestUtils/CreateWorkload.hpp @@ -0,0 +1,2316 @@ +// +// Copyright © 2017 Arm Ltd and Contributors. All rights reserved. +// SPDX-License-Identifier: MIT +// +#pragma once + +#include "TestUtils.hpp" + +#include <Graph.hpp> +#include <Network.hpp> +#include <ResolveType.hpp> + +#include <armnnUtils/DataLayoutIndexed.hpp> +#include <armnn/utility/Assert.hpp> +#include <armnn/utility/IgnoreUnused.hpp> +#include <armnn/utility/PolymorphicDowncast.hpp> + +#include <backendsCommon/TensorHandle.hpp> +#include <backendsCommon/WorkloadData.hpp> +#include <backendsCommon/WorkloadFactory.hpp> + +#include <doctest/doctest.h> + +#include <utility> + +using namespace armnn; + +namespace +{ + +using namespace std; + +// Calls CreateWorkload for a layer, and checks the returned pointer is of the correct type. +template<typename Workload> +std::unique_ptr<Workload> MakeAndCheckWorkload(Layer& layer, + const IWorkloadFactory& factory, + const ModelOptions& modelOptions = {}) +{ + std::unique_ptr<IWorkload> workload = layer.CreateWorkload(factory); + CHECK_MESSAGE(workload.get() == PolymorphicDowncast<Workload*>(workload.get()), + "Cannot convert to derived class"); + std::string reasonIfUnsupported; + layer.SetBackendId(factory.GetBackendId()); + CHECK(factory.IsLayerSupported(layer, layer.GetDataType(), reasonIfUnsupported, modelOptions)); + return std::unique_ptr<Workload>(static_cast<Workload*>(workload.release())); +} + +// Helper function to create tensor handlers for workloads, assuming they all use the same factory. +void CreateTensorHandles(armnn::Graph& graph, + armnn::IWorkloadFactory& factory) +{ + TensorHandleFactoryRegistry tmpRegistry; + for (auto&& layer : graph.TopologicalSort()) + { + layer->CreateTensorHandles(tmpRegistry, factory); + } +} + +///////////////////////////////////////////////////////////////////////////////////////////// +// The following functions are called by backendsCommon/test/CreateWorkload*.cpp +// They build very simple graphs, and then create a workload. +// Some checks are performed on the workload to ensure parameters have been passed correctly. +// They return the created workloads so that backend-specific checks can be performed. +///////////////////////////////////////////////////////////////////////////////////////////// + +template <typename ActivationWorkload, armnn::DataType DataType> +std::unique_ptr<ActivationWorkload> CreateActivationWorkloadTest(armnn::IWorkloadFactory& factory, + armnn::Graph& graph) +{ + // Creates the layer we're testing. + ActivationDescriptor layerDesc; + layerDesc.m_Function = ActivationFunction::Abs; + layerDesc.m_A = 3.5f; + layerDesc.m_B = -10.0f; + + ActivationLayer* const layer = graph.AddLayer<ActivationLayer>(layerDesc, "layer"); + + // Creates extra layers. + Layer* const input = graph.AddLayer<InputLayer>(0, "input"); + Layer* const output = graph.AddLayer<OutputLayer>(0, "output"); + + // Connects up. + armnn::TensorInfo tensorInfo({1, 1}, DataType); + + Connect(input, layer, tensorInfo); + Connect(layer, output, tensorInfo); + + CreateTensorHandles(graph, factory); + + // Makes the workload and checks it. + auto workload = MakeAndCheckWorkload<ActivationWorkload>(*layer, factory); + + ActivationQueueDescriptor queueDescriptor = workload->GetData(); + CHECK(queueDescriptor.m_Inputs.size() == 1); + CHECK(queueDescriptor.m_Outputs.size() == 1); + CHECK(queueDescriptor.m_Parameters.m_A == 3.5f); + CHECK(queueDescriptor.m_Parameters.m_B == -10.0f); + CHECK((queueDescriptor.m_Parameters.m_Function == ActivationFunction::Abs)); + + // Returns so we can do extra, backend-specific tests. + return workload; +} + +template <typename WorkloadType, + typename DescriptorType, + typename LayerType, + armnn::DataType DataType> +std::unique_ptr<WorkloadType> CreateElementwiseWorkloadTest(armnn::IWorkloadFactory & factory, + armnn::Graph & graph) +{ + // Creates the layer we're testing. + Layer* const layer = graph.AddLayer<LayerType>("layer"); + + // Creates extra layers. + Layer* const input1 = graph.AddLayer<InputLayer>(1, "input1"); + Layer* const input2 = graph.AddLayer<InputLayer>(2, "input2"); + Layer* const output = graph.AddLayer<OutputLayer>(0, "output"); + + // Connects up. + armnn::TensorInfo tensorInfo({2, 3}, DataType); + Connect(input1, layer, tensorInfo, 0, 0); + Connect(input2, layer, tensorInfo, 0, 1); + Connect(layer, output, tensorInfo); + CreateTensorHandles(graph, factory); + + // Makes the workload and checks it. + auto workload = MakeAndCheckWorkload<WorkloadType>(*layer, factory); + + DescriptorType queueDescriptor = workload->GetData(); + CHECK(queueDescriptor.m_Inputs.size() == 2); + CHECK(queueDescriptor.m_Outputs.size() == 1); + + // Returns so we can do extra, backend-specific tests. + return workload; +} + +template<typename WorkloadType, + typename DescriptorType, + armnn::DataType DataType> +std::unique_ptr<WorkloadType> CreateSubtractionWithBlobWorkloadTest(armnn::IWorkloadFactory& factory, + armnn::Graph& graph) +{ + // Creates the layer we're testing. + SubtractionLayer* const layer = graph.AddLayer<SubtractionLayer>("layer"); + + auto activationDesc = std::make_shared<ActivationDescriptor>(); + activationDesc->m_A = 10.0f; + activationDesc->m_B = 5.0f; + activationDesc->m_Function = armnn::ActivationFunction::BoundedReLu; + + layer->SetAdditionalInfoForObject(activationDesc); + + // Creates extra layers. + Layer* const input1 = graph.AddLayer<InputLayer>(1, "input1"); + Layer* const input2 = graph.AddLayer<InputLayer>(2, "input2"); + Layer* const output = graph.AddLayer<OutputLayer>(0, "output"); + + // Connects up. + armnn::TensorInfo tensorInfo({2, 3}, DataType); + Connect(input1, layer, tensorInfo, 0, 0); + Connect(input2, layer, tensorInfo, 0, 1); + Connect(layer, output, tensorInfo); + CreateTensorHandles(graph, factory); + + // Check that the additional information can be queried from the layer + std::shared_ptr<ActivationDescriptor> + activationDescPtr = layer->GetAdditionalInformation<ActivationDescriptor>(); + + ARMNN_ASSERT(static_cast<float>(activationDescPtr->m_A) == 10.0f); + ARMNN_ASSERT(static_cast<float>(activationDescPtr->m_B) == 5.0f); + ARMNN_ASSERT( + static_cast<ActivationFunction>(activationDescPtr->m_Function) == armnn::ActivationFunction::BoundedReLu + ); + + // Makes the workload and checks it. + auto workload = MakeAndCheckWorkload<WorkloadType>(*layer, factory); + + DescriptorType queueDescriptor = workload->GetData(); + + const ActivationDescriptor* queueDescBlobPtr = + queueDescriptor.template GetAdditionalInformation<ActivationDescriptor>(); + IgnoreUnused(queueDescBlobPtr); + ARMNN_ASSERT(static_cast<float>(queueDescBlobPtr->m_A) == 10.0f); + ARMNN_ASSERT(static_cast<float>(queueDescBlobPtr->m_B) == 5.0f); + ARMNN_ASSERT( + static_cast<ActivationFunction>(queueDescBlobPtr->m_Function) == armnn::ActivationFunction::BoundedReLu + ); + + CHECK(queueDescriptor.m_Inputs.size() == 2); + CHECK(queueDescriptor.m_Outputs.size() == 1); + + return workload; +} + +template<typename WorkloadType, + typename DescriptorType, + armnn::DataType DataType> +std::unique_ptr<WorkloadType> CreateMultiplicationWithBlobWorkloadTest(armnn::IWorkloadFactory& factory, + armnn::Graph& graph) +{ + // Creates the layer we're testing. + MultiplicationLayer* const layer = graph.AddLayer<MultiplicationLayer>("layer"); + + auto activationDesc = std::make_shared<ActivationDescriptor>(); + activationDesc->m_A = 10.0f; + activationDesc->m_B = 5.0f; + activationDesc->m_Function = armnn::ActivationFunction::BoundedReLu; + + layer->SetAdditionalInfoForObject(activationDesc); + + // Creates extra layers. + Layer* const input1 = graph.AddLayer<InputLayer>(1, "input1"); + Layer* const input2 = graph.AddLayer<InputLayer>(2, "input2"); + Layer* const output = graph.AddLayer<OutputLayer>(0, "output"); + + // Connects up. + armnn::TensorInfo tensorInfo({2, 3}, DataType); + Connect(input1, layer, tensorInfo, 0, 0); + Connect(input2, layer, tensorInfo, 0, 1); + Connect(layer, output, tensorInfo); + CreateTensorHandles(graph, factory); + + // Check that the additional information can be queried from the layer + std::shared_ptr<ActivationDescriptor> + activationDescPtr = layer->GetAdditionalInformation<ActivationDescriptor>(); + + ARMNN_ASSERT(static_cast<float>(activationDescPtr->m_A) == 10.0f); + ARMNN_ASSERT(static_cast<float>(activationDescPtr->m_B) == 5.0f); + ARMNN_ASSERT( + static_cast<ActivationFunction>(activationDescPtr->m_Function) == armnn::ActivationFunction::BoundedReLu + ); + + // Makes the workload and checks it. + auto workload = MakeAndCheckWorkload<WorkloadType>(*layer, factory); + + DescriptorType queueDescriptor = workload->GetData(); + CHECK(queueDescriptor.m_Inputs.size() == 2); + CHECK(queueDescriptor.m_Outputs.size() == 1); + const ActivationDescriptor* queueDescBlobPtr = + queueDescriptor.template GetAdditionalInformation<ActivationDescriptor>(); + IgnoreUnused(queueDescBlobPtr); + ARMNN_ASSERT(static_cast<float>(queueDescBlobPtr->m_A) == 10.0f); + ARMNN_ASSERT(static_cast<float>(queueDescBlobPtr->m_B) == 5.0f); + ARMNN_ASSERT( + static_cast<ActivationFunction>(queueDescBlobPtr->m_Function) == armnn::ActivationFunction::BoundedReLu + ); + + return workload;// Returns so we can do extra, backend-specific tests. +} + +template<typename WorkloadType, + typename DescriptorType, + armnn::DataType DataType> +std::unique_ptr<WorkloadType> CreateAdditionWithBlobWorkloadTest(armnn::IWorkloadFactory& factory, + armnn::Graph& graph) +{ + // Creates the layer we're testing. + AdditionLayer* const layer = graph.AddLayer<AdditionLayer>("layer"); + + auto activationDesc = std::make_shared<ActivationDescriptor>(); + activationDesc->m_A = 10.0f; + activationDesc->m_B = 5.0f; + activationDesc->m_Function = armnn::ActivationFunction::BoundedReLu; + + layer->SetAdditionalInfoForObject(activationDesc); + + // Creates extra layers. + Layer* const input1 = graph.AddLayer<InputLayer>(1, "input1"); + Layer* const input2 = graph.AddLayer<InputLayer>(2, "input2"); + Layer* const output = graph.AddLayer<OutputLayer>(0, "output"); + + // Connects up. + armnn::TensorInfo tensorInfo({2, 3}, DataType); + Connect(input1, layer, tensorInfo, 0, 0); + Connect(input2, layer, tensorInfo, 0, 1); + Connect(layer, output, tensorInfo); + CreateTensorHandles(graph, factory); + + // Check that the additional information can be queried from the layer + std::shared_ptr<ActivationDescriptor> + activationDescPtr = layer->template GetAdditionalInformation<ActivationDescriptor>(); + + ARMNN_ASSERT(static_cast<float>(activationDescPtr->m_A) == 10.0f); + ARMNN_ASSERT(static_cast<float>(activationDescPtr->m_B) == 5.0f); + ARMNN_ASSERT( + static_cast<ActivationFunction>(activationDescPtr->m_Function) == armnn::ActivationFunction::BoundedReLu + ); + + // Makes the workload and checks it. + auto workload = MakeAndCheckWorkload<WorkloadType>(*layer, factory); + + DescriptorType queueDescriptor = workload->GetData(); + const ActivationDescriptor* queueDescBlobPtr = + queueDescriptor.template GetAdditionalInformation<ActivationDescriptor>(); + IgnoreUnused(queueDescBlobPtr); + CHECK(queueDescriptor.m_Inputs.size() == 2); + CHECK(queueDescriptor.m_Outputs.size() == 1); + ARMNN_ASSERT(static_cast<float>(queueDescBlobPtr->m_A) == 10.0f); + ARMNN_ASSERT(static_cast<float>(queueDescBlobPtr->m_B) == 5.0f); + ARMNN_ASSERT( + static_cast<ActivationFunction>(queueDescBlobPtr->m_Function) == armnn::ActivationFunction::BoundedReLu + ); + + return workload; +} + +template <typename WorkloadType, + typename DescriptorType, + armnn::DataType DataType> +std::unique_ptr<WorkloadType> CreateElementwiseUnaryWorkloadTest(armnn::IWorkloadFactory & factory, + armnn::Graph & graph, + armnn::UnaryOperation op) +{ + ElementwiseUnaryDescriptor desc = ElementwiseUnaryDescriptor(op); + Layer* const layer = graph.AddLayer<armnn::ElementwiseUnaryLayer>(desc, "layer"); + + Layer* const input = graph.AddLayer<InputLayer>(0, "input"); + Layer* const output = graph.AddLayer<OutputLayer>(0, "output"); + + armnn::TensorInfo tensorInfo({ 2, 3 }, DataType); + Connect(input, layer, tensorInfo, 0, 0); + Connect(layer, output, tensorInfo, 0, 0); + CreateTensorHandles(graph, factory); + + auto workload = MakeAndCheckWorkload<WorkloadType>(*layer, factory); + DescriptorType queueDescriptor = workload->GetData(); + + CHECK(queueDescriptor.m_Inputs.size() == 1); + CHECK(queueDescriptor.m_Outputs.size() == 1); + + return workload; +} + +template <typename BatchNormalizationWorkloadType, armnn::DataType DataType> +std::unique_ptr<BatchNormalizationWorkloadType> CreateBatchNormalizationWorkloadTest( + armnn::IWorkloadFactory& factory, armnn::Graph& graph, DataLayout dataLayout = DataLayout::NCHW) +{ + TensorShape tensorShape; + switch (dataLayout) + { + case DataLayout::NHWC: + tensorShape = { 2, 4, 4, 3 }; + break; + case DataLayout::NCHW: + default: + tensorShape = { 2, 3, 4, 4 }; + } + + // Creates the layer we're testing. + BatchNormalizationDescriptor layerDesc; + layerDesc.m_Eps = 0.05f; + layerDesc.m_DataLayout = dataLayout; + + BatchNormalizationLayer* const layer = graph.AddLayer<BatchNormalizationLayer>(layerDesc, "layer"); + + armnn::TensorInfo weightInfo({3}, DataType); + layer->m_Mean = std::make_unique<ScopedTensorHandle>(weightInfo); + layer->m_Variance = std::make_unique<ScopedTensorHandle>(weightInfo); + layer->m_Beta = std::make_unique<ScopedTensorHandle>(weightInfo); + layer->m_Gamma = std::make_unique<ScopedTensorHandle>(weightInfo); + layer->m_Mean->Allocate(); + layer->m_Variance->Allocate(); + layer->m_Beta->Allocate(); + layer->m_Gamma->Allocate(); + + // Creates extra layers. + Layer* const input = graph.AddLayer<InputLayer>(0, "input"); + Layer* const output = graph.AddLayer<OutputLayer>(0, "output"); + + // Connects up. + armnn::TensorInfo tensorInfo(tensorShape, DataType); + Connect(input, layer, tensorInfo); + Connect(layer, output, tensorInfo); + CreateTensorHandles(graph, factory); + + // Makes the workload and checks it. + auto workload = MakeAndCheckWorkload<BatchNormalizationWorkloadType>(*layer, factory); + BatchNormalizationQueueDescriptor queueDescriptor = workload->GetData(); + CHECK(queueDescriptor.m_Parameters.m_Eps == 0.05f); + CHECK(queueDescriptor.m_Inputs.size() == 1); + CHECK(queueDescriptor.m_Outputs.size() == 1); + CHECK((queueDescriptor.m_Mean->GetTensorInfo() == TensorInfo({3}, DataType))); + CHECK((queueDescriptor.m_Variance->GetTensorInfo() == TensorInfo({3}, DataType))); + CHECK((queueDescriptor.m_Gamma->GetTensorInfo() == TensorInfo({3}, DataType))); + CHECK((queueDescriptor.m_Beta->GetTensorInfo() == TensorInfo({3}, DataType))); + CHECK((queueDescriptor.m_Parameters.m_DataLayout == dataLayout)); + + // Returns so we can do extra, backend-specific tests. + return workload; +} + +template <typename BatchNormalizationWorkloadType, armnn::DataType DataType> +std::unique_ptr<BatchNormalizationWorkloadType> CreateBatchNormalizationWithBlobWorkloadTest( + armnn::IWorkloadFactory& factory, armnn::Graph& graph, DataLayout dataLayout = DataLayout::NCHW) +{ + TensorShape tensorShape; + switch (dataLayout) + { + case DataLayout::NHWC: + tensorShape = { 2, 4, 4, 3 }; + break; + case DataLayout::NCHW: + default: + tensorShape = { 2, 3, 4, 4 }; + } + + // Creates the layer we're testing. + BatchNormalizationDescriptor layerDesc; + layerDesc.m_Eps = 0.05f; + layerDesc.m_DataLayout = dataLayout; + + BatchNormalizationLayer* const layer = graph.AddLayer<BatchNormalizationLayer>(layerDesc, "layer"); + + armnn::TensorInfo weightInfo({3}, DataType); + layer->m_Mean = std::make_unique<ScopedTensorHandle>(weightInfo); + layer->m_Variance = std::make_unique<ScopedTensorHandle>(weightInfo); + layer->m_Beta = std::make_unique<ScopedTensorHandle>(weightInfo); + layer->m_Gamma = std::make_unique<ScopedTensorHandle>(weightInfo); + layer->m_Mean->Allocate(); + layer->m_Variance->Allocate(); + layer->m_Beta->Allocate(); + layer->m_Gamma->Allocate(); + + auto activationDesc = std::make_shared<ActivationDescriptor>(); + activationDesc->m_A = 10.0f; + activationDesc->m_B = 5.0f; + activationDesc->m_Function = armnn::ActivationFunction::BoundedReLu; + + layer->SetAdditionalInfoForObject(activationDesc); + + // Check that the additional information can be queried from the layer + std::shared_ptr<ActivationDescriptor> activationDescPtr = layer->GetAdditionalInformation<ActivationDescriptor>(); + ARMNN_ASSERT(static_cast<float>(activationDescPtr->m_A) == 10.0f); + ARMNN_ASSERT(static_cast<float>(activationDescPtr->m_B) == 5.0f); + ARMNN_ASSERT( + static_cast<ActivationFunction>(activationDescPtr->m_Function) == armnn::ActivationFunction::BoundedReLu + ); + + // Creates extra layers. + Layer* const input = graph.AddLayer<InputLayer>(0, "input"); + Layer* const output = graph.AddLayer<OutputLayer>(0, "output"); + + // Connects up. + armnn::TensorInfo tensorInfo(tensorShape, DataType); + Connect(input, layer, tensorInfo); + Connect(layer, output, tensorInfo); + CreateTensorHandles(graph, factory); + + // Makes the workload and checks it. + auto workload = MakeAndCheckWorkload<BatchNormalizationWorkloadType>(*layer, factory); + BatchNormalizationQueueDescriptor queueDescriptor = workload->GetData(); + const ActivationDescriptor* queueDescBlobPtr = queueDescriptor.GetAdditionalInformation<ActivationDescriptor>(); + IgnoreUnused(queueDescBlobPtr); + ARMNN_ASSERT(static_cast<float>(queueDescBlobPtr->m_A) == 10.0f); + ARMNN_ASSERT(static_cast<float>(queueDescBlobPtr->m_B) == 5.0f); + ARMNN_ASSERT( + static_cast<ActivationFunction>(queueDescBlobPtr->m_Function) == armnn::ActivationFunction::BoundedReLu + ); + + CHECK(queueDescriptor.m_Parameters.m_Eps == 0.05f); + CHECK(queueDescriptor.m_Inputs.size() == 1); + CHECK(queueDescriptor.m_Outputs.size() == 1); + CHECK((queueDescriptor.m_Mean->GetTensorInfo() == TensorInfo({3}, DataType))); + CHECK((queueDescriptor.m_Variance->GetTensorInfo() == TensorInfo({3}, DataType))); + CHECK((queueDescriptor.m_Gamma->GetTensorInfo() == TensorInfo({3}, DataType))); + CHECK((queueDescriptor.m_Beta->GetTensorInfo() == TensorInfo({3}, DataType))); + CHECK((queueDescriptor.m_Parameters.m_DataLayout == dataLayout)); + + // Returns so we can do extra, backend-specific tests. + return workload; +} + +template <typename Convolution2dWorkload, armnn::DataType DataType> +std::unique_ptr<Convolution2dWorkload> CreateConvolution2dWorkloadTest(armnn::IWorkloadFactory& factory, + armnn::Graph& graph, + DataLayout dataLayout = DataLayout::NCHW, + const ModelOptions& modelOptions = {}) +{ + // Creates the layer we're testing. + Convolution2dDescriptor layerDesc; + layerDesc.m_PadLeft = 3; + layerDesc.m_PadRight = 3; + layerDesc.m_PadTop = 1; + layerDesc.m_PadBottom = 1; + layerDesc.m_StrideX = 2; + layerDesc.m_StrideY = 4; + layerDesc.m_BiasEnabled = true; + layerDesc.m_DataLayout = dataLayout; + + Convolution2dLayer* const layer = graph.AddLayer<Convolution2dLayer>(layerDesc, "layer"); + + TensorShape weightShape = (dataLayout == DataLayout::NCHW) ? TensorShape{2, 3, 5, 3} : TensorShape{2, 5, 3, 3}; + TensorShape inputShape = (dataLayout == DataLayout::NCHW) ? TensorShape{2, 3, 8, 16} : TensorShape{2, 8, 16, 3}; + TensorShape outputShape = (dataLayout == DataLayout::NCHW) ? TensorShape{2, 2, 2, 10} : TensorShape{2, 2, 10, 2}; + + layer->m_Weight = std::make_unique<ScopedTensorHandle>(TensorInfo(weightShape, DataType)); + layer->m_Bias = std::make_unique<ScopedTensorHandle>(TensorInfo({2}, GetBiasDataType(DataType))); + + layer->m_Weight->Allocate(); + layer->m_Bias->Allocate(); + + // Creates extra layers. + Layer* const input = graph.AddLayer<InputLayer>(0, "input"); + Layer* const output = graph.AddLayer<OutputLayer>(0, "output"); + + // Connects up. + Connect(input, layer, TensorInfo(inputShape, DataType)); + Connect(layer, output, TensorInfo(outputShape, DataType)); + CreateTensorHandles(graph, factory); + + // Makes the workload and checks it. + auto workload = MakeAndCheckWorkload<Convolution2dWorkload>(*layer, factory, modelOptions); + + Convolution2dQueueDescriptor queueDescriptor = workload->GetData(); + CHECK(queueDescriptor.m_Parameters.m_StrideX == 2); + CHECK(queueDescriptor.m_Parameters.m_StrideY == 4); + CHECK(queueDescriptor.m_Parameters.m_PadLeft == 3); + CHECK(queueDescriptor.m_Parameters.m_PadRight == 3); + CHECK(queueDescriptor.m_Parameters.m_PadTop == 1); + CHECK(queueDescriptor.m_Parameters.m_PadBottom == 1); + CHECK(queueDescriptor.m_Parameters.m_BiasEnabled); + CHECK((queueDescriptor.m_Parameters.m_DataLayout == dataLayout)); + + CHECK(queueDescriptor.m_Inputs.size() == 1); + CHECK(queueDescriptor.m_Outputs.size() == 1); + CHECK((queueDescriptor.m_Weight->GetTensorInfo() == TensorInfo(weightShape, DataType))); + CHECK((queueDescriptor.m_Bias->GetTensorInfo() == + TensorInfo({2}, GetBiasDataType(DataType)))); + + // Returns so we can do extra, backend-specific tests. + return workload; +} + +template<typename Convolution2dWorkload, armnn::DataType DataType> +std::unique_ptr<Convolution2dWorkload> CreateConvolution2dFusedActivationWithBlobWorkloadTest( + armnn::IWorkloadFactory& factory, + armnn::Graph& graph, + DataLayout dataLayout = DataLayout::NCHW, + const ModelOptions& modelOptions = {}) +{ + // Creates the layer we're testing. + Convolution2dDescriptor layerDesc; + layerDesc.m_PadLeft = 3; + layerDesc.m_PadRight = 3; + layerDesc.m_PadTop = 1; + layerDesc.m_PadBottom = 1; + layerDesc.m_StrideX = 2; + layerDesc.m_StrideY = 4; + layerDesc.m_BiasEnabled = true; + layerDesc.m_DataLayout = dataLayout; + + + Convolution2dLayer* const layer = graph.AddLayer<Convolution2dLayer>(layerDesc, "layer"); + + TensorShape weightShape = (dataLayout == DataLayout::NCHW) ? TensorShape{2, 3, 5, 3} : TensorShape{2, 5, 3, 3}; + TensorShape inputShape = (dataLayout == DataLayout::NCHW) ? TensorShape{2, 3, 8, 16} : TensorShape{2, 8, 16, 3}; + TensorShape outputShape = (dataLayout == DataLayout::NCHW) ? TensorShape{2, 2, 2, 10} : TensorShape{2, 2, 10, 2}; + + layer->m_Weight = std::make_unique<ScopedTensorHandle>(TensorInfo(weightShape, DataType)); + layer->m_Bias = std::make_unique<ScopedTensorHandle>(TensorInfo({2}, GetBiasDataType(DataType))); + + layer->m_Weight->Allocate(); + layer->m_Bias->Allocate(); + + auto activationDesc = std::make_shared<ActivationDescriptor>(); + activationDesc->m_A = 10.0f; + activationDesc->m_B = 5.0f; + activationDesc->m_Function = armnn::ActivationFunction::BoundedReLu; + + layer->SetAdditionalInfoForObject(activationDesc); + + // Check that the additional information can be queried from the layer + std::shared_ptr<ActivationDescriptor> activationDescPtr = layer->GetAdditionalInformation<ActivationDescriptor>(); + + ARMNN_ASSERT(static_cast<float>(activationDescPtr->m_A) == 10.0f); + ARMNN_ASSERT(static_cast<float>(activationDescPtr->m_B) == 5.0f); + ARMNN_ASSERT( + static_cast<ActivationFunction>(activationDescPtr->m_Function) == armnn::ActivationFunction::BoundedReLu + ); + + // Creates extra layers. + Layer* const input = graph.AddLayer<InputLayer>(0, "input"); + Layer* const output = graph.AddLayer<OutputLayer>(0, "output"); + + // Connects up. + Connect(input, layer, TensorInfo(inputShape, DataType)); + Connect(layer, output, TensorInfo(outputShape, DataType)); + CreateTensorHandles(graph, factory); + + // Makes the workload and checks it. + auto workload = MakeAndCheckWorkload<Convolution2dWorkload>(*layer, factory, modelOptions); + + Convolution2dQueueDescriptor queueDescriptor = workload->GetData(); + const ActivationDescriptor* queueDescBlobPtr = queueDescriptor.GetAdditionalInformation<ActivationDescriptor>(); + IgnoreUnused(queueDescBlobPtr); + ARMNN_ASSERT(static_cast<float>(queueDescBlobPtr->m_A) == 10.0f); + ARMNN_ASSERT(static_cast<float>(queueDescBlobPtr->m_B) == 5.0f); + ARMNN_ASSERT( + static_cast<ActivationFunction>(queueDescBlobPtr->m_Function) == armnn::ActivationFunction::BoundedReLu + ); + + CHECK(queueDescriptor.m_Parameters.m_StrideX == 2); + CHECK(queueDescriptor.m_Parameters.m_StrideY == 4); + CHECK(queueDescriptor.m_Parameters.m_PadLeft == 3); + CHECK(queueDescriptor.m_Parameters.m_PadRight == 3); + CHECK(queueDescriptor.m_Parameters.m_PadTop == 1); + CHECK(queueDescriptor.m_Parameters.m_PadBottom == 1); + CHECK(queueDescriptor.m_Parameters.m_BiasEnabled); + CHECK((queueDescriptor.m_Parameters.m_DataLayout == dataLayout)); + CHECK(queueDescriptor.m_Outputs.size() == 1); + CHECK((queueDescriptor.m_Weight->GetTensorInfo() == TensorInfo(weightShape, DataType))); + CHECK((queueDescriptor.m_Bias->GetTensorInfo() == + TensorInfo({2}, GetBiasDataType(DataType)))); + CHECK(queueDescriptor.m_Inputs.size() == 1); + + // Returns so we can do extra, backend-specific tests. + return workload; +} + +template <typename Convolution2dWorkload, armnn::DataType DataType> +std::unique_ptr<Convolution2dWorkload> CreateConvolution2dWorkloadFastMathTest(armnn::IWorkloadFactory& factory, + armnn::Graph& graph, + DataLayout dataLayout = DataLayout::NCHW, + const ModelOptions& modelOptions = {}) +{ + // Creates the layer we're testing. + Convolution2dDescriptor layerDesc; + layerDesc.m_PadLeft = 0; + layerDesc.m_PadRight = 0; + layerDesc.m_PadTop = 0; + layerDesc.m_PadBottom = 0; + layerDesc.m_StrideX = 1; + layerDesc.m_StrideY = 1; + layerDesc.m_BiasEnabled = false; + layerDesc.m_DataLayout = dataLayout; + + Convolution2dLayer* const layer = graph.AddLayer<Convolution2dLayer>(layerDesc, "layer"); + + TensorShape weightShape = TensorShape{32, 32, 3, 3}; + TensorShape inputShape = TensorShape{1, 32, 149, 149}; + TensorShape outputShape = TensorShape{1, 32, 147, 147}; + + layer->m_Weight = std::make_unique<ScopedTensorHandle>(TensorInfo(weightShape, DataType)); + layer->m_Bias = std::make_unique<ScopedTensorHandle>(TensorInfo({2}, GetBiasDataType(DataType))); + + layer->m_Weight->Allocate(); + layer->m_Bias->Allocate(); + + // Creates extra layers. + Layer* const input = graph.AddLayer<InputLayer>(0, "input"); + Layer* const output = graph.AddLayer<OutputLayer>(0, "output"); + + // Connects up. + Connect(input, layer, TensorInfo(inputShape, DataType)); + Connect(layer, output, TensorInfo(outputShape, DataType)); + CreateTensorHandles(graph, factory); + + // Makes the workload and checks it. + auto workload = MakeAndCheckWorkload<Convolution2dWorkload>(*layer, factory, modelOptions); + + Convolution2dQueueDescriptor queueDescriptor = workload->GetData(); + CHECK(queueDescriptor.m_Parameters.m_StrideX == 1); + CHECK(queueDescriptor.m_Parameters.m_StrideY == 1); + CHECK(queueDescriptor.m_Parameters.m_PadLeft == 0); + CHECK(queueDescriptor.m_Parameters.m_PadRight == 0); + CHECK(queueDescriptor.m_Parameters.m_PadTop == 0); + CHECK(queueDescriptor.m_Parameters.m_PadBottom == 0); + CHECK((queueDescriptor.m_Parameters.m_DataLayout == dataLayout)); + + CHECK(queueDescriptor.m_Inputs.size() == 1); + CHECK(queueDescriptor.m_Outputs.size() == 1); + CHECK((queueDescriptor.m_Weight->GetTensorInfo() == TensorInfo(weightShape, DataType))); + + // Returns so we can do extra, backend-specific tests. + return workload; +} + +template <typename LstmWorkload> +std::unique_ptr<LstmWorkload> CreateLstmWorkloadTest(armnn::IWorkloadFactory& factory, armnn::Graph& graph) +{ + // This parameter setting is for withCifgWithPeepholeNoProjection + LstmDescriptor layerDesc; + layerDesc.m_ActivationFunc = 4; + layerDesc.m_ClippingThresCell = 0.0f; + layerDesc.m_ClippingThresProj = 0.0f; + layerDesc.m_CifgEnabled = true; + layerDesc.m_PeepholeEnabled = true; + layerDesc.m_ProjectionEnabled = false; + + LstmLayer* const layer = graph.AddLayer<LstmLayer>(layerDesc, "layer"); + unsigned int batchSize = 2; + unsigned int inputSize = 2; + unsigned int numUnits = 4; + unsigned int outputSize = 4; + + layer->m_BasicParameters.m_InputToForgetWeights = std::make_unique<ScopedTensorHandle> + (TensorInfo({ numUnits, inputSize }, DataType::Float32)); + layer->m_BasicParameters.m_InputToCellWeights = std::make_unique<ScopedTensorHandle> + (TensorInfo({ numUnits, inputSize }, DataType::Float32)); + layer->m_BasicParameters.m_InputToOutputWeights = std::make_unique<ScopedTensorHandle> + (TensorInfo({ numUnits, inputSize }, DataType::Float32)); + layer->m_BasicParameters.m_RecurrentToForgetWeights = std::make_unique<ScopedTensorHandle> + (TensorInfo({ numUnits, outputSize }, DataType::Float32)); + layer->m_BasicParameters.m_RecurrentToCellWeights = std::make_unique<ScopedTensorHandle> + (TensorInfo({ numUnits, outputSize }, DataType::Float32)); + layer->m_BasicParameters.m_RecurrentToOutputWeights = std::make_unique<ScopedTensorHandle> + (TensorInfo({ numUnits, outputSize }, DataType::Float32)); + layer->m_BasicParameters.m_ForgetGateBias = std::make_unique<ScopedTensorHandle> + (TensorInfo({ numUnits }, DataType::Float32)); + layer->m_BasicParameters.m_CellBias = std::make_unique<ScopedTensorHandle> + (TensorInfo({ numUnits }, DataType::Float32)); + layer->m_BasicParameters.m_OutputGateBias = std::make_unique<ScopedTensorHandle> + (TensorInfo({ numUnits }, DataType::Float32)); + + layer->m_BasicParameters.m_InputToForgetWeights->Allocate(); + layer->m_BasicParameters.m_InputToCellWeights->Allocate(); + layer->m_BasicParameters.m_InputToOutputWeights->Allocate(); + layer->m_BasicParameters.m_RecurrentToForgetWeights->Allocate(); + layer->m_BasicParameters.m_RecurrentToCellWeights->Allocate(); + layer->m_BasicParameters.m_RecurrentToOutputWeights->Allocate(); + layer->m_BasicParameters.m_ForgetGateBias->Allocate(); + layer->m_BasicParameters.m_CellBias->Allocate(); + layer->m_BasicParameters.m_OutputGateBias->Allocate(); + + + if (layerDesc.m_PeepholeEnabled) + { + layer->m_PeepholeParameters.m_CellToForgetWeights = std::make_unique<ScopedTensorHandle> + (TensorInfo({ numUnits }, DataType::Float32)); + layer->m_PeepholeParameters.m_CellToOutputWeights = std::make_unique<ScopedTensorHandle> + (TensorInfo({ numUnits }, DataType::Float32)); + layer->m_PeepholeParameters.m_CellToForgetWeights->Allocate(); + layer->m_PeepholeParameters.m_CellToOutputWeights->Allocate(); + } + + // create input and output layers + Layer* const input = graph.AddLayer<InputLayer>(0, "input"); + Layer* const outputStateIn = graph.AddLayer<InputLayer>(1, "outputStateIn"); + Layer* const cellStateIn = graph.AddLayer<InputLayer>(2, "cellStateIn"); + Layer* const scratchBuffer = graph.AddLayer<OutputLayer>(0, "scratchBuffer"); + Layer* const outputStateOut = graph.AddLayer<OutputLayer>(1, "outputStateOut"); + Layer* const cellStateOut = graph.AddLayer<OutputLayer>(2, "cellStateOut"); + Layer* const output = graph.AddLayer<OutputLayer>(3, "output"); + + // connect up + armnn::TensorInfo lstmTensorInfo1({ batchSize, inputSize }, DataType::Float32); + armnn::TensorInfo lstmTensorInfo2({ batchSize, numUnits}, DataType::Float32); + armnn::TensorInfo lstmTensorInfo3({ batchSize, outputSize }, DataType::Float32); + armnn::TensorInfo lstmTensorInfoScratchBuff({ batchSize, numUnits * (layerDesc.m_CifgEnabled ? 3 : 4) }, + DataType::Float32); + Connect(input, layer, lstmTensorInfo1, 0, 0); + Connect(cellStateIn, layer, lstmTensorInfo2, 0, 1); + Connect(outputStateIn, layer, lstmTensorInfo3, 0, 2); + Connect(layer, scratchBuffer, lstmTensorInfoScratchBuff, 0, 0); + Connect(layer, outputStateOut, lstmTensorInfo3, 1, 0); + Connect(layer, cellStateOut, lstmTensorInfo2, 2, 0); + Connect(layer, output, lstmTensorInfo3, 3, 0); + + CreateTensorHandles(graph, factory); + + // make the workload and check it + auto workload = MakeAndCheckWorkload<LstmWorkload>(*layer, factory); + LstmQueueDescriptor queueDescriptor = workload->GetData(); + CHECK(queueDescriptor.m_Parameters.m_ActivationFunc == 4); + CHECK(queueDescriptor.m_Parameters.m_ClippingThresCell == 0.0f); + CHECK(queueDescriptor.m_Parameters.m_ClippingThresProj == 0.0f); + CHECK(queueDescriptor.m_Inputs.size() == 3); + CHECK(queueDescriptor.m_Outputs.size() == 4); + + CHECK((queueDescriptor.m_InputToForgetWeights->GetTensorInfo() == TensorInfo({ numUnits, inputSize }, + DataType::Float32))); + CHECK((queueDescriptor.m_OutputGateBias->GetTensorInfo() == TensorInfo({ numUnits }, + DataType::Float32))); + CHECK((queueDescriptor.m_CellBias->GetTensorInfo() == TensorInfo({ numUnits }, DataType::Float32))); + return workload; +} + +template <typename QuantizedLstmWorkload> +std::unique_ptr<QuantizedLstmWorkload> CreateQuantizedLstmWorkloadTest(armnn::IWorkloadFactory& factory, + armnn::Graph& graph) +{ + auto layer = graph.AddLayer<QuantizedLstmLayer>("quantizedLstmlayer"); + unsigned int numBatches = 2; + unsigned int inputSize = 2; + unsigned int outputSize = 4; + + // Scale/Offset for input/output, cellState In/Out, weights, bias + float inputOutputScale = 0.0078125f; + int32_t inputOutputOffset = 128; + + float cellStateScale = 0.00048828125f; + int32_t cellStateOffset = 0; + + float weightsScale = 0.00408021f; + int32_t weightsOffset = 100; + + float biasScale = 3.1876640625e-05f; + int32_t biasOffset = 0; + + // Weights and bias tensor and quantization info + armnn::TensorInfo inputWeightsInfo({outputSize, inputSize}, + armnn::DataType::QAsymmU8, + weightsScale, + weightsOffset); + + armnn::TensorInfo recurrentWeightsInfo({outputSize, outputSize}, + armnn::DataType::QAsymmU8, + weightsScale, + weightsOffset); + + armnn::TensorInfo biasInfo({outputSize}, + armnn::DataType::Signed32, + biasScale, + biasOffset); + + // Weights and bias + layer->m_QuantizedLstmParameters.m_InputToInputWeights = + std::make_unique<ScopedTensorHandle>(inputWeightsInfo); + layer->m_QuantizedLstmParameters.m_InputToForgetWeights = + std::make_unique<ScopedTensorHandle>(inputWeightsInfo); + layer->m_QuantizedLstmParameters.m_InputToCellWeights = + std::make_unique<ScopedTensorHandle>(inputWeightsInfo); + layer->m_QuantizedLstmParameters.m_InputToOutputWeights = + std::make_unique<ScopedTensorHandle>(inputWeightsInfo); + + layer->m_QuantizedLstmParameters.m_RecurrentToInputWeights = + std::make_unique<ScopedTensorHandle>(recurrentWeightsInfo); + layer->m_QuantizedLstmParameters.m_RecurrentToForgetWeights = + std::make_unique<ScopedTensorHandle>(recurrentWeightsInfo); + layer->m_QuantizedLstmParameters.m_RecurrentToCellWeights = + std::make_unique<ScopedTensorHandle>(recurrentWeightsInfo); + layer->m_QuantizedLstmParameters.m_RecurrentToOutputWeights = + std::make_unique<ScopedTensorHandle>(recurrentWeightsInfo); + + layer->m_QuantizedLstmParameters.m_InputGateBias = std::make_unique<ScopedTensorHandle>(biasInfo); + layer->m_QuantizedLstmParameters.m_ForgetGateBias = std::make_unique<ScopedTensorHandle>(biasInfo); + layer->m_QuantizedLstmParameters.m_CellBias = std::make_unique<ScopedTensorHandle>(biasInfo); + layer->m_QuantizedLstmParameters.m_OutputGateBias = std::make_unique<ScopedTensorHandle>(biasInfo); + + // Allocate weights and bias + layer->m_QuantizedLstmParameters.m_InputToInputWeights->Allocate(); + layer->m_QuantizedLstmParameters.m_InputToForgetWeights->Allocate(); + layer->m_QuantizedLstmParameters.m_InputToCellWeights->Allocate(); + layer->m_QuantizedLstmParameters.m_InputToOutputWeights->Allocate(); + + layer->m_QuantizedLstmParameters.m_RecurrentToInputWeights->Allocate(); + layer->m_QuantizedLstmParameters.m_RecurrentToForgetWeights->Allocate(); + layer->m_QuantizedLstmParameters.m_RecurrentToCellWeights->Allocate(); + layer->m_QuantizedLstmParameters.m_RecurrentToOutputWeights->Allocate(); + + layer->m_QuantizedLstmParameters.m_InputGateBias->Allocate(); + layer->m_QuantizedLstmParameters.m_ForgetGateBias->Allocate(); + layer->m_QuantizedLstmParameters.m_CellBias->Allocate(); + layer->m_QuantizedLstmParameters.m_OutputGateBias->Allocate(); + + // Create input and output layers + Layer* const input = graph.AddLayer<InputLayer>(0, "input"); + Layer* const cellStateIn = graph.AddLayer<InputLayer>(1, "cellStateIn"); + Layer* const outputStateIn = graph.AddLayer<InputLayer>(2, "outputStateIn"); + + Layer* const cellStateOut = graph.AddLayer<OutputLayer>(0, "cellStateOut"); + Layer* const outputStateOut = graph.AddLayer<OutputLayer>(1, "outputStateOut"); + + // Input/output tensor info and quantization info + armnn::TensorInfo inputInfo({numBatches , inputSize}, + armnn::DataType::QAsymmU8, + inputOutputScale, + inputOutputOffset); + + armnn::TensorInfo cellStateInfo({numBatches , outputSize}, + armnn::DataType::QSymmS16, + cellStateScale, + cellStateOffset); + + armnn::TensorInfo outputStateInfo({numBatches , outputSize}, + armnn::DataType::QAsymmU8, + inputOutputScale, + inputOutputOffset); + + // Connect input/output slots + Connect(input, layer, inputInfo, 0, 0); + Connect(cellStateIn, layer, cellStateInfo, 0, 1); + Connect(outputStateIn, layer, outputStateInfo, 0, 2); + + Connect(layer, cellStateOut, cellStateInfo, 0, 0); + Connect(layer, outputStateOut, outputStateInfo, 1, 0); + + CreateTensorHandles(graph, factory); + + // Create workload and check layer support + auto workload = MakeAndCheckWorkload<QuantizedLstmWorkload>(*layer, factory); + QuantizedLstmQueueDescriptor queueDescriptor = workload->GetData(); + + // Validate input/output sizes + CHECK(queueDescriptor.m_Inputs.size() == 3); + CHECK(queueDescriptor.m_Outputs.size() == 2); + + // Validate weight tensor info + CHECK((queueDescriptor.m_InputToInputWeights->GetTensorInfo() == inputWeightsInfo)); + CHECK((queueDescriptor.m_InputToForgetWeights->GetTensorInfo() == inputWeightsInfo)); + CHECK((queueDescriptor.m_InputToCellWeights->GetTensorInfo() == inputWeightsInfo)); + CHECK((queueDescriptor.m_InputToOutputWeights->GetTensorInfo() == inputWeightsInfo)); + + CHECK((queueDescriptor.m_RecurrentToInputWeights->GetTensorInfo() == recurrentWeightsInfo)); + CHECK((queueDescriptor.m_RecurrentToForgetWeights->GetTensorInfo() == recurrentWeightsInfo)); + CHECK((queueDescriptor.m_RecurrentToCellWeights->GetTensorInfo() == recurrentWeightsInfo)); + CHECK((queueDescriptor.m_RecurrentToOutputWeights->GetTensorInfo() == recurrentWeightsInfo)); + + CHECK((queueDescriptor.m_InputGateBias->GetTensorInfo() == biasInfo)); + CHECK((queueDescriptor.m_ForgetGateBias->GetTensorInfo() == biasInfo)); + CHECK((queueDescriptor.m_CellBias->GetTensorInfo() == biasInfo)); + CHECK((queueDescriptor.m_OutputGateBias->GetTensorInfo() == biasInfo)); + + return workload; +} + +template <typename QLstmWorkload> +std::unique_ptr<QLstmWorkload> CreateQLstmWorkloadTest(armnn::IWorkloadFactory& factory, + armnn::Graph& graph) +{ + QLstmDescriptor layerDesc; + layerDesc.m_CifgEnabled = true; + layerDesc.m_PeepholeEnabled = false; + layerDesc.m_ProjectionEnabled = false; + layerDesc.m_LayerNormEnabled = true; + + layerDesc.m_CellClip = 0.0f; + layerDesc.m_ProjectionClip = 0.0f; + + layerDesc.m_HiddenStateZeroPoint = 0; + layerDesc.m_HiddenStateScale = 0.007f; + + layerDesc.m_InputIntermediateScale = 0.007059f; + layerDesc.m_ForgetIntermediateScale = 0.007812f; + layerDesc.m_CellIntermediateScale = 0.007059f; + layerDesc.m_OutputIntermediateScale = 0.007812f; + + QLstmLayer* const layer = graph.AddLayer<QLstmLayer>(layerDesc, "qLstm"); + + unsigned int numBatches = 2; + unsigned int inputSize = 4; + unsigned int numUnits = 4; + unsigned int outputSize = 4; + + // Scale/Offset quantization info + float inputScale = 0.0078125f; + int32_t inputOffset = 0; + + // if (!projectionEnabled) outputScale == hiddenStateScale + float outputScale = layerDesc.m_HiddenStateScale; + int32_t outputOffset = layerDesc.m_HiddenStateZeroPoint; + + float cellStateScale = 3.05176e-05f; + int32_t cellStateOffset = 0; + + float weightsScale = 0.00784314f; + int32_t weightsOffset = 0; + + float layerNormScale = 3.05182e-05f; + int32_t layerNormOffset = 0; + + float biasScale = layerNormScale / 1024; + int32_t biasOffset = 0; + + // Weights and bias tensor and quantization info + armnn::TensorInfo inputWeightsInfo({outputSize, inputSize}, + armnn::DataType::QSymmS8, + weightsScale, + weightsOffset); + + armnn::TensorInfo recurrentWeightsInfo({outputSize, outputSize}, + armnn::DataType::QSymmS8, + weightsScale, + weightsOffset); + + armnn::TensorInfo biasInfo({outputSize}, armnn::DataType::Signed32, biasScale, biasOffset); + + armnn::TensorInfo layerNormWeightsInfo({numUnits}, armnn::DataType::QSymmS16, layerNormScale, layerNormOffset); + + // Create and allocate tensors + layer->m_BasicParameters.m_InputToForgetWeights = std::make_unique<ScopedTensorHandle>(inputWeightsInfo); + layer->m_BasicParameters.m_InputToCellWeights = std::make_unique<ScopedTensorHandle>(inputWeightsInfo); + layer->m_BasicParameters.m_InputToOutputWeights = std::make_unique<ScopedTensorHandle>(inputWeightsInfo); + + layer->m_BasicParameters.m_RecurrentToForgetWeights = + std::make_unique<ScopedTensorHandle>(recurrentWeightsInfo); + layer->m_BasicParameters.m_RecurrentToCellWeights = + std::make_unique<ScopedTensorHandle>(recurrentWeightsInfo); + layer->m_BasicParameters.m_RecurrentToOutputWeights = + std::make_unique<ScopedTensorHandle>(recurrentWeightsInfo); + + layer->m_BasicParameters.m_ForgetGateBias = std::make_unique<ScopedTensorHandle>(biasInfo); + layer->m_BasicParameters.m_CellBias = std::make_unique<ScopedTensorHandle>(biasInfo); + layer->m_BasicParameters.m_OutputGateBias = std::make_unique<ScopedTensorHandle>(biasInfo); + + layer->m_LayerNormParameters.m_ForgetLayerNormWeights = + std::make_unique<ScopedTensorHandle>(layerNormWeightsInfo); + layer->m_LayerNormParameters.m_CellLayerNormWeights = + std::make_unique<ScopedTensorHandle>(layerNormWeightsInfo); + layer->m_LayerNormParameters.m_OutputLayerNormWeights = + std::make_unique<ScopedTensorHandle>(layerNormWeightsInfo); + + layer->m_BasicParameters.m_InputToForgetWeights->Allocate(); + layer->m_BasicParameters.m_InputToCellWeights->Allocate(); + layer->m_BasicParameters.m_InputToOutputWeights->Allocate(); + + layer->m_BasicParameters.m_RecurrentToForgetWeights->Allocate(); + layer->m_BasicParameters.m_RecurrentToCellWeights->Allocate(); + layer->m_BasicParameters.m_RecurrentToOutputWeights->Allocate(); + + layer->m_BasicParameters.m_ForgetGateBias->Allocate(); + layer->m_BasicParameters.m_CellBias->Allocate(); + layer->m_BasicParameters.m_OutputGateBias->Allocate(); + + layer->m_LayerNormParameters.m_ForgetLayerNormWeights->Allocate(); + layer->m_LayerNormParameters.m_CellLayerNormWeights->Allocate(); + layer->m_LayerNormParameters.m_OutputLayerNormWeights->Allocate(); + + // Input and output layers + Layer* const input = graph.AddLayer<InputLayer>(0, "input"); + Layer* const outputStateIn = graph.AddLayer<InputLayer>(1, "outputStateIn"); + Layer* const cellStateIn = graph.AddLayer<InputLayer>(2, "cellStateIn"); + + Layer* const outputStateOut = graph.AddLayer<OutputLayer>(0, "outputStateOut"); + Layer* const cellStateOut = graph.AddLayer<OutputLayer>(1, "cellStateOut"); + Layer* const output = graph.AddLayer<OutputLayer>(2, "output"); + + // Input/Output tensor info + armnn::TensorInfo inputInfo({numBatches , inputSize}, + armnn::DataType::QAsymmS8, + inputScale, + inputOffset); + + armnn::TensorInfo cellStateInfo({numBatches , numUnits}, + armnn::DataType::QSymmS16, + cellStateScale, + cellStateOffset); + + armnn::TensorInfo outputStateInfo({numBatches , outputSize}, + armnn::DataType::QAsymmS8, + outputScale, + outputOffset); + + // Connect layers to slots + Connect(input, layer, inputInfo, 0, 0); + Connect(outputStateIn, layer, outputStateInfo, 0, 1); + Connect(cellStateIn, layer, cellStateInfo, 0, 2); + + Connect(layer, outputStateOut, outputStateInfo, 0, 0); + Connect(layer, cellStateOut, cellStateInfo, 1, 0); + Connect(layer, output, outputStateInfo, 2, 0); + + CreateTensorHandles(graph, factory); + + // Create and check workload + auto workload = MakeAndCheckWorkload<QLstmWorkload>(*layer, factory); + QLstmQueueDescriptor queueDescriptor = workload->GetData(); + CHECK(queueDescriptor.m_Parameters.m_CellClip == 0.0f); + CHECK(queueDescriptor.m_Parameters.m_ProjectionClip == 0.0f); + CHECK(queueDescriptor.m_Inputs.size() == 3); + CHECK(queueDescriptor.m_Outputs.size() == 3); + + CHECK((queueDescriptor.m_InputToForgetWeights->GetTensorInfo() == inputWeightsInfo)); + CHECK((queueDescriptor.m_InputToCellWeights->GetTensorInfo() == inputWeightsInfo)); + CHECK((queueDescriptor.m_InputToOutputWeights->GetTensorInfo() == inputWeightsInfo)); + + CHECK((queueDescriptor.m_RecurrentToForgetWeights->GetTensorInfo() == recurrentWeightsInfo)); + CHECK((queueDescriptor.m_RecurrentToCellWeights->GetTensorInfo() == recurrentWeightsInfo)); + CHECK((queueDescriptor.m_RecurrentToOutputWeights->GetTensorInfo() == recurrentWeightsInfo)); + + CHECK((queueDescriptor.m_ForgetGateBias->GetTensorInfo() == biasInfo)); + CHECK((queueDescriptor.m_CellBias->GetTensorInfo() == biasInfo)); + CHECK((queueDescriptor.m_OutputGateBias->GetTensorInfo() == biasInfo)); + + return workload; +} + +template <typename Convolution2dWorkload, armnn::DataType DataType> +std::unique_ptr<Convolution2dWorkload> CreateDirectConvolution2dWorkloadTest(armnn::IWorkloadFactory& factory, + armnn::Graph& graph) +{ + // Creates the layer we're testing. + Convolution2dDescriptor layerDesc; + layerDesc.m_PadLeft = 1; + layerDesc.m_PadRight = 1; + layerDesc.m_PadTop = 1; + layerDesc.m_PadBottom = 1; + layerDesc.m_StrideX = 1; + layerDesc.m_StrideY = 1; + layerDesc.m_BiasEnabled = true; + + Convolution2dLayer* const layer = graph.AddLayer<Convolution2dLayer>(layerDesc, "layer"); + + float inputsQScale = DataType == armnn::DataType::QAsymmU8 ? 1.0f : 0.0; + float outputQScale = DataType == armnn::DataType::QAsymmU8 ? 2.0f : 0.0; + + layer->m_Weight = std::make_unique<ScopedTensorHandle>(TensorInfo({ 2, 3, 3, 3 }, DataType, inputsQScale)); + layer->m_Bias = std::make_unique<ScopedTensorHandle> + (TensorInfo({2}, GetBiasDataType(DataType), inputsQScale)); + layer->m_Weight->Allocate(); + layer->m_Bias->Allocate(); + + // Creates extra layers. + Layer* const input = graph.AddLayer<InputLayer>(0, "input"); + Layer* const output = graph.AddLayer<OutputLayer>(0, "output"); + + // Connects up. + Connect(input, layer, TensorInfo({2, 3, 6, 6}, DataType, inputsQScale)); + Connect(layer, output, TensorInfo({2, 2, 6, 6}, DataType, outputQScale)); + CreateTensorHandles(graph, factory); + + // Makes the workload and checks it. + auto workload = MakeAndCheckWorkload<Convolution2dWorkload>(*layer, factory); + + Convolution2dQueueDescriptor queueDescriptor = workload->GetData(); + CHECK(queueDescriptor.m_Parameters.m_StrideX == 1); + CHECK(queueDescriptor.m_Parameters.m_StrideY == 1); + CHECK(queueDescriptor.m_Parameters.m_PadLeft == 1); + CHECK(queueDescriptor.m_Parameters.m_PadRight == 1); + CHECK(queueDescriptor.m_Parameters.m_PadTop == 1); + CHECK(queueDescriptor.m_Parameters.m_PadBottom == 1); + CHECK(queueDescriptor.m_Parameters.m_BiasEnabled == true); + + CHECK(queueDescriptor.m_Inputs.size() == 1); + CHECK(queueDescriptor.m_Outputs.size() == 1); + CHECK((queueDescriptor.m_Weight->GetTensorInfo() == TensorInfo({2, 3, 3, 3}, + DataType, inputsQScale))); + CHECK((queueDescriptor.m_Bias->GetTensorInfo() + == TensorInfo({2}, GetBiasDataType(DataType), inputsQScale))); + + // Returns so we can do extra, backend-specific tests. + return workload; +} + +template <typename DepthwiseConvolution2dFloat32Workload, armnn::DataType DataType> +std::unique_ptr<DepthwiseConvolution2dFloat32Workload> CreateDepthwiseConvolution2dWorkloadTest( + armnn::IWorkloadFactory& factory, armnn::Graph& graph, DataLayout dataLayout = DataLayout::NCHW) +{ + // Creates the layer we're testing. + DepthwiseConvolution2dDescriptor layerDesc; + layerDesc.m_PadLeft = 1; + layerDesc.m_PadRight = 2; + layerDesc.m_PadTop = 1; + layerDesc.m_PadBottom = 2; + layerDesc.m_StrideX = 1; + layerDesc.m_StrideY = 1; + layerDesc.m_BiasEnabled = false; + layerDesc.m_DataLayout = dataLayout; + + DepthwiseConvolution2dLayer* const layer = graph.AddLayer<DepthwiseConvolution2dLayer>(layerDesc, "layer"); + + layer->m_Weight = std::make_unique<ScopedTensorHandle>(TensorInfo({1, 4, 4, 2}, DataType)); // [ 1, H, W, I*M ] + layer->m_Weight->Allocate(); + + // Creates extra layers. + Layer* const input = graph.AddLayer<InputLayer>(0, "input"); + Layer* const output = graph.AddLayer<OutputLayer>(0, "output"); + + TensorShape inputShape = (dataLayout == DataLayout::NCHW) ? + TensorShape{ 2, 2, 5, 5 } : TensorShape{ 2, 5, 5, 2 }; + TensorShape outputShape = (dataLayout == DataLayout::NCHW) ? + TensorShape{ 2, 2, 5, 5 } : TensorShape{ 2, 5, 5, 2 }; + + // Connects up. + Connect(input, layer, TensorInfo(inputShape, DataType)); + Connect(layer, output, TensorInfo(outputShape, DataType)); + CreateTensorHandles(graph, factory); + + // Makes the workload and checks it. + auto workload = MakeAndCheckWorkload<DepthwiseConvolution2dFloat32Workload>(*layer, factory); + + DepthwiseConvolution2dQueueDescriptor queueDescriptor = workload->GetData(); + CHECK(queueDescriptor.m_Parameters.m_StrideX == 1); + CHECK(queueDescriptor.m_Parameters.m_StrideY == 1); + CHECK(queueDescriptor.m_Parameters.m_PadLeft == 1); + CHECK(queueDescriptor.m_Parameters.m_PadRight == 2); + CHECK(queueDescriptor.m_Parameters.m_PadTop == 1); + CHECK(queueDescriptor.m_Parameters.m_PadBottom == 2); + CHECK(queueDescriptor.m_Parameters.m_BiasEnabled == false); + CHECK((queueDescriptor.m_Parameters.m_DataLayout == dataLayout)); + + CHECK(queueDescriptor.m_Inputs.size() == 1); + CHECK(queueDescriptor.m_Outputs.size() == 1); + CHECK((queueDescriptor.m_Weight->GetTensorInfo() == TensorInfo({1, 4, 4, 2}, DataType))); + + // Returns so we can do extra, backend-specific tests. + return workload; +} + +template <typename FullyConnectedWorkload, armnn::DataType DataType> +std::unique_ptr<FullyConnectedWorkload> CreateFullyConnectedWorkloadTest(armnn::IWorkloadFactory& factory, + armnn::Graph& graph) +{ + // Creates the layer we're testing. + FullyConnectedDescriptor layerDesc; + layerDesc.m_BiasEnabled = false; + layerDesc.m_TransposeWeightMatrix = true; + + FullyConnectedLayer* const layer = graph.AddLayer<FullyConnectedLayer>(layerDesc, "layer"); + + float inputsQScale = DataType == armnn::DataType::QAsymmU8 ? 1.0f : 0.0; + float outputQScale = DataType == armnn::DataType::QAsymmU8 ? 2.0f : 0.0; + + // As optimization isn't run member variables need to be updated. + layer->m_Weight = std::make_unique<ScopedTensorHandle>(TensorInfo({7, 20}, DataType, inputsQScale, 0)); + layer->m_Weight->Allocate(); + + armnn::TensorInfo weightsTensorInfo({7, 20}, DataType, inputsQScale); + weightsTensorInfo.SetConstant(); + + // Creates extra layers. + Layer* const input = graph.AddLayer<InputLayer>(0, "input"); + auto const weights = graph.AddLayer<ConstantLayer>("weights"); + Layer* const output = graph.AddLayer<OutputLayer>(0, "output"); + + weights->m_LayerOutput = std::make_unique<ScopedTensorHandle>(weightsTensorInfo); + weights->m_LayerOutput->Allocate(); + + // Connects up. + Connect(input, layer, TensorInfo({3, 1, 4, 5}, DataType, inputsQScale), 0, 0); + Connect(weights, layer, weightsTensorInfo, 0, 1); + Connect(layer, output, TensorInfo({3, 7}, DataType, outputQScale)); + CreateTensorHandles(graph, factory); + + // Makes the workload and checks it. + auto workload = MakeAndCheckWorkload<FullyConnectedWorkload>(*layer, factory); + + FullyConnectedQueueDescriptor queueDescriptor = workload->GetData(); + CHECK(queueDescriptor.m_Parameters.m_TransposeWeightMatrix == true); + + CHECK(queueDescriptor.m_Inputs.size() == 2); + CHECK(queueDescriptor.m_Outputs.size() == 1); + + // Returns so we can do extra, backend-specific tests. + return workload; +} + +template <typename FullyConnectedWorkload, armnn::DataType DataType> +std::unique_ptr<FullyConnectedWorkload> CreateFullyConnectedWithBlobWorkloadTest + (armnn::IWorkloadFactory& factory, + armnn::Graph& graph) +{ + // Creates the layer we're testing. + FullyConnectedDescriptor layerDesc; + layerDesc.m_BiasEnabled = true; + layerDesc.m_TransposeWeightMatrix = true; + + FullyConnectedLayer* const layer = graph.AddLayer<FullyConnectedLayer>(layerDesc, "layer"); + + float inputsQScale = DataType == armnn::DataType::QAsymmU8 ? 1.0f : 0.0; + float outputQScale = DataType == armnn::DataType::QAsymmU8 ? 2.0f : 0.0; + + // As optimization isn't run member variables need to be updated. + layer->m_Weight = std::make_unique<ScopedTensorHandle>(TensorInfo({7, 20}, DataType, inputsQScale, 0)); + layer->m_Bias = std::make_unique<ScopedTensorHandle>(TensorInfo({7}, GetBiasDataType(DataType), inputsQScale)); + layer->m_Weight->Allocate(); + layer->m_Bias->Allocate(); + + armnn::TensorInfo weightsTensorInfo({7, 20}, DataType, inputsQScale); + armnn::TensorInfo biasesTensorInfo({7}, GetBiasDataType(DataType), inputsQScale); + weightsTensorInfo.SetConstant(); + biasesTensorInfo.SetConstant(); + + auto activationDesc = std::make_shared<ActivationDescriptor>(); + activationDesc->m_A = 10.0f; + activationDesc->m_B = 5.0f; + activationDesc->m_Function = armnn::ActivationFunction::BoundedReLu; + + layer->SetAdditionalInfoForObject(activationDesc); + + // Check that the additional information can be queried from the layer + std::shared_ptr<ActivationDescriptor> activationDescPtr = layer->GetAdditionalInformation<ActivationDescriptor>(); + ARMNN_ASSERT(static_cast<float>(activationDescPtr->m_A) == 10.0f); + ARMNN_ASSERT(static_cast<float>(activationDescPtr->m_B) == 5.0f); + ARMNN_ASSERT(static_cast<ActivationFunction>(activationDescPtr->m_Function) == + armnn::ActivationFunction::BoundedReLu); + + // Creates extra layers. + Layer* const input = graph.AddLayer<InputLayer>(0, "input"); + auto const weights = graph.AddLayer<ConstantLayer>("weights"); + auto const biases = graph.AddLayer<ConstantLayer>("biases"); + Layer* const output = graph.AddLayer<OutputLayer>(0, "output"); + + weights->m_LayerOutput = std::make_unique<ScopedTensorHandle>(weightsTensorInfo); + weights->m_LayerOutput->Allocate(); + biases->m_LayerOutput = std::make_unique<ScopedTensorHandle>(biasesTensorInfo); + biases->m_LayerOutput->Allocate(); + + // Connects up. + Connect(input, layer, TensorInfo({3, 1, 4, 5}, DataType, inputsQScale), 0, 0); + Connect(weights, layer, weightsTensorInfo, 0, 1); + Connect(biases, layer, biasesTensorInfo, 0, 2); + Connect(layer, output, TensorInfo({3, 7}, DataType, outputQScale)); + CreateTensorHandles(graph, factory); + + // Makes the workload and checks it. + auto workload = MakeAndCheckWorkload<FullyConnectedWorkload>(*layer, factory); + + FullyConnectedQueueDescriptor queueDescriptor = workload->GetData(); + + const ActivationDescriptor* queueDescBlobPtr = queueDescriptor.GetAdditionalInformation<ActivationDescriptor>(); + IgnoreUnused(queueDescBlobPtr); + + ARMNN_ASSERT(static_cast<float>(queueDescBlobPtr->m_A) == 10.0f); + ARMNN_ASSERT(static_cast<float>(queueDescBlobPtr->m_B) == 5.0f); + ARMNN_ASSERT( + static_cast<ActivationFunction>(queueDescBlobPtr->m_Function) == armnn::ActivationFunction::BoundedReLu + ); + + CHECK(queueDescriptor.m_Parameters.m_BiasEnabled == true); + CHECK(queueDescriptor.m_Parameters.m_TransposeWeightMatrix == true); + CHECK(queueDescriptor.m_Inputs.size() == 3); + CHECK(queueDescriptor.m_Outputs.size() == 1); + + // Returns so we can do extra, backend-specific tests. + return workload; +} + +template <typename FullyConnectedWorkload, armnn::DataType DataType> +std::unique_ptr<FullyConnectedWorkload> CreateFullyConnectedWorkloadWeightsBiasesAsInputsTest + (armnn::IWorkloadFactory& factory, + armnn::Graph& graph) +{ + // Creates the layer we're testing. + FullyConnectedDescriptor layerDesc; + layerDesc.m_BiasEnabled = true; + layerDesc.m_TransposeWeightMatrix = true; + layerDesc.m_ConstantWeights = false; + + FullyConnectedLayer* const layer = graph.AddLayer<FullyConnectedLayer>(layerDesc, "layer"); + + float inputsQScale = DataType == armnn::DataType::QAsymmU8 ? 1.0f : 0.0; + float outputQScale = DataType == armnn::DataType::QAsymmU8 ? 2.0f : 0.0; + + // Creates extra layers with weights and biases as input layers. + Layer* const input = graph.AddLayer<InputLayer>(1, "input"); + Layer* const weights = graph.AddLayer<InputLayer>(2, "weights"); + Layer* const biases = graph.AddLayer<InputLayer>(3, "biases"); + Layer* const output = graph.AddLayer<OutputLayer>(0, "output"); + + // Connects up. + Connect(input, layer, TensorInfo({3, 1, 4, 5}, DataType, inputsQScale), 0, 0); + Connect(weights, layer, TensorInfo({7, 20}, DataType, inputsQScale), 0, 1); + Connect(biases, layer, TensorInfo({7}, GetBiasDataType(DataType), inputsQScale), 0, 2); + Connect(layer, output, TensorInfo({3, 7}, DataType, outputQScale)); + CreateTensorHandles(graph, factory); + + // Makes the workload and checks it. + auto workload = MakeAndCheckWorkload<FullyConnectedWorkload>(*layer, factory); + + FullyConnectedQueueDescriptor queueDescriptor = workload->GetData(); + + CHECK(queueDescriptor.m_Parameters.m_BiasEnabled == true); + CHECK(queueDescriptor.m_Parameters.m_TransposeWeightMatrix == true); + CHECK(queueDescriptor.m_Parameters.m_ConstantWeights == false); + CHECK(queueDescriptor.m_Inputs.size() == 3); + CHECK(queueDescriptor.m_Outputs.size() == 1); + + // Returns so we can do extra, backend-specific tests. + return workload; +} + + +template <typename NormalizationWorkload, armnn::DataType DataType> +std::unique_ptr<NormalizationWorkload> CreateNormalizationWorkloadTest(armnn::IWorkloadFactory& factory, + armnn::Graph& graph, + DataLayout dataLayout = DataLayout::NCHW) +{ + // Creates the layer we're testing. + NormalizationDescriptor layerDesc; + layerDesc.m_NormChannelType = NormalizationAlgorithmChannel::Across; + layerDesc.m_NormMethodType = NormalizationAlgorithmMethod::LocalBrightness; + layerDesc.m_NormSize = 3; + layerDesc.m_Alpha = 0.5f; + layerDesc.m_Beta = -1.0f; + layerDesc.m_K = 0.2f; + layerDesc.m_DataLayout = dataLayout; + + NormalizationLayer* layer = graph.AddLayer<NormalizationLayer>(layerDesc, "layer"); + + // Creates extra layers. + Layer* const input = graph.AddLayer<InputLayer>(0, "input"); + Layer* const output = graph.AddLayer<OutputLayer>(0, "output"); + + TensorShape inputShape = (dataLayout == DataLayout::NCHW) ? + TensorShape{ 3, 5, 5, 1 } : TensorShape{ 3, 1, 5, 5 }; + TensorShape outputShape = (dataLayout == DataLayout::NCHW) ? + TensorShape{ 3, 5, 5, 1 } : TensorShape{ 3, 1, 5, 5 }; + + // Connects up. + armnn::TensorInfo inputTensorInfo(inputShape, DataType); + armnn::TensorInfo outputTensorInfo(outputShape, DataType); + Connect(input, layer, inputTensorInfo); + Connect(layer, output, outputTensorInfo); + CreateTensorHandles(graph, factory); + + // Makes the workload and checks it. + auto workload = MakeAndCheckWorkload<NormalizationWorkload>(*layer, factory); + + NormalizationQueueDescriptor queueDescriptor = workload->GetData(); + CHECK((queueDescriptor.m_Parameters.m_NormChannelType == NormalizationAlgorithmChannel::Across)); + CHECK((queueDescriptor.m_Parameters.m_NormMethodType == NormalizationAlgorithmMethod::LocalBrightness)); + CHECK(queueDescriptor.m_Parameters.m_NormSize == 3); + CHECK(queueDescriptor.m_Parameters.m_Alpha == 0.5f); + CHECK(queueDescriptor.m_Parameters.m_Beta == -1.0f); + CHECK(queueDescriptor.m_Parameters.m_K == 0.2f); + CHECK((queueDescriptor.m_Parameters.m_DataLayout == dataLayout)); + + CHECK(queueDescriptor.m_Inputs.size() == 1); + CHECK(queueDescriptor.m_Outputs.size() == 1); + + // Returns so we can do extra, backend-specific tests. + return workload; +} + +template <typename Pooling2dWorkload, armnn::DataType DataType> +std::unique_ptr<Pooling2dWorkload> CreatePooling2dWorkloadTest(armnn::IWorkloadFactory& factory, + armnn::Graph& graph, + DataLayout dataLayout = DataLayout::NCHW) +{ + // Creates the layer we're testing. + Pooling2dDescriptor layerDesc; + layerDesc.m_PoolType = PoolingAlgorithm::Average; + layerDesc.m_PoolWidth = 3; + layerDesc.m_PoolHeight = 3; + layerDesc.m_PadLeft = 2; + layerDesc.m_PadRight = 2; + layerDesc.m_PadTop = 1; + layerDesc.m_PadBottom = 1; + layerDesc.m_StrideX = 2; + layerDesc.m_StrideY = 3; + layerDesc.m_OutputShapeRounding = OutputShapeRounding::Floor; + layerDesc.m_DataLayout = dataLayout; + + Pooling2dLayer* const layer = graph.AddLayer<Pooling2dLayer>(layerDesc, "layer"); + + // Create extra layers + Layer* const input = graph.AddLayer<InputLayer>(0, "input"); + Layer* const output = graph.AddLayer<OutputLayer>(0, "output"); + + TensorShape inputShape = (dataLayout == DataLayout::NCHW) ? TensorShape{3, 2, 5, 5} : TensorShape{3, 5, 5, 2}; + TensorShape outputShape = (dataLayout == DataLayout::NCHW) ? TensorShape{3, 2, 2, 4} : TensorShape{3, 2, 4, 2}; + + // Connect up + Connect(input, layer, TensorInfo(inputShape, DataType)); + Connect(layer, output, TensorInfo(outputShape, DataType)); + CreateTensorHandles(graph, factory); + + // Make the workload and checks it + auto workload = MakeAndCheckWorkload<Pooling2dWorkload>(*layer, factory); + + Pooling2dQueueDescriptor queueDescriptor = workload->GetData(); + CHECK((queueDescriptor.m_Parameters.m_PoolType == PoolingAlgorithm::Average)); + CHECK((queueDescriptor.m_Parameters.m_OutputShapeRounding == OutputShapeRounding::Floor)); + CHECK(queueDescriptor.m_Parameters.m_PoolWidth == 3); + CHECK(queueDescriptor.m_Parameters.m_PoolHeight == 3); + CHECK(queueDescriptor.m_Parameters.m_StrideX == 2); + CHECK(queueDescriptor.m_Parameters.m_StrideY == 3); + CHECK(queueDescriptor.m_Parameters.m_PadLeft == 2); + CHECK(queueDescriptor.m_Parameters.m_PadRight == 2); + CHECK(queueDescriptor.m_Parameters.m_PadTop == 1); + CHECK(queueDescriptor.m_Parameters.m_PadBottom == 1); + CHECK((queueDescriptor.m_Parameters.m_DataLayout == dataLayout)); + + CHECK(queueDescriptor.m_Inputs.size() == 1); + CHECK(queueDescriptor.m_Outputs.size() == 1); + + // Return so we can do extra, backend-specific tests + return workload; +} + +template <typename SoftmaxWorkload, armnn::DataType DataType> +std::unique_ptr<SoftmaxWorkload> CreateSoftmaxWorkloadTest(armnn::IWorkloadFactory& factory, + armnn::Graph& graph) +{ + // Create the layer we're testing. + SoftmaxDescriptor softmaxDescriptor; + // Set Axis to -1 if CL or Neon until further Axes are supported. + if (factory.GetBackendId() == armnn::Compute::CpuAcc || factory.GetBackendId() == armnn::Compute::GpuAcc) + { + softmaxDescriptor.m_Axis = -1; + } + + Layer* const layer = graph.AddLayer<SoftmaxLayer>(softmaxDescriptor, "layer"); + // Create extra layers. + Layer* const input = graph.AddLayer<InputLayer>(0, "input"); + Layer* const output = graph.AddLayer<OutputLayer>(0, "output"); + + // Connect up + armnn::TensorInfo tensorInfo({4, 1}, DataType); + if (DataType == armnn::DataType::QAsymmU8) + { + tensorInfo.SetQuantizationOffset(0); + tensorInfo.SetQuantizationScale(1.f / 256); + } + else if (DataType == armnn::DataType::QAsymmS8) + { + tensorInfo.SetQuantizationOffset(-128); + tensorInfo.SetQuantizationScale(1.f / 256); + } + + Connect(input, layer, tensorInfo); + Connect(layer, output, tensorInfo); + CreateTensorHandles(graph, factory); + + // Make the workload and checks it. + auto workload = MakeAndCheckWorkload<SoftmaxWorkload>(*layer, factory); + + SoftmaxQueueDescriptor queueDescriptor = workload->GetData(); + CHECK(queueDescriptor.m_Inputs.size() == 1); + CHECK(queueDescriptor.m_Outputs.size() == 1); + + // Return so we can do extra, backend-specific tests. + return workload; +} + +template<typename SplitterWorkload, armnn::DataType DataType> +std::unique_ptr<SplitterWorkload> + CreateSplitterWorkloadTest(armnn::IWorkloadFactory& factory, armnn::Graph& graph) +{ + // Create the layer we're testing. + // NOTE: need three dimensions channels, height/y, width/x because the Compute + // library restricts subtensors to have the same x and y dimensions as + // their parent tensors, and therefore the origin on the x and y dimension + // has to be zero for any view. So we need a third dimension to split... + // NOTE: arguments are: number of views, number of dimensions. + ViewsDescriptor layerDesc(3, 3); + // NOTE: arguments are: view, dimension, value. + layerDesc.SetViewOriginCoord(0, 0, 0); + layerDesc.SetViewOriginCoord(1, 0, 1); + layerDesc.SetViewOriginCoord(2, 0, 3); + + Layer* const layer = graph.AddLayer<SplitterLayer>(layerDesc, "layer"); + + // Adds extra layers. + Layer* const input = graph.AddLayer<InputLayer>(0, "input"); + Layer* const output0 = graph.AddLayer<OutputLayer>(0, "output0"); + Layer* const output1 = graph.AddLayer<OutputLayer>(1, "output1"); + Layer* const output2 = graph.AddLayer<OutputLayer>(2, "output2"); + + // Connects up. + armnn::TensorInfo tensorInfo({5, 7, 7}, DataType); + Connect(input, layer, tensorInfo); + + armnn::TensorInfo output0Info({1, 7, 7}, DataType); + armnn::TensorInfo output1Info({2, 7, 7}, DataType); + armnn::TensorInfo output2Info({2, 7, 7}, DataType); + + Connect(layer, output0, output0Info, 0, 0); + Connect(layer, output1, output1Info, 1, 0); + Connect(layer, output2, output2Info, 2, 0); + + CreateTensorHandles(graph, factory); + + // Makes the workload and checks it. + auto workload = MakeAndCheckWorkload<SplitterWorkload>(*layer, factory); + + SplitterQueueDescriptor queueDescriptor = workload->GetData(); + CHECK(queueDescriptor.m_Inputs.size() == 1); + CHECK(queueDescriptor.m_Outputs.size() == 3); + CHECK(queueDescriptor.m_ViewOrigins.size() == 3); + + CHECK(queueDescriptor.m_ViewOrigins[0].m_Origin[0] == 0); + CHECK(queueDescriptor.m_ViewOrigins[1].m_Origin[0] == 1); + CHECK(queueDescriptor.m_ViewOrigins[2].m_Origin[0] == 3); + CHECK(queueDescriptor.m_ViewOrigins[0].m_Origin[1] == 0); + CHECK(queueDescriptor.m_ViewOrigins[1].m_Origin[1] == 0); + CHECK(queueDescriptor.m_ViewOrigins[2].m_Origin[1] == 0); + CHECK(queueDescriptor.m_ViewOrigins[0].m_Origin[2] == 0); + CHECK(queueDescriptor.m_ViewOrigins[1].m_Origin[2] == 0); + CHECK(queueDescriptor.m_ViewOrigins[2].m_Origin[2] == 0); + + // Returns so we can do extra, backend-specific tests. + return workload; +} + +/// This function constructs a graph with both a splitter and a concat, and returns a pair of the workloads. +template<typename SplitterWorkload, typename ConcatWorkload, armnn::DataType DataType> +std::pair<std::unique_ptr<SplitterWorkload>, std::unique_ptr<ConcatWorkload>> + CreateSplitterConcatWorkloadTest(armnn::IWorkloadFactory &factory, armnn::Graph &graph) +{ + armnn::TensorInfo inputTensorInfo({ 1, 2, 100, 10 }, DataType); + + armnn::TensorInfo splitTensorInfo1({ 1, 1, 100, 10 }, DataType); + armnn::TensorInfo splitTensorInfo2({ 1, 1, 100, 10 }, DataType); + + //Constructs the graph. + Layer* const input = graph.AddLayer<InputLayer>(0, "input"); + + armnn::ViewsDescriptor splitterViews(2); + splitterViews.SetViewOriginCoord(0, 0, 0); + splitterViews.SetViewOriginCoord(0, 1, 0); + splitterViews.SetViewOriginCoord(0, 2, 0); + splitterViews.SetViewOriginCoord(0, 3, 0); + + splitterViews.SetViewOriginCoord(1, 0, 0); + splitterViews.SetViewOriginCoord(1, 1, 1); + splitterViews.SetViewOriginCoord(1, 2, 0); + splitterViews.SetViewOriginCoord(1, 3, 0); + + // create splitter layer + Layer* const splitter = graph.AddLayer<SplitterLayer>(splitterViews, "splitter"); + CHECK(splitter); + + armnn::OriginsDescriptor concatViews(2); + concatViews.SetViewOriginCoord(0, 0, 0); + concatViews.SetViewOriginCoord(0, 1, 1); + concatViews.SetViewOriginCoord(0, 2, 0); + concatViews.SetViewOriginCoord(0, 3, 0); + + concatViews.SetViewOriginCoord(1, 0, 0); + concatViews.SetViewOriginCoord(1, 1, 0); + concatViews.SetViewOriginCoord(1, 2, 0); + concatViews.SetViewOriginCoord(1, 3, 0); + + // create concat layer + Layer* const concat = graph.AddLayer<ConcatLayer>(concatViews, "concat"); + CHECK(concat); + + Layer* const output = graph.AddLayer<OutputLayer>(0, "output"); + + // Adds connections. + // connect input to splitter + Connect(input, splitter, inputTensorInfo, 0, 0); + // connect splitter[0] to concat[1] + Connect(splitter, concat, splitTensorInfo1, 0, 1); // The splitter & concat are connected up. + // connect splitter[1] to concat[0] + Connect(splitter, concat, splitTensorInfo2, 1, 0); // So that the outputs are flipped round. + // connect concat to output + Connect(concat, output, inputTensorInfo, 0, 0); + + // created tensor handles + CreateTensorHandles(graph, factory); + + // created splitter workload + auto workloadSplitter = MakeAndCheckWorkload<SplitterWorkload>(*splitter, factory); + CHECK(workloadSplitter); + // created concat workload + auto workloadConcat = MakeAndCheckWorkload<ConcatWorkload>(*concat, factory); + CHECK(workloadConcat); + + return {std::move(workloadSplitter), std::move(workloadConcat)}; +} + + +/// This function constructs a graph with a splitter with two outputs. Each of the outputs is then +/// connected to two different activation layers +template<typename SplitterWorkload, typename ActivationWorkload, armnn::DataType DataType> +void CreateSplitterMultipleInputsOneOutputWorkloadTest(armnn::IWorkloadFactory& factory, armnn::Graph& graph, + std::unique_ptr<SplitterWorkload>& wlSplitter, + std::unique_ptr<ActivationWorkload>& wlActiv0_0, + std::unique_ptr<ActivationWorkload>& wlActiv0_1, + std::unique_ptr<ActivationWorkload>& wlActiv1_0, + std::unique_ptr<ActivationWorkload>& wlActiv1_1) +{ + armnn::TensorInfo inputTensorInfo ({ 1, 3, 100, 50 }, DataType); + armnn::TensorInfo splitTensorInfo1({ 1, 1, 100, 50 }, DataType); + armnn::TensorInfo splitTensorInfo2({ 1, 2, 100, 50 }, DataType); + + //Constructs the graph. + Layer* const input = graph.AddLayer<InputLayer>(0, "input"); + + armnn::ViewsDescriptor splitterViews(2); + + splitterViews.SetViewOriginCoord(0, 0, 0); + splitterViews.SetViewOriginCoord(0, 1, 0); + splitterViews.SetViewOriginCoord(0, 2, 0); + splitterViews.SetViewOriginCoord(0, 3, 0); + + splitterViews.SetViewOriginCoord(1, 0, 0); + splitterViews.SetViewOriginCoord(1, 1, 1); + splitterViews.SetViewOriginCoord(1, 2, 0); + splitterViews.SetViewOriginCoord(1, 3, 0); + + Layer* const splitter = graph.AddLayer<SplitterLayer>(splitterViews, "splitter"); + + armnn::ActivationDescriptor activationDesc; + + Layer* const activ0_0 = graph.AddLayer<ActivationLayer>(activationDesc, "activ0_0"); + Layer* const activ0_1 = graph.AddLayer<ActivationLayer>(activationDesc, "activ0_1"); + Layer* const activ1_0 = graph.AddLayer<ActivationLayer>(activationDesc, "activ1_0"); + Layer* const activ1_1 = graph.AddLayer<ActivationLayer>(activationDesc, "activ1_1"); + + Layer* const output1 = graph.AddLayer<OutputLayer>(1, "output1"); + Layer* const output2 = graph.AddLayer<OutputLayer>(2, "output2"); + Layer* const output3 = graph.AddLayer<OutputLayer>(3, "output3"); + Layer* const output4 = graph.AddLayer<OutputLayer>(4, "output4"); + + // Adds connections. + Connect(input, splitter, inputTensorInfo, 0, 0); + Connect(splitter, activ0_0, splitTensorInfo1, 0, 0); + Connect(splitter, activ0_1, splitTensorInfo1, 0, 0); + + Connect(splitter, activ1_0, splitTensorInfo2, 1, 0); + Connect(splitter, activ1_1, splitTensorInfo2, 1, 0); + + Connect(activ0_0, output1, splitTensorInfo1, 0, 0); + Connect(activ0_1, output2, splitTensorInfo1, 0, 0); + Connect(activ1_0, output3, splitTensorInfo2, 0, 0); + Connect(activ1_1, output4, splitTensorInfo2, 0, 0); + + CreateTensorHandles(graph, factory); + + auto workloadSplitter = MakeAndCheckWorkload<SplitterWorkload>(*splitter, factory); + auto workloadActiv0_0 = MakeAndCheckWorkload<ActivationWorkload>(*activ0_0, factory); + auto workloadActiv0_1 = MakeAndCheckWorkload<ActivationWorkload>(*activ0_1, factory); + auto workloadActiv1_0 = MakeAndCheckWorkload<ActivationWorkload>(*activ1_0, factory); + auto workloadActiv1_1 = MakeAndCheckWorkload<ActivationWorkload>(*activ1_1, factory); + + wlSplitter = std::move(workloadSplitter); + wlActiv0_0 = std::move(workloadActiv0_0); + wlActiv0_1 = std::move(workloadActiv0_1); + wlActiv1_0 = std::move(workloadActiv1_0); + wlActiv1_1 = std::move(workloadActiv1_1); +} + +template <typename ResizeWorkload, armnn::DataType DataType> +std::unique_ptr<ResizeWorkload> CreateResizeBilinearWorkloadTest(armnn::IWorkloadFactory& factory, + armnn::Graph& graph, + DataLayout dataLayout = DataLayout::NCHW) +{ + TensorShape inputShape; + TensorShape outputShape; + + switch (dataLayout) { + case DataLayout::NHWC: + inputShape = { 2, 4, 4, 3 }; + outputShape = { 2, 2, 2, 3 }; + break; + case DataLayout::NCHW: + default: + inputShape = { 2, 3, 4, 4 }; + outputShape = { 2, 3, 2, 2 }; + } + + // Creates the layer we're testing. + ResizeDescriptor resizeDesc; + armnnUtils::DataLayoutIndexed dimensionIndices = dataLayout; + resizeDesc.m_Method = ResizeMethod::Bilinear; + resizeDesc.m_TargetWidth = outputShape[dimensionIndices.GetWidthIndex()]; + resizeDesc.m_TargetHeight = outputShape[dimensionIndices.GetHeightIndex()]; + resizeDesc.m_DataLayout = dataLayout; + Layer* const layer = graph.AddLayer<ResizeLayer>(resizeDesc, "resize"); + + // Creates extra layers. + Layer* const input = graph.AddLayer<InputLayer>(0, "input"); + Layer* const output = graph.AddLayer<OutputLayer>(0, "output"); + + // Connects up. + armnn::TensorInfo inputTensorInfo(inputShape, DataType); + armnn::TensorInfo outputTensorInfo(outputShape, DataType); + Connect(input, layer, inputTensorInfo); + Connect(layer, output, outputTensorInfo); + CreateTensorHandles(graph, factory); + + // Makes the workload and checks it. + auto workload = MakeAndCheckWorkload<ResizeWorkload>(*layer, factory); + + auto queueDescriptor = workload->GetData(); + CHECK(queueDescriptor.m_Inputs.size() == 1); + CHECK(queueDescriptor.m_Outputs.size() == 1); + CHECK(queueDescriptor.m_Parameters.m_DataLayout == dataLayout); + + // Returns so we can do extra, backend-specific tests. + return workload; +} + +template <typename BatchToSpaceNdWorkload, armnn::DataType DataType> +std::unique_ptr<BatchToSpaceNdWorkload> CreateBatchToSpaceNdWorkloadTest(armnn::IWorkloadFactory& factory, + armnn::Graph& graph) +{ + BatchToSpaceNdDescriptor desc; + Layer* const layer = graph.AddLayer<BatchToSpaceNdLayer>(desc, "batchToSpace"); + + // Creates extra layers. + Layer* const input = graph.AddLayer<InputLayer>(0, "input"); + Layer* const output = graph.AddLayer<OutputLayer>(0, "output"); + + // Connects up. + armnn::TensorInfo tensorInfo({1, 1, 1, 1}, DataType); + + Connect(input, layer, tensorInfo); + Connect(layer, output, tensorInfo); + + CreateTensorHandles(graph, factory); + + // Makes the workload and checks it. + auto workload = MakeAndCheckWorkload<BatchToSpaceNdWorkload>(*layer, factory); + + BatchToSpaceNdQueueDescriptor queueDescriptor = workload->GetData(); + CHECK(queueDescriptor.m_Inputs.size() == 1); + CHECK(queueDescriptor.m_Outputs.size() == 1); + + return workload; +} + +template <typename LogSoftmaxWorkload, armnn::DataType DataType> +std::unique_ptr<LogSoftmaxWorkload> CreateLogSoftmaxWorkloadTest(armnn::IWorkloadFactory& factory, + armnn::Graph& graph) +{ + // Create the layer we're testing. + LogSoftmaxDescriptor logSoftmaxDescriptor; + // Set Axis to -1 if CL or Neon until further Axes are supported. + if (factory.GetBackendId() == armnn::Compute::CpuAcc || factory.GetBackendId() == armnn::Compute::GpuAcc) + { + logSoftmaxDescriptor.m_Axis = -1; + } + + Layer* const layer = graph.AddLayer<LogSoftmaxLayer>(logSoftmaxDescriptor, "layer"); + // Create extra layers. + Layer* const input = graph.AddLayer<InputLayer>(0, "input"); + Layer* const output = graph.AddLayer<OutputLayer>(0, "output"); + + // Connect up + armnn::TensorInfo tensorInfo({4, 1}, DataType); + + Connect(input, layer, tensorInfo); + Connect(layer, output, tensorInfo); + CreateTensorHandles(graph, factory); + + // Make the workload and checks it. + auto workload = MakeAndCheckWorkload<LogSoftmaxWorkload>(*layer, factory); + + LogSoftmaxQueueDescriptor queueDescriptor = workload->GetData(); + CHECK(queueDescriptor.m_Inputs.size() == 1); + CHECK(queueDescriptor.m_Outputs.size() == 1); + + // Return so we can do extra, backend-specific tests. + return workload; +} + +template <typename L2NormalizationWorkload, armnn::DataType DataType> +std::unique_ptr<L2NormalizationWorkload> CreateL2NormalizationWorkloadTest(armnn::IWorkloadFactory& factory, + armnn::Graph& graph, DataLayout dataLayout = DataLayout::NCHW) +{ + // Creates the layer we're testing. + L2NormalizationDescriptor layerDesc; + layerDesc.m_DataLayout = dataLayout; + + Layer* const layer = graph.AddLayer<L2NormalizationLayer>(layerDesc, "l2norm"); + + // Creates extra layers. + Layer* const input = graph.AddLayer<InputLayer>(0, "input"); + Layer* const output = graph.AddLayer<OutputLayer>(0, "output"); + + TensorShape inputShape = (dataLayout == DataLayout::NCHW) ? + TensorShape{ 5, 20, 50, 67 } : TensorShape{ 5, 50, 67, 20 }; + TensorShape outputShape = (dataLayout == DataLayout::NCHW) ? + TensorShape{ 5, 20, 50, 67 } : TensorShape{ 5, 50, 67, 20 }; + + // Connects up. + armnn::TensorInfo inputTensorInfo(inputShape, DataType); + armnn::TensorInfo outputTensorInfo(outputShape, DataType); + Connect(input, layer, inputTensorInfo); + Connect(layer, output, outputTensorInfo); + CreateTensorHandles(graph, factory); + + // Makes the workload and checks it. + auto workload = MakeAndCheckWorkload<L2NormalizationWorkload>(*layer, factory); + + L2NormalizationQueueDescriptor queueDescriptor = workload->GetData(); + CHECK((queueDescriptor.m_Parameters.m_DataLayout == dataLayout)); + CHECK(queueDescriptor.m_Inputs.size() == 1); + CHECK(queueDescriptor.m_Outputs.size() == 1); + + // Returns so we can do extra, backend-specific tests. + return workload; +} + +template <typename ReshapeWorkload, armnn::DataType DataType> +std::unique_ptr<ReshapeWorkload> CreateReshapeWorkloadTest(armnn::IWorkloadFactory& factory, + armnn::Graph& graph) +{ + // Creates the layer we're testing. + TensorShape outputShape({ 1, 4 }); + ReshapeDescriptor reshapeDesc; + reshapeDesc.m_TargetShape = outputShape; + Layer* const layer = graph.AddLayer<ReshapeLayer>(reshapeDesc, "layer"); + + // Creates extra layers. + Layer* const input = graph.AddLayer<InputLayer>(0, "input"); + Layer* const output = graph.AddLayer<OutputLayer>(0, "output"); + + // Connects up. + armnn::TensorInfo inputTensorInfo({ 4, 1 }, DataType); + armnn::TensorInfo outputTensorInfo(outputShape, DataType); + Connect(input, layer, inputTensorInfo); + Connect(layer, output, outputTensorInfo); + CreateTensorHandles(graph, factory); + + // Makes the workload and checks it. + auto workload = MakeAndCheckWorkload<ReshapeWorkload>(*layer, factory); + + ReshapeQueueDescriptor queueDescriptor = workload->GetData(); + CHECK(queueDescriptor.m_Inputs.size() == 1); + CHECK(queueDescriptor.m_Outputs.size() == 1); + + // Returns so we can do extra, backend-specific tests. + return workload; +} + +template <typename ConvertFp16ToFp32Float32Workload> +std::unique_ptr<ConvertFp16ToFp32Float32Workload> CreateConvertFp16ToFp32WorkloadTest( + armnn::IWorkloadFactory& factory, armnn::Graph& graph) +{ + // Creates the layer we're testing. + ConvertFp16ToFp32Layer* const layer = graph.AddLayer<ConvertFp16ToFp32Layer>("Fp16ToFp32Converter"); + + // Creates extra layers. + Layer* const input = graph.AddLayer<InputLayer>(0, "input"); + Layer* const output = graph.AddLayer<OutputLayer>(0, "output"); + + // Connects up. + armnn::TensorInfo inputTensorInfo({1, 3, 2, 3}, armnn::DataType::Float16); + armnn::TensorInfo outputTensorInfo({1, 3, 2, 3}, armnn::DataType::Float32); + Connect(input, layer, inputTensorInfo); + Connect(layer, output, outputTensorInfo); + CreateTensorHandles(graph, factory); + + // Makes the workload and checks it. + auto workload = MakeAndCheckWorkload<ConvertFp16ToFp32Float32Workload>(*layer, factory); + + ConvertFp16ToFp32QueueDescriptor queueDescriptor = workload->GetData(); + CHECK(queueDescriptor.m_Inputs.size() == 1); + CHECK(queueDescriptor.m_Outputs.size() == 1); + + // Returns so we can do extra, backend-specific tests. + return workload; +} + +template <typename ConvertFp32ToFp16Float16Workload> +std::unique_ptr<ConvertFp32ToFp16Float16Workload> CreateConvertFp32ToFp16WorkloadTest( + armnn::IWorkloadFactory& factory, armnn::Graph& graph) +{ + // Creates the layer we're testing. + ConvertFp32ToFp16Layer* const layer = graph.AddLayer<ConvertFp32ToFp16Layer>("Fp32ToFp16Converter"); + + // Creates extra layers. + Layer* const input = graph.AddLayer<InputLayer>(0, "input"); + Layer* const output = graph.AddLayer<OutputLayer>(0, "output"); + + // Connects up. + armnn::TensorInfo inputTensorInfo({1, 3, 2, 3}, armnn::DataType::Float32); + armnn::TensorInfo outputTensorInfo({1, 3, 2, 3}, armnn::DataType::Float16); + Connect(input, layer, inputTensorInfo); + Connect(layer, output, outputTensorInfo); + CreateTensorHandles(graph, factory); + + // Makes the workload and checks it. + auto workload = MakeAndCheckWorkload<ConvertFp32ToFp16Float16Workload>(*layer, factory); + + ConvertFp32ToFp16QueueDescriptor queueDescriptor = workload->GetData(); + CHECK(queueDescriptor.m_Inputs.size() == 1); + CHECK(queueDescriptor.m_Outputs.size() == 1); + + // Returns so we can do extra, backend-specific tests. + return workload; +} + +template <typename MeanWorkload, armnn::DataType DataType> +std::unique_ptr<MeanWorkload> CreateMeanWorkloadTest(armnn::IWorkloadFactory& factory, armnn::Graph& graph) +{ + // Reduce along the first and second dimensions, and do not keep the reduced dimensions. + MeanDescriptor descriptor({ 1, 2 }, false); + + // Creates the layer we're testing. + Layer* const layer = graph.AddLayer<MeanLayer>(descriptor, "mean"); + + // Creates extra layers. + Layer* const input = graph.AddLayer<InputLayer>(0, "input"); + Layer* const output = graph.AddLayer<OutputLayer>(0, "output"); + + // Connects up. + armnn::TensorInfo inputTensorInfo({ 1, 3, 7, 4 }, DataType); + armnn::TensorInfo outputTensorInfo({ 1, 4 }, DataType); + Connect(input, layer, inputTensorInfo); + Connect(layer, output, outputTensorInfo); + CreateTensorHandles(graph, factory); + + // Makes the workload and checks it. + auto workload = MakeAndCheckWorkload<MeanWorkload>(*layer, factory); + + MeanQueueDescriptor queueDescriptor = workload->GetData(); + CHECK(queueDescriptor.m_Parameters.m_Axis == descriptor.m_Axis); + CHECK(queueDescriptor.m_Parameters.m_KeepDims == descriptor.m_KeepDims); + CHECK(queueDescriptor.m_Inputs.size() == 1); + CHECK(queueDescriptor.m_Outputs.size() == 1); + + // Returns so we can do extra, backend-specific tests. + return workload; +} + +template<typename ConcatWorkload, armnn::DataType DataType> +std::unique_ptr<ConcatWorkload> CreateConcatWorkloadTest(armnn::IWorkloadFactory &factory, + armnn::Graph &graph, + const armnn::TensorShape &outputShape, + unsigned int concatAxis) +{ + armnn::TensorInfo inputTensorInfo({ 2, 3, 2, 5 }, DataType); + armnn::TensorInfo outputTensorInfo(outputShape, DataType); + + // Constructs the graph. + Layer* const input0 = graph.AddLayer<InputLayer>(0, "input0"); + Layer* const input1 = graph.AddLayer<InputLayer>(1, "input1"); + armnn::OriginsDescriptor descriptor; + + std::vector<armnn::TensorShape> inputShapes{{ 2, 3, 2, 5 }, { 2, 3, 2, 5 }}; + + descriptor = CreateDescriptorForConcatenation(inputShapes.begin(), + inputShapes.end(), + concatAxis); + + // create concat layer + Layer* const concat = graph.AddLayer<ConcatLayer>(descriptor, "concat"); + CHECK(concat); + + Layer* const output = graph.AddLayer<OutputLayer>(0, "output"); + + // Adds connections. + // connect input0 to concat + Connect(input0, concat, inputTensorInfo, 0, 0); + // connect input1 to concat + Connect(input1, concat, inputTensorInfo, 0, 1); + // connect concat to output + Connect(concat, output, outputTensorInfo, 0, 0); + + // create tensor handles + CreateTensorHandles(graph, factory); + + // create concat workload + auto workloadConcat = MakeAndCheckWorkload<ConcatWorkload>(*concat, factory); + CHECK(workloadConcat); + + return workloadConcat; +} + +template <typename PreCompiledWorkload, armnn::DataType dataType> +std::pair<armnn::IOptimizedNetworkPtr, std::unique_ptr<PreCompiledWorkload>> CreatePreCompiledWorkloadTest( + armnn::IWorkloadFactory& factory, + armnn::Graph& graph, + bool biasEnabled = false) +{ + IgnoreUnused(graph); + + // build up the structure of the network + armnn::INetworkPtr net(armnn::INetwork::Create()); + + // Add an input layer + armnn::IConnectableLayer* const inputLayer = net->AddInputLayer(0, "input layer"); + CHECK(inputLayer); + + // ArmNN weights tensor shape is OIHW (out channels, in channels, height, width) for NCHW + // ArmNN weights tensor shape is OHWI (out channels, height, width, in channels) for NHWC + // this test is using NHWC, so the weights shape is OHWI + TensorInfo weightsTensorInfo(TensorShape({16, 1, 1, 16}), dataType, 0.9f, 0, true); + unsigned int weightsLength = weightsTensorInfo.GetNumElements(); + + using WeightType = armnn::ResolveType<dataType>; + std::vector<WeightType> convWeightsData(weightsLength); + for (unsigned int i = 0; i < weightsLength; ++i) + { + convWeightsData[i] = static_cast<WeightType>(i); + } + + armnn::ConstTensor weights(weightsTensorInfo, convWeightsData); + + // Add a layer that can be used in the PreCompiled layer + armnn::Convolution2dDescriptor convDesc2d; + convDesc2d.m_StrideX = 1; + convDesc2d.m_StrideY = 1; + convDesc2d.m_BiasEnabled = biasEnabled; + convDesc2d.m_DataLayout = armnn::DataLayout::NHWC; + + armnn::IConnectableLayer* convLayer = nullptr; + const std::string convLayerName("conv layer"); + + if (biasEnabled) + { + constexpr armnn::DataType biasDataType = ( dataType == armnn::DataType::QAsymmU8) ? + armnn::DataType::Signed32 : armnn::DataType::Float32; + + TensorInfo biasTensorInfo(TensorShape({16}), biasDataType, 0.9f * 0.9f, 0, true); + unsigned int biasLength = biasTensorInfo.GetNumElements(); + + using BiasType = armnn::ResolveType<biasDataType>; + std::vector<BiasType> biasData(biasLength); + std::fill(biasData.begin(), biasData.end(), static_cast<BiasType>(0)); + + armnn::ConstTensor biases(biasTensorInfo, biasData); + + // Create convolution layer with biases + convLayer = net->AddConvolution2dLayer(convDesc2d, + weights, + Optional<ConstTensor>(biases), + convLayerName.c_str()); + } + else + { + // Create convolution layer without biases + convLayer = net->AddConvolution2dLayer(convDesc2d, + weights, + EmptyOptional(), + convLayerName.c_str()); + } + + CHECK(convLayer); + + // Add an output layer + armnn::IConnectableLayer* const outputLayer = net->AddOutputLayer(0, "output layer"); + CHECK(outputLayer); + + // set the tensors in the network (NHWC format) + TensorInfo inputTensorInfo(TensorShape({ 1, 16, 16, 16 }), dataType); + if (dataType == armnn::DataType::QAsymmU8) + { + inputTensorInfo.SetQuantizationOffset(0); + inputTensorInfo.SetQuantizationScale(0.9f); + } + + TensorInfo outputTensorInfo(TensorShape({1, 16, 16, 16}), dataType); + if (dataType == armnn::DataType::QAsymmU8) + { + outputTensorInfo.SetQuantizationOffset(0); + outputTensorInfo.SetQuantizationScale(0.9f); + } + + // Connect the layers + inputLayer->GetOutputSlot(0).Connect(convLayer->GetInputSlot(0)); + inputLayer->GetOutputSlot(0).SetTensorInfo(inputTensorInfo); + + convLayer->GetOutputSlot(0).Connect(outputLayer->GetInputSlot(0)); + convLayer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); + + // Optimize the network for the backend supported by the factory + std::vector<armnn::BackendId> backends = {factory.GetBackendId()}; + armnn::IRuntime::CreationOptions options; + armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options)); + armnn::OptimizerOptions optimizerOptions; + armnn::IOptimizedNetworkPtr optimizedNet = armnn::Optimize(*net, backends, runtime->GetDeviceSpec(), + optimizerOptions); + CHECK(optimizedNet != nullptr); + + // Find the PreCompiled layer in the optimised graph + armnn::Graph& optimisedGraph = GetGraphForTesting(optimizedNet.get()); + Layer* preCompiledLayer = nullptr; + for (auto& layer : optimisedGraph) + { + if (layer->GetType() == LayerType::PreCompiled) + { + preCompiledLayer = layer; + } + } + CHECK(preCompiledLayer != nullptr); + + // Create the TensorHandles. + CreateTensorHandles(optimisedGraph, factory); + + // Make the workload and check it. + auto workload = MakeAndCheckWorkload<PreCompiledWorkload>(*preCompiledLayer, factory); + + PreCompiledQueueDescriptor queueDescriptor = workload->GetData(); + CHECK(queueDescriptor.m_Inputs.size() == 1); + CHECK(queueDescriptor.m_Outputs.size() == 1); + + // Returns the workload so we can do extra, backend-specific tests. + // NOTE: We need to return the optimised network as well, otherwise it gets + // out of scope and the tensor handles get destructed + return std::make_pair(std::move(optimizedNet), std::move(workload)); +} + +template<typename ConstantWorkload, armnn::DataType DataType> +std::unique_ptr<ConstantWorkload> CreateConstantWorkloadTest(armnn::IWorkloadFactory& factory, + armnn::Graph& graph, + const armnn::TensorShape& outputShape) +{ + armnn::TensorInfo outputTensorInfo(outputShape, DataType); + + // create constant layer + auto constant = graph.AddLayer<ConstantLayer>("constant"); + CHECK(constant); + constant->m_LayerOutput = std::make_unique<ScopedTensorHandle>(outputTensorInfo); + + Layer* const output = graph.AddLayer<OutputLayer>(0, "output"); + + // Adds connections. + // connect constant to output + Connect(constant, output, outputTensorInfo, 0, 0); + + // create tensor handles + CreateTensorHandles(graph, factory); + + // create Constant workload" + auto workloadConstant = MakeAndCheckWorkload<ConstantWorkload>(*constant, factory); + CHECK(workloadConstant); + + return workloadConstant; +} + +template <typename PreluWorkload> +std::unique_ptr<PreluWorkload> CreatePreluWorkloadTest(armnn::IWorkloadFactory& factory, + armnn::Graph& graph, + const armnn::TensorShape& inputShape, + const armnn::TensorShape& alphaShape, + const armnn::TensorShape& outputShape, + armnn::DataType dataType) +{ + // Creates the PReLU layer + Layer* const layer = graph.AddLayer<PreluLayer>("prelu"); + CHECK(layer != nullptr); + + // Creates extra layers + Layer* const input = graph.AddLayer<InputLayer> (0, "input"); + Layer* const alpha = graph.AddLayer<InputLayer> (1, "alpha"); + Layer* const output = graph.AddLayer<OutputLayer>(0, "output"); + CHECK(input != nullptr); + CHECK(alpha != nullptr); + CHECK(output != nullptr); + + // Connects up + armnn::TensorInfo inputTensorInfo (inputShape, dataType); + armnn::TensorInfo alphaTensorInfo (alphaShape, dataType); + armnn::TensorInfo outputTensorInfo(outputShape, dataType); + Connect(input, layer, inputTensorInfo, 0, 0); + Connect(alpha, layer, alphaTensorInfo, 0, 1); + Connect(layer, output, outputTensorInfo, 0, 0); + CreateTensorHandles(graph, factory); + + // Makes the workload and checks it + auto workload = MakeAndCheckWorkload<PreluWorkload>(*layer, factory); + + PreluQueueDescriptor queueDescriptor = workload->GetData(); + CHECK(queueDescriptor.m_Inputs.size() == 2); + CHECK(queueDescriptor.m_Outputs.size() == 1); + + // Returns so we can do extra, backend-specific tests. + return workload; +} + +template <typename SpaceToDepthWorkload, armnn::DataType DataType> +std::unique_ptr<SpaceToDepthWorkload> CreateSpaceToDepthWorkloadTest(armnn::IWorkloadFactory& factory, + armnn::Graph& graph) +{ + SpaceToDepthDescriptor desc; + desc.m_BlockSize = 2; + Layer* const layer = graph.AddLayer<SpaceToDepthLayer>(desc, "spaceToDepth"); + + // Creates extra layers. + Layer* const input = graph.AddLayer<InputLayer>(0, "input"); + Layer* const output = graph.AddLayer<OutputLayer>(0, "output"); + + // Connects up. + armnn::TensorInfo inputTensorInfo({ 1, 2, 2, 1 }, DataType); + armnn::TensorInfo outputTensorInfo({ 1, 1, 1, 4 }, DataType); + + Connect(input, layer, inputTensorInfo); + Connect(layer, output, outputTensorInfo); + + CreateTensorHandles(graph, factory); + + // Makes the workload and checks it. + auto workload = MakeAndCheckWorkload<SpaceToDepthWorkload>(*layer, factory); + + SpaceToDepthQueueDescriptor queueDescriptor = workload->GetData(); + CHECK(queueDescriptor.m_Inputs.size() == 1); + CHECK(queueDescriptor.m_Outputs.size() == 1); + + return workload; +} + +template <typename StackWorkload, armnn::DataType DataType> +std::unique_ptr<StackWorkload> CreateStackWorkloadTest(armnn::IWorkloadFactory& factory, + armnn::Graph& graph, + const armnn::TensorShape& inputShape, + const armnn::TensorShape& outputShape, + unsigned int axis, + unsigned int numInputs) +{ + armnn::TensorInfo inputTensorInfo(inputShape, DataType); + armnn::TensorInfo outputTensorInfo(outputShape, DataType); + + // Constructs the Stack layer. + armnn::StackDescriptor descriptor(axis, numInputs, inputShape); + Layer* const stackLayer = graph.AddLayer<StackLayer>(descriptor, "stack"); + CHECK(stackLayer != nullptr); + + // Constructs layer inputs and output. + std::vector<Layer*> inputs; + for (unsigned int i=0; i<numInputs; ++i) + { + inputs.push_back(graph.AddLayer<InputLayer>( + static_cast<int>(i), + ("input" + std::to_string(i)).c_str() + )); + CHECK(inputs[i] != nullptr); + } + Layer* const output = graph.AddLayer<OutputLayer>(0, "output"); + CHECK(output != nullptr); + + // Adds connections. + for (unsigned int i=0; i<numInputs; ++i) + { + Connect(inputs[i], stackLayer, inputTensorInfo, 0, i); + } + Connect(stackLayer, output, outputTensorInfo, 0, 0); + + CreateTensorHandles(graph, factory); + + auto stackWorkload = MakeAndCheckWorkload<StackWorkload>(*stackLayer, factory); + StackQueueDescriptor queueDescriptor = stackWorkload->GetData(); + CHECK(queueDescriptor.m_Inputs.size() == numInputs); + CHECK(queueDescriptor.m_Outputs.size() == 1); + + return stackWorkload; +} + +} // Anonymous namespace |