From 10b4dfd8e9ccd7a03df7bb053ee1c644cb37f8ab Mon Sep 17 00:00:00 2001 From: David Beck Date: Wed, 19 Sep 2018 12:03:20 +0100 Subject: IVGCVSW-1897 : build infrastructure for the src/backends folder Change-Id: I7ebafb675ccc77ad54d1deb01412a8379a5356bb --- src/backends/test/CreateWorkloadNeon.cpp | 455 +++++++++++++++++++++++++++++++ 1 file changed, 455 insertions(+) create mode 100644 src/backends/test/CreateWorkloadNeon.cpp (limited to 'src/backends/test/CreateWorkloadNeon.cpp') diff --git a/src/backends/test/CreateWorkloadNeon.cpp b/src/backends/test/CreateWorkloadNeon.cpp new file mode 100644 index 0000000000..fbe064e1c4 --- /dev/null +++ b/src/backends/test/CreateWorkloadNeon.cpp @@ -0,0 +1,455 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// +#include "backends/NeonWorkloadFactory.hpp" +#include "backends/NeonWorkloadUtils.hpp" +#include "backends/NeonWorkloads.hpp" +#include "backends/MemCopyWorkload.hpp" +#include "backends/NeonTensorHandle.hpp" + +#include "test/CreateWorkloadClNeon.hpp" + +BOOST_AUTO_TEST_SUITE(CreateWorkloadNeon) + +namespace +{ + +bool TestNeonTensorHandleInfo(armnn::INeonTensorHandle* handle, const armnn::TensorInfo& expectedInfo) +{ + using namespace armnn::armcomputetensorutils; + + const arm_compute::ITensorInfo* handleInfo = handle->GetTensor().info(); + const arm_compute::TensorInfo expectedAclInfo = BuildArmComputeTensorInfo(expectedInfo); + + if (handleInfo->data_type() != expectedAclInfo.data_type()) + { + return false; + } + + if (handleInfo->num_dimensions() != expectedAclInfo.num_dimensions()) + { + return false; + } + + if (handleInfo->quantization_info() != expectedAclInfo.quantization_info()) + { + return false; + } + + for (std::size_t d = 0; d < expectedAclInfo.num_dimensions(); ++d) + { + if (handleInfo->dimension(d) != expectedAclInfo.dimension(d)) + { + return false; + } + } + + return true; +} + +} // namespace + +template +static void NeonCreateActivationWorkloadTest() +{ + Graph graph; + NeonWorkloadFactory factory; + auto workload = CreateActivationWorkloadTest + (factory, graph); + + // Checks that inputs/outputs are as we expect them (see definition of CreateActivationWorkloadTest). + ActivationQueueDescriptor queueDescriptor = workload->GetData(); + auto inputHandle = boost::polymorphic_downcast(queueDescriptor.m_Inputs[0]); + auto outputHandle = boost::polymorphic_downcast(queueDescriptor.m_Outputs[0]); + BOOST_TEST(TestNeonTensorHandleInfo(inputHandle, TensorInfo({1, 1}, DataType))); + BOOST_TEST(TestNeonTensorHandleInfo(outputHandle, TensorInfo({1, 1}, DataType))); +} + +#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC +BOOST_AUTO_TEST_CASE(CreateActivationFloat16Workload) +{ + NeonCreateActivationWorkloadTest(); +} +#endif + +BOOST_AUTO_TEST_CASE(CreateActivationFloatWorkload) +{ + NeonCreateActivationWorkloadTest(); +} + +template +static void NeonCreateArithmethicWorkloadTest() +{ + Graph graph; + NeonWorkloadFactory factory; + auto workload = CreateArithmeticWorkloadTest(factory, graph); + + DescriptorType queueDescriptor = workload->GetData(); + auto inputHandle1 = boost::polymorphic_downcast(queueDescriptor.m_Inputs[0]); + auto inputHandle2 = boost::polymorphic_downcast(queueDescriptor.m_Inputs[1]); + auto outputHandle = boost::polymorphic_downcast(queueDescriptor.m_Outputs[0]); + BOOST_TEST(TestNeonTensorHandleInfo(inputHandle1, TensorInfo({2, 3}, DataType))); + BOOST_TEST(TestNeonTensorHandleInfo(inputHandle2, TensorInfo({2, 3}, DataType))); + BOOST_TEST(TestNeonTensorHandleInfo(outputHandle, TensorInfo({2, 3}, DataType))); +} + +#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC +BOOST_AUTO_TEST_CASE(CreateAdditionFloat16Workload) +{ + NeonCreateArithmethicWorkloadTest(); +} +#endif + +BOOST_AUTO_TEST_CASE(CreateAdditionFloatWorkload) +{ + NeonCreateArithmethicWorkloadTest(); +} + +#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC +BOOST_AUTO_TEST_CASE(CreateSubtractionFloat16Workload) +{ + NeonCreateArithmethicWorkloadTest(); +} +#endif + +BOOST_AUTO_TEST_CASE(CreateSubtractionFloatWorkload) +{ + NeonCreateArithmethicWorkloadTest(); +} + +#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC +BOOST_AUTO_TEST_CASE(CreateMultiplicationFloat16Workload) +{ + NeonCreateArithmethicWorkloadTest(); +} +#endif + +BOOST_AUTO_TEST_CASE(CreateMultiplicationFloatWorkload) +{ + NeonCreateArithmethicWorkloadTest(); +} + +template +static void NeonCreateBatchNormalizationWorkloadTest() +{ + Graph graph; + NeonWorkloadFactory factory; + auto workload = CreateBatchNormalizationWorkloadTest(factory, graph); + + // Checks that outputs and inputs are as we expect them (see definition of CreateBatchNormalizationWorkloadTest). + BatchNormalizationQueueDescriptor queueDescriptor = workload->GetData(); + auto inputHandle = boost::polymorphic_downcast(queueDescriptor.m_Inputs[0]); + auto outputHandle = boost::polymorphic_downcast(queueDescriptor.m_Outputs[0]); + BOOST_TEST(TestNeonTensorHandleInfo(inputHandle, TensorInfo({2, 3, 1, 1}, DataType))); + BOOST_TEST(TestNeonTensorHandleInfo(outputHandle, TensorInfo({2, 3, 1, 1}, DataType))); +} + +#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC +BOOST_AUTO_TEST_CASE(CreateBatchNormalizationFloat16Workload) +{ + NeonCreateBatchNormalizationWorkloadTest(); +} +#endif + +BOOST_AUTO_TEST_CASE(CreateBatchNormalizationFloatWorkload) +{ + NeonCreateBatchNormalizationWorkloadTest(); +} + +template +static void NeonCreateConvolution2dWorkloadTest() +{ + Graph graph; + NeonWorkloadFactory factory; + auto workload = CreateConvolution2dWorkloadTest(factory, graph); + + // Checks that outputs and inputs are as we expect them (see definition of CreateConvolution2dWorkloadTest). + Convolution2dQueueDescriptor queueDescriptor = workload->GetData(); + auto inputHandle = boost::polymorphic_downcast(queueDescriptor.m_Inputs[0]); + auto outputHandle = boost::polymorphic_downcast(queueDescriptor.m_Outputs[0]); + BOOST_TEST(TestNeonTensorHandleInfo(inputHandle, TensorInfo({2, 3, 8, 16}, DataType))); + BOOST_TEST(TestNeonTensorHandleInfo(outputHandle, TensorInfo({2, 2, 2, 10}, DataType))); +} + +#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC +BOOST_AUTO_TEST_CASE(CreateConvolution2dFloat16Workload) +{ + NeonCreateConvolution2dWorkloadTest(); +} +#endif + +BOOST_AUTO_TEST_CASE(CreateConvolution2dFloatWorkload) +{ + NeonCreateConvolution2dWorkloadTest(); +} + +template +static void NeonCreateFullyConnectedWorkloadTest() +{ + Graph graph; + NeonWorkloadFactory factory; + auto workload = CreateFullyConnectedWorkloadTest(factory, graph); + + // Checks that outputs and inputs are as we expect them (see definition of CreateFullyConnectedWorkloadTest). + FullyConnectedQueueDescriptor queueDescriptor = workload->GetData(); + auto inputHandle = boost::polymorphic_downcast(queueDescriptor.m_Inputs[0]); + auto outputHandle = boost::polymorphic_downcast(queueDescriptor.m_Outputs[0]); + BOOST_TEST(TestNeonTensorHandleInfo(inputHandle, TensorInfo({3, 1, 4, 5}, DataType))); + BOOST_TEST(TestNeonTensorHandleInfo(outputHandle, TensorInfo({3, 7}, DataType))); +} + +#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC +BOOST_AUTO_TEST_CASE(CreateFullyConnectedFloat16Workload) +{ + NeonCreateFullyConnectedWorkloadTest(); +} +#endif + +BOOST_AUTO_TEST_CASE(CreateFullyConnectedFloatWorkload) +{ + NeonCreateFullyConnectedWorkloadTest(); +} + +template +static void NeonCreateNormalizationWorkloadTest() +{ + Graph graph; + NeonWorkloadFactory factory; + auto workload = CreateNormalizationWorkloadTest(factory, graph); + + // Checks that outputs and inputs are as we expect them (see definition of CreateNormalizationWorkloadTest). + NormalizationQueueDescriptor queueDescriptor = workload->GetData(); + auto inputHandle = boost::polymorphic_downcast(queueDescriptor.m_Inputs[0]); + auto outputHandle = boost::polymorphic_downcast(queueDescriptor.m_Outputs[0]); + BOOST_TEST(TestNeonTensorHandleInfo(inputHandle, TensorInfo({3, 5, 5, 1}, DataType))); + BOOST_TEST(TestNeonTensorHandleInfo(outputHandle, TensorInfo({3, 5, 5, 1}, DataType))); +} + +#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC +BOOST_AUTO_TEST_CASE(CreateNormalizationFloat16Workload) +{ + NeonCreateNormalizationWorkloadTest(); +} +#endif + +BOOST_AUTO_TEST_CASE(CreateNormalizationFloatWorkload) +{ + NeonCreateNormalizationWorkloadTest(); +} + +template +static void NeonCreatePooling2dWorkloadTest() +{ + Graph graph; + NeonWorkloadFactory factory; + auto workload = CreatePooling2dWorkloadTest + (factory, graph); + + // Checks that outputs and inputs are as we expect them (see definition of CreatePooling2dWorkloadTest). + Pooling2dQueueDescriptor queueDescriptor = workload->GetData(); + auto inputHandle = boost::polymorphic_downcast(queueDescriptor.m_Inputs[0]); + auto outputHandle = boost::polymorphic_downcast(queueDescriptor.m_Outputs[0]); + BOOST_TEST(TestNeonTensorHandleInfo(inputHandle, TensorInfo({3, 2, 5, 5}, DataType))); + BOOST_TEST(TestNeonTensorHandleInfo(outputHandle, TensorInfo({3, 2, 2, 4}, DataType))); +} + +#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC +BOOST_AUTO_TEST_CASE(CreatePooling2dFloat16Workload) +{ + NeonCreatePooling2dWorkloadTest(); +} +#endif + +BOOST_AUTO_TEST_CASE(CreatePooling2dFloatWorkload) +{ + NeonCreatePooling2dWorkloadTest(); +} + +BOOST_AUTO_TEST_CASE(CreatePooling2dUint8Workload) +{ + NeonCreatePooling2dWorkloadTest(); +} + +template +static void NeonCreateReshapeWorkloadTest() +{ + Graph graph; + NeonWorkloadFactory factory; + auto workload = CreateReshapeWorkloadTest(factory, graph); + + // Checks that outputs and inputs are as we expect them (see definition of CreateReshapeWorkloadTest). + ReshapeQueueDescriptor queueDescriptor = workload->GetData(); + auto inputHandle = boost::polymorphic_downcast(queueDescriptor.m_Inputs[0]); + auto outputHandle = boost::polymorphic_downcast(queueDescriptor.m_Outputs[0]); + BOOST_TEST(TestNeonTensorHandleInfo(inputHandle, TensorInfo({4, 1}, DataType))); + BOOST_TEST(TestNeonTensorHandleInfo(outputHandle, TensorInfo({1, 4}, DataType))); +} + +#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC +BOOST_AUTO_TEST_CASE(CreateReshapeFloat16Workload) +{ + NeonCreateReshapeWorkloadTest(); +} +#endif + +BOOST_AUTO_TEST_CASE(CreateReshapeFloatWorkload) +{ + NeonCreateReshapeWorkloadTest(); +} + +BOOST_AUTO_TEST_CASE(CreateReshapeUint8Workload) +{ + NeonCreateReshapeWorkloadTest(); +} + +template +static void NeonCreateSoftmaxWorkloadTest() +{ + Graph graph; + NeonWorkloadFactory factory; + auto workload = CreateSoftmaxWorkloadTest(factory, graph); + + // Checks that outputs and inputs are as we expect them (see definition of CreateSoftmaxWorkloadTest). + SoftmaxQueueDescriptor queueDescriptor = workload->GetData(); + auto inputHandle = boost::polymorphic_downcast(queueDescriptor.m_Inputs[0]); + auto outputHandle = boost::polymorphic_downcast(queueDescriptor.m_Outputs[0]); + BOOST_TEST(TestNeonTensorHandleInfo(inputHandle, TensorInfo({4, 1}, DataType))); + BOOST_TEST(TestNeonTensorHandleInfo(outputHandle, TensorInfo({4, 1}, DataType))); +} + +#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC +BOOST_AUTO_TEST_CASE(CreateSoftmaxFloat16Workload) +{ + NeonCreateSoftmaxWorkloadTest(); +} +#endif + +BOOST_AUTO_TEST_CASE(CreateSoftmaxFloatWorkload) +{ + NeonCreateSoftmaxWorkloadTest(); +} + +BOOST_AUTO_TEST_CASE(CreateSplitterWorkload) +{ + Graph graph; + NeonWorkloadFactory factory; + auto workload = CreateSplitterWorkloadTest(factory, graph); + + // Checks that outputs are as we expect them (see definition of CreateSplitterWorkloadTest). + SplitterQueueDescriptor queueDescriptor = workload->GetData(); + auto inputHandle = boost::polymorphic_downcast(queueDescriptor.m_Inputs[0]); + BOOST_TEST(TestNeonTensorHandleInfo(inputHandle, TensorInfo({5, 7, 7}, DataType::Float32))); + + auto outputHandle0 = boost::polymorphic_downcast(queueDescriptor.m_Outputs[0]); + BOOST_TEST(TestNeonTensorHandleInfo(outputHandle0, TensorInfo({1, 7, 7}, DataType::Float32))); + + auto outputHandle1 = boost::polymorphic_downcast(queueDescriptor.m_Outputs[1]); + BOOST_TEST(TestNeonTensorHandleInfo(outputHandle1, TensorInfo({2, 7, 7}, DataType::Float32))); + + auto outputHandle2 = boost::polymorphic_downcast(queueDescriptor.m_Outputs[2]); + BOOST_TEST(TestNeonTensorHandleInfo(outputHandle2, TensorInfo({2, 7, 7}, DataType::Float32))); +} + +BOOST_AUTO_TEST_CASE(CreateSplitterMerger) +{ + // Tests that it is possible to decide which output of the splitter layer + // should be lined to which input of the merger layer. + // We tested that is is possible to specify 0th output + // of the splitter to be the 1st input to the merger, and the 1st output of the splitter to be 0th input + // of the merger. + + Graph graph; + NeonWorkloadFactory factory; + + auto workloads = + CreateSplitterMergerWorkloadTest(factory, graph); + + auto wlSplitter = std::move(workloads.first); + auto wlMerger = std::move(workloads.second); + + //Checks that the index of inputs/outputs matches what we declared on InputDescriptor construction. + armnn::INeonTensorHandle* sOut0 = dynamic_cast(wlSplitter->GetData().m_Outputs[0]); + armnn::INeonTensorHandle* sOut1 = dynamic_cast(wlSplitter->GetData().m_Outputs[1]); + armnn::INeonTensorHandle* mIn0 = dynamic_cast(wlMerger->GetData().m_Inputs[0]); + armnn::INeonTensorHandle* mIn1 = dynamic_cast(wlMerger->GetData().m_Inputs[1]); + + BOOST_TEST(sOut0); + BOOST_TEST(sOut1); + BOOST_TEST(mIn0); + BOOST_TEST(mIn1); + + bool validDataPointers = (sOut0 == mIn1) && (sOut1 == mIn0); + + BOOST_TEST(validDataPointers); +} + +BOOST_AUTO_TEST_CASE(CreateSingleOutputMultipleInputs) +{ + // Tests that it is possible to assign multiple (two) different layers to each of the outputs of a splitter layer. + // We created a splitter with two outputs. That each of those outputs is used by two different activation layers + + Graph graph; + NeonWorkloadFactory factory; + std::unique_ptr wlSplitter; + std::unique_ptr wlActiv0_0; + std::unique_ptr wlActiv0_1; + std::unique_ptr wlActiv1_0; + std::unique_ptr wlActiv1_1; + + CreateSplitterMultipleInputsOneOutputWorkloadTest(factory, graph, wlSplitter, wlActiv0_0, wlActiv0_1, + wlActiv1_0, wlActiv1_1); + + armnn::INeonTensorHandle* sOut0 = dynamic_cast(wlSplitter->GetData().m_Outputs[0]); + armnn::INeonTensorHandle* sOut1 = dynamic_cast(wlSplitter->GetData().m_Outputs[1]); + armnn::INeonTensorHandle* activ0_0Im = dynamic_cast(wlActiv0_0->GetData().m_Inputs[0]); + armnn::INeonTensorHandle* activ0_1Im = dynamic_cast(wlActiv0_1->GetData().m_Inputs[0]); + armnn::INeonTensorHandle* activ1_0Im = dynamic_cast(wlActiv1_0->GetData().m_Inputs[0]); + armnn::INeonTensorHandle* activ1_1Im = dynamic_cast(wlActiv1_1->GetData().m_Inputs[0]); + + + BOOST_TEST(sOut0); + BOOST_TEST(sOut1); + BOOST_TEST(activ0_0Im); + BOOST_TEST(activ0_1Im); + BOOST_TEST(activ1_0Im); + BOOST_TEST(activ1_1Im); + + bool validDataPointers = (sOut0 == activ0_0Im) && (sOut0 == activ0_1Im) && + (sOut1 == activ1_0Im) && (sOut1 == activ1_1Im); + + BOOST_TEST(validDataPointers); +} + +BOOST_AUTO_TEST_CASE(CreateMemCopyWorkloadsNeon) +{ + NeonWorkloadFactory factory; + CreateMemCopyWorkloads(factory); +} + +BOOST_AUTO_TEST_SUITE_END() -- cgit v1.2.1