| // |
| // Copyright © 2017 Arm Ltd. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "WorkloadTestUtils.hpp" |
| |
| #include <armnn/Exceptions.hpp> |
| |
| #include <backendsCommon/CpuTensorHandle.hpp> |
| #include <backendsCommon/Workload.hpp> |
| |
| #include <reference/workloads/RefWorkloads.hpp> |
| #include <reference/RefWorkloadFactory.hpp> |
| |
| #include <boost/test/unit_test.hpp> |
| |
| using namespace armnn; |
| |
| BOOST_AUTO_TEST_SUITE(WorkloadInfoValidation) |
| |
| |
| |
| BOOST_AUTO_TEST_CASE(QueueDescriptor_Validate_WrongNumOfInputsOutputs) |
| { |
| InputQueueDescriptor invalidData; |
| WorkloadInfo invalidInfo; |
| //Invalid argument exception is expected, because no inputs and no outputs were defined. |
| BOOST_CHECK_THROW(RefWorkloadFactory().CreateInput(invalidData, invalidInfo), armnn::InvalidArgumentException); |
| } |
| |
| BOOST_AUTO_TEST_CASE(RefPooling2dFloat32Workload_Validate_WrongDimTensor) |
| { |
| armnn::TensorInfo inputTensorInfo; |
| armnn::TensorInfo outputTensorInfo; |
| |
| unsigned int inputShape[] = {2, 3, 4}; // <- Invalid - input tensor has to be 4D. |
| unsigned int outputShape[] = {2, 3, 4, 5}; |
| |
| outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32); |
| inputTensorInfo = armnn::TensorInfo(3, inputShape, armnn::DataType::Float32); |
| |
| Pooling2dQueueDescriptor invalidData; |
| WorkloadInfo invalidInfo; |
| |
| AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr); |
| AddInputToWorkload(invalidData, invalidInfo, inputTensorInfo, nullptr); |
| |
| // Invalid argument exception is expected, input tensor has to be 4D. |
| BOOST_CHECK_THROW(RefPooling2dWorkload(invalidData, invalidInfo), armnn::InvalidArgumentException); |
| } |
| |
| BOOST_AUTO_TEST_CASE(SoftmaxQueueDescriptor_Validate_WrongInputHeight) |
| { |
| unsigned int inputHeight = 1; |
| unsigned int inputWidth = 1; |
| unsigned int inputChannels = 4; |
| unsigned int inputNum = 2; |
| |
| unsigned int outputChannels = inputChannels; |
| unsigned int outputHeight = inputHeight + 1; //Makes data invalid - Softmax expects height and width to be 1. |
| unsigned int outputWidth = inputWidth; |
| unsigned int outputNum = inputNum; |
| |
| armnn::TensorInfo inputTensorInfo; |
| armnn::TensorInfo outputTensorInfo; |
| |
| unsigned int inputShape[] = { inputNum, inputChannels, inputHeight, inputWidth }; |
| unsigned int outputShape[] = { outputNum, outputChannels, outputHeight, outputWidth }; |
| |
| inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32); |
| outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32); |
| |
| SoftmaxQueueDescriptor invalidData; |
| WorkloadInfo invalidInfo; |
| |
| AddInputToWorkload(invalidData, invalidInfo, inputTensorInfo, nullptr); |
| AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr); |
| |
| //Invalid argument exception is expected, because height != 1. |
| BOOST_CHECK_THROW(RefSoftmaxWorkload(invalidData, invalidInfo), armnn::InvalidArgumentException); |
| } |
| |
| BOOST_AUTO_TEST_CASE(FullyConnectedQueueDescriptor_Validate_RequiredDataMissing) |
| { |
| unsigned int inputWidth = 1; |
| unsigned int inputHeight = 1; |
| unsigned int inputChannels = 5; |
| unsigned int inputNum = 2; |
| |
| unsigned int outputWidth = 1; |
| unsigned int outputHeight = 1; |
| unsigned int outputChannels = 3; |
| unsigned int outputNum = 2; |
| |
| // Define the tensor descriptors. |
| armnn::TensorInfo inputTensorInfo; |
| armnn::TensorInfo outputTensorInfo; |
| armnn::TensorInfo weightsDesc; |
| armnn::TensorInfo biasesDesc; |
| |
| unsigned int inputShape[] = { inputNum, inputChannels, inputHeight, inputWidth }; |
| unsigned int outputShape[] = { outputNum, outputChannels, outputHeight, outputWidth }; |
| unsigned int weightsShape[] = { 1, 1, inputChannels, outputChannels }; |
| unsigned int biasShape[] = { 1, outputChannels, outputHeight, outputWidth }; |
| |
| inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32); |
| outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32); |
| weightsDesc = armnn::TensorInfo(4, weightsShape, armnn::DataType::Float32); |
| biasesDesc = armnn::TensorInfo(4, biasShape, armnn::DataType::Float32); |
| |
| FullyConnectedQueueDescriptor invalidData; |
| WorkloadInfo invalidInfo; |
| |
| ScopedCpuTensorHandle weightTensor(weightsDesc); |
| ScopedCpuTensorHandle biasTensor(biasesDesc); |
| |
| AddInputToWorkload(invalidData, invalidInfo, inputTensorInfo, nullptr); |
| AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr); |
| invalidData.m_Weight = &weightTensor; |
| invalidData.m_Bias = &biasTensor; |
| invalidData.m_Parameters.m_BiasEnabled = true; |
| invalidData.m_Parameters.m_TransposeWeightMatrix = false; |
| |
| |
| //Invalid argument exception is expected, because not all required fields have been provided. |
| //In particular inputsData[0], outputsData[0] and weightsData can not be null. |
| BOOST_CHECK_THROW(RefFullyConnectedWorkload(invalidData, invalidInfo), armnn::InvalidArgumentException); |
| } |
| |
| |
| BOOST_AUTO_TEST_CASE(NormalizationQueueDescriptor_Validate_WrongInputHeight) |
| { |
| constexpr unsigned int inputNum = 5; |
| constexpr unsigned int inputHeight = 32; |
| constexpr unsigned int inputWidth = 24; |
| constexpr unsigned int inputChannels = 3; |
| |
| constexpr unsigned int outputNum = inputNum; |
| constexpr unsigned int outputChannels = inputChannels; |
| constexpr unsigned int outputHeight = inputHeight + 1; //Makes data invalid - normalization requires. |
| //Input and output to have the same dimensions. |
| constexpr unsigned int outputWidth = inputWidth; |
| |
| |
| armnn::TensorInfo inputTensorInfo; |
| armnn::TensorInfo outputTensorInfo; |
| |
| unsigned int inputShape[] = {inputNum, inputChannels, inputHeight, inputWidth}; |
| unsigned int outputShape[] = {outputNum, outputChannels, outputHeight, outputWidth}; |
| |
| inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32); |
| outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32); |
| |
| |
| armnn::NormalizationAlgorithmMethod normMethod = armnn::NormalizationAlgorithmMethod::LocalBrightness; |
| armnn::NormalizationAlgorithmChannel normChannel = armnn::NormalizationAlgorithmChannel::Across; |
| float alpha = 1.f; |
| float beta = 1.f; |
| float kappa = 1.f; |
| uint32_t normSize = 5; |
| |
| NormalizationQueueDescriptor invalidData; |
| WorkloadInfo invalidInfo; |
| |
| AddInputToWorkload(invalidData, invalidInfo, inputTensorInfo, nullptr); |
| AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr); |
| invalidData.m_Parameters.m_NormChannelType = normChannel; |
| invalidData.m_Parameters.m_NormMethodType = normMethod; |
| invalidData.m_Parameters.m_NormSize = normSize; |
| invalidData.m_Parameters.m_Alpha = alpha; |
| invalidData.m_Parameters.m_Beta = beta; |
| invalidData.m_Parameters.m_K = kappa; |
| |
| //Invalid argument exception is expected, because input height != output height. |
| BOOST_CHECK_THROW(RefNormalizationWorkload(invalidData, invalidInfo), armnn::InvalidArgumentException); |
| } |
| |
| BOOST_AUTO_TEST_CASE(SplitterQueueDescriptor_Validate_WrongWindow) |
| { |
| constexpr unsigned int inputNum = 1; |
| constexpr unsigned int inputHeight = 32; |
| constexpr unsigned int inputWidth = 24; |
| constexpr unsigned int inputChannels = 3; |
| |
| constexpr unsigned int outputNum = inputNum; |
| constexpr unsigned int outputChannels = inputChannels; |
| constexpr unsigned int outputHeight = 18; |
| constexpr unsigned int outputWidth = inputWidth; |
| |
| |
| armnn::TensorInfo inputTensorInfo; |
| armnn::TensorInfo outputTensorInfo; |
| |
| unsigned int inputShape[] = {inputNum, inputChannels, inputHeight, inputWidth}; |
| unsigned int outputShape[] = {outputNum, outputChannels, outputHeight, outputWidth}; |
| |
| inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32); |
| outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32); |
| |
| SplitterQueueDescriptor invalidData; |
| WorkloadInfo invalidInfo; |
| |
| AddInputToWorkload(invalidData, invalidInfo, inputTensorInfo, nullptr); |
| AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr); |
| |
| // Invalid, since it has only 3 dimensions while the input tensor is 4d. |
| std::vector<unsigned int> wOrigin = {0, 0, 0}; |
| armnn::SplitterQueueDescriptor::ViewOrigin window(wOrigin); |
| invalidData.m_ViewOrigins.push_back(window); |
| |
| BOOST_TEST_INFO("Invalid argument exception is expected, because split window dimensionality does not " |
| "match input."); |
| BOOST_CHECK_THROW(RefSplitterWorkload(invalidData, invalidInfo), armnn::InvalidArgumentException); |
| |
| // Invalid, since window extends past the boundary of input tensor. |
| std::vector<unsigned int> wOrigin3 = {0, 0, 15, 0}; |
| armnn::SplitterQueueDescriptor::ViewOrigin window3(wOrigin3); |
| invalidData.m_ViewOrigins[0] = window3; |
| BOOST_TEST_INFO("Invalid argument exception is expected (wOrigin3[2]+ outputHeight > inputHeight"); |
| BOOST_CHECK_THROW(RefSplitterWorkload(invalidData, invalidInfo), armnn::InvalidArgumentException); |
| |
| |
| std::vector<unsigned int> wOrigin4 = {0, 0, 0, 0}; |
| armnn::SplitterQueueDescriptor::ViewOrigin window4(wOrigin4); |
| invalidData.m_ViewOrigins[0] = window4; |
| |
| std::vector<unsigned int> wOrigin5 = {1, 16, 20, 2}; |
| armnn::SplitterQueueDescriptor::ViewOrigin window5(wOrigin4); |
| invalidData.m_ViewOrigins.push_back(window5); |
| |
| BOOST_TEST_INFO("Invalid exception due to number of split windows not matching number of outputs."); |
| BOOST_CHECK_THROW(RefSplitterWorkload(invalidData, invalidInfo), armnn::InvalidArgumentException); |
| } |
| |
| |
| BOOST_AUTO_TEST_CASE(ConcatQueueDescriptor_Validate_WrongWindow) |
| { |
| constexpr unsigned int inputNum = 1; |
| constexpr unsigned int inputChannels = 3; |
| constexpr unsigned int inputHeight = 32; |
| constexpr unsigned int inputWidth = 24; |
| |
| constexpr unsigned int outputNum = 1; |
| constexpr unsigned int outputChannels = 3; |
| constexpr unsigned int outputHeight = 32; |
| constexpr unsigned int outputWidth = 24; |
| |
| |
| armnn::TensorInfo inputTensorInfo; |
| armnn::TensorInfo outputTensorInfo; |
| |
| unsigned int inputShape[] = {inputNum, inputChannels, inputHeight, inputWidth}; |
| unsigned int outputShape[] = {outputNum, outputChannels, outputHeight, outputWidth}; |
| |
| inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32); |
| outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32); |
| |
| ConcatQueueDescriptor invalidData; |
| WorkloadInfo invalidInfo; |
| |
| AddInputToWorkload(invalidData, invalidInfo, inputTensorInfo, nullptr); |
| AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr); |
| |
| // Invalid, since it has only 3 dimensions while the input tensor is 4d. |
| std::vector<unsigned int> wOrigin = {0, 0, 0}; |
| armnn::ConcatQueueDescriptor::ViewOrigin window(wOrigin); |
| invalidData.m_ViewOrigins.push_back(window); |
| |
| BOOST_TEST_INFO("Invalid argument exception is expected, because merge window dimensionality does not " |
| "match input."); |
| BOOST_CHECK_THROW(RefConcatWorkload(invalidData, invalidInfo), armnn::InvalidArgumentException); |
| |
| // Invalid, since window extends past the boundary of output tensor. |
| std::vector<unsigned int> wOrigin3 = {0, 0, 15, 0}; |
| armnn::ConcatQueueDescriptor::ViewOrigin window3(wOrigin3); |
| invalidData.m_ViewOrigins[0] = window3; |
| BOOST_TEST_INFO("Invalid argument exception is expected (wOrigin3[2]+ inputHeight > outputHeight"); |
| BOOST_CHECK_THROW(RefConcatWorkload(invalidData, invalidInfo), armnn::InvalidArgumentException); |
| |
| |
| std::vector<unsigned int> wOrigin4 = {0, 0, 0, 0}; |
| armnn::ConcatQueueDescriptor::ViewOrigin window4(wOrigin4); |
| invalidData.m_ViewOrigins[0] = window4; |
| |
| std::vector<unsigned int> wOrigin5 = {1, 16, 20, 2}; |
| armnn::ConcatQueueDescriptor::ViewOrigin window5(wOrigin4); |
| invalidData.m_ViewOrigins.push_back(window5); |
| |
| BOOST_TEST_INFO("Invalid exception due to number of merge windows not matching number of inputs."); |
| BOOST_CHECK_THROW(RefConcatWorkload(invalidData, invalidInfo), armnn::InvalidArgumentException); |
| } |
| |
| BOOST_AUTO_TEST_CASE(AdditionQueueDescriptor_Validate_InputNumbers) |
| { |
| armnn::TensorInfo input1TensorInfo; |
| armnn::TensorInfo input2TensorInfo; |
| armnn::TensorInfo input3TensorInfo; |
| armnn::TensorInfo outputTensorInfo; |
| |
| unsigned int shape[] = {1, 1, 1, 1}; |
| |
| input1TensorInfo = armnn::TensorInfo(4, shape, armnn::DataType::Float32); |
| input2TensorInfo = armnn::TensorInfo(4, shape, armnn::DataType::Float32); |
| input3TensorInfo = armnn::TensorInfo(4, shape, armnn::DataType::Float32); |
| outputTensorInfo = armnn::TensorInfo(4, shape, armnn::DataType::Float32); |
| |
| AdditionQueueDescriptor invalidData; |
| WorkloadInfo invalidInfo; |
| |
| AddInputToWorkload(invalidData, invalidInfo, input1TensorInfo, nullptr); |
| AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr); |
| |
| // Too few inputs. |
| BOOST_CHECK_THROW(RefAdditionWorkload(invalidData, invalidInfo), armnn::InvalidArgumentException); |
| |
| AddInputToWorkload(invalidData, invalidInfo, input2TensorInfo, nullptr); |
| |
| // Correct. |
| BOOST_CHECK_NO_THROW(RefAdditionWorkload(invalidData, invalidInfo)); |
| |
| AddInputToWorkload(invalidData, invalidInfo, input3TensorInfo, nullptr); |
| |
| // Too many inputs. |
| BOOST_CHECK_THROW(RefAdditionWorkload(invalidData, invalidInfo), armnn::InvalidArgumentException); |
| } |
| |
| BOOST_AUTO_TEST_CASE(AdditionQueueDescriptor_Validate_InputShapes) |
| { |
| armnn::TensorInfo input1TensorInfo; |
| armnn::TensorInfo input2TensorInfo; |
| armnn::TensorInfo outputTensorInfo; |
| |
| unsigned int shape1[] = {1, 1, 2, 1}; |
| unsigned int shape2[] = {1, 1, 3, 2}; |
| |
| // Incompatible shapes even with broadcasting. |
| { |
| input1TensorInfo = armnn::TensorInfo(4, shape1, armnn::DataType::Float32); |
| input2TensorInfo = armnn::TensorInfo(4, shape2, armnn::DataType::Float32); |
| outputTensorInfo = armnn::TensorInfo(4, shape1, armnn::DataType::Float32); |
| |
| AdditionQueueDescriptor invalidData; |
| WorkloadInfo invalidInfo; |
| |
| AddInputToWorkload(invalidData, invalidInfo, input1TensorInfo, nullptr); |
| AddInputToWorkload(invalidData, invalidInfo, input2TensorInfo, nullptr); |
| AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr); |
| |
| BOOST_CHECK_THROW(RefAdditionWorkload(invalidData, invalidInfo), armnn::InvalidArgumentException); |
| } |
| |
| // Output size not compatible with input sizes. |
| { |
| input1TensorInfo = armnn::TensorInfo(4, shape1, armnn::DataType::Float32); |
| input2TensorInfo = armnn::TensorInfo(4, shape1, armnn::DataType::Float32); |
| outputTensorInfo = armnn::TensorInfo(4, shape2, armnn::DataType::Float32); |
| |
| AdditionQueueDescriptor invalidData; |
| WorkloadInfo invalidInfo; |
| |
| AddInputToWorkload(invalidData, invalidInfo, input1TensorInfo, nullptr); |
| AddInputToWorkload(invalidData, invalidInfo, input2TensorInfo, nullptr); |
| AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr); |
| |
| // Output differs. |
| BOOST_CHECK_THROW(RefAdditionWorkload(invalidData, invalidInfo), armnn::InvalidArgumentException); |
| } |
| } |
| |
| BOOST_AUTO_TEST_CASE(MultiplicationQueueDescriptor_Validate_InputTensorDimensionMismatch) |
| { |
| armnn::TensorInfo input0TensorInfo; |
| armnn::TensorInfo input1TensorInfo; |
| armnn::TensorInfo outputTensorInfo; |
| |
| constexpr unsigned int input0Shape[] = { 2, 2, 4, 4 }; |
| constexpr std::size_t dimensionCount = std::extent<decltype(input0Shape)>::value; |
| |
| // Checks dimension consistency for input tensors. |
| for (unsigned int dimIndex = 0; dimIndex < dimensionCount; ++dimIndex) |
| { |
| unsigned int input1Shape[dimensionCount]; |
| for (unsigned int i = 0; i < dimensionCount; ++i) |
| { |
| input1Shape[i] = input0Shape[i]; |
| } |
| |
| ++input1Shape[dimIndex]; |
| |
| input0TensorInfo = armnn::TensorInfo(dimensionCount, input0Shape, armnn::DataType::Float32); |
| input1TensorInfo = armnn::TensorInfo(dimensionCount, input1Shape, armnn::DataType::Float32); |
| outputTensorInfo = armnn::TensorInfo(dimensionCount, input0Shape, armnn::DataType::Float32); |
| |
| MultiplicationQueueDescriptor invalidData; |
| WorkloadInfo invalidInfo; |
| |
| AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr); |
| AddInputToWorkload(invalidData, invalidInfo, input0TensorInfo, nullptr); |
| AddInputToWorkload(invalidData, invalidInfo, input1TensorInfo, nullptr); |
| |
| BOOST_CHECK_THROW(RefMultiplicationWorkload(invalidData, invalidInfo), armnn::InvalidArgumentException); |
| } |
| |
| // Checks dimension consistency for input and output tensors. |
| for (unsigned int dimIndex = 0; dimIndex < dimensionCount; ++dimIndex) |
| { |
| unsigned int outputShape[dimensionCount]; |
| for (unsigned int i = 0; i < dimensionCount; ++i) |
| { |
| outputShape[i] = input0Shape[i]; |
| } |
| |
| ++outputShape[dimIndex]; |
| |
| input0TensorInfo = armnn::TensorInfo(dimensionCount, input0Shape, armnn::DataType::Float32); |
| input1TensorInfo = armnn::TensorInfo(dimensionCount, input0Shape, armnn::DataType::Float32); |
| outputTensorInfo = armnn::TensorInfo(dimensionCount, outputShape, armnn::DataType::Float32); |
| |
| MultiplicationQueueDescriptor invalidData; |
| WorkloadInfo invalidInfo; |
| |
| AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr); |
| AddInputToWorkload(invalidData, invalidInfo, input0TensorInfo, nullptr); |
| AddInputToWorkload(invalidData, invalidInfo, input1TensorInfo, nullptr); |
| |
| BOOST_CHECK_THROW(RefMultiplicationWorkload(invalidData, invalidInfo), armnn::InvalidArgumentException); |
| } |
| } |
| |
| BOOST_AUTO_TEST_CASE(ReshapeQueueDescriptor_Validate_MismatchingNumElements) |
| { |
| armnn::TensorInfo inputTensorInfo; |
| armnn::TensorInfo outputTensorInfo; |
| |
| // The input and output shapes should have the same number of elements, but these don't. |
| unsigned int inputShape[] = { 1, 1, 2, 3 }; |
| unsigned int outputShape[] = { 1, 1, 1, 2 }; |
| |
| inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32); |
| outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32); |
| |
| ReshapeQueueDescriptor invalidData; |
| WorkloadInfo invalidInfo; |
| |
| AddInputToWorkload(invalidData, invalidInfo, inputTensorInfo, nullptr); |
| AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr); |
| |
| // InvalidArgumentException is expected, because the number of elements don't match. |
| BOOST_CHECK_THROW(RefReshapeWorkload(invalidData, invalidInfo), armnn::InvalidArgumentException); |
| } |
| |
| |
| BOOST_AUTO_TEST_CASE(LstmQueueDescriptor_Validate) |
| { |
| armnn::DataType dataType = armnn::DataType::Float32; |
| |
| float qScale = 0.0f; |
| int32_t qOffset = 0; |
| |
| unsigned int batchSize = 2; |
| unsigned int outputSize = 3; |
| unsigned int inputSize = 5; |
| unsigned numUnits = 4; |
| |
| armnn::TensorInfo inputTensorInfo({batchSize , inputSize}, dataType, qScale, qOffset ); |
| armnn::TensorInfo outputStateInTensorInfo({batchSize , outputSize}, dataType, qScale, qOffset); |
| armnn::TensorInfo cellStateInTensorInfo({batchSize , numUnits}, dataType, qScale, qOffset); |
| |
| // Scratch buffer size with CIFG [batchSize, numUnits * 4] |
| armnn::TensorInfo scratchBufferTensorInfo({batchSize, numUnits * 4}, dataType, qScale, qOffset); |
| armnn::TensorInfo cellStateOutTensorInfo({batchSize, numUnits}, dataType, qScale, qOffset); |
| armnn::TensorInfo outputStateOutTensorInfo({batchSize, outputSize}, dataType, qScale, qOffset); |
| armnn::TensorInfo outputTensorInfo({batchSize, outputSize}, dataType, qScale, qOffset); |
| |
| armnn::TensorInfo tensorInfo3({outputSize}, dataType, qScale, qOffset); |
| armnn::TensorInfo tensorInfo4({numUnits}, dataType, qScale, qOffset); |
| armnn::TensorInfo tensorInfo4x5({numUnits, inputSize}, dataType, qScale, qOffset); |
| armnn::TensorInfo tensorInfo4x3({numUnits, outputSize}, dataType, qScale, qOffset); |
| armnn::TensorInfo tensorInfo3x4({outputSize, numUnits}, dataType, qScale, qOffset); |
| |
| LstmQueueDescriptor data; |
| WorkloadInfo info; |
| |
| AddInputToWorkload(data, info, inputTensorInfo, nullptr); |
| AddInputToWorkload(data, info, outputStateInTensorInfo, nullptr); |
| AddInputToWorkload(data, info, cellStateInTensorInfo, nullptr); |
| |
| AddOutputToWorkload(data, info, scratchBufferTensorInfo, nullptr); |
| AddOutputToWorkload(data, info, outputStateOutTensorInfo, nullptr); |
| AddOutputToWorkload(data, info, cellStateOutTensorInfo, nullptr); |
| // AddOutputToWorkload(data, info, outputTensorInfo, nullptr); is left out |
| |
| armnn::ScopedCpuTensorHandle inputToInputWeightsTensor(tensorInfo4x5); |
| armnn::ScopedCpuTensorHandle inputToForgetWeightsTensor(tensorInfo4x5); |
| armnn::ScopedCpuTensorHandle inputToCellWeightsTensor(tensorInfo4x5); |
| armnn::ScopedCpuTensorHandle inputToOutputWeightsTensor(tensorInfo4x5); |
| armnn::ScopedCpuTensorHandle recurrentToForgetWeightsTensor(tensorInfo4x3); |
| armnn::ScopedCpuTensorHandle recurrentToInputWeightsTensor(tensorInfo4x3); |
| armnn::ScopedCpuTensorHandle recurrentToCellWeightsTensor(tensorInfo4x3); |
| armnn::ScopedCpuTensorHandle recurrentToOutputWeightsTensor(tensorInfo4x3); |
| armnn::ScopedCpuTensorHandle cellToInputWeightsTensor(tensorInfo4); |
| armnn::ScopedCpuTensorHandle inputGateBiasTensor(tensorInfo4); |
| armnn::ScopedCpuTensorHandle forgetGateBiasTensor(tensorInfo4); |
| armnn::ScopedCpuTensorHandle cellBiasTensor(tensorInfo4); |
| armnn::ScopedCpuTensorHandle outputGateBiasTensor(tensorInfo4); |
| armnn::ScopedCpuTensorHandle cellToForgetWeightsTensor(tensorInfo4); |
| armnn::ScopedCpuTensorHandle cellToOutputWeightsTensor(tensorInfo4); |
| armnn::ScopedCpuTensorHandle projectionWeightsTensor(tensorInfo3x4); |
| armnn::ScopedCpuTensorHandle projectionBiasTensor(tensorInfo3); |
| armnn::ScopedCpuTensorHandle inputLayerNormWeightsTensor(tensorInfo4); |
| armnn::ScopedCpuTensorHandle forgetLayerNormWeightsTensor(tensorInfo4); |
| armnn::ScopedCpuTensorHandle cellLayerNormWeightsTensor(tensorInfo4); |
| armnn::ScopedCpuTensorHandle outputLayerNormWeightsTensor(tensorInfo4); |
| |
| data.m_InputToInputWeights = &inputToInputWeightsTensor; |
| data.m_InputToForgetWeights = &inputToForgetWeightsTensor; |
| data.m_InputToCellWeights = &inputToCellWeightsTensor; |
| data.m_InputToOutputWeights = &inputToOutputWeightsTensor; |
| data.m_RecurrentToInputWeights = &recurrentToInputWeightsTensor; |
| data.m_RecurrentToForgetWeights = &recurrentToForgetWeightsTensor; |
| data.m_RecurrentToCellWeights = &recurrentToCellWeightsTensor; |
| data.m_RecurrentToOutputWeights = &recurrentToOutputWeightsTensor; |
| data.m_CellToInputWeights = &cellToInputWeightsTensor; |
| data.m_InputGateBias = &inputGateBiasTensor; |
| data.m_ForgetGateBias = &forgetGateBiasTensor; |
| data.m_CellBias = &cellBiasTensor; |
| data.m_OutputGateBias = &outputGateBiasTensor; |
| data.m_CellToForgetWeights = &cellToForgetWeightsTensor; |
| data.m_CellToOutputWeights = &cellToOutputWeightsTensor; |
| data.m_ProjectionWeights = &projectionWeightsTensor; |
| data.m_ProjectionBias = &projectionBiasTensor; |
| |
| data.m_InputLayerNormWeights = &inputLayerNormWeightsTensor; |
| data.m_ForgetLayerNormWeights = &forgetLayerNormWeightsTensor; |
| data.m_CellLayerNormWeights = &cellLayerNormWeightsTensor; |
| data.m_OutputLayerNormWeights = &outputLayerNormWeightsTensor; |
| |
| // Flags to set test configuration |
| data.m_Parameters.m_ActivationFunc = 4; |
| data.m_Parameters.m_CifgEnabled = false; |
| data.m_Parameters.m_PeepholeEnabled = true; |
| data.m_Parameters.m_ProjectionEnabled = true; |
| data.m_Parameters.m_LayerNormEnabled = true; |
| |
| // check wrong number of outputs |
| BOOST_CHECK_THROW(data.Validate(info), armnn::InvalidArgumentException); |
| AddOutputToWorkload(data, info, outputTensorInfo, nullptr); |
| |
| // check wrong cifg parameter configuration |
| data.m_Parameters.m_CifgEnabled = true; |
| armnn::TensorInfo scratchBufferTensorInfo2({batchSize, numUnits * 3}, dataType, qScale, qOffset); |
| SetWorkloadOutput(data, info, 0, scratchBufferTensorInfo2, nullptr); |
| BOOST_CHECK_THROW(data.Validate(info), armnn::InvalidArgumentException); |
| data.m_Parameters.m_CifgEnabled = false; |
| SetWorkloadOutput(data, info, 0, scratchBufferTensorInfo, nullptr); |
| |
| // check wrong inputGateBias configuration |
| data.m_InputGateBias = nullptr; |
| BOOST_CHECK_THROW(data.Validate(info), armnn::InvalidArgumentException); |
| data.m_InputGateBias = &inputGateBiasTensor; |
| |
| // check inconsistant projection parameters |
| data.m_Parameters.m_ProjectionEnabled = false; |
| BOOST_CHECK_THROW(data.Validate(info), armnn::InvalidArgumentException); |
| data.m_Parameters.m_ProjectionEnabled = true; |
| data.m_ProjectionWeights = nullptr; |
| BOOST_CHECK_THROW(data.Validate(info), armnn::InvalidArgumentException); |
| data.m_ProjectionWeights = &projectionWeightsTensor; |
| |
| // check missing input layer normalisation weights |
| data.m_InputLayerNormWeights = nullptr; |
| BOOST_CHECK_THROW(data.Validate(info), armnn::InvalidArgumentException); |
| data.m_InputLayerNormWeights = &inputLayerNormWeightsTensor; |
| |
| // layer norm disabled but normalisation weights are present |
| data.m_Parameters.m_LayerNormEnabled = false; |
| BOOST_CHECK_THROW(data.Validate(info), armnn::InvalidArgumentException); |
| data.m_Parameters.m_LayerNormEnabled = true; |
| |
| // check invalid outputTensor shape |
| armnn::TensorInfo incorrectOutputTensorInfo({batchSize, outputSize + 1}, dataType, qScale, qOffset); |
| SetWorkloadOutput(data, info, 3, incorrectOutputTensorInfo, nullptr); |
| BOOST_CHECK_THROW(data.Validate(info), armnn::InvalidArgumentException); |
| SetWorkloadOutput(data, info, 3, outputTensorInfo, nullptr); |
| |
| // check correct configuration |
| BOOST_CHECK_NO_THROW(data.Validate(info)); |
| } |
| |
| BOOST_AUTO_TEST_CASE(BiasPerAxisQuantization_Validate) |
| { |
| constexpr unsigned int nInput = 1u; |
| constexpr unsigned int cInput = 3u; |
| constexpr unsigned int hInput = 3u; |
| constexpr unsigned int wInput = 3u; |
| |
| constexpr unsigned int nOutput = nInput; |
| constexpr unsigned int cOutput = cInput; |
| constexpr unsigned int hOutput = 1u; |
| constexpr unsigned int wOutput = 1u; |
| |
| const TensorShape inputShape { nInput, cInput, hInput, wInput }; |
| const TensorShape outputShape{ nOutput, cOutput, hOutput, wOutput }; |
| const TensorShape weightShape{ cOutput, cInput, hInput, wInput }; |
| const TensorShape biasShape { cOutput }; |
| |
| constexpr DataType dataType = DataType::QuantisedAsymm8; |
| constexpr DataType biasType = DataType::Signed32; |
| |
| constexpr float perTensorScale = 1.5f; |
| const TensorInfo inputInfo (inputShape, dataType, perTensorScale); |
| const TensorInfo outputInfo(outputShape, dataType, perTensorScale); |
| |
| const std::vector<float> weightPerAxisScales = { 2.50f, 3.50f }; |
| const TensorInfo weightInfo(weightShape, dataType, weightPerAxisScales, 0); |
| |
| Convolution2dQueueDescriptor queueDescriptor; |
| queueDescriptor.m_Parameters.m_BiasEnabled = true; |
| |
| WorkloadInfo workloadInfo; |
| AddInputToWorkload(queueDescriptor, workloadInfo, inputInfo, nullptr); |
| AddOutputToWorkload(queueDescriptor, workloadInfo, outputInfo, nullptr); |
| |
| ScopedCpuTensorHandle weightTensor(weightInfo); |
| queueDescriptor.m_Weight = &weightTensor; |
| |
| // Test 1: correct per-axis quantization values |
| const std::vector<float> biasPerAxisScales1 = { 3.75f, 5.25f }; |
| const TensorInfo biasInfo1(biasShape, biasType, biasPerAxisScales1, 0); |
| |
| ScopedCpuTensorHandle biasHandle1(biasInfo1); |
| queueDescriptor.m_Bias = &biasHandle1; |
| |
| BOOST_CHECK_NO_THROW(queueDescriptor.Validate(workloadInfo)); |
| |
| // Test 2: wrong per-axis quantization values |
| const std::vector<float> biasPerAxisScales2 = { 4.00f, 5.00f }; |
| const TensorInfo biasInfo2(biasShape, biasType, biasPerAxisScales2, 0); |
| |
| ScopedCpuTensorHandle biasHandle2(biasInfo2); |
| queueDescriptor.m_Bias = &biasHandle2; |
| |
| BOOST_CHECK_THROW(queueDescriptor.Validate(workloadInfo), InvalidArgumentException); |
| |
| // Test 3: mismatched number of quantization scales |
| const std::vector<float> biasPerAxisScales3 = { 3.75f, 5.25f, 5.25f }; |
| const TensorInfo biasInfo3(biasShape, biasType, biasPerAxisScales3, 0); |
| |
| ScopedCpuTensorHandle biasHandle3(biasInfo3); |
| queueDescriptor.m_Bias = &biasHandle3; |
| |
| BOOST_CHECK_THROW(queueDescriptor.Validate(workloadInfo), InvalidArgumentException); |
| } |
| |
| BOOST_AUTO_TEST_SUITE_END() |