// // Copyright © 2017 Arm Ltd. All rights reserved. // SPDX-License-Identifier: MIT // #include "WorkloadTestUtils.hpp" #include #include #include #include #include #include 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 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 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 wOrigin4 = {0, 0, 0, 0}; armnn::SplitterQueueDescriptor::ViewOrigin window4(wOrigin4); invalidData.m_ViewOrigins[0] = window4; std::vector 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 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 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 wOrigin4 = {0, 0, 0, 0}; armnn::ConcatQueueDescriptor::ViewOrigin window4(wOrigin4); invalidData.m_ViewOrigins[0] = window4; std::vector 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::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_SUITE_END()