// // Copyright © 2017, 2023 Arm Ltd and Contributors. All rights reserved. // SPDX-License-Identifier: MIT // #include "LstmTestImpl.hpp" #include #include #include #include #include #include #include #include #include #include namespace { template> void LstmUtilsVectorBatchVectorAddTestImpl( std::vector& vec, std::vector& batchVec, uint32_t vSize, uint32_t nBatch, std::vector& expectedOutput, armnn::TensorShape& expectedShape) { float qScale = 1.0f; int32_t qOffset = 0; armnn::TensorInfo tensorInfo({nBatch, vSize}, ArmnnType, qScale, qOffset ); // Make encoder and decoder std::unique_ptr> vecDecoder = armnn::MakeDecoder(tensorInfo, vec.data()); std::unique_ptr> batchVecDecoder = armnn::MakeDecoder(tensorInfo, batchVec.data()); std::unique_ptr> batchVecEncoder = armnn::MakeEncoder(tensorInfo, batchVec.data()); VectorBatchVectorAdd(*vecDecoder, vSize, *batchVecDecoder, nBatch, *batchVecEncoder); // check shape and compare values auto result = CompareTensors(batchVec, expectedOutput, expectedShape, expectedShape); CHECK_MESSAGE(result.m_Result, result.m_Message.str()); // check if iterator is back at start position batchVecEncoder->Set(1.0f); CHECK(batchVec[0] == 1.0f); } template> void LstmUtilsZeroVectorTestImpl( std::vector& input, uint32_t vSize, std::vector& expectedOutput, armnn::TensorShape& expectedShape) { float qScale = 1.0f; int32_t qOffset = 0; armnn::TensorInfo tensorInfo({vSize}, ArmnnType, qScale, qOffset ); // Make encoder for input std::unique_ptr> outputEncoder = armnn::MakeEncoder(tensorInfo, input.data()); // call ZeroVector ZeroVector(*outputEncoder, vSize); // check shape and compare values auto result = CompareTensors(input, expectedOutput, expectedShape, expectedShape); CHECK_MESSAGE(result.m_Result, result.m_Message.str()); // check if iterator is back at start position outputEncoder->Set(1.0f); CHECK(input[0] == 1.0f); } template> void LstmUtilsMeanStddevNormalizationTestImpl( std::vector& input, uint32_t vSize, uint32_t nBatch, std::vector& expectedOutput, armnn::TensorShape& expectedShape) { float qScale = 1.0f; int32_t qOffset = 0; armnn::TensorInfo tensorInfo({nBatch, vSize}, ArmnnType, qScale, qOffset ); // Make encoder and decoder for input std::unique_ptr> inputDecoder = armnn::MakeDecoder(tensorInfo, input.data()); std::unique_ptr> outputEncoder = armnn::MakeEncoder(tensorInfo, input.data()); MeanStddevNormalization(*inputDecoder, *outputEncoder, vSize, nBatch, 1e-8f); // check shape and compare values auto result = CompareTensors(input, expectedOutput, expectedShape, expectedShape); CHECK_MESSAGE(result.m_Result, result.m_Message.str()); // check if iterator is back at start position outputEncoder->Set(1.0f); CHECK(input[0] == 1.0f); } template> void LstmUtilsVectorBatchVectorCwiseProductTestImpl( std::vector& vec, std::vector& batchVec, uint32_t vSize, uint32_t nBatch, std::vector& expectedOutput, armnn::TensorShape& expectedShape) { float qScale = 1.0f; int32_t qOffset = 0; armnn::TensorInfo tensorInfo({nBatch, vSize}, ArmnnType, qScale, qOffset ); // Make encoder and decoder std::unique_ptr> vecDecoder = armnn::MakeDecoder(tensorInfo, vec.data()); std::unique_ptr> batchVecDecoder = armnn::MakeDecoder(tensorInfo, batchVec.data()); std::unique_ptr> batchVecEncoder = armnn::MakeEncoder(tensorInfo, batchVec.data()); VectorBatchVectorCwiseProduct(*vecDecoder, vSize, *batchVecDecoder, nBatch, *batchVecEncoder); // check shape and compare values auto result = CompareTensors(batchVec, expectedOutput, expectedShape, expectedShape); CHECK_MESSAGE(result.m_Result, result.m_Message.str()); // check if iterator is back at start position batchVecEncoder->Set(1.0f); CHECK(batchVec[0] == 1.0f); } // Lstm Layer tests: // *********************************** // template> LayerTestResult LstmNoCifgNoPeepholeNoProjectionTestImpl( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory, const std::vector& input, const std::vector& outputExpected, const armnn::TensorShape& inputShape, const armnn::TensorShape& outputExpectedShape, float qScale = 1.0f, int32_t qOffset = 0, armnn::DataType constantDataType = armnn::DataType::Float32) { IgnoreUnused(memoryManager); unsigned int batchSize = armnn::numeric_cast(inputShape[0]); unsigned int inputSize = armnn::numeric_cast(inputShape[1]); unsigned int outputSize = armnn::numeric_cast(outputExpectedShape[1]); // cellSize and outputSize have the same size when there is no projection. unsigned numUnits = outputSize; armnn::TensorInfo inputTensorInfo({batchSize , inputSize}, ArmnnType, qScale, qOffset ); armnn::TensorInfo cellStateInTensorInfo({batchSize , numUnits}, ArmnnType, qScale, qOffset); armnn::TensorInfo outputStateInTensorInfo({batchSize , outputSize}, ArmnnType, qScale, qOffset); armnn::TensorInfo scratchBufferTensorInfo({batchSize, numUnits * 4}, ArmnnType, qScale, qOffset); armnn::TensorInfo cellStateOutTensorInfo({batchSize, numUnits}, ArmnnType, qScale, qOffset); armnn::TensorInfo outputStateOutTensorInfo({batchSize, outputSize}, ArmnnType, qScale, qOffset); armnn::TensorInfo outputTensorInfo({batchSize, outputSize}, ArmnnType, qScale, qOffset); std::vector inputVector; inputVector.assign(input.data(), input.data() + (batchSize * inputSize)); std::vector cellStateInVector(batchSize * numUnits, T()); std::vector outputStateInVector(batchSize * outputSize, T()); std::vector scratchBufferVector(batchSize * numUnits * 4, T()); std::vector outputStateOutVector(batchSize * outputSize, T()); std::vector cellStateOutVector(batchSize * numUnits, T()); std::vector actualOutput(outputTensorInfo.GetNumElements()); std::vector outputVector; outputVector.assign(outputExpected.data(), outputExpected.data() + (batchSize * outputSize)); std::unique_ptr inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo); std::unique_ptr cellStateInHandle = tensorHandleFactory.CreateTensorHandle(cellStateInTensorInfo); std::unique_ptr outputStateInHandle = tensorHandleFactory.CreateTensorHandle(outputStateInTensorInfo); std::unique_ptr scratchHandle = tensorHandleFactory.CreateTensorHandle(scratchBufferTensorInfo); std::unique_ptr outputStateOutHandle = tensorHandleFactory.CreateTensorHandle(outputStateOutTensorInfo); std::unique_ptr cellStateOutHandle = tensorHandleFactory.CreateTensorHandle(cellStateOutTensorInfo); std::unique_ptr outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo); armnn::LstmQueueDescriptor data; armnn::WorkloadInfo info; AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get()); AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get()); AddOutputToWorkload(data, info, scratchBufferTensorInfo, scratchHandle.get()); AddOutputToWorkload(data, info, outputStateOutTensorInfo, outputStateOutHandle.get()); AddOutputToWorkload(data, info, cellStateOutTensorInfo, cellStateOutHandle.get()); AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); armnn::TensorInfo tensorInfo4({numUnits}, constantDataType , qScale, qOffset); armnn::TensorInfo tensorInfo8({numUnits, 2}, constantDataType, qScale, qOffset); armnn::TensorInfo tensorInfo16({numUnits, 4}, constantDataType, qScale, qOffset); std::vector inputToInputWeights = {-0.45018822f, -0.02338299f, -0.0870589f, -0.34550029f, 0.04266912f, -0.15680569f, -0.34856534f, 0.43890524f}; std::vector inputToForgetWeights = { 0.09701663f, 0.20334584f, -0.50592935f, -0.31343272f, -0.40032279f, 0.44781327f, 0.01387155f, -0.35593212f}; std::vector inputToCellWeights = { -0.50013041f, 0.1370284f, 0.11810488f, 0.2013163f, -0.20583314f, 0.44344562f, 0.22077113f, -0.29909778f}; std::vector inputToOutputWeights = { -0.25065863f, -0.28290087f, 0.04613829f, 0.40525138f, 0.44272184f, 0.03897077f, -0.1556896f, 0.19487578f}; std::vector recurrentToInputWeights = {-0.0063535f, -0.2042388f, 0.31454784f, -0.35746509f, 0.28902304f, 0.08183324f, -0.16555229f, 0.02286911f, -0.13566875f, 0.03034258f, 0.48091322f, -0.12528998f, 0.24077177f, -0.51332325f, -0.33502164f, 0.10629296f}; std::vector recurrentToForgetWeights = { -0.48684245f, -0.06655136f, 0.42224967f, 0.2112639f, 0.27654213f, 0.20864892f, -0.07646349f, 0.45877004f, 0.00141793f, -0.14609534f, 0.36447752f, 0.09196436f, 0.28053468f, 0.01560611f, -0.20127171f, -0.01140004f}; std::vector recurrentToCellWeights = { -0.3407414f, 0.24443203f, -0.2078532f, 0.26320225f, 0.05695659f, -0.00123841f, -0.4744786f, -0.35869038f, -0.06418842f, -0.13502428f, -0.501764f, 0.22830659f, -0.46367589f, 0.26016325f, -0.03894562f, -0.16368064f}; std::vector recurrentToOutputWeights = { 0.43385774f, -0.17194885f, 0.2718237f, 0.09215671f, 0.24107647f, -0.39835793f, 0.18212086f, 0.01301402f, 0.48572797f, -0.50656658f, 0.20047462f, -0.20607421f, -0.51818722f, -0.15390486f, 0.0468148f, 0.39922136f}; std::vector cellToInputWeights = {0., 0., 0., 0.}; std::vector inputGateBias = {0., 0., 0., 0.}; std::vector forgetGateBias = {1., 1., 1., 1.}; std::vector cellBias = {0., 0., 0., 0.}; std::vector outputGateBias = {0., 0., 0., 0.}; armnn::ScopedTensorHandle inputToInputWeightsTensor(tensorInfo8); armnn::ScopedTensorHandle inputToForgetWeightsTensor(tensorInfo8); armnn::ScopedTensorHandle inputToCellWeightsTensor(tensorInfo8); armnn::ScopedTensorHandle inputToOutputWeightsTensor(tensorInfo8); armnn::ScopedTensorHandle recurrentToInputWeightsTensor(tensorInfo16); armnn::ScopedTensorHandle recurrentToForgetWeightsTensor(tensorInfo16); armnn::ScopedTensorHandle recurrentToCellWeightsTensor(tensorInfo16); armnn::ScopedTensorHandle recurrentToOutputWeightsTensor(tensorInfo16); armnn::ScopedTensorHandle cellToInputWeightsTensor(tensorInfo4); armnn::ScopedTensorHandle inputGateBiasTensor(tensorInfo4); armnn::ScopedTensorHandle forgetGateBiasTensor(tensorInfo4); armnn::ScopedTensorHandle cellBiasTensor(tensorInfo4); armnn::ScopedTensorHandle outputGateBiasTensor(tensorInfo4); AllocateAndCopyDataToITensorHandle(&inputToInputWeightsTensor, inputToInputWeights.data()); AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, inputToForgetWeights.data()); AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, inputToCellWeights.data()); AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, inputToOutputWeights.data()); AllocateAndCopyDataToITensorHandle(&recurrentToInputWeightsTensor, recurrentToInputWeights.data()); AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, recurrentToForgetWeights.data()); AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, recurrentToCellWeights.data()); AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, recurrentToOutputWeights.data()); AllocateAndCopyDataToITensorHandle(&cellToInputWeightsTensor, cellToInputWeights.data()); AllocateAndCopyDataToITensorHandle(&inputGateBiasTensor, inputGateBias.data()); AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, forgetGateBias.data()); AllocateAndCopyDataToITensorHandle(&cellBiasTensor, cellBias.data()); AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, outputGateBias.data()); 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_InputGateBias = &inputGateBiasTensor; data.m_ForgetGateBias = &forgetGateBiasTensor; data.m_CellBias = &cellBiasTensor; data.m_OutputGateBias = &outputGateBiasTensor; // Flags to set test configuration data.m_Parameters.m_ActivationFunc = 4; data.m_Parameters.m_CifgEnabled = false; data.m_Parameters.m_PeepholeEnabled = false; data.m_Parameters.m_ProjectionEnabled = false; std::unique_ptr workload = workloadFactory.CreateWorkload(armnn::LayerType::Lstm, data, info); inputHandle->Allocate(); outputStateInHandle->Allocate(); cellStateInHandle->Allocate(); scratchHandle->Allocate(); outputStateOutHandle->Allocate(); cellStateOutHandle->Allocate(); outputHandle->Allocate(); CopyDataToITensorHandle(inputHandle.get(), inputVector.data()); CopyDataToITensorHandle(outputStateInHandle.get(), outputStateInVector.data()); CopyDataToITensorHandle(cellStateInHandle.get(), cellStateInVector.data()); workload->Execute(); CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get()); return LayerTestResult(actualOutput, outputVector, outputHandle->GetShape(), outputTensorInfo.GetShape()); } template> LayerTestResult LstmLayerNoCifgWithPeepholeWithProjectionTestImpl(armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory, const std::vector& input, const std::vector& outputExpected, float qScale = 1.0f, int32_t qOffset = 0, armnn::DataType constantDataType = armnn::DataType::Float32) { IgnoreUnused(memoryManager); unsigned int batchSize = 2; unsigned int outputSize = 16; unsigned int inputSize = 5; unsigned numUnits = 20; armnn::TensorInfo inputTensorInfo({batchSize , inputSize}, ArmnnType, qScale, qOffset); armnn::TensorInfo cellStateInTensorInfo({batchSize , numUnits}, ArmnnType, qScale, qOffset); armnn::TensorInfo outputStateInTensorInfo({batchSize , outputSize}, ArmnnType, qScale, qOffset); // Scratch buffer size without CIFG [batchSize, numUnits * 4] armnn::TensorInfo scratchBufferTensorInfo({batchSize, numUnits * 4}, ArmnnType, qScale, qOffset); armnn::TensorInfo cellStateOutTensorInfo({batchSize, numUnits}, ArmnnType, qScale, qOffset); armnn::TensorInfo outputStateOutTensorInfo({batchSize, outputSize}, ArmnnType, qScale, qOffset); armnn::TensorInfo outputTensorInfo({batchSize, outputSize}, ArmnnType, qScale, qOffset); std::vector inputVector; inputVector.assign(input.data(), input.data() + (batchSize * inputSize)); std::vector cellStateInVector(batchSize * numUnits, T()); std::vector outputStateInVector(batchSize * outputSize, T()); std::vector scratchBufferVector(batchSize * numUnits * 4, T()); std::vector outputStateOutVector(batchSize * outputSize, T()); std::vector cellStateOutVector(batchSize * numUnits, T()); std::vector actualOutput(outputTensorInfo.GetNumElements()); std::vector outputVector; outputVector.assign(outputExpected.data(), outputExpected.data() + (batchSize * outputSize)); std::unique_ptr inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo); std::unique_ptr cellStateInHandle = tensorHandleFactory.CreateTensorHandle(cellStateInTensorInfo); std::unique_ptr outputStateInHandle = tensorHandleFactory.CreateTensorHandle(outputStateInTensorInfo); std::unique_ptr scratchHandle = tensorHandleFactory.CreateTensorHandle(scratchBufferTensorInfo); std::unique_ptr outputStateOutHandle = tensorHandleFactory.CreateTensorHandle(outputStateOutTensorInfo); std::unique_ptr cellStateOutHandle = tensorHandleFactory.CreateTensorHandle(cellStateOutTensorInfo); std::unique_ptr outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo); armnn::LstmQueueDescriptor data; armnn::WorkloadInfo info; AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get()); AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get()); AddOutputToWorkload(data, info, scratchBufferTensorInfo, scratchHandle.get()); AddOutputToWorkload(data, info, outputStateOutTensorInfo, outputStateOutHandle.get()); AddOutputToWorkload(data, info, cellStateOutTensorInfo, cellStateOutHandle.get()); AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); armnn::TensorInfo tensorInfo16({outputSize}, constantDataType, qScale, qOffset); armnn::TensorInfo tensorInfo20({numUnits}, constantDataType, qScale, qOffset); armnn::TensorInfo tensorInfo20x5({numUnits, inputSize}, constantDataType, qScale, qOffset); armnn::TensorInfo tensorInfo20x16({numUnits, outputSize}, constantDataType, qScale, qOffset); armnn::TensorInfo tensorInfo16x20({outputSize, numUnits}, constantDataType, qScale, qOffset); std::vector inputToInputWeights = {0.021393683f,0.06124551f, 0.046905167f,-0.014657677f,-0.03149463f, 0.09171803f, 0.14647801f,0.10797193f, -0.0057968358f,0.0019193048f, -0.2726754f, 0.10154029f, -0.018539885f, 0.080349885f, -0.10262385f, -0.022599787f,-0.09121155f, -0.008675967f, -0.045206103f,-0.0821282f, -0.008045952f,0.015478081f, 0.055217247f, 0.038719587f, 0.044153627f, -0.06453243f,0.05031825f, -0.046935108f, -0.008164439f, 0.014574226f, -0.1671009f, -0.15519552f, -0.16819797f,-0.13971269f,-0.11953059f, 0.25005487f, -0.22790983f, 0.009855087f, -0.028140958f, -0.11200698f, 0.11295408f, -0.0035217577f, 0.054485075f, 0.05184695f, 0.064711206f, 0.10989193f, 0.11674786f, 0.03490607f, 0.07727357f, 0.11390585f, -0.1863375f, -0.1034451f, -0.13945189f, -0.049401227f, -0.18767063f, 0.042483903f, 0.14233552f, 0.13832581f, 0.18350165f, 0.14545603f, -0.028545704f,0.024939531f,0.050929718f,0.0076203286f,-0.0029723682f, -0.042484224f, -0.11827596f, -0.09171104f, -0.10808628f,-0.16327988f, -0.2273378f, -0.0993647f, -0.017155107f,0.0023917493f,0.049272764f, 0.0038534778f, 0.054764505f, 0.089753784f, 0.06947234f, 0.08014476f, -0.04544234f, -0.0497073f,-0.07135631f, -0.048929106f,-0.004042012f, -0.009284026f, 0.018042054f, 0.0036860977f,-0.07427302f, -0.11434604f, -0.018995456f, 0.031487543f, 0.012834908f,0.019977754f,0.044256654f, -0.39292613f, -0.18519334f, -0.11651281f,-0.06809892f, 0.011373677f }; std::vector inputToForgetWeights = {-0.0018401089f, -0.004852237f,0.03698424f, 0.014181704f,0.028273236f, -0.016726194f, -0.05249759f,-0.10204261f, 0.00861066f,-0.040979505f, -0.009899187f,0.01923892f,-0.028177269f, -0.08535103f,-0.14585495f, 0.10662567f,-0.01909731f,-0.017883534f,-0.0047269356f,-0.045103323f, 0.0030784295f,0.076784775f,0.07463696f, 0.094531395f,0.0814421f, -0.12257899f, -0.033945758f,-0.031303465f, 0.045630626f,0.06843887f, -0.13492945f, -0.012480007f,-0.0811829f, -0.07224499f,-0.09628791f, 0.045100946f,0.0012300825f, 0.013964662f, 0.099372394f,0.02543059f, 0.06958324f, 0.034257296f, 0.0482646f, 0.06267997f,0.052625068f, 0.12784666f, 0.07077897f, 0.025725935f, 0.04165009f,0.07241905f, 0.018668644f, -0.037377294f,-0.06277783f,-0.08833636f,-0.040120605f, -0.011405586f,-0.007808335f,-0.010301386f,-0.005102167f,0.027717464f, 0.05483423f, 0.11449111f, 0.11289652f,0.10939839f, 0.13396506f, -0.08402166f,-0.01901462f, -0.044678304f,-0.07720565f,0.014350063f, -0.11757958f, -0.0652038f, -0.08185733f,-0.076754324f,-0.092614375f, 0.10405491f, 0.052960336f, 0.035755895f,0.035839386f,-0.012540553f, 0.036881298f, 0.02913376f, 0.03420159f,0.05448447f,-0.054523353f, 0.02582715f, 0.02327355f, -0.011857179f,-0.0011980024f,-0.034641717f, -0.026125094f,-0.17582615f,-0.15923657f,-0.27486774f,-0.0006143371f, 0.0001771948f, -8.470171e-05f, 0.02651807f,0.045790765f,0.06956496f }; std::vector inputToCellWeights = { -0.04580283f, -0.09549462f, -0.032418985f, -0.06454633f, -0.043528453f, 0.043018587f, -0.049152344f, -0.12418144f, -0.078985475f, -0.07596889f, 0.019484362f, -0.11434962f, -0.0074034138f, -0.06314844f, -0.092981495f, 0.0062155537f, -0.025034338f, -0.0028890965f, 0.048929527f, 0.06235075f, 0.10665918f, -0.032036792f, -0.08505916f, -0.10843358f, -0.13002433f, -0.036816437f, -0.02130134f, -0.016518239f, 0.0047691227f, -0.0025825808f, 0.066017866f, 0.029991534f, -0.10652836f, -0.1037554f, -0.13056071f, -0.03266643f, -0.033702414f, -0.006473424f, -0.04611692f, 0.014419339f, -0.025174323f, 0.0396852f, 0.081777506f, 0.06157468f, 0.10210095f, -0.009658194f, 0.046511717f, 0.03603906f, 0.0069369148f, 0.015960095f, -0.06507666f, 0.09551598f, 0.053568836f, 0.06408714f, 0.12835667f, -0.008714329f, -0.20211966f, -0.12093674f, 0.029450472f, 0.2849013f, -0.029227901f, 0.1164364f, -0.08560263f, 0.09941786f, -0.036999565f, -0.028842626f, -0.0033637602f, -0.017012902f, -0.09720865f, -0.11193351f, -0.029155117f, -0.017936034f, -0.009768936f, -0.04223324f, -0.036159635f, 0.06505112f, -0.021742892f, -0.023377212f, -0.07221364f, -0.06430552f, 0.05453865f, 0.091149814f, 0.06387331f, 0.007518393f, 0.055960953f, 0.069779344f, 0.046411168f, 0.10509911f, 0.07463894f, 0.0075130584f, 0.012850982f, 0.04555431f, 0.056955688f, 0.06555285f, 0.050801456f, -0.009862683f, 0.00826772f, -0.026555609f, -0.0073611983f, -0.0014897042f }; std::vector inputToOutputWeights ={-0.0998932f, -0.07201956f, -0.052803773f,-0.15629593f,-0.15001918f, -0.07650751f,0.02359855f, -0.075155355f, -0.08037709f, -0.15093534f, 0.029517552f, -0.04751393f, 0.010350531f,-0.02664851f, -0.016839722f, -0.023121163f, 0.0077019283f, 0.012851257f, -0.05040649f,-0.0129761f, -0.021737747f,-0.038305793f,-0.06870586f, -0.01481247f,-0.001285394f, 0.10124236f, 0.083122835f, 0.053313006f,-0.062235646f,-0.075637154f, -0.027833903f, 0.029774971f, 0.1130802f, 0.09218906f, 0.09506135f, -0.086665764f,-0.037162706f,-0.038880914f,-0.035832845f,-0.014481564f, -0.09825003f,-0.12048569f,-0.097665586f,-0.05287633f, -0.0964047f, -0.11366429f, 0.035777505f, 0.13568819f, 0.052451383f,0.050649304f, 0.05798951f, -0.021852335f,-0.099848844f,0.014740475f,-0.078897946f, 0.04974699f, 0.014160473f, 0.06973932f, 0.04964942f, 0.033364646f, 0.08190124f, 0.025535367f, 0.050893165f, 0.048514254f,0.06945813f, -0.078907564f,-0.06707616f, -0.11844508f, -0.09986688f,-0.07509403f, 0.06263226f, 0.14925587f, 0.20188436f, 0.12098451f,0.14639415f, 0.0015017595f, -0.014267382f, -0.03417257f,0.012711468f,0.0028300495f, -0.024758482f, -0.05098548f,-0.0821182f, 0.014225672f, 0.021544158f, 0.08949725f, 0.07505268f, -0.0020780868f, 0.04908258f,0.06476295f, -0.022907063f,0.027562456f,0.040185735f, 0.019567577f,-0.015598739f, -0.049097303f, -0.017121866f, -0.083368234f,-0.02332002f,-0.0840956f }; std::vector inputGateBias = {0.02234832f, 0.14757581f, 0.18176508f, 0.10380666f, 0.053110216f, -0.06928846f, -0.13942584f, -0.11816189f, 0.19483899f, 0.03652339f, -0.10250295f, 0.036714908f, -0.18426876f, 0.036065217f, 0.21810818f, 0.02383196f, -0.043370757f, 0.08690144f, -0.04444982f, 0.00030581196f }; std::vector forgetGateBias ={0.035185695f, -0.042891346f, -0.03032477f, 0.23027696f, 0.11098921f, 0.15378423f, 0.09263801f, 0.09790885f, 0.09508917f, 0.061199076f, 0.07665568f, -0.015443159f, -0.03499149f, 0.046190713f, 0.08895977f, 0.10899629f, 0.40694186f, 0.06030037f, 0.012413437f, -0.06108739f }; std::vector cellBias = { -0.024379363f, 0.0055531194f, 0.23377132f, 0.033463873f, -0.1483596f, -0.10639995f, -0.091433935f, 0.058573797f, -0.06809782f, -0.07889636f, -0.043246906f, -0.09829136f, -0.4279842f, 0.034901652f, 0.18797937f, 0.0075234566f, 0.016178843f, 0.1749513f, 0.13975595f, 0.92058027f }; std::vector outputGateBias ={0.046159424f, -0.0012809046f, 0.03563469f, 0.12648113f, 0.027195795f, 0.35373217f, -0.018957434f, 0.008907322f, -0.0762701f, 0.12018895f, 0.04216877f, 0.0022856654f, 0.040952638f, 0.3147856f, 0.08225149f, -0.057416286f, -0.14995944f, -0.008040261f, 0.13208859f, 0.029760877f}; std::vector recurrentToInputWeights = { -0.001374326f, -0.078856036f, 0.10672688f, 0.029162422f, -0.11585556f, 0.02557986f, -0.13446963f, -0.035785314f, -0.01244275f, 0.025961924f, -0.02337298f, -0.044228926f, -0.055839065f, -0.046598054f, -0.010546039f, -0.06900766f, 0.027239809f, 0.022582639f, -0.013296484f, -0.05459212f, 0.08981f, -0.045407712f, 0.08682226f, -0.06867011f, -0.14390695f, -0.02916037f, 0.000996957f, 0.091420636f, 0.14283475f, -0.07390571f, -0.06402044f, 0.062524505f, -0.093129106f, 0.04860203f, -0.08364217f, -0.08119002f, 0.009352075f, 0.22920375f, 0.0016303885f, 0.11583097f, -0.13732095f, 0.012405723f, -0.07551853f, 0.06343048f, 0.12162708f, -0.031923793f, -0.014335606f, 0.01790974f, -0.10650317f, -0.0724401f, 0.08554849f, -0.05727212f, 0.06556731f, -0.042729504f, -0.043227166f, 0.011683251f, -0.013082158f, -0.029302018f, -0.010899579f, -0.062036745f, -0.022509435f, -0.00964907f, -0.01567329f, 0.04260106f, -0.07787477f, -0.11576462f, 0.017356863f, 0.048673786f, -0.017577527f, -0.05527947f, -0.082487635f, -0.040137455f, -0.10820036f, -0.04666372f, 0.022746278f, -0.07851417f, 0.01068115f, 0.032956902f, 0.022433773f, 0.0026891115f, 0.08944216f, -0.0685835f, 0.010513544f, 0.07228705f, 0.02032331f, -0.059686817f, -0.0005566496f, -0.086984694f, 0.040414046f, -0.1380399f, 0.094208956f, -0.05722982f, 0.012092817f, -0.04989123f, -0.086576f, -0.003399834f, -0.04696032f, -0.045747425f, 0.10091314f, 0.048676282f, -0.029037097f, 0.031399418f, -0.0040285117f, 0.047237843f, 0.09504992f, 0.041799378f, -0.049185462f, -0.031518843f, -0.10516937f, 0.026374253f, 0.10058866f, -0.0033195973f, -0.041975245f, 0.0073591834f, 0.0033782164f, -0.004325073f, -0.10167381f, 0.042500053f, -0.01447153f, 0.06464186f, -0.017142897f, 0.03312627f, 0.009205989f, 0.024138335f, -0.011337001f, 0.035530265f, -0.010912711f, 0.0706555f, -0.005894094f, 0.051841937f, -0.1401738f, -0.02351249f, 0.0365468f, 0.07590991f, 0.08838724f, 0.021681072f, -0.10086113f, 0.019608743f, -0.06195883f, 0.077335775f, 0.023646897f, -0.095322326f, 0.02233014f, 0.09756986f, -0.048691444f, -0.009579111f, 0.07595467f, 0.11480546f, -0.09801813f, 0.019894179f, 0.08502348f, 0.004032281f, 0.037211012f, 0.068537936f, -0.048005626f, -0.091520436f, -0.028379958f, -0.01556313f, 0.06554592f, -0.045599163f, -0.01672207f, -0.020169014f, -0.011877351f, -0.20212261f, 0.010889619f, 0.0047078193f, 0.038385306f, 0.08540671f, -0.017140968f, -0.0035865551f, 0.016678626f, 0.005633034f, 0.015963363f, 0.00871737f, 0.060130805f, 0.028611384f, 0.10109069f, -0.015060172f, -0.07894427f, 0.06401885f, 0.011584063f, -0.024466386f, 0.0047652307f, -0.09041358f, 0.030737216f, -0.0046374933f, 0.14215417f, -0.11823516f, 0.019899689f, 0.006106124f, -0.027092824f, 0.0786356f, 0.05052217f, -0.058925f, -0.011402121f, -0.024987547f, -0.0013661642f, -0.06832946f, -0.015667673f, -0.1083353f, -0.00096863037f, -0.06988685f, -0.053350925f, -0.027275559f, -0.033664223f, -0.07978348f, -0.025200296f, -0.017207067f, -0.058403496f, -0.055697463f, 0.005798788f, 0.12965427f, -0.062582195f, 0.0013350133f, -0.10482091f, 0.0379771f, 0.072521195f, -0.0029455067f, -0.13797039f, -0.03628521f, 0.013806405f, -0.017858358f, -0.01008298f, -0.07700066f, -0.017081132f, 0.019358726f, 0.0027079724f, 0.004635139f, 0.062634714f, -0.02338735f, -0.039547626f, -0.02050681f, 0.03385117f, -0.083611414f, 0.002862572f, -0.09421313f, 0.058618143f, -0.08598433f, 0.00972939f, 0.023867095f, -0.053934585f, -0.023203006f, 0.07452513f, -0.048767887f, -0.07314807f, -0.056307215f, -0.10433547f, -0.06440842f, 0.04328182f, 0.04389765f, -0.020006588f, -0.09076438f, -0.11652589f, -0.021705797f, 0.03345259f, -0.010329105f, -0.025767034f, 0.013057034f, -0.07316461f, -0.10145612f, 0.06358255f, 0.18531723f, 0.07759293f, 0.12006465f, 0.1305557f, 0.058638252f, -0.03393652f, 0.09622831f, -0.16253184f, -2.4580743e-06f, 0.079869635f, -0.070196845f, -0.005644518f, 0.06857898f, -0.12598175f, -0.035084512f, 0.03156317f, -0.12794146f, -0.031963028f, 0.04692781f, 0.030070418f, 0.0071660685f, -0.095516115f, -0.004643372f, 0.040170413f, -0.062104587f, -0.0037324072f, 0.0554317f, 0.08184801f, -0.019164372f, 0.06791302f, 0.034257166f, -0.10307039f, 0.021943003f, 0.046745934f, 0.0790918f, -0.0265588f, -0.007824208f, 0.042546265f, -0.00977924f, -0.0002440307f, -0.017384544f, -0.017990116f, 0.12252321f, -0.014512694f, -0.08251313f, 0.08861942f, 0.13589665f, 0.026351685f, 0.012641483f, 0.07466548f, 0.044301085f, -0.045414884f, -0.051112458f, 0.03444247f, -0.08502782f, -0.04106223f, -0.028126027f, 0.028473156f, 0.10467447f }; std::vector recurrentToForgetWeights = {-0.057784554f, -0.026057621f, -0.068447545f, -0.022581743f, 0.14811787f, 0.10826372f, 0.09471067f, 0.03987225f, -0.0039523416f, 0.00030638507f, 0.053185795f, 0.10572994f, 0.08414449f, -0.022036452f, -0.00066928595f, -0.09203576f, 0.032950465f, -0.10985798f, -0.023809856f, 0.0021431844f, -0.02196096f, -0.00326074f, 0.00058621005f, -0.074678116f, -0.06193199f, 0.055729095f, 0.03736828f, 0.020123724f, 0.061878487f, -0.04729229f, 0.034919553f, -0.07585433f, -0.04421272f, -0.044019096f, 0.085488975f, 0.04058006f, -0.06890133f, -0.030951202f, -0.024628663f, -0.07672815f, 0.034293607f, 0.08556707f, -0.05293577f, -0.033561368f, -0.04899627f, 0.0241671f, 0.015736353f, -0.095442444f, -0.029564252f, 0.016493602f, -0.035026584f, 0.022337519f, -0.026871363f, 0.004780428f, 0.0077918363f, -0.03601621f, 0.016435321f, -0.03263031f, -0.09543275f, -0.047392778f, 0.013454138f, 0.028934088f, 0.01685226f, -0.086110644f, -0.046250615f, -0.01847454f, 0.047608484f, 0.07339695f, 0.034546845f, -0.04881143f, 0.009128804f, -0.08802852f, 0.03761666f, 0.008096139f, -0.014454086f, 0.014361001f, -0.023502491f, -0.0011840804f, -0.07607001f, 0.001856849f, -0.06509276f, -0.006021153f, -0.08570962f, -0.1451793f, 0.060212336f, 0.055259194f, 0.06974018f, 0.049454916f, -0.027794661f, -0.08077226f, -0.016179763f, 0.1169753f, 0.17213494f, -0.0056326236f, -0.053934924f, -0.0124349f, -0.11520337f, 0.05409887f, 0.088759385f, 0.0019655675f, 0.0042065294f, 0.03881498f, 0.019844765f, 0.041858196f, -0.05695512f, 0.047233116f, 0.038937137f, -0.06542224f, 0.014429736f, -0.09719407f, 0.13908425f, -0.05379757f, 0.012321099f, 0.082840554f, -0.029899208f, 0.044217527f, 0.059855383f, 0.07711018f, -0.045319796f, 0.0948846f, -0.011724666f, -0.0033288454f, -0.033542685f, -0.04764985f, -0.13873616f, 0.040668588f, 0.034832682f, -0.015319203f, -0.018715994f, 0.046002675f, 0.0599172f, -0.043107376f, 0.0294216f, -0.002314414f, -0.022424703f, 0.0030315618f, 0.0014641669f, 0.0029166266f, -0.11878115f, 0.013738511f, 0.12375372f, -0.0006038222f, 0.029104086f, 0.087442465f, 0.052958444f, 0.07558703f, 0.04817258f, 0.044462286f, -0.015213451f, -0.08783778f, -0.0561384f, -0.003008196f, 0.047060397f, -0.002058388f, 0.03429439f, -0.018839769f, 0.024734668f, 0.024614193f, -0.042046934f, 0.09597743f, -0.0043254104f, 0.04320769f, 0.0064070094f, -0.0019131786f, -0.02558259f, -0.022822596f, -0.023273505f, -0.02464396f, -0.10991725f, -0.006240552f, 0.0074488563f, 0.024044557f, 0.04383914f, -0.046476185f, 0.028658995f, 0.060410924f, 0.050786525f, 0.009452605f, -0.0073054377f, -0.024810238f, 0.0052906186f, 0.0066939713f, -0.0020913032f, 0.014515517f, 0.015898481f, 0.021362653f, -0.030262267f, 0.016587038f, -0.011442813f, 0.041154444f, -0.007631438f, -0.03423484f, -0.010977775f, 0.036152758f, 0.0066366293f, 0.11915515f, 0.02318443f, -0.041350313f, 0.021485701f, -0.10906167f, -0.028218046f, -0.00954771f, 0.020531068f, -0.11995105f, -0.03672871f, 0.024019798f, 0.014255957f, -0.05221243f, -0.00661567f, -0.04630967f, 0.033188973f, 0.10107534f, -0.014027541f, 0.030796422f, -0.10270911f, -0.035999842f, 0.15443139f, 0.07684145f, 0.036571592f, -0.035900835f, -0.0034699554f, 0.06209149f, 0.015920248f, -0.031122351f, -0.03858649f, 0.01849943f, 0.13872518f, 0.01503974f, 0.069941424f, -0.06948533f, -0.0088794185f, 0.061282158f, -0.047401894f, 0.03100163f, -0.041533746f, -0.10430945f, 0.044574402f, -0.01425562f, -0.024290353f, 0.034563623f, 0.05866852f, 0.023947537f, -0.09445152f, 0.035450947f, 0.02247216f, -0.0042998926f, 0.061146557f, -0.10250651f, 0.020881841f, -0.06747029f, 0.10062043f, -0.0023941975f, 0.03532124f, -0.016341697f, 0.09685456f, -0.016764693f, 0.051808182f, 0.05875331f, -0.04536488f, 0.001626336f, -0.028892258f, -0.01048663f, -0.009793449f, -0.017093895f, 0.010987891f, 0.02357273f, -0.00010856845f, 0.0099760275f, -0.001845119f, -0.03551521f, 0.0018358806f, 0.05763657f, -0.01769146f, 0.040995963f, 0.02235177f, -0.060430344f, 0.11475477f, -0.023854522f, 0.10071741f, 0.0686208f, -0.014250481f, 0.034261297f, 0.047418304f, 0.08562733f, -0.030519066f, 0.0060542435f, 0.014653856f, -0.038836084f, 0.04096551f, 0.032249358f, -0.08355519f, -0.026823482f, 0.056386515f, -0.010401743f, -0.028396193f, 0.08507674f, 0.014410365f, 0.020995233f, 0.17040324f, 0.11511526f, 0.02459721f, 0.0066619175f, 0.025853224f, -0.023133837f, -0.081302024f, 0.017264642f, -0.009585969f, 0.09491168f, -0.051313367f, 0.054532815f, -0.014298593f, 0.10657464f, 0.007076659f, 0.10964551f, 0.0409152f, 0.008275321f, -0.07283536f, 0.07937492f, 0.04192024f, -0.1075027f }; std::vector recurrentToCellWeights = { -0.037322544f, 0.018592842f, 0.0056175636f, -0.06253426f, 0.055647098f, -0.05713207f, -0.05626563f, 0.005559383f, 0.03375411f, -0.025757805f, -0.088049285f, 0.06017052f, -0.06570978f, 0.007384076f, 0.035123326f, -0.07920549f, 0.053676967f, 0.044480428f, -0.07663568f, 0.0071805613f, 0.08089997f, 0.05143358f, 0.038261272f, 0.03339287f, -0.027673481f, 0.044746667f, 0.028349208f, 0.020090483f, -0.019443132f, -0.030755889f, -0.0040000007f, 0.04465846f, -0.021585021f, 0.0031670958f, 0.0053199246f, -0.056117613f, -0.10893326f, 0.076739706f, -0.08509834f, -0.027997585f, 0.037871376f, 0.01449768f, -0.09002357f, -0.06111149f, -0.046195522f, 0.0422062f, -0.005683705f, -0.1253618f, -0.012925729f, -0.04890792f, 0.06985068f, 0.037654128f, 0.03398274f, -0.004781977f, 0.007032333f, -0.031787455f, 0.010868644f, -0.031489216f, 0.09525667f, 0.013939797f, 0.0058680447f, 0.0167067f, 0.02668468f, -0.04797466f, -0.048885044f, -0.12722108f, 0.035304096f, 0.06554885f, 0.00972396f, -0.039238118f, -0.05159735f, -0.11329045f, 0.1613692f, -0.03750952f, 0.06529313f, -0.071974665f, -0.11769596f, 0.015524369f, -0.0013754242f, -0.12446318f, 0.02786344f, -0.014179351f, 0.005264273f, 0.14376344f, 0.015983658f, 0.03406988f, -0.06939408f, 0.040699873f, 0.02111075f, 0.09669095f, 0.041345075f, -0.08316494f, -0.07684199f, -0.045768797f, 0.032298047f, -0.041805092f, 0.0119405f, 0.0061010392f, 0.12652606f, 0.0064572375f, -0.024950314f, 0.11574242f, 0.04508852f, -0.04335324f, 0.06760663f, -0.027437469f, 0.07216407f, 0.06977076f, -0.05438599f, 0.034033038f, -0.028602652f, 0.05346137f, 0.043184172f, -0.037189785f, 0.10420091f, 0.00882477f, -0.054019816f, -0.074273005f, -0.030617684f, -0.0028467078f, 0.024302477f, -0.0038869337f, 0.005332455f, 0.0013399826f, 0.04361412f, -0.007001822f, 0.09631092f, -0.06702025f, -0.042049985f, -0.035070654f, -0.04103342f, -0.10273396f, 0.0544271f, 0.037184782f, -0.13150354f, -0.0058036847f, -0.008264958f, 0.042035464f, 0.05891794f, 0.029673764f, 0.0063542654f, 0.044788733f, 0.054816857f, 0.062257513f, -0.00093483756f, 0.048938446f, -0.004952862f, -0.007730018f, -0.04043371f, -0.017094059f, 0.07229206f, -0.023670016f, -0.052195564f, -0.025616996f, -0.01520939f, 0.045104615f, -0.007376126f, 0.003533447f, 0.006570588f, 0.056037236f, 0.12436656f, 0.051817212f, 0.028532185f, -0.08686856f, 0.11868599f, 0.07663395f, -0.07323171f, 0.03463402f, -0.050708205f, -0.04458982f, -0.11590894f, 0.021273347f, 0.1251325f, -0.15313013f, -0.12224372f, 0.17228661f, 0.023029093f, 0.086124025f, 0.006445803f, -0.03496501f, 0.028332196f, 0.04449512f, -0.042436164f, -0.026587414f, -0.006041347f, -0.09292539f, -0.05678812f, 0.03897832f, 0.09465633f, 0.008115513f, -0.02171956f, 0.08304309f, 0.071401566f, 0.019622514f, 0.032163795f, -0.004167056f, 0.02295182f, 0.030739572f, 0.056506045f, 0.004612461f, 0.06524936f, 0.059999723f, 0.046395954f, -0.0045512207f, -0.1335546f, -0.030136576f, 0.11584653f, -0.014678886f, 0.0020118146f, -0.09688814f, -0.0790206f, 0.039770417f, -0.0329582f, 0.07922767f, 0.029322514f, 0.026405897f, 0.04207835f, -0.07073373f, 0.063781224f, 0.0859677f, -0.10925287f, -0.07011058f, 0.048005477f, 0.03438226f, -0.09606514f, -0.006669445f, -0.043381985f, 0.04240257f, -0.06955775f, -0.06769346f, 0.043903265f, -0.026784198f, -0.017840602f, 0.024307009f, -0.040079936f, -0.019946516f, 0.045318738f, -0.12233574f, 0.026170589f, 0.0074471775f, 0.15978073f, 0.10185836f, 0.10298046f, -0.015476589f, -0.039390966f, -0.072174534f, 0.0739445f, -0.1211869f, -0.0347889f, -0.07943156f, 0.014809798f, -0.12412325f, -0.0030663363f, 0.039695457f, 0.0647603f, -0.08291318f, -0.018529687f, -0.004423833f, 0.0037507233f, 0.084633216f, -0.01514876f, -0.056505352f, -0.012800942f, -0.06994386f, 0.012962922f, -0.031234352f, 0.07029052f, 0.016418684f, 0.03618972f, 0.055686004f, -0.08663945f, -0.017404709f, -0.054761406f, 0.029065743f, 0.052404847f, 0.020238016f, 0.0048197987f, -0.0214882f, 0.07078733f, 0.013016777f, 0.06262858f, 0.009184685f, 0.020785125f, -0.043904778f, -0.0270329f, -0.03299152f, -0.060088247f, -0.015162964f, -0.001828936f, 0.12642565f, -0.056757294f, 0.013586685f, 0.09232601f, -0.035886683f, 0.06000002f, 0.05229691f, -0.052580316f, -0.082029596f, -0.010794592f, 0.012947712f, -0.036429964f, -0.085508935f, -0.13127148f, -0.017744139f, 0.031502828f, 0.036232427f, -0.031581745f, 0.023051167f, -0.05325106f, -0.03421577f, 0.028793324f, -0.034633752f, -0.009881397f, -0.043551125f, -0.018609839f, 0.0019097115f, -0.008799762f, 0.056595087f, 0.0022273948f, 0.055752404f }; std::vector recurrentToOutputWeights = { 0.025825322f, -0.05813119f, 0.09495884f,-0.045984812f, -0.01255415f, -0.0026479573f,-0.08196161f,-0.054914974f,-0.0046604523f, -0.029587349f, -0.044576716f, -0.07480124f, -0.082868785f, 0.023254942f, 0.027502948f, -0.0039728214f, -0.08683098f, -0.08116779f, -0.014675607f, -0.037924774f, -0.023314456f, -0.007401714f, -0.09255757f, 0.029460307f, -0.08829125f, -0.005139627f, -0.08989442f, -0.0555066f, 0.13596267f, -0.025062224f, -0.048351806f, -0.03850004f, 0.07266485f, -0.022414139f, 0.05940088f, 0.075114764f, 0.09597592f, -0.010211725f, -0.0049794707f, -0.011523867f, -0.025980417f, 0.072999895f, 0.11091378f, -0.081685916f, 0.014416728f, 0.043229222f, 0.034178585f, -0.07530371f, 0.035837382f, -0.085607f, -0.007721233f, -0.03287832f, -0.043848954f, -0.06404588f, -0.06632928f, -0.073643476f, 0.008214239f, -0.045984086f, 0.039764922f, 0.03474462f, 0.060612556f, -0.080590084f, 0.049127717f, 0.04151091f, -0.030063879f, 0.008801774f, -0.023021035f, -0.019558564f, 0.05158114f, -0.010947698f, -0.011825728f, 0.0075720972f, 0.0699727f, -0.0039981045f, 0.069350146f, 0.08799282f, 0.016156472f, 0.035502106f, 0.11695009f, 0.006217345f, 0.13392477f, -0.037875112f, 0.025745004f, 0.08940699f, -0.00924166f, 0.0046702605f, -0.036598757f, -0.08811812f, 0.10522024f, -0.032441203f, 0.008176899f, -0.04454919f, 0.07058152f, 0.0067963637f, 0.039206743f, 0.03259838f, 0.03725492f, -0.09515802f, 0.013326398f, -0.052055415f, -0.025676316f, 0.03198509f, -0.015951829f, -0.058556724f, 0.036879618f, 0.043357447f, 0.028362012f, -0.05908629f, 0.0059240665f, -0.04995891f, -0.019187413f,0.0276265f, -0.01628143f, 0.0025863599f, 0.08800015f, 0.035250366f, -0.022165963f, -0.07328642f, -0.009415526f, -0.07455109f, 0.11690406f, 0.0363299f, 0.07411125f, 0.042103454f, -0.009660886f, 0.019076364f, 0.018299393f, -0.046004917f, 0.08891175f,0.0431396f, -0.026327137f, -0.051502608f, 0.08979574f, -0.051670972f, 0.04940282f, -0.07491107f, -0.021240504f, 0.022596184f, -0.034280192f, 0.060163025f, -0.058211457f, -0.051837247f, -0.01349775f, -0.04639988f, -0.035936575f, -0.011681591f, 0.064818054f, 0.0073146066f, -0.021745546f, -0.043124277f, -0.06471268f, -0.07053354f, -0.029321948f, -0.05330136f, 0.016933719f, -0.053782392f, 0.13747959f, -0.1361751f, -0.11569455f, 0.0033329215f, 0.05693899f, -0.053219706f, 0.063698f, 0.07977434f, -0.07924483f, 0.06936997f, 0.0034815092f, -0.007305279f, -0.037325785f, -0.07251102f, -0.033633437f, -0.08677009f, 0.091591336f, -0.14165086f, 0.021752775f, 0.019683983f, 0.0011612234f, -0.058154266f, 0.049996935f, 0.0288841f, -0.0024567875f, -0.14345716f, 0.010955264f,-0.10234828f, 0.1183656f, -0.0010731248f, -0.023590032f,-0.072285876f,-0.0724771f, -0.026382286f, -0.0014920527f, 0.042667855f, 0.0018776858f, 0.02986552f, 0.009814309f, 0.0733756f, 0.12289186f, 0.018043943f, -0.0458958f, 0.049412545f, 0.033632483f, 0.05495232f, 0.036686596f, -0.013781798f, -0.010036754f, 0.02576849f, -0.08307328f, 0.010112348f, 0.042521734f, -0.05869831f, -0.071689695f, 0.03876447f, -0.13275425f, -0.0352966f, -0.023077697f, 0.10285965f, 0.084736146f, 0.15568255f, -0.00040734606f, 0.027835453f, -0.10292561f, -0.032401145f, 0.10053256f, -0.026142767f, -0.08271222f, -0.0030240538f, -0.016368777f, 0.1070414f, 0.042672627f, 0.013456989f, -0.0437609f, -0.022309763f, 0.11576483f, 0.04108048f, 0.061026827f, -0.0190714f, -0.0869359f, 0.037901703f, 0.0610107f, 0.07202949f, 0.01675338f, 0.086139716f, -0.08795751f, -0.014898893f, -0.023771819f, -0.01965048f, 0.007955471f, -0.043740474f, 0.03346837f, -0.10549954f, 0.090567775f, 0.042013682f, -0.03176985f, 0.12569028f, -0.02421228f, -0.029526481f, 0.023851605f, 0.031539805f, 0.05292009f, -0.02344001f, -0.07811758f, -0.08834428f, 0.10094801f, 0.16594367f, -0.06861939f, -0.021256343f, -0.041093912f, -0.06669611f, 0.035498552f, 0.021757556f, -0.09302526f, -0.015403468f, -0.06614931f, -0.051798206f, -0.013874718f, 0.03630673f, 0.010412845f, -0.08077351f, 0.046185967f, 0.0035662893f, 0.03541868f, -0.094149634f, -0.034814864f, 0.003128424f, -0.020674974f, -0.03944324f, -0.008110165f, -0.11113267f, 0.08484226f, 0.043586485f, 0.040582247f, 0.0968012f, -0.065249965f, -0.028036479f, 0.0050708856f, 0.0017462453f, 0.0326779f, 0.041296225f, 0.09164146f, -0.047743853f, -0.015952192f, -0.034451712f, 0.084197424f, -0.05347844f, -0.11768019f, 0.085926116f, -0.08251791f, -0.045081906f, 0.0948852f, 0.068401024f, 0.024856757f, 0.06978981f, -0.057309967f, -0.012775832f, -0.0032452994f, 0.01977615f, -0.041040014f, -0.024264973f,0.063464895f, 0.05431621f}; std::vector cellToInputWeights = {0.040369894f, 0.030746894f, 0.24704495f, 0.018586371f, -0.037586458f, -0.15312155f, -0.11812848f, -0.11465643f, 0.20259799f, 0.11418174f, -0.10116027f, -0.011334949f, 0.12411352f, -0.076769054f,-0.052169047f, 0.21198851f, -0.38871562f, -0.09061183f, -0.09683246f, -0.21929175f}; std::vector cellToForgetWeights = {-0.01998659f,-0.15568835f,-0.24248174f, -0.012770197f, 0.041331276f, -0.072311886f, -0.052123554f,-0.0066330447f,-0.043891653f,0.036225766f, -0.047248036f, 0.021479502f,0.033189066f, 0.11952997f, -0.020432774f, 0.64658105f, -0.06650122f, -0.03467612f, 0.095340036f, 0.23647355f}; std::vector cellToOutputWeights = { 0.08286371f, -0.08261836f, -0.51210177f, 0.002913762f, 0.17764764f, -0.5495371f, -0.08460716f, -0.24552552f, 0.030037103f, 0.04123544f, -0.11940523f, 0.007358328f, 0.1890978f, 0.4833202f, -0.34441817f, 0.36312827f, -0.26375428f, 0.1457655f, -0.19724406f, 0.15548733f}; std::vector projectionWeights={-0.009802181f, 0.09401916f, 0.0717386f, -0.13895074f, 0.09641832f, 0.060420845f, 0.08539281f, 0.054285463f, 0.061395317f, 0.034448683f, -0.042991187f, 0.019801661f, -0.16840284f, -0.015726732f, -0.23041931f, -0.024478018f, -0.10959692f, -0.013875541f, 0.18600968f, -0.061274476f, 0.0138165f, -0.08160894f, -0.07661644f, 0.032372914f, 0.16169067f, 0.22465782f, -0.03993472f, -0.004017731f, 0.08633481f, -0.28869787f, 0.08682067f, 0.17240396f, 0.014975425f, 0.056431185f, 0.031037588f, 0.16702051f, 0.0077946745f, 0.15140012f, 0.29405436f, 0.120285f, -0.188994f, -0.027265169f, 0.043389652f, -0.022061434f, 0.014777949f, -0.20203483f, 0.094781205f, 0.19100232f, 0.13987629f, -0.036132768f, -0.06426278f, -0.05108664f, 0.13221376f, 0.009441198f, -0.16715929f, 0.15859416f, -0.040437475f, 0.050779544f, -0.022187516f, 0.012166504f, 0.027685808f, -0.07675938f, -0.0055694645f, -0.09444123f, 0.0046453946f, 0.050794356f, 0.10770313f, -0.20790008f, -0.07149004f, -0.11425117f, 0.008225835f, -0.035802525f, 0.14374903f, 0.15262283f, 0.048710253f, 0.1847461f, -0.007487823f, 0.11000021f, -0.09542012f, 0.22619456f, -0.029149994f, 0.08527916f, 0.009043713f, 0.0042746216f, 0.016261552f, 0.022461696f, 0.12689082f, -0.043589946f, -0.12035478f, -0.08361797f, -0.050666027f, -0.1248618f, -0.1275799f, -0.071875185f, 0.07377272f, 0.09944291f, -0.18897448f, -0.1593054f, -0.06526116f, -0.040107165f, -0.004618631f, -0.067624845f, -0.007576253f, 0.10727444f, 0.041546922f, -0.20424393f, 0.06907816f, 0.050412357f, 0.00724631f, 0.039827548f, 0.12449835f, 0.10747581f, 0.13708383f, 0.09134148f, -0.12617786f, -0.06428341f, 0.09956831f, 0.1208086f, -0.14676677f, -0.0727722f, 0.1126304f, 0.010139365f, 0.015571211f, -0.038128063f, 0.022913318f, -0.042050496f, 0.16842307f, -0.060597885f, 0.10531834f, -0.06411776f, -0.07451711f, -0.03410368f, -0.13393489f, 0.06534304f, 0.003620307f, 0.04490757f, 0.05970546f, 0.05197996f, 0.02839995f, 0.10434969f, -0.013699693f, -0.028353551f, -0.07260381f, 0.047201227f, -0.024575593f, -0.036445823f, 0.07155557f, 0.009672501f, -0.02328883f, 0.009533515f, -0.03606021f, -0.07421458f, -0.028082801f, -0.2678904f, -0.13221288f, 0.18419984f, -0.13012612f, -0.014588381f, -0.035059117f, -0.04824723f, 0.07830115f, -0.056184657f, 0.03277091f, 0.025466874f, 0.14494097f, -0.12522776f, -0.098633975f, -0.10766018f, -0.08317623f, 0.08594209f, 0.07749552f, 0.039474737f, 0.1776665f, -0.07409566f, -0.0477268f, 0.29323658f, 0.10801441f, 0.1154011f, 0.013952499f, 0.10739139f, 0.10708251f, -0.051456142f, 0.0074137426f, -0.10430189f, 0.10034707f, 0.045594677f, 0.0635285f, -0.0715442f, -0.089667566f, -0.10811871f, 0.00026344223f, 0.08298446f, -0.009525053f, 0.006585689f, -0.24567553f, -0.09450807f, 0.09648481f, 0.026996298f, -0.06419476f, -0.04752702f, -0.11063944f, -0.23441927f, -0.17608605f, -0.052156363f, 0.067035615f, 0.19271925f, -0.0032889997f, -0.043264326f, 0.09663576f, -0.057112187f, -0.10100678f, 0.0628376f, 0.04447668f, 0.017961001f, -0.10094388f, -0.10190601f, 0.18335468f, 0.10494553f, -0.052095775f, -0.0026118709f, 0.10539724f, -0.04383912f, -0.042349473f, 0.08438151f, -0.1947263f, 0.02251204f, 0.11216432f, -0.10307853f, 0.17351969f, -0.039091777f, 0.08066188f, -0.00561982f, 0.12633002f, 0.11335965f, -0.0088127935f, -0.019777594f, 0.06864014f, -0.059751723f, 0.016233567f, -0.06894641f, -0.28651384f, -0.004228674f, 0.019708522f, -0.16305895f, -0.07468996f, -0.0855457f, 0.099339016f, -0.07580735f, -0.13775392f, 0.08434318f, 0.08330512f, -0.12131499f, 0.031935584f, 0.09180414f, -0.08876437f, -0.08049874f, 0.008753825f, 0.03498998f, 0.030215185f, 0.03907079f, 0.089751154f, 0.029194152f, -0.03337423f, -0.019092513f, 0.04331237f, 0.04299654f, -0.036394123f, -0.12915532f, 0.09793732f, 0.07512415f, -0.11319543f, -0.032502122f, 0.15661901f, 0.07671967f, -0.005491124f, -0.19379048f, -0.218606f, 0.21448623f, 0.017840758f, 0.1416943f, -0.07051762f, 0.19488361f, 0.02664691f, -0.18104725f, -0.09334311f, 0.15026465f, -0.15493552f, -0.057762887f, -0.11604192f, -0.262013f, -0.01391798f, 0.012185008f, 0.11156489f, -0.07483202f, 0.06693364f, -0.26151478f, 0.046425626f, 0.036540434f, -0.16435726f, 0.17338543f, -0.21401681f, -0.11385144f, -0.08283257f, -0.069031075f, 0.030635102f, 0.010969227f, 0.11109743f, 0.010919218f, 0.027526086f, 0.13519906f, 0.01891392f, -0.046839405f, -0.040167913f, 0.017953383f, -0.09700955f, 0.0061885654f, -0.07000971f, 0.026893595f, -0.038844477f, 0.14543656f}; std::vector projectionBiasVector(outputSize, 0.f); armnn::ScopedTensorHandle inputToInputWeightsTensor(tensorInfo20x5); armnn::ScopedTensorHandle inputToForgetWeightsTensor(tensorInfo20x5); armnn::ScopedTensorHandle inputToCellWeightsTensor(tensorInfo20x5); armnn::ScopedTensorHandle inputToOutputWeightsTensor(tensorInfo20x5); armnn::ScopedTensorHandle recurrentToForgetWeightsTensor(tensorInfo20x16); armnn::ScopedTensorHandle recurrentToInputWeightsTensor(tensorInfo20x16); armnn::ScopedTensorHandle recurrentToCellWeightsTensor(tensorInfo20x16); armnn::ScopedTensorHandle recurrentToOutputWeightsTensor(tensorInfo20x16); armnn::ScopedTensorHandle cellToInputWeightsTensor(tensorInfo20); armnn::ScopedTensorHandle inputGateBiasTensor(tensorInfo20); armnn::ScopedTensorHandle forgetGateBiasTensor(tensorInfo20); armnn::ScopedTensorHandle cellBiasTensor(tensorInfo20); armnn::ScopedTensorHandle outputGateBiasTensor(tensorInfo20); armnn::ScopedTensorHandle cellToForgetWeightsTensor(tensorInfo20); armnn::ScopedTensorHandle cellToOutputWeightsTensor(tensorInfo20); armnn::ScopedTensorHandle projectionWeightsTensor(tensorInfo16x20); armnn::ScopedTensorHandle projectionBiasTensor(tensorInfo16); AllocateAndCopyDataToITensorHandle(&inputToInputWeightsTensor, inputToInputWeights.data()); AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, inputToForgetWeights.data()); AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, inputToCellWeights.data()); AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, inputToOutputWeights.data()); AllocateAndCopyDataToITensorHandle(&recurrentToInputWeightsTensor, recurrentToInputWeights.data()); AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, recurrentToForgetWeights.data()); AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, recurrentToCellWeights.data()); AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, recurrentToOutputWeights.data()); AllocateAndCopyDataToITensorHandle(&cellToInputWeightsTensor, cellToInputWeights.data()); AllocateAndCopyDataToITensorHandle(&inputGateBiasTensor, inputGateBias.data()); AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, forgetGateBias.data()); AllocateAndCopyDataToITensorHandle(&cellBiasTensor, cellBias.data()); AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, outputGateBias.data()); AllocateAndCopyDataToITensorHandle(&cellToForgetWeightsTensor, cellToForgetWeights.data()); AllocateAndCopyDataToITensorHandle(&cellToOutputWeightsTensor, cellToOutputWeights.data()); AllocateAndCopyDataToITensorHandle(&projectionWeightsTensor, projectionWeights.data()); AllocateAndCopyDataToITensorHandle(&projectionBiasTensor, projectionBiasVector.data()); 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; // 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; std::unique_ptr workload = workloadFactory.CreateWorkload(armnn::LayerType::Lstm, data, info); inputHandle->Allocate(); outputStateInHandle->Allocate(); cellStateInHandle->Allocate(); scratchHandle->Allocate(); outputStateOutHandle->Allocate(); cellStateOutHandle->Allocate(); outputHandle->Allocate(); CopyDataToITensorHandle(inputHandle.get(), inputVector.data()); CopyDataToITensorHandle(outputStateInHandle.get(), outputStateInVector.data()); CopyDataToITensorHandle(cellStateInHandle.get(), cellStateInVector.data()); workload->Execute(); CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get()); return LayerTestResult(actualOutput, outputVector, outputHandle->GetShape(), outputTensorInfo.GetShape()); } template> LayerTestResult LstmLayerWithCifgWithPeepholeNoProjectionTestImpl( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory, const std::vector& input, const std::vector& outputExpected, const armnn::TensorShape& inputShape, const armnn::TensorShape& outputExpectedShape, float qScale = 1.0f, int32_t qOffset = 0, armnn::DataType constantDataType = armnn::DataType::Float32) { IgnoreUnused(memoryManager); bool cifgEnabled = true; bool peepholeEnabled = true; bool projectionEnabled = false; // These are not the input and the output of Lstm yet unsigned int batchSize = armnn::numeric_cast(inputShape[0]); unsigned int inputSize = armnn::numeric_cast(inputShape[1]); unsigned int outputSize = armnn::numeric_cast(outputExpectedShape[1]); const unsigned int cellSize = outputSize; // Decide the shape of all input tensors armnn::TensorInfo inputTensorInfo({batchSize , inputSize}, ArmnnType, qScale, qOffset); // change to ArmnnType armnn::TensorInfo outputStateInTensorInfo({batchSize, outputSize}, ArmnnType, qScale, qOffset); armnn::TensorInfo cellStateInTensorInfo({batchSize, cellSize}, ArmnnType, qScale, qOffset); unsigned int scratchBufferSize = cifgEnabled ? cellSize * 3 : cellSize * 4; armnn::TensorInfo scratchBufferTensorInfo({batchSize, scratchBufferSize}, ArmnnType, qScale, qOffset); armnn::TensorInfo outputStateOutTensorInfo({batchSize, outputSize}, ArmnnType, qScale, qOffset); armnn::TensorInfo cellStateOutTensorInfo({batchSize, cellSize}, ArmnnType, qScale, qOffset); armnn::TensorInfo outputTensorInfo({batchSize, outputSize}, ArmnnType, qScale, qOffset); // List of inputs std::vector inputData; inputData.assign(input.data(), input.data() + batchSize*inputSize); std::vector outputStateInVector(batchSize * outputSize, 0.f); std::vector cellStateInVector(batchSize * cellSize, 0.f); // Prepare all the weights in the descriptor for LSTM armnn::LstmQueueDescriptor data; armnn::TensorInfo tensorInfoInput({cellSize, inputSize}, constantDataType, qScale, qOffset); armnn::TensorInfo tensorInfoOutput({cellSize, outputSize}, constantDataType, qScale, qOffset); armnn::TensorInfo tensorInfoNumUnits({cellSize}, constantDataType, qScale, qOffset); std::vector inputToCellWeights = { -0.49770179f, -0.27711356f, -0.09624726f, 0.05100781f, 0.04717243f, 0.48944736f, -0.38535351f, -0.17212132f }; std::vector inputToForgetWeights = { -0.55291498f, -0.42866567f, 0.13056988f, -0.3633365f, -0.22755712f, 0.28253698f, 0.24407166f, 0.33826375f }; std::vector inputToOutputWeights = { 0.10725588f, -0.02335852f, -0.55932593f, -0.09426838f, -0.44257352f, 0.54939759f, 0.01533556f, 0.42751634f }; std::vector cellBias = {0.f, 0.f, 0.f, 0.f}; std::vector forgetGateBias = {1.f, 1.f, 1.f, 1.f}; std::vector outputGateBias = {0.f, 0.f, 0.f, 0.f}; std::vector recurrentToCellWeights = { 0.54066205f, -0.32668582f, -0.43562764f, -0.56094903f, 0.42957711f, 0.01841056f, -0.32764608f, -0.33027974f, -0.10826075f, 0.20675004f, 0.19069612f, -0.03026325f, -0.54532051f, 0.33003211f, 0.44901288f, 0.21193194f }; std::vector recurrentToForgetWeights = { -0.13832897f, -0.0515101f, -0.2359007f, -0.16661474f, -0.14340827f, 0.36986142f, 0.23414481f, 0.55899f, 0.10798943f, -0.41174671f, 0.17751795f, -0.34484994f, -0.35874045f, -0.11352962f, 0.27268326f, 0.54058349f }; std::vector recurrentToOutputWeights = { 0.41613156f, 0.42610586f, -0.16495961f, -0.5663873f, 0.30579174f, -0.05115908f, -0.33941799f, 0.23364776f, 0.11178309f, 0.09481031f, -0.26424935f, 0.46261835f, 0.50248802f, 0.26114327f, -0.43736315f, 0.33149987f }; std::vector cellToForgetWeights = {0.47485286f, -0.51955009f, -0.24458408f, 0.31544167f}; std::vector cellToOutputWeights = {-0.17135078f, 0.82760304f, 0.85573703f, -0.77109635f}; armnn::ScopedTensorHandle inputToCellWeightsTensor(tensorInfoInput); armnn::ScopedTensorHandle inputToForgetWeightsTensor(tensorInfoInput); armnn::ScopedTensorHandle inputToOutputWeightsTensor(tensorInfoInput); armnn::ScopedTensorHandle cellBiasTensor(tensorInfoNumUnits); armnn::ScopedTensorHandle forgetGateBiasTensor(tensorInfoNumUnits); armnn::ScopedTensorHandle outputGateBiasTensor(tensorInfoNumUnits); armnn::ScopedTensorHandle recurrentToCellWeightsTensor(tensorInfoOutput); armnn::ScopedTensorHandle recurrentToForgetWeightsTensor(tensorInfoOutput); armnn::ScopedTensorHandle recurrentToOutputWeightsTensor(tensorInfoOutput); armnn::ScopedTensorHandle cellToForgetWeightsTensor(tensorInfoNumUnits); armnn::ScopedTensorHandle cellToOutputWeightsTensor(tensorInfoNumUnits); AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, inputToCellWeights.data()); AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, inputToForgetWeights.data()); AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, inputToOutputWeights.data()); AllocateAndCopyDataToITensorHandle(&cellBiasTensor, cellBias.data()); AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, forgetGateBias.data()); AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, outputGateBias.data()); AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, recurrentToCellWeights.data()); AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, recurrentToForgetWeights.data()); AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, recurrentToOutputWeights.data()); AllocateAndCopyDataToITensorHandle(&cellToForgetWeightsTensor, cellToForgetWeights.data()); AllocateAndCopyDataToITensorHandle(&cellToOutputWeightsTensor, cellToOutputWeights.data()); data.m_InputToCellWeights = &inputToCellWeightsTensor; data.m_InputToForgetWeights = &inputToForgetWeightsTensor; data.m_InputToOutputWeights = &inputToOutputWeightsTensor; data.m_CellBias = &cellBiasTensor; data.m_ForgetGateBias = &forgetGateBiasTensor; data.m_OutputGateBias = &outputGateBiasTensor; data.m_RecurrentToCellWeights = &recurrentToCellWeightsTensor; data.m_RecurrentToForgetWeights = &recurrentToForgetWeightsTensor; data.m_RecurrentToOutputWeights = &recurrentToOutputWeightsTensor; data.m_CellToForgetWeights = &cellToForgetWeightsTensor; data.m_CellToOutputWeights = &cellToOutputWeightsTensor; // other parameters for the descriptor data.m_Parameters.m_CifgEnabled = cifgEnabled; data.m_Parameters.m_ProjectionEnabled = projectionEnabled; data.m_Parameters.m_PeepholeEnabled = peepholeEnabled; data.m_Parameters.m_ActivationFunc = 4; data.m_Parameters.m_ClippingThresProj = 0.0; data.m_Parameters.m_ClippingThresCell = 0.0; // List of outputs std::vector scratchBufferVector(batchSize * scratchBufferSize, T()); LayerTestResult ret0(scratchBufferTensorInfo); // Output state for a certain time step std::vector outputStateOutVector(batchSize * outputSize, T()); LayerTestResult ret1(outputStateOutTensorInfo); // Cell state for a certain time step std::vector cellStateOutVector(batchSize * cellSize, T()); LayerTestResult ret2(cellStateOutTensorInfo); // Output for a certain time step std::vector outputData; outputData.assign(outputExpected.data(), outputExpected.data() + batchSize*outputSize); LayerTestResult ret3(outputTensorInfo); ret3.m_ExpectedData = outputData; std::vector actualScratchBufferOutput(scratchBufferTensorInfo.GetNumElements()); std::vector actualOutputStateOutput(outputStateOutTensorInfo.GetNumElements()); std::vector actualCellStateOutput(cellStateOutTensorInfo.GetNumElements()); std::vector actualOutput(outputTensorInfo.GetNumElements()); // Prepare the inputs and outputs for the workload std::unique_ptr inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo); std::unique_ptr outputStateInHandle = tensorHandleFactory.CreateTensorHandle(outputStateInTensorInfo); std::unique_ptr cellStateInHandle = tensorHandleFactory.CreateTensorHandle(cellStateInTensorInfo); std::unique_ptr scratchBufferHandle = tensorHandleFactory.CreateTensorHandle(scratchBufferTensorInfo); std::unique_ptr outputStateOutHandle = tensorHandleFactory.CreateTensorHandle(outputStateOutTensorInfo); std::unique_ptr cellStateOutHandle = tensorHandleFactory.CreateTensorHandle(cellStateOutTensorInfo); std::unique_ptr outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo); armnn::WorkloadInfo info; AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get()); AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get()); AddOutputToWorkload(data, info, scratchBufferTensorInfo, scratchBufferHandle.get()); AddOutputToWorkload(data, info, outputStateOutTensorInfo, outputStateOutHandle.get()); AddOutputToWorkload(data, info, cellStateOutTensorInfo, cellStateOutHandle.get()); AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); std::unique_ptr workload = workloadFactory.CreateWorkload(armnn::LayerType::Lstm, data, info); inputHandle->Allocate(); outputStateInHandle->Allocate(); cellStateInHandle->Allocate(); scratchBufferHandle->Allocate(); outputStateOutHandle->Allocate(); cellStateOutHandle->Allocate(); outputHandle->Allocate(); CopyDataToITensorHandle(inputHandle.get(), inputData.data()); CopyDataToITensorHandle(outputStateInHandle.get(), outputStateInVector.data()); CopyDataToITensorHandle(cellStateInHandle.get(), cellStateInVector.data()); CopyDataToITensorHandle(scratchBufferHandle.get(), scratchBufferVector.data()); CopyDataToITensorHandle(outputStateOutHandle.get(), outputStateOutVector.data()); CopyDataToITensorHandle(cellStateOutHandle.get(), cellStateOutVector.data()); workload->Execute(); CopyDataFromITensorHandle(actualScratchBufferOutput.data(), scratchBufferHandle.get()); CopyDataFromITensorHandle(actualOutputStateOutput.data(), outputStateOutHandle.get()); CopyDataFromITensorHandle(actualCellStateOutput.data(), cellStateOutHandle.get()); CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get()); ret0.m_ActualData = actualScratchBufferOutput; ret1.m_ActualData = actualOutputStateOutput; ret2.m_ActualData = actualCellStateOutput; ret3.m_ActualData = actualOutput; return ret3; } template> LayerTestResult LstmLayerNoCifgWithPeepholeWithProjectionWithLayerNormTestImpl(armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory, const std::vector& input, const std::vector& outputExpected, float qScale = 1.0f, int32_t qOffset = 0, armnn::DataType constantDataType = armnn::DataType::Float32) { IgnoreUnused(memoryManager); unsigned int batchSize = 2; unsigned int outputSize = 3; unsigned int inputSize = 5; unsigned numUnits = 4; armnn::TensorInfo inputTensorInfo({batchSize , inputSize}, ArmnnType, qScale, qOffset); armnn::TensorInfo cellStateInTensorInfo({batchSize , numUnits}, ArmnnType, qScale, qOffset); armnn::TensorInfo outputStateInTensorInfo({batchSize , outputSize}, ArmnnType, qScale, qOffset); // Scratch buffer size without CIFG [batchSize, numUnits * 4] armnn::TensorInfo scratchBufferTensorInfo({batchSize, numUnits * 4}, ArmnnType, qScale, qOffset); armnn::TensorInfo cellStateOutTensorInfo({batchSize, numUnits}, ArmnnType, qScale, qOffset); armnn::TensorInfo outputStateOutTensorInfo({batchSize, outputSize}, ArmnnType, qScale, qOffset); armnn::TensorInfo outputTensorInfo({batchSize, outputSize}, ArmnnType, qScale, qOffset); std::vector inputVector; inputVector.assign(input.data(), input.data() + (batchSize * inputSize)); std::vector cellStateInVector(batchSize * numUnits, 0.f); std::vector outputStateInVector(batchSize * outputSize, 0.f); std::vector scratchBufferVector(batchSize * numUnits * 4, 0.f); std::vector outputStateOutVector(batchSize * outputSize, 0.f); std::vector cellStateOutVector(batchSize * numUnits, 0.f); std::vector actualOutput(outputTensorInfo.GetNumElements()); std::vector outputVector; outputVector.assign(outputExpected.data(), outputExpected.data() + (batchSize * outputSize)); std::unique_ptr inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo); std::unique_ptr cellStateInHandle = tensorHandleFactory.CreateTensorHandle(cellStateInTensorInfo); std::unique_ptr outputStateInHandle = tensorHandleFactory.CreateTensorHandle(outputStateInTensorInfo); std::unique_ptr scratchHandle = tensorHandleFactory.CreateTensorHandle(scratchBufferTensorInfo); std::unique_ptr outputStateOutHandle = tensorHandleFactory.CreateTensorHandle(outputStateOutTensorInfo); std::unique_ptr cellStateOutHandle = tensorHandleFactory.CreateTensorHandle(cellStateOutTensorInfo); std::unique_ptr outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo); armnn::LstmQueueDescriptor data; armnn::WorkloadInfo info; AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get()); AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get()); AddOutputToWorkload(data, info, scratchBufferTensorInfo, scratchHandle.get()); AddOutputToWorkload(data, info, outputStateOutTensorInfo, outputStateOutHandle.get()); AddOutputToWorkload(data, info, cellStateOutTensorInfo, cellStateOutHandle.get()); AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); armnn::TensorInfo tensorInfo3({outputSize}, constantDataType, qScale, qOffset); armnn::TensorInfo tensorInfo4({numUnits}, constantDataType, qScale, qOffset); armnn::TensorInfo tensorInfo4x5({numUnits, inputSize}, constantDataType, qScale, qOffset); armnn::TensorInfo tensorInfo4x3({numUnits, outputSize}, constantDataType, qScale, qOffset); armnn::TensorInfo tensorInfo3x4({outputSize, numUnits}, constantDataType, qScale, qOffset); std::vector inputToInputWeights = {0.5f, 0.6f, 0.7f, -0.8f, -0.9f, 0.1f, 0.2f, 0.3f, -0.4f, 0.5f, -0.8f, 0.7f, -0.6f, 0.5f, -0.4f, -0.5f, -0.4f, -0.3f, -0.2f, -0.1f}; //{numUnits, inputSize} std::vector inputToForgetWeights = { -0.6f, -0.1f, 0.3f, 0.2f, 0.9f, -0.5f, -0.2f, -0.4f, 0.3f, -0.8f, -0.4f, 0.3f, -0.5f, -0.4f, -0.6f, 0.3f, -0.4f, -0.6f, -0.5f, -0.5f}; //{numUnits, inputSize} std::vector inputToCellWeights = {-0.4f, -0.3f, -0.2f, -0.1f, -0.5f, 0.5f, -0.2f, -0.3f, -0.2f, -0.6f, 0.6f, -0.1f, -0.4f, -0.3f, -0.7f, 0.7f, -0.9f, -0.5f, 0.8f, 0.6f}; //{numUnits, inputSize} std::vector inputToOutputWeights = {-0.8f, -0.4f, -0.2f, -0.9f, -0.1f, -0.7f, 0.3f, -0.3f, -0.8f, -0.2f, 0.6f, -0.2f, 0.4f, -0.7f, -0.3f, -0.5f, 0.1f, 0.5f, -0.6f, -0.4f}; //{numUnits, inputSize} std::vector inputGateBias = {0.03f, 0.15f, 0.22f, 0.38f}; //{numUnits} std::vector forgetGateBias = {0.1f, -0.3f, -0.2f, 0.1f}; //{numUnits} std::vector cellBias = {-0.05f, 0.72f, 0.25f, 0.08f}; //{numUnits} std::vector outputGateBias = {0.05f, -0.01f, 0.2f, 0.1f}; //{numUnits} std::vector recurrentToInputWeights ={-0.2f, -0.3f, 0.4f, 0.1f, -0.5f, 0.9f, -0.2f, -0.3f, -0.7f, 0.05f, -0.2f, -0.6f}; //{numUnits, outputSize} std::vector recurrentToCellWeights = {-0.3f, 0.2f, 0.1f, -0.3f, 0.8f, -0.08f, -0.2f, 0.3f, 0.8f, -0.6f, -0.1f, 0.2f}; //{numUnits, outputSize} std::vector recurrentToForgetWeights = { -0.5f, -0.3f, -0.5f, -0.2f, 0.6f, 0.4f, 0.9f, 0.3f, -0.1f, 0.2f, 0.5f, 0.2f}; //{numUnits, outputSize} std::vector recurrentToOutputWeights = { 0.3f, -0.1f, 0.1f, -0.2f, -0.5f, -0.7f, -0.2f, -0.6f, -0.1f, -0.4f, -0.7f, -0.2f}; //{numUnits, outputSize} std::vector cellToInputWeights = {0.05f, 0.1f, 0.25f, 0.15f}; //{numUnits} std::vector cellToForgetWeights = {-0.02f, -0.15f, -0.25f, -0.03f}; //{numUnits} std::vector cellToOutputWeights = {0.1f, -0.1f, -0.5f, 0.05f}; //{numUnits} std::vector projectionWeights = {-0.1f, 0.2f, 0.01f, -0.2f, 0.1f, 0.5f, 0.3f, 0.08f, 0.07f, 0.2f, -0.4f, 0.2f}; //{outputSize, numUnits} std::vector projectionBiasVector(outputSize, 0.f); //{outputSize} std::vector inputLayerNormWeights = {0.1f, 0.2f, 0.3f, 0.5f}; //{numUnits} std::vector forgetLayerNormWeights = {0.2f, 0.2f, 0.4f, 0.3f}; //{numUnits} std::vector cellLayerNormWeights = {0.7f, 0.2f, 0.3f, 0.8f}; //{numUnits} std::vector outputLayerNormWeights = {0.6f, 0.2f, 0.2f, 0.5f}; //{numUnits} armnn::ScopedTensorHandle inputToInputWeightsTensor(tensorInfo4x5); armnn::ScopedTensorHandle inputToForgetWeightsTensor(tensorInfo4x5); armnn::ScopedTensorHandle inputToCellWeightsTensor(tensorInfo4x5); armnn::ScopedTensorHandle inputToOutputWeightsTensor(tensorInfo4x5); armnn::ScopedTensorHandle recurrentToForgetWeightsTensor(tensorInfo4x3); armnn::ScopedTensorHandle recurrentToInputWeightsTensor(tensorInfo4x3); armnn::ScopedTensorHandle recurrentToCellWeightsTensor(tensorInfo4x3); armnn::ScopedTensorHandle recurrentToOutputWeightsTensor(tensorInfo4x3); armnn::ScopedTensorHandle cellToInputWeightsTensor(tensorInfo4); armnn::ScopedTensorHandle inputGateBiasTensor(tensorInfo4); armnn::ScopedTensorHandle forgetGateBiasTensor(tensorInfo4); armnn::ScopedTensorHandle cellBiasTensor(tensorInfo4); armnn::ScopedTensorHandle outputGateBiasTensor(tensorInfo4); armnn::ScopedTensorHandle cellToForgetWeightsTensor(tensorInfo4); armnn::ScopedTensorHandle cellToOutputWeightsTensor(tensorInfo4); armnn::ScopedTensorHandle projectionWeightsTensor(tensorInfo3x4); armnn::ScopedTensorHandle projectionBiasTensor(tensorInfo3); armnn::ScopedTensorHandle inputLayerNormWeightsTensor(tensorInfo4); armnn::ScopedTensorHandle forgetLayerNormWeightsTensor(tensorInfo4); armnn::ScopedTensorHandle cellLayerNormWeightsTensor(tensorInfo4); armnn::ScopedTensorHandle outputLayerNormWeightsTensor(tensorInfo4); AllocateAndCopyDataToITensorHandle(&inputToInputWeightsTensor, inputToInputWeights.data()); AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, inputToForgetWeights.data()); AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, inputToCellWeights.data()); AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, inputToOutputWeights.data()); AllocateAndCopyDataToITensorHandle(&recurrentToInputWeightsTensor, recurrentToInputWeights.data()); AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, recurrentToForgetWeights.data()); AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, recurrentToCellWeights.data()); AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, recurrentToOutputWeights.data()); AllocateAndCopyDataToITensorHandle(&cellToInputWeightsTensor, cellToInputWeights.data()); AllocateAndCopyDataToITensorHandle(&inputGateBiasTensor, inputGateBias.data()); AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, forgetGateBias.data()); AllocateAndCopyDataToITensorHandle(&cellBiasTensor, cellBias.data()); AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, outputGateBias.data()); AllocateAndCopyDataToITensorHandle(&cellToForgetWeightsTensor, cellToForgetWeights.data()); AllocateAndCopyDataToITensorHandle(&cellToOutputWeightsTensor, cellToOutputWeights.data()); AllocateAndCopyDataToITensorHandle(&projectionWeightsTensor, projectionWeights.data()); AllocateAndCopyDataToITensorHandle(&projectionBiasTensor, projectionBiasVector.data()); AllocateAndCopyDataToITensorHandle(&inputLayerNormWeightsTensor, inputLayerNormWeights.data()); AllocateAndCopyDataToITensorHandle(&forgetLayerNormWeightsTensor, forgetLayerNormWeights.data()); AllocateAndCopyDataToITensorHandle(&cellLayerNormWeightsTensor, cellLayerNormWeights.data()); AllocateAndCopyDataToITensorHandle(&outputLayerNormWeightsTensor, outputLayerNormWeights.data()); 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; std::unique_ptr workload = workloadFactory.CreateWorkload(armnn::LayerType::Lstm, data, info); inputHandle->Allocate(); outputStateInHandle->Allocate(); cellStateInHandle->Allocate(); scratchHandle->Allocate(); outputStateOutHandle->Allocate(); cellStateOutHandle->Allocate(); outputHandle->Allocate(); CopyDataToITensorHandle(inputHandle.get(), inputVector.data()); CopyDataToITensorHandle(outputStateInHandle.get(), outputStateInVector.data()); CopyDataToITensorHandle(cellStateInHandle.get(), cellStateInVector.data()); workload->Execute(); CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get()); return LayerTestResult(actualOutput, outputVector, outputHandle->GetShape(), outputTensorInfo.GetShape()); } LayerTestResult QuantizedLstmTestImpl( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory, const std::vector& input, const std::vector& outputExpected, const armnn::TensorShape& inputShape, const armnn::TensorShape& outputExpectedShape) { IgnoreUnused(memoryManager); auto numBatches = armnn::numeric_cast(inputShape[0]); auto inputSize = armnn::numeric_cast(inputShape[1]); auto outputSize = armnn::numeric_cast(outputExpectedShape[1]); // 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; // Input/Output tensor 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); // Input0 std::vector inputVector; inputVector.assign(input.data(), input.data() + (numBatches * inputSize)); // Input1 std::vector cellStateInVector = {876, 1034, 955, -909, 761, 1029, 796, -1036}; // 13 // Input2 std::vector outputStateInVector = {136, 150, 140, 115, 135, 152, 138, 112}; // 14 // Output0 std::vector cellStateOutVector = {1485, 1177, 1373, -1023, 1019, 1355, 1097, -1235}; // 0 // Output1 std::vector outputVector; // 1 outputVector.assign(outputExpected.data(), outputExpected.data() + (numBatches * outputSize)); std::vector actualOutput(outputStateInfo.GetNumElements()); // Create tensor handles std::unique_ptr inputHandle = tensorHandleFactory.CreateTensorHandle(inputInfo); std::unique_ptr cellStateInHandle = tensorHandleFactory.CreateTensorHandle(cellStateInfo); std::unique_ptr outputStateInHandle = tensorHandleFactory.CreateTensorHandle(outputStateInfo); std::unique_ptr cellStateOutHandle = tensorHandleFactory.CreateTensorHandle(cellStateInfo); std::unique_ptr outputHandle = tensorHandleFactory.CreateTensorHandle(outputStateInfo); armnn::QuantizedLstmQueueDescriptor data; armnn::WorkloadInfo info; // Add inputs and outputs to workload AddInputToWorkload(data, info, inputInfo, inputHandle.get()); AddInputToWorkload(data, info, cellStateInfo, cellStateInHandle.get()); AddInputToWorkload(data, info, outputStateInfo, outputStateInHandle.get()); AddOutputToWorkload(data, info, cellStateInfo, cellStateOutHandle.get()); AddOutputToWorkload(data, info, outputStateInfo, outputHandle.get()); // 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 tensor data std::vector inputToInputWeights = {146, 250, 235, 171, 10, 218, 171, 108}; std::vector inputToForgetWeights = {24, 50, 132, 179, 158, 110, 3, 169}; std::vector inputToCellWeights = {133, 34, 29, 49, 206, 109, 54, 183}; std::vector inputToOutputWeights = {195, 187, 11, 99, 109, 10, 218, 48}; std::vector recurrentToInputWeights = {254, 206, 77, 168, 71, 20, 215, 6, 223, 7, 118, 225, 59, 130, 174, 26}; std::vector recurrentToForgetWeights = {137, 240, 103, 52, 68, 51, 237, 112, 0, 220, 89, 23, 69, 4, 207, 253}; std::vector recurrentToCellWeights = {172, 60, 205, 65, 14, 0, 140, 168, 240, 223, 133, 56, 142, 64, 246, 216}; std::vector recurrentToOutputWeights = {106, 214, 67, 23, 59, 158, 45, 3, 119, 132, 49, 205, 129, 218, 11, 98}; std::vector inputGateBias = {-7876, 13488, -726, 32839}; std::vector forgetGateBias = {9206, -46884, -11693, -38724}; std::vector cellBias = {39481, 48624, 48976, -21419}; std::vector outputGateBias = {-58999, -17050, -41852, -40538}; // ScopedTensorHandles armnn::ScopedTensorHandle inputToInputWeightsTensor(inputWeightsInfo); armnn::ScopedTensorHandle inputToForgetWeightsTensor(inputWeightsInfo); armnn::ScopedTensorHandle inputToCellWeightsTensor(inputWeightsInfo); armnn::ScopedTensorHandle inputToOutputWeightsTensor(inputWeightsInfo); armnn::ScopedTensorHandle recurrentToInputWeightsTensor(recurrentWeightsInfo); armnn::ScopedTensorHandle recurrentToForgetWeightsTensor(recurrentWeightsInfo); armnn::ScopedTensorHandle recurrentToCellWeightsTensor(recurrentWeightsInfo); armnn::ScopedTensorHandle recurrentToOutputWeightsTensor(recurrentWeightsInfo); armnn::ScopedTensorHandle inputGateBiasTensor(biasInfo); armnn::ScopedTensorHandle forgetGateBiasTensor(biasInfo); armnn::ScopedTensorHandle cellBiasTensor(biasInfo); armnn::ScopedTensorHandle outputGateBiasTensor(biasInfo); // Allocate and copy data AllocateAndCopyDataToITensorHandle(&inputToInputWeightsTensor, inputToInputWeights.data()); AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, inputToForgetWeights.data()); AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, inputToCellWeights.data()); AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, inputToOutputWeights.data()); AllocateAndCopyDataToITensorHandle(&recurrentToInputWeightsTensor, recurrentToInputWeights.data()); AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, recurrentToForgetWeights.data()); AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, recurrentToCellWeights.data()); AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, recurrentToOutputWeights.data()); AllocateAndCopyDataToITensorHandle(&inputGateBiasTensor, inputGateBias.data()); AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, forgetGateBias.data()); AllocateAndCopyDataToITensorHandle(&cellBiasTensor, cellBias.data()); AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, outputGateBias.data()); // Setup queue descriptor 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_InputGateBias = &inputGateBiasTensor; data.m_ForgetGateBias = &forgetGateBiasTensor; data.m_CellBias = &cellBiasTensor; data.m_OutputGateBias = &outputGateBiasTensor; // Create workload and allocate tensor handles std::unique_ptr workload = workloadFactory.CreateWorkload(armnn::LayerType::QuantizedLstm, data, info); inputHandle->Allocate(); outputStateInHandle->Allocate(); cellStateInHandle->Allocate(); cellStateOutHandle->Allocate(); outputHandle->Allocate(); CopyDataToITensorHandle(inputHandle.get(), inputVector.data()); CopyDataToITensorHandle(outputStateInHandle.get(), outputStateInVector.data()); CopyDataToITensorHandle(cellStateInHandle.get(), cellStateInVector.data()); workload->Execute(); CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get()); return LayerTestResult(actualOutput, outputVector, outputHandle->GetShape(), outputStateInfo.GetShape()); } // QLSTM: CIFG, LayerNorm LayerTestResult QLstmTestImpl( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory, const std::vector& input, const std::vector& outputExpected) { IgnoreUnused(memoryManager); unsigned int numBatches = 2; unsigned int inputSize = 5; unsigned int outputSize = 4; unsigned int numUnits = 4; bool cifgEnabled = true; bool peepholeEnabled = false; bool projectionEnabled = false; bool layerNormEnabled = true; // Scale/Offset quantization info float inputScale = 0.0078125f; int32_t inputOffset = 0; int32_t hiddenStateZeroPoint = 0; float hiddenStateScale = 0.007f; // if (!projectionEnabled) outputScale == hiddenStateScale float outputScale = hiddenStateScale; int32_t outputOffset = 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; float inputIntermediateScale = 0.007059f; float forgetIntermediateScale = 0.007812f; float cellIntermediateScale = inputIntermediateScale; float outputIntermediateScale = forgetIntermediateScale; float cellClip = 0.0f; float projectionClip = 0.0f; // 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); LayerTestResult ret(outputStateInfo); // Input tensors std::vector inputVector; inputVector.assign(input.data(), input.data() + (numBatches * inputSize)); std::vector cellStateInVector = {0, 0, 0, 0, 0, 0, 0, 0}; std::vector outputStateInVector = {0, 0, 0, 0, 0, 0, 0, 0}; // Output tensors std::vector cellStateOutVector = {-11692, 9960, 5491, 8861, -9422, 7726, 2056, 13149}; std::vector outputVector; outputVector.assign(outputExpected.data(), outputExpected.data() + (numBatches * outputSize)); std::vector actualOutput(outputStateInfo.GetNumElements()); // Create tensor handles std::unique_ptr inputHandle = tensorHandleFactory.CreateTensorHandle(inputInfo); std::unique_ptr cellStateInHandle = tensorHandleFactory.CreateTensorHandle(cellStateInfo); std::unique_ptr outputStateInHandle = tensorHandleFactory.CreateTensorHandle(outputStateInfo); std::unique_ptr outputStateOutHandle = tensorHandleFactory.CreateTensorHandle(outputStateInfo); std::unique_ptr cellStateOutHandle = tensorHandleFactory.CreateTensorHandle(cellStateInfo); std::unique_ptr outputHandle = tensorHandleFactory.CreateTensorHandle(outputStateInfo); armnn::QLstmQueueDescriptor data; armnn::WorkloadInfo info; // Add inputs and outputs to workload AddInputToWorkload(data, info, inputInfo, inputHandle.get()); AddInputToWorkload(data, info, outputStateInfo, outputStateInHandle.get()); AddInputToWorkload(data, info, cellStateInfo, cellStateInHandle.get()); AddOutputToWorkload(data, info, outputStateInfo, outputStateOutHandle.get()); AddOutputToWorkload(data, info, cellStateInfo, cellStateOutHandle.get()); AddOutputToWorkload(data, info, outputStateInfo, outputHandle.get()); // 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); // Weights and bias tensor data std::vector inputToForgetWeights = {-77, -13, 38, 25, 115, -64, -25, -51, 38, -102, -51, 38, -64, -51, -77, 38, -51, -77, -64, -64}; std::vector inputToCellWeights = {-51, -38, -25, -13, -64, 64, -25, -38, -25, -77, 77, -13, -51, -38, -89, 89, -115, -64, 102, 77}; std::vector inputToOutputWeights = {-102, -51, -25, -115, -13, -89, 38, -38, -102, -25, 77, -25, 51, -89, -38, -64, 13, 64, -77, -51}; std::vector recurrentToForgetWeights = {-64, -38, -64, -25, 77, 51, 115, 38, -13, 25, 64, 25, 25, 38, -13, 51}; std::vector recurrentToCellWeights = {-38, 25, 13, -38, 102, -10, -25, 38, 102, -77, -13, 25, 38, -13, 25, 64}; std::vector recurrentToOutputWeights = {38, -13, 13, -25, -64, -89, -25, -77, -13, -51, -89, -25, 13, 64, 25, -38}; std::vector forgetGateBias = {2147484, -6442451, -4294968, 2147484}; std::vector cellBias = {-1073742, 15461883, 5368709, 1717987}; std::vector outputGateBias = {1073742, -214748, 4294968, 2147484}; std::vector forgetLayerNormWeights = {6553, 6553, 13107, 9830}; std::vector cellLayerNormWeights = {22937, 6553, 9830, 26214}; std::vector outputLayerNormWeights = {19660, 6553, 6553, 16384}; // ScopedTensorHandles armnn::ScopedTensorHandle inputToForgetWeightsTensor(inputWeightsInfo); armnn::ScopedTensorHandle inputToCellWeightsTensor(inputWeightsInfo); armnn::ScopedTensorHandle inputToOutputWeightsTensor(inputWeightsInfo); armnn::ScopedTensorHandle recurrentToForgetWeightsTensor(recurrentWeightsInfo); armnn::ScopedTensorHandle recurrentToCellWeightsTensor(recurrentWeightsInfo); armnn::ScopedTensorHandle recurrentToOutputWeightsTensor(recurrentWeightsInfo); armnn::ScopedTensorHandle forgetGateBiasTensor(biasInfo); armnn::ScopedTensorHandle cellBiasTensor(biasInfo); armnn::ScopedTensorHandle outputGateBiasTensor(biasInfo); armnn::ScopedTensorHandle forgetLayerNormWeightsTensor(layerNormWeightsInfo); armnn::ScopedTensorHandle cellLayerNormWeightsTensor(layerNormWeightsInfo); armnn::ScopedTensorHandle outputLayerNormWeightsTensor(layerNormWeightsInfo); // Allocate and copy data AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, inputToForgetWeights.data()); AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, inputToCellWeights.data()); AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, inputToOutputWeights.data()); AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, recurrentToForgetWeights.data()); AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, recurrentToCellWeights.data()); AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, recurrentToOutputWeights.data()); AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, forgetGateBias.data()); AllocateAndCopyDataToITensorHandle(&cellBiasTensor, cellBias.data()); AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, outputGateBias.data()); AllocateAndCopyDataToITensorHandle(&forgetLayerNormWeightsTensor, forgetLayerNormWeights.data()); AllocateAndCopyDataToITensorHandle(&cellLayerNormWeightsTensor, cellLayerNormWeights.data()); AllocateAndCopyDataToITensorHandle(&outputLayerNormWeightsTensor, outputLayerNormWeights.data()); // Setup queue descriptor data.m_InputToForgetWeights = &inputToForgetWeightsTensor; data.m_InputToCellWeights = &inputToCellWeightsTensor; data.m_InputToOutputWeights = &inputToOutputWeightsTensor; data.m_RecurrentToForgetWeights = &recurrentToForgetWeightsTensor; data.m_RecurrentToCellWeights = &recurrentToCellWeightsTensor; data.m_RecurrentToOutputWeights = &recurrentToOutputWeightsTensor; data.m_ForgetGateBias = &forgetGateBiasTensor; data.m_CellBias = &cellBiasTensor; data.m_OutputGateBias = &outputGateBiasTensor; data.m_ForgetLayerNormWeights = &forgetLayerNormWeightsTensor; data.m_CellLayerNormWeights = &cellLayerNormWeightsTensor; data.m_OutputLayerNormWeights = &outputLayerNormWeightsTensor; data.m_Parameters.m_CifgEnabled = cifgEnabled; data.m_Parameters.m_PeepholeEnabled = peepholeEnabled; data.m_Parameters.m_ProjectionEnabled = projectionEnabled; data.m_Parameters.m_LayerNormEnabled = layerNormEnabled; data.m_Parameters.m_InputIntermediateScale = inputIntermediateScale; data.m_Parameters.m_ForgetIntermediateScale = forgetIntermediateScale; data.m_Parameters.m_CellIntermediateScale = cellIntermediateScale; data.m_Parameters.m_OutputIntermediateScale = outputIntermediateScale; data.m_Parameters.m_HiddenStateZeroPoint = hiddenStateZeroPoint; data.m_Parameters.m_HiddenStateScale = hiddenStateScale; data.m_Parameters.m_CellClip = cellClip; data.m_Parameters.m_ProjectionClip = projectionClip; // Create workload and allocate tensor handles std::unique_ptr workload = workloadFactory.CreateWorkload(armnn::LayerType::QLstm, data, info); inputHandle->Allocate(); outputStateInHandle->Allocate(); cellStateInHandle->Allocate(); outputStateOutHandle->Allocate(); cellStateOutHandle->Allocate(); outputHandle->Allocate(); CopyDataToITensorHandle(inputHandle.get(), inputVector.data()); CopyDataToITensorHandle(outputStateInHandle.get(), outputStateInVector.data()); CopyDataToITensorHandle(cellStateInHandle.get(), cellStateInVector.data()); workload->Execute(); CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get()); return LayerTestResult(actualOutput, outputVector, outputHandle->GetShape(), outputStateInfo.GetShape()); } // QLSTM: Projection, LayerNorm LayerTestResult QLstmTestImpl1( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory, const std::vector& input, const std::vector& outputExpected) { IgnoreUnused(memoryManager); unsigned int numBatches = 2; unsigned int inputSize = 5; unsigned int outputSize = 3; unsigned int numUnits = 4; bool cifgEnabled = false; bool peepholeEnabled = false; bool projectionEnabled = true; bool layerNormEnabled = true; // Scale/Offset quantization info float inputScale = 0.0078125f; int32_t inputOffset = 0; int32_t hiddenStateZeroPoint = 0; float hiddenStateScale = 0.007f; // if (!projectionEnabled) outputScale == hiddenStateScale float outputScale = 3.05176e-05f; int32_t outputOffset = 0; 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; float projectionWeightsScale = 0.00392157f; float inputIntermediateScale = 0.007059f; float forgetIntermediateScale = 0.007812f; float cellIntermediateScale = inputIntermediateScale; float outputIntermediateScale = forgetIntermediateScale; float cellClip = 0.0f; float projectionClip = 0.0f; // 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); // Input tensors std::vector inputVector; inputVector.assign(input.data(), input.data() + (numBatches * inputSize)); std::vector cellStateInVector = {0, 0, 0, 0, 0, 0, 0, 0}; std::vector outputStateInVector = {0, 0, 0, 0, 0, 0}; // Output tensors std::vector cellStateOutVector = {-14650, 8939, 5771, 6715, -11843, 7847, 1508, 12939}; std::vector outputVector; outputVector.assign(outputExpected.data(), outputExpected.data() + (numBatches * outputSize)); std::vector actualOutput(outputStateInfo.GetNumElements()); // Create tensor handles std::unique_ptr inputHandle = tensorHandleFactory.CreateTensorHandle(inputInfo); std::unique_ptr cellStateInHandle = tensorHandleFactory.CreateTensorHandle(cellStateInfo); std::unique_ptr outputStateInHandle = tensorHandleFactory.CreateTensorHandle(outputStateInfo); std::unique_ptr outputStateOutHandle = tensorHandleFactory.CreateTensorHandle(outputStateInfo); std::unique_ptr cellStateOutHandle = tensorHandleFactory.CreateTensorHandle(cellStateInfo); std::unique_ptr outputHandle = tensorHandleFactory.CreateTensorHandle(outputStateInfo); armnn::QLstmQueueDescriptor data; armnn::WorkloadInfo info; // Add inputs and outputs to workload AddInputToWorkload(data, info, inputInfo, inputHandle.get()); AddInputToWorkload(data, info, outputStateInfo, outputStateInHandle.get()); AddInputToWorkload(data, info, cellStateInfo, cellStateInHandle.get()); AddOutputToWorkload(data, info, outputStateInfo, outputStateOutHandle.get()); AddOutputToWorkload(data, info, cellStateInfo, cellStateOutHandle.get()); AddOutputToWorkload(data, info, outputStateInfo, outputHandle.get()); // Weights and bias tensor and quantization info armnn::TensorInfo inputWeightsInfo({numUnits, inputSize}, armnn::DataType::QSymmS8, weightsScale, weightsOffset); armnn::TensorInfo recurrentWeightsInfo({numUnits, outputSize}, armnn::DataType::QSymmS8, weightsScale, weightsOffset); armnn::TensorInfo biasInfo({numUnits}, armnn::DataType::Signed32, biasScale, biasOffset); armnn::TensorInfo layerNormWeightsInfo({numUnits}, armnn::DataType::QSymmS16, layerNormScale, layerNormOffset); armnn::TensorInfo projectionWeightsInfo({outputSize, numUnits}, armnn::DataType::QSymmS8, projectionWeightsScale, 0); // Weights and bias tensor data std::vector inputToInputWeights = {64, 77, 89, -102, -115, 13, 25, 38, -51, 64, -102, 89, -77, 64, -51, -64, -51, -38, -25, -13}; std::vector inputToForgetWeights = {-77, -13, 38, 25, 115, -64, -25, -51, 38, -102, -51, 38, -64, -51, -77, 38, -51, -77, -64, -64}; std::vector inputToCellWeights = {-51, -38, -25, -13, -64, 64, -25, -38, -25, -77, 77, -13, -51, -38, -89, 89, -115, -64, 102, 77}; std::vector inputToOutputWeights = {-102, -51, -25, -115, -13, -89, 38, -38, -102, -25, 77, -25, 51, -89, -38, -64, 13, 64, -77, -51}; std::vector recurrentToInputWeights = {-25, -38, 51, 13, -64, 115, -25, -38, -89, 6, -25, -77}; std::vector recurrentToForgetWeights = {-64, -38, -64, -25, 77, 51, 115, 38, -13, 25, 64, 25}; std::vector recurrentToCellWeights = {-38, 25, 13, -38, 102, -10, -25, 38, 102, -77, -13, 25}; std::vector recurrentToOutputWeights = {38, -13, 13, -25, -64, -89, -25, -77, -13, -51, -89, -25}; std::vector inputGateBias = {644245, 3221226, 4724464, 8160438}; std::vector forgetGateBias = {2147484, -6442451, -4294968, 2147484}; std::vector cellBias = {-1073742, 15461883, 5368709, 1717987}; std::vector outputGateBias = {1073742, -214748, 4294968, 2147484}; std::vector inputLayerNormWeights = {3277, 6553, 9830, 16384}; std::vector forgetLayerNormWeights = {6553, 6553, 13107, 9830}; std::vector cellLayerNormWeights = {22937, 6553, 9830, 26214}; std::vector outputLayerNormWeights = {19660, 6553, 6553, 16384}; std::vector projectionWeights = {-25, 51, 3, -51, 25, 127, 77, 20, 18, 51, -102, 51}; // ScopedTensorHandles armnn::ScopedTensorHandle inputToInputWeightsTensor(inputWeightsInfo); armnn::ScopedTensorHandle inputToForgetWeightsTensor(inputWeightsInfo); armnn::ScopedTensorHandle inputToCellWeightsTensor(inputWeightsInfo); armnn::ScopedTensorHandle inputToOutputWeightsTensor(inputWeightsInfo); armnn::ScopedTensorHandle recurrentToInputWeightsTensor(recurrentWeightsInfo); armnn::ScopedTensorHandle recurrentToForgetWeightsTensor(recurrentWeightsInfo); armnn::ScopedTensorHandle recurrentToCellWeightsTensor(recurrentWeightsInfo); armnn::ScopedTensorHandle recurrentToOutputWeightsTensor(recurrentWeightsInfo); armnn::ScopedTensorHandle inputGateBiasTensor(biasInfo); armnn::ScopedTensorHandle forgetGateBiasTensor(biasInfo); armnn::ScopedTensorHandle cellBiasTensor(biasInfo); armnn::ScopedTensorHandle outputGateBiasTensor(biasInfo); armnn::ScopedTensorHandle inputLayerNormWeightsTensor(layerNormWeightsInfo); armnn::ScopedTensorHandle forgetLayerNormWeightsTensor(layerNormWeightsInfo); armnn::ScopedTensorHandle cellLayerNormWeightsTensor(layerNormWeightsInfo); armnn::ScopedTensorHandle outputLayerNormWeightsTensor(layerNormWeightsInfo); armnn::ScopedTensorHandle projectionWeightsTensor(projectionWeightsInfo); // Allocate and copy data AllocateAndCopyDataToITensorHandle(&inputToInputWeightsTensor, inputToInputWeights.data()); AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, inputToForgetWeights.data()); AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, inputToCellWeights.data()); AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, inputToOutputWeights.data()); AllocateAndCopyDataToITensorHandle(&recurrentToInputWeightsTensor, recurrentToInputWeights.data()); AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, recurrentToForgetWeights.data()); AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, recurrentToCellWeights.data()); AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, recurrentToOutputWeights.data()); AllocateAndCopyDataToITensorHandle(&inputGateBiasTensor, inputGateBias.data()); AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, forgetGateBias.data()); AllocateAndCopyDataToITensorHandle(&cellBiasTensor, cellBias.data()); AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, outputGateBias.data()); AllocateAndCopyDataToITensorHandle(&inputLayerNormWeightsTensor, inputLayerNormWeights.data()); AllocateAndCopyDataToITensorHandle(&forgetLayerNormWeightsTensor, forgetLayerNormWeights.data()); AllocateAndCopyDataToITensorHandle(&cellLayerNormWeightsTensor, cellLayerNormWeights.data()); AllocateAndCopyDataToITensorHandle(&outputLayerNormWeightsTensor, outputLayerNormWeights.data()); AllocateAndCopyDataToITensorHandle(&projectionWeightsTensor, projectionWeights.data()); // Setup queue descriptor 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_InputGateBias = &inputGateBiasTensor; data.m_ForgetGateBias = &forgetGateBiasTensor; data.m_CellBias = &cellBiasTensor; data.m_OutputGateBias = &outputGateBiasTensor; data.m_InputLayerNormWeights = &inputLayerNormWeightsTensor; data.m_ForgetLayerNormWeights = &forgetLayerNormWeightsTensor; data.m_CellLayerNormWeights = &cellLayerNormWeightsTensor; data.m_OutputLayerNormWeights = &outputLayerNormWeightsTensor; data.m_ProjectionWeights = &projectionWeightsTensor; data.m_Parameters.m_CifgEnabled = cifgEnabled; data.m_Parameters.m_PeepholeEnabled = peepholeEnabled; data.m_Parameters.m_ProjectionEnabled = projectionEnabled; data.m_Parameters.m_LayerNormEnabled = layerNormEnabled; data.m_Parameters.m_InputIntermediateScale = inputIntermediateScale; data.m_Parameters.m_ForgetIntermediateScale = forgetIntermediateScale; data.m_Parameters.m_CellIntermediateScale = cellIntermediateScale; data.m_Parameters.m_OutputIntermediateScale = outputIntermediateScale; data.m_Parameters.m_HiddenStateZeroPoint = hiddenStateZeroPoint; data.m_Parameters.m_HiddenStateScale = hiddenStateScale; data.m_Parameters.m_CellClip = cellClip; data.m_Parameters.m_ProjectionClip = projectionClip; // Create workload and allocate tensor handles std::unique_ptr workload = workloadFactory.CreateWorkload(armnn::LayerType::QLstm, data, info); inputHandle->Allocate(); outputStateInHandle->Allocate(); cellStateInHandle->Allocate(); outputStateOutHandle->Allocate(); cellStateOutHandle->Allocate(); outputHandle->Allocate(); CopyDataToITensorHandle(inputHandle.get(), inputVector.data()); CopyDataToITensorHandle(outputStateInHandle.get(), outputStateInVector.data()); CopyDataToITensorHandle(cellStateInHandle.get(), cellStateInVector.data()); workload->Execute(); CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get()); return LayerTestResult(actualOutput, outputVector, outputHandle->GetShape(), outputStateInfo.GetShape()); } // QLSTM: Projection, CIFG, LayerNorm LayerTestResult QLstmTestImpl2( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory, const std::vector& input, const std::vector& outputExpected) { IgnoreUnused(memoryManager); unsigned int numBatches = 2; unsigned int inputSize = 5; unsigned int outputSize = 3; unsigned int numUnits = 4; bool cifgEnabled = true; bool peepholeEnabled = false; bool projectionEnabled = true; bool layerNormEnabled = true; // Scale/Offset quantization info float inputScale = 0.0078125f; int32_t inputOffset = 0; int32_t hiddenStateZeroPoint = 0; float hiddenStateScale = 0.007f; // if (!projectionEnabled) outputScale == hiddenStateScale float outputScale = 3.05176e-05f; int32_t outputOffset = 0; 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; float projectionWeightsScale = 0.00392157f; float inputIntermediateScale = 0.007059f; float forgetIntermediateScale = 0.007812f; float cellIntermediateScale = inputIntermediateScale; float outputIntermediateScale = forgetIntermediateScale; float cellClip = 0.0f; float projectionClip = 0.0f; // 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); // Input tensors std::vector inputVector; inputVector.assign(input.data(), input.data() + (numBatches * inputSize)); std::vector cellStateInVector = {0, 0, 0, 0, 0, 0, 0, 0}; std::vector outputStateInVector = {0, 0, 0, 0, 0, 0}; // Output tensors std::vector cellStateOutVector = {-14650, 8939, 5771, 6715, -11843, 7847, 1508, 12939}; std::vector outputVector; outputVector.assign(outputExpected.data(), outputExpected.data() + (numBatches * outputSize)); std::vector actualOutput(outputStateInfo.GetNumElements()); // Create tensor handles std::unique_ptr inputHandle = tensorHandleFactory.CreateTensorHandle(inputInfo); std::unique_ptr cellStateInHandle = tensorHandleFactory.CreateTensorHandle(cellStateInfo); std::unique_ptr outputStateInHandle = tensorHandleFactory.CreateTensorHandle(outputStateInfo); std::unique_ptr outputStateOutHandle = tensorHandleFactory.CreateTensorHandle(outputStateInfo); std::unique_ptr cellStateOutHandle = tensorHandleFactory.CreateTensorHandle(cellStateInfo); std::unique_ptr outputHandle = tensorHandleFactory.CreateTensorHandle(outputStateInfo); armnn::QLstmQueueDescriptor data; armnn::WorkloadInfo info; // Add inputs and outputs to workload AddInputToWorkload(data, info, inputInfo, inputHandle.get()); AddInputToWorkload(data, info, outputStateInfo, outputStateInHandle.get()); AddInputToWorkload(data, info, cellStateInfo, cellStateInHandle.get()); AddOutputToWorkload(data, info, outputStateInfo, outputStateOutHandle.get()); AddOutputToWorkload(data, info, cellStateInfo, cellStateOutHandle.get()); AddOutputToWorkload(data, info, outputStateInfo, outputHandle.get()); // Weights and bias tensor and quantization info armnn::TensorInfo inputWeightsInfo({numUnits, inputSize}, armnn::DataType::QSymmS8, weightsScale, weightsOffset); armnn::TensorInfo recurrentWeightsInfo({numUnits, outputSize}, armnn::DataType::QSymmS8, weightsScale, weightsOffset); armnn::TensorInfo biasInfo({numUnits}, armnn::DataType::Signed32, biasScale, biasOffset); armnn::TensorInfo layerNormWeightsInfo({numUnits}, armnn::DataType::QSymmS16, layerNormScale, layerNormOffset); armnn::TensorInfo projectionWeightsInfo({outputSize, numUnits}, armnn::DataType::QSymmS8, projectionWeightsScale, 0); // Weights and bias tensor data std::vector inputToForgetWeights = {-77, -13, 38, 25, 115, -64, -25, -51, 38, -102, -51, 38, -64, -51, -77, 38, -51, -77, -64, -64}; std::vector inputToCellWeights = {-51, -38, -25, -13, -64, 64, -25, -38, -25, -77, 77, -13, -51, -38, -89, 89, -115, -64, 102, 77}; std::vector inputToOutputWeights = {-102, -51, -25, -115, -13, -89, 38, -38, -102, -25, 77, -25, 51, -89, -38, -64, 13, 64, -77, -51}; std::vector recurrentToForgetWeights = {-64, -38, -64, -25, 77, 51, 115, 38, -13, 25, 64, 25}; std::vector recurrentToCellWeights = {-38, 25, 13, -38, 102, -10, -25, 38, 102, -77, -13, 25}; std::vector recurrentToOutputWeights = {38, -13, 13, -25, -64, -89, -25, -77, -13, -51, -89, -25}; std::vector forgetGateBias = {2147484, -6442451, -4294968, 2147484}; std::vector cellBias = {-1073742, 15461883, 5368709, 1717987}; std::vector outputGateBias = {1073742, -214748, 4294968, 2147484}; std::vector forgetLayerNormWeights = {6553, 6553, 13107, 9830}; std::vector cellLayerNormWeights = {22937, 6553, 9830, 26214}; std::vector outputLayerNormWeights = {19660, 6553, 6553, 16384}; std::vector projectionWeights = {-25, 51, 3, -51, 25, 127, 77, 20, 18, 51, -102, 51}; // ScopedTensorHandles armnn::ScopedTensorHandle inputToForgetWeightsTensor(inputWeightsInfo); armnn::ScopedTensorHandle inputToCellWeightsTensor(inputWeightsInfo); armnn::ScopedTensorHandle inputToOutputWeightsTensor(inputWeightsInfo); armnn::ScopedTensorHandle recurrentToForgetWeightsTensor(recurrentWeightsInfo); armnn::ScopedTensorHandle recurrentToCellWeightsTensor(recurrentWeightsInfo); armnn::ScopedTensorHandle recurrentToOutputWeightsTensor(recurrentWeightsInfo); armnn::ScopedTensorHandle forgetGateBiasTensor(biasInfo); armnn::ScopedTensorHandle cellBiasTensor(biasInfo); armnn::ScopedTensorHandle outputGateBiasTensor(biasInfo); armnn::ScopedTensorHandle forgetLayerNormWeightsTensor(layerNormWeightsInfo); armnn::ScopedTensorHandle cellLayerNormWeightsTensor(layerNormWeightsInfo); armnn::ScopedTensorHandle outputLayerNormWeightsTensor(layerNormWeightsInfo); armnn::ScopedTensorHandle projectionWeightsTensor(projectionWeightsInfo); // Allocate and copy data AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, inputToForgetWeights.data()); AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, inputToCellWeights.data()); AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, inputToOutputWeights.data()); AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, recurrentToForgetWeights.data()); AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, recurrentToCellWeights.data()); AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, recurrentToOutputWeights.data()); AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, forgetGateBias.data()); AllocateAndCopyDataToITensorHandle(&cellBiasTensor, cellBias.data()); AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, outputGateBias.data()); AllocateAndCopyDataToITensorHandle(&forgetLayerNormWeightsTensor, forgetLayerNormWeights.data()); AllocateAndCopyDataToITensorHandle(&cellLayerNormWeightsTensor, cellLayerNormWeights.data()); AllocateAndCopyDataToITensorHandle(&outputLayerNormWeightsTensor, outputLayerNormWeights.data()); AllocateAndCopyDataToITensorHandle(&projectionWeightsTensor, projectionWeights.data()); // Setup queue descriptor data.m_InputToForgetWeights = &inputToForgetWeightsTensor; data.m_InputToCellWeights = &inputToCellWeightsTensor; data.m_InputToOutputWeights = &inputToOutputWeightsTensor; data.m_RecurrentToForgetWeights = &recurrentToForgetWeightsTensor; data.m_RecurrentToCellWeights = &recurrentToCellWeightsTensor; data.m_RecurrentToOutputWeights = &recurrentToOutputWeightsTensor; data.m_ForgetGateBias = &forgetGateBiasTensor; data.m_CellBias = &cellBiasTensor; data.m_OutputGateBias = &outputGateBiasTensor; data.m_ForgetLayerNormWeights = &forgetLayerNormWeightsTensor; data.m_CellLayerNormWeights = &cellLayerNormWeightsTensor; data.m_OutputLayerNormWeights = &outputLayerNormWeightsTensor; data.m_ProjectionWeights = &projectionWeightsTensor; data.m_Parameters.m_CifgEnabled = cifgEnabled; data.m_Parameters.m_PeepholeEnabled = peepholeEnabled; data.m_Parameters.m_ProjectionEnabled = projectionEnabled; data.m_Parameters.m_LayerNormEnabled = layerNormEnabled; data.m_Parameters.m_InputIntermediateScale = inputIntermediateScale; data.m_Parameters.m_ForgetIntermediateScale = forgetIntermediateScale; data.m_Parameters.m_CellIntermediateScale = cellIntermediateScale; data.m_Parameters.m_OutputIntermediateScale = outputIntermediateScale; data.m_Parameters.m_HiddenStateZeroPoint = hiddenStateZeroPoint; data.m_Parameters.m_HiddenStateScale = hiddenStateScale; data.m_Parameters.m_CellClip = cellClip; data.m_Parameters.m_ProjectionClip = projectionClip; // Create workload and allocate tensor handles std::unique_ptr workload = workloadFactory.CreateWorkload(armnn::LayerType::QLstm, data, info); inputHandle->Allocate(); outputStateInHandle->Allocate(); cellStateInHandle->Allocate(); outputStateOutHandle->Allocate(); cellStateOutHandle->Allocate(); outputHandle->Allocate(); CopyDataToITensorHandle(inputHandle.get(), inputVector.data()); CopyDataToITensorHandle(outputStateInHandle.get(), outputStateInVector.data()); CopyDataToITensorHandle(cellStateInHandle.get(), cellStateInVector.data()); workload->Execute(); CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get()); return LayerTestResult(actualOutput, outputVector, outputHandle->GetShape(), outputStateInfo.GetShape()); } } // anonymous namespace #if defined(ARMNNREF_ENABLED) // The LSTM test units are run only for the reference backend at the moment void LstmUtilsZeroVectorTest() { armnn::TensorInfo inputDesc({4}, armnn::DataType::Float32); std::vector input = {2., 3., 3., 4.}; std::vector expectedOutput = {0., 0., 0., 0.}; return LstmUtilsZeroVectorTestImpl(input, 4, expectedOutput, inputDesc.GetShape()); } void LstmUtilsMeanStddevNormalizationNoneZeroInputTest() { uint32_t batchSize = 2; uint32_t vecSize = 4; armnn::TensorInfo inputDesc({batchSize, vecSize}, armnn::DataType::Float32); std::vector input = { 0.1f, 0.2f, 0.3f, 0.4f, //batch 0 0.9f, 1.0f, 1.1f, 1.2f }; //batch 1 std::vector expectedOutput = { -1.34164071f, -0.447213531f, 0.44721365f, 1.34164071f, //batch 0 -1.34163153f, -0.447210163f, 0.447211236f, 1.3416326f }; //batch 1 return LstmUtilsMeanStddevNormalizationTestImpl(input, vecSize, batchSize, expectedOutput, inputDesc.GetShape()); } void LstmUtilsMeanStddevNormalizationAllZeroInputTest() { uint32_t batchSize = 2; uint32_t vecSize = 4; armnn::TensorInfo inputDesc({batchSize, vecSize}, armnn::DataType::Float32); std::vector input = { 0.0f, 0.0f, 0.0f, 0.0f, //batch 0 0.0f, 0.0f, 0.0f, 0.0f }; //batch 1 std::vector expectedOutput = { 0.0f, 0.0f, 0.0f, 0.0f, //batch 0 0.0f, 0.0f, 0.0f, 0.0f }; //batch 1 return LstmUtilsMeanStddevNormalizationTestImpl(input, vecSize, batchSize, expectedOutput, inputDesc.GetShape()); } void LstmUtilsMeanStddevNormalizationMixedZeroInputTest() { uint32_t batchSize = 2; uint32_t vecSize = 4; armnn::TensorInfo inputDesc({batchSize, vecSize}, armnn::DataType::Float32); std::vector input = { 0.0f, 0.0f, 0.0f, 0.0f, //batch 0 0.1f, 0.2f, 0.3f, 0.4f }; //batch 1 std::vector expectedOutput = { 0.0f, 0.0f, 0.0f, 0.0f, //batch 0 -1.34164071f, -0.447213531f, 0.44721365f, 1.34164071f }; //batch 1 return LstmUtilsMeanStddevNormalizationTestImpl(input, vecSize, batchSize, expectedOutput, inputDesc.GetShape()); } void LstmUtilsVectorBatchVectorCwiseProductTest() { uint32_t batchSize = 4; uint32_t vecSize = 29; armnn::TensorInfo vecDesc({vecSize}, armnn::DataType::Float32); std::vector vector = { 1.1f, 2.2f, 3.3f, 4.4f, 5.5f, 6.6f, 7.7f, 8.8f, 9.9f, 10.1f, 11.11f, 12.12f, 13.13f, 14.14f, 15.15f, 16.16f, 17.17f, 18.18f, 19.19f, 20.2f, 21.21f, 22.22f, 23.23f, 24.24f, 25.25f, 26.26f, 27.27f, 28.28f, 0.0f}; armnn::TensorInfo batchVecDesc({batchSize, vecSize}, armnn::DataType::Float32); std::vector batchVector = { /* batch 0 */ 1.1f, 2.2f, 3.3f, 4.4f, 5.5f, 6.6f, 7.7f, 8.8f, 9.9f, 10.1f, 11.11f, 12.12f, 13.13f, 14.14f, 15.15f, 16.16f, 17.17f, 18.18f, 19.19f, 20.2f, 21.21f, 22.22f, 23.23f, 24.24f, 25.25f, 26.26f, 27.27f, 28.28f, 0.0f, /* batch 1 */ -1.1f, -2.2f, -3.3f, -4.4f, -5.5f, -6.6f, -7.7f, -8.8f, -9.9f, -10.1f, -11.11f, -12.12f, -13.13f, -14.14f, -15.15f, -16.16f, -17.17f, -18.18f, -19.19f, -20.2f, -21.21f, -22.22f, -23.23f, -24.24f, -25.25f, -26.26f, -27.27f, -28.28f, 0.0f, /* batch 2 */ 1.1f, -2.2f, 3.3f, -4.4f, 5.5f, -6.6f, 7.7f, -8.8f, 9.9f, -10.1f, 11.11f, -12.12f, 13.13f, -14.14f, 15.15f, -16.16f, 17.17f, -18.18f, 19.19f, -20.2f, 21.21f, -22.22f, 23.23f, -24.24f, 25.25f, -26.26f, 27.27f, -28.28f, 0.0f, /* batch 3 */ -1.1f, 2.2f, -3.3f, 4.4f, -5.5f, 6.6f, -7.7f, 8.8f, -9.9f, 10.1f, -11.11f, 12.12f, -13.13f, 14.14f, -15.15f, 16.16f, -17.17f, 18.18f, -19.19f, 20.2f, -21.21f, 22.22f, -23.23f, 24.24f, -25.25f, 26.26f, -27.27f, 28.28f, 0.0f}; // Expect output = input * output + output. std::vector expectedOutput = { /* batch 0 */ 1.210000f, 4.840000f, 10.889999f, 19.360001f, 30.250000f, 43.559998f, 59.289997f, 77.440002f, 98.009995f, 102.010010f, 123.432091f, 146.894394f, 172.396896f, 199.939606f, 229.522491f, 261.145599f, 294.808899f, 330.512421f, 368.256134f, 408.040039f, 449.864075f, 493.728363f, 539.632874f, 587.577576f, 637.562500f, 689.587585f, 743.652954f, 799.758423f, 0.000000f, /* batch 1 */ -1.210000f, -4.840000f, -10.889999f, -19.360001f, -30.250000f, -43.559998f, -59.289997f, -77.440002f, -98.009995f, -102.010010f, -123.432091f, -146.894394f, -172.396896f, -199.939606f, -229.522491f, -261.145599f, -294.808899f, -330.512421f, -368.256134f, -408.040039f, -449.864075f, -493.728363f, -539.632874f, -587.577576f, -637.562500f, -689.587585f, -743.652954f, -799.758423f, 0.000000f, /* batch 2 */ 1.210000f, -4.840000f, 10.889999f, -19.360001f, 30.250000f, -43.559998f, 59.289997f, -77.440002f, 98.009995f, -102.010010f, 123.432091f, -146.894394f, 172.396896f, -199.939606f, 229.522491f, -261.145599f, 294.808899f, -330.512421f, 368.256134f, -408.040039f, 449.864075f, -493.728363f, 539.632874f, -587.577576f, 637.562500f, -689.587585f, 743.652954f, -799.758423f, 0.000000f, /* batch 3 */ -1.210000f, 4.840000f, -10.889999f, 19.360001f, -30.250000f, 43.559998f, -59.289997f, 77.440002f, -98.009995f, 102.010010f, -123.432091f, 146.894394f, -172.396896f, 199.939606f, -229.522491f, 261.145599f, -294.808899f, 330.512421f, -368.256134f, 408.040039f, -449.864075f, 493.728363f, -539.632874f, 587.577576f, -637.562500f, 689.587585f, -743.652954f, 799.758423f, 0.000000f}; return LstmUtilsVectorBatchVectorCwiseProductTestImpl(vector, batchVector, vecSize, batchSize, expectedOutput, vecDesc.GetShape()); } void LstmUtilsVectorBatchVectorAddTest() { uint32_t batchSize = 2; uint32_t vecSize = 3; armnn::TensorInfo vecDesc({vecSize}, armnn::DataType::Float32); std::vector vector = { 0.0f, -0.5f, 1.0f}; armnn::TensorInfo batchVecDesc({batchSize, vecSize}, armnn::DataType::Float32); std::vector batchVector = { 1.0f, 2.0f, 3.0f, //batch 0 4.0f, 5.0f, 6.0f //batch 1 }; std::vector expectedOutput = { 1.0f, 1.5f, 4.0f, 4.0f, 4.5f, 7.0f }; return LstmUtilsVectorBatchVectorAddTestImpl(vector, batchVector, vecSize, batchSize, expectedOutput, batchVecDesc.GetShape()); } #endif LayerTestResult LstmLayerFloat32WithCifgWithPeepholeNoProjectionTest( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory) { armnn::TensorInfo inputDesc({ 2, 2 }, armnn::DataType::Float32); std::vector input = { 2., 3., 3., 4. }; armnn::TensorInfo outputDesc({ 2, 4 }, armnn::DataType::Float32); std::vector expectedOutput = {-0.36444446f, -0.00352185f, 0.12886585f, -0.05163646f, -0.42734814f, -0.00478661f, 0.13455015f, -0.03560682f}; return LstmLayerWithCifgWithPeepholeNoProjectionTestImpl( workloadFactory, memoryManager, tensorHandleFactory, input, expectedOutput, inputDesc.GetShape(), outputDesc.GetShape()); } LayerTestResult LstmLayerFloat32NoCifgWithPeepholeWithProjectionTest( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory) { armnn::TensorInfo inputDesc({ 2, 5 }, armnn::DataType::Float32); std::vector input = {0.787926f, 0.151646f, 0.071352f, 0.118426f, 0.458058f, 0.295743f, 0.544053f, 0.690064f, 0.858138f, 0.497181f}; armnn::TensorInfo outputDesc({ 2, 16 }, armnn::DataType::Float32); std::vector expectedOutput = {-0.00396806f, 0.029352f, -0.00279226f, 0.0159977f, -0.00835576f, -0.0211779f, 0.0283512f, -0.0114597f, 0.00907307f, -0.0244004f, -0.0152191f, -0.0259063f, 0.00914318f, 0.00415118f, 0.017147f, 0.0134203f, -0.013869f, 0.0287268f, -0.00334693f, 0.00733398f, -0.0287926f, -0.0186926f, 0.0193662f, -0.0115437f, 0.00422612f, -0.0345232f, 0.00223253f, -0.00957321f, 0.0210624f, 0.013331f, 0.0150954f, 0.02168f}; return LstmLayerNoCifgWithPeepholeWithProjectionTestImpl( workloadFactory, memoryManager, tensorHandleFactory, input, expectedOutput); } LayerTestResult LstmLayerFloat32NoCifgNoPeepholeNoProjectionTest( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory) { armnn::TensorInfo inputDesc({2, 2}, armnn::DataType::Float32); std::vector input = {2., 3., 3., 4.}; armnn::TensorInfo outputDesc({2, 4}, armnn::DataType::Float32); std::vector expectedOutput = {-0.02973187f, 0.1229473f, 0.20885126f, -0.15358765f, -0.0185422f, 0.11281417f, 0.24466537f, -0.1826292f}; return LstmNoCifgNoPeepholeNoProjectionTestImpl( workloadFactory, memoryManager, tensorHandleFactory, input, expectedOutput, inputDesc.GetShape(), outputDesc.GetShape()); } LayerTestResult LstmLayerFloat32NoCifgWithPeepholeWithProjectionWithLayerNormTest( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory) { armnn::TensorInfo inputDesc({ 2, 5 }, armnn::DataType::Float32); std::vector input = {0.7f, 0.8f, 0.1f, 0.2f, 0.3f, //batch 0 0.3f, 0.2f, 0.9f, 0.8f, 0.1f}; //batch 1 armnn::TensorInfo outputDesc({ 2, 3 }, armnn::DataType::Float32); std::vector expectedOutput = { 0.0244077f, 0.128027f, -0.00170918f, //batch 0 -0.00692428f, 0.0848741f, 0.063445f}; //batch 1 return LstmLayerNoCifgWithPeepholeWithProjectionWithLayerNormTestImpl( workloadFactory, memoryManager, tensorHandleFactory, input, expectedOutput); } LayerTestResult LstmLayerInt16NoCifgNoPeepholeNoProjectionTest( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory) { const float qScale = 1.0f; const int32_t qOffset = 0; const armnn::DataType datatype = armnn::DataType::QSymmS16; const armnn::DataType constantDatatype = armnn::DataType::QAsymmU8; armnn::TensorInfo inputDesc({2, 2}, datatype); std::vector input = armnnUtils::QuantizedVector({ 2.f, 3.f, 3.f, 4.f }, qScale, qOffset); armnn::TensorInfo outputDesc({2, 4}, datatype); std::vector expectedOutput = armnnUtils::QuantizedVector( { -0.02973187f, 0.12294730f, 0.20885126f, -0.15358765f, -0.01854220f, 0.11281417f, 0.24466537f, -0.18262920f }, qScale, qOffset); return LstmNoCifgNoPeepholeNoProjectionTestImpl( workloadFactory, memoryManager, tensorHandleFactory, input, expectedOutput, inputDesc.GetShape(), outputDesc.GetShape(), qScale, qOffset, constantDatatype); } LayerTestResult LstmLayerInt16WithCifgWithPeepholeNoProjectionTest( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory) { const float qScale = 1.0f; const int32_t qOffset = 0; const armnn::DataType datatype = armnn::DataType::QSymmS16; const armnn::DataType constantDatatype = armnn::DataType::QAsymmU8; armnn::TensorInfo inputDesc({ 2, 2 }, datatype); std::vector input = armnnUtils::QuantizedVector({ 2.f, 3.f, 3.f, 4.f }, qScale, qOffset); armnn::TensorInfo outputDesc({ 2, 4 }, datatype); std::vector expectedOutput = armnnUtils::QuantizedVector( { -0.36444446f, -0.00352185f, 0.12886585f, -0.05163646f, -0.42734814f, -0.00478661f, 0.13455015f, -0.03560682f }, qScale, qOffset); return LstmLayerWithCifgWithPeepholeNoProjectionTestImpl( workloadFactory, memoryManager, tensorHandleFactory, input, expectedOutput, inputDesc.GetShape(), outputDesc.GetShape(), qScale, qOffset, constantDatatype); } LayerTestResult LstmLayerInt16NoCifgWithPeepholeWithProjectionTest( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory) { const float qScale = 2.0f; const int32_t qOffset = 0; const armnn::DataType datatype = armnn::DataType::QSymmS16; const armnn::DataType constantDatatype = armnn::DataType::QAsymmU8; armnn::TensorInfo inputDesc({ 2, 5 }, datatype); std::vector input = armnnUtils::QuantizedVector( { 0.787926f, 0.151646f, 0.071352f, 0.118426f, 0.458058f, 0.295743f, 0.544053f, 0.690064f, 0.858138f, 0.497181f }, qScale, qOffset); armnn::TensorInfo outputDesc({ 2, 16 }, datatype); std::vector expectedOutput = armnnUtils::QuantizedVector( { -0.00396806f, 0.02935200f, -0.00279226f, 0.01599770f, -0.00835576f, -0.02117790f, 0.02835120f, -0.01145970f, 0.00907307f, -0.02440040f, -0.01521910f, -0.02590630f, 0.00914318f, 0.00415118f, 0.01714700f, 0.01342030f, -0.01386900f, 0.02872680f, -0.00334693f, 0.00733398f, -0.02879260f, -0.01869260f, 0.01936620f, -0.01154370f, 0.00422612f, -0.03452320f, 0.00223253f, -0.00957321f, 0.02106240f, 0.01333100f, 0.01509540f, 0.02168000f }, qScale, qOffset); return LstmLayerNoCifgWithPeepholeWithProjectionTestImpl( workloadFactory, memoryManager, tensorHandleFactory, input, expectedOutput, qScale, qOffset, constantDatatype); } LayerTestResult LstmLayerInt16NoCifgNoPeepholeNoProjectionInt16ConstantTest( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory) { const float qScale = 1.0f; const int32_t qOffset = 0; const armnn::DataType datatype = armnn::DataType::QSymmS16; // datatype & constants set to QSymm16 armnn::TensorInfo inputDesc({2, 2}, datatype); std::vector input = armnnUtils::QuantizedVector({ 2.f, 3.f, 3.f, 4.f }, qScale, qOffset); armnn::TensorInfo outputDesc({2, 4}, datatype); std::vector expectedOutput = armnnUtils::QuantizedVector( { -0.02973187f, 0.12294730f, 0.20885126f, -0.15358765f, -0.01854220f, 0.11281417f, 0.24466537f, -0.18262920f }, qScale, qOffset); return LstmNoCifgNoPeepholeNoProjectionTestImpl( workloadFactory, memoryManager, tensorHandleFactory, input, expectedOutput, inputDesc.GetShape(), outputDesc.GetShape(), qScale, qOffset, datatype); } // // QuantizedLstm // LayerTestResult QuantizedLstmTest( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory) { armnn::TensorInfo inputDesc({2, 2}, armnn::DataType::QAsymmU8); std::vector input = {166, 179, 50, 150}; armnn::TensorInfo outputDesc({2, 4}, armnn::DataType::QAsymmU8); std::vector expectedOutput = {140, 151, 146, 112, 136, 156, 142, 112 }; return QuantizedLstmTestImpl(workloadFactory, memoryManager, tensorHandleFactory, input, expectedOutput, inputDesc.GetShape(), outputDesc.GetShape()); } // QLSTM LayerTestResult QLstmTest( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory) { armnn::TensorInfo inputDesc({2, 5}, armnn::DataType::QAsymmS8); std::vector input = {90, 102, 13, 26, 38, 102, 13, 26, 51, 64}; armnn::TensorInfo outputDesc({2, 4}, armnn::DataType::QAsymmS8); std::vector expectedOutput = {-15, 21, 14, 20, -15, 15, 5, 27}; return QLstmTestImpl(workloadFactory, memoryManager, tensorHandleFactory, input, expectedOutput); } LayerTestResult QLstmTest1( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory) { armnn::TensorInfo inputDesc({2, 5}, armnn::DataType::QAsymmS8); std::vector input = {90, 102, 13, 26, 38, 102, 13, 26, 51, 64}; armnn::TensorInfo outputDesc({2, 3}, armnn::DataType::QAsymmS8); std::vector expectedOutput = {127, 127, -108, -67, 127, 127}; return QLstmTestImpl1(workloadFactory, memoryManager, tensorHandleFactory, input, expectedOutput); } LayerTestResult QLstmTest2( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory) { armnn::TensorInfo inputDesc({2, 5}, armnn::DataType::QAsymmS8); std::vector input = {90, 102, 13, 26, 38, 102, 13, 26, 51, 64}; armnn::TensorInfo outputDesc({2, 3}, armnn::DataType::QAsymmS8); std::vector expectedOutput = {127, 127, 127, -128, 127, 127}; return QLstmTestImpl2(workloadFactory, memoryManager, tensorHandleFactory, input, expectedOutput); }