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Diffstat (limited to 'src/backends/backendsCommon/test/layerTests/UnidirectionalSequenceLstmTestImpl.cpp')
| -rw-r--r-- | src/backends/backendsCommon/test/layerTests/UnidirectionalSequenceLstmTestImpl.cpp | 1030 |
1 files changed, 1030 insertions, 0 deletions
diff --git a/src/backends/backendsCommon/test/layerTests/UnidirectionalSequenceLstmTestImpl.cpp b/src/backends/backendsCommon/test/layerTests/UnidirectionalSequenceLstmTestImpl.cpp new file mode 100644 index 0000000000..ac22d5df48 --- /dev/null +++ b/src/backends/backendsCommon/test/layerTests/UnidirectionalSequenceLstmTestImpl.cpp @@ -0,0 +1,1030 @@ +// +// Copyright © 2021 Arm Ltd and Contributors. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#include "UnidirectionalSequenceLstmTestImpl.hpp" + +#include <armnn/utility/NumericCast.hpp> + +#include <backendsCommon/TensorHandle.hpp> + +#include <backendsCommon/test/TensorCopyUtils.hpp> +#include <backendsCommon/test/WorkloadTestUtils.hpp> + +#include <ResolveType.hpp> + +namespace { + +template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> +LayerTestResult<T, 3> UnidirectionalSequenceLstmLayerFloat32TestImpl( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::ITensorHandleFactory& tensorHandleFactory, + const std::vector<T>& input, + const std::vector<T>& outputExpected, + const armnn::TensorShape& inputShape, + const armnn::TensorShape& outputExpectedShape, + float qScale = 0.0f, + int32_t qOffset = 0, + armnn::DataType constantDataType = armnn::DataType::Float32) { + IgnoreUnused(memoryManager); + unsigned int batchSize = armnn::numeric_cast<unsigned int>(inputShape[0]); + unsigned int timeSize = armnn::numeric_cast<unsigned int>(inputShape[1]); + unsigned int inputSize = armnn::numeric_cast<unsigned int>(inputShape[2]); + unsigned int outputSize = armnn::numeric_cast<unsigned int>(outputExpectedShape[2]); + unsigned numUnits = outputSize; + + armnn::TensorInfo inputTensorInfo({batchSize, timeSize, inputSize}, ArmnnType, qScale, qOffset); + armnn::TensorInfo cellStateInTensorInfo({batchSize, numUnits}, ArmnnType, qScale, qOffset); + armnn::TensorInfo outputStateInTensorInfo({batchSize, outputSize}, ArmnnType, qScale, qOffset); + + armnn::TensorInfo outputTensorInfo({batchSize, timeSize, outputSize}, ArmnnType, qScale, qOffset); + + std::vector<T> inputVector; + inputVector.assign(input.data(), input.data() + (batchSize * timeSize * inputSize)); + + std::vector<T> cellStateInVector(batchSize * numUnits, T()); + std::vector<T> outputStateInVector(batchSize * outputSize, T()); + + std::vector<T> actualOutput(outputTensorInfo.GetNumElements()); + + std::vector<T> outputVector; + outputVector.assign(outputExpected.data(), outputExpected.data() + (batchSize * timeSize * outputSize)); + + std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo); + std::unique_ptr<armnn::ITensorHandle> cellStateInHandle = + tensorHandleFactory.CreateTensorHandle(cellStateInTensorInfo); + std::unique_ptr<armnn::ITensorHandle> outputStateInHandle = + tensorHandleFactory.CreateTensorHandle(outputStateInTensorInfo); + + std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo); + + armnn::UnidirectionalSequenceLstmQueueDescriptor 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, outputTensorInfo, outputHandle.get()); + + armnn::TensorInfo tensorInfo4({numUnits}, constantDataType, qScale, qOffset); + armnn::TensorInfo tensorInfo12({numUnits, 3}, constantDataType, qScale, qOffset); + armnn::TensorInfo tensorInfo16({numUnits, 4}, constantDataType, qScale, qOffset); + + std::vector<float> inputToInputWeights = { -0.49536117f, -0.0556083915f, -0.102400711f, + -0.117484632f, 0.3298470976f, -0.1179017122f, + 0.214305695f, 0.42135173085f, 0.003878414626f, + -0.348303917f, -0.1881275477f, 0.0343011027f }; + + std::vector<float> inputToForgetWeights = { 0.2415594226f, 0.15400093799f, 0.4566498398f, + -0.3810434485f, 0.268383264f, -0.009807467424f, + -0.3522925403f, -0.24275735512f, -0.28344226125f, + 0.13512269116f, -0.4932442977f, -0.10039821991f }; + + std::vector<float> inputToCellWeights = { -0.2504855627f, 0.184490025045f, -0.2480507493f, + 0.386399507f, -0.259465157985f, -0.16545993089f, + -0.4230232555f, 0.341664791103f, -0.18127849691f, + -0.2277662414f, -0.55275535589f, 0.34184026718f }; + + std::vector<float> inputToOutputWeights = { 0.2303854227f, 0.5218806862f, -0.4865379333f, + 0.53969591851f, 0.23393625035f, -0.27140527306f, + 0.50009280443f, 0.07511717046f, 0.3998299249f, + -0.51717478049f, 0.1889653282f, -0.367323637f }; + + std::vector<float> recurrentToInputWeights = { -0.128009796112f, 0.1995525098f, -0.07745539397f, 0.1558421701f, + -0.265254765766f, -0.38837709614f, -0.05636804124f, 0.4259087456f, + 0.17628988623f, 0.3877420127f, 0.53300309181f, -0.0959980934f, + 0.00302857416f, 0.3266998827f, -0.142509296562f, -0.04433270756f }; + + std::vector<float> recurrentToForgetWeights = { -0.09499983487f, -0.08814888417f, -0.04834804721f, 0.1516668247f, + -0.3967529535f, -0.06463699788f, 0.4952811002f, 0.003274492938f, + -0.0968840941f, 0.17928104102f, 0.0031281141592f, -0.3387276584f, + -0.3587934076f, 0.06705895066f, 0.22463923692f, 0.1961955726f }; + + std::vector<float> recurrentToCellWeights = { -0.21938985582f, -0.3023648226f, -0.1170005202f, -0.3509177422f, + -0.4286288613f, 0.2726137042f, 0.09216640889f, -0.06551410215f, + 0.20453298098f, 0.2393476665f, 0.11846517771f, 0.2630801796f, + 0.3954237699f, -0.19407111404f, 0.30412107706f, -0.27342408554f }; + + std::vector<float> recurrentToOutputWeights = { -0.32921677827f, 0.32624614238f, -0.1388191282f, -0.17879831790f, + -0.15185534954f, -0.16918526583f, -0.10087361183f, -0.5436913968f, + 0.016758225858f, 0.30454617738f, -0.41493862867f, -0.005565764375f, + -0.12584099173f, -0.12319286912f, 0.2407919466f, -0.08879069983f }; + + std::vector<float> inputGateBias = { 0., 0., 0., 0. }; + + std::vector<float> forgetGateBias = { 1., 1., 1., 1. }; + + std::vector<float> cellBias = { 0., 0., 0., 0. }; + + std::vector<float> outputGateBias = { 0., 0., 0., 0. }; + + armnn::ScopedTensorHandle inputToInputWeightsTensor(tensorInfo12); + armnn::ScopedTensorHandle inputToForgetWeightsTensor(tensorInfo12); + armnn::ScopedTensorHandle inputToCellWeightsTensor(tensorInfo12); + armnn::ScopedTensorHandle inputToOutputWeightsTensor(tensorInfo12); + armnn::ScopedTensorHandle recurrentToInputWeightsTensor(tensorInfo16); + armnn::ScopedTensorHandle recurrentToForgetWeightsTensor(tensorInfo16); + armnn::ScopedTensorHandle recurrentToCellWeightsTensor(tensorInfo16); + armnn::ScopedTensorHandle recurrentToOutputWeightsTensor(tensorInfo16); + 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(&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_ClippingThresCell = 10; + data.m_Parameters.m_ClippingThresProj = 0; + data.m_Parameters.m_ActivationFunc = 4; + data.m_Parameters.m_CifgEnabled = false; + data.m_Parameters.m_PeepholeEnabled = false; + data.m_Parameters.m_ProjectionEnabled = false; + data.m_Parameters.m_TimeMajor = false; + + std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateUnidirectionalSequenceLstm(data, info); + inputHandle->Allocate(); + outputStateInHandle->Allocate(); + cellStateInHandle->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<T, 3>(actualOutput, + outputVector, + outputHandle->GetShape(), + outputTensorInfo.GetShape()); +} + +template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> +LayerTestResult<T, 3> +UnidirectionalSequenceLstmLayerFloat32TimeMajorTestImpl( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::ITensorHandleFactory& tensorHandleFactory, + const std::vector<T>& input, + const std::vector<T>& outputExpected, + const armnn::TensorShape& inputShape, + const armnn::TensorShape& outputExpectedShape, + float qScale = 0.0f, + int32_t qOffset = 0, + armnn::DataType constantDataType = armnn::DataType::Float32) { + IgnoreUnused(memoryManager); + unsigned int batchSize = armnn::numeric_cast<unsigned int>(inputShape[1]); + unsigned int timeSize = armnn::numeric_cast<unsigned int>(inputShape[0]); + unsigned int inputSize = armnn::numeric_cast<unsigned int>(inputShape[2]); + unsigned int outputSize = armnn::numeric_cast<unsigned int>(outputExpectedShape[2]); + unsigned numUnits = outputSize; + + armnn::TensorInfo inputTensorInfo({timeSize, batchSize, inputSize}, ArmnnType, qScale, qOffset); + armnn::TensorInfo cellStateInTensorInfo({batchSize, numUnits}, ArmnnType, qScale, qOffset); + armnn::TensorInfo outputStateInTensorInfo({batchSize, outputSize}, ArmnnType, qScale, qOffset); + + armnn::TensorInfo outputTensorInfo({timeSize, batchSize, outputSize}, ArmnnType, qScale, qOffset); + + std::vector<T> inputVector; + inputVector.assign(input.data(), input.data() + (batchSize * timeSize * inputSize)); + + std::vector<T> cellStateInVector(batchSize * numUnits, T()); + std::vector<T> outputStateInVector(batchSize * outputSize, T()); + + std::vector<T> actualOutput(outputTensorInfo.GetNumElements()); + + std::vector<T> outputVector; + outputVector.assign(outputExpected.data(), outputExpected.data() + (batchSize * timeSize * outputSize)); + + std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo); + std::unique_ptr<armnn::ITensorHandle> cellStateInHandle = + tensorHandleFactory.CreateTensorHandle(cellStateInTensorInfo); + std::unique_ptr<armnn::ITensorHandle> outputStateInHandle = + tensorHandleFactory.CreateTensorHandle(outputStateInTensorInfo); + + std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo); + + armnn::UnidirectionalSequenceLstmQueueDescriptor 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, outputTensorInfo, outputHandle.get()); + + armnn::TensorInfo tensorInfo4({numUnits}, constantDataType, qScale, qOffset); + armnn::TensorInfo tensorInfo12({numUnits, 3}, constantDataType, qScale, qOffset); + armnn::TensorInfo tensorInfo16({numUnits, 4}, constantDataType, qScale, qOffset); + + std::vector<float> inputToInputWeights = { 0.27277296781539917f, 0.3813590407371521f, -0.394489049911499f, + 0.2782636880874634f, -0.3793870210647583f, -0.018918335437774658f, + 0.2724653482437134f, -0.19314253330230713f, -0.2947450876235962f, + -0.30253493785858154f, 0.4241350293159485f, -0.22560018301010132f }; + + std::vector<float> inputToForgetWeights = { -0.2667974531650543f, -0.05505800247192383f, -0.20932340621948242f, + -0.14345619082450867f, 0.09666192531585693f, -0.2604355812072754f, + -0.2681812047958374f, -0.3314584493637085f, 0.4485899806022644f, + -0.23467743396759033f, 0.5072842240333557f, -0.4192768931388855f }; + + std::vector<float> inputToCellWeights = { -0.15782442688941956f, -0.027530014514923096f, 0.4789854884147644f, + 0.23227906227111816f, 0.28259342908859253f, -0.030095696449279785f, + 0.10071521997451782f, -0.08535495400428772f, 0.18563997745513916f, + -0.3049069046974182f, -0.478048175573349f, 0.025234103202819824f }; + + std::vector<float> inputToOutputWeights = { -0.04584759473800659f, -0.2716066539287567f, 0.012970447540283203f, + -0.4729190170764923f, -0.37422770261764526f, 0.49352723360061646f, + 0.3163864016532898f, -0.436781644821167f, -0.33074596524238586f, + -0.32885751128196716f, -0.40959352254867554f, -0.2124689817428589f }; + + std::vector<float> recurrentToInputWeights = { 0.23788475990f, -0.24948765337f, 0.50044941902f, 0.14431896805f, + -0.115940228137f, -0.717082679f, -0.17208620906f, 0.17850610617f, + -0.16702319684f, -0.11384502053f, -0.309785276245f, -0.3316611672f, + 0.52380162477f, -0.06839632987f, -0.391478359627f, -0.10756178963f }; + + std::vector<float> recurrentToForgetWeights = { 0.11383482068f, 0.1676601767f, -0.08550968004f, 0.03399394089f, + 0.08042152225f, -0.2133381964f, 0.05182432704f, 0.38161808255f, + -0.5018365979f, -0.08043262364f, 0.07894329014f, -0.07547105155f, + 0.12047368288f, 0.2986997961f, 0.0485043078f, -0.13372567296f }; + + std::vector<float> recurrentToCellWeights = { 0.0433832928545f, 0.07587072294f, -0.120520234107f, 0.604576051f, + -0.434353142986f, 0.009314475068f, 0.005085289478f, 0.08488202038f, + -0.00025437487886f, 0.15245915082f, -0.1936587542f, 0.004754020f, + -0.1582719236f, 0.3307867646f, 0.0236605107784f, 0.307716339826f }; + + std::vector<float> recurrentToOutputWeights = { -0.079031050201f, 0.041414566286f, -0.583727357285f, 0.1025384515f, + -0.172372072937f, 0.09214124082f, 0.178184121827f, -0.2439443916f, + 0.104485116899f, 0.2600405514f, 0.064414866268f, 0.24141204357f, + 0.281875759363f, -0.14234502664f, 0.15126448862f, -0.24421440064f }; + + std::vector<float> inputGateBias = { 0., 0., 0., 0. }; + + std::vector<float> forgetGateBias = { 1., 1., 1., 1. }; + + std::vector<float> cellBias = { 0., 0., 0., 0. }; + + std::vector<float> outputGateBias = { 0., 0., 0., 0. }; + + armnn::ScopedTensorHandle inputToInputWeightsTensor(tensorInfo12); + armnn::ScopedTensorHandle inputToForgetWeightsTensor(tensorInfo12); + armnn::ScopedTensorHandle inputToCellWeightsTensor(tensorInfo12); + armnn::ScopedTensorHandle inputToOutputWeightsTensor(tensorInfo12); + armnn::ScopedTensorHandle recurrentToInputWeightsTensor(tensorInfo16); + armnn::ScopedTensorHandle recurrentToForgetWeightsTensor(tensorInfo16); + armnn::ScopedTensorHandle recurrentToCellWeightsTensor(tensorInfo16); + armnn::ScopedTensorHandle recurrentToOutputWeightsTensor(tensorInfo16); + 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(&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_ClippingThresCell = 10; + data.m_Parameters.m_ClippingThresProj = 0; + data.m_Parameters.m_ActivationFunc = 4; + data.m_Parameters.m_CifgEnabled = false; + data.m_Parameters.m_PeepholeEnabled = false; + data.m_Parameters.m_ProjectionEnabled = false; + data.m_Parameters.m_TimeMajor = true; + + std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateUnidirectionalSequenceLstm(data, info); + inputHandle->Allocate(); + outputStateInHandle->Allocate(); + cellStateInHandle->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<T, 3>(actualOutput, + outputVector, + outputHandle->GetShape(), + outputTensorInfo.GetShape()); +} + +} // anonymous namespace + +LayerTestResult<float, 3> UnidirectionalSequenceLstmLayerFloat32Test( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::ITensorHandleFactory& tensorHandleFactory) { + armnn::TensorInfo inputInfo({3, 2, 3}, armnn::DataType::Float32); + std::vector<float> input = { 1., 2., 3., 4., 5., 4., + 3., 2., 1., 2., 3., 4., + 5., 4., 3., 2., 1., 2. }; + + armnn::TensorInfo outputInfo({3, 2, 4}, armnn::DataType::Float32); + std::vector<float> expectedOutput = { -0.07149004f, -0.1621171f, -0.17516759f, -0.0232934225f, + -0.16810727f, -0.41412935f, -0.5498753f, -0.00803578f, + -0.06687349f, 0.204077631f, -0.4276504f, -0.03123213f, + -0.12000261f, -0.0941918f, -0.45639035f, -0.02870186f, + -0.03429216f, 0.20824050f, -0.6569892f, -0.004152651f, + -0.10493034f, 0.14210969f, -0.58347696f, -0.03297536f }; + return UnidirectionalSequenceLstmLayerFloat32TestImpl<armnn::DataType::Float32>( + workloadFactory, memoryManager, tensorHandleFactory, + input, expectedOutput, inputInfo.GetShape(), outputInfo.GetShape()); +} + +LayerTestResult<float, 3> UnidirectionalSequenceLstmLayerFloat32TimeMajorTest( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::ITensorHandleFactory& tensorHandleFactory) { + armnn::TensorInfo inputInfo({2, 3, 3}, armnn::DataType::Float32); + std::vector<float> input = { 1., 2., 3., 4., 5., 4., + 3., 2., 1., 2., 3., 4., + 5., 4., 3., 2., 1., 2. }; + + armnn::TensorInfo outputInfo({2, 3, 4}, armnn::DataType::Float32); + std::vector<float> expectedOutput = { 0.135657698f, 0.124672532f, 0.0212090332f, -0.0530203655f, + 0.106138252f, 0.0404792242f, 0.0151643595f, -0.00675163185f, + -0.0128514022f, 0.0644884035f, 0.0709072053f, -0.0454045124f, + 0.16288602f, 0.16649379f, 0.02770456f, -0.03698075f, + 0.11171641f, 0.043119f , 0.0762981f , -0.01228541f, + 0.10439701f, 0.21439962f, 0.11919238f, -0.08390583f }; + return UnidirectionalSequenceLstmLayerFloat32TimeMajorTestImpl<armnn::DataType::Float32>( + workloadFactory, memoryManager, tensorHandleFactory, + input, expectedOutput, inputInfo.GetShape(), outputInfo.GetShape()); +} + +LayerTestResult<float, 3> UnidirectionalSequenceLstmLayerNoCifgWithPeepholeWithProjectionTest( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::ITensorHandleFactory& tensorHandleFactory) +{ + IgnoreUnused(memoryManager); + unsigned int batchSize = 2; + unsigned int timeSize = 3; + unsigned int outputSize = 5; + unsigned int inputSize = 4; + unsigned numUnits = 6; + + armnn::TensorInfo inputTensorInfo({batchSize, timeSize, inputSize}, armnn::DataType::Float32); + armnn::TensorInfo cellStateInTensorInfo({batchSize , numUnits}, armnn::DataType::Float32); + armnn::TensorInfo outputStateInTensorInfo({batchSize , outputSize}, armnn::DataType::Float32); + armnn::TensorInfo outputTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32); + + const std::vector<float> inputVector = { 1., 2., 3., 4., 5., 4., + 3., 2., 1., 2., 3., 4., + 5., 4., 3., 2., 1., 2., + 1., 2., 3., 4., 5., 4.}; + + std::vector<float> cellStateInVector(batchSize * numUnits, 0.f); + std::vector<float> outputStateInVector(batchSize * outputSize, 0.f); + + std::vector<float> actualOutput(outputTensorInfo.GetNumElements()); + + const std::vector<float> expectedOutput = { -0.0135612f, -0.0263441f, 0.0314008f, -0.00883455f, 0.00763052f, + -0.00126877f, -0.0292959f, 0.0449957f, -0.00976195f, -0.00492338f, + -0.0175702f, -0.0431753f, 0.0597117f, -0.0169154f, 0.0142087f, + 0.00472515f, -0.0196355f, 0.0342524f, -0.00407936f, -0.0253189f, + -0.00512944f, -0.0293754f, 0.0512771f, -0.0151874f, -0.0246433f, + -0.00744986f, -0.0345103f, 0.0450666f, -0.00944991f, 0.0127171f }; + + std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo); + std::unique_ptr<armnn::ITensorHandle> cellStateInHandle = + tensorHandleFactory.CreateTensorHandle(cellStateInTensorInfo); + std::unique_ptr<armnn::ITensorHandle> outputStateInHandle = + tensorHandleFactory.CreateTensorHandle(outputStateInTensorInfo); + std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo); + + armnn::UnidirectionalSequenceLstmQueueDescriptor 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, outputTensorInfo, outputHandle.get()); + + armnn::TensorInfo tensorInfo5({outputSize}, armnn::DataType::Float32); + armnn::TensorInfo tensorInfo6({numUnits}, armnn::DataType::Float32); + armnn::TensorInfo tensorInfo6x4({numUnits, inputSize}, armnn::DataType::Float32); + armnn::TensorInfo tensorInfo6x5({numUnits, outputSize}, armnn::DataType::Float32); + armnn::TensorInfo tensorInfo5x6({outputSize, numUnits}, armnn::DataType::Float32); + + std::vector<float> 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 }; + + std::vector<float> 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}; + + std::vector<float> 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 }; + + std::vector<float> 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 }; + + std::vector<float> inputGateBias = { 0.02234832f, 0.14757581f, 0.18176508f, + 0.10380666f, 0.053110216f, -0.06928846f }; + + std::vector<float> forgetGateBias = { 0.035185695f, -0.042891346f, -0.03032477f, + 0.23027696f, 0.11098921f, 0.08989442f }; + + std::vector<float> cellBias = { -0.024379363f, 0.0055531194f, 0.23377132f, + 0.033463873f, -0.1483596f, 0.029460307f }; + + std::vector<float> outputGateBias = { 0.046159424f, -0.0012809046f, 0.03563469f, + 0.12648113f, 0.027195795f, 0.35373217f }; + + std::vector<float> 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 }; + + std::vector<float> 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 }; + + std::vector<float> 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 }; + + std::vector<float> 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 }; + + std::vector<float> cellToInputWeights = { 0.040369894f, 0.030746894f, 0.24704495f, + 0.018586371f, -0.037586458f, -0.15312155f }; + + std::vector<float> cellToForgetWeights = { -0.01998659f, -0.15568835f, -0.24248174f, + -0.012770197f, 0.041331276f, -0.072311886f }; + + std::vector<float> cellToOutputWeights = { 0.08286371f, -0.08261836f, -0.51210177f, + 0.002913762f, 0.17764764f, -0.5495371f }; + + std::vector<float> 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 }; + + std::vector<float> projectionBiasVector(outputSize, 0.f); //{outputSize} + + armnn::ScopedTensorHandle inputToInputWeightsTensor(tensorInfo6x4); + armnn::ScopedTensorHandle inputToForgetWeightsTensor(tensorInfo6x4); + armnn::ScopedTensorHandle inputToCellWeightsTensor(tensorInfo6x4); + armnn::ScopedTensorHandle inputToOutputWeightsTensor(tensorInfo6x4); + armnn::ScopedTensorHandle recurrentToForgetWeightsTensor(tensorInfo6x5); + armnn::ScopedTensorHandle recurrentToInputWeightsTensor(tensorInfo6x5); + armnn::ScopedTensorHandle recurrentToCellWeightsTensor(tensorInfo6x5); + armnn::ScopedTensorHandle recurrentToOutputWeightsTensor(tensorInfo6x5); + armnn::ScopedTensorHandle cellToInputWeightsTensor(tensorInfo6); + armnn::ScopedTensorHandle inputGateBiasTensor(tensorInfo6); + armnn::ScopedTensorHandle forgetGateBiasTensor(tensorInfo6); + armnn::ScopedTensorHandle cellBiasTensor(tensorInfo6); + armnn::ScopedTensorHandle outputGateBiasTensor(tensorInfo6); + armnn::ScopedTensorHandle cellToForgetWeightsTensor(tensorInfo6); + armnn::ScopedTensorHandle cellToOutputWeightsTensor(tensorInfo6); + armnn::ScopedTensorHandle projectionWeightsTensor(tensorInfo5x6); + armnn::ScopedTensorHandle projectionBiasTensor(tensorInfo5); + + 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; + data.m_Parameters.m_LayerNormEnabled = false; + data.m_Parameters.m_TimeMajor = false; + data.m_Parameters.m_ClippingThresCell = 10.0f; + + + std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateUnidirectionalSequenceLstm(data, info); + inputHandle->Allocate(); + outputStateInHandle->Allocate(); + cellStateInHandle->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<float, 3>(actualOutput, + expectedOutput, + outputHandle->GetShape(), + outputTensorInfo.GetShape()); +} + +LayerTestResult<float, 3> UnidirectionalSequenceLstmLayerNoCifgWithPeepholeWithProjectionWithLayerNormTest( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::ITensorHandleFactory& tensorHandleFactory) +{ + IgnoreUnused(memoryManager); + unsigned int batchSize = 3; + unsigned int timeSize = 2; + unsigned int outputSize = 4; + unsigned int inputSize = 3; + unsigned numUnits = 5; + + armnn::TensorInfo inputTensorInfo({batchSize, timeSize, inputSize}, armnn::DataType::Float32); + armnn::TensorInfo cellStateInTensorInfo({batchSize , numUnits}, armnn::DataType::Float32); + armnn::TensorInfo outputStateInTensorInfo({batchSize , outputSize}, armnn::DataType::Float32); + armnn::TensorInfo outputTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32); + + const std::vector<float> inputVector = { 1., 2., 3., 4., 5., 4., + 3., 2., 1., 2., 3., 4., + 5., 4., 3., 2., 1., 2. }; + + std::vector<float> cellStateInVector(batchSize * numUnits, 0.f); + std::vector<float> outputStateInVector(batchSize * outputSize, 0.f); + + std::vector<float> actualOutput(outputTensorInfo.GetNumElements()); + + const std::vector<float> expectedOutput = { 0.0642256f, 0.0343966f, 0.184122f, 0.114717f, + 0.11458f, 0.0407109f, 0.300327f, 0.174301f, + 0.0864761f, 0.0362912f, 0.178635f, 0.115689f, + 0.108008f, 0.0386623f, 0.273471f, 0.167115f, + 0.0859545f, 0.0331481f, 0.186051f, 0.11888f, + 0.106649f, 0.0276847f, 0.229863f, 0.166958f }; + + std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo); + std::unique_ptr<armnn::ITensorHandle> cellStateInHandle = + tensorHandleFactory.CreateTensorHandle(cellStateInTensorInfo); + std::unique_ptr<armnn::ITensorHandle> outputStateInHandle = + tensorHandleFactory.CreateTensorHandle(outputStateInTensorInfo); + + std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo); + + armnn::UnidirectionalSequenceLstmQueueDescriptor 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, outputTensorInfo, outputHandle.get()); + + armnn::TensorInfo tensorInfo4({outputSize}, armnn::DataType::Float32); + armnn::TensorInfo tensorInfo5({numUnits}, armnn::DataType::Float32); + armnn::TensorInfo tensorInfo5x3({numUnits, inputSize}, armnn::DataType::Float32); + armnn::TensorInfo tensorInfo5x4({numUnits, outputSize}, armnn::DataType::Float32); + armnn::TensorInfo tensorInfo4x5({outputSize, numUnits}, armnn::DataType::Float32); + + std::vector<float> inputToInputWeights = { -0.49536117f, -0.0556083915f, -0.102400711f, + -0.117484632f, 0.3298470976f, -0.1179017122f, + 0.214305695f, 0.42135173085f, 0.003878414626f, + -0.348303917f, -0.1881275477f, 0.0343011027f, + -0.38837709614f, -0.05636804124f, 0.4259087456f}; + + std::vector<float> inputToForgetWeights = { 0.2415594226f, 0.15400093799f, 0.4566498398f, + -0.3810434485f, 0.268383264f, -0.009807467424f, + -0.3522925403f, -0.24275735512f, -0.28344226125f, + 0.13512269116f, -0.4932442977f, -0.10039821991f, + 0.2726137042f, 0.09216640889f, -0.06551410215f}; + + std::vector<float> inputToCellWeights = { -0.2504855627f, 0.184490025045f, -0.2480507493f, + 0.386399507f, -0.259465157985f, -0.16545993089f, + -0.4230232555f, 0.341664791103f, -0.18127849691f, + -0.2277662414f, -0.55275535589f, 0.34184026718f, + 0.3954237699f, -0.19407111404f, 0.30412107706f}; + + std::vector<float> inputToOutputWeights = { 0.2303854227f, 0.5218806862f, -0.4865379333f, + 0.53969591851f, 0.23393625035f, -0.27140527306f, + 0.50009280443f, 0.07511717046f, 0.3998299249f, + -0.51717478049f, 0.1889653282f, -0.367323637f, + -0.12584099173f, -0.12319286912f, 0.2407919466f}; + + std::vector<float> inputGateBias{ 0.03f, 0.15f, 0.22f, 0.38f, 0.05f }; + std::vector<float> forgetGateBias{ 0.1f, -0.3f, -0.2f, 0.1f, 0.4f }; + std::vector<float> cellBias{ -0.05f, 0.72f, 0.25f, 0.08f, 0.1f }; + std::vector<float> outputGateBias{ 0.05f, -0.01f, 0.2f, 0.1f, -0.2f }; + + std::vector<float> recurrentToInputWeights = { -0.128009796112f, 0.1995525098f, -0.07745539397f, 0.1558421701f, + -0.265254765766f, -0.38837709614f, -0.05636804124f, 0.4259087456f, + 0.17628988623f, 0.3877420127f, 0.53300309181f, -0.0959980934f, + 0.00302857416f, 0.3266998827f, -0.142509296562f, -0.04433270756f, + 0.54066205f, -0.32668582f, -0.43562764f, -0.56094903f }; + + std::vector<float> recurrentToForgetWeights = { -0.09499983487f, -0.08814888417f, -0.04834804721f, 0.1516668247f, + -0.3967529535f, -0.06463699788f, 0.4952811002f, 0.003274492938f, + -0.0968840941f, 0.17928104102f, 0.0031281141592f, -0.3387276584f, + -0.3587934076f, 0.06705895066f, 0.22463923692f, 0.1961955726f, + 0.01841056f, -0.32764608f, -0.33027974f, -0.10826075f }; + + std::vector<float> recurrentToCellWeights = { -0.21938985582f, -0.3023648226f, -0.1170005202f, -0.3509177422f, + -0.4286288613f, 0.2726137042f, 0.09216640889f, -0.06551410215f, + 0.20453298098f, 0.2393476665f, 0.11846517771f, 0.2630801796f, + 0.3954237699f, -0.19407111404f, 0.30412107706f, -0.27342408554f, + 0.19069612f, -0.03026325f, -0.54532051f, 0.33003211f }; + + std::vector<float> recurrentToOutputWeights = { -0.32921677827f, 0.32624614238f, -0.1388191282f, -0.17879831790f, + -0.15185534954f, -0.16918526583f, -0.10087361183f, -0.5436913968f, + 0.016758225858f, 0.30454617738f, -0.41493862867f, -0.005565764375f, + -0.12584099173f, -0.12319286912f, 0.2407919466f, -0.08879069983f, + 0.11178309f, 0.09481031f, -0.26424935f, 0.46261835f }; + + std::vector<float> cellToInputWeights { 0.05f, 0.1f, 0.25f, 0.15f, -0.02f }; + std::vector<float> cellToForgetWeights { -0.02f, -0.15f, -0.25f, -0.03f, 0.15f }; + std::vector<float> cellToOutputWeights { 0.1f, -0.1f, -0.5f, 0.05f, 0.01f }; + + std::vector<float> 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, + 0.5f, -0.4f, 0.3f, -0.2f, + 0.3f, 0.08f, -0.07f, 0.2f}; + + std::vector<float> projectionBiasVector(outputSize, 0.f); //{outputSize} + + std::vector<float> inputLayerNormWeights{ 0.1f, 0.2f, 0.3f, 0.5f, 0.8f }; + std::vector<float> forgetLayerNormWeights{ 0.1f, 0.2f, 0.3f, 0.5f, 0.2f }; + std::vector<float> cellLayerNormWeights{ 0.7f, 0.2f, 0.3f, 0.8f, 0.5f }; + std::vector<float> outputLayerNormWeights{ 0.6f, 0.2f, 0.2f, 0.5f, 0.1f }; + + armnn::ScopedTensorHandle inputToInputWeightsTensor(tensorInfo5x3); + armnn::ScopedTensorHandle inputToForgetWeightsTensor(tensorInfo5x3); + armnn::ScopedTensorHandle inputToCellWeightsTensor(tensorInfo5x3); + armnn::ScopedTensorHandle inputToOutputWeightsTensor(tensorInfo5x3); + armnn::ScopedTensorHandle recurrentToForgetWeightsTensor(tensorInfo5x4); + armnn::ScopedTensorHandle recurrentToInputWeightsTensor(tensorInfo5x4); + armnn::ScopedTensorHandle recurrentToCellWeightsTensor(tensorInfo5x4); + armnn::ScopedTensorHandle recurrentToOutputWeightsTensor(tensorInfo5x4); + armnn::ScopedTensorHandle cellToInputWeightsTensor(tensorInfo5); + armnn::ScopedTensorHandle inputGateBiasTensor(tensorInfo5); + armnn::ScopedTensorHandle forgetGateBiasTensor(tensorInfo5); + armnn::ScopedTensorHandle cellBiasTensor(tensorInfo5); + armnn::ScopedTensorHandle outputGateBiasTensor(tensorInfo5); + armnn::ScopedTensorHandle cellToForgetWeightsTensor(tensorInfo5); + armnn::ScopedTensorHandle cellToOutputWeightsTensor(tensorInfo5); + armnn::ScopedTensorHandle projectionWeightsTensor(tensorInfo4x5); + armnn::ScopedTensorHandle projectionBiasTensor(tensorInfo4); + + armnn::ScopedTensorHandle inputLayerNormWeightsTensor(tensorInfo5); + armnn::ScopedTensorHandle forgetLayerNormWeightsTensor(tensorInfo5); + armnn::ScopedTensorHandle cellLayerNormWeightsTensor(tensorInfo5); + armnn::ScopedTensorHandle outputLayerNormWeightsTensor(tensorInfo5); + + 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; + data.m_Parameters.m_TimeMajor = false; + data.m_Parameters.m_ClippingThresCell = 10.0f; + + std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateUnidirectionalSequenceLstm(data, info); + inputHandle->Allocate(); + outputStateInHandle->Allocate(); + cellStateInHandle->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<float, 3>(actualOutput, + expectedOutput, + outputHandle->GetShape(), + outputTensorInfo.GetShape()); +} + +LayerTestResult<float, 3> UnidirectionalSequenceLstmWithCifgWithPeepholeNoProjectionTest( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::ITensorHandleFactory& tensorHandleFactory) +{ + IgnoreUnused(memoryManager); + unsigned int batchSize = 3; + unsigned int timeSize = 2; + unsigned int inputSize = 3; + unsigned int outputSize = 4; + unsigned numUnits = outputSize; + + armnn::TensorInfo inputTensorInfo({batchSize, timeSize, inputSize}, armnn::DataType::Float32); + armnn::TensorInfo cellStateInTensorInfo({batchSize, numUnits}, armnn::DataType::Float32); + armnn::TensorInfo outputStateInTensorInfo({batchSize, outputSize}, armnn::DataType::Float32); + + armnn::TensorInfo outputTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32); + + std::vector<float> inputVector = { 1., 2., 3., 4., 5., 4., + 3., 2., 1., 2., 3., 4., + 5., 4., 3., 2., 1., 2. }; + + std::vector<float> cellStateInVector(batchSize * numUnits, 0.f); + std::vector<float> outputStateInVector(batchSize * outputSize, 0.f); + + std::vector<float> actualOutput(outputTensorInfo.GetNumElements()); + + std::vector<float> outputVector = { -0.0129257f, -0.070531f, -0.153508f, -0.0392391f, + -0.0300169f, -0.195717f, -0.528679f, -0.0818106f, + -0.0332748f, 0.155429f, -0.353966f, -0.0801505f, + -0.032312f, -0.0407911f, -0.435053f, -0.0932317f, + -0.0108233f, 0.165584f, -0.640424f, -0.0447535f, + -0.031675f, 0.125987f, -0.526695f, -0.110093f }; + + std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo); + std::unique_ptr<armnn::ITensorHandle> cellStateInHandle = + tensorHandleFactory.CreateTensorHandle(cellStateInTensorInfo); + std::unique_ptr<armnn::ITensorHandle> outputStateInHandle = + tensorHandleFactory.CreateTensorHandle(outputStateInTensorInfo); + + std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo); + + armnn::UnidirectionalSequenceLstmQueueDescriptor 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, outputTensorInfo, outputHandle.get()); + + armnn::TensorInfo tensorInfo4({numUnits}, armnn::DataType::Float32); + armnn::TensorInfo tensorInfo12({numUnits, 3}, armnn::DataType::Float32); + armnn::TensorInfo tensorInfo16({numUnits, 4}, armnn::DataType::Float32); + + std::vector<float> inputToForgetWeights = { 0.2415594226f, 0.15400093799f, 0.4566498398f, + -0.3810434485f, 0.268383264f, -0.009807467424f, + -0.3522925403f, -0.24275735512f, -0.28344226125f, + 0.13512269116f, -0.4932442977f, -0.10039821991f }; + + std::vector<float> inputToCellWeights = { -0.2504855627f, 0.184490025045f, -0.2480507493f, + 0.386399507f, -0.259465157985f, -0.16545993089f, + -0.4230232555f, 0.341664791103f, -0.18127849691f, + -0.2277662414f, -0.55275535589f, 0.34184026718f }; + + std::vector<float> inputToOutputWeights = { 0.2303854227f, 0.5218806862f, -0.4865379333f, + 0.53969591851f, 0.23393625035f, -0.27140527306f, + 0.50009280443f, 0.07511717046f, 0.3998299249f, + -0.51717478049f, 0.1889653282f, -0.367323637f }; + + std::vector<float> recurrentToForgetWeights = { -0.09499983487f, -0.08814888417f, -0.04834804721f, 0.1516668247f, + -0.3967529535f, -0.06463699788f, 0.4952811002f, 0.003274492938f, + -0.0968840941f, 0.17928104102f, 0.0031281141592f, -0.3387276584f, + -0.3587934076f, 0.06705895066f, 0.22463923692f, 0.1961955726f }; + + std::vector<float> recurrentToCellWeights = { -0.21938985582f, -0.3023648226f, -0.1170005202f, -0.3509177422f, + -0.4286288613f, 0.2726137042f, 0.09216640889f, -0.06551410215f, + 0.20453298098f, 0.2393476665f, 0.11846517771f, 0.2630801796f, + 0.3954237699f, -0.19407111404f, 0.30412107706f, -0.27342408554f }; + + std::vector<float> recurrentToOutputWeights = { -0.32921677827f, 0.32624614238f, -0.1388191282f, -0.17879831790f, + -0.15185534954f, -0.16918526583f, -0.10087361183f, -0.5436913968f, + 0.016758225858f, 0.30454617738f, -0.41493862867f, -0.005565764375f, + -0.12584099173f, -0.12319286912f, 0.2407919466f, -0.08879069983f }; + + std::vector<float> cellToForgetWeights{ 0.47485286f, -0.51955009f, -0.24458408f, 0.31544167f }; + + std::vector<float> cellToOutputWeights{ -0.17135078f, 0.82760304f, 0.85573703f, -0.77109635f }; + + std::vector<float> forgetGateBias = { 1., 1., 1., 1. }; + + std::vector<float> cellBias = { 0., 0., 0., 0. }; + + std::vector<float> outputGateBias = { 0., 0., 0., 0. }; + + armnn::ScopedTensorHandle inputToForgetWeightsTensor(tensorInfo12); + armnn::ScopedTensorHandle inputToCellWeightsTensor(tensorInfo12); + armnn::ScopedTensorHandle inputToOutputWeightsTensor(tensorInfo12); + armnn::ScopedTensorHandle recurrentToForgetWeightsTensor(tensorInfo16); + armnn::ScopedTensorHandle recurrentToCellWeightsTensor(tensorInfo16); + armnn::ScopedTensorHandle recurrentToOutputWeightsTensor(tensorInfo16); + armnn::ScopedTensorHandle cellToForgetWeightsTensor(tensorInfo4); + armnn::ScopedTensorHandle cellToOutputWeightsTensor(tensorInfo4); + armnn::ScopedTensorHandle forgetGateBiasTensor(tensorInfo4); + armnn::ScopedTensorHandle cellBiasTensor(tensorInfo4); + armnn::ScopedTensorHandle outputGateBiasTensor(tensorInfo4); + + 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(&cellToForgetWeightsTensor, cellToForgetWeights.data()); + AllocateAndCopyDataToITensorHandle(&cellToOutputWeightsTensor, cellToOutputWeights.data()); + AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, forgetGateBias.data()); + AllocateAndCopyDataToITensorHandle(&cellBiasTensor, cellBias.data()); + AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, outputGateBias.data()); + + 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_CellToForgetWeights = &cellToForgetWeightsTensor; + data.m_CellToOutputWeights = &cellToOutputWeightsTensor; + data.m_ForgetGateBias = &forgetGateBiasTensor; + data.m_CellBias = &cellBiasTensor; + data.m_OutputGateBias = &outputGateBiasTensor; + + // Flags to set test configuration + data.m_Parameters.m_ClippingThresCell = 10; + data.m_Parameters.m_ClippingThresProj = 0; + data.m_Parameters.m_ActivationFunc = 4; + data.m_Parameters.m_CifgEnabled = true; + data.m_Parameters.m_PeepholeEnabled = true; + data.m_Parameters.m_ProjectionEnabled = false; + data.m_Parameters.m_TimeMajor = false; + + std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateUnidirectionalSequenceLstm(data, info); + inputHandle->Allocate(); + outputStateInHandle->Allocate(); + cellStateInHandle->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<float, 3>(actualOutput, + outputVector, + outputHandle->GetShape(), + outputTensorInfo.GetShape()); +} |