Narumol Prangnawarat | e5339e7 | 2021-07-28 17:33:28 +0100 | [diff] [blame] | 1 | // |
| 2 | // Copyright © 2021 Arm Ltd and Contributors. All rights reserved. |
| 3 | // SPDX-License-Identifier: MIT |
| 4 | // |
| 5 | |
| 6 | #include "UnidirectionalSequenceLstmTestImpl.hpp" |
| 7 | |
| 8 | #include <armnn/utility/NumericCast.hpp> |
| 9 | |
| 10 | #include <backendsCommon/TensorHandle.hpp> |
| 11 | |
| 12 | #include <backendsCommon/test/TensorCopyUtils.hpp> |
| 13 | #include <backendsCommon/test/WorkloadTestUtils.hpp> |
| 14 | |
| 15 | #include <ResolveType.hpp> |
| 16 | |
| 17 | namespace { |
| 18 | |
| 19 | template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| 20 | LayerTestResult<T, 3> UnidirectionalSequenceLstmLayerFloat32TestImpl( |
| 21 | armnn::IWorkloadFactory& workloadFactory, |
| 22 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 23 | const armnn::ITensorHandleFactory& tensorHandleFactory, |
| 24 | const std::vector<T>& input, |
| 25 | const std::vector<T>& outputExpected, |
| 26 | const armnn::TensorShape& inputShape, |
| 27 | const armnn::TensorShape& outputExpectedShape, |
| 28 | float qScale = 0.0f, |
| 29 | int32_t qOffset = 0, |
| 30 | armnn::DataType constantDataType = armnn::DataType::Float32) { |
| 31 | IgnoreUnused(memoryManager); |
| 32 | unsigned int batchSize = armnn::numeric_cast<unsigned int>(inputShape[0]); |
| 33 | unsigned int timeSize = armnn::numeric_cast<unsigned int>(inputShape[1]); |
| 34 | unsigned int inputSize = armnn::numeric_cast<unsigned int>(inputShape[2]); |
| 35 | unsigned int outputSize = armnn::numeric_cast<unsigned int>(outputExpectedShape[2]); |
| 36 | unsigned numUnits = outputSize; |
| 37 | |
| 38 | armnn::TensorInfo inputTensorInfo({batchSize, timeSize, inputSize}, ArmnnType, qScale, qOffset); |
| 39 | armnn::TensorInfo cellStateInTensorInfo({batchSize, numUnits}, ArmnnType, qScale, qOffset); |
| 40 | armnn::TensorInfo outputStateInTensorInfo({batchSize, outputSize}, ArmnnType, qScale, qOffset); |
| 41 | |
| 42 | armnn::TensorInfo outputTensorInfo({batchSize, timeSize, outputSize}, ArmnnType, qScale, qOffset); |
| 43 | |
| 44 | std::vector<T> inputVector; |
| 45 | inputVector.assign(input.data(), input.data() + (batchSize * timeSize * inputSize)); |
| 46 | |
| 47 | std::vector<T> cellStateInVector(batchSize * numUnits, T()); |
| 48 | std::vector<T> outputStateInVector(batchSize * outputSize, T()); |
| 49 | |
| 50 | std::vector<T> actualOutput(outputTensorInfo.GetNumElements()); |
| 51 | |
| 52 | std::vector<T> outputVector; |
| 53 | outputVector.assign(outputExpected.data(), outputExpected.data() + (batchSize * timeSize * outputSize)); |
| 54 | |
| 55 | std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo); |
| 56 | std::unique_ptr<armnn::ITensorHandle> cellStateInHandle = |
| 57 | tensorHandleFactory.CreateTensorHandle(cellStateInTensorInfo); |
| 58 | std::unique_ptr<armnn::ITensorHandle> outputStateInHandle = |
| 59 | tensorHandleFactory.CreateTensorHandle(outputStateInTensorInfo); |
| 60 | |
| 61 | std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo); |
| 62 | |
| 63 | armnn::UnidirectionalSequenceLstmQueueDescriptor data; |
| 64 | armnn::WorkloadInfo info; |
| 65 | |
| 66 | AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); |
| 67 | AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get()); |
| 68 | AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get()); |
| 69 | |
| 70 | AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); |
| 71 | |
| 72 | armnn::TensorInfo tensorInfo4({numUnits}, constantDataType, qScale, qOffset); |
| 73 | armnn::TensorInfo tensorInfo12({numUnits, 3}, constantDataType, qScale, qOffset); |
| 74 | armnn::TensorInfo tensorInfo16({numUnits, 4}, constantDataType, qScale, qOffset); |
| 75 | |
| 76 | std::vector<float> inputToInputWeights = { -0.49536117f, -0.0556083915f, -0.102400711f, |
| 77 | -0.117484632f, 0.3298470976f, -0.1179017122f, |
| 78 | 0.214305695f, 0.42135173085f, 0.003878414626f, |
| 79 | -0.348303917f, -0.1881275477f, 0.0343011027f }; |
| 80 | |
| 81 | std::vector<float> inputToForgetWeights = { 0.2415594226f, 0.15400093799f, 0.4566498398f, |
| 82 | -0.3810434485f, 0.268383264f, -0.009807467424f, |
| 83 | -0.3522925403f, -0.24275735512f, -0.28344226125f, |
| 84 | 0.13512269116f, -0.4932442977f, -0.10039821991f }; |
| 85 | |
| 86 | std::vector<float> inputToCellWeights = { -0.2504855627f, 0.184490025045f, -0.2480507493f, |
| 87 | 0.386399507f, -0.259465157985f, -0.16545993089f, |
| 88 | -0.4230232555f, 0.341664791103f, -0.18127849691f, |
| 89 | -0.2277662414f, -0.55275535589f, 0.34184026718f }; |
| 90 | |
| 91 | std::vector<float> inputToOutputWeights = { 0.2303854227f, 0.5218806862f, -0.4865379333f, |
| 92 | 0.53969591851f, 0.23393625035f, -0.27140527306f, |
| 93 | 0.50009280443f, 0.07511717046f, 0.3998299249f, |
| 94 | -0.51717478049f, 0.1889653282f, -0.367323637f }; |
| 95 | |
| 96 | std::vector<float> recurrentToInputWeights = { -0.128009796112f, 0.1995525098f, -0.07745539397f, 0.1558421701f, |
| 97 | -0.265254765766f, -0.38837709614f, -0.05636804124f, 0.4259087456f, |
| 98 | 0.17628988623f, 0.3877420127f, 0.53300309181f, -0.0959980934f, |
| 99 | 0.00302857416f, 0.3266998827f, -0.142509296562f, -0.04433270756f }; |
| 100 | |
| 101 | std::vector<float> recurrentToForgetWeights = { -0.09499983487f, -0.08814888417f, -0.04834804721f, 0.1516668247f, |
| 102 | -0.3967529535f, -0.06463699788f, 0.4952811002f, 0.003274492938f, |
| 103 | -0.0968840941f, 0.17928104102f, 0.0031281141592f, -0.3387276584f, |
| 104 | -0.3587934076f, 0.06705895066f, 0.22463923692f, 0.1961955726f }; |
| 105 | |
| 106 | std::vector<float> recurrentToCellWeights = { -0.21938985582f, -0.3023648226f, -0.1170005202f, -0.3509177422f, |
| 107 | -0.4286288613f, 0.2726137042f, 0.09216640889f, -0.06551410215f, |
| 108 | 0.20453298098f, 0.2393476665f, 0.11846517771f, 0.2630801796f, |
| 109 | 0.3954237699f, -0.19407111404f, 0.30412107706f, -0.27342408554f }; |
| 110 | |
| 111 | std::vector<float> recurrentToOutputWeights = { -0.32921677827f, 0.32624614238f, -0.1388191282f, -0.17879831790f, |
| 112 | -0.15185534954f, -0.16918526583f, -0.10087361183f, -0.5436913968f, |
| 113 | 0.016758225858f, 0.30454617738f, -0.41493862867f, -0.005565764375f, |
| 114 | -0.12584099173f, -0.12319286912f, 0.2407919466f, -0.08879069983f }; |
| 115 | |
| 116 | std::vector<float> inputGateBias = { 0., 0., 0., 0. }; |
| 117 | |
| 118 | std::vector<float> forgetGateBias = { 1., 1., 1., 1. }; |
| 119 | |
| 120 | std::vector<float> cellBias = { 0., 0., 0., 0. }; |
| 121 | |
| 122 | std::vector<float> outputGateBias = { 0., 0., 0., 0. }; |
| 123 | |
| 124 | armnn::ScopedTensorHandle inputToInputWeightsTensor(tensorInfo12); |
| 125 | armnn::ScopedTensorHandle inputToForgetWeightsTensor(tensorInfo12); |
| 126 | armnn::ScopedTensorHandle inputToCellWeightsTensor(tensorInfo12); |
| 127 | armnn::ScopedTensorHandle inputToOutputWeightsTensor(tensorInfo12); |
| 128 | armnn::ScopedTensorHandle recurrentToInputWeightsTensor(tensorInfo16); |
| 129 | armnn::ScopedTensorHandle recurrentToForgetWeightsTensor(tensorInfo16); |
| 130 | armnn::ScopedTensorHandle recurrentToCellWeightsTensor(tensorInfo16); |
| 131 | armnn::ScopedTensorHandle recurrentToOutputWeightsTensor(tensorInfo16); |
| 132 | armnn::ScopedTensorHandle inputGateBiasTensor(tensorInfo4); |
| 133 | armnn::ScopedTensorHandle forgetGateBiasTensor(tensorInfo4); |
| 134 | armnn::ScopedTensorHandle cellBiasTensor(tensorInfo4); |
| 135 | armnn::ScopedTensorHandle outputGateBiasTensor(tensorInfo4); |
| 136 | |
| 137 | AllocateAndCopyDataToITensorHandle(&inputToInputWeightsTensor, inputToInputWeights.data()); |
| 138 | AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, inputToForgetWeights.data()); |
| 139 | AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, inputToCellWeights.data()); |
| 140 | AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, inputToOutputWeights.data()); |
| 141 | AllocateAndCopyDataToITensorHandle(&recurrentToInputWeightsTensor, recurrentToInputWeights.data()); |
| 142 | AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, recurrentToForgetWeights.data()); |
| 143 | AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, recurrentToCellWeights.data()); |
| 144 | AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, recurrentToOutputWeights.data()); |
| 145 | AllocateAndCopyDataToITensorHandle(&inputGateBiasTensor, inputGateBias.data()); |
| 146 | AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, forgetGateBias.data()); |
| 147 | AllocateAndCopyDataToITensorHandle(&cellBiasTensor, cellBias.data()); |
| 148 | AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, outputGateBias.data()); |
| 149 | |
| 150 | data.m_InputToInputWeights = &inputToInputWeightsTensor; |
| 151 | data.m_InputToForgetWeights = &inputToForgetWeightsTensor; |
| 152 | data.m_InputToCellWeights = &inputToCellWeightsTensor; |
| 153 | data.m_InputToOutputWeights = &inputToOutputWeightsTensor; |
| 154 | data.m_RecurrentToInputWeights = &recurrentToInputWeightsTensor; |
| 155 | data.m_RecurrentToForgetWeights = &recurrentToForgetWeightsTensor; |
| 156 | data.m_RecurrentToCellWeights = &recurrentToCellWeightsTensor; |
| 157 | data.m_RecurrentToOutputWeights = &recurrentToOutputWeightsTensor; |
| 158 | data.m_InputGateBias = &inputGateBiasTensor; |
| 159 | data.m_ForgetGateBias = &forgetGateBiasTensor; |
| 160 | data.m_CellBias = &cellBiasTensor; |
| 161 | data.m_OutputGateBias = &outputGateBiasTensor; |
| 162 | |
| 163 | // Flags to set test configuration |
| 164 | data.m_Parameters.m_ClippingThresCell = 10; |
| 165 | data.m_Parameters.m_ClippingThresProj = 0; |
| 166 | data.m_Parameters.m_ActivationFunc = 4; |
| 167 | data.m_Parameters.m_CifgEnabled = false; |
| 168 | data.m_Parameters.m_PeepholeEnabled = false; |
| 169 | data.m_Parameters.m_ProjectionEnabled = false; |
| 170 | data.m_Parameters.m_TimeMajor = false; |
| 171 | |
| 172 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateUnidirectionalSequenceLstm(data, info); |
| 173 | inputHandle->Allocate(); |
| 174 | outputStateInHandle->Allocate(); |
| 175 | cellStateInHandle->Allocate(); |
| 176 | |
| 177 | outputHandle->Allocate(); |
| 178 | |
| 179 | CopyDataToITensorHandle(inputHandle.get(), inputVector.data()); |
| 180 | CopyDataToITensorHandle(outputStateInHandle.get(), outputStateInVector.data()); |
| 181 | CopyDataToITensorHandle(cellStateInHandle.get(), cellStateInVector.data()); |
| 182 | |
| 183 | workload->Execute(); |
| 184 | |
| 185 | CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get()); |
| 186 | |
| 187 | return LayerTestResult<T, 3>(actualOutput, |
| 188 | outputVector, |
| 189 | outputHandle->GetShape(), |
| 190 | outputTensorInfo.GetShape()); |
| 191 | } |
| 192 | |
| 193 | template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| 194 | LayerTestResult<T, 3> |
| 195 | UnidirectionalSequenceLstmLayerFloat32TimeMajorTestImpl( |
| 196 | armnn::IWorkloadFactory& workloadFactory, |
| 197 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 198 | const armnn::ITensorHandleFactory& tensorHandleFactory, |
| 199 | const std::vector<T>& input, |
| 200 | const std::vector<T>& outputExpected, |
| 201 | const armnn::TensorShape& inputShape, |
| 202 | const armnn::TensorShape& outputExpectedShape, |
| 203 | float qScale = 0.0f, |
| 204 | int32_t qOffset = 0, |
| 205 | armnn::DataType constantDataType = armnn::DataType::Float32) { |
| 206 | IgnoreUnused(memoryManager); |
| 207 | unsigned int batchSize = armnn::numeric_cast<unsigned int>(inputShape[1]); |
| 208 | unsigned int timeSize = armnn::numeric_cast<unsigned int>(inputShape[0]); |
| 209 | unsigned int inputSize = armnn::numeric_cast<unsigned int>(inputShape[2]); |
| 210 | unsigned int outputSize = armnn::numeric_cast<unsigned int>(outputExpectedShape[2]); |
| 211 | unsigned numUnits = outputSize; |
| 212 | |
| 213 | armnn::TensorInfo inputTensorInfo({timeSize, batchSize, inputSize}, ArmnnType, qScale, qOffset); |
| 214 | armnn::TensorInfo cellStateInTensorInfo({batchSize, numUnits}, ArmnnType, qScale, qOffset); |
| 215 | armnn::TensorInfo outputStateInTensorInfo({batchSize, outputSize}, ArmnnType, qScale, qOffset); |
| 216 | |
| 217 | armnn::TensorInfo outputTensorInfo({timeSize, batchSize, outputSize}, ArmnnType, qScale, qOffset); |
| 218 | |
| 219 | std::vector<T> inputVector; |
| 220 | inputVector.assign(input.data(), input.data() + (batchSize * timeSize * inputSize)); |
| 221 | |
| 222 | std::vector<T> cellStateInVector(batchSize * numUnits, T()); |
| 223 | std::vector<T> outputStateInVector(batchSize * outputSize, T()); |
| 224 | |
| 225 | std::vector<T> actualOutput(outputTensorInfo.GetNumElements()); |
| 226 | |
| 227 | std::vector<T> outputVector; |
| 228 | outputVector.assign(outputExpected.data(), outputExpected.data() + (batchSize * timeSize * outputSize)); |
| 229 | |
| 230 | std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo); |
| 231 | std::unique_ptr<armnn::ITensorHandle> cellStateInHandle = |
| 232 | tensorHandleFactory.CreateTensorHandle(cellStateInTensorInfo); |
| 233 | std::unique_ptr<armnn::ITensorHandle> outputStateInHandle = |
| 234 | tensorHandleFactory.CreateTensorHandle(outputStateInTensorInfo); |
| 235 | |
| 236 | std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo); |
| 237 | |
| 238 | armnn::UnidirectionalSequenceLstmQueueDescriptor data; |
| 239 | armnn::WorkloadInfo info; |
| 240 | |
| 241 | AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); |
| 242 | AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get()); |
| 243 | AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get()); |
| 244 | |
| 245 | AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); |
| 246 | |
| 247 | armnn::TensorInfo tensorInfo4({numUnits}, constantDataType, qScale, qOffset); |
| 248 | armnn::TensorInfo tensorInfo12({numUnits, 3}, constantDataType, qScale, qOffset); |
| 249 | armnn::TensorInfo tensorInfo16({numUnits, 4}, constantDataType, qScale, qOffset); |
| 250 | |
| 251 | std::vector<float> inputToInputWeights = { 0.27277296781539917f, 0.3813590407371521f, -0.394489049911499f, |
| 252 | 0.2782636880874634f, -0.3793870210647583f, -0.018918335437774658f, |
| 253 | 0.2724653482437134f, -0.19314253330230713f, -0.2947450876235962f, |
| 254 | -0.30253493785858154f, 0.4241350293159485f, -0.22560018301010132f }; |
| 255 | |
| 256 | std::vector<float> inputToForgetWeights = { -0.2667974531650543f, -0.05505800247192383f, -0.20932340621948242f, |
| 257 | -0.14345619082450867f, 0.09666192531585693f, -0.2604355812072754f, |
| 258 | -0.2681812047958374f, -0.3314584493637085f, 0.4485899806022644f, |
| 259 | -0.23467743396759033f, 0.5072842240333557f, -0.4192768931388855f }; |
| 260 | |
| 261 | std::vector<float> inputToCellWeights = { -0.15782442688941956f, -0.027530014514923096f, 0.4789854884147644f, |
| 262 | 0.23227906227111816f, 0.28259342908859253f, -0.030095696449279785f, |
| 263 | 0.10071521997451782f, -0.08535495400428772f, 0.18563997745513916f, |
| 264 | -0.3049069046974182f, -0.478048175573349f, 0.025234103202819824f }; |
| 265 | |
| 266 | std::vector<float> inputToOutputWeights = { -0.04584759473800659f, -0.2716066539287567f, 0.012970447540283203f, |
| 267 | -0.4729190170764923f, -0.37422770261764526f, 0.49352723360061646f, |
| 268 | 0.3163864016532898f, -0.436781644821167f, -0.33074596524238586f, |
| 269 | -0.32885751128196716f, -0.40959352254867554f, -0.2124689817428589f }; |
| 270 | |
| 271 | std::vector<float> recurrentToInputWeights = { 0.23788475990f, -0.24948765337f, 0.50044941902f, 0.14431896805f, |
| 272 | -0.115940228137f, -0.717082679f, -0.17208620906f, 0.17850610617f, |
| 273 | -0.16702319684f, -0.11384502053f, -0.309785276245f, -0.3316611672f, |
| 274 | 0.52380162477f, -0.06839632987f, -0.391478359627f, -0.10756178963f }; |
| 275 | |
| 276 | std::vector<float> recurrentToForgetWeights = { 0.11383482068f, 0.1676601767f, -0.08550968004f, 0.03399394089f, |
| 277 | 0.08042152225f, -0.2133381964f, 0.05182432704f, 0.38161808255f, |
| 278 | -0.5018365979f, -0.08043262364f, 0.07894329014f, -0.07547105155f, |
| 279 | 0.12047368288f, 0.2986997961f, 0.0485043078f, -0.13372567296f }; |
| 280 | |
| 281 | std::vector<float> recurrentToCellWeights = { 0.0433832928545f, 0.07587072294f, -0.120520234107f, 0.604576051f, |
| 282 | -0.434353142986f, 0.009314475068f, 0.005085289478f, 0.08488202038f, |
| 283 | -0.00025437487886f, 0.15245915082f, -0.1936587542f, 0.004754020f, |
| 284 | -0.1582719236f, 0.3307867646f, 0.0236605107784f, 0.307716339826f }; |
| 285 | |
| 286 | std::vector<float> recurrentToOutputWeights = { -0.079031050201f, 0.041414566286f, -0.583727357285f, 0.1025384515f, |
| 287 | -0.172372072937f, 0.09214124082f, 0.178184121827f, -0.2439443916f, |
| 288 | 0.104485116899f, 0.2600405514f, 0.064414866268f, 0.24141204357f, |
| 289 | 0.281875759363f, -0.14234502664f, 0.15126448862f, -0.24421440064f }; |
| 290 | |
| 291 | std::vector<float> inputGateBias = { 0., 0., 0., 0. }; |
| 292 | |
| 293 | std::vector<float> forgetGateBias = { 1., 1., 1., 1. }; |
| 294 | |
| 295 | std::vector<float> cellBias = { 0., 0., 0., 0. }; |
| 296 | |
| 297 | std::vector<float> outputGateBias = { 0., 0., 0., 0. }; |
| 298 | |
| 299 | armnn::ScopedTensorHandle inputToInputWeightsTensor(tensorInfo12); |
| 300 | armnn::ScopedTensorHandle inputToForgetWeightsTensor(tensorInfo12); |
| 301 | armnn::ScopedTensorHandle inputToCellWeightsTensor(tensorInfo12); |
| 302 | armnn::ScopedTensorHandle inputToOutputWeightsTensor(tensorInfo12); |
| 303 | armnn::ScopedTensorHandle recurrentToInputWeightsTensor(tensorInfo16); |
| 304 | armnn::ScopedTensorHandle recurrentToForgetWeightsTensor(tensorInfo16); |
| 305 | armnn::ScopedTensorHandle recurrentToCellWeightsTensor(tensorInfo16); |
| 306 | armnn::ScopedTensorHandle recurrentToOutputWeightsTensor(tensorInfo16); |
| 307 | armnn::ScopedTensorHandle inputGateBiasTensor(tensorInfo4); |
| 308 | armnn::ScopedTensorHandle forgetGateBiasTensor(tensorInfo4); |
| 309 | armnn::ScopedTensorHandle cellBiasTensor(tensorInfo4); |
| 310 | armnn::ScopedTensorHandle outputGateBiasTensor(tensorInfo4); |
| 311 | |
| 312 | AllocateAndCopyDataToITensorHandle(&inputToInputWeightsTensor, inputToInputWeights.data()); |
| 313 | AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, inputToForgetWeights.data()); |
| 314 | AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, inputToCellWeights.data()); |
| 315 | AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, inputToOutputWeights.data()); |
| 316 | AllocateAndCopyDataToITensorHandle(&recurrentToInputWeightsTensor, recurrentToInputWeights.data()); |
| 317 | AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, recurrentToForgetWeights.data()); |
| 318 | AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, recurrentToCellWeights.data()); |
| 319 | AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, recurrentToOutputWeights.data()); |
| 320 | AllocateAndCopyDataToITensorHandle(&inputGateBiasTensor, inputGateBias.data()); |
| 321 | AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, forgetGateBias.data()); |
| 322 | AllocateAndCopyDataToITensorHandle(&cellBiasTensor, cellBias.data()); |
| 323 | AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, outputGateBias.data()); |
| 324 | |
| 325 | data.m_InputToInputWeights = &inputToInputWeightsTensor; |
| 326 | data.m_InputToForgetWeights = &inputToForgetWeightsTensor; |
| 327 | data.m_InputToCellWeights = &inputToCellWeightsTensor; |
| 328 | data.m_InputToOutputWeights = &inputToOutputWeightsTensor; |
| 329 | data.m_RecurrentToInputWeights = &recurrentToInputWeightsTensor; |
| 330 | data.m_RecurrentToForgetWeights = &recurrentToForgetWeightsTensor; |
| 331 | data.m_RecurrentToCellWeights = &recurrentToCellWeightsTensor; |
| 332 | data.m_RecurrentToOutputWeights = &recurrentToOutputWeightsTensor; |
| 333 | data.m_InputGateBias = &inputGateBiasTensor; |
| 334 | data.m_ForgetGateBias = &forgetGateBiasTensor; |
| 335 | data.m_CellBias = &cellBiasTensor; |
| 336 | data.m_OutputGateBias = &outputGateBiasTensor; |
| 337 | |
| 338 | // Flags to set test configuration |
| 339 | data.m_Parameters.m_ClippingThresCell = 10; |
| 340 | data.m_Parameters.m_ClippingThresProj = 0; |
| 341 | data.m_Parameters.m_ActivationFunc = 4; |
| 342 | data.m_Parameters.m_CifgEnabled = false; |
| 343 | data.m_Parameters.m_PeepholeEnabled = false; |
| 344 | data.m_Parameters.m_ProjectionEnabled = false; |
| 345 | data.m_Parameters.m_TimeMajor = true; |
| 346 | |
| 347 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateUnidirectionalSequenceLstm(data, info); |
| 348 | inputHandle->Allocate(); |
| 349 | outputStateInHandle->Allocate(); |
| 350 | cellStateInHandle->Allocate(); |
| 351 | |
| 352 | outputHandle->Allocate(); |
| 353 | |
| 354 | CopyDataToITensorHandle(inputHandle.get(), inputVector.data()); |
| 355 | CopyDataToITensorHandle(outputStateInHandle.get(), outputStateInVector.data()); |
| 356 | CopyDataToITensorHandle(cellStateInHandle.get(), cellStateInVector.data()); |
| 357 | |
| 358 | workload->Execute(); |
| 359 | |
| 360 | CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get()); |
| 361 | |
| 362 | return LayerTestResult<T, 3>(actualOutput, |
| 363 | outputVector, |
| 364 | outputHandle->GetShape(), |
| 365 | outputTensorInfo.GetShape()); |
| 366 | } |
| 367 | |
| 368 | } // anonymous namespace |
| 369 | |
| 370 | LayerTestResult<float, 3> UnidirectionalSequenceLstmLayerFloat32Test( |
| 371 | armnn::IWorkloadFactory& workloadFactory, |
| 372 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 373 | const armnn::ITensorHandleFactory& tensorHandleFactory) { |
| 374 | armnn::TensorInfo inputInfo({3, 2, 3}, armnn::DataType::Float32); |
| 375 | std::vector<float> input = { 1., 2., 3., 4., 5., 4., |
| 376 | 3., 2., 1., 2., 3., 4., |
| 377 | 5., 4., 3., 2., 1., 2. }; |
| 378 | |
| 379 | armnn::TensorInfo outputInfo({3, 2, 4}, armnn::DataType::Float32); |
| 380 | std::vector<float> expectedOutput = { -0.07149004f, -0.1621171f, -0.17516759f, -0.0232934225f, |
| 381 | -0.16810727f, -0.41412935f, -0.5498753f, -0.00803578f, |
| 382 | -0.06687349f, 0.204077631f, -0.4276504f, -0.03123213f, |
| 383 | -0.12000261f, -0.0941918f, -0.45639035f, -0.02870186f, |
| 384 | -0.03429216f, 0.20824050f, -0.6569892f, -0.004152651f, |
| 385 | -0.10493034f, 0.14210969f, -0.58347696f, -0.03297536f }; |
| 386 | return UnidirectionalSequenceLstmLayerFloat32TestImpl<armnn::DataType::Float32>( |
| 387 | workloadFactory, memoryManager, tensorHandleFactory, |
| 388 | input, expectedOutput, inputInfo.GetShape(), outputInfo.GetShape()); |
| 389 | } |
| 390 | |
| 391 | LayerTestResult<float, 3> UnidirectionalSequenceLstmLayerFloat32TimeMajorTest( |
| 392 | armnn::IWorkloadFactory& workloadFactory, |
| 393 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 394 | const armnn::ITensorHandleFactory& tensorHandleFactory) { |
| 395 | armnn::TensorInfo inputInfo({2, 3, 3}, armnn::DataType::Float32); |
| 396 | std::vector<float> input = { 1., 2., 3., 4., 5., 4., |
| 397 | 3., 2., 1., 2., 3., 4., |
| 398 | 5., 4., 3., 2., 1., 2. }; |
| 399 | |
| 400 | armnn::TensorInfo outputInfo({2, 3, 4}, armnn::DataType::Float32); |
| 401 | std::vector<float> expectedOutput = { 0.135657698f, 0.124672532f, 0.0212090332f, -0.0530203655f, |
| 402 | 0.106138252f, 0.0404792242f, 0.0151643595f, -0.00675163185f, |
| 403 | -0.0128514022f, 0.0644884035f, 0.0709072053f, -0.0454045124f, |
| 404 | 0.16288602f, 0.16649379f, 0.02770456f, -0.03698075f, |
| 405 | 0.11171641f, 0.043119f , 0.0762981f , -0.01228541f, |
| 406 | 0.10439701f, 0.21439962f, 0.11919238f, -0.08390583f }; |
| 407 | return UnidirectionalSequenceLstmLayerFloat32TimeMajorTestImpl<armnn::DataType::Float32>( |
| 408 | workloadFactory, memoryManager, tensorHandleFactory, |
| 409 | input, expectedOutput, inputInfo.GetShape(), outputInfo.GetShape()); |
| 410 | } |
| 411 | |
| 412 | LayerTestResult<float, 3> UnidirectionalSequenceLstmLayerNoCifgWithPeepholeWithProjectionTest( |
| 413 | armnn::IWorkloadFactory& workloadFactory, |
| 414 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 415 | const armnn::ITensorHandleFactory& tensorHandleFactory) |
| 416 | { |
| 417 | IgnoreUnused(memoryManager); |
| 418 | unsigned int batchSize = 2; |
| 419 | unsigned int timeSize = 3; |
| 420 | unsigned int outputSize = 5; |
| 421 | unsigned int inputSize = 4; |
| 422 | unsigned numUnits = 6; |
| 423 | |
| 424 | armnn::TensorInfo inputTensorInfo({batchSize, timeSize, inputSize}, armnn::DataType::Float32); |
| 425 | armnn::TensorInfo cellStateInTensorInfo({batchSize , numUnits}, armnn::DataType::Float32); |
| 426 | armnn::TensorInfo outputStateInTensorInfo({batchSize , outputSize}, armnn::DataType::Float32); |
| 427 | armnn::TensorInfo outputTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32); |
| 428 | |
| 429 | const std::vector<float> inputVector = { 1., 2., 3., 4., 5., 4., |
| 430 | 3., 2., 1., 2., 3., 4., |
| 431 | 5., 4., 3., 2., 1., 2., |
| 432 | 1., 2., 3., 4., 5., 4.}; |
| 433 | |
| 434 | std::vector<float> cellStateInVector(batchSize * numUnits, 0.f); |
| 435 | std::vector<float> outputStateInVector(batchSize * outputSize, 0.f); |
| 436 | |
| 437 | std::vector<float> actualOutput(outputTensorInfo.GetNumElements()); |
| 438 | |
| 439 | const std::vector<float> expectedOutput = { -0.0135612f, -0.0263441f, 0.0314008f, -0.00883455f, 0.00763052f, |
| 440 | -0.00126877f, -0.0292959f, 0.0449957f, -0.00976195f, -0.00492338f, |
| 441 | -0.0175702f, -0.0431753f, 0.0597117f, -0.0169154f, 0.0142087f, |
| 442 | 0.00472515f, -0.0196355f, 0.0342524f, -0.00407936f, -0.0253189f, |
| 443 | -0.00512944f, -0.0293754f, 0.0512771f, -0.0151874f, -0.0246433f, |
| 444 | -0.00744986f, -0.0345103f, 0.0450666f, -0.00944991f, 0.0127171f }; |
| 445 | |
| 446 | std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo); |
| 447 | std::unique_ptr<armnn::ITensorHandle> cellStateInHandle = |
| 448 | tensorHandleFactory.CreateTensorHandle(cellStateInTensorInfo); |
| 449 | std::unique_ptr<armnn::ITensorHandle> outputStateInHandle = |
| 450 | tensorHandleFactory.CreateTensorHandle(outputStateInTensorInfo); |
| 451 | std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo); |
| 452 | |
| 453 | armnn::UnidirectionalSequenceLstmQueueDescriptor data; |
| 454 | armnn::WorkloadInfo info; |
| 455 | |
| 456 | AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); |
| 457 | AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get()); |
| 458 | AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get()); |
| 459 | AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); |
| 460 | |
| 461 | armnn::TensorInfo tensorInfo5({outputSize}, armnn::DataType::Float32); |
| 462 | armnn::TensorInfo tensorInfo6({numUnits}, armnn::DataType::Float32); |
| 463 | armnn::TensorInfo tensorInfo6x4({numUnits, inputSize}, armnn::DataType::Float32); |
| 464 | armnn::TensorInfo tensorInfo6x5({numUnits, outputSize}, armnn::DataType::Float32); |
| 465 | armnn::TensorInfo tensorInfo5x6({outputSize, numUnits}, armnn::DataType::Float32); |
| 466 | |
| 467 | std::vector<float> inputToInputWeights = { 0.021393683f, 0.06124551f, 0.046905167f, -0.014657677f, |
| 468 | -0.03149463f, 0.09171803f, 0.14647801f, 0.10797193f, |
| 469 | -0.0057968358f, 0.0019193048f, -0.2726754f, 0.10154029f, |
| 470 | -0.018539885f, 0.080349885f, -0.10262385f, -0.022599787f, |
| 471 | -0.09121155f, -0.008675967f, -0.045206103f, -0.0821282f, |
| 472 | -0.008045952f, 0.015478081f, 0.055217247f, 0.038719587f }; |
| 473 | |
| 474 | std::vector<float> inputToForgetWeights = { -0.0018401089f, -0.004852237f, 0.03698424f, 0.014181704f, |
| 475 | 0.028273236f, -0.016726194f, -0.05249759f, -0.10204261f, |
| 476 | 0.00861066f, -0.040979505f, -0.009899187f, 0.01923892f, |
| 477 | -0.028177269f, -0.08535103f, -0.14585495f, 0.10662567f, |
| 478 | -0.01909731f, -0.017883534f, -0.0047269356f, -0.045103323f, |
| 479 | 0.0030784295f, 0.076784775f, 0.07463696f, 0.094531395f}; |
| 480 | |
| 481 | std::vector<float> inputToCellWeights = { -0.04580283f, -0.09549462f, -0.032418985f, -0.06454633f, |
| 482 | -0.043528453f, 0.043018587f, -0.049152344f, -0.12418144f, |
| 483 | -0.078985475f, -0.07596889f, 0.019484362f, -0.11434962f, |
| 484 | -0.0074034138f, -0.06314844f, -0.092981495f, 0.0062155537f, |
| 485 | -0.025034338f, -0.0028890965f, 0.048929527f, 0.06235075f, |
| 486 | 0.10665918f, -0.032036792f, -0.08505916f, -0.10843358f }; |
| 487 | |
| 488 | std::vector<float> inputToOutputWeights = { -0.0998932f, -0.07201956f, -0.052803773f, -0.15629593f, |
| 489 | -0.15001918f, -0.07650751f, 0.02359855f, -0.075155355f, |
| 490 | -0.08037709f, -0.15093534f, 0.029517552f, -0.04751393f, |
| 491 | 0.010350531f, -0.02664851f, -0.016839722f, -0.023121163f, |
| 492 | 0.0077019283f, 0.012851257f, -0.05040649f, -0.0129761f, |
| 493 | -0.021737747f, -0.038305793f, -0.06870586f, -0.01481247f }; |
| 494 | |
| 495 | std::vector<float> inputGateBias = { 0.02234832f, 0.14757581f, 0.18176508f, |
| 496 | 0.10380666f, 0.053110216f, -0.06928846f }; |
| 497 | |
| 498 | std::vector<float> forgetGateBias = { 0.035185695f, -0.042891346f, -0.03032477f, |
| 499 | 0.23027696f, 0.11098921f, 0.08989442f }; |
| 500 | |
| 501 | std::vector<float> cellBias = { -0.024379363f, 0.0055531194f, 0.23377132f, |
| 502 | 0.033463873f, -0.1483596f, 0.029460307f }; |
| 503 | |
| 504 | std::vector<float> outputGateBias = { 0.046159424f, -0.0012809046f, 0.03563469f, |
| 505 | 0.12648113f, 0.027195795f, 0.35373217f }; |
| 506 | |
| 507 | std::vector<float> recurrentToInputWeights = { -0.001374326f, -0.078856036f, 0.10672688f, 0.029162422f, |
| 508 | -0.11585556f, 0.02557986f, -0.13446963f, -0.035785314f, |
| 509 | -0.01244275f, 0.025961924f, -0.02337298f, -0.044228926f, |
| 510 | -0.055839065f, -0.046598054f, -0.010546039f, -0.06900766f, |
| 511 | 0.027239809f, 0.022582639f, -0.013296484f, -0.05459212f, |
| 512 | 0.08981f, -0.045407712f, 0.08682226f, -0.06867011f, |
| 513 | -0.14390695f, -0.02916037f, 0.000996957f, 0.091420636f, |
| 514 | 0.14283475f, -0.07390571f }; |
| 515 | |
| 516 | std::vector<float> recurrentToCellWeights = { -0.037322544f, 0.018592842f, 0.0056175636f, -0.06253426f, |
| 517 | 0.055647098f, -0.05713207f, -0.05626563f, 0.005559383f, |
| 518 | 0.03375411f, -0.025757805f, -0.088049285f, 0.06017052f, |
| 519 | -0.06570978f, 0.007384076f, 0.035123326f, -0.07920549f, |
| 520 | 0.053676967f, 0.044480428f, -0.07663568f, 0.0071805613f, |
| 521 | 0.08089997f, 0.05143358f, 0.038261272f, 0.03339287f, |
| 522 | -0.027673481f, 0.044746667f, 0.028349208f, 0.020090483f, |
| 523 | -0.019443132f, -0.030755889f }; |
| 524 | |
| 525 | std::vector<float> recurrentToForgetWeights = { -0.057784554f, -0.026057621f, -0.068447545f, -0.022581743f, |
| 526 | 0.14811787f, 0.10826372f, 0.09471067f, 0.03987225f, |
| 527 | -0.0039523416f, 0.00030638507f, 0.053185795f, 0.10572994f, |
| 528 | 0.08414449f, -0.022036452f, -0.00066928595f, -0.09203576f, |
| 529 | 0.032950465f, -0.10985798f, -0.023809856f, 0.0021431844f, |
| 530 | -0.02196096f, -0.00326074f, 0.00058621005f, -0.074678116f, |
| 531 | -0.06193199f, 0.055729095f, 0.03736828f, 0.020123724f, |
| 532 | 0.061878487f, -0.04729229f }; |
| 533 | |
| 534 | std::vector<float> recurrentToOutputWeights = { 0.025825322f, -0.05813119f, 0.09495884f, |
| 535 | -0.045984812f,-0.01255415f, -0.0026479573f, |
| 536 | -0.08196161f, -0.054914974f, -0.0046604523f, |
| 537 | -0.029587349f, -0.044576716f, -0.07480124f, |
| 538 | -0.082868785f, 0.023254942f, 0.027502948f, |
| 539 | -0.0039728214f, -0.08683098f, -0.08116779f, |
| 540 | -0.014675607f, -0.037924774f, -0.023314456f, |
| 541 | -0.007401714f, -0.09255757f, 0.029460307f, |
| 542 | -0.08829125f, -0.005139627f, -0.08989442f, |
| 543 | -0.0555066f, 0.13596267f, 0.025062224f }; |
| 544 | |
| 545 | std::vector<float> cellToInputWeights = { 0.040369894f, 0.030746894f, 0.24704495f, |
| 546 | 0.018586371f, -0.037586458f, -0.15312155f }; |
| 547 | |
| 548 | std::vector<float> cellToForgetWeights = { -0.01998659f, -0.15568835f, -0.24248174f, |
| 549 | -0.012770197f, 0.041331276f, -0.072311886f }; |
| 550 | |
| 551 | std::vector<float> cellToOutputWeights = { 0.08286371f, -0.08261836f, -0.51210177f, |
| 552 | 0.002913762f, 0.17764764f, -0.5495371f }; |
| 553 | |
| 554 | std::vector<float> projectionWeights = { -0.009802181f, 0.09401916f, 0.0717386f, -0.13895074f, 0.09641832f, |
| 555 | 0.060420845f, 0.08539281f, 0.054285463f, 0.061395317f, 0.034448683f, |
| 556 | -0.042991187f, 0.019801661f, -0.16840284f, -0.015726732f, -0.23041931f, |
| 557 | -0.024478018f, -0.10959692f, -0.013875541f, 0.18600968f, -0.061274476f, |
| 558 | 0.0138165f, -0.08160894f, -0.07661644f, 0.032372914f, 0.16169067f, |
| 559 | 0.22465782f, -0.03993472f, -0.004017731f, 0.08633481f, -0.28869787f }; |
| 560 | |
| 561 | std::vector<float> projectionBiasVector(outputSize, 0.f); //{outputSize} |
| 562 | |
| 563 | armnn::ScopedTensorHandle inputToInputWeightsTensor(tensorInfo6x4); |
| 564 | armnn::ScopedTensorHandle inputToForgetWeightsTensor(tensorInfo6x4); |
| 565 | armnn::ScopedTensorHandle inputToCellWeightsTensor(tensorInfo6x4); |
| 566 | armnn::ScopedTensorHandle inputToOutputWeightsTensor(tensorInfo6x4); |
| 567 | armnn::ScopedTensorHandle recurrentToForgetWeightsTensor(tensorInfo6x5); |
| 568 | armnn::ScopedTensorHandle recurrentToInputWeightsTensor(tensorInfo6x5); |
| 569 | armnn::ScopedTensorHandle recurrentToCellWeightsTensor(tensorInfo6x5); |
| 570 | armnn::ScopedTensorHandle recurrentToOutputWeightsTensor(tensorInfo6x5); |
| 571 | armnn::ScopedTensorHandle cellToInputWeightsTensor(tensorInfo6); |
| 572 | armnn::ScopedTensorHandle inputGateBiasTensor(tensorInfo6); |
| 573 | armnn::ScopedTensorHandle forgetGateBiasTensor(tensorInfo6); |
| 574 | armnn::ScopedTensorHandle cellBiasTensor(tensorInfo6); |
| 575 | armnn::ScopedTensorHandle outputGateBiasTensor(tensorInfo6); |
| 576 | armnn::ScopedTensorHandle cellToForgetWeightsTensor(tensorInfo6); |
| 577 | armnn::ScopedTensorHandle cellToOutputWeightsTensor(tensorInfo6); |
| 578 | armnn::ScopedTensorHandle projectionWeightsTensor(tensorInfo5x6); |
| 579 | armnn::ScopedTensorHandle projectionBiasTensor(tensorInfo5); |
| 580 | |
| 581 | AllocateAndCopyDataToITensorHandle(&inputToInputWeightsTensor, inputToInputWeights.data()); |
| 582 | AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, inputToForgetWeights.data()); |
| 583 | AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, inputToCellWeights.data()); |
| 584 | AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, inputToOutputWeights.data()); |
| 585 | AllocateAndCopyDataToITensorHandle(&recurrentToInputWeightsTensor, recurrentToInputWeights.data()); |
| 586 | AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, recurrentToForgetWeights.data()); |
| 587 | AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, recurrentToCellWeights.data()); |
| 588 | AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, recurrentToOutputWeights.data()); |
| 589 | AllocateAndCopyDataToITensorHandle(&cellToInputWeightsTensor, cellToInputWeights.data()); |
| 590 | AllocateAndCopyDataToITensorHandle(&inputGateBiasTensor, inputGateBias.data()); |
| 591 | AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, forgetGateBias.data()); |
| 592 | AllocateAndCopyDataToITensorHandle(&cellBiasTensor, cellBias.data()); |
| 593 | AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, outputGateBias.data()); |
| 594 | AllocateAndCopyDataToITensorHandle(&cellToForgetWeightsTensor, cellToForgetWeights.data()); |
| 595 | AllocateAndCopyDataToITensorHandle(&cellToOutputWeightsTensor, cellToOutputWeights.data()); |
| 596 | AllocateAndCopyDataToITensorHandle(&projectionWeightsTensor, projectionWeights.data()); |
| 597 | AllocateAndCopyDataToITensorHandle(&projectionBiasTensor, projectionBiasVector.data()); |
| 598 | |
| 599 | data.m_InputToInputWeights = &inputToInputWeightsTensor; |
| 600 | data.m_InputToForgetWeights = &inputToForgetWeightsTensor; |
| 601 | data.m_InputToCellWeights = &inputToCellWeightsTensor; |
| 602 | data.m_InputToOutputWeights = &inputToOutputWeightsTensor; |
| 603 | data.m_RecurrentToInputWeights = &recurrentToInputWeightsTensor; |
| 604 | data.m_RecurrentToForgetWeights = &recurrentToForgetWeightsTensor; |
| 605 | data.m_RecurrentToCellWeights = &recurrentToCellWeightsTensor; |
| 606 | data.m_RecurrentToOutputWeights = &recurrentToOutputWeightsTensor; |
| 607 | data.m_CellToInputWeights = &cellToInputWeightsTensor; |
| 608 | data.m_InputGateBias = &inputGateBiasTensor; |
| 609 | data.m_ForgetGateBias = &forgetGateBiasTensor; |
| 610 | data.m_CellBias = &cellBiasTensor; |
| 611 | data.m_OutputGateBias = &outputGateBiasTensor; |
| 612 | data.m_CellToForgetWeights = &cellToForgetWeightsTensor; |
| 613 | data.m_CellToOutputWeights = &cellToOutputWeightsTensor; |
| 614 | data.m_ProjectionWeights = &projectionWeightsTensor; |
| 615 | data.m_ProjectionBias = &projectionBiasTensor; |
| 616 | |
| 617 | // Flags to set test configuration |
| 618 | data.m_Parameters.m_ActivationFunc = 4; |
| 619 | data.m_Parameters.m_CifgEnabled = false; |
| 620 | data.m_Parameters.m_PeepholeEnabled = true; |
| 621 | data.m_Parameters.m_ProjectionEnabled = true; |
| 622 | data.m_Parameters.m_LayerNormEnabled = false; |
| 623 | data.m_Parameters.m_TimeMajor = false; |
| 624 | data.m_Parameters.m_ClippingThresCell = 10.0f; |
| 625 | |
| 626 | |
| 627 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateUnidirectionalSequenceLstm(data, info); |
| 628 | inputHandle->Allocate(); |
| 629 | outputStateInHandle->Allocate(); |
| 630 | cellStateInHandle->Allocate(); |
| 631 | outputHandle->Allocate(); |
| 632 | |
| 633 | CopyDataToITensorHandle(inputHandle.get(), inputVector.data()); |
| 634 | CopyDataToITensorHandle(outputStateInHandle.get(), outputStateInVector.data()); |
| 635 | CopyDataToITensorHandle(cellStateInHandle.get(), cellStateInVector.data()); |
| 636 | |
| 637 | workload->Execute(); |
| 638 | |
| 639 | CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get()); |
| 640 | |
| 641 | return LayerTestResult<float, 3>(actualOutput, |
| 642 | expectedOutput, |
| 643 | outputHandle->GetShape(), |
| 644 | outputTensorInfo.GetShape()); |
| 645 | } |
| 646 | |
| 647 | LayerTestResult<float, 3> UnidirectionalSequenceLstmLayerNoCifgWithPeepholeWithProjectionWithLayerNormTest( |
| 648 | armnn::IWorkloadFactory& workloadFactory, |
| 649 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 650 | const armnn::ITensorHandleFactory& tensorHandleFactory) |
| 651 | { |
| 652 | IgnoreUnused(memoryManager); |
| 653 | unsigned int batchSize = 3; |
| 654 | unsigned int timeSize = 2; |
| 655 | unsigned int outputSize = 4; |
| 656 | unsigned int inputSize = 3; |
| 657 | unsigned numUnits = 5; |
| 658 | |
| 659 | armnn::TensorInfo inputTensorInfo({batchSize, timeSize, inputSize}, armnn::DataType::Float32); |
| 660 | armnn::TensorInfo cellStateInTensorInfo({batchSize , numUnits}, armnn::DataType::Float32); |
| 661 | armnn::TensorInfo outputStateInTensorInfo({batchSize , outputSize}, armnn::DataType::Float32); |
| 662 | armnn::TensorInfo outputTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32); |
| 663 | |
| 664 | const std::vector<float> inputVector = { 1., 2., 3., 4., 5., 4., |
| 665 | 3., 2., 1., 2., 3., 4., |
| 666 | 5., 4., 3., 2., 1., 2. }; |
| 667 | |
| 668 | std::vector<float> cellStateInVector(batchSize * numUnits, 0.f); |
| 669 | std::vector<float> outputStateInVector(batchSize * outputSize, 0.f); |
| 670 | |
| 671 | std::vector<float> actualOutput(outputTensorInfo.GetNumElements()); |
| 672 | |
| 673 | const std::vector<float> expectedOutput = { 0.0642256f, 0.0343966f, 0.184122f, 0.114717f, |
| 674 | 0.11458f, 0.0407109f, 0.300327f, 0.174301f, |
| 675 | 0.0864761f, 0.0362912f, 0.178635f, 0.115689f, |
| 676 | 0.108008f, 0.0386623f, 0.273471f, 0.167115f, |
| 677 | 0.0859545f, 0.0331481f, 0.186051f, 0.11888f, |
| 678 | 0.106649f, 0.0276847f, 0.229863f, 0.166958f }; |
| 679 | |
| 680 | std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo); |
| 681 | std::unique_ptr<armnn::ITensorHandle> cellStateInHandle = |
| 682 | tensorHandleFactory.CreateTensorHandle(cellStateInTensorInfo); |
| 683 | std::unique_ptr<armnn::ITensorHandle> outputStateInHandle = |
| 684 | tensorHandleFactory.CreateTensorHandle(outputStateInTensorInfo); |
| 685 | |
| 686 | std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo); |
| 687 | |
| 688 | armnn::UnidirectionalSequenceLstmQueueDescriptor data; |
| 689 | armnn::WorkloadInfo info; |
| 690 | |
| 691 | AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); |
| 692 | AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get()); |
| 693 | AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get()); |
| 694 | |
| 695 | AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); |
| 696 | |
| 697 | armnn::TensorInfo tensorInfo4({outputSize}, armnn::DataType::Float32); |
| 698 | armnn::TensorInfo tensorInfo5({numUnits}, armnn::DataType::Float32); |
| 699 | armnn::TensorInfo tensorInfo5x3({numUnits, inputSize}, armnn::DataType::Float32); |
| 700 | armnn::TensorInfo tensorInfo5x4({numUnits, outputSize}, armnn::DataType::Float32); |
| 701 | armnn::TensorInfo tensorInfo4x5({outputSize, numUnits}, armnn::DataType::Float32); |
| 702 | |
| 703 | std::vector<float> inputToInputWeights = { -0.49536117f, -0.0556083915f, -0.102400711f, |
| 704 | -0.117484632f, 0.3298470976f, -0.1179017122f, |
| 705 | 0.214305695f, 0.42135173085f, 0.003878414626f, |
| 706 | -0.348303917f, -0.1881275477f, 0.0343011027f, |
| 707 | -0.38837709614f, -0.05636804124f, 0.4259087456f}; |
| 708 | |
| 709 | std::vector<float> inputToForgetWeights = { 0.2415594226f, 0.15400093799f, 0.4566498398f, |
| 710 | -0.3810434485f, 0.268383264f, -0.009807467424f, |
| 711 | -0.3522925403f, -0.24275735512f, -0.28344226125f, |
| 712 | 0.13512269116f, -0.4932442977f, -0.10039821991f, |
| 713 | 0.2726137042f, 0.09216640889f, -0.06551410215f}; |
| 714 | |
| 715 | std::vector<float> inputToCellWeights = { -0.2504855627f, 0.184490025045f, -0.2480507493f, |
| 716 | 0.386399507f, -0.259465157985f, -0.16545993089f, |
| 717 | -0.4230232555f, 0.341664791103f, -0.18127849691f, |
| 718 | -0.2277662414f, -0.55275535589f, 0.34184026718f, |
| 719 | 0.3954237699f, -0.19407111404f, 0.30412107706f}; |
| 720 | |
| 721 | std::vector<float> inputToOutputWeights = { 0.2303854227f, 0.5218806862f, -0.4865379333f, |
| 722 | 0.53969591851f, 0.23393625035f, -0.27140527306f, |
| 723 | 0.50009280443f, 0.07511717046f, 0.3998299249f, |
| 724 | -0.51717478049f, 0.1889653282f, -0.367323637f, |
| 725 | -0.12584099173f, -0.12319286912f, 0.2407919466f}; |
| 726 | |
| 727 | std::vector<float> inputGateBias{ 0.03f, 0.15f, 0.22f, 0.38f, 0.05f }; |
| 728 | std::vector<float> forgetGateBias{ 0.1f, -0.3f, -0.2f, 0.1f, 0.4f }; |
| 729 | std::vector<float> cellBias{ -0.05f, 0.72f, 0.25f, 0.08f, 0.1f }; |
| 730 | std::vector<float> outputGateBias{ 0.05f, -0.01f, 0.2f, 0.1f, -0.2f }; |
| 731 | |
| 732 | std::vector<float> recurrentToInputWeights = { -0.128009796112f, 0.1995525098f, -0.07745539397f, 0.1558421701f, |
| 733 | -0.265254765766f, -0.38837709614f, -0.05636804124f, 0.4259087456f, |
| 734 | 0.17628988623f, 0.3877420127f, 0.53300309181f, -0.0959980934f, |
| 735 | 0.00302857416f, 0.3266998827f, -0.142509296562f, -0.04433270756f, |
| 736 | 0.54066205f, -0.32668582f, -0.43562764f, -0.56094903f }; |
| 737 | |
| 738 | std::vector<float> recurrentToForgetWeights = { -0.09499983487f, -0.08814888417f, -0.04834804721f, 0.1516668247f, |
| 739 | -0.3967529535f, -0.06463699788f, 0.4952811002f, 0.003274492938f, |
| 740 | -0.0968840941f, 0.17928104102f, 0.0031281141592f, -0.3387276584f, |
| 741 | -0.3587934076f, 0.06705895066f, 0.22463923692f, 0.1961955726f, |
| 742 | 0.01841056f, -0.32764608f, -0.33027974f, -0.10826075f }; |
| 743 | |
| 744 | std::vector<float> recurrentToCellWeights = { -0.21938985582f, -0.3023648226f, -0.1170005202f, -0.3509177422f, |
| 745 | -0.4286288613f, 0.2726137042f, 0.09216640889f, -0.06551410215f, |
| 746 | 0.20453298098f, 0.2393476665f, 0.11846517771f, 0.2630801796f, |
| 747 | 0.3954237699f, -0.19407111404f, 0.30412107706f, -0.27342408554f, |
| 748 | 0.19069612f, -0.03026325f, -0.54532051f, 0.33003211f }; |
| 749 | |
| 750 | std::vector<float> recurrentToOutputWeights = { -0.32921677827f, 0.32624614238f, -0.1388191282f, -0.17879831790f, |
| 751 | -0.15185534954f, -0.16918526583f, -0.10087361183f, -0.5436913968f, |
| 752 | 0.016758225858f, 0.30454617738f, -0.41493862867f, -0.005565764375f, |
| 753 | -0.12584099173f, -0.12319286912f, 0.2407919466f, -0.08879069983f, |
| 754 | 0.11178309f, 0.09481031f, -0.26424935f, 0.46261835f }; |
| 755 | |
| 756 | std::vector<float> cellToInputWeights { 0.05f, 0.1f, 0.25f, 0.15f, -0.02f }; |
| 757 | std::vector<float> cellToForgetWeights { -0.02f, -0.15f, -0.25f, -0.03f, 0.15f }; |
| 758 | std::vector<float> cellToOutputWeights { 0.1f, -0.1f, -0.5f, 0.05f, 0.01f }; |
| 759 | |
| 760 | std::vector<float> projectionWeights{ -0.1f, 0.2f, 0.01f, -0.2f, |
| 761 | 0.1f, 0.5f, 0.3f, 0.08f, |
| 762 | 0.07f, 0.2f, -0.4f, 0.2f, |
| 763 | 0.5f, -0.4f, 0.3f, -0.2f, |
| 764 | 0.3f, 0.08f, -0.07f, 0.2f}; |
| 765 | |
| 766 | std::vector<float> projectionBiasVector(outputSize, 0.f); //{outputSize} |
| 767 | |
| 768 | std::vector<float> inputLayerNormWeights{ 0.1f, 0.2f, 0.3f, 0.5f, 0.8f }; |
| 769 | std::vector<float> forgetLayerNormWeights{ 0.1f, 0.2f, 0.3f, 0.5f, 0.2f }; |
| 770 | std::vector<float> cellLayerNormWeights{ 0.7f, 0.2f, 0.3f, 0.8f, 0.5f }; |
| 771 | std::vector<float> outputLayerNormWeights{ 0.6f, 0.2f, 0.2f, 0.5f, 0.1f }; |
| 772 | |
| 773 | armnn::ScopedTensorHandle inputToInputWeightsTensor(tensorInfo5x3); |
| 774 | armnn::ScopedTensorHandle inputToForgetWeightsTensor(tensorInfo5x3); |
| 775 | armnn::ScopedTensorHandle inputToCellWeightsTensor(tensorInfo5x3); |
| 776 | armnn::ScopedTensorHandle inputToOutputWeightsTensor(tensorInfo5x3); |
| 777 | armnn::ScopedTensorHandle recurrentToForgetWeightsTensor(tensorInfo5x4); |
| 778 | armnn::ScopedTensorHandle recurrentToInputWeightsTensor(tensorInfo5x4); |
| 779 | armnn::ScopedTensorHandle recurrentToCellWeightsTensor(tensorInfo5x4); |
| 780 | armnn::ScopedTensorHandle recurrentToOutputWeightsTensor(tensorInfo5x4); |
| 781 | armnn::ScopedTensorHandle cellToInputWeightsTensor(tensorInfo5); |
| 782 | armnn::ScopedTensorHandle inputGateBiasTensor(tensorInfo5); |
| 783 | armnn::ScopedTensorHandle forgetGateBiasTensor(tensorInfo5); |
| 784 | armnn::ScopedTensorHandle cellBiasTensor(tensorInfo5); |
| 785 | armnn::ScopedTensorHandle outputGateBiasTensor(tensorInfo5); |
| 786 | armnn::ScopedTensorHandle cellToForgetWeightsTensor(tensorInfo5); |
| 787 | armnn::ScopedTensorHandle cellToOutputWeightsTensor(tensorInfo5); |
| 788 | armnn::ScopedTensorHandle projectionWeightsTensor(tensorInfo4x5); |
| 789 | armnn::ScopedTensorHandle projectionBiasTensor(tensorInfo4); |
| 790 | |
| 791 | armnn::ScopedTensorHandle inputLayerNormWeightsTensor(tensorInfo5); |
| 792 | armnn::ScopedTensorHandle forgetLayerNormWeightsTensor(tensorInfo5); |
| 793 | armnn::ScopedTensorHandle cellLayerNormWeightsTensor(tensorInfo5); |
| 794 | armnn::ScopedTensorHandle outputLayerNormWeightsTensor(tensorInfo5); |
| 795 | |
| 796 | AllocateAndCopyDataToITensorHandle(&inputToInputWeightsTensor, inputToInputWeights.data()); |
| 797 | AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, inputToForgetWeights.data()); |
| 798 | AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, inputToCellWeights.data()); |
| 799 | AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, inputToOutputWeights.data()); |
| 800 | AllocateAndCopyDataToITensorHandle(&recurrentToInputWeightsTensor, recurrentToInputWeights.data()); |
| 801 | AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, recurrentToForgetWeights.data()); |
| 802 | AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, recurrentToCellWeights.data()); |
| 803 | AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, recurrentToOutputWeights.data()); |
| 804 | AllocateAndCopyDataToITensorHandle(&cellToInputWeightsTensor, cellToInputWeights.data()); |
| 805 | AllocateAndCopyDataToITensorHandle(&inputGateBiasTensor, inputGateBias.data()); |
| 806 | AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, forgetGateBias.data()); |
| 807 | AllocateAndCopyDataToITensorHandle(&cellBiasTensor, cellBias.data()); |
| 808 | AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, outputGateBias.data()); |
| 809 | AllocateAndCopyDataToITensorHandle(&cellToForgetWeightsTensor, cellToForgetWeights.data()); |
| 810 | AllocateAndCopyDataToITensorHandle(&cellToOutputWeightsTensor, cellToOutputWeights.data()); |
| 811 | AllocateAndCopyDataToITensorHandle(&projectionWeightsTensor, projectionWeights.data()); |
| 812 | AllocateAndCopyDataToITensorHandle(&projectionBiasTensor, projectionBiasVector.data()); |
| 813 | |
| 814 | AllocateAndCopyDataToITensorHandle(&inputLayerNormWeightsTensor, inputLayerNormWeights.data()); |
| 815 | AllocateAndCopyDataToITensorHandle(&forgetLayerNormWeightsTensor, forgetLayerNormWeights.data()); |
| 816 | AllocateAndCopyDataToITensorHandle(&cellLayerNormWeightsTensor, cellLayerNormWeights.data()); |
| 817 | AllocateAndCopyDataToITensorHandle(&outputLayerNormWeightsTensor, outputLayerNormWeights.data()); |
| 818 | |
| 819 | data.m_InputToInputWeights = &inputToInputWeightsTensor; |
| 820 | data.m_InputToForgetWeights = &inputToForgetWeightsTensor; |
| 821 | data.m_InputToCellWeights = &inputToCellWeightsTensor; |
| 822 | data.m_InputToOutputWeights = &inputToOutputWeightsTensor; |
| 823 | data.m_RecurrentToInputWeights = &recurrentToInputWeightsTensor; |
| 824 | data.m_RecurrentToForgetWeights = &recurrentToForgetWeightsTensor; |
| 825 | data.m_RecurrentToCellWeights = &recurrentToCellWeightsTensor; |
| 826 | data.m_RecurrentToOutputWeights = &recurrentToOutputWeightsTensor; |
| 827 | data.m_CellToInputWeights = &cellToInputWeightsTensor; |
| 828 | data.m_InputGateBias = &inputGateBiasTensor; |
| 829 | data.m_ForgetGateBias = &forgetGateBiasTensor; |
| 830 | data.m_CellBias = &cellBiasTensor; |
| 831 | data.m_OutputGateBias = &outputGateBiasTensor; |
| 832 | data.m_CellToForgetWeights = &cellToForgetWeightsTensor; |
| 833 | data.m_CellToOutputWeights = &cellToOutputWeightsTensor; |
| 834 | data.m_ProjectionWeights = &projectionWeightsTensor; |
| 835 | data.m_ProjectionBias = &projectionBiasTensor; |
| 836 | |
| 837 | data.m_InputLayerNormWeights = &inputLayerNormWeightsTensor; |
| 838 | data.m_ForgetLayerNormWeights = &forgetLayerNormWeightsTensor; |
| 839 | data.m_CellLayerNormWeights = &cellLayerNormWeightsTensor; |
| 840 | data.m_OutputLayerNormWeights = &outputLayerNormWeightsTensor; |
| 841 | |
| 842 | // Flags to set test configuration |
| 843 | data.m_Parameters.m_ActivationFunc = 4; |
| 844 | data.m_Parameters.m_CifgEnabled = false; |
| 845 | data.m_Parameters.m_PeepholeEnabled = true; |
| 846 | data.m_Parameters.m_ProjectionEnabled = true; |
| 847 | data.m_Parameters.m_LayerNormEnabled = true; |
| 848 | data.m_Parameters.m_TimeMajor = false; |
| 849 | data.m_Parameters.m_ClippingThresCell = 10.0f; |
| 850 | |
| 851 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateUnidirectionalSequenceLstm(data, info); |
| 852 | inputHandle->Allocate(); |
| 853 | outputStateInHandle->Allocate(); |
| 854 | cellStateInHandle->Allocate(); |
| 855 | outputHandle->Allocate(); |
| 856 | |
| 857 | CopyDataToITensorHandle(inputHandle.get(), inputVector.data()); |
| 858 | CopyDataToITensorHandle(outputStateInHandle.get(), outputStateInVector.data()); |
| 859 | CopyDataToITensorHandle(cellStateInHandle.get(), cellStateInVector.data()); |
| 860 | |
| 861 | workload->Execute(); |
| 862 | |
| 863 | CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get()); |
| 864 | |
| 865 | return LayerTestResult<float, 3>(actualOutput, |
| 866 | expectedOutput, |
| 867 | outputHandle->GetShape(), |
| 868 | outputTensorInfo.GetShape()); |
| 869 | } |
| 870 | |
| 871 | LayerTestResult<float, 3> UnidirectionalSequenceLstmWithCifgWithPeepholeNoProjectionTest( |
| 872 | armnn::IWorkloadFactory& workloadFactory, |
| 873 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 874 | const armnn::ITensorHandleFactory& tensorHandleFactory) |
| 875 | { |
| 876 | IgnoreUnused(memoryManager); |
| 877 | unsigned int batchSize = 3; |
| 878 | unsigned int timeSize = 2; |
| 879 | unsigned int inputSize = 3; |
| 880 | unsigned int outputSize = 4; |
| 881 | unsigned numUnits = outputSize; |
| 882 | |
| 883 | armnn::TensorInfo inputTensorInfo({batchSize, timeSize, inputSize}, armnn::DataType::Float32); |
| 884 | armnn::TensorInfo cellStateInTensorInfo({batchSize, numUnits}, armnn::DataType::Float32); |
| 885 | armnn::TensorInfo outputStateInTensorInfo({batchSize, outputSize}, armnn::DataType::Float32); |
| 886 | |
| 887 | armnn::TensorInfo outputTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32); |
| 888 | |
| 889 | std::vector<float> inputVector = { 1., 2., 3., 4., 5., 4., |
| 890 | 3., 2., 1., 2., 3., 4., |
| 891 | 5., 4., 3., 2., 1., 2. }; |
| 892 | |
| 893 | std::vector<float> cellStateInVector(batchSize * numUnits, 0.f); |
| 894 | std::vector<float> outputStateInVector(batchSize * outputSize, 0.f); |
| 895 | |
| 896 | std::vector<float> actualOutput(outputTensorInfo.GetNumElements()); |
| 897 | |
| 898 | std::vector<float> outputVector = { -0.0129257f, -0.070531f, -0.153508f, -0.0392391f, |
| 899 | -0.0300169f, -0.195717f, -0.528679f, -0.0818106f, |
| 900 | -0.0332748f, 0.155429f, -0.353966f, -0.0801505f, |
| 901 | -0.032312f, -0.0407911f, -0.435053f, -0.0932317f, |
| 902 | -0.0108233f, 0.165584f, -0.640424f, -0.0447535f, |
| 903 | -0.031675f, 0.125987f, -0.526695f, -0.110093f }; |
| 904 | |
| 905 | std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo); |
| 906 | std::unique_ptr<armnn::ITensorHandle> cellStateInHandle = |
| 907 | tensorHandleFactory.CreateTensorHandle(cellStateInTensorInfo); |
| 908 | std::unique_ptr<armnn::ITensorHandle> outputStateInHandle = |
| 909 | tensorHandleFactory.CreateTensorHandle(outputStateInTensorInfo); |
| 910 | |
| 911 | std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo); |
| 912 | |
| 913 | armnn::UnidirectionalSequenceLstmQueueDescriptor data; |
| 914 | armnn::WorkloadInfo info; |
| 915 | |
| 916 | AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); |
| 917 | AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get()); |
| 918 | AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get()); |
| 919 | |
| 920 | AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); |
| 921 | |
| 922 | armnn::TensorInfo tensorInfo4({numUnits}, armnn::DataType::Float32); |
| 923 | armnn::TensorInfo tensorInfo12({numUnits, 3}, armnn::DataType::Float32); |
| 924 | armnn::TensorInfo tensorInfo16({numUnits, 4}, armnn::DataType::Float32); |
| 925 | |
| 926 | std::vector<float> inputToForgetWeights = { 0.2415594226f, 0.15400093799f, 0.4566498398f, |
| 927 | -0.3810434485f, 0.268383264f, -0.009807467424f, |
| 928 | -0.3522925403f, -0.24275735512f, -0.28344226125f, |
| 929 | 0.13512269116f, -0.4932442977f, -0.10039821991f }; |
| 930 | |
| 931 | std::vector<float> inputToCellWeights = { -0.2504855627f, 0.184490025045f, -0.2480507493f, |
| 932 | 0.386399507f, -0.259465157985f, -0.16545993089f, |
| 933 | -0.4230232555f, 0.341664791103f, -0.18127849691f, |
| 934 | -0.2277662414f, -0.55275535589f, 0.34184026718f }; |
| 935 | |
| 936 | std::vector<float> inputToOutputWeights = { 0.2303854227f, 0.5218806862f, -0.4865379333f, |
| 937 | 0.53969591851f, 0.23393625035f, -0.27140527306f, |
| 938 | 0.50009280443f, 0.07511717046f, 0.3998299249f, |
| 939 | -0.51717478049f, 0.1889653282f, -0.367323637f }; |
| 940 | |
| 941 | std::vector<float> recurrentToForgetWeights = { -0.09499983487f, -0.08814888417f, -0.04834804721f, 0.1516668247f, |
| 942 | -0.3967529535f, -0.06463699788f, 0.4952811002f, 0.003274492938f, |
| 943 | -0.0968840941f, 0.17928104102f, 0.0031281141592f, -0.3387276584f, |
| 944 | -0.3587934076f, 0.06705895066f, 0.22463923692f, 0.1961955726f }; |
| 945 | |
| 946 | std::vector<float> recurrentToCellWeights = { -0.21938985582f, -0.3023648226f, -0.1170005202f, -0.3509177422f, |
| 947 | -0.4286288613f, 0.2726137042f, 0.09216640889f, -0.06551410215f, |
| 948 | 0.20453298098f, 0.2393476665f, 0.11846517771f, 0.2630801796f, |
| 949 | 0.3954237699f, -0.19407111404f, 0.30412107706f, -0.27342408554f }; |
| 950 | |
| 951 | std::vector<float> recurrentToOutputWeights = { -0.32921677827f, 0.32624614238f, -0.1388191282f, -0.17879831790f, |
| 952 | -0.15185534954f, -0.16918526583f, -0.10087361183f, -0.5436913968f, |
| 953 | 0.016758225858f, 0.30454617738f, -0.41493862867f, -0.005565764375f, |
| 954 | -0.12584099173f, -0.12319286912f, 0.2407919466f, -0.08879069983f }; |
| 955 | |
| 956 | std::vector<float> cellToForgetWeights{ 0.47485286f, -0.51955009f, -0.24458408f, 0.31544167f }; |
| 957 | |
| 958 | std::vector<float> cellToOutputWeights{ -0.17135078f, 0.82760304f, 0.85573703f, -0.77109635f }; |
| 959 | |
| 960 | std::vector<float> forgetGateBias = { 1., 1., 1., 1. }; |
| 961 | |
| 962 | std::vector<float> cellBias = { 0., 0., 0., 0. }; |
| 963 | |
| 964 | std::vector<float> outputGateBias = { 0., 0., 0., 0. }; |
| 965 | |
| 966 | armnn::ScopedTensorHandle inputToForgetWeightsTensor(tensorInfo12); |
| 967 | armnn::ScopedTensorHandle inputToCellWeightsTensor(tensorInfo12); |
| 968 | armnn::ScopedTensorHandle inputToOutputWeightsTensor(tensorInfo12); |
| 969 | armnn::ScopedTensorHandle recurrentToForgetWeightsTensor(tensorInfo16); |
| 970 | armnn::ScopedTensorHandle recurrentToCellWeightsTensor(tensorInfo16); |
| 971 | armnn::ScopedTensorHandle recurrentToOutputWeightsTensor(tensorInfo16); |
| 972 | armnn::ScopedTensorHandle cellToForgetWeightsTensor(tensorInfo4); |
| 973 | armnn::ScopedTensorHandle cellToOutputWeightsTensor(tensorInfo4); |
| 974 | armnn::ScopedTensorHandle forgetGateBiasTensor(tensorInfo4); |
| 975 | armnn::ScopedTensorHandle cellBiasTensor(tensorInfo4); |
| 976 | armnn::ScopedTensorHandle outputGateBiasTensor(tensorInfo4); |
| 977 | |
| 978 | AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, inputToForgetWeights.data()); |
| 979 | AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, inputToCellWeights.data()); |
| 980 | AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, inputToOutputWeights.data()); |
| 981 | AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, recurrentToForgetWeights.data()); |
| 982 | AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, recurrentToCellWeights.data()); |
| 983 | AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, recurrentToOutputWeights.data()); |
| 984 | AllocateAndCopyDataToITensorHandle(&cellToForgetWeightsTensor, cellToForgetWeights.data()); |
| 985 | AllocateAndCopyDataToITensorHandle(&cellToOutputWeightsTensor, cellToOutputWeights.data()); |
| 986 | AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, forgetGateBias.data()); |
| 987 | AllocateAndCopyDataToITensorHandle(&cellBiasTensor, cellBias.data()); |
| 988 | AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, outputGateBias.data()); |
| 989 | |
| 990 | data.m_InputToForgetWeights = &inputToForgetWeightsTensor; |
| 991 | data.m_InputToCellWeights = &inputToCellWeightsTensor; |
| 992 | data.m_InputToOutputWeights = &inputToOutputWeightsTensor; |
| 993 | data.m_RecurrentToForgetWeights = &recurrentToForgetWeightsTensor; |
| 994 | data.m_RecurrentToCellWeights = &recurrentToCellWeightsTensor; |
| 995 | data.m_RecurrentToOutputWeights = &recurrentToOutputWeightsTensor; |
| 996 | data.m_CellToForgetWeights = &cellToForgetWeightsTensor; |
| 997 | data.m_CellToOutputWeights = &cellToOutputWeightsTensor; |
| 998 | data.m_ForgetGateBias = &forgetGateBiasTensor; |
| 999 | data.m_CellBias = &cellBiasTensor; |
| 1000 | data.m_OutputGateBias = &outputGateBiasTensor; |
| 1001 | |
| 1002 | // Flags to set test configuration |
| 1003 | data.m_Parameters.m_ClippingThresCell = 10; |
| 1004 | data.m_Parameters.m_ClippingThresProj = 0; |
| 1005 | data.m_Parameters.m_ActivationFunc = 4; |
| 1006 | data.m_Parameters.m_CifgEnabled = true; |
| 1007 | data.m_Parameters.m_PeepholeEnabled = true; |
| 1008 | data.m_Parameters.m_ProjectionEnabled = false; |
| 1009 | data.m_Parameters.m_TimeMajor = false; |
| 1010 | |
| 1011 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateUnidirectionalSequenceLstm(data, info); |
| 1012 | inputHandle->Allocate(); |
| 1013 | outputStateInHandle->Allocate(); |
| 1014 | cellStateInHandle->Allocate(); |
| 1015 | |
| 1016 | outputHandle->Allocate(); |
| 1017 | |
| 1018 | CopyDataToITensorHandle(inputHandle.get(), inputVector.data()); |
| 1019 | CopyDataToITensorHandle(outputStateInHandle.get(), outputStateInVector.data()); |
| 1020 | CopyDataToITensorHandle(cellStateInHandle.get(), cellStateInVector.data()); |
| 1021 | |
| 1022 | workload->Execute(); |
| 1023 | |
| 1024 | CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get()); |
| 1025 | |
| 1026 | return LayerTestResult<float, 3>(actualOutput, |
| 1027 | outputVector, |
| 1028 | outputHandle->GetShape(), |
| 1029 | outputTensorInfo.GetShape()); |
| 1030 | } |
Narumol Prangnawarat | bd575b2 | 2021-08-31 16:53:54 +0100 | [diff] [blame^] | 1031 | |
| 1032 | LayerTestResult<float, 3> UnidirectionalSequenceLstmLayerInt8Test( |
| 1033 | armnn::IWorkloadFactory& workloadFactory, |
| 1034 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 1035 | const armnn::ITensorHandleFactory& tensorHandleFactory) |
| 1036 | { |
| 1037 | IgnoreUnused(memoryManager); |
| 1038 | unsigned int batchSize = 3; |
| 1039 | unsigned int timeSize = 2; |
| 1040 | unsigned int inputSize = 3; |
| 1041 | unsigned int outputSize = 4; |
| 1042 | unsigned numUnits = outputSize; |
| 1043 | |
| 1044 | armnn::TensorInfo inputTensorInfo({batchSize, timeSize, inputSize}, armnn::DataType::Float32); |
| 1045 | armnn::TensorInfo cellStateInTensorInfo({batchSize, numUnits}, armnn::DataType::Float32); |
| 1046 | armnn::TensorInfo outputStateInTensorInfo({batchSize, outputSize}, armnn::DataType::Float32); |
| 1047 | |
| 1048 | armnn::TensorInfo outputTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32); |
| 1049 | |
| 1050 | const std::vector<float> inputVector = { 0.1f, 0.2f, 0.3f, 0.4f, 0.5f, 0.4f, |
| 1051 | 0.3f, 0.2f, 0.1f, 0.2f, 0.3f, 0.4f, |
| 1052 | 0.5f, 0.4f, 0.3f, 0.2f, 0.1f, 0.2f }; |
| 1053 | |
| 1054 | std::vector<float> cellStateInVector(batchSize * numUnits, 0.f); |
| 1055 | std::vector<float> outputStateInVector(batchSize * outputSize, 0.f); |
| 1056 | |
| 1057 | std::vector<float> actualOutput(outputTensorInfo.GetNumElements()); |
| 1058 | |
| 1059 | const std::vector<float> outputVector = { -0.0142517f, -0.0198845f, -0.0120569f, -0.0116868f, |
| 1060 | -0.0350714f, -0.0343202f, -0.047504f, -0.0569789f, |
| 1061 | -0.0146346f, 0.0106663f, -0.0247238f, -0.0319502f, |
| 1062 | -0.0294759f, -0.0129935f, -0.0444175f, -0.0444354f, |
| 1063 | -0.0280855f, 0.00545101f, -0.051422f, -0.0463838f, |
| 1064 | -0.0310702f, 0.00915739f, -0.0625207f, -0.0482648f }; |
| 1065 | |
| 1066 | std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo); |
| 1067 | std::unique_ptr<armnn::ITensorHandle> cellStateInHandle = |
| 1068 | tensorHandleFactory.CreateTensorHandle(cellStateInTensorInfo); |
| 1069 | std::unique_ptr<armnn::ITensorHandle> outputStateInHandle = |
| 1070 | tensorHandleFactory.CreateTensorHandle(outputStateInTensorInfo); |
| 1071 | |
| 1072 | std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo); |
| 1073 | |
| 1074 | armnn::UnidirectionalSequenceLstmQueueDescriptor data; |
| 1075 | armnn::WorkloadInfo info; |
| 1076 | |
| 1077 | AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); |
| 1078 | AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get()); |
| 1079 | AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get()); |
| 1080 | |
| 1081 | AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); |
| 1082 | |
| 1083 | armnn::TensorInfo tensorInfoNumFp({numUnits}, armnn::DataType::Float32); |
| 1084 | armnn::TensorInfo tensorInfoNumInput({numUnits, inputSize}, armnn::DataType::QAsymmS8, 0.1f, 0); |
| 1085 | armnn::TensorInfo tensorInfoNumOutput({numUnits, outputSize}, armnn::DataType::QAsymmS8, 0.1f, 0); |
| 1086 | |
| 1087 | std::vector<int8_t> inputToInputWeights = { -4, -1, -1, -2, 3, -2, 2, 4, 1, -4, -2, 3 }; |
| 1088 | std::vector<int8_t> inputToForgetWeights = { 2, 1, 4, -4, 3, -1, -3, -2, -3, 1, -4, -1 }; |
| 1089 | std::vector<int8_t> inputToCellWeights = { -2, 1, -2, 4, -3, -2, -4, 3, -2, -2, -6, 3 }; |
| 1090 | std::vector<int8_t> inputToOutputWeights = { 2, 5, -4, 5, 2, -3, 5, 7, 3, -5, 1, -4 }; |
| 1091 | |
| 1092 | std::vector<int8_t> recurrentToInputWeights = { -1, 1, -1, 1, -3, -4, -1, 4, 2, 3, 5, -1, 1, 3, -1, -1 }; |
| 1093 | std::vector<int8_t> recurrentToForgetWeights = { -1, 1, -1, 1, -3, -4, -1, 4, 2, 3, 5, -1, 1, 3, -2, -1 }; |
| 1094 | std::vector<int8_t> recurrentToCellWeights = { -2, -3, -1, -3, -4, 2, 1, -1, 2, 2, 1, 2, 3, -2, 3, -3 }; |
| 1095 | std::vector<int8_t> recurrentToOutputWeights = { -3, 3, -1, -2, -2, -2, -1, -5, 1, 3, -4, -1, -1, -1, 2, -1 }; |
| 1096 | |
| 1097 | std::vector<float> inputGateBias = { 0., 0., 0., 0. }; |
| 1098 | std::vector<float> forgetGateBias = { 1., 1., 1., 1. }; |
| 1099 | std::vector<float> cellBias = { 0., 0., 0., 0. }; |
| 1100 | std::vector<float> outputGateBias = { 0., 0., 0., 0. }; |
| 1101 | |
| 1102 | armnn::ScopedTensorHandle inputToInputWeightsTensor(tensorInfoNumInput); |
| 1103 | armnn::ScopedTensorHandle inputToForgetWeightsTensor(tensorInfoNumInput); |
| 1104 | armnn::ScopedTensorHandle inputToCellWeightsTensor(tensorInfoNumInput); |
| 1105 | armnn::ScopedTensorHandle inputToOutputWeightsTensor(tensorInfoNumInput); |
| 1106 | armnn::ScopedTensorHandle recurrentToInputWeightsTensor(tensorInfoNumOutput); |
| 1107 | armnn::ScopedTensorHandle recurrentToForgetWeightsTensor(tensorInfoNumOutput); |
| 1108 | armnn::ScopedTensorHandle recurrentToCellWeightsTensor(tensorInfoNumOutput); |
| 1109 | armnn::ScopedTensorHandle recurrentToOutputWeightsTensor(tensorInfoNumOutput); |
| 1110 | armnn::ScopedTensorHandle inputGateBiasTensor(tensorInfoNumFp); |
| 1111 | armnn::ScopedTensorHandle forgetGateBiasTensor(tensorInfoNumFp); |
| 1112 | armnn::ScopedTensorHandle cellBiasTensor(tensorInfoNumFp); |
| 1113 | armnn::ScopedTensorHandle outputGateBiasTensor(tensorInfoNumFp); |
| 1114 | |
| 1115 | AllocateAndCopyDataToITensorHandle(&inputToInputWeightsTensor, inputToInputWeights.data()); |
| 1116 | AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, inputToForgetWeights.data()); |
| 1117 | AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, inputToCellWeights.data()); |
| 1118 | AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, inputToOutputWeights.data()); |
| 1119 | AllocateAndCopyDataToITensorHandle(&recurrentToInputWeightsTensor, recurrentToInputWeights.data()); |
| 1120 | AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, recurrentToForgetWeights.data()); |
| 1121 | AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, recurrentToCellWeights.data()); |
| 1122 | AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, recurrentToOutputWeights.data()); |
| 1123 | AllocateAndCopyDataToITensorHandle(&inputGateBiasTensor, inputGateBias.data()); |
| 1124 | AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, forgetGateBias.data()); |
| 1125 | AllocateAndCopyDataToITensorHandle(&cellBiasTensor, cellBias.data()); |
| 1126 | AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, outputGateBias.data()); |
| 1127 | |
| 1128 | data.m_InputToInputWeights = &inputToInputWeightsTensor; |
| 1129 | data.m_InputToForgetWeights = &inputToForgetWeightsTensor; |
| 1130 | data.m_InputToCellWeights = &inputToCellWeightsTensor; |
| 1131 | data.m_InputToOutputWeights = &inputToOutputWeightsTensor; |
| 1132 | data.m_RecurrentToInputWeights = &recurrentToInputWeightsTensor; |
| 1133 | data.m_RecurrentToForgetWeights = &recurrentToForgetWeightsTensor; |
| 1134 | data.m_RecurrentToCellWeights = &recurrentToCellWeightsTensor; |
| 1135 | data.m_RecurrentToOutputWeights = &recurrentToOutputWeightsTensor; |
| 1136 | data.m_InputGateBias = &inputGateBiasTensor; |
| 1137 | data.m_ForgetGateBias = &forgetGateBiasTensor; |
| 1138 | data.m_CellBias = &cellBiasTensor; |
| 1139 | data.m_OutputGateBias = &outputGateBiasTensor; |
| 1140 | |
| 1141 | // Flags to set test configuration |
| 1142 | data.m_Parameters.m_ClippingThresCell = 10; |
| 1143 | data.m_Parameters.m_ClippingThresProj = 0; |
| 1144 | data.m_Parameters.m_ActivationFunc = 4; |
| 1145 | data.m_Parameters.m_CifgEnabled = false; |
| 1146 | data.m_Parameters.m_PeepholeEnabled = false; |
| 1147 | data.m_Parameters.m_ProjectionEnabled = false; |
| 1148 | data.m_Parameters.m_TimeMajor = false; |
| 1149 | |
| 1150 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateUnidirectionalSequenceLstm(data, info); |
| 1151 | inputHandle->Allocate(); |
| 1152 | outputStateInHandle->Allocate(); |
| 1153 | cellStateInHandle->Allocate(); |
| 1154 | |
| 1155 | outputHandle->Allocate(); |
| 1156 | |
| 1157 | CopyDataToITensorHandle(inputHandle.get(), inputVector.data()); |
| 1158 | CopyDataToITensorHandle(outputStateInHandle.get(), outputStateInVector.data()); |
| 1159 | CopyDataToITensorHandle(cellStateInHandle.get(), cellStateInVector.data()); |
| 1160 | |
| 1161 | workload->Execute(); |
| 1162 | |
| 1163 | CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get()); |
| 1164 | |
| 1165 | return LayerTestResult<float, 3>(actualOutput, |
| 1166 | outputVector, |
| 1167 | outputHandle->GetShape(), |
| 1168 | outputTensorInfo.GetShape()); |
| 1169 | } |
| 1170 | |
| 1171 | LayerTestResult<float, 3> UnidirectionalSequenceLstmLayerInt8TimeMajorTest( |
| 1172 | armnn::IWorkloadFactory& workloadFactory, |
| 1173 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 1174 | const armnn::ITensorHandleFactory& tensorHandleFactory) |
| 1175 | { |
| 1176 | IgnoreUnused(memoryManager); |
| 1177 | unsigned int batchSize = 3; |
| 1178 | unsigned int timeSize = 2; |
| 1179 | unsigned int inputSize = 3; |
| 1180 | unsigned int outputSize = 4; |
| 1181 | unsigned numUnits = outputSize; |
| 1182 | |
| 1183 | armnn::TensorInfo inputTensorInfo({timeSize, batchSize, inputSize}, armnn::DataType::Float32); |
| 1184 | armnn::TensorInfo cellStateInTensorInfo({batchSize, numUnits}, armnn::DataType::Float32); |
| 1185 | armnn::TensorInfo outputStateInTensorInfo({batchSize, outputSize}, armnn::DataType::Float32); |
| 1186 | |
| 1187 | armnn::TensorInfo outputTensorInfo({timeSize, batchSize, outputSize}, armnn::DataType::Float32); |
| 1188 | |
| 1189 | const std::vector<float> inputVector = { 0.1f, 0.2f, 0.3f, 0.4f, 0.5f, 0.4f, |
| 1190 | 0.3f, 0.2f, 0.1f, 0.2f, 0.3f, 0.4f, |
| 1191 | 0.5f, 0.4f, 0.3f, 0.2f, 0.1f, 0.2f }; |
| 1192 | |
| 1193 | std::vector<float> cellStateInVector(batchSize * numUnits, 0.f); |
| 1194 | std::vector<float> outputStateInVector(batchSize * outputSize, 0.f); |
| 1195 | |
| 1196 | std::vector<float> actualOutput(outputTensorInfo.GetNumElements()); |
| 1197 | |
| 1198 | const std::vector<float> outputVector = { -0.0142517f, -0.0198845f, -0.0120122f, -0.0116868f, |
| 1199 | -0.0261295f, -0.0188487f, -0.0345463f, -0.049733f, |
| 1200 | -0.0146346f, 0.0106663f, -0.0247238f, -0.0319502f, |
| 1201 | -0.0291863f, -0.0369402f, -0.0354071f, -0.0296529f, |
| 1202 | -0.0419539f, -0.00617731f, -0.0814796f, -0.0804005f, |
| 1203 | -0.0244737f, 0.0119905f, -0.0457527f, -0.0331862f }; |
| 1204 | std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo); |
| 1205 | std::unique_ptr<armnn::ITensorHandle> cellStateInHandle = |
| 1206 | tensorHandleFactory.CreateTensorHandle(cellStateInTensorInfo); |
| 1207 | std::unique_ptr<armnn::ITensorHandle> outputStateInHandle = |
| 1208 | tensorHandleFactory.CreateTensorHandle(outputStateInTensorInfo); |
| 1209 | |
| 1210 | std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo); |
| 1211 | |
| 1212 | armnn::UnidirectionalSequenceLstmQueueDescriptor data; |
| 1213 | armnn::WorkloadInfo info; |
| 1214 | |
| 1215 | AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); |
| 1216 | AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get()); |
| 1217 | AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get()); |
| 1218 | |
| 1219 | AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); |
| 1220 | |
| 1221 | armnn::TensorInfo tensorInfoNumFp({numUnits}, armnn::DataType::Float32); |
| 1222 | armnn::TensorInfo tensorInfoNumInput({numUnits, inputSize}, armnn::DataType::QAsymmS8, 0.1f, 0); |
| 1223 | armnn::TensorInfo tensorInfoNumOutput({numUnits, outputSize}, armnn::DataType::QAsymmS8, 0.1f, 0); |
| 1224 | |
| 1225 | std::vector<int8_t> inputToInputWeights = { -4, -1, -1, -2, 3, -2, 2, 4, 1, -4, -2, 3 }; |
| 1226 | std::vector<int8_t> inputToForgetWeights = { 2, 1, 4, -4, 3, -1, -3, -2, -3, 1, -4, -1 }; |
| 1227 | std::vector<int8_t> inputToCellWeights = { -2, 1, -2, 4, -3, -2, -4, 3, -2, -2, -6, 3 }; |
| 1228 | std::vector<int8_t> inputToOutputWeights = { 2, 5, -4, 5, 2, -3, 5, 7, 3, -5, 1, -4 }; |
| 1229 | |
| 1230 | std::vector<int8_t> recurrentToInputWeights = { -1, 1, -1, 1, -3, -4, -1, 4, 2, 3, 5, -1, 1, 3, -1, -1 }; |
| 1231 | std::vector<int8_t> recurrentToForgetWeights = { -1, 1, -1, 1, -3, -4, -1, 4, 2, 3, 5, -1, 1, 3, -2, -1 }; |
| 1232 | std::vector<int8_t> recurrentToCellWeights = { -2, -3, -1, -3, -4, 2, 1, -1, 2, 2, 1, 2, 3, -2, 3, -3 }; |
| 1233 | std::vector<int8_t> recurrentToOutputWeights = { -3, 3, -1, -2, -2, -2, -1, -5, 1, 3, -4, -1, -1, -1, 2, -1 }; |
| 1234 | |
| 1235 | |
| 1236 | std::vector<float> inputGateBias = { 0., 0., 0., 0. }; |
| 1237 | std::vector<float> forgetGateBias = { 1., 1., 1., 1. }; |
| 1238 | std::vector<float> cellBias = { 0., 0., 0., 0. }; |
| 1239 | std::vector<float> outputGateBias = { 0., 0., 0., 0. }; |
| 1240 | |
| 1241 | armnn::ScopedTensorHandle inputToInputWeightsTensor(tensorInfoNumInput); |
| 1242 | armnn::ScopedTensorHandle inputToForgetWeightsTensor(tensorInfoNumInput); |
| 1243 | armnn::ScopedTensorHandle inputToCellWeightsTensor(tensorInfoNumInput); |
| 1244 | armnn::ScopedTensorHandle inputToOutputWeightsTensor(tensorInfoNumInput); |
| 1245 | armnn::ScopedTensorHandle recurrentToInputWeightsTensor(tensorInfoNumOutput); |
| 1246 | armnn::ScopedTensorHandle recurrentToForgetWeightsTensor(tensorInfoNumOutput); |
| 1247 | armnn::ScopedTensorHandle recurrentToCellWeightsTensor(tensorInfoNumOutput); |
| 1248 | armnn::ScopedTensorHandle recurrentToOutputWeightsTensor(tensorInfoNumOutput); |
| 1249 | armnn::ScopedTensorHandle inputGateBiasTensor(tensorInfoNumFp); |
| 1250 | armnn::ScopedTensorHandle forgetGateBiasTensor(tensorInfoNumFp); |
| 1251 | armnn::ScopedTensorHandle cellBiasTensor(tensorInfoNumFp); |
| 1252 | armnn::ScopedTensorHandle outputGateBiasTensor(tensorInfoNumFp); |
| 1253 | |
| 1254 | AllocateAndCopyDataToITensorHandle(&inputToInputWeightsTensor, inputToInputWeights.data()); |
| 1255 | AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, inputToForgetWeights.data()); |
| 1256 | AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, inputToCellWeights.data()); |
| 1257 | AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, inputToOutputWeights.data()); |
| 1258 | AllocateAndCopyDataToITensorHandle(&recurrentToInputWeightsTensor, recurrentToInputWeights.data()); |
| 1259 | AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, recurrentToForgetWeights.data()); |
| 1260 | AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, recurrentToCellWeights.data()); |
| 1261 | AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, recurrentToOutputWeights.data()); |
| 1262 | AllocateAndCopyDataToITensorHandle(&inputGateBiasTensor, inputGateBias.data()); |
| 1263 | AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, forgetGateBias.data()); |
| 1264 | AllocateAndCopyDataToITensorHandle(&cellBiasTensor, cellBias.data()); |
| 1265 | AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, outputGateBias.data()); |
| 1266 | |
| 1267 | data.m_InputToInputWeights = &inputToInputWeightsTensor; |
| 1268 | data.m_InputToForgetWeights = &inputToForgetWeightsTensor; |
| 1269 | data.m_InputToCellWeights = &inputToCellWeightsTensor; |
| 1270 | data.m_InputToOutputWeights = &inputToOutputWeightsTensor; |
| 1271 | data.m_RecurrentToInputWeights = &recurrentToInputWeightsTensor; |
| 1272 | data.m_RecurrentToForgetWeights = &recurrentToForgetWeightsTensor; |
| 1273 | data.m_RecurrentToCellWeights = &recurrentToCellWeightsTensor; |
| 1274 | data.m_RecurrentToOutputWeights = &recurrentToOutputWeightsTensor; |
| 1275 | data.m_InputGateBias = &inputGateBiasTensor; |
| 1276 | data.m_ForgetGateBias = &forgetGateBiasTensor; |
| 1277 | data.m_CellBias = &cellBiasTensor; |
| 1278 | data.m_OutputGateBias = &outputGateBiasTensor; |
| 1279 | |
| 1280 | // Flags to set test configuration |
| 1281 | data.m_Parameters.m_ClippingThresCell = 10; |
| 1282 | data.m_Parameters.m_ClippingThresProj = 0; |
| 1283 | data.m_Parameters.m_ActivationFunc = 4; |
| 1284 | data.m_Parameters.m_CifgEnabled = false; |
| 1285 | data.m_Parameters.m_PeepholeEnabled = false; |
| 1286 | data.m_Parameters.m_ProjectionEnabled = false; |
| 1287 | data.m_Parameters.m_TimeMajor = true; |
| 1288 | |
| 1289 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateUnidirectionalSequenceLstm(data, info); |
| 1290 | inputHandle->Allocate(); |
| 1291 | outputStateInHandle->Allocate(); |
| 1292 | cellStateInHandle->Allocate(); |
| 1293 | |
| 1294 | outputHandle->Allocate(); |
| 1295 | |
| 1296 | CopyDataToITensorHandle(inputHandle.get(), inputVector.data()); |
| 1297 | CopyDataToITensorHandle(outputStateInHandle.get(), outputStateInVector.data()); |
| 1298 | CopyDataToITensorHandle(cellStateInHandle.get(), cellStateInVector.data()); |
| 1299 | |
| 1300 | workload->Execute(); |
| 1301 | |
| 1302 | CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get()); |
| 1303 | |
| 1304 | return LayerTestResult<float, 3>(actualOutput, |
| 1305 | outputVector, |
| 1306 | outputHandle->GetShape(), |
| 1307 | outputTensorInfo.GetShape()); |
| 1308 | } |
| 1309 | |
| 1310 | LayerTestResult<float, 3> UnidirectionalSequenceLstmLayerInt8NoCifgWithPeepholeWithProjectionTest( |
| 1311 | armnn::IWorkloadFactory& workloadFactory, |
| 1312 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 1313 | const armnn::ITensorHandleFactory& tensorHandleFactory) |
| 1314 | { |
| 1315 | IgnoreUnused(memoryManager); |
| 1316 | unsigned int batchSize = 3; |
| 1317 | unsigned int timeSize = 2; |
| 1318 | unsigned int outputSize = 4; |
| 1319 | unsigned int inputSize = 3; |
| 1320 | unsigned numUnits = 4; |
| 1321 | |
| 1322 | armnn::TensorInfo inputTensorInfo({batchSize, timeSize, inputSize}, armnn::DataType::Float32); |
| 1323 | armnn::TensorInfo cellStateInTensorInfo({batchSize , numUnits}, armnn::DataType::Float32); |
| 1324 | armnn::TensorInfo outputStateInTensorInfo({batchSize , outputSize}, armnn::DataType::Float32); |
| 1325 | armnn::TensorInfo outputTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32); |
| 1326 | |
| 1327 | const std::vector<float> inputVector = { 0.1f, 0.2f, 0.3f, 0.4f, 0.5f, 0.4f, |
| 1328 | 0.3f, 0.2f, 0.1f, 0.2f, 0.3f, 0.4f, |
| 1329 | 0.5f, 0.4f, 0.3f, 0.2f, 0.1f, 0.2f }; |
| 1330 | |
| 1331 | std::vector<float> cellStateInVector(batchSize * numUnits, 0.f); |
| 1332 | std::vector<float> outputStateInVector(batchSize * outputSize, 0.f); |
| 1333 | |
| 1334 | std::vector<float> actualOutput(outputTensorInfo.GetNumElements()); |
| 1335 | |
| 1336 | const std::vector<float> expectedOutput = { 0.612103f, 1.56788f, 0.31966f, 1.42956f, |
| 1337 | 0.909718f, 3.07916f, -0.560586f, 3.8907f, |
| 1338 | 0.753671f, 1.77485f, 0.365122f, 1.60077f, |
| 1339 | 0.812644f, 2.79092f, -0.605396f, 3.61742f, |
| 1340 | 0.791857f, 1.64353f, 0.316588f, 1.55192f, |
| 1341 | 0.807265f, 2.47012f, -0.539598f, 3.25654f }; |
| 1342 | |
| 1343 | std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo); |
| 1344 | std::unique_ptr<armnn::ITensorHandle> cellStateInHandle = |
| 1345 | tensorHandleFactory.CreateTensorHandle(cellStateInTensorInfo); |
| 1346 | std::unique_ptr<armnn::ITensorHandle> outputStateInHandle = |
| 1347 | tensorHandleFactory.CreateTensorHandle(outputStateInTensorInfo); |
| 1348 | std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo); |
| 1349 | |
| 1350 | armnn::UnidirectionalSequenceLstmQueueDescriptor data; |
| 1351 | armnn::WorkloadInfo info; |
| 1352 | |
| 1353 | AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); |
| 1354 | AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get()); |
| 1355 | AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get()); |
| 1356 | AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); |
| 1357 | |
| 1358 | armnn::TensorInfo tensorInfoOut({outputSize}, armnn::DataType::Float32); |
| 1359 | armnn::TensorInfo tensorInfoNumFp({numUnits}, armnn::DataType::Float32); |
| 1360 | armnn::TensorInfo tensorInfoNum({numUnits}, armnn::DataType::QAsymmS8, 0.1f, 0); |
| 1361 | armnn::TensorInfo tensorInfoNumInput({numUnits, inputSize}, armnn::DataType::QAsymmS8, 0.1f, 0); |
| 1362 | armnn::TensorInfo tensorInfoNumOutput({numUnits, outputSize}, armnn::DataType::QAsymmS8, 0.1f, 0); |
| 1363 | armnn::TensorInfo tensorInfoOutNum({outputSize, numUnits}, armnn::DataType::QAsymmS8, 0.1f, 0); |
| 1364 | |
| 1365 | std::vector<int8_t> inputToInputWeights = { -4, -1, -1, -2, 3, -2, 2, 4, 1, -4, -2, 3 }; |
| 1366 | std::vector<int8_t> inputToForgetWeights = { 2, 1, 4, -4, 3, -1, -3, -2, -3, 1, -4, -1 }; |
| 1367 | std::vector<int8_t> inputToCellWeights = { -2, 1, -2, 4, -3, -2, -4, 3, -2, -2, -6, 3 }; |
| 1368 | std::vector<int8_t> inputToOutputWeights = { 2, 5, -4, 5, 2, -3, 5, 7, 3, -5, 1, -4 }; |
| 1369 | |
| 1370 | std::vector<int8_t> recurrentToInputWeights = { -1, 1, -1, 1, -3, -4, -1, 4, 2, 3, 5, -1, 1, 3, -1, -1 }; |
| 1371 | std::vector<int8_t> recurrentToForgetWeights = { -1, 1, -1, 1, -3, -4, -1, 4, 2, 3, 5, -1, 1, 3, -2, -1 }; |
| 1372 | std::vector<int8_t> recurrentToCellWeights = { -2, -3, -1, -3, -4, 2, 1, -1, 2, 2, 1, 2, 3, -2, 3, -3 }; |
| 1373 | std::vector<int8_t> recurrentToOutputWeights = { -3, 3, -1, -2, -2, -2, -1, -5, 1, 3, -4, -1, -1, -1, 2, -1 }; |
| 1374 | |
| 1375 | std::vector<float> inputGateBias = { 0.02234832f, 0.14757581f, 0.18176508f, 0.10380666f}; |
| 1376 | std::vector<float> forgetGateBias = { 0.035185695f, -0.042891346f, -0.3032477f, 0.23027696f}; |
| 1377 | std::vector<float> cellBias = { -0.124379363f, 0.55531194f, 0.23377132f, 0.033463873f }; |
| 1378 | std::vector<float> outputGateBias = { 0.046159424f, -0.12809046f, 0.03563469f, 0.12648113f }; |
| 1379 | |
| 1380 | std::vector<int8_t> cellToInputWeights = { 5, 10, 25, 15 }; |
| 1381 | std::vector<int8_t> cellToForgetWeights = { -5, 15, 25, 3 }; |
| 1382 | std::vector<int8_t> cellToOutputWeights = { 10, -10, -5, 50 }; |
| 1383 | |
| 1384 | std::vector<int8_t> projectionWeights = { -25, 51, 3, -5, 25, 127, 77, 20, 18, 51, -10, 51, -25, 88, 77, -13 }; |
| 1385 | |
| 1386 | std::vector<float> projectionBiasVector(outputSize, 0.f); //{outputSize} |
| 1387 | |
| 1388 | armnn::ScopedTensorHandle inputToInputWeightsTensor(tensorInfoNumInput); |
| 1389 | armnn::ScopedTensorHandle inputToForgetWeightsTensor(tensorInfoNumInput); |
| 1390 | armnn::ScopedTensorHandle inputToCellWeightsTensor(tensorInfoNumInput); |
| 1391 | armnn::ScopedTensorHandle inputToOutputWeightsTensor(tensorInfoNumInput); |
| 1392 | armnn::ScopedTensorHandle recurrentToForgetWeightsTensor(tensorInfoNumOutput); |
| 1393 | armnn::ScopedTensorHandle recurrentToInputWeightsTensor(tensorInfoNumOutput); |
| 1394 | armnn::ScopedTensorHandle recurrentToCellWeightsTensor(tensorInfoNumOutput); |
| 1395 | armnn::ScopedTensorHandle recurrentToOutputWeightsTensor(tensorInfoNumOutput); |
| 1396 | armnn::ScopedTensorHandle cellToInputWeightsTensor(tensorInfoNum); |
| 1397 | armnn::ScopedTensorHandle inputGateBiasTensor(tensorInfoNumFp); |
| 1398 | armnn::ScopedTensorHandle forgetGateBiasTensor(tensorInfoNumFp); |
| 1399 | armnn::ScopedTensorHandle cellBiasTensor(tensorInfoNumFp); |
| 1400 | armnn::ScopedTensorHandle outputGateBiasTensor(tensorInfoNumFp); |
| 1401 | armnn::ScopedTensorHandle cellToForgetWeightsTensor(tensorInfoNum); |
| 1402 | armnn::ScopedTensorHandle cellToOutputWeightsTensor(tensorInfoNum); |
| 1403 | armnn::ScopedTensorHandle projectionWeightsTensor(tensorInfoOutNum); |
| 1404 | armnn::ScopedTensorHandle projectionBiasTensor(tensorInfoOut); |
| 1405 | |
| 1406 | AllocateAndCopyDataToITensorHandle(&inputToInputWeightsTensor, inputToInputWeights.data()); |
| 1407 | AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, inputToForgetWeights.data()); |
| 1408 | AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, inputToCellWeights.data()); |
| 1409 | AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, inputToOutputWeights.data()); |
| 1410 | AllocateAndCopyDataToITensorHandle(&recurrentToInputWeightsTensor, recurrentToInputWeights.data()); |
| 1411 | AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, recurrentToForgetWeights.data()); |
| 1412 | AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, recurrentToCellWeights.data()); |
| 1413 | AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, recurrentToOutputWeights.data()); |
| 1414 | AllocateAndCopyDataToITensorHandle(&cellToInputWeightsTensor, cellToInputWeights.data()); |
| 1415 | AllocateAndCopyDataToITensorHandle(&inputGateBiasTensor, inputGateBias.data()); |
| 1416 | AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, forgetGateBias.data()); |
| 1417 | AllocateAndCopyDataToITensorHandle(&cellBiasTensor, cellBias.data()); |
| 1418 | AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, outputGateBias.data()); |
| 1419 | AllocateAndCopyDataToITensorHandle(&cellToForgetWeightsTensor, cellToForgetWeights.data()); |
| 1420 | AllocateAndCopyDataToITensorHandle(&cellToOutputWeightsTensor, cellToOutputWeights.data()); |
| 1421 | AllocateAndCopyDataToITensorHandle(&projectionWeightsTensor, projectionWeights.data()); |
| 1422 | AllocateAndCopyDataToITensorHandle(&projectionBiasTensor, projectionBiasVector.data()); |
| 1423 | |
| 1424 | data.m_InputToInputWeights = &inputToInputWeightsTensor; |
| 1425 | data.m_InputToForgetWeights = &inputToForgetWeightsTensor; |
| 1426 | data.m_InputToCellWeights = &inputToCellWeightsTensor; |
| 1427 | data.m_InputToOutputWeights = &inputToOutputWeightsTensor; |
| 1428 | data.m_RecurrentToInputWeights = &recurrentToInputWeightsTensor; |
| 1429 | data.m_RecurrentToForgetWeights = &recurrentToForgetWeightsTensor; |
| 1430 | data.m_RecurrentToCellWeights = &recurrentToCellWeightsTensor; |
| 1431 | data.m_RecurrentToOutputWeights = &recurrentToOutputWeightsTensor; |
| 1432 | data.m_CellToInputWeights = &cellToInputWeightsTensor; |
| 1433 | data.m_InputGateBias = &inputGateBiasTensor; |
| 1434 | data.m_ForgetGateBias = &forgetGateBiasTensor; |
| 1435 | data.m_CellBias = &cellBiasTensor; |
| 1436 | data.m_OutputGateBias = &outputGateBiasTensor; |
| 1437 | data.m_CellToForgetWeights = &cellToForgetWeightsTensor; |
| 1438 | data.m_CellToOutputWeights = &cellToOutputWeightsTensor; |
| 1439 | data.m_ProjectionWeights = &projectionWeightsTensor; |
| 1440 | data.m_ProjectionBias = &projectionBiasTensor; |
| 1441 | |
| 1442 | // Flags to set test configuration |
| 1443 | data.m_Parameters.m_ActivationFunc = 4; |
| 1444 | data.m_Parameters.m_CifgEnabled = false; |
| 1445 | data.m_Parameters.m_PeepholeEnabled = true; |
| 1446 | data.m_Parameters.m_ProjectionEnabled = true; |
| 1447 | data.m_Parameters.m_LayerNormEnabled = false; |
| 1448 | data.m_Parameters.m_TimeMajor = false; |
| 1449 | data.m_Parameters.m_ClippingThresCell = 10.0f; |
| 1450 | |
| 1451 | |
| 1452 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateUnidirectionalSequenceLstm(data, info); |
| 1453 | inputHandle->Allocate(); |
| 1454 | outputStateInHandle->Allocate(); |
| 1455 | cellStateInHandle->Allocate(); |
| 1456 | outputHandle->Allocate(); |
| 1457 | |
| 1458 | CopyDataToITensorHandle(inputHandle.get(), inputVector.data()); |
| 1459 | CopyDataToITensorHandle(outputStateInHandle.get(), outputStateInVector.data()); |
| 1460 | CopyDataToITensorHandle(cellStateInHandle.get(), cellStateInVector.data()); |
| 1461 | |
| 1462 | workload->Execute(); |
| 1463 | |
| 1464 | CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get()); |
| 1465 | |
| 1466 | return LayerTestResult<float, 3>(actualOutput, |
| 1467 | expectedOutput, |
| 1468 | outputHandle->GetShape(), |
| 1469 | outputTensorInfo.GetShape()); |
| 1470 | } |
| 1471 | |
| 1472 | LayerTestResult<float, 3> UnidirectionalSequenceLstmLayerInt8NoCifgWithPeepholeWithProjectionWithLayerNormTest( |
| 1473 | armnn::IWorkloadFactory& workloadFactory, |
| 1474 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 1475 | const armnn::ITensorHandleFactory& tensorHandleFactory) |
| 1476 | { |
| 1477 | IgnoreUnused(memoryManager); |
| 1478 | unsigned int batchSize = 3; |
| 1479 | unsigned int timeSize = 2; |
| 1480 | unsigned int outputSize = 4; |
| 1481 | unsigned int inputSize = 3; |
| 1482 | unsigned numUnits = 5; |
| 1483 | |
| 1484 | armnn::TensorInfo inputTensorInfo({batchSize, timeSize, inputSize}, armnn::DataType::Float32); |
| 1485 | armnn::TensorInfo cellStateInTensorInfo({batchSize , numUnits}, armnn::DataType::Float32); |
| 1486 | armnn::TensorInfo outputStateInTensorInfo({batchSize , outputSize}, armnn::DataType::Float32); |
| 1487 | armnn::TensorInfo outputTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32); |
| 1488 | |
| 1489 | const std::vector<float> inputVector = { 1., 8., 3., 4., 5., 4., |
| 1490 | 3., 2., 1., 2., 3., 4., |
| 1491 | 5., 4., 3., 2., 1., 2. }; |
| 1492 | |
| 1493 | std::vector<float> cellStateInVector(batchSize * numUnits, 0.f); |
| 1494 | std::vector<float> outputStateInVector(batchSize * outputSize, 0.f); |
| 1495 | |
| 1496 | std::vector<float> actualOutput(outputTensorInfo.GetNumElements()); |
| 1497 | |
| 1498 | const std::vector<float> expectedOutput = { 0.0471276f, 0.0168155f, 0.0789885f, 0.16550f, |
| 1499 | 0.0643133f, -0.0400722f, 0.100593f, 0.197722f, |
| 1500 | 0.0465562f, -0.0600682f, 0.0622087f, 0.115053f, |
| 1501 | 0.056287f, -0.0566218f, 0.0856832f, 0.148484f, |
| 1502 | 0.0457859f, -0.0588112f, 0.0623636f, 0.114333f, |
| 1503 | 0.0509271f, -0.0754262f, 0.058600f, 0.0801288f }; |
| 1504 | |
| 1505 | std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo); |
| 1506 | std::unique_ptr<armnn::ITensorHandle> cellStateInHandle = |
| 1507 | tensorHandleFactory.CreateTensorHandle(cellStateInTensorInfo); |
| 1508 | std::unique_ptr<armnn::ITensorHandle> outputStateInHandle = |
| 1509 | tensorHandleFactory.CreateTensorHandle(outputStateInTensorInfo); |
| 1510 | |
| 1511 | std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo); |
| 1512 | |
| 1513 | armnn::UnidirectionalSequenceLstmQueueDescriptor data; |
| 1514 | armnn::WorkloadInfo info; |
| 1515 | |
| 1516 | AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); |
| 1517 | AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get()); |
| 1518 | AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get()); |
| 1519 | |
| 1520 | AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); |
| 1521 | |
| 1522 | armnn::TensorInfo tensorInfoOut({outputSize}, armnn::DataType::Float32); |
| 1523 | armnn::TensorInfo tensorInfoNumFp({numUnits}, armnn::DataType::Float32); |
| 1524 | armnn::TensorInfo tensorInfoNum({numUnits}, armnn::DataType::QAsymmS8, 0.1f, 0); |
| 1525 | armnn::TensorInfo tensorInfoNumInput({numUnits, inputSize}, armnn::DataType::QAsymmS8, 0.1f, 0); |
| 1526 | armnn::TensorInfo tensorInfoNumOutput({numUnits, outputSize}, armnn::DataType::QAsymmS8, 0.1f, 0); |
| 1527 | armnn::TensorInfo tensorInfoOutNum({outputSize, numUnits}, armnn::DataType::QAsymmS8, 0.1f, 0); |
| 1528 | |
| 1529 | std::vector<int8_t> inputToInputWeights = { -4, -1, -1, -2, 3, -2, 2, 4, 1, -4, -2, 3, 2, 2, -4 }; |
| 1530 | std::vector<int8_t> inputToForgetWeights = { 2, 1, 4, -4, 3, -1, -3, -2, -3, 1, -4, -1, -3, -2, -4 }; |
| 1531 | std::vector<int8_t> inputToCellWeights = { -2, 1, -2, 4, -3, -2, -4, 3, -2, -2, -6, 3, 2, 5, -4 }; |
| 1532 | std::vector<int8_t> inputToOutputWeights = { 2, 5, -4, 5, 2, -3, 5, 7, 3, -5, 1, -4, -4, -1, -1 }; |
| 1533 | |
| 1534 | std::vector<float> inputGateBias = { 0.03f, 0.15f, 0.22f, 0.38f, 0.05f }; |
| 1535 | std::vector<float> forgetGateBias = { 0.1f, -0.3f, -0.2f, 0.1f, 0.4f }; |
| 1536 | std::vector<float> cellBias = { -0.05f, 0.72f, 0.25f, 0.08f, 0.1f }; |
| 1537 | std::vector<float> outputGateBias = { 0.05f, -0.01f, 0.2f, 0.1f, -0.2f }; |
| 1538 | |
| 1539 | std::vector<int8_t> recurrentToInputWeights = { -1, 1, -1, 1, -3, -4, -1, 4, 2, 3, |
| 1540 | 5, -1, 1, 3, -1, -1, -1, 4, 2, 3 }; |
| 1541 | |
| 1542 | std::vector<int8_t> recurrentToForgetWeights = { -1, 1, -1, 1, -3, -4, -1, 4, 2, 3, |
| 1543 | 5, -1, 1, 3, -2, -1, -1, 2, 2, 1 }; |
| 1544 | |
| 1545 | std::vector<int8_t> recurrentToCellWeights = { -2, -3, -1, -3, -4, 2, 1, -1, 2, 2, |
| 1546 | 1, 2, 3, -2, 3, -3, -1, -5, 1, 3 }; |
| 1547 | |
| 1548 | std::vector<int8_t> recurrentToOutputWeights = { -3, 3, -1, -2, -2, -2, -1, -5, 1, 3, |
| 1549 | -4, -1, -1, -1, 2, -1, 5, 1, -3, -4 }; |
| 1550 | |
| 1551 | std::vector<int8_t> cellToInputWeights = { 5, 3, 8, -5, 2 }; |
| 1552 | std::vector<int8_t> cellToForgetWeights = { -2, -7, 5, -3, 4 }; |
| 1553 | std::vector<int8_t> cellToOutputWeights = { 9, -10 , -5, 5, 1 }; |
| 1554 | |
| 1555 | std::vector<int8_t> projectionWeights = { -1, 2, 1, -2, 1, 5, 3, 8, 7, 2, |
| 1556 | -4, 2, 5, -4, 3, -2, 3, 8, -7, 2 }; |
| 1557 | |
| 1558 | std::vector<float> projectionBiasVector(outputSize, 0.f); //{outputSize} |
| 1559 | |
| 1560 | std::vector<float> inputLayerNormWeights = { 0.1f, 0.2f, -0.3f, -0.1f, 0.5f }; |
| 1561 | std::vector<float> forgetLayerNormWeights = { -0.1f, 0.2f, 0.3f, 0.5f, 0.2f }; |
| 1562 | std::vector<float> cellLayerNormWeights = { 0.5f, 0.2f, 0.3f, 0.4f, -0.5f }; |
| 1563 | std::vector<float> outputLayerNormWeights = { 0.6f, -0.2f, -0.2f, 0.5f, 0.1f }; |
| 1564 | |
| 1565 | armnn::ScopedTensorHandle inputToInputWeightsTensor(tensorInfoNumInput); |
| 1566 | armnn::ScopedTensorHandle inputToForgetWeightsTensor(tensorInfoNumInput); |
| 1567 | armnn::ScopedTensorHandle inputToCellWeightsTensor(tensorInfoNumInput); |
| 1568 | armnn::ScopedTensorHandle inputToOutputWeightsTensor(tensorInfoNumInput); |
| 1569 | armnn::ScopedTensorHandle recurrentToForgetWeightsTensor(tensorInfoNumOutput); |
| 1570 | armnn::ScopedTensorHandle recurrentToInputWeightsTensor(tensorInfoNumOutput); |
| 1571 | armnn::ScopedTensorHandle recurrentToCellWeightsTensor(tensorInfoNumOutput); |
| 1572 | armnn::ScopedTensorHandle recurrentToOutputWeightsTensor(tensorInfoNumOutput); |
| 1573 | armnn::ScopedTensorHandle cellToInputWeightsTensor(tensorInfoNum); |
| 1574 | armnn::ScopedTensorHandle inputGateBiasTensor(tensorInfoNumFp); |
| 1575 | armnn::ScopedTensorHandle forgetGateBiasTensor(tensorInfoNumFp); |
| 1576 | armnn::ScopedTensorHandle cellBiasTensor(tensorInfoNumFp); |
| 1577 | armnn::ScopedTensorHandle outputGateBiasTensor(tensorInfoNumFp); |
| 1578 | armnn::ScopedTensorHandle cellToForgetWeightsTensor(tensorInfoNum); |
| 1579 | armnn::ScopedTensorHandle cellToOutputWeightsTensor(tensorInfoNum); |
| 1580 | armnn::ScopedTensorHandle projectionWeightsTensor(tensorInfoOutNum); |
| 1581 | armnn::ScopedTensorHandle projectionBiasTensor(tensorInfoOut); |
| 1582 | |
| 1583 | armnn::ScopedTensorHandle inputLayerNormWeightsTensor(tensorInfoNumFp); |
| 1584 | armnn::ScopedTensorHandle forgetLayerNormWeightsTensor(tensorInfoNumFp); |
| 1585 | armnn::ScopedTensorHandle cellLayerNormWeightsTensor(tensorInfoNumFp); |
| 1586 | armnn::ScopedTensorHandle outputLayerNormWeightsTensor(tensorInfoNumFp); |
| 1587 | |
| 1588 | AllocateAndCopyDataToITensorHandle(&inputToInputWeightsTensor, inputToInputWeights.data()); |
| 1589 | AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, inputToForgetWeights.data()); |
| 1590 | AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, inputToCellWeights.data()); |
| 1591 | AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, inputToOutputWeights.data()); |
| 1592 | AllocateAndCopyDataToITensorHandle(&recurrentToInputWeightsTensor, recurrentToInputWeights.data()); |
| 1593 | AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, recurrentToForgetWeights.data()); |
| 1594 | AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, recurrentToCellWeights.data()); |
| 1595 | AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, recurrentToOutputWeights.data()); |
| 1596 | AllocateAndCopyDataToITensorHandle(&cellToInputWeightsTensor, cellToInputWeights.data()); |
| 1597 | AllocateAndCopyDataToITensorHandle(&inputGateBiasTensor, inputGateBias.data()); |
| 1598 | AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, forgetGateBias.data()); |
| 1599 | AllocateAndCopyDataToITensorHandle(&cellBiasTensor, cellBias.data()); |
| 1600 | AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, outputGateBias.data()); |
| 1601 | AllocateAndCopyDataToITensorHandle(&cellToForgetWeightsTensor, cellToForgetWeights.data()); |
| 1602 | AllocateAndCopyDataToITensorHandle(&cellToOutputWeightsTensor, cellToOutputWeights.data()); |
| 1603 | AllocateAndCopyDataToITensorHandle(&projectionWeightsTensor, projectionWeights.data()); |
| 1604 | AllocateAndCopyDataToITensorHandle(&projectionBiasTensor, projectionBiasVector.data()); |
| 1605 | |
| 1606 | AllocateAndCopyDataToITensorHandle(&inputLayerNormWeightsTensor, inputLayerNormWeights.data()); |
| 1607 | AllocateAndCopyDataToITensorHandle(&forgetLayerNormWeightsTensor, forgetLayerNormWeights.data()); |
| 1608 | AllocateAndCopyDataToITensorHandle(&cellLayerNormWeightsTensor, cellLayerNormWeights.data()); |
| 1609 | AllocateAndCopyDataToITensorHandle(&outputLayerNormWeightsTensor, outputLayerNormWeights.data()); |
| 1610 | |
| 1611 | data.m_InputToInputWeights = &inputToInputWeightsTensor; |
| 1612 | data.m_InputToForgetWeights = &inputToForgetWeightsTensor; |
| 1613 | data.m_InputToCellWeights = &inputToCellWeightsTensor; |
| 1614 | data.m_InputToOutputWeights = &inputToOutputWeightsTensor; |
| 1615 | data.m_RecurrentToInputWeights = &recurrentToInputWeightsTensor; |
| 1616 | data.m_RecurrentToForgetWeights = &recurrentToForgetWeightsTensor; |
| 1617 | data.m_RecurrentToCellWeights = &recurrentToCellWeightsTensor; |
| 1618 | data.m_RecurrentToOutputWeights = &recurrentToOutputWeightsTensor; |
| 1619 | data.m_CellToInputWeights = &cellToInputWeightsTensor; |
| 1620 | data.m_InputGateBias = &inputGateBiasTensor; |
| 1621 | data.m_ForgetGateBias = &forgetGateBiasTensor; |
| 1622 | data.m_CellBias = &cellBiasTensor; |
| 1623 | data.m_OutputGateBias = &outputGateBiasTensor; |
| 1624 | data.m_CellToForgetWeights = &cellToForgetWeightsTensor; |
| 1625 | data.m_CellToOutputWeights = &cellToOutputWeightsTensor; |
| 1626 | data.m_ProjectionWeights = &projectionWeightsTensor; |
| 1627 | data.m_ProjectionBias = &projectionBiasTensor; |
| 1628 | |
| 1629 | data.m_InputLayerNormWeights = &inputLayerNormWeightsTensor; |
| 1630 | data.m_ForgetLayerNormWeights = &forgetLayerNormWeightsTensor; |
| 1631 | data.m_CellLayerNormWeights = &cellLayerNormWeightsTensor; |
| 1632 | data.m_OutputLayerNormWeights = &outputLayerNormWeightsTensor; |
| 1633 | |
| 1634 | // Flags to set test configuration |
| 1635 | data.m_Parameters.m_ActivationFunc = 4; |
| 1636 | data.m_Parameters.m_CifgEnabled = false; |
| 1637 | data.m_Parameters.m_PeepholeEnabled = true; |
| 1638 | data.m_Parameters.m_ProjectionEnabled = true; |
| 1639 | data.m_Parameters.m_LayerNormEnabled = true; |
| 1640 | data.m_Parameters.m_TimeMajor = false; |
| 1641 | data.m_Parameters.m_ClippingThresCell = 10.0f; |
| 1642 | |
| 1643 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateUnidirectionalSequenceLstm(data, info); |
| 1644 | inputHandle->Allocate(); |
| 1645 | outputStateInHandle->Allocate(); |
| 1646 | cellStateInHandle->Allocate(); |
| 1647 | outputHandle->Allocate(); |
| 1648 | |
| 1649 | CopyDataToITensorHandle(inputHandle.get(), inputVector.data()); |
| 1650 | CopyDataToITensorHandle(outputStateInHandle.get(), outputStateInVector.data()); |
| 1651 | CopyDataToITensorHandle(cellStateInHandle.get(), cellStateInVector.data()); |
| 1652 | |
| 1653 | workload->Execute(); |
| 1654 | |
| 1655 | CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get()); |
| 1656 | |
| 1657 | return LayerTestResult<float, 3>(actualOutput, |
| 1658 | expectedOutput, |
| 1659 | outputHandle->GetShape(), |
| 1660 | outputTensorInfo.GetShape()); |
| 1661 | } |
| 1662 | |
| 1663 | LayerTestResult<float, 3> UnidirectionalSequenceLstmInt8WithCifgWithPeepholeNoProjectionTest( |
| 1664 | armnn::IWorkloadFactory& workloadFactory, |
| 1665 | const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| 1666 | const armnn::ITensorHandleFactory& tensorHandleFactory) |
| 1667 | { |
| 1668 | IgnoreUnused(memoryManager); |
| 1669 | unsigned int batchSize = 3; |
| 1670 | unsigned int timeSize = 2; |
| 1671 | unsigned int inputSize = 3; |
| 1672 | unsigned int outputSize = 4; |
| 1673 | unsigned numUnits = outputSize; |
| 1674 | |
| 1675 | armnn::TensorInfo inputTensorInfo({batchSize, timeSize, inputSize}, armnn::DataType::Float32); |
| 1676 | armnn::TensorInfo cellStateInTensorInfo({batchSize, numUnits}, armnn::DataType::Float32); |
| 1677 | armnn::TensorInfo outputStateInTensorInfo({batchSize, outputSize}, armnn::DataType::Float32); |
| 1678 | |
| 1679 | armnn::TensorInfo outputTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32); |
| 1680 | |
| 1681 | const std::vector<float> inputVector = { 0.1f, 0.2f, 0.3f, 0.4f, 0.5f, 0.4f, |
| 1682 | 0.3f, 0.2f, 0.1f, 0.2f, 0.3f, 0.4f, |
| 1683 | 0.5f, 0.4f, 0.3f, 0.2f, 0.1f, 0.2f }; |
| 1684 | |
| 1685 | std::vector<float> cellStateInVector(batchSize * numUnits, 0.f); |
| 1686 | std::vector<float> outputStateInVector(batchSize * outputSize, 0.f); |
| 1687 | |
| 1688 | std::vector<float> actualOutput(outputTensorInfo.GetNumElements()); |
| 1689 | |
| 1690 | const std::vector<float> outputVector = { -0.0072104f, -0.00991171f, -0.00650478f, -0.00713055f, |
| 1691 | -0.0191782f, -0.0161269f, -0.0233683f, -0.054299f, |
| 1692 | -0.00783725f, 0.00635271f, -0.0126718f, -0.022613f, |
| 1693 | -0.0161351f, -0.00775868f, -0.021054f, -0.0339778f, |
| 1694 | -0.0146392f, 0.00330261f, -0.0258733f, -0.0407797f, |
| 1695 | -0.0174297f, 0.0050105f, -0.0266275f, -0.0362564f }; |
| 1696 | |
| 1697 | std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo); |
| 1698 | std::unique_ptr<armnn::ITensorHandle> cellStateInHandle = |
| 1699 | tensorHandleFactory.CreateTensorHandle(cellStateInTensorInfo); |
| 1700 | std::unique_ptr<armnn::ITensorHandle> outputStateInHandle = |
| 1701 | tensorHandleFactory.CreateTensorHandle(outputStateInTensorInfo); |
| 1702 | |
| 1703 | std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo); |
| 1704 | |
| 1705 | armnn::UnidirectionalSequenceLstmQueueDescriptor data; |
| 1706 | armnn::WorkloadInfo info; |
| 1707 | |
| 1708 | AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); |
| 1709 | AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get()); |
| 1710 | AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get()); |
| 1711 | |
| 1712 | AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); |
| 1713 | |
| 1714 | armnn::TensorInfo tensorInfoNumFp({numUnits}, armnn::DataType::Float32); |
| 1715 | armnn::TensorInfo tensorInfoNum({numUnits}, armnn::DataType::QAsymmS8, 0.1f, 0); |
| 1716 | armnn::TensorInfo tensorInfoNumInput({numUnits, inputSize}, armnn::DataType::QAsymmS8, 0.1f, 0); |
| 1717 | armnn::TensorInfo tensorInfoNumOutput({numUnits, outputSize}, armnn::DataType::QAsymmS8, 0.1f, 0); |
| 1718 | |
| 1719 | std::vector<int8_t> inputToForgetWeights = { 2, 1, 4, -4, 3, -1, -3, -2, -3, 1, -4, -1 }; |
| 1720 | std::vector<int8_t> inputToCellWeights = { -2, 1, -2, 4, -3, -2, -4, 3, -2, -2, -6, 3 }; |
| 1721 | std::vector<int8_t> inputToOutputWeights = { 2, 5, -4, 5, 2, -3, 5, 7, 3, -5, 1, -4 }; |
| 1722 | |
| 1723 | std::vector<int8_t> recurrentToForgetWeights = { -1, 1, -1, 1, -3, -4, -1, 4, 2, 3, 5, -1, 1, 3, -2, -1 }; |
| 1724 | std::vector<int8_t> recurrentToCellWeights = { -2, -3, -1, -3, -4, 2, 1, -1, 2, 2, 1, 2, 3, -2, 3, -3 }; |
| 1725 | std::vector<int8_t> recurrentToOutputWeights = { -3, 3, -1, -2, -2, -2, -1, -5, 1, 3, -4, -1, -1, -1, 2, -1 }; |
| 1726 | |
| 1727 | std::vector<int8_t> cellToForgetWeights = { 47, -52, -24, 31 }; |
| 1728 | std::vector<int8_t> cellToOutputWeights = { -17, 82, 85, -77 }; |
| 1729 | |
| 1730 | std::vector<float> forgetGateBias = { 1., 1., 1., 1. }; |
| 1731 | std::vector<float> cellBias = { 0., 0., 0., 0. }; |
| 1732 | std::vector<float> outputGateBias = { 0., 0., 0., 0. }; |
| 1733 | |
| 1734 | armnn::ScopedTensorHandle inputToForgetWeightsTensor(tensorInfoNumInput); |
| 1735 | armnn::ScopedTensorHandle inputToCellWeightsTensor(tensorInfoNumInput); |
| 1736 | armnn::ScopedTensorHandle inputToOutputWeightsTensor(tensorInfoNumInput); |
| 1737 | armnn::ScopedTensorHandle recurrentToForgetWeightsTensor(tensorInfoNumOutput); |
| 1738 | armnn::ScopedTensorHandle recurrentToCellWeightsTensor(tensorInfoNumOutput); |
| 1739 | armnn::ScopedTensorHandle recurrentToOutputWeightsTensor(tensorInfoNumOutput); |
| 1740 | armnn::ScopedTensorHandle cellToForgetWeightsTensor(tensorInfoNum); |
| 1741 | armnn::ScopedTensorHandle cellToOutputWeightsTensor(tensorInfoNum); |
| 1742 | armnn::ScopedTensorHandle forgetGateBiasTensor(tensorInfoNumFp); |
| 1743 | armnn::ScopedTensorHandle cellBiasTensor(tensorInfoNumFp); |
| 1744 | armnn::ScopedTensorHandle outputGateBiasTensor(tensorInfoNumFp); |
| 1745 | |
| 1746 | AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, inputToForgetWeights.data()); |
| 1747 | AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, inputToCellWeights.data()); |
| 1748 | AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, inputToOutputWeights.data()); |
| 1749 | AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, recurrentToForgetWeights.data()); |
| 1750 | AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, recurrentToCellWeights.data()); |
| 1751 | AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, recurrentToOutputWeights.data()); |
| 1752 | AllocateAndCopyDataToITensorHandle(&cellToForgetWeightsTensor, cellToForgetWeights.data()); |
| 1753 | AllocateAndCopyDataToITensorHandle(&cellToOutputWeightsTensor, cellToOutputWeights.data()); |
| 1754 | AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, forgetGateBias.data()); |
| 1755 | AllocateAndCopyDataToITensorHandle(&cellBiasTensor, cellBias.data()); |
| 1756 | AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, outputGateBias.data()); |
| 1757 | |
| 1758 | data.m_InputToForgetWeights = &inputToForgetWeightsTensor; |
| 1759 | data.m_InputToCellWeights = &inputToCellWeightsTensor; |
| 1760 | data.m_InputToOutputWeights = &inputToOutputWeightsTensor; |
| 1761 | data.m_RecurrentToForgetWeights = &recurrentToForgetWeightsTensor; |
| 1762 | data.m_RecurrentToCellWeights = &recurrentToCellWeightsTensor; |
| 1763 | data.m_RecurrentToOutputWeights = &recurrentToOutputWeightsTensor; |
| 1764 | data.m_CellToForgetWeights = &cellToForgetWeightsTensor; |
| 1765 | data.m_CellToOutputWeights = &cellToOutputWeightsTensor; |
| 1766 | data.m_ForgetGateBias = &forgetGateBiasTensor; |
| 1767 | data.m_CellBias = &cellBiasTensor; |
| 1768 | data.m_OutputGateBias = &outputGateBiasTensor; |
| 1769 | |
| 1770 | // Flags to set test configuration |
| 1771 | data.m_Parameters.m_ClippingThresCell = 10; |
| 1772 | data.m_Parameters.m_ClippingThresProj = 0; |
| 1773 | data.m_Parameters.m_ActivationFunc = 4; |
| 1774 | data.m_Parameters.m_CifgEnabled = true; |
| 1775 | data.m_Parameters.m_PeepholeEnabled = true; |
| 1776 | data.m_Parameters.m_ProjectionEnabled = false; |
| 1777 | data.m_Parameters.m_TimeMajor = false; |
| 1778 | |
| 1779 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateUnidirectionalSequenceLstm(data, info); |
| 1780 | inputHandle->Allocate(); |
| 1781 | outputStateInHandle->Allocate(); |
| 1782 | cellStateInHandle->Allocate(); |
| 1783 | |
| 1784 | outputHandle->Allocate(); |
| 1785 | |
| 1786 | CopyDataToITensorHandle(inputHandle.get(), inputVector.data()); |
| 1787 | CopyDataToITensorHandle(outputStateInHandle.get(), outputStateInVector.data()); |
| 1788 | CopyDataToITensorHandle(cellStateInHandle.get(), cellStateInVector.data()); |
| 1789 | |
| 1790 | workload->Execute(); |
| 1791 | |
| 1792 | CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get()); |
| 1793 | |
| 1794 | return LayerTestResult<float, 3>(actualOutput, |
| 1795 | outputVector, |
| 1796 | outputHandle->GetShape(), |
| 1797 | outputTensorInfo.GetShape()); |
| 1798 | } |