| // |
| // Copyright © 2021, 2023-2024 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "UnidirectionalSequenceLstmTestHelper.hpp" |
| |
| #include <doctest/doctest.h> |
| |
| namespace armnnDelegate |
| { |
| |
| void UnidirectionalSequenceLstmTest(const std::vector<armnn::BackendId>& backends = {}) |
| { |
| int32_t batchSize = 3; |
| int32_t timeSize = 2; |
| int32_t inputSize = 3; |
| int32_t outputSize = 4; |
| // cellSize and outputSize have the same size when there is no projection. |
| int32_t numUnits = outputSize; |
| |
| //tensorInfo12, |
| bool hasInputToInputWeights = true; |
| std::vector<float> inputToInputWeights = { -0.49536117f, -0.0556083915f, -0.102400711f, |
| -0.117484632f, 0.3298470976f, -0.1179017122f, |
| 0.214305695f, 0.42135173085f, 0.003878414626f, |
| -0.348303917f, -0.1881275477f, 0.0343011027f }; |
| |
| std::vector<float> inputToForgetWeights = { 0.2415594226f, 0.15400093799f, 0.4566498398f, |
| -0.3810434485f, 0.268383264f, -0.009807467424f, |
| -0.3522925403f, -0.24275735512f, -0.28344226125f, |
| 0.13512269116f, -0.4932442977f, -0.10039821991f }; |
| |
| std::vector<float> inputToCellWeights = { -0.2504855627f, 0.184490025045f, -0.2480507493f, |
| 0.386399507f, -0.259465157985f, -0.16545993089f, |
| -0.4230232555f, 0.341664791103f, -0.18127849691f, |
| -0.2277662414f, -0.55275535589f, 0.34184026718f }; |
| |
| std::vector<float> inputToOutputWeights = { 0.2303854227f, 0.5218806862f, -0.4865379333f, |
| 0.53969591851f, 0.23393625035f, -0.27140527306f, |
| 0.50009280443f, 0.07511717046f, 0.3998299249f, |
| -0.51717478049f, 0.1889653282f, -0.367323637f }; |
| |
| //tensorInfo16, |
| bool hasRecurrentToInputWeights = true; |
| std::vector<float> recurrentToInputWeights = { -0.128009796112f, 0.1995525098f, -0.07745539397f, 0.1558421701f, |
| -0.265254765766f, -0.38837709614f, -0.05636804124f, 0.4259087456f, |
| 0.17628988623f, 0.3877420127f, 0.53300309181f, -0.0959980934f, |
| 0.00302857416f, 0.3266998827f, -0.142509296562f, -0.04433270756f }; |
| |
| std::vector<float> recurrentToForgetWeights = { -0.09499983487f, -0.08814888417f, -0.04834804721f, 0.1516668247f, |
| -0.3967529535f, -0.06463699788f, 0.4952811002f, 0.003274492938f, |
| -0.0968840941f, 0.17928104102f, 0.0031281141592f, -0.3387276584f, |
| -0.3587934076f, 0.06705895066f, 0.22463923692f, 0.1961955726f }; |
| |
| std::vector<float> recurrentToCellWeights = { -0.21938985582f, -0.3023648226f, -0.1170005202f, -0.3509177422f, |
| -0.4286288613f, 0.2726137042f, 0.09216640889f, -0.06551410215f, |
| 0.20453298098f, 0.2393476665f, 0.11846517771f, 0.2630801796f, |
| 0.3954237699f, -0.19407111404f, 0.30412107706f, -0.27342408554f }; |
| |
| std::vector<float> recurrentToOutputWeights = { -0.32921677827f, 0.32624614238f, -0.1388191282f, -0.17879831790f, |
| -0.15185534954f, -0.16918526583f, -0.10087361183f, -0.5436913968f, |
| 0.016758225858f, 0.30454617738f, -0.41493862867f, -0.005565764375f, |
| -0.12584099173f, -0.12319286912f, 0.2407919466f, -0.08879069983f }; |
| // tensorInfo4 |
| bool hasCellToInputWeights = false; |
| std::vector<float> cellToInputWeights; |
| bool hasCellToForgetWeights = false; |
| std::vector<float> cellToForgetWeights; |
| bool hasCellToOutputWeights = false; |
| std::vector<float> cellToOutputWeights; |
| |
| bool hasInputGateBias = true; |
| std::vector<float> inputGateBias = {0., 0., 0., 0.}; |
| std::vector<float> forgetGateBias = {1., 1., 1., 1.}; |
| std::vector<float> cellBias = {0., 0., 0., 0.}; |
| std::vector<float> outputGateBias = {0., 0., 0., 0.}; |
| |
| bool hasProjectionWeights = false; |
| std::vector<float> projectionWeights; |
| bool hasProjectionBias = false; |
| std::vector<float> projectionBias; |
| |
| bool hasInputLayerNormWeights = false; |
| std::vector<float> inputLayerNormWeights; |
| bool hasForgetLayerNormWeights = false; |
| std::vector<float> forgetLayerNormWeights; |
| bool hasCellLayerNormWeights = false; |
| std::vector<float> cellLayerNormWeights; |
| bool hasOutputLayerNormWeights = false; |
| std::vector<float> outputLayerNormWeights; |
| |
| std::vector<float> inputValues = { 1., 2., 3., 4., 5., 4., |
| 3., 2., 1., 2., 3., 4., |
| 5., 4., 3., 2., 1., 2. }; |
| std::vector<float> expectedOutputValues = { -0.0714901f, -0.162117f, -0.175168f, -0.0232934f, |
| -0.168107f, -0.414129f, -0.549875f, -0.00803579f, |
| -0.0668735f, 0.204078f, -0.42765f, -0.0312321f, |
| -0.120003f, -0.0941918f, -0.456391f, -0.0287019f, |
| -0.0342921f, 0.20824f, -0.656989f, -0.00415265f, |
| -0.10493f, 0.14211f, -0.583478f, -0.0329754f }; |
| |
| tflite::ActivationFunctionType activationFunction = tflite::ActivationFunctionType_TANH; |
| float clippingThresCell = 10.f; |
| float clippingThresProj = 0.f; |
| bool isTimeMajor = false; |
| |
| UnidirectionalSequenceLstmTestImpl<float>(::tflite::TensorType_FLOAT32, |
| batchSize, |
| timeSize, |
| inputSize, |
| outputSize, |
| numUnits, |
| hasInputToInputWeights, |
| inputToInputWeights, |
| inputToForgetWeights, |
| inputToCellWeights, |
| inputToOutputWeights, |
| hasRecurrentToInputWeights, |
| recurrentToInputWeights, |
| recurrentToForgetWeights, |
| recurrentToCellWeights, |
| recurrentToOutputWeights, |
| hasCellToInputWeights, |
| cellToInputWeights, |
| hasCellToForgetWeights, |
| cellToForgetWeights, |
| hasCellToOutputWeights, |
| cellToOutputWeights, |
| hasInputGateBias, |
| inputGateBias, |
| forgetGateBias, |
| cellBias, |
| outputGateBias, |
| hasProjectionWeights, |
| projectionWeights, |
| hasProjectionBias, |
| projectionBias, |
| hasInputLayerNormWeights, |
| inputLayerNormWeights, |
| hasForgetLayerNormWeights, |
| forgetLayerNormWeights, |
| hasCellLayerNormWeights, |
| cellLayerNormWeights, |
| hasOutputLayerNormWeights, |
| outputLayerNormWeights, |
| inputValues, |
| expectedOutputValues, |
| activationFunction, |
| clippingThresCell, |
| clippingThresProj, |
| isTimeMajor, |
| backends); |
| } |
| |
| void UnidirectionalSequenceLstmTimeMajorTestImpl(int32_t timeSize, |
| std::vector<float>& inputValues, |
| std::vector<float>& expectedOutputValues, |
| const std::vector<armnn::BackendId>& backends = {}) |
| { |
| int32_t batchSize = 3; |
| int32_t inputSize = 3; |
| int32_t outputSize = 4; |
| // cellSize and outputSize have the same size when there is no projection. |
| int32_t numUnits = outputSize; |
| |
| std::vector<int32_t> inputShape = {timeSize, batchSize, inputSize}; |
| std::vector<int32_t> cellStateInTensorInfo = {batchSize, numUnits}; |
| std::vector<int32_t> outputStateInTensorInfo = {batchSize, outputSize}; |
| |
| std::vector<int32_t> outputTensorInfo = {timeSize, batchSize, outputSize}; |
| |
| //tensorInfo12 |
| bool hasInputToInputWeights = true; |
| std::vector<float> inputToInputWeights = { 0.27277296781539917f, 0.3813590407371521f, -0.394489049911499f, |
| 0.2782636880874634f, -0.3793870210647583f, -0.018918335437774658f, |
| 0.2724653482437134f, -0.19314253330230713f, -0.2947450876235962f, |
| -0.30253493785858154f, 0.4241350293159485f, -0.22560018301010132f }; |
| |
| std::vector<float> inputToForgetWeights = { -0.2667974531650543f, -0.05505800247192383f, -0.20932340621948242f, |
| -0.14345619082450867f, 0.09666192531585693f, -0.2604355812072754f, |
| -0.2681812047958374f, -0.3314584493637085f, 0.4485899806022644f, |
| -0.23467743396759033f, 0.5072842240333557f, -0.4192768931388855f }; |
| |
| std::vector<float> inputToCellWeights = { -0.15782442688941956f, -0.027530014514923096f, 0.4789854884147644f, |
| 0.23227906227111816f, 0.28259342908859253f, -0.030095696449279785f, |
| 0.10071521997451782f, -0.08535495400428772f, 0.18563997745513916f, |
| -0.3049069046974182f, -0.478048175573349f, 0.025234103202819824f }; |
| |
| std::vector<float> inputToOutputWeights = { -0.04584759473800659f, -0.2716066539287567f, 0.012970447540283203f, |
| -0.4729190170764923f, -0.37422770261764526f, 0.49352723360061646f, |
| 0.3163864016532898f, -0.436781644821167f, -0.33074596524238586f, |
| -0.32885751128196716f, -0.40959352254867554f, -0.2124689817428589f }; |
| |
| //tensorInfo16 |
| bool hasRecurrentToInputWeights = true; |
| std::vector<float> recurrentToInputWeights = { 0.23788475990f, -0.24948765337f, 0.50044941902f, 0.14431896805f, |
| -0.115940228137f, -0.717082679f, -0.17208620906f, 0.17850610617f, |
| -0.16702319684f, -0.11384502053f, -0.309785276245f, -0.3316611672f, |
| 0.52380162477f, -0.06839632987f, -0.391478359627f, -0.10756178963f }; |
| |
| std::vector<float> recurrentToForgetWeights = { 0.11383482068f, 0.1676601767f, -0.08550968004f, 0.03399394089f, |
| 0.08042152225f, -0.2133381964f, 0.05182432704f, 0.38161808255f, |
| -0.5018365979f, -0.08043262364f, 0.07894329014f, -0.07547105155f, |
| 0.12047368288f, 0.2986997961f, 0.0485043078f, -0.13372567296f }; |
| |
| std::vector<float> recurrentToCellWeights = { 0.0433832928545f, 0.07587072294f, -0.120520234107f, 0.604576051f, |
| -0.434353142986f, 0.009314475068f, 0.005085289478f, 0.08488202038f, |
| -0.00025437487886f, 0.15245915082f, -0.1936587542f, 0.004754020f, |
| -0.1582719236f, 0.3307867646f, 0.0236605107784f, 0.307716339826f }; |
| |
| std::vector<float> recurrentToOutputWeights = { -0.079031050201f, 0.041414566286f, -0.583727357285f, 0.1025384515f, |
| -0.172372072937f, 0.09214124082f, 0.178184121827f, -0.2439443916f, |
| 0.104485116899f, 0.2600405514f, 0.064414866268f, 0.24141204357f, |
| 0.281875759363f, -0.14234502664f, 0.15126448862f, -0.24421440064f }; |
| // tensorInfo4 |
| bool hasCellToInputWeights = false; |
| std::vector<float> cellToInputWeights; |
| bool hasCellToForgetWeights = false; |
| std::vector<float> cellToForgetWeights; |
| bool hasCellToOutputWeights = false; |
| std::vector<float> cellToOutputWeights; |
| |
| bool hasInputGateBias = true; |
| std::vector<float> inputGateBias = {0., 0., 0., 0.}; |
| std::vector<float> forgetGateBias = {1., 1., 1., 1.}; |
| std::vector<float> cellBias = {0., 0., 0., 0.}; |
| std::vector<float> outputGateBias = {0., 0., 0., 0.}; |
| |
| bool hasProjectionWeights = false; |
| std::vector<float> projectionWeights; |
| bool hasProjectionBias = false; |
| std::vector<float> projectionBias; |
| |
| bool hasInputLayerNormWeights = false; |
| std::vector<float> inputLayerNormWeights; |
| bool hasForgetLayerNormWeights = false; |
| std::vector<float> forgetLayerNormWeights; |
| bool hasCellLayerNormWeights = false; |
| std::vector<float> cellLayerNormWeights; |
| bool hasOutputLayerNormWeights = false; |
| std::vector<float> outputLayerNormWeights; |
| |
| tflite::ActivationFunctionType activationFunction = tflite::ActivationFunctionType_TANH; |
| float clippingThresCell = 10.f; |
| float clippingThresProj = 0.f; |
| bool isTimeMajor = true; |
| |
| UnidirectionalSequenceLstmTestImpl<float>(::tflite::TensorType_FLOAT32, |
| batchSize, |
| timeSize, |
| inputSize, |
| outputSize, |
| numUnits, |
| hasInputToInputWeights, |
| inputToInputWeights, |
| inputToForgetWeights, |
| inputToCellWeights, |
| inputToOutputWeights, |
| hasRecurrentToInputWeights, |
| recurrentToInputWeights, |
| recurrentToForgetWeights, |
| recurrentToCellWeights, |
| recurrentToOutputWeights, |
| hasCellToInputWeights, |
| cellToInputWeights, |
| hasCellToForgetWeights, |
| cellToForgetWeights, |
| hasCellToOutputWeights, |
| cellToOutputWeights, |
| hasInputGateBias, |
| inputGateBias, |
| forgetGateBias, |
| cellBias, |
| outputGateBias, |
| hasProjectionWeights, |
| projectionWeights, |
| hasProjectionBias, |
| projectionBias, |
| hasInputLayerNormWeights, |
| inputLayerNormWeights, |
| hasForgetLayerNormWeights, |
| forgetLayerNormWeights, |
| hasCellLayerNormWeights, |
| cellLayerNormWeights, |
| hasOutputLayerNormWeights, |
| outputLayerNormWeights, |
| inputValues, |
| expectedOutputValues, |
| activationFunction, |
| clippingThresCell, |
| clippingThresProj, |
| isTimeMajor, |
| backends);} |
| |
| void UnidirectionalSequenceLstmTimeMajorTest(const std::vector<armnn::BackendId>& backends = {}) |
| { |
| int32_t timeSize = 2; |
| |
| std::vector<float> inputValues = { 1., 2., 3., 4., 5., 4., |
| 3., 2., 1., 2., 3., 4., |
| 5., 4., 3., 2., 1., 2. }; |
| |
| std::vector<float> expectedOutputValues = { 0.135658f, 0.124673f, 0.021209f, -0.0530204f, |
| 0.106138f, 0.0404792f, 0.0151644f, -0.00675166f, |
| -0.0128514f, 0.0644884f, 0.0709072f, -0.0454045f, |
| 0.162886f, 0.166494f, 0.0277046f, -0.0369807f, |
| 0.111716f, 0.043119f, 0.0762981f, -0.0122854f, |
| 0.104397f, 0.2144f, 0.119192f, -0.0839058f }; |
| |
| UnidirectionalSequenceLstmTimeMajorTestImpl(timeSize, |
| inputValues, |
| expectedOutputValues, |
| backends); |
| } |
| |
| void UnidirectionalSequenceLstmTimeMajorSingleTimeTest(const std::vector<armnn::BackendId>& backends = {}) |
| { |
| int32_t timeSize = 1; |
| |
| std::vector<float> inputValues = { 1., 2., 3., |
| 4., 5., 6., |
| 7., 8., 9. }; |
| |
| std::vector<float> expectedOutputValues = { 0.13565768f, 0.12467254f, 0.02120903f, -0.05302038f, |
| 0.1053334f, 0.08508634f, 0.00667238f, -0.00356043f, |
| 0.05638668f, 0.02924093f, 0.00119751f, -0.00017249f }; |
| |
| UnidirectionalSequenceLstmTimeMajorTestImpl(timeSize, |
| inputValues, |
| expectedOutputValues, |
| backends); |
| } |
| |
| void UnidirectionalSequenceLstmNoCifgWithPeepholeWithProjectionTest(const std::vector<armnn::BackendId>& backends = {}) |
| { |
| int32_t batchSize = 2; |
| int32_t timeSize = 3; |
| int32_t inputSize = 4; |
| int32_t outputSize = 5; |
| int32_t numUnits = 6; |
| |
| std::vector<int32_t> inputShape = {batchSize, timeSize, inputSize}; |
| std::vector<int32_t> cellStateInTensorInfo = {batchSize, numUnits}; |
| std::vector<int32_t> outputStateInTensorInfo = {batchSize, outputSize}; |
| |
| std::vector<int32_t> outputTensorInfo = {batchSize, timeSize, outputSize}; |
| |
| //tensorInfoInputSize, |
| bool hasInputToInputWeights = true; |
| std::vector<float> inputToInputWeights = { 0.021393683f, 0.06124551f, 0.046905167f, -0.014657677f, |
| -0.03149463f, 0.09171803f, 0.14647801f, 0.10797193f, |
| -0.0057968358f, 0.0019193048f, -0.2726754f, 0.10154029f, |
| -0.018539885f, 0.080349885f, -0.10262385f, -0.022599787f, |
| -0.09121155f, -0.008675967f, -0.045206103f, -0.0821282f, |
| -0.008045952f, 0.015478081f, 0.055217247f, 0.038719587f }; |
| |
| std::vector<float> inputToForgetWeights = { -0.0018401089f, -0.004852237f, 0.03698424f, 0.014181704f, |
| 0.028273236f, -0.016726194f, -0.05249759f, -0.10204261f, |
| 0.00861066f, -0.040979505f, -0.009899187f, 0.01923892f, |
| -0.028177269f, -0.08535103f, -0.14585495f, 0.10662567f, |
| -0.01909731f, -0.017883534f, -0.0047269356f, -0.045103323f, |
| 0.0030784295f, 0.076784775f, 0.07463696f, 0.094531395f}; |
| |
| std::vector<float> inputToCellWeights = { -0.04580283f, -0.09549462f, -0.032418985f, -0.06454633f, |
| -0.043528453f, 0.043018587f, -0.049152344f, -0.12418144f, |
| -0.078985475f, -0.07596889f, 0.019484362f, -0.11434962f, |
| -0.0074034138f, -0.06314844f, -0.092981495f, 0.0062155537f, |
| -0.025034338f, -0.0028890965f, 0.048929527f, 0.06235075f, |
| 0.10665918f, -0.032036792f, -0.08505916f, -0.10843358f }; |
| |
| std::vector<float> inputToOutputWeights = { -0.0998932f, -0.07201956f, -0.052803773f, -0.15629593f, |
| -0.15001918f, -0.07650751f, 0.02359855f, -0.075155355f, |
| -0.08037709f, -0.15093534f, 0.029517552f, -0.04751393f, |
| 0.010350531f, -0.02664851f, -0.016839722f, -0.023121163f, |
| 0.0077019283f, 0.012851257f, -0.05040649f, -0.0129761f, |
| -0.021737747f, -0.038305793f, -0.06870586f, -0.01481247f }; |
| |
| //tensorInfoOutputSize, |
| bool hasRecurrentToInputWeights = true; |
| std::vector<float> recurrentToInputWeights = { -0.001374326f, -0.078856036f, 0.10672688f, 0.029162422f, |
| -0.11585556f, 0.02557986f, -0.13446963f, -0.035785314f, |
| -0.01244275f, 0.025961924f, -0.02337298f, -0.044228926f, |
| -0.055839065f, -0.046598054f, -0.010546039f, -0.06900766f, |
| 0.027239809f, 0.022582639f, -0.013296484f, -0.05459212f, |
| 0.08981f, -0.045407712f, 0.08682226f, -0.06867011f, |
| -0.14390695f, -0.02916037f, 0.000996957f, 0.091420636f, |
| 0.14283475f, -0.07390571f }; |
| |
| std::vector<float> recurrentToForgetWeights = { -0.057784554f, -0.026057621f, -0.068447545f, -0.022581743f, |
| 0.14811787f, 0.10826372f, 0.09471067f, 0.03987225f, |
| -0.0039523416f, 0.00030638507f, 0.053185795f, 0.10572994f, |
| 0.08414449f, -0.022036452f, -0.00066928595f, -0.09203576f, |
| 0.032950465f, -0.10985798f, -0.023809856f, 0.0021431844f, |
| -0.02196096f, -0.00326074f, 0.00058621005f, -0.074678116f, |
| -0.06193199f, 0.055729095f, 0.03736828f, 0.020123724f, |
| 0.061878487f, -0.04729229f }; |
| |
| std::vector<float> recurrentToCellWeights = { -0.037322544f, 0.018592842f, 0.0056175636f, -0.06253426f, |
| 0.055647098f, -0.05713207f, -0.05626563f, 0.005559383f, |
| 0.03375411f, -0.025757805f, -0.088049285f, 0.06017052f, |
| -0.06570978f, 0.007384076f, 0.035123326f, -0.07920549f, |
| 0.053676967f, 0.044480428f, -0.07663568f, 0.0071805613f, |
| 0.08089997f, 0.05143358f, 0.038261272f, 0.03339287f, |
| -0.027673481f, 0.044746667f, 0.028349208f, 0.020090483f, |
| -0.019443132f, -0.030755889f }; |
| |
| std::vector<float> recurrentToOutputWeights = { 0.025825322f, -0.05813119f, 0.09495884f, |
| -0.045984812f,-0.01255415f, -0.0026479573f, |
| -0.08196161f, -0.054914974f, -0.0046604523f, |
| -0.029587349f, -0.044576716f, -0.07480124f, |
| -0.082868785f, 0.023254942f, 0.027502948f, |
| -0.0039728214f, -0.08683098f, -0.08116779f, |
| -0.014675607f, -0.037924774f, -0.023314456f, |
| -0.007401714f, -0.09255757f, 0.029460307f, |
| -0.08829125f, -0.005139627f, -0.08989442f, |
| -0.0555066f, 0.13596267f, 0.025062224f }; |
| // tensorInfoNumUnits |
| bool hasCellToInputWeights = true; |
| std::vector<float> cellToInputWeights = { 0.040369894f, 0.030746894f, 0.24704495f, |
| 0.018586371f, -0.037586458f, -0.15312155f }; |
| bool hasCellToForgetWeights = true; |
| std::vector<float> cellToForgetWeights = { -0.01998659f, -0.15568835f, -0.24248174f, |
| -0.012770197f, 0.041331276f, -0.072311886f }; |
| bool hasCellToOutputWeights = true; |
| std::vector<float> cellToOutputWeights = { 0.08286371f, -0.08261836f, -0.51210177f, |
| 0.002913762f, 0.17764764f, -0.5495371f }; |
| |
| bool hasInputGateBias = true; |
| std::vector<float> inputGateBias = { 0.02234832f, 0.14757581f, 0.18176508f, |
| 0.10380666f, 0.053110216f, -0.06928846f }; |
| std::vector<float> forgetGateBias = { 0.035185695f, -0.042891346f, -0.03032477f, |
| 0.23027696f, 0.11098921f, 0.08989442f }; |
| std::vector<float> cellBias = { -0.024379363f, 0.0055531194f, 0.23377132f, |
| 0.033463873f, -0.1483596f, 0.029460307f }; |
| std::vector<float> outputGateBias = { 0.046159424f, -0.0012809046f, 0.03563469f, |
| 0.12648113f, 0.027195795f, 0.35373217f }; |
| |
| bool hasProjectionWeights = true; |
| std::vector<float> projectionWeights = { -0.009802181f, 0.09401916f, 0.0717386f, -0.13895074f, 0.09641832f, |
| 0.060420845f, 0.08539281f, 0.054285463f, 0.061395317f, 0.034448683f, |
| -0.042991187f, 0.019801661f, -0.16840284f, -0.015726732f, -0.23041931f, |
| -0.024478018f, -0.10959692f, -0.013875541f, 0.18600968f, -0.061274476f, |
| 0.0138165f, -0.08160894f, -0.07661644f, 0.032372914f, 0.16169067f, |
| 0.22465782f, -0.03993472f, -0.004017731f, 0.08633481f, -0.28869787f }; |
| |
| bool hasProjectionBias = true; |
| std::vector<float> projectionBias(outputSize, 0.f); |
| |
| bool hasInputLayerNormWeights = false; |
| std::vector<float> inputLayerNormWeights; |
| bool hasForgetLayerNormWeights = false; |
| std::vector<float> forgetLayerNormWeights; |
| bool hasCellLayerNormWeights = false; |
| std::vector<float> cellLayerNormWeights; |
| bool hasOutputLayerNormWeights = false; |
| std::vector<float> outputLayerNormWeights; |
| |
| std::vector<float> inputValues = { 1., 2., 3., 4., 5., 4., |
| 3., 2., 1., 2., 3., 4., |
| 5., 4., 3., 2., 1., 2., |
| 1., 2., 3., 4., 5., 4.}; |
| std::vector<float> expectedOutputValues = { -0.0135612f, -0.0263441f, 0.0314008f, -0.00883455f, 0.00763052f, |
| -0.00126877f, -0.0292959f, 0.0449957f, -0.00976195f, -0.00492338f, |
| -0.0175702f, -0.0431753f, 0.0597117f, -0.0169154f, 0.0142087f, |
| 0.00472515f, -0.0196355f, 0.0342524f, -0.00407936f, -0.0253189f, |
| -0.00512944f, -0.0293754f, 0.0512771f, -0.0151874f, -0.0246433f, |
| -0.00744986f, -0.0345103f, 0.0450666f, -0.00944991f, 0.0126895f }; |
| |
| tflite::ActivationFunctionType activationFunction = tflite::ActivationFunctionType_TANH; |
| float clippingThresCell = 10.f; |
| float clippingThresProj = 0.f; |
| bool isTimeMajor = false; |
| |
| UnidirectionalSequenceLstmTestImpl<float>(::tflite::TensorType_FLOAT32, |
| batchSize, |
| timeSize, |
| inputSize, |
| outputSize, |
| numUnits, |
| hasInputToInputWeights, |
| inputToInputWeights, |
| inputToForgetWeights, |
| inputToCellWeights, |
| inputToOutputWeights, |
| hasRecurrentToInputWeights, |
| recurrentToInputWeights, |
| recurrentToForgetWeights, |
| recurrentToCellWeights, |
| recurrentToOutputWeights, |
| hasCellToInputWeights, |
| cellToInputWeights, |
| hasCellToForgetWeights, |
| cellToForgetWeights, |
| hasCellToOutputWeights, |
| cellToOutputWeights, |
| hasInputGateBias, |
| inputGateBias, |
| forgetGateBias, |
| cellBias, |
| outputGateBias, |
| hasProjectionWeights, |
| projectionWeights, |
| hasProjectionBias, |
| projectionBias, |
| hasInputLayerNormWeights, |
| inputLayerNormWeights, |
| hasForgetLayerNormWeights, |
| forgetLayerNormWeights, |
| hasCellLayerNormWeights, |
| cellLayerNormWeights, |
| hasOutputLayerNormWeights, |
| outputLayerNormWeights, |
| inputValues, |
| expectedOutputValues, |
| activationFunction, |
| clippingThresCell, |
| clippingThresProj, |
| isTimeMajor, |
| backends); |
| } |
| |
| void UnidirectionalSequenceLstmWithCifgWithPeepholeNoProjectionTest(const std::vector<armnn::BackendId>& backends = {}) |
| { |
| int32_t batchSize = 3; |
| int32_t timeSize = 2; |
| int32_t inputSize = 3; |
| int32_t outputSize = 4; |
| // cellSize and outputSize have the same size when there is no projection. |
| int32_t numUnits = outputSize; |
| |
| //tensorInfo12 |
| bool hasInputToInputWeights = false; |
| std::vector<float> inputToInputWeights{}; |
| |
| std::vector<float> inputToForgetWeights = { 0.2415594226f, 0.15400093799f, 0.4566498398f, |
| -0.3810434485f, 0.268383264f, -0.009807467424f, |
| -0.3522925403f, -0.24275735512f, -0.28344226125f, |
| 0.13512269116f, -0.4932442977f, -0.10039821991f }; |
| |
| std::vector<float> inputToCellWeights = { -0.2504855627f, 0.184490025045f, -0.2480507493f, |
| 0.386399507f, -0.259465157985f, -0.16545993089f, |
| -0.4230232555f, 0.341664791103f, -0.18127849691f, |
| -0.2277662414f, -0.55275535589f, 0.34184026718f }; |
| |
| std::vector<float> inputToOutputWeights = { 0.2303854227f, 0.5218806862f, -0.4865379333f, |
| 0.53969591851f, 0.23393625035f, -0.27140527306f, |
| 0.50009280443f, 0.07511717046f, 0.3998299249f, |
| -0.51717478049f, 0.1889653282f, -0.367323637f }; |
| |
| //tensorInfo16 |
| bool hasRecurrentToInputWeights = false; |
| std::vector<float> recurrentToInputWeights{}; |
| |
| std::vector<float> recurrentToForgetWeights = { -0.09499983487f, -0.08814888417f, -0.04834804721f, 0.1516668247f, |
| -0.3967529535f, -0.06463699788f, 0.4952811002f, 0.003274492938f, |
| -0.0968840941f, 0.17928104102f, 0.0031281141592f, -0.3387276584f, |
| -0.3587934076f, 0.06705895066f, 0.22463923692f, 0.1961955726f }; |
| |
| std::vector<float> recurrentToCellWeights = { -0.21938985582f, -0.3023648226f, -0.1170005202f, -0.3509177422f, |
| -0.4286288613f, 0.2726137042f, 0.09216640889f, -0.06551410215f, |
| 0.20453298098f, 0.2393476665f, 0.11846517771f, 0.2630801796f, |
| 0.3954237699f, -0.19407111404f, 0.30412107706f, -0.27342408554f }; |
| |
| std::vector<float> recurrentToOutputWeights = { -0.32921677827f, 0.32624614238f, -0.1388191282f, -0.17879831790f, |
| -0.15185534954f, -0.16918526583f, -0.10087361183f, -0.5436913968f, |
| 0.016758225858f, 0.30454617738f, -0.41493862867f, -0.005565764375f, |
| -0.12584099173f, -0.12319286912f, 0.2407919466f, -0.08879069983f }; |
| // tensorInfo4 |
| bool hasCellToInputWeights = false; |
| std::vector<float> cellToInputWeights; |
| bool hasCellToForgetWeights = true; |
| std::vector<float> cellToForgetWeights = {0.47485286f, -0.51955009f, -0.24458408f, 0.31544167f}; |
| bool hasCellToOutputWeights = true; |
| std::vector<float> cellToOutputWeights = {-0.17135078f, 0.82760304f, 0.85573703f, -0.77109635f}; |
| |
| bool hasInputGateBias = false; |
| std::vector<float> inputGateBias; |
| std::vector<float> forgetGateBias = {1., 1., 1., 1.}; |
| std::vector<float> cellBias = {0., 0., 0., 0.}; |
| std::vector<float> outputGateBias = {0., 0., 0., 0.}; |
| |
| bool hasProjectionWeights = false; |
| std::vector<float> projectionWeights; |
| bool hasProjectionBias = false; |
| std::vector<float> projectionBias; |
| |
| bool hasInputLayerNormWeights = false; |
| std::vector<float> inputLayerNormWeights; |
| bool hasForgetLayerNormWeights = false; |
| std::vector<float> forgetLayerNormWeights; |
| bool hasCellLayerNormWeights = false; |
| std::vector<float> cellLayerNormWeights; |
| bool hasOutputLayerNormWeights = false; |
| std::vector<float> outputLayerNormWeights; |
| |
| std::vector<float> inputValues = { 1., 2., 3., 4., 5., 4., |
| 3., 2., 1., 2., 3., 4., |
| 5., 4., 3., 2., 1., 2. }; |
| std::vector<float> expectedOutputValues = { -0.0129257f, -0.070531f, -0.153508f, -0.0392391f, |
| -0.0300169f, -0.195717f, -0.528679f, -0.0818106f, |
| -0.0332748f, 0.155429f, -0.353966f, -0.0801505f, |
| -0.032312f, -0.0407911f, -0.435053f, -0.0932317f, |
| -0.0108233f, 0.165584f, -0.640424f, -0.0447535f, |
| -0.031675f, 0.125987f, -0.526695f, -0.110093f }; |
| |
| tflite::ActivationFunctionType activationFunction = tflite::ActivationFunctionType_TANH; |
| float clippingThresCell = 10.f; |
| float clippingThresProj = 0.f; |
| bool isTimeMajor = false; |
| |
| UnidirectionalSequenceLstmTestImpl<float>(::tflite::TensorType_FLOAT32, |
| batchSize, |
| timeSize, |
| inputSize, |
| outputSize, |
| numUnits, |
| hasInputToInputWeights, |
| inputToInputWeights, |
| inputToForgetWeights, |
| inputToCellWeights, |
| inputToOutputWeights, |
| hasRecurrentToInputWeights, |
| recurrentToInputWeights, |
| recurrentToForgetWeights, |
| recurrentToCellWeights, |
| recurrentToOutputWeights, |
| hasCellToInputWeights, |
| cellToInputWeights, |
| hasCellToForgetWeights, |
| cellToForgetWeights, |
| hasCellToOutputWeights, |
| cellToOutputWeights, |
| hasInputGateBias, |
| inputGateBias, |
| forgetGateBias, |
| cellBias, |
| outputGateBias, |
| hasProjectionWeights, |
| projectionWeights, |
| hasProjectionBias, |
| projectionBias, |
| hasInputLayerNormWeights, |
| inputLayerNormWeights, |
| hasForgetLayerNormWeights, |
| forgetLayerNormWeights, |
| hasCellLayerNormWeights, |
| cellLayerNormWeights, |
| hasOutputLayerNormWeights, |
| outputLayerNormWeights, |
| inputValues, |
| expectedOutputValues, |
| activationFunction, |
| clippingThresCell, |
| clippingThresProj, |
| isTimeMajor, |
| backends); |
| } |
| |
| void UnidirectionalSequenceLstmNoCifgWithPeepholeWithProjectionWithLayerNormTest( |
| const std::vector<armnn::BackendId>& backends = {}) |
| { |
| int32_t batchSize = 3; |
| int32_t timeSize = 2; |
| int32_t inputSize = 3; |
| int32_t outputSize = 4; |
| int32_t numUnits = 5; |
| |
| //tensorInfo15 |
| bool hasInputToInputWeights = true; |
| std::vector<float> inputToInputWeights = { -0.49536117f, -0.0556083915f, -0.102400711f, |
| -0.117484632f, 0.3298470976f, -0.1179017122f, |
| 0.214305695f, 0.42135173085f, 0.003878414626f, |
| -0.348303917f, -0.1881275477f, 0.0343011027f, |
| -0.38837709614f, -0.05636804124f, 0.4259087456f}; |
| |
| std::vector<float> inputToForgetWeights = { 0.2415594226f, 0.15400093799f, 0.4566498398f, |
| -0.3810434485f, 0.268383264f, -0.009807467424f, |
| -0.3522925403f, -0.24275735512f, -0.28344226125f, |
| 0.13512269116f, -0.4932442977f, -0.10039821991f, |
| 0.2726137042f, 0.09216640889f, -0.06551410215f}; |
| |
| std::vector<float> inputToCellWeights = { -0.2504855627f, 0.184490025045f, -0.2480507493f, |
| 0.386399507f, -0.259465157985f, -0.16545993089f, |
| -0.4230232555f, 0.341664791103f, -0.18127849691f, |
| -0.2277662414f, -0.55275535589f, 0.34184026718f, |
| 0.3954237699f, -0.19407111404f, 0.30412107706f}; |
| |
| std::vector<float> inputToOutputWeights = { 0.2303854227f, 0.5218806862f, -0.4865379333f, |
| 0.53969591851f, 0.23393625035f, -0.27140527306f, |
| 0.50009280443f, 0.07511717046f, 0.3998299249f, |
| -0.51717478049f, 0.1889653282f, -0.367323637f, |
| -0.12584099173f, -0.12319286912f, 0.2407919466f}; |
| |
| //tensorInfo20 |
| bool hasRecurrentToInputWeights = true; |
| std::vector<float> recurrentToInputWeights = { -0.128009796112f, 0.1995525098f, -0.07745539397f, 0.1558421701f, |
| -0.265254765766f, -0.38837709614f, -0.05636804124f, 0.4259087456f, |
| 0.17628988623f, 0.3877420127f, 0.53300309181f, -0.0959980934f, |
| 0.00302857416f, 0.3266998827f, -0.142509296562f, -0.04433270756f, |
| 0.54066205f, -0.32668582f, -0.43562764f, -0.56094903f }; |
| |
| std::vector<float> recurrentToForgetWeights = { -0.09499983487f, -0.08814888417f, -0.04834804721f, 0.1516668247f, |
| -0.3967529535f, -0.06463699788f, 0.4952811002f, 0.003274492938f, |
| -0.0968840941f, 0.17928104102f, 0.0031281141592f, -0.3387276584f, |
| -0.3587934076f, 0.06705895066f, 0.22463923692f, 0.1961955726f, |
| 0.01841056f, -0.32764608f, -0.33027974f, -0.10826075f }; |
| |
| std::vector<float> recurrentToCellWeights = { -0.21938985582f, -0.3023648226f, -0.1170005202f, -0.3509177422f, |
| -0.4286288613f, 0.2726137042f, 0.09216640889f, -0.06551410215f, |
| 0.20453298098f, 0.2393476665f, 0.11846517771f, 0.2630801796f, |
| 0.3954237699f, -0.19407111404f, 0.30412107706f, -0.27342408554f, |
| 0.19069612f, -0.03026325f, -0.54532051f, 0.33003211f }; |
| |
| std::vector<float> recurrentToOutputWeights = { -0.32921677827f, 0.32624614238f, -0.1388191282f, -0.17879831790f, |
| -0.15185534954f, -0.16918526583f, -0.10087361183f, -0.5436913968f, |
| 0.016758225858f, 0.30454617738f, -0.41493862867f, -0.005565764375f, |
| -0.12584099173f, -0.12319286912f, 0.2407919466f, -0.08879069983f, |
| 0.11178309f, 0.09481031f, -0.26424935f, 0.46261835f }; |
| // tensorInfo5 |
| bool hasCellToInputWeights = true; |
| std::vector<float> cellToInputWeights = { 0.05f, 0.1f, 0.25f, 0.15f, -0.02f }; |
| bool hasCellToForgetWeights = true; |
| std::vector<float> cellToForgetWeights = { -0.02f, -0.15f, -0.25f, -0.03f, 0.15f }; |
| bool hasCellToOutputWeights = true; |
| std::vector<float> cellToOutputWeights = { 0.1f, -0.1f, -0.5f, 0.05f, 0.01f }; |
| |
| bool hasInputGateBias = true; |
| std::vector<float> inputGateBias = { 0.03f, 0.15f, 0.22f, 0.38f, 0.05f }; |
| std::vector<float> forgetGateBias = { 0.1f, -0.3f, -0.2f, 0.1f, 0.4f }; |
| std::vector<float> cellBias = { -0.05f, 0.72f, 0.25f, 0.08f, 0.1f }; |
| std::vector<float> outputGateBias = { 0.05f, -0.01f, 0.2f, 0.1f, -0.2f }; |
| |
| bool hasProjectionWeights = true; |
| std::vector<float> projectionWeights = { -0.1f, 0.2f, 0.01f, -0.2f, |
| 0.1f, 0.5f, 0.3f, 0.08f, |
| 0.07f, 0.2f, -0.4f, 0.2f, |
| 0.5f, -0.4f, 0.3f, -0.2f, |
| 0.3f, 0.08f, -0.07f, 0.2f}; //{outputSize, numUnits} |
| bool hasProjectionBias = true; |
| std::vector<float> projectionBias(outputSize, 0.f);; |
| |
| bool hasInputLayerNormWeights = true; |
| std::vector<float> inputLayerNormWeights = { 0.1f, 0.2f, 0.3f, 0.5f, 0.8f }; |
| bool hasForgetLayerNormWeights = true; |
| std::vector<float> forgetLayerNormWeights = { 0.1f, 0.2f, 0.3f, 0.5f, 0.2f }; |
| bool hasCellLayerNormWeights = true; |
| std::vector<float> cellLayerNormWeights = { 0.7f, 0.2f, 0.3f, 0.8f, 0.5f }; |
| bool hasOutputLayerNormWeights = true; |
| std::vector<float> outputLayerNormWeights = { 0.6f, 0.2f, 0.2f, 0.5f, 0.1f }; |
| |
| std::vector<float> inputValues = { 1., 2., 3., 4., 5., 4., |
| 3., 2., 1., 2., 3., 4., |
| 5., 4., 3., 2., 1., 2. }; |
| std::vector<float> expectedOutputValues = { 0.0642256f, 0.0343966f, 0.184122f, 0.114717f, |
| 0.11458f, 0.0407109f, 0.300327f, 0.174301f, |
| 0.0864761f, 0.0362912f, 0.178635f, 0.115689f, |
| 0.108008f, 0.0386623f, 0.273471f, 0.167115f, |
| 0.0859545f, 0.0331481f, 0.186051f, 0.11888f, |
| 0.106649f, 0.0276847f, 0.229863f, 0.166958f }; |
| |
| tflite::ActivationFunctionType activationFunction = tflite::ActivationFunctionType_TANH; |
| float clippingThresCell = 10.f; |
| float clippingThresProj = 0.f; |
| bool isTimeMajor = false; |
| |
| UnidirectionalSequenceLstmTestImpl<float>(::tflite::TensorType_FLOAT32, |
| batchSize, |
| timeSize, |
| inputSize, |
| outputSize, |
| numUnits, |
| hasInputToInputWeights, |
| inputToInputWeights, |
| inputToForgetWeights, |
| inputToCellWeights, |
| inputToOutputWeights, |
| hasRecurrentToInputWeights, |
| recurrentToInputWeights, |
| recurrentToForgetWeights, |
| recurrentToCellWeights, |
| recurrentToOutputWeights, |
| hasCellToInputWeights, |
| cellToInputWeights, |
| hasCellToForgetWeights, |
| cellToForgetWeights, |
| hasCellToOutputWeights, |
| cellToOutputWeights, |
| hasInputGateBias, |
| inputGateBias, |
| forgetGateBias, |
| cellBias, |
| outputGateBias, |
| hasProjectionWeights, |
| projectionWeights, |
| hasProjectionBias, |
| projectionBias, |
| hasInputLayerNormWeights, |
| inputLayerNormWeights, |
| hasForgetLayerNormWeights, |
| forgetLayerNormWeights, |
| hasCellLayerNormWeights, |
| cellLayerNormWeights, |
| hasOutputLayerNormWeights, |
| outputLayerNormWeights, |
| inputValues, |
| expectedOutputValues, |
| activationFunction, |
| clippingThresCell, |
| clippingThresProj, |
| isTimeMajor, |
| backends); |
| } |
| |
| void UnidirectionalSequenceLstmInt8Test(const std::vector<armnn::BackendId>& backends = {}) |
| { |
| int32_t batchSize = 3; |
| int32_t timeSize = 2; |
| int32_t inputSize = 3; |
| int32_t outputSize = 4; |
| // cellSize and outputSize have the same size when there is no projection. |
| int32_t numUnits = outputSize; |
| |
| //tensorInfo12 |
| bool hasInputToInputWeights = true; |
| std::vector<int8_t> inputToInputWeights = { -4, -1, -1, -2, 3, -2, 2, 4, 1, -4, -2, 3 }; |
| |
| std::vector<int8_t> inputToForgetWeights = { 2, 1, 4, -4, 3, -1, -3, -2, -3, 1, -4, -1 }; |
| |
| std::vector<int8_t> inputToCellWeights = { -2, 1, -2, 4, -3, -2, -4, 3, -2, -2, -6, 3 }; |
| |
| std::vector<int8_t> inputToOutputWeights = { 2, 5, -4, 5, 2, -3, 5, 7, 3, -5, 1, -4 }; |
| |
| //tensorInfo16 |
| bool hasRecurrentToInputWeights = true; |
| std::vector<int8_t> recurrentToInputWeights = { -1, 1, -1, 1, -3, -4, -1, 4, 2, 3, 5, -1, 1, 3, -1, -1 }; |
| |
| std::vector<int8_t> recurrentToForgetWeights = { -1, 1, -1, 1, -3, -4, -1, 4, 2, 3, 5, -1, 1, 3, -2, -1 }; |
| |
| std::vector<int8_t> recurrentToCellWeights = { -2, -3, -1, -3, -4, 2, 1, -1, 2, 2, 1, 2, 3, -2, 3, -3 }; |
| |
| std::vector<int8_t> recurrentToOutputWeights = { -3, 3, -1, -2, -2, -2, -1, -5, 1, 3, -4, -1, -1, -1, 2, -1 }; |
| |
| // tensorInfo4 |
| bool hasCellToInputWeights = false; |
| std::vector<int8_t> cellToInputWeights; |
| bool hasCellToForgetWeights = false; |
| std::vector<int8_t> cellToForgetWeights; |
| bool hasCellToOutputWeights = false; |
| std::vector<int8_t> cellToOutputWeights; |
| |
| bool hasInputGateBias = true; |
| std::vector<float> inputGateBias = { 0., 0., 0., 0. }; |
| std::vector<float> forgetGateBias = { 1., 1., 1., 1. }; |
| std::vector<float> cellBias = { 0., 0., 0., 0. }; |
| std::vector<float> outputGateBias = { 0., 0., 0., 0. }; |
| |
| bool hasProjectionWeights = false; |
| std::vector<int8_t> projectionWeights; |
| bool hasProjectionBias = false; |
| std::vector<float> projectionBias; |
| |
| bool hasInputLayerNormWeights = false; |
| std::vector<float> inputLayerNormWeights; |
| bool hasForgetLayerNormWeights = false; |
| std::vector<float> forgetLayerNormWeights; |
| bool hasCellLayerNormWeights = false; |
| std::vector<float> cellLayerNormWeights; |
| bool hasOutputLayerNormWeights = false; |
| std::vector<float> outputLayerNormWeights; |
| |
| std::vector<float> inputValues = { 0.1f, 0.2f, 0.3f, 0.4f, 0.5f, 0.4f, |
| 0.3f, 0.2f, 0.1f, 0.2f, 0.3f, 0.4f, |
| 0.5f, 0.4f, 0.3f, 0.2f, 0.1f, 0.2f }; |
| |
| std::vector<float> expectedOutputValues = { -0.0142517f, -0.0198845f, -0.0120569f, -0.0116868f, |
| -0.0350714f, -0.0343202f, -0.047504f, -0.0569789f, |
| -0.0146346f, 0.0106663f, -0.0247238f, -0.0319502f, |
| -0.0294759f, -0.0129935f, -0.0444175f, -0.0444354f, |
| -0.0280855f, 0.00545101f, -0.051422f, -0.0463838f, |
| -0.0310702f, 0.00915739f, -0.0625207f, -0.0482648f }; |
| |
| tflite::ActivationFunctionType activationFunction = tflite::ActivationFunctionType_TANH; |
| float clippingThresCell = 10.f; |
| float clippingThresProj = 0.f; |
| bool isTimeMajor = false; |
| |
| UnidirectionalSequenceLstmTestImpl<int8_t>(::tflite::TensorType_INT8, |
| batchSize, |
| timeSize, |
| inputSize, |
| outputSize, |
| numUnits, |
| hasInputToInputWeights, |
| inputToInputWeights, |
| inputToForgetWeights, |
| inputToCellWeights, |
| inputToOutputWeights, |
| hasRecurrentToInputWeights, |
| recurrentToInputWeights, |
| recurrentToForgetWeights, |
| recurrentToCellWeights, |
| recurrentToOutputWeights, |
| hasCellToInputWeights, |
| cellToInputWeights, |
| hasCellToForgetWeights, |
| cellToForgetWeights, |
| hasCellToOutputWeights, |
| cellToOutputWeights, |
| hasInputGateBias, |
| inputGateBias, |
| forgetGateBias, |
| cellBias, |
| outputGateBias, |
| hasProjectionWeights, |
| projectionWeights, |
| hasProjectionBias, |
| projectionBias, |
| hasInputLayerNormWeights, |
| inputLayerNormWeights, |
| hasForgetLayerNormWeights, |
| forgetLayerNormWeights, |
| hasCellLayerNormWeights, |
| cellLayerNormWeights, |
| hasOutputLayerNormWeights, |
| outputLayerNormWeights, |
| inputValues, |
| expectedOutputValues, |
| activationFunction, |
| clippingThresCell, |
| clippingThresProj, |
| isTimeMajor, |
| backends, |
| 0.1f); |
| } |
| |
| void UnidirectionalSequenceLstmInt8TimeMajorTest(const std::vector<armnn::BackendId>& backends = {}) |
| { |
| int32_t batchSize = 3; |
| int32_t timeSize = 2; |
| int32_t inputSize = 3; |
| int32_t outputSize = 4; |
| // cellSize and outputSize have the same size when there is no projection. |
| int32_t numUnits = outputSize; |
| |
| //tensorInfo12 |
| bool hasInputToInputWeights = true; |
| std::vector<int8_t> inputToInputWeights = { -4, -1, -1, -2, 3, -2, 2, 4, 1, -4, -2, 3 }; |
| |
| std::vector<int8_t> inputToForgetWeights = { 2, 1, 4, -4, 3, -1, -3, -2, -3, 1, -4, -1 }; |
| |
| std::vector<int8_t> inputToCellWeights = { -2, 1, -2, 4, -3, -2, -4, 3, -2, -2, -6, 3 }; |
| |
| std::vector<int8_t> inputToOutputWeights = { 2, 5, -4, 5, 2, -3, 5, 7, 3, -5, 1, -4 }; |
| |
| //tensorInfo16 |
| bool hasRecurrentToInputWeights = true; |
| std::vector<int8_t> recurrentToInputWeights = { -1, 1, -1, 1, -3, -4, -1, 4, 2, 3, 5, -1, 1, 3, -1, -1 }; |
| |
| std::vector<int8_t> recurrentToForgetWeights = { -1, 1, -1, 1, -3, -4, -1, 4, 2, 3, 5, -1, 1, 3, -2, -1 }; |
| |
| std::vector<int8_t> recurrentToCellWeights = { -2, -3, -1, -3, -4, 2, 1, -1, 2, 2, 1, 2, 3, -2, 3, -3 }; |
| |
| std::vector<int8_t> recurrentToOutputWeights = { -3, 3, -1, -2, -2, -2, -1, -5, 1, 3, -4, -1, -1, -1, 2, -1 }; |
| |
| // tensorInfo4 |
| bool hasCellToInputWeights = false; |
| std::vector<int8_t> cellToInputWeights; |
| bool hasCellToForgetWeights = false; |
| std::vector<int8_t> cellToForgetWeights; |
| bool hasCellToOutputWeights = false; |
| std::vector<int8_t> cellToOutputWeights; |
| |
| bool hasInputGateBias = true; |
| std::vector<float> inputGateBias = { 0., 0., 0., 0. }; |
| std::vector<float> forgetGateBias = { 1., 1., 1., 1. }; |
| std::vector<float> cellBias = { 0., 0., 0., 0. }; |
| std::vector<float> outputGateBias = { 0., 0., 0., 0. }; |
| |
| bool hasProjectionWeights = false; |
| std::vector<int8_t> projectionWeights; |
| bool hasProjectionBias = false; |
| std::vector<float> projectionBias; |
| |
| bool hasInputLayerNormWeights = false; |
| std::vector<float> inputLayerNormWeights; |
| bool hasForgetLayerNormWeights = false; |
| std::vector<float> forgetLayerNormWeights; |
| bool hasCellLayerNormWeights = false; |
| std::vector<float> cellLayerNormWeights; |
| bool hasOutputLayerNormWeights = false; |
| std::vector<float> outputLayerNormWeights; |
| |
| std::vector<float> inputValues = { 0.1f, 0.2f, 0.3f, 0.4f, 0.5f, 0.4f, |
| 0.3f, 0.2f, 0.1f, 0.2f, 0.3f, 0.4f, |
| 0.5f, 0.4f, 0.3f, 0.2f, 0.1f, 0.2f }; |
| |
| std::vector<float> expectedOutputValues = { -0.0142517f, -0.0198845f, -0.0120122f, -0.0116868f, |
| -0.0261295f, -0.0188487f, -0.0345463f, -0.049733f, |
| -0.0146346f, 0.0106663f, -0.0247238f, -0.0319502f, |
| -0.0291863f, -0.0369402f, -0.0354071f, -0.0296529f, |
| -0.0419539f, -0.00617731f, -0.0814796f, -0.0804005f, |
| -0.0244737f, 0.0119905f, -0.0457527f, -0.0331862f }; |
| |
| tflite::ActivationFunctionType activationFunction = tflite::ActivationFunctionType_TANH; |
| float clippingThresCell = 10.f; |
| float clippingThresProj = 0.f; |
| bool isTimeMajor = true; |
| |
| UnidirectionalSequenceLstmTestImpl<int8_t>(::tflite::TensorType_INT8, |
| batchSize, |
| timeSize, |
| inputSize, |
| outputSize, |
| numUnits, |
| hasInputToInputWeights, |
| inputToInputWeights, |
| inputToForgetWeights, |
| inputToCellWeights, |
| inputToOutputWeights, |
| hasRecurrentToInputWeights, |
| recurrentToInputWeights, |
| recurrentToForgetWeights, |
| recurrentToCellWeights, |
| recurrentToOutputWeights, |
| hasCellToInputWeights, |
| cellToInputWeights, |
| hasCellToForgetWeights, |
| cellToForgetWeights, |
| hasCellToOutputWeights, |
| cellToOutputWeights, |
| hasInputGateBias, |
| inputGateBias, |
| forgetGateBias, |
| cellBias, |
| outputGateBias, |
| hasProjectionWeights, |
| projectionWeights, |
| hasProjectionBias, |
| projectionBias, |
| hasInputLayerNormWeights, |
| inputLayerNormWeights, |
| hasForgetLayerNormWeights, |
| forgetLayerNormWeights, |
| hasCellLayerNormWeights, |
| cellLayerNormWeights, |
| hasOutputLayerNormWeights, |
| outputLayerNormWeights, |
| inputValues, |
| expectedOutputValues, |
| activationFunction, |
| clippingThresCell, |
| clippingThresProj, |
| isTimeMajor, |
| backends, |
| 0.1); |
| } |
| |
| void UnidirectionalSequenceLstmInt8NoCifgWithPeepholeWithProjectionTest( |
| const std::vector<armnn::BackendId>& backends = {}) |
| { |
| int32_t batchSize = 3; |
| int32_t timeSize = 2; |
| int32_t inputSize = 3; |
| int32_t outputSize = 4; |
| int32_t numUnits = 4; |
| |
| bool hasInputToInputWeights = true; |
| std::vector<int8_t> inputToInputWeights = { -4, -1, -1, -2, 3, -2, 2, 4, 1, -4, -2, 3 }; |
| |
| std::vector<int8_t> inputToForgetWeights = { 2, 1, 4, -4, 3, -1, -3, -2, -3, 1, -4, -1 }; |
| |
| std::vector<int8_t> inputToCellWeights = { -2, 1, -2, 4, -3, -2, -4, 3, -2, -2, -6, 3 }; |
| |
| std::vector<int8_t> inputToOutputWeights = { 2, 5, -4, 5, 2, -3, 5, 7, 3, -5, 1, -4 }; |
| |
| //tensorInfo16 |
| bool hasRecurrentToInputWeights = true; |
| std::vector<int8_t> recurrentToInputWeights = { -1, 1, -1, 1, -3, -4, -1, 4, 2, 3, 5, -1, 1, 3, -1, -1 }; |
| |
| std::vector<int8_t> recurrentToForgetWeights = { -1, 1, -1, 1, -3, -4, -1, 4, 2, 3, 5, -1, 1, 3, -2, -1 }; |
| |
| std::vector<int8_t> recurrentToCellWeights = { -2, -3, -1, -3, -4, 2, 1, -1, 2, 2, 1, 2, 3, -2, 3, -3 }; |
| |
| std::vector<int8_t> recurrentToOutputWeights = { -3, 3, -1, -2, -2, -2, -1, -5, 1, 3, -4, -1, -1, -1, 2, -1 }; |
| |
| // tensorInfo4 |
| bool hasCellToInputWeights = true; |
| std::vector<int8_t> cellToInputWeights = { 5, 10, 25, 15 }; |
| bool hasCellToForgetWeights = true; |
| std::vector<int8_t> cellToForgetWeights = { -5, 15, 25, 3 }; |
| bool hasCellToOutputWeights = true; |
| std::vector<int8_t> cellToOutputWeights = { 10, -10, -5, 50 }; |
| |
| bool hasInputGateBias = true; |
| std::vector<float> inputGateBias = { 0.02234832f, 0.14757581f, 0.18176508f, 0.10380666f}; |
| std::vector<float> forgetGateBias = { 0.035185695f, -0.042891346f, -0.3032477f, 0.23027696f}; |
| std::vector<float> cellBias = { -0.124379363f, 0.55531194f, 0.23377132f, 0.033463873f }; |
| std::vector<float> outputGateBias = { 0.046159424f, -0.12809046f, 0.03563469f, 0.12648113f }; |
| |
| bool hasProjectionWeights = true; |
| std::vector<int8_t> projectionWeights = { -25, 51, 3, -5, 25, 127, 77, 20, 18, 51, -10, 51, -25, 88, 77, -13 }; |
| bool hasProjectionBias = true; |
| std::vector<float> projectionBias(outputSize, 0.f); |
| |
| bool hasInputLayerNormWeights = false; |
| std::vector<float> inputLayerNormWeights; |
| bool hasForgetLayerNormWeights = false; |
| std::vector<float> forgetLayerNormWeights; |
| bool hasCellLayerNormWeights = false; |
| std::vector<float> cellLayerNormWeights; |
| bool hasOutputLayerNormWeights = false; |
| std::vector<float> outputLayerNormWeights; |
| |
| std::vector<float> inputValues = { 0.1f, 0.2f, 0.3f, 0.4f, 0.5f, 0.4f, |
| 0.3f, 0.2f, 0.1f, 0.2f, 0.3f, 0.4f, |
| 0.5f, 0.4f, 0.3f, 0.2f, 0.1f, 0.2f }; |
| |
| std::vector<float> expectedOutputValues = { 0.612103f, 1.56788f, 0.31966f, 1.42956f, |
| 0.909718f, 3.07916f, -0.560586f, 3.8907f, |
| 0.753671f, 1.77485f, 0.365122f, 1.60077f, |
| 0.812644f, 2.79092f, -0.605396f, 3.61742f, |
| 0.791857f, 1.64353f, 0.316588f, 1.55192f, |
| 0.807265f, 2.47012f, -0.539598f, 3.25654f }; |
| |
| tflite::ActivationFunctionType activationFunction = tflite::ActivationFunctionType_TANH; |
| float clippingThresCell = 10.f; |
| float clippingThresProj = 0.f; |
| bool isTimeMajor = false; |
| |
| UnidirectionalSequenceLstmTestImpl<int8_t>(::tflite::TensorType_INT8, |
| batchSize, |
| timeSize, |
| inputSize, |
| outputSize, |
| numUnits, |
| hasInputToInputWeights, |
| inputToInputWeights, |
| inputToForgetWeights, |
| inputToCellWeights, |
| inputToOutputWeights, |
| hasRecurrentToInputWeights, |
| recurrentToInputWeights, |
| recurrentToForgetWeights, |
| recurrentToCellWeights, |
| recurrentToOutputWeights, |
| hasCellToInputWeights, |
| cellToInputWeights, |
| hasCellToForgetWeights, |
| cellToForgetWeights, |
| hasCellToOutputWeights, |
| cellToOutputWeights, |
| hasInputGateBias, |
| inputGateBias, |
| forgetGateBias, |
| cellBias, |
| outputGateBias, |
| hasProjectionWeights, |
| projectionWeights, |
| hasProjectionBias, |
| projectionBias, |
| hasInputLayerNormWeights, |
| inputLayerNormWeights, |
| hasForgetLayerNormWeights, |
| forgetLayerNormWeights, |
| hasCellLayerNormWeights, |
| cellLayerNormWeights, |
| hasOutputLayerNormWeights, |
| outputLayerNormWeights, |
| inputValues, |
| expectedOutputValues, |
| activationFunction, |
| clippingThresCell, |
| clippingThresProj, |
| isTimeMajor, |
| backends, |
| 0.1f); |
| } |
| |
| void UnidirectionalSequenceLstmInt8WithCifgWithPeepholeNoProjectionTest( |
| const std::vector<armnn::BackendId>& backends = {}) |
| { |
| int32_t batchSize = 3; |
| int32_t timeSize = 2; |
| int32_t inputSize = 3; |
| int32_t outputSize = 4; |
| // cellSize and outputSize have the same size when there is no projection. |
| int32_t numUnits = outputSize; |
| |
| //tensorInfo12, |
| bool hasInputToInputWeights = false; |
| std::vector<int8_t> inputToInputWeights; |
| |
| std::vector<int8_t> inputToForgetWeights = { 2, 1, 4, -4, 3, -1, -3, -2, -3, 1, -4, -1 }; |
| |
| std::vector<int8_t> inputToCellWeights = { -2, 1, -2, 4, -3, -2, -4, 3, -2, -2, -6, 3 }; |
| |
| std::vector<int8_t> inputToOutputWeights = { 2, 5, -4, 5, 2, -3, 5, 7, 3, -5, 1, -4 }; |
| |
| //tensorInfo16, |
| bool hasRecurrentToInputWeights = false; |
| std::vector<int8_t> recurrentToInputWeights; |
| std::vector<int8_t> recurrentToForgetWeights = { -1, 1, -1, 1, -3, -4, -1, 4, 2, 3, 5, -1, 1, 3, -2, -1 }; |
| |
| std::vector<int8_t> recurrentToCellWeights = { -2, -3, -1, -3, -4, 2, 1, -1, 2, 2, 1, 2, 3, -2, 3, -3 }; |
| |
| std::vector<int8_t> recurrentToOutputWeights = { -3, 3, -1, -2, -2, -2, -1, -5, 1, 3, -4, -1, -1, -1, 2, -1 }; |
| |
| // tensorInfo4 |
| bool hasCellToInputWeights = false; |
| std::vector<int8_t> cellToInputWeights; |
| bool hasCellToForgetWeights = true; |
| std::vector<int8_t> cellToForgetWeights = { 47, -52, -24, 31 }; |
| bool hasCellToOutputWeights = true; |
| std::vector<int8_t> cellToOutputWeights = { -17, 82, 85, -77 }; |
| |
| bool hasInputGateBias = false; |
| std::vector<float> inputGateBias; |
| std::vector<float> forgetGateBias = { 1., 1., 1., 1. }; |
| std::vector<float> cellBias = { 0., 0., 0., 0. }; |
| std::vector<float> outputGateBias = { 0., 0., 0., 0. }; |
| |
| bool hasProjectionWeights = false; |
| std::vector<int8_t> projectionWeights; |
| bool hasProjectionBias = false; |
| std::vector<float> projectionBias; |
| |
| bool hasInputLayerNormWeights = false; |
| std::vector<float> inputLayerNormWeights; |
| bool hasForgetLayerNormWeights = false; |
| std::vector<float> forgetLayerNormWeights; |
| bool hasCellLayerNormWeights = false; |
| std::vector<float> cellLayerNormWeights; |
| bool hasOutputLayerNormWeights = false; |
| std::vector<float> outputLayerNormWeights; |
| |
| std::vector<float> inputValues = { 0.1f, 0.2f, 0.3f, 0.4f, 0.5f, 0.4f, |
| 0.3f, 0.2f, 0.1f, 0.2f, 0.3f, 0.4f, |
| 0.5f, 0.4f, 0.3f, 0.2f, 0.1f, 0.2f }; |
| |
| std::vector<float> expectedOutputValues = { -0.0072104f, -0.00991171f, -0.00650478f, -0.00713055f, |
| -0.0191782f, -0.0161269f, -0.0233683f, -0.054299f, |
| -0.00783725f, 0.00635271f, -0.0126718f, -0.022613f, |
| -0.0161351f, -0.00775868f, -0.021054f, -0.0339778f, |
| -0.0146392f, 0.00330261f, -0.0258733f, -0.0407797f, |
| -0.0174297f, 0.0050105f, -0.0266275f, -0.0362564f }; |
| |
| tflite::ActivationFunctionType activationFunction = tflite::ActivationFunctionType_TANH; |
| float clippingThresCell = 10.f; |
| float clippingThresProj = 0.f; |
| bool isTimeMajor = false; |
| |
| UnidirectionalSequenceLstmTestImpl<int8_t>(::tflite::TensorType_INT8, |
| batchSize, |
| timeSize, |
| inputSize, |
| outputSize, |
| numUnits, |
| hasInputToInputWeights, |
| inputToInputWeights, |
| inputToForgetWeights, |
| inputToCellWeights, |
| inputToOutputWeights, |
| hasRecurrentToInputWeights, |
| recurrentToInputWeights, |
| recurrentToForgetWeights, |
| recurrentToCellWeights, |
| recurrentToOutputWeights, |
| hasCellToInputWeights, |
| cellToInputWeights, |
| hasCellToForgetWeights, |
| cellToForgetWeights, |
| hasCellToOutputWeights, |
| cellToOutputWeights, |
| hasInputGateBias, |
| inputGateBias, |
| forgetGateBias, |
| cellBias, |
| outputGateBias, |
| hasProjectionWeights, |
| projectionWeights, |
| hasProjectionBias, |
| projectionBias, |
| hasInputLayerNormWeights, |
| inputLayerNormWeights, |
| hasForgetLayerNormWeights, |
| forgetLayerNormWeights, |
| hasCellLayerNormWeights, |
| cellLayerNormWeights, |
| hasOutputLayerNormWeights, |
| outputLayerNormWeights, |
| inputValues, |
| expectedOutputValues, |
| activationFunction, |
| clippingThresCell, |
| clippingThresProj, |
| isTimeMajor, |
| backends, |
| 0.1); |
| } |
| |
| void UnidirectionalSequenceLstmInt8NoCifgWithPeepholeWithProjectionWithLayerNormTest( |
| const std::vector<armnn::BackendId>& backends = {}) |
| { |
| int32_t batchSize = 3; |
| int32_t timeSize = 2; |
| int32_t inputSize = 3; |
| int32_t outputSize = 4; |
| int32_t numUnits = 5; |
| |
| bool hasInputToInputWeights = true; |
| std::vector<int8_t> inputToInputWeights = { -4, -1, -1, -2, 3, -2, 2, 4, 1, -4, -2, 3, 2, 2, -4 }; |
| |
| std::vector<int8_t> inputToForgetWeights = { 2, 1, 4, -4, 3, -1, -3, -2, -3, 1, -4, -1, -3, -2, -4 }; |
| |
| std::vector<int8_t> inputToCellWeights = { -2, 1, -2, 4, -3, -2, -4, 3, -2, -2, -6, 3, 2, 5, -4 }; |
| |
| std::vector<int8_t> inputToOutputWeights = { 2, 5, -4, 5, 2, -3, 5, 7, 3, -5, 1, -4, -4, -1, -1 }; |
| |
| bool hasRecurrentToInputWeights = true; |
| std::vector<int8_t> recurrentToInputWeights = { -1, 1, -1, 1, -3, -4, -1, 4, 2, 3, |
| 5, -1, 1, 3, -1, -1, -1, 4, 2, 3 }; |
| |
| std::vector<int8_t> recurrentToForgetWeights = { -1, 1, -1, 1, -3, -4, -1, 4, 2, 3, |
| 5, -1, 1, 3, -2, -1, -1, 2, 2, 1 }; |
| |
| std::vector<int8_t> recurrentToCellWeights = { -2, -3, -1, -3, -4, 2, 1, -1, 2, 2, |
| 1, 2, 3, -2, 3, -3, -1, -5, 1, 3 }; |
| |
| std::vector<int8_t> recurrentToOutputWeights = { -3, 3, -1, -2, -2, -2, -1, -5, 1, 3, |
| -4, -1, -1, -1, 2, -1, 5, 1, -3, -4 }; |
| |
| // tensorInfo5 |
| bool hasCellToInputWeights = true; |
| std::vector<int8_t> cellToInputWeights = { 5, 3, 8, -5, 2 }; |
| bool hasCellToForgetWeights = true; |
| std::vector<int8_t> cellToForgetWeights = { -2, -7, 5, -3, 4 }; |
| bool hasCellToOutputWeights = true; |
| std::vector<int8_t> cellToOutputWeights = { 9, -10 , -5, 5, 1 }; |
| |
| bool hasInputGateBias = true; |
| std::vector<float> inputGateBias = { 0.03f, 0.15f, 0.22f, 0.38f, 0.05f }; |
| std::vector<float> forgetGateBias = { 0.1f, -0.3f, -0.2f, 0.1f, 0.4f }; |
| std::vector<float> cellBias = { -0.05f, 0.72f, 0.25f, 0.08f, 0.1f }; |
| std::vector<float> outputGateBias = { 0.05f, -0.01f, 0.2f, 0.1f, -0.2f }; |
| |
| bool hasProjectionWeights = true; |
| std::vector<int8_t> projectionWeights = { -1, 2, 1, -2, 1, 5, 3, 8, 7, 2, |
| -4, 2, 5, -4, 3, -2, 3, 8, -7, 2 }; |
| bool hasProjectionBias = true; |
| std::vector<float> projectionBias(outputSize, 0.f); |
| |
| bool hasInputLayerNormWeights = true; |
| std::vector<float> inputLayerNormWeights = { 0.1f, 0.2f, -0.3f, -0.1f, 0.5f }; |
| bool hasForgetLayerNormWeights = true; |
| std::vector<float> forgetLayerNormWeights = { -0.1f, 0.2f, 0.3f, 0.5f, 0.2f }; |
| bool hasCellLayerNormWeights = true; |
| std::vector<float> cellLayerNormWeights = { 0.5f, 0.2f, 0.3f, 0.4f, -0.5f }; |
| bool hasOutputLayerNormWeights = true; |
| std::vector<float> outputLayerNormWeights = { 0.6f, -0.2f, -0.2f, 0.5f, 0.1f }; |
| |
| std::vector<float> inputValues = { 1., 8., 3., 4., 5., 4., |
| 3., 2., 1., 2., 3., 4., |
| 5., 4., 3., 2., 1., 2. }; |
| |
| std::vector<float> expectedOutputValues = { 0.0471276f, 0.0168155f, 0.0789885f, 0.16550f, |
| 0.0643133f, -0.0400722f, 0.100593f, 0.197722f, |
| 0.0465562f, -0.0600682f, 0.0622087f, 0.115053f, |
| 0.056287f, -0.0566218f, 0.0856832f, 0.148484f, |
| 0.0457859f, -0.0588112f, 0.0623636f, 0.114333f, |
| 0.0509271f, -0.0754262f, 0.058600f, 0.0801288f }; |
| |
| tflite::ActivationFunctionType activationFunction = tflite::ActivationFunctionType_TANH; |
| float clippingThresCell = 10.f; |
| float clippingThresProj = 0.f; |
| bool isTimeMajor = false; |
| |
| UnidirectionalSequenceLstmTestImpl<int8_t>(::tflite::TensorType_INT8, |
| batchSize, |
| timeSize, |
| inputSize, |
| outputSize, |
| numUnits, |
| hasInputToInputWeights, |
| inputToInputWeights, |
| inputToForgetWeights, |
| inputToCellWeights, |
| inputToOutputWeights, |
| hasRecurrentToInputWeights, |
| recurrentToInputWeights, |
| recurrentToForgetWeights, |
| recurrentToCellWeights, |
| recurrentToOutputWeights, |
| hasCellToInputWeights, |
| cellToInputWeights, |
| hasCellToForgetWeights, |
| cellToForgetWeights, |
| hasCellToOutputWeights, |
| cellToOutputWeights, |
| hasInputGateBias, |
| inputGateBias, |
| forgetGateBias, |
| cellBias, |
| outputGateBias, |
| hasProjectionWeights, |
| projectionWeights, |
| hasProjectionBias, |
| projectionBias, |
| hasInputLayerNormWeights, |
| inputLayerNormWeights, |
| hasForgetLayerNormWeights, |
| forgetLayerNormWeights, |
| hasCellLayerNormWeights, |
| cellLayerNormWeights, |
| hasOutputLayerNormWeights, |
| outputLayerNormWeights, |
| inputValues, |
| expectedOutputValues, |
| activationFunction, |
| clippingThresCell, |
| clippingThresProj, |
| isTimeMajor, |
| backends, |
| 0.1); |
| } |
| |
| TEST_SUITE("UnidirectionalSequenceLstmTestTests") |
| { |
| |
| TEST_CASE ("UnidirectionalSequenceLstmTest_Test") |
| { |
| std::vector <armnn::BackendId> backends = {armnn::Compute::CpuRef}; |
| UnidirectionalSequenceLstmTest(backends); |
| } |
| |
| TEST_CASE ("UnidirectionalSequenceLstmTimeMajorTest_Test") |
| { |
| std::vector <armnn::BackendId> backends = {armnn::Compute::CpuRef}; |
| UnidirectionalSequenceLstmTimeMajorTest(backends); |
| } |
| |
| TEST_CASE ("UnidirectionalSequenceLstmTimeMajorSingleTimeTest_Test") |
| { |
| std::vector <armnn::BackendId> backends = {armnn::Compute::CpuRef}; |
| UnidirectionalSequenceLstmTimeMajorSingleTimeTest(backends); |
| } |
| |
| TEST_CASE ("UnidirectionalSequenceLstmNoCifgWithPeepholeWithProjectionTest_Test") |
| { |
| std::vector <armnn::BackendId> backends = {armnn::Compute::CpuRef}; |
| UnidirectionalSequenceLstmNoCifgWithPeepholeWithProjectionTest(backends); |
| } |
| |
| TEST_CASE ("UnidirectionalSequenceLstmWithCifgWithPeepholeNoProjectionTest_Test") |
| { |
| std::vector <armnn::BackendId> backends = {armnn::Compute::CpuRef}; |
| UnidirectionalSequenceLstmWithCifgWithPeepholeNoProjectionTest(backends); |
| } |
| |
| TEST_CASE ("UnidirectionalSequenceLstmNoCifgWithPeepholeWithProjectionWithLayerNormTest_Test") |
| { |
| std::vector <armnn::BackendId> backends = {armnn::Compute::CpuRef}; |
| UnidirectionalSequenceLstmNoCifgWithPeepholeWithProjectionWithLayerNormTest(backends); |
| } |
| |
| TEST_CASE ("UnidirectionalSequenceLstmInt8Test_Test") |
| { |
| std::vector <armnn::BackendId> backends = {armnn::Compute::CpuRef}; |
| UnidirectionalSequenceLstmInt8Test(backends); |
| } |
| |
| TEST_CASE ("UnidirectionalSequenceLstmTimeInt8TimeMajorTest_Test") |
| { |
| std::vector <armnn::BackendId> backends = {armnn::Compute::CpuRef}; |
| UnidirectionalSequenceLstmInt8TimeMajorTest(backends); |
| } |
| |
| TEST_CASE ("UnidirectionalSequenceLstmInt8NoCifgWithPeepholeWithProjectionTest_Test") |
| { |
| std::vector <armnn::BackendId> backends = {armnn::Compute::CpuRef}; |
| UnidirectionalSequenceLstmInt8NoCifgWithPeepholeWithProjectionTest(backends); |
| } |
| |
| TEST_CASE ("UnidirectionalSequenceLstmInt8WithCifgWithPeepholeNoProjectionTest_Test") |
| { |
| std::vector <armnn::BackendId> backends = {armnn::Compute::CpuRef}; |
| UnidirectionalSequenceLstmInt8WithCifgWithPeepholeNoProjectionTest(backends); |
| } |
| |
| TEST_CASE ("UnidirectionalSequenceLstmInt8NoCifgWithPeepholeWithProjectionWithLayerNormTest_Test") |
| { |
| std::vector <armnn::BackendId> backends = {armnn::Compute::CpuRef}; |
| UnidirectionalSequenceLstmInt8NoCifgWithPeepholeWithProjectionWithLayerNormTest(backends); |
| } |
| |
| } //End of TEST_SUITE("UnidirectionalSequenceLstmTest") |
| |
| } // namespace armnnDelegate |