19 template<armnn::DataType ArmnnType,
typename T = armnn::ResolveType<ArmnnType>>
21 UnidirectionalSequenceLstmTimeMajorSingleBatchTestImpl(
25 const std::vector<T>& input,
26 const std::vector<T>& outputExpected,
37 unsigned numUnits = outputSize;
39 armnn::TensorInfo inputTensorInfo({1, batchSize , inputSize}, ArmnnType, qScale, qOffset );
40 armnn::TensorInfo cellStateInTensorInfo({batchSize , numUnits}, ArmnnType, qScale, qOffset);
41 armnn::TensorInfo outputStateInTensorInfo({batchSize , outputSize}, ArmnnType, qScale, qOffset);
42 armnn::TensorInfo outputStateOutTensorInfo({ batchSize, 1, outputSize }, ArmnnType, qScale, qOffset);
43 armnn::TensorInfo cellStateOutTensorInfo({ batchSize, 1, outputSize }, ArmnnType, qScale, qOffset);
44 armnn::TensorInfo outputTensorInfo({1, batchSize, outputSize}, ArmnnType, qScale, qOffset);
46 std::vector<T> inputVector;
47 inputVector.assign(input.data(), input.data() + (batchSize * inputSize));
49 std::vector<T> cellStateInVector(batchSize * numUnits, T());
50 std::vector<T> outputStateInVector(batchSize * outputSize, T());
52 std::vector<T> actualOutputStateOut(outputStateOutTensorInfo.GetNumElements());
53 std::vector<T> actualCellStateOut(cellStateOutTensorInfo.GetNumElements());
54 std::vector<T> actualOutput(outputTensorInfo.GetNumElements());
56 std::vector<T> outputVector;
57 outputVector.assign(outputExpected.data(), outputExpected.data() + (batchSize * outputSize));
59 std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.
CreateTensorHandle(inputTensorInfo);
60 std::unique_ptr<armnn::ITensorHandle> cellStateInHandle =
62 std::unique_ptr<armnn::ITensorHandle> outputStateInHandle =
65 std::unique_ptr<armnn::ITensorHandle> outputStateOutHandle =
67 std::unique_ptr<armnn::ITensorHandle> cellStateOutHandle =
69 std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.
CreateTensorHandle(outputTensorInfo);
74 AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
75 AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get());
76 AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get());
78 AddOutputToWorkload(data, info, outputStateOutTensorInfo, outputStateOutHandle.get());
79 AddOutputToWorkload(data, info, cellStateOutTensorInfo, cellStateOutHandle.get());
80 AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
86 std::vector<float> inputToInputWeights = {-0.45018822f, -0.02338299f, -0.0870589f,
87 -0.34550029f, 0.04266912f, -0.15680569f,
88 -0.34856534f, 0.43890524f};
90 std::vector<float> inputToForgetWeights = { 0.09701663f, 0.20334584f, -0.50592935f,
91 -0.31343272f, -0.40032279f, 0.44781327f,
92 0.01387155f, -0.35593212f};
94 std::vector<float> inputToCellWeights = { -0.50013041f, 0.1370284f, 0.11810488f, 0.2013163f,
95 -0.20583314f, 0.44344562f, 0.22077113f,
98 std::vector<float> inputToOutputWeights = { -0.25065863f, -0.28290087f, 0.04613829f,
99 0.40525138f, 0.44272184f, 0.03897077f,
100 -0.1556896f, 0.19487578f};
102 std::vector<float> recurrentToInputWeights = {-0.0063535f, -0.2042388f, 0.31454784f,
103 -0.35746509f, 0.28902304f, 0.08183324f,
104 -0.16555229f, 0.02286911f, -0.13566875f,
105 0.03034258f, 0.48091322f, -0.12528998f,
106 0.24077177f, -0.51332325f, -0.33502164f,
109 std::vector<float> recurrentToForgetWeights = { -0.48684245f, -0.06655136f, 0.42224967f,
110 0.2112639f, 0.27654213f, 0.20864892f,
111 -0.07646349f, 0.45877004f, 0.00141793f,
112 -0.14609534f, 0.36447752f, 0.09196436f,
113 0.28053468f, 0.01560611f, -0.20127171f,
116 std::vector<float> recurrentToCellWeights = { -0.3407414f, 0.24443203f, -0.2078532f,
117 0.26320225f, 0.05695659f, -0.00123841f,
118 -0.4744786f, -0.35869038f, -0.06418842f,
119 -0.13502428f, -0.501764f, 0.22830659f,
120 -0.46367589f, 0.26016325f, -0.03894562f,
123 std::vector<float> recurrentToOutputWeights = { 0.43385774f, -0.17194885f, 0.2718237f,
124 0.09215671f, 0.24107647f, -0.39835793f,
125 0.18212086f, 0.01301402f, 0.48572797f,
126 -0.50656658f, 0.20047462f, -0.20607421f,
127 -0.51818722f, -0.15390486f, 0.0468148f,
130 std::vector<float> cellToInputWeights = {0., 0., 0., 0.};
132 std::vector<float> inputGateBias = {0., 0., 0., 0.};
134 std::vector<float> forgetGateBias = {1., 1., 1., 1.};
136 std::vector<float> cellBias = {0., 0., 0., 0.};
138 std::vector<float> outputGateBias = {0., 0., 0., 0.};
190 std::unique_ptr<armnn::IWorkload> workload
192 inputHandle->Allocate();
193 outputStateInHandle->Allocate();
194 cellStateInHandle->Allocate();
196 outputStateOutHandle->Allocate();
197 cellStateOutHandle->Allocate();
198 outputHandle->Allocate();
212 outputHandle->GetShape(),
213 outputTensorInfo.GetShape());
216 template<armnn::DataType ArmnnType,
typename T = armnn::ResolveType<ArmnnType>>
221 const std::vector<T>& input,
222 const std::vector<T>& outputExpected,
233 unsigned numUnits = outputSize;
235 armnn::TensorInfo inputTensorInfo({batchSize, timeSize, inputSize}, ArmnnType, qScale, qOffset);
236 armnn::TensorInfo cellStateInTensorInfo({batchSize, numUnits}, ArmnnType, qScale, qOffset);
237 armnn::TensorInfo outputStateInTensorInfo({batchSize, outputSize}, ArmnnType, qScale, qOffset);
238 armnn::TensorInfo outputStateOutTensorInfo({batchSize, timeSize, outputSize}, ArmnnType, qScale, qOffset);
239 armnn::TensorInfo cellStateOutTensorInfo({batchSize, timeSize, outputSize}, ArmnnType, qScale, qOffset);
240 armnn::TensorInfo outputTensorInfo({batchSize, timeSize, outputSize}, ArmnnType, qScale, qOffset);
242 std::vector<T> inputVector;
243 inputVector.assign(input.data(), input.data() + (batchSize * timeSize * inputSize));
245 std::vector<T> cellStateInVector(batchSize * numUnits, T());
246 std::vector<T> outputStateInVector(batchSize * outputSize, T());
248 std::vector<T> actualOutputStateOut(outputStateOutTensorInfo.GetNumElements());
249 std::vector<T> actualCellStateOut(cellStateOutTensorInfo.GetNumElements());
250 std::vector<T> actualOutput(outputTensorInfo.GetNumElements());
252 std::vector<T> outputVector;
253 outputVector.assign(outputExpected.data(), outputExpected.data() + (batchSize * timeSize * outputSize));
255 std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.
CreateTensorHandle(inputTensorInfo);
256 std::unique_ptr<armnn::ITensorHandle> cellStateInHandle =
258 std::unique_ptr<armnn::ITensorHandle> outputStateInHandle =
261 std::unique_ptr<armnn::ITensorHandle> outputStateOutHandle =
263 std::unique_ptr<armnn::ITensorHandle> cellStateOutHandle =
265 std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.
CreateTensorHandle(outputTensorInfo);
270 AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
271 AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get());
272 AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get());
274 AddOutputToWorkload(data, info, outputStateOutTensorInfo, outputStateOutHandle.get());
275 AddOutputToWorkload(data, info, cellStateOutTensorInfo, cellStateOutHandle.get());
276 AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
279 armnn::TensorInfo tensorInfo12({numUnits, 3}, constantDataType, qScale, qOffset);
280 armnn::TensorInfo tensorInfo16({numUnits, 4}, constantDataType, qScale, qOffset);
282 std::vector<float> inputToInputWeights = { -0.49536117f, -0.0556083915f, -0.102400711f,
283 -0.117484632f, 0.3298470976f, -0.1179017122f,
284 0.214305695f, 0.42135173085f, 0.003878414626f,
285 -0.348303917f, -0.1881275477f, 0.0343011027f };
287 std::vector<float> inputToForgetWeights = { 0.2415594226f, 0.15400093799f, 0.4566498398f,
288 -0.3810434485f, 0.268383264f, -0.009807467424f,
289 -0.3522925403f, -0.24275735512f, -0.28344226125f,
290 0.13512269116f, -0.4932442977f, -0.10039821991f };
292 std::vector<float> inputToCellWeights = { -0.2504855627f, 0.184490025045f, -0.2480507493f,
293 0.386399507f, -0.259465157985f, -0.16545993089f,
294 -0.4230232555f, 0.341664791103f, -0.18127849691f,
295 -0.2277662414f, -0.55275535589f, 0.34184026718f };
297 std::vector<float> inputToOutputWeights = { 0.2303854227f, 0.5218806862f, -0.4865379333f,
298 0.53969591851f, 0.23393625035f, -0.27140527306f,
299 0.50009280443f, 0.07511717046f, 0.3998299249f,
300 -0.51717478049f, 0.1889653282f, -0.367323637f };
302 std::vector<float> recurrentToInputWeights = { -0.128009796112f, 0.1995525098f, -0.07745539397f, 0.1558421701f,
303 -0.265254765766f, -0.38837709614f, -0.05636804124f, 0.4259087456f,
304 0.17628988623f, 0.3877420127f, 0.53300309181f, -0.0959980934f,
305 0.00302857416f, 0.3266998827f, -0.142509296562f, -0.04433270756f };
307 std::vector<float> recurrentToForgetWeights = { -0.09499983487f, -0.08814888417f, -0.04834804721f, 0.1516668247f,
308 -0.3967529535f, -0.06463699788f, 0.4952811002f, 0.003274492938f,
309 -0.0968840941f, 0.17928104102f, 0.0031281141592f, -0.3387276584f,
310 -0.3587934076f, 0.06705895066f, 0.22463923692f, 0.1961955726f };
312 std::vector<float> recurrentToCellWeights = { -0.21938985582f, -0.3023648226f, -0.1170005202f, -0.3509177422f,
313 -0.4286288613f, 0.2726137042f, 0.09216640889f, -0.06551410215f,
314 0.20453298098f, 0.2393476665f, 0.11846517771f, 0.2630801796f,
315 0.3954237699f, -0.19407111404f, 0.30412107706f, -0.27342408554f };
317 std::vector<float> recurrentToOutputWeights = { -0.32921677827f, 0.32624614238f, -0.1388191282f, -0.17879831790f,
318 -0.15185534954f, -0.16918526583f, -0.10087361183f, -0.5436913968f,
319 0.016758225858f, 0.30454617738f, -0.41493862867f, -0.005565764375f,
320 -0.12584099173f, -0.12319286912f, 0.2407919466f, -0.08879069983f };
322 std::vector<float> inputGateBias = { 0., 0., 0., 0. };
324 std::vector<float> forgetGateBias = { 1., 1., 1., 1. };
326 std::vector<float> cellBias = { 0., 0., 0., 0. };
328 std::vector<float> outputGateBias = { 0., 0., 0., 0. };
378 std::unique_ptr<armnn::IWorkload> workload
380 inputHandle->Allocate();
381 outputStateInHandle->Allocate();
382 cellStateInHandle->Allocate();
384 outputStateOutHandle->Allocate();
385 cellStateOutHandle->Allocate();
386 outputHandle->Allocate();
400 outputHandle->GetShape(),
401 outputTensorInfo.GetShape());
404 template<armnn::DataType ArmnnType,
typename T = armnn::ResolveType<ArmnnType>>
406 UnidirectionalSequenceLstmLayerFloat32TimeMajorTestImpl(
410 const std::vector<T>& input,
411 const std::vector<T>& outputExpected,
422 unsigned numUnits = outputSize;
424 armnn::TensorInfo inputTensorInfo({timeSize, batchSize, inputSize}, ArmnnType, qScale, qOffset);
425 armnn::TensorInfo cellStateInTensorInfo({batchSize, numUnits}, ArmnnType, qScale, qOffset);
426 armnn::TensorInfo outputStateInTensorInfo({batchSize, outputSize}, ArmnnType, qScale, qOffset);
429 armnn::TensorInfo outputTensorInfo({timeSize, batchSize, outputSize}, ArmnnType, qScale, qOffset);
431 std::vector<T> inputVector;
432 inputVector.assign(input.data(), input.data() + (batchSize * timeSize * inputSize));
434 std::vector<T> cellStateInVector(batchSize * numUnits, T());
435 std::vector<T> outputStateInVector(batchSize * outputSize, T());
437 std::vector<T> actualOutputStateOut(outputStateOutTensorInfo.GetNumElements());
438 std::vector<T> actualCellStateOut(cellStateOutTensorInfo.GetNumElements());
439 std::vector<T> actualOutput(outputTensorInfo.GetNumElements());
441 std::vector<T> outputVector;
442 outputVector.assign(outputExpected.data(), outputExpected.data() + (batchSize * timeSize * outputSize));
444 std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.
CreateTensorHandle(inputTensorInfo);
445 std::unique_ptr<armnn::ITensorHandle> cellStateInHandle =
447 std::unique_ptr<armnn::ITensorHandle> outputStateInHandle =
450 std::unique_ptr<armnn::ITensorHandle> outputStateOutHandle =
452 std::unique_ptr<armnn::ITensorHandle> cellStateOutHandle =
454 std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.
CreateTensorHandle(outputTensorInfo);
459 AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
460 AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get());
461 AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get());
463 AddOutputToWorkload(data, info, outputStateOutTensorInfo, outputStateOutHandle.get());
464 AddOutputToWorkload(data, info, cellStateOutTensorInfo, cellStateOutHandle.get());
465 AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
468 armnn::TensorInfo tensorInfo12({numUnits, 3}, constantDataType, qScale, qOffset);
469 armnn::TensorInfo tensorInfo16({numUnits, 4}, constantDataType, qScale, qOffset);
471 std::vector<float> inputToInputWeights = { 0.27277296781539917f, 0.3813590407371521f, -0.394489049911499f,
472 0.2782636880874634f, -0.3793870210647583f, -0.018918335437774658f,
473 0.2724653482437134f, -0.19314253330230713f, -0.2947450876235962f,
474 -0.30253493785858154f, 0.4241350293159485f, -0.22560018301010132f };
476 std::vector<float> inputToForgetWeights = { -0.2667974531650543f, -0.05505800247192383f, -0.20932340621948242f,
477 -0.14345619082450867f, 0.09666192531585693f, -0.2604355812072754f,
478 -0.2681812047958374f, -0.3314584493637085f, 0.4485899806022644f,
479 -0.23467743396759033f, 0.5072842240333557f, -0.4192768931388855f };
481 std::vector<float> inputToCellWeights = { -0.15782442688941956f, -0.027530014514923096f, 0.4789854884147644f,
482 0.23227906227111816f, 0.28259342908859253f, -0.030095696449279785f,
483 0.10071521997451782f, -0.08535495400428772f, 0.18563997745513916f,
484 -0.3049069046974182f, -0.478048175573349f, 0.025234103202819824f };
486 std::vector<float> inputToOutputWeights = { -0.04584759473800659f, -0.2716066539287567f, 0.012970447540283203f,
487 -0.4729190170764923f, -0.37422770261764526f, 0.49352723360061646f,
488 0.3163864016532898f, -0.436781644821167f, -0.33074596524238586f,
489 -0.32885751128196716f, -0.40959352254867554f, -0.2124689817428589f };
491 std::vector<float> recurrentToInputWeights = { 0.23788475990f, -0.24948765337f, 0.50044941902f, 0.14431896805f,
492 -0.115940228137f, -0.717082679f, -0.17208620906f, 0.17850610617f,
493 -0.16702319684f, -0.11384502053f, -0.309785276245f, -0.3316611672f,
494 0.52380162477f, -0.06839632987f, -0.391478359627f, -0.10756178963f };
496 std::vector<float> recurrentToForgetWeights = { 0.11383482068f, 0.1676601767f, -0.08550968004f, 0.03399394089f,
497 0.08042152225f, -0.2133381964f, 0.05182432704f, 0.38161808255f,
498 -0.5018365979f, -0.08043262364f, 0.07894329014f, -0.07547105155f,
499 0.12047368288f, 0.2986997961f, 0.0485043078f, -0.13372567296f };
501 std::vector<float> recurrentToCellWeights = { 0.0433832928545f, 0.07587072294f, -0.120520234107f, 0.604576051f,
502 -0.434353142986f, 0.009314475068f, 0.005085289478f, 0.08488202038f,
503 -0.00025437487886f, 0.15245915082f, -0.1936587542f, 0.004754020f,
504 -0.1582719236f, 0.3307867646f, 0.0236605107784f, 0.307716339826f };
506 std::vector<float> recurrentToOutputWeights = { -0.079031050201f, 0.041414566286f, -0.583727357285f, 0.1025384515f,
507 -0.172372072937f, 0.09214124082f, 0.178184121827f, -0.2439443916f,
508 0.104485116899f, 0.2600405514f, 0.064414866268f, 0.24141204357f,
509 0.281875759363f, -0.14234502664f, 0.15126448862f, -0.24421440064f };
511 std::vector<float> inputGateBias = { 0., 0., 0., 0. };
513 std::vector<float> forgetGateBias = { 1., 1., 1., 1. };
515 std::vector<float> cellBias = { 0., 0., 0., 0. };
517 std::vector<float> outputGateBias = { 0., 0., 0., 0. };
567 std::unique_ptr<armnn::IWorkload> workload
569 inputHandle->Allocate();
570 outputStateInHandle->Allocate();
571 cellStateInHandle->Allocate();
573 outputStateOutHandle->Allocate();
574 cellStateOutHandle->Allocate();
575 outputHandle->Allocate();
589 outputHandle->GetShape(),
590 outputTensorInfo.GetShape());
601 std::vector<float> input = {2., 3., 3., 4.};
604 std::vector<float> expectedOutput =
605 {-0.02973187f, 0.1229473f, 0.20885126f, -0.15358765f,
606 -0.0185422f, 0.11281417f, 0.24466537f, -0.1826292f};
608 return UnidirectionalSequenceLstmTimeMajorSingleBatchTestImpl<armnn::DataType::Float32>(
609 workloadFactory, memoryManager, tensorHandleFactory,
610 input, expectedOutput, inputDesc.GetShape(), outputDesc.GetShape());
618 std::vector<float> input = { 1., 2., 3., 4., 5., 4., 3., 2., 1. };
621 std::vector<float> expectedOutput = { -0.0714901f, -0.162117f, -0.175168f, -0.0232934f,
622 -0.0424661f, -0.231802f, -0.513374f, -0.00680323f,
623 -0.0668735f, 0.204078f, -0.42765f, -0.0312321f };
624 return UnidirectionalSequenceLstmLayerFloat32TestImpl<armnn::DataType::Float32>(
625 workloadFactory, memoryManager, tensorHandleFactory,
626 input, expectedOutput, inputInfo.GetShape(), outputInfo.GetShape());
634 std::vector<float> input = { 1., 2., 3., 4., 5., 4.,
635 3., 2., 1., 2., 3., 4.,
636 5., 4., 3., 2., 1., 2. };
639 std::vector<float> expectedOutput = { -0.07149004f, -0.1621171f, -0.17516759f, -0.0232934225f,
640 -0.16810727f, -0.41412935f, -0.5498753f, -0.00803578f,
641 -0.06687349f, 0.204077631f, -0.4276504f, -0.03123213f,
642 -0.12000261f, -0.0941918f, -0.45639035f, -0.02870186f,
643 -0.03429216f, 0.20824050f, -0.6569892f, -0.004152651f,
644 -0.10493034f, 0.14210969f, -0.58347696f, -0.03297536f };
645 return UnidirectionalSequenceLstmLayerFloat32TestImpl<armnn::DataType::Float32>(
646 workloadFactory, memoryManager, tensorHandleFactory,
647 input, expectedOutput, inputInfo.GetShape(), outputInfo.GetShape());
655 std::vector<float> input = { 1., 2., 3., 4., 5., 4.,
656 3., 2., 1., 2., 3., 4.,
657 5., 4., 3., 2., 1., 2. };
660 std::vector<float> expectedOutput = { 0.135657698f, 0.124672532f, 0.0212090332f, -0.0530203655f,
661 0.106138252f, 0.0404792242f, 0.0151643595f, -0.00675163185f,
662 -0.0128514022f, 0.0644884035f, 0.0709072053f, -0.0454045124f,
663 0.16288602f, 0.16649379f, 0.02770456f, -0.03698075f,
664 0.11171641f, 0.043119f , 0.0762981f , -0.01228541f,
665 0.10439701f, 0.21439962f, 0.11919238f, -0.08390583f };
666 return UnidirectionalSequenceLstmLayerFloat32TimeMajorTestImpl<armnn::DataType::Float32>(
667 workloadFactory, memoryManager, tensorHandleFactory,
668 input, expectedOutput, inputInfo.GetShape(), outputInfo.GetShape());
677 unsigned int batchSize = 2;
678 unsigned int timeSize = 3;
679 unsigned int outputSize = 5;
680 unsigned int inputSize = 4;
681 unsigned numUnits = 6;
690 const std::vector<float> inputVector = { 1., 2., 3., 4., 5., 4.,
691 3., 2., 1., 2., 3., 4.,
692 5., 4., 3., 2., 1., 2.,
693 1., 2., 3., 4., 5., 4.};
695 std::vector<float> cellStateInVector(batchSize * numUnits, 0.f);
696 std::vector<float> outputStateInVector(batchSize * outputSize, 0.f);
698 std::vector<float> actualOutputStateOut(outputStateOutTensorInfo.GetNumElements());
699 std::vector<float> actualCellStateOut(cellStateOutTensorInfo.GetNumElements());
700 std::vector<float> actualOutput(outputTensorInfo.GetNumElements());
702 const std::vector<float> expectedOutput = { -0.0135612f, -0.0263441f, 0.0314008f, -0.00883455f, 0.00763052f,
703 -0.00126877f, -0.0292959f, 0.0449957f, -0.00976195f, -0.00492338f,
704 -0.0175702f, -0.0431753f, 0.0597117f, -0.0169154f, 0.0142087f,
705 0.00472515f, -0.0196355f, 0.0342524f, -0.00407936f, -0.0253189f,
706 -0.00512944f, -0.0293754f, 0.0512771f, -0.0151874f, -0.0246433f,
707 -0.00744986f, -0.0345103f, 0.0450666f, -0.00944991f, 0.0127171f };
709 std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.
CreateTensorHandle(inputTensorInfo);
710 std::unique_ptr<armnn::ITensorHandle> cellStateInHandle =
712 std::unique_ptr<armnn::ITensorHandle> outputStateInHandle =
715 std::unique_ptr<armnn::ITensorHandle> outputStateOutHandle =
717 std::unique_ptr<armnn::ITensorHandle> cellStateOutHandle =
719 std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.
CreateTensorHandle(outputTensorInfo);
724 AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
725 AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get());
726 AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get());
728 AddOutputToWorkload(data, info, outputStateOutTensorInfo, outputStateOutHandle.get());
729 AddOutputToWorkload(data, info, cellStateOutTensorInfo, cellStateOutHandle.get());
730 AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
738 std::vector<float> inputToInputWeights = { 0.021393683f, 0.06124551f, 0.046905167f, -0.014657677f,
739 -0.03149463f, 0.09171803f, 0.14647801f, 0.10797193f,
740 -0.0057968358f, 0.0019193048f, -0.2726754f, 0.10154029f,
741 -0.018539885f, 0.080349885f, -0.10262385f, -0.022599787f,
742 -0.09121155f, -0.008675967f, -0.045206103f, -0.0821282f,
743 -0.008045952f, 0.015478081f, 0.055217247f, 0.038719587f };
745 std::vector<float> inputToForgetWeights = { -0.0018401089f, -0.004852237f, 0.03698424f, 0.014181704f,
746 0.028273236f, -0.016726194f, -0.05249759f, -0.10204261f,
747 0.00861066f, -0.040979505f, -0.009899187f, 0.01923892f,
748 -0.028177269f, -0.08535103f, -0.14585495f, 0.10662567f,
749 -0.01909731f, -0.017883534f, -0.0047269356f, -0.045103323f,
750 0.0030784295f, 0.076784775f, 0.07463696f, 0.094531395f};
752 std::vector<float> inputToCellWeights = { -0.04580283f, -0.09549462f, -0.032418985f, -0.06454633f,
753 -0.043528453f, 0.043018587f, -0.049152344f, -0.12418144f,
754 -0.078985475f, -0.07596889f, 0.019484362f, -0.11434962f,
755 -0.0074034138f, -0.06314844f, -0.092981495f, 0.0062155537f,
756 -0.025034338f, -0.0028890965f, 0.048929527f, 0.06235075f,
757 0.10665918f, -0.032036792f, -0.08505916f, -0.10843358f };
759 std::vector<float> inputToOutputWeights = { -0.0998932f, -0.07201956f, -0.052803773f, -0.15629593f,
760 -0.15001918f, -0.07650751f, 0.02359855f, -0.075155355f,
761 -0.08037709f, -0.15093534f, 0.029517552f, -0.04751393f,
762 0.010350531f, -0.02664851f, -0.016839722f, -0.023121163f,
763 0.0077019283f, 0.012851257f, -0.05040649f, -0.0129761f,
764 -0.021737747f, -0.038305793f, -0.06870586f, -0.01481247f };
766 std::vector<float> inputGateBias = { 0.02234832f, 0.14757581f, 0.18176508f,
767 0.10380666f, 0.053110216f, -0.06928846f };
769 std::vector<float> forgetGateBias = { 0.035185695f, -0.042891346f, -0.03032477f,
770 0.23027696f, 0.11098921f, 0.08989442f };
772 std::vector<float> cellBias = { -0.024379363f, 0.0055531194f, 0.23377132f,
773 0.033463873f, -0.1483596f, 0.029460307f };
775 std::vector<float> outputGateBias = { 0.046159424f, -0.0012809046f, 0.03563469f,
776 0.12648113f, 0.027195795f, 0.35373217f };
778 std::vector<float> recurrentToInputWeights = { -0.001374326f, -0.078856036f, 0.10672688f, 0.029162422f,
779 -0.11585556f, 0.02557986f, -0.13446963f, -0.035785314f,
780 -0.01244275f, 0.025961924f, -0.02337298f, -0.044228926f,
781 -0.055839065f, -0.046598054f, -0.010546039f, -0.06900766f,
782 0.027239809f, 0.022582639f, -0.013296484f, -0.05459212f,
783 0.08981f, -0.045407712f, 0.08682226f, -0.06867011f,
784 -0.14390695f, -0.02916037f, 0.000996957f, 0.091420636f,
785 0.14283475f, -0.07390571f };
787 std::vector<float> recurrentToCellWeights = { -0.037322544f, 0.018592842f, 0.0056175636f, -0.06253426f,
788 0.055647098f, -0.05713207f, -0.05626563f, 0.005559383f,
789 0.03375411f, -0.025757805f, -0.088049285f, 0.06017052f,
790 -0.06570978f, 0.007384076f, 0.035123326f, -0.07920549f,
791 0.053676967f, 0.044480428f, -0.07663568f, 0.0071805613f,
792 0.08089997f, 0.05143358f, 0.038261272f, 0.03339287f,
793 -0.027673481f, 0.044746667f, 0.028349208f, 0.020090483f,
794 -0.019443132f, -0.030755889f };
796 std::vector<float> recurrentToForgetWeights = { -0.057784554f, -0.026057621f, -0.068447545f, -0.022581743f,
797 0.14811787f, 0.10826372f, 0.09471067f, 0.03987225f,
798 -0.0039523416f, 0.00030638507f, 0.053185795f, 0.10572994f,
799 0.08414449f, -0.022036452f, -0.00066928595f, -0.09203576f,
800 0.032950465f, -0.10985798f, -0.023809856f, 0.0021431844f,
801 -0.02196096f, -0.00326074f, 0.00058621005f, -0.074678116f,
802 -0.06193199f, 0.055729095f, 0.03736828f, 0.020123724f,
803 0.061878487f, -0.04729229f };
805 std::vector<float> recurrentToOutputWeights = { 0.025825322f, -0.05813119f, 0.09495884f,
806 -0.045984812f,-0.01255415f, -0.0026479573f,
807 -0.08196161f, -0.054914974f, -0.0046604523f,
808 -0.029587349f, -0.044576716f, -0.07480124f,
809 -0.082868785f, 0.023254942f, 0.027502948f,
810 -0.0039728214f, -0.08683098f, -0.08116779f,
811 -0.014675607f, -0.037924774f, -0.023314456f,
812 -0.007401714f, -0.09255757f, 0.029460307f,
813 -0.08829125f, -0.005139627f, -0.08989442f,
814 -0.0555066f, 0.13596267f, 0.025062224f };
816 std::vector<float> cellToInputWeights = { 0.040369894f, 0.030746894f, 0.24704495f,
817 0.018586371f, -0.037586458f, -0.15312155f };
819 std::vector<float> cellToForgetWeights = { -0.01998659f, -0.15568835f, -0.24248174f,
820 -0.012770197f, 0.041331276f, -0.072311886f };
822 std::vector<float> cellToOutputWeights = { 0.08286371f, -0.08261836f, -0.51210177f,
823 0.002913762f, 0.17764764f, -0.5495371f };
825 std::vector<float> projectionWeights = { -0.009802181f, 0.09401916f, 0.0717386f, -0.13895074f, 0.09641832f,
826 0.060420845f, 0.08539281f, 0.054285463f, 0.061395317f, 0.034448683f,
827 -0.042991187f, 0.019801661f, -0.16840284f, -0.015726732f, -0.23041931f,
828 -0.024478018f, -0.10959692f, -0.013875541f, 0.18600968f, -0.061274476f,
829 0.0138165f, -0.08160894f, -0.07661644f, 0.032372914f, 0.16169067f,
830 0.22465782f, -0.03993472f, -0.004017731f, 0.08633481f, -0.28869787f };
832 std::vector<float> projectionBiasVector(outputSize, 0.f);
898 std::unique_ptr<armnn::IWorkload> workload
900 inputHandle->Allocate();
901 outputStateInHandle->Allocate();
902 cellStateInHandle->Allocate();
904 outputStateOutHandle->Allocate();
905 cellStateOutHandle->Allocate();
906 outputHandle->Allocate();
920 outputHandle->GetShape(),
921 outputTensorInfo.GetShape());
930 unsigned int batchSize = 3;
931 unsigned int timeSize = 2;
932 unsigned int outputSize = 4;
933 unsigned int inputSize = 3;
934 unsigned numUnits = 5;
943 const std::vector<float> inputVector = { 1., 2., 3., 4., 5., 4.,
944 3., 2., 1., 2., 3., 4.,
945 5., 4., 3., 2., 1., 2. };
947 std::vector<float> cellStateInVector(batchSize * numUnits, 0.f);
948 std::vector<float> outputStateInVector(batchSize * outputSize, 0.f);
950 std::vector<float> actualOutputStateOut(outputStateOutTensorInfo.GetNumElements());
951 std::vector<float> actualCellStateOut(cellStateOutTensorInfo.GetNumElements());
952 std::vector<float> actualOutput(outputTensorInfo.GetNumElements());
954 const std::vector<float> expectedOutput = { 0.0642256f, 0.0343966f, 0.184122f, 0.114717f,
955 0.11458f, 0.0407109f, 0.300327f, 0.174301f,
956 0.0864761f, 0.0362912f, 0.178635f, 0.115689f,
957 0.108008f, 0.0386623f, 0.273471f, 0.167115f,
958 0.0859545f, 0.0331481f, 0.186051f, 0.11888f,
959 0.106649f, 0.0276847f, 0.229863f, 0.166958f };
961 std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.
CreateTensorHandle(inputTensorInfo);
962 std::unique_ptr<armnn::ITensorHandle> cellStateInHandle =
964 std::unique_ptr<armnn::ITensorHandle> outputStateInHandle =
967 std::unique_ptr<armnn::ITensorHandle> outputStateOutHandle =
969 std::unique_ptr<armnn::ITensorHandle> cellStateOutHandle =
971 std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.
CreateTensorHandle(outputTensorInfo);
976 AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
977 AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get());
978 AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get());
980 AddOutputToWorkload(data, info, outputStateOutTensorInfo, outputStateOutHandle.get());
981 AddOutputToWorkload(data, info, cellStateOutTensorInfo, cellStateOutHandle.get());
982 AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
990 std::vector<float> inputToInputWeights = { -0.49536117f, -0.0556083915f, -0.102400711f,
991 -0.117484632f, 0.3298470976f, -0.1179017122f,
992 0.214305695f, 0.42135173085f, 0.003878414626f,
993 -0.348303917f, -0.1881275477f, 0.0343011027f,
994 -0.38837709614f, -0.05636804124f, 0.4259087456f};
996 std::vector<float> inputToForgetWeights = { 0.2415594226f, 0.15400093799f, 0.4566498398f,
997 -0.3810434485f, 0.268383264f, -0.009807467424f,
998 -0.3522925403f, -0.24275735512f, -0.28344226125f,
999 0.13512269116f, -0.4932442977f, -0.10039821991f,
1000 0.2726137042f, 0.09216640889f, -0.06551410215f};
1002 std::vector<float> inputToCellWeights = { -0.2504855627f, 0.184490025045f, -0.2480507493f,
1003 0.386399507f, -0.259465157985f, -0.16545993089f,
1004 -0.4230232555f, 0.341664791103f, -0.18127849691f,
1005 -0.2277662414f, -0.55275535589f, 0.34184026718f,
1006 0.3954237699f, -0.19407111404f, 0.30412107706f};
1008 std::vector<float> inputToOutputWeights = { 0.2303854227f, 0.5218806862f, -0.4865379333f,
1009 0.53969591851f, 0.23393625035f, -0.27140527306f,
1010 0.50009280443f, 0.07511717046f, 0.3998299249f,
1011 -0.51717478049f, 0.1889653282f, -0.367323637f,
1012 -0.12584099173f, -0.12319286912f, 0.2407919466f};
1014 std::vector<float> inputGateBias{ 0.03f, 0.15f, 0.22f, 0.38f, 0.05f };
1015 std::vector<float> forgetGateBias{ 0.1f, -0.3f, -0.2f, 0.1f, 0.4f };
1016 std::vector<float> cellBias{ -0.05f, 0.72f, 0.25f, 0.08f, 0.1f };
1017 std::vector<float> outputGateBias{ 0.05f, -0.01f, 0.2f, 0.1f, -0.2f };
1019 std::vector<float> recurrentToInputWeights = { -0.128009796112f, 0.1995525098f, -0.07745539397f, 0.1558421701f,
1020 -0.265254765766f, -0.38837709614f, -0.05636804124f, 0.4259087456f,
1021 0.17628988623f, 0.3877420127f, 0.53300309181f, -0.0959980934f,
1022 0.00302857416f, 0.3266998827f, -0.142509296562f, -0.04433270756f,
1023 0.54066205f, -0.32668582f, -0.43562764f, -0.56094903f };
1025 std::vector<float> recurrentToForgetWeights = { -0.09499983487f, -0.08814888417f, -0.04834804721f, 0.1516668247f,
1026 -0.3967529535f, -0.06463699788f, 0.4952811002f, 0.003274492938f,
1027 -0.0968840941f, 0.17928104102f, 0.0031281141592f, -0.3387276584f,
1028 -0.3587934076f, 0.06705895066f, 0.22463923692f, 0.1961955726f,
1029 0.01841056f, -0.32764608f, -0.33027974f, -0.10826075f };
1031 std::vector<float> recurrentToCellWeights = { -0.21938985582f, -0.3023648226f, -0.1170005202f, -0.3509177422f,
1032 -0.4286288613f, 0.2726137042f, 0.09216640889f, -0.06551410215f,
1033 0.20453298098f, 0.2393476665f, 0.11846517771f, 0.2630801796f,
1034 0.3954237699f, -0.19407111404f, 0.30412107706f, -0.27342408554f,
1035 0.19069612f, -0.03026325f, -0.54532051f, 0.33003211f };
1037 std::vector<float> recurrentToOutputWeights = { -0.32921677827f, 0.32624614238f, -0.1388191282f, -0.17879831790f,
1038 -0.15185534954f, -0.16918526583f, -0.10087361183f, -0.5436913968f,
1039 0.016758225858f, 0.30454617738f, -0.41493862867f, -0.005565764375f,
1040 -0.12584099173f, -0.12319286912f, 0.2407919466f, -0.08879069983f,
1041 0.11178309f, 0.09481031f, -0.26424935f, 0.46261835f };
1043 std::vector<float> cellToInputWeights { 0.05f, 0.1f, 0.25f, 0.15f, -0.02f };
1044 std::vector<float> cellToForgetWeights { -0.02f, -0.15f, -0.25f, -0.03f, 0.15f };
1045 std::vector<float> cellToOutputWeights { 0.1f, -0.1f, -0.5f, 0.05f, 0.01f };
1047 std::vector<float> projectionWeights{ -0.1f, 0.2f, 0.01f, -0.2f,
1048 0.1f, 0.5f, 0.3f, 0.08f,
1049 0.07f, 0.2f, -0.4f, 0.2f,
1050 0.5f, -0.4f, 0.3f, -0.2f,
1051 0.3f, 0.08f, -0.07f, 0.2f};
1053 std::vector<float> projectionBiasVector(outputSize, 0.f);
1055 std::vector<float> inputLayerNormWeights{ 0.1f, 0.2f, 0.3f, 0.5f, 0.8f };
1056 std::vector<float> forgetLayerNormWeights{ 0.1f, 0.2f, 0.3f, 0.5f, 0.2f };
1057 std::vector<float> cellLayerNormWeights{ 0.7f, 0.2f, 0.3f, 0.8f, 0.5f };
1058 std::vector<float> outputLayerNormWeights{ 0.6f, 0.2f, 0.2f, 0.5f, 0.1f };
1138 std::unique_ptr<armnn::IWorkload> workload
1140 inputHandle->Allocate();
1141 outputStateInHandle->Allocate();
1142 cellStateInHandle->Allocate();
1144 outputStateOutHandle->Allocate();
1145 cellStateOutHandle->Allocate();
1146 outputHandle->Allocate();
1152 workload->Execute();
1160 outputHandle->GetShape(),
1161 outputTensorInfo.GetShape());
1170 unsigned int batchSize = 3;
1171 unsigned int timeSize = 2;
1172 unsigned int inputSize = 3;
1173 unsigned int outputSize = 4;
1174 unsigned numUnits = outputSize;
1183 std::vector<float> inputVector = { 1., 2., 3., 4., 5., 4.,
1184 3., 2., 1., 2., 3., 4.,
1185 5., 4., 3., 2., 1., 2. };
1187 std::vector<float> cellStateInVector(batchSize * numUnits, 0.f);
1188 std::vector<float> outputStateInVector(batchSize * outputSize, 0.f);
1190 std::vector<float> actualOutputStateOut(outputStateOutTensorInfo.GetNumElements());
1191 std::vector<float> actualCellStateOut(cellStateOutTensorInfo.GetNumElements());
1192 std::vector<float> actualOutput(outputTensorInfo.GetNumElements());
1194 std::vector<float> outputVector = { -0.0129257f, -0.070531f, -0.153508f, -0.0392391f,
1195 -0.0300169f, -0.195717f, -0.528679f, -0.0818106f,
1196 -0.0332748f, 0.155429f, -0.353966f, -0.0801505f,
1197 -0.032312f, -0.0407911f, -0.435053f, -0.0932317f,
1198 -0.0108233f, 0.165584f, -0.640424f, -0.0447535f,
1199 -0.031675f, 0.125987f, -0.526695f, -0.110093f };
1201 std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.
CreateTensorHandle(inputTensorInfo);
1202 std::unique_ptr<armnn::ITensorHandle> cellStateInHandle =
1204 std::unique_ptr<armnn::ITensorHandle> outputStateInHandle =
1207 std::unique_ptr<armnn::ITensorHandle> outputStateOutHandle =
1209 std::unique_ptr<armnn::ITensorHandle> cellStateOutHandle =
1211 std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.
CreateTensorHandle(outputTensorInfo);
1216 AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
1217 AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get());
1218 AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get());
1220 AddOutputToWorkload(data, info, outputStateOutTensorInfo, outputStateOutHandle.get());
1221 AddOutputToWorkload(data, info, cellStateOutTensorInfo, cellStateOutHandle.get());
1222 AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
1228 std::vector<float> inputToForgetWeights = { 0.2415594226f, 0.15400093799f, 0.4566498398f,
1229 -0.3810434485f, 0.268383264f, -0.009807467424f,
1230 -0.3522925403f, -0.24275735512f, -0.28344226125f,
1231 0.13512269116f, -0.4932442977f, -0.10039821991f };
1233 std::vector<float> inputToCellWeights = { -0.2504855627f, 0.184490025045f, -0.2480507493f,
1234 0.386399507f, -0.259465157985f, -0.16545993089f,
1235 -0.4230232555f, 0.341664791103f, -0.18127849691f,
1236 -0.2277662414f, -0.55275535589f, 0.34184026718f };
1238 std::vector<float> inputToOutputWeights = { 0.2303854227f, 0.5218806862f, -0.4865379333f,
1239 0.53969591851f, 0.23393625035f, -0.27140527306f,
1240 0.50009280443f, 0.07511717046f, 0.3998299249f,
1241 -0.51717478049f, 0.1889653282f, -0.367323637f };
1243 std::vector<float> recurrentToForgetWeights = { -0.09499983487f, -0.08814888417f, -0.04834804721f, 0.1516668247f,
1244 -0.3967529535f, -0.06463699788f, 0.4952811002f, 0.003274492938f,
1245 -0.0968840941f, 0.17928104102f, 0.0031281141592f, -0.3387276584f,
1246 -0.3587934076f, 0.06705895066f, 0.22463923692f, 0.1961955726f };
1248 std::vector<float> recurrentToCellWeights = { -0.21938985582f, -0.3023648226f, -0.1170005202f, -0.3509177422f,
1249 -0.4286288613f, 0.2726137042f, 0.09216640889f, -0.06551410215f,
1250 0.20453298098f, 0.2393476665f, 0.11846517771f, 0.2630801796f,
1251 0.3954237699f, -0.19407111404f, 0.30412107706f, -0.27342408554f };
1253 std::vector<float> recurrentToOutputWeights = { -0.32921677827f, 0.32624614238f, -0.1388191282f, -0.17879831790f,
1254 -0.15185534954f, -0.16918526583f, -0.10087361183f, -0.5436913968f,
1255 0.016758225858f, 0.30454617738f, -0.41493862867f, -0.005565764375f,
1256 -0.12584099173f, -0.12319286912f, 0.2407919466f, -0.08879069983f };
1258 std::vector<float> cellToForgetWeights{ 0.47485286f, -0.51955009f, -0.24458408f, 0.31544167f };
1260 std::vector<float> cellToOutputWeights{ -0.17135078f, 0.82760304f, 0.85573703f, -0.77109635f };
1262 std::vector<float> forgetGateBias = { 1., 1., 1., 1. };
1264 std::vector<float> cellBias = { 0., 0., 0., 0. };
1266 std::vector<float> outputGateBias = { 0., 0., 0., 0. };
1313 std::unique_ptr<armnn::IWorkload> workload
1315 inputHandle->Allocate();
1316 outputStateInHandle->Allocate();
1317 cellStateInHandle->Allocate();
1319 outputStateOutHandle->Allocate();
1320 cellStateOutHandle->Allocate();
1321 outputHandle->Allocate();
1327 workload->Execute();
1335 outputHandle->GetShape(),
1336 outputTensorInfo.GetShape());
1345 unsigned int batchSize = 3;
1346 unsigned int timeSize = 2;
1347 unsigned int inputSize = 3;
1348 unsigned int outputSize = 4;
1349 unsigned numUnits = outputSize;
1358 const std::vector<float> inputVector = { 0.1f, 0.2f, 0.3f, 0.4f, 0.5f, 0.4f,
1359 0.3f, 0.2f, 0.1f, 0.2f, 0.3f, 0.4f,
1360 0.5f, 0.4f, 0.3f, 0.2f, 0.1f, 0.2f };
1362 std::vector<float> cellStateInVector(batchSize * numUnits, 0.f);
1363 std::vector<float> outputStateInVector(batchSize * outputSize, 0.f);
1365 std::vector<float> actualOutputStateOut(outputStateOutTensorInfo.GetNumElements());
1366 std::vector<float> actualCellStateOut(cellStateOutTensorInfo.GetNumElements());
1367 std::vector<float> actualOutput(outputTensorInfo.GetNumElements());
1369 const std::vector<float> outputVector = { -0.0142517f, -0.0198845f, -0.0120569f, -0.0116868f,
1370 -0.0350714f, -0.0343202f, -0.047504f, -0.0569789f,
1371 -0.0146346f, 0.0106663f, -0.0247238f, -0.0319502f,
1372 -0.0294759f, -0.0129935f, -0.0444175f, -0.0444354f,
1373 -0.0280855f, 0.00545101f, -0.051422f, -0.0463838f,
1374 -0.0310702f, 0.00915739f, -0.0625207f, -0.0482648f };
1376 std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.
CreateTensorHandle(inputTensorInfo);
1377 std::unique_ptr<armnn::ITensorHandle> cellStateInHandle =
1379 std::unique_ptr<armnn::ITensorHandle> outputStateInHandle =
1382 std::unique_ptr<armnn::ITensorHandle> outputStateOutHandle =
1384 std::unique_ptr<armnn::ITensorHandle> cellStateOutHandle =
1386 std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.
CreateTensorHandle(outputTensorInfo);
1392 AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
1393 AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get());
1394 AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get());
1396 AddOutputToWorkload(data, info, outputStateOutTensorInfo, outputStateOutHandle.get());
1397 AddOutputToWorkload(data, info, cellStateOutTensorInfo, cellStateOutHandle.get());
1398 AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
1404 std::vector<int8_t> inputToInputWeights = { -4, -1, -1, -2, 3, -2, 2, 4, 1, -4, -2, 3 };
1405 std::vector<int8_t> inputToForgetWeights = { 2, 1, 4, -4, 3, -1, -3, -2, -3, 1, -4, -1 };
1406 std::vector<int8_t> inputToCellWeights = { -2, 1, -2, 4, -3, -2, -4, 3, -2, -2, -6, 3 };
1407 std::vector<int8_t> inputToOutputWeights = { 2, 5, -4, 5, 2, -3, 5, 7, 3, -5, 1, -4 };
1409 std::vector<int8_t> recurrentToInputWeights = { -1, 1, -1, 1, -3, -4, -1, 4, 2, 3, 5, -1, 1, 3, -1, -1 };
1410 std::vector<int8_t> recurrentToForgetWeights = { -1, 1, -1, 1, -3, -4, -1, 4, 2, 3, 5, -1, 1, 3, -2, -1 };
1411 std::vector<int8_t> recurrentToCellWeights = { -2, -3, -1, -3, -4, 2, 1, -1, 2, 2, 1, 2, 3, -2, 3, -3 };
1412 std::vector<int8_t> recurrentToOutputWeights = { -3, 3, -1, -2, -2, -2, -1, -5, 1, 3, -4, -1, -1, -1, 2, -1 };
1414 std::vector<float> inputGateBias = { 0., 0., 0., 0. };
1415 std::vector<float> forgetGateBias = { 1., 1., 1., 1. };
1416 std::vector<float> cellBias = { 0., 0., 0., 0. };
1417 std::vector<float> outputGateBias = { 0., 0., 0., 0. };
1467 std::unique_ptr<armnn::IWorkload> workload
1469 inputHandle->Allocate();
1470 outputStateInHandle->Allocate();
1471 cellStateInHandle->Allocate();
1473 outputStateOutHandle->Allocate();
1474 cellStateOutHandle->Allocate();
1475 outputHandle->Allocate();
1481 workload->Execute();
1489 outputHandle->GetShape(),
1490 outputTensorInfo.GetShape());
1499 unsigned int batchSize = 3;
1500 unsigned int timeSize = 2;
1501 unsigned int inputSize = 3;
1502 unsigned int outputSize = 4;
1503 unsigned numUnits = outputSize;
1512 const std::vector<float> inputVector = { 0.1f, 0.2f, 0.3f, 0.4f, 0.5f, 0.4f,
1513 0.3f, 0.2f, 0.1f, 0.2f, 0.3f, 0.4f,
1514 0.5f, 0.4f, 0.3f, 0.2f, 0.1f, 0.2f };
1516 std::vector<float> cellStateInVector(batchSize * numUnits, 0.f);
1517 std::vector<float> outputStateInVector(batchSize * outputSize, 0.f);
1519 std::vector<float> actualOutputStateOut(outputStateOutTensorInfo.GetNumElements());
1520 std::vector<float> actualCellStateOut(cellStateOutTensorInfo.GetNumElements());
1521 std::vector<float> actualOutput(outputTensorInfo.GetNumElements());
1523 const std::vector<float> outputVector = { -0.0142517f, -0.0198845f, -0.0120122f, -0.0116868f,
1524 -0.0261295f, -0.0188487f, -0.0345463f, -0.049733f,
1525 -0.0146346f, 0.0106663f, -0.0247238f, -0.0319502f,
1526 -0.0291863f, -0.0369402f, -0.0354071f, -0.0296529f,
1527 -0.0419539f, -0.00617731f, -0.0814796f, -0.0804005f,
1528 -0.0244737f, 0.0119905f, -0.0457527f, -0.0331862f };
1529 std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.
CreateTensorHandle(inputTensorInfo);
1530 std::unique_ptr<armnn::ITensorHandle> cellStateInHandle =
1532 std::unique_ptr<armnn::ITensorHandle> outputStateInHandle =
1535 std::unique_ptr<armnn::ITensorHandle> outputStateOutHandle =
1537 std::unique_ptr<armnn::ITensorHandle> cellStateOutHandle =
1539 std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.
CreateTensorHandle(outputTensorInfo);
1545 AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
1546 AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get());
1547 AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get());
1549 AddOutputToWorkload(data, info, outputStateOutTensorInfo, outputStateOutHandle.get());
1550 AddOutputToWorkload(data, info, cellStateOutTensorInfo, cellStateOutHandle.get());
1551 AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
1557 std::vector<int8_t> inputToInputWeights = { -4, -1, -1, -2, 3, -2, 2, 4, 1, -4, -2, 3 };
1558 std::vector<int8_t> inputToForgetWeights = { 2, 1, 4, -4, 3, -1, -3, -2, -3, 1, -4, -1 };
1559 std::vector<int8_t> inputToCellWeights = { -2, 1, -2, 4, -3, -2, -4, 3, -2, -2, -6, 3 };
1560 std::vector<int8_t> inputToOutputWeights = { 2, 5, -4, 5, 2, -3, 5, 7, 3, -5, 1, -4 };
1562 std::vector<int8_t> recurrentToInputWeights = { -1, 1, -1, 1, -3, -4, -1, 4, 2, 3, 5, -1, 1, 3, -1, -1 };
1563 std::vector<int8_t> recurrentToForgetWeights = { -1, 1, -1, 1, -3, -4, -1, 4, 2, 3, 5, -1, 1, 3, -2, -1 };
1564 std::vector<int8_t> recurrentToCellWeights = { -2, -3, -1, -3, -4, 2, 1, -1, 2, 2, 1, 2, 3, -2, 3, -3 };
1565 std::vector<int8_t> recurrentToOutputWeights = { -3, 3, -1, -2, -2, -2, -1, -5, 1, 3, -4, -1, -1, -1, 2, -1 };
1568 std::vector<float> inputGateBias = { 0., 0., 0., 0. };
1569 std::vector<float> forgetGateBias = { 1., 1., 1., 1. };
1570 std::vector<float> cellBias = { 0., 0., 0., 0. };
1571 std::vector<float> outputGateBias = { 0., 0., 0., 0. };
1621 std::unique_ptr<armnn::IWorkload> workload
1623 inputHandle->Allocate();
1624 outputStateInHandle->Allocate();
1625 cellStateInHandle->Allocate();
1627 outputStateOutHandle->Allocate();
1628 cellStateOutHandle->Allocate();
1629 outputHandle->Allocate();
1635 workload->Execute();
1643 outputHandle->GetShape(),
1644 outputTensorInfo.GetShape());
1653 unsigned int batchSize = 3;
1654 unsigned int timeSize = 2;
1655 unsigned int outputSize = 4;
1656 unsigned int inputSize = 3;
1657 unsigned numUnits = 4;
1666 const std::vector<float> inputVector = { 0.1f, 0.2f, 0.3f, 0.4f, 0.5f, 0.4f,
1667 0.3f, 0.2f, 0.1f, 0.2f, 0.3f, 0.4f,
1668 0.5f, 0.4f, 0.3f, 0.2f, 0.1f, 0.2f };
1670 std::vector<float> cellStateInVector(batchSize * numUnits, 0.f);
1671 std::vector<float> outputStateInVector(batchSize * outputSize, 0.f);
1673 std::vector<float> actualOutputStateOut(outputStateOutTensorInfo.GetNumElements());
1674 std::vector<float> actualCellStateOut(cellStateOutTensorInfo.GetNumElements());
1675 std::vector<float> actualOutput(outputTensorInfo.GetNumElements());
1677 const std::vector<float> expectedOutput = { 0.612103f, 1.56788f, 0.31966f, 1.42956f,
1678 0.909718f, 3.07916f, -0.560586f, 3.8907f,
1679 0.753671f, 1.77485f, 0.365122f, 1.60077f,
1680 0.812644f, 2.79092f, -0.605396f, 3.61742f,
1681 0.791857f, 1.64353f, 0.316588f, 1.55192f,
1682 0.807265f, 2.47012f, -0.539598f, 3.25654f };
1684 std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.
CreateTensorHandle(inputTensorInfo);
1685 std::unique_ptr<armnn::ITensorHandle> cellStateInHandle =
1687 std::unique_ptr<armnn::ITensorHandle> outputStateInHandle =
1690 std::unique_ptr<armnn::ITensorHandle> outputStateOutHandle =
1692 std::unique_ptr<armnn::ITensorHandle> cellStateOutHandle =
1694 std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.
CreateTensorHandle(outputTensorInfo);
1699 AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
1700 AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get());
1701 AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get());
1703 AddOutputToWorkload(data, info, outputStateOutTensorInfo, outputStateOutHandle.get());
1704 AddOutputToWorkload(data, info, cellStateOutTensorInfo, cellStateOutHandle.get());
1705 AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
1714 std::vector<int8_t> inputToInputWeights = { -4, -1, -1, -2, 3, -2, 2, 4, 1, -4, -2, 3 };
1715 std::vector<int8_t> inputToForgetWeights = { 2, 1, 4, -4, 3, -1, -3, -2, -3, 1, -4, -1 };
1716 std::vector<int8_t> inputToCellWeights = { -2, 1, -2, 4, -3, -2, -4, 3, -2, -2, -6, 3 };
1717 std::vector<int8_t> inputToOutputWeights = { 2, 5, -4, 5, 2, -3, 5, 7, 3, -5, 1, -4 };
1719 std::vector<int8_t> recurrentToInputWeights = { -1, 1, -1, 1, -3, -4, -1, 4, 2, 3, 5, -1, 1, 3, -1, -1 };
1720 std::vector<int8_t> recurrentToForgetWeights = { -1, 1, -1, 1, -3, -4, -1, 4, 2, 3, 5, -1, 1, 3, -2, -1 };
1721 std::vector<int8_t> recurrentToCellWeights = { -2, -3, -1, -3, -4, 2, 1, -1, 2, 2, 1, 2, 3, -2, 3, -3 };
1722 std::vector<int8_t> recurrentToOutputWeights = { -3, 3, -1, -2, -2, -2, -1, -5, 1, 3, -4, -1, -1, -1, 2, -1 };
1724 std::vector<float> inputGateBias = { 0.02234832f, 0.14757581f, 0.18176508f, 0.10380666f};
1725 std::vector<float> forgetGateBias = { 0.035185695f, -0.042891346f, -0.3032477f, 0.23027696f};
1726 std::vector<float> cellBias = { -0.124379363f, 0.55531194f, 0.23377132f, 0.033463873f };
1727 std::vector<float> outputGateBias = { 0.046159424f, -0.12809046f, 0.03563469f, 0.12648113f };
1729 std::vector<int8_t> cellToInputWeights = { 5, 10, 25, 15 };
1730 std::vector<int8_t> cellToForgetWeights = { -5, 15, 25, 3 };
1731 std::vector<int8_t> cellToOutputWeights = { 10, -10, -5, 50 };
1733 std::vector<int8_t> projectionWeights = { -25, 51, 3, -5, 25, 127, 77, 20, 18, 51, -10, 51, -25, 88, 77, -13 };
1735 std::vector<float> projectionBiasVector(outputSize, 0.f);
1801 std::unique_ptr<armnn::IWorkload> workload
1803 inputHandle->Allocate();
1804 outputStateInHandle->Allocate();
1805 cellStateInHandle->Allocate();
1807 outputStateOutHandle->Allocate();
1808 cellStateOutHandle->Allocate();
1809 outputHandle->Allocate();
1815 workload->Execute();
1823 outputHandle->GetShape(),
1824 outputTensorInfo.GetShape());
1833 unsigned int batchSize = 3;
1834 unsigned int timeSize = 2;
1835 unsigned int outputSize = 4;
1836 unsigned int inputSize = 3;
1837 unsigned numUnits = 5;
1846 const std::vector<float> inputVector = { 1., 8., 3., 4., 5., 4.,
1847 3., 2., 1., 2., 3., 4.,
1848 5., 4., 3., 2., 1., 2. };
1850 std::vector<float> cellStateInVector(batchSize * numUnits, 0.f);
1851 std::vector<float> outputStateInVector(batchSize * outputSize, 0.f);
1853 std::vector<float> actualOutputStateOut(outputStateOutTensorInfo.GetNumElements());
1854 std::vector<float> actualCellStateOut(cellStateOutTensorInfo.GetNumElements());
1855 std::vector<float> actualOutput(outputTensorInfo.GetNumElements());
1857 const std::vector<float> expectedOutput = { 0.0471276f, 0.0168155f, 0.0789885f, 0.16550f,
1858 0.0643133f, -0.0400722f, 0.100593f, 0.197722f,
1859 0.0465562f, -0.0600682f, 0.0622087f, 0.115053f,
1860 0.056287f, -0.0566218f, 0.0856832f, 0.148484f,
1861 0.0457859f, -0.0588112f, 0.0623636f, 0.114333f,
1862 0.0509271f, -0.0754262f, 0.058600f, 0.0801288f };
1864 std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.
CreateTensorHandle(inputTensorInfo);
1865 std::unique_ptr<armnn::ITensorHandle> cellStateInHandle =
1867 std::unique_ptr<armnn::ITensorHandle> outputStateInHandle =
1870 std::unique_ptr<armnn::ITensorHandle> outputStateOutHandle =
1872 std::unique_ptr<armnn::ITensorHandle> cellStateOutHandle =
1874 std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.
CreateTensorHandle(outputTensorInfo);
1879 AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
1880 AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get());
1881 AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get());
1883 AddOutputToWorkload(data, info, outputStateOutTensorInfo, outputStateOutHandle.get());
1884 AddOutputToWorkload(data, info, cellStateOutTensorInfo, cellStateOutHandle.get());
1885 AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
1894 std::vector<int8_t> inputToInputWeights = { -4, -1, -1, -2, 3, -2, 2, 4, 1, -4, -2, 3, 2, 2, -4 };
1895 std::vector<int8_t> inputToForgetWeights = { 2, 1, 4, -4, 3, -1, -3, -2, -3, 1, -4, -1, -3, -2, -4 };
1896 std::vector<int8_t> inputToCellWeights = { -2, 1, -2, 4, -3, -2, -4, 3, -2, -2, -6, 3, 2, 5, -4 };
1897 std::vector<int8_t> inputToOutputWeights = { 2, 5, -4, 5, 2, -3, 5, 7, 3, -5, 1, -4, -4, -1, -1 };
1899 std::vector<float> inputGateBias = { 0.03f, 0.15f, 0.22f, 0.38f, 0.05f };
1900 std::vector<float> forgetGateBias = { 0.1f, -0.3f, -0.2f, 0.1f, 0.4f };
1901 std::vector<float> cellBias = { -0.05f, 0.72f, 0.25f, 0.08f, 0.1f };
1902 std::vector<float> outputGateBias = { 0.05f, -0.01f, 0.2f, 0.1f, -0.2f };
1904 std::vector<int8_t> recurrentToInputWeights = { -1, 1, -1, 1, -3, -4, -1, 4, 2, 3,
1905 5, -1, 1, 3, -1, -1, -1, 4, 2, 3 };
1907 std::vector<int8_t> recurrentToForgetWeights = { -1, 1, -1, 1, -3, -4, -1, 4, 2, 3,
1908 5, -1, 1, 3, -2, -1, -1, 2, 2, 1 };
1910 std::vector<int8_t> recurrentToCellWeights = { -2, -3, -1, -3, -4, 2, 1, -1, 2, 2,
1911 1, 2, 3, -2, 3, -3, -1, -5, 1, 3 };
1913 std::vector<int8_t> recurrentToOutputWeights = { -3, 3, -1, -2, -2, -2, -1, -5, 1, 3,
1914 -4, -1, -1, -1, 2, -1, 5, 1, -3, -4 };
1916 std::vector<int8_t> cellToInputWeights = { 5, 3, 8, -5, 2 };
1917 std::vector<int8_t> cellToForgetWeights = { -2, -7, 5, -3, 4 };
1918 std::vector<int8_t> cellToOutputWeights = { 9, -10 , -5, 5, 1 };
1920 std::vector<int8_t> projectionWeights = { -1, 2, 1, -2, 1, 5, 3, 8, 7, 2,
1921 -4, 2, 5, -4, 3, -2, 3, 8, -7, 2 };
1923 std::vector<float> projectionBiasVector(outputSize, 0.f);
1925 std::vector<float> inputLayerNormWeights = { 0.1f, 0.2f, -0.3f, -0.1f, 0.5f };
1926 std::vector<float> forgetLayerNormWeights = { -0.1f, 0.2f, 0.3f, 0.5f, 0.2f };
1927 std::vector<float> cellLayerNormWeights = { 0.5f, 0.2f, 0.3f, 0.4f, -0.5f };
1928 std::vector<float> outputLayerNormWeights = { 0.6f, -0.2f, -0.2f, 0.5f, 0.1f };
2008 std::unique_ptr<armnn::IWorkload> workload
2010 inputHandle->Allocate();
2011 outputStateInHandle->Allocate();
2012 cellStateInHandle->Allocate();
2014 outputStateOutHandle->Allocate();
2015 cellStateOutHandle->Allocate();
2016 outputHandle->Allocate();
2022 workload->Execute();
2030 outputHandle->GetShape(),
2031 outputTensorInfo.GetShape());
2040 unsigned int batchSize = 3;
2041 unsigned int timeSize = 2;
2042 unsigned int inputSize = 3;
2043 unsigned int outputSize = 4;
2044 unsigned numUnits = outputSize;
2053 const std::vector<float> inputVector = { 0.1f, 0.2f, 0.3f, 0.4f, 0.5f, 0.4f,
2054 0.3f, 0.2f, 0.1f, 0.2f, 0.3f, 0.4f,
2055 0.5f, 0.4f, 0.3f, 0.2f, 0.1f, 0.2f };
2057 std::vector<float> cellStateInVector(batchSize * numUnits, 0.f);
2058 std::vector<float> outputStateInVector(batchSize * outputSize, 0.f);
2060 std::vector<float> actualOutputStateOut(outputStateOutTensorInfo.GetNumElements());
2061 std::vector<float> actualCellStateOut(cellStateOutTensorInfo.GetNumElements());
2062 std::vector<float> actualOutput(outputTensorInfo.GetNumElements());
2064 const std::vector<float> outputVector = { -0.0072104f, -0.00991171f, -0.00650478f, -0.00713055f,
2065 -0.0191782f, -0.0161269f, -0.0233683f, -0.054299f,
2066 -0.00783725f, 0.00635271f, -0.0126718f, -0.022613f,
2067 -0.0161351f, -0.00775868f, -0.021054f, -0.0339778f,
2068 -0.0146392f, 0.00330261f, -0.0258733f, -0.0407797f,
2069 -0.0174297f, 0.0050105f, -0.0266275f, -0.0362564f };
2071 std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.
CreateTensorHandle(inputTensorInfo);
2072 std::unique_ptr<armnn::ITensorHandle> cellStateInHandle =
2074 std::unique_ptr<armnn::ITensorHandle> outputStateInHandle =
2077 std::unique_ptr<armnn::ITensorHandle> outputStateOutHandle =
2079 std::unique_ptr<armnn::ITensorHandle> cellStateOutHandle =
2081 std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.
CreateTensorHandle(outputTensorInfo);
2086 AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
2087 AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get());
2088 AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get());
2090 AddOutputToWorkload(data, info, outputStateOutTensorInfo, outputStateOutHandle.get());
2091 AddOutputToWorkload(data, info, cellStateOutTensorInfo, cellStateOutHandle.get());
2092 AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
2099 std::vector<int8_t> inputToForgetWeights = { 2, 1, 4, -4, 3, -1, -3, -2, -3, 1, -4, -1 };
2100 std::vector<int8_t> inputToCellWeights = { -2, 1, -2, 4, -3, -2, -4, 3, -2, -2, -6, 3 };
2101 std::vector<int8_t> inputToOutputWeights = { 2, 5, -4, 5, 2, -3, 5, 7, 3, -5, 1, -4 };
2103 std::vector<int8_t> recurrentToForgetWeights = { -1, 1, -1, 1, -3, -4, -1, 4, 2, 3, 5, -1, 1, 3, -2, -1 };
2104 std::vector<int8_t> recurrentToCellWeights = { -2, -3, -1, -3, -4, 2, 1, -1, 2, 2, 1, 2, 3, -2, 3, -3 };
2105 std::vector<int8_t> recurrentToOutputWeights = { -3, 3, -1, -2, -2, -2, -1, -5, 1, 3, -4, -1, -1, -1, 2, -1 };
2107 std::vector<int8_t> cellToForgetWeights = { 47, -52, -24, 31 };
2108 std::vector<int8_t> cellToOutputWeights = { -17, 82, 85, -77 };
2110 std::vector<float> forgetGateBias = { 1., 1., 1., 1. };
2111 std::vector<float> cellBias = { 0., 0., 0., 0. };
2112 std::vector<float> outputGateBias = { 0., 0., 0., 0. };
2159 std::unique_ptr<armnn::IWorkload> workload
2161 inputHandle->Allocate();
2162 outputStateInHandle->Allocate();
2163 cellStateInHandle->Allocate();
2165 outputStateOutHandle->Allocate();
2166 cellStateOutHandle->Allocate();
2167 outputHandle->Allocate();
2173 workload->Execute();
2181 outputHandle->GetShape(),
2182 outputTensorInfo.GetShape());
bool m_ProjectionEnabled
Enable/disable the projection layer.
const ConstTensorHandle * m_InputGateBias
float m_ClippingThresProj
Clipping threshold value for the projection.
const ConstTensorHandle * m_RecurrentToCellWeights
LayerTestResult< float, 3 > UnidirectionalSequenceLstmLayerInt8Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)
LayerTestResult< float, 3 > UnidirectionalSequenceLstmInt8WithCifgWithPeepholeNoProjectionTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)
const ConstTensorHandle * m_InputToInputWeights
bool m_TimeMajor
Enable/disable time major.
void IgnoreUnused(Ts &&...)
LayerTestResult< float, 3 > UnidirectionalSequenceLstmLayerFloat32Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)
LayerDescriptor m_Parameters
void AllocateAndCopyDataToITensorHandle(armnn::ITensorHandle *tensorHandle, const void *memory)
const ConstTensorHandle * m_OutputGateBias
LayerTestResult< float, 3 > UnidirectionalSequenceLstmLayerInt8TimeMajorTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)
const ConstTensorHandle * m_ProjectionBias
const ConstTensorHandle * m_InputToOutputWeights
const ConstTensorHandle * m_InputToCellWeights
LayerTestResult< float, 3 > UnidirectionalSequenceLstmLayerFloat32TimeMajorSingleBatchTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)
const ConstTensorHandle * m_InputToForgetWeights
LayerTestResult< float, 3 > UnidirectionalSequenceLstmLayerNoCifgWithPeepholeWithProjectionTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)
const ConstTensorHandle * m_CellLayerNormWeights
LayerTestResult< float, 3 > UnidirectionalSequenceLstmLayerNoCifgWithPeepholeWithProjectionWithLayerNormTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)
const ConstTensorHandle * m_CellToInputWeights
const ConstTensorHandle * m_ProjectionWeights
std::shared_ptr< IMemoryManager > IMemoryManagerSharedPtr
LayerTestResult< float, 3 > UnidirectionalSequenceLstmWithCifgWithPeepholeNoProjectionTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)
bool m_PeepholeEnabled
Enable/disable peephole.
void CopyDataFromITensorHandle(void *mem, const armnn::ITensorHandle *tensorHandle)
LayerTestResult< float, 3 > UnidirectionalSequenceLstmLayerInt8NoCifgWithPeepholeWithProjectionTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)
uint32_t m_ActivationFunc
The activation function to use.
float m_ClippingThresCell
Clipping threshold value for the cell state.
LayerTestResult< float, 3 > UnidirectionalSequenceLstmLayerFloat32BatchMajorSingleBatchTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)
const ConstTensorHandle * m_ForgetLayerNormWeights
const ConstTensorHandle * m_OutputLayerNormWeights
bool m_CifgEnabled
Enable/disable cifg (coupled input & forget gate).
const ConstTensorHandle * m_ForgetGateBias
LayerTestResult< float, 3 > UnidirectionalSequenceLstmLayerFloat32TimeMajorTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)
void CopyDataToITensorHandle(armnn::ITensorHandle *tensorHandle, const void *memory)
const ConstTensorHandle * m_CellBias
bool m_LayerNormEnabled
Enable/disable layer normalization.
std::enable_if_t< std::is_unsigned< Source >::value &&std::is_unsigned< Dest >::value, Dest > numeric_cast(Source source)
LayerTestResult< float, 3 > UnidirectionalSequenceLstmLayerInt8NoCifgWithPeepholeWithProjectionWithLayerNormTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)
const ConstTensorHandle * m_RecurrentToInputWeights
Contains information about TensorInfos of a layer.
const ConstTensorHandle * m_CellToForgetWeights
const ConstTensorHandle * m_RecurrentToOutputWeights
virtual std::unique_ptr< IWorkload > CreateWorkload(LayerType type, const QueueDescriptor &descriptor, const WorkloadInfo &info) const
virtual std::unique_ptr< ITensorHandle > CreateTensorHandle(const TensorInfo &tensorInfo) const =0
const ConstTensorHandle * m_RecurrentToForgetWeights
const ConstTensorHandle * m_CellToOutputWeights
const ConstTensorHandle * m_InputLayerNormWeights