ArmNN
 21.08
Functions
UnidirectionalSequenceLstmTestImpl.hpp File Reference
#include "LayerTestResult.hpp"
#include <armnn/backends/IBackendInternal.hpp>
#include <backendsCommon/WorkloadFactory.hpp>

Go to the source code of this file.

Functions

LayerTestResult< float, 3 > UnidirectionalSequenceLstmLayerFloat32Test (armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)
 
LayerTestResult< float, 3 > UnidirectionalSequenceLstmLayerFloat32TimeMajorTest (armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)
 
LayerTestResult< float, 3 > UnidirectionalSequenceLstmLayerNoCifgWithPeepholeWithProjectionTest (armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)
 
LayerTestResult< float, 3 > UnidirectionalSequenceLstmLayerNoCifgWithPeepholeWithProjectionWithLayerNormTest (armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)
 
LayerTestResult< float, 3 > UnidirectionalSequenceLstmWithCifgWithPeepholeNoProjectionTest (armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)
 

Function Documentation

◆ UnidirectionalSequenceLstmLayerFloat32Test()

LayerTestResult<float, 3> UnidirectionalSequenceLstmLayerFloat32Test ( armnn::IWorkloadFactory &  workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr &  memoryManager,
const armnn::ITensorHandleFactory &  tensorHandleFactory 
)

Definition at line 370 of file UnidirectionalSequenceLstmTestImpl.cpp.

References armnn::Float32.

Referenced by TEST_SUITE().

373  {
374  armnn::TensorInfo inputInfo({3, 2, 3}, armnn::DataType::Float32);
375  std::vector<float> input = { 1., 2., 3., 4., 5., 4.,
376  3., 2., 1., 2., 3., 4.,
377  5., 4., 3., 2., 1., 2. };
378 
379  armnn::TensorInfo outputInfo({3, 2, 4}, armnn::DataType::Float32);
380  std::vector<float> expectedOutput = { -0.07149004f, -0.1621171f, -0.17516759f, -0.0232934225f,
381  -0.16810727f, -0.41412935f, -0.5498753f, -0.00803578f,
382  -0.06687349f, 0.204077631f, -0.4276504f, -0.03123213f,
383  -0.12000261f, -0.0941918f, -0.45639035f, -0.02870186f,
384  -0.03429216f, 0.20824050f, -0.6569892f, -0.004152651f,
385  -0.10493034f, 0.14210969f, -0.58347696f, -0.03297536f };
386  return UnidirectionalSequenceLstmLayerFloat32TestImpl<armnn::DataType::Float32>(
387  workloadFactory, memoryManager, tensorHandleFactory,
388  input, expectedOutput, inputInfo.GetShape(), outputInfo.GetShape());
389 }

◆ UnidirectionalSequenceLstmLayerFloat32TimeMajorTest()

LayerTestResult<float, 3> UnidirectionalSequenceLstmLayerFloat32TimeMajorTest ( armnn::IWorkloadFactory &  workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr &  memoryManager,
const armnn::ITensorHandleFactory &  tensorHandleFactory 
)

Definition at line 391 of file UnidirectionalSequenceLstmTestImpl.cpp.

References armnn::Float32.

Referenced by TEST_SUITE().

394  {
395  armnn::TensorInfo inputInfo({2, 3, 3}, armnn::DataType::Float32);
396  std::vector<float> input = { 1., 2., 3., 4., 5., 4.,
397  3., 2., 1., 2., 3., 4.,
398  5., 4., 3., 2., 1., 2. };
399 
400  armnn::TensorInfo outputInfo({2, 3, 4}, armnn::DataType::Float32);
401  std::vector<float> expectedOutput = { 0.135657698f, 0.124672532f, 0.0212090332f, -0.0530203655f,
402  0.106138252f, 0.0404792242f, 0.0151643595f, -0.00675163185f,
403  -0.0128514022f, 0.0644884035f, 0.0709072053f, -0.0454045124f,
404  0.16288602f, 0.16649379f, 0.02770456f, -0.03698075f,
405  0.11171641f, 0.043119f , 0.0762981f , -0.01228541f,
406  0.10439701f, 0.21439962f, 0.11919238f, -0.08390583f };
407  return UnidirectionalSequenceLstmLayerFloat32TimeMajorTestImpl<armnn::DataType::Float32>(
408  workloadFactory, memoryManager, tensorHandleFactory,
409  input, expectedOutput, inputInfo.GetShape(), outputInfo.GetShape());
410 }

◆ UnidirectionalSequenceLstmLayerNoCifgWithPeepholeWithProjectionTest()

LayerTestResult<float, 3> UnidirectionalSequenceLstmLayerNoCifgWithPeepholeWithProjectionTest ( armnn::IWorkloadFactory &  workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr &  memoryManager,
const armnn::ITensorHandleFactory &  tensorHandleFactory 
)

Definition at line 412 of file UnidirectionalSequenceLstmTestImpl.cpp.

References AllocateAndCopyDataToITensorHandle(), CopyDataFromITensorHandle(), CopyDataToITensorHandle(), ITensorHandleFactory::CreateTensorHandle(), IWorkloadFactory::CreateUnidirectionalSequenceLstm(), armnn::Float32, armnn::IgnoreUnused(), LstmDescriptor::m_ActivationFunc, UnidirectionalSequenceLstmQueueDescriptor::m_CellBias, UnidirectionalSequenceLstmQueueDescriptor::m_CellToForgetWeights, UnidirectionalSequenceLstmQueueDescriptor::m_CellToInputWeights, UnidirectionalSequenceLstmQueueDescriptor::m_CellToOutputWeights, LstmDescriptor::m_CifgEnabled, LstmDescriptor::m_ClippingThresCell, UnidirectionalSequenceLstmQueueDescriptor::m_ForgetGateBias, UnidirectionalSequenceLstmQueueDescriptor::m_InputGateBias, UnidirectionalSequenceLstmQueueDescriptor::m_InputToCellWeights, UnidirectionalSequenceLstmQueueDescriptor::m_InputToForgetWeights, UnidirectionalSequenceLstmQueueDescriptor::m_InputToInputWeights, UnidirectionalSequenceLstmQueueDescriptor::m_InputToOutputWeights, LstmDescriptor::m_LayerNormEnabled, UnidirectionalSequenceLstmQueueDescriptor::m_OutputGateBias, QueueDescriptorWithParameters< LayerDescriptor >::m_Parameters, LstmDescriptor::m_PeepholeEnabled, UnidirectionalSequenceLstmQueueDescriptor::m_ProjectionBias, LstmDescriptor::m_ProjectionEnabled, UnidirectionalSequenceLstmQueueDescriptor::m_ProjectionWeights, UnidirectionalSequenceLstmQueueDescriptor::m_RecurrentToCellWeights, UnidirectionalSequenceLstmQueueDescriptor::m_RecurrentToForgetWeights, UnidirectionalSequenceLstmQueueDescriptor::m_RecurrentToInputWeights, UnidirectionalSequenceLstmQueueDescriptor::m_RecurrentToOutputWeights, and LstmDescriptor::m_TimeMajor.

Referenced by TEST_SUITE().

416 {
417  IgnoreUnused(memoryManager);
418  unsigned int batchSize = 2;
419  unsigned int timeSize = 3;
420  unsigned int outputSize = 5;
421  unsigned int inputSize = 4;
422  unsigned numUnits = 6;
423 
424  armnn::TensorInfo inputTensorInfo({batchSize, timeSize, inputSize}, armnn::DataType::Float32);
425  armnn::TensorInfo cellStateInTensorInfo({batchSize , numUnits}, armnn::DataType::Float32);
426  armnn::TensorInfo outputStateInTensorInfo({batchSize , outputSize}, armnn::DataType::Float32);
427  armnn::TensorInfo outputTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32);
428 
429  const std::vector<float> inputVector = { 1., 2., 3., 4., 5., 4.,
430  3., 2., 1., 2., 3., 4.,
431  5., 4., 3., 2., 1., 2.,
432  1., 2., 3., 4., 5., 4.};
433 
434  std::vector<float> cellStateInVector(batchSize * numUnits, 0.f);
435  std::vector<float> outputStateInVector(batchSize * outputSize, 0.f);
436 
437  std::vector<float> actualOutput(outputTensorInfo.GetNumElements());
438 
439  const std::vector<float> expectedOutput = { -0.0135612f, -0.0263441f, 0.0314008f, -0.00883455f, 0.00763052f,
440  -0.00126877f, -0.0292959f, 0.0449957f, -0.00976195f, -0.00492338f,
441  -0.0175702f, -0.0431753f, 0.0597117f, -0.0169154f, 0.0142087f,
442  0.00472515f, -0.0196355f, 0.0342524f, -0.00407936f, -0.0253189f,
443  -0.00512944f, -0.0293754f, 0.0512771f, -0.0151874f, -0.0246433f,
444  -0.00744986f, -0.0345103f, 0.0450666f, -0.00944991f, 0.0127171f };
445 
446  std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo);
447  std::unique_ptr<armnn::ITensorHandle> cellStateInHandle =
448  tensorHandleFactory.CreateTensorHandle(cellStateInTensorInfo);
449  std::unique_ptr<armnn::ITensorHandle> outputStateInHandle =
450  tensorHandleFactory.CreateTensorHandle(outputStateInTensorInfo);
451  std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo);
452 
453  armnn::UnidirectionalSequenceLstmQueueDescriptor data;
454  armnn::WorkloadInfo info;
455 
456  AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
457  AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get());
458  AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get());
459  AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
460 
461  armnn::TensorInfo tensorInfo5({outputSize}, armnn::DataType::Float32);
462  armnn::TensorInfo tensorInfo6({numUnits}, armnn::DataType::Float32);
463  armnn::TensorInfo tensorInfo6x4({numUnits, inputSize}, armnn::DataType::Float32);
464  armnn::TensorInfo tensorInfo6x5({numUnits, outputSize}, armnn::DataType::Float32);
465  armnn::TensorInfo tensorInfo5x6({outputSize, numUnits}, armnn::DataType::Float32);
466 
467  std::vector<float> inputToInputWeights = { 0.021393683f, 0.06124551f, 0.046905167f, -0.014657677f,
468  -0.03149463f, 0.09171803f, 0.14647801f, 0.10797193f,
469  -0.0057968358f, 0.0019193048f, -0.2726754f, 0.10154029f,
470  -0.018539885f, 0.080349885f, -0.10262385f, -0.022599787f,
471  -0.09121155f, -0.008675967f, -0.045206103f, -0.0821282f,
472  -0.008045952f, 0.015478081f, 0.055217247f, 0.038719587f };
473 
474  std::vector<float> inputToForgetWeights = { -0.0018401089f, -0.004852237f, 0.03698424f, 0.014181704f,
475  0.028273236f, -0.016726194f, -0.05249759f, -0.10204261f,
476  0.00861066f, -0.040979505f, -0.009899187f, 0.01923892f,
477  -0.028177269f, -0.08535103f, -0.14585495f, 0.10662567f,
478  -0.01909731f, -0.017883534f, -0.0047269356f, -0.045103323f,
479  0.0030784295f, 0.076784775f, 0.07463696f, 0.094531395f};
480 
481  std::vector<float> inputToCellWeights = { -0.04580283f, -0.09549462f, -0.032418985f, -0.06454633f,
482  -0.043528453f, 0.043018587f, -0.049152344f, -0.12418144f,
483  -0.078985475f, -0.07596889f, 0.019484362f, -0.11434962f,
484  -0.0074034138f, -0.06314844f, -0.092981495f, 0.0062155537f,
485  -0.025034338f, -0.0028890965f, 0.048929527f, 0.06235075f,
486  0.10665918f, -0.032036792f, -0.08505916f, -0.10843358f };
487 
488  std::vector<float> inputToOutputWeights = { -0.0998932f, -0.07201956f, -0.052803773f, -0.15629593f,
489  -0.15001918f, -0.07650751f, 0.02359855f, -0.075155355f,
490  -0.08037709f, -0.15093534f, 0.029517552f, -0.04751393f,
491  0.010350531f, -0.02664851f, -0.016839722f, -0.023121163f,
492  0.0077019283f, 0.012851257f, -0.05040649f, -0.0129761f,
493  -0.021737747f, -0.038305793f, -0.06870586f, -0.01481247f };
494 
495  std::vector<float> inputGateBias = { 0.02234832f, 0.14757581f, 0.18176508f,
496  0.10380666f, 0.053110216f, -0.06928846f };
497 
498  std::vector<float> forgetGateBias = { 0.035185695f, -0.042891346f, -0.03032477f,
499  0.23027696f, 0.11098921f, 0.08989442f };
500 
501  std::vector<float> cellBias = { -0.024379363f, 0.0055531194f, 0.23377132f,
502  0.033463873f, -0.1483596f, 0.029460307f };
503 
504  std::vector<float> outputGateBias = { 0.046159424f, -0.0012809046f, 0.03563469f,
505  0.12648113f, 0.027195795f, 0.35373217f };
506 
507  std::vector<float> recurrentToInputWeights = { -0.001374326f, -0.078856036f, 0.10672688f, 0.029162422f,
508  -0.11585556f, 0.02557986f, -0.13446963f, -0.035785314f,
509  -0.01244275f, 0.025961924f, -0.02337298f, -0.044228926f,
510  -0.055839065f, -0.046598054f, -0.010546039f, -0.06900766f,
511  0.027239809f, 0.022582639f, -0.013296484f, -0.05459212f,
512  0.08981f, -0.045407712f, 0.08682226f, -0.06867011f,
513  -0.14390695f, -0.02916037f, 0.000996957f, 0.091420636f,
514  0.14283475f, -0.07390571f };
515 
516  std::vector<float> recurrentToCellWeights = { -0.037322544f, 0.018592842f, 0.0056175636f, -0.06253426f,
517  0.055647098f, -0.05713207f, -0.05626563f, 0.005559383f,
518  0.03375411f, -0.025757805f, -0.088049285f, 0.06017052f,
519  -0.06570978f, 0.007384076f, 0.035123326f, -0.07920549f,
520  0.053676967f, 0.044480428f, -0.07663568f, 0.0071805613f,
521  0.08089997f, 0.05143358f, 0.038261272f, 0.03339287f,
522  -0.027673481f, 0.044746667f, 0.028349208f, 0.020090483f,
523  -0.019443132f, -0.030755889f };
524 
525  std::vector<float> recurrentToForgetWeights = { -0.057784554f, -0.026057621f, -0.068447545f, -0.022581743f,
526  0.14811787f, 0.10826372f, 0.09471067f, 0.03987225f,
527  -0.0039523416f, 0.00030638507f, 0.053185795f, 0.10572994f,
528  0.08414449f, -0.022036452f, -0.00066928595f, -0.09203576f,
529  0.032950465f, -0.10985798f, -0.023809856f, 0.0021431844f,
530  -0.02196096f, -0.00326074f, 0.00058621005f, -0.074678116f,
531  -0.06193199f, 0.055729095f, 0.03736828f, 0.020123724f,
532  0.061878487f, -0.04729229f };
533 
534  std::vector<float> recurrentToOutputWeights = { 0.025825322f, -0.05813119f, 0.09495884f,
535  -0.045984812f,-0.01255415f, -0.0026479573f,
536  -0.08196161f, -0.054914974f, -0.0046604523f,
537  -0.029587349f, -0.044576716f, -0.07480124f,
538  -0.082868785f, 0.023254942f, 0.027502948f,
539  -0.0039728214f, -0.08683098f, -0.08116779f,
540  -0.014675607f, -0.037924774f, -0.023314456f,
541  -0.007401714f, -0.09255757f, 0.029460307f,
542  -0.08829125f, -0.005139627f, -0.08989442f,
543  -0.0555066f, 0.13596267f, 0.025062224f };
544 
545  std::vector<float> cellToInputWeights = { 0.040369894f, 0.030746894f, 0.24704495f,
546  0.018586371f, -0.037586458f, -0.15312155f };
547 
548  std::vector<float> cellToForgetWeights = { -0.01998659f, -0.15568835f, -0.24248174f,
549  -0.012770197f, 0.041331276f, -0.072311886f };
550 
551  std::vector<float> cellToOutputWeights = { 0.08286371f, -0.08261836f, -0.51210177f,
552  0.002913762f, 0.17764764f, -0.5495371f };
553 
554  std::vector<float> projectionWeights = { -0.009802181f, 0.09401916f, 0.0717386f, -0.13895074f, 0.09641832f,
555  0.060420845f, 0.08539281f, 0.054285463f, 0.061395317f, 0.034448683f,
556  -0.042991187f, 0.019801661f, -0.16840284f, -0.015726732f, -0.23041931f,
557  -0.024478018f, -0.10959692f, -0.013875541f, 0.18600968f, -0.061274476f,
558  0.0138165f, -0.08160894f, -0.07661644f, 0.032372914f, 0.16169067f,
559  0.22465782f, -0.03993472f, -0.004017731f, 0.08633481f, -0.28869787f };
560 
561  std::vector<float> projectionBiasVector(outputSize, 0.f); //{outputSize}
562 
563  armnn::ScopedTensorHandle inputToInputWeightsTensor(tensorInfo6x4);
564  armnn::ScopedTensorHandle inputToForgetWeightsTensor(tensorInfo6x4);
565  armnn::ScopedTensorHandle inputToCellWeightsTensor(tensorInfo6x4);
566  armnn::ScopedTensorHandle inputToOutputWeightsTensor(tensorInfo6x4);
567  armnn::ScopedTensorHandle recurrentToForgetWeightsTensor(tensorInfo6x5);
568  armnn::ScopedTensorHandle recurrentToInputWeightsTensor(tensorInfo6x5);
569  armnn::ScopedTensorHandle recurrentToCellWeightsTensor(tensorInfo6x5);
570  armnn::ScopedTensorHandle recurrentToOutputWeightsTensor(tensorInfo6x5);
571  armnn::ScopedTensorHandle cellToInputWeightsTensor(tensorInfo6);
572  armnn::ScopedTensorHandle inputGateBiasTensor(tensorInfo6);
573  armnn::ScopedTensorHandle forgetGateBiasTensor(tensorInfo6);
574  armnn::ScopedTensorHandle cellBiasTensor(tensorInfo6);
575  armnn::ScopedTensorHandle outputGateBiasTensor(tensorInfo6);
576  armnn::ScopedTensorHandle cellToForgetWeightsTensor(tensorInfo6);
577  armnn::ScopedTensorHandle cellToOutputWeightsTensor(tensorInfo6);
578  armnn::ScopedTensorHandle projectionWeightsTensor(tensorInfo5x6);
579  armnn::ScopedTensorHandle projectionBiasTensor(tensorInfo5);
580 
581  AllocateAndCopyDataToITensorHandle(&inputToInputWeightsTensor, inputToInputWeights.data());
582  AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, inputToForgetWeights.data());
583  AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, inputToCellWeights.data());
584  AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, inputToOutputWeights.data());
585  AllocateAndCopyDataToITensorHandle(&recurrentToInputWeightsTensor, recurrentToInputWeights.data());
586  AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, recurrentToForgetWeights.data());
587  AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, recurrentToCellWeights.data());
588  AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, recurrentToOutputWeights.data());
589  AllocateAndCopyDataToITensorHandle(&cellToInputWeightsTensor, cellToInputWeights.data());
590  AllocateAndCopyDataToITensorHandle(&inputGateBiasTensor, inputGateBias.data());
591  AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, forgetGateBias.data());
592  AllocateAndCopyDataToITensorHandle(&cellBiasTensor, cellBias.data());
593  AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, outputGateBias.data());
594  AllocateAndCopyDataToITensorHandle(&cellToForgetWeightsTensor, cellToForgetWeights.data());
595  AllocateAndCopyDataToITensorHandle(&cellToOutputWeightsTensor, cellToOutputWeights.data());
596  AllocateAndCopyDataToITensorHandle(&projectionWeightsTensor, projectionWeights.data());
597  AllocateAndCopyDataToITensorHandle(&projectionBiasTensor, projectionBiasVector.data());
598 
599  data.m_InputToInputWeights = &inputToInputWeightsTensor;
600  data.m_InputToForgetWeights = &inputToForgetWeightsTensor;
601  data.m_InputToCellWeights = &inputToCellWeightsTensor;
602  data.m_InputToOutputWeights = &inputToOutputWeightsTensor;
603  data.m_RecurrentToInputWeights = &recurrentToInputWeightsTensor;
604  data.m_RecurrentToForgetWeights = &recurrentToForgetWeightsTensor;
605  data.m_RecurrentToCellWeights = &recurrentToCellWeightsTensor;
606  data.m_RecurrentToOutputWeights = &recurrentToOutputWeightsTensor;
607  data.m_CellToInputWeights = &cellToInputWeightsTensor;
608  data.m_InputGateBias = &inputGateBiasTensor;
609  data.m_ForgetGateBias = &forgetGateBiasTensor;
610  data.m_CellBias = &cellBiasTensor;
611  data.m_OutputGateBias = &outputGateBiasTensor;
612  data.m_CellToForgetWeights = &cellToForgetWeightsTensor;
613  data.m_CellToOutputWeights = &cellToOutputWeightsTensor;
614  data.m_ProjectionWeights = &projectionWeightsTensor;
615  data.m_ProjectionBias = &projectionBiasTensor;
616 
617  // Flags to set test configuration
618  data.m_Parameters.m_ActivationFunc = 4;
619  data.m_Parameters.m_CifgEnabled = false;
620  data.m_Parameters.m_PeepholeEnabled = true;
621  data.m_Parameters.m_ProjectionEnabled = true;
622  data.m_Parameters.m_LayerNormEnabled = false;
623  data.m_Parameters.m_TimeMajor = false;
624  data.m_Parameters.m_ClippingThresCell = 10.0f;
625 
626 
627  std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateUnidirectionalSequenceLstm(data, info);
628  inputHandle->Allocate();
629  outputStateInHandle->Allocate();
630  cellStateInHandle->Allocate();
631  outputHandle->Allocate();
632 
633  CopyDataToITensorHandle(inputHandle.get(), inputVector.data());
634  CopyDataToITensorHandle(outputStateInHandle.get(), outputStateInVector.data());
635  CopyDataToITensorHandle(cellStateInHandle.get(), cellStateInVector.data());
636 
637  workload->Execute();
638 
639  CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get());
640 
641  return LayerTestResult<float, 3>(actualOutput,
642  expectedOutput,
643  outputHandle->GetShape(),
644  outputTensorInfo.GetShape());
645 }
armnn::LstmDescriptor::m_ProjectionEnabled
bool m_ProjectionEnabled
Enable/disable the projection layer.
Definition: Descriptors.hpp:959
armnn::LstmDescriptor::m_TimeMajor
bool m_TimeMajor
Enable/disable time major.
Definition: Descriptors.hpp:963
armnn::IgnoreUnused
void IgnoreUnused(Ts &&...)
Definition: IgnoreUnused.hpp:14
armnn::IWorkloadFactory::CreateUnidirectionalSequenceLstm
virtual std::unique_ptr< IWorkload > CreateUnidirectionalSequenceLstm(const UnidirectionalSequenceLstmQueueDescriptor &descriptor, const WorkloadInfo &info) const
Definition: WorkloadFactory.cpp:1889
armnn::LstmDescriptor::m_PeepholeEnabled
bool m_PeepholeEnabled
Enable/disable peephole.
Definition: Descriptors.hpp:957
AllocateAndCopyDataToITensorHandle
void AllocateAndCopyDataToITensorHandle(armnn::ITensorHandle *tensorHandle, const void *memory)
Definition: TensorCopyUtils.cpp:19
CopyDataFromITensorHandle
void CopyDataFromITensorHandle(void *memory, const armnn::ITensorHandle *tensorHandle)
Definition: TensorCopyUtils.cpp:14
armnn::LstmDescriptor::m_ActivationFunc
uint32_t m_ActivationFunc
The activation function to use.
Definition: Descriptors.hpp:949
armnn::LstmDescriptor::m_ClippingThresCell
float m_ClippingThresCell
Clipping threshold value for the cell state.
Definition: Descriptors.hpp:951
armnn::LstmDescriptor::m_CifgEnabled
bool m_CifgEnabled
Enable/disable cifg (coupled input & forget gate).
Definition: Descriptors.hpp:955
armnn::LstmDescriptor::m_LayerNormEnabled
bool m_LayerNormEnabled
Enable/disable layer normalization.
Definition: Descriptors.hpp:961
armnn::WorkloadInfo
Contains information about TensorInfos of a layer.
Definition: WorkloadInfo.hpp:16
armnn::UnidirectionalSequenceLstmQueueDescriptor::m_RecurrentToOutputWeights
const ConstTensorHandle * m_RecurrentToOutputWeights
Definition: WorkloadData.hpp:732
armnn::ITensorHandleFactory::CreateTensorHandle
virtual std::unique_ptr< ITensorHandle > CreateTensorHandle(const TensorInfo &tensorInfo) const =0
armnn::UnidirectionalSequenceLstmQueueDescriptor::m_RecurrentToForgetWeights
const ConstTensorHandle * m_RecurrentToForgetWeights
Definition: WorkloadData.hpp:730
CopyDataToITensorHandle
void CopyDataToITensorHandle(armnn::ITensorHandle *tensorHandle, const void *memory)
Definition: TensorCopyUtils.cpp:9

◆ UnidirectionalSequenceLstmLayerNoCifgWithPeepholeWithProjectionWithLayerNormTest()

LayerTestResult<float, 3> UnidirectionalSequenceLstmLayerNoCifgWithPeepholeWithProjectionWithLayerNormTest ( armnn::IWorkloadFactory &  workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr &  memoryManager,
const armnn::ITensorHandleFactory &  tensorHandleFactory 
)

Definition at line 647 of file UnidirectionalSequenceLstmTestImpl.cpp.

References AllocateAndCopyDataToITensorHandle(), CopyDataFromITensorHandle(), CopyDataToITensorHandle(), ITensorHandleFactory::CreateTensorHandle(), IWorkloadFactory::CreateUnidirectionalSequenceLstm(), armnn::Float32, armnn::IgnoreUnused(), LstmDescriptor::m_ActivationFunc, UnidirectionalSequenceLstmQueueDescriptor::m_CellBias, UnidirectionalSequenceLstmQueueDescriptor::m_CellLayerNormWeights, UnidirectionalSequenceLstmQueueDescriptor::m_CellToForgetWeights, UnidirectionalSequenceLstmQueueDescriptor::m_CellToInputWeights, UnidirectionalSequenceLstmQueueDescriptor::m_CellToOutputWeights, LstmDescriptor::m_CifgEnabled, LstmDescriptor::m_ClippingThresCell, UnidirectionalSequenceLstmQueueDescriptor::m_ForgetGateBias, UnidirectionalSequenceLstmQueueDescriptor::m_ForgetLayerNormWeights, UnidirectionalSequenceLstmQueueDescriptor::m_InputGateBias, UnidirectionalSequenceLstmQueueDescriptor::m_InputLayerNormWeights, UnidirectionalSequenceLstmQueueDescriptor::m_InputToCellWeights, UnidirectionalSequenceLstmQueueDescriptor::m_InputToForgetWeights, UnidirectionalSequenceLstmQueueDescriptor::m_InputToInputWeights, UnidirectionalSequenceLstmQueueDescriptor::m_InputToOutputWeights, LstmDescriptor::m_LayerNormEnabled, UnidirectionalSequenceLstmQueueDescriptor::m_OutputGateBias, UnidirectionalSequenceLstmQueueDescriptor::m_OutputLayerNormWeights, QueueDescriptorWithParameters< LayerDescriptor >::m_Parameters, LstmDescriptor::m_PeepholeEnabled, UnidirectionalSequenceLstmQueueDescriptor::m_ProjectionBias, LstmDescriptor::m_ProjectionEnabled, UnidirectionalSequenceLstmQueueDescriptor::m_ProjectionWeights, UnidirectionalSequenceLstmQueueDescriptor::m_RecurrentToCellWeights, UnidirectionalSequenceLstmQueueDescriptor::m_RecurrentToForgetWeights, UnidirectionalSequenceLstmQueueDescriptor::m_RecurrentToInputWeights, UnidirectionalSequenceLstmQueueDescriptor::m_RecurrentToOutputWeights, and LstmDescriptor::m_TimeMajor.

Referenced by TEST_SUITE().

651 {
652  IgnoreUnused(memoryManager);
653  unsigned int batchSize = 3;
654  unsigned int timeSize = 2;
655  unsigned int outputSize = 4;
656  unsigned int inputSize = 3;
657  unsigned numUnits = 5;
658 
659  armnn::TensorInfo inputTensorInfo({batchSize, timeSize, inputSize}, armnn::DataType::Float32);
660  armnn::TensorInfo cellStateInTensorInfo({batchSize , numUnits}, armnn::DataType::Float32);
661  armnn::TensorInfo outputStateInTensorInfo({batchSize , outputSize}, armnn::DataType::Float32);
662  armnn::TensorInfo outputTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32);
663 
664  const std::vector<float> inputVector = { 1., 2., 3., 4., 5., 4.,
665  3., 2., 1., 2., 3., 4.,
666  5., 4., 3., 2., 1., 2. };
667 
668  std::vector<float> cellStateInVector(batchSize * numUnits, 0.f);
669  std::vector<float> outputStateInVector(batchSize * outputSize, 0.f);
670 
671  std::vector<float> actualOutput(outputTensorInfo.GetNumElements());
672 
673  const std::vector<float> expectedOutput = { 0.0642256f, 0.0343966f, 0.184122f, 0.114717f,
674  0.11458f, 0.0407109f, 0.300327f, 0.174301f,
675  0.0864761f, 0.0362912f, 0.178635f, 0.115689f,
676  0.108008f, 0.0386623f, 0.273471f, 0.167115f,
677  0.0859545f, 0.0331481f, 0.186051f, 0.11888f,
678  0.106649f, 0.0276847f, 0.229863f, 0.166958f };
679 
680  std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo);
681  std::unique_ptr<armnn::ITensorHandle> cellStateInHandle =
682  tensorHandleFactory.CreateTensorHandle(cellStateInTensorInfo);
683  std::unique_ptr<armnn::ITensorHandle> outputStateInHandle =
684  tensorHandleFactory.CreateTensorHandle(outputStateInTensorInfo);
685 
686  std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo);
687 
688  armnn::UnidirectionalSequenceLstmQueueDescriptor data;
689  armnn::WorkloadInfo info;
690 
691  AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
692  AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get());
693  AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get());
694 
695  AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
696 
697  armnn::TensorInfo tensorInfo4({outputSize}, armnn::DataType::Float32);
698  armnn::TensorInfo tensorInfo5({numUnits}, armnn::DataType::Float32);
699  armnn::TensorInfo tensorInfo5x3({numUnits, inputSize}, armnn::DataType::Float32);
700  armnn::TensorInfo tensorInfo5x4({numUnits, outputSize}, armnn::DataType::Float32);
701  armnn::TensorInfo tensorInfo4x5({outputSize, numUnits}, armnn::DataType::Float32);
702 
703  std::vector<float> inputToInputWeights = { -0.49536117f, -0.0556083915f, -0.102400711f,
704  -0.117484632f, 0.3298470976f, -0.1179017122f,
705  0.214305695f, 0.42135173085f, 0.003878414626f,
706  -0.348303917f, -0.1881275477f, 0.0343011027f,
707  -0.38837709614f, -0.05636804124f, 0.4259087456f};
708 
709  std::vector<float> inputToForgetWeights = { 0.2415594226f, 0.15400093799f, 0.4566498398f,
710  -0.3810434485f, 0.268383264f, -0.009807467424f,
711  -0.3522925403f, -0.24275735512f, -0.28344226125f,
712  0.13512269116f, -0.4932442977f, -0.10039821991f,
713  0.2726137042f, 0.09216640889f, -0.06551410215f};
714 
715  std::vector<float> inputToCellWeights = { -0.2504855627f, 0.184490025045f, -0.2480507493f,
716  0.386399507f, -0.259465157985f, -0.16545993089f,
717  -0.4230232555f, 0.341664791103f, -0.18127849691f,
718  -0.2277662414f, -0.55275535589f, 0.34184026718f,
719  0.3954237699f, -0.19407111404f, 0.30412107706f};
720 
721  std::vector<float> inputToOutputWeights = { 0.2303854227f, 0.5218806862f, -0.4865379333f,
722  0.53969591851f, 0.23393625035f, -0.27140527306f,
723  0.50009280443f, 0.07511717046f, 0.3998299249f,
724  -0.51717478049f, 0.1889653282f, -0.367323637f,
725  -0.12584099173f, -0.12319286912f, 0.2407919466f};
726 
727  std::vector<float> inputGateBias{ 0.03f, 0.15f, 0.22f, 0.38f, 0.05f };
728  std::vector<float> forgetGateBias{ 0.1f, -0.3f, -0.2f, 0.1f, 0.4f };
729  std::vector<float> cellBias{ -0.05f, 0.72f, 0.25f, 0.08f, 0.1f };
730  std::vector<float> outputGateBias{ 0.05f, -0.01f, 0.2f, 0.1f, -0.2f };
731 
732  std::vector<float> recurrentToInputWeights = { -0.128009796112f, 0.1995525098f, -0.07745539397f, 0.1558421701f,
733  -0.265254765766f, -0.38837709614f, -0.05636804124f, 0.4259087456f,
734  0.17628988623f, 0.3877420127f, 0.53300309181f, -0.0959980934f,
735  0.00302857416f, 0.3266998827f, -0.142509296562f, -0.04433270756f,
736  0.54066205f, -0.32668582f, -0.43562764f, -0.56094903f };
737 
738  std::vector<float> recurrentToForgetWeights = { -0.09499983487f, -0.08814888417f, -0.04834804721f, 0.1516668247f,
739  -0.3967529535f, -0.06463699788f, 0.4952811002f, 0.003274492938f,
740  -0.0968840941f, 0.17928104102f, 0.0031281141592f, -0.3387276584f,
741  -0.3587934076f, 0.06705895066f, 0.22463923692f, 0.1961955726f,
742  0.01841056f, -0.32764608f, -0.33027974f, -0.10826075f };
743 
744  std::vector<float> recurrentToCellWeights = { -0.21938985582f, -0.3023648226f, -0.1170005202f, -0.3509177422f,
745  -0.4286288613f, 0.2726137042f, 0.09216640889f, -0.06551410215f,
746  0.20453298098f, 0.2393476665f, 0.11846517771f, 0.2630801796f,
747  0.3954237699f, -0.19407111404f, 0.30412107706f, -0.27342408554f,
748  0.19069612f, -0.03026325f, -0.54532051f, 0.33003211f };
749 
750  std::vector<float> recurrentToOutputWeights = { -0.32921677827f, 0.32624614238f, -0.1388191282f, -0.17879831790f,
751  -0.15185534954f, -0.16918526583f, -0.10087361183f, -0.5436913968f,
752  0.016758225858f, 0.30454617738f, -0.41493862867f, -0.005565764375f,
753  -0.12584099173f, -0.12319286912f, 0.2407919466f, -0.08879069983f,
754  0.11178309f, 0.09481031f, -0.26424935f, 0.46261835f };
755 
756  std::vector<float> cellToInputWeights { 0.05f, 0.1f, 0.25f, 0.15f, -0.02f };
757  std::vector<float> cellToForgetWeights { -0.02f, -0.15f, -0.25f, -0.03f, 0.15f };
758  std::vector<float> cellToOutputWeights { 0.1f, -0.1f, -0.5f, 0.05f, 0.01f };
759 
760  std::vector<float> projectionWeights{ -0.1f, 0.2f, 0.01f, -0.2f,
761  0.1f, 0.5f, 0.3f, 0.08f,
762  0.07f, 0.2f, -0.4f, 0.2f,
763  0.5f, -0.4f, 0.3f, -0.2f,
764  0.3f, 0.08f, -0.07f, 0.2f};
765 
766  std::vector<float> projectionBiasVector(outputSize, 0.f); //{outputSize}
767 
768  std::vector<float> inputLayerNormWeights{ 0.1f, 0.2f, 0.3f, 0.5f, 0.8f };
769  std::vector<float> forgetLayerNormWeights{ 0.1f, 0.2f, 0.3f, 0.5f, 0.2f };
770  std::vector<float> cellLayerNormWeights{ 0.7f, 0.2f, 0.3f, 0.8f, 0.5f };
771  std::vector<float> outputLayerNormWeights{ 0.6f, 0.2f, 0.2f, 0.5f, 0.1f };
772 
773  armnn::ScopedTensorHandle inputToInputWeightsTensor(tensorInfo5x3);
774  armnn::ScopedTensorHandle inputToForgetWeightsTensor(tensorInfo5x3);
775  armnn::ScopedTensorHandle inputToCellWeightsTensor(tensorInfo5x3);
776  armnn::ScopedTensorHandle inputToOutputWeightsTensor(tensorInfo5x3);
777  armnn::ScopedTensorHandle recurrentToForgetWeightsTensor(tensorInfo5x4);
778  armnn::ScopedTensorHandle recurrentToInputWeightsTensor(tensorInfo5x4);
779  armnn::ScopedTensorHandle recurrentToCellWeightsTensor(tensorInfo5x4);
780  armnn::ScopedTensorHandle recurrentToOutputWeightsTensor(tensorInfo5x4);
781  armnn::ScopedTensorHandle cellToInputWeightsTensor(tensorInfo5);
782  armnn::ScopedTensorHandle inputGateBiasTensor(tensorInfo5);
783  armnn::ScopedTensorHandle forgetGateBiasTensor(tensorInfo5);
784  armnn::ScopedTensorHandle cellBiasTensor(tensorInfo5);
785  armnn::ScopedTensorHandle outputGateBiasTensor(tensorInfo5);
786  armnn::ScopedTensorHandle cellToForgetWeightsTensor(tensorInfo5);
787  armnn::ScopedTensorHandle cellToOutputWeightsTensor(tensorInfo5);
788  armnn::ScopedTensorHandle projectionWeightsTensor(tensorInfo4x5);
789  armnn::ScopedTensorHandle projectionBiasTensor(tensorInfo4);
790 
791  armnn::ScopedTensorHandle inputLayerNormWeightsTensor(tensorInfo5);
792  armnn::ScopedTensorHandle forgetLayerNormWeightsTensor(tensorInfo5);
793  armnn::ScopedTensorHandle cellLayerNormWeightsTensor(tensorInfo5);
794  armnn::ScopedTensorHandle outputLayerNormWeightsTensor(tensorInfo5);
795 
796  AllocateAndCopyDataToITensorHandle(&inputToInputWeightsTensor, inputToInputWeights.data());
797  AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, inputToForgetWeights.data());
798  AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, inputToCellWeights.data());
799  AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, inputToOutputWeights.data());
800  AllocateAndCopyDataToITensorHandle(&recurrentToInputWeightsTensor, recurrentToInputWeights.data());
801  AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, recurrentToForgetWeights.data());
802  AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, recurrentToCellWeights.data());
803  AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, recurrentToOutputWeights.data());
804  AllocateAndCopyDataToITensorHandle(&cellToInputWeightsTensor, cellToInputWeights.data());
805  AllocateAndCopyDataToITensorHandle(&inputGateBiasTensor, inputGateBias.data());
806  AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, forgetGateBias.data());
807  AllocateAndCopyDataToITensorHandle(&cellBiasTensor, cellBias.data());
808  AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, outputGateBias.data());
809  AllocateAndCopyDataToITensorHandle(&cellToForgetWeightsTensor, cellToForgetWeights.data());
810  AllocateAndCopyDataToITensorHandle(&cellToOutputWeightsTensor, cellToOutputWeights.data());
811  AllocateAndCopyDataToITensorHandle(&projectionWeightsTensor, projectionWeights.data());
812  AllocateAndCopyDataToITensorHandle(&projectionBiasTensor, projectionBiasVector.data());
813 
814  AllocateAndCopyDataToITensorHandle(&inputLayerNormWeightsTensor, inputLayerNormWeights.data());
815  AllocateAndCopyDataToITensorHandle(&forgetLayerNormWeightsTensor, forgetLayerNormWeights.data());
816  AllocateAndCopyDataToITensorHandle(&cellLayerNormWeightsTensor, cellLayerNormWeights.data());
817  AllocateAndCopyDataToITensorHandle(&outputLayerNormWeightsTensor, outputLayerNormWeights.data());
818 
819  data.m_InputToInputWeights = &inputToInputWeightsTensor;
820  data.m_InputToForgetWeights = &inputToForgetWeightsTensor;
821  data.m_InputToCellWeights = &inputToCellWeightsTensor;
822  data.m_InputToOutputWeights = &inputToOutputWeightsTensor;
823  data.m_RecurrentToInputWeights = &recurrentToInputWeightsTensor;
824  data.m_RecurrentToForgetWeights = &recurrentToForgetWeightsTensor;
825  data.m_RecurrentToCellWeights = &recurrentToCellWeightsTensor;
826  data.m_RecurrentToOutputWeights = &recurrentToOutputWeightsTensor;
827  data.m_CellToInputWeights = &cellToInputWeightsTensor;
828  data.m_InputGateBias = &inputGateBiasTensor;
829  data.m_ForgetGateBias = &forgetGateBiasTensor;
830  data.m_CellBias = &cellBiasTensor;
831  data.m_OutputGateBias = &outputGateBiasTensor;
832  data.m_CellToForgetWeights = &cellToForgetWeightsTensor;
833  data.m_CellToOutputWeights = &cellToOutputWeightsTensor;
834  data.m_ProjectionWeights = &projectionWeightsTensor;
835  data.m_ProjectionBias = &projectionBiasTensor;
836 
837  data.m_InputLayerNormWeights = &inputLayerNormWeightsTensor;
838  data.m_ForgetLayerNormWeights = &forgetLayerNormWeightsTensor;
839  data.m_CellLayerNormWeights = &cellLayerNormWeightsTensor;
840  data.m_OutputLayerNormWeights = &outputLayerNormWeightsTensor;
841 
842  // Flags to set test configuration
843  data.m_Parameters.m_ActivationFunc = 4;
844  data.m_Parameters.m_CifgEnabled = false;
845  data.m_Parameters.m_PeepholeEnabled = true;
846  data.m_Parameters.m_ProjectionEnabled = true;
847  data.m_Parameters.m_LayerNormEnabled = true;
848  data.m_Parameters.m_TimeMajor = false;
849  data.m_Parameters.m_ClippingThresCell = 10.0f;
850 
851  std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateUnidirectionalSequenceLstm(data, info);
852  inputHandle->Allocate();
853  outputStateInHandle->Allocate();
854  cellStateInHandle->Allocate();
855  outputHandle->Allocate();
856 
857  CopyDataToITensorHandle(inputHandle.get(), inputVector.data());
858  CopyDataToITensorHandle(outputStateInHandle.get(), outputStateInVector.data());
859  CopyDataToITensorHandle(cellStateInHandle.get(), cellStateInVector.data());
860 
861  workload->Execute();
862 
863  CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get());
864 
865  return LayerTestResult<float, 3>(actualOutput,
866  expectedOutput,
867  outputHandle->GetShape(),
868  outputTensorInfo.GetShape());
869 }
armnn::LstmDescriptor::m_ProjectionEnabled
bool m_ProjectionEnabled
Enable/disable the projection layer.
Definition: Descriptors.hpp:959
armnn::LstmDescriptor::m_TimeMajor
bool m_TimeMajor
Enable/disable time major.
Definition: Descriptors.hpp:963
armnn::IgnoreUnused
void IgnoreUnused(Ts &&...)
Definition: IgnoreUnused.hpp:14
armnn::IWorkloadFactory::CreateUnidirectionalSequenceLstm
virtual std::unique_ptr< IWorkload > CreateUnidirectionalSequenceLstm(const UnidirectionalSequenceLstmQueueDescriptor &descriptor, const WorkloadInfo &info) const
Definition: WorkloadFactory.cpp:1889
armnn::LstmDescriptor::m_PeepholeEnabled
bool m_PeepholeEnabled
Enable/disable peephole.
Definition: Descriptors.hpp:957
AllocateAndCopyDataToITensorHandle
void AllocateAndCopyDataToITensorHandle(armnn::ITensorHandle *tensorHandle, const void *memory)
Definition: TensorCopyUtils.cpp:19
CopyDataFromITensorHandle
void CopyDataFromITensorHandle(void *memory, const armnn::ITensorHandle *tensorHandle)
Definition: TensorCopyUtils.cpp:14
armnn::LstmDescriptor::m_ActivationFunc
uint32_t m_ActivationFunc
The activation function to use.
Definition: Descriptors.hpp:949
armnn::LstmDescriptor::m_ClippingThresCell
float m_ClippingThresCell
Clipping threshold value for the cell state.
Definition: Descriptors.hpp:951
armnn::LstmDescriptor::m_CifgEnabled
bool m_CifgEnabled
Enable/disable cifg (coupled input & forget gate).
Definition: Descriptors.hpp:955
armnn::LstmDescriptor::m_LayerNormEnabled
bool m_LayerNormEnabled
Enable/disable layer normalization.
Definition: Descriptors.hpp:961
armnn::WorkloadInfo
Contains information about TensorInfos of a layer.
Definition: WorkloadInfo.hpp:16
armnn::UnidirectionalSequenceLstmQueueDescriptor::m_RecurrentToOutputWeights
const ConstTensorHandle * m_RecurrentToOutputWeights
Definition: WorkloadData.hpp:732
armnn::ITensorHandleFactory::CreateTensorHandle
virtual std::unique_ptr< ITensorHandle > CreateTensorHandle(const TensorInfo &tensorInfo) const =0
armnn::UnidirectionalSequenceLstmQueueDescriptor::m_RecurrentToForgetWeights
const ConstTensorHandle * m_RecurrentToForgetWeights
Definition: WorkloadData.hpp:730
CopyDataToITensorHandle
void CopyDataToITensorHandle(armnn::ITensorHandle *tensorHandle, const void *memory)
Definition: TensorCopyUtils.cpp:9

◆ UnidirectionalSequenceLstmWithCifgWithPeepholeNoProjectionTest()

LayerTestResult<float, 3> UnidirectionalSequenceLstmWithCifgWithPeepholeNoProjectionTest ( armnn::IWorkloadFactory &  workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr &  memoryManager,
const armnn::ITensorHandleFactory &  tensorHandleFactory 
)

Definition at line 871 of file UnidirectionalSequenceLstmTestImpl.cpp.

References AllocateAndCopyDataToITensorHandle(), CopyDataFromITensorHandle(), CopyDataToITensorHandle(), ITensorHandleFactory::CreateTensorHandle(), IWorkloadFactory::CreateUnidirectionalSequenceLstm(), armnn::Float32, armnn::IgnoreUnused(), LstmDescriptor::m_ActivationFunc, UnidirectionalSequenceLstmQueueDescriptor::m_CellBias, UnidirectionalSequenceLstmQueueDescriptor::m_CellToForgetWeights, UnidirectionalSequenceLstmQueueDescriptor::m_CellToOutputWeights, LstmDescriptor::m_CifgEnabled, LstmDescriptor::m_ClippingThresCell, LstmDescriptor::m_ClippingThresProj, UnidirectionalSequenceLstmQueueDescriptor::m_ForgetGateBias, UnidirectionalSequenceLstmQueueDescriptor::m_InputToCellWeights, UnidirectionalSequenceLstmQueueDescriptor::m_InputToForgetWeights, UnidirectionalSequenceLstmQueueDescriptor::m_InputToOutputWeights, UnidirectionalSequenceLstmQueueDescriptor::m_OutputGateBias, QueueDescriptorWithParameters< LayerDescriptor >::m_Parameters, LstmDescriptor::m_PeepholeEnabled, LstmDescriptor::m_ProjectionEnabled, UnidirectionalSequenceLstmQueueDescriptor::m_RecurrentToCellWeights, UnidirectionalSequenceLstmQueueDescriptor::m_RecurrentToForgetWeights, UnidirectionalSequenceLstmQueueDescriptor::m_RecurrentToOutputWeights, and LstmDescriptor::m_TimeMajor.

Referenced by TEST_SUITE().

875 {
876  IgnoreUnused(memoryManager);
877  unsigned int batchSize = 3;
878  unsigned int timeSize = 2;
879  unsigned int inputSize = 3;
880  unsigned int outputSize = 4;
881  unsigned numUnits = outputSize;
882 
883  armnn::TensorInfo inputTensorInfo({batchSize, timeSize, inputSize}, armnn::DataType::Float32);
884  armnn::TensorInfo cellStateInTensorInfo({batchSize, numUnits}, armnn::DataType::Float32);
885  armnn::TensorInfo outputStateInTensorInfo({batchSize, outputSize}, armnn::DataType::Float32);
886 
887  armnn::TensorInfo outputTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32);
888 
889  std::vector<float> inputVector = { 1., 2., 3., 4., 5., 4.,
890  3., 2., 1., 2., 3., 4.,
891  5., 4., 3., 2., 1., 2. };
892 
893  std::vector<float> cellStateInVector(batchSize * numUnits, 0.f);
894  std::vector<float> outputStateInVector(batchSize * outputSize, 0.f);
895 
896  std::vector<float> actualOutput(outputTensorInfo.GetNumElements());
897 
898  std::vector<float> outputVector = { -0.0129257f, -0.070531f, -0.153508f, -0.0392391f,
899  -0.0300169f, -0.195717f, -0.528679f, -0.0818106f,
900  -0.0332748f, 0.155429f, -0.353966f, -0.0801505f,
901  -0.032312f, -0.0407911f, -0.435053f, -0.0932317f,
902  -0.0108233f, 0.165584f, -0.640424f, -0.0447535f,
903  -0.031675f, 0.125987f, -0.526695f, -0.110093f };
904 
905  std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo);
906  std::unique_ptr<armnn::ITensorHandle> cellStateInHandle =
907  tensorHandleFactory.CreateTensorHandle(cellStateInTensorInfo);
908  std::unique_ptr<armnn::ITensorHandle> outputStateInHandle =
909  tensorHandleFactory.CreateTensorHandle(outputStateInTensorInfo);
910 
911  std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo);
912 
913  armnn::UnidirectionalSequenceLstmQueueDescriptor data;
914  armnn::WorkloadInfo info;
915 
916  AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
917  AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get());
918  AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get());
919 
920  AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
921 
922  armnn::TensorInfo tensorInfo4({numUnits}, armnn::DataType::Float32);
923  armnn::TensorInfo tensorInfo12({numUnits, 3}, armnn::DataType::Float32);
924  armnn::TensorInfo tensorInfo16({numUnits, 4}, armnn::DataType::Float32);
925 
926  std::vector<float> inputToForgetWeights = { 0.2415594226f, 0.15400093799f, 0.4566498398f,
927  -0.3810434485f, 0.268383264f, -0.009807467424f,
928  -0.3522925403f, -0.24275735512f, -0.28344226125f,
929  0.13512269116f, -0.4932442977f, -0.10039821991f };
930 
931  std::vector<float> inputToCellWeights = { -0.2504855627f, 0.184490025045f, -0.2480507493f,
932  0.386399507f, -0.259465157985f, -0.16545993089f,
933  -0.4230232555f, 0.341664791103f, -0.18127849691f,
934  -0.2277662414f, -0.55275535589f, 0.34184026718f };
935 
936  std::vector<float> inputToOutputWeights = { 0.2303854227f, 0.5218806862f, -0.4865379333f,
937  0.53969591851f, 0.23393625035f, -0.27140527306f,
938  0.50009280443f, 0.07511717046f, 0.3998299249f,
939  -0.51717478049f, 0.1889653282f, -0.367323637f };
940 
941  std::vector<float> recurrentToForgetWeights = { -0.09499983487f, -0.08814888417f, -0.04834804721f, 0.1516668247f,
942  -0.3967529535f, -0.06463699788f, 0.4952811002f, 0.003274492938f,
943  -0.0968840941f, 0.17928104102f, 0.0031281141592f, -0.3387276584f,
944  -0.3587934076f, 0.06705895066f, 0.22463923692f, 0.1961955726f };
945 
946  std::vector<float> recurrentToCellWeights = { -0.21938985582f, -0.3023648226f, -0.1170005202f, -0.3509177422f,
947  -0.4286288613f, 0.2726137042f, 0.09216640889f, -0.06551410215f,
948  0.20453298098f, 0.2393476665f, 0.11846517771f, 0.2630801796f,
949  0.3954237699f, -0.19407111404f, 0.30412107706f, -0.27342408554f };
950 
951  std::vector<float> recurrentToOutputWeights = { -0.32921677827f, 0.32624614238f, -0.1388191282f, -0.17879831790f,
952  -0.15185534954f, -0.16918526583f, -0.10087361183f, -0.5436913968f,
953  0.016758225858f, 0.30454617738f, -0.41493862867f, -0.005565764375f,
954  -0.12584099173f, -0.12319286912f, 0.2407919466f, -0.08879069983f };
955 
956  std::vector<float> cellToForgetWeights{ 0.47485286f, -0.51955009f, -0.24458408f, 0.31544167f };
957 
958  std::vector<float> cellToOutputWeights{ -0.17135078f, 0.82760304f, 0.85573703f, -0.77109635f };
959 
960  std::vector<float> forgetGateBias = { 1., 1., 1., 1. };
961 
962  std::vector<float> cellBias = { 0., 0., 0., 0. };
963 
964  std::vector<float> outputGateBias = { 0., 0., 0., 0. };
965 
966  armnn::ScopedTensorHandle inputToForgetWeightsTensor(tensorInfo12);
967  armnn::ScopedTensorHandle inputToCellWeightsTensor(tensorInfo12);
968  armnn::ScopedTensorHandle inputToOutputWeightsTensor(tensorInfo12);
969  armnn::ScopedTensorHandle recurrentToForgetWeightsTensor(tensorInfo16);
970  armnn::ScopedTensorHandle recurrentToCellWeightsTensor(tensorInfo16);
971  armnn::ScopedTensorHandle recurrentToOutputWeightsTensor(tensorInfo16);
972  armnn::ScopedTensorHandle cellToForgetWeightsTensor(tensorInfo4);
973  armnn::ScopedTensorHandle cellToOutputWeightsTensor(tensorInfo4);
974  armnn::ScopedTensorHandle forgetGateBiasTensor(tensorInfo4);
975  armnn::ScopedTensorHandle cellBiasTensor(tensorInfo4);
976  armnn::ScopedTensorHandle outputGateBiasTensor(tensorInfo4);
977 
978  AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, inputToForgetWeights.data());
979  AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, inputToCellWeights.data());
980  AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, inputToOutputWeights.data());
981  AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, recurrentToForgetWeights.data());
982  AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, recurrentToCellWeights.data());
983  AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, recurrentToOutputWeights.data());
984  AllocateAndCopyDataToITensorHandle(&cellToForgetWeightsTensor, cellToForgetWeights.data());
985  AllocateAndCopyDataToITensorHandle(&cellToOutputWeightsTensor, cellToOutputWeights.data());
986  AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, forgetGateBias.data());
987  AllocateAndCopyDataToITensorHandle(&cellBiasTensor, cellBias.data());
988  AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, outputGateBias.data());
989 
990  data.m_InputToForgetWeights = &inputToForgetWeightsTensor;
991  data.m_InputToCellWeights = &inputToCellWeightsTensor;
992  data.m_InputToOutputWeights = &inputToOutputWeightsTensor;
993  data.m_RecurrentToForgetWeights = &recurrentToForgetWeightsTensor;
994  data.m_RecurrentToCellWeights = &recurrentToCellWeightsTensor;
995  data.m_RecurrentToOutputWeights = &recurrentToOutputWeightsTensor;
996  data.m_CellToForgetWeights = &cellToForgetWeightsTensor;
997  data.m_CellToOutputWeights = &cellToOutputWeightsTensor;
998  data.m_ForgetGateBias = &forgetGateBiasTensor;
999  data.m_CellBias = &cellBiasTensor;
1000  data.m_OutputGateBias = &outputGateBiasTensor;
1001 
1002  // Flags to set test configuration
1003  data.m_Parameters.m_ClippingThresCell = 10;
1004  data.m_Parameters.m_ClippingThresProj = 0;
1005  data.m_Parameters.m_ActivationFunc = 4;
1006  data.m_Parameters.m_CifgEnabled = true;
1007  data.m_Parameters.m_PeepholeEnabled = true;
1008  data.m_Parameters.m_ProjectionEnabled = false;
1009  data.m_Parameters.m_TimeMajor = false;
1010 
1011  std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateUnidirectionalSequenceLstm(data, info);
1012  inputHandle->Allocate();
1013  outputStateInHandle->Allocate();
1014  cellStateInHandle->Allocate();
1015 
1016  outputHandle->Allocate();
1017 
1018  CopyDataToITensorHandle(inputHandle.get(), inputVector.data());
1019  CopyDataToITensorHandle(outputStateInHandle.get(), outputStateInVector.data());
1020  CopyDataToITensorHandle(cellStateInHandle.get(), cellStateInVector.data());
1021 
1022  workload->Execute();
1023 
1024  CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get());
1025 
1026  return LayerTestResult<float, 3>(actualOutput,
1027  outputVector,
1028  outputHandle->GetShape(),
1029  outputTensorInfo.GetShape());
1030 }
armnn::LstmDescriptor::m_ProjectionEnabled
bool m_ProjectionEnabled
Enable/disable the projection layer.
Definition: Descriptors.hpp:959
armnn::LstmDescriptor::m_ClippingThresProj
float m_ClippingThresProj
Clipping threshold value for the projection.
Definition: Descriptors.hpp:953
armnn::LstmDescriptor::m_TimeMajor
bool m_TimeMajor
Enable/disable time major.
Definition: Descriptors.hpp:963
armnn::IgnoreUnused
void IgnoreUnused(Ts &&...)
Definition: IgnoreUnused.hpp:14
armnn::IWorkloadFactory::CreateUnidirectionalSequenceLstm
virtual std::unique_ptr< IWorkload > CreateUnidirectionalSequenceLstm(const UnidirectionalSequenceLstmQueueDescriptor &descriptor, const WorkloadInfo &info) const
Definition: WorkloadFactory.cpp:1889
armnn::LstmDescriptor::m_PeepholeEnabled
bool m_PeepholeEnabled
Enable/disable peephole.
Definition: Descriptors.hpp:957
AllocateAndCopyDataToITensorHandle
void AllocateAndCopyDataToITensorHandle(armnn::ITensorHandle *tensorHandle, const void *memory)
Definition: TensorCopyUtils.cpp:19
CopyDataFromITensorHandle
void CopyDataFromITensorHandle(void *memory, const armnn::ITensorHandle *tensorHandle)
Definition: TensorCopyUtils.cpp:14
armnn::LstmDescriptor::m_ActivationFunc
uint32_t m_ActivationFunc
The activation function to use.
Definition: Descriptors.hpp:949
armnn::LstmDescriptor::m_ClippingThresCell
float m_ClippingThresCell
Clipping threshold value for the cell state.
Definition: Descriptors.hpp:951
armnn::LstmDescriptor::m_CifgEnabled
bool m_CifgEnabled
Enable/disable cifg (coupled input & forget gate).
Definition: Descriptors.hpp:955
armnn::WorkloadInfo
Contains information about TensorInfos of a layer.
Definition: WorkloadInfo.hpp:16
armnn::UnidirectionalSequenceLstmQueueDescriptor::m_RecurrentToOutputWeights
const ConstTensorHandle * m_RecurrentToOutputWeights
Definition: WorkloadData.hpp:732
armnn::ITensorHandleFactory::CreateTensorHandle
virtual std::unique_ptr< ITensorHandle > CreateTensorHandle(const TensorInfo &tensorInfo) const =0
armnn::UnidirectionalSequenceLstmQueueDescriptor::m_RecurrentToForgetWeights
const ConstTensorHandle * m_RecurrentToForgetWeights
Definition: WorkloadData.hpp:730
CopyDataToITensorHandle
void CopyDataToITensorHandle(armnn::ITensorHandle *tensorHandle, const void *memory)
Definition: TensorCopyUtils.cpp:9