ArmNN
 21.08
QLstmLayer.cpp
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1 //
2 // Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
3 // SPDX-License-Identifier: MIT
4 //
5 #include "QLstmLayer.hpp"
6 
7 #include "LayerCloneBase.hpp"
8 
9 #include <armnn/LstmParams.hpp>
10 #include <armnn/TypesUtils.hpp>
13 
14 namespace armnn
15 {
16 
17 QLstmLayer::QLstmLayer(const QLstmDescriptor& param, const char* name)
18  : LayerWithParameters(3, 3, LayerType::QLstm, param, name)
19 {
20 }
21 
22 std::unique_ptr<IWorkload> QLstmLayer::CreateWorkload(const IWorkloadFactory& factory) const
23 {
24  QLstmQueueDescriptor descriptor;
25 
26  // Basic parameters
34  descriptor.m_CellBias = m_BasicParameters.m_CellBias.get();
36 
37  // CIFG parameters
39  {
43  }
44 
45  // Projection parameters
47  {
50  }
51 
52  // Peephole parameters
54  {
56  {
58  }
59 
62  }
63 
64  // Layer normalisation parameters
66  {
68  {
70  }
74  }
75 
76  SetAdditionalInfo(descriptor);
77 
78  return factory.CreateQLstm(descriptor, PrepInfoAndDesc(descriptor));
79 }
80 
82 {
83  auto layer = CloneBase<QLstmLayer>(graph, m_Param, GetName());
84 
87  layer->m_BasicParameters.m_InputToCellWeights = m_BasicParameters.m_InputToCellWeights ?
89  layer->m_BasicParameters.m_InputToOutputWeights = m_BasicParameters.m_InputToOutputWeights ?
91  layer->m_BasicParameters.m_RecurrentToForgetWeights = m_BasicParameters.m_RecurrentToForgetWeights ?
93  layer->m_BasicParameters.m_RecurrentToCellWeights = m_BasicParameters.m_RecurrentToCellWeights ?
95  layer->m_BasicParameters.m_RecurrentToOutputWeights = m_BasicParameters.m_RecurrentToOutputWeights ?
97  layer->m_BasicParameters.m_ForgetGateBias = m_BasicParameters.m_ForgetGateBias ?
99  layer->m_BasicParameters.m_CellBias = m_BasicParameters.m_CellBias ?
100  m_BasicParameters.m_CellBias : nullptr;
101  layer->m_BasicParameters.m_OutputGateBias = m_BasicParameters.m_OutputGateBias ?
103 
104  if (!m_Param.m_CifgEnabled)
105  {
106  layer->m_CifgParameters.m_InputToInputWeights = m_CifgParameters.m_InputToInputWeights ?
108  layer->m_CifgParameters.m_RecurrentToInputWeights = m_CifgParameters.m_RecurrentToInputWeights ?
110  layer->m_CifgParameters.m_InputGateBias = m_CifgParameters.m_InputGateBias ?
112  }
113 
114  if (m_Param.m_ProjectionEnabled)
115  {
116  layer->m_ProjectionParameters.m_ProjectionWeights = m_ProjectionParameters.m_ProjectionWeights ?
118  layer->m_ProjectionParameters.m_ProjectionBias = m_ProjectionParameters.m_ProjectionBias ?
120  }
121 
122  if (m_Param.m_PeepholeEnabled)
123  {
124  if (!m_Param.m_CifgEnabled) {
125  layer->m_PeepholeParameters.m_CellToInputWeights = m_PeepholeParameters.m_CellToInputWeights ?
127  }
128 
129  layer->m_PeepholeParameters.m_CellToForgetWeights = m_PeepholeParameters.m_CellToForgetWeights ?
131  layer->m_PeepholeParameters.m_CellToOutputWeights = m_PeepholeParameters.m_CellToOutputWeights ?
133  }
134 
135  if (m_Param.m_LayerNormEnabled)
136  {
137  if (!m_Param.m_CifgEnabled) {
138  layer->m_LayerNormParameters.m_InputLayerNormWeights = m_LayerNormParameters.m_InputLayerNormWeights ?
140  }
141 
142  layer->m_LayerNormParameters.m_ForgetLayerNormWeights = m_LayerNormParameters.m_ForgetLayerNormWeights ?
144  layer->m_LayerNormParameters.m_CellLayerNormWeights = m_LayerNormParameters.m_CellLayerNormWeights ?
146  layer->m_LayerNormParameters.m_OutputLayerNormWeights = m_LayerNormParameters.m_OutputLayerNormWeights ?
148  }
149 
150  return std::move(layer);
151 }
152 
153 std::vector<TensorShape> QLstmLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const
154 {
155  ARMNN_ASSERT(inputShapes.size() == 3);
156 
157  // Get input values for validation
158  unsigned int batchSize = inputShapes[0][0];
159  unsigned int outputSize = inputShapes[1][1];
160  unsigned int numUnits = inputShapes[2][1];
161 
162  std::vector<TensorShape> outShapes;
163  outShapes.push_back(TensorShape({ batchSize, outputSize })); // outputStateOut
164  outShapes.push_back(TensorShape({ batchSize, numUnits })); // cellStateOut
165  outShapes.push_back(TensorShape({ batchSize, outputSize })); // output
166 
167  return outShapes;
168 }
169 
171 {
173 
174  const TensorShape& outputShape = GetOutputSlot(0).GetTensorInfo().GetShape();
175 
177 
178  auto inferredShapes = InferOutputShapes(
179  {
181  GetInputSlot(1).GetConnection()->GetTensorInfo().GetShape(), // previousOutputIn
182  GetInputSlot(2).GetConnection()->GetTensorInfo().GetShape() // previousCellStateIn
183  });
184 
185  ARMNN_ASSERT(inferredShapes.size() == 3);
186 
187  // Check if the weights are nullptr for basic params
189  "QLstmLayer: m_BasicParameters.m_InputToForgetWeights should not be null.");
191  "QLstmLayer: m_BasicParameters.m_InputToCellWeights should not be null.");
193  "QLstmLayer: m_BasicParameters.m_InputToOutputWeights should not be null.");
195  "QLstmLayer: m_BasicParameters.m_RecurrentToForgetWeights should not be null.");
197  "QLstmLayer: m_BasicParameters.m_RecurrentToCellWeights should not be null.");
199  "QLstmLayer: m_BasicParameters.m_RecurrentToOutputWeights should not be null.");
201  "QLstmLayer: m_BasicParameters.m_ForgetGateBias should not be null.");
203  "QLstmLayer: m_BasicParameters.m_CellBias should not be null.");
205  "QLstmLayer: m_BasicParameters.m_OutputGateBias should not be null.");
206 
207  if (!m_Param.m_CifgEnabled)
208  {
210  "QLstmLayer: m_CifgParameters.m_InputToInputWeights should not be null.");
212  "QLstmLayer: m_CifgParameters.m_RecurrentToInputWeights should not be null.");
214  "QLstmLayer: m_CifgParameters.m_InputGateBias should not be null.");
215 
216  ValidateAndCopyShape(outputShape, inferredShapes[0], m_ShapeInferenceMethod, "QLstmLayer");
217  }
218  else
219  {
221  "QLstmLayer: m_CifgParameters.m_InputToInputWeights should not have a value when CIFG is enabled.");
223  "QLstmLayer: m_CifgParameters.m_RecurrentToInputWeights should "
224  "not have a value when CIFG is enabled.");
226  "QLstmLayer: m_CifgParameters.m_InputGateBias should not have a value when CIFG is enabled.");
227 
228  ValidateAndCopyShape(outputShape, inferredShapes[0], m_ShapeInferenceMethod, "QLstmLayer");
229  }
230 
232  {
234  "QLstmLayer: m_ProjectionParameters.m_ProjectionWeights should not be null.");
235  }
236 
238  {
239  if (!m_Param.m_CifgEnabled) {
241  "QLstmLayer: m_PeepholeParameters.m_CellToInputWeights should not be null "
242  "when Peephole is enabled and CIFG is disabled.");
243  }
244 
246  "QLstmLayer: m_PeepholeParameters.m_CellToForgetWeights should not be null.");
248  "QLstmLayer: m_PeepholeParameters.m_CellToOutputWeights should not be null.");
249  }
250 
252  GetOutputSlot(1).GetTensorInfo().GetShape(), inferredShapes[1], m_ShapeInferenceMethod, "QLstmLayer", 1);
254  GetOutputSlot(2).GetTensorInfo().GetShape(), inferredShapes[2], m_ShapeInferenceMethod, "QLstmLayer", 2);
255 
257  {
259  {
261  "QLstmLayer: m_LayerNormParameters.m_InputLayerNormWeights should not be null.");
262  }
264  "QLstmLayer: m_LayerNormParameters.m_ForgetLayerNormWeights should not be null.");
266  "QLstmLayer: m_LayerNormParameters.m_CellLayerNormWeights should not be null.");
268  "QLstmLayer: m_LayerNormParameters.m_UutputLayerNormWeights should not be null.");
269  }
270 }
271 
273 {
283 
284  // Cifg parameters
288 
289  // Projection parameters
292 
293  // Peephole parameters
297 
298  // Layer normalisation parameters
303 }
304 
305 void QLstmLayer::Accept(ILayerVisitor& visitor) const
306 {
307  LstmInputParams inputParams;
317 
318  // Cifg parameters
322 
323  // Projection parameters
326 
327  // Peephole parameters
331 
332  // Layer normalisation parameters
337 
338  ConstTensor inputToInputWeightsTensor;
340  {
341  ConstTensor inputToInputWeightsTensorCopy(managedInputToInputWeights.GetTensorInfo(),
342  managedInputToInputWeights.Map());
343  inputToInputWeightsTensor = inputToInputWeightsTensorCopy;
344  inputParams.m_InputToInputWeights = &inputToInputWeightsTensor;
345  }
346 
347  ConstTensor inputToForgetWeightsTensor;
349  {
350  ConstTensor inputToForgetWeightsTensorCopy(managedInputToForgetWeights.GetTensorInfo(),
351  managedInputToForgetWeights.Map());
352  inputToForgetWeightsTensor = inputToForgetWeightsTensorCopy;
353  inputParams.m_InputToForgetWeights = &inputToForgetWeightsTensor;
354  }
355 
356  ConstTensor inputToCellWeightsTensor;
358  {
359  ConstTensor inputToCellWeightsTensorCopy(managedInputToCellWeights.GetTensorInfo(),
360  managedInputToCellWeights.Map());
361  inputToCellWeightsTensor = inputToCellWeightsTensorCopy;
362  inputParams.m_InputToCellWeights = &inputToCellWeightsTensor;
363  }
364 
365  ConstTensor inputToOutputWeightsTensor;
367  {
368  ConstTensor inputToOutputWeightsTensorCopy(managedInputToOutputWeights.GetTensorInfo(),
369  managedInputToOutputWeights.Map());
370  inputToOutputWeightsTensor = inputToOutputWeightsTensorCopy;
371  inputParams.m_InputToOutputWeights = &inputToOutputWeightsTensor;
372  }
373 
374  ConstTensor recurrentToInputWeightsTensor;
376  {
377  ConstTensor recurrentToInputWeightsTensorCopy(
378  managedRecurrentToInputWeights.GetTensorInfo(),
379  managedRecurrentToInputWeights.Map());
380  recurrentToInputWeightsTensor = recurrentToInputWeightsTensorCopy;
381  inputParams.m_RecurrentToInputWeights = &recurrentToInputWeightsTensor;
382  }
383 
384  ConstTensor recurrentToForgetWeightsTensor;
386  {
387  ConstTensor recurrentToForgetWeightsTensorCopy(
388  managedRecurrentToForgetWeights.GetTensorInfo(),
389  managedRecurrentToForgetWeights.Map());
390  recurrentToForgetWeightsTensor = recurrentToForgetWeightsTensorCopy;
391  inputParams.m_RecurrentToForgetWeights = &recurrentToForgetWeightsTensor;
392  }
393 
394  ConstTensor recurrentToCellWeightsTensor;
396  {
397  ConstTensor recurrentToCellWeightsTensorCopy(
398  managedRecurrentToCellWeights.GetTensorInfo(),
399  managedRecurrentToCellWeights.Map());
400  recurrentToCellWeightsTensor = recurrentToCellWeightsTensorCopy;
401  inputParams.m_RecurrentToCellWeights = &recurrentToCellWeightsTensor;
402  }
403 
404  ConstTensor recurrentToOutputWeightsTensor;
406  {
407  ConstTensor recurrentToOutputWeightsTensorCopy(
408  managedRecurrentToOutputWeights.GetTensorInfo(),
409  managedRecurrentToOutputWeights.Map());
410  recurrentToOutputWeightsTensor = recurrentToOutputWeightsTensorCopy;
411  inputParams.m_RecurrentToOutputWeights = &recurrentToOutputWeightsTensor;
412  }
413 
414  ConstTensor cellToInputWeightsTensor;
416  {
417  ConstTensor cellToInputWeightsTensorCopy(managedCellToInputWeights.GetTensorInfo(),
418  managedCellToInputWeights.Map());
419  cellToInputWeightsTensor = cellToInputWeightsTensorCopy;
420  inputParams.m_CellToInputWeights = &cellToInputWeightsTensor;
421  }
422 
423  ConstTensor cellToForgetWeightsTensor;
425  {
426  ConstTensor cellToForgetWeightsTensorCopy(managedCellToForgetWeights.GetTensorInfo(),
427  managedCellToForgetWeights.Map());
428  cellToForgetWeightsTensor = cellToForgetWeightsTensorCopy;
429  inputParams.m_CellToForgetWeights = &cellToForgetWeightsTensor;
430  }
431 
432  ConstTensor cellToOutputWeightsTensor;
434  {
435  ConstTensor cellToOutputWeightsTensorCopy(managedCellToOutputWeights.GetTensorInfo(),
436  managedCellToOutputWeights.Map());
437  cellToOutputWeightsTensor = cellToOutputWeightsTensorCopy;
438  inputParams.m_CellToOutputWeights = &cellToOutputWeightsTensor;
439  }
440 
441  ConstTensor inputGateBiasTensor;
442  if (m_CifgParameters.m_InputGateBias != nullptr)
443  {
444  ConstTensor inputGateBiasTensorCopy(managedInputGateBias.GetTensorInfo(),
445  managedInputGateBias.Map());
446  inputGateBiasTensor = inputGateBiasTensorCopy;
447  inputParams.m_InputGateBias = &inputGateBiasTensor;
448  }
449 
450  ConstTensor forgetGateBiasTensor;
451  if (m_BasicParameters.m_ForgetGateBias != nullptr)
452  {
453  ConstTensor forgetGateBiasTensorCopy(managedForgetGateBias.GetTensorInfo(),
454  managedForgetGateBias.Map());
455  forgetGateBiasTensor = forgetGateBiasTensorCopy;
456  inputParams.m_ForgetGateBias = &forgetGateBiasTensor;
457  }
458 
459  ConstTensor cellBiasTensor;
460  if (m_BasicParameters.m_CellBias != nullptr)
461  {
462  ConstTensor cellBiasTensorCopy(managedCellBias.GetTensorInfo(),
463  managedCellBias.Map());
464  cellBiasTensor = cellBiasTensorCopy;
465  inputParams.m_CellBias = &cellBiasTensor;
466  }
467 
468  ConstTensor outputGateBias;
469  if (m_BasicParameters.m_OutputGateBias != nullptr)
470  {
471  ConstTensor outputGateBiasCopy(managedOutputGateBias.GetTensorInfo(),
472  managedOutputGateBias.Map());
473  outputGateBias = outputGateBiasCopy;
474  inputParams.m_OutputGateBias = &outputGateBias;
475  }
476 
477  ConstTensor projectionWeightsTensor;
479  {
480  ConstTensor projectionWeightsTensorCopy(managedProjectionWeights.GetTensorInfo(),
481  managedProjectionWeights.Map());
482  projectionWeightsTensor = projectionWeightsTensorCopy;
483  inputParams.m_ProjectionWeights = &projectionWeightsTensor;
484  }
485 
486  ConstTensor projectionBiasTensor;
488  {
489  ConstTensor projectionBiasTensorCopy(managedProjectionBias.GetTensorInfo(),
490  managedProjectionBias.Map());
491  projectionBiasTensor = projectionBiasTensorCopy;
492  inputParams.m_ProjectionBias = &projectionBiasTensor;
493  }
494 
495  ConstTensor inputLayerNormTensor;
497  {
498  ConstTensor inputLayerNormTensorCopy(managedInputLayerNormWeights.GetTensorInfo(),
499  managedInputLayerNormWeights.Map());
500  inputLayerNormTensor = inputLayerNormTensorCopy;
501  inputParams.m_InputLayerNormWeights = &inputLayerNormTensor;
502  }
503 
504  ConstTensor forgetLayerNormTensor;
506  {
507  ConstTensor forgetLayerNormTensorCopy(managedForgetLayerNormWeights.GetTensorInfo(),
508  managedForgetLayerNormWeights.Map());
509  forgetLayerNormTensor = forgetLayerNormTensorCopy;
510  inputParams.m_ForgetLayerNormWeights = &forgetLayerNormTensor;
511  }
512 
513  ConstTensor cellLayerNormTensor;
515  {
516  ConstTensor cellLayerNormTensorCopy(managedCellLayerNormWeights.GetTensorInfo(),
517  managedCellLayerNormWeights.Map());
518  cellLayerNormTensor = cellLayerNormTensorCopy;
519  inputParams.m_CellLayerNormWeights = &cellLayerNormTensor;
520  }
521 
522  ConstTensor outputLayerNormTensor;
524  {
525  ConstTensor outputLayerNormTensorCopy(managedOutputLayerNormWeights.GetTensorInfo(),
526  managedOutputLayerNormWeights.Map());
527  outputLayerNormTensor = outputLayerNormTensorCopy;
528  inputParams.m_OutputLayerNormWeights = &outputLayerNormTensor;
529  }
530 
531 
532  visitor.VisitQLstmLayer(this, GetParameters(), inputParams, GetName());
533 }
534 
535 
537 {
538  std::vector<ConstTensor> constTensors;
548 
549  // Cifg parameters
553 
554  // Projection parameters
557 
558  // Peephole parameters
562 
563  // Layer normalisation parameters
568 
569  // First add mandatory/basic parameters
571  {
572  constTensors.emplace_back(ConstTensor(managedInputToForgetWeights.GetTensorInfo(),
573  managedInputToForgetWeights.Map()));
574  }
576  {
577  constTensors.emplace_back(ConstTensor(managedInputToCellWeights.GetTensorInfo(),
578  managedInputToCellWeights.Map()));
579  }
581  {
582  constTensors.emplace_back(ConstTensor(managedInputToOutputWeights.GetTensorInfo(),
583  managedInputToOutputWeights.Map()));
584  }
586  {
587  constTensors.emplace_back(ConstTensor(
588  managedRecurrentToForgetWeights.GetTensorInfo(),
589  managedRecurrentToForgetWeights.Map()));
590  }
592  {
593  constTensors.emplace_back(ConstTensor(
594  managedRecurrentToCellWeights.GetTensorInfo(),
595  managedRecurrentToCellWeights.Map()));
596  }
598  {
599  constTensors.emplace_back(ConstTensor(
600  managedRecurrentToOutputWeights.GetTensorInfo(),
601  managedRecurrentToOutputWeights.Map()));
602  }
603  if (m_BasicParameters.m_ForgetGateBias != nullptr)
604  {
605  constTensors.emplace_back(ConstTensor(managedForgetGateBias.GetTensorInfo(),
606  managedForgetGateBias.Map()));
607  }
608  if (m_BasicParameters.m_CellBias != nullptr)
609  {
610  constTensors.emplace_back(ConstTensor(managedCellBias.GetTensorInfo(),
611  managedCellBias.Map()));
612  }
613  if (m_BasicParameters.m_OutputGateBias != nullptr)
614  {
615  constTensors.emplace_back(ConstTensor(managedOutputGateBias.GetTensorInfo(),
616  managedOutputGateBias.Map()));
617  }
618 
619  // Add cifig parameters
621  {
622  constTensors.emplace_back(ConstTensor(managedInputToInputWeights.GetTensorInfo(),
623  managedInputToInputWeights.Map()));
624  }
626  {
627  constTensors.emplace_back(ConstTensor(
628  managedRecurrentToInputWeights.GetTensorInfo(),
629  managedRecurrentToInputWeights.Map()));
630  }
631  if (m_CifgParameters.m_InputGateBias != nullptr)
632  {
633  constTensors.emplace_back(ConstTensor(managedInputGateBias.GetTensorInfo(),
634  managedInputGateBias.Map()));
635  }
636 
637  // Add peephole parameters
639  {
640  constTensors.emplace_back(ConstTensor(managedCellToInputWeights.GetTensorInfo(),
641  managedCellToInputWeights.Map()));
642  }
644  {
645  constTensors.emplace_back(ConstTensor(managedCellToForgetWeights.GetTensorInfo(),
646  managedCellToForgetWeights.Map()));
647  }
649  {
650  constTensors.emplace_back(ConstTensor(managedCellToOutputWeights.GetTensorInfo(),
651  managedCellToOutputWeights.Map()));
652  }
653 
654  // Add projection parameters
656  {
657  constTensors.emplace_back(ConstTensor(managedProjectionWeights.GetTensorInfo(),
658  managedProjectionWeights.Map()));
659  }
661  {
662  constTensors.emplace_back(ConstTensor(managedProjectionBias.GetTensorInfo(),
663  managedProjectionBias.Map()));
664  }
665 
666  // Add norm parameters
668  {
669  constTensors.emplace_back(ConstTensor(managedInputLayerNormWeights.GetTensorInfo(),
670  managedInputLayerNormWeights.Map()));
671  }
673  {
674  constTensors.emplace_back(ConstTensor(managedForgetLayerNormWeights.GetTensorInfo(),
675  managedForgetLayerNormWeights.Map()));
676  }
678  {
679  constTensors.emplace_back(ConstTensor(managedCellLayerNormWeights.GetTensorInfo(),
680  managedCellLayerNormWeights.Map()));
681  }
683  {
684  constTensors.emplace_back(ConstTensor(managedOutputLayerNormWeights.GetTensorInfo(),
685  managedOutputLayerNormWeights.Map()));
686  }
687  strategy.ExecuteStrategy(this, GetParameters(), constTensors, GetName());
688 }
689 
690 } // namespace armnn
const ConstTensorHandle * m_CellLayerNormWeights
const ConstTensorHandle * m_ProjectionWeights
virtual void VisitQLstmLayer(const IConnectableLayer *layer, const QLstmDescriptor &descriptor, const LstmInputParams &params, const char *name=nullptr)=0
Function a QLstm layer should call back to when its Accept(ILayerVisitor&) function is invoked...
const ConstTensor * m_ProjectionWeights
Definition: LstmParams.hpp:55
const ConstTensorHandle * m_ForgetGateBias
const ConstTensor * m_CellBias
Definition: LstmParams.hpp:53
const ConstTensorHandle * m_InputToOutputWeights
QLstmDescriptor m_Param
The parameters for the layer (not including tensor-valued weights etc.).
const TensorShape & GetShape() const
Definition: Tensor.hpp:191
QLstmOptProjectionParameters m_ProjectionParameters
Definition: QLstmLayer.hpp:85
const ConstTensor * m_CellToOutputWeights
Definition: LstmParams.hpp:50
std::shared_ptr< ConstTensorHandle > m_OutputGateBias
A unique pointer to represent 1D bias tensor with dimensions [num_units] (int32). ...
Definition: QLstmLayer.hpp:35
bool m_PeepholeEnabled
Enable/disable peephole.
virtual void ExecuteStrategy(const armnn::IConnectableLayer *layer, const armnn::BaseDescriptor &descriptor, const std::vector< armnn::ConstTensor > &constants, const char *name, const armnn::LayerBindingId id=0)=0
const ConstTensor * m_CellToInputWeights
Definition: LstmParams.hpp:48
const ConstTensorHandle * m_InputToInputWeights
std::shared_ptr< ConstTensorHandle > m_RecurrentToInputWeights
A unique pointer to represent 2D weights tensor with dimensions [input_size, num_units] (QSymmS8)...
Definition: QLstmLayer.hpp:61
const ConstTensorHandle * m_CellToOutputWeights
std::shared_ptr< ConstTensorHandle > m_ForgetLayerNormWeights
A unique pointer to represent 1D weights tensor with dimensions [num_units] (QSymmS16).
Definition: QLstmLayer.hpp:71
const ConstTensor * m_InputGateBias
Definition: LstmParams.hpp:51
const ConstTensorHandle * m_CellToInputWeights
virtual std::unique_ptr< IWorkload > CreateQLstm(const QLstmQueueDescriptor &descriptor, const WorkloadInfo &info) const
std::shared_ptr< ConstTensorHandle > m_InputToOutputWeights
A unique pointer to represent 2D weights tensor with dimensions [num_units, inputSize] (QSymmS8)...
Definition: QLstmLayer.hpp:21
std::shared_ptr< ConstTensorHandle > m_InputLayerNormWeights
A unique pointer to represent 1D weights tensor with dimensions [num_units] (QSymmS16).
Definition: QLstmLayer.hpp:69
const ConstTensor * m_RecurrentToCellWeights
Definition: LstmParams.hpp:46
const ConstTensor * m_ForgetLayerNormWeights
Definition: LstmParams.hpp:58
void VerifyShapeInferenceType(const TensorShape &outputShape, ShapeInferenceMethod shapeInferenceMethod)
Definition: Layer.cpp:433
const ConstTensor * m_CellToForgetWeights
Definition: LstmParams.hpp:49
const TensorInfo & GetTensorInfo() const
Copyright (c) 2021 ARM Limited and Contributors.
const IOutputSlot * GetConnection() const override
Definition: Layer.hpp:199
std::shared_ptr< ConstTensorHandle > m_CellToOutputWeights
A unique pointer to represent 1D weights tensor with dimensions [num_units] (QSymmS16).
Definition: QLstmLayer.hpp:53
void ValidateAndCopyShape(const TensorShape &outputShape, const TensorShape &inferredShape, const ShapeInferenceMethod shapeInferenceMethod, const std::string &layerName, const unsigned int outputSlotIndex=0)
Definition: Layer.cpp:393
std::shared_ptr< ConstTensorHandle > m_InputToCellWeights
A unique pointer to represent 2D weights tensor with dimensions [num_units, inputSize] (QSymmS8)...
Definition: QLstmLayer.hpp:19
const ConstTensor * m_OutputGateBias
Definition: LstmParams.hpp:54
QLstmOptLayerNormParameters m_LayerNormParameters
Definition: QLstmLayer.hpp:87
QLstmLayer * Clone(Graph &graph) const override
Creates a dynamically-allocated copy of this layer.
Definition: QLstmLayer.cpp:81
const ConstTensorHandle * m_ForgetLayerNormWeights
std::shared_ptr< ConstTensorHandle > m_CellBias
A unique pointer to represent 1D bias tensor with dimensions [num_units] (int32). ...
Definition: QLstmLayer.hpp:33
std::shared_ptr< ConstTensorHandle > m_ProjectionWeights
A unique pointer to represent 2D weights tensor with dimensions [output_size, num_units] (QSymmS8)...
Definition: QLstmLayer.hpp:41
void VerifyLayerConnections(unsigned int expectedConnections, const CheckLocation &location) const
Definition: Layer.cpp:349
std::shared_ptr< ConstTensorHandle > m_InputToForgetWeights
A unique pointer to represent 2D weights tensor with dimensions [num_units, inputSize] (QSymmS8)...
Definition: QLstmLayer.hpp:17
const InputSlot & GetInputSlot(unsigned int index) const override
Get a const input slot handle by slot index.
Definition: Layer.hpp:316
std::shared_ptr< ConstTensorHandle > m_InputGateBias
A unique pointer to represent 1D weights tensor with dimensions [num_units] (int32).
Definition: QLstmLayer.hpp:63
const ConstTensor * m_InputLayerNormWeights
Definition: LstmParams.hpp:57
bool m_LayerNormEnabled
Enable/disable layer normalization.
std::vector< std::reference_wrapper< std::shared_ptr< ConstTensorHandle > >> ConstantTensors
Definition: Layer.hpp:393
const ConstTensor * m_RecurrentToOutputWeights
Definition: LstmParams.hpp:47
std::shared_ptr< ConstTensorHandle > m_CellLayerNormWeights
A unique pointer to represent 1D weights tensor with dimensions [num_units] (QSymmS16).
Definition: QLstmLayer.hpp:73
#define ARMNN_ASSERT_MSG(COND, MSG)
Definition: Assert.hpp:15
std::shared_ptr< ConstTensorHandle > m_ForgetGateBias
A unique pointer to represent 1D bias tensor with dimensions [num_units] (int32). ...
Definition: QLstmLayer.hpp:31
virtual std::unique_ptr< IWorkload > CreateWorkload(const IWorkloadFactory &factory) const override
Makes a workload for the QLstm type.
Definition: QLstmLayer.cpp:22
std::shared_ptr< ConstTensorHandle > m_CellToInputWeights
A unique pointer to represent 1D weights tensor with dimensions [num_units] (QSymmS16).
Definition: QLstmLayer.hpp:49
const ConstTensor * m_ProjectionBias
Definition: LstmParams.hpp:56
std::shared_ptr< ConstTensorHandle > m_CellToForgetWeights
A unique pointer to represent 1D weights tensor with dimensions [num_units] (QSymmS16).
Definition: QLstmLayer.hpp:51
const ConstTensorHandle * m_InputToForgetWeights
A tensor defined by a TensorInfo (shape and data type) and an immutable backing store.
Definition: Tensor.hpp:327
const ConstTensorHandle * m_CellBias
#define ARMNN_ASSERT(COND)
Definition: Assert.hpp:14
A QLstmDescriptor for the QLstmLayer.
const ConstTensorHandle * m_InputLayerNormWeights
void ValidateTensorShapesFromInputs() override
Check if the input tensor shape(s) will lead to a valid configuration of QLstmLayer.
Definition: QLstmLayer.cpp:170
QLstmLayer(const QLstmDescriptor &param, const char *name)
Constructor to create a QLstmLayer.
Definition: QLstmLayer.cpp:17
Layer::ConstantTensors GetConstantTensorsByRef() override
Retrieve the handles to the constant values stored by the layer.
Definition: QLstmLayer.cpp:272
#define CHECK_LOCATION()
Definition: Exceptions.hpp:197
std::shared_ptr< ConstTensorHandle > m_OutputLayerNormWeights
A unique pointer to represent 1D weights tensor with dimensions [num_units] (QSymmS16).
Definition: QLstmLayer.hpp:75
std::shared_ptr< ConstTensorHandle > m_RecurrentToCellWeights
A unique pointer to represent 2D weights tensor with dimensions [num_units, outputSize] (QSymmS8)...
Definition: QLstmLayer.hpp:26
QLstmBasicParameters m_BasicParameters
Definition: QLstmLayer.hpp:83
void ExecuteStrategy(IStrategy &strategy) const override
Apply a visitor to this layer.
Definition: QLstmLayer.cpp:536
std::shared_ptr< ConstTensorHandle > m_RecurrentToOutputWeights
A unique pointer to represent 2D weights tensor with dimensions [num_units, outputSize] (QSymmS8)...
Definition: QLstmLayer.hpp:28
const ConstTensorHandle * m_InputToCellWeights
void SetAdditionalInfo(QueueDescriptor &descriptor) const
Definition: Layer.cpp:245
const ConstTensor * m_CellLayerNormWeights
Definition: LstmParams.hpp:59
const ConstTensor * m_ForgetGateBias
Definition: LstmParams.hpp:52
const ConstTensor * m_InputToCellWeights
Definition: LstmParams.hpp:42
std::shared_ptr< ConstTensorHandle > m_InputToInputWeights
A unique pointer to represent 2D weights tensor with dimensions [input_size, num_units] (QSymmS8)...
Definition: QLstmLayer.hpp:59
const ConstTensor * m_InputToOutputWeights
Definition: LstmParams.hpp:43
This layer represents a QLstm operation.
Definition: QLstmLayer.hpp:79
const ConstTensorHandle * m_CellToForgetWeights
const ConstTensor * m_RecurrentToForgetWeights
Definition: LstmParams.hpp:45
const ConstTensorHandle * m_ProjectionBias
const ConstTensorHandle * m_RecurrentToCellWeights
bool m_ProjectionEnabled
Enable/disable the projection layer.
const ConstTensorHandle * m_InputGateBias
const ConstTensor * m_RecurrentToInputWeights
Definition: LstmParams.hpp:44
WorkloadInfo PrepInfoAndDesc(QueueDescriptor &descriptor) const
Helper function to reduce duplication in *LayerCreateWorkload.
const OutputSlot & GetOutputSlot(unsigned int index=0) const override
Get the const output slot handle by slot index.
Definition: Layer.hpp:318
void Accept(ILayerVisitor &visitor) const override
Apply a visitor to this layer.
Definition: QLstmLayer.cpp:305
virtual const TensorInfo & GetTensorInfo() const =0
QLstmOptCifgParameters m_CifgParameters
Definition: QLstmLayer.hpp:84
QLstmOptPeepholeParameters m_PeepholeParameters
Definition: QLstmLayer.hpp:86
const char * GetName() const override
Returns the name of the layer.
Definition: Layer.hpp:311
const ConstTensorHandle * m_OutputGateBias
std::shared_ptr< ConstTensorHandle > m_ProjectionBias
A unique pointer to represent 1D weights tensor with dimensions [output_size] (int32).
Definition: QLstmLayer.hpp:43
const ConstTensor * m_OutputLayerNormWeights
Definition: LstmParams.hpp:60
const void * Map(bool blocking=true)
RAII Managed resource Unmaps MemoryArea once out of scope.
std::vector< TensorShape > InferOutputShapes(const std::vector< TensorShape > &inputShapes) const override
By default returns inputShapes if the number of inputs are equal to number of outputs, otherwise infers the output shapes from given input shapes and layer properties.
Definition: QLstmLayer.cpp:153
std::shared_ptr< ConstTensorHandle > m_RecurrentToForgetWeights
A unique pointer to represent 2D weights tensor with dimensions [num_units, outputSize] (QSymmS8)...
Definition: QLstmLayer.hpp:24
const TensorInfo & GetTensorInfo(const ITensorHandle *tensorHandle)
float32 helpers
const ConstTensorHandle * m_OutputLayerNormWeights
const TensorInfo & GetTensorInfo() const override
Definition: Layer.cpp:63
const ConstTensorHandle * m_RecurrentToOutputWeights
const ConstTensorHandle * m_RecurrentToInputWeights
bool m_CifgEnabled
Enable/disable CIFG (coupled input & forget gate).
ShapeInferenceMethod m_ShapeInferenceMethod
Definition: Layer.hpp:408
const ConstTensorHandle * m_RecurrentToForgetWeights
const ConstTensor * m_InputToForgetWeights
Definition: LstmParams.hpp:41
LayerType
When adding a new layer, adapt also the LastLayer enum value in the enum class LayerType below...
Definition: Types.hpp:405
const ConstTensor * m_InputToInputWeights
Definition: LstmParams.hpp:40