ArmNN
 20.05
QLstmLayer.cpp
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1 //
2 // Copyright © 2020 Arm Ltd. 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  return factory.CreateQLstm(descriptor, PrepInfoAndDesc(descriptor));
77 }
78 
80 {
81  auto layer = CloneBase<QLstmLayer>(graph, m_Param, GetName());
82 
84  std::make_unique<ScopedCpuTensorHandle>(*m_BasicParameters.m_InputToForgetWeights) : nullptr;
85  layer->m_BasicParameters.m_InputToCellWeights = m_BasicParameters.m_InputToCellWeights ?
86  std::make_unique<ScopedCpuTensorHandle>(*m_BasicParameters.m_InputToCellWeights) : nullptr;
87  layer->m_BasicParameters.m_InputToOutputWeights = m_BasicParameters.m_InputToOutputWeights ?
88  std::make_unique<ScopedCpuTensorHandle>(*m_BasicParameters.m_InputToOutputWeights) : nullptr;
89  layer->m_BasicParameters.m_RecurrentToForgetWeights = m_BasicParameters.m_RecurrentToForgetWeights ?
90  std::make_unique<ScopedCpuTensorHandle>(*m_BasicParameters.m_RecurrentToForgetWeights) : nullptr;
91  layer->m_BasicParameters.m_RecurrentToCellWeights = m_BasicParameters.m_RecurrentToCellWeights ?
92  std::make_unique<ScopedCpuTensorHandle>(*m_BasicParameters.m_RecurrentToCellWeights) : nullptr;
93  layer->m_BasicParameters.m_RecurrentToOutputWeights = m_BasicParameters.m_RecurrentToOutputWeights ?
94  std::make_unique<ScopedCpuTensorHandle>(*m_BasicParameters.m_RecurrentToOutputWeights) : nullptr;
95  layer->m_BasicParameters.m_ForgetGateBias = m_BasicParameters.m_ForgetGateBias ?
96  std::make_unique<ScopedCpuTensorHandle>(*m_BasicParameters.m_ForgetGateBias) : nullptr;
97  layer->m_BasicParameters.m_CellBias = m_BasicParameters.m_CellBias ?
98  std::make_unique<ScopedCpuTensorHandle>(*m_BasicParameters.m_CellBias) : nullptr;
99  layer->m_BasicParameters.m_OutputGateBias = m_BasicParameters.m_OutputGateBias ?
100  std::make_unique<ScopedCpuTensorHandle>(*m_BasicParameters.m_OutputGateBias) : nullptr;
101 
102  if (!m_Param.m_CifgEnabled)
103  {
104  layer->m_CifgParameters.m_InputToInputWeights = m_CifgParameters.m_InputToInputWeights ?
105  std::make_unique<ScopedCpuTensorHandle>(*m_CifgParameters.m_InputToInputWeights) : nullptr;
106  layer->m_CifgParameters.m_RecurrentToInputWeights = m_CifgParameters.m_RecurrentToInputWeights ?
107  std::make_unique<ScopedCpuTensorHandle>(*m_CifgParameters.m_RecurrentToInputWeights) : nullptr;
108  layer->m_CifgParameters.m_InputGateBias = m_CifgParameters.m_InputGateBias ?
109  std::make_unique<ScopedCpuTensorHandle>(*m_CifgParameters.m_InputGateBias) : nullptr;
110  }
111 
112  if (m_Param.m_ProjectionEnabled)
113  {
114  layer->m_ProjectionParameters.m_ProjectionWeights = m_ProjectionParameters.m_ProjectionWeights ?
115  std::make_unique<ScopedCpuTensorHandle>(*m_ProjectionParameters.m_ProjectionWeights) : nullptr;
116  layer->m_ProjectionParameters.m_ProjectionBias = m_ProjectionParameters.m_ProjectionBias ?
117  std::make_unique<ScopedCpuTensorHandle>(*m_ProjectionParameters.m_ProjectionBias) : nullptr;
118  }
119 
120  if (m_Param.m_PeepholeEnabled)
121  {
122  if (!m_Param.m_CifgEnabled) {
123  layer->m_PeepholeParameters.m_CellToInputWeights = m_PeepholeParameters.m_CellToInputWeights ?
124  std::make_unique<ScopedCpuTensorHandle>(*m_PeepholeParameters.m_CellToInputWeights) : nullptr;
125  }
126 
127  layer->m_PeepholeParameters.m_CellToForgetWeights = m_PeepholeParameters.m_CellToForgetWeights ?
128  std::make_unique<ScopedCpuTensorHandle>(*m_PeepholeParameters.m_CellToForgetWeights) : nullptr;
129  layer->m_PeepholeParameters.m_CellToOutputWeights = m_PeepholeParameters.m_CellToOutputWeights ?
130  std::make_unique<ScopedCpuTensorHandle>(*m_PeepholeParameters.m_CellToOutputWeights) : nullptr;
131  }
132 
133  if (m_Param.m_LayerNormEnabled)
134  {
135  if (!m_Param.m_CifgEnabled) {
136  layer->m_LayerNormParameters.m_InputLayerNormWeights = m_LayerNormParameters.m_InputLayerNormWeights ?
137  std::make_unique<ScopedCpuTensorHandle>(*m_LayerNormParameters.m_InputLayerNormWeights) : nullptr;
138  }
139 
140  layer->m_LayerNormParameters.m_ForgetLayerNormWeights = m_LayerNormParameters.m_ForgetLayerNormWeights ?
141  std::make_unique<ScopedCpuTensorHandle>(*m_LayerNormParameters.m_ForgetLayerNormWeights) : nullptr;
142  layer->m_LayerNormParameters.m_CellLayerNormWeights = m_LayerNormParameters.m_CellLayerNormWeights ?
143  std::make_unique<ScopedCpuTensorHandle>(*m_LayerNormParameters.m_CellLayerNormWeights) : nullptr;
144  layer->m_LayerNormParameters.m_OutputLayerNormWeights = m_LayerNormParameters.m_OutputLayerNormWeights ?
145  std::make_unique<ScopedCpuTensorHandle>(*m_LayerNormParameters.m_OutputLayerNormWeights) : nullptr;
146  }
147 
148  return std::move(layer);
149 }
150 
151 std::vector<TensorShape> QLstmLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const
152 {
153  ARMNN_ASSERT(inputShapes.size() == 3);
154 
155  // Get input values for validation
156  unsigned int batchSize = inputShapes[0][0];
157  unsigned int outputSize = inputShapes[1][1];
158  unsigned int numUnits = inputShapes[2][1];
159 
160  std::vector<TensorShape> outShapes;
161  outShapes.push_back(TensorShape({ batchSize, outputSize })); // outputStateOut
162  outShapes.push_back(TensorShape({ batchSize, numUnits })); // cellStateOut
163  outShapes.push_back(TensorShape({ batchSize, outputSize })); // output
164 
165  return outShapes;
166 }
167 
169 {
171 
172  auto inferredShapes = InferOutputShapes(
173  {
175  GetInputSlot(1).GetConnection()->GetTensorInfo().GetShape(), // previousOutputIn
176  GetInputSlot(2).GetConnection()->GetTensorInfo().GetShape() // previousCellStateIn
177  });
178 
179  ARMNN_ASSERT(inferredShapes.size() == 3);
180 
181  // Check if the weights are nullptr for basic params
183  "QLstmLayer: m_BasicParameters.m_InputToForgetWeights should not be null.");
185  "QLstmLayer: m_BasicParameters.m_InputToCellWeights should not be null.");
187  "QLstmLayer: m_BasicParameters.m_InputToOutputWeights should not be null.");
189  "QLstmLayer: m_BasicParameters.m_RecurrentToForgetWeights should not be null.");
191  "QLstmLayer: m_BasicParameters.m_RecurrentToCellWeights should not be null.");
193  "QLstmLayer: m_BasicParameters.m_RecurrentToOutputWeights should not be null.");
195  "QLstmLayer: m_BasicParameters.m_ForgetGateBias should not be null.");
197  "QLstmLayer: m_BasicParameters.m_CellBias should not be null.");
199  "QLstmLayer: m_BasicParameters.m_OutputGateBias should not be null.");
200 
201  if (!m_Param.m_CifgEnabled)
202  {
204  "QLstmLayer: m_CifgParameters.m_InputToInputWeights should not be null.");
206  "QLstmLayer: m_CifgParameters.m_RecurrentToInputWeights should not be null.");
208  "QLstmLayer: m_CifgParameters.m_InputGateBias should not be null.");
209 
210  ConditionalThrowIfNotEqual<LayerValidationException>(
211  "QLstmLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.",
213  inferredShapes[0]);
214  }
215  else
216  {
218  "QLstmLayer: m_CifgParameters.m_InputToInputWeights should not have a value when CIFG is enabled.");
220  "QLstmLayer: m_CifgParameters.m_RecurrentToInputWeights should "
221  "not have a value when CIFG is enabled.");
223  "QLstmLayer: m_CifgParameters.m_InputGateBias should not have a value when CIFG is enabled.");
224 
225  ConditionalThrowIfNotEqual<LayerValidationException>(
226  "QLstmLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.",
228  inferredShapes[0]);
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 
251  ConditionalThrowIfNotEqual<LayerValidationException>(
252  "QLstmLayer: TensorShape set on OutputSlot[1] does not match the inferred shape.",
254  inferredShapes[1]);
255  ConditionalThrowIfNotEqual<LayerValidationException>(
256  "QLstmLayer: TensorShape set on OutputSlot[2] does not match the inferred shape.",
258  inferredShapes[2]);
259 
261  {
263  {
265  "QLstmLayer: m_LayerNormParameters.m_InputLayerNormWeights should not be null.");
266  }
268  "QLstmLayer: m_LayerNormParameters.m_ForgetLayerNormWeights should not be null.");
270  "QLstmLayer: m_LayerNormParameters.m_CellLayerNormWeights should not be null.");
272  "QLstmLayer: m_LayerNormParameters.m_UutputLayerNormWeights should not be null.");
273  }
274 }
275 
277 {
287 
288  // Cifg parameters
292 
293  // Projection parameters
296 
297  // Peephole parameters
301 
302  // Layer normalisation parameters
307 }
308 
309 void QLstmLayer::Accept(ILayerVisitor& visitor) const
310 {
311  LstmInputParams inputParams;
312 
313  ConstTensor inputToInputWeightsTensor;
315  {
316  ConstTensor inputToInputWeightsTensorCopy(m_CifgParameters.m_InputToInputWeights->GetTensorInfo(),
318  inputToInputWeightsTensor = inputToInputWeightsTensorCopy;
319  inputParams.m_InputToInputWeights = &inputToInputWeightsTensor;
320  }
321 
322  ConstTensor inputToForgetWeightsTensor;
324  {
325  ConstTensor inputToForgetWeightsTensorCopy(m_BasicParameters.m_InputToForgetWeights->GetTensorInfo(),
327  inputToForgetWeightsTensor = inputToForgetWeightsTensorCopy;
328  inputParams.m_InputToForgetWeights = &inputToForgetWeightsTensor;
329  }
330 
331  ConstTensor inputToCellWeightsTensor;
333  {
334  ConstTensor inputToCellWeightsTensorCopy(m_BasicParameters.m_InputToCellWeights->GetTensorInfo(),
336  inputToCellWeightsTensor = inputToCellWeightsTensorCopy;
337  inputParams.m_InputToCellWeights = &inputToCellWeightsTensor;
338  }
339 
340  ConstTensor inputToOutputWeightsTensor;
342  {
343  ConstTensor inputToOutputWeightsTensorCopy(m_BasicParameters.m_InputToOutputWeights->GetTensorInfo(),
345  inputToOutputWeightsTensor = inputToOutputWeightsTensorCopy;
346  inputParams.m_InputToOutputWeights = &inputToOutputWeightsTensor;
347  }
348 
349  ConstTensor recurrentToInputWeightsTensor;
351  {
352  ConstTensor recurrentToInputWeightsTensorCopy(
355  recurrentToInputWeightsTensor = recurrentToInputWeightsTensorCopy;
356  inputParams.m_RecurrentToInputWeights = &recurrentToInputWeightsTensor;
357  }
358 
359  ConstTensor recurrentToForgetWeightsTensor;
361  {
362  ConstTensor recurrentToForgetWeightsTensorCopy(
365  recurrentToForgetWeightsTensor = recurrentToForgetWeightsTensorCopy;
366  inputParams.m_RecurrentToForgetWeights = &recurrentToForgetWeightsTensor;
367  }
368 
369  ConstTensor recurrentToCellWeightsTensor;
371  {
372  ConstTensor recurrentToCellWeightsTensorCopy(
375  recurrentToCellWeightsTensor = recurrentToCellWeightsTensorCopy;
376  inputParams.m_RecurrentToCellWeights = &recurrentToCellWeightsTensor;
377  }
378 
379  ConstTensor recurrentToOutputWeightsTensor;
381  {
382  ConstTensor recurrentToOutputWeightsTensorCopy(
385  recurrentToOutputWeightsTensor = recurrentToOutputWeightsTensorCopy;
386  inputParams.m_RecurrentToOutputWeights = &recurrentToOutputWeightsTensor;
387  }
388 
389  ConstTensor cellToInputWeightsTensor;
391  {
392  ConstTensor cellToInputWeightsTensorCopy(m_PeepholeParameters.m_CellToInputWeights->GetTensorInfo(),
394  cellToInputWeightsTensor = cellToInputWeightsTensorCopy;
395  inputParams.m_CellToInputWeights = &cellToInputWeightsTensor;
396  }
397 
398  ConstTensor cellToForgetWeightsTensor;
400  {
401  ConstTensor cellToForgetWeightsTensorCopy(m_PeepholeParameters.m_CellToForgetWeights->GetTensorInfo(),
403  cellToForgetWeightsTensor = cellToForgetWeightsTensorCopy;
404  inputParams.m_CellToForgetWeights = &cellToForgetWeightsTensor;
405  }
406 
407  ConstTensor cellToOutputWeightsTensor;
409  {
410  ConstTensor cellToOutputWeightsTensorCopy(m_PeepholeParameters.m_CellToOutputWeights->GetTensorInfo(),
412  cellToOutputWeightsTensor = cellToOutputWeightsTensorCopy;
413  inputParams.m_CellToOutputWeights = &cellToOutputWeightsTensor;
414  }
415 
416  ConstTensor inputGateBiasTensor;
417  if (m_CifgParameters.m_InputGateBias != nullptr)
418  {
419  ConstTensor inputGateBiasTensorCopy(m_CifgParameters.m_InputGateBias->GetTensorInfo(),
420  m_CifgParameters.m_InputGateBias->Map(true));
421  inputGateBiasTensor = inputGateBiasTensorCopy;
422  inputParams.m_InputGateBias = &inputGateBiasTensor;
423  }
424 
425  ConstTensor forgetGateBiasTensor;
426  if (m_BasicParameters.m_ForgetGateBias != nullptr)
427  {
428  ConstTensor forgetGateBiasTensorCopy(m_BasicParameters.m_ForgetGateBias->GetTensorInfo(),
430  forgetGateBiasTensor = forgetGateBiasTensorCopy;
431  inputParams.m_ForgetGateBias = &forgetGateBiasTensor;
432  }
433 
434  ConstTensor cellBiasTensor;
435  if (m_BasicParameters.m_CellBias != nullptr)
436  {
437  ConstTensor cellBiasTensorCopy(m_BasicParameters.m_CellBias->GetTensorInfo(),
438  m_BasicParameters.m_CellBias->Map(true));
439  cellBiasTensor = cellBiasTensorCopy;
440  inputParams.m_CellBias = &cellBiasTensor;
441  }
442 
443  ConstTensor outputGateBias;
444  if (m_BasicParameters.m_OutputGateBias != nullptr)
445  {
446  ConstTensor outputGateBiasCopy(m_BasicParameters.m_OutputGateBias->GetTensorInfo(),
448  outputGateBias = outputGateBiasCopy;
449  inputParams.m_OutputGateBias = &outputGateBias;
450  }
451 
452  ConstTensor projectionWeightsTensor;
454  {
455  ConstTensor projectionWeightsTensorCopy(m_ProjectionParameters.m_ProjectionWeights->GetTensorInfo(),
457  projectionWeightsTensor = projectionWeightsTensorCopy;
458  inputParams.m_ProjectionWeights = &projectionWeightsTensor;
459  }
460 
461  ConstTensor projectionBiasTensor;
463  {
464  ConstTensor projectionBiasTensorCopy(m_ProjectionParameters.m_ProjectionBias->GetTensorInfo(),
466  projectionBiasTensor = projectionBiasTensorCopy;
467  inputParams.m_ProjectionBias = &projectionBiasTensor;
468  }
469 
470  ConstTensor inputLayerNormTensor;
472  {
473  ConstTensor inputLayerNormTensorCopy(m_LayerNormParameters.m_InputLayerNormWeights->GetTensorInfo(),
475  inputLayerNormTensor = inputLayerNormTensorCopy;
476  inputParams.m_InputLayerNormWeights = &inputLayerNormTensor;
477  }
478 
479  ConstTensor forgetLayerNormTensor;
481  {
482  ConstTensor forgetLayerNormTensorCopy(m_LayerNormParameters.m_ForgetLayerNormWeights->GetTensorInfo(),
484  forgetLayerNormTensor = forgetLayerNormTensorCopy;
485  inputParams.m_ForgetLayerNormWeights = &forgetLayerNormTensor;
486  }
487 
488  ConstTensor cellLayerNormTensor;
490  {
491  ConstTensor cellLayerNormTensorCopy(m_LayerNormParameters.m_CellLayerNormWeights->GetTensorInfo(),
493  cellLayerNormTensor = cellLayerNormTensorCopy;
494  inputParams.m_CellLayerNormWeights = &cellLayerNormTensor;
495  }
496 
497  ConstTensor outputLayerNormTensor;
499  {
500  ConstTensor outputLayerNormTensorCopy(m_LayerNormParameters.m_OutputLayerNormWeights->GetTensorInfo(),
502  outputLayerNormTensor = outputLayerNormTensorCopy;
503  inputParams.m_OutputLayerNormWeights = &outputLayerNormTensor;
504  }
505 
506 
507  visitor.VisitQLstmLayer(this, GetParameters(), inputParams, GetName());
508 }
509 
510 } // namespace armnn
const ConstCpuTensorHandle * m_CellToForgetWeights
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 ConstTensor * m_CellBias
Definition: LstmParams.hpp:53
QLstmDescriptor m_Param
The parameters for the layer (not including tensor-valued weights etc.).
const TensorShape & GetShape() const
Definition: Tensor.hpp:88
const ConstCpuTensorHandle * m_ProjectionWeights
QLstmOptProjectionParameters m_ProjectionParameters
Definition: QLstmLayer.hpp:85
const ConstTensor * m_CellToOutputWeights
Definition: LstmParams.hpp:50
std::unique_ptr< ScopedCpuTensorHandle > m_OutputGateBias
A unique pointer to represent 1D bias tensor with dimensions [num_units] (int32). ...
Definition: QLstmLayer.hpp:35
std::unique_ptr< ScopedCpuTensorHandle > m_RecurrentToInputWeights
A unique pointer to represent 2D weights tensor with dimensions [input_size, num_units] (QSymmS8)...
Definition: QLstmLayer.hpp:61
std::unique_ptr< ScopedCpuTensorHandle > m_CellLayerNormWeights
A unique pointer to represent 1D weights tensor with dimensions [num_units] (QSymmS16).
Definition: QLstmLayer.hpp:73
bool m_PeepholeEnabled
Enable/disable peephole.
std::unique_ptr< ScopedCpuTensorHandle > m_InputLayerNormWeights
A unique pointer to represent 1D weights tensor with dimensions [num_units] (QSymmS16).
Definition: QLstmLayer.hpp:69
const ConstCpuTensorHandle * m_ProjectionBias
const ConstCpuTensorHandle * m_ForgetLayerNormWeights
const ConstTensor * m_CellToInputWeights
Definition: LstmParams.hpp:48
std::unique_ptr< ScopedCpuTensorHandle > m_InputToInputWeights
A unique pointer to represent 2D weights tensor with dimensions [input_size, num_units] (QSymmS8)...
Definition: QLstmLayer.hpp:59
const ConstCpuTensorHandle * m_CellLayerNormWeights
std::unique_ptr< ScopedCpuTensorHandle > m_CellToOutputWeights
A unique pointer to represent 1D weights tensor with dimensions [num_units] (QSymmS16).
Definition: QLstmLayer.hpp:53
const ConstTensor * m_InputGateBias
Definition: LstmParams.hpp:51
std::unique_ptr< ScopedCpuTensorHandle > m_ProjectionBias
A unique pointer to represent 1D weights tensor with dimensions [output_size] (int32).
Definition: QLstmLayer.hpp:43
std::unique_ptr< ScopedCpuTensorHandle > m_ProjectionWeights
A unique pointer to represent 2D weights tensor with dimensions [output_size, num_units] (QSymmS8)...
Definition: QLstmLayer.hpp:41
const ConstCpuTensorHandle * m_RecurrentToCellWeights
virtual std::unique_ptr< IWorkload > CreateQLstm(const QLstmQueueDescriptor &descriptor, const WorkloadInfo &info) const
const ConstCpuTensorHandle * m_RecurrentToInputWeights
std::unique_ptr< ScopedCpuTensorHandle > m_InputToForgetWeights
A unique pointer to represent 2D weights tensor with dimensions [num_units, inputSize] (QSymmS8)...
Definition: QLstmLayer.hpp:17
std::unique_ptr< ScopedCpuTensorHandle > m_CellBias
A unique pointer to represent 1D bias tensor with dimensions [num_units] (int32). ...
Definition: QLstmLayer.hpp:33
const ConstTensor * m_RecurrentToCellWeights
Definition: LstmParams.hpp:46
const ConstTensor * m_ForgetLayerNormWeights
Definition: LstmParams.hpp:58
const ConstCpuTensorHandle * m_OutputGateBias
const ConstTensor * m_CellToForgetWeights
Definition: LstmParams.hpp:49
const ConstCpuTensorHandle * m_CellBias
Copyright (c) 2020 ARM Limited.
std::unique_ptr< ScopedCpuTensorHandle > m_InputToOutputWeights
A unique pointer to represent 2D weights tensor with dimensions [num_units, inputSize] (QSymmS8)...
Definition: QLstmLayer.hpp:21
const IOutputSlot * GetConnection() const override
Definition: Layer.hpp:199
std::unique_ptr< ScopedCpuTensorHandle > m_OutputLayerNormWeights
A unique pointer to represent 1D weights tensor with dimensions [num_units] (QSymmS16).
Definition: QLstmLayer.hpp:75
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:79
const ConstCpuTensorHandle * m_CellToOutputWeights
std::unique_ptr< ScopedCpuTensorHandle > m_RecurrentToForgetWeights
A unique pointer to represent 2D weights tensor with dimensions [num_units, outputSize] (QSymmS8)...
Definition: QLstmLayer.hpp:24
const ConstCpuTensorHandle * m_OutputLayerNormWeights
void VerifyLayerConnections(unsigned int expectedConnections, const CheckLocation &location) const
Definition: Layer.cpp:339
std::unique_ptr< ScopedCpuTensorHandle > m_CellToInputWeights
A unique pointer to represent 1D weights tensor with dimensions [num_units] (QSymmS16).
Definition: QLstmLayer.hpp:49
const InputSlot & GetInputSlot(unsigned int index) const override
Get a const input slot handle by slot index.
Definition: Layer.hpp:310
const ConstTensor * m_InputLayerNormWeights
Definition: LstmParams.hpp:57
bool m_LayerNormEnabled
Enable/disable layer normalization.
const ConstTensor * m_RecurrentToOutputWeights
Definition: LstmParams.hpp:47
#define ARMNN_ASSERT_MSG(COND, MSG)
Definition: Assert.hpp:15
virtual std::unique_ptr< IWorkload > CreateWorkload(const IWorkloadFactory &factory) const override
Makes a workload for the QLstm type.
Definition: QLstmLayer.cpp:22
const ConstCpuTensorHandle * m_InputToForgetWeights
const ConstTensor * m_ProjectionBias
Definition: LstmParams.hpp:56
std::unique_ptr< ScopedCpuTensorHandle > m_CellToForgetWeights
A unique pointer to represent 1D weights tensor with dimensions [num_units] (QSymmS16).
Definition: QLstmLayer.hpp:51
A tensor defined by a TensorInfo (shape and data type) and an immutable backing store.
Definition: Tensor.hpp:199
std::unique_ptr< ScopedCpuTensorHandle > m_InputGateBias
A unique pointer to represent 1D weights tensor with dimensions [num_units] (int32).
Definition: QLstmLayer.hpp:63
#define ARMNN_ASSERT(COND)
Definition: Assert.hpp:14
A QLstmDescriptor for the QLstmLayer.
std::unique_ptr< ScopedCpuTensorHandle > m_ForgetLayerNormWeights
A unique pointer to represent 1D weights tensor with dimensions [num_units] (QSymmS16).
Definition: QLstmLayer.hpp:71
void ValidateTensorShapesFromInputs() override
Check if the input tensor shape(s) will lead to a valid configuration of QLstmLayer.
Definition: QLstmLayer.cpp:168
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:276
#define CHECK_LOCATION()
Definition: Exceptions.hpp:192
std::unique_ptr< ScopedCpuTensorHandle > m_InputToCellWeights
A unique pointer to represent 2D weights tensor with dimensions [num_units, inputSize] (QSymmS8)...
Definition: QLstmLayer.hpp:19
QLstmBasicParameters m_BasicParameters
Definition: QLstmLayer.hpp:83
const ConstCpuTensorHandle * m_CellToInputWeights
const ConstTensor * m_CellLayerNormWeights
Definition: LstmParams.hpp:59
const ConstTensor * m_ForgetGateBias
Definition: LstmParams.hpp:52
const ConstTensor * m_InputToCellWeights
Definition: LstmParams.hpp:42
const ConstTensor * m_InputToOutputWeights
Definition: LstmParams.hpp:43
This layer represents a QLstm operation.
Definition: QLstmLayer.hpp:79
std::unique_ptr< ScopedCpuTensorHandle > m_ForgetGateBias
A unique pointer to represent 1D bias tensor with dimensions [num_units] (int32). ...
Definition: QLstmLayer.hpp:31
const ConstTensor * m_RecurrentToForgetWeights
Definition: LstmParams.hpp:45
const ConstCpuTensorHandle * m_RecurrentToOutputWeights
std::unique_ptr< ScopedCpuTensorHandle > m_RecurrentToCellWeights
A unique pointer to represent 2D weights tensor with dimensions [num_units, outputSize] (QSymmS8)...
Definition: QLstmLayer.hpp:26
bool m_ProjectionEnabled
Enable/disable the projection layer.
const ConstTensor * m_RecurrentToInputWeights
Definition: LstmParams.hpp:44
WorkloadInfo PrepInfoAndDesc(QueueDescriptor &descriptor) const
Helper function to reduce duplication in *LayerCreateWorkload.
const ConstCpuTensorHandle * m_InputGateBias
std::unique_ptr< ScopedCpuTensorHandle > m_RecurrentToOutputWeights
A unique pointer to represent 2D weights tensor with dimensions [num_units, outputSize] (QSymmS8)...
Definition: QLstmLayer.hpp:28
const OutputSlot & GetOutputSlot(unsigned int index=0) const override
Get the const output slot handle by slot index.
Definition: Layer.hpp:312
void Accept(ILayerVisitor &visitor) const override
Apply a visitor to this layer.
Definition: QLstmLayer.cpp:309
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:305
const ConstCpuTensorHandle * m_InputLayerNormWeights
const ConstCpuTensorHandle * m_RecurrentToForgetWeights
const ConstTensor * m_OutputLayerNormWeights
Definition: LstmParams.hpp:60
std::vector< std::reference_wrapper< std::unique_ptr< ScopedCpuTensorHandle > >> ConstantTensors
Definition: Layer.hpp:363
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:151
const ConstCpuTensorHandle * m_ForgetGateBias
const TensorInfo & GetTensorInfo() const override
Definition: Layer.cpp:63
const ConstCpuTensorHandle * m_InputToOutputWeights
bool m_CifgEnabled
Enable/disable CIFG (coupled input & forget gate).
const ConstCpuTensorHandle * m_InputToInputWeights
const ConstTensor * m_InputToForgetWeights
Definition: LstmParams.hpp:41
const ConstTensor * m_InputToInputWeights
Definition: LstmParams.hpp:40
const ConstCpuTensorHandle * m_InputToCellWeights